esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Finite Element Numerical Simulation
### Joint Finite Element Modeling
#### 4.2.3 Boundary Conditions and Loading  
In the experiments in this paper, the bottom of the column of the beam-column joint specimen was restrained using a steel section element to limit its translation and rotation, which was a fixed support. Therefore, in the finite element model, the bottom plane of the column was set up with fully fixed constraints and placed in the initial analysis step. The loading point at the beam end was connected to the actuator using steel plates and anchors, and the loading was displacement-controlled. In the finite element simulation, the displacement loading part amplitude is generally entered directly to simplify the loading process, facilitate the operation, and achieve better convergence. The load application area was divided at the corresponding position at the end of the beam, and a reference point RP-1 was set. The reference point was constrained with all six degrees of freedom in the divided area using the coupling command, and the displacement loading amplitude was assigned to RP-1 to achieve the application of low-cycle loading at the end of the beam. In the experiments, axial pressure was applied at the top of the column using a large hydraulic jack. Therefore, in the finite element model, a reference point RP-2 was set at the top surface of the column, and all six degrees of freedom at the top surface of the column were coupled to the reference point (coupling), and then all six degrees of freedom at the reference point were constrained with respect to the top plane of the column. This ensured that when an axial force was applied to the reference point, the vertical displacement of the column top plane was equal to the vertical displacement of the reference point, achieving complete fixation of the reference point to the column top plane. The boundary conditions and loading settings are shown in Figure 22.
## 3 Proposed Methodology
### Focal Node Identification Using Distributed Fuzzy Cognitive Map  
Fuzzy cognitive maps have a broad spectrum of application domains. Their ability to model uncertainties and improve the accuracy of the modelled system makes them an ideal choice to model extremely simple to highly complex systems. A fuzzy cognitive map (FCM) is a directed graph that contains weighted edges with signed values that represent the fuzzy causal relationships between its nodes; hence, an FCM can be adopted to represent complex networks that comprise nodes and relationships between the nodes. The three components of an FCM are:  
* Concepts (\(C_{i}\))
* Concepts are the fundamental components of a system that have a significant role in resolving the issue at hand.
* State Vector (\(A\)) [0, 1]
* A vector is formulated through the process of integrating the values of each individual concept from the given system, which usually falls within the range of 0 to 1.
* Weight Matrix (\(W_{ij}\))
* The weight matrix represents a collection of weights that corresponds to all the causal relationships within the system. The presence of a link between the concepts is represented by its weight value; otherwise, it is 0. The diagonal elements are always  
Figure 1: The genetic algorithm framework for community detection.  
zero. The weight values can be positive or negative. A positive weight value indicates a positive causality between the concepts, and a negative value indicates a negative causality.  
The steps in using an FCM for focal node identification are initializing the state vector, initializing the weight matrix, applying FCM learning, and finally identifying the focal nodes. An initial state vector that contains the initial values of all the system concepts is determined either by expert knowledge or computationally. The initial state vector is obtained by examining all of the system's characteristics. The initial state vector is subjected to FCM learning until it converges to provide the desired outcome.
## 3 RL-Based Resource Allocation Algorithm
### Algorithm Flow  
In this paper, the observed value of the \(j\)-th V2V link in the time step \(t\in\{1,2,\ldots,T\}\) is defined as \(O_{j}^{i}\). At this time, the observed values of all V2V links at the time step \(t\) can be expressed as \(O_{t}=\left\{O_{j}^{t}\right\},\forall j\in J\). According to literature [16], the approximate target value of return can be expressed as:\[y_{t}=R_{t+1}+\gamma\text{max}_{a}Q(O_{t+1},a;\theta^{-}), \tag{13}\]  
Then the update process of DQN located at BS can be expressed as:  
\[\theta\leftarrow\theta+\beta\sum\
olimits_{t\in D}\frac{\partial Q(O_{t},a_{t}; \theta)}{\partial\theta}[y_{t}-Q(O_{t},a_{t};\theta)], \tag{14}\]  
where \(\beta\) refers to the time step length in the strategy gradient iteration.  
At each episode \(t\), each V2V link observes the local data \(O_{j}^{t}\) first, then uses it as input to DNN to get feedback \(f_{j}^{t}\), which is then transmitted to BS. The BS will then serve \(\left\{f_{j}^{t}\right\}\) as the input to the DQN to generate the decision \(a_{t}\), which will be broadcast to all V2 Vs. Finally, each V2V link selects its own spectrum according to the decision result.
## 2 Measurements and Quality Control Processes
### Seasonal Variation of Longwave Irradiances  
The monthly statistics of the longwave and shortwave irradiances and their associated standard deviations and extreme values are presented in Table 4. The monthly means of all the radiation components are also shown in Figure 7. The monthly means of \(L_{dn}\) range between 280 and 387 W/m\({}^{2}\) with the lowest values in the winter months and the highest during the summer months. Similar variation is observed for the upward longwave irradiance ranging from 355 to 529 W/m\({}^{2}\). The net longwave irradiance is always negative, ranging from \(-68\) in the winter months to \(-140\) W/m\({}^{2}\) in the summer months. The lower negative values during the summer months are associated with the higher ground surface temperatures. The mean values of global irradiance are comparable to the upward longwave irradiances (\(L_{up}\)). However, the maximum of global irradiances may exceed the value of 1000 W/m\({}^{2}\), while \(L_{up}\) reached the value of 738 W/m\({}^{2}\). Furthermore, the monthly variability of longwave irradiances expressed by their standard deviation is much lower than the respective values of global irradiance. The mean values of the reflected irradiances range mainly between 50 and 130 W/m\({}^{2}\) with the highest occurring in the summer months. The net shortwave irradiance is positive throughout the year, ranging from 195 to 444 W/m\({}^{2}\). However, the net allwave irradiances are positive during the first 10 months of the year and slightly below zero during the last two months of the year (November and December).  
Figure 6: _Conts._  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Rad.** & **Stat.** & **Var.** & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** & **All** \\ \hline LW & & \(L_{dn}\) & 286 & 280 & 290 & 304 & 327 & 367 & 387 & 387 & 359 & 339 & 300 & 295
## 3 Path Planning Algorithm Based on the Offset Curve
### Generation of the Offset Curve on a Triangular Mesh Surface
#### 3.2.1 Outline of CODP  
Figure 6 shows the algorithm flow of the CODP.  
The first step is to generate discrete points on the original curve. The spacing value of the discrete points is set to one-tenth of the offset distance.  
The second step is to judge and eliminate the invalid points on the original curve. A judgment method would be used here. The invalid points would be removed to avoid local self-intersection and global self-intersection.  
Figure 5: Process of the offset curve path planning (OCPP). VRML: virtual reality modeling language.  
Figure 6: Algorithm flow of the curve offsetting based on a discrete point (CODP).  
The third step is to generate offset points according to the remaining points on the original curve.  
The last step is to generate the offset curve with the offset points. A connection surface would be constructed to generate the connected lines.
## 3 Materials and Methods
### Anomaly Detection Module  
The anomaly detection module of the CM-DANA model combines the relevant input data from different modalities using the adaptive attention weights to detect suspicious abnormal behavior. The Theil-Sen Regressor was used as an anomaly detection module in the CM-DANA architecture. The Theil-Sen Regressor is a robust linear regression algorithm that estimates the slope and intercept of a linear relationship between input features and target variables. While it is primarily used for regression tasks, it can also be adapted for anomaly detection by setting a threshold on the residuals used for outlier detection.  
Specifically, after the dynamic attention module obtains the attention vector, it serves as the input to the anomaly detection module. The Theil-Sen Regressor fits a linear regression model to the attention vector and estimates the slope and intercept of the linear relationship. During the anomaly detection phase, it calculates the residuals by comparing the predicted values from the Theil-Sen Regressor with the actual values of the attention vector. Finally, a dynamic threshold on the residuals identifies instances where the deviation from the predicted values is significant. Data instances with residuals above the threshold are considered anomalous. Figure 3 is an example of how to fit a line through almost linear data. The orange Theil-Sen Regressor outperforms the blue linear regressor.
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 2 Advanced Television System Committee (ATSC) 3.0 System Overview
### System Model
Similarly, the \(EL2\) symbols, represented by \(\hat{X}_{EL2}(l)\), can be demodulated by performing the single-tap frequency-domain equalization. The complete \(EL\) symbols for fixed service are obtained by combining \(EL1\) and \(EL2\) in the order of puncturing. Eventually, the bit data for fixed reception can be acquired by BICM decoding the \(EL\) symbols. The transmitter block diagram of LDM-Ex-FDM is shown in Figure 2. In Figures 2 and 3, in addition to the modules defined in the traditional \(LDM\) scheme, the additional modules with full-color shadows are added to handle fixed service. The operating mechanism of the puncturing module in the additional module is detailed below. In this paper, the BICM encoder are used to generate quadrature phase-shift keying (QPSK) and non-uniform quadrature amplitude modulation (NU-QAM). The mobile service is carried by the \(CL\) of the \(LDM\) while the fixed service is carried by the \(EL\). Figure 4 shows the representation of \(CL\), \(EL1\), \(EL2\) in the multiplexer (Mux) output, followed by subcarriers mapping, pilot insertion, inverse fast Fourier transform (IFFT) used in the frequency domain, and CP padding. Finally, the composite LDM-Ex-FDM signal is formed and transmitted over the air interface. by subtracting the \(CL\) symbols from \"Buffering1\" \(LDM\) symbols. The complete \(EL\) signal, which is the data symbols of the fixed service before puncturing, can be formed by combining \(EL1\) symbols and \(EL2\), the \"Buffering2\" non-\(LDM\) symbols. Finally, the bit data of fixed service is acquired by decoding the data symbols of fixed service.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 7 Interoperability  
Interoperability is another crucial attribute of electrical substations, especially for VPAC systems, ensuring that communication and functionality exist among the diverse devices and systems. Although interoperability has not been well studied in general, emerging standards such as IEC 61869-13:2021 Instrument transformers Part 13: Stand-alone merging unit (SAMU) [112, 113] have begun to contribute toward this important requirement through the stipulation of requirements from sensors to new sampled values merging units (SAMU). These standards are critical for achieving a uniform integrated result for different aspects of the substation. Interoperability is enabled by the UCA International Users Group [114] by providing devices compliant to the IEC 61850 standard. This certification process is important because any vendor, although in the process of developing their merging units, can claim IEC 61850 compliance, which may or may not be true. Certification by the UCA ensures that devices implement the IEC 61850 standard; thus, a better interoperability between devices from different manufacturers is assured [115]. It will bring much more reliability and efficiency into substation operations, without the risks of integration problems compromising the performance of systems. While the road to complete interoperability in VPAC systems requires the navigation of evolving standards and certification processes, the efforts put in by standardization bodies and industry groups chart a course toward more cohesive and dependable substation environments. Interoperability continues to hold a place in the spotlight as the industry will continuously evolve, and it will become an important focal point for future electrical infrastructure development [116, 117].  
Interoperability in smart grids faces several key challenges. First, a Common Information Model (CIM) is required to accommodate evolving business needs, but integrating new models into legacy systems remains complex. Second, harmonization between different standards like CIM and IEC 61850 is problematic due to overlapping functionalities and inconsistent data exchange formats. Lastly, validation is crucial to ensuring accurate information exchange, yet differences in system configurations and a lack of standardized validation methods create obstacles. Addressing these issues requires improved semantic models, automated ontology mapping, and refined data validation techniques [118].
## 4 Results and Experiments
### Experiment's Establishment  
The models were developed on the Kaggle platform 1.6.17 using TensorFlow 2.17.0 and Keras 3.4.1. The experimental configuration was equipped with hardware that included an Nvidia GeForce RTX 1050 graphics card and operated on Windows 10. Throughout the data resampling process, only the training set was utilized, while the evaluation dataset was reserved as the testing set. The training process involved executing the models for 500 epochs, with validation accuracy monitored throughout the training.
## 1 Introduction
The TFSTP method [25] utilizes the short-term plasticity to distinguish the static and motion areas to further enhance the reconstruction results; however, it may introduce unexpected noise. Motivated by successful applications of the coarse-to-fine strategy for various tasks, such as deep video coding [26] and optical flow estimation [27], this paper proposes a CFSR framework to reconstruct the image with region-adaptive-based spike distinction. This paper analyzes the distribution characteristics of spikes and the difference between adjacent inter-spike intervals (DAISI) in fixed time to distinguish real-time spike states. Then, we utilize these different distributions to adopt the TFP [18] and TFI [20] methods to reconstruct dynamic and static regions in coarse-grained and fine-grained reconstructions, respectively. Finally, we fuse the reconstruction results of dynamic and static regions to obtain final results. Our contributions are summarized as follows: (1) We first propose a lightweight coarse-to-fine high-speed motion scene reconstruction (CFSR) framework with region-adaptive-based spike distinction. (2) We propose the coarse-grained distinction and the fine-grained distinction based on the spatial distribution for the inter-spike interval (ISI) and the difference between adjacent inter-spike intervals (DAISI), respectively. (3) Before the fine-grained distinction, we take an adaptive threshold scene reconstruction (ATSR) to fuse the static and dynamic region reconstruction. (4) Experimental results on the PKU-SPIKE-RECON dataset demonstrate that our CFSR can achieve high dynamic range and high image quality in reconstructing high-speed scenes. This paper is organized as follows. Section 2 introduces the related works of this paper. Section 3 proposes the CFSR framework in detail. Section 4 shows experimental results, and Section 5 concludes the paper.
## 3 Results
### Question 5: How to Calculate the Individual IEQ Indexes
#### 3.5.4 Visual Quality Model  
For the visual quality models, almost all of them used illuminance as an independent variable but in various forms, such as the linear regression model (e.g., Equation (14) [10,42]), logistic regression model (e.g., Equation (15) [14]), quadratic regression model (e.g., Equation (16) [13]), and complex logistic quadratic regression model (e.g., Equation (17) [15]). Likewise, similar to the acoustic quality model that involved five indicators, [PERSON] et al. [56] also used five lighting-related indicators to develop a holistic visual comfort model (see Equation (18)), wherein ALQ, ALS, ALG, NLR, and NLQ represented the indexes of artificial lighting quality, artificial lighting sources, artificial lighting glares, natural lighting quality, and natural lighting reflection, respectively.  
\[\theta_{Vi}=0.0013 Lux-0.0409 \tag{14}\]\[\theta_{Vi}=1-\frac{1}{1+e^{(-14.13+0.9058E)}} \tag{15}\]  
\[\theta_{Vi}=-5\times 10^{-7}E^{2}+0.0011E-0.106 \tag{16}\]  
\[\theta_{Vi}=-176.16\left[ln(ln(E))\right]^{2}+738.4 ln(ln(E))-690.29 \tag{17}\]  
\[\theta_{Vi}=\frac{0.2 ALQ+0.1 ALS+0.2(10-ALG)+0.3(10-NLR)+0.3 NLQ}{10} \tag{18}\]
## 2 Materials and Methods
### Instruments  
The tools employed in this study included MARS 4.0 software, an Excel/VBA spreadsheet (including VBA script), and a force platform. Kistler MARS 4.0 (Measurement, Analysis, and Reporting Software) encompasses various kinetic analysis modules, including ones specifically for jump analysis (e.g., CMJ, Squat, Drop Jump, and Hop Jump) [10]. In this study, in addition to the MARS 4.0 analysis itself, the data for each jump were imported into the custom-made Excel/VBA spreadsheet. A selected set of variables was then calculated and compared with the MARS 4.0 outcomes: (i) jump height computed from take-off velocity, (ii) jump height computed from flight time, and (iii) force impulse between maximal force and take-off. This last variable was used as an approximation of the propulsive impulse, since MARS 4.0 does not provide this variable directly, and it was assumed that the propulsive phase begins approximately when the force reaches its maximum positive value. The force platform utilized in this research was a Kistler 9286 BA (60 \(\times\) 40 \(\times\) 3.5 cm; Winterthur, Switzerland), a portable piezoelectric force platform with a maximum sampling frequency of 1000 Hz, a 16-bit AD converter, and a voltage range of \(\pm\)10 volts. Kistler is an internationally recognized brand, and its platforms have been employed as benchmark systems in studies validating jump height assessment instruments [8,22].  
The following subsection provides a detailed explanation of the use of the ad hoc Excel/VBA spreadsheet, enabling other researchers to effectively implement it. The structure and usage of the spreadsheet will be thoroughly discussed. Additionally, the spreadsheet, accompanied by two video tutorials, some CMJ force data examples (in.txt format), and a pdf document including a list of the variables provided with percentiles (based on the 105 jumps recorded for this study) is available as Supplementary Material.
## 3 Experimental Results and Discussion  
### Crack Distribution and Failure Mode  
Figure 5 shows the crack distribution and failure mode of the test beam. As Figure 5 shows, the failure mode of all test beams is typically appropriate reinforcement failure, i.e., concrete cracks first appear in the tension zone, then the tensile steel bar reaches the yield strength, and finally the concrete in the compression zone is crushed. During the failure process, a large number of uniform cracks appear in the beam. Compared with the intact test beam (WS beam), the crack spacing of the mid-span for pre-cracked test beams (i.e., the ZSS1, ZSS2 and ZSS3 beam) is larger, and the main crack develops from the prefabricated crack at the mid-span. The crack distribution of pre-cracked test beams at 1/4 (the DSS1, DSS2 and DSS3 beam) is similar to that of the WS beam. Due to the stress concentration effect at 1/4 position prefabricated cracks, beams DSS1, DSS2 and DSS3 have inclined cracks here, which develop along the beam height direction. The largest cracks in the test beam occur in the mid-span, and all the inclined cracks of the T-beam develop towards the loading point, resulting in a dense distribution of cracks near the top loading point. Figure 6 shows the typical failure mode of the test beams.  
The maximum crack width of the WS beam is 2.02 mm, and the maximum crack widths of ZSS1, ZSS2 and ZSS3 are 6.44 mm, 6.35 mm and 7.09 mm, respectively, which are 3.19 times, 3.14 times and 3.51 times, respectively, that of the WS beam. The results indicate that the prefabricated crack in mid-span significantly weakens the cross-section stiffness of the test beam. In addition, the height of the prefabricated crack in the ZSS2 beam is 60 mm, and the concrete in the tension zone will exit the work earlier, resulting in the tension steel bar bearing the tensile stress induced by the load earlier. Therefore, the maximum crack width of the ZSS2 beam is slightly smaller, compared with the ZSS1 beam. As the diameter of the steel bar at the ZSS3 beam decreases, the stress of the steel bar is greater under the same load, and its elastic deformation also increases. Therefore, the maximum crack width of the ZSS3 beam increases by 11.7%, compared with the ZSS2 beam.  
Compared with the mid-span pre-cracked test beam, the prefabricated cracked beam at the \(1/4\) position has little effect on the stiffness of the test beam. The maximum crack widths of DSS1, DSS2 and DSS3 are 1.83 mm, 1.91 mm and 2.63 mm, respectively, which are 0.94 times, 0.95 times and 1.31 times, respectively, that of the WS beam. As the height of the prefabricated crack in the DSS3 beam is relatively large, and the diameter of steel bar at the prefabricated crack decreases, some oblique cracks are generated and expanded here (Figure 5g), which has a great impact on the integrity of the test beam. Therefore, the maximum crack width of the DSS3 beam is increased by 30.2%, compared with the WS beam.