esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbiological and Sensory Quality of Artisanal Sour Cream
## 3 Results and Discussion
### Sensory Analysis of Sour Cream Samples
#### 3.3.4 Texture Properties  
The results shown in Figure 3 indicate considerable differences between the samples in the sensory evaluation of the cream texture.  
Firmness scores varied from 0 to 5, while viscosity ranged from 2 to 8, averaging between 5 and 6. Adhesiveness was moderate across samples, with ratings between 2 and 3. Chalkiness, potentially caused by the addition of milk powder or whey-based ingredients, varied from 0 to 4. Creaminess ranged from 4 to 8, while fizziness, which is undesirable in cream, was rated 0 to 4. This fizziness may result from gas production by lactic acid bacteria or yeasts, indicating possible microbial contamination [2]. Higher milk fat and dry matter contents were associated with better creaminess, viscosity, and firmness, contributing to superior texture scores of sour cream samples in our study. This is in accordance with the results by [PERSON] et al. [36], who noted that creams with higher fat content typically exhibit improved viscosity and firmness. Moreover, the technological process, particularly homogenisation, significantly affects mouthfeel. Homogenisation improves the uniformity and distribution of fat globules, enhancing creaminess and reducing separation. The perception of creaminess, or mouth-coating richness, is closely linked to milk fat flavour, which is primarily evaluated via olfaction. In low-fat and fat-free sour cream products, this perception becomes more challenging. In order to achieve a creamy perception with low-fat and fat-free sour cream, it is crucial to take the milk fat flavour into account. For these products, the evaluation of texture is more important than the sense of smell, although the perception of creaminess can be improved by multisensory approach [38].  
Figure 3: The texture profile of sour cream samples.
## 3 Decomposition and Assignment of Observation Missions
### Motion Characteristic of Marine Vehicles  
The characteristics, including communication capability, vehicle endurance, and propulsion system are different for USVs, AUVs, and AUGs. USVs having communications capabilities are close to land robot vehicles can connect to the onshore center in real time with high speed. The endurance of USVs depends on the propulsion systems and energy supply. AUVs communicate with each other with acoustic signals, or cooperate in a pre-planned form. AUVs have a relatively low endurance but good controllability and maneuverability. As a new kind of AUV vehicle, AUGs can take advantage of the hydrodynamic force (lift force) from the main wing and net buoyancy adjustment mechanism to glide up and down, so AUGs have long endurance, but low velocity and maneuverability. AUGs connect with the control center through a satellite when it floats to the sea surface. We select the shore-based centralized control center in planning and optimizing marine vehicles' trajectory, and transmit the control commands to the vehicles through Iridium or communications transmitters. With the analysis of the marine vehicles' motion property, three kinds of typical observing missions are defined as follows (Figure 2):  
1. USV surface observation: These vehicles are suitable for high-precision observations on the sea surface. USVs can be applied for rapid response, emergency management. For example, if satellite communication is lost between a USV and an onshore server, the USV can temporarily store sample data and transfer the information of AUVs and AUGs.  
* AUV hierarchical observations: These vehicles are suitable for high-precision observation of different water layers under the sea surface, thanks to their good controllability and maneuverability. AUVs are also preferred for regional observations such as eddies or emergency observations, extreme weather conditions, storm surges, _etc_.
* AUG coverage sampling and vertical profile observations: AUGs can be utilized for vertical profile observation due to their vertical gliding up and down motion. On the other side, AUGs can glide for months with low power consumption. Therefore AUGs are suitable for long duration observations.  
It is worth mentioning that vehicle selection and observation mission matching cannot be separated absolutely. With the cooperation of different kinds of vehicles, a variety of observation capabilities can be derived. Considering the endurance of AUGs, we focus on AUG coverage sampling and ocean feature tracking observations, and take USVs and AUVs as additional observing vehicles.
## 4 Experimental Results
### Case Three  
In the third experimental study, we aimed for the moving table to move from the starting coordinates \(x=0,\ y=0,\ z=15\), and \(\theta=0^{o}\) to the reference coordinates \(x=0,\ y=15\) cm, \(z=15\) cm, and \(\theta=0^{o}\) in 1.5 s. In other words, the movable table was intended to move 15 cm to the right only on the y-axis at a height of \(z=15\) cm without any orientation. In order for this reference to be realized by the system, according to the kinematic equations, the first and third motors must turn to the right at an angular position  
Figure 10: (**a**) Position change of the motors for case two. (**b**) Position change of the motors for case two.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case 2** & **Settling Time (s)** & **MSE** & **ISE** \\ \hline Motor 1 SMC & 0.784 & 0.0017 & \(2.92\times 10^{-6}\) \\ Motor 3 SMC & 0.783 & 0.0018 & \(3.24\times 10^{-6}\) \\ Motor 1 PID & 0.829 & 0.0057 & \(3.25\times 10^{-5}\) \\ Motor 3 PID & 0.816 & 0.0049 & \(2.45\times 10^{-5}\) \\ Motor 2 SMC & 0.65 & 0.0012 & \(1.46\times 10^{-6}\) \\ Motor 4 SMC & 0.648 & 0.0011 & \(1.26\times 10^{-6}\) \\ Motor 2 PID & 0.697 & 0.0044 & \(1.93\times 10^{-5}\) \\ Motor 4 PID & 0.683 & 0.0038 & \(1.46\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Outcomes of the case two applications.  
of 11.45 radians, and the second and fourth motors must turn to the right at an angular position of 23.11 radians. According to the experimental results, the angular position changes of the motors are presented in Figure 11a,b. A similar situation was obtained in this experiment, and the obtained values are presented in Table 4. In particular, the MSE and ISE values were lower in the sliding mode controller, and the targeted reference values were reached in a shorter time, resulting in lower settling times.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case 3** & **Settling Time (s)** & **MSE** & **ISE** \\ \hline Motor 1 SMC & 0.471 & 6.30 \(\times\) 10\({}^{-4}\) & 3.96\(\times\) 10\({}^{-7}\) \\ Motor 3 SMC & 0.468 & 5.59 \(\times\) 10\({}^{-4}\) & 3.13\(\times\) 10\({}^{-7}\) \\ Motor 1 PID & 0.532 & 0.0034 & 1.16 \(\times\) 10\({}^{-5}\) \\ Motor 3 PID & 0.518 & 0.0026 & 6.87 \(\times\) 10\({}^{-6}\) \\ Motor 2 SMC & 0.95 & 0.0039 & 1.49 \(\times\) 10\({}^{-5}\) \\ Motor 4 SMC & 0.946 & 0.0035 & 1.20 \(\times\) 10\({}^{-5}\) \\ Motor 2 PID & 0.959 & 0.0114 & 1.29 \(\times\) 10\({}^{-4}\) \\ Motor 4 PID & 0.951 & 0.0095 & 9.07 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Outcomes of the case three applications.  
Figure 11: (**a**) Position change of the motors for case three. (**b**) Position change of the motors for case three.
## 5 Experimental Results and Analysis
### Model Optimization Comparison  
To improve the performance of the Linge network model we proposed, this article adjusted the Linge network model's relevant parameters in training the prediction model using the \"Acute Ischemic Stroke MRI\" data set published on Kaggle. The experimental comparison results before and after adjusting the Linge network model parameters are shown in Figure 8.  
In optimizing model parameters in this article, we mainly adjusted the learning rate, batch size, epochs, activation function, optimization algorithm, and Dropout ratio and conducted related ablation experiments. When conducting ablation experiments, our evaluation metrics are accuracy, precision, recall, specificity, F1 score, and AUC value. To obtain the optimal step size updated in each iteration, we conducted comparative experiments on learning rate and finally set the learning rate to 0.0001. To obtain the optimal number of samples used to update parameters in each iteration, we conducted comparative experiments on the batch size and set the batch size to 16. During the model training process, we found that the LOSS of the model began to converge at epochs 6300. In the comparative experiment of activation functions, we found that when using the SiLU activation function, the evaluation indicators of the model are relatively the highest. In the comparative experiment of optimization algorithms, we found that when using the Adam optimizer, the evaluation indicators of the model are relatively the highest. In the Dropout proportion comparison experiment, we found that when the random deactivation is set to 0.78, the evaluation indicators of the model are relatively the highest. Figure 8 shows that various evaluation indicators of the prediction model have improved to a certain extent after adjusting relevant parameters.  
This section describes in detail the experimental process of our proposed distributed intelligent edge computing technology for disease risk prediction from sample data, evaluation indicators, prediction results, model performance indicators, and model optimization.
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 4 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **Rank** & **Reservoir Name** & **Province** & **City** & **Reservoir Area (km\({}^{2}\))** & **Capacity** & **Power** & **GHC** & **NPV** & **Pay** \\  & & & & & & **Area (km\({}^{2}\))** & **(MW)** & **Production** & **Reduction** & **(10,000)** & **Back** \\ \hline  & 1 & Yedang & Chungcheongnam-do & Yesan & 8.83 & 75.85 & 107.58 & 47,494.86 & 17,513.99 & 4.42 \\
**Floating PV** & 2 & Seomin & Jedolham-do & Ismail & 10.11 & 75.25 & 102.39 & 45,205.39 & 16,191.54 & 4.70 \\
**installed in** & 3 & Najuo & Jedolham-do & Naju & 7.19 & 54.94 & 80.51 & 35,544.71 & 13,392.70 & 4.21 \\
**proportion to the** & 4 & Topjeong & Chungcheongnam-do & Nonsan & 5.82 & 47.20 & 67.77 & 29,921.57 & 11,125.65 & 4.34 \\
**area of the** & 5 & Tokyo & Gangwowo-d & Chewon & 3.92 & 30.28 & 41.21 & 18,195.17 & 6518.30 & 4.69 \\
**reservoir** & 6 & Miho & Chungcheongbuk-do & Inichen & 4.42 & 26.56 & 36.71 & 16,206.52 & 5896.99 & 4.59 \\
**(High power** & 7 & Gosam & Gyeong-do & Anseong & 3.56 & 24.60 & 34.20 & 15,101.31 & 54938.58 & 4.55 \\
**production)** & 8 & Bujapp & Jedolham-do & Youngwang & 3.20 & 21.44 & 31.70 & 13,996.68 & 5304.63 & 4.15 \\
**production)** & 9 & Yidong & Gyeonggi-do & Yongin & 2.87 & 22.00 & 30.31 & 13,382.42 & 4836.23 & 4.61 \\  & 10 & Dekolong & Gyeongonggi-do & Gyeonggi & 3.11 & 20.48 & 29.08 & 12,836.74 & 4736.96 & 4.41 \\ \hline  & 1 & Naesan & Jedolham-do & Haeman & 0.11 & 0.10 & 0.16 & 69.08 & 27.08 & 3.82 \\
2 & 2 & Janagan & Jedolam-do & Shuan & 0.14 & 0.10 & 0.16 & 69.04 & 27.06 & 3.82 \\
3 & Gyeong & Jedolham-do & Haeman & 0.08 & 0.10 & 0.16 & 68.86 & 26.95 & 3.84 \\
**Floating PV with** & 4 & Cheongsu & Jedolham-do & Muan & 0.15 & 0.10 & 0.16 & 68.54 & 26.75 & 3.86 \\
**100 kW capacity** & 5 & Illo & Jedolham-do & Muan & 0.75 & 0.10 & 0.15 & 68.39 & 26.66 & 3.88 \\
**(High efficiency)** & 6 & Gwangdae & Joelham-do & Shuan & 0.41 & 0.10 & 0.15 & 68.21 & 26.55 & 3.89 \\
7 & 7 & Sniede & Jedolam-do & Yeongam & 0.13 & 0.10 & 0.15 & 68.20 & 26.54 & 3.89 \\
8 & 8 & Doyoyong & Jedolham-do & Jindo & 0.11 & 0.10 & 0.15 & 68.09 & 26.48 & 3.90 \\
9 & Sungdong & Jedolham-do & Muan & 0.17 & 0.10 & 0.15 & 68.02 & 26.43 & 3.91 \\
10 & Sungyang & Jedolham-do & Yeongam & 0.40 & 0.10 & 0.15 & 67.77 & 26.28 & 3.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The analysis results of the top 10 reservoirs with the highest power production and with the highest efficiency.
## 3 Theoretical Framework
### Implementation Contexts and Demographic Details of CILS  
The CILS has been applied across various educational levels and language programs, demonstrating its adaptability and effectiveness in diverse settings. Specifically, the CILS has been implemented in elementary, secondary, and tertiary education sectors, facilitating language learning in English, Spanish, Mandarin, and French, among others. This widespread application underscores the system's utility in enhancing linguistic and cultural competence across different educational stages and linguistic backgrounds.  
In terms of geographic deployment, the CILS has been actively used in North America, Europe, and Asia, where the educational policies and objectives vary significantly. For instance, in Asia, particularly in countries like China and Japan, there is a strong emphasis on acquiring English proficiency at all levels of education due to its global significance. Similarly, in Europe, multilingualism is promoted, with students often learning two or more foreign languages as part of their formal education curriculum.  
Each implementation of the CILS has been tailored to the specific linguistic and cultural needs of the population it serves. Demographic details of the learners involved in CILS studies include a broad age range from young children in primary schools to adults in university settings. The educational backgrounds vary, with some participants beginning their language learning journey and others enhancing their advanced skills.  
Statistical figures related to the use of CILS reflect comprehensive data involving these diverse demographics. Every figure provided in this study includes detailed annotations about the population size, age distribution, educational level, and language proficiency of the participants. This detailed demographic information ensures that the findings presented are accurately contextualized and can be reliably interpreted with regard to their scope and applicability.  
By documenting the specific educational cycles, languages involved, and the demographic makeup of the learner populations, this study not only enhances its transparency but also provides valuable insights into the factors that influence the effectiveness of AI-driven language learning systems like the CILS.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 4 Experiments and Results
### Verification Experiment under Simulated Smoke and Water Mist Environment
#### 4.3.2 Verification Experiment on Attitude Positioning Accuracy of Cross Laser-Pattern  
The experimental schematic diagram is shown in Figure 25a, and the experiment procedure is shown Figure 25b:  
In this experiment, the image acquisition system was placed on an electrical turntable with an accuracy of \(\pm\)4\({}^{\prime\prime}\). The cross laser-pattern was fixed as the measurement datum. The image acquisition system was rotated by the electrical turntable along the projection direction of the cross laser. With the proposed method, the measured rolling angles were obtained under the smoke and water mist environment, and the reference rolling angle values were obtained from the electrical turntable controlling software.  
The repeatability experiment error curves under smoke and water mist environment are shown in Figure 26:  
Based on the repeatability error curve under smoke and water mist environment, the error average value was obtained, and the error model was established by using linear fitting method. The related models and results are shown in Figure 27:  
Figure 26: Error curves of repeatability experiment under smoke and water mist environment.  
Figure 25: (**a**) Experimental schematic diagram. (**b**) Experiment procedure picture under smoke and water mist environment.  
The related linear error model was used to determine error compensation; the compensated result is shown in Figure 28:  
According to the result, the system angle measurement accuracy, under smoke and water mist environment, is better than \(\pm 15\)\" and the repeatability accuracy is better than \(\pm 15\)\".
## 4 Conclusions  
The present study concluded that: (i) That the biosorbents YP, OPB, CH, CP, CCR are potentially good for adsorbing Cd\({}^{2+}\), at the optimal experimental conditions found: temperature of 55 \({}^{\circ}\)C, particle size 0.5 mm and quantity of adsorbent 0.03 g, and that the variable with the greatest influence on the process was the dose of adsorbent, achieving adsorption capacities between 76 and 197 mg/g. (ii) The SEM-EDS analysis suggests that the Cd\({}^{2+}\) adsorption mechanisms were ion exchange and complexation with the -COOH and -OH groups present in the structure of the cellulose, lignin, pectin and hemicellulose molecules of the biomasses studied. (iii) The adsorption kinetics of Cd\({}^{2+}\) was fast in the initial minutes due to the availability of active centers, reaching equilibrium at 60 min, being the PSO and Elovich models the ones that best fit the experimental data, suggesting that the process occurs by chemical reaction. (iv) The adsorption isotherms using YP and OPB were adjusted using the Langmuir model, indicating that the metal is adsorbed on the metal surface in a monolayer; while the balance of adsorption in CH, CCR and CP is adjusted by means of the Freundlich model, establishing that the process occurs by chemical reaction in multilayers. The Dubinin-Radushkevich model suggests that the Cd\({}^{2+}\) ion adsorption process is controlled by the fission absorption mechanism. (v) From the calculation of the thermodynamic parameters it was shown that the process is endothermic and not spontaneous using YP and CP, and that it is exothermic, spontaneous and irreversible when OPB, CH and CCR are used. Based on these findings, the biomasses studies reached promising adsorption capacities in the removal of cadmium ions; however, the materials from oil palm bagasse and cocoa husks received showed remarkable performance compared to the peels and corn residues.  
[PERSON], [PERSON] and [PERSON] conceived and designed the paper and wrote the Introduction and Materials and Methods. [PERSON] and [PERSON] wrote the Results and prepared figures and tables. Discussions and Conclusions were the collective work of all authors. The writing-review and editing was performed by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available in the open literature.  
**Acknowledgments:** The authors express their gratitude to Universidad de Cartagena for providing equipment and reagents to conduct this research.
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 4 Results
### Performance of 1D-CNN on Structured Data  
The previous highest accuracy on the TCCPD was 82.94% [56], achieved with an XGboost classifier having min-max scaler for pre-processing of the 28 features. This result is reported without using cross validation. Looking at Table 3, our models gained results less than that mentioned in the above point. However, with M-1D-CNN-1-28\({}^{*}\), we achieved 82.64% accuracy and a decent precision of 71.11%. Our highest accuracy with traditional ML classifiers is 79.93% with precision of 66.16% using logistic regression on all the 28 features. Random forest is the classifier which gave 71.72% of precision with 77.73% accuracy. Table 4 shows the results we achieved with the proposed model on the credit-card fraud dataset. Our model (M-1D-CNN-1-31\({}^{*}\)) achieved best results in terms of accuracy. This data is highly imbalanced, and due to its complexity, the precision is slightly low in comparison to [57]. However, its overall accuracy is less than ours by a margin. Our model also shows better results compared to others reported in [58,59]. These two models (M-1D-CNN-1-28\({}^{*}\) and M-1D-CNN-1-31\({}^{*}\)) are used along with LRP to visualise the features of TCCPD and CCFDD datasets, respectively.  
Although 1D-CNN for structured data shows a good classification result, however a possible limitation where 1D-CNN may struggle will be the lack of translation invariant in the the situation where different features have similar value ranges and repeats/overlaps in the same order in various positions. A potential solution will be adding positional information in the form of unique delimiters that will make sure that features with the same value will not be mixed up with one another as the associated feature map will include the unique ID in some way considering that even if the features are shuffled the unique features will also be moved with respective real features.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 4 Materials and Methods
### Cloning and Sequencing of S. mussotii SmlS1 and SmlS2 Genes  
Total RNA was isolated from the leaves of _S. mussotii_ plants using an Eastep Super Total RNA Extraction Kit (Promega, Shanghai, China) according to the manufacturer's instructions and then eluted in RNase-free water. The quality, purity and concentration of the RNA was estimated spectrophotometrically using A260 and A280 measurements (NanoDrop, Thermo Scientific, Wilmington, MA, USA) followed by visualization on ethidium bromide-stained agarose gels. Quality was then assessed using agarose gel electrophoresis (Luyi, Beijing, China). Transcriptome sequencing was performed by Novogene Bioinformatics Technology (Beijing, China) and the methods for data processing, assembly and annotation were described in [25]. The transcriptome analysis of _S. mussotii_ will be published separately.  
Based on the whole transcriptome data of leaves in _S. mussotii_ before the flowering period, which were obtained using the BLAST similarity search program ([[http://www.ncbi.nlm.nih.gov/BLAST](http://www.ncbi.nlm.nih.gov/BLAST)]([http://www.ncbi.nlm.nih.gov/BLAST](http://www.ncbi.nlm.nih.gov/BLAST))), the entire coding sequences of SmlS1 and SmlS2 were amplified using the primer pairs as shown in Table 3. cDNA synthesis was performed using PrimeScript(tm) RT Master Mix (Dalian TaKaRa, Dalian, China) and oligo dT primers. For PCR, we used cDNA as the template with TransStart FastPfu Fly DNA Polymerase (TransGen Biotech, Beijing, China). Thermal cycling was performed using the following program: one cycle of initial denaturation for 5 min at 94 \({}^{\circ}\)C, 30 cycles consisting 30 s at 94 \({}^{\circ}\)C, 30 s at 60 \({}^{\circ}\)C, and 90 s at 72 \({}^{\circ}\)C, and a final step for 10 min at 72 \({}^{\circ}\)C. The obtained PCR productswere subcloned into the pEASY-Blunt Simple vector (TransGen Biotech, Beijing, China). The fragments were sequenced at the Beijing Genomics Institute (BCI), Shenzhen, China.