esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 3 Results  
To obtain a high confidence of the detection and counting results, a three step validation process was performed: (i) a validation in controlled conditions (laboratory); (ii) a simulated moth validation; and (iii) a field validation. As presented before, the sound characteristic of the _Lobesia botrana_ was obtained in previous experiments.
## 3 Numerical Simulation of Early-Age Behaviour of Small Specimens to Actual Rc Deck Slabs
### Application of the Analytical Model on a Mock-Up Slab Specim  
To represent a restraint condition as close to reality as possible, two I-section girders were placed at the concrete supports as they would be placed on the actual steel girders whereas half of the actual specimen were used for the finite element model as indicated in Figure 14. The replicated specimen was cast with 'BFS44' mix design and water-cured for 42 days on the top surface before exposure to the outside environment. For Figure 14b, the diameters of the reinforcing bars were 19 and 16 mm in both top and bottom sections as coloured and shown with the reinforcement ratio. The environmental data for Rikuzentaka in Iwate prefecture were obtained from the Japan Meteorological Agency and identically input to the analysis as shown in Figure 15. To trace the expansion and contraction behaviour of the deck in a quantitative manner, strain gauges were also embedded at the locations shown in Figure 16. These strain gauges perform measurements in transverse, longitudinal and vertical directions. As portrayed in Figure 16, four finite-element models of the mock-up specimen were established to conduct mesh sensitivity analysis to minimise the calculation efforts with acceptable accuracy in the following large-scale bridge model. This element discretisation process was necessary because the convergence in computation of thermodynamically microscopic aspects in DuCOM had to be ensured to provide reasonably accurate results for its counterpart, COM3, to analyse the macroscopic structural behaviour.  
Figure 12: FE model of one fourth of the specimen.  
Figure 13: Concrete shrinkage strain in 400 \(\times\) 400 \(\times\) 200 mm\({}^{3}\) RC specimens (M-Measured and A-Analytical).  
Due to issues with the embedded strain gauges after removal of the formwork, the measured data from the 42 nd and 56 th days were lost. Nonetheless, the whole trend of the strain could still be captured, whereby the rearrangement of the strain gauges might have affected the strain progress and resulted in a slightly greater shrinkage strain value. Two main conclusions could be drawn from the result of this analysis. First, the computational model can capture the strain progress with only minimal differences. Good agreement was also achieved between the measured and analytical values for the concrete temperature. Second, the differences between the computed strains of the four finite-element models with distinctive aspect ratios and element sizes were also negligible. Thus, it can be confirmed that the element with a length of around 300 mm could reasonably be implemented in the large-scale analysis.
# Glass-Ceramic Foams from Alkali-Activated Vitrified Bottom Ash and Waste Glasses
## 3 Results and Discussion
The transformation of gehlenite into anorthite (Ca feldspar) in the presence of silica and alumina (Ca\({}_{2}\)Al\({}_{2}\)SiO\({}_{7}\) + 3 SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) \(\to\) 2 CaAl\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)) is already known [24]. We suggest that the formation of nepheline and labradorite, as the products of VBA-glass reaction, replaced gehlenite, the standard product of VBA crystallization. Figure 5 shows microstructural details of foams from VBA-SLG and VBA-BSG mixtures in the green state. In the case of SLG addition (Figure 5a), foams were less homogeneous, with coarser cells compared to those formed with BSG addition (Figure 5b). The morphology of samples did not change upon sintering, as an effect of the viscous flow freezing, caused by the crystallization (Figure 5c,d). Figure 4: Mineralogical analysis of (**a**) VBA–SLG glass-ceramic foams and (**b**) VBA–BSG glass-ceramic foams. Higher-magnification details for SLG addition are shown in Figure 6. The increasing firing temperatures did not cause any coarsening of the cellular structures (from 900 \({}^{\circ}\)C (Figure 6a) 950 \({}^{\circ}\)C (Figure 6c) and 1000 \({}^{\circ}\)C (Figure 6e)). This was due to the previously mentioned limitation of viscous flow caused by crystallization. In the case of BSG addition, the increase of firing temperature did not cause any coarsening (Figure 7a-f). Well-consolidated struts appeared at the temperatures starting from 900 \({}^{\circ}\)C (Figure 7c,d). The smooth areas indicate the presence of some residual glass phase. Figure 5: Micrographs of foams before and after firing at 950 \({}^{\circ}\)C: (**a,c**) VBA–SLG; (**b,d**) VBA–BSG. Figure 6: SEM pictures of VBA–SLG foams fired at 900 \({}^{\circ}\)C: (**a,b**), 950 \({}^{\circ}\)C (**c,d**), and 1000 \({}^{\circ}\)C (**e,f**). Table 2 provides information on the mechanical properties and porosity of the prepared glass-ceramic foams. All foams were characterized by abundant porosity (total porosity \(>\)70 vol %), which was almost completely open. The strength/porosity correlation was studied via [PERSON] and [PERSON]'s model [27], calculating the scaling of compressive strength with relative density for open-celled foams, according to the bending strength of the solid phase (\(\sigma_{\text{bend}}\)), as follows: \(\sigma_{\text{comp}}=\sigma_{\text{bend}}\cdot[0.2\cdot(\rho_{\text{bend}})^{ 3/2}]\). From the experimental data of compressive strength and density, we could calculate the bending strength exceeding 100 MPa, comparing well with that of monolithic glass-ceramics of similar composition [23], for BSG addition. The addition of SLG did not lead to particularly strong foams. The much lower strength-to-density values for SLG could be justified by the above-mentioned different degree of interaction between VBA and glass type, which led to different phase assemblages, and internal stresses upon cooling from sintering temperature. In particular, melilites (including gehlenite) have much higher thermal expansion coefficients [28] than wollaston
## 3 Results and Discussion
### Oil Displacement Tests  
Figure 16 shows the comparison between the oil recovery curves for the systems based on deionized water, OADA surfactant solution and nanoparticles suspensions. The shaded area is related to the secondary oil recovery, i.e., water flooding.  
Figure 15: Breakthrough curves for suspensions of 12 sulf20 SPSNP and 30 sulf20 SPSNP and OADA solution (0.2 \(m\%\)/\(n\%\)).  
The water flooding process produced a recovery in the range of 40-46% of the original oil in place in the same way as reported by other studies using sandstone as a porous medium [11; 32; 34]. In order to better discuss the system's performance, Figure 17 displays the incremental oil recovery results from the chemical flooding stage, i.e., considering only the pore volumes injected after the secondary oil recovery.  
It was observed that the OADA solution was not able to displace a satisfactory oil amount and this could be related to the fact that there is a great tendency of surfactant adsorption onto sand particles, shown by the transport test and discussed in the previous section, which causes losses during the flooding and therefore a worse oil displacement performance.  
The 30 sulf20 nanoparticles presented a similar performance to the one presented by the OADA solution, promoting a discrete oil recovery. For this system, it is believed that the main factor could be from adsorption onto the porous medium, as suggested by the observed delay in its elution during the transport test, as discussed in the previous section.  
In contrast, the suspensions containing the 12 sulf20 nanoparticles enabled an incremental oil recovery of about 13% in relation to the water flooding oil recovery. This could be explained by the fact that the 12 sulf20 SPSNP presented a high capacity to permeate through the porous medium,  
Figure 16: Oil recovery as a function of pore volume injected. The tertiary oil recovery began after the injection of 3 PVs of water.  
Figure 17: Incremental oil recovery as function of pore volumes after the secondary oil recovery.  
as shown in Figure 15, a significant IFT reduction (Figure 12) and a higher amount of OADA available (21.2 mg/g). In comparison with OADA solution, this surfactant nanocarrier conducted to a lower surfactant adsorption and consequently may have released a higher surfactant amount to the water/oil interface, reducing its IFT to lower values and enabling a higher displacement of residual oil. Therefore, the 12 sulf20 SPSNP is the best surfactant delivery system developed in this work. In the same way, other studies in the literature observed that complex (dextrin-SDS) carrier systems were more efficient, achieving a similar incremental oil recovery in relation to the SDS flooding recovery in a sand porous media [32; 34].
## 4 Results
### Evaluating Numerical Efficiency
#### 4.2.2 Hybrid Model  
While the hybrid model is effective, it has certain limitations. One of the main challenges is its complexity. The model integrates two different fracture approaches, CZM and CPFM, within a single finite element framework. This requires additional pre-processing effort as it involves the development of two sets of tools. The first set is used to generate CIEs, which in this study was performed using Python scripts. The second set involves  
Figure 12: A quantitative analysis is represented, featuring: (**a**) the reaction force–displacement curves acquired through finite element simulations of the two-dimensional single inclusion model, employing the CZM, CPFM, and the hybrid approach; and (**b**) a comparative study of the \(l_{c}\) sensitivity analysis, conducted utilizing both the hybrid and the CPFM fracture models.  
Figure 13: A comparative study focused on the numerical efficiency between the CPFM, CZM, and the hybrid model.  
the creation of a user-defined element subroutine that implements the cohesive phase-field fracture method within Abaqus/CAE.  
In terms of computational cost, the hybrid model combines CZM and CPFM, which increases the number of iterations required for the numerical solver to converge at each time step (see Figure 13). As a result, the integration of these two methods results in a higher computational cost, as more iterations are required for the solver to achieve convergence.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 3 MAC Layer and Beamforming for mmWave
### MAC Layer
The network operating environment may also influence the MAC protocols that are best suited for deciding which MAC is the most appropriate for a case [30]. Scheduling based on only a partial understanding of the network architecture results in a considerable reduction in network throughput, around 33% as documented in [31]. However, discovering the topology (even partial information) necessitates the exchange of multiple control messages. Due to the uniqueness of the physical control channel in a mmWave network, the transmission of these control messages can be overwhelming [10]. In most cases, the physical control channel has a substantially reduced transmission rate as opposed to the data channel, owing to the increased resilience and robustness [30].
## 2 Data Description and Preparation  
### Data Source and Characteristics  
This study utilizes a publicly available dataset from Equinor's Volve field, which provides high-resolution time series drilling data essential for analyzing stuck pipe incidents [44]. The Volve field, located in the Norwegian sector of the North Sea, was developed based on production from the Maersk Inspirer jack-up rig, as illustrated in Figure 1. The dataset includes real-world operational parameters recorded during drilling, which offers a robust foundation for anomaly detection in stuck pipe prediction.  
The dataset comprises a wide range of critical drilling parameters that influence the wellbore conditions and potential stuck pipe occurrences. These include hookload, weight on bit, rate of penetration, rotary speed, surface torque, standpipe pressure, and mud flow rate, among others. These values capture information about the mechanical and hydraulic wellbore conditions that are crucial in recording an early indication of stuck pipe occurrences. Table 2 provides a complete picture of the drilling parameters and corresponding units [45]. To ensure sound and organized analysis, the dataset was  
\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
[39] & \begin{tabular}{c} Automated DL \\ Model \\ \end{tabular} & \
\begin{tabular}{c} Hole cleaning efficiency \\ evaluation \\ \end{tabular} &
\begin{tabular}{c} ROP improved by \\ 52\% \\ \end{tabular} & 3 & Normal \\ \hline
[40] & \
\begin{tabular}{c} Fuzzy Expert \\ System \\ \end{tabular} & Early stuck pipe detection &
\begin{tabular}{c} 92\% of stuck cases \\ predicted \\ \end{tabular} & 7 & Normal \\ \hline
[41] & \
\begin{tabular}{c} Autoencoder \\ (Unsupervised) \\ \end{tabular} & Early stuck pipe detection &
\begin{tabular}{c} Promising results \\ for anomaly \\ detection \\ \end{tabular} & 15 & Normal \\ \hline
[42] & DNN, GA, GRU &
\begin{tabular}{c} Flow anomaly detection in \\ offshore wells \\ \end{tabular} & F1-score of 0.97 & 20 & Abnormal \\ \hline
[43] & CNN + CWT & \
\begin{tabular}{c} Pipeline leak size \\ classification \\ \end{tabular} &
\begin{tabular}{c} Achieved 95\% \\ accuracy \\ \end{tabular} & 8 & Abnormal \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._preprocessed and labeled according to expert verification. Experts manually prepared the \"Stuck Cases\" column by labeling drilling operations as \"no stuck\" or \"stuck\" in accordance with real operational assessments. Expert-labeled classification contributes to the dataset accuracy and usability in training machine learning algorithms.
# Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault
## 5 Analysis of Short-Circuit Fault Simulation Data
### Analysis of Speed Difference at a Low-Frequency Connection of the Short-Circuit Fault  
The simulation results of the speed difference between the low-voltage rotor and the generator rotor show that the transient speed difference caused by the three-phase short-circuit on the grid side is the largest, and the transient extremum fluctuation is 74.8 rpm. In contrast, the speed fluctuation caused by the two-phase short-circuit on the grid side is the smallest, and the transient extreme fluctuation is 20 rpm. The velocity fluctuation under the above four short-circuit fault conditions is shown in Table 6, and the corresponding velocity fluctuation curve is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fault**} & \multirow{2}{*}{\begin{tabular}{c} **Two-Phase** \\ **Short-Circuit on** \\ **the Grid Side** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Three-Phase** \\ **Short-Circuit on** \\ **the Grid Side** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Two-Phase** \\ **Short-Circuit at** \\ **Generator** \\ **Terminals** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Three-Phase** \\ **Short-Circuit at** \\ **Generator** \\ **Terminals** \\ \end{tabular} } \\ \hline Maximum & & & & \\ forward speed & 20.1 & 74.8 & 31.2 & 41.7 \\ variable (rpm) & & & & \\ maximum & & & & \\ negative speed & 22.3 & 87.0 & 46.2 & 37.6 \\ variable (rpm) & & & & \\ Extreme speed & 22.2 & 90.3 & 47.9 & 48.0 \\ difference (rpm) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Known value under 80% load of generator.  
Figure 11: Maximum impulse current under different short-circuit faults.  
Of the four short-circuit fault conditions affecting the speed fluctuation, the positive speed fluctuation and transient speed difference generated by the grid-side three-phase short-circuit are the largest. The difference between the speed fluctuations caused by the two-phase short-circuit on the grid side and the transient speed is the smallest.
## 6 Concrete-Filled Steel Tubes under Combined Axial Force-Bending Moment
### Circular Composite Sections
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **No.** & **L (mm)** & **D (mm)** & **t (mm)** & **f\({}_{\mathbf{y}}\) (MPa)** & **f\({}_{\mathbf{c}}\) (MPa)** & **P\({}_{\mathbf{exp}}\) (kN)** & **M\({}_{\mathbf{exp}}\) (kNm)** \\ \hline \multirow{10}{*}{[45]} & I & 2000.0 & 300.0 & 4.25 & 438 & 66 & 3070 & 329.0 \\  & II & 2000.0 & 300.0 & 5.83 & 420 & 64 & 1932 & 348.0 \\  & III & 2000.0 & 300.0 & 5.83 & 420 & 62 & 3217 & 326.0 \\  & IV & 2000.0 & 300.0 & 5.65 & 403 & 62 & 2981 & 318.0 \\  & V & 2000.0 & 300.0 & 5.65 & 419 & 66 & 4560 & 256.0 \\  & VI & 2000.0 & 300.0 & 6.23 & 436 & 66 & 1961 & 418.7 \\  & VII & 2000.0 & 300.0 & 5.65 & 419 & 66 & 3256 & 402.0 \\  & VIII & 2000.0 & 300.0 & 6.16 & 406 & 66 & 3285 & 405.0 \\  & IX & 2000.0 & 300.0 & 5.70 & 431 & 66 & 3305 & 391.0 \\  & X & 2000.0 & 300.0 & 5.70 & 431 & 66 & 4629 & 327.0 \\  & XI & 2000.0 & 165.2 & 4.35 & 588 & 66 & 1285 & 99.0 \\  & XII & 2000.0 & 165.2 & 4.35 & 588 & 66 & 1285 & 99.0 \\ \hline \multirow{10}{*}{[31]} & XIII & 2120.0 & 152.0 & 1.70 & 328 & 92 & 470 & 29.7 \\  & XIV & 2120.0 & 152.0 & 1.70 & 328 & 92 & 570 & 32.1 \\  & XV & 2120.0 & 152.0 & 1.70 & 328 & 92 & 270 & 30.1 \\  & XVII & 2120.0 & 152.0 & 1.70 & 328 & 92 & 270 & 30.8 \\  & XVIII & 1071.0 & 152.0 & 1.70 & 328 & 92 & 670 & 34.8 \\  & XVIII & 1071.0 & 152.0 & 1.70 & 328 & 92 & 1273 & 21.4 \\  & XIX & 1071.0 & 152.0 & 1.70 & 328 & 92 & 1451 & 13.8 \\ \hline \multirow{10}{*}{[33]} & XXI & 580.5 & 165.0 & 2.82 & 363 & 48 & 1525 & 10.7 \\  & XXII & 661.0 & 190.0 & 1.94 & 256 & 41 & 1533 & 13.2 \\  & XXIII & 580.0 & 165.0 & 2.82 & 363 & 48 & 1123 & 19.3 \\  & XXIV & 664.0 & 190.0 & 1.94 & 256 & 41 & 1284 & 20.8 \\  & XXV & 662.0 & 190.0 & 1.52 & 306 & 48 & 1260 & 19.5 \\  & XXVI & 579.5 & 165.0 & 2.82 & 363 & 80 & 1940 & 18.2 \\  & XXVII & 662.5 & 190.0 & 1.94 & 256 & 75 & 2203 & 22.0 \\  & XXVIII & 579.5 & 165.0 & 2.82 & 363 & 80 & 1653 & 29.6 \\  & XXIX & 663.0 & 190.0 & 1.94 & 256 & 75 & 1730 & 36.0 \\  & XXX & 578.5 & 165.0 & 2.82 & 363 & 113 & 2246 & 15.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Data for circular concrete-filled steel tube beam–column members. Figure 10: Circular CFT beam–columns; comparison of proposed axial force-bending moment (P-M) interaction curves with experimental results (P\({}_{\text{exp}}\)-M\({}_{\text{exp}}\)).
# Research on the Auto-Exposure Method of an Aerial TDI Camera Based on Scene Prediction
## 5 Conclusions  
In order to obtain a high-quality image of an aerial TDI camera under low-illumination conditions, we analyzed the reasons affecting the image quality and proposed an auto-exposure method for an aerial TDI camera based on scene prediction. Specifically, the camera used TDI CCD to collect the first N rows of data of the target scene in advance as the basis, and then adjusted and determined the exposure parameters with the median gray value as the target; thus, in the case of no additional sensors, the auto-exposure of TDI CCD can be realized. To evaluate the effect of auto-exposure, three methods of image power spectrum variance, image histogram, and image information entropy were used. The test results showed that, after auto-exposure, the variance in the image power spectrum increased by 0.4362, the entropy of image information increased by 1.7064, and the distribution of the image histogram was more uniform than that before auto-exposure. This showed that the effect of auto-exposure was good and it had better scene adaptation, allowing for it to meet the requirements of auto-exposure imaging under low-illumination conditions in aerial TDI cameras.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA17010205 and the APC was funded by the Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA17010205.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average Gray** & **Standard Deviation** & **Information** & **Power Spectrum** \\  & **Value** & **of Gray Value** & **Entropy** & **Variance** \\ \hline a1 & 34.1 & 27.5 & 5.4282 & 7.0004 \\ a2 & 93.07 & 40.89 & 7.1346 & 7.4366 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of objective evaluation data before and after automatic exposure.  
Figure 8: (**a1,a2**) are the images before and after automatic exposure, (**b1,b2**) are the corresponding power spectra, (**c1,c2**) are the corresponding three-dimensional power spectra, and (**d1,d2**) are the corresponding image histograms.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors are grateful for the anonymous reviewers' critical comments and constructive suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.