esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Computer-Aided Engineering in the Design of Ground-Improvement Technologies
## 5 Results and Discussion  
### Validation of Analytical Ground Model  
Figure 6 shows the results of the stress-strain curve obtained by the simulation of the unconfined compression tests for the material parameters given in Table 2, compared with the two sets of real unconfined compression test data. The simulated result shows the soil yields at a compressive strength of 61.24 kN/m\({}^{2}\) when 12.5% strain is experienced. The output compressive strength result in comparison with known data is approximately equal. The green/square plots are the results of the unconfined compression tests for the soil sample from Tsugaru City in Aomori Prefecture. The sample was extracted from a depth of 7 to 7.85 m below the ground and is silty soil with an average soil diameter of 0.0036 mm. The soil sample yielded at 64.69 kN/m\({}^{2}\) when it experienced a total strain of 4.73%. The triangular/orange plots are the results of the unconfined compression tests for the soil sample from Mikawa Town in Yamagata Prefecture. The core sample was taken from a depth of 4 to 4.7 m below the ground. The soil sample is sandy soil with an average soil diameter of 0.034 mm. The result shows that the soil sample yielded a value of 61.55 kN/m\({}^{2}\) when it experienced a total strain of 0.55%. The main reason for higher strain in the simulated sample might be an assumption of the unrealistic sample of 3 mm soil particles possessing compressive strength of 60 kN/m\({}^{2}\).  
Figure 4: Top views of probe region at beginning and end.  
Figure 5: Front views of probe region at beginning and end.  
A detailed parametric study was conducted to recreate the most accurate ground model. It was found that the simulation result for different densities, but all other parameters kept the same, produced the same yield value and strain ratio, though the soil particle size and plastic viscosity considered during the simulation had an influence on the output result. Figure 7 shows the stress-strain relation comparison for three different sizes of soil. For the 0.5 mm soil sample, a yield value of 61.07 kN/m\({}^{2}\) and 2.11% strain value were obtained at 5500 Pa-s input plasticity viscosity. For the 1 mm soil sample, a yield value of 61.03 kN/m\({}^{2}\) and a strain value of 3.8% were obtained at 9000 Pa-s input plasticity viscosity. For a 2 mm soil sample, when compared to a 3 mm soil sample, the parametric change was negligible but the calculation time increased by 1.5-times. Meanwhile, for the 1 mm and 0.5 mm samples, the calculation times increased by 2- and 42-times, respectively. Although the lower soil particle-sized model was more accurate, the simulation might have failed due to the high calculation load, so this study was conducted with the 3 mm soil sample.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 5 Assessment of the Impact of Biofouling and Maintenance Optimization
### Results of Model-1  
Model-1 acts as a reference performance baseline, representing the clean vessel condition, and it be used to evaluate conditions with fouling. According to Table 3, the two datasets corresponding to advanced biofouling are Datasets B and D2. The difference observed between the actual power and the values predicted by Model-1, when applied to these datasets, serves as an estimate of the hull and propeller deterioration due to biofouling.  
Dataset B was about two years away from sea trials and no hull cleaning or propeller polishing had been performed in the meantime. Thus, it represents a period in which severe marine growth has occurred, and, as a result, the hydrodynamic performance has deteriorated. In Figure 14, a scatter plot of the actual power and that predicted by Model-1 is shown, as well as a scatter plot of the power increase indicator.  
Dataset D2 was extracted about a year away from the previous hull cleaning event, three months before the propeller was polished. In this period, biofouling was expected to have caused a considerable power penalty, even if not as significant as that in Dataset B. Indeed, when Model-1 was applied to Dataset D2, the actual power values were greater than the predicted values, which translated into a significant power increase, as shown in Figure 15. Moreover, Table 12 presents the mean power increase (PI) for both cases, which was estimated to be 21.7% and 10.8%, respectively. However, the limitations of Model-1 should be noted, as described in the previous section. Specifically, Figure 15b shows a negative power increase from May 19 to June 1, 2023, which was likely due to the model making predictions outside its training data range. Upon further investigation, it appears that during this voyage, the vessel was sailing in ballast condition with a single mean draft value of 8.2 m. However, the training Dataset D1 used to develop Model-1 did not contain any samples with draft values close to 8.2 m, according to Figure 10.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value (Dataset B)** & **Value (Dataset D2)** \\ \hline Mean actual SHP [kW] & 6342 & 5546 \\ \hline Mean predicted SHP [kW] & 5209 & 4984 \\ \hline Difference in means [kW] & 1133 & 562 \\ \hline Mean PI & 21.7\% & 11.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Application results of Model-1 to Datasets B and D2, showing the mean values of the actual and predicted SHP, as well as the mean PI value.
# Seismic Response and Damage Analysis of an Isolated Bridge Subjected to Near-Fault Ground Motion
## 2 Finite Element Model of Bridge
### Analysis Model of the Bridge  
The analysis model of the bridge [18] is shown in Figure 1. The bridge is a continuous girder bridge with a span of 30 m and 5 spans. The girder adopts a single box and three chambers section with a height of 1.88 m and a width of 8.025 m. The pier height is 6.6 m, and the diameter is 1.219 m. The section of the girder and the pier is shown in Figure 2.  
Figure 1: Analysis model of the bridge.  
The actual bridge girders are mostly prestressed reinforced concrete structures, and seldom undergo plastic deformation or damage behavior under earthquake ground motions. In the analysis, the linear elastic beam element is used to simulate the girders, and each span is divided into 15 elastic beam elements. The pier will show strong nonlinear behaviors, such as plasticity and damage under earthquake ground motions, and it is simulated using the fiber beam-column element.  
LRB700-140 isolation bearings are selected in the isolated bridge [18]. The LRB bearing (shown in Figure 3a) diameter is 700 mm, the lead diameter is 140 mm, the total thickness of the rubber layer is 110 mm, and the total thickness of the steel plate layer is 75 mm. The pre-yield stiffness of the bearing is 17.771 kN/mm, the post-yield stiffness is 1.367 kN/mm, and the yield force is 94.2 kN. The design critical load is 4618 kN. The LRB bearings are simulated by the isolation elements. The laminated bearings (shown in Figure 3b) can be used in the non-isolated bridge. In the analysis, the laminated bearings are simulated by the linear spring elements [21; 22]. The pre-yield stiffness of the LRB bearing is taken as the spring constant (the stiffness of the bearing) [18; 22].
# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
###### Abstract  
For the single crystal furnace used in the photovoltaic industry, growth problems occur frequently due to dislocations during the shouldering and cylindrical growth steps of the Czochralski (CZ) crystal growth. Detecting the dislocation phenomenon in the cylindrical growth step is very important for entire automation of the CZ crystal furnace, since this process usually lasts for more than 48h. The irregular nature of different patterns of dislocation would impose a big challenge for a traditional machine vision-based detection method. As almost no publications have been dedicated to detecting this phenomenon, to address this issue, after analyzing the characteristics of the silicon ingot image of this process, this paper proposes a kind of deep learning-based dislocation detection method along with tracking strategy to simulate manual inspection. The model has a good detection effect whether there is occlusion or not, the experimental results show that the detection accuracy is 97.33%, and the inference speed is about 14.7 frames per second (FPS). It can achieve the purpose of reducing energy consumption and improving process automation by monitoring this process.  
Keywords:crystal furnace; cylindrical growth; habit line; deep learning; convolutional neural networks; transfer learning +
Footnote †: journal: Applied Sciences
## 2 Preliminaries  
### Notation  
In this paper, we use the standard notation for describing probabilistic algorithms and experiments, which was introduced in [25]. Given a probabilistic algorithm \(A(\cdot,\cdot,\ldots)\), we write \(a\gets A(x,y,\ldots)\) to denote the experiment of running \(A\) on input \(x,y,\ldots\), producing as output \(a\), where the probability is taken over the randomness used by the algorithm \(A\). Given a set \(X\), we write \(x\gets X\) to denote the experiment of randomly choosing an element \(x\) from \(X\). On the other hand, if \(w\) is neither an algorithm nor a set, then we write \(x\gets w\) to denote the assignment of \(w\) to \(x\). We say that a function \(\epsilon:N\to R\) is _negligible_ if, for any constant \(c>0\), there exists an integer \(\tau_{c}\) such that for all \(\tau\geq\tau_{c}\), \(\epsilon(\tau)<\tau^{-c}\).
## 2 Materials and Methods
### Data
#### 2.1.3 Meteorological Data  
Temperature data, in order to estimate the crop phenological stages through the calculation of growing degree days (GDD) in zones II, V, and VI, was obtained from 30 openly accessible regional government meteorological stations (www.meteo.navarra.es/estaciones), shown in Figure 2. Rainfall data was also obtained from the same 30 meteorological stations distributed throughout the three agricultural zones under study. The GDDs for each of the two varieties for rain-fed management in Navarre is shown in Table 1[35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenology (Zadocks Scale)** & **Camargo (GDD)** & **Macropolo (GDD)** \\ \hline
**Tilering (26–30)** & 598 & 563 \\
**Heading (55–59)** & 1060 & 1150 \\
**Ripening (75–99)** & 1783 & 1819 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Growing degree days (GDD) and Zadoks scale [36] for the two analyzed varieties.  
Figure 2: Meteorological station locations from which temperature and rainfall data was obtained for the three zones (II, V and VI). UTM coordinates are expressed in meters.  
Following average minimum and maximum mean temperatures of the 30 stations at the three zones, the accumulated GDD was calculated (Equation (1)) following calculations by [PERSON] [37] to estimate zonal phenological dates for 2018 and 2019 seasons. The reported dates that adjust to the phenological stage regarding GDD (Table 1) are reported in Table 2; as Camargo and Marcopolo had slightly different GDD for each stage, the average of both was considered to estimate the zonal phenological date.  
\[GDD=\sum_{junt}^{nov}\frac{Tmax+Tmin}{2}-\textit{Thase} \tag{1}\]  
where GDD is the growing degree days, \(\sum_{junt}^{nov}\) indicates the sum throughout the season, November to June, of daily maximum and minimum temperatures (Tmax and Tmin) divided by 2, minus the base temperature, which in this case was considered to be 0 \({}^{\circ}\)C.
# Experimental and Modeling Study for the Solar-Driven CO2 Electrochemical Reduction to CO
## 2 Materials and Methods
### Numerical Model
#### 2.3.3 PV Model  
The photovoltaic component was integrated into the model using the one-diode equivalent circuit equation (the diode equation), a robust method to represent solar cells electrically [22]. This equation accounts for various electrical parameters of the PV module and is expressed as follows:  
\[I\ =\ I_{ph}-I_{0}\Big{[}exp\left(\frac{V+IR_{s}}{nV_{t}}\right)-1\Big{]}-\frac{ V+IR_{s}}{R_{sh}} \tag{3}\]  
where \(I_{ph}\) is the photogenerated current, representing the current produced by the absorption of light; \(I_{0}\) is the saturation current, describing leakage under reverse bias conditions; \(R_{s}\) the series resistance, which accounts for internal resistive losses; \(R_{sh}\) is the shunt resistance, capturing leakage pathways within the device; \(n\) is the ideality factor, related to the recombination mechanisms within the cell; \(V_{t}\) is the thermal voltage, approximately 25 mV at room temperature.
###### Abstract  
With the rapid development of satellite technology and the need to satisfy the increasing demand for location-based services, in challenging environments such as indoors, forests, and canyons, there is an urgent need to improve the position accuracy in these environments. However, traditional algorithms obtain the position solution through time redundancy in exchange for spatial redundancy, and they require continuous observations that cannot satisfy the real-time location services. In addition, they must also consider the clock bias between the satellite and receiver. Therefore, in this paper, we provide a single-satellite integrated navigation algorithm based on the elimination of clock bias for broadband low earth orbit (LEO) satellite communication links. First, we derive the principle of LEO satellite communication link clock bias elimination; then, we give the principle and process of the algorithm. Next, we model and analyze the error of the system. Subsequently, based on the unscented Kalman filter (UKF), we model the state vector and observation vector of our algorithm and give the state and observation equations. Finally, for different scenarios, we conduct qualitative and quantitative analysis through simulations, and the results show that, whether in an altimeter scenario or non-altimeter scenario, the performance indicators of our algorithm are significantly better than the inertial navigation system (INS), which can effectively overcome the divergence problem of INS; compared with the medium earth orbit (MEO) constellation, the navigation trajectory under the LEO constellation is closer to the real trajectory of the aircraft; and compared with the traditional algorithm, the accuracy of each item is improved by more than 95%. These results show that our algorithm not only significantly improves the position error, but also effectively suppresses the divergence of INS. The algorithm is more robust and can satisfy the requirements of cm-level real-time location services in challenging environments.  
clock bias; single-satellite positioning; PNTRC; integrated navigation; UKF; LEO; communication link +
Footnote †: journal: remote sensing
# Portable and Easily-Deployable Air-Launched GPR Scanner
## 4 Discussion  
Results presented in Section 3 prove the need for a proper choice of the antennas in the field of buried target-detection using GPR systems. In case of shallow landmines or IEDs, range resolution is demanded to clearly distinguish between the reflections from the targets and the reflection from the air-ground interface. In case of targets buried deeper, penetration capabilities must be prioritized at the expense of losing range resolution. Furthermore, the frequency response of the antennas must be  
Figure 17: Reflectivity normalized amplitude, in dB. Metal box buried at 25 cm depth. Applying LPA phase center compensation: cut \(z=-160\) cm (**a**), cut x = 40 cm (**b**), cut y = -42 cm (**c**). Without applying LPA phase center compensation: cut \(z=-160\) cm (**d**), cut \(x=40\) cm (**e**), cut \(y=-42\) cm (**f**). The \(z=0\) position is defined at the plane where the LPA antennas are attached to the payload.  
taken into account, in order to be able to compensate, if needed, the dispersion introduced along the working frequency band.  
A summary of the results presented in Section 3 is shown in Table 1. It can be noticed, for example, that the reflectivity of the metallic box decreases 3 dB within a depth of 8 cm, i.e., 37.5 dB/m approximately. This value is in agreement with the values provided in Figure 5 of [29], where the attenuation for a silt loam soil with 30-40% moisture is around 40-50 dB/m at the frequency of 900 MHz.  
Another effect that can be observed in the reflectivity results corresponding to the loamy soil tests is that the maximum of the air-soil reflectivity happens on a position located above the buried target. The area corresponding to the surface of the hole dug to bury the targets (mortar grenade, plastic landmine, metallic box) has no grass, whereas the surrounding, unaltered scenario, has. The grass randomly scatters the incident field, so the reflectivity at the air-soil interface can be expected to be stronger if the interface has no grass. In the case of the sandy soil (Figure 12), the reflectivity at the air-sand interface is more homogeneous in the entire area-under-test.  
With respect to the time required to perform the measurements with the proposed portable scanner, it must be taken into account that the survey of 1 m \(\times\) 1 m with the setup took around ten minutes because the box where the radar was placed was manually moved. However, UAV flights can be performed at considerably higher speeds, while ensuring a proper sampling of the inspected area. In this regard, it should be noted that the positioning system included in the payload has an update rate of up to 100 Hz.
# Secure Beamforming in 5G-Based Cognitive Radio Network
## Abbreviations  
CRN cognitive radio network  
NOMA non-orthogonal multiple access  
MIMO multiple-input-multiple-output  
5G fifth generation  
SUs secondary users  
PUs primary users  
SC subcarrier  
C cooperative jamming  
SSPA smart sensor protocol algorithm  
PLS physical layer security  
ZFBF zero forcing beamforming  
BF beamforming  
CSTN cognitive satellite terrestrial network  
SDA software defined architecture  
MISOME-SWIPT multiple input, single output multiple-eavesdropper simultaneous wireless  
information and power transferring  
SEE secrecy energy efficiency  
CSI channel state information  
UPA uniform planar array  
BS base station  
AoA angle of arrival  
Eves eavesdropper
## 4 Performance Analysis of U-Shaped Frame Optimized Structure  
To confirm if the topology-optimized model satisfies the static and dynamic performance design requirements, finite element analysis and a comparison are performed between the empirically designed U-shaped frame structure and the topology-optimized final structure. Static and modal analysis steps are executed for both design structures, as shown in Figure 10a,b, respectively, employing identical load and boundary conditions for performance evaluation and comparative analysis. Mass check is applied  
Figure 10: U-shaped frame design structure comparison chart.  
Figure 9: The convergence curves of the objective and constraint functions.  
to the topology-optimized final design structure to assess compliance with lightweight design requirements.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 4 Results
Figure 9: Normalized error prediction of each person ID by different scenarios. The MAE without rPPG screening for each person ID and scenario is presented in Figure 10[29]. In most cases, the largest MAE is observed for Motion with talk and Motion with large movements, as shown in the graph. This observation is consistent with the size of the errors predicted by the model in Figure 9. Furthermore, it can be observed that in Still (Ideal environment), where noise is minimal, the majority of the errors are under 5 bpm. This correlation between the two graphs supports our hypothesis that our proposed rPPG screening model can improve the accuracy of predicting rPPG through the identification and removal of video regions with significant errors.
## 2 Materials and Methods
### Satellite Imagery  
During the first steps of this study, the photointerpretation of low-resolution satellite data (LANDSAT 7 ETM+, ASTER, and ESA Sentinel 2) allowed us to map 357 possible sinkholes in the Azrou plateau (Figure 3). The low resolution of these satellite images, however, did not allow us to extract detailed morphometric information on these collapses. In this study, we tested the Pleiades tri-stereo product (ground resolution of panchromatic data 0.5 m), provided by Airbus Defence and Space, to produce a very high-resolution DEM for a detailed morphological analysis.  
As Pleiades tri-stereo data of the area of interest were not available, we planned an acquisition on-demand of a dedicated tri-stereo product. In order to use the high-resolution imagery for the photointerpretation of geomorphological features, ortho-images completed the list of data used in this study (Table 2). Data were acquired in 2018 and 2019 in the framework of an ESA-funded project (European Space Agency \(\copyright\) CNES 2018, distributed by Airbus DS).  
Figure 2: (**a**) Localization of the study area in Central Morocco. On the DEM on the right image (**b**), volcanic cones and calderas are clearly recognizable. The sinkholes analyzed in this research are shown in red, whereas the other sinkholes are shown in green.  
Figure 3: Sentinel 2 imagery acquired on 23 July 2016 of the Plateau of Azrou: in yellow the 357 identified sinkholes through photointerpretation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Product ID** & **Processing Level** \\ \hline DS\_PHR1A\_201810251114401\_FR1\_PX\_W006N33\_1108\_01721 & SENSOR \\ DS\_PHR1A\_201810251114039\_FR1\_PX\_W006N33\_1109\_01574 & SENSOR \\ DS\_PHR1A\_20180125115071\_FR1\_PX\_W006N33\_1109\_01646 & SENSOR \\ DS\_PHR1A\_201908191122060\_FR1\_PX\_W006N33\_1206\_02715 & ORTHO \\ DS\_PHR1B\_201907131107426\_FR1\_PX\_W006N33\_1216\_00866 & ORTHO \\ DS\_PHR1A\_201908191122060\_FR1\_PX\_W006N33\_1108\_00986 & ORTHO \\ DS\_PHR1B\_201907131107426\_FR1\_PX\_W006N33\_1116\_00824 & ORTHO \\ DS\_PHR1A\_201906281122179\_FR1\_PX\_W006N33\_0910\_02480 & ORTHO \\ DS\_PHR1A\_201908191122494\_FR1\_PX\_W006N33\_0904\_01190 & ORTHO \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of the Pleiades data acquired in this study.  
Pleiades products were delivered in DIMAP V2 format. This file format provides, in addition to the Image file (JPEG 2000 or GeoTIFF), the RPCs (Rational Polynomial Coefficients), allowing to extract the DEM, and to apply the orthorectification and geometric processing. Moreover, a KMZ file for the localization in Google Earth environment, and the masks on data quality and cloud cover complete the data.