esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation and Data Set  
Sampling of experimental measuring data originating from damaged structures is a difficult task with respect to parameter variance, i.e., damage position, size, sensor positions, and so on, and ground truth labeling. Therefore, for this study, we used simulated GUW time-resolved signal data. The signals were simulated using an extended version of the SimNDT simulator [16] based on an elasto-dynamic finite integration technique [18]. A transmission GUW experiment commonly utilizes two transducers, one generator (pitch signal), and one sensor (catch signal). The generator signal was a sine wave of base frequency 40 kHz and a Gaussian mask window (5 cycles). The simulation was carried out with a time step of 0.06 \(\upmu\)s, a total of 5000 steps (300 \(\upmu\)s), with each tenth step recorded. In total, 7 \(\times\) 6 damage positions were simulated. Circular damage (air, 30 mm diameter) placed at a specific center location (_x,y_) modifies the GUW signals, as shown in Figure 13. The host material was a plate of 500 \(\times\) 500 mm with high absorbing damping material at each plate side (to minimize wave reflections at edges and plate sides).
## 3 Methods
### CSSR Joint Decoding with LID  
There are similar pronunciation units across two languages; therefore, units of one language may be incorrectly identified as units with similar pronunciation to that of another language. Figure 5 shows an example in our experiment.  
This problem may be related to the fact that LID is not used in decoding. As indicated in Figure 6, we integrate LID into the decoding process by conditionally modifying the ASR output probabilities \(p_{att}(y_{t}^{k})\) with the LID output probabilities \(p_{att}(z_{t}^{m_{k}})\):  
* Firstly, ASR branch decoding and LID branch decoding are carried out simultaneously to obtain the ASR label \(\hat{y}_{l}\) and the LID label \(\hat{z}_{l}\) of the \(l\)-th step;
* \(\hat{y}_{l}\) can be uniquely mapped to \(\hat{z}_{l}^{\prime}\), since there is no intersection between the Chinese modeling unit set and the English modeling unit set;
* If \(\hat{z}_{l}^{\prime}\) is not in \(\{`E^{\prime},`M^{\prime}\}\) or \(\hat{z}_{l}\) is not in \(\{`E^{\prime},`M^{\prime}\}\), \(p_{att}(y_{l}^{k})\) will not be modified.  
Figure 4: Frameworks for different MTL strategies.  
Figure 5: An example of units in one language being incorrectly identified as units in another language.  
If both \(\hat{z}^{\prime}_{l}\) and \(\hat{z}_{l}\) are in \(\{\hat{`E','M'\}\}\), and \(\hat{z}^{\prime}_{l}\) is different from \(\hat{z}_{l}\), then, modification and normalization will be added to \(p_{att}(y^{k}_{l})\).  
\[p^{{}^{\prime}}_{att}(y^{k}_{l}) =\frac{p_{att}(y^{k}_{l})\times p_{att}(z^{m_{k}}_{l})}{\sum_{k}p_{ att}(y^{k}_{l})p_{att}(z^{m_{k}}_{l})} \tag{10}\] \[p_{att}(y^{k}_{l}) =p^{{}^{\prime}}_{att}(y^{k}_{l}),\]  
where \(k=1,\ldots,d^{units}\), \(d^{units}\) is the number of ASR decoder output units, and \(y^{k}_{l}\) is the \(k\)-th output unit of \(l\)-th step, and \(m_{k}\) is the \(k\)-th ASR task output units mapping to the LID task output units; therefore, \(z^{m_{k}}_{l}\) is the \(m_{k}\)-th output unit of \(l\)-th step of the LID task, \(p_{att}(y^{k}_{l})\) is the probability of the \(k\)-thASR output unit of the \(l\)-th step, \(p_{att}(z^{m_{k}}_{l})\) is the probability of the \(m_{k}\)-th LID output unit of the \(l\)-th step.  
If \(z^{m_{k}}_{l}\) is different to the corresponding language of \(y^{k}_{l}\), then, after normalization, the value of \(p_{att}(y^{k}_{l})\) will decrease, which can reduce the probability of selecting the current error unit.
## 5 Experimental Results and Analysis  
### Analysis of Results before and after Improvement  
The article contrasts the MEAG-YOLO model before and after improvements to ascertain its efficacy, as presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **mAP (\%)** & **Parameters (M)** \\ \hline YOLOv8n & 96.1 & 93.3 & 94.7 & 96.1 & 3.0 \\ MEAG-YOLO & 98.4 & 94.2 & 96.3 & 96.5 & 2.8 \\ \(\Delta\) & +2.4\% & +0.9\% & +1.7\% & +0.4\% & \(-\)6.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of PPE detection results.  
In comparison to YOLOv8n, the MEAG-YOLO model achieves a precision increase of 2.4% and a 6.7% reduction in the parameter count. These advancements are primarily attributed to the MSCA module, which enhances feature extraction in complex backgrounds. Moreover, the model's parameter count has been effectively controlled by integrating the lightweight design of GhostConv and the innovative EC2f module. The EC2f module focuses on extracting key features while simplifying background complexities. The inclusion of the ASF module further improves the model's performance by amalgamating multi-scale features, thereby enhancing the overall effectiveness of the model. Consequently, the MEAG-YOLO model has achieved significant improvements in detection accuracy and has efficiently reduced the number of parameters.  
To more clearly demonstrate the detection capabilities, precision-recall curves for the YOLOv8n and MEAG-YOLO models are displayed in Figure 11. The graphs reveal the MEAG-YOLO model's superior performance over the conventional YOLOv8n model. Furthermore, MEAG-YOLO exhibits greater precision as recall rises.
## 3 Results
### Bench-Top Fermentation Trials
#### 3.2.2 Main Fermentation Parameters in Natural Grape Juice (NGJ)  
The main fermentation parameters determined at the end of fermentation are reported in Table 1.  
In relation to the ethanol content, in comparison with _S. cerevisiae_ pure culture, _S. bombicola_/_S. cerevisiae_ air flow exhibited an ethanol reduction of 1.46% (\(v/v\)). Whereas, _S. bombicola_/_S. cerevisiae_ static condition led an ethanol reduction of 0.21% (\(v/v\)). This trend was reflected by the ethanol yield that was significant significantly lower in _S. bombicola_/_S. cerevisiae_ with air flow in comparison with other fermentation trials. While, volatile acidity amounts were comparable among the fermentations, sequential fermentation led a general increase in final glycerol content. In particular, _S. bombicola_ sequential fermentation air flow supplied showed a significant increase in this compound (more than 3-fold of _S. cerevisiae_). However, the results also exhibited an increase in glycerol content in static sequential fermentations, indicating the effect of _S. bombicola_ in glycerol production. Aeration condition also determined a significant increase in succinic acid.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Fementation Trials**} & **Sugar Consumed** & **Ethanol** & **Ethanol Yield** & **Glycerol** & **Volatile acidity** & **Succinic Acid** \\  & **(g/L)** & **(\% v/v)** & **(g/g) \%** & **(g/L)** & **(as Acetic Acid g/L)** & **(g/L)** \\ \hline _S. cerevisiae_ pure culture & 218 \(\pm\) 0.00 a & 12.12 \(\pm\) 0.11 a & 44.03 \(\pm\) 0.46 a & 3.08 \(\pm\) 0.27 c & 0.35 \(\pm\) 0.01 a & 0.25 \(\pm\) 0.21 b \\ _S. bombicola_/_S. cerevisiae_ & 216.44 \(\pm\) 0.47 a & 11.91 \(\pm\) 0.11 b & 43.45 \(\pm\) 1.13 a & 7.30 \(\pm\) 0.12 b & 0.29 \(\pm\) 0.02 b & 0.61 \(\pm\) 0.14 b \\ _S. bombicola_/_S. cerevisiae_ & 215.03 \(\pm\) 0.99 b & 10.66 \(\pm\) 0.08 c & 38.99 \(\pm\) 0.73 b & 10.50 \(\pm\) 0.12 a & 0.29 \(\pm\) 0.00 b & 2.69 \(\pm\) 0.10 a \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of NGJ fermentation trials. The initial sugar concentration was 218 g/L. Data are means \(\pm\) standard deviations from three independent experiments. Data with different superscript letters (a,b,c) within each Column are different according to Duncan tests (0.05%).  
Figure 3: Sugar consumption kinetics in sequential fermentation trials _S. bombicola_/_S. cerevisiae_ and control _S. cerevisiae_ on natural grape juice (NGJ) in static and aeration condition. (b,c) _S. cerevisiae_; (d,e) _S. cerevisiae_; (e) _S. bombicola_/_S. cerevisiae_ static and (e) _S. bombicola_/_S. cerevisiae_ 20 mL/L/min oxygen.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 3 Reviewing Spatial Coverage Quality
### Area Coverage  
Spatial coverage quality for area coverage applications requires the computation of the total amount of area sensed by all visual sensor nodes together. Thus, the quality metric in area coverage applications (the coverage rate) is the percentage of area covered in relation to the total area of interest. The coverage area should be the sum of the area of the field of view of all visual nodes, considering adequately the overlapped area, as showed in Figure 3. That way, it is possible to use the Inclusion-Exclusion Principle in order to compute the coverage area. This approach is a counting technique from combinatorial mathematics and it computes the quantity of elements in a union of sets [48; 49; 50; 51].  
Spatial coverage quality for area coverage applications is more difficult to assess than for target coverage ones. Since the monitored area is a continuous space, it is difficult to state which region has been covered by which visual sensors. Discrete solutions using grid approaches have been proposed to approximately compute area coverage for WVSN [6; 17; 35; 38; 48; 50; 52]. That way, the area of interest can be divided into smaller regions, each one defined as identical rectangles. Then, the coverage area is defined by the area of the total blocks viewed by at least one visual sensor node.  
A block can be said to be covered if all vertices of a block are within the field of view of a visual sensor, or if a specific point representing the block (its centroid or a predetermined vertex) is within the field of view. Whatever the situation, area coverage problem can be approximated by manifold target coverage problems, being each one of these points of interest a target with infinitesimal size. This is a relevant approximation objecting at higher efficiency, while keeping the computational cost low [6; 17; 50].  
Furthermore, the discrete viewing approach presents an efficient way to address coverage redundancy and occlusion assessment. It is natural that, in the sense of achieving a larger area coverage to get higher quality, the monitoring methods search for area maximization, which entails redundancy and occlusion minimization [17; 48; 49; 52]. These goals can be achieved by network redeployment processes.
## 4 Description of the Physical System
### Mesh Generation  
The computational domain was divided into four zones to generate an unstructured hexahedral mesh in each section using the meshing tool from ANSYS Workbench. The hexahedral mesh was generated using the Multizone method; furthermore, a refinement was applied using the local \"Inflation\" control to be able to predict the boundary layer adequately in the geometrical models in the zones where it is necessary to analyze flow conditions, such as the inlet, the bottom of the conic section and the vortex finder. Figure 2 illustrates the mesh for each of the models.  
Various parameters allow for determining the quality of the mesh; the Aspect Ratio (AR) indicates the proportion between the dimensions of the elements, Skewness (S) determines the mesh quality through its obliqueness, measured in a scale from 0 to 1, where 0 indicates a good quality mesh also the Orthogonal Quality (OQ) is evaluated on a scale from 0 to 1, being 1 is for an appropriate mesh. Table 2 shows all the information on the mesh quality parameters for each cyclone model.
## 3 Methodology
### Data Reliability Procedures  
In the experiment, a single researcher selected and annotated 144 sample data trajectories. The analytical model analysed 32,377 trajectories. The reliability of the analytical model in recognising switch and trap was assessed, against the 144 sample data trajectories that were manually labelled with switch and trap. As such, the reliability measurement is similar to the way inter-observer consistency measurement is performed. The consistency is analysed for the subset of 144 random possessions that were manually annotated. Reliability ratios are evaluated according to the percent agreement value which was \(>\)0.7 for all categories [37]. It was deemed that [PERSON]'s Kappa statistic was not appropriate in this instance as one observer is a deterministic computer model, which does not show uncertain annotation behaviour expected in human annotators.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 2 Problem Statement and Model Setup
### Predictive Frameworks
#### 2.2.3 Model M3: The Hybrid Prediction Model  
As mentioned above, \(k\)NN serves as a predictive model with good interpolation abilities, while linear regression serves as a predictive model with good extrapolation abilities. However, the accuracy and efficacy of these two methods are heavily influenced by the underlying data structure. Therefore, we propose a hybrid prediction framework leveraging the strengths of both interpolation and extrapolation while mitigating their limitations.  
The centrality of a new observation to the original dataset is paramount in selecting the most appropriate predictive approach for it. This centrality, determined by our previous optimization model M0, classifies observations as either interior or exterior to the known dataset. If a new observation is a point interior to the known dataset, we can use prediction methods with good interpolation abilities, such as \(k\)NN. If a new observation is an exterior point but is not too far away from the known dataset, we may combine different prediction methods by leveraging both interpolation and extrapolation abilities. If a new observation is an exterior point and is significantly far away from the known dataset, we can use prediction methods with good extrapolation abilities, such as linear regression.  
By solving model M0, we know whether the new observation \(x_{0}\) is exterior or interior to the given dataset. Taking the advantages of \(k\)NN and linear regression, we propose a hybrid prediction framework to improve prediction quality. Let us consider the prediction outcomes from the two predictive models as \(f^{k\text{NN}}(x_{0})\) (from \(k\)NN) and \(f^{\text{LR}}(x_{0})\) (from linear regression). The hybrid framework computes a weighted average of these two outcomes as follows:  
**Model M3**:  
\[f^{\text{hybrid}}(x_{0})=(1-\theta[d^{*}(x_{0})])f^{k\text{NN}}(x_{0})+\theta[ d^{*}(x_{0})]f^{\text{LR}}(x_{0}), \tag{12}\]  
where \(\theta[d^{*}(x_{0})]\) is a weighting parameter driven by the centrality coefficient \(d^{*}(x_{0})\) of \(x_{0}\). The choice of \(\theta[d^{*}(x_{0})]\) reflects our confidence in either of the predictive models:  
\[\theta[d^{*}(x_{0})]=\begin{cases}\begin{array}{rl}0,&\text{if }d^{*}(x_{0})=0\\ \frac{d^{*}(x_{0})}{h},&\text{if }d^{*}(x_{0})\in(0,h)\\ 1,&\text{if }d^{*}(x_{0})\geq h.\end{array}\end{cases} \tag{13}\]  
Here, \(h\) is a hyperparameter determining the boundary across which we transition from \(k\)NN to linear regression, and the optimal parameter \(h^{*}\) can be determined by validation (to be introduced in Section 3.3). Theoretically, a bigger \(h\) means that M1 (i.e., \(k\)NN) plays a more important role in determining the final prediction outcome. Conversely, a smallermeans that M2 (i.e., linear regression) plays a greater role in determining the final prediction outcome.
## 4 Results and Discussion
### FCS-MPC: Variable-Switching-Frequency Operation
#### 4.1.2 Real-Time Results with Step Change in the Frequency of the Capacitor Voltage Reference  
For this case, a step change in the frequency reference of the capacitor voltage to be tracked by the predictive control algorithm was applied. The step change in the frequency for the capacitor voltage reference ranged from 50 to 80 Hz. The waveforms for capacitor voltages using linear and non-linear loads are shown in Figures 8a and 8b, respectively.  
It can be seen that the waveforms obtained for the capacitor voltage with both loads properly follow the reference voltage imposed by the frequency of the control algorithm.  
In addition, the waveforms obtained for the load current (\(i_{a}\)) for the cases of linear and non-linear loads are shown in Figures 8c and 8d, respectively.  
As for the waveforms obtained for the capacitor voltages, when faced with a step change in the frequency voltage reference, the system delivers currents to the isolated load, tracking the frequency imposed on the controller.  
Figure 7: Real-time results of FCS-MPC variable-frequency operation with steps in voltage references. (**a**) Linear load: capacitor voltages; (**b**) non-linear load: capacitor voltages; (**c**) linear load: load current; (**d**) non-linear load: load current.
###### Abstract  
The laser-induced damage threshold of optics is an issue of essential importance in high-power laser applications. However, the complex and partially interacting mechanisms as well as the underlying reasons for laser damage of glass surfaces are not yet fully understood. The aim of the present work is to contribute to a better understanding of such damage mechanisms by providing original results on the impact of classical glass surface machining on the laser-induced damage threshold. For this purpose, glass samples were prepared with well-defined process conditions in terms of the used lapping and polishing agents and suspensions. Further, the samples were post-processed by atmospheric pressure plasma for precision cleaning. The laser-induced damage threshold and surface contamination by residues from the manufacturing process were determined before and after plasma post-processing. It is shown that the polishing suspension concentration has a certain impact on the laser-induced damage threshold and surface contamination by residues from used working materials. The highest damage threshold of 15.2 J/cm\({}^{2}\) is found for the lowest surface contamination by carbon which occurs in the case of the highest polishing suspension concentration. After plasma treatment for merely 60 s, this value was increased to 20.3 J/cm\({}^{2}\) due to the removal of surface-adherent carbon. The results thus imply that the laser-induced damage threshold can notably be increased by first choosing appropriate process parameters during classical manufacturing and second plasma post-processing for surface finishing.  
Keywords:polishing; glass; optics manufacturing; surface contamination; laser-induced damage threshold; plasma cleaning +
Footnote †: journal: Applied Soft. 2018, 8, 1556; doi:10.3390/app8091556
## 3 Results and Discussion
### Schizochytrium Imacinum Growth on Synthetic VFAs and Resulting Fatty Acid Profile
#### 3.1.2 Effect on Fatty Acid Production  
After 72 h of growing experiment, the culture medium was centrifuged and freeze dried for biomass and fatty acid profile evaluation. According to the chromatographic results obtained from batch experiments, propionic acid reached a higher unsaturated fatty acids ratio (63.8%), close to the butyric acid experiment that showed 58.7% unsaturated fatty acids. Regarding acetic and valeric acid, an equal distribution between unsaturation and saturation was obtained, but significant differences can be observed in the type of fatty acid in each group. Palmitic acid (C16:0) was the principal contributor to total saturated  
Figure 1: Evolution of VFA (%) during experiments on batch cultivation of S. Limacinum at 10 g/L VFA.  
Figure 2: Evolution of carbon source consumption on valeric acid feed 10 g/L.  
fatty acids concentration during the acetic experiment (40.6% of TFA), while for the valeric acid experiment, OCFA (C15:0 and C17:0) were the main contributors to saturation on fatty acid composition. The 20.34% TFA corresponded to C15:0 and 16.03% corresponded to C17:0 fatty acid. Moreover, the use of propionic acid reported the highest DHA ratio, 50% of TFA (Figure 3). The use of propionic acid and valeric acid (C3 and C5) as a carbon source can result in a higher concentration of DHA and OCFA. However, the biomass productivity obtained using these carbon sources was low and consequently it would be needed to introduce acetic or butyric acid to increase the productivity. In comparison with a standard feed of glucose, the results are very promising. A similar fatty acid profile was observed when glucose, acetic or butyric acid was used for culture growing, but the contribution of OCFA was significatively lower when glucose is used as feedstock. A combination of the different VFAs could enhance the production of DHA and OCFA.  
Once the effect of each VFA was evaluated in 250 mL flasks, fed-batch cultivation of _Schizochytrium limacinum_ in 2 L bioreactor on a synthetic mixture of VFA in a similar ratio as that usually found in dark effluents was performed [45]. The purpose of this study was to evaluate the possibility of competition between VFAs that could result in any growth inhibition or any change in expected final fatty acid composition. Synthetic VFA media was prepared according to Section 2.3.  
In the bioreactor, the use of a mixture of synthetic VFA resulted in a good biomass productivity (12.3 g DCW/L), high lipid content 45.9 %, and a high quality fatty acid distribution, with 18.3% of DHA and 19.2% of OCFA (Table 2). In comparison to the results obtained by using separated VFA in batch experiments, the fatty acid profile distribution seemed to follow the same pattern. Acetic and butyric acid contributed to the biomass growth, C16:0 and DHA production, meanwhile, valeric and propionic acid contributed to the production of OCFA, C15:0 and C17:0 (Figure 3).
# Bias and Variance Analysis of Contemporary Symbolic Regression Methods
## 2 Bias/Variance Decomposition
Depending on the used machine learning algorithm, different training samples of \(D\) will result in different models and therefore cause, to a certain extent, bias and variance. Figure 2: _Cont_. In Figure 2 we draw 1000 sets of samples \(D\) and learn one polynomial for each training set for three algorithm settings. 20 exemplary polynomials are shown for each setting in Figure 1(a),c,e. The distribution of the difference \(d\) between the ground truth and the model output at \(x=4\) is shown as histogram in Figure 1(b),d,f. These plots also compare the probability density function of \(\epsilon\) and a normal distribution with the mean and standard deviation (sd) of \(d\) as parameters. Figure 1(a),b show polynomials of degree 2, which cannot capture oscillations in the data. The variance is high, and the average error \(\overline{d}\) clearly differs from the ground truth \(f(x=4)\). Figure 1(c),d show polynomials of degree 9 with the highest variance and the smallest bias in this example. The error of single predictions at that point is often very high, which makes a single model unusable. However, their average fits the ground truth well, as there is nearly no bias. Figure 1(e),f show a polynomial of degree 4, which appears to be an appropriate setting with low bias and variance close to the error variance. As described before, the bias and variance of an algorithm is directly linked to its model generalization capabilities, as the prediction error on previously unseen data can be decomposed into the square of its bias and its variance [16]. Therefore, we expect algorithms with models that generalize well to be both low in bias and in variance. Of course, not every algorithm is equally capable to produce high quality results, as shown in SRBench. While the error of models is well measured, it is often unclear whether it is primarily caused by variance or bias. As described in Section 1, GP-based algorithms exhibit variance even when there is no change in the training data. On the other hand, deterministic algorithms like FFX and AIFeynman produce equal results on equal inputs. Therefore, we expect more similar models and less variance for those algorithms.