esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fast Handover Algorithm
### Process of the Fast Handover Algorithm
#### 3.4.1 Speed Decision  
When the moving speed \(v\) of UE is less than \(V_{\text{thr}}\), that is to say that Equation (10) is satisfied. When the service quality of NCell is better than that of SCell, and the duration meets the \(TTT\), then the handover of A3 is started. The handover condition of the same frequency band is  
\[M_{\text{n}}+Ocn-Hys>M_{\text{s}}+Ocs+Off \tag{9}\]  
where \(M_{\text{n}}\) is the SS-RSRP measurement value of NCell, \(M_{\text{s}}\) is the SS-RSRP measurement value of the SCell, \(Ocn\) is the offset value of NCell, \(Ocs\) is the offset value of the source cell, \(Hys\) is the handover hysteresis, and \(Off\) is the handover bias. When the moving speed of UE is no less than \(V_{\text{thr}}\), that is, when Equation (11) is satisfied, location recognition is performed.  
\[v<V_{\text{thr}} \tag{10}\]  
\[v\geq V_{\text{thr}} \tag{11}\]
# Slot Waveguide Enhanced Bloch Surface Waves
## 3 Results
### Waveguide Integration
Note that in a classical slot waveguide scheme (only one material constituting the rails), the enhancement, coming from the field discontinuity at the interface, is proportional to \(\frac{1}{n^{2}}\), where \(n\) is the refractive index of the rail medium. Increasing the refractive index of the rails leads to an increase of the _slot effect_. Between the cases presented in Figure 3a,c, the multilayer is replaced by a lower refractive index than TiO\({}_{2}\). Considering here, that the observed decreased intensity in the gap is only due to the change of material, a ratio of about 1.7 should be measured. However, the maximum intensity reaches a level much lower than in the case of the multilayer. It proves an enhancement of about 5 of the _slot effect_, which is due to the superimposition of the BSWs.
# Comparative Study on Load Monitoring Approaches
## 3 Advantages and Disadvantages of Intrusive and Non-Intrusive Load Monitoring
### Advantages and Disadvantages of Non-Intrusive Load Monitoring  
#### 3.2.1 Advantages  
Non-intrusive load monitoring (NILM) has some interesting advantages, including:  
Lower acquisition cost: NILM does not require the installation of sensors on each electrical device. Therefore, the number of sensors is reduced to one, and the cost is significantly reduced compared to ILM.  
Easy installation: Unlike ILM, where installation is complex and cumbersome, NILM is much easier to install because it does not require special electrical knowledge.  
Absence of intrusion: For its start-up, the NILM does not integrate any intrusion into the consumer. Indeed, it does not imply the installation of sensors respectively on each electrical appliance as is done in the ILM. In this way, the building occupants are truly reassured of their privacy.  
In addition, the non-intrusive approach is more scalable, as it can be used to monitor the energy consumption of many devices at once, without requiring physical changes to the building's electrical infrastructure. Another important point of this approach is that NILM systems have a wide communication bandwidth that allows them to cover a large number of loads, unlike intrusive systems, where the communication bandwidth limits the coverage of loads.  
In addition, the accuracy with which NILM breaks down total energy into individual consumptions also informs the performance of the associated algorithms [60] and the presence of the utility at the customer's end, leading to changes in consumers' energy consumption patterns [61].  
NILM does not have only advantages. As it is still a maturing technique, the limitations identified are mostly technical and are continuously being improved.
## 6 Results, Analysis and Discussion
### Statistical Analysis of Performance Metrics  
The methodology used to present the following results is the box and whisker method, which shows the distribution of data for each of the evaluated metrics and allows us to have a representation that describes the dispersion and symmetry characteristics. It represents three quartiles (\(Q_{1},Q_{2},Q_{3}\)) and the minimum and maximum data values on an aligned rectangle, horizontally or vertically. \(Q_{1}\), the first quartile, is the value greater than 25% of the values of the distribution. \(Q_{2}\), the second quartile, is the median of the distribution; it is the value of the variable that takes center place in a set of ordered data. \(Q_{3}\), the third quartile, is the value that exceeds 75% of the values of the distribution. The interquartile range is \(Q_{3}\)-\(Q_{1}\), and it represents 50% of the nodes. In order to develop this method, we followed the standard approach described in [20].  
For the purpose of the results analysis, it is important to note that the graphs of the box and whisker method has been built with a whisker coefficient of \(1.5\). The extremes in each box represented by a line show the maximum and minimum values of observed data. The ends represented by X are the 1% at the bottom and the 99% at the top of the box. The filled squares shown in the boxes represent the mean of the data.
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 6 Simulations
### Template Generation  
In order to generalize the approach to different motor sizes, a numerical study will be given in the sequel. This study will determine whether the optimal flux trajectory depends on motor size and torque steps and find the value of the anticipation time \(\Delta t_{\rm a}\). The optimization procedure described in Section 3 was therefore conducted multiple times for different motor parameters and speed ramps.  
Figure 11 shows the results for different motor sizes. Starting from a given motor speed \(\omega_{\rm Rm,1}\) and given load torque \(T_{\rm L}\), at time \(t=t_{1}\), a speed ramp to \(\omega_{\rm Rm,2}\) in the interval \(t\in[t_{1}\quad t_{2}]\), \(t_{1}\geq 0\) and \(t_{2}\leq t_{\rm f}\) is commanded. The MATLAB fmincon solver generates optimal flux and current trajectories subject to minimizing the objective functional (15). It can be observed that in the optimal solution, the flux increases prior to the torque. A deeper analysis shows that the anticipation time \(\Delta t_{\rm a}\) for different motor sizes increases with the rotor time constant. The overall shape of the optimal flux trajectory does not change significantly for different motors. The relationship between the torque step size and the flux rise anticipation time is analyzed based on the motor from Figure 11 with a rated power of 370 W in Figure 12. It can be observed that the anticipative flux rise time does not significantly depend on the torque levels. The flux weakening during speed ramps above the rated speed to satisfy the voltage limit constraint can also be observed from this result. The optimal anticipation time is found to be \(\Delta t_{\rm a}=2.5t_{\rm R}\).  
Figure 11: Simulation of optimal flux trajectories and torque for various motor sizes.  
Figure 10: Control structure.
## 3 Results and Discussion
### Influence of Cavitation on Flow Characteristics
value, then as the speed continues to increase, the volumetric efficiency starts to decrease, and the flow curve slope begins to slow down significantly. This situation is beyond the pump's working capacity, which not only reduces the volumetric efficiency but may also cause damage to the normal operation of the gear pump and shorten its service life. Gear pumps with other tooth numbers show similar trends: the seven-teeth gear pump's volumetric efficiency peaks at 87.27% at 9000 RPM, while the eight- and nine-tooth gear pumps reach their highest volumetric efficiencies of 88.55% and 90.73% at 10,000 RPM, respectively. The conclusion is that as the number of teeth increases, so does the peak value of volumetric efficiency, and the critical speed corresponding to the peak volumetric efficiency also increases accordingly. This is due to the increase in the number of teeth, resulting in the gear volume ratio increasing, while the effective volume of the pump working is reduced, so that the theoretical displacement from six to nine teeth shows a downward trend, but this has a positive impact on the volumetric efficiency. It can be seen from Figures 7-9 that under the same conditions of inlet and outlet pressure difference, the gear pump speed increases within a certain range, and the volumetric efficiency and theoretical outlet flow per unit time are increased. However, once the speed reaches its respective critical values, the volume fraction of gas in the pump chamber will increase significantly, and the cavitation phenomenon will intensify, resulting in the volumetric efficiency beginning to decline. High speed will enhance the cavitation tendency of the fluid in the cavity, which will have an adverse influence on the output flow.
## Appendix A Description on Datasets and Extracted Features  
### Problem 1  
[PERSON] measured ECG, EDA, EMG, and respiration of drivers and extracted 22 features to detect stress (Table A1) [33,34]. ECG is the signal caused by heart activity, and RR intervals can be obtained from the ECG. The RR intervals contain information on our autonomic nervous system, and the features extracted from RR intervals were used. EDA represents the activity of sweat glands in the skin measured on the hand and foot, and electrical activity from skeletal muscles was recorded as EMG in the research. Problem 1 uses the feature data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signal** & **Features** \\ \hline ECG & Normalized mean and variance of RR intervals, ratio of the low (0–0.08 Hz) to the high-frequency (0.15–0.5 Hz) spectral power for RR intervals \\ \hline EDA (hand) & Normalized mean and variance, the number of orienting responses, sum of the startle magnitudes, sum of the response duration, sum of the areas under responses \\ \hline EDA (foot) & Same as EDA (hand) \\ \hline EMG & Normalized mean \\ \hline Respiration & Normalized mean and variance, spectral power (0–0.1, 0.1–0.2, 0.2–0.3, and 0.3–0.4 Hz) \\ \hline \hline \end{tabular}
\end{table}
Table A1: Extracted features for problem 1.
## 6 Results and Discussion
### Material Handling System Control  
The results obtained in the second experimental environment show that it is not possible to identify the best control architecture in absolute terms. Specifically, as expected, the results obtained in the first experimental case (Figure 7a) show that the centralized control is, for almost all the non-compliant product percentages, better than the heterarchical control. Except for the 50% of non-compliant products, the throughput time resulting from centralized control was always lower than the throughput time resulting from heterarchical control (Figure 7a). When AGV failures occur (second and third experimental case), the results of the runs do not show a homogeneous behavior (Figure 7b,c). Sometimes centralized control is better than heterarchical controls and vice versa. However, the throughput time results of the runs obtained under heterarchical control present a standard deviation lower than centralized control architectures. This means that this configuration is able to manage more efficiently uncertainties and to operate the system more steadily in not predictable scenarios. Especially in vague situations, it can be beneficial to make the behavior respectively the outcome of a system more predictable because promises towards customers would be more reliable, subsequent processes could be aligned more easily, and less effort would be needed for adjustments to keep the system stability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Experimental Case Number and Throughput**} \\  & & \multicolumn{6}{c}{**Time Results**} \\ \cline{3-8}  & & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{2}{*}{Mean throughput time} & Centralized control & 4.937 & 4.932 & 25.504 & 25.578 & 83.882 & 33.502 \\  & Heterarchical control & 5.957 & 4.931 & 25.409 & 25.335 & 85.932 & 37.333 \\ \hline \multirow{2}{*}{Non-manufactured finished products number} & Centralized control & 0 & 0 & 0 & 0 & 82 & 80 \\  & Heterarchical control & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results for the assembly station control. Throughput time expressed in minutes.  
Figure 7: Simulation results for the material handling system control; (**a**) No AGV failures; (**b**) AGV failures: MTBF = 3 min and MTTR = 1 min; (**c**) AGV failures: MTBF = 9 min \(-\) MTTR = 1 min.
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 4 Experimental Results
### Discussion
Moreover, the viewpoint is introduced into the subspace fuzzy clustering, which can better guide the clustering process of each cluster subspace;
* Third, the fuzzy weight is introduced, which can overcome the influence of outliers on cluster analysis and speed up the convergence of clustering. Our algorithm adds weights to each data as a whole to indicate the degree of contribution to clustering and assigns smaller weights to noise points to reduce their participation in clustering, thus weakening their impact on clustering results;
* Finally, the separation term between clusters is introduced into the objective function. The reference point for separation between clusters is the average value of the initial cluster centers, which is combined with the distribution of weights to improve the accuracy of the algorithm by enhancing the ability to process data spatial distance. Compared with the ESSC in [28], the proposed VSFCM algorithm has the following advantages:  
* The reference point for inter-cluster separation has been improved. In this study, \(v_{0}\) is used instead of \(x_{0}\). When we initialize the cluster center, outliers are automatically removed, which weakens their influence of them on the reference points so that each cluster can be separated more clearly;
* We use the viewpoint to guide the convergence of each projection subspace of soft subspace clustering, which conforms to the data structure of clustering and accelerates the clustering process. Compared with the DVPFCM of [32], the proposed VSFCM algorithm has the following advantages:  
* The cut-off distance is optimized. The selection of cut-off distance is very important and directly affects the clustering accuracy of the algorithm. Generally, the cut-off distance should be selected so that the sphere space of the cut-off radius contains 1% to 2% of the total number of datasets. However, the calculation formula of the cut-off radius of HDCCI has no corresponding rigorous scientific proof, which makes it difficult to initialize the cluster center completely in line with the concept of DPC. In contrast, the improved cut-off distance achieved better results while meeting the DPC standards;
* Fuzzy weights are introduced. By assigning smaller weights to features with a larger proportion of noise points and outliers, the influence of them on clustering results can be indirectly weakened;
* Subspace clustering is employed. When faced with high-dimensional data, traditional clustering algorithms have no effective processing measures. However, the algorithm combined with subspace clustering has better adaptability to it. By continuously adjusting the weight of each subspace, the contribution of each subspace to the clustering can be accurately described, making the clustering results better.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 4 Actuation
### Net Motor Torque  
The steering law obtained in the preceding subsection must be used in each arc that composes the overall attitude maneuver (i.e., tracking and slewing arcs, cf. Sections 5 and 6). The commanded torque \(\underline{T}_{c}\) is discontinuous across adjacent arcs, and this implies that \(\dot{\underline{\theta}}\) is discontinuous as well. To improve the accuracy in modeling the dynamical system at hand, the steering law (34) is used to yield the commanded value of \(\dot{\underline{\theta}}\), denoted with \(\dot{\underline{\theta}}_{c}\). The real value of \(\dot{\underline{\theta}}\) is found by integrating  
\[\ddot{\underline{\theta}}=\dfrac{\dot{\underline{\theta}}_{c}-\dot{\underline {\theta}}}{\tau_{act}} \tag{35}\]  
where \(\tau_{act}\) (set to 0.05 s) represents the time constant related to the reaction delay of the SGCMGs, while \(\dot{\underline{\theta}}_{c}\) is obtained from Equation (34), with \(\dot{\underline{\theta}}_{c}\) in place of \(\dot{\underline{\theta}}\). Equation (35) is also useful for evaluating the angular accelerations of the gimbal axes, which allows determining the net motor torque \(g_{1}^{(j)}\) applied to device \(j\)[30], i.e.,  
\[g_{1}^{(j)}=I_{T}^{(j)}\,\ddot{\theta}_{1}^{(j)}+I_{T}^{(j)}\,\dot{\Omega}_{1 }^{(j)}+(I_{S}^{(j)}-I_{T}^{(j)})\dot{\Omega}_{2}^{(j)}\,\dot{\Omega}_{3}^{( j)}+I_{S}^{(j)}\,\dot{\Omega}_{2}^{(j)}\,\dot{\theta}_{2}^{(j)} \tag{36}\]  
where \(\underline{\Omega}\) and \(\underline{\underline{\Omega}}\) are (\(3\times 1\))-vectors that include the components of the spacecraft angular velocity in \(\underline{\underline{W_{0}}}\) and \(\underline{\underline{W_{1}}}\), respectively.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 5 Solutions of the CPS Communication Hub
### Solutions for Data Plane Negotiation
#### 5.6.2 Data Plane Negotiation  
The purpose of the actual data plane negotiation is to share information on communication-related parameters of the specific data plane protocol to be used between the physical resources. The basic operation of the data plane negotiation is depicted in Figure 17. The endpoint willing to communicate with the other endpoint, and knowing other endpoint's XMPP JID, sends a specific Data Plane Protocol request (DPP-req) to that JID. The recipient of the request then creates the response message (DPP-resp) that contains the requested information. The recipient of the request can consult trusted JID lists in verifying that the endpoint sending the request is acceptable. The request coming from an endpointnot found in the list should be discarded. Trusted list usage is recommended because, although \"trust\" relationship creation is required before negotiation, an endpoint can still send the DPP-req request to any JID without earlier \"trust\" relationship creation. Below, the endpoint sending the DPP-req is called the DPP-client, whereas the recipient is called the DPP-server. As data plane protocols may vary a lot, only the most common and generic parameters can be shared using this approach. IP address or hostname, port number, and identification of data plane protocol belong to the set that could be shared.  
Request/response implementation is utilizing XMPP message stanza and \"Data Form\" XMPP extension [29]. That extension is identified by XML namespace called \"jabber:x:data\". Fields with example values of the request message are expressed here in plain text.  
By the \"suggested-protocol-type\" field, a DPP-client can express which protocol it is supporting and willing to use. Fields with example values of the response message are in plain text.  
The response message described in XML format is as follows:  
Figure 17: Data plane negotiation procedure.  
where the \"protocol-type\" field states which protocol shall be used, while the \"Status\" field is used to inform the DPP-client whether data plane protocol negotiation was successful.
## 2 Preliminaries
### Digital Twins and Healthcare Systems
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref \#** & **Year** & **Objective** & **Method Used** \\ \hline
[30] & 2019 & To explore the role of big data analytics and AI in healthcare. & Systematic mapping study \\ \hline
[31] & 2021 & To evaluate the performance of machine learning algorithms in predicting heart disease. & Supervised machine learning algorithms \\ \hline
[32] & 2020 & To develop a healthcare recommendation system using big data analytics. & Big data analytics \\ \hline
[33] & 2021 & To develop a comprehensive solution using ML to handle data incompleteness. & Machine learning analysis \\ \hline
[34] & 2021 & To propose a knowledge-based mining approach for analyzing medical data. & Knowledge-based mining \\ \hline
[35] & 2023 & To discuss the impact of Industry 4.0 and digitalization on healthcare. & Review of Industry 4.0 and digitalization \\ \hline
[36] & 2023 & To propose a federated learning approach for medical image retrieval. & Federated learning \\ \hline
[37] & 2023 & To analyze the role of AI in healthcare and its future scope. & Review of AI in healthcare \\ \hline
[38] & 2024 & To use IoT sensors and sequence processing models in the prediction of cardiovascular disease. & The recommendation system \\ \hline
[39] & 2024 & To provide a systematic literature review of quantum computing applications in healthcare. & Review of quantum computing in healthcare \\ \hline
[40] & 2025 & To develop an accurate and interpretable EEG-based framework for epileptic seizure detection. & Hybrid deep learning with SHAP (XAI) \\ \hline
[41] & 2025 & To propose secure Internet of Medical Things (IoMT) healthcare systems with federated learning utilizing blockchain concepts. & Blockchain-based solution \\ \hline
[42] & 2025 & To propose a medical blockchain data sharing method based on asynchronous federated learning. & Federated blockchain system \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Application of artificial intelligence in healthcare. medical conditions, such as epileptic seizures [43], mental depression [44], Parkinson's disease [45], Alzheimer's disease [46], and Autism detection [47], in real time. Also, integrating IoT technology with medical systems facilitates more reliable real-time monitoring which can help to advance remedial methods, particularly in terms of managing brain activity, and supporting areas like emotion and depression [48; 49]. However, the privacy and safety of the patient and their data are of great importance; this aspect has become paramount due to the linking of EEG data with new technology [50; 51].