esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Higher Layer Protocol Evaluation  
In the previous section we have reached interesting conclusions about the influence of the propagation models, so in this section we are going to evaluate their impact on different higher layer protocols, MAC and routing protocols, using an accurate propagation model like the one defined by Bellhop and taking into account several physical parameters related to the network scenario and environmental conditions.  
Figure 10: Propagation loss (**a**) and goodput (**b**) values varying physical scenario parameters.  
In the following evaluation we will reuse the same network scenarios define in previous sections but in two different operational modes: One-Hop (OH) and Multi-Hop (MH). In the first one, OH, all network nodes are able to directly reach gateway node (packet destination); whereas the later mode, MH, some network nodes require relaying their packets through other nodes to reach the gateway.  
The difference between OH and MH modes is focused in the allocated transmission power level to network nodes, which define their coverage area, so in OH network scenarios we adjust transmission power level to reach gateway node from farthest nodes (the same as in previous section simulation experiments). However, for MH network scenarios we will reduce the power transmission of nodes in such a way that they will be able to reach only the nodes of the adjacent cells. In MH network scenarios, a routing protocol is required to let the packets travel across the network towards their destination (gateway node). By default, in MH network scenarios we define a static routing protocol. In Figure 11 we can see the connections between nodes in both operational modes.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 2 Materials and Methods
### Two Approaches to Utilize Masks  
The Ideal Ratio Mask (IRM) is a soft mask and is given as Equation (4):  
\[I_{IRM}(n,k)=\frac{\left|X_{d}(n,k,ref_{m})\right|}{\left|Y(n,k,ref_{m})\right|}, \tag{4}\]  
where \(ref_{m}\) denotes the reference microphone and \(m\) denotes the corresponding neural beamformer. \(Y(n,k,ref_{m})\) denotes the signal recorded at the reference microphone, and \(X_{d}(n,k,ref_{m})\) represents the estimated clean speech signal. For a noise mask, its value can be represented as \(1-I_{IRM}\).  
Two different approaches for incorporating a mask to obtain a desired signal are discussed below.
## 1 Introduction  
### Generalized Langton's Ant  
The virtual ant defined by [PERSON] [1; 2; 3] is the following cellular automaton. The \"ant\", represented by a pair \((z,D)\) of a lattice point \(z\in\mathbb{Z}^{2}\coloneqq\mathbb{Z}+i\mathbb{Z}\) and a direction \(D\in\left\{e^{0},\frac{z}{e^{2}},e^{\pi i},e^{\pi i},e^{\frac{3\pi}{2}}\right\}\)moves around on a two-dimensional square lattice \(\mathbb{Z}^{2}\), where each lattice point, referred to as a \"cell\", is colored by white or black (later we will also introduce gray cells). Initially, the ant is sitting on a given cell with a given direction, say \(z=0\) and \(D=1\). It proceeds to travel from cell to cell according to the following rule (see Figure 1): the ant \((z,D)\) moves to \(\left(z+D,\frac{z}{e^{2}}D\right)\) when \(z+D\) is a white cell, and to \(\left(z+D,\,e^{\frac{\pi}{2}}D\right)\) when \(z+D\) is a black cell; the cell \(z+D\) then reverses its color. In other words, the ant moves in the direction it is heading; when it lands on a white (or black) cell it rotates its direction \(90^{\circ}\) to the right (left); after that, the color of the cell changes to black (or white).  
[PERSON]'s ant has been investigated independently as one model of Lorentz Lattice Gas Cellular Automata (LLGCA). [PERSON]'s ant corresponds to the Flipping Rotator (FR) model on \(\mathbb{Z}^{2}\)[4]. In more general terms, the FR model was investigated on a triangular lattice \(\mathbf{T}^{2}=\mathbb{Z}+e^{\frac{\pi}{3}}\mathbb{Z}\)[5] and on a hexagonal lattice \(\mathbf{H}^{2}=\mathbf{T}^{2}-\sqrt{3}e^{\frac{\pi}{6}}\mathbf{T}^{2}\)[6], too. On \(\mathbf{H}^{2}\) the ant \((z,D)\), where \(z\in\mathbf{H}^{2}\) and \(D\in\left\{e^{\frac{2\pi}{3}}\colon\,k=0,1,2\right\}\), moves to \(\left(z+D,\,e^{\frac{2\pi}{3}}D\right)\) and \(\left(z+D,\,e^{-\frac{2\pi}{3}}D\right)\) when \(z+D\) is white and black, respectively (see Figure 1), and the cell \(z+D\) then reverses its color. Another generalization of [PERSON]'s ant on \(\mathbb{Z}^{2}\) is to introduce a third type of cell, called \"gray\" cell [7]. The ant \((z,D)\) moves to \((z+D,D)\) if \(z+D\) is a grey cell, and the cell \(z+D\) never changes its color but remains gray forever (see Figure 1). In LLGCA models, gray cells were introduced naturally as empty lattice points. We remark that the \(\mathbf{H}^{2}\) topology does not allow having such a gray cell.  
We denote by R, L and S the ant's valid moves corresponding to the Right-turn, Left-turn and Straight-ahead respectively under these transaction rules; e.g., by R, L and S, the ant \((z,D)\) on \(\mathbb{Z}^{2}\) moves to \(\left(z+D,\,e^{\frac{\pi}{2}}D\right)\), \(\left(z+D,\,e^{-\frac{\pi}{2}}D\right)\) and \((z+D,D)\), respectively.
###### Abstract  
In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process.  
soft measurement; Czochralski crystal growth; neural networks; system identification; the diameter of tail growth
# An Experimental Study of the Leakage Mechanism in Static Seals
## 4 Results and Analysis
### Effect of Sealing Device Parameters on Leak Rate
#### 4.2.3 Effect of Size on Leak Rate  
In the experiment, as shown in Figure 9, the inner radius of the gasket was fixed to 2.5 mm, while the outer radius of the gasket was changed. In addition, the contact load to 500 N (with the dash line denoting the calculation results obtained from the theoretical model, and the asterisks denoting the experimental data), 400 N (with the dotted line denoting the calculation results obtained from the theoretical model, and the triangles denoting the experimental data). For the contact load is 500 N, when the outer radius is less than 31 mm, the amount of deformation in the leakage channel plays a dominant role in the leak rate because, when the outer radius increases, the pressure per unit area decreases, resulting in a less compressed leakage channel, and consequently an increased leak rate. When the inner radius is larger than 31 mm, the length of the leakage channel instead plays a dominant role in the leak rate because, as the outer radius increases, the length of the leakage channel increases, resulting in a decreased leak rate. Our study shows that, for metal end face seals, when processing technology and structural strength satisfy the requirements, the smaller the outer diameter, the higher the sealing efficiency, which is consistent with engineering experience.
# The Low-Loss Spot Size Converter for Alignment with Cleaved Single Mode Fiber
## 3 Results  
The whole mode field distributions in the SSC are shown in Figure 8. The MFD of the light source is set to 8 \(\upmu\)m and 10 \(\upmu\)m, and the wavelength is set to 1550 nm. The simulation results of the SSC are presented by the FDTD solution using the Lumerical software. It can be seen that the beam is finally coupled from the cleaved SMF into the Si waveguide through the SSC. The coupling loss of the TE mode is 0.29 dB, and that of the TM mode is 0.27 dB for the light source with an 8 \(\upmu\)m MFD. When the MFD of the light source is set to 10 \(\upmu\)m, the coupling loss is limited to 0.58 dB for TE mode and 0.56 dB for TM mode. The simulation results show that the proposed SSC achieves a very low coupling loss of 0.27 dB with a cleaved SMF, and the good simulation results provide a basis for fabricating a low-loss SSC for alignment with a cleaved SMF.  
In addition to low coupling loss, the SSC we designed has a low PDL and large alignment tolerances. Figure 9 shows the simulation results of the 1-dB alignment tolerances  
Figure 8: The mode distribution of the SSC simulated by 3D-FDTD. (**a**) TE mode field distribution for 8 \(\upmu\)m MFD; (**b**) TM mode field distribution for 8 \(\upmu\)m MFD; (**c**) TE mode field distribution for 10 \(\upmu\)m MFD; (**d**) TM mode field distribution for 10 \(\upmu\)m MFD.  
at the MFD of 8 \(\upmu\)m. For TE mode, the tolerance is \(-2.1\)\(\upmu\)m\(\sim\)1.9 \(\upmu\)m in the vertical direction (\(Z\)-axis) and \(-1.8\)\(\upmu\)m\(\sim\)1.8 \(\upmu\)m in the horizontal direction (\(Y\)-axis). For TM mode, the tolerance is \(-1.9\)\(\upmu\)m\(\sim\)2.1 \(\upmu\)m in the vertical direction (\(Z\)-axis) and \(-1.8\)\(\upmu\)m\(\sim\)1.8 \(\upmu\)m in the horizontal direction (\(Y\)-axis). The PDL between the two polarization states is less than 0.25 dB, which demonstrates good polarization insensitivity.  
The conventional SSCs align with the lensed fiber, which increases the difficulty of alignment and inhibits large alignment tolerances. Therefore, one of the trends in SSC is to change from alignment with lensed fibers to alignment with cleaved SMFs. Table 1 shows the comparison between our work and other SSCs aligned with cleaved SMFs (MFD: 8-10 \(\upmu\)m). Comparing with other SSCs, our designed SSC achieves lower loss coupling between the cleaved SMF and Si waveguides while maintaining the tapered waveguide tip width of 200 nm. This demonstrates the possibility of mass production of our proposed SSC on the current commercial silicon photonics fab, thus reducing the cost.
## Discussion
### Analyzing GPP Variation over a Century  
This is the first time that we witnessed the GPP spatial distribution more than 100 years ago at a fine scale. From 1895 to 2013, the landscape pattern in our study area was comparatively stable where high GPP clusters were concentrated in the west and the east of our study area (Figure 5A,D,E), but dramatic GPP changes can be found. In severe drought years (Figure 5B,C), the high GPP clusters were not visible compared to the map in 1974, but in the center of our study area, there were only some subtle GPP variations. In wet years, (Figure 4D,E), GPP was on the rise in the east and west of the study site.  
The historical satellite images since 1980 could explain many variations in GPP. Our study found that GPP for grassland and for the whole area showed increasing tendencies (Figure 5F) with both datasets (combined GPP dataset and real GPP dataset). However, there are still some ecological processes like forest growth or ecological succession that may need more time to monitor [36; 37]. When further studies are about local ecosystems and their restoration, the long-term dataset (combined dataset) can be more helpful. Our work demonstrates the feasibility of creating fine spatial scale GPP maps for large areas that extend over the time period of the tree-ring chronologies. As we develop longer tree-ring chronologies in a denser network, we will be able to push this type of reconstruction further back over larger areas.  
In the long term (1895-2013), GPP for forests showed a decreasing tendency (GPP = \(-0.1007\times\) Year + 3041.1), while in the short term (1986-2013), the GPP tendency was increasing (GPP = \(1.7789\times\) Year \(-\) 607.92). Even for the same increasing tendencies of GPP for grassland and for the whole area, the increasing magnitudes were also different between the long-term dataset and short-term dataset. In recent decades (1986-2013), the increasing rate was incredibly high, while during the full 119 years (1895-2013), the tendencies were flat. Our research could not judge which one was correct, but the long-term data provided us another dimension to observe GPP variation earlier over a much longer period of time. Within 119 years, we could not find any similar magnitude of GPP variations in the most recent three decades (1986-2013).
###### Abstract
This idea is referred to as \"LEO as a space monitoring station\" in this paper. Compared to ground monitoring stations, the global distribution of LEO constellations enables the monitoring of global navigation satellites, which is particularly advantageous for the BDS. One the other hand, LEO satellites can also act as navigation satellites that transmit navigation signals, increasing the number of visible satellites for users. This idea is called \"LEO as a navigation satellite\" within this work. In-orbit experiments have demonstrated that LEO satellites can not only receive GNSS signals but also transmit navigation signals [31]. On this basis, there have been various studies focusing on improving GNSS navigation accuracy and the convergence speed of precise point positioning with LEO satellites [32; 33]. Currently, there is little research explaining the relationship between autonomous integrity monitoring in medium- and high-orbit satellites and the integrity enhancement in LEO satellites from a systemic perspective. Additionally, there is no quantitative analysis detailing the requirements for medium-orbit, high-orbit, and LEO satellites to meet integrity demands represented by CAT-I. Therefore, this paper first reviews the current status and development trends in satellite navigation system integrity both domestically and internationally. Next, focusing on the integrity requirements represented by civil aviation CAT-I, this paper proposes a space-ground integrated integrity monitoring method that includes two types of satellites, namely LEO satellites as monitoring stations and LEO satellites as navigation satellites. The paper conducts a simulation analysis of the integrated integrity monitoring method with the reliability of LEO satellites as a variable, providing the minimum reliability requirements for navigation satellite schemes under simulation conditions. Finally, it offers development suggestions for enhancing the integrity of the next-generation BDS. This research is expected to provide theoretical support for enhancing the integrity of the next-generation satellite navigation system.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods
### Spatial Position Matching  
In target tracking and detection tasks, IoU (Intersection over Union) [16] is a commonly used metric to evaluate the positional matching degree between two rectangular boxes (usually the target detection box and the tracking box). IoU value measures the ratio of the overlapping area of the two rectangular boxes to their union area and is typically used to measure the matching degree between the predicted bounding box and the ground truth bounding box.  
Spatial position matching calculation steps:  
For two consecutive frames of images, suppose that in frame \(t\) there is a set of detection boxes \(A=\left\{A_{1},A_{2},\ldots,A_{m}\right\}\), and based on frame \(t-1\), the predicted box set for frame \(t\) is \(B=\left\{B_{1},B_{2},\ldots,B_{n}\right\}\). All elements within set \(A\) are the \((x,y,w,h)\) of each detection box, while all elements within set \(B\) are the \((x,y,w,h)\) of each predicted box.  
Figure 2: Principle of average optical flow of pixel points.  
Calculate the IoU values for each pair of detection boxes and predicted boxes, as shown in Equation (7).  
\[\text{IoU}(A,B)=\frac{A_{p}\cap B_{q}}{A_{p}\cup B_{q}},\quad p=(1,2,3,..,m.),q=(1,2,3,\ldots,n) \tag{7}\]  
Distance representation of IoU values:  
IoU values range between 0 and 1, where values closer to 1 indicate greater similarity between the two bounding boxes. To convert the IoU value into a \"distance\" form, we can use the following transformation Formula (8).  
\[\text{R-IoU}=1-\text{IoU} \tag{8}\]  
This transformation method leverages the complement set portion of the IoU. If two boxes are perfectly aligned, IoU = 1, the complement set portion is 0. If two boxes do not overlap at all, IoU = 0, and the complement set portion is 1. Similarly to the optical flow method, a threshold is set. When the complementary set exceeds this threshold, the two boxes are considered non-overlapping. Smaller values of the complementary set indicate higher overlap. This process generates an R-IoU similarity matrix, similar to the optical flow method.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model
### Overlap Avoidance for Enhanced Gauss-Markov Mobility Model
#### 3.4.2 Proposed Overlap Avoidance Scheme for Enhanced Gauss-Markov  
We redesign the Enhanced Gauss-Markov (EGM) model for avoiding overlap by applying the guidance vector scheme as shown in Figure 4. As same in Section 3.4.1, the overlap avoidance scheme is initiated when the protected zones of UAVs are duplicated. The turn direction of each UAV is also determined as the direction to the guidance vector from the current direction.  
For the purpose of avoiding overlaps, we use the boundary avoidance scheme of Enhanced Gauss-Markov model. When the UAV decides to turn left (or right) after detecting overlap of protected zones, the mean direction deviation is set to \(-\delta\) for continuous left turn (or \(+\delta\) for right turn). Let us note that we use 2.5 for \(\delta\) as shown in Figure 5b. Then, UAVs try to turn to different directions so as to avoid overlapped reconnaissance. After no protected zone is duplicated, the mean direction deviation is set to 0, as usual.
## 4 Simulation and Verification
### Verification Points
As a result, the accuracy of the Case B simulation drops quickly as simulation time progresses. During the simulation process, individual traffic jams first appear on the fringe of the city, and then spread to the entire city. In contrast to Case B's excessive traffic, Case A has the problem of insufficient traffic. The black lines in Figure 7 always remains at a lower vehicle quantity. Case C shows the closest result to the real data in the comparison of the three cases. To facilitate the calculation, the detectors in the simulations record vehicle quantity every 600 s. If the simulated traffic volume is more than the minimum real traffic volume of the same time period and less than the maximum real traffic volume of the same time period of the recorded week, the time period of this case is counted by \"in real range\". Figure 8 shows the calculation of the proportion of the simulation results \"in real range\" as simulation time increases. This figure can also represent the simulation accuracy of Case A, B, and C. Case C has always the best performance comparing to Case A and B. For the first eight simulation hours, the best reproduction rate of Case C is 72.22%. Eventually, the simulations of city Duisburg faced the problem of large-scale traffic congestion. Therefore, the simulation after eight hours is not representative. The large-scale traffic congestion in simulation can result in several reasons. First, the traffic light plans can be a major. In this work, traffic light plan data are not available, and all the traffic lights in the simulation are using default plans in SUMO. In reality, Duisburg has both static and dynamic control traffic lights, the reasonably planned traffic lights make urban roads still run well under heavy traffic. In this work with limited traffic light data, only hours with low traffic flow are simulated together. Moreover, the road network has been modified  
Figure 6: Traffic volume of an induction loop. manually according to the street view, but there may be still mistakes in the road network, causes insufficient access roads between two OD matrix areas.
# Metal Release and Cell Viability of 316L Stainless Steel Sputter-Coated with N-Doped a-C:H Coatings
## 2 Materials and Methods
### Coatings Characterization  
The surface and cross-section morphology of the deposited coatings, as well as thickness measurements, were assessed by high-resolution field-emission Scanning Electron Microscopy (SEM) by using a ZEISS Merlin Compact/VP Compact equipment. The cross-section elemental distribution maps were obtained by using an Energy Dispersive Spectroscopy (EDS) detector (X-Max Oxford Instruments, Oxford, UK) coupled to the SEM system.  
The evolution of the structure and chemical bonding of the coatings upon immersion was studied through visible Raman in a LabRAM HR 800 Evolution spectrometer (Horiba, Venissieux, France), equipped with a 532 nm external laser source and coupled with an Olympus BXFM confocal microscope (100\(\times\) objective, yielding a spot size of \(\sim\)1 \(\upmu\)m, and a power of \(\sim\)10 mW at the sample). The Raman spectra were curve-fitted into two main Gaussian functions after linear background subtraction, corresponding to the D and G characteristic bands of amorphous carbon materials [14; 15].  
Prior to wettability analysis, all samples were ultrasonically cleaned in ethanol and distilled water, sequentially. The static water contact angles (WCA) were measured with distilled water drops (4 \(\upmu\)L/s) in a CAM100 equipment (KSV Instruments Ltd., Helsinki, Finland). A minimum of four measurements per sample was performed.  
The coating's surface topography was assessed by Atomic Force Microscopy (AFM), by acquiring 3D images and calculating the average roughness (Ra) in a Veeco DiInnova equipment running in intermittent mode (4 \(\times\) 4 \(\upmu\)m\({}^{2}\) area).
## 3 Results and Discussion
### Comparative Performance of Different DESs for Antioxidant Extraction
Furthermore, buckwheat hull extracts prepared with 20% and 50% ethanol exhibited significantly higher DPPH (81.8-86.5%) and ABTS (85.9-95.4%) radical scavenging activities than those extracted by the other solvents such as 80% ethanol (DPPH = 71.3-72.2%, ABTS = 83.8-92.2%), ethanol (DPPH = 38.7-39.9%, ABTS = 65.1-74.0%), methanol (DPPH = 50.3-57.1%, ABTS = 67.1-74.1%), acetone (DPPH = 8.92-23.4%, ABTS = 21.0-32.6%),  
Figure 2: Effect of DES type on total antioxidant potential of four herbs: chamomile (Cha) (**a**), lemon balm (LB) (**b**), mint (M) (**c**), and nettle (N) (**d**) and two agro-food wastes: buckwheat husk (BH) (**e**) and chokeberry pomace (ChoP) (**f**). and water (DPPH = 67.7-72.1%, ABTS = 75.9-77.6%) for 2 h in a water bath [18]. Interestingly, the methanol extract of buckwheat hull had two and eight times higher DPPH values than the acetone and water extracts (DPPH = 165.8, 73.5, and 19.9 mmol TE/g, respectively) [19].