esa-sceva/satcom-chunk-collection huggingface dataset:
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 5 Results and Discussion
### Radiation Characteristics  
The antenna's radiation pattern measurements in an anechoic chamber are given in Figure 22a. The pattern was obtained by exciting one port and deactivating all the other ports with the matched condition for both the xz and yz planes. As shown in Figure 22b, the antenna had an average peak gain and radiation efficiency of 4 dBi and 90% within the working band of interest. Figure 23 displays the co- and cross-polar radiation characteristics of the principal planes at the 5.03 GHz resonance frequency. In the principal planes (E and H), the proposed antenna provided bidirectional as well as omnidirectional characteristics, respectively. A dual-ridge horn antenna that operates between 1-18 GHz was utilized in this study as the reference antenna. Figure 23 reveals that the proposed antenna exhibited cross polarization values of \(-22\) dB in the E plane and \(-23\) dB in the H plane, indicating the effective and desired antenna radiation.  
Figure 19: Fabrication prototype of the proposed antenna.  
Figure 20: Scattering parameter measurement of the proposed antenna using a vector network analyzer.
## 5 Simulation Method Uncertainties
### Angular Error
#### 5.2.4 Changing Position over Time  
Finally, since the measurements are integrated over 573 ms and the aircraft moves in that time, the measurement is really of the integrated RCS across these angles and orientations. Since the IMU update rate (20 Hz) is far higher than the integration time, we know the location and aircraft at 11-12 points for each measurement time. We can therefore calculate the variation of the incidence and scattering angles over these times for our flight. The median variation in the azimuth angle of the transmitter is 0.12\({}^{\circ}\) and to the receiver of 0.023\({}^{\circ}\). The equivalent median elevation variations are 0.00031\({}^{\circ}\) and 0.016\({}^{\circ}\), respectively.
## 4 Experimental Section and Results Analysis
### Experimental Setup  
As a baseline comparison algorithm, this paper selects three other incremental learning algorithms, adaptive Hoeffding tree [32], SAM-kNN regressor [33] and adaptive random forest [34], for comparing the predictive and process parameter design performance of OASVR, as well as OASVR with regular SVR and AONSVR. Both tree-based models have high learning performance and low demands with respect to input preparation and hyperparameter tuning. And, the size of the sliding window for SAM-kNN can be continuously adapted during training.  
The online model is trained incrementally with each incoming data point. At timestamp \(t\), the model predicts the data at timestamp \(t\) based on the training data from timestamp \(1\) to \(t-1\). The performance of the model is evaluated by calculating the difference between the actual and predicted values.  
We use RMSE and \(R^{2}\) as evaluation metrics for the model. RMSE stands for root mean square error and is a commonly used metric in statistics and machine learning to measure the accuracy of predictions of the model. It quantifies the difference between predicted values and actual values, providing a single number that represents the overall error of the model. The equation for RMSE is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
where \(n\) is the number of the dataset, \(y_{i}\) is the actual value for the \(i\)-th data and \(\hat{y}_{i}\) is the predicted value. The lower the RMSE, the better the model performance.  
The coefficient of determination, often denoted as \(R^{2}\), is a statistical measure that assesses the proportion of the variance in the dependent variable that can be explained by the independent variables in a regression model. The formula for \(R^{2}\) is given as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\bar{y })^{2}} \tag{11}\]  
where \(\bar{y}\) is the mean of the actual values, and other variables have the same meanings as the variables in the RMSE formula. A higher \(R^{2}\) value suggests that the model is better at explaining the variability in the dependent variable.  
The experiment was conducted on a system running the Windows 10 operating system with an AMD Ryzen 7 4800H CPU with 16 GB of memory. Python version 3.8 and scikit-multiflow version 0.5.3 were employed.
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 2 Dual-Window Superpixel Data Augmentation (DWS)
### Patch Transformation  
Several augmentation techniques are introduced in this paper using the patch subdivision principle described in the previous section in combination with the traditional rotate and flip transformations. They can be divided into techniques with transformations applied to the inner region (prefixed with the term inner) and techniques with transformations applied to both the outer and inner regions independently (prefixed with the term dual).  
Figure 4 shows examples of the outputs of all the techniques considered in this paper. None indicates that no data augmentation is applied to the input patch. Random occlusion (RO), as presented in [13], performs selective data erasing. Rotate and flip are the traditional data augmentation techniques where the homonymous operation is applied over the whole patch. In addition to those, the following techniques are proposed:  
1. Inner-Rotate (4\(\times\)): A set of 90, 180, and 270-degree rotation operations are applied to the inner region only, yielding up to four samples.
2. Inner-flip (4\(\times\)): A set of three flip or mirroring operations is applied to the inner region only, yielding up to four samples.
3. Dual-rotate (4\(\times\)/16\(\times\)): A set of 90, 180 and 270-degree rotation operations are applied to the full patch, yielding four samples. Afterward, for each of those four samples, the same operations are applied again to only the inner region, producing up to sixteen samples per input patch.
4. Dual-flip (4\(\times\)/16\(\times\)): A set of three flip or mirroring operations is applied to the full patch, yielding four samples. Afterward, for each of those samples, the same operations are applied again to only the inner region, producing up to sixteen samples per input patch.  
Figure 4: List of data augmentation techniques considered. The red orientation marks (dots) are shown to indicate the orientation of the data relative to the original image.  
Figure 3: Details of the patch extraction and patch subdivision stages.  
The dual-flip 16\(\times\) technique is illustrated in Figure 5. The arrows next to the patches indicate flip transformations performed on certain axes. Long arrows each represent a flip applied to the outer patch, whereas small arrows each represent the same operation applied to the inner region. During dual-flip 16\(\times\) patch transformation, the following operations take place: first, flip transformations over the horizontal axis, vertical axis and a combination of both are applied to the full patch producing patches 1, 5, 9 and 13. Next, for each of the outputs from that step, the same flip transformations are applied only to the inner region (row of transformations at the bottom of the figure).
## 3 Materials and Methods
### FDA Analysis  
Fluorescein diacetate is a compound that can be hydrolyzed by several enzymes, like protease, lipase, and esterase. The hydrolysis product is fluorescein, characterized by a strong yellow color and, therefore, easily detected by spectrophotometry. In this way, an overall estimation of microbial activity can be obtained.  
Figure 1: Scheme of time-domain reflectometry (TDR) acquisition set-up. (a) Soil column with the TDR probe; (b) data-logger; (c) external PC for data processing.  
Methodology reported by [PERSON] and [PERSON] [36] and modified according to [PERSON] and [PERSON] [37] was used. Two solutions were used:  
* Potassium phosphate buffer--8.7 g/L of K\({}_{2}\)HPO\({}_{4}\) and 1.3 g/L of KH\({}_{2}\)PO\({}_{4}\). The solution pH is 7.57, which falls within the acceptable range to get the FDA hydrolysis reaction, namely between pH 7-8;
* FDA stock solution in acetone at a concentration of 2 g/L.  
Samples of 2 g of soil (on a wet basis) were mixed with 15 mL of phosphate buffer and 100 \(\upmu\)L of FDA stock solution in acetone. Each sample was agitated at 50 rpm for 1 h. Then, to stop the hydrolysis reaction, 15 mL of acetone were added. Subsequently, the samples were centrifuged at 6000 rpm for 5 min and then filtered through a 1.2 \(\upmu\)m filter to remove any colloidal particles. Absorbance was measured by spectrophotometry at 490 nm. Two replicas were prepared for each sample and one blank containing only the potassium phosphate solution.  
A series of samples at known concentrations of pure fluorescein (dissolved in potassium phosphate buffer) was prepared to obtain a calibration line that correlated the absorbance measured with the spectrophotometer with the quantity of fluorescein diacetate hydrolyzed by the enzymatic activity of microorganisms.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 6 Conclusions  
### Summary of Key Findings  
The main objective of this study was to determine which of the selected ML methods is best suited for identifying anomalies in IoT networks while ensuring the understandability and reliability of the obtained results. The key findings of the paper are as follows:  
* The importance of choosing the right method depending on the specifics of the data, available computational resources, and requirements for accuracy and speed of detection was reiterated;
* A review of popular ML algorithms such as Random Forest, Isolation Forest, Autoencoder, as well as neural networks (CNN, RNN, LSTM), gradient methods (GB, XGBoost), K-NN, SVM, and decision trees was presented. Their advantages and disadvantages in the context of detecting anomalies in IoT networks were described;
* A comparative analysis of available studies using IoT-23 and CIC-IoT 2023 datasets was conducted, taking into account supervised and unsupervised approaches to detecting anomalies. The results of these studies indicate that Random Forest and Autoencoder are highly effective in this task;
* Limitations of traditional centralized ML approaches, such as privacy threats and scalability issues for large IoT networks were described;Further research directions were proposed, including the use of federated learning, hybrid models combining rule-based systems with ML, and the development of XAI.  
Overall, the study demonstrated the effectiveness of selected ML methods in detecting anomalies in IoT networks and emphasized the importance of data preprocessing and addressing data imbalances.
## 5 Discussion  
This section discusses the results of the synthetic dataset, the real datasets and the ablation experiment in Section 4. First, the quantitative analysis of six different algorithms on the synthetic dataset, Jasper Ridge dataset and Samson dataset found that compared with other algorithms, the abundance results obtained using FCLSU on the three datasets were not satisfactory, and it could be seen that the effect fluctuated according to the complex distribution of real ground objects. On the other hand, according to the visual effect of the abundance map, it can be seen that the visualization quality of the three methods--GLNMF, GraphL and gtvMBO--fluctuated greatly, especially for GLNMF. Due to the SV caused by various factors in the synthetic dataset, the visualization effect of GLNMF was the most affected, which also showed that GLNMF could not handle the influence of SV well. The method based on deep learning achieved good results on the above three datasets, which showed its potential in the task of unmixing. Due to the dimensionality reduction factor, DAEU lost the rich spatial-spectral information in HSI, and the abundance map obtained by DAEU displayed the phenomenon of identification error, especially in the Samson dataset, which was less affected by noise. The result of EGU-pw was excellent, but its use of the feature information of HSI was still insufficient.  
On the other hand, by making full use of spatial-spectral information, the EACNN's unmixing performance was improved. By sharing the parameters of two networks, the overall network obtained more comprehensive feature information and reduced the influence of SVs. Experiments on synthetic, real datasets and the final ablation experiments showed that our model displayed good performance.  
Of course, the proposed method also had some shortcomings, and it also had some dependence on the extraction effect of the endmember bundle. In the future, we will seek a more simple and efficient way, such as a multi-modal method, to improve the precision of HU while balancing performance and efficiency.
## 3 Results
### Vegetation Recovery in the Vegetation Damage Area
#### 3.3.2 The Relationship between Vegetation Recovery and Earthquake Intensity  
The proportion of the recovered fully type in the damage area was noticeably negatively associated with earthquake intensity. In areas where evidence of earthquake intensity increased (more heavily damaged in the damage area), vegetation recovery was slower. The level of recovered fully type was lowest in the intensity-XI (high intensity) zone, which indicates that only 22.15% had recovered to the level before the earthquake. The recovered fully type in the intensity-VII zone was highest, where 58.45% had recovered to the level before the earthquake. Vegetation with not recovering type accounted for 10.65% in the intensity-XI zone, and vegetation with not recovering type increased from 4.87% to 15.07% in intensity zones X to VII, respectively. (Figure 7 and Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**County**} & \multirow{2}{*}{**Total Area**} & \multicolumn{5}{c}{**Vegetation Recovery Types**} \\ \cline{3-6}  & & **Damaged** & **Not Recovering** & **Recovering** & **Recovering** & **Recovered** \\  & **(ha)** & **Area (ha)** & **(\%)** & **Slightly (\%)** & **Largely (\%)** & **Fully (\%)** \\ \hline Qingchuan & 321,643 & 17,316 & 2.17 & 4.05 & 21.08 & 72.69 \\ Lixian & 432,007 & 95,766 & 11.60 & 8.04 & 21.15 & 59.21 \\ Pingwu & 595,201 & 18,548 & 3.88 & 5.44 & 35.01 & 55.67 \\ Jiangyou & 272,271 & 9991 & 16.07 & 15.63 & 27.31 & 40.99 \\ Maoxian & 389,930 & 60,710 & 3.68 & 9.66 & 46.48 & 40.18 \\ Mianzhu & 124,786 & 27,704 & 6.26 & 11.59 & 45.11 & 37.05 \\ Wenchuan & 408,621 & 138,140 & 8.25 & 17.23 & 38.41 & 36.11 \\ Beichuan & 308,516 & 24,285 & 6.24 & 13.02 & 49.90 & 30.85 \\ Pengzhou & 142,327 & 24,604 & 13.84 & 24.50 & 33.12 & 28.54 \\ Shifang & 82,214 & 18,048 & 9.26 & 23.63 & 41.57 & 25.54 \\ Dujiangyan & 121,027 & 22,798 & 20.84 & 22.57 & 32.56 & 24.03 \\ Anxian & 118,352 & 17,779 & 6.45 & 26.17 & 48.29 & 19.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Differences in vegetation recovery by county.  
Figure 7: The relationship between vegetation recovery and earthquake intensity.
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### The Influence of Cluster Setup
#### 4.5.1 The Influence of the Cluster Load  
The cluster load is controlled by job_rate. For example job_rate = 0.7 means that the probability of a job arriving at each time step is 0.7. The average slowdown of each scheduler varies with the cluster load, as shown in Figure 10. The results showed that as the cluster load increases, the average slowdown of all schedulers gradually increases. Figure 10 shows that without using the kill policy, both DeepRM and RLSchert based on DRL are better than traditional heuristic algorithms. Figure 10 shows that when using the kill policy, RLSchert is significantly better than the other compared schedulers. When the cluster load is too low, the job can be executed at any time without queuing, so all schedulers show a similar average slowdown. The higher the cluster load, the more tasks are queued, and RLSchert gradually shows its advantages. In addition, it can be observed from the figure that among all the static algorithms, the SAF performs best under different loads.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Method & Tetris & Random & SJF & Tetris* & SAF & DeepRM & **RLSchert** \\ \hline \multicolumn{8}{c}{_Scheduling **without** kill policy_} \\ \hline AveSlowdown & 13.77 & 12.21 & 10.29 & 10.24 & 10.23 & 9.63 & **8.93** \\ \hline \multicolumn{8}{c}{_Scheduling **with** kill policy_} \\ \hline AveSlowdown & 14.75 & 11.2 & 10.25 & 10.41 & 10.33 & - & **8.32** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The policy quality of the average slowdown on the test set.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Method & Tetris & Random & SJF & Tetris* & SAF & DeepRM & **RLSchert** \\ \hline \multicolumn{8}{c}{_Scheduling **without** kill policy_} \\ \hline completion time & 98.66 & 98.15 & 80.36 & 80.37 & 80.3 & 77.18 & **76.27** \\ \hline \multicolumn{8}{c}{_Scheduling **with** kill policy_} \\ \hline completion time & 105.66 & 94.95 & 80.82 & 80.93 & 81.13 & - & **76.18** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The policy quality of the average completion time on the test set.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 4 Simulation Results and Discussion
### Transmit Power Saving  
Without power control, each user transmits a signal with its own maximum power. The spectral efficiency of this conventional scheme is low despite its high power consumption. The proposed power control scheme reduces the transmit power of each user according to the power control criterion so that the total transmit power of the proposed scheme is considerably lower than that of the conventional scheme without power control. The consumption of transmit power per user is simulated in Figures 13 and 14. The conventional scheme without power control uses a constant transmit power. In contrast, the transmit power of the proposed scheme decreases as the noise power at the BS decreases. Even with a reduced transmit power, the proposed method outperforms the conventional methods as shown in the previous figures (Figures 7-11).  
Figure 12: Spectral efficiency for 64 QAM-2/3 versus noise power with different values of \(\gamma\).  
Figure 13: Transmit power of QPSK-1/2 versus noise power.
# Taxonomy of Scheduling Problems with Learning and Deterioration Effects
## 2 Materials and Methods
### Research Questions  
With the objective of identifying approaches related to scheduling problems with learning and deteriorating effects, a general research question was defined as follows: _To what extent have scheduling problems been modeled and solved to deal with learning and deteriorating effects in job processing times?_  
To properly address the above question, five specific research questions are formulated as follows:  
\(RQ1\): What are the directions toward which researchers are focusing scheduling problems with learning and deterioration effects?  
\(RQ2\): What are the system configurations studied in the literature?  
\(RQ3\): Which have been the most common objective functions considered?  
\(RQ4\): What approximations have been used in the scientific literature to model both learning and deterioration effects?  
\(RQ5\): What solution methods have been used to solve the problems?
## 3 Theoretical Analysis and Numerical Model
### Comparison between Measured and Simulated  
Due to the possible differences between the tunnel model and the construction site, in order to verify the accuracy of the tunnel model, the measured values of E, F, and G measurement points at the human breathing height of 1.6 m in Figure 2 were compared with the simulated values, as shown in Figure 7. As can be seen in Figure 7, the relative error of the measured value and the numerical simulation value of each measurement point are within \(\pm\)10%, and the air flow velocity and numerical simulation results at each measurement point of the double-hole tunnel are basically the same, and the tunnel flow field obtained by numerical simulation can be considered to be accurate.  
Figure 8 is the vector diagram of the air flow of the tunnel at \(L_{\mathrm{t}}\) = 130 m. It can be seen from Figure 8 that there is a vortex area in the air flow of the tunnel in the advanced side tunnel, which flows to the undercut side tunnel along the top of the traffic cross passage, and the air flow in the middle and lower part of the vortex is redirected and flows back into the advanced side tunnel; \(K\) = 22.49 \(>\) 1 is obtained by extracting the flow and resistance of each branch of the double-hole tunnel. Also, the air flow can be clearly determined from the undercut side tunnel to the advance side tunnel, which is consistent with the direction of smoke flow diffusion at the scene in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Set Options** & **Parameter Setting** \\ \hline \multirow{4}{*}{Boundary conditions} & Inlet boundary conditions & Velocity inlet \\  & Outlet boundary conditions & Pressure outlet \\  & & inlet1 (m/s) & 8.16 \\  & & inlet2 (m/s) & 16.18 \\  & & inlet1 (m/s) & 16.18 \\  & & inlet2 (m/s) & 8.16 \\ \hline \multirow{4}{*}{Solver settings} & Time & stable \\  & Solver type & Pressure-based \\  & Energy equation & Off \\  & Turbulence model & Realizable \(k\)-\(\varepsilon\) \\ \cline{1-1}  & Pressure-velocity coupling scheme & simple \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters in the numerical simulation.  
Figure 7: Comparison of simulated and measured data and error analysis.