esa-sceva/satcom-chunk-collection huggingface dataset:
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 5 Proposed VDTN Solution
### SCF Vehicle Selection  
This procedure is the fundamental operation of DTN routing and depends on the active contacts of every bundle's host vehicle. Bundles are relayed between vehicles when better detected SCF vehicles are met according to the fitness evaluation of bundle towards its destination from the host using either FA or GSO techniques.  
Each candidate SCF vehicle is evaluated based on a predefined score formula in Equation (9) which defines both FA fitness and GSO fitness evaluation procedures. This fitness value is calculated based on the following evaluation parameters:  
* Number of relayed bundles (\(Nb_{Relays}\)): indicates the ability of node to participate in bundles forwarding.
* Average buffer time (\(Avg_{Buffer\_time}\)): indicates also the degree of participation of node in relaying bundles between nodes.
* Number of active contacts (\(Nb_{Contacts}\)): indicates the connectivity degree of node to the network.
* Average lifetime of active contacts (\(Avg_{Lifetime}\)): indicates also the connectivity consistency of the node to its neighborhood.
* Relative speed difference (\(Rel_{Delta\_Spead}\)) of candidate SCF node: including the absolute speed difference and the direction angle to the destination which indicates the geographic forwarding quality of the node. \[\begin{split} Swarm_{fitness}=(W_{1}\times Nb_{Relays})+(W_{2} \times Avg_{Buffer\_time})+(W_{3}\times Nb_{Contacts})+\\ (W_{4}\times Avg_{Lifetime})+(W_{5}\times Rel_{Delta\_Spead}) \end{split}\] (9)  
The weights \(W_{1}\), \(W_{2}\), \(W_{3}\), \(W_{4}\), \(W_{5}\) are affected to the fitness parameters consecutively. Equal weight values are affected to the fitness parameters: \(W_{1}=0.2\), \(W_{2}=0.2\), \(W_{3}=0.2\), \(W_{4}=0.2\) and \(W_{5}=0.2\).  
The calculated \(Swarm_{fitness}\) value constitutes the evaluation score of each candidate relay vehicle for every buffered bundle in the framework of the SCF selection.  
It is worth noting that there may be some cases where there is no better candidate nodes. Then, the host vehicle continues to carry the bundle until two changes occur:  
* Either the new contact opportunity is met, and the host vehicle compares its fitness with the new contact to check a likely change about the best SCF vehicle,
* otherwise, a periodic update is performed to refresh the comparison of the host with its active contact so that any change about the best SCF vehicle is stated.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Time Overheads  
In this section, we study the training time and the localization prediction time (letter rate). Figure 12 shows the training time cost of DECC on datasets of different scales described in the previous section. We set the number of sessions per case under the dataset partition to 200. We found that the change in time consumption is basically linear, with 1440 data points training in approximately 34 min, 4320 data points in 1 h and 44 min, and 11,568 data points in 4 h and 36 min. We also tested how fast DECC can calculate 3D position (real-time localization). We give DECC 1000 localization tasks (batch size set to 16) to calculate \"time per thousand localizations\" (TPT). When the test was repeated 200 times, the average TPT was 2.78 s, indicating that each task took less than 0.002 s. These results show that the DECC method can achieve a positioning refresh rate of more than 500 Hz, which is sufficient to meet the real-time positioning requirements of objects moving at a medium speed.  
Figure 11: Performance in different scales of dataset scale.  
Figure 12: Training time overheads compared with a straight reference line.
## 4 Technology
### Distributed Generation  
Distributed generation (DG) is a driving factor for the smart grid implementation. Its integration in the energy network proved to bring many benefits [PERSON] et al. (2010) for customers, energy efficiency, and network operation itself. This integration is enabled through a number of different technologies [PERSON] et al. (2010), some of which are discussed in this survey:  
* Advanced Metering Infrastructure (see Section 4.2);
* Energy Storage Systems (see Section 5);
* Advanced Distributed Management Systems (see Section 3).  
[PERSON] _et al_. [PERSON] et al. (2010) provide a methodology for the integration of DG in a smart grid Network. It is based upon the connection of a distributed generator with a feeder, combined with an Automatic Voltage Control (AVC) system and a Dynamic Line Rating (DLR) function. In their study, they also provide an economic feasibility study, as a series of steps, which may be extended to a general project involving DG technologies. The defined steps are:  
* Define the installation, operation and maintenance costs of the project;
* Define additional financial parameters like electricity rates, discount rates, inflation rate, _etc._;
* Quantify additional benefits brought to the network, in terms of a premium to the electricity rate per output unit;
* Evaluate externalities, such as Greenhouses gases (GHG) reduction, to add them as a benefit;
* Calculate the economic parameters internal rate of return (IRR) and net present value (NPV) to evaluate the feasibility of the project.  
Another project worth to be mentioned is SmartGen [PERSON] et al. (2010), an Italian project driven by several industries and two different research institutes (University of Bologna and Genova). This project aims at finding and implementing industrial solutions for smart grid management. The authors propose the definition of a DMS (Distribution Management System) for each portion of the grid, able to control and optimize power flows, distributed generation and load balancing. The base function of a DMS can be divided into:* Supervisory Control and Data Acquisition (SCADA);
* Control Stations.  
The SCADA system provides specific monitoring and real-time control operations, in an automated way, while the Control Stations allow human operators to interact with the system.
## 3 Model Parameter Estimation
### Propulsion Noise
#### 3.2.2 Order Analysis  
From the resampled signals, information about the engine orders is extracted. Therefore, a filter bank consisting of one bandpass filter per considered engine order is generated and applied to the signal. Eighth order Butterworth filters centered around the engine order frequency \(F_{\
u}\) with a 6-Hz bandwidth are employed. Figure 12 shows the magnitude frequency response of the filter bank. At the output of each filter, the corresponding order level, \(L_{\text{prop,ord,\
u}}\) in Equation (14), is calculated as an equivalent continuous level (Leq). Figure 13 exemplifies measured order levels at idling engine and full load recorded at the rear of a VW Touran running at 1000 and 3000 rpm.  
The order phases are detected using the cross-correlation function. Since the above-described infinite impulse response (IIR) filter bank introduces phase shifts, the outputs of the filter bank are time reversed and sent once again through the same filter bank and, finally, time reversed. In doing so, a zero-phase forward and reverse digital IIR filtering is implemented. This signal, \(g_{v}(t)\), is cross-correlated with a prototype function \(\cos(2\pi F_{v}t)\) to obtain the time shift:  
\[\kappa_{v}=\operatorname*{arg\,max}_{\tau}\left\{\int g_{v}(t+\tau)\cos(2\pi F _{v}t)dt\right\} \tag{40}\]  
from which the phase shift of Equation (15) can be derived as:  
\[\phi_{v}=-2\pi F_{v}\kappa_{v} \tag{41}\]  
Figure 12: Magnitude frequency response of the engine order analysis filter bank (**a**) and engine order suppression filter (**b**) for engine speed \(n=1000\) rpm, engine orders \(\
u=1,1.5,2,...,15\) and \(N_{\text{cyl}}=4\).  
Figure 13: Comparison of engine order levels with idling engine (white) and full load (black) at 1000 (**a**) and 3000 rpm (**b**). Recorded at the rear of a VW Touran 1.6 FSI.  
Figure 14 compares the sound pressure signals of a recording and the corresponding synthesis consisting of engine orders with constant phases, which were estimated by Equation (41).
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 3 Results  
### Flight Experiment Result  
On 4 October 2022, a near-space high-altitude-based coronagraph flight experiment was conducted in the Dchaidan District, Qinghai Province, China. This experiment started in the morning, and the balloon was launched at 8:54. The entire experiment system before the balloon was launched is shown in Figure 9. The balloon reached an altitude of 30 km and began flat flying at 9:26. The coronagraph began to work, and the status of the observation platform during the flight after a total flight time of 6 h and 32 min, including a flight of 4 h and 50 min above 29 km, is shown in Figure 10. The platform landed at 15:26, and the landing status is shown in Figure 11. After the experiment, the security control cabin, payload cabin, scientific payload, and experimental data were all recovered in good condition.
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Mobility Models  
Of the existing mobility models those supported by the NS3 simulator are mentioned in Section 1. Depending on the requirements set by the simulated UAV network use-case, as well as the behaviour defined by the routing protocol, different, and in some occasions more unique mobility models are used to describe the FANETs mobility; e.g., despite its general effectiveness, the Gauss-Markov Mobility Model does not describe circular swarm mobility (common in monitoring use-cases) as effectively as the Semi Random Circular Mobility Model, which is otherwise of minimal emulative value. Generally, the most effective (and thus popular) ones in terms of accurate FANET mobility emulation are the following:  
* Gauss-Markov Mobility Model [18]
* Paparazzi Mobility Model [19]
* Semi Random Circular Mobility Model [52]
* Random Waypoint Mobility Model [18]
###### Abstract  
Vacuum microgrippers are devices used to handle and manipulate small objects. Despite their simple working principle and low cost, they show low efficiency in detaching performance, especially when the object to be grasped is very small. In this work, a particular design for vacuum microgrippers with an incorporated automatic release tool is considered. The final goal of this study was to present a numerical model that can supply reliable estimates of the aerodynamic force acting on the release tool and of the air flow rate inside the gripper as a function of geometric parameters and the outlet pressure value. A complete CFD analysis of a simplified model of the device is presented. Grid independence analysis was also performed to define a suitable grid and guarantee a good trade-off between accuracy and computing time. According to Design of Experiments (DOE) techniques, 81 simulations were performed, changing the values of the outlet pressure (\(p_{2}\)), the body inner diameter (\(D\)), the lateral holes' diameter (\(d\)) and the releasing mass length (\(L\)). Every design variable could assume three different values. Linear regression, based on the least square method, was employed to determine mass flow rate and lifting force empirical correlations.  
vacuum microgripper; CFD analysis; impinging jet; annular jet; axially symmetric back-facing step; empirical correlations Article  
A Semi-Empirical Fluid Dynamic Model of a Vacuum Microgripper Based on CFD Analysis  
[PERSON],  
and to assist with the object detachment. So-called passive release strategies aim to reduce the forces acting at the microscale: coating the gripper surface with a conductive layer connected to the ground to reduce the electrostatic force [8] is an example. Active release strategies provide supplementary forces to overcome adhesion, such as vibration [10], positive pressure pulse [9] or electric fields [11]. The goal of this paper is to present the CFD analysis of vacuum microgrippers with an integrated automatic detaching tool (releasing mass) [12]. Simulations have been performed for 27 different gripper configurations, which were defined according to Design of Experiments (DOE) techniques. The values of three geometrical gripper parameters were changed in the simulations. Since each configuration was investigated for three outlet pressure values, 81 CFD simulations were carried out. CFD results were analyzed in order to find out how gripper geometry affects the gripping performance. Empirical correlations based on numerical results are proposed to predict the air mass flow rate inside the gripper and the aerodynamic force exerted on the releasing mass. The correlations should speed up the design phase.
## 3 Results and Discussion
### Physical Stability of Components of the Capsules  
Furthermore, the characterisation of the capsule's components was also evaluated. Physical separation of the O/W components (i.e., WCO and alginate biopolymer solution)  
Figure 3: Experimental characterisation of WCO capsules: (**a**) Optical image of capsule; (**b**) SEM image of capsule; (**c**) SEM detail image of polynuclear surface structure of capsule; (**d**) SEM-EDS observation on the capsule surface; (**e**) Frequency histogram of the size of capsules with Normal fitting; (**f**) Frequency histogram of the pore size of internal structure of capsules with Log-normal fitting; (**g**) SEM image of the multicavity (egg-box) structure into capsules; (**h**) SEM image of an individual broken capsule by uniaxial compression; (**i**) Detail by SEM image of the fracture type in the broken capsule; (**j**) Force and deformation average curves of the compression tests; and (**k**) Average values of the max. compression force and deformation registered by the capsules tested by a load cell of 1 kN at a speed rate of 0.2 mm/min.  
is described by the creaming process in Figure 4a. Figure 4b shows that the average creaming index results increased with time, and after 30 h the creaming index was 83.02% corresponding to the total separation of the O/W emulsion. This phenomenon was mainly attributed to the increase in size of the WCO droplets with time by means of coalescence, favouring the ascension of the oil droplets to form a creamed layer. Evidence of this aggregation phenomenon between the oil droplets is shown by the microscopic fluorescence images in Figure 4c-e, and further quantified by the droplet size frequency histograms in Figure 4f-h. For the successful synthesis of the WCO capsules, physically stable O/W emulsion must be used during the encapsulation process, and so, the creaming effect should be controlled. Thus, based on this analysis, the encapsulation of the O/W emulsion should take place for a time no longer that 2 h since the emulsion is fabricated and kept in response at ambient temperature.
## 2 Problem Description
### Simulation Challenges
#### 2.1.3 Simulation Efficiency/Scalability  
Combining packet-level network simulators and simulators for the physical system is a convenient and less error-prone way of creating complex simulations for cyber-physical systems. This convenience, however, comes at the cost of scalability and simulation efficiency. Network simulations involving a large number of nodes are still challenging for detailed packet-level simulators. To gain comprehensive insight into the performance of the simulated system, potentially a large number of independent Monte Carlo trials need to be evaluated to obtain satisfactory confidence in the results for different network geometries, channel models and, specifically for cyber-physical systems, different physical disturbances that might affect the system. If there is a physical part to the simulated system, then the physical simulator requires appropriate integration and tight time synchronization with the network simulation engine. These requirements aggravate the complexity/scalability issues with detailed packet-level simulators. Therefore, for practical purposes, evaluation methods are needed that resort to abstraction mechanisms of acceptable complexity to decrease the simulation complexity, while not compromising the accuracy and credibility of the results. Furthermore, eliminating the synchronization overhead between coupled simulation engines might also lead to performance improvement in terms of simulator scalability.
## 3 Results  
### Synthesis of the BS-GMA-EDA-Q and BS-GMA-PHEA-Q  
The RIGP technique is mainly dominated by free radical mechanism. The total amounts of free radicals generated in the substrates and the monomer is the main factor affecting the GY. Figure 2a shows the radiation dose effect on the GY. The GY increases with the dose, and it reached a maximum of 239.6% at 60 kGy. However, the GY of GMA decreased when the radiation dose increased above 60 kGy. These phenomena can be explained by the decay mechanism of the trapped radicals. The grafting polymerization was mainly controlled by the total amount of free radicals formed in the substrates. The high adsorbed dose will initiate more free radicals, thus introducing a high GY. Nevertheless, the grafting occurred only at the interface of the substrate and solution, so GY no longer increases with the dose increasing, the decomposition of cellulose molecules was also intensified [28]. The multiple influence factors result in the decrease of the GY. In this study, the grafted BS with the GY 239.6% at 60 kGy were selected for further experiment.  
The BS-GMA with the GY 239.3% is immersed into EDA or PEHA with the DMF solution for the epoxy ring-opening reaction. The effect of the monomer concentration on DA is shown in Figure 2b. DA increased with monomer concentration and reached 31%  
Figure 2: Effect of radiation dose on GY (**a**) and effect of monomer concentration on DA (**b**).  
for both amine monomer at a monomer concentration 20 wt%, implying that BS-GMA is aminated with the same DA by EDA and PEHA. The BS-GMA -EDA and BS-GMA-PEHA at a monomer concentration of 20 wt% were selected for further experiment.
## 4 Numerical Study of SC Walls Perforation  
This section describes the extensive numerical investigations on SC wall perforations with different concrete panels and steel plates. SC walls with different front concrete panel and rear steel plate thickness combinations are investigated in this section considering protective structure analysis.
## 1 Introduction
In addition, DIM was selected for comparison with DSM in an analysis of PPP-RTK positioning performance for typical medium- and large-scale reference networks. We first introduce the algorithms of the interpolation models and the extraction of server-side atmospheric delay and constraint of user-side atmospheric delay. The data processing strategies are then presented. Finally, we present an analysis of the accuracy of the proposed interpolation model versus other models and the performance of PPP-RTK at the user end for different scenarios.
## 3 Pulsed Ion Beams (Protons and Carbon Ions): Laser-Driven Source Features
### Spectral Control (with Lasers and Targets)
A similar outcome could be more easily achieved using small quadrupole or pulsed solenoid magnets, an approach that is being developed by several groups including the authors of this paper [56; 57; 58] also showing how the proton beam can be captured and transported. More about transport of laser produced proton beams will be discussed in Section 4. Figure 9: **(a)** A laser incident on a proton dot deposited on a titanium substrate; **(b)** Experimental peaked proton spectrum (black dots) and PIC (particle in cell) simulation (red curve). Reproduced from [51]. Copyright 2006, Macmillan Publishers. For other examples see [52]. A number of other approaches to produce quasi-monoenergetic heavy ions have been reviewed previously e.g., monoenergetic deuterons from heavy water droplets [15]. We now discuss very different target types that can be considered for achieving higher proton energies and proton focusing. [PERSON], [PERSON] and co-workers have recently shown [59] that enhanced proton acceleration can be produced with structured dynamic plasma targets. These targets were engineered by depositing snow on sapphire substrates which mimicked the bristles on a brush. In this case the acceleration regime was not typically TNSA (Figure 11). The laser interacted with one or more bristles to provide proton energies greater than that anticipated based on normal TNSA with thin film targets. This behaviour is shown in Figure 12. At laser powers greater than 1 PW, the extrapolated simulation predicts proton energies near 200 MeV. Figure 11: Cartoon indicating how a laser beam interacts with snow pillars about 100 \(\upmu\)m in size. The laser may interact with one or more pillars (purple arrows) and the protons come off (green arrows) from the target on the same side from which the laser approaches the target. This is not a TNSA process [60]. Figure 10: An ultrafast laser-driven microlens to focus and energy select MeV protons. This technique is in principal scalable to high-energy protons. Reproduced from [55]. Copyright 2006, Association for the Advancement of Science. It should be emphasized the novel work of [PERSON] and his team is based on an acceleration mechanism that is very different from normal TNSA acceleration. \"Targetry\" is a rapidly advancing and critical part of ILDIAS source technology. Recent results have been published based on simulations of laser-irradiated two-layer targets with a linearly polarized laser pulse. Such targets have an outer foam layer added to a thin solid foil. 2D PIC simulations predict 200 MeV proton yields using a realistic Gaussian laser pulse focused to an intensity of \(6\times 10^{20}\) W\(\cdot\)cm\({}^{-2}\)[61].
###### Abstract  
Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key challenge. To address this issue, this paper introduces a hybrid load forecasting model that integrates the Long Short-Term Memory Network (LSTM) with the Stochastic Configuration Network (SCN). We first verify the Universal Approximation Property of SCN through experiments on two regression datasets. Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. These extracted feature vectors are then used as inputs for SCN-based STLF. Finally, we evaluate the performance of the LSTM-SCN model against other baseline models using the Australian Electricity Load dataset. We also select five high-volatility regions in the test set to validate the LSTM-SCN model's advantages in such scenarios. The results show that the LSTM-SCN model achieved an RMSE of 56.970, MAE of 43.033, and MAPE of 0.492% on the test set. Compared to the next best model, the LSTM-SCN model reduced errors by 6.016, 8.846, and 0.053% for RMSE, MAE, and MAPE, respectively. Additionally, the model consistently outperformed across all five high-volatility regions analyzed. These findings highlight its contribution to improved power system management, particularly in challenging high-volatility scenarios.  
Short-Term Load Forecasting; high-volatility regions; Long Short-Term Memory; Stochastic Configuration Network; universal approximation property +
Footnote †: journal: applied sciences