esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Magnetotelluric Fundamentals  
The theoretical principles of the method lie in [PERSON]'s equations, which describe the behavior of electric and magnetic fields, as well as their interaction.  
Based on the assumptions of this method [6; 21] and the electromagnetic induction phenomena described by [PERSON]'s and [PERSON]'s laws, we define _skin depth_ (\(\delta\)). This concept describes the distance, in-depth, which the electromagnetic wave has traveled when its amplitude has decreased by a factor of 1/e, assuming a homogeneous half-space. This value depends on the resistivity of the medium (\(\rho\)) and period (\(T\)) of the electromagnetic wave. Using SI units, the _skin depth_ is approximated as:  
\[\delta\approx 500\sqrt{\rho T}, \tag{1}\]  
Remembering that period (\(T\)), frequency (\(f\)), and angular frequency (\(\omega\)) are related by \(T=1/f=2\pi/\omega\), we can use this last equation to analyze the behavior of the resistivity (\(\rho\)) as a function of the period (\(T\)), and thus obtain a first approximation of the depth of investigation. This depth (\(\delta\)) is used to approximate the magnetotelluric data.  
Transfer Function and Apparent Resistivity \(\rho_{a}\)  
The transfer function term involves an Earth model describing a linear system with a predictable input and output. Here, the transfer function \(C\) of Schmucker-Weidelt [6; 22; 23] was used, which depends on the frequency and can be calculated from the field measurements in their orthogonal directions--in other words, by measuring the electric field in the E-W component (\(E_{x}\)-HQE) and the magnetic field in the N-S component (\(B_{y}\)-HFN), or with their equivalents (\(E_{y}\)-HQN, \(B_{x}\)-HFE), as follows:\[C(\omega)=\frac{E_{x}}{i\omega B_{y}}=-\frac{E_{y}}{i\omega B_{x}} \tag{2}\]  
Then, we can calculate the \(\rho_{a}\), defined as the resistivity's average of a homogeneous half-space. Like \(C\), the \(\rho_{a}\) is also expressed as a function of frequency [6]:  
\[\rho_{a}(\omega)=|C(\omega)|^{2}\mu_{0}\omega, \tag{3}\]  
where \(\mu_{0}\) is the magnetic permeability of free space.  
In this study, we use the apparent resistivity \(\rho_{a}\). However, it is essential to note that for MT soundings, being \(C\)-complex, it is also important to obtain the phase parameter, which, like these previous functions, will also depend on the frequency:  
\[\phi_{1-D}(\omega)=\tan^{-1}\bigl{(}E_{x}(\omega)/B_{y}(\omega)\bigr{)} \tag{4}\]  
Note that these MT parameters are usually plotted as a function of the period (\(T\)), as seen in the Results and Discussion Section.
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
## 3 Data and Methods
### Calibration and Core Sampling  
Simultaneously with the GPR data acquisition, cores were also extracted for calibration (Figure 1). In all, 18 cores were extracted from Section I. Twelve cores were extracted after laying the base course layer, and six cores were extracted after laying the binder course. In terms of Section II., twelve cores were extracted after laying the binder course, as shown in Figure 1. GPS recorded the position of the cores. The cores were subjected to laboratory measurements using a SIR 3000 GPR system with a 1 GHz ground-coupled antenna. For the measurements, the cores were placed in a plastic box with a metal plate on the bottom, leading to a complete base reflection. The plastic box was then filled with crushed andesite to lessen wave diffraction and ensure that the ground-coupled 1 GHz antenna was placed on a flat surface. The \(\varepsilon\) was calculated using the TOF method.
## 2 System
### Adjustment of Data Obtained from Sensors
#### 2.2.2 Angular Velocity Sensor  
The values obtained from the angular velocity sensor require offset adjustment.  
Offset Adjustment  
When the sensor is placed and kept stationary, all the values of the angular rate sensors should be 0. In practice, however, a deviation exists in either the positive or negative direction. Therefore, the offset must be set to a value with the opposite sign to the deviated value. The method for determining the offset is the same as in the case of acceleration (Section 2.2.1).
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission
### Comparison of Power Loss Factors  
As discussed in the previous sub-sections, there are several power losses experienced in FSO links. While geometrical loss (see Section 2.1) and atmospheric attenuation (see Section 2.2) determine the average received power, turbulence-induced fading (see Section 2.3) and pointing error (see Section 2.4) affect the instantaneous received power values. In this section, we present our main observations:  
* For very low visibility (e.g., \(V=1\) km), the attenuation losses caused by fog exceed 100 dBs and will dominate all other losses. (See Figure 6a).
* In clear weather conditions, the losses are typically a few dBs, which are comparable to the losses caused by rain, weak pointing error, and atmospheric turbulence;
* In moderate visibility, the losses are typically several dBs. These are comparable to the power losses caused by strong pointing errors;
* The geometrical loss for distances of tens of kilometers falls within the range of losses caused by clear weather's atmospheric attenuation and rain. However, for satellite links, geometrical losses can reach tens of decibels, which is significantly greater than the rain loss (i.e., several decibels). For such long distances, it is important to consider the combined impact of geometrical loss and strong pointing errors;
* Rain-induced losses are independent of the wavelength, whereas both fog-induced losses and turbulence losses vary with the wavelength;
* Longer wavelengths tend to experience lower turbulence-induced losses compared to shorter wavelengths. For all cases, the turbulence loss is a few dBs on average.
## 4 Numerical Analyses Result and Discussion
### Effect of Using EPS Together with Thermoplastic Pipe  
The stress values determined around the reference pipe were less than the 30 m fill stress (30 \(\times\) 20 = 600 kPa) with a unit volume weight of 20 kN/m\({}^{3}\). This shows that some positive arching develops due to the vertical deflection of the thermoplastic pipe under the applied stress. However, when EPS Geofoam is used in the ITI and ETI models, the stresses determined at the pipe crown are smaller than the reference. It can be said that the use of EPS Geofoam material together with the pipe increases positive soil arching regardless of pipe deflection and has a positive effect on pipe behavior.  
The uniform placement of the backfill material around the pipe affects the local and general stability of the pipe. While using mechanical tools to place and compact the backfill, care was taken not to damage the pipes. The backfilling process was controlled to prevent  
Figure 16: The 1.524 m diameter HDPE pipe deflections. (**a**) Horizontal. (**b**) Vertical.  
excessive and asymmetrical deformation. Non-uniform stresses around the pipe can cause non-uniform deformation, local bending, and damage by exceeding the performance limits. [PERSON] et al. [1] emphasized that pipe deflection and strain vary according to the type of backfill material and the compaction equipment's efficiency, especially the uniform placement of the backfill around the pipe. Buried pipe with EPS Geofoam in the ETI model will not require very controlled backfill placement, and more uniform stress distribution will be created around the pipe than in the ITI model.  
According to [PERSON] et al. [31], the flexible pipe at the low embankment thickness will deform elastically and form the soil arching itself without the necessity of any soft inclusions. However, under high embankment fills, the use of EPS at the top of thermoplastic pipes can help with load reduction and safe design.  
In [PERSON] et al. [43], it is stated that the ITI method provides a better reduction in stress and deflection around the corrugated steel pipe crown, while the ETI method provides a better reduction in soil stress around the pipe spring line.
# Numerical Study on Dynamic-Stall Characteristics of Finite Wing and Rotor
## 1 Introduction
In 1995, [PERSON] [20] developed an upwind-biased iterative-flow solver to investigate an unsteady 3D flowfield over an oscillating wing. [PERSON] [21] researched the 3D dynamic-stall characteristics of a wing by using the CFD method. Numerical results indicated that an \(\Omega\)-topology vortex formed due to the interaction of the wing-tip vortex and dynamic-stall vortex. It was also illustrated that the geometry of the tip had significant influence on the dynamic-stall characteristics. [PERSON] [22] investigated the dynamic-stall characteristics of a rotor in forward flight by employing an experimental and a computational method, and this research captured the radial-jet layer caused by radial acceleration. In 2012, [PERSON] [9] conducted a rotational-effect investigation on deep dynamic stall by using CFD. The numerical results illustrated that rotational effects can reduce aerodynamic-load peaks and cause earlier airflow reattachment. Later, [PERSON] [23] simulated the dynamic-stall phenomenon on an oscillating finite wing. The numerical results were in good agreement with the experimental results, and revealed an \(\Omega\)-shape vortex. [PERSON] [24] simulated the dynamic-stall characteristics of a finite wing by employing CFD. It was indicated that the numerical method could correctly capture the physical characteristics of dynamic stall. [PERSON] [25] also investigated the dynamic stall of a finite-span wing by employing NASA's OVERFLOW flow solver and ONERA's elSA flow solver. The numerical results indicated that the transition model could improve static-angle prediction, but it could also lead to separated-flow overpredictions. Recently, [PERSON] [26] performed an investigation on the spanwise end effects on the dynamic stall of a finite wing. It was illustrated in this study that spanwise end and aspect ratio can both significantly affect the dynamic-stall flow structure. However, the influence of spanwise flow on the dynamic-stall vortex was seldom considered in previous studies. As a result, some dynamic-stall potential problems of a rotor still exist due to the 3D effects. In order to obtain a deeper understanding of these 3D effects on dynamic stall, the unsteady aerodynamic characteristics of a finite wing are studied under spanwise flow and nonspanwise flow by using the URANS CFD method. A study on unsteady aerodynamic characteristics of a rotor in forward flight was also performed.
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 4 Results and Discussion
### POD Residuals Analysis  
Because the change in daily residuals of POD is basically similar for the HY2D satellite, we present the residual variation of observations for two days of data calculated by the above method and model strategies. Additionally, the selected days are, respectively, July 10 and 21, 2021 for BDS and GPS. Figure 4 shows the residual variation in spaceborne BDS phase observations from HY2D satellite on 10 July 2021. As can be seen from the figure, about 96.41 percent of residual values are located within \(\pm\)15.00 mm. The average value and standard deviation of residuals are 0.036 mm and 9.12 mm, respectively, which show that these residuals have no significant deviations.  
Moreover, Figure 5 shows the residuals variation in spaceborne GPS phase observations from the HY2D satellite on 21 July 2021. As can be seen from the figure, about 97.21 percent of residuals values are located within \(\pm\)15.00 mm, similar to BDS. After the statistical calculation of these residuals, their average value and standard deviation are 0.026 mm and 8.53 mm, respectively, and there are also no obvious systematic errors in the residuals of GPS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Project** & **Selection/Description** \\ \hline \multicolumn{2}{c}{SLR validation for the calculated HY2D orbit} \\ \hline Cut-off angle & 20\({}^{\circ}\) \\ SLR station coordinate & SLRF2014 \\ Troposphere delay correction & Mendes-Pavlis delay model [37] \\ Relativity correction (propagation path) & IERS Conventions 2010 [34] \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: Variation of residuals in the POD for HY2D using spaceborne BDS data.
# A Personalized Recommendation Algorithm Based on the User's Implicit Feedback in E-Commerce
## 3 User's Similarity Calculation by Combining User's Purchase Behavior and Comparison Behavior
### User's Similarity Calculation  
Each user has their own item-pair sets, and different item-pairs have different weights for the user, to calculate user's similarity. An example is shown in Table 4.  
In the field of natural language processing (NLP), the TF-IDF algorithm is a statistical method to assess the importance of the same word for one file or one corpus. The corpus is similar for user groups. Therefore, in this paper, we tried to apply TF-IDF to a recommendation algorithm. We regarded the user groups as a corpus, the user's item-pair sets as one file, and an item-pair as a word.  
**Definition 3**.: _Item-pair Frequency (IPF). This concept describes the times that an item-pair appears in the item-pair sets of a user. The larger the number of times is, the more important the item-pair is to the user._  
**Definition 4**.: _Inverse item-pair Frequency (IIPF). This concept describes how many user's item-pair sets contain the item-pair. The larger the number of times that an item-pair appears in all user's item-pair sets, the worse the item-pair is for distinguishing the user when calculating the user's similarity._  
Figure 1 shows the process of IPF-IIPF, and the weight of the item-pair for each user can be calculated by Equation (3):  
\[IPF-IIPF_{w(p_{i},u_{j})}=IPF_{(i,j)}\times IIPSF_{(i,j)}=\frac{n_{p_{i},u_{j}} }{\sum_{k}n_{p_{k},u_{j}}}\times\log\Biggl{(}\frac{|U|}{1+|j:p_{i}\in u_{j}|} \Biggr{)}, \tag{3}\]  
where \(p_{i}\) means an item-pair, \(u_{j}\) means a user, \(n_{p_{i},u_{j}}\) means the number of occurrences of \(p_{i}\) in the item-pair set of \(u_{j}\), and \(|U|\) means all users.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**User** & **Item-Pairs Set** \\ \hline \(u_{1}\) & \(P_{(u_{1},a>b)}\), \(P_{(u_{1},a>c)}\), \(P_{(u_{1},c>b)}\), \(P_{(u_{1},c>d)}\)....... \\ \(u_{2}\) & \(P_{(u_{2},a>b)}\), \(P_{(u_{2},c>a)}\), \(P_{(u_{2},c>b)}\  
Then, the user's similarity can be calculated by Equation (4):  
\[sim_{pair\_item}(u,v)=\frac{\sum_{p_{j}\in IP_{u,v}}^{k}\left(w_{u,p_{j}}-\overline {w_{u}}\right)\left(w_{v,p_{j}}-\overline{w_{v}}\right)}{1+\sqrt{\sum_{p_{i} \in I_{u}}^{n}\left(w_{u,p_{i}}-\overline{w_{u}}\right)^{2}}\sum_{p_{j}\in I_{v }}^{n}\left(w_{v,p_{j}}-\overline{w_{v}}\right)^{2}}, \tag{4}\]  
where \(IP_{u,v}\) means the common item-pairs of user \(u\) and user \(v\), \(p_{i}\), \(p_{j}\) means the item-pair, and \(\overline{w_{u}}\) means the average weight of the user's item-pairs.
## 3 Application of Modified Rockfall Hazard Rating System  
### Study Area  
The study area is located in the Kohistan district and is a 78 km stretch of the KKH in the Indus Valley. (Figure 3). The study area is bounded within the longitudes 72\({}^{\circ}\)51\({}^{\prime}\)49.44\({}^{\prime\prime}\) E, 73\({}^{\circ}\) 13\({}^{\prime}\)19.59\({}^{\prime\prime}\) E and latitudes 34\({}^{\circ}\)54\({}^{\prime}\)42.48\({}^{\prime\prime}\) N, 35\({}^{\circ}\)16\({}^{\prime}\)22.46\({}^{\prime\prime}\) N. Altitude in the area ranges from 568 m ASL at Besham to 4888 m snowy peaks on both sides of the Indus River. The world's ninth highest mountain peak, Nanga Parbat, with an elevation of 8125 m, is located in this region. Nanga Parbat Haramosh Syntax, located over a peninsula of the basement of the Indian plate, divides the Kohistan and Ladakh Island Arc terranes. The valleys in Kohistan have steep slopes and thin dispersed alpine-type vegetation, with snow cover mostly in the winter season [51]. The Indus River is the main drainage artery of the area.  
Figure 3: (**A**) Regional tectonic framework of the northwest Himalaya. The study area is shown with a red box. (**B**) Location map and point inventory of 30 rockfall stations in the study area. Active faults, Kamila-Jal structural features, and seismicity are the dominant factors controlling the distribution of rockfalls from the Besham to Dasu section.  
The study area boundaries fall in the upper and Lower Kohistan districts and is categorized as being in the medium-rainfall region where average annual rainfall is in the range of 600-1000 mm. Monsoon in this region usually arrives in early June, but its cycle can vary due to climate change patterns.  
The stretch from Besham to Dasu is vulnerable to catastrophic flash floods every year during monsoon, and its frequency increases with the rapid weather change. Due to the lack of early warning systems, infrastructure, community training for advanced mitigation options, and financial constraints, the adaptive capacity of Pakistan to natural disasters, including flood hazards, Glacial Lake Outburst Flood (GLOF), and landslides is very limited [52]. According to the 2019 Provincial Disaster Management Authority (PDMA) of Khyber Pakhtunkhwa province, 32%, 22%, and 60% of the Upper, Lower and Pattan Kohistan areas, respectively, are at risk of flash floods. Intensive short-duration rainfall coupled with a steep slope and rugged topography initiates these flash floods as well as other mountain hazards, including rockfalls and debris slides.
## 3 Results and Discussion
### The Behavior of VOCs in the Food Samples After Irradiation
#### 3.2.2 Ketones  
The compound 2,3-butanedione, with an intense oily odor, is primarily formed by the degradation of proteins and by autolysis of the glycogen polysaccharide present in the  
Figure 5: Dependencies of the relative concentration of the aldehydes acetaldehyde (**A**), hexanal (**B**), and butanal,3-methyl (**C**); ketones 2,3-butanedione (**D**) and acetone (**E**); the alcohol ethanol (**F**); and sulfur compound methanethiol (**G**) on irradiation dose.  
muscle tissue of animal products [45]. Most products with a high protein content, such as beef, chicken, and salmon, demonstrated an increase in the concentration of 2,3-butanedione with a higher dose. An 80-90% decrease in the concentration of 2,3-butanedione in turkey samples with an increase in the irradiation dose ranging from 0.25 kGy to 2 kGy may result from its oxidation during irradiation, and at the same time, 2,3-butanedione may also form due to oxidation of alcohol 2,3-butanediol present in the non-irradiated turkey samples.  
The compound 2-butanone,3-hydroxy, which was identified only in turkey meat samples, has a strong buttery-creamy odor that can be attributed to enzymatic processes in the product or oxidation of the alcohol 2,3-butanediol [46; 47]. It was found that 2-butanone,3-hydroxy content was reduced by 40-90% with an increase in the irradiation dose, which may indicate effective suppression of microorganisms potentially present in chilled turkey meat.  
Acetone, a ketone resulting from fatty acid peroxidation, was detected in all food samples, and showed different dependencies of the concentration on the irradiation dose. In beef samples, acetone content decreased by 10-30% when the beef samples were irradiated with a dose ranging from 0.25 kGy to 5 kGy. In salmon, however, a 60% increase in the acetone concentration was observed at 2 kGy, which further decreased to the level of the non-irradiated salmon samples with an increase in the irradiation dose up to 5 kGy. The acetone concentration showed a 50% decrease in turkey samples irradiated with doses ranging from 0.25 kGy to 2 kGy; however, acetone content demonstrated no significant changes with respect to the non-irradiated samples after irradiation within 0.25-2 kGy. It should be noted that plant products, such as potatoes, showed a 40-60% increase within 0.1-1 kGy.
## 5 Conclusions  
The present study showed that running kinematics can be predicted with ANNs consisting of GPS-based IMU, treadmill-derived running speed, and anthropometric data. Accurate estimations of sagittal plane joint/segment angles and peak segment velocities were achieved. The highest estimation accuracy was found for flexion/extension angles of the thorax, pelvis and hip, and peak thigh flexion/extension and vertical velocities. Our findings offer sports science and medical practitioners working with this data a method of conducting field-based analyses of running kinematics. The proposed method could have practical implications for measuring biomechanical variables associated with performance and injury rehabilitation in the field.  
The contributions by the authors are as follows: Conceptualization, M.L., R.A.N., R.N. and N.C.; Methodology, M.L. and R.N.; Software, M.L. and R.A.N.; Formal Analysis, M.L., R.N. and R.A.N.; Writing--Original Draft Preparation, M.L. and R.N.; Writing--Reviewing and Editing, R.A.N., R.N. and N.C.; Visualization, M.L.; Supervision, R.N. and N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Ethical clearance for this testing procedure was granted by the Staffordshire University ethical committee (Reference number: LAWSON-TAGBIB-RN, approval date: 13 June 2018).  
**Informed Consent Statement:** All participants gave informed written consent prior to testing.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Middlesbrough Football Club. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.