esa-sceva/satcom-chunk-collection huggingface dataset:
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 3 The Key Management Scheme for Cluster-Based WSNs
### Mobile Sensors Subscheme for Hierarchical WSNs
#### 3.4.2 Session Key Update Process  
However, when \(S_{0}\) moves to the junction of two adjacent clusters, for example \(C_{0}\) and \(C_{2}\) in Figure 5, it may receive key update messages from CH\({}_{0}\) and CH\({}_{2}\) at the same time. It should be noted that \(S_{0}\) only knows the previous session key \(SK_{0}\) of cluster0, and it is unaware of the previous session key of cluster2. Thus, \(S_{0}\) can only decrypt the broadcasting message from CH\({}_{0}\) to update \(SK_{0}\). After joining cluster2, \(S_{0}\) can obtain the present session key \(SK_{2}\) from the base station and wait for key updating to repeat.
# Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data
## 3 Methods Proposed
### Clustering  
The first method for applying the model is based on clustering, i.e., intervals of 0.5 m/s are defined and, in each one, a mean value for the output power is obtained. In every case, the mean output power is assigned to the identifier of the interval and, as a final result, pairs of values are obtained. After this, an optimization process is performed considering a specific model. In this paper, 3 PLE one is used but the procedure is valid for any other. The 3 PLE model follows the expression given by Equation (2), where \(\alpha\), \(\beta\) and \(\gamma\) are the only parameters. In order to a better fitting of the model, only pairs of values greater than the cut-in wind speed ones are considered.  
\[P(v)=\frac{\alpha}{1+\exp(-\beta*(v-\gamma))} \tag{2}\]  
The optimization is performed by using the above mentioned Interior Point Algorithm and the results are the values of the parameters that best approximate the pairs of values. The optimization process has, as its objective function, the Least Squares Error, due to its performance in this type of algorithms.  
The Interior Point Algorithm solves optimization problems by going through the middle of the solid defined by the problem rather than around its surface. The possible solution evolves on each iteration inside the feasible set to search the optima and it is stopped when the solution obtained cannot be optimized any more by the algorithm.  
The algorithm is applied using the \"optimtools\" package provided by MATLAB, where some maxima and minima for the parameters need to be defined in order to accelerate the process. The application of the model based on clustering is shown in Figure 3.  
Figure 3: 3 PLE model applied to the power curve based on clustering.  
Figure 2: Spline model applied to the power curve over cloud data.
## 2 Numerical Methods and Preparations
### Real-Space Dynamical Mean-Field Theory
These parameters are determined from the projection of the non-interacting impurity Green's function  
\[\mathcal{G}_{i,n_{s}}^{(0)}(\sigma;\mathrm{i}\omega_{n})=\mathrm{i}\omega_{n} +\mu_{\sigma}-V(x_{i},y_{i})+\sum_{\alpha=1}^{n_{s}}\frac{|V_{\alpha,\sigma}| ^{2}}{\mathrm{i}\omega_{n}-E_{\alpha,\sigma}} \tag{6}\]  
to \(\mathcal{G}_{i}^{(0)}(\sigma;\mathrm{i}\omega_{n})\) given by the local Dyson equation. This projection is done in practice by numerically minimizing the objective function:  
\[F(\{E_{\alpha,\sigma}\},\{V_{\alpha,\sigma}\})=\sum_{n}\frac{1}{\omega_{n}} \left|\mathcal{G}_{i}^{(0)}(\sigma;\mathrm{i}\omega_{n})-\mathcal{G}_{i,n_{s} }^{(0)}(\sigma;\mathrm{i}\omega_{n})\right|^{2}\,, \tag{7}\]  
where the factor \(1/\omega_{n}\) is introduced to give more weight to the low-frequency part. Once the parameters are determined, the exact diagonalization of \(\mathcal{H}_{i,n_{s}}\) straightforwardly provides the local self-energy \(\Sigma_{i}(\sigma;\mathrm{i}\omega_{n})\) by using either the Lehmann representation at finite temperature, or the Lanczos method at zero temperature [72; 80]. Constructing and solving the impurity Hamiltonian for every site is the second time-consuming part of the R-DMFT interaction. The computational time is reduced by parallelizing the calculations in the lattice site domain. The local self-energies collected after sweeping over all sites are sent back to the lattice Dyson solver in Equation (2). This closes one R-DMFT iteration that goes on until the local self-energies are converged.
## 2 Background  
### Overview of PMIPv6  
The foundation of PMIPv6 is based on MIPv6 in the sense that it extends MIPv6 signaling and reuses many concepts such as the home agent (HA) functionality. However, PMIPv6 is designed to provide network-based mobility management support to a Mobile Node (MN) in a topologically localized domain. Therefore, an MN is free from participation in any mobility-related signaling, and the proxy mobility agent in the serving network performs mobility-related signaling on behalf of the MN. Once an MN enters its PMIPv6 domain and performs access authentication, the serving network ensures that the MN is always on its home network and can obtain its home address on any access network. The serving network assigns a unique home network prefix to each MN, and conceptually this prefix always follows the MN wherever it moves within a PMIPv6 domain. From the perspective of the MN, the entire PMIPv6 domain appears as its home network. Accordingly, it is needless to configure the care of address at the MN. The new functional entities of PMIPv6 are the mobile access gateway (MAG) and local mobility anchor (LMA). The MAG typically runs on the access router (AR). The main role of the MAG is to detect the MN's movements and initiate mobility-related signaling with the LMA on behalf of the MN. In addition, the MAG establishes a tunnel with the LMA for enabling the MN to use an address from it home network prefix and emulates the MN's home network on the access network for each MN. On the other hand, the LMA is similar to the HA in MIPv6. However, it has additional capabilities required to support PMIPv6. The main role of the LMA is to maintain reachability to the MN's address while it moves around within a PMIPv6 domain, and the LMA includes a binding cache entry for each currently registered MN. The binding cache entry maintained at the LMA is more extended than that of the HA in MIPv6 with some additional fields such as the MN identifier, the MN's home network prefix, a flag indicating a proxy registration, and the interface identifier of the bidirectional tunnel between the LMA and MAG. Such information associates an MN with its serving MAG, and enables the relationship between the MAG and LMA to be maintained [9].
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
## 3 Practical Application  
### System Fabrication  
A prototype of the measurement system was fabricated to prepare it for practical use. A schematic diagram of the system is shown in Figure 3a. The microwave-integrated circuit (MIC) configured on a dielectric board consisted of a voltage-controlled oscillator (VCO) and multipliers in the transmission section, and a low-noise microwave/millimeter-wave amplifier and a detector in the receiver section. The output frequency of the VCO was 8-12 GHz and was multiplied up to 32-48 GHz using two multipliers. The microwave output suppressed to less than 1 mW was fed to a transmitting antenna. The sweep time of the incident frequency was 100 usec, and the sweep cycle was 1 msec, which resulted in a duty cycle of 1/10. The reflected wave picked up by an identical antenna was fed to a  
Figure 1: (**a**) Model of Fabry–Perot interferometry; (**b**) model of present subject.  
low-noise amplifier and a detector to provide the envelope of reflected power. Substrate (tapered-slot) antennas were used as a transmitter and a receiver, as shown in Figure 3b. The angle between the transmitting and the receiving antennas was 30 degrees (the incident angle was 15 degrees). Compared to the case of 0 degrees, this causes a 3-4% change in the peak frequency interval due to the Fabry-Perot interferometry effect. This does not affect the results. The distance between the antennas and the tiles was set to 5 mm. Since the directivity of the antennas was poor, the reflected waves became very small at larger distances.  
Figure 3: (**a**) Schematic of the prototype system; (**b**) picture of the transmitting and receiving antennas.  
Figure 2: Measurement of reflected wave vs. frequency and numerical calculation of Fabry–Perot interferometry.  
A PC control unit was responsible for identifying the measurement positions, transmitting the incident wave, detecting the reflected waves and quantitatively evaluating the adhesive state between a tile and the concrete base. The total system was installed on an X-Y stage. The signal for the frequency sweep emitted from the DA output of the control unit was fed to the microwave circuit, as well as the X-Y stage. The entire system was housed in a shield box in order to satisfy the weak power condition for leakage power from the system to be applied as a consumer appliance under the Administration of Radio, and the Ministry of Internal Affairs and Communications (MIC), Japan [14].  
In the test experiment, the system consisting of an assembled MIC board and antennas, together with an automatic X-Y stage, was placed close to a tiled surface and scanned in a two-dimensional space. The scanning was performed every 1 mm in the _X-Y_ axis using the meander scan style. Figure 4 shows the scanning process of the tiled surface. First, the tiled surface is scanned along the \(X\) axis from the initial position, then along the \(Y\) axis by 1 mm, followed by the negative \(X\) direction movement, and so on.
# Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network
## 4 Experiment Study
### Data Collection  
This research uses the WISDM dataset [12] as described in Table 1. A total of 1,098,209 examples are present in the dataset. A total of 36 participants participated in the experiment. To collect data for this experiment, every participant was required to carry a smartphone running on the Android operating system during the performance of each of the six activities. The subjects were required to carry their Android phones in a pocket situated in the front portion of their pants. Furthermore, they were advised to sit, walk, stand, jog, climb upstairs and go downstairs for specific durations of time.  
An Android application, running on the smartphone, controlled the data collection from the accelerometer during activity performance by the subjects. The application used in the experiment comprised a graphical user interface, which permitted the experiment supervisor to record the subject's name and label the activity that the subject performed. Furthermore, the application also provided a feature to start and stop collecting data. In addition, the application enabled the supervisor to control the type of data from different sensors (e.g., gyroscope, accelerometer) and to set the frequency of data collection. Overall, the data from the accelerometer were collected every 50 milliseconds at the rate of 20 samples per second.  
For example, Table 3 shows a part of the dataset with the activity details of participant with user ID 33. The participant is performing the jogging activity. The time column consists of the timestamps in numeric form, which is generally the phone's uptime in nanoseconds. The corresponding coordinate values of the three axes are also included in the table.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## Appendix A Power Loss Calculation  
Power Loss Calculation for PFC is calculated using the following--Equation [48]:  
\[P_{L,PFC}=P_{L,ind}+P_{L,co}+P_{L,sw}+P_{L,cond}\]  
Furthermore, the power loss components are calculated as follows:  
\[P_{L,ind} =I_{inf,rms}^{2}\times DCR \tag{11}\] \[P_{L,co} =I_{co,rms}^{2}\times ESR\] (12) \[P_{L,sw} =0.5\times V_{DC-link}\times I_{DC-link}\times(t_{r}+t_{don}+t_ {f}+t_{doff})\times f_{sw}\] (13) \[P_{L,cond} =R_{DSon}\times I_{inf,avg}^{2}\times D \tag{14}\]  
where \(I_{inf,rms}\), \(I_{inf,avg}\), \(I_{co,rms}\), \(I_{DC-link}\), and \(D\) are the rms interfacing inductor current, average interfacing inductor current, rms DC-link capacitor current, PFC output current, and duty ratio, respectively. Parameters DCR (DC resistance) and ESR (equivalent series resistance) have been selected from the data sheet of the selected interfacing inductor and DC-link capacitor, respectively. Additionally, \(t_{r}\) (rise time), \(t_{don}\) (turn-on delay time), \(t_{doff}\) (turn-off delay time), and \(t_{f}\) (fall time) are the data sheet parameters of the SiC devices chosen for power loss calculation using an online tool, i.e., UnitedSiC FET-Jet Calculator.
## 2 Radar Jamming Signal Analysis and Modeling
### Smart Noise Jamming
#### 2.2.2 Noise Product Jamming  
The principle behind Noise Product Jamming (NPJ) is similar to NCJ. In NPJ, the jamming system multiplies the intercepted radar transmission signal with a noise signal, generating a noise product jamming. Similar to convolution modulation, NP jamming also carries characteristics of the radar transmission signal. By emitting high-power jamming signals, NPJ achieves suppression of the echo signal. The mathematical modeling process of NPJ is as follows:  
Let the radar signal input to the DRFM be \(s(t)\), after delay the output is \(s(t-\tau)\), where \(\tau\) is the delay time of the DRFM. The selected noise \(n(t)\) is a narrowband Gaussian noise signal. \(s(t-\tau)\) and \(n(t)\) are used for noise product modulation to obtain the output deception jamming signal:  
\[J(t)=s(t-\tau)n(t) \tag{5}\]
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Multi-Modal-Based SLAM
Combining sonar for large-area coverage, cameras for detailed imagery, and inertial sensors for stability results in more accurate and adaptable SLAM solutions. For instance, fusing stereo vision and multi-beam sonar can improve feature tracking [45], while visual-LiDAR approaches help overcome aggressive motion and poor lighting conditions [77]. Unmanned surface vehicles equipped with multiple sensors have demonstrated the capability to produce detailed above-and-below-water maps [78]. Nevertheless, multi-modal SLAM introduces challenges. Integrating different sensors requires complex algorithms, high computational resources, and careful real-time data processing. Calibrating and synchronizing heterogeneous sensors add operational complexity, and interpreting fused data demands specialized expertise. Despite these difficulties, multi-modal SLAM holds significant promise. By drawing on multiple sensors and leveragingrefined initialization strategies (as in [75]) or advanced data association methods (as in [76]), SLAM systems can better adapt to low visibility, fluctuating conditions, and extended missions. Table 6 provides a concise overview of recent advancements and methodologies in sensor fusion for SLAM systems. It summarizes key research contributions, highlighting the problems addressed, methods employed, key findings, and limitations of various sensor fusion approaches  
In complex underwater environments, no single sensor modality is sufficient to address all challenges. Adaptive sensor fusion techniques have emerged as a key approach to overcoming these limitations by dynamically combining data from multiple sensors, such as acoustic, optical, and inertial systems. For instance, DVLs provide accurate velocity estimates in feature-depleted regions, while vision-based systems excel in areas with sufficient texture and lighting. When fused, these systems can compensate for each other's weaknesses, ensuring reliable localization and mapping under diverse conditions. Advanced fusion algorithms also incorporate real-time environmental feedback, enabling context-aware sensor prioritization. For example, in turbid or low-visibility waters, acoustic sensors dominate, whereas optical sensors take precedence in clearer conditions. This adaptability not only enhances SLAM performance but also reduces computational overhead by focusing processing resources on the most reliable sensor data. Such innovations are critical for enabling robust and efficient SLAM in unpredictable underwater scenarios.
## 4 Experimental Analysis and Verification
### Preparation of the Experimental Data
#### 4.1.2 Feature Extraction  
Since the denoising and feature extraction of the signals can affect the fault diagnosis of the model, wavelet packet decomposition, threshold filtering, and signal reconstruction on the data were performed to remove the noise. Then, the time-frequency features of the reconstructed signal are extracted, forming a vector Z. The Morlet wavelet was suitable for decomposing the bearing vibration signals [30], thus, it was selected as the wavelet basis function in this section.  
To better highlight the fault information of the motor bearings, eight obvious features of faulty bearings were extracted: the mean value, standard deviation, root mean square, pulse index, margin index, skewness index, kurtosis index, and waveform entropy. These features reflect the various characteristics of the vibration signals and demonstrate robust discriminative ability and stability across different fault conditions. Then, the above time  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of** & **Working** & **Rotational** & & \\
**Bearing** & **Conditions** & **Speeds** & **Loads** & **Health Status** \\ \hline \multirow{6}{*}{SKF6205} & A & 1797 r/min & 0 HP & \\  & B & 1772 r/min & 1 HP & \\ \cline{1-1}  & C & 1750 r/min & 2 HP & Normal state \\ \cline{1-1}  & D & 1730 r/min & 3 HP & Inner ring fault \\ \cline{1-1}  & E & 1797 r/min & 0 HP & Rolling element fault \\ \cline{1-1}  & F & 1772 r/min & 1 HP & Outer ring fault \\ \cline{1-1}  & G & 1750 r/min & 2 HP & \\ \cline{1-1}  & H & 1730 r/min & 3 HP & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bearing dataset.  
frequency features were combined as the feature vector Z. The expressions for the above features are shown in Table 2.  
We took 120,000 consecutive sampling points from all datasets A, B, C, D, E, F, G, and H, and divided them into 400 samples according to the time series. Each sample contained 300 consecutive sampling points. Then, we extracted eight features for each sample based on the expressions in Table 2 and formed a vector.
Sensors_**2012**, _12_, 15438-15466; doi:10.3390/s121115438  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Energy-Efficient Strategy for Accurate Distance Estimation in Wireless Sensor Networks**  
**[PERSON] *, [PERSON] and [PERSON]**  
Data Processing and Simulation Group, Universidad Politecnica de Madrid, ETSI. Telecomunicacion, Avda. Complutense 30, 28040, Madrid, Spain; E-Mails: [EMAIL_ADDRESS] (A.M.B.); [EMAIL_ADDRESS] (J.R.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-9145-33535; Fax: +34-9133-65876.  
_Received: 5 September 2012; in revised form: 31 October 2012 / Accepted: 31 October 2012 / Published: 9 November 2012_  
**Abstract:** In line with recent research efforts made to conceive energy saving protocols and algorithms and power sensitive network architectures, in this paper we propose a transmission strategy to minimize the energy consumption in a sensor network when using a localization technique based on the measurement of the strength (RSS) or the time of arrival (TOA) of the received signal. In particular, we find the transmission power and the packet transmission rate that jointly minimize the total consumed energy, while ensuring at the same time a desired accuracy in the RSS or TOA measurements. We also propose some corrections to these theoretical results to take into account the effects of shadowing and packet loss in the propagation channel. The proposed strategy is shown to be effective in realistic scenarios providing energy savings with respect to other transmission strategies, and also guaranteeing a given accuracy in the distance estimations, which will serve to guarantee a desired accuracy in the localization result.  
**Keywords:** energy consumption; distance estimation; sensor networks
## 4 CFA for EMG and HRV Feature Fusion
### Fusion Results Comparison
Other than that, to evaluate the proposed method, the results of the proposed method are compared with the work done in [8] and [9] and are shown in Table 6. [PERSON]'s and [PERSON]'s used  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Feature Group** & **Fusion Method** & **NB (\%)** & **1-NN (\%)** & **3-NN (\%)** & **5-NN (\%)** & **J48 (\%)** & **SVM (\%)** & ** CFA (\%)** \\ \hline \multirow{3}{*}{(A)} & A(5,1) & 81.36 & 79.66 & 79.66 & 77.97 & 86.44 & 74.58 & **95.49** \\  & A(5,2) & 81.36 & 77.97 & 83.05 & 86.44 & 84.75 & 74.58 & 93.53 \\ \multirow{3}{*}{5-feature set} & A(5,3) & 76.27 & 76.27 & 76.27 & 81.36 & 84.75 & 72.88 & 93.73 \\  & A(5,4) & 83.05 & 76.27 & 77.97 & 79.66 & 84.75 & 74.58 & 95.27 \\ \hline \multirow{3}{*}{(B)} & Average & 80.51 & 77.54 & 79.24 & 81.36 & 85.17 & 74.16 & **94.51** \\ \cline{2-9}  & B(5,1) & 64.41 & 69.49 & 69.49 & 69.49 & 74.58 & 67.8 & 78.36 \\ \multirow{3}{*}{(B)} & B(5,2) & 81.36 & 77.97 & 74.58 & 77.97 & 77.97 & 79.67 & 77.75 \\  & B(5,3) & 84.75 & **86.44** & 83.05 & 77.97 & 72.88 & 74.58 & 80.34 \\ \cline{1-1}  & B(5,4) & 62.71 & 71.19 & 77.97 & 76.27 & 74.58 & 67.8 & 77.85 \\ \hline \multirow{3}{*}{(C)} & Average & 73.31 & **76.27** & **76.27** & 75.43 & 75.00 & 72.46 & **78.58** \\ \cline{1-1}  & C(7,1) & 83.05 & 79.66 & 86.44 & 86.44 & 77.97 & 74.58 & 91.80 \\ \cline{1-1}  & C(7,2) & 81.36 & 77.97 & 74.58 & 79.66 & 79.66 & 72.88 & **93.22** \\ \cline{1-1}  & C(7,3) & 83.05 & 76.27 & 79.66 & 84.75 & 76.27 & 77.97 & 92.92 \\ \cline{1-1}  & C(7,4) & 81.36 & 72.88 & 76.27 & 81.36 & 77.97 & 74.58 & 92.99 \\ \hline \multirow{3}{*}{(D)} & Average & 82.21 & 76.70 & 79.24 & 83.05 & 77.97 & 75.00 & **92.73** \\ \cline{1-1}  & D(9,1) & 84.75 & 76.27 & 83.05 & 88.14 & 72.88 & 72.88 & 89.50 \\ \multirow{3}{*}{9-feature set} & D(9,2) & 79.66 & 77.97 & 79.66 & 83.05 & 72.88 & 74.58 & 90.28 \\ \cline{1-1}  & D(9,3) & 79.66 & 77.97 & 79.66 & 88.14 & 77.97 & 72.88 & **91.33** \\ \cline{1-1}  & D(9,4) & 81.36 & 81.36 & 79.66 & 74.58 & 76.27 & 71.19 & 90.49 \\ \hline \multirow{3}{*}{(E)} & Average & 81.36 & 78.39 & 80.51 & 83.48 & 75.00 & 72.88 & **90.40** \\ \cline{1-1}  & E(11,1) & 83.05 & 76.27 & 83.05 & 84.75 & 76.27 & 72.88 & 90.50 \\ \cline{1-1}  & E(11,2) & 79.66 & 76.27 & 84.75 & 86.44 & 76.27 & 74.58 & 90.10 \\ \cline{1-1}  & E(11,3) & 81.36 & 79.66 & 81.36 & 88.14 & 76.27 & 74.58 & 88.60 \\ \cline{1-1}  & E(11,4) & 86.44 & 77.97 & 83.05 & 86.44 & 77.97 & 77.97 & **91.20** \\ \hline \multirow{3}{*}{(E)} & Average & 82.63 & 77.54 & 83.05 & 86.44 & 76.70 & 75.00 & **90.10** \\ \cline{1-1}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\a face biometric and Hybrid Gabor filters and binary features of an image for gender recognition. Table 6 shows that our proposed method outperformed the work done in [8; 9].