esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion  
In this paper, the performances of the proposed scheme, compared with the conventional scheme SB_DSICED3_W/OF, are illustrated in terms of the MSE, BER, and processing time by using Monte Carlo simulations in MATLAB. For the 5G QC-LDPC codes, the base graph matrix **BG2** was chosen for all simulations. The index of the iterations for the joint iterative decoding and the 5G QC-LDPC decoding is denoted as \(i\) and \(j\), respectively. Many simulations with various channel models and the number of channel taps (ranging from 3-8 taps) were implemented. However, the varying of the channel models did not significantly change the performance results. Due to the limitation and not to overload the paper, all of the configurations and figures are not provided for reasons of readability of the paper and better understanding. Therefore, the SI channel was fixed with three taps, while the intended propagation channel was fixed with four taps according to the ITU-R Pedestrian test environment channel model [54]. These channels were generated independently in each transmission frame. The simulation parameters are summarised in Table 2. It was also noticed that the semi-blind without feedback scheme SB_DSICED3_W/OF needed up to 20 iterations (\(j_{max}=20\)) to converge and to reach the saturation floor, while the feedback scheme JISB_DSICED3 required only four iterations (\(i_{max}=4\)) to reach that floor.
## 3 Proposed Algorithm
### Evolution of the Mainstream Market Population
The probability values \(p_{a}\) and \(p_{r}\) for each of the combinations are calculated as follows. First, \(c_{1}\) is used to determine \(p_{a}\) (\(p_{a}=c_{1}\)), and \(c_{2}\) is used to determine \(p_{r}\) (\(p_{r}=(1-c_{1})\times c_{2}\)). For instance, if \(c_{1}=0.2\) and \(c_{2}=0.25\) (the second combination in Table 1), then \(p_{a}=c_{1}=0.2\) and \(p_{r}=(1-c_{1})\times c_{2}=0.2\). Figure 3: The modified partially mapped crossover operator (MPMX). The values of \(p_{a}\) and \(p_{r}\) for the combinations in Table 1 demonstrate the purpose of selecting the \(11\) combinations. A combination with a larger \(p_{a}\) and a smaller \(p_{r}\) implies that the probability that a member will be added is larger than the probability that a member will be removed. On the contrary, for a combination with a smaller \(p_{a}\) and a larger \(p_{r}\), the probability that a member will be removed is larger than the probability that a member will be added. The first combination (\(c_{1}\) = 0.0 and \(c_{2}\) = 0.0) is used for the case where no member is added or removed. The last two combinations (\(c_{1}\) = 1.0 and \(c_{2}\) = 0.0, and \(c_{1}\) = 0.0 and \(c_{2}\) = 1.0) are used for the cases where a member is always subjected to the MAF (\(p_{a}\) = 1.0) or the MRF (\(p_{a}\) = 0.0 and \(p_{r}\) = 1.0), respectively. The MAF is implemented with a probability of \(p_{a}\); this function randomly generates a solution and applies the DC procedure to shake it and then adds it to the population. The MRF is implemented with a probability of \(p_{r}\); this function eliminates the member with the worst objective value. This search process of the phase continues until a phase termination criterion is satisfied. To increase the diversity of the population, the two-stage MAF and MRF mechanism is proposed. This two-stage mechanism means that in the first half of all the execution time, we use a set of control probability and then use another set of control probability in the last half of the execution time to do MAF and MRF.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
###### Abstract  
This study integrates systematic laboratory testing with advanced machine learning techniques to predict the unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iasi, Romania. Laboratory experiments generated 185 UCS measurements, examining the effects of cement content, curing period, and compaction velocity on strength development. Fourteen regression algorithms were initially screened, with the top three performers subsequently evaluated using nested cross-validation and Bayesian hyperparameter optimization via the Optuna framework. Correlation analysis identified cement content as the primary factor, with curing period as moderately influential and compaction rate having minimal impact when target density was achieved. Random Forest emerged as the optimal algorithm, providing robust and accurate UCS predictions. Beyond standard predictions, a two-stage uncertainty quantification system was implemented, allowing for both central estimates and reliable confidence intervals. SHAP analysis confirmed the dominant roles of cement content and curing period and enabled mechanistic interpretation of parameter contributions. The complete predictive system is available as a public web application, enabling geotechnical engineers to obtain rapid UCS predictions with quantified uncertainty, supporting efficient ground improvement design and risk assessment.  
soil-cement; unconfined compression strength; soil stabilization; cement treatment; machine learning; ground improvement 2025  
7 May 2025
# Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics
## 3 Objectives  
Aiming at expanding upon the shortcomings of the existing research, this paper combines the occupational characteristics of bus drivers to construct a prediction model for the probability of accidents. The results were analyzed and interpreted intuitively. The main objectives are the following:  
1. Combining bus drivers' occupational characteristics to enrich their feature set of accident probability prediction--Based on the existing predictors of bus driver accident probability, this paper adds occupational driving characteristics to fill the gaps in existing research. The occupational driving characteristics considered included the cumulative effects of working hours and driving hours, peak driving hours, and the operating characteristics at each bus stop.
2. Making bus driver accident probability predictions to help companies implement accident prevention--This paper attempts to combine the MICEForest multiple interpolation method with the CatBoost method for predicting the probability of bus driver accidents. The MICEForest multiple interpolation method was used to fill in the missing data in the dataset. Several boosted tree models were compared to select a more suitable method for this data. Meanwhile, Bayesian hyperparameter optimization was used to improve the accuracy and applicability of the model. In addition, a dataset using mean interpolation is presented, and the model's performance was compared with a dataset interpolated using the MICEForest method. In this way, the importance of data integrity and appropriate interpolation methods are demonstrated.  
Visualizing important outcomes that affect the probability of accidents to guide management in practice--Interpretable SHAP methods were used to interpret and analyze black-box predictive models. This can help managers understand the propensity of bus driver accidents. At the same time, the results of this study of important factors and influencing mechanisms can help companies prevent accidents accurately.
# Cloud Storage Data Verification Using Signcryption Scheme
## 2 Preliminaries
### Zss Signature  
ZSS is a short signature based on bilinear pairing first proposed by [PERSON], [PERSON], and [PERSON] [18]. The main idea is to construct a signature that is difficult to CDH problem in a group \(G\). This signature required less pairing operation than other short signatures, such as the Boneh-Lynn-Shacham (BLS) signature, making it more efficient [18]. There are four steps in the ZSS signature:  
1. **ParamGen**. The system parameters are \(G_{1},G_{2},e,q,P,H\).
2. **KeyGen**. Randomly selects \(x\in_{R}Z_{q}^{*}\), and computes \(P_{pub}=xP\). The public key is \(P_{pub}\).
3. **Sign**. Given a secret key \(x\), and a message \(m\), computes signature \(S=(H(m)+x)^{-1}P\).
4. **Ver**. Given a public key \(P_{pub}\), a message \(m\), and a signature \(S\), verify if \(e(H(m)P+P_{pub},S)=e(P,P)\). The verification works because of the following Equation (1). \[\begin{split} e(H(m)P+P_{pub},S)&=e((H(m)+x)P,(H(m )+x)^{-1}P)\\ &=e(P,P)^{(H(m)+x)\cdot(H(m)+x)^{-1}}\\ &=e(P,P)\end{split}\] (1)
## 1 Introduction
Considering the weaknesses of the upright frames under compressive loads, in recent years several methods have been proposed in order to improve the compressive behavior of racking uprights. Partial reinforcement of open sections using spacers was proposed by [PERSON] and [PERSON] [20]. They concluded that by installing spacers at appropriate intervals, the capacity of the uprights is improved and the mode of failure and buckling can be changed at the same time. [PERSON] and [PERSON] [21] also studied the effect of partially closing CFS thin-walled sections. They focused on increasing the torsional stiffness of these sections when used as columns in structures. An investigation for analyzing the behavior of thin-walled channels with partially closed sections under axial forces using different stiffener plates was conducted by [PERSON] and [PERSON] [22]. The result indicated that by partially reinforcing the sections using cover plates, the buckling mode changes from distortional to a combination of local and flexural torsional buckling. Recently, a few studies on improving the upright axial capacity have also been carried out by other researchers [23; 24; 25; 26; 27] which are mainly based on numerical parametric studies. Review of the past studies shows that a great number of research projects have been conducted on the investigation of buckling modes for CFS uprights and little attention has been given to address the weaknesses of the uprights under compressive load. Although some attempts have been made in order to increase the ultimate capacity of open CFS sections using partially closed methods, extensive experimental studies for increasing upright capacity is still required. Therefore, this study aims to propose a new approach in order to improve the strength of the uprights in racking systems and to control buckling issues using reinforcements along the upright length. The main idea is to gain solutions that can be implemented by the industry immediately without any major modifications to the way the industry operates or to the assembly procedure. Hereinto, a total of 81 full-scale experimental tests were conducted on uprights with different heights and thicknesses in order to examine the compressive behavior and strength of upright frames with and without reinforcement. The results include the deformation modes, the failure mechanisms, and ultimate capacities, followed by a discussion on the experimental data. A finite element (FE) model was also employed to investigate the effect of different reinforcement spacing on the upright strength.
## 1 Introduction  
Direct pulp-capping is a common procedure for the treatment of unintentional or unavoidable pulpal exposures in clinical practice. Pulp-capping ensures tooth vitality by preserving the integrity of the hard tissues and protective responses to injury [1]. Pulp-capping involves the placement of a material on the pulpal exposure site followed by an intermediate or permanent restoration. The material should stimulate the pulpal cells to produce a reparative dentinal bridge, thereby sealing the pulp. Ideally, these materials are biocompatible and bioactive (stimulating pulpal cells), aid the formation of apatite crystals and provide an adequate seal [2; 3; 4; 5]. Table 1 summarizes the ideal properties of a pulp-capping agent.  
Dycal (calcium hydroxide) is the most used material due to its low price and wide availability. The major limitations of Dycal are that it has a lower percentage of complete dental bridge formation and an increased risk of tunnel defects, which in turn increases permeability to microbes. In addition, the clinical handling of the material is less desirable [6; 7; 8; 9].  
Mineral trioxide aggregate (MTA) is a silicate material that is said to aid in faster bridge formation with fewer tunnel defects, less pulpal inflammation and the maintenance of pulpal integrity. It is widely used in pediatric dentistry due to its excellent sealing properties. Its disadvantages include slow setting time, difficulty in handling and tooth staining [10; 11; 12; 13; 14; 15; 16].  
Synthetic bioceramics were developed in recent years to offset the drawbacks of naturally sourced materials such as MTA [17; 18; 19; 20; 21; 22; 23]. Biodentine is composed of tricalcium silicate, calcium carbonate and zirconium oxide. It is claimed to have better handling properties, faster dentinal bridge formation and higher strength compared to MTA, but outcome-based clinical trials have not yet been conducted [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. EndoSequence root repair material (ERRM) is a bioceramic material recommended for perforation repair, pulp-capping and periapical surgery. This agent is aluminum-free, has a high pH, is radiopaque and has shown similar results to MTA in dentinal bridge formation and pulp viability. Long-term clinical trials involving ERRM are not yet available [35; 36; 37; 38; 39; 40].  
There is a public health incentive in determining the most effective product in the management of pulpal exposure. Thus, the present study was therefore undertaken to compare the clinical efficacy of pulp-capping using calcium hydroxide, MTA, Biodentine and EndoSequence root repair material (ERRM), employing cone-beam CT and histological methods. A literature search revealed that such a split-mouth study employing these four pulp-capping agents in human participants has not been published.
# Considering the Consequences of Cybersickness in
## 3 Results
### Study Details
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attribute** & \multicolumn{4}{c}{**Total SSQ**} \\ \cline{2-5}  & **n** & **\%** & **Mean** & **95\% CI** \\ \hline Age & & & & \\
0–30 & 8 & 30.7 & 20.508 & [13.086, 27.931] \\
30–60 & 9 & 34.6 & 20.853 & [14.138, 27.569] \\ Above 60 & 8 & 30.7 & 16.036 & [12.699, 19.372] \\ \hline Symptom & & & & \\ Brain injuries & 7 & 26.9 & 21.058 & [15.357, 26.760] \\ Cognitive & 7 & 26.9 & 19.242 & [11.294, 27.189] \\ impairment & 12 & 46.2 & 18.156 & [12.778, 23.534] \\ \hline Inducement & & & & \\ YES & 19 & 73.1 & 19.430 & [15.678, 23.181] \\ NO & 7 & 26.9 & 8.519 & [5.599, 11.438] \\ \hline Time & & & & \\
0–10 min & 3 & 11.5 & 14.474 & [9.165, 19.784] \\
10–20 min & 16 & 61.5 & 15.695 & [11.559, 19.830] \\ Above 20 min & 5 & 19.2 & 26.862 & [13.883, 39.841] \\ \hline Content & & & & \\ Exergame & 11 & 42.3 & 21.090 & [15.695, 26.484] \\ Training & 8 & 30.7 & 16.258 & [10.063, 22.2453] \\ Video & 4 & 15.4 & 35.964 & [15.833, 56.905] \\ Scene & 3 & 11.6 & 14.446 & [6.548, 22.345] \\ \hline Locomotion & & & & \\ Sitting & 6 & 23.1 & 23.044 & [10.847, 35.240] \\ Standing & 7 & 26.9 & 24.122 & [14.779, 33.464] \\ Walking & 11 & 42.3 & 13.686 & [10.173, 17.199] \\ Bicycling & 2 & 7.7 & 22.330 & [14.310, 30.349] \\ \hline Control & & & & \\ Controller-based & 9 & 34.6 & 17.537 & [12.274, 22.800] \\ Gesture-based & 6 & 23.1 & 21.451 & [11.341, 31.562] \\ Gaze-directed & 9 & 34.6 & 23.953 & [12.580, 35.326] \\ \hline Display Type & & & & \\
3 DoF & 16 & 61.5 & 21.307 & [14.402, 28.213] \\
6 DoF & 10 & 38.5 & 19.114 & [14.7005, 23.523] \\ \hline All studies & 26 & 100 & 19.430 & [15.678, 23.181] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SSQ total scores.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 3 A specification-Based IDS for RPL to Detect the Topology Attacks
### Design and Implementation of the Specification-Based IDS for RPL-Based Network
#### 3.2.2 IDS Data Collection
We will use the reserved bytes in the DIO and DIS message format (readers can refer to [1] for the format of DIO and DIS message) for this purpose, so the actual size of such messages will remain the same. The synchronization issue is solved because the sequence number indicates specifically which packets the information belongs to; hence the agent can cross-check only the sources that have the same sequence to eliminate any mistake caused from the unsynchronized communication. Finally, the IDS agent will record the following information for each of its members:  
* DIS sequence, number of DIS received
* List of neighbors, each neighbor has \(\circ\) Node ID \(\circ\) Rank \(\circ\) The sequence of the DIO that provides this info \(\circ\) DIS sequence, number of DIS received \(\circ\) DAO sequence, number of DAO received, and a parent bit (if there is no DAO message sent, or if there is a DAO message require to remove the parent relationship, then the parent bit is 0, otherwise it will be set to 1)
* Preferred parent ID  
Figure 4: Synchronization issue in [PERSON]’s solution. Figure 5: Synchronization issue in [PERSON]’s solution. Such information will be collected and sent periodically to the IDS agents. Upon receiving the IDS data, the IDS agents will apply relevant algorithms to detect any malicious behaviors of the cluster members.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 6 Open Issues and Research Challenges/ Future Challenges and Research Opportunities
### Battery Overcharge and Overheating  
_What is the challenge?_  
Lithium-ion batteries are considered to be the most common battery used in EVs. Battery overcharge can occur due to the incompatibility of the communication protocols required for EV charging, as discussed in the previous research challenge. Battery overcharge needs to be diagnosed to further prevent the battery overheating issues. Battery overcharging leads to the overheating of the electric motor of the EV, which can also be the reason for the failure of the insulation of the charging equipment. Therefore, protection measures should be adapted to maintain and control the burden of EV battery charging.  
_Possible solution: Overcharge protection method_  
The overcharge protection method is a credible solution that can be used to reduce the risk of overcharging and overheating while an EV is charged at a CS. Several researchers studied the overcharging mechanism in the lithium-ion battery and tried to mitigate thermal overheating in the charging equipment and components [138]. However, these research works do not support the upgraded lithium-ion battery. Therefore, EVs can be protected from overcharging with the help of a protection method with upgraded features for lithium-ion batteries.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 5 Experimental Results  
In this section, we evaluate the performance of our proposed algorithm for 5D conflict detection. In the experiment, the tested filter databases were synthesized by ClassBench [33], which is widely used in packet classification and conflict detection research for test simulation. ClassBench can generate filter databases with different properties by using 12 seed files, including three practical application types: access control lists (ACL), firewalls (FW), and IP chains (IPC). For each seed file, we generate three filter sets containing 5 K, 10 K, and 20 K filters, respectively. The performance metrics is the average conflict detection time in microseconds (\(\upmu\)s) required for each filter. In our simulation experiment, we compared our proposed algorithm with those existing 5D conflict detection of two other types. The first part of the experiment tested the difference in performance between the non-prefix field comparison using our algorithm and the non-prefix field comparison using exact comparison, under the condition that the existing 2D conflict detection algorithm was extended to a 5D conflict detection algorithm without any pre-computed information. Because the comparison focused on the efficiency of detecting non-prefix fields, no noticeable difference was observed between our algorithm and any existing 2D conflict detection algorithm for comparison of prefix fields. We then compared our algorithm with the _FastDetect_ algorithm [6] because the authors have shown how their algorithm performs conflict detection for non-prefix fields without additional data structure, and it is helpful to compare the performance between the existing 2D algorithms and our algorithm for non-prefix fields. The second part of the experiment compared our algorithm with the existing detection algorithm which can be extended from 2D detection to 5D detection. To support conflict detection for non-prefix fields, this latter type of algorithm requires a new auxiliary data structure. Therefore, we tested the differences in time and space performance between non-prefix field conflict detection using the original comparison mode and that using our algorithm. Here, the SBV algorithm was used for comparison [7]. All algorithms were implemented in C++ and benchmarked on an Intel Core i5-4440 3.1-GHz processor with 12 GB memory.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 5 Discussion
### Factors Must Be Considered during the Implementation Process  
* Minimizing latency is essential for achieving a smooth user experience by ensuring efficient data processing and voice synthesis.
* Voice Quality: Clear and natural-sounding speech requires the use of high-quality TTS systems.
* NPC Behavior: NPCs should be programmed to display authentic behaviors and reactions, hence boosting the overall immersion of the VR environment.  
The incorporation of the Shopping Assistance Automatic Suggestion (SAAS) paradigm with Virtual Reality Non-Player Characters (NPCs) for voice-based recommendations signifies a notable progression in the development of tailored and engaging shopping experiences. Through the utilization of AI-powered suggestions and immersive virtual reality technologies, individuals can experience a more captivating and efficient shopping experience.