esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Application of an Improved Sparrow Search Algorithm
## 1 Introduction
More and more researchers and scholars consider association rule mining as a multi-objective problem to evaluate the mined association rules with multiple objective functions. However, many studies omit to consider how to further enhance the value of the set objective function, and it is a challenge for the optimization ability of heuristic algorithms to simultaneously consider multiple objective functions for association rule mining under large-scale datasets. To address this problem, this paper proposes the adaptive Weibull distributed sparrow search algorithm (AWSA) to enhance the performance of heuristic algorithms. In order to verify the performance of the improved algorithm, we first conducted experiments on 10 standard functions. The purpose of this was to evaluate the optimization ability of the AWSA. Finally, in order to test the actual performance of the AWSA in multi-objective association rule mining, five different-sized datasets were selected, and experimental simulations were conducted with the objectives of support, confidence, and enhancement, which aimed to evaluate the quality of the rules generated by the AWSA under different-sized datasets. The main contributions of this paper are as follows: (1) We propose an adaptive Weibull distribution sparrow search algorithm that improves the optimization and convergence ability of the sparrow search algorithm by combining three strategies, namely, adaptive Weibull distribution, augmented search, and multidirectional learning. (2) By applying the improved algorithm to multi-objective association rule mining, there is a significant improvement in the value of the objective function, which further improves the quality of the mined rules and provides more accurate and excellent solutions for decision makers.
## 3 Case Study: Measurement Campaign Goal and Setup
### Imaged Area  
All acquired RADARSAT-2 scenes imaged an area around the DLR site at Oberpfaffenhofen, Germany. The transponder was built up right on the DLR premises, allowing easy wiring and monitoring. The 1.5 m corners were installed in the immediate vicinity on the protected grasslands surrounding the airstrip at Oberpfaffenhofen. The site names for sites on which the 1.5 m corners are installed begin with _D26_. The locations are shown in Figure 2. The 3.0 m corners were located within a larger rectangular area of about 14 km \(\times\) 8 km.  
The site names for locations with a 3.0 m corner are _D24_, _D25_, and _D27_ to _D30_. An overview of the 3.0 m corner sites is shown in Figure 3.
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 4 Illustrative Examples
### Smart Buildings  
The very basic feature of an energy system using IoT can be introduced as a consumer of energy sending information to a data collector via a communication system. This appears in a smart building.  
Figure 3: IoT-based system architecture.  
In this case, the number of components is limited, and they are close to each other. Hence, short-range communication systems like ZigBee is suitable for this application.  
In designing of an IoT-based smart building, the very first step is to address every physical object by a virtual one [122; 123; 124]. In this manner, a virtual environment will map every object related to the real-world environment. For developing smart energy systems in buildings, the three main issues should be considered: energy consumption monitoring using smart meters and smart devices, designing a model to analysis it, and implementing an appropriate IoT-based system considering the designed model to analyze energy and needs. For example, using LAN is more appropriate in comparison with a WAN for a small house. The latter one needs attention to every detail including how to manage BD, determine location-based energy management, etc., and all of the IoT-based smart buildings are studied. In order to make the information secure, some policies should be agreed on by energy consumers. As an example, details about the location of end-users must be available to authorities, and security constraints should not negatively affect this accessibility.  
The main motivator of employing IoT for Buildings is reducing sensors' prices and developing applications that are user friendly [125]. This means that, by using a mobile device, every component that is connected to the IoT system can be monitored and controlled. In [126], an example of IoT-based smart home is presented. In this attempt, data sense by some sensors, and then information is sent to a network using ZigBee technology in JavaScript Object Notation (JSON) format. Then, applications like monitoring home conditions, managing home conditions, and controlling home access are applied through the Internet. Physical components are equipped by RFID tags, and, in this manner, physical objects are introduced into the cloud as virtual objects.  
One of the main applications in IoT-based smart buildings is the internet-based demand response, which can be introduced as incentive-based or price-based schemes [127]. Communication infrastructure is mentioned as the key feature in developing DR in smart buildings.
# Controllable Height Hopping of a Parallel Legged Robot
## 5 Simulation and Experiment  
### Simulation and Results  
The main goal of the simulations was to find a numerical fitting solution for function \(f\), while we were not clear about its specific analytical form before experiment. Firstly, we performed preliminary experiments to calibrate the basic system parameters, e.g., static friction in the actuator and constant friction \(f_{g}\) on the guide rail. In the calibration, we used simple facility to fitted approximate linear relation of the motor current and output torque, as in Figure 2b, to solve for the friction \(f_{S}\) in (4). We could then control \(\tau_{S}\) directly, as introduced previously. In the next calibration, according to the illustration in Section 3.3, we designed a free falling experiment to estimate the friction \(f_{g}\) of the guide rail. In the results, the equivalent acceleration on the rail is approximately \(11\) m/s\({}^{2}\).  
In simulation, we constructed a mathematical dynamic model as described in Section 3 and used \(Matlab\) to implement the numerical calculations. Each simulated period only consists of the takeoff phase from the bottom position until the takeoff moment, and the final objective is to find the function \(f\) introduced in Section 4.3. One round of simulation framework is shown in Figure 7, and the specific simulation parameters are according to actual robot prototype. Based on this framework, ranges of virtual stiffness \(k_{vl}\) and initial bottom height \(h_{B}\) values were input to the model for circulation and the corresponding summit hopping heights were obtained. After many simulations (about 5000), series of input and output values were collected, and we used the fitting method to get approximation function \(f\). We found that the fitting relation for \(k_{vl}\), \(h_{B}\), and \(h_{smt}\) (function value of \(f\)) can be approximately expressed as a quadratic polynomial, as follows:  
\[h_{smt(i)}=p_{0}+p_{1}k_{i}+p_{2}h_{B(i)}+{p_{3}}{k_{i}}^{2}+p_{4}k_{i}h_{B(i) }+p_{5}{h_{B(i)}}^{2}+\epsilon_{i} \tag{17}\]  
where \(h_{smt(i)}\), \(k_{i}\), and \(h_{B(i)}\) are \(h_{smt}\), \(k_{vl}\), and \(h_{B}\) in \(i\)th simulation, respectively. Then, we could use the optimization function in (18) to find the appropriate parameter value \(p_{0}\) to \(p_{5}\).  
\[\min\,\sum(f(h_{B(i)},k_{i})-(p_{0}+p_{1}k_{i}+p_{2}h_{B(i)}+p_{3}{k_{i}}^{2}+p _{4}k_{i}h_{B(i)}+p_{5}{h_{B(i)}}^{2}))^{2}=\sum\epsilon_{i}^{2} \tag{18}\]  
Finally, \(p_{0}\)-\(p_{5}\) were optimized as \(0.426\), \(0.00156\), \(-5.341\), \(2.2\times 10^{-8}\) (negligible in actual control calculation), \(-0.00356\), and \(10.68\), respectively. From the fitting function, R-square is \(0.9997\), which basically satisfied our needs.
## 5 Platform Testing and Validation
### Platform Validation
#### 5.4.1 End-User Validation  
Mekorot ([[https://www.mekorot-int.com/](https://www.mekorot-int.com/)]([https://www.mekorot-int.com/](https://www.mekorot-int.com/)), accessed on 5 June 2021) is a National Water utility located in Israel and one of the end users that tested and validated our proposed platform. As part of the STOP-IT project, Mekorot selected the Sorek Desalination Intake Plant ([[https://www.water-technology.net/projects/sorek-desalination-plant/](https://www.water-technology.net/projects/sorek-desalination-plant/)]([https://www.water-technology.net/projects/sorek-desalination-plant/](https://www.water-technology.net/projects/sorek-desalination-plant/)), accessed on 5 June 2021) as its demonstration site. The Sorek intake facility connects the Sorek Seawater Desalination Plant to the Mekorot national water system and is located close to the Tel-Aviv metropolis. The facility is constructed of pumping stations (5 Units), 2 \(\times\) 75,000 m\({}^{3}\) storage pools, a local network - PLCs and HMI, and a remote connection (MDLC protocol) to Mekorot SCADA. Its primary goal is to overcome the gap between supply (24/7) and demand (according to the domestic consumers' behavior) since the national water system does not include a large storage volume.  
The testing and validation phase occurred both physically (at the Sorek Intake Facility) and remotely (offline) using recorded real data. No live data or connection was made to the Mekorot SCADA system out of security and operational policy considerations. Mekorot's command and control system is managed by nine control centers covering the entire land surface of Israel, and the system operates 3000 remote sites by radio, cellular, and satellite links. For this reason, the Sorek intake facility premises and operational data served as a representative environment for the Mekorot SCADA system, whose main interest is to protect its SCADA system from physical intrusions and cyber-attacks and to improve the system's performance due to its wide-ranging dependency SCADA platform.
## 3 Results
### Cytotoxicity  
The cytotoxicity of each extract was evaluated at a concentration of 40 mg/mL. This concentration was chosen because it was the highest concentration tested among all assays, especially the antibacterial and antibiofilm assays. At this high concentration, it was possible to determine that _P. ostreatus_ and _P. erygii_ showed no cytotoxic activity (negative percentages indicate an increase in metabolic activity) while all _A. cylindracea_ extracts were cytotoxic (metabolic inhibition percentages superior to 30%), especially M2A and M2B (Table 4).  
The results for _P. ostreatus_ and _P. erygii_ are in line with other studies. For instance, [PERSON] et al. [41] showed that _P. ostreatus_ showed no cytotoxicity at 2% (\(w/v\)) against Caco-2 cells. Additionally, _P. ostreatus_ and _P. erygii_ extracts also did not show mutagenic activity, demonstrating their safety for food applications. The cytotoxicity observed in extracts from _A. cylindracea_ occurred at high concentrations, highlighting the need for further studies to determine the safe concentration limits for potential applications. The goal should be to identify non-toxic concentrations that retain the desired bioactivities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**ABSTS (mg AAE/g of**} & \multicolumn{2}{c}{**ORAC (mg TE/g of**} \\  & & **Dry Extract)** & **Dry Extract)** \\ \hline  & M1 & 5.78 \(\pm\) 0.31 \({}^{\text{a}}\) & 31.92 \(\pm\) 0.85 \({}^{\text{a}}\) \\ _P. ostreatus_ & M2A & 6.33 \(\pm\) 0.83 \({}^{\text{a}}\) & 33.52 \(\pm\) 4.09 \({}^{\text{a}}\) \\  & M2B & 4.39 \(\pm\) 1.12 \({}^{\text{a}}\) & 37.37 \(\pm\) 1.32 \({}^{\text{a}}\) \\ \hline  & M1 & 5.48 \(\pm\) 0.03 \({}^{\text{a}}\) & 39.29 \(\pm\) 2.28 \({}^{\text{a}}\) \\ _P. erygii_ & M2A & 2.23 \(\pm\) 0.15 \({}^{\text{b}}\) & 34.31 \(\pm\) 5.58 \({}^{\text{a}}\) \\  & M2B & 3.87 \(\pm\) 0.39 \({}_{\text{a}}\) & 44.25 \(\pm\) 0.05 \({}^{\text{b}}\) \\ \hline  & M1 & 5.91 \(\pm\) 0.45 \({}^{\text{a}}\) & 65.70 \(\pm\) 2.82 \({}^{\text{c}}\) \\ _A. cylindtracea_ & M2A & 12.05 \(\pm\) 1.26 \({}^{\text{c}}\) & 60.57 \(\pm\) 3.47 \({}^{\text{c}}\) \\  & M2B & 6.78 \(\pm\) 0.44 \({}^{\text{a}}\) & 61.88 \(\pm\) 4.85 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} Extract M1—hot extraction (1 g of mushroom dry matter (DM); 20 mL of water; 90 \({}^{\text{c}}\)C, 700 rpm, 1 h); extract M2A—room-temperature extraction (1 g of mushroom DM; 20 mL of water); extract M2B—hot extraction of residue from M2A (1 g of mushroom DM; 20 mL of water; 90 \({}^{\text{c}}\)C, 700 rpm, 1 h).  
\end{table}
Table 3: The antioxidant activity of extracts M1, M2A, and M2B from _P. ostreatus_, _P. erygii_, and _A. cylindracea_. The results are presented as the mean \(\pm\) SD. Different letters in each column and for each antioxidant assay indicate significant differences among the mean values of each extract (\(p\) < 0.05).
# Design of Zoom Optical System from Visible to NIR-II for Vivo Fluorescence Imaging Device
## 5 Conclusions  
In this paper, we designed a wide-band zoom optical system with a working spectrum from 400 nm to 1700 nm using ZEMAX. According to the aberration theory, the layout is adjusted by various methods and optimized several times. The MTF of the optical system in each field of view is greater than 0.3 at 100 lp/mm, and RMS is controlled below 5 \(\upmu\)m, indicating that the imaging quality is good. The zoom optical system only uses spherical lenses instead of aspheric lenses and diffractive optical elements for design. The results of the tolerance analysis show that the tolerance distribution is reasonable, and the system is easy to process, so there is a good prospect of application in this optical system.  
Conceptualization, F.C.; methodology, F.C.; formal analysis, S.G. and L.F.; investigation, S.G. and L.F.; data curation, S.G.; writing--original draft preparation, S.G.; writing--review and editing, L.F. and F.C.; visualization, S.G.; supervision, F.C.; project administration, F.C.; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The animal study protocol was approved by Animal Welfare and Ethics Group, Department of Laboratory Animal Science Fudan University (protocol code 202108005S, 2021.08.8).  
Not applicable.  
We would like to thank [PERSON]'s team for their contribution and inspiration on application scenarios and for determining the parameters of the imaging device; we would also like to thank [PERSON] for guidance in writing the paper.  
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.  
Figure 6: Zoom curves.
## 2 Materials and Methods
### Simulation Details  
The purpose of the Monte Carlo wave-optics simulations is to validate the above analysis and to demonstrate how to apply it, specifically Equations (16) and (19), to generate a desired PCS. Before proceeding to the results, a brief discussion of the set-up is warranted.  
To simulate the output of a MMF fed by a broadband laser [25; 26; 27], \(W^{\text{src}}\) took the form  
\[W^{\text{src}}\left(\mathbf{\rho}_{1},\mathbf{\rho}_{2}\right)=\text{circ}\left( \frac{\rho_{1}}{D_{c}/2}\right)\text{circ}\left(\frac{\rho_{2}}{D_{c}/2} \right)\text{jinc}\left(\frac{k}{2}\text{NA}\left|\mathbf{\rho}_{1}-\mathbf{\rho}_{2} \right|\right), \tag{20}\]  
where \(\text{circ}\left(x\right)\) is the circle function defined by [PERSON] [22], \(\text{jinc}\left(x\right)=2J_{1}\left(x\right)/x\), and \(J_{1}\) is a first-order Bessel function of the first kind. The fiber's core diameter and numerical aperture were \(D_{c}=105\)\(\upmu\)m and \(\text{NA}=0.22\), respectively. The simulated wavelength was \(\lambda=635\) nm. Instances of \(U^{\text{src}}\) were synthesized from \(W^{\text{src}}\) using the method described in [28].  
The focal lengths of the lenses comprising the spatial filter were \(f_{1}=100\) mm and \(f_{2}=500\) mm (see Figure 1). The source, focal, and output planes were discretized using 512 points per side with grid spacings equal to 0.8203 \(\upmu\)m, 0.1512 mm, and 4.102 \(\upmu\)m, respectively. All propagations were performed using fast Fourier transforms [29].  
The desired, output SDoC \(\mu^{\text{out}}\) (after filtering) was  
\[\mu^{\text{out}}\left(\mathbf{\rho}_{1}-\mathbf{\rho}_{2}\right)=\text{sinc}^{2}\left( \pi\frac{x_{1}-x_{2}}{2D_{c}}\right)\text{sinc}^{2}\left(\pi\frac{y_{1}-y_{2} }{4D_{c}}\right), \tag{21}\]  
where \(\text{sinc}\left(x\right)=\sin\left(x\right)/x\). This SDoC was chosen to show that a source with a rotationally invariant CSD function (see Equation (20)) can be transformed (via filtering) into one with different, specifiedand \(y\) correlation lengths. The required \(T\) to synthesize a source with this SDoC was determined using the five steps enumerated above. Figure 2a shows this \(T\).  
The simulated output SD \(S^{\text{out,sim}}\) and \(\mu^{\text{out,sim}}\) were computed from 2000 propagated instances of \(U^{\text{src}}\). These results were then compared to the theoretical predictions given in Equation (16). All simulations were performed using MATLAB(r) version R2017a (The MathWorks, Inc., Natick, MA, USA); the scripts (.m files) are included as Supplementary Materials.
# Experimental Study on the Efficacy of Water Infusion for Underground Mining of a Coal Seam
## 2 Stress Re-Distribution Ahead of Working Coalfaces of a Coal Seam with Water Infusion  
In situ stress  
The reduction of pressure acting on the upper part of the coal seam can be explained by theories of soil mechanics (i.e., only for a soft coal seam). Before water infusion, the coal seam is a three-phase material, consisting of a solid phase (i.e., coal particles), liquid phase (i.e., the injected water), and gas-phase, which is the same as a general soil. According to the principle of effective stress with saturated soil, stresses due to external forces (overburden stresses) can be distributed into two components: (1) effective stress (i.e., stress acting at the points of contact of soil particles) and (2) pore water pressure (i.e., the pressure of water in-between soil particles), as shown in Figure 2. In the case of no water infusion in the coal seam, the effective stress is much higher than the pore water pressure. As water is injected with a certain pressure, the coal seam might still be a three-phase material (i.e., partially saturated condition), or might become a two-phase material (i.e., fully saturated condition), consisting of only the solid phase and the liquid phase. This leads to a decrease in effective stress due to injected water, which might cause an increase in pore water pressure (i.e., with the same overburden stress). In this sense, the stress concentration ahead of working coalfaces of a coal seam is re-distributed (i.e., with a considerable reduction of the acting pressure on the upper part of the coal seam).  
Figure 1: Stress distribution ahead of working coalfaces [2; 15]. I—Stress relief zone before water infusion; II—stress concentration zone before water infusion; III—in situ stress zone before water infusion; III’—stress concentration zone after water infusion; III’—in situ stress zone after water infusion.
# Optimal Trajectories of Diffractive Sail to Highly Inclined Heliocentric Orbits
## 1 Introduction
This characteristic distinguishes it significantly from a more conventional reflective solar sail, which, on the other hand, in the same Sun-facing orientation can only generate a radial thrust. Therefore, the plane of the reflective sail must be tilted by some angle from the normal direction to generate a transverse component of the thrust. As a result of this, attitude control of a diffractive sail is much simpler than for a reflective sail and in principle could also be implemented passively by designing the sail with a slightly conical shape [18]. In general, therefore, a nearly Sun-facing diffractive sail may prove to be a very promising propulsion device and relatively simple to use in terms of guidance, navigation, and control mechanisms [19; 20]. The diffractive sail surface consists of a polarization grating whose period, if appropriately chosen, can convert a relevant fraction (greater than 80%) of the solar black body spectrum into spacecraft momentum [15]. Specifically, a Sun-facing ideal diffractive sail is capable of generating a thrust inclined by an angle of 45 deg with respect to the radial direction and thus generating a component in the plane of the sail of the modulus equal to the radial one. The high value of the transverse thrust component of the diffractive sail underlies its potential advantages over a reflective sail. More precisely, comparing a diffractive and a reflective sail on the same reference mission for the same area-to-mass ratio, the diffractive sail allows the target to be reached in a shorter flight time or, for the same mission time, the diffractive sail allows the use of a smaller area-to-mass ratio than the reflective sail [21]. Recently, the performance of this type of propellantless systems has been studied in a number of mission scenarios [22], including orbit raising [19], interplanetary transfers [23], and the generation of heliocentric non-Keplerian orbits [24]. The aim of this work is to assess the performance of a diffractive sail in a mission to observe the Sun's polar regions, and thus to evaluate the ability of this means of propulsion to generate orbits with a high inclination with respect to the Ecliptic. Since the calculation of the optimal trajectories for missions of this type is a non-trivial problem to solve, the fundamental contribution of this work is to provide a simplified solution methodology, consisting in the use of a two-phase strategy, which consists of dividing the calculation of the minimum-time trajectory towards the target orbit into a phase of approach to the Sun and a phase of rotation of the orbital plane. This strategy proves to be near-optimal in the sense that the mission times determined in this way deviate by only a few percentage points from an overall optimal trajectory.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Current Status of In-Vehicle Communication Protocol
#### 3.1.4 Distribution of Published Conference Papers  
The distribution of published conference papers and journal articles demonstrates how impactful research continues to be concentrated in a limited number of publication outlets, particularly in the connected vehicles cybersecurity domain. Figure 4, generated using MS Excel, shows the distribution of published conference papers. Most strikingly, _IEEE Transactions on Vehicular Technology_ is cited three times among the top-cited papers, with 556, 494, and 336 citation figures. This underscores its reputation as a leading outlet for research and innovation in vehicular networks, communication systems, and AI optimization engineering. Also noteworthy are the _Privacy, Security, and Trust_ (PST) conferences held in 2018, showing an increased focus on cybersecurity for vehicular communication systems. This keen interest is equally shared in journals such as _Sensors, IEEE Communications Surveys and Tutorials_, and _IEEE Transactions on Information Forensics and Security_, which stand out for integrating sensor systems, secure computing, and literature surveys. The two recently established journals, _Vehicular Communications_ and _IEEE Transactions on Intelligent Transportation Systems_, highlight the growing importance of specialized transportation and communication disciplines in the field. This distribution showcases reliance on specialized and multidisciplinary sources to disseminate research on intelligent transportation systems and vehicular cybersecurity, as conducted in [17].