esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Other Possible Future Research Directions
#### 10.4.2 Zero-Trust Security  
To incorporate zero-trust security concepts into smart grid infrastructure, a rigorous access control architecture that requires verification and authentication of all users, devices, and apps before granting them access to any resources must be used. This strategy monitors and authenticates identities and devices continuously throughout their life cycle, assuming that threats may come from both internal and external sources. Reducing the attack surface and containing possible breaches can be achieved by applying strategies like micro-segmentation to establish security zones and imposing least privilege access regulations. Furthermore, integrating real-time reaction capabilities, anomaly detection, and continuous monitoring improves the capacity to quickly identify and address security risks, guaranteeing resilient defense against constantly changing cyber threats in smart grid settings. **Hardware Security:** This is essential and requires more focus on hardware-based solutions such as securing processors, substations, transmission lines, antennas, grids, and other critical infrastructure, especially from criminal activities, terror-related attacks, and sabotage acts. In addition, these solutions should ensure resistance against hardware attacks such as side-channel and fault attacks.
## 2 Materials and Methods
### Fatigue Material Properties
#### 2.5.4 Fatigue Crack Growth Rate  
Fatigue crack growth rate \(da/dN\) reflects how fast a fatigue crack propagates, and stable crack growth in the medium rate region is usually expressed by the Paris formula, as in Equation (4). Relevant test results of the fatigue crack growth rate of Q345 qD steel at room temperature and \(-60\)\({}^{\circ}\)C using standard C(T) specimens [16] are listed in the last two columns of Table 2.  
Table 2 shows that constant \(C\) under low temperature is only one-fifth of that at room temperature, the intercept of the \(da/dN\)-\(\Delta K\) curve on the \(da/dN\) axis decreasing significantly with a decrease in temperature. Therefore, for a given \(\Delta K\) action, the fatigue crack growth rate at low temperature is lower than that at room temperature. However, the slope \(m\) of the low-temperature crack growth rate curve is slightly larger than that of room temperature. With an increase in \(\Delta K\), the low-temperature crack growth rate curve tends to approach the room temperature curve [16].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature (\({}^{\circ}\)C)** & \(\Delta_{m}\) **(mm)** & \(\Delta K_{th}\) **(MPa\(\cdot\sqrt{m}\))** & \(C\) **(mm/cycle)** & \(m\) \\ \hline
20 & 1.172 & 3.11 & 3.99 \(\times\) 10\({}^{-8}\) & 2.472 \\ \(-50\) or \(-60\) & 0.383 & 3.89 & 7.18 \(\times\) 10\({}^{-9}\) & 2.818 \\ \hline \end{tabular}
\end{table}
Table 2: Fracture parameters and crack growth rate of Q345 steel under different temperatures.
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 1 Introduction
Currently, most research [5; 6] on the use of sound signals for bearing fault diagnosis is conducted in controlled, quiet laboratory environments. While sound-based diagnostic methods offer distinct advantages over vibration signal methods--such as non-contact measurement, flexibility in sensor placement, and lower costs for large-scale monitoring--these methods face challenges in noisy real-world environments. Sound signals are particularly susceptible to interference from environmental noise, exhibit complex spectral characteristics, and require extensive data processing, all of which can limit diagnostic accuracy. Furthermore, many FMD-based noise reduction methods [21; 22; 23] optimize only two of the four parameters, which may diminish their effectiveness in practical applications. To address this issue, this paper investigates the feasibility of sound-based bearing fault diagnosis in high-noise environments by optimizing all four FMD parameters to enhance the extraction of fault information. The Secretary Bird Optimization Algorithm (SBOA) [26] is employed to adaptively optimize the FMD parameters with the objective of minimizing envelope entropy. Subsequently, the ISEI metric introduced in this study is utilized to select the optimal pattern for extracting fault features. Simulation experiments and tests demonstrate that, compared to the EMD [10], CEEMD [11], fixed-parameter FMD [20], and adaptive VMD [16] methods, this approach is more effective in capturing subtle feature information related to early faults in bearing sound signals, exhibiting improved performance and robustness under noisy conditions. The structure of this paper is organized as follows: the Section 1 presents a literature review and the relevant research background; the Section 2 discusses the relevant algorithms; the Section 3 elaborates on the proposed fault diagnosis method along with its technical details; the Section 4 details the simulation tests; and the Section 5 focuses on experimental design and result analysis. Finally, the conclusion summarizes the main contributions of this article.
## 2 Models
### Statistical  
A classical approach to energy price forecasting involves using statistical models, such as AR, ARMA, ARIMA, and related methods, based on mathematical operations on historical prices.  
1. ARIMA Autoregressive integrated moving average (ARIMA) is a statistical time-series forecasting method combining an auto-regressive part [41], differentiating, and a moving average process. In this model, the future value is assumed to be a linear function of past observations and random errors. ARIMA models are widely used due to advantages such as simple structure and low computational complexity, as well as stable forecasting performance and capability to incorporate the seasonality factor prevailing in electricity price developments. In the presence of spikes, however, statistical methods perform relatively poorly. In addition, they struggle to capture the nonlinear fluctuation of market prices [42; 43; 10; 44].
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 6 Time-Domain Characterization
### Group Delay  
The amount of distortion in the SWB antenna can be defined in terms of the group delay of the device. A uniform sustained group delay is always desirable for the entire SWB frequency range. The group delay of the proposed antenna system was steady, except at the notch bands. There was a sudden variation in the group delay at the notch frequency for the band-notched SWB antenna system due to the appearance of phase irregularities. From Figure 17, it can be seen that a high group delay was observed at the notched frequencies (2, 6, and 10 GHz), whereas for passband frequencies, group delays for the face-to-face and side to side arrangements were within acceptable limit of less than 1 ns.  
Figure 17: Group delay for proposed band notch antenna for side-by-side and face-to-face configurations.
## 2 Bearing Load Testing Method Based on Redundant Strain and Jacking Data Fusion
### Testing Procedure  
The workflow of the bearing load testing method based on the fusion of redundant strain and jacking data is as follows, and the workflow chart of the fused testing method is shown in Figure 2.  
Firstly, a set of strain gauges are attached to the upper and lower surfaces of the shaft segments at N design measurement section positions (Considering both spatial constraints and economic aspects, it is recommended that N be equal to 4) and connected to the strain gauges using a full bridge circuit, as shown in Figure 3. According to references [18; 19], in order to minimize errors, the positions of the design measurement sections should be chosen at locations with significant bending moments on the shaft cross-section, while keeping a certain distance from the bearings under test and from each other.  
Figure 1: Sensor layout diagram for the bearing load testing method based on redundant strain data and jacking data.  
Figure 2: Flowchart of the fused testing method.  
Once the strain gauges are installed, the shaft is rotated in both the clockwise and counterclockwise directions to eliminate the internal stress of the gauges. The strain at the position of the strain gauges indicated in Figure 1 is considered the initial strain state. The shaft is turned 180\({}^{\circ}\) multiple times in both directions, and the strain at each cross-section is recorded. After the strain measurement is completed, the shaft must be rotated to position the strain gauges as shown in Figure 1, in the same plane as the jacking force.  
A jack and a displacement sensor were installed at a specific jacking position, which could be obtained from the Class-approved Alignment Calculation Document. We used the jacking device to slowly lift the shaft with a certain amount of lifting step by step until the bearing was detached from the shaft neck, and then continued to lift it to a certain height, but not to the upper bearing shell. Then, we lowered the shaft with the same amount of lifting step by step.  
The jacking process is divided into two phases: before the disengagement of the bearing and shaft and after the disengagement of the bearing and shaft. During the bearing disengagement phase, we recorded the jacking force \(P_{j}\), jacking displacement \(S_{j}\), and strain data \(\varepsilon_{i,j}\), respectively.  
The measurement data obtained throughout the entire measurement phase are depicted in Figure 4. All measurement data will be transmitted to the data fusion center for centralized processing. The data include: the strain from \(N\) sections provided by \(N\) sets of strain gauges at both ends of the bearing under test, where \(\varepsilon_{i}\) is the strain measured during the strain measurement phase, and \(\varepsilon_{i,j}\) is the strain measured during the jacking phase when the bearing is disengaged; the jacking force \(P_{j}\) provided by the jacking force sensor; and the jacking displacement \(S_{j}\) provided by the jacking displacement sensor.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 4 Results and Discussion
### Case 3: Implementation of the S-ANC  
Case 3 examined the combination of S-MPC and AR-LSTM models. Integrating these advanced models aimed to enhance the predictive capabilities of the control system. The S-ANC predicted the last month of the year using the first eleven months as training data. Using metrics such as train-test (Figure 8a) and grid consumption prediction with AR regression and S-ANC (Figure 8b), the performance of this merged approach was evaluated. This integration (S-MPC and AR-LSTM) increased the precision of forecasting and the precision of power flow optimization. The enhanced prediction capabilities of the AR-LSTM models allowed the control system to anticipate future energy requirements and adjust the operation of the MG accordingly. In Case 3, the S-MPC controller was improved by combining it with AR and RNN-LSTM models. This integration aimed to improve the precision of predictions and the overall performance of the control system. Two primary figures were generated for analysis: a comparison between the training and the test, and a prediction of grid consumption using AR regression and S-ANC.  
Figure 7: Visualization of (**a**) the behavior of the lagged target feature over time, (**b**) the adaptability of the AR models to various patterns and tendencies  
The train-test comparison diagram visually represents the AR-LSTM models' capacity to generalize effectively for unobserved data. It compares the predicted grid consumption values during the testing phase with the actual values, indicating the AR-LSTM models' ability to capture the hybrid MG's complex patterns and dynamics (Figure 8b). The diagram depicts the performance of the combined S-MPC, AR, and RNN-LSTM models on the training and testing datasets (Figure 8a). According to our simulation, the model could generalize well for new data and the integration strategy's effectiveness. Moreover, the prediction of grid consumption using AR regression and S-ANC illustrated the ability of the combined method to optimize power flows, while accurately predicting future power demands. By leveraging the predictive capabilities of AR-LSTM models within the S-MPC framework, the control system could more precisely estimate grid consumption, allowing for more effective EM and enhancing the MG's adaptability to load demands and renewable energy generation fluctuations. The grid consumption forecast graph (Figure 8b) depicts the projections generated by the AR and S-ANC models. This enables a comparison of the two methods and highlights the advantages of the S-ANC method, which employs the AR-LSTM model for accurate predictions, while reducing computational time. The computational time of the model was reduced by nearly 214 s.
# Enhancing Online Learning Through Multi-Agent Debates for CS University Students
## Appendix A Pre-Test and Post-Test Questions
### Regularization Pre-Test Questions  
1. Overfitting usually manifests itself as a high variance problem. (T/F)
2. The regularization parameter \(\lambda\) serves to increase model complexity. (T/F)
3. In machine learning and deep learning, L2 regularization is achieved by adding a regularization term to the loss function, which is 1. The cube of the L2 norm of the model parameters (usually referred to as weights w) 2. The square of the L2 norm of the model parameters (usually referred to as weights w) 3. Minus the cube of the L2 norm of model parameters (usually referred to as weights w) 4. Minus the cube of the L2 norm of model parameters (usually referred to as weights w) 4. Which of the following options are correct in the description of L2 regularization in neural networks (Multiple choice)? 1. L2 regularization is achieved by adding a term to the loss function that is proportional to the square of the weights 2. L2 regularization terms usually do not include bias terms 3. During gradient descent, L2 regularization causes the weights to shrink towards zero, thus preventing overfitting 4. The larger the L2 regularization factor (\(\lambda\)), the weaker the effect of regularization 5. L2 regularization is achieved by directly modifying the weights during the forward propagation of the neural network
5. What is the main difference between L1 regularization and L2 regularization in deep learning? 1. L1 regularization takes into account the square of the weights, while L2 regularization does not take into account the square of the weights. 2. There is no difference in effect between L1 regularization and L2 regularization. 3. L1 regularization focuses on preventing overfitting, while L2 regularization is usually used for feature selection. 4. L1 regularization tends to produce sparse weight matrices, while L2 regularization makes the weights smoother.
6. There is a growing tendency to use L2 regularization when training networks to better achieve the effect of avoiding overfitting, does it mean that L1 regularization has gradually lost its usefulness? Please write down what you think.
# Friction Factor Correlation for Regenerator Working
## 2 Literature Review
[PERSON]'s permeability, \(K\), and Forchheimer inertial coefficient, \(F\), are empirical constants. As empirical constants, both permeability and inertial coefficient are uniquely dependent on the porosity and tortuousity of the porous structure (regenerator). The values determine the momentum losses occurring in the porous media. In experimental practice, the momentum loss can be obtained from the pressure drop measured across the regenerator. On the other hand, to model a system with regenerator, one will need to determine the permeability, \(K\), and Forchheimer inertial coefficient, \(F\), beforehand. This becomes a challenge especially due to the lack of friction factor correlations suitable for oscillating flows in porous media. [PERSON] et al. [PERSON] et al. (2008) reported a CFD-assisted method for determining the permeability and inertial coefficient of a porous regenerator in an oscillating flow. The regenerator in their study, limited to a few sizes of mesh screens, is tested in a small sized device where the inertial effect could be significant. The oscillating flow friction factor is shown to deviate from the steady flow data of [PERSON] et al. [PERSON] et al. (2008) when the permeability Reynolds number, \(Re_{K}=\rho uK^{1/2}/\mu\), is greater than 0.1. [PERSON] et al. [PERSON] et al. (2008) followed the same CFD procedure in establishing the hydrodynamic properties of the regenerator in an oscillating flow but with small mesh fillers suitable for miniature cryocoolers. In a different study, the porous medium coefficients were also derived by [PERSON] et al. [PERSON] et al. (2008). However, the derived equation is inappropriate for general use as the friction factor applied in defining the pressure drop is the equation specifically built for cryogenic conditions.
## 1 Introduction
### Literature Review  
The classical SCA algorithm mainly consists of the two-stage method and the TF masking method. The two-stage method first estimates the mixing matrix and then uses signal sparsity to recover the source signal. [PERSON] [7] utilized the sparse coding technique to discover the 1-D subspace from the set of TF points, then estimated the mixing matrix by grouping vectors in the subspace through hierarchical clustering, and finally used the least-squares method to achieve source signal recovery; [PERSON] [8] firstly estimated the mixing matrix based on the hyperplane normal vector clustering algorithm, and then used the classical algorithm called orthogonal matching pursuit in compressed sensing to realize the separation of the linear frequency modulated radar mixed signals; [PERSON] [9] realized the recovery of the source signal using a new subspace complementary matching pursuit algorithm. The algorithm reduced the computational complexity by selecting multiple atoms per iteration and also used \(L_{2}\) parametric minimization instead of \(L_{0}\) parametric minimization in order to improve the recovery accuracy.  
The other is the TF masking method, i.e., the source signal is separated by designing a TF mask matrix. [PERSON] [10] first detected the peak energy point of the mixed signals and then calculated the cosine distance between all energy points and the peak energy point, afterward obtained the binary mask based on a threshold to realize the reconstruction of the vibration signal; [PERSON] [11] proposed a maximum a posteriori decision criterion to design a binary mask matrix for the separation of mixed speech signals; [PERSON] [12] first detected the active single-source points by calculating the mixing ratio, and then found the cluster centers by a dynamic routing algorithm as a way to design a binary mask.  
The above algorithms usually assume that the signal is sparse in some domains, such as the time domain, frequency domain, etc. However, in the wireless communication scenarios, the signals are inevitably overlapped in both time and frequency domains. In such cases, it may cause a reduction in separation accuracy or even complete failure when the classical SCA-based method is applied directly to the separation of TF overlapped communication signals. The two-stage method could not guarantee the estimation accuracy of the mixing matrix and the performance of the source signal recovery, while reconstructing the source signals through the TF binary mask will bring certain distortions [13; 14; 15]. Obviously, for the communication mixed signals with TF aliasing, better separation performance can be obtained by reasonably estimating the TF soft mask to reconstruct the source signals [16].
## 4 Computational Validation
### Results in the 85-Bus Grid  
When the proposed hyperbolic power flow approximation was applied to the 85-bus system, the following results were obtained: (i) When the neutral wire was solidly grounded, the total power losses for this system were 452.2981 kW, i.e., 6.76% of the total power consumption; however, when the neutral wire was non-grounded, these power losses increased to 489.5759 kW, i.e., 7.32% of the net power consumption. Hence, there was an additional 37.2778 kW of power losses. (ii) The total numbers of iterations were about four for the solidly grounded case and 13 for the non-grounded case.  
The voltage profiles in the 85-bus system for the positive and negative poles considering the solidly grounded and non-grounded operation cases are presented in Figure 6.  
Figure 5: Behavior of the voltage profiles for the meshed configuration of the 21-bus system with the grounded and non-grounded connections of the neutral wire.  
The voltage profiles in Figure 6 show that: (i) For the solidly grounded case, the operative costs of the minimum voltage in the positive pole were 0.9189 pu at node 54, and for the negative pole, they were 0.8950 pu at the same node. (ii) In the non-grounded scenario, these values were 0.9204 pu for the positive pole and 0.8925 pu for the negative pole.  
Note that when the positive and negative voltage profiles are compared (see Figure 6a,b), it can be observed that the positive pole had a similar behavior to that of the negative pole, which was very similar to the reflection of the voltage profile with respect to the axis of the nodes; even if the system were to be perfectly balanced, then \(v_{p}\) must be equal to the absolute value of \(v_{n}\).
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 5 Application Scenario
Each cluster is composed of several underwater nodes and one SB that acts as a gateway. Anchored at different depths, the nodes would cover an area of approximately 500 m starting in the low tide limit with a depth of 2 m and getting into the sea up to a depth of 10 m. The nodes will measure at 1 m, 2 m, 5 m and 10 m. The first cluster in the port area serves as a reference for the others. Figure 15 shows the approximate network deployment. Specifically, the sensors will measure: temperature, salinity, pH, turbidity, dissolved oxygen, dissolved nitrogen, dissolved nutrients (nitrate, nitrite, phosphate, silicate) and chlorophyll. All of these sensors may work on demand based on the flow injection analysis chemistry principles.