esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Digital Twin in the Industry of Axial Hollow-Wall Pipes
## 3 Establish a Digital Twin of the Pipe End-Forming Process
### Establish a Digital Twin Model
After 70 s of heating, the temperature in the expanded section of the pipe rose to 130-142 \({}^{\circ}\)C. The yield strength decreased to 18.956 MPa, and the fracture elongation rate decreased to 124-192%. At this stage, the pipe end exhibited outward bending and deformation of the hollow structure at the pipe opening, but no cracks were observed. This phenomenon indicated that, although the fracture elongation rate decreased, it still met the requirements. However, the increase in temperature led to a rapid decrease in the elastic modulus, causing the cross-sectional stiffness of the pipe material to no longer meet the requirements for expansion deformation, resulting in deformation and damage to the pipe opening structure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **\(\mu\)s** & **\(\mu\)s, Lubrication** & **\(\mu\)k** & **\(\mu\)k, Lubrication** \\ \hline PVC-Steel & 0.022 & 0.006 & 0.018 & 0.004 \\ PVC-Rubber & 0.51 & 0.18 & 0.47 & 0.13 \\ \hline \hline \end{tabular} \(\mu\)s: static friction coefficient, \(\mu\)k: dynamic friction coefficient.  
\end{table} 
Table 2: Frictional coefficient. Figure 13: Simulated cloud map of the flaring process. After 80 s of heating, the temperature in the expanded section of the pipe increased to 143-162 \({}^{\circ}\)C. At this stage, the yield strength further decreased to 11.342 MPa, and the fracture elongation rate decreased rapidly to 65-106%. Large deformation cracks appeared when the pipeline expanded to the position of the sealing ring placement, and the structure of the pipe opening was damaged with significant folding. The low elastic modulus and fracture elongation rate prevented the pipe material from withstanding the deformation caused by the expansion, which aligned with the occurrence of cracks penetrating the inner and outer walls and the extensive flaring of the pipe opening observed in the experiments. After 90 s of heating, the temperature in the expanded section of the pipe rose to 155-170 \({}^{\circ}\)C. The yield strength decreased to 8.257 MPa, and the fracture elongation rate decreased to 41-75%. In the 90 s group, cracking occurred at the early stage of expansion due to an excessively low fracture elongation rate, resulting in the termination of the calculation. The experimental and simulation studies revealed that the temperature-fracture elongation rate performance of the pipe material had a significant impact on the expansion-forming quality of the PVC-U hollow pipes. The numerical simulation results aligned with the experimental findings, and, based on this model, a preliminary digital twin was established. Subsequently, the digital twin was fed back to the production end of the factory for industrial production through the digital twin platform. Real-time monitoring and adjustments were performed, based on various production data through integration with the automation monitoring system, resulting in a yield rate of the pipe of up to 95% for the qualified products.
# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
###### Abstract  
Accurate indoor localization is essential for Internet of Things (IoT) systems and autonomous navigation in the 6G communication system. However, achieving precision in environments affected by signal multipath effects and interference remains a challenge for 6G communication systems. We employ a Residual Neural Network (ResNet) augmented with channel and spatial attention mechanisms to enhance indoor localization performance using time-domain data. Through extensive experimentation, our models, when equipped with an attention mechanism, can achieve accurate location under 20% interference. Numerical results show that the ResNet with a Channel Local Attention Block (CLAB) can reduce the localization error by about 12% even when the interference is high. Similarly, the ResNet with a Spatial Local Attention Block (SLAB) can also improve the localization accuracy. While a ResNet combining both CLAB and SLAB can reduce the position error to about 7 cm.  
Keywords:residual neural network; 6G communication system; channel local attention block; spatial local attention block; indoor localization +
Footnote †: journal: Electronics
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
###### Abstract  
Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080 TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080 TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.  
weapon detection; object detection; deep learning; optimization; computer vision +
Footnote †: journal: _Applied_  
_sciences_
## 3 Results and Discussion  
### Moisture Determination by Oveen-Dry Method  
The high moisture content is attributed to the high binding water capacity of the apple varieties' starch, which is associated with weak molecular forces between the starch granules [27]. On the other hand, the relatively low moisture content in purees can be due to the low binding capacity of starch in apples resulting in the excessive loss of moisture during ripening and storage [28; 29; 30].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Definition** \\ \hline \hline
**Firmness** & N & The force at the maximum peak during the compression test \\ \hline \hline
**Deformation** & mm & The displacement at the maximum force during the puncture compression test \\ \hline \hline
**Adhesiveness** & mJ & Area under the Load vs. Distance curve, measured from where cycle 1 first reaches zero load to where it ends \\ \hline \hline
**Cohesiveness** & n.a. & Indicates the strength of internal bonds of the product, being computed as Hardness Work Done Cycle 2/Hardness Work Done Cycle 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter definition and calculation for texture analysis.  
Following the determination, it was found that the highest moisture of the untreated puree was 87.27% for the Baujade apple variety, which decreased by 84.59%, and the lowest moisture value was 80.72% for the Juliana variety, which decreased by 78.14% (Table 3).  
After sterilization, the puree moisture decreased in all varieties, but the largest decrease was observed in the Parmen Clone apple variety, from 82.05% to 76.54%.  
Other researchers obtained similar values for baby foods pureses, where moisture decreased between 1-5% in different types of apple pureses [31; 32; 33].
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 4 Maximizing Data Rate  
Now that the received power has been accurately estimated using the proposed path-loss model, the noise detected at the receiver also needs to be accounted for to determine the maximum achievable rate. The signal-to-interference-plus-noise ratio (SINR) needs to be kept sufficiently large to ensure correct demodulation with a given bit error probability threshold required by the application. To this end, the system's noise power density \(N_{sys}\) (i.e., the sum of all unwanted signals that contaminate the signal of interest) in the deployment site needs to be determined. The SINR is given by  
\[SINR=\frac{P_{r}}{N_{sys}}=\frac{RSSSI}{N_{A}+N_{fg}}, \tag{15}\]  
where \(P_{r}\) and \(RSS\) denote the received power and \(N_{fg}\) is the noise figure of the receiver. In this case, for the used CC1310 WMCU, \(N_{fg}=7\) dB [58].  
Using the BER performance curve [59; 60] of the used modulation scheme (i.e., Gaussian frequency shift keying (GPSK) in this case), the required energy per bit to noise ratio can be determined to achieve the target BER. Then, using  
\[\left[\frac{P_{r}}{N_{sys}}\right]_{dB}=\left[\frac{E_{b}}{N_{sys}}\right]_{ dB}+[D_{R}]_{dB}, \tag{16}\]  
the achievable data rate can be determined. An example is included in Section 6.2.  
It should be noted that in WSN applications, \(D_{R}\). and \(P_{t}\). are dynamically changed during operation [10; 46]. However, in this work, it is also important to make estimations early in the design process in order to make high-level decisions about the hardware and node configuration to use.  
Now that the link parameters are adjusted for a better energy consumption efficiency, communication range and throughput, packet loss needs to be mitigated in order to achieve the required reliability target. Moreover, the latter must be met with latency and energy costs in mind.
# A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
## 2 Materials and Methods
### Design for the YOLOv8-GGi Model
#### 2.3.2 Global Attention Mechanism  
The attention mechanism, a fundamental data algorithmic approach in machine learning, excels in natural language processing, image processing, and speech recognition [38]. Its ability to focus on specific regions significantly mitigates the impact of background noise and irrelevant objects, enhancing detection accuracy. We leverage the Global attention mechanism (GAM) [39] in the YOLOv8n backbone network to address challenges posed by spotted leaf drop disease and rust in apples, characterized by irregular shapes and subtle features. Illustrated in Figure 8, GAM amplifies the global interactions within the network, thereby enhancing the detection and differentiation of small, easily overlooked targets. By enhancing the feature extraction capabilities and feature integration techniques, GAM ensures precise focus on relevant areas, substantially boosting overall detection performance.  
The global attention mechanism (GAM) is an innovative attention module proposed after the convolutional block attention module (CBAM) [40]. GAM combines channel attention and spatial attention, which can reduce the dispersion of information while enhancing the interaction characteristics of the global dimension. By introducing GAM, the model can better capture the key information in the image, thereby achieving more accurate disease identification under complex background and lighting conditions [41]. GAM consists of spatial and channel attention, operating sequentially from channel to space. In preserving the channel information in the input features, \(\text{M}_{\text{C}}\) arranges the dimensions of the original features from \(\text{C}\times\text{W}\times\text{H}\) to \(\text{W}\times\text{H}\times\text{C}\) and derives the channel attention coefficients \(\text{M}_{\text{C}}(\text{F}_{1})\) by structuring the MLP in two consecutive layers which are then activated by a sigmoid function. In extracting the spatial information of the spatial attention features, two \(7\times 7\) convolutions are used firstly to achieve spatial information fusion; secondly, the channel dimensions are used for compression, and the pooling layers are removed. Then, the spatial attention coefficient \(\text{M}_{\text{C}}\) (F\({}_{2}\)) is finally obtained by sigmoid function activation. The GAM is shown in Equations (7) and (8).  
\[F_{2}=M_{\text{C}}(F_{1})\otimes F_{1}, \tag{7}\]  
Figure 8: Global Attention Mechanism (GAM); \(\text{M}_{\text{C}}\) is channel attention, and \(\text{M}_{\text{S}}\) is spatial attention.  
\[F_{3}=M_{S}(F_{2})\otimes F_{2}, \tag{8}\]  
\(F_{1}\) is the given input image, \(F_{2}\) is the intermediate map feature, \(F_{3}\) is the output image, \(M_{\text{C}}\) is the channel attention mechanism, and \(M_{\text{S}}\) denotes the spatial attention mechanism. \(\otimes\) is the multiplication operation by element.
## 2 Previous Research and Background
### Change Analysis in Forest Management  
A forest changes continuously, whether as a result of forest operations such as thinning or clear-cutting or due to forest damage caused by heavy winds, fire, and other natural disasters. The human-made changes can be reported and easily updated. However, changes induced by nature must be spotted differently. The automatic detection of changes in the forest using aerial imagery shows promising results in areas where severe changes have happened, such as clear-cutting and intense storm damage. However, moderate changes such as thinning are much harder to discover [10]. When using airborne laser scanning (ALS) as the data source to detect changes, [PERSON] et al. [11] were able to identify the removal of individual trees. Nonetheless, acquiring aerial images or even more expensive ALS data is often performed at considerable intervals. Hence, a more interesting remote sensing platform for this problem is satellites. Due to their higher temporal resolution, satellites can provide much more timely data, as is often required in disaster response. In particular,SAR data can provide data even on overcast days. Although weather conditions influence the signal, this noise can be filtered, as [56] demonstrated, and therefore were able to detect changes in forest areas greater than 1 ha with Sentinel-1 data.
## 2 Methodology
### Data Collection and Preprocessing
#### 2.2.1 Weibo Multimodal Data Acquisition  
Social media data for this study were sourced from the Sina Weibo platform. Given that the Weibo Application Programming Interface (API) typically does not allow filtering by time range or specific topics, we utilized Weibo's advanced search feature. A data collection tool was developed in Python to filter and collect data using keywords such as \"Haikui\", \"Fuzhou\", and \"rainstorm\", along with specified time ranges. The collected data included the posting time of the Weibo post, textual content, image URLs, and geotagged locations. Image data were downloaded locally via the image URLs for further analysis in the case study of Typhoon Haikui. Weibo accounts are classified into personal and official categories, and it was observed that official accounts tend to share more information regarding disaster damage than personal accounts. A total of 20,875 Weibo posts were collected and plotted as a time-series curve (Figure 5), showing daily variations in post volume. The timeline of the Typhoon Haikui event was divided into three phases: the warning phase, the emergency phase, and the post-disaster phase, based on the typhoon's landfall in Fujian Province and the termination of its numbering. The warning phase lasted from 3 September to 5:00 a.m. on 5 September; the emergency phase spanned from 5:00 a.m. on 5 September to 5:00 p.m. on 6 September; and the post-disaster phase covered from 5:00 p.m. on 6 September to 10 September. Only posts that explicitly indicated the location of the disaster or contained images with clear location references were retained for this study. Manual screening was conducted to ensure the quality and accuracy of the data.
## 5 Discussion
### Relationship between Mechanical Properties and Crack Expansion  
To examine the relationship between macroscopic mechanical properties, crack propagation, and stress field distribution during triaxial compression loading, we monitored changes in the number of microcracks using acoustic emission. In PFC simulations, the interactions between particles promote the initiation and propagation of microcracks. During this process, the released elastic waves are monitored by Acoustic Emission Sensor configured on the particles, enabling the simulation of rock acoustic emissions. To conserve space, we selected a specimen with 2a = 8 mm and \(\alpha\) = 45\({}^{\circ}\) for analysis. Our findings shed light on the behavior of symmetrical fissure-hole sandstone specimens under these conditions.  
Figure 13a-h shows the crack propagation and force chain distribution patterns with large fluctuations in microcracks and stress-strain curves. According to the growth rate of microcracks, the crack propagation process can be divided into three stages: crack initiation, development, and rapid crack propagation. During the a-b stage, there is a stress concentration at the tip of the fissure, which induces the initiation of microcracks near the fissure tip. As stress concentrations gradually extend toward the hole wall in stages b-e, the load-bearing properties of the rock sample are weakened in this area. This process also drives the development of microcracks near the defect. During stages c-d, tensile cracks penetrate the fissure and hole, accelerating crack growth and causing the stress to drop simultaneously. In stages e-h, the force chain thinning zone near the defects expands, leading to a significant reduction in the load-bearing capacity of the specimen after reaching peak strength and experiencing rapid crack expansion. During stages f-g, the crack propagation slows down, possibly due to the partial closure of microcracks under pressure. However, this short enhancement of mechanical properties is quickly followed by rapid destabilization and damage to the specimen. Therefore, as the stress concentration zone gradually expands from the fissure tip to the hole wall during the crack development stage, cracks propagate towards the hole wall with increasing \(\sigma\)1, resulting in a noticeable decrease in macroscopic mechanical properties. This phenomenon is similar  
Figure 12: Effect of fissure length on the final failure modes of the hole-containing sandstone (\(\alpha\) = 45\({}^{\circ}\)).  
to the acoustic emission findings of [PERSON] [14] for sandstone containing fissures and holes (with 2a = 2b = 8 mm). However, the stress drop in their study occurred early on due to a lack of lateral pressure effects.
## 3 Results and Discussion
### Radial Test  
The radial test is a variant of the circumferential test, in which the position of the center of gravity of the pore is replaced by the angle formed, with respect to the X axis, by the line that joins it with an axis parallel to the vertical Z axis passing through the center of gravity of the specimen.  
Analogous to the circumferential test, the pore position is replaced by a numerical value \(\alpha\), which corresponds to the angle obtained according to the following expression (Equation (3)):  
\[\alpha=\tan^{-1}\frac{\left(\frac{y-y_{\text{vgg}}}{b}\right)}{\left(\frac{x-x _{\text{vgg}}}{a}\right)} \tag{3}\]  
In this case, the coefficient \(\alpha\) can take values between 0 and 360.  
Again, if all pores are uniformly distributed, the histogram of \(\alpha\) distribution presents a specific geometry (Figure 10).  
The four peaks observed in the theoretical histogram correspond to the four semi-diagonals of the specimen section.  
In real specimens, the histogram of \(\alpha\) distribution is different (Figures 11 and 12), which helps us to understand how the characterization of the pore distribution inside the specimen.  
Figure 11 shows how, in this case, there is a certain dispersion in the pore distribution, clearly higher than that of the circumferential test. In particular, specimen CUB-06 shows a distribution markedly different from the rest, with a pore concentration above average between approximately the first and second valley (i.e., in the range \(60<\alpha<200\)). In the rest of the domain, the curve is below average. This shows that this specimen has a pore bias in a particular region.  
Moreover, the average histogram is similar to the theoretical value, although, unlike the circumferential test, certain differences can be observed here. There are regions where the porosity is lower than expected while in others it is slightly higher.  
Figure 9: Circumferential test. Comparison between real and theoretical histograms.  
As in the previous case, filters can be applied to the information obtained from the CT-Scan to work with subsets of pores. This can be useful to know if, for example, there is some kind of wall effect depending on the pore size. In addition, the vertical test can also be applied to damaged specimens to check if the cracking distribution is uniform or if there are certain directions of the specimen that show planes of weakness.  
Figure 11: Radial test. Real histogram of empty voxels.  
Figure 10: Radial test. Theoretical histogram of empty voxels according to a random distribution.