esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Application of MHAR-OLSR
### Paths Classification Step  
After detecting the shortest paths in the previous step, we can now classify them. To do so, we apply the classification algorithm (Figure 7) based on the components nature of each route. Then the detected paths are classified as follows:  
* S \(\rightarrow\) V1 \(\rightarrow\) V4 \(\rightarrow\) D, receives the Path_Id = 5, the path comprises just VANETs nodes (ID-Node = 2).  
Figure 10: MHAR -OLSR Hello message format.  
* S \(\rightarrow\) F1 \(\rightarrow\) F2 \(\rightarrow\) V3 \(\rightarrow\) D, is tagged by Path_Id = 4, this route contains the FANET and VANET nodes ( respectively: ID-Node = 3 and ID-Node = 2).
* S \(\rightarrow\) M2 \(\rightarrow\) M3 \(\rightarrow\) V2 \(\rightarrow\) D, takes Path_Id = 6, the presence of MANET and VANET nodes (respectively: ID-Node = 1 and ID-Node = 2).  
The path with identifier 6 is less reliable than the others. It consists of MANET nodes with a limited range and VANET nodes characterized by their high speed. Therefore, there is a risk of losing the link on these itineraries. This is why they have a minimum priority.  
But the second one (Path_Id = 4) remains more reliable because it contains UAVs. These devices are characterized by high power and controlled mobility.
## Appendix A The Algorithm SPKF 1  
Calculate for equivalent cell at sample time \(k\)  
* This forms the augmented state vector at the sampling time, including the estimated states at the sample time \(k-1\)\(\hat{x}_{k-1}^{+}\), the vector of mean values of the system noise \(\overline{\underline{w}}\) and measurement noise \(\overline{\zeta}\): \[\hat{x}_{k-1}^{a,+}=\left[\hat{x}_{k-1}^{+}\,\,\overline{\underline{w}}, \,\overline{\zeta}\right]^{T}\] (1)
* This forms the estimated covariance matrix of the state vector estimation error, including the covariance matrices of the state estimation error at time \(k-1\), the noise error and the measured error, respectively. \[\sigma_{\overline{x},k-1}^{a,+}=diag\Big{(}\sigma_{\overline{x},k-1}^{+}, \sigma_{w},\sigma_{\zeta}\Big{)}\] (2)
* The input sigma point matrix containing \(p+1\) sigma points is established as \[X_{k-1}^{a,+}=\left\{\hat{x}_{k-1}^{a,+},\hat{x}_{k-1}^{a,+}+\gamma\sqrt{ \alpha_{\overline{x},k-1}^{a,+}},\hat{x}_{k-1}^{a,+}-\gamma\sqrt{\alpha_{ \overline{x},k-1}^{a,+}}\right\}\ =\left[X_{k-1}^{x,+},X_{k-1}^{w,+},X_{k-1}^{v,+} \right]^{T}\] (3)
* Calculates state sigma point matrix by using the dynamic state Equation (5): \[X_{k,j}^{x,-}=f\big{(}X_{k-1}^{x,+},\,i_{k},X_{k-1}^{w,+}\big{)}=A_{k}X_{k-1}^ {x,+}+B_{k}u_{k}+X_{k-1}^{w,+}\] (4)* The priori state vector estimation is calculated as: \[\hat{\underline{\varsigma}}_{k}^{-}=\sum_{j=0}^{p}\alpha_{j}^{m}X_{k,j}^{x,-}\] (11)
* Update the covariance matrices of the state vector estimation error: \[\sigma_{\overline{x},k}^{-}=\sum_{j=0}^{p}\alpha_{j}^{c}\Big{(}X_{k,j}^{x,-}- \hat{\underline{\varsigma}}_{k}^{-}\Big{)}\Big{(}X_{k,j}^{x,-}-\hat{\underline {\varsigma}}_{k}^{-}\Big{)}^{T}\] (12)
* Calculate the output sigma point matrix using the output equation of Equation (5): \[Y_{k}=OCV(\hat{\underline{\varsigma}}_{k})+C_{k}X_{k,j}^{x,-}+D_{k}u_{k}+X_{k -1}^{\zeta,+}\] (13)
* Estimate the voltage of the equivalent cell. \[\hat{y}_{k}=\hat{u}_{k}=\sum_{j=0}^{p}\alpha_{j}^{m}Y_{k}\] (14)
* Update the covariance matrices of estimated output voltage. \[\sigma_{\overline{y},k}=\sum_{j=0}^{p}\alpha_{j}^{c}(Y_{k}-\hat{u}_{k})(Y_{k }-\hat{u}_{k})^{T}\] (15)
* Update the covariance matrices of the state vector estimation error and output voltage estimation error: \[\sigma_{\overline{x},\overline{y},k}^{-}=\sum_{j=0}^{p}\alpha_{j}^{c}\Big{(}X _{k,j}^{x,-}-\hat{\underline{\varsigma}}_{k}^{-}\Big{)}(Y_{k}-\hat{u}_{k})^{T}\] (16)
* Calculate the estimation gain matrix as \[L_{k}=\frac{\sigma_{\overline{x},\overline{y},k}^{-}}{\sigma_{\overline{y},k} ^{-}}\] (17)
* Update the priori estimation states by taking into account the output errors, the estimation of state vector as \[\hat{\underline{\varsigma}}_{k}^{+}=\hat{\underline{\varsigma}}_{k}^{-}+L_{k} (y_{k}-\hat{y}_{k})\] (18)
* Update the covariance matrix of the state vector estimation errors: \[\sigma_{\overline{x},k}^{+}=\sigma_{\overline{x},k}^{-}-L_{k}\sigma_{ \overline{y},k}L_{k}^{T}\] (19)
## 2 Theory  
The LAnTeRN measurement leverages more than a decade of prior work by Exelis Inc. (formerly ITT Exelis Corp.) on the MFLL airborne engineering development unit. The initial implementation of MFLL used pure sinusoidal modulation with each channel (wavelength) having a unique amplitude modulation frequency applied to each. This approach worked very well in the absence of signal returns from intermediate scatterers. However, in the presence of clouds or heavy aerosols, the CW measurement was unable to distinguish the return from the target of interest and those from the intermediate scattering sources. In order to address this for the ASCENDS mission we developed a chirped waveform which enables us to distinguish the return signal from different ranges. Although this approach has worked quite well, these more complicated waveforms significantly increased the cost and complexity of the design through higher bandwidth requirements, large data volumes, and significant computational resources required to post process the data. Additionally, the increased bandwidth can have a negative impact on the instrument SNR performance. The desire to maintain the advantages of the original implementation of MFLL, while developing this more complex approach for the ASCENDS mission, led Exelis to the development of the LAnTeRN concept [15].
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 4 Experimental Design
### Dataset 1: Electric Motor  
The first dataset was collected from a test rig built in the laboratory of the Department of Industrial Engineering, University of Bologna, Italy. The platform includes an asynchronous motor, a gearbox consisting of two pulleys that exchange the rotation through a belt, two shafts that share the motion thanks to a couple of gears, and an electromagnetic brake. The full description of the system can be found in [19]. Three triaxial accelerometers, characterized by an acceleration range of 500 Gpeak, are placed on the system. The first one is placed on the bearing's support; the second one is placed next to the second pulley; and the third one is placed close to the gearboxes. Data are collected with a sampling frequency of 12.8 kHz.  
The available dataset includes vibration signals collected during tests simulating four distinct operating conditions: Nominal 1, Nominal 2, Nominal 3, and Fault 1. In the first three conditions, all components of the test rig, i.e., the electric motor, the belt, the pulleys, and the gears, are in optimal conditions. Different values of the distance between the pulleys and the braking torque determine the implemented operating condition. During the fourth test, a sudden failure occurred to the electric motor. The first 5 min of data collected in each condition are depicted in Figure 1.
## 5 Connected Vehicles
### Technological Developments
#### 5.3.2 OnStar  
OnStar is a sub-company of General Motors (GM) that specializes in providing various in-vehicle services based on a Code Division Multiple Access (CDMA) and is also the symbol of a series of functions equipped by GM [108; 109]. These include:  
* Emergency services--auto-collision response, weather forecasting, crash rescue, and curbside assistance;
* Security services--remote ignition control, theft alerts, and stolen vehicle deceleration;
* Navigation services--regional road condition synchronization and optimal route planning;
* Connectivity services--OnStar 4G LTE, Wi-Fi, Bluetooth hands-free, and adaptability of 3 rd party apps;
* Vehicle management--vehicle condition detection, remote access, location tracking, and intelligent driving.  
When the system is triggered manually by a button or automatically by an airbag-detection device, OnStar will collect relevant data based on the onboard diagnostics and built-in GPS and use the CDMA for communication and database transmission [110]. The first OnStar units were deployed in a variety of Cadillacs introduced in 1997, with expansion to other models beginning in 2002. In 2012, stand-alone units were designed to customize individual services, which are now available in the United States, Canada, China, Europe, etc.
## 4 Discussion
### Changes in NDVI in China Under a High-Emission Scenario
In the case of prolonged droughs, extremely high temperatures, or soil moisture depletion, despite short-term increases in NDVI, the ecosystem may exceed its resilience threshold over a longer period, resulting in reduced vegetation cover, loss of biodiversity, or even desertification [100]. [PERSON] et al. also studied this phenomenon. They used CMIP6 data to predict NDVI values under different future SSP scenarios. The study pointed out that globally, during the near-termperiod of 2021-2040, the average NDVI value under the SSP585 scenario is slightly higher than that under SSP245 and SSP370, indicating that significant temperature increases in the short term have a positive effect on vegetation growth. However, by 2081-2100, SSP370 and SSP585 show a downward trend, with the average NDVI value under SSP585 falling below the levels of 1981-2000. A further analysis of data across continents revealed that between 2021 and 2100, under the SSP585 scenario, NDVI values in Africa, South America, and Oceania decrease over time, while Asia shows an increasing trend. Specifically, during the period of 2081-2100, the NDVI values in Asia under the SSP126 scenario decrease compared with other scenarios [101]. This study focuses on the entire China region, which is located in Asia, and its results align with this observation. Therefore, although NDVI shows a short-term increase under high-emission scenarios, long-term climate change may bring more negative impacts. Long-term predictions must account for the risks of ecosystem instability caused by exceeding resilience thresholds. Meanwhile, human activities play a crucial role in changes in vegetation cover. This study primarily focuses on the impact of meteorological factors on NDVI and thus may not fully capture the specific contribution of human activities. In future research, the authors will place greater emphasis on the impact of human activities on NDVI, particularly in the context of approaching or exceeding ecosystem critical points, in order to provide a more comprehensive understanding framework.
## 1 Introduction
In addition, as users select the passive band according to the maximum force they need, users expend an unnecessary amount of energy when they desire to hold a lightweight object that requires a lower pinch force [18]. To overcome these disadvantages, VO devices have been designed to allow adjustments, but only by limited number of discrete levels of grip force [18]. Body-powered hook technology has not evolved significantly over the last years [14; 19]. A research study conducted more than two decades ago [20], reported the design priorities for body-powered technology users, and these user priorities are still present in surveys from more recent studies [1]. This lack of research and development leads to high rejection rates [6; 21]. Despite the obvious need for new body-powered alternatives, only a few new developments have emerged recently, such as the \"Maker hand\" [22] and the \"Delft cylinder hand\" [23]. Both use the hand as a terminal device, which is not frequently used and has the highest rejection rate for body-powered technology [1]. In contrast, the Body-Powered Grip VC \"GRIP 5\" [24] has high functionality due to its VC mechanism, since the user can manipulate the pressure control. However, in daily use, the VC mechanism can cause fatigue and discomfort [13]. Contrarily to body-powered devices, there is a large variety of 3D-printed prostheses [25; 26; 27]. Three-dimensional printing facilitates the development process and allows a great variety of designs without the restrictions of typical manufacturing processes [28]. However, 3D-printed prostheses have not reported user acceptance, functionality, and durability [16]. Therefore, the context and needs of prosthetic users should be considered while designing prostheses [29]. In this study, we present the design and development of a prosthesis that meets the requirements and conditions for the majority of users. The designed \"Carbon HK\" is a body-powered hook VO, which incorporates ultra-light and resistant materials enrichedwith nanoparticles that provide antibacterial and hydrophobic properties. In addition, its installation is easy and presents a novel continuous force regulation system. Therefore, this study presents an appropriate medical device for the needs of the majority of users, which will reduce rejection rates and ensure an effective alternative to improve users' health.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Transformation from Access Structures to Identities  
**AND-gate-only Access Structure Transformation Algorithm**. Here, we introduce the transformation algorithm of AND-gate-only access structure. The definition of AND-gate-only Access Structure is: The universe of attributes is denoted by \(u\) and the size of the universe is \(|u|\). We can use an AND-gate-only access structure \(A\) such as \((\mathit{att}_{1}\) AND... AND \(\mathit{att}_{n}\) ), where \(1\leq n\leq|u|\). It also can be written as a set of attributes, e.g., \(A=\{\mathit{att}_{1},\mathit{att}_{2},\ldots,\mathit{att}_{n}\}\). Let \(S=\{X_{1},\ldots,X_{n}\}\), where \(1\leq n\leq|u|\), be an attribute set of a user. We say that \(S\) satisfies the access structure \(A\) if and only if \(\mathit{att}_{i}=X_{i}\), for all \(1\leq i\leq n\), denoted as \(S\vDash A\).  
The input includes an AND-gate-only access structure \(A=\{X_{1},\ldots,X_{n}\}\) and the universe \(u\) of the attribute. It outputs an \(ID\), which is uniquely corresponding to the access structure \(A\). Algorithm 7 presents the pseudocode.  
```
1 Let \(ID_{A}[i]\) be the i-th bit of \(ID_{A}\)
2 For \(i=1\) to \(|u|\) Do
3 IF \(X_{i}\in A\)
4\(ID_{A}[i]\gets 1\)
5 ELSE
6\(ID_{A}[i]\gets 0\)
7 END For
8 output \(ID_{A}\)
```  
**Algorithm 7**\(\varphi(A,u)\)  
**Generic Access Structure Conversion Algorithm.** Here, we present the transformation algorithm of generic access structure [36]. Let \(P=\{P_{1},P_{2},\ldots,P_{n}\}\) be a set of attributes. A generic access structure is a collection \(A\) of non-empty subsets of \(\{P_{1},P_{2},\ldots,P_{n}\}\), i.e., \(A\subseteq 2^{[P_{1},P_{2},\ldots,P_{n}]}\backslash\{\varnothing\}\). The sets in \(A\) are called the authorized sets and the sets not in \(A\) are called the unauthorized sets. We can also represent the generic access structure as a disjunction of conjunctive clauses, i.e., disjunctive normal form (DNF).  
The input of the conversion algorithm includes a generic access structure \(A=\{A_{1},\ldots,A_{n}\}\subseteq 2^{u}\), as described before and the universe \(u\) of the attribute. It outputs a set of identities \(S=\{ID_{1},ID_{2},\ldots,ID_{n}\}\), which is uniquely corresponding to the access structure \(A\). Algorithm 8 presents the pseudocode.  
```
1 Let \(S\) be a null set, \(n\) be the size of \(A\)
2 For \(i=1\) to n Do
3\(ID_{i}\leftarrow\varphi(A_{i})\)
4\(S\gets S\cup\{ID_{i}\}\)
5 END For
6 output \(S\)
```  
**Algorithm 8**\(\xi(A,u)\)
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 4 Experiments and Discussion
### Comparison between ML Classifiers  
In order to investigate the effect of classifiers and explore better identification accuracy, we employed different ML learners to complete fault-cause classification with HMVFS. Owing to space limitation and performance stability, F_Score and Relieff were used for comparison. The typical individual classifiers CN2, LR, KNN, SVM and ANN, which have been proven effective in fault-cause identification studies, were tested, and the results  
Figure 3: Performance variation of HMVFS with different values for the parameters \(\alpha\) and \(\beta\) in terms of (**a**) Gmean; (**b**) ACC; (**c**) time. are presented in this subsection. Ensemble models promote fault-cause identification by combining individual learners [22], so we also explored the performance of various ensemble models, including random forest (RF), AdaBoosting, stacking ensemble and dynamic ensemble. META-DES, DES-Clustering and KNORA-U are dynamic ensemble techniques based on metalearning, clustering and k-nearest neighbors, respectively. Classification models were developed using Python machine learning library, scikit-learn and DESlib. Table 3 presents the best performance for each combination of feature selection methods and classifiers. Considering some data may be similar, AUC is introduced as a supplement criterion, which is derived from receiver operating characteristic (ROC) analysis and calculated as the area under the ROC curve. As seen from the table, HMVFS outperforms F_Score and ReliefF except with LR and ANN. It is observed that HMVFS always takes fewer features to achieve the best performance in the remaining comparisons. In the group of RF, the best scores of F_Score, ReliefF and HMVFS are very close to each other because RF has the ability of variable selection. Thus the features that function in final classification are similar if selected feature subsets are large enough to contain valuable features. Except for mentioned learners, HMVFS has advantages in both score and feature number.
## 3 Results and Discussion
### Electrochemical Impedance Spectroscopy
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline NAC & \(\rm R_{e}\) & \(\rm R_{ct}\) & \(\rm Q_{dl}\) & & \(\rm C_{dl}\) & \(\rm R_{F}\) & \(\rm Q_{F}\) & & \(\rm C_{F}\) & \(\rm R_{p}\) & \(\rm W\) \\
**conc.** & **(k\(\Omega\)** & **(k\(\Omega\)** & **(\mu\)F** & **(k\(\Omega\)** & **(\(\mu\)F** & **(k\(\Omega\)** & **(mF** & **(mF** & **(k\(\Omega\) & **(S s^{1/2}\)** & **(\%)**)** \\ **(mM)** & **cm\({}^{2}\))** & **cm\({}^{2}\))** & **s\({}^{n-1}\)cm\({}^{-2}\))** & **cm\({}^{2}\))** & **s\({}^{n-1}\)cm\({}^{-2}\))** & **cm\({}^{-2}\))** & **cm\({}^{-2}\))** & **cm\({}^{-2}\))** & **cm\({}^{-2}\))** \\ \hline \multicolumn{11}{c}{**3.5\% NaCl solution**} \\ \hline
0 & 3.4 & 0.51 & 326.9 & 0.755 & 182.6 & 0.27 & 1.38 & 0.676 & 0.86 & 0.78 & 0.0095 & **-** \\
0.6 & 3.5 & 1.08 & 182.5 & 0.781 & 115.6 & 1.58 & 0.66 & 0.500 & 0.68 & 2.66 & & 70.7 \\
3 & 4.8 & 1.37 & 178.1 & 0.797 & 124.4 & 1.77 & 0.62 & 0.526 & 0.60 & 3.14 & & 75.2 \\
6 & 5.3 & 1.51 & 134.0 & 0.809 & 91.9 & 1.81 & 0.51 & 0.500 & 0.46 & 3.32 & & 76.5 \\
12 & 4.7 & 1.96 & 140.4 & 0.791 & 99.8 & 1.90 & 0.64 & 0.500 & 0.77 & 3.86 & & 79.8 \\
18 & 6.5 & 2.93 & 119.2 & 0.796 & 91.1 & 2.06 & 0.52 & 0.500 & 0.57 & 4.99 & & 84.4 \\
36 & 8.0 & 3.49 & 86.2 & 0.810 & 65.0 & 2.39 & 0.41 & 0.500 & 0.39 & 5.88 & & 86.7 \\ \hline \multicolumn{11}{c}{**0.2 g L\({}^{-1}\) NaHCO\({}_{3}\) + 0.2 g L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) + 0.2 g L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) + 0.2 g L\({}^{-1}\) NaNO\({}_{3}\), pH = 3.4 solution**} \\ \hline
0 & 0.2 & 0.22 & 653.1 & 0.600 & 178.2 & 1.01 & 5.51 & 0.738 & 10.2 & 1.23 & 0.0043 & - \\
0.6 & 1.8 & 4.64 & 220.4 & 0.705 & 222.5 & 1.52 & 1.85 & 0.650 & 3.24 & 6.16 & & 80.0 \\
3 & 1.8 & 7.09 & 156.4 & 0.716 & 162.9 & 3.08 & 4.49 & 0.928 & 5.51 & 10.17 & & 87.9 \\
6 & 1.3 & 9.20 & 95.9 & 0.744 & 91.8 & 3.35 & 1.30 & 0.959 & 1.39 & 12.55 & & 90.2 \\
12 & 1.0 & 5.38 & 113.8 & 0.794 & 100.2 & 3.29 & 0.94 & 0.500 & 2.89 & 8.67 & & 85.8 \\
18 & 1.1 & 3.87 & 176.0 & 0.740 & 153.8 & 1.19 & 3.30 & 0.65 & 6.87 & 5.06 & & 77.9 \\
36 & 1.2 & 3.02 & 148.4 & 0.771 & 116.8 & 1.61 & 1.59 & 0.500 & 4.04 & 4.63 & & 75.8 \\ \hline \end{tabular}
\end{table} 
Table 2: Impedance parameters values for the bronze immersed in the two corrosive electrolytes without and with NAC, at different concentrations. concentration above 6 mM in the simulated acid rain solution, a slight acceleration of the faradaic processes occurring on the bronze surface could take place, resulting in higher C\({}_{\text{F}}\) values (Table 2). This may have been due to the partial desorption or reorganization of the inhibitor molecules on the bronze surface [34]. The maximum inhibition efficiency values, \(\eta_{\text{imp}}\), reached 86.1% in the 3.5% NaCl solution and 90.2% in the simulated acid rain (pH = 3.4). These results confirm that the expired _Fluimuncil solution_ had good inhibiting properties on bronze corrosion exposed to both tested electrolytes.
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
## 6 Conclusions
#### Limitations and Broader Impact  
Adversarial learning suffers from unstable training and mode collapse, which can increase the difficulty of hyperparameter tuning in graph condensations. In addition, when there are many subgroups, class-based sampling may not sample all of the nodes from certain groups. This hinders the applicability of GCARe in settings of more fine-grained subgroup divisions. Future work should be conducted on analyzing the adversarial robustness and privacy of condensed graphs to take one step closer toward trustworthy AI.  
Conceptualization, R.M., W.F. and Q.L.; methodology, validation, writing--original draft preparation, R.M.; writing--review and editing, R.M., W.F. and Q.L. 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}{l l l l l l l l} \hline \hline
**Method** & **Metric** & **GCN** & **GraphSage** & **SGC** & **MLP** & **APPNP** & **Cheby** \\ \hline \multirow{3}{*}{DosCond} & _acc_\(\uparrow\) & 74.30 & **74.73** & 74.63 & 74.64 & 75.11 & _77.77_ \\  & \(\Delta_{SP}\downarrow\) & 13.99 & 10.35 & **14.69** & 11.65 & 15.11 & 11.60 \\  & \(\Delta_{EO}\downarrow\) & 11.39 & 8.34 & 12.00 & 9.61 & 12.65 & 8.51 \\ \hline \multirow{3}{*}{GCARe} & _acc_\(\uparrow\) & **74.96** & 73.97 & **75.82** & **75.01** & **75.56** & **78.12** \\  & \(\Delta_{SP}\downarrow\) & **10.90** & **7.47** & 14.76 & **8.70** & **13.11** & **6.54** \\ \cline{1-1}  & \(\Delta_{EO}\downarrow\) & **8.82** & **5.99** & **11.83** & **6.79** & **10.48** & **4.37** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-architecture evaluation on the Credit-defaulter dataset. Different downstream GNNs were evaluated on the graphs condensed by DosCond and GCARe. GCARe achieved superior performances in both utility and fairness, and it showed better cross-architecture generalization on all GNN variants. The best results are marked in bold.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.