esa-sceva/satcom-chunk-collection huggingface dataset:
# Controlling the Focusing Ability of the Parabolic Reflector in Free Space at Microwave Frequencies
###### Abstract  
The studied configuration is a two-dimensional, very thin parabolic reflector made of graphene and illuminated by an H-polarized electromagnetic plane wave. We present basic scattering and focusing properties of such a graphene reflector depending on the graphene parameters at microwave frequencies, using the resistive boundary condition for very thin sheets. The scattering is formulated as an electromagnetic boundary-value problem; it is transformed to a singular integral equation that is further treated with the method of analytical regularization (MAR) based on the known solution of the Riemann-Hilbert Problem (RHP). The numerical results are computed by using a Fredholm second-kind matrix equation that guarantees convergence and provides easily controlled accuracy. Compared to THz range, in microwaves, the scattering pattern of reflector and the field level at geometrical focus can be controlled in a wide range by adjusting the chemical potential of graphene. Even though here no dielectric substrate supporting the graphene is considered, the practical realization can also be possible as a thin layer graphene material in GHz range. As we demonstrate, the variation of the chemical potential from 0 to 1 eV can improve the focusing ability within the factor of three. The high accuracy of the used method and the full wave formulation of the problem support our findings.  
electromagnetic scattering; reflector; thin resistive sheet; graphene +
Footnote †: journal: Applied Physics Letters  
1
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 2 Review of Simulators
### Comparison of Network Simulators  
Some of parameters for comparison of network simulators are presented in the Table 1. For a convenient understanding of the table parameters, filled circles have been introduced. An empty circle means there is no current parameter at all. A half-filled circle indicates a partial presence of the parameter in the network tool. A filled circle indicates the complete presence of the parameter. The abbreviations for parameter names are presented in the \"Abbreviations\" block.  
Based on the results obtained, it was decided to use the NS-3 network simulator for further research work. Such indicators as free, availability of ready-made wireless modules, and attention to the physical layer played an important role in choosing a network simulator.
###### Abstract
It can be seen that only the unilateral meshing of the working face (concave surface of the pinion, convex surface of the driven gear) was considered in most of the existing research on gear fatigue life based on the actual load spectrum, and few scholar have studied the load spectrum of the gears with reverse torque. This may work in the fatigue life analysis of the main reducer gears for traditional buses because the braking torque is applied to the driven gear under friction braking, and it is still the case that the pinion concave surface drives the gear convex surface. That is, the transmission between the reducer gears is always unidirectional from driving to braking, and only the working faces contact. However, the braking mode of the BEUB with the regenerative braking system is different from that of the traditional bus. Electromagnetic braking is based on the reversibility of the driving motor. The motor will convert from the driving state to the power generation state when braking [17], and the resistance torque is generated at the same time. The speed of the driving pinion is suddenly reduced, resulting in the nonworking face (convex surface of the driving pinion, concave surface of the driven gear) participating in meshing, and the pinion convex surface is driven by the gear concave surface, which changes the gear tooth contact and bending stress cycle characteristics, and inevitably affects the fatigue life of the main reducer gears. For this reason, based on the nominal stress method [18], a pair of spiral level gears is taken as the research object, and the contact and bending stress load spectrum of the gears under typical braking conditions were calculated based on the UDDS road cycle condition and the vehicle driving equation. When calculating the load spectrum, on the basis of the existing research, the reverse torque during electromagnetic braking is taken into account to be closer to the BEUB real braking condition so that, after the introduction of the regenerative braking system, the calculation results are more in line with the engineering practice compared with previous studies. The results of finite element static analysis are imported into nCode DesignLife software, and the fatigue lives of the corresponding gears are analyzed by applying the aforementioned typical load spectrum. Furthermore, the variation law of the fatigue lives of the gears under different braking modes is systematically analyzed, a method for calculating the fatigue life of the gears in the vehicle transmission system with the regenerative braking system is provided to provide a theoretical basis for the design and manufacture of the main reducer gears for the BEUB, with high performance and long service life, and more scientific research and design of new energy vehicles.
# Systematic Analysis of Risks in Industry 5.0 Architecture
## 1 Introduction
### Concept of Industry 5.0  
Industry 4.0 was found to be less concerned with people and more with technology, dismissing the role of people in productive systems. As a result, Industry 5.0 has emerged as a complementary and transitional philosophy from a technological Industry 4.0 to a human-centered Industry 5.0, where worker wellbeing is prioritized while preserving productive performance. Moving beyond a profit-centric approach, Industry 5.0 emphasizes sustainability through a dedication to social, environmental, and societal factors. Though it emphasizes workplace safety, human-machine connections, and larger social and environmental responsibilities, the notion acknowledges the power of technology for industrial development while also tying commercial aims and social goals together. Harness in human-machine collaboration, enhancing interaction in complex industrial systems, and empowering people and operators through individual capabilities and skills are all examples of future possibilities for human centricity [19]. Based on the concepts of  
Figure 3: Industry 5.0 [13].  
the 6 R's policy of industrial recycling, Industry 5.0 may be the first to be human-driven in terms of sustainability: Recognize, Rethink, Realize, Reduce, Reuse, and Recycle waste where possible while producing/creating customized, high-quality products. However, there is still a debate about the concept of Industry 5.0, specifically how this strategy might help sustainable development [13].  
Humans manage personalization and critical thinking while machines handle monotonous jobs in Industry 5.0, which integrates humans and technologies as collaborative robots [20]. Industry 5.0 is a symmetric innovation aimed at securing outputs by isolating automated systems, preparing the next generation of global governance [13; 19; 20].  
The creation of the Digital Twin (DT), which depicts a high-fidelity, virtual, physical entity with real-time communication, is a particular aspect of using robots. [19; 21]. These Industry 5.0-identified DT (Digital Twin) systems enable production optimization while conducting operational safety assessments in conjunction with simulation systems [22]. DTs, primarily focused on connectivity and production system modeling, can reduce educational inequality by promoting tele-operability and interactive robot production systems for instruction and learning [19; 21; 23].
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 4 Results
### Simulation
#### 4.2.3 Variation of the Time Interval Length  
In the previous sections, a simulated data generation period of one year was considered. However, dealing with a different period of time can be of interest. In this section, the influence of different intervals of data is estimated.  
This was evaluated for a shorter period of generation of simulated values. It was checked for half a year and for two years. As expected, the greater amount of data influences the performance of the code, taking on average more than twice the time to perform an iteration, with an average of 0.1 s compared to about 0.04 s. On the other hand, a lower amount of data requires less computational time per iteration, with an average of 0.03 s instead of about 0.04 s.  
In addition, as previously commented, the simulation stage is very heavy and requires long calculation periods to reach high accuracies, so performing a simulation with a greater amount of simulated values can make code take too long, going from approximately 20 h to practically 2 days of calculation time in the case of 2 sites, or from around 2 days up to 4 of computing time in the case of 3 locations. However, the reduction in the number of values can lighten the code time, requiring about 16 h instead of 20 h in the case of 2 places, or one day and about 20 h instead of practically 2 days in the case of 3 sites.  
On the other hand, the trends of the correlations remain, so there are values very close to the spatial correlations and around 0 in the temporal correlations after the resolution of the system by the Cholesky decomposition, and values with an error lower than 5% in each of the correlations obtained after the iterative algorithm.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 4 Discussion
A time lag was used to perform cross-correlation between SPI and SDI, which acknowledged the sequence between SPI and SDI and demonstrated that meteorological drought events could be used to predict hydrological droughts in relatively small watersheds with less anthropogenic activities.
## 3 Algorithm Improvement
### Selection of Lightweight Convolutional Network  
#### 3.2.1 Depthwise Separable Convolution  
Depthwise Separable Convolution (DWConv) is an efficient feature extraction module composed of depth and pointwise convolution. It has significantly fewer parameters and computational costs compared to ordinary convolutions while being able to capture more informative feature representations. Compared to the ordinary convolution with \(h\times w\times k^{2}\times c^{2}\) parameters, DWConv reduces the FLOPs to \(h\times w\times k^{2}\times c\). Assuming the input feature map size is \(D_{H}\times D_{w}\times M\) (height \(\times\) width \(\times\) number of channels) when employing a convolution kernel of size \(D_{k}\times D_{k}\times 1\) in YOLOv8, each convolution produces \(M\) feature maps of size \(D_{H}\times D_{w}\). Then, employ \(N\) sets of \(1\times 1\) convolution kernels for convolution, resulting in a final output size of \(D_{H}\times D_{w}\times N\) for the feature map.  
The calculation amount of ordinary convolution is (\(Q_{c}\)):  
\[Q_{c}=D_{k}\cdot D_{k}\cdot M\cdot N\cdot D_{w}\cdot D_{H} \tag{1}\]  
The calculation amount of DWConv is (\(Q_{D}\)):  
\[Q_{D}=D_{k}\cdot D_{k}\cdot M\cdot D_{w}\cdot D_{H}+M\cdot N\cdot D_{w}\cdot D _{H} \tag{2}\]  
The calculation ratio of DWConv to ordinary convolution is:  
\[\frac{Q_{D}}{Q_{C}}=\frac{D_{k}\cdot D_{k}\cdot M\cdot D_{w}\cdot D_{H}+M\cdot N \cdot D_{w}\cdot D_{H}}{D_{k}\cdot D_{k}\cdot M\cdot N\cdot D_{w}\cdot D_{H} }=\frac{1}{N}+\frac{1}{D_{k}^{2}} \tag{3}\]  
It can be seen from Formula (3) that by introducing DWConv, the calculation amount and parameters of the original network can be reduced so that the detection speed can be significantly improved.  
While DWConv can efficiently decrease FLOPs, it cannot outright substitute the standard convolution, as this could result in a considerable drop in accuracy. In practice, when expanding the DWConv's network width from \(c\) to \(c1\) (where \(c<c1\)) to recoup for the precision loss, it also adds to the memory requirement for computations, thereby decelerating the overall computational velocity. For hardware devices in deployment, the count of memory accesses is enhanced to [21]:  
\[h\times w\times 2c^{\prime}+k^{2}\times c^{2}\approx h\times w\times 2c^{\prime} \tag{4}\]  
Higher than normal convolution, i.e.,  
\[h\times w\times 2c+k^{2}\times c^{2}\approx h\times w\times 2c \tag{5}\]  
where \(h\) and \(w\) denote the dimensions of the input image feature in terms of width and height, \(c\) signifies the network's width, and \(k\) denotes the convolution kernel size.
## 2 Materials and Methods
### PML-V2.1 Model
#### 2.2.1 Energy Balance at Urban Land Surface  
Based on the surface energy balance, the net radiation (\(R_{n}\)) can be balanced by the latent heat flux (\(LE\)), sensible heat flux (\(H\)) and ground heat flux (\(G\)). As for a biweekly or longer estimation, the \(G\) is often negligible (\(G\ll H+LE\)), then the \(H\) is given by  
\[H=R_{n}-LE-G\approx R_{n}-LE \tag{3}\]  
The net radiation at the surface is the sum of the net shortwave downward radiation and the net longwave downward radiation,  
\[R_{n}=(1-\alpha)SW+\left(LW-e\sigma T_{a}{}^{4}\right) \tag{4}\]  
where the shortwave downward radiation \(SW\) (W m\({}^{-2}\)), the longwave downward radiation \(LW\) (W m\({}^{-2}\)) and the surface air temperature \(T_{a}\) (K) are from atmospheric forcing input data [10]. The shortwave albedo \(\alpha\) (-) and the longwave emissivity \(\epsilon\) (-) are from satellite-based estimations. \(\sigma\) is the Stefan-Boltzmann constant (5.67 \(\times\) 10\({}^{-8}\) W m\({}^{-2}\) K\({}^{-4}\)). The latent heat flux (\(LE\), W m\({}^{-2}\)) is calculated by \(LE=\frac{1}{c}\lambda ET\) and  
\[ET=E_{c}+E_{s}+E_{i}+E_{u}+E_{w} \tag{5}\]  
where \(\lambda=2500-2.2(T_{a}-273.15)\) is the latent heat of vaporization (kJ kg\({}^{-1}\)) at \(T_{a}\), and \(c\) (=86.4) is a conversion factor for units from (MJ m\({}^{-2}\) d\({}^{-1}\)) to (W m\({}^{-2}\)). \(ET\) is the evapotranspiration (mm d\({}^{-1}\)) summed from the canopy transpiration (\(E_{c}\)) and soil evaporation (\(E_{s}\)), interception evaporation (\(E_{i}\)), impervious surface evaporation (\(E_{u}\)) and open-water evaporation (\(E_{w}\)).
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Routing Protocol Requirements Analysis
#### 6.1.4 Loop-Freeness  
In RPL, the DAG is constructed when each node selects its parent, calculates its rank accordingly, and broadcasts it to the neighbours via DIO messages. In this way, each node selects a node as a parent if it possesses the minimum rank value. Rank is a scalar representation of the node location within the DODAG. The rank value increases from the root towards the leaf node monotonically. Thus, the rank computation in RPL routing is sufficient to fulfil the condition of loop-freeness and consistency. Similarly, the calculation of valid and optimal paths and consistency ensures loop-freeness. In accordance with the analysis, SMTrust is consistent and hence loop-free, as it follows the standard RPL operation for rank computation. Additionally, when a path is selected, the rank is calculated by the source node based on the selected parent rank. So, when a DIO message is received from another node with a greater rank value, the receiver node discards that DIO message, thus avoiding the loops formation. However, there in the scenario where the routing loops occur because of a distributed Bellman-Ford algorithm [75], then the RPL protocol offers the global and local repair mechanisms accordingly.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 7 Discussion and Algorithm Variations  
The algorithm presented here was designed to converge under all geometries for ocean reflections and low lying land reflections with mild topography with no specific tuning needed. However, there are a number of variations of the algorithm with potential advantages over the base line approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(K\) & **Snell Angle Tolerance** & **Mean Delay Error** & **Max Delay Error** & **Mean Iter.** & **Max Iter.** \\ \hline \(1.0\times 10^{6}\) & 0.1 degrees & 0.13 chips & 0.56 chips & 8.6 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table summary of global performance of the specular point and delay tracking algorithms.  
Figure 8: The distribution of the mean number of iterations needed to converge on the specular reflection point. Without customized algorithm tuning, higher iterations will generally occur at higher incidence angles (for CYGNSS >38 deg).  
It should be noted that because the delay and Doppler tracking values are normally fed into a real-time control loop, it is necessary to perform a linear or higher-order extrapolation of the transmitter and receiver positions based on simple dynamic models or more complex orbital predictions. Additional enhancements that could be made to improve the performance of the open loop tracking algorithms presented here are listed in the remainder of this section.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 5 Conclusions  
We introduced a novel two-step approach for time series anomaly detection, combining bandpass filtering with deep learning methods, specifically Functional Neural Network-based Autoencoders. The application of a bandpass filter in the preprocessing stage was shown to significantly increase the performance of the deep learning models by isolating relevant frequency components and removing noise. Our proposed method consistently outperformed traditional approaches across several benchmark datasets, demonstrating its efficacy in detecting anomalies in complex time series data.  
The integration of the bandpass filter with the FNN offers a robust framework for handling a diverse and complex time series, providing a practical solution for anomaly detection in real-world applications. Our findings suggest that the preprocessing step of bandpass filtering generally boosts the performance, making it a versatile enhancement technique for time series analysis.  
We plan to extend this approach to handle more challenging types of time series data, such as an intermittent time series using smoothing, spatiotemporal data, where standard anomaly detection methods often struggle, along with exploring other preprocessing methods (different signal processing approaches, functional principal components, and basis expansion), and incorporate some explainable AI techniques. Another direction would be to reduce the model complexity and computation using classical approaches or basis expansion in FNNs. Additionally, exploring more recent methods like Transformers, CNNs, and in particular, Variational Autoencoders (VAE), as a replacement for the standard Autoencoder is a promising direction. A VAE would allow us to model the distribution of a normal time series more effectively, providing a probabilistic framework for anomaly detection. This would enhance the robustness of the detection process, especially in environments with high uncertainty or varying patterns. Furthermore, probabilistic models can help refine anomaly scores and reduce false positives, thus improving the overall reliability of the system.  
Conceptualization, J.B., A.R.R. and C.V.; methodology, J.B. and A.R.R.; validation, J.B. and A.R.R.; formal analysis, J.B. and A.R.R.; investigation, J.B. and A.R.R.; writing--original draft, J.B. and A.R.R.; writing--review and editing, J.B., A.R.R., C.V. and C.G.; supervision, C.V. All authors have read and agreed to the published version of this manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors were employed by the company Industrial AI Lab, Hitachi America, Ltd., R&D.
## 2 Materials and Methods
### Global Body-Joint Angle Measurements  
Prior to testing, five reflective markers (2 cm in diameter) were affixed to the participants' left-side anatomical landmarks: the tragus (T), shoulder (S; acromial shelf), hip (H; greater trochanter), knee (K; lateral epicondyle), and ankle (A; lateral malleolus). These landmarks were identified through palpation by trained experimenters to ensure consistent marker placement. A second examiner verified the placements to enhance accuracy and reliability.  
After participants assumed the assigned sitting strategy--comfortable, habitual, or correct--the sagittal plane joint angles were recorded using a motion analysis system (BTS Smart-D140, Milan, Italy), positioned approximately 5 m laterally and perpendicular to the participant's sagittal plane (see Figure 1). The system was calibrated prior to each session according to manufacturer specifications, captured data at a sampling rate of 100 Hz, and recorded each posture for 5 s. The following three global joint angles were computed:  
Figure 1: Three types of chairs and global body joint angles examined in this study (T: tragus, S: shoulder, H: hip, K: knee, A: ankle).  
* Head inclination (HI): the angle between the vertical line and the line connecting the tragus and shoulder markers.
* Trunk angle (TA): the angle formed by the intersection of the line connecting the shoulder and hip markers with the line connecting the hip and knee markers.
* Knee angle (KA): the angle formed by the intersection of the line connecting the hip and knee markers with the line connecting the knee and ankle markers.  
For each combination of sitting strategy and chair type, five repeated measurements were collected per participant to assess postural variability. From these repetitions, the mean and range (i.e., maximum-minimum) of each joint angle were calculated and used as dependent variables in subsequent analyses, following established protocols [34; 39].