esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of MPTMS Concentration in Effluent  
As mentioned earlier the NP in the nanofluid tend to aggregate, which may lead to resisting the flow. Two strategies were employed to prevent/minimize the agglomeration of NP. Firstly, functionalization or surface modification of the NP. The second is by using a stabilizing fluid that keeps the NP suspended. [PERSON] and [PERSON] [PERSON] and [PERSON] (2007) employed Polyvinylpyrrolidone (PVP) at 1% weight concentration in the nanofluid as a stabilizer for silica based nanofluids. However,  
Figure 2: Calibration curves for detecting nanoparticle (NP) concentration and 3-Mercaptopropyl Trimethoxysilane (MPTMS) concentration. ABS: Absorption.  
an important question that arises is the adsorption of the stabilizing fluid on the mineral during the injection that may take place. This changes the ratio between the fluid and NP, which may induce agglomeration of NP during the injection. In this study a method was developed to determine the adsorption of the stabilizing fluid in the rock during the injection. The method was based on mass balance calculation, where the effluent was analyzed by Ultraviolet light (UV). The used wavelength was 300 nm that gives adequate linear relationship between the absorption and concentration of MPTMS (stabilizing fluid). The constructed calibration curve was then used to estimate the loss in the mass balance i.e., related to the adsorbed MPTMS in the core. For each nanofluid containing MPTMS, calibration curve was constructed prior to the injection experiment by measuring the absorption in a dual beam UV/VIS spectrometer at wavelength 300 nm. Absorption of the MPTMS was measured referenced to DIW (with pH adjusted to 2 by dropwise addition of 0.1 M HCl). The removal of the NP from the effluent fluid was achieved by adjusting the pH of the effluent fluid to about 2, then centrifuging the fluid for 60 min at 10,000 rpm to promote the settling of NP. The absorption of the supernatant was determined. As an example, the UV/VIS calibration curve for 1 g/L nanofluid with 1 g/100 mL MPTMS is presented in Figure 3. In summary, after measuring the NPs concentration in the effluent samples through transmissivity measurements, the pH of the samples was then adjusted to 2 by dropwise addition of 0.1 M HCl followed by centrifuging for 60 min at 10,000 rpm. The absorption of supernatant was measured relative to a reference of DIW (pH adjusted to about 2) in the double beam UV/VIS spectrometer.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 4 Critical Analysis and Discussion
### Single Vector  
From a practical application point of view, single-vector predictive methods face computational challenges. This is especially notable in MPS, as the number of basic VVs grows exponentially with the number of phases [47]. The usual approach to SV-FSMPC is the exhaustive search optimization of the cost function. The computational load is then high, leading to high sampling periods. This reduces the bandwidth of the stator current control.  
Early methods for computational load reduction relied on procedures that reduced the capabilities of SV-FSMPC. For instance, reducing the allowed VVs makes modulation more challenging in some regions of the operating space [10]. Elimination of WFs reduces the flexibility to cope with conflicting objectives. Finally, restrained search produces solutions that are not guaranteed to be optimal.  
A recent development in SV-FSMPC for MPS is the introduction of a region-based method [50]. This approach extends prior works developed for VVVs, such as [52; 53; 54; 55; 56].  
However, ref. [50] only considers the \(\alpha\)-\(\beta\) plane, which leads to the generation of \(x\)-\(y\) currents. This problem is eliminated in [57], where a region-based method is proposed. The method enables the use of a weighting factor for \(x\)-\(y\) content management.  
A similar approach is found late in [58], where current error magnitude and sign alongside the current gradient are used to assess error tendencies.  
In these methods, the VV application time is equal to the entire sampling period. This results in increased harmonic distortion in stator currents. Non-uniform sampling has been proposed in FSMPC by dividing the sampling period into subintervals, allowing both VV selection and application time optimization within a single sampling period [59]. However, the computational load increases considerably. In [60], a region-based method is proposed using variable application times. Both the VV and its application time are found without exhaustive search.  
These recent advances make FSMPC competitive and, arguably, superior to multi-vector solutions, especially considering the ability of SV-FSMPC to incorporate weighting factors in the CF.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 3 Adjustments, Testing and Results
### Cluster Analysis  
The following analysis will examine the quality of the data representation. The variables possess different sizes and during the following steps, we will aim to get the most relevant content from them. PCA is applied to modify the data and the clustering analysis can reveal how the quality of the data has changed.  
In the following evaluations of clustering solutions, we will reduce the data dimension into its intrinsic dimension which was measured by likelihood estimation [29]. Data were reduced from their raw format (time set of measurements as rows \(\times\) vector of measured features) into a dimensionally reduced matrix in order to unify the number of features (columns) and to lower the amount of redundancy and noise in the dataset. In cases of temperature data, the intrinsic dimension was always close to 5, therefore we reduced it into this dimension. We did not reduce the dimension of the voltage data, since it has already been measured in the dimension of 5 or 3 which reflected the number of batteries in the set. From now on, the temperature and voltage samples possess reduced vector sizes and are used as samples in cluster analysis and classification modeling.  
Such data were clustered by k-means algorithm into several clustering solutions [30]. Each solution was defined by a different number of clusters. The quality of these solutions was examined by gap statistic that reveals which clustering solution is the most suitable for the given  
Figure 8: The post-hoc analysis of means of temperature (**left**) and voltage (**right**) ranges during charging period.  
Figure 9: The post-hoc analysis of means of temperature (**left**) and voltage (**right**) ranges during discharging period.  
data and the silhouette score that evaluates the clarity of the solution. The results for charging and discharging process can be seen in Figures 10 and 11.
## 2 Problem Model
### Mathematical Model
#### 2.2.1 Objective Function  
According to the above description of VRPCSC, the whole logistics cost mainly consists of four parts. The first part is the purchase cost of materials (denoted as \(Z_{\text{PUR}}\)). The second part is the delivery cost of materials from suppliers to manufacturers (denoted as \(Z_{\text{STM}}\)). The third part is the processing cost for the manufacturers to process materials into products (denoted as \(Z_{\text{PRO}}\)). The fourth part is the delivery cost for the manufacturers to deliver the products to the warehouses (denoted as \(Z_{\text{MTS}}\)). Then, the calculation methods for each part and the optimization objective of this paper are described in detail.  
The purchase cost of materials can be calculated by the weights and prices of the materials, and the calculation method is shown in Formula (1):  
\[Z_{\text{PUR}}{=}\sum_{s\in SS}\sum_{m\in SS}\sum_{k\in SK}\sum_{p\in SP}\sum_{ l\in SL}{g_{smkp}}^{\prime}{{}_{pl}}{c_{sl}} \tag{1}\]  
On the other hand, since there is a one round-trip path between a supplier and a manufacturer, then \(Z_{\text{STM}}\) can be calculated as follows:  
\[Z_{\text{STM}}{=}\;2\times\sum_{s\in SS}\sum_{m\in SM}{g^{\prime}}_{sm}{d_{sm} }{b_{s}} \tag{2}\]  
It can be seen from Formula (2), that the unit delivery cost of the suppliers' vehicles and the delivery distances are related to \(Z_{\text{STM}}\). Further, several varieties of materials will be processed into products by manufacturers. The processing cost is related to the materials' weights and processing prices, which is shown in Formula (3).  
\[Z_{\text{PRO}}{=}\sum_{m\in SM}\sum_{k\in SK}\sum_{p\in SP}{g^{\prime\prime}}_{ mkp}{\rho_{p}}{c^{\prime}}_{m} \tag{3}\]  
At last, the products can be delivered by the manufacturers' vehicles. Since multiple warehouses' products can be delivered by a vehicle of a manufacturer at the same time, then \(Z_{\text{MTS}}\) can be calculated by Formula (4).  
\[Z_{\text{MTS}}{=}\sum_{m\in SM}\sum_{k\in SK}\sum_{p\in SP}{(y_{mkp}+y^{\prime \prime}}_{mkp}){d^{\prime}}_{mp}{b^{\prime}}_{mk}{+}\sum_{m\in SM}\sum_{k\in SK }\sum_{p\in SP}{\sum_{p^{\prime}\in SP}{y^{\prime}}_{mkp^{\prime}}{d^{\prime} }_{pp^{\prime}}{b^{\prime}}_{mk}} \tag{4}\]  
From Formula (1) to Formula (4), it can be obtained that the objective function is:  
\[Z_{\text{Total}}{=}\;\text{min}(Z_{\text{PUR}}{+}Z_{\text{STM}}{+}Z_{\text{ PRO}}{+}Z_{\text{MTS}}) \tag{5}\]  
The goal of this paper is to minimize the total cost of these four parts.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 3 Decellularization Methods
### Complementary Agents
#### Chemicals  
EDTA is a well-known chelator of di- and trivalent metal ions, commonly used in biochemistry to inhibit proteases. Its addition during exposures to trypsin or main decellularizing agents is a common tactic to prevent ECM degradation by cation-dependent proteases, which are abundant in both intra- and extracellular compartments. It also allows disruption of the protein-protein interactions based on metal cation bounding. Concentrations from 0.05% to 0.1% w/v are commonly applied, but levels up to 0.75% w/v have also been reported [80], without any specific association with tissue or decellularizing agent types. EDTA has been tested as a main decellularizing agent in human AM under various conditions (0.02-0.1%, 0.5-16 h, 4 or 37 \({}^{\circ}\)C), but does not completely decellularize the tissue, with a maximum DNA reduction limited to 50% of the initial level [88]. Other commercial protease inhibitors consisting of mixes of chemicals and small peptides can also be added to prevent ECM damage.  
Hypo- and hypertonic shocks are applied from 1 to 12 h before the main decellularization steps or after to wash tissues and ensure the total lysis of remaining cells. To create hypertonic conditions, NaCl is commonly dissolved at 1 M to weaken cell membranes, as reported in the literature and in patented protocols to process fibroblast sheets, fish skin, and human skin [83; 86; 89]. Hypertonic NaCl also has been used to dissociate epidermis and dermis [90; 91]. A hypertonic solution composed of 1 M KI has been used to process mouse skin [87]. Regarding hypotonic buffers, Tris (tris(hydroxymethyl)laminomethane) is often used at 10 mM, and immersion baths before decellularization and for solubilization of SDS have been reported for porcine bladder wall or fibroblast sheets processing [89; 92].
## 3 Graph Neural Networks for Next-Generation Networks
### Backgrounds on Graph Neural Networks
#### 3.1.1 Spectral-Based Graph Convolutional Networks  
Convolutional neural networks (CNNs) dominate image processing due to their ability to extract information from local neighborhoods of pixels (i.e., receptive field) in images. This property can also be leveraged in designing GNNs, where nodes can be seen as pixels, and a GNN layer acts like a CNN layer, capturing local information from node neighborhoods. However, defining a translation-invariant operator in the vertex domain is challenging. Graph signal processing (GSP) theory offers a solid foundation to formulate convolution operators in the spectral domain [45; 46].  
In GSP, a Graph fourier transformation (GFT) is defined for an undirected graph with a symmetric adjacency matrix \(\mathbf{A}\). The graph signal \(\mathbf{x}\in\mathbb{R}^{n}\) represents the scalar signals of each node in \(V\)[45]. The graph Laplacian matrix \(\mathbf{L}=\mathbf{D}-\mathbf{A}\) is a fundamental operator in GSP, where \(\mathbf{D}\in\mathbb{R}^{n\times n}\) is the diagonal matrix such that \(\mathbf{D}_{ii}=\sum_{j=1}^{n}A_{ij}\), i.e., the \(i\)th node degree. Based on the GFT, [PERSON] et al. [46] propose the \(K\)-order approximation of the convolution, using the Chebyshev expansion to reduce the learning and computational complexity. The graph convolution is approximated as  
\[g_{\theta}(\mathbf{x})\approx\sum_{k=0}^{K}\theta_{k}\mathbf{T}_{k}(\bar{\mathbf{L}})\mathbf{x}, \tag{1}\]  
where, via the recurrence of the Chebyshev polynomials, we compute \(\mathbf{T}_{k}(\bar{\mathbf{L}})=2\bar{\mathbf{L}}\mathbf{T}_{k-1}(\bar{\mathbf{L}})-\mathbf{T}_{k-2}( \mathbf{L})\) and \(\mathbf{L}=2\mathbf{L}/\lambda_{\max}-\mathbf{I}_{n}\) given that \(\lambda_{\max}\) is the largest eigenvalue of the Laplacian, \(\mathbf{T}_{0}=0\), and \(\mathbf{T}_{1}=\bar{\mathbf{L}}\). Parameters \(\theta\) can be updated to optimize the learning objective. [PERSON] et al. [47] extend the spectral-based graph convolution for directed graphs by using the same spectral approach. It is also important to note that \(K\) can be interpreted as the kernel size of the convolution, i.e., the radius of the local neighborhood from the central node.
## 4 Problem Formulation
The final form of the frame minimization problem (FMP), formulated by adding additional constraints imposed on the new variables and an objective function is as follows:  
\[\text{FMP}(\mathcal{C})\text{: min }\ T=\sum_{c\in\mathcal{C}}T_{c} \tag{3a}\] \[\sum_{c\in\mathcal{C}(a)}h_{sb(a)c}\geq n(s)y_{sa},\quad s\in \mathcal{S},a\in\mathcal{A}\] (3b) \[\sum_{s\in\mathcal{S}}h_{suc}\leq B(m(c,w))T_{c},\quad c\in \mathcal{C},w\in\mathcal{W}(c)\] (3c) \[\sum_{a\in\mathcal{S}^{-}(v)}z_{snu}+I(s,w,v)=\sum_{a\in\mathcal{ S}^{+}(v)}z_{snu},\] \[s\in\mathcal{S},w\in\mathcal{D}(s),v\in\mathcal{V}\] (3d) \[z_{snu}\leq y_{sa},\quad s\in\mathcal{S},w\in\mathcal{D}(s),a\in \mathcal{A}\] (3e) \[y_{sa}\in\mathbb{B},\,s\in\mathcal{S},a\in\mathcal{A}\] (3f) \[z_{snu}\in\mathbb{R}_{+},\,s\in\mathcal{S},w\in\mathcal{D}(s),a \in\mathcal{A}\] (3g) \[\sum_{s\in\mathcal{S}}h_{suc}\leq B(m(c,w))T_{c},\quad c\in \mathcal{C},w\in\mathcal{W}(c), \tag{2b}\]0  
Constraints (3d) and (3e) are the new constraints that ensure correctness of the constructed routing trees (indicator \(I(s,w,v)\) used in (3d) equals 1 for \(v=s\), and \(-1\) for \(v=w\)). Finally, the objective function (3a) minimizes the total numbers of time slots used by the frame, i.e., it minimizes the sum of the numbers of time slots assigned to the c-sets actually used in the frame. The so described formulation was originally presented in [17]. It should also be emphasized that the frame minimization problem represented by the above formulation is \(\mathcal{NP}\)-hard, since, its basic version is already \(\mathcal{NP}\)-hard [14]. Note that the above formulation provides an optimal solution only for a given family of c-sets \(\mathcal{C}\) (and not for the whole c-sets family), and it does not consider power control. Both of these aspects will be addressed during solution process.
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 4 Discussion
### Robustness of the System  
As mentioned in Section 2.2, we attempted to reduce the noise resulting from inaccurate QRS detection by adjusting the upper and lower bounds and the quantization factor of the discretized Poincare plot. To investigate the robustness of our PAF prediction system, we added various percentages of random noise to the HRV sequences and recorded the corresponding performance of the system. We note that the percentage of noise added to the HRV sequence was proportional to the median of the sequence, since some abnormal beats may be involved in the sequence, affecting the average and range of the sequence. Figure 7 shows the resulting changes in the prediction accuracy (Acc), true positive percentage (TPP), and true negative percentage (TNP) in the training, validation, and testing  
Figure 5: Feature differences between PAF and non-PAF HRV sequences. The reddish pixels represent heart rate changes in a 400 HRV sequence that appear more frequently in PAF than in non-PAF subjects. Upper panel: (**a**) Inter-person features with \(\tau=2\) and \(q=4\) and (**b**) with \(\tau=1\) and \(q=5\); Lower panel: (**c**) Intra-person features with \(\tau=4\) and \(q=4\) and (**d**) with \(\tau=5\) and \(q=5\). The symbol \(\tau\) denotes the time lag and \(q\) denotes the quantization factor.  
Figure 6: Exemplar non-PAF and PAF HRV sequences. The non-PAF HRV sequences (**a**) often exhibit larger variation than the PAF ones (**b**). However, the PAF HRV sequences occasionally show large heart rate changes.  
datasets, respectively. As shown in the figure, all the prediction accuracies remained \(\geq\)80%, if no larger than 3% of noise was added to the HRV sequences.
###### Abstract  
This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth-space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) over a certain location. This research utilized seasonal rain height data obtained from a recent study and the latest ITU-R P.618-14 guidelines, to compute and analyze XPD variations across six selected frequencies (11.7 GHz to 35 GHz) for different percentages of time exceedance in Pretoria. The study reveals significant seasonal dependencies of rain heights, with XPD reaching its maximum during winter due to lower rain height, and lower rain-induced attenuation and its minimum during summer, characterized by intense convective rainfall and maximum rain height. For instance, the estimated XPD for a 35 GHz signal at 0.01% of the time in the summer, spring, winter, and autumn are 13, 14, 15, and 14 dB, respectively. This implies that radio signals suffer severe attenuation caused by low XPD in the summer. The relationship between CPA and XPD highlights the need for increased XPD margins at higher frequencies to mitigate signal degradation caused by rain depolarization. Practical recommendations include the adoption of adaptive modulation and coding schemes to maintain link reliability during adverse weather conditions, particularly in summer. This research highlights the significance of incorporating frequency-dependent parameters and rain height variability in XPD estimation to enhance the design of satellite communication systems, ensuring optimized performance and reliable operation in a tropical climate.  
earth-space links; inter-terrestrial links; millimetre waves; rain height satellite communication; subtropical region; XPD +
Footnote †: journal: _arxiv_  
_Article_
## 3 Concepts, Models and Technologies  
This section provides a concise overview of the core concepts, models and technologies essential for comprehending this study and its future development.
## 3 MPPT Techniques
### Intelligent MPPT Techniques
#### 3.3.1 Fuzzy Logic Control (FLC)  
The FLC is a robust approach for solving non-linear optimization problems. The fuzzy approach is preferred in MPPT implementation due to advantages such as (i) convenient user interface, (ii) ease of implementation, (iii) flexible operation, and (iv) qualified validation. However, FLC is rarely employed alone; although, it is extremely useful when combined with other algorithms, such as artificial neural networks [35], genetic algorithms, and other traditional methods [110; 111]. For MPP tracking, the FLC approach uses three steps, fuzzification, inference fuzzy rules, and defuzzification. The optimal performance of the system is determined through fuzzy inference and fuzzy rule creation in these processes. However, to build fuzzy rules, a significant amount of information and training is required [38].  
The FLC MPPT uses two inputs and one output for extracting maximum power from the PV system. The input variables are errors and change in errors, as presented in Equations (18) and (19), and the output variable is change in duty, as presented in Equation (20).  
\[E(k)=\frac{\Delta P}{\Delta V}=\frac{P(k)-P(k-1)}{V(k)-V(k-1)} \tag{18}\]  
\[\Delta E(k)=e(k)-e(k-1) \tag{19}\]  
\[\Delta D=D(k)-D(k-1) \tag{20}\]  
where  
\(E(k)\) represents the change in slope of the P-V curve;  
\(\Delta e(k)\) denotes the change in the value of the slope of the P-V curve;  
\(\Delta D\) denotes the change in the duty cycle.  
The membership functions (MFs) of FLC for the input and output variables are presented in Figure 10. For input and output variables, the following five MFs are employed: big positive (BP), small positive (SP), zero (ZE), small negative (SN), and big negative (BN). The rule base of FLC presented in Table 5 is used to decrease the steady-state oscillations and to track MPP with fast tracking speed.  
Figure 10: **(a)** Input; (b) change in input and (c) output membership functions.
# Advancing in RGB-D Salient Object Detection: A Survey
## 4 Performance Evaluation and Comparison  
### RGB-D Dataset  
RGB-D data, captured through different sensors, provides RGB and depth information. Depth data offers spatial cues about object structure and the relative distance between objects and the camera, which is particularly valuable in complex and cluttered environments. Evaluating RGB-D SOD models, especially those enhanced by attention mechanisms, requires meticulously designed experiments to assess their effectiveness and robustness in diverse settings. Comprehensive evaluation necessitates the selection of datasets that represent a range of typical challenges encountered in real-world applications, including variations in object scale, background complexity, and quality of depth information.  
The key datasets utilized in these evaluations include the following: LFSD [8], STERE [47], NJUD [48], NLPR [19], SIP [49], Holo50K [12], SSD [49], DUT [50], DES [49], and GIT [51], as shown in Figure 2.  
These datasets are widely used for their diversity and complexity, making them ideal for testing the robustness of RGB-D SOD models. Using these datasets ensures that the developed models are not only theoretically sound, but also practically viable in various real-world settings.