esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 3 Background Information
### EXplainable Artificial Intelligence (XAI)
#### 3.3.1 The SHAP Explanation Method  
[PERSON] and [PERSON] presented the SHapley Additive exPlanations (SHAP) approach in 2017 [31]. This approach uses game theory concepts to explain model predictions locally. In this framework, the model functions as the game regulations, while the input features act as individuals. Observed features participate, while unobserved features abstain.  
SHAP computes Shapley values by assessing the predictive model using various feature combinations and estimating the average difference in prediction when a feature is included instead of when it is removed. This variant shows how the feature impacts the model's prediction for a given input, yielding numerical Shapley values that measure each feature's contribution. SHAP derives these values by combining linear equations with binary variables to determine whether a particular feature is functional [32].  
The SHAP approach can be summarized as follows: A pre-trained model, f SHAP, approximates a more interpretable model, g, for a given input vector \([x_{1},x_{2},\ldots,x_{p}]\) of \(p\) features. This model emphasizes the contribution of particular features to total predictions of overall potential feature subsets. The following formula in Equation (3) defines the representation of the model g:  
\[g(z)=\mathcal{O}_{0}+\sum_{i=1}^{M}\mathcal{O}_{i}z_{i} \tag{3}\]  
where \(\mathcal{O}_{0}\) represents the model's baseline value (the mean output), and \([z_{1},z_{2},\ldots,z_{p}]\) is a refined representation of the input x, with (\(z_{i}=1\)) indicating if the feature contributes to the prediction and 0 if it does not. The coefficient i\(\in\)R represents the Shapley value of each feature and a weighted average of the contributions from every feature combination. The size of each combination determines the weights.  
Specifically, the following Equation (4) is used to determine \(\mathcal{O}_{i}\):  
\[\mathcal{O}_{i}(f,x)=\sum_{z\leq x^{\prime}}\frac{|z|!(p-|z|-1)!}{p!}[f_{x}(z )-f_{x}(\tfrac{Z}{1})] \tag{4}\]  
In this context, \(\mathcal{O}_{i}\) represents the Shapley value for feature i, f is the model, x is the input data point, and x\({}^{\prime}\) refers to the distilled data input. The term \(|z|\) denotes the count of non-zero elements in z, and the z subset \(x^{\prime}\) includes all z vectors with non-zero components aligning with those in x\({}^{\prime}\). The expression \(f_{x}(z)-f_{x}(\tfrac{Z}{1})\) indicates the contribution of the ith variable to the prediction.  
SHAP yields a set of feature weights that provide insights into the model's predictions, accounting for interactions among features and offering a comprehensive understanding of the model's behavior. While calculating Shapley values for all possible feature combinations is computationally intensive, SHAP can be efficiently implemented for models with tree-like structures.
## 3 Main Framework
### Feature Extraction  
We extract node features from two dimensions, namely local features and global features. The purpose is to ensure that the extracted features can more comprehensively describe the topology information of each dimension of the node. At the same time, considering the need to avoid over-reliance on feature engineering as much as possible, we only selected five types of node topology-related indicators as node features, and most of these indicators are simple and easy to calculate. Among them, local features include  
Figure 1: The structure of bridge nodes detection framework.  
degree centrality, LocalRank [30], and clustering coefficient, and global features include PageRank and betweenness centrality. They are detailed in Table 1.  
In order to improve the generalization ability and convergence speed of the model, we normalize the selected features. For each feature \(X\):  
\[X^{norm}=\frac{X}{max(X)} \tag{5}\]
## 3 Mechanical Parameters and Energy Evolution Characteristics
### Characterization of Energy Evolution
This leads to a significant release of energy to the surroundings, resulting in a gradual increase in dissipated energy. In order to further investigate the variation in dissipative and elastic energies as a percentage of the whole process, it is calculated by the following equations:  
\[\alpha =\frac{U_{ei}}{U_{i}} \tag{13}\] \[\beta =\frac{U_{di}}{U_{i}} \tag{14}\]  
where \(\alpha\) and \(\beta\) denote the ratio of elastic and dissipative energy to total energy, respectively. From Figure 9, it can be seen that the proportion of elastic energy, represented by \(\alpha\), exhibits a \"quadratic parabola\" pattern of initially increasing and then decreasing with the increase in loading cycle level, while the proportion of dissipated energy behaves in the exact opposite manner. During the initial loading, energy is primarily stored as elastic energy within the sandstone. Starting from the 8 th loading cycle level, the proportion of elastic energy begins to decrease, while the proportion of dissipated energy does the opposite. The main reason for this is the initiation, propagation, and connection of sandstone cracks, as well as friction between internal particles, leading to a conversion of elastic energy into dissipated energy, and thus an increase in the proportion of dissipated energy. Consequently, when the load exceeds approximately 65% of the peak load, there is a change in the dominant form of energy [44]. Figure 8: Damage factor. Figure 9: Energy proportion.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 3 Visual SLAM and Visual Odometry Using Deep Learning: Survey
### Deep Learning-Based Module for Visual SLAM and Visual Odometry
#### 3.1.4 DL Modules Add to the Visual SLAM Algorithm
[148] proposed a deep network to build 3D dense mapping called the Learning Kalman Network-based monocular visual odometry (LKN-VO). The input data of the network are monocular RGB images. The dense optical flow is estimated using FlowNet2, and the depth map is estimated using DepthNet. The global pose trajectory is built upon transferring and filtering six-degree-of-freedom (DOF)-relative poses using the SE(3) composition layer. Next, the point cloud data of the image are built based on the depth map and the learned global pose. The output is that a dense 3D map is constructed. [PERSON] et al. [149] proposed a method for constructing an indoor 3D semantic VSLAM algorithm based on the combination of the Mask Regional CNN (RCNN) and ORB feature extraction algorithms. To accurately collect key points, the authors used real-time ORB feature extraction. To detect instance segmentation tasks and semantic association of map points, the proposed method used Mask RCNN. The output in their work was the exact semantic map constructed. Regarding mapping categories, to build a safe path that can avoid obstacles in the environment for robots or autonomous vehicles, geometric maps need to be built based on spatial maps. These include information about the space of the environment, structures to plan movements, paths, and locations in the environment. [PERSON] et al. [154] surveyed building an environmental map. The process of building semantic mapping includes three modules: spatial mapping, acquisition of semantic information, and map representation. [PERSON] et al. [155] proposed a method combining CNNs and Visual SLAM (ElasticFusion) to build dense 3D maps. Therein, Visual SLAM builds a 3D global map based on 2D images, while CNNs perform semantic predictions on multiple views based on probability. The output is a densely annotated semantic 3D map. [PERSON] et al. [156] proposed an approach to building an environment map based on Visual SLAM and DL techniques for object detection and segmentation, thereby creating a semantic map of the environment map.
###### Abstract
Moreover, transparent antennas may also be used in smart cities in which advanced wireless technology is needed to control the power, communication, and roads through data analysis and artificial intelligence. Figure 1 illustrates the common techniques employed to disguise antennas from the general public and make them resemble camouflage patterns incorporated with tree, car screen, and cathedral cross while providing seeminglyless 5G connectivity. Screen printed transparent antennas may also have various applications that include medical devices and electronic wearable devices [6]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Ref.**} & **Freq** & **Substrate** & **Sub** & **Patch** & **Gain** & **Bandwidth** & **Efficiency** & **Transp-** & **Transmi-** \\  & **(GHz)** & **Material** & **Thickness** & **Material** & **(dBi)** & **Bandwidth** & **(\%)** & **arency** & **titance** \\ \hline \multirow{2}{*}{[5]} & \multirow{2}{*}{5–18} & \multirow{2}{*}{PDMS} & \multirow{2}{*}{-} & Fabric & \multirow{2}{*}{3.2–4.2} & 0.1– & \multirow{2}{*}{66–90} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & & & tissue (F) & & & & & & \\ \hline \multirow{2}{*}{[6]} & \multirow{2}{*}{23–30} & Polyester & \multirow{2}{*}{0.35 mm} & Copper & \multirow{2}{*}{4.2} & 24– & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & fabric (F) & & foil & & & & & \\ \hline \multirow{2}{*}{[7]} & \multirow{2}{*}{10} & Alumina & \multirow{2}{*}{100 μm} & Polyaniline & \multirow{2}{*}{1.8, 4.69} & 9.6– & \multirow{2}{*}{56, 98} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & & & /copper & & & & & \\ \hline \multirow{2}{*}{[8]} & \multirow{2}{*}{0.9} & PET (F) & \multirow{2}{*}{0.5 mm} & Copper & \multirow{2}{*}{3} & 0.8– & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & & /CP (F) & & & & & & \\ \hline \multirow{2}{*}{[9]} & \multirow{2}{*}{2.4} & Glass & - & AZO/AgNWs & - & 2–3.5 GHz & - & - & 80.28 \\ \hline \multirow{2}{*}{[10]} & \multirow{2}{*}{45} & Quartz & \multirow{2}{*}{5 mm} & AZO (F) & - & 43– & \multirow{2}{*}{-} & \multirow{2}{*}{86} & \multirow{2}{*}{83} \\  & & & & & & & & & \\ \hline \multirow{2}{*}{[11]} & \multirow{2}{*}{2.4} & Silicon & - & AZO & 4.9 & - & \(-7.3\) dBi & - & - \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of optical transparent antennas using thin film.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 2 Video Deepfake Detection with CNN-LSTM  
This section provides an overview of video deepfake detection using the combination of CNN and LSTM. It starts by discussing how deepfakes are created and detected, followed by a brief introduction to the key features of CNN and LSTM.
## 4 Proposed Scheme
### Data Forwarding
For two-hop availability checking, as shown in Figure 3, when node n0 of the data sends a packet, there are two legitimate nodes \(n_{1}\) and \(n_{2}\) that can transmit it, and then, according to the routing protocol, we check the neighbor table of the \(n_{1}\) and \(n_{2}\) nodes and find that both nodes have legitimate forwarding nodes \(n_{3}\) and \(n_{5}\). Then, when we query the neighbor table of the \(n_{5}\) node, we find that there is no adjacent node of node \(n_{5}\) that can transmit the data to the CH; the protocol then marks \(n_{5}\) as a void node and \(n_{1}\) as a trap node, excluding \(n_{5}\) as well as \(n_{1}\) from the candidate set during data transmission. After removing the void node, the source node n0 will sort the nodes in the candidate set for sending according to the total wiring distance, but because of the variable underwater environment and high complexity, there may be a situation where data transmission to the forwarding node with high priority fails at a certain time, and if it does not select the next priority node as a forwarding node in a timely manner, there will be a phenomenon that consumes energy, occupies bandwidth, and reduces data delivery rate. So, we have designed a supplement to the routing algorithm of the scheme in the supplemental algorithm. We set a timer for the forwarding node. When the priority node expires at its timer time and the source node n0 has not received the ACK message returned by the priority node (which indicates that the packet has been accepted and forwarded to the next node), the source node n0 forwards the message to the next priority node until the data arrive at the CH or BN. Figure 3: Example of two-hop availability check.
# Adaptation of the Marine Bacterium _Shewanella baltica_ to Low Temperature Stress
## 2 Results and Discussion
### Upregulation of the Sulfur Assimilatory Pathway upon Cold Stress
Under anaerobic conditions there is a switch of sulfur metabolism to the dissimilarity pathway in which APS is directly reduced to sulfite and then to sulfide (H\({}_{2}\)S) by a dissimilatory sulfite reductase (pathway not shown on the scheme of sulfur metabolism in Figure 5). The dissimilatory pathway of sulfate reduction leads to anaerobic respiration in which many sulfur compounds may serve as the terminal electron acceptors of the respiratory chain. It was reported that the extraordinary ability to use a variety of respiratory electron acceptors, including iron (III), manganese (IV), chromium (VI), uranium (VI), nitrate, elemental sulfur, sulfite, thiosulfate, and tetrathionate, is the hallmark of the _Shewanella_ genus [75; 76; 77]. A molecular mechanism of anaerobic respiration involving inorganic sulfur compounds in _S. oneidensis_ was suggested, but it is still poorly understood [76]. The dissimilatory sulfur pathway itself was not found in our analysis as affected in any direction. However, _S. baltica_ was found to produce large quantities of inorganic sulfide (H\({}_{2}\)S) as a consequence of fish spoilage during prolonged on-ice storage. This phenomenon has a great implication for the fishery industry [21]; the _Shewanella_ genus was named after a scientist working in the fisheries microbiology, aptly named [PERSON] [78]. It has to be mentioned that H\({}_{2}\)S is released from bacterial cells only through anaerobic respiration of the dissimilatory sulfur pathway. Therefore, there is a link between low temperature conditions and the metabolic switch to the dissimilatory sulfur pathway. It was also reported that anaerobic respiration enzymes, such as the sulfite reductase and sulfate reductase, are significantly elevated at the transcriptional level in _S. algae_ 2736 cells after high salt exposure [79]. Thus, it may be possible that _S. baltica_ has a good reason to keep the tetrathionate and/or thiosulfate conversions to sulfite downregulated, so these compounds could accumulate and serve as electron acceptors in the anaerobic respiration. The question is how can the cold stress or high salt stress lead to anaerobic respiration through the dissimilatory sulfur pathway? Cold or high saline conditions lead to anaerobic conditions in the complex marine environment. Thus, cold stress might be a signal for _S. baltica_ cells that anaerobic conditions are possible to attain. There is a high flow of fresh water from rivers but a limited sea water exchange with the Northern Sea in the Baltic Sea; deep water is ventilated only by large perturbations. Low temperature may cause a vertical gradient of salinity, which suppresses adequate ventilation of deep water, resulting in decreased oxygen and increased hydrogen sulfide concentrations [80].
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
## 2 Materials and Methods
### Speech Communication System Design  
Based on the PZT-42 piezoelectric electret mechanical antenna, a speech communication system is proposed for promoting the practical application of mechanical antennas. In this section, the specific design and implementation of the speech communication system will be described in terms of the hardware system, communication protocol, and software system.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Monostatic Multisensor Data Fusion and Target Recognition
#### 3.3.1 Clustering for Extended Target Management  
Clustering algorithms are commonly employed to manage multiple detections from extended targets, such as vehicles. In this work, we propose combining target state predictions (position and velocity) provided by tracking algorithms with a multistage clustering procedure. Initially, an excision filter can be applied to the soft maps to retain points with  
Figure 4: Fusion of range–angle maps from multiple BSs enables distributed and cooperative sensing, enhancing target detection and localization. Each BS detects nearby targets effectively, and fusion creates a global map of all targets for a comprehensive view.  
higher scores, which likely correspond to target reflections. In the next step, a k-nearest neighbors (k-NN) algorithm is used to cluster points that are likely associated with previously detected targets [29]. A gating strategy is exploited to associate new measurement detections with existing targets. Finally, the remaining points--those selected by the excision filter but discarded by the k-NN gate, i.e., those located too far from previously detected targets--are evaluated using the density-based spatial clustering of applications with noise (DBSCAN) algorithm [30]. DBSCAN defines the maximum distance for points to belong to the same cluster and the minimum number of points required to form a cluster. It is important to highlight that, in this context, DBSCAN is applied to cluster residual points that may represent potential new or emerging targets. Ultimately, the centroids of clusters formed by both k-NN and DBSCAN are stored, representing the target detections extracted from the soft map.
## 2 Description of the Experiment
### Process to Extract Scalar Quantities
Figure 3: Simplified flowchart of the image post-processing for extracting several important spray scalar quantities such as the droplets Sauter mean diameter (SMD), the droplets number density (N), and the liquid volume fraction (LVF). The process is performed after recording spray images via a scanning procedure where the SLIPI-LIF signal, the SLIPI-Mie signal, and the light intensity transmitted through the spray are simultaneously recorded. SLIPI-LIF/Mie Ratio Method for Droplet SMD and Extinction Cross-Section (\(\sigma_{\rm e}\)) Mapping
## 3 Materials and Methods
### Calculation of Energy Consumption
#### 3.4.2 Specific Energy Consumption  
The specific energy consumptions of CD, VMD, and CPD-VMFD were calculated on the basis of the ratio of energy consumed to the initial mass \(m\) (grams) of the leaf samples and the ratio of energy consumed to the mass of water \(W\) (grams) expelled from the leaf samples.  
The specific energy consumptions of CD, represented as \(E_{C_{m}}\left(kJ\ g^{-1}\ fw\right)\) and \(E_{C_{W}}\left(kJ\ g^{-1}\ water\right)\), were calculated using Equations (5) and (6), respectively.  
\[E_{C_{m}}=\frac{E_{C}}{m} \tag{5}\]  
\[E_{C_{W}}=\frac{E_{C}}{W} \tag{6}\]  
Equations (7) and (8) show the specific energy consumptions of VMD, namely, \(E_{VM_{m}}\left(kJ\ g^{-1}\ fw\right)\) and \(E_{VM_{W}}\left(kJ\ g^{-1}\ water\right)\), respectively.  
\[E_{VM_{m}}=\frac{E_{VM}}{m} \tag{7}\]  
\[E_{VM_{W}}=\frac{E_{VM}}{W} \tag{8}\]  
Equations (9) and (10) present the value of the specific energy consumptions of CPD-VMFD, namely \(E_{C-VM_{m}}\left(kJ\ g^{-1}\ fw\right)\) and \(E_{C-VM_{W}}\left(kJ\ g^{-1}\ water\right)\), respectively. Both equations are the ratios of the sum of energy \(E_{C}\) and \(E_{VM}\) to the mass of the initial sample \(m\) (grams) and the ratio of the sum of energy \(E_{C}\) and \(E_{VM}\) to the mass of water (grams) from CPD (\(W_{C}\)) and VMFD (\(W_{VM}\)) removed from the leaf sample, respectively.  
\[E_{C-VM_{m}}=\frac{E_{C}+E_{VM}}{m}; \tag{9}\]  
\[E_{C-VM_{m}}=\frac{E_{C}+E_{VM}}{W_{C}+W_{VM}}. \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Name** & **Model Equation** \\ \hline [PERSON] & \(MR=\exp(-k\cdot t)\) \\ Modified Page & \(MR=a\cdot\exp(-k\cdot t^{a})\) \\ [PERSON] and [PERSON] & \(MR=a\cdot\exp(-k\cdot t)\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mathematical models applied to drying curves of _Strobilanthes crispus_.
## 4 Solving the Vibration Response of Circuit Unit
### Decomposition of the Vibration Response
#### 4.1.1 Transient Response  
The homogeneous part of \(m\ddot{x}(t)+c\dot{x}(t)+kx(t)=F(t)\) is written as follows:  
\[m\ddot{x}(t)+c\dot{x}(t)+kx(t)=0 \tag{3}\]  
The damping coefficient and stiffness are known to be \(c=\xi\times 2\sqrt{mk}\), \(k=m\omega_{n}{}^{2}\), which are obtained by substituting it into Equation (3):  
\[\ddot{x}(t)+2\xi\omega_{n}\dot{x}(t)+\omega_{n}{}^{2}x(t)=0 \tag{4}\]  
Let the general solution be \(x(t)=Xe^{st}\), and substitute it into Equation (4) to obtain the general solution of the equation.  
\[x(t)=e^{-\xi\omega_{n}t}(X_{1}e^{\omega_{n}\sqrt{\xi^{2}-1t}}+X_{2}e^{-\omega_ {n}\sqrt{\xi^{2}-1t}}) \tag{5}\]  
When \(\xi\geq 1\), it is an over-damped case in which the circuit unit will gradually return to the equilibrium position after only one time at most, with no periodic change in the factor and no oscillation characteristics. In this case, the energy input to the system by the initial excitation is quickly consumed by the damping. The system is too late to generate reciprocating vibration and is not conducive to the damping of continuous excitation.  
When \(0<\xi<1\), it is an under-damped case. The transient response of the circuit unit is expanded and collated, using [PERSON]'s formula to obtain:  
\[x(t)=e^{-\xi\omega_{n}t}[(X_{1}+X_{2})cos(\omega_{d}t)+i(X_{1}-X_{2})sin( \omega_{d}t)] \tag{6}\]  
Denoted by  
\[x(t)=Xe^{-\xi\omega_{n}t}cos(\omega_{d}t-\varphi) \tag{7}\]  
where \(X=\sqrt{x_{0}{}^{2}+\frac{(v_{0}+\xi\omega_{n}x_{0})^{2}}{\omega_{d}^{2}}}\) is the vibration amplitude of the transient response and \(\varphi=arctan(\frac{v_{0}+\xi\omega_{n}x_{0}}{x_{0}\omega_{d}})\) is the phase of the transient response.  
This is an under-damped condition in which the circuit unit vibrates back and forth near the system's equilibrium position. Its amplitude decreases with the exponential law \(Xe^{-\xi\omega_{n}t}\). The logarithmic decay rate of the amplitude is introduced here to describe the speed of amplitude decay.  
\[\delta=ln\frac{e^{-\xi\omega_{n}t}}{e^{-\xi\omega_{n}(t+T_{d})}}=\frac{2\pi \xi}{\sqrt{1-\xi^{2}}} \tag{8}\]  
Equation (8) is transformed into an expression of the stiffness and damping coefficients. Denoted as \(\delta(k,c)\):  
\[\delta=\frac{2\pi c}{\sqrt{4 mk-c^{2}}} \tag{9}\]  
For this system, the larger the damping rate, the faster the amplitude decay of the transient response and the shorter the time to reach the steady state. However, too large a damping rate can also result in the excitation of the system by the drill string being applied directly to the circuit unit. This will cause the circuit unit to be subjected to excessive transient forces. In engineering applications, it is generally taken that \(0<\xi<0.2\). That is  
\[0<\frac{c}{2\sqrt{mk}}<0.2 \tag{10}\]