esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparative Analyses Results
### Rain-Gauge vs. Radar Observations
#### 3.1.3 PANTHERE and OPERA vs. RADOME
In order to explain the main differences obtained in PANTHERE and OPERA over the DUR and JUR areas for the April case, we compared the spatiotemporal evolution of the precipitation fields, focussing on the zones watered by more than 2 mm of rain over a 3 h period. Figures 7 and 8 present the results for the DUR (and SAL) and the JUR areas, respectively. Figure 7a-d shows that PANTHERE estimated important rain amounts over the Alps (DUR area) during all of the 23 April 2022 in accordance with the RADOME rain gauges, whereas the rain estimated with the OPERA radar product were insignificant. This difference between the two radar products explains most of the diverging values between OPERA and PANTHERE presented in Figure 6a (see the red dashed line). Figure 7 also presents the range of the different radars which were used either in the PANTHERE product or in the OPERA product (also presented in Figure 1). Thus, over this specific area, PANTHERE was constructed using the observations performed by several X-band radars located in the Alps, whereas OPERA was not [28], leading to a loss of radar spatial coverage and, therefore, a loss of the precipitation visibility over this region. Over the JUR area, PANTHERE and OPERA presented different rain features for a period of approximately 6 h on 24 April 2022 (Figure 8), causing an important overestimation of the OPERA rain amounts (Table 3). As previously discussed for the DUR area, this difference could also be due to the number and position of the radars used in the different products. Indeed, in that case, two additional radars located close to the French-German-Swiss border were considered in the OPERA product (Figure 1): the Feldberg radar in southwestern Germany (47.87\({}^{\circ}\) N, 8.00\({}^{\circ}\) E) and the La Dole radar in Switzerland (46.43\({}^{\circ}\) N, 6.10\({}^{\circ}\) E), which could influence the amounts of rain retrieved by the radar product over this area. Figure 8: Temporal evolution of precipitation over JUR area having more than 2 mm over a 3 h period according to PANTHERE (blue) and OPERA (red) on 24 April 2022 from 9 to 12 UTC (**a**), and 12 to 15 UTC (**b**). The circles present the range of the radars which were used in OPERA (black dotted circles). The small filled black circle symbols represent the position of the rain gauges of the RADOME network that also recorded more than 2 mm of rain over the same 3 h period. Figure 7: Temporal evolution of precipitation over DUR and SAL areas having more than 2 mm over a 3 h period according to PANTHERE (blue) and OPERA (red) on 23 April 2022 from 9 to 12 UTC (**a**), 12 to 15 UTC (**b**), 15 to 18 UTC (**c**) and 18 to 21 UTC (**d**). The circles present the range of the radars which were used either in PANTHERE (green circles) or in OPERA (black dotted circles) (see Figure 1 also). The small filled black circle symbols represent the position of the rain gauges of the RADOME network that also recorded more than 2 mm of rain over the same 3 h period.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 2 OFDMA Transmission
Another issue that restricts the choice of RU and MCS is the fact that channels between the AP and STAs have different power gain, which means, along with the frequency \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & **MCS** & **26-Tone** & **52-Tone** & **106-Tone** & **242-Tone** & **484-Tone** & **996-Tone** \\ \hline
1 & BPSK, 1/2 & 0.8 & 1.7 & 3.5 & 8.1 & 16.3 & 34 \\
2 & QPSK, 1/2 & 1.7 & 3.3 & 7.1 & 16.3 & 32.5 & 68.1 \\
3 & QPSK, 3/4 & 2.5 & 5 & 10.6 & 24.4 & 48.8 & 102.1 \\
4 & 16-QAM, 1/2 & 3.3 & 6.7 & 14.2 & 32.5 & 65 & 136.1 \\
5 & 16-QAM, 3/4 & 5 & 10 & 21.3 & 48.8 & 97.5 & 204.2 \\
6 & 64-QAM, 2/3 & 6.7 & 13.3 & 28.3 & 65 & 130 & 272.2 \\
7 & 64-QAM, 3/4 & 7.5 & 15 & 31.9 & 73.1 & 146.3 & 306.3 \\
8 & 64-QAM, 5/6 & 8.3 & 16.7 & 35.4 & 81.3 & 162.5 & 340.3 \\
9 & 256-QAM, 3/4 & 10 & 20 & 42.5 & 97.5 & 195 & 408.3 \\
10 & 256-QAM, 5/6 & 11.1 & 22.2 & 47.2 & 108.3 & 216.7 & 453.7 \\
11 & 1024-QAM, 3/4 & — & — & — & 121.9 & 243.8 & 510.4 \\
12 & 1024-QAM, 5/6 & — & — & — & 135.4 & 270.8 & 576.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data rates in RUs with different number of tones, Mbps. Reprinted with permission from [8]. Copyright 2018 IEEE. Figure 2: An example of uplink OFDMA transmission in 802.11 ax. selectivity, that the signals arrive with different power. If the signals have significantly different power spectral densities at the AP, the weak signals are received with errors [9]. To avoid this problem, the STAs should adjust their transmission power in such a way that the power level of the signals received at the AP is approximately the same for all simultaneously transmitting STAs.
## 5 Evaluation Results
### Exceptional Cases  
_False Positives_ (FPs) that have occurred during the performance evaluation were analyzed, the result of which is given in Table 13.  
\begin{table}
\begin{tabular}{c|c c c c c c c c} \hline \hline
**Filename** & **IMSI** & **Smf IP** & **Amf IP** & **Upf IP** & **gNB IP** & **BearerCreatedTime** & **Class** \\ \hline BEARER\_R1\_202109 & 450052006000002 & 167.79.186.15 & 167.79.184.70 & 167.79.186.65 & 167.79.184.80 & 3 September 2021 & 1  
Table 14 shows a comparative data analysis between users (identified by IMSI) detected as triggering attacks and normal users at a given time band. It can be verified that the preprocessed data are identical with respect to abnormal traffic and normal traffic at the point in time of the FP occurrence.  
From the image above, abnormal data and normal data at the point-in-time of 16:11 (11 min past 4 in the afternoon) exist concurrently, but there is no difference between the normal and the abnormal since only one traffic signal occurred for a duration of 1 min, which implies that it does not belong to a signaling DDoS attack. It can be anticipated that such an exceptional case is attributable to the effect of impurity the ML classifier has.
# Design and Modelling of an Amphibious Spherical Robot Attached with Assistant Fins
## 2 Mechanical Structure and Electrical Actuation and Control System
### Electrical Actuation and Control System of the Robot  
The electrical actuation and control system consist of two DC motors, a servo motor, two electronic speed controls (ESC) working as motor drive units, a signal receiver, a camera and power supply module. It provides the power and impetus needed for the spherical robot. It receives the control signal transmitted by the RF remote controller and the signal emitter, then outputs impetus to the mechanical structure of the IDU and the spherical shell. Its schematic is shown in Figure 3.  
The signal receiver is used to collect radio frequency signals emitted from the RF remote controller. After some valid signals arrived, it will decode the received signals and then generate control signals for corresponding motors. The signal receiver connects to two DC motors through two ESCs respectively and one servo motor directly. These three motors occupied three of the nine output channels of the signal receiver. The power of the signal receiver is supplied by a battery eliminator circuit (BEC) incorporated in one of the ESCs. The ESC, mainly used for brushed DC motors, acts as motor driver in this application. It converts the digital signals from the signal receiver to voltages exerted upon the motors. Those ESCs are powered by the power supply module. The output voltage of the ESC will change with the variation of the input signal received from the signal receiver. As a result, rotation speed of these DC motors will change at the same time.  
To record videos of the robot's surroundings, a digital camera is mounted inside the spherical shell. The servo motor mounted on the main shaft is used for adjusting the vision field of the camera in horizontal plane. This servo motor is connected directly to the signal receiver because it consumes power within the safety threshold of the signal receiver. As a micro wireless signal transmitter is integrated into the camera, videos produced by the camera can be transmitted to the RF remote controller in time.  
A 24 V lithium battery is used as the power supply module for the robot, and a wireless switch is placed between the power supply module and those power consuming devices. The on-off state of the power supply is then achieved by sending corresponding signals to the wireless switch from an external signal emitter.  
The control block diagram of the robot system is shown in Figure 4. Two PID controllers are implemented in order to control the two system inputs, i.e., \(\tau_{i}\) and \(\tau_{r}\), generated by the rolling motion motor and the spinning motion motor, respectively.  
Figure 3: Schematic of electrical actuation and control system.  
The PID1 controller is used in the basic rolling motion mode, while the PID2 controller is used in the basic spinning motion mode, when the robot needs to adjust its direction. These two controllers work in cooperation when the robot is operating in a curved trajectory.
###### Abstract  
Traditional watermarking methods can remove a watermark from an image, making it possible to see the copyright information about the image owner or to estimate similarities using techniques such as bit error rate and normalized correlation. Deep learning is another examination field in AI, and is utilized to develop a deep network to extract objective elements and afterwards distinguish the general environment. To assure the robustness and security of computerized image watermarking, we propose a novel algorithm using convolutional generative adversarial neural networks. This research proposed a novel technique in digital watermarking, with data hiding based on segmentation and classification, using deep learning techniques. The used input images are medical images, including Magnetic Resonance Images (MRI) and Computed Tomography (CT) images, which have been processed for noise removal, smoothening and normalization. The processed image has been watermarked using the Singular Value Decomposition-based discrete wavelet transform quantization model, being segmented and classified using convolutional generative adversarial neural networks. The experimental analysis has been carried out in terms of bit error rate, Structural Similarity Index Measure (SSIM), Normalized Cross-Correlation (NCC), training accuracy, and validation accuracy. This achieved an attained bit error rate of 71%, an SSIM of 56%, a Normalized Cross-Correlation of 71%, a training accuracy of 98%, and a validation accuracy of 95%.  
digital watermarking; data hiding; segmentation; classification; deep learning Article
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Proposed Model
#### 3.2.1 Training-Time Multi-Branch Residual Attention Block  
Inspired by RepVGG [32], we design a training-time multi-branch structure with an improved batch normalization (BN) strategy. It consists of one \(3\times 3\) convolution, one \(1\times 1\) convolution, and one identity connection, as shown in Figure 3. This parallel multi-branch structure allows the convolutions of different receptive field sizes to extract  
Figure 1: Visual result of the proposed method on NH-Haze (2021) dataset. Our method is able to produce haze-free images with high perceptual quality.  
Figure 2: The efficient RRA-Net architecture. It cascades 6 MRABs, followed by a long skip connection with attention and a fusion tail.  
features at different scales and to cope with the nonuniformity of haze. During inference, the multi-branch convolutions and the following BN are converted into a single-path \(3\times 3\) convolution through the technique of re-parameterization, largely reducing the inference time.  
Furthermore, we do not use BN in each branch, as was used in RepVGG, but rather, we use a single BN layer to stabilize the training after summing up the outputs of 3 convolutions. Since BN actually destroys the internal features of the sample, we believe that if individual BN layers are present in different branches, the connection between the features in different branches will be degraded more seriously, incurring a performance loss.  
The MRAB also utilizes local residual learning with attention. The local residual connection allows features from haze-free areas to pass directly without processing so that the multi-branch convolutions can deal with the hazy areas in the image. Additionally, our attention module, consisting of an improved spatial attention (SA) layer and a channel attention (CA) layer, allows dense and thin hazy areas in the image be attended to differently.  
In Section 3.3, detailed ablation studies are conducted to analyze the MRAB and verify the effectiveness of its structure and the BN strategy. Below, we provide the details of this basic block.
## 7 Fundamentals of ML and Taxonomy of Applications
### Unsupervised Learning and Semi-Supervised Learning  
While in semi-supervised learning, only a tiny fraction of the data is labeled, in supervised learning, there is no need for labeled training data. Figure 6 illustrates how unsupervised learning works. Unsupervised learning and semi-supervised learning are commonly used for classification and clustering tasks. Popular algorithms for these learning strategies include maximum likelihood learning, PCA, k-means clustering (kMC), etc. Unsupervised learning can be used for a variety of tasks, including distribution estimation, feature categorization, distribution-specific sampling, point clustering, and feature extraction, in the extremely dynamic physical layer of the IoT-based wireless network.  
Figure 5: Demonstration of supervised learning principle.  
Figure 6: Demonstration of unsupervised learning principle.  
Unsupervised and semi-supervised learning can be potentially used for the equalization of the channel. The pre-coding/encoding scheme selection for the optimization of performance is one of the potential areas of unsupervised learning. There are many potential areas of semi-supervised and unsupervised learning, like clustering/grouping/pairing of nodes/points for optimal allocation of radio resources management and for the optimal allocation of network resources [95]. In [52], [PERSON] proposed a novel solution named \"novel collaborative distributed Q-learning scheme\" to mitigate the congestion of Random-Access Channels in WSN. This scheme allows the WSN to gradually learn the unique slots for data transmission. Hence, the WSN with concurrent transmissions is minimized. The numerical analysis proved better performance in collision probability, throughput, and convergence time.  
In [96], [PERSON] presented a generative adversarial network (GAN), a unique unsupervised learning technique that uses ANNs to detect intruders. Any anomaly in a WSN is immediately detected by these algorithms. These ML/DL algorithms have great potential to reduce WSN congestion, so researchers should pay close attention to them. More information can be found in [97]. The above potential sectors have both more problems and the potential to reduce congestion in IoT networks [98]. Table 5 compares the advantages and disadvantages of supervised learning, unsupervised learning, and deep learning and their deployment in wireless IoT networks.
## 4 Discussion
### Machine Learning Modeling to Predict the Aroma Profile  
Besides fermentation and drying, other factors that contribute to differences in aromas and flavors in cacao are the genotype, location and growth conditions, ripening, and amount of shade [37]. Therefore, the machine learning model developed in this study based on canopy architecture parameters has a strong basis on the physiological factors affecting the aroma profile. Hence, the high accuracy and performance obtained (R = 0.82; MSE = 0.09) using the canopy architecture information, which is related to the amount of shade, accurately represents the six volatile compounds commonly found in cocoa and that are determinant for its quality (Table 2). Up to date, there are no known studies that have managed to predict the volatile compounds in cacao using the canopy architecture. However, there was a study in grapevines in which flavonoid contents were predicted using crown porosity and LAI using partial least squares (PLS) regression [38].  
The model presented in this paper may be useful by producers to modify the canopy architecture of the cocoa trees according to the desired aroma characteristics on the final product. This would allow them to avoid financial loses or predict the end use of the final product by anticipating the quality based on the aroma profile. This model may be fed with more data from samples of other regions and genotypes to create a general predictive model to be used by any grower. On the other hand, similar models may be developed for other crops to predict and improve the quality at early stages.
## 2 Classification Based on Application Time  
The classification of sensors in earthquakes is largely based on their time period of application, such as seismic monitoring for long-term use, earthquake early warning, life detection during rescue, and network support that provides information support. This study classified the whole process of sensors according to their application times and principles (Table 1). The common life-detection sensors primarily use visible light and infrared detection equipment to record vibration signals to detect the position of trapped individuals. To obtain accurate life signals, most of these sensors are located on the ground to provide a close exploration perspective and avoid interference from background noise. The airborne radar electro-optical composite life-detection system, DN-UAV, realizes two life-detection methods, namely, landing detection and hover detection, through the UAV platform to obtain the position information and field environment of buried personnel in ruins. Earthquake early warning sensors typically require long-term detection to provide continuous earthquake early warning functions. The principle involves detecting sound, vibration signals, gravity, and other factors to detect the buried depth, morphology, and distribution rules of different elastic formation interfaces. A radon meter measures the concentration of radon in the air and dose rate to obtain seismic signals. Communication-support sensors transmit seismic information through radio signals, with their application scenarios mainly located in space. For instance, the transmission between ground communication systems or the use of artificial earth satellites as relay stations to forward radio waves takes place.
## 4 Experimentation and Analysis of Results
### Experimental Results and Analysis
#### 4.3.2 Robustness Experiments
However, as the \(\theta\) angle increases, the signal must take a detour, leading to greater diversity and complexity in the propagation path, significantly enhancing the multipath effect. Although D1 + D2 is constant, path variation due to angle changes may cause minor signal strength fluctuations, but these are less significant than those from distance changes. For scenarios where a secondary person performs random movements behind the primary user during gesture execution, the proposed SE-WiGR framework is well-equipped to maintain accurate recognition. The squeeze-excitation (SE) module embedded within the network adaptively recalibrates channel-wise feature responses in the CSI data, enabling the model to prioritize the signal patterns associated with the primary user's intended gestures. Meanwhile, the hierarchical feature extraction capabilities of the VGG16 network effectively filter out interference from background activities. The robustness demonstrated in previous experiments against environmental noise and occlusions further supports the framework's ability to handle such complex, real-world interactions. In addition, combining the data in Table 8, we can further analyze the mechanism of distance and multipath effect on the recognition accuracy: at closer ranges (e.g., D1 + D2 = 1 m, \(\theta\) = 0\({}^{\circ}\)), the direct signal path dominates with weak multipath interference, enabling CSI to clearly reflect gesture dynamics--\"up/down\" recognition accuracy reaches 94.25%. At 4 m, signal reflection via walls/furniture causes \(\sim\)20 dB CSI amplitude attenuation and fuzzy features due to multipath phase superposition, reducing \"enlarge/narrow\" accuracy to 78.75%, consistent with wireless channel theory. \begin{table}
\begin{tabular}{c c c c c c c c} \hline D1+D2 & 1.0 m & 1.5 m & 2.0 m & 2.5 m & 3.0 m & 3.5 m & 4.0 m \\ D (Rx, Tx) & 0.7 m & 1 m & 1.41 m & 1.77 m & 2.12 m & 2.47 m & 2.83 m \\ \hline
90\({}^{\circ}\) & 91.38\% & 89.78\% & 88.53\% & 86.23\% & 85.24\% & 81.23\% & 78.75\% \\
60\({}^{\circ}\) & 92.49\% & 91.54\% & 89.42\% & 87.58\% & 84.45\% & 81.36\% & 77.52\% \\
30\({}^{\circ}\) & 92.17\% & 91.73\% & 91.77\% & 87.69\% & 84.05\% & 82.11\% & 74.36\% \\
0\({}^{\circ}\) & 94.25\% & 92.07\% & 93.03\% & 89.42\% & 88.39\% & 84.28\% & 80.54\% \\ \hline \end{tabular}
\end{table} 
Table 8: F1 score of location diversity (%).
## 3 Numerical Simulations
### Sensitivity to Spatial Resolution  
The algorithm's sensitivity to spatial resolution is first examined. It is evaluated by comparing the CPU running time of the algorithm and the optimal path for different spatial resolutions. The running time determines the efficiency of the algorithm, and the path length is related to whether the algorithm outputs an optimal path. The simulations are performed on a 2D domain without consideration of ocean currents, so the optimal path length is equivalent to the shortest travel time.  
Figure 5 and Table 3 show the path comparison between different spatial resolutions. For a straight path with no obstacles between the starting and target points, the spatial resolution does not change the optimal path but affects the running time of the algorithm. When the grid size is reduced from 2000 m to 1000 m, the amount of mesh grid nodes increases from 386 to 1591 and the running time increases both by 58 times in Case 2 and Case 3, while the optimal path length is only reduced by less than 2.4%. When the grid size is reduced to 200 m, the amount of mesh grids increases to 37,928, which is 98 times that of the grid size of 2000 m. The algorithm runs for over 13 h, which cannot satisfy the real-time  
\begin{table}
\begin{tabular}{c c c} \hline
**Case** & **Start** & **Target** \\ \hline
1 & 114.6787\({}^{\circ}\) E, 22.7375\({}^{\circ}\) N & 114.6787\({}^{\circ}\) E, 22.4315\({}^{\circ}\) N \\
2 & 114.5813\({}^{\circ}\) E, 22.7375\({}^{\circ}\) N & 114.9123\({}^{\circ}\) E, 22.4675\({}^{\circ}\) N \\
3 & 114.7176\({}^{\circ}\) E, 22.7195\({}^{\circ}\) N & 114.9317\({}^{\circ}\) E, 22.6295\({}^{\circ}\) N \\ \hline \end{tabular}
\end{table}
Table 2: The coordinates of starting and target points in the three cases.  
Figure 4: The tidal current in the experimental area at 17:00:00 6 October 2013 (UTC) was predicted using the FVCOM. It is in an ebb tidal course, in which seawater flows out of Daya Bay.  
requirement. Correspondingly, the length of the optimal path is reduced by 4% in Case 2 and 5% in Case 3, compared with a grid size of 2000 m. When the grid size is 200 m, the optimal path is very close to islands. The AUV may take extra actions to avoid collision on the seafloor.  
Specifically, for our configurations in Daya Bay, when the total grid number is no more than 500, the MDP path planning algorithm achieves a balance between an efficient running time and the shortest path length. In the following simulations, a grid size of 2000 m will be applied.