esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Validation with Experimental Data  
The chiller was allowed to operate from transient to cyclic steady state conditions. The key parameters analyzed in the simulation are the temperature profiles for the four beds, the condenser and the evaporator, the coefficient of performance, the effect of heat recovery cycle and the cooling capacity. The values used in the simulation are presented in Appendix B.  
The proposed model predicted temperatures inside the beds, the evaporator and the condenser at one second intervals; thus, the cooling capacity and COP could be evaluated over the cycle and switching time. It is very difficult experimentally to keep constant inlet temperatures for chilled water and cooling and hot water, so the data were measured with varying inlet conditions due to variable solar load use (CSP). In this model, all inlet temperatures were constant, so different outputs were recorded but with same trend as the experimental readings, as shown in Figure 2. The cooling outlet temperature is higher by 5-7 \({}^{\circ}\)C than the inlet cooling temperature for both model and experimental results. The chilled water outlet temperature of the model is one degree less than that of the experimental results. This is because the model uses many ideal assumptions as mentioned earlier with regard to the internal heat exchange between heating and cooling water flow during switching from heating to cooling during the experiment.  
The experimental data for cooling water inlet temperature were time averaged (semi constant) before use in the model. The chiller average cooling capacity coincides very well for both model and experimental results. The average cooling capacity of the model is 110% that of the experimental results at 30 \({}^{\circ}\)C and 98% at 45 \({}^{\circ}\)C, as shown in Figure 3.
## 2 Materials and Methods
### Multi-Temporal Foliage Projected Cover  
After pre-processing to pan-sharpened surface reflectance, each SPOT5 image was used to model FPC. Due to insufficient field data to calibrate a model, we related the surface-reflectance of the 5 m SPOT5 imagery to a Landsat derived overstorey FPC product [25; 36]. The Landsat FPC product was trained using QLD field measurements of FPC, and applied with a multiple linear regression model to Landsat 5 TM and Landsat 7 ETM+ data [36]. The QLD data and methods were made available through the Joint Remote Sensing Research Program (JRSRP) at the University of Queensland, which was established to improve the consistency of products across state borders. The QLD Landsat FPC model had a relatively low RMSE of 10.26% and 8.95% compared to field and lidar measurements, respectively [36]. A preliminary unpublished validation of the model in NSW indicates that it has a similar accuracy.  
To calibrate between sensors, 2485 data points were collected from a total of 60 SPOT5 images across NSW and QLD (Figure 3A). Image locations were chosen to capture a large range of vegetation communities, land types, and variations in FPC. The multi-spectral SPOT5 images and Landsat FPC images were degraded to an area equivalent to 3 by 3 Landsat pixels (90 m by 90 m), before sample pixels were taken on a regular 1 km by 1 km grid. Only points that were on a slope of less than 5% (determined from the SRTM DEM) and within an area of homogenous tree cover (determined by the Landsat FPC), were used. These homogenous areas were defined where the coefficient of variation of the Landsat FPC in a 5 by 5 pixel window (150 m by 150 m) was less than 0.05. Degrading the images and choosing pixels on homogeneous sites reduced the influence of misregistration between the SPOT5 and Landsat FPC images on the model. Multiple linear regression was used to relate the SPOT5 reflectance to Landsat FPC and the adjusted \(r^{2}\) of the model fit was 0.88. The model included terms for each SPOT5 band (\(\lambda_{i}\)), and interactions between them:  
\[F=\beta_{0}+\sum_{i=1}^{4}\beta_{i}f\left(\lambda_{i}\right)+\sum_{i=1}^{3} \sum_{j=i+1}^{4}\beta_{3+i+j}f\left(\lambda_{i}\right)f\left(\lambda_{j}\right) \tag{1}\]  
where \(F\) is FPC, \(\beta\) are the coefficients, and the function \(f\) used to remove skewness in the distribution [36] was:  
\[f\left(x\right)=log_{e}\left(100x+1\right) \tag{2}\]Although the model was developed using 90 m degraded imagery, it was applied on the 5 m resolution pan-sharpened SPOT5 surface reflectance data. In doing so, we assume that the multispectral surface reflectance and FPC models at 5 m and 90 m are linearly related.
## 4 AI Applications and Their Impact
### Garment Defect Detection
#### 4.1.2 Stitching Defect Detection  
[PERSON] et al. [41] focused on detecting broken stitches during garment production. They developed a CNN-based stitching defect detection algorithm effective in scenarios where fabric and stitch colors are similar, a common challenge in edge detection. The dataset was sourced using low-cost Pi cameras with an 8-megapixel Sony IMX219 sensor and supplemented with online image repositories. Despite its innovative approach, the reliance on low-resolution cameras and limited dataset diversity (using only seven stitched samples and using a straight single-needle lockstitch) may hinder scalability in industrial environments.  
[PERSON] et al. [39] developed a two-stage zipper tape defect detection framework based on fully convolutional networks. In the first stage, they used a multi-scale detection architecture to efficiently identify large context regions likely to contain small-scale defects by fusing shallow and high-level features. In the second stage, they performed fine-grained detection of the small-scale defects within those regions. Their method demonstrated high accuracy, efficiency, and robustness compared to existing approaches.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods
### Training and Statistical Evaluation of Models
#### 2.5.2 Training and Test  
The machine learning model training consisted of the input of the remaining predictor variables after the variables selection process into four regression algorithms: Linear regression (LM); Cubist; eXtreme gradient boosting--linear method (Xgblinear); and eXtreme gradient boosting--tree method (Xgbtree). These models were chosen for having an elevated predictive potential and for performing fast on the training, which was carried out with the aid of the caret package [21]. After training, 8 possible predictions were obtained, 4 for approach 1 and 4 for approach 2, which were submitted to testing for evaluating the assertiveness and statistical error.  
Thirty percent of data extracted from the pivots were used to test the trained algorithms. Statistical analyses for the test were: the coefficient of determination (R\({}^{2}\)), Equation (9); root mean squared error (RMSE), Equation (10); mean absolute error (MAE), Equation (11); mean bias error (MBE), Equation (12).  
\[\text{R}^{2}=\frac{\left[\sum_{i=1}^{n}\left(\text{Pi }-\ \overline{\text{P}}\right)\left(\text{Gi }-\ \overline{\text{O}}\right)\right]^{2}}{\left[\sum_{i=1}^{n}\left(\text{Pi }-\ \overline{\text{P}}\right)^{2}\right]\left[\sum_{i=1}^{n}\left(\text{Gi }-\ \overline{\text{O}}\right)^{2}\right]} \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(\text{Pi }-\ \text{Oi}\right)^{2}}{n}} \tag{10}\]\[\text{MAE}= \frac{1}{n}\sum_{i=1}^{n}\left|\text{Pi}\right.-\left.\text{Oi}\right| \tag{11}\] \[\text{MBE}= \frac{1}{n}\sum_{i=1}^{n}\left(\text{Pi}\right.-\left.\text{Oi}\right) \tag{12}\]  
where Pi is the value predicted by the model; Oi is the observed value; \(\overline{\text{O}}\) is the average observed value; n is the number of data pairs.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 4 Experimental Research
### Experiments of Different Loads  
Wheel-rail contact experiments under large slip conditions with different axle loads are carried out utilizing the test-rig. The target creepage is sinusoidal, varying from 0 to 0.3 with a period of 12 s, while the applied axle load is 14 t, 15 t, 16 t and 17 t. The maximum Hertz contact pressure between rail and wheel is 818.4 MPa, 836.7 MPa, 855.0 MPa and 871.8 MPa. The target vehicle velocity is constant at 60 km/h and the sprinkler flow is 4 L/min. Measured slip-adhesion curves are shown in Figure 14.  
Figure 14: Measured slip–adhesion curves in wheel–rail contact experiments under large slip conditions with axle load of (**a**) 14 t; (**b**) 15 t; (**c**) 16 t; and (**d**) 17 t.  
The shape of slip-adhesion curves in the rising process of creepage in Figure 14 is roughly the same as that described in Figure 13. However, with the increase in axle load, the adhesion coefficient of the second maximum slightly decreases. Thus, adhesion recovery shows a negative correlation with axle load. This phenomenon is in accordance with experimental results in previous research [18]. Another interesting phenomenon is that the trends of adhesion coefficient in the process of the creepage decrease appear to be different. In Figure 13a,b, slip-adhesion curves are above those in the whole process of creepage rising. In Figure 13c,d, adhesion coefficient drops below the first maximum. The same phenomenon was reported in previous experimental investigations [18; 19], but the mechanism of it remains to be studied.
## 3 Dual-PRF Waveform Design and Signal Processing
### Waveform Design under Uplink-Flexible Mode  
As shown in Figure 2, the sensing timing of TD-ISAC is similar to the radar sensing pulse. We design nonuniform sensing timing based on the idea of ambiguity resolution by multiple PRF in a radar system. It can be seen from Figure 1 that the common channel symbols are in the middle of some slots with some reserved OFDM symbols at the head or end of the slots. Hence, we avoid the common channel by allotting a sensing symbol at the last position of one slot.  
Here, we first consider uplink-flexible mode. According to As1, one uplink slot needs to be allocated in every five consecutive slots. We select the dual-PRF method to design the sensing waveform. Figure 3 shows the designed pulse, where the color of yellow represents the last symbol of the slot is allocated for sensing. It can be seen from the figure that there is at least one slot free for uplink in every five consecutive slots which satisfies the assumption As1. The designed dual-PRF sensing waveforms are nonuniform, and the dual-PRFs are \(2T_{r}\) and \(3T_{r}\), respectively. We know the least common multiple of \(2T_{r}\) and \(3T_{r}\) is \(6T_{r}\). Obviously, there are two idle symbols in every six symbols as shown in Figure 4; that is, the sensing idle is \(1/3\). The maximum uplink demand is one slot in five consecutive slots. Obviously, \(1/3>1/5\), the sensing idle of the designed waveform can satisfy the maximum link needs. And from the designed waveform in Figure 3, we can see that the designed sensing waveform satisfies the maximum link needs which is assumed in As3.  
The \(2T_{r}\) and \(3T_{r}\) pulses are both uniform pulses. Similar to (10), the Doppler dimension vector of the two pulses in the received matrix can be respectively expressed as  
\[\mathbf{b}_{2/lx}=\left[1,\ldots,e^{i2\pi f_{d}2T_{r}},\ldots,e^{i2\pi f_{d}(N_{2 T}-1)2T_{r}}\right] \tag{16}\]  
Figure 4: Part of designed waveform.  
Figure 3: Dual\(-\)PRF sensing timing under uplink\(-\)flexible mode.  
and  
\[\mathbf{b}_{3 flx}=\left[1,\dots,e^{j2\pi f_{c}3T_{r}},\dots,e^{j2\pi f_{d}(N_{3T}-1)3T _{r}}\right], \tag{17}\]  
where \(k\) is the \(k\)th pulse of the \(2T_{r}\) sensing waveform, \(l\) is the \(l\)th pulse of the \(3T_{r}\) sensing waveform, and \(N_{2T}\), \(N_{3T}\) are the number of \(2T_{r}\) and \(3T_{r}\) pulses. Then, the unambiguity velocities with the DFT-based method of the two pulses are  
\[v_{u2 Tr}=\pm\frac{c}{2f_{c}}\frac{1}{4T_{r}}=\pm 41.5\ \mathrm{km/h} \tag{18}\]  
and  
\[v_{u3 Tr}=\pm\frac{c}{2f_{c}}\frac{1}{6T_{r}}=\pm 27.7\ \mathrm{km/h}, \tag{19}\]  
respectively. Neither the \(2T_{r}\) nor the \(3T_{r}\) pulse can satisfy the unambiguity velocity in urban traffic. However, based on the dual-PRF ambiguity resolution method, it can obtain the unambiguity velocity [33]  
\[v_{uDPRF}=\pm\frac{c}{2f_{c}}\frac{1}{2(3T_{r}-2T_{r})}=\pm 83.08\ \mathrm{km/h} \tag{20}\]  
Obviously, it can obtain the same unambiguity velocity with uniform sensing timing.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 6 Discussion of Results
### Computational Complexity  
The computational complexity of mutual information estimation using the histogram method is of the order \(\mathcal{O}(N_{f}+b^{2})\), where \(N_{f}\) is the length of the frequency vector used in transmissibility coherence calculations, and \(b\) is the number of bins used in marginal entropy calculations. Note that since \(b^{2}\) must be smaller than \(N_{f}\) for the binning process to be meaningful, the computational complexity can be written as \(\mathcal{O}(N_{f})\). The [PERSON]'s correlation coefficient has the same level of complexity. As a reference, the Discrete Fourier Transform has a complexity of \(\mathcal{O}(N\log N)\), where \(N\) is the number of samples in the time domain. Thus, mutual information is suitable for real-time applications.  
Figure 10: \(p\)-values in MI.
## 3 Edible Coatings
### Active Edible Packaging
#### 3.4.3 Phenolic Acids as Active Compounds
Literature reports also show that gallic acid has valuable antibacterial properties against pathogenic Vibrio species, indicating its great potential for creating packaging materials that limit the development of pathogens and bacteria responsible for food spoilage. In addition, the content of gallic acid in foil-forming materials increases their flexibility, acting as a plasticiser, thus eliminating their brittleness. Also, [PERSON] et al. [147] observed that the antimicrobial activity of the chitosan coating increased with the inclusion of gallic acid. On the other hand, films based on chitosan and syringic acid are characterised by higher density, water solubility, and opacity and have properties that limit the development of various microorganisms. In addition, syringic acid has antioxidant properties, and its addition can extend the shelf life of food by being an active packaging material [148]. [PERSON] et al. [149] conducted research on the properties of chitosan films with the addition of protocatechuic acid, which was characterised by, among others, increased thickness, lower light, and water permeability compared to chitosan films without the addition of protocatechuic acid. Protocatechuic acid is characterised by strong antioxidant properties and is most often found in fruits and vegetables. It has been proven that phenolic compounds can interact with chitosan chains due to ester bonds, electrostatic interactions, and hydrogen bonds, resulting in increased mechanical strength of the film. Hydrogels are also used in active packaging, but this is only the initial phase, requiring further research to improve the cross-linking properties of the hydrogel. In addition, the challenge for researchers and scientists has also become the development of biodegradable packaging materials containing antimicrobial agents, the release of which will be controlled to a greater extent. For this purpose, studies have already been conducted on the release of gallic acid after 15 min, 1.5 h, and 16 h with films containing green tea extract. A slight increase in the release of phenolic compounds was observed in the approximate amount of 84 mg during 1.5-16 h [79]. Phenolic acids may be an interesting group of compounds for the development of functional foods. However, there are still some gaps that should be better explored to increase the possibilities of using these compounds on a large industrial scale and to make their use as effective and efficient as possible. The goal is also to provide the benefits of phenolic acids without causing side effects. There is still a need to investigate methods to increase the stability and preservation of the bioactivity of these compounds during the shelf life of food products.
## 1 Introduction
The main difficulty of UWA communications is the high Doppler effect; we anticipate that this can be addressed by the combination of deep learning and expert knowledge to reduce training requirements. In this paper, a novel receiver structure for underwater vertical acoustic communication is proposed, where the correlation-based estimation bias of the timing offset is learned and then estimated by the DNN to an accuracy that renders the real-time adjustment of the subsequent equalizer unnecessary. The expert knowledge of the ambiguity function of linear frequency modulation (LFM) was utilized in the design of the DNN model to reduce the parameter size for tuning. The model was based on the PM random surface height model with a moderate wind speed and was verified to be applicable for various wind speeds and experimental waveforms. This receiver, embedded with the DNN model, demonstrated lower complexity and better performance than the adaptive equalizer-based receiver. This paper's structure is as follows: Section 2 presents the channel model and the transmission packet structure. Section 3 describes the DNN model for synchronization and the full receiver used in the low-complexity baseband processing. Section 4 shows the results of the simulations and deep-sea experiments. Section 5 presents the study's conclusions.
# Mechanized Grape Harvest Efficiency
###### Abstract  
Due to the low number of employees and the time limit in the field of grape harvesting, we focused in the presented article on evaluating the effectiveness of the deployment of an outboard grape harvester within the conditions of Slovak viticulture. The vineyards are in the Nitra wine-growing region on the southwestern slopes behind the Pivnica Radosina company, Piesfany district, in a total area of 33 ha. The object of the research was a PELLENC 8090 Selective Process trailed grape harvester aggregated with a SAME Fruttetto3 100 tractor. The mechanized harvesting was tested in the vineyards of Pivnica Radosina s. r. on three selected varieties (Pinot Gris, Pinot Blanc, and Tramin Red) for two years. Entry conditions, technical parameters of the equipment, and weather conditions were monitored for all varieties. Data were analyzed with STATISTICA statistical software. As part of the research, post-harvest losses due to mechanized harvesting were monitored. The average losses for all examined varieties reached the value of 2.17% in 2018, and the value of 2.25% in 2017. A significant output was the efficiency of the deployment of the set for mechanized grape harvesting, where in 2018 a minimum value of 146.3 ha was set (the average cost of grapes was 500 Euro-t\({}^{-1}\)). A significant part of the cost was fixed items, as a two-year tractor and a trailed collector (for 2017) were used for mechanized harvesting. The difference compared to previous research was the deployment of a completely new set during a two-year period on varieties that have not yet been evaluated. The economic efficiency over two years was elaborated in detail, which highlighted the benefits of multi-annual use. In terms of examining losses, differences were shown not only between varieties, but also between years, and these data were statistically verified. The paper evaluates the dependence of the use of mechanized harvesting on changes in the purchase price of grapes (increasing it also exponentially increases the required area) and on changing the hourly wage of an employee (increasing it degressively reduces the required area). From the results it can be said that statistically and economically significant outputs were achieved for the deployment of machine collection.  
g r; harvest; mechanization; losses +
Footnote †: journal: Applied Sciences
## 2 General Methods for the Synthesis of 1,2,4,5-tetrazines
### Synthesis of 1,2,4,5-tetrazines Using Ethyl Diazacetate  
The six-membered 1,2,4,5-tetrazine ring can also be constructed via dimerization of ethyl diazoacetate (**29**) in the presence of sodium hydroxide (Scheme 13). The resulting disodium salt of 1,4-dihydro-1,2,4,5-tetrazine-3,6-dicarboxylic acid was neutralized, converted into acid chloride using thionyl chloride, esterified with methanol, and subsequently oxidized to form the 1,2,4,5-tetrazine ring [84; 85]. The carboxyl groups at positions 3 and 6 of the 1,2,4,5-tetrazine core enable further structural modifications, allowing for the synthesis of a variety of derivatives [44; 86]. The clear advantages of this method are its mild dimerization conditions and high yield (97%).  
Synthesis of 1,2,4,5-tetrazine derivatives from ethyl diazoacetate.  
Synthesis of 1,2,4,5-tetrazines Using Arylaldehyde-Derived Arylsulfonylhydrazones and N-Chlorosuccinimide  
Symmetrically substituted 1,2,4,5-tetrazines were synthesized through reactions involvingosyl derivatives of hydrazones (**31**) (Scheme 14). The best result was obtained usingN-chlorosuccinimide (NCS) in the presence of potassium hydroxide, with nitromethane as the solvent at 0 \({}^{\circ}\)C. Subsequent cleavage of the tosyl group from the intermediate product was performed in ethanol using tetra-n-butylammonium fluoride (TBAF), yielding the final product with a 92% yield [87]. Table 3 summarizes the acyclic precursors and reaction conditions for constructing the 1,2,4,5-tetrazine ring.
# Reliability-Based Design of Driven Piles Considering Setup Effects
## 4 Parametric Analysis
### Effect of \(\beta_{\rm T}\) on the Resistance Factor and the Factor of Safety  
\(\beta_{\rm T}\) is one of the most important factors for reliability-based design of driven piles. The six allowable values of \(\beta_{\rm T}\) are 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0, respectively, and it is indisputably accepted to study the effect of \(\beta_{\rm T}\) on the resistance factor and the factor of safety for driven piles. Meanwhile, some parameters are determined as follows, \(M_{\rm setup}=1.0\), \(\rho=3.69\)([PERSON] et al., 2019; [PERSON] et al., 2019). It should be noted that the correlation between \(R_{0}\) and \(R_{\rm setup}\) is not considered in this section. Using the above parameters, the resistance factor and the factor of safety are  
Figure 1: Resistance factor and factor of safety with \(\rho\) for driven piles in clay and sand.  
calculated by Equations (17) and (19), and the relationships between the resistance factor and the factor of safety against \(\beta_{\rm T}\) are shown in Figure 2.  
In the distribution curve of \(\phi\) versus \(\beta_{\rm T}\), it is obvious that the \(\phi\) of driven piles in clay and sand both decrease with the increase in \(\beta_{\rm T}\). The \(\phi\) of driven piles decreases tardily with \(\beta_{\rm T}\) increasing from 1.5 and 2.5, and by contrast, decreases rapidly with increasing \(\beta_{\rm T}\) from 2 to 4. These phenomena illustrate that \(\phi\) of driven piles in clay and sand are sensitive to \(\beta_{\rm T}\). When \(\beta_{\rm T}\) increases from 1.5 to 4.0, \(\phi\) nearly decreases from 0.52 to 0.12 in clay, with a decrement of about 77%; when \(\beta_{\rm T}\) increases from 1.5 to 4, \(\phi\) nearly decreases from 0.43 to 0.09 in sand, with a decrement of about 79%.  
In the distribution curve of FOS versus \(\beta_{\rm T}\), it is obvious that the value of the FOS of driven piles in clay and sand both increase with the increase in \(\beta_{\rm T}\), and the FOS of driven piles decreases rapidly with increasing \(\beta_{\rm T}\) from 2.5 to 4. These phenomena indicate that the higher the target reliability of driven piles is, the safer the structure is.  
By comparing the two figures in Figure 2, it can be concluded that \(\beta_{\rm T}\) has a significant effect on the resistance factor and factor of safety. Therefore, the value of \(\beta_{\rm T}\) is one of the most important factors for the reliability-based design of driven piles.