esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Capacitor Design  
Considering the geometries that can be printed with a 3D printer, it was decided that the geometry suitable for experimental production with minimum error should have a planar capacitor design. To enhance the capacitance by increasing the effective surface area while maintaining compactness, the design was modified into the interdigitated structure shown on the right. In Figure 1, the planar capacitor, which is the source of the design, and the design of the prototype capacitor are shared.  
The electric potential is uniform throughout Conductor A, and similarly, it is uniform throughout Conductor B. Surface area (\(A\)) is defined as the total area of the surfaces of Conductor A that face and correspond to Conductor B. The distance between the conductor surfaces and the insulator thickness (\(d\)) defines the distance between the opposite faces of the conductors where the insulator is located. Accordingly, the capacitance value of the planar capacitor is calculated with the formulation in Equation (1).  
\[C=\frac{\varepsilon_{r}\varepsilon_{0}\cdot A}{d} \tag{1}\]  
Here, \(\varepsilon_{r}\) is the relative dielectric constant of the insulator used in the capacitor, \(\varepsilon_{0}\) is the dielectric constant of the vacuum (8.854 \(\times\) 10\({}^{-12}\) F/m), \"\(A\)\" is the surface area of the planar electrode, and \"\(d\)\" is the distance between the planar electrodes. The designs were performed with SolidWorks 2023(r) software. The \"Qidi Tech Q1 Pro 3D Printer (Wenzhou, China)\" was used for printing according to the CAD designs. The nozzle output diameter of this printer is 0.20 mm. Therefore, the dimensions, ratios, and wall thicknesses used in the drawing were calculated as multiples of 0.20. The thickness of the material (\(x\) and \(y\)) that will act as an insulator between two conductive materials was selected as 1.00 mm. This value is proportional to the nozzle diameter and is 5 times. A wall thickness of 1.00 mm was selected, assuming that it would be sufficient for the dielectric strength of the material to be printed. The dimensions of the outer edges of the capacitor are chosen as 50 \(\times\) 50 mm.  
The distance between the fingers of the capacitor (\(x\)) and the distance between the fingertip and the wall (\(y\)) are adjusted according to the desired capacitance value. The corresponding surface area (\(A\)) is determined according to a capacitor with a capacitance value of 1 nF with a material with a dielectric coefficient of 3. However, a tolerance value of 10% has been determined to prevent errors that may occur during 3D printing. Thus, the capacitance value is expected to be between 0.9 and 1.1 nF. The height of the fingers (\(h\)) was set as 50 mm to obtain sufficient surface area. A 5.00 mm gap was left between the fingers and the outer wall height at the top to prevent jumps and to place a cover. The completed design of the capacitor specimen is shown in Figure 2.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Materials
#### 2.1.4 Topographic Data  
ETOPO2v2 is a global topographic model developed by the National Geophysical Data Center (NGDC), part of the National Oceanic and Atmospheric Administration (NOAA). It includes the topography of the world's land and ocean, with a spatial resolution of 0.03\({}^{\circ}\).
## 5 Results and Discussion
### Two-Dimensional Global/Local Analyses
Considering Local Model 2, the Global/Local analyses provides accurate results after very few iterations (see also Figure 15) when the number of nodes on the Global Model side of the interface is 85% of the number of nodes on the Local Model side of the interface. Figure 14: Global/Local performance with respect to the reference Model—comparison between different levels of non–conformal meshes—Local Model 2. Figure 15: Global/Local performance with respect to the Reference Model—conformal mesh and optimal non–conformal mesh—Local Model 2. The present iterative procedure for Local Model 3 is very efficient since \(\eta_{c}\) rapidly decreases after only two iterations; for instance, the configuration with \(\
u_{n}=0.85\) is such that, after only two iterations, \(\eta_{\sigma}\) and \(\eta_{c}\) values become lower than 2 % and 0.3 %, respectively. The configurations with \(\
u_{n}=0.5\) and \(\
u_{n}=0.7\) ensure good results even if \(\eta_{c}\) slightly increases after the first iteration (see Figures 16 and 17). Figure 16: Global/Local performance with respect to the reference Model—comparison between different levels of non–conformal meshes—Local Model 3. Figure 17: Global/Local performance with respect to the Reference Model—conformal mesh and optimal non–conformal mesh—Local Model 3. With regard to local models 4 and 5, the results, in terms of convergence history, \(\eta_{\sigma}\) and \(\eta_{\varepsilon}\), are very similar to those obtained with \"Local Model 3\". Table 10 summarizes the \(\eta_{\varepsilon}\) values obtained when adopting local models 4 and 5.
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 2 Methods
### Pseudo-3D Deghosting Framework with the Data Coherence along the Shot Direction
\begin{bmatrix}\mathbf{D}_{1}(x_{1}, \omega_{k})&\cdots&\mathbf{D}_{1}(x_{j},\omega_{k})&\cdots&\mathbf{D}_{1}(x_{ ntr},\omega_{k})\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ \mathbf{D}_{i}(x_{1},\omega_{k})&\cdots&\mathbf{D}_{i}(x_{j},\omega_{k})& \cdots&\mathbf{D}_{i}(x_{ntr},\omega_{k})\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ \mathbf{D}_{ns}(x_{1},\omega_{k})&\cdots&\mathbf{D}_{ns}(x_{j},\omega_{k})& \cdots&\mathbf{D}_{ns}(x_{ntr},\omega_{k})\\ \end{bmatrix}  \tag{6}\]  
where \(ns\) represents the number of common shot gathers (CSGs) and \(ntr\) represents the number of common offset gathers (COGs). Since the offsets and receiver depths of the  
Figure 1: Schematic illustration of ghost propagation. (**a**) The propagation of ghost. (**b**) The estimation of ghost delay time. different shot gathers data are consistent, the ghost delay operator in Equation (1) is applicable to the data slice described in Figure 2:  
\[\mathbf{D}_{\text{\rm{pse-3D}}}(s_{i},x_{j},\omega)=\sum_{ip=1}^{np}\mathbf{M}_{ \text{\rm{pse-3D}}}(s_{i},p_{ip},\omega)\cdot e^{-i\omega p_{ip}x_{j}}\cdot(1-e^ {-i\omega\Delta t_{ip}}) \tag{7}\]  
where \(s_{i}\) and \(x_{j}\) represent the location of the source and offset, respectively. As shown in Equation (7), the deghosting case for multi-shot gathers is integrated into a linear system of equations, which can be simplified as:  
\[\mathbf{D}_{\text{\rm{pse-3D}}}=\mathbf{G}\mathbf{M}_{\text{\rm{pse-3D}}} \tag{8}\]  
where \(\mathbf{G}\) represents the ghost delay operator. In this way, we can simultaneously deghost for the data cube. In the source direction of the data cube, each slice represents a COG. The characteristics of events in COGs are similar to those of the underground interface, which means the COGs are composed as several smooth and approximately linear events [26]. The event coherence in COGs provides the basis for the introduction of sparse representations, and we can utilize the data coherence along the shot direction as an inversion constraint. In the common offset gathers, more information that can be used for coherence analysis is obtained [27], which effectively improves the inversion accuracy.
# Inter-Story Drift Ratio Detection of High-Rise Buildings Based on Ambient Noise Recordings
## 4 Results and Analysis
### Results and Analysis of \(\theta_{1}\)  
A polarimetric analysis was performed on the data from 24 stations over 19 days from 20 November 2021 to 9 December 2021.  
The processing results for the stations that can display the full time and full spectrum are listed in Table 1, where \(\Delta\theta_{1}\) is the difference value between the \(\theta_{1}\) at 10:15 on 30 November 2021 and the median of \(\theta_{1}\).  
As seen in Table 1, except for unacceptable floors 1-9 (obtained from Section 4.1), the listed floor was evenly distributed on the remaining part of the building, providing good data support for the fitting. The distribution of the polarization parameters shows the following: (1) the \(\Delta\theta_{1}\) parameters of the middle floors rapidly increased and (2) the \(\Delta\theta_{1}\) parameters of high-rise floors decreased slightly.  
The reason for these distribution characteristics is as follows: (1) To a certain extent, \(\Delta\theta_{1}\) has a positive correlation with IDRs. (2) Based on the assumption of the cantilever beam in Section 3.2.2, the bottom of the building should not have any deformation as a fixed end. This makes IDRs and \(\Delta\theta_{1}\) equal to 0 when the height is 0. (3) The middle floors are the places where the maximum IDR occurs, which was determined by the bending characteristics of the building structure. The flexural deformation was angular deformation, which was inherited by the upper floor, so it was cumulative. The shearing deformation was not cumulative. In the middle floors, although the bending moment and shear both decreased, the accumulated speed of flexural deformation between adjacent floors was so fast that the decrease in shearing deformation was covered. Therefore, the IDRs and \(\Delta\theta_{1}\) in these floors also increased quickly and peaked here. (4) In high-rise floors, the accumulated speed of flexural deformation slowed down, but shear deformation still decreased rapidly. This makes the total deformation between adjacent floors decrease, resulting in decreasing IDRs.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 4 Simulation Experiments
### Evaluation Index  
This study used precision, recall, and F1-score, three prevalent evaluation metrics in the field of FDIA detection, to verify the feasibility and effectiveness of this method. First, the following variables were defined:  
True Negative (TN) denotes the number of normal samples correctly identified as normal samples.  
False Negative (FN) denotes the number of samples misidentified as FDIA samples from normal measurements.  
True Positive (TP) denotes the number of FDIA samples correctly identified as FDIA samples.  
False Positive (FP) denotes the number of FDIA samples that are misidentified as normal measurements.  
In this paper, accuracy represents the proportion of correctly classified samples in the total number of samples; precision is defined as the probability that all samples predicted to be attacked are actually attacked; recall is defined as the probability that the samples actually attacked are predicted to be attacked; the F1-score is a composite indicator that represents the balance between accuracy and recall. Accuracy, precision, recall, and F1-score are expressed as follows:  
\[\begin{split}\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{ TP}+\text{FP}+\text{TN}+\text{FN}}\\ \text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\\ \text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\\ \text{F}_{1}\text{-score}=2\times\frac{\text{Precision}-\text{ Recall}}{\text{Precision}+\text{Recall}}\end{split} \tag{12}\]  
Figure 6: Topology of the IEEE 14-bus test system.
# Cross-Platform UAV Swarm Key Management in Denied Environments
## 2 Preliminaries and Definitions
### Preliminaries
#### 2.1.1 Hash Function  
The Hash function [10] is a popular encryption algorithm widely used in security applications and network protocols like message authentication, digital signature, password protection, intrusion detection, virus detection, and construction of pseudorandom functions. The security requirements of the Hash function \(h(\cdot)\) are as follows:  
* Unidirectionality (preimage resistance): \(\forall x\Rightarrow y=h(x)\), and \(y,h^{-1}(y)\
Rightarrow x\) is given.
* Weak collision resistance (second preimage resistance): for \(\forall x\), it is computationally impossible to find \(y\
eq x\) with \(h(x)=h(y)\).
* Strong collision resistance (collision resistance): it is computationally impossible to find some pair \((x,y)\) such that \(H(x)=H(y)\).
# Research on Bone Stick Text Recognition Method with Multi-Scale Feature Fusion
## 4 Experimental Analysis and Discussion
### Experimental Validation of the Effectiveness of the Focal Loss Function  
In order to show that the average performance of the methods in this paper is meaningful, the experiments are deliberately divided into different test subsets, with the number of text images in each category in the test set being three. The F1 value is used as the evaluation criterion for each method. It can be seen that the MS-ResNet network model has a higher F1 value and performs optimally in different test subsets. The results are shown in Table 3.  
### Experimental Validation of the Effectiveness of the Focal Loss Function  
In order to verify the effectiveness of the improved multi-scale MS-ResNet model and the incorporation of the focal loss function for ablation experiments, the MSF-ResNet (multi-scale and focal loss ResNet) model combining the two achieved the highest recognition accuracy of 90.5%. Compared with the classical ResNet34 residual network model, the recall, precision, F1 score, and accuracy are improved by 4.9%, 1.1%, 3.2%, and 1.9%, respectively. Compared with the ResNet network model using only the focal loss function, the above indexes are improved by 3.4%, 1.0%, 2.4%, and 1.6%, respectively. Compared with the multi-scale network model without the focal loss function, the above indexes are improved by 0.8%, 0.4%, 0.7%, and 0.2%, respectively. The experimental results are shown in Table 4.  
Figure 11: Change in loss value of bone stick recognition by different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **F1/\%** & **Model** & **F1/\%** & **Model** & **F1/\%** \\ \hline AlexNet & 76.8 & S1+S5 & 82.0 & S1+S3+S5 & 80.0 \\ DenseNet & 74.7 & S2+S5 & 80.6 & S1+S2+S5 & 81.2 \\ GooLNet & 78.8 & S3+S5 & 80.3 & S2+S3+S5 & 84.5 \\ Vgg16 & 81.6 & S4+S5 & 83.8 & S2+S3+S4+S5 & 84.0 \\ ResNet & 82.4 & S2+S3+S4 & 81.8 & MS-ResNet & 88.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of different subsets.  
The focus loss function can effectively balance the recognition rate of small sample categories, such as in Figure 12, where the\"Zhi\" data sample size of 18 can be expanded to 90. However, for \"Man\" there are 166 training samples, and after the expansion there are 830. After adding the focal loss function, the category weight of small samples is increased, and the small samples that were incorrectly identified before can be correctly identified, strengthening the accuracy of small sample text recognition.  
The MSF-ResNet model with multi-scale feature fusion using focal loss function enhances the feature extraction ability while strengthening the ability to balance small samples, enabling better bone stick text model recognition applications.
## 4 Experimental Environment and Results  
### The Dataset  
The dataset used in this study was obtained from ADNI ([[http://adni.loni.usc.edu](http://adni.loni.usc.edu)]([http://adni.loni.usc.edu](http://adni.loni.usc.edu))) (accessed on 22 December 2020). The ADNI study contains AD, MCI, and NC subjects and its main purpose is to establish clinical, imaging, biochemical, and genetic markers for the early detection of AD. For this study, participants with baseline, plus a-year follow-up 18F-fluorodeoxyglucose (FDG)-PET images were used. Of 256 subjects in total, there are 148 NC subjects, 83 MCI subjects, and 25 AD subjects. Demographic and clinical information (Clinical Dementia Rating--CRD, Mini-Mental State Examination--MMSE) is shown in Table 1. For the training stage, the normal class dataset is split 80-20% for training and inference (e.g., AD vs NC, MCI vs NC, AD vs MCI). One hundred percent of the abnormal class is used for inference, meaning, the model is not trained with the abnormal class.  
Figure 3: Class activation maps of all three classes; (**a**) AD, (**b**) MCI, and (**c**) NC. Despite the CNN in all cases cover a larger activation area, the activation values are lower than the DCN activations. Furthermore, it can be seen that the class activation maps highlight the different parts of the brain.  
The detailed information about the dataset split and the numbers can be seen in Table 2. Each patient's axial scan has 96 images, and each image is grayscale has the size of 160 \(\times\) 160 pixels. The images were downscaled to 64 \(\times\) 64 to reduce the computational cost.
# Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
## 4 Discussion
\[P_{OUT,AVE}/element=Tx\ EIRP-Antenna\ Gain_{1\ element}-20\cdot log_{10}(N) \tag{3}\]  
\[P_{OUT,AVE}/total=Tx\ EIRP-Antenna\ Gain_{1\ element}-10\cdot log_{10}(N) \tag{4}\]  
Using Equations (1) and (4), Figure 9 is created to show that the Tx EIRP of a phased array system is found by multiplying (adding in dB scale) the antenna array gain with \(P_{OUT,AVE}\)/_total_, which both depend on \(N\) for our case using \(N\) FEMs and \(N\) antenna elements. When using a smaller array, the array gain decreases, which then requires the FEMs to produce greater \(P_{OUT,AVE}\)/_total_ for the overall system to maintain the same Tx EIRP. Figure 10 is constructed using Equations (2) and (3) to show the relationship between the HPBW of an antenna array and \(P_{OUT,AVE}\)/_element_ when using \(N\) FEMs with \(N\) antenna elements. Assuming a conservative 10 dB power back-off (PBO) that is safer for testing the CMOS PAs, a FEM consisting of our Si CMOS PA that can produce \(P_{OUT,SAT}\) \(\sim\)14 dBm, an RF switch with flat 1.5 dB loss, and a uniform square array of patch antennas can provide average \(P_{OUT}\) (i.e., \(P_{OUT,AVE}\)) of \(\sim\)2 dBm. In order for the Si FEMs to reach Tx EIRP \(>\) 65 dBm, they would need to be implemented in an array consisting of \(>\)768 elements, which would be realizable in a 1024-element uniform square array having HPBW of 3.2\({}^{\circ}\). Likewise, the medium-power GaN PA with \(P_{OUT,SAT}\) \(\sim\)24 dBm would be realizable in a 256-element array having HPBW of 6.3\({}^{\circ}\), and the Watt-level GaN PA with \(P_{OUT,SAT}\) \(\sim\)30 dBm would be realizable in a 128-element array with HPBW of 9\({}^{\circ}\). Realizable phased array systems using our PAs are summarized in Table 3. The HPBW would be \(\sim\)2\(\times\) wider for a 256-element array using our medium-power GaN PAs, and \(\sim\)2.8\(\times\) wider for a 128-element array consisting of our Watt-level PAs compared to the 1024-element phased array transceiver using our FD-SOI CMOS PAs. At a fixed distance from the Tx antenna array, a signal with HPBW \(\sim\)2-3 times wider should be able to pass around LOS blockers that are \(\sim\)2-3 times wider [46]. It has been observed that antennas with lower gain and wider HPBW can work better in non-LOS communication [47], and antenna arrays that can employ wide-range continuous beamwidth control (WCBC) have been developed for mm-Wave communications to  
Figure 9: Antenna array gain plotted with \(P_{OUT,AVE}\)/_total_ of an \(N\)-element 2-\(D\) uniform square \(\sqrt{N}\times\sqrt{N}\) array assuming spacing between the elements is \(d=\lambda/2\) with the beam pointing towards broadside plotted, targeting a Tx EIRP of 65 dBm (antenna gain per element = 5 dBi; one FEM per element). improve robustness [48]. Additionally, the radiation patterns for smaller antenna arrays should have less pronounced minor lobes, making them less likely to cause interference.
## 2 Material and Experimental Methods
### Temperature Measurement on the Surface along the Keyhole Wall  
The keyhole surface was taken by a high temperature measurement system including a thermal camera and Thermera-HS software (Phantom Co., Ltd., Tokyo, Japan). In this experiment, the keyhole image was captured from the topside perpendicular to the welding direction. A schematic illustration of the longitudinal section of the keyhole and the KPAW weld pool is provided in Figure 3. The experimental setup of temperature distribution measurement on the surface inside the keyhole is depicted in Figure 4. Figure 5 provides a schematic illustration of temperature measurement on the surface along the keyhole wall. Here, the keyhole wall is limited from the top and the bottom surfaces with the diameter on the top surface being larger than on the bottom surface. We used a thermal camera (Miroex, Phantom Co., Ltd., Tokyo, Japan) including three red (\(R\)), green (\(G\)), and blue (\(B\)) color sensors to take the keyhole surface images, which was set up at a rate of 2000 fps. We set the distance from the camera lens to the center line of keyhole to 230 mm and the tilt angle between the optical axis of camera and the surface plane of base metal was set to 45\({}^{\circ}\). At this inclined camera angle, the whole keyhole wall including the top and the bottom surfaces could be captured simultaneously, as shown in Figure 3. A lens (sigma DG28-300, Nikkon Co., Ltd., Osaka, Japan) was used to adjust the magnification of keyhole images. Other welding conditions were same as the convection flow observation described in Section 2.1. During the welding process, excessive arc brightness can impair the camera, so the images were immediately taken after the main arc was switched off from a welding current of 120 A. The keyhole wall image was captured within 2 ms after the cutting main arc. The main arc and the pilot arc completely disappeared within 1.0 ms, which corresponds to two frames, because the current sharply decreased to zero without a ramp down profile. The measured temperature was slightly lower than that at full current. In addition, it was demonstrated in a previous paper that the drop in temperature is negligible for times less than 2 ms after the arc disappears [24]. Afterward, the temperature was evaluated using the two-color pyrometry method.  
Figure 3: Schematic illustration of longitudinal section of the keyhole and the weld pool in keyhole plasma arc welding (KPAW).