esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 3D FDEM Modeling of Demolition of a Small-scale RC Structure  
### Modeling Procedure of Small-Scale RC Structure Collapse  
In this section, we show the blasting demolition of a small-scale RC structure model to investigate the applicability of the developed 3D FDEM code with a rebar model to the RC structure demolition. As shown in Figure 14a, the RC structure is modeled by piling up to three stories as a \(1\times 1\) bay structure, including one slab, four beams, two sub-columns and two main columns. The ground was also modeled as a fixed thin rigid plate consisting of TET4s to sustain RC structure. The rebar is embedded in each part of the RC structure, as shown in Figure 14b. The RC structure is discretized as 22,624 of TET4s for concrete, and the RE and RJE rebar elements are discretized as 841 and 1680, respectively. Although the developed code can deal with a massive number of elements using GPGPU, here, the main focus is not on the computational performance but rather on verification and validation using small-scale models, and the FDEM simulation of demolition of massive RC structure is considered as our future work. The input parameters listed in Tables 2 and 3 are again used to simulate the demolition of RC structure. The numerical simulation is performed with two stages (i.e., initial static analysis and dynamic analysis). At the first stage, to realize the initial static stress state in the RC structure under gravity, the gravity acceleration (=9.81 m/s\({}^{2}\)) is applied to the RC structure along the negative y-direction in Figure 14 until the stress equilibrium statement is achieved. Here, to accelerate the convergence to the static stress state, the local damping scheme coupled with an optimized mass scaling technique is applied [30]. Then, at the second stage for dynamic analysis, to mimic the real blasting demolition of the RC structure, its two-main columns in the first floor are instantaneously removed at the beginning of the simulation, which triggers the collapse of the RC structure due to the loss of stress equilibrium. During the dynamic analysis, gravity is remained to be applied to the RC structure.
# Structure Design, Kinematics Analysis, and Effect Evaluation of a Novel Ankle Rehabilitation Robot
## 1 Introduction
Third, evaluations of patients' comfort, safety, and training effects are lacking. An ankle rehabilitation robot with all degrees of ankle movements and without axis offset is needed to greatly improve the safety in rehabilitation movements. In this work, we propose a novel hybrid manipulator for ankle rehabilitation based on a 2-CRS+PU parallel mechanism, which can generate rotation analogous to human ankles without axis offset and provide all degrees of freedom according to the ankle movement mode. Meanwhile, the robot can meet the size requirements of different adults' lower limbs. The remainder of this paper is organized as follows. Section 2 proposes the type synthesis and structure design based on the ankle joint movement model. Section 3 analyzes the kinematics, including inverted solutions and the Jacobian matrix. Section 4 simulates the performance of the mechanism used for the rehabilitation exercise. Section 5 analyzes the dexterity, with the relevant parameters. Section 6 evaluates the patients' comfort and rehabilitation effects using AnyBody software (Thaitu Software Technology Co., LTD, 2019 (Shanghai, China)). Finally, conclusions are drawn in Section 7.
## 5 Results and Discussion  
### IGBP Stable Time Series  
Table 2 shows results of applying SCD and BFAST to the IGBP stable time series (_i.e._, no land-cover change over time) for each land-cover type. The values of the two parameters in SCD were 0.01 for the significance level (\(\alpha\)) and 2 for the scale factor (\(\beta\)) based on training with one-fifth of the stable time series. False changes using SCD are very few (zero or one) for each land-cover type given the size of test samples. In contrast, the BFAST method detected more false changes. This indicates that the SCD does not tend to flag false land-cover change.
## 4 Experiments
### Experimental Setup
#### 4.2.2 Implementation Details  
From the training set \(D_{r}\), we sample a subset of \(U=20\) images to construct the state representation. To extract data from these images, a Convolutional Neural Network (CNN) feature extractor is utilized, which is then concatenated into a state. The policy network's action corresponds to the index number of the chosen image within \(U\). The split \(D_{r}\) is used to optimize the hyper-parameters, which are chosen based on the best configuration for our technique as well as the baseline methods, and to create the rewards for the DQN. The MSE difference between the victim models' and the surrogate model's predictions is what determines the reward, and it is always negative. In order to solve the overestimation problem inside the policy network, we employ \(10\) surrogate models and calculate the reward by computing the mean of 10 MSE. The victim model selected for our study is ExquisiteNetV2 (2021) [60], which achieved an accuracy of 99.71% on the MNIST dataset.  
In synthetic data generation attacks, we continue to utilize images from \(D_{r}\). An image is randomly selected for perturbation using the previously mentioned techniques. This perturbed image is then fed into the victim model to produce a prediction. Subsequently, the surrogate model is trained using pairs of the perturbed image and its corresponding prediction. Regarding the data-free model extraction method, we employed randomly generated noise images as inputs to train the surrogate model, while the remaining implementation followed a similar approach to the other techniques. Additionally, we employed the entropy method, a purely active learning approach devoid of reinforcement learning, to serve as a benchmark against the synthetic data generation techniques.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 4 Quantitative Model
### Indexed Probability Model  
The QIS of N frame speech is denoted as  
\[QIS=\left\{S_{1},S_{2},\cdots,S_{j}\cdots,S_{N}\right\},S_{j}\in[0,255],j\in[1,N] \tag{4}\]  
Any one of the index values in the index sequence can get any integer value between 0 and 255, which can be expressed as  
\[S_{j}=k,k\in[0,255],P\left(S_{j}=k\right) \tag{5}\]  
\[P\left(S_{j}=k\right)=\frac{1}{N}\sum\left(S_{j}=k\right) \tag{6}\]  
where \(\sum\left(S_{j}=k\right)\) indicates the number of occurrences of the index \(S_{j}=k\) in N speech frames. It can calculate the probability distribution matrix \(A\) according to the index.  
\[A=\left(P(S_{1}),P(S_{2}),\cdots,P(S_{j}),\cdots,P(S_{N})\right)^{T}\] \[= \left(\begin{array}{cccc}P(S_{1}=0)&P(S_{1}=1)&\cdots&P(S_{1}= 255)\\ P(S_{2}=0)&P(S_{2}=1)&\cdots&P(S_{1}=255)\\ \vdots&\vdots&\vdots&\vdots\\ P(S_{N}=0)&P(S_{N}=1)&\cdots&P(S_{N}=255)\end{array}\right) \tag{7}\]  
Figure 2: Quantization sequence distribution.  
The \(\mathbf{A}\) is N \(\times\) 256, which represents the probability of the index value. It shows the long-term characteristics of the index distribution.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 2: OCSVM + Deep Autoencoder (dAE)
#### 4.3.1 Input Data and Classification  
In the context of normal traffic, represented by zDATA\"[1], the output from the OCSVM classifies normal traffic, supplying essential data for the dAE neural network, and the dAE emerges as a deep learning classifier. The dAE is trained primarily on non-attack packet data, improving its capacity to identify normal patterns and behaviors effectively.
## 4 System Model  
### Methodology  
This section describes the proposed ISO strategy for future ICN networks in TL. The proposed strategy achieves maximum gain when the stretch is least that increases the QoT, QoS, and QoE. Thus, it only discusses the stretch reduction via following the optimal path from consumer to producer and vice versa. Conclusively, we proposed a reinforcement learning-based model, and the primary goal is to reduce stretch for the ICN framework. Firstly, we formulate the markov decision process (MDP) of our system and then solve it with the help of Q-learning. We have described RL, Q-learning, and MDP, which we have used as a learning approach for our problem statement. Moreover, we describe our system model in detail while considering a case scenario of AR/VR application to understand our approach better.  
Our ISO algorithm mainly focuses on selecting the optimal path, i.e., the path with fewer hopes or ICN routers from RCR to BS. As RL learns from the experience, it will converge to a better policy after iterations. Thus, proposed ISO algorithm aims to reach the optimal routing policy after learning from different routing paths. Our scenario has the following entities:  
* AR User/ VR User
* A content router in RCR having the contents that are of interest of a user
* BS  
Our proposed system model has been shown in Figure 3. We supposed that router \(H_{1}\) has the content according to the user connected with router \(H_{9}\). We have observed the mesh network of routers in RCR. All the routers \(H_{1}\)-\(H_{9}\) are connected just like a mesh network. Consumers are associated with BS1. A mesh topology connects almost every routing device. The total number of hops encountered in each step by the agent from its present or current hope \(H_{1}\) to terminal hope \(H_{9}\) helps to compute the stretch reward \(S_{ij}\).
###### Abstract  
Crystal-based X-ray interferometry (CXI) detects X-ray phase shifts by using the superposition of waves, and its sensitivity is the highest among the other X-ray phase-detecting methods. Therefore, phase-contrast X-ray imaging (PCXI) using CXI has the highest density resolution among the PCXI methods and enables fine, non-destructive observation with a density resolution below sub-mg/cm\({}^{3}\). It has thus been applied in a wide range of fields, including biology, medicine, geology, and industry, such as visualization of the testis and brains of aged rats with tumors, human embryos at each Carnegie stage, air hydrates in old Antarctic ice, and ion distribution in electrolytes. Novel imaging methods have also been developed to take advantage of its high sensitivity, such as visualization of the effective atomic number (\(Z_{\text{eff}}\)) and the three-dimensional temperature of samples. This article reviews the principles and history of PCXI and crystal-based X-ray interferometers, as well as a CXI system using synchrotron radiation and its potential applications from biomedical to industrial.  
X-rays; crystal-based X-ray interferometer; phase-contrast X-ray imaging; synchrotron radiation +
Footnote †: journal: Physics Letters B
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 4 System Design
### Frame Design  
In other NVIS studies, such as [8; 9; 49; 50; 51], narrowband modulations have proven that communications are quite reliable in a wide range of scenarios. So far, HF standards (military or otherwise) have used 3 kHz channels with single-carrier modulations such as PSK and QAM. An OFDM protocol was designed and tested in [47] for low-power applications with this same channel bandwidth.  
The SC-FDE design follows almost the same structure as OFDM, only the SC-FDE concept distributes the symbols shorter in the time domain but longer in the frequency domain. The symbol length was calculated by simply dividing the symbol length defined in [20] by the number of subcarriers defined due to the coherence bandwidth. We kept the same CP due to the maximum delay spread of 3 ms. In fact, the study performed in [36] concluded that Delay Spread values for the Ordinary channel reached around 2.7 ms during the ionosphere availability. However, if we consider the mean, the values were about 0.5, so this fact caused the CP to have a very large error margin.  
Finally, we used six SC-FDE symbols and only one extra symbol to obtain known sequences in order to have enough samples to perform an equalization. For our case, we chose the MMSE equalization, which is ideal for low SNR because it balances the channel and noise estimation.  
Figure 4: Near-vertical incidence skywave (NVIS) transmission scheme (transmitter on the left and receiver on the right).  
With this configuration, a bit-rate of almost 4 kbps (2% more than the OFDM) and PAPR around 2 dB (more than 5 dB less than the OFDM) could be achieved, taking into account that it could have been even higher if the CP were variable and set to the mean Delay Spread (Ds) of the channel at a given time.  
In Table 1, all the parameters of the SC-FDE design are summarized. Additionally, they are compared to the OFDM design proposed in [20] since they have a similar configuration.
## 2 Materials and Methods
### Measurement Environments  
Measurements took place in two different scenarios: An ordinary auditorium used for teaching activities and a corridor, both at the School of Telecommunication Engineering at the University of Vigo. During measurements the presence of people moving within the environments was not allowed, only the antennas changed their position. This results in a static environment.  
The auditorium is a flat classroom furnished with chairs and tables divided into four big blocks. It has a brick wall covered by plaster on one of the sides, while the opposite wall has plenty of large windows. At the back of the auditorium there is another wall with a couple of glass doors and windows. The last wall has a blackboard and some more windows. The transmitting antenna was located at the ceiling, near the front of the room, and the three receiver antennas were on the moving stand of the rail. Figure 3 depicts this first environment, including the location of transmitting antenna and the paths for the receiving antenna during measurements.  
Figure 3: _Cont._  
The second scenario is a narrow corridor, with brick walls covered by plaster. The floor plans are depicted in Figure 4. This scenario hosted two different environments: LOS and OLOS, depending on the location of the transmitting antenna and its visibility from the receiving ones. The receiving antenna paths were at the exact center of the 2.20-m width corridor, whereas the transmitting antenna location varied depending on the configured environment: For LOS the antenna was at the ceiling, also in the middle of the corridor; for the OLOS, a wall corner obstructed the line of sight between the antenna (again at the ceiling) and the receiving path. Figure 5 shows a picture taken during the measurements.  
Figure 3: Description of the auditorium environment: (**a**) Picture; (**b**) floor plan with dimensions in m.  
Figure 4: Floor plans of the corridor, all dimensions in meters; (**a**) 40 GHz LOS, (**b**) 60 GHz LOS, (**c**) OLOS.
## 3 Edge-Cloud Collaborative Architecture  
### System Model and Assumptions  
In this paper, the architecture for IRMSs is designed as a local edge-cloud three-layer as shown in Figure 1 in which industrial robot-monitoring workflows can run, and the tasks in the workflow can be allocated computing resources in all three layers. The monitor in this system can connect and monitor multiple industrial robots, the monitor itself also has few computing resources, multiple monitors form a local monitoring network, the monitor cloud share its computing resources in the local monitoring network, the network shares the occupation of local computing resources, the monitor have smaller computing capacity, but its computing price can be disregarded. The monitor connects to the edge server through a fabric network. The edge server, which is typically a metropolitan-area-level computing service provider, has medium computing capacity and is more expensive, but has low latency for data transmission. The monitor connects to the cloud center. The cloud center, which is a wide-area-level computing service provider, has higher computing capacity and lower cost than the edge server, but higher latency for data transmission. The edge servers are also connected to the cloud center. The latency of data transfer within the local, edge, and cloud centers is low.  
In the operation of the monitoring system, when the monitor generates a monitoring workflow that exceeds its own computing capacity, it will allocate the resources for the generated monitoring workflow according to the current computing resources of the local edge-cloud network environment. Subtasks in the workflow will then run in the allocated computing resources, and each subtask will transfer the completed processed data to the next subtask, and when the last subtask is completed, it will return the computation results  
Figure 1: IRMS Edge–Cloud Architecture.  
to the monitor. Real-time tasks in industrial environments are executing actions or sensing data [24]. Here all monitoring workflow tasks are assumed to be non-real-time or soft-real-time monitoring tasks which performed based on standard communication protocols (TCP or UDP/IP) such as switching data, preprocessing data, compression, extracting features. The engineers program and compile the subtask logic code in advance according to the monitoring task characteristics, and program the work order and data dependencies between the subtasks, and they run in the memory of the computing resources as docker processes. The monitoring system does not involve and interfere with the industrial robot's own real-time control system.
# A TAM-Based Study of the Attitude towards Use
## 3 Research Method
### Data Handling
#### 3.5.2 Reliability Analysis  
In this study, we employed Cronbach's alpha (alpha reliability) to test the internal consistency of questionnaires. The results showed that the Cronbach's alpha of perceived usefulness was 0.862, indicating the internal consistency of questionnaires was good; of perceived ease of use was 0.888, indicating good; of use attitude was 0.891, indicating good; and of use intention reached 0.911, indicating excellent. The reliability analysis is shown on the right of Table 3.
## 3 Proposed Approach
\(\beta_{\ddagger}\) is a vector that represents the regression parameters, the penalty component of the equation above denoted by \(P_{\alpha}\), the level of expression for the gene of sample \(i\) is represented by \(g_{i}\), and the response value \(y_{i\ddagger}\) for sample \(i\). The penalty of LASSO regression can be achieved by setting \(\alpha=1\) in Equation (3). The reason that LASSO was chosen as the penalty term is that it uses the total actual parameters' values used in the model, which are constrained to be lower than a predetermined threshold. In statistics, the chi-square test determines if two events are independent [28]. Equation (4) shows the calculation of chi-Square statistics where we can obtain the actual count O and the expected value E from two variables' data. Chi-Square determines the discrepancy between the anticipated count E and the actual count O. While choosing features. Our goal is to select those that depend heavily on the outcome. The observed count will be relatively close to the expected count if the two features are independent. Hence, the value of the Chi-Square statistics will be smaller. A high Chi-Square statistic indicates that the independence hypothesis is not true. As a result, features with higher Chi-Square statistics will be selected for training the model. \[\chi_{c}^{2}=\sum\frac{\left(O_{i}+\ E_{i}\right)^{2}}{E_{i}} \tag{4}\]  
Random Forests (RF) is a learning method based on an ensemble approach that builds many decision trees during training and returns each tree's mean prediction or mode of the classes [29]. Problems involving classification and regression are both addressed by RF. RF chooses the most crucial features based on the impurity reduction they offer, and the most vital characteristics offer the most significant impurity reduction. The RF algorithm procedure is presented in Algorithm 1[25]. ```
Training Phase: for rf = 1 to C do  Draw \(B_{rf}\) a size \(N\) bootstrap sample from the training data \(D_{rf}\). Call GrowTree (\(B_{rf}\)) End for GrowTree (\(B\)) If \(B\) includes observations of only one class, then return Else  From the \(p\) genes in \(B\), choose \(g\) possible splitting genes at random. Using an impurity score, choose the best \(G\) gene to divide on. Create \(f\) child nodes of B, \(B_{1},...,B_{f}\), where \(G\) has \(f\) possible values \(G_{1},...,G_{f}\). for rf = 1 to \(f\)do  Set the contents of \(B_{rf}\) to \(D_{rf}\), where \(D_{rf}\) is all observations in \(B\) that match \(G_{rf}\)  Call GrowTree (\(B_{rf}\)) End for End if
```  
**Prediction Phase:**  
To predict a new sample \(S\): let \(\hat{C}_{rf}(s)\) the probability assigned by classifier \(rf^{th}\) random forest tree. Therefore, the \(\hat{C}_{RF}(s)=majority\)\( vote\left\{C_{rf}(s)\right\}_{rf=1}^{\hat{C}}\) ```  
**Algorithm 1**Random Forests Pseudocode
## 4 Results and Discussion
### Performance Metrics  
Various performance metrics are utilized in this work to evaluate the performance of the projected framework. These metrics include precision, recall, f-measure, accuracy, false positive rate (FPR), kappa score as well as Matthews correlation coefficient (MCC), and the mathematical formulations are as follows:  
\[P=\frac{tp}{tp+fp} \tag{32}\]  
\[R=\frac{tp}{tp+fn} \tag{33}\]  
\[FM=\frac{2*P*R}{P+R} \tag{34}\]  
\[A=\frac{tp+tn}{tp+tn+fp+fn} \tag{35}\]  
\[FPR=\frac{fp}{tn+fn} \tag{36}\]  
\[MCC=\frac{tp*tn-fp*fn}{\sqrt{(tp+fp)(tp+fn)(tn+fp)(tn+fn)}} \tag{37}\]  
where \(P\) indicates precision, \(R\) indicates recall, \(FM\) is the f-measure, \(A\) indicates accuracy, \(FPR\) indicates FPR, \(tp\) is the true positives, \(tn\) is the true negatives, \(fp\) is the false positives and \(fn\) is the false negatives.