esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Dominant Frequency Attenuation of Blasting Vibration for Ultra-Small-Spacing Tunnel
## 5 Data Analysis
### Spectrum Analysis of Entire MS Delay Blast Vibration  
In the blasting works of tunnels, the MS delay blasting technique was typically adopted to restrict the charge weight in a single detonated sequence for meeting the control criterion of safety [30]. Two typical vibration velocity waveforms of an MS delay blasting are illustrated in Figure 8, and the Arabic numerals marked at the top of each figure are the delay sequence. The PPV in the curve in Figure 8a occurs in MS12, whereas in the Figure 8b curve, it is observed in MS1, which is mainly related to the average explosive weight in each delay sequence. It can also be seen from the figure that the complete vibration velocity waveform of the MS delay blasting is composed of multiple vibration waveforms that are detonated in different delays. On converting the vibration velocity waveform from the time domain to the frequency domain, the power spectrum contains several frequency components that are contributions of multiple vibration waveforms (as shown in Figure 9).  
The Fourier power spectrum of the waveform in Figure 8 is shown in Figure 9. There are several peak amplitudes in the power spectrum, indicating that the complete vibration velocity waveform is a superposition of different frequency vibrations. When the MS delay blasting was used in excavation, if a power spectrum reflecting all sequences respectively could be obtained, it may better present the true scenario of the entire vibration compared with a global frequency of all delays [30].  
Figure 8: Vibration velocity waveforms of blasting vibration waves. (a) PPV occurs in MS12 and (b) PPV occurs in MS1.
# E-Waste Reverse Supply Chain: A Review and Future Perspectives
## 3 Reverse Supply Chain
### Reverse Supply Chain Processes  
A closed-loop supply chain consists of both forward and reverse supply chains as shown in Figure 1. The flow of a forward supply chain starts with the purchasing of raw materials from different suppliers. Manufacturing plants are well designed with the necessary technology and responsible for producing various parts that are then assembled into finished products. After that, distribution centres deliver these products to end users [31] (seen in the dashed line). In contrast, the flow of reverse supply chain begins with product acquisition to redistribution and sales (seen in the solid line). The reverse supply chain is arranged by five primary steps: product acquisition, reverse logistics, inspection and disposition, refurbish, and distribution and sales. These five steps are further detailed as follows:
## 5 Conclusions  
In this paper, 3D microstructures of intact Q3, Q2 loess, and remolded loess (i.e., F1, C1) from the GMRL project and MECC project in the Loess Plateau were established using micron-scale computed tomography. Through quantitative analysis and comparison of the 3D microstructure volumes of different sizes, several key conclusions were drawn:  
1. A robust workflow for determining the representative volume elements (RVE) of intact and remolded loess is introduced. In general, the intact loess has the smallest RVE size. 600 \(\upmu\)m \(\times\) 600 \(\upmu\)m \(\times\) 600 \(\upmu\)m cube is the smallest size to analyze the 3D microstructure;  
Figure 14: Images of the tested specimens (**a**) the Q3 specimen; (**b**) the Q2 specimen; (**c**) the F1 specimen; (**d**) the C1 specimen.  
2. Intact loess has more overhead pores, contributing to the formation of pore channels. Additionally, intact loess displays a more uniform microstructure and superior connectivity compared to remolded specimens;
3. Remolded loess is much more compacted than intact loess, with the loess from the MBCC project showing greater uniformity than that from the GMRL project;
4. The significant difference in the microstructure of intact and remolded loess is attributed to the substantial variations in their formation processes.  
These results are of urgent practical and scientific significance to understand the difference between compacted loess and natural loess or remolded loess from macro and micro multi-scale, to systematically study the evolution mechanism of the three-dimensional microstructure of compacted loess under the action of water and force, and to reveal the micromechanical behavior and deformation mechanism of compacted loess, which helps to provide a scientific and reasonable theoretical basis for the foundation stability evaluation of GMRL and MECC project in loess area.  
L.M.: Investigation, Data curation, Formal analysis, Writing-original draft, Writing--review and editing. L.Z.: Investigation, Methodology, Project administration, Writing--review and editing. X.H.: Project administration, Writing--review and editing. S.G.: Investigation, Writing--review and editing. S.Q.: Conceptualization, Funding acquisition, Supervision, Investigation, Review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundations of China, grant numbers 42141009 and 42107188.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors wish to offer their great gratitude and regards to senior engineer [PERSON] from Institute of Geology and Geophysics, Chinese Academy of Sciences for his technical support, and the reviewers and the editors for their valuable comments.  
The authors declare no conflicts of interest.
# An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking
## 3 The Proposed Demosaicking Algorithm
### The Calculation Process of Directionally Estimated Pixel Value  
In Step 1, the [PERSON] and [PERSON]'s interpolation equation [15] can be considered as a linear model, as shown in Figure 4a. The linear color model can be replaced by MLRI [21]. The locations of R and B are symmetrical and the amount of R pixels and B pixels are same. To simplify the explanation, we explain the estimation of the R pixel values in the horizontal direction in detail, as shown in Figure 4b.  
Figure 4: _Cont._  
Figure 3: The outline of the proposed algorithm.  
The linear model in [PERSON]'s interpolation equation can be expressed as:  
\[R_{i,j}^{\rm H}=(R_{i,j-1}+R_{i,j+1})/2+(2\times G_{i,j}-G_{i,j-2}-G_{i,j+2})/4, \tag{6}\]  
where \(R^{\rm H}\) is the horizontally estimated R pixel value, the suffix \((i,j)\) represents the target pixel coordinates. This interpolation equation can be considered the linear interpolation, which is shown in Equation (7):  
\[R_{i,j}^{\rm H}-G_{i,j}=(R_{i,j-1}-G_{i,j-1}^{\rm H})/2+(R_{i,j+1}-G_{i,j+1}^{ \rm H})/2. \tag{7}\]  
Here \(G^{\rm H}\) is the G pixel value horizontally estimated at the R pixel and \(G^{\rm H}\) is calculated as follows:  
\[G_{i,j-1}^{\rm H}=(G_{i,j-2}+G_{i,j})/2, \tag{8}\]  
\[G_{i,j+1}^{\rm H}=(G_{i,j}+G_{i,j+2})/2. \tag{9}\]  
Similarly, we can calculate \(G^{\rm H}\) and \(G^{\rm V}\) at the R and B pixels. MLRI uses the tentative estimations to replace of these estimated G pixel values as shown in Figure 4b. As a result, Equation (6) can be rewritten as:  
\[R_{i,j}^{\rm H}-\overset{\sim}{R}_{i,j}^{\rm H}=(R_{i,j-1}-\overset{\sim}{R} _{i,j-1})/2+(R_{i,j+1}-\overset{\sim}{R}_{i,j+1})/2, \tag{10}\]  
where \(\overset{\sim}{R}^{\rm H}\) is the tentative estimated R image.  
To sum up, the horizontally estimated G pixel value at the location of R pixel can be calculated by Equations (8) and (9) at first. Then the interpolated \(G^{\rm H}\) image is used as the guided image. As a result, the tentative estimation \(\overset{\sim}{R}^{\rm H}\) is generated by guided filter. The residual can be calculated after subtracting the tentative estimation \(\overset{\sim}{R}^{\rm H}\) value from the observed R pixel values. After that, residuals are interpolated by Equation (10) at each pixel. We use the residuals to add the tentative estimation \(\overset{\sim}{R}^{\rm H}\) and generate the horizontally estimated R pixel value \(R^{\rm H}\).  
\(R^{\rm V}\), \(B^{\rm H}\), and \(B^{\rm V}\) at G pixel can be similarly generated by Step 1.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Noise Robustness Test  
The main objective of this paper is to showcase the potential of the modified Attention U-Net for seismic velocity modeling. Consequently, all previous numerical simulations were conducted in a noise-free environment. However, in practical applications, recorded microseismic data often contain significant amounts of noise. Therefore, in the final numerical test, Gaussian noise with a signal-to-noise ratio (SNR) of 15 was added to the multi-event test dataset of the multi-layer random stratigraphic models to evaluate the model's robustness to noise. Figure 19 demonstrates that even with the presence of added  
Figure 18: Comparison of modeling results using different methods: (**a**) FWI result with linear initial model; (**b**) modeling result from this paper; (**c**) FWI result with (**b**) as initial model; (**d**) true velocity model.  
noise, the model can still accurately predict simple two-layer velocity models. In the modeling results of multi results of multi-layer velocity models, the velocity distribution generally remains accurate, although there is a noticeable deviation in representing the undulations of deeper geological layers. Future research endeavors will focus on improving microseismic data denoising techniques and fortifying the model against complex geological structures' noise interference.
# An Understanding of the Vulnerability of Datasets to Disparate Membership Inference Attacks
## 3 Results of Vulnerability Classification
### Proportion of Binary Features  
The proportion of binary features was included in the dataset feature set to understand how different types of features and different ratios of feature types can affect dataset vulnerability to membership inference attacks. Understanding how these types and ratios of feature types relate to vulnerability can assist data owners in the development and setup of their datasets dependent on security vs. utility needs. The proportion of binary features provides the ratio of the number of binary features to the total number of features. Table 8 shows that vulnerable datasets have, on average, a higher number of binary features. In addition, the largest diversion occurs in the upper 50% of the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Descriptive Statistic** & **Full Dataset** & **Non-Vulnerable** & **Vulnerable** \\ \hline \multirow{6}{*}{**Average of Label**} & **Mean** & 65.17 & 17.45 & 137.01 \\ \cline{2-5}  & **Standard Deviation** & 101.79 & 19.58 & 129.68 \\ \cline{2-5}  & **Minimum** & 2.14 & 2.14 & 2.31 \\ \cline{2-5}  & **25\% Quartile** & 13.13 & 11.50 & 18.92 \\ \cline{2-5}  & **50\% Quartile** & 18.92 & 18.92 & 23.77 \\ \cline{2-5}  & **75\% Quartile** & 23.77 & 18.92 & 273.40 \\ \cline{2-5}  & **Maximum** & 325.20 & 325.20 & 325.20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Descriptive Statistic** & **Full Dataset** & **Non-Vulnerable** & **Vulnerable** \\ \hline Mean & 0.21 & 0.13 & 0.34 \\ \hline Standard Deviation & 0.49 & 0.32 & 0.65 \\ \hline Minimum & 0.00 & 0.00 & 0.01 \\ \hline
25\% Quartile & 0.01 & 0.01 & 0.06 \\ \hline
50\% Quartile & 0.05 & 0.02 & 0.24 \\ \hline
75\% Quartile & 0.28 & 0.10 & 0.31 \\ \hline Maximum & 5.36 & 4.32 & 5.36 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Descriptive statistics of the _width ratio after one-hot encoding on the class-based data subset_ feature for the overall dataset, the non-vulnerable observations, and the vulnerable observations.
## 1 Introduction  
Laser in-situ keratomileusis (LASIK) is a commonly performed surgical procedure used to correct the refractive error, and while visual outcomes after surgery tend to be excellent, a potential side effect of the procedure is the onset of persistent dry eye (DE) symptoms. The Patient Reported Outcomes with LASIK (PROWL) study reported that up to 27% of individuals reported mild or greater DE symptoms six months after LASIK (58 of 216), with new DE symptoms reported in 20% of patients (23/118) [1]. Such figures, within the published range of 20-55% occurrence, have been reported in several retrospective studies [2; 3; 4; 5; 6; 7; 8]. Post-LASIK DE symptoms were initially attributed to ocular surface dryness; however, recent evidence suggests that these symptoms may result from corneal nerve damage induced by LASIK; in fact, a recent review highlighted that corneal nerve density might not return to baseline levels, even years after surgery [9].  
Further support for the role of nerve dysfunction in post-LASIK DE comes from the use of neuropathic pain descriptors in many individuals. The concept of neuropathic pain as applied to the eye represents a manifestation of pathological neuroplasticity of the trigeminal somatosensory system and higher pathways associated with the spontaneous firing of primary afferents (peripheral sensitization), trigeminal, and/or higher sensory neurons (central sensitization) [10]. Specific DE-related complaints in post-LASIK patients include spontaneous burning pain, pain evoked by wind or light (i.e., hyperalgesia and allodynia), and discordance between ocular pain symptoms and signs of corneal disease [11]. While most individuals experience only mild symptoms, some individuals experience more severe symptoms that can negatively impact the quality of life [1]. Even further, some patients experience such severe pain that they must pursue invasive procedures, including trigeminal nerve stimulation or intrathecal drug delivery, to obtain relief from pain [12].  
No current strategies exist to prevent the development of DE symptoms post-LASIK. However, given that DE symptoms, due to LASIK resemble persistent postoperative pain (PPOP), which is known to occur after other surgical procedures, strategies used to prevent PPOP may also be useful to patients undergoing LASIK [13]. Pre-emptive analgesia is one such strategy, which entails anti-nociceptive treatment around the time of surgery to prevent the onset of post-surgical chronic pain [14]. Gabapentin and pregabalin (\(\alpha 28\) ligands) are the most commonly used agents in this regard, and have been found to reduce the incidence and severity of persistent pain after many other surgeries, including mastectomy, thoracotomy, and hernia repair [14]. However, it is not known whether \(\alpha 28\) ligands can decrease the presence of painful DE symptoms after LASIK. Based on this rationale, we designed a pilot study to assess whether preemptive analgesia with pregabalin could reduce the incidence of post-LASIK painful DE symptoms.
## 3 Results  
### Photosynthetic and Carbon-Lptake Characteristics  
The diurnal dynamics of NEE, GEP and NPP were similar and sharp \"u\" curves were predominant for all land uses (Figure 3). The ecosystem NEE of the three use patterns showed little difference in daytime, whereas during the night, the NEE of the cultivated ecosystem was significantly higher than that of grazed steppe and grazing-excluded steppe (Figure 3a). The GEP showed a noon break under three different uses between 12:00 and 1:00 and the GEP peak before noon was slightly higher than that in the afternoon (Figure 3b). The noon break was most evident in the cultivated land-use treatment.  
Figure 3: Daily variations of net ecosystem exchange (NEE), gross ecosystem productivity (GEP) and net primary production (NPP) on the steppes under different use patterns during the peak growing period. (**a**) NEE; (**b**) GEP; (**c**) NPP.  
The accumulated daily GEP of the three use patterns of steppe varied between approximately 30\(-\)35 g CO\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\). In comparison with the grazing steppe, the cropping and grazing-excluded steppes tended to increase GEP; in particular, the cropping significantly increased by 12.7% (Figure 4, \(p<\) 0.05). However, cropping also significantly decreased the ecosystem NPP compared with grazed steppe by 13.0% (Figure 4, \(p<\) 0.05). There was no significant difference between the grazing-excluded steppe and the grazed steppe (Figure 4, \(p>\) 0.05). The impact of steppe-use patterns on net ecosystem productivity (NEP) was similar to that on NPP; cultivated steppe was significantly lower than grazing and grazing-excluded steppe (Figure 4, \(p<\) 0.05). The 51.2% decrease of NEP on the cultivated steppe was larger than the decrease of NPP (19.7%).
## 4 Results
### Generating Landslide Susceptibility Maps  
The RF and BRT models were used to develop LSMs in the study area. The LSMs were prepared by generating landslide susceptibility indices (LSIs) and reclassifying the class. The LSIs were calculated based on the trained RF and BRT models. Using the natural breaks method, the LSMs were reclassified into five susceptibility classes: very high, high, moderate, low, and very low (Figure 4). Overall, the distribution of LSI for each susceptibility class was similar between the LSM produced by RF (RF LSM) and that produced by BRT (BRT LSM). The \"high\" and \"very high\" susceptibility classes covered about 30% of the total area. The RF model had a value of 34.69%, and the BRT model had the lower value of 31.11%.  
Figure 3: Analysis of the receiver operating characteristic (ROC) curve for the two landslide susceptibility maps: (**a**) success rate curve using the training dataset and (**b**) prediction rate curve using the validation dataset.  
The LSMs produced from the two models were validated based on the landslide density (LD) of each susceptibility class on the LSMs. The LD is the ratio of the percentage of landslide pixels to the percentage of all pixels for each susceptibility class shown on the map [56]. LD was calculated by overlaying the five LSMs and the landslide inventory map. Generally, for the study area, the value of LD increased gradually, from very low to very high susceptibility (Table 3). At the \"very high\" class, the RF and BRT models had LD values of 3.799 and 2.721, respectively. Overall, the models used in this study are suitable for LSM.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 4 NN-Based 8 FSK Demodulator and Its Evaluation  
To evaluate the impact of channel gain estimation error on covert signal demodulation (consequently the imperfect SIC) we used a test scheme presented in Figure 10. This scheme shows that correlation-based (Figure 10, case (a)) and NN-based (Figure 10, case (b)) demodulators were tested under the same conditions, including hard and soft decoding.  
confidence of each bit, according to the received symbols. The commonly used method of soft-decision is the estimation method, based on the logarithmic likelihood ratio (LLR). When the parameters of the channel and noise are unknown, the unknown parameters can be replaced by their maximum likelihood estimates (this leads to the so-called generalized likelihood ratio (GLR)). The output of the demodulator is then transferred to the channel decoder. As a result, it can improve the posterior probability of data decoding and reduce the bit error rate.  
The block diagram of the 8 FSK hard and soft demodulator is presented in Figure 11. The MFSK modulated signal \(s_{2}(t)\) for the order M=8 is composed of eight elementary components [11], as shown in Equation (6):  
\[s_{2,j}(t)=\big{[}s_{2,1}(t),s_{2,2}(t),\ldots,s_{2,j}(t)\big{]},\quad j=1, \ldots,M \tag{6}\]  
Every elementary component is given by [28], as shown in Equation (7):  
\[s_{2,j}(t)=\exp(-j2\pi mf_{j}t) \tag{7}\]  
and frequency separation equals \(\Delta f_{0}\).  
The output of the \(j-th\) correlator \(w_{i}\) is given by [28], as shown in Equation (8):  
\[w_{j}=\bigg{|}\frac{1}{T}\int_{T}\hat{s}_{2}(t)\cdot conj\big{(}s_{2,j}(t) \big{)}dt\bigg{|}. \tag{8}\]  
The hard-decision calculator determines the maximum value among all \(w_{j}\); hence, the symbol value \(c_{i}\)[12], as shown in Equation (9):  
\[c_{i}=max(w_{j}^{2}). \tag{9}\]  
Each received symbol represents \(log_{2}M\) coded bits. The soft-decision of coded symbols are generated by the demodulator, in the form of a log-likelihood ratio (LLR), and passed to the decoder. The LLR of the \(j\)th coded bit \(b_{j}\) is defined as [12,28], as shown in Equation (10).  
\[L_{j}\triangleq\log\left(\frac{\Pr[b_{j}=0|\mathbf{w}]}{\Pr[b_{j}=1|\mathbf{w}]}\right) \tag{10}\]  
where \(\mathbf{w}=[w_{1},w_{2},\ldots,w_{M}]\) is a vector of the outputs of all correlators.  
In our case, because of unknown channel parameters, we use GLR metrics given by Equation (11):  
\[L_{j}(\mathbf{w})=log\left(\frac{\sum_{i:b_{j}=1}\left(\sum_{k=0,\,k\
eq i}^{M-1} w_{k}^{2}\right)^{-M}}{\sum_{i:b_{j}=0}\left(\sum_{k=0,\,k\
eq i}^{M-1}w_{k}^{2} \right)^{-M}}\right) \tag{11}\]  
Figure 11: Block diagram of the 8 FSK correlator-based hard and soft demodulator.
## 4 Dynamic Model
### Fractional Order PID Controller  
A fractional order controller (\(PI^{\lambda}D^{\delta}\)) is derived by the differential equation  
\[u(t)=k_{p}e(t)+k_{l}D_{l}^{-\lambda}e(t)+k_{d}D_{l}^{\delta}e(t), \tag{42}\]  
where \(e(t)\) is the error term. The continuous state transfer function is obtained using the Laplace transform (\(\mathcal{L}\)) on Equation (42):  
\[G_{c}(s)=k_{p}+k_{l}s^{-\lambda}+k_{D}s^{\delta}. \tag{43}\]  
As shown in Equation (43), the design of the FOPID controller requires three gain parameters (\(k_{p},k_{l},k_{D}\)) and two order values (\(\lambda,\delta\)). As mentioned earlier, the FO controller provides better flexibility and adaptability to achieving control objectives as it is a generalised form of traditional Integer order calculus. Clearly, \((\lambda=1),(\delta=1)\) is a case of an integer order controller.  
The simulations on the 2 DSFJ and 2 DSFL controlled by the IOPID controller and FOPID controller are carried out on basis of the parameters provided in Section 5 with the limitation of maximum torque and input current (i). As stated earlier, \(N\) is the order of approximation on which the desired performance of the approximated transfer function strongly relies. As mentioned earlier, lower values of \(N\) cause ripples in both phase and gain behaviours but are simpler to approximate, and vice versa; this can be removed by increasing the order, leading to computationally heavier approximation. The fractional order transfer function of the controller is then approximated using an Outsalup approximation of the integer order transfer function. It turns out that the order of the integer order transfer function is \(15\). On converting the transfer function into state space notation, the state matrix is of the order \(15\times 15\). In order to reduce complexity, the order of the obtained transfer function is reduced using the inbuilt MATLAB model reducer toolbox. The \"truncate\" state elimination method is used in a frequency range of \((\omega_{l}=0.01,\omega_{h}=10)\) with an absolute tolerance of \(1\times 10^{-6}\). The order of the reduced transfer function is chosen to be \(4\).  
The transfer function obtained after the reduction is given as  
\[G_{c}(s)=\frac{2.131s^{4}+2.08s^{3}+0.2952s^{2}+0.0046s-6.29\times 10^{-05}}{s^{ 4}+0.8902s^{3}+0.1049s^{2}+0.001004s-1.797\times 10^{-04}}. \tag{44}\]  
Figures 2 and 3 show the frequency response for the fractional order controller, the reduced version of the FO controller and the integer order controller. As our working limit is in frequency range of [0.01, 10], form Figure 2, it can be stated that the reduced transfer function of the FOPID and the original transfer function of the FOPID have nearly the same responses. Both provide significant gain and phase margins to the plant model, whereas in the case of the integer order PID controller, from Figure 3, it can be stated that there are insufficient margins in the desired frequency range of [0.01, 10]. Further comparisons and discussions are shown in Section 5.