esa-sceva/satcom-chunk-collection huggingface dataset:
# Typhoon Track Prediction Based on Deep Learning
## 2 Materials and Methodology
### Typhoon Dataset
#### 2.1.1 Dataset Acquisition
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Name** & **Description** & **Variable Name** & **Description** \\ \hline SID & Tropical cyclone number & & \\ SEASON & Year & BASIN & Ocean area code where the typhoon center is located, WP \\ \hline NUMBER & The base number of the system & & \\ \hline IFLAG & Identifies the observation point & & Maximum instantaneous wind speed near the center (i.e., gust). \\  & that provides data at the current moment & & Unit: knot \\ \hline WIND & Maximum sustained wind speed at the current moment. Unit: knot & & The direction of the quadrant corresponds to the radius. \\ \hline PRES & Minimum sea level pressure. & R\_DIR & NE-Northeast, SE-Southeast, \\  & Unit: hPa & & SW-Southwest, NW-Northwest \\ \hline SUBREGION & Sub-region code where the typhoon center is located & R\_LONG & The radius of maximum wind. \\ \hline NAME & International name of the typhoon & R\_SHORT & Minimum wind radius. Unit: nm \\ \hline ISO\_TIME & ISO format time. Recorded at & & \\  & 3-h intervals & & Track\_TYPE \\ \hline  & cyclone nature or category. & SPEED & The speed at which the center of a typhoon moves. Unit: knot \\  & DS-disturbance, TS-tropical storm, & & \\  & SS-subtropical storm, & & \\  & ET-extrapolation, NR-unreported, & & \\  & MM-mixed (conflicting reports & & \\  & among multiple agencies) & & \\ \hline LAT & Latitude at which the typhoon is & & \\  & currently located & & \\ \hline LANDFALL & Used to determine whether & & \\  & landfall is possible within 6 h & & \\ \hline DIST2 LAND & Distance from the current position & & \\  & to the nearest land. Unit: km & & \\ \hline  & SID & object PRES & object \\  & SEASON & int64 R50\_DIR & object \\  & NUMBER & int64 R50\_LONG & object \\  & BASIN & object R50\_SHORT & object \\  & SUBBASIN & object R30\_DIR & object \\  & NAME & object R30\_LONG & object \\  & IS0\_TIME & object R30\_SHORT & object \\  & CAT & object & \\  & LAT & float64 POCI & object \\  & LON & float64 POCI & object \\  & TRACK\_TYPE & object RNT & object \\  & DIST2 LAND & int64 EYE & object \\  & LANDFALL & object GUSTS & object \\  & IFLAG & object STORM\_SPEED & object \\  & WIND & object STORM\_DIR & object \\ \hline \end{tabular}
\end{table} 
Table 1: The specific composition of the best path data set for South China Sea typhoons. Figure 2: The data type of each data in the dataset.
## 2 Methods
### Nonlinear Frequency Response Analysis (NFRA)  
With NFRA, a sinusoidal input signal with high current amplitudes \(I_{AC}\) is applied to the system in a defined frequency range from mHz to kHz. Changes in the output voltage \(U_{AC}\) in the time domain are measured. By applying a Fast Fourier Transformation (FFT), the sinusoidal voltage output signal \(U_{AC}\) is transferred from the time domain to the frequency domain. However, not only the voltage signal corresponding to input frequency \(Y_{1}\), but also higher harmonic responses \(Y_{n}\) with \(n\geq 1\) are detected. Higher harmonic amplitudes of the responses \(Y_{2}\) to \(Y_{n}\) are observed at multiples of the fundamental frequency \(f_{1}\) of the sinusoidal input signal:  
\[f_{n}=n\cdot f_{1}. \tag{1}\]  
Even \(Y_{2n}\) and odd harmonics \(Y_{2n+1}\) have different characteristic responses. We examined these LIB characteristics in our previous work by investigating individual higher harmonic responses \(Y_{n}\) as well as the corresponding sum \(\sum\limits_{i=2}^{n}Y_{i}\) over the frequency [24]. In general, the values for Nonlinear Frequency Responses (NFRs) correspond to the sum of nonlinearities \(\sum\limits_{i=2}^{5}Y_{i}\) of the investigated cell.  
The working principle of NFRA as well as the processing of the voltage output from the time in frequency domain using an FFT is illustrated in Figure 3.  
Figure 3: NFRA-scheme making use of higher-order harmonics.
# Classification of Systems and Maintenance Models
## 9 Conclusions  
Novel classifications of systems for selecting maintenance effectiveness indicators, and the mathematical models used to calculate them, have been developed. The proposed system classification substantially facilitated the selection of appropriate maintenance indicators during the operational stage. This enabled us to select indicators based on the values of specified features. The proposed classification of maintenance models enables the systematization of existing models and the development of new models depending on the set of classification signs. The joint use of system and maintenance model classifications can significantly simplify the process of assessing the maintenance effectiveness of systems for various purposes. For the first time, we developed general mathematical models for calculating CBM effectiveness indicators, and criteria for the joint determination of an optimum inspection schedule and replacement thresholds for all classified systems.  
In the future, we plan to apply the proposed approach to develop mathematical models for the other subgroups of the one hundred and sixty-eight classified maintenance models in this study with different signs of the system structure in terms of reliability, type of inspection, degree of system restoration, and external manifestations of system failure.  
Conceptualization, V.U.; methodology, V.U. and A.R.; software, V.U.; validation, V.U. and A.R.; formal analysis, V.U.; investigation, V.U. and A.R.; data curation, V.U. and A.R.; writing--original draft preparation, V.U. and A.R.; writing--review and editing, V.U.; visualization,V.U. and A.R.; supervision, V.U.; project administration, A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations exist in the manuscript:  
AMCUT Average maintenance cost per unit of time  
AOPUT Average operating profit per unit of time  
APFFO A posteriori probability of failure-free operation  
CBM Condition-based maintenance  
CM Corrective maintenance  
FN False negative  
FP False positive  
ILS Instrument landing system  
ISO International Organization for Standardization  
LRU Line-replaceable unit  
MRS Maintenance and repair system  
OM Object of maintenance  
OPFFO Operational probability of failure-free operation  
RUL Remaining useful life  
SATCOM Satellite communication  
TN True negative  
TP True positive  
UC Unit costs
## 1 Introduction
To overcome this drawback, AE signal analysis is required using governing equations that take into account the structural characteristics of the storage vessel and the propagation dynamics of AE caused by leaks on the vessel surface. In addition, this method can only be used if the structure has a moderate attenuation of the AE signal. This technique can provide a simple solution where the exact location of the leak source is not critical. Over the past few decades, numerous studies have been conducted to solve the problems of pipeline leak localization using acoustic-based methods [9; 10]. However, there are still limitations and gaps in fundamental research dealing with signal modeling, nonlinear effects and environmental uncertainty in AE signals from leaks [9]. Previously, a mathematical formula was formulated for AE generated by a point source (PS) as an internal defect in cylindrical structures [20]. The PS was treated as a concentrated time harmonic force (CF). In cylindrical geometry, the CF produces three potential functions representing a compression wave (P) and two split shear waves, known as a vertically polarized (SV) wave and a horizontally polarized (SH) wave, which are referred to as the concentrated force-incorporated potential (CFIP). The displacements responsible for the three waves were developed by introducing the CFIPs into the Navier-Lame (NL) equation. Previously, the CFIPs for the pinhole leakage were derived as an excitation AE source, associated with a fluctuating Reynolds stress (FRS) [21]. As the FRS acts on the pinhole wall, the CFIP only results in axial and tangential displacements due to the FRS. A limited investigation of the angular and axial dependence of the AE signals for the pinhole leakage source has been carried out. The characteristics of the observed AE signals due to the pinhole leakage were well simulated by the proposed theoretical formula. The aim of this study is to investigate the feasibility of applying the proposed mathematical model to leakage source localization in cylindrical geometries. Detailed measurements of the AE signals generated by leakages through pinholes of different diameters in gas cylinders were performed. For application to leak source (LS) localization, the theoretical formula was modified into two semi-empirical equations to describe the angular and axial dependence of the observed AE amplitude, respectively. Finally, the proposed two semi-empirical equations and experimental results were compared to verify the accuracy of the leak source localization.
# Occurrence of Zeralenone and Enniatin B in Swiss Wheat Grains and Wheat Flours
## 3 Results  
The results of both ZEA and ENB contamination in the wheat grains and wheat flours are shown in Figure 1.  
ZEA was detected and quantified in 5 out of 10 samples. When found, the amount of ZEA was below the regulated value of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/kg in unprocessed cereals other than maize [20] for four samples and exceeded the regulation in sample G3 in which the quantity of ZEA reached 147.7 \(\mathrm{\SIUnitSymbolMicro g}\)/kg (average of three replicates). ZEA was not detected in the commercial wheat flours.  
On the other hand, all investigated samples were found to contain ENB at several micrograms per kilogram, ranging from 3.3 to 937.8 \(\mathrm{\SIUnitSymbolMicro g}\)/kg. The relatively high standard deviation for ENB in samples G3 and G4 could be explained by a non-homogeneous contamination of the grains, giving for each extraction replicate a different value. The results obtained here are in line with the quantities of ENB found in wheat grains previously [10]. Interestingly, wheat flours bought in the supermarket were also contaminated with ENB; the amounts found were between 97.6 and 200.9 \(\mathrm{\SIUnitSymbolMicro g}\)/kg. The whole grain and pastry (type 405) flours had similar ENB concentrations, and type 1050 (first clear flour) showed a significantly higher contamination (\(p<0.01\), _t_-test), showing that the refining grade of the flours does not prevent the emerging mycotoxin ENB from being found in the final products.  
No correlations could be found between the agricultural system, labelling, or grain quality classification and the amount of mycotoxin contamination. However, when classifying the results according to the harvest year, the grains harvested in 2021 (samples G1 to G5) were significantly more contaminated with ENB than the samples harvested in 2020 (\(p<0.01\), _t_-test). For ZEA, no contamination could be detected in wheat grains harvested in 2020, where ZEA was detected in all wheat grains harvested in 2021.  
Climatic variables such as temperature, precipitations, and humidity are known to play a determining role in fungal growth and therefore on the potential production of  
As can be seen in Figure 2, even though the mean temperatures were quite similar in 2020 and 2021, the total precipitation in May 2021 was higher than in May 2020 (143.9 mm in 2021 vs. 86 mm in 2020). During the harvest month in July, 2021 also showed higher precipitation (186.5 mm vs. 20.7 mm in 2020) and higher humidity (80% vs. 58% in 2020). Therefore, it is thought that the particularly humid conditions during the summer of 2021 led to a higher contamination of wheat grains by the mycotoxins ZEA and ENB in our study, as seen in Figure 1.
## 3 Results
### Light and Temperature Production Potential, Yield and Water Consumption  
The JG treatment showed the lowest \(\rm ET_{c}\) and the highest yield and WUE (Figure 7); however, no significant differences were detected in these parameters between the NF and WG treatments. The value of \(\rm ET_{c}\) was essentially the same in JG, NF, and WG treatments. Values of \(\rm Y_{c}\) in the NF and WG treatments were 16.18% and 13.32% higher than those in the JG treatment, respectively (\(p\) <0.05). However, maize yield in the JG treatment was 9.53% and 12.10% higher than that in the NF and WG treatments (\(p\) <0.05). In the current study, \(\rm ET_{c}\) was approximately 500 mm in the JG, NF, and WG treatments during the entire growth season; no significant differences were detected among them, probably because of the thickness of maize straw in the JG treatment and organic fertilizer in the NF treatment, which requires further investigation. Moreover, this difference was closely related to differences in the region, maize variety, and irrigation approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicator** & **Model** & **Regression Coefficient** & **t** & \(\mathbf{P}\) & \(\mathbf{\rm R^{2}}\) \\ \hline \(\rm P_{y}\) & (Constant) & 0.696 & 13.358 & 0.000 & \\ \(\rm Y\) & \(-3.320\times 10^{-5}\) & \(-9.648\) & 0.000 & 0.920 \\  & (Constant) & 299.478 & 102.482 & 0.000 & \\ \(\rm S_{p}\) & \(\rm Y_{c}\) & 0.057 & 2029.233 & 0.000 & 0.988 \\ \(\rm ET_{c}\) & \(-0.151\) & \(-1866.520\) & 0.000 & 0.988 \\ \(\rm ET_{c}\) & \(-0.237\) & 3.857 & 0.012 & \\ \hline \hline \end{tabular} Notes: \(\rm P_{y}\) represents water saving potential per unit yield, \(\rm m^{3}\) kg\({}^{-1}\); \(\rm S_{p}\) represents water saving potential per unit area, \(\rm m^{3}\) h\({}^{-2}\); \(\rm Y_{c}\) represents light and temperature production potential of crop, kg \(\rm hm^{-2}\); \(\rm Y\) represents grain yield, kg \(\rm hm^{-2}\); \(\rm ET_{c}\) represents water consumption, mm. t represents significance test values of regression parameters. \(P\) represents significant value.  
\end{table}
Table 2: Stepwise regression coefficient of indexes affecting water saving potential of maize.  
Figure 6: Water saving potential per unit yield and per unit area of maize under various surface mulching patterns. Note: JG represents straw mulching; NF represents organic fertilizer mulching; WG represents no mulching; \(\rm P_{y}\) represents water saving potential per unit yield, \(\rm m^{3}\) kg\({}^{-1}\); \(\rm S_{p}\) represents water saving potential per unit area, \(\rm m^{3}\) h\({}^{-2}\). Bars labeled with different letters (lowercase) differed significantly among the treatments (\(p\) <0.05).  
In the JG, NF, and WG treatments, correlation coefficients between water saving potential (P\({}_{y}\) and S\({}_{\text{P}}\)) of maize and indexes affecting water saving potential were greater than 0.80 (Table 3), indicating a high correlation. Values of P\({}_{y}\) and S\({}_{\text{P}}\) changed significantly with yield (R >0.90).
###### Abstract  
In this work, the performance of coded continuous phase modulation (CPM) transceivers for orthogonal frequency-division multiplexing (OFDM) in underwater acoustic channels is evaluated. The proposed technique employs the Bose-Chaudhuri-Hocquenghem (BCH) class of cyclic codes, and a CPM-based mapper is being used in place of a traditional OFDM mapper. Bit-error-rates (BERs) for various distances are evaluated. We use the Bellhop acoustic channel simulator that is considered to be very close to a real underwater channel. The performance of the proposed system is evaluated for 23 values of the CPM modulation index \(h\) as a function of the distance between the transmitter (Tx) and receiver (Rx). Based upon the error performance, we identified several best-performing CPM indices. We also observed that for Tx-Rx distances of 100 and 250 m, the proposed system gives acceptable performance without the need for equalization. We also compared the out-of-band radiation of the proposed system with PSK-OFDM and observed that the uncoded CPM-OFDM has a better out-of-band (OOB) performance than the traditional OFDM. Moreover, a comparison of BERs with the traditional PSK-OFDM system shows that the proposed system outperforms the traditional OFDM system by a wide margin.  
CPM; OFDM; UWA; underwater acoustic channel; BCH
## 3 Results and Discussion
### Measurement of the Predicted Drift Distance of the Droplets Deposition Center  
According to Section 2.4, a single airflow and multi-airflow spray operation were carried out, respectively. The crosswind speed, the spray pressure, and V-shaped wind speed at the scene were measured, which were 2.2 m/s, 0.52 MPa, and 20.8 m/s, respectively. By collecting the liquid volume in each test tube, the droplet deposition distribution is shown in Figure 15.  
As shown in Figure 15, the distribution range of droplet deposition of multi-airflow synergy is more concentrated than that of a single airflow. The droplets are mainly dis  
Figure 14: Correlation between the predicted values and the simulated values.  
Figure 15: Distribution of droplet deposition in two states.  
tributed in tubes No. 28-50. The anti-drift performance is measured by the droplet drift rate; it is as follows:  
\[\alpha=\frac{V_{z}-\sum\limits_{i=1}^{n}V_{i}}{V_{z}}\times 100\% \tag{9}\]  
where \(\alpha\) is the droplet drift rate (%), \(i\) is the number of droplet collecting troughs, \(n\) is the total number of droplet collecting troughs, \(V_{i}\) is the volume of liquid collected in the \"\(i\)\"th droplet collecting trough (mL), \(V_{z}\) is the total spray volume (mL).  
By calculation, the droplet drift rate of multi-airflow synergy is 29.2% lower than that of single-airflow.  
The tube with the largest droplet volume was recorded as the droplet deposition center. The drifting distance of droplet deposition center (\(d\)) is as follows:  
\[d=d_{v}-d_{m} \tag{10}\]  
where \(d_{v}\) is the center line of the droplet collecting trough corresponding to the tube with the largest droplet volume and \(d_{m}\) is the center line of the droplet collecting plate.  
As shown in Figure 15, the 38 th tube collecting droplet volume is the largest. According to Section 2.4, the distance between the two adjacent droplet collecting troughs is 2 cm, and the width of the droplet collecting plate is 140 cm. Therefore, \(d\) is 5 cm. When the parameters were set as the same as the experiment, simulations were carried out by the method of CFD, and the results are shown in Figure 16.  
As shown in Figure 16, the drifting distance of the droplet's deposition center is 4.6 cm. Substituting experimental parameters into Formula (8), the predicted drifting distance of the droplet deposition center is 4.5 cm. These results show that the method of CFD is feasible for analyzing droplet characteristics, the prediction model of the drifting distance of the droplet deposition center is correct, and multi-airflow synergy can improve the droplet deposition. However, in the process of the experiment, sudden changes in wind values and direction influence the spray system. It is necessary to continue the research on the sensitivity of the proposed spray system to sudden changes in wind values and direction.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 6 Results Analysis and Interpretation
### Model Performance Analysis  
The results from our machine learning analysis demonstrate that CatBoost and XGBoost are the most suitable models for fatigue life prediction. These models exhibited high predictive accuracy, with R\({}^{2}\) values exceeding 0.94 on the test set, alongside relatively low error metrics (MSE, MAE, and MAPE), indicating strong generalization capability. Their ability to effectively capture complex, non-linear relationships make them well suited for fatigue life modeling, where interactions between material properties, welding effects, and operational stress conditions significantly influence failure cycles. While Decision Tree and Random Forest models also performed well, they exhibited higher error rates compared to boosting methods, suggesting a need for further optimization through hyperparameter tuning and pruning to prevent overfitting. Linear regression, on the other hand, proved inadequate, as it assumes a linear relationship between input variables and fatigue life, which contradicts the inherently non-linear nature of fatigue damage accumulation in materials subjected to cyclic loading.  
Notably, support vector regression (SVR) and artificial neural networks (ANNs) failed to deliver reliable predictions. SVR struggled to identify meaningful support vectors, leading to poor generalization and high prediction errors. The ANN model exhibited severe instability and overfitting, likely due to an improper network architecture, insufficient data, or a lack of regularization, resulting in an extreme negative R\({}^{2}\) value during validation. These findings emphasize the importance of careful model selection and tuning, particularly when dealing with fatigue life data, where non-linear dependencies and feature interactions are crucial.  
In conclusion, gradient boosting methods (CatBoost and XGBoost) offer the best balance between accuracy, stability, and interpretability, making them the preferred choice for fatigue life prediction. On the other hand, while tree-based models like CatBoost and XGBoost demonstrate strong predictive performance, the use of machine learning in engineering applications comes with certain limitations that must be acknowledged. Primary concerns include the interpretability of ML models, their dependency on high-quality training data, the challenge of generalization to unseen conditions, and the need for ethical considerations in safety-critical applications. To address these concerns, a correlation analysis based on SHAP, feature importance analysis, and partial dependence plots was performed to enhance interpretability and is outlined in the following section.
## 2 Background on SSI
### Components & Terminology  
Put together, SSI implements [PERSON]'s design goals by having the user--usually combining the roles of holder and subject--in the center of all transactions and in control of their data through a digital wallet. The wallet can be on a smartphone, in the cloud, or both. Holders receive verifiable credentials describing specific claims from issuers, which are usually organizations, such as government agencies, universities, insurance companies, and banks. The user can then show the verifiable credentials as proof to service providers--acting as verifiers--to gain access to their services. Agents and hubs can be used to provide users with persistent and always-online endpoints. Information about the participating entities, such as decentralized identifiers (DIDs), is stored in a decentralized manner. This could be a DLT, such as a blockchain, or any other decentralized form of storage. Some SSI solutions may also integrate smart contracts to automate processes on the DLT.  
For the system described above, we apply the terminology based on [PERSON] et al. [12] and [PERSON] et al. [13]:  
* Verifiable Credentials: A collection of metadata and claims about an entity that can be verified by a proofing mechanism.
* Claim: Statement about an attribute of an entity, such as age or email address.
* Proof: Data (digital signature) allowing a verifiable credential to be verified by a verifier.
* Wallet: Software to store private keys, verifiable credentials, and other documents of an entity.
* Verifier: Allows access to a service after receiving the requested information or attributes of a holder.
* Issuer: Trusted parties that verify attributes or claims of an entity.  
* Subject: The entity the claims within the verifiable credential are made about.
* Holder: Owner of the claims within a verifiable credential, stored in a wallet, and usually the same entity as the subject.
* Agent and hub: Technical endpoints and trustees for identifiers, which enable communication between entities.
* Smart contract: A computer program or transaction protocol that is executed automatically according to the terms of a contract or an agreement.