esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microstructural Analysis  
To analyze the microstructure of SWCNTs used in this work, HR-TEM images were obtained, as depicted in Figure 3. As observed, the original pristine SWCNTs show bundles with diameters in the micron range due to strong interactions among SWCNTs by the Van der Waals forces, and diameter size distribution close to 10 nm. On the other hand, the micrographs of the cryofractured surface of the NR-SWCNTs composite cross-linked with DCP, obtained at different magnifications (Figure 4), exhibit good dispersion state of SWCNTs, which are well embedded within the NR matrix (Figure 4a). Additionally, it is possible to observe individualized SWCNTs (Figure 4b) that show not only a physical interaction, but also interaction with the rubber phase (Figure 4c). Regarding the dispersion method used in our composites, we consider that it achieved an apparently good state of dispersion of carbon nanostructures in the NR and bundles are not easily appreciated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{_t.v._ **(s) (\% Time Reduction)**} \\ \cline{2-5}  & **Sulfur (160 \({}^{\circ}\)C)** & **T29 (129 \({}^{\circ}\)C)** & **DCP (160 \({}^{\circ}\)C)** & **T145 (173 \({}^{\circ}\)C)** \\ \hline Calculated 1  & - & 382 & 255 & 368 \\ Neat & 600 & 380 & 252 & 368 \\ CB & 480 (20) & 312 (18.3) & 240 (5.9) & 360 (2.2) \\ SWCNTs & 300 (50) & 261 (31.7) & 198 (22.4) & 306 (16.8) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vulcanization times (_t.v._) obtained for the vulcanization of NR composites.
## 4 Measurement of the 8-Channel T/R Module with 7-Bit TTD
### Ttd  
The TTD units in active MMICs are basically composed of SPDT switches, DPDT switches and time delay elements such as CLC \(\pi\)-networks. TTD is calculated by the unwrapped phase, which was measured through Agilent vector network analyzer N5244A.  
Firstly, zero-centering is carried out based on the unwrapped phase of the middle frequency, as shown in Equation (1).  
\[P(f)=P(f_{x})-P(f_{0}), \tag{1}\]  
\(P(f_{0})\) is the unwrapped phase of the middle frequency and \(P(f_{x})\) is the unwrapped phase of each frequency within the bandwidth.  
Secondly, the delay wavelength of each frequency \(n_{x}\) is calculated based on the middle frequency, as shown in Equation (2).  
\[n_{x}=P(f)/\big{[}360\times(1-f_{x}/f_{0})\big{]}, \tag{2}\]  
Figure 7: Simulation results of the LTCC-based stripline 2-way power divider: (**a**) insertion loss S21 and S31; (**b**) return loss S11, S22 and S33; (**c**) isolation S23.  
Thirdly, the least square method is adopted to fit \(n_{x}\) and the fitted \(n\) is further used to calculate the TTD value \(\Delta P(f)\), as shown in Equation (3).  
\[\Delta P(f)=P(f)-n\times 360\times(1-f_{x}/f_{0}), \tag{3}\]  
Measured TTD and the error with a theoretical value are listed in Table 1. For the 4-bit TTD, the maximum relative error is approximately 15%. For the 3-bit TTD, the maximum relative error is approximately 10%.
## 3 Preliminaries  
### Hash Functions  
Hash functions are fundamental components in cryptography. Ideally, a hash function yields the following properties: (i) it is collision-free; that is, for a hash function \(H(\cdot)\), it is infeasible to find \(x_{1}\) and \(x_{2}\) such that \(H(x_{1})=H(x_{2})\); (ii) it is hiding, which means, given a hash value \(y_{1}=H(x_{1})\), it is infeasible to find the corresponding \(x_{1}\); and (iii) it is quick to compute the hash value \(H(x)\) for any input \(x\). Therefore, hash functions are handy tools for verifying the integrity of messages transmitted through V2X communications. We can determine whether the message is changed by comparing the hash values of messages (usually with a small and fixed length, and thus labeled message digests) calculated before and after the transmission, no matter how large the raw message is.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Performance Evaluation  
The developed model is evaluated to measure how well it supports a solution to the problem. The prototype model is evaluated using the test set images from collected dataset images. The major components, such as the classification of disease, are evaluated. We used a confusion matrix to describe the classification efficiency of the model. Subsequently, we used precision, recall, and F1-score values to evaluate and discuss classification accuracy. Equations (3)-(6) present the formulas for calculating the mentioned metrics.  
Accuracy is defined as the ratio of correctly recognized to total samples.  
\[\frac{TP+TN}{TP+FP+FN+TN} \tag{17}\]  
Classification error/misclassification is defined as the ratio of incorrectly recognized (either positive or negative) to total samples.  
\[\frac{(FP+FN)}{(TP+TN+FP+FN)}\times 100 \tag{18}\]  
Precision (or positive predictive value) is the proportion of predicted positives that are actually positive.  
\[\frac{TP}{TP+FP} \tag{19}\]  
Recall is the proportion of actual positives that are predicted as positive.  
\[\frac{TP}{TP+FN} \tag{20}\]  
We evaluated the improvement of preprocessing techniques using the following performance metrics.  
Peak signal-to-noise ratio (PSNR) is used between the original and the reconstructed image as a quality indicator. The higher the PSNR, the better the quality of the compressed or reconstructed image [26].  
Mean square error (MSE) is the average of the squared error that is used as the loss function for least-squares regression. There are no acceptable limits for MSE except that the lower the MSE, the higher the accuracy [26; 27].  
Structural similarity (SSIM) is a method used to measure the similarity between two images. The SSIM values range from 0 to 1, where 1 means a perfect match between the reconstructed image and the original one [26].
## 4 Demodulation Method
### Low Complexity Demodulation Methods  
In the reader of existing Wi-Fi backscatter system, the Euclidean distance between the received CSI of preamble and the received CSI of data was utilized for demodulation [12]. Since the Euclidean distance calculation can include meaningless sub-carriers assigned to the CSI, the method of using all sub-carriers may not be effective. The performance of the system was maintained properly, and the computational complexity can be reduced, when operations were performed that contained only good sub-carriers without unnecessary sub-carriers. In order to reduce the computational complexity, we tried various approaches that observed the CSI of received preamble backscatter symbol and used only the appropriate sub-carriers for the operation. We also studied how to use fewer multiplication operations because multiplication operations are more computationally complex than addition operations. As a result, we devised three low complexity demodulation methods.
## 4 Results
### Evaluation of Streamflow Simulations
#### 4.2.1 Daily Streamflow
When the XAJ model was driven by the original 3B42V7 and IMERG products, the percentage of high flow underestimation was worsened to 29.5%-39.3% and 39.1%-48.9%, mainly because of the large negative biases of heavy rain estimates in these two satellite products (Table 3). The bias correction of the satellite products effectively improved the high-flow simulations. In the simulations using the corrected 3B42V7 and IMERG precipitation inputs, the computed high flow was 9.3%-16.6% and 11.5%-25.8% lower than the observations. Table 4 shows that in terms of CC, the high flow simulations using the corrected 3B42V7 precipitation were more accurate than that using the gauge-based precipitation. It also indicates that the performance of the high-flow simulations using the original (corrected) 3B42V7 product was generally better than that using the original (corrected) IMERG precipitation for BIAS and CC statistical indices. Among the five low-flow simulation runs using the different precipitation inputs, the gauged-based runs generally had the highest accuracy (Figure 10 and Table 4). The bias correction of the 3B42V7 product effectively reduced the low-flow errors at all hydrological stations, and the performance of the corrected 3B42V7-based simulations was slightly worse than the gauge-based runs. However, the bias correction of the IMERG precipitation estimates improved the low-flow simulation at the Hkamti station but not for the other four stations. As discussed above, the corrected IMERG precipitation at Homalin and Kalewa in May 2015 was largely overestimated due to the uncertainty of the bias-correction method. Consequently, the simulated daily discharge was considerably higher than the observations at the Homalin, Mawlaik, Kalewa, and Monywa stations in May-June 2015, during which a few low flows actually occurred (Figure 8b). These errors further diminished the general performance of low-flow simulations at these stations. Overall, the low-flow simulations using the original (corrected) 3B42V7 product generally performed better than those using the original (corrected) IMERG precipitation. Figure 9: Scatter plots of the simulated high flow events using the gauge-based precipitation data, the original 3B42V7 and IMERG precipitation estimates, and their corrected data sets against the observed high flow at the five streamflow stations in the Chindwin River basin (1 April 2014–31 December 2015). The terms in the figure are the same as in Figure 8.  
Figure 10: Scatter plots of the simulated low-flow events using the gauge-based precipitation data, the original 3B42V7 and IMERG precipitation estimates, and their corrected data sets at the five streamflow stations in the Chindwin River basin (1 April 2014–31 December 2015). The terms in the figure are the same as in Figure 8.
## 3 Results
### Underwater Photogrammetric Model
#### 3.2.1 Camera Calibration  
Camera calibration represents an essential step in underwater photogrammetry. Our experience revealed that using the Agisoft Metashape 2.1.4 for automatic calibration in underwater context led to significant challenges, particularly the inability to reconstruct a realistic underwater model. The relative orientation of underwater frames produced a distorted, spiral-like representation of the sea bottom, to be attributed to inaccurate camera calibration parameters. To overcome this issue, we performed manual underwater calibration using a chess board and applied the resulting parameters during the image processing (Figure 9).  
This approach effectively mitigates refraction and distortion effects in the images, ensuring the accuracy of the underwater photogrammetric reconstruction and providing reliable results that are essential for accurate modeling and analysis.
## 3 Simulation Implementation
### The BER Varying with Distance for Different Rates under Different Duty Cycle Conditions  
This section conducted three sets of comparative simulations to examine the relationship between BER and distance for different communication rates (300 K, 200 K, and 150 K) under ideal conditions, tailing effects, and varying duty cycles (45%, 47%, and 48%). This was performed to verify whether changing the transmission signal duty cycle could improve communication BER. The simulation results are shown in following Figure 10.  
Figure 10: (**a**) The performance of BER at 150 K speed in simulation. (**b**) The performance of BER at 200 K speed in simulation. (**c**) The performance of BER at 300 K speed in simulation.  
Figure 9: (**a**) The relationship between BER and distance under different rates for ideal scenarios. (**b**) The relationship between BER and distance under different rates for tailing scenarios.  
In Figure 10a, it can be observed that, at a rate of 150 K, the BER performances for all three sets of varying duty cycles are superior to the tailing scenario, with 48% being the optimal choice and 45% slightly outperforming tailing. In Figure 10b, for a communication rate of 200 K, 47% is the optimal duty cycle, with 48% and 45% showing similar performance, both better than tailing. In Figure 10c, for a communication rate of 300 K, 45% is the optimal duty cycle, with 48% slightly better than tailing.  
The phenomenon where the optimal BER performance corresponds to different duty cycles for different communication rates is attributed to the fact that at higher communication rates, the duration of individual symbols and photon energy is smaller compared to lower rates. However, the duration of LED tailing remains constant regardless of the communication rate change. The pulse width of a 6 k low-speed frame is tens of times larger than that of a 300 k high-speed frame. Therefore, changing the emission duration by the same proportion has a much greater impact on the low-speed frame than on the high-speed frame. Under the same tailing effect, high-speed frames require a lower transmission duty cycle to achieve minimal BER, compensating for the impact. In contrast, low-speed frames, due to their longer per-bit transmission time, are less affected by tailing, requiring only slight adjustments to the duty cycle to achieve minimal BER. When both high-speed and low-speed frames achieve minimal BER, the duty cycle for low-speed frames is closer to the original 50% setting. This indicates that LED emission tailing has a greater impact on high-speed frames than on low-speed frames. To improve communication quality by adjusting the duty cycle, it is necessary to set the duty cycle for low-speed frames closer to the 50% range.  
Based on the above simulation results, it is verified that the digital signal processing approach of this scheme can optimize the problem of increased BER of the communication system due to LED tailing under OOK modulation. This also provides guidance for hardware design.
## 2 Overview of Multi-Constellation GNSS PPP Technique
[PERSON] and [PERSON] [30] tested the performance of kinematic multi-GNSS PPP technique with different groups of constellations in a marine environment and showed that quad-constellation solution increases the position accuracy compared to other combinations. In [31] the performance of the multi-GNSS kinematic PPP in UAV photogrammetry was analyzed. The study showed that the results obtained from PPP were comparable to those obtained from the classical relative RTK method. In this case study, unlike the above-mentioned studies, a realistic kinematic experiment was carried out on a dam lake situated in a deep valley and surrounded by high hills covered with dense trees. The performance of the kinematic multi-constellation GNSS PPP was analyzed in terms of the accuracy using different GNSS constellations (single, dual, triple, and quad-constellation) with different precise products. The multipath and signal loss due to obstruction of satellite signals were expected through the kinematic experiment that were carried out in such a challenging environment. The results of this study will guide in the selection of the most suitable satellite constellation and precise products for the more accurate PPP solution especially in inland water and marine surveys with challenging environmental conditions and severe terrain obstructions. However, as of May 2020, there are roughly 100 operational satellites in orbit from global satellite systems, and soon about 120 satellites and more will be available when all global and regional satellite systems reach full constellations, which means that any user typically can observe on average more than 20 satellites at any time. From this point of view, it is now usually possible to observe more satellites than users need and the important factor is which satellite constellations and which precise products should be used for an accurate and reliable solution.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 3 Proposed Method
### Precise Location
#### 3.3.2 Modified NCC Matching Algorithm  
The computational complexity for traditional NCC is \(O((M-m)\times(N-n)\times m\times n)\), where \(M\), \(N\) is the size of the image to be matched and \(m\), \(n\) is the size of the template. There is a huge calculation and computing resources cost when inputting a large image. In order to save computing resources and realize online defect detection, a modified NCC algorithm was carried out according to the following equations:  
\[\epsilon(x,y,i,j)=\left|\left(f(x+i,y+j)-\overline{f}\right)-(w(i,j)-\overline {w})\right| \tag{14}\]  
\[\overline{f}=\frac{1}{m\times n}\sum_{s=1}^{m-1}\sum_{t=1}^{n-1}f(x+s,y+t) \tag{15}\]  
\[\overline{w}=\frac{1}{m\times n}\sum_{s=0}^{m-1}\sum_{t=0}^{n-1}w(s,t) \tag{16}\]  
Figure 5: Chip template: (**a**) template for color picture; (**b**) template for coaxial picture.  
if:  
\[\left\{H\left|\min_{1\leq H\leq Numth}\left[\sum_{h=1}^{H}\varepsilon(x,y,i,j) \geq Th\right]\right\}\
eq\varnothing \tag{17}\]  
\[R(x,y)=0 \tag{18}\]  
or else:  
\[R(x,y)=\frac{\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left\{\left[f(x+i,y+j)-\overline {f}\right]\times\left[w(i,j)-\overline{w}\right]\right\}}{\sqrt{\sum_{i=0}^{m- 1}\sum_{j=0}^{n-1}\left[f(x+i,y+j)-\overline{f}\right]^{2}\times\sum_{i=0}^{m- 1}\sum_{j=0}^{n-1}\left[w(i,j)-\overline{w}\right]^{2}}} \tag{19}\]  
where \(w\) is a template with the size of \(m\times n,f\) is the test picture to be matched, \(\overline{f},\overline{w}\) are the average values of the sub-image and template, respectively, \(R\) is a matrix consisting of normalized cross-correlation value, \(NumTh\) is set as the threshold for the number of pixels, \(Th\) is set as the error threshold, \(\varepsilon\) is the absolute error of a pixel randomly selected in the sub-image, and so \(H\) is the minimum number of pixels with the sum of absolute errors exceeding \(Th\) in the sub-image. As shown in Figure 6, the two thresholds \(Th\) and \(NumTh\) divide the space into four blocks; only the curve passing through block 4 needs further calculation.  
It can be seen from the formula that when the similarity is poor, the matching process is ended early, which reduces the calculation for subsequent pixels. It is equivalent to having a rough match first, so that the matching result is the same as the traditional NCC. Additionally, since the average value is subtracted, both the test picture and the template are standardized, which means that the result will not be affected by the change in light intensity. The matching results are shown in Figure 7c,d, where the brighter the result, the better the match. Finally, the coordinate of the maximum value was selected as the precise location result.  
Figure 6: Illustration of the condition.  
Figure 7: Modified NCC matching result: (**a**) color area to be matched; (**b**) coaxial area to be matched; (**c**) color area matching result; (**d**) coaxial area matching result.
## 2 Proposed System Structure  
The structure of the proposed CPT system is shown in Figure 2a. Switches (S\({}_{1}\)-S\({}_{2}\)) construct a half-bridge inverter (full bridge can be used) to convert DC input voltage (\(V_{D}\)) into an AC signal. Inductor \(L\) is placed in series to compensate for the capacitive interface, where \(C_{c1}\) and \(C_{c2}\) represent the capacitive coupling interface. Diodes (D\({}_{1}\)-D\({}_{4}\)) construct the full wave rectifier to obtain DC voltage. The DC-DC buck converter supplies the voltage and current to the load.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 4 Laser Background Theory  
The Gaussian beam model was used to describe the propagation of the laser beam in free space and taken into account for the determination of the power budget. There are many parameters that can be used for the design of a laser beam [56]. Since we are mostly interested in irradiance (power transmitted per unit area) and the size of the beam at the end of propagation, we have taken these variables into account in this work. The irradiance distribution of a 2D Gaussian laser beam \(I(r,z)\) is given by the following formula:  
\[I(r,z)=I_{0}\bigg{(}\frac{w_{0}}{w(z)}\bigg{)}^{2}\exp\bigg{(}\frac{-2r^{2}}{ w(z)^{2}}\bigg{)} \tag{1}\]  
where \(r\) is the radial distance from the center axis of the beam, \(z\) is the axial distance from the beam's waist, \(w(z)\) is the radius at which the optical intensity falls to \(\frac{1}{e^{2}}\) of the peak intensity on the plane \(z\) and \(w_{0}=w(0)\) is the beam waist radius. \(I_{0}\) and \(w(z)\) are defined as follows:  
\[I_{0}=\frac{2P_{0}}{\pi w_{0}^{2}} \tag{2}\]  
\[w(z)=w_{0}\sqrt{1+\bigg{(}\frac{z\lambda}{z_{R}}\bigg{)}^{2}} \tag{3}\]  
where \(P_{0}\) is the total power of the beam and \(z_{R}\) is the Rayleigh range, defined as  
\[z_{R}=\frac{\pi w_{0}^{2}n}{\lambda} \tag{4}\]  
where \(\lambda\) is the laser's operating wavelength and \(n\) is the refractive index of the medium.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 3 Proposed WMSCCN for Fault Diagnosis
### Model Parameters of WMSCCN  
Margin Loss is the loss function in the proposed WMSCCN algorithm. On the one hand, the process of reducing loss through training makes the digit capsules \(c\), representing the current fault type, tend to become longer. On the other hand, Margin Loss supports the output of multi-classification results, which is of great significance for the future popularization of the model on composite faults. The corresponding formula is shown below.  
\[L_{c}=T_{c}\text{max}\Big{(}0,m^{+}-\|\mathbf{v}_{c}\|\Big{)}^{2}+\lambda(1- T_{c})\text{max}(0,\|\mathbf{v}_{c}\|-m^{-})^{2}, \tag{5}\]  
where \(L_{c}\) represents the Margin Loss of digit capsule \(c\) and \(\mathbf{v}_{c}\) represents the output of digit capsule \(c\). The \(\lambda\) down-weighting of the loss for absent digit classes stops the initial learning from shrinking the lengths of the activity vectors of all the digit capsules. In the experiment, \(\lambda\) is set to 0.5. The \(T_{c}\) indicates whether the fault c exists. When the fault \(c\) exists, \(T_{c}=1\) and the absence c is 0. The \(m^{+}\) is 0.9, which can punish false positives. When the fault \(c\) is present but the prediction result is non-existent, it will result in a large loss value. The \(m^{-}\) is 0.1, which can punish false negatives. When the fault \(c\) does not exist but the prediction result is present, it will also lead to large loss values.  
The model structure parameters of WMSCCN are shown in Table 1. It is worth noting that the multi-scale convolutional layer uses the zero-padding technique introduced in Section 3.1 to ensure the consistent size of the feature maps. The Adam optimization algorithm [30] is used in the training process. It uses the first moment estimation and second moment estimation of the gradient to dynamically adjust the learning rate of each parameter. The Adam algorithm has the advantages of a high computational efficiency and lower memory requirements. Therefore, it is suitable for the training of neural networks with a large number of parameters. The training parameters of the proposed WMSCCN algorithm are set as follows: the batch size is 100 and the learning rate is 0.001.  
In addition, dropout is usually used as a trick for training deep networks and is also used in the proposed WMSCCN algorithm to reduce the risk of overfitting. Dropout technology refers to that in the forward propagation of each training batch; some neurons are ignored with probability \(p\)--that is, some node values of hidden layers are 0. After the training, it is equivalent to obtain an integrated model composed of multiple neural networks with different network structures, which can effectively reduce the risk of overfitting. In the proposed WMSCCN algorithm, dropout is used between the multi-scale convolutional layer and the primary capsule layer with a drop rate of 0.3--that is, 30% of the nodes in the multi-scale convolutional layer have a value of 0.