esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Prediction of HL by the Artificial Bee Colony (ABC)-ANN Model  
For the HL predictions by the ABC-ANN model, a process of the development of the hybrid model was conducted, similar to those models above (e.g., ICA-ANN, PSO-ANN, GA-ANN). Accordingly, the ABC algorithm was applied to optimize the parameters of the selected ANN model (i.e., ANN 8-24-18-1 model), for predicting HL of EEB systems. The initial setting for the ABC algorithm is necessary, as with those set for the previous models (e.g., ICA-ANN, PSO-ANN, GA-ANN), including the number of bees (\(N_{\textit{bees}}\)), the number of food sources (\(N_{\textit{foodsource}}\)), the limit of a food source (\(M_{\textit{foodsource}}\)), the boundary of the parameters (\(b\)), and the maximum number of repetitions for optimization (\(nround\)). Similar to the GA, PSO, and ICA, a TAE procedure for in the ABC algorithm was conducted, with \(N_{\textit{bees}}\) = 100, 200, 300, 400, 500, respectively. The other parameters of the ABC algorithm were set as follow: \(N_{\textit{foodsource}}\) = 50; \(M_{\textit{foodsource}}\) =100; \(b\) = [\(-\)10;10], and \(nround\) = 1000. Once the parameters of the ABC algorithms were established, the initialization ANN model 8-24-18-1 model was optimized by the global search of the bee colony. RMSE was also used to evaluate the efficiency of the optimization of the ABC-ANN model, with the optimal ABC-ANN model corresponding to the lowest RMSE. Figure 18 presents the performance of the optimization process of the ABC-ANN model in estimating the HL of EEB systems. Finally, the optimal ABC-ANN model was defined with the optimal weights and biases, as shown in Figure 19.  
Figure 17: HL predictions on the testing dataset of the ICA-ANN model.  
Figure 18: Artificial bee colony (ABC)-ANN performance for estimating HL of EEB systems in the training process.  
It should be noted that although Figures 7, 11, 15 and 19 are the same, their structure is different since the weights and biases of the models are different. In addition, similar techniques as those used for the ICA-ANN, PSO-ANN, and GA-ANN models were also applied for the development of the ABC-ANN model (i.e., back-propagation algorithm, min-max scale [\(-\)1,1]). Figure 20 shows the HL predictions of the ABC-ANN model on the training dataset. Then, 165 experimental datasets were predicted based on the developed ABC-ANN models, as shown in Figure 21.  
Figure 19: Structure of the ABC-ANN model for estimating HL of EEB systems.  
Figure 20: HL predictions on the training dataset of the ABC-ANN model.
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
## 2 Methods  
### Hyperbolic Radon Transform  
In the layered subsurface model, the reflection events fit the hyperbolic trajectory in the common midpoint (CMP) gather, \(d(t,x)\). Therefore, the hyperbolic Radon transform can be used to process the CMP gather data. The transform formula is as follows  
\[m\big{(}\tau_{i},p_{j}\big{)}=\sum_{k=1}^{K}d\Big{(}t=\sqrt{\tau_{i}^{2}+p_{j} ^{2}x_{k}^{2}},x_{k}\Big{)}dx \tag{1}\]  
\[d(t_{i},x_{k})=\sum_{j=1}^{J}m\Big{(}\tau=\sqrt{\tau_{i}^{2}-p_{j}^{2}x_{k}^{ 2}},p_{j}\Big{)}dp \tag{2}\]  
where \(t\) is the zero-offset two-way time, \(x\) is the offset, \(m(\tau,p)\) is the hyperbolic Radon domain, \(\tau\) is intercept time and \(p\) is the parameter that defines the curvature. \(k=1,2\ldots K\), \(K\) is the number of the offset \(x\), \(n=1,2\ldots N\), \(N\) is the number of the curvature \(p\) and \(i\) is the discretize sequence number for the time \(t\). Generally, in seismic data processing, the hyperbolic Radon transform first defines the inverse transform and then obtains the forward transform formula using the least-squares principle. In matrix-vector notation, the inverse hyperbolic Radon transform formula can be written as:  
\[\mathbf{d}=\mathbf{L}\mathbf{m} \tag{3}\]  
where \(\mathbf{d}\) is the CMP gather, \(\mathbf{L}\) is the forward hyperbolic Radon transform operator and \(\mathbf{m}\) is the hyperbolic Radon model. The objective function of the least-squares solution is as follows:  
\[J=||\mathbf{d}-\mathbf{L}\mathbf{m}|| \tag{4}\]  
The least-squares solution of the forward hyperbolic Radon transform is:  
\[\mathbf{m}=\left[\left(\mathbf{L}^{T}\mathbf{L}\right)^{-1}\mathbf{L}^{T} \mathbf{d}\right] \tag{5}\]  
\(\mathbf{L}^{T}\) is the adjoint hyperbolic Radon operator. A sparse constraint in the time domain can improve the resolution of the Radon panel. To obtain a high-resolution result, the mixed normed is introduced to constrain the inversion. The optimization problem can be written as:  
\[J=||\mathbf{d}-\mathbf{L}\mathbf{m}||_{2}^{2}+\lambda||\mathbf{m}||_{1} \tag{6}\]  
where the \(l_{2}\) norm constrains the misfit of the inversion, the \(l_{1}\) norm constrains the sparsity of the solution and \(\lambda\) is the tradeoff parameter, which controls the sparsity of the Radon panel. If \(\lambda\) increases, then the sparsity of the Radon panel would be improved, that is, a high resolution of the Radon panel would be obtained. However, due to averaging along the offset direction, the sparsity of the Radon panel does not preserve the AVO.
## 3 Results
### Effectiveness Analysis of the PSCDR Method  
Table 2 shows the performance of common regression loss functions and the proposed PSCDR method on a range of well-known point cloud models, as well as the proposed BMP_Net. In many models, when the loss function is replaced with the proposed total loss function \(\Lambda_{\text{sum}}^{\text{PSCDR}}\), the performance of most models improves on the SGCBP dataset. This result is expected because, in regression tasks, the core objective is to predict target values from input data. Regardless of the data modality, the data is encoded into feature vectors by the encoder in the network. By considering the differences between label values and feature values, and adopting a regression-like approach, it is possible to achieve some level of clustering and relative positioning of positive and negative samples in the feature space.  
While PSCDR considers these factors, it also takes advantage of the unique characteristics of point cloud data by using Chamfer distance to measure the similarity between two point cloud samples. The information contained in the feature vectors produced by the encoding process is determined by the model's point cloud encoder. When using common regression loss functions, both PointMixer and the proposed BMP_Net also perform well.  
However, the performance improvement of the PSCDR method on the DGCNN model is not significant. This may be because the DGCNN model utilizes graph convolution operations based on local connections, which primarily focus on capturing local structural information of the point cloud data. This local connectivity approach may limit the model's ability to capture global features of the point cloud data. Although the PSCDR method considers the similarity between global features, the local connectivity nature of the DGCNN model might not fully leverage the global feature similarity loss in the PSCDR method, thus restricting the performance improvement of PSCDR on this model.
## 4 Analyses of the Applications of Digital Twin Models
### Medicine  
The Digital Twin applicability in medicine has become a subject of interest in recent years, the proof being represented by the number of articles published in this field. Thus, the DT can contribute to the development of personalized medicine by continuous monitoring of the patient's health and identifying pathologies from the incipient phases. Also, different pathological scenarios or treatment suggestions can be simulated. The results of the search for the concept of \"Digital Twin in medicine\" show a minimum number of articles on IEEE Explore--114, followed by 161--Web of Science, 307--Scopus, 273,000--Google Scholar. Thus, the presence of a total number of 273,582 articles suggests a tendency to coagulate interests in personalized medicine.
## 2 Methods
### Trajectory Preprocess  
#### 2.1.1 Trajectory Preprocessing Structure  
In order to implement the online preprocessed work, we designed a ship trajectory preprocessing structure (Figure 2). The structure contains three parts: the original points queue, the compression path tree, and the sample points queue. The original points queue is responsible for storing uncompressed points and cleaning the trajectory. The compressed path tree describes the shortest path through which each point can reach the first point, and it can be constructed using the trajectory simplification algorithm. The sample points queue stores sample points used to form simplified trajectories [30]. When a complete simplified trajectory is formed, it is output to the cluster algorithm. The details of these three parts are given in Sections 2.1.2-2.1.4.  
Figure 2: Trajectory preprocessing structure. MMSI is used as the unique identification code to exchange data. ID is used to identify a single trajectory. The original points queue is used to store the trajectory points, noise points are removed at this stage. The compression path tree is used to output the simplified trajectory, redundant points are removed at this stage. The sample points queue is used to store the simplified trajectory, when a stay point is detected, a complete simplified trajectory is output for clustering. The black circle represents a new received trajectory point. The dark blue circles represent the points ready to be cleaned. The green circles represent the sample points of the simplified trajectories. The gray circles represent noise points or redundant points. The red circles represent the stay points.
## 3 Results
### HT-qPCR Analysis
#### 3.1.2 Antibiotic Resistance Genes and Drug Classes  
A total of 122 antibiotic-resistant gene elements (100 ARGs, 5 integrins, and 18 MGEs) were present in the 12 sediment samples collected from Kuwait's shoreline. The genes originated from seven major drug classes such as aminoglycos, beta-lactam, phenicol, sulfonamide, tetracycline, trimethoprim, macrolide limcosamide streptogram B (MLSB), quinolone, and others. The most abundant were beta-lactamase (23%) with 25 gene IDs namely \(\mathit{bla}_{\mathit{TEM},1}\), \(\mathit{bla}_{\mathit{CARB}}\), \(\mathit{bla}_{\mathit{GES}}\), \(\mathit{cfxA}\), \(\mathit{bla}_{\mathit{OXASES},2}\), \(\mathit{bla}_{\mathit{ROB}}\), \(\mathit{bla}_{\mathit{VEB}}\), \(\mathit{cphA}_{\mathit{VEB}}\), \(\mathit{cphA}_{\mathit{J},1}\), \(\mathit{bla}_{\mathit{MRR}}\), \(\mathit{cphA}_{\mathit{2}}\), \(\mathit{bla}_{\mathit{CMY},2}\), \(\mathit{bla}_{\mathit{SHY11}}\), \(\mathit{bla}_{\mathit{OXA10},1}\), \(\mathit{bla}_{\mathit{CMY},1}\), \(\mathit{bla}_{\mathit{MOX}}\)/\(\mathit{bla}_{\mathit{CMY}}\), \(\mathit{bla}_{\mathit{BL-nonmobile}}\), \(\mathit{ampC}_{\  
and _aphA3\(1\). Tetracycline (_tetPA_, _tetA_2_, _tetX_, _tetW_, _tetM_, _tetQ_, _tetG_, _tetO_2_, _tet32_, _tet39_, _tet44_, _tetL_2_, and _tetE_) and MLSB (_InuF_, _mefA_1_, _meFA_1_, _emB_2_, _ereA_, _InuC_, _ermB_3_, _meFB_, _ermF_, _erm42_, _mphA_, _IsaC_, and _msrE_) were next in order with 13 genes (12%) each. A total of eight genes (7%) were detected in the drug class of phenicol (_cmLA_2_, _catQ_, _cmLA_4_, _catB3_, _catB8_, _catB2_, _matdL_, and _floR_1_). Six genes (5%) were confirmed in trimethoprim (_drA1\(1\), _drA15_, _drB_, _drA17_, _drA27_, and _drA12_) and quinolone (_qnrC1_, _vC3_, _vC6_, _qnrS_1_, _qnrB_2_, _qnrB_, and _qnrD_) each. In sulfonamide 5 genes (3%), _sul1\(1\), _sul1\(2\), _sul2\(1\), _sul2\(2\), and _sul4_ were recorded. About eight genes (7%) _merA_, _qacE_A_3_, _qacE_A_1_1, _ar2_, _crAss64_, _crAss56_, _sat4_, and _mcr1_ were classified as others. Mobile genetic elements (_tmpA_1_, _tmpA_6_, _tmpA_2_, _ISPs_, _ISI247_-2_, _tmpA_3_, _lmcporiT_, _tmpA_5_, _ISI247_1, _tmpA_4_, _IS613_, _IncN_rep_, _Tp614_, _ISAba3_, _IncQ_oriT_, _or37-IS26_, and _Tn5_) and integrons (_intl3_, _intl2_, _intl1_, _intl3_, and _intl3_) were also found along with the ARGs resistant to above drug classes. All the ARGs are listed in Table 1. The relative abundance of each gene ID is given in Table S1.
# Space-Efficient, Fast and Exact Routing in Time-Dependent Road Networks +
## 2 Materials and Methods
### Preliminaries
For each node \(v\), instead of the earliest arrival time a tentative travel time function \(f_{v}\) from \(s\) to \(v\) is maintained. Initially, \(f_{v}(\tau)\) is set to \(\infty\) for all \(\tau\in T\) and to zero for \(s\). In the priority queue, nodes are ordered by the current lower bound of their travel time function. The queue is initialized with \((s,0)\). When a node is popped from the queue, outgoing arcs are relaxed. Here, relaxing an arc \(uv\) means linking \(f_{u}\) with \(f_{uv}\) and merging the result with the travel time function of \(v\): \(f_{v}=\min(f_{v},f_{uv}\circ f_{u})\). If the travel time to \(v\) can be improved, its label and queue position will be updated accordingly. This algorithm is not label-setting. Nodes may be popped several times from the queue. The algorithm can terminate as soon as the minimum key in the queue is greater than \(\max_{\tau\in T}f_{t}(\tau)\). We refer to this algorithm as _TD-Profile-Dijkstra_. The \(A^{*}\)_algorithm_[11] is an extension to [PERSON]'s algorithm. It reduces the number of explored nodes by guiding the search towards \(t\). Each node \(u\) has a potential \(\rho_{t}(u)\) which is an estimate of the distance to \(t\). The priority queue is then ordered by \(\mathrm{ea}_{u}+\rho_{t}(u)\).
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 5 Discussion  
Within the framework of the scheme that has been proposed, we present a proposal for a GEO satellite signal that is equipped with an IRS, together with the necessary channel modeling and system characterization. The characteristics of the GFN were evaluated to classify M-QAM and M-PSK signals. In the last step of the process, a performance evaluation of the proposed classifier structure was carried out both with and without the IRS. It used the cascaded channel for the modulation classification, and from Table 5, it is clear that the performance of the proposed classifier is better than that of existing classifiers. As mentioned earlier in the Contributions section, the proposed method is, to the best of our knowledge, the first of its kind; it employs the cascaded channel to classify the modulation. Existing classifiers make use of AWGN and fading channels such as Rayleigh and Rician, but in this scenario, the cascaded channel is the one that is being examined.
# SPEKS: Forward Private SGX-Based Public Key Encryption with Keyword Search
## 5 Analysis
### Security Analysis
In order to guarantee forward privacy, the past search queries should not be directly associated with the updated files. In our SPEKS scheme, we use a search counter that is supposed to be updated after each search. The search queries are generated only with private key and the search counter. Since the ciphertexts are created with the current newly updated search counters, past queries generated with past search counter values cannot match with newly updated files. Therefore, forward privacy is guaranteed in our proposed scheme.
# Active Contours in the Complex Domain for Salient Object Detection
## 2 Related Work
### Chan-Vese Model  
[PERSON] and [PERSON] [29] define a region-based model under the framework of Mumford-Shah [42]. The Chan-Vese (CV) model is based on the level set. The contour of the object corresponds to the zero-level set of the Lipschitz function \(C=\Big{\{}x\in\Omega:\phi(x)=0\Big{\}}\). [PERSON] and [PERSON] defined the energy minimization functional as  
\[E^{cv}(C,c_{1},c_{2})=\mu.L(C)+\upsilon A(C)+\lambda_{1}\int_{x\in In(C)} \big{|}I(x)-c_{1}\big{|}^{2}dx+\lambda_{2}\int_{x\in Out(C)}\big{|}I(x)-c_{2} \big{|}^{2}dx \tag{6}\]  
where \(\mu,\upsilon,\lambda_{1},\lambda_{2}\) are positive weights, \(L\) extracts the contour length and \(A\) counts the region enclosed by the contour. The constant values \(c_{1}\) and \(c_{2}\) are the average intensities for the foreground and background, respectively. The average intensity \(c_{1}\) is used for the region inside the contour, \(In(C)=\Big{\{}x:\phi(x)>0,x\in\Omega\Big{\}}\) and \(c_{2}\) for the region outside the contour, \(Out(C)=\Big{\{}x:\phi(x)<0,x\in\Omega\Big{\}}\). Given the level set function, we can obtain \(c_{1}\) and \(c_{2}\) as then, the optimal solution for the CV model is as follows  
\[c_{1}=\frac{\int_{x\in\Omega}I(x).H(\phi)dx}{\int_{x\in\Omega}H(\phi)dx} \tag{7}\]  
\[c_{2}=\frac{\int_{x\in\Omega}I(x).(1-H(\phi))dx}{\int_{x\in\Omega}(1-H(\phi))dx} \tag{8}\]  
\[\frac{\partial\phi}{\partial t}=\partial\phi\Bigg{[}\mu\
abla\Bigg{(}\frac{ \
abla\phi}{\big{|}\
abla\phi}\Bigg{)}-\upsilon-\lambda_{1}(I-c_{1})^{2}- \lambda_{2}(I-c_{2})^{2}\Bigg{]} \tag{9}\]  
where \(\
abla\) is the gradient operator, \(H(\phi)\) is the Heaviside function, and \(\delta\phi\) is the Dirac delta function. \(\lambda_{1}\) and \(\lambda_{2}\) are fixed parameters, \(\mu\) controls the smoothness of the zero-level set, and \(\upsilon\) is used for speeding up the dynamic contour. The driven force is controlled by \(\lambda_{1}\) and \(\lambda_{2}\). The Heaviside function \(H\) and Dirac measure \(\delta\) are given by \(H(z)=\left\{\begin{array}{cc}1,&z\geq 0\\ 0,&z<0\end{array}\right.\) and \(\delta(z)=\frac{d}{dz}H(z)\), respectively. They are usually approximated by  
\[\left\{\begin{array}{cc}H_{\varepsilon}(z)=\frac{1}{2}\Big{(}1+\frac{1}{2} \mathrm{arctan}\Big{(}\frac{z}{\varepsilon}\Big{)}\Big{)},&z\in R\\ \delta_{\varepsilon}(z)=\frac{1}{\pi}\frac{\varepsilon}{\varepsilon^{2}+z^{2 }}.&\end{array}\right. \tag{10}\]  
The CV model has constant intensities for the background and foreground. Although it is robust against noise, it fails when inhomogeneity is present.
###### Abstract  
Background. The aim of this study was to propose and establish the proof of concept of an approach to synchronize 3D Electromagnetic Articulography (3D-EMA) with Surface Electromyography (SEMG) based on the standard components of this equipment. Methods. The appropriate equipment and instruments were selected according to specifications stablished for this study. Once the necessary equipment was gathered, the proper conditions to synchronize the signals were created. Thus, we selected a SEMG with a switch module incorporated to be able to achieve synchronization of the signals. After the system setup was stablished, chewing tasks were recorded on a healthy volunteer, collecting a proof-of-concept database. The variability among recordings of the database were analyzed in terms of its standard deviation in order to detect possible interferences. Results. The analysis of the chewing task recordings obtained with the synchronized 3D EMA and SEMG signals in the present study did not reveal significant distortions, and all values were within those that had been given by the manufacturers of both of the systems. The method presented the advantage of using only components that are already included with the equipment employed. Conclusion. The method of analysis described in this paper is an effective tool that facilitates the investigation of mandibular movements synchronized in two domains: articulatory movements and electromyographic activity. Thus, it seems promising that it can be applied in different clinical situations to improve the analysis of the complexity of masticatory activity in addition to being able to generate new insights on this topic.  
electromagnetic articulography; surface electromyography; biomechanics; mandible; chewing
## 3 Methodology
### Alternatives Ranking Employing Different MCDM Approaches  
In the present study, four MCDM methods were chosen for the assessment of alternatives: ARAS [29], SAW [37], TOPSIS [27], and PROMETHEE [28]. The same decision matrix D and AHP-MEREC weight vector \(w^{*}_{j}\) (\(j=1,2,\ldots,m\)) should be applied for these calculations. All the applied methods are described briefly in Table 2.  
The Method Stability Coefficient Calculation to Analyse Mcdm Robustness toward the Variation in the Parameters  
The practical significance of mathematical models involves their robustness toward the chosen parameters. In this paper, the solution for aggregating the alternative ranking results produced by \(\tau\) different MCDM methods is proposed. The method stability coefficients \(\Phi_{\tau}\) can be calculated to determine the complex final ranking of the alternatives. It can be interpreted as its sensitivity to the variation of the parameters provided for it. Consider \(D=\left(d_{ij}\right)\) as the decision matrix where \(i=1,\dots,n\) are the alternatives assessed against the criteria \(j=1,\dots,m\). The importance of separate criteria is denoted as the weight vector \(\omega=\left(\omega_{j}\right)\), where \(j=1,\dots,m\) and the sum of the weights is equal to 1. The following steps can be used to describe the proposed solution:  
Step 1. Primary data processing. The decision matrix \(D\) and the weight vector \(\omega\) are used to assess alternatives \(A_{i}\), \(i=1,\dots,\ n\) using \(\tau\) different MCDM methods. Step 2. For reach of the \(\tau\) approaches, data matrices \(M=\left(m_{vi}\right)\) are created to store the results. In each of them, \(i=1,\dots,\ n\) defines the alternatives assessed, and rows \(v=0,\dots,\ 10,\!000\) determine their numerical values gathered by applying the specific MCDM method to the original decision matrix \(D\) and its variations. Step 3. Obtaining a modified decision matrix \(D^{*}\) and weight vector \(\omega^{*}\) with slight changes. Without knowing the distribution of the parameters, a uniform distribution is applied to generate random values of \(x_{new}\) from the \([X_{min},\,X_{max}]\) interval, where \(x_{new}=X_{min}+\overset{\sim}{q}_{\xi}\cdot(X_{max}-X_{min})\), \(\overset{\sim}{q}_{\xi}\in[0,1]\).
# A Case Study of a Large Unstable Mass Stabilization:
## 2 Materials and Methods
### Corrective Measures  
A series of corrective measures were proposed to be performed on the slope to stabilize the landslide. Those measures (Figure 3) were not aimed to stop the hillside movement immediately and absolutely but to slow it down and prevent any damage to surrounding infrastructures. Eventually, the measures projected were expected to completely stabilize  
Figure 2: Location of the boreholes performed in the previous study [15] (“old” boreholes, conducted in 2005, reprinted with permission from ref. [15], Copyright Year 2005, Copyright Owner’s [PERSON]) and the new geotechnical investigation (“new” boreholes, conducted in 2016); profile I-I\({}^{\prime}\) relates to Figure 5.  
the hillside and stop the movement (or reduce it to a minimal value) after some years' time. Corrective measures applied included:  
* A slight modification of the slope geometry by means of the excavations of intermediate areas (benches) on the slope.
* Installation of deep drainage measures (Californian drains) as well as ditches to improve slope drainage and remove water coming from seepage and runoff; in total, 47 single drains were installed with a separation of 15 m; the horizontal slope was 6% following Serrano and Gomez [29] and Forrester [30]. Drains were executed with slotted PVC pipes with a thickness of 1.5 mm and a diameter of 75 mm.
* Implementation of slope conservation and stabilization measures such as slope profiling and re-vegetation.
* Performance of a flexible retaining wall at the slope toe; wall height was 6 m from the ground and 9.2 m from the bottom of the foundation and its width was found between 3.5 and 4.1 m; its vertical slope was 6:10.