esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Different Pretreatments on the GABA Content of
## 3 Results and Discussion
### Effect of HPP and Ultrasonic Treatment on GABA and Antioxidants of Germinated Brown Rice
\begin{table}
\begin{tabular}{c c c c} \hline
**Germination Treatment** & **Total Polyphenols** & **Flavonoids** & **Scavenging DPPH** \\
**Treatment** & **(mg Gallic Acid Equivalent/g)** & **Equivalent/g)** & **Free Radicals (\%)** \\ \hline Ungermination & 0.91 \(\pm\) 0.01 \({}^{\text{c}}\) & 3.68 \(\pm\) 0.00 \({}^{\text{c}}\) & 87.01 \(\pm\) 0.11 \({}^{\text{d}}\) \\ Water & 0.84 \(\pm\) 0.01 \({}^{\text{d}}\) & 4.41 \(\pm\) 0.00 \({}^{\text{ab}}\) & 84.61 \(\pm\) 2.61 \({}^{\text{e}}\) \\ \hline \multicolumn{3}{c}{Soaking/HPP (100 MPa, 10 min)} \\ Water & 0.90 \(\pm\) 0.02 \({}^{\text{c}}\) & 3.92 \(\pm\) 0.42 \({}^{\text{c}}\) & 86.62 \(\pm\) 1.23 \({}^{\text{d}}\) \\
0.1\% CaCl\({}_{2}\) & 0.94 \(\pm\) 0.01 \({}^{\text{c}}\) & 5.15 \(\pm\) 1.27 \({}^{\text{a}}\) & 89.58 \(\pm\) 0.71 \({}^{\text{c}}\) \\
0.1\% Glu & 0.89 \(\pm\) 0.04 \({}^{\text{c}}\) & 3.68 \(\pm\) 0.00 \({}^{\text{c}}\) & 86.83 \(\pm\) 1.48 \({}^{\text{d}}\) \\
0.2\% CaCl\({}_{2}\) & 0.93 \(\pm\) 0.02 \({}^{\text{c}}\) & 3.92 \(\pm\) 0.74 \({}^{\text{c}}\) & 89.14 \(\pm\) 0.05 \({}^{\text{c}}\) \\
0.2\% Glu & 1.00 \(\pm\) 0.06 \({}^{\text{b}}\) & 4.90 \(\pm\) 0.42 \({}^{\text{ab}}\) & 86.33 \(\pm\) 0.44 \({}^{\text{d}}\) \\ \hline \multicolumn{3}{c}{Soaking/ultrasonic treatment (400 W, 20 min)} \\ Water & 1.06 \(\pm\) 0.02 \({}^{\text{a}}\) & 3.92 \(\pm\) 0.42 \({}^{\text{c}}\) & 92.50 \(\pm\) 0.51 \({}^{\text{ab}}\) \\
0.1\% CaCl\({}_{2}\) & 1.06 \(\pm\) 0.01 \({}^{\text{a}}\) & 3.94 \(\pm\) 0.00 \({}^{\text{c}}\) & 93.10 \(\pm\) 0.10 \({}^{\text{ab}}\) \\
0.1\% Glu & 1.08 \(\pm\) 0.03 \({}^{\text{a}}\) & 3.92 \(\pm\) 0.42 \({}^{\text{c}}\) & 93.21 \(\pm\) 0.09 \({}^{\text{a}}\) \\
0.2\% CaCl\({}_{2}\) & 1.08 \(\pm\) 0.02 \({}^{\text{a}}\) & 4.15 \(\pm\) 0.46 \({}^{\text{bc}}\) & 93.08 \(\pm\) 0.08 \({}^{\text{a}}\) \\
0.2\% Glu & 1.06 \(\pm\) 0.01 \({}^{\text{a}}\) & 4.41 \(\pm\) 0.00 \({}^{\text{ab}}\) & 93.00 \(\pm\) 0.21 \({}^{\text{a}}\) \\ \hline \multicolumn{3}{c}{Vitamin C} & 91.72 \(\pm\) 0.00 \({}^{\text{ab}}\) \\ BHA & & 91.06 \(\pm\) 0.00 \({}^{\text{b}}\) \\ \hline \end{tabular} Data were expressed as mean and error bars of \(\pm\) S.D. (\(n\) = 3). Means with different superscript letters in the same column are significantly different (\(p\) < 0.05). The concentrations of the sample, vitamin C, and BHA for the DPPH assay were 20 mg/mL.  
\end{table} 
Table 5: Effects of HPP and ultrasonic treatment on the content of antioxidants in germinated brown rice.
# Ion Current Rectification in Extra-Long Nanofunnels
## 1 Introduction
Exploiting the high surface-to-volume ratio of this structure, we succeeded in detecting, by fluorescence measurements, probe-target molecule interactions even for very dilute solutions (nearly 1 pg/mL). Moreover, we exploited the same geometry to implement ion current rectifiers [52] and studied how analytes accumulate inside the long funnel. The promising results we obtained in both fields (immunonsensing and rectifiers) motivated us to further investigate the electrokinetic phenomena occurring inside such long structures in order to further improve the combination of accumulation and detection in the same area of a device. Here, we study the distribution of ions inside an extra-long (nearly 90 \(\upmu\)m) funnel terminating with an 80 nm wide and nearly 1 \(\upmu\)m long nanochannel. Our investigation was carried out by numerically solving the Poisson-Nernst-Planck equations in order to characterize the electrokinetic behavior of the structure for both positive and negative voltages and to estimate the rectification ratio (RR) in the range (-10, 10 V).
# Cascaded Dual-Inpainting Network for Scene Text
## 3 Methodology
### Training and Inference Procedure  
A two-stage training strategy [29] is adopted in order to fully leverage the structural guidance of text components during the inpainting process. (1) Independent Pre-training: the SGM and the SRM are initially trained separately. (2) Joint Fine-tuning: after 10 epochsof independent pre-training, both modules are jointly optimized with a composite loss function by Equation (11):  
\[L=\gamma_{1}\cdot L_{SGM}+\gamma_{2}\cdot L_{SRM} \tag{11}\]  
where \(\gamma\)-terms are balancing weights. This phased approach ensures stable convergence: the SGM first learns preliminary text structure priors to provide structure guidance, while the SRM focuses on detailed refinement.  
To standardize inputs, all images are resized to 64 \(\times\) 256 pixels during inference, ensuring consistent spatial dimensions for batch processing. This preprocessing step eliminates scale variance and aligns with the model's trained resolution.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 5 Simulation Module Implementation
### Network Topology 1 Simulation Results
#### 5.3.2 Case 2  
Figure 5a presents the average throughput for the same \(3\times 3\) network, but with the maximum number of NICs equal to three, while Figure 5b presents the number of channel reassignments for the same case. In these simulations, we found that the ideal value for \(\lambda\) was 0.9 to balance between the average throughput and the number of channel reassignments. Clearly, we can say that the \(\lambda\) value is a very critical point to obtain the maximum benefits of the proposed adaptive channel allocation. Even with one different parameter, such as the maximum number of NICs per AP, an observable change of the results and the optimal \(\lambda\) occurred.
## 1 Introduction
For example, an authorized receiver can first extract one secret message vector, \(\mathbf{d_{sb}}\), from dual stego images and successfully restore the stego image. In addition, if a commissioned secret key is issued, a legitimate receiver with a higher rank of authority can first restore the stego image and then extract the secret messages, \(d_{wm}\) and \(\mathbf{d_{du}}\), from the restored image. To the best of our knowledge, our scheme is the first in the extant literature to offer a multi-hider approach of message embedding, extraction, and stego-image restoration. The experimental results confirm that our scheme enables the associate recipients to gain access to a specific secret message but still ensures that this secret message is only accessible under appropriate security constraints. In addition, our scheme can resist RS and PVD steganalytic attacks. The comparison confirms that the proposed method can achieve high embedding capacity, while maintaining good visual quality, outperforming our counterparts. We organize this paper as follows. Section 2 introduces the weighted modulus method. In Section 3, we present our proposed method. Section 4 presents our experiment results and analysis. Finally, we offer conclusions in Section 5.
###### Abstract  
Due to extreme environmental loadings and aging conditions, maintaining structural integrity for offshore structures is critical to their safety. Non-destructive testing of risers plays a key role in identifying defects developing within the structure, allowing repair in a timely manner to mitigate against failures which cause damage to the environment and pose a hazard to human operators. However, in order to be cost effective the inspection must be carried out in situ, and this poses significant safety risks if undertaken manually. Therefore, enabled by advancements in automation and communication technologies, efforts are being made to deploy inspection systems using robotic platforms. This paper proposes a distributed networked communication system to meet the control requirements of a precision rotary scanner for inspection of underwater structures aimed at providing a robotic inspection system for structural integrity in an offshore environment. The system is configured around local control units, a fieldbus network, and a supervisory control system accounting for the environment conditions to provide enhanced control of actuators for automated inspection of offshore structures.  
inspection systems; structural integrity; fieldbus; controlled area network; distributed control 2019
# Adaptive Parameters for LoRa-Based Networks Physical-Layer
###### Abstract
In order to answer this question, we propose and evaluate a novel technique, named _SlidingChange_, to support automated decision-making on frequency shifts for maximizing SNR, decreasing the Bit Error Rate (BER), and the number of frequency changes. In earlier work [14], the authors presented the _InstantChange_ method to improve SNR, BER, and frequency change parameters. However, the _InstantChange_ method only considers the values of the most recent SNR samples of LoRa messages in 433 MHz and 915 MHz frequencies for decision making. In this article, we extend our previous work with the proposal of a novel _SlidingChange_ algorithm considering an average of \(W_{n}-1\) (\(W_{n}\) representing the size of the sliding window to be used), previous measurements, and the current measurement to dampen punctual effects and impulsive noise that can happen in the transmission environment. We also evaluate the effectiveness of the _SlidingChange_ algorithm in LoRa networks to optimize the SNR considering the shifts and overhead in the decision-making process, opening an interesting research area complementary to Cognitive Radios in LoRa and LoRaWAN Physical-Layer optics. The main contributions of this article can be summarized as follows:  
* Verify the possibility of changing the configuration 'parameters' in LoRa on the fly to increase the network performance in terms of SNR and BER;
* Propose and validate a novel algorithm with adaptive parameters considering the history of configuration shifts, creating a smarter cognitive-radio for LoRa;
* Compare the state of the art with the proposal using a real dataset. The remainder of this article is organized as follows: Section 2 presents the related work. Section 3 describes the proposed SlidingChange mechanism to improve the quality of LoRa communication in a theoretical format. Section 4 illustrates the results of practical experiments based on the theory presented for the proposal of sliding windows. Finally, Section 5 presents the conclusion and future research directions.
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
###### Abstract  
With the recent evolution of deep learning, machine translation (MT) models and systems are being steadily improved. However, research on MT in low-resource languages such as Vietnamese and Korean is still very limited. In recent years, a state-of-the-art context-based embedding model introduced by Google, bidirectional encoder representations for transformers (BERT), has begun to appear in the neural MT (NMT) models in different ways to enhance the accuracy of MT systems. The BERT model for Vietnamese has been developed and significantly improved in natural language processing (NLP) tasks, such as part-of-speech (POS), named-entity recognition, dependency parsing, and natural language inference. Our research experimented with applying the Vietnamese BERT model to provide POS tagging and morphological analysis (MA) for Vietnamese sentences, and applying word-sense disambiguation (WSD) for Korean sentences in our Vietnamese-Korean bilingual corpus. In the Vietnamese-Korean NMT system, with contextual embedding, the BERT model for Vietnamese is concurrently connected to both encoder layers and decoder layers in the NMT model. Experimental results assessed through BLEU, METEOR, and TER metrics show that contextual embedding significantly improves the quality of Vietnamese-Korean NMT.  
Keywords:neural machine translation; part-of-speech; word-sense disambiguation; Korean-Vietnamese machine translation; bidirectional encoder representations from transformers +
Footnote †: 2021: 11, 11119. [[https://doi.org/10.3390/app11231119](https://doi.org/10.3390/app11231119)]([https://doi.org/10.3390/app11231119](https://doi.org/10.3390/app11231119))
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 3 Results  
To show the merits of this approach, some well-designed simulations were conducted using a PC with an Intel Core i5 3230m 2.6 GHz processor, 12 GB RAM, and a 64-bit Windows 7 Professional operating system. The following global parameters were used in these simulations: \(\varepsilon=3000\), \(\eta_{d}=2000\), \(\eta_{s}=3000\),000, \(\eta_{e}=2000\), \(\tau=0.3\) nautical mile (nm), \(\lambda=0.001\), \(R_{\infty}=0.5\) nm, \(d_{\textit{safe}}=1\) nm, \(\rho_{0}=3\)-\(5\) nm, \(maxturn=5^{\circ}\), \(D_{A}=0.3\) nm, \(T_{A}=6\) min, \(r_{k}=r_{g}=0.25\) nm, \(t_{c}=4\) min, \(K_{p}=1\), \(K_{d}=10\) and time step \(=15\) s. It should be noted that these parameters should be adjusted according to specific ship dynamics in real operations.  
The results are illustrated in multiple figures, representing snapshots of situations. A two-dimensional Cartesian coordinate system presents the distance in nautical miles (nm); the vertical axis in the positive direction shows North \(000^{\circ}\), and the horizontal axis in the positive direction is \(090^{\circ}\). The following symbols and color codes are applied:  
Figure 10: Flowchart of the PGHAPF method; WPT; checking range (CR); target ships (TSs); DCPA; time to the closest point of approach (TCPA); proportional derivative (PD).  
* The black dotted line with the red box as the starting point and the blue triangle as the endpoint is the resulting trajectory of the own ship, and the green line is the prior path of the own ship, which may be short but not necessarily safe.
* To illustrate the CA actions with dynamic TSs, the own ship heading at every time step is plotted with a blue dot, which makes it easy to find the course alteration points.
* The paths of multiple TSs are identified by corresponding TS numbers and different colors.
* The range (scope 0-6 nm) between the own ship and any TS at each time step is chosen to display in various colors.
* The paths of the own ship and all TSs are labeled at regular intervals of 50 time steps.
* The boundary contours of static obstacles are black solid lines, whose potential field is colored from red (high potential) to blue (low potential) and is drawn at an interval of 0.01.
# Determination and Risk Assessment of Flavor Components in Flavored Milk
## 3 Results and Discussion
### Composition and Content of Flavor Samples  
In flavor samples, 285 flavor components were identified and the basic information of flavor compounds is shown in Table S3. As shown in Figure 2a, the flavor samples mainly consisted of esters (32.17%), alcohols (11.19%), olefins (9.09%), aldehydes (8.39%), ketones (7.34%), aromatic compounds (4.20%), and pyrazines (3.85%). Various fruit flavors (68.2%) made up the majority of flavor samples, and studies showed that fruit volatile compounds were mostly composed of esters, alcohols, aldehydes, and ketones [32].  
Figure 1: Heat map analysis of Chinese residents’ preference in milk flavors.  
The detection rate was calculated by the ratio of flavor component detection times to the number of flavor samples. Compounds with a detection rate \(>\) 35% were as follows: methyl palmitate (90.91%) \(>\) ethyl butyrate (81.82%) \(=\) dipentene (81.82%) \(>\) ethyl laurate (77.27%) \(>\)\(\gamma\)-decalactone (72.73%) \(>\) isoamyl acetate (68.18%) \(=\) benzaldehyde (68.18%) \(>\) linabol (63.64%) \(=\) hexyl acetate (63.64%) \(>\) ethyl 2-methylbutyrate (54.55%) \(=\) peach aldehyde (54.55%) \(>\) ethyl1 phenylacetate (50.00%) \(=\) dodecanol (50.00%) \(>\) citrated acetone (45.45%) \(=\) nonanal (45.45%) \(>\) benzyl acetate (40.91%) \(=\)\(\alpha\)-terpineol (40.91%) \(>\) ethyl octaoat (36.36%) \(=\) leaf alcohol (36.36%) \(=\) lauryl alcohol (36.36%) \(=\) ethyl caprate (36.36%). Methyl palmitate was the most common compound in flavors and fragrance [33]. Alphonso mango flavor (26.14%), pineapple flavor (54.4%), and passion fruit flavor (24.67%) were mainly composed of allyl hexanoate. Strawberry concentrate flavor (18.02%), golden mango flavor (37.49%), peach flavor (24.77%), and coconut flavor (16.18%) were mainly made up of \(\gamma\)-decalactone. Although different flavor samples shared many volatile compounds, each flavor had a distinctive aroma depending upon the volatile mixture, concentration, and perception threshold of individual volatile compounds (Figure 2c).  
Figure 2: Visualized analysis of the volatile compounds in flavor samples: (**a**) content ratios in types of volatile compounds among all flavor samples; (**b**) bar plot diagram analysis of the compound types in seven flavor samples; (**c**) classification by PCA of the seven flavor samples; (**d**) cluster heat map analysis of the compound content of the different types.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Topology Management
#### 5.2.1 Scalability and Localization Management
The localization algorithm to be implemented and managed can either be centralized or distributed depending on the mapping information and level of accuracy provided by the algorithm. More recently a localization node selection algorithm based on the SDN and the Cramer-Rao Lower Bound (CRLB) metric has been investigated and proposed [100]. The algorithm makes use of the global network view that the SDN controller has while satisfying the need for energy conservation and the simulation results presented a significant improvement in the localization performance.
## 2 Beyond 100 Gb/S Era
### Why the Need for a Simplified System?  
The aforementioned 100 G Ethernet standard and the existing commercial products are based on either WDM or spatial multiplexing to realize the aggregate bit rate. However, there is a growing consensus that the realization of the anticipated 400 Gb/s and 1 Tb/s depends mainly on the employment of inexpensive, power-efficient, and low-footprint components in the achievement of 100 Gb/s on a single wavelength. The WDM approach in which lower rates such as 25 or 50 Gb/s are multiplexed in the wavelength is challenging due to the required large number of laser sources. For instance, the realization of 400 Gb/s and 1 Tb/s using 8 \(\times\) 50 Gb/s and 20 \(\times\) 50 Gb/s, respectively, based on the required amount of lasers, hinders the overall performance of the transceiver. Furthermore, the additional stringent requirements being imposed on the laser wavelength stability and the transceiver packaging both optically and thermally are more demanding. Similarly, regarding the spatial multiplexing approach in which multiple fiber lanes per cable are employed, there will be some challenges concerning packaging, as well as end-to-end interfacing between the parallel fiber lanes and the transceiver. To minimize the required amount of laser, it is advisable to employ (4 \(\times\) 100) Gb/s based on four colors for the realization of 400 Gb/s solutions. In this, each wavelength will be supporting 100 Gb/s, and all wavelengths can be launched on a coarse WDM (CWDM) grid (20 nm spacing) or LAN-WDM (4.5 nm spacing) [63].  
Furthermore, for the system scalability from 400 Gb/s to 1 Tb/s, ten lasers will be required to be packaged in either a CFP, CFP2, or CFP4 form factor, and this is relatively challenging. On a CWDM grid, it is impractical to fit ten lasers in the \(O\)-band. Consequently, an alternative solution is to employ a LAN-WDM grid. Another factor to be considered in this situation is that the development of uncooled transmitters will be very demanding as the lasers normally have a 0.1 nm/\({}^{\circ}\)C wavelength temperature coefficient. Therefore, a scheme that helps minimize the required number of colors while enhancing the bit rate per wavelength is a promising solution for attending to the system demand. The scheme can exploit additional DoFs to double the realizable bit rate per wavelength. One such viable solution is the polarization division multiplexing (PDM) schemes [63].
## 4 Case Study and Result
### Examples of Data Exploration with the Geovisual Analytical System
#### 4.2.2 Advanced Analysis on Temporal Patterns  
Maps are used to view the spatial variations whereas temporal variations can be detected more clearly from time series analysis (Figure 8). The global mean of surface air temperature (tsurf) of a single run is used as the example. In the line plot, the blue line represents the change of monthly values and the red line represents annual mean values. We find a flat slope on the trend of the annual mean values which is unsurprising given the model simulation configuration.
# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 1 Introduction
The random search mechanism from the whale optimization algorithm is introduced to improve the global search ability of the algorithm. The problem of local optimum is solved by Cauchy variation. The rest of this paper is organized as follows: In Section 2, this part summarizes the current research work on application scheduling and resource allocation. In Section 3, the system model is described, the task success rate model is formulated, and the TBID scheduling algorithm and the improved sparrow search algorithm are described. The performance of the satellite edge resource allocation method proposed in this paper is experimented with in Section 4. Finally, the related work is summarized in Section 5.
## 1 Introduction
[38] explained the inherent mechanism of this effect. Since traditional linear acoustic methods, which are based on wave energy dissipation, have limitations, there is a need for new methods or indices to analyze the nonlinear characteristics of the signal and address the phenomenon of energy saturation. Second-order acoustic nonlinearity has been identified and emphasized in the field of ultrasonic health monitoring [39; 40]. For example, [PERSON] et al. [41] compared the linear wave energy dissipation (WED) method with the nonlinear vibration-acoustic modulation (VAM) method and found that the VAM-based nonlinear method is more sensitive for monitoring loosening. In addition, new damage indices (DIs) have been developed. [PERSON] et al. introduced a new entropy-based DI using the multiscale multivariate sample entropy (MMSE) algorithm [42] and the multiscale permutation entropy algorithm [43]. By extracting nonlinear features from stress wave signals, [PERSON] et al. [44] demonstrated that the recursive entropy index could effectively mitigate energy saturation and fluctuation. This enhancement improved the performance of the piezoceramic-enabled active sensing method in early loosening monitoring. Due to the high design value of bridge prestress, monitoring the entire range of bridge prestress is undoubtedly a significant challenge. [PERSON] et al. [45; 46] utilized the piezoceramic active sensing method, which is based on time reversal and the wavelet packet energy method, to monitor the prestress in the anchorage connection of the bridge. Their study demonstrated a certain potential for monitoring small prestress values, but it still falls short of the design value for bridge prestress. In this paper, two proposals are suggested to solve the problem of monitoring bridge prestress. (1) The contact area of the contact interface does not increase under a certain preload force, which is influenced by the roughness of the contact interface. Therefore, a \"smart anchor washer\" is designed to increase the roughness of the contact interface and delay the saturation of signal energy. (2) Since the entropy method has shown potential in analyzing the underlying complexity of time series signals, a new entropy-based DI is developed to address the issue of signal energy saturation. The rest of the paper is organized as follows: Section 2 introduces the design of the \"smart anchor washer\" and explains the basic principles of the methodology. Section 3 describes the experimental setup and procedure. In Section 4, the energy of the received signal and the entropy-based DI are calculated and demonstrated to be effective. Finally, Section 5 concludes the paper.