esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Windowed RMS Analysis
### 100 kHz RMS Plots and Discussion  
The RMS plots for the healthy and disbonded panels with a 100 kHz excitation are presented in Figure 9. The time window of 10 \(\upmu\)s-20 \(\upmu\)s shows the wave packet starting to interact with the stiffeners. In this time window, the only mode present is the S\({}_{0}\) mode. The healthy panel shows clearly the presence of the stiffener as very little energy is transmitted through the stiffener. The panel with the disbonded region however shows an indication of a defect but not as clear or definitive as the later time windows. There is evidence of wave energy \"bleeding\" into the disbonded region which is initiating a low energy transmitted conical pattern. This is most likely a result of the S\({}_{0}\) mode interacting with the defect.  
As the wave continues to interact with the stiffener it is apparent from both the healthy panel and the panel with the disbonded region that some of the energy is reflected back from the stiffener towards the source. This is shown in Figure 9 by the \"tiger-stripe\" interference fringes on the left of the stiffener. In the results from the panel with the disbonded region, this interference pattern is disrupted. This is clearly shown in the 50 us-60 us window by the circular shape of the reflection pattern to the left of the disbonded region. This reflected conical pattern to the left of the disbonded region has higher RMS values than the neighbouring reflected signal and the reflected signal on the healthy panel. Conversely, as with the plots presented in Figure 4 there is an area of low energy to the right of the disbonded region. This area of low energy is shown by a transmitted conical region of low RMS values as the A\({}_{0}\) mode interacts with the stiffener. This transmitted conical fringe pattern suggest that at this excitation frequency, a greater amount of the Lamb wave energy is reflected back towards the source than is transmitted through the disbonded region. The shape of the defect is also more prominent as the shorter wavelength of the A\({}_{0}\) mode interacts.
## 1 Introduction
### Analysis of Demagnetization Fault Identified Signal
#### 2.3.4 Analysis of the Second Auxiliary Identification Signal  
Take the second electrical cycle signal of \(SI_{\text{a2}}\) (\(SI_{\text{a2-2}}\)) as an example to analyze the waveform characteristics corresponding to Mode 7D and Mode 8H. The \(SI_{\text{a1-2}}\) under these two fault modes is calculated through (6) and normalized through (7); the results are shown in Figure 6.  
As shown in Figure 6, the waveform of normalized \(SI_{\text{a2}}\) under Mode 7D is different from the waveform under Mode 8H. Therefore, the \(SI_{\text{a2}}\) can identify Mode 7D and Mode 8H, and then, determine the healthy condition of PM1, PM2, PM3, and PM4.  
According to the above analysis, two conclusions can be drawn.  
(1) It can be clarified that by combining the signals \(SI_{\text{m}}\), \(SI_{\text{a1}}\), and \(SI_{\text{a2}}\), any demagnetization fault mode can be accurately diagnosed, including uniform demagnetization and local demagnetization with different numbers, locations and severities. Modes 1 to 5 can be identified through \(SI_{\text{m}}\). Mode 6H, Mode 6D, Mode 7H, and Mode 8D can be identified through \(SI_{\text{m}}\) and \(SI_{\text{a1}}\). Mode 7D and Mode 8H can be identified through \(SI_{\text{m}}\), \(SI_{\text{a1}}\), and \(SI_{\text{a2}}\).  
(2) The healthy conditions of PM(2p), PM1, and PM2 can be accurately diagnosed through the first electrical cycle signal of DFISs. Thus, the healthy conditions of the first pair PM (PM1 and PM2) are identified through the first electrical cycle signal. The healthy conditions of PM2, PM3, and PM4 can be accurately diagnosed through the second electrical cycle signal of DFISs. The healthy condition of PM2 is already identified by the first electrical cycle signal, and thus, the healthy conditions of the second pair of PMs (PM3 and PM4) can be accurately diagnosed through the second electrical period signal. Accordingly, the healthy conditions of the \(i\)th-pair PMs (PM(\(2i-1\)) and PM(\(2i\))) are identified through the \(i\)th electrical period signal of DFISs; the corresponding relationship between them is shown in Figure 7.  
Figure 5: The waveforms of normalized \(SI_{\text{a1-2}}\). (**a**) Mode 6H, Mode 7H, Mode 6D, Mode 8H, and Mode 8D. (**b**) Mode 7D and Mode 8H.  
Figure 6: The waveforms of \(SI_{\text{a2-2}}\) under Cas 7D or Mode 8H.  
In summary, the proposed demagnetization fault diagnosis method is as follows: compare and analyze the actual measured waveform of the demagnetization fault identified signal with the various fault mode waveforms in Figures 4-6. When the measured waveform completely matches a certain fault mode waveform, it can be determined that the fault mode is the demagnetization fault mode of the corresponding permanent magnet pairs. Demagnetization faults in DDPSM can be accurately diagnosed by sequentially identifying the condition of each pair of permanent magnets. Thus, the healthy conditions of all of the permanent magnets can be determined through one complete rotation revolution.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 3 Uplink Power Optimization Problem  
In this section, the multi-objective UL power optimization problem, \(\mathbb{P}_{1}\), is formalized in a general correlated Rayleigh fading channel context with the OSLP scheme. From Equations (16) and (17), the decision variables of the optimization problem are the power coefficients allocated to each UE \(k\), i.e., \(p_{k}\), or in vector form, \(\mathbf{p}\). These are restricted between 0 and a maximum value \(P_{max},\forall k\). The MO approach includes two metrics, which are operationalized as OFs to maximize the overall system capacity, while ensuring minimum user bit rates. The solution given by the bi-objective optimization problem allows to grasp the trade-offs between the throughput and the fairness. To maximize the SE of the worst UE and maximize the total sum SE, SE values must be evaluated at the CPU, i.e., the values of Equation (16) for \(l=L\). Therefore, \(\mathbb{P}_{1}\) can be formulated as  
\[\mathbb{P}_{1}:\max_{p_{k}} f_{1}(\mathbf{p})=\sum_{k=1}^{K}\text{SE}_{kL}\quad\text{( Throughput)} \tag{18}\] \[\max_{p_{k}} f_{2}(\mathbf{p})=\min_{k=1,\cdots,K}\text{SE}_{kL}\quad\text{( Fairness)}\] s.t. \[0\leq p_{k}\leq P_{max},k=1,\cdots,K.\]  
The throughput OF is non-convex and, therefore, the global optimum is usually approximated through the employment of successive approximation algorithms that find locally optimal solutions [2]. The fairness OF is quasi-linear and can be solved to optimality by a bisection approach for a given tolerance \(\epsilon>0\)[2; 4]. This means that, for both OFs, the process of obtaining the global optimal power distributions can be time consuming and computationally complex as the number of UE grows. The need to identify non-dominated solutions increases the computational effort with respect to just having the optimal solution to each SO problem. Therefore, instead of pursuing the global optimal solution, a less complex yet accurate approach based on the DE MH is employed [21; 22; 23].
# Research Progress on Router Devices for the OAM Optical Communication
## 1 Introduction
Therefore, the multiplexing of OAM beams with different topological charges without interference is possible, which provides a potential solution to the capacity crisis in optical communication. Currently, the OAM beam exhibits a vast range of potential applications in the fields of optics [33; 34; 35; 36; 37; 38; 39; 40], electromagnetism [41; 42; 43; 44; 45; 46; 47; 48; 49; 50], acoustics [51; 52; 53; 54; 55; 56; 57; 58; 59; 60], and mechanics [61; 62; 63], thus garnishing significant attention from researchers. In the field of optical communication, the applications of OAM beams mainly include two areas: OAM-shift keying (OAM-SK) [64; 65; 66; 67; 68; 69; 70; 71] and OAM-division multiplexing (OAM-DM) [72; 73; 74; 75; 76; 77; 78]. OAM-SK is a mapping technique that assigns digital signals to differentiate OAM beams based on the unique value of each OAM mode (with each mode representing a data bit). Meanwhile, OAM-DM utilizes the carrier of an OAM beam to modulate signals and achieve channel multiplexing via the orthogonality between various beams, resulting in exponential increases in the channel capacity [80; 81; 82]. For a mature OAM optical communication system, the manipulation and transformation of different channels play a crucial role in enhancing the flexibility and practicality of the system [83; 84; 85]. Currently, the research focus in OAM optical communication lies in the development of routing technology that enables end-to-end signal transmission path transformations [86; 87; 88; 89; 90; 91]. Therefore, extensive research and development have been conducted on the OAM router devices (OAM-RDs) with significant performance enhancements [92; 93; 94]. This review focuses on the field of OAM-RDs and provides a detailed account of recent advancements in OAM router schemes, which are centered around OAM-RDs that enable channel multicasting, channel switching, channel filtering, channel hopping, and channel adding/extracting functionalities, as illustrated in Table 1. Finally, this study also summarizes the research process of OAM-RDs while examining their potential for future development.
## 5 Results and Discussion
### Objective Evaluations
Specifically, when comparing the WER from baseline S4 with our proposed method, there was a substantial relative reduction of 21.07%. However, there was a slight degradation in the WER from baseline S2, amounting to 3.98%. These results demonstrate that the conversion model trained on a non-parallel corpus performs comparably, and in some cases better, than its counterparts trained on a parallel corpus. This shows the effectiveness of our method in improving speech quality and reducing pronunciation errors. However, it is essential to note that the direct comparison of parallel and non-parallel VC methods is not entirely fair, as the non-parallel VC only uses the target speaker's data during training. Figure 2 illustrates improvements in spectrogram visualization by comparing the converted speech between the proposed and baseline (S1) systems. Our observations indicate that the proposed method effectively mitigates speech artifacts. However, for the S1 system, the reconstructed speech exhibits audible artifacts, occasionally appearing within non-speech segments.
## 4 Experiments and Discussion
### Lab Test Based on the PVDF Piezoelectrc Film  
#### 4.3.1 Calibration of the PVDF  
The mechanical and the electrical behaviors of PVDF piezoelectric film are expected to be coupled with each other. The piezoelectric equation is used to describe a quantitative relation between its electrical behavior and mechanical behavior. The output of the electric charge from PVDF piezoelectric film is the response of polarization direction caused by strain in all directions. The relationship between the electric charge from the PVDF and the deformation of the monitored structure is calculated by formula (4):  
\[V_{0}=\sum g_{3_{j}}\sigma_{j}t \tag{4}\]  
where \(V_{0}\)is the PVDF's output voltage, \(g_{3_{j}}\)is piezoelectric voltage constant of the PVDF, \(\sigma_{j}\)is the stress on PVDF, \(t\)is the thickness of the PVDF, and \(j=1\sim 3\). Generally, the PVDF's can work in two modes: the stretching mode and the thickness mode. A cantilever beam with a uniform strength, as illustrated in Figure 15, is a flexible structure and the PVDF piezoelectric film only deforms in the direction of tension. Thus, the test beam works in a stretching mode. The output of the PVDF in a stretching mode can be calculated by formula (5):  
\[V_{0}=(g_{3_{1}}\sigma_{1}+g_{3_{2}}\sigma_{2})t \tag{5}\]  
As shown in Figure 15, the cantilever beam could be oscillated under the sinusoidal loads. The electrical charge amplifier transfers the signal from the PVDF to the A/D acquisition system, while the dynamic strain amplifier transfers the signal from strain gauge. The A/D transfer and data acquisition system collects and stores both signals from the PVDF and from the strain gauge. The PVDF's sensitivity \(K\) can be calculated as:  
\[K=\Delta V/\Delta\varepsilon \tag{6}\]  
where \(\Delta V\) is the output variation of PVDF and \(\Delta\varepsilon\) is the output variation of strain gauge.  
Taking 5 Hz loading frequency for example, \(\Delta V\) and \(\Delta\varepsilon\) were recorded under different amplitudes, and \(K\) was calculated and listed in Table 6. With the linear fitting of \(\Delta V\) and \(\Delta\varepsilon\), the sensitivity of the PVDF can be obtained under a loading frequency of 5 Hz. Figure 16 shows corresponding sensitivity of the PVDF, \(K=6.14\) mV/\(\mu\)e, and a good linearity was observed.  
Figure 15: Scheme of sensitivity calibration test of PVDF.  
Similarly, \(K\) could also be acquired under different loading frequencies, as listed in Table 7, for the PVDF's sensitivities in various frequencies. The sensitivity shows small difference under various loading frequencies.
## 3 Results  
### Gas Porosity and Permeability after Thermal Exposure  
Before and after the combustion tube test, the entire set of core samples underwent testing on a standard gas permeameter used for determining the reservoir properties such as porosity and permeability. In addition, all core plugs were photographed after the combustion in order to record the visual changes (Figure 2).  
Figure 2: Photographs of cylindrical samples after the combustion tube experiment (modified after [37]).  
The photographs of samples subject to thermal exposure illustrate the visible changes in the rock matrix. The images show how rock samples have changed color: two samples (#2 and #3) of Oil field 1 changed color to coral-red, which may be caused by the increased pyrite content in the samples and its oxidation. In samples #16 and #18 of Oil field 3, drops of bitumen are observed. In sample #14 of Oil field 2, we can notice a newly formed fracture.  
The first non-contact characterization method was porosity determination by nitrogen flow and pressure decay. We obtained total porosity and permeability for 10 samples before and after combustion (Figure 3). We observe the significant increase in porosity values defined by nitrogen injection due to increased void space in the core cylinders. It may be caused by emptied void space and the occurrence of artificial fractures.  
The full data of porosity and permeability is summarized in Table 4. We can note the tendency of a significant increase in both parameters at high exposure temperatures. This confirms the front propagation and reflects the importance of reached temperature on porosity and pore connectivity evolution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample ID** & **Section** & **Temperature (°C)** & \begin{tabular}{c} **Porosity before** \\ **CT (\%)** \\ \end{tabular} & \begin{tabular}{c} **Permeability** \\ **before CT (mD)** \\ \end{tabular} & \
\begin{tabular}{c} **Porosity after** \\ **CT (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Permeability** \\ **after CT (mD)** \\ \end{tabular} \\ \hline
1 & 2.1 & 300 & 1.07 & 0.004 & 4.04 & 0.062 \\
2 & 2.2 & 368 & 1.28 & 0.003 & 1.91 & 0.034 \\
3 & 3.1 & 430 & 0.54 & 0.033 & 8.27 & 0.769 \\
10 & 6.1 & 207 & 0.95 & 0.003 & 0.84 & 0.014 \\
12 & 4.1 & 462 & 1.41 & 0.003 & 7.24 & 0.030 \\
13 & 2.2 & 368 & 0.58 & 0.024 & 26.07 & 5.770 \\
14 & 3.4 & 461 & 0.47 & 0.012 & 31.92 & 4.277 \\
15 & 4.2 & 422 & 0.62 & 0.007 & 1.27 & 0.085 \\
16 & 2.1 & 299 & 2.81 & 0.053 & 3.30 & 1.591 \\
18 & 3.1 & 429 & 2.43 & 0.065 & 18.39 & 0.577 \\ \hline Avg. & & & 1.22 & 0.021 & 10.33 & 1.321 \\ SD & & & 0.81 & 0.02 & 11.18 & 2.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gas porosity and permeability before and after the combustion.  
Figure 3: Change in gas porosity before and after the combustion.
## 3 Results and Discussion
### Antioxidant Capacities of Porcine Organ Hydrolyzates
#### 3.3.3 Hydroxyl Radical Scavenging Activity  
Hydroxyl radical scavenging activity is widely used to determine antioxidant capacity because hydroxyl radicals are among the reactive free radicals present in living organisms [26]. Regardless of the type of porcine organ, the hydroxyl radical scavenging activity of the hydrolyzates was higher than that of the non-hydrolyzed control, even at concentrations as low as 1 mg/mL (Figure 5a-e). As a result, with the exception of the porcine liver, the trypsin-mediated hydrolyzates exhibited over 50% lower IC\({}_{50}\) values than their control counterparts. Notably, in the porcine kidney and spleen, trypsin outperformed Alcalase in terms of improving hydroxyl radical scavenging activity. The lowest OH radical IC\({}_{50}\) value (1.06 mg/mL) was recorded for trypsin-mediated porcine spleen hydrolyzate (\(p<0.05\)). The  
Figure 3: DPPH radical scavenging activity (**a–e**) and DPPH radical IC\({}_{50}\) value (**f**) of Alcalase- and trypsin-mediated protein hydrolyzates from porcine organs. H, heart; K, kidney; S, spleen; V, liver; L, lung. Each error bar indicates the standard deviation (\(n=3\)). a–c means with the same letter within the result for IC\({}_{50}\) value are not significantly different (\(p\geq 0.05\)).  
porcine spleen has recently been identified as a novel source for producing antioxidant peptides [27; 28]. The trypsin-treated spleen hydrolyzate, composed of low-molecular-weight peptides (<3000 Da), exhibited strong hydroxyl radical scavenging activity (396.40 U/mL), and its antioxidant mechanism may involve the Keep1-Nrf2 pathway [28].
# Asymmetric Encryption of Invisible Structured Light 3D Imaging
## 3 Simulation Results and Performance Analyses
### Histogram Analysis  
The histogram is used to judge the security of image encryption by analyzing the number of occurrences of each gray level in the image. The more uniform the distribution of the gray histogram and with no obvious features, the more resistant it is to the attack of statistical analysis. The following figure shows the histogram of the original image and ciphertext image. Figure 9a-h show the histograms of the eight original images. Figure 10a-h show the histograms of the eight ciphertext images. Obviously, the ciphertext images are evenly distributed and have no obvious features. Therefore, ciphertext can effectively resist statistical analysis attacks.
## 4 Experiment and Result Analysis
### Experimental Results
As shown in Figure 9, in order to demonstrate the effectiveness of RS-Aug, this article compares the PointPillars of GT-Aug and RS-Aug, respectively. Red bounding boxes represent cars, blue bounding boxes represent cyclists, and green bounding boxes represent pedestrians. From Figure 9c, it is evident that the PointPillars using GT-Aug exhibit some misdetections (in the red boxes) and missed detections in certain scenarios. Conversely, as shown in Figure 9d, the PointPillars based on RS-Aug demonstrate superior performance in detecting pedestrians and cyclists with sparse and fewer surface point clouds, effectively reducing the instances of missed detections. Figure 9: Recognition results of PointPillars using different data augmentation algorithms: (**a**) scene; (**b**) ground truth; (**c**) prediction of GT-Aug-PointPillars; (**d**) prediction of RS-Aug-PointPillars. Cars are represented by red bounding boxes, cyclists by blue bounding boxes, and pedestrians by green bounding boxes.
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 5 Experiments
### Evaluation Metrics  
We evaluate the performances of our model and baselines in terms of accuracy and calibration. Following previous works [3,22], we use Recall and MRR as evaluation metrics to measure the recommendation accuracy.  
* **Recall@K** (Rec@K) is a widely used metric in recommendation and information retrieval areas. Recall@K computes the proportion of sequences whose next behaviors are included in the recommendation lists. \[Recall@K=\frac{1}{N}\sum_{s}1(x_{n+1}\in RL_{s})\] (23) where \(1(\cdot)\) is an indication function whose value equals 1 when the condition in brackets is satisfied and 0 otherwise. \(N\) is the number of testing cases.
* **MRR@K** is another important metric that considers the rank of correct items. The score is computed by the reciprocal rank when the rank is within K; otherwise the score is 0. \[MRR@K=\frac{1}{N}\sum_{s}\frac{1}{rank(x_{n+1},RL_{s})}\] (24)  
To evaluate the effectiveness in terms of calibration, we adopt \(C_{KL}\) which is a common metric used for calibrated recommendation [17]. The \(C_{KL}\) compares the consistency between two distribution:  
\[C_{KL}(RL,s)=\frac{1}{N}\sum_{s}\sum_{g\in G}p(g\mid s)\frac{p(g\mid s)}{ \widetilde{q}(g\mid s)} \tag{25}\]  
The lower \(C_{KL}\) value means we provide more calibrated recommendation lists. To avoid the division-by-zero error, we use \(\widetilde{q}(g\mid s)=(1-\alpha)(g\mid s)+\alpha p(g\mid s)\) to replace the original preference distribution \(q(g\mid s)\). The value of \(\alpha\) also equals to 0.01 according to [17,18,20,21].  
In addition, to better compare performances of our model and baseline models, we define the improvement as follows:  
\[Improv.=\frac{Metric_{DACSR}-Metric_{Baseline}}{Metric_{Baseline}} \tag{26}\]  
where _Metric_ can be any metric mentioned above.
## 5 Analyses
### Performance Evaluation
#### 5.3.1 Computation Cost Analysis
Subsequently, ECLAS has an overall computation load of 1.7682 \(ms\), which is better than the rest as shown in Figure 2.  
The relationship of verification time delay for particular number of aggregate signatures that RSU takes to compute for the schemes [2; 13; 27; 55] is portrayed in the Figure 3.  
As a requirement in VANETs, vehicles have to broadcast their messages every 100-300 \(ms\), thus it entails that an RSU or AS can receive about 180 messages every 300 \(ms\). Therefore, in one second an RSU is expected to verify about 600-2000 messages [23]. In Figure 3, it endeavors to illustrate the time it takes to do batch verification for 2000 signa \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Schemes** & **Message Signing** & **Individual Verify** & **Aggregate Verify** \\ \hline Horng & \(3T_{e.m}\approx 1.326\ ms\) & \(3T_{bp}+T_{e.m}+T_{H}\) & \(3T_{bp}+nT_{e.m}+nT_{H}\) \\  & & \(\approx 17.481\ ms\) & \(\approx 12.633+4.4198n\ ms\) \\ \hline Cui & \(T_{e.m}+T_{e.a}+T_{h}\) & \(3T_{e.m}+2T_{e.a}+2T_{h}\) & \((n+2)T_{e.m}+4 nT_{e.a}\) \\  & \(\approx 0.4439\ ms\) & \(\approx 1.3298\ ms\) & \(+nT_{H}+nT_{h}\) \\  & & & \(\approx 6.2973n\ ms\) \\ \hline Xiong & \(3T_{bp.m}+2T_{bp.a}+T_{h}\) & \(3T_{bp}+2T_{bp.m}+T_{bp.a}\) & \(3T_{bp}+2 nT_{bp.m}+nT_{bp.a}\) \\  & \(\approx 5.1413\ ms\) & \(\approx 19.2262\ ms\) & \(\approx 12.633+7.8312n\ ms\) \\ \hline Tzeng & \(3T_{bp.m}+T_{H}\) & \(2T_{bp}+T_{bp.m}\) & \(2 nT_{bp}+nT_{bp.m}\) \\  & \(\approx 9.533\ ms\) & \(\approx 10.131\ ms\) & \(\approx 10.131n\ ms\) \\ \hline Kamil & \(3T_{e.m}+2T_{e.a}+3T_{h}\) & \(2T_{e.m}+T_{e.a}+T_{h}\) & \(2 nT_{e.m}+nT_{e.a}+nT_{h}\) \\  & \(\approx 1.3297\ ms\) & \(\approx 0.8859\ ms\) & \(\approx 0.8859n\ ms\) \\ \hline ECLAS & \(2T_{e.m}+T_{h}\) & \(2T_{e.m}+T_{h}\) & \(2 nT_{e.m}+nT_{h}\) \\  & \(\approx 0.8841\ ms\) & \(\approx 0.8841\ ms\) & \(\approx 0.8841n\ ms\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Computation Costs for Related Certificate-less aggregate signature (CLAS) Schemes in \(ms\). tures. Thus, the comparative analysis shows that the proposed scheme has less verification time delay for \(n\) signature aggregation and the number of signatures has a direct proportion linear relationship to the verification delay. Figure 2: Computation Cost Comparison Per Unit.