esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Rockburst Monitoring Data Processing  
### Rockburst Monitoring Data Acquisition  
Based on the mine rockburst warning project, the data come from Yuanzigou Coal Mine in Baoji, Shaanxi Province, equipped with microseismic monitoring, stress monitoring, and mine pressure monitoring systems. Multiple rockburst events were monitored from January 2020 to 31 March 2021. Due to a large number of monitoring data indicators, the characteristic variables that can characterize the risk of rockburst are initially selected according to practical applications [18], that is, frequency (X1), diurnal frequency variation (X2), energy sum (X3), energy max (X4), energy anomaly coefficient k (X5), and tons of coal release energy (X6) in the microseismic monitoring system, shallow hole stress max (X7), deep hole stress max (X8), and stress increase max (X9) in the stress monitoring system, bolt stress max (X10) and anchor cable prestress max (X11) in the mine pressure monitoring system, AVG daily footage (X12), daily output (X13), and periodic weighting (X14) in progress monitoring, and expert evaluation of the impact hazard level of the working face. Part of the data is shown in Table 1.
## 3 Physical (PHY) Layer and Media Access Control (MAC) in IoT
### Media Access and PHY Layer  
IoT is implemented using Wi-Fi, Bluetooth, ZigBee, and Z-Wave standards; thus, medium access varies, as well as the number of channels and frequency spectrum usage. Medium is accessed either using the non-beacon mode or the non-slotted CSMA/CA method, or the MAC is performed using a beacon-based slotted CSMA/CA mode. BPSK is used for lower data rates, whereas quadrature amplitude modulation (QAM) is used for elevated data rates in the order of 2n, where n is the number of bits per symbol [27].
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 3 Mathematical Model
### Parameter Optimization of Model
#### 3.4.3 Parameter Optimization Process  
Parameter optimization adopts the way of cross-validation setting and parameter combination traversal. By constructing a KFold object, the cross-validation fold is set to 5, and the random state is clearly specified, so as to ensure the repeatability of the whole process. Use the multi-layer nested loop to traverse all predefined parameter combinations. For each specific set of parameter combinations, the following processes are performed in the splitting process of each cross-validation:  
1. [label=(0)]
2. Accurately obtain the corresponding training set and verification set data according to the split index.
3. Perform bootstrap sampling in turn, divide verification set carefully, create LightGBM data set objects, set model parameters reasonably, conduct sub-model training, and finally, complete the prediction task on the verification set.
4. Accurately calculate the accuracy rate, precision rate, recall rate, and F1 value on this verification set, and save the results of these evaluation indicators.  
For each hyperparameter combination, after all the cross-validation splitting operations are completed, the evaluation index results are averaged, and the average accuracy, average accuracy, average recall and average F1 value under this parameter combination are obtained.  
Through the above-mentioned parameter optimization, the model parameters can be adjusted in a systematic way. This method can improve the performance and generalization ability of the prediction model of heart failure combining bootstrap sampling and LightGBM.
## 2 Materials and Methods
### Analytical Methods  
The method detailed below, used to obtain estimates of soil organic carbon stocks, was based on the principles outlined in [27]. For each sample, this involved obtaining estimates of the bulk density and organic carbon content (% Corg). These values were then used to estimate the carbon stocks in each land use and soil class in MgC/hectare.  
Figure 1: The location of the study area, highlighting the four territorial administrative units where the survey was conducted.  
2.3.1. Soil Bulk Density  
The bulk density was calculated using the mass of the completely dry sample and the volume of the wet sample using the following formula:  
\[Soil\ bulk\ density\left(g/cm^{3}\right)=\frac{mass\ of\ dry\ sample\left(g \right)}{volume\ of\ wet\ sample\left(cm^{3}\right)} \tag{1}\]  
2.3.2. Organic Carbon Content (% Corg)  
This parameter was analysed using Multi N/C Analytic Jena equipment and following the ISO 8245/EN 1484 standard [28,29].  
2.3.3. Total Soil Organic Carbon Stock  
Four distinct steps were followed to assess the total organic carbon stock:  
1. The soil carbon density for each core was calculated using the following formula:  
\[Soil\ carbon\ density\left(g/cm^{3}\right)=dry\ bulk\ density\left(g/cm^{3} \right)\times\left(\%C_{org}/100\right) \tag{2}\]
2. The amount of carbon in each core section was calculated by multiplying the carbon density value obtained in the previous step by the thickness of the sampling interval (60 cm).  
\[Amount\ of\ carbon\ in\ core\ section\left(g/cm^{2}\right)=Soil\ carbon\ density\left(g/cm^{3} \right)\times thickness\ interval\ (\mathrm{cm}) \tag{3}\]
3. The amount of carbon in each section was summed over the total sampling depth of a core.
4. The total core carbon content was converted into the units commonly cited in carbon stock assessments (MgC/hectare) using the following unit conversion factors: 1,000,000 g per Mg (megagram) and 100,000,000 cm\({}^{2}\) per hectare.  
Figure 2: Position of sampling points according to land use type.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 8 Conclusions  
The goal of this research was to evaluate the OpenCL as an HLS language for the implementation of a guided image filter on the HARP platform. We have performed this evaluation by using synthetic benchmarks to measure the maximum bandwidth, the influence of the OpenCL cache as well as SVM, and then using the results as guidelines to implement and optimize the guided image filter. Our implementation of the guided image filter processes RGB images of 1080 \(\times\) 1920 pixels at 74 frames/s.  
Based on our findings it can be stated that the HARP platform offers several architectural structures that benefit the implementation of applications such as the guided image filter. Furthermore, OpenCL for HARP allows the tapping of these benefits and the use of specific optimizations to exploit the architectural structures of HARP that improve the implementation. The main advantages are the shared memory and the high bandwidth. HARP uses shared DRAM memory for the CPU and the FPGA. As this shared memory approach is supported by shared virtual memory in OpenCL, this can be fully exploited. Furthermore, a high CPU-FPGA bandwidth is available to OpenCL developers. Besides the HARP platform, the OpenCL compiler offers performance enhancing advantages. A highly effective feature is the implementation of the OpenCL cache. This cache can provide data at a high data rate, and reduces the amount of memory requests. The OpenCL profiler is useful to evaluate the OpenCL kernel. Performance characteristics are visualized in an uncomplicated way, which allows an easy performance analysis.  
Conceptualization, E.H.D. and B.G.; Methodology, T.F., E.H.D. and B.G.; Software T.F.; Validation T.F. and E.H.D.; Formal Analysis, T.F.; Investigation, T.F., E.H.D. and B.G., Writing--Original Draft Preparation E.H.D., Writing--Review and Editing E.H.D., T.F. and B.G., Supervision E.H.D. and B.G.  
This research received no external funding.  
We appreciate the useful comments and feedback from the anonymous reviewers. We thank Intel for the donation of the development tools and hardware. The presented HARP-2 results were obtained on resources hosted at the Paderborn Center for Parallel Computing (PC2) in the Intel Hardware Accelerator Research Program (HARP2).  
The authors declare no conflicts of interest.
###### Abstract  
High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.  
target tracking; unscented particle filter; partitioned sampling; adaptive sample size
## 3 Case Study  
### Numerical Modeling Construction  
Taking the damage identification of a steel truss structure as an example, the real picture of the steel truss structure is shown in Figure 5 and the basic structure is shown in Figure 6.  
Figure 5: Real picture of steel truss structure.  
In this study, we examine how various forms of damage affect the truss structure under different operational conditions. The structural elements are connected using a coupling method, with one end featuring a fixed hinge and the other a sliding hinge connection. The total length of the structure is 8.4 m, with an inner steel pipe diameter of 12 mm and an outer diameter of 18 mm. To simulate real-world conditions in numerical simulations, the four endpoints at the two ends of the steel truss are anchored on four 150 kg pillars. The properties of the steel pipes are detailed in Table 1.  
Modal parameters of the structure are determined through hammering excitation, allowing for an examination of how damage impacts these parameters. Additionally, acceleration signals are collected at each node, and the structural damage response data is derived through the application of transfer functions. This comprehensive approach enables a thorough investigation into the effects of different types of damage on the entire truss structure.
# Vehicle Trajectory Reconstruction Using Lagrange-Interpolation-Based Framework
## 3 Methodology
### Filter Denoising  
Noise data impact data analysis and calculation. The Kalman filter is used to estimate the state of a dynamic system according to the joint distribution of observed data at different times [40]. Compared with the single observation estimation method, it establishes a state space model for discrete stochastic systems through state equations and observation equations. The method can be defined by Equations (6) and (7).  
\[x(t+1)=\Phi x(t)+\mathrm{B}u(t)+\Gamma w(t) \tag{6}\]  
\[y(t)=Hx(t)+v(t) \tag{7}\]  
where \(x(t)\) is the vector of the state system; \(y(t)\) is the vector of the observation system; \(\Phi,\mathrm{B},\Gamma\) and \(H\) represent, respectively, the state transition matrix, control coefficient matrix, input noise coefficient matrix and observation coefficient matrix; \(u(t)\) is input control, initially set to be 0; \(w(t)\) is input white noise; and \(v(t)\) is observation noise.  
Figure 3: The diagram of a data chain.  
Kalman filtering removes noise through the prediction and update stages. The filter uses the estimated values of the previous state to obtain the prediction values of the current state in the prediction stage. The prediction formulas are defined by Equations (8) and (9).  
\[\hat{x}(t)^{-}=\Phi\hat{x}(t-1) \tag{8}\]  
\[P(t)^{-}=\Phi P(t-1)\Phi^{T}+Q \tag{9}\]  
where \(\wedge\) represents the predicted value; \(-\) represents the prior value; \(P(t)\) is the estimation error covariance, and \(Q\) is the variance matrix of the process noise.  
The values of the observation update are calculated with the current observed values and the predicted values from the prediction stage to obtain more accurate estimated values in the update stage. The update formulas are defined by Equations (10) and (11).  
\[K(t)=P(t)^{-}H^{T}\Big{(}HP(T)^{-}H^{T}+R\Big{)}^{-1} \tag{10}\]  
\[\hat{x}(t)=\hat{x}(t)^{-}+K(t)\Big{(}y(t)-H\hat{x}(t)^{-}\Big{)} \tag{11}\]  
\[P(t)=(I-K(t)H)P(t)^{-} \tag{12}\]  
where \(K(t)\) is Kalman gain, and \(R\) is the variance matrix of the observation noise.  
The paper uses Kalman filtering to repeat every time in order to denoise and make the initial value of the state estimate equal to the initial value in the previous step (Figure 4). The prediction formulas are used to predict the values at each time, and then the update formulas optimize the estimated values.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Experimental Results
#### 6.1.2 Test B  
Figure 7 shows another test result in the normal monitoring case, in which the prediction result was affected by normal random events in daily life. In this test, the previous prediction result was 751 W, but the current load (\(t\)) was 561 W. The two results exhibit two different patterns. Therefore, according to the detection rule of the proposed pattern matching-based anomaly detector, the cross-entropy loss function is employed to evaluate the classification result of the real load. The loss difference is smaller than 3. Therefore, the result is verified as acceptable. The difference between the prediction result and the real load is caused by normal variations in the residential electrical load. Finally, the predictor forecasts a prediction for the next step (\(t+1\)) to be 596 W.
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 3 Joint Optimization Algorithm for the Relay Transmit Power and PS Ratio
### Case 3
In the single-relay power allocation, given the initial optimal EE \(x^{*}\) and the segment \(s_{i}\) of the EH model, the Dinkelbach iteration algorithm is used to obtain the optimal relay transmit power, the optimal PS ratio and the optimal EE of \(R_{i}\) (i.e., the inner loop algorithm based on the Dinkelbach iteration, given by Algorithm 2). The workflow of this power allocation algorithm is shown in Figure 3.  
In the inner loop algorithm, in the worst case, the Dinkelbach iteration is \(I_{\max 2}\), and it needs to be performed \((L+1)\) times. The outer loop algorithm contains the \(I_{\max 1}\) inner loop algorithm, where the fixed-point iteration is \(N\) in the worst case. Therefore, the complexity is \(O((L+1)NI_{\max 1}I_{\max 2})\). ```
1:Initialize the maximum iterations \(I_{\text{max1}}\) and the maximum error tolerance \(\delta\). Set the iteration index \(m=0\), the optimal relay transmission power \(P_{i}^{0}\) and the optimal PS ratio \(\rho_{i}^{0}\), \((i=1,...,N)\). 2:repeat
3: For fixed \(P_{i}^{m}\) and \(\rho_{m}^{m}\), solve (P3) by Algorithm 2 to get \(P_{m+1}^{m}\), \(\rho_{m+1}^{m}\) and \(\eta^{*},i=1,...,N\)
4: if \(\left|P_{i}^{m+1}-P_{i}^{m}\right|<\delta\) and \(\left|\rho_{i}^{m+1}-\rho_{i}^{m}\right|<\delta\) then
5: Convergence = true
6: return \(P_{i}^{*}=P_{i}^{m+1}\), \(\rho_{i}^{*}=\rho_{i}^{m+1}\)
7: else
8: \(m=m+1\) and Convergence = false
9: end if
10: until Convergence = true or \(m=I_{\text{max1}}\)
```  
**Algorithm 1** Outer loop algorithm based on a fixed-point iteration. ```
1:We set \(\rho_{i}=1\) and Calculate maximum number of segments \(S_{i}\). 2:for \(s_{i}=1:S_{i}\). 3: Initialize the maximum iterations \(I_{\text{max2}}\), the maximum error tolerance \(\delta\) and the optimal energy efficiency \(x^{*}\). Set iteration index \(k=0\). 4:repeat
5:\(\psi_{1}=\sum\limits_{w=1,w\
eq i}^{N}\gamma_{w}\), \(\varphi_{i}=\sum\limits_{w=1,w\
eq i}^{N}P_{w}\), \(\phi_{i}=\sum\limits_{w=1,w\
eq i}^{N}\left(P_{w}+P_{Q}-P_{H}^{w}(\rho_{w})\right)\)
6: if \(s_{i}=L\) then
7: Solve the power allocation by using (16), (18), (20) to get \(P_{i}\), \(\rho_{i}\). 8: else
9: Solve the power allocation by using (22), (24), (26), (28) to get \(P_{i}\), \(\rho_{i}\). 10: end if
11: Update \(x_{k+1}^{*}\). 12: if \(\left|x_{k+1}^{*}-x_{k}^{*}\right|<\delta\) then
13: Convergence = true
14: return \(P_{i}^{s_{i}}=P_{i}\), \(\rho_{i}^{s_{i}}=\rho_{i}\), \(\eta_{s_{i}}=x_{k+1}^{*}\)
15: else
16:\(k=k+1\) and Convergence = false
17: end if
18: until Convergence = true or \(k=I_{\text{max2}}\)
19:end for
20:\([\eta^{*},s_{i}]=\text{max}(\eta_{s_{i}})\)
21: return \(P_{i}^{m+1}=P_{i}^{s}(s_{i})\), \(P_{i}^{m+1}=P_{i}^{s_{i}}(s_{i})\), \(\eta^{*}\)
```  
**Algorithm 2** Inner loop algorithm based on the Dinkelbach iteration.
## 3 Flight Controller Design  
The 3-DoF quadrotor model has been derived in (9)-(11). The system contains two independent control inputs: one is the translational force \(F\) and the other is the rotational torque \(M\). The main design task is to develop proper control laws to achieve the desired flight demands. In this study, the control objective is the trajectory tracking control, namely \((x,z)\rightarrow(x_{d},z_{d})\), and keeps the attitude \(\theta\) stable simultaneously. In a physical sense, the force \(F\) can be independently designed to achieve altitude tracking control. However, the translation \(x\) relies on the change of the attitude \(\theta\) making the system have  
[PERSON]'s inequality gives \(\lambda_{min}(\mathbf{Q})\|\mathbf{e}\|^{2}\leq\mathbf{e}^{\mathsf{T}}\mathbf{Q} \mathbf{e}\leq\lambda_{max}(\mathbf{Q})\|\mathbf{e}\|^{2}\). According to [PERSON]'s inequality, (22), and (29), (26) follows with:  
\[\begin{array}{ll}\dot{V}&\leq-\mathbf{e}^{\mathsf{T}}\mathbf{Q}\mathbf{e}+2 \|\mathbf{e}^{\mathsf{T}}\mathbf{P}\|\|\mathbf{v}\|\\ &\leq-\lambda_{min}(\mathbf{Q})\|\mathbf{e}\|^{2}+2\lambda_{max}(\mathbf{P}) \|\mathbf{e}\|\|\mathbf{v}\|\\ &=-\lambda_{min}(\mathbf{Q})\|\mathbf{e}\|\Big{(}\|\mathbf{e}\|-\frac{2 \lambda_{max}(\mathbf{P})}{\lambda_{min}(\mathbf{Q})}\|\mathbf{v}\|\Big{)}\\ &\leq-\lambda_{min}(\mathbf{Q})\|\mathbf{e}\|(\|\mathbf{e}\|-\varepsilon) \end{array} \tag{30}\]  
where  
\[\varepsilon=\frac{2\lambda_{max}(\mathbf{P})}{\lambda_{min}(\mathbf{Q})}v^{+} \tag{31}\]  
Equation (30) indicates that the stability is guaranteed when \(\|\mathbf{e}\|>\varepsilon\). However, there is no conclusion when \(\|\mathbf{e}\|<\varepsilon\). Hence, it can be deduced that the error state vector \(\mathbf{e}\) will approach a region \(\mathcal{W}\), i.e.,  
\[\|\mathbf{e}\|\to\mathcal{W}\quad\text{as}\quad t\to\infty \tag{32}\]  
where  
\[\mathcal{W}=\varepsilon \tag{33}\]  
The stability result for the general error system (21) is summarized as follows:  
**Theorem 1**.: _Consider the general error system shown in (21). If the unknown perturbation \(\mathbf{v}\) satisfies the assumption (22) and there exists a feasible solution \((\mathbf{P},\mathbf{Q})\) of LMI (25), then it can be guaranteed that the error state vector \(\mathbf{e}\) will approach a region \(\mathcal{W}\) described in (33) in the sense of Lyapunov._
# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 3 Methodology for Comparative Study
### Description of CNN Model and Their Parameters  
For evaluating the traffic sign and object recognition accuracy, we use a CNN model because it was introduced mainly for image processing applications and shows its potential efficacy in object recognition [18]. This model has less computational complexity, which is one of the major requirements for many real-time image recognition applications like live traffic sign detection used in autonomous vehicles. For traditional denoising, in general, a CNN comprises convolutional layers, pooling layers, fully connected layers, and a softmax layer for generating output.  
As per [18], Figure 6 shows that the CNN model contains two convolutional and pooling layers followed by three fully connected layers. A filtering layer can be incorporated in any position of the CNN model but before the Softmax layer. However, in Figure 6 we embed an extra denoising layer of Gaussian or median filter, which is the first layer in the existing CNN model. This layer is embedded before the first convolutional layer of the CNN model. More embedded layers of Gaussian and median filters could be added to the CNN model. However, based on our experiments, we opted for only one embedded layer because the traffic sign and object recognition accuracy are almost the same as with embedding the first layer. Additionally, adding more filtering layers raises the computational complexity of the CNN model.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 3 Related Work
### Reactive and Imperative Approaches in Microservice Performance  
[PERSON] and [PERSON] [23] examined the performance of microservices developed using reactive and imperative approaches by implementing two Java-based microservices with the Spring framework. In their study, they conducted performance tests for operations such as data retrieval and insertion, processing, and file transfer, comparing reactive and imperative microservices under varying load scenarios (100 and 3000 simultaneous users with multiple service instances). Their findings indicate that reactive applications performed better in cases with delays in communication with databases or other services, reducing response time and RAM usage by up to 36% in certain scenarios. However, for CPU-intensive tasks, reactive applications proved slower by up to 46%, suggesting additional complexity in handling reactive streams compared to imperative code.  
Additionally, the study highlights the advantages of reactive applications in managing inter-service communication and optimizing hardware resource usage. Reactive applications required fewer threads due to the event loop model, thereby reducing RAM demands in scenarios such as order processing or barcode generation, with memory usage up to 57% lower. Although reactive applications showed advantages in I/O-intensive scenarios, their limitations for CPU-intensive processing emphasize the importance of assessing the context of use before implementing a reactive system, ensuring that the selected model aligns with the system's performance requirements [23].  
The study suggests that the reactive approach introduces complexity in code readability and maintainability, which can impact development time and debugging processes. This complexity may arise from the asynchronous, declarative nature of reactive programming, potentially leading to challenges such as difficulty in tracing the flow of data or managing complex operator chains. Therefore, while the reactive model offers performance benefits in specific scenarios, developers must consider the extent of improvements against the possibility of increased development complexity, at least in the short term, and ensure that the team's expertise aligns with the selected approach [23].
## 2 Testing Conditions and Scheme  
### Sample Preparation  
The samples used in this test are sourced from the same sandstone block, characterized by good homogeneity to minimize the impact of rock anisotropy on test outcomes. The sample dimensions are 100 mm \(\times\) 100 mm \(\times\) 100 mm cubes, with the end face flatness controlled to within 0.02 mm. Specim surfaces must be polished to a smooth finish and free of any apparent joints or cracks. The actual triaxial sandstone specimens are prepared in strict compliance with the standards set by the International Society for Rock Mechanics. Figure 2 displays some of the prepared test samples.