esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Future Directions
### Answering Research Question One
The availability of a wide range of datasets offers diversified opportunities to analyze EEG data and extract uncovered information that would promote more research. Hence, in any study field, the increased availability of datasets is just as crucial as developing the datasets themselves. The following can be summarized regarding the present context of the research domain: (i) EEG signals for the analysis were captured either from the frontal side of the brain using a single-channel EEG headset or from different parts of the brain via multi-channel EEG headsets; (ii) after acquisition, the EEG signals were preprocessed mainly using the Fast Fourier transformation (FFT) method; and (iii) the preprocessed signals were then classified using supervised ML or DL classification algorithms. The most commonly deployed ML and DL algorithms are shown with their usage percentages in Figures 5 and 6, respectively. Figure 4: Most popular deep learning algorithms used in existing literature. Figure 3: Most popular machine learning algorithms used in existing literature. All works utilized supervised binary classification ML/DL algorithms to identify confused learners from non-confused learners. The average prediction accuracies for the most common machine learning and deep learning algorithms that were trained using [PERSON] et al. 's dataset [5] are illustrated in Figures 3 and 4, respectively. The tendency to use DL algorithms has now increased more than ML. The literature published closer to the present date mainly studies applying different DL algorithms to improve the accuracy of detection, but the cost of ML and DL algorithms were not compared. When considering the nature of the input features used for model training, the features of EEG signals were independently extracted and combined afterward into a single feature vector. The input features were either Power Spectral Density (PSD)/EEG frequency ranges or statistical measures of the EEG signals.
## 3 Hysteresis Curve Characteristics of GRS
### Hysteresis Curve Shape  
Figure 2 illustrates the trend of the hysteresis curve variations for different fines contents without D-W cycles after 500 cyclic loadings. The corresponding observations are summarized as follows: (1) The hysteresis curve changes from the initial shuttle-like to a willow shape as the number of load cycles increases. (2) The largest hysteresis loop lies in the first cycle, and it gradually becomes smaller and denser in the following cycles. GRS exhibits an elastoplastic behavior, and the irreversible plastic strain gradually enlarges with increasing cyclic numbers. (3) As the fines content increases, the axial strain increases significantly. Comparing the axial strain of C0 with that of C30, the latter is nearly three times the former. The reasons behind these observations could be that the fine particles play a lubricating role on the surface of the coarse particles when the fines content increases from 0% to 10%. Moreover, when the fines content further increases to 30%, the fine particles may block the internal drainage channel of pores, resulting in the accumulation of excess pore water pressure. As the pore water pressure accumulates to the limit, the fine particles will be pushed out, and the axial strain will increase. Consequently, the axial strain becomes greater and greater with the increase of the fines content.  
Figure 3 plots the hysteresis curve changes of soil samples without adding fine particles for the 1 st, 3 rd, 5 th, and 7 th D-W cycles under 500 loading cycles. It can be observed that the hysteresis curve changes show a non-monotonic pattern. The axial strain decreases first and then increases significantly when the number of D-W cycles increases. With the results of [PERSON] et al. [51] and [PERSON] et al. [52], it is inferred that such a result in this paper is probably due to the new skeleton formed by fine and coarse particles being denser within three D-W cycles and the axial strain decreases. However, when the number of D-W cycles continuously increases to five, the hydrophilic capacity of clay minerals decreases, and the adhesion between particles weakens. At the same time, the soil structure becomes loose under the combined action of dynamic load and D-W cycles, which eventually leads to significant growth of axial strain.  
Figure 3: Hysteresis curves for different D–W cycles (C0).  
Figure 2: Hysteresis curves for different fines contents (T0).
## 4 Results and Discussion
### Spatial Variation of Precipitation, Maximum Temperature, and Minimum Temperature
#### 4.3.2 Maximum Temperature  
The spatial distribution of average daily maximum temperature over the Upper Bhima sub-basin for the uncorrected output from the ACCESS1-3 CMIP5 GCM model and its corrected datasets, after applying various bias correction methods, are compared against the observed precipitation data, as shown in Figure 10. Figure 10a-e shows the spatial variation of the model-simulated raw maximum temperature data, observed maximum temperature data, LS-corrected, VS-corrected, and DM-corrected datasets, respectively.  
As raw precipitation datasets show a discrepancy in the spatial pattern, this also shows that the maximum temperature also shows dissimilarity in the spatial pattern of the uncorrected data and observed data. The only difference is that, in the case of precipitation, the model data underestimates the precipitation data and, in the case of the temperature data, the model data overestimates the temperature throughout the study area.  
In the spatial variation of the model-simulated raw data, as shown in Figure 10a, there are only three classes of spatial patterns ranging from the lowest temperature of 34 \({}^{\circ}\)C in the Western Ghat region and the highest maximum temperature of 40 to 42 \({}^{\circ}\)C in the eastern part of the study area, which is very high in comparison to the observed data, as shown in Figure 10b.  
Although, after the application of bias correction, there is a comparative improvement in the model output, Figure 10c-e show the spatial variation of the LS, VS, and DM correction methods, showing similar spatial patterns of precipitation over the Upper Bhima sub-basin. The overall spatial pattern of the maximum temperature distribution is the same for all three bias correction methods over the Upper Bhima sub-basin.
# Sustainable Operation of Active Distribution Networks
## 2 Description of the Suggested Multi-Agent System Structure
### Active Distribution Network Management Agent  
The _Active Distribution Network Management Agent_ (ADNM/A) is at the higher level of the proposed hierarchical MAS. ADNM/A gathers the aggregated responses from the cluster agents and also information for the forecasted price of electricity, the node loads of the network, and the power produced by RESs and it exploits an optimization technique to optimally schedule the operation of the ADN. The proposed method aims to reduce the daily operation cost of the network by shifting the power demand in time periods with a low electricity price. At the same time, all the operational constraints of the ADN, building energy systems, and hosted PEVs should be satisfied. The constrained optimal power flow algorithm (OPF) was exploited to keep the voltages of the ADN within the permissible bounds. Then, the active and reactive power set-points resulted from the OPF exploitation were forwarded back to the respective cluster agents to be optimally dispatched to the local agents they supervise.
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 1 Introduction
The significance of this study is in the calculation and application of the geometric parameters of radiographs required for image-guided vascular intervention procedures using the sinogram data obtained during CBCT acquisition in the intervention room. Herein, we aim to improve the accuracy of the calibration method using sinogram data acquired during the interventional procedures without any additional radiation exposure and any significant increase in the computational time. We attempted to obtain robust calibration accuracy along various orientations of the X-ray gantry using sinogram data and only one calibration procedure. Furthermore, we did not want to permanently fix any calibration pattern to obtain a clear object without using any additional calibration markers. A calibration phantom with ball bearings to establish accurate 3D locations was designed for this purpose, and is used before surgery to calibrate the geometry of the X-ray imaging device. This study also focused on executing the calibration method in under a minute, which is an acceptable calculation time for image-induced vascular intervention, and within a 1 mm accuracy.
## 4 Fault-Tolerance Control Strategy
### Simulation Verification
#### 4.4.1 Single Motor Fault Conditions  
When the left steering motor on the tracked vehicle breaks after 2 s of normal operation, the feasibility of the fault-tolerant algorithm is confirmed by contrasting the twoapproaches for both fault-tolerant control and fault-tolerant control alone. Figure 17 details the emulation outcomes.  
As can be seen in Figure 17, a left rear motor failure occurs during the vehicle's track tracking process, at which point the vehicle requires a differential drive operation command. Before 2 s, the vehicle can accurately trail the double lane path. It is obvious that the vehicle lacks fault-tolerant control at this point since the left steering motor has a failed turn angle value of 0 degrees at 2 s. Since the motor failure will cause the vehicle to understeer, the vehicle will find it difficult to follow the course and flee. However, a vehicle with fault-tolerant regulation is able to accurately continue to track the path. The proposed fault-tolerant control regulation for steering by wire can improve the safety and stabilization of the vehicle when a single motor fails.
## 3 Fault Feature Extraction and Enhancement of Rolling Element Bearing Based on MCKD-EWT
### Program of the Proposed Method  
To extract the weak fault feature caused by the rolling element bearings in the heavy noise condition, a novel fault feature extraction and enhancement method is proposed by the combination of MCKD and improved EWT.  
Suppose that the collected vibration is denoted by \(x(t)\), which is composed of bearing fault components, rotor shaft fault components, background noise, etc. For the fault feature extraction of rolling element bearings, rotor shaft rotational frequency, harmonics, and any other interference frequencies should be eliminated. The proposed method solve such problems and the detailed program is listed as the following.  
1. De-noise the signal by MCKD. De-noise the single accidental impact and non-impact components by implementing the MCKD to \(x(t)\). The key issue of the MCKD for a given bearing signal with faults is choosing these four parameters: the period of deconvolution \(T\), the length of filter \(L\), the termination number of iterations, and the number of shift \(M\). The parameter \(T\) can be easily calculated based on the theoretical fault characteristic frequency, whereas \(L\) and \(M\) should be subjectively set without any reference. Whether the extracted fault signal is desired or not is severely influenced by the selected parameters. Therefore, selecting the optimal parameters is extremely important in MCKD. This method often chooses the termination number of iterations as a range from 20 to 30, the length of filter as a range from 100 to 300, the period of deconvolution \(T\) depending on the actual signal, and the shift number as a range from 2 to 5. Additionally, a method for setting parameters is recommended in a previous study [44].
2. Spectrum segmentation. Calculate the envelope curve of the amplitude spectrum of the de-noising signal. Find the maxima and minima of the envelope curve and segment the spectrum on the modified envelope curve.
3. Signal decomposition. Design the wavelet filter banks based on the spectrum segmentation result and decompose the signal into several sub-signals.
4. IMF (Intrinsic Mode Function) selection. Calculate the kurtosis of each sub-signal and choose the best sub-signal with the maximum kurtosis.
5. Feature extraction. Calculate the squared envelope spectrum and teager energy operator spectrum of the chosen mode. Analyze the fault characteristic frequency of the spectrum to determine the fault type and location of the fault.  
The program of the proposed method is simply illustrated in Figure 1.  
Figure 1: Flowchart of the proposed method.
## 5 Discussions
### Gap in the In Situ SSI  
To apply SSI data for climatological monitoring, the WMO recommends an ideal spatial resolution of 25 km and a minimum spatial resolution of 100 km [58]. Korea has an area of approximately 120,000 km\({}^{2}\), and its coverage, including islands and land areas,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Month** & **Jan.** & **Feb.** & **Mar.** & **Apr.** & **May** & **Jun.** & **Jul.** & **Aug.** & **Sep.** & **Oct.** & **Nov.** & **Dec.** \\ \hline Min. (MJ m\({}^{-2}\)) & 4.872 & 7.709 & 14.041 & 18.247 & 16.672 & 16.422 & 13.963 & 11.952 & 10.943 & 10.975 & 6.696 & 5.885 \\ Station & 115 & 115 & 185 & 101 & 172 & 185 & 185 & 108 & 169 & 101 & 127 & 184 \\ Max. (MJ m\({}^{-2}\)) & 9.396 & 13.301 & 16.933 & 21.493 & 20.010 & 21.053 & 20.008 & 17.594 & 16.332 & 14.845 & 11.209 & 10.425 \\ Station & 159 & 159 & 156 & 156 & 184 & 159 & 102 & 258 & 102 & 258 & 159 & 159 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minimum and maximum values of monthly mean daily KMA ASOS in situ SSI from 1 January 2020 to 31 December 2021, where Min. and Max. indicate minimum value and maximum values, respectively.  
Figure 14: The distribution of monthly mean daily SSI, from 1 January 2020 to 31 December 2021, observed from KMA ASOS stations in (**a**) January, (**b**) February, (**c**) March, (**d**) April, (**e**) May, (**f**) June, (**g**) July, (**h**) August, (**i**) September, (**j**) October, (**k**) November, and (**l**) December.  
can be divided into 258 grid points with 25 km resolution and 26 grid points with 100 km resolution (Figure 15). When monitoring Korea for climatological application using only in situ measurements, at a minimum resolution of 100 km, data are obtained for 18 grid points (approximately 69.2%); at an ideal resolution of 25 km, data are obtained for 41 grid points (approximately 15.9%). If climatological monitoring over Korea is required at the minimum resolution, most areas, except for some regions near shorelines, borders, and islands, are covered by in situ observations (Figure 15a). In contrast, when we aim to meet climatological monitoring requirements at the ideal resolution, most areas over Korea would be missed by in situ observations (Figure 15b). For accurately investigating the climatology of Korea, installing more in situ measurement stations is necessary; however, this is limited by the available human and physical resources. The GK2A/AMI-derived SSI data showed good performance in terms of temporal and spatial stability, and there were no limitations to data acquisition and spatial coverage at a high temporal resolution. Compared with the numerical model data, the satellite-derived SSI exhibited a higher agreement with the in situ SSI; this was because spatially and temporally continuous remote-sensed observations were available [59; 60]. Therefore, satellite-derived SSI data can be used as an alternative to in situ SSI measurements for diverse applications, including climatology, renewable energy, and agriculture.
## 7 Unbalanced PSI-CA Protocol Based on Dual-Cloud Assistance
### Definition of Main Participants and Related Symbols  
1. Large medical institution represents the party with a larger dataset and greater computational and storage capabilities.
2. Small health app developer represents the party with a smaller dataset and lesser computational and storage capabilities.
3. Cloud server \(H_{1}\): Acts as an auxiliary server for the small health app developer, handling the majority of computation and storage pressures.  
Figure 4: Unbalanced PSI-CA protocol based on dual-cloud assistance.  
cloud server \(H_{2}\): Another auxiliary server handling substantial computational and storage demands.
* \(X\) and \(Y\) represent the dataset of the large medical institution and the small health app developer, respectively.
* \(Y^{\prime}\) represents the obfuscated dataset sent by the small health app developer to the large medical institution, used to confuse the cloud server and prevent it from obtaining the accurate cardinality of the intersection. \(k\) represents the cardinality of the set \(Y^{\prime}\).
* \(\alpha\) represents the private key of the large medical institution used in the Diffie-Hellman encryption algorithm.
* \(H\): The hash function agreed upon by the small health app developer and the large medical institution for use.
* \(CF\) represents the Cuckoo Filter, where \(CF.insert\) denotes the operation to add elements, and \(CF.check\) checks for the presence of specific elements.
* \(\omega_{j}\): Random exponentials generated by the small health app developer for cloud server \(H_{1}\), \(\beta_{j}\) for cloud server \(H_{2}\).
* \(a\): A secret value held by the small health app developer.
* \(r_{1,j}\): Random numbers used by the small health app developer for sending obfuscated data to cloud server \(H_{1}\), and \(r_{2,j}\) for \(H_{2}\) where \(r_{1,j}+r_{2,j}=a\).
* \(C_{1}\): The ciphertext collection sent from cloud server \(H_{1}\) to the large medical institution, and \(C_{2}\) from \(H_{2}\); \(C_{1,j}\) and \(C_{2,j}\) are specific elements within these collections.
* \(C^{\prime}_{1}\) and \(C^{\prime}_{2}\): Processed ciphertext collections returned to \(H_{1}\) and \(H_{2}\) from the large medical institution; \(C^{\prime}_{1,j}\) and \(C^{\prime}_{2,j}\) are specific elements within these collections.
* \(C^{\prime\prime}_{1}\) and \(C^{\prime\prime}_{2}\): Final processed ciphertext collections at \(H_{1}\) and \(H_{2}\) after receiving data from the large medical institution; \(C^{\prime\prime}_{1,j}\) and \(C^{\prime\prime}_{2,j}\) are specific elements within these collections.
* \(item_{j}\) represents the result of multiplying \(C^{\prime\prime}_{1,j}\) and \(C^{\prime\prime}_{2,j}\) used to query the filter.
* \(sum\) represents the variable used to help the small health app developer obtain the cardinality of the intersection, where \(sum-k\) represents the cardinality of the intersection.
## 5 Mathematical Modeling and the Control of Attitude Dynamics  
This section discusses the design of the attitude controllers for the octocopter. For this purpose, a linearized model of attitude dynamics is presented, rather than full-vehicle dynamics [12; 13]. To create the dynamic model of the octocopter, a body coordinate frame, as illustrated in Figure 7, was used. All forces and torques generated on the octocopter platform can be expressed in the body frame, and are defined as follows:The origin (\(O_{B}\)) of the frame was placed at the center of mass of the octocopter.  
The \(X\)-axis (\(X_{B}\)) is pointing forward along the structure.  
The \(Y\)-axis (\(Y_{B}\)) points along the structure to the right.  
The \(Z\)-axis (\(Z_{B}\)) points downward to complete a right-handed system.
# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 1 Introduction
For example, in the training phase of DaSiamRPN [15], the ImageNet [16] and COCO detection datasets [17] are made into positive sample pairs through data amplification to expand the types of training data sets and improve the generalization ability of the tracker. Moreover, it extracts pictures in different categories, and the same category as negative samples generates various kinds of complex negative samples and improves the discriminative ability of the tracker. The other is to improve the feature expression ability of the feature extraction module. For example, SiamRPN++ [18] introduces deeper Resnet [19] as the backbone network. The extracted features have a more vital expressive ability, which improves the anti-interference ability of the tracker. However, SiamRPN++ is still susceptible to interference from similar objects in a complex background. The Siamese network is essentially a template matching process that takes the first frame of target information as a template and cannot judge interferers in the background similar to the object. Moreover, they use the same classification network for feature extraction. It is impossible to obtain features with different directivity between the template and the search area. As shown in Figure 1, when other objects similar to the target appear in the current frame, the features extracted by the feature extraction network are very similar. As a result, double peaks appear in the response map, it is impossible to resolve the correct target. The Siamese-based algorithms do not have an online update mechanism, so the drifts are generated, and the tracking fails. These methods enhance the ability to express features and improve the ability to distinguish similar objects to a certain extent. However, these algorithms will still lose track when similar objects of the same type have interfered in the scene. SiamRPN++ proves through experiments that the same category has a high response in some specific feature channels. In contrast, the response is shallow in other channels, indicating that the channel can reflect object category information to a certain extent. We propose a robust tracking algorithm based on compensating attention to solve the above problems. The channel-spatial attention mechanism is introduced into the double branches of the Siamese network to strengthen the high-response channel characteristics of specific target categories and at the same time, strengthen the dominant characteristics of a specific area of the object. Then we add a compensation attention model based on the dual-branch attention, introduce the attention of template branch selection into the search branch, and merge the attention of the search area and the template branch selection. It improves the feature discrimination ability of the search branch to the target. It solves the problem that the channel-space attention introduced by the search branch enhances the target characteristics while also enhancing the characteristics of similar objects near the target.