esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spatial Characterization Methods
### Theoretical Distribution Pattern Space
\begin{pmatrix} \frac{1}{m}\sum\limits_{i=1}^{m}\textbf{x}_{i}^{*2}&\frac{1}{m}\sum\limits_{i =1}^{m}\textbf{x}_{i}^{*}\textbf{y}_{i}^{*}&\frac{1}{m}\sum\limits_{i=1}^{m} \textbf{x}_{i}^{*}\textbf{z}_{i}^{*}\\ \frac{1}{m}\sum\limits_{i=1}^{m}\textbf{x}_{i}^{*}\textbf{y}_{i}^{*}&\frac{1} {m}\sum\limits_{i=1}^{m}\textbf{y}_{i}^{*2}&\frac{1}{m}\sum\limits_{i=1}^{m} \textbf{y}_{i}^{*2}\\ \frac{1}{m}\sum\limits_{i=1}^{m}\textbf{x}_{i}^{*}\textbf{z}_{i}^{*}&\frac{1} {m}\sum\limits_{i=1}^{m}\textbf{y}_{i}^{*2}\textbf{z}_{i}^{*}&\frac{1}{m}\sum \limits_{i=1}^{m}\textbf{z}_{i}^{*2}\end{pmatrix}  \tag{12}\]  
where the variances are on the diagonal of the matrix \(\textbf{C}_{S^{*}}\), and the other elements are covariances, as shown in Equation (12). The covariance matrices \(\textbf{C}_{S^{*}}\) and \(\textbf{C}_{S}\) have a relationship as follows:  
\[\textbf{C}_{S^{*}}=\frac{1}{m}\textbf{S}^{*}\textbf{S}^{*T}=\frac{1}{m}( \textbf{WS})(\textbf{WS})^{T}=\textbf{WC}_{S}\textbf{W}^{T} \tag{13}\]  
Therefore, \(\textbf{C}_{S^{*}}\) can be obtained when the matrix \(\textbf{W}\) satisfies the matrix \(\textbf{WC}_{S}\textbf{W}^{T}\) as a diagonal matrix. Since the covariance matrix \(\textbf{C}_{S}\) is a symmetric matrix, a diagonal matrix \(\boldsymbol{\Lambda}\) can be found by  
\[\boldsymbol{\Lambda}=\textbf{E}^{T}\textbf{C}_{S}\textbf{E} \tag{14}\]  
where the \(\textbf{E}\) is the matrix consisting of 3 sets of unit orthogonal vectors. \(\textbf{E}^{T}\) is equal to \(\textbf{W}\). Therefore, the matrix \(\textbf{W}\) can be calculated by solving the eigenvector of the covariance matrix \(\textbf{C}_{S}\). The projected data \(\textbf{S}^{*}\) and its covariance matrix \(\textbf{C}_{S^{*}}\) can then be obtained as well. Furthermore, the theoretical distribution pattern space can be demonstrated in the matrix with the covariance matrix \(\textbf{C}_{S^{*}}\), where the eigenvalues and eigenvectors can be used to calculate the propagation scales and propagation directions of crack, respectively. A schematic of the spatial characterization by the three methods is presented in Figure 3. The black dots are the monitored events, the two red ellipsoids are given by the DTPS method at two different scales, the gray ellipsoid is calculated by the MVEE method, and the blue irregular convex body is formed by the 3D Convex Hull method.
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 4 Simulation Results  
We performed the experiments using OMNeT++ [21]. This simulator is modular, and it is used for modeling wireless and wired communication networks, multiprocessors, microprocessors, and other various types of parallel and distributed computing systems. It is developed using the C++ language, and it is composed of different basic modules that communicate with each other. Furthermore, these basic modules are used to generate large modules [22]. To start our simulation, we consider the following assumption.  
To perform the simulation in OMNeT++, we should understand the C++ language because the commands and coding are very similar to C++. To start work in the simulator, we make a diagram according to the scenario described in the above section. Obtaining results from the simulation software is a bit risky and complicated. Therefore, we have to depend primarily upon the simulation software. We evaluated the performance of our proposed model mainly on three parameters: the delay, packet loss, and comparison between wired and wireless networks.
## 3 Interpretation of Velocity Signals from Impact Echo Method
### Feature Parameters
#### 3.1.3 Direction or Sign of Peaks  
In addition to the number of intermediate peaks due to defect, the direction or sign of the reflected peak could be associated with the type or category of the defect being present, either when the peak was directed upward and thus having a positive sign and increase in impedance, or when the peak was directed downward and thus having a negative sign and decrease in impedance.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 4 Theoretical Model and Validation
### Validation
#### 4.3.2 Rotation of the Drill Tool
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Eccentricity**} & \multirow{3}{*}{**Drilling Fluid Rate**} & \multicolumn{6}{c}{**Critical Start-Up Velocity**} \\ \cline{3-6}  & & & \multicolumn{3}{c}{**Calculated Data**} \\ \cline{3-6}  & & **Depical Particle** & **Error (\%)** & **Non-Spherical Particle (m/s)** & **Error (\%)** \\ \hline
0 & 4.91 & 0.25 & 0.21 & 19.05 & 0.28 & 12.00 \\
0.1 & 6.05 & 0.31 & 0.26 & 19.23 & 0.36 & 16.13 \\
0.2 & 7.06 & 0.36 & 0.30 & 20.00 & 0.41 & 13.89 \\
0.3 & 7.65 & 0.40 & 0.33 & 21.21 & 0.45 & 12.50 \\ \hline  & & & \multicolumn{3}{c}{Average} & 19.87 & Average & 13.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental and calculation data of critical start-up velocity (Type 2, rotating). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Eccentricity**} & \multirow{3}{*}{**Drilling Fluid Rate**} & \multicolumn{6}{c}{**Critical Start-Up Velocity**} \\ \cline{3-6}  & & \multicolumn{3}{c}{**Calculated Data**} \\ \cline{3-6}  & & **Depical Particle** & **Error (\%)** & **Non-Spherical Particle (m/s)** & **Error (\%)** \\ \hline
0 & 4.20 & 0.22 & 0.17 & 22.73 & 0.26 & 18.18 \\
0.1 & 5.13 & 0.26 & 0.21 & 19.23 & 0.29 & 11.54 \\
0.2 & 5.69 & 0.29 & 0.24 & 17.24 & 0.33 & 13.79 \\
0.3 & 6.59 & 0.34 & 0.28 & 17.65 & 0.39 & 14.71 \\ \hline  & & & \multicolumn{3}{c}{Average} & 19.21 & Average & 14.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental and calculation data of critical start-up velocity (Type 1, rotating). intervals still show a narrowing trend, indicating that increasing rotation speed reduces the variability in cuttings migration and improves migration efficiency. Annular eccentricity has a more complex effect on cuttings migration. As eccentricity increases, the confidence intervals widen significantly, reflecting the introduction of greater fluid dynamic complexity, which increases the variability of the data. This means that the higher the eccentricity, the greater the uncertainty in the cuttings migration process, making predictions more challenging.
# Digital Health Technology for Stroke Rehabilitation in Canada: A Scoping Review
## 3 Results
### Mobile and Sensor-Based Training  
Sensor-based rehabilitation technologies have shown feasibility and effectiveness, particularly for home-based training [26; 38]. The phone-monitored home exercise program (H-GRASP) was found to be feasible and effective when participants were sufficiently challenged [38]. Additionally, a haptic-feedback hand function device was reported to improve motivation and social engagement, beyond functional gains [26]. A mobile toolkit for gait training was also found to enhance self-efficacy among users [33].
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 6 Conclusions  
In conclusion, the QTPM has been successfully developed and implemented by integrating QRNG with TPM to enhance IoT security. By incorporating quantum randomness into the TPM framework, we significantly improve the security and unpredictability of cryptographic operations within TPM, which represents a novel and practical step toward quantum-secure IoT systems. Furthermore, the development of the QEAaS framework enables quantum-secure entropy provisioning for legacy IoT devices that cannot integrate QTPM hardware, eliminating the need for costly hardware upgrades. Our comparative evaluation of Xorshift PRNG, Mersenne twister PRNG, and IDQ QRNG highlights the superiority of quantum-generated randomness, particularly in maintaining the high-quality entropy crucial for secure IoT applications. Additionally, our analysis of performance and memory usage demonstrates the trade-offs between QRNG and PRNGs, reinforcing the necessity of optimized implementations for resource-constrained environments. By demonstrating a functioning QTPM prototype and a scalable QEAaS model, our future work will focus on implementing quantum cryptographic modules at the hardware level, optimizing QRNG integration for low-power IoT systems, and deploying the QEAaS platform to further strengthen IoT security protections.  
Conceptualization, G.X.; methodology, G.X.; software, O.A.; validation, G.X. and O.A.; formal analysis, G.X. and O.A.; investigation, G.X.; resources, G.X.; data curation, O.A. and J.M.; writing--original draft preparation, G.X. and O.A.; writing--review and editing, G.X., O.A., J.M., and E.S.; visualization, O.A.; supervision, S.W.; project administration, G.X.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSF grant number 2329053.  
Data Availability Statement: Data available on request due to privacy.  
The authors declare no conflicts of interest.
## 4 Proposed PPO-Based Method
### MDP Formulation  
In this section, the RSU acts as the decision-making agent, collecting state information from task vehicles and generating optimized decisions based on these states. These decisions are transmitted to the task vehicles, which then interact with the environment and receive corresponding rewards, completing the MDP process. The design of the state space, the action space, and the reward function are described in detail below.  
(1) State space: The state \(\mathbf{S(t)}\) in time slot \(t\) consists of the data sizes, the channel gains, and the AoI of each task vehicle, as described by  
\[\mathbf{S(t)}=\{\mathbf{D}^{\text{rem}}(\mathbf{t}),\mathbf{D(t)},\mathbf{D}^{\text{ mec}}(\mathbf{t}),\mathbf{D}^{\text{sv}}(\mathbf{t}),\mathbf{H(t)},\mathbf{AoI(t)}\}, \tag{16}\]  
where \(\mathbf{D}^{\text{rem}}(\mathbf{t})\) is the set of the remaining data amount of the head task in the task queue of each task vehicle in time slot t, \(\mathbf{D(t)}\) is the set of the total data size of tasks in the task queue of each task vehicle, \(\mathbf{D}^{\text{mec}}(\mathbf{t})\) is the set of the sum of the task data to be computed by MEC, \(\mathbf{D}^{\text{sv}}(\mathbf{t})\) is the set of the total sum of task data to be processed by service vehicles in time slot \(t\), \(\mathbf{H(t)}\) gives the channel gains, and \(\mathbf{AoI(t)}\) is the set of AoI for task vehicles in time slot \(t\).  
(2) Action space: The action space includes the offloading ratio \(\xi_{i}\), offloading object \(\beta_{ij}\), and subcarrier allocation \(w_{s,i}\), which is expressed by  
\[\mathbf{A}(t)=\left\{\mathbf{\xi}(t),\mathbf{B}(t),\mathbf{W}(t)\right\}, \tag{17}\]  
where \(\mathbf{\xi}(t)=\left\{\xi_{1},\xi_{2},\ldots,\xi_{n}\right\}\), \(\xi_{i}\) is between 0 and 1; when \(\xi_{i}=0\), the head task belongs to the \(i\)-th task vehicle is only processed locally; when \(\xi_{i}=1\), the head task belongs to the \(i\)-th task vehicle is all offloaded to the service node for computation. \(\mathbf{B}(t)=\left\{\beta_{ij}\mid\beta_{ij}\in\{0,1\},i\in\mathcal{TV},j\in \mathcal{N}\right\}\) and \(\mathbf{W}(t)\) is the subcarrier allocation matrix. \(\xi(t)\) is continuous vector, and \(\mathbf{B}(t)\) and \(\mathbf{W}(t)\) are discrete vectors.  
(3) Reward function: The goal of the offloading and subcarrier allocation policy is to minimize the AoI. Therefore, the reward function is  
\[R(t)=-\sum_{i=1}^{V}AoI_{i}(t). \tag{18}\]
# Behaviour of Uniform Drava River Sand in Drained Condition--A Critical State Approach
## 2 Physical Properties of Tested Material  
### Origin of Drava River sands  
The Drava River sand tested in this study originates from the wider area of the City of Osijek, located in Eastern Croatia, which is a part of the European Pannonian Basin [30]. The City of Osijek belongs to the Drava depression zone, characterized by the lowland relief and complex geological settings [34]. The city area is predominantly composed of heterogeneous mixtures of Quaternary deposits, among which the Pleistocene continental, marshy and lacustrine loess sediments and Holocene Drava River terrace sediments are generally distinguished. The Drava River sand belongs to the multiphase flooded lithological complex of the Holocene terrace, formed at the right bank of the Drava River. The thickness of the river terrace sediments ranges between 2 and 6 m, and the maximum terrace width is approximately 3.3 km [34]. Sands and sandy silts prevail, with subordinate silts. Quartz (approx. 50%), feldspar (approx. 25%) and carbonates (approx. 25%) are the main minerals in the sands, with subordinate muscovite and rock fragments. A high magnification image of tested sand was made and presented in Figure 1, magnified 30 times. For this purpose, an EOL scanning electron microscope with maximum resolution of 0.8 nm was used with the acceleration charge of 10 kV and working distance of 10 mm [35; 36]. It can be observed that the tested sand is generally characterized by the uniformity of particle shape, which are predominantly of low sphericity and angular to subangular [37].  
The main physical properties of soil, such as, specific gravity, particle size distribution, minimum and maximum void ratios of this sand type were tested and are presented in the following subsections.
## 4 Analysis
### Sensitivity Analysis of All Models  
Sensitivity analysis evaluates how changes in key parameters affect model performance [35]. This study applies it to Markov processes, max-flow problems, and traffic flow models to assess the impacts of varying cargo volumes, road capacities, and traffic conditions. Markov processes analyze how delays affect steady-state probabilities. Max-flow problems explore how capacity changes influence cargo flow and bottlenecks. Traffic flow models evaluate how traffic density and slowdowns affect delivery times. The analysis highlights factors critical to improving cargo handling, routing, and connectivity.
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 4 Results  
### Experiments Setup  
This section illustrates the implementation and conducts real-world experiments to show the performance and robustness of ECC in the elevator environment. For the hardware, two Thinkpad X201 laptops equipped with Atheros 9300 NICs are used as the testbed of the proposed method. Each Atheros 9300 NIC is equipped with 2 antennas. The antennas are placed diagonally in the corners of the elevator by using coaxial antenna signal cables to form a \(2\times 2\) multiple-input multiple-output (MIMO) communication system. The signal frequency of NIC is 5.2 GHz, and the bandwidth is 20 MHz.  
For the software, PicoScenes is adopted as the software platform. PicoScenes [37] is a versatile and powerful middleware for CSI-based WiFi sensing with support for up to 27 models of COTS NICs. PicoScenes provides flexibility for transmission parameter selection and a user-friendly approach for CSI data collection. Moreover, PicoScenes provides multiple easy-to-use APIs for plugin development, providing not only CSI data but also other helpful information such as timestamps, RSSI, SNR, etc. In the experiments, 40 groups of real-time data under each passenger number (0\(\sim\)18 passengers) are captured in real elevator scenes, of which 30 sets of data are used to construct the training dataset while the other 10 sets are used to form the testing dataset. The data are captured based on the shortest running time of the elevator (4 s) and set the packet sending interval of 1 ms. Hence, the length of each group of data is 4000.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 2 Harmonic Detection Principles and Methods  
Harmonic detection is the process of identifying and measuring the presence of harmonic frequencies in a signal. Harmonics are multiples of the fundamental frequency of a signal and can be caused by non-linearities in the system generating the signal or by external sources such as power supply interference [9]. The literature [1; 12] explains harmonic detection methods, which are used to extract the harmonic components in the power signal or to estimate each harmonic's parameter information. Accurate and fast harmonic detection is significant for subsequent harmonic analysis and control. There are several methods for detecting harmonics in a signal, including the use of Fourier transform, wavelet transform, and harmonic analysis.  
Fourier transform is a widely used method for harmonic analysis. It decomposes a signal into its individual frequency components and can be used to identify the presence of harmonic frequencies. However, it is not well suited for signals that have time-varying frequencies or non-stationary signals. Wavelet transform is another popular method for harmonic analysis. It can be used to analyze signals with time-varying frequency and non-stationary signals. It can also provide more detailed information about the harmonic content of a signal than the Fourier transform [13].
# Sintering Behavior of a Six-Oxide Silicate Bioactive Glass for Scaffold Manufacturing
## 4 Conclusions  
In the present study, the sintering behavior of a bioactive silica-based glass characterized by good sintering behavior and promising bioactive properties was investigated in order to optimize scaffold mechanical properties for bone tissue engineering applications.  
Isothermal DTA curves revealed the presence of crystallization exothermic peaks for sintering temperatures above the glass crystallization onset, but diffraction peaks associated to the nucleation of combine and tremolite crystals were observed also in the samples sintered at 700 \({}^{\circ}\)C, indicating a mild beginning of glass devitrification. Material densification upon sintering was found to be comparable in all the samples analyzed, regardless of the temperature used. A decrease in sample density as well as a lower volumetric shrinkage were observed for T\({}_{\mathrm{s}}\)= 750 and 800 \({}^{\circ}\)C as a result of the inhibition in viscous flow caused by the progressive crystallization of the glassy matrix.  
All the scaffolds analyzed exhibited mechanical properties comparable to that of human trabecular bone, showing a linear increasing trend as a result of the increase in sintering temperature.  
These results suggest that 47.5B-based scaffolds produced by foam replication and sintered at 700\({}^{\circ}\)C could represent a good compromise between mechanical performances and bioactive behavior  
Figure 5: Compressive strength of 47.5B glass and glass-ceramic scaffolds as a function of the sintering treatment.  
in body fluids, due to the presence of small crystalline nuclei within a predominant amorphous matrix, improving scaffold strength and ion-exchange processes respectively.  
However, it should be noted that all the scaffolds here proposed revealed an attractive potential for bone tissue engineering applications due to the possibility of obtaining highly densified structures able to provide suitable mechanical support to the host tissue while preserving the apatite-forming ability of the material even in a crystallized form.  
Conceptualization, E.F. and F.B.; Methodology, E.F., E.V. and F.B.; Software, E.F. and G.S.; Investigation, E.F., G.S. and F.B.; Resources, C.B., E.V. and F.B.; Data Curation, E.F. and G.S.; Writing-Original Draft Preparation, E.F.; Writing-Review & Editing, G.S., C.B., E.V. and F.B.; Visualization, E.F., G.S. and F.B.; Supervision, C.B., E.V. and F.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest relevant to this article.
## 3 Experiment Verification
### Comparative Experiments and Result Analysis
This observation aligns with the visual examples in Figure 12, where our model effectively retains diverse echo intensity regions and small echo areas. In terms of the PSNR metric, our model achieves a value of approximately 21, while other comparative models range from 20 to 22. It is believed that this is attributable to the inherent dynamic changes in both space and time within radar echo data, which complicate the capture of spatiotemporal dependencies. This complexity becomes even more pronounced during long-term predictions, as the extended timeframe can lead to cumulative errors in the model's forecasts, often resulting in underestimated prediction values. An analysis of the prediction visualization results from various models applied to the SEVIR dataset in Figure 14 reveals that, despite not being trained on the SEVIR dataset, the model proposed in this paper produces predictions that are most closely aligned with the actual radar echo images in the first row, compared to other models. This proposed model demonstrates the capability to predict intricate details effectively, thereby indicating its robust performance across various weather conditions and its potential for generalization in diverse meteorological contexts. Figure 14: Comparative case study on the standard SEVIR dataset.
## 3 Results and Discussion
### Laser Interference Lithography Fabrication
Compared to the previous described strategy, the small-scale structures dominates in this case, probably because the laser exposure dose which corresponds to the large patterns was not high enough to totally polymerized the used SU-8 layer. In addition to SEM images, the surface topography of the fabricated patterns was characterized using AFM. The results are shown in Figure 2. From Figure 2a,b, the structure height of the 5 \(\upmu\)m on 0.5 \(\upmu\)m hierarchical patterns using the first strategy can be measured (two layer of resist, lower layer with the smaller 0.5 \(\upmu\)m hole-like pattern). The overall structure height is in average 0.83 \(\pm\) 0.14 \(\upmu\)m, which corresponds to the sum of the average heights of the structures in each single layer. Considering that each individual layer of the resist had a thickness of \(\sim\)0.72 \(\upmu\)m and that the height of the 5.0 \(\upmu\)m pattern was 0.73 \(\upmu\)m, it can be assumed either that in the case of the small pattern part of the material was partially polymerized at the interference minima positions or that the thickness of each layer was different. The figure also demonstrates that the large-scale pattern dominates the surface. The small-scale structure can be seen only in the holes of the large-scale structure and is located on a lower height level. The results on the surface topography of the strategy consisting on utilizing also two layers of resist, but with the lower layer corresponding to the larger 5.0 \(\upmu\)m hole-like pattern are shown in Figure 2c,d. In this case, the overall structure depth was 0.30 \(\pm\) 0.07 \(\upmu\)m, which is close to the height of the small-scale structure (0.18 \(\pm\) 0.06 \(\upmu\)m). As explained before, the differences can be attributed to the reason, that for the small-scaled pattern, the resist is also polymerized partially at the interference minima positions. Finally, Figure 2e,f shows AFM measurements of the hierarchical pattern fabricated in a single layer of the SU-8 resist. The average structure height was only 0.08 \(\pm\) 0.01 \(\upmu\)m. In this pattern, the 0.5 \(\upmu\)m structure height was \(\sim\)0.03 \(\pm\) 0.01 \(\upmu\)m while the structure height of the 5 \(\upmu\)m pattern (measured as an envelope on top of the 0.5 \(\upmu\)m structure) was 0.05 \(\pm\) 0.01 \(\upmu\)m. Thus, the overall height is the sum of the envelope of the large pattern and the height of the small-scale pattern. Nevertheless, the depth is not comparable to other structures obtained from this exposure setup. This process could be improved, by reducing the exposure dose used for both patterns. Figure 2: AFM pseudo-color images of **(a,b)** 5 \(\upmu\)m on 0.5 \(\upmu\)m hierarchical patterns (using two layers of resist), **(c,d)** 0.5 \(\upmu\)m pattern on top of 5 \(\upmu\)m pattern (also using two layers of resist) and **(e,f)** 5 \(\upmu\)m and 0.5 \(\upmu\)m hole-like patterns exposed in a single layer of SU-8 resist.