esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Bioactive Compounds from _Ulva lactuca_
## 3 Results and Discussion
### Extraction of Phenolics and Carotenoids--Selection of Solvent  
Studies have confirmed that alcoholic solutions and/or hydrophilic solvent mixtures provide extracts with better antioxidant activity [32,33]. This can be attributed to the selective extraction of polar chemical substances, such as phenolic compounds, that exhibit significant antioxidant capacity [11]. In this study, four different solvents (water, ethanol, ethyl acetate, and a mixture of ethanol/water 70:30 _v/v_) were tested in order to evaluate their ability to recover the bioactive compounds of interest.  
According to Figure 1, solvent selectivity was encountered both for carotenoids and phenolics. Water was the best solvent among the four for phenolic compounds and the ethanolic mixture for carotenoids extraction. Pure ethanol resulted in a lower solvation of phenolics, while carotenoids exhibited an extraction ability in between that of water and the ethanol/water mixture. Ethyl acetate exhibited the worst results in all bioactive compounds' extraction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Component** & **Content** \\  & **g g–1 Biomass** \\ \hline Total lipids & 0.035 \\ Polysaccharides & 0.499 \\ Mineral ash & 0.277 \\ Total proteins & 0.084 \\ Other * & 0.105 \\ \hline \hline \end{tabular}  
* Fibers and other carbohydrates.  
\end{table}
Table 2: Biochemical composition of macroalgae _U. lactuca_.  
Figure 1: Total carotenoids (**A**) and phenolics (**B**) content of the _U. lactuca_ extracts (mg g–1) using different solvents (ethanol, water, ethyl acetate, and ethanol/water 70:30 _v/v_). Presented data are mean values of three replications \(\pm\) standard deviation (\(n\) = 3 \(\pm\) s.d).  
According to the above results, it can be concluded that among the four tested solvents the most suitable ones, for the purpose of the present work, are water and the ethanol/water mixture. Nevertheless, the required energy for the recovery of the solvent used, and the acquisition of the dry extract is of great importance, and it should be accounted for the design of a feasible extraction process. Among the two most effective solvents, the ethanol/water 70:30 _v/v_ mixture exhibits a lower enthalpy of vaporization (\(\Delta\)H\({}_{\text{v}}\)) [34]. Therefore, considering the most efficient recovery of all targeted bioactive compounds, environmental impact, GRAS characteristics of solvents, and extraction process feasibility, the ethanol: water 70:30 _v/v_ mixture, would be the recommended solvent for the study of the extraction parameters.  
Effect of Extraction Parameters on Carotenoid and Phenolic Content, Antioxidant Capacity and Extraction Yield  
The effect of the operational conditions, i.e., time, temperature, and biomass to solvent ratio, using ethanol/water as solvent was examined. The obtained extracts are evaluated according to the total carotenoids content, total phenolic content, antioxidant activity, and extraction yield as shown below (Table 3).
# A Comprehensive Review on Music Transcription
## 2 Overview of Music Transcription
### Note-Level Transcription  
The note-based transcription approaches directly estimates notes, including pitches and onsets. This note-based transcription is one level higher than frame-level transcription because the objective of frame-level transcription is to estimate only pitch in a particular time period. But note-level transcription needs to estimate the pitch, onset time, and offset time of a specific note in a particular time period. Figure 1b shows an example of note-level transcription. The blue rectangle shows the note events after the post-processing of frame-level transcription on a continuous pitch based on a hidden Markov model. The visualization for this transcription has also been carried out using Tony [18] from the wave file (opera_male5.wav) of the ADC2004 vocal melody dataset. The note offset in this level of transcription is ambiguous, so they are sometimes neglected during the inference time and only evaluate the pitch and onset time of a note. The work in [26; 27] estimates the pitches and onsets in a single architecture. One example for this is [28], which jointly estimates the different attributes of notes like the pitch, intensity, onset, and duration. It estimates the properties of notes concurrently using harmonic temporal structured clustering. The note-level transcription can also be achieved after the post-processing in frame-level transcription. First, the fundamental frequency is estimated concurrently, and post-processing is applied to estimate the musical notes in the second step. The methods used during the post-processing steps are median filtering, hidden Markov Models, and Neural networks [24; 29; 30]. The work in [24] uses a median filter by comparing the estimated pitch in nearby frames for temporal smoothing. The moving median filter is used with 0.25 s, with a hop size of 0.01 s. This post-processing method is reliable for connecting non-continuous pitch and can effectively delete the isolated one. Similarly, the work in [29] converted the output of the support vector machine to a posterior probability. The steps for pitch smoothing were performed for each note class by using the Viterbi search method on the transition matrix of 2 \(\times\) 2. The note onset and offset were finally gathered from both the posterior probability of the support vector machine and the training data. Note-level transcription can provide the highest level of detail, capturing individual notes' onset and offset along with pitch. It allows for an in-depth analysis of musical elements like melody, harmony, and rhythm. However, there are some disadvantages of note-level transcription. First, transcribing note by note can be time-consuming, which is not practical for longer pieces of music. Second, to make an accurate transcription, expert musicians are required for the annotation of onset, offset, and pitch. Third, it is challenging and difficult to capture the musical nuances like vibrato and articulation in note level-transcription because it is highly subjective and depends on the interpretation and style of the performer.
## 3 Implementation
### Storage Layer
#### 3.1.1 Metadata Database  
This metadata database contains the attributes of the files that are stored in the cluster. Basic metadata can be categorized into three main types: directory entry, inode, and chunk tag, for example, the file name, the file length, the creation date, the change date, the access date, the permission information, etc. In addition to the basic attributes described above, F3 BFS also supports the setting of extended attributes (xattr) for files, for example, the file type, species or tissue type, sample time, etc.  
A cluster's performance depends heavily on the performance of the metadata database since the file metadata is frequently accessed. In order to speed up the metadata database read/write operations and maintain the reliability at the same time, F3 BFS use RocksDB ([[http://rocksdb.org/](http://rocksdb.org/)]([http://rocksdb.org/](http://rocksdb.org/)) (accessed on 21 September 2022)) as its metadata database back-end engine. Known for its high performance and optimization for fast storage (such as NAND storage), RocksDB is a popular NOSQL database that is widely used to store key-value pairs.  
Figure 1: Three layer architecture overview of F3 BFS.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Security Analysis  
The Arnold scrambling algorithm based on the spatial domain achieves the purpose of encrypting images by changing the spatial position of pixel points and destroying the spatial position correlation of adjacent pixel points. As can be seen from Figure 10, after the algorithm scrambling, the information of the original image is completely hidden, and a good scrambling effect is visually achieved. The key space size of the Arnold permutations algorithm is related to the parameters a and b and the number of permutations. The selection of parameters a and b and the number of permutations has relatively few restrictions, and selecting appropriate values can ensure security of the image encryption. In certain cases, adjustments can be made as appropriate.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 4 Numerical Results and Analyses
### Outage Probability (OP) Performance
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Ref.** & **Relay Schemes** & **Channel Model \& Antenna Type** & **No. of Users** & **No. of Relays** & **OP** \\ \hline Proposed & AF & GBSM, multiple antenna TX, & 2 & 1 & \(10^{-1.25}\) \\ model & DF & single RS and UE & & & \(10^{-2.50}\) \\ \hline
[15] & CpF & & & & \\ AF & CF & CBSM, single antenna TX, RS & 2 & 1 & \(10^{-3.40}\) \\  & DF & and UE & & & \(10^{-2.00}\) \\ \hline
[78] & AF & CBSM, multiple antenna TX and & \(M\) & 1 & \(10^{-5.38}\) \\  & RS, single antenna UE & & & & \\ \hline
[117] & AF & CBSM, single antenna TX, RS & 2 & \(M\) & \(10^{-1.00}\) \\  & DF & and UE & & & \(10^{-0.80}\) \\ \hline
[118] & DF & CBSM, multiple antenna TX, single antenna RS and UE & & & \\ \hline
[119] & DF & CBSM, multiple antenna TX and UE, single antenna RS & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Outage Probability with prior works at SNR of 15 dB. Figure 9: Outage probability comparison of two users using AF and DF schemes of the proposed channel models with **(a)** CA and **(b)** URA.
## 6 Conclusions  
A novel method called TRL using space decomposition has been proposed in this paper, which is designed for PV systems to obtain the maximum attainable solar energy under PSC, whose contributions can be summarized as follows:  
1. Through space decomposition, TRL can efficiently learn the knowledge for MPPT with PSC in real time; thus a high-quality optimum can be obtained to ensure that the PV system produces more energy under various environmental conditions;
2. The knowledge transfer can effectively avoid a blind/random search and provide a beneficial guidance to TRL, which results in a fast convergence and a high convergence stability. Therefore, not only can the output power be maximized for the PV system under various scenarios, but the power fluctuation can also be significantly reduced as the weather condition varies;
3. Compared with the conventional INC and other typical meta-heuristic algorithms, the TRL-based MPPT algorithm can produce the largest amount of output energy in the presence of PSC and other time-varying atmospheric conditions, which can bring about considerable economic benefit for operation in the long term.  
**Author Contributions:** Preparation of the manuscript was performed by M.D., D.L., C.Y., S.L., Q.F., B.Y., and X.Z.  
**Acknowledgments:** The authors gratefully acknowledge the support of ADN Comprehensive Demonstration Project of Smart Grid Application Demonstration Area in Suzhou Industrial Park and Yunnan Provincial Basic Research Project--Youth Researcher Program (2018 FD036).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization
## 2 Analysis of Non-Isolated Interleaved DC-DC Converter
### Losses of Converter  
Losses of one phase consist of IGBT losses (conduction loss and switching loss), inductor losses (conduction loss and core loss) and air-gap loss. The loss caused by the skin effect can be neglected.
## 4 The Physical Layer Security Performance of the Relay Transmission Model
### Influence of Relay Location for \(P(C_{S}>0)\)
#### 4.2.2 Experimental Results with Various Relay Location  
In this experiment, the transmit power of S and R both are fixed at \(-39\) dBm. S, D and E are located at fixed two-dimensional coordinates (0, 0), (3.6 m, 0) and (0, 3.6 m), respectively. The coordinate of R changes from (1.2 m, 0), (1.8 m, 0) to (2.4 m, 0). The experimental results table are shown in Tables 4 and 5 respectively. The graphic experimental result is shown in Figure 9.  
It can be seen that the experimental \(P(C_{S}>0)\) value in Figure 9 is different from the theoretical curves in Figure 8. The optimal location of the relay is a nearer the destina  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relay Location** & **Left (1.2 m, 0)** & **Middle (1.8 m, 0)** & **Right (2.4 m, 0)** \\ \hline \(P_{D}\) & 327.08 & 237.84 & 383.27 \\ \(\gamma_{M}\) & 2.081 & 2.3814 & 1.9336 \\ \(P_{E}\) & 625.71 & 840.78 & 1174.15 \\ \(\gamma_{W}\) & 1.488 & 1.2289 & 0.9472 \\ \(P(C_{S}>0)\) & 0.5831 & 0.6596 & 0.6712 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results with various relay locations of cooperative behavior.  
Figure 8: Simulation results: \(P(C_{S}>0)\) versus relay location under various behaviors.  
ideal DF case, whereas in theoretical ideal DF analysis, the location is in the middle of the Source to destination link. That phenomenon is also appeared in selfish relay case. For malicious behavior, the near destination malicious relay deteriorates the main channel seriously, but it is not as serious as that in theoretical simulation.
# Low Temperature Combustion Modeling and Predictive Control of Marine Engines
## 3 Results
### Full Cycle Validation of the Nonlinear RCCI COM  
After parametrization of the MKIM, Wiebe and BDM, the RCCI COM was simulated to further validate its coupled submodels and the remaining combustion indicators, i.e., \(\text{IMEP}_{\text{gross}}\) against the UVATZ model for full engine cycle, which was presented earlier in Figure 1.  
Despite the simplicity of the single Wiebe, the COM achieved a feasible accuracy for CHR, HRR, in-cylinder pressure and temperature. Figure 6 presents normalized values of the estimated aforementioned variables for a mid load operating point.  
The overall estimation accuracy of the nonlinear RCCI COM after all of the submodels have been coupled together is presented in Figure 7 and Table 5, respectively. The mean absolute percentage error (MAPE) value for \(\theta_{\text{SOC}}\) and \(\theta_{50}\) is quite low as one could have anticipated from the calibration and validation results. The MAPE of \(\theta_{\text{BD}}\) is slightly higher due to the high calibration error. While the MAPE of \(\text{IMEP}_{\text{gross}}\) falls within the \(\pm 5\%\) range, its relatively higher value compared to other combustion indicators can be attributed to the simplification in estimating \(Q_{c}\) and \(\frac{dQ_{c}}{dQ}\). As shown in Figure 6, \(Q_{c}\) estimated by COM has both a lower trendwise and absolute accuracy after \(\theta_{50}\). That on the other hand affects the estimation of in-cylinder pressure and subsequently the estimation of \(\text{IMEP}_{\text{gross}}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\mathbf{\theta_{\text{SOC}}}\) & \(\mathbf{\theta_{\text{BD}}}\) & \(\mathbf{\theta_{50}}\) & \(\text{IMEP}_{\text{gross}}\) \\ \hline
**MAPE [\%]** & 0.19 & 0.98 & 0.71 & 3.87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall validation results of RCCI COM.
# Optimized Sonar Broadband Focused Beamforming Algorithm
## 2 Broadband Focused Beamforming Algorithm
### Principle of an Optimized Broadband Focused Beamforming Algorithm
The most obvious benefit of the proposed method is the use of the advantages of the RCB algorithm and the SOCP method; at the same time, the optimal focusing frequency problem is considered.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 2 Materials and Methods
### Evaluation Metrics and Hyperparameter Optimization Scheme  
To conduct an effective evaluation of the model, the mean squared error (MSE) and coefficient of determination (\(\mathbb{R}^{2}\)) are adopted as evaluation indicators. The formula for the MSE is as follows:  
\[MSE=\frac{1}{n}\Sigma_{i=1}^{n}(g_{i}-y_{i})^{2} \tag{6}\]  
Figure 6: BP network structure.  
Figure 7: Pi-GRU model structure.  
The formula for R\({}^{2}\) is as follows:  
\[R^{2}=1-\frac{\Sigma(y_{i}-\hat{y}_{i})^{2}}{\Sigma(y_{i}-\overline{y})^{2}} \tag{7}\]  
where n represents the sample quantity, \(y_{i}\) is the true value of the i-th sample, \(\hat{y}_{i}\) is the predicted value of the i-th sample, and \(\overline{y}\) represents the average of all actual values \(y_{i}\). The closer R\({}^{2}\) is to 1, the better the fit of the model is. Optimizing the hyperparameters of a model is crucial for achieving its best performance [23].  
An orthogonal experiment was conducted on the optimal combination of model hyperparameters in this paper. An orthogonal experiment is an efficient experimental design method that can study the influences of multiple factors on test indicators with fewer experimental trials. The L9 (3\({}^{4}\)) orthogonal table design is shown in Table 1. We trained and tested the model under various hyperparameter combinations and compared the resulting MSEs to select the hyperparameter set with the smallest MSE.
## 1 Introduction
[11] proposed an integrated deep learning model for identifying glass defects, which combined a sparse coding classifier with deep convolutional neural networks to improve the model's recognition accuracy to meet engineering applications. [PERSON] et al. [12] established connections between polymer data factors (feature space representation, scope of training data, and learning algorithms) for predicting the glass transition temperature of polymers, yielding promising results. [PERSON] et al. [13] investigated the long-term durability of glass fiber-reinforced polymers in highly alkaline environments, developing a universal, accurate, and optimized tensile strength retention prediction model from a machine learning perspective. Ultimately, they found that machine learning offers practical benefits for long-term durability studies of glass fiber-reinforced polymers. Therefore, machine learning has been proven to be an effective method for identifying and classifying types of glass products. However, two issues still hinder the feasibility of identifying different types of glass. First, the weathering process of glass could change in the internal elemental composition, causing a misjudgment in the classification result [14; 15]. Thus, accurately predicting the chemical composition of glass before weathering is a prerequisite for achieving precise classification. Moreover, although several studies on the classification of glass types have been reported, for example, [PERSON] et al. [16] used one-way analysis of variance to sub-classify glass artifacts. [PERSON] et al. [17] employed a multivariate linear regression model for classifying unknown glass products. [PERSON] et al. [18] utilized a support vector machine approach with the sticky slime mold algorithm strategy for classifying glass products. Further investigations are still needed to construct new classification methods and compare the capability of different machine learning algorithms to improve accuracy. Previous studies have reported that neural networks and random forests algorithms exhibit superior estimation and classification performances [19; 20]. Therefore, to address the aforementioned issues, a set of ancient glass products data with high-potassium glass and lead-barium glass was sampled and the chi-square test, binary logistic regression, neural network, and random forest algorithms were employed. The objectives of this study were to (1) investigate the correlation between the degree of surface weathering of glass artifacts and the glass type, emblazonry, and color; (2) establish a model for predicting the chemical composition of glass prior to weathering based on the machine learning algorithms; and (3) determine the optimal chemical composition for glass classification and achieve an accurate classification of the types of glass products and recognize the effects of weathering on them.
# Lightweight Design and Evaluation of Square Reflector
## 2 Square Reflector Lightweight Design
### Design of Mirror Support Point Position
The simulation results are shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline
**Triangular rib structure** & **Weight: 14.1 kg** & **Fundamental frequency: 1287.5 Hz** \\ \hline
**Square rib structure** & **Weight: 14.5 kg** & **Fundamental frequency: 1235.6 Hz** \\ \hline
**Hexagonal rib structure** & **Weight: 15.7 kg** & **Fundamental frequency: 1248.8 Hz** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of lightweight structure of sandwich. (3) Rib optimization design  
After it is determined that the triangular rib structure is used for the sandwich structure, and when the thickness and length of the mirror are also determined, the rib structure of the mirror with an aperture larger than \(\Phi\)350 mm usually adopts an equal-width design. In this paper, through comparative analysis, the rib width of the reflector is optimized. In order to further reduce the weight of the reflector, grouping is carried out according to the force-bearing path of the reflector, and a gradual reinforcing rib structure is proposed. That is, the length and thickness remain unchanged, and only the width of the reinforcing rib is changed, which is the width of the reinforcing rib of the supporting part of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Triangular rib structure** & **Maximum displacement** & **Maximum stress change:** \\  & **1.2 \(\times\) 10\({}^{-4}\) mm** & **0.09 Mpa** \\ \hline \hline
**Square rib structure** & **Maximum displacement** & **Maximum stress change:** \\  & **1.8 \(\times\) 10\({}^{-4}\) mm** & **0.16 Mpa** \\ \hline \hline
**Hexagonal rib structure** & **Maximum displacement** & **Maximum stress change:** \\  & **1.9 \(\times\) 10\({}^{-4}\) mm** & **0.12 Mpa** \\ \hline \hline
**Zerodur** & **Maximum displacement** & **Maximum stress change:** \\  & **2.3 \(\times\) 10\({}^{-4}\) mm** & **0.22 Mpa** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stress simulation data. reflector. It needs to be wider, and the parts with weak support can be narrower, as shown in Figure 4.  
Through the simulation, the change in the relationship between the weight of the reflector and the width of the rib is shown in Table 4, and the changing trend of the weight is simulated at the same time. It can be seen from the simulation results that when the width of the ribs changes within the range of 2.5 mm to 4 mm, the weight of the mirror does not increase much, but the mode becomes larger, indicating that the rigidity of the mirror becomes better.
## 5 Proposed Method
### Method of Randomly Selecting Relay Nodes  
Similar to the low-energy adaptive clustering method used in sensor networks [45; 46; 47; 48], the idea of this method is to avoid biasing the relay nodes toward some regions or some types of nodes. We propose a method to randomly select relay nodes to activate the relay function (hereafter, \"randomly selecting\"). This method controls the number of relay nodes by setting a \"relay function activation cut-off value \((RCV)\)\" as the percentage of relay nodes among all nodes in the network.  
1. When a node \(i\) is activated, a value from 1 to 100 is randomly assigned as the cut-off value \(C_{i}\).
2. The relay function is operated for the node whose cut-off value \(C_{i}\) is less than the \"relay function activation cut-off value \((RCV)\)\". The node \(i\) that satisfies the conditions of Equation (2) is selected as the relay node. \[C_{i}<RCV\] (2)