esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 3 Experimental Results
### Experimental Results
#### 3.3.2 Qualitative Results  
This section shows examples of mispredicted Cityscapes dataset images among the results classified by the mIoUNet. The qualitative test results show the input image, GT segmentation map, and segmentation map for the testing set at the same time, as well as the GT mIoU, mIoU prediction, mIoU error value and failure detection result of the image.  
False-Negative Image  
Figure 10 presents examples of results classified as false-negative images. Figure 10 (left) displays input images, and Figure 10 (middle) displays segmentation maps. Out of the 500 images, 27 images were detected. The false negative refers to when the GT mIoU is greater than 0.5 and the mIoU prediction is less than 0.5. The MAE values for mIoU of these images are as follows.  
error was 0.5. To determine the cause, the images that make the average error higher are classified separately.  
Figure 12 presents six images with large error values. The first column of Figure 12 displays the input images and the second column of Figure 12 displays the segmentation maps. First, some cases had many pixels in the image that were not considered a class in the segmentation map. In the semantic segmentation process on the right side of each picture, the area shown as black is the area deduced as pixels with no class. This dramatically decreases the GT mIoU value when calculating the mIoU value. This phenomenon is solved by randomly assigning classes by handling exceptions during the semantic segmentation inference or by inference using a better semantic segmentation network. Second, there are few pixels in the general roadway area in the image. For example, one can see narrow roads, roads with severe curvature and alleyways with many other buildings and structures. This problem can be solved by assigning more weight to pixels corresponding to classes, such as roads, people, and obstacles, which are essential in safe driving when the network is learning.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 3 Numerical Model Description
### Governing Equation
 \tag{6}\]  
When the level-set equations are coupled with the flow equations, the velocities in the level-set equations are obtained from the laminar flow. The value of the level-set function (\(\varphi\)) is used in the computation of the flow field properties (\(\rho\), \(\mu\)) and then exported to the flow equations. The space between the substrates can be divided into pores and throats, with the throats being the key factor affecting the seepage performance of the pore medium. Assuming that the pore throat is saturated with the gas phase initially, the throat radius suddenly decreases during the water drive process, leading to an increase in local capillary forces. In hydrophilic environments, capillary forces act as power while in hydrophobic environments, capillary forces function as resisting forces. The capillary force in a single capillary tube can be expressed as follows [31]:  
\[P_{c}=P_{nw}-P_{w}=\frac{2\sigma\cos\theta}{R_{h}} \tag{7}\]  
where \(P_{c}\) is the capillary force; \(P_{nw}\) is the gas-phase pressure, \(P_{w}\) is the water-phase pressure, and \(R_{h}\) is the characteristic scale of the pore throat. The simulation of two-phase flow under different wettability involves setting two parameters, slip length \(\beta\) and contact angle \(\theta_{w}\), in the level-set method. The value of the slip length \(\beta\) is defined in terms of the minimum grid length factor when \(\beta=f_{\mathrm{J}min}\). To prevent the interface from moving too fast, the surface friction \(F_{fr}\) is introduced to constrain the slip length [32]:  
\[F_{fr}=-(\mu/\beta)u \tag{8}\]  
The contact angle \(\theta_{w}\) is the angle at the interface between the wall and the fluid, pointing from the aqueous phase to the gas phase. The contact angle is specified by [PERSON]'s equation. \[\sigma\cos\theta+\gamma_{sw}=\gamma_{sg} \tag{9}\]  
where \(\sigma\) denotes the surface tension coefficient, N/m; \(\gamma_{sw}\) and \(\gamma_{sg}\) are the surface energy densities of the aqueous phase, the gas phase, and the solids. The surface tension at the interface of the two-phase flow can be expressed as follows [33]:  
\[F_{st}=\
abla T \tag{10}\]  
\[T=\sigma\Big{(}I-n\cdot n^{\mathrm{T}}\Big{)}\cdot\delta(\phi) \tag{11}\]  
where \(n\) is denoted as the unit vector perpendicular to the interface; \(\delta(\varphi)\) is the Dirac function, \(1/\mathrm{m}\). The Dirac function is 0 only at the phase interface and can be derived from the following smooth function approximation [34]:  
\[\delta=6|\phi(1-\phi)||\
abla\phi| \tag{12}\]
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 4 Discussion  
The shape recovery (\(\%\)) was calculated using Equation (2) from the shape memory effect from Figure 9 for the commercial and prepared samples. It has been observed that the commercial samples could recover more than 75\(\%\) when samples were heated at 50 \({}^{\circ}\)C from the temporary bending shape to the recovery shape. However, the as-prepared samples show a recovery of more than 65-70\(\%\). The recovery was tested for samples as a function of the number of cycles. The result is presented in Figure 8. Both commercial and prepared samples from the solution casting method effectively show better shape recovery from the bending deformation.  
The future perspective of the work is to integrate either commercial or prepared PMMA foils with a shape memory alloy of NiTi, as the shape setting of NiTi flat samples is effectively modified towards the application of sensors and actuators, combining the polymer with bending deformation for the formation of composite [27; 28]. The adhesion of NiTi alloy with polymer will improve in various ways, beginning from mechanical to chemical bonding [26]. Shape memory alloy with polymer as a composite constitutes as both an actuator and active component. It could mimic the low stiffness and large deformation characteristics of biological tissues using a composite comprising SMA ribbons and a low-hardness polymer matrix. The bending and dynamic behavior of advanced composite plates use a simple shear deformation on plate model [29]. Finite elemental analysis was carried out in static bending and buckling analysis of plates. The buckling effect was studied with deflection and distribution of pores within the composite [30]. They showed that when the SMA is electrically actuated, small strains within the SMA could  
Figure 8: (**a**–**c**) Shape memory effect of PMMA commercial foil and prepared as-cast foils from 24 and 18 mL.  
achieve an order-of-magnitude larger strain within the composite for application in the sensor [31, 32].  
The results obtained by the PMMA foils show that glass transition temperature could easily be tuned by varying the concentration of the solvent. The transformation temperature of NiTi alloy and PMMA foil could easily integrate for the formation of the composite structure. The shape memory behavior of PMMA foil is achieved as a function of bending stiffness and heating cycles. This could lead to more compatible components in composite structures with NiTi foil. These composite structures could reflect the behaviors of bending and recovering in control of heating and cooling cycles. This structure will have application in the actuators field.
## 5 Conclusions  
In this study, we measured multi-angular canopy spectra continuously during the whole 2018 growing season of a maize field. We compared the ability of the single-angle and multi-angle PRI observations at canopy level in estimating LUE. Then, the PRI-based LUE model was evaluated and compared to the SIF-based linear model for their performance in the estimation of GPP during the maize growing season in 2018. The major results are as follows:  
1. the observed PRI varied with sun-view angles and the averaged PRI using the multi-angle observations within a short time exhibited better performance than single-angle observed PRI in the estimation of LUE in the maize field;
2. LUE\({}_{\text{PRI}}\times\)APAR tracked the variations of GPP during the growing season of the maize field in 2018, and it demonstrated a higher ability to capture the diurnal variations of GPP, while SIF was a better fit for the seasonal variations of GPP;
3. RH was the most important factor affecting the utilization of the PRI-based LUE model to estimate diurnal GPP variations, while PAR affected most for the SIF-based linear model. Under most environmental conditions, the performance of the SIF-based linear model was not as good as the PRI-based LUE model except for clear days (Q \(>\) 2).  
Conceptualization, J.C., S.W., and B.C.; methodology, J.C., B.C., and Q.Z.; formal analysis, J.C. and L.M.; investigation, Q.Z., L., X.Z., and Y.W.; writing--original draft preparation, J.C.; writing--review and editing, Q.Z., S.W., B.C, Y.Z., and R.A.M.; visualization, J.C.; supervision, S.W. and Y.Z.; funding acquisition, S.W., Y.Z., and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFC0503803; the National Natural Science Foundation of China, grant number 41571192 and 41701393; and the Natural Science Foundation of Jiangsu Province for Youth, grant number BK20170641. Q.Z. was supported by the China Scholarship Council (CSC), grant number 201906195015.  
The authors declare no conflict of interest.
## 5 Results
### Speech-to-Text Evaluation
#### 5.1.1 Open-Set Condition Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Team** & **G1** & **G3** & **G5** & **G6** & **G7** & **G14** & **G21** \\ \hline
**TV Show** & & & & & & & & \\
**AFI** & 27.25 & 18.35 & 15.35 & 21.41 & **14.06** & 16.04 & 26.11 \\
**AV** & 53.38 & 46.67 & 39.69 & 39.95 & **25.93** & 28.69 & 41.83 \\
**DH** & 56.82 & 60.05 & 42.76 & 49.7 & **34.62** & 43.32 & 51.16 \\
**EC** & 19.72 & 16.75 & 14.36 & 16.39 & **10.47** & 11.95 & 20.89 \\
**LA24H** & 13.11 & 14.56 & 7.49 & 11.14 & **7.27** & 8.21 & 13.97 \\
**LM** & 34.65 & 38.00 & 26.26 & 30.93 & **19.79** & 22.59 & 41.53 \\
**LT24 HTer** & 25.23 & 27.76 & 19.97 & 22.31 & 15.75 & **15.42** & 29.65 \\
**SG** & 25.01 & 31.86 & 20.1 & 23.45 & **15.11** & 15.20 & 20.95 \\
**Overall TNWER** & 27.36 & 28.1 & 20.21 & 23.69 & **15.69** & 17.26 & 29.59 \\
**Overall WER** & 29.27 & 28.72 & 20.92 & 24.52 & **16.45** & 18.63 & 31.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Speech-to-text open-set condition. Text normalized word error rate (TNWER) (%) for the primary systems per show and team. Team and system descriptions are in Section 4.1.1. See Table 4 for show descriptions. Best results by TV show are in bold. Figure 1: Boxplot with means (x) of the WER by shows for the best system in the open-set condition. See Table 4 for show descriptions. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Team** & **G1** & **G3** & **G5** & **G6** & **G7** & **G14** & **G21** & \\ \hline
**System** & P & C1 & P & P & P & C1 & C2 & P & P & C1 & C2 \\
**TV Show** & & & & & & & & & & & \\
**AFI** & 29.79 & 30.39 & 19.72 & 16.35 & 22.37 & 28.47 & 25.99 & **15.91** & 17.65 & 28.22 & 31.48 & 29.57 \\
**AV** & 54.67 & 54.68 & 47.13 & 39.97 & 40.49 & 48.36 & 42.17 & **23.94** & 28.90 & 39.14 & 54.75 & 50.21 \\
**DH** & 56.53 & 56.58 & 59.18 & 41.50 & 49.44 & 56.77 & 51.30 & **34.45** & 43.06 & 51.24 & 58.50 & 53.82 \\
**EC** & 21.86 & 22.54 & 17.99 & 15.59 & 17.64 & 23.68 & 20.81 & **11.38** & 13.54 & 22.19 & 25.60 & 23.32 \\
**LA24H** & 14.75 & 15.94 & 15.41 & 8.23 & 11.87 & 16.69 & 12.74 & **7.43** & 9.43 & 14.70 & 16.53 & 14.99 \\
**LM** & 36.74 & 37.58 & 38.35 & 27.10 & 31.72 & 44.69 & 34.40 & **21.94** & 23.96 & 45.94 & 47.70 & 46.43 \\
**LT24 HTer** & 27.37 & 28.57 & 28.37 & 20.61 & 23.34 & 31.14 & 24.82 & 18.97 & **17.41** & 32.90 & 39.18 & 37.29 \\
**SG** & 25.43 & 27.28 & 31.47 & 19.66 & 22.81 & 33.82 & 22.65 & 15.97 & **14.77** & 21.32 & 21.10 & 20.16 \\
**Overall WER** & 29.27 & 30.19 & 28.72 & 20.92 & 24.52 & 33.00 & 26.66 & **16.45** & 18.63 & 31.61 & 35.80 & 33.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Speech-to-text open-set condition. Word error rate (WER) (%) per TV show and team. Team and system descriptions are in Section 4.1.1. See Table 4 for show descriptions. P, primary; C#, contrastive #). Best results by TV show are in bold.
## 2 Previous Works
### Mathematical Modeling of SPVS  
Several studies have focused on developing mathematical models to simulate the performance of SPVSs. These models aim to accurately predict the output of Photovoltaic (PV) modules under various environmental conditions. For instance, [PERSON]. [7] improved and validated a model for PV array performance, while [8] developed a cell-to-module-to-array detailed model for PV panels. [9] proposed reduced-form models for efficient computation of parameters and identification of the one-diode model for PV modules from datasheet values.  
[PERSON] and [PERSON] [10] introduced the Elastic Net, a hybrid approach that combines the strengths of both Lasso and RR. This method addresses the limitations of Lasso, particularly in cases where predictors are highly correlated. By applying both L1 and L2 penalties, the Elastic Net not only performs effective variable selection but also maintains model accuracy in high-dimensional settings. The authors demonstrated through empirical studies that the Elastic Net outperforms traditional methods in terms of predictive accuracy and model stability, making it a crucial tool for handling complex data structures in fields like genomics and finance.  
[PERSON] et al. [11] introduced an efficient analytical approach for obtaining a five-parameters model of PV modules using only reference data, simplifying the modeling process. [PERSON]. [12] took a numerical approach to calculate single-diode solar-cell modeling parameters, providing a foundation for accurate SPVS simulations.  
In a comprehensive review, [PERSON] et al. [13] examined various cell modeling and model parameter estimation techniques for PV simulator applications, highlighting the diverse methodologies available for SPVS simulation. [PERSON] et al. [14] focused on series resistance estimation techniques for different silicon-based solar PV modules, contributing to the optimization of electrical performance in these systems.  
[PERSON] et al. [15] reviewed the modeling, design methodology, and size optimization of PV-based water-pumping, standalone, and grid-connected systems, addressing the practical applications and scalability of SPVS technology. [PERSON] et al. [16] modeled and experimentally analyzed heterojunctions with intrinsic thin-layer PV modules under various atmospheric conditions, providing valuable data on their performance in different environments.  
The configuration of bypass diodes in PV modules was studied by [PERSON] and [PERSON] [17], which examined their impact on module performance. [PERSON] et al. [18] combined theoretical analysis and experimental verification to deepen the understanding of PV module behavior, while [PERSON] et al. [19] provided a comprehensive review of SPVS technologies, summarizing the advancements and challenges in the field.
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 3 Experiments Setup
### Data Selection and Preparation  
In this paper, the authors conducted various experiments using four distinct datasets. Prior to initiating each experiment, the initial step involves selecting a sample from each dataset. As deep learning algorithms demand significant hardware resources, such as CPU, memory, and GPU, for data processing and training. To handle this, a subset of data points encompassing all categories of normal and anomalous traffic from each dataset was meticulously selected.  
To maintain uniform distribution of instances (both attacks and non-attacks) within the dataset samples, the authors repeat this selection process ten times for each dataset. The subset selection process, inclusive of all attack types, was executed with specific measures to counter imbalanced distributions and biases. The following measures were implemented:  
* Stratified sampling: The subset selection utilized stratified sampling techniques to preserve a proportional representation of each attack type, ensuring a balanced distribution of attacks within the subset.
* Class balancing: Additional measures were implemented to balance the representation of different attack types in the subset. These actions might include oversampling the minority classes or undersampling the majority classes to address issues related to imbalanced distributions.
* Randomization: Randomization techniques were used during the subset selection process to reduce potential biases. This approach ensured that the selection was not influenced by any specific order or predetermined biases.  
After each selection iteration, the authors plot the data distributions for each sample and compare them to ensure equality. This step guarantees that the random selection encompasses all potential observations present in the dataset.  
Following the data selection, each sample undergoes a series of preprocessing steps to ensure consistency, eliminating errors, gap filling, outliers' removal, and irrelevant data types. We utilized simple linear interpolation to fill in any gaps present in the dataset.  
To facilitate processing by machine learning algorithms, certain features in the dataset require encoding. One-hot encoding is employed for this purpose. Subsequently, the dataset undergoes scaling using the MinMaxScaler from the Scikit-learn library, which scales the values to the interval [0,1].
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 4 An INS Cyber-Physical Range
### Cybersecurity-Specific Components  
Real equipment mentioned in Section 3.1 may be used for cybersecurity research. Even though the use of real equipment in a testbed can be costly, it is useful to create a realistic test environment. Other than real equipment, some software, such as Bridge Command [131], or NMEA Simulator [141] could be utilized to transmit mimic IEC 61162-1 (i.e., NMEA 0183) messages [8]. The NMEA Simulator consists of 18 types of IEC 61162-1 message. The Bridge Command consists of 10 types of IEC 61162-1 message. Given that 124 types of message are available in the standard [84], the capabilities of the testbed could be limited due to few number of mimic messages transmitted by both software. However, the test capability could be extended with alternative free or paid software (e.g., NemaStudio [142]). For AIS research, the AIS BlackToolkit consists of several features, such as the AIVDM message encoder [143]. Moreover, AIS data offered by service providers can be used in a cyber-physical range. For instance, the Norwegian Coastal Administration provides local AIS data in real-time free of charge over an IP address and port number [144]. As an open source chartplotter, the OpenCPN [128] which is compatible with the Bridge Command, could be used [35; 145]. In the testbed, vulnerability scanners (e.g., Nessus Professional [39]) may be utilized to detect vulnerabilities of components and network [105]. An INS network may be created using network simulation tools (e.g., OPNET Network Simulator [146]) [147]. The Kali Linux is a Debian-derived Linux operating system and comes with more than 300 tools related to information security and penetration testing [148]. These convenient tools can be used on the network for security analysis. For instance, one of the pre-installed tools is Wireshark [149], which could be used to monitor and analyze the network in real-time [150].
## 4 Experimental Results
### Feature Analysis
#### 4.3.2 Change in Major Part Characteristics  
The cumulative distribution function (CDF) of major variables that exhibit a high Gini index so that the major changes are found within the intervals of 30,000-50,000, 50,000-60,000, and 60,000-70,000 km is displayed. The CDF plot illustrates how the values of a variable compose the entire dataset by displaying the cumulative density probability. Through the CDF plots, it can be observed how the features form the distribution. For example, the mean of the auxiliary battery voltages (denoted as Auxiliary.Battery.Vol.mean) has various values for 30,000 km, but a limited value near 14.4 for 50,000 km, which is higher than the value for 30,000 km. When comparing 30,000 and 50,000 km, major features exhibit much different distributions. The intake air temperature, ambient temperature, and inverter temperature show high skewness for 30,000 km, but the range of values increases for 50,000 km, as shown in Figure 6a.  
Figure 6b,c show the CDF plots of important variables that have been changed significantly in the 50,000-60,000 km and 60,000-70,000 km mileage intervals.  
In this interval, a change in several parts occurred compared to the other intervals. The most important variables included auxiliary battery voltage and temperature, engine intake temperature, motor inverter temperature, and battery intake temperature.  
Considering the principal parts whose characteristics vary according to the mileage interval, changes in the battery intake temperature and auxiliary battery temperature, followed by the voltage and temperature of the battery pack can be found.  
\begin{table}
\begin{tabular}{c c c} \hline
**30,000 vs. 50,000 km** & **50,000 vs. 60,000 km** & **60,000 vs. 70,000 km** \\ \hline Intake.Air.Temp.mean & Intake.Air.Temp.mean & Auxillary.Battery.Vol.mean \\ Intake.Air.Temp.max & Intake.Air.Temp.max & Inhaling.Air.Temp.max \\ Inverter.Temp.MG1.mean & Intake.Air.Temp.min & Inhaling.Air.Temp.mean \\ Intake.Air.Temp.min & Inverter.Temp.MG1.mean & Ambient.Temp.mean \\ Ambient.Temp.mean & Calculated.load.max & Inverter.Temp.MG1.mean \\ Temp.MG1.max & & Inhaling.Air.Temp.min \\ \hline \end{tabular}
\end{table}
Table 7: Important features per mileage interval change.  
Figure 6: CDF plot of major features of vehicle parts showing many differences: (**a**) from 30,000 to 50,000 km; (**b**) from 50,000 to 60,000 km; (**c**) from 60,000 to 70,000 km.