esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Phytochemistry of _Artemisia annua_ L. (Qinghao)
### Sesquiterpenenoids
#### 2.6.2.3. Humulanes and Caryophyllanes
\begin{table}
\begin{tabular}{|l|l|l|l|} \hline
**Name** & **Alternative Name(s)** & **CAS Number** & **References** \\ \hline _Humanalenes_ & & & \\ \hline \(\alpha\)-Humulene (**401**) & 2,6,9-Humulatriene & [6753-98-6] & [19, 27, 28, 32, 40, 43, 66] \\ \hline
14-Hydroxy-\(a\)-humulene (**402**) & & [108043-85-2] & [27] \\ \hline Humulene epoxide I (**403**) & 2,3-Epoxy-6,9-humuladiene & [19888-33-6] & [45] \\ \hline Humulene epoxide II (**404**) & 6,7-Epoxy-2,9-humuladiene & [19888-34-7] & [27, 45] \\ \hline _Caryophyllanes_ & & & \\ \hline \(\beta\)-Caryophyllene (**405**) & (_E_)-3(15),6-Caryophylladiene & [87-44-5] & [19, 22, 23, 24, 26, 27, 29, 32, 36, 37, 40, 41-43, 45, 48, 49, 51, 66, 72- 74, 112, 140] \\ \hline \(\gamma\)-Caryophyllene (**406**) & Isocaryophyllene & [118-65-0] & [31] \\  & (_Z_)-3(15),6-Caryophylladiene & & \\ \hline (1\(R\),3Z_,9S)-Bicyclo[7.2.0]undec-3-ene, 4,11,11-trimethyl-8-methylene (**407**) & & [136296-35-0] & [50] \\ \hline CaryophyllladenI (**408**) & Caryophyllenol & [19431-80-2] & [28, 32, 43] \\  & Bicyclo[7.2.0]undecan-5-ol, 10,10-dimethyl-2,6-bis(methylene)-, (15,55,9R)-Caryophylla-4(12),8(13)-dien-5\(\beta\)-ol & & \\ \hline CaryophyllladenI (**409**) & Caryophyllenol & [19431-79-9] & [28, 32, 43] \\  & Bicyclo[7.2.0]undecan-5-ol, 10,10-dimethyl-2,6-bis(methylene)-, (15,5,9R)-Caryophylla-4(12),8(13)-dien-5\(\beta\)-ol & [38284-26-3] & \\ \hline Caryophyllene oxide (**410**) & Isocaryophyllenic oxide & [1139-30-6] & [9, 19, 23, 25, 27, 30, 32, 34, 43, 45, 48, 49, 17627-43-9] & [9, 19, 23, 25, 27, 30, 32, 34, 43, 45, 48, 49, 17627-43-9] & [9, 23, 34, 45, 48, 49, 17627-43-9] & [9, 19, 23, 25, 27, 30, 32, 34, 45, 48, 49, 17627-43-9] & [17, 23, 34, 45, 48, 49, 17627-43-9] & [17, 23, 35, 66] \\ \hline _cis_-Caryophyllene oxide (**411**) & 5-Oxatricyclo[8.2.0.04,6]dodecane, 4,12,12-trimethyl-9-methylene-, (1\(R\),45,6_,105)- & [60594-23-2] & [32] \\ \hline \end{tabular}
\end{table} 
Table 33: Humulane Sesquiterpenoids (1,1,4,8-Tetramethyllycyloundecan, 9 CI) and Caryophyllane Sesquiterpenoids (2,6,10,10-Tetramethylbicyclo[7.2.0]undecan, 9 CI).
## 4 Discussion  
This study aimed to provide position-specific (defenders vs. forwards) reference data for the IHCT. The comparison of the field players revealed only one relevant difference in the 30 m backward sprint without a puck (\(p\) \(<\) 0.001, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.097, d = 0.74) in favor of the defenders (5.37 \(\pm\) 0.21 s vs. 5.55 \(\pm\) 0.28 s). Clear (d \(>\) 0.4) but not relevant differences were also found in 10 m backward sprint without a puck (d = 0.57), weave agility with a puck (d = 0.46), heart rate recovery minute 10 (d = 0.42), and goals after test (d = 0.41). In general, forwards seem to have advantages regarding speed, agility parameters, and the ability to recover. In contrast, defenders showed more frequently higher slap shot velocities. For the anthropometric parameters, there was only a weak group difference (\(p\) = 0.004, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.060) in height. Forwards (1.81 \(\pm\) 0.05 m) displayed a lower height compared to defenders (1.84 \(\pm\) 0.06 m). The same is valid for body mass (85.3 \(\pm\) 8.56 kg vs. 87.1 \(\pm\) 8.48 kg). At the start of the discussion, it should be mentioned that this is an update from the already published reference data for the ice hockey-specific complex test [(16)]. Previous studies examined individual player position-specific load parameters [(2; 3; 5; 24; 25; 26; 27; 28; 29)]. However, their results are difficult to compare due to differences in study methodology (e.g., sample size, length of career, class of competition, location of tests, definition of performance). Most importantly, however, the validation of these tests based on the match performance during prospective data collection has been missing so far. [PERSON] et al. [(29)] supported the need for implementing the technical aspect [(13; 30)] in a complex on-ice diagnostic. The authors postulated that the specific technique (skating vs.
## 7 Real World-Use Cases
### Environments for Learning  
Regarding possible educational uses, ShinyAnonymizer could be a valuable resource for students with a talent for manipulating data and health analytics. It links students to real-world health data and helps them learn critical management and anonymization skills. This application benefits people by giving them more real-world experience and boosting their chances of success in healthcare data management. Ultimately, the ShinyAnonymizer Enhanced Version shows its significance when improving healthcare data management. Its ability to be utilized in various scenarios shows how flexible and effective it is at ensuring privacy, while delivering intelligent data analysis. It seems helpful in this kind of scenario for scientists, businesses, and healthcare professionals who have trouble managing sensitive patient data.  
Managing all kinds of health data is an essential part of its use. Due to various mechanisms, types of data, and guidelines, electronic medical data can be complex. The ShinyAnonymizer Enhanced Version's usage in the current healthcare sector is increased by its ability to manage and anonymize organized and unorganized data. A notable feature of its use is the tool's ability to manage multiple kinds of health data. Due to their distinctive features, particular data types and standards in health data systems can be complex. Lastly, the fully interactive and simple UI of the ShinyAnonymizer Enhanced Version contributes to its greater usefulness in real-world scenarios. Scientists and healthcare professionals taking part in exploring data, statistical evaluation, and clear visualizations might encourage educated, informed decision-making. It helps effectively communicate study results by creating attractive and practical visuals.
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 3 Simulation Results and Statistical Analysis
Based on \(\zeta_{1}\), \(\zeta_{2}\), \(\zeta_{3}\), and \(\zeta_{4}\), the highest scores are 2, 3, 2, and 2, respectively, the score is 0 in 0, 0, 68, and 28 games, respectively, and the score is 2 in 23, 50, 1, and 5 games, respectively. Figure 11c shows the CDF curves of impact score on the north post. The score on \(\zeta_{4}\) is the highest in most games because no defense station is placed nearby. The score on \(\zeta_{2}\) is slightly higher than that on \(\zeta_{3}\) in most games. The score on \(\zeta_{1}\) is the lowest in most games because two nearby defense stations are placed up front, attracting some attacker upon themselves. Based on \(\zeta_{1}\), \(\zeta_{2}\), \(\zeta_{3}\), and \(\zeta_{4}\), the highest scores are 1, 3, 4, and 5, respectively, and the lowest scores are 0, 1, 1, and 2, respectively. The score is 0 in 91 games on \(\zeta_{1}\), is 1 in 21 games and 2 in 73 games on \(\zeta_{2}\), is 1 in 39 games and 2 in 51 games on \(\zeta_{3}\), and is 2 in 48 games on \(\zeta_{4}\). Figure 11d shows that the number of lost defense stations is the highest on \(\zeta_{3}\), second on \(\zeta_{1}\), third on \(\zeta_{4}\), and the lowest on \(\zeta_{2}\). Based on \(\zeta_{1}\), \(\zeta_{2}\), \(\zeta_{3}\), and \(\zeta_{4}\), the highest numbers are 6, 5, 7, and 7, respectively, and the lowest numbers are 4, 1, 5, and 2, respectively. The number of lost defense stations is 4 or 5 in 85 games on \(\zeta_{1}\), 2 or 3 in 61 games on \(\zeta_{2}\), 5 or 6 in 97 games on \(\zeta_{3}\), and 3 or 4 in 63 games on \(\zeta_{4}\). Figure 11e shows that the number of launched interceptors is insensitive to the type of defense cost function. The CDF curves have steep slopes, which imply that a similar number of interceptors are fired in all the games. The number is 63 to 72 on \(\zeta_{1}\), 66 to 71 on \(\zeta_{2}\), 62 to 68 on \(\zeta_{3}\), and 63 to 68 on \(\zeta_{4}\). Figure 11f shows that the number of lost attackers is similar in all four of the cases, which is 26 to 35 on \(\zeta_{1}\), 25 to 36 on \(\zeta_{2}\), 26 to 33 on \(\zeta_{3}\), and 25 to 34 on \(\zeta_{4}\). Slightly fewer attackers are lost when the attack stations are in pincer formation than in slant formation. The simulations are run with MATLAB R2019a on a PC with i7-3.00 GHz CPU and 32 GB memory. A typical game takes about 6.5-7.1 and 8.5-8.7 CPU hours when the attack stations are in slant and pincer formation, respectively, with kill probability of 0.7.
# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 3 Data Acquisition and Pretreatment  
### Data Acquisition  
The brake system of hoists includes disc brakes and hydraulic stations. The hoist brake system used in the laboratory has one brake disc on both sides of the drum, and also includes eight brake shoes and two hydraulic pipes. In the laboratory, by using displacement sensors, temperature sensors, pressure sensors, etc., to monitor or calculate the key parameters of the brake system directly or indirectly, the corresponding data is obtained. The specific monitoring parameters are shown in Table 1.  
After collecting the monitoring data, the SPSS data pretreatment method in the next subsection is used to analyze and process the fault eigenvalues, which can reduce the data dimension and reduce the computational workload.
## 4 Discussion
### Spectral Sensitivity of HSI Image to Soil Alkalinity and Salinity
Thus, the linear models were suitable to describe the relationship between reflectance and soil salinity. Compared with the methods, ANN performed as well as PLSR but might take a longer time than PLSR. The PLSR model could result in an identical calibration model [27]. Some studies reported that MARS was a more suitable technique than PLSR to estimate and map soil salinity, especially in areas with high levels of salinity [58]. When satellite imagery with improved quality becomes available in future studies, we will perform a comparative analysis of the various techniques in assessing soil salinity and alkalinity in the study region.
# The XMUSPEECH System for Accented English Automatic Speech Recognition
###### Abstract
Firstly, the features of the hidden layer of TDNN-F are not only related to the input at the current moment but also with the input at the past and future moments, definitely, the context information is fully modeled. Secondly, the TDNN-F is a 1-D convolution with time translation invariance, for different accents, the same phoneme is pronounced with sounds of different lengths, therefore, the TDNN-F could capture the common features between different accents. We further developed it with multistream CNN and attention mechanism. The idea of multistream CNN [14] comes from the multistream self-attention architecture [15] but without the multi-headed self-attention layers, it processes the input speech frames in multiple streams in parallel, which allows the system to have better robustness. Inspired by speaker recognition, an accent-dependent method is introduced to speech recognition [16; 17; 18]. [PERSON] et al. analyzed the suitability of x-vectors for ASR adaptation in detail. Furthermore, [PERSON] et al. [17] combined accent embeddings with semi-supervised LF-MMI training to train an acoustic model and achieved almost the same results as the fully supervised approach. In this paper, we extracted four types of representations (i.e., spk-ivector, accent-ivector, spk-xvector, and accent-xvector) and compared their efficiency in the accented ASR system. We found that x-vector embedding performed better than i-vector embedding. Finally, language model rescoring was employed in order to obtain a lower WER. Our best system achieved relative WER improvements of 40.6% on the development set and 35.6% on the evaluation set compared with the baseline. The rest of this paper is structured as follows. In Section 2, we describe the details of our systems. In Section 3, we provide the experimental setups and discuss the results from various approaches. Finally, we conclude our work in Section 4.
# Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
###### Abstract  
The use of underwater recording is widely implemented across different marine ecology studies as a substitute for more invasive techniques. This is the case of the Deep Scattering Layer (DSL), a biomass-rich layer in the ocean located between 400 and 600 m deep. The data processing of underwater videos has usually been carried out manually or targets organisms above a certain size. Marine snow, or macroscopic amorphous aggregates, plays a major role in nutrient cycles and in the supply of organic material for organisms living in the deeper layers of the ocean. Marine snow, therefore, should be taken into account when estimating biomass abundance in the water column. The main objective of this project is to develop a new software application for the automatic detection and analysis of biomass abundance relative to time in underwater videos, taking into consideration small items. The application software is based on a pipeline and client-server architecture, developed in Python and using open source libraries. The software was trained with underwater videos of the DSL recorded with low-cost equipment. A usability study carried out with end-users shows satisfaction with the user-friendly interface and the expected results. The software application developed is capable of automatically detecting small items captured by underwater videos. In addition, it can be easily adapted to a web application.  
biological software tools; underwater image processing; biomass abundance; deep scattering layer; marine snow +
Footnote †: journal: Applied
## 2 Materials and Methods
### Characterization Methods
#### 2.5.3 Mechanical Properties Testing  
Tensile tests were conducted to evaluate the mechanical properties of the wet-spun PCL and PCL/CNC filaments. Testing was performed using a Hounsfield Tinius Olsen H100 KS machine (Tinius Olsen, Redhill, UK) equipped with a 250 N load cell. Filament samples of 70 mm in length were tested at a strain rate of 25 mm/min following ASTM D5035 [39] guidelines. Each sample was tested in triplicate to ensure reproducibility. Key mechanical parameters, including tensile strength, strain at break, and Young's modulus, were derived from stress-strain curves. The tensile strength corresponds to the maximum stress recorded before the filament fractured. The elongation at break was determined from the total strain at failure. The stress-strain curves were obtained by recording the applied force and the corresponding elongation of the filament until failure. Stress (\(\sigma\)), strain (\(\epsilon\)), and Young's modulus (\(E\)) were calculated using Equations (1)-(3):  
\[\sigma=\frac{F}{A} \tag{1}\]  
\[\epsilon=\frac{\Delta L}{L_{0}} \tag{2}\]  
\[E=\frac{\Delta\sigma}{\Delta\epsilon} \tag{3}\]  
where \(F\) is the applied force (N), \(A\) is the initial cross-sectional area of the filament (mm\({}^{2}\)), \(\Delta L\) is the change in filament length (mm), \(L_{0}\) is the initial length of the sample (mm), and \(\Delta\sigma\) and \(\Delta\epsilon\) are the changes in stress and strain within the linear elastic deformation range.
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### LFS-Based Sentiment Classification Algorithm for Audovisual Scenes  
#### 2.2.1 Movie Scene Emotion Clip Information Statistics  
The dataset was a movie scene emotion clip database that consists of 21 clips in 5.1 audio format. The 5.1 audio channels include six channels: the center channel, front left channel, front right channel, rear left surround channel, rear right surround channel, and a 0.1 channel for LFS (SW). The SW channel was specifically designed for the subwoofer and had a frequency range set at 20 Hz-120 Hz. Each movie scene clip was annotated with an emotion category label, and the quantity and duration ranges of clips for each emotion category are shown in Table 2. Seven clips from the shock category and eight clips from other emotion categories were extracted from the movie scene emotion clip database as the training and validation sets, with a ratio of 7:3 for training and validation. The accuracy of the validation set was used to evaluate the performance of the model. The remaining three clips from the shock emotion category and three clips from other emotion categories were used as the test set to evaluate the generalization ability of the classification model and its feasibility in practical engineering applications.
## 2 Related Work
### Security Computing Methods Based on Noise or Frameworks  
#### 2.2.1 Differential Privacy  
Differential privacy (DP) [21] is a technique designed to protect individual privacy during data analysis by adding a certain amount of noise to the data processing, making it impossible to accurately distinguish or identify individual information from the statistical results. The core of this method lies in introducing sufficient \"uncertainty\" into the dataset results to protect the individuals participating in the dataset from being identified, even when faced with infinite auxiliary information [22]. The main principle of differential privacy is to ensure that the output results are nearly identical regardless of whether a particular individual's data is included. This is achieved by adding controlled random noise to the query results with the amount of noise typically depending on the sensitivity of the query [23], which is the maximum impact a single record in the dataset can have on the query results. Mathematically, an algorithm \(\mathcal{A}\) satisfies \(\epsilon\)-differential privacy if for any two adjacent datasets \(D\) and \(D^{\prime}\) (differing by at most one element) and for all \(S\subseteq\mathrm{Range}(\mathcal{A})\), the following holds:  
\[\Pr[\mathcal{A}(D)\in S]\leq\epsilon^{\epsilon}\Pr[\mathcal{A}(D^{\prime})\in S] \tag{3}\]where \(\epsilon\) is a non-negative parameter known as the privacy budget, determining the amount of noise added and thus the strength of privacy protection. A smaller \(\epsilon\) value means more noise is added, leading to higher privacy protection but potentially lower data accuracy and usability. Differential privacy is widely applied in data statistics, data mining, and machine learning [24]. By using differential privacy techniques, researchers can publish useful statistical information and research results without compromising individual privacy in government statistical data releases, social science research, and commercial data analysis [25; 26]. However, the main challenge in implementing differential privacy protection measures lies in designing appropriate noise addition mechanisms and finding the optimal balance between enhancing privacy protection and maintaining data usability. Inappropriate privacy parameter settings can result in overly blurred data outcomes that are ineffective for decision making, while overly relaxed settings may fail to provide the necessary privacy protection.
## 2 Background
### Singular Value Decomposition  
For a discrete sampled signal \(\mathbf{X}\in\mathbb{R}^{N}\), its Hankel matrix is constructed:  
\[\mathbf{H}=\left[\begin{array}{cccc}x(1)&x(2)&\cdots&x(n)\\ x(2)&x(3)&\cdots&x(n+1)\\ \vdots&\vdots&\ddots&\vdots\\ x(N-n+1)&x(N-n+2)&\cdots&x(N)\end{array}\right] \tag{5}\]  
Let \(m=N-n+1\), in order to obtain the best denoising effect, when the signal length is even, we should take the number of columns \(n=N/2\) and the number of rows \(m=N/2+1\). While \(N\) is odd, the number of columns \(n=(N+1)/2\) and the number of rows \(m=(N+1)/2\) should be selected.  
The singular value decomposition of \(\mathbf{H}\) is:  
\[\mathbf{H}=\mathbf{U}\mathbf{S}\mathbf{V}^{T} \tag{6}\]  
The diagonal elements of \(\mathbf{S}\) are called singular values of \(\mathbf{H}\), denoted as \(\sigma_{1},\sigma_{2},\ldots,\sigma_{n}\). These singular values are shown as the former \(k\) values are large, and the later singular values are very close to 0. The difference spectrum can describe the change of the singular value sequence. As long as the superposition of the former \(k\) components is selected and the singular value after \(k\) is set to zero, the noise reduction signal can be obtained.
## 4 Innovative Approach to Building a Metaverse Classroom  
Building a Metaverse classroom is an innovative step in modern education, combining technology and strategic planning to create engaging and inclusive virtual learning spaces [16]. The process begins with defining clear educational goals that address challenges such as equitable access, student engagement, diverse learning needs, and career readiness [144]. Identifying the target audience--including age groups, subject areas, and unique requirements like assistive technologies or multilingual support--ensures the classroom design aligns with real-world applications and student needs [16]. Choosing the right Metaverse platform is critical to ensure the classroom supports 3D environments, VR/AR capabilities, and scalability across devices, from VR headsets to smartphones [56]. Platforms like Unity, Unreal Engine, Mozilla Hubs, or AltspaceVR enable the creation of immersive virtual spaces that are accessible and secure. Prioritizing data security and compliance with regulations like GDPR and FERPA protects student information and ensures the classroom is a safe learning environment [220]. A versatile, secure platform makesthe Metaverse classroom adaptable to various educational contexts while safeguarding privacy [70].  
After selecting a platform, the next step is developing the virtual classroom infrastructure, including themed breakout rooms and interactive environments [221]. Tools like Blender and SketchUp allow developers to design spaces ranging from realistic science labs to historical sites, promoting experiential learning [222]. AI-driven features personalize learning by adjusting content to individual student needs, providing real-time feedback and analytics [136]. Gamified elements, such as quizzes and achievement badges created with tools like Kahoot, encourage student engagement while helping educators track progress and adjust instruction based on performance [134]. The classroom should integrate interactive learning tools and assistive technologies to ensure inclusivity. VR/AR tools within platforms like Unity and Unreal Engine enable 3D simulations and holographic displays for hands-on experiences [53]. Assistive technologies like closed captioning, voice-to-text, and real-time translation tools from Microsoft Azure or Google Cloud make the virtual space accessible to students with disabilities or language barriers [7]. Additionally, providing offline access allows students with limited internet connectivity to download materials and engage with content asynchronously [131]. By prioritizing accessibility, personalization, and adaptability, the Metaverse classroom broadens educational access and enhances learning experiences. This holistic approach ensures all students benefit from an inclusive, comprehensive, and future-ready educational environment. Figure 2 provides an overview of the innovative framework for constructing an immersive Metaverse classroom environment.