esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
A segmentation model can be trained with an available aerial image dataset with thousands of images, but these datasets do not include aerial images with distortions. Pretrained segmentation models produce erroneous results for a distorted aerial image because they have not learnt about them. The expansion of the learning space of an existing segmentation model can be done so that already learned parameters are not changed. In this work, the main task is to fuse a limb of extra deep CNNs prior to a segmentation model while keeping the compatibility over the conjunction, so that the whole structure will be collaboratively acting as a segmentation model. Throughout the work, the possibility of implementing the proposed method without exceeding the capabilities of software-based radio hardware was a high concern. The inadequacy of distorted data prevents the proposed method from using the multiple network training strategies which were used in [32; 33; 34; 35]. Because each participating network must be trained simultaneously, the accuracy will be a low value for unseen data as the training distorted aerial image set is insufficient.  
The extra CNN to be fused prior to the pre-trained segmentation model is an approximator model which has the duty of producing a more applicable input to the segmentation model. Through the programmatically made conjunction, its output is presented to the segmentation model. In Section 5, the performance of the proposed approximator model will be tested. The following are details of the proposed combined method.  
* The approximator model and the segmentation model must keep compatibility at their connecting point. The expected output of the approximator model is given to the segmentation model.
* Each model is composed of modules. Combinations of different approximators and different segmentation models are possible.
* The proposed method follows a modular approach, according to the user's preference, so different approximators and different segmentation models can be used.
## 2 Materials and Methods
### Laboratory Analysis  
Before chemical analysis, sediments were freeze-dried to a constant weight. After removing the gravel and shells, the dried sediments were ground and sieved through a 96 \(\upmu\)m stainless-steel sieve. Sediment samples (0.2000 g) for the measurement of Hg and As were digested with 10.0 mL of a mixture of acid (HNO\({}_{3}\)+HCl). Sediment samples (0.1000 g) for the measurement of Cu, Zn, Cd, and Pb were digested with a mixture of concentrated HNO\({}_{3}\) (1.0 mL) and HClO\({}_{4}\) (2.0 mL). HMs (Cu, Pb, Zn, and Cd) in the sediment samples were determined using a flame atomic absorption spectrometer (Analytik Jenna ContrAA 700). As and Hg in the sediment samples were tested using an atomic fluorescence spectrometer (Beijing Haiguang AFS 9560). Sulfide and TOC in sediment samples were determined by methylene blue spectrophotometry and potassium dichromate volumetric method, respectively. Oils were analyzed using a UV spectrophotometer (Shimadzu UV-2450). Eh and pH values of sediments were measured with a potentiometer and a pH meter, respectively. Although As is a metalloid that exhibits intermediate properties between those of metals and non-metals, this text refers to it as a metal. Organisms were sorted, counted, and identified to species level.  
Quality assurance and quality control were evaluated using duplicates, blanks, and standard reference material (GB W07333) from the National Research Center for Standard of China. The detection limits of Hg, As, Cu, Pb, Cd, and Zn were 0.002, 0.06, 0.1, 0.1, 0.02, and 0.2 mg/Kg, respectively. All chemicals used for the analysis were of analytical grade or above. Blanks and duplicates were run for each batch of 10 samples. The blank values are below the detection limit. From the values of the duplicates, the relative errors of Hg, As, Cu, Pb, Cd, and Zn were below 6.5%, 1.8%, 2.5%, 3.7%, 5.9%, and 2.1%, respectively. The measured values of the standard reference material were within the error allowed. The results of quality assurance and quality control indicate that the accuracy and precision are acceptable.  
Figure 1: Sampling stations at the dumping area and adjacent sea areas.
## 5 Discussion
### Accuracy Comparison of the Proposed Model with 2D-DC Versus 3D-DC  
To validate the effectiveness of integrating 2D-DC and 3D-DC in the HDCDF network, we develop two supplementary models for comparative analysis. The first model exclusively uses 3D-DC by replacing the 2D-DC in two branches of the proposed model, while maintaining the output sizes of the feature-extracted branches as in the original model. The second model employs 2D-DC exclusively, following a similar design. The comparative OA results, illustrated in Figure 18, demonstrate that the proposed hybrid depth-wise separable convolution structure consistently achieves superior classification performance across varying numbers of training samples in three datasets. Generally, the classification accuracies of models using 2D-DC or 3D-DC alone are comparable to the original model, with the 2D-DC-only model showing slightly lower performance. This reduction in performance is attributable to 2D-DC's inability to capture the inter-spectral band information of HSI, directly resulting in lower classification accuracy.  
Figure 18: The classification performance of the proposed method employing hybrid depth-wise separable convolution, that using only 3D-DC, and that using only 2D-DC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **IP** & **UP** & **SV** \\ \hline Attention Branch & 93.42 & 95.71 & 96.03 \\ Residual Branch & 81.26 & 88.68 & 90.19 \\ HDCDF & 95.73 & 96.50 & 97.84 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification results of each single branch and HDCDF.  
Overall, HDCDF derives more distinctive features by means of the hybrid integration of 2D-DC and 3D-DC, substantially enhancing the final classification performance.
# Breeding Tomato Hybrids for Flavour: Comparison of GWAS Results Obtained on Lines and F1 Hybrids
## 1 Introduction  
Tomato is the vegetable crop that is most consumed and produced worldwide [1]. Nevertheless, tomato flavour has been widely criticized [2, 3]. Organoleptic quality can be summarized through flavour and texture. Flavour is a complex characteristic related to the integrated perception of taste (sugars and acids) and aroma through olfaction and retronasal olfaction of volatile organic compounds (VOCs) [4]. Up to now, more than 400 VOCs have been identified in tomato fruits [5]. These compounds belong to several metabolic pathways [6, 7] and derive from: benzenoids (B-der); branched chain amino acids (BCAA-der); carotenoids (C-der); lipids (L-der); phenylalanine (Phe-der); sulphur containing compounds (S-der); and terpenoids (T-der). A subset of approximately 30 VOCs (listed in Table 1) has been suggested to impact tomato flavour, based on their odour threshold, their abundancy or their correlation to consumer liking [5, 8]. The flaws of abundancy and odour unit as indicators of VOC contribution to flavour have been underlined in several studies. For example, although (Z)-3-hexenal is the most abundant volatile in tomato fruit, it does not correlate with consumer liking [8]. Consumer sensitivity to VOCs depends on the receptors they possess, with high allelic variability in the sequenceof olfactive receptor genes leading to variable odour perception [9]. Moreover, cross-talk exists between taste and VOC perception, making it possible to enhance sweetness perception by increasing 2-phenylethanol content, for example [10; 11]. Thus, flavour is a combination of human individual specificities, and misunderstood interactions between olfactory and taste receptors. VOC release also depends on their interaction with the tomato matrix [12], this characteristic being modulated by the ripening stage and the plant genotype [13]. In addition to being expensive, VOC quantification is technically demanding and requires both gas chromatography performance and a broad database to link VOC identity to the chemical structure and retention time measured with the chromatogram. Such a challenge is illustrated with the terpenoid-derived volatiles that are rarely quantified, although they show potential for tomato aroma improvement and are crucial to some white grape species and most Citrus species sensory quality [6; 8]. Organoleptic quality-evaluated through both flavour and texture-depends on the variety choice, the growth conditions and post-harvest storage conditions [17]. However, our major interest lies in the genetic aspect of flavour-related traits and more specifically in VOC synthesis in fruits. The highly polygenic nature of VOC content has been revealed through multiple studies. The genetic control of 18 VOCs and sensory traits was first inves
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 3 Results and Discussion
### Biology
#### 3.2.1 Evaluation of the Antibacterial Activity by the Determination of MIC and MBC Values
MIC values that were lower by 1 log\({}_{2}\) dilution (62.5 \(\mathrm{\SIUnitSymbolMicro m}\)L) in comparison with hydrazide **3** were achieved by the azole derivatives **24**, **25**, and **26** for coccus, while only pyrazole **24** repeated the same antibacterial activity against _E. coli_. Pyrrole **25** and 5-oxopyrrolidine **26** had identical MIC values equal to 125 \(\mathrm{\SIUnitSymbolMicro m}\)L against Gram-positive _B. subtilis_ and Gram-negative _E. coli_ and _S. enteritidis_. Analyzing the relationship between the structure and activity, it can be stated that the incorporation of 3,5-dimethylpyrazole, 2,5-dimethylpyrrole, and 5-oxopyrrolidine fragments in the molecules improved the growth inhibition activity of _S. aureus_, and 3,5-dimethylpyrazole (comp. **24**) also selectively inhibited the Gram-negative strain of _E. coli_. The MBC values of pyrazole **24**, Pyrrole **25**, and 5-oxopyrrolidine **26** for _S. aureus_, _E. coli_, and _S. enteritidis_ ranged from 125 \(\mathrm{\SIUnitSymbolMicro m}\)L to 500 \(\mathrm{\SIUnitSymbolMicro m}\)L. Compound **26** showed the lowest bactericidal activity, and its MBC value of 500 \(\mathrm{\SIUnitSymbolMicro m}\)L was found for all of the tested strains. Pyrrole **25** did not show higher bactericidal activity against the tested strains of bacteria. Compared to compound **26**, hydrazide **3** and pyrrole derivative **25** showed higher efficacy only for the _B. subtilis_ strain. Compared to hydrazide **3** and pyrrole **25**, pyrazole **24**had a higher bactericidal activity, with a difference of 1 log\({}_{2}\) dilution only against _E. coli_ when compared with hydrazide 3. SAR analysis showed that the formation of 3,5-dimethyllypyrazole and 2,5-dimethyllypyrrole structures in molecules **24** and **25** maintains the bactericidal effect of hydrazide **3** against _B. subtilis_, and only 3,5-dimethyllypyrazole **24** improves the bactericidal effect against _E. coli_. The comparison of MBC and MIC values for _B. subtilis_ showed that the ratios of MBC/MIC were \(\leq\)4 for all compounds, and such effects were considered the bactericidal ones. Meanwhile, the ratios of MBC/MIC values for _S. aureus_ and _S. enteritidis_ ranged from 4 to 8, whereas for _E. coli_, they ranged from 2 to 32. When the MBC/MIC ratio is >4, the effect of the compound is bacteriostatic (Figure 2). Eighteen derivates had bactericidal effects for all of the tested bacteria, whereas derivates **4**, **12**, **22**, **23**, **24**, **25**, and **26** had bacteriostatic ones for some bacteria strains. The effect of derivates **24**, **25**, and **26** was bacteriostatic only for _S. aureus_, while **22** showed such effect for _E. coli_. The derivates **4** and **12** were bacteriostatic for two tested bacteria, _S. aureus_ and _E. coli_, while **23** showed such an effect for three tested bacteria strains, _S. aureus_, _E. coli_, and _S. enteritidis_.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 6 Evaluation
### Utility  
As for privacy, we measure TU as the utility metric for the four individuals. Figure 4 shows the results for this metric. In addition to this overview, Figure 5 provides a scatter plot for continuous \(\epsilon\) values. To maintain readability, there is only one scatter plot for one individual. Nevertheless, the overall structure looks the same for the remaining participants. We anticipate that TU converges to the optimal value of 1 by using higher epsilon values for generating synthetic data, since less noise is added in the step of the mechanism employed.  
Figure 3: Box plots showing the Tachycardia Privacy (TP) for four individuals using MWEM, DP-CTGAN, and PATE-CTGAN at 20 runs.  
Overall, the TU for MWEM is out of competition for all individuals. It remains at 0 for any \(\epsilon\) and shows no improvement for larger \(\epsilon\) values. This is indicated by the first black line above the values of DP-CTGAN in orange. In addition, the observation can be confirmed  
Figure 4: Box plots showing the Tachycardia Utility (TU) for four individuals using MWEM, DP-CTGAN, and PATE-CTGAN at 20 runs.  
Figure 5: Scatter plot illustrating the continuous evaluation of the Tachycardia Utility (TU) for an individual using MWEM, **DP-CTGAN,1** and PATE-CTGAN at 20 runs.  
through the scatter plot in Figure 5, in which MWEM is a straight line. Moreover, there is no dispersion at all compared to the other technologies. For all individuals, it can be seen that PATE-CTGAN shows an improvement trend with a larger \(\epsilon\). The TU is around 0 for small \(\epsilon\) values, and only starts to show usable values for \(\epsilon=5\). At \(\epsilon=5\), they are closely around 0.5 and 0.8. However, the largest \(\epsilon=10\) reaches significantly worse TU scores than with \(\epsilon=5\), indicating a saturation trend in terms of utility. This can be also seen in more detail with the continuous \(\epsilon\) values of the scatter plot in Figure 5. The scatter plot indicates that there are two optimal \(\epsilon\) values around 6 and 11 and that the TU decreases between these optima. In addition, there is a larger dispersion of the TU values in this phase. DP-CTGAN also shows the expected upward trend for TU for all individuals. However, there is a local optimum around \(\epsilon=0.6\) beyond which the utility declines. This can be seen in the scatter plot from Figure 5. After the TU moves away from the optimal value, the values slowly converge to 0.4 afterwards. This is consistent with the expectation that a higher \(\epsilon\) leads to a saturated TU. It remains to be noted that DP-CTGAN has the highest dispersion of all technologies regardless of \(\epsilon\). The scatter plot confirms this observation. Participant number 4, shown in Figure 4 iv, again has its typical outlier role with clearly deviating TU values. However, the trend observed with the other individuals for PATE-CTGAN remains the same.
## 2 Materials and Methods
### Microwave Free-Space Calibration Procedure  
Before doing any measurement, the instrumentation must be calibrated. This calibration step consists to determine the three complex terms \(\Gamma_{1}\), \(\Gamma_{2}\) and \(T_{1}T_{2}\). This calibration process is based on the modeling of the empty fixture (without the sample under test) considering multiple distance separations between the antenna and the metallic base. The procedure is inspired by the well-known SOL (Short-Open-Load) found in traditional on-wafer or guided microwave measurements, but by considering 3 known distance separations between the antenna and a material standard [11]. As the antenna operates close to the sample, spread propagation losses due to the spherical nature of the propagation waves induce amplitude variation of the propagating waves a function of the distance. Consequently, this calibration procedure provide accurate data only if the calibration standards are close to the material reference plane. The measured empty complex reflection coefficient  
Figure 2: (**a**) Schematic diagram of the free-space monostatic microwave measurement instrumentation. (**b**) Flow-chart of the wave propagation.  
\(S_{11}^{E}\) measured by the VNA can be expressed as a function of the complex reflection \(S_{11}\) at distance \(\Delta_{r}\) from the reference plane by  
\[S_{11}^{E}=\Gamma_{1}+\frac{T_{1}T_{2}S_{11}}{1\Gamma_{2}S_{11}} \tag{3}\]  
where \(S_{11}=e^{-2\gamma_{0}(\Delta r)}\) with \(\gamma_{0}\) denotes the free-space propagation constant.  
A linear system of three equations of type (3) is then solved to determine the three error terms.
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 3 Model Structure
### Model Architecture
#### 3.6.1 Classification Model Structure  
Our classification model is shown in Figure 5 (top branch). We start by sampling a subset of 512 points from the point cloud by using the farthest point sampling (FPS) method and we also cluster \(k\) neighbouring points for each central point by the \(k\)-NN method for extracting the local geometric feature in the following layer of MLP Shuffled Group Convolution (SGC) with filters (64, 128). Similarly, 128 points are then subsampled, followed by an SGC unit with filters (128, 256). The third layer is used to aggregate the local features by using an MLP layer having 256 dimension. For the fourth layer, the global feature of the point cloud is extracted by a max pooling operation. At last, three fully-connected layers (512, 256, 40) are used to transform the global feature to 40 categories. A dropout operation with a keep probability of 0.5 is also used in the last three fully connected layers. ReLU was used as an activation function and batch normalization is used for all the edge convolution operations and the fully-connected layers. In addition, the numbers \(k\) of nearest neighbours for the first, second and third layer are 20, 10 and 10, respectively.  
Figure 5: **ShufflePointNet architecture:** The architecture uses _PointNet++_ as backbone, and it contains classification structure (top branch) and semantic segmentation structure (bottom branch) for point cloud. In detail, \(N,F\) indicates the number of input points, and corresponding feature channels, respectively. \(k\) is the number of the neighbours. In addition, the number in the brace \(|\) indicates the MLP filters. The classification model recursively applies Farthest Point Sampling (FPS) and a Shuffled Group Convolution (SGC) module to capture downsampled local features, followed by a shared full-connected layer to generate a global feature, which is used to predict classification scores. The segmentation model extends the classification model by two more SGC modules and a feature interpolation module to obtain a category score for each point.
# Environmental Remediation of Metribuzin Herbicide by Mesoporous Carbon--Rich from Wheat Straw
## 3 Results and Discussion
### Adsorption Performance  
#### 3.4.1 The Effect of Metribuzin Concentration and Time  
We assess the wheat straw biochar for its adsorption properties, using metribuzin pesticide, suggesting a greater affinity between metribuzin and biochar surface. Figure 4 presents the influence of different initial concentrations (C\({}_{o}\)) of metribuzin on the adsorption  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Pseudo-First-Order**} & \multicolumn{4}{c}{**Pseudo-Second-Order**} \\ \cline{2-7}  & **q** **exp** & **q** **est** & **K\({}_{1}\)** & **R\({}^{2}\)** & **q** **est** & **K\({}_{2}\)** & **K\({}_{2}\)** \\  & **(mg:g\({}^{-1}\))** & **(mg:g\({}^{-1}\))** & **(mg g\({}^{-1}\).min\({}^{-1}\))** & **R\({}^{2}\)** & **(mg:g\({}^{-1}\))** & **(mg:g\({}^{-1}\).min\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline WS & 1.453 & 5.901 & 0.005 & 0.726 & 1.431 & 0.956 & 0.996 \\ \hline WSB & 1.565 & 6.083 & 0.009 & 0.531 & 1.541 & 0.735 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic model parameters for metribuzin and WS/WSB system.  
Figure 3: Kinetic models of metribuzin adsorption.  
tion performance at equilibrium (q\({}_{\text{e}}\)) of the biochar at room temperature. A reduction in metribuzin concentration was noticed with the increment of time until equilibrium was attained within 3 and 5 h for the initial metribuzin concentrations of 5 mg\(\cdot\)L\({}^{-1}\) and 100 mg\(\cdot\)L\({}^{-1}\), respectively. The increase in time required to reach the equilibrium with the increase of initial metribuzin concentration indicates that chemical interactions between pesticide molecules and superficial functional groups of adsorbents are predominantly involved in the studied adsorption process [78].  
Overall, metribuzin molecules were adsorbed fast at the initial stage and then slowed down gradually. The initially high adsorption rate in the concentration of metribuzin may be caused by the WSB available binding sites' existence. On the other hand, as these sites were occupied, a slow metribuzin adsorption rate was observed over time, establishing and consolidating the equilibrium state (Figure 5).  
As the initial metribuzin concentration increased from 5 to 50 mg L\({}^{-1}\) a decrease with 89 and 86% in the metribuzin adsorption rate was associated. For 100 mg L\({}^{-1}\) metribuzin solution, this reduction decreased to 65% due to the high percentage of metribuzin molecules compared to the available active sites from the surface of WSB [59]. At a higher metribuzin concentration, the available superficial groups of WSB are limited and held; hence, the diffusion of metribuzin molecules on the functional groups are limited.