esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Recent Progress in Accelerator
The peak brightness for self-seeded hard X-ray pulses was enhanced to 10 times greater than that of the SASE FEL over hard X-ray ranges. For example, the peak brightness of an X-ray at 9.7 keV was \(3.2\times 10^{35}\) photons/ (s\(\cdot\)mm\({}^{2}\cdot\)mrad\({}^{2}\cdot\)0.1% BW). This is the highest peak brightness ever achieved for FEL pulses. Thanks to the ultrabright X-ray pulse with a narrow bandwidth and superior spectral purity, the SFX experiment results using the seeded FEL showed better data quality with high resolution compared with the SASE FEL [38]. Figure 3: Measured saturation curve of 20 keV FEL with 20 undulators. The optical laser pump and X-ray probe methods are widely used in the XFEL experiments for molecular and structure dynamics studies, and this method is being extended to the X-ray pump and X-ray probe experiment. Two-color FEL generation was proposed for the first time in 1994, using an infrared FEL with an optical cavity [39]. After that, two-color FEL generation has evolved into two directions depending on the generation methods of different photon energies. The former uses groups of undulators tuned in different ways, and the latter uses different energies of electron beams or different energy parts of an electron beam. In the former case, an electron beam with a small energy spread is sent through two groups of undulators with different undulator parameters K, to generate different wavelength FELs. The magnetic chicane between two undulator groups can be used to control the delay between two FEL pulses. Hard X-ray two-color FELs were obtained in the LCLS for the first time [40, 41]. A drawback of this method is that the wavelength tuning range is limited to the variable range of the undulator parameter K. The LCLS undulators were planar, permanent-magnet based, fixed gap undulators. The magnet poles had canted angles to change the undulator parameter K from 3.47 to 3.51. The small range of the undulator parameters K limited the wavelength difference of the two-color FELs within 2%. In the SACLA, variable gap undulators were used to increase the wavelength separation by more than 30% [42]. Two-color FEL generation was tried at the PAL-XFEL to extend the research capabilities to the X-ray pump and X-ray probe experiment. Figure 5 shows the measured photon energy and intensity of generated two-color FELs using a single electron bunch. The pump pulse was generated with eight upstream undulators of the self-seeding section, and the probe pulse was generated with 12 downstream undulators. The energy separation of the two FEL pulses was 200 eV at a 12.7 keV photon energy. The measured FEL pulse energies were 10.7 and 55.8 \(\upmu\)J for the pump and probe pulse, respectively. The time delay between the two pulses was controlled from 0 to 120 fs using the magnetic chicane installed at the self-seeding section. Figure 4: Energy spectrum vs. average intensity of self-seeding FEL. Red and blue lines represent self-seeding and SASE FEL, respectively.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### First Set of Tests
#### 5.2.3 Method Three  
The average values are calculated automatically by taking the mean of the percentage of active sensors in each iteration; thus, they are fixed in both thresholding versions. The same as method two, this method can detect the defects without any false alarms in tests 1-5. However, only tests with an excitation frequency of 34 kHz can use the average value, since using it for other excitation frequencies would result in detecting outliers. The limits of this method are shown in Figure 13. The tests where the average value is between the minimum and maximum are when the automatic detection method is successful. Furthermore, the averages values achieved using all of the different frequencies are between 0.602-0.554, and tend to decrease slightly when the frequency is increased.  
To conclude, this method can automatically detect defects of up to 2% CSA, which are buried in the noise level using a 34-kHz excitation frequency. Furthermore, with regard to the detection length, it can also be said that 34 kHz is the optimum excitation frequency in the first set of tests.
# A Model Based Framework for IoT-Aware Business Process Management
## 3 Background
### Digital Twins  
A _Digital Twin (DT)_[26; 27] is a dynamic digital representation of a physical system that can be seen as a virtual instance of the physical system, often referred to as the _Physical Twin (PT)_. In a traditional M&S approach, simulation models of a given system (i.e., the system under study) can be developed at different steps of the system life-cycle for addressing various objectives:  
* At _execution time_, an existing system is observed and a corresponding simulation model is specified. A simulation-based analysis is performed to predict system behavior under given conditions and to ultimately obtain guidance to properly address critical situations (see Figure 1a);
* At _design time_, a simulation model can be specified starting from models of the system to be developed. Simulation-based analysis allows developers to compare different design alternatives and evaluate the system behavior before its actual implementation (see Figure 1b).  
In order to be coherent with the system under study, a simulation model needs to be _parameterized_. Indeed, the various properties of the real-world environment have to be used for deriving the relevant configuration parameters of the simulation model. One of the most relevant drawbacks of a traditional M&S approach is that the simulation model does not take into account the dynamic evolution of the physical system during its operational life. Changes in the properties of the physical system might invalidate the simulation model with respect to the actual behavior of the system.  
In this respect, the DT paradigm aims at overcoming such issue. DTs are constantly updated according to actual performance, health status and context data of the PT [5], thus keeping the DT continuously coherent with its physical counterpart.  
Due to their novelty and relevance, DTs are increasingly being used in several application domains [28].  
As discussed in Section 4, the framework proposed in this paper introduces an MDA-based approach to ease the development of DT and support the analysis of IoT-aware BPs.  
Figure 1: Traditional M&S Approaches vs. Adoption of a Digital Twin.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 4 Receiver Design  
In this section, the radar and communication receivers are discussed with respect to the DFRC transmitter in detail.
## 4 Discussion  
### Particle Velocity and Impact Degradation  
It has been recognized that particle impact velocity, impact angle, and particle concentrations are key factors in particle impact degradation [10]. Particle impact velocities at the conveying bends in a pneumatic conveyor can be significantly different in terms of the type of materials conveyed and the operating conditions used. In this study, two case studies show that the same materials (wood pellets) can have different degradation at the same operation conditions because of different moisture contents and surface properties. For different materials (wood pellets and coffee beans), the same operating conditions give a significantly different particle acceleration in the pipeline and, alternatively, different particle impact velocities.  
Previously in most cases, air velocities at the bends were used for the estimated impact velocity of the particles. This study shows that a slip velocity (difference between the particle velocity and the air velocity) can be as big as the particle velocity if the bends are not evenly distributed or if the particles are not allowed to fully accelerate (wood pellets). Even for a lighter material (coffee beans), the slip velocity was found to be significant in such pipe settings, whereas coffee beans traveled at air velocity when fully accelerated. Using the air velocities (either measured or calculated) for evaluation of the degradation results in a significant overestimate of impact velocity. It reveals that measurements of particle velocities in pneumatic conveyors can be important if it is feasible, and we can predict how it changes with changing solid flow rate and pipe layout.  
In pneumatic conveying, particle concentration has little effect on the degradation of the solid particles. The study shows that a higher concentration of solids expects a lower level of degradation. The trend in wood pellets degradation was clear. However, compared to particle impact velocity, the effect of particle concentration is not significant.
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 2 Indoor Pseudolite Fingerprint Positioning Method  
### Selection of Feature Fingerprints for Pseudolite Observations  
The original observation information output by the pseudolite receiver includes the pseudorange, carrier phase, integral Doppler, carrier-to-noise ratio, and other observational values of each epoch [28]. The location specificity and stability of each observation are different and need to be analyzed and selected.  
(1) Pseudorange observations  
Pseudorange observation is the most basic observation in GNSS positioning, and is a necessary condition for absolute single-point positioning. In complex indoor environments, it can directly reflect the distance between the pseudolite transmitting antenna and the receiving antenna, but the pseudorange measurement value has low precision and poor stability due to the influence of multipath propagation.  
(2) Carrier Phase Observations  
The carrier phase observation is another basic observation output by the receiver, which represents the difference between the phase of the carrier signal reproduced by the receiver and the phase of the carrier signal received by the receiver. The carrier phase difference between channels is more stable than the pseudorange difference, and is basically a constant value, that is, the distance difference between the pseudolite antennas and the receiving antenna. Carrier phase difference can eliminate the effects of receiver clock differences, but cannot eliminate the effects of space-specific multipath, so it can be added to the signature database as an information source.  
(3) Carrier-to-noise ratio Observations  
The carrier-to-noise ratio (\(C/N_{0}\)) observation is the ratio between the received carrier signal strength and the local noise, describing the quality of the signal received by the receiver. Although the observed value of \(C/N_{0}\) has a certain relationship with the signal processing capability and signal bandwidth of the receiver itself, \(C/N_{0}\) can clearly reflect the distance or occlusion between the receiver and the transmitter when the same receiver is used to receive the same system signal.  
Since no dynamic measurements are involved, Doppler observations are not concerned. Considering the convenience of measurement and the stability of data, we use the simulating channel parameters TOA and RSS, corresponding to the carrier phase and \(C/N_{0}\) in the measured CSI data as eigenvalues for position prediction. Through actual measurements in an indoor environment, the data stability and location specificity are shown in Figure 1. It can be seen that both have good spatial resolution and stability, clearly indicating that these two parameters can be used as the input of the neural network.
###### Abstract  
Addressing the problem that traditional methods cannot reliably monitor surface subsidence in coal mining, a novel method has been developed for monitoring subsidence in mining areas using time series unmanned aerial vehicle (UAV) photogrammetry in combination with LiDAR. A dynamic subsidence basin based on the differential digital elevation model (DEM) was constructed and accuracy of the proposed method was verified, with the uncertainty of the DEM of difference (DoD) being quantified via co-registration of a dense matching point cloud of the time series UAV data. The root mean square error calculated for the monitoring points on the subsidence DEM was typically between 0.2 m and 0.3 m with a minimum of 0.17 m. The relative error between the maximum subsidence value of the extracted profile line on the main section after fitting and the measured maximum subsidence value was not more than 20%, and the minimum value was 0.7%. The accuracy of the UAV based method was at the decimeter level, and high accuracy in monitoring the maximum subsidence value was attained, confirming that an innovative strategy for monitoring mining subsidence was realized.  
subsidence monitoring; UAV photogrammetry; airborne LiDAR; co-registration; dynamic subsidence basin Article
# Influence and Control of SARS-CoV-2 Transmission under Two Different Models
## 3 Results
### Equilibrium Stability and Basic Reproduction Number
\[E_{1}(0,0,0,\frac{A}{d_{h}},0,0)\]  
\[E_{2}(\frac{K(b_{w}-d_{w})}{b_{w}},0,0,\frac{A}{d_{h}},0,0)\]  
\[E_{3}(0,\frac{N(b_{v}-d_{v})}{b_{v}},0,\frac{A}{d_{h}},0,0)\]  
\[E_{4}(0,0,0,S_{h}^{1},E_{h}^{1},I_{h}^{1})\]  
\[E_{5}(\frac{K(b_{w}-d_{w})}{b_{w}},0,0,S_{h}^{1},E_{h}^{1},I_{h}^{1})\]  
\[E_{6}(0,\frac{N(b_{v}-d_{v})}{b_{v}},0,S_{h}^{1},E_{h}^{1},I_{h}^{1})\]  
\[E_{7}(0,\frac{d_{v}+d_{1}}{\beta_{v}},\frac{\beta_{v}Nb_{v}-\beta_{v}Nd_{v}-b_ {v}d_{1}-b_{v}d_{v}}{\beta_{v}(\beta_{v}N+b_{v})},S_{h}^{*},E_{h}^{*},I_{h}^{*})\]  
\[E_{8}(\frac{K(b_{w}-d_{w})}{b_{w}},S_{v}^{**},I_{v}^{**},S_{h}^{**},E_{h}^{**},I_ {h}^{**})\]  
The representation of each symbol is shown in Appendix A. We use a next-generation matrix to calculate the basic reproduction number of model 1. There is a disease-free equilibrium point \(E_{1}(0,\frac{A}{d_{h}},0,0)\). From model 1, it can be obtained that:  
\[F= \begin{bmatrix}\beta_{h}S_{h}&\beta_{h}S_{h}&\beta_{w}S_{h}\\ 0&0&0\\ 0&0&0\end{bmatrix} ;\ V= \begin{bmatrix}\alpha+d_{h}&0&0\\ -\alpha&d_{h}+d&0\\ 0&0&d_{w}-b_{w}+\frac{2b_{w}W}{K}\end{bmatrix} \]  
\[R_{0}=\rho(FV^{-1})=\frac{\beta_{h}S_{h}(\alpha+d+d_{h})}{(d+d_{h})(\alpha+d_{h})}\]  
As in model 1, we obtained the following for model 2:  
\[F= \begin{pmatrix}\beta_{h}S_{h}&\beta S_{h}&\beta_{h}S_{h}&0\\ 0&\beta_{w}S_{v}&0&\beta_{w}S_{v}\\ 0&0&0&0\\ 0&0&0&0\end{pmatrix} \]  
\[V= \begin{pmatrix}\alpha+d_{h}&0&0&0\\ 0&d_{v}+d_{1}&0&0\\ -\alpha&0&d_{h}+d&0\\ 0&0&0&d_{w}-b_{w}+\frac{2 Wb_{w}}{K}\end{pmatrix} \]  
For the disease-free equilibrium point \(E_{3}(0,\frac{N(b_{v}-d_{v})}{b_{v}},0,\frac{A}{d_{h}},0,0)\), the basic reproduction number of model 2 is: \(R=\max\{R_{1},R_{2}\}\), where \(R_{1}=\frac{\beta_{h}S_{h}(\alpha+d+d_{h})}{(d+d_{h})(d+d_{h})}\), \(R_{2}=\frac{\beta_{h}S_{v}}{d_{v}+d_{1}}\). In the long term, if nothing is added, the model will eventually stabilize the coexistence of the disease. In the actual process, when the spread of the disease was isolated, disinfection, treatment, vaccine and other effective measures were used to balance the condition. We do not pay attention to how this model functions in the stable state; instead, we pay more attention to the incipient stage of the disease. In the next section, we compare the numerical solutions of the two models, focusing on the transmission status of the disease at the beginning stage, and via a comparison, we investigate whether changes occur in the early stage of infection when SARS-CoV-2 has a new transmission route of person-object-person, and how to control such changes.
## 4 Results
### Mapping Spectral Features of Alteration Zonation  
The mineral composition of altered surrounding rocks in the Gongchangling rich iron deposit is relatively simple. In consideration of alteration classification scheme proposed by [PERSON] and [PERSON] [4], combining the research habits of most domestic scholars, the alteration zonation was divided into the most abundant minerals in the altered rock: i.e., a chlorite-garnet zone, a garnet-chloride zone and a chlorite zone.  
There are many spectral parameters in the short-wave infrared spectrum, and the spectral information such as wavelength position and absorption depth are most used at present. The wavelength is usually sensitive to the chemical compositions of specific minerals, while the absorption depth is often related to the mineral abundance. Based on the macroscopic zonation, the spectral parameters of the extracted single mineral were analyzed to obtain more detailed characteristic information. Since garnet and chlorite are closely related to the mineralization of high-grade iron ore, their spectral features can provide subtle information on the physiochemical conditions of ore-forming processes. Therefore, the spectral characteristics at ca. 1200 nm, which are considered to be caused by Fe\({}^{2+}\)[51,52] being shared by the two minerals, have been selected to characterize the intrinsic chemical and/or structural variations of the specific minerals. To highlight the transformation regular, a threshold was set, according to the spectral features at 1200 nm of chlorite and garnet, for the classifying spectrum. The threshold was 20 nm for wavelength position, and 0.02 for absorption depth.  
In the garnet-chlorization zone, the wavelength position of chlorite ranges from 1210.0 to 1230.0 nm and 1250.0 to 1270.0 nm, and absorption depth from 0.02 to 0.06; as for garnet, the wavelength position ranges from 1190.0 to 1210.0 nm and 1250.0 to 1270.0 nm, and absorption depth from 0.02 to 0.06, a very small part of 0.06-0.08 (Figure 8). The overall spectral features can be summarized as \"intermediate wavelength of garnet-chloride; intermediate depth of garnet-chloride.\"  
In the chloritization zone, the wavelength position of chlorite ranges from 1250.0 to 1270.0 nm, and absorption depth from 0.02 to 0.04; as for garnet, the wavelength position ranges from 1190.0 to 1210.0 nm and 1250.0 to 1270.0 nm, and absorption depth from 0.02 to 0.04 (Figure 8). In comparation with those in the other two zones, the overall spectral features can be summarized as \"longer wavelength and small depth of chlorite.\" Garnet's concentration area can be seen as a little shallow spectrally, and the scattered visible part of chlorite at the intersection of chlorite and garnet has greater absorption depth.
## 2 Basic Mechanism
Single-photon absorption and two-photon absorption single-event effects tests also involve different wavelengths. When the photon energy is greater than the semiconductor band gap (1.12 eV in silicon), it is absorbed directly, resulting in electron hole pairs [10]. The correlation between the photon energy and light wavelength is [10]:  
\[E_{photon}=\frac{hc}{\lambda} \tag{5}\]  
where \(h\) is Planck's constant and c is the speed of light. It can be concluded that the wavelength value of electron hole pair induced in undoped pure silicon is about 1108 nm. For the SPA test method, since the silicon wafer is not very thin, the wavelength is usually selected at 1064 nm, which is sufficiently long to penetrate deep enough to reach the sensitive volume. For the TPA test method, the pulsed laser can pass through the silicon material up to the focal position of the interior in order to induce the generation of carriers. Therefore, by changing the distance between the measured device and the focusing lens, the position of the generation of the carriers can be changed [11; 12]. At present, the SPA and TPA test methods are commonly used for silicon materials with pulse wavelengths of 1064 nm and 1260 nm. However, SPA technology is not used in SEE tests for wide-band-gap materials, such as SiC, because the photon energy is smaller than the band gap of the material. Therefore, for materials with a wide band gap, the pulse laser wavelength for the TPA test method should vary from 400 nm to 600 nm. Based on the above analysis, this paper mainly presents experimental research conducted on single-photon absorption- and two-photon absorption-induced single-event effects and offers a comparative analysis.
## 3 Initial Results
#### 3.2.1 Conceptual Foundations, Contrasting AI and Blockchain  
Blockchain technologies and AI are considered to be complementary and converging technologies. On the other hand, both technologies operate within their own fundamentally distinct domains, each serving their own unique purposes with very diverging operational principles. Blockchain is inherently designed with a focus on establishing trust, verification, and transparency in decentralized systems that require secure and immutable record-keeping. As such, blockchain technologies achieve this by employing decentralized consensus mechanisms, e.g., Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT), and so on [118]. The goal is to ensure data integrity without any reliance on a central authority as a trusted third party. Thus, this way, blockchain yields the ability to provide auditability, traceability, and tamper-proof transaction histories [119]. Consequently, blockchain technologies have become indispensable in applications such as digital identity management, secure data sharing, and transparent financial transactions.  
AI, on the other hand, has its roots in data-driven intelligence with a focus on predictive analysis, pattern recognition, and automated decision-making. Thus, AI employsmachine-learning algorithms and, additionally, large-scale datasets as well to discover insights, predict outcomes, and adapt to complex scenarios in real-time [120]. Different from blockchain, AI systems typically operate without transparency in their decision-making processes and instead, excel in domains such as computational adaptability and dynamic responsiveness [121]. AI is currently very widely applied in domains that require rapid and context-sensitive analysis, e.g., in fraud detection, personalized recommendations, and autonomous systems [122].  
Despite these differences between blockchain technologies and AI, as well as building on previous studies [111; 112; 113; 114; 115; 116; 117], the integration exploration in this position paper does not aim to simply merge these two distinct domains into a single framework. Instead, this position paper aims to discover their complementary strengths. Blockchain's trust mechanisms ensure that data integrity, secure provenance, and decentralized governance are critical aspects of applications that handle sensitive information [123]. On the other hand, the adaptive intelligence of AI for pattern identification and the generation of actionable insights addresses dynamic and evolving challenges, e.g., digital threats [70]. Without compromising these technologies, the combination of blockchains and AI yields the possibility of addressing complex social issues, e.g., sextortion, privacy violations, and digital exploitation [24]. The goal of such an integrated approach is to ensure that the inherent strengths of blockchain and AI reinforce one another, and thus to create more robust and trustworthy systems [124].