esa-sceva/satcom-chunk-collection huggingface dataset:
# Few-Cycle, \(\mu\)J-Class, Deep-UV Source from Gas Media
## 3 Results and Discussion
The beam divergence is augmented by plasma-induced refraction at the leading edge of the target profile. The plasma formation is more localized (Figure 6e) compared to when the gas cell is at \(z_{0}=-14\) mm (Figure 6a). The overall on-axis NIR intensity is reduced by a factor of four, as can be seen in Figure 6g. Due to the significant ionization at \(z-z_{0}\approx-5\) mm and  
Figure 5: (**a,c**) The experimental spectra taken at the dashed lines in Figure 3c,d, respectively. (**b,d**) DUV spectra obtained from simulation performed using the experimentally observed laser parameters. More details on the simulation results are discussed in Figure 6. Dashed lines in (**b,d**) show an analytic solution of the third-harmonic spectrum obtained by Fourier transforming the cube of the fundamental laser \(E(t)\) in the temporal domain. the localized plasma, the NIR fundamental spectrum becomes significantly blue-shifted at this point already, as indicated by the dashed line in Figure 6g. The frequency-shifted laser then propagates through the gas medium and generates DUV light through the third-order nonlinear process. Therefore, the blue-shifted DUV yield increases gradually after \(\mathrm{z}-\mathrm{z}_{0}\approx-5\) mm (Figure 6h). In both cases of \(\mathrm{z}_{0}=\pm 14\) mm, the DUV yield gradually increases as the beam propagates through the medium. The presence of plasma generated in the gas cell has a notable impact on the DUV efficiency through the phase mismatch. As discussed above, the dashed lines in Figure 3a,b indicate the higher yield for \(\mathrm{z}_{0}>0\). When the cell is located before the laser focus (\(\mathrm{z}_{0}<0\)), the plasma is distributed over the entire interaction region (Figure 6a,b), which makes the phase mismatching dominant. In the case of \(\mathrm{z}_{0}>0\), as can be seen in Figure 6e,f, the plasma is localized at around \(\mathrm{z}-\mathrm{z}_{0}\approx-5\) mm, with a relatively high electron density. The plasma-induced phase mismatching occurs only at the localized position.
## 4 Analysis of Existing Solutions
### Overview of Existing Educational Applications in Education  
The development of educational applications has seen significant growth in recent years, especially considering the accelerated technological advancement and increased availability of digital devices. Educational applications that utilize gamification and XR technologies enable students to engage in interactive learning and create a dynamic and engaging educational environment.
## 1 Introduction
### Modeling of Cell Separation/Partitioning
#### 1.2.1 Lattice Boltzmann Method
A schematic and step by step comparison between LBM and conventional CFD algorithms can be seen in Figure 3 below for a better understanding of the procedure in each numerical method. This method has been massively used in simulations of RBCs to study cell mechanical deformation behaviors under certain circumstances and/or suspension of these particles in various channel shapes representing the blood vessels to help the sorting application of microfluidic devices. A few examples of how LBM method performed in the real applications are presented in the following. Deformability of RBCs has been studied using LBM in simple channel conditions [60; 61] and/or under more complex environments [62; 63]. A few examples of results obtained from simulations using LBM method to simulate phenomena related to RBCs are presented in Figure 4. For a good review of such simulations in the past and more examples, one can refer to [64], where the researchers have presented results obtained from LBM simulations of RBCs-related phenomena. Figure 3: Conventional computational fluid dynamics (CFD) methods versus lattice Boltzmann methods (LBM) algorithms.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 4 Conclusions  
The purity of hybrid maize seeds was detected quickly and non-destructive based on near-infrared spectral imaging technology and a machine learning algorithm. Established models based on PLS-DA, KNN, NB, RF, SVM-Linear, SVM-Polynomial, SVM-RBF, and SVM-Sigmoid for classification and identification. The accuracies of the models were improved by multivariate scattering correction of the spectrum. Principal component analysis was used to select characteristic wavelengths to reduce the calculation time of the model. The confusion matrix and its evaluation factors were used to evaluate the classification effect of different models. The experiments showed that the support vector machine based on the polynomial kernel function has the highest classification accuracy. It can be seen from the analysis results that the discrimination accuracy of completely different maize seeds is very high. Even the seeds with the same maternal parent can be well distinguished. This study provides a theoretical basis and method for rapid and non-destructive testing of the purity of maize seeds and a methodological basis for further research on the quality detection of maize seeds.  
Conceptualization, X.X.; Data curation, Y.Y.; Formal analysis, H.X.; Funding acquisition, N.Z.; Investigation, Y.Y.; Methodology, H.X.; Project administration, Y.L.; Resources, H.X.; Software, H.X.; Supervision, N.Z.; Validation, Y.Y.; Writing--original draft, H.X.; Writing--review & editing, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Provincial Key Research and Development Project (Grant No. 20210201029 GX) and the General Free Exploration Project of the Jilin Provincial Department of Science and Technology (Grant No. YDZJ202201 ZYTS419).  
Not applicable.  
**Data Availability Statement:** All relevant data presented in the article are stored according to institutional requirements and, as such, are not available online. However, all data used in this manuscript can be made available upon request to the authors.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Recognition Algorithm** & **2490** & **2780** & **GD5** & **XX8** & **XY335** \\ \hline PLS-DA & 56 & 26 & 0 & 0 & 12 \\ KNN & 0 & 46 & 12 & 8 & 0 \\ NB & 0 & 0 & 0 & 10 & 0 \\ RF & 0 & 0 & 8 & 16 & 6 \\ SVM (Linear) & 2 & 8 & 4 & 2 & 0 \\ SVM (Polynomial) & 0 & 4 & 4 & 0 & 0 \\ SVM (RBF) & 4 & 10 & 6 & 4 & 4 \\ SVM (Sigmoid) & 16 & 10 & 12 & 12 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The omission error.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 3 Network Model
### Optical Data Collection Link  
For the optical link, the path loss \(PL\) of the underwater wireless optical link can be expressed as [37]  
\[PL\approx 10\log\left(\left(\frac{D_{r}}{2\theta d_{op}}\right)^{2}e^{-cd_{op }\left(\frac{D_{r}}{Rd_{op}}\right)^{\zeta}}\right), \tag{5}\]  
where \(D_{r}\) represents the aperture diameter of the receiver and \(\theta\) denotes half of the transmitter beamwidth, \(d_{op}\) represents the distance between transceivers, and \(c\) and \(\zeta\) represent the extinction coefficient and turbidity of water quality, respectively. Subject to the optical-to-electric conversion efficiency of the receiver, a minimum received power per bit is defined as \(P_{rec}^{op}\). Then, the transmission power of the UOC is expressed as  
\[P_{trans}^{op}=\frac{P_{rec}^{op}}{PL}. \tag{6}\]  
In order to ensure robust optical communication, it is necessary to control the minimum \(SNR_{\text{min}}^{op}\) requirements [38].  
\[SNR_{\text{min}}^{op}=\left[\frac{P_{trans}^{op}e^{-cd_{op}}D_{r}^{2}\cos\varphi }{(\tan^{2}\theta)4d_{op}^{2}NEP}\right]^{2}, \tag{7}\]where \(NEP\) represents the noise equivalent power and \(\varphi\) is the offset angle between transceivers. According to the Lambert \(W\) function [39], the maximum underwater optical communication distance while satisfying the communication \(SNR_{\min}^{op}\) can be obtained by  
\[\text{d}_{op}=\frac{2W\left(\frac{\zeta}{4}\left[\frac{(SNR_{\min}^{op})^{\frac{ 1}{2}}NEP\text{tan}^{2}\theta}{P_{trans}^{trans}D_{i}^{2}\cos\varphi}\right]^{- \frac{1}{2}}\right)}{c}. \tag{8}\]  
Since optical modems are usually directional, in order to receive optical signals from any direction, we assume that an omni-directional optical modem can be achieved by using multiple LEDs [40].
## 3 Results and Discussion
### WVTR Measurements
In accordance with a previous study [41], this result could be further explained considering both that water vapor diffusion coefficient in an amorphous or semicrystalline polymer depends on molecular dynamic or amorphous regions segmental movements, and the different glass transition temperatures of the biopolymers (PCL has a lower Tg compared to PHBV), but the presence and arrangement of substituent groups (such as methyl or ethyl groups) as in PHBV case, which tend to have a minor chain flexibility on account of steric effect, and therefore, a lower WVTR, but the lack of information does not allow to elaborate additional considerations. Compared to PHBV-based coatings, plasticizer addition did not significantly affect WVTR in PCL coatings: the ones with 10% and 20% resulted to be more permeable (though not significantly) to water vapor compared to pure PCL. In general, coated samples, in particular PCL ones, showed an improvement of barrier to vapor compared to base paper; nevertheless, barrier properties can be further improved using other application techniques: processing method plays a key role in film production and can do the same in the case of coatings. Long drying required by solvent casting process can contribute to the formation of more open microstructures and channels for easy diffusion of water vapor molecules [42]. Biopolymers hot processing may allow to achieve denser and more resistant structures.
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Craniofacial Superimposition
#### 3.4.2 Skull-Face Overlay  
Several proposals have been presented in the literature to perform the SFO task. The most natural way to deal with the SFO problem is to replicate the original scenario of the AM photograph in which the living person was in a given pose somewhere inside the camera's field. Regarding computer-aided automatic methods, the task of replicating on a skull the pose and the remaining acquisition parameters of a given facial photograph is the main goal of the SFO process. This is similar to the classic CV problem of replicating the pose of a 3D object from a photo based on some reference points. Technically, we are given \(n\) points having 3D positions \(a_{1}\),..., \(a_{n}\) and target 2D positions \(b_{1}\),..., \(b_{n}\). The goal is to find a projection P so that every P(\(a_{i}\)) is as close as possible to \(b_{i}\).  
The first computer-aided approach for the SFO task was proposed by [PERSON] et al. in 1991 [166]. The landmarks of the 3D skull mesh were selected to calculate the affine and perspective transformation (rotation, scaling and translation) to map with the landmarks of the 2D face image using real-coded genetic algorithms. Another automatic approach was presented in [169], in which they considered two different neural networks for the implementation of an objective assessment of the symmetry between two nearly frontal 2D images (skull and facial image). In the last decade, several works have tackled the SFO automation using evolutionary algorithms and fuzzy sets [173; 174; 177] (see Figure 7). These approaches are based on overlaying a 3D model of the skull over a facial photograph by minimizing the distance among pairs of landmarks while handling the imprecision introduced by the facial landmark location [40; 188]. The minimization process involves the search for a specific projection of the skull model leading to the best possible matching between corresponding landmarks. More recently, [PERSON] et al. [184] proposed a novel automatic SFO algorithm called POSEST-SFO. Unlike prior approaches, POSEST-SFO algorithm solves a system of polynomial equations relating the distances between the points before and after the projection. This latter algorithm was tested on a synthetic data set comprised of 9 CBCTs from 9 different subjects and 60 simulated photographs, i.e., 540 SFOs. This method is extremely fast, since it lasts 78 milliseconds to automatically perform a single SFO. In the most realistic scenario, considering soft tissue thickness (the mean distance was employed) and \(\pm\)5 pixels of error in facial landmarks, the mean back-projection error was 2.0 mm and 3.2 mm in frontal and lateral photos, respectively. However, this algorithm, contrary to previous publications [177; 179], does not address the sources of uncertainty, i.e., the articulation of the mandible, the estimation of the soft tissue thickness, and the intra- and inter-error in landmark location.
## 3 RAD System
### Main Modules of RAD System
#### 3.2.3 Rule Manager  
The rule manager generates the proper rule set for route information received from the traffic engineer module and pushes them to each switch. The following are example general route and action rules after attacks have been detected:  
(1) **Switch Rule**:  
_If This is a general routing rule on switch S5 for host H1(10.0.0.1), which sends traffic to H5(10.0.0.5). The '_actions': _output:1 represents H1 uses port 1 to send traffic to H5. The similar rules are pushed on other switches in the generated paths (Refer to network topology in Figure 2)_.  
{'ipv4_src': '10.0.0.1', 'cookie': '0', 'actions': output: 1, 'active': 'true', 'eth_src': '3a:e4:de:ed:02:ac', 'ipv4_dst': '10.0.0.5', 'eth_dst': '72:f1:22:9a:95:91', 'name': 'flow1', 'priority': '0','switch':00:00:00:00:00:00:00:05', 'eth_type': '0X0800', 'in_port': '3'}
**Switch Rule**:  
_If If a host H1(10.0.0.1) connected to switch S5 is identified as an agent to attack a host H22(10.0.0.22), the following rule is pushed into switch S5 for dropping the attack traffic._  
{'ipv4_src': '10.0.0.1', 'cookie': '0', 'actions': 'drop', 'active': 'true', 'eth_src': '3a:e4:de:ed:02:ac', 'ipv4_dst': '10.0.0.22', 'eth_dst': '72:f1:22:9a:95:91', 'name': 'flow1', 'priority': '0','switch': '00:00:00:00:00:00:00:05', 'eth_type': '0X0800', 'in_port': '3'}
# A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
## 2 Materials and Methods
### Segmentation Model Training
#### 2.3.2 SegNet  
SegNet [31] is a convolutional neural network architecture tailored for semantic image segmentation. Its distinctive features include a conventional encoder-decoder structure, where the encoder captures hierarchical features, and the decoder reconstructs the segmented output through up-sampling layers. Notably, SegNet utilizes max-pooling indices from the encoder during decoding to recover spatial information lost during down-sampling, contributing to accurate segmentation. The network leverages feature maps from the encoder for precise localization. Employing a class-specific softmax activation in the final layer enables pixel-wise classification. Although SegNet lacks skip connectionsbetween the encoder and decoder, its design, particularly the incorporation of pooling indices, makes it well-suited for tasks demanding detailed pixel-wise segmentation.  
In this study, the segmentation experiments have utilized the SegNet architecture in conjunction with VGG16 [32; 33]. In this context, VGG16 plays a role as a feature extractor, capturing high-level semantic information from the input images. It complements the segmentation capabilities of SegNet, contributing to an enhanced overall performance of the segmentation model.
# Design of a Broadband Transition from a Coaxial Cable to a Reduced-Height Rectangular Waveguide
###### Abstract  
For miniaturization, rectangular waveguides with a reduced height are often required, along with a coaxial transition for signal launching. We present a simulation-based design of a broadband transition from a coaxial cable to a rectangular waveguide with the height(\(b\))-to-width(\(a\)) ratio \(b/a\) ranging from 0.125 to 0.375. The proposed transition consists of a coaxial probe with a cylindrical head or a disk and two symmetrically placed tuning posts. To extend the operating frequency range, three sections of the rectangular waveguide are employed with properly chosen dimensions. Design examples are presented for the WR75 waveguide transition with a \(b/a\) of 0.125, 0.25, and 0.375, having a bandwidth of 83.4%, 92.7%, and 84.4%, respectively. Compared with previous works, our design offers the largest bandwidth in a right-angle coaxial-to-rectangular waveguide transition employing the aforementioned structure.  
transition; coaxial-to-waveguide; broadband; simulation-based design +
Footnote †: journal:  
difference between the coaxial cable and the waveguide. It is, therefore, crucial to design CWTs for a broadband operational bandwidth and a reduced waveguide height.  
In view of the above observation, the purpose of this study was to design a right-angle coaxial-to-rectangular waveguide with a simple disk probe and two tuning posts. The design goal was to obtain record-breaking bandwidth with the aforementioned structure. To this end, we employed the bandwidth extension method of [PERSON] [3] and the wideband right-angle CWT structure of [PERSON] and coworkers [4]. The end result is a new wideband coaxial transition for reduced-height rectangular waveguides. In the following, we will describe the initial design and optimization of the proposed transition.
# Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure
## 1 Introduction
Rather than focusing on the absolute values for the real processing time, the strategies for parallel processing were assessed with respect to the speedup. Speedup is a relative measure, defined as the ratio of the real processing time for the sequential program to the real processing time for the program running in parallel. Aside from the optimal combination of parallel processing strategies, the deployment of any workflow on a given platform might require adaptation of the code and, therefore, limit portability from one platform to another. This can be addressed by making sure that the implementation relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access. Another issue is linked to the implementation details of the workflow. Results highly depend on the specific software and libraries used. In this study, the open-source Python library pyjeo [8; 9] was used. A Dockerfile is also available to enable the software to be deployed and run in a variety of virtual environments [9]. This paper is organized as follows. In Section 2, the different strategies for parallel processing are presented. The workflow on which the current study is based is introduced in Section 3. In Section 4, we show that the openEO API [10] can be used to effectively abstract any platform dependency. We also describe the platform on which openEO was deployed for conducting the experiments. Results are presented in Section 5, and conclusions are drawn in Section 6.
## 2 Materials and Methods
### Characterization Techniques  
The BRUKER D8 (Bruker, Karlsruhe, Germany) advance diffractometer was used for the X-ray study of various mixed solids. The patterns were run at 40 kV with Cu Korradiation and 40 mA with 2 theta 2\({}^{\circ}\) min\({}^{-1}\) scanning speeds. The average crystallite size (D), dislocation density (\(\delta\)) and strain (\(\varepsilon\)) of Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) present in the solid investigated were determined using Equations (1)-(3) and based on X-ray diffraction line broadening as follows [19]:  
\[\mathrm{D}=0.89\ \lambda/\beta\ \mathrm{cos}\theta \tag{1}\]  
\[\delta=1/\mathrm{D}^{2} \tag{2}\]  
\[\varepsilon=\beta\ \mathrm{cos}\theta/4 \tag{3}\]  
Where D is the average crystallite size of the phase under examination, 0.89 is the Scherrer constant, \(\beta\) is the full width at half the height of the peaks in radians, \(\theta\) is the diffraction angle in radians, and \(\lambda\) is the wavelength of the X rays in nanometers.  
The Fourier-transform infrared spectrum (FTIR) of the investigated solid was calculated using a PerkinElmer Spectrophotometer (type 1430, PerkinElmer, Unit a Lantrisant CF72 United Kingdom). The IR spectrum was collected at wavelengths from 4000 to 400 cm\({}^{-1}\). A total of 200 mg of potassium bromide (KBr) grade vacuum-dried IR was blended with 2 mg of solid samples. Dispersed for 3 min by grinding in a vibratory ball mill, the mixture was inserted as a disk with 13 mm diameter into a steel die and subjected to 12 ton pressure. In the double grating IR spectrometer holder, the sample disk was inserted.  
Scanning electron microscope (SEM) and transmittance electron micrograph (TEM) were recorded with the help of JEOL JAX-840A and JEOL Model 1230 (JEOL, Tokyo, Japan), respectively, and in order to disperse individual particles over mount setup and Cupper grids, the samples were dispersed in ethanol and then treated ultrasonically for a few minutes.  
Energy dispersive X-ray analysis (EDS with Mapping) was performed with a Delta kevex device attached to an electron microscope, JED- 2200 Series (JEOL, Tokyo, Japan). The following parameters were used: 20 kV accelerating voltage, 120 s accumulation time, and 6 um window width. Using the Asa procedure, Zaf-correction, and Gaussian approximation, the surface molar composition was calculated.  
The vibrating sample magnetometer (VSM) (9600-1 LDJ, Weistron Co., Ltd., West Hollywood, CA, USA), was used to investigate the magnetic properties of the solids evaluated in a maximum field of 20 kOe. Loops of hysteresis, magnetization of saturation (Ms), magnetization of remanence (Mr) and coercivity (Hc) were established.  
Figure 1: Experimental flow chart for the synthesis of Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) nanoparticles (NPs).
## 4 Experiments
### Results
#### 4.3.4 Multi-Source Feature Fusion Inference Results
As illustrated in Figure 9, the method demonstrates strong capabilities in regard to person localization and behavior recognition, even under conditions involving severe occlusion. In Figure 9a, despite the mobile phone being nearly indistinguishable from the background, it is suc \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**AFAR/\%**} & \multirow{2}{*}{**CCA/\%**} \\ \cline{2-2} \cline{4-5}  & **All** & **Smoking** & **Playing on a Phone** & \\ \hline TSM–ResNet50 & 74.46 & 76.55 & 71.3 & 80.36 \\ Improved TSM & 76.3 & 77.57 & 74.38 & 81.84 \\ Multi-Source Feature Fusion & 83.37 & 84.47 & 81.69 & 83.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of anomalous behavior detection results using different methods. Figure 8: Behavior inference results: (**a**) standing, normal; (**b**) sitting, non-driving (smoking); and (**c**) standing, distracted driving (playing on a phone). cessfully detected. By combining this detection with the distinct postural cue of the driver bowing their head, the system accurately identifies two drivers playing on a phone. Similarly, in Figure 9b, the method detects a cigarette near the crew member's mouth. By incorporating contextual information from the control panel, the model correctly classifies the behavior as standing-distracted driving (smoking). These results demonstrate the robustness and practical applicability of the proposed approach in regard to complex operational environments. The multi-level feature fusion strategy effectively addresses key challenges such as target occlusion overlapping and weakened feature representation in dense scenes.