esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Effect Analysis of Roof Self-Stability Factors
### Effect Analysis of Roof Self-Stability Factors along Strike
The roof bending moment above the goat presents asymmetric M-shaped curves due to the existence of the backfilling hydraulic support and unsupported-roof distance in the support system. The maximum roof bending moment continuously decreases with increased supporting-peak loads. When the support peak load rises from 0.62 to 4.62 MPa, the maximum roof bending moment is decreased by 33.34% (from 37.37 to 24.91 MN/m). The peak load reaches a maximum near the front coal wall, where the roof is damaged at first. Therefore, the support force of hydraulic support can be appropriately increased to ensure roof stability. Effect analysis of unsupported-roof distances  
The unsupported-roof distance is not more than 1.5 m according to the actual situation of the slope. It is set to be 0.5, 1.0, and 1.5 m, respectively. Figure 5 shows the correlation curves among the unsupported-roof distance, roof deflection, and bending moment through mechanical calculations. The effect of the unsupported-roof distance on roof subsidence can be ignored. A positive correlation exists between the unsupported-roof distance and roof bending moment. When the unsupported-roof distance increases from 0.5 to 1.5 m, the maximum roof bending moment is increased by 9.9% (from 31.42 to 34.52 MN/m). The overall roof stability is insignificantly impacted by the unsupported-roof distance. However, in the poor condition of the roof, the empty top distance is large, and the supported area of the roof is reduced, which will also cause various disaster problems. Figure 4: Relationship among supporting-peak loads, roof subsidence, and bending moments. Effect analysis of advancing distance of working face  
The advancing distance of the working face is set to be 47.37, 57.37, and 67.37 m, respectively. Figure 6 shows the correlation curves among the advancing distance of the working face, roof deflection, and bending moment through mechanical calculations. The maximum roof subsidence and maximum bending moment increase with the increased advancing distance of the working face. When the advancing distance increases from 47.37 to 67.37 m, the roof maximum subsidence is increased by 4.00% (from 0.225 to 0.234 m); the maximum roof bending moment is increased by 22.20% (from 34.41 to 42.05 MN-m). The advancing distance of the working face slightly impacts roof self-stability under dense backfilling. The backfilling body reaches a certain strength to support the roof after dense tamping, which suppresses the roof movement. The stress concentration coefficient is small in the initial and subsequent mining process of the working face. Besides, the support stress area does not increase significantly with the advancement of the working face.
## 2 Study Area and Datasets
### Seabed Classification Data  
Seabed type information was derived from the seabed geological maps and databases published by the Geological Survey of Ireland (www.gsi.ie). These maps, produced by interpreting multibeam bathymetry and backscatter data, inform about seabed type and geomorphological factors. Data points showing similar characteristics are grouped into discrete classes (see Table 1). Sediment samples are used to label these classes with geological descriptors. This dataset provides a further predictor variable in our study models. Fine-grained sediments (classes 3, 4 and 5) account for over 92% of the Bay's seafloor. Hardgrounds (class 1) and Channel class (class 2) account for 4% each.
## 2 The Proposed Methodology
### The Construction of Slope Stability Evaluation Model
#### 2.2.3 GNN-TreeNet Network Structure
The weight was the attribute value of the edge (if it existed, otherwise it was 1). The update simply used the weighted sum of the node embeddings in the previous layer, so the specific propagation formula for it was the following:  
\[\mathrm{h_{v}(i)}=\sigma\Bigg{(}\mathrm{W_{i}\sum_{u\in\mathrm{N(v)}}\frac{ \omega_{u}h_{u}(i-1)}{|N(v)|}+B_{i}h_{v}(i-1)}\Bigg{)} \tag{2}\]  
The activation function uses a Gaussian error linear unit, and its function form is as follows:  
\[\mathrm{GELU(X)}=\frac{\mathrm{X}}{2}\Bigg{\{}1+\tanh\Bigg{[}\sqrt{\frac{2}{ \pi}}\Big{(}\mathrm{X}+0.044715\mathrm{X}^{3}\Big{)}\Bigg{]}\Bigg{\}} \tag{3}\]  
According to the \"Technical Specification for Geological Hazard Risk Assessment\" (DB11/T 893--2021) [45] and \"Specifications for risk assessment of geological hazard\" (GB/T 40112--2021) [46], the slope stability can be divided into four categories in the probability range of 0-1: stable, relatively stable, basically stable, and unstable. The degree of stability is expressed by a probability value (\(p=0.0\) is the most unstable, and \(p=1.0\) is the most stable). The prediction vector is a one-dimensional vector, and the cross entropy is used as the loss function:  
\[\mathrm{loss}=\sum_{v\in\mathrm{V}}\log\big{(}\sigma\big{(}\mathrm{h_{v}^{ \prime}\theta}\big{)}\big{)}+(1-\hat{y}_{v})\log\big{(}1-\sigma\big{(}\mathrm{ h_{v}^{\prime}\theta}\big{)}\big{)} \tag{4}\]  
where \(\mathrm{h_{v}}\) is the n-dimensional embedding vector of the top node, and \(\theta\) is a matrix that transforms the n-dimensional embedding vector into a one-dimensional prediction vector. Figure 3: Structural diagram of the GNN-TreeNet network.
## 5 Example  
### Calculation Based on Finite Element  
As an example, this paper establishes a model of a pipeline that suffers from the seismic excitation of a non-random process. The length of the pipeline is 200 m and the material of the pipeline  
\begin{table}
\begin{tabular}{c c c} \hline
**Grades of Damage** & **Description** & **Indices** \\ \hline essential integrity & good condition & \(\sigma<[\sigma_{\text{t}}]\) \\ minor damage & non-destructive or appears micro-cracks locally. & \([\sigma_{\text{t}}]<\sigma<\frac{[\sigma_{\text{t}}]+2[\sigma_{\text{t}}]}{3}\) \\ medium damage & joint fracture, local deformation or cracking of & \(\frac{[\sigma_{\text{t}}]+2[\sigma_{\text{t}}]}{3}<\sigma<\frac{2[\sigma_{ \text{t}}]+[\sigma_{\text{t}}]}{3}\) \\ severe damage & severe deformation or fracture, cracked weld, & \(\frac{2[\sigma_{\text{t}}]+[\sigma_{\text{t}}]}{3}<\sigma<[\sigma_{\text{b}}]\) \\ heavy leakage, difficult to repair & & \(\frac{2[\sigma_{\text{t}}]+[\sigma_{\text{t}}]}{3}<\sigma<[\sigma_{\text{b}}]\) \\ destruction & breakage, severe damage to the interface weld, & \([\sigma_{\text{b}}]<\sigma\) \\ severe leakage, no repair value & & \([\sigma_{\text{b}}]<\sigma\) \\ \hline \end{tabular}  
* \(\sigma_{\text{t}}\) is the ultimate compressive strength; \(\sigma_{\text{b}}\) is the ultimate tensile strength.  
\end{table}
Table 1: Division of degree of damage to pipelines.  
Figure 11: Assessment process.  
is X60 steel. By using Abaqus (Dassault aircraft company, Vaucresson, France), a finite element software, a three-dimensional finite element model of pipe-soil interaction is established by nonlinear surface-to-surface contact. The outer wall of the pipeline with high rigidity is selected as the main surface, and the soil with small rigidity is selected as the surface. The normal contact behavior adopts \"penalty\", and the friction coefficient is 0.5. The soil is simulated by a solid element with a depth of 3 m and the constitutive relation is the Drucker-Prager (D-P) model. The mechanical properties of X60 refer to literature [38], and the basic parameters of the materials are shown in Table 2.  
After applying the load, as shown in Equation (38) [39; 40], the Von Mises response of the pipeline can be obtained by using the finite element calculation, as shown in Figure 12. By combining with theoretical analysis, the result of the interval of response is taken as: [229.63 MPa, 242.28 MPa].  
\[g(t)=\left\{\begin{array}{l}\left[-4.671(t/5)^{2},3.417(t/5)^{2}\right],0\leq t <5\\ \left[-4.671,3.417\right],5\leq t<10\\ \left[-4.671e^{-0.3(t-10)},3.417e^{-0.3(t-10)}\right],t_{2}\leq 10\end{array}\right. \tag{38}\]
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 4 Image Data and Training Procedure  
### Image Data Generation Method  
Circular grid pattern images were extracted by recording the tensile test on video using a digital single-lens reflex (DSLR) camera. Additionally, to validate the proposed algorithm, data measured with the traditional DIC method were compared, and lighting was set up to capture images without reflections by reducing specimen reflection.  
From the video of the tensile test process, circular image data were extracted from each frame. The procedure for extracting circular image data is shown in Figure 8, where the tensile specimen was identified using Yolo v4 training on the recorded original image, and the ROI of the detected tensile specimen was designated. Image binarization was applied for contour detection, and image inversion and morphological operations, specifically open operations, were performed to reduce unnecessary noise. To detect only the contours of the circular grid pattern, the pixel size detected was limited by the ratio of the gauge length to the circle diameter. As a result, the circular grid pattern was detected as shown in the pattern ROI of Figure 7, and the circular image ROI was obtained using the coordinates acquired at that time. The acquired circular ROI image was extracted from the original image to prevent information loss during the image preprocessing step that detects the circular pattern and was binarized for training application. The extracted circular images had different sizes depending on the size of each circle. For AI training, all images must be the same size, so the extracted circular images must be resized uniformly. Simply enlarging the extracted images to match the size can deform the circular image itself, leading to different results in strain calculations. To prevent this problem, the circle detected in the first frame of the video was designated as the initial circular image, and a white image twice the diameter of the circle was created. The binarized circular image was combined with the generated white image centered to create an image of the same size without losing the information about the circle size. Additionally, the images used for training were enlarged to a size of 112 \(\times\) 112; because the resolution of the extracted circular image was low at about 20 \(\times\) 20 pixels, super resolution was applied to obtain additional high-resolution images. From the images obtained through the preprocessing step, the initial circular image detected in the first frame and the circular images detected in frames 2 to N were used as input data for the AI application.
###### Abstract  
Due to CPU and memory limitations, mobile IoT devices face challenges in handling delay-sensitive and computationally intensive tasks. Mobile edge computing addresses this issue by offloading tasks to the wireless network edge, reducing latency and energy consumption. UAVs serve as auxiliary edge clouds, providing flexible deployment and reliable wireless communication. To minimize latency and energy consumption, considering the limited resources and computing capabilities of UAVs, a multi-UAV and multi-edge cloud system was deployed for task offloading and UAV trajectory optimization. A joint optimization model for computing task offloading and UAV trajectory was proposed. During model training, a UAV communication mechanism was introduced to address potential coverage issues for mobile user devices through multiple UAVs or complete coverage. Considering the fact that decisions made by UAVs during trajectory planning may lead to collisions, a MADDPG algorithm with an integrated safety layer was adopted to obtain the safest actions closest to the joint UAV actions under safety constraints, thereby avoiding collisions between UAVs. Numerical simulation results demonstrate that the optimization method based on safety reinforcement learning considering communication cost outperforms other optimization methods. Communication between UAVs effectively addresses the issue of redundant or incomplete coverage for mobile user devices, reducing computation latency and energy consumption for task offloading. Additionally, the introduction of safety reinforcement learning effectively avoids collisions between UAVs.  
mobile edge computing; UAV path planning; task offloading; communication; collision avoidance +
Footnote †: journal: Applied sciences
## 2 The Current State of Quality Control in the Tire Industry  
In the tire industry, ensuring the quality of products is of paramount importance. Manufacturers have traditionally relied on manual inspections and quality control processes to detect any defects or imperfections in tires before they reach the market. However, with advances in technology, the tire industry is using AI to revolutionize its quality control procedures.  
Traditionally, tire quality control involved visual inspections and manual measurements. Skilled technicians meticulously examined each tire for abnormalities such as sidewall deformities, tread pattern inconsistencies, or other visual defects. While this method was effective to some extent, it had limitations in terms of speed, accuracy, and consistency.  
With the integration of artificial intelligence, tire manufacturers are now able to use automated inspection systems that use ML algorithms to assess tire quality more efficiently and accurately. These AI-based systems use advanced imaging techniques and sophisticated algorithms to detect even the smallest defects that may not be visible to the human eye.  
One important aspect of tire quality control is the final production stage, where rigorous checks are carried out to ensure that each tire meets certain standards. According to information provided by [[https://www.opony.com.pl/](https://www.opony.com.pl/)]([https://www.opony.com.pl/](https://www.opony.com.pl/)) (accessed on 20 June 2023) [16], this final inspection phase involves a comprehensive test of various tire parameters.  
At the final stage of production, AI-based systems are used to analyze tire dimensions, tread depth, sidewall structure, and other critical factors. These systems are able to measure various parameters quickly and accurately, enabling manufacturers to identify any deviations from the desired specifications.  
What's more, AI algorithms under development can detect surface defects, such as blisters, bubbles, or foreign bodies, which can potentially compromise tire performance and safety. By automating these inspections, tire manufacturers can significantly streamline their quality control processes, reduce human error, and improve overall production efficiency.  
Integrating AI into tire quality control not only streamlines the production process but also ensures that customers receive reliable, high-quality products. Using ML algorithms, tire manufacturers can identify potential defects and anomalies with greater precision, minimizing the risk of defective products reaching consumers [16].
## 2 Survey Methodology  
There has been a growing interest among researchers to enhance the performance of solar PV monitoring systems using various technologies. In line with this matter, various data processing modules and data transmission protocols have been introduced in the last decade. Figure 1 denotes the number of papers published between 2010 and 2020. A sharp upward trend is visible in the number of articles accepted from 2010 to 2020 which implies the importance and potential deployment of various modules and communication protocols in solar PV applications. A total of 443 papers were published between 2010 and 2020. Among the total number of manuscripts, 43.57% of the articles were published in the first seven years (2010-2016). However, the number of articles published in the next four years (2017-2020) increased at a greater rate. In this period, 56.43% of the articles were published which is 12.86% higher than the previous seven years.  
Figure 1: Research trend of solar PV monitoring systems from 2010 to 2020.  
The goal of this review is to gather all the recent information, conduct an analysis, and provide a critical discussion of the monitoring technologies for solar PV systems. Firstly, Google scholar, IEEEXplore, MDPI, and ScienceDirect databases were utilized to explore the related work for this paper. Secondly, keywords, abstract, paper content, novelty, and the main topic of interest of the journal were applied to search for the relevant publications within the scope of this paper including solar PV, wireless monitoring systems, sensors, data processing board, and data transmission protocols. The final suitable works were investigated by assessing the impact factors, the review process, and the citations.  
The outcome of the screening process can be categorized into four sections. Firstly, the review of solar PV monitoring systems based on data processing modules with its design features, implementation, comments or suggestions, and limitations is presented. Secondly, various data transmission protocols are studied for solar PV monitoring systems. Thirdly, the review investigates various issues and shortcomings of the monitoring technology for solar PV systems. Finally, the review delivers some selective future prospects for the further enhancement of solar PV monitoring systems. The review process can be divided into two phases as shown in Figure 2 and summarized as follows:  
Figure 2: Schematic diagram of the survey methodology.
## 5 Discussion
### Limitations of the Approach  
Through model validation, incorporating dynamic deformation information to refine traditional machine learning models can improve the model and make the susceptibility zoning more accurate and precise, yielding results that better align with real-world conditions. However, this approach also has certain limitations. (a) InSAR technology is affected by terrain and vegetation conditions. In areas with steep terrain and dense vegetation, coherence loss may occur, leading to missing data in the study area. (b) Data sources can limit the acquisition of long-term data, preventing a comprehensive understanding of long-term slope deformation data in the research area. (c) The occurrence of landslide disasters in the study area is closely related to the rainy season and typhoons. However, the InSAR technology may not adequately reflect the subsidence changes before and after extreme rainfall events, thus failing to capture changes in susceptibility zoning following extreme rainfall. Therefore, the next phase of our research will introduce InSAR data before and after extreme weather events to enhance the applicability of the IVCI model in the area.
## 6 Simulation Setup and Results
### Downlink Use Case
#### 6.6.1 Baseline  
Figure 6 shows the SINR graphs of the FWA receivers, NGSO-ESIMs, and the GSO-ESIM. The FWA links experience an SINR drop of 2.75 dB in the case of FWA 9, and no drop for FWA 8. However, both FWA links remain operational, as the safety margin for correct reception SINR for them is around 9 dB. On the other hand, the GSO-ESIM receiver suffers on multiple occasions. In the presented SINR graphs, where measurements are dropping below the reception SINR threshold of 9 dB, this threshold is represented with a red dashed line. The first substantial dip below the reception SINR threshold of 9 dB (see dashed red line in Figure 6a is because of the alignment of all NGSO and GSO communication paths around the 100 th second of the simulation run (see blue area). The second dip (green area) happens around the 210 th second of the simulation time because of reaching the receiver FWA node 9. At that time, the GSO-ESIM receiver is perfectly aligned with the signal of FWA node 9, impacting its SINR and dropping the connection.  
In SINR graphs of NGSO links, we can again distinguish the two major interference events, the NGSO-GSO geometry alignment at around the 80 th second (blue area), and then the FWA 9-10 link interference effect at around the 170 th second, as the ESIMs pass behind FWA receiver node 9. There is another slight impact for both of them around 170-200 s that corresponds to both of ESIMs crossing the second FWA link at different times. The impact of interference is substantially higher in NGSO-ESIM 3 (yellow area) than in NGSO-ESIM 2 (green area) because NGSO-ESIM 3 actually stops at the FWA receiver and thus suffers interference for a longer time than NGSO-ESIM 2.  
The fact we can see two or more lines per SINR measurement is because of the condensed measurement of SINRs at the ms scale. We can see multiple levels of SINR measurements that are grouped per slot corresponding to multiple interference events impacting the signal. For instance, some slots might experience interference differently than others based on their ACI and co-channel interference. Hence, we often observe two or more lines in most SINR graphs.  
In Figure 7a, the overall system PER graph is presented. It is clear that PER increases in the specific areas of the SINR graphs, where SINR drops below 9 dB for the GSO and NGSO links. NGSO 3 cannot recover after 120 s, as in the scenario it is designed that it stops close to FWA node 9 at that time and never moves again, therefore it is clearly interfered for the remaining of the scenario from the FWA link.
## 3 Platform for Real-Life Testing of Communication Protocols
### Testbed Architecture  
The developed testing platform for GNU radio applications has a specific architecture, illustrated in Figure 8. The figure shows a situation in which the transmission chain under test is simulated, and the two transceiver modules of the system under test are connected through a simulated channel. In this scenario, the system under test implements a full duplex transmission.  
Figure 6: Static ARP entries.  
Figure 7: Probe packets sent with the ping utility to the far end of the tunnel.  
The framework comprises two primary components: the application process and the support processes/services. The application process comprises the Net IO blocks and GNU radio simulator blocks. The support processes/services include the traffic generator, traffic sniffer, broker, database, and data visualization processes.  
The testbed architecture needs to be modified to test the GNU radio application under real channel conditions using SDR boards, as shown in Figure 9. As explained in Section 3.1, when using SDR boards, the environment only consists of a single Net IO block. Traffic is either read from or written to this block, depending on whether the transmitter or receiver part of the application is being tested. The channel can be accessed through SDR IOs, i.e., source/sink blocks. The support services remain the same as when testing the GNU radio applications through simulation. If the two machines running the GNU radio application pair are different, they need to be synchronized to time-align the monitoring data.