esa-sceva/satcom-chunk-collection huggingface dataset:
# Anaerobic Digestion of Duckweed Used to Remediate Water Contaminated with Zinc and Ammonium
## 3 Results
### Methane Production Using the Solid and Liquid Fractions of the Duckweed
#### 3.3.1 Methane Production
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Duckweed** & **Treatment** & **M\({}_{\rm max}\)** & **R\({}_{\rm max}\)** & \(\lambda\) & **T\({}_{\rm max}\)** & **R\({}^{2}\)** \\
**Fraction** & **Condition** & **M\({}_{\rm max}\)** & **R\({}_{\rm max}\)** & **\(\lambda\)** & **T\({}_{\rm max}\)** & **R\({}^{2}\)** \\ \hline \multirow{8}{*}{Duckweed} & T1 & 46.196 & 5.602 & 5.261 & 8.295 & 0.997 \\  & T2 & 56.066 & 7.138 & 10.500 & 13.400 & 0.993 \\  & T3 & 28.130 & 3.039 & 11.126 & 14.531 & 0.999 \\  & T4 & 153.409 & 3.364 & 11.763 & 28.539 & 0.997 \\  & T5 & 46.791 & 2.253 & 7.514 & 15.154 & 0.997 \\  & T6 & 56.395 & 2.316 & 2.922 & 11.880 & 0.992 \\ \hline \multirow{8}{*}{Duckweed} & T1 & 194.689 & 18.936 & 0.174 & 3.956 & 0.982 \\  & T2 & 112.207 & 5.448 & \textless{}0.01 & 6.079 & 0.957 \\ \cline{1-1}  & T3 & 95.569 & 6.097 & \textless{}0.01 & 4.422 & 0.945 \\ \cline{1-1}  & T4 & 141.182 & 8.309 & \textless{}0.01 & 4.765 & 0.944 \\ \cline{1-1}  & T5 & 117.230 & 7.749 & \textless{}0.01 & 5.329 & 0.978 \\ \cline{1-1}  & T6 & 126.647 & 6.584 & \textless{}0.01 & 5.590 & 0.951 \\ \hline \end{tabular} \(\overline{\text{M}_{\rm max}}\)—maximum methane yield (mL/g COD), R\({}_{\rm max}\)—maximum methane production rate (mL/g COD d), \(\lambda\)—lag phase (d), T\({}_{\rm max}\)—peak time (d), T\({}_{\rm max}\) is defined as ((M\({}_{\rm 0}\)/R\({}_{\rm max}\))/e) + \(\lambda\).  
\end{table} 
Table 2: Estimated kinetic parameters from the modified Gompertz model simulating methane production in the solid and liquid fractions of cultured duckweeds. kinetics, limiting their practicality unless pretreatment is optimized. Overall, Gompertz modeling offers crucial insights into the bioconversion efficiency and system design needs for duckweed-based biogas systems.
# Future Trend in Wearable Electronics in the Textile Industry
## 4 Technical Limitations in Wearable Technology
#### Mobility  
A major limitation to smart textile use is related to batteries: their weight and bulkiness as well as the charging process [44]. In the commercial market, there are already many wearable non-textile products, for instance, smartwatches and wrist bands, which are used to monitor activity and the wearer's health parameters. However, electronic devices integrated into textiles can offer several advantages, such as enhanced mobility and comfort, and for the user, these can offer effective solutions for wearers who seek more detailed data about their fitness and performance [68; 69]. Smart textiles can also eliminate the use of bulky equipment such as chest straps. Since athletes and major league players constantly strive to improve performance, an opportunity for storing data for analysis by lightweight devices that can be embedded in their sportswear offers a high potential for further performance enhancement [70].
###### Abstract  
The outlet conduit is an important construction connecting the outlet of the pump guide vane and the outlet pool; in order to study the hydraulic performance of the straight outlet conduit of the axial-flow pump device, this paper adopts the method of numerical simulation and analyzes the influence of the division pier on the pressure and velocity distribution inside and near the wall of the straight outlet conduit based on three design schemes. Four pressure pulsation measuring points were arranged in the straight outlet conduit, and the low-frequency pulsation characteristic information inside the straight outlet conduit with and without the division pier was extracted by wavelet packet reconstruction. The results show that the addition of a division pier has an effect on the hydraulic loss, near-wall pressure and velocity distribution in the straight outlet conduit. A small high-pressure zone is formed near the wall at the starting position of the division pier, and a large high-speed zone is formed on the left side at the starting position of the division pier. The length of the division pier has no significant effect on the flow distribution of the straight outlet conduit and the pressure and velocity distribution near the wall. Under different working conditions, each monitoring point has the maximum energy in the sub-band (0-31.25 Hz). With the increase of the flow rate, the total pressure energy of the straight outlet conduit decreases gradually. Under each condition, the difference of the energy proportion of the horizontal monitoring points of the straight outlet conduit is small, and the difference of the energy proportion of the two monitoring points at the top and bottom of the outlet channel is relatively large. The energy of the two monitoring points in the straight outlet conduit with a division pier is smaller than that of the two monitoring points in the straight outlet conduit without a division pier. There are differences in the main frequency and the power spectrum corresponding to the main frequency of the monitoring points in the straight outlet conduit, and the reasonable setting of the division pier is conducive to reducing the pressure pulsation of the flow in the straight outlet conduit and is beneficial to the safe and stable operation of the pump device.  
Keywords:axial-flow pump device; straight outlet conduit; division pier; numerical calculation; model test; pressure pulsation +
Footnote †: journal: Applied Sciences
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 4 Results
### Spatial Distributions of Paddy Rice Maps  
#### 4.4.1 Comparisons of Spatial Distributions of Paddy Rice Maps  
The visual comparison of paddy rice maps from RF-, SVM-, PMI (NIR)-, and PMI (SAR)-derived classification at both sites appears in Figures 7 and 8. Machine learning-based paddy rice maps (Figures 7c,d and 8c,d) were produced through scheme 6, which resulted in the best performance among the six schemes (Tables 4 and 5) using RF and SVM classifiers. There is not much difference between two classifiers at site A, while the SVM result shows clearer paddy rice parcels than RF at site B. Paddy rice was not well detected and underestimated at site B because paddy rice and field are mixed within a pixel, especially at site B (Figure 8c-f).  
Figure 6: Threshold of Paddy rice Mapping Index (PMI) (near-infrared (NIR) based) and box plot of PMI threshold for 10 years.  
Figure 7: Spatial distribution of paddy rice maps at site A. (**a**) Landsat 5 RGB image on 26 July 2009; (**b**) reference map; (**c**) Random forest (scheme 6)-derived map; (**d**) Support Vector Machine (SVM) (scheme 6)-derived map; (**e**) PMI (NIR)-derived map; and, (**f**) PMI (Synthetic Aperture Radar (SAR))-derived map. Paddy rice fields are shown in yellow.  
PMI based paddy rice maps were calculated using NIR and SAR backscattering coefficients (Equations (5) and (6)) and, the thresholds applied were 0.2 (NIR) and \(-\)0.15 (SAR). The threshold of SAR based PMI was not fully optimized because the availability of time series of SAR data was very limited, so the threshold, (\(-\)0.15), was derived by averaging one year PMI optimized thresholds from two sites. SAR based PMI produced more noisy paddy rice maps than NIR based PMI due to speckle, which is one of the limitations of SAR images [69]. The overall accuracy of binary classification (paddy rice or non-paddy rice) for the entire study areas (3,354,085 pixels at site A and 2,694,510 pixels at site B) was calculated. That classification results with two machine learning classifiers (\(\sim\)95% at site A and \(\sim\)90% at site B) were more accurate than PMI (NIR) based results (\(\sim\)87% at site A and \(\sim\)83% at site B).  
Figure 8: Spatial distribution of paddy rice maps at site B. (**a**) Landsat 5 RGB image on 21 September 2003; (**b**) reference map; (**c**) Random forest (scheme 6)-derived map; (**d**) SVM (scheme 6)-derived map; (**e**) PMI (NIR)-derived map; and, (**f**) PMI (SAR)-derived map. Paddy rice fields are shown in yellow.
## 4 Experimental Results
### Implementation Details  
This study employed a collaborative approach to train the IDP-YOLOV9 network architecture. In the initial phase, the YOLOV9 detection network was trained without prior knowledge and underwent transfer learning alongside the dataset proposed in this study. The DLIE parameters were reconfigured by utilizing the convolutional block of YOLOV9 up to the fifth layer, and joint training was conducted using a mixed data method. This collaborative training strategy aimed to facilitate maximum information exchange between the image processing module and the object detection network, thereby enhancing overall performance.  
To further enhance the generalization capability for various adverse weather conditions, IDP-YOLOV9 dynamically adjusted the scale during training. Initially, a range of scales was established, and the selection of image size was dynamically adapted based on the content and image complexity. This enabled the model to accommodate various input sizes in each iteration, enhancing its robustness. The experiments were carried out utilizing the PyTorch framework and implemented on GPUs.
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 4 Discussion  
### Misclassification in the F-ALS-Estimated Distortions  
The P-NG method depends on a pre-classification step obtained with the F-ALS method. The F-ALS method can distinguish areas affected by foreshortening from areas affected by active layover/shadow. Whereas, the method only calculates the relationship between the radar incidence angle and slope angle, and thus cannot consider the passive distortion that might occur in the flat area. This will inevitably lead to misclassification in the F-ALS-estimated results. As shown in Figure 6a,b, the points of the passive layover are found in the F-ALS-estimated foreshortening and active shadow, respectively. Obviously, the overlap area should be redefined as the passive layover according to the interaction of the SGD described in the P-NG method. In addition, it can be found in Figure 6d,e that the points of the passive shadow lie in the foreshortening and active layover, respectively. The overlap area is a misclassified result of the F-ALS method, but can be redefined as the passive shadow by the P-NG method. Therefore, the misclassification of the F-ALS method will lead to the incorrect coverage of the derived SGD and will limit the application of the SAR interferometry in the mountainous areas, such as landslide monitoring. As mentioned, the P-NG method can correct such misclassifications due to the exploitation of neighbor gradient between active and passive distortions.
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 3 Optimization Methods  
This section reviews the optimization methods whose results are further discussed in the paper. First, we introduce a detailed description of the VNDMOPSO algorithm. Then, we briefly review the state-of-the-art VND algorithms VLMOPSO and VNDGDE3 used as reference methods in this study.
# On-Road Driver Emotion Recognition Using Facial Expression
## 3 Materials and Methods
### Augmentation-Based Re-Sampling Module (ABR)  
The primary intent of this work is emotion recognition using the data collected in the on-road driving experiment. In this study, labeled data with seven emotion class labels from the FD module are the original images. The original images of the emotion classes are imbalanced, which means that the image quantity varies from different classes. Therefore, the originally extracted faces required further processing. To settle this problem, related research on imbalanced data [69; 70; 71] has proposed methods that deal with long-tailed data, for instance: re-weighting, re-sampling, etc. As shown in Algorithm 1, this work proposed an augmentation-based re-sampling algorithm based on the re-sampling method to alleviate the imbalance of the dataset presented and enhance the model's generalization ability.  
In detail, ABR implements different augmentation and sampling methods for the images of each class, including random augmentation, over-sampling, and random under-sampling. In general, the ABR module receives the input of the original driver face grayscale dataset, and outputs the augmented re-sampled dataset.  
```
Input: images from dataset Output: augmented and re-sampled dataset dataset \(\mathit{dataset}\) contains cropped faces from FD module fordirectory in datasetdo if\(\mathit{directory}=Neutral\)then  perform undersampling \(\mathit{sample}\)\(\mathit{rate}\)\(\leftarrow\) 0.1 \(\mathit{random}\)\(\mathit{sample}\)\(\mathit{sample}\)\(\mathit{rate}\) elseif\(\mathit{directory}=\mathit{Disgust}\) or \(\mathit{Happy}\)then  perform augmentation-based oversampling \(\mathit{sample}\)\(\mathit{rate}\)\(\leftarrow\) 0.2  random sample(sample rate) for\(\mathit{sample}\) in random sampledo  one of: \(\mathit{Random}\)\(\mathit{Rotate}(-20^{\circ}\leq\mathit{angle}\leq 20^{\circ})\) \(\mathit{Random}\)\(\mathit{Bright}(\mathit{brightness}\)\(\mathit{limit}=0.3)\)  end for else  perform augmentation-based oversampling \(\mathit{sample}\)\(\mathit{rate}\)\(\leftarrow\) 0.3  s1 \(\leftarrow\)\(\mathit{random}\)\(\mathit{sample}\)\(\mathit{rate}\)\(\mathit{s2}\)\(\leftarrow\)\(\mathit{random}\)\(\mathit{sample}\)\(\mathit{sample}\)\(\mathit{rate}\)\(\mathit{s3}\)\(\leftarrow\)\(\mathit{random}\)\(\mathit{sample}\)\(\mathit{rate}\) for\(\mathit{sample}\) in s1 do \(\mathit{Random}\)\(\mathit{Rotate}(-20^{\circ}\leq\mathit{angle}\leq 20^{\circ})\) end for for\(\mathit{sample}\) in s2 do \(\mathit{Random}\)\(\mathit{Bright}(\mathit{brightness}\)\(\mathit{limit}=0.3)\) end for for\(\mathit{sample}\) in s3 do \(\mathit{Random}\)\(\mathit{Contrast}(\mathit{contrast}\mathit{limit}=0.1)\) end for end if end for return augmented and re-sampled dataset
```  
**Algorithm 1** Augmentation-based re-sampling algorithm
## 3 Error Characterization and Simulation Method
### Errors Description
#### 3.1.2 Calibration Errors
Using the Planck function to convert the radiance to brightness temperature, the antenna temperature can be described [31; 32; 33]:  
\[T_{A}^{\prime}=\eta T_{A}+(1-\eta)T_{sp}\;, \tag{6}\]  
\[T_{A}=\eta_{a}T_{B}+(1-\eta_{a})T_{S}\;, \tag{7}\]  
where \(T_{A}^{\prime}\) represents the antenna temperature, \(T_{A}\) represents the earth scene component of the antenna temperature, \(T_{SP}\) represents the spillover emission, \(\eta\) represents the coefficient of spillover correction. In addition, \(T_{B}\) is the atmospheric emission brightness temperature, \(\eta_{a}\) is the main beam efficiency and \(T_{S}\) is the sidelobe contribution. Antenna pattern plays an important role in atmospheric limb sounding, since the atmospheric profiles varies rapidly in some regions. The antenna patterns of TALIS are shown in Figure 3, and the fields of view (FOVs) are 5.5 km, 3.8 km, 3.3 km and 0.96 km, respectively. Usually, a 3 dB beamwidth (full width between half-power points) of the antenna pattern is considered as the main beam. However, the sidelobe contribution is not easily estimated, and this has a large impact on the measured radiance, especially in the limb sounding. Thus, how the antenna pattern affects the retrievals should be evaluated. In this simulation study, 2.5 times the main beamwidth of the antenna patterns is used to calculate the measured radiances, since it is usually used to represent the whole antenna pattern in calculation. The antenna patterns of the main beamwidth are used in retrieval in order to estimate the retrieval errors that may be induced by the antenna. The assumed errors are summarized in Table 4.  
Figure 3: The antenna pattern of TALIS: (**a**) the 118 GHz antenna pattern; (**b**) the 190 GHz antenna pattern; (**c**) the 240 GHz antenna pattern; (**d**) the 643 GHz antenna pattern.
## 4 Hybrid Ant Colony Optimization for Optimization of the Machining Line Balancing Problem
### Heuristic Ant Colony Search Mechanism  
The ant colony optimization (ACO) algorithm is abstracted from the communication behaviours of ants, which are communicated by pheromones, to determine the best path from the starting point to the destination [37; 41; 42]. The ant colony optimisation used in this problem selects the task from the task set in each step, when the task set is established by the heuristic rules introduced in Section 4.1.  
To satisfy the tightness constraint, it is introduced into the ant colony search rule. Therefore, a hybrid heuristic search mechanism was formed by combining tightly constrained priority, random search, and heuristic task information. The mechanism is as follows:  
\[s=\left\{\begin{array}{rl}\rm{I_{1}}:&\rm{tight\ constraint\ priority\ }j\in\rm{J_{s}}\\ \rm{I_{2}}:&P_{js}=\frac{\left(\sum\
olimits_{b=1}^{s}\tau_{h}\right)^{s} \left(w_{j}\right)^{\beta}}{\sum\
olimits_{j\in\rm{T_{s}}}\left(\sum\
olimits_ {b=1}^{s}\tau_{h}\right)^{s}\left(w_{j}\right)^{\beta}}\ 0\leq r\leq r_{1}\\ \rm{I_{3}}:&\rm{rad\ search\ }j\in\rm{T_{s}}\ r_{1}<r\leq 1\\ \end{array}\right. \tag{18}\]  
\(s\): task sequence number  
\(P_{js}\): the probability that task \(j\) will be selected at the position of task sequence \(s\)  
\(r\): random number between (0, 1)  
\(r_{1}\): between (0, 1), rule switchover threshold from \(\rm{I_{2}}\) to \(\rm{I_{3}}\)  
\(\alpha\),\(\beta\): parameters that determine pheromone intensity and the relative importance of heuristic information  
\(\tau_{hhhhhhhh}\): pheromone of task \(j\) at sequence place \(h\)  
\(\sum\limits_{h=1}^{s}\tau_{jh}\): cumulative value of information elements of element \(j\) at sequence positions from 1 to \(s\)  
\(w_{j}\): heuristic information of task \(j\), \(w_{j}=1/t_{j}\), the ant colony search will be affected by the processing time of tasks, and tasks with short processing times will more likely be selected.  
The improved ant colony optimisation integrates \(\rm{I_{1}}\), \(\rm{I_{2}}\), and \(\rm{I_{3}}\) rules, which are referred to as ACOPRO in this study. The common ant colony optimisation only integrates the \(\rm{I_{1}}\) and \(\rm{I_{2}}\) rules, which are represented by ACO in the following paper. The improved ant colony optimisation is more likely to prevent the algorithm from falling into local optimisation and has a better global search ability because of the introduction of random search rules \(\rm{I_{3}}\).
# Design and Performance Analysis of BDS-3 Integrity Concept
###### Abstract  
Compared to the BeiDou regional navigation satellite system (BDS-2), the BeiDou global navigation satellite system (BDS-3) carried out a brand new integrity concept design and construction work, which defines and achieves the integrity functions for major civil open services (OS) signals such as BIC, B2a, and B1I. The integrity definition and calculation method of BDS-3 are introduced. The fault tree model for satellite signal-in-space (SIS) is used, to decompose and obtain the integrity risk bottom events. In response to the weakness in the space and ground segments of the system, a variety of integrity monitoring measures have been taken. On this basis, the design values for the new BIC/B2a signal and the original B1I signal are proposed, which are \(0.9\times 10^{-5}\) and \(0.8\times 10^{-5}\), respectively. The hybrid alarming mechanism of BDS-3, which has both the ground alarming approach and the satellite alarming approach, is explained. At last, an integrity risk analysis and verification work were carried out using the operating data of the system in 2019. The results show that the actual operation of the system is consistent with the conceptual design, which satisfies the integrity performance promised by BDS-3 in the ICAO SAPRs.  
BDS; BeiDou; integrity; risk tree; FMEA; satellite service failure; constellation service failure +
Footnote †: journal: remote sensing  
Article  
Design and Performance Analysis of BDS-3 Integrity Concept
# Removal of Antibiotics by Biochars: A Critical Review
## 2 Biochars and Adsorption: A Bibliometric Survey
The adsorption of antibiotics with biochars is a major issue, especially in the last decade, considering the efficiency and cost advantage of biochars. The number of adsorption studies with the keywords \"biochar\" and \"antibiotics\" on the Web of Science has increased in the last eleven years, particularly after 2021, indicating the importance of the topic (Figure 1). The specific search keywords \"biochar\" and \"antibiotic\" resulted in a total of 233 documents, where 94% were research articles and 4% were review articles. Environmental sciences (56%), environmental engineering (27%), and chemical engineering (17%) were the main contributors to these studies. China-based researchers dominated the studies, performing 78.5% of them, followed by researchers from the USA (12.5%) and South Korea (6.9%). The UN Sustainable Development Goal \"clean water and sanitation\" was the principal aim, making up 42.9% of the biochar and antibiotic studies. The broader search keywords \"antibiotic\" and \"adsorption\" yielded a total of 527 documents, where 93% were research articles and 3% were review articles. The scientific areas mentioned above were also dominant in these studies. China-based researchers were also the principal contributors to the antibiotic and adsorption studies, with a 51.0% share, but the studies were distributed more evenly among a large number of countries, where Iran (7.4%), India (6.0%), and the USA (5.7%) were important. The most relevant research topics in biochar and adsorption studies can be visualized by using a co-occurrence network map [55] (Figure 2). In this figure, it can be seen that tetracycline, quinolone, and sulfonamide antibiotics were frequently studied for adsorption. Antibiotic resistance genes, removal of antibiotics by magnetic biochar and activated  
Figure 1: Number of publications with the keywords “antibiotic + biochar”, “antibiotic + removal”, and “antibiotic + adsorption” on the Web of Science (WOS). carbon, and advanced antibiotic removal methods such as photocatalytic degradation were trending research topics.