esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 HPMSM System with Output LC Filter
As shown in the diagram, although the introduction of the LC filter enhances the system's ability to suppress high-frequency harmonics, it leads to the appearance of the resonance in the control system at high frequency and also produces phase changes by \(-180\) degrees. This phase change brings a pair of closed-loop poles in the right half of the s-plane, leading to instability of the system. To solve this problem, the resonant frequency needs to be determined. Based on (14), at high frequencies, the system can be reduced to a series connection of an integral link and a second-order oscillation link. The equivalent transfer function is  
\[\phi(s)\approx\frac{1}{s(LL_{1}Cs^{2}+RL_{1}Cs+L+L_{1})}. \tag{22}\]  
Figure 3: Bode diagram of HPMSM control system with LC filter. From (18), it can be derived that the resonant frequency is  
\[f=\frac{1}{2\pi}\sqrt{\frac{L+L_{1}}{LL_{1}C}-\frac{R^{2}}{2L^{2}}}. \tag{23}\]  
The system's Bode diagram is depicted in Figure 4 when the inductance of the LC filter varies. The cut-off frequency and resonance peak of the system decrease with the increase of the inductance, so it seems that the system resonance can be suppressed by simply increasing the inductance. In reality, however, increasing the filter inductance will enlarge the volume and weight of the LC filter, which is not conducive to the design of the drive system. Therefore, the resonance peak can be suppressed using alternative techniques. The most commonly used methods include passive and active damping methods.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### UWB  
This is a radio technology utilizing low energy level over a large part of the radio spectrum for short-range communications. The UWB is intended for wireless personal area networks that provide communication distance from a host device to other devices in its nearest vicinity up to a distance of nearly 10 m [50]. However, with the short communication distance, the UWB can still achieve a relatively high data rate [51]. The UWB radio has been deployed for some indoor positioning system applications. Unfortunately, UWB positioning systems are costly because of the high precision clocks needed to evaluate the time gaps between radio signals that arrive at different receivers in the environment [52], so, UWB technology is still very costly.
## 4 Experiments and Results
### Results and Discussion for Protocol Classification
Multiple performance metrics based on the confusion matrix, such as F-score (The F-score, also called the F1-score), recall, and precision, have also been used to evaluate the proposed model's performance. A sample of the accuracy confusion matrices for the proposed classifier model across different datasets is presented in Figure 7.  
Our proposed DL classifier model achieves an average accuracy of 95.44% for identifying a wireless technology that falls in the range of SNR 10 dB to 20 dB _across all the channel propagation effects_. The average accuracy of the wireless identification in intense noise environments considering the propagation effect scenarios' effect is 82.55% for SNR range between 0 dB and 5 dB. The maximum achieved accuracy of our emitter classifier based on CDAE weights is 100%, and the average accuracy is 91% across all datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier Name** & **Type** & **Number of Layers** \\ \hline MLP & MLP & 4 \\ MLP + AE & MLP + CDAE weights & 15 \\ \hline CNN1 & CNN & 6 \\ CNN1 + AE & CNN + CDAE weights & 17 \\ \hline CNN2 & CNN & 8 \\ CNN2 + AE & CNN + CDAE weights & 19 \\ \hline CNN3 & CNN & 13 \\
**CNN3 + AE** & CNN+ CDAE weights & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classifier configurations. The proposed classifier architecture is highlighted.
# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 5 Discussion
Short-term gait tracking is a potential application for gait recognition based on COP trajectory, for example in the context of high-security buildings. The idea is to partially counteract gait inconstancies by collecting reference gait samples on a daily basis. Let us imagine the following scenario. A high-tech company has a research center in which future high-profit products are developed. The company wants to protect its scientific assets from industrial espionage with minimal constraints for the scientists. For the sake of demonstration, let us also imagine that scientists are reluctant to be filmed via a camera network because they feel that continuous spying harms their freedom of research. At the building entrance, an individual is first identified (through ID card, face recognition, or other means), and a reference sample of his or her gait is collected on an instrumented walkway. The recognition system thereby acquires a gallery of gaits of all individuals currently authorized to be in the building. Strategic areas of halls and hallways are equipped with pressure-sensitive floors that continuously track the gaits of people walking by. Specific doors can be opened on the fly for authorized personal without user interaction. Access to high-security rooms can be audited and potential intruders rapidly localized. Privacy is respected, because only employees' localization when they are walking in some specific places is monitored and not what they are doing. In this scenario, the footwear inconstancy issue is bypassed, because it can be expected that individuals keep the same shoes on throughout the day. The technology for this biometric approach is already available, but a cost-benefit analysis must be conducted.
## 5 Results and Discussion
### Leakage Location Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(L\) (m) & 3.24 & 3.24 & 3.24 & 4.52 & 4.52 & 4.52 & 4.52 & 5.70 & 5.70 & 5.70 & 5.70 \\ \hline \(f\) (kHz) & 124.2 & 123.8 & 123.4 & 125.4 & 105.5 & 103.4 & 107.2 & 106.7 & 104.7 & 104.3 & 104.7 & 103.5 \\ \(v\) (m/s) & 2404 & 2400 & 2397 & 2415 & 2200 & 2173 & 2222 & 2215 & 2190 & 2186 & 2190 & 2175 \\ \(l_{1}\) (m) & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 & 2.16 \\ \(\hat{l}_{1}\) (m) & 2.19 & 2.18 & 2.20 & 2.25 & 2.26 & 2.09 & 2.25 & 2.27 & 1.99 & 2.23 & 2.28 & 1.98 \\ \(\Delta l_{1}\) (m) & 0.03 & 0.02 & 0.04 & 0.09 & 0.10 & 0.07 & 0.09 & 0.11 & 0.17 & 0.07 & 0.12 & 0.18 \\ \(\delta l_{1}\) (\%) & 1.4 & 0.9 & 1.9 & 4.2 & 4.6 & 3.2 & 4.2 & 5.1 & 7.9 & 3.2 & 5.6 & 8.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The location results obtained by the MLPNN locator.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm
### Algorithm Used in the e-RLRP to Reduce the Overhead
#### 3.2.1 Definition of the Upper and Lower Limits of Broadcast Interval  
The higher the value of the \(BI\) parameter, the lower the frequency of sending Hello messages, and consequently the overhead is reduced. However, it is necessary to define a limit to that value does not grow indefinitely.  
The \(BI_{\text{UL}}\) cannot be greater or equal than 7 s due to the Check Expired Neighbors function. Otherwise, the network nodes will be eliminated when the function timeout will be reached. Considering that the \(BI_{\text{UL}}\) value must be lower than 7 s and also the latency of the existing network, the value 6 s is defined in order to guarantee that neighbors are not erroneously removed.  
To define the \(BI_{\text{LL}}\), 3 values of \(BI\) lower that 2 s are tested in the scenario called Programmed that is described in Section 4.1. The overhead is calculated considering the source and destination node. The \(BI\) value of 2 s, defined in the RLRP, is also tested in the same scenario, and the overhead obtained was 1.42 MB. The \(BI\) values 0.5, 1.0 and 1.5 were tested. Table 5 shows the overhead results for each BI value.  
Table 5 shows the increase in overhead of the tested values in relation to the default value used in RLRP. The \(BI\) value of 1.5 had a gain of 2.12%, the value of 1.0 presented an increase of 4.22%. The value of 0.5 obtained the highest increase, a gain of approximately 12.67%. Considering this value as a high increase in overhead compared to the previous ones, the value of 0.5 is discarded. Hence, we opted for the intermediate tested value, and \(BI_{\text{LL}}\) is set to 1.0.
# Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks
## 4 Proposed Transmit Strategy
In this work, we consider the ensemble of BCH codes presented in Table 1[33]. Depending on the application requirements in terms of frame size, different BCH codes may be used. Let us recall that the a single packet of \(p\) bits at the output of the L3 encoder is encapsulated in \(N_{p}=\lceil p/k\rceil\) BCH codewords, where \(\lceil x\rceil\) denotes the nearest integer greater than or equal to \(x\). The probablity that such a L3 packet is wrongly decoded at L2 decoding of the receiver is  
\[P_{f}^{PHY}=1-(1-P_{f}^{BCH})^{N_{p}}, \tag{18}\]  
Figure 3: Fountain decoder structure (adapted from [15]). where \(P_{f}^{BCH}\) is defined according to Equation (17). Therefore, at the receiver side, the packet success delivery probability at the L3 layer perceives a FER given by \(P_{f}=P_{f}^{PHY}\), which can be plugged into Equation (16) to compute the error probability at the L3 decoding. The transmit energy consumption necessary to recover the \(M\) information-bearing packets is  
\[\mathcal{E}=\left(\frac{1}{r}\right)\frac{P_{t}^{el}}{R_{b}}\left(\frac{N}{M} \right). \tag{19}\]  
The above  
\(E_{b}\). The last one refers to the additional energy consumption imposed by the fountain encoder. It is important to point out that the energy consumption defined by Equation (19) is a simplified energy model. Just for simplification, without affecting the relative results, we assume a negligible baseband processing consumption. A precise model for terrestrial wireless sensor networks can be found in [8], which includes the start-up, baseband processing and reception energy consumption. Our goal is to minimize the energy consumption given by Equation (19), formally defined as  
\[ \begin{split}\underset{r=k/n}{\text{minimize}}& \mathcal{E},\\ \text{subject to}& P_{s}^{*}<1,\ M,\ p,\end{split}  \tag{20}\]  
where \(P_{s}^{*}\) denotes the target packet success delivery probability. The energy consumption can be optimized in terms of the required received SNR or communication link distance.
## 5 Simulative Assessment
### Simulation Results
#### 5.2.3 Scalability Assessment
The results of this assessment can be exploited by the network designer during the network configuration phase. In fact, based on both the number of EDs in the network and the application requirements, the networks designer can combine the results plotted in the Figures 5-7 with the e2 eD values in the Tables 10-12 to choose the best combination of the MAC parameters for the network under consideration. For example, assuming a network with 200 EDs, a required average MDelivR of 82% and a required average e2 eD of 3.0 s, the network designer from the graphs in Figures 5-7 and the values in Tables 10-12 can pick the combinations \((MAC^{max}_{Backoff},U_{backoff})\) that correspond to the targeted MDelivR and e2 eD, i.e., either (i) \(MAC^{max}_{Backoff}=10\) (Figure 6) and \(U_{backoff}=10\) ms (Table 11) or (ii) \(MAC^{max}_{Backoff}=15\) (Figure 7) and \(U_{backoff}=5\) ms (Table 12). When multiple options are available, as in this case, the designer can make the choice also on the basis of other factors, e.g., selecting the configuration with the lowest \(MAC^{  
simulation results in [62] show that the MLossR increases with the number of EDs. This confirms the trend of MDelivR obtained in this work, i.e., the higher the number of EDs the lower the MDelivR. However, in [62] only one backoff value is used, consequently it is not possible to analyze in details the performance varying the LBT parameters. Moreover, [62] does not provide any result in terms of message e2 eDs. The work in [54] compares four different LBT-based medium access protocols for LoRaWAN, using random payload-size and random transmission times. The simulation results show that using random transmission parameters three of the four assessed LBT-based approaches obtained a MDelivR higher than 80% with 1400 EDs. However, in [54] the EDs send their message according to a random interval in the order of one hour, so quite sparingly compared to our work, where the EDs generate messages following an exponential distribution with a mean of 60 s. As the assessment in [54] does not provide results varying the MAC parameters and EDs are not mobile, it is not possible to directly compare the results of [54] with the ones presented in this paper. The work in [55] provides simulative evaluations of a LoRaWAN network in which some EDs adopt ALOHA and others the LBT-based MAC protocol. Due to the different channel model parameters and the different scenario, as in [55] there are no mobile EDs, it is not possible to compare the MDelivR results obtained by [55] with the ones presented in this paper. However, with the same number of EDs the average e2 eDs are comparable with the ones obtained in this paper. While in this paper we demonstrate that the backoff parameters significantly affect the average e2 eDs, the work in [55] does not address the influence of the MAC parameters on the e2 eD.
## 2 Materials and Methods
### Black Box Data Management
The security and transparency of theblockchain provide a foundation for various participants in the electric vehicle ecosystem to confidently share and utilize data. **Adoption of standard cryptographic technologies:** The proposed system uses verified standard cryptographic technologies, such as RSA and SHA-256, ensuring compliance with related regulations and legal requirements while securing long-term safety and flexibility. RSA is standardized by major international standardization bodies, like IETF and NIST, and SHA-2 is also established as the Federal Information Processing Standards (FIPS) 180-2 standard, which is widely used globally. These technologies have been proven safe through extensive research over many years and are also advantageous for transitioning to post-quantum cryptography (PQC) schemes in preparation for the advent of quantum computers. In fact, NIST has been pushing for PQC standardization since 2017, and the fourth round of candidates includes lattice-based cryptography and multivariate equation-based signatures, which could replace RSA and SHA-256. The use of these verified and flexible cryptographic primitives in the system is significant for ensuring long-term reliability, especially since data related to electric vehicles can be directly linked to sensitive issues such as personal privacy breaches and technology leaks. Therefore, having a cryptographic foundation that can proactively respond to emerging security threats and regulatory changes is essential. **Conclusion:** In summary, the proposed black box data management technique ensures security and transparency across the utilization of electric vehicle data through systematic and sophisticated encryption and integrity verification mechanisms. The RSA public key encryption system secures data confidentiality, while storing SHA-256 hash values on the blockchain enables permanent integrity proof. Particularly, the method of sharing only the ciphertext and hash value effectively balances the conflicting requirements of privacy protection and integrity assurance. This multi-layered data security system fundamentally resolves the vulnerabilities inherent in traditional centralized management structures and provides a reliable collaboration foundation. Additionally, by utilizing international standard cryptographic primitives, it simultaneously secures regulatory compliance and long-term safety. If new cryptographic technologies like PQC are incorporated in the future, it could further enhance adaptability in the rapidly changing mobility environment. Ultimately, the black box data management technique proposed in this study can contribute to enhancing the security capabilities of the entire eco-friendly mobility industry, including electric vehicles, as a data infrastructure supporting the transition to intelligent transportation systems. It is also expected to act as a catalyst for data-driven innovation, establishing an optimal information management paradigm that balances privacy, accountability, and availability.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 2 System Model
### Block Diagonalization (BD) Scheme
\right]\widetilde{ \mathbf{V}}_{j}^{zero} \tag{6}\] \[=\mathbf{0}.\]  
From Equation (6), it can be seen that \(\widetilde{\mathbf{V}}_{j}^{zero}\) is in the null space of \(\widetilde{\mathbf{H}}_{S,UE\;j}\), that is, when a signal is transmitted in the direction of \(\widetilde{\mathbf{V}}_{j}^{zero}\), all but the \(j\)-th UE receives no signal at all. In order to satisfy the condition shown in the Equation (2), \(\mathbf{W}_{j}=\widetilde{\mathbf{V}}_{j}^{zero}\) must be satisfied. Thus, \(\mathbf{W}_{j}=\widetilde{\mathbf{V}}_{j}^{zero}\) can be used for pre-coding matrix the \(j\)-th UE signal.
## 4 Bearing Fault Detection Using YAMNet and Transfer Learning
### CWRU Dataset  
The CWRU test bench (Figure 6) consists of an electric motor, a dynamometer, and a torque transducer with encoder. Two ball bearings support the motor shaft: one of the bearings (SKF 6205-2 RS JEM) is placed on the motor drive end (DE), whereas the other (SKF 6203-2 RS JEM) is located at the motor fan end (FE).  
The CWRU experimental campaign considered localized faults in DE and FE bearings, which were damaged by means of electro-discharge machining. The faults were introduced separately at the inner race (IR), outer race (OR), and rolling elements (B), and tests were run for motor powers of 0, 1, 2, and 3 hp with a shaft speed ranging between 1721 and 1796 rpm. This study considers the fault diameters of 0.007, 0.014, 0.021, and 0.028 inches for the DE bearing. Vibrations signals were extracted in normal and fault conditions by means of accelerometers placed on the motor housing. In the case hereby examined, normal data were sampled at 48 kHz, whereas DE fault data were sampled at 12 kHz. The OR fault condition with 0.028 in damage was not analyzed since data was not available.
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 2 Time-Varying Meshing Stiffness Calculation Model  
### The TCA Environment  
TCA is capable of reflecting the contact performance of gears, providing relevant parameters such as meshing position and overlap. Integrating contact analysis, calculating the time-varying meshing stiffness of gears, and conducting dynamic analysis allow for a more realistic representation of gear meshing performance and vibration characteristics.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms  
We discuss the architecture of prominent deep learning models for solar irradiance forecasting, which include Artificial Neural Networks (ANN), Gated Recurrent Units (GRU), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), as well as the hybrid models that were discussed in the literature. In this section, detailed information on the architecture and training techniques of related models is provided.
## 2 Methods
### Mitigation of Ionospheric Errors on SAR Interferogram  
SAR signals travelling through the ionosphere layer are delayed along their paths by interactions with the free electrons. This delay leads to a group delay and a phase shift, which are equal in  
low-latitude regions are processed. Table 1 lists the interferometric parameters for the collected SAR images over the experimental regions.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 3 Results
### Full-Channel Performance  
Figure 8a-c displays \(P_{\text{PD}}\), \(P_{\text{PD cross}}\) and \(P_{\text{PD PP}}\) at a lens-to-lens distance of 12.5 mm for the hybrid lens. The values are determined by misaligning the receiving transceiver, whereas the transmitting one is placed at the origin. Table 3 shows numerical values for different misalignments in negative x-direction. Figure 8a shows a rotationally symmetric performance in its fundamental structure. However, \(P_{\text{PD}}\) is not fully homogeneous within the FOV for a revolution. The largest variation \(\Delta P_{\text{PD}}\) over one revolution is reached if the misalignment is between 2 mm and 3 mm. There, \(\Delta P_{\text{PD}}\) is in the range of 3 dB to 3.29 dB. The crosstalk power \(P_{\text{PD cross}}\) in Figure 8b is similarly distributed as \(P_{\text{PD}}\) with a power level which is about 10 dB lower than \(P_{\text{PD}}\). As a result, the optical power with applied power penalty \(P_{\text{PD PP}}\) in Figure 8c is similar to \(P_{\text{PD}}\). This can also be observed in Table 3: the difference between \(P_{\text{PD}}\) and \(P_{\text{PD PP}}\) is in the range of 0.1 dB... 0.9 dB (1.023...1.230). The crosstalk \(P_{\text{PD cross}}\) consists of two parts: a constant part \(P_{\text{PD cross 1}}\) of \(-\)32.4 dBm and an alignment-depended part \(P_{\text{PD cross 2}}\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Merit** & **Unit** & **LD** & **H TX-RX** & **S TX-RX** \\ \hline \(E_{\text{min}}\) & \(\frac{\mu W}{\text{mm}^{2}}\) & 14.6 & 117 & 25.9 \\ \(\eta_{\text{TX}}\) & \% & 89.5 & 63.2 & 51.4 \\ \(\eta_{\text{TX eff}}\) & \% & 7.2 & 57.6 & 12.8 \\ \(\zeta_{\text{TX M}}\) & \% & 10.5 & 24.7 & 36.0 \\ \(\zeta_{\text{TX F}}\) & \% & 0 & 12.1 & 12.6 \\ \(\zeta_{\text{TX H}}\) & \% & 82.3 & 5.6 & 38.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TX merits for the laser diode (LD), the hybrid TX-RX system (H TX-RX) and the separated TX-RX lens system (S TX-RX).  
Figure 8: Incident power \(P_{\text{PD}}\) and \(P_{\text{PD cross}}\) over misalignment at \(z=12.5\) mm. The \"x\" marks the axis of rotation. (**a**) \(P_{\text{PD}}/\text{dBm}\) for the hybrid lens. (**b**) \(P_{\text{PD cross}}/\text{dBm}\) for the hybrid lens. (**c**) \(P_{\text{PD PP}}/\text{dBm}\) for the hybrid lens. (**d**) \(P_{\text{PD}}/\text{dBm}\) for the system with fully separated TX and RX (Section 2.3) in worst-case orientation, i.e., TX lens facing TX lens and RX lens facing RX lens. Note: The graphs are clipped below \(-\)30 dBm to highlight features within the FOV. Therefore, \(P_{\text{PD cross 1}}\) cannot be seen in Figure 8b.  
The system with fully separated TX and RX is shown in Figure 8d. It suffers from a displacement of the communication-area in x-direction. The effect can be seen in Table 3; a communication is only possible for a misalignment of 0.5 mm... 1 mm, depending on the data rate.