esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 5 Experiments and Results
### Results and Discussion
The first one was not due to the calibration procedure per se, but it was due to the intrinsic tracking accuracy of the inside-out mechanism adopted in our study, which, as anticipated, was affected by the angular resolution of the tracking camera (i.e., \(\approx\)4.8 arcmin/pixel). This choice was dictated by the need to offer sufficient stereo overlap between the two tracking cameras, albeit at the expense of a reduced pixel density. It should be noted that this value of angular resolution had a detrimental effect on the calibration results as it increased the uncertainty in estimating the position of the validation checkerboard. This measure was used both for calibrating the eye-display model for the reference position of the viewpoint camera and also for computing the translation vector \(\lx@math@degree{{}_{\circ}}\)t. We believe that in the future, the accuracy of the off-line phase of the calibration would benefit from the selection of tracking cameras with a higher value of angular resolution, achievable by selecting cameras with a higher image resolution or with longer focus lenses. A second source of inaccuracy arose from not considering, in our solution, the contribution of the display optical distortion. Indeed, the collimation optics of any OST HMDs, despite being essential to provide a reasonable FOV and a comfortable viewing distance for the virtual image, is a source of optical distortions such as radial and tangential distortions [35]. The same can be said for the optical combiner, particularly for waveguide based OST displays [20]. These optical distortions not only affect the shapes of the virtual objects perceived by the user, but they also distort the pattern of binocular disparities between the left and right virtual images, and therefore, they alter the stereoscopic perception of the virtual content [36]. To counter this problem, we are currently working on a solution that integrates a non-linear optimization step in the calibration algorithm. This further step can enable us to estimate the optical distortion parameters (radial and tangential). By doing so, we can correctly compensate for the optical distortions through a predistortion technique based on a 2D non-linear mapping of the virtual image on the imaging plane, as done in [25]. In order for the distortion correction, as well as the transformation (17) to be efficient and real time, a GPU based texture mapping approach (i.e., late warping) should be also adopted [37]. This mechanism would require the use of an eye tracking camera. On account of this, our calibration procedure potentially can be integrated in the low level rendering mechanism of commercial OST HMDs to encompass the linear and non-linear 2D warping of the ideal imaging plane yielded by the intrinsic optical properties of the display and by the user's eye position [38].
## 3 Result and Discussion
### Statistical Analysis  
According to the ANOVA results as represented in Tables 6 and 7, the Model F-value of 41,217.33 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. \(p\)-values less than 0.0500 indicate model terms are significant. In this case A, B, AB, A\({}^{2}\), B\({}^{2}\), A\({}^{2}\)B, AB\({}^{2}\), and A\({}^{3}\) (A stands for load and B stands for blend ratio) are significant model terms, where the load has the biggest impact on BP, followed by the blend ratio. The \(p\)-value reflects this, and the inference made is within sizable error bounds. The surface plot of BP (percentage of energy) versus blend and load (percentage) is shown in Figure. It demonstrates that the maximum BP is observed at the highest load; the surface plot can be used to observe the relative variation of BP, and load. The lack-of-fit F-value of 0.04 implies that the lack-of-fit is not significant relative to the pure error. There is a 99.98% chance that a lack-of-fit F-value this large could occur due to noise. A non-significant lack of fit is good. Adeq. Precision measures the signal to noise ratio. A ratio greater than four is desirable. Our ratio of 562.754 indicates an adequate signal. This model can be used to navigate the design space.  
Figure 13: Exergy balance of Diesel and biodiesel blends at 1500 RPM and full loading conditions.
# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 3 Materials and Methods
### Development and Implementation of the Pix2 Body Algorithm  
#### 3.2.2 Pix2 Pix  
The baseline model for the algorithm developed in this study is Pix2 Pix [44], an image-to-image translation algorithm. Image-to-image translation is a subfield of computer vision that attempts to match an input image to an output image. The conventional generative adversarial network (GAN) model is composed of generator and discriminator models, with the generator producing a random image from the noise z. However, the discriminator evaluates whether a generated image is real or fake, rather than generating an image itself. By contrast, Pix2 Pix's generator takes one image as input and creates an image as an auto-encoder. Because Pix2 Pix's generator has a U-net structure, it reduces the information loss in the input image by connecting features in the encoder's down-sampling layer to the up-sampling layer. This enables Pix2 Pix to generate output images that are associated with the input image rather than generate random images, which is consistent with the objective of this study of generating unclothed body shapes from clothed body shapes. However, Pix2 Pix alone relies solely on texture-level supervision and lacks the ability to handle spatial structure under occlusions. Therefore, we propose Pix2 Body, a novel extension that incorporates pose-derived joint coordinates and surface geometry to more accurately reconstruct body shapes even under loose or occlusive garments. Our proposed framework is built upon a conditional generative adversarial network (cGAN) architecture. In this structure, the generator creates human body shapes conditioned on 2D joint coordinates and front/back normal maps, while the discriminator evaluates the realism of the generated outputs under the same conditions. Unlike standard GANs that generate outputs from random noise, cGANs enable structurally consistent generation aligned with the given inputs, making them highly suitable for tasks such as body shape reconstruction under occlusion. This architectural decision reflects the limitations of conventional CNN or YOLO-style networks in spatially constrained image generation and enhances Pix2 Body's ability to infer anatomically plausible body structures.  
We propose the Pix2 Body algorithm as a baseline model by integrating pose estimation with the Pix2 Pix algorithm. The proposed Pix2 Body algorithm is divided into two steps. The first step is to extract body shape information via pose estimation, and the second step is to generate a human body using the body information retrieved via the generator. The input and output data for each step are shown in Table 1.  
Figure 2: Key Points in BlazePose.
## 3 Main Result  
In this section, we introduce the general framework of nonlinear fractional-order systems (FOSs) and explore two distinct stabilization scenarios, addressing practical challenges such as input saturation and time delays. These scenarios provide a comprehensive foundation for advancing the stabilization theory of nonlinear FOSs under realistic constraints.  
The first scenario focuses on deriving novel stability theorems to achieve asymptotic stabilization of nonlinear FOSs subjected to input saturation. To accomplish this, two robust methodologies are utilized. In Section 3.1, the first methodology employs a Lyapunov-like function with diffusive realization, originally proposed in [40] and subsequently extended in 2019 for analyzing the external stability of Caputo FOSs [34]. This approach provides a systematic framework for analyzing the stability of nonlinear FOSs with saturation constraints. The second methodology leverages the vector Lyapunov function method,a powerful tool particularly suited for the stability analysis and stabilization of FOSs experiencing time-delay effects.  
In Section 3.2, the second scenario builds on the first by incorporating time-delay effects into the stability analysis. A novel stability theorem is proposed for achieving asymptotic stabilization of delayed nonlinear FOSs, explicitly considering the influence of input saturation. This extension significantly enhances the theoretical relevance and practical applicability of the proposed stabilization strategies, enabling their deployment in real-world control systems with complex dynamic behaviors.  
Our study considers the general form of delayed nonlinear FOS, which can be expressed as follows.  
\[{}^{C}D_{t}^{\mathrm{p}}x_{i}=a_{i}x_{i}+f_{i}(x)+g_{i}(x_{i}(t-\tau))+b_{i} sat(u_{i}),i\in\{1,2,\ldots,N\} \tag{12}\]  
Let \(x\in\mathbb{R}^{n}\) and \(u\in\mathbb{R}^{m}\) denote the state and input vectors, respectively, where \(x_{i}\) and \(u_{i}\) represent the \(i^{th}\) state and input. The functions \(f_{i}\) and \(g_{i}\) correspond to the linear or nonlinear components of the system. Here, \(N\) denotes the number of states, \(\tau\) represents the time delay, and \(\mathrm{sat}(\cdot)\) is the saturation function that constrains the input control, defined as \(\mathrm{sat}(u)=\mathrm{sign}(u)\cdot\min(|u|,u_{0})\), where \(u_{0}\) is the saturation bound. Additionally, \(a_{i}\) and \(b_{i}\) are specific real constants, and it is assumed that the system is controllable. Figure 1 presents a detailed block diagram of the system under consideration, providing a clear visualization of its structure and functionality.  
**Assumption 1**.: _The nonlinear functions \(f_{i}\) and \(g_{i}\) are continuously differentiable in time and satisfy the local Lipchitz condition with Lipchitz parameters \(l_{1}\) and \(l_{2}\), respectively._  
**Lemma 5** ([41]).: _The saturation function satisfies the Lipchitz condition with a Lipchitz parameter \(l_{\mathrm{sat}}\)._
# Deep Learning of GNSS Acquisition
## 3 Deep Learning Method for GNSS Acquisition
### Noncoherent Integration through Fusion of Classifiers
A qualitative example of how the fusion rule impacts the performance of the classifier is provided in Figure 5. On the one hand, Figure 5a shows the CAF delay/Doppler map used in standard signal acquisition without any noncoherent integration and just 1 ms coherent integration. It can be seen, as it is known from the GNSS literature, that outside the true peak (denoted with a red circle) the noise floor is relatively spiky and can cause substantial false alarms, particularly at low \(C/N_{0}\) values. On the other hand, the proposed data-driven method takes the CAF values and processes them to produce the so-called probability ratio maps, as defined on the right-hand side of (23). The probability ratio map resulting from processing the CAF in Figure 5a can be observed in Figure 5b, where it is clear that the variability in the noise floor was reduced, although residual spikes can still be detected at delay/Doppler bins where no signal was present. This effect is smoothed further with the fusion method, as shown in Figure 5c, where \(K=6\) noncoherent integrations were considered. Notice that the NN uses subimages as inputs to produce a class probability pair, as depicted in Figure 4. As a consequence, the posterior probabilities are taking into consideration the delay/Doppler correlations of the CAF around the signal peak, in contrast to the standard method which only considers the maximum value of the CAF, thus neglecting the waveform arising from the noise form (i.e., the autocorrelation function of the corresponding spreading code). Figure 5: Comparison of the delay/Doppler grid for (**a**) standard CAF map with coherent integration only, (**b**) probability map produced by the data-driven classifier with coherent integration only, and (**c**) probability map after fusing \(K=6\) noncoherent classifier outputs. The GNSS signal had a \(C/N_{0}\) of 42 dB-Hz and the red circled highlights the location of the peak generated by the GNSS signal.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 3 Results
### Cohesion  
#### 3.2.1 Relationship Between Cohesion and Water Content  
Figure 6a illustrates the relationship curves between the cohesion (\(c\)) of fault gouge and water content under different degrees of cementation. The experimental results indicate that, with increasing water content, the cohesion of fault gouge exhibits a \"first increase, then decrease\" trend at cementation degrees of 0% and 3%. Specifically, at a cementation degree of 0%, the cohesion increases from 27 kPa at 10% water content to 83 kPa at 15% water content but then sharply decreases to 6.5 kPa at 30% water content. For a cementation degree of 3%, the cohesion increases from 27.5 kPa at 10% water content to 164.5 kPa at 25% water content, before slightly decreasing to 138 kPa at 30% water content. The reason for this phenomenon is that with a moderate increase in water content, the adsorption of water enhances the bonding forces between particles, leading to an increase in cohesion. However, as water content continues to increase, excessive water fills the pores between particles. Additionally, the thickness of the adsorbed water layers on the surfaces of clay mineral particles and cementing substances in the fault gouge increases [41]. This weakens the connections between particles as well as between particles and cementing materials, resulting in a decrease in cohesion [42].  
In contrast, the samples with a cementation degree of 1% exhibit a different trend in cohesion with increasing water content. As the water content increases, the cohesion increases from 24.5 kPa to an initial peak of 59 kPa. Instead of decreasing directly, it fluctuates within the range of 40-80 kPa. During this stage, the water, cementing materials, and clay particles within the sample appear to reach an equilibrium, maintaining the overall cohesion at a relatively stable level.
## 3 Methodologies
### Machine Learning Models  
#### 3.5.1 Logistic Regression  
Logistic regression (LR) [32] is a multivariate regression model that utilizes multiple independent variables to predict a single dependent variable, making it a widely adopted method for the spatial prediction of geological disasters. The core principle of LR is to transform domain values from negative to positive infinity into a probability range between 0 and 1. Values closer to 0 represent one class, whereas values closer to 1 represent another, indicating the non-occurrence and occurrence of geological disaster events, respectively. This transformation is achieved using the sigmoid function:  
\[g(z)=\frac{1}{1+e^{-z}}, \tag{3}\]  
where \(g(z)\) represents the estimated probability of the occurrence of geological disasters, varying from 0 to 1 along an S-shaped curve; and \(z\) is a linear combination:  
\[z=b_{0}+b_{1}x_{1}+b_{2}x_{2}+\ldots+b_{n}x_{n}, \tag{4}\]  
where \(x_{1},x_{2},\ldots\ldots,x_{n}\) represent the independent variables that influence the occurrence of geological disasters; and \(b_{0},b_{1},\ldots\ldots,b_{n}\) denote the regression coefficients associated with these variables.
## 1 Introduction  
### Amplitude-Sensitive Eddy-Current Method for Measuring the Thickness of Coating  
To measure the thickness \(d\) of a non-conductive coating on non-magnetic electrically conductive base metals, the amplitude-sensitive eddy-current method of non-destructive testing is provided [1, 2]. The operation principle of eddy-current devices is based on the fact that a high-frequency electromagnetic field generated by an eddy-current probe (ECP) produces eddy currents in a conductive base metal beneath the coating on which the ECP is placed (Figure 1). The density of eddy currents depends on the distance between the excitation coil and the surface of the conductive base metal. These eddy currents cause a change in the electromagnetic field surrounding the coil of the ECP and affect the impedance of the coil.  
The change in the impedance permits obtaining a set of calibration values by using reference standards for thickness of coating, and then, to calculate the thickness of a coating on an electrically conductive base metal.  
In most practical applications, the single ECP excitation coil is a part of a self-oscillating circuit, as shown in Figure 2.  
The output signal of the self-oscillating circuit is the resonance frequency \(f\), determined by the formulas:  
\[\begin{array}{c}f=\frac{1}{2\pi\sqrt{L}}\\ C=\frac{C1+C2}{C1+C2}\end{array} \tag{1}\]  
ECP of this type is called parametric, the parameter determining the output signal being the inductance \(L\) of the ECP coil, depending on the thickness of a coating \(d\). The ohmic resistance \(r\) of the ECP coil does not affect the output signal of the self-oscillating circuit directly. The value \(f\) is a measure of \(d\), and it is converted to \(d\) by means of a graduation curve.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 2 Literature Review
### Smart Factory Cyberattack Structure Diagram  
In a smart factory environment where security solutions are difficult to apply, a cyberattack can be initiated through all network links and various other entry points, such as corporate connections, connections through other networks at the control network layer, or field device level connections. For example, various IIoT devices in the smart factory may be directly connected to the external Internet network and communication may be eavesdropped or infected with a virus. An attacker may also visit for a firmware upgrade or facility maintenance check for malicious purposes and distribute malware by utilizing a USB. Figure 2 depicts a smart factory cyberattack structure diagram. It was constructed by referring to a five-layer IIoT architecture [51] considering various cyberattacks that may occur in smart factories. The five layers are Physical Sensing, Network/Protocol, Transport, Application, and Data and Cloud Services. The Physical Sensing Layer includes various sensors and IoT devices and is subject to side-channel, eavesdropping, and physical attacks. The Network/Protocol Layer includes various wired and wireless network protocols related to IIoT systems--such as Ethernet, 5 G, Wi-Fi, Bluetooth, and ZigBee--and is subject to DoS and MiTM attacks. The Transport Layer includes transmission-related protocols--such as TCP/IP and UDP/IP--and is subject to DoS, malware, and data tampering attacks. The Application Layer includes application protocols developed for low-power and low-capacity IIoT requirements--such as constrained application protocol (CoAP), advanced
###### Abstract  
Urbanization projects, understood as those supplying basic services for cities, such as drinking water, sewers, communication services, power, and lighting, are normally short-term extremely scattered actions, and it can be difficult to track their environmental impact. The present article's main contribution is to employ the project budgets of public urbanization work to provide an instrument for environmental improvement, thereby helping public procurement, including sustainability criteria. Two urban projects in Seville, Spain are studied: the first substitutes existing services, and the second also includes gardens and playgrounds in the street margins. The methodology finds the construction elements that must be controlled in each project from the perspective of three indicators: carbon, water footprints, and embodied energy. The main impacts found are due to only four construction units: concrete, aggregates, asphalt, and ceramic pipes for the sewer system, that represent 70% or more of the total impact in all indicators studied. The public developer can focus procurement on those few elements in order to exert a lower impact and to significantly reduce the environmental burden of urbanization projects.  
ecological management; urbanization; environmental product declaration; cost; carbon footprint; water footprint; embodied energy +
Footnote †: journal:  
The Budget as a Basis for Ecological Management of Urbanization Projects. Case Study in Seville, Spain  
[PERSON], [PERSON], [PERSON], and [PERSON]  
1 Department of Architectural Constructions II, School of Building Engineering, University of Seville, Avenue [PERSON] 4, 41012 Seville, Spain; [EMAIL_ADDRESS] (M.R.R.-P); [EMAIL_ADDRESS] (M.D.A.-R); [EMAIL_ADDRESS] (C.R.-C.); [EMAIL_ADDRESS] (M.M.) 1  
M.R.  
Greenhouse gas (GHG): Any gas that has the property of absorbing infrared radiation (net heat energy) emitted from Earth's surface and reradiating it back to Earth's surface, thus contributing to the greenhouse effect.  
Green public procurement (GPP): the process by which a public organization buys supplies produced in a way that is not harmful to the environment.  
Life Cycle Assessment (LCA): Compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle.  
Life Cycle Inventory (LCI): Quantifies the inputs and outputs of a system, material, and energy flows.  
Sustainable Urban Drainage Systems (SUDS): Systems designed to efficiently manage the drainage of surface water in the urban environment that use and enhance natural processes, i.e., infiltration, evapotranspiration, filtration and re-use.  
Unit costs (UC): The price that corresponds to a construction element formed by a combination of basic or auxiliary elements that make up a unit of work.  
Water footprint (WF): Measures the amount of water used to produce each of the goods and services we use.