esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Over the past several decades, high-harmonic generation (HHG) has been extensively studied for enabling table-top production of coherent pulses of extreme ultraviolet (XUV) radiation. Due to the spectral, spatial, and temporal properties of HHG radiation, its applications range from fundamental atomic and molecular physics to material science, biology, and medicine [1; 2; 3], and great effort is being put into further source development [4; 5; 6] and scaling to high repetition rates and high laser pulse energies [7; 8]. Since high-order harmonics are generated from an infrared laser pulse by nonlinear interaction with a gaseous medium, one task is to filter out the fundamental infrared beam to ensure a pure interaction of the sample with XUV radiation [9; 10; 11]. This becomes even more important if a part of the infrared laser pulses is used to initiate a dynamic process, and its subsequent evolution is monitored with delayed XUV pulses (Figure 1). Independent of whether, in such optical-pump XUV-probe experiments, the transmitted XUV radiation is measured [12; 13], the emitted photoelectrons [14] are monitored, or additional temporal information will be gained by streaking the emitted photoelectrons with an intense infrared (IR) laser field [15], residual IR radiation in the probe arm can significantly complicate these experiments. Additionally, intensity fluctuations of IR laser pulses can result in an ionization-induced blue shift of the harmonic lines [16], or alter the amplitudes of the harmonic lines due to different phase matching conditions. If this happens during the exposure time, a high-resolution microscopy image will, for instance, be degraded, or an additional energy shift will appear in photoelectron spectra. Therefore, for high-quality experiments with HHG radiation, special care must be taken in the suppression of residual IR radiation and in ensuring the stability of the XUV spectra during these measurements. This implies that the signals must be continuously monitored, without interfering with the experiment.  
In this paper, we present reflection measurements of cesium iodide (CsI)-coated and uncoated steel surfaces with shaped XUV pulses. We measure the spectrally integrated signal from both surfaces and show that the ratio of the two measured signals is a sensitive probe of changes in the incident XUV spectrum. Additionally, a disagreement between the measured ratio and the calculated number can be explained by a leakage of IR radiation through one of the metal filters. This simple setup allows for the monitoring of the IR leakage and spectral stability simultaneously. The paper is organized as follows: In Section 2, we introduce the method, describe the fabrication of the coated surfaces, and outline the experimental setup. The results of the experiments are presented in Section 3, and the context of XUV instrumentation is discussed. Conclusions and possible applications follow in Section 4.
## 4 Results and Discussion
Therefore, the overall average calculated in AoI B region (0.16 \(\upmu\)m) is probably a systematic underestimation of the true thickness, which can be due to many factors, such as the presence of a resin adhesive (in the resin-less region the calculated mean thickness is 0.23 \(\upmu\)m) or, on a second approach, simply due to roughness effects (not taken into consideration). For the latter, variations in the impinging radiation and output angles change the absolute path travelled by the X-ray beam and fluorescence  
Figure 5: Influence of background contribution in the thickness calculation for various simulated gold leaves applied to a lead white substrate. Thick values on the top left corner represent the measured matrix _thick ratio_ for the substrate with and without the continuum contribution. lines, respectively. This variation, even small, can introduce errors in the \(\left[\left(I_{K_{\alpha}}/I_{K_{g}}\right)/\left(I_{K_{\alpha}}/I_{K_{g}}\right)_{ thick}\right]\) factor that are emphasized by the logarithmic function in Equation (1), which, compared to the considerably small value being evaluated, may appear considerably high. In addition, the histogram shown as the possible distribution is a truncated one, which can be presumed because all the values are calculated normalizing the peaks ratio to an estimated value \(\left(I_{K_{\alpha}}/I_{K_{g}}\right)_{thick}\). Area of interest C, devoid of resin, presented better results with a relatively symmetric histogram with a large right tail. The tailing may be due to the concentration of greater thickness values observed at the rightmost portion of AoI C (Figure 6), which in turn may have resulted from fabrication defects present in the substrate (lower Ti signal and darker region in the rightmost part--Figure 2). The presence of resin disturbs the results, causing a systematic error. Furthermore, when recalculating the mean thickness for AoI B considering a _thick ratio_, \(\left(I_{K_{\alpha}}/I_{K_{g}}\right)_{thick}\) term in Equation (1), sampled from region A (Figure 2), the mean value decreases only slightly. The calculated statistical error in the thickness determination is about 0.07 \(\upmu\)m (Equation (3)). Therefore, what we observe in the measured thicknesses in both AoI B and C is mostly statistical fluctuation of data. However, the observed mean thickness shift (Table 1), which is less than the standard deviation, is still valid. It is worth noting that the 0.07 \(\upmu\)m shift, exactly the same as the statistical error, would be hardly measured with traditional spot sampling, but due to the large amount of data from MA-XRF scanning, it can be detected, as shown by the results. Figure 6: 3D heightmap of calculated thickness values for AoIs B and C.  
Figure 7: Histograms of measured thicknesses in AoI B (**A**) and C (**B**) corresponding to the regions with and without resin, respectively.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 6 LoRa Performance: Using Error Correction and Filtering  
Following our performance evaluation of LoRa, focusing on PER with CSS modulation, we will explore this further in this section. We will introduce complementary techniques, in particular the Hamming correction code and the IIR and FIR filters, to enrich our understanding of performance dynamics. Our aim is to identify possible improvements or variations in LoRa's PER by incorporating these elements.
# A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction
## 2 Materials and Methods
### Methods  
As shown in Figure 4, this paper combines several classical models and statistical machine learning (SML) models to obtain a high-accuracy prediction model. The prediction and validation of the combined model constructed in this paper mainly include four processes:  
Step 1: Based on the statistical learning method, F10.7 and SSN were used to predict TEC.  
Step 2: The IRI model was used to obtain TEC data in the prediction region.  
Step 3: The BP-NN model is to predict final prediction results by training the data from the above IRI and statistical learning models, wherein the determination of the model using multi-mutation and multi-crossover adaptive genetic algorithm optimization algorithm.  
Step 4: Compare the deviation between the TEC data predicted by the proposed model and the TEC data calculated by other models and the actual observed data, and analyze the capability gap between the proposed model and the existing model.
# Chromosome Cluster Type Identification Using a Swin Transformer
## 5 Results and Analysis
### Confusion Matrix  
The results reported so far indicate the overall classification performance for all the classes. Next, we assess the per-class classification performance. To achieve this, we plot the confusion matrices obtained for all five folds (see Figure 4). We observe that across all folds, on average, the touching class appears to be the most challenging for classification and is often misclassified as the overlapping or instance class. Similarly, we find that the overlapping class, when misclassified, is most likely to be classified as the instance class. These results are intuitive as such confusions across classes are likely to be made by a human expert as well.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Accuracy** & **F1** & **Sensitivity** & **Specificity** \\ \hline MobileNetV2 [(40)] & 81.83 & 83.41 & 77.52 & 76.85 & 94.47 \\ DenseNet121 [(41)] & 85.59 & 87.65 & 82.23 & 81.68 & 95.88 \\ ResNet-50 [(42)] & 88.30 & 90.15 & 86.08 & 85.68 & 96.72 \\ ResNet-101 [(42)] & 90.65 & 91.89 & 88.32 & 87.92 & 97.30 \\ ResNet-152 [(42)] & 90.71 & 91.97 & 89.09 & 88.79 & 97.32 \\ ResNeXt-101-32\(\times\)8d [(43)] & 90.79 & 92.27 & 89.36 & 89.10 & 97.42 \\ ResNeXt-WSL [(10)] & 93.35 & 94.13 & 92.41 & 92.20 & 98.04 \\ Dual-ViT [(44)] & 94.07 & 94.10 & 94.05 & 94.10 & 97.69 \\ SupCAM [(39)] & 93.25 & 94.99 & 92.26 & 92.81 & 98.12 \\
**CCT (Proposed)** & **95.02** & **95.30** & **95.02** & **95.03** & **98.26** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed CCT with the state of the art. Bold values represent the best results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Fold** & **Precision** & **Accuracy** & **F1** & **Sensitivity** & **Specificity** \\ \hline \multirow{8}{*}{\begin{tabular}{c} ResNeXt- \\ WSL [(10)] \\ \end{tabular} } & 1 & 94.28 & 94.77 & 93.58 & 93.43 & 98.26 \\  & 2 & 91.52 & 92.73 & 90.54 & 90.33 & 97.58 \\  & 3 & 92.70 & 93.63 & 91.21 & 90.82 & 97.88 \\  & 4 & 94.42 & 94.99 & 93.42 & 93.23 & 98.33 \\  & 5 & 93.82 & 94.53 & 93.31 & 93.20 & 98.18 \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Mean (\(\pm\)std) \\ \end{tabular} } & Mean (\(\pm\)std) & 93.35 \(\pm\) 2.19 & 94.13 \(\pm\) 1.68 & 92.41 \(\pm\) 2.55 & 92.20 \(\pm\) 2.68 & 98.04 \(\pm\) 0.56 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} CCT \\ **(Proposed)** \\ \end{tabular} } & 1 & 95.37 & 95.75 & 95.36 & 95.36 & 98.44 \\  & 2 & 94.52 & 94.84 & 94.55 & 94.59 & 98.09 \\ \cline{1-1}  & 3 & 95.00 & 95.07 & 95.02 & 95.01 & 98.15 \\ \cline{1-1}  & 4 & 94.84 & 95.29 & 94.83 & 94.81 & 98.29 \\ \cline{1-1}  & 5 & 95.37 & 95.53 & 95.36 & 95.36 & 98.32 \\ \hline \multicolumn{2}{c}{**Mean (\(\pm\)std)**} & **95.02 \(\pm\) 0.32** & **95.30 \(\pm\) 0.31** & **95.02 \(\pm\) 0.31** & **95.03 \(\pm\) 0.30** & **98.26 \(\pm\) 0.12** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-validation performance of the proposed CCT compared to ResNeXt-WSL [(10)].
## 3 Marine-Derived Carotenoid Screening
### In Vitro Carotenoid Screening
#### 3.1.2 Culture-Independent Approaches  
Culture-independent microbial screening methods involve directly extracting and sequencing microbial genetic material present in a sample without the need for cultivation. A common example of this is metabarcoding, which involves the amplification and sequencing of genetic barcodes--specifically the 16S rRNA gene in prokaryotes--directly from a sample. The sequence data may be analyzed for further screening purposes. This particular method provides several advantages: (1) the ability to detect species missed by culture-dependent methods, (2) the high specificity involved in further screening, (3) the straightforward and relatively rapid processing involved, and (4) the option to freeze samples for later analysis [62]. However, PCR-based methods intrinsically present an amplification bias. This bias could be overcome by employing an alternative culture-independent approach: shotgun metagenomics, which involves the indiscriminate sequencing of all microbial genomes present in a sample without previous amplification [63].  
Shotgun metagenomics has generally been considered a viable method for conducting large-scale analyses of complex microbiota [63]. However, this method is not without limitations, as shotgun metagenomic analysis necessitates additional processing to overcome host or non-target DNA dominance [58]. In general, the limitations and advantages of culture-dependent and culture-independent methods may complement one another in terms of providing an optimal and accurate description of microbial communities during secondary metabolite production screening. These combined methods can be used in screening carotenoid biosynthetic gene clusters that are hidden within a variety of marine bacteria.
## 4 Results  
### Training Results of NARX Network  
During the NARX network's training phase, RMSE is used as the model's evaluation criterion. Therefore, the effectiveness of training and the degree of convergence are evaluated by observing the RMSE during the training process.  
Figure 9 illustrates the trend of RMSE changes during model training: it rapidly decreases at the beginning of the training phase (0 to 10 epochs) and gradually slows down until it stabilizes. When the RMSE remains steady (after 20 epochs), it indicates that the model has converged, and further training will have minimal benefit. At this point, training can be stopped, and the transition to the prediction phase can begin. The overall trend of RMSE changes shows no significant fluctuations, indicating that the model does not exhibit evident underfitting. The training results are thus considered satisfactory.
###### Abstract  
When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.  
QoE in immersive video; depth map quality; no-reference quality evaluation +
Footnote †: journal:
###### Abstract  
The growing demand for sustainable and environmentally friendly materials has highlighted the need for innovative alternatives to traditional plastics and composites. This study explores the development of polyurethane-based biocomposites synthesized using hydrogenated safflower oil (HSO), hydrogen gas, and a nickel catalyst. Safflower oil was hydrogenated via a catalytic hydrogenation mechanism, transforming carbon-carbon double bonds into saturated fatty acids. The process, optimized using response surface methodology (RSM), was conducted at 225 \({}^{\circ}\)C, 8 atmospheres, and 6 h, achieving a yield of 67%. Hydrogenation improved the physical and chemical properties of the biocomposites. Biocomposites were synthesized by combining HSO with methylene diphenyl diisocyanate (MDI) as a crosslinker. Optimal performance was observed at 2.5% HSO content, enhancing material properties. However, higher HSO content negatively affected biocomposite density and hardness, and surface morphology analyses revealed that increased HSO content led to irregular pore structures. These findings underscore the balance required in material composition to achieve optimal performance. This study presents a novel approach to producing hydrogenation-modified HSO-based biocomposites, providing an eco-friendly and sustainable alternative for industries such as packaging, construction, and automotive applications. The advancements contribute to reducing environmental impact and enhancing the performance of renewable material technologies.  
Keywords:hydrogenated safflower oil; polyurethane based-biocomposites; catalytic hydrogenation; sustainable materials +
Footnote †: journal: Applied sciences
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Metaheuristic System of Classifications
#### 2.6.10 Parallel Processing  
The capacity to perform parallel processing is another characteristic of the metaheuristics, and it can be seen as an improvement to explore the search space more efficiently. The metaheuristics can be classified into (1) mono processing and (2) multiple processing. The mono processing is not prepared to perform parallel processing. On the other hand, the multi-processing has an architecture prepared to perform parallel processing and consolidate these multiple searches [2; 6; 16]. It is an option to improve any metaheuristic because they can incorporate and activate this capability when needed. In the literature, there are several strategies of parallelization to reduce the time of the search and explore more areas of the search space simultaneously.  
2.6.11. Capacity to Alter the Fitness Function  
Considering the capacity to manipulate the fitness function, the MH can be subdivided into two categories, (1) static and (2) dynamic [9; 11; 82]. The static category is the standard way, where the MHs use the objective function as the problem designed it. In contrast, some algorithms may change the OF behavior by incorporating some obtained knowledge during the search process. The main idea behind this is trying to escape local optima [87]. Guided Local Search (GLS) is an example of MH with this capacity.
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Priority Rule** & **RF** & **Z for j30** & **Z for j60** & **Z for j90** & **Overall Z** \\ \hline \multirow{8}{*}{ADP} & \multirow{4}{*}{Random} & 0.25 & \(-94.42\) & \(242.17\) & \(503.54\) & \(217.10\) \\ \cline{3-7}  & & 0.50 & \(-94.79\) & \(223.66\) & \(459.50\) & \(196.12\) \\ \cline{3-7}  & & 0.75 & \(-100.79\) & \(210.90\) & \(418.82\) & \(176.31\) \\ \cline{3-7}  & & 1.00 & \(-95.87\) & \(204.44\) & \(436.40\) & \(181.66\) \\ \cline{3-7}  & & 0.25 & \(-81.94\) & \(265.69\) & \(536.65\) & \(240.13\) \\ \cline{3-7}  & & 0.50 & \(-81.43\) & \(250.52\) & \(494.32\) & \(221.14\) \\ \cline{3-7}  & & 0.75 & \(-87.34\) & \(234.15\) & \(451.11\) & \(199.31\) \\ \cline{3-7}  & & 1.00 & \(-81.03\) & \(299.904\) & \(469.18\) & \(229.35\) \\ \cline{3-7}  & & 0.25 & \(-84.32\) & \(239.36\) & \(526.28\) & \(227.11\) \\ \cline{3-7}  & & 0.50 & \(-86.06\) & \(221.01\) & \(484.56\) & \(206.50\) \\ \cline{3-7}  & & 0.75 & \(-93.46\) & \(201.69\) & \(440.58\) & \(182.94\) \\ \cline{3-7}  & & 1.00 & \(-86.59\) & \(198.68\) & \(458.11\) & \(190.07\) \\ \hline \multirow{8}{*}{ADP–DCS} & \multirow{4}{*}{Random} & 0.25 & \(-69.54\) & \(277.07\) & \(550.84\) & \(252.79\) \\ \cline{3-7}  & & 0.50 & \(-71.90\) & \(258.76\) & \(503.66\) & \(230.17\) \\ \cline{3-7}  & & 0.75 & \(-77.61\) & \(241.69\) & \(458.55\) & \(207.54\) \\ \cline{3-7}  & & 1.00 & \(-70.51\) & \(235.77\) & \(475.59\) & \(213.62\) \\ \cline{3-7}  & & 0.25 & \(-67.11\) & \(277.85\) & \(551.04\) & \(253.93\) \\ \cline{3-7}  & & 0.50 & \(-69.67\) & \(258.54\) & \(505.59\) & \(231.49\) \\ \cline{3-7}  & & 0.75 & \(-74.97\) & \(243.67\) & \(461.72\) & \(210.14\) \\ \cline{3-7}  & & 1.00 & \(-67.89\) & \(238.69\) & \(479.39\) & \(216.73\) \\ \hline \multirow{8}{*}{ACO} & \multirow{4}{*}{ACO} & 0.25 & \(-69.06\) & \(273.40\) & \(544.29\) & \(249.54\) \\ \cline{3-7}  & & 0.50 & \(-72.89\) & \(253.55\) & \(497.70\) & \(226.12\) \\ \cline{1-1} \cline{2-7}  & & 0.75 & \(-80.14\) & \(235.21\) & \(452.63\) & \(202.57\) \\ \cline{1-1} \cline{2-7}  & & 1.00 & \(-73.29\) & \(231.42\) & \(469.76\) & \(209.30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average values of the NPV for the j30, j60, and j90 projects for six different algorithms in terms of RF.
# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference
### Space-Time Environment for Planning in Mobility Applications
#### 2.3.1 Intuition of Spatio-Temporal Action Zones  
Consider a vehicle agent \(v\) that has an empty route. Consider also a new traveller \(c\) described by: \(n\) a node, \([e,l]\) a time window, \(s\) a service time, and \(q\) a quantity. For \(v\) to fit \(c\) into its schedule, \(l\) must be large enough to allow \(v\) to be in \(n\) without violating its time constraints (if \(e\) is too small, \(v\) will have to wait until \(e\)). More precisely, the current time \(t\) plus the travel time from the depot to \(n\) must be less than or equal to \(l\). Based on this observation, we define the action zone of a vehicle agent as the set of pairs \(<\)\(n,t\)\(>\) of the space-time network that remains valid given its current route (\(n\) can be visited by the vehicle at \(t\)). The conical shadow in Figure 5 illustrates the action zone of a vehicle agent with an empty route.  
When a vehicle agent inserts a traveller into its route, it has to recalculate its action zone. Indeed, some pairs \(<\)\(node,time\)\(>\) become unfeasible. In Figure 6, a new traveller is inserted into the vehicle route. The vehicle agent's action zone after the traveller's insertion is represented by the inside contour of the bold lines, which represent the space-time nodes that remain feasible after the insertion of the traveller.  
Figure 5: Initial spatio-temporal action zone.  
The insertion price sent by a vehicle agent \(v\) to a traveller agent \(c\) corresponds to the hypothetical decrease in the action zone of \(v\) following the insertion of \(c\) in its route, i.e., the number of space-time nodes that would no longer be feasible. The idea is that the vehicle chosen for the insertion of a traveller is the one that keeps the maximum chance of being a candidate for the insertion of future travellers. Thus, the maximised criterion by the vehicle agents' fleet is the sum of their action zones, i.e., the capacity of the MAS to react to the appearance of travellers without mobilizing new vehicles.