esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Material Removal Mechanism of Solid-Phase Oxidant
#### 4.3.1 Solid-Phase Oxidant Sodium Hydroxide (NaOH)  
(1) The surface phases of SiC after polishing all contain oxygen, and the surface compounds are SiO\({}_{2}\) and silicon oxides. On the one hand, the solid-phase oxidant NaOH reacts with CO\({}_{2}\) in the air under the action of friction to form Na\({}_{2}\)CO\({}_{3}\) and H\({}_{2}\)O, see Equation (6).On the other hand, the SiC reacts with H\({}_{2}\)O in the air to form SiO\({}_{2}\) on its surface, see Equations (18)-(20).The CO and CO\({}_{2}\) generated by the reaction escape into the air, and the generated C is removed by friction; the SiO\({}_{2}\) generated is attached to the SiC surface. Figures 8 and 9 show the surface morphology of SiC after dry plishing with NaOH.
2. Under the action of friction, the suspended silicon bonds of SiC also undergo a tribochemical reaction with NaOH, see Equation (16). The reaction produces soluble Na\({}_{2}\)SiO\({}_{3}\), which is removed from the SiC surface. As can be seen from the SEM inspection of the enlarged Figures 7c and 9b, after polishing, more small pits appear on the SiC surface with a smoother edge, not just brittle fracture removal. It can be shown that the Na\({}_{2}\)SiO\({}_{3}\) produced by the reaction is removed by dissolution.  
4.3.2 Solid-Phase Oxidant Sodium Percarbonate (Na\({}_{2}\)Co\({}_{3}\)--1.5 H\({}_{2}\)O\({}_{2}\))  
The solid-phase oxidant Na\({}_{2}\)CO\({}_{3}\)--1.5 H\({}_{2}\)O\({}_{2}\) decomposes into Na\({}_{2}\)CO\({}_{3}\), H\({}_{2}\)O, and O\({}_{2}\) under the action of friction, see Equation (2), and the products, in turn, produce a tribochemical reaction with SiC, as shown in Equations (17)-(20).The generated SiO  
Figure 8: Surface morphology after dry polishing with NaOH.  
Figure 9: Surface pits after dry polishing with NaOH. (**a**) White light interferometer detection of surface pits. (**b**) SEM inspection of surface pits. (**c**) Partial X coordinate shape. (**d**) Local Y coordinate shape. (**e**) Local enlargement of the pit.  
adheres to the surface of SiC, the generated CO and CO\({}_{2}\) escape into the air, and the generated C is removed by friction.The material removal rate consists of the products C, CO, and CO\({}_{2}\); however, the Si atoms in the SiC are not lost, but partly oxidized to SiO\({}_{2}\).Therefore, the removal rate is lower than that of the solid-phase oxidant NaOH, see Figure 5.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 4 Discussion and Conclusions
([PERSON]), M.B.H., K.B., D.D.L., A.E.O., S.B. ([PERSON]), Y.P., J.L.G., E.H.-L., G.J. and T.A.J. ; Writing--review and editing, S.B. ([PERSON]), M.B.H., R.B., D.D.L., [PERSON], [PERSON] ([PERSON]), [PERSON]-J., [PERSON], [PERSON], [PERSON] and [PERSON], [PERSON], M.O., P.G., [PERSON], F.S., G.J. and T.A.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Research Council of Norway through the Centre of Excellence funding scheme NTNU AMOS (Grant No. 223254), MASSIVE (Grant No. 270959), HYPSCI (Grant No. 325961), and Green-Platform (Grant No. 328724), NO Grants 2014-2021 under Project ELO-Hyp (Contract No. 24/2020), and the Norwegian Space Agency and the European Space Agency through PRODEX (No. 4000132515). **Data Availability Statement:** Not applicable. **Acknowledgments:** Special thanks goes to [PERSON] for performing the automatic analysis and providing the results for the spatial resolution statistics in Section 3.1. See [37,38] for details on their method. The authors would also like to thank the full HYPSO-1 team, including all students that have been a part of the project, everyone contributing in the Frohavet and Ny-Alesund campaigns, NanoAvionics for support with the satellite, and KSAT for extra downlink opportunities through the KSATlite network. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 1 Introduction
### Study Objective  
In this study, we propose a method to overcome the problems in previous research to estimate the friction coefficient used in the ELSD system of actual mass-produced high-performance vehicles. Therefore, an algorithm with a small computational load is constructed using only the driving torque and acceleration data that can be used in mass-produced vehicles. To operate the ELSD before the tire slips, we propose a method to predict the friction coefficient as accurately as possible, not only in the tire-road friction limit area but also in a section smaller than the friction limit.  
Since the various traction control and stability control methods in vehicles operate for a long time in the region of the grip limit, the friction coefficient calculated at this time is close to the actual friction value. However, it is necessary to estimate the friction coefficient for ELSD at all times while in operation. Therefore, in order to estimate the friction coefficient as accurately as possible, even in a situation where an acceleration smaller than the grip limit acts on the vehicle, the algorithm is designed to store and use the largest friction coefficient value calculated as the sum of the largest accelerations experienced so far.  
This study aims to improve the agility of high-performance vehicles, which differs from the ELSD study of sports utility vehicles. To enhance the driving performance of high-performance vehicles, the operation time delay and performance of the ELSD system play a crucial role. Therefore, precise estimation of tire-road friction coefficients is important in improving vehicle agility.  
In research related to electronic differential device control, an electronic differential device controller is designed to improve the performance of high-performance electric vehicles [16; 17]. Slip angle proportional-integral-derivative feedback control is performed to improve cornering stability through side-slip angle reduction and yaw-rate optimization. When controlling the electronic differential device, since the limited performance of the vehicle and the control intervention point are not considered, the driving torque feedback control to satisfy the target behavior may cause unstable vehicle behavior. Another problem is that the system may find it difficult to intervene when control is required because of the delay time up to the maximum torque when operating the drive motor actuator. The predictive control algorithm proposed in this study can solve this problem by predicting the driving wheel spin in advance through tire-road friction estimation and enable improved control.
## 3 Results and Discussion
### Case 2  
Case B is a scenario where the power system is rendered unstable by applying a continuous line tripping. The voltage instability index from applying this scenario was observed and analyzed. The contingencies occur every 10 s. Table 1 shows the succession contingency scenario on the transmission lines.  
As shown in Table 1, the contingency lists to be applied in the case-2 simulation are double circuit outages of 345-kV and 765-kV transmission lines. After the contingency occurs, the power system develops into a serious situation. Figure 15 shows that the bus voltage variation depends on the virtual bus in the power sending part.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**List** & **Time (s)** & **Contingency Line** & **Circuits** \\ \hline
1 & 10 & 1020-5010 & 2 \\
2 & 20 & 4010-6030 & 2 \\
3 & 30 & 4400-6950 & 2 \\
4 & 40 & 4650-4950 & 2 \\
5 & 50 & 4450-4650 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Series contingency scenario on the transmission lines.  
Figure 14: Progress of the index in case 1. (**a**) \(Z_{Th}\) and \(Z_{L}\). (**b**) \(Z\) index.  
As shown in Figure 15, the voltage magnitude decreases stepwise. This simulation terminated as voltage collapse occurred at approximately t = 88 s. Figure 16 shows the \(Z_{Th}\), \(Z_{L}\), and the Z index to monitor the power system situation.  
As shown in Figure 16, the power system becomes unstable gradually because the \(Z_{Th}\) increases while the \(Z_{L}\) decreases. Their values match at approximately t = 82 s; it can be observed that the index increases as the transmission lines are tripping and finally indicates 1.0. Consequently, this implies that the index can be sufficient for monitoring the voltage instability as it demonstrates all contingencies. The results of the voltage instability index were similar to those of case 1.
## 8 Simulation and Evaluation
### Throughput Analysis  
ANCR proposed in this paper is to maximize the network throughput by minimizing the total number of transmission of the network. In this part, we compare the performance of NFC and ANCR in increasing network throughput and analyse ANCR in improving the performance.  
Figure 6 shows the comparison of network throughput between NCR and ANCR under the number of data streams and packets increasing respectively. In Figure 6a, there are only two data streams having no intersections, which is the worst case that ANCR is faced with, so the two approaches have the same performance. When the number of data streams is five, we can find the difference from Figure 6b. It shows when the number of packets is in the range of 100 to 250, the throughput of ANCR is lower than that of NCR. The main reason is that in the initial stage, ANCR needs to arrange the transmission time, which means that some packets need to wait for the other packets to participate in coding together (the waiting time is set to 3 \(\upmu\)s). There is a time delay compared with NCR. However, with the number of packets increasing, the time to wait for coding is gradually reducing and tending to be stable. When the number of packets of each data stream increases to greater than 250, the throughput of ANCR performs better than that of NCR. In Figure 6c, it is clearly indicated that ANCR has the better throughput than NCR in the case of the twenty data streams. The throughput of ANCR is about 2 times that of NCR. The main reason is analysed as following: Compared with NCR, when ANCR is used in a network having a large number of data streams, more network coding opportunities would be generated. In addition, with the increasing of packets at the node having a coding opportunity, the number of transmission of ANCR decreases more significantly than that of NCR. This directly leads to a greater improvement of the network throughput.  
Figure 5: Comparison of delay distributions of packets between NCR and ANCR. (**a**) NCR: 220 packets, 2 data streams; (**a**) ANCR: 220 packets, 2 data streams; (**b**) NCR: 660 packets, 5 data streams; (**b**) ANCR: 660 packets, 5 data streams; (**c**) NCR: 880 packets, 20 data streams; (**c**) ANCR: 880 packets, 20 data streams.
## 4 Iterative Trust and Reputation Computation Model
### Model of Total Trust and Reputation
The total trust value can be calculated as  
\[T^{(j)}(t_{i+1})=\beta(T^{(j)}(t_{i})+C^{(j)}*T^{(j)}_{\ \ c}(t_{i})*(\alpha^{(j)}-( \frac{e^{a^{(j)}t_{i}}-e^{-a^{(j)}t_{i}}}{e^{a^{(j)}t_{i}}+e^{-a^{(j)}t_{i}}})^ {2})+(1-\beta)r(t_{i}) \tag{17}\]  
where the weight factor \(\beta\in[0,1]\) balances the values of trust and reputation value at time \(t_{i}.\)  
In real life, the changes of the reputation of a services provider are derived from the psychological trust status of a service client. Therefore, the reputation value of the services provider is updated by the trust values of \(N\) service clients. When the mean algorithm is adopted, the iterative reputation computation is shown as  
\[r(t_{i+1})=r(t_{i})+\frac{1}{N}\sum_{j=1}^{N}(T^{(j)}(t_{i+1})-T^{(j)}(t_{i})) \tag{18}\]  
Combining Equations (17) and (18), the iterative trust and reputation computation model is shown as  
\[\left\{ \begin{array}{l}T^{(j)}(t_{i+1})=(1-\beta)r(t_{i})+\beta(T^{(j)}(t_{ i})+C^{(j)}*T^{(j)}_{\ \ c}(t_{i})*(\alpha^{(j)}-(\frac{e^{a^{(j)}t_{i}}-e^{-a^{(j)}t_{i}}}{e^{a^{(j)} t_{i}}+e^{-a^{(j)}t_{i}}})^{2}),j=1,2,...,N.\\ r(t_{i+1})=r(t_{i})+\frac{1}{N}\sum_{j=1}^{N}(T^{(j)}(t_{i+1})-T^{(j)}(t_{i})).\end{array} \right. \tag{19}\]  
If initial trust values of the total \(N\) service clients and parameters of the derivative functions of trust values are given, the trust values of the total \(N\) service clients and the reputation of the services provider at the following time can be calculated by using the iterative model Equation (19). Similarly, if the difference form of Equation (5) is used to simulate the decrement processes of total trust values \(T^{(j)}(t_{i})\) and \(r(t_{i}),\) the iterative total trust and reputation computation model is similarly shown as  
\[\left\{ \begin{array}{l}T^{(j)}(t_{i+1})=(1-\beta)r(t_{i})+\beta(T^{(j)}(t_{ i})+C^{(j)}*T^{(j)}_{\ \ c}(t_{i})*(-(a^{(j)}-(\frac{e^{-a^{(j)}t_{i}}}{e^{-a^{(j)}t_{i}}})^{2})),j=1,2,...,N.\\ r(t_{i+1})=r(t_{i})+\frac{1}{N}\sum_{j=1}^{N}(T^{(j)}(t_{i+1})-T^{(j)}(t_{i}) ).\end{array} \right. \tag{20}\]  
Equations (19) and (20) are a set of difference equations with varying coefficients which describe the dynamical processes of trust and reputation in B2C e-commerce.
# Radioactivity in Soils of Kosovo and Radiological Implications
## 4 Conclusions  
This study aimed to determine the levels of natural (\({}^{226}\)Ra, \({}^{232\text{Th}}\), and \({}^{40}\)K) and artificial (\({}^{137}\)Cs) radioactivity of soil samples from Kosovo. It was done by using a high-purity germanium gamma-ray detector. The reported activity concentrations in the present study were within the proposed limits of international radiation protection agencies. In addition to activity measurements, the radiological hazard parameters: radium equivalent activity (Ra\({}_{\text{eq}}\)), gamma index (I\({}_{\gamma}\)), external hazard index (H\({}_{\text{ex}}\)), absorbed dose rate in the air (D), annual gonadal dose rate (AGDE), the annual effective dose rate (AEDE), and excess lifetime cancer risk (ELCR) were calculated, and the obtained results were lower than the world means for each mentioned parameter. Therefore, radioactivity concentration in the Kosovo soils is similar to the worldwide average and represents no radiological hazard for the population. The results obtained by this study will serve as a reference for possible future changes. Also, it provides a good baseline for the setting up of natural and artificial radioactivity mapping for monitoring possible radioactivity pollution.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12199520/s1](https://www.mdpi.com/article/10.3390/app12199520/s1)]([https://www.mdpi.com/article/10.3390/app12199520/s1](https://www.mdpi.com/article/10.3390/app12199520/s1)), ref. [50] is cited in the supplementary materials.  
Conceptualization, S.K., S.D. and G.H.; data curation, C.M.Y.: formal analysis, C.M.Y.; investigation, S.D. and G.H.; methodology, S.D. and G.H.; project administration, S.K. and H.A.; resources, S.D. and S.K.; software, C.M.Y.; supervision, S.K.; validation, S.K. and G.H.; writing--original draft, S.D. and C.M.Y.; writing--review and editing, H.A. and G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistics** & **Ra\({}_{\text{eq}}\)** & **I\({}_{\text{g}}\)** & **H\({}_{\text{ex}}\)** & **AGDE** & **ADR** & **AEDE** & **ELCR** \\  & & & **(uS/v/y)** & **(nGy/h)** & **(μSy/y)** & **(\(\times\)10\({}^{-3}\))** \\ \hline Minimum & 27.27 & 0.20 & 0.07 & 91.92 & 13.47 & 16.51 & 0.06 \\ Maximum & 100.40 & 0.74 & 0.27 & 335.11 & 48.77 & 59.81 & 0.21 \\ Average & 80.13 & 0.61 & 0.22 & 269.81 & 39.3 & 48.22 & 0.17 \\ World average & 370 & \(\leq\)1 & \(\leq\)1 & 300 & 60 & 70 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The values of radiologic hazard parameters calculated for the soil samples of Kosovo.  
Figure 7: Distributions of radiological health risks and calculated parameters ADR, AGDE, AEDE, and ELCR.
# Evaluation of Deviations Produced by Soft Tissue Fitting in Virtually Planned Orthopathic Surgery
## 2 Materials and Methods
### Surgical Planning and Surgery  
This study was carried out using computer-aided (partially guided) planning. The German Vincent Maxillofacial Prosthesis Laboratory was therefore contacted once the patient's complete preoperative study was available. In collaboration with the laboratory, a VSP was carried out using Dolphin Imaging and Management System software (version 11.8; Dolphin Imaging and Management, Chatsworth, CA, USA). Maxillofacial measurements were recorded for each surgery, including superior incisive anteroposterior and vertical displacement, pogonion anteroposterior and vertical displacement, and glabella-pogonion and glabella-superior incisive anteroposterior displacement. Once the desired advancements and movements had been planned, the German Vincent laboratory provided us with biocompatible resin surgical guide occlusion splints with which to perform the surgical intervention. These splints were modelled according to digital occlusion simulation and were the only surgical guides used in the procedure. No guide was used for mandibular and maxillary repositioning.  
Orthognathic surgery was carried out according to the VSP, with surgical occlusion splints used and the soft tissue fitting evaluated by the surgeon during the procedure. The soft tissue fit was evaluated by the surgeon during surgery by visual inspection while the repositioning was being performed. If a poor functional or aesthetic soft tissue fit was detected, the surgeon modified the position of the hard tissue to achieve a good soft tissue fit, without compromising guided occlusion, according to his own criteria. Otherwise, the VSP position was fully respected.  
The occlusal splints were manufactured using a Formlabs Form 2 3D printer with Formlabs Surgical Guide Resin, following the manufacturer's printing parameters (Formlabs Inc., Somerville, MA, USA). The post-processing protocol was followed according to the proposed Formlabs workflow for surgical guide resin, with the addition of an ultrasonic cleaning process in an IPA bath. UV curing was performed for 30 min at room temperature in a desiccant environment. Once finished, the splints were sterilized by the HSP Sterilisation Facility and were ready for use by the surgeon. The manufacturing tolerances declared with this work protocol are less than 0.2 mm.
## 3 Selection of Methods for Methodology
### Visual Characteristics (Features) of the Satellite Image Content
Jagged edges in satellite or aerial imagery can often be created naturally by tree lines, especially when trees have irregular or densely packed canopies (Figure 6d). If the edges between regions are overly smooth, meaning they lack sufficient contrast or sharpness (Figure 6c), it can result in regions being merged or fused, leading to under-segmentation. Several other features can be considered. If segmentation is based on color, the number of clusters is equivalent to different colors in the segmented image. This feature is influenced by contrast and roughness features. When used as an image segmentation quality metric, the context-dependent nature of such criterion presents challenges that make it less suitable for assessing image segmentation quality. While reducing the number of clusters may be desirable in some cases to simplify an image or emphasize dominant objects, in other cases, maintaining a larger number of clusters can be equally valid, especially when preserving fine details or capturing subtle color variations. The number of clusters also influences the computational complexity of the segmentation process, increasing processing time. Although comparison and optimization studies [15; 40] would need execution time as a criterion, time depends on the number of pixels to be processed, the algorithm (or its implementation), and computer specifications. Consequently, this variability can make it more challenging to compare the results obtained by another research directly. Finally, the shape of clusters can influence the segmentation quality. Different segmentation algorithms have varying sensitivities to cluster shapes. For example, K-means is  
Figure 6: Smoothness (continuity) of region edges. (a) “mountain_5.png”, (b) “mountain_7.png”, (c) “beach_290.png”, (d) “river_287.png”. Images from the AID. suited for spherical (convex) clusters, while other methods, such as DBSCAN, are suited for nonconvex clusters [23]. The shape of clusters feature is again context-dependent, and what might be considered an appropriate cluster shape can differ based on the goals of segmentation or application requirements.
# Quantum Modular Adder over \(GF(2^{n}-1)\) without Saving the Final Carry
## 5 Conclusions  
We designed an efficient quantum modular adder algorithm for \(GF(2^{n}-1)\) by utilizing the difference by one bit and one between \(2n\) and \(\sum_{i=0}^{n-1}2^{i}\). Because of these correlations, the modular operation for \(2n-1\) was performed by truncating the \(n\)-th bit as a final carry and adding \(1\) to the remaining value. As a result, the proposed circuit of the quantum modular addition over \(GF(2^{n}-1)\) reduced the number of gates and the depth compared to the existing circuits of quantum modular addition over \(GF(2^{n}-1)\). In particular, in the case of \(GF(2^{5}-1)\), which is one of the fields in multivariate quadratic-based post-quantum cryptography, the cost of the proposed circuit reduced the number of gates by \(71.1\%\) and the depth by \(73.6\%\). These results show that the proposed circuit can be extended to multiplication, exponentiation, cumulative sum, and cumulative multiplication over \(GF(2^{n}-1)\) and improve the efficiency of quantum information processing for data on \(GF(2^{n}-1)\).  
Investigation, S.-M.C.; validation, S.L.; software, S.-M.C. and C.-B.S.; methodology, A.K.; writing--original draft preparation, A.K.; writing--review and editing, S.-H.S.; supervision, S.-H.S. All authors read and agreed to the published version of the manuscript. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (<Q| Crypton>, no. 2019-0-00033, Study on Quantum Security Evaluation of Cryptography based on Computational Quantum Complexity); by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01417) supervised by the IITP ; and by a research fund from Hanyang University (HY-2018-N). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments). The authors declare no conflicts of interest.