esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 3 The Channel Calibration Method for Bistatic ISAR System
### Calibration Process for Bistatic ISAR Imaging System  
To illustrate the process clearly, the flow chart of proposed method for bistatic ISAR is shown in Figure 4.  
The specific steps are as follows:  
1. Collect the \(i\)-th baseband echo of calibration satellite \(s_{i\_ball}\), and compensate the intra-pulse velocity to eliminate the influence of high speed motion.
2. Use a matched filter to achieve the pulse compression, and obtain the peak position \(P_{i}\) and the corresponding phase \(\varphi_{i}\).
3. Baseband echo data of \(s_{i\_ball}\) is multiplied by \(e^{j(\varphi_{i}-\varphi_{1})}\) to eliminate the influence of the random phase, the echo data center is aligned according to the peak position difference compared with the first period \(\Delta P=P_{i}-P_{1}\), and the adjusted echo \(s_{r\_i}\) is obtained.
4. Repeat step 1-3 until the \(N\)-th baseband echo signal is adjusted.
5. Coherently accumulate the adjusted baseband signals in time-domain and obtain the accumulated signal \(s_{Acc}(t)=\sum\limits_{i=1}^{N}s_{r\_i}(t)\).
6. Use MATLAB to generate the standard LFM baseband signal \(s_{b}(t)\) with the same time width, bandwidth, sampling rate and number of sampling points.
7. Generate the calibration coefficient: \(C_{bj}=FFT[s_{b}(t)]/FFT[s_{Acc}(t)]\).
8. Before pulse compression, multiply the echo frequency spectrum Equation (8) with the calibration coefficient to achieve the channel calibration.  
The calibration coefficient needs to be generated in advance. The characteristics of the channel are influenced by temperature, weather, humidity and other factors, it is not fixed. Therefore, the calibration coefficient needs to be updated before imaging.  
Figure 4: Channel calibration flow chart for a bistatic ISAR system based on a calibration satellite.
## 2 System Modeling
### The Dynamics of the Lifting Mechanism  
As observer from Figure 1, \(O\) denotes the gyration center; \(O_{1}\) and \(O_{3}\) stand for rotation centers of the lower and upper ears of the hydraulic cylinder severally; \(O_{2}\) denotes the barycenter of the lifting mechanism; \(\theta\) stands for the rotary angle of the lifting mechanism arm, whose range is 0\(\sim\)90\({}^{\circ}\); \(u\) stands for the control input; \(Q_{1}\) and \(Q_{2}\) stand for the return flow and supplied flow of the hydraulic cylinder severally; \(P_{r}\) and \(P_{s}\) stand for the return pressure and supply pressure severally; \(G\) and \(F\) stand for the force acting on the arm and the gravity of the arm severally. In addition, define \(OO_{1}=d_{1}\), \(OO_{2}=d_{4}\), \(OO_{3}=d_{3}\), \(O_{1}O_{3}=d_{2}\), \(O_{1}O_{3}=d_{2}\), \(O_{1}O_{3}=d_{2}\), \(\angle O_{3}OO_{2}=\theta\), \(\angle O_{3}OO_{1}=\theta_{0}\), \(\angle O_{3}OO_{2}=\theta_{0}\) and \(\angle O_{1}O_{3}O=\alpha\).  
The dynamics of the lifting mechanism is expressed by  
\[\tilde{J}\theta\ =\ Ed_{3}\sin(\alpha)-mgd_{4}\cos(\beta_{0}+\theta)-A_{f}S_{f}( \hat{\theta})-B\hat{\theta}+\Delta(t) \tag{1}\]  
where \(J\) stands for the rotary inertia; \(m\) stands for the mass of the lifting mechanism arm; \(F=A_{1}P_{1}-A_{2}P_{2}\), where \(P_{1}\) and \(P_{2}\) stand for the pressure values of two chambers in the hydraulic cylinder severally, \(A_{1}\) and \(A_{2}\) stand for the effective areas of two chambers in the hydraulic cylinder severally; \(A_{f}\) and \(S_{f}\) stand for the amplitude and approximated shape function of the Coulomb friction; \(B\) stands for the viscous friction coefficient; \(\Delta(t)\) stands for the unmodelled disturbances.  
By applying the cosine law, it yields  
\[d\ =\ \sqrt{d_{1}^{2}+d_{3}^{2}-2d_{1}d_{3}\cos(\theta_{0}+\theta)} \tag{2}\]  
Defining the cylinder displacement as \(x_{s}\ =\ d-d_{2}\), one has  
\[\frac{\partial x_{s}}{\partial\theta}\ =\ \frac{d_{1}d_{3}\sin(\theta_{0}+ \theta)}{\sqrt{d_{1}^{2}+d_{3}^{2}-2d_{1}d_{3}\cos(\theta_{0}+\theta)}} \tag{3}\]  
Figure 1: The schematic diagram of the lifting mechanism.  
Based on the sine law, there is  
\[\frac{d_{1}}{\sin(\alpha)}\ =\ \frac{d}{\sin(\theta_{0}+\theta)} \tag{4}\]  
Involving the Equations (1)-(4), it is easy to obtain that  
\[\dot{J}\ =\ \frac{\partial x_{s}}{\partial\theta}(A_{1}P_{1}-A_{2}P_{2})- mgd_{4}\cos(\beta_{0}+\theta)-A_{f}S_{f}(\dot{\theta})-B\dot{\theta}+\Delta(t) \tag{5}\]
# Transaction Mechanism Based on Two-Dimensional Energy and Reliability Pricing for Energy Prosumers
## 4 Case Study
### Performance Evaluation
#### 4.2.1 Prosumer Revenue  
Figure 8 shows the revenue enhancement by the conventional pricing and the proposed ERT when the size of EES is increased to 30% of the installed generation capacity. The EES is operated to maximize prosumer revenue by reducing the error between the generated forecasting and the actual generation, so the revenue is enhanced in the dynamic prices. The revenue converges on a 4% enhancement over the conventional pricing, but the value in the proposed ERT grows continuously, up to a value three times higher than that under the conventional pricing. This shows that the proposed ERT is designed to work well in relation to prosumer dynamics, giving the prosumer greater motivation.
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
## 5 WMS-2f Measurement and Correction  
Wavelength modulation spectroscopy (WMS) was applied to restrain a variety of background noise during measurement. Due to the low absorption of CO\({}_{2}\) in the near-infrared, the second harmonic (2f) signal was directly proportional to concentration [39]. When the optical path and pressure were constant, the WMS-2f peak amplitude was related to gas concentration and the wavelength modulation index \(m\), which is dependent on the modulation current \(I_{\text{mod}}\). Therefore, an appropriate choice of \(I_{\text{mod}}\) was required to improve the detection precision.  
Figure 10: Absolute value of the relative error changes with concentration and temperature. (**a**) Before and (**b**) after compensation.  
Figure 13 merges the inferred concentration values of all measurements at temperature 298-338 K and concentration 10-20%. The error bars represent the repeatability of three repeated experiments by SD. After compensation, the normal repeatability of CO\({}_{2}\) values of DA and WMS were 5.55% and 1.79% at a SD (1\(\sigma\)). The mean relative error, which was caused by the uncertainties of temperature, pressure, and absorption length measurements, decreased from 3.06% to 1.17%. For the wavelength modulation, the mean relative errors were 1.44% and 1.20%, before and after compensation respectively. The relative standard deviation (
## 3 Experiment Setup
### Classifier Evaluation Metrics  
The ANSI/AAMI EC57:2012 R2020 standard [25] specifies the indicators that should be used to evaluate the performance of arrhythmia classifiers, including accuracy, precision or positive hits, sensitivity (or recall), and specificity, which can be calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{11}\]  
where \(TP\) indicates true positives, \(TN\) indicates true negatives, \(FP\) indicates false positives and \(FN\) indicates false negatives.  
\begin{table}
\begin{tabular}{c|c c} \hline \hline \multirow{2}{*}{**Class**} & **Original** & **Training Data with Data** \\  & **Training Data** & **Augmentation** \\ \hline N & 71,503 & 71,503 \\ S & 1314 & 30,000 \\ V & 5604 & 10,000 \\ \hline Total: & 78,421 & 111,503 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data augmentation for training classes in the intrapatient paradigm.  
\begin{table}
\begin{tabular}{c|c c} \hline \hline \multirow{2}{*}{**Class**} & **Original** & **Training Data with Data** \\  & **Training Data** & **Augmentation** \\ \hline N & 4781 & 45,781 \\ S & 975 & 30,000 \\ V & 3786 & 10,000 \\ \hline Total: & 50,542 & 85,781 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data augmentation for training classes in the interpatient paradigm.  
Additionally, as frequently observed in the literature and recommended for evaluation in the case of highly unbalanced databases, the harmonic mean between precision and sensitivity, called the \(F1\ Score\), can be used:  
\[F1\ Score=2.\frac{Precision*Recall}{Precision+Recall} \tag{12}\]  
Each of these indicators contribute to a meaningful interpretation of the automatic ECG signal classification results:  
* Accuracy: Accuracy is the percentage of correct diagnoses of cardiac arrhythmias (or normal cases). Importantly, in unbalanced databases, such as the MIT-BIH, the majority of cases are the normal class, requiring other indicators to better evaluate the classifier.
* Precision: Precision is important in scenarios where the cost of false positives is high (for example, an incorrect diagnosis of arrhythmia). High precision means that the model produces fewer false positives.
* Recall or Sensitivity: Recall is essential in situations where it is important to minimize false negatives, i.e., ensure that arrhythmias are detected, even if there are some false positives.
* Specificity: Specificity is important in evaluating false positives, indicating whether the model erroneously classifies a healthy patient as having an arrhythmia.
* F1 score: The F1 score is a metric that balances the ability of the model to correctly identify arrhythmias (recall) with the lowest possible number of false positives (precision). A high F1 score indicates a good balance between detecting arrhythmias and avoiding false positives.
## 3 Results and Discussion
### Analysis of Volatile Components in NFC Goji Juice
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**CAS**} & **Molecular** & **Retention** & **Retention** & **Drift** & **Fresh-Processed** & **Processed** \\  & & & **Formula** & **Index** & **Time (s)** & **Time (s)** & **Juice** & **Juice** \\ \hline
1 & Ethanol-M & C64175 & C2H0O & 46.1 & 489.9 & 1.0418 & 0.86 & 0.61 \\
2 & Pentanal-M & C110623 & C3H0O & 86.1 & 700.1 & 1.1887 & 0.42 & 0.57 \\
3 & Nonanal & C124196 & C3H8O & 142.2 & 1104.3 & 1.4808 & 0.24 & 0.18 \\
4 & 2-Butanone & C78933 & C4H8O & 72.1 & 580.5 & 1.0587 & 1.32 & 1.4 \\
5 & Ethylmethylpropanoate & C79621 & C4H1O2 & 116.2 & 769.7 & 1.1909 & 0.34 & 0.33 \\
6 & 1-Butanol & C71363 & C4H1O & 74.1 & 649.9 & 1.176 & 1.07 & 1.16 \\
7 & 2-Hexenal-M & C505577 & C4H0O & 98.1 & 846.2 & 1.1822 & 1.56 & 1.63 \\
8 & Propyl acetate & C109604 & C3H10O2 & 102.1 & 717.6 & 1.172 & 0.46 & 0.49 \\
9 & Methyl acetate & C79209 & C3H4O2 & 74.1 & 510.2 & 1.1989 & 0.65 & 0.87 \\
10 & Ethanol-D & C64175 & C2H0O & 46.1 & 451.8 & 1.1168 & 4.02 & 5.06 \\
11 & 3-Pentanone-M & C96220 & C3H0O & 86.1 & 684.3 & 1.1148 & 1.04 & 1.08 \\
12 & 6-Methyl-Septept-2 ome & C110930 & C3H4O & 126.2 & 984.4 & 1.18 & 0.4 & 0.36 \\
13 & Hexanal-M & C66251 & C4H2O & 100.2 & 792.7 & 1.2638 & 1.32 & 1.4 \\
14 & Hexanetitrile & C628739 & C4H1N & 97.2 & 880.6 & 1.2658 & 0.73 & 1.09 \\
15 & Hexanal-D & C66251 & C4H2O & 100.2 & 782.9 & 1.5636 & 4.2 & 3.13 \\
16 & 2-Butanone-D & C78933 & C4H8O & 72.1 & 579.3 & 1.2475 & 3.71 & 3.91 \\
17 & 2-Hexenal-D & C505577 & C4H0O & 98.1 & 847.9 & 1.521 & 3.77 & 3.23 \\
18 & Heptanal-M & C111717 & C7H10O & 114.2 & 890.6 & 1.3318 & 1.32 & 0.83 \\
19 & 2-Methylbutanal & C96173 & C5H40O & 86.1 & 680.2 & 1.3947 & 0.42 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Qualitative analysis information of volatile substances in non-processed and processed NFC goji juice. Figure 7: Gallery plot of NFC goji juice by different processing.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 4 Multi-User Detection of TTMMA via Message-Passing
### Pre-Prediction Method  
In the first stage of demodulation, the first tones of the tone-paths should be detected. However, the tone-space expansion cannot apply to the superposed tones in the first symbol. So, we propose the pre-prediction method for demodulation of the first tone.  
In order to perform the pre-prediction, the receiving UE copies the \(M\times N\) receiving trellis matrix \(\mathbf{Y}\) and inverts the order of the columns so that the order of the columns is inverted to form the reverse-direction trellis matrix \(\left[\mathbf{y}_{N},\mathbf{y}_{N\text{-}1},\ldots,\mathbf{y}_{1}\right]\). This reverse-direction trellis matrix is then connected to the existing received trellis matrix \(\mathbf{Y}\) to form an expanded trellis matrix \(\left[\mathbf{y}_{N},\,\mathbf{y}_{N\text{-}1},\ldots,\,\mathbf{y}_{2}, \mathbf{y}_{1},\,\mathbf{y}_{2},\,\cdots,\,\mathbf{y}_{N}\right]\). When performing messages-passing for the opposite part of the extended trellis matrix \(\left[\mathbf{y}_{N},\,\mathbf{y}_{N\text{-}1},\,\ldots,\,\mathbf{y}_{1}\right]\), the existing trellis diagram \(\mathbf{T}\) uses the graph shown in the opposite direction. Through the pre-prediction, it is possible to confirm whether the first symbol is overlapped and thus the tone-space can be expanded.
# Implementing GDPR-Compliant Surveys Using Blockchain
## 4 System Design
### Methods  
Our GDPR-compliant system stores all personal information off-chain using the InterPlanetary File System (IPFS). These data can thus be modified or removed from the IPFS at the DS request without compromising resilience, thanks to its distributed nature. Additionally, a hash of this data, together with a user ID, timestamp, and consent, is stored on a blockchain (immutable) record. This hash is a kind of proof of the validity of the off-chain data to maintain verifiability, auditability, and tamper resistance. Storing a hash of the personal data on the blockchain guarantees that, although the data can be deleted from the off-chain systems and consequently rectified, it cannot be tampered with. Any alteration to the data can be detected by comparing the hash stored in the blockchain with the hash of the actual file stored off-chain. When data are deleted from IPFS, the hash remains in the blockchain; however, recovering the original data it corresponded to is impossible. In practice, this approach satisfies GDPR's right to be forgotten.  
To support surveys, we added (a) individual IPFS files for each answer of each user to each questionnaire, (b) a field to the user records in the blockchain with the surveys that the user answered, (c) a pointer to the IPFS file with the personal data (to enable edition),and (d) a separate database of surveys in the blockchain with the survey IDs, description, deadline, and pointers to all the IPFS files relative to the survey.  
This architecture enables the users to choose whether to answer each survey or not and to consent to the processing of each after reading and agreeing with the description and purpose. Since these are stored in the blockchain, they cannot be altered or tampered with. All the surveys have a deadline, after which all the data relative to the survey is deleted. The data processor must store all the results of the processed data (but not the data itself) in another system (if desired) before the survey expires.  
Each user in the system has different permissions: The DS can answer, edit, and delete the participation in each survey; the DC can create and delete the surveys; the DP can process all the collected data without knowing the identity of the users that answered the survey.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 3 The Proposed Indoor Localization Method
### The Identification Model of LOS/NLOS
#### 3.2.2 The Determination of \(\upmu\)  
\(\upmu\) is the mean of RSSIs collected when the distance between an MT and FT is determined. According to the propagation model of electromagnetic waves, in Equation (5), the true distance is exponential with RSSI, so the exponential model was used to fit the ranging results and RSSIs.  
A Wi-Fi device was arranged at a fixed point as the FT. The experimenter walked in a straight line with the mobile phone within 23 m from the FT. One thousand, eight hundred groups of ranging results and RSSIs were collected and used as training data. The collected ranging results and RSSIs were used as training data. The fitting of the single exponential model is shown in Figure 7a. It can be seen that fitting over 15 m performs poorly. Here, the mean square error (MSE) was used to evaluate the fitting, calculated as per Equation (8), where N is the number of RSSI used for the fitting; \(RSSI^{(i)}\) is the i-th RSSI for the fitting; and \(RSSI^{(i)}_{fit}\) is the i-th fitting result. By calculating the MSE of the fitting from 0 m to a different distance, as shown in Figure 7b, it was found that within 0-15 m, as the distance increases, the training data increase, and the MSE decreases. Between 15 and 20 m, the MSE becomes larger, and the fitting effect is not good. Over 20 m, the MSE tends to be stable. It can be concluded that the RSSIs collected at the distance over 15 m have a different tendency from those collected within 15 m.  
Figure 5: Experiment for estimating the accuracy of the ranging results.  
Figure 6: Ranging errors of the LOS/NLOS signals.  
Therefore, the ranging result RSSI model was changed into a double exponential model to calculate \(\mu\), as shown in Equation (9). The data in the range of 0-15 m and 15-25 m were fitted, respectively. The result of fitting using the double exponential model is shown in Figure 7c. The MSE of the fitting, using the double exponential from 0 m to different distances, is shown in Figure 7d. It can be seen that the MSE at different distances is lower than that of the single exponential, except at a distance of 4 m. The average MSE of the single exponential model was 4.85 dBm\({}^{2}\), and that of the improved double exponential model was 4.15 dBm\({}^{2}\). Therefore, the double exponential model is more in line with the ranging result-RSSI relationship.  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(RSSI^{(i)}-RSSI^{(i)}_{fit}\right) \tag{8}\]  
\[\mu=\begin{cases}a_{1}+b_{1}*lg(d)&d<15m\\ a_{2}+b_{2}*lg(d)&d\geq 15m\end{cases} \tag{9}\]  
Figure 7: Experimental results of the fitting. (**a**) Comparison of the fitting data using the single exponential model and the raw RSSI data. (**b**) Mean square error (MSE) of the fitting, from 0 m to different distances, using the single exponential model. (**c**) Comparison of the fitting of data using the double exponential model and the raw RSSI data. (**d**) MSE of the fitting, from 0 m to different distances, using the single exponential model.
## 3 Research Method
### Development of the Research Hypotheses
#### 3.1.4 Relationship between Customer Satisfaction and E-Trust  
CS serves as a cornerstone for building e-trust between customers and companies and lays the groundwork for lasting relationships. Numerous studies have highlighted the strong correlation between CS and e-trust across diverse industries. [PERSON] et al. [45] and [PERSON] and [PERSON] [46] found a significant association between CS and e-trust in websites. [PERSON] et al. [47] supported this correlation using e-banking platforms. [PERSON] et al. [4] and [PERSON] et al. [48] explored the positive effects of e-SQ on e-trust through online shopping and e-commerce, respectively. Consequently, the current study hypothesizes the following relationship between CS and e-trust in e-commerce marketplace platforms:  
**Hypothesis 4 (H4).**_CS is positively related to e-trust._
## 2 Literature Review
### Classification of WBAN Routing Protocol
#### 2.1.2. Postural Movement Routing  
Communication in WBAN is affected by the postural movement of the human body. The communication between nodes is usually disconnected by frequent body movement. Several research studies have been made in order to define the cost function for optimal routing to sink. Some of the postural movement-based routing protocols are ETPA [29], PSR [30], DVRPLC [31], and PRPLC [18].
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 3 The Proposed Method
### Loss Functions
The second term is multi-scale SSIM (MS_SSIM), suggested by [PERSON] et al. [36] for image restoration, as defined in Equation (6). \[L_{\rm MS\_SSIM}(\mathbf{x},\ \mathbf{y})=1-{\rm MS\_SSIM}(\mathbf{x},\ \mathbf{y}) \tag{6}\]  
MS_SSIM well preserves the structural similarity in different scales and facilitates smoother output. Third, we apply the gradient difference loss (GDL) function [12] for combating the motion blur. The GDL loss, which considers the relative difference of neighboring pixels between the generated output \(\mathbf{x}\) and ground truth \(\mathbf{y}\), is calculated as follows  
\[L_{\rm GDL}(\mathbf{x},\ \mathbf{y})=\sum_{i,j}\left\|x_{i,j}-x_{i-1,j}\right\|-\left| y_{i,j}-y_{i-1,j}\right\|^{\alpha}+\left\|x_{i,j}-x_{i,j-1}\right\|-\left|y_{i,j}-y_ {i,j-1}\right\|^{\alpha}, \tag{7}\]  
where \(\alpha\) is an integer greater or equal to 1 and we set \(\alpha\) to two in our implementation.
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
###### Abstract  
In this study, under the meteorological conditions of Sanluurfa, Turkey, some parameter studies were conducted to more efficiently utilize Building Integrated Photovoltaic BIPV systems placed on different facades of a building. For this purpose, one single-sided (monofacial) panel was placed on both the roof and the east facade. As an innovation brought about by this study, both bifacial and monofacial panels with the same production potential were compared under the same conditions on the south facade. In addition, to enhance the production performance of the rear surface of the bifacial panel, a reflector was placed on the wall surface by leaving a gap between the wall and the panel. The experimental study was conducted between February and July. In addition, the building model created experimentally was analyzed monthly using the PVsyst program for a duration of one year. According to the study results, in the 6-month BIPV experimental application, the electrical production of the bifacial panel was found to be 15.1% higher than that of the monofacial panel under the same conditions. In addition, based on the 1-year results in the PVsyst analysis, the bifacial panel demonstrated a 5.86% higher production performance compared with the monofacial panel. This demonstrates that the efficiency of the bifacial panel in the experimental setup was enhanced by placing a reflective surface on the structure wall behind it. According to the complete annual analysis results obtained from the PVsyst analysis, the bifacial panel in the south produced 401.65 kWh, the monofacial panel produced 379.41 kWh, the panel on the eastern facade produced 313.34 kWh, and the rooftop panel, where the highest production was recorded, generated 505.64 kWh of energy. Therefore, it is anticipated that the use of bifacial panels with reflective surfaces on the roof under the meteorological conditions of Sanluurfa will demonstrate the highest performance for the BIPV system.  
building integrated photovoltaic; PVsyst; bifacial panel; monofacial panel; reflective surface +
Footnote †: journal: Applied Sciences