esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Channel Modeling
### Outdoor Physical Channel Model  
This model considers UMi environments for frequencies of 28 and 73 GHz. The encompassing mmWave channel modeling outlined in Section 4.1 can be extended to outdoor environments by making appropriate adjustments to specific system parameters like \(n\) and \(\sigma\), as per the outdoor environment. When adapting the indoor channel model for outdoor settings, the main difference lies in the channel connecting the RIS to the UE.  
In the outdoor setting, this channel may experience small-scale fading, similar to indoor environments, but with the additional characteristic of having an arbitrary number of distinctive clusters. The LoS dominant channel, as described in (18), remains applicable when \(\ d_{RIS-UE}\) is relatively short and has a high \(\mu\). However, in the real world, when distance and environmental circumstances fluctuate, this LoS dominance assumption may not hold true. To effectively predict channel behavior, a more comprehensive technique is required.  
The channel vector \(\mathbf{g}\) for the outdoor scenario can be generally formulated as follows [25; 61]:  
\[\mathbf{g}=\widehat{\gamma}\sum_{c=1}^{C}\sum_{s=1}^{S_{c}}\theta_{c,s}\sqrt{ G_{e}\left(\theta_{c,s}^{RIS-UE}\right)L_{c,s}^{RIS-UE}}\textbf{a}\left(\phi_{c,s}^{ RIS-UE},\theta_{c,s}^{RIS-UE}\right)+\mathbf{g}_{\text{LoS}} \tag{21}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **NLoS** & **LoS** \\ \hline \(n\) & 3.19 & 1.73 \\ \(\sigma\) (dB) & 8.29 & 3.02 \\ \(z\) & 0.06 & 0 \\ \(f_{0}\left(GHz\right)\) & 24.2 & 24.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Path loss attributes for InH [25; 61].  
Here, \(\bar{C}\) and \(\bar{S}_{c}\) denote cluster count and sub-rays per cluster for the RIS-UE link, respectively. \(\widetilde{\gamma}=\sqrt{\frac{1}{\Sigma_{c-1}^{\ast}\bar{S}_{c}}}\) is the normalization factor. \(G_{e}\big{(}\theta_{c,s}^{RIS-UE}\big{)}\) is the gain of RIS elements, and \(L_{\mathcal{CS}}^{RIS-UE}\) is the large-scale attenuation in the direction of the \((c,s)^{th}\) scatterer. \(\mathbf{a}\big{(}\theta_{c,s}^{RIS-UE},\theta_{c,s}^{RIS-UE}\big{)}\) represents the array response vector for the associated azimuth and elevation angles. The channel parameters such as \(n,\ z,\ \sigma\) for the outdoor propagation environment are tabulated in Table 3[25; 61].
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 5 Empirical Research and Result Analysis
### Comparison and Analysis of Long-Term Forecast Results
The BA-SVR model has better stock index closing price forecasting performance, and the results are still roughly robust compared with the polynomial kernel function SVR model without optimized free parameters and the Sigmoid kernel function SVR model without optimized free parameters. The results are still roughly robust compared to the polynomial kernel SVR model without optimized free parameters and the Sigmoid kernel SVR model without optimized free parameters. However, the SVR model with free parameters optimized by the bat algorithm has higher predictive power for short-term stock index closing prices compared to the medium-term and long-term, while the two control models do not significantly exhibit this property.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### Energy Readrage Techniques
#### 2.3.4 Solar Energy  
Renewable energy, such as solar power, is a sustainable alternative for powering UAVs for long-duration operations. Integrating solar panels with advanced batteries, such as lithium-ion or solid-state batteries, enables the storage of energy generated during daylight hours for use at night or in low-light conditions. Leading solar-powered UAVs, such as Helio and Zephyr, have demonstrated their effectiveness in this area. The Helio achieved a continuous flight of more than 18 h, while the Zephyr set a record by flying continuously for 64 consecutive days, reaching altitudes of up to 20,000 m [27]. However, these systems face seasonal challenges due to the variability in the length of day and night. During the summer solstice, a 7.44 kg battery is sufficient to ensure night flight. In contrast, during the winter solstice, when nights are longer, a 12.4 kg battery is required to maintain continuous operation [27].
## 3 Image Processing Approaches Used for EAT and PAT Segmentation
### Atlas-Based Methods  
While active contours rely on geometrical constraints to segment shapes, atlas-based segmentation methods take into account the spatial relationship between several structures [31]. This is done by forming an atlas, a map of structures found in an image. Atlases are usually formed by an expert who manually segments an image, labeling different structures on the image. In medical imaging, the specific locations and shapes of anatomical structures vary from patient to patient, so multiple atlas images are usually collected and fused into one atlas image. Once an atlas is acquired, it can be used to segment new input images that were not manually segmented. This is achieved using a registration algorithm. In general, registration deforms and transforms an image so that the structures within the image map onto the same structures on a different image. Atlas-based registration treats the atlas as an image and maps the yet unseen image onto the atlas. The correct way to map the image is determined by a similarity measure that measures how well the shapes and features of one image match the other. Registration is often done in a non-rigid way with the ability to deform the image to account for shape irregularities in different input images. Once an image is mapped onto the atlas, structures can easily be segmented from the image by reading the atlas value for the corresponding mapped image pixel. An example of using a registration algorithm to deform an image is shown in Figure 6.  
Atlas-based methods offer robust segmentation and consider the spatial relationships of different structures, making them a good fit for medical imaging [32]. On a medical image, the same tissue usually has the same grey levels. However, it can be located in different body areas, so a segmentation method that can be sensitive to location and grey levels can provide good results. The downside of atlas-based methods is that they require selecting and acquiring accurate atlas images via manual image segmentation. The collected atlas can be an inadequate representation of real-world data, as images can differ based on patient backgrounds, genetics, medical conditions, and used equipment. Additionally, the registration process can fail due to noise or significant differences in the input image and the atlas and thus entirely prevent further segmentation.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 4 Establishment of Performance Indicator Evaluation Model
### Performance Indicators of the Performance Evaluation  
In order to judge the accuracy of the results of the performance evaluation model, this paper focuses on the reliability of the assessment method from the perspectives of compatibility degree and deviation. A higher degree of compatibility indicates that the results of the evaluation method are more closely aligned with those of other methods, rendering them more representative and leading to more accurate assessment outcomes. Assuming that \(n\) motorized processes are assessed by \(m\) methods, the degree of compatibility is defined as [29]:  
\[r(i)=\frac{1}{m-1}\sum_{i\
eq j}^{m}r(i,j) \tag{27}\]  
\[r(i,j)=1-\frac{6}{n(n^{2}-1)}\sum_{k=1}^{n}\left(d_{i}(k)-d_{j}(k)\right)^{2} \tag{28}\]  
In Equations (27) and (28), \(r(i)\) represents the compatibility degree of the \(i\) method, \(r(i,j)\) represents the rank correlation coefficient of the \(i\) and the \(j\) methods. \(d_{i}(k)\) and \(d_{j}(k)\) represent the ranking numbers of the results of the \(k\) maneuvering process in the \(i\) and the second methods, respectively.  
Deviation refers to the extent to which the assessed value of the performance of a method differs from the assessed value of the results of other methods. The smaller the value of the deviation, the more reliable the assessment is as the method does not deviate significantly from the results of other methods. The outlier can be defined as the square of the difference and the mean value, which can be calculated:  
\[D(i)=\frac{1}{n(m-1)}\sum_{k=1}^{n}\sum_{j\
eq i}^{m}\left(e_{i}(k)-e_{j}(k) \right)^{2} \tag{29}\]  
In Equation (29), \(e_{i}(k)\) and \(e_{j}(k)\) represent the evaluated values of the performance of the \(k\) maneuver in the \(i\) and \(j\) methods.  
In this section, an improved principal component weight determination method based on generalized grey numbers is proposed. This method has better adaptability and flexibility compared with the traditional principal component analysis method, and can deal with the data uncertainty problem caused by complex factors such as space orbit environment. Based on this, a grey-target decision evaluation model is established by using the grey-target decision theory, which enables performance evaluation of the satellite attitude control system to be achieved in tracking mode.
## 2 Numerical Modeling
### Mathematical Model
#### 2.2.2 Deposition Shape Calculation Model  
In this study, we present a computational model for the morphology of the deposition by integrating the VOF technique with the powder feeding equation, whose model equation form is as follows:  
\[\frac{\partial(\rho\phi)}{\partial t}+div(\rho\textbf{L}\textbf{\emph{U}} \phi)=m_{p} \tag{9}\]  
where \(\phi\) is the VOF value in the calculation unit, \(m_{p}\) is the powder feeding flow rate (kg/s/m\({}^{3}\)), and the first term on the left-hand side of the equation reflects the non-stationary component for the time-dependent change in the VOF value of the cell. The second component is the convection term that influences the unit's VOF value; the right end of the equation is the additional source term caused by the powder feed.  
The powder spatial concentration distribution in the powder convergence space is nearly Gaussian; therefore the powder feeding flow rate \(m_{p}\) fulfills the equation.  
\[m_{p}=3v_{p}/\left(\pi r_{p}^{2}\Delta z\right)exp(\frac{-3d_{p}^{2}}{r_{p}^{ 2}}) \tag{10}\]  
where \(v_{p}\) represents the powder feeding rate (kg/s), \(r_{p}\) represents the powder feeding radius (m), \(d_{p}\) represents the distance from the current unit to the center of the powder feeding laser head (m), and \(\Delta z\) represents the unit height (m).
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 5 Application Examples
### Settings
The joint use of MU-MIMO and OFDMA (**b**) creates more multi-user transmissions, resulting in a higher sum-rate. improve performance; according to the results, the sum-rate for Case 1 is \(5/3\) (\(=2/3+1\)), and for Case 2 is \(7/3\) (\(=2/3+5/3\)), i.e., a larger value than in Case 1. Since multiple links are activated at the same time, they may suffer from interference; however, as can be seen from the results, most potential interference issues are resolved by allocating different subchannels. From the results for Case 2, we can identify several cases in which multiple links use the same subchannel, but these do not experience any interference issues: link \(7\to 8\) and link \(4\to 3\) are assigned to subchannel 6 at time slot 2, but they are out of range of interference with each other, and link \(3\to 9\) and link \(4\to 9\), which use the same subchannel 8 at time slot 3, are valid MU-MIMO transmissions, not interference. Figure 3c,d depict the resource allocation results for the two cases where the nodes are restricted to using either one of two technologies. As expected, these results are quite different from those for previous two cases. For example, in Case 3, node 7 does not transmit to node 5 directly, different to the other three cases. In Case 4, all transmissions of flow 1 and flow 2 use a single spatial stream since MU-MIMO is not available. Please note that the reason the two links for flow 1 (links \(7\to 5\) and \(5\to 3\)) have a data rate of 2 is because they are assigned to multiple subchannels. Such a difference, as a result, affects the overall performance: the sum-rate for Case 3 (MIMO only) is \(5/3\) (\(=2/3+1\)) and that for Case 4 (OFDMA only) is \(4/3\) (\(=2/3+2/3\)) which is the smallest value. More specifically, the data rate for flow 2 is reduced since the nodes of flow 2 (i.e., nodes 3, 4 and 9) cannot take advantage of MU-MIMO anymore in Case 4. This result is consistent with the fact that MU-MIMO is of much benefit in terms of increasing the network capacity as mentioned in Section 3.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 4 Optimization Problem
### Joint Torques  
After knowing the generalized coordinates, velocities, and accelerations, one can calculate the joint torques by inverse dynamic equations.  
In SSP, it is quite easy to obtain the joint torques using Equation (1) because matrix \(B\) is an identity matrix in the case of chosen generalized coordinates.  
In DSP, the biped robot model has three degrees of freedom and five independent actuators; therefore, it is over-actuated. For a given state and acceleration, the solutions of joint torques are not unique. Let \(F=[F_{2x};F_{2x}]\) be the constraint forces compatible with constraints (2). By using the constraints (2), the joint torques and constraint forces can be derived. To this end, generalized coordinates can be divided into two parts--unconstrained coordinates \(q_{nc}\in R^{3}\) and constrained coordinates \(q_{c}\in R^{2}\). We can denote this as  
\[q=A\left[\begin{array}{c}q_{nc}\\ q_{c}\end{array}\right], \tag{31}\]  
where \(A\) is an invertible constant matrix that rearranges the components of vector \([q_{nc};q_{c}]\). By deriving the constraint Equation (2), we can obtain  
\[J_{DSP}\dot{q}=\overline{J}_{nc}\dot{q}_{nc}+\overline{J}_{c}\dot{q}_{c}=0, \tag{32}\]where \(\overline{J}_{nc}\in R^{2\times 3}\) and \(\overline{J}_{c}\in R^{3\times 3}\) are submatrices of \(\overline{J}=J_{DSP}=\left[\overline{J}_{nc};\overline{J}_{c}\right]\). With appropriate choice of \(q_{nc}\) and \(q_{c}\), \(\overline{J}_{c}\) could be an invertible matrix. Thus, one can obtain  
\[\dot{q}=H(q)\dot{q}_{nc}, \tag{33}\]  
where \(H\in R^{5\times 3}\) is  
\[H=A\left[\begin{array}{c}I\\ -\overline{J}_{c}^{-1}\overline{J}_{nc}\end{array}\right] \tag{34}\]  
and \(I\in R^{3\times 3}\) is an identity matrix.  
Multiplying dynamic Equation (3) by \(H^{T}\) from the left-hand side and noticing \(JH=0\), one can obtain  
\[H^{T}(M\ddot{q}+\mathbb{C}\dot{q}+G)=H^{T}B\tau. \tag{35}\]  
Note that Equation (35) has three equations but five unknowns \(\tau=[\tau_{1};\tau_{2};\tau_{3};\tau_{4};\tau_{5}]\). Therefore, the dynamics system is over-actuated during DSP, and the solution of Equation (35) is not unique. In this study, among all solutions, we specify the solution that has the least sum of squares.  
\[\tau=B_{nc}^{+}[H^{T}(M\ddot{q}+\mathbb{C}\dot{q}+G)] \tag{36}\]  
where \(B_{nc}=H^{T}B\) and \(B_{nc}^{+}\) is the pseudo-inverse matrix of \(B_{nc}\).
# Flexural Behavior of a 30-Meter Full-Scale Simply Supported Prestressed Concrete Box Girder
## 1 Introduction
These studies reviewed above were conducted to research the flexural behavior, such as material properties, failure mode, and ultimate capacity, of PC box girders from decommissioned bridges [39; 40; 41]. However, investigation of the flexural behavior of a new precast box girder at a construction site has been rarely reported [42; 43]. Therefore, the flexural behavior of a 30 m full-scale simply supported PC box girder is studied in the present work. The nonlinear behavior in the ultimate limit state of a destructive full-scale model provides a valuable reference for the designing of new bridges [44]. At present, commercial finite element software [45; 46] and some professional procedures [47] are used to conduct analysis of concrete structures. However, it is still difficult to calculate the descending section of the load-deflection curve. To achieve this goal, a nonlinear numerical model is developed using the finite strip method (FSM) in this paper. FSM was originally introduced by [PERSON] [48; 49] and has been proven to be efficient and widely used in the analysis of structural members [50; 51; 52]. This article presents some tentative work to apply FSM to full-scale PC box girders. Based on FSM, the load-deflection curve of the structure can be obtained based on the bending-curvature relationship, so that the bearing capacity of the section can be checked directly. Parametric analyses are conducted with the proposed numerical model, and the design is optimized. The rest of this paper is organized as follows. Section 2 introduces the engineering background and materials. Section 3 describes and discusses the flexural behavior of the 30 m full-scale simply supported PC box girder. Section 4 illustrates the nonlinear numerical model and presents parametric analyses of the ratio of reinforcement. Finally, Section 5 concludes the article and gives some recommendations for future work.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 3 Research Progress in Flyer Velocity
### Two-Dimensional (2D) VISAR  
The VISAR system has been limited to measuring motion at points or lines across a target. The 2D-VISAR is introduced for monitoring motion across a 2D image. Unlike the line-VISAR, the 2D-VISAR uses a framing camera to record the 2D spatially resolved interference fringes of the entire surface at multiple times. The recording image plane and the target plane have a one-to-one corresponding object-image relationship to achieve continuous spatial resolution. The Doppler frequency shift of each point on the target plane is different, resulting in different phases of each point of the comb fringe. The phase of each point of the fringe can be obtained through data processing. The relationship between phase and velocity is given by Celliers [40].  
Figure 12: Schematic diagram of line-VISAR.  
[PERSON] et al. developed a framing-imaging VISAR with a spatial resolution of 5 \(\upmu\)m and a velocity sensitivity of 15 m/s. This technique was employed to measure a 2D spatial velocity field of the laser-driven flyer and to reveal the continuous evolution of flyers [41].
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Enhanced Sidelink  
The short range connectivity between vehicles and roadside infrastructure, pedestrians or other vehicles was originally based on LTE-based sidelink. The use of PC5 interface in LTE sidelinks for C-V2X was specified in both Rel-14 and Rel-15, with limitations associated with legacy LTE technologies. A very detailed overview of sidelink under Rel-14 can be found in [20]. The limitations of Rel-14 satisfying the QoS requirements of several crash scenarios are described in [21]. In Rel-16, a 5G NR sidelink was introduced as a complement to LTE-based sidelink [22]. This brings the higher reliability and low latency benefits of NR to the sidelines, and provides flexibility in spectrum usage for the sidelink by using a dedicated NR spectrum or dynamic sharing of the LTE-Uu or NR-Uu spectrum.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 3 Methodology  
### Research Design  
This study adopts a quantitative modeling approach to predict the short-term movement of the U.S. 10-year Treasury bond yield using multiple machine learning (ML) techniques. The research compares four predictive models, linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP), based on their performance using historical macroeconomic indicators as predictors. The primary goal is to identify the model that offers the highest predictive accuracy for short-term yield forecasting, defined as a 1- to 3-month horizon.
# Malicious Traffic Detection on Tofino Using Graph Attention Model
## 3 Maltof
### Overall Architecture  
Maltof is an efficient online malicious traffic detection architecture that covers two key processes: training and detection, as shown in Figure 1. In the training phase, Maltof combines RF models and EGAT-based deep learning models for training. After training, the model is distributed to the P4 switch to achieve real-time threat detection and response at the edge of the network. In this way, Maltof can effectively improve network security and respond to potential malicious traffic threats in a timely manner.  
In the detection phase, Tofino in the P4 switch is first responsible for pre-screening the traffic and filtering packets through the five-tuple filtering mechanism: traffic matching illegal five-tuples will be immediately discarded, traffic matching legal five-tuples will  
Figure 1: The architecture design of Maltof.  
pass directly, and packets that do not match any entries will enter further screening. Subsequently, the system uses the P4-based RF model to analyze these unmatched traffic, filter out potential suspicious packets, and clone them to the deep detection module to avoid deep learning detection of all traffic. The deep detection module runs on the embedded CPU of the switch, and uses a sliding window mechanism [25] to manage suspicious data packets. It dynamically adjusts the window size according to the real-time network load, balances the difference between data input and processing speed, and prevents data loss. Subsequently, multiple EGAT model instances run in parallel on different CPU cores to process batch data. When feature extraction is completed or the waiting time reaches the threshold, the idle EGAT model is selected for deep detection. The detection results are fed back to the filter list through a fast update mechanism for dynamic adjustment of filtering rules. This mechanism can quickly apply the detected threat information to the pre-screening stage, thereby improving the system's defense capabilities against new attacks and reducing potential security risks.