esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Efficacy of Segment Anything Model for
## 3 Methodology
### Validation of Results  
To evaluate the segmentation accuracy for different image types in SAM, we utilize a simple random sampling approach, which has also been used in previous studies [38]. This involves the random selection of 50 uniformly distributed checkpoints per image as sampling locations. We then used the highest resolution images for visual analysis-based comparison between the segmentation results and the actual land cover to determine the segmentation accuracy. Equation (1) shows the formula for this accuracy assessment is as follows:  
\[\text{SAE}=\frac{K}{50} \tag{1}\]  
where segmentation accuracy evaluation (SAE) represents the degree of accuracy, with values ranging from 0 to 1. \(K\) represents the number of points out of a total of 50 sample points falling within the correctly segmented boundary.  
Considering that the primary objective of this paper is to evaluate the segmentation performance of SAM for agricultural and UGS areas, we are aiming for binary segmentation, i.e., achieving delineation between agriculture/UGS areas and all the other land cover classes. To achieve this, we will predominantly focus on farmland/grassland as the primary agricultural objects and parks/gardens as UGS objects, given their uniform dimensions and distribution characteristics. Other land cover classes, such as woodlands, buildings, water bodies, and so forth, are classified as 'others.' We only test the simpler binary classification problem since, after all, currently, conditional segmentation on SAM can only extract a single type of image region when processing an image. In the unsu  
Figure 6: Examples of test areas considered in this study. We have used the red arrows to mark the types of segmentation we need to test.  
pervised segmentation process, the model automatically generates numerous segments. We, therefore, manually consolidated the agricultural/UGS objects and created binary partitioned datasets for further evaluation. We aim to incorporate unsupervised segmentation into our considerations as we seek to comprehensively assess SAM's capability to recognize and segment agricultural or UGS regions under the current models trained on a vast dataset. Exploring unsupervised segmentation and extraction of agricultural or UGS regions could be a significant avenue for future research. After all, dealing with a large number of datasets can become cumbersome if heavy reliance on the manual addition of even a small number of samples is still required.
## 2 Analysis Method of Seepage and Stability
### Finite Element Limit Analysis with Strength Reduction Technology
#### 2.4.3 Strength Reduction Technology  
The two most common methods for slope stability analysis are the limit equilibrium method and the finite element strength reduction technology. Due to the clear concept of the limit equilibrium method, it has been widely used by engineers and researchers to assess slope stability. However, there are some shortcomings when using the limit equilibrium method, which gives different FOSs based on different assumptions. More comparisons between the limit equilibrium method and finite element strength reduction method can be found in [PERSON] et al. [43].  
The FOS of this embankment slope is calculated by the strength reduction method. Comparing with the limit equilibrium method, the strength reduction technology has the advantage that it does not need to assume the position of the shear failure surface in advance. The shear failure surface can be searched automatically during the process of strength reduction. In this method, to obtain the minimum strength reduction factor \(f_{\mathrm{s}}\) of the embankment slope, a series of \(f_{\mathrm{s}}\) values were set to determine the actual FOS until the slope failed. For simplicity, the Mohr-Coulomb yield model is used to describe the failure behavior. The strength reduction process in the Mohr-Coulomb model is shown in Figure 3 and follows the Formulas (11) and (12).  
\[c^{\prime}=c/f_{\mathrm{s}} \tag{11}\]  
\[\varphi^{\prime}= \mathrm{arctan}(\tan\varphi/f_{\mathrm{s}}) \tag{12}\]  
where \(c^{\prime}\) and \(\varphi^{\prime}\) are the strength parameters after reduction and \(f_{\mathrm{s}}\) is the strength reduction factor. The slope is stable if \(f_{\mathrm{s}}>1\), in a critical state if \(f_{\mathrm{s}}=1\) and unstable if \(f_{\mathrm{s}}<1\).
## 3 Results
### Sterols  
The use of kiwi extract promoted the accumulation of a small (but not negligible) quantity of phytosterols: 1.00 and 3.81 mg/100 g of total lipids in buffalo and sheep cheeses, respectively. On the other hand, the sterol profile of the rennet cheeses was characterized by cholesterol alone (Table 3).  
Figure 1: Total polyphenol content of cheese and whey produced with kiwifruit extract and calf rennet.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Buffalo**} & \multicolumn{4}{c}{**Sheep**} \\ \cline{2-9}  & \(\mathbf{BM_{C}}\) & \(\mathbf{BM_{K}}\) & \(\mathbf{SEM}\) & \(\mathbf{S}\) & \(\mathbf{SM_{C}}\) & \(\mathbf{SM_{K}}\) & \(\mathbf{SEM}\) & \(\mathbf{S}\) \\ \hline Total polyphenols & 32.13 & 149.94 & 7.65 & *** & 68.02 & 189.28 & 13.40 & *** \\ Gallic acid & 15.39 & 15.76 & 1.99 & ns & 34.24 & 15.18 & 4.97 & * \\ Caffeic acid & 4.16 & 35.97 & 2.72 & * & 6.32 & 11.52 & 1.50 & * \\ Coumaric acid & - & 21.60 & 1.93 & ne & - & 33.35 & 6.32 & ne \\ Cinnamic acid & - & 10.78 & 1.31 & ne & - & 27.33 & 6.08 & ne \\ Quercetin & 2.93 & 9.19 & 0.91 & * & 10.83 & 10.03 & 2.25 & ns \\ Catechin & 5.49 & 24.03 & 4.44 & * & 5.17 & 43.49 & 3.28 & * \\ Rutin & 4.15 & 43.85 & 2.91 & *** & - & 58.61 & 9.85 & ne \\ Sterols & & & & & & & & \\ \hline Cholesterol & 5.71 & 5.51 & 0.14 & ns & 5.96 & 5.76 & 0.18 & ns \\ Stigmasterrol & - & 0.17 & 0.00 & ne & - & 0.83 & 0.04 & ne \\ Campesterol & - & 0.20 & 0.00 & ne & - & 0.69 & 0.02 & ne \\ \(\beta\)-sitosterol & - & 0.63 & 0.02 & ne & - & 2.29 & 0.15 & ne \\ \hline \hline \end{tabular} BMC, buffalo–calf rennet cheese; BMK, buffalo-kiwifruit cheese; SMC, sheep–calf rennet cheese; SMK, sheep–kivirruit cheese; SEM, standard error medium; S, significance. *: 0.01 \(<P<\) 0.05; **: 0.01 \(<P<\) 0.001; ***: \(P<\) 0.001; ns: not significant; ne: not estimable.  
\end{table}
Table 3: Polyphenol and sterol content of chees made with kiwi extract or calf rennet (mg/100 g of cheese).  
The phytosterols we found in cheeses were stigmasterrol, campesterrol and \(\beta\)-sitosterol, which represent the most abundant sterols in kiwifruit [30].
## 4 Avenues and Approaches for Improving the Biotransformation of Limonene and Alpha Pinene
### Pathways of Terpenes Biotransformation
#### 4.3.2 Monocyclic Monoterepenes  
Limonene (C10H16) is the most abundant monocyclic monoterepene and the second most abundant volatile organic compound (VOC) indoors, after toluene [23]. It is a major component of essential oils derived from citrus plants such as lemon and orange. Biotransformation of limonene start with activation of geranyldiphosphate by linalool synthase, which triggers ionization and rearrangement of geranyldiphosphate. The positively charged geranyl cation will be hydrolyzed by to form linalool and myrcene. For GPP to form limonene or terpineol, it must be activated in its cyclic form to give alpha terpinyl cation [50]. _Rhodococcus erythropo_ DCL14 transforms limonene into limonene-1,2-epoxide, which is then converted to limonene-1,2-diol by a limonene-1,2-monooxygenase and a limonene-1,2-epoxide hydrolase, respectively. A specific dehydrogenase forms the ketone, 1-hydroxy-2-oxolimonene, which is further oxidised to a lactone by a 1-hydroxy-2-oxolimonene 1,2-monooxygenase. Enzyme activities were only detected in limonene-induced cells, indicating tight regulation of limonene degradation. _R. erythropolis_ DCL14 possesses a second pathway for limonene degradation in which limonene is hydroxylated to trans-carveol by a NADPH-dependent limonene 6-monooxygenase. _R. opacus_ PWD4 follows the same pathway, converting limonene to trans-carveol. Biomass from a glucose-toluene chemostat culture of _R. opacus_ PWD4 transformed limonene into trans-carveol, which was further oxidised to carvone by a trans-carveol dehydrogenase [31]. In _Pseudomonas gladidi_, studies on limonene metabolism identified \(\alpha\)-terpineol and perillyl alcohol as major metabolites. However, none of the involved enzymes have been purified or further characterised. A \(\alpha\)-terpineol dehydatase from _P. gladidi_ was isolated and partially purified. The hydration reaction of limonene to form \(\alpha\)-terpineol as the sole product was observed [51]. _Geobacillus stearothermophilus_ (formerly _Bacillus_) has been shown to grow on limonene as the sole carbon source. The main transformation product of limonene in this case is perillyl alcohol, while \(\alpha\)-terpineol and perillyl aldehyde are present in minor concentrations.
## 1 Introduction
Our contribution:  
* By applying the external time-gated technology to the receiving front end, SPAD is only activated within the set gate-ON time. According to the characteristics as dead time, gate-ON time and blocking time of AQ-SPAD, the photon counting model and BER model are constructed. * Taking the minimum BER as the optimization objective and the gate-ON time as the independent variable, the optimal gate-ON time is obtained by weighing the signal photons, background photons and blocking time, which is verified by experiments. According to the research content, this paper is organized as follows: In Section 2, considering the gate-ON time, dead time and ISI distortion, the photon counting model of time-gated AQ-SPAD is constructed; in Section 3, we build the BER model of time-gated SPAD, and improve the receiver performance by optimizing the gate-ON time; the influence of gate-ON time on SPAD performance is analyzed, and the performance of the time-gated scheme and the free-running scheme is compared in Section 4. Conclusions are drawn in Section 5.
# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 3 Methodology and Data
### Methodology of the Study
#### 3.2.2 Methodology of Modelling--Artificial Neural Networks
* _Weighted voting_: Similar to voting, this approach averages predictions from each model but assigns weights to them based on a specific metric, such as reliability. Combining models is an effective technique for enhancing the accuracy and robustness of predictive models. By leveraging the strengths of multiple models, composite models often outperform individual models, providing more reliable predictions. In this study, we used the _boosting_ method to create an ensemble model to enhance the models' accuracy. Specifically, for each type of vehicle, we developed an ensemble model composed of several neural network component models. Each ensemble's maximum number of these component models was set to 10. Each component model was built using a multi-layer perceptron (MLP) neural network. The number of hidden layers and the size of each MLP component model (i.e., the number of synapses) were determined automatically by the software. After creating the component models, the final ensemble model was formed by combining them through a _voting_ technique that averaged their predictions. Using this methodology, we created eight ensemble models--one for each type of vehicle and one of both directions--and an additional model that combined all vehicle types and directions, each containing up to ten component models. Due to the computational complexity involved in manually designing and fine-tuning the neural network models, we opted for the automatic creation of the models. This decision was made to optimise the process and avoid the complexities and potential biases associated with manually selecting the number of neurons. Manually building each model would have had an unwanted level of error in determining the optimal architecture, including the number of layers, nodes, and connections, and would have been extremely time-consuming. It was more important to obtain accurate predictions from the model. By relying on automatic model creation, we ensured a more efficient and systematic approach to generating well-optimised models, allowing us to focus on the analysis and interpretation of results rather than on the intricate details of model configuration. This approach also provided a consistent and unbiased method for determining the best-performing model architecture across different vehicle types and directions. For all statistical tests in this study, a significance level of 0.05 was used. Therefore, even if not directly mentioned, the tests' conclusions were always formulated using this significance level.
## 6 Simulations and Discussion  
### Simulations  
Several simulations were conducted using the OMNeT++ simulator [26] to validate the analyzed performance and to evaluate the traffic performance of the DSP.  
We conducted two simulations to evaluate and validate the DSP algorithm.  
1. Simulation 1: Fault-tolerant performance. This simulation was performed to validate and evaluate the fault-tolerant capability provided by the DSP algorithm when it was applied to wireless networks such as WSNs.
2. Simulation 2: Network traffic performance. This simulation was conducted to validate and compare the network traffic performance of the DSP-based approach with the standard HSR protocol and some state-of-the-art techniques when it was applied to HSR networks.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Effect of Variation in Feeder Parameters  
The proposed algorithm was tested for variations in the parameters of the feeder between nodes 632 and 671. The algorithm was tested for line to ground (AG) fault with the impedance of this feeder kept equal to 100%, 80%, 110%, 120% and 150% and the values of the fault index corresponding to the faulty phase (phase-A) were observed, respectively, as \(5.8\times 10^{9}\), \(7.1\times 10^{9}\), \(5.6\times 10^{9}\), \(5.4\times 10^{9}\), and \(4.2\times 10^{9}\). Further, the FI corresponding to the healthy phases-A & B was found to be below the threshold value. Hence, the algorithm effectively works with the variations in the parameters. The algorithm also effectively detected the LL, LLG and LLLG faults with variable parameters of the feeder between nodes 632 and 671.
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
###### Abstract  
This paper presents a novel 13-level switched capacitor multilevel inverter, which uses less devices to achieve six-fold voltage gain. The proposed topology structure consists of twelve transistors, two diodes, and three capacitors. It is worth mentioning that characteristics as having five complementary switch pairs and self-balanced electric capacity voltages are conducive to simplifying the control strategy. Moreover, the above components constitute the switched capacitor unit and L-type unit. The inverter can acquire more voltage levels and a higher voltage gain by using multiple L-type units with fewer elements. Furthermore, the cost function is employed to comprehensively appraise the performance of the proposed inverter. The comparison with other existing 13-level inverters shows that the proposed multilevel inverter can effectively decrease the value of the cost function. Finally, the simulation and experimental results are presented to demonstrate the feasibility and effectiveness of the 13-level inverter.  
multilevel inverter; switched capacitor; boosting; reduced components; extension +
Footnote †: journal: Applied  
0000-0002-1318-9955
## 3 Results  
Table 4 shows the results obtained from DL-based models with different input images. For the DM images, the pre-trained AlexNet model outperforms both cuCNN and the non-pre-trained AlexNet across every performance measure. It consistently achieves the highest values of Ac (74.72%), Se (73.99%), and Sp (75.17%). In the RP images analysis, cuCNN shows relatively good performance across Ac (68.72%), Se (66.52%), Sp (70.07%). Both versions of AlexNet perform similarly: slightly lower than cuCNN in Se and Sp. Regarding CWT images, the cuCNN model displays the lowest overall performance: Ac (64.11%), Se (68.52%), Sp (62.39%). In addition, the non-pre-trained AlexNet model performs better than cuCNN but falls short compared to the pre-trained version, which maintains a slight advantage.  
On the other hand, Table 5 shows the results obtained from ML-based models accordingly to the input features. In all cases, the SVM shows higher Sp and lower Se. With five features, the SVM model displays the highest Sp (89.06%) and the lowest Se (21.69%) among the rest. In general, however, as the number of features increases, the performance metrics tend to stabilize with slight fluctuations but no special differences between them.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Model**} & **Ac** & **Se** & **Sp** & **PPV** & **PPV** & **PPV** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline DM images & cuCNN & 68.11 & 66.44 & 69.14 & 57.05 & 76.96 & 61.39 \\  & AlexNet & 64.86 & 68.29 & 62.74 & 53.06 & 76.23 & 59.72 \\  & AlexNet (pre-trained) & 74.72 & 73.99 & 75.17 & 65.02 & 82.41 & 69.40 \\ \hline RP images & cuCNN & 68.72 & 66.52 & 70.07 & 57.82 & 77.23 & 61.87 \\  & AlexNet & 68.97 & 62.95 & 72.69 & 58.71 & 76.08 & 60.75 \\  & AlexNet (pre-trained) & 67.65 & 62.54 & 70.80 & 56.92 & 75.39 & 59.60 \\ \hline CWT images & cuCNN & 64.11 & 68.52 & 62.39 & 52.26 & 40.78 & 59.30 \\  & AlexNet & 60.94 & 81.50 & 48.25 & 49.28 & 80.87 & 61.42 \\  & AlexNet (pre-trained) & 64.14 & 60.48 & 66.39 & 52.61 & 35.98 & 56.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of DL results with DM, RP, and CWT images. Models trained with MIT-BIH + UCD-DB, and tested with Apnea-ECG.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 6 Tests on New Methods for Energy Reconstruction
### Energy Resolution  
Energy resolution is computed as follows. We divided our data set in 16 log bins between \(E_{rec}=10^{-0.7}\) and \(E_{rec}=10^{2.5}\) TeV, according to the reconstructed energy. In each bin, we built the \((E_{rec}-E_{true})/E_{true}\) distribution, where \(E_{rec}\) is the reconstructed energy and \(E_{true}\) is the true (simulated) energy of the events. The distribution has an almost Gaussian trend within the interval \([MEAN-k_{1}\times RMS,MEAN+k_{2}\times RMS]\) where \(MEAN\) is the average value of the distribution and RMS is the root mean square of the distribution. However, to take into account non-Gaussianity, we chose different values for \(k_{1}\) and \(k_{2}\): \(k_{1}=1.5\) and \(k_{2}=0.75\). With a Gaussian fit in the range considered, we extracted the values:  
* Energy resolution = sigma of the Gaussian fit;
* Energy bias = mean of the Gaussian fit.  
The energy resolution curve is therefore given by the sigma obtained with the Gaussian fits of the distributions for each reconstructed energy bin.  
Both for the default method implemented in A-SciSoft and for the new tested methods, energy resolution is computed after applying a set of cuts that includes multiplicity cut, gammaness cut, and theta2 cut. A different set of cuts is performed in each energy bin.  
Figure 11: Heatmap of energy Stacking Ensembles. The figure presents the percentage of each Machine Learning model in five different Stacking Ensembles consisting of XGB, Extra Trees (ET), Random Forest (RF), and HGB models. The ensembles and their weights were selected to optimize the bias. Our best model is Ensemble 4. The heatmap visualization provides a clear representation of the relative contribution of each model to each ensemble, with darker shades of blue indicating a higher percentage of the model.  
It is worth noting that the set of cuts applied to the new methods is the same as the one optimized for the sample fully reconstructed by A-SciSoft default methods [7]. The reason behind this approach is that the distribution of gammaness is similar for both the default and new methods. As a result, the comparison between the default method and the new methods can be considered conservative, since the cuts were not optimized for the new methods.  
In Figure 12, we present the results of our analysis, comparing the performance of different Machine Learning methods and two different sets of parameters against the A-SciSoft benchmark. When considering energy resolution alone, our top-performing method is XGB. However, it is crucial to note that this method exhibits a significant negative bias, as we will discuss in detail in the following section (see Figure 13).  
Figure 12: Energy resolution for different Machine Learning methods and two different sets of parameters against the benchmark (A-SciSoft).  
Figure 13: Energy bias for different Machine Learning methods and two different sets of parameters against the benchmark (A-SciSoft).
## 5 Results and Discussion
### Pareto Front Observation  
Even though some conclusions can be extracted from the visual analysis of these graphs, a closer look at the scenarios that belong to the Pareto front has been performed, in order to check the ones that are displayed to the user at the end of the process.  
The first analysis of the graphs enabled the identification of the scenarios belonging to the Pareto front. An analysis of the Pareto fronts obtained was performed based on numerical values where each pair of schemes was compared among each other to detect the major differences.  
To serve as a summary of the results obtained through the analysis performed [39] the following table is presented. The different schemes considered are shown, as well as the number of scenarios in their respective Pareto fronts, the targets and boundaries they have reached, together with their maximum and minimum cost and benefit values.  
From the table below (Table 10) it can be deduced that the pairs of prioritisation weighting schemes 1 and its complementary economic one (1A) experience an expected variation in the cost values, which sometimes can be highly influential in the cost values obtained and in the number of scenarios in the Pareto front. To this latter aspect, it is also noticeable that a higher number of scenarios appear in the Pareto front for the more economically driven prioritisation schemes (1A, 2A, 3A and 4A).  
When comparing the minimum benefit values among each other and the maximum benefit values it can be observed that the deviation among these is not great. However, when doing the same exercise with minimum cost values and maximum cost values, it can be seen that there are major differences.  
These can stem from the DPIs considered in each cost and benefit group: whereas in the cost list (ENV01, ENV04, ECO02.2, ECO03 and ECO05) the DPIs are highly different from each other, in the benefits (ENE01, ENE02, ENE06, ENE09, ENE13 and COM01) DPIs evolve in the same direction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Number of** & **Min. Benefit** & **Max. Benefit** & **Min. Cost** & **Max. Cost** \\  & **Scenarios** & **Value** & **Value** & **Value** & **Value** \\ \hline \(1\) & \(11\) & 0,047 & 0,486 & 0,774 & 1,597 \\ \hline \(1\)A & \(20\) & 0,047 & 0,486 & 0,549 & 2,545 \\ \hline \(2\) & \(9\) & 0,043 & 0,460 & 0,788 & 1,558 \\ \hline \(2\)A & \(19\) & 0,043 & 0,460 & 0,586 & 2,035 \\ \hline \(3\) & \(20\) & 0,057 & 0,451 & 0,314 & 1,669 \\ \hline \(3\)A & \(24\) & 0,057 & 0,451 & 0,265 & 2,259 \\ \hline \(4\) & \(20\) & 0,057 & 0,533 & 0,314 & 1,669 \\ \hline \(4\)A & \(24\) & 0,057 & 0,533 & 0,265 & 2,259 \\ \hline \(6\)A & \(20\) & 0,057 & 0,451 & 0,604 & 3,323 \\ \hline \end{tabular}
\end{table}
Table 10: Summary of Pareto front and cost and benefit value observation.  
Figure 7: 6A (environmental issues + economic aspects).  
This implies that greater changes will be experienced in the costs whenever a parameter changes in the definition of the scenarios or in the DPI results, whereas in the benefits side, it will remain quite invariable.