esa-sceva/satcom-chunk-collection huggingface dataset:
# Deformation Study of Strongly Structured Clays Considering Damage Effects
## 3 Test Results and Analysis
### Determination of Sample Disturbance Degree  
To quantitatively describe the influence of internal engineering parameter variations on the deformation characteristics of soil under different disturbance levels, a calculation formula for the disturbance degree was introduced. As shown in Figure 1, the structural yield stress of the disturbed soil specimens decreased as the disturbance level increased (see Table 3). Therefore, the variation in structural yield stress was considered an appropriate parameter to characterize the disturbance degree of the specimens.  
Scholars usually employ the traditional Cassagrande method to determine the production pressure. However, this method has flaws, especially in the difficulty of determining the maximum curvature point, which leads to errors. When using the double logarithmic coordinate system of \(ln(1+e)-lg\)\(p\), the compression curve can be well represented by two straight lines, and the intersection point of these two lines corresponds to the vertical pressure, which is the structural yield stress of the soil. [PERSON] et al. [18] Based on the \(ln(1+e)-lg\)\(p\) coordinate system, soil sample compression curves were analyzed, and a calculation formula for the degree of disturbance was further proposed, and it was defined as follows:  
\[\text{Disturbance degree }D\text{: }D=\frac{C_{CLB}}{C_{CLR}}\times 100\% \tag{1}\]  
In this formula, \(C_{CLB}\) and \(C_{CLR}\) represent the slopes of the compression curves for disturbed soil specimens and remolded soil specimens, respectively, in the \(ln(1+e)\) vs. \(-lg\)\(p\) coordinate system prior to yield.  
The disturbance degree indicators for the 14 groups of specimens were quantitatively assessed (see Table 4). Due to unavoidable minor disturbances during the sampling process, the disturbance degree of the undisturbed soil specimens was found to be 10.4%. For specimens subjected to vibration, the disturbance degree exhibited a gradual increase with rising vibration frequency when the vibration duration remained constant. Conversely, at a fixed vibration frequency, the disturbance degree also progressively increased with extended vibration time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sample Group** & \(\mathbf{A_{1}\text{-}A_{3}}\) & \(\mathbf{A_{4}\text{-}A_{6}}\) & \(\mathbf{A_{7\text{-}A_{9}}}\) & \(\mathbf{A_{10}\text{-}A_{12}}\) & \(\mathbf{A_{13}\text{-}A_{15}}\) & \(\mathbf{A_{16}\text{-}A_{18}}\) \\ \hline Yield Pressure & 779.41 & 751.09 & 726.15 & 705.38 & 756.62 & 718.34 \\
**Sample Group** & \(\mathbf{A_{19}\text{-}A_{21}}\) & \(\mathbf{A_{22}\text{-}A_{24}}\) & \(\mathbf{A_{25}\text{-}A_{27}}\) & \(\mathbf{A_{28}\text{-}A_{30}}\) & \(\mathbf{A_{31}\text{-}A_{33}}\) & \(\mathbf{A_{34}\text{-}A_{36}}\) \\ Yield Pressure & 703.12 & 682.23 & 726.15 & 687.34 & 651.44 & 617.65 \\ \hline \end{tabular}
\end{table}
Table 3: Yield stress values of 12 disturbed soil samples.
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 3 Results and Data Analysis
### The SNR of the SWIR Imaging System  
This section concerns with the imaging performance of the SWIR sensor system and it is implemented by an AIM's 384 \(\times\) 288 pixel CMT array as detector [17]. The dark current of the sensor is estimated using the Tennant's Rule 07 [18], by assuming the operating temperature of 150 K. Similar to that of the Teledyne detector, this is considered as a lower limit of the dark current and that it can be exceeded as according to the Tennant's rule.  
Figure 8: Shows the SNR behavior of the conventional spectrograph sensing vs. the CA multiplexed sensing for the integrated visible-SWIR sensor system.  
Figure 7: Depicts the variation of the SNR as a function of frame rate for conventional spectrograph and CA multiplexed sensors under a range of sensing parameters: (**a**) ground albedo 10% and 60\({}^{\circ}\) illumination angle, (**b**) well depth reduced to 700 K electrons, (**c**). illumination at 0\({}^{\circ}\) from zenith, (**d**) 120 bands.  
The efficiency of the gratings is approximated as according to [PERSON] et al.'s work [19]. The peak efficiency of the gratings is set at 0.9, and a blazing wavelength of 1700 nm is set for the first order diffraction operation. This gratings is capable to achieve a diffraction efficiency of 15% at 1000 nm, and reasonable efficiency (\(\sim\)10%) even at the long-wavelength end of the spectrum (\(>\)2000 nm). The transmission characteristics of the objective lens has been estimated from the manufacturer's data sheet (SWIR lens of Optec S.p.A.).  
Figure 9 shows the performances of the conventional and the multiplexed HSI sensing in terms of SNR, when the ground reflectance (albedo) are at 50% and 10% under the same irradiance geometry of 60\({}^{\circ}\) from zenith in these two scenarios. Similar to that of the integrated system presented in the last section, it is seen that the conventional HSI sensing outperforms the multiplexed one for all frame rates regardless of reflectance levels. However, it is noted that the multiplexed SWIR sensor here exhibits slightly better performance than that of the Teledyne detector (see Figure 8) which has much higher well depth than that of the AIM's CMT detector, especially at low frame rates regions. This may be due to the higher maximum frame rate in the AIM's CMT detector, which, allows it to aggregate more frames during the image requisition process. This may also suggest that detector arrays that with fast readout capability may favour the multiplexed operations further. It is seen from Figure 7 that increasing the number of spectral bands favours the relative performance of multiplexing, however, the effect of changing the number of bands is not intuitively obvious at the moment.
## 4 Results and Discussion
### Model 1--Intentions to Use Public Transport during COVID-19 Pandemic
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & **Estimate** & **Std. Error** & **Wald** & **df** & **Sig.** & \multicolumn{3}{c}{**95\% Confidence Interval**} \\  & & & & & & **Lower** & **Upper** \\  & & & & & & **Bound** & **Bound** \\ \hline How often would you use & Never & 2.763 & 0.847 & 10.654 & 1 & 0.001 & 1.104 & 4.422 \\ public transport while you & Rarely & 4.767 & 0.857 & 30.929 & 1 & 0.000 & 3.087 & 6.447 \\ have covid-19 symptoms? & Sometimes & 6.460 & 0.885 & 53.294 & 1 & 0.000 & 4.725 & 8.194 \\  & Often & 7.622 & 0.950 & 64.356 & 1 & 0.000 & 5.760 & 9.484 \\ Trip frequency & & \(-\)0.275 & 0.088 & 9.737 & 1 & 0.002 & \(-\)0.447 & \(-\)0.102 \\ F1\_attitudes & & \(-\)0.074 & 0.077 & 0.925 & 1 & 0.336 & \(-\)0.226 &.077 \\ F2\_awareness & & 0.053 & 0.081 & 0.421 & 1 & 0.516 & \(-\)0.106 & 0.212 \\ F3\_responsibility & & 0.042 & 0.077 & 0.296 & 1 & 0.587 & \(-\)0.108 & 0.192 \\ F4\_difficulty & & 0.057 & 0.081 & 0.490 & 1 & 0.484 & \(-\)0.102 & 0.215 \\ Gender & Male & \(-\)0.153 & 0.156 & 0.972 & 1 & 0.324 & \(-\)0.459 & 0.152 \\  & Female & 0 a & & & & & \\ Marital Status & Single & \(-\)0.561 & 0.182 & 9.442 & 1 & 0.002 & \(-\)0.918 & \(-\)0.203 \\  & Married & 0 a & & & 0 & & & \\ Age & \(<\)20 & 3.006 & 0.830 & 13.122 & 1 & 0.000 & 1.380 & 4.632 \\  & 21–30 & 1.900 & 0.806 & 5.561 & 1 & 0.018 & 0.321 & 3.478 \\  & 31–40 & 2.154 & 0.794 & 7.366 & 1 & 0.007 & 0.598 & 3.709 \\  & 41–50 & 1.650 & 0.814 & 4.109 & 1 & 0.043 & 0.055 & 3.246 \\  & Above 50 & 0 a & & & & & \\ Car Ownership & Yes & \(-\)0.270 & 0.174 & 2.414 & 1 & 0.120 & \(-\)0.611 & 0.071 \\  & No & 0 a & & & & & \\ Motorbike Ownership & Yes & \(-\)0.077 & 0.151 & 0.259 & 1 & 0.611 & \(-\)0.373 & 0.219 \\  & No & 0 a & & & & & \\ Profession & Student & \(-\)0.355 & 0.290 & 1.498 & 1 & 0.221 & \(-\)0.923 & 0.213 \\  & Civil Employee & 0.016 & 0.245 & 0.004 & 1 & 0.947 & \(-\)0.463 & 0.496 \\  & Private Employee & 0.882 & 0.217 & 16.451 & 1 & 0.000 & 0.456 & 1.308 \\  & Others & 0 a & & & & & \\ Education & High School & 1.888 & 0.272 & 48.219 & 1 & 0.000 & 1.355 & 2.421 \\  & FA/FSc & 0.795 & 0.251 & 10.028 & 1 & 0.002 & 0.303 & 1.287 \\  & BA/BSc & 0.121 & 0.197 & 0.379 & 1 & 0.538 & \(-\)0.265 & 0.508 \\  & Master and Above & 0 a & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model 3—Parameter estimates for the ordinal regression model.
## 4 Active Antiresonance Assignment with No Additional Masses
### Numerical Examples of Active Control
In the case of solving Equation (20) for the assignment of a complex conjugated pair of zeros through _pinv_ the gains vector \(\mathbf{k_{0}}\) is fully populated while exploiting _mldivide_ only two entries of \(\mathbf{k_{0}}\) are not-null, thus matching in this example the solution of the output-feedback solution. It follows that, for assigning a pair of complex conjugated zeros (i.e., an antiresonance), the output-feedback controller, as well as the state-feedback controller obtained through _mldivide_, both lead to a proportional-derivative controller. The open-loop and closed-loop receptances \(h_{3,3}\)/\(j\omega\)) are compared in Figure 8 to confirm the fulfilment of the assignment task. The same control task is performed through the state feedback with integral action by solving the system in Equation (26) [52] and comparing the results provided _pinv_ and _mldivide_. The open-loop and closed-loop zeros of receptance \(h_{3,3}\)/\(j\omega\)) are reported in Table 6; the 2-norm of the control gains are listed in Table 7. The minimum-norm computation (i.e., _pinv_) leads to a proportional-integral-derivative controller. In contrast, computing the controller through _mldivide_ leads to the proportional-derivative controller obtained in Table 5: indeed, such a controller configuration is enough to assign the desired antiresonance. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Gain Vector k**} & **Output-Feedback Method**[53] & **State-Feedback Method**[50] **Solved Through Pinv** & **State-Feedback Method**[50] **Solved Through Mildivide** \\ \hline \multicolumn{3}{c}{**Velocity gains f**} \\ \hline f\({}_{1}\) & \(-0.0125\) & \(-0.0100\) & \(-0.0125\) \\ f\({}_{2}\) & - & \(-0.0060\) & \(0\) \\ f\({}_{3}\) & - & \(0\) & \(0\) \\ f\({}_{4}\) & - & \(0\) & \(0\) \\ f\({}_{5}\) & - & \(0\) & \(0\) \\ f\({}_{6}\) & & \(0\) & \(0\) \\ \hline \multicolumn{3}{c}{**Position gains g**} \\ \hline g\({}_{1}\) & \(-0.500\) & \(-0.4000\) & \(-0.500\) \\ g\({}_{2}\) & - & \(-0.2000\) & \(0\) \\ g\({}_{3}\) & - & \(0\) & \(0\) \\ g\({}_{4}\) & - & \(0\) & \(0\) \\ g\({}_{5}\) & - & \(0\) & \(0\) \\ g\({}_{6}\) & - & \(0\) & \(0\) \\ \hline \(\left\|\mathbf{k_{0}}\right\|_{2}\) & \(0.5002\) & \(0.4774\) & \(0.5002\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The control gains in the numerical example of zeros assignment through unit-rank active control. Figure 8: Open\(-\)loop and closed\(-\)loop receptances \(h_{3,3}\)/\(j\omega\)): comparison with different controllers.
###### Abstract  
Sustainable irrigation strategies in Southeast Spain (one of the most arid regions in Europe) are essential to fight against desertification and climate change mitigation. In this way, _Fondillon_ production is based on rain-based vineyards, over-ripe _Monastrell_ grapes, and non-alcoholtification. Thus, _Fondillon_ is a naturally sweet red wine, protected within the Alicante Denomination of Origin, recognized by the European Union in its E-bachus database. The study aim was to evaluate the effect of the aging (_solera_ factor) on _Fondillon_: (i) basic ecological parameters (e.g., total, and volatile acidity), (ii) chromatic characteristics, (iii) antioxidant activity (ABTS**, FRAP and DPPH**), (iv) total contents of condensed tannins and anthocyanins, (v) volatile composition, (vi) sensory profile, and (vii) overall liking. Experimental data proved that the wine (1960 _solera_) with the highest total contents of condensed tannins and anthocyanins and total antioxidant activity was the most liked by Spanish consumers. Experimental results clearly established a positive relationship among _Fondillon_ chemical composition, its antioxidant activity, and overall consumer liking. Exceptional harvest with grapes having extremely high antioxidant power (e.g., 1960 _solera_) will result, even more than 50 years later, in high quality wines with high consumer acceptance and a high monetary worth.  
affective sensory analysis; antioxidant activity; descriptive sensory analysis; DPPH**; phenolic content; rain-based farming; wine color +
Footnote †: journal: Applied Soft Matter  
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## 5 Conclusions  
An unsupervised intelligent clustering method based on FCM-DSDFP, a fusion method based on PCA dimensionality reduction and noise reduction, and principles of cluster feature analysis were integrated to identify reservoir facies in Fractured-Vuggy dolomite reservoirs. Seven types of reservoir facies were identified in Dengying Formation 2 nd Member and Dengying Formation 4 th Member. Combining drilling core observation, conventional logging data, and identification results of reservoir facies, Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy type, Pore Type I, Pore Type II, Tight Type I, and Tight Type II are divided in the Dengying Formation 4 th Member. Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy Type I, Pore-Vuggy Type II, Pore Type I, Pore Type II, and Tight Type are divided in the Dengying Formation 2 nd Member. The observation results of drilling cores and the prediction of bitumen content are used to test the identification results; the algorithm's accuracy was above 0.90, reflecting a high level of reliability in reservoir facies prediction. Compared with conventional algorithms, this method showed superiority in highly heterogeneous Fractured-Vuggy carbonate reservoirs, providing an approach for precise characterization. However, for reservoirs with extremely complex categories, unsupervised learning classification remains a significant challenge.  
Conceptualization, Y.Y. and Z.J.; methodology, X.L.; validation, Z.W.; formal analysis, Y.G.; investigation, Y.Y. and Z.J.; data curation, Y.G.; writing--original draft preparation, Y.Y. and Z.J.; writing--review and editing, X.L. and Z.W.; visualization, Y.G.; supervision, X.L.; project administration, Z.W. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Comparison of accuracy of different algorithms.  
Funding: The research was supported by the National Natural Science Foundation of China (No. 42202166) and the National Natural Science Foundation of China (No. 41972165). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data are contained within the article. Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that might have influenced the work presented in this article.
# Acoustic Emission and Modal Frequency Variation in Concrete Specimens under Four-Point Bending
## Acoustic Emission Monitoring  
### Critical Conditions and Fracture Modes  
According to the AE technique, it is possible to detect the transient elastic waves related to each stress-induced crack propagation event inside a material. These waves can be captured and recorded by transducers applied on the surface of specimens or structural elements. The transducers are piezoelectric sensors that transform the energy of the elastic waves into electric signals. A suitable analysis of the AE waveform parameters (peak amplitude, duration time, and frequency) obtains detailed information about the damage evolution, such as the cracking pattern, the released energy, the prevalent fracture mode, and the achievement of critical conditions that anticipate the collapse. The last analysis can be performed by calculating the \(b\)-value from the Gutenberg-Richter (GR) law. Even though two different dimensional scales are involved, the GR law can be applied in the same way for earthquake distributions in seismic areas as well as for the structural monitoring by the AE technique [4; 11; 12].  
The connection between fracture mode and recorded waves depends on different factors like geometric conditions, relative orientations, and propagation distances [13]. The identification of the cracking mode may be done with the AE wave's Rise Time (RT) (i.e., the time interval between the wave onset and its maximum amplitude), the value of the peak amplitude \(A\), and the Average Frequency (AF). The ratio between the rise time (expressed in ms) and the peak amplitude (expressed in \(V\)) defines the Rise Angle (RA), as shown in Figure 1[14; 15; 16]. The peak amplitude can be expressed in dB by the equation  
\[A[\mathrm{dB}]=20\cdot\mathrm{Log}_{10}\bigg{(}\frac{V}{V_{0}}\bigg{)} \tag{1}\]  
where \(V\) is the amplitude of the signal, and \(V_{0}\) is the maximum amplitude of the background noise.  
The AF (measured in Hz) is obtained from the AE ring-down count divided by the duration time of the signal. The AE ring-down count corresponds to the number of threshold crossings within the signal duration time [14; 15; 16].  
The fracture mode is then characterized by the shape of the AE waveforms; low RAs and high AFs are typical for tensile crack propagations (Mode I, opening), whereas shear events (Mode II, sliding), usually generate longer waveforms, with higher RAs and lower AFs, as shown in Figure 1[17; 18; 19; 20]. Variations in the RA and AF values during the loading process identify a change in the prevalent failure mode of the specimen.  
In general, a decrease in frequency might also be caused by the formation of large cracks during both tensile and shearing processes. In fact, it is reasonable to assume that high frequency waves are generated from small discontinuities that characterize the beginning of the damage process, whereas the low frequency ones can be produced from large cracks that usually develop during the final collapse [21].
## 1 Introduction  
Improving productivity and preserving product quality are crucial in production systems [1]. One of the most crucial aspects of quality control is the detection of surface defects. Traditional detection techniques rely on expert inspection for control, which has disadvantages such as time wastage, poor productivity, and poor reliability [2]. Specifically, automatic surface defect detection is becoming increasingly important in steel industries having extensive production fields. Due to heat treatment, non-metallic inclusion, corrosion, and emulsification, flat-rolled steel can develop flaws such as cracks, pitted surfaces, scratches, and patches [3]. Evaluating defect geometry and generating a sizable sample of statistical data is crucial for improving the surface defect process. As a consequence, automated detection and classification systems can prevent unforeseen equipment failure.  
Automatic surface defect detection has frequently been accomplished using traditional machine learning and deep learning-based techniques [4]. Conventional classification approaches including model-based, statistical-based, and spectral-based methods typically rely on statistical data and visual features. These techniques, such as thresholding, Sobel, Local Binary Patterns (LBPs), Fourier transform, and Canny's algorithm, are used to convert features obtained manually, but factors including the background, lighting, and camera angle directly impact the effectiveness of defect identification [5]. Additionally, these techniques have limitations when used on various surfaces, making them unsuitable for use in practical situations [6]. Deep learning-based methods are now used to improve defect-detection capabilities in computer vision [7]. When using deep learning techniques, the algorithm can function by producing prompt and precise predictions even in the absence of supervision. Thus, labor and time saving by detecting automatically in the steel factories have made important advantages in comparison with manual control by technicians and engineers [8].
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 7 Distributed Goal Exchange  
The procedure described in Section 5 can be implemented in a distributed manner. Both key components of the goal switching algorithm; computation of the switching function (32) and permutation matrix transformation (33), involve only two agents. Reliable connection between them was needed as the process was conducted in a sequence of steps. After the agents establish the connection one of them (\(i\)-th) sends its position coordinates \((x_{i},y_{i})\) and goal location to the other (\(j\)-th). The second robot computes switching function (32) which is the verdict on the goal exchange. If it is negative the robots disconnect and continue motion to their goals, otherwise robot \(j\) sends its position coordinates \((x_{j},y_{j})\) and goal location to robot \(i\). This part is critical in the process and must be designed carefully to ensure correct task execution.  
To make distributed goal exchange possible, the robots must be equipped with the on-board radio modules allowing inter-agent communication. Even if not all pairs of agents were capable of communicating, the goal exchange algorithm improved the result. On the other hand, without communication between all pairs of agents the algorithm did not fail. In the vast majority of environments there was no problem with the communication range (current technology allows communication through many routers, base stations and peers). The exceptions may be space and oceanic applications. They will not be considered here. The author plans to conduct laboratory experiments where there are no restrictions on communication. From the practical point of view, robots need to know the network addresses of other robots and sequentially attempt to establish connection and, if successful, exchange the goals.