esa-sceva/satcom-chunk-collection huggingface dataset:
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 2 Materials and Methods
### Data Extraction and Analysis  
#### 2.4.1 Spectral Extraction and Preprocessing  
The corrected hyperspectral images extracted the 400-1000 nm spectral data from the region of interest (ROI) for each plant's leaves. Since the bandpass filter blocks any light beyond 750 nm, we used a spectral range between 400-750 nm for further analysis. The mean spectrum of each plant's ROI from all varieties before heat treatment in controlled plants, with 300 extracted spectral data points and 300 spectral data points, both from individual leaves, was obtained from plants after heat treatment in resistant and sensitive plants. Since the first two varieties were sensitive, and the last two were high temperature-resistant, we extracted spectral data from the susceptible varieties and combined them into a single group. The exact process combined the extracted spectra for two resistant varieties. For further analysis, a total of 600 spectral data points (from control, resistant, and sensitive plants) were obtained under both conditions for control and heat-stressed plants. The data were divided into 70% for calibration and 30% for validation. The spectral data were preprocessed to correct random noise caused by non-uniform illumination and light scattering generated by instruments, path length, and non-uniformity. Hence, it was essential to preprocess the extracted spectral data through appropriate mathematical algorithms to visualize the crucial information embedded in the plants and eliminate unwanted variation from the extracted data. Multiple studies have reported the preprocessing for data pretreatment [45; 46; 47; 48; 49]. In the present study, we applied normalization multiplicative scatter correction (MSC), smoothing, standard normal variate (SNV), and Savitzky-Golay first and second derivative approaches, which the detailed information related to each  
Figure 3: Schematic representation showing data and analysis.  
preprocessing reviewed [50]. Figure 4a,b illustrates the preprocessed spectral data plot and visualizes differences between plotted spectral data.
## 2 Numerical Modelling
### Computational Configurations
The cylindrical mesh contains \(96\times 64\times 264\) nodes in the radial, azimuthal and axial directions, respectively. In the radial and axial directions, the cell sizes are distributed similarly as on the Cartesian mesh (\(\Delta r=0.028D\), \(\Delta x=0.039D\)). Along the azimuthal direction, the cell sizes are the function of the \(r\)-coordinate, and at the location \(r=0.6D\), \(\Delta L_{\theta}=0.03D\), where \(L_{\theta}\) is the arc length. A distance \(0\leq r\leq 0.6D\) was covered by 24 computational nodes. The test computations performed on a slightly denser mesh with \(128\times 96\times 264\) nodes did not reveal any important differences; hence, the coarser mesh was used in the simulations.
# Saturation, Allowed Transitions and Quantum Interference in Laser Cooling of Solids
## 5 Conclusions  
Steady-state analysis of ASF laser cooling revealed that improved cooling efficiency becomes possible at elevated pump intensities whenever the saturation intensity of the background absorption is less than that of the coolant ions. Experimental results (Figures 11 and 12) showed that this situation obtains at wavelengths longer than 1000 nm in 10% Yb:LiYF\({}_{4}\). In this crystal, the saturation intensity of the background absorption was determined experimentally to be \(I_{b}\sim\)2.6 \(\times\) 10\({}^{4}\) W/cm\({}^{2}\), a value low enough to be reached with the continuous-wave excitation used in our experiments. Above this intensity, the cooling efficiency doubled at 1035 nm when input power was increased to the maximum available. This affirmed that in some materials, ASF cooling efficiency could improve with higher pump intensity.  
Cooling of bulk sapphire doped with Ti\({}^{3+}\) ions was also demonstrated on the allowed \({}^{2}\)E\(\rightarrow^{2}\)T\({}_{2}\) transition of the dopant. The polarity of the TLS signals reversed when the wavelength of excitation was tuned from near resonance at 532 nm to wavelengths longer than the mean fluorescence value. The use of Brewster-cut sapphire that was highly purified and lightly doped was essential to minimize losses from the crystal coatings and parasitic absorptions from valence-modified titanium pairs or iron impurities [47; 48; 49]. Samples with FOM values less than 800 did not show net laser cooling at any wavelength. Hence, although configuration relaxation on allowed transitions normally contributes to strong heating in optical interactions, cooling is achievable in high-quality Ti\({}^{3+}\):Al\({}_{2}\)O\({}_{3}\) at long wavelengths in the absorption tail. Cooling in sapphire occurred at discrete absorption resonances which were assigned to electronic transitions and optical phonon sidebands. This behavior differed from cooling on forbidden transitions in rare-earth systems where continuous tuning is observed due to the continuous density of states spectrum for the acoustic phonons which mediate refrigeration. Overall, the present results provide accelerated cooling to lower minimum temperatures based on electric-dipole-allowed transitions and optical saturation. These findings should enable Raman cooling, self-cooled lasing without inversion, and other advancements of the field.  
Conceptualization, S.C.R.; methodology, L.B.A., L.C. and S.C.R.; investigation, L.B.A. and L.C.; formal analysis, L.B.A., L.C. and S.C.R.; writing, L.B.A., L.C. and S.C.R.; supervision, S.C.R.; funding acquisition, S.C.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Air Force Office of Scientific Research, MURI grant FA9550-16-1-0383.  
All data sets obtained in this research are available upon request from lead author [PERSON].  
The authors wish to thank [PERSON] for the loan of a crystal of 10% Yb:LiYF\({}_{4}\).  
The authors declare no conflict of interest.
# What Advanced Treatments Can Be Used to Minimize the Production of Sewage Sludge in WWTPs?
## 3 Advanced Treatments and Technologies
### Biological Treatments
#### 3.2.3 Biological Predation (BP)  
BP indicates the consumption of excess sludge by higher organisms such as protozoa and metazoa [94; 34]. This process can be applied both in the water line and in the sludge line. If it is applied in the water line, it requires a two-stage system: (i) the first has a short hydraulic retention time (HRT), in order to favour the proliferation of rapidly growing bacteria for wastewater treatment; (ii) the second has a long SRT in order to favour the growth of predators [25; 95]. For instance, among the protozoa, the ciliate species have a predatory effect on the microfauna of CAS [96]. [PERSON] et al. [95] reported the results of a lab-scale application of a two-stage system with a temperature of 30\({}^{\circ}\)C and a pH of 7. Using bacteria _Pseudomonas fluorescens_ and _Tetrahymena pyriformis_, they observed sludge reduction yields from 12% to 43%. The main disadvantages of this application are the significant volume required for the second stage reactor due to the high SRT [94] and the difficulty controlling the growth and reproduction of protozoa and metazoa in the biological system [25]. Other authors studied the application of this treatment in the sludge line. In this case, metazoa, particularly worms and larvae, are preferred to protozoa. _Eisenia foetida_ and _Hermetia illucens_ are the most used. For instance, [PERSON] and [PERSON] [97] applied _Hermetia illucens_ in order to minimize the production of primary and secondary SS; after 35 days of process, the wet weight had been reduced by 16%. [PERSON] et al. [98] highlighted that, for a full-scale application, the instability of the worm reactor represents the major disadvantage. They reported that some researchers have brought up some possible solutions to this problem. For instance, they explained that a 75% decrease in the amount of TSS was observed in an optimized reactor with _Lumbriculus variegatus._  
In Table 2, several applications of biological treatments are presented.
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 6 Image Distortion Correction
### Results  
#### 6.4.1 Synthetic Data Results  
A synthetic chart, shown in Figure 22, with 19 \(\times\) 19 dots, is generated using random perspective, user variable lens distortion 0.3 and optical centre (4, \(-\)5).  
The PSO algorithm and CMA-ES [35] algorithm were used to find solutions to map the distorted points to undistorted points.  
Both CMA-ES and PSO algorithms give similar results of max pixel error from 0.22 to 0.28 and average pixel error from 0.09 to 0.08.
# Line Clipping in 3D: Overview, Techniques and Algorithms
## 3 Line-Clipping Algorithms in Three-Dimensions
### Three-Dimensional Kolingerova Line Clipping  
In 1994, [PERSON] presented a comparative study on 3D line-clipping algorithms. Based on the Cyrus-Beck algorithm, she proposed improvements for increasing its speed. Some of these improvements were related to the way that the algorithm calculated intersection points, e.g., by using orthogonal planes, since the new methods use the fact that each line can be described as the intersection of two planes. Furthermore, unlike Cyrus-Beck, she proposed ways to end the computation earlier when finding the two intersections of the line segment against the clipping volume.
## 5 Database Content and Analysis Examples  
The FC-BENTEN database serves as a comprehensive repository of experimental synchrotron X-ray data tailored for the advancement of PEFC research. By standardizing protocols across various measurement techniques, such as XAFS, HAXPES, XRD, PDF, and SAXS, the database ensures high-quality and reproducible data essential for driving material innovation. In addition to synchrotron-based techniques, electrochemical measurements such as CV are included, enabling the direct comparison and integration of data from different analytical perspectives.  
Up to January 2025, the database contained extensive datasets encompassing 202 user-submitted samples and 57 standard reference samples. These datasets, summarized in Table 4, reflect a systematic approach to sample preparation and measurement, with each sample undergoing appropriate pretreatment conditions (e.g., AsMade, hydrogen reduction, or electrochemical treatment). This ensured that the collected data were consistent and aligned with the specific analytical goals.  
For example, XAFS measurements systematically capture key absorption edges (e.g., Pt L3, Fe K, and Co K), whereas HAXPES provides comprehensive spectra of the core levels and valence bands across various energy ranges. This meticulous data-acquisition process often results in multiple datasets per sample, contributing to the breadth and depth of the  
Figure 19: FC-BENTEN web interface for data access ([[https://fcbenten.jasri.jp/](https://fcbenten.jasri.jp/)]([https://fcbenten.jasri.jp/](https://fcbenten.jasri.jp/)), accessed on 26 March 2025). The directory structure on the left panel and full-text search functionalities streamline dataset navigation. The upper-right pane lists files, while the lower-right pane displays thumbnails or spectra previews, facilitating rapid and informed dataset selection.  
database. Furthermore, additional datasets derived from the reference samples enhance the utility of the database as a benchmarking tool for validating analytical methodologies.  
Although the database facilitates advanced data analysis, comparing datasets across different measurement techniques remains labor-intensive due to individualized data storage. To address this, a Data Retriever tool was developed, consolidating key analytical parameters into a unified tabular format for each sample and pretreatment condition. This tool, currently reliant on Excel, is designed for future compatibility with hierarchical data formats, such as HDF5 [62], streamlining comparative analyses and fostering insights into the interplay between structural and electronic properties of PEFC materials.
## 2 Experimental Setup
### Data Acquisition and Analysis  
The sensor setup for this experiment consists of an sR1030 radar from IMST GmbH (Kamp-Lintfort, Germany) for radar sensing and a Raspberry Pi for data acquisition, data validation, pre-processing, data storage, and wireless data transfer to a laptop. Despite not being a fully fledged VNA, the radar could provide the same insights, and its smaller size is also better suited for in-flight testing. The radar system was operated via Power-over-Ethernet using a corresponding switch. Since the Raspberry Pi needs 5 V while the PoE-switch needs at least 24 V, a DC-to-DC converter was used to power both parts from the same 24 V source.  
A schematic of the setup is shown in Figure 5. The sR1030 radar system sends out its frequency ramps into the waveguide from 23.5 GHz to 26 GHz with its maximum bandwidth of 2.5 GHz. The setup, which was mounted on the UAV, can be seen in Figure 6. This waveguide is fully integrated into the housing of the drone as shown in Figure 7. For the experiments, the sensor setup was mounted inside the LIBIS UAV from the Polytechnic University of Madrid. For safety reasons, the drone was tied to a laboratory table so that it could hover slightly above the table. Photos of the final setup, including the drone prototype, can be seen in Figure 8.  
The waveguide has screws that can be removed from the top, creating holes through which EW can leak into the surrounding area. This leads to a change in the detected waves at the end of the waveguide. During the measurements, the seven screws are always removed in exactly the same order. Due to the high sensitivity, even partial rotations of the screws already result in signal changes, hence influencing the damage indicator. In each measurement, screws can only be removed one after another, and the trend of the damage indicators can be analyzed.  
Figure 5: Data processing pipeline.  
The time domain data recorded by the radar are sent to the Raspberry Pi using a TCP connection, where the signals are first checked for completeness. If a measured frequency ramp is incomplete, it is discarded and another one is requested from the radar. One measurement consists of 100 ramps. Once these have been recorded, they can be accessed through a Wi-Fi connection from the Raspberry Pi. Further evaluation takes place on a laptop. For the analysis and calculation of damage indicators, the ramps of a measurement are averaged and Fourier-transformed. The averaged ramps in the frequency domain \(s\) can be used to calculate the damage indicator (DI) as the RMS relative to a baseline signal \(b\):  
\[DI=\sqrt{\sum_{i=0}^{N}(s_{i}-b_{i})^{2}}. \tag{1}\]  
In this equation, \(N\) denotes the number of samples. A higher damage indicator here means greater damage.  
Figure 8: **(left)** Photo showing the radar, Raspberry Pi, and waveguide; **(right)** photo of the complete drone prototype.  
Figure 6: The setup of the experiment before it is mounted on the UAV.  
Figure 7: Photo of the top of the waveguide with seven screws that can be removed.
# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Hybrid
The algorithms used included particle swarm optimization (PSO), ant colony optimization (ACO), the artificial bee colony algorithm, the firefly algorithm, the gray wolf optimizer and the whale optimization algorithm. Regarding the results, the use of SI techniques such as PSO and ACO showed promising results regarding the optimization of wireless communication systems, improving D2D transmission rate, user association, load balancing, MIMO detection, beamforming and channel estimation. SI techniques have been applied to enhance network security, UAV communication designs and clustering and routing in IoT networks within 5G networks, showcasing their potential in optimizing network performance and security. Load-Aware Self-Organizing User-Centric Dynamic CoMP Clustering for 5G Networks [63]: In this paper, the authors focused on introducing innovative load-aware, user-centric Coordinated Multi-Point (CoMP) clustering algorithms designed for 5G networks, with the primary aim of enhancing spectral efficiency, load balancing and user contentment through the dynamic adjustment of cluster sizes in response to network conditions and user behaviors. Key features include a load-aware, user-centric CoMP clustering algorithm, which expands the cluster size in heavily loaded cells to maximize capacity and incorporates a re-clustering mechanism to migrate users from overloaded cells to less burdened ones, thereby mitigating user dissatisfaction and augmenting system throughput, which is particularly beneficial in densely populated deployment scenarios. The simulation results demonstrated significant enhancements in user satisfaction, load distribution and system throughput, effectively mitigating load imbalances in hotspot areas, optimizing cluster utilization in highly loaded cells and improving overall system performance.
## 3 Results and Discussion  
The main result of this work has been to obtain the 3D parametric design of a handle for the graspers used in laparoscopic surgery, in which the parameter \(\rm D_{M}\) allows the design to be scaled to adapt the size of the handle to each surgeon's hand size. In particular, it was found that the anthropometric measurement of the hand that is most appropriate to relate the handle size to the surgeon's hand size is that defined by PLM. To obtain a 3D design, first the surgeon's PLM value is taken. This value is then divided by the coefficient 2.967 (see Table 4) to give the most appropriate value of the median diameter of the handle (\(\rm D_{M}\)). With this value, the 3D parametric model of the handle is scaled to the surgeon's hand size. Figure 5 shows some examples of 3D CAD models scaled to different surgeons among the participants in this study. The curve drawn in a dashed line on the different customized models is in this way adapted to the surgeon's hand as shown by the equivalent dashed line in Figure 3b.  
To check the results, several 3D CAD parametric designs for different surgeons were fabricated by additive manufacturing (for different sexes and hand sizes). Figure 6 shows one of these handles made for a particular value of PLM, and therefore with a characteristic value of \(\rm D_{M}\) (a value that determines the complete design of the handle). For this, first the surgeon's hand was scanned, to then obtain the palm length (PLM), in this case giving a value of 96.55 mm. The optimal diameter was calculated from the anthropometric ratio \(\rm PLM/D_{M}\) resulting in a custom value of \(\rm D_{M}=32.54\) mm, which was used to generate the parametric model of the handle (Figure 6).  
To be fully functional for the validation tests, the 3D CAD model was designed with a hollow interior. This allowed the shaft (the metallic clip) to be inserted together with the opening and closing mechanisms of the grasper and the shaft's rotation mechanism (Figure 6).  
Figure 5: Custom 3D Computer Architectural Drawings (CAD) models of the surgical handle depending on each surgeon’s PLM measurement.  
Figure 7 shows a surgeon using the prototype (D\({}_{\text{M}}\) = 32.54 mm) during the validation test. At the conclusion of the test, the surgeon confirmed that their experience using the handle was completely satisfactory [18]. Identical results were obtained with the surgeons whose handles corresponded to those illustrated in Figure 5.
## 1 Introduction
The specific implementation involves using the disentangled representation (DR) method to disentangle the image into content and style, where the content part is utilized for the segmentation branch, thereby encouraging the segmentation branch to focus more on spatial information relevant to the segmentation task while suppressing irrelevant information. In general, we propose a new network called UNet-MSFE-DR for retinal vessel segmentation, based on multi-scale feature extraction and disentangled representation. The main contributions of this paper are summarized as follows:  
* A multi-scale feature extraction (MSFE) module is designed to replace the simple skip connection, which can enhance the ability of the network to capture multi-scale information and effectively improve segmentation performance. * An image reconstruction auxiliary branch is designed to assist in the segmentation task. The utilization of the disentangled representation method enables the segmentation branch to encompass spatial information relevant to the segmentation task while suppressing redundant information. * Experiments are conducted on three public datasets, and the results demonstrate the superiority of our method compared to other approaches. The rest of this paper is organized as follows. Section 2 discusses related works, covering current deep learning-based retinal segmentation methods and disentangled representation methods. Section 3 details our approach, describing the designed network. Section 4 and Section 5 describe the experiments and results, and the final section draws the conclusions.
## 2 Numerical Calculation Model
### The Boundary Condition Setting of the Calculation  
#### 2.3.1 Boundary Conditions of Flow Pulsation  
The pressure pulsation in the cooling system originates from the pump that supplies the low-temperature coolant. In the cooling system of the DCM, commonly used pump types include centrifugal pumps and plunger pumps, both of which induce fluid pulsations in the system. The pressure pulsation caused by centrifugal pumps is related to the pump's blades. The oscillation frequency of the fluid is equal to the product of the pumping frequency and the number of blades, and the pulsation characteristics of the fluid over time follow the changing trend of a trigonometric function [12; 13]. The fluid oscillation induced by plunger pumps is related to the driving mechanism, which employs a crank-slider mechanism. When the crank angular velocity is constant, the displacement of the slider and the crank angle have a nonlinear relationship [14], causing periodic variations in the fluid pressure supplied by the pump. Therefore, this paper introduces a velocity boundary condition with a sinusoidal trend as the pulsed excitation for the cooling system. The expression for the velocity boundary condition is given by: \(v_{t}=v_{a}+A\sin(2\pi\cdot f\cdot t)\). Here, \(v_{t}\) is the instantaneous velocity at the inlet, \(v_{a}\) is the average flow velocity of the cooling system, \(A\) is the amplitude of velocity pulsation caused by the pump, \(f\) is the pulsation frequency, and \(t\) is the moment in time. The flow rate provided by the liquid nitrogen supply system during rated power operation is 3 L/min, corresponding to a flow velocity of 0.995 m/s. As shown in Table 2, when studying the influence of pulsation frequency on the stability of the DCM, the pulsating flow velocity frequency \(f1\) is the excitation in the low-frequency range, and \(f2\) is the high-frequency range (40-400 Hz nearly covers the range of flow pulsations typically induced by low-temperature liquid nitrogen pumps; for the sake of study and analysis, this range is divided into two research sections in this paper). When investigating the influence of pulsation amplitude on the stability of the DCM, the flow velocity under the rated power of the liquid nitrogen supply system is taken as the reference, and a velocity pulsation with an amplitude A (ranging from 0.1% to 1% of the average flow velocity) is chosen as the excitation. In this section, single phase simulation is used, excluding two-phase gas-liquid flow.