esa-sceva/satcom-chunk-collection huggingface dataset:
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 5 Simulation Experiments and Analyses
### Comparative Analysis of Algorithm Performance  
In order to verify the performance of the SOA-DWA algorithm, in the simulation comparison experiments shown in Figure 14, this paper introduces a unified evaluation index system, including path length, search time, path smoothness, and obstacle avoidance efficiency, to comprehensively evaluate the algorithm's path optimisation capability, real-time performance and obstacle avoidance stability. Under the same experimental conditions, the SOA-DWA algorithm is quantitatively compared and analysed with the fusion algorithms in [24; 25], and the experimental results are shown in Figure 15.  
Figure 14: Experimental comparison of different algorithms. (**a**) Algorithm from [24] in scenario 1; (**b**) algorithm from [25] in scenario 1; (**c**) SOA-DWA algorithm in scenario 1; (**d**) algorithm from [24] in scenario 2; (**e**) algorithm from [25] in scenario 2; (**f**) SOA-DWA algorithm in scenario 2.  
From Figures 14 and 15, it can be seen that in dynamic and static scenario 1, the SOA-DWA algorithm reduces the path length by 13.8% and 7.5%, reduces the search time by 38% and 47%, and improves the smoothing degree by 26% and 16.7%, respectively, compared with the algorithms in [24; 25], which effectively improves the distances to the added obstacles; in dynamic and static scenario 2, the path length reduces the path length by 12.1% and 7%, and the search time by 37.8% and 46%, respectively, and the search time is reduced by 37.8% and 46%, and the smoothness is improved by 31.4% and 19.3%, respectively. Specifically, the lack of constraints on the locally planned paths in the algorithms of [24; 25] leads to their deviation from the globally optimal paths and the lack of fine-grained optimisation of the local paths during the dynamic obstacle avoidance process, which affects the efficiency of the planned obstacle avoidance. In contrast, by combining the global path planning algorithm and the local path planning algorithm, the SOA-DWA algorithm dynamically corrects the local paths on the basis of the globally optimal paths, which not only significantly shortens the path length and search time, but also effectively improves the obstacle avoidance efficiency by adjusting the robot's trajectory in real time. The experimental results show that the SOA-DWA algorithm is able to respond quickly to environmental changes and maintain a safe distance from obstacles during the dynamic obstacle avoidance process, and at the same time reduces the number of path turns, which further improves the smoothness and safety of the path planning, and fully reflects its superiority in complex environments.
## 3 Results and Discussion
### Optimization of PAL Defect Density  
The defects of the absorption layer can interfere when the carriers in the device move to electrodes [71]. The PAL defect density was changed from \(10^{10}\) to \(10^{20}\) cm\({}^{-3}\) to determine its effect on the PSC characteristics. Figures 10 and 11 show the current density-voltage (\(J\)-\(V\)) graphs and changes in \(PCE\) according to the PAL defect densities. With an increase in defect density up to \(10^{15}\) cm\({}^{-3}\), the \(PCE\) remains at \(\sim\)17%; however, with a further increase in defect density, \(PCE\) decreases to 13%. The Shockley-Read-Hall model shows that an increase in carrier life can reduce recombination [72; 73; 74].  
\[\tau=\frac{1}{\sigma V_{th}N_{t}} \tag{8}\] \[\mathrm{L}=\sqrt{D\tau} \tag{9}\]  
Figure 10: The current density–voltage (\(J\)–\(V\)) curves for various PAL defect densities.  
Figure 9: Effects of ETL thickness on (**a**) \(V_{\alpha\epsilon}\), (**b**) \(I_{sc}\), (**c**) \(FF\), and (**d**) \(PCE\).  
\[\mathrm{D}=\frac{\mu kT}{q} \tag{10}\]  
Here, \(\tau\), \(\sigma\), \(V_{th}\), \(D\), and \(\mathrm{L}\) are the minority carrier lifetime, capture cross-section, thermal velocity, diffusion coefficient, and diffusion length, respectively.  
A low defect density prolongs carrier life and extends diffusion length, thereby reducing recombination (Equations (8) and (9)). Therefore, the defects in the absorption layer should be minimized to inhibit recombination.
###### Abstract  
High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework's capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects.  
high-speed railways (HSRs); spoil disposal areas (SDAs); remote sensing (RS); geographic information systems (GIS); global positioning systems (GPS); circular economy +
Footnote †: journal: Applied sciences  
_Article_
# An Analysis of Differential Evolution Population Size
## 3 Differential Evolution
### Basic Differential Evolution  
Several DE variations have been proposed in the literature. The notation DE/x/y/z is used to define specific DE strategies. In this notation, x is the method of selecting the target vector, y indicates the number of difference vectors used, and z indicates the crossover method used. The basic DE/rand/1/bin is used in this paper, where the target vector is randomly selected, one difference vector is used in the mutation operation, and binomial crossover is used. The DE/rand/1/bin strategy is summarized in Algorithm 1.  
```
1: Set the generation counter, \(\mathsf{t}=0\);
2: Initialize the control parameters, \(N_{P}\), \(F\) and \(CR\);
3: Initialize the population, \(G(0)\), of \(N_{P}\) individuals;
4:while stopping condition(s) not true do
5:for each individual, \(\mathbf{x}_{i}(t)\in G(t)\)do
6: Evaluate the fitness, \(f(\mathbf{x}_{i}(t))\);
7: Select three random integers \(i_{1}\), \(i_{2}\), \(i_{3}\), \(\in\{1,\ldots,N_{P}\}\) such that \(i\
eq i_{1}\
eq i_{2}\
eq i_{3}\)
8: Select a random integer \(j_{rand}\in\{1,2,\ldots,D\}\)
9:for each parameter \(j\)do
10:\(x^{\prime}_{ij}(t)=\begin{cases}u_{ij}(t)&\text{if }(rand(0,1)\leq CR\mid \mid j=j_{rand})\\ x_{ij}(t)&\text{otherwise}\end{cases}\)
11:endfor
12:if\(f(\mathbf{x}^{\prime}_{i}(t))\) is better than \(f(\mathbf{x}_{i}(t))\)then
13: Add \(\mathbf{x}^{\prime}_{i}(t)\) to \(G(t+1)\);
14:endif
15:else
16: Add \((\mathbf{x}_{i}(t))\) to \(G(t+1)\);
17:endfor
18:\(G(t)=G(t+1)\);
19:endwhile
20: Return the individual with the best fitness as the solution;
```  
**Algorithm 1** Differential Evolution DE/rand/1/bin
## 2 HSI Procedure
### Preprocessing
#### 2.2.4 Orthogonal Signal Correction (OSC)  
OSC is one of the powerful preprocessing methods, removing irrelevant variations from spectral data, which improves the predictive accuracy of machine learning models [59]. By filtering out the unrelated spectral components for a target variable, the relationship between the spectral data and the variable of interest is enhanced [60]. It works particularly well in combination with other preprocessing techniques, since its integration with CARS showed large enhancements in the coefficients of determination and a decrease in RMSE with the combined SNV-OSC-CARS-SVR model [61]. For instance, in the case of [PERSON] et al. (2024), OSC was used with SNV and CARS on the HSI data in predicting protein content in dried laver. Similarly, [PERSON] et al. (2024) demonstrated the application of the OSC-CARS-Support Vector Machine model for the SSC prediction of Agaricus bisporus slices during ultrasound-assisted osmotic dehydration. By improving model accuracy, OSC has become a useful tool in non-destructive food analysis [60], with applications across a wide range of agricultural products. In this case, studying the usefulness of OSC in products like fruits and vegetables can be considered for evaluation.  
Although OSC's advantages are obvious, it can lead to misconceptions regarding the separation of predictive and non-predictive information, potentially confusing model interpretation [62].
## 2 Background and Related Work  
### Wireless Smart Sensors in Movement Monitoring  
Advanced wireless smart sensor systems are playing a key role in monitoring lower limb movement and biomechanical parameters, offering real-time biofeedback that can optimize performance and prevent injuries [25]. Inertial measurement units (IMUs), which measure angular rate, magnetic field, and acceleration, provide critical data on movement dynamics that help in understanding biomechanical performance and adjusting training [26; 27]. Electromyographic (EMG) sensors, on the other hand, track muscle activity, enabling the analysis of muscle fatigue, activation patterns, and overall muscle health, which is crucial for optimizing training [28; 29]. For lower extremity monitoring, smart insoles like the Pedar and PODOSmart(r) systems capture pressure distribution and the center of pressure (CoP), offering detailed insights into foot biomechanics [30; 31]. Similarly, smart socks like DAid(r) integrate piezoresistive textile sensors, which improve comfort while providing accurate biomechanical data. These systems are advantageous in sports medicine due to their affordability and ease of integration into training protocols, despite not offering the precision of laboratory-grade equipment [20; 22; 30; 32; 33]. However, these sensor systems face some challenges. When multiple wireless devices are used simultaneously, synchronizing data flow can become complex and time-consuming. Signal overlap and inaccuracies may occur and managing device activation timing adds to the complexity [34]. DAid(r) smart socks stand out by offering an unobtrusive design with a minimal impact on movement, making them a promising tool for more natural and effective monitoring [20; 22; 23; 35]. Despite these advances, current smart textile technologies have yet to address the gap in biofeedback systems for injury prevention, particularly for youth football players. Addressing this gap could significantly enhance the effectiveness of movement correctness monitoring and injury prevention strategies for, e.g., by utilizing virtual reality-based solutions. Integrating real-time biofeedback from technologies like DAid(r) smart socks into the injury prevention programs like the FIFA 11+ program in a VR environment offers an opportunity to address these challenges. By using the smart socks  
Figure 1: Conceptual framework of the smart socks-based biofeedback system.  
to monitor plantar pressure and the CoP, players can receive immediate visual feedback through a head-mounted display (HMD) if their foot alignment or weight distribution is off. For example, during exercises like the single-leg stance or squats, a red arrow could appear if there is an inward foot roll, prompting the player to adjust their posture [36]. This integration of biofeedback into injury prevention exercises can significantly improve movement precision, ensuring that players make the necessary adjustments to prevent injury.
# A Novel Optical-Based Methodology for Improving Nonlinear Fourier Transform
## 3 The Inverse Nonlinear Fourier Transform Method  
In following section, we will use the scattering data found in the previous section to find its relationship with the solution \(u(t,z)\) of the NLSE equation. We will first give without proof the following theorem that will be an important step towards the Gel'fand-Levitan-Marchenko equation [49].  
Theorem 3.1: _The solutions \(\varphi(t,\lambda)\) and \(\overline{\varphi}(t,\lambda)\) of Lax Equation (8) also satisfying (10), will be represented as follows:_  
\[\varphi(t,\lambda)=\binom{0}{1}e^{i\lambda t}+\int_{t}^{\infty}K(t,s)e^{i \lambda t}ds \tag{39}\]  
\[\overline{\varphi}(t,\lambda)=\binom{1}{0}e^{-i\lambda t}+\int_{t}^{\infty} \overline{K(t,s)}e^{-i\lambda t}ds \tag{40}\]  
_with \(K(t,s)\) and \(\overline{K(t,s)}\) being independent from \(\lambda\), such that:_  
\[K(t,s)=-\frac{1}{2}u(t) \tag{41}\]  
\[\overline{K(t,s)}=\frac{1}{2}u(t)^{*} \tag{42}\]  
In order to obtain the Gel'fand-Levitan-Marchenko equation, we have to consider that \(u(t)\) should decay fast enough in order to extend Equation (12) to the upper part of the complex plane in such a way as to satisfy the following relation of the Jost functions and continuous data scattering:  
\[\frac{1}{a(\lambda)}\phi(t,\lambda)=\frac{b(\lambda)}{a(\lambda)}\varphi(t, \lambda)+\overline{\varphi}(t,\lambda) \tag{43}\]  
If we substitute (39) and (40) in (43) and we also define the function \(F(t)=\frac{1}{2\pi}\int\frac{b(\lambda)}{a(\lambda)}e^{\lambda t}d\lambda\), we can easily recover the Gel'fand-Levitan-Marchenko equation given as:  
\[K(t,y)+F^{*}(t+y)-\iint_{t}^{\infty}K(t,r)F(r+s)F^{*}(s+y)drds=0 \tag{44}\]  
The important case studied in this paper is when the discrete scattering data do not exist, \((a(\lambda)\
eq 0,\mathfrak{I}(\lambda)\geq 0)\), and \(F(t)\) is defined as the inverse Fourier transform of \(\frac{b(\lambda)}{a(\lambda)}\). The ratio \(\frac{b(\lambda)}{a(\lambda)}\) is also called the _reflection coefficient_ or _spectral amplitude_ of the nonlinear spectrum. Fast Fourier transform (FFT) can be used to compute it.  
Making the following variable change in (44), \(\beta=y-t\) will transform the Gel'fand-Levitan-Marchenko equation into two equations called _Marchenko systems_, which will highly simplify our numerical simulation. If we define: \(B_{2}(t,0;z)=-2u(t,z)\) and \(B_{1}(t,\beta)=\int_{0}^{\infty}B_{2}(\beta,r)F(r+\beta+2t)dr\) as our two auxiliary functions, we can substitute Equation (44) equivalently with the following _Marchenko system_:  
\[\left\{\begin{array}{c}B_{1}(t,\beta)=\int_{0}^{\infty}B_{2}(t,r)F(r+\beta+ 2t)dr\\ B_{2}(t,\beta)=\int_{0}^{\infty}B_{2}(t,r)F^{*}(r+\beta+2t)dr-F^{*}(2t+\beta) \end{array}\right. \tag{45}\]  
Equation (45) should be now solved numerically. Observe that in this way the quadrature formulas that we will use for the approximation of the two integrals that will be applied on the same interval and on the same variable. Note that to apply such quadrature formulas, it will be necessary to approximate the domain of function \(F\), as explained in the next subsection.
## 5 Conclusions  
As a result, protein-based direct conversion of BDFs into bovine myoblasts and myotubes using 30 MNR was not confirmed; however, the delivery of the bMyoD gene using a retrovirus showed effective direct conversion (Figure 6). For direct reprogramming of myogenic cells using a viral approach, enhanced expression of not only early myogenic markers (MyoD and MyoG), but also late myogenic markers (MYH3, MYMK, Desmin) was significantly observed. Further, the morphological change in cells was clear. In Figure 5A,C,E, though all the cells were experimented on simultaneously with the same condition, cellular morphologies were shown differently, depending upon the staining target. Since the images were obtained by capturing the fluorescence-positive region among the whole cells, it is possible that cells with up-regulated MyoG elongated and fused, resulting in enhanced formation of myotubule structures compared with cells with up-regulated MyoD and Desmin.  
Although successful reprogramming was not achieved in cell-penetrating protein-based methods, the 30 Kc19 protein enabled mass production of the transcription factor bMyoD in a soluble form, also able to increase the stability of the bMyoD and induce localization into the nuclei. Thus, this study could have important implications in the following research of direct conversion using transgene-free methods, represented as protein-based conversion. Besides, BDFs were successfully converted into myogenic cells by transduction through retrovirus, delivering the bMyoD gene. It would be a great development from the related former studies in terms of diminishing the delivering factor to only one. Moreover, by expanding the results of direct reprogramming from existing murine or human cell-based research to bovine cell-based study, the results could be widely used in the field of not only regenerative medicine but also the cultured meat industry in the future.
## 4 The Arrowhead-EP of a Real Use Case  
In this section, we present a smart heating system inspired by a real business case as an example to demonstrate the advantages of adopting the Arrowhead Engineering Process model in a real SoS application. Smart boilers are becoming very popular for the advanced features offered to the final user for improved safety, the reduced costs, and the limited environmental footprint of this category of appliances. The digitalisation of a heating system is a complex process, implying the life cycle management of a complex SoS, with a value chain composed of several stakeholders, each adopting different EPs to design, develop, and operate the components of the system.  
A smart boiler system (SBS) is an end-to-end solution for the consumer market, intended to extend a standard boiler for home usage with connectivity and smart functionalities; it is then integrated into an IoT infrastructure and provides added-value services for the manufacturer, installer, and final user. For the manufacturer, the SBS, and the associated EP, simplify, improve, and optimise the management of a fleet of boilers, improve the quality of the product, reduce maintenance costs, and increase the return on investment. It also simplifies the everyday activities of maintenance operators and improves the quality and safety of their job. For the final user, SBS improves boiler safety through continuous remote monitoring by qualified personnel, and it increases the comfort level while reducing energy consumption and the carbon footprint. Thus, SBS is a relevant example to illustrate the complex value chain that supports an apparently simple consumer application.
## 3 Results
### Fourier Transform Infrared (FTIR) Spectroscopy  
Figure 7 shows FTIR spectra of samples A01 to A04 and A10. The samples are measured in the transmission mode within the wavenumber region of 400-4000 cm\({}^{-1}\). The signatures are observed with maximum transmittance (or absorption) at \(\sim\)585 cm\({}^{-1}\), \(\sim\)1635 cm\({}^{-1}\), \(\sim\)2365 cm\({}^{-1}\), \(\sim\)2856 cm\({}^{-1}\), \(\sim\)2930 cm\({}^{-1}\), and \(\sim\)3430 cm\({}^{-1}\) for all the samples. The band at 585 cm\({}^{-1}\) is due to stretching vibration in the TiO\({}_{6}\) group, while the band around 1635 cm\({}^{-1}\) is attributed to Zn-O bond formation. The third signature around 2365 cm\({}^{-1}\), which is observed in four samples only, is attributed to the artifact caused by the spectrometer, in accordance with the earlier report by [PERSON] et al. [19]. We also observed a similar signature in our recent results on CaZrO\({}_{3}\) samples [20]. The origin of FTIR spectra at 2856 and 2930 cm\({}^{-1}\) is unclear at this stage. The broad band with a maximum at 3430 cm\({}^{-1}\) is due to the presence of molecular water in Zn\({}_{2}\)TiO\({}_{4}\). It is observed that despite annealing at 1000 \({}^{\circ}\)C, the samples show the presence of water in the lattice, suggesting that the water is absorbed by the samples from the air and is hygroscopic in nature. No major difference in the FTIR spectra of samples A02 and A10 is observed, as in one sample there is an excess of ZnO while the other is a ZnO-deficient Zn\({}_{2}\)TiO\({}_{4}\) sample.  
Figure 7: The FTIR transmission pattern for five different samples.
## 4 Validation and Comparison  
In this section, we validate the proposed model's performance by comparing it to several baseline methods, including the Close-In (CI) model, CNN, RNN, GNN, Transformer, and AutoML. Additionally, we evaluate the ensemble models with GA-optimized and PSO-optimized output weights. The goal is to assess the reliability and robustness of the neural network ensemble model optimized by the WOA for path loss prediction in HSR seats. We examine how integrating multiple networks and output weight optimization enhances prediction accuracy. The models are tested on the same dataset, and key performance metrics such as RMSE, MAE, MAPE, and R\({}^{2}\) are used for evaluation.
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
###### Abstract  
The limited field of view, cumulative inter-frame jitter, and dynamic parallax interference in handheld video stitching often lead to misalignment and distortion. In this paper, we propose an end-to-end, unsupervised deep-learning framework that jointly performs real-time video stabilization and stitching. First, collaborative optimization architecture allows the stabilization and stitching modules to share parameters and propagate errors through a fully differentiable network, ensuring consistent image alignment. Second, a Markov trajectory smoothing strategy in relative coordinates models inter-frame motion as incremental relationships, effectively reducing cumulative errors. Third, a dynamic attention mask generates spatiotemporal weight maps based on foreground motion prediction, suppressing misalignment caused by dynamic objects. Experimental evaluation on diverse handheld sequences shows that our method achieves higher stitching quality, lower geometric distortion rates, and improved video stability compared to state-of-the-art baselines, while maintaining real-time processing capabilities. Ablation studies validate that relative trajectory modeling substantially mitigates long-term jitter and that the dynamic attention mask enhances stitching accuracy in dynamic scenes. These results demonstrate that the proposed framework provides a robust solution for high-quality, real-time handheld video stitching.  
unsupervised deep learning; end-to-end differentiable network; video stitching; Markov trajectory smoothing Article  
* 2025