esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization
## 3 The KbP-LaF-CMAES Algorithm
### Bi-Population Cooperative Evolution Strategy  
The goal of methods designed for MMOPs is to discover sufficient global and local optima within the optimization problem's landscape. This paper proposes a new method: a bi-population cooperative approach with a KbP strategy. This method leverages two distinct populations that evolve cooperatively, as depicted in Figure 3.  
In the bi-population method, individuals are updated using the LaF method, as outlined in Section 2. New solutions are juxtaposed with corresponding solutions in the follower population to maintain population diversity. The LaF strategy mitigates comparison bias associated with elitism and effectively identifies promising basins. In instances of stagnation, perturbations based on multimodal knowledge are implemented.  
In the KbP strategy, the population is perturbed using a random value determined by the multimodal characteristics of the problem. Subsequently, the search process of the LaF strategy is restarted from the perturbed solutions. The contribution of the KbP strategy in multimodal problems is illustrated in Figure 4. For convenience, the 2D Rastrigin problem serves as an example to demonstrate the influence of KbP. As shown in Figure 4a, a small perturbation value is added to the original solutions, with the new solution remaining in the same basin. This perturbation does not affect the exploitation process. In Figure 4b,c,  
Figure 3: The framework of the bi–population cooperative evolution strategy.  
the perturbation value corresponds to the size of the basin, allowing solutions to explore adjacent basins. These figures indicate that the direction of perturbation is crucial for exploration. In Figure 4d, if the perturbation size is too large, the new solution's basin becomes random.  
In the KbP strategy, the perturbation value is determined by the basin size of the multimodal problem. This knowledge is acquired through the linear search method. A detailed description is provided in Section 2. The minimum basin size in a specific dimension is used to generate the magnitude of the perturbation. During the KbP strategy, solutions are perturbed by a random value related to the basin size.
## 4 Discussion
### UV Radiation Stimulates the Biosynthesis of Anthocyanins  
According to our data, UV-A and UV-C irradiation had a positive effect on the anthocyanin content in basil leaves, while the combination of UV-A and UV-B did not change the anthocyanin content. It is known that repeated exposure to UV-C enhances the accumulation of phenolic compounds [50], and anthocyanin flavonoids have a function as a protective filter of the cell against the destructive photodynamic action of light and UV irradiation [45; 46; 53]. The absorption maximum of anthocyanin refers to 265-280 nm and 510-560 nm.  
Fluxonoids are of interest due to their antioxidant activity, anti-inflammatory and anticarcinogenic effects [54]. It is known that the biosynthesis of flavonoids is influenced by UV-B, UV-A and visible radiation (400-700 nm) [25]. UV-A radiation at a wavelength of 373 nm increased the accumulation of anthocyanins in young lettuce leaves [55]. At the same time, in green-leaved lettuce varieties, UV-A irradiation increased the content of nitrites [56]. UV-A improves antioxidant properties (affects the total content of phenol, anthocyanins and ascorbic acid) in basil plants [44]. In our case, UV-C radiation had the greatest effect on the biosynthesis of flavonoids. The anthocyanin content in this variant was higher than others. In our previously conducted research [57], the \"Red Ruby\" basil variety also showed a strong dependence on the average anthocyanin concentration in leaves on the type of UV radiation. The maximum influence was caused by UV-C on the 60 th day of cultivation, which indicates a greater response to UV stress during the budding and flowering stages.
## 6 Conclusions  
According to gathered sources, only a few attempts have been made to predict abrasion resistance for cementitious composites. As proven by a similar study [2], it is possible to predict the abrasion resistance for self-consolidating concrete based on the component's content. However, no research on the prediction of the abrasion resistance for mortars with fly ash and/or granite powder as a partial cement replacement was found. The modeling of such material is important and is becoming more popular recently. Fly ash can contribute to the improvement of several mortar properties while granite powder can contribute to a reduction in the cement content, together with carbon footprint, which is in compliance with the circular economy.  
Current knowledge of examining abrasion resistance for such mortars involves several disadvantages, including the application of devices that destroy the surface with the need to repair it afterward and examining samples created especially for testing to avoid damaging the pavement, which makes them differ in properties from the main structure or long-lasting laboratory tests. The application of an artificial neural network in abrasion resistance prediction can contribute to the optimization of the process. The assumption of input values into the network or random forest algorithm as mainly mortar components avoid much of the experimental part and allow the end result to be predicted even before the mix is made. These input parameters were the content ratio of cement, granite powder, and fly ash, combined with the number of cycles and the mass of the standardized sample before the test. The accuracy of the model is similar to other models in the field characterized by a good performance of prediction.  
Taking into account that there are minor differences in the results of the tests between the reference samples and the samples of the mixtures containing granite powder and fly ash, it can be stated that in terms of abrasion resistance, these two materials do not deteriorate the property of the material. It is very beneficial when designing eco-friendly cementitious composites. Further research might be conducted by implementing other mineral materials suitable as additives for cementitious composites. It might be beneficial in terms of reducing the carbon footprint and getting closer to the zero-emission goal in the future.  
Conceptualization, S.C. and S.M.; methodology, A.C., S.C. and S.M.; software, S.C.; validation, S.C.; formal analysis, S.C.; investigation, A.C., S.M. and S.C.; resources, L.S.; data curation, S.C.; writing--original draft preparation, S.C., A.C., S.M. and L.S.; writing--review and editing, S.C. and L.S.; visualization, S.M. and S.C.; supervision, L.S.; project administration, L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
The authors received funding from the project supported by the National Centre for Research and Development, Poland (grant No. LIDER/35/0130/L-11/19/NCBR/2020 \"The use of granite powder waste for the production of selected construction products\").  
Not applicable.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 2 Related Works
### Radar Systems  
A low-cost commercial FMCW millimeter-wave radar is used for gesture recognition experiments. The picture of the radar is shown in Figure 1a. The frequency range is 75-81 GHz with a maximum sweep bandwidth of 3 GHz. It has four transmit and four receive antennas which can form sixteen virtual receiving channels by working in a Time Division Multiplex Multiple-Input Multiple-Output (TDM-MIMO) mode. The radar chip is packaged using Antenna in Package (AiP) technology, which greatly reduces the radar size in order to apply it in compact scenarios such as automotive sensing, gesture recognition, and human detection [27].  
The radar hardware is mainly formed by an RF front-end chip, a controller chip, and some peripheral circuits. The structural diagram of the simplified FMCW radar with a single transmitter and receiver is shown in Figure 1b.  
This radar is designed with a compact AiP antenna, which can be easily integrated in small devices. The configuration of the transmit and receive antennas is illustrated in Figure 2a, with the transmit antenna depicted in red and the receive antenna in blue. The receiver antenna spacing is set at half of the carrier wavelength. By employing the TDM-MIMO mode, it is possible to obtain 16 virtual array elements, as illustrated in Figure 2b.  
The radar system parameters are shown in Table 1. According to the parameters, the range resolution is 5 cm, the velocity resolution is 1.14 m/s, and the angle resolution is 1/6 rad.  
The millimeter-wave radar emits an FMCW waveform, which is reflected by the target and received by the receive antenna. After mixing, filtering, and other operations, the radar extracts the intermediate frequency signal from the echo, which contains the target information regarding range, velocity, and angle. Finally, by collecting multiple frames of radar data, the continuous motion trajectory is obtained to achieve gesture recognition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Pulse Repetition Time (PRT) & 110 μs \\ Sweep Bandwidth (BW) & 3 GHz \\ Carrier Frequency (fc) & 75 G \\ ADC Sample Rate (SR) & 3.63 MHz \\ Sweep Slope & 42.5 MHz/ μs \\ Sample Numbers per Pulse & 256 \\ Pulse Numbers per Frame & 16 \\ Virtual Element Numbers & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radar system parameters.  
Figure 1: Physical image (**a**) and structural diagram (**b**) of millimeter-wave radar.  
Figure 2: Schematic diagram of radar antennas. (**a**) L-shaped distribution of transmitting and receiving antennas. (**b**) Schematic diagram of 16 channel virtual array elements.
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 3 Procedure
### Characterization Test Parameters  
As a proof-of-concept of the presented photogrammetric method, a 236 mm (9 / 32 in) long, 22.2 mm (7/8 in) diameter PAM was selected for initial testing and analysis. The material composition of this PAM, as detailed in Table 1, is identical to that of PAMs used in other research (e.g., [25]) resulting in comparable actuation characteristics.  
Photogrammetric data was acquired while the isobaric force-contraction response of the PAM was tested in the MTS machine. This test is performed by cyclically stroking the PAM between its resting length state, and its pressure-dependent minimum length state (concentric and eccentric motion). The PAM is cycled three times at each tested pressure to ensure accuracy and consistency of the characterization data. To ensure a quasi-static characterization, the PAM was stroked at 1.02 mm/s (0.04 in/s). For the photogrammetric analysis, photographs were taken at 1 s intervals for the first cycle of testing, resulting in shape profile data for every 1.02 mm (0.04 in) change in length of the PAM. Testing was performed at isobaric pressures of 6.89-689 kPa (10-100 psi) in 6.89 kPa (10 psi) increments, for a total of ten tests. Pressure was controlled to be constant during testing using the air pressure control system developed in previous work [26].
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 3 Results and Discussion
### Simulation Results and Analysis  
Numerical simulation results, represented by the DPM concentration on the wafer surface, specifically analyzed the concentration distribution and the size of the deposition spots. The following sections examine the impact of different deposition chamber configurations on deposition outcomes from four key aspects. Additionally, the deposition behavior of PSL particles with varying sizes under the optimized chamber configuration is discussed.
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
###### Abstract
It should be noted that in the recent computer science literature, the terms task and cloudlet scheduling are also adopted for the process of mapping submitted end-users' task to the available VMs. Many algorithms and techniques for resource scheduling in cloud computing environments are available. For example, in some systems, classical (deterministic) algorithms are used. However, classical optimization approaches are not efficient due to the fact that deterministic algorithms are not capable of generating satisfying, nor optimal or near optimal solutions within a reasonable computational time for NP hard challenges. Due to the search space complexity and exponential number of possible solutions, classical approaches are not able to evaluate every potential solution from the search domain in polynomial time. When tackling NP hard tasks, such as cloud computing resource scheduling, instead of using classical optimization techniques, methods that evaluate only promising parts of the search space (not the whole search domain) by using a smart mechanism when choosing which solutions to evaluate next, should be utilized. One of the most efficient ways to tackle the resource scheduling problem is the implementation of heuristics and metaheuristics based approaches that do not guarantee finding an optimal solution, but in practice, they proved able to generate satisfying solutions within the polynomial time. According to the literature survey, metaheuristics, especially those that are inspired by nature (nature-inspired, bio-inspired), have proven to be robust approaches that can efficiently tackle the cloud computing resource scheduling issue [2; 3; 4]. One class of the most well-known metaheuristics that are inspired by natural phenomena is swarm intelligence. Swarm intelligence approaches are population-based, stochastic and iterative. Many real-life problems, such as the localization problem in wireless sensor networks (WSNs) [5; 6], drone placement [7], robot path planning [8; 9], the network planning problem in radio frequency identification (RFID) networks [10], machine learning optimization [11], image processing [12], computer aided diagnostic systems [13; 14; 15], portfolio optimization [16], and more have been successfully tackled by utilizing swarm intelligence. In addition to numerous applications, swarm intelligence algorithms have been constantly modified, hybridized [17; 18; 19] and parallelized [20; 21] with the aim of achieving the best possible results.
## 3 Problem Description and Modeling
### Problem Formulation  
This paper aims to enhance the service experience for delay-sensitive tasks while minimizing overall energy consumption. Let \(x_{si}\) be the decision variable, such that \(x_{si}=1\) if task \(t_{i}\) is offloaded to satellite \(s\) and otherwise, \(x_{si}=0\). Another variable is \(y_{sij}=1\) if satellite \(s\) needs to perform task \(t_{i}\) before \(t_{j}\), and \(y_{sij}=0\) otherwise. Decision variables \(x_{si}\) and \(y_{sij}\) describe the offloading assignment \(\theta\). Then, the MILP model of the considered problem is presented as follows:  
\[F(\theta)=\underset{x_{si},y_{sij}}{\min}\sum_{i\in D}\left(\alpha\cdot\max\{0,\tau_{i}^{F}-\sigma_{i}\}+\beta\cdot\sum_{s\in S}E_{i,s}x_{si}\right) \tag{10}\]  
\[\sum_{s\in S}x_{si}=1,\,\forall i\in D \tag{11}\]  
\[\sum_{i\in D}\lambda_{i}x_{si}\leq MA_{s},\,\,\forall s\in S \tag{12}\]  
\[\sum_{i\in D}E_{i,s}x_{si}\leq H_{s},\,\,\forall s\in S \tag{13}\]  
\[\sum_{j\in D}y_{sij}x_{si}\leq 1,\,\forall s\in S,\forall i\in D \tag{14}\]  
\[\sum_{i\in D}y_{sij}x_{sj}\leq 1,\,\forall s\in S,\forall j\in D \tag{15}\]  
\[y_{sij}(\tau_{j}^{S}-\tau_{i}^{F})\geq 0,\,\,\forall s\in S,\forall i,j\in D \tag{16}\]  
\[x_{si}\in\{0,1\},y_{sij}\in\{0,1\},\,\forall s\in S,\forall i,j\in D \tag{17}\]  
\[\alpha+\beta=1,\,\,\alpha\in[0,1],\,\,\beta\in[0,1] \tag{18}\]  
where \(x_{ki}\) and \(y_{kij}\) are decision variables representing offloading assignment \(\theta\). \(\alpha\) and \(\beta\) are weight factors. Equation (10) represents the objective function, which aims to minimize the weighted sum of deadline violation and energy usage of all tasks. Equation (11) indicates that each task can only be offloaded to a specific satellite. Equation (12) states that all offloaded tasks on \(s\) should not surpass the available buffer size \(MA_{s}\), to avoid task overflow, and we have \(MA_{s}=M_{s}\), initially. Equation (13) indicates that the energy consumption for processing all offloaded tasks on \(s\) should not exceed the energy consumption limitation \(H_{s}\). Equations (14) and (15) suggest that each task has, at most, one predecessor and one successor in its sequence. Equation (16) illustrates temporal relations among tasks offloaded to the same satellite. Equations (17) and (18) state the domains of variables.
## 2 SITHON
### Goals and Specifications of the airborne thermal imaging system  
The SITHON project aimed at conducting further R&D in the area of using new smaller and less expensive IR uncooled cameras for fire detection and monitoring purposes, and post-fire management (i.e., mapping of burnt areas, assessment of fire impact). SITHON also focused in the area of image processing coupled with Geographic Information System (GIS), Global Positioning System (GPS) and Inertial Navigation System (INS) technologies to assist in the dynamic translation of the raw image data collected (video outputs or thermal images) to usable products. The SITHON airborne system was designed to offer high quality colour (video RGB) and infrared imagery. Each frame acquisition is coupled with kinematic GPS and inertial platform data ingested by a precise MIDG II Inertial Measuring Unit (IMU) on board the airplane. The integration of the GPS, the IMU and camera data along with a digital elevation model (DEM) allows for real time orthorectification and geo-positioning of the acquired frames without using ground surveyed control points.  
The ultimate aims in SITHON system design were directed to meet operational tactical requirements and products generation which are useful for crisis management including:  
* hot spot identification, geo-positioning and alarm generation,
* fire front location and representation on a map,
* isothermal contour lines generation and mapping,
* geo-referenced video (RGB) and thermal image frames projected to the Hellenic Geodetic Reference System (EGSA87) or any other cartographic projection system used by the fire managers in the area of interest,
* synthetic cartographic products integrating georeferenced thermal and/or video images with existing background maps of assets, transportation networks and facilities situated in the area of operations.  
Timeliness is of paramount importance for the success of such a system. The system is designed to ensure automatic fire detection. It is mountable on any airborne platform and can be operated within 15 to 20 minutes after the first fire announcement. Once on the platform, SITHON is supported by a fully automated control system, which manages the frame acquisition, the radiometric image calibration and thresholding, as well as the dynamic hot spot/fire front identification and geo-positioning within 50-100 m error with the lack of any operating GPS station on the ground. To be noted that the off-line processing of stereo pairs of images with the incorporation of GPS ground reference data, improves the geo-positioning accuracy up to the order of 1-3 m (\(\sim\)1,5-2 pixels).  
Clearly, the post-processing of stereo pairs of images is useful only for post-crisis management to allow precise mapping and assessment of the damages in the impacted zones. During crisis, the imaging system is integrated with telemetry equipment ready to transmit the output products to fire-fighting forces in real-time. The platform of SITHON was a CESSNA 310Q two-engine aircraft own and operated by AEROPHOTO Ltd (Figure 1).  
**Figure 1**. The CESSNA 310Q two-engined aircraft which is the platform of SITHON
## 4 Result Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Algorithm** & \begin{tabular}{c} **Information** \\ **Entropy** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Gradient** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **SSMI** \\ \end{tabular} \\ \hline \multirow{3}{*}{Scenario 1} & Equalization & 7.0618 & 21.0541 & 74.7433 & 0.7938 \\  & Traditional & 6.0503 & 22.6988 & 57.5973 & 0.2491 \\  & Ours & 7.2803 & 23.8238 & 37.6737 & 0.7959 \\ \hline \multirow{3}{*}{Scenario 2} & Equalization & 7.0639 & 23.7906 & 74.8634 & 0.8125 \\  & Traditional & 6.5632 & 18.6107 & 58.9985 & 0.4558 \\  & Ours & 6.8527 & 16.3776 & 18.7265 & 0.8884 \\ \hline \multirow{3}{*}{Scenario 3} & Equalization & 6.6994 & 37.9909 & 75.0269 & 0.6617 \\  & Traditional & 6.2549 & 24.9865 & 66.4276 & 0.4091 \\  & Ours & 7.0238 & 29.5858 & 28.8814 & 0.813691 \\ \hline \multirow{3}{*}{Scenario 4} & Equalization & 7.2683 & 26.6495 & 73.8097 & 0.7970 \\  & Traditional & 6.2721 & 18.7083 & 57.7610 & 0.6676 \\ \cline{1-1}  & Ours & 6.7742 & 21.8333 & 12.0983 & 0.9191 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dehazed Image Evaluation Metrics Table. Figure 15: _Cont._
## 5 Conclusions  
This paper seeks to reveal the cause of a raising deformation accident of SCS in the construction period. The accident is numerically reproduced, and the results agree well with the in-situ measured data at multiple key aspects, such as the raising deformation of the upper stay ring and the upper flange. This numerical study has identified two important factors: (a) the construction condition (unwatered or pressurized), and (b) the shape of SSC.  
The conventional design philosophy of temporary IWP is to create an initial gap between the SSC and the concrete. However, the findings of this study show that the temporary IWP also helps a lot in controlling the raising deformation of the structure. Even under such high external load of this accident, the temporary IWP managed to reduce the raising deformation to approximately one third of that in the unwatered condition. Moreover, it should be noted that the post-accident stresses of the SSC are still well below the yield point of steel. However, considering the need for safe and reliable operation of turbine set, such severe deformation is still unacceptable, and measures should be taken to restore the original state of steel structures. Accordingly, all the second-phase concrete should be removed, and a thorough check of steel structures is needed. Stronger restrains and better ventilation should be adopted for construction, and in-situ monitoring should be enhanced as well.  
Meanwhile, the study has shown that the shape of SSC is also a major factor accounting for the severe deformation. Under temporary IWP, the SSC with circle sections tends to maintain its original shape with slight lateral displacement, while the SSC with non-circle sections tends to get rounded, leading to severe deformation. As for the upper stay ring, the displacement of the non-circle case is larger than that of the circle case, and the location of the maximum displacement is different. There is, therefore, a definite need to strengthen the restrains on the stay ring in the construction period, even for a conventional circle-section SSC.  
The major limitation of this study is that this study merely focuses on the construction period. If such SCSs were to be used in a wider range, a follow-up study focusing on the operation period should be considered. Despite its exploratory nature, this work offers valuable insights into the construction of non-circle M-type SCSs, thus helping practitioners acquire precious lessons from this accident and prevent similar accidents in the future.  
Formal analysis, X.G. and D.F.; methodology, X.G. and D.F.; supervision, D.F. and H.W.; writing--original draft, X.G. and D.F.; writing--review and editing, D.F. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.