esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 4 Experiment Design
### Source Test Cases  
We have selected random source test cases using strong equivalence class testing and grouped the attributes of MRI images into five classes: horizontal dimension, vertical dimension, bit depth, resolution, and the image type. Each class is further divided into multiple sub-classes as shown in Table 2  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Classes** & **Sub-Classes** \\ \hline \multirow{3}{*}{Horizontal Dimension} & h\({}_{1}\): 1–300 \\  & h\({}_{2}\): 301–650 \\  & h\({}_{3}\): 651+ \\ \hline \multirow{3}{*}{Vertical Dimension} & v\({}_{1}\): 1–350 \\  & v\({}_{2}\): 351–700 \\  & v\({}_{3}\): 701+ \\ \hline \multirow{3}{*}{Resolution} & r\({}_{1}\): 1–90 dpi \\  & r\({}_{2}\): 91–99 dpi \\  & r\({}_{3}\): 100–450 dpi \\ \hline \multirow{3}{*}{Bit Depth} & b\({}_{1}\): 8 \\  & b\({}_{2}\): 24 \\ \hline \multirow{3}{*}{Type of Image} & t\({}_{1}\): T1-weighted images \\  & t\({}_{2}\): T2-weighted images \\  & t\({}_{3}\): flair images \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classes using strong equivalence class testing.  
Figure 6: Edge detection algorithm.  
According to Table 2, \"Horizontal Dimension\" is a class which is further divided into three sub-classes. \"Vertical Dimension\" is divided into three classes as well. \"Resolution\" and \"Type of Image\" have three sub-classes each. \"Bit Depth\" is a class which is further divided into two sub-classes.  
As discussed earlier, we have used strong equivalence class testing for the generation of source test cases. In strong equivalence class testing, we will make all possible combinations from these classes for the generation of source test cases. The total numbers of classes generated through these combinations are: 3 \(\times\) 3 \(\times\) 3 \(\times\) 2 \(\times\) 3 = 162. Out of 162 classes, only 95 classes (33, T1; 29, T2; 33, flair) are obtained with a few with an 8-bit depth value. There are few images in each image type (T1, T2, flair) with 8-bit depth that are selected. All missing 67 classes with 8-bit depth value are unavailable. The results of 8-bit depth value or lower show either very dark (T1 and flair) images or very bright (T2) images which make it very difficult to detect the lesions accurately.
# Cellular Entry, Cytotoxicityicity, and Antifungal Activity of Newly Synthesized Dendrimers
## 3 Results  
### Size and Zeta (\(\zeta\)) Potential of DAB and DAB-Br  
The colloidal characteristics of both DAB and DAB-Br dispersions were assessed. According to the obtained DLS profiles, DAB-Br exhibited a larger average size of 222 nm and a higher \(\zeta\)-potential of \(+\)35.2 mV compared to DAB, which had an average size of 118 nm and a \(\zeta\)-potential of \(+\)28.7 mV (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline
**Dendrimer Dispersions** & **Average Size** & **Apparent \(\zeta\)-Potential** \\
**(40 \(\upmu\)g/mL)** & **(nm)** & **(mV)** \\ \hline DAB (1 st day) & 118 \(\pm\) 66 ** & 28.7 \(\pm\) 0.4 ** \\ DAB (3 rd day) & 123 \(\pm\) 45 ** & 26.5 \(\pm\) 1.6 ** \\ DAB (7 th day) & 123 \(\pm\) 54 ** & 26.5 \(\pm\) 2.7 ** \\ \hline DAB-Br (1 st day) & 222 \(\pm\) 39 ** & 35.2 \(\pm\) 1.9 ** \\ DAB-Br (3 rd day) & 234 \(\pm\) 82 ** & 31.6 \(\pm\) 2.4 ** \\ DAB-Br (7 th day) & 265 \(\pm\) 72 ** & 34.5 \(\pm\) 1.7 ** \\ \hline \end{tabular}
\end{table}
Table 1: Average size and apparent zeta potential values of DAB and DAB-Br at a concentration of 40 \(\upmu\)g/mL on the 1 st, 3 rd, and 7 th day after dispersion preparation. Two independent samples were measured, with three replicates per sample. (**) indicates a statistically significant difference between DAB and DAB-Br (\(p\) < 0.01).  
To evaluate dendrimer stability, the colloidal properties were monitored on the 3 rd and 7 th days after preparation. By day 7, the dispersions demonstrated very good colloidal stability over the one-week period. No statistically significant differences were observed in the properties of DAB between days 1, 3, and 7. A similar trend was observed for DAB-Br. Based on these results, all subsequent experiments were conducted using dendrimer solutions no older than one week.
# Paint Removal with Pulsed Laser: Theory Simulation and Mechanism Analysis
## 2 Materials and Methods  
Figure 1 shows a finite element model for laser cleaning. To replicate the actual materials, the model was made of an LY12 aluminum alloy substrate measuring 1 mm \(\times\) 1 mm \(\times\) 2 mm, an oxide layer with thickness of 7 \(\upmu\)m, and a 50 \(\upmu\)m polyacrylate resin base paint layer. The moving pulse laser scans the sample in a single pass in the positive direction along the X-axis. The paint layer directly absorbs the laser energy during the cleaning process, and heat is thermally transferred to the oxide layer and the surface of the substrate. By referring to the pre-experiment and experimental equipment conditions, a simulation calculation was carried out using a set of parameters with a fixed wavelength of 1064 nm, pulse width 1 \(\upmu\)s, energy density 17.26 J/cm\({}^{2}\), spot diameter 78 \(\upmu\)m, laser repetition frequency 20 kHz, and scanning speed 600 mm/s.  
Laser cleaning experiments were carried out using a fiber laser with the same parameters. The beam was in Gaussian mode, and spot diameter after pulse laser focusing was about 78 \(\upmu\)m.  
Figure 1: Finite element model and mesh samples for laser cleaning.  
The base material used was LY12 aluminum alloy with size of 15 mm \(\times\) 15 mm \(\times\) 2 mm. A yellow polyacrylate resin base (polyisocyanate resin curing agent with a nanoparticle additive such as TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\)) paint layer on an aluminum alloy surface with thickness of 50 \(\upmu\)m was used for the experiment. The surface of the aluminum alloy was anodized to form an oxide layer with thickness of about 7 \(\upmu\)m. Table 1 shows the composition of the paint layer, the oxide layer, and the aluminum alloy. The molecular structure of polyacrylate resin and polyisocyanate resin is shown in Figure 2. A silicon wafer was used as the substrate to collect particles, measuring 2 mm \(\times\) 2 mm, approximately 17 mm from the sample, and parallel to the surface of the sample, facing the ablation port. The silicon wafer was ultrasonically cleaned in an ethanol solution for 5 min to remove surface contaminants.  
The surface of the paint layer on the 2 mm thick aluminum alloy plate was placed on the laser focal plane. With oscillation of the X and Y axis galvanometers in the scanning galvanometer system, an orderly laser pattern was applied to the paint layer. The surface of the paint layer was scanned once to remove the paint layer. The laser beam emitted by the fiber laser was in the form of a series of discrete circular spots, distributed at a given frequency.  
Fourier transform infrared spectroscopy (FT-IR, Nicolet 5700) and X-ray photoelectron spectroscopy (XPS, ESCALAB 250 xi) were used to evaluate the functional groups and the chemical valence of C, O, N, Al, and Ti. Scanning electron microscopy (SEM) was used to observe and analyze particle morphology and the morphology of the cleaning surface and the original surface.
# Monitoring Forest Phenology in a Changing World
## 3 Emerging Technological Advancements  
### High Resolution Satellite Remote Sensing  
Until recently, satellites were considered too coarse in their spatial and temporal resolution for many phenological monitoring requirements--such as monitoring individual tree crown changes in the tropics--but recent years have seen deployments of high spatial and temporal resolution satellites that will put satellite based remote sensing at the forefront of future phenological monitoring once more. Although not an emerging technology, the integration of these high resolution remote sensing satellites into phenological monitoring studies is an emerging process, especially in the tropics. Nevertheless, there are already several recent studies that have shown the use of satellite constellations such as PlanetScope and Sentinel-2 to monitor and map phenology at almost daily intervals and at a spatial resolution of 3 to 10 m, respectively [165; 166; 152], including a study of phenology in an Amazonian evergreen tropical forest [124]. We envisage that in the coming years we will see the increased use of these satellites, alongside even higher resolution satellites such as GeoEye-1 (0.41 m), WorldView-4 (0.31 m) and Pleiades (0.50 m), in phenological monitoring studies.  
High resolution satellites can still limited by the high cost of acquiring their data, the availability of on-the-ground validation data [28], the expertise often required for processing and interpretation. However, the increased deployment and use of satellite constellations will in time reduce the costs [167]. Furthermore, the opportunity for researchers to access freely available data through organisations such as the ESA and NASA will hopefully result in a greater body of trained practitioners that can interpret and analyse the data.
###### Abstract  
The proliferation of mobile terminal applications and the increasing energy consumption of chips have raised concerns about insufficient power in mobile user terminals. In response to this issue, this paper proposes a joint optimization algorithm for UAV-assisted caching and charging based on non-orthogonal multiple access (NOMA) within the context of mobile edge caching scenarios. The proposed algorithm considers the revenue generated from UAVs providing caching and charging services to users, as well as the cost associated with leasing cache files and the UAV energy consumption. The optimization problem aimed at maximizing UAV utility is established under constraints related to power and cache capacity. To address this mixed-integer programming problem, we divided it into two parts. The first part uses the Stackelberg-Bertrand game to optimize file pricing and the UAV cache strategy. In the second part, the block coordinate descent (BCD) method is used to optimize the UAV transmission power distribution, positioning, and user pairing. The joint optimization problem is divided into three subproblems, which use the Lagrange multiplier method, a simulated annealing algorithm, and a particle swarm optimization algorithm. Simulation results demonstrate that the proposed algorithm effectively reduces user transmission delay while also improving overall revenue generated by UAVs.  
unmanned aerial vehicle; mobile edge caching; wireless energy harvesting; non-orthogonal multiple access; Stackelberg game Article
## 2 Methods
### Robustness and White Noise Gain  
One of the problems that beamforming algorithms often have is their lack of robustness. This property is related to a resistance to the presence of spatially white noise and can be impaired by deviations from the specified microphone characteristics and microphone position errors. These imperfections affect the beamformer in a manner similar to a recorded spatially white noise that is amplified. Hence, the White Noise Gain (WNG) is a measure commonly used for quantifying the robustness of a beamformer design. The WNG shows the ability of a beamformer to suppress spatial white noise, because it expresses the gain of the beamformer in the desired look direction relative to the amplification of spatially white noise.  
The WNG \(A(f_{p})\) is defined as follows:  
\[A(f_{p})=\frac{\left|b_{steer}(f_{p})\right|^{2}}{\mathbf{w}_{n}^{H}(f_{p}) \mathbf{w}_{n}(f_{p})} \tag{5}\]  
where \(b_{steer}(f_{p})\) denotes the value of the directivity pattern in steer direction [7]. A high value of the WNG \(A(f_{p})>1\) corresponds to a robust beamforming design, whereas a small value \(A(f_{p})<1\)effectively corresponds to an amplification of spatial white noise [7]. The maximum possible value of the WNG is equal to the number of microphones used:  
\[\max(A(f_{p}))=N \tag{6}\]  
which corresponds to a uniform filter [7]:  
\[|w_{n}(f_{p})|=\frac{1}{N} \tag{7}\]
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Period** & **Distance Separation**[6] & **Distance Separation**[15] & **Distance Separation**[15] & **(Proposed)** \\ \hline
10 & 11.2 & 14.1 & 29.3 \\
11 & 10.4 & 15.6 & 28.7 \\
12 & 10.1 & 17.2 & 27 \\
13 & 9.6 & 15.3 & 26.5 \\
14 & 8.3 & 13.1 & 25.2 \\
15 & 7.4 & 12.5 & 24.6 \\
16 & 6.9 & 11.9 & 23.2 \\
17 & 5.1 & 10.1 & 22.7 \\
18 & 4.4 & 9.3 & 21.8 \\
19 & 4 & 8.4 & 20.6 \\
20 & 3.8 & 8.1 & 19.2 \\
21 & 3.2 & 7.8 & 18 \\
22 & 2.6 & 7.5 & 17.5 \\
23 & 2.2 & 6.7 & 16.3 \\
24 & 1.26 & 6.1 & 15.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
From Figure 7 and Table 6, it can be observed that the setup cost is plotted for both the existing [6,15] and proposed method. In this comparison, the case number of clusters is varied, thus increasing the cost of implementation from initial to last state. Consequently, the proposed method implements much lower cost as compared to the existing method, for which, in the initial demonstration state, the process building cost only 1000 dollars. Then, updating with a greater number of clusters added extra cost with beneficial systems to the public, varying up to 6500 dollars. Under the same circumstances, the existing method adds a total cost of 7800 dollars, which is much higher due to the surplus infrastructure in the integration process.
## 3 Algorithm Description
### Comparison with AERONET AOD and SSA
Figure 9: Comparison of (**a**) GEMS and AERONET AOD, (**b**) GEMS and OMAERUV AOD, (**c**) OMAERUV and AERONET AOD and (**d**) GEMS a priori and OMAERUV AOD (443 nm). Colors indicate the aerosol type distinguished by the satellite algorithm shown on the \(y\)-axis. Black: HAF (smoke for OMAERUV), red: dust and blue: NA. Dashed lines indicate an uncertainty envelope of \(\pm\)30% in AOD and dotted lines are 1:1 lines. Black solid lines are linear regression lines for which the equations are shown in the upper left of each panel.
## 4 Experimental Verification of Control Algorithm  
In this study, a velocity closed-loop experiment was performed, based on the servo turntable pitch axis system for ground-based laser communication. The experimental setup is shown in Figure 8. The experimental devices include the pitch axis motor, which is a DC torque motor (J175 LYX04, Chengdu Precision Motor Factory, China), and a C01 motor drive module as the power amplifier (Chengdu Precision Motor Factory, China). The angle value of the pitch motor is set using an encoder (RA26 BEA115B05F, Renishaw, UK). The digital controller is composed of a DSP (TMS320F2812) and FPGA (Altera EP1C12Q240). The DSP was used to store the control algorithm, which calculated the control and attitude signals. The FPGA was used to receive and transmit command signals to achieve logic control of the circuit.  
The experimental structure is shown in Figure 1, and the input speed signals are fixed, sinusoidal, or closed-loop sweep frequency signals for the speed closed-loop performance test. The classical incremental digital PID controller, the chatter-reduced sliding mode controller of Ref. [22], and the proposed discrete sliding mode controller with disturbance compensation algorithm were tested for  
Figure 8: Ground-based laser communication servo turntable experimental platform: (**a**) master computer; (**b**) ground-based laser communication servo turntable; (**c**) digital controller (power amplifier).  
Figure 7: Sliding mode trajectory contrast curves.  
application as the speed loop controller. The parameters of the incremental PID controller were set as \(k_{p}=0.8\) and \(k_{i}=0.16\). The variable structure function coefficients of the proposed and Ref. [22] controllers were set to \(\kappa T=0.56\), and the other parameters were set in accordance with the sliding mode simulation setting.
# Trepenoid Biosynthesis Dominates among Secondary Metabolite Clusters in _Mucoromycotina_ Genomes
## 3 Results and Discussion
### Cluster Types  
The greatest diversity of SMCs types was identified in _Mucorales_, which is likely a result of organism diversity and the relative abundance of assemblies for this order [42]. Canonical NRPS clusters are present predominantly in _Mortierellomycotina_ (\(n=66\)), whereas NRPS-like clusters with a phosphopantetheine attachment site (PP-binding, PF00550) or male sterility protein (NAD_binding_4, PF07993) and adenylate-forming (AMP-binding, PF00501) domains were identified in most of the analysed _Mucormycotina_ assemblies. The largest category of SMCs is terpene clusters, which have diverse domain composition and potentially highly variable products. A uniform low-copy distribution of siderophore clusters was observed among most assemblies except for _Umhelopsis_ spp.  
Canonical PKS or PKS/NRPS clusters were not encoded by the analysed genomes except for _Hesseltinella vesiculosa_ NRRL 3301, _Bifiguratus adelaidae_ AZ0501, _Gigaspora margarita_ BEG34 and all isolates of _Syncephalstrum racemosum_. It has been recently noticed that several of the putative clusters described in _Mucorinae_ are actually fatty acid synthase (FAS) Type I genes [16]. However, our results show very few clusters related to fatty acid synthesis, with only 10 4'-phosphopantetheinyl transferase superfamily (ACPS, PF01648) domain copies in total. They were marked as \"other\" by the classifier and rejected from the final list.  
Single SMCs of bacteriocin, betalactone, fungal-RiPP and ectoine were identified. Few of these were classified as hybrid SMCs with NRPS cluster fragments due to the presence of adenylation and condensation domains.
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## Abbreviations  
\begin{tabular}{l l} MTT & Multi-target tracking \\ ET & Extended target \\ RM & Random matrix \\ RHM & Random hypersurface model \\ GP & Gaussian process \\ RFS & Random finite set \\ MB & Multi-Bernoulli \\ DP & Distance partition \\ EM & Expectation maximization \\ PHD & Probability hypothesis density \\ GM & Gaussian mixture \\ GIW & Gaussian-inverse-Wishart \\ CV & Constant velocity \\ PMF & Probability mass function \\ EOs & Extended objects \\ OSPA & Optimal sub-pattern assignment \\ PDF & Probability density Function \\ \end{tabular}
###### Abstract  
The interaction between an ultrastrong laser and a cone-like target is an efficient approach to generate high-power radiations such as attosecond pulses and terahertz waves. The objective is to study the \(\gamma\)-ray generation under this configuration with the help of 2D particle-in-cell simulations. It is deciphered that electrons experience three stages, including injection, acceleration and scattering, to emit high-energy photons via nonlinear Compton scattering (NCS). These spatial-separated attosecond \(\gamma\)-ray pulses own high peak brilliance (\(>\)10\({}^{22}\) photons/(s\(\cdot\)mm\({}^{2}\cdot\)mrad\({}^{2}\cdot\)0.1%BW)) and high energy (6 MeV) under the case of normalized laser intensity \(a_{0}=30\) (I = \(2\times 10^{21}\) W/cm\({}^{2}\)). In addition, the cone target turns out to be an order of magnitude more efficient in energy transfer compared to a planar one.  
laser-plasma interaction; \(\gamma\)-ray generation; particle-in-cell (PIC) simulation 2022200220220220220220202202020220202202020220202202202202202202220  
intense attosecond pulses carrying orbital angular momentum by irradiating a cone target with a circularly polarized laser, which results in an electric-field component normal to an off-axis laser field obliquely incident, and relativistic surface oscillations convert the laser pulses to harmonic radiation via the ROM mechanism. Furthermore, a recent study has shown that the cone target structure can be employed to generate terahertz pulses efficiently because using the cone target improves the hot electrons' conversion efficiency and the energy of the electrons, both of which contribute to a stronger terahertz source with better collimation [21].  
However, the \(\gamma\)-ray generated from a cone-like target has not been systematically studied yet, and it promises to be a highly efficient \(\gamma\)-ray source. In this work, we propose an all-optical scheme in which a linearly polarized laser irradiates a cone target to generate attosecond pulses when the laser reaches the sides of the cone target, similar to the laser irradiating obliquely on the planar target. By comparing 2D-PIC simulations of the cone's different open angles, we found that the laser-to-photon energy conversion efficiency is the highest at 90\({}^{\circ}\), the cut-off energy of the \(\gamma\)-ray obtained reaches 6 MeV, the FWHM (full width at half maximum) of a single pulse can reach 267 as and the peak brilliance can reach \(>\)10\({}^{22}\) photons/(s\(\cdot\)mm\({}^{2}\cdot\)mrad\({}^{2}\cdot\)0.1%BW). In addition, the simulation comparison between the 90\({}^{\circ}\) cone target and a planar target shows that the laser-to-photon energy conversion efficiency under a cone target is 12 times that of a planar target and is an order of magnitude higher than that in ref. [22].
## 4 Results and Discussion
### Predictive Model Performance and Interpretation
This robustness in the face of input sparsity is particularly evident in emission estimates where despite the skewed distribution of grid factors in the input data (as discussed in Section 2.2), the GBR model preserved predictive stability without overfitting to dominant clusters. The implications of these results are significant for infrastructure design and planning. Reliable predictions across the entire operational domain enable scenario testing and real-time feedback during project-level sustainability assessments. Furthermore, the high alignment of predictions with actual results provides strong justification for deploying GBR as the core engine in the subsequent optimization phase. Unlike black-box deep learning alternatives, GBR offers a transparent and interpretable modeling structure when combined with SHAP-based analysis, further reinforcing its suitability for policy-driven, material logistics decision support systems. Figure 5: Calibration curve for GBR predictions: (**a**) cost prediction, (**b**) CO\({}_{2}\) emission. While the visual accuracy is high, it is also important to acknowledge potential residual structure under certain conditions, such as combined extremes in moisture content and transport distance. Future research could benefit from incorporating higher-order interaction terms explicitly, or exploring hybrid GBR-neural ensemble structures to resolve such localized performance plateaus. Nonetheless, in its current form, the GBR model presents a technically sound and operationally feasible predictive framework for quantifying both cost and emission consequences of steel slag utilization.
# A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions
## 2 Topology, Operation Principle and Optimization Strategy of Proposed PV/ES System
### Energy Optimization Strategy  
From Figure 2, to realize the MPPT of the PV module, the ES module should provide energy to the PV module via a DC/DC converter, compensating the current difference between the PV module and the DC bus. However, because of the inconsistency of the light intensity under partial shading conditions, the currents of the PV modules are usually different, causing SOC differences among the ES modules. In addition, to realize the constant active power from the PV/ES system to the grid in a certain time period, the ES modules should be further used to compensate the power fluctuations caused by random variations of light intensity. Hence, an energy optimization strategy should be designed for the coordination of all of the ES modules under their own capacity constraints, realizing the independent MPPT of the PV modules and the constant grid-connected active power for the PV/ES system.