esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Trajectory Prediction
#### 2.2.3 Speed-Driven Flight Trajectory Prediction (S-FTP)  
S-FTP is proposed to predict the flight trajectory for performing speed instructions in the horizontal plane, i.e., acceleration or deceleration. As shown in Figure 5, in the flight operational progress, the aircraft accelerates from minimum takeoff speed to its cruise speed and further decelerates to the maximum landing speed. Unlike the A-FTP, a DGP model cannot be optimized from flight positions directly since the speed instructions can be performed at any time and location. To this end, a three-stage S-FTP approach is proposed in this work, which is summarized in detail below:  
1. The flight time corresponding to the aircraft speed is firstly predicted by a DGP model, i.e., \(\left\{\left(v_{ir},f_{i}\right)\to t_{i}\right\}_{i=ur}^{vb}\), in which \(va\) and \(vb\) are the starting and target speed of the aircraft, respectively. Since the aircraft performance (acceleration) is limited by aerodynamics, \(fl_{i}\) is also considered to build discriminative characteristics for speed regulating at different flight levels.
2. Based on the predicted speed and aircraft heading, the positional offset of the flight trajectory is predicted regarding the flight time by another DGP, as shown below: \[\Delta Tr=\left\{\left(t_{i},v_{xi},v_{yi}\right)\rightarrow\left(\Delta x_{ i},\Delta y_{i},\Delta z_{i}\right)\right\}_{i=1}^{t_{z\to zb}}\] (14)
3. Based on the current flight position \(Tr_{0}=\left[x_{0},y_{0},z_{0}\right]^{\text{T}}\), the final predicted trajectory can be accumulated as follows, which is the final predicted trajectory for speed regulating flights: \[\begin{array}{l}Tr=\left[Tr_{0},\cdots,Tr_{i}\right]^{\text{T}},\forall i \in\left[1,t_{zu\to vb}\right]\\ Tr_{i}=\left[x_{i-1}+\Delta x_{i},y_{i-1}+\Delta y_{i},z_{i-1}+\Delta z_{i} \right]^{\text{T}}\end{array}\] (15)
## 3 Methodology
### Calculation of Landscape Metrics in R  
Landscape metrics are made up of functions that calculate landscape metrics and take raster data as input. The study is primarily based on the well-known raster package. Metrics on all available levels, including patch, class, and landscape level, are included in the study. Patch-level metrics are used to describe each patch in a landscape (a patch being defined as contiguous cells belonging to the same land-cover class). All patches belonging to a specific land-cover class are described by class-level metrics. Finally, landscape-level measurements represent the landscape's overall properties. Landscape metrics can also be categorized based on the qualities of the landscape they (conceptually) describe. Shape metrics, core area metrics, aggregation metrics, diversity metrics, and complexity metrics are all examples of area and edge metrics. The script was created in R software. First, a reading of the input raster was conducted, and then a grid (50 \(\times\) 50 cells) was created to create the boundaries of a landscape. Creating a 50 \(\times\) 50 grid for computing landscape metrics standardizes spatial units and simplifies the analysis by dividing a complex landscape into smaller, manageable cells. This grid setup allows for a uniform approach to calculating metrics like patch density and edge length within each cell rather than across the entire landscape, making it possible to capture and interpret natural variability more effectively. A grid also maintains scale consistency, ensuring that each metric is based on an equal area size, which is crucial for comparing patterns across cells. Additionally, grids facilitate spatial analysis by providing a framework to track ecological patterns and changes systematically across space, which is essential for understanding how landscape features influence ecological processes. Using a 50 \(\times\) 50 grid, in particular, balances detail with broader landscape coverage, enabling insights at a practical scale for multi-scale or comparative analyses. After that, an overlay was placed on the newly created grid on top of the input raster. The calculation of landscape metrics for each cell can be performed, and selected metrics specified according to the study area in the script and calculated metrics. Next, to visualize the results, values were connected to the grid. Selected metrics are discussed below in the tables. The landscape heterogeneity was analyzed with R software. Five patch-level measures, five landscape-level measures, and nine class-level measures were selected, as shown in Tables 4-6. These measures were chosen because of their widespread usage in landscape research and well-documented effectiveness in characterizing spatial patterns. The percentage of each land cover class' area occupied in each pixel was used to determine the landscape composition of each pixel. A broader set of variables was used to quantify the landscape layout. In this study, graphical and pictorial descriptions of landscape fragmentation were compiled.
# Behavior of Primary Human Oral Keratinocytes Grown on Invisalign(r) SmartTrack(r) Material
###### Abstract  
Orthodontic clear aligner treatment is gaining tremendous popularity. The world market leader is Align Technology(r) and its product Invisalign(r). Although numerous patients are treated with Invisalign(r) aligners, only little is known about the cellular effects of aligner material on oral epithelial cells. In the present study, we aimed to investigate the effects of SmartTrack(r) clear aligner material on directly cultured primary human oral keratinocytes (HOKs). Cell morphology and behavior were investigated by scanning electron microscopy and bright field microscopy. Aligner effects on viability were detected by cell-counting-kit (CCK)-8 and live/dead staining. Gene expression of several inflammatory and barrier proteins was assessed by qPCR. Cells cultured on tissue culture plastic served as control. Cell proliferation/viability was significantly lower in cells cultured on aligner material (\(p<0.05\)) in comparison to control. Live/dead staining did not reveal an increase in the number of dead cells on aligner surfaces. After two and seven days of incubation, interleukin (IL)-6 expression decreased, and IL-8 expression increased in HOKs cultured on aligner surfaces. The expression of intercellular adhesion molecule 1 (ICAM-1) significantly decreased after seven days. Gene expression of epithelial barrier markers showed that integrin (ITG)-\(\alpha\)6 significantly decreased after two and seven days. A significant decrease in ITG-\(\beta\)4 and E-cadherin expression levels compared to control could only be seen after seven days. We did not find any cytotoxic effect, but alterations in the cell's barrier functions and inflammatory reaction were obvious. Clinical studies are required to give further insights into clinical reactions on the underlying aligner material of this quickly expanding orthodontic appliance.  
Orthodontics; aligner; proliferation; epithelial barrier; inflammation; human oral keratinocytes; in vitro; Invisalign; SmartTrack
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 4 Channel Model
### Atmospheric Turbulence  
The atmospheric turbulence present in Neom city, Tabuk, Saudi Arabia, is considered, as shown in Figure 3. Due to its location, Neom experiences unique climatic conditions that differ from the surrounding desert area. One notable aspect of this city is its geographical diversity, which includes both mountain ranges and sandy beaches. This geographic variation contributes to the presence of atmospheric turbulence in the region, which can impact wireless communication systems. The interaction between the mountainous and coastal environments can lead to changes in atmospheric conditions, such as varying wind patterns and turbulence levels [33; 34]. Numerous statistical models have been put forward for characterizing atmospheric fading, aiming to investigate the impact of turbulence-induced fading on the performance of FSO systems. These models are employed to study and analyze the effects caused by atmospheric turbulence on FSO system performance. For weak-to-moderate turbulence conditions, the intensity fluctuation is modeled as a log-normal distribution. For strong turbulence conditions, a Gamma-Gamma distribution is used for the atmospheric fading model (we agree that the adaptation of the LN and GG channels was necessary for a fair comparison with the models in the literature [30], as these models also consider these channels. By employing consistent channel models, we can accurately compare the performance and advantages of different systems). As a result, the Probability Density Function (PDF) is defined as [17]:  
\[f_{h_{t}}(h_{t})= \frac{1}{h_{t}\sqrt{8\pi\sigma_{x}^{2}}}\exp\left(-\frac{(\ln(h_{t })-2\mu)^{2}}{8\sigma_{x}^{2}}\right),\text{ for LN}\] \[\frac{2(\alpha\beta)}{\Gamma(\alpha\lambda)\Gamma(\beta)h_{t}}h_{t}^{\frac{ \alpha+\beta}{2}}\ K_{\alpha-\beta}\left(2\sqrt{\alpha\beta h_{t}}\right), \text{ for GG}\]  
where \(\Gamma(a)=\int_{0}^{\infty}u^{a-1}e^{-u}du\) and \(K_{j}(.)\) are the Gamma function and the modified Bessel function of the second kind of order j, respectively, \(\sigma_{X}^{2}=1.23\ C_{n}^{2}\ k^{\frac{7}{6}}\ z^{\frac{11}{6}}\) is the log irradiance variance, \(\mu=-\sigma_{X}^{2}\), \(C_{n}^{2}\) is the refraction structure's index, and \(k=2\pi/\lambda\) is the optical wave number with wavelength \(\lambda\). Also, the parameters \(\alpha\) and \(\beta\) are the effective number of large-scale and small-scale eddies of scattering environment, respectively [34].  
\[\alpha=\left[\exp\left(\frac{0.49\sigma_{x}^{2}}{\left(1+1.11\sigma_{x}^{12/5} \right)^{\frac{7}{6}}}\right)-1\right]^{-1} \tag{7a}\]\[\beta=\left[\exp\left(\frac{0.51\sigma_{\mathrm{x}}^{2}}{\left(1+1.11\sigma_{ \mathrm{x}}^{12/5}\right)^{\frac{7}{6}}}\right)-1\right]^{-1} \tag{7b}\]
# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 3 System Framework and Model Training  
### System Framework Design  
The generator in WGAN-GN is made up of four completely connected networks with 10, 50, 100, and 200 neurons per layer, respectively. The discriminator has four fully connected layers as well with 100, 50, 10, and 1 neurons per layer. ReLU is chosen as the activation function, with the dimension of random noise set to 10. The SAE module is used to classify faults, and the structure contains three layers with 200, 100, and 4 neurons. The SAE-connected classifier is then implemented using Softmax regression. The SAE weights are updated by the BP algorithm, which also allows parameters to be adjusted. The adaptive moment estimation (ADAM) optimization algorithm is used for model updating.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 2 Related Work  
### INS and Its Components  
The academic literature appears to be poor in sources on the composition and functionality of the INS. In [11] the results of a survey of INSs provided by 35 vendors is presented. The findings have been used to develop a simplified model of a prototypical INS that is subsequently used as a basis for discussing cryptographical measures to improve the protection of the integrity of navigation data in INSs.  
The IMO-Vega Database [15] is an online platform which includes up-to-date IMO requirements. Two resolutions define the performance standards of the INS, namely Resolution MSC.252(83) \"Adoption of the Revised Performance Standards for Integrated Navigation Systems (INS)\" [5; 6; 7], and Resolution MSC.86(70) Annex 3 \"Recommendation on Performance Standards for an Integrated Navigation System (INS)\" [16]. Resolution MSC.86(70) Annex 3 is valid for the INS installed on or after 1 January 2000 and before 1 January 2011. If the INS is installed on or after 1 January 2011, it should comply with Resolution MSC.252(83). In the following we focus on Resolution MSC.252(83), that consists of three parts:  
* Resolution MSC.252(83) \"Adoption of the Revised Performance Standards for Integrated Navigation Systems (INS) Introduction, Contents, Module A-B\" [5];
* Resolution MSC.252(83) \"Adoption of the Revised Performance Standards for Integrated Navigation Systems (INS)--Module C-D\" [7];
* Resolution MSC.252(83) \"Adoption of the Revised Performance Standards for Integrated Navigation Systems (INS)--Appendices\" [6].  
Resolution MSC.252(83) directly indicates some components of the INS. It also points to two IMO-related documents (Paragraphs of 3.7.1, 10.1.1, and 13.7.2 refer to MSC/Circ.982, and paragraphs of 3.5.1, 8.1.1, and 8.1.2 refer to Safety of Life at Sea (SOLAS) Convention regulation V/19). These are:  
* SOLAS Chapter V \"Safety of Navigation\", Regulation 19 \"Carriage requirements for shipborne navigational systems and equipment\";
* MSC/Circ.982 \"Guidelines on Ergonomic Criteria for Bridge Equipment and Layout\".  
\"Chapter V: Safety of Navigation\" of the SOLAS convention includes regulations regarding navigation requirements of vessels including life-saving signals, ship reporting systems, navigational warnings, vessel traffic services, etc. \"Regulation 19: Carriage requirements for shipborne navigational systems and equipment\" that defines applications, requirements, shipborne navigational equipment, and systems for ships; and Appendix 2 of the IMO MSC/Circ.982 (\"Proposed Equipment for Workstations\") also include information useful for identifying components of the INS.  
A model of INSs with detailed information on components and their interactions is not available.
# Quality Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR
## 2 Theory of Wasteless Mining Methods
### Planning Principles for Wasteless Mining  
The essence of wasteless mining is to fully recycle the mineral resources. To reduce the adverse environmental impact of mining activities, wasteless mining can be realized by the reuse of the generated waste. To this end, the principles of wasteless mining planning include seven main aspects:  
1. Waste reduction. Under the premise of safety and economic rationality, the number of mining activities and the output of waste rock should be reduced in the preliminary design of the mine, and the mining parameters should be optimized to reduce the loss depletion rate and stabilize the ore input grade;
2. Comprehensive utilization of resources. For poor ore or ore with high mining difficulty, reasonable ore blending is required to improve the utilization rate of mineral resources; the waste rock, tailing sand and wastewater should be comprehensively recovered to realize the waste treatment;
3. In situ processing and utilization. The waste rock can be treated in the field to avoid waste transportation, and the transportation distance can be reduced as much as possible;
4. Control of secondary pollution. The useful components in the tailings and wastewater treatment can be recovered; the mineral processing and wastewater treatment-related technologies can be further explored to improve the degree of the resourcefulness of waste materials; the pollution generated by the treatment should be reduced to avoid secondary pollution;
5. Using advanced equipment and techniques. The advanced mining equipment and techniques should be developed in wasteless mining to achieve the mechanization, automation, and intelligent transformation of mines. Finally, the mining efficiency and mining enterprises' comprehensive benefits can be greatly improved;
6. The unity of economic, environmental and social benefits. Wasteless mining can be used to ensure the environmental and social benefits, as well as the economic benefits of the enterprise;
7. Adaptation to local conditions and circumstances. During the wasteless mining, optimal planning of wasteless mining for the mining enterprise can be obtained by analyzing the mining enterprise's own situation and the current economic market [13].
## 2 Security Requirement and S-Box Design Methods
### Combination Method  
The Hierocrypt [40] block cipher uses two different S-boxes, namely a high-level S-box and lower-level S-box. For high-level S-box, the construction is based on bit permutation, followed by a power polynomial of \(x^{247}\) over \(\mathbb{F}_{28}\) using the irreducible polynomial of \(z^{8}+z^{6}+z^{5}+z+1\). Lastly, an affine transformation over \(\mathbb{F}_{2}\) is applied on the output. The construction for a lower-level S-box is not described; therefore, we conclude that they used the heuristic approach for lower-level S-box. Using the same approach, the CLEFIA [41] block cipher employs two different S-boxes constructed using inverse function over \(\mathbb{F}_{28}\) and using four 4-bit random S-boxes for the second construction. Therefore, Hierocrypt's and CLEFIA's S-box construction are combination methods because they use the mathematical function approach for the first S-box and heuristic approach for the second S-box.  
The KALYNA [42] block cipher adopted the S-box construction proposed by [PERSON] et al. [43], which was constructed based on gradient descent. This S-box construction is basically an enhancement of the improved hill climbing method proposed by [PERSON] et al. [44] in 2010. Generally, they generate an initial solution, \(S\), which is a bijective S-box with a minimum value of DU based on function \(F\). The function \(F\) is basically determined as an inverse function over finite field \(\mathbb{F}_{28}\). Then, they randomly swap a number of values in \(S\) to generate \(S_{t}\) as the final S-box. Therefore, we classify this construction as a combination method since they combine the mathematical function approach with the random generation approach.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 4 Application of SOI Mushroom Microdosimeters  
Radiobiological effects in particle therapy can be predicted from experimental microdosimetry with silicon microdosimeters. In order to investigate the performance of newly developed SOI microdosimeters at CMRP, different experiments have been carried out at a number of proton and heavy ion therapy centres.
## 4 Results and Discussion
### Effects of Different Mediums
When the Reynolds number changes, Figure 14 shows the comparison results of the numerical calculation and model prediction of three mediums under different Reynolds numbers in Region III and Region IV. Similar to the cases of different relative gaps, the relative deviation of Region III is still large at different Reynolds numbers in the gap. The maximum relative deviation of critical CO\({}_{2}\) reaches 17.7% when the Reynolds number is 2.0 \(\times\) 10\({}^{5}\). The relative deviation has no positive or negative regular pattern but decreases with the increase of the Reynolds number. In Region IV, the relative deviation has no regular changes. Only when the Reynolds number is 1.0 \(\times\) 10\({}^{8}\), the relative deviation is greater than 1% and the relative deviation of air is 6.6%. In fact, in the experiment of Ref. [3], the cases of Reynolds numbers greater than 10\({}^{7}\) are not included. Therefore, the decrease in model accuracy under high Reynolds numbers is tolerable. Figure 12: Comparison of various mediums with different \(S/R_{0}\). (**a**) Region III. (**b**) Region IV. Figure 14: Comparison of various mediums with different \(Re\). (**a**) Region III. (**b**) Region IV. Figure 13: Velocity and vorticity distribution in the gap. (**a**) Velocity distribution in the gap of \(S/R_{0}\) = 0.005 (Region III, air, \(Re\) = 1.0 \(\times\) 10\({}^{6}\)). (**b**) Velocity distribution in the gap of \(S/R_{0}\) = 0.025 (Region III, air, \(Re\) = 1.0 \(\times\) 10\({}^{6}\)). (**c**) Vorticity distribution in the gap under different mediums (\(S/R_{0}\) = 0.015, \(Re\) = 1.0 \(\times\) 10\({}^{6}\)). IV is very high. Combined with the above research, it can be concluded that in Region III, with the increase of the relative gap and Reynolds number, the model becomes more and more accurate when the working point is closer to Region IV. This provides a direction for the aerodynamic design and structural design of radial inflow turbomachinery in practical engineering applications. In the design of standard operating conditions, the Reynolds number can be selected concerning the range mentioned above. The corresponding Reynolds number range can also be referred to the structural design. The power loss of turbomachinery is obtained by a more accurate model to guide the aerodynamic design.
## 4 Discussion
### Secondary Stability  
MRT is the highest reaction force that occurs during the removal of the miniscrew from the bone [55]. MRT measurements in dental implants are generally considered a reliable method for the assessment of osseointegration. They have been frequently used in studies evaluating the secondary stability of orthodontic miniscrews [20].  
When the acid-etched method was evaluated in this systematic review, in only one study the acid-etched group was found to be more stable than the control group. In the study by [PERSON] et al. [33], the MRT values of the acid-etched group without immersion in calcium chloride were found to be higher than the control group. Miniscrews with acid-etched surfaces showed better stability when they were immersed in calcium chloride solution, or combined with sandblasting/grit-blasting methods.  
There was a contradiction when comparing miniscrews with anodized surfaces in this study. In the study by [PERSON] et al. [27], miniscrews with anodized surfaces were found to be more stable than the control group according to the MRT values measured 6 weeks after placement. However, in the study by [PERSON] et al. [37], there was no difference between the control group and the anodized surfaces according to the MRT values measured at 3 weeks and 12 weeks. The use of anodized surfaces in combination with cyclic pre-calcification and heat treatment increased the secondary stability of miniscrews.  
According to the study of [PERSON] et al. [36], SLA surfaces are more stable than machined surfaces. In contrast to this article, [PERSON] et al. [39] found no significant differences between SLA surfaces and machined surfaces. Similarly, [PERSON] et al. [38] found no differences between the stability of SLA surfaces and machined surfaces. [PERSON] et al. [28] found the combination of SLA surfaces with anodic oxidation to be more stable than SLA and the control group, according to MRT values.  
Similar to the SLA method, the RBM method is also applied by acid etching and sandblasting to change the surface topography of titanium implants. The difference between these two methods is that the SLA method utilizes non-resorbable particles such as alumina for sandblasting, whereas the RBM method utilizes resorbable particles such as HA or calcium phosphate [56]. This provides a low risk of debris contamination and rapid osseointegration in the RBM technique [57]. In a study by [PERSON] et al. [29], MRT values at week 2 were significantly lower in the machined surface group than in the RBM group, which was attributed to greater bone resorption in the early period. At week 4, there was no significant difference in MRT values between the groups. The authors suggested that although new bone formation occurred more on the machined surface than on the RBM surface after initial bone resorption, the torque values increased faster with the RBM surface.