esa-sceva/satcom-chunk-collection huggingface dataset:
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 2 Model 1 for Antenna Dimension Prediction
Correspondingly, the antenna efficiency at 26 GHz, depicted in Figure 8, is around 81%, making it a viable solution for reliable 5G communication. To validate the reliability of our AI-optimized and simulated antenna design, a prototype of the proposed structure was fabricated, as shown in Figure 9. The antenna was tested in an anechoic chamber to ensure accurate and interference-free measurements. Figure 5: S11 comparison between simulation and prediction of the proposed antenna. Figure 6: S11 comparison between prediction and analytical method. Figure 8: Efficiency of the proposed antenna. Figure 7: Gain comparison between prediction and analytical method. Figure 9: Fabricated prototype of the single antenna. As presented in Figure 10, a comparison between the full-wave simulation and the measured results reveals excellent agreement across the operating frequency band. The S11 curves exhibit a very close match, with only a slight frequency shift observed, likely due to fabrication tolerances and environmental factors. This close correlation between the simulated, AI-predicted, and measured performances strongly confirms the effectiveness and practical feasibility of our design methodology. These results demonstrate that the proposed antenna not only performs well in simulation, but also maintains its characteristics under real-world conditions. To demonstrate the generalization of our machine learning framework and the quality of the constructed database, we show that it is possible to predict the optimal design parameters of a rectangular microstrip patch antenna printed on an FR4 substrate at n257 (28 GHz) band. So, our dataset generated from high-fidelity HFSS simulations captures the intricate relationship between frequency, the antenna's dimensions, and coefficient S11. As illustrated in Figure 11, our algorithm successfully identified a set of patch dimensions that achieved a return loss S11 below \(-40\) dB at 28 GHz (n257 band), demonstrating negligible deviation from full-wave simulation results. This remarkable alignment not only confirms the model's generalization capability across the 20-34 GHz range, but also underscores its ability to pinpoint optimal geometries for any target frequency between 20 and 34 GHz without requiring additional time-consuming electromagnetic simulations. The dimensions of the antenna dedicated to the 28 GHz frequency are illustrated in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Ls** & **Ws** & **Lp** & **Wp** & **Lf** & **Wf** \\ \hline Value (mm) & 11.50 & 11.50 & 2.95 & 6.20 & 5.54 & 2.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dimensions of the patch antenna for the 28 GHz band. Figure 10: Comparison of simulated and measured coefficient reflexion S11 for the single antenna.
## 2 Materials and Methods
### Ozone Experiment Setup and Samples Analysis  
A bench-scale experiment shown in Figure 1 was used to test pathogen inactivation under different treatment conditions. The experiment setup has various components including an ozone generator, ozone monitor, ozone distributor, ozone concentration controller, and treatment chamber. The O\({}_{3}\) was generated using an O\({}_{3}\) generator (Model NANO, AbsoluteOzone(r), Edmonton, AB, Canada). The concentration of O\({}_{3}\) was controlled by controlling the inflow of ultra-pure air. Mixing of ozone with air produced different levels of ozone, and concentrations of ozone were monitored continuously using an O\({}_{3}\) monitor (Model 106-H Ozone Monitor(tm),2B Technologies, Boulder, CO, USA). Three round bottom flasks (each flask volume of 250 mL named as a treatment chamber) with 20 mL of feedstock (i.e., liquid dairy manure) in each chamber were connected with the ozone tubes, which provided continuous and constant ozone flow to the chamber. The O\({}_{3}\) concentration was set as 3%, 6%, and 9%, which were equal to 43.26, 87.40, and 132.46 mg\(\cdot\)L\({}^{-1}\), respectively. We selected this range because the ability of the ozone generator was to produce ozone concentrations within this range. The time of ozone exposure to manure was set to 0, 30, 60, and 120 min at each concentration of ozone. In the inoculated sterile manure, 1 mL per 10 mL of feedstock (1/10 dilution) of the pathogen solution (either _E. coli_ or _Salmonella_) was added 12 h prior to O\({}_{3}\) treatment and incubated for 12 h statically at 37 \({}^{\circ}\)C to obtain \(\sim 10^{6-8}\) CFU/mL of each pathogen prior to treatment.  
After the O\({}_{3}\) treatment, 1 mL sample from the treatment chamber was collected, transferred to the centrifuge tube, and diluted 1/10 with 9 mL PBS. Subsequently, pathogens in the samples were examined by plating serially diluted samples onto specific agar plates. To enumerate _S. typhimurium_ LT2, samples were plated on Difco XLD Agar (Becton, Dickinson, and Company) plates. To determine _E. coli_ levels, samples were plated on MacConkey II Agar (Becton, Dickinson, and Company). The concentrations of the bacteria were reported as colony-forming units (CFU)/mL of dairy manure. The moisture of the samples was analyzed by weight loss on 102-105 \({}^{\circ}\)C heating. Total organic matter was determined by weight loss on ignition of dried ground samples at 450 \({}^{\circ}\)C.
## 3 Online 3D Fuzzy Modeling
### Spatial MSVR Learned 3D Fuzzy Model
\tag{13}\]  
For spatial multi-output nonlinear optimization problems, the kernel function \(K\left(x^{j},x^{k}\right)=\left<\Phi\left(x^{j}\right),\Phi\left(x^{k}\right) \right>=\Phi^{T}\left(x^{j}\right)^{T}\Phi\left(x^{k}\right)\) is introduced to enable the system to handle nonlinear dynamics, then the nonlinear regression optimization problem is:  
\[max-\frac{1}{2}\sum\limits_{i=1}^{q}\sum\limits_{j,k=1}^{N}\left(\alpha_{i}^{ j}-\hat{\alpha}_{i}^{j}\right)\left(a_{i}^{k}-\hat{\alpha}_{i}^{k}\right)K\left(x ^{j},x^{k}\right)+\sum\limits_{i=1}^{q}\sum\limits_{k=1}^{N}y_{i}^{k}\left( \alpha_{i}^{k}-\hat{\alpha}_{i}^{k}\right)-\varepsilon_{i}\left(\alpha_{i}^{k} +\hat{\alpha}_{i}^{j}\right) \tag{14}\]  
\[s.t.\quad\left\{ \begin{array}{rl}\sum\limits_{k=1}^{N}\left(\alpha_{i}^{j}- \hat{\alpha}_{i}^{k}\right)=0\\ 0\leq\alpha_{i}^{j},\hat{\alpha}_{i}^{k}\leq C\end{array} \right. \tag{15}\]  
Then, the expression for the spatial multi-output model is:  
\[f_{i}(x)=\sum\limits_{k=1}^{N}\left(\alpha_{i}^{k}-\hat{\alpha}_{i}^{k}\right) K(x_{k},x)+b_{i}=\sum\limits_{k\in SV}\left(\alpha_{i}^{k}-\hat{\alpha}_{i}^{k} \right)K(x_{k},x)+b_{i} \tag{16}\]  
where the samples corresponding to the non-zero values in \(\left(\alpha_{i}^{k}-\hat{\alpha}_{i}^{k}\right)\) are the spatial support vectors (\(SVs\)). According to Appendix A, the nonlinear mathematical expression of the 3D fuzzy model with a bias term is as follows:  
\[\hat{y}(z_{i},t)=\sum\limits_{l=1}^{NR}\varphi_{l}(z_{i})\psi^{l}(t)+b_{i} \tag{17}\]  
Comparing (16) and (17), let \(\left(\alpha_{i}^{k}-\hat{\alpha}_{i}^{k}\right)=\varphi_{i}(z_{i})\), \(K(x_{k},x)=\psi^{i}(t)\). Then, we can have the following equivalent relationship:  
\[\hat{y}(z_{i},t)=f_{i}(x) \tag{18}\]The SFBF of the 3D fuzzy model is mapped to the spatial kernel function of MSVR, and the support vector is used to determine the membership function center of the antecedent of the fuzzy rule. Secondly, the spatial basis function of the consequent is generated by the Lagrange multiplier method. In this way, the design of the 3D fuzzy model can be transformed into the training problem of optimizing the spatial MSVR. Therefore, under the above specific conditions, the decision function of MSVR is equivalent to the 3D fuzzy model. In order to improve the adaptability of the model in different environments, the adaptive update of the regularization parameter \(C\) and the error tolerance \(\varepsilon\) in the support vector is crucial. The optimization of these parameters plays a key role in controlling the complexity and accuracy balance of the model, so they must be dynamically adjusted according to real-time data. In addition, we propose an online update strategy based on the SGD method to adjust the learning parameters in real time, further improving the training efficiency and accuracy of the model.
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 5 Methodology
### Dynamic Query Window-Based Selector for Stream Rate Change
#### 5.3.1 Problem Formulation  
Many factors affect stream query processing performance, such as varied stream rates, workload requirements, and resource limitations. According to our windows-based stream analysis, the window configuration in the streaming query (i.e., type and size) significantly affects the performance of the query engine. Assuming that the stream structure remains fixed and the query and the required resources are available according to the data application needs.  
A proper window configuration is required for stream query running on various stream rates by estimating factors related to the stream analytic process. These factors could be called input parameters for querying window configuration over different stream rates. Some parameters could be identified priory using collected statistics from the past analytic process (i.e., queries) such as schema, stream rate behavior and previously executed job statistics. Some of the parameters will be identified by users such as resource capacity and application requirements, i.e., latency, accuracy, data enrichment, etc. The other parameters values could be captured during running stream queries on a stream processing system such as live stream rate and current job status. These parameters could be described in vectors as follows.  
\[Stat_{histo}=[streamrate,streamjobstatus]\]  
\[Stat_{live}=[streamrate,streamjobstatus]\]  
\[\mathit{Req}=[rq_{1},rq_{2},...rq_{i},...,rq_{n}],1<=i<m\]  
\[\mathit{Res}=[rs_{1},rs_{2},...rs_{i},...,rs_{n}],1<=i<n\]  
where \(Stat_{histo}\) and \(Stat_{live}\) vectors denote the collected historical stream statistics from the past streaming jobs behaviour and the live streaming statistics including stream rate itself and current streaming job status respectively. \(\mathit{Req}\) denotes the application requirements for the desired analysis, such as the maximum number of produced tuples known as data enrichment. Data enrichment is a process of data collection to provide a richer profile that will be used for further data analysis, such as machine learning and deep learning models that require large sets of data. \(\mathit{Res}\) denotes the number of given resources to the stream processing system including CPU, memory and network bandwidth etc.
## 3 Results and Discussion
### Computational Overhead
#### 3.1.4. Break-Glass Approach  
In lightweight break-glass, the ECM requires 0.021S for performing the complete decryption with two hash functions. However, in the proposed method, the complete break glass process is performed within 0.018 s. This shows that the proposed MCP-ABE-BG requires minimal computational time compared to the multi-authority multi-ABE and lightweight break-glass approach. Therefore, the proposed method MCP-ABE-BG is computationally effective for the smart healthcare environment.
###### Abstract  
Little is known about the temperature dependence of electron transfer occurring at real metal surfaces. For iron surfaces scratched in seven environments, we report Arrhenius activation energies obtained from the data of photoelectron emission (PE) and X-ray photoelectron spectroscopy (XPS). The environments were air, benzene, cyclohexane, water, methanol, ethanol, and acetone. PE was measured using a modified Geiger counter during repeated temperature scans in the 25-339 \({}^{\circ}\)C range under 210-nm-wavelength light irradiation and during light wavelength scans in the range 300 to 200 nm at 25, 200, and 339 \({}^{\circ}\)C. The standard XPS measurement of Fe 2\(p\), Fe 3\(p\), O 1\(s\), and C 1\(s\) spectra was conducted after wavelength scan. The total number of electrons counted in the XPS measurement of the core spectra, which was called XPS intensity, strongly depended on the environments. The PE quantum yields during the temperature scan increased with temperature, and its activation energies (\(\Delta E_{\text{AUp1}}\)) strongly depended on the environment, being in the range of 0.212 to 0.035 eV. The electron photoemission probability (\(\alpha A\)) obtained from the PE during the wavelength scan increased with temperature, and its activation energies (\(\Delta E_{\text{\alpha A}}\)) were almost independent of the environments, being in the range of 0.113-0.074 eV. The environment dependence of the PE behavior obtained from temperature and wavelength scans was closely related to that of the XPS characteristics, in particular, the XPS intensities of O 1\(s\) and the O\({}^{2-}\) component of the O 1\(s\) spectrum, the acid-base interaction between the environment molecule and Fe-OH, and the growth of non-stoichiometric Fe\({}_{x}\)O. Furthermore, the origin of the \(\alpha A\) was attributed to the escape depth of hot electrons across the overlayer.  
iron surface; scratching environments; X-ray photoelectron spectroscopy (XPS); photoelectron emission; temperature scan; wavelength scan; electron transfer; Arrhenius activation energy; electron photoemission probability +
Footnote †: journal: Applied Physics Letters
## 5 Future Work  
Although CMLs, as opportunistic sensors, have the advantages of a low cost, high spatial and temporal resolution and wide coverage over traditional rain sensors, there is still a lot of room for improvement, as mentioned in Section 4. As shown in Table 3, the focus of our future work may need to concentrate on the following points, which are detailed in the remainder of this section.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Future Works** & & **Highlights** \\ \hline \multirow{3}{*}{Enhanced Collaboration with Mobile Network Operators} & 1. & Improve the sampling frequency and strategy for CML data [174]. \\  & 2. & Promote data sharing. \\  & 3. & Obtain information other than signal strength information (e.g., phase) [100; 118; 175]. \\  & 4. & Mutual benefits for researchers and CML operators [176]. \\ \hline \multirow{6}{*}{Improvement Processing Procedures} & 1. & Improve the accuracy of dry and wet period classification. \\  & 2. & Investigate baseline dynamics [177]. \\  & 3. & Establish WAA models that take into account the characteristics of CML. \\  & 4. & Improve the accuracy of rainfall inversion by combining deep learning and power-law relationship [178]. \\ \cline{1-1}  & 5. & Develop rainfall mapping methods that consider the unique topology and measurement characteristics of CMLs [179; 180]. \\ \cline{1-1}  & 6. & Investigate efficient and cost-effective methods for calibrating CML processing procedures. \\ \hline \multirow{2}{*}{Data Assimilation} & 1. & Assimulate CML data from different operators [181]. \\  & 2. & Assimulate rainfall observations from CML with other rain sensors [91]. \\ \hline \hline \multicolumn{2}{c}{Error Analysis of CMLs Applied to} & Study the effects of unique topographic and climatic conditions in mountainous areas on Mountainous Areas & \\ \cline{1-1}  & & rainfall measurement techniques for CMLs. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Potential future work regarding rainfall measurement techniques based on CMLs.
# Compressive Ghost Imaging of the Moving Object Using the Low-Order Moments
###### Abstract  
Ghost imaging reconstructs the image based on the second-order correlation of the repeatedly measured light fields. When the observed object is moving, the consecutive sampling procedure leads to a motion blur in the reconstructed images. To overcome this defect, we propose a novel method of ghost imaging to obtain the motion information of moving object with a small number of measurements, in which the object could be regarded as relatively static. Our method exploits the idea of compressive sensing for a superior image reconstruction, combining with the low-order moments of the images to directly extract the motion information, which has the advantage of saving time and computation. With the gradual motion estimation and compensation during the imaging process, the experimental results show the proposed method could effectively overcome the motion blur, also possessing the advantage of reducing the necessary measurement number for each motion estimation and improving the reconstructed image quality.  
g 2020; 10.7941; doi:10.3390/app10217941
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 3 System Framework  
This study proposes a novel portfolio decision-making framework utilizing a Spatial-Temporal Graph Attention Network (STGAT). As illustrated in Figure 1, the architecture employs a seasonal-trend decomposition approach (see details in Section 3.1) to dissect raw stock temporal data into distinct components encapsulating diverse market information. Subsequently, modules, including a graph attention module (see details in Section 3.3.1), a temporal convolution module (see details in Section 3.3.2), and a multi-layer perceptron (MLP), are applied to process these decomposed components. The integration of these processed features yields a comprehensive spatial-temporal representation that simultaneously captures inter-stock correlations and temporal dynamics. The model culminates with a secondary feature refinement through an additional temporal convolution module and an output module, ultimately generating precise stock price predictions.  
Additionally, the prediction result and stock volatility analysis result are further combined as a convincing indicator with which to evaluate a stock's potential and select low-risk stocks as portfolios to simulate the trading process in a test dataset. Judging by  
Figure 1: Architecture of STGAT.  
the results, STGAT effectively selects those stocks that balance high return potential with minimized investment risk and achieves high cumulative returns, offering a sophisticated approach to portfolio optimization.
## 3 Experimental Setup and Results
### Range and Velocity Estimation Using Radar-Mote  
We have conducted experiments with a variety of conditions as shown in Table 2 and determined the target range and velocity using data of the Doppler radar-mote. Figure 6 shows examples of range-velocity intensity plots when the target was moving towards and away from the radar at a distance of 1.2 m with a speed of 0.69 m/s (dial position 80 on the control unit). Negative velocity indicates that the target is moving towards the radar.  
Table 4 summarizes the estimated range and velocity of the target using Doppler radar measurements for all experimental configurations. Each entry in Table 3 gives the average range and velocity of 20 runs with the standard deviation of the runs. The table also shows the range as knows from the  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline \multirow{2}{*}{Velocity (m/s)} & \multirow{2}{*}{0.8 m} & \multirow{2}{*}{1.2 m} & \multirow{2}{*}{1.6 m} & \multirow{2}{*}{2.0 m} \\  & & & \\ \hline \(0.74\pm 0.02\) & V: \(0.76\pm 0.04\) & V: \(0.74\pm 0.04\) & V: \(0.76\pm 0.03\) & V: \(0.74\pm 0.07\) \\ _[D = 100]_ * & R: \(0.89\pm 0.08\) & R: \(1.29\pm 0.14\) & R: \(1.62\pm 0.14\) & R: \(2.01\pm 0.12\) \\ \(0.69\pm 0.01\) & V: \(0.68\pm 0.04\) & V: \(0.68\pm 0.04\) & V: \(0.67\pm 0.04\) & V: \(0.65\pm 0.03\) \\ _[D = 80]_ * & R: \(0.86\pm 0.08\) & R: \(1.25\pm 0.09\) & R: \(1.56\pm 0.14\) & R: \(1.97\pm 0.17\) \\ \(0.56\pm 0.01\) & V: \(0.57\pm 0.03\) & V: \(0.57\pm 0.03\) & V: \(0.56\pm 0.03\) & V: \(0.57\pm 0.03\) \\ _[D = 60]_ * & R: \(0.83\pm 0.07\) & R: \(1.26\pm 0.12\) & R: \(1.57\pm 0.18\) & R: \(1.97\pm 0.19\) \\ \(0.40\pm 0.01\) & V: \(0.47\pm 0.01\) & V: \(0.47\pm 0.01\) & V: \(0.49\pm 0.03\) & V: \(0.45\pm 0.02\) \\ _[D = 40]_ * & R: \(0.85\pm 0.08\) & R: \(1.24\pm 0.13\) & R: \(1.61\pm 0.14\) & R: \(1.95\pm 0.17\) \\ \hline \end{tabular}  
* D indicates the Dial position of the velocity control unit in the range 100 (max) and 40 (min) used in the present experiments.  
\end{table}
Table 4: Velocity (V) and Range (R) estimation from Doppler radar data.  
Figure 6: Examples of range-velocity intensity plots as the target was moving towards and away from the radar at a distance of 1.2 m at a speed of 0.69 m/s. The front of the target was covered with a metallic tetrahedron reflector. (**a**) Case of forward movement; (**b**) Backward movement.  
markings of the triggering mode position, as well as the velocity independently determined from known distance and time stamp measurements. We conclude that radar measurements determine the range and velocity with good accuracy in all experiments. There is an increased error in the case of the lowest velocity value of 0.4 m/s, which is attributed to the actual variation and of the train movement during slower lower velocities.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 1 Introduction
Therefore, designing fast and efficient anomaly detection algorithms according to the characteristics of traffic data has become an urgent goal in the field of network security. In this paper, we study network intrusion detection based on the deep learning method in order to reduce the workload of security practitioners and enhance the network situational awareness of security systems, and provide strong support for the improvement of China's network security technology system and the construction of the network power strategy. The main contributions of this paper can be summarized as follows:  
1. In this paper we propose an unsupervised anomaly detection algorithm based on the one-class long short-term memory (OC-LSTM) network. The model is an end-to-end single-class neural network with a specially designed loss function equivalent to the optimization objective of a single-class SVM. 2. By directly adopting the objective of representation learning for anomaly detection, OC-LSTM can directly process raw data without using unsupervised transfer learning for further feature extraction. This will help to discern complex anomalies in large datasets, especially when the decision boundary between normal and anomalous data is highly nonlinear. The rest of the paper is structured as follows. In Section 2, a detailed overview of related research regarding one-class classification and anomaly detection is given. Section 3 outlines the main models of the proposed OC-LSTM method. The experimental setup, including the dataset used, the compared methods, and the evaluation metrics, is described in Section 4. An in-depth discussion and analysis of the obtained experimental results regarding OC-LSTM and other state-of-the-art methods are the focus of Section 5. Section 6 provides the conclusions of this paper, as well as the main points and directions of future work.