esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
## 3 The Newton Iterative Parameter Estimation Algorithm  
The gradient method uses the first derivative, and its convergence rate is slow. The Newton method is discussed here, which requires the second derivative, with fast convergence speed. The following derives the Newton iterative algorithm to estimate the parameters of dual-frequency signal models. According to the criterion function \(J_{1}(\mathbf{\theta})\) in Equation (2). Calculating the second partial derivative of the criterion function \(J_{1}(\mathbf{\theta})\) with respect to the parameter \(\mathbf{\theta}\) gives the Hessian matrix  
\[\mathbf{H}(\mathbf{\theta}):=\frac{\partial^{2}J_{1}(\mathbf{\theta})}{\partial\mathbf{ \theta}\partial^{\varepsilon}}=\frac{\partial\text{grad}[J_{1}(\mathbf{\theta})]} {\partial\mathbf{\theta}^{\varepsilon}}. \tag{17}\]  
Let \(k=1,2,3,\cdots\) be an iterative variable and \(\hat{\theta}_{k-1}\) be the estimate of \(\mathbf{\theta}\) at iteration \(k-1\). Based on the Newton search, we can derive the Newton iterative (NI) parameter estimation algorithm of dual-frequency signal models:  
\[\hat{\theta}_{k} = \hat{\theta}_{k-1}-\mathbf{H}^{-1}(\hat{\theta}_{k-1})\text{grad}[J_ {1}(\hat{\theta}_{k-1})], \tag{18}\] \[Y(L) = [y(1),y(2),\cdots,y(L)]^{\tau},\] (19) \[\hat{\mathbf{\Phi}}_{k} := \mathbf{\Phi}(\hat{\theta}_{k-1},L)=[\mathbf{\varphi}(\hat{\theta}_{k-1},1),\mathbf{\varphi}(\hat{\theta}_{k-1},2),\cdots,\mathbf{\varphi}(\hat{\theta}_{k-1 },L)]^{\tau},\] (20) \[\hat{F}_{k} := \mathbf{F}(\hat{\theta}_{k-1},L)=[f(\hat{\theta}_{k-1},1),f(\hat{ \theta}_{k-1},2),\cdots,f(\hat{\theta}_{k-1},L)]^{\tau},\] (21) \[\hat{\mathbf{\varphi}}_{k} := \mathbf{\varphi}(\hat{\theta}_{k-1},t_{i})\] (22) \[\hat{f}_{k} := f(\hat{\theta}_{k-1},t_{i})=\hat{a}_{k-1}\cos(\hat{\omega}_{1,k -1}t_{i})+\hat{b}_{k-1}\cos(\hat{\omega}_{2,k-1}t_{i}),\] (23) \[H(\hat{\theta}_{k-1}) = \left[ \begin{array}{ccc}h_{11}(\hat{\theta}_{k-1})&h_{12}( \hat{\theta}_{k-1})&h_{13}(\hat{\theta}_{k-1})&h_{14}(\hat{\theta}_{k-1})\\ h_{21}(\hat{\theta}_{k-1})&h_{22}(\hat{\theta}_{k-1})&h_{23}(\hat{\theta}_{k-1 })&h_{24}(\hat{\theta}_{k-1})\\ h_{31}(\hat{\theta}_{k-1})&h_{32}(\hat{\theta}_{k-1})&h_{33}(\hat{\theta}_{k-1 })&h_{34}(\hat{\theta}_{k-1})\\ h_{41}(\hat{\theta}_{k-1})&h_{42}(\hat{\theta}_{k-1})&h_{43}(\hat{\theta}_{k- 1})&h_{44}(\hat{\theta}_{k-1})\end{array}
## 3 Results
### Characterization of NDVI Change
#### 3.2.2 Spatial Variation in NDVI  
Figure 8 shows the spatial variation of NDVI in Inner Mongolia from 1982 to 2020. In the past 39 years, the NDVI values in 74.22% of Inner Mongolia showed an increasing trend  
Figure 6: Variation trend of NDVI mean value of overall and each grassland type in Inner Mongolia from 1982 to 2020.  
Figure 7: Mutation analysis of NDVI in Inner Mongolia from 1982 to 2020.  
(Figure 8a), of which 49.82% showed a significant increase, mainly in Hulunbeier, Xing'an Meng, Tongliao, Chifeng, Erdos, and Hohhot. In addition, 23.62% of the areas showed a decreasing trend in NDVI values during 1982-2020, mainly in the western part of Xilingol league, the northern part of Ulanqab city, Baotou city, Bayannur, and most of Alxa league. Among the five vegetation types, except for desert steppe and desert, NDVI is the largest area of significant increasing level, desert steppe and desert areas have the largest area of stable and constant level of NDVI, and desert is the area with the largest decrease in NDVI value. It indicates that the vegetation in the southeastern part of Inner Mongolia showed a greening scenario during 1982-2020, while the vegetation in the central and northern parts of the country showed little fluctuation, and the largest area of vegetation turning brown in Inner Mongolia was in the west (Figure 8b).
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 4 Traffic Prediction Method of the AT-GRU Satellite Network
### Loss Function  
The loss function can be used to show the difference between the predicted value and the real data. The loss function in this paper adopts the mean square error function:  
\[loss=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\widetilde{y}_{i})^{2}, \tag{23}\]  
where \(N\) represents the number of samples, \(y_{i}\) represents the actual flow value, and \(\widetilde{y}_{i}\) represents the predicted flow value of the model proposed in this paper.  
At the same time, Adam [25; 26; 27] optimizer is used to optimize the model parameters, and the loss function is reduced by updating the neuron weight matrix and offset value.
## 1 Introduction  
In recent years, the use of pedicle screw instrumentation systems in the fusion of the lumbar spine has been on the rise [1, 2]. Instrumented lumbar fusion is an effective and durable treatment strategy for numerous spinal pathologies, including degenerative lumbar conditions [3]. Implementation of pedicle screw rods has been associated with increased fusion rates and has been shown to facilitate the mobilization of postoperative patients without bracing [4, 5, 6]. Despite overall clinical success of the technique, associated complications have been reported, such as pedicle violation, neurologic injuries, pseudo-dourrhosis, instrumentation failures, and facet joint violation [7, 8, 9, 10, 11, 12, 13]. Among these, the superior segment facet joint violation of the pedicle screw could promote symptomatic adjacent-segment pathology (ASP) [2; 14; 15; 16].  
Injury to the superior facet joints during placement of pedicle screws causes facet joint stiffness, rigidity, and osteoarthritis. Moreover, facet joint violation may contribute to increasing stress at the adjacent segment, leading to biomechanical changes including abnormal facet joint loading and aberrant motion, which can accelerate ASP [17]. Abnormal loading and increased mobility in adjacent segments may explain the mechanism behind the development of ASP; however, it is unclear whether it is caused by fusion sequelae or whether it is the result of natural degeneration. Moreover, the exact incidence of ASP and its risk factors are a subject of controversy [18].  
The purpose of this study was to investigate the relationship between superior segment facet joint violation by transpecicular screws and the development of ASP. Following, we aimed at predicting the risk of ASP through quantitative evaluation of facet joint violation.
## 4 Results and Analysis  
In this section, an example of the original observation data at various layers is firstly shown to demonstrate the consistency of the data. Afterwards, the change trend of the indicators, including the MODIS BRDF_R and NDVI, the average wind speed at 1 m height, and the in-situ AR, were presented on the eight-day average scale to ensure the synchronization of the ground measurements and remote sensing observations, followed by the relationship analysis between the in-situ AR and these remote sensing indicators.
###### Abstract  
The present study examined the relationship between upper limb muscle power and shooting velocity in elite male youth risk hockey players. Seventeen participants (age: 18.2 \(\pm\) 1.44 years) underwent assessments of upper limb power and shooting velocity. Upper limb power was evaluated through bench press exercises, including indirect 1 RM, mean power, and peak power. Shooting velocity was measured using a shooting test, including static drive shot, static slap shot, dynamic drive shot, and dynamic slap shot techniques. Results showed significant positive correlations between upper limb power and shooting velocity in static slap shot (r = 0.62, \(p\) = 0.04) and dynamic slap shot (r = 0.86; \(p\) < 0.01). Dynamic slap shot also correlated significantly with peak power (r = 0.63; \(p\) = 0.03). Differences in shooting velocity were observed among the techniques (F\({}_{(3,6,6)}\) = 23.7; \(p\) < 0.01, \(\eta_{\rm P}\)\({}^{2}\) = 0.56), with dynamic slap shot displaying the highest velocity and static drive shot the lowest. These findings highlight the positive association between upper limb muscle power and shooting velocity in elite youth risk hockey players. Developing upper limb power can enhance shooting performance. The choice of shooting technique significantly affects shooting velocity, underscoring the importance of optimizing technique for maximizing performance. These findings provide practical insights for coaches and practitioners, informing the design of targeted training programmes aimed at improving shooting velocity in risk hockey players.  
roller hockey; biomechanics; shooting technique; performance enhancement Article  
Reliability between Upper Limb Muscle Power and Shooting Velocity in Elite Male Youth Risk Hockey Players
## 3 Near-Field Simulation of Metasurfaces with Varying Dimensions
### Near-Field Simulation of Large-Scale Metasurfaces
#### 3.3.2 Normal Incidence of a Circularly Polarized Plane Wave on a Large-Scale Metasurface  
The design simulation of a 40 \(\times\) 40 rectangular metasurface vortex wave in the full-wave simulation software is illustrated in Figure 12. The design mode is \(l=-5\) and the  
Figure 11: Near-field calculation of vortex waves generated by the \(l=-5\) metasurface. (**a**) Noiseless. (**b**) The noise amplitude is 0.2. (**c**) The noise amplitude is 0.3.  
Figure 10: Near-field simulation of a large-scale metasurface. (**a**) The designed 40 \(\times\) 40 metasurface; (**b**) vector electric field; (**c**) magnitude of the electric field.  
incident wave is circularly polarized. The calculated frequency is 12 GHz. Figure 9a displays the OAM spectrum observed at \(z=120\) mm. It can be observed that \(LH=-5\) dominates as the main polarization component, while RH right-hand circular polarization remains low as a cross-polarization component. A high-purity vortex wave with mode \(LH=-5\) is generated, exhibiting a maximum electric field amplitude of 1.81 V/m in Figure 12b. Figure 12c,d depict the calculated ring-shaped intensity of the left-polarized electric field and five-period helical phase, respectively, confirming that the metasurface generates an expected mode vortex wave characterized by a regular ring-shaped electric field amplitude. Figure 12e,f present both the intensity and phase of the calculated right-handed polarization electric field, demonstrating sufficiently low undesired right-handed circular polarization amplitudes and verifying the high conversion efficiency achieved by the designed metasurface.  
Figure 12: Near-field simulation results of vortex waves generated by the \(l=-5\) metasurface with a frequency of 12 GHz. (**a**) OAM spectrum; (**b**) magnitude of E field from full wave software; (**c**,**d**) intensity and phase of left-handed circular polarization electric field; (**e**,**f**) intensity and phase of right-handed circular polarization electric field.
## 2 Theories
### Orb-Slam3  
ORB-SLAM3 [3] is a highly efficient system designed for Visual SLAM. It operates through three concurrent threads: tracking, local mapping, and loop and map merging, alongside an independent full bundle adjustment (BA) thread for map enhancement.  
Bundle adjustment [20] is a non-linear optimization method utilized in the ORB-SLAM3 algorithm; it estimates accurate camera 3D locations and poses by minimizing the reprojection error with respect to the matched key points on the digital image. The SE(3), Special Euclidean Group, represents the special Euclidean group within Lie group algebra. It is a six-dimensional vector encompassing three rotational degrees of freedom and three translational degrees of freedom. The SE(3) manifold possesses a topological structure that renders it differentiable. Consequently, SE(3) can effectively determine an alignment. The error can be formulated as  
\[\mathbf{e}_{i,j}=\mathbf{x}_{i,j}-\pi_{i}(\mathbf{T}_{iu},\mathbf{X}_{w,j}) \tag{2}\]  
and the minimized cost function is represented as Equation (3),  
\[C=\sum_{i,j}p_{h}(\mathbf{e}_{i,j}^{T}\mathbf{\Omega}_{i,j}^{-1}\mathbf{e}_{i,j}) \tag{3}\]  
where \(\rho_{h}\) is the Huber robust cost function and \(\mathbf{\Omega}_{i,j}=\sigma_{i,j}^{2}\mathbf{I}_{2\times 2}\) is the covariance matrix related to the scale of the detected key point.  
In ORB-SLAM3, the tracking thread processes image data, extracts key feature points, and matches them between consecutive frames to determine the current frame's position. To enhance pose estimation, this thread employs a motion-only BA, refining the accuracy of the calculated pose. Additionally, keyframes are discerned through predefined criteria. The local mapping thread implements the tracking thread's decisions, generating new keyframes and updating the local map. A culling mechanism is employed to systematically eliminate redundant keyframes and points, thereby bolstering the robustness of tracking. Furthermore, the local bundle adjustment optimizes all points within the local area for map accuracy. The loop and map merging thread operates within the atlas multi-map system, searching for similar scenes to achieve loop closure and integrate maps smoothly. Lastly, the full BA thread independently performs pose-graph optimization to refine the entire map without compromising real-time performance. By coordinating these threads, ORB-SLAM3 delivers real-time and optimal performance for diverse applications requiring accurate and up-to-date maps.
# Effects of Photobiomodulation in Sports Performance: A Literature Review
## 4 Discussion  
The aim of this literature review was to examine the current scientific evidence on the efficacy of PBMT application as a treatment in sport and sports performance. The search strategy resulted in 15 articles being selected, all of which were randomized clinical trials.  
In 10 of the studies, significant differences were observed in at least one of the variables related to sports performance in the group which had received PBMT before or after training when compared to the group that had received placebo.  
The study interventions can be divided into three different groups. The first group is formed by the intervention being a cardiovascular exercise test, the second group is based on the participants carrying out a strength exercise and in the third the participants had to combine cardiovascular and strength training in the session.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 3 Results and Discussion
### Foaming Properties and Oil-Liquid IFT of Surfactants
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Surfactant** & \begin{tabular}{c} **Concentration** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **V0** \\ **(mL)** \\ \end{tabular} & \begin{tabular}{c} **Vt** \\ **(mL)** \\ \end{tabular} & \begin{tabular}{c} **t1/2** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **t0** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **FCI** \\ **(mL-min)** \\ \end{tabular} &
\begin{tabular}{c} **IFT** \\ **(mN/m)** \\ \end{tabular} \\ \hline \multirow{3}{*}{THPB} & 0.05 & 210 & 65 & 151 & 175 & 23,782.5 & 0.4068 \\  & 0.1 & 265 & 35 & 158 & 180 & 31,402.5 & 0.5533 \\  & 0.15 & 235 & 40 & 168 & 54 & 29,610 & 0.6883 \\ \hline \multirow{3}{*}{DHPB} & 0.05 & 190 & 50 & 110 & 92 & 15,675 & 0.3245 \\  & 0.1 & 215 & 25 & 129 & 103 & 20,801 & 0.2834 \\  & 0.15 & 235 & 15 & 131 & 115 & 23,088 & 0.2214 \\ \hline \multirow{3}{*}{3-SDB} & 0.05 & 155 & 65 & 110 & 57 & 12,787.5 & 0.8971 \\  & 0.1 & 200 & 50 & 127 & 52 & 19,050 & 0.7324 \\  & 0.15 & 185 & 50 & 176 & 67 & 24,420 & 0.6021 \\ \hline \multirow{3}{*}{CTAB} & 0.05 & 190 & 45 & 60 & 35 & 8550 & 0.2914 \\  & 0.1 & 240 & 40 & 57 & 28 & 10,260 & 0.2429 \\  & 0.15 & 180 & 55 & 60 & 31 & 8100 & 0.3755 \\ \hline \multirow{3}{*}{AN} & 0.05 & 140 & 60 & 120 & 33 & 12,600 & 0.8395 \\  & 0.1 & 130 & 65 & 96 & 30 & 9360 & 0.8524 \\  & 0.15 & 145 & 50 & 80 & 23 & 8700 & 0.9455 \\ \hline \multirow{3}{*}{AOS} & 0.05 & 255 & 35 & 83 & 135 & 15,873.75 & 1.2123 \\  & 0.1 & 280 & 20 & 90 & 160 & 18,900 & 1.1084 \\  & 0.15 & 285 & 15 & 73 & 104 & 15,603.75 & 1.3633 \\ \hline \multirow{3}{*}{SO} & 0.05 & \multicolumn{4}{c}{No bubble} & 0.0025 \\  & 0.1 & 35 & 95 & 140 & 84 & 3675 & 0.0016 \\  & 0.15 & 60 & 80 & 600 & 90 & 27,000 & 0.0038 \\ \hline \multirow{3}{*}{AES} & 0.05 & 110 & 60 & 92 & 100 & 7590 & 2.3112 \\  & 0.1 & 265 & 25 & 78 & 129 & 15,502.5 & 2.2219 \\  & 0.15 & 260 & 30 & 90 & 133 & 17,550 & 2.3794 \\ \hline \multirow{3}{*}{SDS} & 0.05 & 250 & 25 & 90 & 77 & 16,875 & 0.795 \\  & 0.1 & 260 & 25 & 108 & 70 & 21,060 & 0.792 \\  & 0.15 & 275 & 20 & 99 & 73 & 20,419 & 1.0666 \\ \hline \multirow{3}{*}{SDBS} & 0.05 & 175 & 25 & 18 & 42 & 2362.5 & 0.7434 \\  & 0.1 & 220 & 20 & 19 & 43 & 3135 & 0.6241 \\ \cline{1-1}  & 0.15 & 220 & 20 & 22 & 52 & 3630 & 0.6432 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Foaming properties and oil-liquid interfacial tension (IFT) of individual surfactants.
# Sensorless Posture Detection of Reluctance Spherical Motor Based on Mutual Inductance Voltage
###### Abstract  
In this paper, a sensorless rotor posture detection method based on the mutual inductance voltage of the stator coil is proposed to simplify the position detection element of a reluctance spherical motor. Firstly, the numerical relationship between the stator/rotor pole misalignment angle and the mutual inductance voltage of the stator coil is analyzed, which is used as the basis for judging the spatial position of the rotor. Secondly, an experimental platform is designed to verify the consistency between the calculated value and the experimental value of the mutual inductance voltage and to determine the appropriate excitation signal. Thirdly, based on the real-time voltages generated by the stator coil mutual inductance, an intelligent algorithm is used to invert the 3-DoF (degree-of-freedom) position angle of the spherical rotor combined with the motor structure constraints. The experimental results show that the detection method has a good on-line detection effect, and the population standard deviation is within 1.8\({}^{\circ}\) Therefore, the developed technique can be used for replacing the position detection method with sensors.  
spherical motor; attitude detection; switched reluctance; mutual inductance voltage; particle swarm optimization
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### Hybrid Lens  
#### 2.2.1 Concept  
In order to achieve separation in radial direction, the lens is divided into a TX and RX part, i.e., a center part and a surrounding one. Generally, both parts consist of nonrotationally freeform surfaces at the top and bottom of the lens to form a constant irradiance pattern \(E_{\text{TX}}\) and provide a homogeneous gain \(g\). In order to limit the lens thickness \(t\), Fresnel-structures could be applied to the top and bottom surface. However, it is favorable to keep the top flat to improve reliability, since cavities tend to fill up with particles.  
There are two possible arrangements of TX and RX:  
1. The emitter is placed centrally and the PD is positioned off-axis as depicted in Figure 4a. In this case only the emission profile of the emitter has to be adjusted. This includes the homogenization of the profile and an adjustment of the angle \(\theta_{\text{TX}}\). This can ideally be achieved with a single  
Figure 3: Schematic illustration of different radial separations of TX (blue) and RX (green) with an optical element (grey). (**a**) Direct integration of TX and RX into a plane. (**b**) Stacking an emitter chip onto a PD. (**c**) An optical element is used to homogenize the TX ray bundle and move it to the optical axis.  
freeform interface. Therefore, the lower surface can be used for beam shaping and the top surface can be flat. The downside of this approach is a challenging RX lens design: the focus point of the RX lens part is off-axis.
2. The PD is placed centrally and the emitter is positioned off-axis as shown in Figure 4b. The TX lens part fulfills two tasks: first, it compensates the displacement of the ray bundle regarding the optical axis. Second, it reshapes the ray bundle to the anticipated FOV. Because two surfaces are required, the top aperture of the lens cannot be flat. On the other hand, the design of the RX lens part is simplified, because the focal point is on the optical axis. If the TX part is neglected, the RX lens can be designed to be rotationally symmetrical. However, the shadowing effect of the TX part introduces a nonrotationally symmetric factor. Theoretically, this can be partly compensated by a nonrotationally symmetric RX lens part. The shadowing effect is also mitigated by minimizing the size of TX.  
We choose Approach 2, due to the simplified RX lens design. In this configuration, the lens thickness \(t\) typically results from the vertical distance between both TX surfaces. A low \(t\) is desirable for size, weight and cost reduction. The costs are reduced, because less material is needed and due to faster processing [36, 37]. Nevertheless, a certain thickness \(t\) is required to keep the refraction angles at the TX surfaces low in order to limit undesirable Fresnel-reflections. If the RX part determines \(t\), the surface can be split into several Fresnel-grooves to reduce \(t\).
## 3 GLRT-Based Fusion Rule
### GLRT Detector  
Based on the quantization scheme, due to the unknown parameter \(\sigma_{1}\), we apply the GLRT [18] to solve the detection problem in (1). The GLRT detector can be obtained by replacing the unknown parameter with its maximum likelihood (ML) estimate, _i.e._,  
\[T(\mathbf{y})=\frac{P_{\mathbf{Y},1}(\mathbf{y};\hat{\sigma_{1}})}{P_{\mathbf{ Y},0}(\mathbf{y};\sigma_{0})}\underset{H_{0}}{\approx}\eta \tag{7}\]  
where \(\mathbf{y}=[y_{1},y_{2},\ldots,y_{N}]\) contains the received data at the FC, \(T(\mathbf{y})\) denotes the test statistic, \(P_{\mathbf{Y},i}(\mathbf{y};\hat{\sigma_{i}})\), \(i=0\) or 1, denotes the PMF or likelihood function of \(\mathbf{y}\) under hypothesis \(i\) corresponding to the non-regular quantization scheme, \(\hat{\sigma_{1}}\) is the ML estimate under \(H_{1}\) and \(\eta\) is a detection threshold.  
Specifically, since \(\{y_{n}\}\) are independent, the likelihood function under \(H_{i}\) is given by:  
\[P_{\mathbf{Y},i}(\mathbf{y};\sigma_{i})=\prod_{n=1}^{N}P_{Y,i}(y_{n};\sigma_{ i}) \tag{8}\]  
The ML estimate \(\hat{\sigma}_{1}\) is obtained by maximizing the likelihood function under \(H_{1}\), _i.e._,  
\[\hat{\sigma}_{1}\triangleq\arg\max_{\sigma_{1}}P_{\mathbf{Y},1}(\mathbf{y}; \sigma_{1}) \tag{9}\]For the considered quantizer, the ML estimate \(\hat{\sigma}_{1}\) cannot be computed in closed-form. However, it can be efficiently obtained through a one-dimensional (1D) search (e.g., gradient search). Substituting the ML estimate \(\hat{\sigma}_{1}\) in (7), we can obtain the GLRT detector.