esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 3 Raw Data Validity  
The effective distance of the lidar pulse signal is related to weather conditions, especially the aerosol content in the air, such as rain, snow, fog, and haze, which may change the aerosol content and affect the transmission of the echo signal. In order to improve the quality and reliability of the data, the following screening work was performed for the original data before data analysis:  
1. Abnormal value data shall be removed; that is, when the wind speed or direction exceeds its measurement range of 0-75 m/s or 0-360\({}^{\circ}\), the wind speed or direction is \"999\", and it shall be removed.
2. Eliminate unreliable data. According to the suggestions of the equipment manufacturer, the larger the SNR, the more reliable the data will be. When the SNR is less than 10 dB, there may be strong noise and disturbance, which will reduce the reliability of the measured data, so the data shall be eliminated.
3. Conduct data validity analysis for data with good continuity and reliability within the scope of concern, such as 0-810 m.  
The data effective ratio is defined as follows: Taking 24 h per day as the unit time, the total numbers of wind speed data at different heights and the total numbers of data excluded are counted to obtain the total numbers of effective data. Then, the total numbers of effective data are divided by the total numbers of data to obtain the effective ratio of data at different heights, as shown in Equation (1).  
\[\beta=\frac{N-N_{error}}{N} \tag{1}\]  
where \(\beta\) is the effective ratio, \(N\) is the total numbers of data at a measured height in a day, and \(N_{error}\) is the total numbers of excluded data at a measured height in the day.  
As can be seen from Figure 6 and Table 2, the measurement range of the lidar is between 0-810 m per day, and the effective ratio of the data fluctuates greatly. Combined with historical weather forecasts, it was light rain on 20 January and 5 March, the data efficiency was the worst, and the data efficiency of each height was less than 0.8. It was cloudy on 19 December and the data quality was better than that on 20 January. On 25 March the day was clear and the effective ratio varied from 0.772 to 0.992 and had good performance. It can be found that lidar data has a higher quality when measured on a fine day. This may help to improve the application efficiency of lidar based on weather forecasts.
## 4 Design of a DT System for Multi-Robot Cooperative CBFMS
### Data Communication Mechanisms  
Data communication, a central part of the DT system, provides the data basis for updating and optimizing the DT system. The DT system for multi-robot collaboration is dynamic and will be generated with the movement of the robot. It contains physical data, behavioral data, visual data, rule data, etc. The physical data include the joint angles and positions generated by the robot movement; the visual data include the size, color, and shape of all the models. The behavioral data include the productivity and status of the entire shop floor. How the physical cell sends the data to the virtual system is key to building the twin system. The mechanisms of data interaction are shown in Figure 24. The DT system needs to process the joint angle, speed, route, time, and other data received from the robot and then drive the DT model to move. The communication between the DT system and the physical workshop is connected through the middleware. The physical workshop controls the actual system through the drive equipment, collects the data generated by the robot controller, and transmits the collected data to the PLC through ethernet. Finally, the network protocol conversion is sent to the virtual system. The virtual system receives and processes the data through the data exchange engine. By analyzing and optimizing the production line data, the workshop service system finally realizes the operation and dynamic display of the DT system. Meanwhile, the optimized data are conveyed to the database for storage.  
Figure 24: Twin system data communication mechanisms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time/h**} & \multicolumn{3}{c}{**Memory**} & \multirow{2}{*}{**FPS**} & \multicolumn{3}{c}{**Number of Respective Faults**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Utilization (\%)**} & & & & & \\ \cline{2-7}  & **Traditional** & **Optimized** & **Traditional** & **Optimized** & **Traditional** & **Optimized** & **Optimization Rate (\%)** \\ \hline
1 & 26 & 12 & 79.2 & 163.5 & 3 & 1 & 66.6 \\
2 & 39 & 23 & 61.7 & 148 & 6 & 3 & 50 \\
3 & 48 & 27 & 45.3 & 125.7 & 8 & 5 & 37.5 \\
4 & 55 & 32 & 32.7 & 117.8 & 9 & 6 & 33.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of system operation data before and after optimization.
## 4 Results
### Threshold-Based--Experimental Results
#### 4.1.2 Orientation Results
The values in the examples appear reasonable when taking into account the uses for these surface capabilities. The orientation along the line might, in fact, optimize palletizing or later re-handling operations that do not require extremely precise positioning while shipping packages or transporting materials. In summary, both the sorting and orientation operations employ a unified threshold-based control framework triggered by AprilTag detection, yet they differ fundamentally in actuation logic and feedback integration. The sorting task implements an on-off control scheme: upon tag ID recognition within the ROI, a discrete column-wise reorientation command is issued, without further continuous feedback. In contrast, the orientation task adopts a hybrid  
Figure 9: Trajectory (**a**) and orientation angle (**b**) of Box A with orientation control [22]. threshold-plus-feedback strategy, continuously estimating the package's angular state and actuating or locking entire module rows once the setpoint threshold is crossed, following a predictive on-off logic informed by angular velocity and acceleration. This comparative analysis highlights the capacity of the same under-actuated modular surface, equipped with minimal sensing and discrete actuation, to realize both discontinuous and quasi-continuous control strategies. Such versatility underscores the platform's robustness and adaptability, demonstrating its suitability for a range of intralogistics tasks under a common control paradigm.
## 5 Towards Enhanced Use of High Resolution EO Data in Earth System Modelling  
In this section, an analysis of data availability with the most common challenges in utilising the data is presented with the aim of identifying virtuous pathways to data inclusion in global modelling for forecasting and monitoring applications. The volume of Earth Observations of the surface creates an issue which calls for data reduction and data streaming with a clear set of applications in mind. A too passive role of EO data users (awaiting new observations without listing modelling needs), or assuming that a web portal will multiply by itself the use of satellite products, has the risk of limited user-uptake of EO data. There is the potential of creating a gap between availability and exploitation of data. Closer connection between EO surface data and Earth surface modelling, as part of weather and environmental prediction applications, can multiply users of the data by orders of magnitude.  
Earth Observations ultimately drive model development, via data assimilation and via exposing systematic model errors. However, the balance between model resolution, the degree of sophistication of represented processes and the size of ensemble configurations (Figure 27) is a trade-off considering that computing resources are limited. The Earth surface cannot be easily observed nor modelled if coarse resolution sensors or models are utilised because the subgrid variability becomes too large. The use of ensembles with a large number of members enables representing the uncertainty of the forecasts [16] attributed to the modelling steps as well as to the analyses used as initial conditions that encompass EO observability and representativity issued.
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 1 Introduction
[13] proposed a lightweight network based on YOLOv4 to improve the detection speed of conveyor belt damage, but its detection accuracy was only 93.22%. [PERSON] et al. [14] proposed a novel multiclassification conditional CycleGAN (MCC-CycleGAN) method for the detection of conveyor-belt-surface damage. At present, deep-learning-based conveyor-belt-detection methods are generally improved in one of the aspects of detection accuracy or speed. When improving detection accuracy, it often leads to a slower detection speed and, conversely, increasing detection speed causes a decrease in detection accuracy. Therefore, improving detection accuracy while also improving detection speed is still a challenge. Since there is no publicly available high-quality conveyor-belt-defect dataset, it is difficult to obtain a sufficient amount of data to train the deep-learning network, which makes it difficult to guarantee the detection and generalization performance of the model. In addition, due to the performance limitations of the equipment in actual production, how to balance and trade-off between detection accuracy and speed is also an important issue. In this paper, we focus on the above two problems in practical applications and propose a new conveyor-belt-defect detection method based on knowledge distillation. Section 2 presents our proposed data enhancement method with the improvement of the YOLOv5 model using model pruning and knowledge distillation methods. Section 3 shows the experimental setting with the relevant parameters and the results of the proposed improved method in this paper. Finally, in Section 4, we conclude with a discussion and summary of the methods used in this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **[EMAIL_ADDRESS]/\%** & **Inference Time/ms** & **Test Equipment** \\ \hline EfficientNet-YOLOv3 ([PERSON] et al., 2021) & 97.26 & 23.81 & NVIDIA RTX2060s \\ Yolov-BTFPN ([PERSON] et al., 2022) & 98.45 & 25.6 & NVIDIA GTX1660 Ti \\ MobilenetV3-YOLOv4 ([PERSON] et al., 2021) & 93.08 & 22.71 & NVIDIA RTX2060s \\ MCC-CycleGAN ([PERSON] et al., 2022) & 96.88 & 22.57 & NVIDIA RTX3070 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the detection effect of some methods.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 3 Radiation-Hardened SRAM Cell Design  
### Dual Interlocked Storage Cell (DICE)  
Figure 2 shows a schematic of 12 T DICE [12], which is one of the most popular radiation-hardened SRAM cells. Unlike the conventional 6 T SRAM cell, there are four nodes to store the data,--A, B, C, and D--where A and C are to have the same logic level, while B and D have the same level for the given data. For example, when the cell stores data as \"1\" with a supply voltage of VDD, A = C = VDD and B = D = 0 V. For the read or write operation, the four access transistors N1-N4 are turned on by WL = VDD to connect the storage nodes to bit-line pairs BL and BLB, while WL is kept as 0 for the hold operation. The read and write operations are performed in a similar manner to the conventional 6 T SRAM cell. For the read operation, the stored data are submitted to BL and BLB. For the write operation, BL and BLB are first driven by the external write driver; then, the data are submitted to the storage node through the access transistors.  
Figure 1: Schematic of the conventional 6 T SRAM cell.  
The key structural feature of DICE is that each storage node is driven by one pFET and one nFET, which are separately controlled by two different neighboring storage nodes. For example, A is driven by pFET P1 and nFET N5, whose gates are connected to D and B, respectively. This unique configuration means that the cell is immune to any error caused by a single node upset.  
The SEU recovery behavior of DICE is as follows. For example, when the datum \"1\" is stored, A = C = VDD and B = D = 0 V. In this condition, the upset can occur on A, changing the voltage from VDD to 0. This turns P2 on, which starts to raise B. However, because C is still VDD, N6 is kept on, which limits the pull-up of B. Moreover, because D is still 0 V, P1 is kept on. Thus, A is recovered to VDD, and B stops being pulled up by P2. This means that the stored data are not flipped but remain as \"1\", even though there is an event that erroneously pulls down the node A.  
Nearly a 2-2.5-times greater overhead in terms of area and power consumption is inevitable. In addition, if multiple nodes are upset simultaneously, the data flip cannot be prevented in DICE.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results
### Results for Generation Tasks  
In this section, we present the experimental results of generation tasks and investigate the impact of different divergences on perceptual quality. Here, we consider two specific realizations of the divergence function for semantic distance, Wasserstein distance and reverse KL divergence. This choice aligns with our theoretical analysis of Gaussian semantic rate-distortion functions. We also compare our framework with the VAE-based semantic coding paradigm, where a variational codec is optimized for MSE distortion and standard KL divergence.  
More specifically, we conduct experiments on two widely used benchmark datasets: MNIST and CIFAR10. The MNIST dataset, consisting of \(28\times 28\) grayscale handwritten digits, serves as a simple yet effective semantic source to demonstrate the tradeoffs between rate, distortion, and perceptual quality. To further evaluate the scalability of our approach, we also employ the more complex CIFAR10 dataset, which contains \(32\times 32\) color images across 10 object categories. For both datasets, the semantic source \(S\) corresponds to the original images, while the indirect observation \(U\) is their quantized version.
# Multi-Label Classification Based on Associations
## 2 Literature Review
### Utilizing AC in MLC  
According to the previous studies, relatively few efforts to solve the MLC issue have used AC. Multi-class multi-label associative classification (MMAC) is among the first methods [43] to try to use AC in MLC. MMAC turns the original multi-label dataset into a single-label one by replicating each instance associated with more than one class label a number of times equals to the number of the class label it is associated with, using or without using a weight. Hence, the dataset becomes SL dataset but, with more instances than the original one. After that, MMAC applies any SL classifier such as CBA or msCBA on the newly transformed dataset as described in Section 2.3. MMAC then generates its rules by combining the outcomes of single-label rules with the same antecedent ending with multi-label rules. Unfortunately, MMAC has only been tested on datasets with single label, and it may be too complicated if the original dataset has many labels as well as high number of instances [44]. A novel multi-label method based on AC is presented in [45]. The multi-label classifier based on associative Classification (MCAC) developed a revolutionary rule discovery approach that creates multi-label rules from a single-label dataset without the need for learning. These multi-label rules reflect important information that most earlier AC algorithms often disregard. The correlative lazy associative classifier (CLAC) method, described in [46], is a hybrid algorithm that combines the principles of AC and lazy learning. CLAC generates classification association rules (CARs) that are graded according to their support and confidence ratings. Each class predicted by CLAC is immediately modified as a new characteristic to predict a different class. In comparison to the BoosTexter method, CLAC performed well on three textual datasets. The authors of [47] presented an identical AC-based method to the MMAC algorithm. In contrast to MMAC, the suggested method has been examined using one multi-label dataset (Scene) and emphasizes the importance of adopting AC in addressing the MLC issue.
## 5 Conclusions  
In this article, IRS use was analyzed and evaluated in the indoor VLC system to identify possible improvements in the performance of the VLC environment. Furthermore, analytical expressions were presented to include the effect of IRS on the light beam, the probability that the LoS link will not be blocked, and proposed more realistic theoretical optical components. The performance of the VLC system was evaluated and compared in terms of CIR and BER by varying the number of rectangular optical smart metasurface patches in the IRS. Specifically for \(n_{m}=16\), we have maximum and minimum CIR values of \(2.2\times 10^{-6}\) and \(0.25\times 10^{-6}\), respectively, which are the best values obtained compared to the scenarios for \(n_{m}=8\) and \(n_{m}=4\). The results obtained through computer simulations of the VLC scenario show that having a greater number of rectangular optical smart metasurface patches in the IRS, also increases the probability that light beams contributing to the Non-LoS component will reach the receiver optimally. Therefore, the system improves its performance in terms of the metrics evaluated. This would imply that the presented solution is quite promising to implement an efficient VLC system as an enabling technology for 6G. Finally, as future work, the position of the IRS and the \(\phi_{m}\) parameter will be optimized in scenarios based on experimental test beds to generate more efficient practical applications in indoor environments. In addition, more accurate channel models for IRS systems with direct and non-direct optical links will be studied and developed.  
Conceptualization, M.R.C. and P.P.J.; methodology, M.R.C. and P.P.J.; software, M.R.C. and P.P.J.; validation, J.G.-M., W.V.-C. and C.A.-M.; formal analysis, M.R.C. and P.P.J.; investigation, M.R.C. and P.P.J.; resources, J.G.-M., W.V.-C. and C.A.-M.; data curation, M.R.C. and P.P.J.; writing--original draft preparation, M.R.C. and P.P.J.; writing--review and editing, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON], [PERSON] and [PERSON]; project administration, [PERSON], [PERSON] and [PERSON]; funding acquisition, [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ANID FONDECYT Regular No. 1211132, and Facultad de Ingenieria, Pontificia Universidad Catolica del Ecuador.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 5: CIR curves for different values of \(P\).  
**Acknowledgments:** This work was supported by SENESCYT \"Convocatoria abierta 2014-primera fase, Acta CIBAE-023-2014\"; Escuela de Ingenieria en Ciberseguridad, FICA, Universidad de Las Americas, and Escuela de Informatica y Telecomunicaciones, Universidad Diego Portales. The authors thank [PERSON] for his help and insightful discussion of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Sink-oriented Dynamic Location Service
### Eight-Direction Anchor System  
Although some location services have been proposed to address location tracking, they suffer from inefficient energy consumption or their specifications are not detailed in. Especially, ALS [23] suffers from concentrated energy consumption, as it always uses border nodes, when it provides the sink location. Though SLS [24] generates lower overhead than existing protocols such as flooding, TTDD, and ALS, they do not consider both location response time and resilience in case one query message fails. Therefore, we propose the Eight-Direction Anchor (EDA) system to solve these problems. EDA is built using the following procedures.  
1. Once an event occurs, a source node selects the nearest grid node as its source agent (see Figure 4, source node and selected source agent) and distributes the location information of the source node.
2. The source agent broadcasts the location of the source node in eight directions (East, West, South, North, Northeast, Southwest, and Southwest) through anchor setup messages.
3. The anchor setup messages are relayed by intermediate sensor nodes between two neighboring grid nodes and the recruited anchors store a copy of the location of the source node.  
These anchor setup messages are generated by a geographical routing protocol. That is, the source agent distributes the location information of the source node to its eight-direction neighboring grid nodes until the message reaches the network border. EDA can prevent intensive energy consumption at the border sensor nodes and considers location response time and success rate to obtain location information. Algorithm 1 explains how to make an Eight-Direction Anchor system at source nodes.  
**Algorithm 1. Pseudo-code to make an Eight-Direction Anchor system at source nodes.**  
**Procedure:**  
01: initially startNode, middleNode, endNode = 0;  
02: source node finds the nearest grid node as source agent  
/* source node floods a query within a local area about a cell size. */  
03: startNode = source agent;  
04: **for** (source agent's each neighbor grid node) /* to eight-direction */  
05: endNode = neighbor grid node;  
06: **while** (endNode!= border node)  
07: middleNode = **forward** (startNode, endNode);  
/* finding the nearest neighbor node to reach the destination node */  
08: **if** (middleNode == grid node)  
09: **saveLocationServer** (source node's location information);  
10: **end if**  
11: startNode = middleNode;  
12: **if** (startNode == endNode)  
13: endNode = startNode.neighbor grid node;  
/* i.e.) if the message is from the east, startNode's neighbor grid node is same direction. */  
14: **end if**  
15: **end while**  
16: **end for**  
Figure 4: The anchor selection and the query and data dissemination processes.