esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Other Techniques  
#### 3.3.1 Plasma Technique  
Plasma is the collection of positive and negative electric particles that consist of electrons, ions, radicals, and neutral particles, which are electric and electroneutral, thus called the fourth state beyond gas, liquid, and solid electrically. Diverse from most AOPs and conventional techniques, plasma techniques could convert water into highly active substances, involving 'OH, O, H\(\cdot\), HO\(\cdot\), O\({}_{2}\)\({}^{\bullet-}\), H\({}_{2}\), O\({}_{2}\), H\({}_{2}\)O\({}_{2}\), and e\({}_{\text{aq}}\)\({}^{-}\)[56]. When using the plasma technique for PFASs elimination, the degradation process was gradually reduced to intermediates, and then intermediates, perfluoroalkyl radicals, and perfluoro alcohols/ketones-perfluoroalkyl were oxidized [57]. However, this technique was hardly  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Catalyst** & **Catalyst Dose** & **PFASs** & **Experiment Condition** & **Removal Efficiency (\%)** & **References** \\ \hline Fe\({}^{3+}\) & 5 mmol/L & PFBA & UV & 49.9 & [47] \\  & 960 mmol/L & Short-chain PFSAs & UV, 350 \({}^{\circ}\)C & 95 & [49] \\  & & (C2–C6) & 20 MPa & & \\ \hline TiO\({}_{2}\) & 0.66 g/L & PFOA & 254 nm & 100\% & [50] \\  & & & pH \(<\) 3 & & \\ \hline TiO\({}_{2}\)-MWCNT & 0.4 g/L & PFOA & 300 W, 365 nm & 100\% & [52] \\  & & & pH = 5 & & \\ \hline TiO\({}_{2}\)-rGO & 0.1 g/L & PFOA & 150 W, 254 nm & 93 \(\pm\) 7\% & [55] \\  & & & pH = 3.8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Photocatalytic efficiency of PFASs with different catalysts.  
applied and is invalid in the degradation for short-chain PFASs. For example, [PERSON] et al. adopted BaTiO\({}_{3}\) iron beads as a filling medium to degrade C\({}_{2}\)F\({}_{6}\); the degradation efficiency was only 20% [58]. Moreover, short-chain PFASs, fluorine ions, and CO\({}_{2}\) by-products were produced during the plasma treatment of PFASs.
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 3 Results  
### Dry Matter, TPC, and FRAP Content  
The mean, SD (standard deviation), minimum, maximum, and CV (coefficient of variation) of the DM, TPC, FRAP, and CUPRAC of the pepper fruit are presented in Table 2. As shown in Table 2, the DM content of the chili samples ranged from 8.39-12.64%, with a mean DM content of 10.52%. As shown by the CV of 4.8%, the range of DM contents was not as wide as that reported by previous researchers [20; 21]; however, it represented the typical range of DM content found in pepper fruits around the point of harvest [11].  
The TPC ranged from 898-2617 mg GAE/100 g, a slightly larger range than that obtained by [PERSON] et al. [6] (919-1803 mg GAE/100 g), with a much higher CV (20.5%). The FRAP and CUPRAC showed a similarly wide variation, with CVs of 23.7 and 19.8%, respectively. The mean FRAP (296 mg TE/100 g) was slightly higher than that previously found in green pepper pulp by [PERSON] et al. [31] (107 mg TE/100 g), although lower than the value of \(\sim\)425 mg TE/100 g found by [PERSON] et al. [32]. The mean CUPRAC (1917 mg TE/100 g) was also higher than the range of CUPRAC values (882-1280 mg TE/100 g) found by [PERSON] et al. [33] in a range of pepper varieties.
## 3 Results and Discussions
### Impacts of pH on the ROUR  
The effects of pH on the ROUR behaved as a bell shape, as illustrated in Figure 6. The ROUR peaked at pH 6-10 and achieved above 80% comparing to that in the absence of toxic substances. It declined dramatically to below 10% under extremely acidic (pH < 4) or alkaline (pH > 10) conditions. The activated sludge was reported to be sensitive to pH, as most bacteria can only survive in a relatively narrow range of pH [72] and the activities of some microorganisms were inhibited under  
Figure 4: The ROUR in treatments with different mixed liquid suspended solids (MLSS) post exposure to toxic substances. (**a**) Zn\({}^{2+}\), (**b**) Ni\({}^{2+}\), and (**c**) phenol.  
Figure 5: The ROUR in treatments with different hydraulic retention time (HRT) post exposure to toxic substances. (**a**) Zn\({}^{2+}\), (**b**) Ni\({}^{2+}\), and (**c**) phenol.  
low or high pH conditions [73]. For example, pH from 5.1 to 6.1 markedly depressed the metabolisms of the activated sludge [74]. Additionally, pH control was critical for optimizing the performance of WWTPs [75]. Our results suggested that the ROUR was quite stable within a broad range of pH (6-10), covering most of the operational pH in WWTPs, and was suitable as an indicator for online early-warning system measuring the shock load of industrial wastewater.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quartile** & **With Damage** & **Without Damage** & \(p\)**Value** \\ \hline  & \multicolumn{2}{c}{Medial meniscus} & \multicolumn{2}{c}{**<0.001 ***} \\ \hline Q1 & 30 (23.62) & 99 (76.74) & & \\ Q2 & 43 (33.86) & 84 (66.14) & **0.008 *** \\ Q3 & 50 (43.48) & 65 (56.52) & **<0.001 *** \\ Q4 & 60 (51.28) & 57 (48.72) & **<0.001 *** \\ \hline  & \multicolumn{2}{c}{Lateral meniscus} & & \\ \hline Q1 & 22 (17.05) & 107 (82.95) & 0.761 \\ Q2 & 25 (19.69) & 102 (80.31) & & \\ Q3 & 22 (18.26) & 94 (81.74) & & \\ Q4 & 19 (16.24) & 198 (83.76) & & \\ \hline  & \multicolumn{2}{c}{Knee chondropathy} & & **<0.001 *** \\ \hline Q1 & 97 (36.43) & 81 (63.57) & & \\ Q2 & 104 (81.89) & 23 (18.11) & **<0.001 *** \\ Q3 & 92 (84.35) & 18 (15.65) & **<0.001 *** \\ Q4 & 106 (90.6) & 11 (9.4) & **<0.001 *** \\ \hline \hline \end{tabular} Data are presented as absolute values with the corresponding percentages (in parentheses). “Without damage” refers to the absence of injury at that specific site; however, patients had lesions (sometimes multiple) in other compartments analyzed. \(p\)-values in the rightmost column represent Chi-square comparisons across all four quartiles for each injury type (overall significance) Additional \(p\)-values listed within each injury sub-table reflect post hoc Chi-square comparisons with Bonferroni correction, comparing each quartile against the reference group (Q1). Marked bold values (*) show significant differences (Chi-square tests, ***—\(p\) < 0.001, ***—\(p\) < 0.01, *—\(p\) < 0.05).  
\end{table} 
Table 3: Prevalence of different types of knee joint injuries in age quartiles. frequency of this condition in the lower quartile was significantly below those measured in the other quartiles. The correlations between the outcomes analyzed in this study are shown in Table 4. The prevalence of medial meniscal damage correlated significantly with the occurrence of lateral meniscal damage across all age quartiles. The strength of these relationships increased with age. The former variable was also positively associated with the frequency of knee chondropathy, but only from the second age quartile onward. The magnitude of these correlations was relatively weaker compared with that noted between those previously mentioned, remaining constant across the upper three quartiles. In contrast, no significant correlations were observed between lateral meniscal damage and any patellar damage.
## 4 Simulation and Analysis
### BP Neural Network Simulation and Analysis  
Using the established BP neural network prediction model for ice copper grade for several simulation experiments, the final optimal parameters of the network were obtained as follows: the number of iterations \(l=100\), the learning rate \(\eta=0.1\), the error accuracy of training \(E_{\text{P}}=0.00004\), and the model prediction data and expectation data are given in Table 2.  
Figure 5 shows a line graph of the output data of the matte-grade neural network prediction model compared to the desired output data.  
From the results obtained in Table 2 and Figure 5, it can be analyzed that the BP neural network prediction model of matte grade has a good ability to mine the implied regulation in the samples, and the model has a certain generalization ability by using the training samples to train the neural network. Meanwhile, the network output data of the test samples and the expected output data were controlled within the error range.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Cu (t)** & **S (t)** & **Fe (t)** & **SiO\({}_{2}\) (t)** & **CaO (t)** & **Oxygen (Nm\({}^{3}\))** & **Blast (Nm\({}^{3}\))** & **Flux (t)** & **Matte Grade (\%)** \\ \hline
22.916 & 25.297 & 23.620 & 9.241 & 1.663 & 209,300 & 152,660 & 35 & 56.65\% \\
20.918 & 22.631 & 22.209 & 11.559 & 2.268 & 199,852 & 173,140 & 39 & 61.69\% \\
21.245 & 24.429 & 23.853 & 10.598 & 1.333 & 212,448 & 151,890 & 42 & 56.43\% \\
21.780 & 24.488 & 23.971 & 10.460 & 1.181 & 206,088 & 161,237 & 47.7 & 57.06\% \\
22.154 & 23.558 & 22.906 & 10.914 & 1.555 & 207,382 & 170,853 & 49 & 60.09\% \\
22.039 & 23.965 & 23.195 & 10.627 & 1.651 & 205,373 & 164,912 & 50 & 57.72\% \\
20.966 & 23.745 & 22.541 & 10.527 & 1.583 & 209,857 & 170,418 & 46 & 62.72\% \\
22.945 & 23.208 & 22.385 & 11.197 & 2.114 & 203,196 & 162,327 & 40 & 51.46\% \\
22.612 & 25.328 & 24.269 & 9.597 & 1.158 & 202,332 & 169,972 & 43.5 & 57.81\% \\
22.297 & 23.572 & 22.688 & 10.721 & 1.791 & 199,668 & 162,570 & 42 & 51.79\% \\ \hline \end{tabular}
\end{table}
Table 1: Ten groups of training samples taken from a copper smelting enterprise.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Number** & **Network Output** & **Expected Output** & **Absolute Error** & **Relative Error** \\ \hline
1 & 52.71\% & 50.88\% & 1.83\% & 3.60\% \\
2 & 58.15\% & 60.28\% & 2.13\% & 3.66\% \\
3 & 56.36\% & 57.13\% & 0.77\% & 1.37\% \\
4 & 56.81\% & 57.57\% & 0.76\% & 1.34\% \\
5 & 56.63\% & 57.78\% & 1.15\% & 2.03\% \\
6 & 54.92\% & 53.11\% & 1.81\% & 3.41\% \\
7 & 51.36\% & 53.35\% & 1.99\% & 3.87\% \\
8 & 56.06\% & 56.68\% & 0.62\% & 1.11\% \\
9 & 55.71\% & 56.11\% & 0.40\% & 0.72\% \\
10 & 56.92\% & 56.64\% & 0.28\% & 0.49\% \\ \hline \end{tabular}
\end{table}
Table 2: The simulated predicted data and expected output values of BP neural network prediction model for matte grade.
## 3 Results and Discussion  
### Model Validation  
#### 3.1.1 CO\({}_{2}\) Distribution Inside the Chamber  
The measured CO\({}_{2}\) concentrations were compared with the simulated CO\({}_{2}\) distribution inside the chamber. Model simulation accuracy was evaluated by comparing each measured and simulated point's percentage errors of Table 3. The MAPE of the left, middle, and right parts of the chamber were 1.85%, 3.43%, and 0.43%, respectively, and the RMSE of left, middle, and right parts were 11.20, 16.99, and 2.17 ppm, respectively, according to the CFD model for CO\({}_{2}\) distribution.  
The results showed that employing a tomato plant as a porous medium considered the photosynthetic process could reasonably predict CO\({}_{2}\) distribution. In comparison to the actual canopy of the tomato plant, the model tomato plant has been simpler. That is, the dense leaves were considered homogenous for the entire plant. Thus, the CO\({}_{2}\) concentration shows a decrease inside the porous medium (corresponding to tomato plant), where the air, including CO\({}_{2}\), may go through the canopy of the tomato plant; then, the CO\({}_{2}\) is absorbed by the process of photosynthesis (Figure 8).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Canopy Layer**} & \multicolumn{2}{c}{**Measured**} & \multicolumn{4}{c}{**Simulation**} & \multicolumn{4}{c}{**Percentage error (\%)**} \\ \cline{2-10}  & **Left** & **Middle** & **Right** & **Left** & **Middle** & **Right** & **Left** & **Middle** & **Right** \\ \hline
**Top** & 420 & – & 440 & 439 & 442 & 442 & 4.53 & – & 0.36 \\
**Middle** & 440 & 420 & 440 & 444 & 443 & 443 & 0.84 & 5.59 & 0.76 \\
**Bottom** & 450 & 455 & 450 & 449 & 449 & 449 & 0.17 & 1.27 & 0.17 \\ \hline
**MAPE (\%)** & 1.85 & 3.43 & 0.43 & & & & & & \\ \hline
**RMSE (ppm)** & 11.20 & 16.99 & 2.17 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of CO\({}_{2}\) concentration between measured and simulated data for model validation in the chamber.  
Figure 8: The CO\({}_{2}\) distribution inside the chamber: The values are the measured data of the CO\({}_{2}\) concentration.  
The CO\({}_{2}\) concentration distribution according to the chamber measurement and simulation for each height were compared to evaluate numerical simulation properties (Figures 8 and 9). These figures showed the simulation results of CO\({}_{2}\) concentration at the left and middle part were slightly overestimated. However, the simulation was still reasonable and can be used for greenhouse numerical simulation.
## 3 Proposed System  
In this proposed system, a BBD-based precoding is utilized to improve the BER performances in the indoor MIMO-VLC system. The precoding utilized in the proposed system leads to the reduction of the interferences occurring in the communications. Additionally, the QAM is executed during data transmission which has the efficient usage of bandwidth. The main elements present in the proposed system are QAM modulation, LED as transmitter, PD as receiver and QAM demodulation. The block diagram of the proposed indoor MIMO-VLC system is in Figure 1.  
Figure 1: Proposed Indoor MIMO-VLC system. (**a**) Transmitter. (**b**) Receiver.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Decoding on Sentences of Various Lengths  
In Figure 8, we summarize the WERs according to the numbers of words in sentences for clean and noisy reverberant data. For both the data, our models achieved better recognition performance than the others in most cases. All the experimented models obtained poor performance for short sentences of three and four words because appropriate contexts could not be extracted in these sentences.  
\begin{table}
\begin{tabular}{l c c l} \hline \hline
**Models** & **Modality** & **Objective** & **Transcription** \\ \hline Ground truth & & & and it’s even rarer to find one that hasn’t been dug into by antiquarans \\ TM-seq2 seq & V & CE & and it’s even rarer to find one that hasn’t **bin diagnosed** by **asking quarries** \\ TM-seq2 seq & A & CE & and it’s **equal rare two** find one that hasn’t been dug into by **anti crayons** \\ TM-seq2 seq & AV & CE & and it’s even rarer to find one that hasn’t been dug into by **antique areas** \\ TM-av\_align & AV & CE & and it’s even rarer to find one that hasn’t been dug into by **antiquarists** \\ TM-DCM & AV & CE & and it’s even rarer to find one that hasn’t been dug into by **antiquarists** \\ TM-DCM & AV & H & and it’s even rarer to find one that hasn’t been dug into by antiquarans \\ \hline Ground truth & & & home to an animal that is right at the top of the food chain \\ TM-seq2 seq & V & CE & home to an animal **has raised in some** of the **future in** \\ TM-seq2 seq & A & CE & home to an animal **bad is rights into top off** a food chain \\ TM-seq2 seq & AV & CE & home to an animal that is right at the **top over** food chain \\ TM-av\_align & AV & CE & home to an animal that is right at the **top of a** food chain \\ TM-DCM & AV & CE & home to an animal that is right at the top of the food chain \\ TM-DCM & AV & H & home to an animal that is right at the top of the food chain \\ \hline Ground truth & & & and would eventually marry her after his wife \\ TM-seq2 seq & V & CE & and would eventually **the most american hundreds of** \\ TM-seq2 seq & A & CE & and would eventually marry **him got the** his wife \\ TM-seq2 seq & AV & CE & and would **emit actually** marry her after his wife \\ TM-av\_align & AV & CE & and would **emitting** her after his wife \\ TM-DCM & AV & CE & and would eventually marry her after his wife \\ TM-DCM & AV & H & and would eventually marry her after his wife \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some decoding results for 0-dB-SNR noisy reverberant data. The boldface words denote wrong prediction. Abbreviation to understand the table: A, audio-only modality; V, video-only modality; AV, audio–visual modalities; CE, cross-entropy loss only; H, hybrid CTC/attention loss.
# Remote Sensing for Irrigation of Horticultural Crops
## 3 Sensors and Platforms
### Remote Sensing Sensors  
Different remote sensing sensors have been developed for monitoring crop growth and yield, as well as the spatial variability within fields. Many factors may affect crop growth and yield variability, such as soil characteristics, soil fertility, soil moisture, canopy volume, biomass and architecture, water content, and biotic and abiotic stress. Different types of sensors can monitor and measure these factors; [PERSON] et al. [48] discussed electronic sensors, spectroradiometers, machine vision, multispectral and hyperspectral sensors, thermal sensors, and machine olfaction systems. A detailed review on sensors related to agricultural crop was reported by [PERSON] et al. [49]. The authors provided an overview of sensing technologies and application as: (i) electromagnetic sensors, spectroscopic sensors, and computer vision; (ii) mechanical contact and acoustic sensors; (iii) biosensors; and (iv) wireless-sensor networks. [PERSON] et al. [50] studied recent development and future perspective of wireless sensors in agriculture and the food industry.  
Sensors can be divided into several broad categories of basic sensor system types such as active and passive. Passive sensors (photographic, electro-optic radiometers, passive microwave systems visible, infrared, and thermal imaging systems, etc.) are the most common for vegetation correlated remote sensing. These sensors provide helpful information for crop monitoring, but their major limitation is the strong relationship among data acquisition and lighting (time of day and year; latitude) and weather conditions (cloud cover). Data from passive sensors requires accurate measurements of solar radiation and correction for \"atmospheric effects\" [34; 40].  
Active sensors use an artificial source of energy; in active remote sensing, humans can control the nature (wavelength, power, duration) of the source energy. Active sensors (Radar systems, LiDAR) do not need sun light and perfect weather conditions (less dependence on environmental factors); therefore, they can be more dedicated and targeted toward the remote sensing objectives even at night or under adverse conditions (haze, clouds, rain, etc.) [34; 40].
###### Abstract  
This study aims to present the development of a wearable prototype device consisting of soft robotic gloves (SRGs), its integration into a wearable robotics platform for sensorimotor rehabilitation, and the device's validation experiments with individuals suffering from impaired hand motor function due to neurological lesions. The SRG is tested and evaluated by users with spinal cord injury (SCI) and stroke. The proposed system combines multiple-sensor arrays with pneumatic actuation to assist finger movement during grasping tasks. Evaluations on SCI and stroke patients revealed that the gloves consistently improved finger and grip performance. Detailed analyses indicated observable differences in sensor-derived features during actuation versus non-actuation, with statistically significant modifications appearing in both time-domain and frequency-domain metrics. Although the stroke participants exhibited greater variability, all participants were able to use the system reporting low discomfort and effort. The findings underscore the potential for personalized calibration to further optimize therapeutic outcomes. In summary, the study validates the utility of these gloves as assistive and rehabilitative modalities, and future research will focus on refining the device in the context of multimodal wearable robotics and individualized neurorehabilitation strategies.  
soft robotics; wearable robotics; robotic gloves; neurorehabilitation; spinal cord injury; stroke +
Footnote †: journal: Applied Sciences  
Article
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 2 Isolation of Proteins and Functional Peptides from Microalgae
### Protein Hydrolysis  
Apart from proteins, bioactive peptides are one of the most commercially attractive microalgae products, with a wide range of potential uses in pharmacy, cosmetics and the production of food and feed. Because peptides remain inactive in the primary structure of proteins, they have to be released in gastrointestinal tract during food processing to become biologically active. The commonly used methods for the production of biologically active peptides are chemical or enzymatic hydrolysis and microbial fermentation [133].  
Chemical hydrolysis of proteins is performed at higher temperatures (over 40 \({}^{\circ}\)C) and an extreme pH. The advantages of this method are low costs, simplicity and a short processing time, but on the other hand, the process lacks sensitivity and specificity, and some amino-acids can be destroyed [134].  
Enzymatic hydrolysis is usually carried out in a reactor with a controlled pH and temperature by adding a protease or protease mixture (containing trypsin, pepsin, papain and \(\alpha\)-chymotrypsin) to the protein concentrate. Compared to chemical hydrolysis, enzymatic hydrolysis is performed in lower temperatures, and the process has higher specificity, higher yields and a higher purity of the product, so it is a preferred hydrolysis technique in food and the pharmaceutical industry. However, peptidases are expensive, and it is difficult to adjust the desired pH and temperature during the whole process.  
Microbial fermentation was evaluated as an eco-friendly method suitable for protein hydrolysis on a large scale. Other advantages include the elimination of hyper-allergic and anti-nutritional factors. For the purpose of protein hydrolysis by fermentation, lactic acid bacteria such as _Lactobacillus brevis_, _Bacillus subtilis_, _Enterococcus gallinarum_ or _Pedicococcus acidilactii_ are frequently used [133]. As discussed by [PERSON] and colleagues recently [135], \"fermented foods comprise very complex ecosystems consisting of enzymes from raw ingredients that interact with the fermenting microorganisms' metabolic activities. Fermenting microorganisms provide a unique approach towards food stability via physical and biochemical changes in fermented foods. These fermented foods can benefit consumers compared to simple foods in terms of antioxidants, production of peptides, organoleptic and probiotic properties, and antimicrobial activity\".