esa-sceva/satcom-chunk-collection huggingface dataset:
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 3 Proposed Methodology
### Trained Models  
In this work, a total of 28 classifiers were trained to classify patient comments. Our classifiers fall into three categories: BiLSTM-, BiGRU-, and BERT-based models. BiLSTM and BiGRU are basically upgraded forms of the recurrent neural network (RNN). The RNN is a feed-forward neural network algorithm that complements deep learning approaches with sequential awareness. It can be described as an algorithm with memory, due to its ability in maintain the previous state of a sequence of a fed vector. The nature of NLP problems requires modeling that has the ability to comprehend a long-term sequence of information. RNN is recurrent, as it feeds the output of its function to the same function as an input, which holds the history of all previous states. Despite having a memory, RNN lacks the ability to maintain information for long-term periods, causing historical information to be forgotten, which means that it cannot be utilized for prediction. This problem is called the _vanishing gradient_.  
LSTM and GRU are variations of the RNN that were introduced as solutions to the vanishing gradient problem. Moreover, the two architectures were further enhanced to process the input sequence in both forward and backward directions in order to allow the context to be captured more effectively. BiLSTM and BiGRU demonstrated state-of-the-art performance in many NLP tasks.
## 5 Simulation Results and Performance Evaluation
### Simulations Results and Discussion
As shown in Figure 10, the outage thresholds at 0.6 and 0.9 yields density thresholds at 0.5 and 0, respectively. Figure 10: The individual user interest as a function of the arrival rate with varying user delay and outage requirements (\(U=3\) and \(P_{T}=0.5W\)). Figure 9: The total user interest as a function of the transmission delay with varying power constraint \(P_{T}\) (\(\lambda=0.5\) and \(\xi=0.9\)).
# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
## 3 Self-Similarity of Processes  
The usefulness of multifractal analysis in quantifying network traffic in supporting anomaly detection and in developing other applications to protect the security of transmission is presented. Multifractality is commonly observed in complex natural and socioeconomic systems. The multifractal analysis provides powerful tools for understanding the complex non-linear nature of time series in various fields. The approaches used to solve the problem of anomaly detection depend on the nature of the data collected. Network data can be obtained at many levels of detail, e.g., based on the end-user or based on the network. End-user data contains information that characterizes the end application (refers to the TCP: transmission control protocol and the UDP: user datagram protocol). Network data describes the operation of network devices and includes information collected from network devices (routers, switches). Traffic numbers obtained from both types of data can be used to generate the time series to which statistical signal processing techniques can be applied.  
Self-similarity and fractals are concepts initiated by [PERSON]. Self-similarity can be associated with \"fractals\", which are objects with an unchanged appearance at various scales. Self-similarity means that network traffic displays shape similarity on an extended time scale. Hurst parameter (abbreviated as H) is an important index to measure the similarity, which is often used in traffic congestion control and access control. Therefore, estimating the Hurst parameter accurately and rapidly has significance in network management and control [4; 16].  
Stochastic processes are considered as sequences of variables, which can be characterized by using average value, variance, process probability distribution value, and higher stochastic moments. A stationary process, on the other hand, is a stochastic process for which the process probability distribution value does not change. A characteristic element of a part of stochastic processes is the fact that the values are mutually dependent on time. However, such a process can be a stationary process, because a process' probability distribution value in a moment t is determined without any premises about the time preceding the process. A process with long-range dependencies is a process for which time is infinite. The autocorrelation function for such a process is a slowly vanishing function [17; 18].  
The multifractal spectrum curves represent the distribution of intersection densities between the different regions. This is a clear indicator of how most of the network is evolving to become more similar through its different areas. Multifractal spectrums can be determined in many different ways. The two primary methods are: Legendre transformation of the split function and establishing spot metering histogram boundaries. In particular, we have presented the details of these methods at work [4].
## 5 Routing Scheme and Attack Types  
### Communication Flow  
The presented clustering routing algorithm based on game theory can be separated into three stages: initialization, cluster establishment, and stable communication, as shown in Figure 1. Specifically, the initialization stage mainly gathers the network details in the monitoring area, such as node location, energy, ID, etc.; the cluster establishment stage comprises CH selection and cluster formation; the stable communication stage implements the information transmission from cluster members to CHs and transfers the fused data to the BS.  
1. Initialization phase  
First, the BS adopts flooding mode to broadcast the 'networking' message to the nodes within the monitoring area. Each node documents the distance from the BS and adjusts the appropriate transmitting power for dispatching individual information. Based on the location information, the BS divides the entire area into \(\omega\) clusters, and \(\omega\) is computed as follows  
\[\omega=\sqrt{\frac{N}{2\pi}}\frac{L}{\overline{d}} \tag{22}\]  
where \(\overline{d}\) is the average distance between all nodes and the BS.  
Figure 1: Flowchart of routing scheme.  
Secondly, according to the node information from each cluster, the BS calculates the invulnerability and cost of each node as CH and the cost of becoming a common node.  
Finally, the BS computes the optimal probability \(p_{i}\)* that each node in every cluster becomes CH and transfers the probability data of individual cluster to the local information table per node in the cluster for storage;  
2. Cluster establishment phase  
At this stage, every node in the \(\omega\) clusters has three states: common node, candidate CH, and CH. These nodes with the highest probability are nominated as the candidate CHs for each cluster. Assuming that merely one candidate CH is in a cluster, the node declares itself as CH; Moreover, in the case that there is more than one candidate CH, the dominance function \(g_{opt}(i)\) of all candidate CHs in a cluster is calculated by  
\[g_{opt}(i)=\frac{E_{i}}{E}\times\frac{1}{\left(d^{\prime}\right)^{2}} \tag{23}\]  
where \(d^{\prime}\) is the distance between CH and the BS.  
Then, the candidate CH with the highest dominance function value is designated as CH. If multiple candidate CHs have the same maximum value, the candidate CH with the smallest ID is assigned as CH. Furthermore, after completing the CH selection, each chosen CH sends a clustering message containing the ID of CH to the nodes in the corresponding cluster. The common nodes in a cluster return a reply message to CH to apply for cluster membership following the reception of the message. Until all nodes in the monitoring area join the clusters, the cluster establishment stage terminates;  
3. Stable communication phase  
During the phase, CHs send the TDMA scheduling table to the corresponding cluster members to coordinate the data transmission for avoiding data conflict. Following the information acquisition from cluster members, CHs implement data fusion to further transmit the data packets to the BS in single-hop mode.
###### Abstract  
The accurate prediction of subway passenger flow is crucial for managing urban transportation systems. This research introduces a hybrid forecasting approach that combines an enhanced TimesNet model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Variational Mode Decomposition (VMD) to improve passenger flow prediction. The method decomposes time series data into Intrinsic Mode Functions (IMFs) using VMD, followed by adaptive predictions for each IMF with TimesNet and SARIMA. The dataset spans from 1 January to 25 January 2019, encompassing 70 million records processed into five-minute intervals. The results show that the VMD preprocessing effectively extracts features, enhancing prediction performance (13.25% MAE, 19.7% RMSE improvements). The hybrid method excels during peak times (52.75% MAE, 50.61% RMSE improvements) and outperforms baseline models like Informer and Crossformer, achieving 66.14% and 63.24% improvements in the MAE and RMSE, respectively. This research offers a reliable tool for predicting subway passenger flow, supporting the smart evolution of urban transport systems.  
subway passenger flow prediction; intelligent transportation; TimesNet; SARIMA; variational mode decomposition Article
# A Mathematical Model for Scheduling and Assignment of Customers in Hospital Waste Collection Routes
## 4 Results
### Computational Experiments and Findings
#### 4.1.1 Scheduling and Assignment of Customers--Maule Instance  
The proposed mixed-integer linear programming model was applied to 85 customers from the Maule region. Two trucks with a 16-container capacity each were used. Because of the geographic location of customers, the \(\theta max\) in the Maule instance was \(\pi/9\). The maximum number of customers serviced daily by truck (\(CusMax\)) was seven. The performance of each solver with the described parameters are presented below.  
The GUROBI solver explored 2070 nodes and iterated 422,658 times in 91.15 s to obtain the optimal solution (Table 3). CPLEX solved the model with a maximum time of 35,000 s (Table 4). The performance of GUROBI vs CPLEX is presented in Figure 2. Note that CPLEX could not find the optimal solution but obtain a feasible solution with a minimal gap of 0.01 percent, which is considered a very good solution.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Attention  
Attention mechanisms serve a crucial function in forcing models to hone in on prominent features but suppress irrelevant ones. In spatiotemporal feature enhancement for lip-reading, attention-based methods include channel attention, hybrid attention, self-attention, and multi-attention combinations. Figure 10 shows the schematic diagram of spatiotemporal feature enhancement based on attention.  
Channel attention is essential for achieving recognition performance, as channels possess diverse discriminative strengths. By bestowing different weights upon each channel, the model can prioritize those of greater significance while elegantly suppressing the inconsequential, thereby elevating overall efficacy.  
In contrast, hybrid attention deftly intertwines channel and spatial focus, allowing the model to engage with multiple features in concert. This approach, often manifested through the Convolutional Block Attention Module (CBAM), proves particularly effective in the realm of lip-reading.  
Meanwhile, the emergence of self-attention and the Transformer architecture has transformed the landscape of lip-reading, empowering models to navigate and emphasize pertinent features across various dimensions.  
The fusion of multiple attention mechanisms breaks free from the limitations of a single perspective, broadening the scope of feature extraction and significantly enhancing the effectiveness of recognition methods.  
As depicted in Figure 10, this enhancement leverages various attention architectures, such as Squeeze-and-Excitation (SE), the Convolutional Block Attention Module (CBAM), and the Transformer, to illuminate vital spatiotemporal features. This spotlighting is further complemented by the modeling of temporal sequences, employing frameworks like DCC-TCN, MS-TCN, and Transformer. This approach creates a rich fabric of insights, deepening our understanding of the data.  
Figure 10: _Cont._
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 3 Related Work  
To distinguish our contributions from prior work, we provide a comparative analysis of existing surveys in Table 1, evaluating them across six dimensions. The first dimension, EMF exposure in pre-5G networks, was addressed briefly in [2; 11], with a more detailed discussion in [12], which focused on evaluating EMF levels for legacy systems. The second dimension, EMF exposure in 5G networks, was introduced in [13], while [11; 14; 15; 16] explored in greater depth the effects of technologies such as MIMO with beamforming on EMF exposure. The third dimension examines resource allocation (RA) techniques incorporating EMF constraints, which aim to optimize parameters like transmit power or precoding weights to maintain exposure below safety thresholds. This was thoroughly reviewed in [11], while [13; 17] offer more limited discussion. The fourth dimension, the use of RISs to manage EMF exposure, has been explored only briefly in the literature. While [17; 18] touch upon its potential to influence EMF levels, comprehensive studies on RIS-assisted EMF-aware network optimization remain sparse. The fifth dimension, focusing on exposure metrics, examines how different measures, such as near-field and far-field radiation, are used to evaluate compliance with safety standards. Finally, the sixth dimension involves EMF evaluation frameworks, where [19] provides a broad exploration of network planning strategies, integrating multi-objective optimization and multi-criteria decision-making techniques to enhance antenna placement and manage growing network demand. The key contributions of this paper are summarized as follows:  
1. Comprehensive review of EMF exposure from 5G technologies: * This paper provides a comprehensive and updated review of recent research on EMF exposure evaluation in 5G networks. It captures the latest trends, evaluation metrics, methodologies, and the impacts of emerging wireless technologies, offering valuable insights into 5G EMF exposure assessment and management.
2. EMF-aware radio resource management: * This paper enhances our understanding of EMF-aware radio resource management for 5G and beyond by synthesizing recent strategies that incorporate EMF constraints into resource allocation. It examines techniques such as power control, beamforming, subcarrier allocation, and user association to balance network performance and EMF safety, with a focus on the dynamic role of RIS technology in reshaping EMF patterns to minimize exposure.  
* Identification of research challenges and opportunities:
* This paper highlights key emerging research challenges and opportunities in EMF-aware radio resource management for 5G networks and beyond, providing a forward-looking roadmap to guide future explorations and innovations in this evolving field.
# Piezoelectric Sensor with a Helical Structure on the Thread Core
## 5 Discussion  
To suggest the possible applications of the proposed sensor, a concept schematic of the sensor woven to clothing measuring the joint angle of the elbow is introduced in Figure 9. Through the output voltage of the woven sensor, elbow flexion and extension can be measured. In addition, the proposed sensor can be better adapted to clothing in textile form by utilizing electric connections such as conductive wires [42] or printed electrodes [43]. Piezoelectric materials have been widely adapted to measure biometric signals from humans' daily activity of living. For example, biometric signals such as walking motion [44] and hand motion [45] are measured through piezoelectric materials. Unlike the previous studies, the proposed sensor is capable of measuring biometric signals such as joint angles of humans with better comfortability and repeatability without external pockets. Additionally, by utilizing an interface board reported in the previous study [46], the proposed sensor can be developed for a real-time joint angle measuring system without an extra measuring system. In future work, we will focus on the electrical response of the textile-based piezoelectric sensor according to coupled flexural-torsional loading. We hope that the sensor structure can be adapted to a variety of fields that require smart clothing such as biometrics [23; 25] and health monitoring [44; 47; 48; 49].
# A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System
## 4 Case Study
### Validation of the ITCA Model  
The accuracy of the obtained ITCA model is quantified using the testing dataset. Five concatenated datasets are formed to represent the five periods of time in the same manner as the data preparation of the training datasets. The mean and the standard error for each period are calculated using the time causality rule and 1000 random data samples; each has a size of 135 observations that provides a 95% confidence level, as shown in Figure 10. Based on the error in each period, an average error distribution is generated over the five periods.  
From another point of view, the variables T2, P2, P15, epr, farB, Nf_dmd and PC-NfR_dmd are not considered in the ITCA model of Figure 8 because they have a constant trend over time (see Table 1). However, the variables NC, NRc, htBleed, W31 and W32 have a changeable trend, but are not included in the ITCA model. To investigate this situation, the correlation matrix between those omitted variables and those already considered in the ITCA model are measured, as depicted in Table 2. In each column, the bold value shows the maximum correlation value. The variables NRc, htBleed, and both W31 and W32 are  
Figure 10: Accuracy of the ITCA model.  
Figure 9: Probability calculations of the HPC fault mode.  
correlated to the variables phi, Nrf and Ps30, respectively, with a correlation value that is higher than 0.6. Except for the variable NC, which measures the physical core speed, and is correlated to P30 with the highest absolute value of 0.17. Accordingly, it seems to be relevant for the HPC degradation. This could be overlooked by the ITCA model.
# Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route
## 3 Preliminaries  
### Paillier Cryptosystem  
The Paillier cryptosystem [15] is a public-key cryptosystem with the property of additive homomorphism based on the composite residuosity assumption [1]. We briefly describe it as follows:  
\(\textit{KeyGen}()\rightarrow(pk,sk)\): Choose two large primes \(p\) and \(q\), and let \(n=pq\). Then compute the least common multiple \(\lambda=lcm(p-1,q-1)\) and select \(g\in Z_{n^{2}}^{*}\), ensuring the greatest common divisor \(gcd\big{(}L\big{(}g^{\lambda}\big{(}mod~{}n^{2}\big{)}\big{)},n\big{)}=1\), in which \(L(x)=(x-1)/n\). Then the public key is \(pk=(n,g)\) and the secret key \(sk=\lambda\).  
\(\textit{Encryption}\): \(E(m,pk)\to c\). Given a plaintext \(m\in Z_{n}^{*}\), randomly select \(r\in Z_{n}^{*}\) and compute the ciphertext as follows:  
\[c=g^{m}r^{n}\Big{(}mod~{}n^{2}\Big{)} \tag{1}\]  
\(\textit{Decryption}\): \(D(c,sk)\to m\). Given a ciphertext \(c\in Z_{n^{2}}\), the corresponding plaintext can be computed as follows:  
\[m=\frac{L\big{(}c^{\lambda}mod~{}n^{2}\big{)}}{L\big{(}g^{\lambda}mod~{}n^{2 }\big{)}}~{}mod~{}n \tag{2}\]  
The Paillier cryptosystem described above is semantically secure. Besides, it has the following homomorphism properties:  
\[E(m_{1})E(m_{2})=E(m_{1}+m_{2}) \tag{3}\]  
\[E(m_{1})g^{m_{2}}=E(m_{1}+m_{2}) \tag{4}\]
## 3 Experiments and Analysis
### Comparison of Haze-Free Detection Algorithms
#### 3.1.2 Objective Data Comparison  
When employing the mean Intersection over Union (mIoU) as the standard for comparative experiments, the Intersection over Union (IoU) for a single class, considering positive cases, is computed using the following formula:  
\[IoU_{1}=\frac{TP}{FN+FP+TP} \tag{15}\]  
Figure 10: Comparison of experimental results in complex scenes.  
Figure 9: Comparison of experimental results in simple scenes.  
The mean Intersection over Union (mIoU) is calculated by taking the average of the IoU for each class. The formula is as follows:  
\[\text{mIoU}=\frac{1}{\text{k}+1}\sum_{i=1}^{k}\left(\frac{\text{TP}}{\text{FN}+ \text{FP}+\text{TP}}\right) \tag{16}\]  
The detection results of the four networks were evaluated using the test set, and the frame rate (FPS) was introduced as a parameter to assess the real-time performance of the models. The results are shown in Table 3 below:  
Among the four algorithms, the proposed algorithm achieves the highest mean Intersection over Union (mIoU), while PSP-Net has the highest frame rate (FPS). However, the proposed algorithm's average frame rate of 39.4 meets the real-time requirements of the task.
## 5 Simulation Results  
### Simulation Environment  
We evaluated and compared our protocol's performance with a recognized asymmetry-based MAC protocol ASYM-MAC with simulations done using OMNeT++. The metrics evaluated were the average energy consumption in the network, in addition to energy consumption per node, average delay per packet, Packet Reception Ratio (PRR), Packet Delivery Ratio (PDR), System Throughput, and Control packet overhead with link asymmetry. The network simulated was made up of of a sink node and either 27 or 54 nodes for two scenarios in a 1000 \(\times\) 1000 m\({}^{2}\) region. Both protocols ran under the same parameters. The parameters are listed in Table 4. The data generation time was distributed uniformly between 0.01 and 0.99 s. In this research work, we considered all the applications that acquire data periodically, usually for early detection of natural calamities such as earthquakes, volcanic eruptions, land slides, etc. [56; 57; 58], therefore have a uniform distribution of inter-arrival time. Such applications require continuous monitoring of the environment, and generate packets at a fixed interval. Furthermore, sensor nodes used in some IoT based applications also use such uniform distribution [59]. The time needed to transition from receiver-initiated mode to sender-initiated mode was 1 s using ASYM-MAC, which is equal to the waiting time (\(t\)) of the proposed protocol.  
Figure 8: Data loss situation.
## 6 Metrics for TSN Schedule Evaluation
### Reasons for Nondeterminism in TSN
#### 6.1.1 Synchronization Errors  
TSN switches must be synchronized for coherent network schedules. Using gPTP, as defined in IEEE 802.1 AS [127], TSN-enabled switches can achieve a very low synchronization error in the order of 10 ns. The gPTP messages exchanged between network entities are part of the network configuration traffic and, therefore, they are not considered the highest priority. Ref. [127] suggests the prioritization of synchronization messages but only stipulates a nonzero traffic class. Therefore, a nonoptimized GCL configuration in some of the switches could cause a large synchronization error or even the loss of synchronization due to timeouts. Synchronization errors can be problematic and may lead to frames missing the allocated cycle interval or the interleaving of frames, which could cause QoS violations.