esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Absorption Bandwidth through Vertically Oriented Metamaterials
## 8 Conclusions  
Based on our investigation, it is evident that the use of multiple resonator geometries within a unit cell is key to enhancing and manipulating the spectral response of metamaterial devices. Unit cells containing a single VSRR, single PSRR, and multiple SRRs were examined to better understand inter-cell and intra-cell coupling. While inter-cell coupling is not discussed, intra-cell coupling between the orthogonal SRR components can be switched on and off based on the orientation of the vertical split-ring resonator.  
The parameter space for a single SRR (not to mention two or more SRRs within a single unit cell) remains large and complex. Additional work is needed to gain deeper insight into parameter sensitivity for a single SRR, as well as the electromagnetic coupling between orthogonal or back-to-back resonators [24].  
All authors contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.  
This research was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories and by the Defense Advanced Research Projects Agency Defense Sciences Office (DSO) Program: DARPA/DSO EXTREME; Agreement No. HR0011726711.  
This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA003525. This article describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government.  
The authors declare no conflict of interest.
# Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System
## 2 Proposed AIoT System with Compressed Sensing and Pose Estimation  
### Description of an AIoT System  
The proposed AIoT system is shown in Figure 1, where an IoT-sensing device used a random sampling matrix function of the CS for data traffic mitigation, and an AIoT edge server used CS recovery and domain transformation matrix functions of the CS and a PoseNet model for pose estimation.  
First, the IoT-sensing device extracts large amounts of data such as images or videos (**O**) from a high-definition (HD) camera sensor and then uses a random sampling matrix function (\(\mathbf{\Phi}\)) to obtain a compressed image (**c**) according to the CS rate (\(r\)). The compressed image is forwarded to the IoT client application modeled, as an application-dedicatednode-application entity (ADN-AE) in oneM2M specifications [26]. Then, the IoT client application transmits the compressed image to an IoT edge middleware (MW) in the AIoT edge server. The IoT edge MW, modeled as an infrastructure node-common service entity (IN-CSE) in oneM2M specifications, forwards the compressed image to a CS recovery function. The CS recovery function generates a sparse transform domain representation (\(\hat{x}\)) of the original image \(\mathbf{O}\) via Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) [27; 28; 29]. After a domain transformation matrix function (\(\mathbf{\
abla}\)) inversely transforms \(\hat{x}\) to produce the recovered image (\(\hat{\mathbf{O}}\)), we estimate human poses from the recovered image \(\hat{\mathbf{O}}\) using the PoseNet model with the MobileNet v1 architecture where the human keypoints on the body were used [30; 31; 32]. The MobileNet v1 function takes the recovered image with red, green, and blue (RGB) channels and outputs a feature map [25]. In the feature map, the keypoint heatmaps function predicts a heatmap as a binary classification task. The short-range offsets function improves keypoint positioning accuracy by predicting a short-range offset vector pointing from the image location within the keypoint disk to the keypoint of the nearest human instance. The mid-range pairwise offsets function predicts a separate pairwise mid-range 2D offset vector, which is the target regression vector for each directional edge connecting adjacent pair of keypoints in a human tree-structured kinematics graph. The multi-person pose decoding function uses Hough voting to compute heatmaps and short-range offsets into a 2D Hough score map and finally outputs the estimated human poses.
## 5 Experimental Analysis
### Analysis of Results
#### 5.3.1 BikeNYC  
For the BikeNYC case study STREED-Net parameters have been set as follows. The number \(n\) of input frames has been set to 4, the number \(L\) of encoding and decoding blocks has been set to 2. This decision has been dictated by the size of the grid (\(16\times 8\)): setting \(L\) greater than 2 (for example 3) would result in an encoder output tensor of size \(4\times 2\times 1\times C\), which would be too small to allow the CMU block to effectively capture the time dependencies in the section located between the encoder and decoder. After some preliminary tests, the number of convolutional filters has been set to 64 in the first layer of the encoder and in the subsequent blocks, while in the last layer it has been set equal to 16. In this way, the dimensionality of the input vector goes from \(I\in\mathbb{R}^{4\times 16\times 8\times 2}\) to \(O\in\mathbb{R}^{4\times 4\times 2\times 16}\) as the encoder output. Symmetrically, the convolutions within the decoder use 64 filters, except for the final layer which uses only 2 filters to generate the prediction of the Inflow and Outflow channels. The parameters corresponding to the dimensionality of the convolution kernel (kernel size equal to 3, batch size equal to 16 and learning rate equal to 0.0001. The number of epochs is set to 150), to the batch size and to the learning rate have been optimized with the _Bayesian optimization_ technique.  
As for the models from the literature, they have been arranged and trained following carefully the parameter values and indications reported in the respective publications.  
As shown in Table 1, STREED-Net outperforms all other considered approaches for all evaluation metrics. In addition, the small standard deviation values are evidence of the robustness of the proposed approach. Nonetheless, it is worth observing that all learning-based approaches return similar results. We believe this is mainly due to the reduced size of the data set that does not allow the models to be adequately trained. Moreover, the tessellation used in this case study (widely used in the literature), with a small grid of dimensions (\(16\times 8\)), tends to level off the metrics and hinder a more precise performance assessment.
## 3 Results and Discussion
### Hardness  
Figure 8 depicts values of Rockwell B and Vickers hardness, respectively, depending on their positions on the drawn grid. The average Rockwell B hardness value was approximately 55 \(\pm\) 4 HRB, which is lower than that of the monolithic AISI 316L (80 HRB [39]), with a deviation of 32%. Regarding the Vickers hardness, the average value (132.2 \(\pm\) 3.8 HV) was approximately 15% lower with respect to the value of the monolithic AISI 316L (155 HV [39]).  
Figure 7: XRD patterns of the ME 316L stainless steel.  
It is reasonable to assume that the difference between the percentage deviations (32% vs. 15%) depends on the correlation between the sample porosity and test methodologies. The Vickers hardness test concentrates the stress on the surface of the specimen, with a limited influence on thickness, leaving a shallow indentation with reduced dimensions, compared to the indentation made during the Rockwell hardness test. Consequently, the Rockwell hardness test is more affected by the presence of porosities, even if they are not outcropped on the surface, as the larger size of the indenter and the greater load applied lead to a greater probability of intercepting non-emerging porosities compared to the probability of finding porosities in the indentations of the Vickers hardness test.  
Furthermore, the uniform distribution of the hardness values, both Rockwell B and Vickers, along the growth direction may suggest the homogeneity of the material.
# Analytical Selection of Leakage Inductance for Single-Phase Dual Active Bridge Converters
## 4 Design of High-Frequency Transformer
### Inductance Calculation  
The leakage inductance of a transformer is influenced by several design aspects, such as core shapes and winding styles. An estimate of the leakage inductance can be calculated via [23]  
\[L_{\sigma}=\mu_{0}\frac{N_{T}^{2}}{H_{w}}\left(\frac{D_{w}}{3}+D_{o}\right) \tag{33}\]  
where the permeability of the vacuum is denoted by \(\mu_{0}\), while \(N_{T}\) represents the total number of turns, and \(D_{0}\), \(D_{w}\), and \(H_{w}\) stand for the core's airgap, width, and height, respectively. The resulting leakage inductance for the presented method equals 130.61 \(\mu\)H, satisfying the condition outlined in Equation (30).
# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Miscellaneous Applications  
ML has been found to be useful in versatile EM applications. [PERSON] et al. [135] described a dual-antenna approach by optimizing the antenna parameters using GA, to avoid the pathloss and increase the signal power effectively. A dual-band Planar Inverted F-Antennas (PIFA) antenna for 5G mobile communications was proposed utilizing a GA and Bayesian convolution incorporated hybrid algorithm in an earlier study [136]. ANN was used to optimize the dual-band antenna with the desired return loss and frequencies of operation [137], which are ideal for 5G communication.  
For better configuration of the number of subcarriers and the size of constellation symbols in the case of mm-Wave communications, KNN was used in another study [138] to maximize the system throughput. The system was evaluated using four antenna configurations resulting in a uniform cylindrical array with the highest data rate. A KNN-based power allocation algorithm was proposed in [139] for a distributed antenna system. Thirteen ML algorithms including GP, SVM, KNN, Naive bayes, and ANN were adopted in another study [140] for performance testing of fingerprint-based mobile terminal localization, which improved the efficiency of KNN over other algorithms, producing the least mean squared error.  
[PERSON] and [PERSON] [141] studied a deep RNN-based approach including the long-short-term memory (LSTM) and the gated recurrent unit to estimate the channel condition. ML has been utilized in different antenna-related Internet of Things (IoT) applications [142]. The single-input multiple-output (SIMO) system is a great example of a modern IoT application where ANN enabled significant improvements in learning the modulation-demodulation schemes for multipath channels [143]. To achieve energy efficiency while reserving the quality of service in the case of a shared wireless medium, base stations must choose the best subset of antennae to utilize the spatial diversity of multiple users. Deep Learning is also used in distributed antenna systems for optimal power allocation to achieve spectral and energy efficiency with reduced computational complexity [144]. ML has also been used to perform different medical applications, such as breast cancer detection by EM radiation [145]. Moreover, ML is used to generate models for the experimentation of antenna performance of wireless devices adjacent to the human body, such as radiation patterns, return loss, impedance, etc. [146]. It has been utilized to predict breast cancer by estimating the S-parameter of an ultra-MIMO sensor antenna using PCA [147]. Figure 8 illustrates the versatility of ML in EM applications involving the design, synthesis, recognition, and optimization of various EM-based systems.
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 4 Cybersecurity Frameworks Used for Tools Classification
### NCSC CyBOK
_Authentication, Authorisation, and Accountability_: Mechanisms for verifying the identities of users, controlling access to resources, and maintaining audit trails for accountability purposes. 5. Software and Platform Security 1. _Software Security_: Language-based security techniques aimed at preventing vulnerabilities applied to system design and implementation: type systems, memory management, code generation, and others. 2. _Web and Mobile Security_: Security challenges specific to web and mobile applications, including secure communication protocols and protections against common threats such as CSRF, XSS, and SQL Injection. 3. _Secure Software Lifecycle_: Ensuring software security by integrating security software engineering techniques throughout the development lifecycle. 6. Infrastructure Security 1. _Applied Cryptography_: Cryptographic techniques applied in securing infrastructure components. 2. _Network Security_: Securing network infrastructure and communications, SDN and NFV security, network access control, and zero trust networking. 3. _Hardware Security_: Secure element, smart card, and trusted platform module (TPM). 4. _Cyber Physical Systems_: Securing industrial control systems, electrical power and smart grids, autonomous vehicles, robotics, medical devices, and IoT. 5. _Physical Layer and Telecommunications Security_: Securing telecommunications networks and physical communication channels, NFC, air traffic communication networks, cellular networks, and others. The CyBOK can be applied in various ways to enhance the security posture of businesses. It can be used to assess skills, develop workforces, design curricula in higher education, and for certification programs [44, 45].
## 2 Materials and Methods
### Geo-Positional Transformer-CNN
#### 2.5.3. GPTransCNN  
As illustrated in Figure 6, GPTransCNN is designed to incorporate geographic positional encoding while effectively integrating both global and local features of environmental covariates to capture their relationship with SOM and improve prediction accuracy. Hyperparameter optimization was conducted using the Optuna framework [47], and the optimal hyperparameter configuration was as follows: the input feature dimension of the GPTransformer branch was set to \(d_{model}=32\), the number of attention heads to \(h=16\), and the number of encoder module to 5. The batch size was set to \(batch=32\), and the model was trained using the Adam optimizer [48] with a learning rate of \(lr=0.001\). The mean squared error (MSE) loss was used as the loss function. The training process was run for a maximum of 500 epochs, with early stopping applied to prevent overfitting [49].  
First, the environmental covariates are simultaneously fed into both the GPTransformer branch and the 1D-CNN branch, where each branch extracts a feature vector of shape (batch, 1). These feature vectors are then concatenated along the feature dimension via a Concat Layer, resulting in a fused feature vector of shape (batch, 2). This fused vector is then passed through a fully connected layer and mapped to a feature vector of shape (batch,  
Figure 5: The architecture of the 1D-CNN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Filter Size** & **Stride** & **Activation** & **Shape** \\ \hline Conv1d\_1 & 3 & 1 & - & (32, 16, 34) \\ BatchNorm\_1 & - & - & ReLU & (32, 16, 34) \\ Conv1d\_2 & 5 & 1 & - & (32, 32, 30) \\ BatchNorm\_2 & - & - & ReLU & (32, 32, 30) \\ Conv1d\_3 & 7 & 1 & - & (32, 64, 24) \\ BatchNorm\_3 & - & - & ReLU & (32, 64, 24) \\ Conv1d\_4 & 11 & 1 & - & (32, 128, 14) \\ BatchNorm\_4 & - & - & ReLU & (32, 128, 14) \\ Flatten & - & - & - & (32, 1792) \\ FC\_1 & - & - & ReLU & (32, 32) \\ FC\_2 & - & - & - & (32, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of each layer in the 1D-CNN model. Note: Shapes are denoted as (batch size, channels, length) for Conv1d and BatchNorm, and (batch size, feature dimension) for Flatten and FC.  
64). A ReLU activation function is subsequently applied to introduce nonlinearity. To alleviate overfitting, a Dropout Layer is employed after the activation function, randomly suppressing 20% of neuron outputs. Finally, the model outputs the predicted SOM content through another fully connected layer.
## Material and Method
### Study Site and Data
By contrast, the SWIR channel differences compared to VGT2-VGTFor _MODIS_, the annual average data of MOD1A1 and MOD13Q1 from May 2016 to May 2017 were obtained from the National Snow and Ice Data Center (NSIDC) Distributed Data Archive [25] and used as the basic data product to build downscaling factors. The _MODIS_ were based on Terra and Aqua and contained 36 spectral bands, including visible and infrared wavelengths. MOD11A1 denoted the surface temperature product of the _MODIS_ Terra satellite, namely grid data organized by tiles. The projection was sinusoidal, with daily time resolution and a spatial resolution of 0.93 km. MYD13Q1 data consisted of a 250-m L3 data products with a sinusoidal projection containing the Enhanced Vegetation Index (EVI); the effects of aerosols were corrected through the blue band. Figure 1: (**a**) Location of (**b**) the study site shown in grayscale from Soil Moisture Active Passive (SMAP) data, and (**c**) spatial distribution of sample plot (sample for validating estimated soil moisture). Data source: Western China Environmental and Ecological Science Data Center.
## 3 Results
### SLR Residuals  
To further investigate the effect of the a priori cuboid+ECOM1 model applied for BDS-3 satellites, SLRhas been used to investigate the orbits. BDS-3 C20, C21, C29 and C30 are the current ongoing missions of the International Laser Ranging Service (ILRS) [43], and the SLR range observations can be downloaded from the ILRS EUROLAS Data Center (EDC). The system eccentricities and station coordinates have been obtained through [[https://cddis.nasa.gov/archive/slr/slrocc/ecc_une.snx](https://cddis.nasa.gov/archive/slr/slrocc/ecc_une.snx)]([https://cddis.nasa.gov/archive/slr/slrocc/ecc_une.snx](https://cddis.nasa.gov/archive/slr/slrocc/ecc_une.snx)) (accessed on 25 July 2022) and [[https://cddis.nasa.gov/archive/slr/products/pos+eop](https://cddis.nasa.gov/archive/slr/products/pos+eop)]([https://cddis.nasa.gov/archive/slr/products/pos+eop](https://cddis.nasa.gov/archive/slr/products/pos+eop)) (accessed on 25 July 2022). BDS-3 satellites' retro-reflector offsets are obtained from CSNO. The SLR normal points (NP) from 1 June 2021 to 31 May 2022 collected by 15 SLR stations (Yarragadee, Changchun, Herstmonceux, Mount Stromlo, Beijing, Monument P, Graz, Shanghai, Grasse, Papeete, Potsdam, Zimmerwald, Simeiz, Katzively and Greenbelt) are processed. Meanwhile, three BDS-2 IGSOs (C08, C10, C13) and one BDS-2 MEO (C11) are also validated. The statistics of the SLR validation for BDS-3 and BDS-2 satellites are demonstrated in Table 9 and shown in Figures 20 and 21. The result coincide with the radial part of the external orbit comparison. The mean RMS value for BDS-3 four MEOs is approximately 4.53 cm, and the biases are approximately \(-\)4 cm and 3 cm for BDS-3 SECM and CAST MEOs, respectively. The mean RMS value for BDS-2 four satellites is approximately 4.73 cm, and the biases are approximately 1-2 cm.
## 3 Methodology
### Model Evaluation System
For the \(k\)-th class, Precision, Recall, and the F1-score are calculated as follows:  
\[\text{Precision}_{k}=\frac{TP_{k}}{TP_{k}+FP_{k}} \tag{12}\]  
\[\text{Recall}_{k}=\frac{TP_{k}}{TP_{k}+FN_{k}} \tag{13}\]  
\[\text{F1}_{k}=\frac{2*Precision*Recall}{Precision+Recall} \tag{14}\]  
where \(FP_{k}\) denotes the number of samples incorrectly predicted as class \(k\). The overall F1-score of the model is computed as the average F1-score across all classes, as follows:  
\[F1=\frac{1}{N}\sum_{k=1}^{N}F1_{k} \tag{15}\]  
A higher F1-score indicates a better balance between precision and recall, reflecting a model's effectiveness in handling both false positives and false negatives. This is particularly valuable in lithology prediction tasks, where misclassifications may lead to significant geological and operational consequences.
## 3 Background
### Anomaly Types  
An anomaly is an observation that deviates so much from the other observations as to arouse suspicious [27]. In practice, detecting anomalies is non-trivial because a significant amount of noise may be contained in the data, making the separation between noise and anomaly difficult to define. Therefore, anomalies are often determined according to application-specific criteria and the interest of the analyst. Recently, several studies [10; 14; 28; 29] have attempted to define abnormal behaviors in time series data. Deriving from the previous works, we summarize the anomaly types as univariate-based and multivariate-based, as shown in Figure 2.  
In univariate-based anomaly type, abnormal behaviors can be determined by a single time series. According to [26], anomalies are classified into point and pattern-wise behaviors. Point-wise anomalies occur only on individual time points. In that, global anomalies are the points that spike significantly from the rest of the points, while contextual anomalies glitch from their neighboring points. On the other hand, a pattern-wise behavior is caused by a sequence of continuous points. The major causes might be dissimilar shapelets, seasonality changes, and trend alternations. Shapelet anomalies are the sequences with dissimilar basic shapelets compared to the normal shapelet. Despite having similar basic shapelets, seasonal anomalies are caused by unusual seasonalities compared to the overall seasonality. Trend anomalies can be determined by a permanent shift in the mean of the data.  
Unlike the univariate-based anomaly type, multivariate-based anomalies occur when the relationships among multiple metrics are violated compared to the historical relationships, as mentioned in [10].  
Figure 1: Window sample \(w_{t}\in\mathbb{R}^{M\times L}\).  
Figure 2: Anomaly types in multivariate time series.