esa-sceva/satcom-chunk-collection huggingface dataset:
Vibration test specifications designed for the evaluation of mechanical systems (such as ISO 16750-3) generally stipulate either random or harmonic test modes [19]. However, some vehicle components are mounted at a location that may be exposed to both random and harmonic excitations simultaneously. Therefore, one spectral excitation pattern may not replicate the required test severity of these components from the perspective of fatigue damage. Hence, current testing standards, such as ISO 16750-3, have some limitations in coping with such multi-spectral pattern cases. An advanced vibration test method is presented by the defense industry in MIL-810G, where the sine-on-random excitation profile is defined for the evaluation of sub-components within defense systems and military equipment [21]. For transient spectral loading cases, such as shock response, a shock loading analysis may be necessary to carry out the fatigue analysis of vehicle components, but this transient loading issue is not included in this study. In this paper, the necessity of a sine-on-random test for a ground vehicle component is investigated for the case where the component of interest is exposed to multi-pattern excitation that includes both harmonic and random patterns. Several vehicle components belonging to those spectral loading cases, such as electronic devices on a combustion engine, powertrain, and chassis frame, are sub-components of the driving motor system for hybrid vehicles. Three different excitation modes were applied in the uniaxial excitation test of a notched simple specimen: the conventional random test, harmonic test, and sine-on-random test. The damage counting was performed using only the acceleration data for two different methods: the pseudo-damage counting explained in ISO-16750-3 and the physical damage derived using the energy isocline function. We verified the necessity of the sine-on-random test through a comparison of the accumulated fatigue damage values over three different spectral loading spectra. Finally, the vibration test guidelines were constructed for vehicle components based on these results.
# Study on the Ablation Behavior of High-Intensity Lasers in Vacuum
demonstrated that the combination of millisecond and nanosecond laser ablation not only improves the roundness of the ablation hole but also reduces the energy required for drilling by an order of magnitude [24]. Another method involves using two sets of nanosecond lasers for hole ablation. [PERSON] et al. investigated the ablation efficiency of dual-nanosecond pulse lasers and found that, in air, the drilling rate of dual-pulse lasers is an order of magnitude higher than that of a single-pulse laser, exceeding 2 \(\upmu\)m/pulse. The increased drilling efficiency of the dual-pulse method is attributed to the reduction in environmental pressure caused by the second laser beam [25]. However, [PERSON] et al. conducted a detailed analysis of the underlying mechanism, suggesting that the spherical shock wave generated by the first pulse may expel the atmosphere near the interaction area, leading to a decrease in particle density near the ablation point and the formation of a brief vacuum effect. This, in turn, enhances the interaction between the second laser beam and the target material [26]. In summary, whether by directly altering the environmental pressure or using specific ablation methods to modify it, it is clear that the vacuum environment plays a crucial role in the ablation process of pulse lasers. Although high-intensity laser technology has found widespread applications in materials processing and scientific research, systematic studies on its ablation behavior in a vacuum environment remain relatively scarce. The existing literature primarily focuses on the interaction between lasers and matter under conventional atmospheric conditions or specific gas environments. However, under vacuum conditions, this interaction mechanism may exhibit distinct characteristics and patterns. Therefore, an in-depth exploration of the ablation behavior of high-intensity lasers in a vacuum environment holds significant theoretical importance and practical application value. The ablative behavior of materials during continuous ablation by nanosecond pulsed lasers has not been thoroughly investigated. This study uses titanium alloy, a typical material, as the research subject and explores the relationship between the ablation depth and volume of titanium alloy under nanosecond laser ablation in a vacuum environment, considering factors such as pulse energy, pulse number, and defocusing distance. The average single-pulse ablation depth and average volume ablation efficiency are measured. Additionally, the three-dimensional morphology of the ablation holes is analyzed, and the ablation plume is captured to support the research findings. The research on the interaction between high-intensity lasers and matter holds significant application value in the fields of materials science, plasma physics, and laser processing technology.
# Compressed Communication Complexity of _Hamming Distance_
## 3 Compressed Communication Complexity of _Hamming Distance_  
In this section, we show a Monte-Carlo randomized protocol for Problem 1 that asks for the _Hamming distance_\(\mathsf{d_{H}}(A,B)\) of strings \(A\) and \(B\) that are compressed by non-self-referencing LZ77. Our protocol achieves \(\langle O(d\log z),O(d\log\ell_{\max})\rangle\) communication complexity, where \(d=\mathsf{d_{H}}(A,B)\), \(z=\mathsf{zn}(A)\), and \(\ell_{\max}\) is the largest value returned by the sub-protocol of the LCP problem for two strings compressed by non-self-referencing LZ77.  
The basic idea is to apply the so-called _Kangaroo jumping_ method, namely if \(d\) is the number of mismatching positions between \(A\) and \(B\), then one can compute \(d=\mathsf{d_{H}}(A,B)\) with at most \(d+1\) LCP queries. More specifically, let \(1\leq i_{1}<\cdots<i_{d}\leq n\) be the sequence of mismatching positions between \(A\) and \(B\). By using the protocol of Theorem 1 as a black box, and using the fact that \(\mathsf{zn}(S)\geq\mathsf{zn}(S[1..j])\) for any prefix \(S[1..j]\) of any string \(S\), we immediately obtain the following:  
Lemma 1: _Suppose that the alphabet \(\Sigma\) and the length \(n\) of strings \(A\) and \(B\) are known to both [PERSON] and [PERSON]. Then, there exists a randomized public-coin protocol which solves Problem 1 with communication complexity \(\langle O(\sum_{k=1}^{d}\log\mathsf{zn}(A[i_{k}+1..])),O(d\log\ell_{\max})\rangle\), where \(\ell_{\max}=\max_{1<k\leq d}\{i_{k}-i_{k-1}+1\}\)._
## 4 Experimental Plan  
The performance of MINDS was evaluated using a simulation technique. Real MA route planning applications (e.g., drones) need additional resources (e.g., infrastructure and equipment) and coordination, especially in a wide rural and/or hard-to-access areas. Hence, simulations are usually used to test MA route planning approaches and evaluate results and performance. OMNET++ [34] is a well-known component-based framework for network communication simulation (e.g., WSN). This utilises an INET [35] framework which supports node mobility patterns, MAC protocols and communication models. This supports mobile ad-hoc networks in which MAs are simulated to move throughout the network to perform particular tasks. In addition, there are implementations of well-known MA itinerary planning protocols--mainly ZMA and TBID in OMNET++. A metropolitan grid (M-Grid), the Manhattan model [36], was used for LDS localisation. The simulation parameters are discussed in the next section.  
There are four metrics that were measured in this research: average end-to-end delay, data robustness, path hop-count and total transmitted traffic. According to the literature [3], these metrics are commonly used to measure and evaluate the performances of MA route planning protocols.
## 5 Results and Discussion
### Intermodal Scheduling Case Study
#### 5.2.1 Scheduling Performance Comparison  
The intermodal container scheduling model is solved using six algorithms. Each algorithm independently generates a complete transport scheme based on the same input data and parameter settings. In practical decision-making, cost is usually the primary concern. Environmental impact and time efficiency are considered secondary but remain important. Therefore, the objective weights for cost, time, and carbon emissions are set to 0.5, 0.25, and 0.25, respectively. The results are presented in Table 6. This includes the objective function values, detailed transport routes, and selected transport modes. In the table, a, b, and c represent highways, railways, and waterways, respectively. The corresponding computation times are also reported. Figure 13 illustrates the total cost, carbon emissions, and transport time associated with the solutions produced by each algorithm, providing a comprehensive comparison of scheduling performance.  
Figure 12: Function exploitation and exploration progress. (**a**) Schwefel 2.22 function; (**b**) Rosenbrock function.  
Algorithm solving time is an important indicator of computational performance. While the IGWOHHO algorithm requires slightly more time than the SSA algorithm, it outperforms the original WOA and DBO algorithms in terms of solving speed. In terms of cost optimization, IGWOHHO achieves a reduction of over 12% in total cost compared to the GWO and HHO algorithms. Regarding carbon emissions, it reports the lowest total emissions, which are 42.1% lower than those of the DBO algorithm. These results highlight its environmental advantages. Overall, the IGWOHHO algorithm demonstrates well-balanced performance in economic efficiency, environmental sustainability, and computational cost. Its improved objective function value further confirms its effectiveness in solving multi-objective optimization problems.
## 2 Digital Twin-Assisted Gearbox Monitoring  
Power generation relies on converting mechanical motion into electricity using generators driven by inexhaustible natural forces such as wind, ocean currents, and waves [34]. These generators, in their simplest form, as shown in Figure 1, consist of aerodynamic blades designed to convert the directional movement of currents into rotational motion. This rotational motion is transmitted to the generator through a gearbox, enabling the conversion of mechanical power into electricity. This critical component serves to multiply the input shaft speed (from the blades) to the output shaft speed (toward the generator) [43]. In the gearbox, motion transmission occurs as follows: the input shaft, supported by a bearing, delivers motion to the pinion. Through the contact between the pinion teeth and the gear teeth, the motion is transferred to the output shaft, which is also supported by a bearing. This entire transformation chain is supported by the yaw system, which orientsthe turbine in the direction of the incoming wind. Figure 1 also shows the potential sensor locations for monitoring the transmission system, numbered from 1 to 8, from the blade to the generator.  
In terms of reliability, the gearbox consists of a series of interconnected components, where the failure of a single element directly affects the overall performance. In addition to its critical role, it has an annual failure rate ranging from 0.1 to 0.15 failures/turbine/year, with an average repair time of 6 to 15 days [14]. This leads to the replacement of the gearbox within a period of 5 to 6 years, despite the wind turbine's total estimated lifespan of 25 years [7]. The two most critical components in gearboxes, due to their potential for damage and high failure rates, are the bearings and gears. Bearing-related faults are the most common, accounting for 76.2% of all gearbox failures, followed by gear faults at 17.3%. The remaining 6.5% are attributed to other components [44,45]. In the following section, an approach for implementing a digital twin (DT) strategy to monitor these components is discussed. As explained in the Introduction, a DT integrates a modeling aspect and a data-driven aspect, both of which are described in detail.
## 3 Methodology  
The whole transformation and optimized airfoil were generated using an integrated aerodynamic shape optimization MATLAB code for airfoil parametrization (i.e., CST and PARSEC aerodynamic performance evaluation and genetic algorithm-based optimization). For this study, a well-known existing airfoil, the NREL S-809, was selected instead of designing a new airfoil with the common application. The NREL S-809 airfoil generates a high lift-to-drag ratio at a stall angle, which is beneficial for the blade design of a small wind turbine. In this study, aerodynamic shape parameterization by the PARSEC and CST methods was employed for the airfoils in the MATLAB environment. These parameterization methods were further linked with a genetic algorithm in the in-house MATLAB code for optimization purposes. XFOIL was used for the evaluation of the flow field analysis, which used the panel technique to calculate the coefficient of lift, drag, and pressure data for optimized airfoil geometries for angles of attack from 0\({}^{\circ}\) to 6.2\({}^{\circ}\). On the other hand, for the NREL S-809 airfoil, a CFD computational study on the ANSYS CFX using structured mesh was performed for both the baseline and optimized airfoil obtained after the parameterization and optimized process from the MATLAB-based method at a 6.2\({}^{\circ}\) AOA. The overall methodology is organized in a flowchart as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline Geometric Constraints & Trailing edge offset in a vertical sense (\(y_{te}\)) and trailing edge thickness (\(\Delta y_{te}\)) are kept zero \\ Aerodynamic Constraints & Lift coefficient should be greater than the original airfoil \\  & Wind angle of attack = 0\({}^{\circ}\) and 6.2\({}^{\circ}\) \\ Objective & Maximize lift coefficient (\(C_{l}\)) and lift-to-drag ratio (\(L\)/\(D\)) \\ Termination Criteria & No change in maximum fitness value for 20 generations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization criteria and constraints.  
Figure 4: Optimized airfoil shape profile by a genetic algorithm (GA).
# An Anchor-Free Location Algorithm Based on Transition Coordinates
## 5 Simulation Result
### The Relationship Between Positioning Accuracy and Number of Iterations  
The positioning accuracy of the proposed algorithm can be changed according to the actual application requirements, and the positioning accuracy is one of the most important factors to evaluate the quality of an algorithm. The positioning accuracy set in the first experiment is 1%, which can meet most of the positioning requirements under normal circumstances. Next, we improve the positioning accuracy to 1%, that is, the error ratio is 0.001, and the other simulation conditions remain unchanged, and analyze the relationship between the positioning accuracy of the proposed algorithm and the number of iterations. The simulation effect is shown in Figure 4.  
As can be seen from Figure 4, when the positioning accuracy of the algorithm is increased from 10% to 1%, 1%, and 0.1%, the number of iterations increases from 12 by 7, 14, and 35 times, respectively, that is, the number of iterations increases by 58.33%, 116.66%, and 291.66%, respectively. Therefore, the proposed algorithm in this paper can achieve high positioning accuracy, because the proposed algorithm does not generate error accumulation in the positioning process, only by constantly correcting the position increment between the transition coordinates and the real coordinates, so the algorithm can quickly and accurately locate the position coordinates of each member during positioning.  
Figure 3: The topological structure of the initial coordinates and the number of iterations: (**a**) the real position several members; (**b**) initial hypothetical positions of several member iterations; (**c**) the first iteration; (**d**) the 22 nd iteration; (**e**) when the initial topology is replaced from square to arbitrary topology, the relationship between the number of iterations of the algorithm and EP.
## 3 Results
### Eye-Tracking Results
Next, we extracted results for the average fixation duration, i.e., how much time on average that participants focused on each of the advertisements. For the right rail, users stabilized their gaze for an average of 1.888 s; for the nonmodal, 0.967 s; for the modal, 2.681 s; for the intraconent without content reorganization, 1.715 s; for the banner, 1.144 s; for the intraconent with content reorganization, 0.808 s; and for the animated right rail, 0.456 s. It is evident that the modal, right rail, and intraconent without content reorganization advertisements are those in which users focus their attention for a longer time. Then, the revisit counts metric allowed us to draw conclusions about how many times users returned their gaze to each advertisement. For the right rail, we see that on average, they looked back at the advertisement 3.44 times. For the nonmodal, the metric showed 1.15 times; for the modal, 1.1 times; for the intraconent without content reorganization, 4.11 times; for the banner form, 1.65 times; for the intraconent with content reorganization, 1.28 times; and for the animated right rail, 0.25 times. Finally, users revisited the right rail and intraconent without content reorganization forms more often. The reasons why a user might return to look at something on a website vary. It could be because of something that piqued their interest either positively or negatively, something that bothered them a lot and they looked again to see if they could hide it from the page, or even because their gaze fell randomly in case of movement. The next metric was dwell time, which shows the percentage of time dedicated to each advertisement compared to the time the advertisement was active, i.e., visible on the page. As long as participants navigated the website, they could scroll up or down, and the advertisement may have disappeared from their field of view. For the right rail advertisement, then, the dwell time percentage reached 6.72%; for the nonmodal, 8.83%; the modal advertisement, 53.18%; intraconent without content reorganization, 5.28%; the banner, 20.16%; the intraconent with content reorganization, 13.82%; and the animated right rail, 8.51%. We see that the modal form and, subsequently, the banner and intraconent with content reorganization forms, hade the highest percentages. We can, therefore, conclude that for these specific forms of advertisement, participants' interest was more focused. The way these particular advertisements were designed played a very important role in the results we received for this metric. The last metric was fixation count, which presents the average number of fixations detected on each advertisement and is an indication of how well the user examined the content of each advertisement. The average number of times users' gaze was stabilized
## 2 Antenna Design
### Optimization for On-Body Communication  
To utilize a wearable antenna for on-body communication, it is important to optimize the antenna to work while positioned on the body and lossy tissues, such as the chest or arm. In this section, the antenna's performance on human chest and arm models will be presented. A 3D-layered body model in CST MWS, containing skin, fat, and muscle layers, was utilized for the chest as a flat body model, while for an arm a layered cylindrical model was used to examine the antenna's performance. Taking the simulation time and accuracy into consideration, a 3D-layered flat body model with the dimensions of 200 \(\times\) 200 \(\times\) 50 mm\({}^{3}\) and thickness (skin = 4 mm, fat = 8 mm, and muscle = 40 mm) was used, with muscle's dielectric constant of 52.7 and 48.2, and conductivity of 1.95 and 6 S/m at the lower and upper frequency bands, respectively. For the cylindrical model (human arm), a radius of 50 mm and length of 150 mm were chosen, following the on-body procedure as mentioned by [PERSON]. [PERSON] et al. in [26,27]. In free-space communication, the antenna showed a good impedance matching in the lower and upper bands. Furthermore, the air-gap between the antenna and body was adjusted at 3, 5, and 10 mm. This choice of air-gap is to mimic utilizing comparable thicknesses of multiple textile layers, which were modeled as an air-gap because textile materials usually have a permittivity close to air. As the antenna was adjusted on a body chest model and the arm of a cylindrical model with an air-gap of d = 3 mm, impedance matching was considerably affected due to the strong mutual coupling. However, as the air-gap was increased, the impedance matching was not much affected. The antenna was nearly decoupled from the body at d = 10 mm; thus, the air-gap was adjusted at this distance. Figure 5 illustrates the simulated reflection coefficient (S\({}_{11}\)) of the meander line antenna at different distances from the body model. The optimized dimensions are highlighted as above in Table 2.  
Figure 4: Variation in the S\({}_{11}\) by changing the length (\(l_{1}\) and \(l_{2}\)) of the proposed antenna design.  
Figure 5: Optimization of S\({}_{11}\) for meander line monopole antennas (MMAs) in free-space, on-chest flat model, and arm model with various locations on the body (3 mm, and 5 mm).
## 5 Conclusions  
PV power output possesses high volatility and intermittency because of its great dependency on environmental factors. Hence, a reliable forecast of solar PV power output is indispensable for efficient operations of energy management systems. This paper compares eight deep learning-driven forecasting methods for solar PV power output modeling and forecasting. The considered models can be categorized into two categories: supervised deep learning methods, including RNN, LSTM, BiLSTM, GRU, and ConvLSTM, and unsupervised methods, including AE, VAE, and RBM. We also compared the performance of the deep learning methods with two baseline machine learning models (i.e., LR and SVR). It is worth highlighting that this study introduced the VAE and RBM methods to forecast PV power. For efficiently managing the PV system, both single- and multi-step-ahead forecasts are considered. The forecasting accuracy of the ten models has been evaluated using two real-world datasets collected from two different PV systems. Results show the domination of the VAE-based forecasting methods due to its ability to learn higher-level features that permit good forecasting accuracy.  
To further enhance the forecasting performance, in future study, we plan to consider multivariate forecasting by incorporating weather data. Also, these deep learning models can be applied and compared using data from other renewable energy systems, such as forecasting the power generated by wind turbines. Further, it will be interesting to conduct comparative studies to investigate the impacts of data from different technologies, such as monocrystalline, and polycrystalline.  
**Author Contributions:** A.D.: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing-original draft, Writing-review & editing F.H.: Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Writing-original draft, Writing-review & editing Y.S.: Investigation, Conceptualization, Formal analysis, Methodology, Writing-review & editing, Funding acquisition, Supervision. S.K.: Investigation, Conceptualization, Formal analysis, Methodology, Writing- original draft. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.  
**Conflicts of Interest:** The authors declare no conflict of interest.