esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Processes
### Indicator Construction  
A normalization method was necessary to transform proxy variables generally measured at different scales and units into a common domain. This study adopts the min-max normalization method as used in previous studies [19; 22; 25; 27; 30; 31; 35; 38; 40], and all proxy variables ranged from 1 to 100 by Equations (5) and (6). Note that 1.0 was taken for the normalized minimum score to avoid the zero composite outcomes by multiplicative aggregation forms in Equations (1)-(4). Equation (5) transforms variables with the functional relationship for increasing vulnerability such as proxy variables for the exposure and sensitivity components, while proxy variables for the adaptability component decreasing vulnerability are transformed by Equation (6):  
\[u_{i}=s_{min}+\frac{x_{i}-\min(x_{i})}{\max(x_{i})-\min(x_{i})}(s_{max}-s_{min}), \tag{5}\]  
\[u_{i}=s_{max}-\frac{x_{i}-\min(x_{i})}{\max(x_{i})-\min(x_{i})}(s_{max}-s_{min }), \tag{6}\]  
where \(u_{i}\) is the normalized value of the original value \(x_{i}\) for the proxy variable \(i\), \(\max(x_{i})\) and \(\min(x_{i})\) are the maximum and the minimum values, respectively of the proxy variable \(i\), and \(s_{max}\) and \(s_{min}\) are the maximum of 100 and the minimum of 1, respectively of the normalized value \(u_{i}\).  
Following previous studies [19; 22; 27; 29; 23; 24; 25; 26; 27; 28; 29; 30; 35; 38; 39], the normalized proxy variables are then linearly aggregated into each component indicator such as exposure \(E\), sensitivity \(S\), lack of adaptability \(A\), potential impact \(PI\), livelihood vulnerability \(LV\), or internal factor \(IF\), respectively with the weighting factor \(w_{i}\) for the total number \(k\) of proxy variables for each component indicator:  
\[(E;S;A;PI;LV;IF)=\sum_{i=1}^{k}w_{i}u_{i}, \tag{7}\]where equal weights are applied to compiling each component indicator in Equation (7) to avoid unexpected effects of weights on flood vulnerability outcomes in this study. Note that it can be valid to assign equal weights to assessment components in the lack of information, resources, or consensus for different weights [38; 46].  
The four types of flood vulnerability indicators \(FVI_{1}\), \(FVI_{2}\), \(FVI_{3}\), and \(FVI_{4}\) are finally constructed by the four aggregation frameworks in Equations (1), (2), (3), and (4), respectively.
# Determination and Profiling of Human Skin Odors Using Hair Samples
## 3 Results
### Analysis of Human Hair as a Matrix for Skin Odor Profile Determination by HS-SPME/GC-MS  
A total of 37 VOCs, including the five quality markers, were identified in the nine hair samples collected from three volunteers (Table 1). These samples were obtained for the investigation of the laboratory applicability of the use of human hair as a matrix for skin odor analysis by HS-SPME/GC-MS. The identified VOCs belonged to seven different classes of organic compounds: Alcohols, aldehydes, carboxylic acids, ketones, esters, ether and hydrocarbons. Twelve out of the 37 identified VOCs (32.43%) were classified as aldehydes, followed by 8 (21.62%) classified as hydrocarbons, 6 (16.22 %) classified as alcohols, 7 (18.92%) classified as carboxylic acids, 3 (8.11%) classified as ketones, and1 (2.7%) classified as an ether (Figure 5).  
Field Applicability of the Standardized Method for Skin Odor Profile Determination and Laboratory Analyses  
The evaluation of the field applicability of the standardized method for skin odor collection and its laboratory analysis was performed using 99 hair samples obtained from 33 volunteers from Corte de Pedra, Bahia. A total of 42 VOCs were identified, including the quality markers (Table 1). These VOCs can be distributed among six functional groups: Aldehydes (14/42; 33.33%), hydrocarbons (8/42; 19.05%), alcohols (7/42; 16.67%), carboxylic acids (5/42; 11.9%), ketones (4/42; 9.52%), andesters (4/42; 9.52%), as shown in Figure 5. The percentage of VOCs belonging to each of these functional groups was similar for laboratory and field sampling (\(P=0.9988\); Df = 5).  
Figure 5: Comparison between the percentages of volatile organic compounds, distributed among seven different organic functional groups, obtained from the HS-SPME/GC-MS analysis of samples collected under laboratory and field conditions.
## 3 LED-PHOTON AE Implementation
The state matrix is passed through the permutation blocks for the main authentication operations. The \(RC\) controls the selections of the multiplexers and permutation rounds. The output hash function is generated after 48 rounds for the absorbing phase and 60 rounds for the squeezing phase to absorb the 64-bit input message and squeeze the 80-bit hash output. For the proposed AE scheme of LED-PHOTON, the design of the internal state follows PHOTON because it has a larger state size than LED. Thus, the state size is 100 bits organized in a 5 \(\times\) 5 matrix. As in a substitution permutation network, the permutation function consists of four operations: AddConstants (AC), SubstitutionBox (SB) or SubCells (SC), ShiftRows (SR), and MixColumns (MC).
## 4 Speaker Tracking  
We extend our previous work in Reference [24] in order to investigate the effect of RBM vectors on a speaker tracking task. We implemented a two stage speaker tracking system, that is, speaker segmentation and speaker identification. Figure 3 shows the basic steps of speaker segmentation, RBM vector extraction and identification. First of all, the audio is segmented according to the speaker change points. The speaker change points are detected using _'the sliding window and searching for speaker change'_ approach. A fixed length window is slid over the audio with a very small shift and speaker change is detected using some distance metric. We have used the Divergence Shape distance as a distance metric in this paper. The distance is thresholded in order to decide if the neighboring windows are spoken by the same speaker or whether there exists a speaker change. As a result of these speaker change points, the audio is segmented. In the next stage, a speaker identification of the target speakers against the segments is performed in order to know _'to which target speaker the corresponding segment belongs?'_ All the target speakers and segments are transformed into a vector based representation by means of RBMs, that is, RBM vectors. These RBM vectors are scored using cosine and PLDA scoring methods. In the following sections, the two stages of our speaker tracking system are discussed in detail.
# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 6 Discussion  
A robot vision system needs to overcome similar situations that a human visual system may have in daily life. However, it pays very high computational costs to fulfil the task, especially when it suffers from ill-posed views, wheel slippage, movement vibrations, and accident recovery. The following are some examples of constraints placed on the system:  
1. The quality of the photo is heavily affected by the body pose of the robot. Most laboratory robots are not tall enough to get a human-level view. The Pioneer 2-DX8 robot used in the experiment is lower than 27 cm. A one-meter tall rod was used to raise the camera on top of the robot body frame (Figure 7C).
2. A surveillance camera has a limited visual range. For example, the face area viewed by the camera is around 40 \(\times\) 40 pixels at a 10 m distance, which is too small to be recognized. The cloud camera (DCS-5222L) was taken as the camera source for the robot in our project. Its rotation range is 340\({}^{\circ}\), and highest photo resolution is 1280 \(\times\) 720 pixels. Most of the face images acquired were side views, and the quality of some photos were poor with low resolution. Hence, in the experiment, multiple snapshot candidates had to be taken to increase the quality.
3. To stabilize the camera on its top, the robot cannot move too fast. Furthermore, it has to wait for the camera to stop rotating before it can acquire a clear image.
4. Limited by its view and line of sight, a self-controlled robot cannot get a good overview of the whole area. It has to patrol around the room step-by-step to search for the target if no location hypotheses from ambient intelligence are supplied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Results**} & \multicolumn{3}{c}{**Distance**} \\ \cline{3-5}  & & **Around 3 m** & **Around 6 m** & **Around 9 m** \\ \hline \multirow{4}{*}{Our method} & Number of faces & \multirow{2}{*}{49} & \multirow{2}{*}{54} & \multirow{2}{*}{45} \\  & recognized & & & \\ \cline{2-5}  & Number of failures & 5 & 9 & 9 \\  & (recognition rate) & (89.80\%) & (83.33\%) & (80.00\%) \\ \hline \multirow{3}{*}{Single SVM} & Number of faces & \multirow{2}{*}{62} & \multirow{2}{*}{64} & \multirow{2}{*}{51} \\  & recognized & & & \\ \cline{1-1} \cline{2-5}  & Number of failures & 10 & 12 & 11 \\ \cline{1-1}  & (recognition rate) & (83.87\%) & (81.25\%) & (78.43\%) \\ \hline \end{tabular}
\end{table}
Table 5: Comparison between our method and the single pre-trained SVM classifier.  
Disturbed by other tasks, for example, obstacle avoidance or self-localization, it is easy for the robot to miss an ideal view of the target. Overarching cameras may be needed to assist the task to improve the efficiency.
## 2 Related Works
### Machine Learning Based Handover Management
The proposed QoE model was similar to the research in literature [7], which evaluated QoE based on the time required to download a file. However, they did not consider the QoS or QoE issue for users being more sensitive with real-time stream services. Therefore, the more complex QoE evaluation criteria for real-time video conference services proposed in our research are applied to evaluate the proposed deep-learning-based handoff mechanism. In [14], a multilayer neural network (MLNN) privacy and security preservation protocol was presented. To facilitate target cell selection, parameters taking user satisfaction, network, UE and service requirements into consideration were deployed so as to enhance both QoS and QoE during and after handover. The authors in [15] presented a machine learning scheme that not only facilitated optimal selection of target cells but also upheld both security and privacy during handovers. The simulation results showed that their proposed handover scheme provided a lower packet drop rate and ping-pong rate. In summary, the research studies in [6; 8; 9; 10; 11; 12; 13; 14; 15] have shown the advantage of predicting optimal handover timing with better results as compared to the original handover mechanism by using the deep learning model. This observation is consistent with the results in this paper. However, this paper further considers the QoE required for the network service and then uses deep learning to predict the optimal handover timing. In addition, the authors also compared the performance results for different deep learning models, such as MLP, CNN, GRU and RNN/LSTM. From the results of [8; 11; 13], the RNN/LSTM-based deep learning model showed much more effective and stable predictions of the handover timing of UE. The above results are consistent with the experimental results in this paper. The experimental results in this paper show that RNN has the best performance among the MLP, CNN, RNN and LSTM models.
# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
## 5 Experimental Results
### Analysis of the CICIDS2017 Dataset
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attack Type** & **Accuracy (\%)** \\ \hline Total malign LSVC & 99.13 \\ Bot & 99.47 \\ DDoS & 99.95 \\ DoSGoldenEye & 97.18 \\ DoSSlowhttpstsci & 96.85 \\ DoSHulk & 99.13 \\ DoSSlowloris & 80.19 \\ FIT\_Patator & 99.74 \\ SSH\_Patator & 98.88 \\ Heartbleed & 100 \\ Infiltration & 50 \\ PortScan & 99.91 \\ Web Attack—BruteForce & 83.39 \\ Web Attack—SqlInjection & 0 \\ Web Attack—XSS & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained by the SVM model on the test set. Figure 16: Comparing accuracy for the two algorithms on the entire test set. Figure 15: The distribution of instances for each attack class in the test set. These results provide valuable insights into the performance of the LSVC model in correctly classifying instances for each attack category. The overall test set accuracy of 99.13% demonstrates that LSVC effectively classifies most instances across various attack types. Notably, some attacks, such as Heartbleed and DDoS, achieved a perfect accuracy of 100%, reflecting the model's exceptional ability to identify these threats. However, lower accuracies were observed for certain attacks, such as Infiltration and Web Attack--SQL Injection, indicating potential areas for improvement. The results obtained by the RF model on the test set, evaluated for all attacks collectively as well as for each individual attack, are summarized in Table 8.  
These results highlight the performance of the RF model in accurately classifying instances across various attack categories on the test set. The overall test set accuracy of 99.91% underscores the model's high effectiveness in identifying different types of attacks. Notably, several attack categories, including Bot, DDoS, FTP_Patator, SSH_Patator, Heartbleed, and Web Attack--SQL Injection, achieved a perfect accuracy of 100%, reflecting the exceptional precision of the RF model for these specific attacks. Figure 17 compares the accuracy of the two algorithms on the entire test set. Figure 17: Comparing the accuracies of the two algorithms for the three most prominent attacks. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attack Type** & **Accuracy (\%)** \\ \hline Total malign LSVC & 99.91 \\ Bot & 100 \\ DDoS & 100 \\ DoSGoldenEye & 99.95 \\ DoSSlowhttptest & 99.81 \\ DoSHulk & 99.99 \\ DoSSlowloris & 99.74 \\ FTP\_Patator & 100 \\ SSH\_Patator & 100 \\ Heartbleed & 100 \\ Infiltration & 83.33 \\ PortScan & 99.98 \\ Web Attack—BruteForce & 86.26 \\ Web Attack—SqIll Injection & 100 \\ Web Attack—XSS & 71.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results obtained by the RF model on the test set.
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 5 Results and Discussion
### Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
#### 5.2.3 Improvement of the Inter-Stage Imbalance of Pressure Drop of Two-Stage Finger Seal  
According to the above analysis results, for the 2 SFS investigated in this paper, the pressure drop of the 2 nd finger seal is higher than that of the 1 st finger seal, especially under high axial pressure difference (1 MPa), the pressure drop of the 2 nd finger seal is 1.71 times of that of the 1 st finger seal. In view of the high pressure drop of the 2 nd finger seal, it's easy to wear due to the large contact pressure between finger foot and rotor, which not only affects the leakage performance of 2 SFS, but is also not beneficial to fully release the performance advantages of low leakage and long working life of 2 SFS under high axial pressure difference.  
Compared with the structure optimization design of finger element of 2 SFS, adopting the existing finger element and reducing the number of finger elements of the 2 nd finger seal while increasing the number of finger elements of the 1 st finger seal may be one of the very convenient measures to solve the inter-stage imbalance of pressure drop. For this purpose, the pressure distributions of 2 SFSs with three kinds of combinations (combination 1: \(n_{1}=5\), \(n_{2}=5\); combination 2: \(n_{1}=6\), \(n_{2}=4\); combination 3: \(n_{1}=7\), \(n_{2}=3\)) under the  
Figure 19: Effect of radial displacement excitation on the inter-stage pressure drop characteristics.  
Figure 18: Effect of side leakage flow on the inter-stage pressure drop characteristics.  
condition of axial pressure difference of 1 MPa are calculated, and the results are shown in Figure 20. It is clear to see that increasing the number of finger elements of the 1 st finger seal and decreasing the number of finger elements of the 2 nd finger seal can significantly increase the percentage of pressure drop of the 1 st finger seal; moreover, the leakage of 2 SFS is little affected by the change of the number of finger elements of each 1 SFS. Therefore, adjusting the number of finger elements of each 1 SFS can improve the inter-stage imbalance of pressure drop with little change in leakage performance.
## 5 Application of Self-Sensing Materials for
### Grounding System  
The grounding system of transportation infrastructures plays a critical role in protecting them against lightning strikes, thereby enhancing safety. However, one of the primary challenges faced is the corrosion of grounding electrodes, which diminishes the efficiency of the grounding system over time during its service life, consequently posing risks to the infrastructures [281]. In addressing this challenge, multifunctional cementitious composites containing conductive fillers offer a potential solution as a grounding system due to their low electrical resistivity. These composites can effectively transfer electrical charges from lightning strikes into the ground owing to their high electrical conductivity, thereby enhancing safety measures [282]. In this context, [PERSON] et al. (2019) [283] developed a reference electrode (RE) using a graphene-based conductive cementitious composite to control the corrosion of reinforced concrete structures. Their findings indicated that an RE incorporating 2 wt.% graphite in cement exhibited the best performance. Building on the promising potential of conductive cementitious composites, [PERSON] et al. (2017) [281] successfully applied CFs-CB-based conductive grounding rods in the foundation of a transmission tower to mitigate the effects of lightning strikes. While electrically conductive cementitious composites show significant potential for use as grounding electrodes instructural foundations, the current body of research in this regard remains limited. Further investigation is necessary to explore their practical applications in field projects.
## 4 Results  
### Surface Deformation by PS Analysis  
Figure 3 shows the Line of Sight (LOS) displacements extracted via the PS time series analysis in the target area. The multiple reference points by the analyses of phase angles of each interferograms were generated on the stable ground surfaces. Therefore, deformation values in booth ascending and descending analyses were fitted on the same spatial and temporal reference frames to be feed forwarded to decomposition. The time series displays of LOS surface deformations (ascending mode in Figure 4 and descending mode in Figure 5) demonstrate that the deformation patterns along geological/landcover units were consistent overall. However, there were strong discrepancies between the ascending and descending mode observations in a few geological units, particularly along the Ulsan fault line. This implies that surface migration along the Ulsan fault was typically governed by horizontal translation. As the accuracy and effectiveness of PS analysis in eradicating the error components needed to be investigated, we conducted an intercomparison test between the GPS and InSAR deformations.  
\begin{table}
\begin{tabular}{l l} \hline \hline Processing engine & Bernese 5.2 \\ \hline \multirow{2}{*}{Data processing strategy} & Precise Point Positioning (PPP) \\  & Relative Positioning (RP) \\ \hline Satellite ephemeris/clocks & CODE Final \\ \hline Ionosphere model file (ION) & CODE ION \\ \hline Reference frame (coordinate, velocity) & ITRF 2014 \\ \hline A priori troposphere/mapping function model & GMF (Global Mapping Function) \\ \hline Ocean tide loading model (BLQ) & FES 2004 \\ \hline Phase center variations (PCV) & PCV\_COD.I14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Processing method/parameters of GPS data.  
Figure 4: Temporal migration of LOS deformation in ascending mode during the 2014-2018 period, where (**i**) and (**p**) are the coseismic displacements of two earthquakes. The referencing time of deformation is 26 November 2014. Note the localized strong deformation in the eastern city area and the locations of two faults, represented as black lines.  
Figure 3: (**a**) Line of sight (LOS) displacements by ascending mode InSAR time series analysis during November 2014 to July 2018 and (**b**) descending mode InSAR time series analysis during May 2015 to June 2018. Note the locations of the Yangsan and Ulsan fault lines, represented as dotted lines.
## 3 Controller Design  
### The Architecture of the MRAC Adaptive Controller  
\(\mathcal{L}_{1}\) adaptive control is developed on the basis of MRAC. Therefore, we consider the architecture of the 6-DOF Stewart-Gough flight simulation platform based on MRAC given by Figure 3 [28].  
The state equation and output equation of the 6-DOF Stewart-Gough flight simulation platform is:  
\[\dot{x}(t)=A_{p}x(t)+B_{p}u_{p}(t)\]  
\[y(t)=c^{T}x(t) \tag{17}\]  
where \(x(t)\in\mathbb{R}^{m}\) denotes the state of the system, \(A_{p}\in\mathbb{R}^{m\times m}\) denotes the state matrix, \(B_{p}\in\mathbb{R}^{m\times n}\) denotes the input matrix, \(u_{p}(t)\in\mathbb{R}^{n}\) denotes the control input, \(c^{T}\in\mathbb{R}^{m\times m}\) denotes the output matrix, and \(y(t)\in\mathbb{R}^{n}\) denotes the output of the system.  
The reference system of the Stewart-Gough flight simulation platform, given by [29]:  
\[\dot{x}(t)=A_{m}x(t)+bk_{g}u_{m}(t)\]  
\[y(t)=c^{T}x(t) \tag{18}\]  
Figure 3: Closed-loop model reference adaptive controller (MRAC) architecture.  
where \(A_{m}\in R^{m\times m}\) denotes a Hurwitz matrix, \(b\in R^{m}\) denotes a known constant vector, \((A_{m},\ b)\) is controllable, and \(k_{g}=\underset{s\to 0}{lim}\left(1/\left(c^{T}(sI-A_{m})^{-1}b\right)\right)\) denotes a known constant, which can ensure that the system has zero steady-state error when the input is constant.  
The model reference adaptive controller is given by:  
\[u_{p}(t)=\hat{K}(t)u_{m}(t)+\hat{F}(t)x(t)+\hat{\delta}(t) \tag{19}\]  
where \(\hat{K}(t)\in\mathbb{R}^{m\times m}\) denotes the feed-forward gain matrix of the system, \(\hat{F}(t)\in\mathbb{R}^{m\times n}\) denotes the feedback compensation matrix of the system, and \(\hat{\delta}(t)\) denotes the input disturbances.