esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
It is well recognised in soil mechanics [1; 2; 3; 4; 5; 6] that granular soils can be prepared into different densities characterised by the mean pressure \(p\) and solid fraction \(\phi\) (solid fraction is equivalent to the void ratio or specific volume, and solid fraction is used in this study). Two densities are distinguished--dense or loose, depending on the distance between the state (\(p\) and \(\phi\)) and a critical \(p\)-\(\phi\) relationship. Dense and loose soil elements have different responses when sheared [7; 8; 9; 10; 11]. These phenomena are also reproduced in discrete element simulations [12; 13; 14; 15; 16; 17]. Some researchers noticed that there is a possible range of \(\phi\) for a fixed pressure \(p\)[18], and it is impossible to prepare samples outside this range. Researchers use empirical equations to define such a range of possible \(p\)-\(\phi\) combinations.  
Parallellly in the field of condensed-matter physics, researchers are also interested in granular materials and they use the name jammed granular matters for solid-state granular assembly [19; 20; 21; 22; 23; 24]. Physicists tried to explain this range of possible \(p\)-\(\phi\) combinations theoretically. In 1989, Sir [PERSON] made the proposition to treat jammed granular materials using a volume ensemble of equiprobable jammed states in analogy to thermal equilibrium statistical mechanics. [PERSON] et al. [25] followed this pathway and proposed a phase diagram to explain the jamming limit of granular assemblies of uniform spherical grains. Their theoretical phase diagram is also validated by simulations with the discrete element method (DEM), in which the possible range of jammed \(p\)-\(\phi\) combinations is studied by isotropic compression simulations. In other studies, researchers found that shearing is also important and can cause unjamming [24]. The critical state is the ultimate flowing state under continuous shearing and is an important reference state for shear deformation. How the range of jammed \(p\)-\(\phi\) combinations found in isotropic compression and extension is related to the important reference state (the critical state) for shear deformation is not studied in the literature. Rather than give a theoretical examination, this short communication aims to answer this question by conducting DEM simulations.  
The paper is organised as follows: in Section 2, the discrete element model (DEM) and the used parameters are briefly explained, and then the method used to study the jamming limit and to prepare specimens is explained in Section 3, i.e., the isotropic compression tests for a periodic cuboidal domain. In Section 4, another method (varying-friction method) for generating specimens is explained and it is found that this method can generate specimens with a larger range of \(p\)-\(\phi\) combinations. A large number of specimens are prepared with different \(p\)-\(\phi\) combinations and conducted continuous isochoric shear tests to find the critical \(p\)-\(\phi\) line, and find that this line is also the loosest jammed state line.
# Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method
## 1 Introduction
In our previous studies [44; 45; 46], a new stochastic optimization method using subset simulation--the so-called subset simulation optimization (SSO)--was developed for both unconstrained and constrained optimization problems. Compared with some well-known stochastic methods, it was found to be promising for exploiting the feasible regions and searching optima for complex optimization problems. Subset simulation was originally developed for reliability problems with small failure probabilities, and then became a well-known simulation-based method in the reliability engineering community [47; 48]. By introducing artificial probabilistic assumptions on design variables, the objective function maps the multi-dimensional design variable space into a one-dimensional random variable space. Due to the monotonically non-decreasing and right-continuous characteristics of the cumulative distribution function of a real-valued random variable, the searching process for optimized design(s) in a global optimization problem is similar to the exploring process for the tail region of the response function in a reliability problem [44; 45; 46]. The general constraints in structural optimization problems should be carefully handled. Since most stochastic optimization methods have been developed as unconstrained optimizers, common or special constraint-handling methods are required [49]. A modified feasibility-based rule has been proposed to deal with multiple constraints in SSO [45]. However, the rule fails to directly detect active constraints. As an enhancement, the dynamic augmented Lagrangian multiplier method (DALMM) [25; 26; 50] is presented and integrated with SSO for the constrained optimization problem in this study, which can automatically detect active constraints. Based on DALMM, the original constrained optimization problem is transformed into a series of unconstrained optimization sub-problems, which subsequently forms a nested loop for the optimization design. The outer loop is used to update both the Lagrange multipliers and penalty parameters; the inner loop aims to solve the unconstrained optimization sub-problems. Furthermore, DALMM can guarantee that the proposed method obtains the correct solution with finite penalty parameters because it already transforms a stationary point into an optimum by adding a penalty term into the augmented Lagrangian function. This paper begins with a brief introduction of SSO for the unconstrained optimization problem, followed by development of the proposed method for structural optimization problems in Section 3. Then, four classical truss sizing optimization problems are used to illustrate the performance of the proposed method in Section 4. Finally, major conclusions are given in Section 5.
# Free Vibration Analysis of Arches with Interval-Uncertain Parameters
## 4 Numerical Simulation and Discussion
### Uncertain Natural Frequency Investigations Considering Different Uncertain Parameters  
#### 4.3.1 Single Uncertain Parameter  
In the first part of this subsection, a single uncertain parameter is considered. Parts of the numerical simulation results are illustrated regarding the effect of the uncertain cross-sectional height in the comparative analysis previously presented, and here the width of the cross-section is considered with the same deviation degree. Taking into consideration material dispersion, [PERSON]'s modulus and the density of mass are regarded as uncertain parameters, with respective deviation degrees of 5%. The simulation results are shown in Figure 6. It is found that the bounds of the changing rate are drawn as straight lines in the presence of each of the uncertainties above, and further investigations demonstrate that these phenomena remain unaffected by variations in radius. Furthermore, the natural frequencies are minimally influenced by the uncertain width of the cross-section; however, they exhibit greater sensitivity to variations in the uncertain cross-sectional height compared with [PERSON]'s modulus and the density of mass. It can be seen that the physical parameters, including Young's modulus and the density of mass, demonstrate a linear relationship with the natural frequencies through eigenvalue decomposition of the free-vibration equation. Additionally, the cross-sectional width can be canceled on both sides of the equation. Moreover, a nonlinear correlation exists between the cross-sectional height and the moment of inertia, subsequently affecting the natural frequency. This observation elucidates the distinct influences of each parameter on the uncertain dynamic characteristics.  
Figure 5: The changing rates of the first fourth-order natural frequencies of the arch with uncertain cross-sectional height and a radius of 3 m using the CPS model and MCS: (**a**) first order; (**b**) second order; (**c**) third order; and (**d**) fourth order.
## 4 Battery Cell-Module-Pack Level Modelling
### Pack Level  
The pack-level model's purpose is to distribute the pack-level electrical and thermal inputs to the module-level model and, in turn, collate the cell-level outputs into the equivalent pack-level ones via the module-level model. A 112s10p pack-level electrical arrangement is adopted. Given the 4s10p module-level electrical arrangement, there are 28 modules (\(N_{mod}\)) connected in series in the pack. Every module adopts the same thermal arrangement as either Figure 8c or Figure 8d and is thermally connected in parallel flow-wise. Hence, every module is considered identical in the electrical and thermal aspects, which is a reduced-order compromise to reduce computational complexity.
## 4 Classification and Results
### S-Classification  
The feature matrix, consisting of 540 samples and 13 features, is utilized for size detection, specifically for the S-Classification. During the testing phase, 10% of the dataset was allocated for evaluation and the 5-folds cross-validation method was selected as the validation scheme. The best performing machine learning classifier models were determined by running various algorithms, and the results are summarized in Table 6.  
Figure 16: R-Classification: the optimization process for the tuned hyperparameters for the network in the case of S4–S5.  
Figure 17: R-Classification: (**a**) confusion matrix for the training phase; (**b**) confusion matrix for the testing phase in the case S4–S5.  
Table 6 showcases that the Ensemble-Subspace KNN algorithm boasts the highest validation accuracy of 94.65%. It is important to note that this algorithm uses the Subspace method for the ensemble and the nearest neighbors learning type, with a total of 30 learners. The Ensemble-Subspace KNN algorithm, alongside the Ensemble-Bagged Trees, Ensemble-Boosted Trees, and Kernel-SVM Kernel, achieved the highest accuracy in the testing phase, which was 98.15%. During the testing phase, the mean F1-score and sensitivity were recorded as 0.990 and 1, therefore. A higher accuracy in the testing phase compared to the validation accuracy suggests that the algorithm has not been overfitted during the training phase. Furthermore, since the accuracy in the testing phase is high enough, no optimization process was carried out to tune the hyperparameters.  
The confusion matrices for the training and testing phases of the Ensemble-Subspace KNN algorithm are shown in the following graphs, Figure 18a,b. It should be emphasized that S-Class 1, S-Class 2, and S-Class 3 are represented by indices 0, 1, and 2, respectively.  
As depicted in the previous graph, out of the 486 samples randomly assigned to the training phase, 26 samples were misclassified. Specifically, 6 samples from S-Class 1 were classified as S-Class 2, 13 samples from S-Class 2 were classified as S-Class 1, 1 sample from S-Class 2 was classified as S-Class 3, 4 samples from S-Class 3 were classified as S-Class 2, and 2 samples from S-Class 3 were classified as S-Class 1. During the testing phase, only 1 sample from S-Class 2 was incorrectly classified as S-Class 1.
## 3 Results
The results showed that high compressive stress patterns were found at the upper frontal process (near point 3) regardless of the IFS, PRT or mini-plate models, and high tensile stress patterns. Figures 7 and 8 should be compared together. It is difficult to observe the stress distribution of the three-dimensional structure by just using the stress distribution in Figure 8. Figure 8 shows that the PSBT model has a similar stress trend as the IFS model, which means the mid-face will have high stability with the PSBT implant reconstruction. In order to simulate the actual clinical reconstruction situation, the orbital plate was used on the PSBT model and the mini-plate model. The orbital plate did not affect the overall stress distribution trend of the PSBT model and mini-plate model, so the stress distribution of the orbital plate was not presented in the results. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & & & & & & \\ \hline \multirow{2}{*}{Unit: MPa} & Compression & & Tensile & & & \\  & (minimum principal stress) & & (maximum principal stress) & & & \\ \multirow{2}{*}{Intact model (IFS model) (IFS model) (PSRT implant) & 6.26 & 3.13 & 4.73 & 5.62 & 6.87 & 4.32 \\  & & & & & & \\ \multirow{2}{*}{PSBT implant} & 5.75 & 3.79 & 4.60 & 5.13 & 5.03 & 3.23 \\  & & & & & & \\ \multirow{2}{*}{Mini-plate} & 4.34 & 7.21 & 2.09 & 0.21 & 0.59 & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stress values of the bone corresponding to the points 1 to 6 positions. Figure 8: The stress distribution of the intact skeleton bone, the ZMC fracture repairing with the PSBT implant and traditional mini-plates. Figure 7: Comparison of the maximum/minimum bone stress value among the IFS model, the ZMC fracture repair with thin implant and traditional mini-plates. A similar stress pattern between the IFS model and repair with the PSBT implant, i.e., convex curves were found while connecting the maximum stress value from points 1 to 3 and points 4 to 6. However, a different corresponding curve trend was found for repairs with mini-plates from points 1 to 3.
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 4 Problem Formulation
### State  
At time slot \(l\), the observed state is defined by a \(4K\) dimensional vector \(s(l)=(v_{1}(l),\cdots,v_{K}(l),d_{1}(l),\cdots,d_{K}(l),\)\(z_{1}(l),\cdots,z_{K}(l),c_{1}(l),\cdots,c_{K}(l))\), where \(v_{k}(l)\) denotes the video bitrates, \(d_{k}(l)\) represents the currently assigned download speeds, \(z_{k}(l)\) tracks the accumulated stall time for the current playing video until slot \(l\), and \(c_{k}(l)\) counts the number of chunks that are downloaded but not yet played for user \(k\in[K]\). For brevity, we use the notation \(s(l)=(\vartheta(l),\tilde{d}(l),\tilde{z}(l),\tilde{c}(l))\), where \(\vartheta(l)=(v_{1}(l),\cdots,v_{K}(l)),\tilde{d}(l)=(d_{1}(l),\cdots,d_{K}( l)),\tilde{z}(l)=(z_{1}(l),\cdots,z_{K}(l)),\tilde{c}(l)=(c_{1}(l),\cdots,c_{K}(l))\). We will expand the corresponding vector when necessary. By considering the variables \(v_{k}(l)\) and \(d_{k}(l)\), the learning model should be able to estimate the downloaded and played video chunk information in the current time slot \(l\), while \(z_{k}(l)\) and \(c_{k}(l)\) provide the objective-related history information.
## 2 Proposed Method
### Proposed Beam Weight Generation Mehtod
\(d\) denotes the antenna spacing normalized by the wavelength of the carrier and is set to 0.5. \(\theta_{p}^{c}\), \(\phi_{p}^{c}\), and \(h_{p}\) denote the AoA, AoD and channel coefficient of the \(p\)-th path in a cell with CID \(c\), respectively. \(\mathbf{w}_{v}\) and \(w_{i,p}\) denote an additive white Gaussian noise vector in the \(v\)-th training symbol and the noise signal received at the \(i\)-th Rx beam, respectively. Also, \(x_{v}^{c}\) denotes the preamble transmitted from the BS with CID \(c\). Here, \(x_{v}^{c}\) is set to 1 to focus on the effect of beam weight in the proposed method. The dispersions in time and frequency domains are not considered in Equation (3) because the channel is assumed to have a strong LOS path. Then, when \(\phi_{p}^{c}\) is 0, \(y_{b,v}^{c}\) becomes discrete Fourier transform (DFT) of ZC sequence with a phase rotation corresponding to the subarray index \(b\). This means that the BSs with different CID and subarray index generate different signals \(y_{b,v}^{c}\) with \(v\) ranging from 0 to \((N_{SA}-1)\). Therefore, \(y_{b,v}^{c}\) with \(v\) ranging from 0 to \((N_{SA}-1)\) can be considered as the training signal transmitted from each BS with CID \(c\) and subarray \(b\). Figure 1 shows an example of Tx beamformers at two neighboring BSs when the proposed beam weight generation method in Equation (1) is used. In this example, the Tx beamformers at the BSs have two subarrays, each consisting of \(N_{Tx}/2\) elements. The weights of Tx beamformers are determined by the values of BS's CID and subarray index \(b\). Note that the beam patterns of BSs in the proposed method are different because their beam weights are generated with different root indices. This enables MSs to distinguish BSs, with the received signals. In the figure, the Rx beam at the MS is ignored for simplicity.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Site Survey and Calibration Phase
#### 4.1.1 Radiomap Generation
Finally, the server stores as text files the fingerprints in the server database with additional configuration information of interest for the investigations (RSS features, real position (room ID) of the tag, orientation, parameter values, _etc._).
## 3 Measurement Procedure  
The measurements are carried out on the noise track of the Applus+ IDIADA company in Spain. This decision was made in order to keep environmental variations, especially temperature changes minimal.  
The measurements track is constructed equivalent to the one described in UN/ECE Regulation No. 117 [19]. Figure 3 shows the different points of the test track. The test track includes two microphones at a height of 1.2 m, one on each side of the track, at a distance of 7.5 m to the center line at the 0 m location of the track.  
Figure 1: First block of experiments.  
Figure 2: Second block of experiments, +1 indicating an additional weight adaption of the vehicle.  
For each tire car combination, rolling noise is measured by driving through the test track with speeds between 40 km h\({}^{-1}\) and 90 km h\({}^{-1}\). Thereby, the speed is increased in steps of 5 km h\({}^{-1}\) leading to a total of 22 idle measurement since every speed is repeated to minimize measurement errors.  
Furthermore, accelerated runs are recorded. Thereby, the speed at P-P' is set to be 50 km h\({}^{-1}\). The car is accelerated at least entering A-A'. The average acceleration between A-A' and B-B' is automatically calculated by the used measurement software (PAK Pass-By 3.2 from Muller-BBM VibroAkustik Systeme GmbH). Considering the average track acceleration, five steps between 1.0 m s\({}^{-2}\) and 3.0 m s\({}^{-2}\) are recorded. This acceleration differs from instantaneous acceleration at P-P', which is why further shown figures also show greater accelerations. In the case of hybrid cars (LSHEV and MSHEV), only three acceleration steps are recorded. The acceleration steps are realised through changing the fixed gear and driving full throttle (ICE) and through attaching small spacers to the gas pedal, to limit the maximal gas pedal position (EV). Each acceleration step is repeated four times.  
For each run, data is recorded at steps of 0.2 m. A data point consists of speed, distance, elapsed time, SPL at both microphones, air temperature, road temperature, wind speed, wind direction, air pressure, and humidity.
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## 3 Results  
### Demonstration of the Edge-Integrated RIS Control Mechanism  
This experiment builds a complete RIS-assisted optimized wireless transmission system. The experimental setup includes a transmitter (TX), receiver (RX), RIS board, FPGA control system, USRP, etc. The overall system architecture is illustrated in Figure 5. In order to verify the hardware architecture of the RIS tracking system and the effectiveness of the dynamic beam tracking algorithm, this experiment is firstly conducted in a non-line-of-sight (NLOS) scenario to test whether the UE is able to successfully receive and demodulate the signal with the assistance of RIS reflection. The TX continuously transmits a 16-QAM modulated signal, which is reflected by the RIS and propagated to the RX. In the initial state, the RIS uses the default beam direction without active beam adjustment. As shown in Figure 6a, the constellation diagram of the receiver is clearly recognizable, which indicates that the UE successfully receives the signal even under NLOS conditions. This result verifies that the RIS can still achieve effective signal coverage under NLOS conditions with the preconfigured beam direction.  
As shown in Figure 6b, when the UE moves, due to the non-NLOS condition in the experimental environment. The UE is no longer in the optimal coverage area of the current RIS beam, resulting in signal distortion. It can be observed from the constellation diagram that the modulation points become scattered and jitter. Since the received signal quality drops below the preset threshold, according to Algorithm 1, the UE triggers the RIS to enter beam scanning mode in order to relocate the optimal reflection direction and restore the signal quality.  
As shown in Figure 6c, after the RIS beam scanning and adjustment, the system finally selects the optimal reflection direction and directs the signal to the new position of the UE. The constellation diagram of the receiver appears clear and focused, indicating successful modulation recovery and BER reduction. The results validate the effectiveness of the system, proving that the RIS is able to dynamically optimize the signal coverage in real time, improve the signal quality, and ensure the stability of communication performance in NLOS scenarios.  
Figure 4: Grid mapping and pattern indexing for RIS control.
# Pyramid Feature Attention Network for Speech Resampling Detection
## 1 Introduction
Combined with the deep learning method, this paper constructs a feature pyramid network (FPN) structure to extract the features of the spectrogram, carrying out feature fusion, and adds the Squeeze and Excitation (SE) attention mechanism [31] to obtain the feature map containing low-level location information and high-level semantic information for resampling-tampered detection. In summary, the contributions of this paper mainly include the following three points:  
1. We propose to use a deep learning method to extract resampling features from spectrograms containing time-frequency information, and apply various models of classical image classification for speech resampling detection;
2. We apply the SE attention mechanism to the feature pyramid fusion network, which can focus on those channels that are more efficient for the resampling classification task and help the network to make resampling decisions;
3. Thorough experimental evaluations show that the proposed method is suitable for speech detection tampered with multiple resampling factors, especially for tampering close to the original speech, and the detection performance is significantly improved. Moreover, the effect of MP3 compression on speech resampling detection is evaluated, and the results show that the proposed model is robust to MP3 compression. The remaining sections of this paper are as follows: the second part focuses on speech preprocessing and introduces the structure of our proposed network model; the third part is about the experimental setup and analysis of the experimental results. Finally, in the fourth part, the paper is summarized and future work is prospected. Figure 1: Visualization of speech signals. (**a**) Speech signal waveforms; (**b**) spectrogram.