esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Analysis and Discussion of Results of Computer Experiments
### Analysis of the Recognition Effectiveness of Word Frequency  
The maximum recognition accuracies on Dataset-0 obtained with word frequencies are shown in Figure 4: from left to right, the first column shows the word selection approaches, and the second column shows the recognition accuracies. It should be noted that in the study, two feature selection procedures were used to select words whose frequencies will be used in word frequency feature groups. For the calculation of word frequencies and for word frequency feature groups, see Sections 3.5 and 5.3, respectively. In the author recognition problem under consideration using one of these procedures, the most frequently and middle frequently used words in the lexicons of the candidate authors were selected. Using another procedure, the most frequently and middle frequently used words in the training set were selected without taking into account the author differences. For feature selection procedures 1 and 2, see Formulas (1)-(4) and (5)-(7), respectively.  
Figure 3: Recognition accuracies obtained with 2-gram frequencies using feature selection procedure 4: (a) 4.1 and 4.2 variants of the feature selection procedure (see Formulas (12) and (13)); (b) 4.2 and 4.3 variants of the feature selection procedure (see Formulas (13) and (14)).  
It can be seen from Figure 4 that the frequencies of words selected using only procedure 2 turned out to be more effective in recognition than the frequencies of words selected using only procedure 1. Recognition accuracy increased when selecting words by manually removing some words--words used in everyday life, names of characters in literature works, and so on from among the words most used by authors, i.e., selected using feature selection procedure 1 (see groups \"procedure 1\" and \"procedure 1, MAN\" in Figure 4). Recognition accuracy decreased when the most common words in the training set, i.e., selected using feature selection procedure 2, were not selected from all words but only from words belonging to auxiliary parts of speech that do not have lexical meaning (see groups \"procedure 2\" and \"procedure 2, AUX\" in Figure 4). When the most frequently used words of authors are selected separately by the authors using feature selection procedure 1, in addition to the usage frequencies of words in a given text, the total usage frequency of words in each candidate author's texts, and the total usage frequency in the training set are used in recognition, recognition effectiveness decreases (see groups \"procedure 1, MAN\" and \"W&WA&WT procedure 1, MAN\" in Figure 4). This may be due to the fact that the frequencies in the authors' texts have already been taken into account when selecting words using procedure 1. In addition to the frequencies of the most frequently used words in the training set without taking into account author differences, total usage frequencies in candidate authors' texts and the overall frequency in the training set are also used, and recognition accuracy increases (see \"procedure 2\" and \"W&WA&WT, procedure 2\" groups in Figure 4).
## 3 Multi-Link Channel Access Schemes and Coexistence Issue
### STR Capability  
Since multiple radio interfaces exist within a single MLD, near-band in-device interference may arise. Figure 3 shows the transmit spectral mask requirements of an IEEE 802.11 transmitter in the 20, 40, and 80 MHz bandwidths [18]. As shown in this figure, for a transmit power of 1 mW, the maximum allowable power leakage to other channels ranges from \(-\)20 dBm (for neighboring channels) to \(-\)40 dBm (for other channels); for a typical level of transmit power (e.g., 20 dBm), this range will be higher. These RF power  
Figure 2: Common UMAC and queuing architecture for packet-level link aggregation.  
leakage levels are higher than the energy detection (ED) threshold of IEEE 802.11, which is \(-62\) dBm. This may cause other links that are contending in other channels to falsely detect such leakage as a busy medium.  
If an MLD is equipped with high-fidelity filters and shielding, this RF power leakage problem can be avoided, thus enabling STR. However, if the level of power leakage to the nontransmitting links of an MLD ruins either their clear channel assessment (CCA), signal reception, or both (We consider the case in which the power leakage of a transmitting link ruins both the CCA and reception of other links in an MLD), it does not have the STR capability. Depending on whether they possess the STR capability, MLDs can be classified into the following two types:  
* STR MLD: A transmission on one link does not affect the operations of frame reception and CCA on other links. Therefore, individual links can operate independently of each other.
* Non-STR MLD: Operation on one link is restricted by operation on another link; a transmission on one link is not allowed if it will cause reception interruption on another link, or a reception or CCA on one link is not allowed if a transmission is ongoing on another link.  
In general, an AP MLD should be capable of STR, while different non-AP MLDs may have different STR capabilities. Therefore, IEEE 802.11 be WLANs support the following cases: STR AP MLDs with STR non-AP MLDs and non-STR non-AP MLDs. It is still important to provide non-STR MLDs with throughput enhancement and latency reduction close to those of STR MLDs.
## 4 Results and Discussion  
Validating the Quantitative Calculation Method for Storage Tank Probability Based on Fuzzy DFT and Neural Network  
In order to validate the feasibility of the developed ANN model, a series of model tests were conducted on the trained ANN model using 30 sets of BEs failure rate data from different scenarios as a test dataset, aiming to simulate the computational process of DFT. The results of the proposed method in this paper were compared with those of the traditional Monte Carlo simulation method, and the results are shown in Figure 7. The green curve in the figure represents the failure probability of the top DFT events obtained from the Monte Carlo simulation, while the orange curve represents the prediction results of the constructed ANN model. The prediction results between the two methods exhibit significant consistency, and the results obtained by the proposed method align with the findings of [PERSON] et al. [17] and [PERSON] et al. [18]. This provides strong evidence to demonstrate the effectiveness and accuracy of the proposed method. In addition, through an estimation of the computation time, we found that using the ANN model significantly improves computational efficiency when handling large amounts of failure data. For example, in this study, when 1000 sets of data were used for computation, the ANN model completed the calculation in just 2 min, while the Monte Carlo model took approximately 2 h. This demonstrates that the ANN model significantly enhances computational efficiency when processing large-scale data, greatly saving computation time.  
Figure 7: Comparison of Fault Probability in Prediction Sets using ANN and Monte Carlo Simulation Methods.  
In order to assess the performance of the ANN model more comprehensively, the average difference value and the maximum difference value between the predicted results of the two models were calculated. The results show that the average difference value between the results calculated by the two methods is 6.61 \(\times\) 10\({}^{-4}\) and the maximum difference value is 1.82 \(\times\) 10\({}^{-3}\). These small difference values further confirm the high accuracy of the ANN model in predicting the probability of failure of the top event.
## 4 Results and Analysis
### Results of the EEG Emotion Dataset
#### 4.2.3 Results of Wrapper-Based Feature Selection on the EEG Emotion Dataset  
We employed a wrapper-based feature selection method for the classification task utilizing SVMs, logistic regression, RFs, and XGBoost algorithms. The primary objective was to enhance the classification performance by identifying and incorporating relevant features into the models.  
Table 5 describes the experimental outputs obtained from wrapper-based feature selection using GA. The exploration of feature selection using the GA yielded insightful results, demonstrating the algorithm's ability to tailor feature subsets for improved machine learning model performance. The baseline assessment without feature selection on the original dataset of 2548 features set a performance benchmark, with RF achieving an accuracy of 98.78%, logistic regression at 96.72%, XGBoost showing 99.30%, and SVM exhibiting a solid accuracy of 96.67%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of** & & & & & \\
**Features** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline \multirow{4}{*}{2548} & RF & 98.78 & 98.79 & 98.78 & 98.78 \\  & Logistic & 96.72 & 96.78 & 96.71 & 96.70 \\  & Regression & 99.30 & 99.30 & 99.30 & 99.30 \\  & SVM & 96.67 & 96.70 & 96.66 & 96.66 \\ \hline \multirow{4}{*}{95} & RF & **99.39** & **99.39** & **99.39** & **99.39** \\  & Logistic & **97.70** & **97.70** & **97.70** & **97.70** \\ \cline{1-1}  & Regression & XGBoost & **99.34** & **99.35** & **99.34** & **99.34** \\ \cline{1-1}  & SVM & **97.80** & **97.80** & **97.79** & **97.79** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of machine learning after applying the embedded-based feature selection method. Improved results compared to models without feature selection are marked in boldface.  
The GA, which is employed for feature selection, showed its efficacy in identifying optimized feature subsets for each model. For RF, a reduction to 1264 features resulted in an increase in accuracy to 99.30%. The improved precision, recall, and F1 score indicate that the algorithm successfully captured the most informative features. Similar trends were observed for logistic regression and XGBoost, with 1274 and 1241 features selected, respectively. Both models exhibited comparable or improved performance across key metrics, emphasizing the effectiveness of the GA in optimizing feature subsets.  
For the RF model, there was a modest increase in accuracy (+0.52%), precision (+0.49%), recall (+0.51%), and F1 score (+0.51%). Logistic regression showed a slight increase in accuracy (+0.92%), precision (+0.85%), recall (+0.93%), and F1 score (+0.94%). XGBoost showed slight changes in accuracy (+0.04%), precision (+0.03%), recall (+0.04%), and F1 score (+0.04%). SVM exhibited an increase in accuracy (+0.36%), precision (+0.32%), recall (+0.36%), and F1 score (+0.37%).
###### Abstract  
Renewable energy generation and nonlinear load devices will generate harmonics and reactive power to power grids, resulting in current distortion and low power factors. To solve the power quality problems, the \(LC\)-coupling hybrid active power filter (\(LC\)-HAPF) is proposed, with lower DC-link voltage and lower cost compared with conventional active power filters (APFs). The LC-HAPF requires a controller to operate, therefore, hysteresis current control (HCC) and proportional current control (PCC) were proposed. However, they both result in significant steady-state error. Hence, linear quadratic regulator control (LQRC) with integral action (LQRIC) is proposed for the \(LC\)-HAPF in this paper to mitigate the steady-state error. The \(d\)-\(q\)-\(0\) coordinate state-space model of the LQRIC-controlled \(LC\)-HAPF is derived, and a detailed design guideline of the weighting matrices Q and R of LQRIC is given. By the state-space model and weighting matrices, the gain matrix K of LQRIC can be acquired by MATLAB, thus a good steady-state performance can be ensured. Finally, the simulation results of different controllers for the \(LC\)-HAPF under 40V and 50V DC-link voltages are given to verify the effectiveness of the proposed LQRIC. The experimental results of LQRIC-controlled \(LC\)-HAPF are also given to verify the feasibility of the proposed LQRIC.  
_ACL_-coupling hybrid active power filter; power quality; current harmonic; linear quadratic regulator control; optimal control
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 2 Data and Problem Analysis
### LiDAR Data Characteristics  
In the task of point cloud clustering, we need to be familiar with the characteristics of the point cloud collected by the LiDAR. For the rotary LiDAR, like Velodyne 64E, the main characteristics of point clouds are as follows:  
(1) **Sparse density in different directions:** For the same object, the point cloud in the horizontal direction is dense, while the vertical direction is relatively sparse. Therefore, the processing point cloud in the horizontal and vertical directions needs different methods to adapt to its density characteristics [37]. For point clouds in the same horizontal channel, if the Euclidean distance is less than a particular threshold value, it can be regarded as the same category. However, if the distance  
Figure 1: The points with different colors represent different traffic objects.  
exceeds the threshold value, other judgment methods should be added to prevent over-segmentation. For points in the same vertical direction, they come from different channels, for which we need to calculate their spatial position relationship, and the selection of the threshold is also different.  
(2) **Distance to the sensor:** The point cloud collected at close range is dense, but the distant object only has sporadic point clouds. With the same angular resolution, the farther the distance is, the greater the distance between the same included angles is [36]. So an object's point cloud density depends on the distance to the sensor. For the point cloud nearby, the distance between ten adjacent vertical angle units is still relatively close, while for the distant point cloud, the difference of several vertical angle units is far in space.  
(3) **Missing point cloud:** Although the number of lasers collected from the LiDAR is same at a certain frequency, the amount of data collected by the receiver is different. The point cloud in some angles is missing, because black areas absorb laser light, or it might be sent to the sky and not return, and these missing data need to be handled properly, preventing it from interfering with clustering speed. Therefore, we need to establish a connection method, which can mark the invalid points and quickly find the effective point clouds on different sides of the invalid points.
## 3 Time-Resolved Serial Femtosecond Crystallography
### TR-SFX with Light-Unresponsive Biomacromolecules
On the other hand, if the photocage is diffusing into the crystal water channels, a time delay of diffusion toward the active site after de-caging is to be expected at the nanosecond scale. Therefore, it is important to test the experimental setup before attempting the time-limited XFEL experiments. To have a good idea of sample and photocage behavior at the beamlines, a good characterization of the following factors is useful: absorption profile, quantum yield and cleavage rate. After verifying the experimental feasibility and the good compromise of the various variables, including pump laser energy, sample laser absorption and de-caging efficiency, it is also useful to test the delivery method in-house. An example of photocage usage for TR-SFX experiments is caged nitric oxide (NO) (Figure 3) [34]. Here, the cleavage rates of under tens of microseconds were observed, even though the crystal structure for that particular study slowed the reaction significantly. The photocage quantum yield was 1.4 (each cage released 2 NO) and the setup had the pump beam split into two sample impinging beams that were 160\({}^{\circ}\) apart for a higher triggering homogeneity. Photocaged soaked crystals were pumped by 6 ns pulses of 308 nm wavelength and 0.31-0.51 mJ/cm\({}^{2}\) energy, achieving a compromise between damage (threshold energy of 60%) and triggering efficiency as only 50% of the sites showed NO occupation. Quality dark frames (or time 0) together with various other experiments validated the structural information that was obtained [34,73]. In conclusion, although photocages enable a method that can provide the fastest timescale triggering of a system (similar accessible timescales up to fs are allowed by the pump-probe method), there is no \"one size fits all\" photocage because different molecules have different combinations of characteristics, meaning that the best overall cage must be found for each study case. It should be noted that the lack of viable photocages can limit the scope of potential TR-SFX studies. However, for each new uncovered or manufactured photocage molecule that is compatible with the field, the TR-SFX toolbox of photocages becomes larger and more effective.
# Hunting Molecules in Complex Matrices with SPME Arrows: A Review
## 5 SPME Arrow Applications
### Environmental Analysis

Table 3: Comparison of different headspace technologies towards the analysis of VOCs. Table reprinted with permission of [PERSON] from Reference [29], 2019. Headspace work with SPME Arrow has been performed for the analysis of volatile amines in waste water. For example, after careful optimization of the extraction conditions, [PERSON] et al. observed that the SPME Arrow produced lower detection limits, and higher recoveries of dimethyl amine (DMA) over the traditional SPME fiber (88% vs. 57%, respectively) [17]. The SPME Arrow has also been used for the analysis of semi-volatile compounds using immersion extraction. Typically, semi-volatile sample preparation for water samples has used liquid-liquid extraction (LLE) or solid phase extraction (SPE). [PERSON] et al. did a comparison of SPME technology versus LLE and SPE, and it was found that the reduction of unwanted matrix interferences, the solventless extraction, the reusability, and the feasibility for high throughput sample analysis made SPME a more attractive technique [47]. [PERSON] et al. did extensive work with freely dissolved polycyclic aromatic hydrocarbons (PAHs) in water [48]. Extraction times and stirring rates were optimized to determine the method detection limits, and these were compared to previously published data for traditional SPME and SBSE (see Table 5). Direct immersion experiments using the SPME Arrow for freely dissolved semi-organic compounds showed detection limits five times lower than the traditional SPME fiber and similar results to stir bar sorptive extraction (SBSE). When comparing the SPME Arrow to SBSE, the authors highlighted the easiness of automation and utilization of shorter extraction times. The SPME Arrow has also been used for environmental air sampling with the in-field analysis of biogenic volatile organic compounds (BVOCs). Monoterpenes (gamma-pinene and d3-carene) and aliphatic aldehydes (octanal and decanal) were selected by [PERSON] et al. to represent expected compounds to be found in field testing [49]. In-laboratory extraction efficiencies showed that the SPME Arrow had two-to-three times more area count than the traditional SPME fiber. Sampling was then performed in a boreal forest in Hyytiala, Finland. [PERSON] et al. determined that the extraction efficiency of the SPME Arrow was two-times higher or greater, depending on compound, than the traditional SPME fiber, allowing for more sensitive testing (see Figure 6) [49]. Figure 6: Comparison between the average mass of identified monoterpenes (\(\alpha\)-pinene, 13-carene, and limonene) and aldehydes (octanal, nonanal, and decanal) collected with different polydimethylsiloxane-divinylbenzene (PDMS-DVB) SPME devices (fiber and Arrow) from ambient air and measured by GC-MS. Figure reprinted with permission of Elsevier from Reference [48], 2019.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 4 RF Waveform Configuration  
The chirp signal duration consists of 128 linear frequency-modulated chirps, each of which can mainly be characterized by a 240 us chirp cycle time and bandwidth B of  
Figure 8: Antennas physical diagrams.  
Figure 10: Simulated and measured impedance and axis ratio bandwidth.  
Figure 9: Simulated and measured radiation patterns.  
1.024 GHz. The frequency of the signal varies over time between the minimum frequency \(f_{\rm min}\) and the maximum frequency \(f_{\rm max}\), as shown in Figure 11. The configuration of the RF chirp and frame affects the performance of the radar [21], and the RF waveform configuration needs to consider the following factors. First, based on the farthest detection distance and the chip sampling frequency limit formula \(R_{\rm max}=\rm{IF}_{\rm max}C/2S\), it can be determined that the maximum FMCW slope does not exceed 10 MHz/us. The range resolution of radar \(\Delta R=C/2B\) means \(\Delta R\) only depends on the frequency modulation bandwidth B of the transmitted waveform, and the frequency modulation bandwidth can be determined to be 1.024 GHz according to the requirements of range resolution. Second, the maximum speed \(V_{\rm max}=\lambda/4T_{\rm c}\) is related to the chirp period. According to the requirements of the speed range and considering that the chirp period also needs to meet the conditions of data sampling, transmission time, and fumeless ranging, the final chirp period is determined to be 240 us. At the same time, the speed resolution only depends on the effective transmission period of the frame, according to the speed resolution \(\Delta V=\lambda/2 NT_{\rm c}\), the frame period is finally determined to be 35 ms. A detailed listing of AWR2243's RF bandwidth, chirp slope, sampling rate, and ADC sampling performance is shown in the Table 3 below.