feat(cw): archive decoded history, polish the waterfall, document licensing

三个用户反馈一并解决:

1. 解码文字不再消失 (核心)
   旧实现: 20 秒环形缓冲满了就静默覆盖最旧样本, 文字随之从屏幕消失。
   新实现: CwDeepBuffer 新增 overflow —— 满时被覆盖的旧样本先进 overflow,
   解码器累积到 15 秒就单独解码一次, 结果追加到 historyText (只增不减)。
   UI 记录区显示 historyText + decodedText (历史稳定 + 当前窗口实时)。
   ICwDecoder 接口新增 historyText StateFlow。

   归档音频已离开主窗口, 不再被 CTC 修正, 故其文本是"最终版", 追加安全。
   归档窗口 15 秒: 内容已在 20 秒窗口里解过多次, 短一点几乎无损, 且推理
   开销小。

2. 瀑布图更好看
   配色从"深蓝->青->黄"换成 matplotlib inferno (黑->紫->品红->橙->黄),
   与静态频谱图保持一致。相邻 bin 之间用水平渐变做线性插值, 消除 65 列
   离散方块的像素感。

3. AGPL 合规补漏 (用户提醒: 仓库许可证没体现 AGPL 组件)
   README 新增 License 章节: 声明项目主体 GPL-3.0 + feature/cw 的 DeepCW
   模型 AGPL-3.0-only, 并说明合并作品按 GPL-3.0 §13 / AGPL-3.0 §13 处理。

验证:
- :core:domain:test => 31 个 CW 测试全绿 (CwDeepBufferTest 新增 3 个
  overflow 归档测试: 顺序/清空/reset)
- :core:domain:compileKotlin + :core:data + :feature:cw:compileDebugKotlin
  => BUILD SUCCESSFUL

core/data/src/main/java/com/rtbishop/look4sat/core/data/cw/CwDeepDecoder.kt

@@ -55,11 +55,18 @@ class CwDeepDecoder(context: Context) : ICwDecoder {
         const val TAG = "CwDeepDecoder"
         const val MODEL_ASSET = "deepcw/model.onnx"
         const val METADATA_ASSET = "deepcw/model.onnx.json"
+
+        /** Evicted audio is decoded into permanent history once this much accumulates. */
+        const val ARCHIVE_SECONDS = 15.0
+        val ARCHIVE_THRESHOLD: Int = (CwDeepSpectrogram.SAMPLE_RATE * ARCHIVE_SECONDS).toInt()
     }
 
     private val _decodedText = MutableStateFlow("")
     override val decodedText: StateFlow<String> = _decodedText.asStateFlow()
 
+    private val _historyText = MutableStateFlow("")
+    override val historyText: StateFlow<String> = _historyText.asStateFlow()
+
     private val _estimatedPitch = MutableStateFlow<Float?>(null)
     override val estimatedPitch: StateFlow<Float?> = _estimatedPitch.asStateFlow()
 
@@ -74,6 +81,16 @@ class CwDeepDecoder(context: Context) : ICwDecoder {
 
     private val buffer = CwDeepBuffer()
 
+    /**
+     * Evicted audio accumulates here until it reaches [ARCHIVE_SECONDS], then
+     * is decoded once and appended to [historyText]. Archiving in ~15 s chunks
+     * keeps the extra inference cheap (short window) while long enough to be
+     * decoded accurately — the content has already been through the 20 s window
+     * many times, so a slightly shorter archive decode loses almost nothing.
+     */
+    private val archiveBuffer = FloatArray(CwDeepBuffer.DEFAULT_MAX_SECONDS.toInt() * CwDeepSpectrogram.SAMPLE_RATE)
+    private var archiveSize = 0
+
     /** Held while inference runs so slow devices skip work instead of queuing it. */
     private val inferenceLock = Mutex()
 
@@ -157,6 +174,21 @@ class CwDeepDecoder(context: Context) : ICwDecoder {
             samples, sampleRate, CwDeepSpectrogram.SAMPLE_RATE
         )
         val shouldRedecode = buffer.append(resampled)
+
+        // Archive audio that scrolled out of the live window. It is decoded once
+        // when a full archive chunk has accumulated, so old text does not vanish.
+        val overflow = buffer.drainOverflow()
+        if (overflow.isNotEmpty()) {
+            for (v in overflow) {
+                if (archiveSize < archiveBuffer.size) archiveBuffer[archiveSize++] = v
+            }
+            if (archiveSize >= ARCHIVE_THRESHOLD) {
+                val audio = archiveBuffer.copyOf(archiveSize)
+                archiveSize = 0
+                archiveDecode(audio)
+            }
+        }
+
         if (!shouldRedecode || !buffer.hasEnoughAudio) return
 
         // Drop this cycle rather than queue when the previous run is still going.
@@ -186,6 +218,37 @@ class CwDeepDecoder(context: Context) : ICwDecoder {
         CwProbe.step("infer_begin frames=${window.size}")
 
         val spectrogram = CwDeepSpectrogram.compute(window)
+        val text = runInference(activeSession, activeEnvironment, spectrogram)
+
+        // Replace, never append: the model rewrites earlier characters as more
+        // context arrives, so appending would leave stale guesses on screen.
+        _decodedText.value = text
+
+        updateSignalMetrics(spectrogram)
+    }
+
+    /**
+     * Decode a chunk of audio that has scrolled out of the live window and
+     * append it to [historyText]. Unlike the live window this never replaces —
+     * the archived audio is final, so its text is permanent.
+     */
+    private suspend fun archiveDecode(audio: FloatArray) = withContext(Dispatchers.Default) {
+        val activeSession = session ?: return@withContext
+        val activeEnvironment = environment ?: return@withContext
+        if (audio.size < CwDeepSpectrogram.FFT_LENGTH) return@withContext
+        val spectrogram = CwDeepSpectrogram.compute(audio)
+        val text = runInference(activeSession, activeEnvironment, spectrogram)
+        if (text.isNotEmpty()) {
+            _historyText.value += text
+        }
+    }
+
+    /** Run the ONNX model over a pre-computed spectrogram and return the decoded text. */
+    private fun runInference(
+        activeSession: OrtSession,
+        activeEnvironment: OrtEnvironment,
+        spectrogram: Array<FloatArray>
+    ): String {
         val frames = spectrogram.size
         val bins = CwDeepSpectrogram.FREQUENCY_BINS
 
@@ -205,12 +268,7 @@ class CwDeepDecoder(context: Context) : ICwDecoder {
         }
         _lastInferenceMs.value = (System.currentTimeMillis() - startedAt).toInt()
         CwProbe.step("infer_done ms=${_lastInferenceMs.value}")
-
-        // Replace, never append: the model rewrites earlier characters as more
-        // context arrives, so appending would leave stale guesses on screen.
-        _decodedText.value = text
-
-        updateSignalMetrics(spectrogram)
+        return text
     }
 
     /**
@@ -248,6 +306,8 @@ class CwDeepDecoder(context: Context) : ICwDecoder {
     override fun reset() {
         buffer.reset()
         _decodedText.value = ""
+        _historyText.value = ""
+        archiveSize = 0
         _estimatedPitch.value = null
         _signalStrength.value = 0f
         _lastInferenceMs.value = 0