esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Distribution of Errors  
In Table 1, we summarised TI model performances, highlighting the added explanatory power offered by TIs. Below, we set out to extend this analysis, and verify that the inclusion of TI features does not induce fat error tails. Fat error tails pose a significant risk to traders that rely on accurate model forecasts to make profitable investment decisions. Because traders are loss-averse, they often prefer models which forecast prices with a lower tail risk, even if this comes at the expense of a marginally higher model RMSE. With this in mind, in Figure 3 we present the distributions of model errors with and without _best TIs_.  
Before analysing the plots of Figure 3, let us describe the distribution shift that successful feature engineering should optimally result in. Ideally, the addition of features with added explanatory power should induce a leftward shift of the error distribution--i.e., towards zero error--and reduce error tail mass. Successful prediction improvement should thus result in a higher central error peak without rousing fatter tails. We observe such error distribution shifts in the plots of Figure 3: _best TIs_ (black) induce notably higher central error peaks, often shifted to the left, with thinner or unchanged tail masses. While in many cases tail masses remain unchanged or get slightly smaller, importantly none become fatter. Overall, Figure 3 reflects both Tables 1 and 2 results. Elaborating, except with RF, we observe significant performance improvements in the error distributions. Further, we observe a greater leftward shift of the central peak with models whose IQR performance was boosted by the inclusion of TI features: notably CONV models. Figure 3 thus supports our findings from Section 3.1, offering evidence that TI features can yield more consistently accurate forecasts, without increasing the frequency of extreme errors. This is a significant finding because it demonstrates that the inclusion of TI features does not increase the tail risk.
## 5 Experimental Validation  
To evaluate the performance of the autonomous landing line system, simulations and experiments were conducted. The performance of the autonomous landing line system was evaluated by analyzing the experimental results. The effective depth estimation range of the binocular camera is approximately 0.3-10 m. If the distance between the FPLIR and the power line exceeds the effective depth range, the depth estimation could be inaccurate. Therefore, it is necessary to set the initial position of the FPLIR in a reasonable manner. It is assumed that the height of the power line is \(I_{H}\), and the distance between the FPLIR and the power line is \(I_{L}\). To improve the efficiency of the FPLIR landing line, in this study, a combined rough and precise landing line strategy is proposed, including fast and precise landing line phases. The aim of the fast landing line phase is to quickly reach the initial height \(I_{H}\) in accordance with the prior structure data of the OPTL, ensuring that the ground wire is in the camera's field of view. During the precise landing line phase, the FPLIR attains a safe landing position by segmenting the power lines and plans a landing trajectory based on the landing line position. Finally, the FPLIR uses the MPC controller to realize that trajectory.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data  
In the last decade, many anomaly detection methods have been proposed to monitor spacecraft telemetry channels. An extensive literature search reveals that anomaly detection for spacecraft telemetry can be grouped into two main families: thresholding methods and artificial intelligence methods. This section is dedicated to describing the relevant anomaly detection methodologies for each category.
## 7 Visible Light Communication and LiFi  
Visible light communication is envisioned to play a crucial role in WLANs in the 6G era with the plethora of unlicensed bandwidth available in the visible light spectrum.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**WiFi Standard** & **IEEE 802.11 ay (60 GHz)** & **IEEE 802.11 ax (WiFi 6)** & **IEEE 802.11 be (WiFi 7)** \\ \hline \multirow{7}{*}{Key features} & \(\bullet\) Multi-Gbps data rates & & \multirow{7}{*}{\
\begin{tabular}{l} \(\bullet\) Supports OFDMA \\ \(\bullet\) Beamforming \\ \(\bullet\) Fast session transfer \\ mode to switch to \\ sub-6 GHz channels \\ \(\bullet\) Less interference from \\ existing systems \\ \end{tabular} } &
\begin{tabular}{l} \(\bullet\) Supports larger bandwidths \\ \(\bullet\) Spatial reuse to increase \\ efficiency \\ \(\bullet\) Power saving modes of \\ IoT devices \\ \(\bullet\) Higher order modulation \\ \(\bullet\) Existence of non-WiFi 6 \\ devices can affect \\ performance \\ \(\bullet\) Complex HARQ modes \\ \(\bullet\) Complex coordination \\ mechanisms \\ \(\bullet\) High power consumption \\ probability due to power \\ saving mechanisms \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key Features and Limitations of Emerging WiFi Technologies.  
The developments in VLC can be categorized into two broad areas: (1) the standardized works that are carried out according to the available VLC standards; and (2) the ongoing research work that does not strictly follow any standardization.  
Most of the research work portrays VLC as a physical layer technology for 6G, where the upper layer architecture is the native mobile communication architecture or an architecture such as backhaul links [65; 66]. However, as the focus of this paper is on WLANs that can augment 6G networks, we will examine the standardized VLC WLANs in this paper. In the following subsections, we discuss different standardizations introduced by IEEE and ITU, as well as their key features, to evaluate how they can support 6G deployments.
## 5 CIDF-WSN
### Motivation  
The rapid development of IoT-based technologies has revamped almost every industry including the agriculture sector. The IoT revolution has revamped the conventional agriculture methods with new technologies such as \"WSNs\" to reduce the environmental impact on crops, attain large production, maintain high quality with minimum labor cost. The WSNs play a backbone role in the agriculture sector, as the sensor nodes sense the environmental conditions such as temperature, humidity, soil moisture level, soil pH, light conditions, and carbon dioxide, etc., and transfer the readings to the monitoring station. These aforementioned readings highly affect the productivity of the crop and require well-timed delivery at the control center to yield better growth. This is because any change in these parameters may directly affect the growth of underlying crop and may require immediate attention (e.g., The 1-degree Celsius increase in the seasonal temperature may lower the overall productivity of rice crop by 9%, while the same increase may lower the productivity of corn crop by 10%) [37]. Therefore, we take into consideration the above-mentioned environmental conditions which have substantial importance in attaining the fine-quality final product.  
The conventional WSNs-based agricultural monitoring systems employ the traditional address-based mechanisms which may induce scalability and efficiency issues in the network. For instance, a user requiring the temperature information from a sensor node in the monitoring region requires complete knowledge of all the deployed sensor nodes along with their data type. This procedure makes the overall management of large-scale networks significantly complex and time-consuming. Moreover, in single-channel WSNs, most of the schemes lack in providing efficient next forwarder selection, Interest, and Data Packet recovery mechanisms. Thus, the main motivation of the proposed scheme is to put forward a robust NDN-based IoT-based agricultural monitoring system that eliminates the aforementioned constraints and in the meantime fulfills the fundamental requirements of the agricultural monitoring system in order to yield better growth.
# Environmental-Based Speed Recommendation for Future Smart Cars +
## 3 Proposed Approach
### Road Status Estimation Based on Weather Information
#### 3.2.2 Stages of Execution  
At this point, we would like to clarify the procedure for creating the classification tree and utilizing it in a vehicle. Specifically, three steps are required:  
Collection of data: The first step focuses on the collection of data. Weather data and road condition information were gathered by RSUs and weather services for a period of one year covering various areas in the United States of America.  
Training on cloud: The second step focuses on the training and the construction of the classifier (classification tree). This step is performed on the cloud (as it is computationally intensive) and it is executed only once. Algorithm 1 presents the algorithmic steps that are needed in order to create the classification tree based on a given dataset.  
Inference on vehicle: After the training of the classifier is completed, the classifier is sent back to the vehicle. Then, the vehicle utilizes it in real time to estimate the weather condition without need for cloud communication, which would add significant delay. This action is called inference. Inputs to the classifier are the attributes from the RSUs and other weather based services.
## 4 Geometric Reconstruction of Topology Optimization Results
### Geometric Reconstruction Approach  
Based on research on geometric reconstruction methods in current engineering applications and extensive validation with numerous examples, the geometric reconstruction approach based on the marching cubes (MC) algorithm [47, 48]-PolyNURBS modeling-SolidWorks Modeling (MPS) was proven to be feasible and efficient. The specific steps of this approach can be itemized in the following form:  
1. _Adjust the MC algorithm's isosurface threshold_: By adjusting the isosurface threshold of the MC algorithm, the lower bound of the density value for intermediate-density units to be retained is controlled. The intermediate-density units above this lower bound are taken as the solid material, whereas those below it are considered the void material. This process lessens or eliminates the undesirable features resulting from TO under a given volume constraint that impacts structural performance.
2. _Use PolyNURBS for solid fitting_: The PolyNURBS method is commonly utilized to perform solid fitting on the TO structural configuration. During such a process, the pre-established geometric reconstruction criteria should be fully taken into account to ensure that the fitted structure meets the design requirements and optimizes performance.
3. _Use SolidWorks for parametric modeling_: In SolidWorks, parametric modeling is performed for non-design regions (such as ear flanges and bolt connection holes) to complement the structure generated by TO and complete the engineering design.
## 4 Case Study and Discussion
### Position and Orientation Deviation Computation of Mechanical Product
#### 4.2.3. Guiding Accuracy Computation of the Sliding Workbench  
The curves of the distance deviation along the X- and Z-axes and the angle deviation around the Y-axis specifically characterize the guiding accuracy of the sliding workbench. These curves can be acquired by computing PODs at different positions within the sliding stroke.  
The acquired variation curves are shown in Figure 14, and they characterize the fluctuation of the position and orientation of PPC in the horizontal and vertical planes within the sliding stroke. These results can help to control the performance of the sliding workbench in the design stage. For instance, the upper and lower limits of the guidance accuracy can be determined based on these curves.
## 5 Performance Evaluation
### Simulation Environment and Parameters
As a result, with increasing the cell radius, the achieved sum throughput of DUEs by all schemes increases gradually, and our proposed DRL algorithm outperforms all the schemes, which can be seen in Figure 7. Increasing the cell radius from 200m to 400m, sum throughput increases by 40.2%, 38.9%, and 33.8% in the proposed DRL, D3 QN DRL, and Only Optimize Power schemes, respectively. Figure 8 shows the relation of the sum throughput of all DUEs with their Tx power. For all schemes, an optimal maximum transmission power results in a significant improvement in the sum throughput of DUEs. Increasing D2D pairs Tx power from 10 dBm to 15 dBm, sum throughput improves 10.5%, 10.2%, and 15.1% in our proposed DRL, D3 QN DRL, and Only Power Optimize schemes, respectively. Only Optimize Power scheme is showing better slop at this point as it has focused power optimization only for DUEs without ensuring minimum QoS requirements for all users. Figure 5: Sum throughput of all CUEs vs. no. of CUEs. Figure 9 shows the sum throughput of all D2D pairs against the minimum QoS requirements, i.e., minimum throughput requirements, to establish a successful link between the users. By increasing the minimum throughput requirement from 2 bps/Hz to 9 bps/Hz, the sum throughput drops from 300 bps/Hz to 148 bps/Hz for the proposed DRL, 107 bps/Hz for D3 QN DRL, 85 bps/Hz for Only Optimize Power and 53 bps/Hz for random policy schemes respectively. The admission constraints become tighter with increasing the QoS requirements, and hence, the sum throughput of DUEs is degraded if a suitable power  
Figure 6: Sum throughput of all DUEs vs. distance between D2D pairs. Figure 7: Sum throughput of all DUEs vs. cell radius. control, mode selection, and resource allocation approach is not applied, which is reflected in our simulation results.
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 3 Model Optimization
When the satellite orbital radius is 6878 km and the orbital inclination is 60\({}^{\circ}\), \(\left|\mathrm{V}_{\beta=0^{\circ}}\right|\) = 7.3833 km/s,\(\left|\mathrm{V}_{\beta=20^{\circ}}\right|\) = 7.3799 km/s, \(\left|\mathrm{V}_{\beta=40^{\circ}}\right|\) = 7.3746 km/s, and \(\left|\mathrm{V}_{\beta=60^{\circ}}\right|\) = 7.3653 km/s. For satellites with low orbital altitudes, when \(\beta\) is different, the change in \(\left|\mathrm{V}_{\beta}\right|\)'s value is less than 0.25%. Therefore, to simplify the calculation, \(\beta\) in Equation (30) can be approximated as the satellite's orbital inclination \(i\), and thus, Equation (30) can be written as  
\[F_{d}=\frac{F_{c}}{c}r(\sqrt{\frac{GM}{r^{3}}}-\frac{2\pi}{T}\cos i)\cos\theta \cos\varphi \tag{31}\]
## 3 Mathematical Modelling
### Solar PV Panels  
For calculation of the solar irradiance based on the geographic location (Oxford, UK) and other technical parameters such as the tilt angle of the panel, a model has been designed in MATLAB function in SIMULINK, which calculates the solar irradiance in various simulation steps. For the calculation of beam radiation incidence angle on a surface (\(\theta\)), Equation (1) is employed [24]:  
\[\begin{split}\cos\theta=\sin\delta\sin\varphi\cos\beta-\sin \delta\cos\varphi\sin\beta\cos\gamma+\cos\delta\cos\varphi\cos\beta\cos \omega\\ +\cos\delta\sin\varphi\sin\beta\cos\gamma\cos\omega+\cos\delta\sin \beta\sin\gamma\sin\omega\end{split} \tag{1}\]  
where \(\delta\), \(\varphi\), \(\beta\), \(\omega\) and \(\gamma\) are declination, latitude, slope, hour angle and surface azimuth angle, respectively [24]. \(\gamma\) and \(\beta\) are assumed to be \(0^{\circ}\) and \(30^{\circ}\), respectively, since most of the houses in the UK has \(30^{\circ}\) slope on their ceilings, where PV panels arebe installed. The equation of Cooper is used for calculation of declination [24]:  
\[\delta=23.45\sin\left(360\frac{284+n}{365}\right) \tag{2}\]  
where \(n\) is the number of days during the year. Further, the radiation on the tilted plane (\(G_{o}\)) could be calculated by Equation (3) [24; 25]:  
\[G_{o}=G_{sc}\left(1+0.0033\cos\frac{360n}{365}\right) \tag{3}\]  
where \(G_{sc}\) is extraterrestrial radiation and assumed as 1367 W/m\({}^{2}\) in this study [1]. To calculate the beam and diffuse radiations transmitted through a clear atmosphere, the following equations are applied based on [PERSON]'s method [1; 24]:  
\[\tau_{b}=a_{0}+a_{1}exp\left(\frac{-k}{\cos\theta_{z}}\right) \tag{4}\]  
\[\tau_{d}=0.271-0.294\tau_{b} \tag{5}\]  
\(\tau_{  
The output current of the PV panel is given by [26]:  
\[I_{pv}=I_{ph}-I_{\rm s}\bigg{(}e^{\frac{V+I*R_{\rm s}}{N*T_{\rm f}}}-1\bigg{)}-I_{ \rm s2}\bigg{(}e^{\frac{V+I*R_{\rm s}}{N*T_{\rm f}}}-1\bigg{)}-\frac{V+I*R_{\rm s }}{R_{p}} \tag{7}\]  
where, \(I_{\rm s}\) and \(I_{\rm s2}\) are diode saturation currents for diodes 1 and 2 shown in Figure 5, respectively. \(V_{\rm f}\) is the thermal voltage, \(N\) and \(N_{\rm 2}\) are diode emission coefficients and \(I_{ph}\) is solar-generated current, respectively. The mentioned PV parameters are obtained from MATLAB SIMULINK Simscape library for Amerisolar PV panel the specifications of which are provided in Table 3[26].
## 5 General Dispersion Material for Individual Composite Antenna Structure  
Based on the mode structure defined in the previous section, the electromagnetic field of the composite antenna can, for any of the following, be presented in the form of a series:  
\[\underline{E}_{t}(x,y,z)=\sum_{k}\left\{v_{k}^{+}\underline{\underline{\Psi}} _{k}exp(+ik_{zk}z)+v_{k}^{-}\underline{\underline{\Psi}}_{k}exp(-ik_{zk}z)\right\} \tag{59}\]  
\[\underline{H}_{t}(x,y,z)=\underline{\underline{u}_{z}}\times\left[\sum_{k} \eta_{k}^{-1}\{+v_{k}^{+}\underline{\underline{\Psi}}_{k}exp(+ik_{zk}z)-v_{k}^ {-}\underline{\underline{\Psi}}_{k}exp(-ik_{zk}z)\}\right] \tag{60}\]  
with the known vector Floquet harmonics \(\Psi_{k}\) and the distribution coefficients remaining to be determined \(v_{k}\)[20; 21]. Given the known field incident on the antenna, determining the total field requires deriving the scattering matrix of the entire system. The solution is obtained by determining the scattering matrix of individual planar system elements, such as the dielectric boundary, metallized periodic plane, and a layer of a homogeneous dielectric medium. In each of these cases, the distribution coefficients \(v_{k}\) correspond to the field amplitudes defined in Figure 2.  
Let us consider any single element of a composite antenna denoted by the index \(j\). Let us create a vector \(\underline{V}_{j}^{(in)}\) of field amplitudes entering (\(in\)) into the system (\(j\)), which in turn consists of a vector \(\underline{V}_{j}^{(inup)}\) of amplitudes of type in on the upper (upper) side of the system (\(j\)) and the vector \(\underline{V}_{j}^{(inlow)}\) of amplitudes of type in from the lower side of the system (\(j\)):  
\[\underline{V}_{j}^{(in)}=\left[\begin{array}{c}\underline{V}_{j}^{(inup)} \\ \underline{V}_{j}^{(inlow)}\end{array}\right]=\left[\begin{array}{c}\underline {a}_{j}\\ \underline{a}_{j}^{\prime}\end{array}\right]. \tag{61}\]  
Similarly, we define the vector \(V_{j}^{(out)}\) of field amplitudes coming from the system (\(j\)):  
\[\underline{V}_{j}^{(out)}=\left[\begin{array}{c}\underline{V}_{j}^{(out)} \\ \underline{V}_{j}^{(out)}\end{array}\right]=\left[\begin{array}{c}\underline {b}_{j}\\ \underline{b}_{j}^{\prime}\end{array}\right]. \tag{62}\]  
What from the asymmetry of the reflection coefficient on the dielectric boundary and the continuity of the field tangent components on the boundary of two dielectrics gives the following matrix form \(S^{(i)}\):  
\[\left[\mathbb{S}^{(i)}\right]=\left[\begin{array}{cc}\mathbb{S}^{(j)}_{11}& \mathbb{S}^{(j)}_{12}\\ \mathbb{S}^{(j)}_{21}&\mathbb{S}^{(j)}_{22}\end{array}\right]=\left[\begin{array} []{cc}+\underline{\Gamma}-\underline{WU}^{-1}\underline{M}(\underline{I}+ \underline{\Gamma})&\underline{I}-\underline{\Gamma}-\underline{WU}^{-1} \underline{M}(\underline{I}-\underline{\Gamma})\\ \underline{I}+\underline{\Gamma}-\underline{WU}^{-1}\underline{M}(\underline {I}+\underline{\Gamma})&-\underline{\Gamma}-\underline{WU}^{-1}\underline{M }(\underline{I}-\underline{\Gamma})\end{array}\right] \tag{68}\]
# Enhancing Asphalt Binder Performance and Storage Stability with Trinidad Lake Asphalt (TLA)
###### Abstract  
This study delves into the transformative effects of Trinidad Lake Asphalt (TLA) on asphalt binders, revealing significant improvements in various performance metrics. Our findings indicate that TLA substantially increases binder viscosity and enhances rutting resistance, as evidenced by elevated G*/sin \(\delta\) values in both original and RTFO-aged states. TLA-modified binders demonstrate superior rheological properties, including improved \(J_{nr}\) and percentage recovery, indicating enhanced resistance to permanent deformation and reduced fatigue cracking. However, an increase in TLA content correlates with higher G*-sin \(\delta\) values, suggesting a trade-off with fatigue resistance and a higher susceptibility to cracking. Additionally, while TLA initially improves binder flexibility at low temperatures, excessive content can increase stiffness, adversely affecting stress relaxation properties. Notably, TLA enhances storage stability, ensuring minimal phase separation and excellent homogeneity. These insights underscore the need for the precise control of TLA content to optimize pavement durability. Future research should focus on fine-tuning TLA incorporation with additives like crumb rubber to maximize the structural integrity and longevity of asphalt pavements.  
TLA; viscosity; rutting resistance; fatigue resistance; low-temperature performance; storage stability +
Footnote †: journal: Applied Sciences  
Article Enhancing Asphalt Binder Performance and Storage Stability with Trinidad Lake Asphalt (TLA)