esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement and Principal Approach
### Number of Errors  
Typically, different error correction codes are applied for the user-data and for meta data. Along with the user-data, a flash controller stores meta information about the life-cycle state, e.g., the number of P/E cycles. Typically, the meta data is protected by a significantly better error correction code than the user-data. Hence, it is possible to correct the errors in the codeword of the meta ECC even at a relatively large offset in the reference voltage. On the other hand, the number of bits for the meta data is small. Consequently, the number of errors in the meta ECC is not always a statistically relevant sample size.  
Figure 6 illustrates the number of errors in the meta-data versus the read threshold at different life-cycle states. We consider a code with length \(n=508\) with a correction capability of up to \(t=21\) errors. The horizontal line in Figure 6 indicates the maximum number of correctable errors. The number of errors for a given voltage provides information about the flash aging. For instance, consider the value \(V_{th}=120\). For the EOL condition, we observed 11 errors on average, whereas the expected number of errors for 1500 P/E cycles  
Figure 4: Error count for 1-to-0 errors and 0-to-1 errors versus the threshold voltage for \(r_{3}\) at EOL.  
Figure 5: Error count for 1-to-0 errors and 0-to-1 errors versus threshold voltage for \(r_{7}\) of fresh flash memory.  
is only about two. Hence, the observed number of errors enables a prediction of the offset to the optimal read voltage. However, the provided information depends on \(V_{th}\). In Figure 6, the slope of the error curves is very low near the optimum. When threshold voltages close to the optimal voltage \(V_{opt}\) are applied, only little information is gained by the number of errors. On the other hand, if the offset between \(V_{th}\) and \(V_{opt}\) is too large then the read data is likely to be undecodable. We use the mutual information between the optimal reference voltage \(V_{opt}\) and the observed number of errors to measure the information that is gained by counting errors.
## 2 GRU-HMM Fault Prediction Model  
### Demand Analysis of GRU-HMM Fault Prediction Model  
The transmission structure of the CRH5 is traction motor-universal shaft-gearbox, and vibration acceleration and temperature data are collected simultaneously during operation. The monitoring data capacity of one primary repair cycle is about 200,000 items, and the data show time series characteristics, which are typical large-capacity and long-range time series data. The Hidden Markov Model (HMM) is capable of modeling probability distributions of time-series data, and the hidden state setting is well suited for multi-component fault prediction studies. However, HMM has a limitation on the length of the input sequence, has weak performance in dealing with dependencies over long distances, and is prone to problems such as gradient vanishing and gradient explosion. Therefore, the fusion of a Gated Recurrent Unit (GRU) with the HMM can solve the shortcomings in the performance of the HMM and provide better access to the dependencies of long sequence data, and its gating mechanism can also effectively solve the problems of gradient vanishing and gradient explosion, which can effectively improve the accuracy of the fault prediction model.
## 2 Materials and Methods
### Datasets
In our experiment, _NIR_ (band 8) and _RED_ (band 4) were carefully chosen from the Sentinel-2 reflectance image to calculate these vegetation indices. The highest vegetation index value in an image was considered as a pure vegetation pixel whereas the lowest value was decided as a pure soil pixel. Consequently, pure pixels for vegetation and nonvegetation (soil) information were identified to calculate the forest _FVC_ images from the vegetation indices calculated using Equation (4). The best vegetation index among vegetation indices for the forest _AGB_ calculation was selected based on the correlation coefficient (\(R\)) and mean absolute error (_MAE_) between the ground forest _FVC_ and image forest _FVC_. \(R\) is the degree of statistical similarity between a pair of variables, whereas _MAE_ is a measure of errors between paired observations. \(R\) and _MAE_ were calculated between the ground _FVC_ and the _FVC_ from Sentinel-2 by _NDVI, MVI, SAVI_, and _MSAVI_ corresponding to ground sample locations. Based on the highest correlation and lowest _MAE_ criteria, the _MSAVI_-based image _FVC_ was chosen in _AGB_ calculation. Further, the trend line by linear regression between the ground and _MSAVI_-based _FVC_ was fitted, which is shown in Equation (13):  
\[S_{vgg}=S_{v}\times F_{c}, \tag{1}\]4  
In Tundi reserved forest, the predominant tree species is _Shorea robusta_ (Sal). Accordingly, based on [27], the ground forest _AGB_ was calculated using the following equation from the field data (Table 2):  
\[S_{vgg}=S_{v}\times F_{c}, \tag{1}\]5  
where _AGB_ is the forest above ground biomass (kg), \(\mu\) (0.667) is specific gravity (tree stem density) (g cm\({}^{-3}\)), \(D\) is the _DBH_ (cm), \(H\) is the tree height (m), and the constant 0.509 obtained from [27]. The calculated ground forest _AGB_ from field data and the selected image forest _FVC_ are shown in \[S_{vgg}=S_{v}\times F_{c}, \tag{1}\]6  
A trend line is obtained between calculated ground forest _AGB_ and image-retrieved forest _FVC_ by linear regression. The following model (Equation (15)) was obtained from 15 ground training samples (Table 5) and the remaining 7 ground samples were used for validation of the results obtained from the developed model (Table 6). \[S_{vgg}=S_{v}\times F_{c}, \tag{1}\]7  
The accuracy of the forest _AGB_ based on Sentinel-2 was assessed by comparing the remaining seven field measurements to the corresponding locations. Kindly note that the samples selected for training were not chosen for the validation to avoid the biasness in the accuracy assessment. Thus, training and validation samples were separated before trend modeling. For data processing and implementation of the proposed procedure and experimentation in forest _AGB_ assessment, mainly the Interactive Data Language (_IDL_) programing and Environment for Visualizing Images (_ENVI_) software were used.
## 3 Imaging Results from Hot Commissioning Stage  
### X-ray Phase Contrast Result  
For data acquisition, the MCT beamline uses a web-browser-based user interface for common experiment types with continuous feedback and monitoring. The input values, such as file name, exposure time, beam mode, X-ray energy, number of projections, and number of darks and flats, can be entered via this user interface. There is also the option to choose 2D (projections) or 3D (tomographic) data acquisition. The produced data product is an hdf5 file, incorporating both image and metadata information.  
The first phase-contrast images produced at the MCT beamline can be seen in Figure 7. A monochromatic beam at 11 keV was used to image an insect, fibrous paper, and eucalyptus leaf, using the 5\(\times\) objective lens of the white-beam detector. The distance between the samples and detector was 70 mm. Phase contrast is clearly present in these images (manifesting itself as black-white fringes enhancing the edge structure of the sample in particular).
# The Influence of Track Irregularity in Front of the Turnout on the Dynamic Performance of Vehicles
## 2 Numerical Approach
### Vehicle-Turnout-Track Coupling  
The traditional moving track model has an advantage in computation efficiency; however, its effective frequency range is within 20 Hz [37]. For the study in this paper, some short-wavelength irregularities are considered, and the traditional moving track model is, obviously, not applicable to the analysis of short-wavelength irregularities at high frequencies. Therefore, to fully reflect the flexibility of the track structure, a rigid-flexible coupled model is simulated in the model. The rails and sleepers are considered as flexible bodies, and modeled as beam elements and solid elements, respectively, by ABAQUS finite element software. The rails and sleepers are connected by a fastener system. The stiffness and damping of the fastener system are 6 \(\times\) 10\({}^{7}\) N/m and 7.5 \(\times\) 10\({}^{4}\) N\(\cdot\)s/m, respectively [38]. The wheelsets, bogie frames, and the vehicle body are considered as rigid bodies in SIMPACK, connected by force elements.  
The deformation of flexible bodies in multi-body dynamics can be represented by modal coordinates [36]. Therefore, the Craig-Bampton modal synthesis method is conducted to describe the elastic deformation of the track system. The modal synthesis method is then combined with a hybrid coordinate method in the multi-body dynamics system to achieve the coupling of rigid and flexible body motions. The coupling process of vehicle and track models is shown in Figure 6. Concerned with the calculation cost, there is no need to consider the whole length as a flexible track. Therefore, the flexible track length is set as 120 m, which takes into account the whole length of the turnout and the length for setting the irregularities in front of the turnout, and avoids the effects of junction position between the rigid and flexible track. The calculation frequency is 500 Hz.  
Figure 5: The semi-Hertzian method.
# A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
## 5 Results
### Computational Cost Comparison  
We also investigated the impact of including environmental data on the model's complexity and inference speed. For the environmental data, we utilized the simplest forms of the LSTM layer and two fully connected layers, resulting in a model with low complexity, as shown in Table 4. However, this model exhibited the lowest \(F1\)-score among the three modalities. Interestingly, when combining the model for environmental data with Resnet 50 for image data, we observed a 2.5%p increase in \(F1\)-score compared to Resnet 50 alone, despite the slight increase in model complexity and computational volume being negligible in comparison.
## 3 Results
### Fuzzy Model Validation  
Fuzzy model validations are commonly conducted using various metrics, among which the determination coefficient or \(r^{2}\) is widely utilized [10; 40; 49; 50]. The \(r^{2}\) can be interpreted as the proportion of the variance in the dependent variable can be predicted from the independent variables. It can also be used to compare the outputs of two models, then observe if there is some linearity. Ranging from 0 to 1, a value of 0 indicates a nonlinear relationship between variables and 1 indicates a perfect linear relationship [50; 51]. It is important to note that an \(r^{2}\) value above 0.5 is considered statistically acceptable [33; 52] and also considered the best metric to evaluate regression models, even better than mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric absolute percentage error (SMAPE) [51].  
In the context of this particular study, it is not possible to compare the results of the fuzzy model with a quality standard due to the model's nature, which is expert knowledge-based and developed using data ranges. As an alternative approach, a linear model was fitted to the processed dataset following the application of the fuzzy model, enabling a comparative analysis.  
To validate the model's performance, a sample was extracted from the complete dataset. The sample size was determined using a 95% confidence level and a 5% margin of error, resulting in a total of 359 rows. The model's effectiveness was assessed by calculating the \(r^{2}\) value, which obtained a value of 0.906, indicating a high degree of linearity between the linear model and the fuzzy model. However, it is important to acknowledge that the results exhibit limited variation, as evidenced by the data distribution depicted in Figure 14a. It has two main clusters of the predicted outputs, one centered at 2.5 and the other at 5 on the SQ scale.  
Figure 14: (**a**) Scatter plot of the fuzzy model compared to the linear regression model using collected data from the stevia crop. (**b**) Scatter plot of the fuzzy model compared to the linear regression model using randomly generated data.  
In order to evaluate the model's generalization capabilities, it was further assessed using a completely random dataset generated using Python programming libraries for the input variables inside the universe of disclosure of each one. The random dataset was processed by the fuzzy model, a linear model was fitted based on the results, and the \(r^{2}\) value was calculated. The obtained \(r^{2}\) value was 0.532, lower than the \(r^{2}\) value obtained from the collected data but still considered acceptable because it exceeds the threshold of 0.5. Notably, the values in the random dataset are now grouped into four different clusters instead of two, with more transition values observed between these clusters, as depicted in Figure 14. The data distribution of the randomly generated dataset is illustrated in Figure 15, where the SQ histogram visually highlights the aforementioned clusters.
# GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & **Per Image** & & & **Overall** \\  & \(n\) & **MAE** & **MAE (\%)** & **MSE** & \(N\) & **MAE** & **MAE (\%)** \\ \hline Chardonnay & 1.0 & 4.69 \(\pm\) 3.53 & 4.23\% \(\pm\) 2.87\% & 4.69 \(\pm\) 3.53 & 112.8 & 4.69 \(\pm\) 3.53 & 4.23\% \(\pm\) 2.87\% \\ Lagrein & 1.4 & 5.41 \(\pm\) 3.23 & 3.36\% \(\pm\) 1.79\% & 5.61 \(\pm\) 3.51 & 228.2 & 4.63 \(\pm\) 3.22 & 2.29\% \(\pm\) 1.68\% \\ Marzemino & 2.6 & 18.48 \(\pm\) 17.43 & 16.29\% \(\pm\) 11.48\% & 21.29 \(\pm\) 18.71 & 307.2 & 19.20 \(\pm\) 16.00 & 8.78\% \(\pm\) 10.02\% \\ Pinot Gris & 5.4 & 9.57 \(\pm\) 3.75 & 6.84\% \(\pm\) 3.10\% & 11.59 \(\pm\) 4.39 & 766.6 & 36.60 \(\pm\) 27.58 & 4.60\% \(\pm\) 3.50\% \\ Pinot Noir & 3.4 & 14.30 \(\pm\) 8.48 & 10.88\% \(\pm\) 5.87\% & 16.08 \(\pm\) 9.33 & 480.0 & 16.37 \(\pm\) 13.82 & 3.68\% \(\pm\) 3.64\% \\ Sauvignon & 3.4 & 21.35 \(\pm\) 7.03 & 19.33\% \(\pm\) 6.22\% & 25.08 \(\pm\) 8.51 & 367.0 & 50.55 \(\pm\) 28.15 & 13.88\% \(\pm\) 6.77\% \\ Traminer & 3.2 & 14.02 \(\pm\) 11.28 & 11.52\% \(\pm\) 9.18\% & 15.88 \(\pm\) 12.72 & 408.8 & 24.02 \(\pm\) 32.51 & 4.95\% \(\pm\) 5.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Application of GBCNet on CR1 dataset with 5-CV. Results are reported for all the varieties used in this work, with the average number of images (_n_) and berries (_N_) per fold. Results are formally reported as mean \(\pm\) sd, where sd may be larger than mean.
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 2 Basic Concept of Mechanical Simulation Technology  
Mechanical simulation technology combines the real physical world with computers. By analysing the data periodically fed back by the equipment and the real-time data obtained during constant operation, MST can grasp the status of objects in the physical world in an accurate and timely manner. The development of computer technology in recent years has made it possible to establish a simulation model and environment for multi-dimensional information interaction. Unlike traditional simulation technology, MST can provide dynamic images based on real-time changing data and draw entities visually in the computer [16].  
MST is emerging in the high-end industrial field. Many companies are keeping pace. For instance, an airline used MST for the health maintenance and sustainment of aerospace vehicles. They established a model of actual aeroplanes in computer and realized the actual state of the aeroplane through synchronization with sensors. By this means, whether the aeroplane needed maintenance or could withstand the next mission load could be analysed and evaluated in a timely manner according to the existing structure and past load after each flight. MST can integrate diachronic maintenance data and related derived data from the health monitoring system. By integrating the above data, MST can continuously monitor and forecast the health condition, the remaining working life, and the probability that the system will continue to work normally. It also indicates the response to critical security incidents of the system.  
MST is used to collect data such as cable force and actuator stroke in the FAST cable net in our system, which not only meets the requirements of low data delay but also acquires enough data for real-time monitoring. Through the calculation, the safe operation status of FAST can be evaluated over time, warn of possible accidents on the structure in advance, and guarantee the stable operation of FAST.
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Experimental Results
#### 4.4.4 Exploration of Hyperparameter Tuning on Model Efficacy  
The profound influence of hyperparameters on model performance necessitates a rigorous examination. Here, we conduct a thorough analysis of the influence of various hyperparameters on the predictive ability of the model, including the number of units and the size of the batch, among others. To assess the model's performance in a methodical manner, We embarked on a suite of tests utilizing the WISDM dataset, utilizing the F\({}_{1}\)-Score as a reliable metric to quantify and compare the performance outcomes.  
(1) Effect of the number of LSTM cells  
Selection of LSTM unit count per layer is pivotal, as a higher number facilitates the extraction of more intricate and profound features, enhancing the model's learning capabilities. However, this augmentation in feature complexity comes at the cost of increased model parameters and potential exacerbation of overfitting issues. Figure 5 succinctly illustrates the trade-off between accuracy gains and parameter growth in the BSTCA-HAR model, emphasizing the strategic importance of optimizing the LSTM unit count. With the increasing number of network parameters, the accuracy of the model does not increase; thus, the F\({}_{1}\)-Score effect is the best when the number of Bi-I-STM units is 32.  
Figure 5: Effect of increasing the number of LSTM layer cells on model performance.  
(2) Effect of Batch Size Variations  
Batch processing with smaller batches is a commonly used data processing technique in deep learning and other machine learning tasks. In this method, the dataset is divided into multiple smaller batches (or subsets), each containing a certain number of samples. In each iteration, the model calculates the gradient of the current batch of data and uses this gradient to update the model's weights. Because batch data are used instead of the entire dataset, this update is typically faster than batch gradient descent using the entire dataset and more stable than stochastic gradient descent using a single sample. Figure 6 demonstrates the trend of model accuracy with five different batch sizes. The results show that the model has the highest accuracy when the batch size is set to 192.  
Additionally, we conducted experiments with other hyperparameters, such as different learning rates and optimizers. The findings indicated that the influence of these hyperparameters on the model's execution was relatively marginal. For instance, we experimented with several optimizers (Adam, SGD, RMSprop) and learning rates (0.001, 0.01, 0.1), discovering that these changes had no discernible effect on the F\({}_{1}\)-Score. Therefore, for the sake of simplicity and emphasis, this paper primarily discusses the impact of the number of filters and batch size on model performance.
# Docker Performance Evaluation across Operating Systems
## 2 Docker Architectures
### Docker on Linux
#### 2.1.6 Control Groups  
In addition to isolation, which is provided mostly through the use of namespaces, a decent virtualization tool needs to use system resources efficiently. Control Groups allow the regulation of CPU, memory, disk and network I/O division between multiple containers and a host machine [21]. This is used to prevent over-consumption from a single container and avoid resource contention. It is possible to achieve predictable performance by limiting resource usage. It is important to know that by default containers can access resources as long as they are available on the host.  
Docker distinguishes two types of limits: soft limit and hard limit. The hard limit specifies the amount of a given resource that a container cannot exceed, while the soft limit defines a limit that works as a hard limit but only, when Docker detects contention or low memory on the host machine. A soft limit must have a lower value, and the container can exceed those limits in opposition to the hard limits [22].  
In order to appreciate cgroups usage in the Docker system, it is important to understand the consequences of OOME--Out Of Memory. When the physical and swap memory of the Linux system is exhausted, the Linux kernel launches the OOM process killer, which is responsible for terminating processes to prevent a system crash [23]. In Docker containers, this idea is extended to individual containers. When an instance of a container runs out of memory, process termination is executed in a separate namespace and avoids interrupting apps in other container and host systems [24].
# Advanced Analysis of Electoretinograms Based on Wavelet Scalogram Processing
## 4 Statistical Analysis of the Electroretinogram Signal Database
### Pediatric Group  
There were 65 signals used in the analysis of a pediatric group. We diagnosed 26 as being healthy, and 39 as subjects with pathology among them. Table 2 presents the obtained amplitude and the latency values for the a-waves and b-waves. According to the ISCEV Standard, the results table contains the median values and standard deviation (STD), to the 5 th and 95 th quantiles (Q5% and Q95%). The data are aggregated by the diagnosis. The boxplots of amplitude and latency values for the a-waves and b-waves are plotted in Figure 5a.  
Figure 4: Illustration of the generalized pipeline of the data processing.  
From the data written in Table 2 and Figure 5a, the following can be concluded: the amplitude values seem to be better indicators for distinguishing between the two diagnostic groups. The subjects with pathology tended to have lower values of amplitude. However, it is worthy to mention that there are \"overlaps\" that make an improbable diagnosis using a single feature.