esa-sceva/satcom-chunk-collection huggingface dataset:
# Recovery Behavior of Fe-Based Shape Memory Alloys under Different Restraints
## 3 Experimental Program
### Activation Method
#### 3.4.3 Partial Restraint  
A number of studies were conducted to apply Fe-SMAs to the couplers used in pipe joining [1, 16]. As seen in Figure 3, an initial gap is present between the inner part of the coupler and the outer part of the pipes at the pipe joint. In the case of activation in the Fe-SMA, the initial gap is reduced by SEM. When the initial gap is fully closed between the Fe-SMA and the pipes, the recovery stress develops in the Fe-SMA. In order to simulate this situation, two types of restraint conditions were considered in the activation process in the Fe-SMA. Figure 4 shows the description of partial restraint. Firstly, the test was arranged with the stress being kept at less than 5 MPa in the specimen. The recovery strain developed freely in the Fe-SMA specimen. In the second step, the behavior of the Fe-SMA was considered as the gap between the coupler and the pipes fully closed. The Fe-SMA specimen was restraint to deform and the stress in the specimen was observed. The test was performed with a pre-strain of 0.04 at 200 \({}^{\circ}\)C for heating temperature. Two types of Fe-SMA (FSMA-A, FSMA-C) were considered and the contact temperatures were varied as listed in Table 3. The first letter A and C represent the type of Fe-SMA, respectively. Next to the symbol \"-\", Arabic numbers 120, 160 and 200 represent the contact temperature (T\({}_{\text{C}}\)) 120 \({}^{\circ}\)C, 160 \({}^{\circ}\)C, and 200 \({}^{\circ}\)C, respectively. Finally, the last letters H and C represent the heating phase and cooling phase at which the Fe-SMA specimen was constrained. The contact temperature (T\({}_{\text{C}}\)) is the temperature at which the initial gap is fully closed in Figure 3 and the recovery strain is constrained by the SME of Fe-SMAs in Figure 4b. The activation method and data acquisition in the process are the same as the test described under the free restraint with varying heating and cooling temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & **Type of** **SMA** & **Pre-Strain** & \begin{tabular}{c} **Heating** \\ **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Stress at Maximum** \\ **Temperature**, \\ **\(\alpha_{\text{m}T}\) (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Recovery Stress**, \\ **\(\sigma_{\text{m}}\) (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Final Recovery** \\ **Stress**, \(\sigma_{\text{f}}\) (MPa)** \\ \end{tabular} \\ \hline \hline C2-T160 & & 0.02 & 160 & 188 & 380 & 342 \\ \hline C4-T120 & & 0.04 & 120 & 154 & 322 & 321 \\ \hline C4-T160 & FSMA-C & 0.04 & 160 & 171 & 377 & 362 \\ \hline C4-T200 & & 0.04 & 200 & 189 & 416 & 401 \\ \hline C4-T240 & & 0.04 & 240 & 213 & 462 & 416 \\ \hline C6-T160 & & 0.06 & 160 & 153 & 376 & 375 \\ \hline C8-T160 & & 0.08 & 160 & 118 & 377 & 377 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Schematics of pipe joining process by SMA.
## 2 Literature Review
### Data Quality Dimensions Specific for the Healthcare Industry
Their aims included the classification of DQDs and the investigation of measurement methods and of ways of improving DQ in general [30]. The point of interest in their work in relation to the research carried out in this paper is that they posited that the dimensions of _accuracy_, _completeness_, and _capture_ are the most relevant DQDs to explore. The work in [30] also made use of an integrative literature review technique approach. The _capture_ DQD seems to be different compared to the normal DQDs usually cited. The authors described the _capture_ dimension as the extent to which all necessary cases that could have been registered have actually been registered. The existence of this _capture_ dimension confirms the assumption that DQ is linked with its context of use, and, therefore, each different context of use might require different DQDs. A recent study within the context of the Ebola outbreak in 2014 pointed to some interesting DQDs [31]. Even if the authors did not directly investigate DQDs, they discussed some of the major challenges to improving data quality for Ebola outbreaks. Focusing on the challenges, the authors cited _accuracy_, _privacy and security_, _heterogeneity_, _provenance and trust_, _availability_, and _completeness_. The authors advocated for the use of different tools for data capture, including websites and mobile apps. Some of the characteristics of Big Data seem to be present as part of the datasets examine; however, at no point in their work did the authors confirm whether they were using Big Data. [PERSON] et al. [32] proposed a framework for DQ in the context of cloud-based information systems. The authors performed a general literature review and then filtered out DQDs for redundancies. The process of filtering was not discussed and, therefore, the rationale for eliminating certain dimensions and how redundancies were defined in this context are not known. The result is a framework consisting of three sections: Information (_accuracy_, _completeness_, _consistency_, _relevance_, _timeliness_, and _usability_), Communication (_provenance_, _interpretability_), and Security. Other authors have carried out research into DQDs in specific areas of health data. One such example is the work concerning DQDs for genome annotations [10]. The complexity and variety of genome annotations make it prone to suffer from DQ issues. The authors applied a scenario-based research method. Out of a total of 17 typical DQDs detected after the analysis of the scenarios, 5 main DQDs were deduced after the statistical analysis performed: _accuracy_, _accessibility_, _usefulness_, _relevance_, and _security_.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 2 Challenges for IoT Applications and IEEE 802.11 ah
### Coverage Range  
Some of the IoT applications require more than 1 km of coverage for their desired operation. Besides the fact that it is using a lower frequency band (sub-1 GHz) with better propagation characteristics, in IEEE 802.11 ah, the extended range requirement is fulfilled by introducing 1 MHz wide transmission and by using a new Modulation and Coding Scheme (MCS) index (MCS10). This scheme is effectively MCS0 (BPSK 1/2) with an addition of 2\(\times\) repetition. Along with 1 MHz channel bandwidth (CBW), IEEE 802.11 ah also supports 2, 4, 8 and 16 MHz (it is expected that early commercial devices support up to 4 MHz). These narrower bandwidths entail a larger symbol duration than legacy IEEE 802.11. With longer symbols (and guard intervals), IEEE 802.11 ah transmissions are more robust to inter-symbol interference found in longer links and outdoor scenarios (large delay spread). By supporting Multiple-Input Multiple-Output (MIMO), IEEE 802.11 ah benefits from spatial diversity, which improves the received signal quality and, hence, makes longer links possible. The specification also considers multi-hop operation with relays or mesh networking to extend coverage.
# Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
## 4 The Proposed Algorithm: MPC_CPDMO-NSGA-II  
### Framework of MPC_Cpdmo-NSGA-II  
In MPC, the controller is required to offer a solution based on current traffic conditions. Thus, the essence of freeway control problem is to realize on-line dynamic multi-objective optimization in model predictive control at each control step. Thus, dynamic multi-objective optimization is used to solve the model predictive control problem to respond to changed environment rapidly, and to improve the performance of control solutions. In this paper, a new model predictive control method based on dynamic multi-objective optimization algorithms, named as MPC_CPDMO-NSGA-II is proposed to solve freeway control problem. Figure 3 shows the framework of the proposed MPC_CPDMO-NSGA-II algorithm. The difference between the MPC_CPDMO-NSGA-II algorithm and traditional MPC method is that it introduces dynamic multi-objective optimization algorithm instead of single objective optimization algorithm in MPC controller design.  
Firstly, MPC_CPDMO-NSGA-II are used to provide more effective optimization solutions by dealing with multiple conflict objectives. Besides, it is more flexible to process constraints in the optimization period, such as being modified to new objectives optimized. It is easier to be realized in complex problems than linear relaxation method. Finally, MPC_CPDMO-NSGA-II can rapidly respond to the variation of traffic flow. For freeway congestion problems, environmental changes typically mean disturbance, weather conditions, traffic flow variations, etc. In this paper, in the disturbance, weather condition is not considered in the model, as the traffic flow is given based on on-site history data. In order to deal with the variation of traffic flow, the dynamic multi-objective genetic algorithm is used to solve the multi-objective optimization control problem in the model predictive control. Compared with static multi-objective genetic algorithm, dynamic multi-objective genetic algorithm can respond to the variation of traffic flow, i.e., environmental changes occur quickly and provide optimal solutions effectively.  
In the MPC_CPDMO-NSGA-II algorithm, the dynamic multi-objective optimization algorithm will search for new optimal solutions by considering processed historical solutions as the initial population. If current optimal solutions are similar to the historical one, algorithms will perform a quick convergence. In contrast to static optimization algorithms, the initial population need to be re-generated randomly. Therefore, static optimization algorithms have lower efficiency and it is easy to acquire inaccurate solutions. In addition, MPC_CPDMO-NSGA-II is universal for application to solve various problems of optimization in the transportation field.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 4 The Proposed Approach  
This work proposes an approach that accurately detects three abnormal behaviors in videos. The overall design of this approach has three main characteristics. First, based on the intuition of each studied behavior presented in the previous section, a video dataset is carefully prepared by selecting videos from public datasets. These videos are then preprocessed to be converted into a suitable shape for the input to a CNN. Second, the approach uses a two-stream CNN architecture that allows us to use a combination of video (RGB) and optical flow streams to enhance the recognition performance. Third, due to the low number of examples found in public video sets related to abnormal behavior detection, transfer learning is applied that benefits from huge video datasets to specialize in abnormal behavior detection. So, the pretrained network is finetuned on the newly developed dataset to identify and classify an abnormal behavior. Specifically, each stream of the network provides a classification score which is finally fused to determine the final prediction. The overall architecture of the approach is shown in Figure 1. The following subsections provide a detailed discussion of the various components and steps involved in the development of the system.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 5 Results
### Effect of Slope on Evacuation Time
The time slope curves at each density were obtained by fitting, and the slope of the curves was used to indicate the degree of influence of slope on evacuation time (Figure 7), which is known as the ratio of time to slope. Figure 8 shows the variation in the average evacuation time at different slopes and the ratio of time to slope for different crowd densities, which leads to the following conclusions:  
1. There is a linear positive correlation between the average evacuation time at different slopes and the crowd density as the crowd density increases. 2. As the crowd density increases, the degree of influence of slope on evacuation time grows in parallel. When the crowd density is in the range of 0.50-1.00 pedestrians/m\({}^{2}\), the curve grows more gently, the degree of influence of the slope on the evacuation time is weak; when the crowd density is greater than 1.00 pedestrians/m\({}^{2}\), the curve begins to steepen, the degree of influence of the slope on the evacuation time is enhanced. In summary, evacuation times are affected by both crowd density and slope. When the crowd density is less than 1.00 pedestrians/m\({}^{2}\), the evacuation process is relatively smooth and the effect of slope on evacuation time is small. This may be due to the fact that at low densities, people have enough space to move freely, and the effect of the slope is masked by other factors (such as individual evacuation intentions and mobility). The effect of slope on evacuation time is significantly enhanced at crowd densities greater than 1.00 pedestrians/m\({}^{2}\), where crowding among personnel may make movement difficult. At high densities, the slope may make it more difficult for people to move, increasing the overall time required for evacuation.
# Parallel Compact Differential Evolution for Optimization Applied to Image Segmentation
## 3 Parallel Compact Differential Evolution  
In this section, we propose a parallel cDE algorithm (pcDE). The purpose of parallel processing is to perform calculations on multiple processors at the same time to produce the same results. Parallelism can improve the speed of search and convergence, and find better solutions faster. In [33], parallel particle swarm optimization (PPSO) and three different communication strategies are simultaneously proposed. A new communication strategy for paralleling gray wolf optimization is proposed in [34]. Parameter adaptive DE(PaDE) is proposed in [35], and parallel heterogeneous meta-heuristic is proposed in [36]. It can be seen that the effect of parallelization is much better than that of non-parallelization. This paper also proposes two parallel strategies. One is to replace the elites in all groups with the most adaptable elites and replace the corresponding \(PV\) value at the same time, called the optimal elite strategy(oe); the other is to average the elites in all groups and replace the \(PV\) with the average value of \(PV\) in all groups at the same time, called the mean elite strategy(me). The difference mechanism rand/1/bin is adopted in this paper, and two different communication strategies are tested under persistent elitism version. The algorithm flow is as follows:  
1. Divide the problem into \(g\) independent groups, and initialize the \(PV\) in each group, where \(\mu_{i}=0,\sigma_{i}=1,i\in[1,n]\). 2. According to the method proposed in Section 2, each group generates an elite. 3. Each group performs mutation and crossover. 4. According to the persistent elitism version, each group compares the generated offspring with the elite, and obtains a new elite and updates the \(PV\) according to the rules mentioned in Section 2. 5. Every time after \(\theta\) (a predefined constant representing the threshold) iterations, the groups communicate with each other. Taking the optimal elite strategy as an example, it firstly calculates the fitness of the elites in each group and then selects the elite with the highest fitness to replace the other elites. \(PV\) will also be replaced with the optimal elite's \(PV\). 6. Repeat 2-5 until the program ends. The pseudocode of pcDE (optimal elite strategy) is shown in Algorithm 4, and the pseudocode of pcDE (mean elite strategy) is shown in Algorithm 5, \(m\in\)[1, \(g\)].
## 5 Results and Discussion  
### The Basic Formal Ontology Model  
The developed basic formal ontology model has a total of 114 classes. Thirty of them utilized classes from the environmental ontology and 84 new classes have been added based on the Revit and Revit libraries. There are also four types of relationships (is a, part of, has a part, composed primarily of). These four relationships were created to connect classes according to description logic, and each class was applied to connect as needed. For the class annotation, each class covers at least four annotations such as definition, definition source, term editor, language, comments, and reference. Figure 5 shows the annotations and description logic. In this study, the developed model partially uses the existing model, so there are superclasses of the building part. Existing superclasses can also be modified, but this model has kept them to show reusability. In the annotation, language can be selected depending on the user preference, display definitions, comments, and if classes can be reused. It also shows where the class is imported from. Figure 6 shows the structure of the significant class of the proposed basic formal ontology model. Since this model is based on BIM, the structure of the BIM software is used as it is. It is divided into five classes, such as architecture, structure, electricity, machinery, and plumbing. Figure 7 shows the multiple subclasses of the main five classes. Although not shown in the figure, other subclasses exist as needed. Each box shown in Figures 6 and 7 with OntoGraph represents classes, and the lines connecting the boxes represent the relationship, and the relationship can be classified according to the color and shape of the line. The proposed basic formal ontology model is a cornerstone of developing application systems for building information modeling by using information and data. The purpose of the development of this domain ontology is that BIM is used for construction automation and information and research, such as artificial intelligence, because it generates a lot of knowledge and information data. Therefore, in order to prepare the base for the knowledge system of BIM-based research, it is recommended to reuse the proposed domain ontology by developing a perfect domain ontology. The developed basic formal ontology model covers building element information, such as that regarding columns, slabs, walls, and so on, based on BIM and delivers the basic interface for linking the ontology and a BIM platform.
## 3 Results and Discussion
### Agricultural Drought Year Detection
[117] argued that the most abrupt change in increasing warm season droughts in Europe has been observed in central Europe in the last 15 years, with dry events identified in agreement with those of the present study. Agriculture is facing changing climatic conditions, and severe droughts are expected to increase in the coming years [3; 118]. The results obtained show an increase in droughts in the most recent decade of study in the German regions. Despite the apparent lower dependence on irrigation in temperate regions, the impacts of drought on agriculture in these areas are considered a major abiotic stress [119]. One of the actions aimed at reducing drought risks is increasing water supply [120; 121]. Irrigation can greatly mitigate adverse impacts resulting from water stress by maintaining higher SM requirements [122]. This fact becomes even more evident and corroborates the evolution of the agricultural drought years identified in this study when analyzing the evolution of the irrigated area in German regions. According to data obtained from the German Federal Statistical Office [60], from 2010 to 2020, the irrigated area increased by 86%, 27% and 86% in BY, NS and NW, respectively. Therefore, there are worrying signs of increasing water stress in areas where this variable was not previously a limiting factor.
## 3 Numerical Examples
### Boundary Randomness
#### 3.2.1 Calculation Model  
One hundred groups of random boundary samples are randomly generated by the Monte Carlo method, which obeys the normal distribution with the mean of radius of 1 times and the coefficient of variation of 0.1 times. The model of the saturated sedimentary valley under the random boundary is shown in Figure 7, with a depth of 220 m, a width of 440 m, a cover width of 200 m and a thickness of 46.8 m. The incident angles \(\theta\) are 0\({}^{\circ}\) and 30\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Vertical**} \\ \cline{2-5} \(\eta\) & **0\({}^{\circ}\)** & **30\({}^{\circ}\)** & **0\({}^{\circ}\)** & **30\({}^{\circ}\)** \\ \hline
0.5 & 0.11 & 0.14 & 0.15 & 0.16 \\
1 & 0.09 & 0.45 & 0.21 & 0.61 \\
2 & 0.14 & 0.48 & 0.42 & 0.64 \\ \hline \end{tabular}
\end{table}
Table 2: Mean variation coefficient of horizontal and vertical displacement amplitude (random permeability coefficient).
## 3 Results and Discussion
### Optimization of Voltammetrical Parameters
#### 3.4.2 Effect of Scanning Rate  
In order to give a deep insight into the oxidation of DA and UA, CVs of 10 uM DA and UA were performed at various scanning rates (Figure 6A). As the scanning rates increased, their anodic peaks shifted positively while the cathodic peaks shifted in the reverse direction. Furthermore, their response peak currents increased with the potentials scanning speeding up. It is noteworthy that the background currents also enhanced synchronously. To pursue high-signal to noise (S/N), 0.1 Vs\({}^{-1}\) was recommended as the optimal scanning rate. As shown in Figure 6B, the redox peak currents of DA were proportional to the square root of the scanning rate (\(v^{1/2}\)), suggesting the electrooxidation of DA is a diffusion-controlled process. There was also a good relationship between the anodic peak currents of UA and the square root of the scanning rate (Figure 6C), indicating a diffusion-limited electrode process for UA oxidation.  
Figure 5: (**A**) Dependence of pH on the anodic peak currents of DA and UA. (**B**) Linear plots of anodic peak potentials of DA and UA against pH (\(n=3\)).
## 3 Results
### Static Speed Tracking of PW Dataset  
Based on the PW dataset, the static speed tracking performances are shown in Figure 8. The tracking performances are pretty good when the speed is less than 3 km/h and we can reach the same conclusion from Figure 7b that the RMSE remains almost unchanged at lower speeds but eventually increases. The preliminary guess is that the force acting on the knee joint at heel-strike or toe-off is greater at higher speeds which bring bigger knee angle tracking errors as shown in Figure 7c. Therefore, the deviation between the inverted pendulum model and the actual model increases which leads to a higher estimation error of  
Figure 6: Experimental results based on the AB dataset. (**a**) Speed tracking results. (**b**) Step frequency estimation of AB06. (**c**) Stride estimation of AB06.  
Figure 7: (**a**) Speed estimation errors at different speeds based on the AB dataset; (**b**) Speed estimation errors at different speeds based on the PW dataset. (**c**) Tracking errors of prosthetic knee angle when subjects are equipped with the lower-limb prosthesis and walk at different speeds. The error band represents one standard deviation.  
walking speed. The results indicate that the control performances of intelligent prostheses affect walking speed estimation.