esa-sceva/satcom-chunk-collection huggingface dataset:
# _photonics_
## 4 Simulation Results
### Practical Exploitation of Distilled Key Bits
#### 4.4.2 AES-256 Key Refresh Time  
The cryptographic algorithm of AES is most frequently supposed to be used as the encryption algorithm using the distilled keys of a QKD link. AES is proven to be quantum-resistant and therefore can be effectively combined with QKD to offer high security in the post-quantum world [62]. In order to further enforce the security of the encryption, a key size of 256 bits, instead of the classical size of 128 bits, was proposed to be used in AES-based methods. Besides the size of the AES key, the amount of the data encrypted by the same key is essential for guaranteeing the algorithm's security [60; 63]. Therefore, it is of high importance that the keys that will support the AES-256 engines are frequently refreshed. Different quantum key rotation times can be selected, depending on the attack  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Distilled Key** & **Sat 1** & **Sat 2** & **Sat 3** & **Sat 4** & **Sat 5** & **Sat 6** & **Sat 7** & **Sat 8** & **Sat 9** & **Sat 10** & **Total** \\ \hline Helmos & 0.136 & 0.143 & 0.147 & 0.145 & 0.126 & 1.152 & 0.144 & 0.147 & 0.144 & 0.147 & 1.435 \\ \hline Skinakas & 0.037 & 0.04 & 0.041 & 0.04 & 0.035 & 0.04 & 0.041 & 0.04 & 0.041 & 0.04 & 0.40 \\ \hline Cholomondas & 0.011 & 0.012 & 0.012 & 0.012 & 0.01 & 0.013 & 0.013 & 0.012 & 0.012 & 0.012 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distilled key bits (Gbits) per satellite per ground station over a period of one year.  
surface that we need to pursue in the link, as well the classical data rates fed into AES-256 cryptographic engines [64].  
In the presented satellite QKD scheme, we assumed that the trusted node communicates between all three OGSs with a single shared key used for encryption of data flows at 10 Gbps data speeds. The lifespan of a single key is depended on many factors [60], with the amount of data encrypted by the same key to be the most crucial parameter. According to [63], the maximum volume of data that can be encrypted by a single key in order to achieve an ultra-low attack success probability of \(2^{-60}\) is calculated to be about 0.3887 terabytes. Therefore, the keys should be frequently refreshed in order not to exceed this threshold. The maximum refresh time that can ensure this constraint for the given data speed was calculated in Appendix B to be 103.65 s or approximately 1.72 min. According to Table 1 and the method described in Section 4.4.1, every ground station is able to share up to 1.43 Gbits over a period of one year with each other. In Appendix C, these calculated bits are sufficient for refresh times down to about 5.64 s. This value is much less than the maximum refresh threshold of 1.72 min, corresponding to attack success probability threshold of \(2^{-60}\). Consequently, this would mean that the security levels of all three OGSs would not be compromised even if the link had an availability as low as 5.4% during nighttime and the key rates would still support the threshold refresh time value of 1.72 min (Appendix C).
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 2 Materials and Methods
### Professional Land Surveying GNSS Receiver  
At the same time, measurements were taken with a professional geodetic GNSS receiver of a proven manufacturer, Leica GS18T [29], to compare the quality of the satellite measurements. It also has advanced technologies, such as the ability to compensate for pole tilts, or advanced measurement applications, while the following tests will take into account the performance only of the satellite measurement.  
Unlike the C099-F9P, the Leica GS18T receiver forms a single unit with the antenna --a \"smart antenna\" device. This one has its advantages and disadvantages, such as not being able to freely change the antenna itself or the high weight of the whole. This also causesthe u-blox to have an advantage in the flexibility of mounting the device (for example, on autonomous cars, drones, machines or buildings).
## 4 Numerical Simulation
### Monte Carlo Analysis  
To rigorously assess the performance of the proposed DRPBC method against defined performance thresholds, we conducted a standard Monte Carlo simulation. This analysis serves dual purposes: first, to ascertain the robustness of our controller against initial positional uncertainties; second, to confirm that the method consistently adheres to the predetermined performance boundaries. Initially, we select random values for the uncertain parameters \(\mathbf{q}_{a}\) and \(\mathbf{q}_{b}\) within \(\pm 1.5\%\) of their nominal values. For each pair of \(\mathbf{q}_{a}\) and \(\mathbf{q}_{b}\), the simulation is executed over 400 s to determine the norms of the steady-state relative position errors [31].  
We categorize the relative position error data into eight evenly spaced bins ranging from the minimum to the maximum observed values. As illustrated in Figure 5, the bin spanning from \(2.880\times 10^{-4}\) to \(2.885\times 10^{-4}\) contains the highest frequency of values, with a central value of \(2.883\times 10^{-4}\). The cumulative probability distribution allows us to estimate that there is approximately an 80% likelihood that the relative orbit error does not exceed \(2.885\times 10^{-4}\). Table 3 presents the percentage difference between the maximum and minimum values relative to the center value, indicating that this difference is smaller than the variation in parameters, thereby demonstrating the robustness of the proposed controller to initial positional uncertainties.  
Figure 6 illustrates the distribution of relative position errors resulting from the randomly selected uncertain parameters, which are compactly aligned into distinct bands. In terms of relative position errors, despite the presence of parameter uncertainty, the steady-state errors align with the magnitude of those observed under precise initial conditions and consistently fall beneath the thresholds required for engineering applications. This substantiates the efficacy of the proposed method in maintaining control performance within defined limits.  
Figure 5: Probability results of Monte Carlo analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Center Value** & **Min** & **Max** & **Difference** \\ \hline Relative position error [\(\times 10^{-4}\) m] & 2.883 & 2.864 & 2.898 & 1.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monte Carlo percentage difference.
## 4 Selective Area Growth Applied on InPSi Wafers  
The main objective of our advanced integration platform based on InP seed-bonding and regrowth is to extend the mature building-blocks from the InP platform in the III-V/Si heterogeneous platform. Among them, the Selective Area Growth (SAG) technique by MOVPE requires an epitaxial step that must be done after the bonding step because of the bonding process specifications (low roughness, low bow). By the precise design of mask geometry, generally two dielectric mask stripes on both side of the SAG region, different active regions, in terms of thicknesses and compositions, can be grown on the same wafer from a single epitaxy step [3; 4; 5]. Due to the significant reduction of fabrication complexity, the introduction of SAG into the heterogeneous silicon photonics platform would enable high flexibility in complex PICs.  
SAG has been widely used for wavelength detuning both for \"longitudinal\" and \"transversal\" integrations of components. In the \"longitudinal\" integration, along the optical waveguide direction, active and passive components such as electro-absorption modulators (EAM), lasers, semiconductor optical amplifiers (SOA) and spot size converters have been successfully integrated [37; 38; 39]. SAG has also demonstrated its high potential for \"transversal\" integration, with large wavelength shift of numerous juxtaposed devices such as lasers in an array [27; 40]. In this context, Coarse Wavelength Division Multiplexing (CWDM) systems are key components for which the SAG technique is particularly adapted. The large inter-channel wavelength shift of 20 nm needed between the laser in the array require a 140 nm-wide PL extension for a 8-channel CWDM system. With respect to a more traditional approach based on discrete DFB lasers fabricated from different epitaxial  
Figure 6: Light-current characteristics comparison between the laser grown on InPSi (blue) and for the one grown on InP (red): (**a**) at 20 \({}^{\circ}\)C; (**b**) evolution of the threshold current density up to 50 \({}^{\circ}\)C.  
growths further integrated on a SOI wafer by means of die-bonding [41], SAG technique allows us to obtain the entire set of emission wavelength from a single growth step.  
In this section, we present a 5-channel laser array achieved by SAG on Si wafer. The aim of this study is to evaluate the performance of SAG based active regions without any influence of the passive functions underneath the SOI. Therefore, AlGaInAs-based MQW laser structures were selectively regrown on InPSi. First, selectively grown AlGaInAs MQW-based structures were characterized by HRXRD and PL measurements. Based on these structures, the 5-channel laser array was fabricated and performance was assessed under CW operation.
## 2 Materials and Methods
### Method  
An overview of the methods is shown in Figure 3, and each step is described in detail in this section.  
We focused on inversing HJ-1 NBAR, based on the MOD43 product inversion method, and analysis of time series characteristics of two types of vegetation land cover: forest and farmland. The change in the vegetation within such areas can be well depicted by the time series using HJ-1 NBAR data, which has less mixed pixels than MODIS.  
The red and NIR bands of HJ-1 reflectivity data and MOD09 GA data were used as input data in the process. We created the HJ-1 fusion data of 16 days as a group, and 8 days as a cycle. In each cycle, it was assumed that the change in the object matter was negligible and the HJ-1 NBAR was reversed.  
Using the inversion of the NBAR and the MODIS NBAR data, we conducted a time series feature analysis in both the forest and the farmland areas of interest. Through the prediction and comparison with surface measured values, we proved the practicality of the data.  
However, referring to the MODIS flags file, which on behalf of the image point were cloud, cloud shadow, ice, and water, and 15 other types of coverage, we excluded unavailable data from HJ-1 data, resulting in insufficient data in the cycle. Therefore, we used the spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to fuse the data, supplemented the missing data, and obtained complete 2-day HJ-1 data for inversion.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 1 Introduction
The multiple models' estimation (MME) algorithm is a research hotspot at present. However, it is hard to realize real-time positioning, because the rapid growth in computational complexity over time. The interacting multiple model (IMM) [24] has the same positioning accuracy and computational complexity as second-order and first-order pseudo Bayesian, respectively. It makes real-time high-precision reliable positioning feasible. Nevertheless, existing IMM filters [25; 26] and variants [27; 28] require process and measurement variance which cannot be accurately obtained in the actual scene [29]. The BICI-IMM multi-sensor positioning algorithm is presented for unknown covariance among local filters, uncertain time-varying noise variance and unknown motion models in challenging environments. The estimation accuracy is increased by the IMM algorithm. Meanwhile, we deduced the batch form of the ICI method which had less conservativeness. Then, the accuracy of global estimation is improved by BICI. The positioning accuracy of the BICI and BICI-IMM multi-sensor fusion positioning algorithms is demonstrated by simulations and experiments in open and semi-open areas.
## 2 Problem Formulation
### Control Objective  
The purpose of this paper is to propose an adaptive compensation-fault-tolerant control scheme that enables the output signal \(\Omega\) to follow the reference signal \(\Omega_{c}\) without resorting to the exact information of inertia matrix and despite the occurrence of eccentric moments, uncertain system parameters, actuator failures and external disturbances. In addition, signals in a closed-loop system are bounded. To do this, the following assumptions are required.  
**Assumption 1**.: _Before and after the occurrence of the unexpected centroid shift, the aerodynamic configuration of the HSV remains the same, that is, there is no variation in the aerodynamic parameters._  
**Assumption 2**.: _The inverse of the inertial matrix \((J^{*}+\Delta J)\) exists._  
**Assumption 3**.: _The complex disturbance \(D_{s}\) satisfies \(\|D_{s}\|\leq\kappa_{s}\), where \(\kappa_{s}\) is an unknown positive real constant. Besides, the uncertainty of quick-loop system \(f(\hat{\rho})\) meets \(\|f(\hat{\rho})\|\leq\kappa_{f}\), where \(\kappa_{f}\) is an unknown constant, satisfying \(\kappa_{f}>0\). The eccentric disturbance moment of HSV satisfies \(\|\Delta\tau\|\leq\kappa_{\tau}\), where \(\kappa_{\tau}\) is the unknown positive real constant. In addition, the disturbance of quick-loop \(d_{f}\) is also bounded._  
**Assumption 4**.: _The desired tracking command signals \(\Omega_{r}\), \(\omega_{c}\) and their time-derivative \(\dot{\Omega}_{r}\), \(\dot{\omega}_{c}\) is continuous and bounded._  
**Remark 1**.: _Under Assumption 2, the inertia matrix \((J^{*}+\Delta J)\) is an invertible matrix, which plays a crucial role in controller; however, the inverse function of \((J^{*}+\Delta\hat{J})\) may not exist because \(\Delta\hat{J}\) is estimated by the estimator. For this challenge, a special negative feedback control is developed in this work without resorting to the exact knowledge of \((J^{*}+\Delta J)^{-1}\). In addition, based on an auxiliary integrator [30] or necessary gain [31], they can be another way to deal with this problem._  
Before the fault-tolerant controller design, a necessary lemma is delivered as follows.  
**Lemma 1** ([32]).: _The higher-order sliding mode differentiator is defined as_  
\[\begin{split}& z_{0}=v_{0}=-\kappa_{0}|z_{0}-f(t)|^{(n/(n+1))} \mathrm{sgn}(z_{0}-f(t))+z_{1}\\ &\vdots\\ &\dot{z}_{i}=v_{i}=-\kappa_{i}|z_{i}-v_{i-1}|^{(n-i/(n-i+1))} \mathrm{sgn}(z_{i}-v_{i-1})+z_{i+1}\\ &\vdots\\ &\dot{z}_{n-1}=v_{n-1}=-\kappa_{n-1}|z_{n-1}-v_{n-2}|^{1/2} \mathrm{sgn}(z_{n-1}-v_{n-2})+z_{n}\\ &\dot{z}_{n}=-\kappa_{n}\mathrm{sgn}(z_{n}-v_{n-1})\end{split} \tag{13}\]  
_where \(z_{i}\) and \(v_{i}\) are the states of higher-order sliding mode differentiator (13) and \(\kappa_{i}\) is the design parameter. Assisted by the proper parameters, \(z_{0}=f(t)\) and \(z_{i}=v_{i}=f^{(i)}(t),i=1,\ldots,n\) are true with no input noise after the finite time \(t_{\sigma}\) of the transient process. In addition, the corresponding solution of this dynamic system is Lyapunov stable, i.e., namely finite time stability._
# Attenuation Zones of Two-Dimensional Periodic Foundations Including the Effect of Vertical Loads
## 2 Computational Method
Applying the [PERSON]'s principle, \(\delta\int_{t_{1}}^{t_{2}}\sum\limits_{e}\left(U^{(e)}+W^{(e)}-T^{(e)}\right) \ dt=0\), gives the dynamic equations of the typical unit cell  
\[\mathbf{\ddot{M}}+(\mathbf{K}+\mathbf{K}^{0})\mathbf{d}=\mathbf{F}, \tag{4}\]  
A harmonic wave with angular frequency \(\omega\) and wave vector \(\mathbf{k}=(k_{x}+\mathrm{i}\varepsilon_{x})\mathbf{e}_{x}+(k_{z}+\mathrm{i} \varepsilon_{z})\mathbf{e}_{z}\) is considered, where the real parts \(k_{x}\) and \(k_{z}\) of the wave vector are phase constants in the \(x\)- and \(z\)-directions, respectively; the imaginary parts \(\varepsilon_{x}\) and \(\varepsilon_{z}\) are attenuation coefficients in the \(x\)- and \(z\)-directions, respectively. By applying the [PERSON] theorem, periodic boundary conditions can be obtained  
\[\mathbf{d}_{r}=\mathrm{e}^{-\varepsilon_{x}a}\mathrm{e}^{\mathrm{i}k_{x}a} \mathbf{d}_{f},\mathbf{d}_{t}=\mathrm{e}^{-\varepsilon_{z}a}\mathrm{e}^{ \mathrm{i}k_{z}a}\mathbf{d}_{b},\]  
\[\mathbf{F}_{r}=-\mathrm{e}^{-\varepsilon_{x}a}\mathrm{e}^{\mathrm{i}k_{x}a} \mathbf{F}_{I},\mathbf{F}_{t}=-\mathrm{e}^{-\varepsilon_{z}a}\mathrm{e}^{ \mathrm{i}k_{z}a}\mathbf{F}_{b}, \tag{5}\]  
where the subscripts \(r\), \(l\), \(t\) and \(b\) refer to the nodes on the right, left, top and bottom edges of the typical unit cell, respectively. From Equation (5), it can be clearly seen that \(\mathrm{e}^{-\varepsilon_{z}a}\) and \(\mathrm{e}^{-\varepsilon_{z}a}\) quantify the attenuation of nodal displacement and nodal force between the opposite edges in the \(x\)- and \(z\)-directions, respectively. Combining Equations (4) and (5) yields  
\[\left[\overline{\mathbf{K}}(\mathbf{k})+\overline{\mathbf{K}}^{0}(\mathbf{k})- \omega^{2}(\mathbf{k})\overline{\mathbf{M}}\right]\left[ \begin{array}{c} \mathbf{d}_{i}\\ \mathbf{d}_{l}\\ \mathbf{d}_{b}\\ \end{array} \right]=\left[ \begin{array}{c}0\\ 0\\ 0\\ \end{array} \right], \tag{6}\]where \(\overline{\mathbf{K}}(\mathbf{k})\), \(\overline{\mathbf{K}}^{0}(\mathbf{k})\), and \(\overline{\mathbf{M}}\) are the corresponding stiffness matrix, geometric stiffness matrix, and mass matrix of the typical unit cell, respectively, after considering the periodic boundary conditions. It should be noted that the mass matrix is independent of the wave vector [23]. The dispersion curves of in-plane waves (coupled \(P\) and \(SV\) waves) in 2D periodic foundations can be constructed from Equation (6) by finding eigenfrequencies \(\omega\) for a given wave vector in the first irreducible Brillouin zone without attenuation coefficients (i.e., \(\mathbf{k}=k_{x}\mathbf{e}_{x}+k_{z}\mathbf{e}_{z}\)). On the other hand, the attenuation coefficients \(\varepsilon_{x}\) and \(\varepsilon_{z}\) can be determined from Equation (6) for a given angular frequency \(\omega\).
# Modeling and Control Simulation of Power Converters in Automotive Applications
## 1 Introduction
### Motivations
During these advancements, the growing significance of accurately modeling and simulating complex systems has become evident. A comprehensive understanding of the intricacies inherent in electric battery charging is particularly crucial for the design of electric vehicles. Consequently, an exceedingly detailed model is presented, encompassing the electrothermal dynamics of an onboard computer (OBC) with a modular design [32; 33; 34; 35]. Developing a precise simulation model for onboard electronic systems is a critical first step in creating innovative, effective gadgets. Model development is often oriented toward fine-grained attention on certain aspects, such as voltage and current behavior, thermal patterns, or the subtleties of component magnetism. Still, this method restricts the scope for a thorough analysis of important scenarios, and 'destructive' testing is, thus, required to investigate difficult operational combinations [36; 37; 38; 39]. By utilizing a multifaceted model that encompasses many components, these constraints are addressed. While it is understood that not every physical component can be precisely represented using traditional software methods, finding a balance between model complexity and other factors is still crucial. This balance makes it easier to quickly and efficiently assess a variety of scenarios and operational circumstances [40; 41; 42; 43]. Hence, the proposed operating protocol advocates the development of a simulation model that integrates two essential aspects: the circuit dynamics and the thermal conductivity of switching components. This approach aims to bridge the divide between comprehensive modeling and practical implementation, utilizing the 1\(\phi\) OBC circuit configuration as a case study. This initiative is poised to usher in a new era of innovation in the realm of electronic power systems for modern automobiles.
## 5 Results and Discussion
### Core Neutron Dynamic Module Parameter Sensitivity Analysis
#### 5.1.2 Parameter Sensitivity Analysis with One Loop Temperature Feedback  
The core neutron dynamic module has fuel temperature and coolant temperature feedbacks with the total feedback coefficient being negative, which is necessary to ensure the reactor self-stability. Supposing the temperature feedback was reduced to one-loop, it could be expressed by the first-order inertia link as follows:  
\[H(s)=\frac{a}{s+d}\times RD \tag{21}\]  
where \(a\) and \(d\) are the constant coefficients. \(RD\) is the temperature feedback coefficient. The transfer function analysis of the core neutron dynamic module with one-loop feedback showed that the reactor temperature feedback coefficient change would change the model gain. It also showed that it would change the model output \(N_{\mathrm{r}}\) under the same input of \(\rho\).  
With typical parameters, it gave changes of \(\pm 10\%\) to \(l\), \(\lambda\), \(\beta\) and \(RD\), respectively, under a step change of \(\rho\) from 0 to 0.001 at 0 s. Then, it recorded the trajectory curve of \(N_{\mathrm{r}}\). Figure 14 shows the trajectory sensitivity analysis.  
From Figures 13 and 14, the track sensitivities of \(\beta\) and \(\lambda\) first became larger due to the effect of the negative temperature feedback. Then, eventually the track sensitivities tended to zero at about 90 s. The effect of \(RD\) always existed with its value reducing gradually from 0, indicating that the temperature feedback coefficient had an evident effect on the steady-state value of \(N_{\mathrm{r}}\).  
To summarize, the reactor temperature feedback coefficient, as well as some design parameters actually changed the model gain. As a result, the output variable steady-state values were changed with the trajectory sensitivities constantly existing. Meanwhile, the effects of other parameters were only reflected in the variation process with the trajectory sensitivities eventually reduced to zero. The trajectory sensitivity analysis of parameters could generally reflect the influence of parameters on the system dynamic response and provide a reference for the parameter identifiability. The parameter was easier to be identified if the parameter trajectory sensitivity had a large value over a long time interval. On the contrary, it was not conducive to parameter identification.
# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
## 2 Materials and Methods
### Dataset
#### 2.1.2 Data Preprocessing  
After the data collection, the obtained data were pre-processed as follows. Firstly, we selected the images with the best shooting angle and cut their useless edges. Secondly, we modified the resolution and the brightness of the images. Thirdly, we delineated the bounding boxes of the seedling targets in the images and classified the categories to enable manual annotation of the seedlings. Then, 660 images were labeled using the image annotation software 'LabelImg' tool based on the smallest surrounding rectangle of a tree seedling to ensure the rectangle covers as small of a background area as possible. After data labeling, the position and classification of the tree seedlings were marked, resulting in JSON annotation files, and then they converted to txt files. Txt files contain information on object class, object coordinates, and the height and width of the labeling frame. Figure 2 shows the manual annotation of the tree seedlings using the 'LabelImg' tool, and Figure 3 shows the three types of tree seedlings after preprocessing.