esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Salience of Environmental Acoustic Events
The inclusion of unseen mixtures within the training database was provided through varying the SNR level of three isolated sound sources in the mix, specifically: \(\{-5,0,+5\}\) dB for cicadas, \(\{-3,0,+3\}\) dB for outside air conditioner, and \(\{-6,-3,0,+3,+6\}\) dB for ambulances. The results show that the classifier improves its overall accuracy in two of the three categories. However, these mixtures were generated without taking into account the real contribution of each sound source in the given acoustic environment. In [19] several typical sound events related to surveillance applications (e.g., scream, glass breaking and gunshot) were artificially mixed with other environmental sounds from indoor and outdoor environments with six different SNRs (from \(+5\) to \(+30\) dB at \(5\) dB step sweep) in order to simulate the occurrence of this kind of event in real and complex environments. In [21], a mixture of sound sources considering road traffic noise plus other type of sound events was also generated using two different SNRs (\(+6\) dB and \(+12\) dB) in order to assess the performance of an anomalous noise event detection algorithm (ANED). In this case, the ANEs were obtained from FreeSound while road traffic noise was recorded in a city ring road in real-life conditions. The experiments analysed different configurations of the ANED algorithm and showed quite good results when discriminating road traffic noise from those significantly salient ANEs. Finally, in [27] a small environmental audio database containing sound mixtures simulated with SimScene software [35] is described. The main argumentation of working with simulated sound mixtures is having a controlled experimental environment where the road traffic level is known beforehand. Unfortunately, there is no information available in this work about the SNR values considered to build the 20 scenes mixing background noise and car, bird and car horn samples. Logically, the main drawback of these approaches is that the synthetic mixtures need not strictly follow real-life patterns. In the best case scenario, they could represent a subsampling of the actual picture of what is generally observed in average for the given acoustic environment. However, in the worst case, the significant difference with real-life conditions may lead to erroneous conclusions (e.g., see [28]).
## 2 Materials and Methods
### Model Setup
In addition, these bathymetry data do not cover the furthest area of the model. The JODC bathymetry data (at 50, 150, and 500 m resolutions) were therefore replaced and combined for these areas. The model grid considered in this research comprises the river mouths of the Ota River in Hiroshima Bay and the Yodo River in Osaka Bay, near which hydrographic charts cannot describe the sea depth. To generate bathymetric information for these river-mouth areas, we utilized the heights of watermarks at water level gauges in each river. These heights were measured from an elevation of zero and were provided by the Disaster Prevention Information for Rivers (www.river.go.jp (accessed on 31 May 2022)) of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT). The heights of gauges distributed along the river were interpolated to the grid by assuming linear depth changes between gauges. Allthe bathymetry data used herein were adjusted in reference to the MSL. The minimum depth was restricted to 3 m due to the present stability of the model. The initial temperature, salinity, SSH, and velocity fields were obtained from the Meteorological Research Institute Community Ocean Model (MRLCOM-JPN) [20]. The MRLCOM-JPN is an ocean model developed by the Japan Meteorological Agency (JMA) with a resolution of approximately 2 km near the environs of all of Japan. If the MRLCOM-JPN model could not precisely describe the coastal areas in which our model demanded initial values, the closest values among the available grid data were used for those areas.
## 4 Agricultural and Research Applications
### Passive and Active Detection of Exotic Species and Diseased Plants  
Our military trials verified bee detection of non-floral and non-pheromone chemicals at parts per billion (PPB)/parts per trillion (PPT) levels (Figure 14), chemicals one might not expect bees to be able to detect at all [12]. We have videos of bees locking onto an airborne vapor trail while still tens of meters away from the vapor odor source, measured at just a scant few PPT. When bees cross a vapor trail of interest to them, they turn toward the source, following the scent upwind. This detection of a scent meters away from  
Figure 13: Honey bee use of plots of varying plant diversity as mapped by use of bee-mapping LIDAR.  
a low (below 15 PPT) concentration source implies instantaneous detection by bees at levels even lower, nominally parts per quadrillion (PPQ).  
This detection behavior by bees following scents and chemical signals known to be attractive to them is passive behavior. However, it also can be used actively, by conditioning them to seek out scents of interest, whether free-flying bees in the field [70, 71, 72] or constrained in a laboratory using PERs [17, 18, 31, 107]. Other investigators use honey bees and bumble bees to carry or vector microbial biocontrol products to control crop diseases and insect pests of crops [108, 109, 110, 111, 112]. [PERSON] at the University of Guelph calls this \"bee-vectoring biocontrol\". He relies on bees to passively deliver biocontrol microbes to specific crops. For example, his team uses _Clonostachys rosea_ to control _Sclerotinia scleroorum_, which causes sunflower head rot, and _Beauveria bassiana_ to control banded sunflower moth [108, 110, 111]. Recent trials reduced severity by 70%-100% and increased sunflower yield by 20% [112] or more.  
Similarly, DARPA has funded research on a variety of scenarios whereby larger insects like cockroaches and moths are employed to carry chemical reactive or electronic sensors [40]. Size, weight requirements for battery power, and information retrieval present logistical challenges with smaller insects such as bees.  
A related and intriguing question that we are studying is whether bees can be used for finding introduced plants and animal species in native habitats. Our collaborators in New Zealand have investigated using bees to detect human diseases ([PERSON] and Sagar [107]) and verified that bees could detect the scent of _Mycobacterium tuberculosis_. We have just now begun to explore whether bees can serve similar functions for detecting infected plants and other biosecurity targets.  
Figure 14: Receive Operating Characteristic curve (ROC) for bee detection of 2,4-DNT in soil, derived from DARPA funded trials, based on a figure we provided to a Rand report on Alternatives to Landmine Detection, 2003 [12].
## 3 Results and Discussion
### Effects of the Momentum Factor \(\alpha\)  
In order to find how the momentum factor affected the RBF ANN model in this study, ten kinds of \(\alpha\) were selected between 0.1 and 1, and they were 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1. The RBF ANN model constructed based on the findings of Section 3.3 were trained ten times according to the \(\alpha\) selected. Although the \(\alpha\) was changing, MSED was almost invariant. The running time of the model expended gradually with the increase of \(\alpha\).
## 3 Results
### A Quantitative Evaluation of the Impact of Irrigation on CWILE in Cropland  
#### 3.2.1 A Dual-Type Randomized Extraction Algorithm to Extract the Study Area  
The extraction of non-irrigated and irrigated cropland in Henan Province from 2001 to 2019 is shown in Figure 6. Areas with unchanged irrigated cropland during this period were primarily located in the central-eastern and northern regions, including Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Shangqiu, Luohe, and Xuchang (Figure 6a), indicating these regions' continued reliance on irrigation for agricultural stability. Except for the Taihang Mountains region in the west, the area of cropland that remained unchanged largely covered all prefecture-level cities in Henan Province (Figure 6b), highlighting the vital foundation for Henan Province as a major agricultural region in maintaining the long-term scale advantage of its 18 cropland resources. Irrigated cropland that remained stable from 2001 to 2019 accounted for approximately 8.97% of the total unchanged cropland area (Figure 6c), underscoring the limited spatial coverage of irrigation infrastructure despite its critical role. Using the dual-type randomized extraction algorithm developed in this study, 100 rectangular vectors representing irrigated and non-irrigated cropland were extracted; their spatial distribution is shown in Figure 6d, ensuring statistically robust comparisons between the two systems in the cropland CWUE analysis.  
Figure 5: Interannual variation trends of CWUE, GPP, NPP, and ET annual averages of cropland in Henan Province from 2001 to 2019. The annual mean change trends of GPP (**a**), NPP (**b**), ET (**c**), \(\mathrm{WUE_{GPP}}\) (**d**), \(\mathrm{WUE_{NPP}}\) (**e**), and CUE (**f**).
## 3 Component Design  
### Structural Design  
The filter-wheel mechanism investigated in this article is applied to a space star sensor. This sensor consists of a reflective optical system and a filter-wheel component. The reflective optical system comprises five mirrors, as depicted in Figure 4a. The filter-wheel component mainly consists of a lens, a double-layer filter-wheel mechanism, detector components, as well as supporting structures and accessories, as shown in Figure 4a,b. Based on the theory and method in Section 2, this article mainly conducted a study on the filter-wheel mechanism.  
Based on the above structure, the upper layer filters are numbered as 1, 2, 3, 4, and 5. Correspondingly, the lower-layer filters are numbered as A, B, C, and D, as shown in Figure 5.  
When the upper-level assembly star sensor is in operation, the filter-wheel component plays a critical role in filtering out background light across various spectral bands. This process is essential for enhancing the signal-to-noise ratio of the target star's detection signal. In order to meet specific working conditions and maximize spectral types, the spectral types have been designed and are detailed in Table 1.  
Figure 4: Design results: (**a**) design of star sensor for filter-wheel applications; (**b**) design of double-layer filter wheel.  
Figure 5: Upper and lower gear numbers of double-layer filter wheel.  
The total number of filter spectrum combinations achieved is 20, with the opaque A1 combination serving as the calibration for the initial position. Table 2 displays all possible spectral combinations. To ensure timely operations, the switching time between adjacent combinations must not exceed 0.15 s, and the maximum allowable switching time for any combination is capped at 0.6 s.
# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 3 Related Concepts
### Graph Structure  
In order to analyze the graphs, it is useful to consider the structural information of the graphs, and therefore, it is important to introduce some basic notions as follows.  
Graph topology is the arrangement and the form in which nodes are connected. Through the topology analysis, we could find some important features for the graph analysis, such as the node's degree, which refers to the number of relationships of a node. In-degree refers to the relationships that enter a node, and out-degree refers to the relationships that leave a node. In undirected graphs, in-degree, out-degree, and degree have the same value.  
Nodes' centrality is used to identify essential or central nodes in a network. A community is used to describe a cluster or a set of highly connected nodes within a network. Similarity is a form to measure or compare how similar two different nodes are in a network considering their relationships, structural information, or attributes. Path is a sequence of nodes that are connected throughout the network.  
Another important concept to understand the herein proposed work is the subgraph, a portion of the original graph consisting of a subset of selected nodes and edges, which reduces the model's size, and therefore, the analysis's complexity. A subgraph might be the result of applying a filter. Two useful operations for analyzing a graph structure are filtering and summarization. A filter can be a helpful tool to select or show a subset of nodes or relationships that meet certain conditions or criteria. Using filters can reduce the size and complexity of the graph and facilitate the analysis and visualization of important information. Filters can be applied based on attributes, degree, community, weight, or distance, among others. Structural attributes of a graph are characteristics that describe the topology. Some examples of structural attributes are degree, community, and path, among others. Graph summarization is a process that allows for a more compact representation of the original graph to facilitate the analysis and understanding of complex graphs. Various algorithms can be used to summarize graphs, by grouping nodes or relationships based on specific attributes or types of entities to present a more concise version of the complete graph.
## 5 Evolution of HCI Mode
### Haptic Feedback  
Haptic feedback refers to reproducing touch sensations for users through forces, vibrations, and other series of actions [66]. Representative products include the Versius surgical robot [67], Senhance surgical system [68; 69], and MiroSurger surgical system [70]. Surgeons perceive and control the size of the forces and the physiological characteristics of tissues during the surgical procedure through haptic feedback [71]. This can effectively compensate for cognitive biases caused by environmental isolation, avoid secondary injuries to patients due to excessive force [72], and alleviate surgeons' muscle tension and fatigue during surgery, thereby improving the stability and safety of the operation [30].  
Current research in this field mainly focuses on the following two aspects:  
1. Verifying the effectiveness of haptic feedback: [PERSON] et al. [73] conducted experiments based on animal models and concluded that the use of haptic feedback could significantly reduce tissue damage during surgery. [PERSON] et al. [74] developed a combined haptic and kinesthetic feedback system and verified the effectiveness of haptic feedback during remote operations.
2. Exploring more usable and lower-cost haptic feedback systems: [PERSON] et al. [75] developed a new robot tactile sensing auxiliary system, which realized force feedback based on magneto-rheological fluid and helped surgeons to better detect the force situation between surgical instruments and the vascular environment. [PERSON] et al. [76] created a grip-type haptic device and integrated it into the surgical robot console, enabling surgeons to have the same tactile experience as traditional surgery. [PERSON] et al. [77] proposed a haptic device designed based on magneto-rheological sponge cells to provide feedback on organ viscoelasticity.  
At present, due to technical limitations, surgical robots cannot truly reproduce the tactile sensation experienced by surgeons during traditional surgery, and there is still potential for improvement in the operation experience.
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
### Remote Sensing of Surface Water and Wetland Analysis in Drought
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Method** & **References** \\ \hline \multirow{6}{*}{Remote Sensing of Vegetation in Drought} & Extended super-resolution convolutional neural & [67] \\  & network (ESRCNN) & [74] \\ \cline{2-3}  & Enhanced Spatial and Temporal Adaptive & \\  & Reflectance Fusion Model (ESTARFM) & [75] \\ \cline{2-3}  & SAFYE & [80] \\ \cline{2-3}  & PROSPECT model & [94] \\ \cline{2-3}  & Red-Edge Position (REP) & [94] \\ \hline \multirow{6}{*}{Remote Sensing of Land Use and Land Cover Change in Drought} & The random forest wrapper approach & [102] \\ \cline{2-3}  & Support vector machine & [102] \\ \cline{2-3}  & Random forest & [102] \\ \cline{2-3}  & k-Nearest Neighbor & [102] \\ \cline{2-3}  & Pixel R-CNN & [108] \\ \hline \multirow{6}{*}{Remote Sensing of Evapotranspiration in Drought} & Surface energy balance models & [114; 115] \\ \cline{2-3}  & FAO-56 Penman-Montecito (FAO-56 PM) & [116; 117; 122] \\ \cline{2-3}  & Soil water balance model & [123] \\ \cline{2-3}  & PBIM & [133] \\ \cline{2-3}  & SADFAT & [134] \\ \cline{2-3}  & Data Mining Sharpening & [135; 136; 137] \\ \cline{2-3}  & Multi-Sensor Data Fusion approach (MSDF-ET) & [138] \\ \cline{2-3}  & Simple Algorithm For ET Retrieving (SAFER) & [140; 141] \\ \hline \multirow{6}{*}{Remote Sensing of Soil Moisture in Drought} & Change Detection Method & [66; 173; 178; 179] \\ \cline{2-3}  & OPtical TRApezoid Model (OPTRAM) & [147; 162; 163; 164; 165] \\ \cline{2-3}  & ANN & [148] \\ \cline{2-3}  & Water Cloud Model (WCM) & [149; 169; 178; 179; 180; 178] \\ \cline{2-3}  & Integral Equation Model (IEM) & [178; 179] \\ \cline{2-3}  & Cost Function & [174] \\ \cline{2-3}  & Support Vector Regression & [175] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarized methods for extracting drought information from Sentinel-2.
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
## 1 Introduction
The final image after processing has clearer and thinner contour edges than that obtained from the traditional backprojection algorithm. It is more conducive to classification and recognition of targets.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results  
In order to ensure the reliability of the experimental results, this study utilizes a five-fold cross-validation method to partition the dataset. Specifically, the dataset is divided into five non-overlapping subsets. These subsets are subsequently combined to maintain the heterogeneity and representativeness of the original dataset.  
Throughout the experimental process, the principle of cross-validation is rigorously applied. In each iteration, four subsets are utilized as training data to construct and train the model, while the remaining subset functions as independent test data to assess the model's performance. This procedure is repeated five times, with each iteration employing a different subset as the test set, thereby ensuring that every sample is evaluated at least once. This methodology offers a thorough evaluation of the model's generalization capability.  
Following the completion of the experiments, the results from the five iterations are averaged to derive the final assessment of the model's performance. This methodology aims to minimize random errors that may arise from data partitioning, thereby improving the stability and reliability of the evaluation through the averaging effect of multiple experimental trials.  
In addition to cross-validation, multiple anti-overfitting strategies were employed during model training to further enhance generalization and mitigate overfitting risks. These include the use of Dropout layers (with a dropout rate of 0.4) in the convolutional modules to randomly deactivate neurons during training and L2 regularization to penalize excessive weight magnitudes. Moreover, early stopping was implemented based on validation loss monitoring, ensuring that training was halted once performance on unseen data plateaued or declined. These techniques jointly serve to prevent the model from memorizing training data and promote its ability to learn robust, transferable representations.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 6 Monitoring Phenomena Other than Rain
### Water Vapor  
[PERSON] et al. [96] used a 4.8 km long E-band mm-wave link in the Xianghe area located in the city of Langfang, Hebei Province, China. As compared to the data of meteorological station, the annual correlation value of the water vapor retrieved from the link is as high as  
Figure 5: GRU structure.  
0.95, RMSE is as low as 0.35, and the average relative error is as low as 0.05. [PERSON] et al. [97] proposed a water vapor retrieval model by using dual-frequency E-band CMLs based on LSTM network, and the results show that the retrieved water vapor density is in good agreement with the results measured by temperature and humidity sensors. [PERSON] et al. [98] also used a longer E-band link to realize the water vapor retrieval because E-band is more sensitive to raindrops and atmospheric gases, and the signal attenuation is sufficiently strong to enable the detection of water vapor at long CMLs. The proposed empirical model does not require in situ calibration. However, the separation of gaseous attenuation from the total losses is more challenging than traditional 15-40 GHz CMLs.
## 2 Methodology
### System Modeling Based on Machine Learning (ML) Method-Gaussian Process Regression (GPR)
Besides, the update for a particle's velocity and position at \(t\) iteration is presented in Figure 3.  
Figure 2: Graphical model for Gaussian process regression (GPR). As mentioned above, PSO could effectively solve single optimization problems. Based on PSO, MOPSO algorithm was developed for MOO problems. The performance of PSO largely depends on its control parameters, in which the inertia weight parameter (\(w\)) is an important parameter. A larger value of \(w\) is beneficial to improve the algorithm's global search capability, while a smaller value of \(w\) will enhance the algorithm's local search capability. The MOPSO algorithm is a continuation of the PSO algorithm in effectively solving single-objective problems. It adds non-dominated ideas to the search principle of the optimal solution. The improved adaptive weight MOPSO (AWMOPSO) algorithm considers the fitness of the overall particle, and establishes a new weight self-renewal strategy [38]. The improved inertia weight is as follows:  
\[w=\left\{ \begin{array}{cc}w_{\min}&-&\frac{(w_{\max}-w_{\min})\times(l-l_{ \min})}{(l_{arg}-l_{\min})},J\leq J_{avg}\\ &&w_{\max},J>J_{avg}\end{array} \right., \tag{15}\]  
where \(w_{\max}\) and \(w_{\min}\) are the maximum and minimum inertia weights, respectively; \(J\) is the current fitness value of the particle; \(J_{\rm avg}\) and \(J_{\min}\) are the average and minimum fitness value, respectively. Similarly, the AWMOPSO seeks the global optimal position according to the positions of an individual particle and particle swarm. Several global optima exist that constitute the Pareto front and the AWMOPSO collects all the non-dominated particles into a repository. Finally, each particle selects its leader in the repository based on different decisions. The flowchart of the proposed method is illustrated in Figure 4, which is the integration AWMOPSO with GPR (i.e., GPR-AWMOPSO). Figure 3: The update of velocity and position at \(t\) iteration regarding a bi-dimensional problem.