esa-sceva/satcom-chunk-collection huggingface dataset:
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 6 Conclusions  
As the number of buildings in the city continues to rise, elevators have become an inevitable device for household environments. However, the long-term imbalance between the counterweight and the actual load during the elevator movement will accelerate the consumption of the device, resulting in safety hazards and energy costs. Hence, a passenger counting method is required to calculate the usage rules of elevators. In this way, the actual load range of the elevator can be inferred, thereby regularly adjusting the counterweight of elevators according to practical requirements to solve the above problem. This paper proposes a device-free passenger counting approach in the elevator called ECC based on commodity WiFi. ECC presents an SLD-PCA method to decorrelate the CSI streams and simultaneously performs the pre-treatment process with CSI segmentation and noise reduction. Then, ECC proposes a TVS feature that adopts radar detection schemes, which can calculate the short-time energy variation of CSI attenuation with the passenger number, then constructs a multi-dimensional feature matrix with AOA and PEM features. Finally, the RF classifier is employed to complete the passenger counting work based on the expended datasets. Extensive real-world experiment results demonstrate that ECC performs well in precision, scalability, and reliability, which can be applied to guide the adjustment of the elevator counterweight.  
Conceptualization, X.M. and Z.C.; Data curation, Z.C. and H.H.; Formal analysis, H.H.; Methodology, X.M., W.X. and Z.C.; Project administration, W.X. and J.Z.; Software, X.M.; Supervision, W.X. and J.Z.; Writing--original draft, X.M.; Writing--review and editing, X.M. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Key R & D Program of China 2019 YFB2102200, NSFC Grant No. 61832008, 62072367, 62176205, and National Science Foundation of Shaanxi Province 2021 JM-025.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CSI & channel state information \\ GDPR & general data protection regulation \\ OFDM & orthogonal frequency division multiplexing \\ COTS & commercial off-the-shelf \\ PCs & principal components \\ AOA & average of amplitude \\ TVS & time-varying spectrum \\ PEM & percentage of non-zero elements \\ WT & wavelet transform \\ SLD-PCA & short-time local detection principal component analysis \\ LPP & locality preserving projection \\ CAV & capture variance \\ WCCDA & wavelet coefficient correlation de-noising algorithm \\ DWT & discrete wavelet transform \\ STFT & short-time Fourier transform \\ \end{tabular}
## 3 SES/MB Framework
### Model Base  
The model base represents the behavioral characteristics of the system and describes procedural characteristics. It is composed of models that provide a dynamic and symbolic means of expression. This base store executes software modules of dynamic system components referenced by SES leaf nodes (Figure 2c) [50]. The models stored in the base can be mutually coupled by a given coupling relationship, and then can be constructed into a simulation model (Figure 2d). The DEVS model, which is a representative formalism for discrete event modeling, measures the behavior of a system for discrete events in a continuous time. An atomic DEVS model is formally defined by the following structure  
\[\langle X,S,Y,\delta_{int},\delta_{ext},\lambda,t_{a}\rangle. \tag{3}\]  
In Equation (3), \(X\) is an external input set, \(S\) is a sequential state set, \(Y\) is an external output set, \(\delta_{int}\) is an internal transition function, \(\delta_{ext}\) is an external transition function, \(\lambda\) is an output function, and \(t_{a}\) is a time advance function.  
Therefore, the SES/MB framework is able to synthesize and execute a structural and hierarchical simulation model by trees of entity structures and atomic DEVS models in bases.
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 3 Materials and Methods
### Standard MCDA
For the final decision on whether or not to engage a civilian vessel, the SAR coordinator may need other complementary data, which are not taken into consideration in the mathematical selection (e.g., vessel's own risk, readiness of the crew/ship to get engaged in the operation, etc.). Prior to performing the calculations referred to above, it is necessary to:  
1. Determine a set of alternatives (here: gather information about vessels in the vicinity of the distress position),
2. Determine a set of assessment criteria (a coherent family of criteria ensuring an exhaustive, coherent and non-excessive evaluation),
3. Model the decision-maker's preferences (determine the criteria evaluation methodology, including, inter alia, define the weight of each criterion and choose the preference function).
## 3 Principals of Routing Protocols and Their Breakdowns
### Acar [57]  
In [57], the authors proposed an ant colony algorithm-based routing (ACAR) protocol for UWSN. The authors made two assumptions: the node depth and residual energy were detectable using the pressure sensor attached to the nodes, and the nodes were stable during the transmission of the packets. The nodes are placed underwater in a dynamic environment during the initialization phase. There was no pheromone present. So the source node releases forward ants for path detection. Each node updates its pheromone level when the ants reach the destination node. For the path selection, the source checks the pressure level of the neighboring node and adds the neighbor only if it is shallower than the source node. The authors introduced an acceptance factor: the nodes decide whether to add the neighbor to the path. Now that the sink has multiple routes, it selects one by calculating the pheromone level and sending backward ants toward the source. The source selects the best route by collecting information about the backward ants. The forward and backward ants are reactive and are unicasted to save energy. The authors introduced an adaptive mechanism to update the pheromone level and path routing. The adaptive mechanism updates routes and finds new routes if the selected path is unavailable. The authors simulated and compared the protocol using the NS2-aqua sim. They compared ACAR with VBF. ACAR outperformed VBF in successful delivery ratio, network lifetime, and end-to-end delay, and ACAR is more energy-balanced. One drawback of this is that they have high computational complexity.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Qualitative Evaluation
#### 3.1.1 Qualitative Evaluation Based on Structural Overdetermination  
Based on the equivalence of structural overdetermination and analytic redundancy, the sensitivity relation between the residuals and the faults is transformed into a sensitivity relation between structural overdetermination and the faults. Thus, the problem of the diagnosability qualitative evaluation is transformed into the issue of the sensitivity relationship between structural overdetermination and the faults. The faults in the structurally overdetermined part can be detected by designing the residuals. If the faults belong to different structurally overdetermined parts, then the faults belong to other residuals. So, the faults in the different structurally overdetermined parts can be isolated.  
(Structural detectability) A fault \(f\) in \(M=(E,V)\) is structurally detectable if and only if:  
\[e_{f}\in E^{+} \tag{11}\]  
(Structural isolability) Faults \(f_{i}\) and \(f_{j}\) in \(M=(E,V)\) are structurally isolable if and only if:  
\[e_{f_{i}}\in\left(E\backslash\left\{e_{f_{j}}\right\}\right)^{+} \tag{12}\]  
(Structural diagnosability) Fault \(f_{i}\) in \(M=(E,V)\) is diagnosable if and only if:  
\[\forall f_{j}\in(F\backslash f_{i}),e_{f_{i}}\in\left(E\backslash\left\{e_{f_ {j}}\right\}\right)^{+} \tag{13}\]  
[PERSON] proposed a structural analysis-based fault diagnosability analysis method, which defined fault diagnosability and optimized the location of sensors to improve the detectability [32].
## 3 Results and Discussion  
The antibacterial mechanism for several ammonium salts, both in the form of low molecular weight and soluble compounds [37; 38; 39], and in the form of macromolecular soluble ammonium salts (polymers, copolymers, and dendrimers) [31; 38; 39; 40; 41; 42] has long been studied. The antibacterial activity of these cationic compounds is due to their ability to kill bacteria thanks to a sequential series of elementary processes. In particular, ammonium salts are first adsorbed on the bacterial cell surface by electrostatic interactions [38; 39] and then spread through the cell wall. As a result of their binding to the cytoplasmic membrane (CM), they significantly damage its integrity and cause the release of cytoplasmic constituents, such as K\({}^{+}\) ions, DNA, and RNA, thus determining the death of the bacterial cell [38; 39]. The antibacterial activity of soluble polymeric phosphonium salts of various structures, as a function of their positive charge density and the length of their chains, has been thoroughly studied. The results showed that precisely the positive charge density (responsible for hydrophilicity and greater solubility in water) and the length of their chains (responsible for the hydrophobic character) represent the key points of the biocidal capacity of cationic macromolecules. It has been shown that the high density of positive charge improves the ability of such molecules to interact with the negatively charged bacterial surface while long hydrophobic chains promote diffusion and strong interaction with CM, thus determining a higher antibacterial/bactericidal activity [38; 39]. Collectively, a correct balance between the hydrophilic and hydrophobic properties is desirable to have a remarkable antibacterial effect. To this end, to obtain a resin with intrinsic powerful bactericidal effects, we strategically used the same M4 cationic monomer that supplied the soluble copolymer P5 [31], endowed with powerful bactericidal activity and therefore an optimal hydrophilic-lipophilic balance (HLB). In this case, to obtain an insoluble resin, we copolymerized M4 with the same co-monomer used to prepare P5 but in the presence of a suitably selected crosslinker. Although, in the literature, it is suggested that polystyrene-based resins (PS resins) crosslinked with divinylbenzene (i.e., P(St-DVB) resins) may be a good choice as a matrix substrate for disinfection agents, including ammonium salts [26], to obtain a low-cost material to purify water with good mechanical strength, high porosity, and absorbency [25; 26; 27; 28; 29; 30], we cross-linked M4 and DMAA with AAEA. Hydrophilic AAEA was used instead of hydrophobic DVB to avoid impairing HLB of M4 and obtain a material that, although insoluble, would be able to interact well with water, by swelling, and therefore with any bacteria present.
## 5 Discussion  
### The Impact of Class Imbalance  
Our experiments revealed a persistent issue of class imbalance in plant datasets, which notably impacts the segmentation accuracy across various plant organ categories. This imbalance primarily stems from the disproportionate distribution of point counts among different organs. For instance, leaves, which are larger and more abundant, dominate the point count, whereas the main stem, being smaller and structurally simpler, contributes fewer points. This disparity often leads to segmentation errors, particularly at the junctures between stems and leaves, where models tend to misclassify boundary points or those in smaller proportions as leaves. As an example, the segmentation results of models on tomato point clouds derived from images were utilized, as illustrated in Table 5 and Figure 6. Our analysis indicates significant performance variations among segmentation models when processing different organ categories of the same crop. These results highlight the critical need for segmentation models to effectively address class imbalance, ensuring accurate representation and segmentation of all plant organs, regardless of their point count or  
Figure 9: Visualization of instance segmentation results by Mask3D. The circled areas indicate incorrectly segmented regions; GT represents the ground truth.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **AP** & **AP50** & **AP25** \\ \hline Soil & 1.000 & 1.000 & 1.000 \\ Maize & 0.817 & 0.994 & 1.000 \\ mAP & 0.909 & 0.997 & 1.000 \\ \hline \end{tabular}
\end{table}
Table 9: Performance metrics of Mask3D.  
structural complexity. Our analysis shows that segmentation models for tomato plants perform less effectively in identifying stems and fruits, with notably lower Intersection over Union (IoU) and accuracy (acc) values, compared to leaves. To explore this, we analyzed the total point counts and their proportions across all labeled categories in the tomato point cloud dataset derived from images. The results show leaves holding the highest point proportion at 0.734, stems at 0.198, and fruits the lowest at 0.068. This marked disparity suggests that imbalances in point distribution significantly impair model performance. However, altering these proportions to balance point counts is not feasible without compromising the natural morphological integrity of the plants, as different structural components inherently vary in size and complexity.
## 4 Experimental Results
### Result Analysis  
In order to compare the performance of different features from the time-frequency domain, wavelet packet space and EEMD space, the performance degradation index of rolling bearings is selected from different feature spaces, which then divided into the training data and the testing data. The experimental results of the WCDPSO-KELM model are shown in Figures 7-9, When rolling bearings run in different working condition, the performance index distribution does not exactly satisfy the same distribution condition, as can be seen in Figure 6.  
Figure 9: Actual outputs and prediction outputs in working condition 3. (**a**) Phase space; (**b**) wavelet packet space; (**c**) EEMD space.  
The performance degradation index of different feature spaces is input to the WCDPSO-KELM model to calculate the MAPE value. The MAPE values of the WCDPSO-KELM model are listed in Table 4 and shown in Figure 10.  
The experimental result shows the MAPE value of EEMD space is smaller than that of the time-frequency domain, wavelet packet space in same working conditions. Therefore, compared with the feature extraction method of wavelet packet or time-frequency algorithms, EEMD can effectively extract the features of the vibration signal of rolling bearings, and takes on higher efficiency.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 4 Model Solution
### Algorithm Flow  
The main steps of the variable neighborhood tabu search algorithm are as follows:  
Step 1. Let the number of iterations be \(K=0\). Randomly generate an initial feasible solution \(\Omega^{0}\), and calculate the initial objective function value \(\tau^{0}\) according to \(\Omega^{0}\) using in Equation (1). Initialize the tabu list \(\mathrm{T_{L}}\). Set both the current solution \(\Omega^{K}\) and the optimal solution \(\Omega^{B}\) as \(\Omega^{0}\), i.e., \(\Omega^{K}\)= \(\Omega^{B}\)= \(\Omega^{0}\), and set the current objective function value \(\tau^{K}\) and the optimal objective function value \(\tau^{B}\) as \(\tau^{0}\), i.e., \(\tau^{K}\)= \(\tau^{B}\) = \(\tau^{0}\). The number of iterations to obtain the feasible solution \(\Omega^{K}\) is \(X\).  
Step 2. Generate a neighborhood solution \(\Omega^{N}\) of \(\Omega^{K}\), and obtain its objective function value \(\tau^{N}\). Determine whether or not the movement operation for generating the neighborhood solution is in \(\mathrm{T_{L}}\). If yes, go to step 4; otherwise, go to step 3.  
Step 3. Set \(\Omega^{K}\)= \(\Omega^{N}\), \(\tau^{K}\)= \(\tau^{N}\). Update \(\mathrm{T_{L}}\). If \(\tau^{K}>\ \tau^{N}\), then set \(\Omega^{B}\)= \(\Omega^{K}\), \(\tau^{B}\)= \(\tau^{K}\), and go to step 4, otherwise, go to step 5.  
Step 4. If \(\tau^{N}>\ \tau^{B}\), cancel the tabu status, let \(\Omega^{K}\)= \(\Omega^{B}\)= \(\Omega^{N}\), \(\tau^{K}\)= \(\tau^{B}\)= \(\tau^{N}\), \(K\) = \(K+1\), and update \(\mathrm{T_{L}}\), go to step 5, otherwise, go to step 2.  
Step 5. If \(\Omega^{K+1}=\Omega^{K}\), let \(X=X+1\). Judge whether \(K\geq\ H_{2}\) or \(X\geq\ H_{1}\) is true; if yes, go to step 6; otherwise, go to step 2.  
Step 6. Output the optimal solutions \(\Omega^{*}\), \(\Omega^{*}\)= \(\Omega^{B}\), and the corresponding optimal objective function value is \(\tau^{*}\)= \(\tau^{B}\).
## 3 Experimental Results
### Performance Evaluation
#### 3.3.3 Dataset of 1200 Videos  
In Table 3, the 1200-video dataset was used to train, validate, and test the models. It can be observed that SynthSecureNet outperformed both MobileNetV2 and ResNet50V2. The results in Table 3 were obtained during the testing phase, and the training duration that was observed during the training phase is also included in Table 3.  
The SynthSecureNet architecture achieved an F1 score of 98.31%, a recall of 98.25%, a precision of 98.37%, and an accuracy of 98.15%, demonstrating a high level of performance for this model in violence detection. On the other hand, both ResNetV2 and MobileNetV2 achieved an F1 score of 91%, indicating that the level of reliability of both models is the same.  
The training duration of the SynthSecureNet model (46 min) took longer than that of ResNet50V2 (26 min) and MobileNetV2 (19 min).  
Figure 4 shows the confusion matrices obtained in the testing process. SynthSecureNet made 7374 correct predictions and only 139 wrong predictions, demonstrating its effectiveness in accurately classifying violent and non-violent activities.
## 2 Basic Theory and Method
### Adjacent Singular Value Difference Method  
The basic principle of the singular value decomposition (SVD) method is as follows: the covariance matrix \(R_{s}\) of n signal sources \(s(t)\) is a square matrix of n*n, and the number of linearly independent columns (rows) is the number of uncorrelated sources (NIS) of \(s(t)\). In general, the covariance matrix \(R_{s}\) cannot be directly obtained (because the source signal \(s(t)\) is unknown), but the observation signal covariance matrix \(R_{x}\) can be directly obtained. Therefore, the SVD method is conducted through \(R_{x}\) to estimate the number of unrelated sources.  
After singular value decomposition, a series of singular values \(\Lambda_{IMF}=diag[\lambda_{1}\geq\cdots\lambda_{n}\geq\lambda_{n+1}\geq \cdots\lambda_{M}\geq 0]\) is obtained. Then, a set of adjacent singular values \(\Lambda_{i,j}=\lambda_{i}-\lambda_{j},i=1,2,\cdots,M-1,j=i+1\) is obtained. When one or both of the \(\lambda_{i}\) and \(\lambda_{j}\) values are singular values of fault signals, \(\Lambda_{i,j}\) is larger. When both \(\lambda_{i}\) and \(\lambda_{j}\) values are singular values of noise signals, \(\Lambda_{i,j}\) is relatively smaller. The difference of the adjacent singular values is detected, the minimum difference corresponds to the interface between the fault signal and noise subspace, and the number of sources can then accordingly be estimated.
## 4 Control Strategies
### Gain Scheduling Control  
The control performed by gain scheduling originated in the development of flight control systems and is considered an adaptive system, i.e., a controller that can modify its behavior in response to changes in the process dynamics and disturbance characteristics--it is a model capable of adapting quickly [41; 42]. It is classified as a feedback control system, in which gains are adjusted using feedforward compensation [43].  
The key point in developing this method is to determine the auxiliary variables that have a good correlation with changes in the process dynamics. Figure 7 represents the logic for the implementation of a gain scheduler.  
Therefore, by changing the parameters to be functions of auxiliary variables, it is possible to obtain the best adaptation of the compensator to different operating conditions of the plant. The following steps make it possible to implement a gain scheduler:  
* Obtain a valid representation or approximation of the nonlinear system for each operating point and perform its linearization;
* Apply linear control techniques to develop a controller that meets the needs of the linearized system;
* Choose the architecture of the gain scheduler (rules used in transitions);
* Validate the performance of the controller.  
It is important to note that gain scheduling is open-loop compensation, so there is no feedback to correct incorrect programming. Additionally, it is worth emphasizing the abundance of operating conditions that must be analyzed to determine the controller's parameters.  
Figure 7: Block diagram representing gain scheduling.  
The main advantage of applying this technique is its rapid response to changes in the process. Since there is no estimation of parameters, only selection structures, the limiting factors depend solely on the response speed of the auxiliary measurements to changes in the process [44; 45].