esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Discussions
### Effect of MetaFoundation
The best performance of the MF is observed under the ABBAR--L, Manjil earthquake in which the PI corresponding to the displacement of impulsive mass, overturning moment on top of foundation, and base shear are 0.742, 0.742, and 0.740, respectively. In terms of the vertical displacement of the free surface, it is seen that the MF has no significant effect. As is seen in Figure 10, the PI of the vertical displacement of the free surface approximately equals one for all of the earthquake ground motions. To demonstrate the filtering effect of the MF, the TR above the MF in the absence of the liquid storage tank is demonstrated in Figure 11.  
A TF equal to zero implies that the displacement output on the top of the foundation equals the input displacement induced at the bottom of the foundation. After that, the TR corresponding to the regions of less than zero indicates that the amplitude of the output displacement is less than the amplitude of the input displacement. This is due to the fact that the incident wave is blocked and cannot pass through the MF. Conversely, when the amplitude of the input displacement increases, the value of TR becomes more than zero; hence, the MF has an adverse effect. With reference to Figure 11, an amplification area is seen for the frequency region below 18.733 Hz, followed by an attenuation region from 18.733 to 18.984 Hz where the wave cannot propagate throughout the MF. The second attenuation zone is seen for frequencies over 22.4 Hz. Based on the obtained responses, the MF reduces the amplitude of waves with frequencies falling in the stop band. Since an earthquake is a random phenomenon with a wide range of frequencies, and its frequency content cannot be predetermined, the effectiveness of MF is limited to those ranges of frequencies which it is designed for.
## 2 Methodology
### Temporal Subsampling
#### 2.2.2 PCA-Based Temporal Subsampling  
A principal component analysis (PCA) is a well-known dimensionality reduction method [33]. In our proposed setup, we used a PCA to project each of the feature vectors into a scalar value. Consequently, the consecutive differences of the projected values were computed and stored in list \(D\). Then, for each difference element \(\mathrm{d}\) in \(D\), we checked it against a threshold and decided whether to retain the underlying video frame. The threshold was based on statistics gathered from the projected feature vector values, as detailed in Algorithm 1.  
The theory behind this is that that smaller differences between principal components belonging to feature vectors of frames indicate a higher similarity between them, which indicates redundancy and allows us to remove one of the frames. For example, for the following frames \([fr_{1},fr_{2},fr_{3},fr_{4},\ldots,fr_{n}]\) and their feature vectors \([v_{1},v_{2},v_{3},v_{4},\ldots,v_{n}]\), the first principle component would appear as \([p_{1},p_{2},p_{3},p_{4},\ldots,p_{n}]\), and the consecutive differences would be \([d_{1},d_{2},d_{3},d_{4},\ldots,d_{n-1}]\), with \(d_{1}\) and \(d_{2}\) being the differences between \(p_{1}-p_{2}\) and \(p_{2}-p_{3}\), respectively. In Algorithm 1, with the calculation of the TH, the mean and std are the mean and standard deviation of all values in \(D\), respectively. If \(d_{1}\) is less than the composite thresholding value, then \(fr_{1}\) is marked for elimination. This continues until all feature vectors are covered.  
This proposed algorithm relies on projecting feature vectors into scalars. The temporal activity threshold is computed based on the means and standard deviations of the differences of these scalar values pertaining to the consecutive feature vectors of a video sequence, hence the use of the first PCA component only.  
Figure 4: Calculation of temporal activity threshold for temporal subsampling with HEVC features.  
```
Input: FVs_train[]: Feature matrix of train data set  FVs_test[]: Feature matrix of test data set  k: Predetermined multiplier Output: IDX_DEL[]: Frame indices to delete // Calculate temporal TH [Projected_FVs, first_PC] = Project FVs_train using PCA into scalar values  D = Consecutive differences of Projected_FVs  mean = Mean of all values in D  std = Standard deviation of all values in D  TH = mean + (k xstd) // Perform temporal subsampling for each FN in FVs, test do \(\rho\) = Project FV using first_PC  if p \(\leq\) TH  Append index of FV to IDX-DEL[]  end  end
```  
**Algorithm 1** PCA-based temporal subsampling of frames.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## Materials and Methods
### Search Strategy  
In the broad field of academic research, data collection is not only an indispensable foundation for literature analysis but also a prerequisite for gaining insight into academic development and cutting-edge dynamics. Therefore, it is imperative to obtain a wide range of literature. In this paper, the English literature was obtained from Web of Science and Engineering Village, and the Chinese literature was obtained from China Knowledge Network (CNKI), which is currently regarded the largest academic journal database in China. It is the largest academic journal database in China. Through this selection, the representativeness and authority of our literature sources are guaranteed. The literature review in this paper was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) scheme. The English and Chinese databases were searched separately. When searching for papers, the keywords \"Digital Twins\", \"Augmented Reality\", and \"Prognostics and health management\" were used in different combinations with the help of the Boolean operators \"AND\", \"OR\", and \"NOT\". For example, the advanced search formula in WOS is as follows: TS = (PHM) OR TS = (PHM * AR) OR TS = (PHM * DT) OR TS = (DT * AR).  
In order to study the progress and development of research studies in PHM, including augmented reality and digital twin technology, in an in-depth and accurate manner, the initially retrieved literature needs to be further screened with the help of pre-determined eligibility criteria so that valuable literature can be included in the study. The eligibility criteria for inclusion in the study are as follows: a publication date from 1 January 2000 to 1 June 2024; type of literature includes journal papers, conference papers, and dissertations; and field of study is engineering, computing, or related engineering and technology fields. Figure 2 provides an overview of the literature screening and selection process. We removed some papers using Endnote X9's own \"Find Duplicates\" function. We categorized the retrieved literature by topic in Endnote, PHM, PHM + DT, PHM + AR, and AR + DT, for detailed analysis of each aspect below.  
Figure 2: PRISMA flowchart showing final results [2].
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques  
The impact of FSO communication channels and the installation scenario of transceiver systems with channel effect mitigation techniques are reviewed in this section.
## 2 Related Works
In this paper, the applied data set was normalized (all values are between 0 and 1); then, by applying a genetic algorithm, the feature selection process was completed. Simulation results proved that the random forest algorithm combined with particle swarm optimization (PSO) achieved better results in terms of accuracy, precision, and recall than the other ML algorithms. [PERSON], [PERSON] [16] proposed a hybrid DL-based model for the SDN environment to detect botnet attacks in the IoMT network. The authors used the Bot-IoT data set for the evaluation of unpredictable attacks in the proposed model. In the first step, data transformation and data normalization were performed in the proposed data set. Evaluation metrics such as accuracy and precision were observed and measured in the proposed model. The experimental results proved the efficiency and scalability of the proposed model. This model using a hybrid DL algorithm provides higher accuracy and precision than the other algorithms. Finally, [PERSON], [PERSON] [17] proposed a new attack detection method in the IoMT network using a deep learning algorithm. The authors suggested a solution for the vanishing gradient problem to rapidly perform the training process. The simulation results demonstrate that the proposed model provides optimal results in terms of evaluation parameters such as accuracy, precision, recall, and f-score and higher detection rates with less computational cost using the recurrent neural networks (RNN) algorithm. The main ideas, data sets, simulation environments, and the proposed prediction approaches in related studies are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Main Idea** & **Data Set** & \begin{tabular}{c} **Simulation** \\ **Environment** \\ \end{tabular} & **Prediction Approach** \\ \hline [PERSON], [PERSON] [8] & \begin{tabular}{c} A tree classifier-based \\ IDS model using DL \\ algorithms in the IoMT \\ network. \\ \end{tabular} & Benchmarked data set & \
\begin{tabular}{c} Simulated in a \\ restricted network \\ environment \\ \end{tabular} &
\begin{tabular}{c} Random forest (RF), \\ GridSearchCV, best \\ estimator, \\ Logistic regression, \\ randomized tree. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main ideas and the prediction approaches of the related works in the fields of ML-based IDS systems for the IoMT network.
## 6 Performance Evaluation
### Performance Metrics
#### 6.2.4 Service Reliability  
Three parameters, namely accessibility, continuity, and performance, are considered in service reliability in cloud computing. Accessibility refers to service availability whenever a customer requires service. Continuity refers to the non-disturbance of services over a particular duration. Performance refers to the complete realization of customer expectation. The service reliability metrics in our proposed work consider the three parameters and are compared with those of an existing method.
# Study of the Constraint Pillar Test and the Application in Gob-Side Entry Retaining
## 2 Experimental Study on Bearing Capacity of Restrained Pillars
### Test Overview
#### 2.1.2 Test Process  
The test was carried out on a 30,000 kN pressure testing machine developed by the laboratory of the General Institute of Coal Science Research. The test bench is controlled by a four-cylinder servo synchronous control platform to achieve loading actions. Each column is equipped with pressure sensors and displacement sensors, which can convert stress into column stress. The software is a data-acquisition software developed by the testing branch that can record the loading time, displacement, and column stress of each oil cylinder. The instrument used to determine the pressure is the CS-series pressure transmitter produced by Zibo Fast Feiyan Measurement and Control Technology Co., Ltd., model CS20 FUHIIEOC3 Lm (Zibo, China), with a measurement range of 0-40 MPa. According to the operating specifications of the testing machine, first fix the movable platform to an appropriate height, then place the support column at the center of the testing platform, and then use a four-cylinder servo synchronous control to gradually load the external loading platform at a speed of 1 mm/s to test the load-bearing capacity of the column. The three groups of specimens are C30, C50 concrete columns, and 1:1 high-water-material pillars. The physical specimens of the columns are shown in Figure 1.  
Figure 1: Pillar design structure and physical images of pillar specimens under different external constraints.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group Number** & **Specimen** & **Ultimate Load** & \begin{tabular}{c} **Amount of** \\ **Compression** \\ **(nm)** \\ \end{tabular} & \begin{tabular}{c} **Compression** \\ **Ratio** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Residual Load** \\ **(kN)** \\ \end{tabular} &
\begin{tabular}{c} **Residual Load** \\ **Rate** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{1} & C30-N & 2608 & 90 & 2.6 & 1523 & 58 \\  & C30-M & 3360 & 95 & 2.8 & 1952 & 58 \\  & C30-P & 4980 & 120 & 3.5 & 2963 & 59 \\  & C30-B & 9460 & 111 & 3.3 & 5950 & 63 \\  & C30-H & 7200 & 95 & 2.8 & 4322 & 60 \\  & C30-S & 11,500 & 105 & 3.1 & 7591 & 66 \\ \hline \multirow{4}{*}{2} & C50-N & 3565 & 85 & 2.5 & 1889 & 53 \\  & C50-B & 11,025 & 94 & 2.8 & 6295 & 57 \\  & C50-S & 13,637 & 92 & 2.7 & 8042 & 59 \\ \hline \multirow{4}{*}{3} & HW-N & 1107 & 230 & 6.8 & 682 & 62 \\  & HW-M & 1530 & 246 & 7.2 & 1079 & 71 \\ \cline{1-1}  & HW-P & 2460 & 238 & 7.0 & 1796 & 73 \\ \cline{1-1}  & HW-B & 6290 & 200 & 5.9 & 4865 & 77 \\ \cline{1-1}  & HW-S & 7230 & 176 & 5.2 & 5678 & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test piece parameters and test results.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 3 System Model  
Figure 1 shows the multiuser uplink communication system with a base station (BS). In this section, first, the channel model and problem illustration are presented. Then, the proposed DNN is described.
## 2 Methods  
### Satellite Observation Probability  
#### 2.1.1 Medium and Low Earth Orbit Satellites  
Medium Earth orbit (MEO) satellites, which are in approximately circular orbits, move around the whole world over time. In the Earth-centered Earth-fixed (ECEF) system, the angular velocity \(\omega_{ij}\) of an MEO satellite at \((\lambda_{i},\phi_{j})\) can be expressed as the following [29; 30; 31]:  
\[\left\{\begin{array}{l}\omega_{\phi_{j}}=\omega_{lj}\sqrt{1-\left(\frac{ \cos i_{\mu\mu}}{\cos\phi_{j}}\right)^{2}}\\ \omega_{\lambda_{i}}=\frac{\omega_{ij}\cos i_{\mu\
u}}{\cos^{2}\phi_{j}}- \omega_{e}\end{array}\right. \tag{1}\]  
where \(\lambda_{i}\) is the geocentric longitude, and \(\phi_{j}\) is the geocentric latitude; \(\omega_{\phi_{j}}\) and \(\omega_{\lambda_{i}}\) are angular velocities in the north-south directions and in the east-west directions, respectively; \(i_{orb}\) is the satellite orbit inclination; and \(\omega_{e}\) is the rotational angular velocity of the Earth.  
[PERSON] et al. [29] suggested that the larger the angular velocity of an MEO satellite at \((\lambda_{i},\phi_{j})\), the smaller observed probability will be. However, due to the convergence of the orbits towards northern and southern latitudes, the observation probability of an MEOsatellite \(P_{M}\) is a function of \(\omega_{\phi_{j}}\) only, which is verified by [PERSON] et al. [30]. Thus, \(P_{M}\) at \((\lambda_{i},\phi_{j})\) can be expressed as:  
\[P_{M}(\lambda_{i},\phi_{j})=\frac{\kappa}{\omega_{\phi_{j}}} \tag{2}\]  
Nevertheless, an MEO satellite cannot be observed when the \(\phi_{j}\) is larger than the orbit inclination \(i_{orb}\); thus, we modified \(P_{M}(\lambda_{i},\phi_{j})\) as:  
\[P_{M}(\lambda_{i},\phi_{j})=\left\{\begin{array}{cc}\frac{\kappa}{\omega_{ \phi_{j}}}&|\phi_{j}|<i_{orb}\\ 0&|\phi_{j}|\geq i_{orb}\end{array}\right. \tag{3}\]  
To calculate the constant \(\kappa\) in Equation (3), the spherical orbit surface of MEO satellites can be divided into \(1^{\circ}\times 1^{\circ}\) grids by \(\lambda_{i}\) and \(\phi_{j}\). Then, the observation probabilities for the MEO satellites for grids satisfy:  
\[\sum_{\lambda_{i}=1}^{360}\sum_{\phi_{j}=-89}^{90}P_{M}(\lambda_{i},\phi_{j}) =n_{\text{MEO}} \tag{4}\]  
where \(n_{\text{MEO}}\) is the total number of MEO satellites.  
The law of LEO satellite motion is similar to that of MEO, the observation probabilities of the LEO satellites \(P_{L}(\lambda_{i},\phi_{j})\) can also be expressed as Equation (3) and satisfy:  
\[\sum_{\lambda_{i}=1}^{360}\sum_{\phi_{j}=-89}^{90}P_{L}(\lambda_{i},\phi_{j}) =n_{\text{LEO}} \tag{5}\]  
where \(n_{\text{LEO}}\) is the total number of LEO satellites.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation
### Case Study of Image Processing with D-SWIM  
With the D-SWIM buffer, an architecture for a specific image application can be easily constructed. This section presents D-SWIM-based architectures for two real-world image applications and their performance study.  
To evaluate the practicability of D-SWIM in real applications, we compare the D-SWIM designs with similar streaming architectures for image processing [7; 10]. In prior studies, [PERSON] et al. [7] improved the HIPA\({}^{\texttt{CC}}\) image-processing framework to generate effective High-level Synthesis (HLS) codes for FPGA with a specific memory architecture, and [PERSON] et al. [10] optimized the OpenCL framework of Intel (Altera) FPGA to a domain-specific language (DSL) for image processing. These works are widely accepted by the community, and were developed based on the latest tools from the industry and academia. Thus, we consider these two as the state-of-the-art works for comparison.
## 2 Materials and Methods
### Exposure Experiments  
Irgarol (2-(tert-butylamino)-4-(cyclo-propylamino)-6-(methyl-thio)-s-triazine) and BPA (99.9% pure) were prepared from Sigma-Aldrich (St. Louis, MO, USA). DEHP solutions were obtained from a solid compound (99%, Junsei Chemical Co. Ltd., Japan). The 10 mg L\({}^{-1}\) stock solutions of BPA, DEHP, and irgarol were prepared by dissolving the chemicals in 99% acetone at room temperature. For working solutions of 1, 10, and 30 \(\upmu\)g L\({}^{-1}\) for each chemical; the stock solution was diluted with seawater. In solvent controls, a solvent concentration was <0.5% acetone.  
For each chemical exposure, the crabs (\(n=120\)) were randomly divided into four experimental groups (1, 10, and 30 \(\upmu\)g L\({}^{-1}\) treatment solutions, as well as solvent controls). Ten crabs were exposed with one of three doses of BPA, DEHP, or irgarol. Treatment times were 24 h, 96 h, and 7 d. Three individuals were subjected to tissue extractions for each time interval in each chemical treatment condition and control crab. The seawater was changed every day by adding the equivalent concentration of each chemical during the experiment. No food was provided during the experimental period. All experiments were run in three replicates with independent samples. Following exposure, tissue samples were immediately extracted from the crabs.
# 5G-MEC Testbeds for V2X Applications
## 2 Background
### Fundamentals of ETSI-MEC
#### 2.2.3 Softwarization Technologies  
Softwarization technologies are rapidly growing as are the network paradigms for network and service providers. SDN and NFV are the key enabling technologies in the network paradigm that can support the 5G-MEC system.  
SDN aims to provide network management, programmability, and flexibility of networks. By separating the control plane from the data plane, SDN allows seamless network access. The decoupling is performed via a logically centralized network intelligence (SDN controller), and the data plane consists of network devices that forward the packets. Google and Facebook have already introduced SDN into their respective data centers [23].  
NFV allows network operators to provide network services using virtual infrastructure. This allows NFV to deploy software network functions on off-the-shelf hardware rather than deploy hardware-based proprietary network functions. NFV provides cost efficiency and the timely benefits of cloud computing. The authors of [24] present an NFV reference architecture in which Virtual Network Functions (VNFs), NFV Infrastructure (NFVI), and Management and Network Orchestration (MANO) are the key elements.
# Shear Behavior of Superposed Perfobond Connectors Considering Lateral Constraints
## 3 Experimental Results
### Strain Analysis of Perfobond Rib and Perforating Rebar  
To study the failure mechanisms of the connectors, the strains of the perfobond rib and perforating rebar were analyzed first. Specimens P-1 and P-4 were taken as representatives of double and single-hole connectors, respectively, and their load-strain curves (after removal of the damaged strain rosette) are shown in Figure 9. The strain value of the perfobond rib near the H-shaped flange exceeded that of the outer one. When the specimen reached its ultimate load, each measuring point of the steel plate remained in the elastic region. Under a continuously increasing slip, the concrete dowel gradually lost functionality, whilst the perforating rebar began to make contact with the steel plate, further increasing the stress. Finally, a small principal strain of the perfobond rib produced a yield value of 1890 \(\upmu\)e, attributable to the thick steel plate used in the test; the perfobond rib exhibited minimal deformation (see Figure 8).  
Figure 8: Inner failure modes of the perfobond connectors: (**a**) Inner failure mode of specimens with circular hole; (**b**) Inner failure mode of specimens with long hole; (**c**) Inner failure mode of specimens with high hole; (**d**) Inner failure mode of specimens without stirrups; (**e**) Inner failure mode of specimens with single hole; (**f**) Inner failure mode of specimens without perforating rebar.  
Figure 10 shows the load-strain curves of the perforating rebar for the P-1 and P-4 specimens. The tensile strain is positive, and the compressive strain is negative. It can be seen that in the initial loading stage, the concrete dowel bore the loads. Hence, the rebar strain value was small. When the load increased, the perforating rebar and concrete dowel resisted shear together. Meanwhile, the rebar strain increased rapidly. In contrast, when the specimens reached the ultimate load, many of the reinforcing bars still did not yield, and the strain of the rebar exceeded the yield strain of 2215 \(\mu\)c by the end of the loading. Simultaneously, the upper and lower parts of the perforating rebar were seen to be tensioned and compressed, respectively, indicating that the perforating rebar was subject to pull-bending action. Hence, it can be concluded that the perforating rebar in the concrete dowel provided both vertical shear contribution and axial force.
## 4 Experiments and Analysis  
### Dataset  
#### 4.1.1 Multi-Granularity Time Dataset  
As shown in Table 1, for the MULTITQ [7] dataset, questions can be classified into simple and complex questions based on whether a single tuple or multiple tuples are required to answer the question. Additionally, questions can be divided into categories such as \"Equal\", \"Before/After\", \"First/Last\", etc., depending on whether the event times in the question are consistent. Alternatively, questions can be classified based on the time granularity, such as year-level, month-level, or day-level questions.
## 5 Conclusions
### Limitations  
This study has some limitations. Firstly, it focuses only on a specific region of Istanbul, namely Beylikduzu-Bakirkoy, which may limit the generalizability of the results. Secondly, it uses data from a specific time period covering a period of 16 months. Including a longer time frame may provide a more comprehensive analysis. Thirdly, the use of geohash fields is associated with a certain level of geographical precision.  
When considering smaller geographical areas, forecasting success may be more challenging, but it can lead to more precise area estimation. On the other hand, if larger geographical areas are taken into account using geohash codes, prediction success may increase, but location accuracy may decrease. Additionally, the study's model selection and evaluation metrics are based on a specific preference. These results could have been different if a specific algorithm or metric had been chosen.