esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
###### Abstract  
The primary challenge in unsupervised learning is training unnormalized density models and then generating similar samples. Few traditional unnormalized models know what the quality of the trained model is, as most models are evaluated by downstream tasks and often involve complex sampling processes. Kernel Stein Discrepancy (KSD), a goodness-of-fit test method, can measure the discrepancy between the generated samples and the theoretical distribution; therefore, it can be employed to measure the quality of trained models. We first demonstrate that, under certain constraints, KSD is equal to Maximum Mean Discrepancy (MMD), a two-sample test method. PT KSD GAN (Kernel Stein Discrepancy Generative Adversarial Network with a Pulling-Away Term) is produced to compel generated samples to approximate the theoretical distribution. The generator, functioning as an implicit generative model, employs KSD as loss to avoid tedious sampling processes. In contrast, the discriminator is trained to identify the data manifold, also known as an explicit energy-based model. To demonstrate the effectiveness of our approach, we undertook experiments on two-dimensional toy datasets. Our results highlight that our generator adeptly captures the accurate density distribution, while the discriminator proficiently recognizes the unnormalized approximate distribution shape. When applied to linear Independent Component Analysis datasets, the log likelihoods of PT KSD GAN improve by about 5% over existing methods when the data dimension is less than 30. Furthermore, our tests on image datasets reveal that the PT KSD GAN excels in navigating high-dimensional challenges, yielding authentically genuine samples.  
hypothesis testing; energy-based model; Kernel Stein Discrepancy; Maximum Mean Discrepancy
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 3 Statistical Techniques in Spectroscopy  
Spectroscopic techniques are increasingly being used as a tool for extracting relevant information in various scientific fields. They enable the identification of substances, analysis of chemical composition, and quality control of raw materials, among other applications [122; 123; 124]. These techniques also play a crucial role in the wine sector, providing powerful tools for detecting fraudulent practices such as mislabeling of geographical origin, adulteration, or misrepresenting grape varieties. Additionally, they provide insights into wine's chemical composition, quality, and sensory properties [1; 125]. Combining information from spectral data, sensory data, and environmental factors can achieve a more holistic understanding of wine and wine byproducts. However, due to the complex and multidimensional nature of the resulting data, advanced statistical and machine-learning techniques are essential for data processing, modeling, and fusion [24; 123; 126; 127]. These techniques ensure reliable interpretation and help to derive actionable insights leading to improved wine classification, quality control, fraud detection, and predictive modeling for producers and consumers.
## Network Setting and Computational Model
### Computation-Offloading Model
#### 3.4.1 sMEC Offloading  
For offloading to a sMEC, the completion time \(T_{mn}^{sbs}\) of task \(I_{m}\) comprises two aspects: transmission time \(T_{mn}^{t}\) and remote computation time \(T_{mn}^{cs}\). Transmission time through the wireless access link when MD \(m\) offloads its task to the sMEC \(n\) is given by  
\[T_{mn}^{t}=\frac{D_{m}}{R_{mn}}. \tag{4}\]  
Let \(f_{mn}\) denote the computing resources of the sMEC \(n\) allocated to the MD \(m\). The computation time required to execute the computational task on the sMEC \(n\) can be calculated as follows:  
\[T_{mn}^{cs}=\frac{C_{m}}{f_{mn}}. \tag{5}\]  
According to Equations (4) and (5), the completion time \(T_{mn}^{sbs}\) can be obtained as follows:  
\[T_{mn}^{sbs}=T_{mn}^{t}+T_{mn}^{cs}=\frac{D_{m}}{R_{mn}}+\frac{C_{m}}{f_{mn}}. \tag{6}\]  
As noted, the energy consumption required to offload computational task \(I_{m}\) for execution at sMEC \(n\) (denoted by \(E_{mn}^{sbs}\)) is equivalent to the energy consumption required to transmit task \(I_{m}\) from MD \(m\) to SBS \(n\), as follows:  
\[E_{mn}^{sbs}=E_{mn}^{t}=p_{m}T_{mn}^{t}=p_{m}\frac{D_{m}}{R_{mn}}. \tag{7}\]
## 4 Results
### Simulation Results  
#### 4.2.1 Effects of the Traffic Load  
Figure 6a illustrates the results of discovery success ratio plotted versus traffic load. The results are evaluated by varying the load. As shown in the figure, the metric of RFAODV is much better than that of AODV. Approximately, there is at least an 18% improvement. DSAODV uses geographic information to limit the forwarding area, and the success rate of route discovery has increased to some extent, but it is not obvious. Because the interference is more from adjacent nodes, if this node is in the forwarding area, most of the adjacent nodes are also in the forwarding area.  
Figure 6b shows packet delivery ratio plotted versus traffic load. The graph results show that the metric decreases as the load increases. The reason is that as the load increases, the probability of conflict increases. The performance of RFAODV is always better than that of AODV and DSAODV, with a performance improvement of about 5%.  
Figure 6c presents protocol overhead plotted versus traffic load. It is clear that the total number of routing control packet of RFAODV significantly less than AODV. Compared with AODV, DSAODV also reduces the protocol overhead. The reason is that RFAODV and DSAODV limits the scope of nodes participating in route discovery.  
Figure 6d depicts the relationship between consumption energy plotted and traffic load. However, with the increase in load, RFAODV has more and more obvious energy-saving advantages.  
Figure 6: Effects of the traffic load. (**a**) Discovery success ratio vs load; (**b**) packet delivery ratio vs load; (**c**) protocol overhead vs load; (**d**) consumption energy vs load.
# Evaluation of Fatigue Life of Asphalt Concrete Mixtures with Reclaimed Asphalt Pavement
## 6 Summary  
Hot recycling of asphalt pavement is currently a very trendy topic in Poland. This article presents the results of the research projects carried out in this field in Poland. As part of the work, tests and analyses were carried out to verify the suitability of bituminous mixtures with an increased content of RAP with regard to fatigue life. Fatigue life was evaluated in terms of mixture durability as well as pavement structure life. The results of laboratory tests of the basic properties of binders and mixtures, as well as the results of advanced tests such as fatigue life and stiffness, are presented. The test results were analyzed and the design life of the pavement structure was calculated by known calculation methods.  
The evaluation of the fatigue life of AC22P and ACWMS16 mixtures with 50% content of reclaimed asphalt indicated their positive properties in this regard. Laboratory tests indicate good fatigue properties of mixtures with RAP. In the case of asphalt concrete AC22P 35/50, fatigue properties were obtained in the range typical for this type of mixture and this binder. However, in the case of ACWMS16 25/55-60, the addition of RAP slightly deteriorated the fatigue properties. The reason for this was unmodified binder in RAP, which largely replaced polymer modified binder (PMB) in the final mixture. Ultimately, the properties of the mixture in terms of fatigue were closer to the results typical for asphalt concrete with unmodified binder. Nevertheless, the mixture fulfilled the requirements of fatigue life according to Polish technical requirements. Regardless of the method used for determining the stiffness or the fatigue criterion, the results of the calculations of the structure design life were higher than the upper limit of the range of the design number of equivalent standard axles for the specific traffic category. The use of bituminous mixtures with reclaimed asphalt developed as part of the project allows the appropriate fatigue life of the pavement structure to be obtained. The results of analyses and tests also indirectly indicate the advantages of an asphalt plant with a separate drum for RAP. With this technology, it is possible to reduce RAP overheating, to limit technological aging process and proper mixing of materials. As a result, a mixture with parameters comparable to a mixture without RAP is obtained.  
In the further part of the project, test sections were made and subjected to testing and observations. This will be described in subsequent publications.  
The tests were performed within the framework of the project entitled: \"Recclaimed asphalt pavement: Innovative technology of bituminous mixtures using material from reclaimed asphalt pavement\" (InnGA) implemented as part of the INNOTECH programme according to agreement No. 225765/NCBR/15 in consortium with the Warsaw University of Technology and the company Budimex S.A.  
The authors declare no conflicts of interest.
# Leading Edge Blowing to Mimic and Enhance the Serration Effects for Aerofoil +
## 2 Experimental Setup
In practice, though there might be pressure loss incurred within the aerofoil internal air channel system, it is assumed negligible. During the quiescent condition (without the mean flow), hot wire sensor was used to measure the spanwise profiles of the air jet mean velocity exiting the straight leading edge aerofoil at several streamwise direction \(x/D\). Note that the coordinate system pertinent to the results in Figure 4b should refer to Figure 2a. Figure 4b shows an example of the velocity profile with the bulk flow rate \(Q\) = 208 L/minute when \(\lambda^{\prime}\) = 5 mm. Each spike in the figures represents the maximum air jet velocity produced by a particular orifice on the leading edge. The measured velocity \(u\) is normalised by \(U_{peak}\), which is the peak velocity measured at \(x/D\) = 1.5 (i.e., closest distance from the orifice exit). This means that the reference velocity is taken as the maximum jet velocity at \(x/D\) = 1.5. The results shown in Figure 4b, therefore, would demonstrate the decay of the maximum jet velocity \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(A\) (mm) & \(\lambda\) (mm) & \(A\)/\(C\) & \(\lambda\)/\(C\) & \(A\)/\(\lambda\) \\ \hline Baseline & - & - & - & - & - \\ \(\lambda\)5\(A\)15 & 15 & 5 & 0.1 & 0.033 & 3 \\ \(\lambda\)5\(A\)30 & 30 & 5 & 0.2 & 0.033 & 6 \\ \(\lambda\)10\(A\)15 & 15 & 10 & 0.1 & 0.067 & 1.5 \\ \(\lambda\)10\(A\)30 & 30 & 10 & 0.2 & 0.067 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometrical parameters of the leading edge serrations. Figure 3: (a) Isometric drawing of the NACA 65(12)-10 aerofoil model with serrated leading edge; (b) comparison between a serrated aerofoil and baseline aerofoil. with respect to the streamwise distance. It can be seen that between \(x/D\) = 1.5 and 4.0, the produced jet from each orifice will decrease by 40%. Further increase of \(x/D\) beyond 4.0 will result in further reduction of the maximum jet velocity, but on a slower rate. In addition, the jet is also found to spread longitudinally, but a fully merged and developed flow would not be attained unless it reaches at least \(x/D\) > 11.5.
# Exchange Flows in Inclined Pipes with Different Viscosity Ratios
###### Abstract  
In this study, we investigated the effects of the viscosity ratio (\(\beta\)) and the angle of inclination (\(\theta\)) on the change in the flow regime of two fluids undergoing exchange flow in a pipe, using a combination of experimental and theoretical methods. In our experiments, we observed that changing either the viscosity ratio or the inclination angle of the pipe causes a change in the fluidity of the two fluids. For the upward-flowing light fluid, we observed four flow regimes: axisymmetric core-annular flow (CAF), eccentric core-annular flow (ECAF), transitional side-by-side flow (eccentric and side-by-side flow at the same time) (TSBS), and side-by-side flow (SBS). In addition, the larger the viscosity ratio, the larger the critical angle at which the flow pattern eventually changes to side-by-side flow. When the tilt angle is larger than \(16^{\circ}\), the flow pattern is a side-by-side flow, regardless of the viscosity ratio, and we find that the viscosity ratio and the inclination angle determine the ratio of the width of the rising fluid to the diameter of the pipe (\(\delta_{\mathrm{s}}\)) and the velocity of the increasing fluid (V). We used the velocity model of the fluid in the pipe to compare with our experimentally measured velocities and found some similarities and differences, which we explained. For the downward-flowing heavy fluid, we divided the viscosity ratios into three ranges for our study and found that the changes in flow regimes were different for different ranges of viscosity ratios.  
exchange flows; inclined pipes; multiphase flow; different viscosity ratios Article
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 5 Transport Management with SDN
### Energy-Saving Based on Traffic  
One of the main challenges in traffic management is energy saving. Managing traffic reduces energy consumption and increases the lifetime of the devices used in the 5G and existing network. The use of an SDN-based MA for managing traffic should consider specific services or applications and energy-saving, which reduces the cost of the public services and applications.  
According to [25], recent research on EE using NOMA within the 5G environment seems to corroborate existing theories. This research used the density function of channel gains, outage performance, and ergodic sum rate with basic theories to improve the EE. In the future, 5G users can justify the claim that a given 5G system is set with an ergodic sum rate; thus, this proposed approach based on these theories will explain why EE is increasing.  
Despite many modulation schemes, employing a novel quadrature space-frequency index modulation in 5G wireless communication will increase the EE and the data rate without any extra cost of energy [26]. Further, this approach can be employed to improve the satellite channels and communication when future 5G wireless systems are deployed in satellite modems.  
According to [27], satellite links and communication require a reliable system with optimal latency and minimal energy consumption. Here, the authors presented a simulated annealing and clustering hybrid algorithm (SACA) to enhance reliability and achieve minimum latency.  
Future 5G+ networks are expected to have a simple and manageable traffic model that should measure the various properties of real-time traffic with maximum accuracy and minimum initial conditions [28].
## 4 Modeling Business Rules
### Transforming Standards into SBVR Rules
#### 4.1.1 Extraction of Conformance Rules from Table  
Let us start with the following Technical Guide table (Figure 3):The presented page above, extracted from the Technical Guide \"The tile roofs\" is a typical example of the difficulties that can be encountered when translating information expressed in natural language to explicit and processable assertions. The example above contains a table with different information relative to the installation of tiles with their dimensional and structural properties. The first column shows different applicable slopes. In each row, we can see for a specific slope, roof covering, and area of installation. There are three different so-called \"climatic zones\" in France (From Zone I to Zone III, mainly based on the average temperature over a year). Each of these zones is divided into three sub categories according to the local situation of the concerned roof. It depends if the roof is exposed or not to the wind or the rain (\"protected\"/\"normal\"/\"exposed\").  
To transform these constraints into SBVR rules we process them in two steps:  
First, we transform the information in rows into textual constraint. Example, from the first row we can extract the information below:  
The applicable tiles slope for a roof covering greater than 8 cm, built in zone 1, in a protected situation is equal to 70%.  
The second step is to rewrite this text into SBVR rules by using the ontology of TDs, the text will read as follows:  
Figure 3: Extract of Technical Guide (TG) “The tile roofs”.  
* If the tile _has_ a slope equal to 70% then it is obligatory that the implementation is in Zone **1**, in a protected situation with a roof covering greater than 8 cm.  
The concepts identified in this fragment are Slope, Zone1 and protected, which belong to the ontology of TD.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 3 Results
### Water Level, Mean Depth, and Volume
Figure 11 shows the time series of inferred volumes for each lake, in \(10^{9}\) m\({}^{3}\) (or 1 km\({}^{3}\)), as well as the total for all lakes. The peak volume (\(26.5\times 10^{9}\) m\({}^{3}\)) was reached in early December 2001. Maximum volumes and the dates of those maxima are listed in Table 7.
## 3 System Modeling
### Orifice Equation of the Mass Flow Rate  
The flow process of the gas passing through the valve port is very complicated. According to the assumptions, the gas flow via the valve port is approximated as one-dimensional isentropic flow of the ideal gas flowing through a orifice [33]. The gas has two states of sonic flow and subsonic flow when passing through the orifice. The mass flow rate of the gas flowing through the proportional valve can be summarized as follows [34].  
\[\dot{M}_{\mathrm{a}}=\left\{\begin{array}{ll}\frac{\mathrm{C}Wx_{\mathrm{v}} p_{\mathrm{b}}}{\sqrt{RT_{\mathrm{s}}}}\sqrt{\frac{2x}{k-1}\left[\left(\frac{p_{ \mathrm{a}}}{p_{\mathrm{b}}}\right)^{\frac{2}{k}}-\left(\frac{p_{\mathrm{a}}} {p_{\mathrm{b}}}\right)^{\frac{k+1}{k}}\right]}&\frac{p_{\mathrm{a}}}{p_{ \mathrm{s}}}\geq c_{0}\\ \frac{\mathrm{C}Wx_{\mathrm{v}}p_{\mathrm{b}}}{\sqrt{RT_{\mathrm{s}}}}\sqrt{ \frac{2x}{k+1}\left(\frac{2}{k+1}\right)^{\frac{2}{k-1}}}&\frac{p_{\mathrm{a} }}{p_{\mathrm{s}}}<c_{0}\end{array}\right. \tag{1}\]  
\[\dot{M}_{\mathrm{b}}=\left\{\begin{array}{ll}\frac{\mathrm{C}Wx_{\mathrm{v}} p_{\mathrm{b}}}{\sqrt{RT_{\mathrm{b}}}}\sqrt{\frac{2x}{k-1}\left[\left(\frac{p_{ \mathrm{e}}}{p_{\mathrm{b}}}\right)^{\frac{2}{k}}-\left(\frac{p_{\mathrm{e}}} {p_{\mathrm{b}}}\right)^{\frac{k+1}{k}}\right]}&\frac{p_{\mathrm{e}}}{p_{ \mathrm{b}}}\geq c_{0}\\ \frac{\mathrm{C}Wx_{\mathrm{v}}p_{\mathrm{b}}}{\sqrt{RT_{\mathrm{b}}}}\sqrt{ \frac{2x}{k+1}\left(\frac{2}{k+1}\right)^{\frac{2}{k-1}}}&\frac{p_{\mathrm{e} }}{p_{\mathrm{b}}}<c_{0}\end{array}\right. \tag{2}\]  
where \(C\) represents the discharge coefficient, \(W\) stands for the area gradient of the spool valve, \(R\) is the gas constant, \(\kappa\) is the ratio of the specific heat at constant pressure to the specific heat at constant volume, \(p_{\mathrm{e}}\) is the atmospheric pressure, and \(c_{0}\) denotes the critical pressure ratio. Then, linearizing Equations (1) and (2) yields  
\[\left\{\begin{array}{ll}\Delta\dot{M}_{\mathrm{a}}=c_{1}\Delta x_{\mathrm{v} }-c_{2}\Delta p_{a}\\ \Delta\dot{M}_{\mathrm{b}}=-c_{1}\Delta x_{\mathrm{v}}-c_{2}\Delta p_{b}\end{array}\right. \tag{3}\]  
where \(c_{1}\) and \(c_{2}\) are both known as zero coefficient. Specifically, \(c_{1}\) is the flow gain, while \(c_{2}\) is the flow-pressure coefficient. Because the spool valve has the same form of the valve port at the left and right ends, the zero coefficients under the initial condition are  
\[\left\{\begin{array}{ll}c_{1}=\left.\frac{\partial\dot{M}_{\mathrm{a}}}{ \partial x_{\mathrm{v}}}\right|&p_{\mathrm{a}}=p_{0}&\approx\left.-\frac{ \partial\dot{M}_{\mathrm{b}}}{\partial x_{\mathrm{v}}}\right|&p_{\mathrm{b}}=p _{0}\\ x_{\mathrm{v}}=0&x_{\mathrm{v}}=0\\ c_{2}=\left.-\frac{\partial\dot{M}_{\mathrm{a}}}{\partial p_{\mathrm{a}}} \right|&x_{\mathrm{v}}=0&\approx\left.-\frac{\partial\dot{M}_{\mathrm{b}}}{ \partial p_{\mathrm{b}}}\right|&x_{\mathrm{v}}=0\\ p_{\mathrm{a}}=p_{0}&p_{\mathrm{b}}=p_{0}\end{array}\right. \tag{4}\]
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 3 Establishing IRT Database
### Establishment of Optimal Infrared Camera Imaging Conditions  
#### 3.3.1 Infrared Camera Imaging Distance  
The dimensions of the specimens used for infrared imaging was 250 mm \(\times\) 200 mm. As a practical consideration, the effective imaging area was set as 190 mm \(\times\) 140 mm, excluding 30 mm from each of the four sides directly affected by open air. For precise testing, the infrared imaging distance was set such that 70% or more of the effective imaging area can be captured, and the number of pixels in 1 mm\({}^{2}\) is 1 or more. In addition, the specification of infrared camera utilized in this research is indicated in Table 6. Ultimately, the imaging distance of the experiments was set at 40 cm, at which 75% of the effective imaging area was captured and the number of pixels was 1.25/mm\({}^{2}\).  
Figure 6: Induced rebar corrosion using the rebar corrosion automation system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cover Thickness (mm)**} & **Expected Corrosion** & **Expected Corrosion** & **Expected Corrosion** & **Expected Corrosion** & **Expected Corrosion** \\  & **Ratio = 1\%** & **Ratio = 3\%** & **Ratio = 5\%** & **Ratio = 7\%** & **Ratio = 10\%** \\ \hline
**10** & 134\%* & 105\% & 98\% & 103\% & 115\% \\
**20** & 57\% & 76\% & 64\% & 61\% & 54\% \\
**30** & 35\% & 73\% & 66\% & 58\% & 44\% \\ \hline \end{tabular}  
* Actual corrosion ratio
* Expected corrosion ratio \(\times\) 100 (average corrosion ratio value for three specimens with the same variables).  
\end{table}
Table 5: Difference between expected corrosion ratio and actual corrosion ratio (preliminary test results).
# Calibration of a Quartz Tuning Fork as a Sound Detector
###### Abstract  
This study compares the performance of a quartz tuning fork (QTF) with a highly sensitive ultrasound microphone in the context of acoustic measurements, applying the substitution calibration method. QTF sensors are increasingly used for high-precision tasks due to their sensitivity and stability, while microphones are still the standard in general acoustic measurements. The aim of this study is to evaluate both technologies across several key performance metrics, including linearity of response, sensitivity, noise characteristics, and acoustic detection limit. Which sensor is better suited to which acoustic and physical condition? The results show that QTFs perform exceptionally well in applications requiring high precision, especially in high-frequency and narrow-band measurements. The signal-to-noise-ratio (SNR) of the QTF at its resonance frequency is 14 dB higher than that of the microphone, whereas the detection limit and linearity are comparable. The findings suggest that QTF sensors are particularly advantageous for specialized applications like photoacoustic spectroscopy (PAS).  
Keywords:QEPAS; measuring microphone; acoustic sensors; QTF; ultrasound; photoacoustic spectroscopy +
Footnote †: journal: Applied Sciences