esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In general, the study explored the 82 VOCs of SS from six regions by using HS-SPME-GC-MS and HS-GC-IMS. Among them, the content of terpenoids accounted for a large proportion of the SS. Through these two kinds of instrument analysis, it was concluded that the terpenoid substance content is higher in Anhui province. In addition, some aldehydes and phenols were found by HS-GC-IMS. The combination of the two analytical methods realized the rapid identification and comprehensive characterization of VOCs in SS. The fingerprint of HS-GC-IMS has the advantage of data visualization, which can directly and conveniently compare VOCs in samples from different regions. The discriminant analysis of SS from six regions was carried out by OPLS-DA. Only parts of SS from different regions can be separated by HS-SPME-GC-MS. HS-GC-IMS could effectively distinguish the six kinds of SS. By random forest analysis, 14 compounds were identified that were beneficial to the classification of SS in different areas. Although HS-GC-IMS was more commonly used to identify food from various sources, it was less commonly used in traditional Chinese medicine. HS-GC-IMS coupled with an appropriate multivariate analysis has potential in the characterization and classification of TCM containing VOCs. However, due to the complex composition of TCM, the application of HS-GC-IMS in the identification of TCM from different origins still needs to be studied with a larger sample size.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules27144393/s1](https://www.mdpi.com/article/10.3390/molecules27144393/s1)]([https://www.mdpi.com/article/10.3390/molecules27144393/s1](https://www.mdpi.com/article/10.3390/molecules27144393/s1)), Table S1: Detailed information of all the samples. Table S2: Peak area of standard product. Table S3: Peak area of sample determined by HS-SPME-GC-MS. Table S4: Peak area of sample determined by HS-GC-IMS.  
Conceptualization, H.Y., J.Y. and L.H.; investigation, L.Z.; writing--original draft preparation, C.L. and H.W.; writing--review and editing, H.Y. and W.Y.; visualization, X.W. and H.C.; project administration, Z.L.; resources, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the financial support of the National Natural Science Foundation of China (Grant No. 81904251).  
Not applicable.  
The data presented in this study are available in Supplementary Materials.  
The authors would like to thank the support from the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (No. ZYYCXTD-D-202002).  
The authors declare no conflict of interest.
## 1 Introduction  
In recent years, deep machine learning has emerged in many fields of engineering. With the advancements in big data and optimization algorithms and with increased computing resources, deep networks are currently the state-of-the-art technique for different problems, from speech processing to computer vision and online gaming [1]. Deep networks have become one of the most popular research directions and play a significant role in many fields, such as machine translation, speech recognition, image recognition and recommendation systems, etc. Researchers are actively attempting to extend these technologies to other domains, including wireless communication [2]. Many researchers have applied deep learning (DL) techniques to communications systems [2; 3; 4].  
In this paper, we propose a deep learning approach to solve the optimization problem in a wireless powered communication network (WPCN). In the literature, many researchers have already proposed solutions that use numerical optimization to solve signal processing tasks [5; 6; 7]. Most of the solutions use iterative algorithms that input parameters of the network, such as channel gain or the signal to noise ratio (SNR), and run a number of iterations to find optimized solutions as outputs [8; 9]. Iterative algorithms are always costly solutions in terms of their complexity and time parameters, and it is not easy to implement those solutions in a real-world paradigm. Thus, it is necessary to find time and complexity-efficient solutions.
# Hamming Distance Optimized Underwater Acoustic OTFS-IM Systems
## 2 OTFS-IM for Underwater Acoustic Communication System
### Detection Algorithm
#### 2.3.1 ML Detection Algorithms  
The ML detection algorithm achieves joint detection of index positions and constellation symbols for each sub-grid group by searching for all possible combinations of indexes and constellation symbols by exhaustive enumeration. Using group \(\beta\) as an example:  
\[\left(\hat{I}_{\beta},\hat{S}_{\beta}\right)=\underset{I_{\beta},S_{\beta}}{ \mathrm{argmin}}\sum_{\hat{I}_{\beta},S_{\beta}}\sum_{\alpha=1}^{k}\left|\tilde{ y}_{\beta}^{DD}\big{(}i_{\beta,\alpha}\big{)}-s_{\beta}(\alpha)\right|^{2} \tag{14}\]  
where \(s_{\beta}(\alpha)\) denotes the \(\alpha\) symbol in the set of legal constellation symbols. After detection, the signal \(\hat{I}_{\beta}\) and \(\hat{S}_{\beta}\) return the \(p_{1}\) bit index information and \(p_{2}\) bit symbol information through index and symbol decoding.
## 5 Photogrammetry: Knowledge Base
### Aerial Photogrammetry: Realization Phase  
Aerial photogrammetry [39] deals with the interpretation and evaluation of digital images that have been created from aircraft or other UAVs. The term UAV photogrammetry defines a remote measurement platform, which can be partially dependent or independent, does not contain a pilot and is equipped with photogrammetric measurement systems. These include small and medium video cameras, thermal or infrared camera systems and LIDAR aeronautical systems. Within the UAV standard, it is possible to monitor and record the position and direction of sensors that are applied in a local or global coordinate system.  
In our case, to create a 3D model of the Kunerad manor it was necessary to implement not only ground but aerial photogrammetry as well. The DJI Mavic 2 PRO drone with an integrated Hasselblad L1D-20c camera with resolution of 20 Mpx and 1 inch CMOS sensor was used for this phase. Captured photos were saved in DNG format for further postprocessing.  
To achieve the same pixel size (GSD) [40] as with the terrestrial photogrammetry, the maximum distance between the UAV and the object was determined to be 20 m. Again, the spatial conditions were considered, where part of the forest was in this radius. For this reason, the object was captured in manual mode for the outer perimeter and from above in automatic mode.  
The photography was performed in a fully automatic mode, since the histogram images were evaluated as uniform during the control flight. A total of eight images was taken from each side, from above the building, and for details at the terraces.  
Since it was not possible to combine two methods of photogrammetry in one day, the weather forecast was monitored for upcoming days and the lighting conditions were evaluated for the same parameters. It was not necessary to change the flight modes and the way of flying, because the wind conditions were very favorable (approx. 5 m/s).  
During the flight, attention was paid to the degree of capture details on terraces, tower, external staircase, main entrance and individual details of the architecture. The essential part was capturing of common goals for the combination of the two methods of digitization.  
The total number of images created during the flight, with the emphasis on even coverage of the object, was 670. Figure 8 shows the UAV positions in which the images were taken.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Initialization
#### 2.1.2 Positional Solving Using a Homography Matrix  
If the homonymous points are in the same plane, then the positional solution can be performed according to the homography matrix, as (2) shows below:  
\[x_{k}^{C_{2}}=\mathbf{H}_{C_{1}}^{C_{2}}x_{k}^{C_{1}} \tag{2}\]  
where \(\mathbf{H}_{C_{1}}^{C_{2}}\) denotes the homography matrix from \(I_{1}\) to \(I_{2}\).  
The SVD decomposition of the homography matrix \(\mathbf{H}_{C_{1}}^{C_{2}}\) is performed as follows in (3):  
\[\mathbf{H}_{C_{1}}^{C_{2}}=\mathbf{U}\mathbf{D}\mathbf{V}^{T}\text{ and }\mathbf{D}= \mathbf{R}_{D}+\frac{t_{D}\mathbf{n}_{D}\mathbf{T}}{d}, \tag{3}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are orthogonal matrices and \(\mathbf{D}\) is a diagonal matrix.  
Here, the SVD decomposition was performed with \(\mathbf{D}\) as the new single response matrix, and \(\mathbf{R}_{C_{2}}^{C_{1}}\) and \(t_{C_{2}}^{C_{1}}\) under the single response relation could be obtained.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### Parameter Analysis
#### 6.4.2 Distance between Antenna and Tag Array  
The configuration of our fixed tags array is unchanged, which is 20 tags with a pitch of 15 cm, in order to explore the influence of the distance between the antenna and tags array on the localization error. Increase the distance between the antenna and tags array in turn, as shown in Figure 19. The localization error is first reduced and then increased. We consider that, when the distance is small, some of the tags are at the edge of the signal coverage area, which result in low data quality, which affects the overall localization error. When the distance gradually increases, more tags enter the signal coverage area. The data quality improves, so the localization error is naturally small. In addition, as mentioned above, the error, with respect to the incident angle, directly affects the localization error, and when the distance is further increased, the influence will become larger.
## 4 Accuracy Evaluation
### Evaluation Methods  
To compare camera calibration errors, the knowledge of how to calculate these errors is needed. Since the calibration mechanism is used by the OpenCV library, the error is also calculated by the method offered by this library.  
The reprojection error when calibrating the camera is calculated as follows. The absolute deviation between the transformation outcome and the corner finding algorithm is calculated. Next, the average of these values for each image is calculated and, thereby, the total average error is calculated. Thus, each calibration will result not only in a calibration matrix, but also in a reprojection error.  
To compare the suggested approach with the classical manual calibration, a series of camera calibrations was performed using the manual approach and the suggested one. After that, the mean values of errors obtained for both approaches were calculated. Thus, it enables the evaluation of the suggested solution against the currently used manual approach, given the order of the error obtained [12].  
As noted earlier, with respect to calibration factors, the approach used to calibrate the camera is not applicable. Therefore, the influence of the coefficient on the robot's trajectory curvature is estimated. To do this, the robot was located at a certain fixed distance from a straight line, along which it was oriented and then moved in manual mode strictly directly to a distance of two meters from the start point along the axis, relative to which it was oriented. Then, the robot stopped and the distance between the initial distance to the line and the final one was calculated. This difference was chosen as a calibration error. Figure 3 represents the end of the test line. The robot was originally set up so that the right edge of the right wheel was aligned with the right side of the line. The distance the robot traveled between the right side of the wheel and the edge of the line was calculated. Since the rubber protrusion of the wheel and the measurement method may not be entirely ideal, the value was rounded to the nearest hundredth of a meter.  
As a reference value of the calibration coefficient, the coefficient hand-picked in the same place, but further tested in a different place, was used. This was carried out in order to exclude the value setting specifically for a certain area of coverage [11].  
It is important to note that, when fully in the automatic mode, the quality of the wheel calibration is affected by the camera calibration, since the process of calibrating the wheels uses a marker recognition mechanism and therefore the calibration matrix. In order to determine the effect of camera calibration on the wheel calibration accuracy, two series of wheel calibration experiments were conducted. In one case, the same camera calibration matrix obtained in a manual mode was used, and in the other, a new camera calibration matrix obtained in an automatic mode each time was used. This made it possible to determine the relationship between the camera calibration quality and the accuracy of the wheel calibration based on it.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### High-Performance Computing Benchmark
#### 3.3.1 Fast Fourier Transformation Benchmark (FFT)  
Fast Fourier transform (FFT) is an algorithm that is more efficient in computing discrete Fourier transform (DFT). It is primarily used in communication and 2D image analysis. For example, in the Automatic Dependent Surveillance Broadcast (ADS-B) system, FFT is applied in a sliding window of 128 points to implement automatic monitoring technology for aircraft position determination through satellite navigation [23].  
In this paper, the FFT benchmark first calculates a batch of predefined sizes of 1D FFT. The size parameter can be used to specify the size of these FFTs. A batch of FFTs is used to increase the overall execution time of the benchmark test, to reduce measurement errors, and to better utilize the kernel pipeline. The benchmark kernel is based on the  
Figure 1: Basic principles of evaluation.  
cuFFT library provided by NVIDIA, with slight modifications to allow execution on the corresponding experimental platform, using complex single-precision floating-point values for computation. The configuration of the cuFFT library is carried out using FFTplan, where a plan defines a single transformation operation to be performed. With the plan, memory and computational resources can be pre-configured based on the size of the input data, allowing the processor to achieve optimal performance during actual computation.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 4 Proposed Spe-Cks Scheme
### Security Proof
If \(R=\hat{e}(g,g)^{\frac{1}{4}}\), \(\mathbb{A}\)'s view is identical to its view in a real attack game and it must satisfy \(|Pr[\beta^{{}^{\prime}}=\beta]-\frac{1}{2}|\geq\epsilon\). If \(R\) is a random number and \(R\in G_{2}^{*}\), then it must have \(|Pr[\beta^{{}^{\prime}}=\beta]|=\frac{1}{2}\). Therefore, we have that:  
\[|Pr[\mathbb{C}(P,xP,x^{2}P,\ldots,x^{q_{T}+1}P,\hat{e}(p,p)^{\frac{1}{x}})=1]-Pr [\mathbb{C}(P,xP,x^{2}P,\ldots,x^{q_{T}+1}P,R)=1]|\geq\frac{\epsilon}{e^{ \epsilon}e^{\epsilon}\mathit{m}q_{T}^{2}}\]  
This means that \(\mathbb{C}\) can solve the decision \((q_{T}+1)\)-BDHI assumption with probability at least \(e^{{}^{\prime}}=\frac{\epsilon}{e^{\epsilon}e^{\epsilon}\mathit{m}q_{T}^{2}}\). We complete the proof of the theorem. The above security proof demonstrates that our scheme is secure against chosen keywords attack. The following paragraph is showing that our scheme can defend against the keyword guessing attack. As figured out by [PERSON] and [PERSON] in Reference [28], the reason SPE schemes inherently suffer from the keyword guessing attacks by adversaries is that they have abilities to execute the \(IndexBuild\) and \(Test\) algorithms simultaneously. If the capability of index building for adversaries is limited, the adversaries fail to launch the keyword guessing attack. In the proposed scheme, for the \(IndexBuild\) and \(Trapdoor\) algorithms, the adversaries only can obtain \(g_{0}\), \(g_{0}^{s}\) and \(g_{0}^{t}\). If the adversaries can calculate the value of \(\phi=g_{0}^{st}\), then it means that the adversaries can solve the CDH problem [31] in a polynomial probabilistic time. Considering that the CDH problem is hard for any polynomial probabilistic time adversaries, the secret \(\phi\) is unknown to anyone except the senders and the receiver. Based on this, neither the outsider attacker nor the malicious insider server is able to produce a correct ciphertext for any keyword set of theirs interest. Thus, we argue that our scheme can defend against the keyword guessing attack.
## 4 Uncertainty Analysis
### Model Selection and Application
#### 4.2.3 Deep Learning Model  
In recent years, an increasing number of scholars have also started exploring the application of deep learning in landslide susceptibility assessment [120; 121; 122]. Deep learning  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** &
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} & **Specificity** & **Sensitivity** & **AUC** \\ \hline RF & 0.948 & 0.887 & 0.956 & 0.927 & 0.985 \\ SVM & 0.942 & 0.871 & 0.983 & 0.868 & 0.984 \\ BPNN & 0.930 & 0.846 & 0.939 & 0.907 & 0.973 \\ Stacking ensemble & 0.958 & 0.908 & 0.982 & 0.932 & 0.988 \\ Blending & 0.947 & 0.878 & 0.956 & 0.910 & 0.980 \\ ensemble & 0.955 & 0.901 & 0.971 & 0.924 & 0.987 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model prediction performance results [119].  
Figure 6: Integrated learning framework [119].  
has the capability to automatically learn landslide features and then model and assess the data. Compared to traditional machine learning models, deep learning possesses stronger expressive power and evaluation accuracy. Currently, typical deep learning methods include convolutional neural networks (CNN) [92; 123; 124], recurrent neural networks (RNN) [125], and long short-term memory networks (LSTM) [126], among others. These methods have different advantages and applicability in various aspects such as data processing, feature extraction, model construction, and evaluation [127].  
[PERSON] et al. [92] were the first to introduce convolutional neural networks (CNN) for landslide susceptibility assessment. The authors developed three different data representation algorithms (1D, 2D, and 3D) to construct three distinct CNN architectures and compared these with traditional machine-learning models (Figure 7). Using OA, MCC, ROC, and AUC metrics for evaluation, the results showed that the CNN-2D model had the highest assessment accuracy. The authors concluded that CNNs are more suitable for landslide prevention and management than traditional machine-learning models. [PERSON] et al. [52] also compared CNN models with traditional machine-learning models. The results indicated that the landslide susceptibility maps generated using CNN models had better consistency and exhibited superior predictive capabilities. However, a limitation that was noted was the relatively small scale of the study.  
Deep learning does offer greater advantages in terms of evaluation accuracy, but it also sacrifices transparency and interpretability, and there is a risk of overfitting in the models. Currently, most applications of deep learning in landslide susceptibility assessment are based on small scales, such as cities or counties with simple ge  
Figure 7: Flowchart of the proposed CNN framework [92].  
to apply deep learning on a larger scale will be a direction for future research. Compared to traditional machine learning models, deep learning models are more complex, making them harder to understand, and learning the models presents a significant challenge for researchers.
## 4 Materials and Methods
### Model Selection and Training  
#### 4.2.1 Model Selection  
While DL methods are traditionally associated with image and sequential data, their application to network traffic analysis is well-justified by the inherent characteristics of DDoS attack patterns and IoT network flows. Sequential connectivity and temporal interconnections in network traffic data demand advanced pattern recognition capabilities. CNNs can capture local feature patterns and complex interactions inside network flow characteristics by considering feature vectors as 1D sequences, which enables the identification of attack signatures across multiple feature dimensions simultaneously. The comprehensive evaluation of both traditional ML and DL methods overcomes the variety of computing needs and deployment scenarios existing in IoT systems. For IoT systems with limited resources, this makes it possible to create hybrid frameworks that combine the computational efficiency of conventional techniques with the advanced pattern recognition abilities of DL. LSTMs are particularly suitable for this domain as DDoS attacks exhibit sequential dependencies and temporal behavioral patterns that traditional ML methods could not totally capture. In this work, five distinct ML and DL models are evaluated: RF, SVM, KNN with varying k values, LSTM, and CNN.
## 3 Proposed Converter Topology  
Bi-directional conversions transmit power in both directions between two DC sources. The bi-directional DC-DC converters are widely utilized in various applications, including large fuel, hybrid cars, and uninterruptible power sources. These devices are constantly backed up and sustained by rechargeable resources like batteries. At the standard level, a bi-directional DC conversion is necessary to allow for energy flow in both directions. These converters are ready to step forward or backward and transfer voltage levels between their input and output. The bi-directional conversion sends photovoltaic energy to the system when the DC bus power is reduced.  
The bi-directional DC-DC converter is shown in Figure 1. The circuit uses inductors, resistors, capacitors, diodes, and switches with two power supplies. The bi-directional model enhances the voltage gain. The boosting conversion is the first part of the network, while the reversible converter is the latter. To balance the inherent resistance level of the photovoltaic panel with the resistance of the loads, any converter (Speed, Bow, or Buckboost) is utilized, which leads to the transfer of high energy. MPPT is attained by adjusting the switching frequency ratio of the DC converters, allowing the PV panel to function at its full capacity. The duty cycle is changed by the MPPT algorithms, whereby the PV array functions at the voltages corresponding to the highest power. In most cases, the supply to the power converter is a steady input voltage in locations other than photovoltaic. The duty factor is computed based on the amplification and the circuit characteristics are determined based on the permissible ripple spectrum.  
First, the boost conversion must be designed to meet the required charge output voltage. The boosted converter's functionality is tested by adjusting the load to a continuous power level. The loading currents and voltages change when the load voltages increase, but the converter's terminal voltage and line current should remain the same. If there is a difference, it implies the existence of losses. The reversible DC conversion is the next level, which can function in both forward and backward directions.