esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wireless Mesh Sensor Network Requirements
A WMSN solution based on the IEEE 802.15.4 protocol stack would satisfy, _a priori_, the compatibility with other solutions which fully employ this same standard, and the inter-connection with multiple wired/wireless technologies by means of commercial gateways available. 6. _Self-organization_: WMSNs should be easily configured and maintained requiring little or no human intervention. This requirement is one of the most attractive aspects of the WMSNs for the end-user because it demands a very low effort in network maintenance tasks. However, self-organization is also one of the major challenges for developers. The reason is that nodesmust implement enough intelligence and autonomy (by means of efficient software/hardware modules) to auto-organize themselves in a mesh topology. Nevertheless, this fact entails a significant trade-off with the energy efficiency. 7. _End-to-end reliability_: This requirement guarantees the correct information delivery at destination. As discussed before, link reliability can be improved by increasing the number of devices within the sensing area as well as the path redundancy. This issue allows us to reduce the ratio of messages lost in WMSNs (caused, for instance, by path loss fading in the wireless propagation medium). However, some applications demand transport layer services to assure the information delivery (e.g., precision asset location or personal health monitoring) or, at least to notify any important event occurred in the network at destination (e.g., detection of intruders in a restricted zone). Therefore, techniques such as the support for data prioritization can be applied to enhance the end-to-end reliability of the network. 8. _Security_: Security is a key issue in WMSNs because wireless communications can be easily eavesdropped, altered (replacement, deletion and/or injection of data) and disrupted by malicious attackers from inside/outside the network in absence of security mechanisms. For instance, a dishonest user can keep sending a large volume of queries to the network to prevent authorized users from accessing the sensor data. Thereby, the main security objectives consist of guaranteeing the _confidentiality_ and _integrity_ of data; the _authentication_ to verify the identity of nodes; and the _availability_ to ensure the survivability of the application facing attacks that disrupt the right operation of the network. 9. _Mobility support_: The majority of WSN and WMSN applications are planned for static nodes, where they usually remain in the same initial location. However, some applications require mobility support of sinks and/or sources. For instance, in large-scale networks, sinks can be displaced along different strategic points to ensure the delivery of data from the sources as well as to improve different metrics such as the transmission delay.
## 4 Laboratory Tests
### Phase 3: Mechanical Behaviour of Soil Mixtures
#### 4.2.2 Compaction Test: Modified Proctor  
The level of compaction is a key parameter in road layer construction. A proper compaction ensures the strength and durability of the road; conversely, if the road has not been adequately compacted, it becomes unstable, which can result in a possible differential settlement. Settlement in a road implies pavement deformation and cracking that, added to storm water infiltration, causes a serious impact on traffic and road safety [35].  
Compaction is studied through the moisture-dry density relationship, which is determined according to the Modified Proctor Test, UNE EN 103501: 1994 standard.  
The moisture-dry density relationship, which is shown in Figure 7, is a very useful tool for understanding compacted soil behaviour. The maximum dry density to the optimum moisture content is obtained, which allowed the samples made to achieve the optimal mechanical behaviour. Additionally, the curve shape indicates the sensitivity of the soil to the water addition. If the curve is flatter, the maximum dry density is less affected by moisture changes; however, if the curve is sharper, small changes in the moisture content greatly affect the maximum dry density [36], as can be observed in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Designation**} & \multicolumn{8}{c}{**Materials (kg/m\({}^{3}\))**} \\ \cline{2-7}  & **CS** & **SS** & **Quicklime** & **OPC** & **N1** & **N2** & **N3** \\ \hline CS + 1.5\%QL & 1590.00 & - & 23.85 & - & - & - & - \\ CS + 1.5\%QL + N1 & 1590.00 & - & 23.85 & - & 0.910 & - & - \\ CS + 1.5\%QL + N2\%N3 & 1590.00 & - & 23  
Analysing Figure 7, SS shows the highest maximum dry density, 2.28 kg/m\({}^{3}\), and minimum optimum moisture content, 6.2%, likely due to its physical properties, such as a higher quantity of coarse particles and its higher density. However, CS stabilised with lime and quicklime plus nanomaterials show values contrary to SS due to the clay gradation, which is composed mainly of fine particles and its great absorption of water, which is a typical characteristic of expansive soil.  
As can be observed, the addition of nanomaterials in the amount used in this article (0.056 w% N1 and 0.12% N1&N2) shows a low effect on the compaction parameters, especially for the dry density, which is consistent with the results obtained in previous studies of soil stabilisation with nanoparticles [29].  
However, [PERSON] et al. [37] showed a decrease in the maximum dry density and an increase in the optimum compaction humidity in a soil stabilised with 5% lime from nano-SiO\({}_{2}\) additions greater than 1%.  
Finally, the AM-1, AM-2 and AM-3 mixtures have an intermediate behaviour among the materials that compose them. A slight increase in the maximum dry density due to the addition of nanomaterials was observed in the AM-2 and AM-3 mixtures, likely due to the interaction of the stabilised soils with nanomaterials and the OPC.
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN
### ML for Protocol Stack Layers
#### 6.2.2 Link and Medium Access Control Layers  
In traditional wireless networks, ML can be used for efficiently and proactively allocating the spectrum. In [47], a DNN is adopted to solve the resource allocation problem in  
Figure 13: Structure of the multi-access edge computing: (**a**) on-board deployment; (**b**) architecture.  
real time. The authors considered the weighted sum-rate (WSR) maximization problem subject to the transmit power constraints. Results show that the approximation obtained by a DNN, trained on input/output pairs of a resource allocation algorithm, can speed up the computation time by orders of magnitude compared to a weighted minimum mean square error (WMMSE) method. It is fair to point out that these results are referred to a Python implementation of both WMMSE and DNN. However, the performance improvement obtained by the Python DNN compared to a C implementation of WMMSE is about 7% on average. It is worth noting that the same resource allocation problem can be formulated by considering the spectrum allocation of the LEO beams or even between different NTEs. As an example, a DNN model can be used to optimize the area throughput (defined as the sum of the throughput of all users belonging to a specific area), by properly allocating the spectrum among different NTEs, or even dynamically adjust the size of the LEO beam footprint, for example enlarging the footprint in low populated areas while reducing it in the most populated areas. This can provide a greater spectrum available for the most requiring users. Then, several inputs for the enhancement by ML can be used, such as the user density in a given area, the grade of user mobility, NTE and UE trajectories or the requested traffic type (e.g., MMB or MTC).  
Another application related to this layer is the UE traffic prediction. ML can take the traffic load and spectrum usage as inputs and predict the traffic generated by UEs in a dynamic way, tolerating high variations on these input factors. In [48], an auto-encoder based model is used for spatial modeling and LSTM units for temporal modeling. The authors have chosen the average error (i.e., prediction vs actual) as their performance metric calculated as mean squared error (MSE), mean absolute error (MAE) and log loss. Simulation results showed a \(30.8\%,20.5\%,33.1\%\) reduction, respectively, compared to the support vector regression (SVR) and \(40.4\%,28.4\%,18.5\%\) reduction, respectively, compared to the auto-regression integrated moving average (ARIMA). While considering ground, air and space elements in a standalone way, these techniques are still applicable to NTNs. However, NTNs introduce novel specific issues, which can be mitigated by the adoption of ML techniques. ML can help in the NTE selection for a given service, taking into account the network congestion and the requested QoS/QoE, for example, choosing the one with the shortest delay or the least congested.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 3 Finite Element Modeling of the Micropump
### Mesh Independence Test for FQPB and Micropump  
A mesh independence test was carried out next. The meshing of FQPB along with the silicon diaphragm and pump chamber was carried out. The meshing of the FQPB actuator varied the number of elements, ranging from 1000 to 50,000. With an increasing number of elements, a change in natural frequency was observed (Figure 2d). It can be seen clearly  
Figure 2: (**a**) Meshing of the FQPB actuator with silicon diaphragm, (**b**) meshing of the complete micropump, (**c**) boundary conditions at various interfaces, (**d**) mesh independency test of the actuator, and (**e**) mesh independency test of the complete micropump.  
that, beyond 28,500 elements, the curve became asymptotic, indicating mesh independence. At 28,500 elements, a natural frequency of 35,338 Hz was observed. An increase in the number of elements beyond 28,500 brought a deviation of the order of 0.09% only, hence, 28,500 elements were considered for the analysis.  
A mesh independence test was also carried out for a complete micropump with solid-fluid interaction. For this purpose, the meshing of the fluid domain was done with a varied number of elements, ranging from 20,000 to 60,400. The results were almost the same, with only 0.61% of pressure deviation beyond 49,000 elements, as shown in Figure 2e.
## 1 Introduction
### Review Methodology
#### 1.2.3 Inclusion and Exclusion Criteria  
Inclusion criteria: (1) Peer-reviewed journal articles and conference papers; (2) technical reports from authoritative institutions; (3) studies focusing on urban road traffic control systems; (4) research addressing connected/automated vehicle integration; (5) publications in English or Chinese languages.  
Exclusion criteria: (1) Studies limited to highway or rural traffic control without urban applicability; (2) pure theoretical works without practical implementation considerations; (3) research focusing solely on vehicle design rather than traffic control systems; (4) duplicate publications or preliminary conference versions of journal articles.
## 3 Results and Discussion
### C-V Response to mAs  
In general, for all the strips used in this study, the C-V signal was linear to mAs variation. The green C-V signal data points were very close to the linear fit hence had the highest R-squared value--0.9784. The R-squared values for the cold white, warm white, red, and blue were 0.9673, 0.6199, 0.9775, and 0.8282 respectively, as observed in Figure 4. Higher R-square values implied higher conformity of the signal data points to the linear fit. This further indicated that the signal linearly increased as the mAs was increased. The magnitude and rate at which the signal increases is however depicted by the linear fit's slope. The slopes for the cold white, warm white, red, green, and blue C-V graphs were 0.0015, 0.0004, 0.0022, 0.0015, and 0.0011 respectively. The red LED strip had the highest signal increment per unit mAs, i.e., 0.0022 mV per unit increase in mAs. Conversely, the warm white had the lowest signal increment per unit mAs, i.e., 0.0004 mV per unit increase in mAs. In all LED strip colors tested, a threshold effect was observed for all tube current values below 20 mAs. No signal is observed for these tube current-time product values (as illustrated in Figure 4).  
The tube current-time product (mAs) is correlated to the quantity of thermionically emitted electrons in the x-ray tube [42]. This corresponds to the number of x-ray photons in the x-ray beam i.e., x-ray beam population. In our study, maintaining a constant kVp (incident photon energy) while varying the mAs (number of photons) produced more linear signals. Therefore, the LED strip C-V response was more sensitive and linear to the number of photons in the x-ray beam (mAs). In other  
Figure 4: The C–V responses of cold white, warm white, red, green, blue LED strips to the milliamperage (mAs) parameter alteration.  
words, the charge drift in LED strip PN junctions was linear to the number of photons in the x-ray beam. The charge drift's linear dependency on x-ray beam photon number thus implies a specific quantity of x-ray photons correspondingly excites a discrete number of electrons resulting in a fixed amount of radiation-induced C-V signal. The threshold effect observed across the LED strips could further imply the minimum quantity of x-ray photons that has to be present in the x-ray beams before charge drift/electron excitation takes place. [PERSON]'s findings [51] coincide with ours since our output signal was linear to the tube current (mAs); thus, radiation dose is linear to the tube current (mAs) and tube potential (kVp) [52; 53].
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### General Applications
#### 6.1.3 Video & Live Streaming
[PERSON] [84] presented an overlay network architecture to supplement the enhanced multimedia broadcast multimedia service (emBMS) standard architecture in order to allow crowdsourcing live video providers to multicast. Figure 31 illustrates the Multcast at edge architecture. The authors designed a Virtual Network Function (VNF) in order to identify possible multicast scenarios, using user request for a live video from a particular locality. Information collection, validation of a multiple multicast framework as well as initialization of emergency multicast service is executed by the VNF application server on the fly. The authors executed a simulation evaluation of the proposed architecture using Facebook live video datasets. The result showed a considerable advantage of the proposed architecture in respect to efficiency, cost and quality of experiment (QoE), and also in saving bandwidth at the backhand, transit and RAN links. [PERSON] [85] proposed an edge-assisted crowdcast framework called Deepcast which carries out intelligent decisions using massive amount of viewers and network real-time  
Figure 31: Multcast at edge architecture [84]. data to aid personalized QoE with reduced system cost. To deal with the high computation complexity involved in this situation, the authors designed a data-driven DRL based solution capable of learning the most appropriate approaches to schedule and transcode users request automatically. The proposed framework was evaluated with a trace-driven experiment using various real-world datasets. The result revealed that the proposed framework and its DRL-based solution was superior.
## 2 Method
### Contour Offset
The direction of rotation is set to counterclockwise, the axis of rotation is the contour tangent vector, and the direction of the curve at the point is the target vector. Whether the point moves in a direction that obeys or resists the direction of the curve is a matter of whether or not to reverse the line segments on the curve. When the angle between the start vector and the target vector is obtuse, the curve needs to be reversed for the case of contraction. When the angle is acute, the curve is reversed for extended cases. Figure 8: Contour offset. Points are moved forward or backward along their constraint curves, thus enabling the contraction or expansion of the contour. Figure 7: The plane intersects a triangular facet. (**a**) Transform the problem into a plane intersecting a line. (**b**) Solve it using mathematical geometric relationships. As shown in Figure 9, self-intersection and sawtooth problems also need to be solved when obtaining the biased contour. Figure 9a shows the redundant rings when the points on the contour are offset and then reconstituted into the contour in the original order. The adjacent points on the contour are formed into line segments, and it is judged whether there is intersection between the adjacent segments, including same-plane intersection and opposite-plane intersection. If it is the former, the intersection point \(Q\) of the two line segments is directly retained and the points \(P_{i+1}\) and \(P_{i+2}\) on the anomalous ring are removed. If the latter, then, each line segment has a pseudo-intersection, taking the intermediate values of \(Q^{\prime}\) and \(Q^{\prime\prime}\) that represent them. Figure 9b shows the phenomenon of sawtooth on a contour. When this occurs, there must be a conflict between the constraint curves of neighboring points. Therefore, when there is an intersection of the offset trajectories of two points, the neighboring point \(P_{i+1}\) is removed.
# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 3 Damage Visualization
### Developed Image Processing Algorithms
#### 3.1.1 Pre-Processing  
The obtained images were converted into greyscale, in which the minimum intensity value was equal to 0, and the maximum intensity value was equal to 255 [34]. Then, the averaging filter and contrast enhancement techniques were used to precede the enhancement of the raw images. Enhancement in this case, consists of smoothing the image while preserving as much of its grey tonality as possible. Moreover, computational efficiency is an essential consideration in image processing, due to the size of data sets. Some filters, such as average filter, can be implemented by division into two simpler filters, lead to substantial time savings [27]. Assume an \(n\times n\) filter that can be separated into two smaller sizes of \(n\times 1\) and \(1\times n\). The performance of the \(n\times n\) filter involves \(n^{2}\) multiplications, and also \(n^{2}-1\) additions for every pixel in an image, while the performance of \  
the grayscale components can be curved to either brighten or darken the intensity [36]. The MATLAB function, imagjust (), was used herein to create Gamma transformations. Since our images were of the class 'uint8', the function multiplied the values by 255 to specify the actual values of an image. After pre-processing of images, cluster analysis was carried out for steel plate (SP), composite materials (CM), and metal plate (MP) images.
# Mechanical Performance of Built-Up Columns
## 4 Numerical Analysis
### Effect of Number of Connector

Table 4: _Cont._Furthermore, the effect of the number of connectors was also analyzed from the aspect of the strength-to-weight (STW) ratio, as listed in Table 4. Note, the strength-to-weight ratio is derived from dividing the ultimate capacity obtained from the numerical calculation by the total weight of the models. As listed in Table 4, for the columns having length of 2400 mm and 3000 mm, when the column length is the same, the STW ratio increases with the increase in the number of connectors. For example, for the column with length of 3000 mm, the ultimate load of the model with 7 connectors is increased by 54% compared with the model without connectors, and the STW ratio is improved by 28%. However, for the columns with length of 600 mm, 1200 mm and 1800 mm, the STW ratio decreases as the number of connectors increases. For example, for the column with length of 1800 mm, the ultimate load of the model with 4 connectors is only increased by 8% compared with the model without connectors, but the STW ratio is reduced by 9%. This shows that the increase in the number of connectors can improve the economic performance of long columns but reduce the economic performance of middle and short columns. Finally, in order to better improve the mechanical performance of the built-up column from the structure, two additional connectors were mounted at both ends of S1. The results of the model with end connectors were listed in Table 4 as L3000S700-5 (end). It can be observed that the bearing capacity of L3000S700-5 (end) is increased by 14.2% compared to that of L3000S700-3 (S1, same spacing between connectors), but decreased by 3.4% compared to that of L3000S500-5 (same number of connectors). This shows that the performance of the built-up column can be improved by mounting connectors at the column ends without changing the spacing between connectors. However, on the premise of the same number of connectors, it has a more obvious effect on improving the mechanical performance to reduce spacing between connectors than to install connectors at column ends.
# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 3 Results and Analysis
### Statistical Analysis  
To conduct a more in-depth evaluation of the proposed method, quantitative indicators such as the number of crossings of visible feature targets and the processing times are selected for analysis. The statistical results of different methods are presented in Table 2.  
In Dataset 1, characterized by a predominantly flat landscape with a few building complexes, the [PERSON] et al. [20] method traversed 87 obvious buildings within a processing time of 30 s. In comparison, [PERSON] et al. [25] traversed fewer buildings (67), while showing that segmentation-based methods can offer advantages over pixel-based methods. The proposed SOSG method demonstrated the least number of crossings of obvious feature targets in this dataset but incurred the highest processing time.  
Dataset 2 features a large number of buildings and some areas of undulating terrain. The [PERSON] et al. [20] method and the [PERSON] et al. [25] method showed comparable seamline performance in this dataset, with [PERSON] et al. [25] requiring more time. The proposed SOSG method passed through significantly fewer obvious objects than other methods in this dataset, although it also exhibited the longest processing time.  
In summary, the statistical results affirm that the SOSG method effectively avoids obvious ground objects, generating optimal seamlines that enhance visual quality and quantitative performance. However, achieving a balance between efficiency and accuracy remains a challenge, and future work will focus on improving the time efficiency of the proposed method.