esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Three-Class Problems
#### 3.2.4 Enhanced Multi-Class MMFT for Three-Class Cross-Subject Classification  
In this section, multi-class MMFT is implemented based on a three-class cross-subject classification problem, mainly to address the challenge of inter-subject variations which sig  
Figure 7: Results for three-class cross-subject classification.  
nificantly constitute NT [42]. The proposed multi-class MMFT firstly discards non-related subjects by identifying the optimal combination of subjects among multiple source domains. After source selection, then utilize the best combination of closely related SDs to perform transfer learning. In a similar manner as the previous experiment based on EEG sessions, the effectiveness of our proposed approach is evaluated based on classification performance before domain selection and after domain selection using nine three-class subjects [32].  
From the resulting Figure 8, a highest classification accuracy of 75.3% was observed before the selection of the optimal combination of source domains, when S3 is the target domain and (S1-S2 and S4-S9) are source domains. However, a 9.4% increase in CA was observed when only (S2 and S7-S8) were optimally selected, then utilized as source domains for TL, as depicted in Table 2. In this case, S3 achieved an accuracy of 84.7% after source selection, as illustrated in Figure 8.  
Moreover, a highest classification accuracy of 67.3% was observed before source selection across S2 as a TD when (S1 and S3-S9) are SDs. Notably, an 18% increase in CA was recorded when (S3, S7-S8) were selected as the optimal combination of closely related source domains, with a highest CA of 85.3% observed across S2 after source selection.  
\begin{table}
\begin{tabular}{c c} \hline
**Target Domain** & **Selected Source Domain(s)** \\ \hline
1 & \(3,8,9\) \\ \hline
2 & \(3,7,8\) \\ \hline
3 & \(2,7,8\) \\ \hline
4 & \(5,8\) \\ \hline
5 & \(1,7,8\) \\ \hline
6 & \(8,9\) \\ \hline
7 & \(2,5,9\) \\ \hline
8 & \(1,4,5,9\) \\ \hline
9 & \(4,8\) \\ \hline \end{tabular}
\end{table}
Table 2: Selected source domains for a three-class cross-subject classification.  
Figure 8: Multi-Class MMFT performance evaluation before and after domain selection based on a three-class cross-subject classification.  
Based on these results, it is worth noting that utilizing all subjects as source domains can increase the prediction rate across individual target domains. However, the same sources can deteriorate IDRs across different individual target domains due to the existence of non-related domains characterized by significant variations in EEG dynamics, which lead to NT. Hence, selecting only the best source domains with high transferability can significantly increase the prediction rate in the target domain when enhanced multi-class MMFT is employed.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 2 Preliminary and Security Requirements
### Security Requirements
#### 2.2.6 Non-Repudiation  
When the receiver receives the message sent by the sender, the sender may deny sending the message. Therefore, the message sent by the sender must be signed with the secret key of the sender. The receiver can verify the received message with the public key of the sender, and then the sender cannot deny sending the message. A blockchain-based secure inter-hospital EMR sharing system should thus achieve nonrepudiation.
## 3 Results  
Cross-correlation coefficients between the Q-angle and relative FML reached values between 0.70 and 0.77 and are illustrated in Figure 1. There were no statistically significant differences in cross-correlation coefficients among the cadences (F(2,42) = 0.642, \(p\) = 0.531, \(\eta^{2}\) = 0.030) and workloads (F(2,42) = 0.458, \(p\) = 0.636, \(\eta^{2}\) = 0.021).  
Absolute FML was statistically significantly different at different cadences (F(2,42) = 4.19, \(p\) = 0.022, \(\eta^{2}\) = 0.166) and workloads (F(2,42) = 3.61, \(p\) = 0.036, \(\eta^{2}\) = 0.147). Post hoc tests revealed statistically significantly higher absolute FML at C75 compared C95 (\(p\) = 0.024, CI: \(-\)15.1-2.3) and at HIGH compared to LOW (\(p\) = 0.030, CI: \(-\)4.1-6.0) conditions.  
There was no statistically significant differences in normalized FML across the three cadences (F(2,42) = 1.44, \(p\) = 0.248, \(\eta^{2}\) = 0.064). On the other hand, there was a statistically significant difference in normalized FML at different workloads (F(2,42) = 4.56, \(p\) = 0.016, \(\eta^{2}\) = 0.179). Post hoc test revealed statistically significant decrease from HIGH to LOW conditions (\(p\) = 0.017, CI: \(-\)2.3-0.5). The results for absolute and normalized FML across the workload and cadence conditions are illustrated in Figure 2.  
Figure 1: Cross correlations coefficients between the medial-lateral force and Q-angle across the three cadence (C75, C85 and C95) and three workload (HIGH, MID and LOW) conditions.
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 3 Measurements
### Measurement System  
Figure 6 shows the measurement system. The tip of the 3.5 mm coaxial connector was set on the calibration plane, measurement samples were inserted, and the transmission characteristic \(S_{21}\) and reflection characteristic \(S_{11}\) were measured in the range of 0.01-10 GHz. The instrument used was a vector network analyzer Agilent E8362C, the IF bandwidth is 1 kHz, and the number of measurement points is 401. In addition, SMA connectors RS 526-5785 are attached to both ends of the substrate for connection with the coaxial calibration plane.
## 3 Beamforming System
#### Classic Beamforming Algorithm  
To access the antenna array capabilities, a simple beamforming algorithm was implemented. This beamforming algorithm points the main lobe to the user-specified elevation, \(\theta_{0}\), and azimuth, \(\phi_{0}\), angles. The following phases between elements are evaluated using the reference frame in Figure 13. Here, \(\beta_{x}\) and \(\beta_{y}\) are the phases between elements, \(d_{x}\) and \(d_{y}\) the distances between elements in the \(x\)- and \(y\)-direction, respectively, and \(\lambda\) is the wavelength of the signals.  
\[\begin{split}\beta_{x}&=-\frac{2\pi d_{x}}{\lambda} \sin\ \theta_{0}\cos\phi_{0}\\ \beta_{y}&=-\frac{2\pi d_{y}}{\lambda}\sin\ \theta_{0}\sin\phi_{0} \end{split} \tag{2}\]  
The effective phase for each element \(\beta_{mm}\) is the accumulative sum of both \(\beta_{x}\) and \(\beta_{y}\) for the element position [22]:  
\[\beta_{mm}=(m-1)\beta_{x}+(n-1)\beta_{y} \tag{3}\]  
where \(m\) and \(n\) are the positions of the nth element in the \(x\)- and \(y\)-directions, respectively. The phase on each element is then converted to the complex phase delay notation to be multiplied with the incoming signal from each antenna [22]:  
\[S_{out_{mm}}=S_{in_{mm}}e^{-j\beta_{mm}}=S_{in_{mm}}\times(\cos(\beta_{mm})+j \,\sin(\beta_{mm})) \tag{4}\]  
where \(S_{in_{mm}}\) and \(S_{out_{mm}}\) are the incoming and phase-shifted complex signals from the antenna element in the nth position, respectively. Finally, the delayed signals are summed, resulting in the beamformed signal.
# Fingerprints Classification through Image Analysis and Machine Learning Method
## 5 Result of Experimentation
### Analysis Results of Experimentation

Table 3: Results of classification algorithms: RF, SVM, CNN, k-NN of NIST-DB4 database. The results of the classification of 3 databases with the criteria of Precision, Recall, Accuracy evaluation of Random Forest and SVM algorithms show that the Random Forest algorithm has the best accuracy (>96.75%) for all 3 criteria. The SVM algorithm and CNN models have the best accuracy (>95.5%) for 2/3 of the database. The K-NN model has the lowest accuracy (<93%). The RF algorithm and CNN models need a lot of training time due to the process of tree construction and the interpretation of many cases.
# Development and Application of MAGIC-f Gel in Cancer Research and Medical Imaging
## 2 Development of MAGIC-f Gel
Later, agarose in the BANANA gel was replaced by gelatin to form BANG (Bis, AAm, nitrogen, and aqueous gelatin) gel, which was patented for commercial use [24; 25], and its in-house version is known as PAG gel [26]. Various compositions and formulations of polymer gel dosimeter have been studied, which brings significant innovation in polymer gel proposed by [PERSON] et al. The interest in the development of polymer gel dosimeters with different compositions and formulations continued. The Fricke-type gel dosimeter's limitation has been addressed by several studies [5; 27]; one major limitation of the Fricke-type dosimeter was the atmospheric oxygen inhibiting polymerization, as the radiation-induced polymerization is ineffective in those parts of the dosimeter contaminated by air. Hence, an oxygen-free environment should be maintained to manufacture the gel dosimeter. [PERSON] et al. addressed this issue by developing a polymer gel dosimeter acronym as MAGIC due to its composition (Methacrylic and Ascorbic acid in Copper-Initiated Gelatin) [28]. The MAGIC gel can be manufactured in a normal laboratory environment, as the problem of oxygen inhibition was resolved by binding atmospheric oxygen in an organic complex (free oxygen found in the matrix of gelatin is bound by ascorbic acid to metallic-organic complexes with the help of metal-composed copper sulfate) [29]. Later, other authors proposed different kinds of antioxidants to manufacture such a class of polymer gel known as normoxic polymer gel [30]; as an example, tetrakis (hydroxymethyl) phosphonium chloride (THPC) was proposed by [PERSON] et al. [31]. Another issue that occurred in polymer gel dosimetry was the melting of the gel samples when kept at average room temperature, which resulted in a lack of knowledge on the dose distribution, hence restricting its use. [PERSON] et al. solved this issue in 2008 by adding formaldehyde to the existing MAGIC formulation, which raised the melting point of gel to 69 \({}^{\circ}\)C and named it MAGIC-f gel [32]. Formaldehyde was applied to the MAGIC gel to increase the matrix's stability through the development of the high number of hydrogen bonds between the gelatin and formaldehyde. The fusion point of MAGIC-f gel doubles if 1% of formaldehyde is used [28; 32]. Hence, the MAGIC-f gel dosimeter is the latest innovation in the field of three-dimensional radiation dosimetry.
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
###### Abstract  
Many kinds of panel structures are proposed in aircraft design. This study presents a topology optimization method to improve the buckling resistance of panel structures. It should be noted that a popular configuration of the present panel structure is that with ribs and frames. Stiffening characteristics (i.e., effects of increasing structural stiffness of a panel structure with ribs and frames) are thus included during analysis of panel structures. After studying the coupling relationship between the dynamic characteristics and buckling behavior of the panel, a developed MMC (moving morphable component) method is proposed for topology optimization to improve the buckling resistance of the panel. It is seen that the coupling relationship between the dynamic characteristics and buckling behavior of the panel is mainly reflected when the compression force acts on the panel, corresponding that dynamic characteristics will vary with the load. If the load acts on the structure, the first-order natural frequency of the panel with ribs and frames in this study decreases with the increase in the load, with the optimization objective of maximizing the first-order natural frequency. Based on the coupling relationship between dynamic characteristics and buckling behavior, the critical buckling load of the panel increases as the first-order natural frequency increases. The present optimization method can reduce computational complexity without changing the accuracy of the calculation. At the same time, the coupling relationship between dynamic characteristics and buckling behavior is applied in topology optimization, which is of great significance to improve the comprehensive performance of panel structures in the engineering design process. This paper improves the dynamic characteristics and buckling resistance of panels with ribs and frames based on the improved MMC method. The proposed method effectively meets the design requirements of flight vehicle design in complex environments.  
dynamic characteristics; buckling; optimization; panel structure; stiffening characteristic
# Exploring the Dynamics of Global Plate Motion Based on the Granger Causality Test
## 3 Data Analysis and Results
### Results of the Causality Test between Plate Boundaries
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Direction of Causality**} & \multicolumn{2}{c}{**Granger Test**} & \multicolumn{3}{c}{**Cointegration Test**} \\ \hline
**From** & **To** & **F-Statistics** & **P** & **Lag Length** & **Statistics** & **P** \\ \hline NZ-PA & PA-PS & 11.68343 & 0.6317 & 14 & & \(-\)6.609 & 0 \\ PA-PS & NZ-PA & 24.52843 & 0.0395 & 14 & & \(-\)6.609 & 0 \\ AN-PA & PA-PS & 21.36072 & 0.8763 & 30 & & \\ PA-PS & AN-PA & 69.31429 & 0.0001 & 30 & \(-\)4.647 & 0.001 \\ NZ-PA & AU-PA & 19.72928 & 0.6582 & 23 & & \\ AU-PA & NZ-PA & 38.60992 & 0.0219 & 23 & \(-\)4.96 & 0.001 \\ AN-PA & AU-PA & 60.61951 & 0.0001 & 24 & & \\ AU-PA & AN-PA & 46.5939 & 0.0038 & 24 & \(-\)7.365 & 0 \\ NZ-PA & NZ-SA & 8.091878 & 0.5249 & 9 & & \\ NZ-SA & NZ-PA & 23.13383 & 0.0059 & 9 & \(-\)5.243 & 0 \\ AN-SA & SA-NA & 43.688 & 0.0039 & 22 & & \\ SA-NA & AN-SA & 49.71639 & 0.0006 & 22 & & \(-\)3.932 & \(-\)0.012 \\ AN-SA & NZ-SA & 31.60144 & 0.1372 & 24 & & \\ NZ-SA & AN-SA & 67.6177 & 0 & 24 & \(-\)4.044 & 0.009 \\ NZ-SA & SA-AF & 66.31497 & 0 & 9 & & \\ SA-AF & NZ-SA & 125.734 & 0 & 9 & \(-\)11.09 & 0 \\ AN-SA & SA-AF & 3.560421 & 0.7359 & 6 & & \\ SA-AF & AN-SA & 16.40422 & 0.0117 & 6 & & \\ AN-AF & AF-SO & 12.18294 & 0.0166 & 4 & & \\ AF-SO & AN-AF & 2.911122 & 0.5728 & 4 & & \\ AN-AF & AF-EU & 6.970334 & 0.1375 & 4 & & \\ AF-EU & AN-AF & 8.452415 & 0.0763 & 4 & & \\ SO-AR & AR-EU & 43.16792 & 0.0899 & 32 & & \\ AR-EU & SO-AR & 45.15261 & 0.0615 & 32 & \(-\)8.652 & 0 \\ PA-PS & PS-EU & 8.290653 & 0.3077 & 7 & \(-\)5.19 & 0 \\ PS-EU & PA-PS & 20.3014 & 0.005 & 7 & & \\ AF-EU & PS-EU & 29.18502 & 0.4555 & 29 & & \\ PS-EU & AF-EU & 58.76574 & 0.0009 & 29 & \(-\)10.875 & 0 \\ IN-EU & PS-EU & 14.77975 & 0.996 & 32 & & \\ PS-EU & IN-EU & 54.48406 & 0.0079 & 32 & \(-\)10.156 & 0 \\ AN-PA & AN-NZ & 15.00008 & 0.0591 & 8 & & \\ AN-NZ & AN-PA & 19.33468 & 0.0132 & 8 & \(-\)10.671 & 0 \\ AN-PA & AN-SA & 112.5939 & 0 & 25 & \(-\)5.414 & 0 \\ AN-SA & AN-PA & 109.1282 & 0 & 25 & & \\ AN-AU & AU-EU & 21.11864 & 0.7358 & 26 & \(-\)2.564 & 0.26 \\ AU-EU & AN-AU & 97.2081 & 0 & 26 & & \\ AN-AU & AU-PA & 28.10634 & 0.0053 & 12 & \(-\)2.492 & 0.29 \\ AU-PA & AN-AU & 45.1501 & 0 & 12 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Granger causality between energy releases at plate boundaries.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 3 Effect of Sub-Mirror Position Error on WFE  
The optical model is established for simulation and analysis according to the optical design of the [PERSON] telescope, which is a coaxial triple-reflector achromatic telescope with the primary mirror split as shown in Figure 1 (hexagonal split), and the layout of the optical system as shown in Figure 3. The different colored arrows in the figure correspond to different fields of view of the light. Among them, the primary mirror (PM), secondary mirror (SM), and tertiary mirror (TM) are designed using the quadratic surface reflector design method, and in order to make the structure of the optical system more compact and reasonable, the fine steering mirror (FSM) is utilized for the folding imaging of the optical path.  
Compared to non-segmented primary mirror optical systems with the same structure, segmented primary mirrors with misalignments cannot achieve the effective aperture size of a monolithic mirror due to the presence of gaps between segments. This causes the MTF of the optical system to decrease at mid-frequency positions, but the segmented mirror increases the cutoff frequency of the entire system. Where, for this optical system, the operating wavelength of the system is 1 \(\upmu\)m. When the wavefront difference RMS of the optical system is less than \(1/14\lambda\), the image quality is considered to be good, and the diffraction limit can be considered to have been approached. Based on this method, the influence of different position errors of sub-mirror on wavefront of optical system is simulated.
# Malicious Powershell Detection Using Graph Convolution Network
## 4 Experimental Results
### Results
#### 4.2.1 Number of Adjacent Nodes  
Figure 7 shows that when the number of adjacent nodes was 0, the detection rate was 88.4% and when the number of adjacent nodes was 1, the detection rate was 89.4%, which was 1% higher than when using the identity matrix. When the number of adjacent nodes was 2, the detection rate was 96.6%, which was 8.2% higher than when using the identity matrix. However, when the number of adjacent nodes was 3, the detection rate was 95%, which was less than when the number of adjacent nodes was 2.  
When the number of adjacent nodes increased, the detection rate also increased because additional feature data could be obtained from adjacent nodes. However, when there were too many adjacent nodes, the detection rate decreased compared to when the number of adjacent nodes was two, even though it was higher than when using the identity matrix.  
Figure 8 shows the FPR. When the number of adjacent nodes was 0, the FPR was 1%. When the number of adjacent nodes was 1, the FPR was 0.8%, which decreased by 0.2% compared to using the identity matrix. However, when the number of adjacent nodes was 2 or 3, the FPR was 2% and it increased by 1%. This showed that if we used adjacent nodes, then the FPR decreased. However, when there were too many adjacent nodes, the FPR increased.  
Figure 6: Identity matrix.  
Figure 9 provides the accuracy. When the number of adjacent nodes was 0, accuracy was 93.7%. When the number of adjacent nodes was 1, it was 94.3%. When the number of adjacent nodes was 2, it was 97.3% and it increased by 3.6% compared to using the identity matrix. When the number of adjacent nodes was 3, it was 96.5% and it was higher than using the identity matrix but was less than when the number of adjacent nodes was 2. This showed that we could increase the accuracy by using adjacent nodes; however, the accuracy decreased when the number of adjacent nodes was high.  
Figure 8: False Positive Rate according to the number of adjacent nodes.  
Figure 7: Recall (Detection rate) according to the number of adjacent nodes.  
Figure 9: Accuracy according to the number of adjacent nodes.  
Table 1 shows the results in each experiment.
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 2 Refrigeration System  
### System Description  
A large-scale cold store, whose products are used for long-term storage reserve, with ammonia as the refrigerant located in Henan, China, is studied to show the feasibility and effectiveness of the proposed strategy. The cold store covers an area of over 4800 m\({}^{2}\) with a storage capacity of over 7000 tons; its warehouse is shown in Figure 1a. The refrigeration system is simplified to a combination of 7 core components (Figure 1b) including the compressor unit, the centralized evaporative condenser, the centralized high-pressure ammonia storage, the throttle valve, the separator, the refrigerant pump, and the evaporator inside the warehouse. As the most essential refrigeration equipment, the main parameters of the compressor are shown in Table 1. The system contains two identical compressors operating in parallel that can be independently controlled, and both are equipped with capacity adjustment devices.  
In the schematic, the direction of the line segment between components indicates the flow direction of the refrigerant in the pipeline, and the color indicates the state of the refrigerant (blue for the liquid, red for the gas, and green for the gas-liquid mixed phase).  
With a separator as the medium, in which gas and liquid refrigerant exist simultaneously, a refrigeration circuit and a cooling circuit are connected, and thus energy exchange can be realized. The function of the refrigeration circuit is to generate low-temperature liquid refrigerant in the separator. The cooling circuit uses the generated refrigerant to provide cooling capacity to maintain the warehouse temperature within a reasonable range. The gas refrigerant in the separator is sucked and compressed by the compressor unit before being cooled in the evaporative condenser. After condensation, the refrigerant is stored in the centralized high-pressure ammonia storage and then throttled to the low-temperature fluid by the throttling valve to return to the separator to complete the refrigeration circuit. Being pumped into the evaporator in the warehouse, the liquid refrigerant evaporates to absorb heat and returns to the separator in the gas-liquid state. The two-phase refrigerant returning to the separator is separated again, among which the gas is used in the refrigeration circuit and the liquid re-enters to the cooling circuit.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 5 Discussion
### Analysis in Relation to the Feature Length of the Fully Connected Layer  
The main function of the fully connected layer is to map the learned representation of features into the sample tag space. Increasing the feature length of the fully connected layer is equivalent to increasing the number of neurons, which can theoretically improve the learning ability of the model. However, if the feature length of the fully connected layer is too long, which, on one hand, will make the learning ability too strong and lead to overfitting, on the other hand, will increase the operation time and reduce the efficiency. In order to test the influence of feature length on classification accuracy, the feature length of the fully connected layers from FC1 to FC6 is set as exponential multiples of 32, 64, 128, 256, 512, 1024 with other parameters unchanged, as shown in Figure 18. When the feature length from FC1 to FC6 is 512, the classification accuracy is the highest on UCM dataset. For the SIRI-WHU dataset, when the feature length from FC1 to FC6 is 64, the convolutional neural network has the best feature expression ability.