esa-sceva/satcom-chunk-collection huggingface dataset:
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods
### CoVe-Pol Model for Right Circular-Vertical (RV) Polarization  
Figure 4 shows the flowchart for the derivation process for the CoVe-Pol model for RV polarization data. As with all GMF functions, an empirical functional relation is used to establish the dependency  
Figure 3: Distribution of months of the collocated data used in this study.  
Figure 2: Distribution of buoy locations used in this study. We indicate the number of synthetic aperture radar (SAR) images overlaying the respective buoys in the legend.  
of the normalized backscatter on wind speed, wind direction, and the incidence angle [5]. The general form of the CMOD function is summarized as:  
\[\begin{split}&\sigma^{0}(\theta,U_{10},\varphi)=\\ & B(c_{0},U_{10},\theta)[1+B1(c_{1},U_{10},\theta)\cos(\varphi)+B2 (c_{2},U_{10},\theta)\cos(2\varphi)]^{1.6}\end{split} \tag{1}\]  
where \(\sigma^{0}\) is the NRCS in linear units, \(\varphi\) is the relative wind direction, which is the angle between local wind direction and radar look direction (both relative to north), \(U_{10}\) is the statistically neutral wind referenced to 10 m height, \(\theta\) is the incidence angle and \(B0\), \(B1\), \(B2\) are coefficients depending on \(U_{10}\), \(\theta\), the radar frequency and polarization. The dominant term, \(B0\), sets the speed scale for a given measurement. The upwind-crosswind asymmetry term \(B2\) allows for a determination of the wind direction, and \(B1\) is used to resolve the remaining \(180^{\circ}\) ambiguity in the wind direction. Coefficients \(c_{i}\) complete the definition of the terms \(B0\), \(B1\), \(B2\). Detailed expressions are shown in Appendix A.
# Preliminary Analysis of Skin Temperature Asymmetries in Elite Young Tennis Players
## 1 Introduction  
Infrared thermography (IRT) is a non-invasive, rapid, safe, and objective method to measure skin temperature (Tsk) through body radiation [1]. This technology has been applied in sports science in different areas, such as injury prevention, thermo-physiology, sports wearing or technical pattern studies [2, 3]. Focusing on asymmetries, researchers have found that contralateral thermal asymmetries over 0.5 \({}^{\circ}\)C can be related to injuries, considering physiological dysfunctions [4, 5]. Moreover, thermal asymmetries are used to monitor muscular involvement in sports technical actions in symmetrical and asymmetrical sports [1].  
Tennis is an asymmetrical sport in which repetitive movements, such as service or groundstrokes, are executed repeatedly during the game [6]. In the upper limbs, significant differences in anthropometric parameters (muscle circumferences and bone diameters) between the dominant and non-dominant arms have been found [7]. As a result, musculoskeletal disorders may appear, causing injuries such as tennis elbow, muscle strain or shoulder injuries, among others [6]. To the best of the author's knowledge, few studies have applied infrared thermography to address thermal asymmetries in tennis players.  
Thermal asymmetries in the upper limbs associated with the sports technique were found in handball and archery [1, 8]. In archery, Tsk was higher on the side where the bow was placed due to greater muscle activation to maintain the shooting position, and only 15% of the athletes presented asymmetry higher than 0.5 \({}^{\circ}\)C [8]. In handball, significantly higher temperatures were found in the dominant arm but without exceeding the established threshold of 0.5 \({}^{\circ}\)C [1, 4]. In the case of symmetrical sports such as scullers or running, no significant differences were found in the Tsk symmetrical area comparisons [1, 2].  
This preliminary study aimed to assess Tsk asymmetries before and after on-court training in elite young tennis players and to analyse the correlation between these asymmetries and demographic data, racquet characteristics, and pain and fatigue variation. We hypothesised that tennis players' Tsk would present thermal asymmetries in post-training measurements and higher Tsk values in the dominant arm due to the technical characteristics of the sport.
###### Abstract  
This study evaluates the potential of biomass fuels (10 wt% and 20 wt%) as partial coal replacements in combustion and their effects on clinker performance. Cement was produced by co-grinding clinker with gypsum, and hydration products were analyzed. Potassium and sodium carbonates were introduced to create highly alkaline conditions, thereby simulating the effect of alkali metals in biomass-derived fuel ash on the mineral phases of clinker under high substitution ratios. The results showed biomass fuels' low ignition point and high volatile matter content improved mixed fuels combustion, increasing the average combustion rate by 0.52%-2.28% and reducing the ignition temperature by up to 56 \({}^{\circ}\)C. At low substitution levels, biomass ash did not adversely affect clinker mineral composition or cement properties. However, the highly alkaline environment suppressed the formation of tricalcium silicate (C\({}_{3}\)S) in the clinker, resulting in an increased content of free calcium oxide(f-CaO). Simultaneously, it promotes the formation of sulfates (K\({}_{2}\)SO\({}_{4}\), Na\({}_{2}\)SO\({}_{4}\)) and sodium silicate (Na\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)).  
Keywords: biomass fuel; combustion characteristic; high alkalinity; alkali content; hydration and microstructure +
Footnote †: journal: Applied sciences
## 3 Methodologies
### Stengography  
Steganography is considered the method of hiding a secret message into a carrier signal such as text, audio, image, or video, mainly to avoid the perception of this secret message from unauthorized parties. The important information, in this case, is not the carrier but the hidden message. There are several methods used to apply steganography in all types of carriers but for the sake of the topic of this paper, we have limited our discussion only to techniques applied in images. In this type of steganography, an image serves as a cover and we can embed into it a secret message. The secret message can be embedded because of image processing operations such as lossy compression, quality alteration format conversion, palette modification, filtering, etc. [28; 29]. In all cases, the stego object (hidden information) must be small enough not to raise any suspicion that the cover has been altered. Watermarking similarly to Steganography uses the same principles. The only difference is that the message used by watermarking is to identify the ownership of the image; meanwhile, in Steganography the message has no relationship with the cover image; most often this cover lies far from the meaning of the secret message. Furthermore, in watermarking, the secret message should be imperceptible in the meaning that should be perceptually indistinguishable from the original work for a human observer, a very important aspect of the cover.  
The stenographic techniques can be implemented in both domains, spatial and frequency domains. The least significant bit (LSB) method, statistical methods, distortion technique or transform domain techniques are some of the main methods to implement steganography. In the spatial domain, most of the techniques alter one or more bits of each byte of the cover image. The most known technique is LSB (least significant bit). It consists of replacing the last bit of each pixel with the message information according to a specific pattern such as column by column, row by row, or in a more advanced approach a random number generator is used for embedding such as ex. zig zig zig. The application of watermarking in the spatial domain is low cost, low complexity and needs fewer low system requirements. However, these methods lack robustness [28]. Steganographic and Watermarking applications in the frequency domain can be applied in several transformations such as Discrete Wavelet, Discrete Cosine or Discrete Fourier Transform. In all cases, the transformation coefficients can be altered to embed the watermark in the image. Each of these transformations has its strengths and weaknesses. DWT makes use of the characteristic of the human visual system but has higher complexity compared to DCT; DCT transformation offers robustness against compression and DFT has an advantage over the others in terms of robustness during rotation, scaling or translation [28; 30].
# Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
## 3 Enhanced YOLO v5 Model
### Attention Mechanism
#### 3.2.2 ECA-Layer Module  
Considering the requirements for model detection performance, the efficient channel attention mechanism ECA-Layer is inserted into the network to effectively enhance model performance. Due to its lightweight design, it does not lead to a significant increase in model parameters. Within the ECA-Layer module, the input feature information is first globally average-pooled to obtain weight information corresponding to channels. Subsequently, it is processed through one-dimensional convolution, followed by treatment with a Sigmoid activation function. The obtained channel attention weights are then multiplied with the original input feature information to generate the output feature information, as illustrated in Figure 7. The ECA-Layer mechanism enhances the performance of convolutional neural networks in computer vision tasks such as image classification and object detection by autonomously learning and selecting critical channel information, amplifying the importance of valuable features, reducing computational overhead, and expanding the network's receptive field.  
For the size of the convolution kernel k, we follow the following process for formula derivation. There is a certain mapping relationship between the one-dimensional convolution kernel size \(k\) and channels \(C\), as shown in Equation (5):  
\[C=\phi(k) \tag{5}\]  
The simplest functional relationship is a linear relationship, as shown in Equation (6):  
\[\phi(k)=\gamma*k-b \tag{6}\]  
Figure 6: PSA module structure.  
Figure 7: ECA-Layer structure.  
Since the number of channels is typically set to a power of 2, we introduce this linear function into a potential nonlinear solution, as shown in Equation (7):  
\[C=\phi(k)=2^{(\gamma\kappa k-b)} \tag{7}\]  
Thus, we reverse calculate the convolution kernel size \(k\) and perform rounding, as shown in Equation (8):  
\[k=\varphi(C)=\left|\frac{\log_{2}C}{\gamma}+\frac{b}{\gamma}\right|_{odd} \tag{8}\]  
where \(\mid\cdot\mid_{odd}\) is the nearest odd number, and \(\gamma\) and \(b\) are the hyperparameters (\(\gamma=2\), \(b=1\)).
# Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment
## 3 Materials and Methods
### Eye-Tracking Measurement and Data Processing  
Visual attention was measured using the eye-tracking device Tobii EYEX [36], which collects eye gaze at 60 Hz. The EyeX SDK for Unity was used to integrate data collection in the virtual environment. The SDK provides user fixations, which is defined as the action of fixing one's eyesight on a particular point of the environment. Number and duration of fixations as well as the established a priori AOIs were interpreted by Unity; it analyzed whether the participant fixed their sight on a particular virtual agent, while considering the agent's role at that moment. Specifically, for each eye-tracking (ET) variable, two feature roles were defined in accordance with the situation and virtual agents. Those roles were (a) basic and (b) behavioral. Basic roles consisted of 3 ET features (active, passive, and background) captured during the entire 3 DSG. Active role refers to the virtual agents and/or virtual elements that were the protagonist in a situation; passive role refers to the virtual agents and/or virtual elements that are present in the situation but without a protagonist role in that situation; and background role refers to the remaining virtual elements in the virtual scenario (Figure 2).  
In addition, five behavioral roles were defined, depending on the situation and on the specific behaviors displayed by each virtual agent in each situation. Those behavioral roles were (a) informative role--participant eye gaze focused on the virtual agents from whom the participant could obtain more information to make the decision; (b) empathy role--participant focused their gaze on those agents who explicitly asked for help or agents who had emotional needs; (c) decision-making role--participant focused eye gaze on those virtual agents with whom they could make a direct decision; (d) authoritarian role; (e) supportive role--participant focused eye gaze on the agent with supportive personality traits. We note that these roles could change throughout the game. For example, Figure 3 shows a situation in which the strategist ([PERSON]) informs the other agents that she wishesto return to the hotel to pick up something; she does not wish to elaborate on it and asks the other agents to consider her feelings (show empathy).  
Several variables were extracted during the course of the game (see Table 1). Specifically, 29 variables pertained to the decisions made by participants in the 10 different situations. These included aspects like which action they chose to perform in a particular situation, which character they preferred to perform an action with, and the number of times they asked the characters questions during a particular situation. Fifty-six variables were extracted from the eye-tracking sensor. These variables collected four different metrics (see Table 2), which could be related to either which virtual agent the participant was looking at or to the role the virtual agent had in that moment.  
Figure 3: Example of behavioral roles.  
Figure 2: Basic roles: active and passive.
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
## 2 Material and Methods
### Summary of the Followed Methodology  
The present study investigates a stand-alone energy system that feeds the total electrical loads of a building. The studied building is placed in Athens, Greece and its thermal loads are calculated using the TRNSYS software [22]. The weather data of the present work have been extracted by the TRNSYS libraries which include weather data from Meteororm. The typical meteorological year (TMY) is used for the location of Athens. Below, some critical results for the weather conditions of the studied location taken from TRNSYS libraries are given. Figure 3 shows the variation in the environment temperature in the studied location, while Figure 4 shows the solar irradiation on the tilted surface for the same location. Moreover, Figure 4 depicts the respective cumulative solar energy expressed in [kWh/m\({}^{2}\)]. It is obvious that Athens has a high solar potential with 1739 kWh/m\({}^{2}\) on a yearly basis.  
Figure 3: The environment temperature variation for the studied location (Athens, Greece).  
The calculation methodology of the electricity for covering the DHW, appliance, and lighting has been described in the previous subsections. Photovoltaic collectors are used for covering the electricity needs of the building and there is a hydrogen storage unit based on an electrolyzer and a fuel cell. Practically, the surplus electricity production from the PV is converted into hydrogen by electrolyzing water. The hydrogen is stored under high pressure and when there is a need, then it feeds a fuel cell for producing electricity for covering the building's demands. In the present work, the first step was the calculation of the thermal loads and the total energy needs for electricity with the TRNSYS tool. The second step was the investigation of the overall configuration together by using the provided equations in a homemade code by exploiting the weather data from TRNSYS libraries for the PV production calculations.  
It is important to state that a critical parameter is an initially stored quantity in the storage tank of the hydrogen, as well as the estimation of the PV area in order to provide a proper system sizing. These two parameters were calculated internationally by taking into account the following constraints: (i) The hydrogen storage quantity at the start of the year to be the same as at the end of the year, and (ii) The minimum stored quantity to be positive in order to cover the building needs successfully. These constraints ensure a proper operation without oversizing the system.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 5 Simulation Results and Analysis
### Outage Probability Simulation Results and Analysis
The plotted curves depict the theoretical and simulated system OP on the average SNR for atmospheric transmission distances of 200 m and 500 m, with the underwater distance fixed at 20 m. The simulation results from the figure clearly indicate that increasing the transmission distance of the FSO link will deteriorate the performance of the system OP. This degradation occurs because a longer FSO link increases the path loss, which, in turn, reduces the received SNR of the system. Figure 4b depicts the OP as a function of the \(\overline{\gamma}\), with the atmospheric transmission distance fixed at 200 m and underwater distances set to 20 m and 30 m. Pointing error  
Figure 4: Effect of different link transmission distances on the outage probability of FSO-UWOC systems: (**a**) changing the FSO link transmission distance \(Z\); (**b**) changing the UWOC link transmission distance \(d\). Figure 3: Outage probability under different pointing errors for mixed FSO-UWOC system under IM/DD scheme (\(\xi_{1}=1.14\), \(\xi_{1}=1.52\), \(\xi_{2}=4.0\)). parameters for both hops are configured as \(\xi_{1}=\xi_{2}=4.0\), representing weak pointing error conditions. Simulation results indicate that increasing the transmission distance of the UWOC link causes the deterioration of the OP performance, which is mainly reflected at low system average SNR. As \(\overline{\gamma}\) increases, the impact of extended UWOC distance on end-to-end OP diminishes, approaching zero in the high-SNR limit. The reason for this behavior can be traced to the asymptotic formula of the OP given by Equation (34). It is clear that the asymptotic line is determined by five system fading parameters--\(\alpha\), \(\beta\), \(ac\), \(\xi_{1}^{2}\), \(\xi_{2}^{2}\)--which characterize the atmospheric and oceanic turbulence, as well as the pointing errors of the two hops. In the simulation, the pointing errors of the dual hops were set at the same value, \(\xi_{1}=\xi_{2}=4.0\), resulting in \(\xi_{1}^{2}\) and \(\xi_{2}^{2}\) being much larger than the other three parameters (with \((\alpha,\beta)=(3.99,1.70)\), \(ac=3.60\)). Consequently, in the high-SNR region, the asymptotic OP is dominated by the atmospheric turbulence parameter \(\beta\)--specific to the FSO link--and becomes independent of UWOC link parameters, including transmission distance. The close alignment between theoretical and asymptotic curves across different detection schemes in the high-\(\overline{\gamma}\) regime further validates the accuracy of the derived closed-form OP expressions and the asymptotic approximations.
## 5 Performance Analysis and Results  
Key space analysis, UACI, NPCR, neighboring pixel correlation analysis, and histogram analysis are only a few of the statistical and security analysis techniques used. A CT scan of the brain (medical) and other images are chosen with different standard gray-scale test images such as Cameraman, Peppers and Lena for system simulation. The test image is \(256\times 256\) pixels. The OFDM parameters include the following: the total number of OFDM sub-carriers is denoted by \(\left(N_{sc}=256\right)\), the FFT length is set to a 256 bit length, and the \(CP\) is set to a 32 bit length. The suggested system performance is tested under the AWGN channel effect at zero mean \(\mu=0\) and at different values of noise variances, \(\left(\sigma^{2}=0.01,0.05,0.10,0.15,0.20\right)\). Also, the proposed system is tested under different signal processing attacks as Salt and Pepper noise and Speckle noise. Analysis of the histogram, neighboring pixel correlation, key space, NPCR, and UACI tests are examples of statistical and security analysis. The multi-secure simulation parameters are displayed in Table 2. The suggested authenticated secure image transmission system simulation parameters are also shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline SK (Hex.) & 4071A20C3 CB340E95E65 AF06549 CCD16220A \\  & COD998 DEE50C22550 EA6637F5 AFA \\ \hline \(x_{0}\) & 0.918347421094373 \\ \hline \(r_{1}^{f}\) & 10.485174284360704 \\ \hline \(r_{2}^{f}\) & 18.936817384042791 \\ \hline \(r_{3}^{f}\) & 9.195731663827418 \\ \hline \(r_{4}^{f}\) & 3.038618376892133 \\ \hline \(v_{1}\) & 0.843728376417384 \\ \hline \(v_{2}\) & 0.172865272648265 \\ \hline \(v_{3}\) & 0.447162948327648 \\ \hline \(v_{4}\) & 0.728395273521837 \\ \hline \(a\) & \(a=256\) bytes \(\left(a_{1},a_{2},\cdots,a_{256}\right)\) \\ \hline \(b\) & \(b=256\) bytes \(\left(b_{1},b_{2},\cdots,b_{256}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The multi-secure parameters used in the simulations.
## 2 Theory and Formulations
### Modelling Beam Element  
The degrees of freedom for a two-node finite element beam model with a total length of \(L\) are shown in Figure 3. It is assumed that the elastic modulus \(E\), density \(\rho\) and Poisson's ratio \(\
u\) of the beam are constant. Figure 3: A finite element with two nodes for the beam in the local coordinate system. In relation to Euler-Bernoulli beam theory, any point can have an axial and transverse displacement, as written in Equations (21) and (22), respectively. \[u(x,z,t)=u_{0}(x,t)-z\frac{\partial w_{0}(x,t)}{\partial x} \tag{21}\]  
\[w(x,z,t)=w_{0}(x,t) \tag{22}\]  
[PERSON]'s law allows you to write the elastic strain energy of an element in Cartesian coordinates in the Euler-Bernoulli beam frame as in Equations (23) and (24):  
\[U=\frac{1}{2}\int_{V}\left\{\varepsilon\right\}^{T}\{\sigma\}dV \tag{23}\]  
\[U=\frac{1}{2}\int_{0}^{L}\left[A_{xx}\left(\frac{\partial u_{0}}{\partial x} \right)^{2}-2B_{xx}\left(\frac{\partial u_{0}}{\partial x}\right)\left(\frac{ \partial^{2}w_{0}}{\partial x^{2}}\right)+D_{xx}\left(\frac{\partial^{2}w_{0} }{\partial x^{2}}\right)\right]dx \tag{24}\]  
where \(\varepsilon\) is strain components and \(\sigma\) is stress components. The other components of Equation (24) are written as in Equation (25a)-(25d). \[A_{xx}=\int_{A}EdA=Ebh \tag{25a}\]  
\[B_{xx}=\int_{A}E\cdot zdA=0 \tag{25b}\]  
\[D_{xx}=\int_{A}E\cdot z^{2}dA=\frac{Ebh^{3}}{12} \tag{25c}\]  
\[A=\int_{A}dA=bh \tag{25d}\]  
Equation (26) gives the beam's kinetic energy in Cartesian coordinates as a function of its bending at any time. Figure 3:0  
\(\dot{u}\) and \(\dot{w}\) are the axial and transverse velocities, respectively. Extending Equation (26) and reducing the volume integral to a one-dimensional integral can result in Equation (27) being written. Figure 3:1  
where  
Figure 3:2  
Figure 3:3  
Figure 3:4  
As shown in Figure 3, every node has three degrees of freedom and all nodal degrees of freedom are given in Equation (29). Figure 3:5  
To express the transverse displacement (\(w(x,t)\)) and axial displacement (\(u(x,t)\)) with the nodal displacements, shape functions are used in matrix notation. \[\{u\}=[N]_{bx}\cdot\left\{ \begin{matrix}q_{3i-2}\\ q_{3j-2}\end{matrix} \right\}_{u} \tag{30}\]  
\[\{w\}=[N]_{w}\cdot\left\{ \begin{matrix}q_{3i-1}\\ q_{3i}\\ q_{3j-1}\\ q_{3j}\end{matrix} \right\}_{w} \tag{31}\]  
Axial displacement and bending have their shape functions defined by \([N]_{bx}\) and \([N]_{w}\), respectively.