esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 4 Combination of Different Types of Dampers
### Performance Evaluation of Combined Dampers Models  
The same analysis was performed for the combined dampers models as for the individual damper-equipped models, and their fragility curves were compared with each individual damper-equipped model, as shown in Figure 10. Figure 10a-c show the probability of exceeding 0.5, 1.0, and 2.0% @max, respectively, for all TS buildings. The 10F_TS_HOHM has the highest seismic performance for all performance levels, as seen in Figure 10a-c.  
In addition, the TS building equipped with a Hysteresis-Oil-Hysteresis damper (10F_TS_HOHM) configuration has almost the same performance as the CS building (10F_CS_FM), as shown in Figure 10d. Therefore, this configuration of dampers in a TS building can significantly improve the seismic performance equivalent to the CS building.  
Moreover, the average rate of energy absorption with increasing values of ground motion intensity was obtained for all of the models that included the combination models, and these values were then compared, as shown in Figure 11. It appears that the combined models use a mixture of the properties of hysteresis and oil dampers, and absorb energy more consistently for all levels of ground motion intensity.  
Figure 9: New combinations (dimensions in mm): (**a**) Hysteresis–Oil–Hysteresis Model; (**b**) Oil–Hysteresis-Oil Model.  
Figure 11: Energy response of all TS models.
## 4 Simulation Results and Discussion
### Effects of the OIS's Load Ratio on Various Aspects of Performance
#### 4.3.4 Manageable Load Percentage of the OIS  
Figure 25 shows the performance of the manageable load percentage of the OIS in relation to its load ratio in various channel environments. When there is no channel fading, in all three methods, the manageable load percentage of the OIS decreases in response to increasing load ratio. This is because the load capacity that can be handled by each SBS is independent of the OIS's load ratio, and hence, an SBS would not exceed its upper load-handling limit even under high OIS load ratios. Moreover, we also observe that the toss-and-catch algorithm has the best performance among the three methods. This is because the toss-and-catch algorithm has a higher connection hold rate than the control groups and has a higher chance of selecting a suitable SBS, which in turn offers more chances of continuously handling the OIS's loads. Therefore, the toss-and-catch algorithm  
Figure 24: Comparing the performance of the OIS’s average RSS in relation to its load ratio using the three methods in (**a**) nonfading environments; (**b**) Nakagami-3 fading environments; (**c**) Nakagami-1 fading environments; and (**d**) Nakagami-1/2 fading environments.  
performed better than the two other methods at various OIS load ratios. In addition, we found that at an OIS load ratio smaller than or equal to 50%, the SBSs selected by the toss-and-catch algorithm are capable of handling over 90% of the OIS's load, mainly because the loads under such conditions are mostly within the load range that most of the SBSs are fully capable of handling.  
When Nakagami-3 fading is employed, the load-handling capacities of the SBSs selected by the toss-and-catch algorithm performed decently, whereas those of the two control groups declined significantly. When Nakagami-1 fading is employed, under most load ratios, the load-handling capacities of the SBSs selected by the toss-and-catch algorithm have exceeded 50% (at an OIS load ratio range of 1% to 83.33%), whereas those of the control groups are lower than 50% (at an OIS load ratio range of 16.67% to 100%). When Nakagami-1/2 fading is employed, the performances of all three methods are below 50% (nonetheless, the performance of the toss-and-catch algorithm was 10% higher than the two control groups in most conditions). This is because the severe fading had reduced the number of SBSs that can be selected by the OIS, as well as the connection hold rate, which lowered the OIS's overall total manageable load percentage.
## 2 Robot Environment Perception for Navigation
### LiDAR and Camera Data Fusion
Another feature map was obtained from the RGB multi-view images using 2D CNNs and projected onto the associated voxels in the volumetric grid to append with respective 3D geometry features. Then, object classes and 3D bounding boxes are predicted by feeding these fused features to a 3D CNN architecture. In the second phase, another 3D CNN estimates the per-pixel object instances using already identified features, object classes and bounding boxes. These voxel-based segmentation methods are constrained by the voxel resolution and require increased computation capabilities with higher grid resolutions. The application of instance segmentation in LiDAR-camera fusion for real-time systems is challenging. Some research studies had limitations, such as the system developed in [100], which does not support dynamic environments. A proposal-free deep learning framework that jointly realises 3D semantic and instance segmentation is presented in [101]. This method performs 3D instance segmentation in the BEV of point clouds. However, this approach is less effective in identifying vertically oriented objects because of the BEV segmentation approach. This method first extracts a 2D semantic and instance map from a 2D BEV representation of the observed point cloud. Then, using the mean shift algorithm [102], and semantic features of the 2D BEV, instance segmentation is achieved by propagating 3D features onto the point cloud. It should be noted that the instance segmentation approaches discussed have been developed to segment 3D point clouds from static indoor environments, and these methods have not shown any segmentation capabilities in dynamic environments. Overall, while significant progress has been made in the perception capabilities of autonomous robots, particularly in regard to object detection and scene segmentation, many of the existing approaches have only been tested in relatively structured indoor environments, and substantial additional work may be required to adapt these techniques to be used in unstructured outdoor environments. Nonetheless, some very useful research directions have been identified that have the potential to significantly advance this field.
# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 3 Results
### Real Routine Verification
#### 3.2.1 Validation of Multi-Index Synchronization Performance Evaluation Model
In view of the complexity and generality of road networks, we randomly select 10 road samples from the Changsha road network to demonstrate the time, economy, and comfort performance and verify its effectiveness. The results of sorting according to type are shown in Table 9.  
Table 9 shows the standard errors \(\sigma\) of the samples in the road landscape of Binjiang road and Third Ring Road, with the minimum errors being 0.014 and 0.062, respectively. The corresponding error vectors for time, economy, and comfort are \([-0.05\),\(0.03\),\(-0.09]\). The errors are within the acceptable value of 0.10. The average standard error of the sample road is 0.036, which meets the quantitative requirement of actual road performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Road Classification** & **Road Density** & **Road** & **Road** & **Environmental** & **Drive Speed** & **Economic Consumption** \\  & **km/km\({}^{2}\)** & **Saturation a.u.** & **Quality a.u.** & **Quality a.u.** & **km/h** & **yuan/km** \\ \hline G-Expressway & 0.42 & 0.40 & 0.97 & \(>\)0.40 & 60–100 & 0.72 \\ Elevated express & 0.06 & 0.48 & 0.97 & \(>\)0.35 & 60-80 & 0.35 \\ Central main road & 1.52 & 0.82 & 0.97 & 0.25-0.30 & 40–50 & 0.58 \\ Peripheral main road & 1.31 & 0.76 & 0.97 & 0.25-0.40 & 40–60 & 0.51 \\ Central sub Road & 1.75 & 1.05 & 0.64 & 0.25-0.35 & 30–40 & 0.63 \\ Peripheral sub road & 1.60 & 0.80 & 0.64 & 0.30-0.40 & 30–50 & 0.52 \\ Access Road & 3.00 & 0.60 & 0.49 & \(>\)0.20 & 20–40 & 0.55 \\ landscape road & 0.15 & 0.55 & 0.64 & \(>\)0.60 & 30–50 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance of road network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Road Level** & **Road Classification** & **T** & **M** & **C** \\ \hline \(1\) & G-Expressway & 0.6–1.0 & 0.12 & 0.77 \\ \(2\) & Elevated Express & 0.6–0.8 & 0.71 & 0.57 \\ \(3\) & Central main road & 0.4–0.5 & 0.26 & 0.48 \\ \(4\) & Peripheral main road & 0.4–0.6 & 0.35 & 0.52 \\ \(5\) & Central sub Road & 0.3–0.4 & 0.20 & 0.40 \\ \(6\) & Peripheral sub road & 0.3–0.5 & 0.34 & 0.44 \\ \(7\) & Access Road & 0.2–0.4 & 0.29 & 0.29 \\ \(8\) & landscape road & 0.3–0.5 & 0.37 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance standard of road time, economy and comfort of each road level.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
###### Abstract
However, when studying large areas in a global aspect, where only lower-detail elevation models are available (5 m, 10 m, 30 m), the passable area is larger with lower-detail models. Processing and adjustment of these elevation models are necessary to obtain reliable CCM analysis results that correspond to the real terrain conditions. The other national models and studies are based on a vector line tactical level CCM analysis [34; 35]. They do not consider the unavailability of detailed data; therefore, they do not fit within the focus of this article. Their focus was on automated navigation in the terrain. From the global point of view, this may be the only option before any global high-resolution elevation database is developed. Elevation models are an important part of the complex analysis of the movement of vehicles in terrain; however, they cannot be considered in analyses without including other data and aspects. The data focus should be put on soil databases. The analysis of the Czech environment is introduced in [36]. The study considered the modelling of geographic and meteorological effects on vehicle movement, focusing on soil conditions and penetrometry; see also [37]. It consisted of an analysis of the characteristics of soil databases. The situation was similar to that of elevation models. National soil databases are relatively detailed enough to support digital terrain models with sufficient accuracy, but the global models are overly generalised and they cannot be used in detailed CCM analyses. Global data can be improved with methods for automatic refinement of soil databases [38] using an algorithm for refining soil data via comparison with relief models in a test grid with a cell of 100 m \(\times\) 100 m.  
Apart from soil databases, vector databases comprising features of the terrain should be taken into account for accurate passability analyses. One of the most used vector databases in NATO is the Multinational Geospatial Co-Production Program (MGCP) [39]. It is not completely a global database, though it has full coverage of the AOI. Since updating vector databases at the global scale can be substantially challenging, a new trend of using open-source data is rising. The OpenStreetMap is an example of a rapidly developing universal vector database [40]. NATO will most probably be using this kind of data more often in the near future. In general, contemporary vector databases do not consist of all features that are needed to enable accurate tactical-level CCM analyses. Currently, there are no DEMs that would meet the requirement of high detail and global coverage at the same time in order to fulfil the needs of NATO and cooperating civilian organisations. The aim of this research was to verify the possibilities of usage of lower detail DEMs in practical tasks, predominantly in CCM analyses. The main asset of this study rested in a proposal for processing elevation models for their efficient evaluation in passability analyses done in larger areas.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 3 Simulations
### Energy Consumption
#### 3.2.1 Simulation Setup in the Energy Consumption Simulation  
The simulation has been conducted with the OPNET simulator. Throughout the entire simulation, 36, 64 and 100 sensors were uniformly deployed in an 180 m \(\times\) 180 m, 240 m \(\times\) 240 m and 300 m \(\times\) 300 m area, respectively. Furthermore, the manet_station model of OPNET is used as the sensor model. Each node transmits a 200-byte packet with a 45-m communication range to the sink node at one of the corners. The packet generation follows a Poisson distribution with a rate of 0.1 packets per second. The optimized link state routing (OLSR) protocol is used in the multi-hop scenario, and DaaG collects the data physically.  
To measure the energy consumption of each node, we used the first order radio model [32]. In this model, the energy consumption for transmitting 1 bit over a distance \(d\) is \(E_{Tx}=E_{elec}+\epsilon_{amp}\times d^{2}\), where \(E_{elec}\) is the energy spent by the transmitter or receiver circuit and \(\epsilon_{amp}\) stands for the energy dissipated in the transmit amplifier. The energy used to receive 1 bit of data is \(E_{Rx}=E_{elec}\). In the series of simulations, \(E_{elec}\) was set to 50 nJ/bit, and \(\epsilon_{amp}\) was set to 100 pJ/bit/m\({}^{2}\)[33; 34]. Each simulation lasted 900 s.
# MDA-Based Approach for Blockchain Smart Contract Development
## 3 MDA-Based Smart Contract Development Method
### Transformation from Blockchain PIM to Blockchain PSM  
Our approach currently has two defined PSMs: Ethereum PSM and Hyperledger Fabric PSM. We chose to demonstrate the multiplatform aspect of our approach by outlining these two PSMs, but the set of PSMs can be further extended by defining the UML profile for other platforms if such a need arises. Blockchain PSMs in MDAsmartCD have their own UML profiles, encompassing platform-specific stereotypes, data types, and validation rules that check model conformance to the required PSM structure. The stereotypes and datatypes of both the Ethereum and Hyperledger PSMs are presented in Figure 5, which also defines their relation to the standard UML metamodel. The profiles also include platform-specific-Ethereum PSM and Hyperledger Fabric PSM--OpaqueBehavior library elements which can be used as code embeddings and are based on the platform documentation examples and ERC standard implementations outlined in [38,39].  
The M2M transformation algorithms to generate Ethereum PSM or Hyperledger Fabric PSM (Figures 6 and 7, respectively) are similar, as they both use the same Blockchain PIM as an input. At the start, a PSM <<contract>> (in Ethereum PSM) or <<chaincode>> (in Hyperledger Fabric PSM) class is created that is appended with model elements that were previously outlined in the Blockchain PIM model. The main difference between the two platforms in this step is the stereotypes applied, and the data types of these elements. In both cases, the stereotype applied to an element denotes the specific structural concept implementation in a particular technology.  
Figure 4: Blockchain CIM to Blockchain PIM transformation.  
After the transformation to either of the two Blockchain PSM variants, the specified models can be extended by additional logic. Such an extension can be executed by specifying interactions (in the form of UML sequence diagrams) or opaqueBehaviors for specific functions, or by incorporating the opaqueBehaviors from the provided curated library of code embeddings based on the platform documentation or community-based ERC application-level standards.  
The final version of Blockchain PSM encompasses UML class diagrams for smart contract structure and can also encompass a combination of several types of behaviour specifications: UML state machine diagrams, UML sequence diagrams, and embedded code as opaqueBehaviors. The resulting Blockchain PSM is validated for conformance to the PSM structure and can then be transformed into smart contract code for the selected platform.
## 5 Rubber Wastes--Used Car Tires  
Rubber waste in geoengineering applications mainly concerns used rubber tires, or rather their crushed parts (Figure 3): pieces \(\geq\) 300 mm, shreds 20-400 mm, chips 10-50 mm, granulate 0.8-20 mm, and powder \(<\) 0.8 mm. The physical and mechanical parameters of the crushed tires depend on the grain size (fraction), the crushing method, and the impurity content (of steel and textile cord). Due to the fact that TDAs are characterized by low particle density (compared to soils), they are a desirable material for the construction of lightweight road embankments on weak-bearing soils [45]. Other applications in geotechnics [46] include as a backfill material behind retaining structures [47], backfills for culverts and underground pipelines [48], drainage and seepage layers (also in landfills), frost protection layers, thermal insulation layers, and vibration insulation layers [49].  
There are many examples of TDA applications in the literature, although the specific investment location is not often indicated. Applications described in more detail include the following:  
* Retaining wall (car tires) at the Lane Cove Tunnel Pacific Highway Exit project in Sydney, Ecoflex International [50].
* Lightweight road embankment (compressed car tires) at the Tampere Western Ring Road project in Tampere, Finland [51].
* Road embankment (shreds) at the Dixon Landing I-880 Project in California (2000) [52].  
Figure 3: Examples of various tire rubber waste used in geoengineering applications (source: photo of [PERSON]).  
Lightweight backfill behind retaining walls (shreds) at the Caltrans 207 Project in Riverside, California [52].
* Insulating layer (geosynthetic with TDA) as a subbase of the BART (Bay Area Rapid Transit) railway embankment at the Warm Springs Extension Light-Rail Project in Fremont, California [52].
* Drainage layer (TDA) in municipal waste landfills--Kiefer in Sacramento, California [52], Tri-Community in Fort Fairfield, Maine [47].
* Drainage layer (shreds) of the East Nippon Expressway in Hokkaido, Japan [49].
* Recultivation (20,000 waste tires) of the Nan-Liao municipal waste landfill in Taiwan [53].  
However, each of the mentioned applications requires a series of tests on soil-rubber mixtures (mainly in terms of optimum moisture content for compaction, swelling parameters, and mechanical properties), especially since the results do not show a constant trend, but are variable depending on numerous factors [54; 55].  
It should be noted that the results of environmental studies [56] did not show significant hazards resulting from the use of TDA in construction work. According to the Regulation of the Climate Minister on the Waste Catalog [12], neither used tires nor products made of them are considered hazardous. However, it is recommended to use worn out tire materials only in an environment with neutral pH and above the level of the groundwater table.
## 4 Empirical Research
### Demographic and Bias Test  
The demographic details of the respondents are shown in Table 1. Among them, 165 (68%) are male respondents and 77 (32%) are female respondents. The respondents' ages are mainly between 31-40 years old (38%), followed by 40-50 years old (31.4%). The education level is mainly a bachelor's degree (39%), followed by high school and below (34.3%). The respondents' positions are mainly middle management (40.5%), followed by top management (34.3%).  
To avoid the non-response bias of demographic data, a paired t-test is adopted to test the data filled in by the top and bottom 25 people who submitted the questionnaire. According to the verification results, there are no noteworthy differences between the two groups.  
Common method bias is a common problem in the questionnaire survey. This study uses two methods to measure common method bias. First, the single factor analysis by [PERSON] was performed [93]. The results showed that the percentage of extracted variables was 12.720% (less than 40%). Then, the common method bias in PLS-SEM was measured according to FLL-VIF [94; 95], and all VIF values were lower than 3.3. The results of the two test methods show that the common method bias in this study is not a serious problem.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Respond Characteristics** & **N = 242** & **\%** \\ \hline
**Gender** & & \\ Female & 77 & 32 \\ Male & 165 & 68 \\
**Age (in years)** & & \\ \textless{}30 & 32 & 13.2 \\
30–40 & 92 & 38.0 \\
41–50 & 76 & 31.4 \\
51–60 & 36 & 14.8 \\ \textgreater{}60 & 6 & 2.4 \\
**Education** & & \\ High school, technical school, and below & 83 & 34.3 \\ Junior college & 28 & 11.6 \\ Bachelor degree & 95 & 39.0 \\ Master’s degree and above & 36 & 14.9 \\
**Job position** & & \\ First-line managers & 61 & 25.2 \\ Middle managers & 98 & 40.5 \\ Top Management & 83 & 34.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic Details of Respondents.
# Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning
## 3 Methods
### Reward Function Design  
In reinforcement learning, a reward function gives an assessment to the actions of agents and can guide the agents to maximize their objectives. The design of the reward function can largely affect the convergence of RL algorithms [52; 53]. Based on our chronological scenario, we design a reward function that can well reflect the tasks in chronological order.  
The reward function consists of three parts. First, the agents need to reach their landmarks as soon as possible, to achieve efficient task completion. Therefore, we give a negative reward at each step according to the distance \(d^{i}\) between agent \(i\) and its corresponding landmark:  
\[r_{dis}^{i}=-d^{i}. \tag{2}\]  
This part of the reward motivates the agents to approach their landmarks as soon as possible to reduce the penalty (negative reward).  
Second, the agents should not collide with each other. Therefore, we give a penalty part if the distance between any two agents is smaller than the sum of their radii.  
\[r_{collide}^{i}=\begin{cases}0&d^{ij}\geq(Rad^{i}+Rad^{j}),\forall j\
eq i\\ -1&d^{ij}<(Rad^{i}+Rad^{j}),\exists j\
eq i\end{cases} \tag{3}\]  
\(d^{ij}\) means the distance between agent \(i\) and agent \(j\) and \(Rad^{i}\) and \(Rad^{j}\) represents their radii.  
The last part is a negative reward that penalizes the agents who break the precedence order. In our setting, the \(N\) agents are required to occupy their landmarks one by one. We represent a precedence constraint as \((v^{1},v^{2},\ldots,v^{N})\). This means that, for any \(i>1\), agent \(v^{i}\) must occupy its landmark after agent \(v^{i-1}\), which is called the precedent agent of agent \(v^{i}\). In our setting, we describe this chronological constraint using the distances between agents and their targeted landmarks. We give a penalty, i.e., a negative reward, to the agent \(i\) (whose precedent agent is agent \(j\)) if the distance from agent \(i\) to its landmark is less than that of agent \(j\). The penalty is described in Equation (4).  
\[r_{order}^{i}=-max(0,d^{i}-d^{i}). \tag{4}\]  
Considering all three penalties together, the reward function in our scenario is:  
\[R^{i}=r_{dis}^{i}+r_{collide}^{i}+\beta r_{order}^{i}, \tag{5}\]where \(\beta\) is the coefficient of the precedence penalty. For simplicity, the coefficients of \(r^{i}_{dis}\) and \(r^{i}_{collide}\) are set to 1 as default as in previous research. Specifically, the third part of the reward varies in tasks with different \(r^{i}_{order}\).
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Dataset Preprocessing
#### 3.2.1 Image Conversion to 8-bit  
The initial step in data preprocessing involves converting the raw medical images into an 8-bit format [87]. This conversion simplifies the pixel intensity values to a range between 0 and 255, which is more manageable for subsequent processing. The 8-bit representation ensures uniformity across all images, regardless of their original bit depths [88]. This simplification is particularly important for consistency in the analysis and facilitates efficient memory usage.