esa-sceva/satcom-chunk-collection huggingface dataset:
# Vector Beams with Only Transverse Intensity at Focus
## 2 Methods  
### Numerical Simulation  
The simulation in this work was carried out by the calculation of the Richards-Wolf integral [23,24], in which the electric field at the focus of a high numerical aperture lens is calculated by the equation:  
\[\begin{split}&\mathbf{E}(p,\psi,z)=-\frac{if}{\lambda}\int\limits \limits_{0}^{\infty}2\pi B(\theta,\varphi)T(\theta)\mathbf{P}(\theta,\varphi) \times\\ &\times\exp\{ik[\rho\sin\theta\cos(\varphi-\psi)+z\cos\theta] \}\sin\theta\,\mathrm{d}\theta\,\mathrm{d}\varphi,\end{split} \tag{1}\]  
where \(B(\theta,\varphi)\) is the electric field of the incident beam (before the focusing lens); \(\theta\) and \(\varphi\) are the polar and the azimuthal angles; \(T(\theta)\) is the apodization function, which characterizes the focusing lens (for example, it is equal to \(T(\theta)=\cos(\theta)^{1/2}\) for aplanatic lenses [24], or \(T(\theta)=\cos(\theta)^{-3/2}\) for zone plates [25]); \(f\) is the focal length; \(k=2\pi/\lambda\) is the wavenumber; \(\lambda\) is the wavelength; \(\alpha\) is the maximum polar angle, which is defined by the numerical aperture of the lens \(\alpha=\arcsin(\mathrm{NA})\); and \(\mathbf{P}(\theta,\varphi)\) is the polarization vector:  
\[\begin{split}\mathbf{P}(\theta,\varphi)=\left[\begin{array}{c}1 +\cos^{2}\varphi(\cos\theta-1)\\ \sin\varphi\cos\varphi(\cos\theta-1)\\ -\sin\theta\cos\varphi\end{array}\right]a(\theta,\varphi)+\\ +\left[\begin{array}{c}\sin\varphi\cos\varphi(\cos\theta-1)\\ 1+\sin^{2}\varphi(\cos\theta-1)\\ -\sin\theta\sin\varphi\end{array}\right]b(\theta,\varphi),\end{split} \tag{2}\]  
where \(a(\theta,\varphi)\) and \(b(\theta,\varphi)\) are functions describing the state of polarization of the \(x\)- and \(y\)-components of the incident beam. The Richards-Wolf integral (1) is used to calculate the components of the electric and magnetic fields at the focus of a high numerical aperture lens because it allows calculation in a vector approximation--it calculates not only the transverse field components but also the longitudinal component. The Richards-Wolf integral is valid if the focal length is much longer than the wavelength of the focused light. For simplicity of calculations, it was assumed in the simulation that the zone plate (\(T(\theta)=\cos(\theta)^{-3/2}\)[25]) with \(\mathrm{NA}=0.95\) focusing the plane wave \(B(\theta,\varphi)=1\) in air \(n=1\).  
In this work, vector beams with sector azimuthal polarization are investigated. Figure 1 shows the direction of polarization of the studied beams: Figure 1a shows the direction for an azimuthally polarized beam; Figure 1b,c show the direction of polarization of a beam with a V-line singularity [26]; Figure 1d shows the polarization of a beam with two V-line singularities. It should be noted that the direction of polarization of the beam in Figure 1b coincides with the direction of polarization of an azimuthally polarized beam that has passed through a binary step [27; 28].
## 3 Results  
The following section will assess urban accessibility to destinations that most people commute to in their daily lives. UrMoAC also has the capability to compute accessibility measurements to other destinations that could be considered to expand the current research and that are indeed very important to fully understand accessibility levels in a given area; nevertheless, they go beyond the scope of work of this research. In the same way, this study analyzed one-way trips as a starting point to evaluate the accessibility of a city, and although it was not implemented in this study, one way to improve the implemented methodology is by including the analysis of round trips [33], activity paths, and travel times into the simulation with the help of other open-source tools such as SUMO [34] and TAPAS [35].  
As the research was based on the concept of the 15 Minute City, the two transport modes used to assess accessibility were walking and cycling. Many cities are encouraging more people to walk or cycle, as they bring about important benefits to society (noise reduction, exercise, safe and comfortable streets), to the environment (reduction of GHG emissions), and even to the economy (pedestrian and cycling infrastructure is less expensive to build and maintain than that of motorized transport). However, there are important factors that can impact the decision to use one transport mode or another, one of them being the geography of the city. The inclusion of such factors is beyond the scope of this article and can be added to the limitations of the study. However, a more realistic estimate could be obtained by adding the \"steepness\" [36] of the streets in the city as another factor to the OSM dataset.  
Nevertheless, it is important to state that, today, cities and societies still rely heavily on motorized transport such as public transport and private vehicles to reach certain destinations. Consequently, this study should be extended by incorporating accessibility measures to the chosen destinations using such modes of transport. To do so, it is necessary to develop datasets that provide the average speed of cars on the entire road network throughout the day, as well as GTFS datasets of the whole public transport system of the given city. Currently, such datasets are not available for both cities, eliminating the possibility to make such comparisons in the following section.
## 5 Simulation Analysis
### Effect of Robot Performance on Formation Transformation  
From Equation (8), the navigation state of the robot itself was shown to have a great influence on the choice of the formation transformation scheme, so this section discusses  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Robot Number** & **Coordinates of the Robots** & **Coordinates of the Target** & **Coordinates of the Obstacles** \\ \hline
1 & (60, 150, 150) & (250, 150, 150) & (100, 0, 0–300) \\
2 & (35, 125, 150) & - & (100, 120, 0–300) \\
3 & (35, 175, 150) & - & (100, 300, 0–300) \\
4 & (10, 100, 150) & - & - \\
5 & (10, 200, 150) & - & - \\
6 & (60, 150, 100) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation experiment environment setup.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(E_{b}\) & \(E_{a}\) & \(v_{g}\) & \(p_{0}\) & \(D_{s}\) \\ \hline Value & \(-20\) & \(20\) & \(4\) & \(70.3\) & \(90\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter setting.  
the influence of different state parameters on the transformation scheme. The performance of the robot is affected by its internal space limitation (limited battery compartment and drive unit, etc.) and water resistance, and the navigation speed was set to within 10 m/s in this paper. Because of the random nature of the taboo search, the data obtained from each trial were not identical, and Table 4 shows several representative sets of results (values are normalized).  
As can be seen from Table 4, the FCC model proposed in this paper can autonomously select dynamic formation transformation schemes according to the different robot navigation parameters (\(v_{g}\) and \(p_{0}\)), and each scheme corresponds to a unique \(\mu\) and \(f\), thus constraining the formation. In addition, the FCC model also leaves an interface for environmental factors such as ocean currents, which can simulate the formation change under ocean current disturbance.
# Developing a GIS Model for Identifying Bear Corridors with Open-Source Data: The Romanian Context
###### Abstract  
The brown bear (_Ursus arctos_) is the only bear species on the Romanian territory, its natural habitat occupying a third of the country's surface, including the entire mountain area as well as the hills in the immediate vicinity. The bear population in Romania is the largest in Europe, increasing every year due to legislative protection, the species being present in Annex II of the Habitats Directive. The fragmentation of the bears' habitat is the main cause of conflict occurrences between bears and the human population, which have occurred increasingly often in recent years. The main aim of this study is the ecological diagnosis of lands populated with bears in Romania to identify the highest quality habitats within the hunting grounds and to identify possible ecological corridors aimed at preserving the connectivity between them, using GIS spatial analysis techniques and taking into account the ecological conditions needed for the brown bear to survive. Following the spatial modelling of the open-source data, it was possible to observe the presence of habitats of the highest quality that could support a bear population outside the areas where bear specimens are concentrated (the counties of Mures, Bistrita, Harghita, Covasna, Neamt, Buzau, Vrancea, Prahova, Brasov, Sibiu, and Arges). The analysis of these habitats, where the bear population has exceeded the optimal level that they can support, highlighted that to allow the passage of bear specimens from one habitat to another, it is necessary to create ecological corridors in several key areas deducted on the basis of mathematical models.  
GIS spatial analysis; ecologic corridor; habitat connectivity; sustainable management Article
## 3 Synchronization and Channel Estimation for TIBWB-OFDM
### Frame Synchronization  
Taking advantage of ZC proprieties, a time-domain correlation operation can be performed between the received preamble signal and known sequence in order to obtain the beginning of each transmitted frame. Hence, by autocorrelating the known preamble sequence, maximal correlation peak \(P_{zc}\) is expected to appear in the \(N_{ZC}\) sample. This value is extremely important since it defines the threshold percentage value for the receiver system markers, thereby affecting the success or miss probability of detection. Figure 3 shows the frame-detection algorithm flow graph.  
In order to better understand Figure 3, Algorithm 1 details in pseudocode the followed procedure for frame detection. Input variables are the TIBWB-OFDM modulated block \(\mathbf{X}\), composed by a Zadoff-Chu preamble sequence, \(\mathbf{S_{ZC}}\), and threshold setting parameter \(\delta_{decision}\). For the output result, it is expected to obtain correlation index peaks of several transmitted preamble frames and stored ZC sequence \(y_{sync}\) that translates the frame beginning sequence.  
Figure 3: Frame-detection algorithm procedure for ZC preamble in TIBWB–OFDM receiver system.  
Figure 2: Preamble-assisted TIBWB–OFDM receiver scheme with IB–DFE equalization.  
The threshold decision device implementation is not straightforward due to the wireless-channel multipath effect. So, to achieve good performance, threshold variable _threshold_ is not fixed, and fluctuates depending on the transmitted correlation preamble amplitude peak. More than one peak may appear in dispersive channels situations due to signal echos. Therefore, after the detection threshold signal \(y_{thold}\), it is necessary to develop a correction algorithm based on a moving window width \(\mathcal{L}\) and the marked threshold indices \(y_{index}\), that accepts the maximal peak index and eliminates the remaining ones inside the respective window. The unique amplitude peak marks the presence of the frame TIBWB-OFDM beginning. However, if we encounter other high amplitude peaks where it is not expected, the algorithm acknowledges this event has a possible noise frame, rejecting the respective block.
## 3 Results and Discussion
### Physicochemical Characteristics of the Peat Profile
The value of the H/C parameter does not change monotonously with depth in the studied part of the deposit, which indicates a slight increase in the proportion of aromatic fragments in organic matter along the vertical profile of the deposit. At the same time, a pronounced peak of the H/C ratio is observed at a depth of 20 cm, which indicates a high content of the aliphatic component with the organic matter of this horizon, probably associated with the supply of low molecular weight organic acids by leaching from the overlying stratum. The C/N ratio is 20-34, which is also typical for moss-type peat [62]. The degree of oxidation of organic matter (\(\omega\)) is in the range of \(-\)0.66--0.29 and also varies non-linearly along the depth of the profile, which is apparently associated with the occurrence of multidirectional redox processes, vertical transfer of organic matter in deposits, and \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Layer, cm**} & **Degree of Decomposition** & **Natural Moisture Content, g/g** & **Bulk Density,** & **Eh\({}_{4}\), mV** & **rH** \\  & **R\%** & **Absolutely Dry Matter** & **g/cm\({}^{3}\)** & **Eh\({}_{4}\), mV** & **rH** \\ \hline
0–10 & 0 & 23.96 \(\pm\) 0.23 & 0.075 \(\pm\) 0.002 & 428 \(\pm\) 6 & 22.5 \\ \hline
10–20 & 5 & 23.57 \(\pm\) 0.04 & 0.095 \(\pm\) 0.001 & 340 \(\pm\) 7 & 19.5 \\ \hline
20–30 & 5 & 24.49 \(\pm\) 0.07 & 0.097 \(\pm\) 0.002 & 200 \(\pm\) 1 & 14.8 \\ \hline
30–40 & 7 & 20.66 \(\pm\) 1.20 & 0.099 \(\pm\) 0.003 & 125 \(\pm\) 3 & 12.2 \\ \hline
40–50 & 7 & 15.19 \(\pm\) 0.11 & 0.109 \(\pm\) 0.004 & 109 \(\pm\) 7 & 11.7 \\ \hline \end{tabular}
\end{table} 
Table 1: Change in the degree of decomposition, natural moisture content, bulk density, ORP (Eh\({}_{4}\)), and the rH-index of peat by depth. Figure 2: Change in the elemental composition of peat profile by depth. dynamics of swamp water level fluctuations. At the same time, the minimum value of this indicator falls on the horizon of 20 cm, which is also distinguished in the section by significant fluctuations in the parameters of ash content, the content and composition of organic matter (Figure 3). The maximum ash content for the 20 cm horizon can be due to both the washing out of the mineral part from the overlying horizons and the anthropogenic dust content of the atmosphere during the formation of the peat deposit. See more Appendix A, Table A3.
## Materials and Methods
### Enzyme-Assisted Extraction of polysaccharides  
Dry powder of naked pumpkin seeds (_Cucurbita moschata_) was obtained after degreasing according to the reported method [15]. 3.0 g of powder was mixed with 120 mL complex enzyme solution (cellulase:papain in the ratio of 1:1) in a beaker and extracted at the designated pH, temperature, time, and enzyme concentration. After extraction, the enzyme activity was inactivated in boiling water for 6 min and then centrifuged (3500 rpm) for 15 min. The extracts were filtered, concentrated and precipitated by the addition of ethanol to a final concentration of 80% (\(v/v\)), and kept overnight at 4 \({}^{\circ}\)C. The precipitant was collected after centrifugation (3500 rpm for 10 min) and dried to obtain the crude polysaccharides (PSP).  
Single factor experiments were employed to investigate the influence of extraction temperature (30, 40, 50, 60, and 70 \({}^{\circ}\)C), complex enzyme concentration (1.5%, 2.0%, 2.5%, 3.0%, and 3.5%), extraction time (20, 30, 40, 50, and 60 min), and pH (3.0, 4.0, 5.0, 6.0, and 7.0) on the yield of PSP. During the single factor experiments, one parameter was altered while the other parameters remained unchanged in all experiments.
## 2 Materials and Methods
### Cleanliness of Working Fluids (Lubricant) Contaminated with Solid Particles  
The primary objective is to analyze the quality of the lubricant used in the gear mechanism, with a particular focus on its contamination and overall cleanliness [19]. Maintaining optimal cleanliness of the lubricant is a key factor in extending the service life of components, improving operational efficiency, prolonging oil life, and reducing costs associated with routine maintenance. A clean lubricant--or a working fluid with the required moisture content--ensures long-term stable performance of the equipment.  
The presence of fine particles in the fluid leads to abrasive wear, whereas larger particles may cause fatigue damage. Contamination of the lubricant with solid particles accelerates its degradation, thereby reducing the overall efficiency and reliability of the system.  
Figure 4: The proposed measurement test rig.  
To monitor the quantity and size of solid particles in hydraulic fluids, several standards have been established. One such standard is NAS 1638 (National Aerospace Standard), which in recent years has been largely replaced by ISO 4406. A comparison of cleanliness class designations according to these standards is provided in Table 1.  
The ISO 4406 standard defines cleanliness codes consisting of three numerical values that categorize the size and distribution of solid particles in the following manner:  
* The first number represents the quantity of particles larger than 4 \(\upmu\)m per mL of fluid;
* The second number represents the quantity of particles larger than 6 \(\upmu\)m per mL of fluid;
* The third number represents the quantity of particles larger than 14 \(\upmu\)m per mL of fluid.  
The recommended cleanliness classes according to ISO 4406 for individual systems are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Cleanliness Class**} & \multicolumn{5}{c}{**Particle Size**} \\ \hline
**Code ISO 4406** & **NAS 1638** & **2–5 (\(\upmu\)m)** & **5–15 (\(\upmu\)m)** & **15–25 (\(\upmu\)m)** & **25–50 (\(\upmu\)m)** & **5  
An analysis of lubricant from gear modules will be evaluated based on ISO 4406 [20] and also compared to NAS 1638 [21] and technical manual NMAIR 01-1A-17 [22].
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 3 Surface Discharge Simulation Using the FDTD Method
### PD Current Source and Point Sensor  
The PD current can be represented by a pulse whose magnitude and risetime depend on various factors such as the PD type, its intensity and speed of propagation, and the surrounding medium. For example, typical risetimes of a protrusion PD in oil range between 0.7 ns and 2.0 ns, whereas a floating particle produces PD pulses having risetimes in the range 2.5-2.7 ns, and bad contact defects produce pulses up to 17 ns [1]. These PD pulses have spectra in the UHF frequency range of 300-3000 MHz [7].  
In this work, a current filament having a Gaussian shape defined by Equation (1) was used to simulate the PD source [21].  
\[i(t)=I_{max}e^{-t^{2}/2\sigma^{2}} \tag{1}\]  
where \(I_{max}\) = magnitude of the peak current and \(\sigma\) = pulse width measured at half of the maximum value. The PD current is expressed in the frequency domain as:  
\[I(\omega)=I_{max}\sigma\sqrt{2\pi}e^{-(\frac{\omega^{2}\sigma^{2}}{2})} \tag{2}\]  
The pulse rise time (\(T\)) of the Gaussian pulse is the time required for signal magnitude to change from 10% to 90% and is calculated as  
\[T=t_{90\%}-t_{10\%}=\sqrt{2}\sigma\ (\sqrt{ln10}-\sqrt{\ln\left(\frac{10}{9} \right)} \tag{3}\]  
Figure 4 shows examples of Gaussian pulses of different risetimes and their respective frequency spectra.  
Figure 4: _Cont._  
The PD excitation is represented by a current filament which extends over several FDTD segments that determine the PD path (Figure 5). The filament is oriented horizontally in the positive \(y\)-axis direction 0.5 mm above the insulating surface. The source of this filament is assumed to be a current source component having 50 \(\Omega\) output impedance. A Gaussian type waveform with a given pulse width and amplitude is associated with this source to excite the frequencies up to the upper limit of the UHF range. A \"point sensor\", which is a data storage facility, is used to store the computed field and current density values at any desired location within the FDTD mesh. The field values returned by this point sensor can be at the edges of the cells if the point sensor location is snapped to the FDTD grid, or extrapolated if it is between two edges.
# 3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
## 1 Introduction
[29] had put forward the V-net architecture, the U-Net 3D variant, to segment 3D images by the direct use of 3D convolutional layers that had an objective function on the basis of the Dice coefficient. A suitable loss function is an important measure for the usefulness of segmentation for an intended task. Nonetheless, the widely used loss functions for 3D FCNs, including cross-entropy or Dice, are proposed on the basis of integrals (summations) in various segmentation volumes. Besides, the regional loss in segmentation with a high imbalance level (which is common in liver and tumor segmentation) leads to substantially different values among different segmentation types, and this possibly affects the training stability and performance. [PERSON] et al. [30] proposed the boundary loss concept, where contour or shape space, rather than regions, was used to form the distance metric. It reduced the regional loss difficulty when there were problems of substantially imbalanced segmentation, since integrals were used along the inter-regional boundary (interface) rather than the imbalanced integrals across regions. Based on similar ideas, [PERSON] et al. [31] proposed Hausdorff (HD) loss based on distance metrics. Although HD was utilized to evaluate the performance of image segmentation algorithms, those algorithms rarely aimed at minimizing HD directly. An \"HD-Inspired\" loss function was proposed in [31] that could be used for a stable training segmentation model with the goal of reducing HD directly. Moreover, [PERSON] et al. [32] proposed a distance-based loss function named the Signed Distance Map (SDM) loss. Due to the rigorous mapping of the signed distance map computed based on the object boundary contour with a binary segmentation map, they took advantage of learning SDM on the basis of the medical scans directly. The task of segmentation was converted into predictive SDM in their method, which retained excellent segmentation performance and had better smoothness and shape continuity. In this paper, an improved V-Net based on distance metric was utilized for the 3D liver and tumor segmentation tasks. Three distance-based loss functions, including BD loss, HD Loss, and SDM Loss, were used in combination with the original V-Net loss, respectively, to solve the problem of a highly unbalanced liver and tumor segmentation. In addition, the algorithm was tested on two databases, and the results proved the effectiveness of improvement.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Sample-Matrix Interference  
Food samples often contain complex matrices--including fats, polysaccharides, polyphenols, and proteins--that can interfere with ionization efficiency and spectrum quality. These matrix effects are particularly pronounced in direct-from-food applications where enrichment or culture-free methods are desired. Such interference can result in poor spectra, weak signals, or background noise, compromising identification accuracy [49; 50; 51; 52; 140].  
Although protocols exist for sample clean-up and matrix optimization, they introduce additional steps, potentially reducing the speed and simplicity that make MALDI-TOF MS so attractive. Robust protocols for high-fat, high-sugar, or polyphenol-rich foods remain under development, and are not yet standardized across laboratories [49; 78; 99].