esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 6 Transportations in 5G and Beyond
### Solutions  
Users and service providers expect to have many solutions for improving transportation services. Although transport solutions depend on technical, business, and political aspects, this section explores the limitations of emerging technologies and technical capabilities of transport solutions. In this section, the following issues will enhance transport solutions.  
The EE of all technical devices requires risk monitoring and confirmation of whether these devices are in working order. When these devices lose reliability and have a decreased lifetime, many connecting components linked with these devices create security issues. Therefore, the calculation of EE is very important for monitoring energy levels and power failures. When energy continues with the same energy level, the reliability and EE secure the components and necessary devices.  
Although EE is one of the 5G+ requirements, energy cost increases with the designs and other factors. As shown in Figure 6, by using intelligent techniques with the emerging SDN technologies, service providers can improve the EE with efficient framework designs.  
Latency not only reduces the delay but also improves the speed of the processing. This means that the timeframe for risks to occur is much smaller for those who deliberately want to attack. Regarding the emerging technologies, future transportation solutions will be dependent on these 5G+ technologies. These include SDN-based 5G networks, IoT-based 5G communication networks, and IoT sensors based on photonic technology.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ITS Levels** & **Traditional** & **SDN** & **SDN** & **SDN** \\  & **e \textgreater{} 0.05** & **E = 0.05** & **e = 0.005** & **e = 0.001** \\ \hline Driver-assisted ADVs & 20\% & 35\% & 40\% & 50\% \\ Partial ADVs & 30\% & 40\% & 45\% & 55\% \\ Limited condition ADVs & 35\% & 45\% & 50\% & 60\% \\ Full ADVs & 25\% & 40\% & 55\% & 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Security with traditional and SDN environments for ITS-based ADVs.  
When the levels of ITS are set with the emerging technologies, latency (Figure 7) is improved with the complexity of algorithms focused on ITS levels. Despite the complexity, ITS Levels depend on many factors, including traffic management with SDN-based MA, as in Figure 5. The secrecy rate of 5G+ and 6G communication will be future challenges in emerging technologies. This can be improved through the efficient design of a framework which focuses on an improved plan of EE and latency.
## 3 Results
### Optimization of In Situ Transesterification Reaction
#### 3.2.2 Effect of Acid Concentration on Reaction Efficiency  
Acid catalysts are widely used for transesterification due to their ability to catalyze all free or linked fatty acids. Base catalysts have prominent advantages compared to acid catalysts, the reaction time is shorter and the requirement for the equipment for the reaction is not as high as that of acid catalysts in case of corrosion. However, base catalysis has high selectivity based on the type of lipids. Base catalysis methods showed lower FAME yield compared to the yield obtained by acid catalysis [19]. The base catalysts may not be able to penetrate through the thick algal cell wall and the existing free fatty acid may be subjected to saponification in base catalyzed transesterification. Therefore, acid catalysis was chosen in our study and the effect of acid concentration on the in situ transesterification is shown in Figure 2. As the concentration of acid catalyst increased, the FAME content was also enhanced. When the acid concentration was 2.5%, FAME yield was extremely low at 8.01% \(\pm\) 0.05%. As the acid concentration increased to 5%, the FAME yield increased to 10.04% \(\pm\) 0.08%. However, upon further increase of the acid concentration from 5% to 11%, no obvious improvement in the FAME yield was observed. Considering the cost and the corrosive characteristic of acid to the equipment, a catalyst concentration of 5% was chosen as the optimum value.  
Figure 1: Effect of the ratio between methanol and biomass on the fatty acid methyl ester (FAME) yield. Reaction conditions: HCl/Methanol (5%, \(v/v\)), reaction temperature: 70 \({}^{\circ}\)C, reaction time: 1.5 h.  
Figure 2: Effect of acid concentration on the FAME yield. Reaction conditions: methanol-to-biomass ratio: 90 mL-g\({}^{-1}\), reaction temperature: 70 \({}^{\circ}\)C, reaction time: 1.5 h.
###### Abstract
Moreover, the I/Q signal quality of QAF networks (roughly 5\({}^{\circ}\) phase imbalance and 0.5 dB magnitude imbalance) cannot satisfy the requirement of an image rejection ratio (IRR) higher than 30 dB (IRR > 30 dB typically requires the phase and magnitude mismatch to be <3\({}^{\circ}\) and <0.25 dB, respectively [12]). Without I/Q signal generation, a front-end with IRR greater than 75 dB was achieved in [15] by properly designing the frequency plan and adopting a high-pass filter in LNA but at the expense of higher noise figure (NF) and design complexity. In [18], ultra-wideband quadrature signal generation was achieved with state-of-art I/Q balance performance by cascading transformer-based quadrature hybrids (without resistive components). Compared with a single-stage transformer-based quadrature hybrid [19; 20], the I/Q imbalance of the quadrature signal generation network in [18] was highly suppressed, and bandwidth was extended significantly, making ultra-wideband, precise and low-loss I/Q signal generation possible. However, relatively long meander lines (for phase matching) and signal routes introduce extra loss and increase chip area and design complexity. Moreover, coupling coefficient k could be further enhanced to reduce loss and minimize layout footprint. Figure 1: Wideband 5G receiver with quadrature signal generation and combination. In this paper, a compact and ultra-wideband quadrature signal generation network (shown in Figure 1) is proposed for 5G applications. The wideband passive balun is designed to achieve minimum loss, and the I/Q generation network is optimized for wideband operation, low loss and compact area. This article is organized as follows. In Section 2, we introduce the characteristics of the GlobalFoundries 45 nm CMOS SOI process. In Section 3, we present the proposed transformer-based quadrature hybrid, its detailed operation principles and circuit implementation. In Section 4, we describe the design details of the proposed ultra-wideband I/Q signal generation network. In Section 5, we present the simulation results and a performance comparison with reported I/Q generation schemes. Finally, in Section 6, we draw conclusions.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.2 Untraceability  
Randomized nonces \((RND_{1},...,\ RND_{7})\) and the current time-stamp are selected arbitrarily throughout the authentication process, to ensure each candidate's messages \((MSG_{1},...,MSG_{4})\) are unique. An attacker cannot identify any connections between the messages delivered by \(SN\) and cannot identify the source. Additionally, genuine identities or pseudonyms are carried out in a protected one-way collision-resistant hash function, rather than being used openly in communications. The suggested approach can thereby attain untraceability.
# Bond Strength Assessment of Concrete-Corroded
## 3 Application of Artificial Neural Network (ANN) Model
### Application of ANN Method on the Cases with the Unconfined Condition  
As the proposed model is assumed to be a reliable method for assessing the bond strength of the corroded RC member, a parametric analysis was conducted to clarify the relationship between the bond strength loss and each parameter.  
For the unconfined condition (without stirrups), the ANN method was used to calculate the bond strength with the same database based on the different parameters, as shown in Figure 9. The increase in the coefficient of determination is observed with an increase in the number of parameters. The input with crack width (Figure 9b) can achieve higher accuracy than that with the corrosion level (Figure 9a); however, the numbers of data are different, and, therefore, the advantage of each parameter is difficult to determine. There is an insignificant increase in the coefficient of determination when using the cover-to-diameter ratio; however, when both the corrosion level and crack width are considered, \(R^{2}\) can be improved to as high as 0.841 and the RMSE can be decreased significantly to as low as 0.127. Further, when more parameters are considered, the predicted results can match the test results perfectly. It shows the advantage of the ANN method that can achieve higher accuracy.  
Figure 8: Comparison between the existing analytical models and ANN method considering both crack width and cover-to-diameter ratio as inputs for specimens without stirrups (unconfined) (note \(\tau_{u}(w)\) is the bond strength of specimen with crack width \(w\), \(\tau_{u}(0)\) is the bond strength of specimen without corrosion damage, \(w\) is the crack width on the surface of specimen and \(c/d\) is the cover-to-diameter ratio): (**a**) [PERSON] et al. (1996) [55]; (**b**) ANN.  
Figure 9: Comparison of the results calculated by ANN method considering different parameters for the unconfined cases: (**a**) corrosion level; (**b**) crack width; (**c**) corrosion level and cover to diameter ratio; (**d**) corrosion level and crack width; (**e**) corrosion level, crack width, cover to diameter ratio and concrete strength.
## 2 Experimental Set-Up and Results
### Temperature Sensing of the SMC-FBG (Stepped-Metal Coated Fiber Bragg Grating)  
Three SMC-FBGs were tested by the controllable temperature oven with a precision of 1 \({}^{\circ}\)C, and their temperature sensing characteristics are shown in Figures 4-6. The spectra of these three SMC-FBGs all split into two resonance peaks with two different temperature sensitivities. We describe the temperature sensitivity difference between the two peaks as \(\Delta K_{T}\) for Type A, \(\Delta K^{\prime}{}_{T}\) for Type B and \(\Delta K^{\prime\prime}{}_{T}\) for Type C. \(\Delta K_{T}\), \(\Delta K^{\prime}{}_{T}\) and \(\Delta K^{\prime\prime}{}_{T}\) are 2.1 pm\({}^{\prime}\)\({}^{\circ}\)C, 6.5 pm/\({}^{\circ}\)C and 0.8 pm/\({}^{\circ}\)C, respectively.  
Figure 3: Schematic of the stepped-metal coated-fiber Bragg gratings (SMC-FBGs). (**a**) appearance diagram, (**b**) axisymmetric diagram of Type A SMC-FBG, (**c**) axisymmetric diagram of Type B SMC-FBG, (**d**) axisymmetric diagram of Type C SMC-FBG.  
Figure 2: Schematic of the stepped-metal electroplating.  
Figure 4: Temperature sensing of Type A (**a**) spectra at 30 to 80 \({}^{\circ}\)C. (**b**) temperature sensitivity.  
Figure 5: _Cont._  
Figure 5: Temperature sensing of Type B (**a**) spectra at 30 to 80 \({}^{\circ}\)C (**b**) temperature sensitivity.  
Figure 6: Temperature sensing of Type C (**a**) spectra at 30 to 80 \({}^{\circ}\)C (**b**) temperature sensitivity.
## 3 Monitoring Results and Analysis
### Internal Force in the Four Bar Lattice Girders  
Figure 17 has presented the dynamic evolution of the internal forces in the four bar lattice girders. Compression was defined as being positive and tension was defined as being negative, and the same definition has been applied below. For the vault monitoring points (i.e., monitoring points A-G), the stresses in the four bar lattice girders reached a higher level at the initial stage, and then either increased or sharply decreased in the first 30 days; as shown in Figure 17a, the part marked by the orange circles. This phenomenon was common on the inner side and outer side of the four bar lattice girders. This showed that the four bar lattice girders play a certain load bearing role after installation, and the rapid deformation of the surrounding rock caused a sharp adjustment of the stresses of them. After a brief period of stability, the stresses of the four bar lattice girders experienced a sharp adjustment again, as shown in Figure 17a, the part marked by black circles. This phenomenon can be attributed to the extrusion deformation of the four bar lattice girders that was caused by the construction of the prestressed anchor cables and bolts. The extrusion effect was more evident closer to the anchor plate; therefore this phenomenon was more noticeable on the inner side of the four bar lattice girders than on the outer side. As the excavation continued, the four bar lattice girders showed a characteristic of the compressive stresses increasing or the tensile stresses decreasing. For the monitoring points of the inverted arch (i.e., Monitoring Points H-L), the stress decreased after a rapid initial increase. This was the result of the construction of the secondary support of the inverted arch which exerted a downward load on the primary support of the inverted arch, thus offsetting part of its convergence deformation. As the construction continued, the change rule of the inverted arch's monitoring points was similar to that of the vault's monitoring points. When the construction had been completed, the values of all the monitoring points almost stopped increasing. At this time, the state of the four bar lattice girders could be considered stable.  
Figure 18 has shown the final distribution of the stresses on the inner and outer sides of the four bar lattice girders. It can be seen that the maximum and minimum values of the stress on the inner side of the four bar lattice girders occurred at the Monitoring Points C and H, with corresponding values of 141.6 MPa and 4.8 MPa, respectively. The maximum and minimum values of the stress on the outer side of the four bar lattice girders appeared at Monitoring Points A and F, with corresponding values of 103.8 MPa and 13.2 MPa, respectively. Except for Monitoring Point J in Figure 18b, the other monitoring points in the four bar lattice girders were in a state of compression.  
Figure 17: Time history plots of the internal forces in the four bar lattice girders: (**a**) inner side; (**b**) outer side.
# Skin Depigmenting Agents in Anti-Aging Cosmetics: A Medicinal Perspective on Emerging Ingredients
## 3 Results and Discussion  
### Prevalence Analysis of Skin Depigmenting Ingredients in Anti-Aging Products  
The preliminary analysis of the presence of skin depigmenting ingredients including 280 anti-aging cosmetics, in a total of 40 brands, indicates an increase in the use of these ingredients from 2011 to 2018. Although in 2011 only 58.8% of the 177 anti-aging cosmetics contained this type of ingredient, a noticeable growth was observed in 2018, where these ingredients were present in 73.8% of the new 103 studied anti-aging products, representing an increase of 25.6 percentage points in the analyzed period of time (Figure 1). Due to the widespread market value of anti-aging products, constant developments are being made to come out with new ingredients and formulations, which may have contributed to the observed market growth.  
Figure 2 allows us to analyze which skin depigmenting ingredients were used in the studied anti-aging cosmetics commercialized over this seven-year span. Overall, the majority of ingredients used in these products are represented in both years. The most used ingredients in both years were alpha-hydroxy acids, ascorbic acid and derivatives, as well as retinol and derivatives. _Vitis vinifera_ preparations are the only ingredient whose use decreased from 2011 to 2018. Noteworthy, ubiquinone was only found in 2011, and tranexamic acid together with bakuchiol and 4-butylresorcin  
Figure 1: The prevalence of skin depigmenting ingredients in anti-aging products marketed in 2011 and 2018.
## 4 Problem Formulation
### Mathematical Model
#### 4.2.2 Objective Function  
Our objectives are to minimize both RBR and crosstalk ratio simultaneously.  
\[\text{min}\textbf{F}(x)=(f_{blocking},f_{XT})^{T} \tag{3}\]  
where  
\[f_{blocking}=N_{R}^{blocking}/|R| \tag{4}\]  
\[f_{XT}=N_{fs}^{XT}/N_{fs}^{occupied} \tag{5}\]  
\[N_{fs}^{XT}=\sum_{\tau_{k}\in\textbf{R}}\sum_{\begin{subarray}{c}\tau_{k}= \mu_{k}^{\prime},\mu_{k}\\ \tau_{k},start\end{subarray}}^{\mu_{k}^{\prime},\mu_{k}}+\tau_{k}+\tau_{k}+ \tau_{k}+\tau_{k}+\tau_{k}+\tau_{k}+\tau_{k}\\ \sum_{\tau_{k}\in\textbf{C}}\sum_{l_{ij}\in P}\beta_{\tau_{k},f_{x}}^{l_{ij},\mu_{k},\mu_{k}} \tag{5a}\] \[N_{fs}^{occupied}=\sum_{l_{ij}\in L}\sum_{\alpha_{n}\in\textbf{C}}\sum_{\tau_{k} \in\textbf{F}}\mu_{l_{ij},\mu_{n}}^{f_{x}} \tag{5b}\]  
The former, shown in Equation (4), denotes the ratio of the total number of blocked requests to the number of all created requests. The latter, in Equation (5), equals the ratio of the total number of crosstalk FSs to the total number of occupied FSs, which are expressed by Equation (5a) and Equation (5b), respectively.
## 4 Results and Discussion
### Evaluation Metrics  
The choice of evaluation metrics is crucial, as appropriate metrics can accurately assess the network's performance and guide adjustments to the network's structure and training parameters. For this study, Precision (P), Recall (R), and Average Precision (AP) were selected as the evaluation metrics, with their calculation formulas provided in Equations (1)-(4):  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{AP}=\int_{0}^{1}\text{P}\times\text{R}\;\text{dr} \tag{3}\]  
\[\text{mAP}_[EMAIL_ADDRESS]}=\frac{1}{10}(\text{AP}_[EMAIL_ADDRESS]}+\text{AP}_[EMAIL_ADDRESS]}+\cdots +\text{AP}_[EMAIL_ADDRESS]}) \tag{4}\]  
True Positives (TPs) refer to positive samples correctly identified by the model, False Positives (FPs) are negative samples incorrectly identified as positive, and False Negatives (FNs) are positive samples incorrectly identified as negative. The value of AP is calculated as the area under the Precision-Recall (P-R) curve, with dr as the integrating variable.  
[EMAIL_ADDRESS] denotes the Average Precision at an Intersection over Union (IoU) threshold of 0.5, meaning the predicted bounding box must overlap with the ground truth box by at least 50%. Similarly, [EMAIL_ADDRESS] represents the Average Precision at an IoU threshold of 0.95, requiring a much higher overlap between the predicted and labeled areas. The IoU measures the ratio of the intersection area to the union area of the predicted and labeled bounding boxes.  
The [EMAIL_ADDRESS]:0.95 metric, introduced by the COCO dataset, calculates the Average Precision over a range of ten IoU thresholds (from 0.5 to 0.95, in increments of 0.05). This criterion provides a comprehensive evaluation by considering both loose and strict matches between predicted and ground truth bounding boxes, making it a robust indicator of model performance in object detection tasks.
# Performance Analysis of Thread Block Schedulers in GPGPU and Its Implications
###### Abstract  
GPGPU (General-Purpose Graphics Processing Unit) consists of hardware resources that can execute tens of thousands of threads simultaneously. However, in reality, the parallelism is limited as resource allocation is performed by the base unit called thread block, which is not managed judiciously in the current GPGPU systems. To schedule threads in GPGPU, a specialized hardware scheduler allocates thread blocks to the computing unit called SM (Stream Multiprocessors) in a Round-Robin manner. Although scheduling in hardware is simple and fast, we observe that the Round-Robin scheduling is not efficient in GPGPU, as it does not consider the workload characteristics of threads and the resource balance among SMs. In this article, we present a new thread block scheduling model that has the ability of analyzing and quantifying the performances of thread block scheduling. We implement our model as a GPGPU scheduling simulator and show that the conventional thread block scheduling provided in GPGPU hardware does not perform well as the workload becomes heavy. Specifically, we observe that the performance degradation of Round-Robin can be eliminated by adopting DFA (Depth First Allocation), which is simple but scalable. Moreover, as our simulator consists of modular forms based on the framework and we publicly open it for other researchers to use, various scheduling policies can be incorporated into our simulator for evaluating the performance of GPGPU schedulers.  
thread block; GPGPU; thread block scheduling; Round-Robin
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 3 Proposed Methodology
### Attack Model  
To contextualize the anomaly detection framework, we define the threat landscape and attacker capabilities as follows:  
* Attacker Access and Capabilities: We assume an attacker who has gained the ability to inject or manipulate BSMs within the V2X communication channel. This could be achieved through various means, such as compromising an OBU of a vehicle or an RSU by employing a rogue device capable of broadcasting V2X messages that mimic legitimate ones. An attacker can also exploit vulnerabilities in the V2X communication stack to alter messages in transit, e.g., a man-in-the-middle [23] scenario. The attacker is assumed to be familiar with the BSM format and content, but not necessarily with the internal parameters of the deployed anomaly detection system. Their goal is to introduce false information regarding a vehicle's state (position, speed, acceleration) to disrupt V2X applications, potentially leading to unsafe conditions or reduced traffic efficiency.
* Resources Used by Attacker: An attacker might utilize software-defined radios (SDRs) [24] or custom-programmed V2X communication modules to craft and transmit anomalous BSMs. For attacks involving compromised OBUs/RSUs, the attacker leverages the existing communication hardware of the compromised unit. The injected anomalies, as simulated in our experiments in Section 4.2, such as constant offsets or targeted vehicle data falsification, represent attempts to broadcast deceptive kinematic information.  
Our framework primarily focuses on detecting anomalies that are indicative of malicious, intelligently crafted attacks aimed at falsifying BSM data. However, the underlying principle of learning normal data patterns and identifying deviations means the system is also capable of flagging anomalies caused by severe sensor malfunctions or significant signal degradation, provided these events cause the BSM data to deviate from the learned norm significantly. The types of anomalies injected in our experiments could manifest from either a sophisticated attack or a critical sensor failure.