esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In areas at the junction of plains and mountains, such as western Hebei, northern Beijing, and older residential areas in the study region, provide effective heavy rain warnings, develop emergency plans for different risk levels, and ensure timely relocation and disaster relief efforts. Additionally, strictly regulate river channel filling, install protective river facilities, stabilize unstable slopes, and build check dams downstream of gullies to reduce the impact of secondary disasters like landslides, thereby maximizing public safety [54]. 3. Provinces and cities should enhance underground network infrastructure, expedite drainage network upgrades in urban areas, boost flood control and drainage capabilities, develop low-impact rainwater systems, and strengthen sponge city construction to better cope with future heavy rain challenges. The choice of model is crucial to the accuracy of heavy rain disaster risk assessment. Compared with the SCV-RF assessment model, the MAML-RF model proposed in this study significantly improves simulation accuracy and training efficiency. This demonstrates that the MAML-RF model possesses practical applicability and operability, for real-world heavy rain disaster risk assessments. However, the RF algorithm can only fully leverage its ensemble learning advantages when data are abundant and evenly distributed. Due to the relatively low actual occurrence frequency of relatively high-risk and high-risk heavy rain disaster events, there is a scarcity of high-risk sample data. Therefore, future work should focus on enriching the database of heavy rain disaster loss events and increasing the number of disaster risk factors to drive more accurate simulations with the MAML-RF model, further improving the accuracy of heavy rain disaster risk simulations.
# Simple and Efficient Non-Contact Method for Measuring the Surface of a Large Aspheric Mirror
## 2 Methodology  
A non-contact probe was developed to co-operate with the laser tracker to scan and measure the large optical surface. As shown in Figure 1, the probe comprises a corner cube retroreflector, an air-bearing pad, the preload, and the air tube.  
A retroreflector is used as a co-operative target for co-ordinate measurement of the laser tracker, and it can be a corner cube retroreflector or an SMR.  
An air-bearing pad is used to isolate the retroreflector from the testing surface. The air-bearing uses a thin film of compressed air (an air gap of several microns) to support a load. Therefore, there is no physical contact between moving parts and the optical surface. The air-bearing provides superior scanning performance compared to traditional mechanical scanning: it has no friction and will not be worn by friction. The fluid film acts to average out small-scale errors (such as residual micrometer-size abrasive particles), so the measurement is less sensitive to surface contaminants. In addition, the distance between the retroreflector's vertex and the pad's air floating surface is a constant value. Therefore, it does not suffer from the centering and sphericity issues of a traditional SMR.  
Some factors must be considered and weighed during non-contact probe design, including pad form and size, preload, and air pressure.  
Figure 1: Schematic diagram of the non-contact probe.
## 2 Materials and Methods
### Pilots Tones and Correction Coefficients  
The signal transmitted in the UAC channel suffers from time dispersion, which manifests itself as selective fading of the signal spectrum. To reduce the negative effects of this phenomenon on the information detection process, some of the OFDM symbols' spectra are used as reference pilot tones to equalise the values of the subcarriers of the symbols which are carrying the data. Every second time domain OFDM symbol is transmitted as a pilot symbol and all of its OFDM subcarriers carry data that is known in the receiver. Such a pilot tone pattern is shown in Figure 1.  
For each subcarrier, a correction coefficient \(C_{H}\) is calculated on the basis of a complex value of a given subcarrier at the transmitter and receiver sides [7]:  
\[C_{H}\left[k,f_{n}\right]=\frac{H_{TX}\left[k,f_{n}\right]}{H_{RX}\left[k,f_{n} \right]},\quad f_{n}=0,2,4,\ldots,N-2;\quad k=0,1,2,\ldots,K-1 \tag{1}\]  
where:  
\(C_{H}\left[k,f_{n}\right]\)--correction coefficient for pilot tone number \(f_{n}\) and symbol number \(k\),  
\(H_{TX}\left[k,f_{n}\right]\)--value of transmitted pilot tone number \(f_{n}\) and symbol number \(k\),  
\(H_{RX}\left[k,f_{n}\right]\)--value of received pilot tone number \(f_{n}\) and symbol number \(k\),  
\(N\)--number of OFDM subcarriers,  
\(K\)--number of OFDM symbols.  
Each \(H_{RX}\left[k+1,f_{n}\right]\) subcarrier is corrected by the mean value of the two neighbouring \(C_{H}\) coefficients: for the subcarrier in the preceding and following OFDM symbols (except for the \(H_{RX}\left[K-1,f_{n}\right]\), for which only \(C_{H}\left[K-2,f_{n}\right]\) is taken into account). Thus, the equalised values of subcarriers are calculated as:  
\[\hat{H}_{RX}\left[k+1,f_{n}\right]=H_{RX}\left[k+1,f_{n}\right]\cdot\left( \frac{C_{H}\left[k,f_{n}\right]+C_{H}\left[k+2,f_{n}\right]}{2}\right) \tag{2}\]  
Such a pilot tone pattern allows the influence of ISI and noise on the received signal to be suppressed, and thus significantly improves a BER of data transmission.
# A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications
###### Abstract  
With the continuous development of indoor positioning technology, various indoor applications, such as indoor navigation and emergency rescue, have gradually received widespread attention. Indoor navigation and emergency rescue require access to a variety of indoor space information, such as accurate geometric information, rich semantic information and indoor spatial adjacency information; hence, a suitable 3D indoor model is needed. However, the available models, such as BIM and CityGML, mainly represent geometric and semantic information of indoor spaces, and rarely describe the topological adjacency relationship of interior spaces. To address the requirements of indoor navigation and emergency rescue, a simplified 3D indoor model is proposed in this research. The building components and indoor functional spaces of buildings are described in a simplified way. The geometric and semantic information are described based on CityGML, and the topological relationships of indoor adjacent spaces are represented by CityGML XLinks. While describing the indoor level of detail (LOD) of buildings in detail, the model simplifies building components and indoor spaces, which can preserve the characteristics of indoor spaces to the maximum extent and serve as a basis for indoor applications.  
BIM; IFC; 3D GIS; CityGML; indoor model; indoor applications +
Footnote †: journal: Accepted: 13 October 2020; Published: 16 October 2020
# A Watermark-Based In-Situ Access Control Model for Image Big Data
## 3 Problem Formulation
### Attack Models
#### 3.2.4 Physical Copy Attack  
In image big data distribution, the most difficult attack to defend against is physical copy attack, in which images are copied by physical manners such as screen capture or outside photo shooting. After accessors gain access to images, those images are totally displayed and out of (access) control. This attack must be tackled, especially if certain areas or layers in images must remain confidential. It cannot be defended against by access control because it is a kind of proactive defense before events.  
This attack can be traced back by watermark-based schemes for further provenance, as that is a kind of reactive defense after events.  
Proposition 3: _Physical copy attack cannot be defended against by any access control schemes, but it can be traced back to the source of image leakers, which is called provenance. The provenance can only be achieved by associated watermarks in images._  
Proof: As images can be uncovered and viewed by authorized accessors, physical copy attack such as screen capture and photo shooting is also possible.  
The provenance can be achieved by embedding watermarks in images, as watermarks are also carried by images during and after physical copy attack.  
Only when some watermarks associated with the identity of originators are embedded with uncovered images can the provenance of originators who exposed the images be accomplished from leaked images.
# Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
## 2 Materials and Methods
### MI-EEG Recognition Based on MTF-CSP
#### 2.2.2 Multi-Time Window and Multi-Frequency Band CSP Strategy for Feature Extraction  
This paper proposes a multi-time and multi-frequency CSP (MTF-CSP) to extract features for MI information. In the MTF-CSP, n time windows of length w are intercepted from the original signal of 3 s, and the interception step length is one s second. For a single time window signal, the sub-band time-amplitude signals of seven frequency bands (u, u-1, u-2, \(\beta\), \(\beta\)-1, \(\beta\)-2, \(\beta\)-3) are separated by inputting the original data into different band-pass filters. These seven frequency bands include the u rhythm frequency band (8-13 Hz) and the \(\beta\) rhythm frequency band (13-30 Hz), with significant MI frequency features as well as the refined frequency bands (u-1, u-2, \(\beta\), \(\beta\)-1, \(\beta\)-2, \(\beta\)-3) to simultaneously capture the global and local frequency features of the signal. The CSP algorithm was then used to extract the features of each frequency band for a single-time window signal in order to obtain a set of eigenvalues with the length of 2 m, which is a feature vector that maximizes the difference between the two classes of signals by mapping the signal to another space. The eigenvalues of the seven frequency bands are connected end to end to form a set of vectors that can be used as the features of a single MI-EEG signals in a single-time window. Thus, the features of the seven frequency bands filtered from a single window can finally combined into a vector with a length of 2 m \(\times\) 7, as shown in Figure 3. The statistical results of the EEG features extracted from the seven frequency bands are also visualized in Figure 4 after dimension reduction takes place.  
Figure 4: The statistical visualization scatter diagram of the multi-band CSP features of nine subjects.  
Figure 3: Multi–band features fusion based on CSP.
## 3 Results
### Tissue RAS-Regulating Enzyme Activities
#### 3.7.2 Renal Medulla  
Figure 12 illustrates the specific enzymatic activity of various peptidases involved in RAS regulation in the renal medulla of WKY and SHRs under the different beverage treatments (TW, NAB, TW/NAB, AB and TW/AB).  
Regarding ACE activity (Figure 12A), no statistically significant differences are observed between treatment groups for either WKY or SHRs, although higher levels of ACE activity are found in WKY animals compared to SHRs.  
Figure 12: _Cont._  
Regarding soluble and membrane-bound APA specific activities, Figure 12B shows significant differences between treatment groups. Notably, SHRs treated with TW/NAB and TW/AB show a significant increase in soluble APA activity compared to those receiving TW, NAB or AB alone. In WKY rats, a similar trend is observed, where APA activity is significantly higher in the TW/NAB and TW/AB-treated groups relative to the TW-treated ones. Conversely, in the membrane-bound fraction (Figure 12C), no significant differences in APA activity are found between treatment groups in either WKY or SHRs.  
Regarding ASAP activity in the soluble fraction (Figure 12D), WKY and SHRs exhibit contrary trends. Significant decreases in ASAP activity are observed in both WKY animals when treated with TW/NAB, AB and TW/AB compared to TW alone. On the contrary, no significant changes were observed in SHRs. In the membrane-bound fraction (Figure 12E), all activities remain undetectable (ND).  
Figure 12: (**A**) Angiotensin-converting enzyme (ACE) activity, and specific activities of soluble (**B**) and membrane-bound (**C**) aminopeptidase A (APA), soluble (**D**) and membrane-bound (**E**) aspartyl aminopeptidase (ASAP), soluble (**F**) and membrane-bound (**G**) aminopeptidase N (APN), and soluble (**H**) and membrane-bound (**I**) aminopeptidase B (APB) in the renal medulla of Wistar–Kyoto rats (WKY) and spontaneously hypertensive rats (SHRs). Experimental groups included animals provided with tap water (TW), alcohol-free beer (NAB), tap water or alcohol-free beer at their discretion (TW/NAB), alcoholic beer (AB), or tap water and alcoholic beer at their discretion (TW/AB). Results are presented as ACE activity in IU/L, and for APA, ASAP, APN and APB, in picomoles of the corresponding aminoacyl-\(\beta\)-naphthylird hydrolyzed per minute per milligram of protein (mean \(\pm\) SEM; n = 8; * \(p<\) 0.05; **+ \(p<\) 0.01).  
Regarding APN and APB, no significant changes were found in the soluble or membrane-bound activities after the different treatments, either in WKY or SHRs (Figure 12F-I).
## 4 Simulations and Experiments
### Simulations
Supposing that the Gaussian beams follow the same equation and only the parts inside the beam waists are calculated, the ratio of the overlapped region and the maximum volume (within the beam waist) is used to appraise the influence. In Figure 8a, all these curves overlapped together, which means the rotational arm, equaling to \(L=(A^{2}+C^{2})^{1/2}/2\), has no relation to the interfering beams. The common optical path heterodyne grating interferometer is reckoned as a special case of spatially separated configuration with the parameter A = 0. Thus, conclusions in this paper are also suitable for the common optical path configuration. According to Figure 8b, the parameter B, representing the length of diffracted beams, caused more changes of the interfering beams. It could also be attributed to a longer distance which generated a larger offset at a certain rotational angle. Figure 8c clearly illustrates that a bigger beam suffers larger spot deviations. In summary, the simulation results demonstrate the influence of rotational angles and stand-off error on the deviation of measurement beams from the quantitative point of view. It also shows that the intensity of the interference signal is independent of the space between beams (parameters A and C) and is affected by the length of the diffracted beams and the spot diameter (parameters B and D).
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
## 3 Convolutional Neural Network for Image Steganography  
### High-order Transformation  
In image steganography, we argue that we should not only focus on where to hide information, which most traditional methods work on, but we should also focus on how to hide it.  
Most traditional steganography methods usually directly embed hidden information into parts of pixels or transformed correspondances. The transformation regularly occurs in _where to hide_, either actively applied in the steganography method or passively applied because of file format. As a result, the payload capacity is highly related and restricted to the area of the texture-rich part of the image detected by the _handcoded_ patterns.  
DCT-based steganography is one of the most famous transform domain steganography. We can consider the DCT process in JPEG lossy compression process as a kind of one-level high-order transformation which works at a block level, converting each \(8\times 8\) or \(16\times 16\) block of pixel information into its corresponding frequency-domain representation. Even hiding in DCT transformed frequency-domain data, traditional works [3; 12] embed hidden information in mid-frequency band via LSB-alike methods, which eventually cannot be eluded.  
While in contrast, deep convolution neural network makes multi-level high-order transformations possible for image steganography. Figure 4 shows the receptive field of one high-level kernel unit in a demo of a three-layer convolutional neural network. After lower-level features are processed by kernels and propagated through activations along middle layers, the receptive field of final higher-level kernel unit is able to absorb 5 lower-level features of the first layer and form its own higher-level feature throughout the training process.
## 2 The Proposed DCLNN Method for Failure Time Prediction
### Convolutional LSTM Neural Network
#### 2.4.3 CLNN: Combination of CNN and LSTM  
The architecture of an CLNN is shown in Figure 7. In addition, the network structure of an CLNN consists of a convolutional layer, a pooling layer, LSTM units, and two fully connected layers.  
Figure 7: The architecture of a convolutional LSTM neural network (CLNN).
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination
#### 2.5.7 Hilbert-Huang Transform  
The Hilbert-Huang Transform (HHT) method is an adaptive method for analyzing nonlinear and nonstationary signals. It combines the Hilbert Transform method with local feature extraction techniques, enabling the local analysis of signals. Thus, it offers an effective way to extract features in signal processing [44].  
\[H(x(t))=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{x(\tau)}{t-\tau}d\tau \tag{7}\]  
Herein, \(H(x(t))\) notes the signal after the Hilbert Transform, where \(x(t)\) is the original signal and \(\tau\) represents the integration variable.  
\[x(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t) \tag{8}\]  
wherein, \(c_{i}(t)\) represents the \(i\) Intrinsic Mode Function (IMF), and \(r_{n}(t)\) denotes the residue. Synthesizing the steps above, the overall process of the HHT method begins with applying the Hilbert Transform to the signal, followed by the Empirical Mode Decomposition of the transformed signal to yield a series of IMFs.  
Upon calculation, the time domain, frequency domain, and eigenvalue signals are all matrices of size 1 \(\times\) 60, while the time-frequency domain signal is size 24 \(\times\) 60. Through amalgamation, a matrix of size 27 \(\times\) 60 is obtained.
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Specific Case Study  
The case study is an EVCS that charges an urban fleet of 89 electric buses at a sampling interval of 15 min. There are as many chargers as buses. The case and associated bus drop-off times are taken from a real-life example. **The original charging schedule can be seen in Figure A1.**  
The maximum power contracted from the grid, \(P_{\text{Gmax}}\), is limited to 2 MW. The energy that each bus requires for a day, \(E_{B}(n)\), is 960 or 1380 kWh, depending on the bus route. The maximum power that a charger can provide, \(P_{\text{Cmax}}\), is limited to 120 kW. This value is consistent with fast DC charging [29]. Regarding the grid tariffs, four specific daily price profiles are taken from the OMIE webpage [30], with the aim of representing the variability in grid tariffs over a year.
## 4 Data Analysis and Interpretation
### Study Scenario 2: Li-Ion Storage System with Low SoC Level  
In this second study scenario, the Li-Ion Storage System began operating at 20% of the full capacity, prioritizing EV charging, as can be seen in Figure 15b. The objective is to ensure that the lithium-ion battery is used in an electric vehicle through a Vehicle-to-Grid (V2G) system. Figure 15b demonstrates that using the MA filter with FLC reduces variance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **(50\%)** \\ **Initial** \\ \end{tabular} & \begin{tabular}{c} **Variance with** \\ **Compensation** \\ \end{tabular} & \begin{tabular}{c} **Variance with** \\ **Compensation** \\ \end{tabular} & \begin{tabular}{c} **Variance** \\ **Reduction** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Renewable Energy** \\ **Delivered without** \\ **Compensation** \\ **(kWh)** \\ \end{tabular} & \
\begin{tabular}{c} **Renewable Energy** \\ **Delivered with** \\ **Compensation** \\ **(kWh)** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Difference** \\ **(kWh)** \\ \end{tabular} \\ \hline MA & 15.66 & 13.07 & 16.55 & 3.21 & 3.17 & 0.03 \\ RR & 15.91 & 13.65 & 14.2 & 3.21 & 3.15 & 0.05 \\ V2 GSUN & 15.62 & 12.58 & 19.43 & 3.23 & 3.20 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of energy generation were obtained by applying the power smoothing methodologies studied.  
Figure 14: Real-time window size updating is featured in the proposal (V2 GSUN).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Variance of Reduction**} \\ **Frequency (\%)** \\ \hline Case base & 1.5617 \\ MA & 1.6263 \\ RR & 1.5610 \\ V2 GSUN & 1.3782 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of frequency variation when applying different power smoothing methodologies in the V2G context.  
by 16.05%. Figure 16 illustrates the Li-Ion battery's SoC beginning to charge and reaching 60%, focused on its recharge over mitigating power fluctuation and effectively utilizing peaks in renewable energy.  
Figure 15: Experimental test outcomes by applying the V2G concept with the proposed “V2 GSUN” filter method for charging priority: (**a**) GD power. (**b**) PV power compensated with Psc. (**c**) generator frequency, and (**d**) generator line voltages.  
Table 4 shows the results of Study 2, which focuses on smoother energy to reduce the initial percentage of beef to decrease the change in beef production. This method prioritizes charging in electric vehicles, from 20% SoC to 60% SoC. This method makes energy production stable, and the power source adjusts according to the current status of SoC. Outcomes show that the variance with the MA filter method compared to V2 GSUN was reduced by 16.05%. Figure 17 establishes the temporal progress with smoother factors in FLC output WS data.