esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient BDS-3 Long-Range Undifferenced Network RTK Positioning Algorithm
## 3 Experimental Results Analysis
### Reference Station AR
\begin{table}
\begin{tabular}{c c c c c c} \hline
**PRN** & \begin{tabular}{c} **Reference Station** \\ **Network** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Observations** \\ \end{tabular} & \
\begin{tabular}{c} **Fixed** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Unfixed Number** \\ **Rate (\%)** \\ \end{tabular} \\ \hline C22 & 1 & 17,464 & 17,385 & 79 & 99.54 \\ C22 & 2 & 14,878 & 14,863 & 15 & 99.89 \\ C27 & 1 & 18,035 & 17,856 & 179 & 99.01 \\ C27 & 2 & 18,035 & 18,035 & 0 & 100 \\ C32 & 1 & 22,195 & 22,120 & 75 & 99.66 \\ C35 & 2 & 22,195 & 22,195 & 0 & 100 \\ C45 & 2 & 8135 & 8135 & 0 & 100 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistical results of the B1C/B2a/B3I AR success rate. \begin{table}
\begin{tabular}{c c c c c c} \hline
**PRN** & \begin{tabular}{c} **Reference Station** \\ **Network** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Observations** \\ \end{tabular} & \
\begin{tabular}{c} **Fixed** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Unfixed Number** \\ **Rate (\%)** \\ \end{tabular} \\ \hline C22 & 1 & 17,464 & 17,389 & 75 & 99.57 \\ C22 & 2 & 14,878 & 14,739 & 139 & 99.06 \\ C22 & 1 & 18,035 & 17,851 & 184 & 98.97 \\ C27 & 2 & 18,035 & 18,035 & 0 & 100 \\ C32 & 1 & 22,195 & 21,998 & 197 & 99.11 \\ C35 & 2 & 22,195 & 22,195 & 0 & 100 \\ C45 & 2 & 20,823 & 20,573 & 250 & 98.79 \\ \hline \end{tabular}
\end{table} 
Table 3: Statistical results of the B1I/B1C/B2a/B3I AR success rate. \begin{table}
\begin{tabular}{c c c c c c} \hline
**PRN** & \begin{tabular}{c} **Reference Station** \\ **Network** \\ \end{tabular}  & \begin{tabular}{c} **Number of** \\ **Observations** \\ \end{tabular}  & \ \begin{tabular}{c} **Fixed** \\ **Number** \\ \end{tabular}  & \begin{tabular}{c} **Unfixed Number** \\ **Rate (\%)** \\ \end{tabular}  \\ \hline C22 & 1 & 17,464 & 17,123 & 341 & 98.04 \\ C22 & 2 & 14,878 & 14,464 & 414 & 97.21 \\ C22 & 1 & 18,035 & 17,752 & 283 & 98.43 \\ C27 & 2 & 18,035 & 17,519 & 516 & 97.13 \\ C32 & 1 & 22,195 & 21,876 & 319 & 98.56 \\ C32
## 3 Methodology
### Data Preprocessing
#### 3.1.2 ROI Selection  
The majority of the images contain a dark background, which is irrelevant to the model. Therefore, the region of interest (ROI) is selected to enhance the model's accuracy. The ROI selection process involves segmenting the image using [PERSON]'s thresholding [49]. For that, the RGB image is first converted into a grayscale image. Subsequently, [PERSON]'s algorithm is applied, which returns a threshold. The process of determining the threshold can be explained via Equation (4). In this equation, in order to determine the threshold, \(k_{\text{Otsu}}\), the algorithm iterates over all possible thresholds, \(k\). Here, \(q_{1}(k)\) and \(\sigma_{1}^{2}(k)\) are the probability variances of the background pixels. Similarly, \(q_{2}(k)\) and \(\sigma_{2}^{2}(k)\) are the probability variances of the foreground pixels, respectively.  
\[k_{\text{Otsu}}=\arg\min_{k}\Bigl{[}q_{1}(k)\cdot\sigma_{1}^{2}(k)+q_{2}(k) \cdot\sigma_{2}^{2}(k)\Bigr{]} \tag{4}\]  
\[I_{\text{output}}(x,y)=\begin{cases}1,&\text{if }I_{\text{input}}(x,y)>k_{\text{ Otsu}}\\ 0,&\text{otherwise}\end{cases} \tag{5}\]  
After calculating the global threshold, as explained in Equation (5), the intensity values above the threshold are then marked white and the rest are black. Since the background is dark, the algorithm can easily segment the background from the retinal section. Based on the segmentation, a bounding box is drawn, covering the white region. This box works as the selection boundary of the ROI. Therefore, the input image is cropped based on the bounding box.
## 4 Proposed Scheme
### Registration Phase  
In the domain of 5G-enabled vehicular networks, vehicle Vi begins the registration process with the local TA by following these steps:  
* After the login password \(PW_{i}\) is chosen, the driver submits the identity of driver \(DID_{i}\) and the identity of vehicle \(ID_{i}\) to the local TA.
* TA computes two secret authentication parameters \(A_{i}=H_{1}(DID_{i}||ID_{i}||s)\) and \(B_{i}=H_{1}(PW_{i})\oplus A_{i}\).
* TA randomly picks a value \(r_{i}\in Z_{q}^{*}\) and computes the corresponding \(R_{i}=r_{i}P\) for \(V_{i}\). It then computes a pseudonym ID \(AID_{i}=ID_{i}\oplus H_{1}(s||R_{i})\) for each vehicle \(V_{i}\).
* TA preloads \(\{p,q,P,E,G,R_{i},Z_{q}^{*},DID_{i},ID_{i},PW_{i},A_{i},B_{i},P_{pub},H_{1},H_{ 2},H_{3}\}\) to the vehicle \(V_{i}\).
* Finally, TA stores \(\{ID_{i}\}\) locally.  
The adversary cannot launch a successful stolen-verified attack because the TA does not store the vehicle's login password.
## 2 Materials and Methods
### Outlier Elimination  
The main challenges of data analysis are associated with data quality. Most of the common drilling data challenges are the presence of missing data, outliers, noises, and also faulty measurements [7; 22]. Statistically, outliers are defined as data that have a low probability of being consistent with the other data in use, i.e., a value that deviates considerably and abruptly from other values [23].  
Identifying and eliminating the outliers is a necessary step in data analysis, as these observations can be biased and distorted, leading to erroneous conclusions [24; 25]. In this sense, a statistical outlier removal method widely used is the Tukey box plot method [26].  
The box plot method is a graphical technique that calculates data distribution through the smallest normal observation (f\({}_{1}\)), largest normal observation (f\({}_{3}\)), lower quartile value (H\({}_{\text{L}}\)), and upper quartile value (H\({}_{\text{U}}\)). The statistical method presented by [PERSON] [26] involves the construction of \"inner fences\" and \"outer fences\", then using estimated interquartile range (IQR) and denominated H-spread (d\({}_{\text{H}}\)), which basically makes up the difference between values of sampling upper (H\({}_{\text{U}}\)) and lower (H\({}_{\text{L}}\)) quartiles, as per Equation (2):  
\[\text{d}_{\text{H}}=\text{H}_{\text{U}}-\text{H}_{\text{L}} \tag{2}\]  
\"Inner fences\" and \"outer fences\" are expressed and calculated as per Equations (3) and (4), respectively:  
\[\text{f}_{1}=\text{H}_{\text{L}}-1.5\cdot\text{d}_{\text{H}} \tag{3}\]  
\[\text{f}_{3}=\text{H}_{\text{U}}+3\cdot\text{d}_{\text{H}} \tag{4}\]  
As a result, any value beyond either limit (f\({}_{1}\) or f\({}_{3}\)) is considered an outlier. The outliers can be considered extreme if beyond either the lower or upper outer boundary (f\({}_{3}\)) [27]. [PERSON]'s box plot method can be seen in Figure 1.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 3 Guidance Law Design Based on Saturation Function
### Longitudinal Guidance Law
Third, compared with the aforementioned two-phase guidance scheme, the proposed guidance profile can be applied to the whole phase of aero capture, making the guidance process more unified and continuous. In each guidance cycle, after the determination of \(t_{mid}\), the complete bank angle magnitude profile can be defined by the above equation. Under the control of this profile, the state at atmospheric exit is obtained by numerical integration of the differential equations of motion, and then the post-aerocapture orbit and the velocity increment required for orbital insertion are determined by Equations (13)-(18). After that, we can correct \(t_{mid}\) according to the deviation between the prediction and the target. In the light of different maneuvering modes, single-impulse maneuvering requires that the post-aerocapture orbit apogee coincides with the target apogee, so it is a single parameter root seeking problem, which can be solved by false position method; The two-impulse maneuver is to minimize total velocity increment, so it is a single-parameter optimization problem, which can be solved by fminbnd function in MATLAB. Considering the problem's complexity, an effective initial guess method is adopted, that is, select the converged solution obtained from the previous guidance period as the initial value. This method will significantly improve the convergence speed of solving \(t_{mid}\). While the symbol of \(\sigma\) is determined by the lateral logic introduced in the following sections. Whenever the bank reversal is required, the guidance system will directly change the symbol of the bank angle.
# Virtual Career Fairs: Perspectives from Norwegian Recruiters and Exhibitors
## 4 Results
### Benefits of Virtual Fairs (RQ1)
#### 4.1.1 Visibility and Branding  
In line with the expert report, branding and visibility appear to be key opportunities identified by the focus group participants. This may be particularly the case when the industry may be less known to candidates (e.g., those who consider a transfer from one sector to another). Presence at these virtual fairs was seen as key to informing candidates about the employer but also to generate positive word of mouth. Attending virtual fairs was seen as a way to be noticed by potential candidates. Being able to gain an overview of exhibitors was also seen as a benefit, while the online format was seen as enabling dialogue with the different visitors. In addition, interest in virtual fairs was driven by motives such as \"employer branding, related to strategy about becoming more digital [...]. It's possible to measure the effect of virtual career fairs, and not traditional ones. And, we can meet with professionals\" in employment as well as students seeking new opportunities. Being seen was another aspect: \"We want to be present where things happen, to show that we are not'slow'\".  
Virtual fairs provide organizations with increased visibility in front of potential visitors (the numbers may be considerable, as Global Forums has seen 3000 to 4000 attendees at their virtual fairs). This kind of presence is of significant value to business as virtual fairs offer employers a novel and innovative branding opportunity. The goal is \"to be visible, spread information about the company, employer branding, not recruitment specifically but connect with people\" and \"to meet more experienced candidates\" who would not usually attend such events. This suggests that the online format was seen as a means to reach talent that may be difficult to recruit or entice to attend alternative events, while also presenting the company in both a \"professional and stylish\" fashion. Visibility was seen as adding value while being able to reach the talent group and costs were equally motivating for the focus group participants.  
Figure 2 outlines the benefits of virtual fairs in terms of location. As the virtual venue can be tailor-made, no exhibitors or recruiters are potentially disadvantaged by being located in a location less likely to be reached by job seekers.
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 5 Conclusions  
This paper compares fragility curves obtained by various known static and dynamic nonlinear procedures. Incremental modal pushover analysis (IMPA) is proposed as an alternative to IDA, which is currently considered the most reliable method, to determinate IMPA curves and thus seismic fragility. Similarly, both MPA and NL-THA are used to determine capacity in the well-known cloud method. For this study, it was necessary to perform a relatively small number of nonlinear time histories using two different data sets. These differ in the range of Joyner-Boore distance (R\({}_{\text{jp}}\)) and are scattered in a range of magnitude. The following conclusions can be drawn from this study that is limited to a simplified 2D frame model and a small set of records:  
* Comparison of fragility curves shows that, in the case of methodologies distinguished by scaling (in terms of accelerograms (IDA) or response spectra (IMPA)), near-fault records and far-field record selections have led to nearly equivalent results. In contrast, the results in terms of fragility when using records without scaling, i.e., in D-CA and MPA-CA, show clear differences in the whole range of intensities;
* Fragility curves that only consider the contribution of the first mode in determining \(DCR_{LS}\) have led to more accurate results in relation to IDA, so the inclusion of higher mode contributions does not seem to be essential for low to medium buildings (up to nine stories [20]);
* A total number of 12 subsets have been extracted from the main 210 set of records and examinated, but results are not fully reported in this paper. The results have shown that D-CA leads to a smaller vulnerability than MPA-CA and IMPA in all selection and for the whole range of intensities. Methodologies based on the pushover analysis, on the contrary, have led to more conservative results, especially for 16% and 50% fractiles;
* IDA shows less sensitivity to record-to-record variability. It should be noted, however, that IMPA, despite its slightly greater sensitivity, has the advantage of a large reduction in the computational effort required to perform the structural analysis. In IMPA, the total time required relates mostly to the post-processing phase, which is no different for small 2D frames or more complex 3D buildings. A more comprehensive validation is needed to confirm the obtained results and draw more general conclusions. Conceptualization, C.P.C., A.P., C.N. and B.B. ; methodology, C.P.C., A.P. C.N. and B.B. ; software, C.P.C. and A.P. ; formal analysis, C.P.C. and A.P. ; data curation, C.P.C. and A.P. ; writing--original draft preparation, C.P.C. and A.P. ; writing--review and editing, C.P.C., B.B., A.P. and C.N. ; visualization, C.P.C. and A.P. ; supervision, C.N. and B.B. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the funding received by The Laboratories University Network of Seismic Engineering (ReLUS): research project ReLUS/DPC 2019-2021 Reinforced Concrete Existing Structures.
# New Approximation Methods Based on Fuzzy Transform for Solving SODEs: [PERSON]*, [PERSON], [PERSON], [PERSON]
## Appendix B Algorithms  
In this Appendix, the algorithms of the approximation methods based on FzT for Sections 3.2 and 3.3 are explained in detail. Pseudocode is used to describe the algorithms and a simplified code that is easy to read. This pseudocode specifies the form of the input to be supplied and the form of the desired output. As a consequence, a stopping technique independent of the numerical technique is incorporated into each algorithm to avoid infinite loops. Two punctuation symbols are used in the algorithms: a period (.) indicates the termination of a step, and a semicolon (.) separates tasks within a step. The integral symbol (integral(function,upper limits,lower limits)) is used to denote a definite integral. The steps in the algorithms follow the rules of structured program construction. They have been arranged so that there should be minimal difficulty translating pseudocode into any programming language suitable for scientific applications. We approximate the solution of SODEs (5) at (\(N+1\)) equally-spaced numbers in the interval \([a,b]\) as follows.  
```
INPUT: \(f(t,x,y)\); \(g(t,x,y)\); endpoints \(a,b\); integer \(N\); initial condition \(y_{1}\). Step 1 Set \(h=(b-a)/N\); \(X_{1}=x_{1}\); \(Y_{1}=y_{1}\); \(t_{1}=a;k=1,\ldots,N+1\); \(t_{k}=a+(k-1)h\). Step 2 Define the generalized uniform fuzzy partitions as \(A_{k}(t)=\frac{1}{2}\left(1+\cos\left(\pi\left(\frac{t-t(k)}{h}\right)\right) \right)\). Step 3 For \(k=1\) to \(N\), do Steps 4-7. Step 4 \(F(k)=\)integral\((f(t,X(k),Y(k))A_{k}(t),t(k-1),t(k+1))/\)integral\((A_{k}(t),t(k-1),t(k+1))\). Step 5 \(G(k)=\)integral\((g(t,X(k),Y(k))A_{k}(t),t(k-1),t(k+1))/\)integral\((A_{k}(t),t(k-1),t(k+1))\). Step 6 \(X(k+1)=\)\(X(k)+hF(k)\). Step 7 \(Y(k+1)=\)\(Y(k)+hG(k)\).  OUTPUT: Approximation \(X\) and \(Y\) to \(x\) and \(y\), respectively, at the (\(N+1\)) values of \(t\).
```  
**Algorithm A2** Two-stage (modified trapezoidal rule) algorithm for the system of ODEs.
## 2 Background and Related Work
### Related Work  
Previous research has identified vulnerabilities in the ESC and proposed countermeasures; however, existing solutions rely on static rules or introduce excessive latency, which is incompatible with real-time requirements [23]. Although lightweight ML frameworks demonstrate the feasibility of ML in embedded devices, their application to microsecond-scale control loops remains largely unexplored [20]. Control theory provides mathematical foundations for adaptive systems, and fuzzy logic offers approaches to handle uncertainty. However, existing frameworks do not address the specific challenges of deploying embedded ML in adversarial environments [24].
## 4 Results and Discussion
### On-Orbit Application of RTDC  
The DN from one scene observed by PSAC-B on 7 November 2020 included 130 scanning cycles of observations. The detector temperature controller and instrument thermal controller of the PSAC operated normally on orbit, and the PST was basically stable at approximately \(-61\)\({}^{\circ}\)C, with a change of approximately 0.06\({}^{\circ}\)C. The radiation intensity information observed at the nadir and the G\({}_{\mathrm{ins}}\)(T) correction-induced DN change in the 2250 nm channel are shown in Figure 20. After G\({}_{\mathrm{ins}}\)(T) correction, the DN values of the 2250 nm channel increased by approximately 1.37%. As shown in Figure 20, the continuous-origin DN value after the 100 th sampling point was constantly less than 1000 because the PSAC nadir was flying over Bohai Bay in China at that time.  
Figure 19: Comparison of two G(T) correction effects. The 1.08% fluctuation in DN when the T decreased from \(-38\)\({}^{\circ}\)C to \(-61\)\({}^{\circ}\)C was corrected to 0.38% and 0.34%, respectively, with G\({}_{\mathrm{det}}\)(T) and G\({}_{\mathrm{ins}}\)(T).  
Figure 18: Comparison of the temperature characteristic consistency for 4 cells at 2250 nm. The experimental results indicated that their responsivity temperature dependences were generally consistent, and the maximum relative deviation was 0.11% in the temperature range of \(-70\)\({}^{\circ}\)C \(<\) T \(<-25\)\({}^{\circ}\)C.
## 4 Results and Discussions  
We have applied several classifiers to identify the best performer among them and compared various performance metrics. Extreme gradient boosting (XGB), random forest (RF), decision tree (DT), K-nearest neighbor (K-NN), and Gaussian Naive Bayes (GNB) algorithms are applied in the TRSP dataset. Among the classifiers, XGB works best for handling the imbalanced dataset in spite of facing some computational complexities. RF is prone to overfitting in comparison with the rest of the classifiers but its ensemble nature sometimes makes it harder to interpret. On the other hand, the decision tree algorithm is well known for its simplicity and interpretability, but it faces some overfitting issues which result in lower accuracy. Though implementing KNN in this study is simple, it struggles with large and high-dimensional data. Lastly, GNB is computationally efficient but the assumption of feature independence leads the accuracy to a minimum value. Furthermore, the comparison of the results and analysis of the models is represented by confusion matrices, precision-recall curves, and ROC curves. We have adopted two approaches to evaluate the performances of our models. The first approach is with the raw dataset and the second one is with the feature-extracted dataset.
# Enhancing Conversational Agent Development Through a Semi-Automatization Development Proposal
The absence of WhatsApp integration limits the utility of Xatkit in key international markets, thus limiting its potential impact in regions where this platform is integral to communication and customer interaction. This work presents an enhancement to the Xatkit framework [6], a chatbot development platform, addressing its prior lack of WhatsApp integration, a crucial gap given WhatsApp's global dominance. Using MDD, Xatkit's metamodel is extended using Ecore models [9] to formally define chatbot properties, allowing for a structured specification of both structural and behavioral aspects. With a metamodel defined in Ecore and implementing Acceleo M2T transformations [10], this approach can automate the generation of Java code for chatbots. This automation can reduce the manual coding effort required by developers, as they are only responsible for defining the chatbot's properties within the model. Automation of the code generation process, a key aspect of MDD, not only can reduce the time needed for manual coding and debugging but also can reduce the probability of human errors, leading to more reliable and maintainable code [11]. Furthermore, since the logic and structure of the chatbot are encapsulated within the model, any updates or modifications can be efficiently made at the model level, with the code being automatically regenerated to reflect these changes. The benefits of MDD have been proved in empirical studies such as [12; 13]. Critically, we integrate the large language model GPT-3 [14], endowing Xatkit with advanced natural language processing capabilities to generate contextually rich and nuanced responses to provide better interaction with the user when the exact answer to its query is not considered in the programmed chatbot. Communication with WhatsApp [15] is facilitated by integrating it with the Twilio API (Application Programming Interface), which provides Xatkit support for WhatsApp. These combined advances--metamodel extension, automated code generation, and GPT-3 integration, coupled with Twilio API [16] integration for WhatsApp connectivity--transform Xatkit into a more powerful, efficient, and versatile platform for developing sophisticated conversational agents on various platforms, now including the widely used WhatsApp. The target audience of this research work refers to, but is not limited to, software engineers, information technology managers, independent research developers in the area of conversational agents, and research students. This article is structured as follows: Section 2 provides the foundational background; Section 3 presents a comprehensive review of related work; Section 4 details the proposed approach; Section 5 presents the results obtained from the Xatkit enhancement, illustrating the proposal through a practical application; and finally, Section 6 offers concluding remarks and outlines ongoing and future work.