esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations  
### Problem Description  
The strip is assumed to be very thin, making the problem a two-dimensional plane stress problem. As shown in Figure 4, \(x\) is the width direction, \(y\) is the rolling direction, and \(B\) is the strip width. From the exit of the deformation zone, the area of \(B\times 2B\) is analyzed. According to [25], \(2B\) is an adequate distance for the velocity distribution, evolving from uneven to even, so at \(y=2B\), the stress \(\sigma_{y}(x)\) induced by the velocity distribution is 0. At \(x=-B/2\) and \(x=B/2\), the surface force of \(F_{x}\) and \(F_{y}\) is 0, and at \(y=0\), the stress \(\sigma_{y}(x)\) induced by the uneven velocity distribution is expressed by Equation (1):  
\[\sigma_{y}(x)=E\frac{\overline{v}_{y}-v_{y}}{\overline{v}_{y}} \tag{1}\]  
where \(E\) is the Young's modulus of the strip, \(v_{y}\) is the velocity distribution at the exit of the deformation zone, and \(\overline{v}_{y}\) is the average velocity at the exit of the deformation zone.  
According to the theory of elasticity [26], the stress function of the plane stress problem can be described as the biharmonic Equation (2):  
\[\frac{\partial^{4}\varphi}{\partial x^{4}}+2\frac{\partial^{4}\varphi}{ \partial x^{2}\partial y^{2}}+\frac{\partial^{4}\varphi}{\partial y^{4}}=0 \tag{2}\]  
Figure 4: Coordinate system of the problem.  
where \(\varphi\) is the stress function.  
The boundary conditions can be described by Equation (3):  
\[\left\{\begin{array}{l}l\frac{\partial^{2}\varphi}{\partial y^{2}}-m\frac{ \partial^{2}\varphi}{\partial x\partial y}=F_{x}\\ m\frac{\partial^{2}\varphi}{\partial x^{2}}-l\frac{\partial^{2}\varphi}{ \partial x\partial y}=F_{y}\end{array}\right. \tag{3}\]  
where \(l,m\) are the directional cosines, and \(F_{x},F_{y}\) are the applied surface force on the boundary.  
To solve the problem with the finite difference method, the strip is divided into \(20\times 40\) small rectangular elements, and the side length of a small rectangular element is \(B/20\). Outside the border of the strip, a layer of imaginary nodes is applied. The nodes are numbered \(i=0\sim 42,j=0\sim 22\).
## 6 Conclusions  
Aimed at the current difficulties in spatial localization and monitoring of the sound power of mobile sound sources, this paper studies the sound source localization method combining a binocular camera and beamforming. This method scans scene information through binocular cameras and outputs depth images, and obtains the structural information of the target area through image segmentation. As the range of the reconstructed sound field is limited on the three-dimensional surface of the target, the combination method can not only realize the three-dimensional localization of the sound source with less computation, but also achieve the sound field reconstruction of the moving target by updating the three-dimensional information of the target continuously. Locating the moving sound source spatially provides the depth variation of the source, and the sound power of the source can be estimated by the sound intensity scaling method. The sound power level is more stable than sound pressure level to monitor the condition of the sound source.  
It was proved by a mathematical derivation and experiment that the use of stereo technology to reduce the number of reconstruction points can effectively improve computational efficiency. With mature technology and a high precision of stereo vision, the accuracy of sound source focusing and reconstruction is improved, which is conducive to obtaining accurate source positioning and sound power value.  
Conceptualization, Y.C. and X.L.; methodology, Y.C., X.L. and Y.X.; software, Y.C. and Y.X.; validation, Y.C., X.L. and Y.X.; writing--review and editing, X.L.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (51875272); Science and Technology Major Project of Yunnan Province (202002 AC080001).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Approximate Sensory Data Collection: A Survey
## 4 Query-Driven Approximate Data Collection Algorithms
### Summary  
The query-driven approximate data collection algorithms are sufficient considering the users' requirements during data collection, and they only transmit the data that are valuable to users so that many transmission costs are saved. However, these methods are only designed for specific kinds of queries. These algorithms do not consider correlations among sensory data and cannot recover original data. Furthermore, the coding-based algorithms still require all of the nodes to transmit their data to the sink, incurring a large communication cost. For the sampling-based algorithms, the core information of the raw data may not be preserved.
## 3 Results and Discussion
### Calculation of Theoretical Bandgap Values for Multicomponent Hybrid Ferroelectric Materials  
The theoretical bandgap values of the hybrid ferroelectric materials were calculated using Materials Studio software, as shown in Figure 6. The specific calculation method and super cell structure diagram (Figure S4) are described in the supporting literature. As the two ferroelectric materials, HDA-Bil\({}_{5}\) and [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]Sbl\({}_{5}\), were mixed, Bi\({}^{2+}\) was replaced by Sb\({}^{2+}\) to form the [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]Bi\({}_{1-x}\)Sb\({}_{x}\)I\({}_{5}\) compound. The crystal structure of this compound was modeled in MS software, the convergence calculations of the cutoff energy were performed, and the bandgap maps of the compound with different doping ratios were calculated and analyzed. As can be seen from Figure 6, the lowest point at the bottom of the conduction band and the highest point at the top of the valence band of the materials doped in various ratios were at the same position; hence, they were all direct bandgap semiconductors. The Fermi energy levels of each material were as follows: [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]Sbl\({}_{5}\), 1.34 eV; 0.2 Bi, 2.03 eV; 0.4 Bi, 2.02 eV; 0.5 Bi, 1.71 eV; 0.6 Bi, 1.70 eV; 0.8 Bi, 2.01 eV; [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]BiI\({}_{5}\), 1.16 eV. When the ratio of Bi to Sb was 0.4:0.6, the narrowest bandgap of the compound was 1.58 eV. Compared with the experimental values of the bandgap in Figure 5, the bandgap values obtained from the theoretical calculations were smaller. This is due to the electronic autocorrelation problem of the pure DFT method, the absence of  
Figure 5: Plot of (ahv)\({}^{1/2}\)-hu for a multicomponent ferroelectric film.  
derivative discontinuity in the exchange-correlation function, and the self-crossing error in the density generalized theory calculation, which usually lead to low prediction results for the bandgap of semiconductors or insulators. In addition, the actual preparation of hybrid films may lead to large experimental bandgap values because the quality of the final film is not completely ideal due to the environment and preparation techniques. However, the trend of the measured bandgap values in the actual experiments with the change in the ratio of Bi and Sb elements is consistent with the theoretical calculations. The purpose of calculating the theoretical bandgap is to obtain the trend of the bandgap as a reference basis for the experimental values, rather than specific values. It can be seen that the mixture of two ferroelectric materials, HDA-Bi\({}_{5}\) and [C\({}_{6}\)N\({}_{2}\)H\({}_{18}\)]Sb\({}_{5}\), led to a decrease in the bandgap, which is more favorable for the absorption of visible light in the film.
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 1 Introduction
However, these features cannot effectively represent the DR, DME and glaucoma regions because of color, size, higher intra-class variations, bright regions other than OD, thus resulting in unsatisfactory results of CAD solutions [26]. For performance optimization of CAD systems, glaucoma classification is performed through a correctly segmented glaucoma region called region of interest (ROI), which enhances the detection ability, as the affected region gives a good representation of the glaucoma characteristics. Therefore, the segmentation is the main phase before the classification for the performance improvement of CAD systems. Various computer vision and deep learning-based approaches were employed for DR detection [14; 15; 27; 28] and classification, DME detection [2; 5; 29; 30; 31] and identify glaucoma signs from the retinal images [32; 33; 34; 35; 36; 37; 38; 39]. A literature review of important methods to detect DR, DME and glaucoma is summarized in Table 1.
# A Simplified Free Vortex Wake Model of Wind Turbines for Axial Steady Conditions
## 2 Simplified Free Vortex Wake
### Velocity Induced by the Near Wake and the Far Wake  
#### 2.4.1 Velocity Induced by the Near Wake  
The vortex filaments of the near wake comprise a series of straight-line vortex elements. The velocity induced by the vortex filaments equals the sum of the velocities induced by the straight-line vortex elements at the control nodes of the vortex elements, which are calculated using the Biot-Savart law as:  
\[\mathbf{V}_{ind}=\frac{\Gamma}{4\pi h}(\cos\theta_{A}-\cos\theta_{B})\frac{\mathbf{r} _{A}\times\mathbf{r}_{B}}{|\mathbf{r}_{A}\times\mathbf{r}_{B}|} \tag{7}\]  
Figure 3: Schematic of the far wake.  
where \(h\), \(\theta_{A}\), \(\theta_{B}\), \(\mathbf{r}_{A}\), and \(\mathbf{r}_{B}\) are as shown in Figure 4. However, if a collocation point is positioned very close to the vortex-line segment, then (\(h\to 0\)) will result in a very high induced velocity. In addition, the self-induced velocity (\(h=0\)) exhibits a logarithmic singularity. These two phenomena cause convergence problems. To avoid these numerical problems, some vortex core models based on the Lamb-Oseen vortex model [26], Ramasamy and Leishman vortex model [27], [PERSON] vortex model [28] and \(\beta\)-Vatistas vortex model [29,30] have been applied in a FVW model. In our recent work, we compared the effects of the \(\beta\)-Vatistas model and Lamb-Oseen model in the FVW model [29] and further studied the application [30] of the \(\beta\)-Vatistas model. In this study, a vortex core model based on the Lamb-Oseen vortex model, which is the most widely adopted and simpler model, was used. To account for the effect of viscous diffusion, the vortex core radius growth is used in the Biot-Savart law and is modified by using an empirical viscous growth model [31]. The stretching effect of the vortex filaments is taken into account by the application of a model developed by [PERSON] and [PERSON] [32].
# Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos
## 4 Sliding Mode Control Design for the General Fractional-Order Chaotic System
### Application to Fractional-Order Chen System  
In this section, we present an illustrative example to verify and demonstrate the effectiveness of the proposed control scheme.  
The control input \(u(t)\) to stabilize Chen system is added to the third state equation to control chaos in the fractional order system in Equation (13). The proposed class of fractional order model in Equation (16) can be described as follows:  
\[\left\{\begin{array}{l}D^{(q_{1})}x(t)=a(y(t)-x(t))\\ D^{(q_{2})}y(t)=(c-a)x(t)-x(t)z(t)+cy(t)+u_{s}(t)\\ D^{(q_{3})}z(t)=x(t)y(t)-bz(t)\end{array}\right. \tag{28}\]  
Using the sliding mode control law in Equation (24), we derive the following control action with \(c_{1}=0.9\) and \(c_{2}=0.5\) to stabilize the fractional-order Chen system,  
\[u(t)=-c\;x(t)-K_{r}\;sgn\left(S(t)\right) \tag{29}\]  
The simulation results are shown in Figures 2 and 3, where the reaching gain \(K_{r}=0.1\), and the initial values \([x,y,z]^{T}=[-9,-5,14]^{T}\) showing the states of the system in Equation (28) under the controller in Equation (29) illustrated in Figure 4.  
The sliding surface in Equation (17) is illustrated in Figure 5, which shows that the sliding mode control law guarantees the sliding surface reaches final stabilization. It should be noted that the control is activated at \(t=5\,\mathrm{s}\).  
Figure 2: _Cont._Figure 3: Phase portrait of the controlled system.  
Figure 2: State trajectories of stabilized fractional-order Chen system.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Aegerolysins from Insecta, Lepidoptera  
Aegerolysins from _Pseudolpusia includes_  
Loopers such as _Chrysodeixis includens_ ([PERSON], 1858) (_Pseudolpusia includes_), _Trichoplusia ni_ ([PERSON], 1803), and _Helicoverpa zea_ ([PERSON], 1850) (Plusiinae) are important defoliators of soybeans, sunflowers, and crucifers, respectively, in countries of the Americas [47]. The polyphagous species larvae, such as the cabbage or soybean moth _C. includes_, feed on a wide range of plants [48].  
Moths and butterflies (_Lepidoptera_) produce four types of hemocytes at the larval stage, which are usually identified by a combination of morphological, antigenic, and functional characteristics. Functionally, granulocytes phagocytize a variety of bacteria and other small organisms, and are required for the encapsulation of certain types of large foreign targets [48]. In last (fifth) instar larvae of _C. includes_, granulocytes comprise 60-70% of circulating hemocytes, are highly adhesive in vitro, and spread symmetrically on flat surfaces such as tissue culture plates. Circulating plasmatocytes differ in their ability to spread on foreign surfaces, and aegerolysin **P23**, named after the molecular mass band, serves as a marker for the subpopulation that is able to encapsulate foreign targets and respond to the plasmatocyte spreading peptide of the ENF peptide family (a consensus sequence of ENF at its amino termini), whose members act as potent activators of plasmatocyte adhesion-dependent activities in several _Lepidoptera_ species [48]. **P23** showed no antibacterial or cytolytic activity against bacteria and mammalian erythrocytes [48]. However, it is noteworthy that the protein sequence of **P23** is most similar to the aegerolysis-like protein **TnAV2c gp029** encoded by _Trichoplusia ni_ asovirus 2c [49; 50].  
From the genome assembly of chromosome 6 of _P. includes_, it can be concluded that there are four aegerolysin genes in a row, 92, 52, 38, and 35% identical to P23, with one more gene between the first two.
# A New Approach to Quantify Soccer Players' Readiness through Machine Learning Techniques
## 6 Discussion
### Effect of Training Load Variations on LEI  
To better understand the effects of training load, we examined the relationship between LEI and weekly load variation. In particular, we considered the workload accumulated in the previous 7 days (total distance, distance > 19.8 km/h, distance > 25.2 km/h, and number of accelerations and number of decelerations). Considering that workload can change from player to player depending on different factors (e.g., playing position), it was individualized using z-score transformation. Three different conditions were identified: (D-WL), the player experienced a decrease in weekly load compared to normal (z-score \(<-1\)); (S-WL), the load remained within a normal range (z-score between \(-1\) and \(1\)); and (I-WL), the player experienced an increase in weekly load compared to normal (z-score > 1). When players registered an increase in weekly load (I-WL) for total distance (ES 0.20-0.63), distance > 25.2 km/h (ES 0.56-0.82), and number of accelerations (ES 0.10-0.32), a significant decrease in LEI was observed. (Figure 6). A similar approach was adopted in a previous study, wherein the authors found an association between a higher acute load and an increase in fatigue [24]. As stated by [PERSON] et al. [32], a high volume of total distance, a very high-intensity running distance, and the number of accelerations could be associated with repeated eccentric muscle contractions, muscle damage, and disturbances in the biochemical milieu, consequently increasing the fatigue state. As such, the use of the LEI could support daily practitioners in assessing players that are responding properly to variations in training load and those that present decreased neuromuscular status; in turn, this would support better workload management for elite soccer players.  
Although the approach developed in the current study showed encouraging results, some limitations must be addressed. First, although the LEI showed an interesting association with the period of the season, day of the week, and weekly load variation, this metric was not corroborated by any objective fatigue markers (e.g., creatine kinase, testosterone-cortisol ratio, Countermovement Jump outputs such as flight time-contraction time ratio). For this reason, future studies should further investigate the relationship between the LEI and objective markers of neuromuscular fatigue. In addition, future research should involve investigating how the interaction of different external load parameters affect the LEI. Second, the ML showed good accuracy with at least 23 weeks of data within the training dataset, implying that predictions made at the beginning of the season could present a certain degree of error. Therefore, future studies could extend the analysis to multiple seasons and increase the size of the dataset.
## 5 Performance Evaluation
### Experiment
#### 5.4.1 Classification Performance of G-TIAG  
We evaluate the classification capability of G-TIAG using the three indicators of precision, recall, and F1-Score with the optimal parameter selection obtained in Section 5.3. The results are shown in Table 7. The results show that the overall classification capability of G-TIAG is strong. The precision of the three attacks, Dos, DDoS, and PortScan, remains around 0.88 to 0.91, the recall remains around 0.87 to 0.89, and the F1-Score remains  
Figure 7: Parameters selection of G-TIAG. (**a**) Epochs; (**b**) Number of GRU hidden units; (**c**) Time interval of construction.  
around 0.87 to 0.90. However, compared to the three attacks mentioned above, the Bot and Web Attacks are around 0.70 in the three indicators. We consider that the reason for this problem is data imbalance because the number of pure traffic for both Bot and Web attacks is less than other types, and the amount of data classified into TP is less than others. This issue will be discussed further in the Discussion subsection.  
To understand the obfuscation relations among the five attack and normal data, we visualize the obfuscation matrix of G-TIAG, as shown in Figure 8. The recall of benign data is 0.9, and the recalls of the five attacks are 0.89, 0.88, 0.87, 0.71, and 0.72, respectively. The experiments show that G-TIAG can effectively distinguish benign data from the five attacks and the Dos, DDoS, and PortScan attacks from other categories of data. However, Bot and Web Attacks are more likely to be misclassified into PortScan and benign data.
# Non-Cooperative Target Ranging Based on High-Orbit Single-Star Temporal-Spatial Characteristics
###### Abstract  
A visible light camera payload with star-sensitive functionality was installed to measure the distance between a non-cooperative target satellite and a high-orbit satellite. The rotation matrix was used to calculate the pointing vector from the center of the satellite's star-sensitive camera axis to the target satellite. Multiple position imaging was achieved, and the moving window approach was used to establish two sets of equations relating the pointing vectors to the positions of binary satellites. To simplify the calculations, the target satellite's eccentricity was assumed to be small (0 to 0.001), allowing elliptical orbits to be approximated as circular. Additionally, short-interval (1-min) imaging measurements were taken, assuming a small inclination of the target satellite (0.0\({}^{\circ}\) to 0.4\({}^{\circ}\)). This resulted in the construction of a ranging model with high accuracy, producing a ranging error of less than 5% of the actual distance.  
non-cooperative target ranging: high-orbit satellites; monocular visual distance measurement +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences  
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Footnote 0: _Appl. Sci._**2024**, 14, 11232. [[https://doi.org/10.3390/appl42311232](https://doi.org/10.3390/appl42311232)]([https://doi.org/10.3390/appl42311232](https://doi.org/10.3390/appl42311232))
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments
### Failure Cases  
Despite its merits, our method fails in three scenarios:  
**Complex Lighting:** Rapid changes or extreme contrasts distort color and depth data, disrupting feature matching and causing inaccurate results.  
**Ultra-high-speed Movement:** Speeds over 10 m/s cause motion blur and aliasing, preventing stable feature tracking and real-time results.  
**Repetitive Environments:** Repetitive patterns yield insufficient unique features, leading to inaccurate pose estimation and poor map quality.
## 3 Proposed Approaches
### Spectrum Attention Mechanism
#### 3.2.1 Frequency Domain Transformation  
Conducting _FFT_ on the input time-series data transforms the time domain signal into the frequency domain, resulting in a spectral plot of the signal. The frequency domain representation offers a more distinct portrayal of the frequency components within the signal, enabling the model to function within the frequency domain, as illustrated in (5). In this equation, \(X_{t}\) denotes the time domain signal, while \(X_{f}\) signifies the frequency domain signal.  
\[X_{f}=FFT(X_{t}) \tag{5}\]
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 4 Experiments
### Thermal View Synthesis  
To compare the quality of novel view synthesis for thermal images across various Gaussian Splatting (GS) methods, we conducted quantitative and qualitative evaluations. As shown in Table 1, our method outperforms existing approaches in visual metrics such as PSNR and LPIPS while consuming significantly less memory. Its running speed is comparable to ThermalGaussian and outperforms other methods. Specifically, our method achieves the best performance with minimal memory consumption of 540 MB, attaining a PSNR of 32.78 dB and LPIPS of 0.092. In comparison, the second-best method (3 DGS [2]) exhibits similar visual metrics--with a marginal difference of 0.27 dB in PSNR and 0.01 in LPIPS--but requires nearly triple the memory footprint at 1658 MB, and more inference time.  
For qualitative comparison, Figure 3 visually contrasts our method with baseline approaches across three representative scenes: Fruits2 (top row), Hairdryer (middle row), and Laptop (bottom row). The results highlight our method's improved visual fidelity. For instance, in the Hairdryer scene, baseline methods exhibit noticeable thermal distortions such as warped nozzle geometry and blurred temperature gradients, whereas our approach preserves structural integrity and radiometric consistency. Similarly, in the Laptop scene, our method accurately reconstructs fine thermal edges including keyboard gaps that are either missing or misaligned in baseline outputs. Notably, this capability relies on the presence of temperature differences between objects. For example, the keyboard gaps in the Laptop scene are discernible only because the active electronics generate localized heat, creating measurable thermal gradients. If all objects in the scene were at equilibrium with ambient temperature (e.g., a static laptop in a thermally uniform environment), thermal imaging would fail to capture structural details such as key boundaries, as no thermal contrast would exist. These visual improvements align with the quantitative metrics, validating our method's robustness in low-light multimodal reconstruction tasks.  
Figure 3: Qualitative evaluation of thermal view synthesis.