esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 GNC Subsystem Architecture
### Guidance and Control
#### 4.3.2 Separation of Control and Estimation Sampling Intervals  
The CubeSat was designed based on the 1U standard of 10 cm \(\times\) 10 cm \(\times\) 10 cm and has a very narrow gap between the sensor and other components. In such a configuration, the concurrent operation of any hardware increases sensor noise and degrades the estimation results. By separating the estimation and control intervals, the actuator should not be activated during attitude estimation. According to [38], when the attitude is estimated using the 2U satellite's fine sun sensor the error converges within 2-3 s. The sampling rate of the orbit control input was set to 0.2 Hz and the attitude was estimated 2 s prior to sampling the thrust vector and then controlled for 3 s immediately after the sampling. The attitude observation data for the estimation and control torque were sampled at 1 Hz. When tracking the thrust vector for orbital maneuvering, the reference angular velocity is zero, and the current attitude does not change significantly if torque is not applied during the estimation interval. However, the relative velocity is not zero during relative orbit control; therefore, the zero-control input causes the deputy to drift away from the chief. Therefore, the thrust must be fired continuously, while the attitude is adjusted from its current state to the desired state.
# Analysis of the Mode Shapes of Kaplan Runners
## 3 Natural Frequencies and Mode Shapes of Simplified Structures
### Modal Characteristics of a Disk-like Structure  
A basic disk, defined by a radius r and a thickness h, is often used as a simplified model of some types of runners. In free vibration condition, its motion is governed by the following differential equation (in polar coordinates) [25]:  
\[\rho h\frac{\partial^{2}w}{\partial t^{2}}+\frac{Eh^{3}}{12(1-v^{2})}\bigg{[} \frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial}{\partial r}+ \frac{1}{r^{2}}\frac{\partial^{2}}{\partial\theta^{2}}\bigg{]}^{2}w=0 \tag{5}\]where: \(\rho\) is the density, \(w\) is the transverse displacement, \(t\) is the time, \(E\) is the Young modulus and \(v\) is the Poisson coefficient. On one hand, by solving Equation (5), the transverse displacement \(w\) can be analytically determined [25; 26; 27].  
On the other hand, natural frequencies and mode shapes can also be obtained by performing a numerical modal analysis.  
As a starting point of this study, a basic disk model, clamped at its center, is analyzed. This disk has an outer diameter D = 400 mm, an inner diameter d = 40 mm and a thickness h = 8 mm (See Figure 1a). To perform a numerical modal analysis a mesh with 41850 hexahedral elements (with a size of 3 mm) is used (see Figure 1b). Moreover, a Fixed Support condition is used and placed in the center of the disk.  
From here, mode shapes and natural frequencies of the disk are obtained. In Table 1 some mode shapes of a disk clamped at its center are shown and sorted by the number of nodal diameters (D) and nodal circles (C). As it was studied by many authors [17; 26; 28; 29; 30; 31], the vibration mode shapes of a disk have combinations of nodal diameters and nodal circles, which increase their number with the mode order. For convenience, in this work, the mode shapes related to the disk will be called modes \"(m, n)\" for modes shapes with m nodal diameter and n nodal circles.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
###### Abstract  
Data networks are naturally prone to interferences that can corrupt messages, leading to performance degradation or even to critical failure of the corresponding distributed system. To improve resilience of critical systems, time-triggered networks are frequently used, based on communication schedules defined at design-time. These networks offer prompt error detection, but slow error recovery that can only be compensated with bandwidth overprovisioning. On the contrary, the Flexible Time-Triggered (FTT) paradigm uses online traffic scheduling, which enables a compromise between error detection and recovery that can achieve timely recovery with a fraction of the needed bandwidth. This article presents a new method to recover transmission errors in a time-triggered Controller Area Network (CAN) network, based on the Flexible Time-Triggered paradigm, namely FTT-CAN. The method is based on using a server (traffic shaper) to regulate the retransmission of corrupted or omitted messages. We show how to design the server to simultaneously: (1) meet a predefined reliability goal, when considering worst case error recovery scenarios bounded probabilistically by a Poisson process that models the fault arrival rate; and, (2) limit the direct and indirect interference in the message set, preserving overall system schedulability. Extensive simulations with multiple scenarios, based on practical and randomly generated systems, show a reduction of two orders of magnitude in the average bandwidth taken by the proposed error recovery mechanism, when compared with traditional approaches available in the literature based on adding extra pre-defined transmission slots.  
flexible time-triggered; fault-tolerance; time-triggered; CAN; server; scheduling; temporal redundancy; real-time systems +
Footnote †: journal: Sensors
## 8 Discussion
Most studies applied the network-based architecture, while the nature of IoT is heavily distributed. It will be more effective if the researchers and developers pay more attention to combining host-based architecture with those that are network-based in a distributed and hierarchical architectural design manner to minimize the detection time, improve the detection accuracy, and decrease the network's overhead.
## 4 Results
### Influence of Image Division and Enhancement on the CPs Extraction
Figure 9 shows the image after geometric correction of the BNU-1 L1A image using the CPs extracted from the original image and the optimal control point extraction scheme (Scheme 2). The CPs extracted from the original image are few and unevenly distributed. If these points are directly used \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Image**} & **Number of CPs** & **Number of CPs** & **Number of CPs** & **Optimal Increment of** \\  & **Extracted from the Original Image** & **Extracted from Scheme 1** & **Scheme 2** & **CPs (\%) 2** \\ \hline A & 1071 & 2100 1 & 1840 & 96 \\ B & 935 & 2280 1 & 1624 & 144 \\ C & 1334 & 2236 1 & 1905 & 68 \\ D & 447 & 580 1 & 579 & 30 \\ E & 245 & 435 & 596 1 & 143 \\ F & 17 & 32 & 48 1 & 182 \\ \hline \hline \end{tabular}  
* represents the optimal number of the extracted CPs for geometric correction. 2 represents the ratio of the difference between the optimal number and the original image number to the original image number.  
\end{table} 
Table 2: The number of the extracted CPs with different image division schemes. Figure 8: The BNU-1 images superimposed on the corresponding MODIS images. (**a**) BNU-1 L1A image; (**b**) BNU-1 L1B image. Red boxes refer to the extent of the BNU-1 L1A/L1B images. Figure 7: Schematic diagram of the number and distribution of the CPs extracted by different division strategies. (**a**) The original image; (**b**) Scheme 1; (**c**) Scheme 2.  
for geometric correction of the BNU-1 L1A image, the corrected image will be severely distorted (Figure 9a). More, and more evenly distributed CPs are extracted of the image divided by Scheme 2 (Figure 9b) compared to the original image (Figure 9a). The corrected BNU-1 image overlaps well with the MODIS image (Figure 9b).
## 4 Applying ADMM to the Ordered Ridge Regression
### Experimental Results of Synthetic Data  
In this section, numerical examples show the convergences of ADMM-O\(\ell_{1,2}\), ADMM-O\(\ell_{2}\), and other methods. A tiny, dense example of an ordered \(\ell_{2}\) regularization is examined, where the feature matrix A has n = 1500 examples and p = 5000 features. Synthetic data is generated as follows: create a matrix \(A\) and choose \(A_{i,j}\) using \(\mathcal{N}(0,1)\) and then normalize columns of the matrix \(A\) to have the unit \(\ell_{2}\) norm. \(x^{0}\in\mathbb{R}^{p}\) is generated such that each sampled from \(x^{0}\sim\mathcal{N}(0,0.02)\) is a Gaussian distribution. Label b is calculated as \(\mathrm{b}=\mathrm{A}^{*}x^{0}+\mathrm{v}\), where \(\mathrm{v}\sim\mathcal{N}(0,10^{-3}*I)\), which is the Gaussian noise. A penalty parameter \(\rho=1.0\), an over-relaxed parameter \(\alpha=1.0\), and termination tolerances \(\epsilon^{abs}\leq 10^{-4}\) and \(\epsilon^{rel}\leq 10^{-2}\) are used. Variables \(u^{0}\in\mathbb{R}^{p}\) and \(z^{0}\in\mathbb{R}^{p}\) are initialized to be zero. \(\lambda\in\mathbb{R}^{p}\) is a non-increasing ordered sequence according to Section 5.1 and Algorithm 2. Figure 1(a),b indicates the convergence of ADMM-O\(\ell_{2}\) and ADMM-O\(\ell_{1,2}\), respectively. Figure 2(a),b shows the convergence of the ordered \(\ell_{1}\) regularization and the lasso, respectively. It can be seen from the Figures 2 and 3 that the ordered \(\ell_{2}\) regularization converges faster than all algorithms. The ordered \(\ell_{1}\), lasso, ordered \(\ell_{1,2}\), and ordered \(\ell_{2}\) take less than 80, 30, 30, and 10 iterations, respectively to converge. Dual is not guaranteed to be feasible. Therefore, a level of infeasibility of dual is also needed to compute. A numerical experiment terminates whenever both the infeasibility (\(\hat{w}\)) and relative primal-dual gap (\(\delta(b)\)) are less than equal to \(\lambda(1)(\mathrm{Tolinfeas}\) (\(\epsilon^{infeas}=10^{-6}\)) and \(\mathrm{TolRelGap}\) (\(\epsilon^{gap}=10^{-6}\)), respectively) the ordered \(\ell_{1}\) regularization harness synthetic data provided by Reference [5]. The same data is generated for the lasso as for the ordered \(\ell_{2}\) regularization except for an initial value of \(\lambda\). For the lasso, set \(\lambda=0.1*\lambda_{max}\), where \(\lambda_{max}=\|A^{T}*b\|_{\infty}\). The researchers also use 10-fold cross-validation (cv) with the lasso. For further details about this step, see Reference [9].  
Figure 1: Illustration of sequence \(\{\lambda_{k}\}\) for p = 5000: The solid line of \(\lambda_{BH^{(k)}}\) is given by Equation (14), while the dashed and dotted lines of \(\lambda_{k}\) are given by Equation (15) for n = p and n = 2p, respectively.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 2 Methods for Estimating the Location of Radio Transmission Sources  
There are two types of systems for estimating the location of radio wave sources such as illegal radio waves and mobile terminals: geometric methods that use various numerical data observed from actual radio wave sources to estimate the location, and statistical methods that estimate the location by comparing the actual observation data with a database obtained using a virtual transmitter in advance. This section describes the geometrical methods currently used to estimate the location of a radio transmitter. In Japan, the DEURAS system [3] is currently used to monitor illegal radio stations. The DEURAS system uses phase differences of the received signals at different antennas to estimate the direction of incoming radio waves. This section briefly explains the methods based on estimation of angle of arrival.
## 4 Discussion
### The Advantages and Disadvantages of This Study  
Based on the above analysis, the responses of carbon-water processes to summer droughts have been revealed deeply. Not only were different biomes but also the abrupt wet-dry shifts and drought timescale were taken into consideration to explore the response mechanisms. Meanwhile, the lower sensitivity of GPP and ET to summer droughts was also discussed with some compensation mechanisms. However, some disadvantages should be paid attention to. For example, the accuracy of GPP and ET in barren areas or low-NDVI regions was limited by the satellite sensor. Next, the seasonal differences of carbon-water process to droughts should be analyzed in depth.
## 4 Support Optimization for High-Level Stressed Mining Roadway
### Support Optimization Design  
The \"anchor bolt-anchor cable-net\" support structure is the original support scheme. However, this scheme is inadequate for addressing high horizontal stress. By comparing the direction of the \(\alpha_{\rm H}\) with the strike of the 6A# Left 2 nd Roadway, it is observed that the included angle between the roadway and the maximum principal stress direction is approximately 50\({}^{\circ}\). The specific direction is illustrated in Figure 14.  
Figure 14: Included angle between roadway strike and the direction of maximum principal stress.  
Taking Ronghua No. 1 Mine as the study subject and building on research on roadway surrounding rock stability, an optimized support scheme is proposed: The spacing distance between the anchor bolts in the roof and the two sides is reduced from 1200 mm to 800 mm, and the length of the roof anchor cables is increased from 6200 mm to 10,300 mm. Additionally, anchor bolts or anchor cables are added at the junctions of the roof and the two sides for reinforcement, and W steel strips are placed on the roof to enhance the stability and integrity of the roof. The schematic of the optimized roadway support system is shown in Figure 15.  
Building on the original support scheme, the optimized scheme is proposed, and numerical simulation software FLAC3D 5.0 is used for modeling and analysis. The specific details are shown in Table 3.  
A roadway model for the 6A# Left 2 nd Roadway is established. By simulating factors such as the spacing of the optimized anchor bolts and anchor cables, the addition of W steel strips on the roof, and the support angles at the junctions of the sidewalls with the roof and floor, the vertical and horizontal deformations exerted on the roadway are analyzed. The optimized support scheme is obtained by fully meeting the safety requirements for roadway support and considering factors such as economy.  
As seen from Figure 16, the deformation in the optimized scheme is significantly reduced: the roof subsidence decreases from 66 mm to 41 mm, a decrease of 37.3%, the floor heave reduces from 38 mm to 19 mm, a decrease of 49.5%, and the side convergence decreases from 81 mm to 53 mm, a decrease of 34.7%. This indicates that the optimized scheme is highly effective in enhancing roadway stability.  
Figure 15: Schematic of the optimized roadway support scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Scheme**} & **Anchor Bolt** & **Anchor Bolt** & **Anchor Cable** & **Anchor Cable** \\  & **Parameters/mm** & **Row \& Line** & **Parameters/mm** & **Row \& Line** \\  & & **Space/mm** & & **Space/mm** \\ \hline Original scheme & \(\Phi 20\times 2000\) & \(1200\times 1000\) & \(\Phi 21.6\times 6200\) & \(1200\times 1000\) \\ Optimized scheme & \(\Phi 20\times 2000\) & \(800\times 1000\) & \(\Phi 21.6\times 103,000\) & \(1000\times 1000\) \\ \hline \end{tabular}
\end{table}
Table 3: Support scheme.
## 2 Materials and Methods  
### Device Fabrication  
Metal organic chemical vapor deposition was employed to create the GaN-based LEDs in this study on a c-plane sapphire substrate. The un-doped GaN layer was grown to a thickness of 1.5 \(\upmu\)m at 1040 \({}^{\circ}\)C after a GaN buffer layer was grown to a thickness of 25 nm at 550 \({}^{\circ}\)C. A 2 \(\upmu\)m n-type GaN layer was then grown at 1100 \({}^{\circ}\)C using silane (SiH\({}_{4}\)) as a doping source. Following that, 5 pairs of In\({}_{0.04}\)Ga\({}_{0.96}\)N/GaN multi-quantum wells and a p-type GaN were grown on a substrate using NH\({}_{3}\), trimethylindium, triethylgallium, and Cp\({}_{2}\)Mg to create a GaN-based LED structure. The Mg dopant of p-type GaN was activated by rapid thermal annealing at 940 \({}^{\circ}\)C in a N\({}_{2}\) environment. To fabricate the LEDs, a photo-lithography procedure and reactive ion etching with BCl\({}_{3}\)/Cl\({}_{2}\) gases were used to implement the mesa pattern structure to obtain an n-type GaN contact layer. Finally, a transparent and conductive electrode of 200 nm of indium tin oxide was formed on the selected region above the p-type GaN using electron beam evaporator, and a Cr and Au bimetal stack was deposited to 50 and 200 nm, respectively, to form n-type and p-type pads for current injection into LEDs.  
To grow the h-BN film on the polycrystalline Cu foil, 0.3 sccm borazine (B\({}_{3}\)N\({}_{3}\)H\({}_{6}\)) and 70 sccm H\({}_{2}\) were introduced in a thermal chemical vapor deposition chamber at 1100 \({}^{\circ}\)C for 5 min at \(7\times 10^{-3}\) torr. The Cu foil was etched with Cu etchant to form a PMMA/h-BN structure after the h-BN/Cu foil was covered with PMMA (polymethylmethacrylate), using a spin-coater at 4000 rpm for 1 min. The PMMA/h-BN structure was rinsed with de-ionized water to remove residual Cu etchant. Finally, the PMMA/h-BN structure was transferred onto the fabricated LEDs, and the PMMA was removed by acetone treatment for 1 h. After h-BN transfer onto LEDs, the h-BN on the n-type and p-type pads was selectively removed using a photo-lithography process, and reactive ion etching with O\({}_{2}\) plasma for 10 s was used for measuring the electrical and optical characteristics of LEDs. Figure 1 shows the h-BN-passivated GaN-based LED structure.
# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 1 Introduction
In this paper, this \"larger road region\" is called the \"outlook region\", and its spatial coverage should be sufficient to accommodate several potential lane change maneuvers of the vehicle. Thanks to recent advancements in vehicle networking and intelligent transportation systems [13,14], vehicles can obtain real-time environmental information beyond the field of view of on-board sensors through other connected vehicles or roadside units, without being affected by obstacles. This provides a foundation for environment perception in the outlook region. Meanwhile, many studies have already begun exploring real-time estimation [15,16] and even short-term prediction [17] of lane-level traffic flow states. Considering that minor fluctuations in the motion states of individual distant vehicles do not significantly impact a driver's decisions, while traffic flow states, which represent collective vehicle motion, are more stable, this paper attempts to characterize the driving environment of the outlook region using high-resolution traffic flow data. This approach also avoids the need to predict the motion states of numerous vehicles in the outlook region. The proposed lane-level driving task planning method can also be viewed as a potential application for future lane-level traffic state prediction methods. In summary, the main contributions of this paper are summarized as follows:  
1. The tactical planning function in existing driving task architectures has been further decomposed and the concept of lane-level driving tasks is clarified;
2. The concept of an \"outlook region\" is applied to the lane-selection strategies of automated vehicles, with decision-making aimed at optimizing the overall efficiency of multiple lane change maneuvers. This can improve the driving performance of automated vehicles in multi-lane scenarios. 3. By representing the driving environment within the outlook region in the form of high-resolution traffic flow, the need for long-term predictions of the motion states of numerous traffic vehicles is avoided. We implement the proposed model base on a directed graph planning algorithm and build several test scenarios in Virtual Test Drive (VTD) for simulation and verification. The results show that the proposed method can reasonably guide an automated vehicle to drive on a multi-lane road segment. The rest of this paper is organized as follows. Section 2 reviews the relevant research work. Section 3 defines the objectives expected to be achieved, as well as the fundamental assumptions adopted. Section 4 describes the representations of the driving environment that form the basis for planning and decision-making. Section 5 presents the modeling approaches for lane-following and lane-change maneuvers. Section 6 discusses the algorithm for solving optimal driving policies. Section 7 introduces the details of the simulation tests, while Section 8 presents the results of the simulation tests along with analysis. Finally, conclusions are given in Section 9.
## 3 Results and Discussion
### Numerical Simulation of Stress-Relief Groove Utility Analysis
#### 3.1.3 Perimeter Rock Condition After Backfilling of Stress-Relief Groove  
An analysis of simulations shows that central grooving with backfilling provides better control over roadway surrounding rock than floor-side corner grooving. The Double-Yield shear model in FLAC 3D 7.0, with a 5% compression ratio for waste rock, simulates post-backfilling conditions, treating waste rock as a continuous granular medium [34]. Results indicate that backfilling reduces maximum sidewall displacement, improving control and addressing issues seen with grooving. Figure 14 shows that the method consistently reduces displacement in both the floor and sidewalls across all groove depths.
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 2 Proposed Fusion Scheme  
The newly proposed multi-focus image fusion framework is shown in Figure 1. Obviously, the fusion method consists of two main steps: generating a decision map and performing fusion. In the first step, multi-focus feature analysis of the low-scale images of the two source images is performed to obtain the corresponding clarity score maps. Then, they are normalized to get the initial decision map, and the spatial frequency method is used to obtain the final decision map. Section 2.1 details the creation of the score maps, and the specific process for further obtaining the initial decision map and the final decision map are described in Section 2.2. In the second step, the fused image based on the final decision graph and the transitional area fused image are obtained, respectively, and the two images above are averaged to obtain the final fused image. Among them, the fusion process of the transitional area is based on sparse representation, which is elaborated in Section 2.3.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 4 5G Communication Status
### Applications and Services  
Although there are numerous applications that are to come out of 5G, there are three major categories all these applications and services can be classified into, Figure 2. The classification is based on the requirements of each application such as number and type/resource of devices, mobility, latency, and speed [18].  
* **Enhanced Mobile broadband (eMBB)**\(\rightarrow\) Traditional mobile broadband applications such as Voice, SMS, and data services fall into this category. While the major new application that can fall in this category include Augmented Reality (AR), Virtual Reality (VR), and Gaming [18; 22].
* **Massive machine-type communication (mMTC)**\(\rightarrow\) Mostly, IoT together with smart home, city, and agriculture are part of this class [18; 23].
* **Ultra-reliable and low-latency communication (URLLC)**\(\rightarrow\) Extremely delay-sensitive applications such as e-health, autonomous vehicles, and industrial automation are associated with this class [18].  
Figure 2: Applications and services in IMT-2020 (5G) [2].  
Approaching all applications with similar network infrastructure is wasteful and costly. For instance, mMTC applications have small data (in Kbps), which means allocating a resource that is Gbps speed is needless. Hence, 5G applies a new network architecture, which is network slicing [24]. Right now, there are four Slicing/Service Types (SSTs) under the 3 GPP document for the 5G specification [25]. The SST values are 1, 2, 3 and 4, for eMBB, URLLC, mMTC, and V2X applications, respectively.