esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Redundant Transform Based on Parseval Frames
## 4 Image Denoising
### Sparsification of Nature Images
This figure shows that the'sparsification error' of the proposed DRTPF model is also converged and insensitive to the initialization matrices. In fact, the loss function of the proposed DRTPF mainly contains two portions: \(\|\mathbf{X}-\mathbf{\Phi}\mathbf{Y}\|_{F}^{2}\) and \(\|\mathbf{Y}-\mathcal{S}_{\lambda}(\mathbf{\Psi}^{T}\mathbf{X})\|_{F}^{2}\). The first portion is the recovery loss (i.e., the loss in temporal domain) and the second portion is the'sparsification error' (i.e., the loss in frequency domain). Our proposed model aims to achieve low error both in temporal domain and frequency domain. To illustrate the behavior of the proposed DRTPF in image representation, we choose six images shown in Figure 4 to train transforms and recover images. The Figure 6 shows the average sparsity curve and the recovery PSNR values with the increase of the sparsity. From the left sub-figure we know that the images are well sparsified along the iterative process. This figure is generated by setting \(\|\mathbf{y}_{i}\|_{0}<5\) and the recovery PSNR is 32.27 dB. For each sample \(\mathbf{x}_{i}\) vectorized by a \(10\times 10\) patch, its corresponding sparse coefficients \(\mathbf{y}_{i}\) is of length 200. It is easy to know that the sparsity rate is lower than 2.5%. Furthermore, less than 5% of the data need to be stored to recover an image with PSNR larger than 32.27 dB. The right sub-figure of Figure 6 shows the average recovery PSNR values with the increase of the sparsity which is a main measurement for the quality of the learnt transform. From the figure we know that in most of the case, our proposed DRTPF can obtain a better image quality in terms of PSNR with lower sparsity than the compared LST [21] method and the classic DCT transform. The ransform for LST [21] method and the classic DCT transform are of size \(64\times 64\). The transform of LST [21]js trained by 4096 \(8\times 8\) samples extracted from every image shown in Figure 6 with the main of the patches removed. The experiment is set as them illustrated in the paper [21]. When the total sparsity of a \(512\times 512\) image is more than 47,000, the recovery results of the proposed DRTPF and the LST [21] are nearly the same. The recovery PSNR at sparsity 47,000 is 37.3 dB.
# Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
## 3 Materials and Methods  
### Materials  
#### 3.1.1 Dataset Description  
The dataset used in this study is specifically curated for the practical application of deep learning models in medical imaging, particularly for cataract detection. Sourced from Kaggle's public repository, this dataset can be accessed at ([[https://www.kaggle.com/datasets/nandanp6/](https://www.kaggle.com/datasets/nandanp6/)]([https://www.kaggle.com/datasets/nandanp6/](https://www.kaggle.com/datasets/nandanp6/)) cataract-image-dataset (10 September 2024)). It contains a total of 609 eye images, comprising 305 normal eye images and 304 cataract-infected eye images. This balanced dataset is well-suited for training neural networks, ensuring reliable classification between normal and cataract-affected eyes. Each image is labeled as either \"normal\" or \"cataract\" and captured under consistent conditions to maintain quality and resolution. The balanced distribution of images minimizes the risk of class imbalance, a common issue in medical datasets, thereby enhancing model training and evaluation. Prior to model training, the images are preprocessed by resizing to a uniform dimension and normalizing pixel values, which helps improve the training efficiency and model convergence. This dataset is representative of a real-world clinical setting, containing variability in patient demographics and eye conditions. The diversity in the data enhances the model's generalization ability across different cases, contributing to the robustness of the trained deep learning framework. By leveraging this dataset, the proposed hybrid deep learning model aims to achieve high accuracy in cataract detection, providing a potential tool for early diagnosis and effective treatment. Figures 1 and 2 provide visual examples of the normal and cataract-infected eye images, respectively.  
Figure 1: Normal eye images.
# On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
## 4 Implementation of Beamforming in Open RAN Networks  
Beamforming has been introduced with numerous benefits to overcome various communication challenges, such as enhancing the accuracy of radio connections, increasing throughput, increasing the number of parallel connections in a given cell area, and saving energy consumption during transmissions. In mmWave transmission, beamforming is particularly beneficial for enhancing the signal-to-noise-to interference ratios (SNIR) through direct targeting of user groups, mainly indoor coverage; moreover, beamforming technology is considered as one of the methods to revoke the interference between UEs singles in high-density environments. Such benefits have meant that the developers, testers, and an tenna designers have had to face the cost of implementation, design complexity, and testing methods that should be carried out, in some cases, in an actual physical environment [27].  
The latency is one of the common challenges in the new applications of 5G with the current O-RAN structure, especially in the ultra-reliable low-latency communication (URLLC) scenarios, where many IoE applications may be positioned [28]. The problem lies in defining whether the front-haul open interface (communicating the O-DU and O-RU) can meet stringent ultra-low latency applications, implying practically zero interferences in the interface.  
We propose implementing beamforming using the channel information method combined with the zero-forcing algorithm to avoid interference challenges. The O-DU can carry out the progress scenario by estimating the channel information between O-RU and users and transferring this information as channel matrix information to O-RU, which the C-Plane carries. This method can be implemented based on Category B in Split option 7-2x, where the precoding is generated in O-RU.
## 3 Validation with Data from International GNSS Service
### Performance of Positioning
What is more, the POP GR, GE and GC solutions are all better than its GPS-only solution, and its GREC fixed solutions enables the highest average positioning accuracy of 1.2, 1.1 and 2.5 cm, while its GPS-only fixed solution is 2.0, 1.8 and 3.3 cm for the east, north and up components, respectively.
# A Digital Framework to Predict the Sunshine Requirements of Landscape Plants
###### Abstract  
Knowledge of the sunshine requirements of landscape plants is important information for the adaptive selection and configuration of plants for urban greening, and is also a basic attribute of plant databases. In the existing studies, the light compensation point (LCP) and light saturation point (LSP) have been commonly used to indicate the shade tolerance for a specific plant; however, these values are difficult to adopt in practice because the landscape architect does not always know what range of solar radiation is the best for maintaining plant health, i.e., normal growth and reproduction. In this paper, to bridge the gap, we present a novel digital framework to predict the sunshine requirements of landscape plants. First, the research introduces the proposed framework, which is composed of a black-box model, solar radiation simulation, and a health standard system for plants. Then, the data fitting between solar radiation and plant growth response is used to obtain the value of solar radiation at different health levels. Finally, we adopt the LI-6400 XT Portable Photosynthetic System (Li-Cor Inc., Lincoln, NE, USA) to verify the stability and accuracy of the digital framework through 15 landscape plant species of a residential area in the city of Wuhan, China, and also compared and analyzed the results of other researchers on the same plant species. The results show that the digital framework can robustly obtain the values of the healthy, sub-healthy, and unhealthy levels for the 15 landscape plant species. The purpose of this study is to provide an efficient forecasting tool for large-scale surveys of plant sunshine requirements. The proposed framework will be beneficial for the adaptive selection and configuration of urban plants and will facilitate the construction of landscape plant databases in future studies.  
digital framework; Solar Analyst; shade tolerance; solar radiation response; landscape plants; geographic information systems (GIS) +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Confirmation Analysis
#### 2.4.2 UPLC-FLD Analysis  
Chromotographic analysis was performed using an Acquity UPLC H-Class Bio System (Waters Chromatography B.V., Etten-Leur, The Netherlands) including a quaternary pump, a thermostated autosampler, a thermostated Acquity UPLC BEH-C18 column (50 \(\times\) 2.1 mm, 1.7 um), and a fluorescence detector. The excitation and emission wavelengths adopted for the fluorescence detection were 370 and 481 nm, respectively. The chromatographic separation was carried out at a flow rate of 0.4 mL min\({}^{-1}\) using a binary gradient based on water (solvent A) and acetonitrile (solvent B). The temperatures of the column and the autosampler were set at 40 and 10 \({}^{\circ}\)C, respectively. The gradient was set as follows: the initial condition was 70% solvent A, solvent A increased to 85% after one minute and then was kept constant up to 5 min. Solvent A was brought to 100% in 3 min, and then the initial conditions were restored followed by a re-equilibration time of 2 min. The total run time was 11 min. The injection volume was 5 \(\mu\)L. Under these conditions, the retention times were 1.9 and 4.0 min for the T-2 and HT-2 toxins, respectively.
## 2 Materials and Methods  
### Materials  
We use the public Sentinel 2 water edges dataset (SWED) [34], which contains multiple coastal geographic locations, comprised of 16 labelled \(10,980\times 10,980\) tiles (the large Sentinel 2 images are known as tiles). The images were selected to have minimal coverage of clouds with no preprocessing apart from bottom-of-atmosphere correction [35]. This dataset is rich in contrast and variety of physical coastal features in each geographical location with the addition of multiple fine-grained physical features such as jetties and bridges. The hypothesis is that we can see the transition of features and the evolution of the manifold with respect to both small features and larger geographical changes. Including water and land also introduces the complexity of the feature transition between both. Most papers within the domain of satellite imagery utilise datasets that are geographical location- or feature-specific as also expressed by the authors of the SWED dataset [34] who also find that water and coastline mapping is restricted to singular regions in the literature as one of their motivations to publish a dataset rich in variety. As the dataset contains a wide range of features, we can also explore the relationship of geographical features and their similarity in regards to how a CNN differentiates them. The atmospheric conditions around the world drastically change the reflection intensity recorded by a satellite for similar features.
## 2 Methodology
### Re-Planned Path Using Potential Fields  
#### 2.3.1 Path Candidates  
From the collision checking process, a possible collision risk point \(\mathbf{p}_{\text{col}}\) along the path is determined. For re-planning on it, one first imagines a tunnel or corridor with a certain radius the UAV has to avoid, centered around \(\mathbf{p}_{\text{col}}\). Around that point, the autonomous UAV lists the intermediate waypoint candidates \(\mathbf{p}_{\text{int},l}\) (\(l=1,2,\cdots,m\)) onto the tunnel surface as follows:  
\[\mathbf{p}_{\text{int},l}=\mathbf{p}_{\text{col}}+\left(\cos\theta_{l}\right) \mathbf{y}+\left(\sin\theta_{l}\right)\mathbf{z}, \tag{11}\]  
where \(\theta_{l}=2\pi l/m+\) perturbation for asymmetry. The origin of the perpendicular plane is \(\mathbf{p}_{\text{col}}\) and the normal vector of the plane is the desired velocity at the origin \(\mathbf{x}=\mathbf{v}_{\text{col}}/\|\mathbf{v}_{\text{col}}\|\). The vertical vector is a unit vector direction to z-axis \(\mathbf{z}=[0,0,1]^{\text{T}}\) in the body frame and the y-axis unit vector follows the right-hand rule \(\mathbf{y}=\mathbf{z}\times\mathbf{x}\), shown in Figure 3. Here,  
Figure 2: Sensor’s range and field-of-view (FOV) on the measured point cloud and the extracted sample points.  
the number of intermediate destinations (i.e., \(m\)) enable variability depending on the availability of computing power.  
Next, the MPs create two distinct path segments with a midpoint candidate: one connects from the current position to the intermediate point and the other from the intermediate point to the final location. To smoothly connect the two path segments at that midpoint, in implementation, the following full states \(\mathbf{s}_{\text{int},l}\) at the \(l\)-th intermediate point are commonly used as each end condition:  
\[\mathbf{s}_{\text{int},l}=\left[\mathbf{p}_{\text{int},l},\mathbf{v}_{\text{ int},l},\mathbf{a}_{\text{int},l}\right]^{\text{T}}, \tag{12}\]  
where \(\mathbf{a}_{\text{int},l}=[0,0,0]^{\text{T}}\) and  
\[\mathbf{v}_{\text{int},l}=\bar{\mathcal{V}}\ \frac{\mathbf{p}_{f}-\mathbf{p}_{ \text{int},l}}{\|\mathbf{p}_{f}-\mathbf{p}_{\text{int},l}\|}. \tag{13}\]  
Among \(m\) route candidates, unsafe intermediate waypoints are rejected by performing the collision checking process again. If all of the path candidates are unsafe, the UAV sets additional route candidates on the surface of another tunnel with a larger radius at the same sample point for bigger detouring and performs the collision checking of new ones.
# Kleene Algebra to Compute Invariant Sets of Dynamical Systems
## 4 Kleene Algebra of Automorphisms
### Automorphisms
We have \[\left(\mathrm{Id}\wedge f\right)(x)=x \Rightarrow \forall i\geq 1,\left(\mathrm{Id}\wedge f\right)^{i}(x)=x\] \[\Rightarrow \left(\mathrm{Id}\wedge f\right)^{\infty}(x)=x\] \[\Leftrightarrow f^{*}(x)=x\] since, from (4), we have \(\left(\mathrm{Id}\wedge f\right)^{\infty}=f^{*}\). Thus, \(\mathrm{Fix}(\mathrm{Id}\wedge f)\subset\mathrm{Fix}(f^{*})\). 2. We now want to prove the inverse inclusion: \(\mathrm{Fix}(f^{*})\subset\mathrm{Fix}(\mathrm{Id}\wedge f)\). We first prove by induction that \[\forall i\geq 0,\left(\mathrm{Id}\wedge f\right)^{i}\geq(\mathrm{Id} \wedge f)^{i+1}.\] (14) From the partial order property of Table 2, we have \[\mathrm{Id}\geq(\mathrm{Id}\wedge f)\Leftrightarrow\mathrm{Id}\wedge(\mathrm{ Id}\wedge f)=\mathrm{Id}\wedge f\] (15) since \(\mathrm{Id}\wedge(\mathrm{Id}\wedge f)=(\mathrm{Id}\wedge\mathrm{Id})\wedge f =\mathrm{Id}\wedge f\), we get that the right-hand side of (15) is true and thus \(\mathrm{Id}\geq\mathrm{Id}\wedge f\) is true. This means that for \(i=0\), (14) is true. Assume now that (14) is true for \(i\), let us check that it is true for \(i+1\). From Proposition 2 (i), we get that \[f\geq g\Rightarrow h\circ f\geq h\circ g.\] With the substitution \(f\to(\mathrm{Id}\wedge f)^{i},g\to(\mathrm{Id}\wedge f)^{i+1},h\to\mathrm{Id}\wedge f\), we get \[(\mathrm{Id}\wedge f)^{i}\geq(\mathrm{Id}\wedge f)^{i+1}\Rightarrow(\mathrm{ Id}\wedge f)\circ(\mathrm{Id}\wedge f)^{i}\geq(\mathrm{Id}\wedge f)\circ( \mathrm{Id}\wedge f)^{i+1}.\] Since (14) is true for \(i\), we get \[(\mathrm{Id}\wedge f)^{i+1}\geq(\mathrm{Id}\wedge f)^{i+2},\] thus, we have proved (14). From (14), we get \[(\mathrm{Id}\wedge f)^{i}\) is a decreasing sequence and thus \[\mathrm{Id}\geq(\mathrm{Id}\wedge f)\geq(\mathrm{Id}\wedge f)^{i}\geq( \mathrm{Id}\wedge f)^{\infty}.\] Take a fixed point \(x\) of \((\mathrm{Id}\wedge f)^{\infty}\), we get \[\left(\mathrm{Id}\wedge f\right)(x)=x \Rightarrow \forall i\geq 1,\left(\mathrm{Id}\wedge f\right)^{i}(x)=x\]0 thus, \((\mathrm{Id}\wedge f)(x)=x\), which implies that \(x\) is also a fixed point of \(\mathrm{Id}\wedge f\). Since \(f\) is a monotonic, so is \(\mathrm{Id}\wedge f\) and \(f^{*}\). From the Knaster-Tarski theorem, we deduce that \(\mathrm{Fix}(f^{*})\) is a complete sublattice of \(\mathcal{L}\).
## 3 The Joint Relay Selection and Power Control Optimization Method via Stackelberg Game
### Existence of Stackelberg Equilibrium  
In the proposed scenario, we consider that the user adopts mixed strategy to fool the jammer due to the randomness of strategy, which can increase anti-jamming performance effectively. Let q denotes the user's mixed strategy, i.e., the probability distribution of the user's optional relay selections and power strategies. Motivated by [4,8], we define the Stackelberg equilibrium (SE) and give the proof of existence of SE in the following.  
**Definition 1**.: _If no player can improve the utility by deviating its optimal strategy unilaterally, the policy profile \((q^{*},J^{*})\) constitutes the SE, which satisfies the following conditions:_  
\[U(\mathrm{q}^{*},J^{*})\geq U(\mathrm{q},J^{*}), \tag{10}\]  
\[V(\mathrm{q}^{*},J^{*})\geq V(\mathrm{q}^{*},J). \tag{11}\]  
**Lemma 1**.: _There exists a user's stationary strategy and a smart jammer's stationary strategy, which constitute a SE [8]._  
Proof.: Inspired by [44,45], every finite strategy game has a mixed strategy equilibrium [46], which means there exists a SE in the formulated game.  
For the jammer, it aims to maximize the utility and makes strategy based on best-response:  
\[J^{*}=\operatorname*{arg\,max}_{J}V(\mathrm{q},J). \tag{12}\]Having known the jammer's optimal strategy, the user's optimal strategy can be obtained as follows:  
\[\mathrm{q}^{*}=\underset{\mathrm{q}}{\arg\max}\;U(\mathrm{q},J^{*}(\mathrm{q})). \tag{13}\]  
Based on the analysis above, the policy profile \((\mathrm{q}^{*},J^{*})\) constitutes the SE.
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 5 Results
### Workload  
The actual throughput for each AP is displayed in Figure 10 to demonstrate the efficacy of the load balancing methods. It is clear that the MPD- and RSSI-based systems display greater load variations amongst the APs and have poorer normalized throughput values. Due to the uneven load distribution among the APs, the same is true for DASA. The CMAS system displays better throughputs, but the loading disparity across the APs is also quite large. Due to the centralized SDN design, the proposed ASLB exhibits superior load  
Figure 9: Average Handoff Performance.  
distribution across all APs. Additionally, the ASLB network performance is enhanced by fewer computations. When compared to CMAS, DASA, MPD and RSSI schemes, ASLB optimizes the workload by 35%, 44%, 57% and 75%, respectively.
# Three-Dimensional Elastodynamic Analysis Employing Partially Discontinuous Boundary Elements
## 2 Time-Domain Boundary Integral Equation of Elastodynamics and Its Numerical Implementation
### Numerical Integration Scheme  
For convenience, we take the numerical integration of an element in Equation (12) as an example. The integral of the \(l\)-th element for the source point \(\mathbf{p}\) is as follows:  
\[f_{ijk}=\int\limits_{S_{l}}F_{ij}^{*}(\mathbf{p},\mathbf{q})Q_{k}(\mathbf{q}) dS_{l}(\mathbf{q}) \tag{13}\]  
in which \(F_{ij}^{*}(\mathbf{p},\mathbf{q})=U_{ij}^{Mm}\) or \(P_{ij}^{Mm}\), \(Q_{k}(\mathbf{q})\) represents the shape function of the partially discontinuous element. Equation (13) is usually numerically calculated by the Gauss-Legendre quadrature method. Before applying the Gauss-Legendre quadrature method to Equation (13), the coordinate system should be transformed from the real 3D space \(xyz\) to the regular space \(\xi\eta w\), as shown in Figure 2.  
Figure 1: Sketch map of the partially discontinuous element. Figure 2: Transformation of the coordinate system. The transformation formula is formulated as follows:  
\[ \begin{array}{l}f_{ijk}=\int\limits_{S_{l}}F_{ij}^{*}(\mathbf{p},\mathbf{q})Q_{ k}(\mathbf{q})dS_{l}(\mathbf{q})\\ =\int\limits_{S_{l}}F_{ij}^{*}(\mathbf{p},\mathbf{q})Q_{k}(\mathbf{q})|\mathbf{ J}(\mathbf{q})|_{local}du(\mathbf{q})dv(\mathbf{q})\\ =\int\limits_{-1}^{1}\int\limits_{-1}^{1}F_{ij}^{*}(\mathbf{p},\mathbf{q})Q_{ k}(\mathbf{q})|\mathbf{J}(\mathbf{q})|_{local}|\mathbf{J}(\mathbf{q})|_{element}d \xi(\mathbf{q})d\eta(\mathbf{q})\end{array}  \tag{14}\]  
where \(|\mathbf{J}(\mathbf{q})|_{local}\) is the determinant of the Jacobian matrix of the transformation from the _xyz_ space to the _uvw_ space, and \(|\mathbf{J}(\mathbf{q})|_{element}\) is the determinant of the Jacobian matrix of the transformation from the _uvw_ space to the \(\xi\eta w\) space. The calculation method of \(|\mathbf{J}(\mathbf{q})|_{local}\) and \(|\mathbf{J}(\mathbf{q})|_{element}\) in the partially discontinuous element is the same as that in the continuous element [1]. Since the interpolation points in partially discontinuous elements are the element nodes after inward shifting, the shape function \(Q_{k}(\mathbf{q})\) is different from the standard shape function \(N_{k}(\mathbf{q})\). As shown in Figure 3, \(d_{k}\) is the position of node \(k\) after moving inside the element.
## 3 Results  
Table 1 shows the means and standard deviations of the angulations recorded by the algorithm and the video recording. In relation to the agreement, all the angulations showed a difference of less than 4% between both instruments (Table 1 and Figure 6). However, the algorithm overestimated the angle in relation to the video, showing significant differences (\(p>0.001\)), with a magnitude between small and medium, in four of the eight angulations. The 90R (3.3 \(\pm\) 0.3\({}^{\circ}\); [PERSON]'s d = 0.94) and 135R (5.7 \(\pm\) 0.5\({}^{\circ}\); [PERSON]'s d = 1.0) angulations were the angulations that showed the greatest difference between both instruments. In the rest of the angulations, the differences between both instruments were trivial. The algorithm showed good reproducibility in all angulations, except at 45L where reproducibility was poor (%CV = 11.7).  
The one-way ANOVA analysis, as well as the post hoc Games-Howell test, only showed significant differences in the angle registered by the algorithm, when the test was performed at 13 km/h or 18 km/h at 45L, where the angle registered was greater than the speed of 18 km/h (mean bias = \(-2.6^{\circ}\); [PERSON]'s d = \(-0.57\)) (Table 2). Precisely at this  
angulation, the reproducibility of the algorithm was poor at both speeds (CV = 11.0% at 13 km/h; 11.9% at 18 km/h). When the CoDs were performed at 45R and at a speed of 18 km/h, the algorithm showed the highest CV of all the combinations (CV = 16.9%).  
Table 3 shows the mean differences and the effect size between the three devices at each angle. The results showed a high level of agreement when the angles recorded by each of the three IMMUs were compared, at all angulations, showing small differences in [PERSON]'s d, only in two combinations of the 24 possible (IMMU1 vs. IMMU2 = [PERSON]'s d = \(-\)0.44; IMMU1 vs. IMMU2 = [PERSON]'s d = \(-\)0.55). At 45L and 45R the three IMMUS presented a poor coefficient of variation, but the same did not occur in the rest of the angulations, where all the IMMUS showed good reproducibility. The three IMMUs showed very similar differences based on the angle obtained by the video at all angulations. The 135R angulation was the direction that showed the highest percentage difference in the three IMMUs in relation to the video (mean bias = 3.9 \(\pm\) 0.45).