esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Taguchi Methodology  
The Taguchi methodology [13] is a statistical approach used in engineering and manufacturing to improve product quality by minimizing variation and deviation from target values. At its core, the methodology focuses on the design of experiments with the aim of making products and processes more robust against variations without significantly increasing costs. The Taguchi method introduces several key concepts to achieve this, notably control factors, noise factors, and the signal-to-noise (S/N) ratio.  
Control factors are the variables within a process that can be controlled or adjusted. By adjusting these factors, it is possible to optimize a desired output or performance measure. Noise factors, in contrast, represent environmental, material, and operating conditions that are difficult or impossible to control but can affect the performance or quality  
Figure 1: Geometry machined in WEDM.  
Figure 2: Example of machined part.  
of a product. These could include humidity, material quality variations, or differences in how a product is used by customers. [PERSON]'s approach designs experiments to vary control and noise factors to observe their effects on the response to identify settings that improve the desired response and minimize the impact of noise.  
The signal-to-noise (S/N) ratio is a metric that quantifies how well a product or process performs relative to the variability or \"noise\" it is exposed to. The S/N ratio helps in identifying the level of control factors that maximize performance stability. [PERSON] introduced specific types of S/N ratios for different kinds of objectives, such as the \"smaller-the-better\" type of response for processes where the goal is to minimize a characteristic, like defects or deviations from a target:  
\[\text{S/N}=-10\log_{10}\left(\frac{1}{n}\sum_{i=1}^{n}y_{i}^{2}\right) \tag{1}\]  
where S/N represents the signal-to-noise ratio for a smaller-the-better type of response, \(n\) is the number of observations or trials, and \(y_{i}\) denotes the value of the \(i\)th observation.
## 3 Concepts and Methods
### Bitmaps and Seeds Synchronization and Task Scheduling
#### 3.3.2 Seeds Synchronization  
In the fuzzing process, testcases generated through the mutation of a particular seed may no longer belong to the original subtask. Suppose the deviations of the new seeds discovered by the fuzzer from the current task are not detected promptly, in that case, the new testcases are likely to deviate entirely from the task assigned to the fuzzer after several iterations. This renders the task allocation strategy practically ineffective. Therefore, it is necessary to introduce a seeds synchronization strategy, whereby after a fuzzer discovers a new seed, it is assessed to which subtask it belongs and then assigned to the fuzzer currently conducting fuzzing on that subtask.  
To this end, a strategy for seeds synchronization was designed: when identifying a testcase as an interesting seed, it is stored in a cache folder instead of the local queue of interesting seeds. The cache folders of all fuzzing instances are monitored by a new seed division module, and when a new seed is detected, the subgraph association and seed mapping mechanism is used to determine the corresponding subtask of this seed, and the seed is synchronized to the seed corpus corresponding to that subtask. The fuzzers in our framework do not directly synchronize new seeds generated by other fuzzing instances anymore.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 5 Long Path Selection  
The next major challenge for detecting SDDs is propagating the fault effect through long paths. So it is necessary to select the long paths accurately as it positively affects the pattern quality. Also, the number of long paths must be lower to reduce the number of patterns and the run time without compromising the test coverage. The long paths are difficult to find because of the process variation, crosstalk, power supply noises, etc. All these effects have to be considered to determine the accurate long paths. Generally, the TA ATPG of commercial tools takes the standard delay format (SDF) files or results from static timing analysis (STA) tools like pin slack information to propagate the fault effect through the long paths. Hence, the process variations and crosstalk information are not taken into consideration directly by the tool, thus leading to many SDD test escapes. The STA finds the delay of the circuit by considering the values of the factors that cause delay variation at a particular process corner, say best, worst or nominal. This worst-case corner overestimates the circuit delay. But in the actual scenario, the factors that cause delay variation are not restricted to a particular process corner value. So, statistical static timing analysis (SSTA) [24] is used to consider the effects of process variation. In SSTA, the gate and interconnect delays, the arrival time, required time, and slack are all considered as a probability distribution rather than a fixed value based on the corner case as in STA. Here, each gate and interconnect's probability density function (pdf) is used to calculate the path delay pdf. Various SSTA-based long path selection methods are available in the literature [24; 25]. For efficient statistical analysis, the SSTA method is classified into two types: path-based [26] and block-based method [27]. In [24], a test quality metric based on the slack of path (considering different process parameters) found through statistical timing analysis and branch and bound algorithm is used to guide the long path selection. But the SSTA does not give information about the pattern-dependent effects like crosstalk, IR drop, etc. Considering these effects is crucial while selecting the long path to increase the SDD or HDD testing accuracy. In [21], the statistical timing analysis considering the pattern-dependent effects is proposed for long path selection and pattern grading.
## 3 INS Measurement--Outline of Some Results
### Conventional Theory: Momentum and Energy Conservation in Two-Body Collisions  
Let us consider in some detail a neutron colliding with an atom (nucleus) of mass \(M\). We use the notations defined in Appendix A. The atomic initial momentum \(\mathbf{P}\), which in general is not zero, causes the following energy transfer \(E=E_{0}-E_{1}\equiv\hbar\omega\) being transferred from the neutron to the atom (\(E_{0}\): neutron's initial energy; \(E_{1}\): neutron's final energy; see Figure 1):  
\[E=E_{0}-E_{1}\equiv\hbar\omega = \frac{(\hbar\mathbf{K}+\mathbf{P})^{2}}{2M}-\frac{\mathbf{P}^{2} }{2M} \tag{1}\] \[= \frac{(\hbar\mathbf{K})^{2}}{2M}+\frac{\hbar\mathbf{K}\cdot \mathbf{P}}{M}\]  
This relation represents the conventional energy conservation. \(\hbar\mathbf{K}\) is the momentum transfer from the neutron to the atom. In the following we use the common notation \(\mid\mathbf{X}\mid=X\), for any vector \(\mathbf{X}\).  
The first term in the right-hand side (rhs) of the last equation is often called recoil energy,  
\[E_{rec}=\hbar\omega_{rec}=\frac{(\hbar K)^{2}}{2M} \tag{2}\]  
It gives the gained kinetic energy of a struck atom being initially at rest. Hence, since it holds \(\langle P\rangle=0\) before collision, we obtain for the involved mean values the relation  
\[\langle E\rangle=\frac{\hbar^{2}\langle K\rangle^{2}}{2M}\equiv E_{rec} \tag{3}\]  
This equation refers to the center of the measured peak. Hence, INS from a gaseous sample of atoms leads to an intensity peak centered at energy transfer \(E_{\text{rec}}\), and having a finite width being caused by the additional term \(\hbar\mathbf{K}\cdot\mathbf{P}/M\). This term represents the so-called Doppler broadening (see also Appendix A). Peak center and peak width are illustrated in Figure 1, which shows data of incoherent neutron scattering from liquid helium, \({}^{4}\)He; for details see [25].  
The above simple formulas of this section capture the features of the so-called Impulse Approximation (IA) [10; 26]. Obviously, the IA describes ultra-fast two-body collisions.  
Figure 1: Schematic representation (blue points) of measured dynamic structure factor \(S(K,E)\) of liquid helium [25]. The red line is the calculated recoil parabola, for the mass of \({}^{4}\)He, shown as a guide to the eye. The white-blue ribbon around the recoil parabola represent data points measured with the time-of-flight spectrometer ARCS [15] (Figure taken from reference [26] with permission of Quanta).
## 4 Discussion
### Comparison with Other Cocoa Maps and Implications for Reporting on Area Estimation  
The comparative analysis of our cocoa map with the maps generated by [PERSON] et al. [34] and [PERSON] et al. [45] showed that solely interpreting the accuracy scores can be misleading for the map's quality. For instance, in terms of the PA for the cocoa class, the scores achieved in our study and these two studies were approximately in agreement: our model achieved 81.9% on average, while [PERSON] et al. and [PERSON] et al. reported 87.2% and 82.9%, respectively [34; 45]. Yet, with a visual inspection, we discovered disparities between the three cocoa maps, highlighting the added value of the qualitative visual assessment. The CNN model by [PERSON] et al. [34] demonstrated robust performance in detecting full-sun cocoa and cocoa agroforestry. Conversely, the random forest by [PERSON] et al. [45] showed a random and poor performance despite the adequate accuracy scores they reported. However, the findings by [PERSON] et al. [45] were consulted in reports with regard to the cocoa area estimation [131; 132], while their map has been distributed in the web-based platform of Copernicus [133]. It becomes apparent that there is a seeming objectivity in quantitative accuracy measures and their interpretation should be approached with caution [28]. Therefore, the synergistic application of quantitative and qualitative accuracy assessment is recommended for communicating map quality in a comprehensive way. Clarity on the (in)accuracies of the classification map is necessary, especially when the outcome is to be used in high-level reporting against global and national policies and commitments, such as zero deforestation.
## 4 Model Architecture Design
### Model Architecture Parameter Configuration
#### 4.1.1 Calculation of Memory Requirements  
* Vocabulary Size and Embedding Matrix: With a vocabulary size of 2,400,000 words and an assumed embedding dimension of 256, the embedding matrix size is 2,400,000 \(\times\) 256 = 614,400,000 floating-point numbers. Using float32 precision, this would require approximately 2.46 GB of GPU memory. However, this is typically not the main source of memory consumption, as the embedding layer can utilize techniques like Embedding Bag or optimized lookup tables to reduce memory usage.
* Memory Usage of Transformer Blocks: Each Transformer block primarily consumes memory from the QKV calculations, i.e., (seq_len \(\times\) d_model) \(\times\) 3 (for Q, K, V). For 8 layers, the total is 256 \(\times\) 256 \(\times\) 3 \(\times\) 8 = 1,536,960 floating-point numbers, approximately 6.23 MB (float32). This is just the basic intermediate state, and with residual connections, layer normalization, FFN, etc., the actual consumption will be greater.
* Full Model Memory Estimation: Considering all layers, residual connections, intermediate outputs, and backpropagation, along with possible batch processing, a safe estimate is several times the above calculations, with additional overhead. For large-scale training, especially with large batch sizes, tens of GB of GPU memory may be required.
## 4 Modeling and Results
### Results
#### 4.3.1 Pretraining the Performance Baseline in the CASIA-HWDB Dataset  
Before conducting the aforementioned experiments, it is essential to first train the Vanilla Preact ResNet-18 model in the CASIA-HWDB dataset, as this step is crucial for implementing the cross-language transfer-learning concept proposed in this paper. This dataset, recognized for its extensive collection of handwritten Chinese character images, serves as an ideal basis for pretraining. Specifically, a Preact ResNet-18 architecture, chosen for its proven performance in image recognition tasks, was trained from scratch. Figure A1 illustrates the training dynamics (accuracy and loss curves) achieved by the Vanilla Preact ResNet-18 model for CASIA-HWDB class recognition. The curves show the initial volatility within the first -10 epochs, common during early learning phases in large datasets, followed by stabilization and convergence toward the optimal performance, which was observed at around the 14 th epoch.  
The performance metrics achieved during this initial training phase are detailed in Table 4, providing a baseline for subsequent transfer-learning experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **mAP** & **mAA** \\ \hline Vanilla Preact & & & \\ ResNet-18 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance indicators for the model that recognizes CASIA-HWDB classes.  
As can be seen from Table 4, the Vanilla Preact ResNet-18 model provided a satisfactory solution, which allowed us to use its weights for the transfer-learning approach. The deeper analysis of the obtained results showed that the minimal and maximal character-specific recognition accuracies varied significantly. The character \(\square\) had the lowest recognition accuracy, at 41.67%, while % and dozens of other Chinese characters achieved a perfect recognition accuracy of 100%.
# Design and Analysis of Pattern Reconfigurable Antenna Based on RF MEMS Switches
## 2 Antenna Structure Design
### Main Patch, Sub-Patches and Parasitic Patches Design
This can achieve the ideal impedance matching effect. The simulation results show that, when \(W_{\rm m}\) = 0.38 mm, the antenna and the 1/4 wavelength impedance transformer can achieve matching, which is exactly the width of the 50 \(\Omega\) microstrip transmission line. Without affecting the impedance matching, \(W_{\rm m}\) was fine-tuned and the simulation results show that the bandwidth of the antenna decreases as \(W_{\rm m}\) increases, as shown in Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Antenna Working State** & **Switch State** \\ \hline
1 & Switch S\({}_{1}\) is on, switch S\({}_{2}\) is off. \\
2 & Switch S\({}_{1}\) is off, switch S\({}_{2}\) is on. \\
3 & Switch S\({}_{1}\) is on, switch S\({}_{2}\) is on. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Corresponding relationship between antenna working state and switch state. Figure 3: Schematic diagram of the reconfigurable antenna (top view in the upper picture and side view in the bottom figure). Two parasitic patches (the blue portion in Figure 6) were added to the antenna to increase its working bandwidth and enhance its functionality. The parasitic patch can form a resonant circuit, which is equivalent to the occurrence of two or more resonant points. The bandwidth of the antenna can be increased when the resonant frequency of the resonant  
Figure 4: The relationship between the size of the main patch, the sub-patch, and the center frequency of the antenna. (**a**) The relationship between L\({}_{0}\) and f\({}_{0}\); (**b**) The relationship between W\({}_{0}\) and f\({}_{0}\); (**c**) The relationship between L\({}_{1}\) and f\({}_{0}\); (**d**) The relationship between W\({}_{1}\) and f\({}_{0}\). Figure 5: The relationship between the width of the impedance transformer and the bandwidth of the antenna. circuit is close to the antenna's own resonant frequency. The size of the parasitic patch can also be modeled, simulated, and fine-tuned using the above method, which was not repeated here. The pattern reconfigurable antenna was thus created. Figure 6 depicts the top view of the antenna, while Table 2 provides the specific dimensions of the antenna.
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 5 Conclusions  
For economical condition monitoring of 3D printers in practical applications, low-cost signal acquisition and efficient computing methods are necessary. In this paper, a method based on the ESN was proposed to monitor the transmission condition of a 3D printer using a low-cost attitude sensor. Firstly, the fault data of the 3D printer transmission was collected using a simple and low-cost data acquisition system. Then, the ESN model was trained by extracting 10 time-domain statistical features and 8 frequency-domain features using the nine channels' data from the attitude sensor. The experimental results showed the effectiveness of the proposed method for condition monitoring of 3D printers. The highest monitoring accuracy of this method was 97.17%. It should be pointed out that SVM, LPPSVM, and PCASVM are invalid methods to monitor 3D printers when using the low-cost attitude sensor for data collection as their highest monitoring accuracy was only 48.87%. Compared with SVM, LPPSVM, and PCASVM, the ESN was a highly accurate and stable method for monitoring 3D printers. Therefore, the proposed data-driven method can be used for effective and economical condition monitoring of 3D printers.  
The topic of this research is designed by [PERSON] and [PERSON]; Experiments is performed by [PERSON] and [PERSON]; Formal analysis is completed by [PERSON], [PERSON] and [PERSON]; Methodology validation is carried out by [PERSON], [PERSON] and [PERSON]; The first version of manuscript is prepared by [PERSON] and [PERSON], and this version of the manuscript is read, approved and substantially contributed by all authors.  
This work was supported in part by the National Natural Science Foundation of China (71801046, 51605406 and 51775112), the Research Program of Higher Education of Guangdong (2016 KZDXM054), and Postdoctoral Science Foundation of China (2019M652881).  
Figure 6: Accuracy comparison among ESN, SVM, LPPSVM and PCASVM using all nine channels data under (**a**) 20%, (**b**) 30%, (**c**) 40%, and (**d**) 50% proportion training samples.  
**Acknowledgments:** We hereby acknowledge that this work was supported by DGUT: Intelligent Manufacturing System Health Monitoring and Maintenance Technology Research Center of Guangdong at Dongguan University of Technology. The valuable comments and suggestions from the editors and the anonymous reviewers are very much appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Landform Analysis and Wave Propagation Modeling  
The locations of the measuring points were presented on various map backgrounds, facilitating further analysis. A high-resolution orthophotomap was utilized for this purpose, depicting the approximate spatial arrangement of the areas under investigation, such as the distribution of greenery and communication routes [57]. Another base map used was the hypsometric map, which represents the surface relief of the terrain using isohypses and corresponding colors. Its purpose is to visualize height differences in land areas. The key colors used on these maps are green for lowlands, yellow for highlands, and orange and brown for mountains. This color system is designed not only to accurately represent altitude but also to convey a three-dimensional impression [58]. Additionally, the hypsometric map is juxtaposed with a digital terrain model. A digital terrain model (DTM) is a digital representation of the Earth's surface, mapped using points with known spatial coordinates (X, Y, Z). The study employed a regular GRID model (meter-by-meter grid), where each node is assigned a height value. With the aid of the DTM, it became possible to determine the height of each measuring point with an accuracy of 0.1 m [59]. Utilizing the above map layers at a later stage enabled the comparison of the measurement results obtained with the meter with various spatial data.  
In the context of the complexity of acoustic wave propagation in a forest environment, a spatial analysis was conducted to illustrate the impact of noise sources in conditions of varied vegetation structure and terrain in Roztocze National Park. Particular emphasis was placed on analyzing the impact of topography and land cover. The Noise Prediction: Calculate Noise Level From Site plugin in the QGIS (3.34.12 version) environment, based on an algorithm compliant with the BS 5228-1:2009 standard [60], was used for modeling. The choice of this standard was justified by its compliance with the requirements of environmental documentation such as the Environmental Impact Statement (EIS) and its direct applicability in the natural environment.  
The model was based on the Digital Terrain Model (DTM) in the PL-EVRF2007-NH system, with a 1 m grid, provided by the Main Office of Geodesy and Cartography (Poland). The sound reception height was set at 1.5 m above ground level and the space was divided into hexagons with sides of 30 m. Soft ground and no reflections (reflection value = 0) were assumed, in accordance with the acoustic properties of a typical forest habitat. The noise source was located by the road, and its emission was determined on the basis of actual field measurements.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on Improved CNN
#### 3.1.3 Preprocessing  
Ship images captured in actual scenarios often contain complex backgrounds. Additionally, differences in illumination and viewing angles influence image-feature extraction. If the ship images are directly fed to CNNs, the classification result may get adversely impacted. In this study, therefore, ship images were subjected to a series of preprocessing procedures that tend to weaken the influence of background noise as well as ensure sample randomness along with an increase in generalization ability and stability of the model.  
The said preprocessing operations involve the use of the bicubic interpolation method to adjust the size of ship images to 64 pixels \(\times\) 64 pixels. Subsequently, all training images were randomly cropped. This helped the elimination of the effect of illumination and viewing angles, thereby enhancing classification performance. However, center cropping is only performed on the set of test images. The image size after random and center cropping equaled 56 pixels \(\times\) 56 pixels.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 4 Discussion
### Importance of Variability in Data Augmentation Strategies  
The introduction of variability through data augmentation is a fundamental aspect that underpins its efficacy in genomic prediction. Genetic variation is a hallmark of biological systems, and capturing this variability is essential for developing predictive models that can effectively generalize across diverse genetic backgrounds. Synthetic data generated through augmentation techniques facilitate the representation of a broader spectrum of genetic variations, thereby enhancing the model's ability to adapt to novel or unseen genotypes. By mitigating the risk of overfitting and expanding the representation of genetic diversity within the training dataset, variability emerges as a pivotal element in augmenting predictive capabilities within genomic selection frameworks. Therefore, careful consideration and integration of variability into the design and implementation of data augmentation strategies are essential to ensure optimal performance and generalizability of predictive models.