Fix the phase-1/2 review findings in recording, calibration and depth replay

What
- record: one packet->row conversion (record/sample.py). A failed compute chain
  records no force value at all, a newton value is written only for an axis that
  really has a calibration curve, and mono_t (not the wall clock) orders history
  with a per-row calibration hash.
- record lifecycle: explicit starting/recording/stopping/stopped/failed states
  owned by the writer thread; stop() reports completed=False on timeout and does
  not write ended_at; consecutive write failures stop acceptance immediately.
- record capacity: byte budget for the video queue (512 MiB default); a full
  queue sheds the *frame* and parks the tiny data row, so a video overflow no
  longer deletes CSV rows. Lost rows leave a dropped=1 index row and are counted
  as dropped_gaps / unrecorded_gaps.
- calibration identity: sha256 content hash instead of a path, minted from the
  calibration the sensor actually loaded (reported before the first frame) and
  confirmed by the acquisition thread; events are timestamped when they happen.
- calibration diagnostics: the fit summary, residual and validation error now
  come from the model that export/apply would use (piecewise_linear); validation
  points are held out of the build; repeated reference loads are merged with an
  n-weighted mean and pooled std.
- calibration record: every point keeps its full three-axis sample series
  (frame id, time, raw, newtons, valid) plus diagnostics, written to an
  independent calibration_session_*.json that can be reopened for inspection;
  save record / export coefficients / apply to device are three separate actions.
- depth replay: the cursor's frame is bound to the depth result by request
  sequence and frame id, so a moved cursor never shows another frame's map; the
  SDK is stepped from its current position instead of re-reading from the start;
  the live chain (box deformation+contact, finger unroll+tactile) is replayed
  with the exact resolved config path; algorithm state is rebuilt from a reset
  marker or from the recorded pre-roll, and says so when it cannot be.

Why
The review (D:/orisys_ws/_review_phase12/review.md) showed the app writing raw
counts into the newton columns, recording a stale value after a failed compute,
losing CSV rows when the video queue filled, reporting success while a writer was
still running, diagnosing a model nobody applies, applying a calibration before
the device confirmed it, dropping the samples behind a calibration point, showing
a stale depth map under a moved cursor and recomputing depth of the wrong frame.

Verification
- D:/orisys_ws/_review_phase12/reproduce.py: all ten acceptance lines flipped
  (log saved as _review_phase12/reproduce_after.log). Including 25/25 rows kept
  with 8 video frames shed, frames [2]->[3], depth 8.0/8.0, fit 2/2 points.
- apps/studio/tools/ui_check.py: 249 checks, 0 failures (twice).
- shared suites: 91 tests, 0 failures (new: test_record_semantics,
  test_record_lifecycle, test_calibration_model, test_calibration_record,
  test_depth_replay).
- apps/studio/tests/test_ui_smoke.py: 23/30; the 7 remaining are harness
  artifacts of the sandbox (4 tmp_path PermissionError, 3 parametrized tests the
  shim cannot drive), unchanged from before this change.
- compileall clean; locale parity zh/en + t() key coverage: 0 failures.

Not in this change
- P2#10 disk-based browsing of a long recording and range CSV export (next
  round, per your choice).
- Stability-triggered calibration sampling, replay fixed colour scale, PyOpenGL
  three-dimensional view.
- Hardware verification: no camera attached, so the SDK's real processing chain
  and the pre-roll's effect on a live tracker were exercised with fakes only.

apps/studio/config/locales/zh_CN.json

@@ -146,12 +146,22 @@
     "depth_config_missing": "无法重算：录制未记录传感器配置",
     "depth_config_file_missing": "无法重算：配置文件的原始路径已不存在",
     "depth_no_video": "无法重算：该录制没有视频分段",
+    "depth_not_recomputed": "当前游标位置尚未重算深度\n（不会沿用其他帧的结果）",
+    "depth_recomputing": "正在为当前游标位置重算深度…",
+    "depth_frame_not_recorded": "该帧未录制：数据行在队列溢出时丢失，只有索引记录",
+    "depth_frame_not_in_video": "该帧未写入视频，无法重算深度",
+    "depth_state_across_segment": "重算无法跨分段延续算法状态：本段从段首重放",
+    "depth_state_approximate": "本段缺少归零或预卷历史，算法参考状态为近似重建",
+    "depth_warnings": "重算提示: {detail}",
     "warnings": "回放提示: {detail}",
     "warn_config_changed": "配置文件与录制时不一致，重算出的深度可能与当时不同",
     "warn_sdk_changed": "SDK 版本与录制时不一致，重算结果可能不同",
     "warn_config_missing": "找不到配置哈希，无法校验重算一致性",
     "warn_depth_recomputed": "深度由离线重算得到，不是当时保存的数据",
-    "warn_rows_skipped": "有无法解析的 CSV 行被跳过"
+    "warn_rows_skipped": "有无法解析的 CSV 行被跳过",
+    "warn_frames_dropped": "有帧在队列溢出时丢失，仅保留索引记录（回放显示为“该帧未录制”）",
+    "warn_state_across_segment": "重算不能跨分段延续算法状态",
+    "warn_state_approximate": "算法参考状态为近似重建"
   },
   "record": {
     "label": "记录",
@@ -180,7 +190,22 @@
     "delete_row": "删除行",
     "clear_channel": "清空通道",
     "clear_all": "全部清空",
-    "export": "导出…",
+    "export": "导出系数…",
+    "export_tip": "只写出 SDK 标定系数文件，不改变运行中的设备",
+    "apply": "应用到设备",
+    "apply_tip": "把最近导出的系数下发到运行中的设备（需再次确认）",
+    "apply_no_export": "还没有导出的系数文件，请先「导出系数」",
+    "save_record": "保存标定记录…",
+    "save_record_tip": "保存本次标定会话的全部点与三轴完整采样（审计数据），与系数文件相互独立",
+    "save_record_empty": "还没有标定点，无法保存标定记录",
+    "save_record_ok": "已保存标定记录 {path}\n{points} 个点，共 {samples} 条三轴采样",
+    "save_record_failed": "保存标定记录失败: {error}",
+    "open_record": "打开标定记录…",
+    "open_record_tip": "重新打开已保存的标定会话，检查每个点的完整采样与诊断状态",
+    "open_record_filter": "标定记录 (*.json)",
+    "open_record_ok": "已载入 {path}（{points} 个点）",
+    "open_record_warnings": "注意: {detail}",
+    "open_record_failed": "打开标定记录失败: {error}",
     "sn": "序列号",
     "sn_placeholder": "序列号（可选）",
     "live_raw_label": "实时原始值",
@@ -207,8 +232,10 @@
     "marked_validation": "该点已标记为验证点",
     "unmarked_validation": "该点已取消验证点标记",
     "fit_insufficient": "拟合需要至少 2 个有效点（当前 {count} 个）",
-    "fit_summary": "拟合 {channel}: N = {gain} × raw + {offset}（{count} 点，最大残差 {residual} N）",
+    "fit_summary": "导出模型 {channel}: {method}（{count} 点 / {breakpoints} 个折点，最大残差 {residual} N）",
     "fit_validation": "独立验证 {count} 点，最大偏差 {error} N",
+    "method_piecewise_linear": "分段线性",
+    "method_linear": "线性",
     "point_title": "标定点 {channel} @ {load} N",
     "point_channel": "加载轴",
     "point_direction": "方向",
@@ -231,7 +258,9 @@
     "apply_title": "应用标定",
     "apply_confirm": "将刚导出的标定应用到当前设备？\n\n设备类型: {sensor_type}\n配置: {config}\n\n选择「否」只保留导出文件，不改动运行中的设备。",
     "apply_skipped": "已导出，未应用到设备",
-    "apply_done": "标定已应用到 {sensor_type}",
+    "apply_pending": "已请求把标定应用到 {sensor_type}，等待设备确认…",
+    "applied": "设备已确认标定生效（版本 {version}）",
+    "export_only": "已导出系数 {path}（未应用到设备）",
     "apply_no_target": "没有可应用的设备（请先运行一次采集）"
   },
   "status": {