esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In practical applications, it is necessary to quickly process a large amount of image data to ensure that the system can respond and handle faults in real time. 3. Diversity and quantity of training data: The performance of target recognition algorithms heavily depends on the diversity and quantity of training data. For specific application scenarios, a large number of annotated data are needed to train the model to ensure its performance in practical applications. However, obtaining a large number of high-quality annotated data in actual operations remains a challenge. 4. Robustness and stability of the algorithm: Under different lighting conditions and environments, the recognition algorithm needs to maintain stable performance. This requires the algorithm to have strong robustness to cope with various complex application scenarios. For example, in outdoor environments, lighting conditions change greatly, and the recognition algorithm needs to work stably under both strong and weak light conditions. At present, research on 3D recognition technology has made significant progress and can be used to solve the problems of complex backgrounds and occlusions encountered in 2D target recognition. Many scholars and research institutions are committed to developing more accurate and efficient pose estimation algorithms. For example: [PERSON] et al. proposed the OpenPose model, which can estimate the 2D pose of multiple people in real time and has been widely used in the field of pose estimation [8]. [PERSON] et al. proposed the DensePose model, which is a pose estimation method that maps every pixel of the human body to the 3D surface, achieving high-precision pose estimation under complex backgrounds [9]. [PERSON] et al. proposed a multi-view convolutional neural network model, which achieves high-precision recognition of 3D shapes by combining image information from different perspectives [10]. [PERSON] et al. proposed the PointNet model, which is a deep learning model based on point sets, used for 3D classification and segmentation tasks, showing superior performance in processing 3D data [11]. These models, through training on large-scale datasets, can achieve high-precision pose estimation and exhibit good robustness and stability in practical applications. Figure 1: Picture of terminal and cable connections. This paper analyzes and finds similarities between the connection relationship of terminals and cables and human posture; therefore, it treats the connection of cables and terminals as a whole and proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. With the help of the YOLOv8-pose model, the problem of identifying the connection relationship between terminals and cables through two-dimensional images under conditions of large-angle perspective distortion has been effectively solved.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 4 System Model
The normalized gain between the serving base station and the user is expressed as \(\frac{\left|\mathbf{H}_{ik}\mathbf{W}_{ik}\right|^{2}}{\left\|\mathbf{W}_{ik} \right\|^{2}}\) where \(\left|\mathbf{H}_{ik}\right|^{2}\) is the precoding matrix at the transmitter, and \(\left|\mathbf{W}_{ik}\right|^{2}\) is the weights' matrix. The gain is given as \(\left|\mathbf{H}_{ik}\mathbf{W}_{ik}\right|^{2}\) with \(\left\|\mathbf{W}_{ik}\right\|^{2}=1\). The instantaneous cell throughput can be calculated as  
\[R=\sum_{k=1}^{K_{c}}r_{k}=\sum_{k=1}^{K_{c}}B_{i}N_{k}\log_{2}\biggl{(}1+ \frac{\mathcal{S}_{k,i}}{\mathcal{I}_{k}+\mathcal{N}_{k}}\biggr{)} \tag{6}\]
# Risk Factor Analysis of Elevator Brake Failure Based on DEMATEL-ISM
## 4 Analysis of Risk Factors for Elevator Brake Failure
### Analysis of Results  
#### 4.2.1 Grey DEMATEL Analysis  
In the DEMATEL method, the Centre Degree mainly indicates the risk factor's influence on the comprehensive degree of the complex system, serving as a core parameter for assessing its importance. A higher Centre Degree indicates greater significance of the factor. Cause Degree indicates the extent of a risk factor's influence on other factors in the system. A factor with a Cause Degree greater than 0 is classified as a Cause Factor, while a factor with a Cause Degree less than 0 is termed a Result Factor.  
Therefore, the 16 influencing factors are divided into four categories, as shown in Figure 3. Category I factors have high Centre Degrees and are classified as strong Cause Factors, indicating that they have a significant impact on both elevator brake failure and other factors. Category II factors have low Centre Degrees and are also Cause Factors, suggesting that they have a minor impact on elevator brake failure but still affect other factors. Category III factors have low Centre Degrees and are classified as strong Result Factors, meaning they have a limited impact on elevator brake failure but are significantly influenced by other factors. Category IV factors have high Centre Degrees but are weak Result Factors, indicating that they have a substantial impact on elevator brake failure while being minimally affected by other factors.  
In summary, from the grey DEMATEL analysis, it can be seen that category I risk factors of elevator brake failure are Safety Knowledge S9, Attitude of Responsibility S10, Policy Planning S11, Management Control S12, Subjective Responsibility S13, and Safety Participation S14, these factors have a greater impact on elevator brake failure, and also exert a significant influence on the other factors, and should be focused on this type of factor, and focus on the measures that can efficiently reduce the risk of elevator brake failure. At the same time, inefficient behaviors with too many inputs are avoided.
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 3 POD Estimation
### Empirical AE Signals Modeling
#### 3.1.1 AE Source Modeling  
The defect is the AE source, which responds to the loading effect and emits signals with a nanosecond duration and can be considered as 8-functions. The number of emitted signals \(N_{\Sigma}\) and the amplitudes of the AE signals \(A_{k}\) depend not only on the defect size, but also on the loading parameters and, to a large extent, on the parameters of the material microstructure. Preliminary studies have shown that the parameters \(N_{\Sigma}\) and \(A_{k}\) can be considered as random variables that do not directly correlate with the defect size. In [12], it was established that the parameters \(N_{\Sigma}\) and \(A_{k}\) correspond to certain laws of probability distribution and can be estimated using probabilistic models.  
The number of signals emitted by a defect during growth per unit length is a parameter of the material emissivity and can be estimated using the multiplicative parameter of the Palmer-Heald model as  
\[N_{\Sigma}=D_{a}\cdot\left[sec(\left(\frac{\pi}{2}\frac{\sigma}{\sigma_{y}} \right)-1)\right], \tag{1}\]  
where \(\sigma\) is the actual stress, \(\sigma_{y}\) is yield stress, \(D_{a}\) is coefficient depending on the characteristics of the material [24]. The parameter \(D_{a}\) is random and corresponds to the normal distribution \(N(\mu,\sigma^{2})\) and the quantitative values of \(\mu\) and \(\sigma^{2}\) for 09G2S and 45 steels are given in [12]. According to the well-known law of \(D_{a}\) probability distribution, based on the Palmer-Heald model, it is possible to estimate the probability that a certain number of AE signals will be recorded when the object with fatigue crack is loaded. For this, the  
Figure 3: Flowchart of AE signal simulation algorithm.  
distribution of \(D_{a}\) is scaled by the coefficient \(\left(sec\left(\frac{\pi}{2}\frac{\sigma_{max}}{\sigma_{y}}\right)-1\right)\) with respect to the value \(\sigma_{max}\).  
In [25], it was shown that the distribution of AE signal amplitudes corresponds to the Weibull law \(W(\lambda,k)\). The parameters of scale \(\lambda\) and shape \(k\) are random values corresponding to the normal distribution law for each of the inspected materials. Considering the results of the preliminary study, a hierarchical two-level algorithm is proposed for modeling the AE source (Figure 4), in which the distribution parameters for \(\lambda\), \(k\) and \(D_{a}\) are estimated first, and then the parameters of the AE source are determined, characterizing its emissivity and amplitude values of radiated AE signals.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 3 Proposed Method: Policy-Based Smart Contract Management for IoT Privacy Preservation
### Model Ingredients
#### 3.1.1 User Side  
The user side encompasses edge and fog layers as follows:  
* Edge layer--IoT devices are owned by the user and have sensing and communication capabilities that enable them to gather sensed data, interact with other devices owned by the user, and connect to the mobile application that administers them. We identify various devices in an IoT ecosystem, including sensors, RFID readers, actuators, mobile phones, and embedded computers [41]. The mobile application can connect with external entities.
* Fog layer--smart contracts are the only means to communicate with blockchain networks. We define multiple smart contracts according to the user's preferences and purposes to preserve their privacy. Thus, the application within the fog device triggers the appropriate smart contracts and establishes a private, secure channel when handling sensitive data. Further details about smart contracts and their management are depicted in Section 3.2.
* Fog layer--application: each user has an application installed on a fog device to submit their preferences and trigger new transactions containing data received from IoT devices. The application transforms the data sent by the IoT devices or user requests to smart contracts executed thereafter to communicate with the private blockchain. Furthermore, the application obtains a list of blocks from the longest chain and filters the transactions from those linked blocks to the particular user. These transactions are represented so that the user can see how many portions they have at any given time.  
Figure 1: Proposed model.
## 4 Performance Evaluation of the Navigation Algorithm through the M&S Software
It can be seen from Figure 18 that the multi-radio integrated navigation system could provide the continuous navigation output due to the use of the KNSS navigation system and the ground-based radio navigation systems. For the variations of jammer power, results of GPS only navigation and the multi-radio navigation system are listed in Table 6. The availability in Table 6 means the percentage value of the time interval in which the navigation outputs could be obtained to the whole mission time interval. It could be observed from the results of the GPS only navigation in Table 6 that the position errors increased as the jammer power increased from 0 to 50 W and decreased at 100 W. This decrease was caused by the fact that the navigation error was not included in the calculation when the number of satellites in view was less than four or the PDOP was greater than 10. It could also be observed in Table 6 that the availability of the GPS only navigation decreased as the jammer power increased. On the other hand, it could be seen that the multi-radio integrated navigation provided 100% of the availability, even though the power of the jammer increased. It can also be observed from the results of the multi-radio integrated navigation system in Table 6 that the position errors increased as the jammer power increased. This was due to the fact that the time interval, at which the GPS measurements were not available, increased as the power of the jammer increased. It could be seen from the test results that the performance evaluation of the multi-radio integrated navigation algorithm under the  
Figure 17: Position error of GPS only navigation with a 50 W jammer. Figure 18: Position error of multi-radio integrated navigation with a 50 W jammer. navigation warfare environment could be effectively carried out through the proposed M&S S/W (software) design method.
###### Abstract  
Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research primarily concentrates on using prototype networks for FSRE and enhancing their performance by incorporating external knowledge. However, these methods disregard the potential interactions among different prototype networks, and each prototype network can only learn and infer from its limited instances, which may limit the robustness and reliability of the prototype representations. To tackle the concerns outlined above, this paper introduces a novel prototype network called SACT (multi-head self-attention and contrastive-center loss), aimed at obtaining more comprehensive and precise interaction information from other prototype networks to bolster the reliability of the prototype network. Firstly, SACT employs a multi-head self-attention mechanism for capturing interaction information among different prototypes from traditional prototype networks, reducing the noise introduced by unknown categories with a small sample through information aggregation. Furthermore, SACT introduces a new loss function, the contrastive-center loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments on FSRE datasets, this paper demonstrates the outstanding performance of SACT, providing strong evidence for the effectiveness and practicality of SACT.  
few-shot; relation extraction; prototype network; multi-head self-attention; contrastive-center loss +
Footnote †: journal:  
Article
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 2 Fundamentals of VOCC System Architecture
### LED Bit Detection in VOCC  
In VOCC systems, transmitted data are obtained through the LED bit detection step, as shown in Figure 2. Upon locating the position of the entire LED panel in the image, the LED panel is divided into equal sub-regions. It is assumed that the number of LEDs in the panel is known beforehand. In Figure 2, the LED panel is divided into 8 \(\times\) 8 equal sub-regions because there are 8 \(\times\) 8 LEDs in the panel. Each of these sub-regions contains the information of an LED in the panel. From each sub-region, two LED features, namely the average greyscale ratio (AGR) and the gradient radial inwardness (GRI), can be obtained [9]. From these two features, algorithms such as Linear Fisher Discriminant Analysis or K-mean clustering [10], can be used to distinguish the status On and Off of each LED.  
Figure 1: VOCC system architecture.  
The AGR is calculated as follows:  
\[AGR=\overline{G_{led}}\big{/}\overline{G_{panel}}, \tag{1}\]  
where \(\overline{G_{led}}\) is the average greyscale of an LED and \(\overline{G_{panel}}\) is the average greyscale of the entire LED panel.  
The GRI is calculated as follows:  
\[GRI=\frac{\sum\
abla f(x,y)\cdot r(x,y)}{number\ of\ pixels}, \tag{2}\]  
where \(\
abla f(x,y)\) denotes the gradient vector at each pixel inside the LED region, \((\cdot)\) denotes the dot product operation, and \(r(x,y)\) denotes the normalized radial inward vector at the corresponding pixel.  
The gradient vector is given as:  
\[\
abla f\left(x,y\right)=\left[\begin{array}{c}f\left(x+1\right)-f\left(x-1 \right)\\ f\left(y+1\right)-f\left(y-1\right)\end{array}\right], \tag{3}\]  
where \(f(x,y)\) is the greyscale at the coordinate \((x,y)\) in the image.  
The normalized radial inward vector is given as:  
\[r(x,y)=\frac{\left(x_{c}-x,y_{c}-y\right)}{\left\|\left(x_{c}-x,y_{c}-y \right)\right\|} \tag{4}\]  
where \((x_{c},y_{c})\) are the coordinates of the center pixel of the LED and \(\left\|\mathbf{v}\right\|\) denotes the magnitude of the vector \(\mathbf{v}\).
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 4 Results and Discussion  
During the model development, one-year observations (since 10 July 2021 to 28 June 2022) were utilized. The model was then applied to GNOS-II data to retrieve soil moisture from July to October 2022. As an illustration, the retrieved FY-3E GNOS-II soil moisture results of one month, from 7 July to 6 August 2022, are shown in Figure 9.  
Figure 8: The histogram of the incidence angles for the observations of FY-3E GNOS-II.  
Figure 9: GNOS-II global soil moisture estimation (7 July–6 August 2022).
## 4 Simulation
### Simulation Results
This leads to reduced energy and increased noise for the desired pixels. Simultaneously, the radiation makes the 2D ISI increase. Figure 6 presents the BER performance of the output signal from the first branch, second branch, and combined branch (the average function branch). The outputs of the first and second branches are similar to the signal \(a[j,k]\). The output of the combined branch can achieve a gain of 2 dB at a BER of \(10^{-4}\). The output of the combined branch can improve  
Figure 4: Learning curve of the RNN model in the first branch. Figure 5: Learning curve of the RNN model in the second branch. the BER performance because the combined branch can balance the information from horizontal and vertical detections. In the following simulation, we compared the proposed model with those of previous studies. Figure 7 presents an interference estimator with a parallel structure, which corresponds to the model introduced in [17]. Serial detection was implemented following the methodology outlined in [16]. Furthermore, a decision feedback equalizer (DFE) with a reliability factor was simulated as described in [27]. 2D SOVA and the 1D GPR target, which were introduced in [15], respectively, are also compared with our model. The results demonstrate that the proposed model can improve the BER performance of HDS systems. In particular, it can achieve a gain of \(\sim\)1 dB at BER = \(10^{-4}\) compared to the model in [17]. Figure 6: BER performance of the proposed model. Figure 7: BER performance comparison with 0% misalignment. Next, we set up 10% misalignment of the HDS channel. Figure 8 demonstrates that the proposed model still achieves the best performance. The gain of the proposed model is 1 dB at a BER of \(10^{-4}\). When increasing the misalignment, the ITI and ISI also grow. However, by estimating the increase in the ITI and ISI, our proposed model can compensate for this change and improve the BER performance of HDS systems. With 2D SOVA and 1D GPR target models, we can see that the performance changed more when increasing the misalignment because the 2D SOVA and 1D GPR target models did not have the ITI/ISI estimators. As shown in Figure 9, we changed the blur from 1.8 to 3 with an SNR of 15 dB and misalignments of 0% and 10%. The simulation results show that our model can enhance the BER performance when the blur is less than 2.6. Therefore, our proposed model can resist the blur up to 2.6. Figure 8: BER performance comparison with 10% misalignment. Figure 9: BER performance according to blur.
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 2 Study Area and Data
### Data
#### 2.2.1 Smap  
SMAP is the first dedicated SM satellite launched by the National Aeronautics and Space Administration (NASA) that provides global maps of land-surface soil moisture and freeze-thaw state [52]. It operates in a near-polar sun-synchronous orbit of 685 km, rising at 6:00 and setting at 18:00 local solar time, covering the globe every 50 h. Its active radar failed on 7 July 2015. The passive microwave product SPL3 SMP from the National Snow and Ice Data Center was used in this study. This dataset contains the L-band global 36 km EASE-Grid 2.0 surface SM, which represents the soil volumetric water content (cm\({}^{3}\)/cm\({}^{3}\)) from the top of the soil column to an average depth of approximately 5 cm [53]. Five predictors, SM, longitude, latitude, day sequence, and AM/PM, were extracted from this dataset.
# Targeting Antigen-Presenting Cells in Multiple Sclerosis Treatment
## 4 MS-Approved Drugs Targeting Immunocompetent Cells
Recombinant soluble CD83 (rsCD83) have demonstrated its potential via immunosuppressive properties observed in animal models of autoimmune diseases, including MS [101]. All of the studies indicate that this receptor is able to suppress immune responses, via inhibition of human monocyte differentiation into DCs, changing the DC cytoskeleton, preventing DC maturation, and reducing DC-mediated T-cell proliferation [102; 103; 104; 105; 106; 107]. Results obtained from EAE studies indicate that administration of rsCD83 prevents EAE development and also reduces the level of T-cell cytokines, such as IFN-\(\gamma\), IL-2, IL-4 and IL-10 [101]. Dimethyl fumarate (DMF) is the first-line oral drug approved for RR-MS patients, with moderate to high efficacy [108]. The main mechanism of action of DMF is an alteration of Th-cells differentiation and total number, induction of regulatory B-cells, and its anti-oxidative potential [109]. The naive and regulatory T-cell subpopulations are elevated, whereas there is a reduction in memory T-cells, as well as in Th1 and Th17 cells [110; 111]. However, it was also shown that DMF may alter activity of APCs, such as monocytes and dendritic cells [108]. Moreover, it has been shown that DMF has ameliorated a B-cell-accutuated EAE by diminishing the capacity of B-cells to act as APCs for T-cells [112]. In the studies conducted on blood obtained from MS patients, it was reported that the total number of B-cells was reduced after DMF treatment, with the emphasis on differentiated cells [113; 114; 115; 116]. The level of mature and memory B-cells, together with plasmablasts, was reduced, whereas the frequency of immature, transitional B-cells increased [112]. One of the therapeutic goals in MS treatment is the induction of long-term, durable, antigen-specific T-cell tolerance. Antigen-specific immunotherapies are emerging to suppress targeted immune responses without altering the global immune system [117; 118]. \begin{table}
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