esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Simulation of Thermal Profiles Using FSC
#### 3.2.4 Approximation of Thermal Profiles and Simulation in FSC
This suggests the material has reached a saturation in crystallinity, meaning that under these conditions, crystallization has died down. The LMWPA copolymer represented by Condition 6 still exhibits a notable increase in crystallinity during the third zone, implying the possibility of crystallization progressing even further if the sample would have remained on the build plate. Hence, the time the part remains on the build plate after printing is crucial to control the eventual crystallinity of the printed part for LMPWA. Although research reported in the literature on PLA mentions a higher degree of crystallinity associated with lower print speed, which could be expected due to the longer printing and thus annealing time, keeping the polymer above its glass transition temperature for a longer time, the results produced in this study do not present these findings [30]. This could be explained by the visible plateau in crystallinity for HMPWA. Despite of the longer printing time for slower print speed, the thermal profiles in Figure 3b suggest a similar cooling cycle for both conditions in the third zone, resulting in a similar plateau value for the crystallinity of both conditions. Figure 10: The effect of applying the linear approximation corrected for nozzle reflection on the melting enthalpies after simulation of the full thermal profiles.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 3 Performance Test
### Optimization of the Number of Layers of Hg\({}^{2+}\) Sensitive Film  
Under the same conditions, fiber reflective SPR sensors with Hg\({}^{2+}\) sensitive films of 5 layers, 10 layers, and 15 layers were fabricated, named as (CS/PAA)\({}_{5}\), (CS/PAA)\({}_{10}\), and (CS/PAA)\({}_{15}\), respectively. These fiber sensors were immersed in 50-\(\upmu\)M Hg\({}^{2+}\) solution, and the output spectra were recorded with the detection time, respectively, as shown in Figure 8. From the experimental results, it can be seen that with the increase of the detection time, the spectral wavelength moves to the long wavelength direction, and finally stops moving. This is because the adsorption of Hg\({}^{2+}\) leads to an increase of the cross-linking degree and an increase of the refractive index of the sensitive film. The wavelength shifts at equilibrium are 15 nm, 20 nm, and 6 nm, respectively. When measuring the same concentration of Hg\({}^{2+}\) solution, the (CS/PAA)\({}_{10}\) sensor has the largest spectral wavelength shift, namely the highest sensitivity. Therefore, the optimal number of layers of the sensitive film was selected as 10 in the following experiments.
## 5 Conclusions  
A multi-rotor wind turbine with multi-generator drive trains was presented. Both the mechanical structure with the calculation of eigenmodes and a control scheme with simulation results were proposed. The control system uses a wind speed observer-based state feedback scheme for each rotor to consider wind speeds, which vary significantly over the entire altitude. The wind speed observer model and control law was presented, and more detailed controller design references were given.  
The results of a purely mechanical structural analysis and mechanical FEM design were presented in the first part. Then, in the second part, a control structure was proposed that mitigates the loads on the tower structure. This approach is based on the mechatronic idea using the synergy of information processing, electronics (software and hardware of the controller), mechanical/aerodynamic modeling, and numerical calculation (FEM and ODE solver). A controller structure was proposed, which acts both in the partial-load operating region by adjusting the generator torque and in the full-load by changing the pitch angle. In both regions, estimating the non-measurable effective wind speed in front of the rotor is utilized. For the partial-load region, the reference speed of the control is calculated, which is necessary to achieve the optimum tip speed ratio, respectively, the maximum power coefficient. In contrast, the full-load controller uses the wind speed estimate to calculate the current scheduling vector and to adapt the controller coefficients via the weighted sum of the controller gains \(K_{x,i}\) and \(k_{I,i}\). Simulation results with artificial speed curves in the partial-load region (for a better assessment of the optimum power operation) and realistic turbulence curves in the full-load region have shown the applicability of the approach.  
Note that the couplings between the rotors via the support arm couplings have not yet been taken into account in the control design. In a recent paper, this is addressed by an extended design model and thus also in the controller design. With the extension of the controller, the calculation of the fatigue strength is then carried out with realistic loads.  
Conceptualization, literature survey, review and editing, U.G.; Investigation, formal analysis, methodology, S.K., H.S.; writing original draft, preparation, H.S.; supervision, project administration, U.G. and H.S.; All authors have read and agreed to the published version of the manuscript. This research was self funded.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
LMI Linear Matrix Inequality  
PDC Parallel Distributed Compensation  
TS Takagi-Sugeno  
WT Wind turbine
## 3 Methodology
### Collection and Annotation of Dataset  
The first step is to collect a comprehensive dataset of fruit and vegetable images, which will serve as the basis for our model training. To achieve this, we have collected 2622 images of 12 different classes of vegetables and fruits. The summary of our dataset including images and instances is shown in Table 1. The images were captured using the camera of a Samsung phone, with varying angles, orientations, and lighting conditions, to ensure the model could generalize well to different scenarios. Furthermore, the images were taken against backgrounds that are similar to our intended usage scenario, which involves the use of a scale to determine the weight of each fruit or vegetable. The preview of our dataset is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Class Name** & **Number of Images** & **Number of instances** \\ \hline
1 & Potato & 368 & 882 \\
2 & Tomato & 338 & 1027 \\
3 & Onion & 250 & 868 \\
4 & Turnip & 246 & 635 \\
5 & Chili & 314 & 885 \\
6 & Garlic & 148 & 710 \\
7 & Carrot & 181 & 301 \\
8 & Cucumber & 310 & 467 \\
9 & Apricot & 155 & 823 \\
10 & Yam & 126 & 731 \\
11 & Lemon & 68 & 826 \\
12 & Plum & 118 & 947 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of datasets with number and name.  
Figure 6: The glimpse of some of the images from dataset.  
After collecting the dataset of fruit and vegetable images, the next step is to manually annotate each image with appropriate labels for each fruit and vegetable. To accomplish this, we used the labelImg framework [29], which provides a user-friendly interface for manual annotation. Using this framework, we manually drew precise bounding boxes around each fruit and vegetable image and assigned the corresponding labels to each bounding box as shown in Figure 7. The dataset was then split into a training set and a validation set in a ratio of 80:20, respectively, resulting in 2225 training set images along with 128 negative images, and a validation set consisting of 525 images. This process ensured that our dataset was accurately annotated and ready for use in training our supervised deep learning model.
## 1 Introduction
As a result, a flow-feedback approach is proposed by [PERSON] and [PERSON] (2019) to supply the jet with a twin spiral case to collect a fraction of the discharge from the cone outlet. The jet is driven by the pressure difference between the cone wall and the pressure at the jet nozzle outlet. The flow-feedback approach was experimentally investigated by [PERSON] et al. (2019) assessing it performances. Ten percent of the jet discharge is delivered by the plain flow feedback and only 2% boost is provided by the ejector pumps. These active control methods act to increase the axial flux of the moment of momentum to stabilize the flow instead of to diminish the swirl level below the critical level (to decrease the tangential flux of the moment of momentum) like the passive control methods mentioned above. [PERSON] and [PERSON] numerically examined a new idea about the water injection method ([PERSON] and [PERSON], 2019), and considered a swirl component superposed over an axial jet in order to improve its effects. Their investigations have shown that the pressure pulsations, velocity fluctuations and the size of the vortex rope decrease when a jet with a flow rate less than 3% is injected from the runner crown. In addition, the axial water and air-water mixture was successfully tested to mitigate the unsteady effects associated to different part load operating points for a pump-turbine by [PERSON] et al. (2019). In this paper, we introduce a novel passive control method to mitigate the pressure fluctuations associated to the vortex rope, relying on the latest knowledge gained from an extensive analysis of the swirling flows. This proof of a concept involves a progressive throttling of the cross-section area using an adjustable diaphragm. The control system with adjustable diaphragm is located at the end of the conical diffuser--Figure 1. All positions of the circular cross-section are centered on the axis of the turbine. The shutter area of the diaphragm has to be correlated with the turbine operating regime. As a result, the diaphragm should be retracted to the cone wall when it is unnecessary to be used (i.e., around to BEP) to avoid any additional hydraulic losses.
## 3 Challenges for Routing Protocols in FANETs
### High Mobility  
UAV nodes have greater mobility than that of MANETs and VANETs [75]. Every UAV node is extremely mobile, traveling at speeds ranging from as low as 30 to as high as 460 km per hour [76]. Mobility models in UAV networks are based on the application and depend on the type of UAV deployed in the field: multi-rotor, vertical take-off and landing (VTOL), or fixed-wing. The considered UAV type has an impact on the most suitable mobility model. The use of global path planning for UAVs is favored in some multi-UAV systems. On the other hand, multi-UAV systems operate independently, with no predetermined path. Mobility models such as the GM model, which allows UAVs to follow flexible trajectories, can be employed in search and rescue operations. In the GM model, UAV movement is dependent on previous directions and speed, which helps UAVs relay networks [77]. Due to its highly dynamic nature, node mobility is considered the greatest challenge in UAV routing.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 3 Fragility Analysis for Human Occupant Comfort
### Uncertainties in Occupant Comfort Problems  
Significant uncertainties arise in wind-excited tall buildings due to aerodynamic wind loading characteristics or the properties of the building system. The statistical components of design wind speeds directly influence the uncertainty of wind loading characteristics. The design wind speed value is uncertain due to the sample mean and sample standard deviation being random variables, with their variation contingent upon the sample size. The uncertainty in estimated design wind speeds may be assessed by analyzing the mean and variance. The mass, stiffness, and damping of a structure all affect its dynamic properties, such as its natural frequency and the shapes of its vibration modes. A decrease in the uncertainty of wind-induced loads and corresponding responses cannot be achieved without addressing the uncertainty in structural damping, which is closely related to the predicted accelerations of a structure and significantly influences its ability to satisfy occupant comfort criteria. Consequently, structural damping, acknowledged as the most uncertain factor, is a significant consideration in the serviceability design of tall buildings. From a practical perspective, mass and stiffness exhibit far less uncertainty than damping and may therefore be regarded as deterministic variables [51]. In this study, while considering uncertainty in both mean wind speed and structural damping, the distributions of the random wind speed and damping were modeled as Gumbel and lognormal probability distribution functions, respectively. Table 8 represents the statistical parameters for wind speeds and damping ratios used to assess the probability of vibration exceedance of the tall building for occupant comfort.
## 3 Optimization-Based Strategies
### Instantaneous Optimization
#### 3.1.2 Model Predictive Control (MPC)
Based on vehicle location, traveling direction, and terrain information for HEVs running in hilly regions, the road grade for stochastic routes was modeled by a finite-state Markov chain model, and the vehicle speed profile was modeled using a similar method. Factors including prediction accuracy, design parameters, and optimization solution will influence the control performance of the MPC strategy, which means that it cannot guarantee robustness and accuracy when the actual driving cycle is quite different from the training driving cycle. Specifically, prediction uncertainty can heavily affect the performance of the MPC-based strategy for unknown routes with unknown driver behavior. Similarly, as for the SMPC strategy, the transition probabilities in the Markov chain are based on collected driving cycles [71], that is, it cannot guarantee prediction accuracy or the optimality of energy management when the real driving conditions differ from the collected data.
## 4 Results and Discussion
### Peeling Work  
Peeling work is defined as the reduction of the free energy of the asphalt binder and aggregate system when the asphalt binder gets separated from its interface with aggregate in the presence of water. For asphalt mixtures, the greater the spalling work, the worse the resistance to spalling--that is, the poorer the moisture damage. Figure 7 is a calculation result of three kinds of asphalt stripping work.  
Figure 6: Influence of aging on adhesion work of aged asphalt.  
Comparing the adhesion work of three kinds of asphalt under anhydrous conditions and the change of water with the participation of the stripping work, it was found that the participation of water greatly reduced the adhesion performance of the asphalt-aggregate. Experiments show that the adhesion performance of asphalt-aggregate depends on the stripping effect of water on asphalt [34]. The addition of water breaks the two-phase stable state of asphalt and aggregate, and unstable water molecules penetrate into the interface between the asphalt and aggregate. By replacing the asphalt film wrapped on the aggregate surface, the contact area between the asphalt and the aggregate is reduced. The stronger the displacement, the greater the peeling effect of water on the asphalt-aggregate, and the more water damage is likely to occur [39; 40].  
Compared with the anti-flaking ability of matrix asphalt and Sasobit warm asphalt in water, Evotherm M1 warm asphalt has the best anti-flaking ability, followed by matrix asphalt. Sasobit WMA and aggregate are more prone to spalling damage with the participation of water, and the anti-flaking  
Figure 7: Influence of aging on spalling work of aged asphalt.  
ability is the worst. Evotherm M1 warming agent is a surfactant. The reversed micelles combined with water and surfactant form a special membrane structure inside the asphalt, which strengthens the chemical bonding, improves the stability of the asphalt, and improves the anti-flaking performance to some extent. Researchers have also found that the stability and aging resistance of asphalt with Evotherm warm mixing agent are better than those without Evotherm warm mixing agent. This proves that Evotherm warming agent has an anti-flaking effect to some extent.  
When considering the influence of water on the adhesion of aged asphalt, it is found that the variation of the stripping work of the three asphalt concrete aggregates is consistent with that of the unaged asphalt. This indicates that water still plays a role in the exfoliation of the asphalt during the adhesion process of the aged asphalt concrete aggregate, and the aging process exacerbates the degree of peeling of the asphalt from the aggregate surface. The comprehensive comparison of the stripping work changes of the three kinds of asphalt after aging in Figure 7 shows that the exfoliation work of the matrix asphalt, Evotherm M1 WMA, and aggregate increases after aging. The spalling work of Sasobit WMA-aggregate first increases and then decreases. The exfoliation work of the matrix asphalt and Evotherm M1 WMA tends to be stable after 150 h of UV aging.
# An Improved Robust Fractal Image Compression Based on M-Estimator
## 3 Robust M-Estimator
### Tukey's Bisquare M-Estimator Figures, Tables and Schemes  
The Tukey's bisquare function can suppress the outlier even further. In [PERSON]'s bisquare case, \(\rho\)-function is defined by Equation (18) as follows:  
\[\rho_{TB}(e)=\left\{\begin{array}{l}\frac{k^{2}}{6}(1-\left[1-(e/k)^{2} \right]^{3}),\text{ for }|x|\leq k\\ \frac{k}{6},\text{ for }|x|>k\end{array}\right. \tag{18}\]  
where, the tuning constant \(t=4.6851\sigma\) for [PERSON]'s Bisquare case in particular [34], where \(\sigma\) is the standard deviation of the errors, and can be obtained by Equation (15).  
The objective function is defined in Equation (10). Note that the Huber objective function increase without bound as the residual \(e\) departs from 0. In contrast, [PERSON]'s bisquare objective function levels eventually level off (for \(|e|>k\)). The weights for the Huber estimator decline when \(|e|>k\), and the weights for [PERSON]'s bisquare are 0 for \(|e|>k\).  
Here \(W_{TB}\) denotes the weight function of [PERSON]'s bisquare case.  
\[w_{TB}(e)=\left\{\begin{array}{l}e[1-(e/k)^{2}]^{2},\text{ for }|e|\leq k\\ 0,\text{ for }|e|>k\end{array}\right. \tag{19}\]  
Considering the general regression model (1), Let \(\varepsilon=e/\sigma\) and \(w_{i}=w(\varepsilon_{i})=w(e/\sigma)\). The main steps of IRLS for the estimation of the parameter using [PERSON]'s bisquare M-estimator are described as Algorithm 2.  
```
Input Initial value of estimates \(\hat{\beta}^{(0)}\), random variable \(\hat{\varepsilon}_{i}{}^{(0)}\), and weight \(w_{i}{}^{(0)}\) Output The final value of \(\hat{\varepsilon}_{i}^{(t-1)}\), \(w_{i}^{(t-1)}\) and \(\hat{\beta}^{(t)}\)
1: Select initial estimates : \(\hat{\beta}^{(0)}\), \(\hat{\varepsilon}_{i}{}^{(0)}\) and \(w_{i}{}^{(0)}\). The least-square can generate the initial estimates.
2:while\(\hat{\beta}\) didn't converge to a convergence value do
3: Compute residuals by \(\hat{\varepsilon}_{i}^{(t-1)}=y_{i}-x_{i}^{T}\hat{\beta}^{(t-1)}\)and weights\(w_{i}^{(t-1)}\) by Equation (19)
4: Compute the new weighted \(-\) least \(-\) squares estimates \(\hat{\beta}^{(t)}\) by Equation (13)
5: Compute the objective value \(E\) based on the objective function by Equation (10)
6:endwhile
```  
**Algorithm 2** The IRLS algorithm using [PERSON]'s bisquare M-estimator
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 3 Standard, Network and Channel Models
### Standard
The 802.15.4 MAC layer allows both inbound and outbound data transfer and management by higher-level entities of the RF and PHY. The MAC sublayer provides access to the upper layers through two Service Access Points (SAPs). MAC-SAP manages the data while MAC Layer Management Entity (MLME-SAP) interface controls and monitors functions. The MAC layer is responsible for generating network beacons that permits devices to find an existing network, or, in the case of Time Division Multiple Access (TDMA) networks, it furnishes a timing indication for client devices to access the channel during both contention-based and contention-free periods. The MAC sublayer also provides interface for executing application-appropriate security mechanisms.
## 2 Materials and Methods  
The performance of the OHS system is presented and analysed using various indicators. The complexity of the OHS system as well as diverse internal and external factors influencing its functioning, necessitates a careful selection and classification of indicators into groups describing the corresponding influential factors.  
The research presented in this paper was based on a methodological procedure designed to identify the influence of indicators on the functioning of the OHS system using multi-criteria analysis methods. Figure 1 presents the methodological procedure. This procedure is supported by earlier research into various factors influencing the OHS system including the environmental factor [32], the organisational factor [34], the human factor [32], and the technological factor [35]. These factors were examined within the framework of social acceptability, risks, and costs.  
The procedure consists of two main phases: a preparatory phase and an execution phase. During the preparatory phase, the indicators are analysed, and the initial classification is defined. Potential experts are then contacted, and those who agree to participate in the analysis are given the opportunity to review the initial proposal and suggest potential modifications. The influence of the experts is determined based on their previous experience, and this influence is presented by corresponding weights. Following this, the execution phase is carried out.  
The execution phase consists of two parts: the selection of key indicators and their ranking. The selection of key indicators is carried out based on the experts' weights using Borda voting. The selected key indicators are obtained as results, ensuring an equal number from each group of indicators that describe the relevant influencing factors. This approach avoids bias in favour of certain groups of indicators over others.  
Figure 1: Methodological procedure.  
Experts receive the results of the key indicator selection, along with the defined hierarchical structure. Following this, the experts evaluate the selected key indicators and influential factors, comparing them in pairs according to the levels of the hierarchical structure using linguistic variables. These linguistic variables represent the corresponding fuzzy numbers. The comparisons defined in this manner are used in the FAHP. Once the necessary calculations with aggregated fuzzy matrices are completed, the weights of the influential factors and key indicators are determined, and the ranking is performed. The results are then presented to the experts. If the experts agree with the results, potential proposals for improving the OHS system are articulated based on the best-ranked key indicators. Optionally, various strategies for improving the OHS system may be defined, and their effects on the rank of key indicators are analysed.  
Details of the aforementioned steps are presented in the following sections.