esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed System
### Pilot Detection Module  
As indicated in Figure 4, action or gesture recognition algorithms are not suitable for direct application to control the UAV, because they may be confused by a similar action caused by another person or produce false alarms on background objects. To address these problems, we developed the pilot detection module to: (1) isolate the real UAV pilot from another person; (2) extract the skeleton points from the real pilot; and (3) determine the correct hand regions for gesture recognition.  
Figure 9 presents an overall diagram of the pilot detection process. Firstly, people in the images will be extracted by YOLOv3 [27], where multiple persons may be detected from the image sequence. Thereafter, to maintain the correct correspondence of multiple people, a deep sort tracking algorithm [30] is used to provide the correct ID of each detected person continuously. The AlphaPose [28] pose estimation algorithm, which is based on Faster R-CNN [31], is applied to the regions of detected people to extract the key skeleton points of human bodies, through which the hand areas can easily be located. Finally, the UAV pilot will be separated from the other persons by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scene LabelStrategy** & **Top Speed (m/s)** & **Acceleration (m/s\({}^{2}\))** & **Maximum Height (m)** & **Possible to Land** \\ \hline indoor & 1 m/s & 0.25 & 2 & Yes \\ \hline buildings & 2 m/s & 0.50 & 10 & Yes \\ \hline woods & 3 m/s & 1.00 & 20 & Yes \\ \hline factory & 2 m/s & 0.75 & 20 & Yes \\ \hline square & 4 m/s & 1.00 & 50 & Yes \\ \hline water surface & 5 m/s & 1.50 & 50 & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAV flight strategy corresponds to scene understanding result in our research.  
Figure 8: Diagram of scene understanding to automatically adjust the flight strategy under different working conditions.  
Figure 7: The structure of Resnet101 [29].  
applying a color filter to their hand regions, because the real pilot is requested to wear orange-colored gloves. The extracted key skeleton points of the pilot are used for the action recognition detailed in the following section.  
Figure 10 presents an example of our pilot detection module, where the background contains similar objects such as human hands, and another human performs similar actions to the real UAV pilot. By using the pose estimation and color similarity measurements, our system can successively remove the noisy background components and distinguish the real pilot from the other persons.
###### Abstract  
In this work a fish species distribution model (SDM) was developed, by merging species occurrence data with environmental layers, with the scope to produce high resolution habitability maps for the whole Mediterranean Sea. The final model is capable to predict the probability of occurrence of each fish species at any location in the Mediterranean Sea. Eight pelagic, commercial fish species were selected for this study namely _Engraulis encrasicolus_, _Sardina pilchardus_, _Sardina aurita_, _Somber colias_, _Somber combrus_, _Spicana smaris_, _Thunus thynnus_ and _Xiphias gladus_. The SDM environmental predictors were obtained from the databases of Copernicus Marine Environmental Service (CMEMS) and the European Marine Observation and Data Network (EMODnet). The probabilities of fish occurrence data in low resolution and with several gaps were obtained from Aquamaps (FAO Fishbase). Data pre-processing involved feature engineering to construct 6830 features, representing the distribution of several mean-monthly environmental variables, covering a time-span of 10 years. Feature selection with the ensemble Reciprocal Ranking method was used to rank the features according to their relative importance. This technique increased model's performance by 34%. Ten machine learning algorithms were then applied and tested based on their overall performance per species. The XGBoost algorithm performed better and was used as the final model. Feature categories were explored, with neighbor-based, extreme values, monthly and surface ones contributing most to the model. Environmental variables like salinity, temperature, distance to coast, dissolved oxygen and nitrate were found the strongest ones in predicting the probability of occurrence for the above eight species.  
species distribution models; fish species; feature extraction; feature selection; XGBoost; habitability maps +
Footnote †: journal: Applied Sciences
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### D2D Networks for Decentralized Federated Learning  
FL is a type of decentralized ML-based technique used to train networks by exploiting local models training and client-server communication [32]. This type of decentralized model is suitable for networks where the training data are distributed over a large number of devices with a fraction of the data. At the same time, those devices exchange their locally-trained models instead of exchanging their private data. Specifically, FL enables a joint ML training over distributed data sets with limited disclosure of local data. In [32], the authors provided an implementation of decentralized stochastic gradient descent (DSGD) technique for large-scale wireless D2D networks. However, exploiting such FL techniques for joint scheduling and resource allocation in D2D networks is a challenging task, especially under channel uncertainty and connection availability in each iteration.
# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
## 2 Methodology
The MPU6050 communicates with the microcontroller via the I2C protocol. The MPU6050 detects inertial movement due to breathing- and non  
Figure 1: (a) Conceptual flow of our wearable multi-sensor system consisting of an inertial measurement unit (IMU) and a flexible resistive sensor connected to a data acquisition module, (b) picture showing our testing process and data collection and analysis experimental setup for breath monitoring. breathing-related body movement. Flex pressure sensor data are collected via one of the analog pins on the board. The flex sensor is connected to the analog pin present in the header. The headers represent various analog and digital pins obtained from the microcontroller. The IMU sensor is connected to the microcontroller via electrical traces on the board. The Micro USB is used to communicate with a laptop on two occasions. First, the breathing sensor was programmed to read all the acceleration and rotation data from the IMU using an Arduino IDE (Version 2.2.1). The programming is performed only once, as the board has the memory to save the code. Second, the breathing sensor was used to read and plot the data continuously via MATLAB (version (R2023b) Update 4)-based code. At the end of each test session with participants, the data were saved in an Excel file. This event is the main data acquisition event and is repeated each time during the experiment. For the spirometry setup, the participants were instrumented with a mouthpiece and nose clip, and data from the spirometer were collected using the PC, as shown in Figure 1b (the picture of the spirometer and its data acquisition system). The mouthpiece was connected to a calibrated respiratory flow head and high-precision spirometer (ADInstruments(r), Colorado Springs, CO, USA). The respiratory rates and volumes were determined in real-time using PowerLab and LabChart Pro software version 8 (ADInstruments(r)).
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 2 Unmanned Ship-Wind and Wave Coupling CFD Model
### Analysis of the Berthing Characteristics of Unmanned Ship
#### 2.5.2 The Influence of Quay Wall Distance  
Numerical simulations were carried out for different quay wall distances at speeds of 0.45 m/s and 3 m/s, and the rule of bow torque and lateral force varying with quay wall distance was recorded, as shown in Tables 2 and 3:  
As can be seen from Tables 2 and 3, the lateral force on the hull changed direction during the berthing process. The reason for this phenomenon is that the bow of the unmanned ship is driven by shore thrust away from the quay wall when the unmanned ship is sailing along the coast, and the shore suction of the mid-ship is caused by the difference in flow velocity on both sides of the ship. Therefore, an offset occurs.
## 2 Theory Background
### Autoencoder (AE) and AE-ED Definition  
An autoencoder (AE) is a common unsupervised learning algorithm [22]. It is able to learn a compressed representation from the input data while preserving as much information as possible from them.  
As shown in Figure 1, an autoencoder (AE) is a special artificial neural network that has two main components: an encoder and a decoder. The function of the encoder is to extract and compress the original high-dimensional input data and map the input vector to a feature space. The function of the decoder is to reconstruct the high-dimensional data according to the feature information. The input data have the same dimensions as the output data reconstructed by the AE, and the label when training the network is the input vector itself.  
The solution formula of the AE is as Equation (1):  
\[f_{,}g=\operatorname{argmin}_{f_{,}g}\lVert X-g[f(X)]\rVert^{2} \tag{1}\]  
where \(f\) is the mapping from the input space to the feature space, \(g\) is the mapping from the feature space to the input space, and \(X\) is the input data.  
The execution process of the autoencoder can refer to the pseudocode in Algorithm 1.  
The autoencoder (AE) can reconstruct data. In the process of the encoder mapping the input data to the feature space, the characteristic information of the data, including the data distribution, is stored in this feature space. The process of decoding is similar to reconstructing data according to the characteristic information.  
Throughout the process, the AE plays the role of feature extractor. An AE is a data-dependent model; that is to say, it can only handle data similar to the training data, while dissimilar data cannot guarantee the model performance. For new input data with high data similarity, the AE can reconstruct the data very well, and the reconstructed data are very close to the original data. For new input data with low data similarity, the AE performs poorly because the features it learns are about the dataset used to train the AE.  
In order to quantify the difference between the new input data and reconstructed data, and also to quantify the data similarity, we define a metric named the AE-ED, which stands for \"the Euclidean Distance between input data and the reconstructed data from the autoencoder model\".  
The calculation method of the AE-ED is as Equation (2):  
\[\text{AE-ED}=\sqrt{\left(X^{R}-\hat{X}^{R}\right)^{2}} \tag{2}\]  
where \(X^{R}\) is a piece of new input data with the dimension R, and \(\hat{X}^{R}\) is the R-dimensional data reconstructed by the autoencoder (AE).  
Data with high degrees of data similarity have smaller AE-ED values; on the contrary, data with low degrees of data similarity have larger AE-ED values.
# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation
## 5 Discussion  
According to the methodology of the developed system and the improvement of experimental performance, the novelty of the proposed method can be shown.  
First, the developed system with Multiple Network Fusion with Low-Rank Representation tackles the problem of insufficient descriptive power with insufficient training data. To solve this problem, two types of solutions have been considered in previous methods. One is to improve the descriptive power of a single feature and the other is to fuse multiple features. To improve the descriptive power of a single feature, deep learning has been proved to be effective to represent images in the past few years. To fuse multiple features, manifold learning, low-rank learning, and so on are proposed. However, there have been few attempts to combine the above two solutions. We successfully combine deep learning and low-rank learning. In addition, we implement age estimation. Therefore, the proposed method is theoretically novel.  
Second, experimental performance indicates the effectiveness of the proposed method, which can be summarized as follows:  
1. We compared different activation functions and different combinations of neural networks to determine the optimal neural network.
2. We compared different feature fusion methods to emphasize the effectiveness of choosing low-rank learning.
3. We compared the proposed method with the state of the art in terms of age estimation to emphasize the overall improvement of the proposed method. It can be concluded that the proposed method improves age estimation performance.
## 3 Results
### Navigation Risk for Cumulative Siltation and Declining Water Levels  
From July to December, the combined influence of cumulative siltation and a gradual decline in water levels results in significant hydro morphological changes within the study  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Siltation** & **July** & **August** & **September** & **October** & **November** & **December** \\ \hline Mean & 0.55 & 0.50 & 0.53 & 0.51 & 0.51 & 0.48 \\ Standard deviation & 0.41 & 0.34 & 0.48 & 0.33 & 0.36 & 0.38 \\ Minimum & 0.06 & 0.10 & 0.08 & 0.13 & 0.11 & 0.18 \\ Maximum & 4.00 & 4.29 & 4.30 & 3.82 & 3.88 & 3.77 \\ Total & 767.49 & 685.59 & 736.13 & 710.37 & 699.91 & 665.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical summary of model-predicted monthly siltation volumes, July–December 2024.  
Figure 7: Model-predicted spatial distribution of monthly siltation for second half of 2024.  
area (see Figure 8). The model predictions suggest that these changes can lead to riverbed exposure and bank erosion, particularly in zones where siltation accumulates more rapidly, thereby threatening the navigability of the main channel.  
The water levels in July are generally sufficient to maintain an adequate depth and width for safe navigation. However, by August, early signs of bank erosion and sediment emergence become apparent along the edges of the navigation route, leading to the narrowing of the navigable corridor. This trend intensifies over time; the sediment-affected areas gradually expand, and by December, large portions of the upstream channel are projected to become non-navigable, disrupting waterway connectivity and posing risks to vessel operation.  
It is important to note that Figure 8 only distinguishes the areas with water levels above or below zero, serving as a basic threshold. However, in reality, navigability requires a minimum effective water depth to ensure vessel clearance. Therefore, the actual area affected by restricted navigation is likely to be larger than what is visually represented in this figure. This underlines the fact that even partial water-level reduction, when compounded by siltation buildup, can significantly impair channel functionality.  
This evolving situation highlights the urgent need for timely sediment management and water level regulation measures. Without proactive intervention--such as targeted dredging, flow regulation, and adaptive navigation planning--the risk of seasonal navigation failure in this section of the Ganjiang River will increase, particularly during low-flow periods.
# The PIV Measurements of Time-Averaged Parameters of the Synthetic Jet for Different Orifice Shapes
## 3 Results and Discussion
### The Turbulent Kinetic Energy of the Synthetic Jet
#### 3.3.1 Impact of the Orifice Shape on the Vorticity  
The time-averaged vorticity field of the SJ observed in Figures 17 and 18 for the circle and square orifices (a and b) was typical. In the figures, we observed two axisymmetric fields with opposite values. These fields correspond to the areas at which the vortices propagate. The SJ coherent structure produces a high vorticity field. At the centerline, the time-averaged vorticity is equal to zero. The vortex ring had a similar shape to the circle and square orifices [12] in the axis on which the measurement was carried out (Figure 2). The difference in flow topology for the circle and square SJ is visible during the diagonal measurement, but these measurements were not carried out in the present study. The dimensionless vorticity was equal to or lower than 1 at a distance around \(x/d=6\) for the circle and square SJs (Figure 17a,b and Figure 18a,b). Thus, it can be concluded that the coherent structure of the SJ was lost at this distance, but it did not mean the disappearance of vortices. The radial vorticity profile at a distance of \(x/d=10\) (Figures 19 and 20) showed that the maximal value of the dimensionless vorticity was equal to 0.3 (Figure 19, \(L_{0}=2\)) and 0.6 (Figure 20, \(L_{0}=10\)). The decrease in vorticity can be caused by the vortex ring coalescing caused by a high Reynolds number, which has already been observed by [PERSON] and [PERSON] [15]. They did not observe the ring coalescing at \(Re=7784\) and \(St=0.012\). The investigated SJ had a lower Reynolds number but a higher Stroud number, which also has a significant impact on topologies on the SJ. The effect of the coalescing of vortex rings at a certain distance from the orifice has also been observed by [PERSON] et al. [25] at \(Re=199\) and \(St=0.22\).  
Figure 19: Dimensionless vorticity radial distribution at \(L_{0}=2\).  
Figure 20: Dimensionless vorticity radial distribution at \(L_{0}=10\).  
The time-averaged vorticity field of the slot SJ contracts for the slot-long side at the distance \(1<x/d<2\) (Figures 17c and 18c) and expands along the slot-short side. The vorticity field expands significantly from the orifice outlet to the distance \(x/d=4\) (Figures 17d and 18d). This shape of the vorticity field is coherent with the evolution of the slot SJ presented by [PERSON] et al. [12].
## 3 Method
### Satellite Data
#### 3.1.2 GF-1 WFV Data  
The GF-1 satellite is the first satellite for the Chinese high-resolution earth observation system of national science and technology major projects. It was launched on 26 April 2013. Two WFV sensors were onboard the GF-1 satellite. It can acquire multispectral images at a resolution of 16 m with four-day intervals (Table 2).
## 4 Discussion
### Elevation Mask Angle  
As we have seen from Figure 10, the calibration results could be degraded as there were almost no observations available at low-elevation regions. Therefore, an appropriate elevation mask angle may be helpful to improve the calibration results. We test three elevation mask angle settings--0\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\)--and the RMSEs of the corresponding results for GPS and BDS are listed in Tables 4 and 5, respectively. On one hand, under the setting of 0\({}^{\circ}\), the average RMSE for the antenna calibration results with GPS was 2.15 times that with BDS. Then it decreased to 1.41 and 1.45 times under the settings of 10\({}^{\circ}\) and 20\({}^{\circ}\), respectively. This indicates that the disadvantage of lack of GPS data at low elevation region is erased. The remaining gap can be attributed to the shorter duration of GPS data collection. Meanwhile, however, it is reasonable to expect that the BDS results could be further improved by extending the first data collection session and filling the south hole with more available observations. On the other hand, within the calibration results of either GPS or BDS, there was a clear trend toward decreasing RMSE as the elevation mask angle increased. This is in line with the larger number of available observations in the high-elevation regions, which we have seen in Figure 6. In conclusion, before the phase pattern fitting process, we can evaluate the distribution of available observations and then set an appropriate elevation mask angle--if there are hardly any available observations below a certain elevation angle,  
Figure 14: The relationship between triple-differenced PCCs and weightings. The bold black line plots the fitted linear function. In addition, the slope and the coefficient of determination (R\({}^{2}\)) of each fitted line are indicated above each subfigure. The unexpected cases, where the slope of the fitted linear function is negative, are marked in red.  
we can set the elevation mask angle to that value. The consequent fitted phase pattern might have a smaller coverage, but its accuracy can be improved.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Evaluation Metrics  
Common performance metrics used to quantify system or model effectiveness include Precision, Accuracy, Recall, and F1-score [34]. These metrics are based on the following components: True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN).  
Precision, in the context of IoT anomaly detection, refers to the ability of a model to correctly identify real threats from among all events classified as potential threats. In other words, high precision ensures that when the model signals an alarm, an attack or other undesirable event has occurred with a high probability [30; 39; 40; 41; 42; 43; 44]. Expressing the precision as a formula [38; 40], we obtain the following:  
\[\mathrm{P}=\frac{\mathrm{TP}}{\left(\mathrm{TP}+\mathrm{FP}\right)}, \tag{1}\]  
Accuracy in the context of IoT anomaly detection measures how often a model correctly classifies data as normal or anomalous. This is a general measure of a model's performance, but can be misleading when there is significant class imbalance (e.g., when the vast majority of data represent normal performance) [30; 39; 40; 41; 42; 43; 44]. Expressing the accuracy as a formula [40], we obtain the following:  
\[\mathrm{Acc}=\frac{\left(\mathrm{TP}+\mathrm{TN}\right)}{\left(\mathrm{TP}+ \mathrm{FP}+\mathrm{TN}+\mathrm{FN}\right)}, \tag{2}\]  
Recall, in the context of IoT anomaly detection, is a measure of how well a model can identify all actual anomalies. In other words, recall answers the following question: 'Out of all existing anomalies, how many were actually detected by the model?' [30; 39; 40; 41; 42; 43; 44]. Expressing the recall as a formula [40], we obtain the following:  
\[\mathrm{R}=\frac{\mathrm{TP}}{\left(\mathrm{TP}+\mathrm{FN}\right)}, \tag{3}\]  
The F1-score is the harmonic mean of precision and recall, providing a balanced assessment of the performance of the IoT anomaly detection model. A higher F1-score indicates a better trade-off between detecting all anomalies (high recall) and minimizing false alarms (high precision) [30; 39; 40; 41; 42; 43; 44]. Expressing the F1-score as a formula [40], we obtain the following:  
\[\mathrm{F1}=2\times\frac{\left(\mathrm{P}\times\mathrm{R}\right)}{\left( \mathrm{P}+\mathrm{R}\right)}, \tag{4}\]
## 2 Materials and Methods
### Hyperspectral Imaging System  
#### 2.2.1 Configuration and Main Components of the System  
Spectral images were acquired in the reflectance mode using a push broom line-scanning HSI system, as shown in Figure 2. The system consisted of a spectrograph (Hyperspec SWIR, Headwall Photonics, Fitchburg, MA, USA), a digital camera with a Peltier-cooled 320 \(\times\) 256 mercury-cadmium-telluride (MCT) detector (MCT-851 XC403, XenICs, Leuven, Belgium), a 30.7 mm front lens (OLES30, Specim, Oulu, Finland), an illumination unit comprised of two tungsten-halogen lamps (Fiber Lite A-240L and A-240P, Dolan-Jenner Industries, Boxborough, MA, USA), a translation stage (TLSR300B, Zaber Technologies, Inc., Vancouver, BC, Canada), a data acquisition software (written inhouse), and a computer.  
Figure 1: Key steps of the experimental procedure. GLCM: gray level co-occurrence matrix; ROI: region of interest; RC: regression coefficients; R\({}_{i}\): raw image; D\({}_{i}\): dark reference; W\({}_{i}\): white reference.  
Figure 2: Hyperspectral imaging (HSI) system.