esa-sceva/satcom-chunk-collection huggingface dataset:
## Notation and Preliminaries
### Naive and Nonminimal Solvers  
In our solver ICOS, the computation of the scale, rotation, and translation without outliers is realized by introducing the fast nonminimal naive solvers below.  
Assume that we have \(M\) correspondences (inliers); we first need to acquire the corresponding 3D vectors, which, in rotation search, are just the original vector correspondences such that:  
\[\widetilde{\mathbf{m}}_{i}=\mathbf{u}_{i},\widetilde{\mathbf{n}}_{i}=\mathbf{v}_{i}, \tag{5}\]while in point cloud registration, this can be represented with the centroid-based translation-free technique [32] such that:  
\[\widetilde{\mathbf{m}}_{i}=\mathbf{P}_{i}-\mathbf{\bar{P}},\ \widetilde{\mathbf{n}}_{i}= \mathbf{Q}_{i}-\mathbf{\bar{Q}}, \tag{6}\]  
where \(\mathbf{\bar{P}}\) and \(\mathbf{\bar{Q}}\) are the centroids in the two frames, respectively, given by:  
\[\mathbf{\bar{P}}=\frac{\sum_{i=1}^{M}\mathbf{P}_{i}}{M},\ \mathbf{\bar{Q}}=\frac{ \sum_{i=1}^{M}\mathbf{Q}_{i}}{M}. \tag{7}\]  
The optimal rotation can be computed in closed-form using Singular-Value Decomposition (SVD) [32] such that:  
\[\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\top}=SVD\left(\sum_{i=1}^{M} \widetilde{\mathbf{m}}_{i}\widetilde{\mathbf{n}}_{i}^{\top}\right), \tag{8}\] \[\mathbf{\hat{R}}=\mathbf{V}\mathbf{U}^{\top}.\]  
In point cloud registration, the best scale can be computed by the weighted representation as in [25] such that:  
\[\hat{s}=\frac{1}{\sum_{i=1}^{M}\omega_{i}}\cdot\sum_{i=1}^{M}\omega_{i}\frac{ \lVert\widetilde{\mathbf{n}}_{i}\rVert}{\lVert\widetilde{\mathbf{m}}_{i} \rVert}, \tag{9}\]  
where \(\omega_{i}=\frac{||\widehat{\mathbf{m}}_{i}||^{2}}{A^{2}}\). With the rotation and the scale obtained, the translation can be recovered using the centroids as:  
\[\mathbf{\hat{t}}=\mathbf{\bar{Q}}-\hat{s}\mathbf{\hat{R}}\mathbf{\bar{P}}. \tag{10}\]
## 3 Four-Port MIMO Antenna
### Results and Discussion
#### 3.2.5 Diversity Gain  
By using a MIMO antenna system, there are transmission power losses that may be examined by looking at diversity gain (DG). In the best-case situation, the value DG = 10 dB; however, in real-world scenarios, a value near 10 dB is taken into account. According to Figure 15, the suggested ultra-wideband MIMO antenna provides DG of about 9.99 dB over an operational bandwidth of 26.5-42.7 GHz. Equation (5) provided in [45; 46] may be used to quantitatively compute the DG of a MIMO antenna system.  
\[DG=10\sqrt{1-\left|ECC\right|^{2}} \tag{5}\]
# A Regionalization Approach Based on the Comparison of Different Clustering Techniques
### Federated Heuristic Optimization Based on Fuzzy Clustering  
Using the centroids of the regions obtained from K-means++ k = 5, TSA, we designed a federated model to optimize the number of regions using genetic algorithms and fuzzy clustering. By applying cross-validation with five blocks, the average number of regions across all iterations (we performed 100 iterations) was seven (see Figure 11).  
Figure 10: Distribution of the silhouette score of the algorithm with index kappa more significant with respect to reference regionalization. Federate: Fuzzy federated algorithm; Infomap: Algorithm with k = 5 and contiguity as a third-degree polynomial. K-means++: Algorithm with k = 5 and contiguity as a third-degree polynomial. PDC: Algorithm with k = 3 and contiguity as a third-degree polynomial.  
The regions obtained through this hybridization of techniques have resulted in heterogeneous areas, particularly for Morocco and the Iberian Peninsula, with only three environmentally defined regions identified in the French territory. We compared this regionalization with [PERSON]'s biomes using the kappa index, resulting in a value of 0.069, and with the ecoregions, we obtained a value of 0.003. Both values indicate a poor spatial coincidence; however, the ordering produced by this hybrid model is the most favorable based on the silhouette index scores presented in Figure 10. Therefore, this regionalization provides a robust solution despite its lack of resemblance to [PERSON]'s regions.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Performance of Proposed Approach Using Real-Time Dataset
#### 5.5.2 Experimental Results  
Table 15 shows the results of deep learning models using the real-time dataset. Deep learning models are used because they show better performance compared to machine learning models. The performance of the proposed CNN model is better than that of other deep learning models. The CNN shows an accuracy score of 0.9534, which is better than MLP, RNN, and LSTM. It is followed by RNN, which has a 0.9449 accuracy score. The proposed CNN has better performance on metrics such as precision and F1 score and obtains the highest recall score at 0.97.
## 2 Related Works
### Multicasting  
With the introduction of M-D2D communications into conventional cellular networks, the door is opened to motivate various use cases. Some of the well-known applications of the M-D2D communication are media-rich applications, such as local multimedia content sharing of photos and music, and also high-quality video sharing applications, such as video multicasting in social platforms. The main issues and challenges in a multicast communications environment are summarized in [26]. The paper classifies the existing multicast protocols based on several distinct features and performance parameters. In [27], the authors discussed the modeling and analysis of M-D2D communication and an optimization problem was formulated based on the mobility and network assistance issues. Comparing the result of this scheme with that of the static network demonstrated the enhancement of the mean number of covered receivers. The problem with this study is that the interference coordination function was not applied. A millimeter wave (mmWave) small cell is becoming popular as a promising solution for D2D communications due to the availability of the abundant spectrum. In [28], the authors proposed an energy-efficient multicast scheduling scheme to achieve high energy efficiency. Also, the transmission power control method of the multi-hop D2D transmission paths was analyzed. The results showed that their proposed scheme improved the energy efficiency in the network. The disadvantage of their scheme is that the mmWave communications have high path loss problems. In [29], the authors proposed an optimized opportunistic multicast scheduling over wireless cellular networks. The homogeneous network was partitioned into rings and uses different channel links. An optimization problem was formulated to achieve the optimal solution and results showed that their proposed scheme achieved a significant performance gain.
## 1 Introduction
It is easy for the algorithm to fall into local optimum when the learning rate is too small, while the convergence is unstable when it is too large. The root mean square propagation (RMS-Prop) algorithm is an optimized neural network algorithm with an adaptive learning rate, which can be dynamically adjusted and therefore avoids the shortcomings of a fixed learning rate. In the past few years, the RMS-Prop algorithm has been used in the active polarization control system of high-power lasers [22; 23]. In 2023, [PERSON] et al. [22] employed an active polarization control technique based on the RMS-Prop algorithm in the narrow linewidth fiber amplifiers and realized a record power of 3.83 kW, with a PER of 12.2 dB. In 2024, utilizing the RMS-Prop algorithm, [PERSON] et al. [23] demonstrated the active polarization control of a high-power linearly polarized ultrafast fiber laser for the first time, with a highest average power of 402.3 W at a repetition rate of 80 MHz and a PER of 11.4 dB. The RMS-Prop algorithm has shown great advantages in the power scaling and the pursuit of a higher PER in high-power fiber lasers. Nevertheless, there exists no report covering the theoretical analysis of polarization control based on the RMS-Prop algorithm. In this paper, in order to provide an instruction for our further experiments, we theoretically analyzed the control accuracy and convergence speed of the RMS-Prop algorithm in the conversion of a random SOP to the linear polarization, which is reported for the first time. When the PER of output laser is used as the cost function, the amplitude of random perturbation is set to 0.06, and the global learning rate is set to 0.6, the algorithm shows the optimal convergence effect. In comparison with the SPGD algorithm, the RMS-Prop algorithm demonstrated its superiority in the acquisition of a higher PER. First, in Section 2, the mathematical model of the active polarization control system based on the RMS-Prop algorithm is established. In Section 3, the impact of different parameters on the convergence characteristics of the RMS-Prop algorithm is systematically analyzed. Finally, the performance of the RMS-Prop algorithm is compared with the SPGD algorithm in Section 4.
###### Abstract
Knockout models and/or treatment with endogenous and xenobiotic AhR ligands results in shifts in the gut microbiome with diverse effects depending on the model and ligand [8; 11; 26; 27; 28]. Shifts in the Firmicutes/Bacteroidetes ratio can differ between AhR ligands, e.g., 2,3,7,8-tetrachlorobenzofuran decreased the ratio [8] whereas TCDD increased it [11]. However, responses in various AhR models are in agreement regarding increased secondary bile acids [8; 9] and effects on segmented filamentous bacteria [8; 11; 27]. AhR knockout models, and treatment with TCDD or other endogenous compounds also demonstrate strong correlations between AhR activation and enrichment of Lactobacillus species, i.e., _L. reuteri_[26; 27; 28; 29; 30]. Tryptophan catabolism to AhR ligands by Lactobacillus species is a proposed mechanism for gut microbial regulation of AhR signaling that modulates intestinal and gut microbiome homeostasis [26]. Bile acids also affect the gut microbiome by exerting antimicrobial activity [31]. Conversely, the gut microbiota play critical roles in host bile acid homeostasis through microbial metabolism that qualitatively and quantitatively impact bile acid composition with consequences for bile acid activated signaling pathways in the host. The gut microbiome performs the first step of bile acid deconjugation with subsequent oxidation, reduction, or dehydroxylation reactions to produce diverse secondary bile acid molecular species [32]. Select secondary bile acids, e.g., glycodeoxycholic acid [GDCA], demonstrate higher inhibition of bacterial growth compared to other primary and secondary bile acids [33]. In regard to the host, some secondary bile acids, e.g., lithochic acid [LCA] and deoxycholic acid [DCA]), exhibit a high affinity for the farnesoid x receptor (FXR) and G protein-coupled bile acid receptor (TGR5, a.k.a., GPBAR1), which regulate glucose, lipid, and bile acid homeostasis [34; 35; 36]. In human NAFLD, the secondary bile acid metabolism is disrupted with bile acid analogs that target the FXR and TGR5 signaling pathways under development for the treatment of liver disease [22; 31; 37]. Previous work demonstrated that the serum levels of LCA and DCA increased following TCDD treatment suggesting enrichment for the microbial bile acid metabolism [9]. To further explore dose-dependent disruptions in the gut microbiome and microbial metabolism relevant to the progression of NAFLD-like pathologies, shotgun metagenomic analysis was used to examine the dose dependent taxonomic and metabolic disruptions elicited by TCDD.
## 4 Results and Discussions
### The Frequency Weighting Factors with Different Holes  
One advantage of the FWMB algorithm is that it can be adaptively optimized for different leakage holes. In practical applications, the causes of leaks are abundant, so the shapes and sizes of leaks are various. For different leaks, the spectral characteristics may vary greatly. In order to verify this problem, 1.5 and 2.0 mm standard circular leaks were selected for testing.  
Because signals below 100 kHz have strong background noises and signals above 500 kHz are very weak, signals between 100 and 500 kHz were chosen for signal processing. The time domain plot, frequency spectrum, and frequency weighting factors for the signals that were sampled at position A of sensor #0 are illustrated in Figure 7.  
The amplitudes of the acoustic emission signals that were generated by the two leaks in the time domain were close, and from the waveform point of view, they were both difficult to obtain effective  
Figure 7: Time domain plot, frequency spectrum, and frequency weighting factors for different holes. (**a**) Time domain plot of a 1.5 mm hole in 100 ms, (**b**) time domain plot of a 2.0 mm hole in 100 ms, (**c**) frequency spectrum of a 1.5 mm hole, (**d**) frequency spectrum of a 2.0 mm hole, (**e**) frequency weighting factor of a 1.5 mm hole, and (**f**) frequency weighting factor of a 2.0 mm hole.  
information from and were confusing. After performing FFT on the signal, the spectrum energy of different frequency bands was integrated at 10 kHz intervals and normalized to obtain its weighting coefficient matrix. In the figure, the center frequency of the band is used as the label (for example, 100-110 kHz is represented as 105 kHz). It can be seen from the results that the acoustic emission signal that was generated by the 1.5 mm leak was in a higher frequency band. This is consistent with the physical understanding that smaller gaps result in sharper and higher frequency sounds.  
In order to further optimize the algorithm, a band energy threshold was set. The bands with energy smaller than 50% of the peak value were discarded in subsequent processing. It can be seen from the figure that the 1.5 mm leak reserved nine frequency bands of 100-190 kHz, while the 2.0 mm leak reserved eight frequency bands of 100-180 kHz. For the above two leaks, although the frequency bands were similar, the weighting coefficients were significantly different. For holes with more specific shapes, larger differences will occasionally be encountered. The above results illustrate the adaptive ability of the method in this paper to different leaks.
## 3 Scheme Design
### Iterative Algorithm Based on FPP-SCA
#### 3.1.3 Summarization of the Proposed Algorithm  
Based on the FPP-SCA algorithm presented above, the iterative algorithm is summarized as Algorithm 1.  
The Algorithm 1 is convergent based on the following Property 1.  
**Property 1**.: _The iterative algorithm based on FPP-SCA is convergent._  
Proof.: In the \(k\)th iteration of the proposed algorithm, we first compute \(\mathbf{F}_{r}^{[k]}\) with the given \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k-1]}\). Since the optimal solution \(\mathbf{F}_{r}^{[k]}\) can be achievable with CVX, where CVX is a Matlab-based available convex programming toolbox [45], we can discover that the objective value corresponding to \(\mathbf{F}_{r}^{[k]}\), \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k-1]}\) is no greater than that to \(\mathbf{F}_{r}^{[k-1]}\), \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k-1]}\). Similarly, \(\mathbf{F}_{1}^{[k]}\) is no larger than that to \(\mathbf{F}_{1}^{[k-1]}\), \(\mathbf{F}_{2}^{[k]}\) is no larger than that to \(\mathbf{F}_{2}^{[k-1]}\), and \(\mathbf{W}_{i}^{[k]}\) is optimally solved and the objective value is descendent. Consequently, the objective value of the original problem monotonically decreases and is lower-bounded by zero, which verifies the convergence of Algorithm 1.
## 3 Results and Discussion  
This study concerned the development of _I. gravelens_/PCL nanofibrous scaffolds using an electrospinning technique. The scaffolding conditions are improved by adjusting the electrospinning parameters, which include the needle measurement, the collector, the voltage, and the pumped polymer quantity. After optimization of conditions [24], bead-free fibers with a minimum diameter were obtained. To that end, the needle's distance to the collector was 20 cm, the voltage was 8 kV, and the polymer flow was 0.5 mL/h, and a blend of 8% PCL and 5% _I. gravelens_ were found to be suitable. The polymeric materials, becoming smaller in scale with a large surface area to volume ratio, can provide the plant extracts with improved properties such as chemical and antimicrobial activity [25]. The major benefit of plant scaffolding is the obvious simplicity with which it can be produced and handled; the design of a variety of sizes and shapes is entirely foldable, easy to cut, shaped, rolled, or stacked. They can also be reused, are easy to use, and are relatively cheap [26]. The principle of plant material Electrospun comprising therapeutic characteristics especially wound healing on the polymer matrix, is impressive. In Figure 1, the SEM micrographs of PCL Electrospun nanofibers and _I. gravelens_/PCL nanofibers showed that nano-scaled fibrous structures without beads were present. It was acquired under the controlled spinning conditions used in this research. The scaffold nanofibers derived from the _I. gravelens_/PCL combination were illustrated with micrographs of the SEM in Figure 1. As seen in the pictures, the fiber diameter decreases when natural polymers are increased. Fiber diameters were 72 \(\pm\) 963 nm in the PCL/_I. gravelens_ ratio 70/30 (V:V). In a similar study, [PERSON] and his coworkers reported that the average fiber diameter of PCL nanofibers was increased with the encapsulation of Tridax Procumbens Extract [27]. A natural _I. gravelens polymer_ is also a polyelectrolyte polymer, which can release ionic groups such as carboxyl groups in the solution. The addition of anionic and cationic polyelectrolytes increases the electric conductance of the solution of electrospinning [28].
## 3 Results and Discussion
### Porosity and Saturation Influence
The distribution of the CO\({}_{2}\) phase and its contact area with the brine do influence the concentration gradient (driving force) at the CO\({}_{2}\)-brine interface which, in turn, influences the dissolution rate [36; 37]. The pore-scale variations of the flow field were found to significantly affect the dissolution rate of individual CO\({}_{2}\) bubbles, especially at low CO\({}_{2}\) saturations [38]. For gaseous CO\({}_{2}\), the dissolution rate was inversely proportional to CO\({}_{2}\) saturation. At higher saturations, the dissolution rate was relatively large. A smaller size of an initial CO\({}_{2}\) cluster will lead to more rapid dissolution [39]. The dissolution rate increased rapidly with the saturation reduction due to the rapid extension of the flow path with the brine injection. This indicates that entrapped CO\({}_{2}\) can result in preferential flow, flow paths can change with time as CO\({}_{2}\) dissolved, and the size and distribution of CO\({}_{2}\) bubbles at early PVs of imbibition affected the degree to which flow bypassing occurs. The relationships between the dissolution rate and saturation were close to supercritical CO\({}_{2}\) after saturations were less than 0.5. Meanwhile, when the saturations were between 0.001 and 0.1, the dissolution rates of CO\({}_{2}\) were dispersed between different saturations with differences of one to two orders of magnitude. It is reported that CO\({}_{2}\) dissolution rates are sensitive to pore scale variations in the size, shape, and spatial distribution of CO\({}_{2}\) bubbles [34]. In the period of residual bubble dissolution (especially at saturations less than 0.01), the dissolution rates were greater at the same PV if the bubbles in the porous media were smaller.
# Device-Free Indoor Activity Recognition System
## 2 Methodology
### Feature Extraction
#### 2.2.2 Feature Extraction  
The human motion in the test area is associated with time domain and frequency domain of raw CSI. In time domain, we choose three features as follows. Firstly, we calculate the maximum peak as the following:  
\[Peak=\max_{n\in w}{(CSI(n))}-\min_{n\in w}{(CSI(n))}, \tag{6}\]  
where \(CSI\) refers to the value of Channel State Information, and \(n\) refers to the sample in \(|W|\) window intervals. Then, we calculate the third central moment \(\gamma\) as delineated in Equation (7):  
\[\gamma=\exp[(CSI(n)-\mu)^{2}], \tag{7}\]  
where \(exp\) is the expectation value, and \(\mu\) is the mean value of CSI amplitude which can be calculated as:  
\[\mu=\frac{\sum_{n=1}^{|W|}{CSI(n)}}{|W|}, \tag{8}\]  
where \(n=1\) is the start point of window \(W\).  
Moreover, we calculate the root of the mean square (RMS) deviation of the CSI to the mean value \(\mu\), as the following expression:  
\[RMS=\sqrt{\frac{(\sum_{n=1}^{|W|}{CSI(n)-\mu})^{2}}{|W|}}. \tag{9}\]  
Furthermore, it is very important to analyze the frequency components to discriminate two different actions. The time domain features may not enough to achieve this goal. Hence, we choose two frequency features as follows. Firstly, we calculate the DC component \(a_{0}\). To calculate \(a_{0}\), we must apply Fast Fourier Transform (FFT) to all \(n\) samples in dynamic window \(W\), so we calculate \(i\)th frequency component as the following expression:  
\[FFT(i)=\sum_{n=1}^{|W|}{CSI(n)}~{}e^{-\frac{j\frac{2\pi}{|W|}in}{|W|}}. \tag{10}\]  
DC component is the average of \(FFT(i)\) and can be calculated as:  
\[a_{0}=\frac{\int_{-\frac{|W|}{2}}^{\frac{|W|}{2}}{CSI(n)}dn}{|W|}. \tag{11}\]  
The last feature is the entropy which can be calculated in chosen points in the \(n\) sample set, as explained in Equation (12):  
\[entropy=\sum_{n=1}^{\frac{|W|}{2}}{P(n)}\cdot\ln({P(n)}), \tag{12}\]  
where \(P\) is the probability of each spectral \(FFT(i)\) band.
## 4 Results and Discussion
### MP-AES Spectra of B, P, and Mo  
Identical wavelengths were used (249.677 nm for B, 213.618 nm for P, and 379.825 nm for Mo) in the measurements of emission signals for both MP-AES and ICP-OES. For the determination of the analyte concentration, a direct comparison of the respective signals in the linear range was followed rather than the conventional method, which relies on rational extrapolations [19]. Analysis of the experimental results revealed that MP-AES provides a wider linear range in the calibrations for B, P, and Mo than FAAS and ICP-OES. These observations demonstrate that MP-AES is comparable to ICP-OES in multi elemental determination and a more convenient method than FAAS because of the advantages already described.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.6 Electra  
Smart network management issues are dealt by the project \"ELECTRA\", which involves participants from 12 countries. Among others, the project's objectives focus on control schemes for smart grids that would control the frequency and voltage while renewable sources of electricity are increasing. In addition, the functional layer of monitoring and control systems is elaborated and the functionality of the future control room is to be addressed [103].
## 4 Major Components of Network Slicing System  
Our proposed system has four major modules: OSM (Open Source MANO), an intent-based networking tool, SDN controller FLEXRAN, and a deep learning (DL) module. This section contains the details of each module and how these modules work together and perform radio and core network slicing efficiently.