esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Similar to the previous test, this one focused on a shorter timeframe, averaging the parameters in the last 15 days to capture more recent trends and fluctuations;
* Test 3 (Differences between maximum and minimum for the previous 30 days). This test evaluated the variability of the features by calculating the differences between the maximum and minimum values of the parameters over the last 30 days, helping to understand the range and extremes of the data;
* Test 4 (Differences between maximum and minimum for the last 15 days). This analysis aimed to determine the variability of the parameters over a more recent period, focusing on the differences between maximum and minimum values over the last 15 days;
* Test 5 (All daily parameters). This test will include each day's air temperature, surface pressure, precipitation, and air relative humidity values. For this test, the magnitude value was set to 1 if an earthquake occurred in the interval. Otherwise, it was set to 0. The test results are presented in Table 1.  
The results in Table 1 show notable differences in EMM performance across the five test conditions, highlighting how variations in data preparation and feature selection impact prediction accuracy:  
* Test 1 (30-Day Averages) used the average values over the last 30 days. It performs relatively poorly across algorithms, with R\({}^{2}\) values mostly below 0.3. This suggests that longer-term averaging may dilute short-term fluctuations, resulting in a limited predictive capability;
* Test 2 (15-Day Averages) shows that reducing the timeframe to 15-day averages slightly improved results, with R\({}^{2}\) values up to 0.3232 (LightGbmRegression). However, performance was still moderate, indicating that averaging over any period still obscure daily variations required for accurate predictions;
* Test 3 (30-Day Range) emphasizes that model performance significantly improved using the difference between maximum and minimum values over the last 30 days, particularly for FastForestRegression and FastTreeRegression, which achieved R\({}^{2}\)
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 3 Results
### Robot's TCP Transformation  
The robotic arm ABB IRB 4600 had a payload of 40 kg and a reach of 2.55 m. It had a position repeatability of 0.06 mm, a path repeatability of 0.28 mm, and an accuracy of 1 mm [15]. With this precision, the reconstruction showed the best performance with an RMSE of 1.7432 mm for the filtered dataset and an RMSE of 1.9651 mm for the unfiltered. In this method, the filtered dataset showed an improved result in relation to the unfiltered dataset.
## 2 Materials and Methods  
### Plant Material and Decoction Preparation  
The plant raw material was obtained from two following sources: collected from wild habitat (self-collected; SC) and purchased at a pharmacy (pharmacopeial raw material; PH). The rhizomes of _P. cuspidatum_ (SC) were collected in the first week of October 2020 from urban environments near Wroclaw, Poland (coordinates: 51\({}^{\circ}\)07.404\({}^{\prime}\) N, 17\({}^{\circ}\)04.146\({}^{\prime}\) E). Only healthy, fully developed rhizomes with diameters ranging from 15 to 30 mm were selected for harvesting. The species identification is described elsewhere [10]. The second type was _Polygoni cuspidati rhizoma et radix_ (_hu zhang_ in pinyin Chinese) (PH), a pharmacopeial raw material, purchased from a Sonnen-Apotheke pharmacy in Germany (Bad Kotzting, Germany). The specification for this product is attached in the Supplementary Materials, along with information on the content of emodin and polydatin (piceid). The collection and curation of plant material is supervised by the Botanical garden of Medicinal Plants at the Wroclaw Medical University.  
Decoctions were prepared according to the recommendations of traditional Chinese medicine [6]. Two weighted portions of 200 g of milled rhizomes of pharmacopeial-grade raw material (PH) and self-collected rhizomes of _P. cuspidatum_ (SC) were soaked in 2000 mL of distilled water for 60 min. The mixture was then heated to a boil for 20 min. Each of the decoctions was centrifuged, and the remaining raw materials were soaked again in 1200 mL of water and brought to a boil for 30 min. The second batch of decoctions was also centrifuged. The supernatants from the first and second batch of decoctions were combined. The solvent was then evaporated under reduced pressure, and the dry residue was lyophilized. The process is illustrated in Figure 1.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 3 Analysis of Numerical Simulation Results
### Intensity Scintillation Effect
Figure 4 shows the variation in the scintillation index of a Gaussian beam as the turbulence strength changes from \(2.5\times 10^{-16}m^{-2/3}\) to \(10^{-13}m^{-2/3}\), while all other parameters remain the same as those in Figure 3. The illustration includes conditions with no aberration, as well as with various aberrations: defocus (\(Z_{4}\)), y-direction coma (\(Z_{7}\)), spherical aberration (\(Z_{11}\)), and secondary astigmatism in the y-direction (\(Z_{25}\)), each tested at different receiver aperture sizes. From Figure 4a-d, it can be observed that under identical turbulence conditions, the scintillation index corresponding to each system aberration (\(Z_{4}\), \(Z_{7}\), \(Z_{11}\), \(Z_{25}\)) decreases as the receiver aperture radius increases. This suggests that the aperture averaging effect can reduce the scintillation index. Compared to the condition with no system aberration, the change patterns in the scintillation index for each aberration (\(Z_{4}\), \(Z_{7}\), \(Z_{11}\), \(Z_{25}\)) differ as follows: (1) At the same weight coefficient, the stronger the turbulence strength or the smaller the receiver aperture radius, the higher the scintillation index for \(Z_{4}\)\(\beta_{I,Z_{4}}^{2}\). When \(C_{n}^{2}\leq 1.5\times 10^{-14}m^{-2/3}\), \(\beta_{I,Z_{4}}^{2}\) is always lower than that of the aberration-free condition \(\beta_{I,no}^{2}\) at any receiver aperture radius, and the difference is so small as to be negligible. When \(C_{n}^{2}>1.5\times 10^{-14}m^{-2/3}\), \(\beta_{I,Z_{4}}^{2}\) is always bigger than \(\beta_{I,no}^{2}\), especially at stronger turbulence strength or a smaller receiver aperture radius. This means the promoting effect of \(Z_{4}\) aberration on the scintillation index becomes more pronounced as turbulence strength increases or the receiver aperture size decreases. (2) Compared to the no-aberration state, \(Z_{7}\), \(Z_{11}\), and \(Z_{25}\) aberrations always have a promoting effect on the on-axis scintillation index, especially at stronger turbulence or higher weight coefficients. However, this promoting effect is not significant, and the maximum differences in the scintillation index are all within 0.01. This means that the effects of \(Z_{7}\), \(Z_{11}\), and \(Z_{25}\) on the scintillation index are limited and can be ignored. Thus, combined with Figures 3 and 4, it is evident that increasing the receiver aperture radius can effectively reduce the promoting effect of aberrations on the scintillation effect.
## 5 Conclusions  
We have presented a methodology to calculate the complex dispersion relation of 2D periodic resonant and non-resonant systems of finite-size via a slight modification of the SLaTCoW method presented in Ref. [11]. By assuming the finite size structures to be mirror symmetric with respect to their median axes along the analyzed direction, the SLaTCoW method is extended to a two-dimensional problem allowing for the calculation of the dispersive and attenuation properties of the system at specific symmetry directions, namely \(\Gamma\)X and \(\Gamma\)M. To properly record the necessary acoustic pressure, which happens to be distributed in a surface map to account for 2D structures, a basic spatial sampling criteria applicable to periodic structures is provided and discussed. Two periodic structures with different properties are considered in the study. First, we validated the results provided by the present methodology by comparing them to those obtained via a well-established technique, the EPWE, exhibiting an excellent agreement for both the real and imaginary parts of the wavevector for the two symmetry directions. Then, we show the potential of the methodology to analyze a 2D acoustic metamaterial including QWRs and accounting for visco-thermal losses. The obtained results also show  
Figure 4: Complex dispersion relation for a periodic resonant system formed by QWRs. Numerical simulations using an infinite structure and solving the corresponding eigenvalue problem in FEM are compared to the results obtained applying SLatCoW from numerical data using a finite structure.  
an excellent agreement with an already validated technique for the real part of the dispersion relation and clearly manifest the typical signature of resonant and Bragg gaps in the imaginary part of the wavenumber. Limitations related to the appearance of air modes in the dispersion results, due to the finite size of the structure and its excitation by plane incident waves are also discussed. As shown in Refs. [11; 12] this method is applicable to systems of different kind, at different frequency ranges, and should also be applicable for other type of waves, such as TE and TM modes for electromagnetic waves, where there is a close similarity with the 2D acoustic wave equation. Finally, the methodology presented in this work is intended to directly serve for the experimental characterization of real 2D periodic and resonant systems.  
Conceptualization, A.C., V.R.-G., J.P.G. Methodology, A.C., V.R.-G., J.P.G.; Original Draft Preparation, A.C., V.R.-G., J.P.G.; Writing Review & Editing, A.C., V.R.-G., J.P.G.; Supervision, A.C., V.R.-G., J.P.G., Numerical simulations: A.C.  
This article is based on work from COST Action DENORMS CA15125, supported by COST (European Cooperation in Science and Technology).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Bayesian Modeling of Traffic Accident Rates in Poland Based on Weather Conditions
## 3 Results
### Model Comparison
#### 3.2.1 County-Level Models  
The county-level analysis revealed differences in predictive performance between the two competing models. As shown in Figure 22 and Tables 4 and 5, no substantial differences were observed between the WAIC and PSIS-LOO criteria; therefore, only the results from the latter are discussed. Model 2 performed better, achieving a higher Expected Log Pointwise Predictive Density (ELPD) of \(-12\),788.50, compared to \(-12\),872.12 for Model 1. The ELPD difference of 83.61 suggests a modest improvement in predictive accuracy. Model 2 also had a higher effective number of parameters (p-LOO = 152.42 vs. 92.02), correctly reflecting its greater complexity. It received a slightly higher model weight (0.54 vs. 0.46), suggesting a mild preference, but no strong evidence of superiority.  
Figure 21: Posterior distributions for Model 4 parameters: (**a**) Intercept posterior density showing a well-defined, narrow distribution with high estimate precision. (**b**) Socioeconomic predictors including population density—positive effect (top), passenger car ownership—strong positive effect (middle), and road density—negative effect (bottom). (**c**) Weather predictors showing consistent effects with Model 3: temperature—positive effect (top), humidity—positive effect (middle), and precipitation—slight negative effect (bottom).  
Figure 22: Comparison of yearly county-level models using the (**a**) PSIS-LOO and (**b**) WAIC criteria. The dashed vertical line indicates the reference point for model comparison based on the expected log pointwise predictive density (ELPD-LOO or ELPD-WAIC). The dashed vertical line indicates the reference point for the best-performing model (Model 2). Both model comparison metrics yield consistent results.  
However, the standard errors--436.22 for Model 2 and 447.29 for Model 1--indicate that the observed ELPD difference falls well within the range of uncertainty. This shows that both models provide comparable predictive performance. While Model 2 may provide added flexibility, Model 1's simpler structure could be advantageous in applications where interpretability or computational efficiency are especially important.
# Statistical Analysis of Cutting Force and Vibration in Turning X5 CrNi18-10 Steel
## 3 Results
### Statistical Analysis
#### 3.4.2 ANOVA Results Between the Feed Rate and AF RMS  
According to the experimental results in Table 4, the first-order regression model of active force RMS as a function of feed was developed at a 95% of confidence level, as shown in Figure 9. The Equation (4), is given by  
\[y=80.09+1420x. \tag{4}\]  
Figure 9 represents the linear regression line graph between the feed and active force, where the circles represent observed values. In Equation (4), y represents one variable, and x represents the other variable, the Equation, \(y=ax+b\), where a is the slope and b is the y-intercept, can be used to predict y based on the observed level of x, which highlights the impact of one variable on another. In Equation (3), x is the feed, and y is the active force RMS; with R\({}^{2}\) = 48.6%, y is the active force RMS in [N], and the feed is in [mm/r]. The analysis of variance (ANOVA) is represented in Table 6. The model is found to be statistically significant with a \(p\)-value (probability of significance) less than 0.05, in this case, 0.002. \({}^{\text{a}}\) Dependent Variable--active force RMS; \({}^{\text{b}}\) predictors--(constant), feed rate; \({}^{\text{d}}\)r'' is degrees of freedom, which shows the number of independent variables in the model. The analysis of variance (ANOVA) of regression was evaluated to check the validity of the model.  
Figure 9: Linear regression line graph between feed rate and active force RMS.
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 3 Anti-Biofilm Agents to Tackle _P. aeruginosa_
### Relevant Anti-Virulence Agent Review
#### 3.1.2 Quorum Sensing Inhibitors  
Considering the major regulating role played by QS in the development of biofilms, the design of quorum sensing inhibitors (QSIs) as anti-biofilm agents appears as a promising and sustainable solution.  
Most of these classes of ABAs target one of the three transcription factors, LasR, RhIR or PqsR, from the _las_, _rhl_ or _pqs_ systems, respectively. Therefore, the comparison of their binding modes with those of acyl-homoserine lactone (AHL) or 2-alkyl-4(\(1H\))-quinolone (AQ) autoinducers appears relevant. For this study, some protein/ligand complexes are characterized and available in the Protein Data Bank (PDB) (Figure 5). The signaling molecules odDHL and BHL bind to their respective receptors LasR and RhIR in a similar manner: (i) the carbonyl group of the lactone moiety establishes a hydrogen bond with a tryptophan residue (Trp60 for LasR and Trp68 for RhIR), (ii) the secondary amine forms a hydrogen bond with an aspartic acid residue (Asp73 for LasR and Asp81 for RhIR) while (iii) the 1-oxo group from the acyl chain is involved in two H-bonds with serine and tyrosine residues (Ser129/Tyr56 for LasR and Ser135/Tyr64 for RhIR). The additional carbonyl group in odDHL enables the establishment of a key H-bond with Arg61. Finally, the alkyl chain is surrounded by hydrophobic residues. Interestingly, while the alkyl chain of odDHL appears to be buried in the hydrophobic pocket of the LasR ligand-binding domain, the shorter chain of BHL is not as tightly surrounded by residues in the corresponding RhIR hydrophobic pocket. This could favor the RhIR binding of molecules other than the native ligand. The ligand-binding domain of PqsR is composed of two pockets, P\({}_{1}\) and P\({}_{2}\). The deep planar-shaped pocket P\({}_{1}\) allows the quinolone moiety of the autoinducer PQS to fit into a unique orientation contacting with the Leu207, Leu208, Ile236, Ile149, Ala168 and Phe221 residues, while the cigar-like long pocket P\({}_{2}\) hosts the heptyl chain thanks to a key interaction with Tyr258 [47; 48; 49]. Moreover, [PERSON] et al. describe the establishment of an H-bond between the quinolone carbonyl group and Leu197 (NH) [50]. Finally, a water molecule acts in the apo protein form as an H-bond donor with Leu208 and Arg209 and as an H-bond acceptor towards Gln194, thus blocking the conformation of the autoinduction site. The interaction of the 3-OH group of PQS with this water molecule could destabilize this binding network and be responsible for the activation of the receptor.  
In the following sections, the most promising anti-biofilm QSIs will be described and divided in two main categories: analogs and non-analogs of native AIs implicated in the _las_, _rhl_ and _pqs_ systems.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Determining the Multi-Objective Deployment Model  
#### 3.2.1 Coverage Model  
For the specific scenario of deploying nodes in complex environments with obstacles in this paper, we can make the following assumptions:  
* Sensor nodes are of the same type and have identical physical characteristics, with a communication radius set to twice the sensing radius.
* Sensor nodes can determine their own positions and can move to specified locations based on the algorithm [29; 30].  
* The positions of obstacles within the monitoring area are known in advance.  
Node coverage rate is one of the important metrics for assessing the overall monitoring quality of WSNs. In this paper, we first gridify the monitoring area, considering the center of each grid as a target monitoring point. Subsequently, the number of target monitoring points covered by the grid representation of the monitoring area is used as the coverage area of the sensor nodes in the monitoring area.  
The probability of node \(s_{i}\) perceiving monitoring point \(P_{j}\) is represented in Formula (2). \(s_{all}\) represents all the nodes in the monitoring area, so the joint perception probability of all sensor nodes for point \(P_{j}\) is defined as  
\[C_{p}(s_{all},P_{j})=1-\prod_{s_{i}\subseteq s}(1-P_{\text{cov}}(s_{i},P_{j})). \tag{4}\]  
The coverage rate of sensor nodes in an environment with obstacles is represented by  
\[Cov(X_{i})=\frac{\sum\limits_{j=1}^{G}C_{P}(s_{all},P_{j})}{G-K} \tag{5}\]  
where \(G\) represents the total number of target monitoring points after the gridding of the monitoring area, and \(K\) indicates the number of vertices in the grid cells occupied by obstacles.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Evaluation Measures  
We used mean absolute error (MAE) and normalized cumulative Lagrangian separation (NCLS). In prior work [8], we also used the Euclidean distance as an evaluation measure. Since it is calculated by a mechanism similar to MAE, the average of the error distance was calculated only using MAE. NCLS, also called the skill score, was calculated by subtracting the error from 1. Lower values, therefore, represent better results for MAE, whereas higher values close to 1 represent better results for NCLS. The calculation for MAE can be expressed as the following Equation.  
\[\frac{1}{m}\sum_{i=1}^{m}(|\textit{pred\_Lat}_{i}\ -\ \textit{observed\_Lat}_{i}|+| \textit{pred\_Lon}_{i}\ -\ \textit{observed\_Lon}_{i}|) \tag{1}\]  
where _pred_Lon_ and _pred_Lat_ represent the longitude and latitude of the predicted data. _Observed_Lon_ and _observed_Lat_ denote the longitude and the latitude of the observed data. Therefore, the difference between these values can be considered as an error. The denominator \(m\) is the number of test datasets. Therefore, the MAE is the average of the errors.  
NCLS is a measurement method that is quite frequently used in trajectory modeling and it is proposed to solve weaknesses in the Lagrangian separation distance in relation to the continental shelf and its adjacent deep ocean. The error of each location is calculated in MAE, whereas NCLS calculates errors by cumulative calculation. Figure 4 shows the calculation process of NCLS. In this process, if \(s\) becomes too large, the skill score may continue to remain zero. If the tolerance threshold \(n\) is set high, this can be solved to some extent. In this study, we set \(n\) to 1 since errors sufficient to make \(s\) relatively large did not frequently occur.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Library** \\ \hline Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) & DEAP \\ Differential Evolution (DE) & DEAP \\ Particle Swarm Optimization (PSO) & PySwarms \\ Multi-Layer Perceptron MLP & WEKA 3 \\ Gaussian Process (GP) & WEKA 3 \\ Support Vector Regression (SVR) & WEKA 3 \\ Radial Basis Function Network (RBFN) & WEKA 3 \\ Deep Neural Network (DNN) & PyTorch \\ Recurrent Neural Network (RNN) & PyTorch \\ Long Short-Term Memory (LSTM) & PyTorch \\ \hline \hline \end{tabular}
\end{table}
Table 2: Methods and library resources.
## 2 Materials and Methods  
The system must be sufficiently compact and portable to fit into a confined space, yet sufficiently accurate to capture fine surface details. The hypothesis is that by integrating active optical techniques (e.g., utilizing structured light) and a suitable mechanical design of the scanning head, it is feasible to obtain 3D data in a space where conventional scanners are unable to access. The significance and contribution of this work lies in its potential to advance the frontiers of non-destructive diagnostics. The proposed technology has the potential to enable the inspection of equipment (e.g., machinery components, nuclear reactors, historical artifacts) without the need for disassembly, thereby reducing both time and cost, and the risk of damage to the objects under investigation. It should be noted that the present research is constrained to laboratory proof-of-concept; it does not extend to direct integration into mass production or automated processing of point clouds into CAD models. It is assumed that the scanned objects remain static during the digitization process and that the surrounding environment does not introduce significant distracting light reflections that would not be accounted for by the controlled illumination. The proposed approach's limitations may stem from the field of view, as the system is unable to capture surfaces that are not visible to both cameras unless the sensor position is altered, a modification not feasible under the stipulated conditions. Notwithstanding these limitations, it is anticipated that this approach will broaden the scope of 3D digitization by enabling its application in hitherto inaccessible locations. Furthermore, it is expected to lay the foundation for a new generation of markerless 3D scanners designed for specific applications. In addition to industrial inspections and reverse engineering, additive manufacturing can also benefit significantly, by verifying the dimensional accuracy or detecting surface defects of 3D printed components. It is critical, and proposed markerless scanning solution provides a convenient pathway to perform such quality control on newly fabricated parts.