esa-sceva/satcom-chunk-collection huggingface dataset:
# McAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects
## 4 Experimental Results and Analyses
### Ablation Experiments  
To validate the effectiveness of the proposed improvements, this section designed ablation experiments for comparative analysis. These experiments evaluated the impact of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defect Category** & \begin{tabular}{c} **Average Width** \\ **/Pixel** \\ \end{tabular} & \begin{tabular}{c} **Average Height** \\ **/Pixel** \\ \end{tabular} & \
\begin{tabular}{c} **Standard Deviation** \\ **Width/Pixel** \\ \end{tabular} &
\begin{tabular}{c} **Standard Deviation** \\ **Width/Pixel** \\ \end{tabular} \\ \hline edge defect & 63.1 & 78.5 & 57.6 & 60.5 \\ angle defect & 73.1 & 54.2 & 79.9 & 74.6 \\ white-dot defect & 10.4 & 10.6 & 4.4 & 4.2 \\ light-block defect & 19.2 & 20.8 & 17.4 & 23.2 \\ dark-block defect & 12.5 & 13.4 & 9.5 & 10.7 \\ aperture defect & 32.5 & 33.7 & 18.1 & 19.9 \\ marking-pen defect & 181.3 & 234.4 & 142.3 & 170.2 \\ scratch defect & 98 & 65.9 & 121.5 & 88.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average size and standard deviation of each category of defects.  
Figure 7: Model loss curve.  
different enhancement strategies on the YOLOv5 model, as shown in Table 2. Specifically, experiments 2 and 3 introduced modules into the model that offered improved efficiency. While achieving nearly identical mAP values, they reduced parameters by approximately 30% and enhanced detection speed. In experiment 4, Light-BiFPN was employed as the feature fusion module, while experiment 5 introduced the MobileViTv3 module to the backbone network. Experiment 6 replaced the original loss function with Wise-IoU.  
In experiment 7, a lightweight MobileVitv3 was introduced after improving the neck component. Although the model parameters were slightly different from experiment 4 by 0.08 M, compared to the base model, this led to a 26.5% reduction in parameters and a 27.9% reduction in computational complexity. Additionally, the mAP value increased by 1.5%, validating the superior performance enhancement achieved by the improvements. Experiment 8, building upon experiment 7, introduced the lightweight attention mechanism NAM. With minimal parameter increase, the mAP value improved by 2.6% compared to the base model. Finally, introducing the Wise-IoU loss function while slightly decreasing detection speed compared to the original model increased the mAP value by 3.1%. Simultaneously, model parameters and computational complexity decreased by 26.5% and 20.9%, confirming the effectiveness of combining Light-BiFPN, MobileViTv3, NAM, and Wise-IoU. Overall, the improved YOLOv5n network achieved model lightweight and enhanced accuracy, making it feasible for deployment on edge terminal devices.  
In order to visually demonstrate the effectiveness of the proposed improvement algorithm, several representative ceramic-tile defects were selected for detection. The visualization of the results is shown in Figure 8. The improved algorithm's detection outcomes were more accurate, enhancing precision and reducing missed detection rates.
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
## 2 Material and Methods
### Sentinel-2 Data  
Sentinel-2 provides high-resolution imagery that is available from the European Space Agency (ESA) for free. With a 290 km field of view, its MultiSpectral Instrument (MSI) carries 13 spectral bands ranging from the visible and near-infrared to short-wave infrared (Table 2). Depending on the spectral band, the spatial resolution ranges from 10 to 60 m. This unique combination of high spatial resolution, wide field of view, and broad spectral coverage, together with their free availability as ready-to-use products in an atmospherically corrected reflectance mode, has opened up a new window in operational ecosystem monitoring [53; 54]. Table 3 provides an overview of the Sentinel-2 images available for the study site during 2016-2019.  
Surface spectral reflectance data were used to calculate twelve vegetation indices: NDVI [55]; SAVI [39]; RVI [56]; PVI [40]; GNDVI [41], kNDVI [48]; NDWI [42]; SLAVI [43]; MCARI [47]; MTCI [46]; SeLI [44]; NDVI70 [45]. The VIs were computed in SNAP ESA's  
Figure 2: FVC assessment in a high-FVC image (**A**) and low-FVC image (**B**) for 257 and 195 plots, respectively, on the 25 th of May 2018. The images on the left show the definition of different categories in some areas: alfalfa in the sun (red), alfalfa in the shade (green), soil in the sun (yellow), and soil in the shade (blue). After the classification of the maximum likelihood supervised algorithm, the images on the right show the classification of the total pixels of the images in the different categories. Unclassified pixels are shown in black.  
software using the equations shown in Table 4. Prior to the study, squared plots were selected to obtain representative VI values in each plot, especially in small plots. Furthermore, a 5 m buffer was applied, reducing its variability and eliminating the border effect.
## 3 Results
### Pre-Flowering
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Concentrations \({}^{\rm z}\)**} & \multicolumn{3}{c}{**Twelve Weeks Growth**} \\ \cline{2-5}  & \multicolumn{3}{c}{**Height \(\gamma\)**} & \multicolumn{3}{c}{**Diameter \(\gamma\)**} \\ \hline  & **Mean** & **SD \({}^{\star}\)** & **Mean** & **SD** \\ \hline
3.75 & 78.10 B & 6.47 & 59.85 C & 6.91 \\ \hline
7.50 & 86.33 AB & 9.95 & 53.04 C & 6.10 \\ \hline
11.25 & 84.94 AB & 2.03 & 62.09 BC & 6.24 \\ \hline
15.0 & 86.76 AB & 6.62 & 64.52 BC & 7.80 \\ \hline
22.50 & 94.76 A & 7.42 & 87.64 A & 12.49 \\ \hline
30.0 & 96.04 A & 4.50 & 77.10 A & 10.21 \\ \hline Significance \({}^{\rm w}\) & *** & *** & *** & *** \\ \hline \hline \end{tabular} \({}^{\rm z}\) P fertility concentrations based on mg-L\({}^{-1}\) (ppm). \({}^{\rm y}\) All height and diameter measurements based on cm. Diameter was calculated by taking the widest two points on a plant taken 90\({}^{\rm z}\) from each other. These numbers were then added together and divided by 2 to obtain the diameter measurement. All dry weights were in grams and taken based on oven dried material. \({}^{\rm x}\) All standard deviation values assumed to be \(\pm\) of the given value. \({}^{\rm w}\)\({}^{\star}\), \({}^{\star}\), or *** indicate statistically significant differences between sample means based on _F_-test (proc GLM) at at \(p\leq 0.05\), \(p\leq 0.01\), or \(p\leq 0.001\), respectively. NS (not significant) indicates the _F_-test difference between sample means was \(p>0.05\). Values with the same letter indicate a lack of statistical significance while values with different letters indicate statistically significant results.  
\end{table} 
Table 5: The impacts of phosphorus (P) fertility on plant growth metrics of _C. sativa_ “BaOx” after four weeks of the induction of the critical night length (CNL). Plant diameter increased with an increase in P concentration with the smallest plant diameter being observed at the 7.50 mg\(\cdot\)L\({}^{-1}\) concentration, and the greatest at the 22.50 mg\(\cdot\)L\({}^{-1}\) concentration. Plant diameters were all similar up to the 15.0 mg\(\cdot\)L\({}^{-1}\) concentration, after which no increase was observed in the higher concentrations (Table 5).
# MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization
## 3 Long Short-Term Memory Networks  
Long short-term memory (LSTM) networks were created as a variant of recurrent neural networks (RNNs) with a differentiated architecture. RNNs were firstly introduced in 1990 [13; 14]. They were inspired by typical feedforward networks, but were tweaked in order to efficiently handle data that were in the form of time steps. In order to achieve that, RNNs are equipped with recurrent (feedback) connections as well. RNNs are able to operate not only on single data patterns (such as images), but also on sequences of data, such as video or speech. Due to this new architecture, RNNs presented the ability to efficiently handle time-series problems. A representation of an RNN is depicted in Figure 1[15]. These networks were found to be difficult to train, as they were prone to numerical instability problems such as the exploding and/or vanishing gradient. This led to the development of the LSTM networks and the so-called bi-directional LSTMs [16] that are used in this study. LSTMs include an extra memory value that is trained on a specific number of time steps. This temporary memory acts as a hidden state that is cycled over the time steps.  
A typical LSTM architecture contains the input layer, a series of hidden layers (according to the depth of the network) and finally, the output layer. The size of the input layer depends on the problem and, specifically, the size of the input vector. The hidden layers present the special architecture of LSTM networks, the memory cells. These cells are characterized by three nodes, known as gates. The first gate, named input, is responsible for introducing a new value to the cell. The second one, called the forget gate, decides which previous state will be dropped by the network. The last one, known as the output gate, decides on the cell's output value. In a time series problem, whenever a new step is introduced to the network, all gates are fed with the new input along with the output value of the previous step that was stored in the networks' memory. Finally, the size of the output layer also depends on the problem. For example, in a classification problem with two classes, the size of the output layer would be equal to two [17]. As LSTMs were gaining more research attention, a new architecture of LSTMs was introduced, named bi-directional LSTMs. The novelty in this approach was that they include two LSTMs functioning at the same time but in reverse directions (forward and backward) and are connected by a merging function. Due to this form, the hidden state repeatedly extracts knowledge from both past and future information fed to the network [18]. Bi-directional LSTMs are very efficient when dealing with problems of time series, as they can take advantage of past and future input values simultaneously.
# Performance Analysis of ICA in Sensor Array
## 3 Performance of ICA _vs._ Practical Factors
### Countermeasure for Singular Mixtures
During our simulations, it is found that generally speaking \(k=\)1 or 2 should be suitable under most scenarios.
## 5 Simulation Results and Analysis
### Performance Analysis on the AWGN Channel  
Table 4, shows the percentage recognition accuracy (PRA) on the AWGN channel with different SNRs and numbers of samples for the considered modulation formats. From Table 4, it can be observed that, for all modulation formats, the PRA was increased by increasing the number of samples and the SNR. The proposed recognizer was able to recognize all of the modulation formats with higher accuracy at lower SNRs and even with smaller numbers of samples. At an SNR of 0 dB, the proposed AMR provided 100% PRA with 2048 and 4096 samples, while it provided around 90% with 512 and 1024 samples. The average PRA for the BPSK, QPSK, QAM, 16-QAM, and 64-QAM was 97.5%, 99.3%, 95%, 99.8%, and 99.5%, respectively, with an SNR of 0 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**PRA for BPSK**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 97 & 100 & 100 \\
1024 & 99.90 & 100 & 100 \\
2048 & 100 & 100 & 100 \\
4096 & 100 & 100 & 100 \\ \hline \multicolumn{3}{c}{**PRA for QPSK**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 86 & 99 & 100 \\
1024 & 94 & 99.99 & 100 \\
2048 & 100 & 100 & 100 \\
4096 & 100 & 100 & 100 \\ \hline \multicolumn{3}{c}{**PRA for QAM**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 86 & 99 & 100 \\
1024 & 94 & 99.99 & 100 \\
2048 & 100 & 100 & 100 \\
4096 & 100 & 100 & 100 \\ \hline \multicolumn{3}{c}{**PRA of 16-QAM**} \\ \hline
**No. of Samples** & **0 dB** & **5 dB** & **10 dB** \\ \hline
512 & 98 & 99.98 & 100 \\
1024 & 99.99 & 100 & 100 \\
2048 & 100 & 100 & 100 \\
4096 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage recognition accuracy on the AWGN channel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Standard Value** \\ \hline No. of Samples & [512, 1024, 2048, 4096] \\ SNR & [0–5] dB \\ Training of Recognizer & 70\% \\ Testing of Recognizer & 20\% \\ No. of Genes & 120 \\ No. of Chromosomes & 1024 \\ Crossover Fraction & 0.25 \\ Crossover & Heuristic \\ Selection & Stochastic Uniform \\ Mutation & Adaptive Feasible \\ Elite Count & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
###### Abstract  
Due to the serious impact of metro vibration on people's lives, it is important to design vibration isolators. In this study, the dynamic characteristics of a thick-walled ring spring are studied first. Through theoretical derivation, a new formula suitable for thick-walled ring springs is proposed. Finite element numerical analysis was performed to study the load-displacement curve and stress of the ring spring and verified the correctness of the formula. According to the studied mechanic characteristics, a novel ring spring isolator is proposed for vibration isolation of the metro superstructure. With the help of a ring spring, the proposed isolator has good energy absorption and self-reset function. The dynamic simulations were conducted in a multi-story building with the ring spring isolator as the isolator to study the vibration performance. It is common knowledge that the vertical natural frequency of the superstructure that is isolated by compression springs is given by the mass of the superstructure and the spring stiffness. In order to obtain vibration attenuation and control the vertical deformation, the spring stiffness needs to be 500-1000 kN/mm. Hence, it is clear that the vibration isolator does reduce the vertical eigenfrequency. By comparing the isolated structure with the non-isolated structure, it is proved that the new isolator can effectively improve a building's serviceability.  
metro vibration; ring spring; vibration isolator; structure vibration isolation +
Footnote †: journal: Applied Sciences
## 2 AC Equivalent First Principle Linear Circuit and Experiments
### Analysis of AC Equivalent Impedances on Electrode-Electrolyte Interfaces by Average Switch Modeling
#### 2.2.2 AC Equivalent First Principle Impedances on Electrode-Electrolyte Interfaces  
Equation (6) implies that equivalent AC impedances on electrode-electrolyte interfaces is equal to half of the sum of charging and discharging impedances on electrode-electrolyte interfaces of both positive and negative electrodes.  
Each element in AC equivalent first principle impedances is non-directional and totally linear. This advantage simplifies the simulation, and it does not need to determine the direction of excitation in advance, nor does it need auxiliary circuits to switch sub-models in simulations [20]. Further, the charging impedances (\(Z_{Cha}e^{i\beta_{Cha}}\)) are strictly separated from discharging impedances (\(Z_{Dis}e^{i\beta_{Dis}}\)). Equations (11) to (13) expand the impedances (\(Z_{Cha}e^{i\beta_{Cha}},Z_{Dis}e^{i\beta_{Dis}},Z_{ex}e^{i\beta_{ai}}\)) to charge-transfer resistors (\(R_{ct,pc}\), \(R_{ct,mc}\), \(R_{ct,pd}\) and \(R_{ct,pd}\)) and double-layer capacitors (\(C_{dl,pc}\), \(C_{dl,mc}\), \(C_{dl,pd}\) and \(C_{dl,pd}\)): Equation (14) is an equivalent transformation for the Nyquist fitting. The physical meanings of elements in the above equations are explained in Table A1 of the Appendix A.  
\[Z_{Cha}e^{i\beta_{Cha}}=\frac{R_{ct,pc}}{1+jR_{ct,pc}\omega C_{dl,pc}}+\frac{R_ {ct,nc}}{1+jR_{ct,nc}\omega C_{dl,nc}}, \tag{11}\]  
\[Z_{Dis}e^{i\beta_{Dis}}=\frac{R_{ct,pd}}{1+jR_{ct,pd}\omega C_{dl,pd}}+\frac{R _{ct,nd}}{1+jR_{ct,nd}\omega C_{dl,pd}}, \tag{12}\]  
\[Z_{ex}e^{i\beta_{eni}}=0.5\times\Bigg{(}\frac{R_{ct,pc}}{1+jR_{ct,pc}\omega C_ {dl,pc}}+\frac{R_{ct,nc}}{1+jR_{ct,nc}\omega C_{dl,nc}}+\frac{R_{ct,pd}}{1+jR_ {ct,pd}\omega C_{dl,pd}}+\frac{R_{ct,nd}}{1+jR_{ct,nd}\omega C_{dl,md}}\Bigg{)} \tag{13}\]  
\[Z_{ex}e^{i\beta_{eni}}=\frac{0.5R_{ct,pc}}{1+(0.5R_{ct,nc})\omega(2C_{dl,pc})}+ \frac{0.5R_{ct,nc}}{1+(0.5R_{ct,nc})\omega(2C_{dl,pc})}+\frac{0.5R_{ct,pd}}{1+ j(0.5R_{ct,pd})\omega(2C_{dl,pd})}+\frac{0.5R_{ct,nd}}{1+(0.5R_{ct,nd})\omega(2C_{dl, pd})}, \tag{14}\]  
Figure 4: Steady-state AC fundamental voltage responses of both DC equivalent first principle linear circuit (\(FPLC_{DCqu}\)) and AC equivalent first principle linear circuit (\(FPLC_{ACqu}\)) are equal in simulations: (**a**) 5A rms current injections at 0.5 Hz; (**b**) 5A rms current injections at 1.0 Hz.
## 6 One vs. More than One Element per Member  
It was commented in Section 1 that in typical seismic analysis of steel frames, the common practice is to use only one element per member (no intermediate nodes), including for the beams. In this section of the paper, the precision of using this practice is quantified. The consistent mass matrix is assumed in these analyses. To this aim, the same response parameters studied in Section 5 are considered, but now discretizing the beams by using one, or two, intermediate nodes; the responses are compared to those obtained without considering intermediate nodes. It is important to mention that some periods, associated to lateral vibration, are significantly modified when intermediate nodes are used in the beams. For the 3-story frame, by example, the first period is essentially the same for the model with and without one intermediate node. The periods for modes 2 and 3, on the other hand, increase 10% and 98%, respectively. For the 9-story model, no increments occur in the periods of modes 1 through 4, but for modes 5 to 9 increments, which range from 28% to 35%, are observed. For the 20-story model, there are no changes for the first eleven periods, but significant increments occur for the remaining nine lateral modes, which range from 11% to 21% if one intermediate node is used.  
It is assumed that the greater the number of elements per member, the better the seismic response since the distribution of mass is more uniform. In the subsequent discussion, the abbreviations \(1E\), \(2E\) and \(3E\) will symbolize the structural models obtained from considering one, two and three elements per beam, respectively.
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
###### Abstract  
Phase-only Spatial Light Modulator (SLM) is one of the most widely used devices for phase modulation. It has been successfully applied in the field with requirements of precision phase modulation such as holographic display, optical tweezers, lithography, etc. However, due to the limitations in the manufacturing process, the grayscale-phase response could be different for every single SLM device, even varying on sections of an SLM panel. A diverse array of calibration methods have been proposed and could be sorted into two categories: the interferometric phase calibration methods and the diffractive phase calibration methods. The principles of phase-only SLM are introduced. The main phase calibration methods are discussed and reviewed. The advantages of these methods are analyzed and compared. The potential methods for different applications are suggested.  
phase measurement; spatial light modulator; calibration; interference; diffraction 1
# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art  
The review is organized in three subsections addressing mobility models, positioning algorithms, and propagation models. All these topics have a clear impact on the completion of FANET as mentioned in the introduction section.
## 7 Case Studies
The explanation, of course, is that a shorter (say, first) shift for BOM leads to a different window content than for B(N)DM for the second window, which may have a larger shift value. This effect depends on the pattern: For the pattern ACCCCC, there is a 52.4% probability that BOM is at least as fast as B(N)DM (in terms of character accesses), while it is 4.9% for ACGTAC, again on texts of length 100. An example text where BOM is faster than B(N)DM while searching for the pattern ACCCCC is shown in Figure 5. Both algorithms read two characters of the first window but prescribe different shifts. The first window ends on TC. BOM recognizes that TC is not a substring of the pattern an shifts the window by five positions, just far enough to exclude this substring. In contrast, B(N)DM determines that neither C nor TC are prefixes of the pattern and shifts the window by six positions. It turns out, however, that a shorter shift of five positions was beneficial in this case as BOM can process the second window with one character access, while B(N)DM uses two character accesses. To assess the effect of DAA minimization before constructing PAAs, we constructed minimized DAAs for all 21840 patterns of lengths 2 to 7 over \(\Sigma=\{\texttt{A},\texttt{C},\texttt{G},\texttt{T}\}\). The minimum, average, and maximum state counts are shown in Table 1. For length 6, Figure 6 contains a detailed histogram. These statistics show that construction and minimization as given in this article lead to smaller automata (and thus better runtimes) than the constructions given in the conference version of this article [18]. It may be conjectured that the worst-case size of the minimal state space grows only polynomially with \(m\) for all of these algorithms, as has been previously proven for the Horspool algorithm [13]. Figure 5: Example of a string for which BOM executes less character accesses than B(N)DM when searching for the pattern \(p=\texttt{ACCCCC}\). The searched windows are indicated below the text; the nearby number gives the number of character accesses executed when processing this window. The algorithms were implemented in JAVA and are available as part of the MoSDi software package available at [[http://mosdi.googlecode.com](http://mosdi.googlecode.com)]([http://mosdi.googlecode.com](http://mosdi.googlecode.com)). They were run on an Intel Core 2 Dual CPU at 2.1 GHz. Computing the distributions shown in Figure 3 took 0.5 to 1.3 seconds for each distribution. Distributions of differences as in Figure 4 were computed in 56 to 97 seconds.
## 3 Research Status of the Multipath Error Data Processing Algorithm  
In GNSS relative positioning, the carrier-phase multipath error at the station end is intricately linked to the surrounding environment of the station, which cannot be eliminated by the double-difference method, and is the main source of the errors affecting the positioning results. Consequently, addressing the carrier-phase multipath errors from the station side remains a challenging and focal point in the field of GNSS positioning error processing. Since the 1990s, scholars globally have been engaged in research aimed at mitigating carrier-phase multipath errors, approaching the issue from various perspectives. Numerous multipath error correction methods, based on software data processing, have been proposed as a result of these endeavors. These correction methods can be categorized into two groups based on different research perspectives: mitigation methods based on the stochastic model and mitigation methods based on the function model.