esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical and Physical Model of Thermal Energy Dissipation in Particle Assembly  
The basic modes of heat transfer include conduction, convection, and radiation [26; 27]. Without considering the kinetic energy generated by particle oscillation on a long-term scale, the heat \(Q_{H}\) absorbed by the particle during forced vibration \(\Delta t\) can be calculated as follows:  
\[Q_{H}=Q_{p}+Q_{A}+Q_{i} \tag{1}\]  
In this case, \(Q_{p}\) is the heat transfer due to thermal convection in the internal part of the particle during vibration, \(Q_{A}\) is the heat transfer between the surface of the particle in the container and the air, and \(Q_{i}\) is the heat radiation from the particle. \(Q_{p}\) is the heat transfer due to thermal convection in the internal part of the particle for solid conduction, which can be obtained using the following formula:  
\[Q_{p}=cm\Delta T \tag{2}\]  
In this case, \(c\) is the specific heat capacity of the particle, \(m\) is the total mass of the particle, and \(\Delta T\) is the change in temperature.  
According to [PERSON]'s cooling law, in a closed container containing the particle, natural convection of air is a heat transfer method with no forced airflow. \(Q_{A}\) is the convective heat transfer rate of air:  
\[Q_{A}=hA\Delta T \tag{3}\]  
Figure 3: Total particle kinetic energy, total particle rotational kinetic energy, and total number of collision times under different excitation conditions.  
\(h\) is the coefficient of convection heat transfer used here, and \(A\) is the surface area of the object exposed to the moving fluid.  
According to the Stefan-Boltzmann law, the heat radiation of a particle can be determined by the following formula:  
\[Q_{i}=\sigma A\Delta T^{4} \tag{4}\]  
In this case, the constant \(\sigma\) is the Stefan-Boltzmann constant, with a value of \(\sigma~{}=~{}5.67\)\(\times~{}10^{-8}~{}\mathrm{W/m^{2}}~{}\cdot~{}\mathrm{K^{4}}\). In the experiment, adding insulation layers can reduce the consumption of heat radiation, and since \(Q_{i}<<Q_{p}\), the thermal radiation of the particle can be ignored.  
The total energy \(Q\) during the forced vibration of a particle is the sum of the energy of the forced vibration in each period. Among them, the experiment can record the variation curve of the reduced acceleration of the particle body with time under different forced vibration states. Because the reduced acceleration curve of the particle damper in each cycle is similar (see Appendix B), the reduced acceleration in multiple cycles is integrally averaged. The average speed of the particle damper in a cycle under different vibration states is calculated. The total energy of each cycle in the forced vibration time is as follows:  
\[Q=\sum_{1}^{n}\frac{1}{2}mv^{2} \tag{5}\]  
where \(n\) is the total number of all periods in the forced vibration time. The ratio of thermal energy dissipation generated by the particle during forced vibration to its total energy is as follows:  
\[\alpha=\frac{Q_{H}}{Q} \tag{6}\]
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Identification Experiments and Results
#### 3.5.3 Analysis of Computational Complexity  
Computational complexity is considered to be another significant indicator of the corrugation identification algorithms. Therefore, in this subsection, the computational time complexity of the proposed IMFLLC algorithm is theoretically analyzed at first and then the practical identification speed is given.  
In general, computational complexity consists of space complexity and time complexity. The reason why only time complexity is considered here is that the time source is more important than memory space for the corrugation identification algorithms. The time complexity of an algorithm is a function that qualitatively describes the running time of the algorithm. In addition, the complexity function is usually represented by \(O(.)\) notation, which excludes the low-order terms and the first coefficient of this function [38].  
At first, the computational time complexity function of IMFLLC is analyzed. As discussed in Section 3.4, IMFLLC-based corrugation identification involves three key steps: (1) rail image multi-feature extraction; (2) vector quantization of extracted features; and (3) rail corrugation identification. In the first step, the Uniform LBP and dense_SIFT feature of the input rail image are extracted, respectively. In terms of the feature extraction principle of Uniform LBP [20] and dense_SIFT [21], the time complexity of the first step is approximately equal to \(O(N)\), where \(N\) is the pixel number of the input rail image. Secondly, by using LLC method, the computational complexity of vector quantization procedure is \(O(M+K^{2})\)[23], in which \(M\) denotes the codebook size and \(K\) stands for the nearest neighbors number. In the last step, based on the linear SVM classifier obtained from the training procedure, the input rail image is judged whether it is a corrugation rail image or not. This procedure using simple linear SVM obtains a constant complexity, so the computational time complexity of rail corrugation identification is \(O(1)\). As a whole, the time complexity of IMFLLC is \((O(N)+O(M+K^{2})+O(1))\).  
After that, the actual time of a corrugation rail image identification is tested. This experiment was carried on a desktop with Intel(R) Core(TM)2 Duo CPU (2.93 GHz) and 4-GB memory. The proposed IMFLLC algorithm is implemented with MATLAB. Furthermore, the program is performed on the test images and the mean of the run time is recorded. The final average identification time of the corrugation rail image is 0.27 s, which indicates that an acceptable corrugation identification speed based on the IMFLLC can be achieved.
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail  
### Introduction  
PAQ is a series of experimental lossless data compression software aiming at the best compression ratio for a wide range of file types without a focus on using few computing resources or keeping backward version compatibility. It was started by [PERSON] and later developed by more than 20 developers in different branches of compression. PAQ8 PX is a branch of PAQ started by [PERSON] in 2009 and that has recently adopted the best image compression models in the series with the help of [PERSON]. In short, we refer to version 167 of PAQ8 PX.  
A detailed description of the software in its current phase is not available in the literature. The reason may be the everchanging filetype specific models and the amount of version branching this software receives, from simplified models for fast compression to platform-specific optimization tests and the generalization of the algorithms used. However, a description of the PAQ series of compressors from the perspective of machine learning is available at [18].  
The description of the overall compression algorithm and the techniques used can be found in [19] and [20]. The PAQ8 PX version has a development thread that can be found at [21]. The source code is written in the C++ programming language and is contained in only one file with more than 12000 lines of code. The logic was not broken into different files in order to make it easier to compile to any platform. The big downside of this is that it makes the code very difficult to read. Another thing that makes the code difficult to develop is that numerous optimization techniques were inserted along the code, which can slow down the understanding of what is going to execute and when.
## 4 Examples and Discussion
### Experimental Design and Parameters Settings  
The simulation experiment's core modules in the RL environment consist of road network setup, agent state space and reward function calculation, simulation execution, and environmental resetting following each run. Road network setup involves determining start and end points and lane segmentation and linking, along with configuring signal timings. Information on simulated vehicles and roads is sourced from Traci for state-space computation. The overall reward in this simulation experiment is calculated by evaluating crucial factors, including energy consumption per unit, travel distance, estimated collision time, and driving comfort. Moreover, the environmental reset process entails clearing all data before each training episode to initialize the simulation environment. In the simulation operation module, the vehicle is programmed to terminate the simulation once it has traversed beyond the entire road length and subsequently returns state and reward values to the main function after each iteration. Data from the simulations are stored in a numerical matrix format and outputted as text files for ease of sorting and analysis.  
Four distinct control methods, namely the Krauss model, TD3, DQN, and DDPG, are employed for longitudinal following of the CAV during intersection navigation. Additionally, the lateral lane-changing behavior of vehicles is governed by the SUMO default lane-changing model (LC2013) [42]. Experiments are conducted to assess and compare the performance of the CAV under each control method to identify the most effective one. To alleviate the impact of the preceding vehicle on the training vehicle and prioritize safety, default values for speed differential and relative distance in state space are set as maximum speed limits and safety thresholds. For structural parameters, current market performance parameters for electric vehicles are used, and drag coefficients for vehicle motion are sourced from existing studies [43]. In addition, the hyperparameter of the RL algorithm is determined through numerous experiments. A hyperparameter in machine learning refers to a configuration setting that influences the behavior and performance of a model during training, yet it cannot be directly learned from the data. Examples include learning rate, batch size, and discount factor. It is imperative to maintain consistency in environmental parameters across simulations, encompassing road and vehicle attributes. These essential parameters for simulation fidelity are detailed in Table 2.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 3 Methods
### Structure of the Linear Equalizer  
The coefficient vector of the equalizer \(\overrightarrow{w}\), representing the optimal values of the linear equalize,\(r\) is based on the minimum mean squared error (MMSE) estimation between the original and the distorted signals. The given coefficients are set according to the input values and calculated using various algorithms, such as LMS or RLS.  
The decision circuit ensures rounding to the nearest value within the used constellation diagram. The error can be determined either based on the knowledge of the used constellation diagram or on the basis of the so-called training sequence, which is transmitted before the actual data stream (Figure 5). If a linear equalizer is used in multi-tone modulations, a separate equalizer is inserted in each output branch of the receiver.
## 6 Conclusions  
In this paper, we provided a framework for QoS self-provisioning for 3G small cells based on a lightly cognitive approach--the network listen mode. The QoS self-provisioning and interference management method utilizes network measurements to optimally configure power configuration of its different downlink channels, as well as the suitable uplink transmit power limits for connected users. The method works for both dedicated and shared carrier deployment of femtocells as well as for both open and closed subscriber group modes. However, in this paper, we have chiefly focused our attention on the most challenging shared channel and restricted access modes.  
It is shown that our algorithm controls the target data rates with high accuracy and good robustness against the modeled dynamic effects which cannot be predicted by the HNB-only based method. Examples have shown only few percent outages of up to 1 Mbps downlink throughput and additionally guaranteed across the whole intended femtocell coverage zone. In uplink, fair rates around 256-512 kbps can be guaranteed, but beyond these rates, tradeoffs need be established. Besides controlling the desired femtocell service levels to tight margins, the solution offers additional benefits in terms of required downlink transmit power. Compared to not using power control and a fixed data to pilot power ratio, the proposed method provides 9 dB transmission power saving and better outage performance. Compared to a 3 GPP reference method, the outage is very similar, but a power saving close to 4 dB is still achieved. By a simple parameter setting (coverage target set to infinity), the method automatically boosts the femtocell range while maintaining the QoS requirements at the cell edge. For the case where a 256 kbps service is sufficient, the method offers approximately 15-meter coverage radius increase compared to a no power control reference with a fixed and typical transmit power ratio.  
The framework was partially verified by field measurements in an existing network deployment with femtocell deployed on same carrier as macro. The results show a good match between predicted and offered coverage in real network with test equipment for macrocell edge deployment of small cell.  
Figure 13: Measured and simulated noise rise in the macrocell.  
Furthermore, the assumptions related to uplink noise rise dimensioning were verified. The predicted NR contribution from a single user connected to the small cell was checked depending on the small cell geographical position during extensive measurement campaign. While not being a full verification of the proposed method, it verifies the main assumptions behind the algorithms. Future work will focus on moving beyond the initial parameter initialization to further enhancements that could be made possible by collecting measurements of end-users session throughputs and measurement reports from connected devices.
## 4 Predictions Using ML Algorithms
### Predictions for Settlement  
Figures 13(a) and 13(a) show the training and testing results of the prediction of the average value of settlement by RF, MLP, and XGBoost, where the solid line indicates equality. It is clear that the accuracy of the testing model is close to that of the training model. As can be seen from the plots, all three methods have learned the input/output mapping effectively, with the XGBoost and RFR models outperforming the MLP model. As shown in the plots, the MLP model gives fairly reasonable predictions for settlements less than 150 mm, but has slightly incorrect estimations for settlements exceeding 150 mm. The training and testing results of the settlement predictions at measurement points ESD-L, ESD-C, and ESD-R are shown in Figures 13(b)-d and 13(b)-d. Similar to the prediction results for the average values of settlements, the XGBoost and RFR models predicted the settlement values of measurement points ESD-L, ESD-C, and ESD-R more effectively than the MLP did.  
Figure 14: Predictive results using training samples: (**a**) ESD-L, (**b**) ESD-C, (**c**) ESD-R, and (**d**) average value.  
Figure 15: Predictive results using testing samples: (**a**) ESD-L, (**b**) ESD-C, (**c**) ESD-R, and (**d**) average value.  
In this research, the data used for machine learning was obtained from field monitoring. The amount of field monitoring data is small. The XGBoost and RFR models perform more effectively when processing the sparse data. Considering that MLP is a deep learning method, the effect of MLP may improve if the sample continues to expand.  
To assess the accuracy of the training and testing models quantitatively, three evaluation indicators were introduced. The performance indicators are the coefficient of determination (R\({}^{2}\)), the maximum average error (MAE), and the root mean square error (RMSE), expressed as Equations (3)-(5), respectively.  
\[\text{R}^{2}=1-\frac{\sum\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2}}{\sum\bigl{(}Y_ {i}-\overline{Y}\bigr{)}^{2}} \tag{3}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2} \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2}} \tag{5}\]  
where \(Y_{i}\) is the ith observed element, \(\hat{Y}_{i}\) is the ith predicted element, \(\overline{Y}\) is the mean of the observed values of \(Y_{i}\), and \(n\) is the number of datasets used. Theoretically, the MSE and RMSE measure the deviation between the measured and predicted data, and lower MSE and RMSE values suggest better performance. R\({}^{2}\) values that approach 1 indicate a better fit of the model to the data.  
The performance indicators of the various models are shown in Table 2. As may be seen when comparing the values of R\({}^{2}\), MSE, and RMSE, XGBoost performs best in the training models, while RF performs better in the testing models. In general, the best predictive model is RF, followed by XGBoost.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 1 Introduction
Figure 2d demonstrates a different scenario where all sensor nodes are far away from each other and no wireless connection can be established between them. In this case, the UAV will reach within each node's communication range to communicate with individual nodes. Then, the existing optimal communication protocols can be used. When the energy consumption of either the UAV or sensor networks is considered, optimization in the physical layer (rather than in the communication protocols) needs to be addressed. Two apparent scenarios exist: (1) through spatial path-energy optimization the UAV finds the optimal flying path  
Figure 2: Conceptual field development of an AG-WSN and opportunistic networking: (**a**) sample field sub-networks; (**b**) a sub-network case wherein all sensors work normally and the UAV receives data directly or via relay without flying to individual sensors; (**c**) a sub-network case wherein some sensor failure is allowed and the UAV needs to be mobile to collect data; and (**d**) a sub-network case wherein the sensors are far with each not allowing relay, and the UAV needs to be fully mobile to fly to individual sensors for data collection. through the geospatially deployed ground sensors, for which it belongs to a typical traveling salesmen problem (TSP) and is being tackled in our recent research [36]; and (2) through a sensor activation approach, as being concentrated on in this paper, such that sensors are only active when the UAV is in its neighborhood.
###### Abstract  
This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for \(\mathcal{H}_{\infty}\) design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.  
fault diagnosis and isolation; multiple sensor faults; LPV observer; permanent magnet synchronous generator +
Footnote †: journal: Applied Sciences
## 5 Experimental Setup
### Training of SSD MobileNet 640 \(\times\) 640 FPNLite  
The \"SSD MobileNet 640 \(\times\) 640 FPNLite\" pre-trained model was obtained from the Tensorflow 2 Model Zoo [63] and underwent retraining on a personal computer equipped with an Intel(R) Core(TM) i7-8750H CPU 2.20 GHz, 8 GB RAM, and an NVIDIA Geforce GTX 1060 6 GB video card (Manufacturer: ASUS; Country of origin: Bucharest, Romania). We updated the video card driver on the PC and installed the CUDA (11.2) and cuDNN (8.1) drivers [64] compatible with the Tensorflow version (2.7.0) to enable the learning process to run on the GPU for better efficiency. We created a virtual environment in Anaconda Prompt where the Tensorflow framework, Object Detection API, and necessary modules were installed [65].  
The dataset used in the previous chapter served as the foundation for training the model, with an 80% split for training and 20% for testing. The training process consisted of 25,000 iterations, with a batch size of 4 and a learning rate specified in Figure 9. The model's overall loss function encompassing regularization error, localization error, and classification error, was minimized to 0.074 within a duration of 2.5 h.  
Figure 8: Unidentified objects.  
Figure 7: Comparing validation loss and accuracy variation for the nighttime model.  
Following the conclusion of the training process, we exported the model using the script \"exporter_main_v2_py\" and subsequently validated using the script \"model_main_tf2.py\". During the testing phase, it became apparent that the model exhibited lower performance on the test dataset, manifesting some false predictions, despite the effective minimization of the loss function during training. This phenomenon, where the model excels on the training dataset but struggles with the test dataset, is known as overfitting [66]. The overfitted model was highly tailored to the training dataset and failed to generalize to new data, resulting in erroneous outcomes and suboptimal decisions.