esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Accurate model identification and parameters extraction of the lumped equivalent circuit for these devices may be difficult to achieve since both rail launchers and compulsators are inherently time-varying and nonlinear electromechanical devices and consequently the parameters that identify the equivalent circuit of one device may depend on the operating conditions of the whole system and on the characteristics of the other device. A strong-coupled 3D electromechanical analysis of the interacting devices seems to be the only option able to provide accurate results. This paper discusses the coupled electromechanical analysis of the whole launch package by using the research code EN4 EM previously developed by the authors. In order to avoid confusion, in the remaining of the paper the phrase \"strong coupling\" will be reserved to the magnetoquasistatic-mechanical problems, arising when analyzing a device with conductors in relative motion. \"Strong coupling\" is necessary when analyzing high speed devices and it is inherently provided by underlying formulation of EN4 EM. The phrase \"strong-interaction\" is reserved to indicate a simultaneous full 3D \"strong coupled\" analysis of the rail launcher and its feeding compulsator. Moreover, the phrase \"weak-interaction\" is reserved for those analyses where one of the devices is substituted with a lumped equivalent circuit and the other is analyzed by a 3D \"strong coupled\" model. The \"equivalent circuit\" is reserved for zero-dimensional voltage-currentdependencies at the terminals of a device. A lumped \"equivalent circuit\" is usually unable to provide information about the spatial distribution of the electromechanical quantities inside the device. Finally, the phase \"equivalent network\" is related to EN4 EM and, as it will be shown in the next section, is used to indicate the internal procedure of the code which builds an electric network whose currents are uniquely related to the current density distribution in a device. To best of the authors' knowledge, scientific literature does not report any \"strong interaction\" analysis between rail launcher and compulsator capable to consider two mechanical degrees of freedom (one rotation for the compulsator and one translation for the launcher) together with high speed sliding contacts. Considering that the components and the materials used in EML technology are heavily stressed from the electrical, mechanical and thermal point of view, a tools which allows an accurate coupled analysis represents a valuable resource. The manuscript is organized as follows. Section 2 briefly summarizes the adopted numerical formulation. Section 3 shows two examples of the \"weak interaction\" analysis and further justifies the motivations of the research by discussing the results of these analyses. In Section 5, the \"strong interaction\" analysis of the whole system is carried on and its results are compare with those by a \"weak interaction\" analysis. Finally, some concluding remarks are reported.
## 4 Results and Discussions
### Simulation Results with Pilot Frequency Varied  
Table 11 shows the BER of SNR with pilot frequency varied.
# Digital Health Technologies to Support At-Home Recovery of People with Stroke: A Scoping Review
## 4 Discussion
### Recommendations for Technologists and Researchers  
Technology-wise, improvements can be made at the hardware and software levels to enhance stroke rehabilitation. For example, the design and usability of devices can be improved by developing flexible, skin-friendly sensors or smart garments, which would not only increase patient comfort but also provide richer data on body movements [34; 35]. Also, the development of comprehensive monitoring tools that incorporate features like pressure mapping, limb configuration, and detailed activity data would make systems more robust and clinically relevant, ultimately improving rehabilitation outcomes [30; 34]. From a software perspective, integrating advanced analytics through machine learning algorithms could significantly improve system functionality, enabling the classification of movements, detection of abnormalities, and real-time alerts for healthcare providers [35].  
Research-wise, expanding the technologies to monitor other body parts and testing them on larger, more diverse patient populations would improve system versatility and validity, enhancing their broader applications [28; 33]. For instance, combining accelerometer-based feedback with behavioral interventions, including exercise programs, could help address learned non-use and better motivate patients toward recovery [27]. Moreover, integrating multiple modalities, such as pairing systems like SECOSP with IMU sensors, would offer a more comprehensive view of patient activity by capturing specific motion percentages and movement intensity throughout the day [29]. Yet, significant gaps remain. Technologies that address cognitive function, ADLs, or whole-body rehabilitation are under-represented [47]. Future studies should emphasize more comprehensive approaches that integrate cognitive and motor rehabilitation, interdisciplinary development, AI integration, and larger, more diverse samples to ensure generalizability and impact [48; 49; 50].
## 3 Results and Discussion
### Comparison of Volatile Components of Rice Grains Stored under Different Conditions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**N0**} & \multirow{2}{*}{**Compound Name**} & \multicolumn{3}{c}{**Storage Conditions (Mean \(\pm\) SD)**} \\ \cline{3-5}  & & **RA** & **RB** & **RC** \\ \hline Alcohols & & & & \\
1 & 2-Ethyl-1-hexanol & 15.78 \(\pm\) 1.41 a & 22.04 \(\pm\) 4.61 a & 15.52 \(\pm\) 0.62 a \\
2 & 2-Hexyl-1-decanol & ND & 5.59 \(\pm\) 0.52 a & 1.81 \(\pm\) 0.13 b \\
3 & 2-Butyl-1-octanol & ND & 4.85 \(\pm\) 2.02 a & ND \\
4 & 2-Isopropyl-5-Methyl-1-heptanol & ND & ND & 5.54 \(\pm\) 1.68 a \\ Acid esters & & & & \\
1 & 1,2-Benzenedicarboxylic acid, & 7.20 \(\pm\) 4.76 a & 4.03 \(\pm\) 1.19 a & 3.61 \(\pm\) 0.46 a \\  & bis(2-methylpropyl) ester & & & \\
2 & Ethyl dodecanoate & 6.6 \(\pm\) 0.06 a & 6.41 \(\pm\) 1.71 a & 6.75 \(\pm\) 1.56 a \\
3 & Ethyl heptanoate & 18.40 \(\pm\) 3.15 a & 16.87 \(\pm\) 6.78 a & 16.02 \(\pm\) 4.06 a \\
4 & Ethyl hexadecanoate & 5.02 \(\pm\) 2.18 a & 23.40 \(\pm\) 7.52 a & 18.20 \(\pm\) 12.49 a \\
5 & Ethyl nonanoate & 48.72 \(\pm\) 2.50 a & 30.17 \(\pm\) 5.67 a & 41.68 \(\pm\) 13.50 a \\
6 & Ethyl octanoate & 22.65 \(\pm\) 7.14 a & 16.61 \(\pm\) 5.63 a & 21.05 \(\pm\) 5.04 a \\
7 & Ethyl myristate & 6.78 \(\pm\) 0.37 a & 5.67 \(\pm\) 2.56 a & 14.74 \(\pm\) 12.67 a \\
8 & 1,2-Benzenedicarboxylic acid, & ND & 3.81 \(\pm\) 0.81 a & 3.06 \(\pm\) 0.25 a \\  & butyl ester & & & \\
9 & Sulfutrous acid, dodecyl & & & \\  & pentyl ester & ND & 9.3 \(\pm\) 0.21 a & ND \\
10 & Ethyl 9-hexadecanoate & ND & ND & 1.58 \(\pm\) 0.47 a \\
11 & Sulfutrous acid, 2-pentyl & 10.98 \(\pm\) 0.71 a & ND & ND \\ Ketone & & & & \\
1 & 6,10,14-Trimethyl-2-pentadecanone & 8.51 \(\pm\) 2.72 a & 6.17 \(\pm\) 1.33 a & 8.67 \(\pm\) 2.81 a \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The content of volatile compounds of rice grains stored under different environments (\(\upmu\)g/kg). Figure 2: The composition of volatile components in rice grains stored under three different conditions. Content (**A**) and proportion (**B**) of different volatile components. “Others” represents aromatic and heterocyclic compounds. RA: room temperature, horizontal ventilation, nitrogen gas atmosphere; RB: room temperature, vertical ventilation, nitrogen gas atmosphere; RC: quasi-low temperature, horizontal ventilation, nitrogen gas atmosphere.
## 6 Objective QoE Evaluation of Cloud Gaming
### Correlation of FR and NR Metrics with Subjective QoE
The use of duplicated packets showed better results than the existing NR models in non-correlated settings in the previous work. We also found how the P-Hash-SSIM (PHS) models, PCC and SCC, as shown in Table 2. Since the number of duplicated packets increases as the MOS goes down, the correlation coefficient obtained is negative. Similar to our prior studies, PHS performed exceptionally well in non-correlated situations. However, it was not as successful for correlated network parameter settings. Nonetheless, it outperformed VMAF in non-correlated cases and was comparable in correlated ones. Despite the fact that this is still an NR metric, as opposed to the other three, the results of this model were impressive, and it would be more time efficient and require less computational power, since it only requires client-side game footage for evaluation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**QoS Type** & **FR Metric** & **FIFA** & \multicolumn{2}{c}{**CSGO**} & \multicolumn{2}{c}{**GTA**} \\ \hline  & & **PCC** & **SCC** & **PCC** & **SCC** & **PCC** & **SCC** \\ \hline \multirow{4}{*}{Non-Correlated} & SSIM & 0.85 & 0.80 & 0.91 & 0.92 & 0.69 & 0.76 \\  & PSNR & 0.81 & 0.85 & 0.93 & 0.94 & 0.61 & 0.75 \\  & VMAF & 0.97 & 0.91 & 0.96 & 0.92 & 0.97 & 0.86 \\  & P-Hash-SSIM (NR) & \(-0.94\) & \(-0.99\) & \(-0.95\) & \(-0.97\) & \(-0.89\) & \(-0.97\) \\ \hline \multirow{4}{*}{Correlated} & SSIM & 0.75 & 0.77 & 0.75 & 0.79 & 0.36 & 0.28 \\  & PSNR & 0.41 & 0.38 & 0.78 & 0.80 & 0.36 & 0.20 \\ \cline{1-1}  & VMAF & 0.88 & 0.77 & 0.87 & 0.81 & 0.88 & 0.83 \\ \cline{1-1}  & P-Hash-SSIM (NR) & \(-0.75\) & \(-0.88\) & \(-0.72\) & \(-0.87\) & \(-0.74\) & \(-0.83\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PCC and SCC of Full-Reference Objective QoE Metrics against the subjective MOS.
## 3 Results and Discussion
### Hardness  
The results of the hardness (Shore D test) are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \(T_{5\%}\) & \(T_{20\%}\) & \(T_{50\%}\) & \(T_{80\%}\) \\ \hline E0 & 300 & 352 & 385 & 568 \\ E/0.01 MWCNT & 301 & 352 & 389 & 574 \\ E/0.05 MWCNT & 312 & 354 & 389 & 582 \\ E/0.1 MWCNT & 319 & 352 & 390 & 563 \\ E/0.5 MWCNT & 326 & 353 & 396 & 598 \\ E/0.5 EG & 316 & 352 & 392 & 548 \\ E/1.0 EG & 338 & 356 & 392 & 552 \\ E/1.5 EG & 310 & 352 & 395 & 580 \\ E/2.0 EG & 315 & 352 & 400 & 682 \\ E/2.5 EG & 305 & 350 & 388 & 656 \\ \hline MWCNTs & 563 & 600 & 625 & 644 \\ EG & 595 & 687 & 741 & 785 \\ \hline \end{tabular} Notation: \(T_{5\%}\), \(T_{20\%}\), \(T_{50\%}\), \(T_{80\%}\)—temperatures (in \({}^{\circ}\)C), corresponding to 5%, 20%, 50%, and 80% mass loss of the sample, respectively. The values for fillers were given for comparison.  
\end{table}
Table 2: Temperatures corresponding to the certain weight loss of epoxy composites during heating.  
The hardness of the neat resin was 78. The addition of MWCNTs slightly increased the hardness. There was no significant increase in the hardness below 0.05 wt.% loading because of relatively low filler concentration. The increase in MWCNT loading above 0.1 wt.% led to a decrease in hardness and it became lower than that of neat resin. This is probably due to the growth of the concentration of air bubbles coming from porous filler added to the resin (Figure 5). The data obtained for epoxy/ MWCNT composites were close to the Shore D hardness of epoxy composite based on multi-wall carbon nanotubes which were subjected to thermoelectr treatment [48]. For example, epoxy composites based on heat-treated MWCNTs showed a hardness ranging from 74-77 (Shore D). Thermoelectr treatment induced expansion of the range of hardness to 76-82.
## 2 Materials and Methods  
### Test Material  
Iron crystal sand colloidal geotechnical material was used as the test simulation material, and the main components contained iron ore powder, barite powder, quartz sand, rosin, alcohol, and gypsum powder [49]. To ensure uniformity, all samples were kept in a humidity box for 14 days at the constant temperature of 25 \({}^{\circ}\)C and 35% humidity. Interface molds with seven different angles (0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\)) were printed using a 3D printer. The mold pedestal thickness was 0.5 cm with the size of 10 cm \(\times\) 10 cm \(\times\) 10 cm (Figure 2). In the process of sample preparation, the air bubbles were fully discharged by vibration at a certain frequency. The sample surface was leveled and placed at room temperature. After eight hours, the sample was removed and placed inside a humidity box at constant temperature for curing. The roughness characteristics are important; therefore, to ensure that each sample had the same roughness, the surface was polished smooth (JRC = 1) using 600 mesh sandpaper.  
In this study, a shallow-buried tunnel was considered as the object of investigation, and the thickness of the cover soil was determined to be less than twice the tunnel span. Therefore, in this study, the tunnel span was 3 cm and set in the middle of a cube. The distance between the tunnel roof and the surface was 3 cm. In shallow-buried tunnels, the lateral failure is mainly manifested by tensile failure under vertical load, because the binding force of lateral plane is small and the tensile strength of rock mass is much lower than that of compressive strength. In order to study the characteristics of lateral deformation and tensile failure of rock mass under the influence of tunnel, no lateral constraint was set in this test.  
The sample numbers and initial compression wave velocities are listed in Table 1. According to the initial wave velocity, the interface samples with different inclination angles have obvious anisotropic characteristics, and the wave velocity decreases as the dip angle increases. The compressional wave velocity represents the resistance of the specimen to compression deformation, and a positive correlation exists between them [50]. The strength of the sample can be preliminarily assessed by a wave velocity test.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 2 Background
### Domain Analysis
#### 2.2.4 Outgoing Dock  
The goods that need to be sent out of the warehouse are transported here; all the sales order details are cross-checked, and the product or bundles of products are checked out of the inventory, while the information is updated in the inventory records. The check-outs can be performed manually or with some partial automation; however, the use of scanners and RFID tags integrated with the software system can make the flow smoother and improve efficiency.
## 3 The Sensor Integration  
The sensor integration can be separated into the strapdown algorithm (SDA) and the Kalman filter update. In the SDA, the highly dynamic movement of the system is determined integrating the angular rates and accelerations of the MEMS IMU in real time. As a result of various errors (initial alignment, inertial sensors, computational, _etc._), the SDA solution is drifting over time. Therefore, the additional sensors, providing long-term, stable measurements, are needed to correct and bound the drift of the inertial sensor integration [39]. The estimation of the SDA errors is realized in an error state space Kalman filter. Some advantages for this approach can, for example, be found in [40,41].  
In the following, the SDA and the Kalman filter update will be summarized. More details can be found in [42, 43, 44]. Here, the navigation equations of the b-frame are expressed in the e-frame. The b-frame to e-frame rotation \(C_{b}^{e}\) is split into a rotation of the b-frame with respect to the local-level navigation frame (n-frame) \(C_{b}^{n}\) and a rotation of the n-frame with respect to the e-frame \(C_{n}^{e}\). Since the axes of the n-frame are aligned with the directions east, north and up, the \(C_{n}^{e}\) rotation is:  
\[\mathbf{C}_{n}^{e}=\begin{bmatrix}-\sin\lambda_{o}&-\sin\varphi_{o}\cos\lambda _{o}&\cos\varphi_{o}\cos\lambda_{o}\\ \cos\lambda_{o}&-\sin\varphi_{o}\sin\lambda_{o}&\cos\varphi_{o}\sin\lambda_{o }\\ 0&cos\varphi_{o}&\sin\varphi_{o}\end{bmatrix} \tag{20}\]  
where \(\lambda_{o}\) and \(\varphi_{o}\) are the ellipsoidal coordinates of the b-frame origin.  
The rotation from the b-frame to the n-frame may be expressed as the product of three separate rotations:  
\[\mathbf{C}_{b}^{n}=\mathbf{C}_{n}^{b,T}=\mathbf{R}_{3}(\gamma)^{T}\cdot \mathbf{R}_{2}(\beta)^{T}\cdot\mathbf{R}_{1}(\alpha)^{T} \tag{21}\]  
using the Euler angles roll \(\alpha\), pitch \(\beta\) and yaw \(\gamma\).  
In the full state vector \(\mathbf{x}\), not the Euler angles, but a quaternion \(\mathbf{q}\) is used for the attitude representation. Further states are the position \(\mathbf{x}_{p}^{e}\), the velocity \(\mathbf{v}_{eb}^{e}\), the accelerometer bias \(\mathbf{b}_{a}^{b}\) and the gyro bias \(\mathbf{b}_{\omega}^{b}\):  
\[\mathbf{x}=\begin{bmatrix}\mathbf{x}_{p}^{e,T}&\mathbf{v}_{eb}^{e,T}&\mathbf{ q}^{T}&\mathbf{b}_{a}^{b,T}&\mathbf{b}_{\omega}^{b,T}\end{bmatrix}^{T} \tag{22}\]
## 4 Discussion
To the best of our knowledge, no other study has analyzed the intensity of a VR game sequence throughout an entire rehabilitation session using the rated perceived exertion scale combined with heart rate. It is believed that this may be an objective way to plan physical training throughout the rehabilitation session and improve the end user's experience throughout the rehabilitation, making the process more pleasant and safe. The results of our study reinforce the use of complementary information from two different instruments to assess the internal load on participants. To reduce musculoskeletal injuries when introducing a sequence of VR rehabilitation exercises with different physical fitness purposes, it is recommended to include in the training session a warm-up and cool-down, stretching exercises, and a gradual progression of the volume and intensity of the session [13]. This is a fundamental consideration for exercise prescription with important implications for decreasing the risk of performing repeated VR exercises. It is well known that a single exercise session should include the following phases: (i) warm-up: 5-10 min of a light-to-moderate-intensity aerobic and muscular endurance activity; (ii) conditioning: 20-60 min of aerobic, resistance, flexibility, and neuromotor exercise; and (iii) cool-down: 5-10 min of light-to-moderate-intensity cardiorespiratory and muscular endurance activities [33]. In this study, we used the RPE and HR monitoring throughout the VR games sequence, aiming to assess the gradual progression of the intensity in the session from warm-up to conditioning phases and a regression (i.e., a decrease in the demand of the exercise) between the conditioning and cool-down phases. Indeed, both metrics analyzed (i.e., RPE and HR) showed a gradual progression and then regression of the intensity of the session, being more evident in the curve of RPE (Figure 1). These results allowed us to validate, in terms of the progression of the intensity, a complete rehabilitation session based on different virtual reality gaming according to the standard components of the exercise training. Some limitations must be acknowledged when interpreting our results. The first concerns the scarcity of prior literature using a similar experimental design that restricts the comparison with our novel results. Second, the students recruited at the academic institution involved in this user-experience pilot study are different from elite or amateur soccer players, where this solution may be implemented. We also did not control whether participants had football-related connections. Different considerations may arise when using other populations at this technological solution's development stage. For safety reasons, the sample comprised only non-injured people, which is a good decision at this development stage. However, some concerns may arise about using the system with injured soccer players that may affect the development of the system. Finally, more longitudinal and experimental studies are needed to reinforce the efficacy of using a VR system to recover from musculoskeletal injuries in this entire rehabilitation session.
# Two-Dimensional Linear Inversion of GPR Data with a Shifting Zoom along the Observation Line
## Appendix A  
This appendix is devoted to the physical interpretation of the quantity in Equation (6). For the sake of simplicity and generality, we have rewritten the relevant equation in a more general form.  
\[g(y)=\int K(y,x)f(x)dx \tag{1}\]  
with its SVD provided through the singular value system \(\{\,u_{n}(y),\sigma_{n},v_{n}(x)\,\}_{n=1}^{\infty}\). The Point Spread Function, for a pulse located at \(x^{\prime}\) can be expressed as:  
\[PSF(x,x^{\prime})=\sum_{n=1}^{\infty}u_{n}(x^{\prime})^{*}u_{n}(x) \tag{2}\]  
and its image in the data space is given by:  
\[\widetilde{g}(y,x^{\prime})=\sum_{n=1}^{\infty}\sigma_{n}u_{n}(x^{\prime})^{* }v_{n}(y) \tag{3}\]  
The integral  
\[E(x^{\prime})=\int\Big{|}\widetilde{g}(y,x^{\prime})\Big{|}^{2}dy \tag{4}\]  
represents the energy of the image of the Point Spread Function for a pulse located at point x\({}^{\prime}\). This quantity can be expressed by exploiting the orthonormality of the functions \(v_{n}(y)\) in the data space as:  
\[E(x^{\prime})=\int|\widetilde{g}(y,x^{\prime})|^{2}dy=\sum_{n=1}^{\infty} \sigma_{n}^{2}|u_{n}(x^{\prime})|^{2} \tag{5}\]  
Now, if we integrate the quantity \(E(x^{\prime})\) over the investigation domain, we obtain the following:  
\[\widetilde{E}=\int E(x^{\prime})dx^{\prime}=\int\sum_{n=1}^{\infty}\sigma_{n} ^{2}|u_{n}(x^{\prime})|^{2}dx^{\prime}=\sum_{n=1}^{\infty}\sigma_{n}^{2} \tag{6}\]  
Therefore, the summation of the squared singular values represents the summation of the energies of the images of the Point Spread Function (PSF) for each generic point x\({}^{\prime}\) in the investigation domain. The same consideration holds when regularization is applied by the Truncated Singular Value Decomposition (TSVD), once we consider (in Equation (6)) the summation truncated at the index (T).
## Appendix A The Bit Error Rate in Non-AWGN Channels
### The Decoding Performance of \(L\) Factor Graphs Is the Same  
Assuming that \(L\) factor graphs have the same decoding performance, each factor graph has the same weight \(w\), and the soft information centroid is \(\frac{\sum\limits_{j=1}^{L}\lambda_{j}}{L}\). According to the central limit theorem, when \(L\) is sufficiently large, \(\sum\limits_{j=1}^{L}\lambda_{j}\) follows a normal distribution with parameters \((L\mu,L\sigma^{2})\), the bit error rate of BPL decoding employing the path selection criterion proposed in this paper is:  
\[p(\frac{\lambda_{1}+\lambda_{2}+\cdots+\lambda_{L}}{L}\leq 0)=p(\lambda_{1}+ \lambda_{2}+\cdots+\lambda_{L}\leq 0)=\Phi(\frac{0-L\mu}{\sqrt{L\sigma}})=\Phi(- \frac{\sqrt{L}\mu}{\sigma}). \tag{1}\]  
When the bit error rate is less than 0.5, \(\mu>0\), \(-\frac{\sqrt{L}\mu}{\sigma}<-\frac{\mu}{\sigma}\). Since the cumulative distribution function of the normal distribution is monotonically increasing, the bit error rate of BPL decoding using the path selection criterion proposed in this paper is less than the original BP decoding bit error rate.