esa-sceva/satcom-chunk-collection huggingface dataset:
# Process Optimization for Antarctic Krill (_Euphausia superba_)
## 3 Results
### Color Detection  
Table 3 shows the color of the A&S under various processing conditions. The addition of salt to the A&S had no effect on its color (\(p\) > 0.05). When no salt was added to the Aks, the \(L^{\star}\) value was 23.84 \(\pm\) 0.07. Increasing the amount of salt resulted in fluctuations. Similarly, \(a^{\star}\) displayed the same up-down situation, yet the values were all less than the starting position (0% addition). The addition of 4.0%, 6.0%, and 8.0% salt resulted in lower \(b^{\star}\) values (\(-\)14.10 \(\pm\) 0.32, \(-\)14.03 \(\pm\) 0.12, and \(-\)14.30 \(\pm\) 0.51, respectively) than those at the beginning point (\(-\)13.96 \(\pm\) 0.09). Therefore, the \(\Delta\)E varied from 75.75 \(\pm\) 0.11 at the beginning to 79.24 \(\pm\) 5.30 after a salt addition of 8.0%.  
Moreover, varying the amount of rock sugar had no obvious effect on the color of the A&S. Lower amounts of rock sugar (0%, 0.8%, and 1.2%) in the A&S resulted in similar \(L^{\star}\) values of 24.87 \(\pm\) 0.03, 24.61 \(\pm\) 0.20, and 24.88 \(\pm\) 0.07, respectively. Higher amounts of rock sugar (1.6% and 2.0%) resulted in higher \(L^{\star}\) values of 25.56 \(\pm\) 0.05 and 27.96 \(\pm\) 0.05, respectively. The \(a^{\star}\) values ranged from 2.58 \(\pm\) 0.10 for low amounts to 2.67 \(\pm\) 0.08 for high amounts. The application of 0% to 1.6% rock sugar resulted in equal \(b^{\star}\) and \(\Delta\)E values. However, an increase of 1.8% resulted in lower \(b^{\star}\) and higher \(\Delta\)E values.  
Increasing the oil-to-material ratio resulted in significantly higher \(L^{\star}\) and \(b^{\star}\) values for the A&S (\(p\) < 0.05). The \(L^{\star}\) values increased from 22.18 \(\pm\) 0.04 at 0.4 to 27.96 \(\pm\) 0.37 at a 1.2 oil-to-material ratio. Similarly, the \(b^{\star}\) increased from 14.54 \(\pm\) 0.56 at 0.4 to \(-\)9.01 \(\pm\) 0.39 at a 1.2 ratio. When the oil-to-material ratio was between 0.4 and 1.0, the \(a^{\star}\) value was approximately 2.93, but the other oil-to-material ratios were approximately 2.76. The \(\Delta\)E values decreased dramatically as the oil-to-material ratio increased from 77.27 \(\pm\) 0.42 at 0.4 to 69.30 \(\pm\) 0.54 at 1.2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multicolumn{3}{c}{**Implicit Variable**} \\ \cline{2-4}  & \(-\)**1** & **0** & **1** \\ \hline A & 0\% & 2.0\% & 4.0\% \\ B & 0.8\% & 1.2\% & 1.6\% \\ C & 0.6:1 & 0.8:1 & 1.0:1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Box–Behnken design factors and levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & **Levels** & \(L^{\star}\) & \(a^{\star}\) & \(b^{\star}\) & \(\Delta\)E \\ \hline \multirow{6}{*}{Salt added/\%} & 0 & 23.84 \(\pm\) 0.07 \({}^{\rm a}\) & 3.07 \(\pm\) 0.17 \({}^{\rm a}\) & \(-\)13.96 \(\pm\) 0.09 \({}^{\rm a}\) & 75.75 \(\pm\) 0.11 \({}^{\  
The single-factor analysis results revealed that salt and rock sugars have no effect on the color of AkS. Sensory scores, therefore, were used as the response values for the response surface design.
# Water Retention Curve of Biocemented Sands Using MIP Results
## 5 Conclusions  
The paper was focused on the definition of the water retention curve of sandy soils, as this is necessary to investigate the effects of biocementation treatment, for example, to prevent soil erosion. This is a novel contribution.  
A model to find the water retention curve based on pore size distribution from mercury intrusion tests was proposed and tested for a sand before and after bioccementation. The model adopted considers the compressibility of the soil during the porosimetry test, caused by the presence of air entrapped in the soil pores. This explains the large differences between the void ratio at preparation and the void ratio computed by considering the total volume of mercury intruded. The model must be tested in other soils where air could be trapped in the soil pores and not removed during MIP tests, such as in the artificially cemented soil investigated.  
The adjustment with the model proposed was able to fit the experimental data of the water retention curve well that were above 0.5 MPa, measured by using conventional methods. Below the air entry value, it is believed that the curves from MIP tests are reliable, as well, because the intrusion pressures are very small and are in accordance with the largest void diameters detected. The shape found for the transition zone is expected, as well, considering the usual pore size distributions of sands.  
The curve in the lowest suctions can be measured by using tensiometers, axis translation, and pressure plate apparatus, but they cannot be measured by using vapor equilibrium (unless the environment is highly controlled), by most of the existing commercial sensors (resistive, capacitive, and inductive), and by water dewpoint potentiometers. Indirect techniques such as through using pore size distribution from mercury intrusion porosimetry (MIP) tests can be investigated to define the water retention curves of such soils, overcoming the difficulties in measuring the points at a very low suction range.  
Conceptualization, R.C.; methodology, R.C. and I.B.; validation, J.V., I.B. and R.C.; formal analysis, J.V. and R.C.; investigation, J.V. and R.C.; resources, I.B.; data curation, J.V.; writing--original draft preparation, J.V.; writing--review and editing, R.C.; visualization, R.C.; supervision, R.C.; project administration, R.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundacao para a Ciencia e a Tecnologia, FCT, I.P, projects BIOSOIL (ref. PTDC/ECI-EGC/32590/2017), and CALCITE (ref. PTDC/ECI-EGC/1086/2021).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### The Fungal Data Set
\begin{table}
\begin{tabular}{c c c c} \hline
**Genus** & **Fungal Name** & **References** & **Environment** \\ \hline \multirow{2}{*}{_Clonostachys_} & _Clonostachys rosea_ & & [58] & C-HE \\  & (syn. _Gliodadium roseum_) & & [58] & C-HE \\ \hline \multirow{2}{*}{_Cochliobolus_} & _Cochliobolus geniculatus_ & [32,76] & NC-HE, C-NHE \\  & (syn. _Curvulata geniculata_) & & \\ \hline \multirow{2}{*}{_Collariella_} & _Collariella bostrychodes_ & [28,32] & NC-HE \\  & (syn. _Chaetomium bostrychodes_) & & \\ \hline \multirow{2}{*}{_Collricia_} & _Colricia_ sp. & [66] & C-NHE \\ \hline \multirow{2}{*}{_Coprrinellus_} & _Coprrinellus aoki_ & & [67] & SC-NHE \\  & (syn. _Coprimus aokii_) & & \\ \hline \multirow{2}{*}{_Coprinopsis_} & _Coprinopsis atmentaria_ & [38] & C-HE \\  & _Coprimopsis culturata_ & & \\  & (syn. _Copritus culturnatus_) & & \\ \hline \multirow{2}{*}{_Cordycps_} & _Cordycps farinosa_ & & \\  & (syn. _Isaria farinosa_) & [68] & C-HE \\ \hline \multirow{2}{*}{_Coriolopsis_} & _Coriolopsis_ sp. & [66] & C-NHE \\ \hline \multirow{3}{*}{_Cunninghamella chitulata_} & [9,28,32,40,55] & NC-HE, C-NHE \\ \cline{2-4}  & _Cunningham elegans_ & [38] & C-HE \\ \cline{2-4}  & _Cunningham_ sp. & [23,24] & C-HE \\ \hline \multirow{5}{*}{_Cureularia australiensis_ (syn. _Drechslen australiensis_)} & [28, 29, 30, 31, 32] & NC-HE, C-NHE \\ \cline{2-4}  & _Curvularis hawaiiensis_ & [28, 29, 30, 31, 32] & NC-HE, C-NHE \\ \cline{2-4}  & _Curvularis lunata_ & [28,30, 31, 32,76,83] & NC-HE, C-NHE \\ \cline{2-4}  & _Curvularis pallescens_ & [29, 30, 31, 32] & NC-HE, C-NHE \\ \cline{2-4}  & _Curvularis spicifera_ & [46] & C-NHE \\ \cline{2-4}  & _Curvularis spicifera_ & [75] & C-NHE \\ \hline \multirow{2}{*}{_Cutaneotrichosporon_} & _Cutaneotrichosporon mucoides_ & [63] & C-NHE \\  & (syn. _Tichosporon mucoides_) & & \\ \hline \multirow{2}{*}{_Cylindrocarbon_} & _Cylindrocarbon_ sp. & [23,24] & C-HE \\ \hline \multirow{2}{*}{_Cyphellophora_} & _Cyphellophora olivacea_ & [42] & C-HE \\ \cline{2-4}  & _Cyphellophora_ sp. & [42] & C-HE \\ \hline \multirow{2}{*}{_Cyphellostereum_} & _Cyphellostereum_ sp. & [66] & C-NHE \\ \hline \multirow{2}{*}{_Cystoderma_} & _Cystoderma sp. & [66] & C-NHE \\ \hline \multirow{2}{*}{_Devriesia_} & _Devriesia_ sp. & [45] & NC-HE \\ \hline \multirow{2}{*}{_Dichotomophililus_} & _Dichotomophililus indicus_ & [55] & C-NHE \\  & (syn. _Chaetomium indicum_) & & \\ \hline \multirow{2}{*}{_Didymella_} & _Didymella glomerata_ & [21,40] & C-NHE \\  & (syn. _Phoma glomerata_) & & \\ \hline \multirow{2}{*}{_Dipodascus_} & _Dipodascus geotrichum_ & [55] & C-NHE \\ \cline{2-4}  & _Dipodascus_ sp. & [47,75] & NC-HE, C-NHE \\ \cline{2-4}  & (syn. _Geotrichum_ sp.) & & \\ \hline \multirow{2}{*}{_Discostroma_} & _Discostroma corticola_ & [20,56] & O-SPE \\  & (syn. _Seimatosporium lichenola_) & & \\ \hline \multirow{2}{*}{_Drechslen_} & _Drechslera_ sp. & [65] & O-SPE \\ \cline{2-4}  & _Drechslen_ sp. & [65] & O-SPE \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results and Discussions
### Spectral Feature Analysis
The EVI index, on the other hand, is shown as an effective indicator in identifying shrubs (brighter spot) from the Gobi Desert background (dark area of vegetation-free zone) (see the bottom left slice in Figure 6).
## 3 Model Calculation Based on Actual Parameters of True Transformer
### Natural Frequencies and Mode Shapes  
By solving the characteristic Equation (5), it can be concluded that the eigenvalue and eigenvector correspond to the natural frequency and vibration mode vector of the axial winding vibration model, respectively.  
Figure 3 shows the natural frequencies of each order of high-voltage winding, which are arranged in ascending order. As shown, the frequency values, which are equal to \(\omega_{nr}=\sqrt{K_{r}/M_{r}}\), change linearly and change with the change of the square root of the ratio of stiffness to mass.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Conductor density & 8.9 & \(\rho\)/[g/cm\({}^{3}\)] \\ Turns of high voltage winding N\({}_{\rm H}\) & 647 & [-] \\ Turns of medium voltage winding N\({}_{\rm M}\) & 226 & [-] \\ Turns of low voltage winding N\({}_{\rm L}\) & 107 & [-] \\ Cross-sectional area of high voltage winding S\({}_{\rm H}\) & 61.44 & [mm\({}^{2}\)] \\ Cross-sectional area of medium voltage winding S\({}_{\rm M}\) & 182.75 & [mm\({}^{2}\)] \\ Cross-sectional area of low voltage winding S\({}_{\rm L}\) & 296.64 & [mm\({}^{2}\)] \\ Total length of high voltage winding L\({}_{\rm H}\) & 2594 & [m] \\ Total length of medium voltage winding L\({}_{\rm M}\) & 700 & [-] \\ Total length of low voltage winding L\({}_{\rm L}\) & 245.5 & [m] \\ Elastic modulus E & 50 & [MPa] \\ Cushion block width a\({}_{\rm H}\) & 50 & [mm] \\ Cushion block width a\({}_{\rm M}\) & 40 & [mm] \\ Cushion block width a\({}_{\rm L}\) & 30 & [mm] \\ Axial height of conductor b\({}_{\rm H}\) & 11.8 & [mm] \\ Axial height of conductor b\({}_{\rm M}\) & 14 & [mm] \\ Axial height of conductor b\({}_{\rm L}\) & 6 & [mm] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transformer parameters.  
Figure 4 shows the typical modal shape diagram of the axial vibration of a winding with 34 disks by solving the system characteristic Equation (5), where the number of modal vectors is equal to the number of disks. It can be concluded from the figure that the lower the natural frequency, the greater the oscillation period of the disk, that is, the greater the impact on the system. Therefore, only the first four order modal vectors having the greatest impact on the system are taken in Figure 3. The first-order vibration mode shows the windings vibrate in the same direction in the radial direction and is distributed in a \"V\" shape; the second-order vibration mode shows the windings can be divided into halves from the middle part of the windings in the radial direction, and the upper half and lower half vibrate in the opposite direction, with an \"N\" distribution; the third-order and fourth-order modes show the higher the number of waves, the higher the distribution of waves in the fourth-order mode.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 3 Structural-Missing Tensor Completion
### ADMM for Solving the Hankel Tensor Completion Problem  
Normally, the optimization problem (10) is difficult to solve because the tensor nuclear norm terms \(\|\boldsymbol{\hat{\mathcal{H}}}\|_{*}\) are non-smooth. Hence, we adopt the alternating direction method of multipliers (ADMM), which can effectively solve large-scale optimization problems with non-smooth objectives [56]. The implementation of the ADMM for solving the Hankel tensor completion is elaborated in the following. Specifically, in order to make sure that the six nuclear norm terms \(\|[\boldsymbol{\hat{\mathcal{H}}}]_{j}\|_{*}\) in the objective of the dimensional-augmented Hankel tensor nuclear norm minimization problem (10) can be independently optimized, we define six auxiliary tensors \(\boldsymbol{\mathcal{Y}}_{j}\) with identical initialization. As such, the dimensional-augmented Hankel tensor nuclear norm minimization problem (10) can be converted into an equivalent form  
\[ \begin{split}\underset{\boldsymbol{\hat{\mathcal{H}}}}{\text{min}} \ \|\boldsymbol{\hat{\mathcal{H}}}\|_{*}=\sum_{j=1}^{6}\alpha_{j}\|[ \boldsymbol{\hat{\mathcal{H}}}]_{j}\|_{*}\\ \text{s.t.}\ \ \text{P}_{\bar{\Omega}}\big{(}\boldsymbol{\hat{ \mathcal{H}}}\big{)}=\text{P}_{\bar{\Omega}}(\boldsymbol{\mathcal{H}}),\\ \boldsymbol{\mathcal{Y}}_{j}-\boldsymbol{\hat{\mathcal{H}}}= \boldsymbol{\mathcal{O}},\,j=1,2,\cdots,6,\end{split}  \tag{11}\]  
where \(\boldsymbol{\mathcal{O}}\) is zero tensor with appropriate size. Then, we introduce six dual variables \(\boldsymbol{\mathcal{G}}_{j}\in\mathbb{C}^{\tau_{1}\times(M-\tau_{1}+1)\times \tau_{2}\times(N-\tau_{2}+1)\times\tau_{2}\times(T-\tau_{3}+1)}\) corresponding to the six dimensions of the optimization variables \(\boldsymbol{\hat{\mathcal{H}}}\) with \(j=1,2,\cdots,6\). Based on that, the augmented Lagrangian function of the dimensional-augmented Hankel tensor nuclear norm minimization problem (11) is defined as  
\[\mathcal{L}\big{(}\boldsymbol{\hat{\mathcal{H}}},\boldsymbol{ \mathcal{Y}}_{j},\boldsymbol{\mathcal{G}}_{j}\big{)}=\|\boldsymbol{\hat{ \mathcal{H}}}\|_{*}+<\boldsymbol{\mathcal{Y}}_{j}-\boldsymbol{\hat{\mathcal{H} }},\boldsymbol{\mathcal{G}}_{j}>+\frac{\rho}{2}\big{\|}\boldsymbol{\mathcal{Y} }_{j}-\boldsymbol{\hat{\mathcal{H}}}\big{\|}_{\text{F}}^{2}, \tag{12}\]  
where \(\rho>0\) is the penalty constant, \(\|\cdot\|_{\text{F}}\) denotes the Frobenius norm, and \(<\cdot,\cdot>\) denotes the inner product between two tensors.
# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
## 2 Description of the Wireless Monitoring System
### Wireless Sensor Nodes  
The sensor nodes described in this work represent an upgrade of the ones described in [36; 37]. The core of the device is the PCB, which is shown in Figure 2. The designed circuit has the function of performing measurements, processing the acquired data, and sending the results to the gateway.  
Figure 1: Schematization of the monitoring system to be mounted on the structure.  
Figure 2: Layout of the PCB and focus on the main components.
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## Methods
### RTK-DGPS Topographic Survey  
The planimetric coordinates of the RTK-DGPS topographic survey were referenced to the World Geodetic System (WGS84) while the vertical datum was referenced to NGF-IGN 1969 datum. A GPS base station was installed in a local geodesic point (located near Capbreton - Figure 1) and provided, in real time, the corrections to the mobile GPS unit via radio-transmission. After the survey, the planimetric coordinates were converted to the same coordinate system as the Pleiades products (WGS 84 UTM 30N), and spikes in the data (erroneous measurements) were eliminated. The processed topographic measurements were subsequently divided into individual profiles and interpolated in the cross-shore direction with 2 m spacing (to match the resolution of the Pleiades DEM). Comparisons between RTK-GPS ground-truth and remotely sensed Pleiades and LiDAR DEMs were performed by extracting values from the DEMs at each profile location (using all RTK-GPS point measurements). This task was performed using QGIS software (Lyon version) and the function \"sample raster maps at point location\" from GRASS-GIS toolbox. The data comparisons included the calculation of statistical parameters such as the correlation coefficient (CC), root mean squared (RMS) error, and bias (BIAS) using all RTK-GPS topographic observations.
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 3 System Model
### Communication and Computation Model
(2) The time when all the data needed by subtask has arrived at server \(k\). (3) The scheduling result of server \(k\). The execution time is according to Equation (3). The time when all the data needed for \(T_{i,j}\) is ready in server \(k\) can be denoted by:  
\[t_{i,j,k}^{D}=\max_{l\in pred(j)}\{\min\{(t_{i,l}^{F}+C_{j,l}^{k}),(t_{i,l^{F}} ^{F}+C_{j,l^{F}}^{k})\} \tag{5}\]  
where the \(pred(j)\) is the immediate predecessor subtasks of subtask \(j\). The ready time of \(T_{i,j}\) is determined by the lasted time when all the predecessors have transmitted its result to server \(k\). Furthermore, each predecessor subtask may have a duplication, and thus the inner \(min\) block in the equation returns the time when the result of a predecessor subtask has transferred to the \(T_{i,j}\) either from the \(T_{i,l}\) or its duplication \(T_{i,l^{\prime}}\), note that if a subtask has no duplications the \(t_{i,l^{\prime}}^{F}\) is infinite. The \(t_{i,l}^{F}\) denotes the actual finish of the subtask \(T_{i,l}\), and it will be defined in the following. Furthermore, the \(C_{j,l}^{k}\) denotes the data exchange time between subtask \(j\) and its predecessor subtask \(l\):  
\[C_{j,l}^{k}=\left\{ \begin{array}{ll}0&\quad\text{if }d_{i,l}=d_{i,j}\\ edge_{ij}/Rate_{i,k}&\quad\text{otherwise}\end{array} \right. \tag{6}\]  
After that, we finally obtain the actual finish time of subtask \(T_{i,j}\):  
\[t_{i,j}^{F}=\min_{k\in\{-i\}\cup\mathcal{M}}\{t_{i,j,k}^{S}+t_{i,j,k}\} \tag{7}\]  
\[t_{i,j,k}^{S}=\max\{t_{i,j^{\prime}}^{k},t_{i,j,k}^{D}\} \tag{8}\]  
The \(t_{i,j}^{k}\) denotes the time server k is available to execute \(t_{i,j}\), which depends on the scheduling result of all the subtask from nearby devices. From Equation (8), we can see that in order to obtain the starting execution time of a task (\(t_{i,j,k}^{S}\)) on a processor, all the data required by the subtask must be received and the processor must be available. Furthermore, the actual finish time of \(T_{i,j}\) is to select the minimum time by enumerating all the possible processors. It is worth mentioning that Equations (7) and (8) are also suitable for the possible duplicate subtask, as the dependency relationship of duplication is the same as the original subtask.
###### Abstract
The model considers the convection and radiation effects of the material, but ignores the conduction of heat of the fixture. [PERSON] et al. [23] established a mathematical model for different power distributions of arc and laser, which simulated the temperature field and velocity field of molten material in the welding pool by considering the effects of phase transition, buoyancy, and convective heat transfer. As we know, most of the studies on the influence mechanism of process parameters on laser-MIG composite welding are focused on experiments, while the simulation studies on welded joints are mainly focused on the microstructure and mechanical properties [24; 25; 26]. In laser-MIG hybrid welding, there are few simulations on the relationship between molten flow, pore behavior, and molten pool depth. Compared with laser welding and arc welding, laser-MIG hybrid welding requires more complex process parameters to be adjusted, so it is necessary to study the welding process through simulation to reduce the experimental workload. In this paper, the relationship between melt flow and keyhole behavior in the sensitive laser power range is studied, and the influence of the keyhole with regular fluctuation on the depth of the molten pool after entering the high-frequency oscillation stage is studied. Laser-MIG hybrid welding technology was used to weld AH36 steel in this experiment. The cross-sectional profile of the welded joint was photographed using a light microscope, and the weld dimensions were statistically analyzed. In order to better study, the Fluent finite element software was used to establish a 3D transient mathematical model based on three conservation equations. This model adopts the Semak V recoil pressure model and comprehensively considers the influence of surface tension, electromagnetic force, buoyancy, recoil pressure, evaporation condensation, evaporation heat transfer, droplet transfer, convection heat transfer, radiation heat transfer, and other factors. In terms of heat source, the combination of a Gaussian cylindrical heat source and a double ellipsoid heat source with linear attenuation along the direction of depth was selected to successfully obtain the \"nail\" morphology of the laser-arc composite welding joint, adjust the mathematical model according to the actual welding process and weld size parameters, and realize the verification of simulation and actual results. The melt pool under the action of dual heat source coupling was simulated under the sensitive laser power range, and the temperature field, velocity field, keyhole, and depth of melt changes were analyzed and verified with experimental results. This experiment studied the relationship between melt flow, keyhole behavior, and weld depth in the sensitive laser power range, providing a theoretical basis for the optimization of the laser-MIG hybrid welding process.