esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Device-to-Device (D2D)
#### 4.2.1 Unique Features of D2D  
1. Single-hop communication: A single hop is required for communication between the devices. Communication in D2 Drequires fewer resources, resulting in the effective use of the spectral. Because proximity users connect directly with one another in D2D communication, latency is significantly decreased. These D2D communication features also assist the operators of mobile networks [87].
2. Reusability of the frequency: When D2D communication is used in cellular networks, the same frequency is shared by both D2D and cellular users. This enhances frequency reuse, hence optimizing the frequency reuse ratio [88].
3. Power levels optimization: The existence of D2D links between close-by devices results in minimum transmission power over a short distance. This extends the device's battery life. As a result, D2D communication in cellular networks can achieve improved energy efficiency (EE) [89; 90].
4. Increased area of coverage: Since D2D communication is feasible via relays, this allows for communication over larger distances, thus expanding the entire coverage area.
# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 3 Methodology
### Evaluating the Interpretability of the Selected ML Algorithms
Correct predictions are highlighted in blue, and incorrect predictions are highlighted in red. The color intensity indicates the number of cases in each cell. The distribution in Figure 12 reveals that SVM predictions were concentrated along several distinct lines, including incorrect predictions aligned parallel to the diagonal. Starting at row 9, column 1, the first red line represents misclassifications related to pump internal leakage, indicating that the SVM struggled to detect this failure mode. Similarly, the second red line reflects instances where the SVM failed to identify a pump external leakage. The third red line shows cases where the SVM did not consistently recognise simultaneous internal and external leakages in the pump. Notably, the confusion matrix for the ANN exhibited a similar pattern, with false negatives concentrated in these three fault classes. Additionally, the logistic regression (LR) frequently produced false negatives for FOHE leakages, often misclassifying faulty cases as normal. In contrast, the decision tree (DT) model generated a high number of false positives for the same failure modes, potentially leading to increased maintenance costs. Among the failure modes in the simplified AFS, blockages were generally easier to detect than leakages. Overall, based on these analyses, the ANN and the SVM demonstrated a superior performance compared to the LR and the DT. Figure 11: F-1 score of the four algorithms across the 32 classes.
## 5 Results
### Result of The Spatial Durbin Model for Hypothesis 1
#### 5.2.2 LM Test
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
\begin{tabular}{c} **Transportation** \\ **Structure Index** \\ \end{tabular} & **Rank** & **City** & **Transportation Function Index** & **Rank** & **City** & **Transportation Integration Index** \\ \hline
31 & Fuyang & 0.476 & 31 & Taizhou(jiangsu) & 0.2467 & 31 & Quzhou & 0.4756 \\
32 & Huaihan & 0.3982 & 32 & Lianyangang & 0.2396 & 32 & Isohua & 0.4679 \\
33 & Chuzhou & 0.3950 & 33 & Huaian & 0.2381 & 33 & Tongling & 0.4643 \\
34 & Liuan & 0.3902 & 34 & Suqan & 0.2215 & 34 & Maanshan & 0.4547 \\
35 & Manashan & 0.3446 & 35 & Anqing & 0.2103 & 35 & Lishui & 0.4520 \\
36 & Wuhu & 0.3115 & 36 & Chizhou & 0.1994 & 36 & Taizhou(jiangsu) & 0.4382 \\
37 & Xuancheng & 0.3011 & 37 & Tongling & 0.1931 & 37 & HuangShan & 0.4348 \\
38 & Tongling & 0.2854 & 38 & Huangshan & 0.1754 & 38 & Huahei & 0.4111 \\
39 & Chizhou & 0.2824 & 39 & Lishui & 0.1483 & 39 & Yancheng & 0.3818 \\
40 & Anqing & 0.2255 & 40 & Haubei & 0.1308 & 40 & Suqian & 0.3714 \\
41 & Huangshan & 0.0000 & 41 & Zhoushan & 0.0426 & 41 & Zhoushan & 0.0213 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Moran Index** & **Year** & **Moran Index** \\ \hline
2011 & 0.2382 *** & 2016 & 0.2191 *** \\
2012 & 0.2341 *** & 2017 & 0.2246 *** \\
2013 & 0.2341 *** & 2018 & 0.2357 *** \\
2014 & 0.2206 *** & 2019 & 0.2682 *** \\
2015 & 0.2179 *** & 2020 & 0.2693 *** \\ \hline \hline \end{tabular} Note: the significance of *** is 1%.  
\end{table} 
Table 4: Spatial autocorrelation test of industrial location entropy under the geographical adjacency spatial weight matrix. Figure 3: Spatial distribution map of transportation integration value in the Yangtze River Delta urban agglomeration in 2020. In view of the above test results, this study adopted the Spatial Durbin Model (2) to study the impact of transportation integration on industrial agglomeration in urban agglomerations.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 5 Burst Data Transmission Scheme
### Frame Format  
The data frame format used is similar to the data frame format of the EPC C1G2 protocol, as shown in Figure 5. The data payload length in the EPC C1G2 protocol is defined by the EPC length field in the Protocol Control (PC) field, which ranges from 16 to 496 bits and is an integer multiple of 16 bits. Since the unit of increasing or decreasing the frame length in our scheme is 8 bits, the field is modified so that each bit represents 8 bits. Considering that the device only supports 96 bits, the maximum value of the field is 96 bits, that is, 01100. The ACK format of EPC C1G2 consists of 2 bits of command bits and 16-bit RN. However, in our proposed scheme, the frame length and the number of redundant frames need to be adjusted according to energy-harvesting and channel conditions, which are fed back to the CRFID by the reader through the ACK. Therefore, two additional 5-bit fields are added in the ACK, respectively indicating the frame length and the number of redundant frames in the next round, and the ACK format is as shown in Figure 6.  
Figure 5: Format of the data frame.  
Figure 6: Format of the ACK message.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 2 Materials and Methods
### Methods
#### 2.3.2 Acquisition of Endmembers Spectral Data  
1. Endmember of Green Tide Algae  
The normalized difference vegetation index (_NDVI_), a green tide extraction algorithm, is widely used because of its reliable and robust performance [47].  
\[NDVI=\frac{NIR-R}{NIR+R}, \tag{3}\]  
where _NIR_ is the reflectance of the near-infrared band, and \(R\) is the reflectance of the red band.  
The theoretical value of threshold T is 0 but fluctuates due to factors such as shallowness and water depth [48]. The threshold T of NDVI was determined by visually interpreting the threshold segmentation and false-color images of NDVI in order to eliminate the influence of thin clouds and coastal waters and to unify the conditions. After many experiments, the threshold was determined to be [\(-0.05\), \(0.80\)]. The results are shown in Figure 3.  
2. Endmember of Clouds  
The apparent atmospheric reflectance images in the R, G and B bands after radiometric calibration were processed and converted into images with distinct cloud features [49]. Then, cloud detection was performed on the cloud feature images. This method could greatly improve the detection accuracy of clouds compared with the general threshold method of cloud detection. The specific process is shown in Figure 4.  
The equation for converting the RGB image into a cloud feature image with specific coefficients is given by  
\[Gray=PM, \tag{4}\]  
\[P=\begin{bmatrix}0.229&0.587&0.114\\ 0.595716&-0.274453&-0.321263\\ 0.211456&-0.522591&0.311135\end{bmatrix} \tag{5}\]  
\[M=\begin{bmatrix}R\\ G\\ B\end{bmatrix},\]  
where \(P\) is the specific coefficient converted to a grayscale image with prominent cloud features [50], and \(R\), \(G\) and \(B\) are the reflectance of the red, green and blue light bands, respectively. The cloud detection results are shown in Figure 5.  
Figure 4: Schematic diagram of the cloud inspection workflow.  
Figure 3: Extraction results and spatial location determination of green tide algae: (**a**) GOCI, (**b**) MODIS and (**c**) OLCI.
# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 6 Conclusions  
With the colossal increase in IoT device usage, there is a need for an efficient system that can deal with the issues of increased load, energy, and delay as a result of more data traffic and processing. In this article, an integrated model is proposed for IoT-fog-cloud to efficiently use the available resources. A three-tier architecture having IoT devices, fog nodes, fog gateways, and cloud devices is used to achieve uniform load balancing among  
Figure 6: Average Delay vs. No. of nodes.  
Figure 7: Response Time vs. No. of nodes.  
-fog and cloud tiers. A novel algorithm is developed which considers energy usage of nodes, delay, network lifetime, and response time. Concretely, a systematic framework is developed to investigate the energy consumption, load balancing, and delay in an IoT-fog-cloud environment. Using the iFogSim simulator, the proposed algorithm is tested for load balancing in an IoT-fog-cloud environment. The results are presented that show the proposed model is outperforming the existing mechanisms under different scenarios. The proposed algorithm works well for a moderate number of devices., but if the IoT data continues increasing exponentially, it will be difficult to handle. Therefore, in future work, we intend to investigate the IoT-fog-cloud environment with more QoS parameters and with more data traffic at each layer.  
Conceptualization, project administration, and validation, M.V.; conceptualization and methodology, S.G.; software, writing--original draft, A.A.; resources, editing and review, M.O.A.; methodology, resources, writing--original draft, S.A.A.; visualization, supervision, J.B.A. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the Centre of Excellence, University of Zululand, Kwadlangezwa 3886, South Africa.  
Not applicable.  
The authors declare no conflict of interest.
## 3 SOH Estimation Methods
### Deep Learning Methods
#### 3.2.2 Long Short-Term Memory (LSTM)  
An RNN can collect the latest information. However, because the memory for long sequences of inputs is shortened, the vanishing and exploding gradient problem can occur. LSTM cells were used to overcome this drawback. LSTM was proposed to address the vanishing and exploding gradient problem [33]. The memory of an LSTM cell has the characteristics of being stored and converted from an input to an output in the cell state. As shown in Figure 6, the LSTM comprises the forget, output, input, and update gates. The forget gate decides what to forget from the previous memory unit, the input gate decides to accept the input, and the output gate generates a new long-term memory. These four main components of LSTM interact with each other by accepting long-term memory, short-term memory, and input sequence values and generating new long-term and short-term memory at a given specific step. The input gate decides which information is to be sent to the cell. Equation (16) is the mathematical equation for the input gate.  
\[i_{t}=\sigma(W_{i}^{s}[h_{t-1},x_{t}]+b_{i}) \tag{16}\]  
In Equation (16), the operator \(*\) represents the multiplication of each element of the vector, \(\sigma\) represents activation function, \(W_{i}\) represents the weight, \(h_{t-1}\) represents the hidden state of a previous point and \(b_{in the previous memory is defined by a mathematical equation for the forget gate, as expressed by Equation (17).  
\[f_{t}=\sigma(W_{f}^{*}[h_{t-1},x_{t}]+b_{f}) \tag{17}\]  
The cell state is updated by the update gate, which is defined by mathematical equations, as expressed in Equations (18) and (19).  
\[\widetilde{C}_{t}=\tanh(W_{c}\big{[}h_{(t-1)}\big{]}+b_{c} \tag{18}\]  
\[c_{t}=f_{t}^{*}c_{t-1}+i_{t}^{*}\widetilde{c}_{t} \tag{19}\]  
The hidden layer of the previous step is updated by the output gate, which updates the output as follows.  
\[O_{t}=\sigma(W_{o}^{*}[h_{t-1},x_{t}]+b_{o} \tag{20}\]  
\[h_{t}=O_{t}^{*}\tanh(c_{t}) \tag{21}\]
# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 3 Core Idea  
### Problem Statement  
**Time Series**: In this paper, time series with the same time interval are abbreviated as:  
\[\text{S}=\langle\text{s}_{1},\text{s}_{2},\dots,\text{s}_{n}\rangle\]  
Time series data describe the changes of various parameters of equipment in work and also contain the law and trend of fault occurrence, which is an important basis for fault detection in this paper.  
**Cluster-Based Fault Diagnosis:** In the fault diagnosis algorithm based on machine learning, it can be divided into two categories: supervised learning and unsupervised learning. Due to the confidentiality of the autonomous unmanned system, the small number of faulty samples, and the difficulty of manual interpretation under massive data, it is impossible to perform supervised learning by effectively labeling the dataset. The learning of unlabeled training samples is needed to reveal the inherent laws and properties of the autonomous unmanned system data. Clustering is often the preferred method for such unsupervised learning.  
The operation of dividing samples into a data set into several disjoint subsets is called clustering, and each subset is a cluster. Formally speaking, the sample set \(\text{D}=\{\text{x}_{1},\text{x}_{2},\dots,\text{x}_{n}\}\) contains n unmarked samples, and for each sample, the goal of the clustering algorithm is to divide the set composed of these samples into several disjoint clusters \(\text{C}=\{\text{c}_{i}\,|\,\text{i}=1,2,\dots,\text{k}\}\), where k is the number of clusters, and \(\text{c}_{i}\cap\text{c}_{i}=\varnothing,\text{D}=\text{U}_{i=1}^{\text{k}} \text{c}_{i}\).  
In order to improve the clustering effect of time series and better discover abnormal data, we need to solve the following problems, denoted as questions Q1-2:  
1. Design a time series dimensionality reduction method to reduce the cost of clustering calculation (Q1);
2. Determine the similarity measure of two time series (Q2).
# Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems
## 1 Introduction
The main contributions of this paper are given as follows. * We investigate a novel application of RSMA systems, where a UAV splits its rate to avoid deteriorating the covert transmission of a ground user while increasing the ESR. To the best of the authors' knowledge, this is the first work that studied the covert communication in UAV-assisted uplink RSMA system. * We derive the closed-form expressions of the ESR and obtain the optimal target rate of UAV which maximizes the ESR of the system. Subjected to minimum detection error probability (DEP) and expected covert rate (ECR) constraints, a joint position and target rate optimization problem is formulated for maximizing the ESR of uplink RSMA systems. * The numerical results show that the proposed scheme outperforms NOMA systems in terms of ESR with the same DEP and ECR and illustrate the effect of constraints on the ESR.
###### Abstract  
Electrical treeing is one of the effects of partial discharges in the solid insulation of high-voltage electrical insulating systems. The process involves the formation of conductive channels inside the dielectric. Acoustic emission (AE) is a method of partial discharge detection and measurement, which belongs to the group of non-destructive methods. If electrical treeing is detected, the measurement, recording, and analysis of signals, which accompany the phenomenon, become difficult due to the low signal-to-noise ratio and possible multiple signal reflections from the boundaries of the object. That is why only selected signal parameters are used for the detection and analysis of the phenomenon. A detailed analysis of various acoustic emission signals is a complex and time-consuming process. It has inspired the search for new methods of identifying the symptoms related to partial discharge in the recorded signal. Bearing in mind that a similar signal is searched, denoting a signal with similar characteristics, the use of artificial neural networks seems pertinent. The paper presents an effort to automate the process of insulation material condition identification based on neural classifiers. An attempt was made to develop a neural classifier that enables the detection of the symptoms in the recorded acoustic emission signals, which are evidence of treeing. The performed studies assessed the efficiency with which different artificial neural networks (ANN) are able to detect treeing-related signals and the appropriate selection of such input parameters as statistical indicators or analysis windows. The feedforward network revealed the highest classification efficiency among all analyzed networks. Moreover, the use of primary component analysis helps to reduce the teaching data to one variable at a classification efficiency of up to 1%.  
Keywords:solid dielectrics; acoustic emission; artificial neural networks; electrical treeing; wavelets; non-destructive testing; high-voltage insulating systems +
Footnote †: journal: Applied Physics Letters
# Abstractive Summarizers Become Emotional on News Summarization
## 3 Emotional Content Measures  
We aim to quantify (i) how frequent an emotion is in a text and (ii) which emotions increase/decrease their frequency in summaries compared to their frequency in articles. We base our study on the methodology introduced in [1].  
Following this methodology, we assume that the presence of an emotional word in a text is enough to convey some degree of an emotion. Although this assumption oversimplifies the problem because of the inherent limitations of lexicons, such as the lack of compositionality or ambiguity, having a moderately accurate fine-grained view of emotions in texts is useful. We use the Nrc lexicon [34] (version 0.92), which contains 27 k words and their associations with the eight basic emotions in [PERSON]'s wheel (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust). Ten thousand of these words were manually annotated through crowdsourcing, and the remaining 17 k words are Wordnet synonyms of the annotated words. We use the Nrc lexicon through the Nrclex Python package to detect words with emotions. Words from texts and the Nrc lexicon are lemmatized to deal with inflections.  
To measure how frequent an emotion \(e\) is in a text \(t\), we use the emotion density defined (\(ED\)) in Equation (1).  
\[ED(e,t)=\frac{count(e,t)}{|t|} \tag{1}\]  
where \(count(e,t)\) is the number of words in the text \(t\) that convey the emotion \(e\) following the Nrc lexicon, and \(|t|\) is the number of words in \(t\). We compute the emotion density on articles, reference summaries, and generated summaries.  
To quantify emotions that appear more/less frequently in a summary than in an article, we use the emotion ratio (\(ER\)). The emotion ratio of an emotion \(e\) in an article-summary pair is defined in Equation (2).  
\[ER(e,a,s)=\frac{ED(e,s)}{ED(e,a)} \tag{2}\]  
where \(a\) is an article and \(s\) a summary. When \(ER(e,a,s)>1\), we say the emotion \(e\) is overemphasized in the summary. On the contrary, when \(ER(e,a,s)<1\), we state that the emotion is underemphasized in the summary. Intuitively, emotions that are more frequent in reference summaries than in source articles should also be more numerous in generated summaries [1]. To measure this, we compute the emotion ratios for both reference summaries and summaries generated by abstractive summarizers.
# Analysis of Different Guide Elements' Designs in Hydraulic Cylinders
## 4 Results
### Guidance with Guide Rings  
The measured frictional forces for the design using guide rings are shown and reported separately for each pressure used in Figures 10-12. At a pressure of 0 bar (Figure 10), the frictional force at the velocities below 0.1 m/s increases with increasing radial load (from 0 kg to 50 kg) to 540 N at 0 kg and 1390 N at 50 kg. In addition, the frictional force decreases rapidly with increasing velocity and reaches its lowest value (270 N at 30 kg) at 0.15 m/s. This decrease in frictional force is greater with higher radial loads. A further increase in velocity leads to a slight increase in the frictional force, which reaches a value of around 500 N for all radial loads used at velocities above 0.5 m/s. At a pressure of 120 bar (Figure 11), the trend of the curves is similar to that for 0 bar of pressure (see Figure 10). At velocities below 0.1 m/s, the frictional forces increase with increasing radial load, being 830 N at 0 kg and 1280 N at 50 kg. With increasing velocity, the frictional force decreases and reaches its lowest value (511 N at 30 kg) at 0.17 m/s. This decrease in frictional force is greater with higher radial loads. A further increase in velocity leads to a slight increase in frictional force at values between 600 N and 800 N for velocities above 0.4 m/s. At a pressure of 240 bar (Figure 12), the trend of the curves is similar to that for 0 bar and 120 bar of pressure (see Figures 10 and 11), but the initial frictional forces are even higher than at pressures of 0 bar and 120 bar, indicating that a higher pressure increases the initial frictional forces. At velocities below 0.1 m/s, the frictional forces increase with increasing radial load, being 1040 N at 0 kg and 1800 N at 50 kg. With increasing velocity, the frictional force decreases rapidly and reaches its lowest value (690 N at 50 kg) at 0.17 m/s. This decrease in frictional force is greater with higher radial loads. A further increase in velocity leads to a slight increase in frictional force at values between 800 N and 950 N for velocities above 0.4 m/s. The graphs in Figures 10-12 show that the pressure has an additional influence on the frictional force, as an increase in pressure in the cylinder also increases the frictional force, both at the beginning and with increasing velocity.  
Figure 10: Measured friction force in relation to velocity at a pressure of 0 bar, for the design using guide rings.
## 3 Research Results  
### Dynamic Evolution of Land Use from 2000 to 2020  
#### 3.1.1 Spatiotemporal Characteristics of Land Use  
Between 2000 and 2020, woodland and arable land were the predominant LU types in the BRB, collectively making up 95.37% of the total area. Woodlands are chiefly found in the northwest, west, and south, whereas cultivated land is predominantly concentrated in the east and northeast. Both land types show a trend of initially expanding and then contracting in area throughout this period. The proportional distribution of different land types is as follows: woodland (75.98%), arable land (19.40%), grassland (3.74%), construction land (0.58%), and water area (0.30%) (Figure 4).