esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Setup and Validation
### Grid Independency  
The computational domain is discretized with tetrahedral elements, while non-uniform computational grids are used for the present simulation. To describe the heat transfer and flow status more accurately, grids are further refined near walls, inlet and outlet, while relatively coarse grids are used for zones away from solid surfaces. The expansion rate between two consecutive cells is no more than 1.12.  
To ensure the first numerical point is located inside the logarithmic layer, the variable \(y^{+}\), termed as the dimensionless wall distance, is controlled in the range of 90-220, satisfy the requirement range of \(30\leq y^{+}\leq 300\) for RNG \(k\)-\(\varepsilon\) model using standard wall functions [24].  
\[y^{+}=\frac{y}{\
u}\sqrt{\frac{\tau_{w}}{\rho}} \tag{5}\]  
where, \(y\) is the vertical distance from wall to the first cell center. \(\
u\) is the kinematic viscosity. \(\tau_{w}\) is the wall shear stress.  
Grid independency is carried out to achieve converged results of the simulations. A set of pre-simulations are carried out to check the same physical parameters by using different grid densities (coarse, normal, and fine), and the grid number is increased until the numerical results will not be affected by the grid size. In this study, the root-mean-square error in the temperature, shown in Equation (16), \(\varepsilon_{r,m,s}\) of less than 2% is used as the criterion for grid independence [25].  
\[\varepsilon_{r,m,s}=\sqrt{\frac{1}{N}\!\sum_{1}^{N}\!\left(\frac{T_{i,f}-T_{i, c}}{T_{r}}\right)^{2}} \tag{16}\]  
where, \(T_{i,f}\) is the temperature in the former grid number, \(T_{i,c}\) is the temperature in the current grid number, the subscript \(i\) represents the \(i\)th sampling point, \(T_{r}=19\ ^{\circ}\)C is the reference temperature, and \(N\) is the number of the examined sample points. Based on the different configurations of building envelope, the resulting unstructured grid numbers of 4.03-5.02 million are used for the simulations.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 3 Parallel Computational Model
### OpenMP  
OpenMP is a parallel programming model for shared memory and distributed shared memory multiprocessors. The OpenMP API supports multi-platform shared-memory parallel programming in C/C++ and Fortran. The OpenMP API defines a portable, scalable model with a simple and flexible interface for developing parallel applications on platforms from the desktop to the supercomputer. OpenMP supports the parallelisation of small portions of a program at a time rather than all at once, a method also known as incremental parallelisation. As a result, it is possible to observe the impact of parallelisation of individual functions on the application [38]. Through OpenMP, the compiler guides the parallelisation of code through directives, easing the burden on programmers of doing it manually [39]. The steps to parallelise the given code using OpenMP have been elaborated in Figure 5. An important aspect is identifying which functions take the longest to execute. The second step involves identifying the code blocks in the function that could be parallelised. This is conducted by ensuring that operations in parallel are not accessed in the middle of their execution and that each result is calculated independently of the others in parallel. After declaring sections as parallel, define variables, as each thread may require a variable only accessible to them. Finally, several test cases must be run to check that the results are identical to sequential code and that there is no race condition.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 3 Results  
As mentioned in Section 2.1, the autonomous monitoring system was operational from 28 July 2020 to 23 May 2021 within the fattening cage, capturing a ventral perspective of the fish. The system is able to provide thousands of accurate automatic measurements per day under ordinary circumstances. However, different factors impede its consistent functioning, primarily including limited visibility and component damage resulting from adverse weather conditions or prolonged exposure to the marine environment. Given these challenges, the recorded data were organised into consecutive-day periods, as detailed in Table 1, with a maximum interval of five days between them. This approach allowed for the accumulation of thousands of measurements within each period.
## 5 Maintenance Decision
### Proactive Maintenance
#### 5.4.2 An E-Maintenance  
Researchers sometimes consider e-Maintenance as a maintenance strategy, a maintenance plan, a maintenance type (e.g., CBM, RCM, RBM), or maintenance support [318]. As an enterprise-level architecture, e-Maintenance includes the resources, services, and management necessary to execute proactive maintenance decisions. This support consists of two parts: e-technologies such as ICT, web technology, new sensors, wireless communications, and so forth, and the other is e-maintenance activities (operations or processes) such as e-monitoring e-diagnosis, e-prognosis, and so forth. [319].  
Through the e-Maintenance, the pertinent DIaK becomes available and usable at the right place, at the right time for making the best (anticipated) maintenance decision. Although E-maintenance has not been applied in ships, it has a broad application prospect in MSAE maintenance because it can make better use of big data and modern information technology.
## 5 Discussion
### Comparisons between the VRPC and Existing Networks  
Although several other validation plans mentioned in the introduction have been launched by research groups or experimental projects, the VRPC is still important for the regional development and validation of remote sensing products in China. Firstly, the observation fields of the VPRC are selected and designed especially for obtaining the \"ground truth\" values for the remote sensing pixels at different scales. However, the existing stations (e.g., CERN) are designed for the particular ecosystem and the observation elements focus on special scales, such as individuals, populations, community, ecosystems. Secondly, the nested multi-scale observation systems are designed and constructed especially for the validation of remote sensing data in VRPC. The existing stations in China don't normally have these kinds of integrated systems. Normally partial instruments are just used by these stations, such as EC and LAS. Thirdly, continuous _in situ_ observation data are necessary to validate and improve remote sensing data. Current validation projects estimate the product accuracy for a single time or over a short time period. However, remote sensing products need to be validated at irregular intervals because of sensor drift and environmental changes. Therefore, proper maintenance, multi-year monitoring and steady funding should be ensured from different departments. Lastly, there are big differences in the observation elements between the VRPC and the other existing stations. Therefore, the construction of the VRPC will be an important complement to the validation networks around the world.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 3 Modulation Techniques for VLC in E-Healthcare Systems  
Modulation techniques are crucial in determining the efficiency, data rate, and power consumption of VLC systems, particularly in healthcare applications where reliability and data transmission security are essential. This section provides an in-depth analysis of different modulation techniques, such as on-off keying (OOK), pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and orthogonal frequency-division multiplexing (OFDM). We will evaluate their suitability for healthcare settings by considering factors such as data rate, power efficiency, and resistance to noise and interference.
## 3 Path Planning Problem
### Path Planning Objectives
#### 3.1.2 Path Safety  
The safe distance of the path reflects the distance from the surrounding obstacles. The set \(O=\{O_{1},O_{2},...,O_{m}\}\) includes all \(m\) obstacles in the workspace. \(MinDis(p_{i}p_{i+1},O_{j})\) is the minimum distance between segment \(p_{i}p_{i+1}\) and obstacle \(O_{j}\). Therefore, \(\underset{0\leq i\leq n\leq j\leq m}{\text{MinDis}(p_{i}p_{i+1},O_{j})}\) is the shortest distance between the obstacles and the path--the larger the value, the safer the path. After adding a negative sign to the value, the maximum optimization safety problem is transformed into the minimum optimization problem. The path safety objective is calculated using Equation (3):  
\[Safety(p)=-\underset{0\leq i\leq n\leq j\leq m}{\text{MinDis}(p_{i}p_{i+1},O _{j})}. \tag{3}\]  
Figure 1: Path representation.
## 3 SPME Process
### Fiber Coatings
It has been used to deposit thin films of SiO\({}_{2}\), TiO\({}_{2}\), SnO\({}_{2}\), ZrO\({}_{2}\) and the three dimensional transition metal oxides V, Cr, Mn, Fe, Co, Ni, Cu, Zn, In (individually or combined) [24]. The hydrothermal growth technique has been used to produce ZnO nanoparticles on the surface of fused silica fiber and stainless steel wire [118, 119]. At first, the procedure consisted of two steps, seeding and growing [24]. A simplification to a single step was realized by [PERSON] _et al._ (2011) [118]. It is possible to coat 50 fibers at once. The hydrothermal method is used to produce organic framework (MOF) SPME coatings which offer high thermal and mechanical stability and exceptionally large surface areas [24]. In their review on procedures for the preparation of SPME coatings, [PERSON] and [PERSON] (2014) conclude that this variety of coatings forms a good basis for the development of more designable structures. Durable and stable SPME devices can be produced by metal supports and chemical bonding between support and sorbent. However, poor selectivity and limited applicability to samples with complex matrices remain problematic [24]. \begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Dipping and physical agglutinating methods** & **Sol-gel technology** & **Chemical grafting** & **Electrochemical methods** & **Electro-spinning** & **Liquid-phase deposition** & **Hydro-thermal methods** \\ \hline
**Carbon nanomaterials [26,120]** & Functionalized or polymer-functionalized carbon nanomaterials [125,126] & Nanomaterials [127,126] & Nanomaterials [138,139] & Nanomaterials [138,139] & **Electrodeposition** & Electrospun & SiO\({}_{2}\), TiO\({}_{2}\), & ZnO \\
**Ordered mesoporous materials** & Ionic liquid-mediated SPME coating [128,129] & Molecularly imprinted polymers [141,142] & Electropolymerized conductive polymers [144,149] & **Electrospun** & **SiO\({}_{2}\), TiO\({}_{2}\), & **ZnO** \\  [121,122] & Sol-gel derived polymeric ionic coatings [130,131] & Substrate-bonded ionic coatings [143] & Conductive polymer-ionic liquid composites [150,151] & **polymer** & **[117]** & **Organic frameworks (MOFs)** \\
# Environmental Risk Source Analysis and Classification of Zones: Subway Construction
## 4 Distribution Characteristics of Environmental Risk Sources of Subway Construction in Study Area
### Environmental Risk Source Zoning Evaluation and Control of Subway Construction  
We redefined the risk level of each partition area to accurately depict the comprehensive influence of each risk source on the environmental risk zoning of subway construction in Urumqi. We first obtained the risk assessment value by calculating the weighted average of the risk scores in each partition area. We then defined areas with risk assessment values in the range of [3.75, 2.5), [2.5, 1.75), and [1.75, 1.25) as low-risk, medium-risk, and high-risk areas, respectively. High-risk areas with three or more class I risk sources were defined as level I high-risk areas, medium-risk areas with two or more level I risk sources were defined as level I medium-risk areas, and low-risk areas with one or more level I risk sources were defined as level I low-risk areas. Based on their risk assessment values, the remaining partition areas were defined as II high-risk areas, II medium-risk areas, and II low-risk areas (Figure 13).  
We intersected the planned spatial location of Urumqi Metro Line 1-7 with the geometric partition layer of the subway construction risk source and combined the risk index table to statistically analyze the situation of railway lines crossing the risk area (Table 7). According to Table 8, the comprehensive risk degree of the main crossing risk areas of Metro Line 1-7 is small. However, all lines cross the high-risk zones, the most significant of which is Metro Line 6. There are six high-risk areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metro Lines** & **Main Crossing Partition** & **Risk Index** &
\begin{tabular}{c} **The Highest Level of** \\ **Risk Zoning** \\ \end{tabular} & **Regional Number** & **Risk Index** \\ \hline Line 1 & 1432 & 2.25 & 1113 & 1 & 1.5 \\ Line 2 & 3433 & 3.25 & 1312 & 2 & 1.75 \\ Line 3 & 2433 & 3 & 1122 & 2 & 1.5 \\ Line 4 & 3424 & 3.25 & 1312 & 1 & 1.75 \\ Line 5 & 1444 & 3.25 & 1232 & 1 & 2 \\ Line 6 & 1433 & 2.75 & 1212 & 5 & 1.5 \\ Line 7 & 3444 & 3.75 & 1222 & 3 & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical analysis of the Urumqi Metro Line 1–7 across the subway construction risk source zones.  
Figure 13: Risk index and classification table for the subway construction risk source zones. The higher the index, the lower the comprehensive risk level of the partition. In other words, the comprehensive impact of the risk source in the region on subway construction is small.
# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 3 Mobility Models and Methodology
### Mobility Models
#### 3.1.2 Gauss-Markov Mobility Model  
This mobility model [20] belongs to the category of temporally dependent mobility models; it applies the historical values of direction and node speed to calculate the next values for both direction and speed. The historical values are used to avoid unexpected changes in the velocity and heading direction. Nodes are randomly placed within the simulation area and given an initial speed and direction. The initial values are used to determine the future values. The model calculates the new speed and direction at each predefined time interval, using different seeds to guarantee randomness.
## 3 Case Study
### Preprocessing  
The vehicle-induced term of the cable tension is valuable as it provides information similar to load-test data, but the exact weight of the vehicle is unknown. Therefore, it is necessary to decouple the multi-source effects and extract the vehicle-induced term. However, this term is non-stationary, as shown in Figure 6a,b, making it difficult to obtain the ideal term by frequency methods alone. Thus, the raw data is segmented into 30-min lengths, and smaller segmentations are suggested [51; 52]. In addition, due to the sparsity of vehicles on the bridge, a larger percentage of the observed data points are near the trend term induced by temperature. Therefore, the violin plot is used as a detrending technique in the preprocessing procedure.  
A violin plot is a data visualization technique that combines the features of a box plot and a kernel density plot. It is used to display the distribution of a continuous variable across various levels of a categorical variable, as illustrated in Figure 6b. By visualizing the data distribution, it is easy to identify that most observed data points are near the trend. Therefore, the median value of a specified segmentation obtained by the violin plot is used as the trend term. Figure 6c shows the trend term obtained by this method in a 30-s segmentation, while Figure 6d shows the trend term obtained for the entire day by segmentations and interpolation, under the smooth assumption of the temperature-induced trend term.  
Figure 6e shows cable tension detail under two occasions of single vehicle loading. Each subgraph in Figure 6e compares the cable tension of the upstream and downstream sides. The left subgraphs show that the vehicle-induced cable tension of the upstream side cables exceeds that of the downstream side cables, while the right subgraphs show the opposite. This indicates that a vehicle is passing in the upstream side direction in the left subgraphs and the downstream side direction in the right subgraphs. Time delay of the vehicle-induced peaks in these two directions is also observed, indicating the nonlinearity in the cable tension of the cable group. Ref. [51] proposed the cable tension ratio feature and a Gaussian Mixture Model (GMM) for damage identification of cables based on cable tension data under single vehicle, from the perspective of cable pairs (cable tension of the upstream and downstream cables). However, this pair-by-pair method cannot infer the exact damaged cable in the suspected pair and may fail in missing data cases. Additionally, the proposed linearity-based ratio fails to model the nonlinearity in the cable tension dataset of the cable group.
## 5 Numerical Results
### The Performance of Weight Adaption  
To test the sensitivity of the DDPG-Agent to the weight of each slice, we set the number of UEs to 45 per slice to simulate the overload of BS. The setting of \(a_{m}^{d}\) is shown in Figure 6b. The corresponding slice-level QoS satisfaction rate of each slice is shown in Figure 6a. The DDPG-Agent tends to allocate more resources to the slices with larger weights for a higher QoS satisfaction rate by observing the weights as part of the state, i.e., S2 UL has the largest weight during steps 40-50, and it has a higher QoS satisfaction rate than any other time. If a slice with the same weight as the others has a very low value in resource use, it may be sacrificed with the same weight as the others, i.e., S3 UL from step 0 to step 50.  
Figure 7a shows the percentage of resources allocated to different slices obtained via different slices versus different schemes, and the corresponding slice-level QoS satisfaction rates versus different schemes are shown in Figure 7b. S2 DL and S3 UL in Table 3 have higher throughput requirements, so the DDPG-Agent allocates more wireless resources to these two parts, which is shown in Figure 7a. Furthermore, the DDPG-Agent allocates more wireless resources to S1 UL than to S1 DL under the same throughput requirements, because the uplink transmission power is lower than the downlink transmission power. Although the heuristic-based DNSIE-nonSUL scheme has similar resource allocation results to that of the DRL-based QGJRA-nonSUL scheme, the DRL-based solution can achieve a better performance in QoS, as guaranteed through dynamic and detailed parameter adjustment. DDPG-driven QGJRA-nonSUL is able to achieve the performance gains of more slices, with a small performance loss of a few slices. The performance of the static-NR-nonSUL scheme is completely determined by preset actions, and it cannot adapt to complex traffic fluctuations, resulting in the worst performance of the BS-level QoS satisfaction rate.  
Figure 5: The average QoS satisfaction rates of all slices versus the number of UEs, increasing from 30 UEs to 120 UEs.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 3 Results
### Strain and Stress at the Subgrade
#### 3.3.2 Plastic Strain and Shear Strain  
Figure 10 illustrates the plastic strain profiles of both unreinforced and geogrid-reinforced pavements over 10,000 and 20,000 loading cycles. It is evident that the geogrid reinforcement results in lower plastic strain compared to the unreinforced section while also distributing the strain at the subgrade's top. This significant plastic strain suggests that the influence of the geogrid layer predominantly affects the formation of plastic strains rather than resilient strains, indicating its efficacy in mitigating permanent deformation.  
Figure 8: Vertical strain at the subgrade’s top.  
Additionally, plastic strain is influenced by subgrade stiffness and base layer thickness, as depicted in Figure 11a,b. Increased base course thickness correlates with reduced vertical plastic strain, while higher CBR values of subgrade soil also lead to lower vertical plastic strain. For instance, increasing the base thickness from 150 mm to 180 mm reduces plastic strain by approximately 30%, while increases to about 47% and 58% are observed when the base thickness changes to 220 mm and 250 mm, respectively.  
Examining shear strain reveals slightly distinct patterns compared to plastic strain. Unlike plastic strain, shear strain registers zero at the centre of the tire imprint, peaking after passing the tire centre, as depicted in Figure 12. Geogrid reinforcement not only decreases shear strains at the subgrade's surface but also improves their distribution. This is primarily attributed to the base/sub-base layer predominantly transferring tensile loads to the geogrid reinforcement rather than the subgrade, thereby reducing shear strains in the subgrade soil. Considering base course thickness, it is clear that shear strain decreases as base thickness increases. For example, increasing the base thickness from 150 mm to 180 mm results in a 23% reduction in shear strain, while increases to about 32% and 37% are observed with base thickness changes to 220 mm and 250 mm, respectively. Similarly, CBR values also impact shear strain, with increasing soil strength leading to a continuous reduction in shear strain.  
Figure 9: Profiles of vertical strain at different depths of the subgrade.  
Figure 11: (**a,b**) Plastic strain developed in the geogrid-reinforced subgrade.  
Figure 10: Plastic strain profiles of unreinforced and geogrid-reinforced pavements.
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 2 Wi-Fi backscatter Uplink Communication  
Figure 1 illustrates the architecture and operation of Wi-Fi backscatter uplink communication. As shown in Figure 1a, the Wi-Fi backscatter includes three types of devices: the Wi-Fi reader, Wi-Fi helper, and Wi-Fi backscatter tag, which are the mobile device, Wi-Fi AP, and RF-powered device, respectively. For uplink communication, the Wi-Fi backscatter tag transmits its tag information to a Wi-Fi reader by either reflecting or not reflecting a Wi-Fi helper signal. Figure 1b shows the operation of Wi-Fi backscatter uplink communication. The Wi-Fi helper consecutively transmits the same size of packets. Then, the Wi-Fi backscatter tag modulates these transmissions by either reflecting or not reflecting the signals. The Wi-Fi reader decodes the modulated transmissions by using the variation of received signal strength indicator (RSSI) or channel state information (CSI) [21,22]. Note that the reflected packets are decoded as \"1\" bits by the Wi-Fi reader or \"0\" bits [6]. As mentioned earlier, the existing Wi-Fi backscatter hardly ensures the consecutive packet transmissions of the Wi-Fi helper when multiple Wi-Fi devices (e.g., legacy Wi-Fi APs and clients) simultaneously transmit their packets in the vicinity, because the Wi-Fi helper should compete with other legacy Wi-Fi devices to access the channel by using CSMA/CA.