esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Applications and Future Research Directions of RIS
### RIS-Assisted Visible Light Communication (VLC) System and Hybrid VLC-RF Networks
#### 7.4.2 Physical Layer Security  
VLC links are susceptible to eavesdropping by malicious users. Therefore, securing LiFi transmissions in public places like shopping malls, airports, libraries, and outdoor vehicular applications [8]. Incorporating RIS into LiFi systems can result in improved PLS in one of the following ways:  
1. Improved secrecy: Dynamic multi-path tuning maximizes channel gain for legal users while reducing eavesdroppers.
2. Jamming: Artificial noise created by randomized multi-path reflections directed at the eavesdropper.  
Finally, RIS-aided VLC systems can provide increased light-based monitoring as well simultaneous light-wave information and power transfer to extend the lifetime of energy-constrained terminals. Each reflected path in RIS-assisted scenarios has its spatial signature, determined by the locations and EM responses of the objects in the environment [117]. Controlling the EM response of each RIS element can shape the reflections from the elements, allowing for better mapping from the position space to the measurement space. Therefore, an accurate localization can be achieved in outdoor and indoor applications, such as measuring the distances between buildings, vehicles, and people for safety and surveillance, physical rehabilitation, automated manufacturing, and gesture recognition [118]. Other possible use cases of RIS are discussed in Section 7.5, Section 7.6, Section 7.7, Section 7.8, Section 7.9.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Lifecycle of an Attack  
Most cyber-physical attacks are driven by a clear motive to target a specific organization or industry. Attackers often collect the greatest amount of data they can by various means (such as eavesdropping attacks, for example) in order to plan an attack ahead of time to maximize its impact. The motives for the attacks are primarily data harvesting and sabotage. Data harvesting concerns the collection of sensitive data from system networks that could provide important information to attackers that can be used to facilitate an attack. Sabotage is aimed at disrupting the functionality of a system and possibly damaging it [3; 8]. These two motives are usually used in combination to ensure an effective attack.  
Besides the fact that there are a number of different possible attacks that can be carried out, they have a lot in common in terms of their execution and lifecycle. Figure 4 illustrates the lifecycle of a typical cyber-attack.  
During the pre-entry phase the attacker identifies a target and acquires any prerequisite knowledge to plan and formulate an attack. The tools and skills required to perform the attack are also identified and acquired during this phase. After the necessary tools, skills and knowledge have been acquired, the entry phase begins by collecting information that will be of use to the attacker to plan the attack. This typically involves basic cyber-attacks such as phishing by means of malicious e-mails, social media platforms and infiltrating the supply chain, where for example, software updates from trusted entities are compromised with malware to harvest information that can be valuable to the attacker. As the harvesting of data continues to delve deeper into the targeted network, network systems with poor security measures are exploited to gain further access deeper into the network by obtaining higher access privileges inside the targeted network. The use of C&C servers are very common during this phase of the attack to harvest more data and to infect the network further with more malware (such as in the case of the Stuxnet, Duqu and Flamer threats) by means of malicious updates.  
When enough information has been harvested and the security of the targeted system has been sufficiently crippled, the main aim of the attack can be reached by carrying out the attack in the operation phase. This is where the true motive of the attack comes into play. Depending on the motive of the attack (harvesting data or sabotage), this phase can last anything from a few minutes to months and even longer if the attack remains undetected. Finally, when the attack has been carried out, the attackers might cover their tracks by clean-up processes in order to minimize the possibility of being detected or traced.  
The success of an attack depends largely on the state of the targeted network's security measures. Since OT systems networks are mostly designed without proper consideration for security, the chances of successful attacks on these networks are vastly higher than those attacks that target IT systems networks.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 2 Problem Formulation
### Objective Function
#### 2.2.2 Bus Punctuality  
We model bus punctuality as the fluctuations in the bus travel time. Many studies have calculated bus punctuality rates based on GPS trajectories. For example, [PERSON] et al. [28] proposed a new user-oriented punctuality measure to evaluate bus performance, and [PERSON] et al. [29] introduced the concept of the adjacent site punctuality rate (ASPR). The proposed bus punctuality model applied extreme value theory between adjacent sites based on a distribution function of the bus travel time. Because our model is based on road segments for bus lane planning, the bus punctuality rate on road segments based on the ASPR is defined in this article and is formulated as Equation (8).  
\[R_{i}=P\big{\{}\big{|}T_{i}-t_{ij}\big{|}>S_{i}\big{\}} \tag{8}\]  
where \(R_{i}\) is the punctuality rate of road segment \(i\), \(t_{ij}\) is the \(j\)th bus travel time on road segment \(i\), \(T_{i}\) is the average value of the travel time, and \(S_{i}\) is the standard deviation. Assuming the prior distribution of ASPR is \(\beta\)(\(a\),\(b\)), and it is able to be inferred that the posterior distribution is \(\pi(R\,|\,m)\sim\beta(m+a,\,n-m+b)\beta(a\),\(b)\), with a given \(1-\alpha\), the confidence interval of punctuality is \([R_{1}{}^{\star},R_{2}{}^{\star}]\). The shortest interval is calculated, and its mean value is used as the punctuality value. More details are discussed in [29].  
Based on the above bus punctuality rate on a road segment, the objective function of the bus punctuality rate can be formulated as Equation (9).  
\[\max\sum_{i\in E^{\prime}}R_{i} \tag{9}\]
## 4 Algorithm Description
### Data Gathering from Proxy Nodes  
When a new data gathering round begins, \(MC\) would visit the proxy nodes one by one according to schedule \(\Psi\). When they are in contact, the sensing data that have been routed and stored at the proxy nodes are gathered by \(MC\). Proxy nodes has high probability to be stationary and hence be able to make a stable connection for data transfer. Yet \(MC\) still has to figure out the location of the proxy node in order to visit a proxy node during its key stationary stay (\(kss\)) according to the schedule.  
For a proxy node \(v\), suppose the key stationary stay \(kss\) is mapped from \(m\) stationary stays from \(m\) epochs, where each stationary stay is composed of a stationary duration \(sd\) and the central point \(C_{sd}\); then the set of \(m\) central points are aggregated according to their locations. A set of central points, e.g., \(Q=\{q_{1},..,q_{k}\}\), could be aggregated into a new point if they satisfy the following conditions:  
\[k=1,\ \ or\ \ distance(q_{i},q_{j})\leq 2R,\ \ i,j=1,..,k \tag{11}\]  
where \(R\) is the radius of communication range. Points in \(Q\) could then represented by a new point: \(C_{Q}=centroid(Q)\), which is defined at Equation (3). Similar with the expected probability of a point defined at Equation (4), the _encounter probability_ of \(C_{Q}\) is then defined as:  
\[p3(C_{Q})=\sum_{q_{k}\in Q}h(q_{k})=\sum_{q_{k}\in Q}\frac{1}{2^{|W|+1-e(q_{k} )}} \tag{12}\]  
where \(e(q_{k})\) is the index of epoch from which the central point \(q_{k}\) is extracted. The data gathering point for proxy node \(v\) is then set to be the point who has the largest encounter probability, denote as \(C_{Q^{*}}\). \(MC\) then just moves to point \(C_{Q^{*}}\) and prepares for encountering the proxy node and gathers the data.  
Although the node movement is assumed to roughly follow some kind of pattern within the network, and nodes would stay at some area during the key stationary duration with high probability, it is also possible that a proxy node deviates from the expected location and moves to other area. At this case the proxy node would periodically broadcast its locations to its neighboring nodes so that \(MC\) is able to get the proxy node's exact locations from neighboring nodes, and move to the new location to pick up the data during the data gathering time slot. \(MC\) also collects data from ordinary nodes when they are in the communication range, and gathers the network meta-data for proxy nodes selection and time slot allocation in the next epoch.
## 2 Configuration and Design of the Proposed UWB-MIMO Antenna  
The fundamental properties of a suggested prototype comprising a modified antipodal Vivaldi (MAVA) patch integrated with ten MTM components are studied, and its capacity to function as a UWB-MIMO transceiver element for 5G and satellite communications is demonstrated. First, a modified antipodal Vivaldi antenna was developed, and its properties were evaluated. The antenna was then loaded with arcs linked to the patch around the intersection of the transmission line and patch to improve the BW and remove the stop band near the X-band (frequency band of 7.25-11 GHz). They were combined. Following that, the antenna was loaded with a number of staircase meander lines. Following that, a cell of MTM was created and characterized at 5G and satellite bands. The MTM components were then combined to enhance the UWB-MIMO antenna radiation qualities by reducing mutual coupling, increasing isolation, and increasing diversity gain, as well as improving the radiation characteristics.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 3 Methodology
#### System Algorithm  
Table 2 illustrates the algorithm that performs the thresholding and frequency partitioning. The algorithm is first run using ISODATA. Then, the thresholding approach is substituted with NIR, and histograms and graphs are generated for each particular method.
## 2 Principles
### Wavelet Packet Energy Method
#### 2.3.2 Wavelet Packet Energy and Shannon Entropy  
The coefficients of the final wavelet packet layer are reconstructed, and the coefficients of the reconstructed wavelet packet, which has the same length as the original data, are obtained. The energy of the signal can be obtained by calculating it using Equations (13) and (14).  
\[E_{jm}=\left\|S_{jm}\right\|_{2} \tag{13}\]  
\[E_{total}=\sum\
olimits_{m=0}^{2^{j}-1}E_{jm} \tag{14}\]  
where \(S_{jm}\) represents the reconstructed subband, \(m=0,1,2\ldots 2^{j}-1\).  
In 1948, [PERSON] introduced the concept of information entropy as a means of quantifying information [50]. In the field of nonlinear time series, it serves as a measure of information uncertainty [51].  
The relative energy of each subband can be calculated as  
\[p_{jm}=\frac{E_{jm}}{E_{total}} \tag{15}\]  
Then, the Shannon entropy is calculated as follows:  
\[H_{j-Shannon}=-\sum\
olimits_{m=0}^{2^{j}-1}p_{jm}\ln\left|p_{jm}\right| \tag{16}\]  
The flowchart of the method proposed in this paper is illustrated in Figure 5. First, the signal received by the PZT patch is decomposed using wavelet packets, and WPD is performed from level 1 to level \(n\). Then, the coefficients of the obtained subbands are reconstructed to calculate the signal energy and the relative energy of the subbands. Finally, the Shannon method is used to calculate wavelet packet entropy. The greater the entropy, the greater the uncertainty, and the lower the stress level at the contact surface [52]. The index used in this paper evaluates the tightness of prestressed steel strands.
## 4 Discussion
This can address inconsistencies or irrationalities in the combination of numerical data, which in the real world might not correlate, even if the numerical result appears satisfactory. Additionally, human experts can correct results related to grouping objects that are numerically similar but not substantively similar, thereby facilitating the retraining and fine-tuning of the system. Specifically, \"Early Validation\" plays a crucial role in the following activities:  
* Detection and Correction of Anomalies: Experts review the system outputs to identify anomalies or inconsistencies that might not be evident through numerical analysis alone. For instance, a property might be grouped based on numerical similarities, but contextual knowledge may reveal these properties to be fundamentally different in significant ways. * Feedback Loop for Continuous Improvement: Human feedback is essential for the continuous learning and improvement of the system. This feedback is then used to adjust the model parameters, enhancing the system's ability to make more accurate predictions and classifications in the future. * Ensuring Practical Relevance: By incorporating expert opinions, the system ensures that the results are not only numerically sound but also practically relevant. This is particularly important in the real estate market, where contextual factors and market nuances play a critical role in decision-making. * Validation of Synthetic Data: The system includes an \"Artificial Homogeneous Property Database with Flaws\" used to test and validate the model. Experts play a crucial role in reviewing this synthetic data to ensure that it accurately reflects real-world conditions and to identify any flaws that need to be addressed. The proposed approach is indispensable for bridging the gap between AI-driven data processing and human expertise. It ensures that the HELIOS Nexus system remains accurate, reliable, and practically applicable in the dynamic and complex real estate market. Looking ahead, future research could explore other language models and natural language processing methods to compare their effectiveness in property clustering, as well as investigate the impact of different data preprocessing strategies on clustering outcomes. Additionally, applying our approach to other aspects of real estate market analysis, such as price forecasting or assessing the impact of economic and social factors on market dynamics, may open up new perspectives for understanding and predicting market trends.
## 3 BERT-BiLSTM-Based Intelligent Recognition Algorithm for Earthquake Information
### BiLSTM Layer  
BiLSTM, the bi-directional long short-term memory neural network, solves the problem of not being able to encode information from back to front based on long short-term memory (LSTM) by capturing longer-distance dependencies. LSTM's unit structure is shown in Figure 4.  
The information to be forgotten and the information to be remembered are selected by calculating the forgetting gate and the memory gate, respectively. Their inputs are both the implicit state \(h_{t-1}\) at the previous moment and the input word \(X_{t}\) at the current moment.  
Figure 4: LSTM cell structure diagram.  
Figure 3: BERT model masking mechanism.  
The output value of the forgetting gate is the value \(f_{t}\) of the forgetting gate, and the output value of the memory gate is the value \(i_{t}\) of the memory gate and the temporary cell state \(i_{t}\).  
The formula for calculating the forgetting gate is as follows. \(W\) in the formula is a weight parameter, and \(b\) is a bias parameter:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},X_{t}]+b_{f}) \tag{1}\]  
The formula for calculating the memory gate is as follows:  
\[f_{i}=\sigma(W_{f}\cdot[h_{t-1},X_{t}]+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=tanh(W_{c}\cdot[h_{t-1},X_{t}]+b_{c}) \tag{3}\]  
After that, the current state of the cell is calculated with the input of the value of the memory gate \(i_{t}\) and the values of the forgetting gate \(f_{t}\), the temporary cell state \(\widetilde{C}_{t}\), and the previous moment cell state \(C_{t-1}\). The output is the cell state \(C_{t}\) at the current moment. The calculation formula is as follows:  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{4}\]  
Finally, the output gate and the hidden state of the current moment are calculated, and the inputs are the hidden state \(h_{t-1}\) of the previous moment, the input word \(X_{t}\) of the current moment, and the cell state \(C_{t}\) of the current moment.  
The calculation formula is as follows:  
\[o_{t}=\sigma(W_{o}\big{[}h_{t-1},X_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}*tanh(C_{t}) \tag{6}\]  
The overall structure of the BiLSTM model is shown in Figure 5.  
The hidden vector ht output by the BiLSTM model can capture the long-range contextual information in the earthquake emergency information text, but there will be a large number of repetitive words in the earthquake emergency information text, and the degree of the same words contributing to semantics in different sentences will be different, resulting in low recognition accuracy for the model. To solve this problem, in this paper, based on the BERT-BiLSTM-CRF model, an attention layer is added between the CRF layer and the BiLSTM layer to assign weights to the word vectors separately.
# Spatial Variations in Microfiber Transport in a Transnational River Basin
## 4 Discussion
### Influencing Factors of Microplastic Contamination
#### 4.2.1 Relationship between Suspended Sediment and Microplastic Transport  
As the Tisza and its tributaries had low stages during the surveys, relatively low SSCs were measured (mean Tisza: 37.5 \(\pm\) 10.9 g/m\({}^{3}\); mean tributaries: 120 \(\pm\) 63.8 g/m\({}^{3}\)). These are consistent with the reported sediment concentrations on Tisza. For instance, the multiannual (1998-2002) mean SSC was 31 g/m\({}^{3}\) during low stages and 110 g/m\({}^{3}\) during floods at the Middle Tisza [68]. Conversely, on the Lower Tisza the mean concentrations are slightly higher (low stages: 35 g/m\({}^{3}\); high stages: 125 g/m\({}^{3}\); [69]), due to the elevated suspended sediment loads transported by the Maros River.  
The suspended sediment concentrations could be employed as an indicator of surface runoff over the watershed into the river channel [70], thus higher SSCs may refer to elevated surface runoff, which is usually associated with an increase in MP concentrations [22]. In our study, no correlation was found between SSC and MP concentrations (Figure 6). For instance, in 2022 the greatest MP concentration in the water was recorded at Site \"u\", though the SSC was the lowest here. Therefore, it is assumed, that at low stages the spatial distribution of MP in the Tisza may be highly related to effluents of WWTPs or tributaries, rather than surface runoff from the watershed. The lack of correlation between SSC and MP could be explained by the low river slope [71], the existence of dams and artificial levees, which block the longitudinal and lateral sediment input into the river. Our results are also consistent with the results of [PERSON] et al. [72], [PERSON] et al. [73], [PERSON] et al. [74], who found no correlation between precipitation/surface runoff and MP concentration in rivers.  
On the other hand, it should be noted, that our measurements were performed at low stages, when the MP transport is governed by sewage input rather than run-off. Therefore, the measurements should be repeated in time, during rising and falling stages of floods to understand the correlation between hydrology, SSC and MP transport.  
Figure 6: Correlation between suspended sediment concentration and transported microplastics= during the low stage.
## 5 Albedo Inversion and Analysis
### Application of FY-2G Albedo Retrieval
Among them, class 1 accounted for more than 90%. This result is slightly different from the statistical result based on MODIS reflectance data, and the proportion of class 2 in the FY-2G albedo inversion is extremely small. It is speculated that the reason for this phenomenon may be due to the difference in performance of the two sensors, the difference in spatial resolution, and the difference in homogenization of the surface during observation. Figure 12: The albedo retrieved using FY-2G satellite data based on the BRDF archetypes. Figure 13: Fitting results for adjustment coefficients A and B. (**a**) Coefficient A. (**b**) Coefficient B.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 4 Experimental Results
### Effect of the Sub-Domain Division  
To demonstrate the effect of the sub-domain division step for SCTV surrogate modeling, the proposed PWTA method is used to build the surrogates for each sub-domain after domain division based on the knowledge of geographical SCTV distribution (denoted as PWTASD) and also used to directly build a surrogate for the global domain (denoted as PWTASD). For PWTASD, 60 training points are sampled in each sub-domain using full-factorial design. For PWTASD, all the samples obtained in PWTASD are used for the global surrogate modeling. Table 5 presents the R\({}^{2}\) correlation coefficients, NRMSE and NMAE metrics of PWTASD and PWTASD in each sub-domain, as well as the summary values in the global domain. The best value in each column is shown in bold for ease of comparison. From Table 5, it can be seen that PWTASD performs better than PWTASD in the eight sub-domains and across the entire domain. It clearly validates the effect of the sub-domain division of the proposed method which can better capture the regional SCTV features by building the independent ensemble for each sub-domain, leading to better approximation quality.
## 2 System Model  
### Proposed MCIM-SR-DCSK-CC System  
A half-duplex cooperative communication system is considered in this paper. The general system model is shown in Figure 1. The proposed system includes a source node S, a relay node R, and a destination node D. In the proposed system, the information bit streams include polarity-modulated bits, code index bits, and code-modulated bits. The polarity-modulated bits are transmitted by S. The code index bits and code-modulated bits are transmitted by R. Each transmission period is composed of two time slots. There are N groups of information bit streams.  
Figure 1: The proposed MCIM-SR-DCSK-CC model.