esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Waste Foundry Sands Characterization  
The diffractograms for 31A and 17A sands are reported in Figure 1. The 31A sand presents peaks corresponding to quartz SiO\({}_{2}\) (Q), microcline K-feldspar KalSi\({}_{3}\)O\({}_{8}\) (K), and zircon ZrSiO\({}_{4}\) (Z). The diffractogram of 17A presents peaks corresponding to quartz SiO\({}_{2}\) (Q), calcite CaCO\({}_{3}\) (C), hematite Fe\({}_{2}\)O\({}_{3}\) (H), and muliteAl\({}_{6}\)Si2O\({}_{13}\) (M). The main phase consists of quartz, SiO\({}_{2}\) (Q), for both sands.
## 4 Results
### Computational Complexity Analysis  
The computational complexity of the proposed algorithm can be divided into the following two main components: (a) the metaheuristic process (Whale Optimization Algorithm, binarization, and repair) and (b) the local search operator, which is executed conditionally when a new global optimum is found. The metaheuristic process, applied at every iteration, involves several key steps. First, the metaheuristic updates the positions and velocities of the population, which incurs a complexity of \(O(N\times M)\), where \(N\) represents the population size and \(M\) is the number of items in the problem. Subsequently, \(k\)-means clustering is applied to the velocity vectors, introducing a cost of \(O(\ell\times N\times k\times M)\), where \(\ell\) is the number of iterations required for \(k\)-means convergence and \(k\) is the number of clusters. Additional operations, such as binarization and solution repair, also exhibit a complexity of \(O(N\times M)\). Combining these steps, the overall complexity for the metaheuristic process over \(T\) iterations can be expressed as follows:  
\[O\Big{(}T\times\big{(}N\times M+\ell\times N\times k\times M\big{)}\Big{)}.\]  
This formulation keeps the metaheuristic component generic, allowing for flexibility in substituting the Whale Optimization Algorithm with other metaheuristic approaches without altering the overall structure of the analysis. The local search operator, outlined in Algorithm 3, is executed only when a new global optimum is found. During its execution, it iteratively applies one of the predefined movements--addition, removal, swap, or a combination of these (mix)--up to a specified iteration limit, _iterationLimit_. Each movement involves examining items in the solution or candidate set. The complexity depends on the type of movement selected, as follows  
* For addItem or dropItem, the worst-case complexity is \(O(M)\) per iteration, as these movements involve checking the inclusion or exclusion of items and evaluating the penalized objective function. * For swapItem, the worst-case complexity increases to \(O(M^{2})\), as this operator evaluates potential exchanges between the items inside and outside the solution. * For mix, which combines all movements probabilistically, the complexity is dominated by \(O(M^{2})\) due to the inclusion of swapItem. Given that the local search runs for at most _iterationLimit_ iterations per execution, the complexity of a single invocation is as follows:  
\[O(iterationLimit\times M)\quad\text{(for addItem or dropItem)}.\]  
\[O(iterationLimit\times M^{2})\quad\text{(for swapItem or mix)}.\]  
The local search operator is not executed during every iteration but only when the algorithm identifies a new global optimum. In practice, the number of such activations (\(\alpha\)) is significantly smaller than the total number of iterations (\(T\)), as the frequency of finding a new global optimum decreases over time. Empirically, this behavior often follows a geometric or exponential decay pattern.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
#### Paper Contributions  
The main contributions of the paper can be summarized as follows.  
* A definition and statistical performance analysis of an intrusion detection system for partial intercept-and-resend attacks to BB84 scheme, also considering quantum system noise.
* An investigation of the role of the interception rate and key lengths on the BB84 security performances, which reflects its possible future application as a QKD scheme.
* A simulative implementation that exploits an open-source library able to model the noise of real quantum computers. This feature allows the analysis of the obtained results using backend simulators inspired by real quantum machines.  
To the best of the authors' knowledge, all of these combined aspects have not been studied.
## Abbreviations  
The following abbreviations are used in this manuscript:  
A Alpha (regularization parameter for ANN)  
ACC Accuracy  
AI Artificial Intelligence  
ANN Artificial Neural Networks  
AT Activation (function)--hyperparameter in ANN (e.g., ReLU, Tanh)  
AUC Area Under the Curve (generic term)  
B1 Beta1 (exponential decay rate for the first moment estimates in Adam optimizer)  
B2 Beta2 (exponential decay rate for the second moment estimates in Adam optimizer)  
BS Batch Size (hyperparameter for ANN training)  
BT Bagging Temperature (CatBoost hyperparameter)  
C (1) Regularization strength (penalty parameter) in Logistic Regression/SVM(2) In the statistical tables, sometimes used to denote \"ROC-AUC\"  
CB CatBoost  
CD Critical Difference (in post-hoc tests/diagrams after Friedman test)  
CIS Community Innovation Survey  
CIS2014 Community Innovation Survey 2014 (Croatian sample used in the paper)  
CL Colsample ByLevel (XGBoost hyperparameter)  
CPU Central Processing Unit (hardware)  
CT Colsample ByTree (XGBoost/LightGBM hyperparameter)  
CV Cross-Validation (general term)  
D (1) Depth (CatBoost hyperparameter)(2) Degree (SVM polynomial kernel hyperparameter)  
Epsilon (e.g., for Adam optimizer in ANN, or for SVM tolerance in some contexts)  
EM Eval Metric (CatBoost hyperparameter)  
ES Early Stopping (ANN hyperparameter)  
F1 F1-score (classification metric, harmonic mean of precision & recall)  
FF Feature Fraction (LightGBM hyperparameter)  
GP Grow Policy (CatBoost hyperparameter)  
G Gamma (SVM or XGBoost hyperparameter, depending on context)  
HLS Hidden Layer Sizes (ANN hyperparameter)  
I Iterations (CatBoost hyperparameter)  
k-fold K-Fold Cross-Validation (general CV technique)  
K Kernel (SVM hyperparameter, e.g., 'rbf', 'poly', 'linear')  
L1 Lasso penalty (L1 regularization)  
L2 Ridge penalty (L2 regularization)  
L2R L2 Leaf Reg (CatBoost hyperparameter for leaf regularization)  
LGBM LightGBM (gradient boosting library/model)  
LEI Leaf Estimation Iteration (CatBoost hyperparameter)  
LR (1) Logistic Regression (model) in the main text and tables(2) Learning Rate (hyperparameter) in boosting/ANN contexts  
MCW Min Child Weight (XGBoost hyperparameter)  
MD Max Depth (common tree-based hyperparameter for RF, XGB, LGBM, etc.) MDL Min Data in Leaf (LightGBM, CatBoost hyperparameter)  
MDS Max Delta Step (XGBoost hyperparameter)  
MF Max Features (Random Forest hyperparameter)
# Study on the Influence of Collision Scene on the Energy Dissipation Process for Train Collision
## 3 Establishment and Accuracy Verification of Longitudinal Collision Dynamics Model of Train  
### Longitudinal Collision Dynamics Model of Train  
The longitudinal collision dynamic model of the train generally calculates the energy dissipation parameters of the multi-level crashworthiness structure of the train [16; 17; 18]. According to previous investigations, the following assumptions are made while establishing the longitudinal collision dynamics mode of the train [19; 20]: (1) the vehicle is equivalent to a homogeneous rigid body, (2) the influence of track irregularity is ignored, (3) the longitudinal impact force of train is much larger than external resistance, and the aerodynamic resistance and wheel-rail friction are ignored, (4) only the longitudinal tension and compression energy absorption characteristics of the energy absorbing structure are considered without the influence of lateral and vertical motion, the fluctuation of the force value in the crushing stage is neglected.  
As shown in Figure 3, a longitudinal collision finite element model of the train is constructed in the collision dynamics software of Ls-Dyna (V971_R10.0) [21], which could reflect the energy absorption characteristics of the end energy absorption area and energy absorption structure between vehicles [22]. During the establishment of the longitudinal collision dynamics model of the train, the end structure of the vehicle is simulated as a nonlinear spring, and the middle passenger area is simplified into a rigid body of a lumped mass model. The mass of the coupler and the anti-climbing energy absorption device is much smaller than the mass of the car body, and its mass is ignored during modeling. Only the connection and buffering function of the coupler and the energy absorption function of the anti-climbing device are considered.  
Figure 2: Typical train collision scenarios.  
The modeling details are as follows: the nonlinear spring element of the end structure is constructed based on the modeling method of Beam discrete beam element combined with Mat119 material in References [23; 24]. Specifically, the mechanical characteristic curves of the coupler buffer device and energy-absorbing structure are given to Mat119 material. The mechanical model of the coupler buffer device, energy absorbing device and non-deformation area of the car body end during the collision process are demonstrated in Figure 3.  
The force-displacement curve during the crushing process of the car body structure is obtained by using the condition of the car body structure impacting the rigid wall. The average value of the equivalent force is taken as the mechanical characteristics input curve of the non-deformation area at the end of the car body [25]. The input curves of the nonlinear spring at the end of the head car and the middle car are shown in Figure 3. The common EMU train (8 units and 16 units) is chosen, and the number of each interface is shown in Figure 3.
# Mechanical Properties of DNA Hydrogels: Towards Highly Programmable Biomaterials
## 5 Dissecting the Mechanical Properties of DNA Hydrogels
### Nanoscale Mechanical Properties  
The nanoscale mechanical properties of DNA [122] are known to influence the bulk mechanical properties of the DNA hydrogels assembled [50; 123]. Indeed, DNA has inherent nanoscale mechanical properties that depend on its structure and assembly [22; 26]. It has an elastic modulus comprised between 0.3 and 1 GPa and a persistence length of 50 nm (~150 bp) in a duplex form [124]. In addition, the presence of nicks, bulges, single stranded DNA, or multiple helix bundles also effects the nanoscale mechanical properties of double stranded DNA linkers and motifs [23; 24; 25], which subsequently effects the overall mechanical properties of the DNA hydrogels formed with these motifs [123]. An illustration of the importance of these nanoscale features has been given by the study from [PERSON] et al. in 2006, which demonstrated that the crosslinking of DNA motifs with ligase to avoid the presence of nicks can drastically increase the mechanical strength of DNA-based hydrogels [50]. Furthermore, hydration around the DNA strands increases stiffness [125], and salt interactions determine the length-scale of this hydration layer resulting in significant swelling of DNA fibers in the absence of salt [60]. Salts act as counterions to stabilize the negative charge of the DNA backbone and govern hole transfer along dsDNA, while the conformational state is responsible for electronic coupling through \(\pi\)-orbital base stacking [126; 127; 128; 129] This mechano-electronic coupling has been utilized to develop a FRET-based nanosensor for mercury [130] and demonstrated an order of magnitude increase in conductivity for A-form DNA relative to its B-form [131]. An alternative mechanoelectric DNA-based probe was developed for potassium detection based on the conformational transition of DNA into a G-quadruplex [132]. Inversely, DNA extension can be programmed to occur above a certain force threshold (<100 pN) and FRET reporter probes have been used to further study mechanotransduction at single cell-ECM and cell-cell adhesion sites [133; 134; 135; 136; 137].
# Predictability of Maximly Expansion with Invisalign(r) First: Treatment Planning vs. Outcome
## 4 Discussion
Predictability in mixed dentition appears to be lower than in permanent dentition, and this could be explained by the essential modification that verify the mixed dentition. No additional aligners were considered in this study, and this aspect could affect the predictability of the outcome, as treatment with clear aligners implies finishing in more phases. Teeth exfoliation and eruption could certainly lead to fitting problems, prolonging the treatment time. In addition, compliance level in younger patients must be evaluated, even though, in the experience of the authors, younger patients undergoing clear aligners treatment usually show a high level of compliance. While comparing these results to RME, it is indeed important to underline that the predictability of traditional expansion techniques is far from high. Typically, a relapse rate of about 30-50% is observed, which deeply affects the efficacy of these treatments [23]. The results observed in the current investigation underline the importance of the development of a correct treatment plan and patient selection. The scientific evidence supports the efficacy and predictability of clear aligners to expand the upper arch; however, clinicians must be aware of the biomechanical limitations to achieve a correct treatment design. Further studies will need to investigate the possible variables linked to staging, attachment design, and manufacturing processes that could influence the predictability of treatments. Therefore, overcorrection represents a key factor when planning upper arch expansion. As seen in other studies, the ClinCheck(r) software tends to overestimate the expansion movement compared to the clinical outcome, and frequently leads to crown tipping movements. In this scenario, the ClinCheck(r) software does not only represent a setup tool, but it should also be intended as a guide for biomechanics, taking into active consideration the attachment's design and overcorrection. These elements are essential in managing complex movements and ensuring that the aligners effectively translate the digital plan into clinical reality. Proper utilization of ClinCheck(r) can optimize outcomes by reducing deviations between the predicted and actual tooth movements, thus enhancing treatment predictability and efficiency and ensuring better control over the biomechanical principles at play.
# An Overview of GEO Satellite Communication Simulation Systems
## 3 Resource Management and Scheduling  
In satellite communication systems, resource management and scheduling play a critical role in improving overall system performance. However, they face increasingly complex challenges as these systems evolve to support multi-service and highly dynamic applications. On one hand, the involved resources, including spectrum, power, time slots, and beam pointing, are diverse and tightly coupled, necessitating highly coordinated and real-time scheduling strategies. On the other hand, management and scheduling objectives have shifted from single-metric optimization (e.g., throughput maximization) to multi-objective joint optimization involving fairness, QoS, and energy efficiency. In this section, we categorize and introduce resource management and scheduling strategies based on key performance metrics, including spectral efficiency, user fairness and QoS, as well as energy efficiency.
## 4 Conclusions  
Quadratic electro-optic effects (Kerr coefficients) were measured for the first time for metal nanoparticles within a transparent dielectric medium. In particular, gold nanoparticles in glass were studied. The measurements were made using a field-induced birefringence method. The magnitudes of the Kerr coefficients for the different sizes of gold nanoparticles in glass were measured and compared with that of subnanometer size metallic particles in non-conjugated conductive polymers. The magnitude was found to rapidly increase (about \(\mathrm{d^{-3}}\)) for smaller diameters (\(\mathrm{d}\)) of nanoparticles. The results are consistent with existing theories and understandings of nonlinear optics in metal nanoparticles. This size dependence was not previously observed in experiments, presumably because of thermal effects. In the present measurements, the thermal effect was insignificant. These results imply a broad range of applications of metal nanoparticles in ultrafast, electro-optic, modulation/switching, low-cost Kerr cells and other important functions in lasers and optoelectronics.  
Conceptualization, supervision, M.T.; experimental investigation, J.V.C.; writing, M.T.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
The purpose of this in vitro study was to evaluate marginal and internal fits of ceramic crowns fabricated with chairside computer-aided design and manufacturing (CAD/CAM) systems. An experimental model based on ISO 12836:2015 was digitally scanned with different intraoral scanners (Omnnicam (CEREC), EZIS PO (DDS), and CS3500 (Carestream)). Ceramic crowns were fabricated using the CAD/CAM process recommended by each system (CEREC, EZIS, and Carestream systems; N = 15). The 3-dimensional (3D) marginal and internal fit of each ceramic crown was measured using a 3D inspection software (Geomagic control X). Differences among the systems and various measurements were evaluated using the Kruskal-Wallis test. Statistically significant differences were validated using pairwise comparisons (\(\alpha\) = 0.05). Occlusal gaps in the CEREC, EZIS, and Carestream groups were 113.0, 161.3, and 438.2 um, respectively (\(p\) < 0.001). The axial gaps were 83.4, 78.0, and 107.9 um, respectively. The marginal gaps were 77.8, 99.3, and 60.6 um, respectively, and the whole gaps were 85.9, 107.3, and 214.0 um, respectively. Significant differences were observed with the EZIS system compared with the other two systems in terms of the marginal gap sizes. The CEREC system showed no significant differences among the four measured regions. However, the EZIS and Carestream systems did show a statistically significant difference (\(p\) < 0.05). All three systems were judged to be capable of fabricating clinically acceptable prostheses, because the marginal gap, which is the most important factor in the marginal fit of prostheses, was recorded to be below 100 um in all three systems.  
marginal and internal fit; ceramic; dentistry; chairside dental CAD/CAM  
15 January 2021
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 5 Conclusions  
Recently, a new framework referred to as spatial crowdsourcing (SC) was introduced to achieve spatial tasks in a crowdsourcing environment. However, there are many challenges in SC, with the core one being task assignment, formulated as the task matching or task scheduling problem.  
Task scheduling is an important issue, where the worker can view their trajectories to complete all tasks that are assigned to them. In real applications, SC usually requires optimizing several objectives, many of which may be conflicting. However, there is a lack of studies that address the multi-objective optimization problem, as well as the task scheduling problem, within an SC environment.  
In this study, we focused on the TS-MOO issue in SC, which aims to maximize the number of completed tasks, minimize the total travel costs, and ensure the balance of the workload between workers. To solve this issue, we proposed a new MOTSO model to find the optimal task scheduling approach based on three conflicting objectives. The MOTSO model includes a modification of two main algorithms related to fitness function and the ranking strategy to enhance its performance.  
We conducted experiments to evaluate the proposed method using a synthetic and a real dataset. The extensive results and statistical analyses showed that our proposed model proved effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Workload Balancing Using MOTSO** \\  & **WLB** \\ \hline
**Case 1** & 55.58566 \\
**Case 2** & 56.42424 \\
**Gowalla** & 58.01609 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The results of the standard deviation of the workload balance (WLB) using the MOTSO model.  
As regards future work, we plan to extend our model to deal with complex tasks that require multiple workers to finish one task. Additionally, we plan to extend our model to deal with heterogeneous tasks in SC.  
Conceptualization, A.A.A. and M.F.A.; methodology, A.A.A.A.; software, A.A.A.; validation, A.A.A. and M.F.A.; formal analysis, A.A.A.; investigation, A.A.A.A.; resources, A.A.A.; data curation, A.A.A.A.; writing--original draft preparation, A.A.A.A.; writing--review and editing, A.A.A. and M.F.A.; visualization, A.A.A.; supervision, M.F.A.; project administration, A.A.A.; funding acquisition, A.A.A. and M.F.A. All authors read and agreed to the published version of the manuscript.  
This paper was supported by King Abdulaziz City for Science and Technology (KACST), grant no. 1-17-00-009-0030.  
Not applicable.  
The authors declare no conflict of interest.
# A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network
## 3 Proposed SEFR Protocol
### Energy Model and Assumptions  
The proposed protocol adopts the energy model as applied in [33]. The energy consumption depends on transmitting and receiving data. The sensor nodes consumed the energy resource \(E_{Tt}(k,D)\) while transmitting the \(k\) data bits over the communication distance \(d\), as given in Equation (1).  
\[E_{Tt}(k,D)=\left\{\begin{array}{ll}E_{e}*k+k*E_{fs}*D^{2},&if\ D\leq d_{t} \\ E_{e}*k+k*E_{amp}*D^{4},&if\ D>d_{t}\end{array}\right. \tag{1}\]  
The bit level energy consumption is shown by \(E_{e}\), \(E_{fs}*D^{2}\) or \(E_{fs}*D^{4}\) is the energy consumption of sensor nodes, and \(d_{t}\) is the distance threshold. The energy consumption \(E_{Rx}(k)\) during the reception of \(k\) data bits can be computed as given in Equation (2).  
\[E_{Rx}(k)=E_{e}*k\]  
Figure 1: Architectural design of the proposed work.  
The following network assumptions are made during the design and development of the SEFR protocol.  
1. The sensor nodes are installed randomly and stand static with limited abilities of resources.
2. Fog nodes are physical devices with powerful and fault-tolerant functionalities.
3. Source and destination nodes are exempt from malicious operations.
4. The malicious nodes flood bogus route reactions so they can be selected as data forwarders and drop the data packets.