esa-sceva/satcom-chunk-collection huggingface dataset:
# Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons
## Appendix A  
For deriving the LSMC control law (8), consider the first case 1.(a) corresponding to \(g(t)>0\), \(\dot{g}<0\), \(G(t)>0\) for which \(u(t)<\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\). Note in the first place that \(-\|\tau(t)-\widetilde{v}(t)\|\leq\tau(t)-\widetilde{v}(t)\leq\|\tau(t)- \widetilde{v}(t)\|\). By the triangle inequality, it is checked that \(\|\tau(t)-\widetilde{v}(t)\|\leq\|\tau(t)\|+\|\widetilde{v}(t)\|\leq\|\tau(t) \|+v_{\max}\) holds. Since \(-\|\tau(t)\|-v_{\max}\leq-\|\tau(t)-\widetilde{v}(t)\|\leq\tau(t)-\widetilde{ v}(t)\), then in order to ensure that \(u(t)<\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\) for \(G(t)>0\), it suffices to select a control that fulfils \(u(t)<\frac{-\|\tau(t)\|-v_{\max}}{G(t)}\). A firm selection is \(u(t)=\frac{-\|\tau(t)\|-v_{\max}}{G(t)}-\delta\) with \(\delta>0\), which is equivalent to  
\[u(t)=-\bigg{(}\frac{\|\tau(t)\|+v_{\max}}{G(t)}+\delta\bigg{)}\text{sgn}(g(t)G (t)).\] (A1)  
The case 1.(b) corresponds to \(g(t)>0\), \(\dot{g}(t)<0\), \(G(t)<0\) for which \(u(t)>\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\). Since \(\|\tau(t)\|+v_{\max}\geq\|\tau(t)+\widetilde{v}(t)\|\geq\tau(t)+\widetilde{v }(t)\), the control which ensures that \(u(t)>\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\) for \(G(t)<0\) and also fulfils \(u(t)>\frac{\|\tau(t)\|+v_{\max}}{G(t)}\) is \(u(t)=\frac{\|\tau(t)\|+v_{\max}}{G(t)}+\delta\), which is equivalent to  
\[u(t)=\bigg{(}\frac{\|\tau(t)\|+v_{\max}}{G(t)}+\delta\bigg{)}\text{sgn}(g(t)G (t)).\] (A2)  
The case 2.(a) refers to \(g(t)<0\), \(\dot{g}(t)>0\), \(G(t)>0\) for which \(u(t)>\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\). It is similar with the case 1.(b) therefore, one control input which fulfils \(u(t)>\frac{\|\tau(t)\|+v_{\max}}{G(t)}>\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\) is found as in (A2).  
Finally, the case 2.(b) is valid whenever \(g(t)<0\), \(\dot{g}(t)>0\), \(G(t)<0\) for which \(u(t)<\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\). Being similar to the case 1.(a), one control input fulfilling the condition \(u(t)<\frac{-\|\tau(t)\|-v_{\max}}{G(t)}<\frac{\tau(t)-\widetilde{v}(t)}{G(t)}\) is expressible as in (A1).  
The control input in all the four subcases will be correctly captured and compactly written by the general form (A1), depending on the sign of the product \(g(t)G(t)\).
## 1 Introduction
At the same time, the jet movement process is simulated based on simulation software. The reliability of the test results is ensured by comparing the experimental results with the simulation results, and the logic is more complete. A two-phase flow model is used in the simulation. The calculation domain of the water tank is the wall boundary, the jet outlet was the velocity boundary, the turbulence model is the k-\(\varepsilon\) model, and the structure grid is used as a divide. Some of the symbols and abbreviations that appear in this paper are defined below Table 1: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Symbol or** \\ **Abbreviation** \\ \end{tabular} & **Definition** & **Units** & \begin{tabular}{c} **Symbol or** \\ **Abbreviation** \\ \end{tabular} & **Definition (Units)** & **Units** \\ \hline \(c_{p}\) & \begin{tabular}{c} Constant Pressure Specific \\ Heat Capacity \\ \end{tabular} & J\(\cdot\)kg\({}^{-1}\)K\({}^{-1}\) & \(\mu_{\textit{eff}}\) & Kinetic Viscosity & m\({}^{2}\)/s \\ \hline \(F_{x},F_{y},F_{z}\) & Volume Forces on the element & N/m\({}^{3}\) & \(\rho\) & Fluid Density & g/cm\({}^{3}\) \\ \hline \(G_{b}\) & \begin{tabular}{c} Turbulent Energy \\ due to Buoyancy \\ \end{tabular} & J/kg & \(\sigma_{k}\) & \begin{tabular}{c} Turbulent Planck Number of \\ Turbulent Kinetic Energy \\ \end{tabular} & - \\ \hline \(G_{k}\) & \begin{tabular}{c} Turbulent Energy due to Mean \\ Velocity Gradient \\ \end{tabular} & J/kg & \(\tau\) & Viscous Stress & N\(\cdot\)s/m\({}^{2}\) \\ \hline \(k\) & Turbulent Kinetic Energy & m\({}^{2}\cdot\)s\({}^{-2}\) & \(\tau_{xx},\tau_{xy},\tau_{xz}\) & Components of the viscous stress & N\(\cdot\)s/m\({}^{2}\) \\ \hline \(p\) & Pressure & p\({}^{a}\) & CCD & Charge Coupled Device & - \\ \hline \(S_{T}\) & Viscous Dissipation Term & - & CFD & Computational Fluid Dynamics & - \\ \hline \(T\) & Temperature & K & DNS & Direct Numerical Simulation & - \\ \hline \(\overline{u}\) & Velocity Vector & m/s & PIV & Particle Image Velocimetry & - \\ \hline \(u,v,w\) & \begin{tabular}{c} Components of the \\ velocity vector \\ \end{tabular} & m/s & PLIF & \begin{tabular}{c} Planar Laser-Induced \\ Fluorescence \\ \end{tabular} & - \\ \hline \(\varepsilon\) & Specific Dissipation Rate & W\(\cdot\)m\({}^{-3}\) & POD & \
\begin{tabular}{c} Proper Orthogonal \\ Decomposition \\ \end{tabular} & - \\ \hline \(\lambda\) & Heat Transfer Coefficient & W\(\cdot\)m\({}^{-2}\)K\({}^{-1}\) & RANS &
\begin{tabular}{c} Reynolds-Averaged \\ Navier–Stokes \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definitions of Symbols and Abbreviations.
# A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
## 2 Proposed Antenna Design Procedure
### MIMO Performance Metrics  
Several MIMO performance metrics were analyzed for the proposed antenna system incorporated with a dielectric layer, such as envelope correlation coefficient (ECC), channel capacity loss (CCL), and diversity gain (DG). In Figure 9, the ECC is demonstrated using the far-field calculation method [31], which conforms with the standard value (<0.5), whereas in Figure 10, DG is analyzed, which is close to the standard value, i.e., 10 dB. Moreover,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Frequency (GHz)**} & \multicolumn{2}{c}{**Simulated Value**} & \multirow{2}{*}{**Frequency (GHz)**} & \multirow{2}{*}{**Measured Value (dB)**} \\  & **(dB)** & & \\ \hline
21.5 & 4.61 & 21.5 & 4.45 \\
22.5 & 5.07 & 22.5 & 4.85 \\
23.5 & 5.48 & 23.5 & 5.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gain measurement of the proposed antenna.  
Figure 5: MIMO antenna incorporated with a dielectric layer.  
in Figure 11, CCL is shown. A CCL value of less than 0.4 bits/s/Hz is achieved across the operating band, which depicts a minimal capacity loss. It is important to analyze this loss because when more than one antenna is incorporated to form an antenna system, the channel capacity is increased, but some losses are encountered due to the correlation between the MIMO links. In [32], a channel capacity of 41 bps/Hz was observed for the sub 6 GHz band, with a low isolation value of \(-\)14.8 dB. CCL was not analyzed, and the size of the proposed design was relatively large, i.e., \(>\)80 mm. An MIMO antenna with a size of 150 mm \(\times\) 80 mm was presented in [33]; a channel capacity of 41 bps/Hz was achieved, but no analysis of CCL was presented. The proposed MIMO antenna achieved an isolation of less than \(-\)15 dB within the 5150-5925 MHz frequency band, which may result in deterioration of the transmission quality. The same idea was implemented in [34], in which CCL was not taken into account and a relatively larger antenna of more than 80 mm was reported to have a low operating bandwidth, i.e., less than 1 GHz.  
Figure 6: S-parameters based on antenna incorporation with a dielectric layer: (**a**) reflection coefficient, (**b**) isolation.  
Figure 8: Antenna polar radiation patterns in: (**a**) E-plane port 1, (**b**) E-plane port 2, (**c**), H-plane port 1, and (**d**) H-plane port 2.  
Figure 7: Proposed MIMO antenna gain comparison with and without a dielectric layer.  
Figure 11: CCL of the proposed antenna system incorporated with a dielectric layer.  
Figure 10: DG of the proposed antenna system incorporated with a dielectric layer.  
Figure 9: ECC of the proposed antenna system incorporated with a dielectric layer.
# FirepanIF: High Performance Host-Side Flash Cache Warm-Up Method in Cloud Computing
## 4 Evaluation
### Experimental Setup  
We use two computing nodes and one shared storage node. Each of the two computing nodes equip six physical cores, 8 GB main memory, a 250 GB SATA SSD for a host operating system, and a 512 GB NVMe SSD for a host cache. The shared storage node equips four physical cores, 8 GB main memory, a 120 GB SATA SSD for an operating system, and a 480 GB NVMe SSD, which is used for the shared storage. Linux kernel 4.15.0-43 is used for all the nodes, and Kernel-base Virtual Machine (KVM) is used to virtualize the computation and storage resources. Three nodes are connected via 10 Gigabit Ethernet.  
Each VM has four virtual cores, 1 GB memory, and 50 GB storage. For the host-side flash cache, each VM is given 10 GB of NVMe SSD. The host-side cache size is 20% of the VM storage that that is enough to store the operating system and workload data of the VM. We configure the workload data size as 20 GB for all the experiments so that the host-side flash cache can store 50% of the VM's workload data.  
EnhancelIO is used for flash cache management. As explained in Section 2.1, EnhanceIO provides three cache write policies: read-only, write-through, and write-back. We use the read-only policy for our experiments because write caching is not preferred in the virtualized environment due to the reliability issue. When the flash cache management subsystem in the host crashes, the data of the VM can be lost with write caching. Please note that although we apply the read-only policy, our models do not depend on the cache policy.  
We use flexible I/O tester (FIO) as a benchmark suite in our experiments [30]. To create a realistic workload with spatial locality, we configure the FIO benchmark as follows: four threads issue I/O requests concurrently, and each thread issues random reads and writes with an 8:2 ratio for an exclusive 5 GB file. The requests are directly delivered to the storage device, and the maximum number of concurrently issued requests is 32 per thread. Because the FIO always tries to issue as many requests as possible, 128 requests from the four threads wait in the storage device simultaneously while the benchmark program is running.  
In addition, we carefully configure the randomness of the FIO. Because the default configuration of the FIO uses the same number for a random seed across executions, the FIO generates the same series of requests for each execution. The behavior is not similar to that of a real file system. Therefore, we configure the FIO to use fully random numbers for the target addresses of the I/O requests. Moreover, we configure the FIO such that 80% of the requests are issued for the first 20% of addresses in the target file to simulate spatial locality. We provide the full configuration of the FIO workload in Figure 5 to help the reproduction of our experiments.
## 3 Data and Methods
### Overview of Machine Learning Models
#### 3.5.1 Random Forest  
Random Forest (RF) is an ensemble algorithm composed of multiple decision trees [73; 74]. This method employs bagging to generate \(k\) decision trees, each constructed using a bootstrap-sampled training subset. At each node split, a random subset of \(m\) features is selected. The final prediction is integrated through majority voting for classification tasks or mean regression for regression tasks. The output of the RF is the class with the highest average probability across all decision trees, as calculated by Equation (1).  
\[p_{c}=\max\left\{p_{i}=\frac{\sum\limits_{j=1}^{m}p_{ij}}{m}|i\in I\right\} \tag{1}\]  
In this equation, \(I\) is the set of all classifications; \(m\) is the number of decision trees; \(p_{i}\) is the probability of an event occurring or existing; \(p_{ij}\) is the probability of the event occurring or existing in the \(j\)-th decision tree; \(p_{c}\) is the probability corresponding to the class chosen by the model.
###### Abstract  
Accurately forecasting sales for fast-moving consumer goods (FMCG) remains a significant challenge due to the volatile and multi-faceted nature of sales data. Existing methods often struggle to capture intricate patterns driven by seasonal trends, external factors, and consumer behavior, hindering effective inventory management and strategic decision-making. To overcome these challenges, we propose STL-DCSInformer-ETS, a hybrid model that integrates three complementary components: STL decomposition, an enhanced DCSInformer model, and the ETS model. The model uses monthly sales data from a FMCG company, with key features including sales volume, product prices, promotional activities, and regulatory factors such as holidays, geographical information, consumer behavior, product factors, etc. STL decomposition partitions time-series data into trend, seasonal, and residual components, reducing data complexity and enabling more targeted forecasting. The enhanced DCSInformer employs dilated causal convolution and a multi-scale feature extraction mechanism to capture long-term dependencies and short-term variations effectively. Meanwhile, the ETS model specializes in modeling seasonal patterns, further refining forecasting precision. To further improve predictive performance, the Random Forest-based Recursive Feature Elimination (RF-RFE) method is applied to optimize feature selection. RF-RFE identifies key predictive factors from multiple dimensions, such as time, geography, and economy, which significantly influence forecasting accuracy. Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. Furthermore, the model effectively captures both medium- and long-term sales trends while addressing short-term fluctuations, leading to more accurate forecasting and improved decision-making for fast-moving consumer goods. This research provides new theoretical insights into hybrid forecasting models and practical solutions for optimizing inventory management and strategic planning in the FMCG industry.  
STL-DCSInformer-ETS; medium- and long-term sales forecasting; fast-moving consumer goods; random forest recursive feature elimination; multi-scale feature extraction; dilated causal convolution +
Footnote †: journal: applied sciences
# Sharp Interface Capturing in Compressible Multi-Material Flows with a Diffuse Interface Method
## 3 Time and Space Discretization
### Space Discretization  
In our study we deal with the Finite Volume method (FVM) [33] discretization of Equations (33)-(37). It is important to remark at this point that here in this section, for the sake of conciseness, we prefer not to highlight the numerical strategy for the FV discretization of the divergence operators in the advection problems' set (17)-(20), we reserve that analysis for Section 5. Further, the two-dimensional (2D) and three-dimensional (3D) versions have been implemented, but at this step, only a 2D version has been validated and thus presented in this paper. We thus consider a two-dimensional (2D) uniform Cartesian mesh [34] with staggered variables for space discretization (in the spirit of [PERSON] finite elements), in order to avoid the pressure oscillations due to the checkerboard problem [33]. Thus, as usual, the normal velocity variables are stored at faces of control volumes, whereas all other scalars (volume fractions and thermodynamic variables, generically noted by \(\Pi\) hereafter) are stored at cell centers, as shown in Figure 1[35].  
After performing a finite volume space discretization of time discrete model (33)-(37), we get a non-linear and coupled algebraic model, which generically can be written as:  
\[\mathbf{Q}^{time}_{space}\left(u^{n+1}_{\phi,face},v^{n+1}_{\phi,face},m^{n+1 }_{\phi,center},V^{n+1}_{\phi,center},e^{n+1}_{\phi,center},T^{n+1}_{\phi, center},p^{n+1}_{\phi,center}\right)=0. \tag{39}\]  
where \(\mathbf{Q}^{time}_{space}\) represents the system of Equations (33)-(37) in the implicit step, and discretized with respect to time and space.  
Further, to find the numerical solution of the above model on 2D Cartesian meshes, we have used the _[PERSON]'s method_[36] from PETSc library (see Section 4), and thus for any phase \(\phi\) we can express it in the general form of a solvable _linear system_ at time \(t^{n+1}\):  
\[\mathbf{J}(\mathbf{x}^{k})(\mathbf{x}^{k+1}-\mathbf{x}^{k})=-(\mathbf{Q}^{ time}_{space})(\mathbf{x}^{k}),k=0,1,..., \tag{40}\]  
where  
\[\mathbf{x}^{k}=\begin{bmatrix}u^{n+1,k}_{\phi,face}\\ v^{n+1,k}_{\phi,face}\\ m^{n+1,k}_{\phi,center}\\ V^{n+1,k}_{\phi,center}\\ e^{n+1,k}_{\phi,center}\\ T^{n+1,k}_{\phi,center}\\ p^{n+1,k}_{\phi,center}\end{bmatrix}\]  
with superscript \(k\) refering to the \(k^{th}\) Newton iteration, and \(\mathbf{X}^{0}\) is an initial approximation given by the values: \((u^{n}_{\phi,face},v^{n}_{\phi,face},m^{n}_{\phi,center},V^{n}_{\phi,center},e^{n}_{\phi,center},T^{n}_{\phi,center},p^{n}_{\phi,center})\). Matrix \(\mathbf{J}=(\mathbf{Q}^{time}_{space})^{\prime}\) is a _block_ and _sparse_**Jacobian** matrix of the non-linear and coupled system (39). Again, _block_ nature of the Jacobian matrix highlights the fact that the algebraic system (39) is coupled.
## 4 Experiments and Results  
### MSTAR Datasets  
To evaluate the performance of the proposed model, we used the moving and stationary target acquisition and recognition (MSTAR) dataset [36] published by the Defense Advanced Research Projects Agency and the Air Force Laboratory of the United States in the mid-1990s.  
The SAR military vehicles in the MSTAR dataset were collected by a high-resolution spotlight synthetic aperture radar with a 0.3 m resolution, operating in the X-band and HH polarization mode. The acquisition conditions can be divided into standard operating conditions (SOCs) and extended operating conditions (EOCs) [37]. SOC refers to situations where the sag angle and aspects of the training set and test set samples are slightly different, but the serial number and target configuration of the samples are the same. Conversely, EOC refers to situations in which the training set and test set had significant differences in pitch angle, target definition, and other factors, making it a more complex classification task than SOC. The MSTAR dataset contains SAR images of various sizes, including 128 \(\times\) 128, 158 \(\times\) 158, and 177 \(\times\) 178 pixels. To maintain consistency in target size, the original images were uniformly cropped to 128 \(\times\) 128 pixels without affecting the target.  
In SOC classification, there are 10 types of vehicle targets, including BMP2 (infantry combat vehicle), BRDM2 (armored reconnaissance vehicle), BTR60 (armored transport vehicle), BTR70 (armored transport vehicle), D7 (bulldozer), T62 (tank), T72 (tank), ZIL131 (cargo truck), ZSU234 (self-propelled anti-aircraft gun), and 2S1 (self-propelled howitzer). The training and test samples were images taken at 17\({}^{\circ}\) and 15\({}^{\circ}\) pitch angles, respectively. Figure 4 shows the optical and SAR images of different vehicle targets in the MSTAR dataset. It is evident from the figure that SAR images have low resolution and blurred target edge information, making it difficult to distinguish each vehicle target visually.  
Figure 4: Ten class vehicle targets for optical images (top) and SAR images (bottom).: (**a**) 2S1; (**b**) BMP2; (**c**) BRDM2; (**d**) BTR60; (**e**) BTR70; (**f**) D7; (**g**) T62; (**h**) T72; (**i**) ZIL.; (**j**) ZSU23 LG.  
In EOC classification, there are large differences between the training and test sets, such as different pitch angles; however, the target categories in the dataset are more similar than in SOC. Therefore, the classification task is more complex, and the model's robustness is more critical than under SOC. Based on previous research [38], this study focused on three types of ground articulated vehicle targets: T72, BRDM2, and ZSU234. The training and test samples were taken at 17\({}^{\circ}\) and 30\({}^{\circ}\) pitch angles, respectively.
## 2 Short-Term and Long-Term Collision Probability Calculation Models  
### Short-Term Collision Probability Calculation Model  
The orbital prediction model in this summary uses the SGP4 model to carry out short-term collision studies. This section establishes a proximity analysis method for large-scale space debris and large-scale satellites to eliminate the dangerous targets. The short-term collision probability computational model is constructed in accordance with the collision probability calculation method for the rendezvous target. This model is used to calculate short-term in-orbit natural evolution collisions in the shell where the constellation is located. A series of methods are used for proximity analysis of space targets, as shown in Figure 1.
# Wide-Area Protection System for Radial Smart Distribution Networks
## 3 Proposed Protection Plan
### Fault Location  
In the proposed method, the CPU is responsible for fault location or fault section identification. The procedure of fault section identification is commenced right after the fault detection signal is received from each of the UFDMs (network relays). In the condition of a fault within the network, relays send one of the following signals:  
* One (+1): A fault current is passing through the relay, and the location of the fault is in the front of the UFDM installation location;
* Mines one (\(-1\)): A fault current is passing through the relay, and the location of the fault is at the back of the UFDM installation location;
* Zero (0): There is a fault in the network, and a no-fault current is passing through the UFDM.  
In the context of the above sentences, the terms 'front' and 'back' delineate whether the fault occurs towards the end side of the feeder, where the relay is positioned (front), or towards the direction of the network's main post (back). This direction can be achieved from the phase angle between the voltage and current.  
The relay signal is 'one' or'minus one' in the case that the fault current is passing through the relay (Classes 1 and 2 of the ANN output) and is 'zero' when there is a fault in the network and a no-fault current is passing through the relay (Class 3 of the ANN output).  
For radial networks with a number of \(N\) protective devices (relays combined with Circuit Breakers), there would be \(N\) protection zones. During faults in each zone, every relay assumes a distinct condition, forming an \(N\times N\) matrix where each row represents the conditions of a relay, and each column represents a fault zone.  
As an example, for the network shown in Figure 2, a fault in Z1 the condition of R1, R2, R3, and R4 would be +1, 0, 0, and \(-1\). A similar procedure for all of the zones will result in the below matrix known as Fault Zone Relay Condition (FZRC)It is evident that the fault location can be determined using an FZRC matrix along with the signals received from the network relays. It is noteworthy that the fault location procedure does not require signals from all the relays during a fault event. This is because each column (representing a zone) of the matrix is uniquely characterized based on the condition of the selected network relays responsible for zone isolation. For instance, the occurrence of a fault in Z4 can be established when R4 transmits a +1 signal, regardless of the signals received from other relays. However, receiving signals from other relays can help in identifying malfunctions and cyber anomalies. Once the faulted zone is determined, break signals are transmitted to the relevant relays to initiate fault isolation.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 3 Proposed Method  
In this section, we propose Two-Layer-Graph (TLG) clustering method, which significantly increases the speed of calculations while ensuring the accuracy of segmentation. We divide the task of point cloud clustering into three parts: (1) storage structures, (2) segmentation standards, (3) category update.