esa-sceva/satcom-chunk-collection huggingface dataset:
# The Fate of MTBE and BTEX in Constructed Wetlands
## 3 Fate of MTBE in Constructed Wetlands
Other investigations at the same area [42; 96] reported similar findings, showing that higher removal of MTBE (up to 33% [96] and 93% [42]) was achieved through microbial community adaptation in the planted gravel bed after 3-4 years of operation. A laboratory-scale CW experiment with the same contaminated source indicated that MTBE removal (as well as benzene and ammonium) is improved through the combination of microbial electrochemical technology with a CW system [97]. Overall, the existing works indicate that MTBE is removed in CWs. Biodegradation appears as the dominant removal process, although with lower degradation rates compared with other petroleum hydrocarbons. The VF CW design seems to be more efficient. However, the number of field studies is relatively low to allow for a complete evaluation of MTBE fate in various CWs.
## 5 Summary and Conclusions  
A Hg Mussel Watch Program was carried out at 52 stations distributed along the intertidal zone of the Estuary and Gulf of St. Lawrence, in the summers of 2016, 2018, and 2019. This survey took place forty years after a previous Mussel Watch survey was performed in this area (1977-1979), at almost all stations currently sampled. The same normalization procedure applied 40 years ago was also applied to the current data set. It allows the minimization of the effects of biological factors on the Hg content of the mussel soft tissues and optimizes the interpretation of Hg distribution in terms of the availability of Hg in coastal waters. Current Hg concentrations in the soft tissue of mussels from the St. Lawrence System are similar to those of the perennial Mussel Watch Programs implemented along the US and French Atlantic coasts [34; 35]. The results indicate that the normalized Hg contents of mussels in the Estuary and the Gulf of St Lawrence were similar at sampling times 40 years apart. In addition, sub-regional Hg distribution patterns indicate a gradual decrease of Hg content in mussels downstream from freshwater inputs, which suggests that the spatial distribution of Hg concentrations in the soft tissues of marine mussels is, in part, governed by riverine Hg sources. Atmospheric Hg deposition and concentration in marine waters of the Atlantic Ocean are known to have decreased in the last decades [44; 64]. However, in coastal environments, the response to these changes does not seem to be rapid. This probably results from the long residence time of Hg in soil and land cover before being exported to coastal areas with freshwater discharges. Indeed, different environmental reservoirs have different time scales as regards Hg mobility [65; 66]. Furthermore, coastal sediment resuspension/sedimentation cycling may also contribute to a longer retention time of Hg in the intertidal marine environments, making mussel habitats conducive to the retention of legacy Hg. The changes in atmospheric Hg inputs are thus damped in terms of Hg availability for sessile benthic animals. A renewed Mussel Watch survey in a few decades may elucidate the long-time trend of Hg load in the St Lawrence Coastal System.  
**Author Contributions:** [PERSON] designed the experiment, performed sampling and analyses, and wrote the article. A.-M.T. participated in sampling and sample preparation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** Thanks are due to [PERSON] for providing some of the sampling material and [PERSON] for his support in mercury analyses performed within the analytical chemistry platform of ISTerre (OSUG-France). Special thanks to [PERSON], [PERSON], [PERSON], and [PERSON] for their comments on the manuscript.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.
###### Abstract  
Aquaculture plays a vital role in meeting global food demands, necessitating technological innovations for sustainable production. This study investigates deep learning-based semantic image segmentation for enhanced monitoring of rainbow trout (_Oncorhynchus mykiss_) in Puno, Peru. We conducted three experiments using UNET and UNETR architectures, varying image resolution, loss functions, and optimizers on a dataset of 1200 high-resolution images. Experiment 1, with UNET and 256 \(\times\) 256 pixel images, achieved an IoU of 0.942854 after 20 epochs, using MSELoss and Adam, demonstrating superior segmentation accuracy. Experiment 2, utilizing UNET with 512 \(\times\) 512 pixel images, resulted in an IoU of 0.803244 after 50 epochs, with L1 Loss and Adam, indicating satisfactory performance despite increased complexity. Experiment 3, employing UNETR with 256 \(\times\) 256 pixel images, yielded lower IoU scores, with a best IoU of 0.253928, highlighting the challenge of training Transformer-based models with limited data. A critical aspect of this study was the use of a coin as a scale reference in all experiments, enabling precise conversion of pixel measurements to physical dimensions. This, combined with OpenCV for contour detection, allowed for accurate fish size estimations, validated by comparisons with real images. The results underscore UNET's effectiveness for aquaculture image segmentation, while also emphasizing data requirements for UNETR. This approach provides a non-invasive, automated method for monitoring fish growth and health, contributing to sustainable aquaculture practices.  
segmentation; trout; UNET; transformer Article  
1E.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Pune 21002, Peru; [EMAIL_ADDRESS] (L.B.); [EMAIL_ADDRESS] (R.C.); [EMAIL_ADDRESS] (E.A.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (F.P.)
## 3 Methodology
### Network Structure  
This study adopts a dual convolutional autoencoder network architecture guided by signal knowledge representation, called the Knowledge-Guided Convolution Autoencoder (KGCAE), to achieve upgoing and downgoing wavefield separation [21]. Figure 6 shows the constructed autoencoder network architecture, where Net-U and Net-D are responsible for predicting the upgoing and downgoing wavefields, respectively. The input was the original VSP record and the output was the predicted upgoing and downgoing wavefields of the same shape as the original record.  
Using Net-U as an example, the specific structures and parameters are listed in Table 1. Here, we assumed that the original input VSP record had the shape (1600, 100, 1), representing 1600 time points, 100 detectors, and 1 channel, and Input data are normalized into the [\(-\)1, 1] interval for better convergence. The overall structure consisted of encoding and decoding layers. The encoding layer is employed for feature extraction and compression of VSP data, as illustrated in Figure 7. It specifically comprises three convolutional layers and two pooling layers. The first convolutional layer is set to 32 channels to capture basic, low-level features; an excessive number of channels would increase computational redundancy. The second layer expands to 64 channels to encode more complex, high-level features, thereby enhancing the feature representation capability. The third layer compresses the channel count back to 32, aiming to avoid excessive feature redundancy while retaining critical information for the decoding layer. To balance the receptive field and simultaneously reduce the loss of boundary information, the convolutional kernel size is set to 3 \(\times\) 3. The pooling layers utilize 2 \(\times\) 2 max pooling to enhance the feature extraction capability. The decoding layer comprised 4 convolutional and 2 upsampling layers for data feature reconstruction and recovery. The channel counts for the convolutional layers were set to 32, 64, 32, and 1, respectively; the final layer outputs single-channel reconstructed data with dimensions consistent with the original input VSP data. The convolutional kernel size was 3 \(\times\) 3. The upsampling layers employed 2 \(\times\) 2 nearest-neighbor upsampling. We use the tanh activation function instead of ReLU because the value interval contains both positive and negative values, The use of the ReLU function would lead to value overflow. Figure 7 shows the upgoing and downgoing wave-separation processes.  
Figure 6: Dual Convolutional Autoencoder Architecture (KGCAE). The input is the original VSP data, X, with Net-U and Net-D predicting the upgoing and downgoing wavefields, respectively. The predicted wavefields, Outputs U and D, are regularized using representation loss, gradient loss, cosine loss, and reconstruction loss derived from knowledge representations.
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
###### Abstract  
Channel estimation scheme for OFDM modulated transmissions usually combines an initial block-pilot-assisted stage with a tracking one based on comb or scattered pilots distributed among user data in the signal frame. The channel reconstruction accuracy in the former stage has a significant impact on tracking efficiency of the channel variations and the overall transmission quality. The paper presents a new block-pilot-assisted channel reconstruction procedure based on the DFT-based approach and the Least Square impulse response estimation. The proposed method takes into account a compressibility feature of the channel impulse response and restores its coefficients in groups of automatically controlled size. The proposition is analytically explained and tested in a OFDM simulation environment. The popular DFT-based methods including compressed sensing oriented one were used as references for comparison purposes. The obtained results show a quality improvement in terms of Bit Error Rate and Mean Square Error measures in low and mid ranges of signal-to-noise ratio without significant computational complexity growth in comparison to the classical DFT-based solutions. Moreover, additional multiplication operations can be eliminated, compared to the competitive, in terms of estimation quality, compressed sensing reconstruction method based on greedy approach.  
O remarkRemarks: 1.1 [leftmargin=*] 1.1 _Academic Editors: [PERSON] and [PERSON]  
18 August 2021  
Accepted: 11 November 2021  
13 November 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright:** **© 2021 by the authors. Licence MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).**
## 2 Proposed Seismic Damage Prediction Model for a Building Structure
### Neural Network Configurations
#### 2.3.1 Model \(M_{0}\)  
The baseline network \(M_{0}\) model: An NLP approach (MFCCs [58]) is used for the hand-crafted feature. It is designed with inspiration from how humans perceive speech signals [59] and physiological evidence directed to the human ear structure. The same features are used for vibration signals [41], which achieve good results. Figure 3b shows the features we extracted by hand, and the specific steps are also shown in Figure 5-\(M_{0}\), where the samples are pre-emphasized, framed (frame length is 0.05 s, frameshift is 0.03 s), windowed (hann window), and then the time domain samples are converted to frequency domain samples by fast Fourier transformation (FFT) [60]. Following, a 40-dimensional Mel-filter set is applied to map the frequency domain samples to Mel-scale, which is calculated as follows:  
\[F(mel)=2595\times\log 10[1+\frac{f}{700}] \tag{1}\]  
here, \(f\) is the signal frequency, and \(F\) is the Mel-scale. The sample is transformed into a linear Mel-spectrum. Then, the log Mel-spectrum is obtained by non-linear activation log, and a signal with a lower amplitude and frequency is amplified in this way. The discrete cosine transform (DCT) is the final step of MFCCs, which aims to compress data dimensionality, and it is calculated as follows:  
\[C(n)=\sum_{m=1}^{M}S(m)cos\Big{(}\frac{n(m-1/2)\pi}{M}\Big{)} \tag{2}\]  
where \(S(m)\) is the log Mel-spectrum, \(m\) is the number of Mel-filters, and \(n\) is the DCT transform corresponding to m. Moreover, short-time energy [61] is added to enhance the time-domain features of samples. In addition, the classification module of the \(M_{0}\) network is composed of some gated recurrent unit (GRU) [62; 63] layers and fully connected layers. It outputs the probability that five damage states occur. In Section 3, the effect of its parameter settings is also analysed.
###### Abstract  
This study examines the optimal design of a tuned mass damper (TMD) in the frequency domain so that the dynamic response of cantilever beams can be decreased. Random vibration theory is applied to identify the mean square acceleration of the endpoint of a cantilever beam as the objective function to be reduced. In addition, to determine the optimal TMD coefficient of mass, stiffness, and damping, a differential evolution (DE) optimization algorithm is employed. The upper and lower limit values of these parameters are taken into account. A majority of the previous studies have concentrated on determining just the stiffness and damping parameters of TMD. Nonetheless, in this study there is also the optimization of TMD mass parameters to determine the mass quantity. In addition, there has been inefficient use of the stochastic DE optimization algorithm method for the optimization of TMD parameters in previous studies. Hence, to obtain optimal TMD parameters, this algorithm is precisely used on the objective function. Tests are carried out on the cantilever beam with the TMD system following this optimization method with harmonic base excitations that resonate the foremost modes of the beam and white noise excitation. The method proposed here is reasonably practical and successful regarding the optimal TMD design. When a TMD is designed appropriately, the response of the cantilever beam under dynamic interactions undergoes a considerable reduction.  
cantilever beam; vibration control; tuned mass damper; transfer function; differential evolution; resilience +
Footnote †: journal: Applied Sciences
## 3 Efficient OFDM BER Floor with Joint Near-Optimal Time Sampling and CFO  
### CFO Abstraction by AWGN  
The impact of CFO on the BER floor (considered to be determined just by the CFO) can be modeled by conventional AWGN link abstraction, for example, as the PAPR was shown to affect the CFO-induced phase distortion \(\Delta\Phi_{\text{kmax}}\) of the \(k\)-th original (pre-OFDM) symbol, as a function of BER, with the CFO \(\Delta f_{\text{CFO}}\) as the parameter [18]. Inversing the relationship \(\Delta\Phi_{\text{kmax}}(BER)\), we obtain:  
\[BER\Big{(}\Delta\Phi_{\text{kmax}}^{2}\Big{)}\approx\frac{4}{\log_{2}M}\cdot Q \left[\sqrt{\frac{3}{M-1}}\cdot\frac{1}{10\cdot\log\left(10^{\frac{2}{30\cdot( \Delta\text{/}_{\text{CFO}})^{\text{M}_{\text{S}}})^{2}}\cdot\Delta\Phi_{ \text{kmax}}^{2}}-10^{\frac{\text{BAC-OFF}}{10}}\right)}\right];\quad 1\leq k\leq M \tag{17}\]  
where \(Q[\cdot]\) is a Gaussian tail function. From Equation (18), it is obvious that its computational burden is quite demanding for network end-points with low processing capacity.  
For HPA back-off of 10 dB, Equation (17) is graphed in Figure 2.
## 1 Introduction
This approach leads to performance improvements of 7.37% and 19.33% over the symmetric matrix mapping method, respectively. These papers used the pattern multiplication theorem to calculate the pattern of the sparse arrays, suppress the sidelobes, and improve the performance of the main beam but did not further calculate the transmission power of the sparse arrays and the power in the target area. Some other research has studied the power optimization problem of sparse arrays in the target area. The primary approach is to obtain the total waveform by superimposing each node in the target area and then calculating the jamming power. The authors in [17] modified the transmission model, more accurately defined the protected area in precise jamming, proposed a new target optimization function, and simplified the complex covariance matrix into a semi-definite programming and mixed Boolean problem, which improved the jamming accuracy and power. The authors in [18] proposed three algorithms to reduce the grating lobe effect based on the Majorization-Minimization (MM) and Alternating Direction Method of Multipliers (ADMM) frameworks. Without relaxation algorithms, a proximal gradient method was designed to maintain the sparsity of the one-norm, achieving beam concentration. The authors in [19; 20] proposed ADMM algorithms and Riemannian space optimization methods to solve non-convex and non-smooth problems under imperfect Channel State Information (CSI). The results showed that both the Signal to Interference Plus Noise Ratio (SINR) and the Combined Power Spectrum (CPS) were improved. However, these papers did not consider the array as a whole when calculating the received power. They needed to calculate the waveform after superposition in each grid sequentially. When the sub-array is a directional antenna, it is also necessary to estimate the angle between the target point and the sub-array to obtain the gain. This calculation method requires many resources and reduces the convergence speed of the algorithm. Combining the contributions of the aforementioned literature, this paper proposes an equivalent transmitted power calculation method for sparse arrays. This algorithm treats the sparse array as a whole and calculates the pattern and gain. By combining the transmitted power of the sub-arrays, the transmitted power of the entire sparse array can be obtained. In the study of coordinated jamming, this method can more quickly calculate the power in the target area based on the array shape, thereby assessing the effectiveness of electronic warfare. The remainder of this paper is organized as follows: Section 2 describes the steps of the beam superposition algorithm for power calculation. Section 3 presents a universal method for calculating the array factor and sub-array pattern, eliminating the need to select different calculation models based on the array shape. Section 4 derives the calculation method for equivalent transmitted power. Section 5 calculates the estimation accuracy and compares the complexity with the beam superposition algorithm. Finally, Section 6 summarizes the entire paper.
# Moving Ad Hoc Networks--A Comparative Study
## 6 Comparison of MANET, VANET, FANET, and SANET (MVFS)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mobile Ad-Hoc Network** & **Vehicular Ad-Hoc Networks** & **Flying Ad-Hoc Networks** & **Ship Ad-Hoc Networks** \\  & **(MANETs)** & **(VANETs)** & **(FANETs)** & **(SANETs)** \\ \hline Topology Change & Topology alters low & Topology alters fast & Topology alters is rapid and very fast & Topology alters medium \\ \hline Radio Propagation & The radio propagation typical & The radio propagation typical & The radio propagation typical, & The radio propagation typical, & The radio propagation typical \\  & close to the ground & close to the ground & close to the ground & close to the ground \\ \hline Power consumes & Power consumes low & Power consumes high & Power consumption is high for mini-UAV/Small UAV not needed. & Power consumes high \\ \hline Power & Limited & High & High & High \\ \hline Mobility & Node/device mobility is low & Node/device mobility is high & Node/device mobility is very high & Node/device mobility is Medium \\ \hline Mobility Model & The mobility model is random & The mobility model is regular & The mobility model is regular & The mobility model is random \\  & & & under the condition & The mobility model is random \\ \hline Line of Sight & Not available for all cases & available in some cases & Available in most cases & available in some cases \\ \hline Security & \(\bullet\) & Data confidentiality & \(\bullet\) & Data confidentiality & \(\bullet\) & Data confidentiality \\  & \(\bullet\) & This high & is high & \(\bullet\) & is high \\  & \(\bullet\) & Data integrity is high & \(\bullet\) & Data integrity is high & \(\bullet\) \\  & \(\bullet\) & Data reliability is high & \(\bullet\) & Data reliability is high & \(\bullet\) \\ \hline Localization & GPS & DGPS, GPS, AGPS & IMU, GPS, AGPS, and DGPS & GPS, DGPS \\ \hline Density & Low & High & Very Low & High \\ \hline Number of Nodes & High & High & Low & Low \\ \hline Update topology & Not Often & Often & Often & Not Often \\ \hline \multirow{5}{*}{Routing Protocol} & Proactive & Proactive & Proactive & Proactive \\  & \(\bullet\) & DSDV & \(\bullet\) & OLSR & \(\bullet\) & DSDV \\  & \(\bullet\) & OLSR & \(\bullet\) & FSR & \(\bullet\) & DSDV \\  & \(\bullet\) & Reactive & Reactive & Reactive \\  & \(\bullet\) & DSR & \(\bullet\) & AODV & \(\bullet\) & DSR \\  & \(\bullet\) & AODV & \(\bullet\) & TORA & \(\bullet\) & AODV \\  & \(\bullet\) & TORA & \(\bullet\) & DSR & \(\bullet\) & TORA \\ \hline \multirow{3}{*}{Simulator} & OMNET++, NS-2, NS-3, & OMNET++, NS-2, NS-3, & OMNET++, NS-2, NS-3, & OMNET++, NS-2, NS-3, & GloMoSim, OPNET, & GloMoSim, OPNET, & GloMoSim, OPNET, \\  & GloMoSim, OPNET, QualNet & SIMITS, MATLAB & QualNet, MATLAB & QualNet, MATLAB & QualNet, MATLAB \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of MVFS ad hoc networks.