esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Results
### Event-Triggered Disturbance Estimation for Relative Motion Dynamics  
Considering the dynamics (1), \(\zeta_{1}(t)\), \(\zeta_{2}(t)\)\(\zeta_{3}(t)\) represent the estimation to \(r_{1}\), \(r_{2}\) and \(r_{3}\) respectively, \(\psi_{1}(t)\), \(\psi_{2}(t)\)\(\psi_{3}(t)\) represent the estimation to \(\dot{r}_{1}\), \(\dot{r}_{2}\) and \(\dot{r}_{3}\) respectively, and \(\phi_{1}(t)\), \(\phi_{2}(t)\)\(\phi_{3}(t)\) represent the estimation to \(\dot{r}_{1}\), \(\dot{r}_{2}\) and \(\ddot{r}_{3}\), respectively. Since the event-triggered extended observer is proposed as:  
\[ \begin{array}{l}\dot{\xi}_{i}(t)=\dot{\psi}_{i}(t)+\ell_{i}\beta_{1}(r_{i}(t _{k})-\zeta_{i}(t))\\ \dot{\psi}_{i}(t)=\dot{\phi}_{i}(t)+\ell_{i}^{2}\beta_{2}(r_{i}(t_{k})-\zeta_{ i}(t))+u_{i}(t)\\ \dot{\phi}_{i}(t)=\ell_{i}^{3}\beta_{3}(r_{i}(t_{k})-\zeta_{i}(t)),i=1,2,3\end{array}  \tag{3}\]  
for any \(t\in[t_{k},t_{k+1})\), where \(u_{i}(t)\) represent the control input for each axis, \(K_{i}(\beta)=\left[ \begin{array}{ccc}\beta_{i1}&\beta_{i2}&\beta_{i3}\end{array}  \right]^{T}\) represents the gain of the observer, \(\ell_{i}\) is the high-gain parameter, \(t_{k}\) (\(k\in\mathbb{N}\)) denotes the sampling moment, and the first sampling happens at the initial time \(t_{0}\). The event-triggered mechanism selectively transmits output information by monitoring at the sensor end, allowing for the separate monitoring and transmission of three-axis output information. The transmission mechanism is designed to trigger sampling based on some positive constants \(\Pi_{1}\), \(\Pi_{2}\), and \(\Pi_{3}\) by design:  
\[|r_{1}(t)-r_{1}(t_{k})|\leq\Pi_{1} \tag{4}\]\[|r_{2}(t)-r_{2}(t_{k})|\leq\Pi_{2} \tag{5}\]  
\[|r_{3}(t)-r_{3}(t_{k})|\leq\Pi_{3} \tag{6}\]  
The estimation errors are defined as \(\xi(t)=r_{i}(t)-\xi(t)\), \(\tilde{\psi}(t)=r_{i}(t)-\psi(t)\) and \(\tilde{\phi}(t)=\dot{r}_{i}(t)-\phi(t)\). Then, the following theorem is obtained. Theorem 1: _Let us consider plant (1), observer (14) and the driven conditions (4)-(6) if there exist some positive definite matrices \(P_{1}\), \(P_{2}\), \(P_{3}\), and some constants \(\theta>0\), \(0<\sigma<1\) such that_  
\[0<\|P_{i}K_{i}(\beta)\|\Pi_{i}\leq\frac{1}{\ell_{i}^{3}}\Bigg{(}\frac{\lambda \sigma}{2}\sqrt{\frac{\varpi}{\varpi}}\theta-\|P_{i}\|M_{i}\Bigg{)},i=1,2,3 \tag{7}\]  
_where \(\varpi\) and \(\underline{\varpi}\) represent the maximum and minimum eigenvalues among \(P_{1}\), \(P_{2}\), and \(P_{3}\), respectively. And then, there exists some \(\beta_{i1}\), \(\beta_{i2}\) and \(\beta_{i3}\) such that the estimation error will meet the bound_  
\[|\tilde{\xi}_{i}(t)|\leq\frac{\theta}{\ell_{i}^{3}} \tag{8}\]  
\[|\tilde{\psi}_{i}(t)|\leq\frac{\theta}{\ell_{i}^{2}} \tag{9}\]  
\[|\tilde{\phi}_{i}(t)|\leq\frac{\theta}{\ell_{i}} \tag{10}\]  
_for any \(t\geq t_{0}+T(\ell)\), where \(\ell=\min\{\ell_{1},\ell_{2},\ell_{3}\}\), and \(T(\ell)\) is some positive constant which satisfies \(\limsup\limits_{\ell\to\infty}T(\ell)=0\)._  
Remark 1: _It is a common conclusion to obtain practical convergence in estimates under bounded disturbances.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 5 Conclusions  
This study presents a method for retrieving Level 2A PWV data from Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. First, the theoretical basis of retrieving PWV from remote sensing images is presented, and lookup tables (LUTs) for PWV retrieval are constructed. Second, the accuracy of the S2-L2A PWV data is validated using the PWV data obtained from the GPS, MOD05, and ESA-L2A. Third, the spatial distribution features of S2-L2A PWV are analyzed. The results show that the PWV retrieved by S2-L2A has a strong correlation and low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. PWV data distribution is affected by land-use type and elevation, and it shows obvious aggregation and gradual change. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; however, as elevation increases, the PWV value tends to decrease. In addition, inversion error increases with AOT value, but decreases with elevation and NDVI. The proposed algorithm can provide PWV data with a high spatial resolution for various applications, such as climate change, hydrologic cycle, and so on in various regions.  
Conceptualization, Y.Z.; Formal analysis, Y.Z.; Funding acquisition, S.L.; Investigation, Y.Z., G.Z., Y.S., C.W., Y.L., Z.S. and W.W.; Methodology, Y.Z.; Project administration, S.L.; Supervision, S.L.; Validation, S.L.; Writing--original draft, Y.Z.; Writing--review & editing, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Green exploitation of coal resources and its environmental effects in Xinjiang (U1903209) and Major projects of Ordos scientific and technological cooperation (2021 EEDSCXQDFZ010).  
We would like to thank [PERSON] and [PERSON] for their help with the data collection process. The authors greatly appreciate the anonymous reviewers' excellent comments in improving the manuscript quality.  
The authors declare no conflict of interest.
## 3 Methodology
### Feature Fusion
```
1:The de-redundant feature subset \({}_{DE}MIC_{FF}\), feature subset \({}_{DE}MIC_{FF}=\{f_{1},f_{2},\ldots,f_{m},R\}\), real RUL value \(R\), \(m\) is the number of features in the subset \({}_{DE}MIC_{FF}\). 2:optimal DI set \(Opt_{DI}\). 3:for\(f_{i}\in_{DE}MIC_{FF}\)do
4: Calculate \(MIC_{FR}\) values and \(Mon_{F-DT}\) values separately, obtaining the \(MIC-Mon\) matrix,
5:for Every value in every row of the \(MIC-Mon\) matrix do
6: Calculate the \(Std_{DI}\) value based on \(Std_{DI}=\frac{MIC_{FR}+Mon_{F-DT}}{2}\),
7:if\(Std_{DI}>0.5\)then,
8: Select the features in subset \({}_{DE}MIC_{FF}\) to form the optimal DI set \(Opt_{DI}\). 9:endif
10:endfor
```  
**Algorithm 1** Optimal DI construction method.
## 3 Use of Microbial Agents in Remediation System
### Bacterial Cultures in Submerged Fermentations
For large volumes of textile water remediation, microbial fermentation performed in batch conditions for 5-6 days is not a practical and economical option; hence, a continuous treatment system should be in operation to deal with vast volumes generated in textile industries. For this purpose, researchers have experimented on immobilising bacterial cultures in a biofilm system for their long-term application in a continuous-flow decolourisation process [18]. Some cheaply available materials, such as solidified highly porous volcanic lava called Kissiris obtained from Greece, seashells, sponge-scrubbers, and nylon-webs, were used to grow biofilms of individual bacteria and bacterial-consortium with capabilities of textile-effluent decolourisation. These biofilm-containing support systems were studied for their efficiency in the decolourisation in a continuous-flow packed-bed-column bioreactor (CFPRR) system. Biofilm support systems using volcanic mineral material, nylon-web, and seashells supported good bacterial growth in the form of actively growing bio-films. These bio-systems were established to decolourise textile effluent, without the release of a noticeable number of bacterial-cells released from biofilm attached to the substratum into the outflow of treated effluent [17; 18]. Therefore, a continuous process for effluent treatment could be performed for a longer period treating a larger volume of effluent compared to a batch treatment process. The effluent generated by textile companies is high in temperature; therefore, [PERSON] et al. [19] studied the isolation of thermophilic bacterial strains capable of growing and decolourising textile dyes present in effluents at higher temperatures.
## 3 Model-Free Robust Control Design
### Adaptive PD RBFNN Controller Design
#### 3.1.2 Effects of Different Parameters on RBF Approximation  
The RBF approximation is affected by different parameters. These include the Gaussian function (related to the design of the center vector \(c_{j}\) and width value \(b_{j}\)) and the number of hidden nets. There are principles for the design of \(c_{j}\) and \(b_{j}\), the details of which are presented in [51].  
After careful consideration, two inputs, five hidden layers, and one output RBF neural network structure \((2-5-1)\) were used; the details are presented below:  
Inputs: \(i=2\).  
In the control system, when RBF is used to approximate \(f\), the system states are chosen as the input of the RBF neural network.  
Output of Gaussian function for five hidden neural nets \((j=5)\), \(h_{j}=\left[\begin{array}{ccc}h_{1}&h_{2}&h_{3}&h_{4}&h_{5}\end{array}\right]\).  
Using the principles to design \(c_{j}\) for the given input, the coordinate values of the center point of the Gaussian function of neural
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### An Overview of Common Run-Time Errors  
#### 2.3.1 Deadlocks  
A deadlock occurs when multiple processes are trapped in a state of waiting for each other to release resources or complete execution, during which neither process progresses at all. Deadlocks are a serious issue in parallel systems as they can bring the entire system to a halt. Although deadlocks are easy to detect once they have occurred, in some cases, they can be difficult to detect early on due to specific interleaving [24]. Furthermore, the execution environment and sequence of any system may or may not create actual or potential deadlocks.  
For example, if an MPI process is dependent on another process for data, a deadlock may occur if the sender is held up in a parallel OpenMP area. Deadlocks can also occur due to workload misbalance or the failure of every thread to approach a barrier. In such cases, the wait time for an MPI process may be infinite.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 2 Preparatory Knowledge
### Common Fault Types of the Yaw System  
During the long-term operation of wind turbines, coupled with prolonged exposure to various harsh environmental conditions, the key components of the yaw system are prone to fatigue damage, inevitably leading to certain mechanical failures. The fault types of the yaw system are as follows:  
(1) Yaw drive mechanical failures: The yaw drive motor is responsible for steering the nacelle to align with the wind, and its failure can lead to inaccurate adjustment of the yaw position. Examples of such failures include yaw gearbox gear tooth damage, yaw motor malfunctions, yaw bearing gear tooth breakage and detachment, etc.  
(2) Yaw brake mechanical failures: excessive wear of friction pads, brake oil leakage, excessive noise during yawing, insufficient yaw braking force, etc.  
(3) Yaw counter mechanical failures: loose connecting bolts, foreign object intrusion, damaged connecting cables, etc.  
(4) Anemoscope failure.  
According to the actual operation of the wind turbine, it can be observed that the failure and shutdown of the yaw system components is an accumulative process, which is particularly evident when analyzing the SCADA data. Initial damage to the yaw components is typically manifested as gear wear, electromagnetic brake pad wear, abnormal motor temperature, etc. This is followed by intermediate-level wear, such as bearing cracking, cam tooth skipping, etc. If timely inspection and maintenance of the yaw system components are not carried out at this stage, it will lead to more serious system failures and even jeopardize the overall operational safety of the turbine [24; 25].  
In the event of primary damage to the yaw system, the change in yaw position is more sensitive. Therefore, this paper considers the yaw position as a prediction parameter of the yaw system. By analyzing the patterns of its variations, it is possible to identify anomalies in the yaw system.
## 4 Discussion
This approach not only prolongs the lifespan of the filter but maintains its operational efficiency, as supported by previous studies that emphasize the importance of pre-treatment methods in filtration systems. Health Potential for Lead Filtration by Our Systems. According to the literature, our systems have the potential to filter out lead from water. Lead was found at extremely high concentrations in one of the tested reservoirs, sometimes up to 1000 times above the permissible level. Most of the experiments presented in this article were conducted on water from the Red Dam, which had the highest turbidity levels. However, there is no lead in this water source, so our systems have not yet been tested for lead removal. The consumption of such high levels of lead is particularly concerning for children. Lead exposure in children can result in severe health consequences, including cognitive impairment, developmental delays, and various physical health issues. Chronic exposure can lead to long-term detriments that affect their academic performance and overall well-being. As indicated by existing research, our sedimentation and biofilm-based slow sand filtration systems can remove lead from water [8; 9]. This capability will be assessed during future expeditions to Tanzania. By implementing these systems, we aim to significantly reduce the lead content in water, thereby providing safer drinking water for the affected communities. Additionally, the use of agricultural residual waste, such as maize stalk material, for removing heavy metals, as suggested by [PERSON] et al., presents an interesting alternative approach [19]. Although not evaluated in this research, such materials could be explored in future studies as a complementary or standalone method for removing elements like lead from contaminated water sources in resource-limited settings. Finally, the success of this project in a Maasai community provides a replicable model for other regions facing similar water quality challenges. The methods and results from this study can serve as a blueprint for other communities, demonstrating that effective water purification can be achieved with minimal resources and local materials. Future projects can build on this foundation, scaling up the approach to benefit larger populations and diverse geographic areas. The recent expedition to Tanzania was cut short due to unforeseen circumstances in October, limiting the scope of our research. In future expeditions, we plan to extend our stay to deepen our investigation. Specifically, we aim to assess the effectiveness of our systems in filtering bacteria from the water and to analyze the characteristics of the biofilm through molecular testing. These additional studies will provide a more comprehensive understanding of the capabilities and limitations of our water filtration systems, ultimately contributing to the improvement of water quality in the affected regions.
## 4 Discussion
### Limitations  
These research results are only indicative, as no definite conclusions can be formed due to the limited amount of research participants. The statistical power of the results remains low. Furthermore, research participants were quite inexperienced regarding VR solutions, which might have affected the results--experiencing virtual reality for the first time can be a positive experience in itself. Participants also reported low levels of stress. It needs to be noted that different sample populations could produce different results--meaning that the effects of the method studied might be significantly different for users with high stress levels or extended experience regarding VR applications.  
Due to the lack of a control group in our study, any possible effect caused by experiencing VR with a head-mounted display cannot be differentiated from the effects caused by Binaural Odyssey. Although this does not affect the results regarding the general applicability of VR solutions for psychiatric care, more research with a control group would be needed in order to confirm that the positive effects found in this study are caused by the method studied in this article.  
In addition, this research provides no data on how long the positive effects last, or how repeated use would affect the results and user experience.  
To summarize, the results of this research should not be used to justify therapeutic use of the methods studied in this article for psychiatric treatment, as more research with more stressed and anxious user populations would be required to verify the suitability of this method for therapeutic care.
###### Abstract  
When the direct visible-light channel is affected by people walking or other obstacles, the shadow effect formed in the receiver space affects communication performance and can even lead to communication interruption. In this paper, two numerical methods for calculating the shadow area of the human body were proposed for an indoor multi-light communication system. The influence of shadows on system performance was analyzed using the mean square errors of illuminance and signal-to-noise ratio, as well as the interrupt probability, as parameters. The results showed that shadows had a great influence on the performance of the visible-light communication system. When the number of light sources was fixed, the shadow effect could be effectively reduced by optimizing the layout of the light sources. These research results can provide a theoretical basis for improving the stability of visible-light communication systems.  
visible-light communication; shadow effect; multiple light sources; optimized layout; quantitative analysis +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China; [EMAIL_ADDRESS]
## 3 Proposed Methodology
### Data Pre-Processing
#### 3.2.1 Data Denoising
The visualizations reveal similar SNR results across all subcarriers of the CSI data after noise reduction using the PCA denoising method. Similar to the previous investigation, the SNR value of the denoised CSI data is higher than the SNR value of the raw data.
## 3 Results
### Chemical Analysis
The main constituents in the EOF were oxygenated monoterepenes (Figure 1, 52.9%), with 1,8-cineole (33.3%), \(\alpha\)-terpinyl acetate (7.0%), and \(\alpha\)-terpineol (3.9%) as the main compounds. The second largest group was represented by monoterepne hydrocarbons (22.4%), with \(\alpha\)-pinene (5.8%), sabinene (5.3%), and \(\beta\)-pinene (4.0%) being most abundant. Within sesquiterpenes (13.6%), \(\beta\)-elemene (5.7%) was predominant. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Compound** & **KI\({}^{\mathbf{a}}\)** & **L. _nobilis leaf_** & **L. _nobilis fruit_** & **L. _nobilis fruit_** \\  & & **EOL (\%)** & **EOF (\%)** & _fatty oil (\%)_ \\ \hline Monoterpenes & & 13.0 & 22.4 & 1.7 \\ Monoterpenoids & & 66.0 & 52.9 & 1.6 \\ Phenylpropanoids & & 5.2 & 2.4 & 6.9 \\ Sesquietrepene & & 8.9 & 13.6 & 6.6 \\ Sesquietrepenoids & & 5.3 & 3.3 & 15.8 \\ Fatty acids & & 0.1 & 1.9 & 39.1 \\ Alcohols/aldehydes & & 0.6 & 3.1 & 1.5 \\ Lactones & & & 23.8 \\ Others & & & 2.4 \\ The percentage of the total chemical compounds & 99.1 & 99.6 & 99.4 \\ \hline \end{tabular} \(\mathrm{K}^{\mathbf{2}}\)—[PERSON] retention index.  
\end{table} 
Table 1: _Cont._  
Figure 1: The main groups of identified components in the investigated samples of EOL, EOF, and fatty oil (obtained from laurel fruits after EO extraction). In addition to the leaf and fruit EOs, fatty oil obtained from the fruit material after hydrodistillation and EO separation was investigated (Figure 1). Results showed that 88 constituents representing 99.2% of the total content were identified in the fatty oil. The chemical composition revealed that the main constituents in the fatty oil were sesquiterpene lactone dehydrocostuslactone (21.7%), saturated fatty acids lauric acid (13.8%), and palmitic acid (8%), and nonsaturated fatty acids with short-chain, monounsaturated \(\omega\)-9 fatty acid--oleic acid (5.4%), and short-chain polyunsaturated fatty acids (SC-PUFA), \(\omega\)-6 linoleic acid (3.1%). In addition, the phenylpropanol derivative of cinnamic acid, (_E_)-2-hexyl-cinnamaldehyde was detected (5.0%).
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 4 Results
### Ablation Study  
In order to verify the effectiveness of the GFLM and the MFFM, this section adds each of these two modules to the original 3D-Unet model. They are compared with the baseline model with respect to accuracy, mIoU, and the time to make predictions for each model in Table 5.  
Compared with the 3D-Unet model, the addition of the GFLM increases the acc and mIoU of the model by 1.4% and 3.3%, respectively, and increases the model prediction time by 12.6/ms. The addition of the MFFM improves the accuracy and mIoU by 1.1% and 2.4%,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Params/M** & **Flops/G** & **Device** & **Time/ms** \\ \hline RPVNet [33] & 24.8 & 239 & Tesla V100 & 168 \\ SPVNAS [9] & **12.5** & 147.6 & GTX 1080 Ti & 259 \\ FusionNet [34] & - & - & GTX 1080 & 900 \\ SqueezeSegV3 [7] & 26.2 & 1030.4 & - & 142 \\ LFNet (ours) & 17.7 & \(\approx\)**64** & GTX 3090 & **94** \\ \hline \hline \end{tabular} The bolded value is the best result for that column.  
\end{table}
Table 4: Efficiency comparison of different methods.  
respectively, and increases the model prediction time by 6.9/ms. With the addition of both modules, LFNet improved in accuracy and mIoU by 1.8% and 7.9%, respectively.  
The experiments show that, although the GFLM and the MFFM increase the prediction time of the model, they both can effectively improve the prediction accuracy and segmentation effect of the model.