esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case-Study: Resource Fitting
### Experiment 1: Comparison of Native Performance of ML Backends
#### 5.1.2 Resource Utilization for FL Clients and Server  
Next, we elaborate on the reasons for the differences in the per-round time duration by analyzing the resource utilization of FL clients and servers for each ML backend. In Figure 5(a), we compare the CPU utilization, memory allocation, and network traffic for different models, frameworks, and datasets on the client services. For clarity, we note that for the network, we account only for the outgoing traffic from the clients to filter noise, such as downloading the dataset. Interestingly, all examples utilize a comparable number of CPUs, with the only exception being the TensorFlow CNN model for the MNIST dataset. We attribute this behavior to the simple data layout of MNIST and the asynchronous capabilities of TensorFlow, which aims to load up the CPUs with as much work as possible and process a larger number of images in a parallel and vectorized fashion.  
Moving now to the FL server resource consumption (Figure 5(b)), we first observe that PyTorch features a slightly higher CPU utilization than TensorFlow. Another observation is that TensorFlow consistently requires more memory and network bandwidth than PyTorch. This suggests that TensorFlow may not be suitable for FL training on extremely constrained IoT or edge devices. Additionally, the TensorFlow deployment exhibits higher memory utilization and network traffic on the server side than PyTorch, as shown in Figure 5(b). These findings indicate that _TensorFlow-based FL pipelines are memory- and network-intensive, while PyTorch-based FL servers require more processing power_.  
Figure 5: FL-level performance overview for different ML backends and datasets. (**a**) Model accuracy after 5 training rounds. (**b**) Mean training round duration after the completion of 5 rounds.  
Figure 6: Resource reservation as requested by PyTorch and TensorFlow server.
# Modeling and Control of an Underactuated System for Dynamic Body Weight Support
## 6 Simulation Research
### Analysis of the Results  
The results of simulation tests indicate that the designed control system meets our objectives and ensures stable operation, regardless of patient movement and the adjusted  
Figure 14: Control signals: (**a**) overall control system and control signal components; (**b**) control during the initial phase of gait; (**c**) control graph with customized scales.  
Figure 15: Follow-up movement errors: (**a**) position error of the active part \(e_{\phi}\); (**b**) position error of the passive part \(e_{x}\); (**c**) speed error of the active part \(e_{dp/dt}\); (**d**) speed error of the passive part \(e_{dx/dt}\).  
weight support load. For the demonstrated approach, the trajectory of the passive part is adjusted over time and the trajectory of the active part can be calculated on the basis of (10)-(12). Control method (8) enables the adjusted trajectory of the passive part to be determined, because the dynamic performance of this system in the adjusted trajectory of the active part is considered. Comparing the results of the simulation tests for the first two variants, we observed differences mainly in the calculated set trajectory of the active part. For the variant with a support force of 300 N, the position of the active part corresponded to the deformation of the spring in the body weight support system, which is needed to compensate for this support force. We also identified differences in the values of the control signal corresponding to the servomotor torque. For the no-load variant, the motor torque values fluctuated around zero, while for the variant with load, negative values during operation (ignoring the start-up phase) were assumed. The negative values here result from the assumed turning of generalized coordinates.  
Application of the assumed control method requires the determination of certain model parameters (inertia, damping, elasticity) and the system's status (movement and speed).  
The system settings selected by way of experiments had small errors. Due to the knowledge of the dynamics of the controlled object, in numerical studies the compensation function ensures very precise control; therefore, the control signal from the PD controller had much lower values. In real objects, with unknown disturbances, the level of compensation will not provide such perfect control and greater influence of the PD control should be expected. Moreover, the simulation did not consider signal processing delays that occur in real conditions, therefore, analysis of the results of empirical studies is necessary [49; 50; 51; 52; 53].
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 4 Condition-Adaptive MPC Controller
### Implementation of Condition-Adaptive MPC Controller  
For different road curvatures and vehicle speeds, MATLAB is utilized to perform table-lookup operations on the optimized results in Table 5 and match the optimal control parameters. Linear interpolation is applied and rounding is performed for conditions between steady-state steering states. Figure 6 presents a pseudo-code snippet illustrating the table-lookup logic for control parameters based on a vehicle speed-curvature table. For curve conditions beyond the optimized results, the time domain parameters of the steady-state steering condition closest to the current condition are adopted, thereby obtaining an adaptive time domain parameter control law for any vehicle speed and road curvature. Figure 7 shows the flow chart of the time domain parameter adaptive MPC algorithm.  
The control law for the optimal time domain parameter combination under various operating conditions is implemented through the S-function and integrated into the MPC co-simulation model, which is established based on CarSim/Simulink. This integration facilitates the design of a working-condition-adaptive MPC controller. The overall system architecture is depicted in Figure 8. Based on the deviation between the reference path and the actual tracking path of the vehicle, the MPC path tracking controller calculates the front wheel angle increment within the current control time domain by solving a quadratic programming problem. The increment is then utilized to feed back and correct the control quantity from the previous time step, which is subsequently applied to the vehicle model. The vehicle's longitudinal speed is controlled by the PID controller. Meanwhile, the time domain parameter adaptive controller takes the current vehicle speed and road curvature radius as real-time input and determines the optimal time domain parameter combination for the current operating condition through the S-function. Finally, the results are fed back to the MPC path tracking controller as parameters for the next time step, thereby achieving closed-loop control for condition-adaptive path tracking.
## 2 Study Area and Data
### Data
#### 2.2.5 Land-Use Data  
The land-use data with a spatial resolution of 1 km used in this study were obtained from the Resource and Environment Science and Data Center ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)), accessed on 1 May 2023). The land-use types identified include farmland, forest, grassland, water, impervious, and others, as depicted in Figure 1.
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 3 Evaluation  
### Experimental Setup  
We performed our experiments in a classical WLAN indoor environment, an open office room with 9 m \(\times\) 8 m \(\times\) 3 m (length \(\times\) width \(\times\) height), as shown in Figure 3. There were a total of eleven APs available with one AP in the room and the other APs from adjacent area of the same floor with none-line-of-sight signal propagation. We collected 100 RSS samples at each reference point with a total of 72 reference points. Each reference point was separated by about one meter. A total of 56 test points with related 5600 testing samples were collected at different locations to test the positioning accuracy. All reference and test points were collected on the two dimensional horizontal floor. We deploy the error distance as the performance metric, which is Euclidean distance between the true coordinate and the estimated one. The cumulative probability distribution of error distance was defined as positioning accuracy. To determine tunable parameters of the proposed E-LFDA method, five-fold cross validation was used during offline training phase. We also carried and compared WKNN, ANN, MLE, PCA, LDA and LFDA (proposed E-LFDA without signal perturbation) method. The number of training RSS samples per reference point was added from 100 to 1000 by the proposed signal perturbation method. In our future work, we will test our approach in some more complicated and larger-scale indoor environments, and also in some public testbeds and datasets, such as UJIIndoorLoc [31].
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 4 Numerical Results  
### BER versus Link Budget  
The characterization of both the FSO link and RF link was substantiated through numerical simulations, and the specific parameters utilized for the simulation were detailed in Table 1. The comparison that obtains the relationship between weather and BER among the modulations is presented in Figures 3 and 4. The BER increases with worsening weather conditions. It is worth noting that the BER remains relatively stable on foggy days due to the strength of the RF link against fog.  
Tables 2 and 3 show the LB thresholds for the modulations we have listed, indicating that the modulation order changes in adverse weather conditions. To achieve the switching of multiple modulations between the FSO link and RF link under rainfall rate and visibility changes, the target BER \(P_{e,obj}\) needs to be set. The BER of these modulations should be lower than or equal to the set target BER \(P_{e,obj}\). In the rain, \(P_{e,obj}\) set on the FSO link is \(10^{-6}\) and its value set on the RF link is \(10^{-3}\). In the fog, \(P_{e,obj}\) is set on the FSO link as \(10^{-9}\). Given that the RF link has less variation on foggy days when using the random forest model, its \(P_{e,obj}\) and threshold range are ignored.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter of FSO Sub-System** & **Value** \\ \hline Link range & 3 km \\ Wavelength & 1550 nm \\ Transmitting power & 50 dBm \\ System loss & 3 dB \\ Divergence angle & 1.25 mrad \\ Receiver sensitivity & \(-\)31 dBm \\ \hline
**Parameter of RF Sub-system** & **Value** \\ \hline Link range & 3 km \\ Carrier frequency & 60 GHz \\ Receiver antenna gain & 44 dBi \\ Transmitting power & 40 dBm \\ Signal-to-noise power ratio & 50 dB \\ Bit rate & 84.1 bps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for Simulation.  
Figure 4: Bit error rate performance of RF system in rainy and foggy weather condition. (**a**) Rainfall rate. (**b**) Visibility.  
Figure 3: Bit error rate performance of FSO system in rainy and foggy weather condition. (**a**) Rainfall rate. (**b**) Visibility.
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
## 5 Imaging Algorithm
### High-Order Imaging Algorithm for High-Resolution Spaceborne SAR  
The block diagram of the proposed high-order imaging algorithm is shown in Figure 15. The imaging process consists of four parts. The first part is data resampling, which is used to transform the non-uniform echo into uniform echo. The second part is azimuth preprocessing, which is performed to remove azimuth aliasing. For high-resolution spaceborne SAR systems, the steering of the antenna beam introduces extra bandwidth. As a result, the insufficient PRF would cause severe azimuth aliasing, especially for a 0.25 m resolution. Using a subaperture partition, nonlinear shift filter, delay phase compensation and sub-aperture recombination, the 2-D signal spectrum data are obtained without aliasing in the azimuth direction. The third part is high-precision focusing within the full swath. With increasing integration time for high-resolution spaceborne SAR, the spatial dependence of the 2-D point scatterer response becomes substantially more significant. By removing the RCM, azimuth modulation, high-order cross-coupling at the reference slant range and range cubic phase filter processing, the coarse focusing within the full swath can be realized. After range compensation and residual phase compensation, the residual RCM and the residual phase error are completely corrected. The last part is resampling processing, which solves the azimuth folding problem in the focused domain. To avoid the azimuth aliasing, an azimuth resampling operation is applied to overcome the constraint on the azimuth swath with a derotation phase function [19].
## 2 Data and Methods
### Analysis and Processing of SWOT-Derived SSH Data
#### 2.3.3 Filter Evaluation  
To evaluate the performance of the filter and its different parameterizations, the following variables are calculated:  
* The radial power spectral density: This variable was calculated to obtain the SWOT spatial spectra. The radially averaged power spectral density (power spectrum) of an image (in our case, the SWOT swath data) is computed.
* The Root Mean Squared Error (RMSE): The RMSE was calculated for the SSH, velocity and vorticity variables as follows: \[RMSE=\sqrt{\frac{\sum(data-estimate)^{2}}{N}},\] (7) where \(N\) is the number of points. Data is taken to be SSH\({}_{model}\) (or its derived variables, i.e., velocity and vorticity) without filtering. An estimate is taken to be the simulated noisy SSH\({}_{obs}\) fields (or its derived variables) without filtering, and filtered with different \(\lambda_{c}\)s. RMSE values are therefore calculated for different estimates.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Asset Management and Maintenance in Operation
#### 3.5.2 Analytics and AI in Asset Health Monitoring  
The study in [89] underlines the significance of utilizing low-cost, non-intrusive sensors combined with AI for asset health monitoring. These technologies provide continuous monitoring and real-time analysis that is essential for predictive maintenance. The integration of AI algorithms can process large datasets from sensors, facilitating early detection and proactive maintenance strategies. This approach not only ensures system reliability but also significantly reduces maintenance costs by avoiding unplanned outages and expensive repairs.
## 4 Experimentation and Results
### Evaluation of Mean Squared Error (MSE)  
The convolutional neural network was trained using 850 iterations in total. Convolutional neural networks generally undergo increases in the number of iterations to guarantee the least amount of learning error possible. Additionally, the learning factor was set to 0.5, and the momentum parameter was changed to 0.7. The convolutional neural network's training process included several moments when the accuracy of the quadratic error prediction was carefully assessed. The graphic in Figure 9 below displays the values of the detected quadratic errors. Given that the quadratic error of the network was 0.02018 after the most recent iteration, it could be inferred that deep learning had little to no impact on the quadratic error of the network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Patch Size & 36, 48, 48 \\ Convolution Layer & \(3\times 5\times 5\) \\ Max Pooling & \(3\times 3\times 3\) \\ Dropout Value & 0.2 \\ Convolution Layer 2 & \(3\times 5\times 5\) \\ Max Pooling 2 & \(2\times 2\times 2\) \\ Dropout Value & 0.2 \\ Fully Connected Layer & 350 nodes \\ Fully Connected Layer 2 (Nodes) & 2 \\ Activation & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed CNN architecture for efficient lung nodule classification.  
Figure 8: A training algorithm for false positive reduction.
## 6 Case Study: Application of the Method on LoA 4 Valet Parking with V2X
### Driver Warning--AVP  
Driver warning may be chosen optionally for AVP in the scope of V2X, depending on the implementation of the functionality. In case LoA 4 AVP for any reason requires human presence in the vehicle during the maneuver, the system is required to inform and/or warn the driver on the current status of the parking maneuver through the infotainment system or other communication channels. This is not a critical element of the LoA 4 functionality, but may be a regulatory or safety requirement of the local government to be fulfilled. Driver warning may still be required if the human operator is not present in the vehicle. In these cases, the communication is done via notification messages, either about a successful/unsuccessful parking, or hazards in the garage or regarding the vehicle. This function requires the capability of the vehicle and the infrastructure to recognize these situations, assess their severity and send information to the human operator.  
Using the summary presented in Table 2, one can plan the components and prepare the design definition of driving functions for an LoA 4 AVP system to address the requirements of this specific use case from the V2X technology point of view, following the steps outlined in Figure 3. Implementation requirements, test cases and test implementation are also integral parts of the automotive software development process, which has been defined and has been widely used in the industry, such as A-SPICE [53].
## 8 Conclusions  
This research's primary contributions and findings arise from integrating machine learning techniques, particularly convolutional neural networks (CNNs), with physics-informed approaches to improve overload forecasting in semi-autogenous grinding (SAG) mills. This study addresses the operational challenges and economic impacts of mill overloads, underscoring the need for effective predictive strategies in the mining sector. Our model's architecture represents a novel contribution, combining CNNs with a loss function that integrates operational and physical rules. This approach enhances the model's interpretability and aligns its outputs with real-world operational contexts. By embedding domain-specific knowledge through a physics-informed framework, the model achieves improved accuracy and reliability in predicting overloads. The inclusion of physical rules during training resulted in significant performance improvements. Ablation studies demonstrated that incorporating physical parameters and operational insights into the learning process enhances the model's predictive capabilities. That highlights the value of combining data-driven techniques with domain knowledge to achieve operationally relevant predictions. Techniques like Grad-Cam++ provided insights into the network's decision-making process, revealing how it prioritizes specific operational features and their interactions over time to predict potential overloads. These findings contribute to academic research on machine learning in industrial applications and offer practical guidance for reducing risks in mill operations. In summary, this study highlights that embedding domain-specific physical laws into the CNN's loss function enables the model to harness the predictive strength of data-driven methods while benefiting from the stability and reliability of physics-informed constraints. This approach allows our methodology to effectively capture the complex interactions and nonlinearities inherent in SAG mill operations, which are often overlooked by conventional methods reviewed in the state of the art.
## 1 Introduction
If a person watches an object with a linear size of S in a distance of D, the visual angle (\(\theta\)) may be calculated using the following equation:  
\[\Theta=2\text{tan}^{-1}\text{(S/2D)} \tag{1}\]  
The Civil Aeronautics Administration [24] has defined visual LOS as the far most distance between an operator and the UAV where the operator can visually catch the UAV without using cameras, telescopes, or other visual aids when observing the UAV. These visual aids do not include the ordinary lens and contact lens to correct the visual acuity of the operator. The fundamental idea of regulating drone operation within LOS is that the operators are believed to be capable of avoiding an impact of the drone with other objects whenever he or she is fully aware of the trajectory of the drone and where the drone is. The LOS is not explicitly defined in terms of either distances or visual angles in the codes of many countries such as the UK and Taiwan. The CAAC [22], however, has defined the LOS in operating a UAV as a radius of 500 m and an altitude of 120 m AGL. The distance between the drone and the operator at this altitude and radius is approximately 514 m. Whether an operator could visually catch a drone in such a distance is questionable. It is apparent that more scientific evidence is needed to define the concept of LOS [18]. There are many factors affecting the distance of the LOS for drone navigation. The visual capability of the observer, design of the drone (size, color, and sound of motors), weather conditions (such as ratio of cloud coverage and orientation to the sun) are among the primary ones. The distance within LOS for drone operation could be quite different depending on these factors. However, how these factors affect the distance of LOS has not been reported in the literature. The study of [PERSON] et al. [28] was the first one and only one studying the LOS distance. In this study, the authors conducted a field test to determine the LOS using a Phantom 4 quadcopter. Their results indicated that the probability to catch their quadcopter 500 m away visually was less than 10%. Assuming a 50% probability of visual capture and the \"definitely or probably yes\" criterion, they recommended a distance of 245 m as the distance of the LOS for small quadcopters. The corresponding visual angle for this LOS distance was approximately 0.065\({}^{\circ}\). Determination of the LOS distance of small drones is complicated. The purpose of this study is to determine the probability of visual detection of a small drone in the air in terms of drone distance. The LOS visual angle of this drone was also determined and discussed. In addition, the CAAC definition of the LOS distance was re-examined to determine whether this distance is appropriate for a commonly adopted small drone.