esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 4 The Implementation and Optimization of Efficient GPU Tensor Sensing
### Optimizations of the GPU Tensor Sensing  
During the computation flow of the GPU tensor sensing, frequent data transfer between the CPU and GPU will significantly degrade system performance. Therefore, we design a data reusing strategy to reduce data transfer overhead and resource consumption. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Meaning** & **Value** \\ \hline transA & operation op(**A**) that is non- or transpose & non-transpose \\ transU & operation op(**U**) that is non- or transpose & transpose \\
**A** & pointer to the **A** matrix corresponding to the first instance of the batch & \(d_{A}\) \\
**U** & pointer to the **U** matrix & \(d_{y}\) \\
**W** & pointer to the **W** matrix & \(d_{W}\) \\ strideA & the address offset between \(\mathbf{A}_{i}\) and \(\mathbf{A}_{i+1}\) & \(M\times N_{1}N_{3}\) \\ strideU & the address offset between \(\mathbf{U}_{i}\) and \(\mathbf{U}_{i+1}\) & 0 \\ strideW & the address offset between \(\mathbf{W}_{i}\) and \(\mathbf{W}_{i+1}\) & \(M\times N_{3}\) \\ batchNum & number of \(gemm\) to perform in the batch & \(N_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters of the gemmStridedBatch() routine in the cuBLAS library. Figure 3: Multiplication of block diagonal matrices on GPU. In the entire tensor sensing flow, we invoke data transfer only twice at the beginning and at the end. At the beginning, the input data are transferred from the CPU to the GPU. At the end, the final result matrix is sent back to the CPU. We optimize the computations in the tensor sensing flow such that they all perform in-place calculations. For instance, in the QR decomposition to solve the least squares problem, the input vector \(\mathbf{y}\) is overrode by the result vectors (\(\text{vec}(\mathbf{U})\) and \(\text{vec}(\mathbf{V})\)). Therefore, we need to reassign the vector \(\mathbf{y}\) at the beginning of each least squares minimization iteration. However, it is an expensive operation to load the original data of vector \(\mathbf{y}\) from the CPU memory every time. Instead, we pre-allocate a memory space named \(dyL\) on the GPU that stores the original data of the vector \(\mathbf{y}\). Every time we need to reassign the \(dy\), we use the GPU device-to-device transfer routine cudaMemcpyDeviceToDevice() to copy the data from \(dyL\) to \(dy\). Since the device-to-device bandwidth of 380 GB/s is much higher than the bandwidth between the CPU and GPU of 12 GB/s, this strategy significantly reduces data transfer overhead. Algorithm 3 and Figure 4 describe the computation flow and data organization of the tensor sensing on the GPU.
## 3 Results
### Retention of EO Content in CE EO and NLC EO During Storage at 4 \({}^{\circ}\)C  
The alterations in the content of EO ingredients in CE EO and NLC EO expressed as mg/mL of emulsion are depicted in Figure 2 and Table S2. The results showed that the content of each EO ingredient in both samples decreased over time at a different rate. In particular, at 30 and 60 days, the eucalypt content in the NLC EO was reduced by 18.99 \(\pm\) 6.78% and 46.06 \(\pm\) 0.85%, respectively. In the CE EO, the eucalypt content reduction was 58.88 \(\pm\) 0.40% at 30 days and reached 81.22 \(\pm\) 0.23% at 60 days of storage. At 90 days, the eucalypt content loss was 91.2 \(\pm\) 0.44% for NLC EO and 98.58% \(\pm\)0.04% for CE EO. The linalool content in the NLC EO was reduced by 38.5 \(\pm\) 1.54% at 30 days and by 70.51 \(\pm\) 1.67% at 60 days of storage. The maximum loss was 79.76 \(\pm\) 5.55% at 90 days of storage. The linalool content in the CE EO was reduced by 66.03 \(\pm\) 0.96% and 97.58 \(\pm\) 0.01% at 30 and 60 days, respectively, reaching 98.7 \(\pm\) 0.23% at 90 days. Three EO components, namely, cymene, trans-linaly oxide (furanoid), and borneol, which were detected only in the NLC EO one day after preparation, were not traceable at 30 days after storage. Similarly, \(\alpha\)-terpineol and methyl clavool, detected only in the CE EO, were not present after 30 days of storage. The total content of EO incorporated in the CE EO was released significantly faster (\(p<0.05\)) compared to that in the NLC EO (Figure 2). The EO content in the NLC EO was initially slightly lower (\(p<0.05\)) than that in the CE EO but decreased at a slower rate over time. At 30 days, the percentage of EO loss for the CE EO was about double compared to that of the NLC EO (64.80 \(\pm\) 0.32% and 32.77 \(\pm\) 0.38%, respectively). At 60 days, 87.75 \(\pm\) 0.55% was released from the CE EO and 54.07 \(\pm\) 0.42% from the NLC EO. At 90 days, almost all EO was released from the CE EO (98.48 \(\pm\) 0.10%), while for the NLC EO, significantly lower loss (83.36 \(\pm\) 0.41%, \(p<0.05\)) was noted (Figure 2). The assessment of the release mechanisms of EO ingredients from the CE EO and NLC EO was achieved based on the R\({}^{2}\) value after fitting the release results to several mathematical models. The Korsmeyer-Peppas model could not be implemented, as the release of EO exceeded 60% at 60 days and in some cases at 30 days of storage [21]. The results of CE EO analysis revealed that the best fitting was achieved using the Higuchi and first-order models for eucalypt and linalool, whereas linaly acetate was best fitted only at the zero-order model. Camphor, on the other hand, did not fit adequately to any of the tested models, as the R\({}^{2}\) value did not exceed 0.8 (Higuchi) (Table 3). The best fit for all EOingredients incorporated in the NLC EO was the zero-order model, and Higuchi was also adequately fitted for linalool and linalyl acetate.
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 2 Kinetics of Liquid Jets and Sheets
### Instability of Liquid Jets  
The liquid jet originated from the nozzle comes in contact with the surrounding gas, and this jet is broken down into drops when put through interferences like pressure variation, surface shift, velocity change of the apparatus or the liquid jet and also variations in temperature, consistency of the liquid and feature of the liquid surface to endure superficial exertion [31; 32; 33]. These interferences cause instability in the liquid jet causing disturbance waves in it and the instability is characterized by studying the development of this wave, droplet emergence and rupture length of the jet [34; 35]. The pressure variation is obtained by using acoustic waves, change in velocity is obtained by vibrating the flow, temperature is varied by providing regular heating and the surface is disturbed by using an electrifying sensor [30]. [PERSON] studied the vulnerability of the liquid jet and he procured an equation to calculate the growth rate of the disturbances that cause instability in the jet. He also hypothesized that interruptions with the highest advancing rate will cause dispersion of the jet into the droplet and the size of this droplet was calculated in the series of wavelengths of the interruptions [12].  
Studies on instability of a liquid jet reveal that a required wavenumber is necessary to generate stable liquid jet and when the wavenumber is less than the required value, it generates an unstable jet leading the breakup of the jet into a large droplet and one or more small drops. Figure 3 represents such a breakup of the liquid jet. By adjusting the interferences, we can generate droplets without the small drops (satellites).  
Later in 1931, [PERSON] introduced the effect of viscosity in the exploration of jet split-up using the 3D partial differential equations of Newtonian viscous fluid and found that viscosity of the fluid plays a role in slowing down the growth rate of the interference and thus elongating the wavelength, without altering the wavenumber [37]. Similarly, [PERSON] [38] and [PERSON] et al. [39] are the two other groups of scientists who provided the theoretical examination of the instability of the liquid jet.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 3 Proposed Approach  
The aim of the proposed approach is to utilize deep learning algorithms to construct One Dimensional Deep Convolutional Autoencoder (i.e., 1D-DCAE) model that can learn the features of normal SIP messages and identify any anomalies (i.e., SPIT attacks). The construction of this model involves two phases: the initial phase entails the extraction of relevant features from SIP messages, while the subsequent phase deploys the 1D-DCAE model for the detection of SPIT attacks.
## 3 Results and Discussion
### Deep Learning Model Assessment  
The proposed deep learning model previously explained in Section 2.3 was tested and evaluated in two experiments. The first experiment involved assessing the results compared to standard benchmarks such as linear, Adaboost, support vector, and multilayer perceptron regressors. The experiment involved splitting the data into 10-fold and calculating the average mean absolute error (MAE) and the average mean squared error (MSE) for all folds using the standard benchmarks and the proposed model. The 10 folds cross-validation assures that the model performs well given different data splits and is not biased to certain data distribution. The results produced by the proposed model outperformed the standard benchmarks, as shown in Table 5.  
Figure 3: Proposed deep learning model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MSE**} \\ \hline  & **Train** & **Test** & **Train** & **Test** \\ \hline Linear Regressor & 2.57 & 2.57 & 10.95 & 10.96 \\ \hline Adaboost Regressor & 1.85 & 1.86 & 5.86 & 5.91 \\ \hline Support Vector Regressor & 0.86 & 0.86 & 3.00 & 3.00 \\ \hline Multilayer Perceptron Regressor & 0.45 & 0.45 & 0.82 & 0.83 \\ \hline Proposed Model & 0.28 & 0.28 & 0.32 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between the results produced by the proposed model and standard benchmarks using 10-fold cross-validation.  
The second experiment involved dividing the dataset into two parts, two-thirds of the records for training the model and one-third for testing. The reason for such a split was to be able to assess the results produced by a single trained model, thus resembling the real-life application with a single model on each device and perform a deep analysis by the output results, which will be presented in the following section. The model was trained for 400 epochs as shown in Figure 4. The figure shows that the change in error was minimal after epoch 250 thus suggesting that the model did not need further training after epoch 400. The results produced when inputting the test data to this model will be presented and analyzed in the following section.
## 6 Experiment Results
### Experimental Results  
To simulate real-world IoV scenarios, the simulation data used in the experiments are randomly generated within a specified range, and multiple simulations are conducted to avoid random errors. To ensure statistical reliability, each experiment was repeated five times with different random seeds. The reported results represent the average performance across all runs. The PER-iSAC policy was trained until the convergence of cumulative rewards was observed. Across all runs, the performance trends were consistent, confirming the stability and robustness of the proposed algorithm.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 5 Results  
To confirm the compass operability and to estimate the expected accuracy we used a prototype model of the described GNSS/IMU gyrocompass (see Section 3.2) installed in a car body (Figure 4). The tests were conducted in urban conditions (St. Petersburg, Russia), with significant distortions in GNSS signal propagation.  
The following data arrays were generated in the prototype tests: inertial sensors' data \(\mathbf{f_{s}}\),\(\mathbf{\omega_{s}^{i,s}}\); IMU rotation angle \(u\); pseudorange \(\rho^{i}\); radial velocity \(\hat{\rho^{i}}\); and single phase difference of carrier \(\Delta\phi^{i}\) received by the spaced antennas for each observed i-th satellite, ephemeris data, and external measurements of linear velocity \(V_{N}^{ext}\), \(V_{E}^{ext}\). The obtained arrays were postprocessd using algorithms described in Section 4.3 implemented in MATLAB (Simulink). It should be mentioned that for the measurements' processing initial heading error in each system start was taken to be \(100^{\circ}\). During data integration we simultaneously used maximum five satellite signals (GPS or GLONASS) to generate phase measurements (12) so that to reduce the computation burden. To generate pseudorange (10) and radial velocity (11) measurements we applied the signals of the whole observable satellite constellation (both GPS and GLONASS).  
The car test path is shown in Figure 6. The figure presents relative displacement coordinates (\(\Delta\)Fi, \(\Delta\)La) in meters calculated from the route initial point.  
Figure 6: Car path during the tests.  
Reference attitude parameters were obtained using the data from reference INS (position 3 in Figure 4) with heading generation accuracy of 6'sec\(\wp\). Heading and heading rate during the route are presented in Figure 7 (ovals denote the maneuvers).  
Below the following results are presented.  
Section 5.1 demonstrates the results with GNSS signals from multiple satellites. The state vector (8) was estimated using differential measurements (10)-(12) including pseudorange, radial velocity, and phase single difference.  
Section 5.2 demonstrates the results with a signal of a single GLONASS satellite suitable for phase measurements generation (12) and with further signal outage. It is shown that with phase measurements (12) of a single satellite in filtering problem, all the components of FOG error model are estimated (3). Then the heading accuracy is slightly degraded as compared with the use of phase data from five satellites. Note that pseudorange (10) and radial velocity (11) measurements were generated by the signals from minimum five satellites.  
Section 5.3 presents the results under total GNSS signal outage. The filtering problem then uses differential measurements (20) generated by data from additional external linear velocity sensors. It is shown that acceptable heading accuracy (max error of 3\({}^{\circ}\)) can be provided for a long time.
# A Survey of Machine Learning Methods for Time Series Prediction
###### Abstract  
This study provides a comprehensive survey of the top-performing research papers in the field of time series prediction, offering insights into the most effective machine learning techniques, including tree-based, deep learning, and hybrid methods. It explores key factors influencing the model performance, such as the type of time series task, dataset size, and the time interval of historical data. Additionally, this study investigates potential biases in model development and weighs the trade-offs between the computational costs and performance. A detailed analysis of the most used error metrics and hyperparameter tuning methods in the reviewed papers is included. Furthermore, this study evaluates the results from prominent forecasting competitions, such as M5 and M6, to enrich the analysis. The findings of this paper highlight that tree-based methods like LightGBM 4.6.0 and deep learning methods like recurrent neural networks deliver the best performance in time series forecasting, with tree-based methods offering a significant advantage in terms of their computational efficiency. This paper concludes with practical recommendations for approaching time series forecasting tasks, offering valuable insights and actionable strategies that can enhance the accuracy and reliability of predictions derived from time series data.  
time series forecasting; machine learning; deep learning; XGBoost; LightGBM; CatBoost 2025155957
## 2 Problem Formulation  
### Single Diode Model  
Single diode model is a very popular model used to simulate the \(I\)-\(V\) characteristic of a solar cell. The output current \(I_{\rm L}\) (A), as depicted in Figure 1, can be formulated as follows according to [PERSON]'s current law.  
\[I_{\rm L}=I_{\rm ph}-I_{\rm d}-I_{\rm sh} \tag{1}\]  
where \(I_{\rm ph}\), \(I_{\rm d}\), and \(I_{\rm sh}\) are the photo generated current (A), diode current (A), and shunt resistor current (A), respectively. \(I_{\rm d}\) and \(I_{\rm sh}\) are calculated by Equations (2) and (3), respectively [24; 51; 52; 53].  
\[I_{\rm d}=I_{\rm sd}\cdot[\exp(\frac{V_{\rm L}+R_{\rm s}\cdot I_{\rm L}}{nV_{ \rm t}})-1] \tag{2}\]  
\[I_{\rm sh}=\frac{V_{\rm L}+R_{\rm s}\cdot I_{\rm L}}{R_{\rm sh}} \tag{3}\]  
\[V_{\rm t}=\frac{kT}{q} \tag{4}\]  
where \(V_{\rm L}\) and \(V_{\rm t}\) represent the output voltage (V) and thermal voltage (V), respectively. \(I_{\rm sd}\) is the reverse saturation current (A). \(R_{\rm s}\) and \(R_{\rm sh}\) denote the series resistance (\(\Omega\)) and shunt resistance (\(\Omega\)), respectively. \(n\) is the diode ideal factor. \(k=1.3806503\times 10^{-23}{\rm J/K}\) is the Boltzmann constant. \(q=1.60217646\times 10^{-19}{\rm C}\) is the electron charge. \(T\) denotes the cell temperature in Kelvin.  
Substituting Equations (2)-(4) into Equation (1), the output current \(I_{\rm L}\) can be written as follows:  
\[I_{\rm L}=I_{\rm ph}-I_{\rm sd}\cdot[\exp(\frac{V_{\rm L}+R_{\rm s}\cdot I_{ \rm L}}{nV_{\rm t}})-1]-\frac{V_{\rm L}+R_{\rm s}\cdot I_{\rm L}}{R_{\rm sh}} \tag{5}\]  
It is observed from Equation (5) that if we know the values of \(I_{\rm ph}\), \(I_{\rm sd}\), \(R_{\rm s}\), \(R_{\rm sh}\), and \(n\), then the \(I\)-\(V\) characteristic of this model can be constructed. Therefore, accurate extraction of these five unknown parameters is the core of this study.
## 2 Materials and Methods
### User Experience  
After 2 months of the in-home rehabilitation program, all patients in the MAR group were contacted via telephone for a 20-min interview on their experience of the MSD design. Closed- and open-ended questions were used to evaluate their actual use of the device and obtain feedback and detailed suggestions.  
The following 4 items were assessed as closed questions on a 0-10 scale ([PERSON]'s alpha = 0.93):  
1. Helpfulness of the MSD in assisting with rehabilitation: \"On a scale of 0 to 10 (0 indicating the least helpful and 10 indicating the most helpful), how would you rate the helpfulness of the wearable motion sensor device in assisting your home-based exercises after knee replacement?\"
2. Ease of operability for the MSD: \"On a scale of 0 to 10 (0 indicating the most difficult and 10 means the easiest), how would you rate the ease of operability for the motion sensor device in your daily exercise?\"
3. Satisfaction with the app design: \"On a scale of 0 to 10 (0 indicating the least satisfied and 10 means the most satisfied), how satisfied are you with the app's design and visual appeal?\"
4. Interests for future use: \"On a scale of 0 to 10 (0 indicating the least interested and 10 means the most interested), how interested would you be in using the device after it is fully developed in assisting your future rehabilitation if needed?\"  
After finishing all interviews, the research team summarized the findings and forwarded them to participants for confirmation and further feedback, if any.
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 4 Experimental Setup and Scenario-Based Simulation
### Scenario-Based Simulation  
The simulator CloudSim Plus [7; 21] is used to present and simulate the scenarios to assess the proposed algorithm and methodology. For the simulation, a virtual host with a Virtual Machine setup matching the instances of Amazon T2.medium is used. The processor used in EC2 T2 is a 2 nd Gen Intel Xeon Scalable processor Ref. [33]. Table 3 displays the reference setup for the VM as used in the simulation.  
One VM is used as a baseline at the beginning of the simulation that is kept constant for all experiments. The power unit for processing the CPU is calculated in Million per second instruction (MIPS). The Geekbench-5 Ref. [34] calculates the MIPS of Amazon EC2 T2. medium instances based on the [PERSON] guidelines Ref. [35]. The provisioning policy is a time-shared policy for both VMs and workloads. The approach equally shares the computing power into workloads simultaneously in a VM.  
The first scenario is to submit a workload (Table 2, Rows 1-4, and Rows 5-8) for ten instances handled by the VM every 15 s. For the first and second case, where \"Definitely No Violation\" and \"Normal\" were the QoS criteria, the calculated decisions rule. In this case, no scalability algorithms were triggered. The experiment is replicated 20 times, with random workload submissions reaching 200.  
The second scenario is to submit a workload (Table 2, Rows 9-12) for ten occurrences, processed by the VM every 15 s. The experiment is replicated 20 times with the submission of variant workloads at random reaching 350. The third option is to apply a workload (Table 2, Rows 13-16) for ten occurrences handled by the VM every 15 s. For this third case, the role of prevention is assigned by the scalable resource allocation followed by the USL model generation for handling the workload resulting in QoS \"Definitely Violated\" due to the under-provisioning. The experiment is replicated 20 times with a minimum of 500 instances of the random workload resulting in a \"Definitely Violation\" decision rule.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 2 Related Works
### Four-Dimensional Radar Perception  
The recent advent of 4D radar and the introduction of height information have made the adaption of 3D point cloud methods, such as frameworks for 3D voxelization, to radar-only detection approaches possible.  
The pillar representation divides a point cloud into an even grid. However, in contrast to voxels, each grid cell has an unlimited height, shaped like a pillar, summarizing all points inside that cell. The reduction of height allows for a 2D bird's-eye representation over the point cloud. Pillar networks apply a 2D backbone to this representation. The multi-scale backbone features are further processed by a network neck, followed by detection heads to output detection predictions. Pillar networks have proven effective, especially for accurate and robust real-time point cloud detection.  
RPFA-Net [26] and VoD [5] make use of this concept and adapt the PointPillars architecture [9] in combination with attention mechanisms to create 2D pseudo-images, which are processed in a BEV perspective by a 2D-CNN. [PERSON] et al. [27] used vector attention [28].  
Four-dimensional data are comparatively new, and for this reason, there is limited research in this area. Four-dimensional radar still suffers from sparsity, and as such, there is a natural imbalance between classes (large objects such as cars have more points than small objects such as pedestrians). Attention mechanisms have been studied for a single layer but remain under-explored in aggregating multi-scale information. VRU detection has been studied with other sensor modalities [3], but it has yet to be studied with 4D radar data.