esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Study
### Comprehensive Study of Launch Velocity  
In the previous section on vibration isolation performance parameter study, it was found that the vibration isolation performance is best when the structural parameters satisfy the quasi-zero stiffness condition. Based on the quasi-zero stiffness characteristics, this section will delve into the impact of structural parameters on launch velocity in detail. According to Equation (12), the structural parameters affecting the launch velocity include vertical spring stiffness \(k_{v}\), vertical spring original length \(l\), transverse spring stiffness \(k_{h}\), and vertical distance \(d\). Under the consideration of quasi-zero stiffness characteristics, the stiffness ratio \(\alpha=k_{v}/k_{h}\) and the length ratio \(\lambda=l/d\) satisfy a definite relationship \(\alpha=1/(\lambda-1)\). Thus, these four parameters are interdependently determined. Then, by keeping \(k_{h}\) and \(d\) unchanged, the effect of stiffness ratio \(\alpha\) on the launch velocity is studied by changing the parameter \(k_{v}\) (with \(l\) changing correspondingly with \(k_{v}\)). Figure 8 shows the launch velocity \(v_{0}\) curve versus pre-compression length \(\delta\) with a different \(\alpha\). It is seen that when \(\delta\) is less than 0.1 m, the launch velocity \(v_{0}\) does not change much with the pre-compression length \(\delta\). In this range, the larger \(\alpha\) is, the smaller the launch velocity \(v_{0}\) is. When \(\delta\) is greater than 0.15, the slope of the second half of the launch velocity curve gradually increases with the increase of \(\alpha\), which means that a small pre-compression \(\delta\) can bring a higher launch velocity \(v_{0}\).  
Figure 7: The vibration transmissibility \(T\) with different \(c_{v}\) of the proposed design.  
Therefore, it can be concluded that when the pre-compression \(\delta\) of the transverse spring is small, a smaller stiffness ratio \(\alpha\) can obtain a higher launch velocity \(v_{0}\). When the pre-compression \(\delta\) of transverse spring is large, the higher stiffness ratio \(\alpha\) can produce the higher launch velocity \(v_{0}\). Moreover, the critical value of stiffness ratio should be determined according to different structural parameters.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
## 3 System Model
### OTFS Modulation and Demodulation
Suppose the filter is a rectangular window time-limited to 0 to \(T\), then the Heisenberg transform becomes an OFDM modulator operating over each column of the signal \(X_{tf}\). This, therefore, reduces the OTFS modulator to a two-dimensional ISFFT-precoded OFDM modulator. Where the 2D ISFFT transforms the Doppler domain to the time domain using IDFT and the delay domain to the frequency domain using the DFT. Finally, the Heisenberg transform converts the TF domain to the time domain using the IDFT. The signal \(x(t)\) is transmitted through a high-mobility channel with delay-Doppler channel impulse response \(h(\tau,\
u)\), which is equivalent to the delay-time channel response given by \(g(\tau,t)\), where \(\tau\) and \(\
u\) represents the delay and Doppler shift, respectively, and \(z(t)\) represents noise. Therefore, the received signal \(y(t)\) is expressed as  
\[\
u_{l}=\frac{l}{NT},\;l=0,1,\ldots,N-1 \tag{15}\]1  
At the receiver, the received signal \(y(t)\) in the time domain is converted to TF domain using the Wigner transform, equivalent to the OFDM demodulator, as shown:  
\[\
u_{l}=\frac{l}{NT},\;l=0,1,\ldots,N-1 \tag{15}\]2  
and the TF domain signal is then converted back to the DD domain using the SFFT as shown:  
\[\
u_{l}=\frac{l}{NT},\;l=0,1,\ldots,N-1 \tag{15}\]3  
effectively recovering the DD domain representation of the transmitted symbols. OTFS modulation can also be implemented using the inverse Zak transform, which is mathematically equivalent to the combination of ISFFT and the Heisenberg transform [6]. The inverse Zak transform provides an alternative and computationally efficient method for transforming the input signal \(x_{dd}\) from the DD domain to the delay-time domain \(X_{dt}\), bypassing the need for the IDFT-DFT pair. Therefore, the input signal \(x_{dd}\) can be converted to the delay time domain \(X_{dt}\) as shown:  
\[\
u_{l}=\frac{l}{NT},\;l=0,1,\ldots,N-1 \tag{15}\]4  
\[\
u_{l}=\frac{l}{NT},\;l=0,1,\ldots,N-1 \tag{15}\]5  
\[\
u_{l}=\frac{l}{NT},\;l=0,1,\ldots,N-1 \tag{15}\]6  
where the superscript \(T\) denotes the transpose. Note that the IFFT-FFT combination cancels out and reduces the operation to an IFFT across the Doppler axis, thereby reducing the computational complexity. The intermediate result \(X_{dt}\) is the inverse Zak transform, and the time-domain signal \(x_{t}\) is obtained by vectorizing \(X_{dt}\)[25].
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 4 Results and Discussion
### Possible Reasons for the Amplified Urbanization Effects at Coarse Spatial Resolutions
In addition, two recent studies indicated that spring phenology of a coarse pixel is prone to be closer to that of a fine pixel with earlier spring phenology and higher vegetation growth speed [35; 63]. Therefore, we assume that the overestimated urbanization effects at coarse spatial resolutions is mainly due to the amplified heterogeneity of vegetation spring phenology of mixed pixels at coarser spatial resolutions, especially in urban areas. Specifically, compared with rural areas, urban areas have higher diversity in terms of spring phenology as deciduous trees in urban areas mainly consist of planted trees with different growth conditions and are located in different environments, for example, street trees and garden trees. Thus, with spatial resolution getting coarser, spring phenology in rural areas remains stable whereas urban vegetation spring phenology shifts to earlier dates, which results in the overestimation of urbanization effects on spring phenology.
# Target Recognition and Navigation Path Optimization Based on NAO Robot
## 3 Navigation Theory and Navigation Map  
### Simultaneous Localization and Mapping Theory  
The SLAM is a theoretical framework to build real-time location and graphs, which are widely used in navigation task. At present, the theory has a lot of development, such as the EKF-SLAM algorithm, the visual SLAM algorithm, and so on. The basic theory of the SLAM is that the robot avoids obstacles automatically and navigates by walking. To pinpoint the robot's current position, the grid theory is introduced. The robot's current position can be seen as one of the grids of the area. The robot walks from the current grid to the next grid. The procedure is shown in Figure 3.  
In the theoretical framework of SLAM, there are two important concepts, the Q-learning algorithm, and the K-means algorithm, to help the robot build an environment map.
# Reaction Forces and Apparent Thrust in Dual Oscillating Control Moment Gyroscopes
## 2 Materials and Methods
### Description of Mechanical Model  
In more detail, we consider a right-handed Cartesian coordinate system \(Gxyz\) originating from the centroid \(G\) of a wheeled cart, where \(y\) is the upward vertical axis. Let ABCD be the top view of a cart (on horizontal plane \(Gxz\)), onto which two contra-rotating gyroscopes are mounted (see, Figure 2). On the upper horizontal surface of the cart, we consider two parallel spindles (with axes \(A^{\prime}D^{\prime}\) and \(B^{\prime}C^{\prime}\)) which contra-rotate in their corresponding cages firmly connected to the cart. Perpendicular to each of these two shafts, the axle of the corresponding gyroscope is pivoted, with the first (\(O_{1}L\)) at the left shaft \(A^{\prime}D^{\prime}\) and the second (\(O_{2}R\)) at the right shaft \(B^{\prime}C^{\prime}\). In other words, the illustrated symbols (L) and (R) refer to the left and right spinning wheels, respectively.  
Although the system is primarily designed for testing vertical thrust, the cart is equipped with wheels to allow for horizontal thrust evaluation as well (toward the \(z\) direction), thereby enhancing generality. The cart supports rigid mounting plates (not shown) that carry the twin control moment gyroscope (CMG) system, as illustrated in Figure 2:  
* The propulsion system consists of two identical gyroscopes that undergo controlled rotation (forced precession) about parallel spindles, positioned at the same height  
Figure 3: Photo of gyroscope with motor driver ([[https://www.gyroscope.com/](https://www.gyroscope.com/)]([https://www.gyroscope.com/](https://www.gyroscope.com/)) (accessed on 17 July 2025)).  
above the horizontal ground. The ideal geometric axis of each spindle is fixed relative to the cart. As is customary, each spinning wheel is housed within a circular ring, with its spin axis mounted on a gimbal (Figure 3). These two frames are mutually perpendicular and behave as a rigid body with _one degree of freedom_\(\theta\) (see the third bullet below for further details).
* Each gyroscope is spun by an electric motor (driver) mounted on the gimbal (Figure 3), maintaining a nearly constant spin rate \(\omega_{\text{rotor}}\).
* The single degree of freedom \(\theta\) for each gyroscope (illustrated in Figure 2b), along with the associated spindle rotation, is actuated by a servomotor (torquer).
* When the axes of the synchronized gyroscopes are coaligned, they share the same magnitude and direction of angular velocity \(\omega\) (see Figure 2a). This means that on the left gyroscope (L), the vector of angular momentum is directed from the pivot \(O_{1}\) toward the spinning wheel, whereas on the right gyroscope (R), it is directed from the spinning wheel to the pivot \(O_{2}\).
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 4 Model Simulations and Discussion
### Development of Retrieval Algorithm  
The ultimate goal of remote sensing development is to achieve an efficient and convenient inversion algorithm. GNSS-R technology, especially spaceborne GNSS-R technology, is feasible in vegetation monitoring, but there is still a certain gap to the quantitative inversion of vegetation parameters. The development and use of an efficient and convenient inversion algorithm will make this technology truly practical in vegetation remote sensing and it is an inevitable development trend in the future. According to the above simulation of the difference in scattering characteristics, it can be seen that when the vegetation parameters (vegetation water content, density, and height) change under different polarizations and different observation angles, the scattering characteristics are different.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Trunk**} & \multicolumn{2}{c}{**Branch**} \\ \cline{2-5}  & **Moisture** & **Permititivity** & **Moisture** & **Permititivity** \\ \hline Aspen1 & 0.6 & 41.84 + 18.93 i & 0.5 & 32.97 + 14.73 i \\ Aspen2 & 0.4 & 25.60 + 11.25 i & 0.3 & 19.29 + 8.28 i \\ Aspen3 & 0.2 & 13.33 + 5.47 i & 0.1 & 6.67 + 2.33 i \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vegetation water content and permittivity for the three scenarios.  
Figure 7: Specular scattering properties of various polarization for different vegetation water contents. (**a**) RR polarization; (**b**) LR polarization; (**c**) VR polarization; (**d**) HR polarization.  
How to effectively identify and reverse the information in the receiver signal is one of the challenges. There is a complex nonlinear relationship between GNSS-R observations and vegetation parameters. At present, it is relatively easy to use machine learning such as neural networks to invert forest parameters [36] because machine learning can model the nonlinear relationship well and achieve relatively good inversion accuracy.  
The later development of inversion algorithms can cover but is not limited to empirical models based on optimization theoretical algorithms and semi-empirical inversion models simplified based on theoretical models. The empirical model can create a function based on the statistical analysis of ground measurement data and ground object parameters. This function mainly describes the function fitting relationship between GNSS-R observations and ground object parameters. The semi-empirical inversion model comes from the simplification of the theoretical model. The measured data are used to partially fit the theoretical model coefficients, which reduce the amount of calculation tasks and complexity, and then a definite analytical solution can be obtained.
## 3 Tolerancing Informatics
### Tolerancing Informatics Workflows
#### 3.4.2 Assembly Adjustments and Advanced Matching Strategies  
Industry 4.0 and the internet of things as well as the increasing digitalization in manufacturing lead to an increased gathering of sensor data from manufacturing machines and processes. In order to use this data for controlling and managing geometrical variations, it can be used as input data in manufacturing process simulations to predict the systematic part variations, which can in turn be further processed. Moreover, as a result of inspection activities, it is known if the parts conform to the specified tolerances and information about the actual part geometries in the form of measurement data are available (also considering uncertainties).  
These results from manufacturing process simulations or measurement data can be processed in tolerance analysis and variation simulation models to predict the behavior of the assembly. In this context, depending on the required simulation model fidelity, measurement point reduction techniques allow for simpler models to be equipped with meaningful measurement data [27], while advanced tolerance analysis and variation simulation methods based on the concept of \"skin model shapes\" [28] allow for a realistic prediction of the assembly behavior considering the complete measurement point set.  
The efficient processing of the verification-related tolerancing information enables the implementation of advanced matching strategies [29], i.e., identifying suitable assembly partners that reduce geometrical variation. Moreover, such data processing allows for building a digital twin for geometrical variations management [15] and geometry assurance [16]. Beside the aforementioned matching strategies, such digital twins allow for the individualizing of locator adjustments [30] or the selective assembly of sheet metals [31].  
However, the implementation of a digital twin for geometry assurance still entails some challenges in industry [17].
## Materials and Methods
### Time-Series Smoothing Methods  
Since it is beyond our capacity to investigate all possible time-series smoothing methods we have selected some common methods from the two broad categories of local vs global methods. From the local category we have chosen adaptive Savitzky-Golay filtering (SG) [11; 61] and adaptive LOESS filtering (LO) [62], and from the global category we have chosen least-squares fits to asymmetric Gaussian functions (AG) [12] and double logistic functions (DL) [13; 14]. In addition, we test spline smoothing (SP) [63; 64] that has capacity to act both as a local and a global smoother.  
SG, AG and DL are available in the current TIMESAT version 3.3 [16; 65]. LO and SP were integrated into a development version of TIMESAT for this study. The adaption to the upper envelope of the original NDVI time-series was applied to all smoothing methods for reducing negative bias of satellite NDVI due to clouds or poor atmospheric conditions [11; 16]. Upper-envelope adaptation makes the smoothed function approach the upper NDVI envelope by iteratively generating new curves with data weights that have lower values for the data points below the previous smoothed curve [11; 12]. This upper-envelope adaptation is in TIMESAT determined by two parameters: the number of iterations \(u\) for which weights are being modified, and the adaptation strength \(a\), which fine-tunes the strength of the upper-envelope weighting in each iteration (Figure 2).  
Figure 1: Elevation of the Ammar region. (Source: National Land Survey of Sweden, Dnr: I2014/00579).
## 2 Propagation Mechanism and Particle Distribution  
To investigate terahertz wave propagation characteristics in rain and fog, it is essential to have an understanding of the electromagnetic wave propagation mechanisms and the physical characteristics of fog and raindrops since these factors are key in affecting terahertz wave attenuation. Moreover, when terahertz waves interact with fog and raindrop particles, these particles scatter and absorb the waves. The particle distribution spectrum, which describes the number of particles of various sizes, aids in accurately calculating terahertz wave attenuation. Therefore, this section first presents the propagation mechanism of electromagnetic waves and the physical characteristics of fog droplets and raindrops. Subsequently, the distribution spectra of fog droplets and raindrops are summarized.
## 3 RCU-Style Non-Blocking Synchronous Algorithms
### Read Log Update (RLU)  
RLU was proposed to overcome the major limitations of RCU, the complexity of using the API, and the synchronization overhead between write threads due to the lock [24]. RLU maintains a write log for each thread, create updates in the log, and commits these updates atomically to release updates to other threads at once. This way, it is possible to provide more straightforward APIs by atomically performing multiple pointer update operations at once. Each update is versioned in the write log according to the commit time. The previous versions need to be cleaned up for log reclamation for each thread's write log, which involves a considerable overhead. Multiple threads can read at the same time without interfering with write threads similar to RCU, since read threads read the latest valid version. In both reading and writing, RLU conducts operations based on a clock. The system maintains a global clock, and each thread reads the moment it entered a critical section (also known as the RLU section) from global clock and keeps it in a thread's metadata as local clock.  
For data consistency, read operations read the most recently updated version depending on the thread's local clock. A copy of a node (a version) to be read may exist in another thread's write log. Once a write thread executes multiple operations for an update and commit it with a write clock, other threads can atomically identify the change. Conducting a commit with a write clock can result in all updates being published to other threads at the same time. While RCU allows a single pointer modification, RLU enables multiple changes atomically with conducting a commit operation specifying a write clock.
## 3 Proposed Method  
### Overall Architecture  
As shown in Figure 1, our study is focused on developing AI-based sports broadcasting.  
First, the model obtains background knowledge required for the game. The background knowledge contains the layout of the court, type of game, number of players, number of referees, player numbers, team names, team colors, and rules of the game. Secondly, the model derives homography from real-time videos by tracking relative positions on court. It extracts features from frames and the applied SIFT (Scale-Invariant Feature Transform) algorithm to match frames during panoramic image shift. SIFT (Scale-Invariant Feature Transform) is an algorithm used to detect and represent significant local features in an image, providing feature extraction that is invariant to scale and rotation. This algorithm identifies key points in the image and generates local descriptors around each key point, enabling tasks such as object matching, image stitching, and object recognition [5]. The position of the camera for broadcasting the main stream of the game is located and rotates in a fixed position for the game. Our model changes the view of the court into a bird's-eye view based on the camera calibration result. For this, with OpenCV, it detects the court by shifting partsof the videos frames into panoramic images, and calculates the error with the actual court size. It then calculates the homography of the actual court size to produce bird's-eye view images. To generate the bird's-eye view image, the left and right videos covering the entire area were utilized, allowing the panorama to be created with a minimal number of images. Third, the model detects and tracks players with the YOLO model. The YOLO8-cls model is fine-tuned to extract the court domain and player positions, allowing detection of the players and the ball in real time. For object tracking, we propose the following algorithm to optimize the system performance. In each frame, the YOLO detection model is used to identify the bounding boxes of players. Subsequently, the Intersection over Union (IoU) is calculated between the bounding boxes from the current frame and those from the previous five frames. The bounding boxes with the highest IoU are then linked together to track the movement paths of the players. Each player's actions, specifically, stop, run, dribble, shoot, and blocking, are detected with the R(2+1)D action recognition model. R(2+1)D is a deep learning model developed for action recognition in videos, which separates the spatial (2D) and temporal (1D) components of 3D convolutional neural networks (CNNs) for processing. By incorporating information about sequentially occurring actions during recognition, it enhances the accuracy of action recognition and was applied in this study to analyze the actions of athletes [19]. Fourth, with the information derived from the video, key events are recognized. Key events contain players, teams, positions, and action information, and GPT-4.0 turbo API allows the generation of a broadcast line, switching it to audio with TTS for natural broadcasting.