esa-sceva/satcom-chunk-collection huggingface dataset:
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 2 Data Preprocessing and Analysis
### Data Analysis
#### 2.2.2 Inconsistent Colors  
Due to the solar elevation angles and weather conditions during image acquisition, there are large differences in irradiance and color distribution between images. Figure 2 shows mosaicked images with adjacent locations, which illustrates the significant differences in brightness and color between images. An ideal mosaic result should have uniform tone and brightness, and the different sub-images should be difficult to distinguish.  
Figure 1: Example of an original HY-1C image of Antarctica. There is uneven lighting in the image.  
Figure 2: Example mosaic result for a group of HY-1C images of Antarctica.  
To illustrate the difference between images, two adjacent images were selected randomly from the dataset. Figure 3 shows their color histograms, means, and standard deviation values. It is clear that the color histogram, mean, and standard deviation values were quite different, whereas the contents of the two images were in fact similar. According to the laws of natural imagery, two images with adjacent locations and similar contents should had similar histogram, mean, and standard deviation values. This example clearly demonstrated the color consistency problem between images.
# Energy Bus-Based Matrix Modeling and Optimal Scheduling for Integrated Energy Systems
## 5 Case Study
### Analysis of Rolling Optimization Results  
In this case, the rolling optimization strategy revises the day-ahead scheduling scheme on the real-time RES and load prediction data, with the objective function defined as the deviation in the electricity purchase scheme and the operating cost for the upcoming hour. The rolling optimization results based on the comparison of the day-ahead stochastic and deterministic optimization schemes are shown in Figure 21. Rolling optimization cannot be implemented when the PGU is turned off, and the results of the two rolling optimization methods are primarily concentrated between 8:00 and 22:00. Rolling optimization based on the day-ahead stochastic optimization scheme has a smaller objective function value, and the standard deviation of its objective value is reduced by 42.79% compared to that of rolling optimization based on the deterministic optimization scheme, indicating smaller revisions to the devices and stronger adaptability to prediction errors. The maximum objective function values of the rolling optimization method based on the day-ahead stochastic optimization scheme and the rolling optimization method based on the deterministic optimization scheme are 40.95 and 70.52, respectively, and their minimum values are 0, indicating that the former has a stronger ability to deal with anomalies. There are still some prediction errors in the RES and load data during rolling optimization, and actual supply and demand imbalances exist. Therefore, in actual operation, the use of storage devices to store excess electricity and cooling energy ensures a real-time supply and demand balance.  
Figure 21: Comparison of results of two rolling optimization methods.  
Figure 20: Revised day-ahead deterministic optimization scheduling results for cooling energy.  
Figures 22 and 23 present the rolling optimization scheduling results for electricity and cooling energy, revised with the use of energy storage units. From 8:00 to 23:00, during each rolling optimization interval, the IES purchases less electricity from the grid, primarily adjusting the power output of the PGU to respond to the stochastic fluctuations in renewable power generation and the electrical load. It also regulates the input/output of the electricity storage unit to achieve an energy supply-demand balance, thereby reducing the fluctuations in the electricity purchases and operating costs. From 23:00 to 8:00 on the next day, when the PGU is not operating, the IES primarily adjusts the amount of electricity purchased to maintain the supply-demand balance. Similarly, in terms of the cooling supply, the system adjusts the real-time output of the electric chiller and the input/output of the chilled water storage unit to ensure a stable supply of cooling energy.
# New Optical Design Method of Floating Type Collimator for Microscopic Camera Inspection
###### Abstract
By placing the collimator in series with a multifocal lens system whose focal distance is varied by electrical means and moving the collimator left and right in accordance with the multifocal lens system, the height of the focus can be changed faster than in previous optical pickup devices [22]. In summary, the collimator has been applied to various inspection devices, creating a virtual target image at a calculated distance or changing the focusing point of the optical systems. Based on our best technical knowledge, the floating-type collimator technique has not been used for camera phone inspection instruments for adjustable the object variance. Because such a conventional collimator can create a virtual image only at a specific location, the resolution inspection distance of the camera optics is fixed [8]. However, as described above, the shooting distance of recently released phone cameras has changed. Therefore, we need a collimator that can change the distance between the virtual image and the target. To do so, a part of the lenses constituting the collimator must move such that the virtual image distance can be changed even in the collimator. However, considering that recent phone cameras can take photographs at a fairly close distance, the distance of the virtual image by the collimator should be the distance that can be photographed with the phone camera. Therefore, it is necessary to design the optics such that the virtual image distance by the collimator changes significantly. To do so, the amount of movement of the lens group must be increased. However, the field curvature of the collimator increases as the movement amount of the lens group increases [24; 25]. In particular, because the angle of view of phone cameras has recently increased, correcting the field curvature is extremely important to the resolution performance of a phone camera. It is therefore necessary to have a structure to move a number of lens groups in a collimator for inspecting a phone camera with an extremely large angle of view and a large change in the shooting distances. An optical system that focuses on moving a large number of lens groups is called a floating system. When the object distance that the camera can take is extremely short and only one lens group is moved, the aberrations of the optical system become quite large. To resolve this issue, several lens groups must be moved to correct aberrations within the shortest close range. Therefore, a floating-type optical system must be configured [26]. In this paper, we discuss an optical design method for a floating collimator. In Section 2, the floating system used to obtain the paraxial optical design of the collimator is analyzed. In Section 3, the distortion analysis, modulation transfer function, and locus analysis, which need to be obtained for a collimator optical system, are described. Finally, Section 4 provides some concluding remarks regarding this research.
# SA-SGRU: Combining Improved Self-Attention and Skip-GRU for Text Classification
## 6 Conclusions  
For text classification, when reading text, especially long text, a large number of words are irrelevant and can be skipped. Therefore, we started with improving GRU and self-attention mechanisms and proposed a text classification model combining skip-GRU and improved self-attention mechanisms. The model not only includes an improved GRU network and self-attention mechanism but also optimizes CNN to further extract richer text features. Different from the traditional model feature extraction method, we use a jump network mechanism to directly skip the information that is not important to the classification results from the text and then transfer the retained important information into the GRU network, which can effectively capture the semantic association between long text sequences. Considering that the GRU network is not sensitive to key pattern information, we use improved self-attention to reassign weight information to the information captured by the GRU network to enhance the expression ability of text features. Simultaneously, in order to further enrich the text's features, optimized CNN is used to extract the text's local key information.  
We tested the performance of the SA-SGRU model on three public data sets and obtained good experimental results. We also compared the SA-SGRU model with some of the most advanced text classification models based on deep learning. In addition to the KATG3 and Lex-AT3 models, the SA-SGRU model proposed in this paper is superior to other comparison models on the three data sets. After an in-depth study, it was found that BERT, which has stronger textual semantic understanding ability, is used in both the KATG3 and Lex-AT3 models in the text vectorization part, while the SA-SGRU model proposed by us uses GloVe in the text vectorization part. Compared with the BERT model, the SA-SGRU model's text semantic understanding ability in the text vectorization part is not so good, resulting in the final performance of the model being slightly worse than the KATG3 and Lex-AT3 models. In order to enhance the semantic understanding ability of the SA-SGRU model in the text vectorization part, we plan to combine the jump network proposed in this paper with BERT. However, considering the large amount of computation in the BERT model, improving the BERT model and reducing the amount of computation will be the focus of our research.  
Funding acquisition, Y.H.; Conceptualization, X.D.; Investigation, J.Y. and Z.H.; Data curation, X.D.; Formal analysis, J.Y. and Z.H.; Methodology, Y.H. and X.D.; Writing--original draft, X.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of China under Grant No. 61772449, in part by the Natural Science Foundation of Hebei Province (Youth) under Grant No. D2021402043, and in part by the Handan Science and Technology Bureau Foundation under Grant No. 21422093285.  
Not applicable.  
The authors declare no conflict of interest.
# Parallel Crossed Chaotic Encryption for Hyperspectral Images
## 2 Chaotic Systems and Chaotic Encryption
### Chaotic Image Encryption Performance  
To measure the performance of the image chaotic encryption algorithms is common to use the information entropy defined in (1), where \(s_{i}\) is the intensity of the pixel \(i\) of the image, \(P(s_{i})\) is the probability of \(s_{i}\) intensity to appear. The \(2^{b}-1\) is the larger number represented in the data (255 for image intensity values), then the maximum theoretical value of entropy for the image of 256 intensity values is 8. Encrypted data that have a value of entropy close to the theoretical maximum is difficult to differentiate from random data, consequently it is secure against cypher-text-only attacks.  
\[H(s)=-\sum_{i=0}^{2^{b}-1}P(s_{i})\log_{2}[P(s_{i})] \tag{1}\]  
The idea of chaotic encryption is that using the chaotic behavior no algorithm could differentiate between an encrypted image and image full of random pixel values. A common technique in cryptanalysis is the differential attack, where we encrypt two different images with the same cipher. If the encrypted images are statistically different, then we could infer information about the ciphersecret key or parameters. In [16], two statistical measures are proposed to verify if cipher algorithm is weak against differential attack. The Number of Changing Pixel Rate (NPCR) is defined in Equation (3), where \(T\) is the total of pixels in the images and \(D(i,j,k)\) is defined in (2), where \(I_{1}\) and \(I_{2}\) are two different images and \(c\) is a cipher algorithm.  
\[D(i,j,k)=\left\{\begin{array}{ll}0&\text{if}\quad c(I_{1}(i,j,k))=c(I_{2}(i,j,k))\\ 1&\text{if}\quad c(I_{1}(i,j,k))\
eq c(I_{2}(i,j,k))\end{array}\right. \tag{2}\]  
\[NPCR(I_{1},I_{2},c)=\sum_{i,j,k}\frac{D(i,j,k)}{T}\times 100\% \tag{3}\]  
The Unified Average Changed Intensity (UACI) is defined in (4), where \(F\) is the maximum intensity value allowed. To ensure that the encrypted image is not differentiable of a image full of random pixel values, the expected values of NPCR and UACI are \(NPCR_{e}=99.6094\%\) and \(UACI_{e}=33.4634\%\).  
\[UACI(I_{1},I_{2},c)=\sum_{i,j,k}\frac{|c(I_{1}(i,j,k))-c(I_{2}(i,j,k))|}{F\cdot T }\times 100\% \tag{4}\]
# Self-Recognition Grasping Operation with a Vision-Based Redundant Manipulator System
## 2 Self-Recognized Grasping Operation  
For self-recognized grasping operation, the core problem is achieving object detection, pose estimation, and pose transformation autonomously. In this section, the process of the self-recognized grasping operation is described. Instance segmentation is used to classify the target object and describe the contour via a RGB-D image. During the region of the contour, pose estimation is used to locate the pose of target object in pixel coordinate system. Then, pose transformation is applied to transfer the pose, expressed in pixel coordinates, into joint angles of the manipulator. Considering the internal parameters of camera, the pose is preferentially transferred from pixel coordinates into Cartesian coordinates, then transferred into the joint space of the manipulator for direct motion control. The process of the self-recognized grasping operation is shown in Figure 1.  
In the self-recognized grasping operation, instance segmentation and pose estimation rely on the RGB-D image obtained by the vision system, using CNN frameworks to achieve self-recognition. The frameworks are described in detail in Figure 2.  
Figure 1: Process of the vision-based self-recognized grasping operation.
## 3 Results
### Sedimentation  
#### 3.3.1 Sedimentation Experiments with Chalks  
The sedimentation experiments were conducted with small (50 mL) falcon tubes to evaluate the turbidity change as a function of the used coagulants (Table S4). Next, larger (0.5-6 L) containers were used to scale up the coagulation-precipitation process and evaluate turbidity reduction (Tables S5-S7).  
Figure 11 illustrates the relationship between chalk (CaCO3) concentration and turbidity levels in water, revealing that turbidity is at its lowest when the chalk concentration is in the 6.67 g per liter range. This suggests that this concentration is optimal for sedimentation processes designed to reduce turbidity among the tested concentration. However, when the chalk concentration increases, turbidity levels begin to rise again, possibly due to particle re-stabilization that reduces the efficiency of the sedimentation process.  
Figure 10: Top view of the filter showing biofilm formation.  
The literature on calcium carbonate precipitation and sedimentation indicates no singular optimal concentration for achieving the best results. Instead, optimal concentration varies significantly depending on several factors, including the acidity (pH) of the system, the temperature at which the process occurs, the level of agitation, and the concentrations of CO2 present during the reaction [18].  
Furthermore, the experiments emphasize the importance of the physical form of the chalk, the volume of water, and the weight of the chalk in the effectiveness of sedimentation. Crumbled chalk was generally more effective at reducing turbidity than larger, broken pieces. Additionally, it was observed that larger quantities of chalk were necessary to achieve lower turbidity in larger volumes of water. A \(t\)-test further confirmed a significant difference in turbidity levels between conditions with and without chalk, reinforcing the impact of chalk on the sedimentation process.
## 3 Results and Discussion
### Adsorption of Ammonium Nitrogen on Biochar
#### 3.2.2 Effect of Activation Temperature on the Adsorption Capacity of Biochar  
Figure 1b shows the yield and NH\({}_{4}\)\({}^{+}\)-N adsorption capacity of KOH-activated biochar obtained at different pyrolysis temperatures. It was found that increasing the pyrolysis temperature resulted in a decline in biochar yield, which could be explained by the release of more volatile matter during the pyrolysis at a higher temperature [28]. However, the NH\({}_{4}\)\({}^{+}\)-N adsorption capacity of the biochar improved when the pyrolysis temperature was increased. The biochar obtained at 550 \({}^{\circ}\)C exhibited an adsorption capacity of about 7.67 mg/g, while the value increased significantly to 12.02 mg/g when the pyrolysis temperature was increased to 750 \({}^{\circ}\)C. Thus, the NH\({}_{4}\)\({}^{+}\)-N capacity of KOH-activated biochar is influenced by the pyrolysis temperature. The pyrolysis temperature has a critical effect on the specific surface area of the prepared biochar and, accordingly, the adsorption capacity of the biochar [28]. The specific surface areas of the KOH-activated biochar pyrolyzed at 550, 650, and 750 \({}^{\circ}\)C were 95.9, 117.7, and 155.3 m\({}^{2}\)/g, respectively. This result is consistent with those of other studies reporting a high surface area in biochar pyrolyzed at 650-800 \({}^{\circ}\)C [30; 31]. The low surface area of biochar pyrolyzed at  
Figure 1: Effect of activators on the yield and NH\({}_{4}\)\({}^{+}\)-N adsorption of biochar from sorghum distillers grain (**a**); Effect of pyrolysis temperature on the yield and NH\({}_{4}\)\({}^{+}\)-N adsorption of KOH-activated biochar from sorghum distillers grain (**b**).  
a lower temperature is caused by the formation of tars, which prevents pore formation during the pyrolysis process. However, when pyrolysis is conducted at a higher temperature, pores are generated easily due to the volatilization of tar components [30].
## 3 Accuracy and Feasibility for Hardware Implementation
### Simulation Signals  
In Figure 13, the plots for the 100 mm length PEC thin wire response and its corresponding CNR-based reconstruction are shown. Subsequently, Figure 14 displays the CNRs in the complex plane. For time domain analysis, the signal processing is that of the late-time response of the object only; however, this part of the response is included in the frequency domain. Therefore, as the calculated CNRs do not have any filtering or preprocessing, they show similar characteristics but are not exactly the same. Considering this CNRs comparison, as the damping factor \(\alpha\), which is the real part of each CNR, approaches zero, its contribution to the signal in the form of an exponential decay becomes more significant, and the complex conjugated component of the CNR is related to the oscillation frequency. This means that two main frequencies can be identified in Figure 14 with the imaginary values 1.45 and 4.26, which also correspond to the expected resonances for this particular thin wire.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Dimensions (mm)** \\ \hline Thin wire & \(l=100\), \(d=2\) \\ \hline Sphere & \(r=50\) \\ \hline Plate & \(l_{1}=100\), \(l_{2}=100\), \(h=1\) \\ \hline Cylinder & \(d=50\), \(l=100\), \(t=1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Canonical objects simulated in three different positions.
## 5 Verification of the Prediction Model for Height of WFFZ
### Model Verification  
The factors affecting the height of the WFFZ of panel 31110X in the Coal Mine A were input into the SSA-Elman neural network, Elman neural network, and BP neural network prediction models. Meanwhile, the corresponding parameters were substituted to calculate the results of the empirical formulas. Table 3 shows a comparison of the prediction results.  
Figure 11: Total water injected at different measuring points in T1.  
Figure 10: Water pressurized into the formation at different measuring points in T1.  
As shown in Table 3, the heights of the water flowing fractured zone are projected to be 49.47-168.9 m by the Elman neural network, BP neural network and empirical formulas. With respect to the in-situ measurement of the height of 126.0 m, the absolute error range is 40.8-76.53 m and the relative error range is 32.38-60.74%. While the SSA-Elman neural network predicted that the height of the WFFZ of coal mine A is 124.9 m, and the relative error range is 0.87%. It is obviously observed that the predicted value of the WFFZ by the SSA-ELMAN model is closer to the measured value, while the values predicted by the empirical formulas, BP neural network, and Elman neural network have a larger error. It is because the empirical formulas that only consider mining height, buried depth, and working face length, the Elman neural network itself has a good learning ability and considers two more factors, the mining method, the ratio of the thickness of hard rock layers and soft rock layers in the bedrock, which makes the prediction more consistent with the in-situ measurement value. Meanwhile, compared with BP neural network, ELMAN neural network has better performance than BP neural network in terms of computational power and stability due to the addition of undertaking layer. Using sparrow search optimization algorithm to optimize ELMAN neural network can effectively improve the convergence speed and stability. In the future, the influence of overlying strata structure on the development of water-flowing fractured zone will be more comprehensively considered. Meanwhile, we consider expanding the collection range of highly correlated data of water flowing fractured zone, further improving the convergence speed and reducing the training time of the model, and improving the accuracy, in order to be applied in a wider range of mining areas.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 2 Materials and Methods
### Materials  
#### 2.2.1 Rainfall Datasets  
The ground rainfall data from 1998 to 2006 used in this study were obtained from the Mekong River Commission (MRC) and the China Meteorological Administration (CMA). Data were collected from a series of 175 rain gauges over the study area (Figure 1a). The GPPs included CPC, TRMM, and CMORPH over the period of 1998-2006, and GSMaP over the period of 2000-2006, which were selected for evaluation in the MRB (Table 1). For global land areas, the CPC data used in the current global/regional gridded datasets were mainly retrieved from the World Meteorological Organization (WMO) Global Telecommunication System (GTS) based on the optimal interpolation (OI) method [35] on a \(0.5\times 0.5^{\circ}\) grid. It should be noted that within the MRB, the number of GTS gauges operating over the period of 1998-2006 was relatively low--30 gauges (1998-2002) and 40 gauges (2002-2006)--compared to 175 rain gauges from the MRC and the CMA. Moreover, these satellite precipitation products (GSMaP, TRMM, and CMORTH) were calibrated with the CPC and the Global Precipitation Climatology Center (GPC) [36] products to obtain higher accuracy.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 4 Experimental Setup and Result
### Implementation Details  
The RASST model was implemented in PyTorch 2.2 and trained on an NVIDIA RTX 3090 GPU with 24 GB of memory. The training procedure used the stochastic gradient descent (SGD) optimizer [26] with a momentum of 0.9 and a weight decay of \(10^{-4}\). The learning rate was initialized at 0.0025 and reduced at predefined intervals during training. Strong and weak data augmentations were employed to increase robustness, with strong augmentations applied to the student model and weak augmentations to the teacher model.  
The training process involved 180,000 iterations, with learning rate adjustments at 120,000 and 160,000 iterations. For the semi-supervised setting, labeled data subsets of 10%, 20%, and 30% were created, while the remaining data were treated as unlabeled. Pseudo-labels were generated by the teacher model and refined using the RAW and GC losses. The confidence threshold for pseudo-labeling was set to 0.7, ensuring that only high-confidence predictions were utilized for training, described in Table 1.