esa-sceva/satcom-chunk-collection huggingface dataset:
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 4 Traffic-Aware Clustering-Based Routing Protocol (TACRP)
### Inter-CH Communication  
The idea of LEACH protocol is used for the process of inter-cluster communication and is recreated according to the network model [33]. At the initial stage, CH is an election and then clusters are created. The CH collects the data from the CM vehicles and then transmits it to other CHs and to the RSUs according to availability. The inter-CH communication is described in Figure 3. In order to reduce the network overhead, the CH merges similar information by consolidating several data into one. The process of inter-CH communication is sequentially explained below.  
The TMU initiates the new rounds in a periodical manner, for each round it gets increments of the round number. The CH is chosen according to the above-mentioned calculations. The CH used in the network is 0 to 1 in numbers. After the election of the CH, it transmits the request message to the vehicles in its coverage area. The CH present in the coverage area of the vehicles receives the reply message from the vehicles. After receiving the confirmation from the CH the vehicles become cluster members CMs. CM vehicles forward sensed data to respective CHs. Then the CHs aggregate the collected information from the CMs. Finally, the data are transmitted to the RSUs/CHs. The RSUs, in turn, forward the data to the TMUs. The same process is repeated in all the rounds until the end of the communication.
## 2 Results and Discussion
### Morphology and Size Distribution  
TEM images were used to demonstrate the morphology, particle size, and particle size distribution of the TiO\({}_{2}\) nanoparticles, as shown in Figure 5. The TiO\({}_{2}\) samples prepared by the modified thermal treatment method at a lower calcination temperature exhibited uniform morphology with adequate particle size distribution. However, at a high calcination temperature of 800 \({}^{\circ}\)C, the TiO\({}_{2}\) particles exhibited irregular morphology due to the agglomeration of primary particles consisting of either some single particles or clusters of particles. The average size and size distribution of nanoparticles were determined from TEM images using Image tool software, considering at least 100 particles for each sample. The average particle size is about 10.1 and 40.5 nm at calcination temperatures of 500 and 800 \({}^{\circ}\)C, respectively. These results indicate that the attained particle size increased as calcination temperature increased due to the fact that, as the temperature rose, many adjacent particles tended to fuse together to form larger particle sizes by melting their surfaces [35]. The average particle size obtained from TEM images was found to be in a good agreement with the results obtained from the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temp. (\({}^{\circ}\)C)** & **20\({}^{\circ}\)** & **Crystallite Size from XRD** & **Particle Size from TEM** \\  & & **(\(\pm\)0.3 nm)** & **(\(\pm\)0.1 nm)** \\ \hline
500 & 25.436 & 8.4 & 10.1 \\
600 & 25.352 & 28.4 & 31.3 \\
700 & 25.300 & 36.7 & 38.0 \\
800 & 25.241 & 42.9 & 40.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The calculated crystallite sizes and particle size of TiO\({}_{2}\) corresponding to different calcination temperatures.  
Figure 4: X-ray diffraction (XRD) pattern of TiO\({}_{2}\) nanoparticles with various calcination temperatures.  
XRD measurements (Table 1) and demonstrate the exponential variation with increasing calcination temperature. Many researchers have already reported the effect of calcination temperature on the crystallite size and specific surface area of TiO\({}_{2}\)[36; 37]; however, in our case, the regular variation in particle size with calcination temperature offers the advantage of the size-controlled preparation of TiO\({}_{2}\) nanoparticles. For instance, to produce a comparable particle size of TiO\({}_{2}\) with reference material (TiO\({}_{2}\)/P25: 21 nm), a calcination temperature of 550 \({}^{\circ}\)C is appropriate.
## 3 Results
### Agronomic Parameters
According to Figure 2, sites with more exchangeable K also tend to have lower exchangeable Ca (r: \(-\)0.79), nitrogen in leaves (r: \(-\)0.5), corncob weight (r: \(-\)0.55), corncob length (r: \(-\)0.58), cob weight (r: \(-\)0.53), cob length (r: \(-\)0.51), plant fresh weight (r: \(-\)0.53), and plant diameter (r: \(-\)0.51). Principal component analysis (PCA) is shown in Figure 3, and the differences between treatments were notable. Dimensions 1 (28.2%) and 2 (15.3%) are the first two principal components; together, they explain 43.5% of the total variation in the data. Figure 3: Biplot of principal component analysis (PCA): (**a**) Individuals and variables for each treatment; (**b**) variables contributions. Plant height (PH), plant diameter (PD), plant fresh weight (PFW), number of leaves per plant (NLPP), total leaf area (TLA), 100-seed weight (100 SW), number of seeds per plant (NSPP), seed length (SL), seed diameter (SD), cob length (CCL), cob weight (CCW), corncob weight (CW), corncob length (CL), corncob diameter (CD), leaf carbon (C_leaf), leaf nitrogen (N_leaf), mesophilic aerobic bacteria (MAB), filamentous fungi and yeast (FFY), _Pseudomonas_ (PSE), actinomycetes (ACT), _Bacillus_ (BAC), soil organic carbon (SOC), exchangeable cations (Ca_Ex, Na_Ex, Mg_Ex, K_Ex), electrical conductivity (EC). The Control treatment (T0) stood out in terms of plant growth and yield attributes. Among the organic amendments, vermicompost leachate (T1) was associated with higher levels of soil nutrients such as potassium, as well as, increased abundance of _Pseudomonas_ spp. Biochar (T2) was notable for improving soil characteristics, microbial communities, and plant biomass. The combined vermicompost leachate plus biochar treatment (T3) exhibited an intermediate effect.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.1 Video Analysis and Monitoring  
The authors [203] propose that a system that demonstrates that real-time video analytics is the killer app for Telco Cloud. The reduction in camera prices and the increase in network speed have resulted in a significant increase in video analysis applications and the deployment of large numbers of camera networks. If the video analysis process is moved to the edge, it will help to reduce energy consumption and increase data processing [204].
# A Novel Visual SLAM Based on Multiple Deep Neural Networks
## 2 Related Work
### Feature Extractions for Visual SLAM  
Many existing visual SLAM systems generate feature pairs for localization and map building by extracting features in each image frame and matching the extracted features between different image frames. For example, ORB-SLAM2 [10] extracts ORB features [15] in each input image frame, constructs corresponding descriptors, and matches these features in the current frame with reference frames and local maps by the Hamming distance between descriptors. Stereo LSD-SLAM [11] treats high-contrast pixels in input image frames as features and matches the features between image frames based on the photoconsistency of all high-contrast pixels. SuperPoint-SLAM [25] uses SuperPoint (a fully convolutional neural network) [26] to extract features and descriptors, and matches these features across image frames by the Euclidean distance between descriptors.
## 2 Field Measurements  
### Introduction to the Case Study  
Located in Gansu Province, China, Jinchuan Group is a large nickel-based mining company that ranked first in China in terms of nickel production in 2021. After more than sixty years of practice and exploration, Jinchuan Group has developed the largest mechanized underhand drift cut-and-fill stopping with the largest continuous recovery area in the world. The third mine of Jinchuan Group was taken as an example, which adopts the mechanized panel area downward layered cement filling mining method. The panel areas are divided along the strike of the ore body, each panel area is 100 m-200 m long and the width is the horizontal thickness of the ore body, and each panel area is connected by a segmented liaison road and a layered liaison road. The height of the drift-type stope is 4.5 m, the span is 5 m and the length generally is not more than 70 m. Cemented paste backfill (CPB) is used to fill the mining-out area, and the compressive strength and tensile strength of CPB are 5 MPa and 0.59 MPa, respectively. As can be seen from Figure 1, workers are exposed to the CPB roof for mining operations, thus the mechanical properties of the CPB roof are extremely important for safe mining. To investigate the stress-strain distribution pattern in the CPB roof during mining, field measurements were conducted in the 40# stope of the third sublevel of the 1110 m sub-section II panel area third mine and the 48# stope of the third sublevel of the third panel area.
# Development of a Wideband Slotted Antenna Array with Low Profile and Low Sidelobe
## 1 Introduction
This issue can be partly resolved by using the unequal T-junction power divider with phase compensation [28]. However, the two output ports are asymmetric, and thus, the design process will be also very complicated when it is used for a complex full-corporate-feed network. In this paper, a wideband 16 \(\times\) 16 slotted cavity waveguide-based antenna array is proposed for K-band applications. The main novelty of the developed study is that by introducing a high-order cavity mode (\(TE_{220}\)), a much more compact broadband antenna array is obtained. Another novelty is that, in order to provide unequal powers while keeping their phases the same, a simple and effective T-junction power divider structure is designed. Additionally, by properly designing the amplitude-tapering feeding network, based on the proposed T-junction power divider, a low SLL radiation pattern is obtained. The proposed topology is validated by using simulations based on the finite element method (FEM). The rest of the paper is organized as follows. In Section 2, the design principles of slotted cavity antenna array are presented, including the design of the subarray and the unequal T-junction-based feeding network. In Section 3, simulated results of the proposed 16 \(\times\) 16 antenna array are discussed. The simulated results of the proposed antenna array are compared with the existing similar designs in the literature in Section 4. Finally, a conclusion is drawn in the final section.
## 3 Drone Vulnerabilities and Attack Vectors
### Signal Jamming  
Signal jamming disrupts drone communication by flooding frequencies with high-intensity signals, severing the link between the drone and its operator. This can cause loss of control, autonomous landing, or system failure, causing risk to critical applications like law enforcement or emergency response. Countermeasures involve frequency-hopping and spread-spectrum technologies and robust fail-safe mechanisms to guide drones to safety during communication loss [108].
# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
### Preliminaries and Assumptions  
In this work, a scenario of multi-hop data communication in a WSN is proposed, in which all the nodes are randomly deployed at finite distances with the central control node, the BS. The network is divided into clusters in such a way that there is no specific number of nodes per cluster. Each cluster can be identifiable by a unique ID as Cluster ID. The total number of clusters at time \(T\) can be represented by \(KT\). Moreover, the nodes can switch to another cluster so the cluster sizes may be changed dynamically. The network can be defined in the terms of a game \(G\) as \(G=\{N,\ S,\ U\}\). Where, \(N\) represents the nodes, \(S\) denotes the strategy set for each node, and the utility function of each node can be denoted by \(U\).  
All the member nodes send the gathered data to their respective CHs. It is the duty of CHs to combine and summarize the observed collected data and forward it to the BS, either directly or through some other nodes. Other nodes can be CHs or CMs of the same or neighboring clusters. The network nodes are assumed to be rational to meet the requirement of game theory. The nodes are independent to either cooperate or not in the network. Moreover, the intelligent nodes can adjust their strategies to become CHs or get a better record for their participation level.  
The fundamental routing mechanism is inspired by the dynamic source routing (DSR) [33] protocol. In DSR, the address of each involved node during the data transmission can be obtained from the received packets. The BS must be capable of investigating the level of involvement of all the network nodes by analyzing the received packets. This act is very essential in order for BS to enlist the selfish or noncooperative nodes in the cluster. A node can be said to be a selfish node when it does not cooperate with other nodes for some personal benefits.  
A WSN with homogenous nodes has been proposed in this scheme. All the nodes have initially equal energy levels and their energy consumption ratios on various operations are also considered to be similar. For optimal accuracy and results, it is preferred to deploy BS near the center of the network. According to the basic principle of WSNs, the immediate nodes to the BS take more load as compared to other nodes. Therefore, it is desired to deploy nodes in such a way that nodes in each upper hop level are very dense as compared to the density of nodes at the lower hop level of the network. For example, the nodes directly connected with the BS, i.e., hop level \(1\), will be more densely deployed than the nodes lying in hop level \(2\).
# Numerical Investigation of the Pile-Soil Interaction Problem under Dynamic Loads
## 4 Parametric Studies
### Effect of Groundwater Level  
In order to investigate the effect of the groundwater level, the condition of the soil being completely clay was investigated numerically. Clay and sand layer thicknesses were not changed in the experimental analysis. For both cases, the groundwater levels at the surface, \(-3\) m, \(-6\) m and \(-9\) m were examined. For both cases, the groundwater level-displacement graph is presented in Figure 12, and the groundwater level-maximum moment graph is presented in Figure 13. Considering the layered soil situation, it is seen that the displacement increases with the increase in the depth of the groundwater level, and there is no significant change in the displacement after the groundwater level reaches \(-6\) m. This situation is caused by the groundwater level approaching the underlying sand layer, and the effect of the groundwater level remaining in the sand on deformation decreases. In the case of only clay, it was observed that the deformation first decreased and then increased as the groundwater level increased. The decrease in the groundwater level causes the upper layer to behave more rigidly and the deformation to decrease up to a certain level. As the water level decreases below the bottom elevation of the pile, the pile behaves flexibly, and its deformation increases. In terms of the maximum moment, in the experimental analysis, if the groundwater is on the surface, the moment takes the lowest value and then shows a very significant change. In the clay case, groundwater does not have a significant effect.
## 3 Ticket Categorization and Visualization Method
### Ticket Vectorization  
Tickets, which include text data and developer data, are converted into vectors. This process can be divided into five steps:  
* Step 1.1. To handle tickets for natural language processing, select the text data columns that indicate the project's features (e.g., ticket title). Multiple columns should be selected due to the small sentence length. For example, commit comments are useful because they may contain information about the features modified.
* Step 1.2. To enhance the accuracy of ticket categorization, parse the extracted text data into terms and filter uninformative terms such as stop words. If terms are not separated by white space, use morphological analysis to parse the sentence into terms (e.g., [37]). For example, if only nouns are extracted from ticket \(I_{1}\), \"Button\" from the title, \"button\" and \"dialog\" from the description, and \"dialog\" from comment 1 are extracted (Figure 2).
* Step 1.3. To obtain the ticket vectors, count the term frequency in each ticket. For example, the term frequency of ticket \(I_{1}\) is \(\{button,dialog\}=\{2,2\}\) (Figure 2).
* Step 1.4. To emphasize the keywords, weigh the term frequency vectors based on the term frequency-inverse document frequency (TF-IDF). TF-IDF weights a term vector to extract the feature terms. The weight of term \(i\) of ticket \(j\) is calculated as \[w_{i,j}=tf_{i,j}\cdot log\frac{N}{df_{i}}\] where \(tf_{i,j}\) is the term frequency of term \(i\) of ticket \(j\), and \(df_{i}\) is the number of tickets containing term \(i\). \(N\) is the number of tickets. Moreover, select important and unimportant terms by clustering the most common terms in each category and weight the vectors of these terms in a second analysis.
* Step 1.5. To consider the relationship between features managed by the same developers, select columns of developer data (e.g., author and assignee of tickets). To concatenate the developer data with the weighted term frequency, convert developer data  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Description** \\ \hline Title & Add Button \\ Description & Add Button on a dialog \\ Assignee & Bob \\ Issue date & yyyyy/mm/02 \\ Updated date & yyyyy/mm/03 \\ Comment 1 & Fix dialog \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of items described in an issue ticket.  
Figure 3: High-level chart of flow of steps in our method.  
into binary data. For example, the developer vector of ticket \(I_{1}\) is \(\{P_{A},P_{B}\}=\{1,0\}\) (Figure 2).
## 4 Discussion
### VF-ABM Performance  
VF-ABM were calibrated for Day 1, Day 2, Day 3 and Day 5 and verified for Day 7, Week 2 and Week 4 with the flow cytometry data. The overall temporal trajectories of neutrophils, macrophages and fibroblasts were found to be in reasonable agreement between empirical and simulated data.  
Statistically, the empirical data of all three cell types fell within the VF-ABM output ranges for Day 7, but slightly outside the 95% confidence interval for remote time points (Week 2 and Week 4 except for neutrophils). Of note, the VF-ABM stimulated data showed a discrepancy of fibroblast count from the empirical trend at Week 4. The simulated fibroblast counts were lower than those of empirically observed by about 14 folds at Week 4. Based on the literature, proliferation rates of fibroblasts are \(15.4\pm 1.1\%\) after 4 days, \(4.1\pm 0.6\%\) after 1 week, and less than 0.5% after 2 weeks from rodent cardiac literature [113]. Since the proliferation rate of fibroblasts greatly depends on the microenvironment such as cytokine levels and ECM contents, the exact proliferation rate for vocal fold fibroblasts was not fully accurate based on the cardiac tissue data. Additional empirical data are thus needed to better estimate the proliferation rates of vocal fold fibroblasts in both homeostatic and injurious conditions. Once the data become available, parameters related to the sprouting amount and sprouting frequency of fibroblasts can be revised to improve the ABM-simulation of fibroblast dynamics in the final stages of wound healing such as wound remodeling.  
We also noticed that VF-ABM neutrophil outputs did not have a perfect match with those from the empirical data in terms of timing and magnitude. (Figure 4A) The estimation of initial parameter range for corresponding sprouting frequencies (i.e., the rate of the cell infiltration when there is tissue damage) was difficult to define for calibration. The infiltration rate of neutrophils was only available as a function of blood flow in the human circulatory system [114]. However, specific information on rates of blood flow and injury-induced vasodilation within the vocal fold lamina propria are not available to date. Such data are necessary to better simulate the transmigration of neutrophils from capillary to mucosal tissue in VF-ABM.
## 2 Materials and Methods
### Biodegradation of MBCs  
For the evaluation of the biodegradability of the bio-composite materials, the concept of the 301F method developed and approved by the OECD was used after partial modification according to the sample's specifics and the objectives of this study. Namely, this includes the complex and non-defined organic content of the samples and the requirement to investigate the behavior of the bio-composites in terms of decomposition in a soil environment.  
OECD 301F is an aerobic biodegradation test that utilizes respiratory as an indication of oxygen consumption and ongoing biodegradation. It aims at screening biodegradable materials within 28 days of degradation by activated sludge collected from WWTP. Among the group of 301 methods, it is the one suitable for testing poorly soluble and non-soluble materials such as the samples that are the subject of this study.  
The degradation percentage is calculated as the ratio of oxygen consumption to the theoretical oxygen demand (ThOD). The chemical formula of the test substance and its purity may be known to calculate the ThOD. If the ThOD cannot be obtained, the chemical oxygen demand (COD) may be experimentally determined and used as an alternative. The validation of the results needs a blank control containing only the inoculum (which, in this study, was a mixed culture of aerobic soil bacteria) without any organic medium for evaluation of the autolysis contribution to the test results measured.
## 3 Field Case Study of Pumping Test
### History Matching  
In the initial model used in the simulations, the permeability and pore compressibility of the aquifer (L-2) were obtained from the 2D radial flow solutions. The other parameters of the aquifer and the other layers were determined based on experimental results. Table 1 lists the optimal hydraulic and mechanical properties determined using the history matching approach. Aided by the aforementioned 2D radial flow analysis results, the determination of the optimal parameters required only a few tuning steps.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Property & Model & L-1 & L-2n & L-2s & L3 & L4 \\ \hline \multirow{2}{*}{Bulk modulus (GPa)} & A & \multirow{2}{*}{0.35} & 0.80 & 0.80 & \multirow{2}{*}{0.7} & 0.6 \\ \cline{2-2} \cline{5-6}  & B & & & 0.80 & & \\ \hline \multirow{2}{*}{Shear modulus (GPa)} & \multirow{2}{*}{0.20} & 0.40 & 0.40 & \multirow{2}{*}{0.4} & \multirow{2}{*}{0.3} \\ \cline{2-2} \cline{5-6}  & & & 0.40 & & & \\ \hline Pore comp., \(\beta_{pr}\) (Pa\({}^{1}\)) & A & \(2.5\times 10^{9}\) & \(3.9\times 10^{9}\) & \(12\times 10^{9}\) & \(2.5\times 10^{9}\) & \(2.5\times 10^{9}\) \\ \hline \end{tabular}
\end{table}
Table 1: Major hydraulic and mechanical properties.  
Figure 8: Geological and numerical models for simulation of the pumping tests at Well-3 and Well-4.