esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 4 Blockage Probability Analysis and Modeling
### Blockage Probability Model in Multi-LED Scenario  
In this subsection, we discussed the modeling of multiple BPs in the multi-LED scenario. To obtain the BP in the multi-LED scenario, the human body shadowing modeling should be conducted for every single LED. In the 3-LED scenario, for every human position in the moving trajectory, three shadowing areas with different shapes and positions exist at the same time. And the eight blockage conditions are the joint effect of the three shadowing areas.  
Figure 10 illustrates the probabilities of the eight blockage conditions in the 3-LED scenario. It is observed that all eight probabilities show a common feature, namely symmetry  
Figure 9: Blockage probability for the single-LED scenario.  
The simulated results and the fitting model of \(p_{12}\) are plotted in Figure 10c. It is found that as the distance to the \(X\)-axis becomes larger, the \(p_{12}\) has a nonlinear increasing trend.  
Figure 10: Blockage probabilities in the 3-LED scenario.  
ultra-high BP at the local position of the receiving plane. This is due to the separation of the transmitter enables the angle of arrival (AoA) of the income signal to expand from a single value to multiple values. And, it is much more difficult to simultaneously block the signals from several AoAs than just block the signals from a single AoA, thereby reducing the probability of complete blockage.  
Based on the multi-wavelength path loss model under the free space propagation and the BP models of the blockage condition, the path loss for the 3-LED scenario can be expressed by a piecewise function with seven cases, which is given by  
\[PL_{multi}(d_{1},d_{2},d_{3};\lambda_{1},\lambda_{2},\lambda_{3})=\left\{\begin{array}[ ]{ll}PL_{multi}^{{}^{\prime}}(d_{1},d_{2},d_{3};\lambda_{1},\lambda_{2},  
**Conflicts of Interest:** Authors [PERSON], [PERSON] were employed by the China Mobile Research Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 4 Experiments and Analysis
### Experimental Results
#### 4.2.2 Results of Different Model Architectures  
The results of replacing the audio and visual extractors with better model architectures are shown in Table 4. It can be seen from the table that the performance of AVSR will be improved by adopting a better audio extractor. On the contrary, if a better video extractor is adopted, the performance of AVSR will degrade. This may be caused by the difference in the amount of information brought by the audio and video data. In order to better analyze these two modalities, we compared the differences between AVSR and ASR in the original VGG/Resnet architecture under different environments in Table 5. As can be seen, when there is considerable TV background noise (i.e., C1, C2, C5), AVSR has a significant improvement over ASR (about 5% absolute values). On the contrary, when there is no background noise (i.e., C7, C8), AVSR does not bring much improvement compared with ASR. In particular, under the C6 conditions, the performance of AVSR is worse than that of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Model** & **Pre-Training** & **Data Processing** & **Eval** \\ \hline \multirow{4}{*}{ASR} & \multirow{2}{*}{E2E} & HS (CAT) & CAT & 36.81 \\ \cline{3-5}  & & HS (SP) & SP & 37.44 \\ \cline{3-5}  & & HS (SP + CAT) & SP + CAT & 35.90 \\ \hline \multirow{4}{*}{AVSR} & \multirow{2}{*}{E2E} & ES (CAT) & CAT & 33.16 \\ \cline{3-5}  & & ES (SP) & CAT & 34.00 \\ \cline{3-5}  & & ES (SP + CAT) & CAT & 32.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results (CER %) of the ES pre-training. HS and ES mean the hybrid system and end-to-end system, respectively. CAT and SP mean splicing the continuous segments and speed perturbation, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Pre-Training** & **Extractor** &
\begin{tabular}{c} **Data** \\ **Processing** \\ \end{tabular} & **Eval** \\ \hline E2E (M = 6, N = 3) & & VGG/Resnet & & 38.27 \\ \hline E2E (M = 6, N = 3) & & & 42.78 \\ \hline E2E (M = 10, N = 4) & & AV-HuBERT-V1 & & 41.85 \\ \hline E2E (M = 12, N = 6) & & & 96.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results (CER %) of pre-trained audio–visual representation on the AVSR task. HS means the hybrid system. CAT means splicing the continuous segments. M and N denote the number of blocks in the encoder and decoder modules, respectively. AV-HuBERT-V1 is the utilized modified AV-HuBERT model.  
ASR, which may be because the lights and TV interfere with the video data, leading to the introduction of wrong information. In general, the video modality only brings an absolute improvement of 2.53% for recognition performance. Therefore, compared with the audio modality, the video modality contains less effective information, which may be the reason why the recognition performances become worse when a better video extractor is used.
# Hydrates for Cold Storage: Formation Characteristics, Stability, and Promoters
## 3 Results and Discussion
### Characteristics of Hydrate Formation
Furthermore, the stirring process only changes the state of the hydrate at the macro level, as it does not affect the microstructures or inherent characteristics. The use of additives can improve the conditions for hydrate formation, change the liquid distribution on the microscopic scale, reduce the interfacial tension between guest molecules and the liquid, increase the solubility and diffusion coefficient of guest molecules in the liquid phase, and enhance contact between water and the guest molecules, thereby contributing to hydrate nucleation [22; 45; 46]. When stirring was combined with graphene, the induction time increased moderately, whereas the hydrate formation rate increased significantly. This may be attributed to changes in the characteristics of hydrate formation due to the nanostructure characteristics of graphene, thereby promoting hydrate formation. The nucleation of crystals includes two pathways, heterogeneous nucleation and homogeneous nucleation, but the former more easily forms the hydrate crystal nucleus [31]. Graphene increased the heterogeneity of the hydrate formation system, thereby providing the condition of heterogeneous nucleation. Meanwhile, graphene with abundant functional groups on its surface can be used to reduce surface tension and increase the solubility and diffusion of the guest molecule in the liquid phase, which is conducive to hydrate nucleation [47]. It also provides a large specific surface area for contact between the TBAB/TBAC mixture and water molecules. The increased specific surface area leads to a higher number of surface molecules, and a greater intermolecular force between water and the guest molecules [48]. The stronger surface adsorption increases the concentration of the mixture molecules at the interface, which promotes nucleation and increases the rate of hydrate formation. In addition, graphene with high thermal conductivity can eliminate the heat from the system, which maintains the system at a low temperature and makes the hydrate growth more stable [49].
## 2 Cutting Test
### Analysis of the Effect of Cutting Parameters on Surface Roughness
#### 3.2.3 Effect of Cutting Depth on Surface Roughness  
The bar graph of the variation of the machined surface roughness of the workpiece with cutting depth under the parameters of Table 4 is given in Figure 11. The parameters of cutting speed of 4 m/min, feed of 18 \(\upmu\)m/rev, ultrasonic amplitude of \(A_{y}=4.5\upmu\)m and \(A_{z}=1.2\upmu\)m, and a tool nose radius of 0.5 mm were selected. It can be observed from the provided histogram that the surface roughness of the workpiece increases gradually with the increase in cutting depth. When the cutting depth was increased from 2 \(\upmu\)m to 4 \(\upmu\)m, the surface roughness increased linearly, However, a significant increase in surface roughness was observed when the cutting depth was increased from 4 \(\upmu\)m to 5 \(\upmu\)m, indicating that deeper depths of cut had exceeded the benefits provided by the ultrasonic vibrations, resulting in less efficient removal of material during the cutting process, increased cutting forces, and an accumulation of heat of cutting, which significantly deteriorated the surface quality.  
To further analyze the influence factors of surface roughness with a cutting depth, Figure 12 shows the surface roughness maps obtained by white-light interferometer at cutting depths of 2 \(\upmu\)m, 3 \(\upmu\)m, 4 \(\upmu\)m, and 5 \(\upmu\)m. From the figure, it can be seen that as the cutting depth increases from 2 \(\upmu\)m to 5 \(\upmu\)m, uneven bumps and burrs are formed on the surface of the workpiece, which increases the surface irregularity and damage, making the quality of the workpiece surface worse and worse. The change in the height of the workpiece is more significant.  
During ultrasonic elliptical vibratory cutting, the variation of workpiece surfaces roughness with cutting depth is related to the magnitude of cutting force. As the cutting depth continues to increase, the area of the diamond tool in contact with the workpiece increases,  
Figure 11: Surface roughness change with cutting depth in UEVC.  
the actual cutting area increases, and more material needs to be removed, which directly leads to an increase in cutting force. Higher cutting forces can lead to severe deformation of the workpiece surface during the cutting process. They may also cause vibration of the tool and workpiece, making the cutting process unstable and increasing surface roughness. The increased cutting area means greater deformation forces and friction, increasing heat between the tool and the workpiece, affecting the cutting results. Ultrasonic elliptical vibration can effectively reduce the contact time between the tool and the workpiece, thereby reducing heat buildup and friction and helping to improve cutting stability and surface quality. The proper cutting depth allows the cutting forces to be kept within reasonable limits while taking advantage of ultrasonic elliptical vibration to improve machining efficiency and surface finish quality [32].
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 5 Test and Results
### Complexity Analysis  
The module of principal component analysis can be regarded as a part of the singular value decomposition (SVD): the calculate process of the right singular matrix and the singular value of original matrix \(X_{\left[\begin{array}{cc}m&n\\ m&n\end{array}\right]^{\prime}}\) therefore the algorithm complexity of principal component analysis module is O(n\(\cdot\)3). According to the properties of SVD, when \(n<m\), the computation complexity of the right singular matrix is half of the computation complexity of SVD for \(X_{\left[\begin{array}{cc}m&n\\ m&n\end{array}\right]^{\prime}}\). Therefore, the algorithm complexity and delay of PCA are far less than those of SVD. In the Intel i5-5200U processor, 4 GB memory, 2.2 GHz work memory, it takes 20 ms to finish one part of PCA. Given the time reduction of parametric stereo, the delay of PCA-based codec algorithm is in the acceptable range. In the part of multi-frame joint PCA, the forming of the original matrix takes 40 ms. When the first frame finishes MDCT, the process of forming original matrix will begin. Besides, the thread of PCA is different from matrix construction, and MDCT windowing also belongs to the calculating thread. Suppose the time for MDCT of first frame is \(t_{1}\); the whole delay can be regarded as around \(40+t_{1}\) ms, which is around 50 ms. The delay of the algorithm proposed in this paper still has space to be improved, and we can  
Figure 19: MUSHRA score of per item test. (PCA_2 represents the PCA-based codec signal that is transmitted over two channels separately (75 kbps), PCA_PS+ represents PCA-based codec signal with improved parametric stereo (55 kbps), PCA_PS represents PCA-based codec signal with traditional parametric stereo (56 kbps), G.719 represents G.719 codec signal with traditional parametric stereo (56 kbps), anchor represents anchor signal; HE-AACv2 represents HE-AACv2 signal (55 kbps), and hidden reference material has been removed. 1–6 represents different test materials).
###### Abstract  
(1) Background: This work aims to assess the human exposure to the RF-EMFs emitted by a wearable antenna. (2) Methods: a wearable antenna tuned at \(i=2.45\) GHz was tested by placing it in six realistic configurations relative to a male and female human model. The exposure assessment was performed by means of computational methods to estimate the SAR\({}_{10g}\) distributions at 1W of input power. (3) Results: (i) for all the configurations the SAR\({}_{10g}\) distributions resulted always mainly concentrated on a superficial area immediately below the antenna itself; (ii) the obtained values have shown that the configuration with the highest exposure value was when the antenna was posed on the arm; (iii) the exposure tends to be higher for male model. (4) Discussion and Conclusions: This work highlights the importance of performing an exposure assessment when the antenna is placed on the human wearer considering the growth of the wearable technology and its wide variety of fields of application, e.g., medical and military.  
wearable device; EM fields; human exposure +
Footnote †: journal: Journal of Environmental Research and Public Health
## 4 Modeling WSN LPP as a Matrix Game
### Incomplete Information of Survival Times  
Uncertainty in \(\mathbf{H}\) matrix is typically referred as incomplete information in payoffs. Both probabilistic and non-probabilistic methods that handle uncertainty in payoffs are available in the literature [35; 36; 37; 38; 39]. Uncertain payoffs in the form of interval-valued payoffs [40; 41; 42; 43] are related to the proposed WSN game.  
In this work, we assume that the uncertainty in estimating parameter \(\mathbf{H}\) exists. Furthermore, we assume that the uncertainty can be represented by a bounded interval. Without loss of generality, let the interval based payoff matrix of defender be expressed as \(\mathbf{\Omega}=(\omega)_{d,a}=([\underline{h}_{d,a},\overline{h}_{d,a}])\), where \((\omega)_{d,a}\) is a closed interval, \(\underline{h}_{d,a}\) is the lower bound on the closed interval, and \(\overline{h}_{d,a}\) is the upper bound of the closed interval.  
Note that the above uncertainty keeps the zero-sum nature of the game intact, since the payoff matrix of the attacker will be nothing but \(-\mathbf{\Omega}=(-\omega)_{d,a}=([-\overline{h}_{d,a},-\underline{h}_{d,a}])\). Similar to the methodology presented in [41] for handling interval-based uncertainty, we transform Formulation (12a-f) into two formulations: one for obtaining lower bound (pessimistic game) and another for obtaining upper bound (optimistic game). The formulations are as follows:  
\[max :\] \[\overline{\lambda} \tag{13a}\] \[s.t. :\] \[\overline{\lambda} \leq\overline{\mathbf{h}}_{a}^{T}\overline{\mathbf{x}}\qquad a=1,\ldots,A\] (13b) \[\mathbf{e}^{T}\overline{\mathbf{x}} =\mathbf{1}\] (13c) \[\mathbf{q}^{T}\overline{\mathbf{x}} \leq\overline{q}_{max}-\varepsilon_{1}\] (13d) \[\mathbf{p}^{T}\overline{\mathbf{x}} \leq\overline{p}_{max}-\varepsilon_{2}\] (13e) \[\overline{\mathbf{x}} \geq\mathbf{0} \tag{13f}\]\[max:\] \[\underline{\lambda} \tag{14a}\] \[s.t.:\] \[\underline{\lambda}\leq\mathbf{h}_{a}^{T}\underline{\lambda}\qquad a=1,\ldots,A\] (14b) \[\mathbf{e}^{T}\underline{\mathbf{x}}=1\] (14c) \[\mathbf{q}^{T}\underline{\mathbf{x}}\leq\underline{q}_{max}- \varepsilon_{1}\] (14d) \[\mathbf{p}^{T}\underline{\mathbf{x}}\leq\underline{p}_{max}- \varepsilon_{2}\] (14e) \[\underline{\mathbf{x}}\geq\mathbf{0} \tag{14f}\]  
where the values of \((\overline{q}_{max},\overline{p}_{max})\) and \((\underline{q}_{max},\underline{p}_{max})\) are obtained using (9) by replacing \(\mathbf{h}_{a}^{T}\) with \(\overline{\mathbf{h}}_{\mathbf{a}}\) and \(\mathbf{h}_{\mathbf{a}}\), respectively. Upon solving Formulations (13a-f) and (14a-f), we obtain the upper bound \(\overline{\lambda}\) and the lower bound \(\underline{\lambda}\), respectively, for a given value of \(\varepsilon_{1}\) and \(\varepsilon_{2}\). To obtain the Pareto surfaces, a grid search on different values of \(\varepsilon_{1}\) and \(\varepsilon_{2}\) is conducted. The solution of the above two models results in two Pareto surfaces. The network manager and/or the decision maker can pick a desired operational point from the Pareto surfaces.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 2 Types of Wastewater Treatment Plants Where _Thiothrix_ Morphotype Can Occur
### Biofilms  
A biofilm is a set of microorganisms located on the surface of wastewater or parts of treatment facilities. One of the most common types of bioreactors that contain microorganisms in the form of a biofilm is the rotating biological contactor (RBC).  
In an RBC, a chain of round plastic discs rotates on a horizontal rod using a motor. This rotation causes the mixing of wastewater volumes, the diffusion of compounds into the biofilm, convection through the pores/medium of the biofilm and the subsequent exchange of products with the reactor and the environment. Oxygen diffusion is obligatory for the oxidation of organic substances. The rotation of the discs creates shear forces that inhibit excess biofilm growth. Excess sludge is transferred to a settling tank [68].  
Filamentous organisms can proliferate at the surface-biofilm interface in RBCs. The presence of filaments of the _Thiodrix_ morphotype in RBC biofilms has been shown many times, although they are much less common in biofilms than in suspension form. They were detected as part of an RBC biofilm in the simultaneous removal of nitrogen and phosphorus [21]. It was shown that the _Thiothrix_ morphotype can be used as an indicator of nitrous and nitric nitrogen's presence in RBCs [69]. In addition, bacteria similar in morphotype to _Thiothrix_ were found in the biofilm of a non-aerated reactor for the treatment of sulfide-containing wastewater [70].
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 2 Literature Review
Although existing studies show a wide application of deep learning in the agricultural context, the use of versions of YOLO has stood out, especially in plant disease and pest-detection tasks. As evidenced in the research by [PERSON] et al. (2021) [28], YOLO is effective in quickly and accurately detecting diseases in coffee leaves, making it crucial for monitoring plants in real time. In addition, YOLOv7 and YOLOv10, used in other agricultural research, such as the studies by [PERSON] et al. (2023) [27] and [PERSON] and [PERSON] (2024) [25], have proved useful for controlling pests and diseases in various agricultural crops. However, the different approaches, including classification techniques and more specialized methods, point to the need for continuous improvements in the accuracy and robustness of these models, especially when dealing with variable lighting conditions and the diversity of diseases that affect plants. Considering that no studies have been found in the literature using recent versions of the YOLO architectures to perform disease and pest detection on coffee leaves, this work focuses on the use of recent versions of YOLO, including YOLOv8, YOLOv9, YOLOv10, and YOLOv11. In order to analyze the performance of each model, experiments are carried out to assess the impact of recent updates to these architectures on the accuracy and speed of detection of specific coffee diseases. In addition, the expert review enhances the BRACOL dataset, strengthening data quality and contributing to the reliability of the trained models. In this way, this study not only expands the application of YOLO to precision agriculture in coffee cultivation, but also offers a detailed analysis of the architecture's advances in the specific application.
# A Structured Recognition Method for Invoices Based on StrucTexT Model
## 4 Conclusions  
In this article, a structured invoice recognition method based on the StrucTexT model is proposed. To address the issue of invoices with multiple attributes, the structured StrucTexT model is used for recognition but is made lightweight and therefore optimized for practical applications. The experimental results show that the proposed method achieves an accuracy rate of over 94% on the publicly available datasets SROIE and FUNSD and an accuracy rate of over 95% on a self-built structured invoice dataset, with a 30% decrease in average processing time. The model's size was reduced by 20%, making it more deployment-friendly. The proposed method has a fast recognition speed and is highly practical, all without sacrificing accuracy, making it applicable to production on a daily basis.  
Conceptualization, Z.L. and W.T.; methodology, Z.L. and W.T.; experiment design, Z.L. and W.T.; experimental validation, Z.L., W.T., Y.L. and H.S.; formal analysis, Z.L., W.T. and C.L.; resources, Z.L.; data collection, Z.L. and C.L.; writing--original draft preparation, W.T.; writing--review and editing, Z.L.; supervision, Z.L. and C.L.; project administration, Z.L. and C.L.; funding acquisition, Z.L. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of the 13 th Five-Year Plan (2019 YED1100904), the National Natural Science Foundation of China (51878536), and the Science and Technology Program for Housing and Urban-Rural Development of Shaanxi Province (2020-K09).  
Not applicable.  
The public dataset SROIE is available at the following link: GitHub--zzDavid/ICDAR-2019-SROIE: ICDAR 2019 Robust Reading Challenge on Scanned Recipts, OCR, and Information Extraction. The public dataset FUNSD is available at the following link: FUNSD (guillaumejaume.github.io) (accessed on 4 July 2022). Self-built datasets cannot be made public due to the privacy of invoices.  
We are grateful for the financial support provided and the valuable comments from the editors and reviewers.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Keyword** & **Precision** \\ \hline Pre-tax amount & 1 \\ Tax rate & 1 \\ Tax amount & 1 \\ Name (seller) & 0.98 \\ Taxpayer identification number (seller) & 0.99 \\ Address telephone (seller) & 0.94 \\ Bank and account number (seller) & 0.93 \\ Name (purchaser) & 0.98 \\ Taxpayer identification number (purchaser) & 0.99 \\ Address telephone (purchaser) & 0.94 \\ Bank and account number (purchaser) & 0.93 \\ Invoice code & 0.99 \\ Invoice number & 0.99 \\ Date & 1 \\ Passenger name & 0.95 \\ Departure place & 0.99 \\ Destination & 0.99 \\ Flight number & 0.98 \\ E-ticket number & 0.97 \\ \hline \end{tabular}
\end{table}
Table 3: Keyword recognition performance.
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 5 Results and Discussion  
In this section, we conduct experiments on natural scenes using our system and method for haze removal. To validate the effectiveness of our approach, we compare it with the original dark channel prior-based haze removal method. We discuss the advantages of our method in terms of image quality, accuracy of atmospheric light estimation, and adaptability to different scenes. Finally, we point out the limitations of our method, providing support for future research.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 2 Overview of Layered Steered Space-Time Codes
### Transmitter Model
Moreover, the SP modulation is especially designed for a correlated Rayleigh fading environment.