esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Architectures of the Modified Deep Neural Networks (DNNs)
### Sub-Networks of DenseNet121  
In ResNet, gradients can flow directly through the identity function from later layers to the earlier layers. To further improve the information flow between layers, DenseNet adopts direct connections from any layer to all its subsequent layers. Consequently, any layer receives a concatenation of the feature maps of all its preceding layers.  
There are three kinds of blocks in the DenseNet implementation. The first is convolution block, which is a basic block of dense block. Convolution block is similar to the identity block in ResNet. The second is dense block, in which convolution blocks are concatenated and densely connected. Dense block is the main component in DenseNet. The last is transition layer, which connects two contiguous dense blocks. Since feature map sizes are the same within the dense block, transition layer reduces the dimensions of the feature map. The technique of bottleneck design is adopted in all the blocks. The architecture of DenseNet121 is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ResNet50** & **Total** & **Trainable** & **Non-Trainable** \\ \hline Ac37 & 7,471,492 & 7,443,972 & 27,520 \\ Ac40 & 8,593,284 & 8,562,692 & 30,592 \\ Ac43 & 14,652,292 & 14,611,460 & 40,832 \\ Ac46 & 19,124,100 & 19,077,124 & 43,976 \\ Ac49 & 23,595,908 & 23,542,788 & 53,120 \\ \hline \end{tabular}
\end{table}
Table 2: The number of parameters in ResNet50 sub-networks.  
Figure 3: (**Left**) ResNet50 architecture. Blocks with dotted line represents modules that might be removed in our experiments. (**Middle**) Convolution block which changes the dimension of the input. (**Right**) Identity block which will not change the dimension of the input.  
In our experiment, we remove some deep convolution blocks in the last dense block and add a new set of classification layers. Consistent with the DenseNet121 implementation in Keras, our sub-networks are named after their last concatenation layer. For conciseness, we use the abbreviation of the layer name. For example, the layer named \"conv5_block16_concat\" in Keras is represented as \"C5_b16\" in the article. In our experiment, \"C5_b16\" represents the sub-network removing no module; \"C5_b14\" represents the sub-network removing last two convolution blocks; \"C5_b12\" represents the sub-network removing last four convolution blocks; \"C5_b10\" represents the sub-network removing last six convolution blocks; \"C5_b8\" represents the sub-network removing last eight convolution blocks; \"C5_b6\" represents the sub-network removing last 10 convolution blocks; \"C5_b4\" represents the sub-network removing last 12 convolution blocks; \"C5_b2\" represents the sub-network removing last 14 convolution blocks. The number of parameters in DenseNet121 sub-networks is listed in Table 3.
## 3 Results and Discussion
### Comparison of Methods
The standard deviations of the various parameters for the oval-shaped sapphires were generally greater than those for the pear-shaped sapphires, indicating that the color differences in the oval-shaped sapphires were larger than those in the pear-shaped sapphires. The standard deviation of the **b** value obtained from the two methods was similar when using the average, median, or mode as the representative value. This indicated that the influence of the two methods on the sapphire's primary hue (blue) was similar. The standard deviation of the **a** value simulated by the function simulation method was greater than that of the **a** value calculated by the statistical method. This showed that the functional simulation method was better than the statistical method in distinguishing red and green hues. The standard deviation of the chroma was larger for the function simulation method than for the statistical method except for the chroma (**C**) of the pear-shaped sapphires that were simulated using the mode. The standard deviation of the lightness for the function simulation method was smaller than that for the statistical method for the other simulated sapphire color, except for the lightness (**L**) of both the oval- and pear-shaped sapphires that were simulated using the average. Among the six sub-methods, the standard deviation of the function simulation method based on the **b** average values was all greater than that of the statistical method. Therefore, the function simulation method using the average of **b** as the representative was able to maximize the dispersion of the obtained sapphire's color parameters (**L**, **a**, **b**, and **C**), which was considered the optimal method. In the process of evaluating the gemstone color with the human eye, it could only be divided into 3-5 levels due to the insensitivity of the human eye to subtle color changes, which is conducive to ensuring the accuracy of the evaluation results. The function simulation method has the ability to grade and evaluate the color of gemstones with minimal color differences, which is beyond the capabilities of the human eye. Moreover, the evaluation results of the function simulation method have better stability and reliability compared to human eyes.
## 4 Proposed System Design
### FSO Channel
#### 4.2.1 Fog Weather Conditions  
Fog is an atmospheric phenomenon that is well known as a cloud of either water particles or smoke particles that reduce the visibility and cause attenuation which degrades the received signal. According to the amount of smoke particles and quantity of water particles suspended in the atmosphere, fog varies from light fog (LF) to moderated fog (MF) to heavy fog (HF). These diverse fog levels cause different attenuation.  
The attenuation of fog increases as the quantity of microscopic water droplets suspended in the atmosphere increases, and that causes fog to become heavier. Thus, different fog levels LF, MF, and HF have different attenuations. Fog attenuation, \(f_{a}\), in dB/km that depends on visibility range, \(R\), in km, can be expressed as [25]:  
\[f_{a}=\frac{3.912}{R}\bigg{(}\frac{\lambda}{550\text{nm}}\bigg{)}^{-b} \tag{9}\]  
where \(\lambda\) is the operating wavelength in (nm) and \(b\) is the size distribution of the scattering particles that can be calculated according to [PERSON]'s model [30].  
Different fog levels with their attenuation are shown in Table 2[39]:  
\[b=\left\{\begin{array}{ccc}1.6&R>50\\ 1.3&6<R<50\\ 0.16R+0.34&1<R<6\\ R-0.5&0.5<R<1\\ 0&R<0.5\end{array}\right. \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Wavelength (nm)** & **1550** & **1550.8** & **1551.6** & **1552.4** & **1553.2** & **1554** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} OAM \\ \end{tabular} } & Channel \#1 & & & 0 & & & 0 & 0 \\ \cline{2-8}  & Channel \#2 & 0 & 1 & 1 & 0 & 1 & 0 \\ \cline{2-8}  & Channel \#3 & 0 & 0 & 1 & 1 & 0 & 1 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} OAM \\ \end{tabular} } & Channel \#4 & 1 & 1 & 0 & & 1 & 0 \\ \cline{2-8}  & Channel \#5 & 0 & 1 & 1 & 0 & 1 \\ \cline{2-8}  & Channel \#6 & 0 & 0 & 1 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wavelengths assigned to the channels of the FRS encoder.
## 5 CT Pre-Processing Core Architecture  
In this section, we describe our CT pre-processing core, which implements the I0-correction. For fulfilling the real-time requirements, the CT pre-processing core is designed as a dataflow architecture, which has a constant delay and throughput. Furthermore, to process data with high clock frequency and to reduce the critical path of the arithmetic operations, the dataflow architecture is pipelined. The depth of the pipeline depends on the data format and prepossessing algorithm, as explained in Section 9.  
The CT pre-processing core, as shown in Figure 6, has the following three main stages:  
* **Sensor-data conversion stage**: This stage obtains the pixel raw sensor data of the I0-image and the current image collected by the data acquisition system (DAS). In this stage, each pixel is converted to the selected pre-processing data format. At synthesis time, a custom configuration for floating-point or fixed-point representation must be selected.
* **Image-processing stage**: In this stage, pixel data are ready to be pre-processed in the selected data representation. This stage has multiple internal stages, and it is scalable for additional pre-processing steps. In this article, we focus on the pre-processing step, based on Formulas (6) and (7).
* **Reconstruction conversion stage**: This stage obtains the pre-processed data (attenuation image) and converts them in the reconstruction data format, defined at the synthesis time. The output results are ready for the reconstruction, and they are forwarded to the data stream unit, which is responsible either for storing or sending them to the reconstruction system.  
Figure 4: Data flow graph representation for the optimized I0-correction.  
Figure 5: Data flow graph representation for the standard I0-correction.  
Figure 6: CT pre-processing core.  
For communicating, the CT pre-processing core uses the AXI4-Stream interface. This flexible interface can be configured with different data widths, so it can be easily used for different data representations, and integrated in any system that uses the AXI4-Stream standard.  
The CT pre-processing core is designed for processing one pixel per clock cycle. If the DAS collects multiple pixels per clock cycle, multiple instances of this core must be added; in this way, all the collected pixels are processed in parallel. For example, in the integration with the DAS of our open-interface CT, we have four instances because the DAS collects four pixels per clock cycle, as is explained in the following Section 6.  
Furthermore, the CT processing core is designed and implemented to be configurable for custom data representations that are defined at synthesis time. In this way, the architecture can be easily used for DSE, as it is explained in Section 7.
# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 3 Results and Discussion  
### Characterization of the Soil Properties  
Table 1 presents a summary of soil properties for 34 soil samples collected from various sites and depths in the Czech Republic. The main criteria for selecting sample sites were based on expected soil composition from geological map surveys with a special focus on sites with high clay content and low organic matter, site history and accessibility for sampling equipment. Preference was given to areas with minimal agricultural or industrial activity to reduce the risk of contamination from external sources. The surface soil layer was excluded from sampling due to the vegetation cover and expected higher OC content. Most of the collected samples originated from the unsaturated zone. Each sample is defined by its site of origin, sampling depth, soil texture, and a comprehensive set of physical and chemical properties. Clay content varied widely, from as low as 0% to as high as 64.9%, while silt ranged from 5.1% to 71.2%, and sand from 5.2% to 88.9% (_w/w_). The relative percentages of sand, silt, and clay were used to classify the samples according to the international soil texture classification standard; this system delineates 12 distinct textural classes. These were broadly categorized into two main groups: (i) fine-grained soils, comprising clay, silt loam, and silty clay loam; (ii) and coarse-grained soils, primarily consisting of sand, sandy loam, and loamy sand. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**No. **} & \multirow{3}{*}{\ \begin{tabular}{c} **Site of** \\ **Origin** \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} **Samp.** \\ **Depth** \\ **(m)** \\ \end{tabular}
## 3 Results and Discussion
### Multi-Objective Optimization of Stress-Relief Groove Parameters  
Previous analyses indicate that a 2 m \(\times\) 2 m stress-relief groove, with central grooving and backfilling, provides optimal control over the surrounding rock. Qualitative analyses of stress and strain after grooving have been completed, highlighting that while central grooving effectively reduces floor displacement, it increases sidewall displacement, creating a conflict. The goal is to minimize floor displacement while limiting sidewall displacement.  
To resolve these conflicts, multi-objective optimization methods can be used. This involves feature engineering to quantify data and define valid evaluation criteria, followed by the application of machine learning models to map inputs to outputs. Multi-objective optimization can then derive optimal solutions, which should be validated against actual values.
## 2 Materials and Methods
### Modal Analysis
As shown in [17], the Fourier transform of a signal at a distance \(z\) from the radiation region is equal to  
\[U(z,\omega)=U(0,\omega)\cdot q_{p}(z,\omega), \tag{8}\]  
where \(u(0,t)\) is the signal emitted by the AE source located in region \(R_{g}\), and \(U(0,\omega)=\mathcal{F}\left[u(0,t)\right]\) is its Fourier transform. The signal at point \(z\) is reconstructed using an inverse Fourier transform:  
\[u(z,t)=\mathcal{F}^{-1}[U(z,\omega)], \tag{9}\]  
It was shown in [17] that when the external force is a point source, the coefficients \(a_{p}(z)\) can be determined using the simplified formula:  
\[a_{p}(z)=\frac{e^{jk_{p}z}}{4P_{p}}. \tag{10}\]  
Considering that acoustic power \(P_{p}\) is constant, we can conclude that when the external excitation is a point force, expression (10) can be used for any guided waves, particularly for Lamb waves. As shown in [18], Lamb wave models could be used to describe the propagation of acoustic emission waves both in plane and cylindrical testing structures. In this case, the wavenumber in Equation (10) is equal to  
\[\hat{\sigma}(r,\theta,z)=\widetilde{\sigma}(r,\theta)e^{\hat{y}\kappa z}= \sigma(r)e^{jn\theta}e^{\hat{y}\kappa z}, \tag{2}\]0  
where \(c_{ph}(f)\) is the phase velocity of the selected Lamb wave [18]. The modeled signal \(u(z,t)\) describes only effects caused by the dispersive propagation of Lamb waves. For the model parameters, the steel type (given by the density, Young's modulus, and Poisson's ratio), wall thickness, and distance between the AE source and sensor could be variated.
## 3 Materials and Methods
### Evaluation of Algorithms  
Owing to constraints in terms of the amount of available data, we considered the technique known as \"walk forward validation\" to evaluate the performance of the classification algorithms [60]. This technique allows us to perform a progressive evaluation of the performance using temporal data as new time-dependent observations are available (refer to Figure 4). In this case, in each iteration, the classifier's performance at the \(i\)-th sample was evaluated by our algorithm according to its features at that time instant. The metrics for evaluating the test samples are the ACC, P, R, and F1:  
\[ACC =\frac{TP+TN}{TP+FP+TN+FN}, \tag{3}\] \[P_{i} =\frac{TP_{i}}{TP_{i}+FP_{i}},\] (4) \[R_{i} =\frac{TP_{i}}{TP_{i}+FN_{i}},\] (5) \[F1_{i} =\frac{2P_{i}R_{i}}{P_{i}+R_{i}}, \tag{6}\]  
where \(TP\) and \(FP\) correspond to the true and false positives, while \(TN\) and \(FN\) correspond to the true and false negatives of classification. Note that \(P\), \(R\), and \(F1\) are calculated for each one of the \(i\)-th classes. In addition, our algorithm evaluates the training error of the classifiers in terms of the zero-one-loss metric (\(\mathcal{L}\)) as follows [61]:  
\[\mathcal{L}(y_{i},y^{\prime}_{i})=\left\{\begin{array}{ll}1&y_{i}\
eq y^{ \prime}_{i}\\ 0&\text{otherwise}\end{array}\right., \tag{7}\]  
where \(y_{i}\) and \(y^{\prime}_{i}\) represent the predictions and actual classes, respectively. To measure the relative importance of the available features in the prediction of COVID-19 incidence, we proposed an iterative wrap-around method that considers the following steps (refer to Figure 5). First, our approach creates a baseline model \(\delta_{b}(\cdot)\) that considers only the communal feature subset to obtain an initial performance. Then, each of the regional and national features is added to the model individually (one at a time) to create a feature space. For each feature added to the model, the algorithm measures the performance difference compared to the performance of the baseline model. Finally, the performance differences are sorted from highest to lowest. A larger difference indicates that this variable is more relevant to the problem because it contributes more to the classifier's performance.  
Figure 3: Proposed deep learning model to estimate the risk of COVID-19 incidence.
## 2 Preliminaries
\(\limsup_{n\to\infty}\gamma_{n}\leq 0\) _or_ \(\sum_{n=0}^{\infty}|\lambda_{n}\gamma_{n}|<\infty\)_._ _Then,_ \(\lim_{n\to\infty}a_{n}=0\)_._  
**Lemma 4** ([24]). : _We suppose \(T\) is a nonexpansive mapping defined on a convex nonempty subset \(C\) of a Hilbert space \(H\). Let \(\lambda\) be a number in \((0,1]\). We suppose \(F\) is a self \(\kappa\)-Lipschitzian and \(\eta\)-strongly monotone mapping on entire space \(H\). Define the mapping \(T^{\lambda}:C\to H\) through_  
\[\|Tx-Ty\|\leq\alpha\|x-y\|,\quad\forall x,y\in C;\]3  
_Then, \(T^{\lambda}\) is a contraction if \(0<\mu<\frac{2\eta}{\kappa^{2}}\); that is,_  
\[\|Tx-Ty\|\leq\alpha\|x-y\|,\quad\forall x,y\in C;\]4  
_where \(\tau=1-\sqrt{1-\mu(2\eta-\mu\kappa^{2})}\in(0,1]\)._  
**Lemma 5**: _Let the mapping \(A:C\to H\) be \(\alpha\)-inverse-strongly monotone. Then, for a given \(\lambda\geq 0\),_  
\[\|Tx-Ty\|\leq\alpha\|x-y\|,\quad\forall x,y\in C;\]5  
_In particular, if \(0\leq\lambda\leq 2\alpha\), then \(I-\lambda A\) is nonexpansive._  
Proof. : \[ \begin{array}{l}\|(I-\lambda A)y-(I-\lambda A)x\|^{2}\\ =\|\lambda(Ay-Ax)\|^{2}-2\langle\lambda(Ay-Ax),y-x\rangle+\|y-x\|^{2}\\ \leq\lambda^{2}\|Ay-Ax\|^{2}-2\lambda\alpha\|Ay-Ax\|^{2}+\|y-x\|^{2}\\ =\lambda\big{(}\lambda-2\alpha\big{)}\|Ax-Ay\|^{2}+\|y-x\|^{2}.\end{array} \]  
Utilizing Lemma 5, we immediately obtain the following lemma. **Lemma 6**: _We suppose the nonself mappings \(B_{1},B_{2}\) is \(\alpha\)-inverse-strongly monotone and \(\beta\)-inverse-strongly monotone defined on convex nonempty closed subset \(C\) of entire space \(H\), respectively. Let the self mapping \(G\) be defined as \(G:=P_{C}(I-\mu_{1}B_{1})P_{C}(I-\mu_{2}B_{2})\). If \(0\leq\mu_{1}\leq 2\alpha\) and \(0\leq\mu_{2}\leq 2\beta\), then \(G:C\to C\) is nonexpansive._  
**Lemma 7** ([25]): _We suppose that \(X\) is a real Banach space with a weakly continuous duality and \(C\) is a convex nonempty closed set in \(X\). Let \(T\) be a self mapping defined the set \(C\) and we also suppose it is asymptotically nonexpansive with a empty fixed-point set. Then, \(T-I\) is demiclosed at zero, i.e., let \(\{x_{n}\}\) be a sequence in set \(C\) converging weakly to some \(x\), where \(x\) in \(C\) and the sequence \(\{(I-T)x_{n}\}\) converges strongly to \(0\), then \((T-I)x=0\), where \(I\) is the identity mapping of \(X\)._  
**Lemma 8** ([26]): _We suppose \(C\) is a convex nonempty closed set in a Hilbert space \(H\) and \(A\) is a monotone and hemicontinuous nonself mapping defined on convex nonempty closed set \(C\) to \(H\). Then, we have_  
1. \(\forall{\rm I}(C,A)=\{x^{*}\in C:\langle Ay,y-x^{*}\rangle\geq 0,\;\forall y\in C\}\)_;_
2. \(\forall{\rm I}(C,A)={\rm Fix}(P_{C}(I-\lambda A))\) _for all_ \(\lambda>0\)_; and_
3. \(\forall{\rm I}(C,A)\) _is singleton, if_ \(A\) _is Lipschitz continuous strongly monotone._
## 3 Results and Discussion
### Antioxidant Activity  
The ability of a compound to inhibit oxidation reactions, expressed as a percentage of inhibition, is known as antioxidant activity. The ability of algae to accumulate antioxidant compounds has attracted the attention of researchers because the environment in which algae grows is harsh, which can lead to the production of strong oxidants [5].  
The antioxidant activity was determined using the DPPH, TAC, and TRC assays, and the results are shown in (Figure 4). The DPPH (1,1-diphenyl-2-picrylhydrazyl) test has been widely used as a stable free radical to evaluate reducing chemicals, and it is a suitable reagent for studying compounds' free radicals [38]. The antioxidant scavenging activity of DPPH was considerably increased in all of the seaweeds tested, as shown in Figure 4. _T. ornata_ showed higher DPPH scavenging activity of 72.48 \(\pm\) 0.17% inhibition, while _S. sciniaoides_ exhibited lower activity of 49.54 \(\pm\) 0.13% inhibition. These results were not only comparable to the ascorbic acid standard (62.35 \(\pm\) 0.07%), but they were much higher in the case of brown species. These findings are in line with those of [PERSON] et al. [39], who observed that brown seaweed _Dictyota dichotomous_ acetone extract has high DPPH radical scavenging activity. The other two approaches, total antioxidant capacity (TAC) and reducing capacity (TRC), were used to assess and evaluate the antioxidant activity of various seaweed components (electron donors). Total antioxidant capacity  
Figure 3: Vitamin C and \(\beta\)-carotene contents (mg/100 g) for studied seaweeds. Values are the mean of three replicates \(\pm\) standard deviations (SD). Different letters refer to significant differences between species (\(p\) < 0.05).  
(TAC) and reducing capacity (TRC) had similar results with different scavenging percentages (Figure 4), which were better in the case of brown seaweeds than red seaweeds and near to the evaluated value of the ascorbic acid standard (17.41 \(\pm\) 0.03 and 57.21 \(\pm\) 0.01, respectively) for two testes.  
_T. ornata_ observed the highest total antioxidant capacity and reducing capacity (15.02 \(\pm\) 0.13% and 53.24 \(\pm\) 0.11%, respectively) while _S. scinaioides_ exhibited lower activity (12.96 \(\pm\) 0.09% and 30.37 \(\pm\) 0.14%, respectively). As a result, all of the studied species are deemed to be high in antioxidants. Brown seaweeds have more antioxidant activity than red seaweeds, according to [PERSON] et al. (2019) and [PERSON] (2019), which explains why brown algae _T. ornata_ and _P. indica_ had the greatest antioxidant activity.  
Antioxidants convert reactive oxygen species (ROS) to non-toxic compounds, prevent their harmful effects, and reduce the risk of diseases, ischemia, diabetes infections, cardiovascular disease, and cancer (2019).  
Biologically, antioxidants protect the body by transferring an electron or a hydrogen (H) atom to reactive substances (2019). As a result, hydrogen atoms transfer (HAT) and single electron transfer tests assess antioxidant capability.