esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermophysical Property Measurements with the Finite Bar
## 2 Physical Foundations
### Solution in the Small Fourier-Number Limit  
At small physical times, when the Fourier-Number \(Fo=at/L^{2}\) is significantly smaller than 1, the temperature rise at the adiabatic end is almost zero. This requires the integral \(\int_{\frac{1}{2\sqrt{at}}}^{\infty}erfc(\mu)d\mu\) in Equation (4) to vanish. The measured temperature profile can then be compared to the analytical temperature profile to determine the factor \(2\dot{q}_{0}\sqrt{a}/\lambda\).  
The temperature rise at the heated end is then given by  
\[\Delta T_{sF}(x=0,t)=\frac{2\dot{q}_{0}\sqrt{t}}{\sqrt{\lambda\rho c_{p}}\sqrt {\pi}} \tag{5}\]  
since the integral \(\int_{0}^{\infty}erfc(\mu)d\mu=1/\sqrt{\pi}\) is constant. With a specific heat flux \(\dot{q}_{0}\) at the heated end (\(x=0\)), it is sufficient to measure the temperature at the heated end to de  
Figure 1: Sketch of the bar with the boundary conditions and the expected temperature profile.  
termine the factor \(\lambda\rho c_{p}\). When the specific heat capacity \(c_{p}\) and density \(\rho\) are known, the conductivity can be extracted from the factor.  
\[\lambda=\frac{1}{\rho c_{p}}\frac{4\dot{q}_{0}^{2}}{\pi}\frac{t}{\Delta T_{sf}^{ 2}(x=0,t)} \tag{6}\]  
The equation for the temperature distribution (Equation (4)) can be simplified when the temperature between the heated and the adiabatic end of the bar \(\Delta T_{sf}\) is known.  
\[T(x,t)=T_{0}+\Delta T_{sf}\sqrt{\pi}\int_{\frac{x}{2\sqrt{\pi}}}^{\infty}erfc( \mu)d\mu \tag{7}\]  
This equation is more suited for the post-processing of the experimental data, since \(\Delta T_{sf}\) can be obtained directly by measurements. When temperature is monitored at several positions along the bar, it is possible to fit the curve to the temperature Equation (7) with the product by variation of \(\eta=x/(2\sqrt{at})\) with known time \(t\) and position \(x\) to extract the thermal diffusivity \(a\).
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 1 Introduction
The coherent scattering component is expected to be more sensitive to SMC because of its improved spatial resolution (limited by approximately the first Fresnel zone) and its scattering properties. This smaller footprint provides an improved estimation on the actual heterogeneity of the surface. As such, the sensitivity of GNSS-R to SMC should increase with higher \(\theta_{\rm e}\) because the size of the first Fresnel zone is lower at this geometry. On the other hand, the coherent scattering component is expected to increase with lower \(\theta_{\rm e}\)[24] because the effective surface roughness is lower at this angular range, in agreement with the Rayleigh criterion. This trade-off is assessed over different surface types using the CyGNSS Level 1 Science Data Record [12, 25]. The remainder of the paper is organized as follows. Section 2 provides an overview of approaches for studies over land. Section 3 describes bistatic scattering properties relevant to GNSS-R. Section 4 describes the methodology. Section 5 presents global-scale results. Section 6 describes the impact of \(\theta_{\rm e}\) as a function of SMC and effective surface roughness parameter with a focus over land. Section 7 includes final discussions, and the conclusions are highlighted in Section 8.
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
## System Model and Preliminaries  
### System Model  
We consider a single-cell TDD massive MIMO coverage with \(N\) single-antenna MTCDs that are served by a BS equipped with \(M\) antennas. Such a cellular network with MTCDs is illustrated in Figure 1. Each time slot finds a certain number of active MTCDs, and the remaining MTCDs are inactive. Assume that the MTCDs share \(\tau_{p}\) mutually orthogonal pilot sequences, and each sequence is \(\tau_{p}\) symbols long. The pilot random access block and data block model is illustrated in Figure 2. We observe the transmission of MTCDs during one frame \(T_{A}\). We call \(T_{A}\) the duration of observation. We divide the duration of observation into \(I_{A}\) discrete random access slots of the length \(\tau_{u}\), where \(\tau_{u}\) is not greater than the channel coherence interval. In each time slot, the pilot random access block is followed by a data block. An MTCD uses the pilot random access block to access randomly to BS, and uses data block to transmit its data to BS [25]. After MTCDs send pilots successfully, it can send its data. We only concentrate on the pilot random access process in this paper. During the duration of observation \(T_{A}\), the \(i\)th time slot is denoted by \(\text{RS}_{i}\), \(1\leq i\leq I_{A}\). Assume each subframe is transmitted in one time slot. Let \(X_{i}\) denote the number of new arrivals during the \(\text{RS}_{i}\). According to [13], \(X_{i}\) follows the distribution of activation traffic \(g(t)\), which is the probability density function of RA requests and the total number of MTCDs \(N\). Thus, \(X_{i}\) is defined as \(X_{i}=N\int_{t_{i-1}}^{t_{i}}g(t)dt,\quad i=1,2,...,I_{A}\).  
Figure 1: Illustration of cellular network with MTCDs.
## 3 Results and Discussion
### XPS Analysis  
The elemental composition and chemical nature of the 1% CuO/TiO\({}_{2}\)@GCN-CA membrane were evaluated using X-ray photoelectron spectroscopy (XPS). The combined survey spectra containing elemental compositions of Ti, Cu, O and N on the surface of 1% CuO/TiO\({}_{2}\)@GCN-CA membrane are presented in Figure 2a. The XPS survey showed no notable evidence of impurities on a wide range spectrum of XPS (0-1200 eV).  
Figure 2b showed five peaks of C 1s appearing at 284.39 and 284.91 eV, which were imputed to C-N and C-C on the GCN and CA, which suggested that the GCN incorporated with 1% CuO/TiO\({}_{2}\) within and on the surface of the CA membrane was achieved. The peak at 286.23 eV was attributed to C-O accredited to the methyl group bonded to an oxygen atom in CA. The peak at 287.57 eV was due to C=O functional group in CA, while the peak at 288.71 eV was due to carbon in the O-C-O functional group found in CA [43].  
Figure 2c displayed three peaks at 333.20, 402.40, 404.00 eV indicating evidence of nitrogen N 1s emanating from the triazine structure. The peak appearing at 333.20 eV was imputed to the C-N=C sp\({}^{2}\)-hybridized N atom found in triazine of GCN. The peak observed at 402.40 eV was attributed to bridged N-(C)\({}_{3}\). The peak at 404.00 eV was associated with the N atom in the amino group (N-H) [44].  
Figure 2d showed two Ti characteristic peaks appearing at 458.04 and 464.00 eV which were assigned to Ti 2p\({}_{3/2}\) and Ti 2p\({}_{1/2}\), respectively emanated by Ti\({}^{4+}\) in TiO\({}_{2}\)[45]. This evinces that Ti\({}^{4+}\) ions in CuO/TiO\({}_{2}\) were incorporated successfully in the CA membrane.  
Figure 2: XPS spectra of 1% CuO/TiO\({}_{2}\)@GCN-CA (CTG–500) membrane survey: (**a**) survey, (**b**) C 1s, (**c**) N 1s, (**d**) Ti 2p, (**e**) O 1s and (**f**) Cu 2p spectra.  
Figure 2e displays O 1s spectrum consisting of two characteristic peaks appearing at 531.81 eV was ascribed to O-C which are found in CA and attributed to hydroxyl groups attached on the surface of the oxide, while the peak at 532.97 eV was ascribed to O=C which were imputed to O\({}^{-}\) ions in CuO or TiO\({}_{2}\)[43; 44; 45; 46].  
Figure 2f displayed Cu 2p spectrum with two key peaks at 978.16 and 1022 eV that were associated with the binding energies of Cu 2p\({}_{1/2}\) and Cu 2p\({}_{3/2}\), respectively which provided evidence of Cu\({}^{2+}\) in CuO [39]. The satellite peak appearing at 1001.42 eV is emanated by Cu\({}^{2+}\), which further confirms the presence of CuO in the CA membrane matrix [45].
Along with the implementation of the GPS modernization program and the development of other global navigation systems, such as Galileo, BeiDou, and GLONASS, binary offset carrier (BOC) and its derivative signals are designed and adopted to achieve significant enhancement but also introduce challenges. Conventional multipath mitigation techniques were generally designed for BPSK-modulated signal whose autocorrelation function is a triangle with single peak, such as GPS coarse-acquisition (C/A) code, whereas for BOC-modulated signals, because of subcarrier, the autocorrelation function has multi-peaks. They will result in frequent false locking and large ranging errors under a severe multipath circumstance [3]. Therefore, developing reliable and effective multipath mitigation techniques for BOC signals, especially high-order BOC signals, becomes a pressing need [4].  
Currently, some multipath mitigation approaches have been presented specifically aiming at BOC signals. They can be further classified into two categories: non-parametric techniques and parametric techniques [5].
## 4 Trust Assessment Using Various Approaches
### Comparative Analysis of Trust Assessment Methods  
Various methodologies have been employed to evaluate trust levels within 6G networks, encompassing Bayesian, weighted sum, subjective logic, Markov chains, SVM, neural networks, and blockchain. Each approach presents distinct advantages and disadvantages, necessitating careful consideration for selection. Bayesian methods offer a versatile probabilistic approach to confidence assessment by integrating prior knowledge and evidence. However, they may struggle with uncertainty and rely heavily on certainassumptions. Weighted sum techniques effectively manage uncertainty and subjective opinions, providing flexibility and customization. Nonetheless, challenges arise in determining the appropriate weights and achieving limited interpretability. Subjective logic excels in handling uncertainty and contextualized evaluation, but demands significant computational resources and encounters difficulties in defining operators. Markov chain models are beneficial for dynamic systems, but may falter in complex scenarios. Support vector machines are suitable for high-dimensional data, but require extensive tuning and lack explainability. Artificial neural networks process complex relationships, but face challenges in terms of interpretability and high computational demands. Blockchain offers decentralization and immutability, but at the expense of high resource consumption and regulatory hurdles. To select the most suitable approach for enhancing trust, security, and privacy within the 6G network ecosystem, it is imperative to weigh these strengths and limitations. Table 1 provides an overview of the advantages and disadvantages of the common trust assessment methods.
## 3 Results and Discussions
### Social NPV  
Table 2 summarizes the values of \(NPV_{s}\) obtained applying the MC simulation. By analyzing all 5000 values of the \(NPV_{s}\) carried out, it was possible to extrapolate the minimum, maximum, and average values of the simulation defined in Table 1. The \(NPV_{s}\) includes the annual cash flow the CO\({}_{2}\) emissions avoided for the renewable energy production and the fee calculated in a previous section.  
The CO\({}_{2}\) emissions avoided for the non-elimination in landfill would be the same in the three scenarios, therefore, they have not been considered. The refore, the \(NPV_{s}\) of Scenario 1 appears to be equal to 0, since the benefit resulting from CO\({}_{2}\) emissions avoided are considered to be null, as assumed in previous sections. The \(NPV_{s}\) of Scenario 2 ranged between 2.49 and 24.81 \(\celsius\)/tonne with an average value of 13.59 \(\celsius\)/tonne. Slightly higher values were obtained for Scenario 3, with an average value for \(NPV_{s}\) of 16.60 \(\celsius\)/tonne with a range between 8.31 and 24.81 \(\celsius\)/tonne. This shows that Scenario 3 is the most socially viable scenario.  
A more in-depth analysis is required for Scenarios 2 and 3, where there are benefits of non-zero CO\({}_{2}\) emissions avoided. As Figure 2 shows, for this analysis, different rank classes were defined in order to cover all outputs of \(NPV_{s}\) outputs extracted from the MC simulation. The rank classes suggested range from \(-\)10 to 36 \(\celsius\)/tonne. In starting to analyze \(NPV_{s}\) for Scenario 2, it can be noted that the density functions have the same trend of fluctuating between 8.41% and 9.72% of probability for the majority  
Figure 1: Fee density and distribution function. (**a**) Scenario 1, Composting; (**b**) Scenario 2, renewable electricity production; (**c**) Scenario 3, biomethane production.  
rank classes considered (covering 89.26% of \(NPV_{s}\) values) ranging from 6 to 24 \(\copyright\)/tonne. Studying the cumulative distribution function, it can be seen that P (\(NPV_{s}>4\)) is 93.10%, becoming 100% in rank class 2. P (\(NPV_{s}>24\)) is 3.85%, becoming 0% in the following rank class of 26.  
By moving to Scenario 3, it can be noted that the rank classes covering the majority of \(NPV_{s}\) values range from 10 to 24 (94.98% of \(NPV_{s}\) values), which is different from the 6 to 24 rank range identified for Scenario 2, meaning that in Scenario 3 the average value of the \(NPV_{s}\) will always be higher than the average value resulting from Scenario 2. Analyzing the cumulative distribution function, it can be seen that P (\(NPV_{s}>10\)) is 89.42%, becoming 100% in rank class 8, and P (\(NPV_{s}>24\)) is 5.02%, becoming 0 in rank class 26.
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 4 Goal-Oriented Requirements Analysis Approach for IoT Software Systems
### Application Example: IoT System for Monitoring Air Quality
#### 4.2.2 User Story  
* As a homeowner concerned about environmental health,
* I want to integrate IoT devices to monitor indoor air quality
* To obtain real-time information on pollutant levels and take proactive measures to improve indoor air quality.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
###### Abstract  
Non-orthogonal multiple access (NOMA) has become a promising technology for 5G. With the support of effective resource allocation algorithms, it can improve the spectrum resource utilization and system throughput. In this article, a new resource allocation algorithm in the NOMA-enhanced cellular network with device-to-device (D2D) communications is proposed, in which we use two new searching methods and an optimal link selection scheme to maximize the system throughput and limit the interferences of the NOMA-based cellular network. In the proposed joint user scheduling, tree-based search power allocation and link selection algorithm, we simplify the solving process of previous methods and  
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+++  
Then we allocate power to the CUs of each SC, which can be updated by Algorithm 2.  
Finally, we assigned the DUs of the system into each SC. In this part, similar to CUs, we assume that each DU has the same power and define \(U_{\max}^{D2D}\left(U_{\max}^{D2D}\leq U_{\max}\right)\) as the maximum number of DUs allocated on each SC. \(U_{un}^{D2D}\) is initialized to record the DUs who have not been allocated to any SC. We calculate the transmission power of each DU by (18) with the condition that \(\mathrm{SINR}_{\mathrm{L},un}^{D2D}=\mathrm{SINR}_{thr}^{D2D}\). In the scheduling process, we need to find the DUs which have larger channel gains than others and access them to the corresponding CUs if the number of CUs multiplexed on this SC is less than \(U_{\max}^{D2D}\). If the number of DUs is equal to \(U_{\max}^{D2D}\), the DUs should be selected from the DU sets \(U_{m,possible}^{D2D}\) which includes all the DUs who prefer to be assigned on the \(m-\)th SC. The DUs which can provide the maximum data rates in this SC will be allocated on this SC. The DUs which have not been allocated on the SC will be returned to \(U_{un}^{D2D}\). This part of algorithm ends when there is no DU left to be allocated.  
Since the maximum CUs and DUs can be multiplexed on the same SC is less than \(U_{\max}\), the global optimal solution can only be obtained by the exhaustive search method (ESA), which has exponential complexity with respect to the number of SCs. In order to lower the computational complexity and increase the processing speed, we propose a tree-based power allocation scheme to for accelerating the searching progress in the next part. The convergence of the proposed algorithm is shown in Section 4.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Identification Experiments and Results  
#### 3.5.1 Introduction to Experiment Dataset  
The dataset used in this paper consists of images taken from Beijing Metro Line 6. These images are captured by the handheld DSLR camera. Additionally, the collected images are characterized in that the track is located in the middle of the image and each image is an RGB image with a resolution of 3456 \(\times\) 4608 pixels. The dataset contains 200 typical corrugation rail images as positive samples and 200 non-corrugation images as negative samples. In the experiment, 70% of the images of each class are used as the training images and the rest is used as the test images.
## 2 Description of Study Area  
The Beichuan River basin, a typical urbanized valley basin, is located in the northern part of the Huangshui River basin (Figure 1). The area is defined by the coordinates ranging from \(101^{\circ}77^{\prime\prime}\) E to \(101^{\circ}54^{\prime}11^{\prime\prime}\) E for longitude, and from \(36^{\circ}38^{\prime}33^{\prime\prime}\) N to \(37^{\circ}14^{\prime}17^{\prime\prime}\) N for latitude, covering a total area of 194.28 km\({}^{2}\). The basin exhibits a slender, elongated form, encircled by mountains on three sides, and has a central valley plain. The region's altitude ranges from 2215 m to 4375 m. The Huangshui River's first-level tributary, the Beichuan River, runs through the entire basin from north to south. This area has a semi-arid plateau continental climate. The average annual temperature is \(5.8~{}^{\circ}\)C, the highest temperature is \(15.0~{}^{\circ}\)C, and the lowest temperature is \(-12.0~{}^{\circ}\)C. The annual precipitation reaches 512.8 mm, with most of the rainfall concentrated in June to September, accounting for about 70% of the yearly precipitation. The annual evaporation amount is 1426.4 mm, nearly three times the rainfall, indicating a high likelihood of drought risk in the basin [47].  
Hydrogeologically, the aquifer system of the basin is mainly composed of two parts. On one hand, there is the porous water of the loose rock types in the central valley plain. On the other hand, there is the phreatic water of the loess and underlying gravel in the hills and gullies of the surrounding mountainous areas, as well as the fissured-porous water of the clastic rocks [48]. Overall, the basin is rich in groundwater resources with aquifers that exhibit good hydraulic properties. The well yield of the aquifers in the valley plain exceeds 100 m\({}^{3}\)/day for individual wells. In areas along the Middle and lower reaches of the Beichuan River where surface water replenishes groundwater, the well yield can even exceed 5000 m\({}^{3}\)/d. In the surrounding mountainous areas, the well yield of the aquifers  
Figure 1: Location of the (**a**) Huangshui Basin, (**b**) the Beichuan River basin, and (**c**) groundwater sampling site within the basin.  
also surpasses 8.64 m\({}^{3}\)/d for individual wells. Abundant groundwater resources play an irreplaceable role in supplying water to local residents, especially during drier periods.  
In recent years, as urbanization in the basin has intensified, both the quantity and quality of local groundwater have faced unprecedented challenges. The advancement of urbanization signifies an increase in population and a rise in water usage for industrial and agricultural purposes [49]. This poses significant pressure on the exploitation of groundwater in the region. Additionally, there is a certain degree of mutual conversion between local groundwater and surface water. This leads to the threat of various industrial and agricultural pollutants to groundwater as well.
## 3 Instrument Validation
### Power Measurement System  
Figure 12 shows the PMS measurements for the flight of December 2015 for all down-looking channels. These are stable, as expected for a sea surface target, although they show small variations that are coincident with the peaks measured in the DC offset correction system. The mean value for all of the PMS measurements is different. This is due to the tolerances of the electronic components building the PMS circuit, and it is not critical, as the differences can be characterized through the calibration procedures of the instrument.  
Figure 12: PMS values for the eight down-looking channels.
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 5 Dunhuang Murals Restoration System for Professional Restorers
### Architecture Design  
The system architecture consists of two components: the restoration end and the server (Figure 8). The restoration end directly interacts with professional restorers and automatically records the operation process, which contains the image restoration model and image database, to accomplish image restoration tasks. The image restoration model includes 15 generators for the two damage restoration methods described in Section 4.3, and the image database includes the original Dunhuang fresco images and images used in the model training.  
First, the user selects whether to crop the mural image to be restored. Then, the image is passed to the server to finish adjusting the image pixels, and the server then stores the damage type of the images as determined by professional restorers. Finally, the server invokes the restoration model. The restoration results are returned to the client according to the model training rounds, thereby completing the restoration.
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 2 Materials and Methods
### Overview of the Whole System  
The single-shot coherent X-ray imaging instrument consists of a sample chamber for sample delivery, a loadlock chamber for fast sample exchange, and a detector part for photon measurement. The sample chamber is widely utilized with tightly focused X-ray pulses and optimized for single-shot-based experiments. Tightly focused X-ray pulses by Kirkpatrick-Baez mirrors (KB mirrors) with a ~5.96 m long focal length are delivered to the sample chamber [18]. Since specimens are destroyed by a micron focused XFEL pulse [19], fresh samples are supplied to the interaction region using thin Si\({}_{3}\)N\({}_{4}\) membranes mounted on high-speed piezo stages. Diffraction patterns are collected by a multiport charge-coupled device (MPCCD) detector. To avoid damage to the detector by a direct beam, Ta metal is used as a beam stopper. Figure 1 shows the single-shot coherent X-ray imaging instrument, including KB mirrors, at the NCI endstation of PAL-XFEL.