esa-sceva/satcom-chunk-collection huggingface dataset:
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 2 Methodology
### System Advisor Model (SAM)  
System Advisor Model (SAM) [8] is provided by the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy. SAM provides a consistent framework both for analyzing and for comparing power system costs and performance across the range of solar technologies and markets, from PV systems for residential and commercial markets to concentrating solar power and large PV systems for utility markets. SAM combines an hourly simulation model with performance, cost, and finance models to calculate energy output, energy costs, and cash flows. The software can also account for the effect of incentives on cash flows. Most of SAM's inputs can be used as parametric variables for sensitivity studies [15; 26; 27; 28].  
SAM input consists of hourly weather data and the software can read files in either TMY2, TMY3 (typical meteorological year [29]), and EPW (energy plus weather data file) formats from over 20 sources [30] and data that the user may introduce. The program provides horizontal infrared radiation from sky, global horizontal radiation, direct normal radiation, and diffuse horizontal radiation, directly from the database, or calculated through various user-selected models, such as [PERSON] [25], [PERSON] [24; 31], and [PERSON] [32].  
SAM includes a wide database of PV panels and inverters and it is possible to define new elements. The complete design of the facility cannot be entered, but the estimated losses in each section can be indicated: DC and AC wiring, transformer efficiency, thermaland power losses from wires and the sun-tracking system. It also implements a position of the panel model to predict shadows in the solar plant.  
As a result, the program provides a summary table with major energy and economic data of the facility and its temporal evolution. It is a very complete free program with great possibilities for the analysis of solar photovoltaic, thermal, concentration, wind, and geothermal energy projects of all sizes and complexity.
## 6 Simulation and Experimental Comparison of New Flexible Web  
### Testing of a New Space Flexible Web for Ground Launch  
The new type of flexible space web is a typical nonlinear and large deformation multi-flexible coupling structure with vast application space in practical engineering practice. Due to its complexity in a low-altitude aerodynamic environment, only numerical analysis results are used to analyze its air deployment performance, which has a specific reference value, but the reliability is not good. Therefore, a ground launch and deployment test of the new space flexible web is carried out to compare and verify the simulation model and analysis results of the new space flexible web and further clarify its traction and expansion characteristics.  
The schematic diagram of the setting and positioning of the test site is shown in Figure 27. The picture taken by a high-altitude UAV is used to illustrate the setting of the site. The figure shows the launching points of the new space flexible web, the erection positions of two high-speed stereo cameras, and four target positioning points. Before the test, the stereo camera was positioned, its field of view was adjusted, and its internal and external orientation elements were adjusted. After the calibration, relevant test measurements were recorded.  
The experimental fuel adopts a 20 g double-base propellant, an electrical signal controls the charge initiation, and the new space flexible web is folded in the storage cage. In the ignition process, the internal energy of the charge is constantly transformed into the kinetic energy of the traction body, and six tungsten steel traction bodies are used to pull the new space flexible web to fly in the air. To visually express the measurement results, the measurement points of the new space flexible web are numbered, and the specific positions are shown in Figure 28 below.  
With ignition after launch, under the action of the traction force, the new flexible space web is gradually pulled and expanded from the ground storage box, molding in the air. When the web rope has tension, the traction body in the web tension retracts, the web surface relaxes, the overall expansion area is reduced, and then, in the rise of a short distance, the web rope loses upward power under the action of gravity and begins to fall. The whole test process is then complete. The new space flexible web is in the launch ascent stage from 0 to 2.75 s and the free fall stage from 2.75 s to 5.79 s. Figure 29 below shows the instantaneous air attitude changes of the new space flexible web at the selected characteristic moments in the whole test process. It is found that due to the influence of air resistance and transverse wind, the flight trajectory of the new space flexible web does not rise and fall in the vertical direction entirely, and a certain degree of deviation occurs in the established trajectory.  
Figure 28: Numbering diagram of new space flexible web physical measurement points.  
Figure 27: Location diagram of test site.
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 5 Optimization Studies  
Winglet design predominantly depends on parameters such as cant angle, twist angle, sweep angle, and taper ratio. These parameters influence the C\({}_{\text{L}}\), C\({}_{\text{D}}\), and C\({}_{\text{M}}\) significantly. However, there is insufficient evidence on how much each parameter influences them. Therefore, a parametric study is conducted using the Taguchi technique to understand the relative performance of each parameter and to determine the combination of parameters for an optimal design. Moreover, ANOVA is used to determine the percentage contribution of each parameter.
###### Abstract  
In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Levy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Levy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.  
Keywords:radial distribution systems; distributed generators; differential evolution; Levy flights; quasi-opposition based learning +
Footnote †: journal: Applied
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 6 Empirical Results and Analyses  
### Problem Description  
The models and algorithms proposed in this study have a wide application potential and can be applied in many practical environments. With the rapid development of e-commerce, the distribution quality requirements of enterprises are increasing, and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Optimization Goals** & \(\mathbf{D_{1}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{D}\) \\ \hline \multirow{3}{*}{R204} & Travel costs & 535.73 & 407.98 & 943.71 \\  & Cost of carbon emissions & 539.84 & 455.69 & 995.53 \\  & Total distribution costs & 436.23 & 307.84 & 744.07 \\ \hline \multirow{3}{*}{RC104} & Travel costs & 589.68 & 466.26 & 1005.94 \\  & Cost of carbon emissions & 618.71 & 474.05 & 1092.76 \\ \cline{1-1}  & Total distribution costs & 412.80 & 406.20 & 819.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation results for different optimization objectives.  
distribution efficiency and cost concerns are becoming increasingly urgent. In order to better verify the practicality of the GA-LNS algorithm, this paper takes a logistics distribution center in Guangxi as a research object. This logistics center mainly faces the following problems in the distribution process:  
1. Improper selection of product distribution routes: when selecting product distribution routes, the distribution personnel mainly relied on electronic maps or personal experience without scientific planning of logistics and distribution routes, which made it easy to take a detour or even travel in the wrong direction, wasting distribution time and increasing distribution costs.
2. The logistics supervision system is not perfect and clear: the logistics center has not established a clear and perfect logistics supervision system, and the sorting, storage, and classification of logistics-related data and logistics-supporting information are not sufficient and complete, resulting in wasted data, which affects the evaluation of customer satisfaction and the quality of distribution.
3. Lack of green logistics concept: The logistics center has a weak concept of low-carbon logistics and does not recognize the importance of developing green logistics for enterprises and society. Logistics managers do not pay enough attention to logistics and distribution cost accounting, and the loading rate of logistics and distribution vehicles and the utilization rate of resources are low, resulting in high distribution costs and increased environmental pollution.
4. Slow update of the industrial model: there is a shortage of logistics personnel, the logistics technology is backward, and technological innovation is not powerful enough to take the traditionally rough logistics road, which, in the long run, is not conducive to the healthy development of high-quality enterprise logistics.
## 5 Discussion on Generation and Stability of Various Soliton Molecules  
### Key Parameters for Generation of Soliton Molecules  
Gain is the most apparent parameter to tailor in order to operate a fibre laser in the regime of more than one pulse within a round trip. The nonlinear interaction is then the most important and most tricky knob to achieve balanced force between soliton atoms. The complex nonlinear interaction is accomplished by abundant terms, namely, nonlinear terms including nonlinearity of passive fibres, nonlinear gain and nonlinear loss, dispersion terms including second-order and higher-order terms, birefringence terms including linear and nonlinear ones, noise term for instance the initial experimental condition and so on. In experiment, a real saturable absorber, rather than effective one like NPE technique or figure-eight-cavity method, enables an independent control of the interactions between pulses by adjustment of PC orientation with properties of mode-locking pulses (temporal and spectral widths, etc.) almost unchanged. The adjustment of PC orientation could incur change of sidebands [65], therefore cause change of interaction forces between pulses [66; 67; 68]. While in other cavities using NPE or NALM techniques, adjustment of wave plates leads to changes of both mode-locker properties and interactions between pulses. The second key reason is the initial conditions. Even though a theoretical solution of in-phase bound states exists in a fibre laser system, an improper initial condition will make the solution unavailable [15]. Before a bound state is generated, control of the initial pulse separation of two-pulse bunch by adjustment of PC orientation is of significant preference. The initial pulse separation could be changed from several nanoseconds to sub-nanosecond (when initial pulse separation was very small, the two-pulse bunch was resolved as a single pulse from the oscilloscope, where real pulse separation is proportional to width of the single pulse displayed by the oscilloscope). Change of the initial pulse separation via PC adjustment may be related to  
Figure 20: **(a)** Pulse evolution when third-order dispersion is considered. **(b)** and **(c)** show temporal amplitude and phase profile of the two bound solitons, respectively. \(\beta_{3}=0.05\) ps\({}^{3}\)/km, \(0.127\) ps\({}^{3}\)/km and \(0.05\) ps\({}^{3}\)/km for SMF1, SMF2 and EDF, respectively.  
electrostrictive effect [69] or gain depletion and recovery [70; 71], as well as interaction force incurred by dispersive waves [66]. Experimental experiences provide an essential evidence that a bound state was frequently obtained when the initial pulse separation was adjusted to be sub-nanosecond or less, although it is impossible to foresee which kind of bound states will be obtained. By doing so, rather than simply increasing pump power from a single soliton or from cw radiation, a variety of bound states can be experimentally discovered.
# Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
## 1 Introduction
The LWC samples were made with the addition of granulated expanded glass (GEGA) and sintered fly ash (GAA). Laboratory experiments and the analysis of the test results have shown, that the use theoretical tools proves to be particularly helpful to predict LWC properties if we use a mix of aggregates in different proportions [18; 19; 20; 23; 24]. It is also possible to precisely determine the composition of the aggregate mixture, so that it is possible to obtain LWC with the desired properties such as density, compressive strength, and thermal insulation. Estimating the share of individual types and sizes of the aggregates with the use of ANN requires a significant number of repetitions in order to train the network.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 5 Implementation
### Software Architecture  
The software that enables the implementation of the above-mentioned strategies and operations is based on ROS 2. The usage of ROS 2 permits the execution of the algorithms in a distributed manner in simulation or in actual deployments. The proposed software architecture is shown at Figure 4.  
* **Stage simulator plus ROS 2 node binding.** This ROS 2 node (executable) carries out physics simulation for the robot models within a determined simulation world.
* **Initial pose node**. The ROS 2 node in charge of publishing the initial poses for each robot in the world reference frame [46]. The existence of this node can be justified due to the differences in the local robot position estimations (odometry) at 'zero time' and the real position in a common spatial reference.
* **Robot controller**. As the name implies, this software component manages the execution state of each robot. It is designed to run on both simulated and real robots. Essentially, the robot controller acts as a wrapper for one or more controller instances, referred to as the _Robot class_. By encapsulating multiple _Robot class_ instances within a single _Robot Controller_ ROS 2 node, the computational load is reduced compared  
Figure 4: Software architecture for the proposed package (contained in dots) plus their functionality.  
to running a dedicated node for each robot. This makes it possible to run large-scale multi-robot simulations more efficiently. Without this wrapping, simulations involving 100 to 200 robots have proven too demanding for our test machines. The advantages of grouping multiple Robot class instances within one _Robot Controller_ are demonstrated in Table 2, which highlights how computational requirements grow with swarm size. In real robots, however, this optimization is not feasible: each _Robot Controller_ can only manage a single _Robot class_ instance.
* **Supervisor**. The supervisor is a simulation mechanism the purposes of which comprise the launching of the deployment phase, the networking simulation, and the negotiation protocol (as right now it is centralized, and only when it applies). Additional tasks the supervisor carries out are those related to metrics collection and visualizing robot positions.  
The software architecture presented above allows to not only take advantage of the already developed ROS 2 tools like simulators, packages, and utilities, but, given its modularity, to modify the number of robots either in simulation or in actual deployments only requiring minor tweaks in the Supervisor configuration files and spawning the correct set of Robot Controllers. Moreover, the recently mentioned ROS 2 configuration files permit a straightforward method to set various simulation parameters, like the forces or the algorithms and strategies to be used. More details about the different parameters used in this work are given in the next section.
# Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
## 1 Introduction
After evaluating the performance of the proposed routing mechanism through simulation, this research showed that the proposed routing algorithm provides guaranteed bandwidth and limited delay with a significant reduction in energy consumption. The key contributions of this paper are the following:  
* We propose an energy-aware and QoS routing algorithm for mesh-connected visual sensor nodes in a hybrid IoT network, as proposed in our previous work [6]. Figure 1: Hybrid IoT network architecture (Reprinted with permission from reference [6]. Copyright 2020 IEEE). * We propose an optimal routing algorithm that provides guaranteed bandwidth and limited delay while minimizing total energy consumption. The energy efficiency is achieved by completely shutting down as many visual sensor nodes as possible without compromising performance. * We formulate the problem as an integer linear program (ILP) and used a branch-and-bound algorithm for obtaining the optimum solution. The rest of the paper is organized as follows. Section 2 presents the existing research on energy optimization and QoS routing in wireless multi-hop networks. Section 3 introduces the system models on which this research is mainly based. Section 4 presents the mathematical formulation of the problem and our solution. Section 5 analyzes the performance of the proposed routing algorithm and compares the results with other algorithms from the literature. Finally, Section 6 presents concluding remarks.
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 1 Introduction  
Transcranial magnetic stimulation (TMS) is a non-invasive technique that uses a magnetic coil placed over the scalp to electromagnetically induce a current over the adjacent brain. It is a well-tolerated and relatively simple way of directly inducing action potentials within conductive structures. TMS is a flexible technique that has the potential to be used for diagnostic, prognostic, and therapeutic purposes in a variety of diseases.  
One disease of particular relevance is multiple sclerosis (MS), a complex autoimmune disease that can have a debilitating effect on the central nervous system. MS typically has a mean onset at 20 to 30 years of age and is more common in females than males, at a ratio of approximately 3:1 [1]. The onset of disease in younger individuals consequently leads to a disproportionate impact on personal and professional development. Depending on the severity and progression, individuals with MS may experience a diminished quality of life stemming from a loss in physical and cognitive function, poorer emotional well-being, and an inability to work or participate in activities [2]. With the prevalence of MS growing on a yearly basis [3], there is a growing need for tools such as TMS that can be used for management and monitoring over the course of the disease. This paper will explore the basic principles of TMS, what measures can be derived from TMS, and the current body of literature relating to the use of TMS in patients with MS.
## 3 Results
### Obstacle Factor Identification  
Through the obstacle degree model, the key barriers affecting the resilience of the BTHUA were identified at the dimension level, with their impact demonstrating significant changes from 2010 to 2022 (Figure 9). Resource endowment (E1) and technological innovation (T2) have consistently ranked among the primary obstacle factors. Specifically, the obstacle degree of E1 increased steadily from 17.31% in 2010 to 22.29% in 2022, making it the most primary constraint by 2022. The third obstacle factor, public service (S2), exhibited a marginal decline in its obstacle degree. From 2010 to 2016, economic strength (S1) was the fourth largest obstacle factor, while from 2017 onward, the obstacle degree of infrastructure (T1) exceeded that of S1, except in 2021 when S1's obstacle degree rebounded slightly. In  
Figure 8: The structure of the coupled coordination space network in 2010, 2014, 2018, and 2022. The bi-directional interactions between cities were denoted as city A to B and city B to A. Here, “city A to B” signified the influence emanating from city A towards city b, while “city B to A” represented the impact that city A receives from city B. The number of connections at each level is shown in parentheses.  
2010, the obstacle degree of landscape pattern (E2) was slightly lower than that of social stability (S3). However, E2 demonstrated a pronounced upward trend, becoming the fourth largest obstacle factor in both 2020 and 2022. Correspondingly, S3's obstacle degree showed a minor decline. Additionally, ecological quality (E3) and technical performance (T3) remained the least influential factors throughout the period, with both showing a consistent downward trend in their obstacle degrees.  
To facilitate a more detailed analysis of disparities, Figure 10 illustrates the obstacle factors for each city in 2010 and 2022. While the major obstacle factors in most cities generally aligned with the regional pattern, distinct urban characteristics emerged, indicating variances in specific challenges and their impacts. For example, while T2 remained a crucial obstacle factor for many cities, its hindrance on Beijing's resilience development gradually diminished, ceasing to be a major obstacle by 2022. In contrast, whereas S3 showed a downward trend in the obstacle degree across most cities, Beijing witnessed an increase in S3's hindrance, which became the third largest obstacle factor in 2022, with an obstacle degree of 14.39%. Notably, despite E1's high obstacle degree at the regional level, it was not a major obstacle factor for Chengde during the period, with its obstacle degree consistently below 10%. Although S1 was not a major regional obstacle factor, it retained significant influence on cities like Chengde, Zhangjiakou, Qinhuangdao, Handan, and Xingtai.  
Figure 9: The obstacle factors of the BTHUA from 2010 to 2022.