esa-sceva/satcom-chunk-collection huggingface dataset:
# Imaging Characteristics of Disturbance Flow Field Surrounding a Hypersonic Target
## 2 Methods
### Flow Refractive Index Computation and Ray Tracing Algorithm
\begin{array}{l}\mathbf{K}_{3}=f\Big{(}t_{i}+\frac{h}{2},\mathbf{T}_{i}+\frac {h}{2}\mathbf{K}_{2}\Big{)}=\mathbf{D}\Big{(}\mathbf{r}_{i}+\frac{h}{2}\mathbf{T}_{i}+\frac{h ^{2}}{4}\mathbf{D}(\mathbf{r}_{i})\Big{)}\\ \mathbf{L}_{3}=g\Big{(}t_{i}+\frac{h}{2},\mathbf{r}_{i}+\frac{h}{2}\mathbf{L}_{2}\Big{)}= \mathbf{T}_{i}+\frac{h}{2}\mathbf{D}\Big{(}\mathbf{r}_{i}+\frac{h}{2}\mathbf{T}_{i}\Big{)} \end{array} \right. \tag{9}\]  
\[\left\{ \begin{array}{l}\mathbf{K}_{4}=f(t_{i}+h,\mathbf{T}_{i}+h\mathbf{K}_{3})=\mathbf{D} \Big{(}\mathbf{r}_{i}+h\mathbf{T}_{i}+\frac{h^{2}}{2}\mathbf{D}(\mathbf{r}_{i})\Big{)}\\ \mathbf{L}_{4}=g(t_{i}+h,\mathbf{r}_{i}+h\mathbf{L}_{3})=\mathbf{T}_{i}+h\mathbf{D}\Big{(}\mathbf{r}_{ i}+\frac{h}{2}\mathbf{T}_{i}+\frac{h^{2}}{4}\mathbf{D}(\mathbf{r}_{i})\Big{)}\end{array} \right. \tag{10}\]  
The Runge-Kutta method for ray tracing is described by Equations (7)-(10), where \(h\) is the step length. If the initial position vector \(\mathbf{r}_{0}=(x_{0},y_{0},z_{0})\), ray vector \(\mathbf{T}_{0}=n[\cos\alpha_{0},\cos\beta_{0},\cos\gamma_{0}]\) and step length \(h\) were specified, the propagation path of the light can be obtained by iterating Equations (6)-(10). Considering the axisymmetry of the model, the two-dimensional refractive index field can be transformed into a three-dimensional refractive index field for ray tracing. It is crucial to calculate the gradient of the refractive index at each point in the disturbance flow field to facilitate the ray tracing procedure. The gradient of the refractive index \(\
abla n\) can be calculated using the Barron gradient operator [21]  
\[\left\{ \begin{array}{l}\frac{\partial n(i,j,k)}{\partial t_{i}}=\frac{1}{1 2\Delta\mathbf{x}}(n(i-2,j,k)-8n(i-1,j,k)+8n(i+1,j,k)-n(i+2,j,k))\\ \frac{\partial n(i,j,k)}{\partial t_{i}}=\frac{1}{12\Delta\mathbf{y}}(n(i,j-2,k) -8n(i,j-1,k)+8n(i,j+1,k)-n(i,j+2,k))\\ \frac{\partial n(i,j,k)}{\partial z}=\frac{1}{12\Delta\mathbf{z}}(n(i,j,k-2)-8n(i,j, k-1)+8n(i,j,k+1)-n(i,j,k+2))\end{array} \right. \tag{11}\]As mesh nodes cannot contain every point in the disturbance flow field, it is required to interpolate the refractive index or refractive index gradient using the distance-weighted averaged interpolation algorithm with the 8 nearest nodes, which can be expressed as [22]  
\[n_{j}=\frac{\sum\limits_{i=1}^{8}n_{i}\prod\limits_{ \begin{subarray}{c}j=i\\ j=1\end{subarray} }^{8}d_{j}}{\sum\limits_{ \begin{subarray}{c}i=1\\ j=1\end{subarray} }^{8}\prod\limits_{ \begin{subarray}{c}j=i\\ j=1\end{subarray} }^{8}d_{j}} \tag{12}\]  
\[d_{j}=\sqrt{(x-x_{j})^{2}+(y-y_{j})^{2}+(z-z_{j})^{2}} \tag{13}\]
## 8 Conclusions  
The above material indicates that the transport properties of ion-exchange membranes are determined primarily by their structure, which in its turn depends on the membrane preparation  
Figure 8: Scheme of concentration profiles in diffusion layers (DL) and a membrane (Mb) established at zero current (**a**) and a current density \(i\) close to \(0.5\)\(i_{\mathrm{lim}}\) (**b**). Counterion 1 is preferably sorbed by and transferred through the membrane as compared to counterion 2. The profiles match the results of calculation using model [330]. Explanations are given in the text.  
method. As shown in a number of publications, there is a trade-off between the membrane permeability/conductivity and the selectivity of the transfer rate of the target component. In this regard, the quality of membrane material can be characterized by means of the relation between the permeability and permselectivity, for example, by the relation between the ionic conductivity and counterion transport number (in electrodialysis) or ionic conductivity and permeability of fuel molecules (crossover in fuel cells). To enhance this relation, a wide range of approaches is used, such as crosslinking, preparation of hybrid membranes with embedded nanoparticles, and surface modification. Their use can significantly increase the selectivity of transport processes. For example, cross-linking can significantly reduce coion transport, doping with nanoparticles can increase ionic conductivity or, conversely, selectivity of transport processes. A very interesting approach is the surface modification, which allows one to achieve the separation of mono- and multivalent ions. A group of synthetic methods that make it possible to eliminate the formation of large membrane pores during the synthesis (obtaining grafted membranes or membranes with a fairly rigid three-dimensional matrix) should be separately noted.  
The use of these approaches offers opportunity for expanding the prospects of ion-exchange membrane use in such a traditional application as electrodialysis, or in rapidly developing hydrogen energy. On the other hand, surface modification can significantly increase the efficiency of extracting valuable components from natural or waste-water.  
The authors declare no conflict of interest.
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 4 System Model
* PoW consensus algorithm is used in the public blockchain to validate the transactions and add the blocks into the blockchain. * Mutual authentication: When two nodes want to communicate with each other, they first need to be recognized before the interaction. The identity of all nodes is stored on the BSs that authenticate the ANs. * Nonrepudiation: The nodes that take part in the communication cannot deny sending the packets. The nonrepudiation scheme is performed on the blockchain. All operations are stored on it, therefore, data tampering cannot be performed. * Integrity: This includes the data packets' integrity, where unauthorized nodes cannot access and illegally tamper with the data packets in the interaction process. The integrity of the data packets is ensured by the authentication process, which is carried out by the public and private blockchains. * Transparency and traceability: The whole process is traceable and transparent because the information of SNs is bound to each data record. Whenever any malicious node exists in the WSN, it can be identified by the traceability feature of the proposed model. The steps included in the proposed model are initialization, registration, authentication and trust evaluation of the nodes.
## 4 Discussion
Clusters 2 and 3 have their tops in the middle troposphere, and they could be resolved by using supplementary information such as cloud coverage, optical thicknesses, water vapor profiles, microphysical top phases or specific cloud type retrievals. However, the use of such tools does not guarantee a clearer resolution of the different types of the clouds involved owing to the fact that different clouds can share the same properties, or there can be multilayered clouds. The latter aspect applies to cluster 4 as well. Another point that is worth mentioning is the use of vertical profiles to establish, through linear regressions, a relationship between temperature and height. Non-linear fits to the temperature profiles could also be conducted. Even though this alternative is expected to improve the outcome that relates the two variables, it is worth emphasizing that the results described in this paper are qualitatively in correspondence with the existing literature. Regarding the partition of cluster 1 into Ci and Cb pixels the results is encouraging considering this was a first attempt. Yet, some types of high clouds (Ccs and Css), or even mixtures of all the high clouds that populate the analyzed cluster, were left unaccounted for due to the A \(>\) 200 km\({}^{2}\) criterion. The number of unaccounted objects peaked in summer. The seasonal differences could be partly diminished by conducting a seasonal analysis. However, the SDs of the D\((\text{P})\) values were low enough to expect a significant reduction in the seasonal differences. Separately, the methodology should be refined in order to include objects with hollow regions (a typical example of this is an entire patch of Ccs) by carrying out morphological operations to cluster 1. Other possible improvements include the use of alternative models to quantify object perimeters and the implementation of multifractal analyses. The paper was aimed at classifying CC in SA and its adjacent oceans. The results regarding the cloud regimes are in agreement both with the different meteorological phenomena in the region and with the existing literature. Future research directions pursue the use of these results in analyses that include the identification of synoptic and sub-synoptic cloud systems and their relationships to multi-scale atmospheric processes.
## 3 Results
### Translation and Validation of RAC-Q  
The translation and validation processes was performed according to the 10 proposed steps. In general, there was no significant difficulty faced during the process. The forward translation was carried out by two independent translators, who produced comparable and highly similar forward translations. An important point that was discussed was the appropriate Malay term for compassion. Both translators chose \"penyayang\" for \"compassion\". Deeper consideration was given, as one of the central values of MOH's corporate culture is \"penyayang\", but it was translated into English as \"caring\". After an in-depth discussion about the connotations of \"budaya penyayang\" in MOH's corporate culture, the committee agreed that \"penyayang\", in the corporate culture, is equivalent to compassion. \"Penyayang\" has also been used in the local healthcare system since the beginning of its service. Moreover, the concept of compassion has emerged relatively recently. No major modification was applied during the cognitive debriefing. The respondents gave feedback indicating that the questionnaire could be easily understood. The questions also sufficiently covered their thoughts about compassion. Some of the respondents suggested providing a short video to explain the survey before the respondents started to answer. Thus, a short introductory video was prepared and distributed during the pretest and actual survey.
## 5 SDN-based VANETs Security Challenges
### Forgery Attack  
This type of attack comprises molding and transmitting bogus admonition messages, so as to taint enormous bits of the roads [86]. For example, the attacker can communicate a fashioned Global Positioning System (GPS)-based signal so as to deceive vehicles to receive off-base area data. Instances of conventional quick assistance against this attacker guarantees the secured confinement. In a study by [PERSON] et al. [87], the authors define the triangulation method to decide the situation of a vehicle. Utilizing this procedure, assailants cannot diminish the separation between two neighboring vehicles. In another study by [PERSON] et al. [88], the authors developed the unquestionable multilateration so as to decide the situation of the vehicle from the lot of reference focuses whose locations are identified  
Figure 13: Distributed denial of service (DDoS) attack in SDN-based VANETs.  
ahead of time. Independent position confirmation [89; 90] is a system to distinguish the effect of misrepresented position data specifically for location based directing conventions at the VANETs. It depends on different ideas, for example, the greatest thickness limit and position guarantee catching, [PERSON] et al. [90] proposed a protected appropriated area check to distinguish vehicles cheating about their positions. The discovery component does not depend on extra equipment yet just on communitarian neighbors.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 3 Experiment Results
### Experiment Setup
#### 3.1.3 Defects and Evaluation  
All images used for this experiment were captured, and several examples of UAV images containing discrete defects are shown in Figure 9. A large dataset was constructed to verify the algorithm, which contains 50 rail images, and each one has a lot of defects (more than 2) on its surface. Rail surface defects are divided into two categories depending on size and maintenance standard of the railway. In general, a defect whose size is larger than 255 mm\({}^{2}\) should be inspected as soon as possible, because it may result in serious accidents [49]. Therefore, in our data set, the defects are divided into two types, according to size \(\Omega\) of defects, as shown in Table 1.  
Figure 8: Experiment environment.  
The dataset includes 208 defects: 126 in T-I and 82 in T-II. These defects are labeled by experts.  
Within the Matlab 2014 compile environment, the designed software is achieved by Matlab program languages, and an inspected defect is automatically marked by a rectangle. Then, the inspected defect is accepted as correct if it matches the marked defect in the corresponding image.  
In information retrieval and pattern recognition, recall and precision are the basic criteria for evaluation of retrieval quality. The two criteria are used for evaluation of our experiment result in this paper. The precision (\(P\)) and recall (\(R\)) are respectively given by:  
\[P=TP/(TP+FP) \tag{20}\]  
\[R=TP/NP \tag{21}\]  
where \(TP\) is the number of defects that were inspected correctly, \(FP\) is the number of wrongly inspected defects, and \(NP\) is the number of marked defects for the corresponding defect category (T-I and T-II). Specifically, the recall is more significant than precision, because a defect which is not detected may have severe consequences.  
It should be noted that each defect in the dataset is labeled with a minimum enclosing rectangle; thereby, the real region of rail surface defects is approximated by the region of its minimum enclosing rectangle. As to the LWLC-GSME model, all these detected defects are also automatically marked by a minimum enclosing rectangle. This is regard as correct inspection if the minimum enclosing rectangle of a detected defect overlaps the corresponding labeled image more than 85%; otherwise, it is error detection. \(TP\) denotes number of defects that were correctly inspected.
## 3 Noise Analysis
### Noise Analysis  
#### 3.5.1 Measurement of Noise around the Path Using AEDT  
The noise maps for the two scenarios shown in the AEDT software are the same as Figure 14. In order to perform noise analysis according to the route, eVTOL operating hours were set at 06:00 to 20:00, including rush hour, and noise analysis was conducted by applying R44, a five-seat helicopter, for that time.  
In the AEDT software, the grid method was selected for the receptor setting, for which the noise map is configured in the range. Since the path is from west to east, a total of 10,000 receptors were placed on the path: 100 from the \(X\)-axis and 100 from the \(Y\)-axis, based on latitude 37.48 and longitude 126.64. The interval between receptors was set to 0.31 nautical miles for the \(X\)-axis and 0.1 nautical miles for the \(Y\)-axis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Gangeo-gu** & **Yapcheon-gu** & **Yeongdeungpo-gu** & **Dongjak-gu** & **Gwanak-gu** & **Seecho-gu** \\ \hline Real area (km\({}^{2}\)) & 41.4 & 17.4 & 24.6 & 16.4 & 29.6 & 47.1 \\ \hline Number of cells & 52 & 22 & 31 & 20 & 38 & 59 \\ \hline Cell area (km\({}^{2}\)) & & & 0.8 & & & \\ \hline Total cells area (km\({}^{2}\)) & 41.6 & 17.6 & 24.8 & 16 & 30.4 & 47.2 \\ \hline Error (\%) & \(-\)0.48 & \(-\)1.1 & –0.8 & 2.4 & –2.7 & –0.2 \\ \hline  & **Gangnam-gu** & **Songpa-gu** & **Mapo-gu** & **Yongsan-gu** & **Seongnam-si** & **Gwacheon-si** \\ \hline Real area (km\({}^{2}\)) & 39.6 & 33.9 & 23.9 & 21.9 & 141.7 & 35.9 \\ \hline Number of cells & 49 & 43 & 30 & 28 & 177 & 45 \\ \hline Cell area (km\({}^{2}\)) & 0.8 & & & & & \\ \hline Total cells area (km\({}^{2}\)) & 39.2 & 34.4 & 24 & 22.4 & 141.6 & 36 \\ \hline Error (\%) & 1.01 & \(-\)1.47 & –0.4 & –2.3 & 0.1 & –0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Characteristics of virtual space.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Helicopter Type** & **Representative Helicopter** & **Number of Operations** & **Proportion (\%)** \\ \hline
1–2 seats piston & Robinson R22 & 1165 & 19.95 \\ \hline
3–5 seats piston & Robinson R44 & 1160 & 23.29 \\ \hline
6–8 seats turbo-shaft & Bell 206B & 3001 & 51.39 \\ \hline
9–12 seats turbo-shaft & Sikorsky S-76 & 314 & 5.38 \\ \hline Total & & 5840 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fleet composition of helicopters by type.  
According to the FAA, 60 dB is the typical noise at the urban level, over 65 dB is the noise outside the critical noise-exposure level, and 55 dB is the quiet noise at the suburban level. Therefore, since this paper aims to analyze noise for eVTOL to operate in urban areas, noise below 55 dB was been considered. In addition, the analysis excludes receptors arising from out-of-path sections where the department line and arrival line are not exactly in line when a path is made via AEDT.
## Appendix A Simulation Example
It is found that when the induced voltages have smaller values (e.g., 0.6 V) or a code word has a smaller block length (e.g., for the symbol size of 2), DTN tends to happen more often. In row 3, at a frequency of 204 MHz, three symbols become corrupted (i.e., at symbols 1, 2, and 7) due to the EMD. However, this number of errors is within the provided RS Codes' correction capability, and the output will be matched with the input (i.e., DFP). In row 4, at a frequency of 200 MHz, at the first sight, everything seems correct, but the channel actually is in control and enforces a logical '1' to all transmitted bits. As mentioned earlier, such a scenario happens by pure luck (i.e., CTP), and if any other code words are transmitted under this disturbance, the output would be always the same and the results are in a false negative. In row 5, at a frequency of 202 MHz, six symbols become corrupted due to the EMD. However, RS Codes falsely assume only one error has occurred as the minimum symbol distance of the received code word to another valid code word (i.e., \([7,7,7,7,7,7,7]\)) is 1. As a result, RS Codes choose this code word as the output, which results in miscorrection (i.e., CTN). Row 6 is similar to row 5, but under the specified disturbance, the transmitted code word becomes corrupted but in a way that there are three valid code words with the same minimum symbol distance (i.e., 5) to the corrupted code word. Consequently, RS Codes fail to produce an output (i.e., CTN). Row 7 is also similar to row 2 but with the difference that the channel is in control and enforces the values (i.e., CFP). In row 8, at a frequency of 200 MHz, all symbols become corrupted, which turn the input code word into another valid code word. In this case, although the corrupted code word is incorrect, RS Codes think that nothing has occurred (i.e syndrome = 0), and it sends the input data directly to the output (i.e., CFN). As can be seen in Figure 11, due to the periodicity of the single-frequency EMD, all bits are turned into '1'. Correspondingly, all symbols are affected and are turned into the same symbol (i.e., in this case, '11' or 7).
# Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
## 4 Determining Priority Probability with Expert Knowledge Uncertainty
The cross-entropy between the individual expert evaluation results and the group expert evaluation results, was calculated using (7): \[D(y_{k},x)=\sum\limits_{i=1}^{n}\left[u_{ki}\ln\frac{u_{ki}}{\frac{1}{2}(u_{ ki}+\mu_{i})}+v_{ki}\ln\frac{v_{ki}}{\frac{1}{2}(v_{ki}+v_{i})}\right]+\sum \limits_{i=1}^{n}\left[u_{i}\ln\frac{\mu_{i}}{\frac{1}{2}(u_{ki}+\mu_{i})}+v_{ i}\ln\frac{v_{i}}{\frac{1}{2}(v_{ki}+v_{i})}\right]\] (7) Then, the cross-entropy weights of one expert \(D_{k}\) was defined as (8): \[r_{k}=\frac{\frac{1}{D(y_{k},x)}}{\sum\limits_{k=1}^{s}\frac{1}{D(y_{k},x)}}\] (8) Among them, \(0\leq r_{k}\leq 1\), \(k=1,2,\ldots,s\), \(\sum\limits_{k=1}^{s}r_{k}=1\). 3. Calculating the intuitionistic fuzzy entropy weight of each expert: [PERSON] and [PERSON] [28] introduced intuitionistic fuzzy entropy weight. According to the definition of entropy, if the entropy value of an individual expert evaluation was larger, it meant that the expert's individual evaluation results contained more uncertain information, and the expert should have been assigned a smaller weight. Conversely, a larger weight was required. The entropy was calculated using (9), and the entropy weight of each expert was obtained using (10). \[E_{k}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{min\{\ u_{ki},v_{ki}\}+\pi_{ki}}{max \{u_{ki},v_{ki}\}+\pi_{ki}}\] (9) \[e_{k}=\frac{(1-E_{k})}{\left(s-\sum\limits_{k=1}^{s}E_{k}\right)}\] (10) Among them, \(\pi_{ki}=1-u_{ki}-v_{ki}\), \(0\leq e_{k}\leq 1\), \(k=1,2,\ldots,s\), \(\sum\limits_{k=1}^{s}e_{k}=1\). 4. Calculating the final weight of each expert: The final weight of each expert had to consider both the cross-entropy and intuitionistic fuzzy weights, which were calculated using (11) [26]. \[\omega_{k}=\frac{1}{2}r_{k}+\frac{1}{2}e_{k}\] (11)Step 3: Determination of root event fault probability  
After obtaining the final weights of each expert, the evaluation results of the individual experts were combined with the final expert weights in order to generate weighted expert group evaluation results. The expected value of the weighted group evaluation TrIFN results was calculated using (12) [24]:  
\[I(\widetilde{a})=\frac{1}{8}\times[(a+b+c+d)\times(1+u_{\widetilde{a}}-v_{ \widetilde{a}})] \tag{12}\]
# Binary Decision Support Using AHP: A Model for Alternative Analysis
## 4 Case Study
### Structuring the Hierarchical Model  
We developed a hierarchical decision model based on the Binary AHP framework to ensure a structured and objective classification process. This model systematicallyassessed whether a given technological solution qualified as an ICT solution according to predefined criteria. The hierarchical structure consisted of three levels: (1) Decision determining whether a given solution qualified as ICT; (2) evaluation criteria and subcriteria factors used to assess the solution's relevance to ICT; and (3) decision alternatives, i.e., the two possible classification outcomes: yes (ICT solution) and no (non-ICT solution).  
We began structuring the model by conducting a document content analysis and reviewing official regulations, policies, and procurement guidelines related to ICT classification. This analysis identified the key criteria and subcriteria that justify an ICT classification. Additionally, input from subject matter experts ensured that the model captured relevant decision factors and aligned with existing administrative and legal frameworks. Based on these findings, the decision criteria were established to reflect the essential characteristics of ICT solutions, such as data-processing requirements, software and hardware dependencies, cybersecurity implications, and technological functionalities. Each criterion was then divided into subcriteria to provide a more detailed evaluation framework.  
Figure 2 presents the resulting hierarchical structure of the decision model and illustrates how the decision goal, criteria, subcriteria, and classification alternatives interrelate. This structured approach enables decision-makers to systematically compare alternatives using pairwise comparisons, ensuring greater transparency and consistency in ICT classification. This hierarchical model forms the foundation for applying the Binary AHP methodology, which we elaborate on in the subsequent sections, including the pairwise comparison process, consistency evaluation, and final classification results.