esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Analysis of an Intensified Batch-Fed Wine Fermentation Process
## 2 Materials and Methods
### Wine Volatile Chemical Analysis  
Wine volatile compounds were analyzed as described by [PERSON] et al. [16]. In summary, samples from each fermentation replicate were prepared and analyzed in triplicate within a month of wine sensorial analyses as follows.  
A total of 3 g sodium chloride (NaCl) (Sigma-Aldrich, St Louis, MO, USA) was placed into 20 mL amber vials, and then 10 mL of wine were transferred into the vials. Moreover, 50 mL of a solution of 2-undecanone (10 mg/L prepared in 100% ethanol) was added to each vial as an internal standard. An automated headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) model 7890A gas controlled by Maestro (version 1.2.3.1, Gerstel Inc., Linthicum, MD, USA) was used to analyze the wines. Samples were exposed to a 1 cm polydimethylsiloxane/divinylbenzene/carboxen (PDMS/DVB/CAR) (Supelco Analytical, Bellefonte, PA, USA), 23-gauge solid-phase microextraction (SPME) fiber for 45 min. Helium was used as carrier gas at a flow of 0.8636 mL/min, in a DB-Wax 231 ETR capillary column (30 m, 0.25 mm, 0.25 mm film thickness) (J&W Scientific, Folsom, CA, USA) column. The method used was retention time-locked for the internal standard 2-undecanone at a constant pressure of 6.69 psi to prevent retention time drifting. Samples were agitated and warmed to 30 \({}^{\circ}\)C for five minutes, following the fiber introduction into the vial headspace in order to adsorb volatile compounds for 45 min. Then, the SPME fiber was desorbed into the column in split mode (10:1 split ratio) and the oven temperature was kept 40 \({}^{\circ}\)C for five minutes, increased to 180 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then heated to 250 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/min with the total run time of 61.67 min. The samples were measured using synchronous scan and selected ion monitoring (SIM mode), the range of which was from 40 \(m/z\) to 300 \(m/z\).  
Data were analyzed by MassHunter Qualitative Analysis software Version B.07.00 (Agilent Technologies). Samples were analyzed semi-quantitatively by normalizing each volatile compound peak area with the peak of 2-undecanone as internal standards. Volatile compounds were identified by mass spectrometry (MS) spectrum of the peaks found at the determined retention times and each peak spectrum was compared to the National Institute of Standards and Technology (NIST) ([[https://www.nist.gov](https://www.nist.gov)]([https://www.nist.gov](https://www.nist.gov)), accessed on 1 July 2020) database for further confirmation. Statistical differences among the wines were analyzed using univariate analysis of variance (ANOVA) and the means were determined by [PERSON]'s least significant differences (LSD). Principal component analysis (PCA) was performed to compare and visualize the relations between wines and volatile compounds. All statistical analyses were performed in XLSTAT (Microsoft Office Professional Plus 2010, version 14.0.7194.5000, Redmond, WA, USA).
# A Decentralized Receiver in Gaussian Interference
###### Abstract  
Bounds are developed on the maximum communications rate between a transmitter and a fusion node aided by a cluster of distributed receivers with limited resources for cooperation, all in the presence of an additive Gaussian interferer. The receivers cannot communicate with one another and can only convey processed versions of their observations to the fusion center through a Local Array Network (LAN) with limited total throughput. The effectiveness of each bound's approach for mitigating a strong interferer is assessed over a wide range of channels. It is seen that, if resources are shared effectively, even a simple quantize-and-forward strategy can mitigate an interferer 20 dB stronger than the signal in a diverse range of spatially Ricean channels. Monte-Carlo experiments for the bounds reveal that, while achievable rates are stable when varying the receiver's observed scattered-path to line-of-sight signal power, the receivers must adapt how they share resources in response to this change. The bounds analyzed are proven to be achievable and are seen to be tight with capacity when LAN resources are either ample or limited.  
distributed reception; communications networks; channel capacity; relay channels; interference mitigation +
Footnote †: journal:  
_Article_  
A Decentralized Receiver in Gaussian Interference
## 4 Experiments
### Objective Metrics  
Three metrics are used when testing the models with the various modeling tasks. Since the mean absolute error depends on the amplitude of the output and target waveforms, before calculating this metric, we normalize the energy of the target and the output and define it as the energy-normalized mean absolute error (_mae_).  
As an objective evaluation for the _Leslie speaker_ time-varying tasks, we propose an objective metric which mimics human perception of amplitude and frequency modulation. The modulation spectrum uses time-frequency theory integrated with the psychoacoustics of modulation frequency perception, thus, providing long-term knowledge of temporal fluctuation patterns [59]. The modulation spectrum mean squared error (_ms_mse_) is based on the audio features from [60] and [61] and is defined as follows:  
* A Gammatone filter bank is applied to the target and output entire waveforms. In total we use 12 filters, with center frequencies spaced logarithmically from 26 Hz to 6950 Hz.
* The envelope of each filter output is calculated via the magnitude of the Hilbert transform and downsampled to 400 Hz.
* A Modulation filter bank is applied to each envelope. In total we use 12 filters, with center frequencies spaced logarithmically from 0.5 Hz to 100 Hz.
* The Fast Fourier Transform (FFT) is calculated for each modulation filter output of each Gammatone filter. The energy is summed across the Gammatone and Modulation filter banks and the _ms_mse_ metric is the mean squared error of the logarithmic values of the FFT frequency bins.  
The evaluation for the nonlinear tasks with short-term and long-term memory corresponds to _mfc_cosine_: the mean cosine distance of the Mel-frequency cepstral coefficients (MFCCs). This metric is calculated as follows:  
* A log-power-melspectogram is computed from the energy-normalized waveforms. This is calculated with 40 mel-bands and audio frames of 4096 samples and 50% hop size.
* 13 MFCCs are computed using the discrete cosine transform and the _mfc_cosine_ metric is the mean cosine distance across the MFCC vectors.
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.2 Leveraging DL for Kelp Forest and Other Seaweed Ecosystems Monitoring
To address these challenges, DL techniques, particularly CNNs and U-Nets, have been applied, significantly enhancing the precision and efficiency of seaweed ecosystem monitoring [235; 236; 237; 216]. When leveraging CNNs to kelp research, [PERSON] et al. [214] analyzed imagery from satellites, aerial systems, and underwater autonomous vehicles, uncovering insights into the growth of kelp in aquaculture farms. [PERSON] et al. [238] compared several CNN models, including MobileNetV2, ResNet18, and Xception, for macroalgal segmentation. ResNet18 achieved the highest accuracy at 91.9% but encountered minor errors at species boundaries. [PERSON] et al. [213] utilized Mask R-CNN to automatically detect kelp forest canopy cover along the coastlines of Southern California and the northeastern Pacific using Landsat imagery. Through cross-validation, data augmentation, and fine-tuning hyperparameters, the optimized model achieved a Dice index of \(0.93\pm 0.04\), demonstrating its reliability for mapping kelp forest canopies. U-Net, a specialized architecture for image segmentation, is another commonly used DL approach for macroalgae mapping [239]. Traditional methods like the Floating Algae Index often produce false positives in nearshore regions due to their complex optical characteristics. To address this issue, [PERSON] et al. [217] developed a method based on the U-Net architecture. Trained and tested on 8518 images from the MODIS sensors, their approach achieved an F\({}_{1}\) score of approximately 92.5%, outperforming the Floating Algae Index's 86.2%. [PERSON] et al. [215] applied U-Net and multispectral data to monitor macroalgae ecosystems and assessed the reliability of crowdsourced labels. Their study revealed that biases from participants impacted the performance of the U-Net models. However, models trained using a combination of in situ and crowdsourced labels outperformed those trained exclusively with in situ labels, demonstrating the value of integrating diverse data sources. [PERSON] et al. [211] used Sentinel-2 optical images and DL to map aquaculture areas. They developed an enhanced deep convolutional generative adversarial network (DCGAN) with convolutional layers in the discriminator and convolutional-transpose layers in the generator. The study applied three semantic segmentation methods--U-Net, DeepLabV3, and SegNet--for seaweed region classification. The improved DCGAN outperformed standard GANs and U-Net achieved the highest overall classification accuracy of 94.56%. Leveraging these DL approaches, researchers have achieved dramatic progress in monitoring kelp and other seaweed ecosystems, offering critical insights into the impacts of climate change on these vital underwater habitats.
## 3 Results
Oxygen content of 0-8 at.% produces a yellow or grey color, whereas 25-40% oxygen in the upper layer produces blue and violet coloration (oxygen content measured by GDOES). As a proof of concept, two-layered coatings were deposited on brass pendants. The lower layer (P) serves protective purposes, whereas the upper layer (D) is decorative. Figure 11 provides photos of pendants before the deposition, after the deposition of the P layer and with a final P + D two-layered coating. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **B, at.\%** & **U, V** & **Corrosion Current, nA/cm\({}^{2}\)** \\ \hline P1 & 0 & 0.808 & 140 \\ P2 & 0.4 & 0.725 & 22 \\ P3 & 2 & 0.660 & 18 \\ P4 & 2.3 & 0.650 & 27 \\ P5 & 7.3 & 0.330 & 36 \\ P6 & 10 & 0.310 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Electrochemical characteristics. Figure 10: The plot of current densities versus the free corrosion potential. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Specimen** & \(\mathbf{Q_{\text{A}}}\)/\(\mathbf{Q_{\text{air}}}\), & \(\mathbf{\tau_{\text{v}}}\) & \multicolumn{6}{c}{**Elemental Composition, at.\%**} & \multicolumn{2}{c}{**Coating**} & \multirow{2}{*}{**Coloration**} \\ \cline{4-9}  & **cm\({}^{3}\)/min** & & **Cr** & **A1** & **Ti** & **B** & **N** & **O** & **C** & **Thickness, \(\mu\)m** \\ \hline D1 & 5/40 & 10 & 27.2 & 5.2 & 13.8 & 5.1 & 16.7 & 27.4 & 4.6 & 0.04 & Violet \\ D2 & 5/40 & 20 & 22.7 & 3.8 & 13.8 & 3.9 & 11.5 & 40.2 & 4.1 & 0.07 & Light-blue \\ D3 & 5/40 & 30 & 4
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 2 Related Works
Apart from UGC videos, another specific category of videos, demanding a unique approach to quality assessment, are nighttime videos, analyzed in [39], where the authors proposed a blind nighttime video quality assessment model based on feature fusion. An attempt to develop a somewhat unified model for VQA can be found in [40], where the authors develop different instances of the AVQBits algorithm. The authors claim that the algorithm can be used to assess video quality in different contexts, such as video service monitoring, evaluation of video encoding quality, gaming video QoE, and omnidirectional video quality. The presented results show that AVQBits predictions closely match subjective ratings of video quality for videos of up to 4K-UHD resolution. We can also report that, recently, there has been significant progress in developing open-access video databases containing diverse video content suitable for testing. Many of these databases also include subjective ratings, making them valuable sources of data for developing VQA algorithms. In turn, scholars from this domain are more frequently focusing on developing models that incorporate subjective ratings into the inference systems, as they can rely on already completed extensive and resource-consuming subjective experimentation. Based on the reviewed studies, we draw the following conclusions:  
* Inference systems of developed models primarily rely on machine learning, neural networks, fuzzy logic, or a combination of these techniques;
* Models based on neural networks are content-domain-dependent and require application-specific training [41];
* Video-related parameters, such as video frame rate, compression, and spatio-temporal properties, have been identified as crucial factors influencing the human perception of video quality;
* Online video databases serve as excellent starting points for developing VQA models, offering rich, diverse, and subjectively rated video content, and adhering to international standards for conducting research in this field;* Given the abundance of diverse video content and streaming scenarios in various network contexts, it is challenging to create a universal VQA model.
# Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution
## 4 Conclusions  
This study compared the selected properties of biochars produced under two different methods (lab tube furnace vs. BioCharlie) and evaluated their performance as a sorbent for Pb\({}^{2+}\). BioCharlie-produced biochars exhibited higher EC and volatile matter than tube-furnace-produced biochars. The batch adsorption tests at 10 mg Pb\({}^{2+}\) L\({}^{-1}\) showed that overall tube-furnace-produced biochars performed better at Pb\({}^{2+}\) removal than the BioCharlie-produced- biochars, except for the brown seaweed biochar. Four biochars (AST, GPT, ASB, and BSB) were top performers in removing Pb\({}^{2+}\) across both production methods. Further investigation determining biochars' adsorption capacity in various ranges of Pb\({}^{2+}\) concentrations (adsorption isotherm), pH and biochar dosage effects on Pb\({}^{2+}\) adsorption, and their performance under the flowing condition is desired to confirm the biochars as an effective sorbent for Pb\({}^{2+}\).  
Conceptualization, J.J.K., E.P. and C.-L.C.; experimental design, J.J.K., E.P. and C.-L.C.; performance of experiments, P.G. and S.M.; data analysis, P.G., S.M. and J.J.K.; writing--original draft preparation, P.G.; writing--review and editing, J.J.K., E.P., C.-L.C. and S.M.; funding acquisition, J.J.K., E.P. and C.-L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the US EPA P3-People, Prosperity and the Planet grant (award no. 83990001), USDA Hispanic Serving Institutions Education Grants Program (award no. 2018-38422-28568), and National Science Foundation GEOPATHS (award no. 1600585).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 4: Percent removal of aqueous lead (Pb\({}^{2+}\)) affected by biomass types and biochar production methods.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## Appendix B Zephyr RTOS
### Bluetooth Support  
Zephyr OS is equipped with the Bluetooth Low-Energy controller (LE link layer) including BLE mesh and a Bluetooth BLE controller.  
Furthermore, the OS supports the Generic Access Profile (GAP) with all LE roles, the Generic Attribute Profile (GATT), and the pairing with secure connections feature from Bluetooth 4.2. It has a smooth and clean HCI driver abstraction. Furthermore, a raw HCI interface can be utilized to operate Zephyr as a controller instead of using a full host stack that makes it extremely configurable.  
Zephyr also offers full mesh support for protocol development, with features such as relay, friend node, Low-Power Node (LPN), and GATT proxy, as well as support for both provisioning bearers (PB-ADV and PB-GATT) and the ability to fit in devices with at least 16 k RAM.  
There are three layers to the Zephyr Bluetooth stack such as the host (GAP, GATT, SM, ATT, L2 CAP), controller (host control interface, link layer), and physical hardware. Furthermore, for application development, Zephyr comes with a number of BLE and mesh APIs.
###### Abstract  
The well installed status of raft vibration isolation is undoubtedly of great significance in marine engineering, especially for submarines. To achieve this, the accurate measurement of the installation space of the vibration isolation is necessary. The traditional measuring technique has many drawbacks. Therefore, simultaneously measuring the multi-point spacing with high precision between two metal surfaces is the focus of this work. Based on eddy current sensing principle, a multi-point spacing measuring system with a simple structure and good measurement accuracy has been developed and reported. The system includes a sensor array component, an integrated controlling component, and a calibration platform. The measured data from multiple points are obtained at the same time through the sensor array and are uploaded to the host computer and a corresponding LabVIEW program was exploited to display, process, and store the spacing results. Furthermore, the least square algorithm has been employed to calculate the flatness of the measured metal surfaces, and the GUM (guide to the expression of uncertainty in measurement) method has been applied to evaluate the flatness error uncertainty. The experimental tests show that each measuring duration only lasts for seconds to get results and the error uncertainty of the measured surface flatness could reduce to less than 1.0 \(\upmu\)m. The developed measuring system has better efficiency and higher precision compared to traditionally manual operations. The measuring and analysis method could also be applied to other related situations.  
research and development; spacing measuring system; eddy current sensor array; surface flatness; measurement uncertainty analysis Article  
Research and Development of a Multi-Point High-Precision Displacement Measuring System for the Installation Space of Vibration Isolation on Submarine Raft  
[PERSON], [PERSON], [PERSON], and [PERSON] 2023
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
## 2 Materials and Methods  
### Chemicals and Solutions  
Standards of phenolic acid, 3-hydroxyphenylacetic acid (3-HPA), 4-hydroxyphenylacetic acid (4-HPA), protocatechic acid (PCA), 4-hydroxybenzoic acid (4-HBA), syringe acid (SyA), and hippuric acid (HA) were purchased from Sigma-Aldrich (Madrid, Spain), Acros Organics (Geel, Belgium), and HWI Analytik (Rulzheim, Germany). Other reagents used for polymerization as ethylene glycol dimethacrylate (EGDMA), 2,2\({}^{\prime}\)-azobis(2-methyl-propionitrile) (AlBN) were obtained from Sigma-Aldrich (Madrid, Spain) and 4-vinylpyridin (4-Vpy) were purchased from Merck-Schuchardt (Hohenbrunn, Germany). HPLC-grade solvents used were acetonitrile (ACN) and methanol (MeOH), they were purchased from Scharlau (Barcelona, Spain). Ultrapure water (18.2 M\(\Omega\)/cm quality) was obtained using a Milli-Q water system (Millipore Iberica, Spain) and used for all dilutions. All other reagents used were of analytical grade or better.  
Stock solution of 6 metabolites (1000 mg/L) was prepared in water and stored at 4 \({}^{\circ}\)C till their usage. Ultrasonic bath was used in preparation solutions to aid analyte dissolution.  
All working solutions of analytes were daily prepared by dilution in water of stock solutions. All solutions were stored in amber-colored flasks to prevent photodegradation.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors
#### 5.3.1 Eavesdropping  
Eavesdropping is the primary threat addressed by encryption. Both protocols provide protection against eavesdropping, although with different security assumptions.  
The PQC standalone PKE protocol protects against eavesdropping by encrypting data using the receiver's public key, which only the receiver, possessing the corresponding private key, can decrypt. The security relies on the post-quantum security of the underlying PQC algorithm.  
PQC-AES Hybrid PKE  
The hybrid protocol provides two layers of protection against eavesdropping. First, the AES-256 key is exchanged securely using the PQC KEM. Second, the data itself is encrypted with AES-256. An eavesdropper would need to break both the PQC KEM and AES-256 to recover the plaintext.
# On Hyperbolic Complex Numbers
## 2 The Basic Hyperbolic Complex Algebraic Structure
### Vector Algebra
: _The vector \(\mathfrak{o}=(0,0)^{T}\) is the additive neutral element. Let \(\mathfrak{e}=(1,0)^{T}.\) If \(z\in\mathfrak{X}\) then_  
\[z\odot_{h}\mathfrak{e}=z. \tag{6}\]  
_That is, within \(\mathfrak{X}\) is \(\mathfrak{e}\) the multiplicative neutral element. On the other hand, if \(z=(x,y)^{T}\in\mathfrak{Y}\) then_  
\[z\odot_{h}\mathfrak{e}= \begin{cases}(x,y)^{T}\text{ with probability }p,p\in(0,1)\\ \text{and}\\ (y,x)^{T}\text{ with probability }1-p.\end{cases} \]  
_Here \(\left( \begin{array}{c}y\\ x\end{array} \right)=\left( \begin{array}{cc}0&1\\ 1&0\end{array} \right)\left( \begin{array}{c}x\\ y\end{array} \right)\in\mathfrak{X}\) is the result of reflecting the point \(\left( \begin{array}{c}x\\ y\end{array} \right)\) at the line \(y=x\)._  
**Definition 4**. : _If for \(z_{1}\) and \(z_{2}\) from \(\mathfrak{X}\) there exists \(z_{3}\in\mathfrak{X}\) such that \(z_{1}\odot_{h}z_{3}=z_{2}\), then we say that \(z_{2}\) can be divided by \(z_{1}\) and call the operation \(z_{2}\oslash z_{1}=z_{3}\) the operation of constrained vector division. If \(\mathfrak{e}\) can be divided by \(z=(x,y)^{T}\), then we call \(\mathfrak{e}\oslash z=z^{-1}\) the inverse of \(z\)._  
Notice that if \(z=(x,y)^{T}\in\mathfrak{X}\) has an inverse, then  
\[z^{-1}=\frac{1}{x^{2}-y^{2}}\left( \begin{array}{c}x\\ -y\end{array} \right).\]  
The vector product \(\odot_{h}\) is obviously commutative and the second up to the fourth hyperbolic vector powers of \(z=(x,y)^{T}\in\mathfrak{X}\) are  
\[\left( \begin{array}{c}x\\ y\end{array} \right)^{2}=\left( \begin{array}{c}x^{2}+y^{2}\\ 2 xy\end{array} \right),\left( \begin{array}{c}x\\ y\end{array} \right)^{3}=\left( \begin{array}{c}x^{3}+3 xy^{2}\\ y^{3}+3 yx^{2}\end{array} \right)\]  
and  
\[\left( \begin{array}{c}x\\ y\end{array} \right)^{4}=\left( \begin{array}{c}x^{4}+6x^{2}y^{2}+y^{4}\\ 4 xy(x^{2}+y^{2})\end{array} \right),\]  
respectively.