esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
## 2 Brake Energy Recovery Control Strategy
### Overall Control Strategy Design for Brake Energy Recovery  
To meet the driver's braking needs, it is necessary to formulate different braking force distribution strategies for each braking mode. First, according to the above analysis, the driver's braking process will be divided into three modes of control system:  
(1) Single-pedal energy recovery mode:  
When the driver gradually releases the accelerator pedal or reduces the opening of the accelerator pedal to decelerate the vehicle, the vehicle only utilizes the power of the electric mechanism to decelerate in this braking phase. It does not step on the brake pedal or participate in mechanical braking; so, it is a single-pedal energy recovery mode.  
(2) Glide energy recovery mode:  
When the driver releases the accelerator pedal and does not step on the brake pedal, both pedal openings are 0. Therefore, according to the different driving conditions, the coasting brake stage is divided into two control modes: downhill and horizontal road. In this process, the driver does not operate the accelerator pedal and brake pedal, and the car is coasting; so, it is the coasting brake energy recovery mode.  
(3) Conventional energy recovery mode:  
When the driver steps on the brake pedal, the vehicle's mechanical braking system and electric mechanism power are involved in the vehicle's deceleration process to respond to the driver's braking needs as quickly as possible. This is the conventional braking energy recovery mode.  
The switching logic of the three modes is shown in Figure 1.  
The design of the brake energy recovery control strategy is qualified according to the braking process:  
1. The braking energy recovery control strategy is strictly based on the driver's operational intent. It is the switching signal between the three modes and controls the vehicle's braking system.
2. When the SOC of the vehicle's HV battery is too high, continuing energy recovery will lead to overcharging, which will seriously impact the battery's service life and performance. To reduce the adverse effects on the battery, when the battery SOC value reaches the maximum value (e.g., >90%), the vehicle will turn off the brake energy recovery system and no longer charge the battery.
3. To avoid the frequent switching of the motor between the two states of the generator and motor, which will reduce the service life of the motor and the HV battery, a 2 s idle period is added when entering the coasting brake energy recovery mode. The coasting brake mode is entered when the driver does not operate the brake pedal and the accelerator pedal for more than 2 s. At the same time, a 1 s gap is defined for each state switch to avoid multiple switching of different modes within 2 s.
4. To avoid the safety hazard of single-pedal mode in emergencies due to misuse, the vehicle can only activate the single-pedal brake energy recovery mode when the accelerator pedal opening is below the minimum value (e.g., <10%).
## 4 E-Waste Management in Serbia--Current Situation  
In Serbia, there is still no systematically organized separate collection and sorting of e-waste, where e-waste should be collected under the supervision of local communities as a special waste process. However, the practice is such that the organized collection of this waste occurs only sporadically [1]. Given that e-waste can have the character of hazardous waste, it should be disposed of either in hazardous waste landfills or neutralized before disposal. Contrary to these facts, this waste most often ends up in municipal waste landfills, together with other waste [32]. During 2022, according to data from the Serbian Environmental Protection Agency, 22,571,038 items of electrical and electronic waste, or 20,226.6 tons, reached the market. In the same year, the reuse of 30,316 tons of e-waste was recorded, while 3451 tons of separated components from this waste were exported, of which 2574 tons had the character of hazardous waste [33]. Based on the reported data, it is estimated that the average amount of collected e-waste is 6.3 kg/inhabitant annually, which is unsatisfactory. There are estimates that 15,000 tons of this waste will be collected in 2024, that is, 38,000 tons in 2031 in Serbia [1].  
There are around 500 operators licensed to collect e-waste in Serbia, and the collection is based on the selective collection of profitable waste. In Serbia, only 5 operators engage in e-waste recycling, 13 operators focus on reuse, 10 operators handle hazardous waste treatment, and 89 operators handle non-hazardous waste from electrical and electronic equipment. Not a single operator in the entire territory of Serbia deals with e-waste disposal [1]. Only in the territory of Belgrade, the largest city in Serbia, are there 58 operators who collect e-waste, while 42 operators, in addition to collection, have storage permits. A total of 34 operators deal with e-waste treatment [34]. According to the interactive map \"Where to Recycle\", which represents a common social good and provides information on all available collection and recycling centers for all types of waste in Serbia, it can be counted that there are as many as 14 recycling centers that collect and/or treat e-waste [35]. Figure 2 shows the approximate schedule of collection and recycling centers for this waste in Serbia.  
In all recycling centers, manual sorting of e-waste according to classes and manual disassembly of macro-components are carried out with the eventual application of magnetic separators for separating ferrous metals and Eddy current devices for separating non-ferrous metals. No center has even considered the use of robots in the separation of e-waste [36].
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 1 Introduction  
The current state of the research field should be _H. pylori_ is well-known as a pathogenic bacterium that causes chronic gastro-duodenal diseases, such as gastric ulcer, duodenal ulcer, and gastric cancer [1]. The strong relationship of _H. pylori_ with gastric cancer, evidenced by numerous cohort and prevalence studies, led the International Agency for Research on Cancer to categorize _H. pylori_ as a class I carcinogen. A high rate of infection increases gastric cancer morbidity and mortality [2; 3]. The eradication of _H. pylori_ is essential to reduce gastric ulcer, duodenal ulcer, and gastric cancer incidence [4]. However,emerging antibiotic resistance in several countries even after following treatment guidelines introduces obstacles for eradicating the infection [5]. Mechanisms for antibiotic resistance to _H. pylori_ indicated a multiple contributing factors. Studies reported including reduced permeability, increased efflux pumping, mutation of target genes, direct modification of antimicrobial agents, and biofilm formation [6; 7]. Biofilm is also an unpredictable factor that can substantially increase the concentration of mutations and resistance. Each year, new studies reveal findings that enhance the understanding of the mechanisms underlying biofilm formation. Currently, the standard triple therapy using clarithromycin and amoxicillin are still the first regiment used [8]. An update on the comprehensive summary of those factors has not been conducted. Therefore, we conducted this extensive review to update our understanding of _H. pylori_ biofilm formation to help in finding targets for biofilm eradication and enhance therapeutic efficacy. (the outline is depicted in Figures 1 and 2 explained the biofilm formation cycle).  
Figure 1: A Conceptual framework of _Helicobacter pylori_ biofilm formation mechanism and eradication strategies. The central theme is the interplay between various factors influencing biofilm development and the need for innovative approaches to disrupt biofilm structure and enhance antibiotic susceptibility.  
Figure 2: Graphic summary of biofilm formation mechanism.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 1 Introduction
Additionally, we introduce various techniques through different heterogeneous parameters that can be applied when utilizing each RSU to maximize the average throughput. Our main contribution in this paper is as follows. * This paper emphasizes the characteristics of RSUs used in V2X based on 3 GPP technical reports to better understand the functionality of V2X within the 5G NR system, and discusses 5G-V2I/V2N modeling in dense urban scenarios. We design a cellular system model in a dense urban environment using the SLS methodology proposed by 3 GPP for V2X performance comparison based on rel. 16. The methodology provides standards for the distribution of each RSU type, road composition, and vehicle arrangement. In addition, it incorporates standardized criteria such as antenna models, channel models, and PHY layer models. * We propose a hybrid RSU approach as a cooperative system between BS/UE-type RSUs in the designed system environment and minimize shadow areas through an RSU allocation method based on SINR received at the V2X application server. The proposed Hybrid RSU approach not only improves the overall received SINR of the VUEs in the simulation, but also shows performance improvements in received interference and average throughput. Moreover, it provides insights into the impact of using different RSU approaches in cooperation on the overall system performance. * The proposed Hybrid RSU technology approach contributes to higher system capacity or better power usage in direct communication between vehicle terminals and RSUs through resource collaboration management, CRE, and 3D beamforming technology. The applied cooperative resource allocation techniques, dynamic ICIC and CS-CoMP are effective methods for mitigating Co-Channel Interference (CCI) and improving the channel situation of cell edge users in dense urban environments. Furthermore, the utilization of 3D beamforming technology is effective in meeting the safety, re liability, and high-quality driving requirements of the V2X environment. Realistic antenna radiation patterns are characterized and applied to evaluate performance in 5G cellular scenarios. * Simulation results focus on analyzing VUE interference, outage probability, and average throughput values by the proposed Hybrid RSU approach. The simulation results demonstrate that the proposed scheme significantly improves the channel quality and overall system reliability. The structure of this paper is as follows: Section 2 reviews prior research and proposed techniques to maximize RSU performance in V2X communication, and Section 3 presents a system model that constitutes a simulation environment. Section 4 introduces how BS/UE-type RSUs collaborate for V2X communication and evaluates performance using system-level simulation (SLS) when applying the techniques proposed in Section 5. Finally, Section 6 provides our conclusion and recommendations.
## 3 Results
### Typography and Typesetting  
#### 3.6.1 Font  
A study by [PERSON] et al. examined the impact of intelligent typesetting on enhancing attention and comprehension in Chinese text reading, leveraging font size, brightness, and hue adjustments to emphasise word importance [10]. Using the TextRank algorithm, key terms were highlighted based on their relevance. Eye-tracking metrics showed that larger font sizes, darker brightness, and warmer hues improved attention, while reading performance tests revealed that brightness-based typography yielded the highest accuracy (57.4%), outperforming standard typesetting (38.9%). Additionally, subjective evaluations confirmed improved readability, reduced fatigue, and greater visual appeal for these intelligent typesetting approaches.  
Similarly, another study examined the impact of text capitalisation on reading performance among Chinese learners, comparing all-capitalised text, text with initial capital letters, and standard text formatting [46]. The study included 46 college students from Nanjing University of Science and Technology, comprising 21 English majors and 22 non-English majors. The comprehension accuracy rates were 92.31% for English majors and 94.87% for non-English majors. The findings revealed that all-capitalised text significantly increased both fixation count and fixation duration compared to normal text. For all-initial-capital text, sentences containing six or more capitalised words led to a marked increase in fixation duration (\(p<0.001\)) and Regressive Saccade Count (\(p<0.001\)). These results indicate that, while comprehension accuracy remained high across all typography types, capitalised formats imposed a significantly greater cognitive effort on participants.  
Another study by [PERSON] et al. highlighted the limitation of word identification to eight characters for both deaf and hearing readers (42 and 60 participants, respectively) [47]. Beyond this limit, reading rates plateaued, and fixation durations ceased to decrease. This underscores the importance of using clear, legible fonts to enhance word recognition within this character boundary.
# Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning
## 4 Results and Discussion
### TSP Example as Proof of Concept
After taking the right action, now the agent is on State S1 based on the sample testing environment matrix in Table 4. The agent keeps track of the previous state and maintain a list. On State S1, the highest reward value is against action 1. When the agent performs the down action from State S1, it reaches State S5, and the agent updates the list to S0, S1, and S5. On State S5, the highest reward value is again on action 1, so the agent performs the down action, reaching State S9, and the list is updated. On State S9, the reward value is maximal on action number 3, which is the right action. When an agent performs the right action from S9, it reaches State S10, the final state of the testing environment matrix. Thus the agent reached the goal state, and the final updated list of state sequences is S0, S1, S5, S9, and S10. The best state navigation sequence of the agent with maximum Q value to reach the goal is S0, S1, S5, S9, and S10. Prioritization algorithm maps that sequence on the testers' log, computes cosine similarity on the logs shown in Equation (3) and then rearranges the test cases according to the similarity. The test case with the highest similarity is prioritized as high and vice versa. If the test cases have the same similarity index, then the length of the test case is considered another parameter. The test case with a large length value is prioritized as high. The prioritized test cases are given in Table 6.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 3 Methodology
### Residual Energy  
The residual energy \(E_{r}\) of a sensor node is the energy that is spent during the transmission and reception of data through the network, and Equation (7) is used to determine the energy trust value of the node [3].  
\[Data\ Receiving\ Power\ y=K\times E_{elec} \tag{7}\]  
where \(K\) is the total number of bits, and \(E_{elec}\) is the electronic energy consumed to receive one bit of data. The total energy for sending the data of node \(y\) is calculated using Equation (8).  
\[\left.\begin{array}{c}Data\ Sending\ Power\ y=\\ \left\{\begin{array}{l}K\times E_{elec}\ +\ K\times e_{fs}\times d^{2}\ where\ d<d^{2}\\ K\times E_{elec}+K\times emp\times d^{4}\ where\ d<d_{0}\end{array}\right\} \end{array}\right\} \tag{8}\]  
Here, \(d_{0}\) represents the threshold distance and calculated using Equation (9), \(E_{elec}\) shows the energy utilization in the electronics for sending or receiving one bit. The terms \(e_{fs}\times d^{2}\) and \(emp\times d^{4}\) are amplifier energy consumptions for a short and long-distance transmissions.  
\(d_{0}\) is calculated using this formula  
\[d_{0}=\sqrt{\frac{e_{fs}}{e_{mp}}} \tag{9}\]  
If it is supposed that the node \(y\)'s initial energy is \(E_{0}\), then its remaining energy is computed via Equation (10).  
\[\text{Remaining energy of the node }y=E_{0}-Data\ Receiving\ Power\ y \tag{10}\] \[-Data\ Sending\ Power\ y\]  
Finally, Equation (11) is used to calculate the energy trust value of each sensor device.  
\[E_{y}=\frac{\text{Remaining energy of the node }y}{E_{0}} \tag{11}\]  
After calculating energy trust value \(E_{y}\), it can be deduced that this node can only take part in data transmission if its residual energy is more than or equal to the threshold energy of the node.
# Temporary Pacing Simulator: A Training Tool for Clinicians
## 2 Materials and Methods
### The Encapsulation of the Simulator on a User Interface
#### 2.5.8 Managing AV Delay in Dual-Chamber Pacing Modes  
When a user selects the DDI or DOO pacing modes, specific steps are executed to manage the AV delay (Figure 10), which is crucial for simulating temporary TP behavior and atrioventricular synchronization.  
Once DDI or DOO is chosen, the user is provided with the option to select or input an AV delay. This step ensures that the user can proceed with configuring the AV delay. The TCPS then searches for available AV delay signals for the selected patient. If such signals are found, they are listed as options in the AV delay dropdown. If no AV delay signals are  
Figure 10: AV Delay Flowchart—This diagram illustrates the AV delay functions and buttons that are enabled when the user chooses a DDI pacing mode.  
Figure 9: Sensitivity Threshold Flowchart: This diagram illustrates the sensitivity flows for both atrial and ventricular responses. Each flow triggers the activation of its respective sensitivity buttons—atrial or ventricular.  
available for the selected patient, the system attempts to find another patient with the same baseline rhythm to retrieve the necessary AV delay signals.  
When an AV delay is selected from the dropdown, the system updates the ECG, ABP, and CVP signals according to the chosen AV delay. Once the AV delay signals are set, the pacing mode dropdown is disabled, preventing the user from changing the pacing mode without resetting the system. The system periodically updates the display of ECG, ABP, and CVP signals, ensuring that the signals are coherently displayed on the plot. When an AV delay is set, the updated paced signals are used instead of the baseline signals. This maintains alignment and accuracy in the display. For DDI and DOO modes, the system ensures that both atrial and ventricular pacing are coordinated according to the selected AV delay. This synchronization is critical for maintaining effective cardiac output and mimicking real TP behavior.  
The proper AV delay is essential in dual-chamber pacing (DDI and DOO) to coordinate the timing between atrial and ventricular contractions, optimizing cardiac efficiency and output. The ability to choose and simulate different AV delays allows the system to realistically mimic clinical scenarios, helping users understand the impact of AV delay adjustments on hemodynamic parameters. The system's design to disable certain controls once AV delay is set prevents conflicting inputs, guiding the user through a logical sequence of steps and ensuring the pacing mode configuration is coherent and clinically relevant. This simulation teaches users about the importance of AV delay in pacing therapy and how different pacing modes interact with intrinsic cardiac activity, providing hands-on experience in a controlled environment.  
Overall, these steps contribute to a robust and educational simulation of temporary TP behavior, highlighting the critical role of AV delay in dual-chamber pacing modes.
## 3 Link the Marginal PDFs of Wind Speed and Load by D-Vine Copulas
### D-Vine Copulas
#### 3.3.3 Generate Correlated Samples by D-Vine Copula  
Consider \(x_{1},\,\ldots,\,x_{n}\) as the variables representing wind speed and load. In fact, when we use CE-IS to assess system reliability, only in the first iteration of the pre-simulation stage we may use D-vine copulas to draw samples. In the subsequent iterations, we draw samples from the suboptimal distribution derived by CE-IS. If there are enough historical observations, we can use the simple random sampling of the observations in the first iteration as well.  
However, to ensure the integrity of the methodology, the procedures of sampling from D-vine copulas is briefed below, based on Quasi-Monte Carlo method [40].  
Step 1: Generate \(n\) independent samples distributed uniformly on \([0,1]\), which are denoted by \(z_{1}\), \(\,\ldots,\,z_{n}\).  
Step 2: Set \(z_{1}=F_{1}(x_{1})\), \(z_{2}=F(x_{2}\,|\,x_{1})\), \(\,\ldots,\,z_{n}=F(x_{n}\,|\,x_{1},\,\ldots,\,x_{n-1})\). Convert \(z_{i}\) into \(x_{i}\) by the inverse function of \(F(x_{i}\,|\,x_{1},\,\ldots,\,x_{i-1})\), which has been saved while the bivariate conditional copulas are constructed by (11). \(F_{1}(x_{1})\) is the marginal CDF of \(x_{1}\) modeled by SNMM.  
When we evaluate the CC of wind energy, we use the data of wind power rather than wind speed. Hence, the wind speed samples are further transformed into the power of the WTG by:  
\[P(x_{i})=\left\{\begin{array}{ll}0&x_{i}\
otin(v_{ci},v_{co})\\ \frac{P_{R}(x_{i}^{3}-v_{co}^{3})}{(v_{co}x_{i}^{3}-v_{co}^{3})}&x_{i}\in(v_{ ci},v_{r})\\ P_{R}&x_{i}\in(v_{r},v_{co})\end{array}\right., \tag{14}\]  
where \(v_{ci}\), \(v_{r}\) and \(v_{co}\) are the cut-in, rated and cut-out wind speed of the WTG. \(x_{i}\) can stand for any element in \(\{x_{1},\,\ldots,\,x_{n}\}\) that represents wind speed. \(P_{R}\) is the rating of the WTG.  
Figure 1: Graphical structure of four-dimensional D-vine copulas.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Securing Authentication for Mobile Networks
The analysis demonstrates that 5G authentication outperforms 4G authentication in several ways [23], including the use of a unified authentication framework that can support a more significant number of user cases, enhanced user equipment identity protection, enhanced home-network control, and increased key separation during key derivation. Additionally, this section highlights the vulnerabilities of 5G authentication and the necessity for it to evolve regularly. Prior generations' security and privacy challenges, notably in radio access networks (RANs), have been thoroughly explored. The following are only a few of the numerous concerns uncovered. 1. Due to the absence of network authentication in 2G, attacks such as network spoofing by faked base stations are possible. For example, a faked base station can advertise a different tracking area code with a stronger signal strength to entice user equipment (UE) away from its legitimate cellular network and register with the faked base station [24]. 2. Inadequ secrecy in certain signaling messages, resulting in a violation of privacy. For example, unencrypted paging information can be employed to detect the presence of a specific user and even trace the person to a precise location [25]. To address these concerns, the 3 rd Generation Partnership Project (3 GPP) provides an Authentication and Key Agreement (AKA) protocol and associated procedures that support entity authentication, message integrity, and message secrecy, among other security aspects [26]. The 3 GPP AKA protocol is a challenge-and-response authentication scheme based on the sharing of a symmetric key between a subscriber and a home network. Following mutual authentication between a subscriber and a home network, cryptographic keying materials are generated to safeguard further communication between the subscriber and a serving network, which includes both signaling messages and user plane data (e.g., over radio channels) [26]. Additionally, because the 5G network is IP-based, it will be vulnerable to all IP-specific vulnerabilities. Based on these findings, ensuring a high level of security and privacy will be one of the most crucial parts of deploying 5G networks successfully. Table 3 is presented a comparative analysis of security and privacy of 3G, 4G and 5G cellular networks.