esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### The BP Neural Network Model  
A backpropagation neural network (BP) is a type of artificial neural network that uses a supervised learning algorithm for training [28]. It consists of multiple layers of interconnected processing units called neurons that work together to process information and make predictions [29]. The BP neural network uses a feedforward architecture where information flows in one direction from the input layer to the output layer through the hidden layers. During the training process, the network receives input data and produces an output based on the weights assigned to each neuron [30]. The difference between the predicted and actual output is measured by a loss function [31], and the weights are adjusted in the opposite direction of the gradient of the loss function using a technique called backpropagation. The backpropagation algorithm is used to update the weights of the neurons in the hidden layers and the output layer, and it uses the chain rule of calculus to propagate the error from the output layer back to the input layer [32]. This allows the network to learn from its errors and improve its predictions over time. BP neural networks are widely used for pattern recognition [33], classification [34], and regression tasks in fields such as image processing [35], natural language processing [36], and finance [37]. Suppose that \(X_{1}\), \(X_{2}\), \(X_{3}\)..., \(X_{n}\) are the independent variables in the BP neural network, which represent the faction that influences safety risk in the underground coal mine. \(\Theta_{1}\) is the output corresponding to the building model for predicting the safety risk in an underground coal mine. In addition, \(\mathrm{T}_{1}\) is the actual value of the safety risk for the corresponding training data; \(w_{ij}\) is the weight for each node in the hidden layer; and \(w_{jk}\) is the weight for each node in the output layer. The number of input nodes in the neural network is \(n\), and \(g\) is the number of hidden nodes. The number of output nodes is \(S\), and the threshold of each node is \(c\). In feed-forward propagation in the BP neural network, the output of the hidden layer is described as follows:  
\[O_{j}=f(\sum_{i=1}^{n}w_{ij}x_{i}-c_{j})\quad j=1,2,\ldots,g \tag{1}\]  
The output of the output layer is as follows:  
\[T_{k}=f\left(\sum_{j=1}^{g}O_{j}w_{jk}-c_{k}\right)\quad k=1,2,\ldots,s \tag{2}\]  
Currently, the performance function in most BP network modeling toolboxes employs the Mean Squared Error (_MSE_) between the actual output and desired output. The learning approach of the BP network is to quickly reduce the weights and thresholds in the direction of the performance function [38]. The function is defined as follows:  
\[E_{k}=\frac{1}{2}\sum_{k}(T_{1}-\Theta_{1})^{2} \tag{3}\]  
BP neural network Error  
By substituting Equations (1) and (2) into (3).
# Shear Lag Effect on Box Steel Beams with Wide Curved Flanges
## 4 Numerical Parametric Analysis of Shear Lag in Steel Beams  
### Introduction to Numerical Parametric Analysis  
A review of the available literature and published scientific papers reveals considerable effort invested in studying the impact of shear lag in girders of various structural systems by varying a large number of the parameters previously mentioned.  
The research in this paper comprises a numerical analysis of a \"family\" of wide-flange beams with curved flanges.  
Numerical simulations were carried out using linear plate elements with ideally elastic structural steel, specified by an elastic Young's modulus of \(2.1\times 10^{5}\) MPa and a Poisson's ratio of 0.3, implying also shear modulus with a value of \(8.1\times 10^{4}\) MPa.  
Although higher-order finite elements provide more accurate results, in this numerical analysis the model is densely discretized, allowing linear elements to also produce results of sufficient accuracy. The potential sources of numerical error are not apparent in this case, as the study handles a geometrically and materially linear model, making it numerically simple and allowing for smooth solver convergence.  
Below is a summary of the conducted analysis (Table 1) where the parameter sets are visible. For all the sets, uniform load along the entire girder is considered. The first varying parameter is the ratio of the effective span to flange width (b/2)/\(\mathrm{L_{e}}\), which is later shown as the horizontal axis in the graphical presentation of results. Next, the impact of flange curvature on the effect of shear lag is also introduced as a second varying parameter (b/2)/R. It is verified that the ratio of web height to beam span has a negligible impact on the results, as does the ratio of flange thickness to web thickness, so varying these parameters is not in the scope of this parametric analysis.  
Extensive analysis demonstrated that the results for a flange curvature ratio of (b/2)/R = 0.05 align with those obtained for a flat flange (where the radius approaches infinite value). Consequently, the results for the flat flange are not presented in the subsequent sections.  
Despite the fact that the model is entirely geometrically and materially linear, in the observed cases involving significant curvature of the flanges, there is an exclusion of the \"non-bearing\" middle part of the cross-section due to buckling caused by the action of skewed (due to shear stresses) principal compressive stresses. In engineering practice, this problem is avoided by adding longitudinal and transversal stiffeners to slender flanges (and/or webs). In this article, the plate elements are modeled with increased longitudinal bending stiffness (stiffness multiplier equals 100) to avoid the buckling of the middle part of the flange caused by the action of compressive longitudinal stresses on the pre-deformed system (rounded flange).  
It is also evident that the gradual rounding of the flange to a certain extent neutralizes the reduction in the cross-section caused by the shear deformability of the flange.
## 2 Methodology
### Ordinary Kriging (OK)  
Kriging is a technique for predicting the values of unobserved points using the weighted combination of known surrounding values. Kriging was developed by [31] and mathematically established by [32]. Kriging defines the spatial interrelationship as a variogram calculated from the given data. The variogram \(2\gamma(h)\) is a measure of spatial autocorrelation that represents the similarity of data present at a constant distance \(h\).  
\[2\gamma(h)=E\Big{[}(z(x)-z(x+h))^{2}\Big{]}, \tag{5}\]  
OK predicts the unknown value of point \(z_{0}\) using \(z_{i}\), which are \(n\) data values already known. OK resolves the bias problem of simple kriging by proposing the sum of weights \(\lambda_{i}\) as \(1\).  
\[E(z_{0})-E(\hat{z_{0}})=E(z_{0})-E\Bigg{(}\sum_{i=1}^{n}\ \lambda_{i}z_{i} \Bigg{)}=0, \tag{6}\]  
\[1-\sum_{i=1}^{n}\ \lambda_{i}=0, \tag{7}\]  
Under this condition, weights are obtained so that the error variance can be minimized. The problem of obtaining the minimum and maximum values under constraints is solved by the Lagrange parameter method. The error variance equation of OK is as follows.  
\[\begin{split} L(\lambda_{1},\lambda_{2},\cdots,& \lambda_{n}:n)=\sigma^{2}-2\sum_{i=1}^{n}\ \lambda_{i}\sigma_{0i}^{2}+\sum_{i=1}^{n}\ \sum_{j=1}^{n}\ \lambda_{i}\lambda_{j}\sigma_{ij}^{2}+2w \Big{(}1-\sum_{i=1}^{n}\ \lambda_{i}\Big{)},\\ &\text{where}^{0}=\text{Var}(z_{0}),\sigma_{0j}^{2}=\text{Cov} \Big{(}z_{0},z_{j}\Big{)},\sigma_{ij}^{2}=\text{Cov}\Big{(}z_{i},z_{j}\Big{)}.\end{split} \tag{8}\]  
where \(w\) is the Lagrangian factor and \(L(\lambda_{1},\lambda_{2},\cdots,\lambda_{n}:n)\) is the Lagrangian objective function. This study used a variogram based on the exponential model. In this study, OK was performed using the krige function of the gstat package [33].
## 2 Introduction of pH Value Regulation System and Analysis of Regulation Process
### Analysis of Liquid Fertilizer pH Regulation Process
#### 2.2.2 Dynamic Model  
Assuming that the liquid volume in the mixing tank is constant and uniformly mixed, ignoring the influence of liquid temperature on pH, the dynamic process of pH neutral ization reaction can be represented by a CSTR process [17; 18; 19]. The dynamic process of acid-base neutralization is shown in Figure 2.  
In the figure, \(V\) is the mixing tank volume; \(F_{w}\) is the irrigation water flow rate; \(C_{w}\) is the irrigation water input concentration; \(F_{a}\) is the acid flow rate input into the mixing tank; \(C_{a}\) is the regulating liquid input concentration; \(F_{f}\) is the liquid fertilizer flow rate input into the mixing tank; \(C_{f}\) is the liquid fertilizer input concentration; \(F_{m}\) is the fertilizer mixture flow rate output from the mixing tank; \(x_{1}\), \(x_{2}\) are the acid and alkali concentrations in the output fertilizer mixture, respectively.  
According to the principle of material conservation, the dynamic equation of each state variable is obtained when the fertilizer mixing process reaches equilibrium.  
\[\left\{\begin{aligned} & V\frac{dx_{1}}{dt}=F_{a}C_{a}-F_{m}x_{1}\\ & V\frac{dx_{2}}{dt}=F_{f}C_{f}+F_{m}C_{w}-F_{m}x_{2}\end{aligned}\right. \tag{17}\]  
The input and output flows should be dynamically balanced, then:  
\[F_{m}=F_{a}+F_{w}+F_{f} \tag{18}\]  
Equations (16)-(18) together form a mathematical model of the pH neutralization process.  
\[\left\{\begin{aligned} -x_{1}+\frac{1}{1+10^{pA_{k}+pH-14}}x_{2}+10^{-pH }-10^{pH-14}=0\\ & V\frac{dx_{1}}{dt}=F_{a}C_{a}-F_{m}x_{1}\\ & V\frac{dx_{2}}{dt}=F_{f}C_{f}+F_{m}C_{w}-F_{m}x_{2}\\ & F_{m}=F_{a}+F_{w}+F_{f}\end{aligned}\right. \tag{19}\]  
From Equation (19), it can be seen that the mathematical model of the pH neutralization process consists of a static model and a dynamic model, and the dynamic process exhibits a slight nonlinearity, which is negligible when it is assumed that the input flow rate of the liquid fertilizer in the mixing tank is much larger than the input flow rate of the conditioning fluid.  
However, Equation (16) expresses the inherent nonlinearity of the pH neutralization process. And in the actual fertilizer mixing process, besides the stirring and mixing process, the delay factors such as the slow flow of liquid in the pipeline and the time lag in the measurement link also have an impact on the pH regulation process.  
Therefore, the pH regulation process is characterized by nonlinearity and time lag, which place higher demands on the performance of the controller.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### Monitor-Aware/Evasive Attack  
Monitor-Evasive Advanced Attacks, by design, aim to evade detection by conventional monitoring systems, including public route collectors. These attacks can significantly benefit from early detection through the use of data from multiple vantage points. The ability to leverage diverse observations across the Internet is crucial for identifying these attacks early, before they achieve their malicious objectives. The evasion tactics used in these attacks make them particularly amenable to detection methods that aggregate and analyze data from a wide array of sources to uncover subtle anomalies.  
These attacks may involve sophisticated coordination across multiple ASs to ensure their evasion tactics are successful. The distributed nature of these attacks, which aim to remain undetected by selectively targeting or avoiding certain monitors, underscores the need for a group-level analysis that can identify coordinated malicious activities across the network.  
Monitor-Evasive Advanced Attacks inherently involve complex interactions across ASs, as attackers must understand the global BGP ecosystem, including the placement and capabilities of monitors, to effectively evade detection. Techniques that can analyze these complex interactions are essential for detecting such advanced evasion tactics. These attacks require an advanced understanding of BGP routing and the operational practices of monitoring systems. Attackers leverage this knowledge to craft attacks that are difficult todetect without analyzing group-level interactions and dynamics, fitting the criterion for sophisticated manipulation.  
The hallmark of Monitor-Evasive Advanced Attacks is their use of stealthy maneuvers designed to evade detection by conventional monitoring systems. This criterion is directly applicable to our study, as these attacks manipulate AS path attributes or selectively announce paths to bypass detection, necessitating advanced detection techniques that can identify such evasive maneuvers.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
###### Abstract  
In this paper, a dual-band RF wireless power transfer (WPT) system with a shared-aperture dual-band Tx array antenna for 2.4 and 5.8 GHz is proposed. The final configuration of the Tx array, which is made up of 2.4 GHz right-handed circular polarization (RHCP) patches and 5.8 GHz RHCP patches, is derived from the optimization of 2.4 and 5.8 GHz thinned arrays, ultimately to achieve high transmission efficiency for various WPT scenarios. The dual-band RF WPT Tx system including the Tx array antenna and a Tx module is implemented, and Rx antennas with a 2.4 GHz patch, a 5.8 GHz patch, and a dual-band (2.4 and 5.8 GHz) patch are developed. To validate the proposed dual-band RF WPT system, WPT experiments using a single band and dual bands were conducted. When transmitting RF wireless power on a single frequency (either 2.482 GHz or 5.73 GHz), the received power according to the distance between the Tx and Rx and the position of the Rx was measured. When the distance was varied from 1 m to 3.9 m and the transmitted power was 40 dBm, the received power value at 2.482 GHz and 5.73 GHz were measured and found to be 24.75-13.5 dBm (WPT efficiency = 2.985-0.224%) and 19.25-6.8 dBm (WPT efficiency = 0.841-0.050%), respectively. The measured results were in good agreement with the calculated results, and it is revealed that the transmission efficiency when wireless power is transmitted via beam-focusing increases more than that with conventional beam-forming. Furthermore, the dual-band WPT experiment proves that 2.482 GHz beam and 5.73 GHz beams can be formed individually and that their wireless power can be transmitted to a dual-band Rx or two different Rx.  
wireless power transfer; microwave power transfer; array antenna; thinned array; dual-band; shared-aperture antenna; beam-focusing; beam-forming +
Footnote †: journal: Applied Optics and Photonics
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 2 Related Work
An example of a non-commercial IBE scheme is proposed in RFC6508, which defines an identity-based encryption (IBE) implementation based on elliptic curves. This IBE scheme is optimized to be used for both the Sakai-Kasahara key encryption (SAKKE) algorithm, described in RFC6508, and the certificateless signatures for identity-based encryption (ECCSI) signature scheme, described in RFC6507. RFC6508 uses IBE to securely disseminate a \"shared secret\" (e.g., the key of a symmetric cipher) to a receiver, whilst RFC6507 provides authentication via certificateless signatures that are based on the Elliptic Curve Digital Signature Algorithm (ECDSA). In particular, RFC6508 (SAKKE) presents a variant of the Sakai-Kasahara key encapsulation mechanism (SK-KEM), optimized to support multi-party communications that have been adopted by the IEEE 1363.3 2013 standard for identity-based cryptography. The RFC6507 standard presents a certificateless variant of the Elliptic Curve Digital Signature Algorithm (ECDSA) that is designed to be used with identity-based encryption; it is compatible with the IBE proposed in RFC6508, and is optimized to have low bandwidth and low computational requirements. In 2016 the National Cyber Security Centre of the UK proposed the MIKEY-SAKKE protocol to provide secure, cross-platform multimedia communications (e.g., voice over IP) for government-related agencies. Their proposal was presented in RFC6509 [40]; it uses IBE/IBC, the Sakai-Kasahara key encryption (SAKKE) algorithm and a variant of the ECDSA adapted to be used with the Sakai-Kasahara protocol.
## 2 Methodology
With the clean frequency data in a continuous arc, the performance of atomic clocks can be calculated, including the frequency accuracy, frequency drift rate (daily), and frequency stability (300 s, 9900 s, and 86,400 s). The HDEV is used to characterize the frequency stability. In the following text, the stability at the averaging time 9900 s is denoted as 10,000 stability. 6. Repeat step 5 for the next data arc, and then the clock assessed results can be derived in the next continuous arc. With the frequency accuracy, drift rate, and stability of each continuous arc, the average values of all arcs can be derived, which is the final assessed result of one atomic clock.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
###### Abstract  
This paper presents a projection-based augmented-reality system (MARVIS) that supports the visualization of internal structures on the surface of a liver phantom. MARVIS is endowed with three key features: tracking of spatial relationship between the phantom and the operator's head in real time, monoscopic projection of internal liver structures onto the phantom surface for 3D perception without additional head-mounted devices, and phantom internal electronic circuit to assess the accuracy of a syringe guidance system. An initial validation was carried out by 25 medical students (12 males and 13 females; mean age, 23.12 years; SD, 1.27 years) and 3 male surgeons (mean age, 43.66 years; SD, 7.57 years). The validation results show that the ratio of failed syringe insertions was reduced from 50% to 30% by adopting the MARVIS projection. The proposed system suitably enhances a surgeon's spatial perception of a phantom internal structure.  
projection-based augmented reality; liver phantom; tracking; motion parallax; glasses-free visualization Article  
Medical augmented-reality visualizer for surgical Training and Education in Medicine  
[PERSON] _et al._ (2019), 9, 2732; doi:10.3390/app9132732
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 4 Discussion
### The Benefit of Unimodal Warnings  
Each individual unimodal warning condition (i.e., visual, auditory, and tactile) was compared with the no-warning condition respectively to determine whether it facilitates drivers' response time in a significant level. The results showed that the implementation of auditory or tactile driving warning systems could significantly reduce drivers' response time. However, visual warnings failed to reduce drivers' response time substantially. Driving is a visually demanding task which requires more than just the visual acuity, contrast sensitivity, and visual field of an individual [102]. With no competition for drivers' limited visual resources, auditory and tactile warnings appear to be more effective in reducing drivers' response time because of a lower level of interference [36].  
As revealed from our results, the effectiveness of the visual warning system is below expectation, making it crucial for us to understand the rationale behind such observation. One of the important visual abilities is visual attention [102], which can be interfered with by many other factors [20,103]. With drivers often being uncertain as to when and where a critical situation would appear, they are required to constantly focus and adapt to the rapidly changing visual world. This extended visual concentration or focus can lead to visual fatigue. If the visual warning is presented while drivers are experiencing visual fatigue, visual attention overload may occur [104], thus reducing the effectiveness of visual warning systems. Another possible explanation is that, whenever drivers experience a divided attention or visual fatigue, they could end up experiencing inattentional blindness, resulting in the failure to notice a readily visible yet unexpected visual stimulus, such as looked-but-failed-to-see-errors [105]. These stimuli not only include the objects and events in the driving scene, but also the visual warning signals that are added to the visual field. Even when the drivers' eyes are fixated on the road or potential danger, the effect of inattentional blindness can still disrupt their visual processing, in which drivers detect the potential risk or visual warning signals but fail to perceive and react to it [14].  
In sum, our meta-analysis demonstrated that the incorporation of auditory and tactile warnings could benefit drivers' performance in comparison to no warning; yet the effect of visual warnings was insignificant potentially due to factors such as visual fatigue and inattentional blindness.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 4 Implementations and Evaluation
### Simulation Analysis  
Since the BDAPS does not have the ability to broadcast all frequency signals. In order to further verify the location performance of multi-frequency fusion ranging, the simulation experiment is carried out with the BDS data in Table 1.  
A total of 24 frequency combination patterns can be found in the above analysis. In order to better verify the different group phase combination of different frequency points on the impact of ranging location, this paper selected nine of the typical combination for simulation analysis. This is shown in the Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Name** & **ME (cm)** & **RMSE (cm)** & **MAX (cm)** \\ \hline FG-GPRE & 20.86 & 8.68 & 43.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Locate the performance profiling list.  
Figure 8: The second group of positioning accuracy comparison test.  
In order to ensure the maximum authenticity of the simulation data, the group phase ranging error and environmental noise are fully considered in the design of the simulation environment. Therefore, in the following analysis, carrier phase measurement data are randomly introduced with 0.02-0.04 cycles of measurement error. On this basis, the following analysis is mainly from the ranging and positioning accuracy improvement.