esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management.  
cropland use; multiple cropping; multi-source remote sensing; rainfed agriculture 25 July 202427 September 202414 October 202415 October 202415 October 202415 October 202415 October 202415 October 202415 October 2024163832 [[https://doi.org/10.3390/rs16203832](https://doi.org/10.3390/rs16203832)]([https://doi.org/10.3390/rs16203832](https://doi.org/10.3390/rs16203832))
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 3 Flexible Packaging and Testing of Sensors
### Testing of Pulse Signals  
By installing sensors in a wristwatch and dynamically adjusting the external pressure to observe the peak-to-peak values of the pulse, when the peak-to-peak value was at its maximum, maintaining the pressure constant was considered to result in a flattened state of the blood vessels. At this point, the relationship between blood pressure and the output of the pressure sensor was linear. The pulse waveform collected by the unpackaged MEMS pressure sensor is shown in Figure 6a, and the pulse waveform plots collected by the sensors encapsulated with PDMS in different ratios (with a uniform thickness of 0.3 mm) are shown in Figure 6b-d.  
From Figure 6a, it can be seen that the pulse signal directly collected by the MEMS pressure sensor was of good quality; the relevant characteristics of the pulse wave signal, such as the pulse onset, the percussion peak, the preceding tidal wave trough, and tidal wave In order to prevent device damage caused by the direct contact between the skin and the sensitive area of the sensor chip, the hardness characteristics of PDMS with different mass ratios were studied. The experimental results showed that when the packaged PDMS was relatively hard (the ratio of curing agent to this agent was 1:30 or 1:35), the sensor chip was easily damaged by hard PDMS, and it was difficult for the harder PDMS to fully contact the curved skin surface to collect a good pulse waveform, as shown in Figure 6b. When the encapsulated PDMS had a softer texture (with a curing agent to base ratio of 1:45 or 1:50), the acquired pulse signal by the sensor, as shown in Figure 6d, was affected due to the close contact between PDMS and the skin surface. The signal was susceptible to interference from microarteries near the radial artery and other tissues, leading to a poor-quality collected pulse signal When the hardness of the packaged PDMS was moderate (the ratio of curing agent to this agent was 1:40), PDMS not only had good adhesion but could also fully adhere to the skin surface, so as to ensure that the sensor chip could be protected by the soft PDMS and, at the same time, collect good-quality pulse wave signals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Before Encapsulation** & **After Encapsulation** & **Change** \\ \hline Accuracy/mmHg & 0.0017 & 0.1962 & 0.1945 \\ Sensitivity & 0.9996 & 0.9972 & 0.0024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of accuracy and sensitivity of the sensor before and after encapsulation.  
Figure 5: Graph of external pressure and sensor output value.
## 2 Methodology  
### Construction of a Refined 3D Computational Model  
The 3D surface of the mine was constructed based on the current topographic survey map of the mine. By extracting contour lines, elevation points, and other surface markers, a digital terrain model (DTM) of the mine surface was generated using the constraint-based line function and DTM functionality. Subsequently, the \"curtain\" function was used to generate the 3D surface entity of the initial model [17].  
The 3D solid models of ore bodies and faults were constructed using drilling data and exploration profile maps from the mine's exploration report. The boundary lines between different ore bodies were extracted to determine the spatial position and orientation of the ore groups.  
Based on the completed 3D solid model, the Griddle plugin was employed to segment the model into pillars, sub-levels, and mining areas according to different simulation scenarios, and a computational grid recognizable by FLAC3D was generated. The detailed steps are as follows:  
Step 1: Select the closed non-manifold multi-surface of the mine to be meshed and choose the equidistant division method in the Griddle plugin.  
Step 2: After selecting the meshing method, set the grid parameters, including grid density and boundary type. Choose appropriate grid parameters based on the shape of the surface and the requirements.  
Step 3: After setting the grid parameters, click the \"Generate Grid\" button to generate the grid. The time required to generate the grid will vary depending on the complexity of the surface and the density of the grid. Once the grid is generated, its shape and details can be viewed in the Griddle plugin interface.  
Step 4: Errors may occur when using the Griddle plugin for meshing. First, review the error message to understand the cause of the error. Errors may be due to invalid input surfaces or multi-surfaces. Check to see if the surface is closed, if there are overlaps, etc.  
Figure 1: Flowchart of rock mass stability analysis method based on detailed geological and mechanical parameter modeling.  
Step 5: Check to see whether the grid parameter settings are correct, such as whether the grid density is too high or too low, or if the boundary type is correct. Try changing the grid parameter settings, e.g., adjusting the grid density to an appropriate size. If errors persist after checking the grid parameter settings, further adjustments, such as altering the grid density or boundary type, may be needed.  
Upon completion of these steps, the refined 3D solid model and mesh model of the mine, including faults, ore bodies, and surrounding rock, were constructed. This refined 3D computational model serves as the basis for the subsequent refined zoning of rock mass quality and numerical simulation. The 3D solid model of the mine can be found in Section 3, while the 3D grid model of the mine and the detailed computational solid model are presented in Section 6.1. The detailed model construction workflow is illustrated in Figure 2.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 3 Results
### Application to SARS-CoV-2 Data from Germany
(**B**): After an initial rise, the SIRD-AKS estimated for the recovery rate \(\gamma_{t}\) stays nearly constant, while the estimated death rate \(\theta_{t}\) shows a dynamic with two peaks at around May–June 2020 and February–March 2021. These time points coincide with periods of high hospitalization states and where mostly older people were infected [71]. (**C**): The SIRD-AKS estimates (black) for the reproduction number are displayed in comparison to the seven-day reproduction number based on Nowcasting data and published by the RKI in magenta [66] as well as the calculated \(\mathcal{R}_{t}^{s,\tau}\) incidence-based method in blue that is based on CSSE data. Both incidence-based methods assume a fixed serial time of \(s=4\) days and averaging window of \(\tau=7\) days. Time points of essential control measures are indicated by colored vertical lines [75]. time scales. This becomes even more prominent when the changes in the reproduction numbers are compared to the time points of new non-pharmaceutical interventions and control measures, as indicated by vertical lines. While the incidence-based \(\mathcal{R}_{t}\) values likewise decrease drastically at the start of the first nation-wide lockdown in March 2020, the end of the first lockdown is only visible as a trend change in the SIRD-AKS reproduction number, but not in the incidence-based approaches. A similar case occurs for the so-called lockdown light in November 2020 and second nation-wide lockdown in December 2020 [75]. Both incidence-based measures barely show any effect on the reproduction number, while the SIRD-AKS estimate shows a fluctuating yet undoubtedly decreasing trend until the approximate time point where the SARS-CoV-2 Alpha variant spread in Germany.
## Materials and Methods
### Classification  
Convolutional neural network (CNN), a well-established and popular deep learning method, has made considerable improvements in image analysis [31; 32]. Particularly, 2D CNNs have been widely used to extract spatial features from the dimensions of width and height for object detection and semantic segmentation of high-resolution images [24; 33; 34]. Another major application of CNNs is hyperspectral image classification, in which CNNs are used to extract spatial-spectral features, through either 1D convolution across the spectral dimension [35], 2D across the spatial dimensions [33], or 3D across the spectral and the spatial dimensions simultaneously [36]. [PERSON] et al. [35] concatenated hyperspectral images from three seasons and applied 1D convolution to the spectral domain for land cover classification. One-dimensional convolutional neural network (Conv1D) has also been used to effectively identify 13 crops through time series images [23]. In this study, we adopted the one-dimensional convolutional neural network (Conv1D) for classification of tree species, which has a good classification effect on continuous sequence data such as speech, multi-temporal data, and text [37; 38]. We selected random forest model as the representative non-deep-learning classifier to compare with the Conv1D algorithm, since random forest classifier is renowned for high performance and is often established as the baseline model in classification tasks [39]. The main steps of the data processing work flow adopted in this study are presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category Code** & **Description** & **Total Number of Parcels** & **Number of Pixels** \\ \hline AL & Amur linden & 22 & 4827 \\ CP & Chinese pine & 13 & 2853 \\ DL & Dahurian larch & 7 & 1536 \\ AP & Aspen & 11 & 2414 \\ WB & White birch & 13 & 2853 \\ MW & Manchurian walnut & 26 & 5705 \\ MA & Manchurian ash & 25 & 5486 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground reference data in units of pixels for each of the classes analyzed.
## 1 Introduction
Drawing on the Lyapunov stability theorem, an RSC current back-stepping controller based on the DFIG decoupling model was designed to replace the traditional PI controller that made it possible simplify the control of the RSC and reduce the SSO input into the RSC system path. At the same time, in order to suppress the variable frequency SSO, an adaptive quasi-resonant controller was designed to track the stator current in the SSO state, and its output was input to the RSC control system as a compensation amount to compensate the output voltage command of the RSC controller, finally eliminating the SSO component in the stator current and achieving a stable output state. Through simulation and experimental verification, the proposed back-stepping SSO suppression strategy based on an adaptive quasi-resonant controller was found to be able to stabilize the generator stator current output at different SSO frequencies and had a good suppression effect on the SSO of the DFIG system.
# Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN
## 4 Algorithm Design
### Reward Function and Update Strategy Design  
In the complex workshop scheduling environment, the state space is huge, and the uncertainty factor is significant. If the reward function design is unreasonable, it is easy to cause the learning direction of the intelligent body to deviate from the target [43], resulting in unstable or even invalid scheduling strategy generated. In the multi-scene DPFSP, the reward function design is as follows:  
\[\text{reward}=\left\{\begin{array}{ll}5,&\text{Overall improvements}\\ 3,&\text{local improvement}\\ -1,&\text{unimproved}\end{array}\right. \tag{1}\]  
For the MSRDPFSP with included fault rates, the same set of sequences will yield different results in this problem for different times of simulation. In order to improve the performance of DDQN in this problem, this study constructs a reward mechanism based on the ordering. This involves running three times of interaction between each set of scheduling sequences and the simulation model, then arranging the final results in ascending order, marking them as superior, medium, and inferior values in order, and making a comparison. The three sets of results corresponding to the old and new states are compared to obtain three sets of reward values. Finally, the weighted reward values are calculated as follows:  
\[\text{reward}=0.4\;\times\;\text{eward}_{\text{best}}+0.2\;\times\;\text{ reward}_{\text{medium}}\;+0.4\;\times\;\text{eward}_{\text{worst}} \tag{2}\]  
The convergence effect of the three-valued sorting algorithm under the MSRDPFSP is shown in Figure 20 below. The update strategy is set to perform population replacement only when the total value improves and the other logics are kept unchanged. In order to enhance the exploratory ability and convergence efficiency of the DDQN in solving the MSRDPFSP, a dynamically adjusted greedy rate strategy is designed in this study. The initial greedy rate (\(\varepsilon\)) is set to 1 to ensure that the intelligent body has sufficient exploratory ability in the early stage of training; with the advancement of training, the greedy rate gradually converges at a rate of 0.2% reduction per iteration round until it is reduced to 0.1. Meanwhile, in order to avoid the exploratory ability loss of new strategies in scenarios of local optimums or training stagnation, when the successive reward obtained is less than or equal to 0 (indicating no improvement in scheduling performance), the greedy rate is increased by 0.1% each time. This adjustment restores the exploration ability of the intelligent body, thus improving the probability of jumping out of the local optimum.  
Figure 20: Three-value sorting iteration.
# A Quantum Detectable Byzantine Agreement Protocol Using Only EPR Pairs
## 2 Preliminaries
### Assumptions and Setting  
In this section we begin the presentation of our EPR-based protocol for Detectable Byzantine Agreement, or EPRQDBA for short. For the sake of completeness, we explicitly state the assumptions that underlie the execution of the EPRQDBA protocol.  
\begin{tabular}{l l} (\(\mathbf{A}_{1}\)) & There is a \"quantum source\" responsible for generating single qubits in the \(|+\rangle\) state and EPR pairs entangled in the \(|\Psi^{*}\rangle\) state. The source distributes these qubits to each general through a quantum channel. \\ (\(\mathbf{A}_{2}\)) & A complete network of pairwise authenticated classical channels connects all generals. \\ (\(\mathbf{A}_{3}\)) & The classical network is synchronous coordinated by a global clock. \\ (\(\mathbf{A}_{4}\)) & The protocol unfolds in rounds. In every round each general may receive messages, perform computations, and send messages. The messages sent during the current round are guaranteed to have arrived to their intended recipients by the beginning of the next round. \\ (\(\mathbf{A}_{5}\)) & All measurements are performed with respect to the computational basis \(B=\{|0\rangle,|1\rangle\}\). \\ \end{tabular}  
We follow the tradition and describe the EPRQDBA protocol as a game. The players in this game are the \(n\) spatially distributed generals. It will be convenient to divide them into two groups. The commanding general alone comprises the first group. From now on the famous [PERSON] will play the role of the commanding general. The \(n-1\) lieutenant generals make up the second group. In the special case of \(n=3\) that we shall examine shortly, the venerable [PERSON] and [PERSON] will play the 2 lieutenant generals. In the general case, where \(n>3\), we will mostly employ generic names such LT\({}_{0}\),..., LT\({}_{n-2}\), LT standing of course for \"lieutenant general.\" Occasionally, to emphasize some point, we may use again [PERSON] or [PERSON]. To make the presentation easier to follow, we shall first show how the EPRQDBA protocol works in the special case of only 3 players, namely [PERSON], [PERSON] and [PERSON].
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 2 Theoretical of Compressed Sensing  
By focusing on the discrete-time where any instance of the input signal \(x\ \in\ \mathbb{R}^{M}\) is represented by its Nyquist-rate samples. The CS framework is based on a sparsity assumption. The transform sparsity can be represented as the linear combination of a few vectors \(x=\Psi s\), where the transformation matrix \(\Psi\) is a given proper basis with the size of \(\mathbb{R}^{M\times N}\), \(M\) represents the number of rows, and \(N\) represent number of columns. The coefficient vector \(s\) has only \(K\leq M\) non-null coefficients [25]. However, due to white Gaussian noise \(v\in R^{N\times 1}\) present in real data, the transform sparsity step translates the signal \(x\) into the sparse signal. The observed linear measurements can be written as follows:  
\[y=\Phi x+v=(\Phi\Psi)s+v=As+v \tag{1}\]  
where \(y\in R^{N\times 1}\) is the measurement vector which represents each realization of \(x\), \(A=\Phi\Psi\) is a \(N\times N\) matrix that links the sparse representation \(s\) to \(y\), and \(v\) is additive noise modeling any random processes that occur in nature and non-idealities, and it is bounded by \(\left\|v\right\|_{2}\leq\epsilon\), where \(\Phi\in\mathbb{R}^{N\times M}\) is a random Gaussian matrix. The compressed measurements are realized by a simple matrix multiplication and the ratio \(R=\frac{N}{M}\) is generally called the compression ratio. Given that \(N<M\), the reconstruction of the signal from \(y\) is an under-constrained problem. Then, the sparse coefficient \(s\) is recovered by solving the \(\ell_{1}\) minimization problem for a given tolerance \(\epsilon\) via convex optimization:  
\[\left(\ell_{r}^{1}\right):\ \left(\hat{s}\text{ to }min\left\|s\right\|_{\ell_{1}} \text{ subject to }\left\|\Phi\Psi s-y\right\|_{2}\leq\epsilon\right. \tag{2}\]  
where \(\left\|*\right\|_{\ell_{1}}\) and \(\left\|*\right\|_{\ell_{2}}\) represent the standard \(\ell_{1}\) and \(\ell_{2}\) norms, respectively, and \(\epsilon\) considers the effect of noise \(\mathbf{v}\). The right compressive ratio can be found via the energy packing property, as explained in Section 3 below.
###### Abstract  
Long-term series monitoring of ground surface settlement by remote sensing has become an effective method. Based on TS-InSAR (time series Interferometric Synthetic Aperture Radar) interferometry, this paper proposes a new model based on the Grey Wolf Optimizer (GWO) and Long Short-Term Memory (LSTM) to monitor and predict the ground surface settlement in Kunming City. The results show that the MAPE (mean absolute percentage error), RMSE (root mean square error), and MAE (mean absolute error) of GWO-LSTM are significantly reduced. R\({}^{2}\) (goodness of fit) in the six sub-study areas of Kunming City is improved in comparison to the LSTM model. The problem of manual parameter selection in the LSTM model is solved by the GWO algorithm to select parameters automatically. This approach not only significantly reduces the model's training time but also identifies the most suitable network parameters. This can bring the best performance. Based on TS-InSAR data, the prediction of urban ground surface settlement by the GWO-LSTM model has good accuracy and robustness, which offers a scientific foundation for monitoring and issuing early warnings about urban land disasters.  
GWO-LSTM; TS-InSAR; ground surface settlement; Kunming +
Footnote †: journal: Applied  
0000-0001-999-22001
## 2 Materials and Methods
### Antioxidant Activities of Yogurt Samples
#### 2.3.1 DPPH (2,2-Diphenyl-1-Picrylhydrazyl) Free Radical Scavenging Assay  
The antioxidant activity of the yogurt samples was determined using DPPH free radical scavenging assay according to [34], with some modifications. Briefly, a 1/10 (\(w/v\)) dilution of the yogurt sample in water was prepared. Then, an aliquot of 0.1 mL of each sample was added to 4 mL of DPPH solution with a concentration of 0.004% in methanol. The mixture was shaken thoroughly and allowed to stand at room temperature for 30 min. The OD of the samples was read at 517 nm against water as blank. The inhibition percent (1%) of the DPPH free radical was calculated. The readings were recorded in triplicates.  
The percentage of radical scavenging activity (RSA) was calculated as follows:  
\[RSA\ (\%)=\frac{A\ (control)-A\ (sample)}{A\ (control)}\times 100 \tag{1}\]
## 3 Results and Discussion
### Methyl Orange Speciation and the Regeneration of the Photocatalyst  
Figure 9 shows the removal of MO by the TC-10 photocatalyst. The absorption spectrum depicts a maximum absorption peak at \(\sim\)465 nm, which decreased as the photocatalytic degradation progressed from 5 min to 120 min. The color of the MO solution changed from orange to colorless. No new peaks were observed throughout the reaction, indicating that there was no formation of any chromophoric compounds [30].  
Figure 8: The photocatalytic degradation (**a**) and first-order kinetic curve (**b**) for the TC-10 photocatalyst in the presence of 5% H\({}_{2}\)O\({}_{2}\).  
Figure 9: Absorption spectra of methyl orange, as degraded by TC-10 over time.  
The photocatalyst's recyclability was evaluated by first conducting photocatalytic experiments for the degradation of MO for 120 min at neutral pH. Thereafter, the photocatalyst was separated from the solution by centrifugation. This was followed by rinsing the catalysts with 0.1 M NaOH and then boiling water to leach out any MO that may have been adsorbed on the surface of the photocatalyst. The photocatalyst was then dried at 60 \({}^{\circ}\)C for 24 h and was reused in the photocatalytic degradation of more methyl orange. This process was performed five times, with the results presented in Figure 10. The activity decreased with reuse: 100% > 95% > 89% > 81% > 76%. This was attributed to the loss of mass each time it was reused [6].