esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electronic Tongue Measurements  
Based on the evaluation of the raw sensor signals, the initial three replicate measurements of the samples were excluded from further analysis, as the sensors of the electronic tongue were found to be unstable. The average of the raw sensor signals of the six selected sensors is presented in Figure 3. It was observed that the majority of sensors exhibited notable differences between at least some of the tested samples.  
Figure 3: Mean and standard deviation of the selected six electronic tongue sensor signals of the tested smoothie samples (\(n=36\)).  
Figure 4 illustrates the Euclidean distances calculated between the raw signals of the electronic tongue sensors of the tested smoothie samples. The greatest distances were observed between the groups of fresh and stored samples, indicating the formation of two principal groups based on the chemical profiles of the tested smoothie samples. As illustrated in Figure 4, the groups of treated fresh samples exhibited shorter distances to the control fresh sample group than after 14 days of storage.  
The results of the principal component analysis calculated based on the drift-corrected results of the six selected sensors are presented in Figure 5. The first two principal components (Figure 5a) represent about 94% of the total variance and result in a clear separation of the six types of samples. The most prominent separation was observed based on PC1 between the groups of fresh and stored samples.  
Figure 5b illustrates the different patterns observed on the initial three PCs highlighted with the different colouring schemes. PC2 demonstrates a clear separation between the control and treated sample groups (Figure 5b, PC1-PC2). Additionally, a separation has been observed between the different treatment types (Figure 5b, PC2-PC3). Figure 5c depicts the contributions of the six selected electronic tongues on the first three PCs, which demonstrates the separation of the aforementioned patterns.  
Figure 4: Euclidean distances calculated between the raw signals of the selected six electronic tongue sensors of the tested smoothie samples (\(n=36\)); a neuron-like graph for the visualisation of the Euclidean distances.
## 2 Proposed Fault Feature Extraction Methodology for the Fault Diagnosis of Rolling Bearings
### Preview of the Mathematical Morphology Analysis and Quantum Signal Processing  
#### 2.3.1 Mathematical Morphology Analysis  
Mathematical morphology methods realize satisfactory signal processing performance. Studies have shown that [35; 36; 37] the lengths of structural elements strongly impact their analysis. Vibration signals are usually composed of one or more groups of discrete signals. The common basic mathematical morphological operators are the expansion, erosion, opening, and closing operators, along with combinatorial variant operators among them. Assuming that \(f(n)\) is a one-dimensional discrete signal, it is defined as \(F=(0,1,\cdots,N-1)\) and \(n\in 0,1,\cdots,N-1\); \(g(m)\) is a structural element; its definition domain is set as \(G=(0,1,\cdots,M-1)\), where \(N\geq M\) and \(m\in 0,1,\cdots,M-1\). Then, the erosion operation and dilation operation of signal \(f(n)\) with respect to \(g(m)\) are defined as follows:  
\[(f\Theta g)(n)=\min[f(n+m)-g(m)], \tag{7}\]  
\[(f\odot g)(n)=\max[f(n-m)+g(m)]. \tag{8}\]  
According to the cascade form of the corrosion operation and dilation operation, the opening operation and closing operation of the signal \(f(n)\) with respect to \(g(m)\) are defined as follows:  
\[(f\circ g)(n)=(f\Theta g\oplus g)(n), \tag{9}\]  
\[(f\bullet g)(n)=(f\oplus g\Theta g)(n). \tag{10}\]  
Since the opening operator can remove the positive pulse sequence and the closing operator can remove the negative pulse sequence, this combined type of filter considers all the filtering of the opening and closing operators. Two morphological operators--namely, the filter operator of opening and closing (FOC) and the filter operator of closing and opening (FCO) of \(f(n)\) with respect to \(g(m)\)--are defined as follows:  
\[FOC(f(n))=(f\circ g\bullet g)(n), \tag{11}\]  
\[FCO(f(n))=(f\bullet g\circ g)(n). \tag{12}\]  
Although the filter of opening and closing and filter of closing and opening can eliminate the positive and negative pulses of a signal simultaneously, statistical bias will occur in practical applications--namely, the output signal amplitude of the operators of opening and closing will decrease, while the output amplitude of the operators of closing and opening will increase, which will directly affect the operation results; hence, the ideal filtering effect cannot be realized by using any of these combined filters alone [24]. To overcome this disadvantage, scholars calculate the difference and arithmetic average of the closing and opening operator and the opening and closing operator as the final filtering results and construct a closed-open-open-closed morphological gradient operator \(MG_{CO\&OC}\) and a closed-open-open-closed combined average operator \(AVG_{CO\&OC}\)[25].  
\[MG_{CO\&OC}(f(n))=FCO(f(n))-FOC(f(n)), \tag{13}\]  
\[AVG_{CO\&OC}(f(n))=\frac{FCO(f(n))+FOC(f(n))}{2}. \tag{14}\]  
\(AVG_{CO\&OC}\) is called the combination morphological filter (CMF).
# Improvements for the Planning Process in the Scrum Method
## 1 Introduction
### Scrum Methodology
#### 1.2.2 Scrum Advantages and Disadvantages  
Scrum, a widely adopted agile framework, is known for its effectiveness in managing complex projects, especially in software development. Its iterative process and emphasis on team collaboration have been praised for delivering high-value products efficiently. However, Scrum also comes with its set of challenges. This chapter explores the advantages and disadvantages of Scrum, providing a comprehensive overview supported by various scientific sources.  
**Advantages of Scrum**  
Scrum enhances team collaboration and communication through regular meetings and defined roles, fostering a collaborative environment. Daily stand-ups and sprint reviews ensure all team members are aligned, leading to better team dynamics and improved project outcomes [8]. The flexibility and adaptability of Scrum allow teams to respond quickly to changes in project requirements or market conditions. This iterative nature, highlighted by [PERSON] and [PERSON] (2022), enables continuous feedback and incremental improvements, making Scrum effective in dynamic and fast-moving project environments [9].  
Frequent testing and reviews integral to the Scrum process help identify and address issues early, leading to higher-quality deliverables and reduced risk of significant defects at the end of the project [10]. The focus on value delivery ensures efficient resource utilization, as described by [PERSON] and [PERSON] (2020), optimizing the use of time and resources by breaking down large projects into manageable sprints [11].  
Customer satisfaction is another significant advantage, as regular feedback loops and stakeholder involvement ensure the final product meets user needs and expectations. This alignment with customer-centric development practices is evident in the iterative approach of Scrum [9].  
**Disadvantages of Scrum**  
Despite its advantages, Scrum can lead to scope creep due to continuous feedback and new requirements, making it challenging to finalize the product [12]. The methodology also heavily relies on the skills and experience of the team members. Inexperienced team members may struggle with the autonomy and responsibility that Scrum requires, leading to suboptimal performance [13].  
Scrum is not always suitable for large and complex projects. The need for frequent communication and collaboration can become cumbersome as team size increases, often necessitating additional frameworks like LeSS or SAFe, which can be challenging to implement [12]. The frequent meetings and continuous feedback loops in Scrum require a high level of commitment from all team members, which can be demanding and may lead to burnout if not managed properly [14].  
Ensuring high quality in Scrum can be challenging, particularly in the early stages of adoption. The iterative process requires rigorous testing and validation, which can be difficult to maintain consistently [13].
## 4 Parametric Study
### Effect of Internal Friction Angle \(\varphi\)  
Figure 10 provides the influence of the internal friction angle \(\varphi\) on \(K_{\rm a}\), \(\alpha_{\rm a}\), and normalized dynamic active earth pressure distributions with \(f_{\rm a}=1.4\), \(\delta=\varphi/2\), \(\theta=20^{\circ}\), \(i=8^{\circ}\), and \(k_{\rm v}=0\).  
It can be observed that the dynamic active earth pressure coefficient \(K_{\rm a}\) decreased with the increase in the internal friction angle \(\varphi\), as shown in Figure 10a. As shown in Figure 10b, it can also be observed that the difference in \(K_{\rm a}\) under various internal friction angles increases with the increase in \(k_{\rm h}\). The existence of cohesion (\(c/\gamma H=0.05\)) reduces \(K_{\rm a}\).  
In Figure 10c, the failure wedge angle \(\alpha_{\rm a}\) in the active condition is increased as \(\varphi\) increases from \(25^{\circ}\) to \(40^{\circ}\). When \(c/\gamma H\) changes from 0 to 0.05, \(\alpha_{\rm a}\) tends to increase. This trend becomes more and more obvious with further increases in \(k_{\rm h}\).  
The typical normalized dynamic active earth pressure distribution for different values of \(\varphi\) with \(k_{\rm h}=0.2\) and \(k_{\rm v}=0\) is shown in Figure 10d. When \(\varphi\) increases from \(25^{\circ}\) to \(40^{\circ}\), in the case \(c/\gamma H=0\), the dynamic active earth pressure at the bottom of the wall decreases from 0.949 to 0.625, which represents a decrease by about 34.1%. When the internal friction angles \(\varphi\) are \(25^{\circ}\), \(30^{\circ}\), and \(35^{\circ}\), respectively, in the case of \(c/\gamma H=0.05\), the normalized critical depth \(z_{\rm c}/H\) is almost constant, with an average of 0.117.
## 2 Materials and Methods
### NIR Hyperspectral Image Acquisition and Preprocessing
#### 2.3.2 Regions of Interest (ROI) Selection and Spectral Information Extraction  
Hyperspectral imaging systems can acquire abundant spatial and spectral information. The ROI tool in ENVI (Research Systems Inc., Boulder, CO, USA) was used to extract spectral information from rapeseed ROI. The regional growing method was employed to select ROI and spectral information was extracted from these regions. After identifying the ROI image, average spectra were calculated for all pixels enclosed in this region and taken as the NIR spectra of each rapeseed sample. All samples followed this procedure to acquire spectral information. As a result, a spectral matrix of 150 rapeseed samples in a row and 271 bands in a column was obtained for the modeling and prediction of the quality parameters of rapeseed.
## 3 Proposed Hybrid Algorithm
### Benefits of the Hybrid Algorithm  
The hybrid algorithm aims to efficiently perform point multiplication on BEC in \(\omega\) coordinates. The benefits of a hybrid approach are as follows:  
* Efficient point multiplication: The algorithm uses a combination of Montgomery ladder and double-and-add methods to perform point multiplication on BEC efficiently.
* Reduced number of point additions: The algorithm performs point addition only when the current bit of the scalar is \(0\). This reduces the number of point additions required as compared to the standard double-and-add algorithm, where point additions are performed at every iteration.
* Reduced number of point doublings: The algorithm performs point doubling only when two consecutive bits of the scalar are \(1\). This further reduces the number of point-doubling operations compared to the standard double-and-add algorithm, where point-doubling is performed at every iteration.  
Overall, the hybrid algorithm provides an efficient way to perform point multiplication on BEC. The Montgomery ladder algorithm provides efficient scalar multiplication without revealing any intermediate values, while the double-and-add algorithm improves the performance of the scalar multiplication by avoiding unnecessary additions and doublings.
# Effect of Pore Size Distribution and Amination on Adsorption Capacities of Polymeric Adsorbents
## 3 Results and Discussion
### Static Adsorption Equilibrium
Although NDA-2 had the largest specific surface area, its adsorption capacity was far lower than that of NDA-3 and NDA-4. This was because the NDA-2 resin was also affected by pore hindrance. This effect was enhanced by the molecular aggregation of RBB4 [25]. [PERSON] et al. [26] showed that the degree to which some acid dyes aggregate on the surface of activated carbon and bone charcoal could be greater than 10 molecules. Accordingly, it is understandable that the macromolecule RBB4 would experience pore hindrance on, NDA-1 and NDA-2 owing to small pore diameters of these resins. Figure 6: (**a**) Adsorption isotherms of phenol; (**b**) Adsorption isotherms of ABS acid; (**c**) Adsorption isotherms of RBB4. As shown in Figure 6, although NDA-4 and NDA-3 had similar specific surface areas, pore volumes and average pore diameters, the adsorption capacity of NDA-4 was lower than that of NDA-3 for the three adsorbates. This was because the pore diameter of NDA-4 was larger than that of the adsorbates. Thus, pore size that is either too small or too large is not conducive to improving the adsorption capacity of a resin, and there was an optimal adsorption pore size for specific adsorbates. The size distribution of the pores greatly influences the adsorption capacity of a resin. Only when the resin has a proper pore diameter can the adsorbate achieve good diffusion into the channels and be absorbed effectively. If the pore diameter is too large and the specific surface area is small, the resin will have a low adsorption capacity. If the pore diameter is too small, it will limit the diffusion of adsorbates and solvents, which enhances the shielding effect for molecules with larger diameters [27]. When the pore diameter of a micropore is less than several times the diameter of the adsorbate molecule, a potential shielding effect can occur. This can increase the interaction between the solid surface and gas molecules, thus enhancing adsorption [28]. Macropores, mesopores and micropores are typically categorized by pore diameters greater than 50 nm, 2-50 nm, and less than 2 nm, respectively. The surface of macropores contributes little to adsorption and only provides a channel for the diffusion of adsorbates and solvent. Mesopores can adsorb macromolecules and help micromolecules pass through the micropores. Adsorption is related to the chemical structure of resin, regardless of the adsorption method. Regarding the adsorption mechanism, non-polar adsorbent resins adsorb molecules through van der Waals forces. By contrast, adsorption typically occurs via dipole-dipole and electrostatic interactions (including hydrogen bond and donor-acceptor interactions) for medium and strongly polar resins.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 3 Dataset and Methodology
### Data Pre-Processing
#### 3.1.3 Multivariate Multi-Step Sequence Sample Construction  
Given that the MPD data collected in the field have a certain time series correlation, namely the data at the current well depth will have a certain influence on the lower well section, the prediction of BHP can be treated as a multi-variable sequential prediction problem, and the BHP at a certain depth can be predicted through the historical BHP.  
Therefore, prior to training the model, it is necessary to prioritize the collection time of the original data set and convert it into supervised learning samples. Since there are multiple characteristic variables in managed pressure drilling data at each time point, and the predicted label has only one variable BHP, the other 12 characteristic variables can be deleted at the last time point when constructing sequence samples, so as to reduce data dimension and improve model operation efficiency. The fixed sequence of the unit length specified by the sliding window is used to calculate the indicators in the fixed sequence. The window size selected is 10, namely the sequence data of the first 10 s are used to predict the wellbore pressure of the next 10 s. The partial samples of the established multivariate sequence data are shown in Table 3.
# Neural Multivariate Grey Model and Its Applications
## 4 Experiments
### Forecast of China's Total Amount of Renewable Energy
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Total** & **Hydropower** & **Solar Energy** & **Bioenergy** & **Wind Energy** & **Biogas** & **Other** \\ \hline
2013 & 359,516 & 280,440 & 17,759 & 6089 & 76,731 & 193 & 210.53 \\
2014 & 414,651 & 304,860 & 28,399 & 6653 & 96,819 & 310 & 249.13 \\
2015 & 479,103 & 319,530 & 43,549 & 7977 & 131,048 & 331 & 297.73 \\
2016 & 541,016 & 332,070 & 77,809 & 9269 & 148,517 & 350 & 342.69 \\
2017 & 620,856 & 343,775 & 130,822 & 11,234 & 164,374 & 454 & 370.39 \\
2018 & 695,463 & 352,261 & 175,237 & 13,235 & 184,665 & 630 & 401.15 \\
2019 & 788,844 & 358,040 & 204,971 & 16,537 & 209,582 & 799 & 431.91 \\
2020 & 899,625 & 370,280 & 253,964 & 23,583 & 282,113 & 1285 & 462.67 \\
2021 & 1,020,234 & 390,920 & 306,973 & 29,753 & 328,973 & 1711 & 493.43 \\
2022 & 1,160,799 & 413,500 & 393,032 & 34,088 & 365,964 & 1928 & 524.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: China renewable energy data (2013–2022). Figure 8: Predictive curves from five grey models for China’s renewable energy. Figure 6: MAPE of five grey models to China’s renewable energy. Figure 7: APE of five grey models to China’s renewable energy.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 2 Modeling and Analysis of Corner
### Finite Element Analysis of Flux Density Distribution  
The electromagnetic finite element analysis of the inductor is carried out to analyze the flux density distribution when the current \(i\) is from 20 A to 1000 A. At 20 A, the average flux density of the core is 0.085 T. At 1000 A, the average flux density of the core is 1.58 T, which has reached the saturation range. The flux density distribution is shown in Figure 2.  
The flux density distribution is symmetric due to the structural symmetry and uniform in the limb enclosed by the winding. In the corner of the core, the flux density is non-uniformly distributed and the non-uniformity increases with current significantly. In the joint part, between the corner and limb surrounded by winding, the flux density distribution of the area near the geometrically symmetric plane is more uniform, and the flux density distribution of the region near the corner is more uneven. In addition, as the excitation is enhanced, the leakage flux of the core window inner corner increases.  
Taking into account the characteristics of flux density distribution, in this paper, we propose a local model of these three parts of the core, as shown in Figure 3, which are corner, the limb joint  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(w_{s}\) & width of the core window & 67 mm \\ \(w_{i}\) & width of the limb & 23 mm \\ \(w_{w}\) & height of winding & 56 mm \\ \(w_{e}\) & distance of the end of the winding to limb & 5.5 mm \\ \(d_{w}\) & width of winding & 4 mm \\ \(l_{c}\) & depth of the core & 23 mm \\ \(N\) & number of turns of each limb & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of square core-type inductor geometry.  
Figure 1: A square core-type inductor: (**a**) structure parameters; (**b**) electrical schematic.  
Figure 2: Flux density distribution: (**a**) 50 A; (**b**) 100 A; (**c**) 500 A; (**d**) 1000 A.  
parts between winding end and corner named Part B and Part C, and the core window inner corner area named Part D. Since the model structure of this paper is symmetrical, Part B and the Part C are identical.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 6 Conclusions and Perspectives  
As a multicellular architecture surrounded by EPS, mycobacterial biofilm is composed of polysaccharides, proteins, lipids, and eDNA. Several genes and molecules play important roles in mycobacterial biofilm formation and are either relevant to the production of biofilm compositions or involved in inducing biofilm formation. The existence of biofilm in vivo was verified by using calcofluor white staining on cellulose and has posed serious clinical problems, including biofilm-related infections, powerful virulence, high recurrence rates, etc. Therefore, agents that can interfere with biofilm formation or promote biofilm destruction are urgently needed. However, traditional anti-TB drugs exhibit only a small impact on Mtb biofilms. Within the last decade, a variety of novel drugs and agents targeting mycobacterial biofilm have emerged. Most of them intervene with biofilm formation, and some can increase biofilm hydrolysis. When used as an adjunct therapy, these antibiofilm agents can eliminate biofilms, thus enhancing the potency of anti-TB drugs and mitigating inflammation. A few innovative agents have been created to assist antibiofilm drugs in penetrating the robust barrier of bacterial cells and arriving at certain locations. Thus, deeper investigations into the composition of biofilm and novel combination therapies are crucial for TB control.  
However, the literature described in this review has several limitations. Firstly, most of the research mentioned in this review was conducted on Mtb models, like _M. smegmatis_ and _M. bovis_. To ascertain the outcomes, similar experiments using Mtb specimens are required to prove the results. Secondly, in vivo clinical trials are also needed to bridge the gap and adequately use antibiofilm drugs in patients. Thirdly, other methods of using these antibiofilm agents remain to be investigated. For example, when a patient with TB needs a trachea cannula, drugs can be applied to the tube before intubation to prevent biofilm formation.  
Conceptualization, S.S. and Y.M.; writing--original draft preparation, X.L.; writing--review and editing, S.S. and Y.M.; visualization and validation, J.H., W.W., H.Y. and E.T.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Scientific Research Program of Department of Education of Liaoning Province, China (Funding number: LJKZ0846). The authors declare no conflicts of interest.