esa-sceva/satcom-chunk-collection huggingface dataset:
# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 4 Experimental Setup
### Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Descriptions**} & \multicolumn{4}{c}{**Retinal Image Registration Applications**} \\ \cline{2-4}  & **Super-Resolution** & **Image Mosaicking** & **Longitudinal Study** \\ \hline Total images & 71 & 49 & 14 \\ Image size (pixels) & & 2912 \(\times\) 2912 & \\ Total patients & & 39 & \\ Age (Years) & & 19–67 & \\ Pathological cases & & Diabetic retinopathy & \\ Field of view & & 45\({}^{\circ}\) & \\ Year & & 2006 to 2015 & \\ Ground truth images & & 10 corresponding points for each image pair & \\ Anatomical differences 1  & No & No & Yes \\ Scale & \(\approx\)1 & \(\approx\)1 & \(\approx\)1 \\ Overlapping area (\%) & 86–100 & 17–89 & 95–100 \\ Rotation (\({}^{\circ}\)) & 0\({}^{\circ}\)\(-\)12\({}^{\circ}\) & 6\({}^{\circ}\)\(-\)52\({}^{\circ}\) & 1\({}^{\circ}\)\(-\)4\({}^{\circ}\) \\ \hline \hline \end{tabular} \({}^{1}\) Anatomical differences observed between fixed and moving images.  
\end{table} 
Table 3: Descriptions of FIRE dataset for evaluating registration performance. the density of the spatial sampling, which can resolve the blurred edges of the retinal vessels caused by patient movements or improper imaging setup. The image mosaicking application aligns multiple fundus images to generate an image with a wider view of the retina. The wide view image of the retina can be used to view the full extent of the retinal disease in one big picture during diagnosis [50,51] and during the preparation of eye laser treatment for diabetic retinopathy [52]. However, registering image pairs from the image mosaicking application can be challenging as it involves a combination of small overlapping areas and large rotation. The longitudinal study application combines multiple fundus images that are acquired at different screening sessions. Therefore, the anatomical changes due to progression or remission of retinopathy such as increased vessel tortuosity, microaneurysms and cotton-wool spots can be observed between fixed and moving images. The longitudinal study application is essential in monitoring the progression of retinal diseases, such as glaucoma and age-related macular degeneration, which usually undergoes a long degeneration process [53].
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 4 Methodology
### Minimum Spanning Tree Algorithm Based on Fuzzy Cut-Set Optimization  
Given an undirected network \(G(N,E,W)\), where \(N\) denotes the set of vertices, \(E\) is the set of edges, and \(W\) is the weight function. The path \(L\langle p,q\rangle\in E^{\prime}\) is a tree arc between two vertices \(p\) and \(q\) in \(G\). Let \(L\) be a non-tree arc where \(L\langle p,q\rangle\
otin E^{\prime}\). Let \(E^{\prime}\subseteq E\), if \(T(N,E^{\prime})\) is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Residual Energy** & **Resource Utilization** & **Fitness Value** \\ (\(\widetilde{A}_{i}\)) & (\(\widetilde{B}_{i}\)) & (\(\widetilde{R}_{i}\)) \\ \hline Very low & Low & Low fitness \\ Very low & Medium & No fitness \\ Very low & High & No fitness \\ Very low & Very high & No fitness \\ Low & Low & Low fitness \\ Low & Medium & No fitness \\ Low & High & No fitness \\ Low & Very high & No fitness \\ Medium & Medium & Low fitness \\ Medium & High & No fitness \\ Medium & Very high & No fitness \\ High & Medium & Fitness \\ High & Medium & Fitness \\ Very high & High & Low fitness \\ Very high & High & Low fitness \\ Very high & Very high & No fitness \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fuzziness rules. connected and has no loops. Then, \(T(N,E^{\prime})\) is a spanning tree of \(G\). Assuming that each edge \(e\in E\) has a weight \(W(e)\), thus, the sum weight of \(T\) is shown in (8). \[W(T)=\sum_{e\in E^{\prime}}W(e) \tag{8}\]  
Let \(T^{*}\) be the spanning tree of \(G\). If \(W(T^{*})\leq W(T)\), then \(T^{*}\) is the minimum spanning tree (MST) of \(G\). If \(L\langle p,q\rangle\) is a tree arc, removing it from \(T\) divides the set \(N\) into two parts \(X\) and \(\overline{X}=N-X\). \(\left(X,\overline{X}\right)=\left\{(i,j)\in E|i\in X,j\in\overline{X}\right\}\) is the cut set of \(G\). If the cut-set is minimum, then \(T(N,E^{\prime})\) is the MST of \(G(N,E)\). The MST algorithm based on fuzzy cut-set optimization (FCO-MST) continuously expands a subtree \(T=(S^{*},E^{*})\) until all vertices of \(G(N,E)\) are included, where \(S*=N\). In FCO-MST, the path weights between CHs are calculated using triangular fuzzy inference, and the arcs with the most negligible weights in the cut set are sequentially added to form the MST of \(T\). The FCO-MST pseudo code is shown as follows (Algorithm 1).
## 3 Experiment Setup and Evaluation
### Performance Metrics  
To evaluate and validate the algorithms, we used the following metrics: precision (\(P\)), recall (\(R\)), \(F_{1}\)-score, and average precision (\(AP\)). These metrics are commonly used in research studies [39; 40; 41] to analyze the performance of models. The precision, recall, and \(F_{1}\)-score are defined by Equations (2)-(4).  
\[P=TP/(TP+FP), \tag{2}\]  
\[R=TP/(TP+FN), \tag{3}\]  
\[F_{1}=2\times(P\times R)/(P+R), \tag{4}\]  
where \(TP\), \(FP\), and \(FN\) represent the true positives, false positives, and false negatives, respectively. \(AP\) is the area under the precision-recall curve. This measure describes the overall performance of the model across the different confidence thresholds. The \(AP\) is defined in Equation (5).  
\[AP=\sum_{n}\bigl{(}(r_{n+1}-r_{n})\cdot p_{\text{interp}}(r_{n+1})\bigr{)}, \tag{5}\]  
where \(p_{\text{interp}}(r_{n+1})=\max_{r\geq r_{n+1}}p(p)\). The precision value at a specific recall value is denoted by \(p(\tilde{r})\).The \(AP\) values ranged between 0 and 1 based on a specific Intersection over Union (IoU). In this case, IoU was 0.5.
## 3 Results
### Analysis of GPP Estimation Using Multisource SIF Satellite Products
#### 3.2.4 Analysis of GPP/SIF Values under Different Weather Conditions  
From Figure 6, it can be seen that there are differences in GPP/SIF values in the study area under different weather conditions and ecosystems. In the desert vegetation area (Figure 6a), SIF_OCO2_005 and GOSIF exhibited severe overestimation under clear, overcast, and cloudy conditions, while CSIF showed greater overestimation under clear conditions and greater underestimation under overcast and cloudy conditions. RTSIF fluctuated around the 1:1 line, indicating reasonably accurate values. In the pasture grassland area (Figure 6b), SIF_OCO2_005, GOSIF, and CSIF all demonstrated significant overestimation under clear, overcast, and cloudy conditions, whereas RTSIF remained relatively close to the 1:1 line. In the cultivated and farmland area (Figure 6c), SIF_OCO2_005 generally exhibited significant underestimation under clear, overcast, and cloudy conditions, while GOSIF tended to overestimate, and CSIF showed a balance between overestimation and underestimation across different weather conditions. RTSIF maintained fluctuations around the 1:1 line.  
These results indicate that the GPP/SIF values of the four SIF satellite products were overestimated or underestimated differently under different ecosystem and weather conditions in arid regions. SIF_OCO2_005 and CSIF showed varying degrees of severe overestimation and underestimation, whereas GOSIF demonstrated significant overestimation, and RTSIF maintained relatively reasonable values. The GPP/SIF values ranked overall as follows: RTSIF > CSIF > SIF_OCO2_005 > GOSIF. The RTSIF satellite products exhibited the highest overall rationality in GPP/SIF values.  
Figure 6: Differences in GPP/SIF values under different weather conditions. (**a**) Kelameili Station, desert vegetation area. (**b**) Ulastai Station, pasture and grassland area. (**c**) Ulan Usu Station, cultivate land and farmland area.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 1 Introduction
Even though developed methods based on derivatives have improved the demodulation efficiency, noisy perturbations and low amplitude variations between states remain the main limitations of these techniques since derivative greatly depends on the amplitude variations of the signal. Therefore, the main contribution of this article is to propose a new demodulation method, the Zeros Evaluation for Robust and Optimal Edges Selection (ZEROES) method, based on the detection of zeros of the derivative for determining the precise signal edge position using linear functions, achieving better communication performance due to its greater level of robustness in comparison with the demodulation methods previously presented. With experiments conducted in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, the comparison of demodulation efficiency through the measurement of the Bit Error Rate (BER) and the percentage of bits received clearly show a significant improvement of the communication performance when using the ZEROES method. The rest of this paper is organized as follows: Section 2 presents the different processing steps of OCC images. Section 3 describes the ZEROES developed method. Section 4 details the experimental setup employed to perform experiments. Section 5 is dedicated to the obtained results with their discussions. Finally, a conclusion summarizes this work, and a discussion about our future work is presented.
## 4 Experiments  
To thoroughly assess the accuracy and dependability of HUE-Net, we focused on robustness in severe visual conditions and performed multi-faceted tests using both real-world and synthetic benchmarks. Our objectives were fourfold: (1) capture recognition accuracy verbatim, (2) evaluate performance under modality impairment and vexatious occlusion, (3) assess the estimation of uncertainty, and (4) measure the efficiency of inference relative to peers. The assessment tested extreme identification difficulties like illumination,motion blur, facial disfigurement, and cross-modal identification, which are commonplace in altruistic, forensic, and surveillance endeavors.
## 2 Equivalence Model of IBDG
### Analysis of Output Short-Circuit Currents after Injection  
At the instant after faults, it is assumed that the characteristic frequency signals \(a\text{cos}\theta_{1}\) and \(a\text{sin}\theta_{1}\) are injected into the d-axis and q-axis reference currents of the current internal loop control link in the IBDG control system, respectively. The instantaneous values  
Figure 2: The phase relationship between \(\mathbf{I}_{\text{IBDG}}\) and \(\mathbf{U}_{\text{pcc\_d}}\).  
of the three-phase current provided by IBDG after injection are obtained according to Equations (5) and (6), and Park transformation, as shown in Equation (10).  
\[\left[\begin{array}{c}i^{\prime}_{a}\\ \dot{f}_{b}\\ i^{\prime}_{c}\end{array}\right]=\left[\begin{array}{ccc}\cos\theta&\sin \theta\\ \cos(\theta-120^{\circ})&\sin(\theta-120^{\circ})\\ \cos(\theta+120^{\circ})&\sin(\theta+120^{\circ})\end{array}\right]\left[ \begin{array}{c}I_{\text{IBDG,d}}+a\cos\theta_{1}\\ I_{\text{IBDG,q}}+a\sin\theta_{1}\end{array}\right] \tag{10}\] \[=\left[\begin{array}{c}I_{\text{IBDG,d}}\cos\theta+I_{\text{IBDG, q}}\sin\theta+a\cos(\theta_{1}-\theta)\\ I_{\text{IBDG,d}}\cos(\theta-120^{\circ})+I_{\text{IBDG,q}}\sin(\theta-120^{ \circ})+a\cos(\theta_{1}-\theta+120^{\circ})\\ I_{\text{IBDG,d}}\cos(\theta+120^{\circ})+I_{\text{IBDG,q}}\sin(\theta+120^{ \circ})+a\cos(\theta_{1}-\theta-120^{\circ})\end{array}\right]\]  
where, \(\theta=\omega t+\delta_{0}\), \(\theta_{1}=\omega_{1}t\), \(\omega_{1}\) is the characteristic angular frequency and \(a\) is the amplitude of the injected characteristic frequency signal.  
Comparing Equations (5), (6) and (9), after injection, there will be new characteristic frequency signals in the output currents of IBDG. The new characteristic frequency signals are also called characteristic harmonic currents (CHCs). They are three-phase symmetrical. Taking the output current of the A-phase as an example analysis, the CHC is  
\[a\cos(\theta_{1}-\theta)=a\cos((\omega_{1}-\omega)t-\delta_{0})=a\cos(2\pi(f_{ 1}-f)t-\delta_{0}) \tag{11}\]  
where \(f_{1}\) is the frequency of the characteristic signals injected into the d-axis and q-axis reference currents;\(f\) is the fundamental frequency. From Equation (11), the amplitude of the CHC is \(a\), and the frequency of CHC is \((f_{1}-f)\) Hz.
## 3 The Inversion of J-C Constitutive Parameters for Powder Liner
### Inversion from Experimental Data and Evaluation of Results  
Using NSGA-II for the iterative solution of the display expression, about two hundred Pareto optimal solutions can ultimately be obtained. To gain an intuitive understanding of these solutions, they are plotted in Figure 11 using blue dots, with each Pareto optimal solution corresponding to a set of J-C constitutive parameters. As can be seen from the figure, these Pareto optimal solutions exhibit a descending distribution trend, i.e., the deeper the perforating depth, the smaller the corresponding perforating aperture, which is consistent with the empirical rule of perforation hole formation. To obtain the J-C constitutive parameters applicable to the new powder liner, the results of the penetrating steel target experiment are also drawn in Figure 11 with a black triangle, the average value of the experimental results is taken, and the average value is marked as the green triangle; thus, the Pareto optimal solution (red dot) that is closest to the experimental average value can be found. The J-C constitutive parameters corresponding to the red dots are \(A=90\) MPa, \(B=25\) MPa, \(m=0.1\), \(C=0.025\), and \(n=0.31\), respectively, which can be regarded as the optimal J-C constitutive parameters of the new powder liner.  
To verify the reliability of the retrieved J-C constitutive parameters of the powder liner, numerical simulation of the penetration of the steel target by the projectile flow of the shaped charge was carried out based on the retrieved constitutive parameters. The simulation results are shown in Figure 12b. The results were compared with the simulation results based on copper constitutive parameters (Figure 12a) and experimental results (Figure 12c). At the same time, to display the achievements of this research more intuitively, we present the experimental results in Table 5. It was found that the perforation depth obtained based on the inversion parameters was 31.50 cm, and the perforation aperture was 1.10 cm. The perforation depth was 9.2 cm higher than that obtained based on the copper constitutive parameter, and the aperture was reduced by 0.21 cm; both the perforation depth and perforation aperture are closer to the experiment of the penetrating steel target. The error between the perforation depth and the experimental results obtained by the perforation simulation based on the constitutive parameters obtained in this paper is 4.86%, and the error of the perforation aperture is 2.66%--both of which are within 5%, which indicates that the constitutive parameters of the powder liner obtained by the inversion method in this paper are effective and reliable. Therefore, the constitutive parameters obtained from the inversion can be applied to the subsequent numerical simulation of hard rock perforation.  
Figure 11: Pareto optimal solution distribution of target variables.
# Simplified Welch Algorithm for Spectrum Monitoring
## 5 Laboratory Tests and Real-Time Validation  
### Design in Xilinx DSP Generator Library  
The practical part of this research benefits from the digital signal-processing library provided in the Xilinx System Generator integrated in the Simulink environment. The following methodology contains two main steps: system performance and mathematical discovery based on the simulation that followed by transferring all simulation blocks to the Xilinx DSP Generator. The FFT blocks provided in this library are flexible for multiple FFT sample size adjustments even after synthesis while FPGA is running. The idea is that the method proposed must be practical and easily implementable in the existing technology as much as possible. Following this idea in the simulation design step and benefiting from the Xilinx DSP library, the modified design can easily be transferred from simulation to a worthy FPGA design. Thus, one can assume that the real response of the FPGA design will be reflected accurately in the simulation.  
Figure 13a,b represent the most important blocks used in the Xilinx DSP library. The Xilinx Fast Fourier Transform block core computes N-point forward DFT or inverse DFT based on the Cooley-Tukey FFT algorithm [26]. It buffers a vector of N complex values provided on real and imaginary input ports during the N system clocks and generates N\({}_{F}\) frequency samples in a series manner on real and imaginary output ports. For synchronization, this block also provides a synchronization signal, the \"Data Ready pulse\". This signal simply represents the start and end of each calculated FFT packet. Connecting this pulse to the reset port of a free counter recreates the behavior similar to the simulation step. The second block is the RAM, which is one of the main components of the modified Welch algorithm presented in this paper. The RAM size can also be adjusted to increase or decrease the resolution of the spectral representation. The desired frequency resolution can be easily adjusted with the FFT and RAM size. The smoothness and past value dependency can be set up simply by adjusting the \(\alpha\) parameter. These two options give the user unique flexibility for dynamic system adjustment based on the current situation.  
Warning: as mentioned before, sorted values inside the RAM will grow to a certain value, then, the system will converge to a final spectrum. The important point here, is the hardware limitation for saving large values inside the RAM. If the averaging value goes higher than the saturation limit of the user computational capability, then an error will be generated. Xilinx DSP generator allows the designer to adjust the precision and \"fixed point\" output type for each block. For the blocks that are involved in Figure 13, \"number of bits\" and \"binary point\" must be selected carefully to avoid the saturation error. During the real time test, hardware limitation must also be considered for selecting the maximum value for the \(a\) parameter.
## 5 Conclusions  
This study demonstrated that both the manufacturing method and process parameters have a significant impact on the quality and reproducibility of tablets formulatedwith sodium naproxen, dolomite, cellulose, polyvinylpyrrolidone (PVP), and magnesium stearate. The reference formulation, prepared in a conventional V-type mixer, showed the greatest variability and poorest performance across several quality criteria. In contrast, tablets produced by direct compression using a vibrating ball mill exhibited improved uniformity and mechanical properties, particularly when mixed for longer durations. Wet granulation using a pan granulator consistently yielded tablets with excellent dimensional stability, low abrasiveness, and highly reproducible API content, with optimal results observed for the intermediate inclination angle (30\({}^{\circ}\)). Notably, vibratory ball mill mixing proved capable of producing tablets with comparable quality to those obtained via granulation, while requiring fewer processing steps and no solvents.  
The observed tablet properties were closely linked to the functional roles of the formulation components. The use of dolomite enhanced tablet strength, while microcrystalline cellulose contributed to both compressibility and internal porosity. Polyvinylpyrrolidone improved granule cohesion in wet granulation, and magnesium stearate ensured efficient compression. Together with the high solubility of sodium naproxen, these components enabled the development of a simplified formulation with robust performance across different manufacturing methods.  
This finding highlights the potential of this method as a cost-effective and scalable alternative for formulations containing challenging or poorly flowing materials, especially in applications aiming for greener and simpler manufacturing processes.  
Conceptualization, M.P.; methodology, M.P.; software, M.P.; validation, M.P., W.J. and K.J.; formal analysis, M.P.; investigation, M.P., K.J. and W.J.; resources, M.P.; data curation, M.P.; writing--original draft preparation, M.P.; writing--review and editing, M.P.; visualization, M.P.; supervision, M.P.; project administration, M.P. All authors have read and agreed to the published version of the manuscript.  
Financed by the Minister of Science and Higher Education Republic of Poland within the program \"Regional Excellence Initiative\", agreement no. RID/SP/0032/2024/01.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Proposed Frame Aggregation with Block Acknowledgment Mechanism
### The FASBA Approach  
The main objective of the proposed FASBA is to accommodate a large number of life-saving emergency nodes in emergency times when a larger number of nodes report an emergency for channel access. It is useful to design a network protocol that can offer lower packet delays and higher throughput by reducing transmission overheads. Second, the protocol should provide a strict QoS guarantee in message delivery. To meet the objectives mentioned above, FASBA aggregates three frames with a simple BlockAck mechanism for transmissions. The FASBA adopts the idea of frame-aggregation (for reducing protocol's transmission overheads) from BUMA protocol [8]. Moreover, FASBA employs a simple two-bit BlockAck strategy for accommodating more nodes in the network and to provide a strict QoS guaranteed message delivery. This is achieved in the following ways.  
In FASBA, each node (station) maintains a buffer (temporary memory) to combine and hold three packets with a single packet header and a trailer at the MAC layer before transmissions. This allows FASBA to reduce transmission overheads significantly and consequently improve system performance. Figure 1 illustrates the FASBA's frame aggregation mechanism. The figure also shows the acknowledgement mechanism followed by SIFS.  
FASBA's simple block acknowledgement mechanism is illustrated in Table 2. The first column shows the two bits of acknowledgement, followed by purpose, and the next possible bits, which will be sent by the STA. If both ACK bits are 11, it means all three packets (i.e., packet 1, packet 2, and packet 3) are successfully delivered. The transmitting STA will send the subsequent three packets (i.e., packet 4, packet 5, and packet 6). If ACK bits are 01, it means packet 2 and packet 3 are transmitted successfully. The transmitting STA will send packets 1, 4, and 5. Similarly, if ACK bits are 10, it means packets 1 and packet 2 are delivered successfully, and STA will send packets 3, 4, and 5. In all other cases, all three packets will be resent. Though FASBA is designed to support emergency traffic through a modified channel contention mechanism, non-priority traffic can use a standard IEEE 802.11e multi-user contention mechanism to contend for the channel.