esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Working Principle and Kinematic Modeling of Extension-Dominated Mixer
### Working Principle
Consistent with Figure 2, the black waist-shaped figure represents the stator, the bright pink circle represents the rotor, the green circle represents the fixed circle, the light blue circle represents the moving circle, and the dark blue circle represents the trajectory formed by the center of the moving circle. As the rotor rotates, it spins at a constant speed in the opposite direction. The rotation center is denoted as point B, and the trajectory of point B is depicted in dark blue, moving counterclockwise around the stator's geometric center with a radius of \(e\). The turquoise circle represents the moving circle as described in the cycloid formation principle, where points A and C always reside on the diameter of the moving circle, with the circle's center serving as the rotor's rotation center. From the figure, it is evident that the rotor not only orbits around the stator's center but also undergoes rotation at point B, which revolves around the stator's center at a constant speed in reverse. The trajectory of point A, situated at the rotor's center, forms a straight line within the stator's center. Points E and D, as well as F and G, represent points on the same rotor diameter, moving in conjunction with the rotor's rotation and revolution. In MATLAB, trajectories of points on the rotor depicted in Figure 2 [37] were plotted, as demonstrated in Figure 4. It is evident that points A and C, both situated on the moving circle, follow linear trajectories dependent on their initial positions. As the center of the rotor, point A moves linearly along the path of the moving circle. Conversely, point C, positioned differently from point A on the moving circle, also traces a linear trajectory, albeit in a different direction due to its distinct initial position. Although point C lies on the moving circle, it does not align with the rotor's profile, resulting in a linear trajectory. However, due to its off-profile positioning, it does not make direct contact with the fluid. Point B, positioned at the center of the moving circle, undergoes uniform circular motion. Points D and E, located on the same rotor diameter but at different distances from point B, exhibit elliptical motions with different phases. The long axis of the ellipse traced by point D intersects the two centers of the stator profile, while the trajectory of point E's ellipse is tangential to the stator profile. Points F and H, corresponding to two points on the same diameter of the rotor, also trace elliptical trajectories symmetrically with respect to the y-axis, internally tangent to the stator profile. Figure 3: The position diagram of each point at different times when the rotor is moving.
## 3 Results and Discussion
### Case II: Experimental Work
#### 3.2.2 Damage Localization--Results  
The numerical calculations for the results of the experimental measurements were carried out according to the same research programme as for the simulation stage. First of all, the fit factor for the particular vibration forms was analyzed, and the calculation results are presented in Figure 11.  
Figure 10: The experimental set up in laboratory: (**a**)—tested blade and equipment of LSV, (**b**)—head of laser scanning vibrometer, (**c**)—model of the damage.  
In the following order, the fit factor was determined for all available vibration forms averaged by the RMS method for three damage locations. In Figure 12 the curves of the fit factor are shown.  
Figure 11: Results of fit coefficient calculation for modes order from 1 to 10 and the first case of damage localization: (**a-j**) consecutive modes; vertical dashed line—location of the damage; x/L—normalized position.  
Finally, the fit factor was determined for the three damage locations near the centre of the blade midspan, as shown in Figure 13.  
All conclusions that were formulated after the simulation stage were confirmed on experimental data. In the case of single modes analysis, the determination of the damage position was difficult, and only a few vibration forms clearly indicated the damage edges. After the application of averaging by RMS method of all available mode shapes, results were obtained that clearly showed the localization of damage. However, some differences could be seen in relation to the results of the calculation with the use of numerical simulation data. The reasons for these differences should be seen both in the too idealized numerical model, physical limitations of measuring instruments and assumed damage model in the experiment. Even though optimal laboratory conditions were ensured during the tests, the obtained measurement data were noisy. Moreover, the damage model adopted in the experiment (a steel element fixed to the blade surface) was supposed to change the stiffness of the coating locally. However, its additional mass caused a discrepancy in the vibration amplitude for the damaged and reference state, which affected the final calculation results.  
Figure 12: Results of fit coefficient calculation for averaged modes for all cases of damage localization: (**a**) 1 st location; (**b**) 2 nd location; (**c**) 3 rd location; dashed line—location of the damage; x/L—normalized position.  
Figure 13: Results of _fit_ coefficient calculation for RMS value for all given modes shapes and three size of damage in one localization; x/L—normalized position.
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 3 Experimental Results
### Comparison to the State of the Art  
Table 2 presents a comparison between the proposed method and the state-of-the-art methods based on compressive sensing and Gaussian process regression (CS + GP) [9], SURF feature matching, and Gaussian process regression (SURF + GPR) [9], multi-resolution random projection (MR-random-proj) [13], fine-tuning of GoogLeNet [13], and AE fusion of HOG + BOW + LBP features [12]. These results are obtained using the proposed Model 3 with a hidden layer size of 256, and a presence threshold of 0.3, as suggested by the sensitivity analysis.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**Dataset** & \multicolumn{1}{c}{**Method**} & **SEN** & **SPE** & **AVG** & **Time(s)** \\ \hline \multirow{4}{*}{**KSU 1**} & EDCS + GPR [9] & 73.82 & 80.53 & 77.18 & \(>\)1 \\ \cline{2-6}  & MR random projection [13] & 71.65 & 94.81 & 83.23 & 0.035 \\ \cline{2-6}  & Pre-trained GoogLeNet [13] & 73.56 & 97.95 & 85.75 & 0.064 \\ \cline{2-6}  & Fine-tune improved SqueezeNet [ours] & 94.14 & 96.54 & 95.34 & 0.024 \\ \hline \multirow{4}{*}{**KSU 2**} & EDCS + GPR [9] & 64.46 & 72.91 & 68.69 & \(>\) 1 \\ \cline{2-6}  & MR random projection [13] & 70.00 & 92.33 & 81.16 & 0.037 \\ \cline{2-6}  & Pre-trained GoogLeNet [13] & 67.76 & 95.91 & 81.84 & 0.067 \\ \cline{2-6}  & Fine-tune improved SqueezeNet [ours] & 94.51 & 93.79 & 94.15 & 0.029 \\ \hline \multirow{4}{*}{**UTrento1**} & SURF + GPR [9] & 71.16 & 100.00 & 85.58 & 1.17 \\ \cline{2-6}  & EDCS + GPR [9] & 69.66 & 80.19 & 74.93 & 1.08 \\ \cline{2-6}  & MR random projection [13] & 71.16 & 83.20 & 77.18 & 0.037 \\ \cline{2-6}  & Pre-trained GoogLeNet [13] & 61.79 & 96.55 & 79.17 & 0.068 \\ \cline{2-6}  & AE fusion of HOG + BOW + LBP [12] & 89.51 & 81.3 & 85.41 & 0.022 \\ \cline{2-6}  & Fine-tune improved SqueezeNet [ours] & 88.14 & 88.49 & 88.31 & 0.013 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison to state-of-the-art methods.  
Figure 13: Effect of the hidden layer size and the presence thresholds \(T_{p}\) on the AVG score for Model 3: (a) KSU1 dataset, (b) KSU2 dataset, (c) UTrento1, and (d) UTrento2.  
The results in Table 2 clearly show the superiority of the proposed method for all datasets, not only in terms of AVG accuracy but also in terms of execution time per image. This is mainly due to the effectiveness of the image features extracted with the help of the pre-trained SqueezeNet CNN and the improvements that we have proposed for the architecture.
## 2 Experiment and Methods
### Evaluation Indexes of Model Performance  
Performance evaluation indexes include mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). MAE is the average absolute error index, with a range of [0, +\(\infty\)]. The larger the error is, the larger the value will be; otherwise, the value will be smaller. When the predicted value is in complete agreement with the real value, MAE is equal to 0. MAPE is the prediction accuracy index, with a range of [0, +\(\infty\)]. The larger the error is, the larger the value will be. Otherwise, the value will be smaller. When MAPE is 0, the predicted value is in complete agreement with the real value. RMSE is the efficiency index of the prediction process, and the range is [0, +\(\infty\)]. The larger the error is, the larger the value will be, and vice versa.  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|f_{i}-y_{i}\right| \tag{5}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{f_{i}-y_{i}}{y_{i}}\right| \times 100\% \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(f_{i}-y_{i}\right)^{2}} \tag{7}\]
# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 2 Related Work
### Dataset  
The data considered relate to the thermal generation due to dispatch in the northern region of Brazil and are provided by the national system operator (in Portuguese _Operator Nacional do Sistema_--ONS). The original dataset is available at: [[https://dados.ons.org.br/dataset/geracao-termica-despacho-2](https://dados.ons.org.br/dataset/geracao-termica-despacho-2)]([https://dados.ons.org.br/dataset/geracao-termica-despacho-2](https://dados.ons.org.br/dataset/geracao-termica-despacho-2)) (accessed on 25 November 2023). The total generation relates to 16 thermal power plants (Aparecida Parte I, Cristiano Rocha, Geramar I, Geramar II, Jaraqui, Manauara, Maranhao III, Maranhao IV, Maranhao V, Maua 3, MC2 Nova Venecia 2, Parnaiba IV, Parnaiba V, Ponta Negra, Porto do Itaqui, and Tambaqui).  
Scheduled and verified generation data for thermal power plants dispatched by the ONS on an hourly basis are provided. This time series is presented in Figure 2. Given an hourly basis, 2160 records are considered from 18 August (0 h) to 15 November (23 h) of 2023, resulting in a period of evaluation of 90 days. The time series forecasting presented here is one step ahead, meaning that the predictions would be presented one hour earlier.  
The models presented in this paper can be applied to other datasets since the analysis is carried out in relation to the variation in the time series (see Figure 2). Especially concerning the analysis of variation in diesel consumption, it is necessary to understand whether non linearities (noise in the time series) affect the applicability of the model. In these cases, it is necessary to use input filters to attenuate the noise.
## 2 Related Works
### Skyline Query  
Skyline query algorithms are quickly becoming the mainstay of decision support systems. They function as an alternative to top-k algorithms, which are unable to retain the characteristic features of the various dimensions. The skyline approach makes it possible to identify the best result for every dimension, and tailor the results according to criteria designated by the user. Consider a situation in which the user seeks accommodation close to a particular venue. As shown in Table 2, various forms of information pertaining to nearby hotels must be taken into account in the selection of accommodation. If cost and location were the only considerations, then the information could be arranged within a two-dimensional scatter diagram, as shown in Figure 2. The black line is the skyline; it indicates that the cost and distance of C are better than those of D. In other words, C dominates D on both criteria. Unfortunately, a skyline algorithm is unable to choose between A and C, due to the fact that C is closer to the desired venue but A has a lower price. In this situation, the skyline query would return both hotels, leaving the ultimate decision up to the user. This problem becomes increasingly complex when the number of criteria grows. For example, hotels A and C could all be considered skyline hotels, due to the fact that they are not dominated by other hotels.  
Three skyline algorithms are particularly well known: block nested loops (BNL) [39], divide and conquer (DAC) [39], and sort limit skyline (SaLSa) [14]. The BNL algorithm compares a pair of data points and eliminates the point that is dominated. All of the remaining data points are then compared to one another iteratively in order to identify a single dominant candidate. This is the simplest and most intuitive skyline method; however, computational complexity grows exponentially with an increase in the quantity of data.  
The DAC algorithm [39] breaks down data into groups, conducts a separate skyline query in each group, and then combines the group results into a final skyline query to produce a final decision. The elimination of many data points during the initial grouping process enhances the execution speed.  
The SaLSa algorithm [14] uses summation values obtained from the raw data as threshold values to enable the filtering out of unnecessary data points. This algorithm arranges data sequentially according to the summation values and checks whether a given data point falls within the skyline threshold before testing for dominance; that is, only those below the threshold value are tested for dominance. The SaLSa algorithm has proven particularly effective in time and cost reduction.
## 1 Introduction
The COGNIFOG framework introduced in this paper represents an innovative cognitive fog solution tailored to meet the challenges of next-generation computing. Currently in development, it carries the name of the Horizon Europe project COGNIFOG, which will conclude by the end of 2025 [10]. This framework aims to enable the seamless orchestration of diverse distributed computing resources across various IT systems, integrating IoT, edge, and cloud ecosystems. By creating a unified fog continuum, COGNIFOG aspires to achieve the following:  
* Minimize energy consumption and latency by shifting data processing to the edge instead of routing it through communication networks to a data center. * Cut operational expenses and accelerate service delivery by dynamically provisioning computing, storage, and networking resources with minimal human oversight. * Enable the rapid development and deployment of applications by means of open application programming interfaces (APIs). The remainder of this document is organized as follows: Section 2 reviews the current state of the art in IoT-edge-cloud continuum systems and their main challenges. Section 3 presents the architecture, building blocks, and main features of the COGNIFOG framework. The discussion in Section 4 then focuses on connectivity challenges, demonstrating the potential of COGNIFOG to efficiently manage network resources. Section 5 incorporates a preliminary implementation of COGNIFOG in an IoT testbed environment and assesses its benefits in terms of connectivity performance. Lastly, conclusions and future work are discussed in Section 6.
# Application of the Supercapacitor for Energy Storage in China: Role and Strategy
## 3 Development of China's Supercapacitor Field
### Market Trends, Upstream Materials, and Preparation Procession
#### 3.1.2 Preparation Processes  
The preparation of supercapacitors can be divided into three key steps, i.e., electrode fabrication processing, the monomer structure design, and the control of water-oxygen content. The preparation of electrodes is one of the critical factors affecting its performance index. In terms of electrode preparation methods, Chinese supercapacitor companies are far behind the most advanced ones in the world. The coating technology, which uses styrene-butadiene rubber (SBR) and polyvinylidene fluoride (PVDF) as solvent is widely adopted in China. It has a moderate degree of specific capacity and stability as well as good process ability. Compared with sheet technology from Maxwell Ltd., the leading manufacturer of the supercapacitor, the coating process of China has poor environmental protection and produces relatively more impurities, so its investment is lower. Moreover, the coating technology is dependent on workers' experience and slurry properties fluctuate strongly, which leads to defects prone to low consistency and poor reproducibility. As a result of this, it is an urgent task to solve the process control problem of coating technology for the development of the supercapacitor manufacturing industry in China [28].  
The monomer structure design also needs to take into account the current distribution, resistance decrease, electrical contact reliability improvement, and heat dissipation factor and service life. It is the key factor in the preparation to affect the quality of the supercapacitor. The innovative design of the jellyroll structure, including extraction and safe structure, may be the main research direction of monomer structure design to achieve safety pressure release and self-recovery [29]. The control of the water-oxygen content includes the heat treatment of jellyroll and environmental control for electrolyte filling and ball sealing. Water-oxygen content that exceeds the standard causes the monomer voltage to be reduced, resistance to be increased and cycle life to be decreased while assembling supercapacitors. Therefore, the water-oxygen content has to be strictly restricted to 1 ppm in the whole manufacturing process. The control of the water-oxygen content is the shortcoming of China's supercapacitor preparation technology [30].
# Parametric Analysis of Cylinder Drying Process in Association with Various Materials
## 2 Methodology
### Computational Model  
Typically, a cylinder dryer is designed with multi cylinders as schematically presented in Figure 1a,b. Although a dryer contains many cylinders, the heat transfer processes in every single cylinder are the same; it only differs in the initial conditions of the drying material.  
Therefore, in this study, a single-cylinder dryer, which was experimentally reported by [PERSON] and [PERSON] [10], and is also a unit cell of the multi-cylinder dryers, is selected as an examining dryer. The drying process of the dryer in a cylinder is divided into four phases as shown in Figure 1c. The cylinder is driven by a motor, and its rotational speed is controlled by a gear. The cylinder is heated by hot steam flowing inside the cylinder. The drying sheet is fed at the inlet of the cylinder, and it is assumed to move at the same velocity with the cylinder. One surface of the drying sheet is named the basic layer, which is in direct contact with the cylinder in phases I, II, and III, and with the cold air of the ambient environment at phase IV.  
Another surface of the drying sheet is called the topmost layer, which has contact with the cold air of the ambient environment at phases I, III, and IV, and with moving felt at phase II. The distribution of the phases is schematically illustrated in Figure 1c. The drying process will release hot air and vapor to the ambient environment, which is driven to leave the drying section by an upwardly ventilated duct. The thickness of the drying sheet is much smaller than the length of the radius of the cylinder; therefore, a short segment of the drying sheet is assumed to be an approximately straight segment. For calculating the heat transfer process within the textile in the drying process, a unit cell of the textile is selected as a computational model of this study, which comprises a length, \(\Delta\)x, and a thickness, \(\Delta\)y, as presented in Figure 1d.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
###### Abstract  
Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition is proposed in this paper. Firstly, the K-means algorithm was used to cluster samples with similar characteristics together, extract kinematic fragment samples in typical driving conditions, calculate their feature parameters, and carry out principal component analysis on the feature parameters to achieve dimensionality reduction transformation of information. Then, the vehicle velocity prediction sub-neural network models based on long short-term memory (LSTM) with the QPSO algorithm optimized were trained under different driving condition datasets. Furthermore, the kinematic segment recognition and traditional vehicle velocity prediction were integrated to form an adaptive vehicle velocity prediction method based on driving condition identification. Finally, the current driving condition type was identified and updated in real-time during vehicle velocity prediction, and then the corresponding sub-LSTM model was used for vehicle velocity prediction. The simulation experiment demonstrated a significant enhancement in both the velocity and accuracy of prediction through the proposed method. The proposed hybrid method has the potential to improve the accuracy and reliability of vehicle velocity prediction, making it applicable in various fields such as autonomous driving, traffic management, and energy management strategies for hybrid electric vehicles.  
vehicle velocity prediction; neural network (NN); quantum particle swarm optimization (QPSO); long short-term memory (LSTM) +
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
It has been proven that a versatile bio-glue, polydopamine, can firmly bind TiO\({}_{2}\) (titanium dioxide) nanoparticles on thin film composite (TFC) membranes. In this work, the anti-fouling behaviour of this novel polydopamine-TiO\({}_{2}\)-modified membrane is evaluated, based on the static bovine serum albumin (BSA) surface adhesion of the membranes and the relative flux decline. The results show that the anti-fouling performance of this new membrane is significantly improved in dark conditions when compared with the neat TFC membrane and the membranes only modified by polydopamine or TiO\({}_{2}\). When filtrating a 0.5 g\(\cdot\)L\({}^{-1}\) BSA solution in dark conditions, the flux of the polydopamine-TiO\({}_{2}\)-modified membrane remains constant, at 95% of its pure water flux after 30 min filtration for 8 h of the experiment. This indicates a significant increase in anti-fouling performance when compared to the 25% flux decline observed for the neat TFC membrane, and to the 15% flux decline of those only modified by polydopamine or TiO\({}_{2}\). This remarkable anti-fouling behaviour is attributed to an improved and uniform hydrophilicity, due to the presence of TiO\({}_{2}\) and to the regular nanosized papillae structure of the polydopamine-TiO\({}_{2}\) coating. Furthermore, since dopamine-modified TiO\({}_{2}\) has visible light-induced photocatalytic properties, the membrane's photocatalytic performance was also tested in light conditions. However an increase of flux and decrease of retention were observed after 24 h of continuous illumination, indicating that light may also affect the top layer of the membrane.  
Keywords:anti-fouling; visible light photocatalysis; TiO\({}_{2}\) nanoparticles; dopamine; TFC membrane +
Footnote †: journal: Applied Soft 2017