esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Classification Models
#### 4.4.3 Transformers  
This section provides a summary of the top text classification transformer models that perform well on texts of various lengths since they already include word embeddings.  
* **BERT** (Bidirectional Encoder Representations from Transformers): is a powerful language model transformer that has been shown to achieve state-of-the-art performance on a variety of natural language processing tasks [85]. By examining relationships in sequential data, the neural network that helps the transformer architecture to comprehend context and meaning. This information is the words in a sentence when natural language processing (NLP) is used. Encoder-decoder architecture is used in this system. An input sequence's characteristics are extracted by the encoder on the left side of the architecture, which is then used by the decoder on the right to create the output sequence.
* **GPT** (Generative Pretrained Transformer): is a language model comprising an encoder and a decoder as part of a transformer architecture. It has been applied to NLP tasks. As humans, they generate messages, respond to inquiries, and produce photographs and movies. [PERSON] et al. [86] proposed the generative pretrained language model AraGPT2. The model utilizes a self-attention mechanism to identify long-term relationships between sequences over time. The model is trained using a collection of texts, the majority of which are written in Modern Standard Arabic (MSA). The AraGPT2 program has been extensively used in NLP projects such as text creation [87]. In contrast to regular transformers, it extends the self-attention block with a second normalizing layer, which makes it unique among transformers. Both BERT and GPTexel at text classification due to their abilities to capture contextual information. BERT's bidirectional approach allows it to understand word meaning in relation to both preceding and succeeding contexts, while GPT's generative nature enables it to generate coherent text based on the preceding context. These attributes contribute to their effectiveness in text classification tasks, as they provide models with a deeper understanding of language, context, and semantic relationships within texts [88].
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 3 Related Work
Additionally, the impact of integer-only quantization with restricted bit precision has not been extensively analyzed. In this work, we extend prior research by evaluating the performance of an all-integer-quantized min-sum SC decoder with varying bit precision levels. Our study provides insights into the effectiveness of integer-based quantization in maintaining decoding accuracy while reducing computational complexity.
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 4 OLSR Protocol
### The MPR Computation of OLSR  
The OLSR exchanges HELLO messages on a regular interval to sense links between nodes, to discover one-hop and two-hop neighbors and to build a local topology. HELLO messages are exchanged in broadcast manner and they are never forwarded to other nodes. A device informs, within the HELLO messages, its adjacent nodes about its links with other nodes. The MPR set is computed every time a HELLO message is received. A node selects among its one-hop neighbors the minimum number of MPRs to cover its two-hop neighbors. After the MPR computation is completed, all the two-hop neighbors are covered. The MPR collection should be as small as possible. The smaller the MPR collection is, the lower the overhead would be [30]. Let us consider the OLSR network presented in Figure 3, \(N1(x)=\{a,b,c,d\}\) and \(N2(x)=\{e,f,g,h,i\}\). Figure 4 presents the default MPR computation algorithm of original OLSR on the node \(x\) as described in [10].  
1. Input: \(N1(x)=N1\), \(N2(x)=N2\), \(M(x)=MPR\); \(MPR=\{\emptyset\}\)
2. Start with an MPR set made of all members of \(N1\) with willingness equal to WILL_ALWAYS;
3. calculate \(D(y)\), where \(y\in N1\). \(D(y)\) is defined as the number of nodes \(u\in N1(y)\backslash\{N1,x\}\);
4. \(MPR\gets m\) if \(\exists m\in N1\), \(m\) is the only node that covers a node \(v\in N2\);  
Figure 3: MPR computation of original OLSR.  
Figure 2: MPR concept of Optimized Link State Routing protocol.  
Remove from \(N2\) all reached nodes by \(m\);
6. While there are nodes in \(N2\) which are not covered by at least one node in \(MPR\); 1. Calculate \(R(w)\) for each \(w\in N1\). R(w) is the number of nodes in \(N2\) which are not yet covered by a node in \(MPR\) and are reachable through \(w\); 2. \(MPR\gets w\) if \(w\) has the highest willingness with \(R(w)\
eq 0\); 3. In case of multiple choice, \(MPR\gets u\), \(u\) has the maximum reachability. 4. In case of multiple nodes providing the same amount of reachability, \(MPR\gets y\), where \(D(y)\) is greater; Remove \(N2\), the nodes which are now covered by nodes in \(MPR\).  
According to the default MPR computation algorithm in Figure 4, \(x\) adds \(a\) and \(d\) to \(MPR(x)\) because they are the only nodes connected to \(e\) and \(i\), respectively. \(R(b)=2\), \(R(c)=2\), \(D(b)=2\), and \(D(c)=3\). Both \(b\) and \(c\) have the same reachability. However, \(D(c)>D(b)\). The node \(x\) will select node \(c\) as MPR and all nodes in \(N2(x)\) will be covered. This MPR set rises the broadcast zone because nodes \(c\) and \(d\) are close to each other which will increase the collision.
# Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches
## 2 Materials and Methods
### Tumor Feature Definition  
Five traditional features, namely, SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion activity (TLA) and tumor-to-normal contralateral cortex activity ratio (T/N ratio), were defined to quantify the metabolic characteristics of the tumor [18]. SUVmax, SUVmean and TLA represent the maximum, mean and total radioactivity of the tumoral ROI, while the T/N ratio indicates the ratio of the tumor SUVmax to mean SUV of the contralateral brain (Nmean). MTV represents the volume of ROI and remained unchanged before and after postprocessing. Changes of SUVmax, SUVmean, TLA and T/N ratio during postprocessing were calculated to reflect the influence of denoising to the original feature of the tumor, and Pearson correlation coefficients of SUVmax, SUVmean, MTV and TLA with denoising performance were computed to evaluate whether the denoising was tumor feature-dependent.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 2 System Model
### Network Model
#### 2.1.1 Environment  
All simulations were performed in a real 3D environment. The 3D surface scan with 1 m resolution of Flanders, Belgium [21] was used. It is a height map containing buildings as well as vegetation (see Figure 1). The city of Ghent was chosen for the simulations, since it represents a typical European middle-sized city. The environment can be categorized as urban.  
The simulator uses real locations of the base stations provided by BIPT [22]. Nineteen BS in a radius of 750 m around the center of the map were considered. To imitate the mast deployment, the height of the BS was determined by the highest location in a radius of 5 m around the given BS coordinate to guarantee that it is on top of a roof and not in the middle of a street. It was put at 5 m above this point make sure it can be seen at street level, even if put on top of a structure. The BS heights ranged from approximately 23 m to 32 m, with the average height being 27.81 m. In the baseline LTE scenario, every base station was considered to have three sectors, while, for the mmWave scenarios, the number of sectors was increased up to 45 sectors per base station (see Section 2.2). We considered the infrastructure of a single operator. No microcells were considered.  
Figure 1 shows the map with the region of interest marked by the white lines and the base station locations marked as white dots. An area of 1 km\({}^{2}\) (see Figure 1) centered at 512'57\" N 3 deg43'41\" E is used for the analysis.
## 6 Evaluation
### Performance Evaluation
#### 6.2.2 CPU Load Analysis  
The total CPU load during the data transfer and integrity verification is another critical design aspect of the proposed framework. Figure 9 depicts the average CPU load of the proposed CRBF data integrity framework compared to the TPBF and baseline (without integrity). A bar graph represents the average CPU load, and the error bars denote the 99% confidence intervals. These graphs illustrate that the proposed CRBF-based data integrity demands 10% to 15% higher CPU resources than the TPBF-based data integrity. However, the experimental results demonstrate that this overhead does not affect the overall data transfer rate.  
Figure 8: Data transfer performance analysis. (**a**) Data transfer time comparison. (**b**) Data transfer rate comparison for D3 workloads.  
Figure 9: CPU load analysis.
## 4 Results and Discussion
### Hydraulic Conductivity  
A low hydraulic conductivity is an important characteristic for NB as an impermeable material in civil engineering applications; however, the impermeability will be restricted when NB is used in saline water conditions. Figure 16 shows the hydraulic conductivity of NB in fresh water conditions, and that of MB in fresh and saline water conditions. The hydraulic conductivity of NB in saline water conditions was much higher than that in fresh water conditions. After polymer modification, the hydraulic conductivity of MB in saline water conditions was identical to that of NB in fresh water conditions. The hydraulic conductivities of modified bentonite were smaller than 1.0 \(\times\) 10\({}^{-7}\) cm/s under saline conditions. This satisfied the permeability standard requirements for waterproof materials for use in civil engineering works. As has been mentioned, the improvement efficiency was significant and MB exhibited high salt tolerance with the polymer effectively preventing contacting between salt ions and bentonite molecules. The hydraulic conductivity of MB in saline water was similar to that of NB in fresh water.  
Figure 16: Hydraulic conductivity of NB in fresh water and MB in fresh and saline water conditions.  
Figure 15: Relationship between maximum swelling pressure and initial dry density of MB specimen
# Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models
## 3 Methods
### Agent-Based Model  
Reproducing real-world behavior of individuals' daily path in an urban setting is done with the open source agent-based transport simulation MATSim [17]. MATSim focuses on the simulation of individual mobility over the course of one day, in a great level of details. Its pluggable architecture and completely disaggregate formulation are the drivers of its increasing adoption not only by researchers in the transportation planning community but also in artificial intelligence [31], as well as practitioners, such as the Swiss Federal Railways [32]. It relies on the equilibrium assumption, standard in transportation models [33]. Assuming each agent has a fixed sequence of activities, located in space, it wants to perform, the software searches for the best combination of routes, modes, and departure times for each agent, considering congestion created by other agents [34].  
A MATSim run contains a configurable number of iterations, illustrated by the loop of Figure 4. It starts with an initial demand arising from the study area population's daily activity chains. These chains are usually derived from empirical data through sampling or discrete choice modeling. During iterations, this initial demand is optimized individually by each agent. Every agent possesses a memory containing a fixed number of day plans, where each plan is composed of a daily activity chain and an associated score (i.e., econometric utility). In every iteration, prior to the simulation of the network loading with the MATSim mobility simulation (mobsim), each agent selects a plan from its memory. This selection is dependent on the plan scores, which are computed after each mobsim run, based on the executed plans' performance. Plan modification is performed by the re-planning modules, on a copy of the selected plan. When the number of plans exceeds the memory size, the plan with the lowest score is removed. This evolutionary process leads to iterative improvements of the plans of the agent, given what other agents are doing. The iterative process is repeated until the average population score stabilizes [17]. Based on this information, individuals can be identified who share the same location and thus have a chance to infect each other. Since no change in the agent's behavior is considered, the traffic simulation must be conducted only once, and the epidemic spread model can be applied to the analysis phase, drastically decreasing the computational time.
# A Circularly Polarized mmWave Dielectric-Resonator-Antenna Array for Off-Body Communications
## 2 Design Methodology
### 2 \(\times\) 2 DRA-Array Design  
The presented single-DRA element was utilized to design a compact broadside 4-element array that was positioned on a square ground plane with a size of 36.5 mm, on which the cross-slots were etched, as demonstrated in Figure 6a. A square-array geometry was chosen, as illustrated in Figure 6b, since it offers a more compact structure. The feed network is presented in Figure 6c, where it can be observed that a four-way microstrip power-divider line was utilized for the array-feed network [28]. The dimensions were chosen to support the broadside-array-design criteria, including uniform power division and phase distribution. In addition, two microstrip line widths were chosen, \(w_{t1}\) = 0.5 mm and \(w_{t2}\) = 0.25 mm, for characteristic impedances of 50 \(\Omega\) and 100 \(\Omega\), respectively. Furthermore, the lengths of the microstrip line sections were optimized as \(l_{t1}\) = 18.13 mm, \(l_{t2}\) = 2.5 mm, \(l_{t3}\) = 5 mm and \(l_{t4}\) = 5.3 mm in order to achieve the widest impedance bandwidth. It should be noted that a Rogers substrate, RO4350B, was employed in the simulations based on commercial availability.  
Figure 4: Axial ratio of a DRA placed in free space and next to a phantom.  
Figure 5: Gain of a DRA placed in free space and next to a phantom.  
The impact of the separation distance, d\({}_{\rm s}\), between the centers of the adjacent DRA elements was investigated, as demonstrated in Figure 7. For this investigation, d\({}_{\rm s}\) was varied from 6 to 10 mm, which corresponds to a range of 0.558\(\lambda_{\rm o}\) to 0.93\(\lambda_{\rm o}\), where \(\lambda_{\rm o}\) is the free-space wavelength at 28 GHz. As demonstrated in Figure 7, the widest impedance bandwidth was achieved when the antenna elements were separated by 10 mm, which can be understood as a result of the reduced mutual coupling at this distance. It is noteworthy that the widest recorded impedance bandwidth for the array was 28%, which was close to that of the single DRA element. Moreover, Figures 8 and 9 present the axial ratio and gain as functions of d\({}_{\rm s}\), where the widest AR bandwidth of 13% and highest gain of \(\sim\)14 dBic were achieved, again, when d\({}_{\rm s}\) = 10 mm due to the reduced mutual coupling.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 2 Overview of the Three Path Planning Algorithms
### Graph Attention Model  
[PERSON] et al. proposed a machine learning approach to solving routing problems using a graph attention model (GAM) and reinforcement learning [34]. They use GAM to encode  
Figure 1: Diagram of the HAUCS concept. Data from the HAUCS platforms and weather stations are integrated in a control center, and conditions can be predicted to enable efficient aeration and pond maintenance. Adapted from [31].  
a learned representation of the locations and their relationships, which is then used in conjunction with reinforcement learning to identify optimal routes. GAM is a type of graph neural network that uses attention mechanisms to weigh the importance of different parts of the input data, allowing it to focus on the most relevant information when making predictions. To train the GAM, [PERSON] uses a dataset of synthetic routing problems with known optimal solutions, and uses reinforcement learning to learn a policy for selecting actions that lead to the optimal routes. GAM is trained using a variant of the actor-critic algorithm, which involves learning both a policy for selecting actions and a value function that estimates the expected future reward. In our experiments, GAM was built with a 128 dimension embedding and 3 encoder layers, with the same dimensions for the actor-critic reinforcement learning trainer. The results of the experiments in the paper show that GAM is able to learn the highly optimal routes for the synthetic routing problems, and outperforms many traditional optimization methods in terms of both accuracy and speed.
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 4 Algorithms and Evaluation Metrics Used
### Isolation Forest  
Isolation Forest is an anomaly detection algorithm that, like Random Forest, utilizes an ensemble of decision trees, but with a distinct purpose and methodology. It operates on the principle that anomalies are easier to isolate than normal data points, requiring fewer random partitions to separate them from the rest of the data. Unlike traditional methods, Isolation Forest does not require building a profile of normal behavior or density estimation, making it computationally efficient. The algorithm works by randomly selecting features and splits points to create isolation trees, recording the path length required to isolate each data point. It then assigns anomaly scores based on average path lengths across multiple trees, with shorter paths indicating likely anomalies and longer paths suggesting normal observations [5].  
The anomaly score is a value between 0 and 1, where scores closer to 1 indicate a higher likelihood of being an anomaly. Typically, a threshold is used to make the final determination--points with scores above this threshold are flagged as anomalies. In practice, these scores can be used to trigger various actions in a monitoring system. For example, if the proportion of detected anomalies exceeds a predetermined threshold, this could trigger a model retraining process. Additionally, the nature of the detected anomalies can provide insights into potential data drift or concept drift, where the underlying patterns in the data have changed significantly from the training data [5]. Isolation Forest's unsupervised nature also means it can potentially identify novel types of anomalies that might be missed by supervised methods trained on historical data [5].  
In this work, concept drift is measured by tracking shifts in anomaly detection rates. To quantify the severity of the drift, changes in the anomaly score distributions between the baseline and drifted datasets are studied. A significant increase in detected anomalies indicates potential drift.  
Identifying Concept Drift  
To validate concept drift detection, Isolation Forest's performance on the drifted test dataset was compared against its performance on the baseline test set. Changes in anomaly rates and variations in prediction confidence were analyzed to confirm the presence of concept drift and assess the model's robustness against subtle and substantial anomalies. The formulas for the Base Anomaly Rate, New Anomaly Rate, and Drift Factor are in Equations (5), (6), and (7), respectively.  
\[\textit{Baseline Anomaly Rate}=\frac{\#\,\textit{of anomalies in Original Test Data}}{\textit{Total \# of Instances in Original Test Data}} \tag{5}\]  
\[\textit{New Anomaly Rate}=\frac{\#\,\textit{of New Anomalies}}{\textit{Total \# of instances in New Test Data}} \tag{6}\]  
\[\textit{Drift Factor}=\frac{\textit{New Anomaly Rate}}{\textit{Baseline Anomaly Rate}} \tag{7}\]
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model  
The D2D communication-based IoT 5G network has been modelled by a graph \(G(V,\ E)\), wherein the \(V\) denotes the set of devices and \(E\) denotes the links between these devices. The packets are injected from the source device \(i\), \(i\in\ V\) and flow on multiple paths in order to reach their destination. Every device \(i\) can specifically interact with its neighboring devices within its range. If the destination is not on the neighbor list, it will utilize the routing protocol to transmit the data packets toward the destination device. A direct link from device \(i\) to \(j\) is denoted by \((i,\ j)\), while the transmission data rate matrix link is \(\mu_{(i,j)}(t)\) in slotted time \(t\in\{\ t_{1},t_{2}\ldots\}\). The routing variable data rate of a packet destined for device flow on the link \((i,\ j)\) is denoted by \(\mu^{f_{c}}_{(i,j)}(t)\). In this study, the flow, \(f_{c}\in\ V\), has been donated by its destination \(f_{c}\) for clarity. Due to the changes in the device's position, the device's selection to build an optimum path will be dynamically modified as well. In view of the link's route computation and link cost, the graph's optimum path will be selected to transmit the packet from the source to the destination. In order to attain optimal throughput and performance enhancement, the back-pressure strategy has been considered in this study, which is discussed extensively in the next section.
## 5 Implementation of the Adaptive SMC and Detailed Circuital Simulations
### Digital Implementation of the Adaptive SMC
In addition, it was demonstrated that both analog and digital implementations of the adaptive SMC exhibit equivalent performances; hence, the one more suitable for the particular application can be adopted.