esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Establishment and Verification of Mathematical Model
### Establishment of Mathematical Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{\(R^{2}\)} & \multicolumn{4}{c}{**ANOVA**} & \multicolumn{4}{c}{**Parameters Estimated by Function**} \\ \cline{3-8}  & & & **Degree of** & **Degree of** & **Regression** & **Constant** & **Factor 1** & **Factor 2** & **Factor 3** \\ \hline Linear & 0.984 & 181.650 & 1 & 3 & 0.001 & 92.512 & \(-83.500\) & \\ Logarithmic & 0.987 & 219.342 & 1 & 3 & 0.001 & 29.037 & \(-30.480\) & \\ S-shape & 0.903 & 27.794 & 1 & 3 & 0.013 & 3.593 & 0.161 & \\ Exponential & 0.997 & 439.050 & 1 & 3 & 0.000 & 102.187 & \(-1.419\) & \\ Inverse function & 0.936 & 44.014 & 1 & 3 & 0.007 & 31.055 & 9.675 & \\ Power function & 0.973 & 108.389 & 1 & 3 & 0.003 & 34.950 & \(-0.512\) & \\ Quadratic & 0.994 & 172.564 & 2 & 2 & 0.006 & 102.712 & \(-141.786\) & 72.857 & \\ Cubic & 0.998 & 202.028 & 3 & 1 & 0.052 & 80.032 & 58.914 & \(-467.143\) & 450.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitting results of air pressure and SMD. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Function Expression** & **Fitting Formula** & \(R^{2}\) \\ \hline \(p_{\text{air}}\) & \(y_{1}=b_{1}e^{b_{2}x_{1}}\) & \(y_{1}=102.187e^{-1.419x_{1}}\) & 0.993 \\ \(p_{\text{L}}\) & \(y_{2}=b_{1}+b_{2}\ln(x_{2})\) & \(y_{2}=84.978+26.216\ln(x_{2})\) & 0.984 \\ \(d\) & \(y_{3}=b_{1}+b_{2}/x_{3}\) & \(y_{3}=93.65-40.341/x_{3}\) & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fitting formula between single factor and SMD. and the model (1) was input to fit the model coefficient, and then the multivariate nonlinear regression mathematical model can be obtained as follows:  
\[y=54.799+106.504e^{-1.419x_{1}}+25.198\ln(x_{2})-38.033/x_{3} \tag{2}\]  
In the multiple nonlinear regression Equation (2), \(R^{2}=0.962\), indicating the better goodness of fit of the regression model. In order to further improve the goodness of fit and the accuracy of predicted values of the regression model, considering the interactions between various factors, the correction terms were added to the original model. Then, the modified multivariate nonlinear regression mathematical model can be expressed as:  
\[y=b_{1}+b_{2}e^{-1.419x_{1}}+b_{3}\ln(x_{2})+b_{4}/x_{3}+b_{5}x_{1}x_{2}+b_{6 }x_{1}x_{3}+b_{7}x_{2}x_{3} \tag{3}\]  
According to the 25 groups of experimental data in Table 2, the multivariate nonlinear regression was selected again in the SPSS software to modify the prediction model. Then, with Equation (3) we can obtain the modified multivariate nonlinear regression mathematical model as follows:  
\[ \begin{split} y=-14.465+172.912e^{-1.419x_{1}}+20.03\ln(x_{2})-3 0.721/x_{3}\\ +68.262x_{1}x_{2}+22.905x_{1}x_{3}-5.414x_{2}x_{3}\end{split}  \tag{4}\]  
The value of \(R^{2}\) in the modified prediction model was equal to 0.970, indicating that the goodness of fit of the multiple nonlinear regression was improved. The SMD of the nozzle can be calculated according to Equation (4).
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 4 Proposed Scheme
### Packet Types in the Dolphin and Whale Pods Routing  
There are three various types of packets in DOW-PR routing protocol, which are NEIGHBOR REQUEST, ACK and DATA. The source node uses packet _NEIGHBOR REQUEST_ to search its qualified forwarding nodes. Its format is shown as an NR (TID, SID, DP, VA). TID field is a two-bit number that differentiates between the packets. The TID for NR is \"00\". SID abbreviated as ID of the source and it is broadcast in the neighbor request message. DP represents the depth of source node and VA is a one bit number represents the void hole announcement. The value of VA will be true if the source found a void hole. _ACK_ packet is sent in reply to neighbor request means the neighbor node send its information. The format of ACK is ACK (TID, SID, DP). The TID for ACK packet is \"01\", SID presents the identification ID of the current neighbor sensor and DP is the depth of node sending _ACK_ packet. _DATA_ is the real data and it has header and payload. The format of DATA is (TID, SID, DID, DP, PID). The TID value for DATA packet type is \"10\", SID is the source ID, DID represents the destination address, DP represents source depth and PID representing packet sequence number. The neighbor request and Acknowledgment packet has smaller size than the DATA packet.
## 3 Results and Discussion
### Calibration of the Relationship between the Mass Flow Rate and Rotation Speed  
The original relationship model between the mass flow rate and rotation speed was calibrated as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Application Rate (kg/ha)** & **Vehicle Speed (km/h)** & **Test Distance (m)** \\ \hline seeding & fertilizing & & \\
225 & 300 & [2,4] & 300 \\
375 & 525 & [2,4] & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test scheme and operation parameters.  
During each calibration test process, the driving shaft rotation speed of the fluted rollers was controlled to rotate at a constant speed at different set speeds for 3 min. By weighing the actual material collected in the output bins, the relationship model between the mass flow rate and rotation speed was established, as shown in Figure 6. The ratio coefficients of seeds and compound fertilizers were 0.1015 kg per rotation and 0.162 kg per rotation, respectively.
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
###### Abstract  
Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference cases to implement in imaging signal processors with if-then rules and low computation efforts.  
color constancy; illumination estimation; adaptive neuro-network fuzzy inference system (ANFIS); clustering; image enhancement +
Footnote †: journal: Applied Sciences  
**Keywords:** color constancy; illumination estimation; adaptive neuro-network fuzzy inference system (ANFIS); clustering; image enhancement
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### How Wide and Deep a Model for Our Data?
#### 4.4.2 Influence of the Depth  
We now propose here to vary the number of successive layers, i.e., the depth of the network, for the same model complexities. To keep the model complexity constant, we decrease the number of units for deeper networks. More specifically, we consider six architectures composed of one to six convolutional layers with a number of units ranging from 256 to 16, and one dense layer with a number of units ranging from 64 to 2048. Figure 10b shows OA values as a function of number of convolutions.  
Figure 10b shows that the highest accuracies are obtained with the lowest standard deviation for an optimal number of convolutional layers of two or three. For a given complexity (here about 2.5M parameters), the use of an inappropriate number of convolutional layers and number of units may lead to an under-performance of TempCNNs. For a specific task, the selection of a reasonable architecture might be important, and could be optimized via cross-validation or using meta-learning approaches [77; 78; 79].
# Internalization of Phospholipid-Coated Gold Nanoparticles
## 4 Results
### Endocytosis Occurs in C2C12 and A549 Cells and is Cell Type Specific
Image correlation spectroscopy data corresponding to A549 cells (**blue bars**) and C2C12 cells (**yellow bars**) exposed to PCAuNPs for 1, 2, 4, and 24 h. Error bars indicate standard error. ANOVA followed by Bonferroni post-hoc tests were performed for pairwise comparisons. There is no statistically significant change between the time points indicated by the * where \(p>0.0125\). Otherwise, all pairwise comparisons are statistically different and have \(p\leq 0.0125\). Figure 7: Images of phospholipid-coated gold nanoparticles after variable exposure with fixed uptake in A549 Cells. Confocal fluorescence microscopy images of A549 cells exposed to phospholipid-coated gold nanoparticles for 1, 2, 4 and 24 h, washed, then fixed. We observed an increasing trend of the auto correlation amplitude (g(0,0)) with time. The fit for the laser beam width ranges between 0.38-0.48 microns; this is consistent with the images shown in Figure 7 as larger size clusters were observed after 24 h. In previous work, we characterized the sizes of clathrin-mediated endocytic markers to be approximately 0.40 microns [50]. Thus, simply from this observation, one can assume phospholipid-coated gold nanoparticles may be contained in endocytic compartments. Additionally, the average intensity of phospholipid-coated gold nanoparticles increases with time of exposure in A549 cells, consistent with the images presented in Figure 7.  
Table 6 shows the parameters derived from the data in Table 5 corresponding to the average number of clusters containing phospholipid-coated gold nanoparticles, the average number of clusters containing phospholipid-coated gold nanoparticles per square micron, and the average relative degree of aggregation for phospholipid-coated gold nanoparticles per cluster; calculated for a set of 20-30 images per sample. The data in Table 6 suggest that the number of clusters containing phospholipid-coated gold nanoparticles in the observation region of the beam as well as the cluster density decreases with time with a corresponding increase in the relative degree of aggregation. Thus, as more and more phospholipid-coated gold nanoparticles are taken up with ongoing exposure, they aggregate into fewer clusters that each contain many more gold nanoparticles. This is in stark contrast to the decomposition seen after a short exposure, suggesting that the uptake of new PCAuNPs is faster than their processing and decomposition.
## 4 A Pilot Study
### Discussion on the Annotated Data  
Tables 2 and 3 are the descriptive summaries of our proposed annotation procedure. Confidence intervals (CI) with a 95% confidence level were also computed for the total number of tweets with different targets and emotions. To better understand the summaries, the pictorial presentations of the results are also provided in Figures 4-6, respectively. Table 2 provides the results of the annotation process related to key targets and emotions. The far-right column presents the total numbers and percentages (with CI) of posts for ten targets (i.e., the nine key targets identified in annotation and an \"other\" category). The bottom row displays the total numbers and percentages (with CI) of posts conveying each emotion category. The \"other\" category includes targets, such as sports and events. The top three most talked about targets were attractions (manmade or natural), accommodation, and food and beverage, highlighting that these targets matter most to tourists and form the 'backbone' of their trip. The dominance of attraction-related posts reflects their critical role as a pull factor of a destination. In contrast, shopping and city facilities attracted little coverage, confirming earlier work by [PERSON] (2010), who did not reveal these as recurring themes in bloggers' reported tourist experiences. From Table 2, it can further be noted that there were a total of 567 instances of positive emotions, 237 instances of negative emotions, such as sadness, anger, or fear, and 141 posts without expressing any emotions. This result confirmed that people tend to share positive experiences [PERSON] (1996); [PERSON] (2010), but the extent to which this distribution resulted from positive bias among Twitter users requires further investigation. \begin{table}
\begin{tabular}{l c} \hline \hline
**Characteristic** & **Number** \\ \hline Total number of posts & 475 \\ Positive posts & 279 \\ Negative posts & 28 \\ Neutral posts & 168 \\ Total number of sentences & 585 \\ Positive sentences & 342 \\ Negative sentences & 43 \\ Neutral sentences & 200 \\ Tweets composed of 1 sentence & 382 \\ Tweets composed of 2 sentences & 76 \\ Tweets composed of 3 sentences & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive summary of the annotated tourism database.
# Part II--Aroma Profile of Twenty White Grapevine Varieties: A Chemotaxonomic Marker Approach
## 3 Results & Discussion
### Volatile Composition
#### 3.3.1 Free Volatile Fraction
In this study, it could be seen that they were detected in low contents in almost every variety, also contributing at a very low percentage to their free aromatic profile (Figure 1), with 'Garrido Fino' and 'Silveirina' being those varieties that showed the highest values (142 and 124 ug\(\cdot\)L\({}^{-1}\) respectively). 'Albilla do Avia', despite having a lower concentration value than those varieties previously mentioned, showed a similar contribution percentage to its free aromatic profile as the latter ones. Phenols were detected in free form in every variety, except in 'Albilla do Avia', where they were not detected in any vintage. 'Agudelo' was the variety with the highest values with 44 ug\(\cdot\)L\({}^{-1}\), while 'Ratino' was the one showing the lowest one, with 2 ug\(\cdot\)L\({}^{-1}\), and they were only detected in one vintage. However, they were found in low contents and contributed as a low percentage to their free aromatic profiles (Figure 1). Ketones were detected in all varieties and years of study, mainly in the free aromatic fraction. 'Garrido Fino' and 'Treixadura' showed the highest values (193 and 190 ug\(\cdot\)L\({}^{-1}\) respectively) but contributed to a higher percentage in the free aromatic profile of 'Batoca', 'Pirixileira' and 'Torrontes' compared to that of 'Treixadura' (Figure 1). On the contrary, 'Albilla do Avia' showed the lowest value (61 ug\(\cdot\)L\({}^{-1}\)). Methyl isobutyl ketone was the major compound detected in all the studied varieties, identified in other varieties and other kind of foods, such as lemon juice, papaya, or ginger [68]. Referring to PAHs, 'Lado' stood out for its highest value (123 \(\mathrm{\SIUnitSymbolMicro g}\):L\({}^{-1}\)) and 'Italia' with the lowest one (16 \(\mathrm{\SIUnitSymbolMicro g}\):L\({}^{-1}\)). However, they did not contribute, with more than 4%, to the free aromatic profile of any of the studied varieties (Figure 1). Hemimellitene and mesitylene were those major compounds in almost every variety, being isomers of trimethylbenzene, as has already been shown in previous studies [36; 37]. Lactones were detected in every variety, mostly in their free form. 'Dona Branca' stood out for the highest content detected (80 \(\mathrm{\SIUnitSymbolMicro g}\):L\({}^{-1}\)), with furnacel as the major compound, which is related to the strawberry-like aroma, generally found in higher amounts in some _Vitis lambrusca_ or hybrid varieties [69]. The thiol content depends on the variety, and some of them are important providers of varietal wine aroma odors [70]. In this study, they were detected in their free form in 14 out of 20 varieties studied in low contents, not being detected in every year. Major compounds detected were 2-methyl- 2-undecanethiol and 3-(methylthio)-nonanal.
## 4 Conclusions
### Research Prospects  
The reliability analysis and prediction technology of the marine lubricating oil system plays an irreplaceable role in the intelligent operation and maintenance system. However, there are still many challenges and much room for improvement in the current research. Future research will focus on multimodal data fusion, intelligent optimization algorithms, dynamic decision-making modeling, and system-level health management, so as to improve the intelligent level of ship operation and maintenance and ensure the safety and economy of ship operation.  
In the aspect of system reliability evaluation, the traditional Weibull proportional failure rate model has certain parameter dependence, which cannot fully represent the dynamic failure mode of complex systems. Future research can explore reinforcement learning and meta-learning strategies, and dynamically update key failure mode parameters based on Reinforcement Agents to construct an adaptive failure mechanism modeling framework. At the same time, the degradation modeling methods based on non-stationary random processes, such as Markov Decision Process (MDP) and Particle Filtering, are constructed to realize the degradation evolution prediction of equipment under complex environmental factors.  
At the decision level of operation and maintenance optimization, the current optimization objectives of maximizing steady-state availability and minimizing average cost rate still have certain lags and limitations in extreme operating conditions and sudden failures. Therefore, future research can combine Game Theory and the multi-objective evolutionary algorithm (MOEA) to establish a robust operation and maintenance optimization strategy adapted to a multi-scale operating environment. In addition, an intelligent operation and maintenance system based on the Digital Twin architecture is constructed to realize the intelligent control of the whole life cycle from sensor data acquisition, health status analysis, fault prediction to decision optimization, and provide global optimization decision support for the operation and maintenance of intelligent ships.  
Conceptualization, H.X. and L.Q.; methodology, H.X.; software, H.X.; validation, H.X., B.D. and J.S.; formal analysis, S.L.; investigation, L.Q.; resources, R.T.; data curation, D.Z.; writing--original draft preparation, H.X.; writing--review and editing, L.Q.; visualization, R.T.; supervision, L.Q.; project administration, L.Q.; funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by: 1. Research on the Mechanism of Hydrogen-Oxygen Flame Line Heating and Forming of Outer Hull Plate and Intelligent Operation Decision Support System (National Natural Science Foundation of China) grant number 51875270. 2. V-DT-driven an-dAI-enabled Comprehensive Protection Technology for Complex Equipment of Large Ships at Sea Research grant number JC2024021.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Exploring the Benefits of Combining DevOps and Agile
## 2 Literature Review
### DevOps Concepts and Model
CALMS is an acronym for Culture-Automation-Lean-Measurement-Sharing. CALMS was created by [PERSON], co-author of The DevOps Handbook. The acronym highlights the five core elements of CALMS [23]:  
* Culture: a cultural change focusing on collaboration and integration between developers' team and operations' team;* Automation: a high level of automation to achieve continuous delivery running each code change through automated tests;
* Lean: the application of lean principles to increase efficiency and reduce complexity;
* Measurement: keeping key performance indicators for measuring performance and identifying where improvements can be achieved;
* Sharing: knowledge and best practice should be shared in the organization and across organizational boundaries. Security issues concerning DevOps led to the spread of another term: DevSecOps. It adds \"Security\" to \"Development\" and \"Operations\", which were already part of the DevOps term. According to [PERSON] et al. [14], security is often treated as a non-functional requirement, handled at a later stage of the software development life cycle. Under DevSecOps, security should be built into every part of the DevOps life cycle. The purpose of the DevSecOps philosophy is to align the speed of code delivery with building secure code, merged into one streamlined process. The application of DevOps still must deal with some problems and concerns that can limit its use (e.g., resistance to change, organizational vision, legacy systems) [26]. Misuse of the term, lack of clear guidelines and, as already mentioned, the lack of a clear definition creates some ambiguity about how to use DevOps principles. Those principles presuppose that, before DevOps, development teams and operations teams were working independently with almost no knowledge about each other's work. This lack of knowledge across teams is not, in general, as deep as DevOps assume. The whole software development process is improved with better collaboration between teams, as DevOps advocates, but it does not mean that DevOps teams did not previously cooperate. Another concern around DevOps is that its adoption rate is still low. There is a close relation between DevOps and Agile methods in software development. According to [PERSON] et al. [13], DevOps is an evolution of the Agile movement since software development under Agile favors small release iterations with customer reviews.