esa-sceva/satcom-chunk-collection huggingface dataset:
# Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer
## 2 Method
### Transmission Efficiency Analysis  
Assuming that the ATP (Acquisition, Tracking, and Pointing) technique is perfect, the center axis of the beam can be aligned with the center of the receiver telescope. And then, the transmission efficiency from aircraft to the ground station can be calculated when the light is via the boundary layer or not.  
Without the boundary layer effects, the detected events fulfil the Gaussian distribution. When the beam illuminates the receiving telescope, the transmission efficiency \(\eta_{0}\) can be calculated by geometric optics as [31]  
\[\eta_{0}=\left[1-\exp\left[-0.5\left(\frac{D_{R}}{\omega_{\rm LP}}\right)^{2} \right]\right]\cdot\exp[-\beta\cdot\sec(\varphi)], \tag{11}\]  
where \(D_{R}\) is the diameter of the receiving telescope, and \(\beta\) is the extinction optical thickness between sea level and altitude.  
After passing through the boundary layer, the intensity of the beam will be redistributed. Then, by weighting each beam with the Gaussian distribution in Equation (7), the photons distribution probability eventually reaches the receiver telescope will be calculated, when the light is via the boundary layer or not. Then, the photons distribution probability ratio \(E_{boundary}\) can be calculated as  
\[E_{boundary}=\frac{\varepsilon_{boundary}}{\varepsilon_{0}}, \tag{12}\]  
where \(\varepsilon_{boundary}\) is the statistics of photons distribution probability within the range of receiving telescope per unit time with the effect of BL. While \(\varepsilon_{0}\) is the value without the effect of BL.  
After passing through the boundary layer, the transmission efficiency \(\eta_{boundary}\) can be calculated as  
\[\eta_{boundary}=\eta_{0}\cdot\text{SR}\cdot E_{boundary}, \tag{13}\]  
where SR is the Strehl ratio [32]  
\[\text{SR}\approx\exp\left[-\left(\frac{2\pi\text{OPD}_{\text{rms}}}{\lambda} \right)^{2}\right]. \tag{14}\]  
where OPD\({}_{\text{rms}}\) is the RMS of the OPD on the optical aperture.
## 5 Discussion
### Sedimentary Patterns Driven by Astronomical Orbital Cycles  
This study proposes two typical sedimentary patterns in the upper section of the Zhujiang Formation in the Enping Sag, the formation mechanisms of which are closely related to paleoclimate changes controlled by Milankovitch orbital parameters (Figure 9). Pattern I (Figure 9a) develops during the low phase of the 405 kyr long eccentricity cycle, when the decrease in Earth's orbital eccentricity makes the orbit more circular, reducing the distance difference between perihelion and aphelion and thereby attenuating the precession amplitude. The orbital dynamic effects lead to a decrease in the annual mean solar radiation in mid-latitude regions, a reduction in seasonal temperature differences, and a weakening of monsoonal rainfall, creating an arid and hot paleoclimate background. Under these conditions, the source area is dominated by physical weathering, the supply of terrigenous clastics increases, and combined with the shelf exposure effect caused by sea-level fall, a progradational deltaic sedimentary system is formed. This system develops massive sandstone units with thicknesses of 8-15 m, and the sand-shale ratio significantly increases. Pattern II (Figure 9b) corresponds to the high phase of the 405 kyr long eccentricity cycle, when the increase in Earth's orbital eccentricity makes the orbit more elliptical and enhances the precession amplitude, leading to an increase in the seasonal differences in solar radiation  
Figure 8: Characteristics of sea-level changes driven by astronomical orbits and sedimentary responses. **(a).** Stratigraphic boundary ages calibrated by the astronomical timescale (ATS); **(b)** Lithologic profile of Well EP17; **(c)** Tuned GR data curve; **(d)** 405 kyr filtered curve; **(e)** 20 kyr filtered curve, E1–E7 represents 7 eccentricity cycles; **(f)** Average summer insolation at 20\({}^{\circ}\) N [5]; **(g)** Global sea-level change curve [47]; **(h)** Global benthic foraminiferal oxygen isotope curve (818O) [48]; **(i)** DYNOT sea-level change curve; **(j)** \(\rho\)1 sea-level change curve.  
in low-latitude regions. The climate system response is characterized by warm and humid features, with an increase in the chemical weathering index (CIA), reflecting strong chemical weathering and an increase in the clay mineral content. Accompanying the retreat of the source area caused by rising sea levels, a retrogradational deltaic system is formed. This sedimentary unit is dominated by thick mudstone layers (single-layer thickness of 3-5 m) intercalated with thin layers of fine sandstone.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Military Applications
#### 2.1.1 Battlefield Surveillance  
In applications of this type, the sensor nodes of the WSN may be deployed on a battlefield nearby of the paths that enemy forces may use. The main advantage provided is that the WSN not only can be spontaneously positioned but also can function, without need for continuous attendance and maintenance. The terrain of the battlefield in most cases is absolutely variable. This plays an important role for both the coverage and the energy consumption of the sensor nodes. Some of these applications are presented below.  
In [6], the use of WSN technology for ground surveillance is studied. Specifically, the authors propose a system that consists of low-cost common nodes which are capable of sensing magnetic and acoustic signals produced by various moving target objects. The system aims to detect and categorize various targets, such as vehicles and troop movements, based on the spatial differences of the signal strength detected by the sensors. Figure 5 illustrates the architecture of this system.  
In [7] a submarine detection system for Anti-Submarine Warfare (ASW) is presented. The system consists of inexpensive multi-sensing units that combine both active and passive sonars. The system can be scaled to a large number of sensors, which are deployed in littoral waters according to a specific pattern and ocean depth. These sensing units utilize their passive sonar to detect a diesel-electric submarine and their active sonar to confirm their target. The unit that has confirmed a target, notifies its neighboring sensing units by using an alarm signal that contains its ID code. Whenever the units send multiple alarm signals within a predefined period of time, an alert is triggered. Furthermore, the system can acquire low False Alarm Rate (FAR) due to the very low range of the active sonar (50 m) that solves the acoustic multipath problem of the conventional sonobuoys.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 2 System Model  
In this section, we introduce the mathematical model of a general GOMIMO system equipped with \(N_{t}\) LEDs and \(N_{r}\) photodetectors (PDs). The channel model is first described, and then the basic principle of GOMIMO is further reviewed.
# A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests
## 3 Lateral-Directional Control Law Design
### Lateral-Directional Attitude Control Law Based on ADRC and NDI
#### 3.1.2. Yaw Angle Rate Control Loop  
The second part of Equation (9) can be expressed as:  
\[\dot{r}=f_{r}+g_{r}\Delta\delta_{p} \tag{14}\]  
\(g_{r}\) is the control efficiency of the differential propeller thrust:  
\[g_{r}=\Gamma_{6}\rho S_{p}C_{p}\left(2k_{p1}\delta_{p}+k_{p2}\right) \tag{15}\]  
Obviously, the control efficiency of \(\Delta\delta_{p  
where \(\hat{\Delta_{r}}\) is the observed value of \(f_{r}\). \(\beta_{1}\) and \(\beta_{2}\) are the gains of ESO, whose larger parameters lead to a better performance, but easier divergence. \(\text{fal}(\cdot)\) is a nonlinear function defined as:  
\[\text{fal}(e,\sigma,\delta)=\begin{cases}\frac{e}{\delta^{1-\sigma}},&|e|\leq \delta\\ |e|^{\sigma}\text{sign}(e),&|e|>\delta\end{cases} \tag{17}\]  
where \(\text{sign}(\cdot)\) is the signum function.  
Based on the ADRC theory, the feedback control loop is designed as:  
\[\Delta\bar{\delta}_{p}=\left(K_{r}(r_{c}-r)-\hat{\Delta_{r}}\right)g_{r}^{-1} \tag{18}\]  
In practice, \(\delta_{pl}\) and \(\delta_{pr}\) must be in the range of \((0,1]\). It is necessary to limit \(\Delta\delta_{p}\) in \(\Delta\delta_{pmax}\) to prevent \(\delta_{pl}\) and \(\delta_{pr}\) from saturation.  
\[\Delta\delta_{p}=\text{sat}(\Delta\bar{\delta}_{p},-\Delta\delta_{pmax}, \Delta\delta_{pmax}) \tag{19}\]
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 2 Materials and Methods
### Overview of Methodology
#### 2.2.1 Gridding Algorithm and Uncertainty Propagation  
To grid the data, the nearest-neighbour method was used, where the value of a given grid cell, \(\langle L\rangle_{\text{G}}\), is the average of the pixel values, \(L_{i}\), for which it is the nearest grid point, as,  
\[\langle L\rangle_{\text{G}}=\frac{1}{N_{\text{G}}}\sum_{i=1}^{N_{\text{G}}}L_{ i}\, \tag{1}\]  
Figure 1: Example of a SLSTR-A scene from the tandem phase in the nadir view, S8 channel (**left**), and the difference between this scene and the corresponding SLSTR-B scene (**right**) separated in time by 30 s (data: 13 August 2018).  
where \(N_{\text{G}}\) is the number of pixel values contributing to the grid cell. To validate per pixel uncertainties in a comparison of gridded data, the pixel-level uncertainties must be propagated through the gridding process (Equation (1)). This may be achieved following the standard method described in the _Guide to the Evaluation of Uncertainty in Measurement_ (GUM) [11].  
For uncertainty propagation, the uncertainty of a given grid cell may be defined with,  
\[u^{2}(\langle L\rangle_{\text{G}})=u_{\text{ind.}}^{2}(\langle L\rangle_{\text {G}})+u_{\text{com.}}^{2}(\langle L\rangle_{\text{G}})\, \tag{2}\]  
where \(u_{\text{ind.}}(\langle L\rangle_{\text{G}})\) is the uncertainty due to an error that is uncorrelated (i.e., independent) between grid cells and \(u_{\text{com.}}(\langle L\rangle_{\text{G}})\) is the uncertainty due to an error that is fully correlated (i.e., common) between grid cells. This model does not treat error effects that are locally correlated, or structured. This assumption is discussed in more detail below.  
Pixel-level error effects that cause the uncertainty \(u_{\text{ind.}}(\langle L\rangle_{\text{G}})\), for example instrument noise, may be combined together to give a pixel-level independent uncertainty, \(u_{\text{ind.}}(L_{i})\). When pixel-level independent uncertainties are propagated through Equation (1), since independent errors are uncorrelated within \(\{L_{i}\}\), we obtain,  
\[u_{\text{ind.}}^{2}(\langle L\rangle_{\text{G}})=\frac{1}{N_{\text{G}}^{2}} \sum_{i=1}^{N_{\text{G}}}u_{\text{ind.}}^{2}(L_{i}). \tag{3}\]  
If \(u_{\text{ind.}}^{2}(L_{i})\) is constant for all \(i\), this reduces to \(u_{\text{ind.}}^{2}(L_{i})/N_{\text{G}}\).  
Pixel-level error effects that cause the uncertainty \(u_{\text{com.}}^{2}(\langle L\rangle_{\text{G}})\), for example calibration error, may be combined together to give a pixel-level common uncertainty \(u_{\text{com.}}^{2}(L_{i})\). When pixel-level common uncertainties are propagated through Equation (1), since common errors are fully correlated within \(\{L_{i}\}\), we find,  
\[u_{\text{com.}}^{2}(\langle L\rangle_{\text{G}})=\left[\frac{1}{N_{\text{G}}} \sum_{i=1}^{N_{\text{G}}}u_{\text{ind.}}^{2}(L_{i})\right]^{2}. \tag{4}\]  
This is effectively gridding the per pixel common uncertainty and so this does not reduce for increasing \(N_{\text{G}}\).
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 2 Materials and Methods
### Algal Biomass Pretreatment and Reducing Sugar Quantification  
A dilute acid pretreatment using 0.3 N of HCl was performed to extract reducing sugars from 10% (\(w/v\)) of DCB according to the method used by [PERSON] et al. [10]. After centrifugation at 10,950\(\times\)\(g\) for 20 min, the resulting supernatant was used for reducing sugar quantification based on the 3,5-dinitrosalicylic acid (DNS) method.  
Acid type and concentration were selected based on our previous study reporting the efficacy of this acid pretreatment to achieve a maximum fermentable sugar level of 52 g/L. The recovered reducing sugars were identified by HPLC analysis as glucose (46 g/L) and cellobiose (5.94 g/L) [10].
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 2 Crystal Structure and Magnetic Properties of Spinel Iron Oxide
### Magnetic Properties
#### 2.2.2 Structuration in Magnetic Areas  
In order to decrease the magnetostatic energy (denoted form energy), a material in its massive forms can be divided into magnetic areas denoted Weiss' areas separated by walls denoted Bloch's walls. Inside the Weiss' areas, the spins are antiparallel without compensation of magnetic moments giving rise to magnetization. Due to the variation of magnetization from one area to another, the material does not present macroscopic magnetization without an external magnetic field. Under the effect of a magnetic field, the Bloch's walls will move, widening the areas which present a collinear orientation to the external field. There is a progressive orientation of all the spins of the material. It is often necessary to apply an important external magnetic field around several tesla in order to orient all the magnetic spins of the material in the same direction [7]. This phenomenon is at the origin of the remnant magnetization and of the hysteresis observed when the magnetization \(\overset{\rightarrow}{M}\) of a material is measured as a function of the external magnetic field \(\overset{\rightarrow}{H}\) (Figure 4).  
Figure 3: Representation of the arrangement of magnetic moments in materials having different magnetic behaviors. When necessary, the behavior with or without the presence of an external field H is specified.  
In Figure 4, the magnetization of the material reaches the maximum value M\({}_{5}\) (or saturation magnetization) when the imposed field increases enough. All spins are oriented in the same direction. The hysteresis curve also makes it possible to highlight the non-instantaneous nature of the orientation of the magnetic spins within a solid material, thus inducing a delay in magnetization. Two characteristic values of the material can be defined:  
* The coercive field (H\({}_{C}\)) which corresponds to the imposed magnetic field when the magnetization of the material is zero.
* The remnant magnetization (M\({}_{R}\)) which corresponds to the magnetization of the material when the external field is zero.
## 2 Materials and Methods
### Gene Expression Analysis  
The total RNA of the apple fruit tissue was extracted by the cetyltrimethyl ammonium bromide (CTAB) method [19]. DNase was used to remove genomic DNA. The first-strand cDNA from the total RNA was reverse-transcribed using a FastKing RT Kit (Beijing Tiangen Biochemical Technology Co., Ltd., Beijing, China). Real-time quantitative PCR (RT-qPCR) was performed in a StepOne(tm) Plus Real-Time PCR System (Applied Biosystems Inc., Carlsbad, CA, USA) using a SuperReal PreMix Plus kit (Beijing Tiangen Biochemical Technology Co., Ltd., China). Primers for RT-qPCR in Supplementary Table S1 were designed using Primer Premier 5.0 software (PREMIER Biosoft International, San Francisco, CA, USA). The gene-encoding fatty acid metabolism enzymes, _MdLOX1a_, _MdHPL_, _MdADHs_ (_MdADH1_ and _MdADH2_), _MdAATs_ (_MdAT1_ and _MdAAAT2_) were selected. Additionally, the genes related to ethylene biosynthesis (_MdACS_ and _MdACOI_) and signaling (_MdERFa-f_) were also analyzed. _MdGAPDH_ (Locus ID: MDP00007/57565) was used as the housekeeping gene. Relative mRNA expression was calculated using the 2\({}^{-\Delta\text{CT}}\) method [20]. RT-qPCR amplifications were performed in triplicate for each sample.
# The Frequency Response Characteristics of Ge-on-Si Photodetectors Under High Incident Power
## 3 Experiments and Discussion
At incident light powers of 0.5 mW, 1 mW, 1.5 mW, 2 mW, 2.5 mW, 3 mW, 3.5 mW, and 4 mW, the corresponding concentration of photogenerated carriers in the high-field area of the germanium absorption region are 0.38 \(\times\) 10\({}^{16}\) cm\({}^{-3}\), 0.75 \(\times\) 10\({}^{16}\) cm\({}^{-3}\), 1.12 \(\times\) 10\({}^{16}\) cm\({}^{-3}\), 1.51 \(\times\) 10\({}^{16}\) cm\({}^{-3  
The 3 dB bandwidth of the photodetector can be calculated from the RC constant and the carrier transport time [24; 25]. The frequency formula corresponding to the carrier transport time is the following:  
\[f_{tr}=\frac{0.45v_{h}}{d}, \tag{1}\]  
where \(v_{h}\) is the saturation hole drift velocity of the germanium and \(d\) is the thickness of the intrinsic germanium film. The frequency formula corresponding to the \(RC\) is the following:  
Figure 2.0  
where \(R\) is the load resistance and \(C\) is the capacitance. The 3 dB bandwidth of the photodetector can be calculated as follows:  
Figure 2.1  
The measured series resistance was 618 \(\Omega\) and the capacitance was 4.5 fF, resulting in an RC-limited bandwidth of 57.23 GHz. Using the saturation hole drift velocity obtained from the Hall effect measurements experimentally, we calculated the bandwidth at various optical powers, as shown in Figure 2.2 This experimentally determined bandwidth serves as a reference and is consistent with the data presented in Figure 2.3 The bandwidth derived from the saturation hole drift velocity aligns closely with our experimental results confirming that, for incident light powers less than 2.5 mW, the saturation hole drift velocity is the primary factor that influences the bandwidth. Notably, bandwidth values that are calculated using theoretical formulas tend to be slightly higher than those that are observed experimentally. This discrepancy is primarily due to losses and measurement errors during the testing process, which may affect the accuracy of the results. Overall, our findings highlight the critical role of the saturation hole drift velocity in determining photodetector performance under specific optical power conditions. Figure 2.4 Carrier saturation drift velocity versus photogenerated carrier concentration in the germanium layer according to the measured Hall effect. Under high incident optical power, the operating temperature of the photodetector will increase. The increased temperature will enhance lattice vibrations, thereby increasing the probability of carrier scattering, leading to a decrease in carrier saturation drift velocity.
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 5 Performance Evaluation
### Handover Latency  
In this section, we focus on the handover latency--the delay experienced when a mobile node switches from one access point (AP) to another. The goal is to compare the handover performance between RI-SDN and [PERSON] et al. [19] across different grid sizes, with a focus on how quickly the handover occurs and how each method impacts this latency.  
Figure 12 illustrates the handover latency observed in RI-SDN and [19] across various grid sizes (\(2\times 3\), \(3\times 3\), \(3\times 4\), \(4\times 4\), \(4\times 5\)). The handover latency refers to the delay incurred when a mobile node switches from one access point to another. The average handover latencies shown were obtained from 1000 runs of our simulation setup for each grid configuration.  
As shown in this Figure, RI-SDN consistently exhibits lower handover latency compared to HATA. For the smallest grid size (\(2\times 3\)), RI-SDN achieves an average latency of 20 ms, while [19]'s latency is significantly higher at 35 ms. This trend continues across all grid sizes, with RI-SDN maintaining a lower latency as the grid size increases. Note that HATA denotes the proposed method by [PERSON] et al. in [19].  
For larger grids (\(4\times 4\) and \(4\times 5\)), the difference in handover latency narrows slightly, but RI-SDN still outperforms HATA. Specifically, in the \(4\times 5\) grid, RI-SDN reduces the handover latency to 14 ms, compared to HATA's 27 ms. The efficiency of RI-SDN in minimizing route recalculations and focusing on high-bandwidth routes helps to reduce the overall handover time.  
This confirms that in networks with frequent handovers, RI-SDN provides a more reliable and efficient solution, ensuring minimal latency during transitions. Meanwhile, HATA, while offering route diversity, may incur additional delays during handovers, which could impact performance in latency-sensitive applications.  
RI-SDN provides better performance than [19] in terms of handover latency for several reasons. RI-SDN determines optimal routes upstream via the anchor, which reduces the need for recalculation when changing domains, unlike [19], where paths are recalculated at each transition. Moreover, the RI-SDN method places the mobility anchor as close as possible to the correspondent, thus reducing the number of hops required after a handover.
## 3 Results and Analysis
### Results and Comparison
model SC-FMNet proposed in this paper shows better results in improving the integrity of road extraction and can enhance the accuracy of road segmentation to some extent. (2) CHN6-CUG road dataset  
As shown in Figure 10, some of the prediction results of the model trained using the CHN6-CUG dataset in multiple comparison networks are demonstrated. The original high-resolution remote sensing image, the corresponding labeled maps, and the prediction result maps of A2 FPN, BANet, DCSwin, MANet, UNetFormer, ABCNet, and the SC-FMNet network proposed in this paper, respectively, are shown in the figure in order. As can be seen from the comparison results in Figure 10, the networks in Figure 10e,g,h are ineffective for the extraction of both main and branch roads, especially for the narrow and thin roads in the areas covered by trees or buildings, and the extracted roads all have obvious breakpoints and discontinuities. The roads extracted in Figure 10c are relatively high in accuracy and completeness, but there is still the problem of omission. Figure 10d,f are clearer for the main roads, but the roads extracted from the road edges and narrow paths have interrupted and discontinuous phenomena and also misidentified the background as a road. For Figure 10j, for the network proposed in this paper, we can see that Figure 10j in Figure 10j is based on the absence of isolated points, and the extraction of road boundaries are clearer in the absence of sheltered roads extracted from road edges that are smooth; in the presence of sheltered roads, it can be extracted coherently, and there is no obvious omission problem. The extracted road detail information can be expressed well, with good coherence. This section focuses on the comparison of experimental segmentation results using the network model SC-FMNet proposed in this paper with other network models on the CHN6-CUG road dataset. The experimental results of the commonly used evaluation metrics are shown in Table 4.  
Figure 10: Comparison of network test results in the CHN6-CUG dataset. As shown in Table 4, the network SC-FMNet in this paper has an OA of 96.16%, a precision of 79.60%, a recall of 66.86%, an F1-score of 72.61%, as well as an IOU of 63.10%. As a whole, the network SC-FMNet in this paper has advantages in all evaluation indexes and can extract the features of the road well, which makes the model better for the segmentation of remote sensing images with higher extraction accuracy.