esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Groundwater Management  
Surface water is abundant during the monsoon, resulting in seasonal floods; however, there is a deficiency of water during the dry season to meet irrigation, industrial, and other water demands. As a result, the dependency on groundwater in Bangladesh has increased profoundly since the 1960s with the introduction of high yield variety (HYV) rice, which require a timely and assured water supply [33]. The unsustainable extraction of groundwater leads many rural and urban areas of Bangladesh facing water problems, including the drying up of wells during the peak irrigation period and the lowering of the groundwater table. Currently, about 80% of the dry season irrigation water supply is derived from groundwater, which plays a vital role in meeting the growing demand for food production, and nearly 98% of drinking water comes from groundwater [8]. The Dhaka Water Supply and Sewerage Authority (DWASA) sources 78% of its water supplies from groundwater through a network of about 887 production wells, and in several parts of the city ground level has now dropped 60-70 m below the ground surface due to over-exploitation [34].  
Regardless of hydrogeological complexity and an inappropriate understanding of aquifer responses to stresses, groundwater management started from a fragile institutional arrangement like in other developing countries. Scientific and institutional governing approaches are slow to ensure the effective use of groundwater. With the frequent occurrence of water-related conflicts, mainly due to water scarcity, the institutional development to govern the use of water resources, its allocation, and management are not receiving adequate policy attention. Good governance needs a proper matching between the scale of ecological processes and the institutions that deal with groundwater resources [35].  
Besides unplanned exploitation, groundwater resources are increasingly facing quality problems that are both anthropogenic, i.e., pollution from urban, industrial, and agricultural activities, as well as geogenic, i.e., arsenic contamination in floodplains and salinity in coastal areas make the water unacceptable for human consumption for many areas. The  
Figure 2: Water sector institutional framework in Bangladesh [32].  
arsenic contamination of groundwater caused an unprecedented impact on human health in rural areas of Bangladesh for decades [36; 37; 38; 39; 40]. This section collates the information on the history of the identification of arsenic in groundwater in Bangladesh. The section also discusses the water table lowering, which is also a burning issue in Bangladesh, even though it enjoys the benefits of the frequent recharge of groundwater due to rainfall.
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 3 Setup of the Lidars
### Dual Setup  
[PERSON] (pos. 1) and [PERSON] (pos. 3) were configured to match their scans along three horizontal virtual lines at 50, 100 and 150 m ASL from 5 km west to 4 km east (inland) of position 2 (Figure 4). They acquired 45 LOSs per virtual line in 45 s, corresponding to horizontal distances of \(\approx\)200 m between points. Like the PPI scenario in phases 2 and 3, the total scanning time amounted to 145 s, including the 10 s that the scanner heads need to get back to their starting positions. LOS velocities were retrieved separately for each point and lidar and, from these, the horizontal wind speed components were reconstructed over a given period. To ensure the spatial proximity of two opposite range gates for the dual-setup reconstruction, 89 and 91 range gates were acquired per LOS and per system for phases 2 and 3, respectively.  
In order to include all acceptable range-gate combinations, a collocating algorithm filtered out data that did not fulfil a certain distance threshold, resulting in the black points depicted in Figure 4. During phase 2, the range-gate positions were not well collocated in the \(x\)-direction (i.e., west-east), and therefore, the threshold in the \(x\)-direction was set to 51 m. For the \(y\) (north-south) and \(z\) (vertical) directions and during phase 3, the threshold was 10 m. The reconstruction algorithm was applied to the 10, 30 and 60-min averaged LOS velocities of both systems, after filtering out data that did not fulfil the CNR threshold (\(-\)26.5 dB). Reconstructed points situated near the coast using the dual setup cannot be well established. This is due to errors introduced by the very small beam crossing angles. Work is ongoing to quantify the uncertainty of the flow in this area and therefore we do not use the dual-setup measurements near the coast in this article.
## 6 Conclusions and Future Research  
This paper investigates the production and logistics collaborative scheduling problem in a flexible job shop scheduling environment with limited transport resources and charging constraints. To solve this problem, we introduce CRGPPO-TKL, a novel variant of the PPO algorithm. CRGPPO-TKL samples multiple candidate actions to compute a more comprehensive probability ratio, thereby evaluating policy deviations more thoroughly. Furthermore, it incorporates target scatter KL to dynamically adjust the clipping range, balancing exploration and stability. A 13-dimensional state representation capturing the operational status of machines and AGVs is designed, along with an action space comprising 40 composite scheduling rules. A reward function is formulated to maximize makespan reduction. Using CRGPPO-TKL, the optimal number of AGVs is determined for three facility layouts of different scales. Comparative experiments demonstrate superior learning speed and generalization compared to baseline methods.  
Despite these achievements, some limitations remain. The current model assumes deterministic AGV path planning and does not consider path conflicts or dynamic traffic congestion common in high-density shop floors. Additionally, real-world disturbances such as machine breakdowns, order insertions, and stochastic transport delays have not been incorporated into the scheduling framework. Future work will focus on modeling these disturbances and optimizing scheduling accordingly. More accurate nonlinear battery models, including temperature effects, will also be incorporated to improve simulation realism.  
Future research will extend the applicability of the proposed method to diverse manufacturing scenarios, such as multi-variety small-batch production and inter-workshop collaboration, enhancing the adaptability and generalization of scheduling algorithms. With the rapid development of Industrial Internet of Things (IIoT) and edge computing, we plan to investigate deploying scheduling strategies on edge devices to enable real-time response and intelligent coordination, providing more efficient and reliable solutions for flexible manufacturing systems.  
Data curation, W.Y.; funding acquisition, Y.C.; investigation, Z.P.; resources, W.Y. and Z.C.; software, X.H.; supervision, Z.P.; writing--original draft, X.H.; writing--review and editing, X.H. and Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (Key Program): W2411062.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this article are available at GitHub--Adenacademic/Brandimarte-benchmark.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Data and Methods
### Methods
#### 3.3.3 Geo-Detectors Model  
The geo-detectors model is a spatial detection model that is widely applied in the social, economic, and ecological fields, and it is a statistical method that is used to explore the laws of spatial differentiation and reveal the driving forces. It is mainly used to measure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level** & **Very Low** & **Low** & **Moderate** & **High** & **Very High** \\ \hline Index & \(EQI\geq 0.75\) & \(0.75>EQI\geq 0.55\) & \(0.55>EQI\geq 0.35\) & \(0.35>EQI\geq 0.2\) & \(EQI<0.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grades of eco-environmental quality.  
the importance of independent variables relative to dependent variables [50]. In this study, factor detection and interaction detection via the geo-detectors model were used to quantitatively analyze the driving factors of ecosystem quality changes in Henan Province and explore the possible interactions among different factors.  
1. Factor detector: This detects the spatial differences in changes in ecosystem quality and the influence of different factors (\(X\)) on the ecosystem quality index (\(\gamma\)). The expression is: \[q=1-\frac{1}{N\sigma^{2}}\sum_{h=1}^{l}N_{h}\sigma_{h}^{2}\] (5) where \(q\) is the detection index of the impact of driving factors on ecosystem quality; \(j\!=\!1\),..., \(i\); \(i\) is the classification or partition number of the dependent variable or the independent variable; \(N_{h}\) and \(N\) are the number of units of the corresponding layer or class and the number of units of the whole research area, respectively; \(\sigma^{2}_{h}\) and \(\sigma^{2}\) are the ecosystem quality variance of the sub-region and the whole region, respectively. The value range of \(q\) is \([0,1]\), and when \(q=0\), this indicates that the ecosystem quality presents a random distribution. The larger the value of \(q\), the stronger the explanation of the change in ecosystem quality by the independent variable factor and the stronger the spatial heterogeneity of ecosystem quality.
2. Interaction detector: Its main purpose is to analyze the interactions of different driving factors with the dependent variable, to identify whether the two factors affect ecosystem quality independently or together, and to determine whether the change in ecosystem quality is enhanced or weakened when the two factors act together. When evaluating the influence of factors, it is necessary to calculate the \(q\) value of the effects of the two interacting factors on ecosystem quality, i.e., \(q(XI)\) and \(q(X2)\), to calculate the \(q\) value of the combined effect of the two interacting factors on ecosystem quality, i.e., \(q(X1\cap X2)\), and, finally, to compare \(q(XI)\), \(q(X2)\), and \(q(X1\cap X2)\). See Table 4 for the specific interaction relationships.
## 4 Results and Discussion
### Comparison with Polynomial Regression Approach  
This section demonstrates the effectiveness of AI approaches compared to classical statistical approach using polynomial regression technique. A first order polynomial equation was tested and selected as a reference to compare with SVM model. Such equation is in the following form:  
\[\mathrm{O}\ =\ \mathrm{AI_{1}+BI_{2}+CI_{3}+DI_{4}+E} \tag{5}\]  
where O refers to output parameters of the problem (i.e., MS, MF and MQ), whereas I\({}_{1}\), I\({}_{2}\), I\({}_{3}\), I\({}_{4}\) correspond to inputs parameters such as the contents of coarse aggregates, Bitumen 60/70, PMB I bitumen and cellulose fibers, respectively. Figure 10 shows the regression of the predicted outputs using the proposed equation (Equation (5)) with respects to the input dataset.  
The obtained values of R were R = 0.82, 0.76 and 0.73 using Equation (5), whereas using SVM model, they were R = 0.9246, 0.9429 and 0.9085, for predicting MS, MF and MQ, respectively. The constants used in these equations are presented in Table 7.  
It is worth noting that using a higher order polynomial equation could only increase the correlation coefficient by about 3%, but the results fluctuated over a wide range. It could be concluded that with a similar number of samples (i.e., 60 data points), using an AI approach is more efficient in predicting  
Figure 10: Correlation coefficient of the regression of the actual and predicted values.  
the complex nonlinear relation between the Marshall Parameters and the mixture components than classical regression techniques.
## 2 Materials and Methods  
### Dataset  
The electrocardiogram (ECG) is the gold standard for HSS and it is generally considered that the R-peak and end-T-wave of the ECG signal are the corresponding positions of S1 and S2, respectively [13], as shown in Figure 1. Thus, heart sound databases with ECG annotations can be used to verify the performance of the HSS algorithm more realistically than those without ECG annotations.  
The public PhysioNet Computing in Cardiology Challenge 2016 has six training sets [21], but only the training-a set (PN-training-a) contributed by the Massachusetts Institute of Technology heart sounds database (MITHSDB) provides complete synchronous ECG recordings, whereas the other five sets lack these data. The original signals for PN-training-a comprise 409 phonocardiogram (PCG) recordings obtained at nine acquisition locations from 121 subjects, where both the PCG and ECG recordings were sampled at 44,100 Hz with 16-bit quantization. In addition to normal PCG recordings, the types of diseases in the heart sound database include mitral valve prolapse, benign murmurs, aortic disease, and other miscellaneous pathological conditions. Due to noise and interference by diseases with ECG in PN-training-a, we manually mark the positions of S1 and S2 based on synchronous ECG recordings. As a result, six recordings were removed due to excessive noise in the ECG or a lack of ECG, and two other recordings were also truncated due to data quality problems. After manual labeling, 403 heart sounds remained, which ranged from 9 s to 37 s, with 117 normal heart sounds and 286 abnormal heart sounds.  
MITHSDB is also the source database for the training data used in LS-HSMM (LH-training) [13]. The positions of S1 and S2 marked automatically by ECG algorithms are given directly in the LH-training data set, and these are the standard annotations used most widely in HSS studies. After treatment with LR-HSMM, LH-training contained 792 heart sounds from 135 patients, and the recordings ranging from 1 s to 36 s were down-sampled to 1 kHz. However, according to our experience of labeling based on PN-training-a, ECG algorithms usually cannot avoid all errors and there may be a small number of annotation errors in the LH-training data set, so we conducted experiments using PN-training-a with our manual labels and LH-training with the annotations given by LR-HSMM.  
Figure 2: Example of direct classification annotation based on the feature classification results for a noisy heart sound. The feature classification result output from the temporal convolutional network (TCN) comprises the state probabilities for each time point, and the direct classification annotation is the max probability state at each time point.
## 1 Introduction
### RoI Detection  
Using RoI to detect objects has been the focus of research for many years. Almost all RoI algorithms use two approaches: object-based and feature-based [24]. The feature-based approach searches pixels that have significant optical features and then uses analysis and a target to make RoIs. It can collect almost all target pixels based on the similarity of optical features. As all pixels do not have a strong feature that is shown in optical pixels, the detection using feature-based methods is usually not sufficient for identifying the entire target. Conversely, the object-based method can detect  
\begin{table}
\begin{tabular}{c c} \hline RLL Code & Proposed Preamble (SF) \\ \hline Manchester & 011100 \\ \hline
4B6B & 0011111000 \\ \hline
8B10B & 000011111111100000 \\ \hline \end{tabular}
\end{table}
Table 1: Coding scheme and proposed preamble.  
Figure 1: System architecture.  
Rols better than the pixel-to-pixel feature-based method, as it utilizes information about the structure and shapes for detection. This includes many typical approaches, such as matched filter and template matching [24]. However, the method is limited by its many calculation requirements, and it is also unreliable and produces low-quality images.  
In this work, Rol methods were used to detect LED objects in each image, which were captured by a camera. The detection performance may not be as good as Computer vision functions but Rol detection presented its compatible with OWC/OCC system due to the short time processing caused by its simple processes. Rol methods are deployed by only processing a subset of the pixel to detect LEDs in images. From that, the position of signals is detected and demodulated in the receiver side. The concept of Rol-signaling technique for OCC has recently been introduced into IEEE 802.15.7m task group [25].
## 2 The Pulsed Coding Technique: Theory, Modelling and Methods of the Optical Approach
In particular, the control unit accomplishes these operations by regulating the relative time delays (i.e., the timing alignments) between the received pulse train \(U_{R}(t)\) and the recovered clock CLK\({}_{R}\) together with the integration window, from \(T/2\) to \(T\), of the integrator block to correctly recover the data stream \(S_{R}(t)\).
## 3 Errors Identification Principle
### Calculation of the 6 Degrees-of-Freedom Errors
Similarly, the horizontal straightness error \(\delta_{y}(x)\) can be identified by the following equation:  
\[\delta_{y}(x)=Y^{\prime}{}_{Fi}-Y_{Fi}-km\varepsilon_{x}(x_{Fi}). \tag{12}\]When the 1-D artifact is located vertically, the measurement values of the relative angles \(\theta^{\prime\prime}{}_{xi}\) and \(\theta^{\prime\prime}{}_{xi}\) and relative position \(X^{\prime\prime}{}_{Fi}\) and \(Y^{\prime\prime}{}_{Fi}\) of the \(i\)th target can be obtained by the following equations:  
\[X^{\prime\prime}{}_{Fi}=k(x^{\prime\prime}{}_{M0}-x^{\prime\prime}{}_{F0})+ \left(X^{\prime\prime}_{Mi}-X^{\prime\prime}_{M0}\right)+k(x^{\prime\prime}{}_ {Fi}-x^{\prime\prime}{}_{Mi}), \tag{13}\]  
\[Y^{\prime\prime}{}_{Fi}=k\left(y^{\prime\prime}{}_{M0}-y^{\prime\prime}{}_{F0} \right)+\left(Z^{\prime\prime}_{Mi}-Z^{\prime\prime}_{M0}\right)+k\left(y^{ \prime\prime}{}_{Fi}-y^{\prime\prime}{}_{Mi}\right), \tag{14}\]  
where \(x^{\prime\prime}{}_{M0}\), \(y^{\prime\prime}{}_{M0}\), \(x^{\prime\prime}{}_{Mi}\), \(y^{\prime\prime}{}_{Mi}\), \(x^{\prime\prime}{}_{F0}\), \(y^{\prime\prime}{}_{F0}\), \(x^{\prime\prime}{}_{Fi}\), and \(y^{\prime\prime}{}_{Fi}\) are the coordinates of the spot in the image system when measuring vertically. Unlike horizontal layouts, the \(Y\) coordinates of each target are calculated based on the displacement in the \(Z\) direction of the machine. Thus, the \(X\) coordinates \(X^{\prime\prime}_{M0}\) and \(X^{\prime\prime}_{Mi}\) and the \(Z\) coordinates \(Z^{\prime\prime}_{M0}\) and \(Z^{\prime\prime}_{Mi}\) of the measurement point in the machine coordinate system were used to compute the relative positions of targets. Similarly, the yaw error \(\varepsilon_{z}(x)\) and the vertical straightness error \(\delta_{z}(x)\), instead of the pitch error \(\varepsilon_{y}(x)\) and horizontal straightness error \(\delta_{y}(x)\), are caused by deviation in the relative angle \(\theta_{yi^{\prime\prime}}\) and position \(Y_{Fi^{\prime\prime}}\). Therefore, the yaw error \(\varepsilon_{z}(x)\) can be identified by the equation:  
\[\varepsilon_{z}(x)=\theta^{\prime\prime}{}_{yi}-\theta_{yi}, \tag{15}\]  
and the vertical straightness error \(\delta_{z}(x)\) can be identified by the equation:  
\[\delta_{z}(x)=Y^{\prime\prime}{}_{Fi}-Y_{Fi}-km\varepsilon_{x}(x_{Fi}). \tag{16}\]  
Thus, the 6 degrees-of-freedom errors of a linear motion guide rail can all be identified.
## 3 Results
### Linearity-Dilution Effect  
The linearity-dilution effect was studied to select a standard human serum sample with a concentration of 2310.0 ng/L. It was diluted with horse serum and DEA buffer (1 M, pH 10.3) to obtain a series of concentrations (i.e., 1, 1/2, 1/8, 1/32, 1/64, and 1/128 of the original concentration). Each sample was measured in duplicate and in parallel. Data are shown in Tables S4 and S5. The linear regression equation was mapped with the expected value of NT-proBNP as the X-axis and the measured values of NT-proBNP as the Y-axis. The regression equations, Y = 1.005X - 2.377 (R\({}^{2}\) = 0.9998) and Y = 0.9899X - 4.013 (R\({}^{2}\) = 0.9999), were obtained when the serum sample was diluted with horse serum. Similarly, the regression equations, Y = 0.9902X - 1.090 (R\({}^{2}\) = 0.9999) and Y = 1.005X + 3.647 (R\({}^{2}\) = 0.9998), were obtained when the serum sample was diluted with DEA. The aforementioned results present a good correlation, and indicate that the matrix barely affects the binding of NT-proBNP to the antibody.
## 6 Conclusions
Additionally, the estimated standard deviation of the position error is kept comparatively low during GNSS outages, making the position estimation less vulnerable to multipath once GNSS reception is possible again. In order to get plausible values for these estimated standard deviations, the stochastic models of all input data has to match the actual measurement errors as closely as possible. In addition, this will assure the correct weighting of all input data relative to one another. While the measurement noise variance model for GNSS observations presented in Section 2.2 does produce good results in the evaluated data sets, it is not based on a statistical analysis of actual measurement errors. The same holds for the stochastic model of the odometry data, which has not been discussed in this paper. We plan to conduct a thorough analysis of the actual measurement errors for both GNSS and odometry input data with the purpose of developing more realistic stochastic models for the input data. Based on these models it is also possible to implement a more sophisticated method for detection and exclusion of erroneous observations, e.g., due to multipath for GNSS and due to tire slip for odometry. In the case of GNSS, this detection can be facilitated by the fact that some observations remain unused in the current algorithm design: If pseudo-ranges on all three frequencies are available, the ones on L2C (for GPS) and E5b (for Galileo) are not incorporated into the ionosphere-free combination. Consequently, they provide an independent source of information which can be beneficial to outlier detection. The goal of these prospective research areas is to make the algorithm more robust against measurement errors and to enable a continuous quality monitoring in all processing steps from input data to navigation solution. B.R. : conceptualization, methodology, algorithm development, data collection, result generation and interpretation, writing--original draft preparation. M.B. : conceptualization, methodology, supervision, writing--review and editing. S.L. : conceptualization, methodology, data collection, writing--review and editing. **Funding:** This research received no external funding. **Acknowledgments:** We acknowledge support in covering the publishing costs by the German Research Foundation and the Open Access Publishing Fund of Technische Universitat Darmstadt. **Conflicts of Interest:** The authors declare no conflict of interest.
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 2 Data and Methods
### Trends in Regional Vegetation Indices
#### 2.1.3 Vegetation Indices Trend Calculation  
We analyzed the trends of multiple vegetation parameters, including NDVI, EVI, and LAI, with a spatial resolution of \(1\times 1\) degree and time resolution of 15-16 days. After the GP was uniquely defined for each grid cell (Section 2.1.2), the mean of the vegetation indices over the fixed GP length for each year in each grid cell was calculated. Interannual trends in mean GP vegetation indices were defined using least-square linear fitting, and the Mann-Kendal test was used to determine statistical significance of the linear trends in each grid cell.