esa-sceva/satcom-chunk-collection huggingface dataset:
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 4 Experimental Results and Discussion
### Nodule Segmentation
ROC and PR on the LIDC-IDRI test dataset of 91.58%, and 82.02%, respectively, as shown in Figure 10.  
A qualitative comparison of the segmentation results of the AWEU-Net and the six segmentation models is shown in Figure 11. The segmentation results of the input nodule ROIs of CT images with a variety of difficult levels, including illumination variations and irregular shapes and boundaries of the nodule regions, were presented. As shown in Figure 11, four examples from the two datasets along with the ground truth and the predicted mask of the six tested models were compared to the proposed AWEU-Net model. AWEU-Net provides segmentation results very close to the ground truth with an average similarity of \(>\)86% (true positive (TP)). Our segmentation method also provides the lowest  
Figure 8: The (**a**) ROC and (**b**) PR curve for all test samples of the LUNA16 lung nodule segmentation dataset. Figure 7: Boxplots of (**a**) Dice coefficient (DSC) and (**b**) intersection over union (IoU) scores for all test samples of the LUNA16 lung nodule segmentation dataset. Different boxes indicate the score ranges of several methods; the red line inside each box represents the median value, and all values outside the whiskers are considered outliers, which are marked with the (+) symbol. degrees of false negative (FN) and false positive (FP) results compared to the rest of the models. The AWEU-Net model yields more regular borders compared to PSPNet, MANet, FPN, since our model strives for higher accuracy on nodule region boundaries. The resulting segmentation of the six tested models may significantly differ from the ground truth in some cases, e.g., the second example of the LUNA16 dataset. Finally, regarding the model efficiency, the total number of parameters, the sum of all the weights and biases on the proposed model, was around 34.5 million. Our model yielded a reduction of 40% compared to the baseline model, UNet (i.e., number of parameters of around 60 million). In order to assess the computational complexity of the model, we measured the number of resources that the proposed model used in training and inference by computing the multiply-accumulate operation (MACs) in billions of  
Figure 10: The **(a)** ROC and **(b)** PR curve for for all test samples of the LIDC-IDRI lung nodule segmentation dataset. Figure 9: Boxplots of **(a)** Dice coefficient (DSC) and **(b)** intersection over union (IoU) scores for all test samples of the LIDC-IDRI lung nodule segmentation dataset. Different boxes indicate the score ranges of several methods; the red line inside each box represents the median value, and all values outside the whiskers are considered outliers, which are marked with the (+) symbol. operations, (MACs(G)). The proposed model performed 65.3 billion MACs. Furthermore, our proposed model achieved an inference time of 10.8 ms (around 92.3 fps) on an NVIDIA GeForce GTX 1080 GPU. In summary, our framework can be executed on a single GPU, guaranteeing accurate nodule segmentation in real-time.
## 3 Data and Scenarios
### Work Zone Location and Scenarios
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario Date Location** & **Segment Length** & **Video Length** & **Number of Trajectories** \\ \hline
1 & 15 May 2022 & A1 & 443 & 0:10:05 & 209 \\
1 & 15 May 2022 & B1 & 455 & 0:32:12 & 942 \\
1 & 18 May 2022 & A1 & 450 & 0:26:54 & 769 \\
1 & 18 May 2022 & B3 & 454 & 0:18:23 & 506 \\
1 & 18 May 2022 & C1 & 451 & 0:15:00 & 1700 \\
1 & 27 May 2022 & B4 & 497 & 0:13:59 & 480 \\
1 & 1 June 2022 & B4 & 467 & 0:35:39 & 1286 \\
2 & 18 May 2022 & B2 & 456 & 0:25:07 & 825 \\
2 & 1 June 2022 & A3 & 490 & 1:05:29 & 2697 \\
3 & 27 May 2022 & A4 & 472 & 0:22:13 & 349 \\ Total & & 8 & & 4:25:01 & 9763 \\ \hline \end{tabular}
\end{table} 
Table 1: Description of the selected work zone sites.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
###### Abstract  
This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR) data processing. Several nonlinear chirp scaling (NLCS) algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC). However, the azimuth depth of focusing (ADOF) is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS) algorithm that is proposed in this paper uses the method of series reverse (MSR) to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.  
Keywords:synthetic aperture radar (SAR); linear range walk correction (LRWC); generalized nonlinear chirp scaling (GNLCS); azimuth depth of focusing (ADOF); focusing precision +
Footnote †: journal: Sensors
## 3 Data Analysis
### The Perturbations in Electron Density and Temperature from CSES
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Latitudes** & **45–60\({}^{\circ}\) S** & **30–45\({}^{\circ}\) S** & **15–30\({}^{\circ}\) S** & **0–15\({}^{\circ}\) S** & **0–15\({}^{\circ}\) N** & **15–30\({}^{\circ}\) N** & **30–45\({}^{\circ}\) N** & **45–60\({}^{\circ}\) N** \\ \hline r of E/Ne in dayside on 26 August with E at 225 Hz & 0.040 & 0.78 & \(-\)0.42 & 0.83 & **0.91** & **0.93** & \(-\)0.24 & \(-\)0.32 \\ \hline
725 Hz & \(-\)0.32 & 0.61 & \(-\)0.68 & 0.47 & **0.93** & 0.85 & \(-\)**0.95** & \(-\)0.34 \\ \hline
1125 Hz & \(-\)0.51 & 0.81 & \(-\)0.71 & 0.55 & **0.94** & **0.90** & \(-\)**0.96** & \(-\)0.075 \\ \hline r of E/Ne in dayside on 28 August with E at 225 Hz & **0.83** & 0.53 & 0.20 & 0.79 & 0.31 & \(-\)0.33 & \(-\)0.34 & \(-\)0.034 \\ \hline
725 Hz & \(-\)0.27 & 0.18 & 0.60 & \(-\)**0.88** & 0.30 & \(-\)0.58 & \(-\)0.65 & 0.36 \\ \hline
1125 Hz & \(-\)0.54 & 0.088 & \(-\)0.31 & 0.15 & 0.56 & \(-\)0.26 & \(-\)0.70 & 0.43 \\ \hline r of E/Ne in nightside on 26 August with E at 225 Hz & \(-\)0.59 & 0.17 & **0.61** & 0.43 & \(-\)0.044 & \(-\)0.51 & 0.057 & \(-\)**0.65** \\ \hline
725 Hz & \(-\)0.35 & 0.35 & 0.15 & 0.072 & \(-\)0.089 & 0.40 & 0.27 & \(-\)**0.61** \\ \hline
1125 Hz & \(-\)0.48 & **0.64** & 0.13 & 0.42 & 0.049 & 0.036 & 0.20 & \(-\)0.59 \\ \hline r of E/Ne in nightside on 28 August with E at 225 Hz & \(-\)0.50 & \(-\)0.034 & 0.015 & 0.52 & \(-\)0.33 & 0.064 & \(-\)0.069 & \(-\)0.59 \\ \hline
725 Hz & \(-\)0.53 & 0.59 & 0.078 & 0.40 & 0.10 & \(-\)0.24 & 0.14 & \(-\)0.53 \\ \hline \end{tabular}
\end{table} 
Table 2: The correlation r of E/Ne at different latitudes from CSES on 26 and 28 August. The bold values present the maxima.
###### Abstract  
We present analyses of Global Navigation Satellite System (GNSS) carrier phase observations in multiple kinematic scenarios for different receiver types. Multi-GNSS observations are recorded on high sensitivity and geodetic-grade receivers operating on a moving zero-baseline by conducting terrestrial urban and aerial flight experiments. The captured data is post-processed; carrier phase residuals are computed using the double difference (DD) concept. The estimated noise levels of carrier phases are analysed with respect to different parameters. We find DD noise levels for L1 carrier phase observations in the range of 1.4-2 mm (GPS, Global Positioning System), 2.8-4.6 mm (GLONASS, Global Navigation Satellite System), and 1.5-1.7 mm (Galileo) for geodetic receiver pairs. The noise level for high sensitivity receivers is at least higher by a factor of 2. For satellites elevating above 30\({}^{\circ}\), the dominant noise process is white phase noise. For the flight experiment, the elevation dependency of the noise is well described by the exponential model, while for the terrestrial urban experiment, multipath and diffraction effects overlay; hence no elevation dependency is found. For both experiments, a carrier-to-noise density ratio (C/N\({}_{0}\)) dependency for carrier phase DDs of GPS and Galileo is clearly visible with geodetic-grade receivers. In addition, C/N\({}_{0}\) dependency is also visible for carrier phase DDs of GLONASS with geodetic-grade receivers for the terrestrial urban experiment.  
Global Navigation Satellite System (GNSS); kinematic terrestrial and flight experiment; geodetic and high sensitivity GNSS receivers; double difference; relative positioning; stochastic models 2020, 20, 4046; doi:10.3390/s20144046
## 4 Experimental Result
### Qualitative Performance Evaluation  
The test with the FIR camera sensor showed that the proposed method tracks the pedestrian's head accurately. The experimental environment, in which the image contrast changes according to the temperature change in the background, and the temperature and brightness data in the target ROI was visualized as a histogram. The tracking performance of the brightness-based MS and TIR-MS was compared in each camera sequence and the temperature-based method is robust to background and image contrast variations.
## 4 Experimental Results and Discussion
### Sensitivity Analysis for Different Distributor Plates
Due to such configuration, airflow entered the plate and adopted two paths; one is a 90\({}^{\circ}\) straight upward path via smaller holes with higher kinetic heads, and the other is the 45\({}^{\circ}\) angular path through 45\({}^{\circ}\) angular slots. So the smaller, faster bubbles rise straight upward, whereas larger, slower bubbles through slots and adopt swirling angular paths. These two forms of bubbles interact with each other as they ascend upward, and the breakage of larger bubbles and coalescence of smaller bubbles occurred, as illustrated in Figure 4 below. So intermediate-size bubbles were formed, leading to the intermediate minimum fluidization velocity, which falls between the perforated and hybrid plate. Figure 5 illustrates the fluidization phenomena for different distributor plates at 1.5 U\({}_{\text{mf}}\) and an average particle size of 590 \(\upmu\)m. It can be seen from Figure 5a that for perforated plate distributors, the particles are fluidized with central concentration. However, the particle fluidization seemed to be rotational for the 45\({}^{\circ}\) slotted plate due to angular slots openings in distributor plates, as shown in Figure 5b. Moreover, the initial bed height rise ratio is prominent in the 45\({}^{\circ}\) slotted plate case due to the larger bubbles rupturing the bed surface, as shown in Figure 5b. Figure 5c shows fluidization behavior for hybrid plate distributors. Due to the larger open area ratio and a blend of smaller and larger openings in the distributor plate, the more sprinkled particle flow occurs at the bed's surface. It  
Figure 4: Schematic illustration of velocity flow vectors through the angular slot and straight hole of the hybrid distributor plate. moderates the bed height rise that appeared for the said plate. Furthermore, scattered flow appeared due to the blend of angular and straight holes.
## 3 Multipath Parameter Estimate Algorithm  
### Algorithm Description  
In order to handle the problems of heavy computational burden and severe noise challenge that the direct-FFT method faces, we develop an averaging-FFT method. It employs the process of statistical averaging to obtain more clear power spectrum of the incoming signal, which contributes to the significant reduction of both computation load and noise effect. This section explains the main principle of the proposed technique and presents the mathematical derivation.  
Figure 1 illustrates the scheme of the averaging-FFT method. The proposed method can be summarized into two stages: CPSD calculation and multipath parameter estimation. In the stage I \"CPSD calculation\", our goal is to compute the CPSD between incoming signal and local replica.  
We mainly take measures of segmentation and averaging to minimize the impact of noise. Similar way of processing can also be used for signal quality monitoring [30]. An interleaver is used here to store the consecutive baseband signal in rows and extract each column of rows for parallel computing. In practice, interleaver is implemented by mass storage devices like SDRAM in which \(M\) different rows store \(M\) consecutive segments, and \(L\) columns of the rows are sequentially read out for windowing and FFT [31; 32]. Thus, this phase can be stated as follows:  
* Step 1: Let the baseband signal after sampled and down-conversion first pass an interleaver to be divided into \(M\) segments with \(L\)-point length for each segment;
* Step 2: Add the time-domain hamming window to each segment to reduce the effect of leakage and truncation;
* Step 3: Perform FFT to obtain the frequency spectrum of each segment;
* Step 4: Multiply the frequency spectrum by the conjugate spectrum of local replica to compute the CPSD of each segment;
* Step 5: Average the total M segments to get the final CPSD.  
The second stage \"multipath parameter estimation\" can be divided into 3 steps. First, the CPSD obtained from state I is divided by the nominal PSD of local replica in the frequency domain to get the CTF. Then, we take the IFFT of CTF to obtain the channel impulse response function in the time domain. Finally, we search for the time offset and amplitude of each impulse and output the estimated multipath parameters. Next, the thorough mathematical derivation for both two stages is given in the following part.
# Online Streaming Feature Selection via Conditional Independence
## 5 Experiments and Analysis  
### Experimental Setup  
We empirically evaluated the performance of the algorithms. All experiments were conducted on a computer with Intel (R) Xeon (R) CPU E3-1505M 3.0 GHz, 32 GB RAM.  
We tested representative algorithms of _Alpha-investing_ and _OSFS_ on the 14 benchmark datasets in Table 3. The arcene, colon, ionosphere, and leukemia datasets come from the _NIPS 2003_ feature selection challenge [8] and one frequently studied public microarray dataset (wdbc). We also downloaded the datasets from _Causality Workbench_, such as slyva, lung, cina0, reged1, lucas0, marti1, and lucap0. Cina0 is a marketing dataset derived from census data while reged1 is a genomics dataset that could be responsible for lung cancer. Marti1 is obtained from the data generative process of simulated genomic data. Lucas0 is a lung cancer simple set, whereas lucap0 is a lung cancer set with probes. They are used to model a medical application for the diagnosis, prevention, and cure of lung cancer. The number of features ranges from 11 to 10,000, and the number of samples varies from 72 to 145,252. In particular, the number of features in seven datasets--marti1, reged1, lung, prosate_GE, leukemia,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Phase of Filtering** & **Cost** \\ \hline null-conditional independence & \(O(|N|)\) \\ single-conditional independence & \(O(|N|-|N_{i}|)|CFS|) \\ multi-conditional independence & \(O(|M|\,|CFS|\,2^{|CFS|})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The time complexity in the phase of three-layer filtering.  
arcene, and Smk_can_187--is larger than the number of samples. These 14 datasets cover a wide range of real-world application domains, including gene expressions, ecology, and casual discovery. This makes the construction of feature selection extremely challenging. We preprocessed the data, for example deleting similar columns in the leukemia dataset.  
Our comparative study had the following design and compares the _ConInd_ algorithm with two state-of-the-art online feature selection algorithms, namely _Alpha-investing_ and _OSFS_, using 10-fold cross validation on each training dataset. The experiment was traced as follows: (1) analyzing the change in the number of features at every stage of running the _ConInd_; (2) comparing the prediction accuracy of _ConInd_ with _Alpha-investing_ and _OSFS_ through some state-of-the-art classifiers, including Decision Tree, KNN, SVM, and Ensemble using their implementation provided in the MATLAB app tool; (3) analyzing the number of selected features and running time in different algorithms; and (4) analyzing changing trends in the numbers of selected features and running time in different ratios of the streaming features.
## 2 Calculation Methods
### Band Structures Calculation  
We used the relaxed atomic structures shown above to calculate each perovskites band structure. We calculated the band structures along four different high symmetry k-points in the Brillouin Zone, \(\Gamma\) (0,0,0), R (0.5, 0.5, 0.5), X (0, 0.5, 0), and M (0.5, 0.5, 0). Each valence band represents one electron, and we added a total of four conduction bands. The electronic band structure is used to determine the electronic properties of perovskites. We transferred the lattice vectors to reciprocal spaces by using the formula below:  
\[\vec{b_{1}}=2\pi\frac{a_{2}\times a_{3}}{a_{1}\cdot(a_{2}\times a_{3})} \tag{2}\]  
\[\vec{b_{2}}=2\pi\frac{a_{3}\times a_{1}}{a_{2}\cdot(a_{3}\times a_{1})} \tag{3}\]  
\[\vec{b_{3}}=2\pi\frac{a_{1}\times a_{2}}{a_{3}\cdot(a_{1}\times a_{2})} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Atom** & **Electron Configurations** & **Radius Cutoff (Bohr)** \\ \hline O & [He] 2s\({}^{2}\) 2\(p\)4  & 1.4147 \\ Cl & [Ne] 3s\({}^{2}\) 3\(p\)5  & 1.8032 \\ Ca & [Ar] 4\(s^{2}\) & 1.9142 \\ Ti & [Ar] 3\(d^{2}\) 4\(s^{2}\) & 2.3000 \\ Sr & [Kr] 5\({}^{2}\) & 2.2067 \\ Rh & [Kr] 4\(d^{8}\) S\({}^{1}\) & 2.4031 \\ Sn & [Kr] 4\(d^{10}\) 5\({}^{2}\) 5\(p\)2  & 2.5109 \\ I & [Kr] 4\(d^{10}\)5\({}^{2}\)5\(p\)5  & 2.3002 \\ Cs & [Xe] 6s\({}^{1}\) & 2.2066 \\ \hline \end{tabular}
\end{table}
Table 2: Electron configurations and radius cutoff for generate PAW pseudopotentials atom.  
\begin{table}
\begin{tabular}{c c c} \hline
**Compounds** & **Ecut (ha)** & **K Point Mesh** \\ \hline Cssncl3 & 32.0 & 6 6 6 \\ \hline Scsnij3 & 20.0 & 6 6 6 6 \\ \hline Srrho3 & 20.0 & 6 6 6 6 \\ \hline Srtio3 & 20.0 & 4 4 4 \\ \hline \end{tabular}
\end{table}
Table 3: Kinetic energy cut off (ecut), k-point mesh convergence for cubic perovskites.
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
###### Abstract  
In this paper, we present an innovative approach inspired by the _Path-scan_ model to detect paths with \(k\) adjacent edges (_k-path_) exhibiting unusual behavior (synonymous with anomaly) within network communications. This work is motivated by the challenge of identifying malicious activities carried out in vulnerable _k-path_ in a small to medium-sized computer network. Each observed edge (time series of the number of events or the number of packets exchanged between two computers in the network) is modeled using the three-state observed Markov model, as opposed to the _Path-scan_ model which uses a two-state model (active state and inactive state), to establish baselines of behavior in order to detect anomalies. This model captures the typical behavior of network communications, as well as patterns of suspicious activity, such as those associated with brute force attacks. We take a perspective by analyzing each vulnerable _k-path_, enabling the accurate detection of anomalies on the _k-path_. Using this approach, our method aims to enhance the detection of suspicious activities in computer networks, thus providing a more robust and accurate solution to ensure the security of computer systems.  
cypsecurity; cyberattacks; Markovian model; generalized maximum likelihood ratio; computer networks; network traffic +
Footnote †: journal: Advances in Neural Networks  
1
Footnote 1: [[https://doi.org/10.3390/jcp4030022](https://doi.org/10.3390/jcp4030022)]([https://doi.org/10.3390/jcp4030022](https://doi.org/10.3390/jcp4030022))  
2024  
2024  
2024
### Our Contribution  
In this paper, we propose a new technique for privacy-preserving deep neural network offloading for IoT devices. Different from [15], our scheme unleashes IoT devices by eliminating the need for the ever-increasing data storage while enjoying the secure offloading of linear operations. We achieve this with our novel design of an interactive privacy-preserving scalar product (iPPSP) primitive. Specifically, we introduce a constant-size random key pool on each IoT device, from which encryption/decryption keys for iPPSP are continuously generated. The theoretical results show that, compared to local execution, our design is able to accelerate the execution of convolutional layers by an approximation of \(\frac{\hbar k^{2}}{T}\), where \(T\) is a pre-set parameter which is usually a small number, and \(k\) and \(\hbar\) denote the kernel size and input size of each channel, respectively. Experimentalresults on a Raspberry Pi 4 (mode B) show that our design has over 14\(\times\) speedup over local execution of AlexNet over ImageNet.  
Our contributions can be summarized as follows:  
* We propose a new deep neural network inference scheme for IoT devices that supports the secure and efficient outsourcing of 99.9% inference operations from IoT devices to edge devices.
* We introduce a new privacy-preserving scalar-product evaluation technique, namely iPPSP, that supports secure encryption and decryption in a similar fashion of a one-time pad (OTP). In addition to deep learning, iPPSP can be used as a generic lightweight cryptographic primitive for other applications, wherein the interactive evaluation of scalar products is needed.
* A thorough analysis shows that our protocol is secure against key-recovery attack and semantically secure under the chosen-plaintext attack (CPA) model.The theoretical results show that, compared to local execution, our design improves the performance of convolutional layers by approximately \(\frac{\hat{n}k^{2}}{T}\). Extensive experimental evaluation shows that our protocol is able to speed up over 14\(\times\) for CNN inference as compared to local execution.  
This paper is organized as follows. In Section 2, we formulate the problem and present the system and threat models. Section 3 presents the detailed design of our secure inference scheme and the construction of iPPSP. Section 4 provides thorough security analysis and performance evaluation. Section 5 reviews related works, which is followed by conclusion in Section 6.