esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development of Analytical Model  
### Indentation Crack System of Brittle Materials  
The plastic deformation of brittle materials is well known. When the load applied to an indenter is small enough, the elasticity and plasticity behaviors of 4H-SiC single crystals can be verified via the nanoindentation test [28]. As the load is further increased, a median crack appears from the bottom of plastic zone, as shown in Figure 1. This median crack is closed during the process of unloading, initiating a lateral crack which propagates to the sample surface with progressive unloading [29]. The lateral crack length, \(C_{L}\), and the lateral crack depth, \(C_{H}\), are calculated using the following equations [30]:  
\[C_{L}=C_{2}\bigg{(}\frac{1}{\tan\beta}\bigg{)}^{5/12}\bigg{(}\frac{E^{3/4}}{HK _{\text{IC}}(1-\
u^{2})^{1/2}}\bigg{)}^{1/2}F_{N}{}^{5/8} \tag{1}\]  
\[C_{H}=C_{2}\bigg{(}\frac{1}{\tan\beta}\bigg{)}^{1/3}\frac{E^{1/2}}{H}F_{N}{}^{ 1/2} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Applications** & **Properties** & **Instance and Reference** \\ \hline Optical material & \begin{tabular}{c} High specific rigidity, high \\ thermal stability \\ \end{tabular} & \begin{tabular}{c} Space-based astronomy telescope [13], \\ grating [14] \\ \end{tabular} \\ \hline Device & \begin{tabular}{c} Large band gap, high temperature, \\ high frequency \\ \end{tabular} & \begin{tabular}{c} Partial-Oxide-Semiconductor Field-Effect \\ Transistor (MOSFET) [16], Bipolar \\ Junction Transistor (BJT) [17], Schottky \\ diodes [18] \\ \end{tabular} \\ \hline Composite and coating & \begin{tabular}{c} High hardness, high strength, \\ wear resistance \\ \end{tabular} & \begin{tabular}{c} pistons, valve heads, bearings [19], \\ armor [20] \\ \end{tabular} \\ \hline Porous material & \begin{tabular}{c} High porosity, less volume density, \\ high mechanical strength \\ \end{tabular} & \begin{tabular}{c} Water filtration, filters, polymer matrix \\ composites [3,8] \\ \end{tabular} \\ \hline Micro-Electro-Mechanical & \begin{tabular}{c} High temperature, corrosive \\ environment, high radiation \\ \end{tabular} & \
\begin{tabular}{c} Accelerometer [21], gyroscope [22], \\ pressure sensors [23], resonator [24] \\ \end{tabular} \\ \hline Biosensor & Biocompatibility, chemically inert &
\begin{tabular}{c} Biochemical detection systems [25], \\ implantable flexible electronics [26] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The applications and corresponding properties of silicon carbide (SiC).  
where \(C_{2}\) is a dimensionless constant, \(E\) is the elastic modulus, \(H\) is the hardness, \(K_{\rm IC}\) is the fracture toughness, \(\
u\) is the Poisson's ratio, \(F_{N}\) is the normal force applied to the indenter, and \(\beta\) is the half apical angle of the indenter.  
Another crack system, the radial crack, can be caused in brittle material surfaces by sharp-edged indenters, such as Berkovich and Vickers indenters. The radial crack length is calculated using the following equation [31]:  
\[C_{R}=0.12\left(\frac{E}{H}\right)^{1/3}(\cot\beta)^{4/9}\left(\frac{F_{N}}{K_ {IC}}\right)^{2/3} \tag{3}\]
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 2 Problem Formulation
### General Features of Tweets  
Typically, for a classification task using machine learning or deep learning requires extraction of useful features from the dataset. A variety of features can be extracted from Twitter data, for example, four types of features are extracted from Twitter data for the study on spread of anomalous information in social media [9]: user profile features (users' friends and followers count), user network features (users' EgoNet features), temporal features (retweet count), and content features (e.g., whether a tweet has question mark). In the case of content-based features, word embeddings and n-gram model are well known techniques applied for natural language processing tasks. A word embedding represents words in the text in a way that words sharing the similar meaning have a similar representation. An n-gram model transforms text into a sequence of N words. However, based on the theories of rumor propagation, authors in [10] considered temporal features as one of the key properties for studying spread of rumors since rumormongers have a short attention [11]. In this work, for the fast detection of rumors on social media, we solely focused on the temporal features of Twitter data, which are the creation timestamps of tweets. These timestamps can be readily fetched, and our work strictly relies on them for generation of time-series data, which involves simple calculations, i.e., counting of number of tweets for a given time interval limits.
## Appendix A  
```
Input: LiDAR point cloud \(P_{LiDAR}\), RGB image \(I_{RGB}\), calibration file C Output: Minimum depth \(d_{min}\) for each detected object
1\(projectedPoints\leftarrow\) ProjectToImagePlane(\(P_{LiDAR},C\)) Equation (1);
2 Transparent Sparse Depth Map \(\leftarrow(projectedPoints,I_{RGB})\);
3\(z_{\text{normalized}}\leftarrow\) Depth Normalization Equation (2);
4 detections \(\leftarrow\) YOLOv8(\(I_{RGB}\));
5 foreach\(detection\in detections\)do
6\(B\), \(M\leftarrow\) detection BBox, SegMask Equations (3) and (4);
7\(regionPixels\leftarrow\) GetCoordinatesPoints(\(B,M\)) Equations (5) and (8);
8\(D_{Bbox},D_{SegMask}\leftarrow\) Extracted Depth Values from Transparent Sparse Depth Map Equations (6) and (9);
9 if\(regionPixels\
eq(0,0,0,0)\)then
10\(depthValues\gets D_{Bbox},D_{SegMask}\);
11
12 else
13\(None\gets D_{Bbox},D_{SegMask}\);
14\(z_{normalized}\leftarrow\) Depth Normalization;
15 foreach\((x,y,z)\in depthValues\)do
16 if\(d_{min}^{Bbox}[x,y,z]\) and \(d_{min}^{SegMask}[x,y,z]\) are valid Equation (7) and (10) then
17 Append \(d_{min}[z]\) to \(depthValues\);
18
19
20
21 Close object distance \(\leftarrow(Objects,d_{min}[z])\);
22
23 returnClose object distance
```  
**Algorithm A1**Complete Region Depth Extraction (CoRDE)
```
Input: LiDAR point cloud \(P_{\text{LiDAR}}\), RGB image \(I_{\text{RGB}}\), calibration file C Output: Minimum depth \(D_{\text{min}}\) for each detected object
1\(projectedPoints\leftarrow\text{ProjectToImagePlane}(P_{\text{LiDAR}},\text{C})\) Equation (1);
2 Transparent Sparse Depth Map \(\leftarrow(projectedPoints,I_{\text{RGB}})\);
3\(z_{\text{normalized}}\leftarrow\text{Depth Normalization Equation (2)}\);
4\(detections\leftarrow\text{YOLOv8}(I_{\text{RGB}})\);
5 foreach\(detection\in detections\)do
6\(B\), \(M\leftarrow\) detection BBox, SegMask Equations (3) and (4);
7\(coordinatesPoints\leftarrow\text{GetCoordinatesPoints}(B,M)\) Equations (5) and (8);
8\(center_{B}\) and \(center_{M}\leftarrow\text{ComputeCenter}(\text{CoordinatesPoints})\) Equations (11) and (13);
9\(regionPixels\gets center_{B}\) and \(center_{M}\);
10\(D_{\text{Bbox}},D_{\text{SegMask}}\leftarrow(center_{B},n\times n)\), \((center_{M},n\times n)\) Equations (6) and (9);
11 if\(regionPixels\
eq(0,0,0,0)\)then
12\(depthValues\gets D_{\text{Bbox}},D_{\text{SegMask}}\);
13
14 else
15\(None\gets D_{\text{Bbox}},D_{\text{SegMask}}\);
16\((depthValues_{B},depthValues_{M})\gets z_{\text{normalized}}(D_{\text{Bbox}},D_ {\text{SegMask}})\);
17 foreach\((x,y,z)\in(depthValues_{B},depthValues_{M})\)do
18 if\(d_{min}[x,y,z]\) from Bbox and SegMask is valid Equation (12) then
19 Append \(d_{\text{min}}[z]\) to \(depthValues\);
20
21 Close object distance \(\leftarrow\) (Objects, \(d_{\text{min}}[z]\));
22
23 returnClose object distance
```  
**Algorithm A2**Central Region Depth Extraction (CeRDE)
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
###### Abstract  
Recent studies have shown that deep learning achieves superior results in the task of estimating 6D-pose of target object from an image. End-to-end techniques use deep networks to predict pose directly from image, avoiding the limitations of handcraft features, but rely on training dataset to deal with occlusion. Two-stage algorithms alleviate this problem by finding keypoints in the image and then solving the Perspective-n-Point (PnP) problem to avoid directly fitting the transformation from image space to 6D-pose space. This paper proposes a novel two-stage method using only local features for pixel voting, called Region Pixel Voting Network (RPVNet). Front-end network detects target object and predicts its direction maps, from which the keypoints are recovered by pixel voting using Random Sample Consensus (RANSAC). The backbone, object detection network and mask prediction network of RPVNet are designed based on Mask R-CNN. Direction map is a vector field with the direction of each point pointing to its source keypoint. It is shown that predicting an object's keypoints is related to its own pixels and independent of other pixels, which means the influence of occlusion decreases in the object's region. Based on this phenomenon, in RPVNet, local features instead of the whole features, i.e., the output of the backbone, are used by a well-designed Convolutional Neural Networks (CNN) to compute direction maps. The local features are extracted from the whole features through RoIAlign, based on the region provided by detection network. Experiments on LINEMOD dataset show that RPVNet's average accuracy (86.1%) is almost equal to state-of-the-art (86.4%) when no occlusion occurs. Meanwhile, results on Occlusion LINEMOD dataset show that RPVNet outperforms state-of-the-art (43.7% vs. 40.8%) and is more accurate for small object in occluded scenes.  
pose estimation; deep learning; keypoints detection; PnP +
Footnote †: journal: Medical Image Computing  
12 January 2021  
11743339/app11020743
## 5 Tracking Radar
### Early Digitization  
One key aspect in the design of the new tracking radar is the early conversion of the RF signal into a discrete signal vector for further processing. This _software-defined radar_ approach allows us to flexibly test novel signal processing algorithms in the real-time environment of radar target tracking. For this purpose, the RF signal is sampled directly in the bandpass domain.  
To reduce costs and development time, COTS components are procured and customized to our needs. Two synchronized two-channel _ADQ32_ digitizer systems from Teledyne SPDevices are used to sample the four channels of the monopulse system in the second Nyquist zone. This system converts the signal to baseband by digital down-conversion (DDC). The following signal processing steps are performed on a graphical processing unit (GPU) to meet real-time requirements for the closed-loop tracking of targets.  
As the signal is sampled directly after the receiver protection and the low-noise signal amplification module, the demanding environment of the module location on the moving part behind the apex of the antenna paraboloid (red platform in Figure 1) requires modifications to the cooling concept of the COTS signal conversion module, which was built for a regular workstation computer in a controlled lab environment. This module is integrated to an edge computing device developed by DELL, which itself is housed in a custom-designed 19-inch rack shelter.
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
## 5 Implication of Radiation Hormesis for Potential Therapeutic Study
### Diabetes  
Diabetes mellitus is a condition known to be associated with major risk factors for cardiovascular diseases, the incidences of which continue to increase globally. Recent studies have shown that excessive oxidative stress caused by an increased production of ROS, particularly superoxide anion and its derivatives, may account for its pathogenesis, including several complications observed in the early stages of diabetes. Reactive oxygen species play important pathological roles in cardiovascular diseases due to their abilities to alter the function of specific cellular proteins and enzymes, which eventually leads to alteration in the expression of pro-inflammatory molecules and endothelial dysfunction. As described earlier, one of the mechanisms by which LDIR exerts its protective effects is strongly associated with increased antioxidant activities. Consequently, LDIR has been used as an experimental tool in studying the development of diabetes and in the prevention or amelioration of diabetic cardiovascular complications. Animal studies have demonstrated that the anti-diabetic effect of low-dose-rate gamma radiation can ameliorate type II diabetes, at least in part through the increased insulin secretion and pancreatic superoxide dismutase activity [153]. In addition, it has been found that the repetitive exposure of diabetic mice (type I and type II) to whole-body low dose rate X-ray radiation at 0.025 or 0.050 Gy produced remarkable results in alleviating diabetes-induced renal oxidative stress damage and inflammation. This is reported to arise through the increased expression of AKT and renal nuclear factor Erythroid 2-related factor-2 (Nrf-2), a key regulator of cytoprotective responses to oxidative stress [154; 155]. Animal data have indicated that the bio-positive effect of LDIR in diabetes is not only obtained in terms of enhanced insulin sensitivity but also leads to significantly reduced complications, especially in diabetic nephropathy and dyslipidemia and cardiomyopathy through Akt/Nrf2-mediated antioxidant signalling pathways [154; 155; 156]. Clearly, it would not be ethical nor practical to irradiate humans solely for this purpose, especially because of the risk of carcinogenesis (which may be underestimated in small animal experiments), but the development of surrogate drugs might confer therapeutic benefits. Moreover, mice immune systems may not adequately reflect human immune systems. Nevertheless, the results of [PERSON] et al. (2004) [157] support the concept of LDIR protection against diabetes and its complications through the induction of antioxidants. However, it also needs to be mentioned that lifestyle changes and carefully designed targeted drugs are known to achieve effective results.
## 2 Materials and Methods
### Dataset  
In order to fulfill the objectives set for this work, the NSCLC-Radiogenomics Dataset [23] was identified and used, as it comprises three fundamental requirements that were crucial for the development of the present study: thoracic CT scans from NSCLC patients, tumor binary segmentations, and _EGFR_ mutation status labels. This dataset is a publicity available collection of CT images from a NSCLC cohort of 211 subjects collected retrospectively between 2008 and 2012 at Stanford University School of Medicine and Palo Alto Veterans Affairs Healthcare System. The CT scans are paired with patient clinical history and were obtained using different scanner models and scanning protocols, presenting variations in slice thickness from 0.625 to 3 mm (median: 1.5 mm) and X-ray tube current from 124 to 699 mA (mean: 220 mA) at 80-140 kVp (mean: 120 kVp). Additionally, binary nodule segmentation masks are also stored in this database, as well as semantic tumor annotations. This dataset is the only public dataset that comprises paired information on lung-cancer-related gene mutation status and CT data; however, the present study focuses only on _EGFR_ due to its clinical relevance based on approved target therapies. Out of 211 subjects, only 116 lung cancer patients from this database were considered since only these owned tumor binary masks and a _EGFR_ mutation test result (mutant: 20%, wild-type: 80% (see Figure 2)).
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 4 Simulation
### Parameter Setting
In Figures 6 and 7, we compare the performance of the proposed AL-SAC algorithm with two benchmark algorithms: the DDPG algorithm with the penalty involved in the reward function, termed as penalty DDPG [33] and the original soft actor-critic algorithm without constraint handling, termed as SAC [31]. Observe from Figure 6, the SAC algorithm achieves the highest reward, which is much larger than those achieved by AL-SAC and Penalty DDPG. However, from Figure 7, we can see that the overall bandwidth allocated exceeds the maximum bandwidth constraint. Hence, it can be concluded that the high reward achieved by the SAC algorithm is because it cannot handle the constraint of the action sum, resulting in more radio resources being available, i.e., BS cannot satisfy the constraints in (9). More importantly, comparing the rewards achieved by the proposed AL-SAC and Penalty DDPG, we can see that from Figure 7, both of them satisfy the maximum bandwidth, and the proposed AL-SAC algorithm significantly outperforms the Penalty DDPG algorithm due to the larger reward achieved. The reason behind this is that although Penalty DDPG can meet bandwidth constraints relying on the penalty item in the rewards, the proposed AL-SAC has a strong capability in handling much smaller discrete and continuous action spaces by two stage-design, and the introduction of the constraint part can make the optimizing process more effective. Specifically, the proposed AL-SAC algorithm achieves an improvement of around 42.1% in reward compared to the Penalty DDPG algorithm after 5000 episodes. Moreover, from Figure 7, the overall bandwidth allocated of the proposed AL-SAC algorithm is a little larger than the Penalty DDPG, which is both under the maximum bandwidth \(B_{m}=10\) MHz constraint. It shows that the policy of device association and bandwidth allocation trained by the proposed AL-SAC algorithm are more efficient. Figure 5: Total bandwidth allocated for all devices during training process.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 6 Simulation Performance and Analysis
### Result Discussion
#### 6.2.2 Poisson Traffic  
In the real physical world, network traffic can be described by Poisson distribution. In this paper, we test routing protocols under Poisson traffic under various rates. Simulation results are demonstrated in Figure 10. According to the results, similar to CBR traffic, DHBP has the best throughput and the lowest transmission cost. The DDR is higher than that of DBPR and SALB, which indicates a good load-balancing performance.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 3 Materials and Methods
### Construction of Zones
\begin{tabular}{c c} \hline
**S. No.** & **Parameters** & **Definition** & **Description** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{\(Z_{i}\)} & Zone declared in VANET, i.e., Z1 & Every zone defines its communication and provides effective feedback for zone 1, Z2 for zone 2, and so on & Every zone defines its communication and provides effective feedback for zone-based content caching. There are several zones concerning the number of vehicles and RSUs. \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular} 
\begin{tabular}{c c} \hline
**S. No. ** & **Parameters** & **Definition** & **Description** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{\(Z_{i}\)} & Zone declared in VANET, i.e., Z1 & Every zone defines its communication and provides effective feedback for zone 1, Z2 for zone 2, and so on & Every zone defines its communication and provides effective feedback for zone-based content caching. There are several zones concerning the number of vehicles and RSUs. \\ \cline{1-1} \cline{3-4}  & &In Algorithm 1, the network is distributed into multiple zones. Every zone is selected according to zone radius. The radius length depicts the area covered by the zone. Importantly, the zone is affected through ZoR and ZoF. In each zone, we determined two forms of nodes: one type is interior nodes, and the other is boundary nodes. Interior nodes inside the zone are responsible for communication and message dissemination for the safety of the vehicles in V2V and V2I environments. In other words, these are responsible for Intrazone communication. The main goal is to Geocast communication among the zonal members. The boundary nodes are selected for Interzone communication. The pre-caching strategies are implemented for adequate safety and road accident prevention based on the zone selection. Besides all this information, a zone-based routing table is maintained, storing the routing paths from which the messages are forwarded and received inside each Zone. The time complexity of Algorithm 1 is measured for all zones from \(Z_{1}\) to \(Z_{n}\). In every zone, the nodes are selected within boundaries. So, the measured time complexity for Algorithm 1 is O(Log \(Z_{n}\) ), which is good in terms of zone selection time in the moving environment of VANET. On the other hand, the space complexity for the algorithm is about storing parameters from Table 1, which is O(n).
## 2 Dual-Port MIMO Antenna
### Diversity Parameters
#### 2.5.2 Diversity Gain (DG)  
It measures the enhancement in reception achieved through antenna diversity. It is calculated using Equation (3) [25; 26], which relies on the characteristics of ECC. Figure 11 demonstrates that both the simulated and measured DG values remain consistently near the ideal benchmark of 10 dB throughout the full UWB spectrum. In this case, the lowest observed DG is 9.93 dB, and the highest is 9.99 dB, resulting in an average DG of 9.98 dB. Hence the system effectively improves signal quality by utilizing multiple antennas.  
\[\mathbf{DG}=\mathbf{10}\sqrt{\mathbf{1}-\left(\mathbf{ECC}\right)^{2}} \tag{3}\]  
Low ECC values along with high diversity gain demonstrate strong isolation between the antenna elements. This outcome is the result of spatial separation and a well-optimized ground plane structure. These design features limit electromagnetic interaction and suppress surface current coupling.