esa-sceva/satcom-chunk-collection huggingface dataset:
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 4 Conclusions  
AFGN conducts research from two directions to improve classification accuracy. Firstly, it explores the hidden dependencies in features through a BDM module and constructs a suitable graph for the model, with nodes in the graph containing the nonlinear relationships within image features. This method of constructing graphs does not solely rely on image features but enhances the overall expressive ability of the graph based on the interactions between features. It effectively alleviates the impact of noise in images, thereby enhancing the model's performance. Furthermore, we transform spatial domain graph convolution into filtering graph signals in the frequency domain. Compared to single low-pass or high-pass filters designed for graph signals by other GBFS models, we have developed an adaptive filter. This filter can adjust its parameters based on different few-shot tasks, selectively retaining more useful information for the model, thereby improving the model's performance in few-shot classification tasks.  
In the future, we will integrate graph networks and attention mechanisms in few-shot learning to enable the model to adaptively focus on important nodes and edges, thereby improving information transfer efficiency. Additionally, we will continue to explore the combination of multimodality and GBFS, leveraging the graph structure in non-Euclidean spaces to jointly learn various data modalities such as images, texts, and audio. However, handling the heterogeneity and differences in information across different modalities remains a challenge for us.  
Conceptualization, Q.T.; methodology, Q.T.; software, Q.T. and J.L.; validation, J.L. and C.Z.; formal analysis, Z.W.; investigation, Q.T. and J.L.; resources, Z.W. and X.Z.; data curation, Q.T. and C.Z.; writing--original draft preparation, Q.T.; writing--review and editing, Z.W. and X.Z.; visualization, J.L. and C.Z.; supervision, Z.W.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China under Grant (62327808).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Backbone** & **miniImageNet** & **CUB** & **FC100** \\ \hline ResNet-12 & \(\mathbf{84.63\pm 0.28}\) & \(89.53\pm 0.17\) & \(\mathbf{63.27\pm 0.24}\) \\ ResNet-18 & \(83.12\pm 0.37\) & \(\mathbf{92.30\pm 0.41}\) & \(62.04\pm 0.26\) \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of different ResNet models on datasets. 5-way 5-shot average classification accuracy(%) with 95% confidence intervals. The best results in the table are highlighted in bold.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 6 Implementation
### Diagnostic Variables and Test Programs  
For optimizing and testing the program, it is important to know which algorithm is chosen for given \(\omega,\beta\), and how many terms need to be summed. This information is provided by two global variables in the source file kww.c. Programs linked with libkww can access them using extern declarations:  
extern int kww_algorithm; extern int kww_num_of_terms;  
The variable kww_algorithm is set to 1, 2, or 3, to indicate whether the low-\(\omega\) expansion, the numeric integration, or the high-\(\omega\) expansion has been used. The variable kww_num_of_terms counts the evaluations of \(f_{\beta}\).  
The test program runkww (source code runkww.c in directory test/) allows to call the high-level functions of Section 6.2 and the low-level functions of Section 6.3 from the command line. If the program is called without arguments, it prints a help text. Besides the function values \(Q_{\beta}(\omega)\) or \(V_{\beta}(\omega)\), runkww also prints the diagnostic variables described above.  
The script kww_findlims.rb, written in the Ruby programming language, uses bisection to determine the limits \(\omega^{\mathrm{L,H}}\) where the series expansion first fails.  
The program kww_countterms tests the numeric integration within a hardcoded \(\beta\) range and for \(\omega\) within the limits \(\tilde{\omega}^{\mathrm{L,H}}\), and prints the average number of evaluations of \(f_{\beta}\). It has been used to optimize the parameters \(p\) and \(q\) of the kernel \(\eta\) of the double-exponential transform \(\phi\) (Section 5.3).  
The script kww_checks.rb performs scans in \(\omega\) at fixed \(\beta\), or vice versa, and detects points where the used algorithm or the number of function evaluations has changed. It then checks the continuity of \(Y=Q,V,P\) across this border. Results of numerous test runs confirm that violations of monotonicity are extremely rare and never exceed a few \(\delta\).
## 3 Load Distribution Model  
In this section, a new load distribution model for cycloid drives is proposed, which is treated as a three-dimensional elastic body contact problem to demonstrate the effects of both the tooth profile and longitudinal modifications accurately. This semi-analytical model is developed and analyzed under the following assumptions:  
1. The load distributed on one tooth pair does not affect elastic deformations on adjacent tooth pairs.
2. The rotational speed and inertia forces of moving elements are neglected in the quasi-static process.
3. The curvature of the gear tooth surface over the contact region is assumed to remain unchanged.
4. The friction between contacting tooth pairs is neglected.  
With the above assumptions, the overall methodology for the proposed semi-analytical model is shown in Figure 5. The design dimensions, modification amounts, material properties, and applied torque are the input parameters for the proposed model. The cycloid gear tooth and roller profiles described by these parameters are transformed to a global coordinate system to perform the unloaded tooth contact analysis (TCA) and to determine the backlash, unloaded transmission error, contact points, and rotational angle in a mesh circle. Next the tooth compliance model considering tooth contact deformation is developed by combining the Boussinesq force-displacement relationships in elastic half-space with an influence coefficient method. Finally, a group of equilibrium and compatibility conditions are given and solved to calculate the loads, contact stress, contact pattern, loaded transmission error (TE), and gear ratio of the cycloid drive.  
Figure 4: Three types of tooth longitudinal modifications for cycloid gears: (**a**) fully crowned modification (FCM), (**b**) partially crowned modification (PCM), and (**c**) logarithmically crowned modification (LCM).
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 4 Results and Evaluation
### Binary Driver Distraction Classification  
An alternative way to study driver behavior is to detect whether the driver is in non-distracted driving or distracted conditions. This section addresses the driver behavior with two separate binary classification tasks which detect non-distracted driving or distracted driving. In this case, all of the driver behavior classes except the driving class are combined to form a distracted driving class, whereas normal driving is considered as non-distracted driving. Likewise, for multiclass recognition, seven-fold cross-validation was computed to evaluate the performance of the binary classifier. Table 5 reports the performance of the binary classifier across the seven folds and, based on the average result, the classification accuracies were 93.9% and 99.2% for RGB and IR images, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**RGB Images**} & \multicolumn{3}{c}{**IR Images**} \\ \hline
**Driver State** & **Precision (\%)** & **Recall (\%)** & **F\({}_{1}\)-Score (\%)** & **Precision (\%)** & **Recall (\%)** & **F\({}_{1}\)-Score (\%)** \\ \hline Talking & 78.4 & 78.7 & 76.1 & 90.1 & 95.1 & 92.4 \\ Controlling & 85.0 & 87.3 & 83.6 & 95.4 & 96.7 & 95.6 \\ Driving & 95.0 & 87.0 & 89.4 & 98.0 & 98.9 & 98.6 \\ Drinking & 92.4 & 92.1 & 92.3 & 93.9 & 94.6 & 93.7 \\ Fainting & 86.4 & 84.4 & 84.3 & 89.1 & 84.9 & 86.0 \\ Looking & 86.9 & 88.7 & 87.4 & 94.6 & 99.1 & 96.6 \\ outside & 93.1 & 96.3 & 94.7 & 98.3 & 93.0 & 95.3 \\ Texting & 84.7 & 81.1 & 81.7 & 82.7 & 75.0 & 77.4 \\ Smoking & 89.4 & 88.1 & 88.6 & 81.3 & 74.1 & 75.7 \\ \hline \end{tabular}
\end{table}
Table 4: Average Precision, Recall and F\({}_{1}\) score of the nine class categories for RGB images and IR images.  
It is worth noting that the overall performance of the model significantly increased for binary classification. For IR images, similar performance was observed for both non-distracted driving and distracted driving classes. In RGB images, the average accuracy for distracted driving appears to be superior, with a value of 97.7% along with a value of 86.8% for non-distracted driving. The reason could be that the combined distraction class data resulted in superior performance in comparison to non-distracted driving. In fact, in real-life driving scenarios, obtaining a higher detection accuracy for a distracted driving class is more necessary than for non-distracted driving, since the main intent is to identify the distracted driver and provide a warning system to reduce the potential accident risk.
## 4 Numerical Examples
### Cantilever Beam  
This classic example from the literature is presented to validate the formulation presented in this article. The design domain, boundary conditions, and optimal topologies of a cantilever beam with a single, isotropic material are shown in Figures 2 and 3. In Figure 3a, the cantilever is optimized using the BESO method, while Figure 3b shows the same cantilever optimized using the SESO method. The material properties are [PERSON]'s modulus \(E=1\) MPa and [PERSON]'s ratio \(\mu=0.3\). The design domain and boundary conditions are displayed in Figure 2. The left end is fixed, and a concentrated load \(F=1\) kN is applied at the center of the free face. The optimization parameters used are prescribed volume fraction \(\mathbf{V_{f}}=0.20\) and filter radius \(\mathbf{r}=1.5\) mm. The design domain is discretized into a \(64\times 40\times 8\) element mesh, totaling 20,480 hexahedral finite elements with 1 mm edge lengths.  
The optimization procedure with the BESO method was carried out with an evolutionary ratio \(ER=0.03\) with a computational cost of 47.8 min and compliance of 11.3334 N \(\times\) mm. While in the SESO method, a rejection ratio \(RR=0.01\) and an \(ER=0.02\) were used with a computational cost of 32.5 min and compliance of 11.5428 N \(\times\) mm. It can be seen that the upper part of the cantilever is in tension (blue color) while the lower part is compressed.  
Figure 3: _Cont._  
Figure 2: Design domain and boundary conditions.
## 1 Introduction
In the other method, the compensation inductors are integrated into the bipolar main coils (IB method) [30], and the compensation inductors are unipolar coils. Comparing these two design methods, the IU method has a superior misalignment capability and greater load-carrying efficiency than the IB method. In [31], a dual-coupled integrated IPT system involving coupling between the compensation inductors and cross-coupling between the compensation inductors and main coils facilitated an increase in the total output power of a system in misalignment. However, compensation inductors are located in the upper and lower position of the transmitter and receiver coil, which will increase the package thickness. In [32; 33], a fully integrated magnetic structure was proposed to  
Figure 1: Typical stationary EV IPT system. minimize system cost and increase power density. But, in the system misaligned state, the proposed method does not consider the effect of the cross-coupling generated between the compensation inductor and the main coil in the magnetic coupling structure. In this study, a fully integrated IPT system based on the LCCL-S topology is proposed to reduce the magnetic pad volume and focus on solving the door-to-door misalignment problem. In the proposed system, the transmitter and receiver coils are used as unipolar coils, and the extended DD structure coil is integrated into the transmitter coil as a compensation inductor. The advantages of the proposed magnetic coupling structure are mainly demonstrated in two aspects: First, when the system is in the aligned state, the coupling effect of the extended DD coil to the transmitter and receiver coils is eliminated. Another advantage is, when in door-to-door misaligned state, the first magnetic coupling is generated between the transmitter and receiver coils, and a second magnetic coupling is generated between the receiver and the extended DD coils, which increases the total output power of the system. Specifically, this study utilized the finite element method (FEM) simulation tool to optimize the distance between the two unipolar small coils of the extended DD coil, as well as the number of turns and inner diameter of the extended DD coil. The main purpose of the optimization is to maximize the second magnetic coupling and improve the total output power of the misaligned system. Finally, to verify that the proposed magnetic coupling structure has high misalignment tolerance performance, two sets of magnetic coupling structures with the DD coil and the extended DD coil as the compensation inductors were established, and comparative experiments were performed. This study is organized as follows: Section 2 provides an analysis of the fully integrated LCCL-S topology. In Section 3, an analysis of the proposed extended DD coil, as well as its design and optimization, is presented. Section 4 presents how our experiment was conducted along with a comparison of the volume and weight of different compensation inductor structures. Finally, conclusions are drawn in Section 5.
## 3 Results and Discussion
### Fixed-Bed Column Adsorption Performance of EGS@APTES-GT  
The testing of the potential applicability of the EGS@APTES-GT was performed in a column study with respect to the effectiveness of arsenate and Iprodione removal. In order to design and optimize the fixed-bed column adsorption process, different mathematical models were used to predict the breakthrough curve for the studied system. In this study, Yoon-Nelson (Y-N), Bohart-Adams (B-A), and Thomas models were used (SM S2.6) to predict the dynamic behavior of arsenate and Iprodione adsorption onto the EGS@APTES-GT hybrid adsorbent in a fixed-bed system (Table 5).  
The correlation coefficient values for each model are found to be high, suggesting that the experimental data and model predicted data are in agreement with each other (Table 5). The value N\({}_{\text{o}}\) and constant k\({}_{\text{BA}}\), obtained from the Bohart-Adams model, are decreased with the increasing flow rate, which may be attributed to the insufficient time for an effective interaction between the adsorbate and adsorbent. The breakthrough curves of arsenate and Iprodione adsorption onto EGS@APTES-GT at three different flow rates (0.5, 1.0, and 1.5 mL min\({}^{-1}\)) and at fixed-bed height of 5 cm are shown in Figure S8.  
Figure 6: Pseudo-second-order model (**a**) and intra-particle diffusion model plots divided into two distinctive sections (**b**) for the adsorption of arsenate and Iprodione by the EGS@APTES-GT.
## 2 Experiments
### Thermal Shock Test  
The drastic change of ambient temperature in deep space missions is an important factor resulting in internal defects in the packing of electronic devices. In order to study the mechanical properties of this new bonding material in space missions, a thermal shock test was carried out to simulate the aerospace environment, in which the temperature changed from \(-170\)\({}^{\circ}\)C to \(125\)\({}^{\circ}\)C covering the temperature range of the moon, Mars, common asteroids and comets. During the test, samples were placed in a high and low temperature chamber with a thermal shock profile (Figure 3). The soak time of extreme temperature was 15 min, and the frequency was about 30 min/cycle.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Condition** & **Micron Ag Paste** \\ \hline Ag content (wt\%) & in paste & 92–94 \\ Viscosity, E-type 3\({}^{\circ}\)cone (Pa-s) & 5 rpm@RT & 18–22 \\ Thixotropic Index & 0.5 rpm/5 rpm & 6.5 \\ Volume Resistivity (\(\Omega\cdot\)cm) & \(5\times 10^{-6}\) \\ Poisson’s ratio & 0.25 \\ Density (g/cm\({}^{3}\)) & 7.8 \\ Elastic modulus (GPa) & Nanoindentation@RT & 25 \\ CTE (ppm/\({}^{\circ}\)C) & Isotropy & 18 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the pressureless sintered micron silver material.  
Figure 2: SEM image with spectrum spot and EDS analysis.
## 5 Discussion
### Parameter Analysis  
Applications with different requirements should consider the balance of P4 and Pfa. The greater the threshold, the lower the false alarm probability and detection probability. If the system requires a lower false alarm rate, a higher verification time is suggested, and if the system requires faster acquisition, a lower verification time is recommended. For general applications, Nth = 2 is recommended, whereby the false alarm probability would be under \(10^{-5}\), which is low enough for most cases.
## 5 Conclusions
Selection combining presented the best results, improving the mean \(\mathrm{E_{b}}/\mathrm{N_{0}}\) up to 4 dB compared to an individual ionospheric mode, and also remarkably lowering the BER results for the different modulations studied (4 PSK, 4 PSK, and 4 QAM). Furthermore, we can also affirm that the ordinary and extraordinary waves were received with different mean \(\mathrm{E_{b}}/\mathrm{N_{0}}\) values, which differed up to 2 dB. According to our knowledge, no similar studies were performed for NVIS transmissions. The closest study for a channel like ours and the analysis of the different modes of the ionosphere was performed in [28] (a multifrequency study from 2012). Similar results were obtained in the multipath study for both the O and X modes. Our study, in addition, presented the availability of the different modes for NVIS transmissions and their correlation coefficients. The authors of [28] also presented a study of the BER improvement (up to 8% improvement) by the use of polarization diversity. Our system, on the other hand, improved the robustness of NVIS communications much more, up to 33% (4 FSK at 50 W). Investigation, J.M., J.P., T.G., J.M.M., J.L.P. and D.B. ; Methodology, J.L.P. and D.B. ; Software, J.M., J.P., T.G. and J.M.M. ; Supervision, J.L.P. and D.B. ; Validation, J.L.P. and D.B. ; Writing--original draft, J.M., J.P., T.G. and J.L.P. ; Writing--review and editing, J.M., J.P., T.G., J.M.M., J.L.P. and D.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministry on Science, Innovation and University, the Investigation State Agency and the European Regional Development Fund (ERDF) under the grant number RTI2018-097066-B-I00 (MCIU/ AEI/FEDER, UE) for the project \"NVIS SENSOR NETWORK FOR THE SOUTH SMETLAND ISLANDS ARCHIPELAGO\" (SHETLAND-NET). This work received funding from the \"Agencia de Gestio d'Ajuts Universitaris i de Reecerca (AGAUR)\" of \"Generalitat de Catalunya\" (grant identification \"2017 SGR 977\"). Not applicable. The authors declare no conflict of interest.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### OFDM-OQAM Transceiver
#### 4.1.1 OFDM-OQAM Transceiver: NPR Design
# Dual-Band UWB Monopole Antenna for IoT Applications +
## 4 Parametric Analysis  
An analysis of the antenna was performed by first removing the slit from the design and finding the S-parameter. Then, two further analyses were conducted by including the slit in the design. First, the width of the slit was changed, and then length was changed to find out the S-parameter.  
As a result, Figure 2 shows dual bandwidth but not ultrawideband. On the other hand, Figure 2a-c show dual bandwidth with the ultrawideband as the second band, which can eventually be considered better than the one without the slit.
# A Study on Technology Competition of Graphene Biomedical Technology Based on Patent Analysis
## 4 Analysis
### Patent Analysis
#### 4.1.2 Competitive Position of Top Assignees
INNOGRAPHY's PatentStrength(r) is a proprietary algorithm that focuses on patent value as well as relevance, and encourages stakeholders to find leading assignees holding these most valuable patents in TC. There are more than 10 indicators to calculate the patent strength, such as backward citations, forward citations, patent litigation, family numbers, claim numbers, etc. Specifically, INNOGRAPHY's PatentStrength(r) employs 0 to 100 scales to assess the patent strength. The larger the number of scale, the more valuable the patent. General patents cover the lower 30 scale. Patents with patent strength from 30 to 80 would be considered as important patents, while the patent strength of core patents seem to be more than 80. In this study, we obtain 598 important high-strength patents as of March 2019 related to graphene biomedical technology (see Table 1). Table 1 depicts the distribution of top 20 assignees holding high-strength patents. Lockheed Martin Corporation overtakes other assignees, with 83.33% of high-strength patents and a high competitive advantage in TC, followed successively by Harvard University (80.00%), Samsung Electronics Co., Ltd. (77.78%), University of California (57.14%), and Suzhou Institute of Nano-Tech and Nano-Bionics CAS (55.56%), which illustrate their relatively stronger technological innovation capability in TC. US9572918 B2 filed by Lockheed Martin Corporation is the number one patent with strongest patent strength, in which a device isolates a substance from blood. It was filed in 2013. This device may be configured to include a conduit system. It may also be configured to operate according to a reversible cycle. As for the country origin, the percentage of high-strength patents being held by the U.S.'s assignees outstrip assignees from other countries, indicating its leading position in global TC. In addition, half of Chinese assignees hold less than 10.00% high-strength patents. Guangxi Chinese Medicine College, Shanghai University, Jinling Institute of Technology, and Zhengjiang Sci-Tech University don't hold any high-strength patents, which may lead to the loss of competitive advantage in TC marketplace.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Explainable AI
#### 2.3.1 Explainability Scope  
What portion of a model's behavior are we trying to make transparent? The literature converges on two complementary scores: global and local [18].  
* A **global explanation** attempts to convey the broad logic that governs all of a model's predictions. It compresses the mapping \(f:X\to Y\) into a form that remains faithful, at least approximately, over most of the input space. Global summaries may dis  
Figure 6: Recurrent concept drift.  
guise important minority patterns because they average behavior across the whole population [19].
* A **local explanation** zooms in on a single instance, or at most a small neighborhood of similar points, and answers the question, \"Why did the model produce this particular outcome?\" Local narratives are precise but narrow; they tell us nothing about how the model behaves elsewhere, and they can fluctuate if the decision boundary is highly non-linear [20].