esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Crystals and Lasers
### Experimental Setup  
The experimental setup of the diode-pumped Ho: GdVO\({}_{4}\) laser is schematically illustrated in Figure 6. The laser adopted a linear cavity design with a cavity physical length of about 30 mm. The above Ho: GdVO\({}_{4}\) crystal was used as the gain medium, and both ends of the crystal were coated with anti-reflection coatings, resulting in the reflectivity of the end face being less than 0.5% at 1.94 \(\upmu\)m and less than 0.3% at 2 \(\upmu\)m. At the same time, the crystal needed to be sandwiched between two indium foil water-cooled copper heat sinks (thickness: 0.1 mm, cooling water temperature: 18 \({}^{\circ}\)C). A fiber-coupled laser diode (LD) (output power: 20 W, wavelength: 1.94 \(\upmu\)m) was employed to pump the Ho: GdVO\({}_{4}\) crystal. Here, the core diameter of the partial fiber was 600 \(\upmu\)m, and its numerical aperture was 0.22. Similarly, LD also required a water-cooled system to control the temperature at 18 \({}^{\circ}\)C. The output spectral characteristics of LD were monitored with an optical spectrum analyzer (Bristol 721A). The measurement showed that central wavelength of the LD was at 1940 nm with an FWHM linewidth of approximately 7.9 nm. Moreover, we also observed that the wavelength shift of the LD from the threshold to 20 W is about 6 nm. It is worth noting that the red shift of the emission wavelength of the 1.94 \(\upmu\)m LD here is caused by the significant thermal loading of LD under large current driving. At the same time, the pump power absorption of Ho: GdVO\({}_{4}\) crystal after a single cycle of the laser cavity increases from 68-75%. The addition of lens FI(focal length: 25 mm) and F2(focal length: 25 mm) was beneficial to adjust the energy density of the pump beam incident on the crystal. Therefore, the pump beam radius after convergence was about 300 \(\upmu\)m. The input mirror M1 was flat, which had high transmission at 1.94 \(\upmu\)m, and high reflective at 2.05 \(\upmu\)m. Plano-concave mirror M2(curvature radius: 500 mm) served as the output mirror of the laser. M3 was a dichroic mirror with a 45\({}^{\circ}\) beam splitting, which had high transmission at 2.05 \(\upmu\)m and high reflection at 1.94 \(\upmu\)m.
## 4 Results and Discussion
### Influence of Concrete Strength  
Numerical simulations of S-SWJ with different concrete strengths were performed to explore the influence of concrete strength on its shear capacity. In the simulations, the concrete strength ranged from 25 to 55 MPa, with an interval of 5 MPa. The other material properties and geometric parameters of S-SWJ were kept the same as those used in Section 3.  
Figure 11 shows the load-displacement curves of S-SWJ with different concrete strengths. It can be seen that the curves show a linear rise, followed by a nonlinear rise, and the nonlinear rise tends to be faster and higher with the increase of concrete strength. Thus, for S-SWJ with higher concrete strength, the load in the nonlinear rise region is larger, leading to a higher ultimate shear capacity. Finally, the curves decline after reaching the peak load. The red circle in Figure 11 indicates the load corresponding to the concrete-concrete interface debonding of S-SWJ. According to the position of the circles in Figure 11, it can be seen that the debonding strength of S-SWJ increases with increasing concrete strength.  
From Figure 11, it can be seen that the ultimate shear capacity can be enhanced by increasing the concrete strength. The ultimate shear capacity of S-SWJ with different concrete strengths is summarized in Figure 12 for a more specific comparison. It can be seen that the ultimate shear capacity of S-SWJ increases almost linearly with increasing concrete strength. Specifically, the ultimate shear capacity of S-SWJ with concrete strength of 55 MPa is 65.3% higher than that of S-SWJ with concrete strength of 25 MPa. Figure 12 also shows the displacement corresponding to the ultimate shear capacity. As shown in Figure 12, for S-SWJ with concrete strength ranging from 30 to 55 MPa, the displacement fluctuates slightly, and the value is about 0.3 mm. However, S-SWJ with concrete strength of 25 MPa has a relatively larger displacement, the value of which is 0.47 mm.  
Figure 11: Load–displacement curves of S-SWJ with different concrete strength.  
Figure 10: Contour plots of damage for (**a**) S-SWJ and (**b**) its bridge panel end and (**c**) S-CWJ and (**d**) its bridge panel end when the specimen reaches the maximum load.
###### Abstract
Increasing the number of bacteria, including _Porphytomonas gingivials_, _Aggregaitobacter actinomycetesmitans_, _Tancrella forsythia_, _Prevorella intermedia_, _Prevorella nigrescens_, _Fusobacterium_ spp., _Streptococcus mutans_, _Lactobacillus_ spp., and _Candidia albicans_, may lead to the development of gingivitis, periodontitis, caries, white spot lesions and haltisios [4]. Oral microbiome distortion reaches its maximum 3 months after the initiation of FA treatment; however, after another 3 months, the number of bacteria begins to decline [8], but in some patients, the altered microbiota composition persists even after the appliance is removed [4]. It is critical to monitor patient oral hygiene to avoid these complications. Patients should be motivated to better control dental biofilm on their own and, if necessary, perform in-chair professional hygiene with air-powder polishing [9; 10]. Even though FAs are the gold standard of orthodontic treatment, an increased risk of pain, damage and failure of appliances is an inherent part of the treatment. This kind of therapy requires more frequent and longer visits related to FA adjustment and activation. [11; 12]. The increasing popularity of clear aligner (CA) treatment is due to the convenience of use, less discomfort, better esthetics, easier hygiene, lower failure rate and shorter and less frequent visits [12; 13]. Qualitative and quantitative alterations in the biofilm are smaller because CAs are removed for tooth brushing. Moreover, tooth movement is very close to the planned motion, which prevents the biofilm form migrating subgingivally [11]. For this reason, CAs are recommended for adults at increased risk of developing periodontitis [4; 7; 14; 15]. However, bacterial biofilm forms on the three-dimensional architecture of the irregular CA surface, despite daily washing and brushing. The presence of bacterial plaque is observed, in which _Streptococcus mitis/oralis_ predominates. Hence, not only perfect oral hygiene but also proper CA cleaning is extremely important [16]. The limitations of using CA include the following: treatment costs, the need for good patient cooperation--aligners must be worn for a strictly defined period of time, depending on the kind of material they are made of [17]--and limited effectiveness in the treatment of some malocclusions [12]. Therefore, the question arises whether treatment with aligners is a good solution for periodontal patients. To answer this question, it was decided to perform a review of the literature. The null hypothesis was that the benefits for periodontal patients were equal in the case of CA or FA treatment.
## 6 Results and Discussion
### Analysis of Traffic Load in the Network  
Traffic load is measured as the number of packets transmitted in the network. Higher packet transmissions directly affect the bandwidth and energy consumption, these resources are limited and thus their usage should be optimized. Therefore, it is necessary to reduce the frequent packets transmission in the network. Figure 15 illustrates the traffic load pattern generated by the three protocols. As we can see, DT generates high traffic load as compared to LEACH and LEACH-PRO due to the frequent transmissions towards the BS, and sensors die quickly after at most 500 rounds. The traffic load generated in LEACH increases gradually to stabilize after 1500 rounds, for networks of 200 and 500 nodes, and after 1000 rounds for smaller networks. LEACH-PRO has similar pattern to LEACH but the traffic load generated is almost 40% to 50% lower, especially after the first 500 rounds.  
Figure 14: Energy consumption evolution under varying network densities. Network of (**a**) 50, (**b**) 100, (**c**) 200, (**d**) 500 nodes.  
Figure 13: System residual energy under varying network densities. Network of (**a**) 50, (**b**) 100, (**c**) 200, (**d**) 500 nodes.
# Quad-Port Multiservice Diversity Antenna for Automotive Applications
## 2 Antenna Design
### MIMO Antenna  
Multiple antennas are required in automobile applications to receive signals from all directions. The antenna elements in the proposed MIMO antenna are arranged orthogonally to one another to improve isolation, without the use of any decoupling structures. The orthogonal placement also provides polarization diversity.  
The inter-element spacing between the radiators is kept as 0.06\(\lambda_{0}\), and the MIMO antenna dimensions are 56 mm \(\times\) 56 mm. Figures 7 and 8 show the proposed MIMO/diversity antenna and its S-parameters without a connected ground plane, respectively. The MIMO antenna with a common ground plane [32] is shown in Figure 9. The reflection coefficients and mutual coupling of the MIMO antenna with a connected ground plane are presented in Figure 10a-c, respectively. There are no significant differences in the performance of the antenna with or without connected ground. The photograph of the proposed MIMO antenna prototype is shown in Figure 11.  
Figure 8: S-parameters of the MIMO antenna with unconnected ground planes: (**a**) reflection coefficients, (**b**) mutual coupling with respect to port 1 and port 2, (**c**) mutual coupling with respect to port 3 and port 4.  
Figure 9: Layout of the proposed MIMO antenna with a common ground plane: (**a**) front view, (**b**) rear view.  
Figure 10: S-parameters of the MIMO antenna with the connected ground plane: (**a**) reflection coefficients, (**b**) mutual coupling with respect to port 1 and port 2, (**c**) mutual coupling with respect to port 3 and port 4.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 1 Introduction
In the literature, fused Sentinel-2 + ASTER data produced an SVM accuracy of 83.16% [31]; Sentinel-2 data produced MLC accuracy rates of 70% [32] and 76% [33]. So, various ML algorithms outperform one another for different lithological formations when using a variety of satellite data [34]. Due to their nonparametric methodology and capacity to identify complex decision boundaries, SVM, deep learning, and neural network-based algorithms are now the most popular [35]. Lithological unit classification from multispectral and hyperspectral data is often performed at the pixel level. This is achieved by applying analytical techniques that either use spectral parameters, such as the depths and symmetry of the absorption features, spectral slopes at particular wavelengths, and band ratios, or match a series of candidate spectra from a spectral library (in the field or laboratory) with the pixel spectra (in an image) [36]. USGS spectral library values of some common minerals found in lithological units of the study area are shown in Figure 1.  
This study investigated how remotely sensed data are able to assist in the discrimination of the lithological character of geological formations in the K  
Figure 1: The laboratory reflectance spectra of hematite, epidote, calcite, muscovite, illite, and kaolinite extracted from the USGS Spectral library [37]. near the Pakistan-Afghanistan border in KP, Pakistan (Figure 2). The attributes of different lithological units with respect to remotely sensed data have not been thoroughly studied in this region previously. The goal of this research is to determine the suitability of ASTER and Landsat 8 OLI data for discriminating the lithological character of the study area. The study also focuses on determining the best classifier between two common machine learning classifiers: maximum likelihood classification (MLC) and support vector machine (SVM), for lithological categorization of the study area.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors
#### 5.3.4 Chosen-Ciphertext Attacks (CCAs)  
This is relevant to the PQC KEM used in both protocols. NIST's selections are generally CCA-secure. The chosen PQC KEM (e.g., CRYSTALS-Kyber) is designed to be IND-CCA2 secure, meaning it is resistant to chosen-ciphertext attacks. This ensures that an attacker cannot gain information about the plaintext even if they can choose ciphertexts to be decrypted.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Results Based on Parameter Tuning
#### 6.2.3 Mean Route Reliability Results Based on the Different Velocity Values  
In this study, we measured the best route discovery based on varying vehicle speeds to ensure the performance of the JMA algorithm in various scenarios. Based on experimentation, we noticed that higher velocity rates achieve low-quality routes compared with the reduced velocity rates. In addition, we observed that velocity at 20 km/h for vehicles discovers the best route. This study clearly shows that topology becomes stable at lower velocity rates. In the next subsection, results are observed based on average mobility of 20 km/h. It is noticed that the reliability score will not be stable when there are fluctuations in velocity above 20 km/h. With this outcome, we stabilize vehicles in VANET that are fixed at a speed of 20 km/h, to process the routing of data.  
Figure 4: Mating probability Rate \(M_{R}\)—over 1000 Monte Carlo trails.
# Fatigue Resistance Characterization of Warm Asphalt Rubber by Multiple Approaches
## 4 Discussion  
The fatigue behavior (at intermediate temperatures) of Pen60/70, AR and WAR binders, mortars, and mixtures were characterized by binder, mortar, and mixture tests. CRM had remarkable enhancing effects on the fatigue performance at intermediate temperatures. Table 3 briefly summarizes the ranking results of the five fatigue tests. It is noted that results of the LAS, shear fatigue test, and 4 PB test ranked the asphalt binders and their corresponding asphalt mixtures exactly the same (ARS > AR > ARE >  
Figure 6: IIFT measurements for mixtures.  
Figure 7: Four-point beam (4 PB) test results.  
ARA > Pen60/70). Although Sasobit improved the binder stiffness, ARS samples were found to have the best fatigue resistance. This enhanced performance may be attributed to the crystalline network structure of the Sasobit additive, which stabilizes the asphalt fractions. However, the enhancing mechanism of Sasobit is as yet unclear; chemical and micro analyses are recommended for mechanism investigation. Evotherm-DAT and Aspha-min had certain negative effects on fatigue resistance. The liquid components in the chemical and foaming additives may not be completely evaporated during the mixing and compacting processes. The residual water may result in a poorer adhesive capability between binder and aggregates, which leads to poorer fatigue resistance.  
By comparison, the Superpave fatigue factor test and ITFT indicated different conclusions. For the Superpave fatigue test results, AR performed better than ARS, and ARA slightly outperformed ARE. The other rankings were almost same as those of other parameters, apart from those obtained by the ITFT. Previous studies have reported that the G*sin\(\delta\) is not an accurate indicator of asphalt mixtures' fatigue performance [28; 29]. According to its mechanism, G*sin\(\delta\) is a stiffness-based parameter. This parameter is measured under the condition of a small shear strain and a constant loading frequency, and can only undergo several loading cycles without fatigue damage. The simulated condition is obviously different from the complicated fatigue phenomenon observed with large numbers of loading cycles and fatigue damage [29]. The influence of the Sasobit additive on asphalt stiffness may be the reason why the G*sin\(\delta\) value of ARS is larger than that of AR. This study further confirmed the poor performance of the G*sin\(\delta\) test. By comparison, the LAS test was proven to be a better fatigue indicator for rubberized binders.  
It is unexpected to notice that the ITFT provided completely different conclusions as compared to the other tests. One possible explanation for this is that the resilient crumb rubber particles led to the local distortion of the place in touch with the fixers, which resulted in the actual fracture mode being inconsistent with the theoretical mode. Moreover, the loading mode of the ITFT is different from the real loading status under traffic conditions. Therefore, the four-point bending test is a more reliable fatigue characterization for rubberized mixtures.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 5 Solving CVRP with the Hyper-Heuristic Approach
### Creating Dispatching Rules for CVRP  
In addition to the functions defined in Table 1, in CVRP we define the following set of terminals (variables) that GP can use in the creation of candidate priority functions; these are shown in Table 3. All the terminals are self explanatory and correspond to the current vehicle, for which the decision is being made of which customer to visit next. In the table, the terminal _ncc_ gives the value of the distance from the currently considered customer to the closest unserviced customer; i.e., this may be the next customer the vehicle could visit after the one being considered.  
The experiments for CVRP were conducted on benchmark set from [52], which include 20 problem instances with 200 to 480 customers. Out of 20 instances, the first 10 were used as a learning set, while the remaining 10 served as the test set. In accordance with classical cross-validation used in other machine learning applications, the priority functions are first evolved on the learning test set. After that, all the presented results were obtained using only the instances from the test set.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Terminal** & **Description** \\ \hline dist & distance to the customer being considered \\ d & customer demand \\ rc & vehicle remaining capacity \\ drc & ratio of demand to remaining capacity \\ ncc & ’next closest customer’ \\ \hline \hline \end{tabular}
\end{table}
Table 3: GP terminals for CVRP.  
Every learning experiment is repeated in 30 GP runs, and the 30 best individuals from those runs are evaluated on the test set. Both meta-algorithms were tested, but the results for the parallel variant were significantly worse than for the serial counterpart, so they are not presented in this section. This round of experiments is performed in static conditions, where it is expected that the serial meta-algorithm will exhibit better performance. Regardless of that, the parallel algorithm is the only choice when constructing the schedule in dynamic conditions, and the priority functions evolved with the serial algorithm can also be used in that case.  
The results for 10 test cases from the dataset in terms of total travelled distance are given in Table 4. Note that the proposed approach is not meant to generate optimal or near-optimal solutions, but rather to produce adaptive schedules in a short amount of time. This property is additionally examined in the next subsection.
# Rural Fires--Causes of Human Losses in the 2017 Fires in Portugal
## 3 Human Losses in Rural Fires  
Year after year, wildfires are killing people around the world, and some of the fires have led to the loss of a large number of people in a single occurrence.  
The first recorded high number of losses of human life was in October 1871 in Wisconsin, a state in the upper Midwestern United States, when a forest fire caused between 800 and 1200 deaths, according to estimates. The fire, which had broken out in the forest a few days before, destroyed in a few hours a town of 1700 inhabitants and 16 other villages in an area of more than 500,000 hectares [12]. This event has been recognised as the deadliest rural fire event in history.  
Additionally, in Landes, France, in August 1949, 82 people were killed fighting fires. The victims-firefighters, volunteers and 23 military personnel-were caught in a \"cloud of fire\" caused by a sudden change in the direction and intensity of the wind.  
Many other events followed, such as in Australia in 2009, where at least 173 people died in several fires that occurred in what became known as Black Saturday. In Russia in July 2010, during one of the hottest summers ever registered, wildfires killed 60 people. In Matti, Greece, in 2018, around 100 people died when they tried to fight the flames.  
In Portugal, this is a recent reality, remembering the years 2003 and 2005, when, respectively, 21 and 23 people died [13], and the fateful year of 2017, with the loss of 66 casualties in June and 57 in October. The year 2017 was a milestone in history due to the high number of fatalities in two different occurrences that were geographically close by, where most deaths occurred in urban-forest interface areas. Between 2017 and 2021, there was no loss of civilians to forest fires, only the loss of firefighters.  
The loss of human life due to forest fires will always be the greatest loss, regardless of the area burned by the fire or the cumulative number of fires each year.