esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Variable-Stiffness Compliant Skin for a Morphing Leading Edge
## 4 Optimization Method
### Optimization Algorithm  
It is known that conventional gradient-based optimizers are not only inadequate for the current situation, which requires collaborative optimization involving both continuous variables (the actuating loads) and discrete ones (the stiffness distribution over the entire flexible skin, the composite layup sequence, and the actuating positions), but also likely to be trapped in locally optimal solutions. In contrast, the secondary generation of Non-dominated Sorting Genetic Algorithm (NSGA-II) [32] is known for its high efficiency and stability in terms of globally optimal solution search. Therefore, NSGA-II is employed here, and the optimization framework of the entire procedure is illustrated in Figure 8. At the beginning, when a generation of a population of design variables (namely individuals) has been initialized, the main procedure will call the finite element module to generate the same amount of numerical models of morphing leading edge, each of which is customized with the corresponding design variables. All these models are analyzed to simulate the drooping deformation, and its results are evaluated in terms of WLSE (namely the fitness valuation). Afterward, the main procedure updates these design variables according to respective evaluation results, such as selection, crossover, and mutation, and then generates the offspring design variables. So long as one loop is finished, the same procedure above will be repeated until the convergence criterion is met.  
In the algorithm, there are 62 design variables in total: five variables defining layup sequence in each of the 10 design zones, the position of four stringers in terms of normalized length, and the actuating force components in x and y directions in each of four actuating points. According to the recommendation from Reference [32] that the population should be 4 times larger than the design variable quantity, the population here is set as 200. When variations of the optimal and average value in two consecutive generations are both less than 1.0 \(\times\) 10\({}^{-6}\), convergence is regarded to be achieved.  
As illustrated before, there exists a large deformation of the complaint skin during the drooping process, which produces a geometrically linear problem. Specifically, the external aerodynamic force direction in the structural FEM model should change in real time to remain perpendicular to the skin surface. Therefore, a nonlinear finite element analysis (within the right-hand side frame of Figure 8), rather than the linear one, has been employed in the FEM analysis, which will also help to produce movement locus of the actuating points on stringers along optimizing process. Actually, it strongly supports later inner kinematic mechanism design, although it takes relatively longer computational time.
## 3 Results and Discussion
### UV-Visible Spectroscopy  
The diffuse reflectance absorption spectra of the gels (aged at room temperature) in the range of 200-600 nm are shown in Figure 4. These spectra were used for the determination of the absorption edge and optical band gap of the samples. As seen from the figure, the absorption peaks were found to be in the range of 380-410 nm: TiO\({}_{2}\) (380.44 nm), ZnO (384.53 nm), 90 TiO\({}_{2}\)/10 ZnO (382.42 nm), and 10 TiO\({}_{2}\)/90 ZnO (351.73 nm). The UV-Vis spectra shows that the higher amount of TiO\({}_{2}\) leads to the narrowing of the band gap (E\({}_{\text{g}}\)) due to the red shifting in the adsorption edge of the 90 TiO\({}_{2}\)/10 ZnO sample. Such strong absorption in the visible-light range has also been observed by other authors, and this was related to the formation of heterogeneous junctions [32]. The other 10 TiO\({}_{2}\)/90 ZnO gel exhibited shifting of the cut-off toward the lower wavelength of the spectra. The calculated  
Figure 3: EDS spectra of TiO\({}_{2}\) (a), 90 TiO\({}_{2}\)/10 ZnO (b), and 10 TiO\({}_{2}\)/90 ZnO (c) catalysts.  
band gaps are as follows: TiO\({}_{2}\) (3.25 eV), ZnO (3.23 eV), 90 TiO\({}_{2}\)/10 ZnO (3.24 eV), and 10 TiO\({}_{2}\)/90 ZnO (3.52 eV). The lower value of Eg for the sample with higher TiO\({}_{2}\) content may be attributed to the excessively smaller particle size of the TiO\({}_{2}\) catalysts, which favor greater surface defects [33]. Therefore, the obtained results could predict promising photocatalytic properties of the investigated samples.
# On Global Quantum Communication Networking
## 5 Concluding Remarks  
To enable the next generation of quantum-enabled cyber security systems, we proposed a quantum network of satellites that will provide the global coverage. The quantum satellite network will be composed of quantum subnetworks comprised of LEO satellites. Some of these LEO satellite-based quantum subnetworks will be connected to a subnetwork of MEO satellites. The MEO satellite subnetworks will then be interconnected to the global network of GEO satellites. The LEO/MEO satellites will also be used to interconnect terrestrial quantum networks. Each quantum communication subnetwork will be based on the cluster state concept. This quantum global network will allow us to  
Figure 6: Terrestrial quantum communication network to be developed at the University of Arizona.  
Figure 7: Free-space optical transceiver used in outdoor FSO link.  
establish EPR pairs between any two nodes in the global network. It can also be used to distribute the entangled states for quantum-sensing applications and to enable distributed quantum computing on a global scale.  
This research received no external funding.  
The author declares no conflict of interest.
## 3 Results and Discussions
By comparing the probabilities of the most extreme cases in the three structures, it was confirmed that the EHBTFET has the smallest probabilities, and this difference is caused by the difference in the number of metal grains that affect the V\({}_{\text{turn-on}}\). Figure 4: Energy band diagrams of planar TFETs with the highest value of V\({}_{\text{turn-on}}\) at 0.367 V (black solid line) and the lowest value of V\({}_{\text{turn-on}}\) at 0.289 V (red solid line) among the simulated samples when V\({}_{\text{D}}\) is 0.5 V and V\({}_{\text{G}}\) is 0.4 V. The blue dashed line and magenta dashed line show the most extreme cases in theory when the entire gate of the planar TFET is 4.6 and 4.4 eV, respectively. In order to prove that the WFV improvement of the EHBTFET is due to the large number of metal grains, the \(\sigma\)V\({}_{\text{turn-on}}\) of the planar TFET, DG TFET, and EHBTFET was examined while reducing the L\({}_{\text{G}}\) of the EHBTFET, as shown in Figure 6. The WFV effects of the planar TFET and DG TFET were determined by the metal grains located at the edge between the source and the channel. Therefore, the \(\sigma\)V\({}_{\text{turn-on}}\) of the planar TFET and DG TFET is not significantly affected by the change in the gate length. On the other hand, when the L\({}_{\text{G}}\) of the EHBTFET decreases, the number of metal grains in the overlapping areas of MG and SG decreases and the \(\sigma\)V\({}_{\text{turn-on}}\) of the EHBTFET increases accordingly. By comparing when L\({}_{\text{G}}\) was 70 nm and when L\({}_{\text{G}}\) was 30 nm, it can be seen that the \(\sigma\)V\({}_{\text{turn-on}}\) of the EHBTFET increased from 4.0 to 9.9 mV as the number of metal grains affecting the tunneling current decreased from 70 to 14, respectively. Therefore, if L\({}_{\text{G}}\) continues to decrease, the WFV effects of the EHBTFET are expected to be similar to those of other TFET structures. In addition, the electrical performance (e.g., I\({}_{\text{on}}\)) also degrades as the tunneling area (i.e., overlap area between main and sub gates) of the EHBTFET decreases with the smaller L\({}_{\text{G}}\). Consequently, when the scaling down of TFETs continues, it is necessary to maintain the L\({}_{\text{G}}\) by using a vertical channel [38] to maintain the advantages of the EHBTFET in the WFV effects.
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 5 Experimental Results and Analysis
### Design and Analysis of Experiments
#### 5.3.1 Effectiveness of Hybrid Crossover  
With the lower bound of average customer satisfaction fixed at 50%, the number of riders set to 10, and the weights for platform costs (\(\omega_{1}\)) and average rider earnings (\(\omega_{2}\)) each set to 0.5, comparative experiments were conducted by keeping other components of the algorithm unchanged while varying the crossover strategy. Specifically, partial matching crossover, order crossover, and a hybrid crossover that uses both with equal probability were employed for these experiments. The experimental results are shown in Columns 2-4 of Table 2. From the results, it can be observed that, compared to using the partial matching crossover and order crossover strategies alone, the hybrid crossover strategy resulted in a decrease in fitness value by approximately 9.60% and 8.12%, respectively, demonstrating its effectiveness.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 4 Discussion
### Nonrotating Parts  
Regarding the tower: although aesthetically more pleasant, the tapered truncated cone has a larger RCS than the simpler truncated cone design, so the simpler design should be given preference.  
Regarding the nacelle, the box shape causes significant signal return when the flat sides are oriented perpendicularly towards a radar installation. This issue can already be dealt with in current radar system's signal processing. It could also be resolved by coordination with local radar installations: operation data can be exchanged online, so that the wind turbine can avoid assuming or passing through a critical angle while the radar system is \"looking\" in this direction. Future wind-turbine nacelle designs might also prefer rounded shapes, which spread the reflected energy over a bigger solid angle or a bevelling of the sides, which will deflect the radar beam, either downwards or upwards.  
Special radar-absorbing coating, e.g., varnish or paint, can also be applied to reduce the radar echo.
## 5 Study 2: Resilience of Roof-Mounted Solar Panels to High Wind Loads
### Outcomes  
The initial findings indicated a notable decrease in the magnitude of minimum pressures on the roof surface when solar panels were introduced (depicted in Figure 10d), a reduction further accentuated in configurations where the PV array boasted a seamless alignment of modules (as illustrated in Figure 10f).  
Installing solar panels on a building's roof introduces a potential influence on the main wind force resisting system (MWFRS), the primary structure responsible for transmitting all loads to the foundation. An assessment of the PV array's impact on the aggregate load transmitted to the ground was undertaken, involving the collection of pressure coefficients from both external and internal facets of the attached panels. This data was subsequently leveraged to compute an average pressure coefficient value for the underlying Zone 1. This  
Figure 10: Minimum pressure coefficients were observed for taps 1–110 on the roof surface, maintaining a 0-degree orientation across all four experimental trials. Subfigure (**a**) delineates the spatial arrangement of the pressure taps (dots) and prevailing wind direction (arrow). Subfigure (**b**) visually represents the building’s alignment within the open-jet testing facility. Subfigures (**c**) through (**f**) present the minimum pressure coefficients corresponding to distinct roof configurations: (**c**) undororned roof (Case 1), (**d**) roof outfitted with panels (Case 2), (**e**) roof furnished with panels and supporting framework (Case 3), and (**f**) roof featuring panels, supporting framework, and trim (sans interstices) (Case 4).  
analysis, conducted in adherence to ASCE 7 design standards, facilitated an evaluation of the effect of PV arrays on the MWFRS.  
The estimated average \"net\" minimum pressure coefficients for Zone 1 are listed in Table 2. A discernible observation is that installing a PV array does not engender an escalation in the exposure to heightened wind loads on the supporting structure compared to an unadorned roof. This phenomenon is attributed to the net pressures on the panels, exerted downward on the roof, consequently reducing the total roof uplift loads. Specifically, roofs fitted with panels (Case 2) demonstrated a reduction in overall uplift forces ranging from 54% to 60%, while those with panels and racking (Case 3) experienced a 45-52% decrease. The most substantial reduction in uplift wind loads, ranging from 61 to 63%, was achieved in the configuration featuring panels, racking, and trim without gaps (Case 4).
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 2 Proposed Model
### Spatial-Temporal Features Extraction Based on DSTP
#### 2.2.1 First Stage of Attention  
The data from the central site and its matching sites are input into the model together, which can be used to study the exogenous series relationships between them and can improve the accuracy of predicting PM2.5 concentrations. The exogenous series correlation method is used to find matching sites. Given the \(k\)-th feature \(x^{k}\) of the central site collection at time \(t\), the \(k\)-th feature \(x_{k}^{(best)}\) of the exogenous series of the best matching site can be obtained by the exogenous series correlation method [24]. The spatial correlation between the exogenous attributes of the learning central site collection and the matching site in the input attention mechanism is:  
\[f_{t}^{k}=v_{f}^{\text{T}}tanh\Big{(}W_{f}\Big{[}h_{t-1}^{f}:s_{t-1}^{f}\Big{]}+ U_{f}x^{k}+M_{f}x_{k}^{(best)}\Big{)}, \tag{7}\]  
where \([*:*]\) is a concatenation operation, and \(v_{f}\in\mathbb{R}^{S_{W}}\), \(W_{f}\in\mathbb{R}^{S_{W}\times 2m}\), \(U_{f},M_{f}\in\mathbb{R}^{S_{W}\times S_{W}}\) are the parameters to learn; \(h_{t-1}^{f}\in\mathbb{R}^{m}\) and \(s_{t-1}^{f}\in\mathbb{R}^{m}\) are the hidden state and unit state of the encoder LSTM unit at the previous time. After \(f_{t}^{k}\) is calculated, the Softmax function is used to normalize to get the attention weight \(\alpha_{t}^{k}\). \(\alpha_{t}^{k}\) is determined by \(h_{t-1}^{f}\), \(s_{t-1}^{f}\), the \(k\)-th feature \(x^{k}\) of the current input, and the \(k\)-th feature \(x_{k}^{(best)}\) of the _best_-th matching site, which measures the importance of the \(k\)-th feature at time \(t\). \(\widetilde{x}_{t}\) is the combination of all features at moment \(t\), which is defined as follows:  
\[\widetilde{x}_{t}=\Big{(}\alpha_{t}^{1}x_{t}^{1},\alpha_{t}^{2}x_{t}^{2}, \ldots,\alpha_{t}^{n}x_{t}^{n}\Big{)}^{\text{T}}. \tag{8}\]  
Then, the hidden states \(h_{t-1}^{f}\) and \(\widetilde{x}_{t}\) are input into the LSTM layer to update the hidden state of the current moment, and \(\widetilde{x}_{t}\) is input into the attention of the second stage.
## 1 Introduction
In view of the problem-solving strategies, most of the reviewed research works have utilized stochastic programming techniques, however some have applied other methods such as robust optimization or Monte Carlo simulation [20; 21]. In [22], a stochastic AC security-constrained unit commitment problem under wind power uncertainty has been formulated. Also, a stochastic multi-objective framework has been proposed in [23], for joint energy and reserve scheduling in day-ahead however, this reference has not considered AC network, load, EVs and wind power uncertainties. Furthermore, authors in [24] have proposed a multi-objective structure that can optimize objective functions including operation costs of MG, but they have not considered demand and EVs uncertainty in day-ahead scheduling. This paper presents the effect of different types of TBR programs on the MG operation costs and shaping the load profile in presence of RESs and electrical vehicles (EVs). EVs are employed for energy scheduling or peak shaving with fast charging and discharging capabilities, while the responsive loads are used to supply a part of the required MG reserve to compensate RESs uncertainties. Monte-Carlo simulations together with k-means clustering technique are applied to create several scenarios corresponding to renewable generation variations and EVs owners' behaviors. The generated scenarios are then reduced and fed into a two-stage optimization model developed for minimizing the operation costs. In the first stage of optimization, the energy and reserve costs are minimized simultaneously and in the second stage, the cost associated with the rescheduling of generating units (due to the variations in wind turbine (WT) and photovoltaic (PV) output powers) is minimized. Finally, simulation results for co-optimization of energy and reserves in the examined residential MG are presented and compared under different DR programs and operating conditions. As a whole, the main contributions of this paper can be highlighted as:* Optimal management of an islanded MG with RTP-based DR programs using a scenario-based two-stage stochastic programming model. * Simultaneous energy and reserve scheduling of MGs with regard to different DR schemes in an uncertain environment. * Assessment of TBR-based DR programs under different scenarios with/without considering EVs participation. The remainder of this paper is arranged as follows: a network-constrained day-ahead market clearing model is introduced in Section 2 and it is reformulated into a mixed integer programming (MIP) model in Section 3. The case studies are presented in Section 4 and the simulation results are discussed thereafter. Finally, Section 5 concludes this paper with future scope.
## 4 Experiments and Results
### Implementation and Set-Up
#### 4.1.1 Energy Consumption Measurement Configuration  
We have divided the energy consumption tests according to their objectives: The first set of tests tries to validate the election of a message-based communications system versus other alternative communications systems, and the second set evaluates the energy consumption overhead when adding the access control system to the MQTT protocol.  
In the following paragraphs we will show the specific definition of each set of tests:  
* **Validation of the communications system**: 1. Selection of two well-known Internet-based communication systems: REST API vs. message-based communications (MQTT). 2. Configuration of an IoT device as a Web Server to use a REST API and another device as an MQTT client, both with the exact same characteristics. 3. Definition of a common set of message exchange that allows the measurement of the energy consumption in each device. 4. Measurements start when the first message is exchanged and stop when all the messages configured have been successfully delivered.
* **Energy consumption overhead measurements**: 1. Configuration of two IoT devices as MQTT clients. Both devices have the exact same characteristics. 2. Configuration of one Broker as a standard MQTT Mosquito Broker (without authorization) and other as a Resource Server Broker using the access control proposed in this paper. 3. Definition of a common set of message exchange that allows the measurement of the energy consumption in each device. 4. Measurements start when the first message is exchanged and stop when all the messages configured have been successfully delivered.
## 1 Introduction  
Despite the fact that drug-eluting stent (DES) implantation has demonstrated marked reduction in restenosis as compared with the bare metal stent (BMS), the occurrence of late stent thrombosis has been a major concern [1; 2; 3; 4]. Animal studies have demonstrated that DES induced greater fibrin deposition, late neointimal thickening, and delayed endothelialisation compared with BMS [5]. Furthermore, incomplete neointimal coverage of covered struts, the most powerful indicator of endothelialisation, was the best morphometric predictor of late stent thrombosis in a pathologic study [6]. Recently, studies have demonstrated that uncovered and malapposed struts were frequently observed in patientswith very late stent thrombosis, occurring beyond one year after DES implantation [7; 8]. Therefore, assessment of appropriate endothelialisation and late apposition status of DES is the key factor for the prevention of stent thrombosis.  
A large randomized controlled trial, IMProved Reduction of Outcomes: Vytorin Efficacy International Trial (IMPROVE-IT), demonstrated the efficacy of combination therapy with ezetimibe/ simvastatin (Vytorin, manufactured by Merck) on the reduction of low-density lipoprotein (LDL) cholesterol and improving long-term clinical outcomes [9]. Therefore, as of recently, guidelines recommend that the combination of statin and ezetimibe should be considered in patients with statin intolerance [10; 11].  
The biodegradable polymer Biolimus-eluting stent (BioMatrix FlexTM, Biosensors Inc., Newport Beach, CA, USA) consists of a stainless-steel platform coated by an abluminal layer of biodegradable polymer which is degraded by surface hydrolysis to lactic during a period of 6 to 12 months [12]. However, data is scarce on the process of neointimal coverage and late apposition status of the biodegradable polymer Biolimus-eluting stent (BP-BES) when implanted in the highly thrombogenic setting of acute myocardial infarction (AMI) after combination therapy with statin and ezetimibe.  
Intracoronary frequency domain optical coherence tomography (OCT) provides high-resolution (10 \(\upmu\)m) imaging which enables the visualization of various vascular structures and detection of strut coverage, malapposition, and the characterization of neointimal tissue in the setting of AMI when compared with intravascular ultrasound (IVUS) [13; 14; 15; 16; 17; 18; 19]. OCT has almost 10 times higher axial resolution than IVUS. Therefore, we performed this study to compare the neointimal response, assessed by serial OCT, at 12-month follow-up between Vytorin 10/10 mg and 10/40 mg after BP-BES implantation in patients with AMI.