esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Strength Characteristics
#### 3.1.2 Effect of Dynamic Loading Rate under the Same Static Pre-Stress  
Figure 7 shows the effect of dynamic loading rate on the SUCS and the elastic modulus of the rock specimens. We observe different mechanical behaviors with different dynamic loading rates, and a high dependence on strain rate, as shown in Figure 7a. When the static pre-stress is at a low level; taking the 40% of the UCS as an example, the SUCS increases from 78.20 to 107.82 MPa (an increment of 37.88%) when the dynamic loading rate is increased from 2 \(\times\) 10\({}^{-4}\) to 2 \(\times\) 10\({}^{-2}\) s\({}^{-1}\). We found that the logarithmic form is the most consistent when we fit the curve of the experimental data. Some scholars have conducted similar studies using other rock materials [30; 37]. An increase in static pre-stress weakens the strain rate effect on the SUCS. In the case of static pre-stress equal to 50% of the UCS, the SUCS increases from 82.10 to 96.05 MPa (an increment of 17%) as the dynamic loading rate increases from 2 \(\times\) 10\({}^{-4}\) to 2 \(\times\) 10\({}^{-2}\) s\({}^{-1}\). The relationship between the SUCS and the dynamic loading rate can be written as:  
\[S_{4}=134.04+6.7\ln\dot{\epsilon} \tag{5}\]  
\[S_{5}=109.07+3.7\ln\dot{\epsilon} \tag{6}\]  
\[S_{6}=99.85+2.8\ln\dot{\epsilon} \tag{7}\]  
where \(S_{4}\), \(S_{5}\) and \(S_{6}\) are the SUCS when the static pre-stresses are 40% of the UCS, 50% of the UCS and 60% of the UCS, respectively, and \(\dot{\epsilon}\) is the dynamic loading rate. When the static pre-stress is furthered increased to 0.7 UCS, the SUCS does not increase with increasing dynamic loading rate. The laboratory test results indicate that the dynamic loading rate plays a major role in the SUCS at low static pre-stress (less than 40-50% of the UCS), and the SUCS increases significantly with increasing dynamic loading rate. At medium static pre-stress (50-60% of the UCS), both static pre-stress and dynamic loading rate play a major role in the SUCS, and the SUCS increases with increasing dynamic loading rate, but the increased magnitude declines significantly. At high static pre-stress levels (more than 60-70% of the UCS), the SUCS is primarily dependent on static pre-stress, and has a relatively low dependency on the dynamic loading rate.
# A Hierarchical Coordinative Control Strategy for Solid State Transformer Based DC Microgrids
## 4 Simulation Results
Figure 14: The current waveform of the SST under continuous power fluctuation: (**a**) the distribution network current; (**b**) the HVDC-link current; (**c**) the LVDC-link current. Figure 13: The comparison of voltage response under continuous power fluctuation: (**a**) the LVDC bus voltage response; (**b**) the HVDC bus voltage response. It is worth noting that the proposed coordinative control strategy can actually be applied to mitigate dynamic voltage fluctuation conditions. For example, when the system detects a fault in the DC microgrid and trips it off at 2.7 s, the corresponding voltage response is shown in Figure 15. Before removal, the output of the PV power supply is 1 MW. It can be seen from the figure that the SST layer coordinative control at this time can rapidly compensate for the power shortage and restore the DC link voltages to the given value more quickly. When the control strategy shown in Figure 9a is slightly changed to make the super capacitors dynamically compensate for the input current of the output-stage inverter in the island switching process, i.e., \(i_{inv}\) in Figure 4, the coordinative control can also smooth the island switching process. The voltage waveform of the LVDC bus under this condition is shown in Figure 16. Although only the microgrid layer coordinative control plays a role at this time, the bus voltage fluctuation could still be suppressed by a considerable extent. Under the microgrid layer coordinative control, the maximum voltage sag is about 37 V. While under the independent control, the maximum voltage sag is about 90 V, and the voltage needs longer time to recover. The strategies to improve the SST power quality in previous studies are mostly considering the power-side disturbances [30] and load fluctuations [31]. However, power-side disturbances and load fluctuations do not directly act on the DC buses of the SST, and they can always be isolated by adjacent  
Figure 16: The comparison of voltage response during the island switching process. Figure 15: The comparison of voltage response after the DC microgrid is tripped off: (**a**) the LVDC bus voltage response; (**b**) the HVDC bus voltage response. inverters. In both cases, independent control strategies can maintain good power quality. At present, there are few researches on the power quality problem of SST DC ports caused by the output fluctuation of microgrid.
## 4 Autonomous Shovel Technology  
### Rock Pile Identification and Modeling Technology  
The underground intelligent LHD identifies the ore pile and obtains information, such as its shape and outline, through equipment, such as cameras or laser scanners, in the first step, creates a three-dimensional model of the ore pile secondly and then transmits it to the bucket excavation trajectory planning system to assist it in the process for planning of optimal mining trajectories. Therefore, the perception and modeling of the ore pile is a significantly decisive step to realize the autonomous shoveling and loading with the LHD [5,9].  
There is extensive research on mine pile sensing technology at home and abroad, which can be mainly classified into two categories by sensing mechanisms: mine pile identification based on image sensors and that based on distance sensors [5].
## 2 Experimental Model Considering CSI Effect
### CSI Effect Analysis
#### 2.3.3. Considering Higher-Order CSI Effect  
In this case, the relationship between the input and output of the DC motor is:  
\[u=-\frac{L_{a}}{R_{a}}\dot{u}-\frac{K_{b}K_{i}K_{g}^{2}}{R_{a}r_{m}^{2}}\dot{x }_{A}+\frac{K_{i}K_{g}}{R_{a}r_{m}}V \tag{19}\]  
The state equation of this system is:  
\[\dot{Z}=\left[\begin{array}{ccc}0&I&0\\ -\frac{K}{M}&-\frac{C}{M}&\frac{B_{s}}{M}\\ 0&\frac{K_{b}K_{i}K_{g}^{2}}{L_{a}r_{m}^{2}}B_{s}^{T}&-\frac{R_{a}}{L_{a}} \end{array}\right]\dot{Z}+\left[\begin{array}{ccc}0&0\\ \frac{B_{gs}}{M}&0\\ 0&\frac{K_{i}K_{g}}{L_{a}r_{m}}\end{array}\right]\left[\begin{array}{c} \ddot{x}_{g}\\ V\end{array}\right] \tag{20}\]  
Figure 7: Transfer function from control voltage to control force (Reduced-order CSI effect), (**a**) Magnitude; (**b**) Phase.  
where \(\hat{Z}=\left[\begin{array}{cc}X&\dot{X}&u\end{array}\right]^{T}\). The observation equation is:  
\[Y=\left[\begin{array}{ccc}I&0&0\\ 0&I&0\\ -\dfrac{K}{M}&-\dfrac{C}{M}&\dfrac{B_{s}}{M}\\ 0&0&1\end{array}\right]\hat{Z}+\left[\begin{array}{cc}0&0\\ 0&0\\ \dfrac{B_{gs}}{M}&0\\ 0&0\end{array}\right]\left[\begin{array}{c}\tilde{x}_{g}\\ V\end{array}\right] \tag{21}\]  
From Figure 8, with the increasing frequency of the control voltage, the magnitude and phase of the transfer function initially increase and then decrease. This phenomenon indicates that a long time-delay exists when the frequency is relatively large or small.  
From Figures 7 and 8, as the AMD system does not consider the CSI effect, the control force generated by a unit control voltage is the largest, and the time-delay is close to zero. This means that the performance of the servo motor without the CSI effect is preferable to that of the reduced-order CSI effect, and the worst performance is seen by the higher-order CSI effect. When the worst condition occurs, system time-delay always exists (negative phases), and the frequency of the control voltage needs to be decreased or increased properly to near the value which is located at the peak of the magnitude and phase curves. Finally, in order to obtain the largest magnitude and the smallest phase, the frequency of the experimental system is taken as 10 Hz. An influence analysis of the frequency on the magnitude of the transfer function from the control voltage to the control force can be found in much literature. However, the basic conception of its negative influence on the phase has been introduced here. In sum, as a suitable frequency of the control voltage is selected, a unit control voltage can be converted into a relatively large control force, and the additional time-delay is relatively short.  
The influence of external disturbance on an AMD system can also be attributed to an impact on the transfer function from the control voltage to the control force. The magnitude affects the control force, which corresponds to the control gain. The phase corresponds to the time-delay in the control system.
###### Abstract  
The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IFSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery.  
fault diagnosis; rotating machinery; variational mode decomposition (VMD); swarm optimization algorithm (IPSO); probabilistic neural network (PNN)  
2024 14 7380. [[https://doi.org/10.3390/app14167380](https://doi.org/10.3390/app14167380)]([https://doi.org/10.3390/app14167380](https://doi.org/10.3390/app14167380))
## 4 Experiments
### Relational Knowledge Distillation
#### 4.2.1 Comparison of Traditional Knowledge Distillation  
Typically, traditional Knowledge Distillation [10] transforms the output \(z_{i}\) of the teacher into a \(Softmax\) classification probability \(q_{i}\) and trains it on the student model. Giventhe probability \(q_{i}\) of the teacher for the \(i\)-th class, the probability \(p_{i}\) of the student after the same \(Softmax\) transformation and the temperature \(T\), the loss function is composed using a KL Divergence function.  
\[q_{i}=\frac{\exp(z_{i}/T)}{\sum_{j}\exp(z_{j}/T)},\quad\text{loss}=\sum_{j} \text{KL}(q_{i},p_{i}) \tag{4}\]  
We configured student models from the ALBERT-Base and ALBERT-Large teacher models, each having half the original layer reuse number. We performed distillation using LR-Distillation and traditional Knowledge Distillation methods for the ALBERT-Base and ALBERT-Large models and compared their GLUE performances. Figure 4 shows the result.  
Experimental results showed that the model distilled through Relational Knowledge Distillation performed better in the GLUE benchmark score. Existing traditional, probability-based distillation techniques are suitable for distilling the information generated by the head layer for classification. They are not ideal for directly training the knowledge of the complex encoding layer generated by the model. Also, using the \(softmax\) function, information corresponding to a specific range is lost or underestimated. Additionally, it can distill the results of the classification head used for learning the existing language model, but it is inefficient regarding the data set and learning cost.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
###### Abstract  
Polarization sensitive optical coherence tomography (PS-OCT) is an imaging technique based on light scattering. PS-OCT performs rapid two- and three-dimensional imaging of transparent and translucent samples with micrometer scale resolution. PS-OCT provides image contrast based on the polarization state of backscattered light and has been applied in many biomedical fields as well as in non-medical fields. Thereby, the polarimetric approach enabled imaging with enhanced contrast compared to standard OCT and the quantitative assessment of sample polarization properties. In this article, the basic methodological principles, the state of the art of PS-OCT technologies, and important applications of the technique are reviewed in a concise yet comprehensive way.  
Keywords:optical coherence tomography; polarization sensitive devices; biomedical imaging; birefringence; scattering; depolarization +
Footnote †: journal: Applied Physics Letters
## 2 Problem Formulation  
Line losses contribute significantly to increasing the operation costs of the power system. Hence, costs are reduced by reducing the losses. Moreover, for systems with extensive reactive power demand, the reactive power compensation is performed by installing capacitors, which contribute to the installation, operation, and maintenance costs. The objectives considered in this work contain all of the above costs and is given as (1):  
\[F_{Minimize}=C_{P}T_{loss}^{Tot}+C_{q}Q_{loss}^{Tot}+C_{C}Q_{C}^{Tot}. \tag{1}\]  
where,  
\[P_{loss}^{Tot}=\sum_{k=1}^{N_{b-1}}P_{loss}\;,\] \[Q_{loss}^{Tot}=\sum_{k=1}^{N_{b-1}}Q_{loss}\;,\]  
and  
\[Q_{C}{}^{Tot}=\sum_{q=1}^{NC}Q_{cq}\]So,  
\[F_{Minimize}=C_{p}\sum_{k=1}^{N_{b-1}}P_{lossk}+C_{q}\sum_{k=1}^{N_{b-1}}Q_{lossk} +C_{c}\sum_{q=1}^{NC}Q_{Cq} \tag{2}\]  
If \(n\) is the life expectancy of the capacitors, the annual cost of the capacitor is given by (3):  
\[\text{Total cost of capacitors}=\frac{C_{c}Q_{C}{}^{Tot}}{n}\frac{\text{z}}{\text{ Year}} \tag{3}\]  
where z is the cost of single capacitor. The operation and maintenance costs are neglected [32]. The objective function shown in (2) is subjected to the following constraints;  
**Node voltage:** each node voltage should be in its minimum and maximum limits:  
\[V_{q}{}^{min}\leq V_{q}\leq V_{q}{}^{max} \tag{4}\]  
**Size of capacitor:** injected reactive power for compensation through capacitors must be in their defined limits.  
\[Q_{Cq}^{min}\leq Q_{Cq}\leq Q_{Cq}^{max} \tag{5}\]  
**Number of total capacitor:** the number of capacitors to be placed must be equal or less than the possible maximum locations.  
\[N_{C}\leq N_{C}^{max} \tag{6}\]  
**Total reactive power injection:** injection of total reactive power must be less than or equal to the total reactive power load.  
\[Q_{C}^{Tot}\leq Q_{L}^{Tot} \tag{7}\]
# A Novel Inductor-Based Non-Dissipative Equalizer
## 2 Proposed Equalizer Scheme
### Calculation of the Key Parameters  
In this section, the current and the duty cycle of switching waveforms are analyzed. In Figure 4b, when \(t_{d}<t\leq T\), the circuit current can only flow unidirectionally because the discharge circuit passes through the body diode of \(S_{1}\). Therefore, after the inductor \(L_{1}\) discharges all the energy, cell \(B_{1}\) will not reversely charge the inductor \(L_{1}\). T represents the switching period.  
Due to the small values of \(R_{on}\) and \(R_{off}\), \(e^{x}\) is approximately equal to \(1+x\). Hence, the above formula about \(i_{L1}\) can also be written as:  
\[i_{L1}=\left\{\begin{array}{ll}\frac{V_{B2}-V_{D}}{L}t,&0\leq t<t_{on}\\ \frac{V_{B2}-V_{D}}{L}t_{on}-\frac{V_{B1}+V_{D}}{L}(t-t_{on}),&t_{on}\leq t<t_ {d}\\ 0,&t_{d}\leq t<T\end{array}\right. \tag{8}\]  
\[i_{p}=\frac{V_{B2}-V_{D}}{L}t_{on}=\frac{(V_{B2}-V_{D})\cdot D\cdot T}{L} \tag{9}\]  
In Equation (9), \(D\) represents duty cycle. The inductor current has to be reduced to zero within one period. Because \(t_{d}\) represents the moment that the inductor current falls to zero, \(t_{d}<T\), which means that when \(t=T\), the following formula must be satisfied:  
\[\frac{V_{B2}-V_{D}}{L}t_{on}-\frac{V_{B1}+V_{D}}{L}(T-t_{on})<0 \tag{10}\]  
Furthermore:  
\[D<\frac{V_{B1}+V_{D}}{V_{B2}+V_{B1}} \tag{11}\]
## 3 Results and Discussion
### Ionic Liquids
\begin{table}
\begin{tabular}{c c c} \hline
**Contaminants** & **Wavenumber [cm\({}^{-1}\)]** & **Assignments** \\ \hline \multirow{8}{*}{ChCl-Urea} & 3435 & NH\({}_{2}\) asymmetric stretching \\  & 3340 & NH\({}_{2}\) symmetric stretching \\  & 1669 & NH\({}_{2}\) bending vibration \\  & 1597 & OH bending vibration \\  & 1474 & CH\({}_{3}\) rocking \\  & 1152 & C-N stretching \\  & 1062 & CH\({}_{2}\) rocking \\  & 961 & Asymmetric stretching of COO \\  & 790 & C-O bonding \\  & 3180 & O-H stretching \\  & 1681 & C=O stretching \\  & 1581 & Asymmetric stretching of COO \\  & 1282 & C-O stretching vibration \\ ChCl-PHA & 1082 & C-O stretching \\  & 953 & C-N stretching \\  & 861 & CH\({}_{2}\) rocking vibrations \\  & 838 & Aromatic C-H out-of-plane bending \\  & 786 & C-C stretching \\  & 3122 & The stretching vibration of the phenolic O-H \\  & 1667 & The stretching vibration of carbonyl group \\ ChCl–PB & 1272 & Methylene \\  & 1030 & C-H binding \\  & 3126 & Bending vibration of -NH\({}_{2}\) \\  & 1675 & C=O stretch of carboxylic acid \\ ChCl-PCA & 1606 & C=C stretching \\  & 1160 & C-OH stretching \\  & 848 & C-H stretching \\  & 771 & Stretching of the -OH group \\  & 3353 & N-H stretching \& O-H stretching \\  & 2942 & C-H stretching (asymmetric) \\ Cellulase & 2900 & C-H stretching (symmetric) \\  & 1642 & NH\({}_{2}\) scissoring \& C=N vibration \\  & 1334 & C-N stretching \\  & 1036 & C-N stretching \\  & 3351 & N-H stretching \\ \(\beta\)-glucosidase & 1646 & N-H bonding \& C=O stretching \\  & 1432 & N-H stretching \\  & 620 & N-H out of plane bending \\  & 3364 & O-H stretching \\ HMF & 1661 & Carbonyl stretching \\  & 1561 & C=C stretching (furan ring) \\  & 3134 & C-H stretching \\  & 2859 & C-H vibration of aldehyde group \\ Furfural & 1671 & C=O in conjugated carbonyl group \\  & 1465 & C=C stretching offuran ring \\  & 1276/1021 & C-O stretching vibration \\  & 928/884/755 & C-H bending out of plane peaks \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._out-of-plane bending, 786 cm\({}^{-1}\) for C-C stretching [54; 55]. The bands from choline chloride-PB were observed at 3122 cm\({}^{-1}\) for the stretching vibration of the phenolic O-H group exhibiting intermolecular hydrogen bonding, 1667 cm\({}^{-1}\) for the stretching vibration of carbonyl group, 1272 cm\({}^{-1}\) for the methylene, 1030 cm\({}^{-1}\) for C-H binding vibration [56]. The bands from choline chloride--PCA DES were observed at 3126 cm\({}^{-1}\), 1675 cm\({}^{-1}\), 1606 cm\({}^{-1}\), 1160 cm\({}^{-1}\), 848 cm\({}^{-1}\) from bending vibration of -NH\({}_{2}\), C=O stretch of carboxylic acid, C=C stretching, C-OH stretching, C-H stretching and 771 cm\({}^{-1}\) from stretching of the -OH group on the second carbon of the choline chloride [57; 58; 59].
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 6 Discussion
### Limitations  
**Dataset generation:** We utilized a mature SOME/IP generator to produce simulated datasets for experimentation. Due to SOME/IP being proposed by BMW and not widely deployed, researches regarding SOME/IP mostly rely on simulated data. Currently, there is a lack of open-source datasets, with most resources only providing generators or simulators. This leads to a lack of comparisons with benchmark datasets.  
**Single attack:** In real-world attack scenarios, attackers often employ more complex methods to achieve their objectives. Our detection model, targeting a single type of attack (DDoS), has its limitations. We intend to extend our model to cover a broader range of attacks in future research.  
**Single-communication protocol:** To achieve various functions, connected and autonomous vehicles often employ different communication protocols, such as CAN, MOST, FlexRay, LIN, Ethernet, etc. Attackers typically execute attacks involving multiple in-vehicle communication protocols within a comprehensive attack chain. Therefore, our focus on a single communication protocol, SOME/IP, may result in our model failing to capture anomalies that could be reflected in data from other communication protocols.  
**Potential pitfalls:** Due to limitations in available datasets, obtaining actual communication data from in-vehicle networks proved challenging. We needed to use a generator to produce simulated data for all our experiments. We employed a mature generator to simulate real attack scenarios to the best of our ability. On the other hand, the datasets utilized by researchers, including our own, were generated using open-source tools like the SOME/IP generator, potentially leading to sampling bias [46].
# An Architecture for Reliable Transportation of Delicate Goods
## 5 Workflow
### Service Initialization
At any time, anyone with the mobile application installed can connect to the device, as long as it is in advertising mode, and access to the descriptive information carried by the device: merchandise, current operator, next operator, EDD/LDD, and state. The descriptive information that is made available can be useful even for those who are not directly involved in the transport of the merchandise, and the fact that it is available does not compromise the security of the system.
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 2 Spatial Adaptive Filtering Algorithm in Hadoop Environment
### Spatial Adaptive Filtering Algorithm
The KS test identified the SHP corresponding to the row and column data and the identification information of the pixel points in the search window (containing 1 and 0; 1 for SHP and 0 for others), N, M, and W. Output: Interferogram phase values after filtering with a three-dimensional complex list mosaic
1 for each n \(\in\) [0,N] do
2 for each m \(\in\) [0,M] do
3 W[n][m] \(\leftarrow\) Obtain SHP identification information in the search window
4 sum[n][m] \(\leftarrow\) Calculate the sum of the numbers of W[n][i]
5 ifsum[n][m] \(>\) (Filter Threshold)) then
6 phase[n][m] \(\leftarrow\) Obtain the interferogram phase values corresponding to the pixel phase[:,n,m]
7 filter = (phase-W[n][m])/sum[n][m]
8 pnoise[:,n,m] = filter
9 endfor
10 endfor
```  
**Algorithm 1** Serial Spatial Adaptive Filtering Algorithm  
Figure 1: Comparison before and after filtering: (**a**) before filtering and (**b**) after filtering (the resultant figure is after cropping a differential interferogram in the study area). The spatial adaptive filtering algorithm requires the spatial averaging of each pixel of N(N\(-1\))/2 interferograms, which does not require an increased computer performance in terms of performance; however, because it requires sequential pixel-by-pixel processing, when the pixel points are large, the serial approach not only poorly utilizes the computing resources, but also requires a considerable amount of time for the data processing. We propose a parallelized processing scheme using clusters by splitting the different pixel datasets using a YARN cluster built on Hadoop. We use Spark distributed technology to load the split data into the memory and distribute them to each node for filtering, which can fully utilize the idle CPU resources to process the data while relieving the pressure on the memory resources, ensuring no loss of accuracy and improving the data processing performance.