esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microbial Quality  
The effect of increasing salinity on the microbial quality of the halophytes _Disphylma crassifolium_, _Inula crithmoides_, _Mesembryanthemum nodiforum_ and _Suaea maritima_ are shown in Table 1.  
The increase in salinity of the nutritive solution was accompanied by a general rise in the microbial populations studied, namely the total aerobic and filamentous fungi on _D. crassifolium_ and _I. crithmoides_ and the psychotrophic population on _S. maritima_ (_p_ < 0.05). The psychotrophic population was less abundant when compared to the values of the mesophylls. This rise in microbial populations in conditions of salt stress was also observed in _Salicornia ramsissima_[2]. As mentioned above, the increase in the cultivation salinity of the nutritive solution during the growth of the four plants implies the adoption of ionic and osmotic homeostasis strategies, which are certainly different in the plants studied. _D. crassifolium_ loses succinate and increases osmotic metabolite (e.g., proline) production due to salt increase [32]. Similarly, _I. crithmoides_ increases the production of osmolytes, such as glycine betaine, and sugars (arabinose, fructose, and glucose). Still, it also activates a K\({}^{+}\) transportation system, which may contribute to its salt tolerance [33]. _M. nodiforum_, similarly to other _Mesembryanthemum_ species, accumulates salt and water in epidermal bladder cells that act as a water storage reservoir, which helps them to maintain ion sequestration and homeostasis in the leaves [34]. Finally, _S. maritima_ accumulates inorganic ions and proline to adapt to saline conditions, decreasing its transpiration rate and improving its water use efficiency [35; 36]. These different responses to salt stress may result in different degrees of colonisation by microbial populations, as seen in this work. The highest numbers of aerobic microorganisms were observed in _I. crithmoides_ (4.54, 5.18, 5.38 Log CFU g\({}^{-1}\) in the 35, 200, and 465 mM of NaCl, respectively) and the lowest numbers of filamentous fungi on _M. nodiforum_ (2.15, 1.24, and 2.30 Log CFU g\({}^{-1}\) in the three concentrations, respectively). In the work of [PERSON] et al. [12], _M. nodiforum_ extracts showed an increasing trend in phenolic production with salt stress, while _I. crithmoides_ appeared to be more susceptible, losing the ability to produce phenolic compounds at salin
## 3 Material and Methods
### Basin Asymmetric Factor (Af)  
At the drainage basin scale, the basin asymmetric factor index (Af) can be used to assess the presence of tectonic activity. It is a metric for measuring and documenting tectonic tilting in relation to a basin's size [7; 29; 30]. This index is generally applied to a relatively large region. The Af index is defined as follows:  
\[\mathrm{Af}=100\ (\mathrm{Ar}/\mathrm{At})\]  
The parameters of this formula can be defined as follows: Ar is the total of the basin to the right side of the main basin stream, and At measures the entire area of the basin. A change in inclination perpendicular to the direction of the stream might cause the Af  
Figure 5: Mechanism of calculating the SL index (modified after Ref. [19]).  
index to change (Figure 6). An Af index significantly higher or lower than 50 indicates the influence of differential erosion, lithologic control, or active tectonics. One example of this would be the stream gradually eroding through bedding plains [3]. If the Af index is near 50, there is either not much or no tilting perpendicular to the trunk channel's direction (Figure 6). The landforms in tectonically active terrain are distinguished by comparatively steep, mountainous sides and floors. The faults that cause the valley floor to move downward in relation to the surrounding margins or upward in relation to the floor are what cause the steep sides. With an arrow pointing in the direction of asymmetry, we expressed the Af index as the absolute value minus 50 to represent the tilting direction of the study sub-basins.  
In other words, the values of Af represent Af \(-\) 50, or the difference between the neutral value of 50 and the observed values. Therefore, to evaluate the relative tectonic activity, the absolute difference is the most significant value.
# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
## 3 Methodology
### Som-Istm
Calculating the spectral amplitude:  
The output of the FFT is a complex-valued array that contains both amplitude and phase information for each frequency component. The spectral amplitude, which indicates the strength or energy of each frequency, is extracted from this array. The amplitude spectrum is then analyzed to identify the dominant frequencies and their corresponding amplitudes, which are characteristic of the motor's operating condition. Through this FFT-based preprocessing, the raw motor sound signals are transformed into a format that is suitable for further analysis, allowing for more precise monitoring and fault detection. 3. Feature extraction using SOM:  
After preprocessing the motor sound signals with FFT to obtain frequency-domain representations, these spectral data are fed into the self-organizing map (SOM). The input to the SOM consists of the spectral data obtained from FFT analysis. This data includes  
Figure 3: The network structure of SOM-LSTM. the amplitude values of various frequencies for each sound sample. As a result of training, the SOM clusters similar spectral features close to each other on the map, while dissimilar features are positioned further apart. This spatial organization helps visually distinguish different types of motor sounds based on their frequency content. 4. Temporal analysis and classification with LSTM:  
Once the SOM has processed and mapped the spectral features, these mapped features are then used as input for the long short-term memory (LSTM) network. Based on the learned patterns, the LSTM classifies the motor sound into one of four categories: normal operation, out-of-phase fault, unbalanced voltage fault, or misalignment fault. This classification is achieved by evaluating the output state of the LSTM after processing the entire sequence of input features [35]. 5. Evaluation of the output:  
The output of the LSTM is a single-dimensional prediction that indicates the operational state of the motor. By using SOM to extract and map features, followed by LSTM to learn temporal patterns and classify the sounds, this combined approach effectively evaluates the motor's operational state and provides a robust method for monitoring and diagnosing potential faults.
## 2 Fisher-Snedecor \(\mathcal{F}\) Fading Channel Model
### Bivariate Fisher-Snedecor \(\mathcal{F}\) Distribution
#### 2.2.2 Joint PDF of the Instantaneous Output SNR  
Let \(\gamma_{i}=R_{i}^{2}E_{s}/N_{0}\)\((i=1,\,2)\) denote the instantaneous output SNR of the bivariate Fisher-Snedecor \(\mathcal{F}\) composite fading model. By using (9) and a simple variable transformation, the joint PDF of \(\gamma_{1}\) and \(\gamma_{2}\) for the bivariate Fisher-Snedecor \(\mathcal{F}\) composite fading model can be expressed as follows:  
\[\begin{array}{ll}f_{\gamma_{1},\gamma_{2}}(\gamma_{1},\gamma_{2})&=\sum \limits_{k=0}^{\infty}{\sum\limits_{l=0}^{\infty}{\frac{(m_{l})\rho_{\text{N }}^{k}(n_{l})\rho_{\text{G}}^{l}\phi\mathbf{\Theta}_{1}^{n_{1}+k_{2}}\mathbf{ \Theta}_{2}^{m_{2}+k}}{k!\Pi!\Gamma(m)\Gamma(n)B(l+n,k+m)}}}\\ &\times F_{2}\Big{[}\lambda_{2};m_{2}-m_{1},n_{2}-n_{1};m_{2}+k,n_{2}+l;{ \frac{\rho_{\text{N}}\pi_{2}\gamma_{2}}{a_{2}\gamma_{2}+1}},{\frac{\rho_{ \text{G}}}{a_{2}\gamma_{2}+1}}\Big{]},\end{array} \tag{13}\]  
where \(\alpha_{i}=m_{i}(1-\rho_{\text{G}})/((n_{i}-1)(1-\rho_{N})\overline{\gamma}_{ i})\), \(\overline{\gamma}_{i}=\Omega_{i}E_{s}/N_{0}\). By using (12) and the same approach as (13), the joint PDF of \(\gamma_{1}\) and \(\gamma_{2}\) for the special case of identical shaping parameters can be obtained as:  
\[\begin{array}{ll}f_{\gamma_{1},\gamma_{2}}(\gamma_{1},\gamma_{2})&=\frac{ \phi}{B^{2}(m,n)}\bigg{(}\prod\limits_{i=1}^{2}{\frac{\eta_{i}\eta_{i}^{m} \gamma_{i}^{n-1}}{\left(\eta_{i}\gamma_{i}+1\right)^{m+n}}}\bigg{)}\\ &\times F_{4}\bigg{[}m+n,m+n;m,n;\prod\limits_{i=1}^{2}{\frac{\rho_{\text{N}} \eta_{i}\gamma_{i}}{\eta_{i}+1}},\prod\limits_{i=1}^{2}{\frac{\rho_{\text{G }}}{\eta_{i}\gamma_{i}+1}}\bigg{]}.\end{array} \tag{14}\]  
where \(\eta_{i}=m(1-\rho_{\text{G}})/((n-1)(1-\rho_{N})\overline{\gamma}_{i})\), and \(F_{4}[\cdot]\) is the Appell hypergeometric function defined in ([43], Equation (9.180.4)).
# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 5 Performance Evaluation of DPO
Figure 6: Bit error rate (BER) versus optical energy per bit to noise power spectral density (\(E_{b(opt)}/N_{0}\)) for different orthogonal frequency division multiplexing (OFDM) forms.
## 4 Simulation Results  
In this section, the efficiency of the proposed ALADIN-based method to estimate the Laplacian spectrum is evaluated by considering the two following case studies.  
Firstly, it can be said that the Laplacian spectrum can decide the performance of the network since it reveals the connection of the network. For example, the robustness of the network can be estimated before starting the operations to avoid the interruption during these operations and, as a result, enhance the benefits technically and economically.  
On the purpose of monitoring the connection of a large network \(G^{*}(V^{*},E^{*})\), one may face with the numerical issue in step 3 of the ALADIN-based method to solve the linear system (19) due to the huge dimension of the obtained matrix that leads to the common ill-conditioning problem with the inverse matrix calculation.  
In [16], the authors have suggested to partition the large network into \(M\) disjoint sub-networks \(U_{\ell}\), \(\ell=1,2,\cdots,M\), [29]. Let us define \(\mathcal{N}_{i}^{*}=\{j\in V^{*}:(i,j)\in E^{*}\}\) and its cardinality \(|\mathcal{N}_{i}^{*}|\) as the set of neighbors of node \(i\) and its degree in \(G^{*}\), respectively. Each sub-network is monitored by a super-node \(i\) which knows the number \(N_{\ell}\) of agents in the sub-network and the associated average information state \(x_{\ell}\). Node \(i\in U_{\ell}\) is a super-node if it has at least one neighbor in a different subset, i.e., \(\exists\ell^{*}\
eq\ell\) s.t. \(\mathcal{N}_{i}\bigcap U_{\ell^{*}}\
eq\emptyset\). Let us consider that two sub-networks are connected if there exist edges linking at least two agents of these sub-networks. If two sub-networks are connected then their super-nodes are linked as showed in Figure 2. Here, the network can be social network, power system network, molecular network, etc.  
Let \(G=(V,E)\) be the undirected graph representing a network with \(N=|V|\) super-nodes, which are black nodes in Figure 2. \(G\) captures the interaction between sub-networks of \(G^{*}\). Therefore, the large network is to be robust if the partitions are strongly linked to each other and if the critical threshold is high enough in [16]. Then, the Laplacian spectrum of network of super-nodes \(G\) can monitor the connection of the large network \(G^{*}\) via the robustness index.
# Application of Ultrafiltration for Recovery of Bioactive Phenolic Compounds from Rose Wastewater
## 2 Materials and Methods
### Methods
#### 2.2.4 The Determination of Phenolic Compounds  
Total phenolic compounds, phenolic acids and flavonoid phenolic compounds in the initial rose wastewater, retenates, and permeates were determined by a modified Glories method [30]. The procedure involved the preparation of a mixture containing 1 mL of 0.1% HCl in 96% ethanol (\(v/v\)), 18.2 mL of 2% HCl (\(v/v\)), and 1 mL of methanol extract, incubated at room temperature for 15 min. A blank solution, prepared with distilled water, was used as a reference for the spectrophotometric measurements of total phenolic compounds at 280 nm, phenolic acids at 320 nm, and flavonoids at 360 nm. Calibration curves were employed to express the results as mg gallic acid equivalent per liter for total phenolic compounds, mg caffe acid equivalent per liter for phenolic acids, and mg quercetin equivalent per liter for flavonoid phenolic compounds.
## 4 Problem Analysis and Algorithm Description
### Algorithm Description
#### 4.2.1 Optimal Activation Strategy Based on 2D Convex Hull
Moreover, the activation of a sleeping node from the exterior of the 2D convex hull can increase the area of the convex hull, thus forming a new 2D convex hull. In selecting to activate a sleeping node that can maximize the area of convex hull, the node becomes a candidate active node denoted by \(S_{Cand2}\). The next optimal active node must be either \(S_{Cand1}\) or \(S_{Cand2}\), and the optimal activation strategy is shown in Formula (13):  
\[Opt=\left\{ \begin{array}{ll}S_{Cand1},&E\_circle\geq H\_add\\ S_{Cand2},&H\_add<E\_circle\end{array} \right. \tag{13}\]  
where \(E\_circle\) denotes the maximum area of the empty circle when the active node is \(S_{Cand1}\), \(H\_add\) denotes the maximum area increase of the 2D convex hull when the active node is \(S_{Cand2}\), and \(Opt\) denotes the node selected to be activated ultimately. Node \(Opt\) will be added to \(Sa\) and deleted from \(S\)s. Thereafter, in determining whether the current network coverage rate would satisfy the coverage requirement, if the result does not satisfy the requirement, then the abovementioned strategy is adopted to continue activating a new sleeping node until the coverage threshold \(C_{th}\) is reached in this horizontal plane. Thus the whole optimal activation strategy process is completed, and the detailed flowchart of the activation strategy is as shown in Figure 6.
###### Abstract  
The partial-wave method takes advantage of the Christoffel equation's generality to represent waves within a waveguide. More specifically, the partial-wave method is well known for its usefulness when calculating dispersion curves for multilayered and/or anisotropic plates. That is, it is a vital component of the transfer-matrix method and the global-matrix method, which are used for dispersion curve calculation. The literature suggests that the method is also exceptionally useful for conceptual interpretation, but gives very few examples or instruction on how this can be done. In this paper, we expand on this topic of conceptual interpretation by addressing Rayleigh waves, Stoneley waves, shear horizontal waves, and Lamb waves. We demonstrate that all of these guided waves can be described using the partial-wave method, which establishes a common foundation on which many elastodynamic guided waves can be compared, translated, and interpreted. For Lamb waves specifically, we identify the characteristics of guided wave modes that have not been formally discussed in the literature. Additionally, we use what is demonstrated in the body of the paper to investigate the leaky characteristics of Lamb waves, which eventually leads to finding a correlation between oblique bulk wave propagation in the waveguide and the transmission amplitude ratios found in the literature.  
Keywords:partial wave method; slowness curves; lamb wave; stoneley wave; mode sorting; acoustic leakage; rayleigh wave; surface waves; elastodynamics +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Datasets  
In this study, two sets of landscape images were used for HI-based classification: the \"Indian Pines\" and \"Pavia University\" datasets. The AVIRIS sensors were used to acquire the Indian Pines dataset collected over the Northwestern Indiana region. It is 3D-data where the first two dimensions, 145 \(\times\) 145, represent spatial information while the third one corresponds to 202 spectral channels wavelengths ranges from 400-2500 nm. Each pixel has a spectral resolution of 10 nm and represents an area of 20 \(\times\) 20 m\({}^{2}\). There are 16 classes in this dataset with 10,366 instances. Although the dataset is imbalanced, the proposed methodology uses fewer instances for training sessions, thereby relieving the inherent effect of the unbiased nature of the dataset. For regions visualization, an image of spectrum for band (150) in grayscale is shown in Figure 2a.  
The Pavia University dataset has been acquired using a ROSIS sensor by capturing the Pavia University, Italy region. It consists of 3D-data where the first two dimensions, 610 \(\times\) 340, express the spatial resolution while the third dimension represents 103 spectral wavelength channels in the range 430-860 nm. Each pixel has a geometric resolution of 1.3 m. This dataset has 9 classes and 207,400 instances. For regions visualization, the spectral band, namely number 90, in the grayscale channel has been shown in Figure 2b.  
Figure 1: The hybrid learning system for classification and segmentation of the HI landscape dataset.  
Figure 2: Gray level representation of single bands for the HI dataset, (**a**) band number 150 of the Indian Pines dataset, and (**b**) band number 90 of the Pavia University dataset.
## 3 Results and Discussion
### Considerations on Porous Structure Characterization and Durability  
The mechanistic explanations for the observed boiling enhancement, particularly the exceptional performance of the Large Lattice and Small Lattice surfaces, are primarily based on the precisely defined nominal geometries derived from their CAD designs (Section 2.2). The internal geometry of the 3D-printed structures was confirmed through detailed CAD models and high-resolution SEM images of the top surfaces (Figure 6). While metallographic microsections would provide further internal validation, the consistency of the BJ3 DP process and the repeatability of the boiling results provided sufficient confidence in the as-designed pore network.  
Regarding the mechanical durability of the produced porous structures, no independent mechanical tests were conducted. However, throughout the extensive pool-boiling experiments, which involved prolonged heating at high heat fluxes over several hours, the porous layers on all test pieces remained visually intact. There were no visible signs of degradation, delamination from the copper substrate, or mechanical failure observed on the surfaces. This indicates that the high-temperature sintering process created strong bonds between the copper particles and the substrate, resulting in robust and durable structures capable of withstanding the thermal and hydrodynamic stresses encountered during boiling. This inherent durability is a significant advantage for practical applications of these surfaces.
###### Abstract  
In this study, the total electromagnetic dose, i.e., the combined dose from fixed antennas and mobile devices, was estimated for a number of hypothetical network topologies for implementation in Switzerland to support the deployment of fifth generation (5G) mobile communication systems while maintaining exposure guidelines for public safety. In this study, we consider frequency range 1 (FRI) and various user scenarios. The estimated dose in hypothetical 5G networks was extrapolated from measurements in one of the Swiss 4G networks and by means of Monte Carlo analysis. The results show that the peak dose is always dominated by an individual's mobile phone and, in the case of non-users, by the bystanders' mobile phones. The reduction in cell size and the separation of indoor and outdoor coverage can substantially reduce the total dose by \(>\)10 dB. The introduction of higher frequencies in 5G mobile networks, e.g., 3.6 GHz, reduces the specific absorption rate (SAR) in the entire brain by an average of \(-8\) dB, while the SAR in the superficial tissues of the brain remains locally constant, i.e., within \(\pm 3\) dB. Data from real networks with multiple-input multiple-output (MIMO) were not available; the effect of adaptive beam-forming antennas on the dose will need to be quantitatively revisited when 5G networks are fully established.  
dosimetry; radio-frequency; exposure assessment; 5G; base station; mobile networks +
Footnote †: journal: Applied Sciences
# RIS-Assisted Cooperative Time-Division Multiple Access
## 2 RIS-Assisted Cooperative Time-Division Multiple Access
### Beamforming Vector Design
Thus, by applying the eigenvalue decomposition to Equation (14), \(\mathbf{w}_{c,w}^{max}\) for maximizing \(\text{SINR}_{c,w}\) is obtained by  
\[\mathbf{w}_{c,w}^{max}=\text{max. eigenvector of }(\mathbf{H}^{H}\mathbf{\Phi}^{ H}\mathbf{g}_{w}^{H}\mathbf{g}_{w}\mathbf{\Phi H}+\mathbf{h}_{w}^{H} \mathbf{h}_{w}). \tag{15}\]  
The null space of \(\mathbf{g}_{s}\mathbf{\Phi H}+\mathbf{h}_{s}\), \(\mathbf{V}_{null}\), can be obtained by the eigenvectors of \((\mathbf{g}_{s}\mathbf{\Phi H}+\mathbf{h}_{s})^{H}(\mathbf{g}_{s}\mathbf{\Phi H }+\mathbf{h}_{s})\), which is given as  
\[(\mathbf{g}_{s}\mathbf{\Phi H}+\mathbf{h}_{s})^{H}(\mathbf{g}_{s}\mathbf{\Phi H }+\mathbf{h}_{s})=\mathbf{V}\mathbf{\Lambda}\mathbf{V}^{H}, \tag{16}\]  
where \(\mathbf{V}=[\mathbf{v}_{1},\dots,\mathbf{v}_{\alpha},\mathbf{V}_{null}]\), \(\mathbf{v}_{i}\in\mathbb{C}^{M\times 1}\), \(\mathbf{V}_{null}\in\mathbb{C}^{M\times(M-\alpha)}\), \(\alpha\) is the rank of \((\mathbf{g}_{s}\mathbf{\Phi H}+\mathbf{h}_{s})^{H}(\mathbf{g}_{s}\mathbf{\Phi H }+\mathbf{h}_{s})\), and \(\mathbf{\Lambda}\) is the diagonal matrix whose diagonal elements are the decreasing ordered eigenvalues. In other words, \(\mathbf{V}_{null}\) is the space consisting of eigenvectors corresponding to eigenvalue zero. Therefore, the optimal transmit beamforming vector for the weak-channel-conditioned UE, \(\mathbf{w}_{c,w}^{*}\), is given as  
\[\mathbf{w}_{c,w}^{*}=\frac{\mathbf{V}_{null}\mathbf{V}_{null}^{H}\mathbf{w}_ {c,w}^{max}}{\|\mathbf{V}_{null}\mathbf{V}_{null}^{H}\mathbf{w}_{c,w}^{max} \|}. \tag{17}\]