esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Hazard Evaluation and Strain Dynamics in the
## 3 Methodology and Implementation  
### Creating Dataset  
There are three different earthquake catalogs that document the seismic events occurring in the study region: The Kandilli Observatory and Earthquake Research Institute (KOERI), the U.S. Geological Survey (USGS), and the Disaster and Emergency Management Authority (AFAD). These catalogs contain information such as magnitudes, epicenter coordinates, depths, and locations of the earthquakes. While the data in the AFAD and USGS catalogs are scarce, the most comprehensive data for the SFZ are available in the KOERI earthquake catalog. Therefore, the earthquake data from the KOERI catalog were utilized for the analyses conducted within the scope of this study. Since earthquake catalogs primarily contain instrumental period data regarding the seismic activity of faults, the number of damaging earthquakes recorded in these catalogs is quite limited. This situation further highlights the importance of statistical and geodetic studies for seismic hazard analysis. To reduce noise in the catalog containing events recorded in the study region, a Goodness-of-Fit Test [83] was performed, yielding a completeness magnitude (Mc) threshold of 3. Based on this analysis, earthquakes with Mw \(<\) 3 were excluded from the  
Figure 3: Epicentral distribution and focal mechanism solutions of M \(\geq\) 5 earthquakes occurring near the SFZ segments (red lines represent active faults, Abbreviations: AF—Ashhanlar Fault, CF—Cukürören Fault, EF—Erdögmus Fault, NFZ—Nasa Fault Zone, Esf—Eskisehir Fault Zone, GFZ—Gelenbe Fault Zone, HBFZ—Havran-Balkesir Fault Zone, SKFZ—Soma-Kirkagč Fault, GGB—Gediz Graben, CG—Civil Graben; AFAD, [77], KOERI).  
catalog. Furthermore, aftershocks following major earthquakes were also removed from the dataset. For this purpose, earthquakes located within a 25 km radius of the mainshock, occurring up to 6 months after Mw: 5 earthquakes, and taking place within 1 year after Mw: 6 earthquakes were classified as aftershocks. The histogram of the final dataset of more than 4500 earthquakes obtained after this filtering process is presented in Figure 4.  
Different types of earthquake magnitudes (e.g., Md, MJ, Mw) can lead to inconsistencies in seismological studies. Therefore, for seismic hazard analyses, earthquake data must be converted to a common magnitude scale to generate a homogeneous catalog. For magnitude conversion, various quantitative relationships have been developed by researchers and are widely accepted in the literature [84; 85; 86]. However, since these relationships may reflect local characteristics, conversion coefficients specific to the study area were determined to enhance the reliability of the analysis. The moment magnitude (Mw) was chosen as the common magnitude scale, and Orthogonal Regression was employed to compute the conversion coefficients. The values obtained from the regression analysis are presented in Table 3.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Genetic Algorithms
#### 6.1.4 Termination  
The termination condition of the algorithm can be defined by the user limiting the execution time or the number of generations, being the latter the most used since it is proportional to the number of evaluations of the objective function and it is a useful reference parameter for evaluating performance or making comparisons with other techniques. The result of the optimization method is the highest quality solution within the population of individuals of the last computed generation.
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 3 Results and Discussion
### Phaeodactylum Biomass Addition (Part I)
#### 3.1.3 Antioxidative Capability  
Based on the results of primary and secondary oxidation, it is clear that the _Phaedactylum_ biomass addition did not exert an antioxidative effect. In other words, _Phaedactylum_ biomass was not capable of inhibiting fish oil oxidation. Former studies that added microalgal biomass to bulk oil did, however, report an enhancement in oxidative stability [19; 20; 21; 22; 23; 24]. Nonetheless, it has to be remarked that [PERSON] et al. [19], who investigated the addition of _Nannochloropsis_ biomass to soybean oil, based these findings on a Rancimat test operating at temperatures of 120-150 \({}^{\circ}\)C far from real storage conditions, while single PV values after 15 h (without further storage) were even shown to be slightly higher (ca. 2 meq hydroperoxides/kg oil) for the oil with added _Nannochloropsis_ biomass. Moreover, the results of the studies of [PERSON] and [PERSON] [20; 22] require nuance. Although microalgal biomass addition (in this case, _Chorella_ or _Arthospira_) to virgin olive oil did lead to lower PV values (of ca. 40-100 meq hydroperoxides/kg oil lower), this was only the case for the last time point of the 42-day storage experiment that (compared to the earlier time points) still showed a clear increase of ca. 50-100 meq hydroperoxides/kg oil and therefore an oxidation onset for oils both with and without microalgal biomass. Moreover, it has to be kept in mind that, other than in the current research, systems without n-3 LC-PUFA were considered, and also the methods of analyzing lipid oxidation showed some differences since a storage temperature of 60 \({}^{\circ}\)C and a less specific analysis method for secondary oxidation (anisidine value) were used. This was also the case in the study of [PERSON] et al. [24], which investigated the effect of adding both whole-cell _Arthospira_ and _Arthospira_ nanoparticles (after ball-milling) to olive oil, although, in this study, lower PV values (4-58 meq hydroperoxides/kg oil lower) without a clear oxidation onset were observed for all time points (weekly analysis for 7 weeks), making their statement of _Arthospira_ exerting an antioxidative action more powerful. Since in all of these papers, the antioxidative effect is explained by the release of antioxidative compounds from the microalgal biomass into the oil, it raises the question of whether this sufficiently occurred in the current research or whether the reason for the absence of oxidation inhibition lies in the fact that no antioxidants were released from the _Phaedactylum_ biomass. With former research suggesting carotenoids as the most important antioxidants in _Phaedactylum_[18; 29; 31], their presence in the fish oil after mixing with _Phaedactylum_ biomass was investigated and is discussed in the next section.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods
### Image Degradation Modeling via Conditional Normalizing Flow
The training procedure is illustrated by the blue solid lines in Figure 1. For blur degradation modeling, HR images are first encoded by the network of the HR encoder. Clean LR images along with HR encoding features are then processed through a series of flow steps in BDM and transformed to the corresponding latent variables \(\mathbf{z}_{b}\), as shown in Figure 1. For real-world noise modeling, the noise patches are taken as an input of RNM to produce the latent variables of noise \(\mathbf{z}_{\mathbf{n}}\). The variables are later used to calculate NLL losses for model optimization.
# Influence of Soil Particle Size on the Engineering Properties and Microstructure of a Red Clay
## 3 Results and Analysis
### Results of Macro Cracks
#### 3.3.2 The Impact of Dry Density on the Cracks of Red Clay with Different Particle Sizes  
The development of pores and cracks in red clay soil with the dry density values of 1.3 g/cm\({}^{3}\), 1.4 g/cm\({}^{3}\), and 1.5 g/cm\({}^{3}\) after  
It can be seen from Figures 16-18 that after 12 WD cycles, the soil samples with particle sizes of 0.25 mm and 0.50 mm and a dry density of 1.3 g/cm\({}^{3}\) mainly presented cracks. As the dry density increased to 1.4 g/cm\({}^{3}\) and 1.5 g/cm\({}^{3}\), the number of cracks significantly declined. The soil sample with a particle size of 2.00 mm mainly presented cracks and independent pores caused by water loss and shrinkage, and the number of pores and cracks reduced with the increase in dry density. The soil sample with a diameter of 5.00 mm was mainly composed of cracks, pores, and edge cracks. When the dry density increased to 1.4 g/cm\({}^{3}\), the cracks in the middle of the soil sample decreased, and the independent pores decreased when the dry density reached 1.5 g/cm\({}^{3}\). With the same dry density, the larger the particle size of the red clay, the greater the number of independent pores, but the cracks in the soil sample with the smallest particle size of 0.25 mm were the most developed. After quantification, the dry density and total porosity curves were determined (Figure 19), as were the curves of dry density and fractal dimension (Figure 20).  
Figure 19: The fitting curve of dry density and total porosity.  
The dry density displays curves with the total porosity and fractal dimension, as shown in Figures 19 and 20. In other words, the greater the dry density, the smaller the total porosity and fractal dimension of the red clay soil sample. In Figure 19, for a dry density of 1.3 g/cm\({}^{3}\), the average total porosity of different particle sizes is 0.11%. Compared with this, when the dry density is 1.4 g/cm\({}^{3}\) and 1.5 g/cm\({}^{3}\), the average total porosity is reduced by 36.36% and 45.45%, respectively, with a difference of 14.29%. In Figure 20, for a dry density of 1.3 g/cm\({}^{3}\), the average fractal dimension of different particle sizes is 1.324. In contrast, for the dry densities of 1.4 g/cm\({}^{3}\) and 1.5 g/cm\({}^{3}\), the average fractal dimensions are reduced by 5.21% and 8.16%, respectively, with a difference of 3.11%. The main reason is that the higher the dry density, the denser the soil sample, the smaller the internal pores, and the larger the force between the soil particles. As a result, the WD cycle and particle sizes have little effect. In addition, at different dry densities, the total porosity and fractal dimension of soil samples also vary with particle size.
## 1 Introduction  
Three-dimensional (3D) printing technology has become an integral part of modern orthodontics, offering enhanced customization, workflow efficiency, and precision. Initially adopted for the fabrication of dental models, 3D printing has more recently expanded to the realm of surgical guides, temporomandibular joint splints, indirect bonding trays, and custom brackets [1, 2, 3, 4, 5]. Another emerging application of 3D printing in orthodontics is the production of clear aligners.  
Over the past two decades, clear aligner therapy (CAT) has become a popular treatment modality, especially for patients seeking an esthetic and comfortable orthodontic appliance. Advances in computer-aided design and manufacturing technology have improved the clinical efficacy of CAT [6]; however, its overall performance remains inferior to that of fixed appliances [7]. Therefore, there is a need to develop new materials and workflows to improve patient and clinician experience with this evolving treatment modality.  
The conventional method for fabricating clear aligners has involved the thermoforming process, which is error-prone, costly, time-consuming, and environmentally wasteful [8]. Recently, the development of a clear photocurable resin has enabled the production of direct printed aligners (DPA), promising improved accuracy and precision, simplified workflow, and reduced waste. However, there are currently few published studies that have evaluated the forces and moments generated by this new material during simulated orthodontic tooth movement [9; 10; 11]. An improved understanding of the biomechanics involved in CAT using DPA may lead to more predictable and biologically harmonious force application, enhancing treatment outcome and efficiency while minimizing iatrogenic tissue damage.  
The aim of this study was to evaluate the forces and moments exerted by clear aligners during in vitro simulated lingual bodily movement of the maxillary left central incisor. The forces and moments exerted on the central incisor and its adjacent teeth were compared between thermoformed aligners (TFM) and DPA at two activation amounts. The moment-to-force (M/F) ratios of the aligners were also calculated to determine if bodily movement could be achieved with the tested materials.
## 3 Overview and Theoretical Foundations
### Meteorological and Pollution Data Analytics
A proposed platform that aims at making this task more efficient and effortless for the end user, by providing a seamless workflow from data collection to integrated pollution, meteorological and mobility data view is depicted in Figure 4.  
Figure 3: Heuristic overview of pollutions data measuring PM\({}_{10}\) levels across time and location dimensions. Thanks to the automation of technical aspects and the handling of complex tasks such as spatial joins, analysts can retrieve data from any meteorological agency (also capturing pollution data) and mobility databases, select the geographical area of interest and efficiently integrate data to find meaningful insights that assist in making strategic urban planning decisions. The provisioning of an interactive interface for querying the integrated data improves the analysis experience, allows testing sophisticated queries and exporting results to other tasks downstream a big integrated data processing workflow. In the next subsection, we discuss the details of a novel system that we have designed to realize this vision, aiming at simplifying the task of unifying and integrating heterogeneous muti-domain georeferenced data streams.
###### Abstract
The SAS model was originally developed based on the \(k-\sqrt{k}L\) model, and was then transformed into the \(k\)-\(\epsilon\) model, the \(k\)-\(\omega\) model, and the well-known shear stress transport (SST) \(k\)-\(\omega\) model [5]. The most widely used SAS model is based on the SST \(k\)-\(\omega\) model. Among the many RANS models, there is a class of models that are very attractive, namely, the elliptic blending or elliptic relaxation-based models. The basis of this type of model is the \(\overline{v^{2}}-f\) model developed by [PERSON] [7]. Since then, many variants have evolved. These models can be divided into three categories: one stems from the \(k\)-\(\epsilon\) model, the second stems from the Reynolds stress model, and the third stems from the \(k\)-\(\omega\) model. Research on the first two categories began earlier, and many results have been obtained. For detailed reviews of these models, readers can refer to [PERSON] and [PERSON] [8] and [PERSON] [9]. Research on the \(k\)-\(\omega\)-based models started relatively late, and the early developments of these models were reviewed by [PERSON] et al. [10,11]. Recently, [PERSON] et al. [12] developed an elliptic blending lag model based on the conventional \(k\)-\(\omega\) model. [PERSON] and [PERSON] [13] developed an elliptic blending lag model based on the conventional SST \(k\)-\(\omega\) model. The present authors [14] developed an elliptic blending turbulence model by transforming the \(k\)-\(\epsilon\)-based \(\mathrm{BL}-\overline{v^{2}}/k\) model of [PERSON] and [PERSON] [8] into a \(k\)-\(\omega\) system. Additionally, the SST characteristics in the boundary layer were integrated, and the model performance was greatly improved. Generally, \(k\)-\(\omega\)-based models have better stability than others [10,11]. There are very few SAS models based on elliptic blending or elliptic relaxation models. The only one found in the literature was developed by [PERSON] et al. [15]. They proposed an SAS model based on the elliptic relaxation model of [PERSON] et al. [16] (the \(\zeta\)-\(f\) model). The model was validated by computing several generic flow configurations with relatively coarse meshes, including the channel flow, flow over periodic hills, flow over fence, impinging jet, and flow mixing in the T-junction. The results are encouraging. In this study, an SAS model, which is based on an elliptic blending model (denoted as the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model) previously developed by the authors [14], was proposed and validated. In the next section, the formulation of the model and its implementation are briefly described. In the third section, the performance of the model is investigated by applying it to three flow configurations: 3D asymmetric diffuser flow, flow over two-dimensional (2D) periodic hills, and 2D U-turn duct flow. Lastly, the conclusions are presented.
## 5 Conclusions  
In this paper, the codewords of polynomial-based LDPC-CCs are separated into two categories, _i.e._, structured codewords and non-structured codewords. In the distance spectrum, the number of the structured codewords is dominated by the size of the polynomial syndrome former matrix \(\textbf{H}^{T}(D)\), while the number of the non-structured ones depends on the particular monomials or polynomials in \(\textbf{H}^{T}(D)\). For the design of practical LDPC-CCs, an approach of improving the distance spectrum of the mother code is eliminating the low weight non-structured codewords in the super codes.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 3 Applications and Real-World Scenarios
### Domain Adaptation in Robotic Vision: Real-World Applications
#### 3.2.3 Search and Rescue Missions  
Crucial search and rescue operations often entail traversing challenging and hazardous terrains. Here, robotic vision systems, thanks to domain adaptation approaches, exhibit enhanced adaptability, enabling them to perform effectively in unforeseen and unpredictable disaster scenarios. Domain adaptation allows robotic platforms to dynamically adjust their visual perception to varying lighting conditions, structural damage, and debris congestion during search and rescue missions. Domain-adaptive robots can swiftly locate victims and respond to evolving situations, thereby augmenting the effectiveness and success of rescue efforts.  
In conclusion, the applications of domain adaptation techniques in computer and robotic vision are extensive, encompassing domains from agricultural robotics to autonomous driving. Domain adaptation unlocks previously unexplored possibilities for reliable and effective performance in real-world contexts, enabling vision systems to adapt  
Figure 9: An illustration of the assessment of a robot’s performance in an unknown task through domain adaptation.  
to a multitude of environmental variables. As the field continues to evolve through multi-disciplinary collaborations and data-driven methodologies, the relevance and impact of domain adaptation in shaping the trajectory of computer and robotic vision technologies will only continue to grow.
## 4 Discussion
Team's medical and coaching staff can use feasible screening protocols to capture the state of the athletes, thereby enhancing injury prevention. Regarding that, wearable technologies (global position systems (GPS), portable dynamometers, inertial sensors, etc.) and field-based tests enable frequent data collection on a daily, weekly, or monthly timescale [30]. The implementation of practical frequent data collection protocols can inform practitioners of \"early warning signals.\" Capturing the time when an athlete reaches an unstable stage (e.g., due to increased fatigue or microinjuries) is an essential first step for effective injury prevention [30]. Therefore, the team's staff is important in implementing iterative feedback cycles of (re)evaluation, identification, and intervention as a crucial injury-prevention strategy [81; 97]. This is essential because of the dynamic and complex nature of the HI, as proposed by the HI-CLD. Table 4 describes a proposed evaluation protocol and preventive intervention strategies for the aforementioned purposes and the following text provides practical implications. As proposed, the sports team's staff should organize and apply feasible field-based assessment protocols to evaluate athlete conditions. More sophisticated assessments that require costly equipment, such as isokinetic dynamometers, may make implementing continuous screening more difficult. Primary evaluations should focus on higher-order factors that better represent the system's function [30]. As HI-CLD proposes, these factors include workload characteristics, neuromuscular coordination, and psychological conditions. Regarding workload, repeated screening of GPS data can prevent spikes in the applied workload by maintaining an ACWR range of 0.8 to 1.3 and week-to-week changes below 10%. Particularly, week-to-week changes in HSR and distances exceeding 24 kg/h should be prevented. GPS technologies may make it feasible for fitness coaches to record and moderate the applied workload to the desired level. Additionally, using the Borg Rating of Perceived Exertion (RPE) scale, team staff can gauge athlete fatigue. On the other hand, core stability during HSR and overall sprinting biomechanics are essential components. Utilizing two-dimensional assessment or inertial sensors can enhance the evaluation of HSR biomechanics. Furthermore, the team's staff would benefit from adding to the assessment protocol examinations of ballistic function, core endurance tests [103; 104], hamstring endurance [57], hamstring eccentric strength [47], and hamstring flexibility [5]. Moreover, validated questionnaires such as the Athlete Burnout Questionnaire (ABQ) can provide information about the athlete's psychological condition (Table 4) [21; 22; 55].
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Security Evaluation
#### 5.3.3 Attack on Information in Transit  
The transmitted information is vulnerable to eavesdropping, modification, injection, interruption, and traffic analysis. Eavesdropping, modification, injection, and interruption are protected in our solution by assuring authentication, confidentiality, and integrity techniques, and also by using replay protection, which is based on SQs. The sequential number is sent in each frame, and next time it is used, it is increased by 1. The sequence number is always placed in the encrypted part of the frame. Such an approach, admittedly, extends the detection time of the repeated frame, but hides the current value of the number against the attacker.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 5 Security Analysis
### Achieve High Level of Anonymity  
In a wireless environment, messages can be intercepted easily. In [27], the MU sends the authentication message \(\text{m}_{1}\) to the FA in the authentication phase. The message content is not encrypted, and therefore, the attacker easily intercepts \(\text{ID}_{\text{HA}}\) and then determines the relationship among the MA, FA, and HA. When the relationship is known, the level of anonymity of the entire scheme is lowered, and the attacker can successfully use the attacks described in Sections 4.2 and 4.3. In our proposed scheme, when the MU wants to send the message to the FA, it will calculate the shared key EC and then encrypt the message by using EC to achieve the security and integrity requirements. Only the FA possesses the EC, and therefore, other people cannot decrypt this cipher text. Therefore, our scheme can achieve the C3 level requirement of high anonymity.