esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sensor Positioning Principle
### Wi-Fi/Bluetooth Positioning Principle  
Wi-Fi/Bluetooth has the characteristics of low equipment cost and wide coverage. It is a positioning technology used more in indoor positioning. The geometric positioning method of Wi-Fi/Bluetooth mainly uses the RSSI attribute of Wi-Fi/Bluetooth and the propagation model of a Wi-Fi/Bluetooth signal in the air to carry out the inverse ranging of Wi-Fi transmission signal points [26] so as to realize trilateral positioning. However, the accuracy of energy positioning is low, and the positioning effect is poor. It needs a dense array. Combined with inertial sensors, it can realize navigation and positioning with certain accuracy, and the positioning effect is poor when used alone.  
At present, the positioning method of multistation joint direction finding based on array Bluetooth is relatively widely used and has good navigation and positioning effect. The positioning principle based on Bluetooth arrays is to measure the azimuth of the carrier reaching each base station through multiple array base stations. In indoor complex environments, due to the limitation of array aperture and installation structure, the direction finding of Bluetooth arrays generally does not measure the pitch angle of the carrier and generally adopts the method of fixing the height of the carrier. The azimuth measurement adopts high-resolution direction-finding methods, such as MUSIC [27], ESPRIT [28] and other algorithms, which have good direction finding effects. After obtaining the direction finding results of multiple base stations, the least square method can be used to locate the carrier.  
Then the least square positioning method based on the direction finding of multiple Bluetooth base stations can be described as:  
\[\mathbf{s}_{LS}=\left[\mathbf{H}^{\text{T}}\mathbf{H}\right]^{-1}\mathbf{H}^{\text{T}}\mathbf{X} \tag{9}\]  
\[\text{where }\mathbf{H}=\begin{bmatrix}\sin\varphi_{1}&-\cos\varphi_{1}\\ \vdots&\vdots\\ \sin\varphi_{N}&-\cos\varphi_{N}\end{bmatrix}\text{, }\mathbf{X}=\begin{bmatrix}x_{B1} \sin\varphi_{1}-y_{B1}\cos\varphi_{1}\\ \vdots\\ x_{BN}\sin\varphi_{N}-y_{BN}\cos\varphi_{N}\end{bmatrix}\text{, }\mathbf{s}_{LS}= \begin{bmatrix}x&y\end{bmatrix}^{T}\text{ is the carrier position coordinate, }\varphi_{1}\cdots\varphi_{N}\text{ is the azimuth measurement result of }N\text{ base stations and }\begin{bmatrix}x_{B1}&\cdots&x_{BN}\\ y_{B1}&\cdots&y_{BN}\end{bmatrix}\text{ are the position coordinates of }N\text{ base stations.}\]
# Different Object Functions for SWIPT Optimization by SADDE and APSO  
[PERSON]  
1 Department of School of Electric and Information Engineering, Beibu Gulf University, Qinzhou 535000, China; [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (Y.-L.)1 Department of Electrical and Computer and Engineering, Tamkang University, New Taipei City 251301, Taiwan; [EMAIL_ADDRESS] (P.-H.C.); [EMAIL_ADDRESS] (Y.-T.C.)1 Department of Electrical and Electronic, University Tunku Abdul Rahman, Selangor 43000, Malaysia; [EMAIL_ADDRESS] Department of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
1 Department of School of Electric and Information Engineering, Beibu Gulf University, Qinzhou 535000, China; [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (Y.-L.)1 Department of Electrical and Computer and Engineering, Tamkang University, New Taipei City 251301, Taiwan; [EMAIL_ADDRESS] (P.-H.C.); [EMAIL_ADDRESS] (Y.-T.C.)1 Department of Electrical and Computer and Engineering, Tamkang University, New Taipei City 251301, Taiwan; [EMAIL_ADDRESS].  
the optimal analytic solution with the Signal to Interference plus Noise Ratio (SINR) constraint was presented for the narrow band signal.  
To the best of our knowledge, most papers utilize a narrow band system under the SINR constraint with an optimal analytic solution. For wide band signals, the BER and SINR are not completely correlated. The Bit Error Rate (BER) performance shall be considered in the wireless wideband communication system due to the frequency selective effect of the multipath channel and Inter Symbol Interference (ISI) rather than SINR only. In our past research, we attempt to solve this problem with a multiple objective function. In [21], we use the single objective function value to set the weighting for the multiple function. In other words, we cannot find the suitable multiple function unless the maximum single objective function has been found. In [24], we try to use the constraints for optimization. However, the penalty for the algorithm constraint should be adjusted according to the environment. Therefore, we try to improve the objective function for easy adjustment. In this paper, we introduce different objective functions for the BER and power harvesting efficiency for a wideband circular array SWIPT system. We use a novel multiple objective function that does not need to set specific weighting for the BER constraint objective function.  
The system model and the deployment of the antenna array are presented in Section 2. Section 3 explains the SADDE and APSO with the objective function. In Section 4, two different multiple objective functions with APSO and SADDE are compared. The optimization of the object function is achieved. The configured parameters and the SWIPT quality are compared by numerical simulation. The conclusion section summarizes the key points of this paper.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 1 Introduction
Techniques such as block-wise repetition, majority voting, and forward error correction (e.g., BCH or Reed-Solomon codes) have been used to counteract localized data loss. Moreover, temporal redundancy, spreading the watermark across several frames, has been shown to increase robustness against frame dropping, re-encoding, and desynchronization [23,24]. However, without careful selection of embedding blocks and modulation levels, redundancy alone may not yield sufficient gains. In this work, we propose a perceptually guided, fully blind watermarking method that integrates adaptive block selection, JND-based QIM modulation, and multiframe redundancy. By unifying these strategies in a lightweight spatial-domain pipeline, our method achieves strong robustness against noise, compression, and geometric attacks while maintaining high visual fidelity and low computational cost. A detailed summary of our novel contributions is presented in the following subsection.
# Segmented Composite Optical Parametric Amplification
## 3 General Numerical Approach and LiNbO\({}_{3}\) Crystal Simulations
The right-hand panel (**c**) show the signal amplification spectrum for the two cases at the optimum level of pump intensity \(I_{3}\) corresponding to the two horizontal lines in panels (**a,b**), \(I_{3}=0.8\) GW/cm\({}^{2}\) and \(I_{3}=3.75\) GW/cm\({}^{2}\), respectively. The red solid line is for the segmented composite crystal while the blue dashed line for standard QPM only. Figure 1:0 (Color online) Color plots of the signal intensity amplification \(a\) vs. the input pump intensity \(I_{3}\) and the signal wavelength \(\lambda_{1}\) for the case of MgO:LiNbO\({}_{3}\). The initial conditions are \(A_{1}(0)=1\), \(A_{2}(0)=0\), and \(A_{3}(0)=1\) so that \(r=1\) and the remaining parameters are as in Figure 1:1 (**a**) QPM periodic design only. (**b**) Composite crystal with eight segments (8 from Table 1). (**c**) Same as panel (**b**) but with a random error within \(\pm 5\%\) of the segment lengths. The two isolines mark intensity amplification levels of 1.5 and 1.9, respectively. We can conclude from Figures 4-6 that the present composite OPA approach works very well in the depleted pump case, because, in all these cases, one has a significant pump depletion, and thus a signal amplification approaching the maximum theoretically possible.
## 3 Identification and Division of Urban Functional Zones
### Urban Functional Zone Division Based on Density Analysis  
Some scholars identified urban functional zones using density analysis based on urban static data (such as road network data and POI data) since remote sensing data do not adequately reflect urban socioeconomic functions. [PERSON], et al. [33] quantitatively analyzed urban functional zones by calculating the density of POI data in the grid, and mixed functional zones were visually described. Other scholars used this method to determine the influence of the number of grids on the identification of urban functional zones. The results showed that the classification of mixed functional zones in the Xicheng District of Beijing was more accurate when a 25 \(\times\) 25 spatial grid was used than a 50 \(\times\) 50 spatial grid [68]. [PERSON], et al. [34] assigned scores to different levels of POI data and identified urban functional districts by calculating the POI density scores of block units. [PERSON], et al. [45] considered the spatial autocorrelation of POI data and used kernel density estimation (KDE) to weaken the discretization phenomenon of the POI data (Figure 6). The authors calculated the quadrat density of the POI data in the block to improve the identification accuracy of mixed functional zones. On this basis, some studies conducted comparative experiments of different kernel density bandwidths. A larger kernel density bandwidth increased the influence range of the POI points and the mixing degree of the functional zones [69].  
[PERSON], et al. [70] used POI data to analyze and delineate the central business district (CBD) using network KDE. [PERSON], et al. [71] used KDE and head/tail breaks to analyze POI data and identify the spatial distribution and interaction of different functional districts. [PERSON], et al. [72] used urban road network data and POI data and divided the urban functional zones using kriging interpolation and network KDE. [PERSON] and [PERSON] [73] calculated the density of POI data in each block and used SPSS software to perform principal component analysis (PCA) on the standardized block density. Comprehensive indicators were selected to describe the characteristics of the functional zones and visualize the results. [PERSON], et al. [74] used the term frequency-inverse document frequency (TF-IDF) statistical method to identify urban functional regions based on POI data. [PERSON], et al. [75] performed automatic classification of a large number of buildings into different functional types using the weighted frequency density ratio of each POI data type inside and within a distance to a building, improving the traditional quantitative identification of urban functional zones. The classification method based on density analysis is easy to understand. It mainly relies on processing platforms such as ArcGIS or SPSS to realize fast and efficient classification of urban functional zones [56]. However, since most of them only consider single data (POI), there is room for improvement in the accuracy of these division methods [38; 58].
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 3 Variance Characteristics of Surface Albedo for Essential Land Types and Scales
### Albedo Variances for Typical Land Types
#### 3.1.2 Vegetation Albedo  
The change in vegetation canopy albedo has essential interactions with the carbon trade-off [93], atmospheric nitrogen deposition and other environmental dynamics (e.g., temperature and precipitation) [69; 94], especially for forests. Forest canopy albedo mainly varies with species (e.g., evergreen needleleaf forests, evergreen broadleaf forests, deciduous broadleaf forests, and mixed forests) [95; 96], greenness [96], forest structure (e.g., forest density, the leaf area index (LAI), and fractional vegetation cover (FVC)) [97; 98], stand age [95; 99] and disturbance type (e.g., harvest and fire) [95]. Broadleaf-dominated species usually present higher albedo levels than conifer-dominated stands, and these values are more significant under snow-covered conditions [95]. Moreover, forest structure may modulate albedo in most sub-biomes, and there is always a positive albedo response to the LAI and a negative response to the FVC, except for deciduous broadleaf forests in Mediterranean and temperate regions [97]. Notably, leaf structure and biophysical parameters present significant effects on canopy albedo, such as leaf glossiness and/or canopy morphological traits [100] and foliage nitrogen concentration for temperate and boreal forests [94; 101]. In addition, albedos of forest fine structures have also been investigated, such as the vertical albedo profile [102] and coniferous needle and shoot albedo [103].  
Similarly, canopy albedos for various vegetation types in addition to forests (e.g., shrubs, crops and grass) also change with species [96]. Given that these non-forest vegetation types always have considerable intra- and interannual variations, their canopy albedo variations with spatial (regional), temporal (within the growing season), and spectral (visible, near-infrared, and shortwave) characteristics are more complex than those of forests [104]. Numerous studies have focused on crop albedo in agricultural fields [105], and biochar application to Mediterranean agricultural landscapes may reduce climate mitigation in the production of biochar due to reduced albedo [90]. In particular, different albedo variances were found among biofuel crops, such as corn, switchgrass and miscanthus in American agroecological zones [106]. In addition, shrubs in tundra ecosystems of the terrestrial cryosphere can also reduce winter albedo [107], and vegetation canopy albedo responds in opposite ways to soil moisture conditions, such as those of soil [86; 108; 109]. The impact of flowers on canopy albedo has also been investigated [110].
## 3 Results and Discussion  
### Physicochemical Properties of the Seed Powders  
Table 1 presents the physicochemical results of the mango and rambutan seed powders obtained by the CD and FD methods. Freeze-dried seeds had a lower water activity level (a\({}_{\text{w}}<0.03\), for both mango and rambutan seed powders) compared to convection-dried seeds, (0.21 and 0.23 for mango and rambutan, respectively). The lower water activity of freeze-dried powders is mainly due to the method of removing water during this process. Lyophilization allows for the removal of water from the product by sublimation, i.e., direct phase transformation of a solid (ice) into gas (water vapor), bypassing the liquid phase, and then by desorbing water molecules [32; 33]. The water present in the products is free water (and only this part of it can be removed by convection drying) and water associated with the matrix with different forces [33]. The freeze-drying process allows for the removal of both free and bound water during sublimation [34], ensuring a much lower water activity of products dried with this method compared to convection drying. The low water activity of the seeds is necessary due to the possibility of bringing them to powder form; moreover, it ensures the microbiological stability of the powders during storage [27]. The powders were characterized by fairly low values of water absorption (1.22-1.59 g H\({}_{2}\)O/1 g of powders) and low solubility in water (9.5-17.4%). Analyzing the results obtained, it can be observed that the method of drying the seeds did not significantly affect the water-binding capacity of the given powders. Regardless of the drying method, the mango seed powder absorbed about 1.6 g water/1 g d.m. of powder, while the rambutan seed powder absorbed 1.22 g water/1 g FD powder and 1.29 g water/1 g d.m. of CD powder. The drying method of the studied powders had a statistically significant (\(p<0.05\)) effect on their solubility in water. The powders obtained from the sublimation-dried seeds were characterized by a higher solubility index. The freeze-dried mango seed powder had the highest solubility (17.4%), while the lowest solubility was found in convection-dried rambutan seed powder (9.5%). The available literature lacks data on the water-binding capacity and water solubility of exotic fruit seed powders, such as mango and rambutan. Comparing the WHC and WSI results obtained for the seed powders with the literature  
results for fruit powders, it was noticed that these indices show lower values for the powders tested in this work. For comparison, freeze-dried strawberry powder absorbed about 2.8 g water/1 g d.m. of powder, while convection-dried strawberry powder absorbed 2.3 g water/1 g d.m. of powder, and its solubility in water was 62% for freeze-drying and 55% for convection-drying, respectively [35; 36]. Compared to fruit powders, the powders obtained from the tested seeds absorbed about two times less water and their water solubility was about three times lower. These differences may be due to the presence of more carbohydrates and fiber, with higher solubility in the fruit than in the tested seeds [37].
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Ablation Studies
#### 4.4.3 Effectiveness of the Attention Mechanism  
To enhance restoration performance, we introduced spatial and channel attention mechanisms. As shown in (g)-(j) of Table 5, four experiments demonstrate that while each mechanism alone only marginally improves performance, their combination leads to significant gains. This approach overcomes the limitations of the algorithm unfolding model in the presence of non-uniform blur. The two mechanisms improve the model's ability to capture image details and robustness by adaptively adjusting attention to different parts of the image. The spatial attention mechanism focuses on critical regions by generating a weight map that evaluates the importance of different areas, dynamically guiding the model's attention to improve restoration quality. Meanwhile, the channel attention mechanism optimizes restoration by adjusting the weights of feature channels through global pooling, enhancing attention to textures and high-frequency details while suppressing less relevant channels. Together, these mechanisms refine feature-level information and improve the model's flexibility and accuracy, enabling the more effective restoration of images affected by noise or missing details.  
We strategically incorporate the \(L_{0}\) regularizer and attention mechanisms, which not only improve the model's efficiency in image restoration but also enhance its ability to select relevant features. This integration is critical for boosting the model's generalization ability, allowing it to perform well across a diverse range of images and maintain resilience against various types of noise. To validate our approach, we conducted extensive experiments. The results underscore the model's excellent performance across multiple metrics, including PSNR and SSIM, and demonstrate its superiority in both quantitative and qualitative assessments. Our research demonstrates that even with a limited number of parameters  
Figure 8: Blur kernel obtained by different regularizers.  
Figure 7: Experimental results of whether to use \(L_{0}\) regularizer.  
and datasets, the proposed method achieves remarkable performance, showcasing its robustness in handling complex deblurring tasks.
## Materials and Methods
To express the difference between the impacts brought to the threshing gap by both adjustment types accurately, the equations for the threshing gap cross section area with concave adjustment type and threshing drum adjustment type are as follows when the threshing gap is increased:  
\[S_{1}=\pi R^{2}+2R\times\Delta\text{L}-\pi\pi^{2} \tag{1}\]  
\[S_{2}=\pi R^{2}-\pi\left(\text{r}-\Delta\text{L}\right)^{2} \tag{2}\]  
The difference between the two cross-sectional areas \(\Delta S\) is therefore:  
\[\Delta S=S_{2}-S_{1}=\pi\pi^{2}-2\Delta\text{LR}-\pi\left(\text{r}-\Delta \text{L}\right)^{2} \tag{3}\]  
where \(S_{1}\) is the threshing gap cross-section area for the concave adjustment type and \(S_{2}\) is that for the threshing drum adjustment type (mm\({}^{2}\)), \(R\) is the concave grid radius (mm), \(r\) is the threshing drum radius (mm), and \(\Delta\)L is the threshing gap variance (mm).  
The equations for the threshing gap cross section area with concave adjustment type \(S_{1}\) and drum adjustment type \(S_{2}\) are as follows when the threshing gap is to be decreased:  
\[S_{1}=\pi R^{2}-2R\times\Delta\text{L}-\pi\pi^{2} \tag{4}\]  
\[S_{2}=\pi R^{2}-\pi\left(\text{r}+\Delta\text{L}\right)^{2} \tag{5}\]  
The difference between the two cross-section areas is therefore:  
\[\Delta S=S_{2}-S_{1}=\pi\pi^{2}+2R\times\Delta\text{L}-\pi\left(\text{r}+ \Delta\text{L}\right)^{2} \tag{6}\]  
For this combine harvester threshing experiment, the initial concave grid radius \(R=320\) mm, the initial threshing drum radius \(r=300\) mm, and the threshing gap variance \(\Delta\)L = 10 mm, When the threshing gap was increased, the threshing gap cross-section area of the concave adjustment type \(S_{1}\) and threshing drum adjustment type \(S_{2}\) are 45,336 mm\({}^{2}\) and 57,462 mm\({}^{2}\), respectively, \(\Delta S=12\),126 mm\({}^{2}\), the threshing gap cross-section area of the threshing drum adjustment type was 26.7% larger than that of the concave adjustment type. When the threshing gap was decreased, the threshing gap cross-section area of the concave adjustment type \(S_{1}\) and threshing drum adjustment type \(S_{2}\) are 32,536 mm\({}^{2}\) and  
Figure 1: Two threshing gap adjustment types: (**a**) Concave adjustment type; (**b**) Threshing drum adjustment type; (**c**) Cross section of concave adjustment type; (**d**) Cross section of threshing drum adjustment type.  
19,782 mm\({}^{2}\), respectively, \(\Delta S\) = 12,754 mm\({}^{2}\), the threshing gap cross-section area of the threshing drum adjustment type was 39.2% smaller than that of the concave adjustment type [18].