esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Identification Methods
#### 2.3.2 Feature Extraction  
First, as shown in Table 1, six equations were empirically selected from our previous work [23], and used to extract features from the preprocessed measurements. The preprocessed measurements obtained by an individual sensor in a single sampling cycle were substituted into the equations, which resulted in six feature values. Thus, the dimension of extracted features was 48, since there were eight gas sensors in our EN.  
The six features can be categorized into two types. Features 1-3 are statistics in the time space of the original preprocessed data, while features 4 and 5 are calculated based on the first-order derivatives. Feature 6 incorporates both the first- and second-order derivatives of the preprocessed data. The different features in Table 1 characterize the perfume samples from different aspects which could contribute to the PI process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Feature Name** & **Calculation Equation** \\ \hline
1 & Mean of response-values & \(\frac{\sum_{t=0}^{N}V(t)}{N}\) \\ \hline
2 & Maximal response-value & max\(V(t)\) \\ \hline
3 & Response-value at the first-order differential maximum & \(V\big{(}t_{qgn}\big{)},t_{qgn}=\operatorname*{argmax}\frac{dV(t)}{dt}\) \\ \hline
4 & The first-order derivative maximum & max\(\left[\frac{dV(t)}{dt}\right]\) \\ \hline
5 & The first-order derivative average & \(\frac{\sum_{t=0}^{N}\left[dV(t)/dt\right]}{N}\) \\ \hline
6 & Mean curvature of the response curves & \(\left[\sum_{t=0}^{N}\frac{\left[d^{V}(t)/dt\right]^{2}}{\left[1+(dV(t)/dt)^{2} \right]^{\frac{1}{4}}}\right]/N\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The six equations used to extract features from the preprocessed measurements.  
Then, to achieve further feature extraction and dimension reduction, the features extracted using the six equations were used as the input to PCA. The pseudo-code of PCA is sketched in Algorithm 1[19]. In PCA, a projection from the high-dimensional feature space into a relatively low-dimensional feature space is conducted. The input of PCA is a \(n\times d\) feature matrix \(E\). In PCA, a \(d\times d^{\prime}\) transformation matrix \(W\) is constructed using \(d^{\prime}\) (\(d^{\prime}<d\)) top ranking Eigen vectors of the covariance matrix \(EE^{T}\) in the descending order of Eigen values [19]. Then, \(W\) is used to transfer the \(n\times d\) matrix \(E\) to a new \(n\times d^{\prime}\) matrix \(X\). The \(n\) row vectors of \(X\) with lower dimension \(d^{\prime}\) is output as the dimension reduction result.  
```
1 Calculate the covariance matrix \(EE^{T}\) of the original \(n\times d\) feature matrix \(E\).
2 Calculate the Eigen vectors and values of \(EE^{T}\) using Eigen decomposition.
3 Sort the Eigen vectors of \(EE^{T}\) according to the descending order of Eigen values.
4 Construct a matrix \(W\) using the \(d^{\prime}\) top-ranking Eigen vectors.
6 Calculate the new \(n\times d^{\prime}\) feature matrix \(X\) using the equation \(X=EW\).
```  
**Algorithm 1** PCA [19].
# Laser Beam Positioning by Using a Broken-Down Optical Vortex Marker
## 3 Intensity Analysis Methods  
The intensity analysis method can be used for a beam with or without a vortex. A commonly used quadrant detector for a Gaussian beam can be adopted for a vortex beam [31]. The best advantage of this method is its speed, but it is susceptible to asymmetric beams. A very similar situation occurs when a CCD camera (with many more pixels) is used and the center of gravity is calculated. In positioning with the use of OV, the minimum intensity regions are the most important because the optical vortices are in these regions. The minimum intensity localization method was applied for simulated and recorded data using typical image processing operations. The sequence of operations is listed below:  
* Applying a two-dimensional median filter;
* Conversion to binary (black and white) images with the defined threshold level;
* Color inversion, with minimum light intensity represented as white color;
* Grouping pixels with a white color in regions;
* Calculating the center of gravity for each white region.  
As mentioned in the next sections of this article, the intensity analysis method is used only for the coarse localization of OV. Image regions based on coarse localization are used as input data for the artificial neural network.
# On the Hardware-Software Integration in Cryptographic Accelerators for Industrial IoT
## 1 Introduction  
Industrial Internet of Things (IIoT) refers to the application of IoT technologies on industrial automation systems. The aim is to provide capabilities of sensing, Internet-wide communication, intelligent processing, self-organization and self-maintenance within industrial information and control architectures. Among the numerous applications of IIoT, we can mention smart transportation, smart factories, smart grid, and so on. Real-time requirements are often necessary in IIoT, due to the need to maintain the controllability of the automation system, and due to latency criticality in general. As a result of the widespread adoption of IIoT, cybersecurity threats against these systems are rapidly increasing. The risks associated to IIoT cybersecurity incidents are particularly high, since many IIoT systems are considered critical infrastructures. To address both cybersecurity and real-time requirements, we need to deploy cryptographic processing components which are both secure and efficient. Within this context, hardware acceleration of cryptographic functionalities is paramount. However, we must also make an effort to efficiently integrate such hardware with the software chain, otherwise, the intrinsic efficiency of hardware cryptographic accelerators is wasted. This is especially important if we consider that many IIoT devices are so complex nowadays that they need an operating system running on them. The majority of papers that study efficiency aspects of hardware/software cryptographic components typically focus on software only [1; 2; 3; 4] or hardware only [5]. Very little research is dedicated to efficient software-hardware integration.  
In this paper, we study a software-hardware integration of various cryptographic accelerators (namely, the ones presented in [6; 7; 8; 9]) with a Linux operating system. We test the performance of such an integration, and we discuss our design choices and the lessonslearned during the optimization process. As a result, we find that some implementation choices that may look reasonable when developing the hardware in isolation turn out to be sub-optimal when integrating the hardware with existing software architectures.  
Our reference applications are the remote software update of industrial robots (e.g., UGVs) and the remote industrial sensing (e.g., temperature or pressure measurements in plants). We refer to such applications because they represent the extreme endpoints of the spectrum with respect to the size of messages to be secured within IIoT applications. Indeed, UGV firmware can easily reach megabytes in size, while messages sent by temperature or pressure sensors are typically less than a kilobyte.  
The rest of the paper is organized as follows. Section 2 introduces the necessary preliminary concepts and compares with some related work. Section 3 describes our hardware system and our hardware-software integration. Section 4 reports and discusses our experimental results. Finally, Section 5 concludes the paper and reports the lessons learned during the optimization process.
# On the Measurement of Particle Contact Curvature and Young's Modulus Using X-ray \(\mu\)CT
## 3 Contact Curvature Theories  
### The Hertz Contact Theory  
[PERSON] pioneered the contact mechanics by investigating the contact of two elastic isotropic solids [12]. He considered geometrical effects on local elastic deformation properties but neglected surface interactions. The radius of elastic contact \(r_{ce}\) in [12] is given:  
\[{r_{ce}}^{3}=\frac{3R^{*}F}{2E^{*}} \tag{4}\]  
where \(R^{*}\) and \(E^{*}\) are the effective radius and effective modulus of elasticity, respectively, and \(F\) is the contact force.  
The depth of indentation \(\delta\) gives:  
\[\delta=\left\lfloor\frac{9F^{2}}{4{R^{*}}^{2}{(E^{*})}^{2}}\right\rfloor^{\frac {1}{3}} \tag{5}\]  
The relationship between the contact force \(F\) and the maximum pressure \(p_{max}\) is [23]:  
\[F=2\pi\int_{0}^{r_{ce}}p(r_{c})r_{c}dr_{c}=\frac{2}{3}\pi{r_{ce}}^{2}p_{max} \tag{6}\]  
where \(r_{ce}\) is the radius of a circular contact area and \(p(r_{c})\) is the contact pressure:  
\[P_{max}=\frac{3}{2}\frac{F}{\pi r_{ce}^{2}}=\frac{3}{2}p=\frac{3}{2}\frac{F}{ A_{ce}}=\left(\frac{3}{2}\frac{F}{\pi^{3}}\frac{{E^{*}}^{2}}{{R^{*}}^{2}} \right)^{\frac{1}{3}} \tag{7}\]  
It shows that the maximum contact pressure is 1.5 times the mean pressure \(p\) on the elastic contact area \(A_{ce}\). The effective radius \(R^{*}\) of both solids is given by  
\[R^{*}=\left(\frac{1}{R_{1}}+\frac{1}{R_{2}}\right)^{-1} \tag{8}\]  
where \(R_{1}\) and \(R_{2}\) are the radius of the principal curvatures of two solids before flattening, and \(E^{*}\) the effective modulus of elasticity [24]:  
\[E^{*}=2\left(\frac{1-v_{1}^{2}}{E_{1}}+\frac{1-v_{2}^{2}}{E_{2}}\right)^{-1} \tag{9}\]  
where \(v_{1}\) and \(v_{2}\) are Poisson's ratio of the two solids, and \(E_{1}\) and \(E_{2}\) are the elastic moduli of the two solids.  
In the case of a particle under compression by two parallel plates, the equation for calculating the modulus of elasticity, based on the Hertz contact stress, is expressed as [25]:  
\[E=\frac{0.338F\big{(}1-v^{2}\big{)}}{D^{3/2}}\left[K_{u}\bigg{(}\frac{1}{R_{1 }}+\frac{1}{R_{1}^{\prime}}\bigg{)}^{1/3}+K_{L}\bigg{(}\frac{1}{R_{2}}+\frac{ 1}{R_{2}^{\prime}}\bigg{)}^{1/3}\right]^{3/2} \tag{10}\]where \(E\) and \(D\) denote the [PERSON]'s modulus and indentation displacement, respectively, and \(v\) and \(F\) are [PERSON]'s ratio and applied force, respectively. \(R_{1}\) and \(R_{1}^{\prime}\), and \(R_{2}\) and \(R_{2}^{\prime}\) are the radii of principal curvature at the corresponding points of contact, respectively. The sketch of two particles under contact is schematically shown in Figure 4.  
The constants \(K_{u}\) and \(K_{L}\) in Equation (10) are determined by \(cos\theta\). The values of \(K_{u}\) and \(K_{L}\) give inverse correlation with \(cos\theta\); the variation between \(K\) and \(cos\theta\) is given elsewhere [25] and \(cos\theta\) is calculated from the following formula:  
\[cos\theta=\frac{\left[\frac{1}{R_{1}}-\frac{1}{R_{1}^{\prime}}\right]}{\left[ \frac{1}{R_{1}}+\frac{1}{R_{1}^{\prime}}+\frac{1}{R_{2}}+\frac{1}{R_{2}^{ \prime}}\right]} \tag{11}\]
## 3 Results
Control cells appear stained in green, which represents viable cells (see white arrows); while treated cells show a marked spotted red staining. BxPc3 spheroids appear smaller, as per proper cell characteristics. Cell staining appears concentrated on the spheroid margins, as they are compact and have less dye permeable. Quantification of viable and dead cells using Spheroid Sizer (Mathlab) is also reported. Image size for all pictures: 900 \(\times\) 900 \(\mu\)m, acquired under 10\(\times\) magnification. ***\(p\) < 0.001;**\(p\) < 0.01. \(p\)-Values for each investigated frequency results were: 0.19 for 1 MHz, 0.0018 for 2 MHz and 0.0013 for 3 MHz, respectively. Highly significant values were obtained using 3 MHz frequencies, if compared to both 1 and 2 MHz. During the US treatment, total disruption of some spheroids was found with a cadency of 25% within the treated population. The frequency seems to affect only the level of cell scattering, as shown in Figure 8. Overall, the morphology of the spheroids results appeared to be deeply affected by the US treatment. Spheroids appeared more compact in the controls, while cells tended to disaggregate after the treatment, losing their shape or appearing enlarged and round-shaped at the highest frequency of 3 MHz. After treatment, BxPc3 cells tended to disrupt and to move from the center core of the spheroids. Figure 8: Brightfield images of PANC-1, Mia Paca 2 and BxPc3. (**A**) PANC-1 images of spheroids, control (untreated cells), control after 5 min (Control 5 min) of stimulation, cells treated with 1 MHz, 2 MHz and 3 MHz. (**B**) Mia Paca 2 images of spheroids, control (untreated cells), control after 5 min (Control 5 min) of stimulation, cells treated with 1 MHz, 2 MHz and 3 MHz. (**C**) BxPc3 images of spheroids, control (untreated cells), control after 5 min (Control 5 min) of stimulation, cells treated with 1 MHz, 2 MHz and 3 MHz (scale bar: 100 \(\mu\)m). We also analyzed the Annexin/PI staining in all the three cell lines, comparing the results in control conditions, as well as after the application of 3 Hz US. Figure 9 clearly shows that all the three PDAC cells are in late apoptosis after US treatment at 3 MHz, compared to the respective control. In particular, PANC-1 cells show 91.8% of treated cells in Q2 (late apoptosis) compared to 67.9% of Control cells. Mia Paca-2 treated cells have 94.3% of cells in Q2, compared to 37% of Control cells. Finally, BxPc3 has 51.3% of cells in Q2 compared to 38.4% of control cells. A lower effect, but in consistence with the trend, was envisaged in cells treated at lower frequencies, as reported in Figure 9.
# Fracture Behaviour of Concrete with Reactive Magnesium Oxide as Alternative Binder
## 3 Experimental Results and Discussion
### Main Mechanical Properties
[PERSON] et al. [18] found reductions in tensile strength when 10% reactive MgO was added to the fuel ash and metakaolin binary mixes. There is an increase in modulus of elasticity when MgO's C, SO and S1 are used at almost all ratios of substitution. These increases are up to 16%, presenting better results for MgO S1 mixes, with values between 8% and 14%. Mixes with MgO A had an improvement at 5% and 10% MgO ratios, with values of 14% and 8%, respectively, and a reduction of 6% for 20% MgO. This increase can be explained by the expansive nature of MgO, decreasing the shrinkage and the micro-cracks and increasing the bulk density of concrete [28; 47; 48]. Figure 4a shows the relationship between compressive strength and splitting tensile strength in cylindrical specimens for all mixes at 28 days. All mixes were found to be well within the confidence intervals for compressive strength of conventional concrete. Figure 4b shows the theoretical relationship between the compressive strength and the modulus of elasticity of concrete, according to Equation (6) proposed by EC2 [49], where \(\alpha\) = 0.9 for coarse limestone aggregates (the case of this paper). It can be observed that all mixes have values of modulus of elasticity much higher than those proposed by EC2. Thus, it is concluded that the proposal established by EC2 [49] is quite conservative. \[\mathrm{E_{cm}}=\alpha\ 22\left(\frac{\mathrm{f_{cm}}}{10}\right)^{0.3} \tag{6}\]  
Figure 4: Relationship between compressive strength and cylindrical specimens (\(\mathrm{f_{cm,sp}}\)) at 28 days (**a**); relationship between modulus of elasticity and compressive strength (\(\mathrm{f_{cm}}\)) at 28 days (**b**).
## 3 Numerical and Experimental Verification  
The proposed method was validated by means of a numerical example and an experiment. In the numerical example, the measurement error and the error in the ultrasonic transmission velocity are analyzed using simulated data generated under the assumption that the actual traveling velocity of the P-wave coincides exactly to that of Equation (2). In addition, the change in the results is also analyzed according to the variation of the regularization factor. The experiment considers the data obtained from the laboratory test on specimens fabricated by installing anchor bolts in concrete.
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 2 Methodology
### Network Architecture  
The network we used is based on U-Net. U-Net is a fully convolutional network, first proposed by [PERSON] et al. [41], and widely used in various domains due to its outstanding generalization ability. The U-Net is composed of two parts: the contracting path, known as the encoder, is used for extracting features and context information; and a symmetric expanding path responsible for resolution restoration, which can be understood as a decoder. The features extracted by the encoder are incorporated together with the feature maps in the decoder through skip connections. Compared to the standard convolution neural network (CNN), U-Net uses skip connections so that the low- and high-level features of the input data do not decay inside network propagation. These architecture choices collectively contribute to the effectiveness of the U-Net. The structure of the U-Net network used in this paper is shown in Figure 2.  
The input to the network is the reference noise (X- or Y-component data), and the output is expected to be real noise. The encoder on the left consists of four blocks, each containing two convolutional layers and a maximum pooling layer for downsampling,  
Figure 2: The architecture of U-Net used for shear wave attenuation of OBN data.  
with a total of four downsamplings. After one downsample operator, the number of feature channels is doubled, and the length and width of the feature map halved. The decoder on the right contains four blocks consisting of two convolutional layers, a upsampling layer and a splicing of the feature maps from the corresponding decoder. The last layer of the decoder is a convolutional layer. The size of the convolution kernels of all layers is \(3\times 3\), and the number of feature channels in each convolutional computation is shown in Figure 2. Except for the last layer, batch normalization (BN) is applied after each convolutional layer to mitigate over-fitting and reduce the training time [42], and the LeakyReLU activation function [43] is used to enhance the nonlinearity.
## 4 Numerical Simulation Analysis
### Fault Dynamic Response Law under Different Mining Conditions
#### 4.1.1 Shear Stress  
Removing the measuring point 2 and the measuring point 7, according to Figures 11 and 12, it can be seen that the dynamic change in the shear stress at the measuring point can be divided into three categories, namely, almost no effect, small fluctuation and large fluctuation. Measuring points 3, 4 and 8 are located in the fault plane of the non-mining area, and their shear stress does not change and is in a stable state. The shear stress of measuring points 5, 6 and 10 fluctuates greatly, and the shear stress of measuring point 6 increases continuously with the increase in segmental height, which indicates that this stage is the stage of elastic energy accumulation, and the shear stress of measuring point 10 decreases from 3 MPa to 0.8 MPa in the early stage of collaborative mining with a section height of 20 m when using the caving method, indicating that the position of this measuring point may be in a rapid slip stage until it is in a stable state. When the segmented height of the collapse method exceeds 15 m, which is 20 m, the shear stress at measuring point 10 shows a greater decrease.  
From Figure 13, it can be analyzed that points 3, 4, 8 and 11 are in a stable state. The measuring points with the largest fluctuations in shear stress are still 5, 6 and 10. There is a high probability of slippage at the early stage of mining at measuring point 10, and the change form of these measuring points is consistent with the trend of the caving method at different segmental heights, but with the increase in the segmented height of the filling method, the variation range of the shear stress at all measuring points is small. This may indicate that the sublevel caving method without a sill pillar has a greater impact on the stability of the fault than the filling method.
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 2 Object of Study  
### Dust Removal Fan Structure  
Taking a new type of wet dust removal fan for mines as the research subject, the influence of structural parameters of the dust removal fan on its dust removal performance is analyzed. The primary internal structure of the dust removal fan includes the driving impeller, driven impeller, rear guide vane, swirl guide vane, etc. The physical diagram and internal structure diagram of this wet dust removal fan for mining are depicted in Figure 1.  
The driving impeller of the wet dust removal fan is rotated by the motor, causing it to suck in the dust-laden air. Simultaneously, the airflow drives the driven impeller to rotate. Spray droplets are emitted from the nozzle located in front of the driving impeller. The rotation of the driving impeller facilitates the mixing of dust and fog droplets. The rear guide vane improves the airflow state and enhances aerodynamic performance. The swirl guide vane induces a swirling flow, causing the mixture of liquid droplets and dust particles to collide with the inner wall of the dust removal fan and converge into a flow.
## 2 Previous Experimental Work Used for FEM
### Test Data on Slim Floor Assemblies
The test was conducted against ISO-834 standard fire exposure conditions (ISO 834) and the beam assembly was heated for 120 min. The second test, WFRC 67756, was conducted on 4 September 1996 on a slim floor assembly formed using an ASB section and a composite slab [5]. The nominal depth and width of the slim floor assembly were 334 mm and 1000 mm, respectively, Figure 2. The ASB section used was 304 mm deep with a nominal width of the top and bottom flange was 190 mm and 300 mm, respectively. Flanges were 20 mm thick while steel web was 18 mm in thickness. A concrete layer of 30 mm thickness, reinforced with A-142 steel mesh was provided above the top flange. During the test, measured dimensions were found to differ from nominal ones for the steel section as given in Table 1. The measured yield strength of structural steel was 392 MPa, higher than the nominal yield strength of 355 MPa. Similar to the previous test, comprehensive instrumentation was done to record temperatures and deflections via thermocouples and LVDTs installed at different positions along the length of the test assembly. Temperatures recordings were taken in concrete and on the steel decking in addition to those on the steel beam section. The external loads, 85 kN each, were applied on the test assembly at four locations through hydraulic rams placed at an intermediate distance of 530 mm as shown in Figure 2b. The applied load and self-weight of the beam assembly induced a bending moment representing the degree of utilisation of 0.390 of its cold capacity. At ambient conditions, the estimated bending moment capacity of the test assembly was 796 kNm, calculated ignoring the enhanced action between steel and concrete. Like in the case of the previous test, the ASB specimen was tested against standard fire exposure conditions and the heating of the beam assembly continued for 90 min.