esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 2 Related Work
### Vision-Based Automatic Forest Fire Detection Techniques  
The merits of vision-based techniques, characterized by their real-time data capture, extensive detection range, and easy verification and recording capabilities, have positioned them as a focal point in the forest fire monitoring and detection arena [29]. Over the last ten years, research endeavors have extensively employed vision-based UAV systems for fire monitoring and detection. The authors in [30] introduced an innovative method optimized for smart city settings, harnessing YOLOv8 to enhance fire detection precision. In [31], a comprehensive overview explored object detection techniques, focusing on YOLO's evolution, datasets, and practical applications. In [32], the authors refined the detection of forest fires and smoke, offering potential benefits for wildfire management. The work described in [33] advanced forest fire detection reliability using the Detectron2 model and deep learning strategies. Moreover, in [34], the authors suggested enhancing forest fire inspection using UAVs, emphasizing accurate fire identification and location.  
Given the complex and unstructured nature of forest fires, leveraging multiple information sources from diverse locations is imperative. When dealing with large-scale or multiple forest fires, a single drone's update rate might be insufficient. The common features used in existing works include the color, motion, and geometry of detected fires. Color, particularly extracted using trained networks, is used to segment fire areas. In [35], the authors introduced a novel framework combining the color-motion-shape features with machine learning for fire detection. Fire characteristics, extending beyond color to irregular shapes and consistent movement at specific spots, were considered. The authors in [36] presented a multi-UAV system for effective forest fire detection. Their paper described in detail the components of the helicopter UAV, offering a vision-based forest fire detection technique that utilized color and motion analyses for clear-range images. The study also showcased algorithms for ultraviolet and visual cameras. A robust forest fire detection system was proposed in [37], necessitating the precise classification of fire imagery against non-fire images. They curated a diverse dataset (Deep-Fire) and employed VGG-19 transfer learning to enhance the prediction accuracy, comparing several machine learning approaches. Furthermore, a deep learning-based forest fire detection model was presented in [38], capable of identifying fires using satellite images. Utilizing RCNNs to decrease the prediction time, the model achieved a 97.29% accuracy in discerning fire from non-fire images, focusing on unmonitored forest observations.
## 2 Related Work
### Radar Sensor Approach  
Radar sensors have attracted attention for their broad applications in the automotive and robotics industries, growing their ability to locate and detect objects and obstacles in 3D, even in severe weather conditions and limited visibility [15]. Currently, there are several datasets available for researchers to validate algorithms for object detection in various weather conditions such as sun, night, rain, fog, and snow, covering diverse scenarios such as cities, parks, and urban and suburban settings [16; 17; 18]. Radar sensors have been further utilized in innovative collision avoidance systems, which addresses a critical safety issue [19]. The proposed system leverages a microwave-transceiving radar sensor mounted on the vehicle and a curved radio wave reflector installed at the corner to sense potential collision threats. By continuously monitoring the vehicle's speed via an onboard diagnostics (OBD) system, the sensor can distinguish between static objects and approaching ones, effectively detecting threats with no blind spots across a wide range of weather conditions. One widely used approach is the extraction of micro-Doppler (\(\mu D\)) signatures, which provide detailed motion patterns for object differentiation. When processed with neural networks, these signatures allow the classification of dynamic agents such as pedestrians and cyclists [20; 21; 22]. However, when dealing with multiple targets, the results become complex, and only a few studies maintain consistently applicable information. Voxel-based methods have also been explored for activity recognition. In [23], a 3D voxelizationprocess was employed to convert point cloud data into discrete features, which were then analyzed using a time-distributed CNN combined with a bi-directional LSTM network, achieving 90.47% accuracy in controlled experiments. Alternatively, clustering techniques offer computational efficiency; ref. [24] utilized a DBmeans algorithm alongside a recursive Kalman filter (RKF) to track up to five individuals with a low RMSE of 0.1893 m. Another method for detecting objects involves using an extended object tracking Kalman filter to estimate position and shape, integrated with deep learning methods on radar point clouds [25]. Fusion sensors, such as radar-camera and radar-lidar combinations [17], as well as communication between multiple radar sensors [26], significantly enhance detection accuracy through a software framework enabling intercommunication. In [27; 28], the authors utilize the DBSCAN clustering algorithm to segment point clouds, demonstrating the effectiveness of these approaches in providing distance and speed measurements for dynamic objects.
## 5 Results and Discussion
### DMD Convergence Criterion
#### 5.2.1 Convergence of Sampling Frequency
Figure 11: DMD reconstruction relative errors with respect to sampling resolution with a 2-s sampling window (\(T_{w}=2\) s) for two filtered signals: (**left**) \(f_{c}=100\) Hz, (**right**) \(f_{c}=400\) Hz; note that ‘P’ and ‘WS’ refer to pressure and friction, respectively. Figure 12: The power spectral density (PSD) of the DMD-reconstructed pressure drag and friction drag coefficient signals, conditioned with two low-pass filters at \(f_{c}=100\) Hz, and sampled at different frequencies (\(f_{s}=200\), 400, and 1000 Hz). (**a**) Pressure signal (**b**) wall shear stress. Figure 13: The power spectral density (PSD) of the DMD-reconstructed pressure drag and friction drag coefficient signals, conditioned with two low-pass filters at \(f_{c}=400\) Hz and sampled at different frequencies (\(f_{s}=1\) kHz, 2 kHz, and 5 kHz). (**a**) Pressure signal (**b**) wall shear stress.
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 2 Related Work
It does not consider any modification to the SDN controller, and it is not necessary to add a module to the SDN controller. To resolve the problem of path selection, QoSComm uses a multi-objective algorithm, which was designed considering four fundamental parameters in communication networks mentioned in the previous section. In addition, QoSComm can establish communication with one or more SDN controllers and process path selection within a single domain or between two or more domains (distributed data centers), which is the primary goal of our strategy. Table 1 shows the main features of the revised QoS proposals and QoSComm. The BDA scheduler sends a request to QoSComm to get and set the optimal paths according to application network requirements. Once QoSComm gets and configures these paths in both data centers, the application scheduler submits the job to compute nodes in the distributed data centers and meet network requirements, as it is illustrated in Figure 1. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Network Parameters** & **Path Selection Algorithm** & **Multi-Controller** & **Domain** \\ \hline
[19] & Bandwidth, delay, jitter & SPF with delay restriction & No & Single domain \\
[20] & Bandwidth, delay, jitter & SPE with bandwidth and delay as constraints & No & Single domain \\
[21] & Bandwidth & Bandwidth control & No & Single domain \\
[22] & Bandwidth, delay & Bandwidth control, & No & Single domain \\  & user-defined priority & No & Single domain \\
[23] & Bandwidth & SPF with bandwidth as constraint & No & Single domain \\
[24] & Bandwidth & SPF with bandwidth as constraint & No & Single domain \\
[9] & Bandwidth & SPF with bandwidth as constraint & No & Inter-domain \\  & Bandwidth, delay, jitter, & Multi-objective, & Yes & Inter-domain \\  & packet loss & considers application requirements & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: QoS proposals features comparison. Our main objective is to minimize application job completion time, considering the four parameters required by the application to provide network QoS in the data flow allocation process to transfer data flows among geographically distributed data centers. With this in mind, QoSComm considers BDA network requirements and meets QoS parameters, then selects and configures the path with the minimum end-to-end delay between two distributed compute nodes for time-sensitive applications.
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 5 System Completion  
### Sensors Deployment  
The landslide monitoring and early warning system developed in this study was deployed at the Danjiangkou Reservoir. Based on past disaster information, 31 potential landslide sites were selected and the responsible department constructed monitoring sites, which were fitted with a rainfall gauge, inclinometer, water level gauge, osmometer and displacement gauge whose real-time information was transmitted to the monitoring and early warning center using GPRS wireless signal transmitter. The deployment of multi-sensors is shown in Figure 6.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion  
Banglades has 11 soil temperature observation stations. From 2001 to 2022, at a depth of 10 cm (ST10), the soil temperature ranged from 13.4 \({}^{\circ}\)C to 35.2 \({}^{\circ}\)C. At a 30 cm depth (ST30), it ranged from 15 \({}^{\circ}\)C to 34.7 \({}^{\circ}\)C, and at a 50 cm depth (ST50), it ranged from 15.1 \({}^{\circ}\)C to 34.6 \({}^{\circ}\)C. Affected by large oscillations in air temperature, the variability of ST10 was significantly larger than that of ST30 and ST50.  
Figure 2 further illustrates the average daily soil temperature variations at three distinct depths across the 11 soil monitoring stations in Bangladesh. The average daily soil  
Figure 1: Distribution of 11 soil temperature observation stations in Bangladesh (in Green). Source: [40; 41; 42].  
temperature at the 10 cm depth varies from 17.1 \({}^{\circ}\)C (Dhaka station) to 32.9 \({}^{\circ}\)C (Rajshahi station); at the 30 cm depth, it varies from 18.07 \({}^{\circ}\)C (Dinajpur station) to 32.7 \({}^{\circ}\)C (Dhaka station); and at the 50 cm depth, it varies from 18.6 \({}^{\circ}\)C (Dinajpur station) to 32.4 \({}^{\circ}\)C (Faridpur station). The overall range of average temperatures across the stations is from 18.2 \({}^{\circ}\)C to 31.3 \({}^{\circ}\)C for ST10, from 19.09 \({}^{\circ}\)C to 30.9 \({}^{\circ}\)C for ST30, and from 19.7 \({}^{\circ}\)C to 30.6 \({}^{\circ}\)C for ST50. Soil temperatures at Rangamati, Srimongal, Dhaka, and Barisal exhibit nearly identical temperatures across all depths, whereas soil temperatures at various depths at stations like Tangail and Bogra have significant differences. At most of the stations, the soil at the 10 cm depth has higher temperatures in January\(-\)August and lower temperatures in September\(-\)December compared to those at the 30 cm and 50 cm depths. The main reason for this is that surface soil temperature is more susceptible to temperature fluctuations. Specifically, the soil layer at the 10 cm depth exhibits greater sensitivity to short-term fluctuations and seasonal variations due to its proximity to the surface and limited thermal mass, whereas deeper soil layers at 30 cm and 50 cm exhibit more stable temperature trends due to their insulation from surface-level conditions. Aligning with the changing seasons, soil temperature at all depths shows increasing trends from January to April, and decreasing trends from September to December. The soil temperature from May to August is the highest in the whole year, which is consistent with the summer season. These temperature variations are primarily attributed to seasonal shifts in the solar insolation, daylight duration, and thermal inertia of the soil.
## 2 Materials and Methods
For cyclic voltammetry (CV), the cells were prepared by sandwiching the electrolyte pellet, prepared as described above, between a disk of lithium (\(\mathcal{O}\) = 10 mm, thickness = 30 \(\upmu\)m) as a counter/reference electrode and a working electrode prepared as follows. Super P carbon (Timcal, Imers Graphite & Carbon, Paris, France) and poly(vinylidene fluoride), Solef(\(\otimes\)6020 PVDF (Solvay, Brussels, Belgium) were mixed at a ratio of 7:3 wt/wt using \(N\)-methyl-2-pyrrolidone (NMP) as a solvent. The slurry was coated on Al or Cu current collector for anodic and cathodic scans, respectively, and dried at 120 \({}^{\circ}\)C for 1 night. The CV was recorded between OCV (\(\sim\)3.0 V) and 0.01 V vs. Li/Li\({}^{+}\) for the cathodic scan and OCV to 6.0 V vs. Li/Li\({}^{+}\) for the anodic scan. The scan rate was set to 0.1 mV s\({}^{-1}\). Charge-discharge cycling performance of lithium metal cells was evaluated as follows. The cells were similarly prepared as mentioned for CV, but with LiFePO\({}_{4}\) cathode. A cathode sheet of LiFePO\({}_{4}\) deposited on Al foil was purchased from Custom Cells (Itzehoe, Germany), cut into a disk with a diameter of 10 mm, and dried at 120 \({}^{\circ}\)C for 1 night. The specific capacity of the LiFePO\({}_{4}\) is reported to be 150 mAh g\({}^{-1}\) by the supplier. The cycling test was performed at C/20 using a Maccor Series 4000 Battery Test System (Maccor Inc., Tulsa, OK, USA) where 1C is equivalent to 1.08 mA cm\({}^{-2}\). Figure 1: Chemical structure of FSI IL and DFOB IL.
## 4 Result and Discussion
### Prediction and Validation
#### 4.1.2 Predictions via Artificial Neural Network (ANN)  
ANN is manually tuned at initial stages to yield lower error rate compared to SVM. The number of layers is 2 and the number of neurons in each layer is 3. The activation function for input and hidden layer is ReLU and Linear for output layer. The dropout ratio is 0.15 and the number of iterations is 1000. Figure 2 illustrates a comparison of the predicted and observed pressure loss from the experimental setup. The RMSE and MAE are found to be 17.8 kPa and 10.7%, respectively, with R\({}^{2}\) = 0.915 for training data. Similarly, the obtained values for the test data are 21.36 kPa for RMSE and 12.34% for MAE with R\({}^{2}\) = 0.904. The prediction for the test and train data is good below 50 kPa as the values are clustered around the best fit curve, but a drift from the best fit towards the upper region can be observed. Different precautionary measures are taken to avoid overfitting, such as monitoring the RMSE score, early stopping criteria, and dropout regularization. The proposed ANN is shallow in structure, therefore, changes in number of neurons in each layer, number of layers, and the activation functions are also iteratively selected. The lowest RMSE score of unseen test data may leads to generalizing better results.
## 2 The Analytical Model of Mesh Stiffness of Helical Gear  
With the existence of a helical angle, slice theory can be used to compute the mesh stiffness of a helical gear, and the axial effect should also be considered. Thus, the axial and transverse stiffness are calculated, respectively. The stiffness model considering the effect of the relationship between neighboring sliced gears is established.
# Theory of Electrical Breakdown in a Nanocomposite Capacitor
## 1 Introduction  
Recently, the polymer film capacitors have received growing attention due to their advantages of a low cost, facile fabrication, excellent flexibility and high operating voltage [1]. To also attain high energy storage density, one employs nanosized inclusions with a high dielectric constant, thus increasing the effective dielectric permittivity of such polymer-based nanocomposites [2, 3, 4, 5]. This can be performed, however, at the expense of a decrease in the capacitor breakdown strength [6, 7, 8] that diminishes to some extent the advantages of this approach.  
To properly design nanocomposite capacitors, one needs a deep understanding of the factors which control the electrical breakdown in them. For relatively low volume fractions of inclusions, which do not create deep traps for electrons [9, 10, 11, 12, 13], the primary effect of their embedding is a modification of the electric field in the capacitor. Among different approaches which aim to account for this influence, only the Maxwell Garnett approximation [14, 15] follows from first principles, while the other ones are rather phenomenological models (see, for example, a review of different models in Ref. [7]). This approximation treats the spherical inclusions as point dipoles with a dipole moment which is determined by the sphere polarizability. It is applicable, however, to composite media provided they extend throughout a space of dimensions which are much larger than the wavelength of the external field or to unbounded media in the static case. For a nanocomposite capacitor, this criterion is not fulfilled, and one must take into account the polarization of electrodes and, resulting from it, the image potential [16].  
In the present paper, we develop a first-principles approach to the static electric field in a nanocomposite capacitor which is based on a rigorous account of the image potential for point dipoles between parallel metallic electrodes. The obtained results are used to find the parameters which characterize the electrical breakdown in a nanocomposite capacitor.  
The predicted dependencies are compared with the available experimental data on the breakdown field strength as a function of the volume fraction of inclusions and temperature.
# Retinal Nerve Fiber Layer in Patients with Alcohol Use Disorder
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Quadrants (\(\mu\)m)**} & \multicolumn{2}{c}{**Control Subjects**} & **SD Control** & **AUD Patients** & **SD Alcohol** & \multirow{2}{*}{**Difference CI**} & \multirow{2}{*}{\(p\)**Value**} \\  & \(n\) = 21** & **(\(\pm\))** & **\(n\) = 21** & **(\(\pm\))** & & \\ \hline RNFL macula & & & & & & \\ \hline Right & 28.97 & 2.20 & 27.77 & 2.13 & \(-\)0.16 to 2.54 & 0.081 \\ \hline Left & 28.72 & 2.21 & 27.82 & 2.67 & \(-\)0.62 to 2.43 & 0.239 \\ \hline GLC++ \({}^{1}\) & & & & & & \\ \hline Right & 109.13 & 8.46 & 104.00 & 7.23 & 0.22 to 10.04 & 0.041 \({}^{*}\) \\ \hline Left & 109.33 & 6.53 & 105.12 & 7.77 & \(-\)0.27 to 8.68 & 0.065 \\ \hline GLC+ \({}^{2}\) & & & & & & \\ \hline Right & 74.75 & 5.95 & 71.52 & 5.14 & \(-\)0.24 to 6.70 & 0.670 \\ \hline Left & 74.87 & 5.23 & 70.91 & 5.83 & 0.51 to 7.42 & 0.025 \({}^{*}\) \\ \hline \end{tabular} \({}^{1}\) Ganglio cell complex (GCC): RNFL + ganglion cell layer (GCL) + inner plexiform layer (IPL). \({}^{2}\) GLC: RNFL/GLC and IPL/INL (inner nuclear layer). \({}^{*}\) Statistically significant data (\(p<0.05\)).  
\end{table} 
Table 5: RNFL macular and GLC thickness (\(\mu\)m). Figure 6: Box diagram corresponding to peripapillary RNFL. Values are expressed using median, percentiles, and extreme values.
## 4 Fldqn
### Algorithm Description
The actual training starts at line 14, where an agent iterates over the episodes and each episode consists of multiple steps. During each step a random value is generated and compared with \(e\). If the generated random value is smaller than the epsilon \(e\), the agent will perform a random action to explore the environment (line 17); otherwise, the agent will perform an action that is suggested by the Q-network (line 19). The selected action is executed in the SUMO environment (line 21), where the agent obtains the reward \(r_{t}\) and the next state \(s_{t+1}\) based on the action taken. The agent then stores the transition \((s_{t},a_{t},r_{t},s_{t+1})\) in the replay memory \(D\) (line 22). Subsequently, the agent randomly samples a mini-batch of transitions from the replay memory \(D\). The replay memory consists of the agent's experiences: the agent selects a sample mini-batch of transition from \(D\) to optimize the neural network, and a sample mini-match is randomly selected to remove the correlation between recently stored records and old records (6) (line 23). The target calculation is performed next. If the next state \(s_{t+1}\) is terminal, also known as the final state, meaning the episode ends after this state, then target \(y\) is set to the observed reward \(r\). In other words, reaching the terminal state indicates that there is no future reward for the agent to look forward to, so the target value \(y\) for updating the Q-function is simply the reward \(r\) received at this step (line 25). If \(s_{t+1}\) is not terminal, then the target \(y\) is calculated as the sum of the immediate reward \(r\) and the discounted maximum Q-value of the next state (line 27). This approach incorporates the expected future rewards into the current update. Finally, the Q-function updates the parameter \(\Theta\) to minimize the loss between the predicted Q-values and the target values \(y\) using Equation (5) (line 29). After completing the episodes, the updated local model weights are returned to the server for aggregation (line 32). In FLDQN, each agent refines its policy based on its experiences, thereby improving its decision-making capabilities. The server aggregates the local updates from all agents, ensuring that the global model benefits from diverse experiences. FLDQN allows agents to collectively learn and improve their performance over time, leveraging shared knowledge without sharing raw data. This innovative approach enhances the robustness and generalization of the learned policies, enabling agents to make better decisions in dynamic and complex environments.