esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Source Array-Based Multiple Time-Reversal Focusing
## 4 Simulations in Waveguide  
In this section, we investigate the behavior of the adaptive VSA using numerical simulations. As the weight vector, \(\mathbf{w}\), is derived from the constraint matrix, \(\mathbf{M}\), which partially includes the characteristic of the homogeneous medium with constant sound speed, \(c\), the performance of the adaptive VSA might be sensitive if the actual sound speed in the water column is different from the assumed constant sound speed, \(c\). Thus, a fundamental question is whether adaptivity (i.e., MCM) can be applied to VSA-based TR focusing.  
The schematic of the numerical simulations is illustrated in Figure 1. The TRM consists of 27 elements spanning a 78-m aperture with a 3-m element spacing in 100-m deep shallow water. Similarly, the VSA is a 48-m long 17-element vertical array with a 3-m element spacing, located 2 km from the TRM. A normal mode propagation model [14] is used for numerical simulations with the following geoacoustic parameters of the sea floor: density \(\rho_{b}=1.5\) g/cm\({}^{3}\), compressional sound speed \(c_{b}=1600\) m/s, and attenuation \(\alpha_{b}=0.2\) dB/\(\lambda\). A range-independent environment with a simple half-space bottom and a downward-refracting sound speed profile (SSP) shown in Figure 1b is assumed. An average sound speed of \(c=1500\) m/s across the VSA is used for the line-of-sight timedelay, \(t_{n}\). Figure 2 presents the simulated backpropagation results of VSA-based single TR focusing in the ocean waveguide shown in Figure 1. Each of the backpropagated fields is normalized with respect to its own maximum and superimposed with the corresponding snapshot times. A 10-ms pulse with a center frequency of 500 Hz and a Hann window is used for probe transmission. It is clear that the backpropagated synchronized field is steered over the VSA to a selected location (\(r\) = 2.2 km, \(z\) = 100 m) (see Figure 2b) compared to when no time delay is applied (see Figure 2a).  
Figure 1: (Color online) Schematic of virtual source array (VSA)-based time reversal (TR) focusing and waveguide conditions used for numerical simulations. (**a**) Different vertical arrays, denoted by time-reversal mirror (TRM) (blue circle) and VSA (red circle), of 27 and 17 elements with 3-m array spacing are considered in 100-m deep shallow water; (**b**) Sound speed profile used for normal mode simulations.  
Figure 2: (Color online) Simulated VSA-based single TR focusing. Six snapshots of the backpropagated field from the TRM are superimposed with the corresponding snapshot times. Each snapshot is normalized with respect to its own maximum. (**a**) Without time delay (i.e., \(t_{n}=0\)) for a-priori sampled transfer functions; (**b**) Sampled transfer functions are appropriately time delayed; thus, it is clear that the synchronized field is steered over the VSA to a selected location.
## 5 Performance Analysis and Discussion  
### Experiment Environment  
WorkflowSim is a Java-based open source discrete event workflow engine that has been used to model a cloud execution environment for executing scientific workflow applications [32]. This trace-based framework is an extension of CloudSim [33]. WorkflowSim offers support for workflow DAGs and provides execution environment for workflow level resource provisioning, resource management, task clustering and task scheduling. For our experiments, WorkflowSim was adopted to evaluate the performance of the proposed method on real traces under dynamically provisioned on-demand cloud VMs and a pay-per-use model derived from Amazon EC2 pricing model ([[http://aws.amazon.com/ec2](http://aws.amazon.com/ec2)]([http://aws.amazon.com/ec2](http://aws.amazon.com/ec2))). The correctness of WorkflowSim has been proved in [32]. Moreover, it supports the analysis of various scheduling overheads and failures.  
An IaaS provider offering a single data center and six types of VMs was modelled. Table 3 lists the configurations of the VM types based on EC2.  
The workflow generator toolkit, Pegasus, was used to generate synthetic workflows of various sizes for each workflow application in terms of total number of nodes. Five real-world scientific applications were chosen, namely: Cybershake (data-intensive, memory-intensive, resource-intensive),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**VM Type** & **ECU** & **Memory** & **Price (5/h)** \\ \hline m3.medium & 1 & 3.75 & 0.067 \\ c3.xlarge & 4 & 3.75 & 0.21 \\ m3.xlarge & 4 & 15 & 0.266 \\ c3.2 xlarge & 8 & 15 & 0.42 \\ m3.2 xlarge & 16 & 30 & 0.532 \\ c3.4 xlarge & 16 & 30 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: VM types specifications. ECU: EC2 Compute Unit.  
Epigenomics (CPU-bound), LIGO Inspiral Analysis (memory-intensive, resource-intensive), Montage (I/O bound) and SIPHT [30].
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
## 5 Experiments
### Statistical Analysis  
The encoded and decoded images comparison between StegNet and LSB method are presented in Figure 2. They are very similar though, however, there is one critical flaw about the LSB method,  
Figure 13: One batch Steganography Example.  
in that it does not suffer through statistical analysis, and therefore LSB method is usually combined with transformations of the hidden image, i.e., compression, randomization, etc.  
Figure 14 is a comparison of histogram analysis between LSB method and our work. It shows a direct view of robustness of StegNet against statistical analysis, which the StegNet embedded's histogram and the cover image's histogram are much more matching.  
A more all-around test is conducted through StegExpose [45], which combines several decent algorithms to detect LSB-based steganography, i.e., sample pair analysis [46], RS analysis [47], chi-square attack [48] and primary sets [49]. The detection threshold is its hyperparameter, which is used to balance true positive rate and false positive rate of the StegExpose's result. The test is performed with linear interpolation of detection threshold from 0.00 to 1.00 with 0.01 as the step interval.  
Figure 15 is the ROC curve, where true positive stands for an embedded image correctly identified that there are hidden data inside while false positive means a clean figure falsely classified as an embedded image. The figure is plotted in red-dash-line-connected scatter data, showing that StegExpose can only work a little better than random guessing, the line in green. In other words, the proposed steganography method can better resist StegExpose attack.  
Figure 14: Histogram Comparison between StegNet and Plain LSB.  
Figure 15: ROC Curves: Detecting Steganography via StegExpose.
###### Abstract
This article aims at sharing our experience and very limited experimental results in directly using Joint Polar Satellite System (JPSS) Ozone Mapping and Profiler Suite (OMPS) radiances. This article is organized as follows: We introduce the Joint Polar Satellite System in Section 2; the OMPS sensor data record (calibrated radiance) is described in Section 3; Section 4 reviews the community radiative transfer model (CRTM), the core tool for radiance assimilation; Section 5 describes a very simple radiance assimilation scheme; the experimental results of the OMPS radiance assimilation for atmospheric ozone are given in Section 6; the last section leads to the summary and discussions.
## 3 Reducing the Uncertainty at the Output of Kalman Filter by Post Processing  
The estimation of multiple target trajectories by an automotive RADAR sensor supposes the following operations:  
* The measurement of scatters received from each target [7];
* Estimation of range, radial velocity, azimuth and elevation angles, signal to noise (SNR) and other parameters of each target;
* Target detection;
* Data association (association of the measurements with the trajectories);
* Kalman filtering of each trajectory.  
The first three operations depend on the particularities of the RADAR sensor and, more specifically, on the type of waveform used. The last two operations are detailed in the following subsections, representing the methods, which were used to elaborate this paper. These two methods are applied in conjunction because the intermediate results of one of the methods serve as initializations for the other method and reciprocally.
# Fine-Tuning a Personalized OpenBioLLM Using Offline Reinforcement Learning
## 3 Methods
### InfCTI Module  
This module, as shown in Figure 4, aims to generate reward values based on the patient's physiological indicators through inverse reinforcement learning, and provide reward signals for the CQL algorithm. The InfCTI module consists of two deep neural networks, i.e., the Q network and the R network, which are trained together to evaluate the value of state-action pairs and estimate the reward distribution that reflects the clinician's treatment intention. The module takes the patient's physiological indicators and the doctor's treatment decision as input, and then generates the patient's current reward for CQL training.  
Figure 3: The network structure of DTE modules.  
The input of the R network and the Q network is 38-dimensional, which is the patient's current observation. The internal structure of the two networks is similar, both with two hidden layers. The R network has two output values, which are the mean and variance of the current reward distribution; the output value of the Q network is the value of the current action-state pair. The pseudo-code for training the InfCTI module is shown in Algorithm 3.  
```
1: Initialize the Q network \(Q_{\theta}\) and R network \(R_{\phi}\) in the InfCTI module
2: Input: \(\mathcal{D},\mathcal{S},\mathcal{A}\);
3:while Not converged do
4: Sample \(\mathcal{D}_{mini}\) from \(\mathcal{D}\);
5:\(\mathcal{L}(\phi,\theta,\mathcal{D})=\mathbb{E}[\frac{\eta}{b}\mathcal{L}( \phi,\theta,\mathcal{D}_{mini})]\);
6:\((\phi^{\prime},\theta^{\prime})\leftarrow(\phi,\theta)+\eta\
abla_{\phi, \theta}\mathcal{L}(\phi,\theta,\mathcal{D})\);
7:\(\phi,\theta\leftarrow\phi^{\prime},\theta^{\prime}\)
8:endwhile
```  
**Algorithm 3** Training InfCTI  
The loss function is as follows:  
\[\begin{split}\mathcal{L}(\phi,\theta,\mathcal{D})=\sum_{(s, \mathcal{A},\mathcal{A}^{\prime})\in\mathcal{D}}&\left(\log \frac{\exp(Q_{\theta}(s,a))}{\sum_{b\in\mathcal{A}}\exp(Q_{\theta}(s,b))} \right.\\ &\left.-D_{KL}\big{(}r_{\phi}(R(s,a))\parallel p(R(s,a))\big{)} \right.\\ &\left.+\lambda\log r_{\phi}\big{(}Q_{\theta}(s,a)-\gamma Q_{ \theta}\big{(}s^{\prime},a^{\prime}\big{)}\big{)}\right)\end{split} \tag{1}\]
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 3 Results and Discussion
### Noise Sensors  
A scatterplot of the average concentration levels relative to the sound level meter is shown in Figure 8. The low-cost noise sensor measurements were on the one-to-one line compared to the reference instrument for 60 dBA and above. The 30 sensors were averaged and the \(y\)-axis error bars represent the standard deviation between sensors. The average slope, intercept, bias, and \(R^{2}\) for values above 60 dBA were 1.0, \(-\)2 dBA, 1.2% and 1.0, respectively, and the precision for all monitors was 1%.  
The noise sensor results were strongly correlated with the reference instrument for sound levels between 65 and 95 dBA. The maximum sound level of the correlation test was limited by the experimental apparatus available, but we expect that the sensors are reliable up to 114 dBA, which is at the upper limit of the range recommended by OSHA [44], because the hardware and software should be reliable through that range.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
###### Abstract  
With the development of distribution networks, large amounts of distribution terminal units (DTU) are gradually integrated into the power system. However, limited numbers of maintenance engineers can hardly cope with the pressure brought about by the substantial increase of DTU devices. As DTU fault would pose a threat to the stable and safe operation of power systems; thus, it is rather significant to reduce the fault incidence of DTU devices and improve the efficiency of fault elimination. In this paper, a DTU fault analysis method using an association rule mining algorithm was proposed. Key factors of DTU fault were analyzed at first. Then, the main concept of the Eclat algorithm was illustrated, and its performance was compared with FP-growth and Apriori algorithms using DTU fault databases of different sizes. Afterwards, a DTU fault analysis method based on the Eclat algorithm was proposed. The practicality of this method was proven by experiment using a realistic DTU fault database. Finally, the application of this method was presented to demonstrate its effectiveness.  
association rule mining; distribution terminal unit; Eclat algorithm; fault analysis Article
# Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems
## 6 Materials and Methods  
All the results presented in section 7 were obtained using a laptop with an Intel Core i7 10710U CPU, an Nvidia GTX 1650 GPU, and 16 GB of RAM. All algorithms were implemented with Python 3.11, Numpy 1.23, and Scipy 1.14. For GAQS and GAQS SBX, the QC measurements were not done during the execution of the code, but a pool of (more than 10,000) measurements was created beforehand and used in all the executions after being shuffled. The measurements were done using two approaches. The first one used the Aer simulator available as a sub-library in Qiskit (Qiskit-aer 0.12.1).  
The second one used IBM-Sheerbroke, a real quantum computer equipped with the Eagle r3 quantum processor. The connection to the IBM quantum platform had to be done using the latest versions of Qiskit and qiskit-ibm-runtime. When using IBM's quantum computers, the circuit has to be transpiled to accommodate the architecture of the quantum processor.  
The use of a pool of measurements is motivated by the long queues that can be encountered when connecting to IBM quantum computers due to their free and easy access. The algorithms use the parameters presented in Table 2.  
All MTFs were executed with 4 dimensions, the wideband filter was tested with N = 9 and center frequency \(f_{0}\), and the EM absorber used 4 layers of purely dielectric lossy materials with permittivity presented in Table 3. The weight \(w\) was set to 50, the thicknesses of the materials are considered in mm, and the target bandwidth was 2 to 6 GHz. This covers the 2.4 GHz and 5 GHz bands used in most commercial communications systems such as WiFi and Bluetooth, as well as the majority of the non-millimeter wave bands used for cellular telephony in 4G and 5G [20].  
The materials used are fictitious, as the characterization of real materials is outside the scope of this paper. All the tests presented were conducted 200 times, with 200 generations each, to allow for a statistical analysis of the final results and to prevent incorrect conclusions caused by outliers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **GA** & **GAQS** & **GAQS SBX** \\ \hline Population size & 32 & 32 & 32 \\ Mutation rate & \(1/N\) & \(1/N\) & \(1/N\) \\ Number of qubits & & 6 & 6 \\ \(k_{e}\) & & 3 & 3 \\ \(\eta\) & & & 0.5 \\ \(\sigma_{m}\) & & & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the optimization algorithms.
# Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks
###### Abstract
In a Blind SQL injection attack, a query is inserted, requiring the database to respond to the true or false question [11]. Attackers analyze the response to a question and can obtain sensitive information. Out of band SQL injection is a very uncommon attack type, for the most part; it relies on features allowed on the database server to be utilized by the web application. Fundamentally, this attack is performed when an attacker is inept at utilizing the same attack channel to obtain the results. In recent times, deep learning, a subset of machine learning, has played an essential role in the classification of natural language processing and image pattern recognition [12]. Several studies have been conducted on how to benefit from machine learning in cybercurity [13; 14; 15]. A neural network, a part of deep learning that imitates the human brain by making a network of neurons that are combined and modelled as a neural network, has been applied in detecting malicious code attacks [16]. The significant advantages provided by the neural network have led to the elimination of machine learning problems by increasing detection accuracy, and many studies have reported the same. To improve the accuracy of SQL injection attacks, researchers have developed various techniques, including signature-based detection and anomaly-based detection [17]. However, these techniques often have limitations in their ability to accurately detect and prevent SQL injection attacks. The main contribution of this work is the development of a PNN-based model for detecting SQL injection attacks. The model is trained using a large dataset of SQL queries, both legitimate and malicious, and is able to accurately distinguish between the two types of queries. This allows the model to identify and block SQL injection attacks with a high degree of accuracy, while minimizing false positives. Our experimentsuncover how PNN performed better when smoothing parameters were selected using the BAT optimization algorithm. Therefore, it establishes that the proposed approach can effectively detect SQL injection attacks. The rest of the paper is organized as follows: Section 2 provides the details about related work. Section 3 details the overview of the proposed approach for detecting SQL injection using a Probabilistic Neural network. In Section 4, the experimental results and implementation details are provided. Section 5 concludes the work.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 7 Security Analysis  
After original signals are processed by the proposed method, even if attackers obtain the transmitted data, that is \(Y_{c}\), by some sort of deviousness, it is highly likely that they cannot achieve the original signals under the current computing power level in a tolerable time duration, because in the proposed method signals are encrypted with a tremendous key space. Attackers could obtain very little useful information, because the signals operated by the proposed method leak very few statistics messages.
# Novel Fluidic Oscillator Evaluation Considering Dimensional Modifications
## 4 Mass Flow and Pressure Momentum Terms Acting on the Jet as It Enters the Mixing Chamber  
One of the key points when analyzing the performance of an FO is the study of the origin of the self-sustained oscillations. This study involves the evaluation of the lateral forces acting on the jet as it enters the MC; this is why in the present research the different momentum terms on the feedback channels' outlets were determined; see Figure 1. When disregarding the gravitational forces, the momentum equation considers two main terms: the pressure and the mass flow. Both terms were instantaneously and independently evaluated at each of the two FC outlets. The forces considered were those acting in the vertical direction.  
In the present research, the fluid is taken as incompressible, the fluid density is temporally and spatially independent, and the pressure and velocity fields are spatially and temporally dependent. As a result, the momentum equation, once discretized and evaluated at each grid cell belonging to the corresponding surface, was instantaneously applied to each feedback channel outlet. The momentum equation used can be represented as  
\[F=F_{massflow}+F_{pressure}=\rho\sum_{i=1}^{i=n}(S_{i}V_{i}^{2})+\sum_{i=1}^{i=n} P_{i}S_{i} \tag{16}\]  
The subindex \(i\) defines a generic mesh cell belonging to any of the two FC outlet surfaces. The index \(n\) characterizes the number of cells existing on an FC outlet surface. The instantaneous pressure and velocity components acting in the vertical direction and on a given mesh cell are defined as \(P_{i}\) and \(V_{i}\), respectively. \(S_{i}\) characterizes a generic cell surface. The instantaneous momentum term due to the mass flow in any of the two FC outlets was determined by adding the vertical components of each elementary momentum mass flow term from each cell of the chosen surface. The calculation of the vertical instantaneous momentum pressure term was performed by multiplying the static pressure by the cell surface on each cell belonging to the corresponding surface, and then, adding the elemental momentum pressure terms. The net force, also called the net momentum, acting on the jet as it enters the MC considers the application of Equation (16) on both FC outlets at each instant. When considering just the first term of Equation (16) instantaneously applied to both FC outlets, the net mass flow momentum term acting on the jet is obtained, and when performing the same with the second term of Equation (16), the net pressure momentum term is obtained.
## Data and Methods
### Accuracy Assessment and Comparison with Other Forest Maps  
To analyze the consistency and uncertainty of the available forest maps at relatively high spatial resolution in the Loess Plateau, we compared five popular existing moderate resolution forest maps in 2010 (Table 2): (1) FROM-GLG Global Land Cover Data, which was generated by Tsinghua University [48]. (2) GlobalLand30 product, which was generated by the National Geomatics Center of China (NGCC) [49]. (3) The University of Maryland's Global Land Cover Facility Data Center (GLCF) produced a 30 m resolution continuous tree cover product [50] called the Vegetation Continuous Fields (VCF) dataset which was derived from all seven bands of Landsat 5 and 7 images based on rescaling the 250 m MODIS VCF Tree Cover layer([[http://glcf.umd.edu/data/landsatTreecover/](http://glcf.umd.edu/data/landsatTreecover/)]([http://glcf.umd.edu/data/landsatTreecover/](http://glcf.umd.edu/data/landsatTreecover/))). (4) The JAXA forest map generated by Earth Observation Research Center, JAXA, using L-band PALSAR data. (5) Through the integration of PALSAR and MODIS/Landsat data, the University of Oklahoma (OU) [26] generated an updated forest data layer (FDL), referred to as OU-FDL in this paper.  
The mutual ROIs were used to evaluate the accuracy of the PL-based and the other five forest maps in the Loess Plateau, using the confusion matrix method for the estimates of producer accuracy (PA), user accuracy (UA), and overall accuracy (OA). Then, all the ROIs were divided into two parts to evaluate the accuracy of each forest map in the forest region and non-forest region. Note that we also adjusted the accuracy assessment based on the area of each stratum following previous studies [51].  
Figure 5: Spatial distribution of the randomly generated validation Regions of Interest (ROIs) in ArcGIS (**a**). Ground truth samples derived from Google Earth. Two ROIs include the forest region and non-forest region from the very high resolution (VHR) images of Google Earth from around 2010 (**b**). Using the Collect Earth software to verify the results of ROIs (**c**).