esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
With the development of deep learning, convolutional neural networks based on time series data, long short-term memory models, and multi-model hybrid models have been widely used and have achieved excellent performance [12]. Large amounts of data are required for data training of time series prediction models. The early deployment of seismic monitoring equipment and advances in observational monitoring techniques have facilitated the development of multiple types and long-time series of seismic data. These have provided favorable conditions for the application of time series forecasting models in the field of seismology. [PERSON] et al. [13; 14] used the ARIMA algorithm for the short-term prediction of total electron content (TEC) and detected several pre-earthquake anomalies. In another paper published in the same year, the authors reported a better performance of the long short-term memory (LSTM) model than the ARIMA model in detecting seismic ionospheric anomalies. Some researchers have made improvements to the LSTM algorithm. A multi-network-based hybrid long and short-term memory (N-LSTM) model was proposed by [PERSON] et al. [15]; [PERSON] et al. [16] combined the LSTM model and the relative power spectrum method for TEC anomaly detection; [PERSON] et al. [17] proposed an encoder-decoder extended short-term memory expansion model; all these improved models obtained better performance in their article applications. In radon time series studies, [PERSON] et al. [18] used the ARIMA model and Monte Carlo prediction model to find a good correlation between soil radon and micro-seismicity in the study area; [PERSON] et al. [19] used different sets of algorithms to predict time series and detect anomalies for real-time soil radon time series of different scenarios; [PERSON] et al. [20] studied groundwater radon and used an empirical mode decomposition-long short-term memory (EMD-LSTM) model to find multiple possible radon anomalies before earthquakes. In addition to TEC data and radon data, surface temperature, geoelectric seismic signal, seismic energy release, b-value (Gutenberg-Richter law's b-value), groundwater level, OLR, and geomagnetic data with time series properties have all been used by scholars and based on time prediction models to study pre-earthquake sequence anomalies. This paper first describes traditional methods for studying longwave radiation data and analyzing seismic sequence data based on different time series prediction models. The data section presents the data sources and data processing methods used and selects the Yangbi _M_s6.4 earthquake of 21 May 2021 and the Luding _M_s6.8 earthquake of 5 September 2022 as research cases. Conventional anomaly extraction methods were able to detect pre-seismic OLR anomalies for both earthquakes. The Methods section describes time series prediction models and anomaly assessment methods. The Discussion section discusses the strengths and weaknesses of this experiment, how the results compare with those of previous studies, and future directions for improvement. Finally, conclusions were drawn for the whole text.
## 4 Experimental Results
### Electrical Characterization
For a \(\lambda/4\) whip antenna at the transmitter and a dipole antenna at the receiver side, the received power in a free space is shown in Figure 19. The sensitivity of the receiver has been measured to be \(-74\) dBm for a \(BER\) of \(10^{-5}\), close to the estimated theoretical value predicted by Equation (27) of \(-79\) dBm, allowing a transmission range larger than \(30\) m. The system has also been successfully tested for a \(10\) m distance between transmitter and receiver in a hostile environment as a neuroscience laboratory, giving a PA efficiency of about \(7\%\) while delivering a \(-2\) dBm at a \(50\)-\(\Omega\) output load. Disabling the PA, the tank inductor may act as transmitting antenna; in such measurements about \(-70\) dBm was received by a loop antenna placed at approximately \(5\) cm from the tank inductor. The overall performance of the implemented system is provided in Table 3. The power consumption of the presented circuit has been computed by projecting the performance to a complete \(64\) channel system. To this end, the additional budget needed for the missing \(48\) amplifiers has been added to the actual power consumption (\(16.6\) mW) leading to \(17.2\) mW (\(6.7\) mW with the PA turned off). The estimate is fair since all the other circuit blocks work with the same data rate of a full \(64\) channel system. Figure 12:0 Spectrum of the modulated signal. Figure 12:1 Received power as a function of the distance between transmitter and receiver antenna in free-space. A dipole antenna was adopted at the receiver side. \begin{table}
\begin{tabular}{c c} \hline Technology & \(0.35\)\(\mu\)m AMS \\ Supply Voltage & \(3\) V \\ Number of channels & \(64\) (\(16\) available) \\ Pre-amplifier gain & \(65\) dB \\ Pre-amplifier band & HP tunable-\(10.5\) kHz \\ Input-Referred Noise & \(3.05\)\(\mu V_{rms}\) (\(f_{HP}=300\) Hz) \\ ADC DNL-INL & \(<0.45\) LSB; \(<1\) LSB \\ ADC ENOB & \(7.2\) \\ Transmission frequency & \(400\) MHz \\ Bit-rate & \(1.25\) Mbit/s \\ Current Consumption & \\
20-MHz crystal oscillator & \(90\)\(\mu\)A \\ Pre-amplifiers & \(4.3\)\(\mu\)A \(\times 64=275\)\(\mu\)A \\ Line buffers & \(25\)\(\mu\)A \(\times 2=50\)\(\mu\)A \\ VGA & \(50\)\(\mu\)A \\ ADC buffer & \(250\)\(\mu\)A \\ ADC & \(410\)\(\mu\)A \\ DSP+2 kbit RAM & \(400\)\(\mu\)A \\ Modulator (PLL) & \(700\)\(\mu\)A \\ Power amplifier & \(3.5\) mA \\ Total power consumption & \(6.7\) mW (\(17.2\) mW with PA enabled) \\ \hline \end{tabular}
\end{table} 
Table 3: Performance summary.
## 1 Introduction
### Literature Survey  
Table 1 presents a comprehensive summary of recent studies that utilized deep learning approaches for anomaly detection and predictive modeling in oil and gas drilling applications. Various techniques, including artificial neural networks, convolutional neural networks, gated recurrent units, and long short-term memory models, have been used to counter problems such as stuck pipe occurrence, lost circulation, and inefficiency in hole cleaning. Hybrid models that combine machine learning with fuzzy logic or the attention mechanism have been discovered to offer improved prediction performance and robustness in real-time operation environments. The outcomes indicate that deep learning-based models outperform the traditional techniques consistently, with some models achieving more than 90% accuracy in detecting drilling anomalies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & \begin{tabular}{c} **Deep Learning** \\ **Approach** \\ \end{tabular} & **Application/Parameters** & **Key Finding** & **\# Parameters Used** & \begin{tabular}{c} **Training Data** \\ **Type** \\ \end{tabular} \\ \hline \multirow{3}{*}{[32]} & \multirow{3}{*}{ANN, SVM} & \multirow{3}{*}{\
\begin{tabular}{c} Stuck pipe prediction from \\ real-time drilling data \\ \end{tabular} } & ANN & \multirow{3}{*}{19} & ANN \\  & & & &
\begin{tabular}{c} outperformed \\ SVM with 88.89\% \\ accuracy \\ \end{tabular} & & & \\ \hline \multirow{3}{*}{[33]} & \multirow{3}{*}{ML Models} & Gas leak detection via & High classification & \multirow{3}{*}{12} & \multirow{3}{*}{Both} \\  & & sensors \& IR & precision & \\  & & thermography & demonstrated & & \\ \hline \multirow{3}{*}{[34]} & \multirow{3}{*}{CNN, GRU, LSTM} & Lost circulation severity & CNN achieved & \multirow{3}{*}{20} & ANN \\  & & classification & 98\% accuracy & & \\ \hline \multirow{3}{*}{[35]} & Ensemble DL & Subsea pipeline anomaly & Ensemble achieved & \multirow{3}{*}{18} & ANN \\  & Models & classification & up to 99\% accuracy & & \\ \hline \multirow{3}{*}{[36]} & \multirow{3}{*}{ANN, Fuzzy Logic} & Stuck pipe prediction via & F1-score of 0.98, 1\% & \multirow{3}{*}{10} & \multirow{3}{*}{Normal} \\  & & friction probability & false alarms & & \\ \hline \multirow{3}{*}{[37]} & \multirow{3}{*}{RBFNN, MELM} & Filtration volume & RMSE of 0.6396 & \multirow{3}{*}{2} & \multirow{3}{*}{Normal} \\  & & prediction in drilling & mL achieved & & \\ \hline \multirow{3}{*}{[38]} & \multirow{3}{*}{ATT-LSTM} & Stuck pipe detection with & Accuracy & \multirow{3}{*}{10} & \multirow{3}{*}{Normal} \\  & & data augmentation & improved by & & \\ \cline{1-1} \cline{4-4} \cline{6-6} \cline{8-6} \
## 2 Materials and Methods
### Evaluation of Image Performance  
In this study, the CNR and ERD were used as quantitative evaluation methods for CT image quality. The CNR is a representative factor that can be used to observe the contrast and noise levels of an image in combination; the equation is as follows:  
\[\text{CNR}=\frac{|\text{S}_{\text{A}}-\text{S}_{\text{B}}|}{\sqrt{{\sigma_{\text {A}}}^{2}-{\sigma_{\text{B}}}^{2}}} \tag{2}\]  
where S\({}_{\text{A}}\) and \(\sigma_{\text{A}}\) represent the average and standard deviation of the signal strength in the target area, respectively; S\({}_{\text{B}}\) and \(\sigma_{\text{B}}\) represent the average and standard deviation of the signal strength in the background area, respectively. In addition, the ERD is a representative factor that can evaluate the degree to which the boundary part of the image is preserved; it can be defined as the distance between the points representing 10% and 90% of the CT number. The region of interest (ROI) for measuring the CNR and ERD is shown in the CT sample image in Figure 2.  
In addition, to check the overall CT image quality, a blind/referenceless image spatial evaluator (BRISQUE) proposed by [PERSON] et al. was used as a representative factor among the non-reference-based evaluation methods [21]. The formula related to BRISQUE evaluation is as follows: the smaller the value, the more similar it is to the ideal image quality.  
\[\text{f}\Big{(}\text{x};\text{\alpha},\sigma^{2}\Big{)}=\frac{\text{\alpha}}{2 \beta\Gamma(1/\text{\alpha})}\text{e}^{(-(\frac{|\text{x}|}{\beta})^{\text{x}})} \tag{3}\]  
where f is a function based on a generalized Gaussian distribution (GGD) that can measure BRISQUE; \(\alpha\) is a model parameter; \(\sigma\) is a variance; \(\Gamma\) is a gamma function; and \(\beta\) is \(\sigma\sqrt{\frac{\Gamma(1/\text{\alpha})}{\Gamma(3/\text{\alpha})}}\).
# Error-Correction Coding Using Polynomial Residue Number System
## 2 Modular Codes
### Codes of Residue Number System  
Relatively prime numbers, \(m_{1},\ m_{2},\ \ldots,\ m_{n}\), are used as bases in a code of RNS. The integer \(D\) is represented in a code of RNS as a tuple of residues:  
\[X=(x_{1},\ x_{2},\ldots,\ x_{n}) \tag{1}\]  
where \(x_{i}\equiv X\text{mod}m_{i}\) and \(i=1,\ 2,\ldots,\ n\).  
The product of the bases determines the range of possible code combinations, which is called the operating range:  
\[W_{n}=\prod_{i=1}^{n}m_{i} \tag{2}\]  
In order to unambiguously represent the integer \(X\) as a code combination of RNS, the following condition must be met:  
\[X<W_{n} \tag{3}\]  
The Chinese Remainder Theorem gives a bijection between an integer \(X\) that satisfies Condition (3), along with a residues vector, \((x_{1},\ x_{2},\ \ldots,\ x_{n})\). Thanks to this property, it is possible to perform calculations with sets of the short-length residues of long numbers rather than the long numbers themselves. At the same time, calculations for each of the modules can be performed in parallel [1; 2; 3; 4]:  
\[U\bullet X=((u_{1}\bullet x_{1})\text{mod}m_{1},\ldots,\ (u_{n}\bullet x_{n}) \text{mod}m_{n}) \tag{4}\]  
where \(\bullet\) are addition, subtraction, and multiplication operations, \(U\equiv u_{i}\text{mod}m_{i}\), and \(i=1,\ 2,\ldots,\ n\).  
Codes of RNS are able to increase the performance of arithmetic devices because calculations occur in parallel on the bases of the code and the operands have a small bit depth.
## 5 Data Analyses and Results
### Image Pre-Processing  
For each dataset, a number of pre-processing operations were needed to correct, co-register, better define and filter the pre-post image pairs.  
To better calibrate and validate the DIC analyses performed on the absolute SAR amplitude images, a temporal average filtering process was applied to the available ascending and descending CSK datasets (Figures 4 and 5). Hence, the environmental and instrumental background noise has been significantly decreased, leading to an increase in the effective signal. In this way, the signal-to-noise ratio (SNR), related to the horizontal displacement field measurement and estimation, has been increased as highlighted in Reference [39].  
Regarding the DIC analyses performed on the pre-post DTMs, a number of surface operations were performed in the GIS environment, and eight shaded reliefs were generated. The procedure was characterized by the sun-azimuth angle variation in steps of 45 degrees, with contemporary maintenance of the sun-altitude angle at 45 degrees. The DIC analysis was based on the correlation of the different combinations of the hillshade raster data. According to the authors of [33; 71], a COSI-Corr analysis has a higher probability of success for retrieving high-quality displacement results if the correlations are based on the shaded DSMs (or DTMs).  
The US Geological Survey's Earth Resources Observation and Science (EROS) Center [72] directly radiometrically corrected and coregistered the LANDSAT 8 OLI-TIRS images to a cartographic projection with rectifications for terrain displacement, thereby resulting in a standard orthorectified digital image. More information is available on the official website.  
Finally, both HR aerial optical orthophotos were oversampled at a 0.80 m/pixel geometric resolution because of their different initial geometric resolutions.
# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 1 Introduction
[13] devised a non-linear mathematical model to analyse the effects of sea level rise on coastal populations. However, these models are often purely theoretical [13] and may lack the detailed, traceable data required for effective urban planning [14]. Data inconsistencies, complexities with cross-domain data, and the narrow focus and scalability issues of GIS-based tools highlight the need for an improved approach to assessing sea level rise vulnerability, combining the granularity and data traceability of conventional GIS methods with the versatility to provide a multi-perspective analysis. The World Avatar (TWA) project addresses these challenges using a dynamic knowledge graph approach to integrate and utilise multi-domain data [15], which is essential for sea level rise impact analysis. By representing cross-domain information with ontologies as the building blocks, new data and knowledge can be seamlessly added with semantic meaning [16], ensuring transparency and traceability amongst disparate data sources. The purpose of this paper is to provide a multi-domain perspective vulnerability assessment that includes vulnerable infrastructure, affected land plots, population at risk, and vulnerable cultural sites at a country-wide scale and a site-specific scale. To achieve this, the study explores the application of computational agents, knowledge graphs and ontologies to enhance the connectivity of disparate datasets (e.g., government data, industry data, open-source data) and augment buildings with metadata including property usage, estimated construction cost, gross floor area, and building floor. Furthermore, it explores how agents identify and instantiate vulnerable sites based on sea level rise projection scenarios as well as using ontologies to integrate the sea level rise scenario with the instances of each vulnerable site. The sections of this paper are organised as follows: Section 2 details the problem and prior technical efforts; Section 3 introduces the methodology used and relevant data sources; Section 4 highlights the use cases and results; and Section 6 concludes the work.
## 2 Materials and Methods
### Flow Cytometry
#### 2.5.3 Assessment of Mitochondrial Membrane Potential (MMP)  
To assess mitochondrial membrane potential (\(\Delta\Psi_{\mathrm{m}}\)), a cell-permeable cationic and lipophilic dye, JC-1 (5, 5', 6, 6'-tetrachloro-1, 1', 3, 3'-tetraethylbenzimidazolcarbocyanine iodide), was used as previously described ([PERSON], 1993). This probe aggregates within mitochondria and fluoresces red (590 nm) at higher \(\Delta\Psi_{\mathrm{m}}\). However, at lower \(\Delta\Psi_{\mathrm{m}}\), JC-1 cannot accumulate within the mitochondria and instead remains in the cytosol as monomers, which fluoresce green (490 nm). Therefore, the ratio of red to green fluorescence gives a measure of the transmembrane electrochemical gradient. _L. infantum_ promastigotes (106 cells) were exposed to essential oil IC\({}_{50}\) concentrations for 24 h at 26 \({}^{\circ}\)C. Promastigotes were then incubated JC-1 (5 \(\upmu\)g/mL) (Molecular Probes, Invitrogen) in the dark for 15 min at room temperature. Then, cells were washed in PBS, suspended in 400 \(\upmu\)L of PBS, and analyzed by flow cytometry. Data analysis was carried out using the program Paint-a-gate.
## 4 Analysis of Fatigue Crack Propagation Test Results
### Effect of Notch Shape on Crack Propagation Rate
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Numbering** & **Crack Front Marking Technique** & \(a/\)**mm** & \(c/\)**mm** & \(a/c\) & \(N\) \\ \hline \multirow{6}{*}{S-1} & Crack front marking 1 & 0.371 & 1.484 & 0.250 & 20,000 \\  & Crack front marking 2 & 0.795 & 2.862 & 0.278 & 30,000 \\  & Crack front marking 3 & 1.272 & 3.180 & 0.400 & 40,000 \\  & Crack front marking 4 & 1.643 & 4.558 & 0.360 & 50,000 \\  & Crack front marking 5 & 2.544 & 6.572 & 0.387 & 60,000 \\ \hline \multirow{6}{*}{S-2} & Crack front marking 1 & 0.303 & 1.313 & 0.231 & 15,000 \\  & Crack front marking 2 & 0.757 & 2.727 & 0.278 & 22,000 \\ \cline{1-1}  & Crack front marking 3 & 1.414 & 4.444 & 0.318 & 29,000 \\ \cline{1-1}  & Crack front marking 4 & 1.868 & 6.565 & 0.285 & 36,000 \\ \hline \multirow{6}{*}{S-3} & Crack front marking 1 & 0.901 & 4.346 & 0.207 & 11,000 \\ \cline{1-1}  & Crack front marking 2 & 1.590 & 5.088 & 0.313 & 16,000 \\ \cline{1-1}  & Crack front marking 3 & 2.120 & 6.042 & 0.366 & 21,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test data of steel wire crack propagation with notches of different shapes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Numbering** & **Fitting** & **Coefficient** & **Coefficient** & **Fitting** & **Coefficient** & **Coefficient** \\  & **Curve** & **A (10\({}^{-4}\))** & **B** & **Curve** & **A\({}^{\prime}\) (10\({}^{-4}\))** & **B\({}^{\prime}\)** \\ \hline S-1 & \(a\)-\(N\) & 0.519 & \(-\)0.753 & \(c\)-\(N\) & 1.187 & \(-\)1.018 \\ S-2 & \(a\)-\(N\) & 0.765 & \(-\)0.864 & \(c\)-\(N\) & 2.496 & \(-\)2.603 \\ S-3 & \(a\)-\(N\) & 1.219 & \(-\)0.413 & \(c\)-\(N\) & 1.696 & 2.445 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Coefficient table. Figure 26: Variation of crack depth with cyclic numbers. Figure 27 shows the scatter diagram of _a/c_ varying with the cyclic loading numbers during the crack propagation of each group of steel wires. It can be seen that the ratio of a to c of each specimen is between 0.2 and 0.4, which indicates that in the case of uniaxial tension, the surface crack usually remains semi-elliptic during the propagation process. With the increase of cyclic numbers, the ratio of a to c gradually increases, which indicates that the crack propagation rate along the depth direction is higher than that along the width direction, but the shape of the crack front remains semi-elliptic during the crack propagation.
## 4 Results and Discussion
### Mixture Testing Results and Discussion
#### 4.2.2 Relaxation--Phase Angle  
Figure 10 shows the phase angle master curves of four kinds of mixtures. Generally, the higher phase angle indicates the better relaxation capability of mixtures, which can be an indicator of better resistance against cracking. The mixtures with modified binders show the higher phase angle values than the virgin mixtures, except for 4% EMAA, indicating that the 4% EMAA mixture may be more prone to cracking. Among all the mixtures, the 4%EMAA/3%SRB and 3%SBR-modified asphalt mixtures show the highest phase angle, which indicates a higher relaxation capacity under a certain load condition. It should be noted that the phase angle is always more variable than dynamic modulus, especially at higher temperatures/lower frequencies.  
Figure 9: Dynamic modulus master curve for different mixtures.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 4 Establishment of Dynamic Constitutive Model
### Creep Strain of Clay under Long-Term Static Load
On the other hand, bearing in mind the crack evolution and damage of soil skeleton during the loading process, many researchers [47; 48] have introduced damage variables into the viscoplastic body to depict the creep behavior of geotechnical materials. [PERSON] et al. [49] proposed a nonlinear viscoplastic body (NVPB) model to portray creep strain in the accelerating stage for green schist and verified its reliability by experimental data, with the corresponding creep equation given by  
\[\epsilon_{c}(t)=\frac{H(\sigma_{0}-\sigma_{\text{s}})}{\eta}t^{n} \tag{21}\]  
where \(\sigma_{0}\) is the normal stress, \(n\) is the rheological index and \(H\) can be expressed by  
\[H(\sigma_{0}-\sigma_{\text{s}})=\left\{ \begin{aligned} 0& \quad(\sigma_{0}\leq\sigma_{\text{s}})\\ \sigma_{0}-\sigma_{\text{s}}&\quad(\sigma_{0}>\sigma_{ \text{s}})\end{aligned} \right. \tag{22}\]  
It will be apparent that the constitutive model has been established by empirical formula without considering structure damages in the soil. Based on the research methods noted above, the damage variable \(D\) was introduced into the viscosity coefficient \(\eta\) in this paper, where \(D\) assumes damage evolution in the clay rheological process to be a negative exponential function, which can be described as  
\[D=1-e^{-\beta t} \tag{23}\]  
where \(\beta\) is a coefficient related to the properties of the clay, so that the viscoplastic coefficient can be written as  
\[\eta=\eta(1-D)=\eta e^{-\beta t} \tag{24}\]  
Therefore, a new viscoplastic body is proposed as shown in Figure 10. Following to the analysis above, a new creep equation for the accelerating stage is given as follows:  
\[\epsilon_{c}(t)=\frac{H(\sigma_{0}-\sigma_{\text{s}})}{\eta e^{-\beta t}} \tag{25}\]  
Figure 9: Variable-order fractional creep constitutive model. Bringing together the above, a full creep model for clay under long-term static load can now be obtained, as shown in Figure 11, and the corresponding creep equation can also be determined, so that, when \(\sigma_{0}\leq\sigma_{\text{s}}\), the creep constitutive equation is expressed as  
\[\varepsilon_{c}(t)=\frac{\sigma_{0}}{E_{0}}+\frac{\sigma_{0}}{\eta_{1}}\frac{t^ {\mu_{1}}}{\Gamma(\alpha_{1}+1)}\ \left(0<t\leq t_{1}\right) \tag{26a}\] \[\varepsilon_{c}(t)=\frac{\sigma_{0}}{\eta_{2}}\frac{\left(t-t_{1}\right)^{\alpha _{2}}}{\Gamma(\alpha_{2}+1)}\ \left(t_{1}<t\leq t_{2}\right)\] (26b) If \[\sigma_{0}>\sigma_{\text{s}}\] \[\varepsilon_{c}(t)=\frac{H(\sigma_{0}-\sigma_{\text{s}})}{\eta e^{-\beta t}} \ \left(t>t_{2}\right) \tag{26c}\]