esa-sceva/satcom-chunk-collection huggingface dataset:
Hence, this study aimed to do an experimental investigation to study the influence of laser processing parameters on the surface modification of the CP Ti substrate for the production of surface structures and evaluate their influence on the modification of the surface chemistry and development of colourization and wettability. The focus is on the low laser fluence to induce specific morphological changes, wetting behaviours, and alteration of the optical properties on the CP Ti. The focus is also on the wetting behaviour of the laser-structured surfaces of the CP Ti material under three conditions, i.e., before immersion, immediately after immersion, and after storage following immersion. The ps laser was chosen for the fabrication process because of its advantages of high pulse energy, high precision for micro/nanostructures, and low heat accumulation [2]. This research provides more information on how laser processing parameters can be utilised to obtain different surface morphologies and colourization which could have a broad range of uses in laser colour marking and artistic design, among others. It will also lead to more understanding of the wettability development of laser-textured metallic surfaces aimed for corrosion control, cell adhesion, antifouling behaviour, and self-cleaning surfaces, among others.
# A Survey of Machine Learning Methods for Time Series Prediction
## 6 M5 and M6 Forecasting Competitions
### M6 Forecasting Competition  
The M6 forecasting competition from 2022 to 2023 [92] marked a significant evolution in the M series. With a prize pool of USD 300,000, it attracted 226 teams to participate. The challenge revolved around creating investment portfolios using real-time data from 50 U.S. stocks and 50 exchange-traded funds (ETFs). Unlike prior iterations, the M6 competition emphasized both forecasting and investment decision making, awarding prizes for the forecasting accuracy, investment performance, and overall performance. Participants were given flexibility in their data sources and methodologies. While organizers provided an optional dataset of adjusted closing values, teams could choose their own data, frequency, and supporting information, such as economic indicators or news. The competition spanned 12 months, with teams submitting monthly forecasts and investment strategies for the subsequent 20 trading days.  
The winners in forecasting, investment, and the combined \"duathlon\" category all used distinct methods. The top forecasting model used a Bayesian dynamic factor model. The best-performing investment model relied on AutoTS, an open-source library for probabilistic time series forecasting. The duathlon champion employed a meta-learning model using NNs, which ranked fourth in forecasting. Notably, an XGBoost-based approach secured second place in both forecasting and the duathlon. Interestingly, the team that won the investment challenge placed 92 nd in forecasting, and the second-place investment team, which used a type of exponential smoothing model (ATA), ranked 110 th in forecasting. In fact, the researchers found zero correlation between forecasting and the investment performance, emphasizing that a team's ability to accurately predict a stock's future price was not the driving factor in creating the most profitable investment portfolio.  
The researchers were not surprised that the best performing methods included both conventional econometric time series methods as well as sophisticated machine learning methods because of the unique challenges present in financial forecasting including external factors, seasonality, stochastic trends, etc. The key takeaway from the M6 competition is that the choice of data and their usage are as critical as the forecasting techniques themselves in achieving superior results.
## 3 Materials and Methods
### Development of the Radial Basis Function Model (RBF)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **Multilayer Perceptron (MLP)** & **Radial Basis Function (RBF)** \\ \hline Network Structure & Multilayer and feedforward neural network & One hidden layer, radial basis function net \\ \hline Learning Approach & Gradient-based backpropagation algorithm & Competitive learning algorithm followed by least-squares regression \\ \hline Activation Function & Nonlinear functions like sigmoid, ReLU, tanh, etc. & Gaussian radial basis functions \\ \hline Training Speed & Slow to moderate & Fast \\ \hline Training Data & Requires a large amount of labeled data & Requires fewer labeled data \\ \hline Interpretability & Less interpretable than RBF & More interpretable than MLP \\ \hline Performance & Good for complex classification tasks & Good for simple to moderately complex tasks \\ \hline Overfitting & More prone to overfitting & Less prone to overfitting \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison table for MLP and RBF networks [81].
## 5 Conclusions  
This work represents a substantial advancement in the conceptualization, implementation, and validation of DTs for production and service systems, with a special focus on organic waste management. The contributions are categorized into three distinct dimensions:  
(a) Methodological Contributions  
This study introduces a set of methodological innovations that advance the implementation of DTs in low-tech, variable environments:  
* An applicable and operational definition of DTs was proposed, which integrates predictive capabilities, dynamic feedback, and autonomous management. This conceptual clarity facilitates the distinction between Digital Models, Shadows, and Twins, and supports their implementation across different maturity levels.
* A modular and scalable three-layer architecture--composed of physical, cloud, and virtual environments--was designed to enable integration with emerging technologiessuch as IoT, AI, and cloud computing. This structure supports real-time monitoring, simulation, and feedback loops in dynamic contexts.
* The step-by-step methodology, structured in contextual and propositional phases, offers a replicable framework for guiding the development of DTs. It includes stages for system diagnosis, tool selection, simulation modeling, physical integration, and iterative validation.
* A novel approach to gradual DT adoption was established through three levels of implementation: Digital Model (monitoring), Digital Shadow (simulation), and DT(optimization). This staged framework enables organizations to scale up based on their available infrastructure and operational complexity.
* Additionally, the methodology introduces the concept of minimum replicability conditions, outlining the essential requirements for transferring the model to new sectors or environments. These include the availability of sensors, KPI definitions, connectivity, and human-operator engagement.  
This set of contributions provides a robust foundation not only for technical implementation but also for strategic planning of DT adoption in sectors traditionally excluded from digital transformation efforts.  
To consolidate the main contributions of this study, Figure 7 provides an integrative representation of the proposed DT methodology. It illustrates the logical flow of the two-phase framework, contextual and propositional, along with its empirical validation through real-world deployment in a rural composting facility. Additionally, it outlines the methodology's potential adaptability to other domains, including wastewater treatment, smart agriculture, and decentralized bio-processing systems. Beyond serving as a synthesis of the case study, this figure also conveys the generalizable procedure or baseline structure required to replicate and scale the methodology across different sectors. As such, it reinforces the modular nature, strategic sequence, and cross-sectoral transferability of the approach, making it a practical foundation for applying DTs in circular economic contexts with limited digital infrastructure.
###### Abstract  
In this study, wireless sensor networks and time base generators are used to solve the fixed-time containment control problem in multi-agent systems with fixed topologies. A new event-triggered control protocol is proposed, which combines a fully distributed method and a time base generator (TBG). The goal is to converge the states of all followers to the convex hull formed by the leader. The controller reduces communication and improves control efficiency by integrating a fully distributed control mechanism using wireless sensor networks. In addition, a time base generator (TBG) is added to ensure that the dwell time continues to be pre-specified and independent of initial conditions. Using matrix theory, the original system is transformed into an error system, and its stability is analyzed by the Lyapunov method. The necessary and sufficient conditions for solving the time consensus containment control problem in multi-agent systems are determined and Zeno behavior is avoided. The effectiveness of the proposed control algorithm is illustrated by numerical examples.  
actual fixed-time consensus; event-triggered control; wireless sensor networks; time base generator; containment control
## 4 Dual-Comb Doppler Reflectometer System in LHD
### System Test for Doppler Shift Measurement by Rotating Grating  
The most attractive feature of Doppler radar is its ability to measure the velocity of a target object. We conducted a test to confirm whether this dual-comb Doppler reflectometer system can observe the velocity of an object. For this test, a grating drum with a diameter of 500 mm and a grating spacing of 10 mm was prepared, as shown in Figure 9a. Its rotational speed is variable up to 800 rpm by the controller. The relationship between the drum rotation speed \(\omega\) and the Doppler shift frequency \(f_{D}\) can be expressed by \(f_{D}=r\omega\cdot 2k\ \sin\theta\ /2\pi\). Here, \(r\) (=250 mm) is the drum radius and \(\theta\) (=45 degrees) is the angle of incidence with respect to the normal drum surface, as shown in Figure 9b.  
Figure 8: Frequency spectra of reference signal of IQ detection circuit (monitor point 5). **(a)** Entire reference signal and **(b)** 120 MHz component of a portion of the figure in **(a)** (the area enclosed by the dotted square) enlarged. **(c)** Example of phasor plot (In- phase and Quadrature-phase).  
Figure 9: **(a)** Photograph of rotating grid drum and **(b)** Schematic layout of the test for the Doppler shift measurement.  
The received signal of Mixer 3 clearly shows the discrete peaks associated with the grating structure and the spectral frequency shift associated with the rotating drum, as shown in Figure 10a. A Gaussian fit is applied to the observed frequency spectrum to estimate the Doppler shift frequency. The obtained Doppler shift frequencies at different rotating speeds of the drum, are shown in Figure 10b. The linear relationship expected from the above equation is clearly observed, indicating that Doppler shift measurement is possible in this dual-comb system.
## 4 Performance Evaluation and Result Analysis  
### Sensing Matrix Optimization  
The idea behind the proposed approach is to isolate the components of speech and noise signals into different columns of a sensing matrix. The degree of isolation is reflected by the fitness function of the optimization algorithm, where a lower value reflects a higher level of isolation. To validate this concept, we have shown the column contributions of the sensing matrix for speech and noise frames before (Figure 8) and after (Figure 9) optimization. The convergence plot of the optimization process is also shown (Figure 10) to link the fitness function and the level of isolation.  
The convergence plot shows that, when the optimization begins, the average and best fitness values were 0.64 and 0.52, respectively, which resulted in highly overlapping column contributions, whereas at the end of the optimization, the average and best fitness values were 0.27 and 0.25, respectively, which resulted in well-separated column contributions. Figure 9 also shows that after optimization, 95% of the total signal energy was concentrated in half the number of columns.
# Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign
## 5 Discussion  
The execution of the ODT and the processing of the collected data provides an important set of results that give the first significant insights on the overall system performance. First of all, the transmitted signal, after its replica were provided by internal calibration and their similarity to an ideal chirp were checked, showed an almost ideal IRF in range. The compensation of the known phase function, corresponding to the target motion, allowed us to check the phase stability of the system over a long time interval spanning up to 10 min. The analysis over datasets acquired with different azimuth and elevation pointing of the SAOCOM antenna tile allowed the successful validation of the antenna pattern pointing and shape over a large interval including the first and second side-lobes. The main limitations remain the single antenna tile used, thus allowing us to test only the beamforming in the elevation direction, and the target used, which responded only with the co-pol channels. Nevertheless, the collected results allow us to state that the key functionalities of the SAR system are verified, even with the limitations that an on-ground setup unavoidably brings.
## 5 Numerical Experiments
### Parameters Setting  
Table 1 presents the values of important parameters used by _MOWOATS_. These parameters are used to adjust the performance of the algorithm. The _MaxIt_ parameter controls the maximum number of iterations. The value of the _MaxIt_ parameter was chosen to be the least value that ensures the stability of the algorithm. _Np_ parameter represents the number of whales in each swarm. This parameter also was set to the least value that can maintain the stability of the algorithm. Minimizing the values of _MaxIt_ and _Np_ parameters contributes to minimize the running time of the algorithm. The _Max_NonImprove_ parameter stands for the maximum number of iterations without improvement. The _Max_NonImprove_ value was set after different number of runs to ensure obtaining high-quality solutions by the algorithm. The _max_EL_ parameter represents the maximum number of non-dominated solutions that can be stored in _EL_. This parameter was also set to be as minimum as possible to minimize the time needed when adding new solutions to the _EL_ as the algorithm needs to loop over all the solutions stored in _EL_ to eliminate dominated ones.  
_MOWOATS_ starts with a set of random solutions generated from solution space. Then, it applies both intensification and diversification techniques to explore and exploit search space. After running the algorithm, if the number of iterations with no improvement equals _Max_NonImprove_, then either intensification or diversification by crossover are executed to amplify the diversity of swarm members. After the number of iterations equals _MaxIt_, the algorithm terminates and returns the best solutions stored in _EL_.
## 3 The Proposed EATS-MOBFS Method for the 2L-VRPSC
### The Proposed MOBFS Method for Two-Dimensional Loading  
In this study, we investigated four classes of the 2L-VRPSC with respect to different loading configurations. A MOBFS packing heuristic is proposed, which is extended from a bottom-left-fill algorithm and a skyline-based heuristic. The MOBFS method considers not only the area of items but also the length and width of the items as the sorting criterion to generate the loading sequence (Algorithm 5 line 1). When sequential loading constraints are imposed, we will first sort all the items by the reverse visiting order of its customers and then use the above sorting rules to sort the rectangles of each customer (Algorithm 5 line 3). Thereafter, these two heuristics are called in sequence (Algorithm 5 line 4). Specifically, the sophisticated skyline-based heuristic will be called only if the BLF fails to produce a feasible loading solution. If neither heuristic can find a feasible packing solution, a tabu search procedure will be called by swapping two non-tabu items to generate a new sequence (Algorithm 5 line 9). The pseudocode of the presented MOBFS heuristic for two-dimensional loading is shown in Algorithm 5. Detailed improvements in the packing heuristics are described in the following subsections.  
```
Input: Route \(R\) and corresponding items Output: Packing sequence
1 Sort the items to generate three orderings \(\mathit{Ord}_{1},\mathit{Ord}_{2},\mathit{Ord}_{3}\).
2 for each ordering \(\mathit{Ord}_{i}\) of the three orderingsdo
3 Let \(I_{t}\) be the sequence of all rectangular items and \(|I_{t}|\) be the number of items.
4 for\(j=1\)to 2 do
5 if\(\mathit{Heur}_{j}(I_{t})\) places all itemsthen
6 returnsuccess
7 else
8 for\(k=1\)to\(\mathit{MaxIter}\)do
9 Generate \(|I_{t}|\) non-tabu sequences from \(I_{t}\) by swapping two items.
10 Let \(I_{t}^{*}\) be the sequence with the highest area utility using \(\mathit{Heur}_{j}\).
11 if\(\mathit{Heur}_{j}(I_{t}^{*})\) places all itemsthen
12 returnsuccess
13 else
14\(I_{t}=I_{t}^{*}\) and update the tabu list.
15
16 end if
17
18 end for
19
20 end for
```  
**Algorithm 5**The proposed MOBFS algorithm for two-dimensional loading.
## 3 Fault Location Method
### Information Loss Detection Algorithm  
Considering that FTUs and FIs are installed outdoors, information loss often occurs in the process of information collection and transmission. This may cause errors in the line list and lead to a local optimum of the topology search algorithm. Therefore, this section proposes an information loss detection algorithm to detect the FIE with information loss and correct the wrong status in the line list. This can eliminate the possibility of local optimal for the topology search algorithm in advance, so it improves the accuracy of fault location method. Moreover, if a piece of fault-indicating equipment often loses status information, this indicates that the equipment may need to be repaired.  
In this method, there are five variables: \(j\), \(k\), \(m\), \(n\), \(a\). Variable \(k\) represents the equipment number of fault-indicating equipment in the iteration process. Variable \(a\) represents the maximum number of all triggered FIEs. Variables \(j\), \(m\) and \(n\) are iteration parameters to record the times of iterations and judgment conditions. Array \(L[]\) is used to record the equipment number of the FIE with information loss. Array \(B[]\) is used to store the equipment number of a branch during iteration process.  
The procedure for the information loss detection algorithm is explained in detail in the following steps, and the flow chart of the method is shown in Figure 4.  
Figure 3: Flow chart of the topology search algorithm.  
* Determine the maximum number of all triggered FIEs in the line list, and assign the value of quantity to \(a\). Set the initial values of \(k\) and \(m\) as 1; set the initial values of \(j\) and \(n\) as 0;
* If \(k\leq a\), check the status of the \(k\)th FIE and continue to next step; if \(k>a\), go to step 7;
* If the status of the \(k\)th FIE is \"1\", set \(k=k+1\) and go back to step 2; if the status of the \(k\)th FIE is not \"1\", check the status of the FIE which is on the nearest upstream of the \(k\)th FIE; if the status is \"1\", continue to next step; otherwise, go to step 6;
* Check the quantity of the FIEs that are on the nearest downstream of the \(k\)th FIE, and assign the quantity of the FIEs to \(n\). If \(n\
eq 0\), assign the number of the nearest downstream FIE to \(B[j+1]\),..., \(B[j+n]\) separately, then go to step 5; if \(n=0\), go to step 6;
* Set \(k=k+1\), then go back to step 2;
* Stop the iteration and output \(L[]\). Each equipment number in \(L[]\) means that the information of this FIE is lost, then correct the status of this FIE to \"1\". If there are no equipment numbers in \(L[]\), it means there is no information loss equipment.  
Figure 4: Flow chart of the information loss detection algorithm.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 2 Materials and Methods
### UWB Positioning
#### 2.2.1 Chan Algorithm Based on TDOA  
The specific ranging and positioning process of TDOA is as follows:  
The label node sends a pulse signal at time \(t\), and the 3 base stations receive the signal at time \(  
Further, Equation (17) can be simplified as Equation (18):  
\[\begin{cases}x=p_{1}+q_{1}r_{1}\\ y=p_{2}+q_{2}r_{1}\end{cases} \tag{18}\]  
\[p_{1}=\frac{y_{21}r_{31}^{2}-y_{31}r_{21}^{2}+y_{31}(K_{2}-K_{1})-y_{21}(K_{3}- K_{1})}{2(x_{21}y_{31}-x_{31}y_{21})},q_{1}=\frac{y_{21}r_{31}-y_{31}r_{21}}{x_{2 1}y_{31}-x_{31}y_{21}} \tag{19}\]  
\[p_{2}=\frac{x_{21}r_{31}^{3}-x_{31}r_{21}^{2}+x_{31}(K_{2}-K_{1})-x_{21}(K_{3}- K_{1})}{2(x_{21}y_{31}-x_{31}y_{21})},q_{2}=\frac{x_{21}r_{31}-x_{31}r_{21}}{y_{2 1}x_{31}-y_{31}x_{21}} \tag{20}\]  
If Equations (19) and (20) is incorporated into Equation (18), the following is obtained:  
\[Ar^{2}+Br_{1}+C=0 \tag{21}\]  
where \(A=q_{1}^{2}+q_{2}^{2}-1\), \(B=-2[q_{1}(x_{1}-p_{1})+q_{2}(y_{1}-p_{2})]\).  
In sum, two solutions for r can be obtained. One of the solutions can be eliminated based on prior information. The position coordinate \((x,y)\) of the label node can be found by plugging the valid solution into Equation (18).
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 3 Experimental Results  
Experimental tests aim at verifying and comparing the radar imaging performance when UAV positioning data are provided by GPS or CDGPS. The imaging capabilities are evaluated in terms of ability to detect targets, to determine their elevation from the ground (i.e., the air-soil interface) and to estimate their relative distance.  
The tests have been performed at two different sites: a site for amateur UAV flights testing in Acerra, a small town in suburban area of Naples; a site made available by TopView srl [41] in San Nicola la Strada, a rural area closest to the famous Royal Palace of Caserta, Italy. The experimental tests were carried out during summer and winter seasons, with low or moderate wind conditions, and, by using targets with different geometrical and electromagnetic features.