esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The following improvements and extensions have been made:  
* A more extensive review of the related work in the literature for the problem of physical layer authentication (e.g., RAI, SEI or RF-DNA) and on the application of WSST. * In the initial paper, only the identification problem was analyzed. In this paper, we also evaluate the verification/authentication problem. * In the initial paper, only the K nearest neighbor with K = 1 was used to compare the performance of WSST with the other representations. In this paper, the authors have compared the results from different machine learning algorithms. * A more extensive analysis and optimization of the hyperparameters of WSST and machine learning algorithms is performed in this paper. Structure of this paper: The structure of the paper is the following: Section 2 provides a review of the related work on physical layer authentication. Section 3 provides a definition of the WSST. Section 4 provides a description of the methodology used to collect the RF signals and the test bed. Section 5 provides the experimental results and the related analysis where a comparison of different statistical features and machine learning algorithms is performed. In the first part, the results for the identification of wireless devices are provided. The second part presents the results for the verification or authentication of a wireless device. Finally, Section 6 concludes this paper.
# Applicability of Infrared Thermography for the Detection of Phase Transitions in Metal Alloys
## 1 Introduction
In that frame, the IRT method constitutes an interesting alternative particularly fit for the aimed applications but also in materials science [7; 12], especially in metallurgy given the omnipresence of metal alloys in most industries [13; 14; 15] and the need for material screening. The IRT method has been extensively used and validated for the study of phase transitions of organic materials including systems of sugar alcohols [16], fatty acids [9; 10], and fatty alcohols [11]. Given the promising results obtained, it is interesting to investigate its applicability to inorganic materials which could allow the rapid screening of blended PCMs, the development of a large material library, the study of the phase transitions of multicomponent PCMs and the establishment of their phase diagrams. In this work, the applicability of the IRT method, a high-throughput characterization technique, for the detection of phase transitions in metal alloys is explored for a pure sample of Gallium (Ga) and a mixture of Gallium and Indium (In). The results of this preliminary study are compared with Differential Scanning Calorimetry (DSC) measurements and experimental data extracted from literature.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 3 Monotonic Shear Tests  
### Test Results  
#### 3.1.1 Shear Stress-Shear Displacement Curve  
As shown in Figure 5a, when the interface roughness is relatively smooth, the shear stress-shear displacement curve of loose sand or dense sand first grows nonlinearly and then tends to be gentle; that is, the characteristics of strain hardening. The maximum shear stress almost always corresponds to a shear displacement of about 3 mm. Figure 5e shows the interface under rough conditions. For dense sand, there will be obvious peak shear stress; that is, strain softening. For loose sand, the trend is similar to that when the interface is relatively smooth. The performance of the interface roughness at \(R_{n2}\) is similar to that of \(R_{n3}\), but the softening phenomenon is not so obvious. It is observed that as the normal stress or interface roughness increases, the softening phenomenon exhibited by the dense sand becomes more intense.  
If the tangent slope of the initial nonlinear growth phase in the shear stress-shear displacement curve is defined as the initial shear stiffness, whether loose sand or dense sand is on the same roughness interface, as the normal stress increases, the initial shear stiffness increases accordingly, and the shear strength also increases. Under the same normal stress, as the interface roughness increases, its shear strength also increases.
## 2 Project overview and In-Situ Monitoring  
### General Situation  
As an important transportation guarantee line for the 2022 Beijing Winter Olympic Games, Beijing-Zhangjiakou high-speed railway is about 174 km long with 10 stations and has a great strategic significance for connecting Beijing and Zhangjiakou in Hebei province (Figure 1). The New Badaling tunnel is a double-track railway tunnel with a total length of around 12 km (from DK59+260 to DK71+270) and a maximum design speed of 250 km/h. It is the longest tunnel with complex construction technology in the Beijing-Zhangjiakou high-speed railway project. Located within the Changping and Yanqing District of Beijing, the New Badaling tunnel passes through the Badaling Great Wall core area. The exit section of the New Badaling tunnel in the direction of Zhangjiakou is buried shallowly with poor ground quality which undercrosses the Chengjiayao Village and a golf course, sequentially (Figure 2). From DK70+500 to DK70+889, the maximum and minimum buried depth of tunnels are 38 m and 4 m, respectively. The ground is composed of loess with high permeability and poor stability, as well as tuff with diverse weathering degree from the top to the bottom, as shown in Figure 3. Table 1 represents the physical and mechanical properties of the exit section of the New Badaling tunnel in the direction of Zhangjiakou. According to the geological prospecting data, ground quality of the surrounding rock is classified as grade V. Tunnel collapses and obvious ground surface settlements easily occur when tunneling in the surrounding rock of grade V. A simplified relationship of the rock mass qualities between the Chinese classification basic quality (BQ) system and the widely used quality (Q) system is listed in Table 2 [22].  
Figure 1: Map of Beijing-Zhangjiakou high-speed railway.  
Figure 3: Longitudinal geological profile of northwest portal of New Badaling tunnel.  
Figure 2: Aerial photo of the project area.
## 1 Introduction  
Viruses are among the most widespread and abundant entities on Earth, playing a significant role in shaping ecosystems [1]. They are well recognized as major pathogens in crop plants, where their impacts on agriculture and food production have been extensively studied [2]. However, their role in woody plants, particularly forest trees, remains largely unexplored and is an emerging area of research [3]. Ash trees (_Frazinus_ spp., Oleaceae) are among European forests' most ecologically and economically significant broadleaved species [4]. A recent survey revealed that ash trees are also affected by several plant viruses [5]. Among these, ash shoestring-associated virus (ASeV, _Emaravirus frazini_) is a novel emaravirus, first identified in Switzerland in 2016 using next-generation sequencing (NGS) [6]. In _F. exeskip_, AseV was the second most abundant virus after cytorhabdoviruses, with an incidence of 82% in trees showing virus-suspected leaf symptoms [5]. Infected trees exhibited symptoms such as leaf mottling, chlorosis, and abnormal growth patterns, including leaf curling and shoestring formation [6]. AseV belongs to the genus _Emaravirus_ within the order _Elliovirales_ and the family _Finoviridae_[7, 8]. Virions of AseV are enveloped harboring five negative-sense single-stranded RNAs with the conserved 13 nucleotide regions specific for emaraviruses at both termini of each genomic segment [8; 9]. Each genomic fragment encodes a distinct protein: RNA-dependent RNA polymerase (RdRP, 268 kDa), glycoprotein precursor (GPP, 73 kDa), nucleocapsid protein (NP, 35 kDa), movement protein (MP, 41 kDa), and a 26 kDa protein with an unknown function (P5), encoded by RNA1 to RNA5, respectively [6]. As the fastest-evolving plant pathogens, viruses exhibit a high capacity for adaptation, enabling them to infect new host plants, overcome resistance, and alter symptoms and virulence [10; 11].  
Little is known about the genetic variability of ASeV. Understanding genome variability within viral populations is essential to developing more effective detection methods and strategies for viral disease control, such as searching for resistant ash varieties. Therefore, we investigated the genetic diversity of ASeV across different geographic regions and host plants by analyzing near full-length ASeV RNA2, RNA3, RNA4, and RNA5, as well as partial RNA1.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Algorithm  
Optimization of sensor placement is explained by the flowchart shown in Figure 6. It starts by obtaining the input data that defines the structural loading cases. It consists of field data (e.g., strain tensors, temperature fields) for all the load cases. The field data is given in the form of values in a dense grid of points, together with coordinates of those points. Next is the configurations of the genetic algorithm. In this case, the key parameters are population size, number of generations, mutation rate, and crossover rate. These parameters determine how the optimization process evolves across iterations. A set of candidate sensor configurations is randomly generated during initial population generation. Each individual in the population specifies a sensor's 3D position and its orientation. The objective function value is calculated for each individual in the population. First, each sensor is modeled as a line segment in three-dimensional space. The orientation of the line can be determined using azimuth and elevation angles. The sensor line is divided into a number of discrete points. These sample locations simulate the continuous field interaction along the sensor's physical extent and are used in subsequent interpolation. Using the inverse distance weighting (IDW) method, the algorithm interpolates field values at each sampling point along the sensor line, whether it is tensor or scalar data. A conditional operation determines whether the field type is tensor or scalar. If tensor, the system calculates both parallel and perpendicular components using transformation equations. If scalar, it just computes the average value along the sensor line. The objective function for each individual is calculated. Check stop criteria is defined as the maximum number of generations or minimum error threshold. In following step the genetic operators are applied, and the crossover and mutation create new individuals. A new population of individuals is created. Some of the individuals from the previous generation survived (succession) and some are replaced with new ones during the selection process. When the stop criteria are satisfied, the algorithm selects the best individual and outputs the final sensors' configuration with an objective function value.
# Practical Application of Mesh Opportunistic Networks
## 4 Results
### Survey
#### 4.1.1 One-to-One Communication  
If this tool had not been used to carry out individual communications in the context of the proposed experiment, participants indicated that they would have used: WhatsApp (eight participants), Signal (five), Telegram (two) and iMessage (one). Note that some participants proposed more than one instant messaging alternative. In this same context of one-to-one communication, the participants highlighted the following benefits of the system used here compared to the alternative systems: not having to pay for commercial data networks (seven participants), and no need for telephone coverage in order to communicate anywhere (four participants). In addition, the following drawbacks were mentioned: the limitations compared to data network systems as it is impossible to share photos and videos as goTenna devices linked to the mobile still only transmit text and locations; the need to have an additional device as well as the mobile phone, the respective cost; and the initial complexity in configuring the device for an amateur user.
## 3 Special Model: Exponentiated Exponential-Pareto Distribution (EEP)  
The exponential-Pareto (EP) composite distribution was first proposed by [PERSON] and [PERSON] in 2006 [17]. Suppose that \(Y\) is a EP random variable. Then, the pdf of \(Y\) is first defined as follows:  
\[f_{Y}(y;\lambda,\theta)=\begin{cases}c\lambda e^{-\lambda y}&0\leq y<\theta\\ c\alpha\frac{\theta^{\alpha}}{y^{\alpha+1}}&y\geq\theta,\end{cases}\]  
Assuming that the continuity and differentiability conditions hold, we have the following:  
\[\begin{cases}\lim_{y\to\theta^{-}}f_{Y}(y;\lambda,\theta)=\lim_{y\to\theta^{+} }f_{Y}(y;\alpha_{1},\alpha_{2},\theta)\\ \lim_{y\to\theta^{-}}\frac{df_{Y}(y;\lambda,\theta)}{dy}=\lim_{y\to\theta^{+} }\frac{df_{Y}(y;\lambda,\theta)}{dy}.\end{cases}\]  
This generates a system of equations in terms of \(\theta,\alpha\), and \(\lambda\):  
\[\begin{cases}\lambda e^{-\lambda\theta}=\alpha\theta^{-1}\\ -\lambda^{2}e^{-\lambda\theta}=-\alpha(\alpha+1)\theta^{-2}.\end{cases}\]  
By simple calculation, \(\lambda\) can be expressed in terms of \(\alpha\) and \(\theta\), as follows:  
\[\lambda=\frac{\alpha+1}{\theta}.\]  
Additionally, by utilizing the system of equations, the value of \(\alpha\) can be determined as the solution of the following equation:  
\[(\alpha+1)e^{-(\alpha+1)}=\alpha.\]  
Since the shape parameter \(\alpha\) of a Pareto distribution is restricted to being positive, the above equation gives a unique solution:  
\[\alpha=0.349976.\]  
Since we assume that \(f_{Y}(y;\lambda,\theta)\) is a valid pdf, we can also obtain the unique solution for the normalizing constant \(c\) as follows:  
\[c=\frac{1}{2-e^{-(\alpha+1)}}=0.574464.\]  
Thus, the pdf of \(Y\) with one parameter \(\theta\) can be written as follows:  
\[f_{Y}(y;\theta)=\begin{cases}c(\frac{\alpha+1}{\theta})e^{-\frac{(\alpha+1)y} {\theta}}&y<\theta\\ c\alpha\frac{\theta^{\alpha}}{y^{\alpha+1}}&y\geq\theta,\end{cases}\]  
where \(c=0.574464,\alpha=0.349976\).  
With the power transformation \(T=Y^{\frac{1}{\theta}}\), \(T\) is obtained as an exponentiated exponential-Pareto (EEP) composite random variable. The pdf of \(T\) can be expressed as follows:  
\[f_{T}(t;\theta,\eta)=\begin{cases}c(\frac{\alpha+1}{\theta})e^{-\frac{(\alpha +1)t^{\beta}}{\theta}}\eta t^{\eta-1}&0<t<\theta^{\frac{1}{\theta}}\\ c\alpha\frac{\theta^{\alpha}}{(t^{\theta})^{\alpha+1}}\eta t^{\eta-1}&t\geq \theta^{\frac{1}{\theta}},\end{cases} \tag{11}\]  
Figure 1 shows the pdf of EEP distributions with different parameters. Notice that, when \(\eta>1\), the pdf of EEP distribution is hump-shaped; we discuss this in detail in the next subsection.
# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
## 3 Methodology
### Data Capture
#### 3.1.1 Single-Shot Set-Up  
We used a Nikon D610 professional camera with a Sigma 24-105 mm lens placed on a fixed tripod. The distance from the camera lens to the prints was approximately 51 cm. The 2.5D prints and a Macbeth ColorChecker were placed inside the light booth cabinet (VeriWide CAC 60-5, illumination was around 1328 lux) with D65 (diffuse) illumination. We used the following setting parameters for the camera: ISO was 125, the aperture was 4.5, and the shutter speed was 1/80 with manual exposure mode. We worked with the camera jpeg images and the color checker was used for white balancing. The single-shot set-up is further referred to as SS.
## 2 Circuit Implementation of the Proposed Operational Amplifier
### Frequency Response  
The LFP and HFP signals are combined by the current adder in Figure 6. The LFP has a second-order frequency response caused by A\({}_{3}\) and A\({}_{5}\) and the HFP has a first-order frequency response caused by A\({}_{5}\). To smoothly merge the transfer functions of the two paths, the frequency response of the LFP must be made similar to that of a first-order system. To accomplish this, the poles were split by applying the nested Miller compensation to C\({}_{\rm m1}\), C\({}_{\rm m2}\), and C\({}_{\rm m3}\). In the case of C\({}_{\rm m2}\), as there is positive feedback, there is a risk that the operation of the entire amplifier will become unstable by inducing the pole to appear in the right half of the complex-s plane [13]. Therefore, C\({}_{\rm m2}\) is connected to a negative terminal and its signal is implemented in the form of negative feedback.  
The simulation results of the complete transfer function of the proposed operational amplifier are shown in Figure 7. The red line represents the HFP, the blue line represents the LFP, and the black line represents the overall transfer function of the multi-path operational amplifier. The frequency where the loop gain of the LFP and the loop gain of the HFP are equal is called the cross-over frequency and is determined by C\({}_{\rm i1}\) and A\({}_{4}\) in Figure 1. The overall transfer function follows the LFP function at low frequencies and the HFP function at high frequencies, and thus is not affected by the notch of the chopper frequency in the RRL. When the transconductance of A\({}_{2}\) is G\({}_{\rm m1}\), the frequency response of the proposed multi-path operational amplifier is seen as the first-order frequency response, so unit gain bandwidth (UGBW) can be expressed as Equation (8). The simulation results show that the UGBW of the proposed multi-path operational amplifier is 2.2 MHz.  
\[\rm UGBW=\frac{G_{m1}}{2\pi C_{m1}} \tag{8}\]
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Content Store Size and Hit Rate  
The size of a content store of intermediate NDN nodes is set at 10 MB, and the size of a content store of QoS nodes is varied between 50 MB and 250 MB. The overall average of the content store hit rate for the entire network is determined. As mentioned in the simulation setup, 20% of the traffic is priority content. Section 5.3.1 presents the evaluation of how this priority content was treated in the network, and Section 5.3.2 presents the evaluation of how the overall content was treated by the network. It can be observed that QoS traffic obtained better treatment than non-QoS traffic, when both evaluations are compared.  
Figure 4: Content store hit rate vs. percentage of QoS nodes in the network. (**a**) Content store hit rate vs. percentage of QoS nodes in the network for \(|M|\) = 500. (**b**) Content store hit rate vs. percentage of QoS nodes in the network for \(|M|\) = 1000.
## 2 Existing Wi-Fi Backscatter Communication Systems  
A Wi-Fi backscatter system consists of a Wi-Fi helper (AP), a reader (Wi-Fi device, such as a smart phone), and a tag sensor as shown in Figure 1[9]. In Wi-Fi backscatter uplink communications, the information is sent from the backscatter tag sensor to the reader. To transmit one bit datum with one (1) value, the tag sensor just reflects the Wi-Fi RF radio wave fully radiated from the Wi-Fi AP during the period of a Wi-Fi packet. The tag sensor can transmit one bit datum with zero (0) value with absorbing the Wi-Fi RF radio packet. Since the tag sensor has to work with very low power or powerlessness, it uses a low-power switch that controls the antenna impedance, which affects the amount of reflection. The analog switch can be implemented in MOSFET (metal-oxide-semiconductor field-effect transistor) technology and can consume less than 1 uW[9]. Since the one bit information of tag sensor spans over a Wi-Fi packet period, the impedance of antenna has to be maintained over the period of a single Wi-Fi packet and can be changed for every Wi-Fi packet depending on the information. Consequently, the Wi-Fi backscatter tag sensor embeds its information bits to the Wi-Fi channel without harming normal communications between Wi-Fi devices.  
The backscattered signal modulated from the tag sensor changes CSI (in frequency domain) and received a signal strength indicator (RSSI) of a received Wi-Fi packet without directly affecting the data in the Wi-Fi packet. The reader can decode the information of a tag sensor by evaluating the CSI or the RSSI of every received packet. Wi-Fi systems employ an orthogonal frequency division multiplexing (OFDM) technology, and there can be CSI values for 54 sub-carriers in a Wi-Fi packet.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 2 Results
### Hormonal Differences between Normal and Slow Ripening Phenotypes
#### 2.3.3 JA Production  
Linoleic acid is converted to 12-oxo-phytodienoic acid (OPTDA) through the enzymes lipoxygenase (LOX), allene oxide synthase (AOS) and allene oxide cyclase (AOC), then is transformed through the activity of the enzyme 12-oxophytodienoate reductase (OPR) and a series of \(\beta\)-oxidations to form jasmonic acid (Figure 3, green section). The results obtained showed a higher abundance of one _LOX_ and three _OPR_ in normal ripening fruit, suggesting a higher JA production only in T7. The _LOX_ described here had an FC = 9.3 with 613.0 FPKM in the NR siblings, and low expression in the SR individuals (FPKM < 1.0). Although other _LOX_ genes were overexpressed in the SR siblings, their expression levels were considerably lower than those mentioned above (data not shown). On the other hand, there were six genes described as _OPR2_ in the peach genome. Three of them displayed differences in their expression levels at T7 (Figure 3, green section), suggesting higher OPDA accumulation and lower JA biosynthesis in SR individuals.  
Finally, JA needs to be conjugated into JA-Ile through the activity of the enzyme jasmonic acid-amino synthetase (JAR1) to perform its biological function. Our results showed that _JAR1_ abundance increased significantly in T2 and remained high in NR fruit in T7 (Figure 3, green section). The SR fruit presented similar _JAR1_ abundance behavior in T1, T2, and T3, but then showed a significant decrease in T7. These results suggest that SR fruit displays less JA production than regular ripening fruit. Thus, we hypothesize that there might also be less JA conjugation to JA-Ile.