esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Moisture Susceptibility  
#### 3.2.1 Modified Boiling Water Test  
The asphalt binder is shown peeling off the surfaces of the five aggregates after the boiling water test. By using engineering knowledge and visual inspection, it was discovered that the granite's peeling was the worst and that the asphalt at the angles of basalt and limestone showed only minor peeling. Diabase and granite's surface asphalt also showed significant peeling. Granite and asphalt binder clearly adhered the least well, according to the data. To evaluate the outcomes of the boiling water test and verify that the data were visible and quantifiable, the index of peeling rate was used in this test. Figure 8 demonstrates that, with limestone adhesion being superior to granite adhesion, the five aggregates' peeling rates and the adhesion work of the asphalt binder--aggregate system were roughly on par. Through XRF elemental analysis, this is related to the acidity and alkalinity of the aggregate, where limestone is an alkaline aggregate, about 50% of the composition is CaCO\({}_{3}\), so the surface of the stone is positively charged and reacts chemically with the active material with negative charge in the asphalt binder to form the bond strength, while granite is an acidic aggregate, 60% of the composition is SiO\({}_{2}\), and does not have a strong chemical reaction with the asphalt binder material, so under certain conditions of physical adsorption, the chemical reaction produced by limestone can complement and enhance the bonding properties with asphalt binder.
# A Review of the Applications of OCT for Analysing Pharmaceutical Film Coatings
## 2 Overview of OCT Techniques  
OCT is based on low-coherence interferometry, which operates in accordance with the principle interference between two optical beams when the optical path difference (OPD) is within the coherence length of the light source. The coherence length therefore determines the temporal width of the interferometric signal generated by the interferometer and thus defines the axial resolution of an OCT imaging system. There are three types of OCT modalities that have been applied in pharmaceutical film coating imaging: time-domain full-field OCT (FF-OCT), spectral-domain (SD-OCT) and swept-source OCT (SS-OCT). They are implemented with time-domain OCT (TD-OCT) and Fourier-domain OCT (FD-OCT) principles. It should be noted that Fourier transform is used as part of depth profile reconstruction for SD-OCT and SS-OCT [8; 9; 10; 11]. In this paper, OCT performed in SD-OCT and SS-OCT are therefore categorised as FD-OCT. Here we present a brief summary of TD-OCT and FD-OCT technologies.
# Research on Temperature Compensation Method in Crankshaft Online Measurement System
## 4 Experiment  
### Improved DA-BP Model  
Crankshaft online measurement requires fast and high-precision measurement of the journal. To adapt to the instability of the industrial field environment, it is necessary to select a sensor with high resolution, high-speed sampling, good dynamic performance and good stability. To meet the design of the journal multi-probe distribution, a small size is also required for dense arrangement. Solartron AUSB-0.25-S single-chip flexible gauge type LVDT displacement sensor was selected. The thickness of this sensor is only 6 mm, the measuring range is \(\pm\)0.25 mm, the accuracy is better than 1 \(\upmu\)m, resolution and repeatability are high.  
Firstly, the calibration experiment is carried out in combination with the temperature sensor in the range of +5\({}^{\circ}\)C-+60 \({}^{\circ}\)C. Then the displacement measured by the displacement sensor, the temperature measured by the temperature sensor and the output voltage of the temperature sensor are used as input, and the output voltage of the displacement sensor is used as the output. Finally, the IDA-BP model proposed in this paper is used to train 90 sets of measured data, and 30 sets of data are used for prediction. Temperature compensation is taken into the displacement sensor. The prediction results are shown in Figure 7.  
Figure 6: Flowchart of IDA-BP model.  
In order to prove the advantage of this algorithm proposed in this paper, comparisons are taken with the conventional BP neural network and the PSO-BP neural network. The prediction results based on BP are shown in Figure 8 and the prediction results based on PSO-BP are shown in Figure 9.  
Figure 8: Prediction based on BP.  
Figure 7: Prediction based on IDA-BP.  
The residual error comparison of three models is shown in Figure 10. The comparative parameters of three temperature compensation methods are shown in Table 1. The residual error of IDA-BP is 0.6930 mV, which is less than the residual error in BP and in PSO-BP. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are also much lower than BP and PSO-BP. It proves that the search ability and training efficiency of IDA-BP neural network have been improved.  
Figure 10: Residual error comparison of three models.  
Figure 9: Prediction based on PSO-BP.
## 5 Conclusions  
The rapid variation of the underwater acoustic vertical channel was estimated successfully with the proposed DNN-based synchronization method. To decrease the learning burden, the DNN was model-driven and embedded with expert knowledge. For a duration of 7 s, 15 timing offsets obtained at the start of the LFMs with correlation bias were fed into the DNN. Although the DNN model was trained with the surface wave height generator at fixed wind speed, the DNN model showed its robustness in response to a large range of wind speeds and experimental waveforms. Combined with  
Figure 5: Timing offsets before and after interpolation (**a**) and the output of the DNN (**b**) in the vertical communication experiment.  
Figure 6: QPSK phase trajectories and symbol error rates (SERs) in the experimental verification (**a**) Biased LFM with linear interpolation (**b**) Biased LFM with spline interpolation (**c**) DNN-unbiased with linear interpolation (**d**) DNN-unbiased with spline interpolation.  
spline interpolation to simulate real movement, the timing offset compensation could make the channel time-invariant. The successful time-invariant equalization in the symbol-rate sampling using 5000 m depth deep-sea experimental data showed the superiority of the proposed DNN-based synchronization.  
Conceptualization, Y.W. and M.Z.; methodology, Y.W. and N.W.; software, Y.W., Y.Y., and N.W.; investigation, Y.W. and M.Z.; writing--original draft preparation, Y.W. and Y.Y.; writing--review and editing, Y.Y. and M.Z.; supervision, M.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 61971472 and 61471351), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA22030101), the National Key Research and Development Program of China (Grant No. 2016 YFC0300300), and the China Scholarship Council (Grant No. 201904910081).  
The authors declare no conflict of interest.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 7 Discussion
In practical applications, such as vehicular communications with high mobility, where maximizing received signal quality while maintaining reasonable computational complexity is important, the MRC detector could be a suitable option.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 2 Related Works
### Adversarial Defense
#### 2.3.3 Feature Denoising  
Feature denoising [44] mitigates or eliminates disturbances in images and videos. From the perspective of adversarial defense, denoising serves as a protective mechanism against adversarial assault by discarding the perturbations derived from the model. This defense strategy diminishes the effect of adversarial instances by recalibrating them to align with the standard data distribution via a denoising procedure. The ultimate objective of incorporating a denoising module into the AI model is to ensure accurate predictions despite any harmful modifications to the input data.  
Despite their effectiveness, denoising methods apply filtering to all input data uniformly, which can sometimes lead to an inadvertent alteration of the general data. To counteract this, it is essential to ascertain whether the input data are adversarial examples or the original data. Although there are statistical methods such as the'maximum mean discrepancy' [45] and the technique of analyzing the final hidden layer neural network output distribution [46], they have yet to prove their effectiveness as defense mechanisms. Currently, no known statistical methods can accurately identify adversarial examples [47]. Furthermore, if an attacker gains an understanding of the denoising module, they can craft perturbations that consider this module, thereby enabling lethal adversarial attacks. This implies that the denoising technique aligns with the obfuscated gradient properties [22].  
Although the removal of perturbations through denoising is considered a positive defensive strategy, it has the potential to damage the original data unintentionally. To address this issue, researchers proposed a defense technique that merges feature denoising with adversarial training [48]. [PERSON] et al. [48] proposed a two-step process, starting with adversarial training, followed by denoising. From the decision boundary perspective, the classification of the original data remained unaltered. Thus, it opens up the possibility of effective regulation of adversarial examples toward the ground-truth class. Nevertheless, adversarial training comes with its own set of drawbacks, such as decreased classification performance and increased learning time.  
Therefore, this study aims to expand the research of [PERSON] et al. [48]. Instead of directly conducting adversarial training, this study intends to learn adversarial robustness through knowledge distillation. Subsequently, denoising is conducted.
## 5 Performance Evaluation  
In this section, we present a series of experiments to evaluate the effectiveness of the reordering and buffer management method proposed in this paper. The experiments are conducted on the NS-3 simulation platform, with two different network topologies selected for testing. First, the basic performance of the proposed method is assessed in a simple multipath topology. Next, a more complex real-world network topology is used to comprehensively evaluate the method's performance in realistic network conditions.
# Design and Experiment of a Passive Vibration Isolator for Small Unmanned Aerial Vehicles
## 1 Introduction
### Research Trends in Vibration Isolation Devices  
Vibration reduction in general aviation and mechanical systems closely corresponds to the performance of the system, due to which extensive research is being conducted on vibration reduction across various fields. Vibration isolation devices are categorized into the passive, active, and semi-active types [10], with research focused on active-type devices to improve the vibration damping performance. However, due to the limitations of battery-operated drones, passive-type vibration isolation devices are predominantly employed in the aviation industry. In 2014, a patent by [PERSON] and [PERSON] [11] introduced a passive isolation device developed by integrating rubber processed between metal plates.  
Figure 1: Trends of small drone equipment in terms of the weight and precision survey grouped by purpose.  
In 2020, a patent by [PERSON] et al. [12] presented a passive vibration isolation device for UAVs utilizing steel wire rope and a viscous damper.  
Therefore, the basic design of the passive-type vibration isolation device was based on the patents by [PERSON] and [PERSON] [11]. Consequently, research was conducted on the passive-type vibration isolation device by considering the operational environment of the drone (October type) and the weight of the optical equipment.
###### Abstract  
Understanding the dynamics of soil water content (SWC) is essential for effective land management, particularly in regions affected by underground mining. This study investigates the spatial and temporal patterns of SWC and its interaction with topographic and edaphic factors in coal mining and non-coal mining areas of the Chenghe watershed, located in the southeast of the Chinese Loess Plateau, which is divided by a river. Our findings revealed that the capacity to retain moisture in the top layer of coal mining areas is significantly higher (25.21%) compared to non-coal mining areas, although deeper layers exhibit lower SWC, indicating altered moisture dynamics due to underground mining disturbances. Coal mining areas show greater spatial and temporal variability in SWC, suggesting increased sensitivity to moisture fluctuations, which complicates water management practices. Additionally, underground mining activities introduce more intense effects on the relationship between SWC and topographic factors (i.e., GCVR across soil profile of 0-60 cm; slope at depth of 50 cm) or edaphic factors (i.e., soil organic matter and available potassium at depth of 30 cm; pH at depth of 50 cm) compared to non-coal mining areas. This variability is evident in the temporal shifts from positive to negative correlations, particularly in coal mining areas, reflecting modifications in both soil physical and chemical properties resulting from mining activities. In contrast, non-coal mining areas maintain a more stable moisture regime, likely due to preserved natural soil structures and processes. These contrasting findings emphasize the necessity for tailored management strategies in coal mining regions to address the unique challenges posed by altered soil characteristics and water dynamics.  
coal mining area; soil water content (SWC); spatial patterns of SWC; edaphic factors; soil hydrology
# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 2 Methods
### ResNet34 as the Feature Extractor
#### 2.2.1 ResNet34 Architecture  
The degradation problem manifests as the decreased performance of deep neural networks compared to their shallower counterparts. Suppose that a deep neural network reaches its optimal state at a certain depth, in that case, the extra layers beyond that point can be replaced by identity mapping connections, meaning the extra layers are bypassed during computation. In theory, this method can solve the degradation problem, making the deep neural network perform as well as its corresponding shallower counterpart. However, traditional nonlinear representations of multilayer network structures are difficult to represent identity mapping. Inspired by identity mapping, He proposed a deep residual learning framework to solve the degradation problem [34].  
Figure 2 shows the difference in the operation process between residual networks and traditional networks, also called plain networks. Residual networks possess an additional shortcut connection structure compared to plain networks. In plain networks, as shown in Figure 2a, we denote the underlying mapping to be fit by a few stacked weight layers as T(x), where x is the output value of the previous block. However, the computed T(x) cannot remain constant and equal to x, leading to the degradation phenomenon. In contrast, residual networks, as shown in Figure 2b, modify the underlying mapping to H(x), and the stacked layers are assigned to learn a residual mapping: F(x) = H(x) \(-\) x, allowing the network to have two paths during forward propagation, namely F(x) and x, which can also be viewed as parallel circuits. When the network reaches the optimal number of layers, the identity mapping can be realized by setting F(x) to zero when passing through the extra weight layers, so that its underlying mapping can be written as H(x) = x.  
He [34] demonstrated the performance of residual and plain net with the same number of layers and the same hyperparameters for the image classification tasks. Experimental results showed that the network with residual mapping has higher accuracy in the test of ImageNet. He further designed ResNet with different depth layers (18, 34, 50, 101, and 152) to address more complex classification tasks [34]. In this paper, to balance the number of training sets and accuracy, ResNet34 was chosen as the feature extractor. The structure of ResNet34 is shown in Figure 3. ResNet34 is composed of a convolutional layer, four residual blocks, and a fully connected layer, where each residual block represents a conventional feed-forward network with shortcut connections.
## 3 Results and Discussion
### Effect of Occupants on Subjective SRT Results  
Figure 5 presents the subjective SRT results for various speaker-listener position configurations and occupancy modes, showing the means and 95% confidence intervals. SRT is the SNR value that achieves a 50% intelligibility score; thus, a lower SRT value represents that a lower SNR can achieve sufficient speech intelligibility. In other words, a lower SRT value represents better speech intelligibility performance. For the convenience of comparison with the STI results in Figure 4, Figure 5 uses inverse coordinates for the SRT.  
Note that the SRTs with no other passengers always reached minimum values when compared to other occupancy modes (Figure 5), that is, the SRT value increased slightly after additional occupants were added, meaning that the speech intelligibility slightly decreased. However, the differences among various occupancy modes were mostly within 1 dB, except when the speaker was in the driver's seat and the listener was in the BR seat (reaching 2 dB). Overall, the SRT trend across the various occupancy modes was similar to the STI results. When the listener was in a front-row seat, the differences in SRT among the various occupancy modes were larger than those when the listener was in a rear-row seat. The SRT variations among different occupant distributions were also found to be comparable to those among the different numbers of occupants. These findings are both in line with the STI results.  
A repeated-measures analysis of variance with a significance level of 0.05 was performed to examine the effect of occupants on the SRT result for each speaker-listener position configuration. The Geisser-Greenhouse correction was applied to correct the violation of the sphericity assumption. The results indicate that the influence of the occupancy mode on the SRT result was insignificant (\(p>0.3\)), except for the situation in which the speaker or listener were in the BR seat (\(p>0.01\), \(F=3.17\); or \(p=0.007\), \(F=4.76\)). Post-hoc pairwise comparisons revealed significant differences in the SRT results between \"None\" and \"BM\", \"BL\", \"BM+BL\", \"FP+BM\", or \"FP+BM+BL\" (\(p<0.05\)) when the listener was in the BR seat and the speaker was in the driver's seat. There were significant differences between the SRT results for \"None\" and \"FP+BM\" (\(p<0.05\)) when the listener was in the BR seat. The difference between the SRT results for \"None\" and \"FP+BM\" (\(p<0  
the driver's seat and the speaker was in the BR seat. In addition, there were insignificant differences among the various occupancy modes for the other cases.
## 5 Simulation and Performance Analysis
### Classifier Performance Analysis
The AUC values were calculated and compared with the LDM-satellite and LLD methods to validate the comprehensive advantages of the proposed method in detecting packet loss differentiation performance. As depicted in Figure 6, the AUC value of the proposed method is 0.976, surpassing that of the other two methods (0.883 and 0.921), thereby indicating its superior overall efficacy. Moreover, the proposed heterogeneous integration architecture is utilized and the resulting integrations are combined using a voting method, ACC weighting method, and SGD-based weighting method, respectively. The ROC graph in Figure 7 illustrates that the AUC value of the SGD-based weighting method (0.976) surpasses those of the other two methods (0.927 and 0.916), indicating its superiority. Figure 5: Performance comparison of the three weighting algorithms. In order to verify the performance of the proposed method compared with the existing learning model-based packet loss discrimination methods, this experiment uses the same sampled data set to detect packet loss differentiation. The LLD, LDM-satellite, and LDM-HEL methods were selected to assess the F1-score performance and training time of different differentiation methods under various training data ratios. As shown in Figure 8, among the three differentiation methods, the LDM-satellite and LDM-HEL methods show a gradual increase with the growth of the training data ratio, and generally, the F1-score of our LDM-HEL method is superior to the other two methods. Additionally, the comparison of training time is presented in Figure 9. The training time of the three packet loss differentiation methods increases with the rise in the training data ratio. The training time of the proposed method is shorter than LLR and slightly higher than LDM-satellite.
## 2 System Model  
The suggested model simulates the P2P market over the duration of a single day that is divided into \(T_{n}\) number of intervals of equal duration \(\Delta t_{i}\). Additionally, the network consists of \(P=\{1,2,3,\ldots p\}\) sets of prosumers and \(C=\{1,2,3,\ldots c\}\) sets of customers trading with each other such that \(P\cap C=\emptyset\). Note that in all of our discussion, we refer to customers as the participants that consume the PV energy of the prosumers.  
The suggested framework consists of three stages, as shown in Figure 1. The first stage collects the input data (i.e., the load profiles, PV curves of the prosumers, and day-ahead market price). The second stage uses the data from the first stage to compute the winners and the market price range for each time interval using the CDA market. The bid signals refer to the price set up by the customers, whereas the ask values refer the market price set up by the prosumer. Finally, in the third stage, the market operator determines the optimal set of parameters (i.e., the optimal trading quantity and P2P price for the matched orders using the trading approach).  
Any unmatched orders at the end of P2P trading will be cleared at the utility price by the market operator. Prosumers will sell any excess renewable power as a FIT to the market operator. Similarly, the customers will pay the utility price to the market operator for meeting their demand.