esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### YOLO Object Detection
#### 3.2.3 Implementation of YOLO in ROS  
The integration of the You Only Look Once (YOLO) object detection algorithm with the Robot Operating System (ROS) plays a pivotal role in enabling fire detection and localization on our robotic platforms. To facilitate this integration, we utilize the widely adopted \"darknet_ros\" package, which leverages the \"darknet\" framework--a neural network framework developed in C and CUDA.  
The \"darknet_ros\" package provides a seamless ROS interface for utilizing the YOLOv4 object detection algorithm. Specifically, we utilize the yolov4-for-darknet_ros package within the \"darknet_ros\" package. To make use of this package effectively, we provide two key files: the configuration file (.cfg) and the tiny-weights file (.weights) for the pre-trained YOLOv4-Tiny model.  
* **Configuration File (CGF):** This file contains essential information regarding the network architecture and hyperparameters used during both training and testing phases, by configuring these parameters within the YOLOv4-Tiny model in ROS, we optimize the object detection capabilities of our robotic platforms, facilitating efficient fire detection and localization.
* **Weights:** This file is a binary file that contains the learned parameters of the neural network model. This file is utilized to make predictions on new data by applying the knowledge gained from the training process.  
In addition to the 'darknet_ros' package, we also require another ROS package called 'darknet_3d_ros' to enable fire localization relative to the camera. This is crucial in this context because we aim to maintain a safe distance from the fire and position the pump in the correct direction for extinguishing it. The 'darknet_3d_ros' package provides the integration of 3D object detection using YOLO, which uses the RealSense Depth Camera of the Jackal Rover to capture 3D point cloud data and perform fire localization. The main output of the package darknet_3d_ros package is the topic /darknet_ros_3d/markers. The ROS topic /darknet_ros_3d/markers contains visualization markers that represent the position and orientation of fires detected by the YOLOv4-Tiny model in the x, y, and z coordinates. The markers provide a way to visually represent the position, size, and orientation of the detected objects in 3D space. The markers are represented as a set of rectangular cuboids, where each cuboid represents a detected object. The cuboids are positioned and sized according to the 3D bounding box of the object in the scene.
# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 4 Experimental Results
#### 4.1.1 First Scenario  
In our initial experiment, we utilized the Kindle book review dataset to evaluate the performance of the proposed Bi-LSTM model.  
Figure 8 presents the performance metrics obtained from the first scenario. The accuracy metric indicates the overall correctness of the sentiment classification model, with a value of 0.93, indicating a high level of accuracy in predicting sentiment. The f1-score, with a value of 0.94, provides a balanced measure of the model's precision and recall, capturing the trade-off between correctly identifying positive and negative sentiments. The AUC metric, with a value of 0.92, reflects the model's ability to distinguish between positive and negative sentiments, with a higher value indicating a better discriminatory power.
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
###### Abstract
Based on the above considerations, this paper proposes a hyperspectral detection deep learning model based on Diffusion and target suppression constraints, with the intention of enhancing coarse detection's efficiency and the ability to represent the background. The model is named by SID-DN. The main contributions of this paper are as follows:  
1. This paper proposes a two-stream adaptive coarse detection algorithm that enhances the coarse detection process and better distinguishes background samples from target samples. 2. In order to enhance the learning of hyperspectral information by Diffusion, a SID-based Diffusion model is designed to optimize the loss of Diffusion. Compared with the traditional Diffusion, the performance of the algorithm is improved while using prior spectral information. 3. To verify the effectiveness of the method in this paper, the results are compared with the most advanced existing algorithms, which show that our model is superior to existing HTD algorithms. The rest of this paper is organized as follows. Section 2 reviews the necessary knowledge and related works. Section 3 gives a detailed description of the proposed method. Section 4 presents the experimental setup and results on the data set. Section 5 analyses the experiments. Section 6 concludes and summarizes the paper.
## 3 Results and Discussion
### The Electron Transmittance Measurements
(**b**) Electron transmittance as the grid aperture ratio increases from 44% to 90%. (**c**) Cathode currents, gate currents, and anode currents as the anode voltage increases. (**d**) Results of electron transmittance as the anode voltage increases. (**e**) When the aperture ratio remains constant, the variations in the current of each electrode and electron transmittance as the grid wire distance increases. Although the aperture ratio was unchanged, the increase in grid wire distance was more significant than the increase in grid wire width. Figure 5e shows the variation in the currents and electron transmittance as the grid wire distance increased. Figure 5e indicated that the currents at all electrodes decreased slightly as the grid wire distance increased. Figure 5e also showed that as the grid wire distance increased from 103 \(\upmu\)m to 618 \(\upmu\)m, the electron transmittance only slightly increased from 74.6% to 78.5%, with the electron transmittance essentially remaining around 76%. This minimal increase in electron transmittance was attributed to the reduced number of electrons captured by the grid wire. Thus, the electron transmittance remained essentially constant despite the increase in grid wire distance. The grid aperture ratio determined the electron transmittance. When the grid aperture ratio increased, electron transmittance also increased, and when the grid aperture ratio remained constant, electron transmittance also remained almost constant. Therefore, the grid aperture ratio determined the electron transmittance.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
### Problem Formulation  
Despite the presence of various cryptography- and non-cryptography-based algorithms, as well as protocols and solutions, the components of a smart grid remain vulnerable to security threats and vulnerabilities. Therefore, it is essential to continually monitor these systems and devices to enhance safety and security by developing new security standards, strategies, and technologies, particularly those based on machine learning, and introducing tamper-resistant equipment to ensure protection against physical attacks. Moreover, as renewable energy sources are being integrated into the smart grid domain, protecting and securing them will not only protect the grid but also the environment through combating climate change [1].
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods  
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m  
In 2011, MeteoSwiss started the renewal of its weather radar network, which now consists of five identical, dual-polarization, Doppler, C-band (5.5 GHz) radars [16]. One of them is the Monte Lema radar, which is located at an altitude of 1625 m 10 km northwest of Lugano, very close to the Swiss-Italian border. This is the radar that is dealt with in this study.  
Radar hardware monitoring is run continuously: many parameters are monitored and submitted from each radar site to the central server after completion of every single sweep, that is, 20 times in 5 min. These parameters are automatically checked for anomalies and archived for diagnostic analyses. Daily monitoring of the antenna pointing accuracy [9] and of the relative calibration of the receiver has been implemented [11], according to the Dutch/Finnish operational monitoring approach [10; 12].  
In order to guarantee system stability and reproducibility, instrumental calibration of the radar receiver should be implemented using internal microwave equipment. This is performed at MeteoSwiss using a noise source signal [17], which is inserted every 2.5 min in both polarization channels of the receiver path, both before the T/R limiter and at the entrance of the low noise amplifiers.  
The MeteoSwiss state-of-the-art C-band radars determine the standard dual-polarization meteorological quantities (differential reflectivity, \(Z^{dr}\), see Section 2.2.4; copolar correlation coefficient, \(\rho^{HV}\), see Section 2.2.5; differential phase shift, \(\psi_{\mathrm{dp}}\), see Section 2.2.6) from simultaneous horizontal (H) and vertical (V) transmissions. The return signals are measured simultaneously in two parallel H and V receiving channels. The advantage of simultaneously transmitting and receiving both polarizations is that \(Z^{dr}\), \(\rho^{HV}\), and \(\psi_{\mathrm{dp}}\) are determined directly from the same transmitted pulse (which is at zero lag), and are thus not contaminated by Doppler effects that can increase their variance, as is the case when alternating H and V polarizations are transmitted. The approach has the additional advantage of not needing a high-power polarization switch. According to measurements performed at the Site Acceptance Tests (i.e., twisting the transmitter or receiver horns of the Radar Target Simulator to the following four configurations: 0\({}^{\circ}\), 90\({}^{\circ}\) and \(\pm\)45\({}^{\circ}\)), it was possible to verify that the Lema radar is quasi-linear and has a slant of +45\({}^{\circ}\).
# Analysis on Flow and Temperature Field of High-Power Magnetorheological Fluid Transmission Device
## 6 Experiments and Discussions
### Influence of Slip Power and Rotating Speed on Heat Dissipation  
The slip power of the MR transmission device is 50 kW by adjusting the exciting current when the motor velocity is 500 r/min. Besides, the coolant flow maintains at 0.72 m\({}^{3}\)/h. Figure 21 shows the temperature of the coolant outlet and three internal monitoring points, where the radius of point one, two and three is 111 mm, 150 mm and 189 mm, respectively.  
In the 50 kW slip stage, due to the heat generated by slip, the temperature of each internal measuring point rises rapidly, and the temperature of the cooling water outlet also rises accordingly. In the 0 Kw slip stage, there is no slip and heat generation, and the temperature of each point inside drops rapidly due to the action of the cooling water. The highest temperature is 88 \({}^{\circ}\)C, at point two. Under the working condition of the rotating speed of 500 r/min, the temperature obviously take a step up, mainly due to the speed increase leading to the difficulty the cooling water has in moving into the permeability group cooling gap increasing dramatically. After the device stops, the coolant quickly enters the cooling gap and takes away a large amount of heat, causing the temperature rise of the water outlet.
# The First Seismic Imaging of the Holy Cross Fault in the Lysogory Region, Poland
###### Abstract  
The Holy Cross Mountains represent an isolated outcrop of Palaeozoic rocks located in the Trans-European Suture Zone, which is the boundary between the Precambrian East European Craton and Phanerozoic mobile belts of South-Western Europe. Despite extensive structural history studies, high-resolution seismic profiling has not been applied to this region until now. This research introduces near-surface seismic imaging of the Holy Cross Fault, separating two tectonic units of different stratigraphic and deformation history. In our study, we utilize a carefully designed weight drop source survey with 5 m shot and receiver spacing and 4.5 Hz geophones. The imaging technique, combining seismic reflection profiling and travel time tomography, reveals detailed fault geometries down to 400 m. Precise data processing, including static corrections and noise attenuation, significantly enhanced signal-to-noise ratio and seismic resolution. Furthermore, the paper discusses various fault imaging techniques with their shortcomings. The data reveal a complex network of intersecting fault strands, confirming general thrust fault geometry of the fault system, that align with the region's tectonic evolution. These findings enhance understanding of the Holy Cross Mountains' structural framework and provide valuable reference data for future studies of similar tectonic environments.  
H pacs: 1
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 3 GNSS Data Compression Review and Related Work
An additional example can be found in [42], which shows that GNSS information contains many commas between the parameters. Commas are information repeated over and over again within the message [It undoubtedly takes up bandwidth and therefore needs to be more efficiently compressed (e.g., Huffman code). In the algorithm discussed above, compressed information is transferred most of the time, but occasionally full information is transferred to avoid retaining errors over time.
## 3 A Worked Example  
### Mapping Population  
[PERSON] _et al._[36] constructed a linkage map with 76 microsatellite markers for 535 F\({}_{2}\) mice derived from two strains, the Large (LG/J) and Small (SM/J). The total length of this map is \(\sim\)1,500 cM (in [PERSON]'s units) and an average marker interval length is \(\sim\)27.5 cM. The same experiment was repeated by [PERSON] _et al._[37] in which 502 F\({}_{2}\) mice were generated and a linkage map of 1,780 cM long was constructed. In both experiments, each F\({}_{2}\) progeny was measured for their body mass at 10 weekly intervals starting at age seven days. The raw weights were corrected for the effects of each covariate due to dam, litter size at birth, parity and sex. We combine these two F\({}_{2}\) populations for QTL mapping of growth trajectories. Overall, about \(10\%\) of the marker genotypes were randomly missing. The mice with missing data were excluded from the analyses.
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 3 Lagrange Points and Basins of Convergence
### Developing the Searching Algorithm  
The NR method is one of the most common methods used to find zeros of functions. Thus, many studies have employed it to solve Equation (18) [12; 17; 20; 30]. This method is based on using a first-order linear approximation of the function [20]. Then, considering vector \(\mathbf{x}=\left\{x,y\right\}^{T}\) and function \(\mathbf{F}(\mathbf{x})=\left\{\Omega^{*}x_{r},\Omega^{*}y\right\}^{T}\), the NR method is formulated as follows:  
\[\mathbf{F}(\mathbf{x}_{n+1})=\mathbf{F}(\mathbf{x}_{n})+\mathbf{F}^{\prime}( \mathbf{x}_{n})(\mathbf{x}_{n+1}-\mathbf{x}_{n}) \tag{19}\]  
where \(\mathbf{F}^{\prime}(\mathbf{x}_{n})\) represents the Jacobian matrix of function \(\mathbf{F}(\mathbf{x})\) evaluated at point \(\mathbf{x}_{n}\). Then, the zeros of function (\(\mathbf{F}(\mathbf{x}_{n+1})\)) are iteratively obtained as follows:  
\[\mathbf{x}_{n+1}=\mathbf{x}_{n}-(\mathbf{F}^{\prime}(\mathbf{x}_{n}))^{-1} \mathbf{F}(\mathbf{x}_{n}). \tag{20}\]  
The first-order NR method does not always converge towards a specific solution, at least not with an acceptable level of precision, when the complexity of the model is increased.  
However, it has a significantly lower computational cost than that of other methods and performs the calculations faster. These considerations are taken into account when developing the libration point searching methodology.  
Firstly, as mentioned before, a rectangular domain is discretized, and the NR method is applied, with the centroids of each of the cells as the starting points. The solution is considered to converge when the distance between two consecutive iterations is below a precision threshold. Then, the method is used to get as close as possible to the solution. If the method is not precise enough or its convergence velocity is too slow, then the algorithm stops.  
After that, the solutions obtained are analyzed. Two solutions are considered equal if the distance between them is less than a given threshold. Once all possible solutions have been determined, each of them is assigned to a corresponding identifier (called \(L_{i}\)), which corresponds to a Lagrange point. Subsequently, the solution reached in each cell is compared with each one of the Lagrange points so that each convergent cell is assigned to the Lagrange point to which it converges. Representing these identifiers graphically, the BCs of the system are obtained. In regions near the boundaries of the basins of convergence (BCs), where the dynamics are highly sensitive, some points may fail to converge within a fixed number of iterations. These points are assigned as \"non-convergent\" and are excluded from the analysis of the BCs.
## 7 Conclusions  
Conducting image matching while preserving confidentiality is a challenging task. This paper presents a secure scheme that evaluates similarities between image collections of two parties without compromising their privacy. We utilized the homomorphic properties to design a secure protocol to achieve cosine similarity between two feature vector sets. Specifically, we used SURF descriptor to extract feature vectors. Our proposed framework for secure private image matching is not limited to a specific feature vector and instead can work under different features. We improve the performance of the matching time to scale for massive databases. Particularly, we apply the well-known \(k\)-means clustering technique to select representative descriptors for each image because clustering selects fewer descriptors. Thus, distance calculation could be largely reduced and consequently decrease matching costs. The practical value of our work is demonstrated through several experimental results. Our formula implementation over 10,000 images shows that SEPIM generates only minimal losses in search time and precision when placed against existing image search methodologies for plaintexts. Following this line of research, our future work will intend to apply search access control, as it is a great characteristic for the secure image search method and the data owner has greater control over those who can employ the search process.  
Figure 10: Effect of private key on security image matching.  
**Acknowledgments:** This work is supported by National 973 Fundamental Basic Research Program of China under grant No. 2014 CB340600.  
**Author Contributions:** All authors extensively discussed the contents of this paper and contributed to its preparation. [PERSON], [PERSON], and [PERSON] proposed and developed the model, drafted the manuscript, and performed experiments. [PERSON] and [PERSON] performed the literature overview and analyzed results. The revisions of this manuscript were done by [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 2 Methodology
Taking the \(x\)-axis coordinate as an example, if the displacements along the \(x\)-axis and the horizontal rotation angle of the laser beam reference are \(\Delta x\) and \(\Delta\alpha\), respectively, the resulting laser spot position error \(\Delta x^{\prime}\) is as shown in Equation (6):  
\[\Delta x^{\prime}=\Delta x+L\times tan\Delta\alpha=\Delta x+L\times\Delta\alpha. \tag{6}\]  
In order to reduce this error, a data compensation method is proposed here. The data compensation method uses two backward lasers in the laser transmitter module and two fixed distant reference monitoring image acquisition modules with higher stability to monitor the feasibility of the laser transmitter, as shown in Figure 4, where \(Ls\) represents the distance between the laser transmitter module and the image acquisition modules, \(\Delta\alpha\) represents the horizontal rotation angle of the laser transmitter module, and \(\Delta x\) represents the displacement along the \(x\)-axis of the laser transmitter module. Given that \(Ls\) were dozens of meters, small displacement and rotation angle will have no effect on these \(Ls\); that is, the lengths of \(Ls\) were invariable before and after the position change of the laser transmitter module. With the two reference monitoring image acquisition modules, two laser spot position errors \(\Delta x_{1}^{\prime}\) and \(\Delta x_{2}^{\prime}\) can be obtained. The image acquisition module detecting displacement with error is \(\Delta x_{3}^{\prime}\). The equations of two variables \(\Delta x\) and \(\Delta\alpha\) were obtained, as shown in Equation (7):  
\[\left\{ \begin{array}{c}\Delta x_{1}^{\prime}=\Delta x+L_{1}\times\Delta \alpha\\ \Delta x_{2}^{\prime}=\Delta x+L_{2}\times\Delta\alpha\end{array} \right.. \tag{7}\]  
According to Equation (10), \(\Delta x\) and \(\Delta\alpha\) can be determined as shown in Equation (8):  
\[\left\{ \begin{array}{c}\Delta x=\Delta x_{1}^{\prime}-L_{1}\times\frac{ \Delta x_{1}^{\prime}-\Delta x_{2}^{\prime}}{L_{1}-L_{2}}\\ \Delta\alpha=\frac{\Delta x_{1}^{\prime}-\Delta x_{2}^{\prime}}{L_{1}-L_{2}} \end{array} \right.. \tag{8}\]  
Then, the detected displacement can be compensated in Equation (9):  
\[\Delta x_{3}=\Delta x_{3}^{\prime}-\left[\left(\Delta x_{1}^{\prime}-L_{1} \times\frac{\Delta x_{1}^{\prime}-\Delta x_{2}^{\prime}}{L_{1}-L_{2}}\right) +L_{3}\times\frac{\Delta x_{1}^{\prime}-\Delta x_{2}^{\prime}}{L_{1}-L_{2}} \right]. \tag{9}\]  
Therefore, the detected displacement error caused by sudden vibration is compensated successfully.
## 2 Materials and Methods
### System Design
#### 2.1.1 Interaction Period
Once two agents meet, agent \(a\) first determines how willing it is to accept information from agent \(b\), a function of \(a\)'s current certainty, where \(a_{certainty}\) represents \(a\)'s current certainty and \(a_{acceptance}\) represents \(a\)'s willingness to accept information from agent \(b\)  
\[a_{acceptance}=1.0-a_{certainty}. \tag{3}\]  
Agent \(a\) determines how much influence to allow agent \(b\), \(b_{influence}\), a function of agent \(b\)'s confidence, trust_score, and certainty, and agent \(a\)'s acceptance, \(a_{acceptance}\),  
\[b_{influence}=b_{confidence}*a_{acceptance}*b_{trustCertainty} \tag{4}\]  
where \(b_{trustCertainty}\) represents trust_score * certainty,  
\[b_{trustCertainty}=b_{trustScore}*b_{certainty}. \tag{5}\]  
Agent \(b\)'s influence is further modified based on agent \(b\)'s prior_performance, represented by \(b_{priorPerf}\), such that the corrected influence can be represented by \(b_{correctInfluence}\),  
\[b_{correctedInfluence}=b_{priorPerf}*b_{influence}. \tag{6}\]  
If agent \(a\) and agent \(b\) have different predictions, \(b_{correctInfluence}\) will be multiplied by \(-1\) to act against agent \(a\)'s current predictive belief. Agent \(a\)'s updated certainty, \(a_{certainty}\), can be calculated by Equation (7), where \(a\)'s certainty increases if both agents \(a\) and \(b\) have the same prediction and decreases if the predictions are different,  
\[a_{certainty}=a_{certainty}+b_{correctedInfluence}. \tag{7}\]  
If agent \(a\)'s certainty value falls below 0.50, agent \(a\) will change its prediction. If the prediction is changed, agent \(a\)'s certainty is updated by Equation (8),  
\[a_{certainty}=1.0-a_{certainty}. \tag{8}\]