esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Selection of Spectral Indices  
The prediction of water content in vegetation using spectral indices mostly depends on the reflectance behaviour of water molecules in the leaves to various sections of the electromagnetic spectrum [76]. For instance, high foliar water content in vegetation is highly sensitive to water absorption features in the SWIR section, which explains the reflectance of vegetation in these regions [77]. Additionally, the RE region is sensitive to vegetation chlorophyll absorption, which is directly related to water content [78; 79]. In comparison to other indices, water-sensitive as well as RE-based vegetation indices may have an advantage of accurate estimation of grass-leaf-water-related elements. In this regard, this study specifically evaluates the potential of water-sensitive spectral vegetation indices (which provide the absolute measure of plant water content) as well as the RE-region-based spectral vegetation indices in estimating GWC. The vegetation index formulas were identified and selected from the literature, whereas the other formulas were derived from the Index Database available online ([[https://www.indexdatabase.de/](https://www.indexdatabase.de/)]([https://www.indexdatabase.de/](https://www.indexdatabase.de/)), accessed on 10 August 2022) and computed in the GEE platform (Table 1).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**VI** & **VI Formula** & **Sentinel-2 Bands** & **References** \\ \hline Normalized Difference Water Index (NDWI) & \(\frac{NIR-SWIR}{NIR}\) & B8, B12 & [80] \\ Modified Normalized Difference Water Index (MNDWI) & \(\frac{NIR-SWIR}{NIR}\) & B3, B11 & [81] \\ Normalized Difference Infrared Index (NDII) & \(\frac{NIR-SWIR}{NIR}\) & B8, B11 & [82] \\ Moisture Stress Index (MSI) & \(\frac{NIR}{NIR}\) & B11, B8 & [83] \\ Normalized Difference Red Edge (NDRE) & \(\frac{NIR-RF}{NIR}\) & B8, B5 & [84] \\ Red-edge Ratio Index 1 (RRII) & \(\frac{RF}{NIR}\) & B8, B5 & [85] \\ Red-edge 1 (Rededge1) & \(\frac{RFRF}{NIR}\) & B5, B4 & [86] \\ Red-edge 2 (Rededge2) & \(\frac{RFRF}{NIR}\) & B5, B4 & [86] \\ Red-edge Normalized Difference Vegetation index (NDVI\({}_{705}\)) & \(\frac{RFRF}{NIR}\) & B6, B5 & [87] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral vegetation indices used in this study.
## 4 Correlation Analysis
### Forced Lane-Changing Rate Analysis  
According to Figure 8, the curves reveal the variation trend of FLR of bus stops with the flow rate change under different Ra levels. Figure 8a-c shows that the flow rate positively correlates with FLR. At the same time, it can be found that the value of FLR decreases as the penetration of CAVs increases, indicating that FLR is negatively correlated with Ra.  
Comparing the curves of scenarios 1, 2, 3, and 4, it can be found that the relationship between FLR and the number of bus arrivals per hour is obvious. By observing the curves of scenarios 2, 5, 6, and 7, it can be concluded that the FLR value increases with the dwell time, because the longer the dwell time, the higher the possibility of queuing. Comparing scenarios 2, 8, 9, and 10, it can be found that FLR value is highest in the single-beth curbside bus stop, mainly because the curbside stop occupies a road lane when vehicles are parking and queuing, which has a significant impact on the speed of other vehicles and the rate of passengers getting on and off.  
Figure 9 shows the variation in the FLR change rate with the flow rate. It can be seen that there is also a relationship between the FLR change rate and the flow rate. It can be seen from Figure 9a-c that the magnitude of FLR change rate with flow rate slows down as  
Figure 8: FLR of ten scenarios relates to flow rates under different Ra. (**a**) Ra = 0%; (**b**) Ra = 50%; (**c**) Ra = 100%.  
Figure 7: FD of ten scenarios relates to flow rates under different Ra. (**a**) Ra = 0%; (**b**) Ra = 50%; (**c**) Ra = 100%.  
Ra increases. In Figure 9a, the FLR growth rate rises steeply when the flow rate rises from 3000 to 3400 veh/h in almost all single-berth bus stop scenarios, but the rate of change in Figure 9b,c does not change very significantly, or even decreases slightly. This indicates that the involvement of CAVs improves to some extent the stability of the traffic situation near the bus stop and enhances its ability to adapt to larger traffic flow situations, i.e., no large forced lane-changing rate due to increased traffic flow.  
Overall, according to the simulation experiment results, it can be confirmed that there is a positive correlation between FD and FLR and the traffic flow, and the traffic flow is smoother as the penetration rate of CAVs rises, which can better dissipate the impact brought by FD and FLR. In addition, by observing the comparison of the changes in different individual scenarios, we found that the three factors bus arrivals per hour, bus dwell time, and station types all impact FD and FLR.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 4 Experimental Setup
### Methods for Comparison  
The proposed method is compared with three state-of-the-art space object detection methods, which are the channel and space attention U-net (CSAU-Net) [57], the topological sweep (TS) method [58], and the new object detection algorithm using motion information (NODAMI) method [28]. CSAU-Net is a single-frame-based space object detection method that adds attention modules in the traditional encoder and decoder structure to enhance features and better use original feature layers. TS is a multi-frame space object detection method that exploits the geometric duality to find GEO objects from short sequences of optical images. NODAMI is a video-based space object detection method using motion  
Figure 7: Example of a video sequence with SNR = 0.2, diameter = 0.1 m, speed = 4 pixel/frame, direction = 10 \({}^{\circ}\) in SDD dataset (top row). Close-up of space debris marked by a green circle in the image sequence (bottom row).  
information from video satellites. CSAU-Net is a recent deep learning-based method aiming to achieve state-of-the-art performance. The related parameters are set to their default values as described in the original publications. With regard to the deep learning-based methods, the pretrained models provided by the corresponding authors were adopted.
## 4 Experiments and Analysis of Results
### Dataset Construction and Preprocessing  
The self-built dataset consists of 10,000 high-resolution crop pest images captured under controlled environmental conditions in Hebei Province, China, during the 2023-2024 growing seasons using a Canon EOS R5 camera (2560 \(\times\) 1920 resolution, JPEG format) under natural daylight (10:00-14:00 h), which is shown in Figure 6. The dataset includes five main disease categories: rice blast, wheat powdery mildew, corn leaf blight, cucumber downy mildew, and healthy samples, each contributing 2000 images [(20)].  
Disease regions were manually annotated using LabelImg for bounding boxes (\(\geq\)15 \(\times\) 15 pixels) and LabelMe for pixel-level segmentation masks, with quality control involving automatic overlap checks and expert review of 20% randomly selected samples. To enhance model generalization, data augmentation techniques were applied during training, including random rotations (\(\pm\)15 deg), horizontal/vertical reflections, brightness adjustments (\(\pm\)20%), and color jittering (\(\pm\)15% hue/saturation), increasing effective training data by 300% while preserving class distributions.  
Figure 5: Improvement of U-Net network structure diagram.  
The dataset was partitioned into 7000 training, 2000 validation, and 1000 test images using a 70:20:10 ratio with stratified sampling. Additionally, low-resolution (LR) images were generated via bicubic interpolation from high-resolution (HR) originals to simulate real-world deployment challenges, maintaining the same split for controlled evaluation of resolution degradation impacts.
## 4 Methodology
### Robust Factor Analysis (RFA)  
Factor analysis (FA) is a multivariate statistical method that establishes relationships between observed variables and underlying latent variables to improve interpretation. This is accomplished through axis rotation or loading reduction based on a covariance or correlation matrix. However, FA is prone to significant bias when influenced by outliers and is highly sensitive to extreme values [40; 46]. Consequently, if the compositional geochemical data contains outliers and/or extreme values, the results obtained through classical FA are neither reliable nor robust [47]. Robust Factor Analysis (RFA) is designed to mitigate the impact of outliers and minimize the influence of extreme values using the minimum covariance determinant estimator [48]. RFA outputs, visualized in the compositional biplot, are scores representing observations and loadings representing variables [39]. Furthermore, the scores are considered indicative of mineralization presence [47]. The calculation relies on the R Project, a statistical computing software, as well as the robust statistical analysis of compositional data (robComposition) and the StatDA package [49]. A comprehensive explanation of the RFA process for compositional data can be found in previous works [42]. The R language for data analysis and graphics, in conjunction with the StatDA package [49] and robust statistical analysis [32], were used for calculation.
## 3 Results
### APE and MPV Relationship  
Similar results as observed for patients with class IV NYHA HF were also observed in patients with acute pulmonary edema (APE). Based on the logistic regression model, the estimated coefficients for which are represented in Table 2, we can explain the probability that a patient had APE based on the MPV value.  
The applied model is the following: score (of APE happening) = \(-7.9128\) + \(0.7543\)\(\times\) MPV.  
Starting from this model, let us assume that we have a patient with an MPV of 8 fl; applying the above mathematical formula results in a score of \(-1.91\). To be able to interpret this result as a probability, we apply the following equation: \(\mathbf{p}=\frac{1}{1+e^{-\alpha\mathbf{p}}}\). So the probability of the patient with an MPV of 8 fl is 0.14, representing a 14% chance of having APE. The results obtained by applying the above-mentioned mathematical formula are clearly illustrated in the figure below (Figure 3), which shows that there was a tendency for the probability of having APE to increase as the MPV value increased (41% probability for an MPV value of 10 fl versus 14% for an MPV value of 8 fl).  
The mathematical model applied above to patients with APE is statistically significant because the \(p\)-value associated with the coefficient for MPV (0.75) is 0.000.  
Thus, starting from the arguments above, it can be said that, based on the MPV value, we can predict the probability of having APE, and that the higher the value of MPV, the greater the probability that patients have APE. This finding is represented graphically in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dep.** & **APE** & **No. Observations:** & **260** & & \\
**Variable:** & & & & & & \\ \hline Model: & Logit & Df Residuals: & 258 & & & \\ Method: & MLE & Df Model: & 1 & & & \\  & & Pseudo R-squ.: & 0.05015 & & & \\ Converged: & TRUE & LL-Null: & \(-146.21\) & & & \\ \hline  & **coef** & std err & \(Z\) & \(p>|z|\) & [0.025 & 0.975] \\ \hline Intercept & \(-7.9128\) & 1.869 & \(-4.235\) & 0 & \(-11.575\) & \(-4.25\) \\ MPV & **0.7543** & 0.204 & 3.694 & **0** & 0.354 & 1.155 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Logistic regression applied to APE patients based on the MPV value.  
Figure 3: Probability of patients having APE based on MPV value by applying the logistic regression model.  
the figure below, in which three graphs are displayed (Figure 4). All three graphs reveal that patients with APE (1) tended to have a higher MPV value (above 9 fl) compared to those without APE (0).
# Biochar Affects Heavy Metal Uptake in Plants through Interactions in the Rhizosphere
## 2 Materials and Methods
### Polluted Soils and Experiment  
The three soils were collected from the top 0-25 cm after plowing in October 2012, sieved to \(<\)2 mm and air dried. Biochar (2% _w/w_) was then mixed thoroughly into the soils and filled into triplicate freely draining (bottom holes covered with cotton gaze) 1 L polyethylene pots. Soil of each pot was fertilized individually with 69 mg/kg N [as a 50/50 mol ratio of NH4 NO3 and Ca(NO3)2 4H2O], 12 mg/kg P (as Na2 HPO4 2H2O), 90 mg/kg K (as K2 SO4) and 22 mg/kg Mg (as MgSO4 7H2O) and watered to 70% of the soils individual water holding capacities. They were then sown with 10 seeds of barley (_Hordeum vulgare_ L.,) before placing them in a temperature-controlled growth chamber (\(21\pm 1\)\({}^{\circ}\)C; 16 h/8 h light/dark cycle providing 350 umol/m/s photosynthetically active radiation). The number of seedlings was thinned to 5 per pot 4 days after germination and plants watered daily until they were harvested 4 weeks after germination (Figure 1). Shoots and roots were harvested separately, washed with Milli-Q water, oven dried (80 \({}^{\circ}\)C to constant weight) and dry weights recorded. Plant materials were analyzed for Cd, Cu, Pb and Zn after wet digestion with boiling 1 M perchloric acid (8 h). Samples of rhizosphere soil (soil adhering to roots after gentle crushing and shaking, constituting 2-5% of the total soil of the pot) and bulk soil were collected, dried at 60 \({}^{\circ}\)C for 48 h and weighed. Bioavailable fractions of heavy metals were extracted either with 1M NH4 NO3 at pH 4.9 for 2 h (extracting readily exchangeable metal forms) or with 0.11 M acetic acid (CH3 COOH) at pH 2.9 for 16 h, according to the first step of the a sequential extraction procedure of the Standards, Measurements and Testing Program of the European Union (SM&T--formerly BCR) [28], extracting exchangeable, water and acid soluble (e.g., carbonates) metal forms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Biochar** & **Unpolluted Soil** & **Weakly Polluted Soil** & **Highly Polluted Soil** \\ \hline pH & 9.16 & 6.16 & 7.53 & 7.35 \\ Total C (\%) & 79.3 & 0.91 & 1.05 & 1.11 \\ Total H (\%) & 0.47 & - & - & - \\ Total N (\%) & 1.05 & 0.09 & 0.11 & 0.12 \\ Total P (g/kg) & 2.43 & - & - & - \\ Cd (mg/kg) & 0.05 & 0.15 & 1.25 & 1.91 \\ Cu (mg/kg) & 15.0 & 3.20 & 200 & 451 \\ Pb (mg/kg) & 0.62 & 11.1 & 118 & 211 \\ Zn (mg/kg) & 67.1 & 26.7 & 67.2 & 134 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of _Miscanthus_ biochar used for soil amendment and soils used in the pot experiment.  
Figure 1: Pot experiment showing differences in barley (_Hordeum vulgare_ L.) growth between soils and treatments 4 weeks after germination, from left to right: highly polluted soil (control), highly polluted soil with 2% (_w/v_) biochar, weakly polluted soil (control), weakly polluted soil with 2% (_w/v_) biochar, unpolluted soil (control), unpolluted soil with 2% (_w/v_) biochar. Note that only one of the triplicates of each treatment is shown.
## 4 Results and Discussion
### Splitting Tensile Strength  
The splitting tensile test results for various mixes are shown in Figure 8 (some images of samples after splitting tensile strength are also presented in Figure 9). Figure 10 also illustrates the relationship between concrete's splitting tensile strength and its compressive strength at 120 days, highlighting a relatively high correlation coefficient for all specimens at about 0.82.  
For 5% cement replacement, the tensile strength of specimens increased slightly (2%), while for more than 5% ratios, it decreased as the waste content increased. Generally, slight improvements in tensile strength can be related to lower porosity and development of binder and the interfacial transition zone (ITZ) properties, as is noted in works by [PERSON] et al. (2018) and [PERSON] et al. (2018). The smaller size of waste powder particles in comparison with cement grains leads to improved densification of ITZ. However, increasing  
Figure 8: Splitting tensile strength of specimens at 120 days.  
Figure 9: Samples after splitting tensile strength test.  
the waste content by more than 30% considerably decreased the tensile strength (by about 50%), which can be attributed to the pozzolanicity of the used waste powders. While GGBFS showed an acceptable performance in terms of pozzolanic reactions with cement hydration products in previous works, EAFD and RM could not increase the mechanical properties of concrete when used in high amounts. Therefore, as the same amount of EAFD, RM, and GGBFS were used in this study, GGBFS could not govern the pozzolanic reactions, especially in mixes with more than 15% cement replacement.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 6 Evaluation
### Case with Different SNR for All Mobile Devices  
In this section, our proposed scheme is evaluated under the scenario where the SNR values of transmitted signals are different The results are obtained with various SNR ranges.  
Figure 7: Confusion maps of ASensNet, AIC, and MDL for the SNR of 6 dB.  
In Table 6, each element of the 'SNRs [dB]' column consists of the minimum SNR and the maximum SNR. Therefore, the element [6, 8] means that the SNR of a transmitted signal is within [6 dB, 8 dB].  
Table 6 shows the precision and recall with \(d=1\). (The precision and recall of AIC and MDL has a similar tendency to Tables 4 and 5, and thus these results are omitted in this paper.)The precision and recall present a similar tendency to Table 3. Hence, in almost all cases, our ASensNet guarantees that the precision and recall are larger than 0.9.  
Therefore, this realistic evaluation confirms that our proposed scheme can accurately detect the number of asynchronously transmitted signals even if the SNRs of the signals vary.  
Finally, Figure 8 shows case diagrams demonstrating how well each stream-sensing scheme follows the ground truth. Via the results, we also confirm that compared to other methods, our proposal can accurately estimate the number of survivors. Note that for Intel(R) Core(TM) i7-6900K CPU, the processing time for a case is 31.8 milliseconds, and the weight size of our ASensNet is 1.0 gigabyte. Therefore, our proposed scheme can operate in real time with an appropriate memory load.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Precision** & \multicolumn{10}{c}{**The Number of Transmitted Signals**} \\ \hline SNRs [dB] & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline  
\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision and recall of our ASensNet with \(d=1\).
## Methods
### Sample and Living Labs Overview  
The participants were active on-board operators of highly automated shuttle buses in living labs. They were recruited through inquiries made to project partners of the German Aerospace Center (DLR) who are involved in living labs with highly automated shuttle buses, as well as other living labs in Northern Germany. The living labs were HEAT (Hamburg Electric Autonomous Transportation [23]), RealLabHH (Reallabor Hamburg, Real-World Laboratory Hamburg [24]), and TaBuLa (Testerentrum fur automatisiert verkehrende Busse im Kreis Herzogtum Lauenburg, Test Center for Automated Buses in the Duchy of Lauenburg [25]), and were all located in and around Hamburg, Germany (further information in Section 2.3). The interviews were conducted in October 2021 and were carried out in person at the operating sites of the respective living labs. The shuttle types from these living labs are still widely used in research projects across Germany, underlining the persisting relevance of the analysis [26]. Nine on-board operators, three from each of the three different living labs, took part in the interview study. In the subsequent analysis, they are referred to as P1, P2, and P3 for each living lab. The participants' ages ranged from 24 to 53, with an average of \(M=41\) years (\(SD=10.77\) years). The duration of employment as an on-board operator ranged from 6 months to 40 months, with an average duration of \(M=14.56\) months (\(SD=12.16\) months). Table 1 presents work experience as on-board operators and age per living lab.  
The participants took part in this study voluntarily and the interviews were conducted during their work hours. This study was conceptualized and conducted in accordance with  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Laboratory** & \multicolumn{2}{c}{**HEAT**} & \multicolumn{2}{c}{**RealLabHH**} & \multicolumn{2}{c}{**TaBuLa**} \\ \hline  & \(M\) & \(SD\) & \(M\) & \(SD\) & \(M\) & \(SD\) \\ \hline Work Experience (Months) 1  & 8.33 & 3.21 & 6.00 & 0.00 & 29.33 & 9.24 \\ Age (Years) 2  & 29.00 & 6.24 & 44.33 & 6.81 & 49.67 & 5.77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Work experience as on-board operators and age per living lab.  
the Declaration of Helsinki. Informed consent was obtained from all participants before the interview. The participants were allowed to interrupt or end the interview at any point without justification or consequence.
# _Applied_
## 3 Results
### Confirmation of Riboflavin Production of HY7715 In Vivo Using an Animal Model  
After inducing riboflavin deficiency by administering HY7715 to mice, riboflavin was measured in plasma and urine to confirm whether HY7715 can continuously produce and supply riboflavin in vivo. The plasma riboflavin concentration of mice not administered with LAB (D) was 0.21 \(\pm\) 0.64 mg/dL, which was significantly lower than that of the control group (C) 0.65 \(\pm\) 0.98 mg/dL; however, the plasma riboflavin concentration of mice fed with HY7715 was 0.61 \(\pm\) 0.04 mg/dL. The plasma riboflavin concentration of mice that received NCDO1752 was 2.9 \(\pm\) 0.03 mg/dL, which was slightly higher than that of the D group but was the difference was not significant (Figure 8A). The concentration of riboflavin excreted in the urine was 53.49 \(\pm\) 8.11 mg/dL in the C group, 3.82 \(\pm\) 1.53 mg/dL in the NCDO1752 group, and 45 \(\pm\) 5.16 mg/dL in the HY7715 group. The riboflavin concentration in group D urine was 0.75 \(\pm\) 0.09 mg/dL, which was relatively low compared to other groups (Figure 8B). Therefore, RNA was isolated from cecum contents of mice and the ratio of LAB and _ribG_ gene levels were analyzed. From the analysis of the LAB ratio in the cecum, using LAB-specific 165 rRNA primers, it was confirmed that the LAB increased by about four times, as compared to the C group, in both NCDO1752 and HY7715 groups. Group D showed similar LAB ratio to group C (Figure 8C). The mRNA levels of the ribG were about 150 times higher in the HY7715 group than in group C or D and 14 times higher than in the NDCO1752 group (Figure 8D). There were no significant differences in body weight and food intake between groups during the experiment (Figure S2). This indicated the presence of bacterial species that could produce riboflavin in the intestine of HY7715 group, which indirectly showed that HY7715 is settling in the intestine.  
Figure 7: Riboflavin production in a co-culture of lactic acid bacteria and Caco-2 cells. HY7715 produced higher riboflavin than NCDO1752, which was used as a control in a co-culture environment. Data are represented as mean \(\pm\) standard error of means (SEM) of three independent experiments. \(\mathbf{***}\)\(p\) < 0.001 compared with LAB untreated control.