esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Implications from Biomonitoring Perspective  
The use of small prosobranch snails, such as _P. insularum_, as one of the biological indicators in toxicity testing, offers several benefits. First, because these snails are prevalent in still (ponds) and running (streams) waters, they can be utilized as ecologically significant target species in both loci and lentic environments. Secondly, they are affordable, easily harvested and manageable. In addition, they are sensitive indicators of dangerous amounts of heavy metals such as Cu, Pb, Cd, Ni and Zn identified in this study, comparable to that reported by [PERSON] [52] and [PERSON] [62]. They are possibly more susceptible to metals than larger snails, _Brotia hainanensis_, because they possess the same trait [107]. For a realistic approach to pollution consequences, additional research on the acute and chronic toxicity of various environmental contaminants under various environmental and biological circumstances is necessary. It is also necessary to assess the combined toxicity of substances. The mechanisms of contaminants at the cellular and molecular levels in these animals must also be comprehended. Under controlled laboratory conditions, [PERSON] et al. [108] evaluated the effects of Pb (5 or 10 mg/L) on the survival of the freshwater snail _Lymnea stagnalis_ (L.) collected from lead-contaminated or uncontaminated environments. Significantly more animals from the polluted environment survived subsequent acute (up to 24 days) Pb exposure than animals from the unpolluted environment. Acute exposure to Pb (72 h) hindered various behavioral activities, including movement, eating, tentacle elongation and emerging from the shell. Pb bioaccumulated in snail tissues, specifically the buccal mass and the stomach. The freshwater snail is an excellent system for researching the bioaccumulation and development of environmental Pb tolerance. [PERSON] et al. [71] observed that three snail species from western Oregon were exposed to metals: _Juga plicifera_ and _Lithoglyplus virens_, which occupy temperate coastal streams, and _Physa gyrina_, which inhabits ponds in the Willamette Valley. _J. plicifera_ was subjected to Cu and Ni in laboratory flow-through testing, while _L. virens_ was exposed to Cu, and _P. gyrina_ was exposed to Ni and Zn. _J. plicifera_ had a 96-h LC\({}_{50}\) Cu value of 0.015 mg/L, and a no observable effect level (NOEL) of 0.006 mg/L (at which mortality was not substantially different from that in control groups) (30-d survival). The 96-h LC\({}_{50}\) and NOEL for Ni in _J. plicifera_ were 0.23 mg/L and 0.124 mg/L, respectively. The 96-h LC\({}_{50}\) and NOEL for Cu in _L. virens_ were 0.008 mg/L and less than 0.008 mg/L, respectively. The 96-h LC\({}_{50}\) for Ni in _P. gyrina_ was 0.239 mg/L, the 96-h LC\({}_{50}\) for Zn was 1.274 mg/L and the NOEL for Zn was 0.570 mg/L. [PERSON] et al. [88] investigated the acute toxicity of Cd and Cu to _Pomacea_ sp collected from Thailand. The findings revealed the possibility of using the freshwater
# Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks
## 4 Numerical Result
Figure 6a,b shows the ground truth arrow figure shape and the reconstructed shape by DCNN, respectively. It is seen that the reconstructed shape has some missing portions on the upper and right parts. In general, the reconstructed result is not perfect enough, though the size and position of the shape can still be recognized. The RMSE and SSIM are 17.81% and 80.01%, respectively, as listed in Table 1.  
Figure 7a,b shows the ground truth peanut shape and the reconstructed shape by DCNN, respectively. It is seen that the reconstructed shape's contour is slightly protruding. Generally, the reconstructed result is not good enough, though the size and position of the shape can still be recognized. The RMSE and SSIM are 15.10% and 83.82%, respectively, as listed in Table 1.  
Figure 5: Reconstructed image of case 2: (**a**) ground truth; (**b**) DCNN. Figure 6: Reconstructed image of case 3: (**a**) ground truth; (**b**) DCNN. Figure 7: Reconstructed image of case 4: (**a**) ground truth; (**b**) DCNN. Figure 8a,b shows the ground truth four-petal shape and the reconstructed shape by DCNN, respectively. It is seen that the reconstructed shape's petal appearance has little protrusions around it due to its relatively irregular shape, ensuring harder reconstruction. The RMSE and SSIM are 14.14% and 89.27%, respectively, as listed in \[RMSE=\frac{1}{M}\sum_{i=1}^{M}\|Y-Y^{i}\|_{F}/\|Y\|_{F}, \tag{16}\]0  
\[RMSE=\frac{1}{M}\sum_{i=1}^{M}\|Y-Y^{i}\|_{F}/\|Y\|_{F}, \tag{16}\]1 Reconstructed image of case 5: (**a**) ground truth; (**b**) DCNN. Figure 9a,b shows the ground truth three-petal shape and the reconstructed shape by DCNN, respectively. It is seen that the reconstructed shape has a little protrusion on the upper part, but the size and position of the shape can still be recognized. The RMSE and SSIM are 15.24% and 86.73%, respectively, as listed in \[RMSE=\frac{1}{M}\sum_{i=1}^{M}\|Y-Y^{i}\|_{F}/\|Y\|_{F}, \tag{16}\]2  
Above numerical results are given in \[RMSE=\frac{1}{M}\sum_{i=1}^{M}\|Y-Y^{i}\|_{F}/\|Y\|_{F}, \tag{16}\]3 The average RMSE and SSIM of the entire testing set are 14.45% and 82.63%, respectively. Summarizing the above results, we can conclude that irregular conductor shapes, such as the arrow figure, peanut shape, and triple and quadruple petals, are more difficult to reconstruct than round and elliptical shapes. \[RMSE=\frac{1}{M}\sum_{i=1}^{M}\|Y-Y^{i}\|_{F}/\|Y\|_{F}, \tag{16}\]4 Reconstructed image of case 6: (**a**) ground truth; (**b**) DCNN. Figure 10 highlights the effectiveness of the training process by plotting the loss results with respect to the number of epochs. In the beginning, the loss decreases very rapidly in the first 10 epochs. It decreases significantly between 10 and 20 epochs and decreases slowly between 20 and 80 epochs. It decreases very slowly between 80 and 140 epochs, and then slowly converges.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing
### Improvements in Terms of Holding Time  
Holding time is given to nodes with lesser values of respective forwarding function. Holding time calculation is defined by the formula given in equation.  
\[H^{\prime}_{T}=(1-F_{F})\times H_{T} \tag{21a}\]  
\(H_{T}\) is the maximum value of holding time. It is defined in following equation as the ratio of consumed energy per unit power.  
\[H_{T}=\frac{E_{o}-Er}{P_{T}} \tag{21b}\]  
So if a certain node has less value of remaining energy it would have higher value of holding time and refrain to forward the packet.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 5 Experimental Simulation Scenarios Topology
### Low-Voltage Power Line  
The first series of presented simulations follows selected parameters of the BPL communication due to the distance between two virtual BPL modems. The results show several experimental G.hn BPL communications with parametric sweep. The simulation targets were to verify the effects of the parameters on the throughput, and how they depend on the low-voltage line distance. For simplicity, only the results of the communication between the two nodes are demonstrated. The presented results are divided by variable parameters:  
* Modulation sweep--defined QAM modulations with decision points from 4 to 4096.
* Transmit power spectral density (PSD) sweep--based on G.hn definition for the NS-3 spectral model in dBm/Hz.
* Cable cross-section sweep--cable type NAYY for fixed underground installation. NAYY AxB SE where A is the number of cores and B is the nominal cross-section [mm].
* Cable type sweep--cable types designed for distribution in buildings and underground installation.
* Bandwidth sweep--used bandwidths 25, 50 and 100 MHz correspond to the G.hn standard.  
Figure 4: Dependence of transmission speed on distance at different power values.  
Each simulation case uses a G.hn extension that has been modified for simulation purposes. The medium access method is common--CSMA/CA. The point-to-point communication system is full-duplex. The statistical method delta is used to determine the application rate with a confidence interval of 95%. The distance sweep at a step of 30 m. The impedance of both nodes is 100 \(\Omega\). Summary of the simulation setup is seen in Figure 5.
## 3 The Proposed Method  
### Conventional Cuckoo Search Algorithm (CSA)  
CSA is comprised of two techniques for updating new solutions. The first technique is based on Levy flights to expand searching space considering very large step sizes. On the contrary, the second technique narrows searching space nearby current solutions, using a mutation operation similar to that in the DE algorithm. Due to different strategies, the first technique is called the exploration phase, whereas the second technique is known as the exploitation phase. The exploration phase is mathematically expressed as follows:  
\[So_{s}^{new}=So_{s}+\alpha\times(So_{s}-So_{Ghest})\otimes\mathsf{Levy}(\beta) \tag{13}\]  
where \(\alpha\) is the positive scale factor, which can be selected within the range of 0 and 1; Levy(\(\beta\)) is the Levy distribution function [21], and \(So_{Ghest}\) is the best solution of the previous iteration.  
The exploitation phase can be mathematically expressed as the following mutation technique:  
\[So_{s}^{new}=\begin{cases}So_{s}+\delta\times(So_{1}-So_{2}),&rd_{s}<MF\\ So_{s},&otherwise\end{cases} \tag{14}\]  
where \(So_{1}\) and \(So_{2}\) are two randomly generated solutions from the current solutions, \(rd_{s}\) is a randomly generated number within zero and 1, and \(MF\) is the mutation factor, which is selected within the range of 0 and 1.  
In the exploitation phase, there is a possibility that new solutions cannot be updated, i.e., new solutions and old solutions can be the same. This is particularly the case, given that the mutation factor, \(MF\), is selected to be close to zero, and therefore the possibility that the phenomenon happens is very high. Additionally, it is obvious that new solutions are absolutely updated, if \(MF\) is selected to be close to 1.0. Consequently, the searching performance of CSA is highly dependent on the most appropriate value of \(MF\).
## 3 Results and Discussion
### Fourier Transform-Infrared Spectroscopy (FT-IR)  
FTIR analysis was performed to investigate the functional groups of the scaffolds as well as the interactions between the components. The molecular structures of pure PLA, CS, and levodopa and all scaffold samples produced by 3D-printing are given in Figure 2. The main characteristic absorption bands are C=O vibration peak at 1750 cm\({}^{-1}\), CH\({}_{3}\) asymmetric shear at 1456 cm\({}^{-1}\), CH\({}_{3}\) and C-H bending vibrations 1386 and 1358 cm\({}^{-1}\), C-O-C stretching vibration at 1265 cm\({}^{-1}\), C-O-C stretching at 1080 cm\({}^{-1}\), C-CH\({}_{3}\) stretching, OH bending at 950 cm\({}^{-1}\) and C-COO stretching at 873 cm\({}^{-1}\) for pure PLA in Figure 2a, respectively [26]. Figure 2b shows a large peak at approximately 3283 cm\({}^{-1}\), representing hydrogen-bonded O-H or N-H stretch in pure CS, while the peak close to 2869 cm\({}^{-1}\) reflects the C-H vibration. C=O stretch of the primary amide is represented at 1585 cm\({}^{-1}\), while the two peaks corresponding to the CH\({}_{2}\) and O-H bending stress are observed at 1413 and 1375 cm\({}^{-1}\), respectively [27,28]. The O-H stresses at 2980 cm\({}^{-1}\), phenyl group C=C vibrations at 1455, 1404, 1352, 817, and 674 cm\({}^{-1}\), the phenyl group C=C vibrations at 1458, 1404, 817 and 674 cm\({}^{-1}\), and C-H at 2929 and 2982 cm\({}^{-1}\) for pure levodopa are shown in Figure 2c. Furthermore, the NH\({}_{2}\) stresses are observed at 1562 cm\({}^{-1}\) and C=O stresses are observed at 1651 cm\({}^{-1}\)[29]. Figure 2 shows that the characteristic peaks of PLA, CS, and levodopa were obtained in pure and levodopa-loaded PLA/CS scaffolds. Due to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concentration (wt.\%)** & **Viscosity** & **Density** & **Surface Tension** \\
**and Composition** & **(mPa s)** & **(g/mL)** & **(mN m\({}^{-1}\))** & **Scaffold Images** \\ \hline
17 PLA & 2282 & 1.352 & 114.19 \\ \hline
17 PLA/3 CS & 2293 & 1.360 & 114.29 \\ \hline
17 PLA/3 CS/0.01 LEV & 2287 & 1.356 & 114.24 \\ \hline
17 PLA/3 CS/0.03 LEV & 2295 & 1.357 & 114.69 \\ \hline
17 PLA/3 CS/0.05 LEV & 2313 & 1.359 & 115.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rheological properties of the solutions.  
small amount of levodopa loaded in the scaffolds, no clear peaks can be observed. Thus, it was proven that the 3D-printed scaffolds contained the polymers and drugs according to these results.
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation  
In this section, we introduce the implementation of M-Emu. M-Emu presents a Service-Forwarding Architecture, which enables the data plane to process packet scheduling tasks and to deploy arbitrary complex multicast protocols and algorithms. Based on the Service-Forwarding Architecture, M-Emu integrates a Multicast-Emulation Framework. The Multicast-Emulation Framework has a complete multicast tree construction and maintenance mechanism. In addition, we deployed data collection and analysis capabilities for common multicast performance metrics.
## 1 Introduction
Self-etching primers began to be used about 20 years ago because their use was found to result in the formation of a continuum between the adhesive contained and the etched surface by simultaneous acid demineralization and penetration of the treated surface with acidic monomers. Those monomers can be then easily polymerized in situ, so the bonding technique is simplified [8]. Self-etching primers are combinations of etching acids and bonding resins that are manufactured in order to eliminate the etching step and to reduce the chairside working time and the risk of salivary contamination; using the primer reduces the adhesion process to two steps instead of three. According to the manufacturer's (3M Unitek, Monrovia, CA, USA) datasheet, Transbond Plus Color Change adhesive when used together with Transbond Plus Self-Etching Primer (3M) provides a moisture-tolerant bonding system. Transbond Plus Self-Etching Primer (TSEP) contains methacrylate phosphoric acid esters as the main ingredients [9]. The acidic end group of the resin derivate has hydrophilic properties [10]. Contamination may occur frequently in clinical activity after application of the primer; in this case, the ability of the primer to create a chemical bond strong enough to allow the proper adhesion of the brackets is to be considered. Another bonding material used in our study was Fuji Ortho LC, an adhesive made by GC America, Inc.; this dental material has been described by the manufacturer as a light-cured resin-reinforced glass ionomer cement. Resins have been added to ionomeric cements to improve their aesthetic and mechanical characteristics, to increase adhesion and to maintain the fluoride-releasing property [11]. Fuji Ortho LC is a viscous material that becomes hard by polymerization that occurs upon exposure to ultraviolet or natural light. In orthodontics, it has been frequently used for bonding brackets and bands. The study described in this work aimed to evaluate the adhesion created by using two adhesive systems with a different chemical composition for bonding brackets, Transbond Plus Color Change with Transbond Plus Self-Etching Primer and pre-etched Fuji Ortho LC in three enamel conditions achievable in regular dental office activity: dry, contaminated with water and contaminated with saliva.
## 3 Results and Discussion
### Pigments and Astaxanthin Production
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & **Chlorophyll a** & **Chlorophyll b** & **Total** & **Astaaxanthin** & **Lipts (\%)** \\  & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** \\ \hline BBM & 36.59 \(\pm\) 0.26 \({}^{\circ}\) & 31.85 \(\pm\) 0.17 \({}^{\circ}\) & 2.24 \(\pm\) 0.08 \({}^{\circ}\) & 1.70 \(\pm\) 0.14 \({}^{\circ}\) & 34.59 \(\pm\) 4.10 \({}^{\circ}\) \\
10\% WW & 11.49 \(\pm\) 0.01 \({}^{\circ}\) & 4.56 \(\pm\) 0.05 \({}^{\circ}\) & 4.04 \(\pm\) 0.03 \({}^{\circ}\) & 0.71 \(\pm\) 0.14 \({}^{\circ}\) & 30.49 \(\pm\) 1.65 \({}^{\circ}\) \\
20\% WW & 4.10 \(\pm\) 0.09 \({}^{\circ}\) & 1.77 \(\pm\) 0.13 \({}^{\circ}\) & 0.84 \(\pm\) 0.04 \({}^{\circ}\) & 0.14 \(\pm\) 0.02 \({}^{\circ}\) & 24.72 \(\pm\) 2.08 \({}^{\circ}\) \\
50\% WW & 0.47 \(\pm\) 0.01 \({}^{\circ}\) & 0.33 \(\pm\) 0.01 \({}^{\circ}\) & 0.22 \(\pm\) 0.00 \({}^{\circ}\) & 0.03 \(\pm\) 0.00 \({}^{\circ}\) & 19.19 \(\pm\) 1.43 \({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _C. zofingiensis_ biochemical composition in whey wastewater (WW) compared to BBM medium (mean \(\pm\) standard deviation of three different tests, n = 3). Letters indicate statistical order of differences after one way ANOVA and post hoc tests (_p_ \(<\) 0.05): a \(>\) b \(>\) c \(>\) d.  
in 7.5% palm oil mill effluent. [PERSON] and [PERSON] [29] reported that _C. zofingiensis_ cultivated in ozonated domestic wastewater could accumulate astaxanthin at 0.009 mg L\({}^{-1}\).
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 4 C-ResNet Classification Network
### Constructing the C-ResNet Classification Model  
Therefore, given the presence of significant noise information in the time-frequency plots of jamming signals, this section designs a classification network called C-ResNet, based on deep residual networks and spatial-channel attention mechanisms, to enhance classification performance. The network structure and parameters are shown in Figure 5. The network model includes 23 convolutional layers (containing 11 residual blocks), two CBAM attention layers, two pooling layers, and one fully connected layer. When the input and output dimensions of the residual blocks are the same, solid line skip connections are used, and the numbers beside the skip lines indicate the number of identical stacked blocks; when the input and output dimensions of the residual blocks differ, dashed line skip connections are used. The parameters in the diagram denote the kernel size, convolution dimension, number of convolution kernels, and whether an additional linear mapping is needed to match the output dimensions; for example, \(\{3\times 3\), Conv2d, \(128/2\}\) indicates a kernel size of \(3\times 3\), using 2D convolution, with 128 kernels and requiring an additional linear mapping to match the output dimensions. The activation functions are all RELU functions, and the pooling layers include both max pooling and adaptive pooling. To avoid impacting the network structure, a CBAM module is added at both the first and the last layers, which improves classification efficiency and facilitates the loading of pre-trained parameters. In the C-ResNet classification model, the cross-entropy function is used as the loss function to measure the discrepancy between the predicted probability distribution and the actual label probability distribution. The generated time-frequency plots and the real time-frequency plots are combined to form a new dataset, which is then fed into the C-ResNet classification model. The model is trained using the cross-entropy loss function to complete the classification. The cross-entropy loss function is shown in Equation (13).  
\[H(\text{p},q)=-\sum_{i=1}^{n}p(\mathbf{x}_{i})\log(q(\mathbf{x}_{i})) \tag{13}\]where \(p(x_{i})\) represents the probability distribution of the true labels, while \(q(x_{i})\) represents the distribution predicted by the model.
## 3 Results and Discussion
### Membrane Morphology  
Figure 2 displays the SEM images of R-HIPS, R-HIPS-3, and R-HIPS-5. The sublayer surfaces of R-HIPS (Figure 2a) exhibited micro-sized pores, while the sulfonated membranes R-HIPS-3 (Figure 2c) and R-HIPS-5 (Figure 2e) revealed completely dense surfaces. The cross-section morphologies of R-HIPS and sulfonated R-HIPS-3 and R-HIPS-5 revealed a sponge-like matrix with rounded and elongated macroproves (Figure 2b,d,f), with this trend becoming more pronounced as the sulfonation degree increased. This phenomenon can be attributed to a delayed demixing process in the coagulation bath. This effect is due to hydrogen bonding interactions between sulfonic acid groups and water molecules present in the vinegar. According to [PERSON] et al. [32], the presence of hydrogen bonding increases dope viscosity, which slows down the convection and diffusion processes between the solvent and nonsolvent during membrane formation. The increased viscosity can lead to a reduced droplet growth rate, resulting in a smaller pore size distribution within the membrane [33]. The morphology of sulfonated R-HIPS allows for better stability in the as-prepared membranes.  
Table 2 presents the % porosity (\(\epsilon\)) and pore radius (r\({}_{\text{m}}\)) values for R-HIPS and sulfonated R-HIPS membranes prepared in this work and their comparison with some recycled HIPS membranes reported in literature [15; 21]. This study confirmed that the pore radius decreased slightly from 4.0 nm to 3.3 nm with an increasing sulfonation degree (Table 2), which also led to a reduction in membrane porosity (\(\epsilon\)). However, no significant decrease in the finger-like structures within the cross-section of R-HIPS sulfonated membranes was observed, which may be attributed to NaHCO\({}_{3}\) addition that weakened the hydrogen bonding interactions between -SO\({}_{3}\)H groups and water molecules [34]. The membrane porosity (\(\epsilon\)), calculated using the dry-wet weight method, decreased from 75 for R-HIPS to  
Figure 2: SEM images of surface and cross section at 2000\(\times\) and 300\(\times\) of (**a,b**) R-HIPS, (**c,d**) R-HIPS-3, and (**e,f**) R-HIPS-5, respectively.  
63.9% in R-HIPS-3. Increasing sulfonation for R-HIPS-5 showed a slight porosity increase (67.0%) in comparison with R-HIPS-3, which was attributed to the presence of elongated macroopores. As shown in Table 2, for membranes from recycled HIPS reported in literature, termed HIPS-R ([PERSON] et al., 2018), 30% and 35% ([PERSON] et al., 2019) exhibited higher pore radii that led to membranes for ultrafiltration and microfiltration, resulting in increased flux and lower solutes retention. Thus, this larger pore size compromised the membrane selectivity and overall performance. The increase in pore size is attributed to the instantaneous demixing formation that occurs during the fabrication process when water is used as a non-solvent.