esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 6 Operational Changes in the Proposed Architecture
#### Memory Access:  
Executing instructions in the CPU and fetching data require access to primary memory, which needs address translation. Address translation in ASMI is executed as shown in Figure 6, and can be described as follows. The processor/CPU passes the virtual address to the guest/root TLB. If the corresponding entry is identified from TLB, the physical address is given to the Pro-mem unit for validation.  
Figure 6: Address translation in ASMI.  
If a TLB miss occurs, page tables are searched by the guest OS to determine the corresponding physical address. A page hit passes the physical address to the Pro-mem unit for validation. A page miss raises an exception that requests the OS to load the pages into memory through a memory allocation function.  
Currently available simulators, which simulate the processors, memory, disk, and the entire system are written in high-level languages and run in the lowest privilege level. Since the instructions in the hardware-assisted virtualization environments operate at different privilege levels, such simulators are not suitable for the simulation and analysis of ASMI memory architecture [32]. Due to the lack of a proper simulation environment for virtualization-enabled processors, we decided to estimate the time taken for a memory access operation in an ideal memory module, simulated and synthesized using _ModelSim_ and _Xilinx ISE_ software. The following section explains the analysis process.
## 6 Machine Learning and 5G Security
### The Role of Machine Learning in Enhancing 5G Security
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**ML Techniques in 5G Security** & \multicolumn{1}{c}{**Applications**} & \multicolumn{1}{c}{**Strengths**} & \multicolumn{1}{c}{**Weaknesses**} & \multicolumn{1}{c}{**Evaluation Metrics**} \\ \hline Supervised learning & - & IDS & - & High accuracy in known threat detection & - & Requires labeled data for training & - & Accuracy Precision–recall curve \\ \cline{3-6}  & - & Spam filtering & - & Low false-positive rates & - & Susceptible to overfitting & - & Cluster purity \\ \hline Unsupervised learning & - & Anomaly detection & - & No need for labeled data & - & Lower accuracy compared to supervised methods & - & Cluster purity \\ \cline{3-6}  & - & Network traffic clustering & - & Can identify previously unseen threats & - & High computational cost & - & Silhouette score \\ \hline Reinforcement learning & - & Adaptive network configuration & - & Capable of learning optimal policies over time & - & Requires extensive training data & - & Cumulative reward Convergence time \\ \cline{3-6}  & - & Policy-based security measures & - & Adapts to changing network conditions & - & Complexity in setting reward functions & - & Cumulative reward Convergence time \\ \hline Ensemble methods & - & Boosting IDS capabilities & - & Improved generalization classifiers & - & Computationally expensive & - & Cross-validation score \\ \cline{3-6}  & - & Combining multiple classifiers & - & Resistance to overfitting & - & Complexity in interpretation & - & F1 score \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Machine learning security existing solutions in 5G [7; 39; 109; 110; 111; 115; 118; 119].
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 2 Study Area Description and Dataset  
### Study Area Description  
The Wabi Shebelle River Basin (WSRB) is one of the largest basins in Ethiopia, located in the southeastern part of the country. It originates from the Arsi and Bale Mountain ranges 4000 m above sea level and drains to the Indian Ocean after crossing Somalia. The basin's absolute location is within the latitudes 4\({}^{\circ}\)45\({}^{\prime}\)-9\({}^{\circ}\)45\({}^{\prime}\) N and longitudes 38\({}^{\circ}\)45\({}^{\prime}\)-45\({}^{\circ}\)30\({}^{\prime}\) E. The WSRB is characterized by bimodal rainfall seasons due to the southern and northern movement of the intertropical convergence zone (ITCZ) from March to May and from July to September. According to the master plan hydrology report, the highest mean annual rainfall recorded is 1467 mm in Seru Wereda of the Arsi Zone. The lowest mean annual rainfall recorded is 220 mm in the Kelafo Area of the Somali Region [23]. In general, the spatial and temporal distribution of rainfall is not evenly distributed; it is clustered in the upper and urban areas of the basin, and tends to decrease with decreasing altitude as shown in Figure 1.  
Figure 1: Study area description with rain gauge station distribution and topography elevation of Wabi Shebelle River Basin, Ethiopia.
## 2 Smoothing Method Analysis  
### Gaussian Smoothing  
Gaussian smoothing is a technique commonly used to smooth data and remove noise. It can help in bridge deflection prediction by reducing measurement noise and environmental disturbances for smoother data. In Gaussian smoothing, each data point is replaced with a weighted average within its neighborhood, with weights determined by a Gaussian function. Points further away from the center are given less weight, which enables a smoothing of the noise while preserving the main trends [19].  
Since one-dimensional data are used in this paper, a one-dimensional Gaussian filtering kernel is used in this paper, whose Gaussian function formula is shown below:  
\[f(j)=\frac{1}{\sqrt{2\pi\
u}}e^{-\frac{(j-\mu)^{2}}{2\sigma^{2}}} \tag{1}\]\[g(j)=\frac{f(j)}{\sum\limits_{i=-(t-1)/2}^{(t-1)/2}f(i)} \tag{2}\]  
\[j=\frac{-(t-1)}{2},\frac{-(t-1)}{2}+1,\ldots,\frac{(t-1)}{2} \tag{3}\]  
where \(j\) is an integer, \(\mu\) is the template mean, \(\sigma\) is the standard deviation of the template, and \(t\) is the template length. When calculating the Gaussian template, first, select the template length, take the center of the template as the origin, and carry out the calculation by Formula (1); taking the template length of three as an example, the resulting Gaussian template can be \(f=[f[-1],f[0],f[1]]\), as shown in Figure 1. The normalized Gaussian template can then be derived from Equation (2) \(g=[g[-1],g[0],g[1]]\).  
In bridge deflection prediction, deflection data usually come from sensor measurements, which are susceptible to environmental factors (e.g., wind, temperature changes, traffic loads, etc.) and measurement errors, resulting in more high-frequency noise. Modeling directly with these data may result in large model errors. Therefore, Gaussian smoothing as a preprocessing step for data can effectively improve data quality.
## 4 Performance Evaluation
### Benchmark Routing Protocol  
The DDOR protocol has been used in this work as a benchmark for the performance of FPBR. The DDOR protocol was chosen because, as mentioned in the introduction, it was explicitly developed for the deployment of VANETs in highway scenarios. As such, in [39] it was shown that DDOR  
Figure 5: Flow charts for the main processes of the FPBR protocol: (**a**) The Hello mechanism; (**b**) the location service and (**c**) the data dissemination service.  
Figure 6: State machine of the interaction between the different modules of FPBR protocol.  
outperforms AODV, DSR, and GPSR. Furthermore, DDOR includes features specifically designed to deal with some of the impairments imposed by highway scenarios, which are not considered by other V2V protocols such as GPSR-L, LORA-CBF and CAR.  
The DDOR protocol includes a discovery service coupled with a packet retransmission strategy. As previously mentioned, including these strategies in a VANET routing protocol for highways is very important, since the wireless channel constraints imposed by these scenarios may lead to significant packet losses. The DDOR protocol proposes enabling the IEEE 802.11 RTS/CTS mechanism for its location service and for the data dissemination stages. Thus, DDOR implements a unicast strategy for its location service. These unicast packets are sent in all directions in order to discover the geographic position of the destination node. Additionally, DDOR proposes an adaptive beacon mechanism (AB) based on the traveled distance of the vehicle. The AB mechanism goal is to reduce the overhead caused by the hello messages. Shortly, the AB mechanism sends a beacon message every time that a vehicle crosses a predefined milestone. Finally, DDOR includes a location prediction algorithm based on the vehicle relative velocity and the size of the transmitted packet. This location prediction algorithm is used before sending any packet in order to select the next-hop vehicle.  
In order to compare FPBR and DDOR under the same scenario conditions both protocols were implemented in the OPNET Modeler simulator [69]. The FPBR implementation follows the state machine and flowchart introduced in Section 3.6. The DDOR protocol was implemented following the guidelines provided in [39].
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 3 Methodology
### Dual Detection Methods
#### 3.2.2 YOLOv8-Based Road Anomaly Detection  
The latest version of the \"You Only Look Once\" (YOLO) object detection framework, YOLOv8 [43], represents a significant advancement in real-time object detection by offering exceptional accuracy and speed through a sophisticated deep learning architecture. This model is designed to efficiently identify and localize objects in images and videos, making it ideal for applications across various sectors, including industrial automation, autonomous vehicles, and surveillance. Its ability to operate in real-time makes YOLOv8 particularly valuable for safety-critical tasks, such as detecting road anomalies like potholes and cracks. To ensure adaptability across different environments, the model is trained on a comprehensive dataset encompassing various terrains, including urban streets, rural paths, and mountainous areas, enhancing its effectiveness and reliability for practical deployment in real-world scenarios.  
* **Bounding Box Prediction Refinement:** The standard formula for bounding box prediction in YOLOv8 is: \[\begin{split}\hat{x}&=\sigma(t_{x})+c_{x}/p_{x}, \quad\hat{y}=\sigma(t_{y})+c_{y}/p_{y},\\ \hat{w}&=p_{w}\cdot\exp(t_{w}),\quad\hat{h}=p_{h} \cdot\exp(t_{h})\end{split}\] (1)
* **Custom Bounding Box Loss Function:** To address the challenge of detecting small anomalies, a weighted Intersection over Union (IoU) loss function is employed: \[\text{Weighted IoU}=\frac{\text{Area of Overlap}}{\text{Area of Union}+\lambda\cdot\text{Penalty}}\] (2)
* **Class Prediction Optimization:** YOLOv8 uses a refined softmax function for class prediction: \[P(c\mid x)=\frac{\exp(z_{i})}{\sum_{j}\exp(z_{j})}\] (3)
* **Imbalance Handling With Focal Loss:** To handle class imbalance, YOLOv8 adopts focal loss: \[FL(p_{t})=-\alpha_{t}\cdot(1-p_{t})^{\gamma}\cdot\log(p_{t})\] (4)  
Table 3 summarizes key architectural and performance advancements across YOLO versions, highlighting the superior detection accuracy and deployment flexibility of YOLOv8. Our implementation leverages YOLOv8 for real-time anomaly detection on roads, providing the predictive input required for vehicle stabilization control.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Feature** & **YOLOv3** & **YOLOv4** & **YOLOv5** & **YOLOv7** & **YOLOv8** \\ \hline Developer & Redmon & Bochkovskiy & Ultralytics & WongKinYiu & Ultralytics \\ Anchor-based & Yes & Yes & Yes & Yes & No (anchor-free) \\ Backbone & Darknet-53 & CSPDarknetet53 & CSPDarknet & E-ELAN & Custom Ultralytics \\ Neck & None & PANet & PANet & MConv + E-ELAN & FN + PAN \\ Training speed & Moderate & Slow & Fast & Faster & Fastest \\ Inference speed & Moderate & Slow & Fast & Improved & Optimized \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of YOLO Versions.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 4 Experimental Results
### Inference Speed  
The goal of the proposed method is to measure respiration in real time using standard webcams and computing devices without using contact devices. Therefore, an experiment was conducted to measure the computational time required to perform the entire process using the proposed method. To measure the calculation time, the execution time of each step was measured for real-time operation with an actual webcam. The measurement process was repeated a total of 10,000 times. In Table 9, among the steps for respiratory detection, RoI detection, signal purification, and bpm calculation were performed using only the CPU, and respiratory movement detection through the model was the result of using a GPU. Among the entire process for estimating respiration, the process of detecting the torso area with RoI using BlazePose was the longest at 19.57 ms. Other computational processes showed a short time of less than 3 ms. The total processing time, including the RoI detection process, is 21.87 ms, achieving real-time at an arithmetic speed of approximately 45 fps. However, GPUs are difficult to consider as typical computing devices. To discuss more general-purpose utilization, in this study, the speed at which GPUs were not used and only CPU resources were used was also verified through experiments. Table 10 shows the results of performing the operation of the entire process using only the CPU without using the GPU. The average performance speed of the motion measurement step, which performed the operation using the GPU, increased by about 3 ms from 1.96 ms to 4.58 ms. In addition, as the computational load of the CPU increased, the execution time of RoI  
Figure 14: Examples of references and estimated signals on motion noise environment: (**a**) reference, (**b**) ours, (**c**) optical flow-based, and (**d**) intensity-based.  
detection increased by about 5 ms, signal refinement by about 0.04 ms, and RR estimation by about 0.09 ms. The total processing time in this case is 29.5 ms, which is a processing speed of about 34 fps. Given that the standard for commonly used real-time operations is 30 fps, these results show that the proposed method can perform real-time processing even with general computing and webcam hardware.
# Characterization of Light-To-Frequency Converter for Visible Light Communication Systems
## 3 Characterization of the LTF for a VLC System
### Evaluation of the LTF Converter  
The evaluation process for the LTF has been broken down into the following three steps:  
1. constant current signal, \(i_{t}(t)\), is applied to the transmitter LED.
2. Using the Tektronix TDS 3034C oscilloscope, the output frequency \(f_{0}\) and dark frequency \(f_{D}\) are measured for different distance cases between the transmitter LED and the LTF.
3. Next, the incident optical power, \(P_{in}(t)\), is recorded for each case using the optical sensor S120C with aperture diameter 9.5 mm, which is coupled with the THORLABS PM100D instrument. This meter console can deliver measurements of luminous flux and incident irradiance. It is not recommended to use the irradiance measurement as the PM100D instrument considers the area of the sensor S120C rather than the area of the photodiodes integrated into the LTF TCS3200 [21]. Therefore, the useful information of this experiment is the incident optical power flow \(P_{in}(t)\), considering the Ar of the sensor S120C.
### Objective  
Researchers usually use the MEEI database as the standard training database for VPD systems in the research on pathological voice detection and intelligent diagnosis, but ignore the imbalance between normal and pathological classes of this database. As a model evaluation measure, Acc is not suitable for class-imbalanced data, leading to inaccurate model results. The current classification studies of pathological voices only focus on binary classification between normal and pathological voices or binary classification between one pathological voice type and other pathological voices; there are few studies on the multi-classification of pathological voices.  
To tackle these issues, we propose a VPD system that combines the data oversampling-based fuzzy c-means clustering SMOTE method and machine learning models to resolve the multi-classification of pathological voices with imbalanced datasets. Mel-Frequency Cepstral Coefficients (MFCCs) are some of the most effective feature parameters in speech recognition [36; 37; 38] and were proposed based on the human auditory perception structure [39]. Recent studies have shown that the feature extraction of MFCCs has been widely used in VPD systems. Thus, this paper selects MFCCs as the input parameters of the model. Considering the issue of class-imbalance in the MEEI database, we conjunct the c-means clustering algorithm with SMOTE and propose a VPD system in the class-imbalanced pathological voice database called FC-SMOTE. FC-SMOTE finds safe areas where minority class samples are located by the c-means clustering algorithm and oversamples minority class samples in safe areas according to the density of the minority sample distribution. Ultimately, more samples will be generated in sparse minority areas to solve the imbalance within the class. Moreover, FC-SMOTE can detect safe oversampled areas without considering all sample class labels. In the modeling of the VPD system, a set of machine learning models are evaluated and validated using the resulting class-balanced dataset as an input. Some reasonable metrics [40] include Recall, Specificity, \(G\) value, \(F1\) value, and AUC/PRC as evaluation metrics for the classifier model. The FC-SMOTE algorithm is used to select minority and majority classes, and a class-balanced training dataset is constructed to train machine learning models. The results show that the VPD system with FC-SMOTE has shown good results in the detection and multi-classification of pathological voices, which helps in the pathological diagnosis of voices in practical clinical use. For the selected model evaluation measures, FC-SMOTE outperforms other traditional oversampling algorithms, which include SMOTE, Borderline-SMOTE, and ADASYN.
## 4 5G and V2X Deployment Projects
### 5G-Routes  
The main objective of the 5G-ROUTES project is to conduct advanced field trials for innovative Cooperative Awareness Message (CAM) applications along the designated 5G cross-border corridor known as \"Via Baltic-North\", which spans across Latvia, Estonia, and Finland, aiming to validate the latest 5G features and 3 GPP specifications under real-world conditions. The project aims to accelerate the deployment of 5G end-to-end interoperable CAM ecosystems and services in digitized motorways, railways, and shipw throughout Europe.  
The project is built upon three pillars. Firstly, it focuses on formulating and validating a wide range of innovative use cases that directly relate to CAM and cross-border mobility in the \"Via Baltic-North\" corridor. These use cases align with 3 GPP V2X Phase 3 and will undergo validation against more than 150 target 5G Key Performance Indicators (KPIs). Secondly, the project integrates various enabling technologies to facilitate the execution of field trials. These technologies include AI-based network slicing and optimization, AI-based distributed mobile edge computing (MEC) for CAM services, AI-based 5G radio interface for shared spectrum usage, AI-based positioning enhancements for vehicle-to-everything (V2X) communication, and cross-domain integration fabric for multi-domain interaction and service roaming, as well as complementary technologies, like satellite 5G connectivity, vehicular On-Board Units (OBUs), IEEE 802.11p, and the FRMCS (Future Railway Mobile Communication System).  
Lastly, the project leverages a Key Performance Indicator (KPI) visualization system from the 5G-SOLUTIONS project, extending its functionality to incorporate CAM use cases. This system enables the parallel execution of use cases, automated analysis of trial results, and near-real-time presentation through an intuitive, user-friendly dashboard. The project also aligns its implementation roadmap with the latest 3 GPP standardization releases, actively contributing to standardization bodies and promoting a collaborative approach between the telecom and automotive sectors. The project's objectives follow the SMART criteria, including developing innovative CAM use cases, analyzing requirements, advancing enabling technologies, developing infrastructure, conducting field trials, managing exploitation and innovation, contributing to standardization, and ensuring long-term success through dissemination and collaboration.  
Overall, the 5G-ROUTES project aims to validate and optimize 5G technologies for CAM services in a cross-border context, with the ultimate goal of driving the widespread adoption of interoperable CAM ecosystems and services in the transportation sectors across Europe [92].
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 3 Network Datasets and Experimental Settings
### Datasets  
For the dynamic network datasets collection, the 'KONECT Network Dataset' [51] (i.e., the Koblenz Network Collection) and 'Network Repository' [52] were used. KONECT project is run by the Institute of Web Science and Technologies at the University of Koblenzas part of collecting large network datasets to facilitate research in network science and related fields. This study extracted different dynamic network datasets including directed and undirected networks where links between actors are timestamped.
## 2 Results
### AsA Mass Fraction Determination in Selected Soybean Tissues Under Development and Stress Conditions  
To validate the significance of the gene expression data of AsA biosynthesis pathways in soybeans, the AsA mass fraction in dry matter was determined in leaves and seeds across developmental stages and leaves and roots under abiotic stress conditions (Figure 10). During seed germination, AsA mass fractions increased from the initial stages at 12 h to 24 h (Figure 10A) and declined afterward. The comparison of developing leaves in Figure 10B revealed a significantly higher relative accumulation of AsA in leaves at 15 days than in leaves at 4 days. Both roots and leaves under PEG and NaCl presented a decline in AsA mass fractions compared to the control; however, this response varied regarding the time of exposure and stress condition (Figure 10C,D). In leaves, PEG decreased AsA levels only at 24 h, while in roots, NaCl reduced AsA levels only at 48 h (Figure 10C,D). The higher decline in AsA mass fraction under both stress conditions occurred in roots at 48 h (Figure 10C,D).