esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 2 Literature Review
### Deep Learning-Based EEG Data Analysis
\begin{table}
\begin{tabular}{c c c} \hline
**Field** & **Study/Studies** & **Model** & **Accuracy** \\ \hline \multirow{5}{*}{Emotion recognition} & [PERSON] et al. (2017) [91] & Hierarchical network (CNN) & \(\sim\)88.00\% \\ \cline{2-4}  & [PERSON] et al. (2017) [108] & LSTM & \(\sim\)85.00\% \\ \cline{2-4}  & [PERSON] et al. (2018) [99] & Deep belief network–graph convolutional network + LSTM + support vector machine & \(\sim\)87.00\% \\ \cline{2-4}  & [PERSON] et al. (2017) [105] & CNN-LSTM & \(\sim\)90.00\% \\ \hline \multirow{5}{*}{Motor imagery classification} & [PERSON] et al. (2019) [102] & CNN-LSTM & \(>\)90.00\% \\ \cline{2-4}  & [PERSON] et al. (2018) [94] & LSTM & 82.52\% \\ \cline{2-4}  & [PERSON] et al. (2018) [107] & Wavelet transform + CNN & 85.59\% \\ \hline \multirow{2}{*}{Seizure detection} & [PERSON] et al. (2018) [109] & CNN & 88.67\% \\ \cline{2-4}  & [PERSON] et al. (2019) [110] & CNN–recurrent neural network & \(>\)97.00\% \\ \hline \multirow{2}{*}{Sleep stage classification} & [PERSON] et al. (2017) [111] & CNN + bidirectional-LSTM & 74.80\% \\ \cline{2-4}  & [PERSON] et al. (2019) [61] & CNN & \(\sim\)80.00\% \\ \hline Event-related potential detection & [PERSON] et al. (2018) [88] & EEGNet (lightweight CNN) & \(>\)90.00\% \\ \hline \end{tabular}
\end{table} 
Table 8: Studies using electroencephalography data for classification tasks.
# An MLLM-Assisted Web Crawler Approach for Web Application Fuzzing
## 5 Evaluation
### Evaluation of Enhanced Vulnerability Detection Capabilities  
To evaluate CrawlMLM's effectiveness in vulnerability discovery, we conducted experiments using two distinct sets of Web application datasets: the first set comprised three Vulnerable Web Applications (DVWA [25], XVWA [26], and bWAPP [27]), while the second set included seven diverse widely deployed open-source Web applications. For vulnerability assessment, we employed both the commercial scanning tool Burp and the open-source scanner ZAP's audit capabilities. The crawler component utilized three different approaches: Burp's built-in crawler, the Rnd BFS and URL path algorithm, and CrawlMLM.  
Figure 6 presents a comparative analysis of vulnerability detection results across different tool combinations in the Vulnerable Web Application environment. Among all six combinations tested, CrawlMLM+Burg demonstrated superior detection capabilities across all three testing environments, identifying 44 vulnerabilities, 10 more than the next best combination. Notably, in XVWA, which features the least complex structure and lowest page complexity, all combinations exhibited similar performance. However, as application complexity increased progressively from XVWA through DVWA to bWAPP, CrawlMLM's contribution to vulnerability discovery became increasingly pronounced, highlighting its robust adaptability to complex Web applications and its sophisticated comprehension of intricate page structures and functionalities.  
Table 3 presents the vulnerability detection results across three scanning configurations tested on six real-world web applications, with the numbers in parentheses indicating vulnerabilities uniquely discovered by each configuration. The results demonstrate that the CrawlMLM+Burg combination identified a total of 20 vulnerabilities, with 14 being exclusive to this configuration. Consider the OpenEMR system case study: of the 28 documented vulnerabilities in the system, our approach successfully detected 13. The remaining 15 undetected vulnerabilities can be attributed to two primary limitations: (1) crawler coverage  
Figure 6: Comparative Analysis of Vulnerability Detection Results in Vulnerable Web Applications. (**a**) The x-axis depicts three vulnerable web applications, representing the number of vulnerabilities identified by six distinct configurations across each application. (**b**) The x-axis represents six configurations, illustrating the aggregate number of vulnerabilities discovered by each configuration across all three vulnerable web applications.  
constraints, accounting for six undetected vulnerabilities due to insufficient functional exploration within the allocated timeframe (partially addressable through extended crawl durations, though some cases are constrained by CrawlMLM's interaction history capacity limits); (2) tool-specific limitations, where nine vulnerabilities remained undetected by Burp despite successful request data acquisition, due to the tool's inherent detection capabilities.
## 3 Results and Discussion
### \(\text{NH}_{3}\) Emission  
The NH\({}_{3}\) emissions from all treatments mainly occurred during the high temperature period (Figure 5). This was mainly due to the strong mineralization of organic nitrogen into NH\({}_{4}\)+, which was further converted into NH\({}_{3}\) under high temperature and alkaline conditions [10]. Therefore, NH\({}_{3}\) emission peaks were observed for each treatment around day 7 of composting. As the temperature decreases, NH\({}_{3}\) emissions then drop to a lower concentration. As mentioned above, turning can reactivate related microorganisms, restore biodegradation of organic matter, and regulate matrix porosity, thus facilitating the occurrence of NH\({}_{3}\) re-discharge.  
Compared with T1 treatment, T2 treatment can effectively reduce the cumulative NH\({}_{3}\) emission by more than 75% (Figure 5). The main reason was that the addition of GW improved the C/N ratio and biodegradation efficiency of the matrix in the composting process. In fact, T1 treatment inhibited biodegradation efficiency and prolonged the high temperature period (Figure 2), resulting in increased NH\({}_{3}\) production after each turning of the heap to activate the compost matrix between day 7 and day 21.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 3 Results
### HEMP Characteristics
#### 3.1.1 HEMP Characteristics with Free Space  
According to Equation (1), there is a double exponential pulse waveform, which has the characteristics of a short rise time, long fall time, long tailing, and a strong peak field. Figure 5 shows the time-domain and frequency-domain waveforms of the HEMP radiation field respectively.  
In Figure 5, the EMP radiation field energy is mainly concentrated in the low frequency band (within 100 MHz), and the field strength value at each frequency point in the low frequency band is relatively uniform. These constitute the main frequency components that cause a coupling effect in the electronic equipment of the aircraft platform. If the frequency \(f\) is greater than 100 MHz, the amplitude of the frequency components decreases at a rate of about 20 dB per decade as the frequency increases. Hence, the frequency sweep method can be considered useful for necessary calculations, and the calculation frequency range is defined as 1 kHz-50 MHz.
###### Abstract  
Rotation search and point cloud registration are two fundamental problems in robotics, geometric vision, and remote sensing, which aim to estimate the rotation and transformation between the 3D vector sets and point clouds, respectively. Due to the presence of outliers (probably in very large numbers) among the putative vector or point correspondences in real-world applications, robust estimation is of great importance. In this paper, we present Inlier searching using COmputable Structures (ICOS), a novel, efficient, and highly robust solver for both the correspondence-based rotation search and point cloud registration problems. Specifically, we (i) propose and construct a series of _compatible structures_ for the two problems, based on which various _invariants_ can be established, and (ii) design time-efficient frameworks to filter out outliers and seek inliers from the invariant-constrained random sampling based on the compatible structures proposed. In this manner, even with extreme outlier ratios, inliers can be effectively sifted out and collected for solving the optimal rotation and transformation, leading to our robust solver ICOS. Through plentiful experiments over standard datasets, we demonstrated that (i) our solver ICOS is fast, accurate, and robust against over 95% outliers with nearly a 100% recall ratio of inliers for rotation search and both known-scale and unknown-scale registration, outperforming other state-of-the-art methods, and (ii) ICOS is practical for use in real-world application problems including 2D image stitching and 3D object localization.  
rotation search; 3D point cloud registration; robust estimation; invariant; image stitching; object localization +
Footnote †: journal: Applied  
18 August 2021  
1
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 3 Results
### RGB-D Results  
Figure 13 illustrates the RGB-D results obtained for the three experiments. The unrivalled results obtained in experiment one (Figure 13a) produced an r\({}^{2}\) of 0.950 and an RMSE of 12.458 g--the best-performing results presented in this study. The Kinect sensor favoured the controlled conditions of the laboratory, specifically the artificial illumination as a source of light. Applying the same methodology to experiment two (in-situ bunches) resulted in a lower yield estimation performance for both canopy treatments. The FC treatment (Figure 13b) produced an abnormally low r\({}^{2}\) of 0.020, with an RMSE of 8.081 g. A statistical outlier in the data was evident when the results were analysed. With the removal of this outlier, the modified FC results (n = 20) improved drastically, with a new r\({}^{2}\) of 0.609 and an RMSE of 26.790 g (Figure 13c). In contrast, the LR treatment (Figure 13d) resulted in an r\({}^{2}\) of 0.756 and an RMSE of 24.601 g, which aligned with the LR results for bunch-level obtained with RGB imagery (Figure 12c). At plant-level, the unfavourable results of the FC treatment (Figure 13e) generated an r\({}^{2}\) of 0.487 and an RMSE of 673.535 g. However, the LR treatment (Figure 13f) provided some promise for the Kinect sensor at plant-level, achieving an r\({}^{2}\) of 0.594 and an RMSE of 661.739 g. The same effect of the canopy treatment was evident in the RGB-D results as in the RGB results, with the LR treatment producing a better yield estimation agreement. The results of experiment three indicated a limitation within the proposed methodology for RGB-D yield estimation at plant-level. Such limitation could be from the data-acquisition process, or the image segmentation within the data analysis.  
The poor results obtained in experiment two's FC dataset can be attributed to the mesh reconstruction step in the data-analysis process. Exaggerated reconstruction of bunches presents a potential limitation of the _Screened Poisson Surface Reconstruction_ algorithm [30], as depicted in Figure 14. The statistically-outlying bunch circled in red (Figure 13b) produced a Kinect volume of 856 cm\({}^{3}\), as illustrated in Figure 14a, when the actual volume was only 35 cm\({}^{3}\). An example of an accurately reconstructed mesh (Figure 14b) was included for visual comparison. This anomaly was unavoidable during data processing.  
Figure 12: RGB results presented for the three experiments; experiment one (**a**), experiment two FC (**b**) & LR (**c**) and experiment three FC (**d**) & LR (**e**). _[Key: Exp. = experiment; FC = full canopy; LR = leaf removal]._  
Figure 13: Presented RGB-D results of the three experiments; experiment one (**a**), experiment two FC (n = 21) (**b**), experiment two FC\({}^{\star}\) (n = 20) (**c**), experiment two LR (**d**) and experiment three FC (**e**) & LR (**f**). [Key: Exp. = experiment; FC = full canopy; LR = leaf removal; “statistical outlier removed, resulting in 20 bunches].
## 2 Materials and Methods
### Protein and Amino Acid Analysis of Soybeans and Pea Beans
#### Staining and interpretation of bands  
The gel was removed from the plate system and subjected to the fixation step. Fixation means treating the gel with the fixing solution (30 mL of 12% trichloroacetic acid solution) for 15 min. The gels were placed in a crystallizer, and the Coomassie staining reagent was added over them. They were kept under stirring conditions for 12 h. Then, the gels were removed from the staining solution and were destained several times with fractions of the destaining solution until the contrast between the protein bands and the gel background was adequate to read (essentially the reagent that had entered the gel pores was eluted; the protein-bound reagent was stable upon discoloration) for approximately 6 h, with a solution change every 2-3 h. The presence of proteins causes the appearance of horizontal blue bands, and the evaluation of molecular masses was based on the molecular mass marker. Subsequently, it was stained with silver reagent. Silver reagent is generally used for small amounts of protein due to its sensitivity. In the presence of appreciable amounts of proteins, the staining becomes too intense and difficult to interpret, the close bands overlap and the protein fractions cannot be distinguished. However, the advantage was represented by the visualization of some proteins in small amounts, which are not visible after staining with the Coomassieie reagent.  
The Pierce Silver Stain kit was used. Silver coloring steps are as follows:  
1. The gels were washed twice with ultrapure Milli-Q(r) water for 5 min each. Then, the water was removed.
2. They were fixed with ethanol/acetic acid/ultrapure water 3:1:6 \(v/v/v\) solution twice for 15 min. The solution was removed.
3. They were washed with 10% ethanol solution in water twice, 5 min each.
4. The gels were immersed in a solution consisting of 25 mL Milli-Q(r) ultrapure water and 50 \(\mu\)L Silver Stain Sensitizer reagent for exactly 1 min.
5. They were washed with ultrapure water twice for exactly 1 min, after which the water was removed.
6. They were immersed in a solution consisting of 25 mL Milli-Q(r) ultrapure water and 250 \(\mu\)L Silver Stain Enhancer reagent for exactly 5 min.
7. They were washed with ultrapure water twice for exactly 20 s, after which the water was removed.
8. They were immersed in a mixture of 25 mL Silver Stain Developer and 250 \(\mu\)L Silver Stain Enhancer for approximately 30 s until bands became visible.
9. The reaction was quickly stopped with 5% acetic acid solution.  
Stained gels were analyzed on the scanner and with Bio-Rad's ChemiDoc XRS + System software (Bio-Rad, Hercules, CA, USA). Since, after silver staining, differently colored bands were obtained, for their better observation, photos were also captured with a classic camera. All reagents used were of analytical grade from Merck and Sigma-Aldrich.  
All samples were analyzed in triplicate, and the results were expressed as mean \(\pm\) standard deviation (SD).
# An ADS-Based Sparse Optimization Method for Sonar Imaging Sensor Arrays
## 4 Analysis of ADS Thinned Array
### Azimuth Resolution  
In this paper, the MVDR direction spectrum estimation method is used to evaluate the azimuth resolution of the thinned array after applying the ADS method. The azimuth resolution in the radar field is defined as the minimum azimuth angle between two objects that can be distinguished by radar in the same radar detection direction. Using MVDR direction spectrum estimation, we determine the azimuth resolution based on the azimuth angle when the minimum value of the direction spectrum of the two sources is increased by 3 dB, that is, the lowest depression between the direction spectra of two sources is raised by 6 dB. It is important to note that the azimuth resolution has a direct relationship with the width of the main lobe of the beam pattern. Figure 10 displays the MVDR direction spectra estimations of different arrays with the steering angle at \(\varphi=0^{\circ}\) and \(\theta=0^{\circ}\), where the subfigures a and d respectively correspond to the sparse arrays after application of the ADS and CDS methods to the original Mills Cross array with \(40\times 40\) and \(20\times 20\) elements, respectively. Here, we indicate that the ADS and CDS methods still correspondingly use the parameters above. According to Figure 10, the azimuth resolutions of the various arrays in subfigures a and b are roughly \(5.2^{\circ}\), \(5.5^{\circ}\), \(5^{\circ}\), and \(10.2^{\circ}\). To fully describe the azimuth performance of these four arrays, Table 3 gives the average values and standard deviations of the azimuth resolutions of the arrays at the different steering angles. Here, the acoustic wavelength \(\lambda\) is set as 100 KHz.  
Figure 9: Beam power pattern of the obtained array after employing the CDS method. (**a**) 3D pattern; (**b**) 2D section pattern.  
From these results, one can observe that the azimuth resolution of the thinned array using ADS is superior to that using CDS, although the CDS method can produce a better \(PSLL\) of the thinned array. The reason for this disadvantage of the CDS method is that the autocorrelation function of the CDS method has the 'binarity' property. Besides, this work also implies that the same azimuth resolution can be achieved by the ADS method using half the numbers of the original Mills array. With these results, there is reason to believe that the ADS method can produce one sparse array with unchanged beam performance when the right ADS sequence is created.
# Potential of Waste Oyster Shells as a Novel Biofiller for Hot-Mix Asphalt
## Materials and Methods  
### Preparation of Oyster Shell Powder (OSP)  
Specimens of the _Crassostrea gigas_ (Figure 1A) were collected from Tongyeong beach, Gyeongsang province, South Korea. First, the oyster shells were properly cleaned by brushes after discarding the fresh remnant to them. Subsequently, they were well washed with deionized water (DI water) to remove the excess of alkalinity and chloride, and dried in a drying oven (\(T\) = 100 \({}^{\circ}\)C) for ca. 2 days. The dried shells were ground (4 times, 4 h per round) using a dry ball-mill (Oyster Shell Grinding Machine, Zhengzhou Jiangtai Heavy Industrial Machinery Co., Ltd, Weihai, China) to generate a fine powder, which was then sieved in a No. 50 sieve (300 \(\upmu\)m). In practical applications, fillers with a granulometry of less than 75 \(\upmu\)m (i.e., passing ASTM standard sieve No. 200) are used to provide the best homogenous asphalt mixtures [31]. However, the asphalt pavement design may not be the greatest scenario for maximizing pavement performance when pavement distresses such as fatigue cracking, rutting, and moisture damage occur. Therefore, the purpose of this investigation was to determine the influence and effects of a wide range of different particle sizes (up to 300 \(\upmu\)m) on the asphalt-filler mastic performance properties through selected Superpave (i.e., Superior Performance Asphalt Pavement) binder tests. It must be emphasized here that this was done only to learn about the advantages and disadvantages associated with the use of particles larger than the standard. Hence, the aforementioned procedure is not intended to be used in practice. After sieving, the oyster shell powder (labelled as OSP, Figure 1B) was stored at room temperature in a closed container. The particle size and distribution of OSP were performed using a Mastersizer 3000 Smarter Particle Sizing (Malvern Instruments Ltd., Malvern, UK) granulometer. The measurement was carried out at room temperature (ca. 25 \({}^{\circ}\)C) with an Aero S dry powder disperser (\(P\) = 1 bar). The chemical composition was conducted using elemental analyzer (Thermo ScientificTM FLASH 2000 CHNS/O Analyzer, Loughborough, UK), X-ray fluorescence spectrometer (Shimadzu EX-720, Shimadzu Corporation, Chiyoda-ku, Tokyo, Japan), and energy-dispersive spectroscopy (SEM, JSM-6010 LA, JEOL Ltd., Akishima, Tokyo, Japan).  
Figure 1: Oyster shell waste (**A**) and its fine powder (**B**).
## 3 Results  
### Quantitative Results  
The quantitative test results (in terms of \(DSC\), HD95 and ABD metrics) for both MR and US segmentation models (globally, and by dataset) are shown in Table 2. Both models achieve a mean and median \(DSC\) above the 0.91 threshold for all datasets, meaning that they are very strong performers and, more interestingly, that they are robust to the heterogeneity of the various datasets. As an exception, the mean \(DSC\) on the \"Girona\" dataset falls to around 0.90 due to a single relatively weak prediction (\(DSC\) of 0.8467) dragging down the mean of this extremely small set, as evidenced by the otherwise inexplicably high median value. Also, the mean \(DSC\) for the MR segmentation model on the \"Private\" dataset is exceptionally high, probably due to it being the only dataset where GT segmentations were revised. Figure 5: Mask predicted by a prostate segmentation CNN and upscaled along the z-axis three times using: nearest-neighbor interpolation (**left**), and Neural Resolution Enhancement (**right**). These observations are further supported by the HD95 metric. For all MR and US datasets, it sits mostly just below 4 mm in average. Since the slices in a typical prostate MR image are about 3 mm apart, achieving an HD95 below 3 mm is extremely unlikely due to the different criteria regarding how far the base and the apex should extend. Thus, an average HD95 below 4 mm is a very good result. Finally, the ABD metric lies mainly below 1 mm in average for all MR datasets, and below 1.2 mm for the US datasets. For comparison purposes, a second segmentation (GT2) was created for the first three datasets by one of the IVO experts. Table 3 shows the mean \(DSC\) of the predictions of the model against each of the GTs (GT and GT2), as well as the mean \(DSC\) of the GTs against themselves (the inter-expert agreement). As it can be seen, the \(DSC\) of the model against both GT and GT2 surpasses by a large margin the inter-expert agreement (except for one case), suggesting that the model is more robust and reliable than any given expert by itself. Two Wilcoxon tests confirm that these differences in \(DSC\) are statistically significant (at a significance threshold of 0.05). Since most authors focus on performing well in one single dataset, it is difficult to compare these results against other published models. As an exception [16] used a private dataset (with diffusion-weighted MR images and ADC-maps in addition to the T2-weighted MR images) in conjunction with \"Promise12\", achieving an impressive mean \(DSC\) of 0.9511 on their own dataset, but only a mean \(DSC\) of 0.8901 on \"Promise12_test\". Regarding 3D-US image segmentation, few publications were found (most use 2D-US), and none employed more than one dataset. As for recent 3D-US papers, ref. [19] achieved a mean \(DSC\) of 0.90 by leveraging an attention mechanism. Ref. [33] obtained a \(DSC\) 0.919 by using a contour-refinement post-processing step, however, the results are not reported on a proper test set, but rather, using leave-one-out cross-validation. More recently, ref. [34]