esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Principles
### The Principle of Singular Value Decomposition
#### 2.2.1 SVD Decomposition Method  
Assuming that the time-domain signal sequence to be noise reduced is \(x_{i}=[x_{1},x_{2},\)\(\ldots,x_{N}]\), based on the phase-space reconstruction theory, the \(m\times n\) dimensional Hankel matrix is first constructed based on the one-dimensional signal sequence as follows:  
\[H=\left(\begin{array}{cccc}x_{1}&x_{2}&&x_{n}\\ x_{2}&x_{3}&&x_{n+1}\\ \vdots&&\ddots&\vdots\\ x_{m}&x_{m+1}&\cdots&x_{m+n-1}\end{array}\right)=D+W \tag{19}\]  
where \(H\) is the constructed Hankel matrix; \(m\) is the embedding dimension and satisfies \(m+n-1=N\). In a general case, noise reduction is better when m is set to \(m=[N/10,N/2]\) and \(m\geq n\). \(D\) denotes the \(m\times n\) matrix of the effective signal in the reconstructed space, and \(W\) denotes the \(m\times n\) matrix of the noise interference signal in the reconstructed space.  
The essence of noise reduction using SVD is to find the best approximation matrix of the D matrix. A singular value decomposition of the matrix \(H\) is obtained:  
\[H=USV^{T} \tag{20}\]  
where \(U\) and \(V^{T}\) are \(m\times n\) and \(n\times n\) matrices, respectively, and \(S\) is an \(m\times n\) diagonal matrix with the main diagonal elements \(\lambda_{i}(i=1,2,\ldots,k)\), \(k=\min(m,n)\).  
\[S=diag(\lambda_{1},\lambda_{2},\ldots,\lambda_{k}) \tag{21}\]  
where \(\lambda_{1},\lambda_{2},\ldots,\lambda_{k}\) are the singular values of the matrix \(H\) and \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}\ldots\geq\lambda_{k}\), and \(U\) and \(V^{T}\) denote the left and right singular matrices.  
According to the theory of singular value decomposition and the matrix best approximation theorem in the sense of [PERSON] paradigm, it is known that effective signals are mainly represented by the first r larger singular values, while noise signals at high frequencies are characterized by the other singular values that follow. Therefore, the first r singular values characterizing the effective signals are retained so that the other singular values characterizing the noise signals behind them are 0. Then, the inverse operation of singular value decomposition is performed on the processed S matrix to obtain the matrix \(\hat{H}\).  
At this time, the matrix \(\hat{H}\) is the maximum approximation matrix of the matrix \(H\) with rank r. According to the construction method of the Hankel matrix, the inverse operation from the matrix \(\hat{H}\) can extract the noise-canceled time series signal, which can better remove the noise components in the original time series to be noise canceled.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 4 Discussion
### Simulations  
To simulate the transport of photons and heat diffusion in the tissue, we used the finite element method framework (FEM) in combination with the mesh-based Monte Carlo method (MMCM), as described in the main body of this article and Appendix B. The FEM model of the PlatSil SiliGlass type C tissue-mimicking phantom had the following optical and thermal properties. The absorption and scattering coefficients \(\mu_{\mathrm{a}}\) and \(\mu_{\mathrm{s}}\) were 0.035 mm\({}^{-1}\) and 5.9 mm\({}^{-1}\), respectively; the anisotropy factor g was set to 0.7; the refractive index n was 1.44 [49], while the specific heat \(c_{\mathrm{p}}\), thermal conductivity \(k\), and density \(\rho\) were 703 J/kgK, 1.38 W/mK, and 2203 kg/m\({}^{3}\), respectively [49]. The FEM model of the phantom was homogeneous, i.e., had only one tissue type and had 8,697 tetrahedral elements. To assess the absorption coefficients of the epidermis and hair, we used the realistic FEM Model #1 with the optical and thermal properties of different tissue types specified in Tables 1 and 2, respectively, with the \(\mu_{\mathrm{a}}\) for the epidermis and hair obviously missing.  
Verification of the protocol using the PlatSil SiliGlass type C tissue-mimicking phantom.  
Figure 11 shows the temperature distribution map on the surface of the PlatSil SiliGlass type C phantom immediately following irradiation with a fluence of 50 J/cm\({}^{2}\) and a pulse duration of 30 ms. In the center of the irradiated area, the temperature increase amounted to more than 30 K. The time course of the temperature change in the center of irradiation following the pulse is shown in Figure 11, where we can see that experimental measurements agree well with simulation results.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 4 Experimental Results and Discussions
From the Decision Trees to the Neural Networks and Ensemble methods, the algorithms trained with this feature consistently outperformed their counterparts trained solely on Wi-Fi data. This indicates that room-to-room transition time serves as a valuable contextual feature, improving classification accuracy across different learning paradigms. The motivation behind integrating room-to-room transition time in ML algorithms lies in its ability to provide deeper insights into human movement behavior within spatial contexts. By capturing the dynamics of indoor mobility, models trained on Wi-Fi + room-to \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Training Complexity** & **Testing Complexity** \\ \hline Fine Tree & \(O(F\cdot N^{2}\cdot\log N)\) & \(O(N)\) \\ Efficient Logistic Regression & \(O(I\cdot F\cdot N)\) & \(O(F)\) \\ Weighted k-NN & N/A & \(O(N\cdot F+k\cdot\log k)\) \\ Cosine k-NN & N/A & \(O(N\cdot F)\) \\ Coarse k-NN & N/A & \(O(N\cdot F+k\cdot\log k)\) \\ Linear SVM & \(O(\max(N,F)\cdot\min(N,F)\cdot I)\) & \(O(F\cdot M)\) \\ Medium Gaussian SVM & \(O(N^{2}\cdot F)\) & \(O(F\cdot M)\) \\ Wide Neural Network & \(O(I\cdot N\cdot(F\cdot W+W^{2}\cdot L))\) & \(O(F\cdot W+W^{2}\cdot L)\) \\ Ensemble Bagged Trees & \(O(B\cdot N\cdot F\cdot D)\) & \(O(B\cdot D)\) \\ Linear Discriminant Analysis & \(O(F^{2}+F\cdot N)\) & \(O(F)\) \\ \hline \hline \end{tabular} Sample parameters used in calculations: \(F=63\) (number of features); \(N=3000*(90/100)\) (number of training samples-10-fold cross-validation); \(I=100\) (number of iterations, default for Efficient Logistic Regression and Wide Neural Network); \(k=5\) (number of Nearest Neighbors, default for Weighted k-NN and Coarse k-NN); \(W=10\), \(L=3\) (width and layers, defaults for Wide Neural Networks); \(B=100\), \(D=4\) (number of trees and depth, defaults for Ensemble Bagged Trees); \(M\leq N\) (number of support vectors, assumed to be, at most, \(N\)).  
\end{table} 
Table 11: Training and testing time complexities of algorithms (numerical and mathematical) [59]. room datasets exhibit enhanced predictive capabilities, paving the way for more accurate, adaptive, and context-aware ML models applicable to various real-world domains.
## 2 System Model
### Cluster Sparse UWA Channel  
For time instant \(n\), the discrete CIR \(\mathbf{h}(n)=[h_{1}(n),h_{2}(n),\ldots,h_{N}(n)]^{T}\in\mathbb{C}^{N}\)of UWA channel with standard sparse characteristic follows a Bernoulli-Complex Gaussian (B-CG) distribution [26], as:  
\[p(h_{n})=\varsigma\mathcal{CN}(h_{n};\mu_{h},\sigma_{h}^{2})+(1-\varsigma) \delta(h_{n}) \tag{3}\]  
where \(\mu_{h},\sigma_{h}^{2}\) are the mean and variance of the non-zero taps, respectively, \(\varsigma\) is the channel sparsity, \(\delta(\cdot)\) is the Dirac function. Recent research has shown that the propagation paths of sparse UWA channel appear in several distinct clusters [37]. The cluster sparse CIR is constructed as the time-domain superposition of finite clusters, as:  
\[\mathbf{h}(n)=\sum_{i=1}^{C}\mathbf{h}_{\mathbf{\tau}_{i}}(n) \tag{4}\]  
where \(C\) is the cluster number, and \(\{\mathbf{\tau}_{i}\}_{i=1}^{C}\) is the subset of the index set of CIR vector taps \(\mathbf{\Psi}=\{1,2,\cdots,N\}\) and satisfies the following relationship:  
\[\begin{split}\bigcup_{i=1}^{C}\mathbf{\Psi}_{i}=\mathbf{\Psi}\\ \mathbf{\Psi}_{i}\bigcap\mathbf{\Psi}_{j}=\varnothing,\text{when} \quad i\
eq j\end{split} \tag{5}\]  
If the subset of CIR \(\mathbf{h}_{\mathbf{\Psi}_{i}}(n)\) contains any non-zero tap, \(\mathbf{h}_{\mathbf{\tau}_{i}}(n)\) will follow the B-CG distribution corresponding to its index vector \(\mathbf{\Psi}_{i}\):  
\[\begin{cases}h_{\mathbf{\Psi}_{i},k}\sim\text{B-}\mathcal{CN}(\mu_{i},\sigma_ {i}^{2},\varsigma_{i}),&\|\mathbf{h}_{\mathbf{\Psi}_{i}}\|_{1}\
eq 0\\ \mathbf{h}_{\mathbf{\Psi}_{i}}=\mathbf{0}_{\mathbf{\tau}_{i}},&\text{else} \end{cases} \tag{6}\]  
where \(h_{\mathbf{\Psi}_{i},k}\) is the \(k\)-th element of \(\mathbf{h}_{\mathbf{\Psi}_{i}}\).
# Ins and Outs of the Ankle Syndesmosis from a 2D to 3D CT Perspective
## 3 Results
### Conventional 2D Measurements  
In Table 1, a description of all 2D measurements is provided. Table 2 presents the mean, normative reference limit, and ICC per measurement, if available. Figures 2-4 depict the mediolateral translation measurements, fibular rotation measurements, and fibular translation measurements, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**Measurement** & **Description** & **References** \\ \hline \multicolumn{3}{c}{a. Mediolateral translation (diastasis)} \\ \hline Anterior tibiofibular width (A) & The distance between the anterior tibial tubercle and the nearest fibular point. & [5, 6, 13, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32] \\ \hline Posterior tibiofibular width (B) & The distance between the posterior tibial tubercle and the nearest fibular point. & [5, 6, 13, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32] \\ \hline Middle tibiofibular width & The distance between the most central point of the incisura and the nearest fibular point. & [5, 13, 18, 20, 28, 31, 32, 33] \\ \hline Maximum tibiofibular width & The maximal distance between the tibia and fibula, regardless of the location & [32] \\ \hline Minimum tibiofibular width & The minimal distance between the tibia and fibula, regardless of the location. & [26] \\ \hline Syndesmotic area & The surface area, delineated by the medial cortex of the fibula and the lateral cortex of the tibial incisura, and two lines tangential to the anterior and posterior cortices of the tibia and fibula & [13, 26, 33, 34, 35] \\ \hline \multicolumn{3}{c}{b. Fibular rotation} \\ \hline Fibular rotation by Dikos (\(\alpha\)) & The angle between the fibular axis and the tangential line to the anterior and posterior tibial tubercles. A higher angle value indicates internal rotation of the fibula, while a lower angle value indicates external rotation. & [5, 13, 20, 22, 26, 28, 29, 31, 33, 36] \\ \hline Tang ratio & The ratio of distances from the tibial centroid to the most anterior fibular point and from the tibial centroid to the most posterior fibular point. & [23, 26, 29] \\ \hline Ratio A/B & The ratio between the anterior tibiofibular width (A) and posterior tibiofibular width (B). The ratio increases as the fibula externally rotates. & [5, 20, 28] \\ \hline Bimalleolar angle (\(\beta\)) & The angle between the tangential line to the medial cortex of the lateral malleolus and the tangential line to the lateral cortex of the medial malleolus, at the level of the talar dome or more distally. & [28, 37] \\ \hline \end{tabular}
\end{table} 
Table 1: Description of radiological measurements.
## 3 User Clustering and Power Allocation Co-Design
### Pairing-Assignment Strategy for User Clustering
Since this clustering strategy can fully utilize the allocation power of each cluster and assign users based on the consideration of energy efficiency increase, it can effectively improve the energy efficiency of the dynamic programming results.
## 3 Results and Discussion
### Single-Phase Polymer Flooding Results  
#### 3.2.1 Measuring Pressure Drop across the Cores  
Viscoelastic phenomena of polymer solutions in porous media can be observed via a similar approach to that proposed by [PERSON] et al. [81]. This approach measures the pressure drop during polymer flooding experiments plotted against increasing interstitial velocity. [PERSON] observed the Newtonian response for a slope of precisely 45\({}^{\circ}\). However, shear-thinning behavior for a slope below 45\({}^{\circ}\) was observed in his work and a slope greater than 45\({}^{\circ}\) for shear-thickening behavior. The obtained results of our work are in exceptionally good agreement with those reported by [PERSON] et al. [81]. The observed behaviors of HPAM polymer solutions at different temperatures prepared in both brines are illustrated in Figure 7, clearly as follows: shear thinning below 45\({}^{\circ}\), shear thickening behavior above 45\({}^{\circ}\), and a characteristic slope for a Newtonian response at 45\({}^{\circ}\). Deionized water (DW) in Figure 7 represents the pressure drop over interstitial velocity for the Newtonian fluid flow behavior used as baseline. From Figure 7, it can be seen that Flopaam 6035 S depicted the higher viscoelastic properties (based on pressure drop, formed angle) compared to the Hengfloc 63026 at specific temperature and same brine. However, it prevails the need to be investigate whether brine salinity (hardness) or reservoir temperature has the dominant influence to counter polymer viscoelastic properties in porous media. As it can be seen from Figure 7, Flopaam 6035 S, prepared in high-salt brine (B2) at 22 \({}^{\circ}\)C, depicted a higher pressure drop than that of the same polymer prepared in low-salt brine (B1) at 55 \({}^{\circ}\)C. Note that higher-pressure drops at the same flux values represent a strong viscoelastic behavior (base on the formed angle), and this behavior is assumed to be due to the high molecular weight of the polymer and the resistance to flow when flowing in the porous media. Based on these observations, we have then observed that temperature has a stronger impact on decreasing polymer viscoelastic properties in porous media as compared to the brine hardness. As a matter of background, we have plotted two reference points for comparison purposes. The black triangular point in Figure 7 represents a typical interstitial flow velocity of 1 ft/day deep inside the reservoir, whereas the red triangular point represents the flow velocity of 5 ft/day. Figure 7 describes that, far inside the reservoir, is mainly shear-thinning behavior of polymer solutions. However, around the injection and production wells, flux rate is increased to 10-15 ft/day, and the polymer viscoelastic response becomes significant. [PERSON] [82] reported multiples near-wellbore problems associated to the polymer viscoelasticity, for instance, pumps caveating.  
Figure 6: Comparison of steady shear viscosity for Flopaam 6035 S and Hengfloc 63026, at a shear rate of 1 s\({}^{-1}\), measured at 22 and 55 \({}^{\circ}\)C.
# Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit
## 4 Multi-Layer Balancing Strategy
### Cell Balancing Task Formulation  
The goal of equalization is to increase the battery pack's consistency as well as the battery pack's real capacity. The higher the equalization efficiency, the shorter the battery equalization time. The balancing goal can be formulated as:  
\[\mathrm{J}=(\mathrm{SOC}(\mathrm{k})-\overline{\mathrm{SOC}}(\mathrm{k}))^{ \mathrm{T}}(\mathrm{SOC}(\mathrm{k})-\overline{\mathrm{SOC}}(\mathrm{k})) \tag{11}\]  
where \(\overline{\mathrm{SOC}}(\mathrm{k})\) is the average value of SOC of all batteries, which can be expressed as:  
\[\overline{\mathrm{SOC}}(\mathrm{k})=\frac{1}{2^{\mathrm{n}}}\sum_{\mathrm{i =1}}^{2^{\mathrm{n}}}\mathrm{SOC}_{\mathrm{i}}(\mathrm{k}) \tag{12}\]  
where \(\mathrm{SOC}_{\mathrm{i}}(\mathrm{k})\) represents the SOC of the ith battery, the battery pack has \(2^{\mathrm{n}}\) batteries in total, \(1\leq\mathrm{i}\leq 2^{\mathrm{n}}\).  
To improve the equilibrium efficiency, the objective function (11) can be minimized to design a control strategy that satisfies the constraints. The transformed function is:  
\[\underset{\mathrm{I}_{\mathrm{i,j}}(\mathrm{k})}{\mathrm{minimize}}\mathrm{ J}(\mathrm{SOC}(\mathrm{k}+1),\mathrm{I}_{\mathrm{1}}(\mathrm{k}))\]  
subject to  
\[\mathrm{SOC}(\mathrm{k}+1)=\mathrm{SOC}(\mathrm{k})+\mathrm{aI}_{\mathrm{ex} }(\mathrm{k})+\mathrm{dI}_{\mathrm{1}}(\mathrm{k})\]  
\[\mathrm{SOC}_{\mathrm{i,min}}\leq\mathrm{SOC}_{\mathrm{i}}\leq\mathrm{SOC} _{\mathrm{i,max}}\]  
\[\mathrm{I}_{\mathrm{-max}}\leq\mathrm{I}_{\mathrm{i,c}}\leq\mathrm{I}_{\mathrm{ +max}}\]  
\[\mathrm{I}_{\mathrm{-max}}\leq\mathrm{I}_{\mathrm{i,d}}\leq\mathrm{I}_{\mathrm{ +max}}\]  
\[0\leq\mathrm{I}_{\mathrm{m,im}}\leq\mathrm{I}_{\mathrm{m,max}}\]  
The majority of balancing solutions simply evaluate the balancing control of adjacent cells, ignoring the battery pack's influence from external current. Unlike the previous equalization technique, the equalization method proposed in this study considers all the battery current and equalization current constraints and optimizes the equalization current to maintain the battery current within safe limits. However, Equation (12) has a lot of variables, which puts a lot of strain on the processor and makes it difficult to apply in reality.  
Considering the aforementioned flaws, this study presents a layered equalization technique that calculates the equalization current by treating the left and right battery modules attached to each equalizer as two separate batteries. After calculating the upper circuit's equalizing current, it is used as an input to calculate the below equalizing current, layer by layer, until the total equalizing current of all equalizing units is obtained. The equalizing current calculation formula is similar to Equation (1). The layered equalization strategy will be introduced in detail below.
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS
### Scenarios
#### 2.1.2 3D Mapping  
Three-dimensional map generation using JCRS is an advanced and innovative approach that leverages both communication and sensing technologies to create detailed three-dimensional (3D) maps of outdoor and indoor environments. Among other technologies, this concept uses the reflection from the existing communication channels to improve map accuracy, create interactive digital twins of the environment, making it particularly useful in various fields such as factory robotics, autonomous vehicles, augmented reality, virtual reality, and geographic information systems (GIS) [12]. Fifth-generation cellular networks can be used for outdoor 3D mapping. Wireless fidelity (Wi-Fi) or Campus networks are used to enhance the existing indoor infrastructure map. Resource sharing can be done, particularly allocating specific carriers in orthogonal frequency-division multiplexing (OFDM)-based frames for sensing and preserving others for communication.  
By using multiple sensors and communication channels, the system becomes more robust and resilient. Relying on data from different sources, the concept utilizes redundancy, reducing the risk of data loss and ensuring interactive map updates. The concept can be scaled to cover larger areas or complex environments. By adding more sensors and optimizing communication networks, the 3D map generation system can adapt to different scenarios and requirements.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 4 Results and Discussion
### Temperature Plot Verification  
Another target output of this thermal model is the temperature distribution of a pulley under a certain load/speed condition. Layer2 has the greatest radial distance, as shown in Figure 4, this comparison focusses on the Layer2 along the outer surface of the pulley. Figure 17a,b shows the calculated temperature distributions on 76.2 mm and 108 mm pulleys, and five experimental temperatures at different points along the radial direction of each pulley are used to validate the calculated result. The calculated temperature plots are in good agreement with the measurement data,  
Figure 14: Air velocity around the DN pulley under a speed of 4000 RPM and torque load of 10 N-m.  
Figure 15: Curves of \(\mathrm{S_{n}}(\omega_{\mathrm{pn}})\) for three pulleys under various rotation speeds.  
thereby demonstrating that the model can successfully predict the temperature decrease from the inner to the outer radial distances on Layer2. The maximum temperature difference is within 4 \({}^{\circ}\)C in these two plots. Therefore, the thermal model can successfully provide the reliable temperature distribution and heat dissipation for each pulley.  
In the above comparisons, some differences can be noted between the calculated and experimental values. One reason for these differences is the measurement error from the devices, which cannot be eliminated completely even if the measurement data is considered as the average values of test results obtained three times for each condition. The second reason is that the turbulence modeling is slightly different compared to the real airflow, especially in terms of the airflow near the walls of the pulley. This aspect represents a balance between the accuracy and time consumption of the numerical method. Nevertheless, the above figures generally show good agreement between the predicted and experimental values.  
Overall, all the predictions provided by the thermal model require considerably less computational time and resources to be obtained than those required by the existing approaches. The pulley temperature prediction on this system requires less than 5 s on a workstation with a 12-core 3.4 GHz  
Figure 16: Temperature comparisons for three pulleys of two-belt drive systems: belt drive layout (**a**) 1 and (**b**) 2.  
Figure 17: Comparisons of calculated (Calc.) and experimental (Exp.) temperature distributions of Layer 2 of two pulleys at a rotation speed of 6000 RPM and torque load of 10 N-m: pulley with diameter of (**a**) 76.2 mm and (**b**) 108 mm.  
CPU, except for the one-time computational cost of the baseline numerical simulations. In contrast, the current numerical method requires approximately 17 h [29] to realize the temperature prediction for a belt drive system under one working condition. The comparison thus demonstrates that the proposed thermal model can provide reliable and time-efficient temperature prediction.