翻译:Multi-measurements: Analyze and display multiple signals at once The industry’s first multi-measurement capability can configure, execute and display multiple measurements simultaneously. For example, a systems engineer can analyze the coexistence of 5G and LTE signals leveraging Dynamic Spectrum Sharing. A collection of measurements can be created and stored in memory, available to run instantly. Connect to multiple analyzers at the same time, or use a single multi-channel instrument, to acquire signals from different test points or frequency bands in parallel. Powerful display tools enable comparison and correlation of results from different measurements.
时间: 2024-04-01 18:36:44 浏览: 310
多测量:同时分析和显示多个信号
行业首个多测量功能可以同时配置、执行和显示多个测量。例如,系统工程师可以利用动态频谱共享分析5G和LTE信号的共存情况。可以创建和存储一组测量结果,在内存中随时运行。同时连接多个分析仪或使用单个多通道仪器,以并行方式从不同的测试点或频带获取信号。强大的显示工具可以比较和相关不同测量结果。
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Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.
回答:本文提出了一种基于底部类型自适应的卫星深度测量(BA-SDB)方法,以解决传统卫星深度测量模型中假设底部均匀的局限性以及不能充分考虑影响反射率的干扰因素,如浊度等。通过基于聚类的底部反射率自适应分割,并采用PSO-LightGBM算法考虑多种因素,实现了对不同沉积物和水深的准确深度估算,与空中激光测深和多波束回声深度声纳联合实测结果相比,均方根误差值达到0.85m,是传统比值法、多波段法和机器学习方法中最准确的方法。
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