esa-sceva/satcom-chunk-collection huggingface dataset:
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 1 Introduction  
In the past, the car was a mechanical system with the primary function of moving people around efficiently. Therefore, the number of on-board electronic components was small and their impact on the cost of the car was low. In car design, the focus was on vehicle control and the user experience mainly depended on the hardware. Later on, the introduction of novel functions such as Advanced Driver Assistance Systems (ADAS) and infotainment shifted the focus towards including comfort and safety, and therefore, car software started playing an increasingly important role.  
Nowadays cars are cyber-physical systems consisting of a high number of electronic components, called Electronic Control Units (ECUs), interconnected through communication networks to exchange data and control messages. The impact of on-board electronics on the car's design complexity and cost increased significantly. In fact, over the past two decades, the number of ECUs has steadily grown as an effect of both the availability of different types of sensors and of ECU specialization. Today, Automated Driving requires support for time-critical and safety-critical traffic flows and poses compelling requirements on in-car communications, especially in terms of bandwidth, reliability and predictability [1].  
In light of this context, Automotive Ethernet is the prime candidate for the main in-car network technology [2]. In fact, Automotive Ethernet technology offers a broad range of bit rates, from 10 Mbps up to 10 Gbps. Automotive-grade Ethernet physical layers that are able to correctly operate in harsh conditions are standardized, and most of them are already available. Moreover, the IEEE Time-Sensitive Networking (TSN) family of standards [3; 4; 5] offers several features that are suitable for automotive communications, such as fault-tolerant and accurate time synchronization, bounded latency, support for scheduled traffic, high reliability, etc.  
This paper provides a perspective on in-car networking, with a focus on Ethernet-based communications. Starting from the traditional in-car network architectures and technologies, the paper discusses the current status, novel trends and future developments of Ethernet and TSN standards for automotive communications.  
The paper is structured as follows. Section 2 presents an overview of the current in-car network architectures and technologies. Section 3 offers a perspective on Automotive Ethernet and the IEEE Audio Video Bridging (AVB) standards. Section 4 focuses on the TSN standards that are relevant to automotive communications. Section 5 addresses the ongoing evolution of car architecture and the relevant challenges. Section 6 highlights some of the main ongoing developments relevant to automotive TSN-based communications. Finally, Section 7 summarizes the paper and outlines future trends.
# Hash Indexing-Based Image Matching for 3D Reconstruction
## 3 The Proposed Method  
To improve the quality of modern 3D reconstruction, in this paper, we present an efficient image matching method based on hashing indexing, named HashMatch. The pipeline of the HashMatch is depicted in Figure 1, where we first use the SURF feature to find local feature points and compute feature descriptors for given image pairs. Second, Haar wavelet coding is adopted to construct a compact hash table for the located feature points and descriptors. Thirdly, the hash index replaces the traditional KNN to select the corresponding feature point from the reference feature set, and then the wrong feature correspondences are caused by repeating the query process. Note that the ratio test is also integrated into the HashMatch for rejecting outliers. The procedures of the HashMatch are presented in Algorithm 1. More details can be found in the subsequent sections.  
```
Input: Image collection \(I=\{{{l}_{n}}|n\in[1,N]\}\) Output : Feature correspondences \(M_{ij}=\{f(p,q)|p,q\in[1,M]\}\)
```  
**Step 1**: Use the SURF feature to detect feature points for each image in image collection \(I\).  
**Step 2**: Use the SURF feature to compute feature descriptors for each located feature point.  
**Step 3**: Create a hash table for all feature descriptors:  
for idx = 1 to \(N^{\prime}\):  
(1) Compute Haar wavelet coefficients for each descriptor via Equation (8);  
(2) Calculate the values of \(\epsilon_{k1}\), \(\epsilon_{k2}\), \(\epsilon_{k3}\), and \(\epsilon_{k4}\) via Equations (9)-(12);  
(3) Assign values to the members of hash table _table_ via Equations (13)-(16);  
**Step 4**: Indexing hash table for given numbers \(i\) and \(j\):  
(1) Compute the values of \(id_{0}\), \(id_{1}\), and \(id_{2}\) according to Equation (18);  
(2) Calculate the indexing table for locating feature descriptors via Equation (19);  
**Step 5**: Measure the difference between feature descriptor \(d_{p}\) and \(d_{q}\) via L2-distance;  
**Step 6**: Repeat Step 4-Step 5. This should result in a set of feature correspondences for each image pairs.  
Figure 1: Flowchart of the HashMatch. The core of the proposed method consists of two parts: the first involves finding features including keypoint detection and computing feature descriptor, and the second involves matching feature descriptors by searching the created Haar wavelet hash table.
## 2 System and Channel Model
### Generalized Pointing Errors with Nonzero Boresight
In view of the above, the probability density function (PDF) of the random variable \(h_{p,m}\) is given as [4, 19]:  
\[f_{h_{p,m}}(h_{p,m})=\xi_{m}^{2}(A_{0,m}g_{m})^{-\xi_{m}^{2}}h_{p,m}^{\xi_{m}^ {2}-1},\ \ \ 0\leq h_{p,m}\leq g_{m}A_{0,m}, \tag{9}\]  
where the parameter \(g_{m}\) is obtained as [17, 28, 29]:  
\[g_{m}=\exp\left(\frac{1}{\xi_{m}^{2}}-\frac{1}{2\xi_{x,m}^{2}}-\frac{1}{2\xi_ {y,m}^{2}}-\frac{\mu_{x,m}^{2}}{2\sigma_{x,m}^{2}\xi_{x,m}^{2}}-\frac{\mu_{y,m }^{2}}{2\sigma_{y,m}^{2}\xi_{y,m}^{2}}\right), \tag{10}\]  
with \(\xi_{m}=\frac{w_{0,m}}{2\sigma_{mod,m}}\), \(\xi_{x,m}=\frac{w_{0,m}}{2\sigma_{x,m}}\), \(\xi_{y,m}=\frac{w_{0,m}}{2\sigma_{y,m}}\) describing the corresponding misalignment-induced fading strength. In fact, the pointing-error effect becomes more significant for lower values of \(\xi_{m}\). Therefore, for very large values of \(\xi_{m}\),the effect of pointing errors can be considered as a negligible one, [12, 20]. Additionally, it should be noted that considering \(\mu_{x,m}^{2}=\mu_{y,m}^{2}=0\), \(\sigma_{x,m}^{2}=\sigma_{y,m}^{2}=\sigma_{s}^{2}\) and \(g_{m}=1\), the PDF including NZB boresight in Equation (9) reduces to the well-known one for zero-boresight pointing errors presented first in ([27], Equation (11)).
## Appendix A Specific Measures of the Radar Data Quality
### Time Quality Index  
The time of the measurement is evaluated in this QI. More precisely, this QI evaluates the temporal degradation of the radar signal within a single antenna rotation. This is computed as a linear relationship of the difference \(\Delta T\) between the time of the measurement and the given product validity time:  
\[qi_{5,i}=\begin{cases}1-\frac{\Delta T}{\Delta T_{max}};&\Delta T\leq\Delta T_ {max}\\ 0;&\Delta T>\Delta T_{max}\end{cases} \tag{12}\]  
where \(\Delta T_{max}\) is a user-defined maximum allowed time difference.  
This QI aims at quantifying the time interval that is needed to carry out a full PPI scan (plan position indicator: a complete 360\({}^{\circ}\) scan where the elevation angle of the radar beam is set to a constant value). The larger the \(\Delta T\) (i.e., the slower the radar revolves), the more significant the observed change in the rainfall field may be at the start of the rotation and at the same position of the radar after a full 360\({}^{\circ}\) circle, respectively.  
The temporal degradation of the radar signal is also considered in [17]. [PERSON] et al. [20; 21] aimed to catch the temporal dimension of the rainfall field in a different way. They expressed the temporal variability of the rainfall by applying an \(n\)-hour moving window method both backward (i.e., on the past estimates of the rainfall field) and forward (i.e., on its forecasts).
## 4 Discussion
### Challenges Faced by DA in Agricultural Application
#### 4.1.2 Refinement and Dynamic Updating of Complex Crop Growth Models
This poses challenges for optimizing state estimation and parameter calibration, as these processes can involve solving nonlinear and non-Gaussian problems or managing uncertainty and error quantification [262]. Technical challenges in implementing dynamic updates cannot be overlooked, necessitating models with robust learning capabilities and adaptability to evolving environmental conditions. Innovative algorithms, such as the highly adaptive DL-Crop algorithm, have been proposed to support dynamic up dates [146; 263]. As we advance CGMs' sophistication and dynamism, integrating precision agriculture and exploring DL applications in parameter automation and dynamic updating are essential. DL methods like multi-variable time-attention networks and U-nets have proven advantageous in fitting complex nonlinear relations [264]. Bayesian frameworks, including HBMs, HMC, and Bayesian Neural Networks, continue to offer promising avenues for solving nonlinear problems and quantifying uncertainties [264; 265]. Hybrid assimilation methods, such as integrated Kalman and Bayesian filters, should also be considered for their advantages in tackling similar challenges [266]. The synergistic application of these methods holds potential for delivering comprehensive and accurate solutions.
###### Abstract  
This study provides a comprehensive evaluation of the vertical accuracy of ERA5 reanalysis data for boundary layer height and key meteorological variables, based on high-precision observational data from Baotou, located on the Mongolian Plateau, during the winter (January-March) and summer (July-August) months of 2021. Results indicate that ERA5 exhibits significant biases in horizontal wind speed, with deviations ranging from \(-\)5 to 8 m/s at 50 m, primarily driven by sandstorms in winter and convective weather in summer. The most pronounced errors occur below 500 m. Vertical wind speeds are consistently underestimated in both seasons, with biases reaching up to 1 m/s, particularly during active summer convection. ERA5 also struggles to reproduce low-level wind directions accurately. In winter, correlation coefficients range from 0.43 to 0.64 below 200 m and improve to above 0.7 at 500 m. In summer, correlation coefficients are lower, ranging from 0.3 to 0.5 below 200 m, with reduced accuracy at 500 m compared to winter. Temperature deviations increase above 2000 m, with a relative overestimation of 3% at 3000 m. Relative humidity is generally overestimated by 5-20% between 1000 and 2000 m in winter and by 10-30% in summer. For boundary layer heights, ERA5 overestimates daytime mixed-layer heights by up to 2000 m in summer and 500-800 m in winter. In contrast, ERA5 captures nocturnal stable boundary layer heights well during winter. This comprehensive evaluation of the vertical structure accuracy of ERA5 reanalysis data, conducted in a heavily industrialized city on the Mongolian Plateau, offers essential insights for improving meteorological studies and refining climate models in the region. The findings provide valuable reference data for enhancing weather forecasting and supporting climate change research, particularly in complex terrain areas.  
Article  
202517393  
1
# Preliminary Biomechanical Evaluation of a Novel Exoskeleton Robotic System to Assist Stair Climbing
## 3 Results
### Joint Moments in Sagittal Plane  
The net joint moments of the foot, knee, and hip in the sagittal plane are shown for ascent and descent in Figure 8 for the tests with the eBUS (blue) and without the eBUS (red).  
Figure 8: Foot, knee, and hip moment curves in the sagittal plane for ascent and descent without (red) and with (blue) the exoskeleton for Subject 1 (**left**), Subject 2 (**middle**), and Subject 3 (**right**); DSP = double stance phase, SSP = single stance phase, SP = swing phase.  
The curves of the foot or ankle moments without the eBUS are characterized by two peaks for both movements. The foot moment curves with the eBUS already differ qualitatively from those without the eBUS, since only one peak occurs here, except for the descent in Subject 3. Quantitatively, the foot joint moments that are applied without the eBUS, with mean maximum values of up to 1.6 Nm/kg, are significantly higher than the mean maximum values of 1.0 Nm/kg with the eBUS.  
The results of the knee moment without the eBUS already show qualitative differences in the intersubjective comparison during the ascent. During descent, these curves without the eBUS are again similar. On the other hand, the knee moment curves with the eBUS are qualitatively different for each subject, which applies to the ascent and the descent. It is obvious during the descent in the trials with the eBUS that even flexion moments occur in all three subjects during the phase of controlled descent. In the trials without the eBUS, an extension moment always occurs in this phase. During descent, the quantitative maxima with the eBUS are significantly lower compared to the maxima without the eBUS. During the ascent, this again varies from subject to subject.  
During the ascent, the curves of the hip moments coincide qualitatively and quantitatively in the phase of weight acceptance. In the subsequent phases, when the eBUS is used, the extension moment continues to increase for Subject 1 up to a cycle time of 45%, until a maximum of 1.3 \(\pm\) 0.2 Nm/kg is achieved. For Subjects 2 and 3, a plateau is maintained here until the second double stance phase. Subsequently, the curve with the eBUS decreases more rapidly compared to the curve without the eBUS. The swing phase of the ascent with the eBUS is characterized by a lower flexion moment up to 80% cycle time. Subsequently, the flexion moment increases when using the eBUS until a maximum value of 0.5 \(\pm\) 0.1 Nm/kg is achieved at the end of the cycle for all three subjects. During descent, the two trajectories differ both qualitatively and quantitatively in the most frequent phases for all three subjects. In the swing phase, the hip moment without the eBUS increases between 75 and 85% cycle time and then decreases again. In the swing phase, the hip moment curve with the eBUS is exactly the opposite of the curve without the eBUS.
# Rod-Airfoil Interaction Noise Reduction Using Gradient Distributed Porous Leading Edges
## 3 Results
### Effect of Gradient Properties on Noise Reduction
For example, X80Y60Z40 and X80Y50Z20 generally performed better than X40Y40Z40, as shown in Figure 15a. However, a larger gradient difference does not mean increased noise reduction: X80Y50Z20 (gradient difference of 30 ppi from upstream strip to downstream strip) rather than X120Y80Z40 reduced the most noise. Overall sound pressure level reductions of up to 3.07, 6.02, and 5.67 dB were obtained by X80Y50Z20 for the operating conditions of \(L\) = 100 mm with \(\alpha=0^{\circ}\), \(L\) = 200 mm with \(\alpha=0^{\circ}\), and \(L\) = 200 mm with \(\alpha=10^{\circ}\), respectively. We attributed these findings to two candidate factors: (1) Secondary vortex-structure interaction may form when the airflows or shedding vortices pass across the upstream sparse porous area and then encounter the downstream dense porous area. This interaction increases noise emission because vortex-structure interaction increases the noise spectra around the main peak, as shown in Section 3.1. (2) As demonstrated in Figures 6-8, 10 and 11, the porous leading edge with a lower-ppi material reduces more noise and the use of an upstream porous strip results in the smallest noise reduction. Therefore, the gradually more sparsely distributed porous leading edges, which have lower-ppi porous materials in the last two downstream strips, reduced more noise. Figure 11: Typical noise spectra for different porous positions (\(V_{\infty}=50\) m/s): (**a**) \(L=100\) mm, \(\alpha=0^{\circ}\); (**b**) \(L=200\) mm, \(\alpha=0^{\circ}\); (**c**) \(L=200\) mm, \(\alpha=0^{\circ}\); and (**c**) \(L=200\) mm, \(\alpha=10^{\circ}\). Figure 12: \(\Delta\)SPL as a function of frequency and flow velocity for different arrangements of the porous leading edges: (**a**) X80Y50Z20, \(L\) = 100 mm, \(\alpha=0^{\circ}\); (**b**) X80Y60Z40, \(L\) = 200 mm, \(\alpha=0^{\circ}\); (**c**) X80Y60Z40, \(L\) = 200 mm, \(\alpha=10^{\circ}\); (**d**) X40Y60Z80, \(L\) = 100 mm, \(\alpha=0^{\circ}\); (**e**) X40Y60Z80, \(L\) = 200 mm, \(\alpha=0^{\circ}\); and (**f**) X40Y60Z80, \(L\) = 200 mm, \(\alpha=10^{\circ}\).
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 2 Materials and Methods
### Surveying Equipment  
#### 2.2.1 GNSS Receiver Leica GPS900 CS  
Dual-frequency GNSS rover Leica GPS 900 CS equipped with hardware and software for receiving GPS and GLONASS signals was used. In this study, it was used in the coordinates of ground control points (GCP) determining the absolute orientation of photogrammetric images. Quick static GNSS method was used. Horizontal and vertical accuracy is expressed as follows according to the manufacturer data: standard deviation in position \(\sigma_{\text{P}}\) = 5 mm + 0.5 ppm and in height \(\sigma_{\text{h}}\) = 10 mm + 0.5 ppm. GNSS RTK measurement method was also used for the detailed measurement of two reference heap surfaces. Manufacturer declares horizontal and vertical accuracy in terms of relations \(\sigma_{\text{P}}\) = 10 mm + 1 ppm and \(\sigma_{\text{h}}\) = 20 mm + 1 ppm [53].
## 4 An Indoor Positioning Algorithm Combining IMU and LBP-FFT-CNNs
### Inertial-Based Positioning Algorithm Using IMU
#### 4.1.4 Double Integration for Position  
Velocity \(\overset{\rightarrow}{v}(t)\) is obtained by integrating linear acceleration, where \(\overset{\rightarrow}{v}_{0}\) is initial velocity:  
\[\overset{\rightarrow}{v}(t)=\overset{\rightarrow}{v}_{0}+\int\overset{ \rightarrow}{a}_{linear}(t)dt \tag{21}\]  
Position \(\overset{\rightarrow}{p}(t)\) is determined by integrating the velocity, where \(\overset{\rightarrow}{p}_{0}\) is initial position:  
\[\overset{\rightarrow}{p}(t)=\overset{\rightarrow}{p}_{0}+\int\overset{ \rightarrow}{v}(t)dt \tag{22}\]
## 2 Methods
### Topology Optimization
#### 2.1.1 Density-Based Approach  
The density-based approach involves using constant material properties in each element of the discretized design domain. The formulation of this approach was developed based on the pioneering work on homogenization methods [22].  
This approach depends on the Young's modulus (\(E_{e}\)) and the relative density (\(x_{e}\)) of the element and is represented as:  
\[E_{e}(x_{e})=(x_{e})^{p}E\qquad 0\ <x_{\min}\leq x_{e}\leq 1 \tag{4}\]  
where  
\(p\): Penalization factor greater than 1 (e.g., 1.5 \(<p<7\))  
\(E\): [PERSON]'s modulus in the base material  
\(x_{\min}\): Minimum value of the relative density  
When [PERSON]'s modulus of a void material is assigned, the modified Solid Isotropic Material with Penalization (SIMP) method uses non-zero values to avoid the singularity in the finite element stiffness matrix.  
\[E_{e}(x_{e})=E_{\min}+(x_{e})^{p}(E_{0}-E_{\min})\ x_{e}\in\ [0,1] \tag{5}\]  
where \(E_{\min}\) is a very small stiffness, and \(E_{0}\) is the stiffness of the material [22].  
To solve nonlinear programming problems for minimizing stress, compliance, volume, etc., the method of moving asymptotes (MMA), sequential quadratic programming (SQP), or optimality criteria methods have been used in the literature. The optimality criteria method utilizes the Karush-Kuhn-Tucker (KKT) condition to determine the optimal solution [23]. The termination criteria for the optimization process are either reached when the maximum number of iterations is reached without meeting the optimal criteria or when the convergence termination criteria are satisfied. One of the convergence criteria is based on the difference between the new and old values of the design variables (i.e., \(\left|\mathbf{x}^{\text{new}}-\mathbf{x}^{\text{old}}\right|\)). If the difference between these two design variable values is smaller than the tolerance, the topology optimization process is terminated.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 5 Future Challenges
### Handling the Mobility of RISs and Users  
For a large-scale data-centric network, since communication service requirements are highly dynamic and imbalanced among users, it is usually inefficient to deploy RISs at fixed locations. To improve network coverage and serve remote nodes, RISs can be deployed on autonomous systems, such as unmanned aerial vehicles or unmanned ground vehicles for providing flexible channel reconfigurations.  
Furthermore, the locations of users may also dramatically change over time in emerging V2X networks. Due to the passive nature of RISs, they cannot send pilot signals to track the movement of the users, especially when the direct links from the BS to users are blocked [56]. With a mobile RIS or users, the system performance not only depends on the RIS's or users' locations but also on the trajectory itself. Consequently, the dimension of design variables is significantly increased. Mathematically, the time-varying phase shift design of a mobile wireless system can be modeled as a high-dimensional dynamic pro gramming problem, in which Q-learning, temporal difference learning, and policy iteration algorithms in approximate dynamic programming could provide effective solutions [57].  
On the other hand, since the CSI for unvisited places and future time slots are unknown, the prior distribution of channels has to be predetermined via the geometry-based tracing approach. However, as time evolves, the knowledge about the channel distribution should be updated for a better phase shift design. This can be modeled as a partially observable Markov decision process, where the DRL methods can be used to learn the underlying wireless environment while choosing the moving trajectory on the fly. Hence, the state of DRL includes not only the current CSI but also the action from the previous time step. Furthermore, by exploiting the extra partial information (e.g., previous locations and velocities of users or the RIS), the post-decision state algorithm can be used to find an optimized solution in dynamic environments during the training of the DRL model [58].