diff --git a/scripts/get_partition_hash.py b/scripts/get_partition_hash.py
new file mode 100644
index 0000000..0d90e85
--- /dev/null
+++ b/scripts/get_partition_hash.py
@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""Compute the ESP32 partition hash for an RNode firmware binary.
+
+The ESP32 build toolchain appends a SHA256 hash to firmware binaries.
+esp_partition_get_sha256() returns this embedded hash (the last 32 bytes).
+This is different from sha256sum of the entire file.
+
+Usage:
+    python3 get_partition_hash.py <firmware.bin>
+
+    # Flash and set hash:
+    python3 get_partition_hash.py build/esp32.esp32.esp32s3/RNode_Firmware.ino.bin
+    rnodeconf -H <output_hash> /dev/ttyACM0
+"""
+import hashlib
+import sys
+
+def get_partition_hash(firmware_path):
+    with open(firmware_path, "rb") as f:
+        fw = f.read()
+
+    embedded_hash = fw[-32:]
+    calc_hash = hashlib.sha256(fw[:-32]).digest()
+
+    if calc_hash != embedded_hash:
+        print(f"WARNING: Embedded hash doesn't match SHA256(fw[:-32])", file=sys.stderr)
+        print(f"  Calculated: {calc_hash.hex()}", file=sys.stderr)
+        print(f"  Embedded:   {embedded_hash.hex()}", file=sys.stderr)
+        return None
+
+    return embedded_hash.hex()
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print(f"Usage: {sys.argv[0]} <firmware.bin>", file=sys.stderr)
+        sys.exit(1)
+
+    result = get_partition_hash(sys.argv[1])
+    if result:
+        print(result)
+    else:
+        sys.exit(1)
diff --git a/sdrconnect_lora_scanner.py b/sdrconnect_lora_scanner.py
new file mode 100644
index 0000000..2c15c26
--- /dev/null
+++ b/sdrconnect_lora_scanner.py
@@ -0,0 +1,668 @@
+#!/usr/bin/env python3
+"""
+SDRconnect Headless WebSocket client for LoRa signal detection at 868 MHz.
+
+Connects to SDRconnect_headless via its WebSocket API (port 5454 by default),
+tunes the RSPduo to 868 MHz, and monitors signal power + spectrum data to
+detect LoRa activity.
+
+Usage:
+    # First, start SDRconnect_headless:
+    #   /opt/sdrconnect/SDRconnect_headless --websocket_port=5454
+    #
+    # Then run this script:
+    #   python3 sdrconnect_lora_scanner.py
+    #   python3 sdrconnect_lora_scanner.py --host 127.0.0.1 --port 5454
+    #   python3 sdrconnect_lora_scanner.py --save-spectrum spectrum.png
+    #   python3 sdrconnect_lora_scanner.py --iq-capture iq_data.bin --iq-seconds 5
+
+Protocol reference: SDRconnect WebSocket API 1.0.1
+    https://www.sdrplay.com/docs/SDRconnect_WebSocket_API.pdf
+"""
+
+import argparse
+import asyncio
+import json
+import struct
+import sys
+import time
+from collections import deque
+from datetime import datetime
+
+import numpy as np
+
+try:
+    import websockets
+except ImportError:
+    print("ERROR: 'websockets' package required. Install with: pip3 install websockets")
+    sys.exit(1)
+
+
+# --- SDRconnect WebSocket API message helpers ---
+
+def make_msg(event_type: str, property_name: str = "", value: str = "") -> str:
+    """Build a JSON message for the SDRconnect WebSocket API."""
+    return json.dumps({
+        "event_type": event_type,
+        "property": property_name,
+        "value": value
+    })
+
+
+def set_property(prop: str, value) -> str:
+    return make_msg("set_property", prop, str(value))
+
+
+def get_property(prop: str) -> str:
+    return make_msg("get_property", prop)
+
+
+# --- Binary message parser ---
+
+PAYLOAD_TYPE_AUDIO = 1      # Signed 16-bit PCM Stereo @ 48kHz (LRLR)
+PAYLOAD_TYPE_IQ = 2         # Signed 16-bit interleaved IQ (IQIQ)
+PAYLOAD_TYPE_SPECTRUM = 3   # Unsigned 8-bit spectrum FFT bins
+
+
+def parse_binary_message(data: bytes):
+    """
+    Parse a binary WebSocket message from SDRconnect.
+    Returns (payload_type, payload_data).
+
+    Binary messages start with a 2-byte little-endian uint16 payload type:
+        1 = Signed 16-bit PCM Stereo Audio @ 48kHz (LRLR)
+        2 = Signed 16-bit interleaved IQ (IQIQ)
+        3 = Unsigned 8-bit spectrum FFT bins normalised to visible range
+    """
+    if len(data) < 2:
+        return None, None
+    payload_type = struct.unpack_from('<H', data, 0)[0]
+    payload = data[2:]
+    return payload_type, payload
+
+
+class SDRconnectClient:
+    """Async client for SDRconnect WebSocket API."""
+
+    def __init__(self, host: str = "127.0.0.1", port: int = 5454):
+        self.uri = f"ws://{host}:{port}/"
+        self.ws = None
+        self.properties = {}
+        self._property_events = {}  # property name -> asyncio.Event
+        self._property_values = {}  # property name -> latest value
+
+        # Data buffers
+        self.spectrum_bins = deque(maxlen=200)  # last N spectrum snapshots
+        self.iq_buffer = bytearray()
+        self.signal_power_history = deque(maxlen=1000)
+        self.signal_snr_history = deque(maxlen=1000)
+
+        # Stats
+        self.spectrum_count = 0
+        self.iq_sample_count = 0
+
+    async def connect(self):
+        """Establish WebSocket connection."""
+        print(f"Connecting to {self.uri} ...")
+        self.ws = await websockets.connect(
+            self.uri,
+            max_size=10 * 1024 * 1024,  # 10 MB max message
+            open_timeout=10,
+        )
+        print("Connected.")
+
+    async def close(self):
+        if self.ws:
+            await self.ws.close()
+            print("Connection closed.")
+
+    async def send(self, msg: str):
+        """Send a JSON text message."""
+        await self.ws.send(msg)
+
+    async def send_set_property(self, prop: str, value):
+        await self.send(set_property(prop, value))
+
+    async def send_get_property(self, prop: str):
+        await self.send(get_property(prop))
+
+    async def request_property(self, prop: str, timeout: float = 5.0) -> str:
+        """Send get_property and wait for the response."""
+        evt = asyncio.Event()
+        self._property_events[prop] = evt
+        await self.send_get_property(prop)
+        try:
+            await asyncio.wait_for(evt.wait(), timeout=timeout)
+        except asyncio.TimeoutError:
+            print(f"  WARNING: Timeout waiting for property '{prop}'")
+            return None
+        finally:
+            self._property_events.pop(prop, None)
+        return self._property_values.get(prop)
+
+    def _handle_json(self, msg: dict):
+        """Process an incoming JSON message."""
+        event_type = msg.get("event_type", "")
+        prop = msg.get("property", "")
+        value = msg.get("value", "")
+
+        if event_type == "property_changed":
+            self.properties[prop] = value
+            if prop == "signal_power":
+                try:
+                    self.signal_power_history.append(
+                        (time.time(), float(value))
+                    )
+                except (ValueError, TypeError):
+                    pass
+            elif prop == "signal_snr":
+                try:
+                    self.signal_snr_history.append(
+                        (time.time(), float(value))
+                    )
+                except (ValueError, TypeError):
+                    pass
+            # Wake up anyone waiting for this property
+            if prop in self._property_events:
+                self._property_values[prop] = value
+                self._property_events[prop].set()
+
+        elif event_type == "get_property_response":
+            self._property_values[prop] = value
+            if prop in self._property_events:
+                self._property_events[prop].set()
+
+    def _handle_binary(self, data: bytes):
+        """Process an incoming binary message."""
+        payload_type, payload = parse_binary_message(data)
+
+        if payload_type == PAYLOAD_TYPE_SPECTRUM:
+            bins = np.frombuffer(payload, dtype=np.uint8)
+            self.spectrum_bins.append((time.time(), bins.copy()))
+            self.spectrum_count += 1
+
+        elif payload_type == PAYLOAD_TYPE_IQ:
+            self.iq_buffer.extend(payload)
+            # Count IQ sample pairs (each pair = 4 bytes: I16 + Q16)
+            self.iq_sample_count += len(payload) // 4
+
+    async def receive_loop(self, duration: float = None):
+        """
+        Receive messages in a loop. If duration is set, stop after that
+        many seconds. Otherwise run until cancelled.
+        """
+        start = time.time()
+        try:
+            async for message in self.ws:
+                if isinstance(message, str):
+                    try:
+                        msg = json.loads(message)
+                        self._handle_json(msg)
+                    except json.JSONDecodeError:
+                        print(f"  WARNING: Non-JSON text message: {message[:100]}")
+                elif isinstance(message, bytes):
+                    self._handle_binary(message)
+
+                if duration and (time.time() - start) >= duration:
+                    break
+        except websockets.ConnectionClosed as e:
+            print(f"Connection closed: {e}")
+
+    # --- High-level commands ---
+
+    async def select_device(self, index: int = 0):
+        """Select device by index."""
+        print(f"Selecting device index {index} ...")
+        await self.send(make_msg("selected_device", "", str(index)))
+        await asyncio.sleep(1)
+
+    async def start_device_streaming(self):
+        """Start the device hardware streaming."""
+        print("Starting device streaming ...")
+        await self.send(make_msg("device_stream_enable", "", "true"))
+        await asyncio.sleep(1)
+
+    async def stop_device_streaming(self):
+        """Stop the device hardware streaming."""
+        await self.send(make_msg("device_stream_enable", "", "false"))
+
+    async def enable_spectrum(self, enable: bool = True):
+        val = "true" if enable else "false"
+        print(f"Spectrum streaming: {val}")
+        await self.send(make_msg("spectrum_enable", "", val))
+
+    async def enable_iq_stream(self, enable: bool = True):
+        val = "true" if enable else "false"
+        print(f"IQ streaming: {val}")
+        await self.send(make_msg("iq_stream_enable", "", val))
+
+    async def tune(self, center_freq: int, vfo_freq: int = None,
+                   sample_rate: float = 2e6, bandwidth: int = None):
+        """
+        Tune the radio.
+        center_freq: LO frequency in Hz
+        vfo_freq: VFO frequency in Hz (defaults to center_freq)
+        sample_rate: sample rate in Hz
+        bandwidth: filter bandwidth in Hz
+        """
+        if vfo_freq is None:
+            vfo_freq = center_freq
+
+        print(f"Setting center frequency: {center_freq/1e6:.3f} MHz")
+        await self.send_set_property("device_center_frequency", center_freq)
+        await asyncio.sleep(0.3)
+
+        print(f"Setting VFO frequency: {vfo_freq/1e6:.3f} MHz")
+        await self.send_set_property("device_vfo_frequency", vfo_freq)
+        await asyncio.sleep(0.3)
+
+        print(f"Setting sample rate: {sample_rate/1e6:.1f} MHz")
+        await self.send_set_property("device_sample_rate", int(sample_rate))
+        await asyncio.sleep(0.3)
+
+        if bandwidth:
+            print(f"Setting filter bandwidth: {bandwidth/1e3:.0f} kHz")
+            await self.send_set_property("filter_bandwidth", bandwidth)
+            await asyncio.sleep(0.3)
+
+    async def configure_for_lora_868(self, sample_rate: float = 2e6):
+        """
+        Configure for LoRa monitoring at 868 MHz (EU ISM band).
+
+        LoRa EU868 channels:
+            868.1 MHz, 868.3 MHz, 868.5 MHz (common 3-channel)
+            867.1, 867.3, 867.5, 867.7, 867.9 MHz (additional)
+            869.525 MHz (downlink RX2)
+
+        With 2 MHz sample rate centered at 868.3 MHz, we cover
+        ~867.3 - 869.3 MHz which includes the 3 primary channels.
+        """
+        center = 868_300_000  # 868.3 MHz - center of primary LoRa channels
+        await self.tune(
+            center_freq=center,
+            vfo_freq=center,
+            sample_rate=sample_rate,
+            bandwidth=int(sample_rate),
+        )
+        # Use NFM demodulator (closest to what we want for monitoring)
+        print("Setting demodulator: NFM")
+        await self.send_set_property("demodulator", "NFM")
+        await asyncio.sleep(0.2)
+
+    async def print_device_info(self):
+        """Query and print current device state."""
+        props = [
+            "device_center_frequency", "device_vfo_frequency",
+            "device_sample_rate", "filter_bandwidth",
+            "lna_state", "lna_state_min", "lna_state_max",
+            "demodulator", "started", "can_control",
+            "signal_power", "signal_snr",
+        ]
+        print("\n--- Device State ---")
+        for p in props:
+            val = await self.request_property(p, timeout=3.0)
+            if val is not None:
+                self.properties[p] = val
+                # Format frequency values nicely
+                if "frequency" in p and val and val.isdigit():
+                    freq_mhz = int(val) / 1e6
+                    print(f"  {p}: {val} ({freq_mhz:.3f} MHz)")
+                elif "sample_rate" in p and val:
+                    try:
+                        sr = float(val)
+                        print(f"  {p}: {val} ({sr/1e6:.3f} MHz)")
+                    except ValueError:
+                        print(f"  {p}: {val}")
+                else:
+                    print(f"  {p}: {val}")
+            else:
+                print(f"  {p}: (no response)")
+        print("---\n")
+
+
+async def monitor_lora(args):
+    """Main monitoring routine."""
+    client = SDRconnectClient(host=args.host, port=args.port)
+
+    try:
+        await client.connect()
+
+        # Select device (index 0 = first device)
+        await client.select_device(args.device_index)
+
+        # Start hardware streaming
+        await client.start_device_streaming()
+        await asyncio.sleep(1)
+
+        # Tune to 868 MHz LoRa band
+        await client.configure_for_lora_868(sample_rate=args.sample_rate)
+        await asyncio.sleep(1)
+
+        # Query current state
+        # Start receive loop in background to handle property responses
+        recv_task = asyncio.create_task(client.receive_loop())
+        await asyncio.sleep(0.5)  # let loop start
+
+        await client.print_device_info()
+
+        # Enable spectrum streaming
+        await client.enable_spectrum(True)
+
+        # Optionally enable IQ streaming
+        if args.iq_capture:
+            await client.enable_iq_stream(True)
+
+        # Cancel the generic receive loop, we'll run our own timed one
+        recv_task.cancel()
+        try:
+            await recv_task
+        except asyncio.CancelledError:
+            pass
+
+        # --- Main monitoring loop ---
+        print(f"\nMonitoring 868 MHz LoRa band for {args.duration} seconds ...")
+        print(f"  Center: 868.3 MHz | BW: {args.sample_rate/1e6:.1f} MHz")
+        print(f"  Coverage: ~{(868.3 - args.sample_rate/2e6):.1f} - "
+              f"{(868.3 + args.sample_rate/2e6):.1f} MHz")
+        print()
+
+        start_time = time.time()
+        last_report = start_time
+        report_interval = 2.0  # Print status every N seconds
+
+        try:
+            async for message in client.ws:
+                now = time.time()
+                elapsed = now - start_time
+
+                if isinstance(message, str):
+                    try:
+                        msg = json.loads(message)
+                        client._handle_json(msg)
+                    except json.JSONDecodeError:
+                        pass
+                elif isinstance(message, bytes):
+                    client._handle_binary(message)
+
+                # Periodic status report
+                if now - last_report >= report_interval:
+                    last_report = now
+                    # Get latest signal power/SNR from property_changed events
+                    power = client.properties.get("signal_power", "?")
+                    snr = client.properties.get("signal_snr", "?")
+                    spec_count = client.spectrum_count
+                    iq_count = client.iq_sample_count
+
+                    ts = datetime.now().strftime("%H:%M:%S")
+                    line = (f"[{ts}] t={elapsed:.0f}s | "
+                            f"Power: {power} dB | SNR: {snr} dB | "
+                            f"Spectrum frames: {spec_count}")
+                    if args.iq_capture:
+                        line += f" | IQ samples: {iq_count}"
+                    print(line)
+
+                    # Detect LoRa-like activity: sudden power increases
+                    if client.signal_power_history:
+                        recent = [p for t, p in client.signal_power_history
+                                  if now - t < 5.0]
+                        if len(recent) >= 2:
+                            avg = sum(recent) / len(recent)
+                            peak = max(recent)
+                            if peak - avg > 6:  # 6 dB spike
+                                print(f"  >>> SIGNAL DETECTED: "
+                                      f"peak={peak:.1f} dB, avg={avg:.1f} dB, "
+                                      f"delta={peak-avg:.1f} dB")
+
+                if elapsed >= args.duration:
+                    break
+
+        except websockets.ConnectionClosed as e:
+            print(f"Connection closed during monitoring: {e}")
+
+        # --- Post-capture analysis ---
+        print(f"\n--- Capture Summary ---")
+        print(f"Duration: {args.duration} seconds")
+        print(f"Spectrum frames captured: {client.spectrum_count}")
+        print(f"IQ samples captured: {client.iq_sample_count}")
+
+        if client.signal_power_history:
+            powers = [p for _, p in client.signal_power_history]
+            print(f"Signal power: min={min(powers):.1f} dB, "
+                  f"max={max(powers):.1f} dB, "
+                  f"mean={sum(powers)/len(powers):.1f} dB")
+
+        if client.signal_snr_history:
+            snrs = [s for _, s in client.signal_snr_history]
+            print(f"Signal SNR: min={min(snrs):.1f} dB, "
+                  f"max={max(snrs):.1f} dB, "
+                  f"mean={sum(snrs)/len(snrs):.1f} dB")
+
+        # Save spectrum plot if requested
+        if args.save_spectrum and client.spectrum_bins:
+            save_spectrum_plot(client, args)
+
+        # Save IQ data if requested
+        if args.iq_capture and client.iq_buffer:
+            save_iq_data(client, args)
+
+        # Analyze spectrum for LoRa signatures
+        if client.spectrum_bins:
+            analyze_spectrum_for_lora(client, args)
+
+        # Cleanup
+        await client.enable_spectrum(False)
+        if args.iq_capture:
+            await client.enable_iq_stream(False)
+        await client.stop_device_streaming()
+
+    finally:
+        await client.close()
+
+
+def save_spectrum_plot(client, args):
+    """Save a waterfall/spectrum plot of captured data."""
+    try:
+        import matplotlib
+        matplotlib.use('Agg')
+        import matplotlib.pyplot as plt
+    except ImportError:
+        print("WARNING: matplotlib not available, skipping spectrum plot")
+        return
+
+    # Build waterfall from spectrum history
+    timestamps = []
+    spectra = []
+    for t, bins in client.spectrum_bins:
+        timestamps.append(t)
+        spectra.append(bins)
+
+    if not spectra:
+        print("No spectrum data to plot.")
+        return
+
+    # Normalize to same length (take the most common length)
+    lengths = [len(s) for s in spectra]
+    target_len = max(set(lengths), key=lengths.count)
+    spectra = [s for s in spectra if len(s) == target_len]
+    if not spectra:
+        return
+
+    waterfall = np.array(spectra, dtype=np.float32)
+    # FFT bins are uint8 normalised to visible range by SDRconnect
+    # Higher values = stronger signal
+
+    sr = args.sample_rate
+    center = 868.3  # MHz
+    freq_axis = np.linspace(center - sr/2e6, center + sr/2e6, target_len)
+    time_axis = np.arange(len(waterfall))
+
+    fig, axes = plt.subplots(2, 1, figsize=(12, 8), gridspec_kw={'height_ratios': [1, 2]})
+
+    # Top: average spectrum
+    avg_spectrum = np.mean(waterfall, axis=0)
+    max_spectrum = np.max(waterfall, axis=0)
+    axes[0].plot(freq_axis, avg_spectrum, 'b-', alpha=0.7, label='Mean')
+    axes[0].plot(freq_axis, max_spectrum, 'r-', alpha=0.5, label='Peak')
+    axes[0].set_xlabel('Frequency (MHz)')
+    axes[0].set_ylabel('Magnitude (0-255)')
+    axes[0].set_title(f'868 MHz LoRa Band Spectrum ({len(waterfall)} frames)')
+    axes[0].legend()
+    axes[0].grid(True, alpha=0.3)
+
+    # Mark LoRa channel frequencies
+    lora_channels = [868.1, 868.3, 868.5]
+    for ch in lora_channels:
+        if freq_axis[0] <= ch <= freq_axis[-1]:
+            axes[0].axvline(x=ch, color='g', linestyle='--', alpha=0.5,
+                          label=f'{ch} MHz' if ch == lora_channels[0] else '')
+            axes[0].text(ch, axes[0].get_ylim()[1] * 0.95, f'{ch}',
+                        ha='center', va='top', fontsize=8, color='green')
+
+    # Bottom: waterfall
+    extent = [freq_axis[0], freq_axis[-1], len(waterfall), 0]
+    axes[1].imshow(waterfall, aspect='auto', extent=extent,
+                   cmap='viridis', interpolation='nearest')
+    axes[1].set_xlabel('Frequency (MHz)')
+    axes[1].set_ylabel('Time (frame #)')
+    axes[1].set_title('Waterfall')
+    for ch in lora_channels:
+        if freq_axis[0] <= ch <= freq_axis[-1]:
+            axes[1].axvline(x=ch, color='r', linestyle='--', alpha=0.5)
+
+    plt.tight_layout()
+    outfile = args.save_spectrum
+    plt.savefig(outfile, dpi=150)
+    print(f"Spectrum plot saved to: {outfile}")
+    plt.close()
+
+
+def save_iq_data(client, args):
+    """Save captured IQ data to a binary file."""
+    outfile = args.iq_capture
+    with open(outfile, 'wb') as f:
+        f.write(bytes(client.iq_buffer))
+    n_samples = len(client.iq_buffer) // 4  # 2 bytes I + 2 bytes Q
+    print(f"IQ data saved to: {outfile} ({len(client.iq_buffer)} bytes, "
+          f"{n_samples} IQ samples)")
+    print(f"  Format: signed 16-bit little-endian interleaved IQ (IQIQ)")
+    print(f"  To load in Python:")
+    print(f"    raw = np.fromfile('{outfile}', dtype=np.int16)")
+    print(f"    iq = raw[0::2] + 1j * raw[1::2]  # complex IQ")
+
+
+def analyze_spectrum_for_lora(client, args):
+    """Analyze captured spectrum data for LoRa-like signals."""
+    if not client.spectrum_bins:
+        return
+
+    # Get spectra of consistent length
+    lengths = [len(s) for _, s in client.spectrum_bins]
+    target_len = max(set(lengths), key=lengths.count)
+    spectra = [s for _, s in client.spectrum_bins if len(s) == target_len]
+    if not spectra:
+        return
+
+    waterfall = np.array(spectra, dtype=np.float32)
+    sr = args.sample_rate
+    center = 868.3e6
+    n_bins = target_len
+
+    # Map bin indices to frequencies
+    freqs = np.linspace(center - sr/2, center + sr/2, n_bins)
+
+    # LoRa channel frequencies
+    lora_channels = {
+        "EU868 ch1": 868.1e6,
+        "EU868 ch2": 868.3e6,
+        "EU868 ch3": 868.5e6,
+    }
+
+    print(f"\n--- LoRa Channel Analysis ---")
+    print(f"Spectrum bins: {n_bins}, frames: {len(spectra)}")
+
+    # For each LoRa channel, check for activity
+    # LoRa BW is typically 125 kHz or 250 kHz
+    lora_bw = 125e3  # Hz, typical LoRa BW
+
+    avg_spectrum = np.mean(waterfall, axis=0)
+    noise_floor = np.median(avg_spectrum)
+    print(f"Noise floor (median): {noise_floor:.1f}")
+
+    for name, ch_freq in lora_channels.items():
+        # Find bins covering this channel +/- half BW
+        ch_mask = (freqs >= ch_freq - lora_bw) & (freqs <= ch_freq + lora_bw)
+        if not np.any(ch_mask):
+            print(f"  {name} ({ch_freq/1e6:.1f} MHz): outside capture range")
+            continue
+
+        ch_avg = np.mean(avg_spectrum[ch_mask])
+        ch_max = np.max(avg_spectrum[ch_mask])
+        ch_peak_frame = np.max(waterfall[:, ch_mask])
+
+        # Check for intermittent signals (LoRa is bursty)
+        ch_per_frame = np.mean(waterfall[:, ch_mask], axis=1)
+        above_noise = ch_per_frame > noise_floor + 10  # 10 units above noise
+        active_frames = np.sum(above_noise)
+        duty_cycle = active_frames / len(spectra) * 100
+
+        delta = ch_avg - noise_floor
+        print(f"  {name} ({ch_freq/1e6:.1f} MHz):")
+        print(f"    Avg level: {ch_avg:.1f} (delta from noise: {delta:+.1f})")
+        print(f"    Peak level: {ch_max:.1f} (peak any frame: {ch_peak_frame:.1f})")
+        print(f"    Active frames: {active_frames}/{len(spectra)} "
+              f"({duty_cycle:.1f}% duty cycle)")
+
+        if delta > 15:
+            print(f"    ** STRONG signal activity detected **")
+        elif delta > 5:
+            print(f"    * Possible signal activity *")
+        else:
+            print(f"    (no significant activity)")
+
+    print()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Monitor 868 MHz LoRa band via SDRconnect WebSocket API",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Prerequisites:
+  1. Start SDRconnect_headless:
+     /opt/sdrconnect/SDRconnect_headless --websocket_port=5454
+
+  2. Run this script:
+     python3 %(prog)s --duration 30 --save-spectrum lora_868.png
+
+Examples:
+  # Basic 30-second scan
+  %(prog)s --duration 30
+
+  # Save spectrum plot and IQ data
+  %(prog)s --duration 60 --save-spectrum spectrum.png --iq-capture iq.bin
+
+  # Different host/port
+  %(prog)s --host 192.168.1.100 --port 5454 --duration 30
+        """
+    )
+    parser.add_argument("--host", default="127.0.0.1",
+                        help="SDRconnect WebSocket host (default: 127.0.0.1)")
+    parser.add_argument("--port", type=int, default=5454,
+                        help="SDRconnect WebSocket port (default: 5454)")
+    parser.add_argument("--duration", type=float, default=30,
+                        help="Monitoring duration in seconds (default: 30)")
+    parser.add_argument("--sample-rate", type=float, default=2e6,
+                        help="Sample rate in Hz (default: 2000000)")
+    parser.add_argument("--device-index", type=int, default=0,
+                        help="Device index to select (default: 0)")
+    parser.add_argument("--save-spectrum", metavar="FILE",
+                        help="Save spectrum/waterfall plot to PNG file")
+    parser.add_argument("--iq-capture", metavar="FILE",
+                        help="Capture raw IQ data to binary file")
+
+    args = parser.parse_args()
+    asyncio.run(monitor_lora(args))
+
+
+if __name__ == "__main__":
+    main()