esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Publication** & **Task** & **Methods** & **Performance** \\ \hline [PERSON] et al. (2011) [39] & 2010 & 2b2/VA challenge on concepts, assertions, and relations in clinical text [38] & SVM-based supervised learning algorithm & Best performance with an F1-score of 0.737 \\ \hline [PERSON] et al. (2016) [76] & Clinical RE on 2010 & Domain invariant CNN on multiple features & Decent performance: filter combination of [4; 6] performed the best (F1-score: 0.7116) amongst CNNs \\ \hline [PERSON] et al. (2016) [79] & Disease-mutation RE on biomedical texts & C4.5 decision trees on various features & State-of-the art performance thus far; F1-score of 0.880 and 0.845 on prostate and lung disease mutations \\ \hline [PERSON] et al. (2016) [95] & Clinical RE on 2010 & Deep autoencoder-based model and sparse deep autoencoder-based model & Sparse deep autoencoder-based model performed better with an F1-score above 80\% \\ \hline [PERSON] et al. (2017) [80] & Disorder Recognition in the 2013 CLEF task-1 dataset & multi-label structured SVM & Improved Performance: F1-score: 0.7343, i.e., 0.1428 more than the baseline BIOHD1234 scheme. \\ \hline [PERSON] et al. (2017) [73] & Clinical RE based on the categories of medical concepts & Feature-oriented SVM-based supervised learning & Better performance (F1-score: 0.86) than the rule-based approach (F1-score: 0.79) \\ \hline [PERSON] et al. (2018) [72] & Clinical RE for biological pathway & Node2 vec to learn the features from texts in networks & Best performance for type 2 diabetes pathway \\ \hline [PERSON] et al. (2018) [96] & Clinical RE towards drug surveillance & SVM model and a deep learning-based LSTM model & SVM performed better (89.1\% F1-score) than all of the LSTM models \\ \hline [PERSON] et al. (2019) [97] & Clinical RE on 2010 & NNs to model the shortest dependency path between entities and sentences & Resulted in an improved performance with an F1-score of 74.34\% \\ \hline [PERSON] et al. (2019) [81] & Clinical RE on 2010 & Multi-class SVM & Poor performance (F1-score: 0.70) compared with the previous models \\ \hline [PERSON] et al. (2020) [79] & Extraction of the adverse drug events and medications relations relations & An ensemble deep learning method & F1-score of 0.9472 and 0.8765 for RE and end-to-end RE, respectively \\ \hline [PERSON] et al. (2020) [82] & Clinical RE on 2010 & Deep learning methods such as CNN, GCN, GCN-CDT, ResNet, and BiLSTM & BiLSTM performed the best with a nine-class F1-score of 0.8808 and a six-class F1-score of 0.8894 \\ \hline [PERSON] et al. (2021) [70] & Adverse drug event extraction on 2018 & Sentence-CNN and segment-CNN & F1-score: 0.78 and macro-average F1-score: 0.77) than the other models mentioned \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the machine learning-based approaches for clinical RE.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 5 Experiment and Result
### Dataset Preprocessing  
We collected 60380 raw benign and malicious samples with a total size of more than 3.1 GB. To prevent duplication of data from multiple data sources, we calculated the MD5 of each sample to ensure that each sample is unique.  
As for malicious samples, 29893 files (Table 4) were collected from HynekPatrak [48], geeksonsecurity [49] and [PERSON]'s dataset [50]. Most of these malicious samples are JavaScript files. For HTML files, we manually extracted the JavaScript code from the \(<\)script\(>\) tags and stored them as a JavaScript file. Almost all samples were obfuscated, e.g., through data obfuscation or encoding obfuscation.  
As for benign samples, we collected JavaScript files in Alexa [51] top sites. We believe that the JavaScript files from these sites are safe, because subjectively these sites have no motive to do evil, and objectively there are a large number of security engineers to protect these sites. For each website, we first visited their homepage, then visited the same domain link on the homepage to collect dynamically generated scripts. For inline scripts in HTML, we extracted the JavaScript code and stored it as a JavaScript file in the original order. In this way, we obtained 30487 unique benign samples. In order to ensure that these JavaScript files are benign, we checked them with anti-virus software. [PERSON] et al. [52] indicate that more than 30% of developers obfuscate their scripts and more than 55% of third-party scripts are obfuscated. Therefore, there should also be a certain proportion of obfuscated scripts in our benign samples.  
We constructed two new datasets, DB_DeOb and DB_Ob, based on the collected raw data (called DB_Raw). The dataset DB_DeOb contains only deobfuscated scripts. We combined deobfuscation tools and manual analysis to deobfuscate 1227 malicious samples and 1516 benign samples. It should be noted that there is currently no strict definition of obfuscated code and deobfuscated code, so we consider the code that meets the following two conditions to be successful in deobfuscating:  
Condition 1: The variables and strings in the code are not encoded by Unicode, ASCII, etc.  
Condition 2: Compared with before processing, the clarity of the processed code logic has been significantly improved.  
The dataset DB_Ob consists only of obfuscated codes. In order to construct DB_Ob, we collected 9892 benign samples and 9721 malicious samples from DB_Raw with conspicuous feature of obfuscation. Table 5 gives the details of these datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **\#JS** \\ \hline HynekPatrak & 27458 \\ \hline geeksonsecurity & 1751 \\ \hline WangWei & 684 \\ \hline Total & 29893 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of malicious JavaScript.
## 4 Results
### Invention Classification
#### 4.2.5 Fuel Cell Integration  
Patents in this category address the core challenge of efficiently embedding hydrogen fuel cell systems into aircraft architectures while managing heat, water, and energy flows under dynamic flight conditions. These inventions propose modular, lightweight, and fault-tolerant designs that integrate fuel cells with propulsion systems, cooling networks, and aircraft infrastructure. Common solutions include advanced thermal management using multi-loop systems, cryogenic reactant reuse, liquid hydrogen vaporization, and airflow-based heat exchangers. Several concepts in this category combine fuel cells with auxiliary systems like turbines, compressors, and emergency power units to maximize onboard energy efficiency and redundancy. Integration strategies range from nacelle-embedded systems and Prandtl wings for drones to blended wing bodies and linear motor propulsion for larger aircraft. Innovations also enable onboard hydrogen generation, adaptive oxidizer supply, gray water electrolysis, and environmental conditioning using fuel cell byproducts. Collectively, these patents reflect a shift toward highly integrated, multi-functional systems that enhance performance, endurance, and safety in hydrogen-electric aviation.  
The following examples illustrate how the integration of fuel cells into aviation platforms requires both thermal sophistication and diagnostic reliability. [PERSON] et al. (2024) developed a high-efficiency thermodynamic fuel cell system that incorporates hydrogen expansion stages, regenerative heat exchangers, and closed-loop water recovery to maximize energy use and thermal stability at high altitudes ([PERSON] et al., 2024). [PERSON] et al. (2024) addressed the operational reliability of such systems by embedding ultrasound and ionization-based mass spectrometry sensors to detect faults based on surface condition changes or gas emission profiles ([PERSON] et al., 2024). Together, these inventions exemplify how performance enhancement and early failure detection converge to make fuel cells more viable for commercial flight.
###### Abstract  
This paper reviews recent progress on different high-speed optical short- and medium-reach transmission systems. Furthermore, a comprehensive tutorial on high-performance, low-cost, and advanced optical transceiver (TRx) paradigms is presented. In this context, recent advances in high-performance digital signal processing algorithms and innovative optoelectronic components are extensively discussed. Moreover, based on the growing increase in the dynamic environment and the heterogeneous nature of different applications and services to be supported by the systems, we discuss the reconfigurable and sliceable TRxs that can be employed. The associated technical challenges of various system algorithms are reviewed, and we proffer viable solutions to address them.  
5G network; coherent detection; data center network; digital signal processing; direct detection; intensity modulation; Kramers-Kronig algorithm; minimum-phase signals; self-coherent detection scheme; short-reach communication; Stokes vector detection +
Footnote †: journal: Applied Sciences  
_Applied Sciences_  
_Review_  
A Review of Self-Coherent Optical Transceivers: Fundamental Issues, Recent Advances, and Research Directions
## 3 Joint Simulation of Crack Propagation on Channel Surface of Silicon Nitride Ceramic Ball Bearing
### Motion Model of Joint Simulation  
Figure 6 illustrates the schematic diagram detailing the operational dynamics of a silicon nitride ceramic ball bearing. The ring channel surface experiences both the positive pressure, \(p(x,y)\), arising from the vertical channel surface due to the rolling contact of the ceramic ball, and the opposing friction traction force, \(q(x,y)\), acting in the opposite direction of the rolling motion. The contact load within the working mechanism follows a cyclic pattern, transitioning from a position distant from the crack to proximity through the crack and then gradually moving away from the crack. Consequently, to investigate the stress intensity factor of the crack front at various positions of the contact load, five distinct load contact positions, as depicted in Figure 7, were chosen around the crack. Each of these load contact positions was evenly spaced by 0.1 mm. The numerical calculation expressions for \(p(x,y)\) and \(q(x,y)\) are as follows:  
\[p(x,y)=p_{0}\!\left(1-\frac{x^{2}+y^{2}}{R^{2}}\right)^{1/2} \tag{8}\]  
\[q(x,y)=fp(x,y) \tag{9}\]  
where \(p_{0}\) is the maximum positive pressure, \(R\) is the radius of the load circle, and \(f\) is the friction coefficient. The friction coefficient of the joint simulation is 0.0417, which is obtained from the above rolling contact friction experiment.  
The width of the initial crack is \(a=0.2\) mm, \(\theta\) is the inclination angle of the initial crack, and the depth \(b\) of the initial crack is 0.1 mm, 0.15 mm, and 0.2 mm, respectively. The JH - 2 constitutive model was adopted for the joint simulation of silicon nitride material, and its parameter settings are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Material Parameter**} & \multicolumn{4}{c}{**Damage Constant**} & \multicolumn{4}{c}{**Equation of State Constant**} \\ \hline \(\rho\) & \(E\) & \(D_{1}\) & \(D_{2}\) & \(FS\) & \(K_{I}\) & \(K_{II}\) & \(K_{III}\) & BETA \\ \hline
3 & g/cm\({}^{3}\) & 320 & GPa & 0.35 & 0.74 & 1 & 264 & GPa & 0 & GPa & & 1 & \\ \hline \multicolumn{8}{c}{Strength constant} \\ \hline A & B & C & D & M & N & EPSI & SFMAX & HEL & PHEL \\ \hline
0.95 & 0.35 & 0 & 1 & 0.67 & 1 & 0.7 & Gpa & 0.8 & Gpa & 15 & Gpa & 6 & Gpa \\ \hline \hline \end{tabular}
\end{table}
Table 2: Silicon nitride JH-2 material parameters table.
## 4 Results
### Hypotheses Evaluation  
In terms of research hypotheses, the direct effects of social factors were examined. It was found that adoption of EESS at the level of the network of friends, colleagues, or family (SI_FF) has a positive effect on EESS perceived ease of use (PEOU), perceived usefulness (PU), hedonic motivation to use EESS (HM), and EESS expected performance (PE). Therefore, H1a, H3a, H5a, and H7a were supported. In contrast, promotion of the use of EESS in the public space (SI_PS) may only determine an increased expected performance of EESS (PE), thus giving support only for the other hypotheses formulated in this sense; specifically, H1b, H3b, and H5b were not supported by the empirical results because the direct effects were not statistically significant.  
Furthermore, we were also interested in the indirect effects of social factors on the behavioral intention to use EESS (BIOU). The bootstrapping procedure with 5000 subsamples revealed that the adoption of EESS at the level of the network of friends, colleagues, or family (SI_FF) also has an indirect effect on the behavioral intention to use EESS (BIOU), which is mediated by PEOU, HM, and PE; PU does not significantly mediate the relationship. Therefore, H2a, H6a, and H8a were supported, and H4a was not. Instead, promotion of the use of EESS in the public space (SI_PS) has an indirect effect on a BIOU mediated only by PE, thus supporting H8b. However, H2b, H4b, and H6b were not supported. All the direct and indirect effects of social factors, their associated metrics, and the decision on our research hypotheses are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constructs** & \(\mathbf{R^{2}}\) & \(\mathbf{p}\)**Value** & \(\mathbf{Q^{2}}\) \\ \hline PEOU & 0.208 & 0.000 & 0.196 \\ \hline PU & 0.179 & 0.000 & 0.167 \\ \hline HM & 0.255 & 0.000 & 0.242 \\ \hline PE & 0.313 & 0.000 & 0.302 \\ \hline BIOU & 0.782 & 0.000 & 0.310 \\ \hline \hline \end{tabular} \(R^{2}\)—Coefficient of determination; \(p\) value—the statistical significance; \(\mathrm{Q^{2}}\)—The blindfolding-based cross-validated redundancy measure. Source: authors with SmartPLS 4 [50].  
\end{table}
Table 5: Model predictive power.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 5 Results  
This section presents the quality assessment of the acquired MLS point clouds. In applying our methodology, we distinguish between three different approaches and objectives.  
**Multi-pass approach:**: We investigate the agreement of MLS point clouds obtained with one scanner in several successive passes. Apart from satellite constellation and availability, we assume that the conditions are the same for all passes. The MMS setup, scanning geometry, and surface properties remain unchanged during acquisition.  
Figure 9: Separation between forward and backward passes indicates a lower trajectory quality near the tunnel portal. RGB point cloud of RW1 and discrete color coded points with separation in east (**left**, blue line in right plot); separation in all three components plotted versus time (**right**). Note: Driving direction is opposite to the viewing direction in the left 3D visualization.  
Therefore, the comparison of these point clouds provides information on how well georeferencing of the point clouds was accomplished.
**Ground truth verification:**: In addition to repeatability analysis, we attempt to analyze the accuracy of the point clouds using reference data. For this purpose, we use TLS data registered with high accuracy surveyed GCPs. In addition, direct comparison of these GCPs with manually picked locations in the MLS point cloud allows for independent verification.
**Multi-scanner approach:**: Finally, we compare point clouds of two scanners mounted on one platform. The goal is to verify the co-registration of the scanners. It is worth noting that direct georeferencing can also impact the co-registration of multiple scanners. This is especially true if the time difference between scans is large (e.g., at low travel speeds) or if the quality of the trajectory varies significantly. In the case of non-symmetrically designed systems, different scan geometries (i.e., incidence angle and distances) can lead to further deviations.
## 1 Introduction
[PERSON] [22] suggested the equation to calculate the seismic gap based on the structural vibration periods and damping ratios of buildings. [PERSON] [23]recommended using an effective structural period in the formula for the optimum gap size. The minimum seismic gap between adjacent non-linear structures was analyzed by [PERSON] [24]. [PERSON] et al. [25] used the inelastic vibration phase theory for seismic rounding mitigation. The accuracy of different criteria for a separation large enough to prevent collisions between nonlinear hysteretic structural systems was evaluated by [PERSON] and [PERSON] [26]. A number of other studies concerned also different methods to control relative displacements between adjacent structures, reducing the bounding hazard by the application of various types of dampers, links, etc. (see [27; 28; 29; 30; 31], for example). Moreover, [PERSON] et al. [32] evaluated the adequacy of the seismic gap prescribed by diffident building design codes and load standards (see also [33; 34; 35; 36]). They conducted nonlinear response history analyses on many buildings with different lateral force resisting systems. [PERSON] et al. [32] illustrated that, in many cases, the seismic gap prescribed by building design codes is exceeded significantly, especially when soil-structure interaction effects are taken into account. The aim of the present paper is to show the results of the investigation focused on using an artificial neural network (ANN) method to conduct the evaluation of the optimum gap size between adjacent buildings, so as to prevent their collisions during seismic excitations. In the study, two structures with a different number of stories have been subjected to different earthquake records. To predict the appropriate distance between structures, the earthquake characteristics and the parameters of structures have been defined as inputs in the ANN model.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Network Model  
Our network topology models an IEEE 802.11-based multi-rate WLAN, which contains N STAs and M APs. Each AP has a limited coverage area and neighboring APs are assigned to operate at interference-free channels. Adjacent APs have overlapping coverage areas. Let A and S respectively denote the set of APs and the set of STAs. Each AP serves only STAs that reside in its coverage area. At any given time, a STA can be allowed to choose one and only one AP to associate with whereas each AP can serve multiple STAs simultaneously. The coefficient \(x_{ij}\) is intended to indicate the association relationship between STA \(i\) and AP \(j\), which is defined as follows:  
\[x_{ij}=\left\{\begin{array}{l}1,\;if\;\text{STA $i$ is associated $tp$ AP $j$,}\\ 0,\;otherwise.\end{array}\right. \tag{1}\]  
where \(i\) = 1, 2,..., N, \(j\) = 1, 2,..., M. It is assumed that the optimal data rate of downlink transmission can be estimated by STAs according to the RSSI of Beacon packets from every AP candidate, and it is further assumed that the channel condition between a STA and an AP is symmetric, so the optimal data rate of downlink and uplink transmissions is identical.
## 4 Results Analysis
### Resultant Velocity Variation  
The resultant velocity, which is the vector sum of \(V_{\rm w}\) and \(U\), is depicted as a function of the azimuthal angle in Figure 12. Other than the free wind velocity, the resultant velocity is the effective flow velocity acting on the blade, which is another factor, together with the AoA, that determines the magnitude of all of the forces. The magnitude of the resultant velocity is determined by the tangential flow velocity and flow velocity going through the stream tube according to the local velocity triangle. At the same rotation speed as the VP-VAWT and FP-VAWT, in the upwind half-cycle, the pitch angle does not affect the local tangential flow velocity and wind velocity. This causes slight changes in the resultant velocity in the upwind half-cycle, as shown in Figure 12. The slight changes are also observed when the TSR = 4 and 4.5 in the downstream half-cycle, and large changes occur when the TSR = 5 and 5.5 (Figure 12). The VP-approach decreases the wake velocities of the upwind stream tubes, and changes the velocity of the stream flowing through the stream tubes and the axial induction factor of the downwind half-cycle. The changed stream velocity and axial induction factor determine the different resultant velocities. As depicted in Figure 12, when the TSR = 5.5, the resultant velocity has a tendency to be even in a wide zone of the downwind region.
## 4 Conclusions  
Ti-6 Al-4V samples were successfully fabricated by metal injection molding from both blended and prealloyed powders. Although the rheological experiment indicates that the viscosity of the feedstock prepared by the blended powder is lower than that prepared by the prealloyed powder, both feedstocks can be molded successfully. Subsequently, the green samples were debound according to the thermal analysis result, and sintered at 1200 or 1300 \({}^{\circ}\)C. The density and mechanical properties of samples fabricated from blended powder are very close to those prepared from prealloyed powder. The samples fabricated from blended powder have a sintering density of 4.26 g/cm\({}^{3}\), yield strength of 810 MPa, UTS of 927 MPa, and elongation of 4.6%. In order to demonstrate industrial application potentials of the technology, watch cases with complex shapes were fabricated by injection molding of blended Ti-6 Al-4V powder without any post processing. The sintered parts exhibit little distortion and good dimensional consistence.  
Conceptualization, P.Y.; Methodology, S.Y., W.M., Y.L., and Z.W.; Investigation, S.Y. and W.M.; Data Curation, S.Y.; Writing--Original Draft Preparation, S.Y.; Writing--Review & Editing, P.Y. and C.T.K.  
This research was funded by the Shenzhen Science and Technology Innovation Committee (grant number SGLH20161212155758670, KQJSCX2018032215242453 and JCYJ20170818161702462) and the National Natural Science Foundation of China (grant number 5180010371).  
The Ti-6 Al-4V parts provided by ElementPlus Co., Ltd. (Shenzhen, China) are appreciated.  
The authors declare no conflict of interest.  
Figure 10: The Ti-6 Al-4V watch case fabricated by metal injection molding (MIM) from blended powder.