esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Evaluation Method for Echelon Usage of Double-Life Equipment Considering Calendar Life
## 3 Analysis and Solution
### An Extreme Special State: \(M=n\)  
The number of double-life equipment items in a unit is \(N\). The equipment number is \(i,i\in[1,N]\) (the maximum number of the current number is incremented). The remaining calendar life is \(\mathit{Tcl}_{i}\). The working life and the calendar life of all equipment are shown in Figure 2, respectively. The unit undertakes a fixed task every month, and the working life monthly usage is \(\mathit{Ty}\). One new piece of equipment will be added every month. The calendar life of the new equipment is \(N\) months, and the remaining working life is \(\mathit{Ty}\). The number of units used each time is \(M=N\).  
From the number of units used, \(M=N\), it can be seen that the working life consumption of each equipment item per month is \(Ty\div M\).  
To meet the requirement of no waste of working life, the relationship between the working life and calendar life of each equipment item will meet the following requirements:  
\[Twl_{i}\leq Tcl_{i}\times\frac{Ty}{M}\]  
To meet the requirement of sufficient equipment, the relationship between the working life and calendar life of each equipment item will meet the following requirements:  
\[Twl_{i}\geq Tcl_{i}\times\frac{Ty}{M}\]  
At the same time, there is no waste of working life and there are enough pieces of equipment to use. The relationship between the working life and calendar life of each set will meet the following requirements:  
\[\left\{\begin{array}{l}Twl_{i}\leq Tcl_{i}\times\frac{Ty}{M}\\ Twl_{i}\geq Tcl_{i}\times\frac{Ty}{M}\end{array}\right.\Rightarrow Twl_{i}= Tcl_{i}\times\frac{Ty}{M}\]  
Therefore, the optimal distribution curve of the extreme special state can be obtained:  
\[Twl=\frac{Ty}{M}\times Tcl\]  
Since all equipment items are used, there is only one choice, that is, all equipment is used, so the controllable range is also the optimal distribution curve. The relationship between the working life of all of the equipment and the calendar life must be within the controllable range to ensure that there is no waste of working life or calendar life. As shown in Figure 2, if the diagonal part of equipment No.03 is used according to the current specific rules, the working life of this part must not be used up, which is bound to cause a waste of working life. If equipment No.04 is used according to the current specific rules, its working life will be consumed in the fourth month, resulting in insufficient equipment.
## 3 Results and Discussion
### Figure of Merit and Results  
As shown in Table 2, the analytical features of the proposed method revealed linearity over the range of 7-250 ng/mL with a detection limit of 4 ng/mL. The relative standard deviation (RSD) obtained was 1.9% for seven samples of 10 ng/mL Pb(II). A comparison between the proposed method and others reported in literature based on the cloud point extraction of lead are summarized in Table 3[31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. The table presents a good enrichment factor, high sensitivity, and good linearity range of this presented method. The calibration graph for the pre-concentration of Pb\({}^{2+}\) with DLNL was linear with a correlation coefficient of 0.9981 at the range of 10-250 ng/mL. The obtained regression equation was Abs = 6.3123(Pb\({}^{2+}\) ng/mL) + 0.0523. In order to determine the enhancement factor (EF), calibration curves were prepared without CPE. The calibration equation obtained was Abs = 0.1262(Pb\({}^{2+}\) ng/mL) + 0.0101 (R\({}^{2}\) = 0.9995). The experimental enhancement factor calculated as the ratio of slopes of the calibration graphs with and without pre-concentration was 50. The detection limit (LOD) and quantification limit (LOQ) were calculated as under 3 and 10 \(s/m\), respectively, where \(s\) is the standard deviation (n = 10) of the blank and \(m\) is slope of the calibration graph. The LOD and LOQ were calculated as 4.0 and 7.0 ng/mL, respectively. The analytical characteristics, i.e., precision of methods, were expressed as the % relative standard deviation (%RSD) of a minimum six independent analyses of standard Pb\({}^{2+}\) samples, after CPE of Pb\({}^{2+}\) was found to be 1.9%.
# Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
## 3 Methodology
### Classification with Modified VGG16  
Proposed by the Visual Geometry Group at the University of Oxford [31], the VGG16 model is a CNN architecture that is characterized by a depth of 16 layers, including 13 convolutional layers and three fully connected layers. VGG-16 is renowned for its simplicity, versatility, and enviable performance on a range of computer vision tasks, including image classification and object recognition. As a result, it remains a popular choice for many DL applications.  
In this study, to further improve its performance, the VGG16 model was previously trained on ImageNet, excluding the original fully connected layers by setting the include_top parameter to False. After initialization on the ImageNet dataset, the convolutional and MaxPooling layers of the model were set to non-combining (frozen), which reduced the learning of redundant information. Additionally, to enhance the learning process and accelerate classification, several new layers were introduced into the architecture. These can be ordered as follows:  
* A dense layer with 256 distributions and ReLU activation, followed by a 50% dropout layer;
* A second dense layer with 128 distributions and ReLU activation, followed by a 50% dropout layer;
* Finally, an output security layer with softmax activation, with lengths up to the number of classes.  
The add-on layers can be characterized as flattened, dense, and dropout layers. The flattening layer is necessary to feed the output into the fully connected layers, and the dense layer is added as a fully connected layer using the ReLU activation function. The dense layer is also adjusted according to the number of classes in the training data, as it is the classification layer. The dropout is used to prevent overfitting. By adding a flattened dense and dropout layer, the desired level of learning was achieved, which in turn shortened the classification time.  
The model is customized to successfully tackle the specific task of classifying pepper leaf diseases by leveraging the advantages of the large and complex VGG16 model, combined with the newly added layers. This approach also ensures faster training and more efficient performance.  
Figure 6: Colour enhancement examples.  
The training batch size was 32. During training, Adam optimizer was utilized to adjust learning rates due to its high performance on large datasets and complex models, resulting from efficient memory usage and automatic adaptation of all parameters after switching to new learning rates. The algorithm was used with a learning rate of \(\lambda=0.0001\). _Categorical cross-entropy_ was used as the loss function, while accuracy was used as the performance metric. _ReduceLROnPlateau_ and _EarlyStopping_ callbacks were implemented to increase training stability and prevent overfitting.
## 2 Problem Definition and Literature Review
For instance, the authors [51], used stochastic DP method to solve projects that involved only 17 activities. To address this issue in an uncertain or stochastic RCPSP, the authors of [19; 22] utilized rollout algorithms [49] to approximate the policy functions for DP, thus converting it to ADP. Their conversion of DP to an ADP algorithm was based on three core techniques. First, in each decision stage, the sub-problem is constructed by approximating the recursive cost-to-go function in DP. Second, sample paths from MC simulations were used for forward iteration, bypassing the exhaustive state enumeration in traditional DP. Finally, deterministic scheduling techniques were applied within each iteration of ADP. It is should be noted that the efficiency of ADP is highly dependent on the policy (or rollout policy). An analysis of the method combining rollout and lookup table algorithms, including look-back and look-ahead using the idea of parallel rollout [49], was explored in [19]. Constraint programming [52] to solve sub-problems was also explored as the quality of a sub-problem influenced the policy estimation. For combinatorial problems like RCPSP (RCPSPDC), it is recommended to use some heuristic based policy [32] in place of exact cost/benefit-to-go function [53] as promising results have been reported in [54; 55]. The dynamic and Markov nature of ADP enables several advantages for RCPSP (RCPSPDC-PEO). First, it always maintains a feasible schedule as the scheduled jobsare not disturbed. Second, as it sequentially schedules jobs in the forward iteration, the computation complexity reduces as the project progresses. Finally, MC simulation of it can be parallelized on multi-core machines. Although the lookup table as explored in [19] serves basic requirements, it is our belief that using distribution-based algorithms would improve the solution further. However, the policies generated in ADP are approximate, leading to sub-optimal solutions. In summary, various metaheuristics and their hybrids were proposed in the literature to solve RCPSPDC, which can be customized to solve RCPDPDC-POE. In addition, ADP was successfully used in non-deterministic RCPSP, which can also be customized to solve our problem. However, there are numerous drawbacks to both sets of methods. The main reason for our proposed hybrid architecture is to overcome these challenges. A detailed explanation of our hybrid algorithm is presented in the following section.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods  
Steady-state modes are divided into supervised processes and unsupervised processes. If prior knowledge of the process is sufficient (i.e., the mode of the sample data during modeling is known), it is referred to as a supervised process; if not, it is referred to as an unsupervised process.
# Architectural Design for Digital Twin Networks
## 3 Digital Twin Network
### Reference Architecture
#### 3.2.1 Real Network Layer  
This layer includes the actual network components, consisting of physical and virtual entities such as routers, switches, virtual network functions. The real network exchanges data and control messages with the twin through interfaces, allowing for real-time synchronization and control. It can span across various domains like mobile access networks, data centers, or IIoT networks.
## 5 Conclusions  
Most current research into sun-induced chlorophyll fluorescence (SIF)-estimated Gross primary production (GPP) focuses on flux towers and at small regional scales. GPP can be obtained based on the relationship between SIF and GPP, but there are still complex basic physiological processes that need to be constrained. Therefore, it is difficult to quantitatively evaluate the factors that affect the relationship between SIF and GPP.  
In this study, global GPP was estimated from GOSIF SIF data using the gradient boosting regression trees (GBRT) model at a spatial resolution of 0.05\({}^{\circ}\). In this model, we also used land cover, DEM, NIRv, and other data as input variables while SIF was far more important than other variables. This is a new method for estimating GPP using SIF on a global scale. Subsequently, we evaluated the performance of three GPP products \(\text{GPP}_{\text{SIF}}\), \(\text{GPP}_{\text{MODIS}}\), and \(\text{GPP}_{\text{GLASS}}\), when estimating GPP from flux towers and for different vegetation types. Our results show that SIF can estimate global GPP very well, and the GPP estimated based on GOSIF is similar to the results of that derived from FLUXNET sites (R\({}^{2}\) = 0.58, RMSE = 2.74 g C\(\cdot\)m\({}^{-2}\), bias = \(-\)0.34 g C\(\cdot\)m\({}^{-2}\)). Furthermore, the GPP that we estimated using GOSIF demonstrated reasonable spatial and seasonal changes on a global scale. Although we have revealed that it is possible to estimate GPP from SIF at the satellite scale, challenges remain in estimating GPP directly from SIF at the regional or global scale. In the future, the accuracy of estimations of GPP using SIF could be improved by obtaining higher-quality SIF remote sensing datasets.  
[PERSON] and [PERSON] designed the experiment. [PERSON] collected and preprocessed the data. [PERSON], [PERSON] and [PERSON] performed the experiment and conducted the analysis. All authors have read and agreed to the published version of the manuscript.  
This study was partially funded by the National Key Research and Development Program of China (no. 2016 YFA0600101).  
**Data Availability Statement:** All data used in this study were from open access satellite products or published field work cited in this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### Federated Learning  
FL is a decentralized training framework that allows multiple participants to collaboratively train models without exchanging their private raw data [25]. Most FL methods are based on the federated averaging (FedAvg) algorithm [17], which aggregates local model updates to form a global model. FedAvg is extensively used because it minimizes communication costs and effectively aggregates model updates. This property is critical in FL environments with numerous participants and limited communication bandwidth.  
For FL to become practical in real-world applications, several key challenges must be addressed. A primary challenge is the presence of nonindependent and identically distributed (non-IID) data. Variations in data distribution across clients can result in biased models that favor certain participants [26; 27]. In addition, system heterogeneity and scalability pose significant challenges. Differences in the computational and communication capabilities of clients can result in uneven contributions to model training. As the number of participants increases, efficient aggregation algorithms and scalable infrastructure are essential for effectively managing global model updates.  
FL's future development will be driven by theoretical advancements and practical applications across various industries. In particular, FL is expected to play a crucial role in artificial intelligence solutions that prioritize user privacy and data sovereignty.
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Real Data
#### 3.2.2 Reference Values  
The reference values for assessing the accuracy of the system were obtained by using an alignment piece, shown in Figure 15, made of a transparent polymer that had been engraved with alignment axes. Reference values were obtained by manually positioning the alignment piece in the aligned position and acquiring the transformation provided by the manipulator's CS, \(T_{W}^{TCR}\).  
Figure 14: Example image. The bead arrays can be seen as significantly brighter spots in an otherwise underexposed background.  
Figure 13: Experimental setup.  
Provided that the robot's TCP has been assigned to the cross-hair, the transformation between the TCP and the target origin is, in the aligned position, the identity matrix. The definition of the TCP position is a built-in function of the KUKA robot controller, where the transformation between the robot's flange and the alignment piece is estimated through the XYZ Four-Point TCP Calibration Method. This method involves bringing the tool center to a known position and recording several points as the position is approached from different orientations.  
For a given test position, \(i\), the rigid body transformation between the optical axis of the camera and the coordinate system of the target, \(T_{\text{C1}_{i}}^{K}\), is given as follows:  
\[T_{\text{C1}_{i}}^{K}=\left(T_{TCP}^{\text{C1}}\right)^{-1}\left(T_{W}^{TCP_ {i}}\right)^{-1}\,T_{W}^{TCP_{0}}\,T_{TCP_{0}}^{K}\,, \tag{13}\]  
where \(T_{W}^{TCP_{i}}\) is provided by the robot's CS at position \(i\) and \(T_{TCP}^{\text{C1}}\) is the calibrated hand-eye transformation. The transformation between the tool center point, \(T_{\text{T}\text{C}_{\text{I}_{\text{I}_{\text{I}_{\text{I}_{\text{I}_{\text{I }_{\text{I}_{\text{I}_{\text{I}}}}}}}}}}^{K}}^{K}\), is the identity matrix. In our experiments, for each test position, we estimate \(T_{\text{C1}_{i}}^{K}\) and compare it with the reference value described above.
## 3 Design Patterns of Vectorized Programming for FIR Image Filtering
### Kernel Loop Unrolling  
In kernel loop unrolling, reference pixels in a kernel are vectorized to calculate kernel convolution processing for a pixel of interest in parallel. Figures 4c and 5b indicate the code and vectorization approach to kernel loop unrolling. If the kernel width, which depends on parameters, is a multiple of the size of the SIMD register, reference pixels are able to be efficiently loaded into the SIMD register. However, in most cases, kernel width is not a multiple of the SIMD register size. In such a case, residual processing is necessary for residual reference pixels, which generally occur at the lateral edge of the kernel. The set/gather instruction or scalar operations, which do not assume sequential access, are used for residual processing. The kernel loop steps incrementally; thus, the loading memory address must cross unaligned addresses. In this pattern, weights are vectorially calculated by reference pixels. If the kernel weight depends only on the position relative to the pixel of interest, it is possible to efficiently load the weight into the SIMD register. Because only reference pixels are vectorized, no restriction exists on image size. In this pattern, reference pixels are required to have continuous loading; thus, kernel shape is constrained. Kernel subsampling, where reference pixels are discontinuous, cannot use the pattern (see Figure 5c). The set/gather instruction is used for kernel subsampling. We call kernel loop unrolling with the set/gather instruction arbitrary kernel loop unrolling. Arbitrary kernel loop unrolling is slower than kernel loop unrolling because the set/gather instruction is inefficient.  
This pattern can be achieved on SoA, but the image data structure is usually AoS. Therefore, color channel splitting must be performed before processing. Output data structure should be AoS, and this pattern outputs scalar data; thus, the data are stored with scalar instructions. The pattern has more constraints than color loop unrolling, but the vectorization efficiency of the pattern is significantly better than that of color loop unrolling.
# Methodology for Evaluating the Generalization of ResNet
## 5 Experimental Analysis
### Robustness Testing  
#### 5.4.1 CAM Method Replacement Testing  
To thoroughly validate the evaluation approach by calculating the ratio of task-relevant features using IoU values, we used GradCAM++ [27] and SmoothGradCAM++ [28] to recalculate the IoU of the ResNet18 models, and calculated the correlation between IoU and the generalization gap.  
Table 6 displays the experimental findings, highlighting a consistent strong correlation between computed IoU values and the generalization gap across different CAM calculation methods, including GradCAM, GradCAM++, and SmoothGradCAM++. This suggests that the IoU-based evaluation approach is largely invariant to the CAM method chosen, underscoring its robustness in assessing model generalization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy Threshold** & \(\geq\)**95** & \(\geq\)**90** & \(\geq\)**85** & \(\geq\)**80** \\ \hline IoU (Using GradCAM++) & \(-\)0.9127 & \(-\)0.9105 & \(-\)0.9004 & \(-\)0.8879 \\ IoU (Using SmoothGradCAM++) & \(-\)0.8872 & \(-\)0.8873 & \(-\)0.8731 & \(-\)0.8582 \\ IoU (Using GradCAM) & \(-\)0.9144 & \(-\)0.9096 & \(-\)0.8996 & \(-\)0.8911 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The correlation between IoU and generalization gap based on using different CAM calculation methods on the ResNet18 models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy Threshold** & \(\geq\)**95** & \(\geq\)**90** & \(\geq\)**85** & \(\geq\)**80** \\ \hline ResNet18 & 76.10\% & 75.74\% & 75.14\% & 74.67\% \\ ResNet34 & 73.81\% & 73.25\% & 72.83\% & 72.57\% \\ ResNet50 & 72.78\% & 72.30\% & 72.01\% & 71.71\% \\ ResNet101 & 76.67\% & 76.28\% & 76.00\% & 75.77\% \\ Total & 74.36\% & 74.18\% & 73.97\% & 73.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of using IoU to select models with strong generalization ability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ResNet18** & **ResNet34** & **ResNet50** & **ResNet101** & **Total** \\ \hline Random Model & \(-\)0.1544 & 0.1820 & 0.2246 & \(-\)0.0585 & \(-\)0.0094 \\ Trained Model & \(-\)0.8911 & \(-\)0.8149 & \(-\)0.8054 & \(-\)0.8520 & \(-\)0.8159 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the correlation between IoU and model generalization ability between trained and randomly initialized models.
## 5 Conclusions  
First of all, we show the good performances of the nonlinear regression model (Equation (2)) for estimating Sentinel-2 LAI based on the strong exponential relationship between NDVI and LAI and the PML-V2.1 model of estimating ET and GPP at 10m resolution using Sentinel-2 LAI and land cover map. This Sentinel-2-based technology using the  
Figure 8: Energy fluxes in June 2018 for the Beijing Sponge City. (**a**) Downward shortwave radiation; (**b**) Surface net radiation; (**c**) Latent heat flux; (**d**) Sensible heat flux. Units are in W m\({}^{-2}\).  
PML-V2.1 model with surface meteorological forcing data as the input for estimating ET provides a new framework to evaluate city-level hydrological dynamics in urban ecosystems. Secondly, we find that plant transpiration from greenbells with trees and grassland play an important role in most impervious areas for Beijing Sponge City. Thirdly, mixed forests, forest parks and croplands due to their high ET have much smaller surface heat contribution than the impervious and grasslands, providing better ecosystem services (e.g., cooling) for the sponge city.  
Conceptualization, X.Z.; methodology, X.Z.; software, X.Z. and P.S.; validation, X.Z. and P.S.; formal analysis, X.Z.; investigation, X.Z.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z. and P.S.; visualization, X.Z. and P.S.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Municipal Natural Science Foundation (grant number 8212017), the National Natural Science Foundation of China (grant number 42001019) and the Shanghai Sailing Program (grant number 19 YF1413100). The APC was funded by the Beijing Municipal Natural Science Foundation (grant number 8212017).  
The resulting dataset in this study is available at [[https://doi.org/10.6084/m9.figshare.14387630.v2](https://doi.org/10.6084/m9.figshare.14387630.v2)]([https://doi.org/10.6084/m9.figshare.14387630.v2](https://doi.org/10.6084/m9.figshare.14387630.v2)) (accessed on 8 April 2021). More data and codes can be accessible from the corresponding author upon request.  
We acknowledge the Beijing Water Science and Technology Institute (BWSTI) for providing technical and financial supports to this work. We thank the three anonymous reviewers and editors for their constructive suggestions and comments.  
The authors declare no conflict of interest.