esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data  
### Study Area  
The Tibetan Plateau (Figure 1), referred to as the \"Roof of the World\", is a unique and critical research region due to its vast size and diverse environmental conditions. Located in Central Asia, the Tibetan Plateau covers over 2.5 \(\times\) 10\({}^{6}\) km\({}^{2}\) and has average elevation exceeding 4500 m, making it the highest and largest plateau in the world. The region's topography is uneven, with elevation decreasing from west to east, featuring flat valleys and plains alongside towering mountain ranges [9]. The Tibetan Plateau is rich in frozen soil, snow, and glacial resources, playing a pivotal role in the Asian hydrological cycle. It serves as the source of major rivers, such as the Yangtze, Yellow, and Mekong, earning its designation as the \"Water Tower of Asia\". The plateau also supports a wide range of ecosystems with unique flora and fauna that have adapted to its harsh climate, characterized by cold temperatures, strong winds, and fluctuating diurnal temperatures. Moreover, the Tibetan Plateau is extremely sensitive to global climate change. Over the past 50 years, the mean annual temperature on the Tibetan Plateau has increased by 0.3 \({}^{\circ}\)C per decade, a rate significantly higher than in other regions of China [35]. This rapid warming has profound effects on the alpine vegetation, including an advanced spring phenology and enhanced plant carbon fixation capacity. The major vegetation types on the Tibetan Plateau include alpine grasslands, alpine meadows, shrubs, and forests [36]. Alpine grasslands dominate the plateau, consisting primarily of hardy grasses and sedges adapted to high altitudes and cold conditions. The GPP is a key focus of study in this region, as it represents the total carbon fixed by photosynthetic organisms, offering critical insights into carbon sequestration and ecosystem productivity.
## 5 Performance Evaluation
### Performance Matrices
#### 5.2.2 Percentage of Requests Satisfying QoS  
The percentage of requests satisfying QoS is calculated as the ratio of the successfully completed services to the total number of arrived requests. A higher value indicates that a higher number of service requests have been processed by the system satisfying the QoS requirements, hence higher user satisfaction.  
\[q\delta s_{r}=\frac{R^{e}}{R^{a}}\times 100\%, \tag{18}\]  
where \(R^{e}\) denotes the number of QoS satisfied requests and \(R^{a}\) is the total number of requests.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of BBU & 5 \\ BBU processing speed & 20\(\sim\)50 MHz \\ Number of RRH & 10 \\ Incoming data per request to be processed & 20\(\sim\)600 KB \\ Maximum allowable delay (QoS) & 20\(\sim\)200 ms \\ RSSI value & \(-\)15\(\sim\)\(-\)75 dB \\ Simulation Duration & 500 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
## 4 Discussion
### False Readings  
Although the in situ regressions poorly fit, false negative and positive error rates were useful for comparing XRF in situ and AAS results. According to the USEPA Superfund soil screening guidance, a study should aim for a false negative rate of 5% or below and a false positive rate of 20% or below [16]. The 0-2 cm depth had the highest false-negative rate (8.33%) (Table 3). Two samples were false negatives for all depths, both of which were along waterways fed by mining wastewater and in areas prone to flooding. These samples were taken at depths 0-20 cm and 0-2 cm. It is possible that both samples contained elevated levels of Hg below 1 cm XRF in situ depth), but not on the surface, possibly due to being located in a flood-prone area contaminated by water with elevated Hg concentrations. False negatives can also be caused by different analytical techniques between XRF and AAS, and because XRF in situ results were not at the same locations where ex situ samples were collected for AAS analysis. XRF analyzes heavy metal concentrations for a point on the soil (up to 1 cm deep), and multiple analyses are used to determine an average for the whole subarea (Section 2.1). On the contrary, ex situ samples are randomly taken in a subarea without any consideration of in situ analysis locations. Thus, the soil analyzed in situ is not the same as the soil analyzed ex situ (Section 2.1).  
Because the 0-2 cm depth false negative percentage is based on a low number of samples (N = 12), the false negative rate of all depths (N = 49) was used. Using all depths, the false negative rate was 4.08%, satisfying the USEPA Superfund recommendation.  
False positive calculations were computed for three different criteria including the in-field criterion (20 mg/kg), the US RSL (23 mg/kg), and the Kyrgyz Hg in soil standard (2.1 mg/kg). Using all criteria for Hg in soil, all depths were less than the USEPA's Superfund false positive rate recommendation of 20%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & **Total** & **In-Field** & **US RSL** & **Kyrgyz** & **Notes** \\  & **Samples** & **Criterion** & **Percent** & **(23 mg/kg)** & **Percent** \\  & & **(20 mg/kg)** & & **(2.1 mg/kg)** & \\ \hline
0–2 & 97 & 2.1 & 2.1 & 9.3 & NA \\ \hline
2–10 & 76 & 5.3 & 1.3 & 14.5 & NA \\ \hline Combo & 212 & 2.8 & 1.4 & 10.4 & Excludes Litter \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of false positives for all depths compared to three criteria. The in-field criterion column is the physical soil sampling criterion (Section 2.1). The Kyrgyz and US RSL columns refer to the Kyrgyz standard and US RSL for Hg in soil, respectively.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology
### Continuous Wavelet Transform  
CWT is a mathematical tool utilized to examine non-stationary signals by decomposing them into time-frequency components. It provides a representation of the signal at multiple scales and resolutions, making it particularly useful for detecting transient features. A source wavelet function is used by the CWT to transform a TD signal into the time-frequency domain. This source, often known as the \"mother\" wavelet, is typically a brief time-based  
Figure 1: Proposed methodology architecture.  
signal that vibrates in cycles. The decomposition process starts with the mother wavelet and divides the complex signal into coefficients that are scaled and translated according to certain parameters [36]. The CWT is collected by summing scaled and shifted copies of the wavelet function across the entire time domain. The CWT of a X (t) signal is defined mathematically as follows:  
\[CWT_{x}\left(\tau,s\right)=\frac{1}{\sqrt{|s|}}\ \int_{-\infty}^{+\infty}X\left(t \right)\cdot\psi^{*}\cdot\left(\frac{t-\tau}{s}\right)dt \tag{1}\]  
where the wavelet's center frequency and window length are determined by s, and its position in the time domain is indicated by \(\tau\). Greater detail is revealed by smaller scales, which correspond to higher frequencies, while larger scales provide general information about the signal by matching to lower frequencies.  
The Morlet wavelet as \"amor\" was selected as the basis function for CWT due to its ability to balance time and frequency resolution effectively [37]. Its Gaussian envelope minimizes spectral leakage, which is essential for analyzing transient AE signals. This choice ensures precise localization of leak-related features in the time-frequency domain. The frequency range of interest was set to [1 Hz, 500 kHz] to enclose all relevant spectral components of AE signals. By defining this range, the CWT algorithm dynamically determines the scales, ignoring the need to explicitly set decomposition levels and ensuring optimal representation of the AE signal features.  
CWT generates a 2D transformation matrix when it is applied to real AE data from a pipeline. The scale of the AE signal is represented by each row in this matrix, while the translation or size of the pipeline AE signal is represented by each column. These two-dimensional transformation matrices can be seen as what are known as AE scalograms, which are pictures where the color intensities represent the different wavelet energy levels across time and frequency, representing variations in the pipeline's operating conditions.  
Different energy regions that correlate to variations in pipeline conditions are clearly seen in the AE images. Figure 2a,b show scalogram images of pipelines working under normal and defective conditions, respectively.
## 1 Introduction
This model is then applied to structural damage identification using the acceleration response data from multi-sensor measurement points on a steel truss structure as an example. In different operating conditions, multiple sets of raw acceleration vibration response signal data were acquired from sensors installed on a steel truss bridge structure. The acquired data was subjected to noise contamination and then decomposed using empirical mode decomposition (EMD) and variational mode decomposition (VMD). Comparative evaluation of the obtained sets of intrinsic mode functions (IMFs) based on time-frequency representations confirmed the data value of the IMF components derived from VMD decomposition. Subsequently, the selected IMFs were concatenated and augmented with the original data. The resulting dataset was then fed into a convolutional neural network (CNN) model with the integration of a self-attention mechanism for feature extraction and damage identification. The model was capable of assessing the extent of damage and performing damage localization. By conducting a comparative analysis between variational mode decomposition (VMD) and empirical mode decomposition (EMD), we employed the VMD-CSANN model to process signal data containing typical levels of background noise, and subsequent comparison with CNN, LSTM, and GRU models. The study confirms the effectiveness and feasibility of the VMD-CSANN model in the identification of engineering structural damage.
## 3 Results
### Gas Hydrate Dissociation Enthalpies (\(\Delta H_{diss}\))  
The participation of inhibitors in the hydrate crystalline structure could be evaluated by measuring the gas hydrate dissociation enthalpy, \(\Delta\)H\({}_{\text{diss}}\)[15]. The \(\Delta\)H\({}_{\text{diss}}\) of the systems tested in this study is presented in Table 7.  
It is well established that CO\({}_{2}\) gas hydrates form structure I hydrates [7; 51; 52; 53]. The main parameters that alter the dissociation enthalpy of hydrates are the hydrogen bonding of the clathrate structure and the cage occupancy of the guest molecule [54]. The results obtained showed that the \(\Delta\)H\({}_{\text{diss}}\) value did not vary significantly in the presence of DES, which signifies DES molecules having no contribution in the hydrate crystallization process. As the enthalpy values were almost similar without the inhibitor, it can be deduced that THIs behavior shown by the studied DESs are mainly due to the hydrogen bonding ability.  
A key advantage of DES is also the ability to solve the corrosion issues in the pipeline. It is well known the most gas hydrate inhibitors often have antagonistic behavior, which promotes corrosion while suppressing the hydrate formation [12]. The detailed corrosivity study by [PERSON] et al. [55] on the CO\({}_{2}\) saturated DES system and CO\({}_{2}\) saturated MEA system had a corrosion rate of 0.027 mm/year and 0.54 mm/year, respectively. The results prove that the DESs of Cholinium Chloride and Levulinic Acid are good candidates for corrosion inhibition in the fuel pipelines. There are several potentials to be exploited with the introduction of Deep Eutectic Solvents.
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 3 Extension of Rank-Based Ant System
### Pheromone Smoothing Mechanism
In order to incorporate the smoothing strategy into an ACO algorithm, it is also necessary to define when the pheromone smoothing is triggered. For this purpose, it is necessary to select a measure of how close the algorithm is to stagnation and based on this measure, define a criterion that triggers the pheromone smoothing strategy. Several measures can be used to describe the amount of exploration an ACO algorithm performs in order to detect stagnation situations, such as the standard deviation of the length of the tours, the average number of arcs that are not common in every pair of ant tours, and the average \(\lambda\)-branching factor [14]. Despite these last two criteria being good indicators of the size of the search space effectively being explored, they require a high computational cost. For instance, the average \(\lambda\)-branching factor is used in [11] for triggering occasional pheromone initialization of MMAS, but due to its high computational cost, it is only calculated every 100 algorithm iterations. On the contrary, the standard deviation between the fitness of ant's tours is a more efficient way to measure the diversity among the solutions generated at an iteration. The standard deviation, \(\sigma_{L}\), of the ant tours of an iteration decreases when the search starts exploiting a concrete region of the search space. Additionally, in the case where \(\sigma_{L}\) reaches a null value, we can assume that all ants are taking the same paths (as it is very unlikely that different tours have the same length) and thus, \(\sigma_{L}=0\) is an indicator that the algorithm has fallen in a stagnation situation. Due to its lower computational requirement, \(\sigma_{L}\) is the criterion selected for the proposed ACO algorithm. Figure 2: A visual representation of the pheromone matrix: (**left-right, top-bottom**) the pheromone values obtained with AS\({}_{Rank}\) applied to the TSPLIB instance _burn14_ with 14 cities, after 0, 5, 10 and 100 iterations, respectively. Lastly, it is worth clarifying the difference between the discussed dissimilarity functions, which measure the dissimilarity of a group of ants tours, with respect to the originality function presented in Section 3.1, which measures the dissimilarity between a unique solution with respect to several of tours. Additionally, while the former are generally used with the intention of detecting the approach to stagnation situation, the latter is proposed with the intention of promoting solutions that diversify the search.
# Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems
## 4 Experiments
### Comparison Results  
Table 2 presents a comparison between our proposed E-MTAM and baseline methods across three typical vehicle routing problems: (1) TSP; (2) CVRP; and (3) OP. Our results demonstrate that E-MTAM consistently outperforms existing state-of-the-art baseline methods. For TSP, E-MTAM achieves the best optimality gaps of **0.26%**, **0.35%**, and **0.77%** for problem sizes of 20, 50, and 100 nodes, respectively. Notably, as the problem size increases, the advantage of E-MTAM becomes more pronounced. For the most challenging TSP100, E-MTAM reduces the gap by **0.65%** compared to the second-best method, POMO. This significant advantage is also observed in the CVRP experiments, where E-MTAM achieves gap reductions of **4.26%**, **5.11%**, and **6.39%** compared to the milestone method (AM) for CVRP20, CVRP50, and CVRP100, respectively. In comparison to heuristic algorithms (i.e., ACO and EMA), E-MTAM attains gap reductions of **7.93%** and **6.84%** for CVRP100 while  
requiring less computational time. For OP, E-MTAM clearly outperforms other DRL-based methods, achieving the highest reward values of **5.35**, **16.06**, and **32.85** for OP20, OP50, and OP100, respectively. Regarding running time, while the encoder introduces additional computational overhead due to the aggregation of edge information, and the multi-decoder architecture of E-MTAM results in slightly slower computation compared to AM, E-MTAM still outperforms exact and heuristic algorithms by orders of magnitude in terms of speed. Furthermore, compared to other DRL-based methods, E-MTAM strikes a better balance between effectiveness and efficiency. To facilitate a more comprehensive comparison, we present the training curves of E-MTAM and AM on CVRP20, CVRP50, and CVRP100. As shown in Figure 4, the average tour length of both models steadily decreases during the training process and eventually
## 2 Study Area & Data Sets
### InSAR & MAI Data Processing
To measure MAI deformation using COSMO-SkyMed SAR data, it was necessary to correct the phase oscillation along the azimuth direction. Unlike other SAR images, a COSMO-SkyMed MAI interferogram exhibits an additional oscillation phase pattern. The fluctuation range of this sinusoidal phase is approximately \(\pm\)0.044 radians. This corresponds to an approximately \(\pm\)4 cm displacement in the along-track direction. We did not identify the main reason for phase oscillation in the along-track direction, but the most likely causes are a clock error in the satellite or an oscillation in the altitude of the satellite. The magnitude of the fluctuation is approximately \(\pm\)4 cm, but the oscillation phase patterns were different in each MAI interferogram. We attempted to correct the oscillation phase with its own values for each MAI interferogram. The phase screen for the correction was computed by averaging the uncorrected MAI phase with the range direction within the stable region. Some residual errors remained after this approach, but they were reduced in further processing _via_ stacking. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Ascending Pairs**} & \multicolumn{3}{c}{**Descending Pairs**} \\ \hline
**Acquisition Date** & **B\(\pm\)(m)** & **B\({}_{\text{t}}\)(Day)** & **Acquisition Date** & **B\(\pm\)(m)** & **B\({}_{\text{t}}\)(Day)** \\ \hline
20150206\_20150522 & 291.89 & 105 & 20150209\_20150601 & –241.63 & 112 \\
20150206\_20150525 & –73.29 & 108 & 20150209\_20150618  
The 3D components of surface deformation were retrieved by combining multi-stacked InSAR and MAI measurements from ascending and descending data sets. The east (\(u_{e}\)), north (\(u_{n}\)), and up (\(u_{u}\)) components were retrieved using the equations defining InSAR (\(d_{{}_{los}}\)) and MAI (\(d_{{}_{MAI}}\)) measurements in previous studies [9]. \[(u_{n}\sin\varphi-u_{e}\cos\varphi)\sin\theta+u_{u}\cos\theta=d_{{}_{los}} \tag{1}\]  
\[u_{n}\cos\varphi+u_{e}\sin\varphi=d_{{}_{MAI}} \tag{2}\]  
where the track angles (\(\varphi\)) of \(-10.53\)\({}^{\circ}\)and \(-169.58\)\({}^{\circ}\)and look angles \(\theta\) of 38.76 \({}^{\circ}\)and 41.34 \({}^{\circ}\)were used for the ascending and descending data, respectively.
## 4 Numerical Simulation
### Damage Identification Based on the Proposed Method
#### 4.6.1 Damage Case 1  
In the optimization process, the initial value of the damage factor (usually \(\mathbf{\mu}_{0}=[1,1,\cdots,1]\), i.e., the undamaged state) is first selected. After calculating the value range of the objective function of mode shape and frequency respectively, the weight coefficients of mode shape and frequency in optimization are estimated: \(\alpha_{p}=2.3208\times 10^{5},\alpha_{\omega}=129.8002\). Then, the damage factor is updated iteratively using the fmincon optimization toolbox in MATLAB until the objective function reaches its minimum value. The gradient of the objective function, as derived in Section 3.3.4, is included for faster or more reliable computations. The identification results of damage factors are shown in Figure 6 and Table 3. It can be seen from Table 3 that the results identified using the improved and traditional objective function are both close to the theoretical values, and the error is less than 0.03, which meets the requirements of damage identification. The improved objective function proposed contains the measured modes of actual damaged structures and the FRF constructed by the theoretical FE model using the SVDM. The damage identification method based on the frequencies and mode shapes has been validated in numerical and experimental investigation and the references are given in the introduction. The SVDM utilized in the improved method has also been verified by experimental research in Reference [39]. In the simulation, the accuracy of the improved method is proved to be consistent with the traditional method by the theoretical research, so the effectiveness of the improved method is verified and confirmed.  
Moreover, the computational efficiency is discussed. In the numerical simulation, there are 144 DOFs of the FE model. When the traditional objective function is adopted, all DOFs should be calculated to obtain the structural modes, so the computational cost is of the order of O(144\({}^{3}\)). However, the number of damage factors to be identified is eight, and the number of main virtual distortions is about 50. So the computational cost based on the improved objective function is of the order of O(6 \(\times\) 50\({}^{3}\)), which shows that the proposed method using an improved objective function is more efficient. In practical calculation, the time of optimization is about 0.83 s by using the improved objective function, while by using the traditional objective function the time of optimization is about 2.73 s. As a result, the computational efficiency is increased approximately three times using the method proposed in this numerical simulation.
###### Abstract
These surrogates are extensively studied to elucidate fundamental combustion data and validate kinetic models [13]. Consequently, research on LBV for aviation kerosene primarily focuses on n-alkanes within the C10-C14 range, with considerably limited investigation into non-key components, particularly iso-alkanes and cyclic compounds [14]. Reducing aviation carbon emissions is essential for the industry's long-term sustainability. The International Air Transport Association (IATA) has announced its commitment to achieving Fly Net Zero by 2050 [15], and the development of SAF technology stands as the most crucial means to achieving this goal [16]. Unlike conventional petroleum-based aviation kerosene, SAFs can be derived from biomass, waste oils, or solid waste. Consequently, SAFs differ in composition and distribution from traditional aviation kerosene. For instance, in Fischer-Tropsch hydroprocessed synthesized paraffinic kerosine (FT-SPK), synthesized paraffinic kerosine hydroprocessed esters and fatty acids (HEFA-SPK), synthesized iso-paraffins (SIPs), alcohol-to-jet synthetic paraffinic kerosene (AT)-SPK), and hydroprocessed hydrocarbons, esters, and fatty acids (HC-HEFAs), the maximum mass fraction of aromatics cannot exceed 0.5%. Particularly in SIPs, the mass fraction of saturated hydrocarbons must exceed 98% [17]. Due to the presence of aromatic compounds, petroleum-derived fuels exhibit lower LBV compared to SAFs [18]. This leads to differences in combustion flame lengths and changes in thermal loads on combustion chamber walls or in the recirculation zone, thereby affecting engine performance [19]. To minimize the pronounced influence of hydrocarbon fuel composition and proportion on engine functionality, certified SAFs must be blended with conventional aviation kerosene, with a maximum blending ratio of 50% [20]. To meet airworthiness requirements, ensuring aircraft and aviation engine safety and performance remain at the same level as traditional fuels [21], in-depth research on the LBV of SAFs and its individual components is essential.