esa-sceva/satcom-chunk-collection huggingface dataset:
# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 2 Methodology of 1D CNN Seismic Damage Assessment  
### 1D CNN and 2D CNN Approaches  
The 2D CNN-based seismic damage assessment used as a reference is briefly summarized before the 1D CNN approach is presented. Figure 1 shows the pipeline of the 2D CNN approach (dash arrows) as summarized from the previous studies [22; 23; 25]. The 1D GMRs had to be transformed into 2D GMR images through an encoding technique (e.g., WT encoding) before they were inputted into the 2D CNN model for post-earthquake damage assessment. As a widely used image encoding technique in GMR classification, WT encoding achieved the highest prediction accuracy among three image encoding techniques [25] and was thus used to encode GMR images in this study via the toolbox developed by [39]. In contrast, the 1D CNN approach (solid arrows), in Figure 1, directly used the recorded GMRs in the 1D CNN model and predicted their resultant damage states. The heavy preprocessing of WTs in the 2D CNN approach was avoided in the 1D CNN approach. Therefore, the 1D CNN approach would be preferred if it could maintain a comparable or higher prediction accuracy, as compared to the 2D CNN approach.
## 5 Results and Discussions
Overall, the average coverage rate of ECSO-OBL is improved by 14.54%, 8.55%, 19.48%, 11.73% compared with the ICS-PSO-OBL, CD-CSO, ACSO, and PSO-CSO algorithms, respectively. In a WSN given 40 sensor nodes and with the same network dimensions, Figure 8 presents the maximum time taken for the execution of various coverage algorithms compared with the proposed ECSO-OBL algorithm. It is proved that the time taken by the ECSO-OBL algorithm is improved by 65.45%, 60.36%, 66.43%, and 55.54%. The reason why ECSO-OBL has superior performance is that the algorithm converges stably in fewer iterations and achieves the global optimal solution. Figure 9 represents the total time consumption of various coverage algorithms, and they are compared with the proposed ECSO-OBL algorithm. In a network of 80 sensor nodes, the execution time of the ECSO  
Figure 4: Sensor node deployment and random coverage optimization before and after ECSO-OBL algorithm execution. OBL algorithm is improved by 33.89%, 17.98%, 20.65%, and 29.12% compared with the ICS-PSO-OBL, CD-CSO, ACSO, and PSO-CSO algorithms, respectively. Similarly, with a network size of 100 nodes, the time consumption of the ECSO-OBL algorithm decreases by 58.29%, 48.78%, 51.77%, and 46.14% compared with the ICS-PSO-OBL, CD-CSO, ACSO, and PSO-CSO algorithms, respectively. This is due to the better optimal iterative tendency and search accuracy of the ECSO-OBL algorithm compared with the other CSO-based algorithms. Overall, the proposed ECSO-OBL algorithm proved that its convergence time is superior to existing nascent techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Network Dimensions**} \\ \cline{3-4}  & & \(\mathbf{50~{}m^{2}}\) & \(\mathbf{100~{}m^{2}}\) & \(\mathbf{200~{}m^{2}}\) \\ \hline
**N** & & \(\mathbf{20}\) & \(\mathbf{40}\) & \(\mathbf{80}\) \\ \hline \multirow{6}{*}{Iterations for convergence} & ICS-PSO-OBL [11] & 158 & 194 & 417 \\ \cline{2-4}  & CD-CSO [9] & 147 & 181 & 436 \\ \cline{1-1} \cline{2-4}  & ACSO [15] & 182 & 214 & 524 \\ \cline{1-1} \cline{2-4}  & PSO-CSO [10] & 226 & 289 & 559 \\ \cline{1-1} \cline{2-4}  & ECSO-OBL [proposed] & 112 & 143 & 314 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between the proposed ECSO-OBL algorithm and the other existing algorithms. Figure 5: Fitness function values of ECSO-OBL algorithm towards convergence. Figure 6: Average coverage rates of different algorithms in a WSN with 40 sensor nodes and a terrain area of 100 m\({}^{2}\). Figure 8: WSN with 40 sensor nodes and a terrain area of 100 m\({}^{2}\). Figure 7: Maximum coverage of different algorithms in a WSN with 40 sensor nodes and a terrain area of 100 m\({}^{2}\). Figure 9: Comparison of total time consumption by different algorithms with different network sizes.
## 3 Routing in Satellite Networks
### Routing Protocols for Satellite Networks  
Satellite networks are among the communication technologies whose prevalence is increasing due to advantages such as wide coverage area, providing access to areas where communication is limited, providing service in times of natural disasters, providing continuous service, and accessibility to areas with insufficient infrastructure. Nowadays, satellite technologies are widely used in many fields, such as meteorological services, navigation systems, internet access, Global Positioning System (GPS) applications, agricultural field traceability, and product quality determination. The use of satellite networks is increasing steadily. The Starlink satellite, which was placed in space by SpaceX (a private spaceflight company) to provide internet access, has already passed 2000 units. It is aimed to increase this number to 42,000 [96]. With the increasing use of satellite networks, it is also of great importance that they can be integrated with new, emerging technologies. Routing approaches play a critical role in order to ensure high velocity and low latency, which is one of the most basic features of the next epoch of communication technologies. Concerning the framework of a satellite network system, satellite network routing can be categorized into three components [97]:  
* Boundary routing;
* Access routing;
* Inter-satellite routing.
# A Review on Methods and Systems for Remote Collaboration
## 4 Visual Display Technologies
### 2D View  
A range of existing systems facilitate remote collaboration on physical tasks using 2D videos, as shown in the research articles [15; 16; 17; 21; 22; 23; 24; 25].  
Most remote collaboration systems use video cameras installed at the local user's (worker's) space (environment). The video captured by these cameras is shared in 2D on a display such as a PC, TV, or smartphone. However, this method does not allow the remote user to understand the spatial relationship between the objects; thus, communication is more difficult.
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 1 Introduction
However, in most of these studies, the water contact angle (CA) values are on the lower threshold limit for superhydrophobicity (CA = 150\({}^{\circ}\)). Moreover, to our knowledge, no study has characterized the stability of fabricated LIG superhydrophobic surfaces under extreme conditions. Indeed, such characterizations are necessary to ascertain the stability of the superhydrophobic property for applications. In this work, an LIG-based strain sensor was fabricated on a polyimide tape directly adhered onto a carbon fiber composite (CFRP) material, obtaining a gauge factor (GF) of about 40. Additionally, the laser parameters were selected such that the LIG sensor was rendered superhydrophobic with high CA (\(\sim\)165\({}^{\circ}\)). Finally, the stability of superhydrophobicity was demonstrated by continuous immersion underwater for 25 h, droplet impact and water-jet impact. The combined properties of high sensitivity with a highly stable superhydrophobic state could boost the commercial applicational prospects of LIG.
## 3 RTS/CTS Scheme for IEEE 802.15.6
### Proposed RTS/CTS Scheme
Figure 4: RTS/CTS scheme between two nodes in IEEE 802.15.6 CSMA/CA based networks. ```
Input :\(CW_{i,min}\), \(CW_{i,max}\), \(N\), \(P_{L}\) Initialization: \(CW=CW_{i,min}\), C=random(1,\(CW\)), \(N=0\) \(N++\) for each available frame(i)do ifchannel is idlethen if\(T_{sf}>T_{f}\) and \(T_{pl}>T_{f}\)then  C -  end if ifC=0 then  Send RTS  end if ifCTS receivedthen  Send frame(i) else ifframe(i)=5 and \(P_{L}<P_{Lmax}\)then \(P_{L}++\) else \(P_{L}\)=\(P_{Lmax}\)  end if  end if iftransmission failsthen ifC is eventhen \(CW=min(2 CW,CW_{i,max})\) else \(CW\) is not changed  end if  end if else  Freeze counter until the channel is idle  end if  end if
```  
**Algorithm 1** Proposed RTS/CTS scheme with power level adjustment for IEEE 802.15.6 CSMA/CA protocol.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 4 Results
### Model Assessment  
#### 4.2.1 Model Results for Old Landslide Detection  
After the experiment, the improved YOLOv8 model was compared with the original YOLOv8 model, and the results are shown in Table 2. The YOLOv8 model, after adding a CBAM attention mechanism and replacing the IoU loss function, performs better than the original YOLOv8 model. Among them, the YOLOv8-CW2 model using the WIoUv2 loss function performs the best. The F1 score is 0.74, and the precision rate is increased by 10.9%, the recall rate by 6%, and mAP by 11% compared to the original model. Compared with the YOLOv8-CW1 model and the YOLOv8-CW3 model using WIoUv1 and WIoUv3 functions, respectively, the YOLOv8-CW2 model also achieved better experimental results. The improved YOLOv8 is compared with classic models such as YOLOv5 and Retinanet, demonstrating its superior detection ability. The test results reveal a significant increase in F1 scores of 9.4% and 10.2%, respectively, validating the suitability of the YOLOv8-CW2 model for old landslide detection.  
Figure 9 shows a comparison of the detection results between the original YOLOv8 algorithm and the improved YOLOv8 algorithm applied to the old landslide dataset in the study area. From the graph, it is evident that the original YOLOv8 model exhibits differences in detection performance compared to the improved YOLOv8 model, both for small and large old landslides. The complexity of the background environment in high-resolution remote sensing images is much greater than that of natural images, leading  
to the original YOLOv8 model incorrectly identifying certain objects in the background environment, such as exposed soil that resembles landslide image characteristics.  
Comparing the detection results of the three improved YOLOv8 models, it is observed that the YOLOv8-CW2 model achieves highly accurate detection for both small and large old landslides. In the case of large old landslides, the YOLOv8-CW3 model performs exceptionally well, with higher accuracy compared to the other two improved models. However, the YOLOv8-CW3 model shows a higher number of overlapping detection boxes when detecting small old landslides, leading to inaccurate detection of the actual locations of the landslides.  
Overall, the improved YOLOv8 models demonstrate better performance in detecting old landslides in the study area, especially for small and large landslides, indicating their effectiveness in addressing the complexities of high-resolution remote sensing images.
## 3 System Model
### Topology Description  
We considered an IAB network having one IAB-donor and \(N-1\) IAB-nodes numbered from 2 to \(N\). We say that the network consists of \(N\) network nodes and refer to the IAB-donor as Node 1. We further assumed that network node \(n=1,\ldots,N\) has \(A_{n}\) antenna sectors numbered from 1 to \(A_{n}\). Each antenna sector provides uplink and downlink communication to UEs and can support one wireless backhaul connection to another network node.  
The network topology was assumed fixed and tree-shaped with the IAB-donor, i.e., Node 1, at its root. In other words, a single specific route exists between the IAB-donor and each IAB-node. Such a topology can be represented by a matrix \(\mathbf{M}=(M_{ij})_{i,j=1,\ldots,N}\), where entry \(M_{i,j}\in\{0,1,\ldots,A_{i}\}\) is either the antenna sector in node \(i\) through which it communicates with node \(j\
eq i\) or 0 if there is no link between these nodes. Note that if \(M_{ij}=0\), then \(M_{j,i}=0\), and conversely, if \(M_{i,j}>0\), then \(M_{j,i}>0\). We let \(M_{i,j}=0\) for all \(i=1,\ldots,N\).  
An example of an IAB RAN with \(N=3\), \(A_{1}=4\), and \(A_{n}=3\) for \(n=2,3\) is depicted in Figure 3. We say that a network node belongs to tier \(k\) if its distance (the number of links) from Node 1 is \(k-1\). Node 1 is hence always the only node in Tier 1. Node 2 in Figure 3 belongs to Tier 2. It is connected to Node 1, which corresponds to the IAB-donor, through its Sector 1 and to Node 3 through Sector 2. The backhaul topology of the network in Figure 3 is given by  
\[\mathbf{M}=\begin{vmatrix}0&3&0\\ 1&0&2\\ 0&3&0\end{vmatrix}. \tag{3}\]
## 5 Discussion  
### The Influence of Stress Wave Vibration Frequency  
The frequencies of stress waves radiated by different mine tremors are diverse because of the difference in the fracture scale during underground extraction. According to [PERSON]'s research [34], the dominant frequency of mine tremors shifts to lower frequencies with an increase in the fracture scale. To investigate the influence of the compression wave frequency on the dynamic stress concentration around the hole, the frequency was varied (1, 2, 3, 4, and 5 kHz) while maintaining the velocity peak value at 4 m/s. That is, the wave periods were 1000 \(\upmu\)s, 500 \(\upmu\)s, 333 \(\upmu\)s, 250 \(\upmu\)s, and 200 \(\upmu\)s, respectively (see Figure 17). The corresponding dynamic stress concentration factors (DSCFs) were calculated and compared.  
The DSCF around the hole was distributed similarly and symmetrically, as shown in Figure 18a. The maximum DSCF with a positive value is at the angles of 90\({}^{\circ}\) and 270\({}^{\circ}\), thereby showing a compressive stress concentration. Furthermore, its minimum with a negative value is at the angles of 0\({}^{\circ}\) and 180\({}^{\circ}\), which is characterized by tensile stress concentration. Apparently, the DSCF around the hole shows a decreasing trend with a decrease in the incident wave frequency. The maximum compressive stress concentration factor (MCSCF) and maximum tensile stress concentration factor (MTSCF) decrease with frequency. [PERSON] et al. observed that a sufficiently low frequency causes dynamic loading to become quasi-static loading [18]. According to field investigations, the low-frequency components of mine tremors induce rockbursts owing to the attenuation of high-frequency components propagating in coal and rock media. Therefore, the disturbance effect of low-frequency mine tremors on mining can be considered from the perspective of static-stress loading.  
Figure 17: Incident wave with different frequencies.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 4 Human Activity Signatures by Using a Distributed MIMO FMCW Radar System
### Implications on the Performance of an RF-Based Step Counter
We can conclude that a distributed \(2\times 2\) MIMO radar system is sufficient to capture the human activity signatures in all directions.
## 4 Channel Model  
We consider a radio channel that is affected by path-loss and shadowing effects. We intend to calculate the received power \(P_{r}\) over the distance \(d_{\alpha}\) between the source-destination pair for a given transmitted power, \(P_{t}\) using [6]  
\[P_{r}=\frac{P_{t}G_{r}G_{t}}{10^{\frac{\omega}{10}}d^{\alpha}} \tag{10}\]  
We denote the gains of transmitter and receiver as \(G_{t}\), and \(G_{r}\), respectively. The signal is attenuated by the path-loss as \(d^{-\alpha}\) which is usually bounded as \(2\leq\alpha\leq 6\) multiplied by the shadowing factor \(\omega\) in dB, which is a Gaussian random variable with zero mean and standard deviation \(\sigma(\text{dB})\).  
To quantify these variations, we calculated the attenuation among collaborating and connectable sensors denoted by \(\beta\). The expected value of \(\mathbf{P}_{r}\) increases as \(\varepsilon\times N\), where \(\varepsilon\) is defined as a function of the phase distribution \(0\leq\varepsilon\leq 1\)[41]. When there is no phase error meaning that beamforming with \(\aleph\) sensors gives a power gain over transmission as with single element as \(\mathbb{E}[\mathbf{P_{r}}=\aleph]\)[42]. Thus, any improvement or degradation is caused by the distribution of \(\varepsilon\). As long as keeping the distribution of connectivity of collaborative sensors is contained in such a way as to keep \(\varepsilon\approx 1\), large gains can be realized using distributed beamforming. The power attenuation between two collaborating sensors denoted by \(\beta\) is calculated as \[\beta=\frac{P_{t}}{P_{r}}=\frac{10^{\frac{\alpha}{10}}d^{\alpha}}{G_{r}G_{t}} \tag{11}\]  
This means that two collaborating sensors are successfully connected if the power attenuation is not greater than a threshold \(\beta(d_{\max})<\beta_{0}\). According to [6], if we replace \(\beta\) in Equation (10) with \(\beta_{0}\), the maximum transmission range \(d_{max}\) is given as \(d_{max}=\sqrt[\alpha]{\frac{G_{r}G_{t}\beta_{0}}{10^{\frac{\alpha}{10}}}}\). From Equation (10), the probability of having no direct connection between two sensors separated by \(d_{\max}\) is given by  
\[P(\beta\geq\beta_{th}) =P(\frac{d^{\alpha}}{G_{r}G_{t}}\geq\beta_{0}) \tag{12a}\] \[=P((\beta_{0}G_{t}G_{r})^{\frac{1}{2}}\leq d) \tag{12b}\]  
The sensor can communication with all its randomly distributed neighbors within a radius \(R\) given by  
\[R=(\beta_{0}G_{t}G_{r})^{\frac{1}{2}} \tag{13}\]  
Substituting Equation (13) into Equation (12), we get \(P(\beta\geq\beta_{0})=P(R\leq d_{\max})\). From this analysis, we can derive the first constraint in Equation (14) of the optimization problem which depends on the antenna gains of each connected source-destination pair. The constraint can vary with the number of connectable and collaborative sensors with different directions since \(G_{r}\) and \(G_{t}\) are also affected and vary in different directions [40].  
\[0\leqq d_{max}\leqq d_{x} \tag{14}\]
## 3 Experiments Results and Discussion
### Effect of Proposed Loss Function  
We explored the effect of our involved loss function by observing the dice rate on the training and validation datasets, as shown in Figure 7. On the plot of dice rate on the training dataset, \(\mathcal{L}_{\text{Dice}}\)(Dice loss) achieved the best performance, while \(\mathcal{L}_{\text{total}}\) (BCE + Dice +  
Figure 6: The segmentation results of WDTISeg having different values of \(\lambda\) by given the same user interaction points.  
SC loss) came second. The reason why \(\mathcal{L}_{\text{Dice}}\) performed well on the training set is that the network used dice as the evaluation metric, and the network maximizes dice by optimizing the network structure during training. However, the dice rate of \(\mathcal{L}_{\text{Dice}}\) on the validation dataset had a sharp oscillation. This is mainly because \(\mathcal{L}_{\text{Dice}}\) is a region-dependent loss, and if some pixels of a small target are incorrectly predicted, then it will lead to a significant change in the loss value, which will result in a drastic change in the gradient.  
Compared with other three loss functions, \(\mathcal{L}_{\text{total}}\) incorporating prior knowledge achieves optimality on the validation set and does not show more intense oscillations after epochs greater than 30. The BCE loss used for dichotomous classification is insensitive to category imbalance, so it can prevent the oscillation due to \(\mathcal{L}_{\text{Dice}}\) to some extent. On the other hand, the loss function \(\mathcal{L}_{\text{SC}}\) based on compact shape constraint utilizes prior information of tumor shape, and thus can improve the accuracy of segmentation. Note that \(\mathcal{L}_{\text{SC}}\) converges to 1 at the minimum when the tumor is circular, so it cannot be used as a segmentation loss function alone.  
The purpose of introducing a subjective human into the segmentation process is to use human's prior knowledge as a supplement to improve the segmentation accuracy. In the interactive segmentation task, human is both the participant in the interactive segmentation process, the prior information provider, and also the evaluator of segmentation results without the ground truth. In the interaction segmentation task, we may learn from the few-shot learning which has been widely used machine learning classification task. Human guides segmentation on a few simple images so that the network can master the segmentation skills, further reducing the training time and human interaction time.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 5 Conclusions
100-150, 150-200, 250-300, 350-400, and >400 m distances from the busbar. For each distance category, 100 different faults were created, and it was investigated whether these faults were correctly located in each category. The highest number of faulty locations was observed in the 0-50 m range, which represented the closest distance to the busbar. At a fault impedance of 100 ohms, the number of faulty detections in this range was 69 for single-phase-ground faults, 65 for three-phase faults, 63 for phase-phase faults, and 49 for two-phase-ground faults using noisy data. The performance of the ORR-RVFLN, Cauchy-M-RVFLN, RVFLN, CNN, LSTM, SVM, and ELM methods was compared for fault location. At the end of this comparison, it was found that the ORR-RVFLN method showed the best performance, followed by the Cauchy-M-RVFLN, RVFLN, CNN, LSTM, SVM, and ELM methods, respectively. The ORR-RVFLN method predicted the single-phase ground faults about 10% better than the ELM method, which was the worst method, and about 3% better than the Cauchy-M-RVFLN method, which was the closest method. Similar results were obtained for other fault types. In addition, in this study, the average detection success rate of the ORR-RVFLN method was 92.2% for all fault impedances with noise added to the data. Conceptualization, C.H., H.K., M.T.O. and B.G. ; methodology, C.H., H.K., M.T.O. and B.G. ; software, C.H., H.K. and M.T.O. ; validation, C.H. and B.G. ; formal analysis, C.H. and H.K. ; investigation, C.H. and H.K. ; resources, C.H. ; data curation, C.H. and H.K. ; writing--original draft preparation, C.H. ; writing--review and editing, C.H., H.K., M.T.O. and B.G. ; visualization, C.H. and H.K. ; supervision, M.T.O. and B.G. All authors have read and agreed to the published version of the manuscript. This study was financially supported by Firat University with FUBAP-MF.24.125. Not applicable. Data Availability Statement: Data is contained within the article. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request. This study was supported by the Scientific Research Projects Coordination Unit of Firat University. Project number: MF.24.125. The authors declare no conflicts of interest.