esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes
## 4 Analysis

Table 6: Moderate damage scenario simulation results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Travel Time (min)** & **Speed (km/h)** & **Traffic Volume** \\ \hline
**No.1 bridge 2 Lanes 10 km/h** & 17.81 & 2.0 & 28,052 \\ \hline
**No.2 bridge 2 Lanes 10 km/h** & 10.11 & 4.5 & 16,450 \\ \hline
**No.3 bridge 2 Lanes 10 km/h** & 7.71 & 3.5 & 16,420 \\ \hline
**No.4 bridge 2 Lanes 20 km/h** & 12.58 & 2.2 & 43,211 \\ \hline
**No.5 bridge 2 Lanes 10 km/h** & 1.14 & 11.8 & 3915 \\ \hline
**No.6 bridge 2 Lanes 10 km/h** & 0.45 & 9.4 & 1792 \\ \hline
**No.7 bridge 2 Lanes 10 km/h** & 0.51 & 9.5 & 1730 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Major damage scenario simulation results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Travel Time (min)** & **Speed (km/h)** & **Traffic Volume** \\ \hline
**No.1 bridge 8 Lanes 40 km/h** & 1.37 & 27.6 & 32,387 \\ \hline
**No.2 bridge 4 Lanes 40 km/h** & 1.3 & 35.2 & 10,598 \\ \hline
**No.3 bridge 4 Lanes 40 km/h** & 0.93 & 27.7 & 14,418 \\ \hline
**No.4 bridge 2 Lanes 60 km/h** & 2.24 & 12.6 & 31,009 \\ \hline
**No.5 bridge 2 Lanes 30 km/h** & 0.51 & 25.2 & 3539 \\ \hline
**No.6 bridge 4 Lanes 30 km/h** & 0.16 & 27.5 & 4428 \\ \hline
**No.7 bridge 4 Lanes 40 km/h** & 0.14 & 35.3 & 5672 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Minor damage scenario simulation results. Figure 4: Travel time comparison. Figure 5: Travel speed comparison. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **No.1** & **No.2** & **No.3** & **No.4** & **No.5** & **No.6** & **No.7** \\  & **Bridge** & **Bridge** & **Bridge** & **Bridge** & **Bridge** & **Bridge** \\ \hline
**Minor Damage** & \(-39\%\) & \(-38\%\) & \(-46\%\) & \(-75\%\) & \(-32\%\) & \(-41\%\) & \(-38\%\) \\ \hline
**Moderate Damage** & \(-81\%\) & \(-78\%\) & \(-84\%\) & \(-85\%\) & \(-53\%\) & \(-64\%\) & \(-71\%\) \\ \hline
**Major Damage** & \(-96\%\) & \(-92\%\) & \(-93\%\) & \(-95\%\) & \(-68\%\) & \(-80\%\) & \(-84\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Travel speed comparison. Table 10 shows traffic volume comparisons for the selected scenarios. It is apparent that some traffic volumes were diverted to other bridges. For example, higher number of traffic volume passed through Bridges 2 and 4, while there was significant diversion on Bridges 6 and 7. Vehicles that used Bridges 6 or 7 might have taken alternative routes to minimize their travel times. This diversion can be studied with further information of traffic volume on nearby highway links.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 6 Possible Applications and Improvements
### Applications of the Channel Models  
The channel models reported in the literature can be applied for various purposes and scenarios. For example, the channel model in [9] is applicable for UAVs that want to carry out ocean surveillance and report to the ship using the duct layer communication principles. The channel model in [10] is applicable for high-speed water vehicles. The channel model in [12] can support the 5G network architecture and is; thereby, applicable in such a scenario where the 5G network is deployed. The channel model presented in [8] can be used to support highly mobile water vehicles. Sea surfaces are assumed to be smooth in most of the channel models in the literature. However, the channel model presented in [5] went beyond the paradigm of the smooth sea surface and worked upon the rough sea surface. In [13], authors designed a channel model applicable for a scenario where bLoS propagation is needed. It has also been noticed that there are channel models that are only applicable for specific sea regions, as the environment and parameters vary a lot in different places. References [14, 15, 18] have been subsequently designed for wireless wave propagation in the South China Sea, the sea region encompassing Malaysian shores, and Diaoyu islands in China, respectively. The channel model proposed in [16] is especially applicable for transmitting the data gathered from the ad hoc sensor nodes for environmental monitoring purposes. A mathematical model for calculating the fading margin applicable only for the South China Sea was designed in [14]. The probable applications of channel models discussed in this paper are listed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channel Model** & **Application** \\ \hline Ref. [8] & Applicable to vessel-to-vessel communication system where both the transmitter and receiver are mobile. \\ Ref. [9] & UAV to surface vessel based communication. \\ Ref. [10] & Applicable to high-speed watercraft transmitter or receiver. \\ Ref. [11] & Channel model can be applied to a mobile vessel to sea shore based communication. \\ Ref. [12] & Applicable to the 5G network architecture. \\ Ref. [5] & Applicable for vessels in both smooth and rough sea surfaces. \\ Ref. [13] & Applicable to a scenario in which the transmitter and receiver have bLoS propagation. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applications of the channel models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
## 5 Discussion
### Benefits of AR in E-learning Contexts
[47] found that AR-centered approaches to learning tended to increase students' eagerness, interest, and engagement (also referred to as motivation) when compared to non-AR methods. For instance, when AR-style gameplay was introduced in a learning context, the learners' intrinsic motivation increased. Similarly, students who used AR books appeared to be much more eager to read and learn than students who used non-AR books. The students who used AR were also found, in the included studies, to be more proactive compared to non-AR students and were often willing to continue to learn through the technology even after the end of the learning session. The empirical and theoretical literature on the benefits of AR in e-learning contexts cite a variety of other positive implications, see Table 3. Refs. [49; 50] found that in similar subject areas, students who learned using AR tended to score higher on tests compared to those who learned using conventional methods because AR was found to improve students' knowledge retention. Such findings suggest that AR also promotes improvement in students' learning curve compared to other traditional non-AR contexts, with students studying similar subjects and taking similar tests. The study by [51] found that AR methods support creative learning, and thus, produce creative learners compared to non-AR methods. This benefit was related to the method's capacity to facilitate the absorption of new knowledge while solving problems in settings that were more realistic. Other benefits, such as increased interactivity, information accessibility, and improved collaborative and student-centered learning were also reported in studies by [52; 53].
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 5 Performance Evaluation
### Network Performance Based on a Number of Vehicles
The overall performance achieved by the proposed TACRP routing protocol is higher than the earlier works like ECHS and NRHCS protocols in terms of efficiency, throughput and delivery ratio with very low network delay.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Authentication
#### 3.6.3 Account Lockout  
Account Lockout protects user accounts from automated password-guessing attacks by implementing a limit on incorrect password attempts before further attempts are disallowed.  
A high-level diagram is provided in Figure 17, while the steps of an authentication attempt are presented below:  
1. The client is provided with a transaction form or login screen requiring both a user-name and a password.
2. The user provides the username and password and submits the request for a protected resource.
3. The mediator checks the username and, if valid, retrieves the account information. If the username is invalid, it returns a generic failed login message.
4. The mediator checks if this user's account is locked out (number of successive failed logins exceeds the threshold) and not yet cleared (last failed login time + reset duration > current time). If locked, it returns a generic failed login message.
5. If the user's account is not locked, the mediator checks the validity of the password. If the password is not valid, it increments the number of failed login attempts against the account and sets the last failed login time to the current time. It then returns a generic failed login message.
6. If the password is valid, it resets the number of failed logins to 0 and executes the request against the protected resource.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 5 Performance Evaluation
### Packet Delivery Ratio  
\(OFQS\) achieves 91.8% of Packet Delivery Ratio (PDR) compared to 85.7% for \(MRHOF/OF0\). This shows that \(OFQS\) overpasses \(MRHOF/OF0\) in terms of reliability. Firstly, \(HC\) has no link  
Figure 8: Network lifetime variation.  
Figure 9: Remaining energy distribution among the nodes after 30 min.  
reliability mechanisms in the route selection which causes packet loss by selecting congested paths. Moreover, although \(ETX\) considers the link reliability, \(mOFQS\) still overpasses it by considering the delay of sending a packet in one hop which reflects the interference and the queuing delay on that hop by multiplying \(ETX\times d\), allowing then more reliable routes to be chosen.
## 2 Literature Review
### The Need for Temporal Models and the Markov Chain Foundation  
Human proficiency is best understood as a dynamic process rather than a static trait, necessitating a model that can interpret and process temporal data. Operators experience fluctuations in skill levels due to factors such as learning, fatigue, and task complexity. Static models that capture a single point in time do not align with this understanding, emphasizing the need for temporal models that accommodate changing states over time.  
Markov chains provide a foundational framework for these temporal models. A Markov Chain is a statistical model in which the probability of transitioning to the next state relies solely on the current state, allowing the assessment of transitions between proficiency states, such as from Novice to Intermediate. This transition probability can yield insights into learning rates and the stability of proficiency levels [18; 59]. However, traditional Markov Chain models assume that these states are directly observable, while true proficiency is inherently latent and inferred from observable behavior rather than measured directly [57].  
The limitation of observing only behavior emphasizes the necessity of advanced models that can infer latent constructs. As pointed out in the literature, combining objective metrics with hidden states can enrich the understanding of underlying proficiency mechanisms [19]. Therefore, leveraging temporal models, such as the Markov Chain, while addressing their inherent limitations regarding latently measured states can enhance our ability to understand human proficiency dynamics in real-time settings.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 5 Future Research Directions
### Optimizing Smart Contract Efficiency  
Enhancing blockchain system efficiency requires optimizing smart contracts. Firstly, the design of smart contracts should focus on efficiency and simplicity to avoid complex logic and unnecessary data storage. For example, it is possible to design contracts that can be deleted or updated. Such contracts allow updates or removals without affecting the immutability of the blockchain, thus reducing the need for long-term data storage. Secondly, the execution efficiency of smart contracts can be improved through various optimization techniques. For example, adopting deferred or on-demand computation strategies, which execute computation-intensive operations only when necessary, not only reduces the resources needed for contract execution but also lowers the pressure on the blockchain network.  
Furthermore, specific parts of the contract logic can be executed utilizing state channels or sidechain technologies. These innovations enable the performance of certain contract segments off the main blockchain network, with outcomes relayed back to the main network at pivotal moments. This strategy significantly reduces the burden on the main chain, thereby improving the network's overall efficiency. Additionally, adopting a modular approach to smart contract design facilitates their reuse across different contexts, enhancing development productivity while minimizing the duplication of code. Another critical aspect of smart contract optimization involves bolstering security measures. Employing advanced encryption methods and conducting rigorous security audits are critical measures for mitigating contract vulnerabilities and protecting against security risks, ensuring the blockchain system's integrity and the security of user assets. In essence, optimizing smart contracts in terms of design and execution can reduce unnecessary data storage on the blockchain, thereby improving the efficiency and security of contract operations and significantly improving the blockchain system's performance and user experience.
# The Effects of Activated Carbon Toothpastes on Orthodontic Elastomeric Chains--An In Vitro Study
## Results  
### Baseline Discrepancy between the Three Brands of ECs  
Table 1 displays the mean values and SD of the baseline tensile strength of the three EC brands recorded on day zero (when the ECs were stretched by 50% of their original length). The ANOVA revealed significant differences in the mean values of tensile strength (\(p=0.003\)) and resistance to rupture (\(p<0.001\)) between the three brands of ECs before using any toothpaste (\(p\leq 0.003\)). [PERSON]'s multiple range post hoc test showed that the Ortho Classic(r) EC (3.717 \(\pm\) 0.11 N) had a tensile strength significantly greater than the 3M Unitek(r) (3.287 \(\pm\) 0.14 N) and Ormco(r) (3.327 \(\pm\) 0.09 N) EC brands. Furthermore, [PERSON]'s HSD post hoc test revealed a significant difference in the resistance to rupture between the ECs of the three brands in the following order: Ormco(r) (42.628 \(\pm\) 0.213 mm), 3M Unitek(r) (36.352 \(\pm\) 0.604 mm), and Ortho Classic(r) (31.323 \(\pm\) 0.210 mm).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**EC Brand** & **t0** & **Toothpaste** & **t28** & **ATS (t28 - t0)** & **Loss \%** \\ \hline \multirow{6}{*}{3M Unitek®} & \multirow{3}{*}{\(3.287\pm 0.143\)} & \multirow{3}{*}{\(3.287\pm 0.143\)} & \multirow{3}{*}{\(1.448\pm 0.014\)} & \multirow{3}{*}{\(-1.84\)\({}^{\mathrm{h}c}\) (\(\pm\)0.55)} & \multirow{3}{*}{\(-55.93\)} \\ \cline{1-1}  & & & Colgate Total® & & & \\ \cline{1-1}  & & & Colgate Max White® & \(1.484\pm 0.026\) & \(-1.80\)\({}^{\mathrm{h}c}\) (\(\pm\)0.56) & \(-54.86\) \\ \cline{1-1}  & & & Dr Organic® & \(1.406\pm 0.014\) & \(-1.88\)\({}^{\mathrm{h}c}\) (\(\pm\)0.58) & \(-57.23\) \\ \hline \end{tabular}
\end{table}
Table 1: Mean values and standard deviation for three tested elastic chain (EC) brands of tensile strength (ATS) (N) (stretched for 50%) on day zero and day 28 following exposure to different toothpastes. The percentage of the declining tensile strength at each time is also presented (15 EC specimen, per group).
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 1 Introduction
The downlink transmission power level of the femto base station (FBS) affects the users in its vicinity, which have no access to the femtocell. The high downlink transmission power of the femtocell provides more coverage and good signal quality; at the same time, it creates terrific interference to other cell-edge users of the other femtocell or macrocell networks. In this paper, we proposed an adaptive network sensing (ANS) technique for downlink power control, to avoid ICI. The simulation results show that the ANS power control technique successfully decreases the cell-edge macro user's interference and enhances the throughput performance, whilst also optimizing the femtocell coverage and capacity. When compared to the femto user equipment (FUE)-assisted and MUE-assisted power control technique, the proposed technique provides a good tradeoff in reducing interference in MUE and maintaining the QoS requirement of the femtocell users. The paper is divided further into the following sections. Related work has been discussed in Section 2. In Sections 3 and 4, we describe the system model and power control techniques (previous and proposed techniques), respectively. In Section 5, we analyze the simulation results. Finally, in Section 6, we draw our conclusion.
## 3 Methodology
### Feature Selection Optimization using Bio-Inspired Algorithms  
For the purpose of this paper, we decided to use EvoloPy-FS framework [20; 21; 22; 23; 24; 25] and measure the performance of several popular Swarm Intelligence algorithms on the feature selection optimization problem. EvoloPy-FS, an easy to use Python framework, developed by its authors to help researchers in solving optimization problems using Swarm intelligence algorithms. The main component of the framework is the Optimizer, where we set up our experiment along with the initial configurations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Possible Values** \\ \hline Day & 0, 1, 2, 3, 4, 5, 6 \\ Time & 1, 2, 3, 4,..., 24 \\ User & String Type \\ PC & String Type \\ Activity (1) & 1, 2, 3, 4, 5, 6, 7 \\ \hline \end{tabular}
\end{table}
Table 2: Encoded features at pre-processing phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Day** & **Time** & **User** & **PC** & **Activity (1)** \\ \hline
5 & 2 & 4512 & 4512 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Encoded features at pre-processing phase.  
In the optimizer, we define the dataset to use, the optimizers, the number of runs and number of iterations. For each implementation of the included optimizers, there is a separate Python script, since the framework is Open Source and all included components are transparent.  
The Optimizers are used to generate a near-optimal subset of features from the original dataset, free from unnecessary features, to improve the model's anomaly prediction performance.  
In our approach, for feature selection optimization we selected Binary Particle Swarm Optimization (BPSO), Binary Gray Wolf Optimizer (BGWO), Binary Bat Algorithm (BBAT), Binary Multi-Verse Optimizer (BMVO), Binary Mod-Flame Optimizer (BMFO), Binary Whale Optimization Algorithm (BWOA) and Binary Firefly Algorithm (BFFA) as optimizers, as they are available in EvoloPy-FS framework. We employed these bio-inspired models in the feature selection optimization process, to generate the optimal subset and fit the Machine Learning algorithm on it, to get better results compared with the original dataset.
# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 2 Methods
### Establishment of Vehicle Detection Model of Two-Way Two-Lane Roads  
#### 2.1.1 Collection and Labeling of Data Set  
In order to train the vehicle detection model suitable for two-way two-lane roads, this paper used the traffic flow atlas of the real two-way two-lane roads as the data set. The total number of samples in the data set is 5516, including 3925 traffic flow pictures of urban and rural two-way two-lane roads taken from the BDD100K public data set and 1591 self-made traffic flow pictures of on-campus two-way two-lane roads. Each sample contains at least one forward vehicle and one opposite vehicle. Partial samples of the data set are shown in Figure 2a. The open-source image labeling tool LabelImg was used to manually label the samples; each vehicle was labeled with a rectangular box, and a tag file in TXT data format was generated. The tag file included the file name of the sample, the category, the coordinates of the rectangular box, the length and width of the rectangular box, and other information. In this paper, the object categories were forward vehicles and opposite vehicles. According to the driving characteristics of the vehicles on two-way two-lane roads, the vehicle showing its back was labeled as 'back', and the vehicle showing its front was labeled as 'front'. A total of 13,774 vehicles were labeled, 7832 of which were forward vehicles and 5942 of which were opposite vehicles. Examples of labeling are shown in Figure 2b.  
Figure 1: Overall framework of the method.
## 3 Proposed Optimization Algorithm
### BS Transmit Power Allocation
#### 3.1.1 Fractional Programming Transform  
Problem (P1) is a sum-of-ratios problem that can be equivalently re-written as follows:  
\[\underset{\mathbf{P},\mathbf{\rho}\succeq 0}{\text{max}} \sum\limits_{n\in\mathcal{N}}\rho_{n}\] (23) s.t. \[\ \ \ \frac{U_{n}}{D_{n}}\geq\rho_{n},\ \forall n\in \mathcal{N}, \tag{24}\] \[\ \ \ 0\leq P_{m,n}\leq P_{m}^{c},\ \forall m\in\mathcal{M},\ \forall n\in \mathcal{N}. \tag{25}\]  
**Theorem 1**.: _If \((\mathbf{P}^{*},\mathbf{\rho}^{*})\) is the solution of the problem (23), then there exists \(\mathbf{\mu}=\{\mu_{n}\}\) such that \(\mathbf{P}^{*}\) is a solution of the following parameterized problem with parameters \((\mathbf{\mu},\mathbf{\rho})=(\mathbf{\mu}^{*},\mathbf{\rho}^{*})\):_  
\[(P1^{\prime}):\ \ \ \underset{\mathbf{P}}{\text{max}} \sum\limits_{n\in\mathcal{N}}\mu_{n}(U_{n}-\rho_{n}D_{n})\] (26) s.t. \[\ \ 0\leq P_{m,n}\leq P_{m}^{c},\ \ \ \ \forall m\in\mathcal{M},\ \forall n\in \mathcal{N}. \tag{27}\]  
_And \(\mathbf{P}^{*}\) also satisfies the following conditions when \((\mathbf{\mu},\mathbf{\rho})=(\mathbf{\mu}^{*},\mathbf{\rho}^{*})\):_  
\[\mu_{n}=\frac{1}{D_{n}}, \tag{28}\] \[U_{n}-\rho_{n}D_{n}=0. \tag{29}\]Proof of Theorem 1.: See ([40], Lemma 2.1).  
From Theorem 1, we learn that, when \((\mathbf{\mu},\mathbf{\rho})=(\mathbf{\mu}^{*},\mathbf{\rho}^{*})\), the parametric problem \((\mathrm{P1}^{\prime})\) and the sum-of-ratios maximization problem (P1) have the same optimal solution. Here, \((\mathbf{\mu}^{*},\mathbf{\rho}^{*})\) represents the optimal parameter values that satisfy the conditions (28) and (29) together with solution \(\mathbf{P}^{*}\) to problem \((\mathrm{P1}^{\prime})\).