esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Evaluation Criteria  
The results of the automated segmentation method are quantitatively evaluated by using three popular evaluation criteria including clustering accuracy, entropy and purity. We refer to the class labels as the ground truth and the results of clustering algorithms as the clusters [35; 36]. We refer to class as the ground truth and cluster as the results of clustering algorithms. Clustering accuracy is the most intuitive measure to evaluate the performance of clustering, which is defined as follows  
\[accuracy=\sum_{i=1}^{4}\frac{n_{ii}}{n} \tag{5}\]  
where \(n_{ii}\) represents the number of common samples in cluster \(i\) and class \(i\), \(i\in\{1,2,\ldots,4\}\), and \(n\) is the size of the data set.  
Entropy is an information theoretic measure and is defined as  
\[entropy=-\sum_{i=1}^{4}\frac{n_{i}}{n}\sum_{j=1}^{4}\frac{n_{ij}}{n_{i}}\log_{ 2}\frac{n_{ij}}{n_{i}} \tag{6}\]  
where \(n_{ij}\) indicates the number of common samples in cluster \(i\) and class \(j\), \(n_{i}\) stands for the number of samples in cluster \(i\).  
Purity is computed to measure the degree of clusters containing a single class. The purity is calculated as follows  
\[purity=\sum_{i=1}^{4}\frac{n_{i}}{n}\text{max}(\frac{n_{ij}}{n_{i}}) \tag{7}\]  
Figure 3: The scheme of random forest algorithm. The final prediction is obtained by taking a majority vote of the predictions from all the trees in the forest.
## 3 Numerical Results and Discussion
### Optimization of the BPS Filter Length  
After the optimization of the BPS number of phases \(B=32\), the next step was to investigate the optimum length \(N\) of the BPS algorithm averaging filter. The model A simulator, at a baud rate of 25 GBd, was used to compute the OSNR penalty by sweeping both the phase noise and the BPS filter length \(N\). Figure 6 shows the results for 16-QAM and 64-QAM modulation formats. QPSK and 32-QAM formats yielded similar results and are not plotted here to save space. For both modulation formats shown, \(N<6\) produces a high penalty because of the inability of the BPS algorithm to produce good phase estimates due to large estimation noise. Larger values of \(N\) produce good phase estimates and hence lower penalties. However, for a fixed value of the phase noise, large values of \(N\) yield larger penalties due to the inability of the filter to track quick changes of the phase noise. The larger the phase noise, the lower the filter length \(N\) should be to produce optimum phase estimates. As a compromise, we choose the optimum value of \(N=10\) for all subsequent simulations.
## 5 Performance Analyses
### Impact of STO on the Interference  
This section analyzes how the increase of \(\Delta\) may affect the interference that disturbs the data communication. To do so, we rely on the Frobenius norm of (10), which is a matrix that captures the interference introduced by the CP length violation and the STO in the mapped vector. The Frobenius norm is given by  
\[||\underline{\mathbf{H}}_{-i}||_{\text{F}}=\sqrt{\text{Tr}\left\{\underline{ \mathbf{H}}_{-i}\underline{\mathbf{H}}_{-i}^{\dagger}\right\}}. \tag{42}\]  
As this equation informs the magnitude of \(\underline{\mathbf{H}}_{-i}\), it can quantify the level of degradation yielded by the interference from the \((l-i)\)th 2\(N\)-length symbol block into the /th  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(N\) & 256 \\ \hline \(B\) & 500 kHz \\ \hline \(L_{h}\) & 30 \\ \hline \(L_{\text{CP}}\) & \(14,18,30\) \\ \hline \(\Delta\) & \(0,3,7\) \\ \hline Y & 0 dB \\ \hline \(\mathcal{B}\) & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The main simulation parameters.  
\(2N\)-length symbol block. Moreover, we consider \(\mathbf{h}\) to be normalized (i.e., \(||\mathbf{h}||^{2}=1\)) because it facilitates the visualization of how the [PERSON] norm of \(\overline{\mathbf{H}}_{-i}\) behaves. Figure 7 shows the curves of \(||\overline{\mathbf{H}}_{-1}||_{\text{F}}\), \(||\overline{\mathbf{H}}_{1}||_{\text{F}}\), and \(||\overline{\mathbf{H}}_{-1}||_{\text{F}}+||\overline{\mathbf{H}}_{1}||_{ \text{F}}\) for \(\Delta\in[0,12]\), \(L_{\text{CP}}\in\{14,18,30\}\). For all curves of \(||\overline{\mathbf{H}}_{-1}||_{\text{F}}\) (i.e., all values of the CP), as expected, there is a decrease of its value with the increase of \(\Delta\) until it reaches a value of zero. For instance, \(||\overline{\mathbf{H}}_{-1}||_{\text{F}}=0\) when \(\Delta=11\) and \(L_{\text{CP}}=18\), which follows Theorem 1. However, the increase of \(\Delta\) also increases \(||\overline{\mathbf{H}}_{1}||_{\text{F}}\) in the same manner for all \(L_{\text{CP}}\) since it does not affect \(\overline{\mathbf{H}}_{1}\). To conclude, the most interesting result is to analyze the sum of \(||\overline{\mathbf{H}}_{1}||_{\text{F}}+||\overline{\mathbf{H}}_{-1}||_{ \text{F}}\). Indeed, this sum shows that the choice of \(\Delta\) can be used to mitigate the interference caused by the CP length violation in spite of the one originating from STO. In this context, \(\Delta\) equal to 0, 2, and 7 results in the minimum values of \(||\overline{\mathbf{H}}_{1}||_{\text{F}}+||\overline{\mathbf{H}}_{-1}||_{ \text{F}}\) with an \(L_{\text{CP}}\) of 30, 18, and 14, respectively, yielding Frobenius norms of 0, 0.29, and 0.56. Sections 5.4 and 5.5 discuss how this insight can benefit the achievable data rate and BEP, respectively.
## 2 Materials and Methods
### Selection and Assessment of Mesh Bags for Holding WTR  
Several mesh materials were investigated. The constraints of continuous submerged conditions, solar radiation, and temperature fluctuations ruled out many of the materials. Polyester was selected over nylon due to less brittleness after being submerged and potential to autoclave if the mesh needed to be sterilized [28; 29; 30]. Four different mesh sizes of polyester mesh (Component Supply Company, Sparta, TN, USA) were selected with nominal pore diameters: 53, 70, 120, and 160 \(\upmu\)m. The mesh is used to hold the WTR inside the cartridges at each gate. Four different combinations were investigated: one with a single layer and three double layer variations combining two different mesh sizes (a larger mesh size on the outside and the smaller mesh size on the inside). The four combinations investigated were 53 \(\upmu\)m, 53/120 \(\upmu\)m, 70/120 \(\upmu\)m, and 70/160 \(\upmu\)m.  
A 1.83-m piece of rebar was installed in Eckert's Ditch at Dawley road approximately 4.58 m north of the culvert in the center of the stream channel. Eight 0.25-cm-long mesh bags were sewn and fastened from the pole using cable ties. The first four mesh bags filled with Al-WTR were weighed, and then placed into Eckert's Ditch for 48 days. Upon removal, it was determined that the cotton used to sew the mesh bags had deteriorated and was not suitable for use in this experiment.  
The thread was replaced with a water and UV resistant nylon thread, and pre-weighed WTR filled meshed bags were again placed in Eckert's Ditch, this time for 53 days. Upon removal, the bags were air-dried and reweighed to determine the mass loss. The mass lost was extrapolated to calculate the potential mass loss of a full-scale 11.35-kg bag. This weight was chosen as the maximum weight of the cartridge after consulting with the watershed personnel; anything heavier was deemed to be impractical due to the necessity of manually removing the underwater cartridges while in a canoe. This amount was then multiplied by the total number of bags that would be contained in eight gates, each comprised of four mesh-bags containing cartridges to estimate the mass lost for the entire system over a one-month period. One month was selected as the timeframe as it was anticipated that the WTR in the full-size cartridges would have to be replaced monthly. Overall, the 53/120 mesh released the least amount of Al-WTR and was selected for use in the full scale system, with the 53/120 mesh estimated (Table 1) to lose roughly 2.03 kg over one month. This amount of loss was the least concerning as it is less than the amount of sediment introduced from runoff during a single storm event. Nor does it introduce any foreign contamination into the system as the WTR was made from the watershed it is being utilized within.
# Simple HPLC-PDA Analysis to Determine Illegal Synthetic Dyes in Herbal Medicines
###### Abstract  
Various synthetic dyes are artificially added to herbal medicines for the purpose of visual attraction. In order to monitor the illegal usage of synthetic dyes in herbal medication, a rapid and straightforward analysis method to determine synthetic dyes is required. The study aimed to develop and validate a high-performance liquid chromatography (HPLC) analysis to determine ten synthetic dyes in Hawthorn fruit, Cornus fruit, and Schisandra fruit. Ten synthetic dyes such as Tartrazine, Sunset yellow, Metanil yellow, Auramine O, Amaranth, Orange II, Acid red 73, Amaranth, New Coccine, Azorubine, and Erythrosine B, were extracted using 50 mM ammonium acetate in 70% MeOH; then separated by gradient elution with a mobile phase consisting of acetonitrile and 50 mM ammonium acetate in distilled water using a photodiode array detector (PDA) at 428 nm or 500 nm. In addition, this study established the LC-MS/MS method to confirm the existence of synthetic dyes in the positive sample solution. The HPLC analysis had good linearity (r2 > 0.999). The recoveries of this method ranged from 74.6-132.1%, and the relative standard deviation (RSD) values were less than 6.9%. Most of the samples fulfilled the acceptance criteria of the AOAC guideline. This study demonstrates that the HPLC analysis can be applied to determine ten synthetic dyes in herbal medication.  
herbal medicine; HPLC; LC-MS/MS; synthetic dye; Hawthorn fruit; Cornus fruit; Schisandra fruit +
Footnote †: journal: Applied Sciences
# Natural Virtual Reality User Interface to Define Assembly Sequences for Digital Human Models
## 1 Introduction
### Problem Statement  
As shown above, it is necessary to use digital human models in virtual product development to assure human factors such as ergonomics. These simulations, however, involve a lot of manual configuration effort using WIMP interfaces. On the other hand, there are reality-based interfaces, which allow a situation, in this case, assembly activities, to be simulated. But the instruction of this DHM is still complicated. Users still have to perform up to 55 actions to instruct [PERSON] to grab an object with two hands and move it to another place [22].  
However, to this day, it is not possible to interact with the virtual scene and use the interactions as input for the manikin. Thus, it is only possible in VR to review the model and the simulation results. The authors thereby transfer the idea of user and environment tracking into the virtual action tracking system (VATS) and create a natural user interface, which allows direct manipulation. Our approach minimizes the necessary menu items, which means that users do not have to navigate through menus in the ergonomic simulation software. This enables them to manipulate the virtual environment directly and thus to define the input for the DHM. We want to use the approach of RBI in our system to provide an effective way to work with DHMs and substitute mouse and keyboard with an interactive VR system. Our goal is to develop a user-friendly application to instruct a digital human model that significantly improves the control of the digital systems' functionality through intuitive human interaction features. We formulated the following hypotheses:  
**Hypothesis 1**: _The time to define an assembly sequence with the VATS is significantly faster than with the existing WIMP Interface._Hypothesis 2: The time to learn to define an assembly sequence with the VATS is significantly faster than with the existing WIMP Interface.  
Hypothesis 3: The subjective workload with the VATS is less than with the WIMP interface.  
Another exploratory question is to what extent users make mistakes when using the two different applications. An error, using the VATS, is when a user selects a wrong grip type. In the WIMP interface, however, the question is whether the user can complete the complex handle definition at all. Comparable error analyses are not possible, because on the one hand, VATS is always terminated automatically, and on the other hand, the instructions for the WIMP interface specify the grip type. Nevertheless, the two types of errors are illustrated descriptively.
# Spectral Raman Database of Modern Acrylic Paints at 532 nm and 633 nm Laser Excitation Wavelengths
## 2 Experimental  
### Materials  
Figures 1 and 2 show the analysed samples, which consist of eighteen pigments, mixed with acrylic binder, (supplied in tubes form by Maimeri, from the Brera Acrylics Extrafine line) deposited on wooden and glass supports, respectively.  
To simulate real cases, the pigments were applied onto three wooden supports (30 cm \(\times\) 3 cm), each precoated with four layers of an acrylic plaster preparatory base (produced by Zecchi, product code n. 3030/750). To ensure homogeneity, the preparatory layer was sanded using varying grit sizes of sandpaper. Each pigment covers an area of 3 \(\times\) 2.5 cm\({}^{2}\) on the wood substrate, with varying layer thicknesses [25]. The beechwood support was used. A detailed description of how the samples have been made can be found elsewhere [25]. The same pigments have been deposited on glass support for studying the effect of the substrate on the pigments. Key properties of the studied pigments are summarised in Table 1. A subset of these pigments, previously investigated via optical techniques and discussed elsewhere [25], were also examined in this work using Raman spectroscopy. The molecular structures drawn in this work were made by using ChemDraw Ultra software version 12.0.2.1076, CambridgeSoft.com.  
Figure 1: Acrylic colours from the Acrylics Brera line supplied by Industria Maimeri: Cadmium Red Medium (CRM), Cadmium Yellow Medium (CYM), Cerulean Blue (CeB), Cobalt Blue (CB), Cobalt Blue (Hue) (CB(H)), Indanthrone Blue (IB), Iridescent Silver (IS), Lemon Yellow (LY), Permanent Blue Light (PBL), Permanent Green Light (PGL), Phthalo Blue (PB), Phthalo Green (PG), Primary—Cyan Blue (PBC), Primary Red Magenta (PRM), Titanium White (TW), Ultramarine (UL), Van Dyke Brown (VB), Zinc White (ZW) deposited on wooden panels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nature** & **Commercial Name** & **Pigment** & \
\begin{tabular}{c} **Colour** \\ **Index (CI)** \\ \end{tabular} &
\begin{tabular}{c} **Composition** \\ \end{tabular} \\ \hline \multirow{7}{*}{Organic} & Phthalo Blue (PB) & PB15:1 & 74,160:3 & Phthalocyanine \& ((\)C\({}_{32}\)H\({}_{6}\)CuN\({}_{8})\) \\  & Primary—Cyan Blue (PBC) & PB15:3 & 74,160:3 & Phthalocyanine \& \((\)C\({}_{32}\)H\({}_{6}\)CuN\({}_{8})\) \\  & Indanthrone Blue (IB) & PB60 & 69,800 & 6,15-Dihydro-5,9,14,18-anthz
###### Abstract  
With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions--routing protocols, security control, path planning, resource management, and UAV deployment--highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions.  
6G; wireless communication; Space-Air-Ground Integrated Networks; unmanned aerial vehicle +
Footnote †: journal: Advances in Neural Information Processing Systems
## 4 The Concept Alignment Example Setup and Modeling
### CAE-ATG Systems Behavior Specification
#### 4.4.2 Firefighting Machine CS Behavior  
The firefighting machine's modal behavior is depicted in Figure 6b. The car starts in an _IDLE_ state and when an _evGo_ event occurs, it moves first to a _MOVING_ state and then to a _WORKING_ state. After the fire has been successfully extinguished (transition from _WORKING_ to _REPORTING_ state) it moves back to the fire station and reports its presence to the district. Each firefighting machine is subject to a fault event (_evCarFailure_) that inhibits the normal operative mode of the car until a corresponding restore event (_evCarFailureRestore_) happens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Firefighting Machine Block**} \\ \hline  & \multicolumn{2}{c}{**Event**} & \multicolumn{1}{c}{**Destination/Source Block**} \\ \hline
**Name** & **Send/Received** & **Description** & **Destination/Source Block** \\ \hline _evGo_ & Received & Dispatch command from the fire station & Fire station \\ \hline _evEst_ & Sent & Communication of ceased fire alarm (fire extinguished) & District \\ \hline _evCarReturned_ & Sent & Operation completion notification & District \\ \hline _evCarFailure_ & Received &
\begin{tabular}{c} Machine fault occurrence. When a machine is in failure \\ and under maintenance, it cannot be dispatched to \\ the district. \\ \end{tabular} & None \\ \hline _evCarFailureRestore_ & Received & Machine restoration after an _evCarFailure_ & None \\ \hline \hline \end{tabular}
\end{table}
Table 3: Events list of the firefighting machine CS block with event name, direction (send/received), description, and its destination/source block.
## 3 Results
### Consolidation Analysis Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**Model Summary b**} \\ \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**R**} & \multirow{3}{*}{**Square**} & \multirow{3}{*}{**Adjusted**} & \multirow{3}{*}{**Std. Error of**} & \multicolumn{4}{c}{**Change Statistics**} \\ \cline{3-10}  & & & & **R Square** & & **R Square** & **R Square** & **F** & **R Square** & **R Square** \\ \hline
1 & 0.920 a & 0.846 & 0.435 & 0.24882 & 0.846 & 2.058 & 8 & 3 & 0.299 \\ \hline \end{tabular}  
* \({}^{\text{a}}\): Predictors: (Constant), cohesion, sieve no. 200, water content, excavation level, sieve no. 10, unit volume weight, groundwater level, internal friction angle; b: Dependent Variable: Settlement.  
\end{table} 
Table 8: Multiple linear regression analysis results for Settlement. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**ANOVA a**} \\ \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**Model**} & \multirow{3}{*}{**Sum of Squares**} & \multirow{3}{*}{**df**} & \multirow{3}{*}{**Mean Square**} & \multirow{3}{*}{**F**} & \multirow{3}{*}{**Sig.**} \\ \cline{3-10}  & & & & & & & & & & \\ \cline{3-10}  & & Regression & 1.019 & 8 & 0.127 & 2.058 & 0.299 b  \\ \cline{2-10}  & & Residual & 0.186 & 3 & 0.062 & & & \\ \cline{2-10}  & & Total & 1.205 & 11 & & & & & \\ \hline \end{tabular}  
* \({}^{\text{a}}\): Dependent variable: Settlement; b: Predictor Parameters: (Constant), cohesion, sieve no. 200, water content, excavation level, sieve no. 10, unit volume weight, groundwater level, internal friction angle.  
\end{table} 
Table 9: Analysis of Variance (ANOVA) results for Settlement. The linear model produced a moderate R\({}^{2}\) value, indicating limited predictive accuracy due to its inability to capture possible nonlinear relationships. In contrast, the quadratic model showed improved fit, reflecting its capacity to incorporate curvature in the relationship between soil parameters and settlement. Among the models, the cubic predictive model provided the highest R\({}^{2}\) value, indicating its superior ability to represent the complex interactions between parameters affecting settlement. These findings highlight the importance of selecting an appropriate prediction model for accurate settlement predictions, especially in scenarios involving nonlinear behavior. Further validation with a larger dataset and inclusion of additional parameters could increase the reliability of these models. Figure 8: Quadratic prediction model and R\({}^{2}\) value for Settlement. Figure 9: Cubic estimation model and R\({}^{2}\) value for Settlement.
# Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
## 7 Conclusions
The authors have already designed a methodology of recording both room impulse responses (RIRs) that carry the overall pressure room response, as well as spatial intensity impulse responses (SIIRs), which carry the directional room responses, telling us at which angle each sample was registered. For processing such impulse responses, the authors also have a complete methodology, focused on segmentation of PIRs using three-dimensional directional responses [68]. The whole concept revolves around enabling users to virtually combine theirpersonalized HRIRs with known acoustic characteristics of a specific room to simulate any virtual immersive sound space, tailored specifically to their ears. There is no question that personalized BRIRs enhance the spatial accuracy and naturalness of sound reproduction by adjusting acoustic outputs to the unique anatomical features of each user's ears. This customization allows for precise sound localization and a more immersive auditory experience, which is particularly beneficial in applications requiring high levels of audio realism. Overall, the application of immersive two-channel recordings based on personalized BRIRs is poised to revolutionize various industries by enhancing the realism and effectiveness of auditory experiences. As technology continues to evolve, the potential for even more innovative applications of immersive audio will grow, solidifying its role as a key component in the future of audio technology and industry. Conceptualization, K.K. ; methodology, W.M. ; validation, W.M. ; investigation, K.K. and W.M. ; resources, K.K. ; writing--original draft preparation, K.K. ; writing--review and editing, W.M. ; visualization, K.K. ; supervision, W.M. ; project administration, W.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
HRTF Head-Related Transfer Function  
HRIR Head-related impulse response  
BRIR Binaural room impulse response  
RIR Room impulse response  
IVAS Immersive Voice and Audio Services  
HOA Higher-order ambisonics  
VSE Virtual sound environment  
HoH Hard of hearing