esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### Trajectory Optimization  
Finding the optimal location for the UAV is the goal in order to reduce the amount of power consumed for transmissions overall, i.e.,  
\[\begin{split}&\underset{x,y,z}{\min}P_{ttp}+P_{tp}\\ &\text{subject to}\sum\limits_{q=1}^{2}\frac{\iint P_{q}^{\min}( x,y)dxdy}{P_{q}}=A\end{split} \tag{7}\]  
where \(P_{q}^{\min}\) indicates the lowest transmission power (\(q\in 1\), \(2\)) that the UAV should arrange for communication with the users at (\(x,y\)), and \(A\) is a constant. \(P_{ttp}\) is the total transmission power of the UAV required to fulfill the data demand of the users. Without sacrificing generality, we assume that the UAV's maximum transmission power is enough to fulfill the user demand for data. \(P_{tp}\) denotes the total power of the UAV. \(P_{q}^{\min}\) can be shown as  
\[P_{q}^{\min}(x,y)=B_{q}d_{q}^{2}(x,y)P_{L}(x,y)(2^{\kappa/B_{q}}-1). \tag{8}\]  
\(P_{ttp}\) can be calculated as  
\[P_{ttp}=\sum\limits_{q=1}^{2}\iint P_{q}^{\min}(x,y)dxdy. \tag{9}\]  
\(P_{tp}\) can be provided by  
\[P_{tp}=\sum\limits_{q=1}^{2}\sqrt{\left(x-x_{q}\right)^{2}+\left(y-y_{q}\right) ^{2}+z^{2}}. \tag{10}\]This equation is focused on a high-altitude UAV, where \(z^{2}>(x-x_{q})^{2}+(y-y_{q})^{2}\). Equation (7) is clearly a convex function relating to \(x\) and \(y\)[39]. Initial partial derivatives are set to zero, and the optimal location of the UAV can be provided as  
\[\begin{split} x^{*}&=\frac{2}{q-1}\frac{\iint xg_{q} d_{q}^{2}(x,y)P_{L}(x,y)(2^{x/q}-1)dxdy}{\iint(2^{x/q}-1)dxdy},\\ y^{*}&=\frac{\iint yg_{q}d_{q}^{2}(x,y)P_{L}(x,y)(2^{x/q }-1)dxdy}{\iint(2^{x/q}-1)dxdy}.\end{split} \tag{11}\]  
Even though the function of objective (7) is convex with regard to \(x\) and \(y\), it is challenging to find a closed-form answer that minimizes a UAV's transmission and mobility power. However, it is easy to discover the numerical solution to (11) using Wolfram Mathematica.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Building Reconstruction  
With the extracted contours of representative layers (see Figure 19a), we assembled these contours to generate 3D building models. However, directly extruding contours to create the building model causes a jagged, unaligned mesh as shown in Figure 19. Instead, we registered each contour layer-by-layer from bottom to top through averaging the location of edges with the predefined distance \(d_{m}\) (e.g., 0.05 m) and angle \(\theta_{m}\) (e.g., 5\({}^{\circ}\)). Figure 19 contains the final reconstructed models overlapping with the original point clouds (Figure 19d,f) and labeled vertical point clouds (Figure 19c,e). For the sake of clarity, we truncated the ceiling to show the performance of the interior reconstruction in Figure 19c,e.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2 Route Setup
##### 9.1.2.1 Spectrum Sensing  
Spectrum sensing is the process whereby the nodes obtain spectral awareness of their environment in terms of free channels and the behavior of the PU on those channels. It is important to note that sensing is not restricted to discovering spectrum opportunities. Sensing is also used to detect the presence of incumbent users and to vacate the spectrum to avoid interfering with PU communication. Because the nodes we are considering have only single transceivers, the activity of both sending and sensing becomes very challenging, unlike in the case of multiple transceivers wherein the duties can be shared between the various radio interfaces [50]. However, the fact that the power of sensor transmission rarely affects the transmission of a PU downplays the issue somewhat. In the case of sensing for spectrum awareness, we can base the route setup on full spectrum awareness or local spectrum awareness scenarios.  
Figure 9: Illustration of a typical routing scenario in a CRSN.
## 4 PPP Accuracy Evaluation  
To investigate the positioning performance of multi-GNSS in the Asia-Pacific region, 10 MGEX stations were selected to conduct static and kinematic PPP experiments. For the data processing strategies, the sampling interval of observation data is 30 s, and the period is from DOY 283 to 289 in 2021. At present, several MGEX analysis centers can provide precise satellite orbit and clock offset products for the five systems, and the consistency between WUM orbit and clock offset products and other MGEX analysis centers is about 3-10 cm and 0.1-0.3 ns, respectively, showing better consistency with the products from other analysis centers. The positioning performance in the Asia-Pacific region can be reflected using the WUM orbit and clock offset products [28; 29]. Therefore, the multi-GNSS final satellite orbit, clock offset, and earth rotation parameter (ERP) products generated from Wuhan University are applied in this study [30]. The satellite antenna phase center variation (PCV) and phase center deviation (PCO) values are used from igs14.atx [31]. The dual-frequency ionosphere-free is employed to eliminate the effect of the first-order ionosphere and the higher-order ionosphere is ignored [32]. The zenith hydrostatic delay of the troposphere is corrected using the Saastamoinen model [33], while the zenith wet delay is estimated as the parameter. The carrier phase ambiguities are estimated as float solutions [34]. The station coordinates of the static PPP are estimated as a constant, while it is estimated as white noise in the kinematic model. The receiver clock offset is estimated as white noise. In addition, the relativistic effects, satellite antenna phase wind-up, and station tides are weakened or eliminated using existing models [35; 36; 37]. To compare the positioning performance differences between single-system and multi-GNSS combinations in the Asia-Pacific region, six mode combinations in the static and kinematic PPP experiments were conducted, which is: GPS(G), GPS/QZSS combination (GJ), BDS (C), BDS/QZSS combination (CJ), GLONASS (R), and Galileo (E), respectively. The convergence time and positioning accuracy are used to evaluate the positioning performance. The convergence time is that the current epoch with 20 consecutive epochs is better than 10 cm, and the positioning accuracy is the RMS of the positioning error after convergence [38]. It is noted that the positioning error is the positioning difference between the PPP solutions and IGS weekly solution [39].
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 1 Introduction  
Lossless image compression is addressed as one of the most challenging tasks among the research community. Lossless coding methods are employed to retain the quality of an image in visual quality critical applications. Among the fields like remote sensing, business documentation, digital radiography, medical industry and satellite imagery, work is being done to achieve significant error-free compression [1; 2; 3].  
Lossless image compression is also utilized in techniques such as information hiding and RASTER map compression [4; 5]. Satellite and medical imagery are often high-resolution and at times require the original data to be protected from any loss. Different types of images such as grayscale, multi-spectral images, hyperspectral images, electrocardiogram (EEG), and Magnetic Resonance Imaging (MRI) have been processed to achieve lossless compression [6; 7; 8; 9; 10; 11; 12; 13].  
Lossless compression is majorly achieved due to the redundancy of intensity levels in the image thus making it a tedious task to bring down the size without compromising its quality [14; 15; 16]. By transforming the image data earlier to compression, higher image data redundancy can be achieved which in turn leads to higher compression [14]. Highly correlated image pixels regions offer greater information redundancy. This inter-pixel redundancy is utilized for effective lossless compression.  
This research work is oriented around the enhancement of lossless compression of colour images based on the Bi-level Burrows-Wheeler Compression Algorithm (BBWCA) utilizing colour space transformations. The BBWCA developed by [PERSON] et al. in [PERSON] et al. (2018) utilized Reversible Colour Transform (RCT) focusing on its extensive possibilities of applications given in [PERSON] et al. (2018). In the proposed technique, various colour space transformations were analyzed for their increase in inter-pixel redundancy leading to higher compression.  
Section 2 presents the various techniques employed for lossless compression with a major focus on the literature related to the Burrows-Wheeler Compression Algorithm (BWCA). RCTs are presented in Section 3. Experimental results in comparison with benchmark schemes and different image types are given in Section 4. Conclusions, contributions and future directions for the study are presented in Section 5.
## 2 Literature Review
### Production Management  
Production management plays an increasingly important role in modern industry. A key aspect of production management is determining the optimal number of machines and operators needed to meet the total demand for various products in a [27]. According to [28], researchers have long sought to solve the challenge of determining the optimal number of products to produce using a shared resource (machine) with limited capacity.  
A central model in this area is the economic production quantity (EPQ) model, introduced by [29]. This model uses one machine to determine the optimal production quantity for a single product. Over time, the EPQ model has been adapted to handle more complex situations. For instance, ref. [30] extended the EPQ model to account for fluctuating demand over time rather than assuming a constant demand. Refs. [31; 32] presented multi-product economic order quantity (EOQ) inventory models for reusable items, incorporating real-world constraints such as budget, warehouse space, and holding costs. These studies also provide a comprehensive review of extensions to the EOQ and EPQ models.  
A particularly important extension is the economic lot scheduling problem (ELSP), which addresses the scheduling of multiple product types on a single machine while minimizing total costs, including setup and inventory holding costs [33]. This problem highlights trade-offs between production sequencing and inventory management and emphasizes the need for intelligent scheduling strategies [34; 35]. Several studies have extended EPQ to tackle ELSP, focusing on lot size determination and production scheduling to reduce costs and avoid overlapping [28]. However, as noted by [PERSON] et al. (2017) [28], many of these models lack explicit consideration of scheduling complexities, such as sequence-dependent setups or workforce allocation.  
To address these complexities, recent works have incorporated labor constraints and physical layout considerations. For example, ref. [36] proposed a mixed-integer linear programming (MILP) model for lot-sizing and scheduling using reconfigurable machine tools (RMTs), accounting for manpower limitations. Ref. [37], as well as [38], showed that limited labor availability could significantly affect scheduling efficiency. Ref. [3] further developed a scheduling model that integrates the physical distance between workers on parallel machines, aiming to maximize workforce utility while maintaining safety and productivity.  
Overall, ELSP remains a foundational framework in production management, continuously evolving to reflect the real-world constraints of modern manufacturing systems, including multi-product environments, human resource limitations, and dynamic production demands.
## 3 Methodology
### Measures
#### 3.1.4. Quality of Experience (QE)  
Quality of experience is the construct concerning how well a system provides functions for a user to complete particular tasks [44]. In [PERSON] and [PERSON]'s study, they suggested 10 items to measure the user's perceived value of quality of experience, such as the relevance of information, search engine, available information, and so on. Since this study focused on English learning on the Internet-based resources, 6 relevant items were chosen to evaluate the quality of experience. In addition, the items were modified for student's learning English scenarios. For instance, there was an item like \"I can have the relevant English learning information through Internet-based resources\". In addition, designed items stated as \"I have a sufficient amount of English learning information available on Internet-based resources\", \"I can easily use the browser to learn English\", and \"I have a reliable Internet connection\".
# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 2 Materials and Methods
### Increase the Coverage of WSNs
* Determine the WSN cluster heads  
The improved whale algorithm will be used for clustering, assuming that each whale contains the position of m cluster heads (m is the optimal number of cluster heads). The structure of each whale is as follows:  
\[x_{i}=\{a_{i1},a_{i2},a_{i3},\cdots,a_{im}\}\]  
Among them, \(a_{ij}\) refers to the \(j\)-th cluster head vector of the \(i\)-th whale in cluster \(c_{ij}\). Each whale represents a clustering method, and the entire whale population represents multiple clustering methods. The improved whale algorithm takes the \(fitness\) function as the fitness function, takes its minimum value as the optimal value, and finds the position of the whale when \(fitness\) takes the minimum value. At this time, the position of the \(m\) cluster heads in the optimal whale is the optimal clustering method. The specific steps for WSN cluster head selection based on the improved whale algorithm are as follows:  
* Initialize the whale population; the position of the \(i\)-th whale is as follows: \[x_{i}=\{a_{i1},a_{i2},a_{i3},\cdots,a_{im}\}\] where m is the optimal number of cluster heads. Set the termination condition for the algorithm;
* Calculate the fitness value of each whale;
* Record the optimal individual and location;
* If the termination condition is met, proceed to step 6; otherwise, proceed to step 5;
* Update \(a\) and \(p\) based on the number of iterations;
* Update the whale position and proceed to step 2;
* Output the optimal solution and obtain the optimal cluster head. Generate 200 two-dimensional data samples in Matlab2023a, randomly distributed in a square of 200 m \(\times\) 200 m.  
Within the scope, as shown in Figure 11, conduct ten control experiments on ten different sets of data, and compare them with the traditional K-means performance. Compare the clustering performance of the LEACH-C and EEUC algorithms to verify the superiority of the improved K-means algorithm in this paper. The black \"*\" in the figures below represents ordinary nodes, and the red \"*\" represents cluster heads. Figures 12-15, respectively, show the traditional K-means, LEACH-C, EEUC, and improved algorithms. \[X(t+1)=D^{\prime}\times e^{bl}\times\cos(2\pi l)+X^{*}(t)\]0 Scene diagram. \[X(t+1)=D^{\prime}\times e^{bl}\times\cos(2\pi l)+X^{*}(t)\]1 K-means clustering. \[X(t+1)=D^{\prime}\times e^{bl}\times\cos(2\pi l)+X^{*}(t)\]2 EEUC clustering. \[X(t+1)=D^{\prime}\times e^{bl}\times\cos(2\pi l)+X^{*}(t)\]3 LEACH-C clustering. The clustering effect of the K-means algorithm in experimental scenarios shows that the improved K-means algorithm has a better clustering effect than the traditional K-means, LEACH-C, and EEUC algorithms. This conclusion also demonstrates the universality of the improved K-means algorithm in optimizing the clustering effect of WSN networks.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 3 Experiments
### Results
#### 3.4.2 Results for the Botswana Dataset  
To evaluate the reconstruction effect of our model on the Botswana dataset, we chose to compare it with seven comparison models, as shown in Table 3, respectively. The visual analysis of the reconstructed images from the Botswana dataset revealed that, among the machine learning algorithms, the IHS algorithm was the best; the PCA algorithm was the second best; and the GS algorithm reconstructed the most distorted images, losing a large amount of spectral information. Among the deep learning algorithms, the MSDCNN algorithm reconstructs images with the lowest spatial resolution, and the PNN and SRPNN reconstructed images show local detail distortion. Due to the Swin transformer's global self-attentive process, the proposed Swin-MRDB method maintains both local and global features of the reconstructed images, and no local distortion is produced.  
The objective evaluation parameters for the fusion results of the Botswana dataset are shown in Table 4. With essentially the same results as with the Pavia dataset, the deep learning approach still has better reconstructions, but it is worth noting that GS maintains high CC values on both the Pavia and Botswana datasets, demonstrating that the images reconstructed using the GS algorithm have a high linear correlation with the reference image. In terms of deep learning algorithms, MSDCNN fared the poorest; PNN, SRPPNN, and TFNET achieved similar reconstruction results, while the Swin-MRDB method proposed in this paper outperformed the other algorithms in all evaluation metrics. The most significant improvement in the evaluation metric is SAM, where the results of other deep learning models are distributed between 1.72 and 2.35, while our proposed model achieves a final result of 1.483, which is a 13.7% improvement compared to the best-performing model. Next was RMSE, which received a 12.4% boost, followed by ERGAS, which received an 8.9% boost. The metric with the smallest improvement was CC, which gained 1.6%, 1.5%, 3.8%, and 1.7% over PNN, TFNET, MSDCNN, and SRPPNN, respectively.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Hnfib-mody (mody 5)  
MODY 5 is a rare type of the disease caused by hepatocyte nuclear factor 1B (_HNF1B_) gene mutations [40]. _HNF1B_ is a transcription factor of the homeodomain-containing transcription factor superfamily and is found in a wide range of tissues, such as liver, intestine, stomach, lung, and pancreas [34; 40; 64]. It is involved in many developmental processes, including the development of the nephron and the embryonic pancreas [65; 66]. Patients with _HNF1B_-MODY often have significant histological abnormalities, such as renal cysts and diabetes (RCAD) syndrome. _HNF1B_-MODY has variable multisystemic phenotypes with a wide range of pancreatic and extrapolate clinical manifestations [34; 67; 68]. Severe kidney disease results from mutations in this gene, which can appear before glucose tolerance is impaired [69]. MODY 5 can cause complications like vaginal aplasia, a rudimentary uterus, hyperglycemia, gout, and a reduction in infant birth weight (900 g) [66; 70; 71]. _HNF1B_-MODY patients exhibit hepatic insulin resistance [30], sulfonylurea therapy is inefficient, and early insulin administration may be necessary [72].
# Peroxidase-Like Metal-Based Nanozymes: Synthesis, Catalytic Properties, and Analytical Application
## 3 Results and Discussion  
### Obtaining and Characterizing the Best Peroxidase-Like Nanozymes  
First, different NPs were synthesized and screened for their reaction to H\({}_{2}\)O\({}_{2}\) in solution. PO-like activities were measured with \(o\)-dianisidine as the chromogenic substrate. The experiments demonstrated that the synthesized NPs, especially Au/Cu, Ag/Ce\({}^{S}\), Fe/Ce\({}^{S}\), and Zn/Ce, possessed significant PO-like activities in solution (Table 2). These results are in agreement with the data of other researchers [9; 10].  
The most catalytically active NZs were characterized by SEM coupled with X-ray microanalysis (SEM-XRM). SEM provided information on the size, distribution, and shape of the tested sample. Figure S1 presents the overall morphology of the formed hybrid particles. The XRM images of the synthesized NZs showed the characteristic peaks for metals of the composites.  
The obtained Pd/Ce NPs have the shape of a sphere with a diameter of approximately 200 nm. The size of synthesized Pt/Cu NPs is near 500 nm. XRM data for Pt/Cu NPs proved the presence of Pt\({}^{0}\) and Cu\({}^{0}\) forms, with characteristic peaks K\(\alpha\) at 2.4 keV and 1.4 keV, respectively (Figure S1). The size of synthesized Pd/Cu NPs is in the range of  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Nanozyme** & **Synthesis Method** & **Specific Activity, Units /mg** \\ \hline
1 & Fe/Ce \({}^{S}\) & Na\({}_{2}\)S & 1.86 \(\pm\) 0.16 \\
2 & Cu/Ce \({}^{S}\) & NaBH\({}_{4}\)/Na\({}_{2}\)S & 1.27 \(\pm\) 0.11 \\
3 & Fe/Mn & Na\({}_{2}\)S & 0.12 \(\pm\) 0.01 \\
4 & Ag/Ce \({}^{S}\) & Na\({}_{2}\)S & 0.05 \(\pm\) 0.004 \\
5 & Pt/Cu & Ascorbic acid & 1.84 \(\pm\) 0.14 \\
6 & Fe/Ce & NH\({}_{4}\)OH & 3.06 \(\pm\) 0.26 \\
7 & Zn/Ce & NH\({}_{4}\)OH & 2.28 \(\pm\) 0.21 \\
8 & Cu/Ce & NH\({}_{4}\)OH & 0.90 \(\pm\) 0.007 \\
9 & Pd/Ce & NaBH\({}_{4}\) & 0.72 \(\pm\) 0.06 \\
10 & Pd/Cu & NaBH\({}_{4}\) & 0.4 \(\pm\) 0.03 \\
11 & Ag/Ce & NaBH\({}_{4}\) & 2.50 \(\pm\) 0.22 \\
12 & Au/Cu & NaBH\({}_{4}\) & 4.22 \(\pm\) 0.34 \\
13 & Au & NaBH\({}_{4}\) & 2.62 \(\pm\) 0.19 \\
14 & Ag/Cu & NaBH\({}_{4}\) & 0.24 \(\pm\) 0.02 \\
15 & Pt/Ag & NaBH\({}_{4}\) & 1.40 \(\pm\) 0.11 \\
16 & Ag/Zn & NaBH\({}_{4}\) & 0.79 \(\pm\) 0.06 \\ \hline \end{tabular} \({}^{S}\)–NPs obtained by a sulfide method.  
\end{table}
Table 2: Peroxidase (PO)-like activities of the synthesized nanozymes NZs in solution.  
500 nm-1 \(\upmu\)M. The sole peak at 1.8 keV demonstrates the full covering of CuNPs with the PdNPs.  
SEM imaging and XRM data proved the formation of needlelike hybrid Fe/Ce and Au/Cu micro particles with the size ranging from 10 to 20 \(\upmu\)M (Figure S1). The XRM image of Au/Cu NPs shows the K\(\alpha\) peaks of Cu\({}^{0}\) and Au\({}^{0}\), confirming the presence of both CuNPs and AuNPs. It was shown that the Fe/Ce and Au/Cu NPs tested look like irregular shaped clusters of microfibrillar structures. The best of catalytically active NZs, namely, Cu/CeS, Au/Cu, Ag/Ce, Fe/Ce, Ag/Zn, Pd/Ce, and Pt/Cu NPs were chosen for further investigation.