esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 3 Materials and Methods
### A Deep Learning Model for Gunshot Detection
#### 3.1.2 Normalization and Feature Extraction  
The audio files are then normalized with the min-max normalization method using the Pydub library [35]. The stereo audio samples are made single channel audio by taking only the left channel data. Then, feature extraction is performed by converting the time-domain audio signal to the Mel Frequency Cepstral Coefficients (MFCCs) [36, 37]. The main principle behind MFCC is to condense essential information into a concise set of coefficients, inspired by the human ear's auditory perception. To compute the MFCC, the time-domain audio signal is first partitioned into frames lasting 20-40 milliseconds each. For each frame, the power spectrum is determined. Subsequently, triangular-shaped Mel filter banks are computed and applied to the power spectra, generating a spectrogram. Notably,the human ear exhibits superior sensitivity to subtle pitch changes in lower frequencies (below 1 kHz) compared to higher frequencies. To account for this sensitivity discrepancy, the first ten filters in the Mel filter bank are linearly spaced approximately 100, 200, \(\ldots\), and 1000 Hz. Beyond 1 kHz, these filters are distributed according to the logarithmic Mel scale. Following this, the logarithm of all filter bank energies is calculated, and their discrete cosine transform (DCT) is performed to decorrelate the filter bank coefficients. This process effectively captures the salient characteristics of the sound, making it suitable for subsequent sound classification tasks.  
In this project, the sound sample is segmented into frames lasting 30 milliseconds each. To extract meaningful features, the MFCCs are computed using the SpeechPy [38] library. For this purpose, 32 filters are employed in the filter bank, and the Fast Fourier Transform (FFT) is applied with 512 points. The resulting MFCC representation consists of 32 cepstral coefficients. In Figure 2, we can observe examples of a gunshot sample and a non-gunshot sample presented both in the time domain and their corresponding MFCC representations. Remarkably, after applying MFCC calculations, the one-dimensional time-domain sound signal is transformed into a two-dimensional signal with a size of 32 \(\times\) 32, effectively resembling an image. Leveraging this MFCC-based image representation, we can employ image classification deep learning architectures, such as convolutional neural networks (CNNs), to classify the sample images. Consequently, a dataset comprising 670,000 MFCC images is assembled from the sound samples, serving as the input for the deep learning network during the classification process. The MFCC data and their associated class labels are then randomly shuffled maintaining the correspondence between data and label.  
Figure 2: (a) Time-domain gunshot sound; (b) MFCC of the gunshot sound in (a); (c) time-domain non-gunshot sound of door knocking; (d) MFCC of the non-gunshot sound in (c).
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
###### Abstract  
Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.  
image quality assessment; no reference; visual perception; support vector regression; region of interest; generalized Gauss distribution (GGD) +
Footnote †: This paper is an extended version of our paper published in the International Symposium on Computer, Consumer and Control, Xi’an, China, 4-6 July 2016.
## 3 Proposed Effectiveness Evaluation Method of Safety Barrier  
In this section, the proposed method for the effectiveness evaluation of SBs based on the integration of IVIFNs and bow-tie methodology is described in detail. The method aims to evaluate the effectiveness of SBs in improving system safety. The point-to-point guide to this method is shown in Figure 3, which mainly consists of four critical procedures, and a brief overview of each critical procedure is as follows: (1) Preparatory work: This initial step involves identifying the system under evaluation, forming an expert group, and constructing the corresponding BT model with SBs. (2) System failure probability analysis: In this procedure, the possibility of system failure events and the effectiveness of preventive SBs are assessed using IVIFNs. (3) System failure consequence analysis: The severity of failure consequences and the effectiveness of protective SBs are evaluated using the IVIF-AHP and IVIFNs, respectively. (4) Safety barrier analysis: Finally, the failure risk of the system with and without SBs is computed, and the effectiveness of various safety barrier configurations is analyzed to provide recommendations for future safety planning. In summary, this method takes the BT model with SBs as the basis of safety evaluation and optimizes the calculation process and analysis results of the model using IVIFNs. This IVIF-BT method can further improve the reliability of the evaluation results.
## 4 Experimental Validation
### Datasets
#### 4.1.2 RGB Canopy Images  
The dataset consists of 2386 zenithal RGB images of experimental plots, each containing either a single vegetable species or a mixture of two different vegetables. From the dataset, 280 images were randomly extracted to be manually annotated and serve as a test set. There are four vegetable species in total: fennel, dwarf bean, cabbage, and kale. The dataset includes images of each species in pure culture as well as all possible combinations of two vegetables. We are interested in differentiating plant and non-plant pixels.  
Each plot was photographed four times over the course of the four-month (July, August, September, and October 2021) experiment, resulting in images of the plants at different stages of growth and under varying sunlight conditions. The photos were taken in Belgium at either the Lauzelle farm (50.680, 4.618) or one of 20 volunteer market gardeners' plots. The resulting images exhibit a wide range of contexts, including variations in soil types, crop arrangements, and non-plant objects present on the plots, as well as differences in cultivation techniques among the market gardeners. The cameras used consist of 2 OEM OV2640 (Omnivision) camera modules (OmniVision, Santa Clara, CA, USA), RGB, 2 Mpixel, embedded in an ESP32-Cam module (Espressif Systems, Shanghai, China), installed parallel to each other.
## 2 Materials and Methods
### Model Development  
The prediction models for wearing-off were developed using the 15 s and 15 min datasets for each participant. For this initial case study, we built individual-level models because each participant experienced PD differently. Thus, we wanted to discover and optimize a prediction model for each individual to understand their PD situation fully.  
In this subsection, the features and the different prediction models are discussed in detail (1) to understand how each model estimates the wearing-off phenomenon, and (2) to interpret how they use different features in the prediction. We also explain how we trained, evaluated, and optimized the models using the nested cross-validation approach. We used the Python programming language with PhotonAI [37] and Scikit-Learn [38] as the main libraries.  
The following features extracted from the processed dataset were used to develop the prediction model:  
* \(x_{1}\): Heart rate (HR);
* \(x_{2}\): Step count (Steps);
* \(x_{3}\): Stress score (Steps);
* \(x_{4}\): Awake duration during the estimated sleep period (Awake);
* \(x_{5}\): Deep sleep duration (Deep);
* \(x_{6}\): Light sleep duration (Light);
* \(x_{7}\): REM sleep duration (REM);
* \(x_{8}\): Total non-REM sleep duration (NonREMTotal);
* \(x_{9}\): Total sleep duration (Total);
* \(x_{10}\): Total non-REM sleep percentage (NonREMPercentage);
* \(x_{11}\): Sleep efficiency (SleepEfficiency);
* \(x_{12}\): Time elapsed from the last drug taken (DrugElapsed);
* \(y\): Wearing-off.  
In this study, five machine learning algorithms were applied to develop the prediction models. These algorithms were Logistic Regression (LR), Linear Support Vector Machine (L. SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB) classifiers. We used these machine learning models to interpret the importance of each feature to the wearing-off prediction.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
###### Abstract  
In order to fulfill the increasing demand for computation power to process a boundless data concurrently within a very short time or real-time in many areas such as IoT, AI, machine learning, smart grid, and big data analytics, we need exa-scale or zetta-scale computation in the near future. Thus, to have this level of computation, we need a massively parallel computer (MPC) system that shall consist of millions of nodes; and, for the interconnection of these massive numbers of nodes, conventional topologies are infeasible. Thus, a hierarchical interconnection network (HIN) is a rational way to connect huge nodes. Through this article, we are proposing a new HIN, which is a tori-connected flattened butterfly network (TFBN) for the next generation MPC system. Numerous basic modules are hierarchically interconnected as a toroidal connection, whereby the basic modules are flattened butterfly networks. We have studied the network architecture, static network performance, and static cost-effectiveness of the proposed TFBN in detail; and compared static network and cost-effectiveness performance of the TFBN to those of TTN, torus, TESH, and mesh networks. It is depicted that TFBN possesses low diameter and average distance, high arc connectivity, and temperate bisection width. It also has better cost-effectiveness and cost-performance trade-off factor compared to those of TTN, torus, TESH, and mesh networks. The only shortcoming is that the complexity of wiring of the TFBN is higher than that of those networks; this is because the basic module necessitates some extra short length link to form the flattened butterfly network. Therefore, TFBN is a high performance and cost-effective HIN, and it will be a good option for the next generation MPC system.  
m  
massively parallel computer system; hierarchical interconnection network; TFBN; cost performance trade-off factor; static network performance; static cost effective factor; time cost effective factor; smart grid; energy storage system
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 2 Problem Formulation
### Monotone Functions between ISDRs and the DOA
\(\phi_{s}\) is away from \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\), we can obtain all of the \(I_{uv}\), \(I_{vu}\) and \(I_{uv0}\) values properly. To visualize the discussions above, a decision tree of handling the special angles in computing \(\textbf{I}_{\text{isdr}}\) is plotted in Figure 3.  
When \(\mathbf{I}_{\text{isdr}}=[I_{uv}\), \(I_{vu}\), \(I_{uv}]\) has been computed properly, with simple manipulation from Equations (23)-(25), we get:  
\[\phi_{\text{s}}(\tau,\omega) =\text{arccot}(I_{uv}(\tau,\omega)-\varepsilon_{1}(\tau,\omega)) \tag{28}\] \[\phi_{\text{s}}(\tau,\omega) =\text{arctan}(I_{vu}(\tau,\omega)-\varepsilon_{2}(\tau,\omega))\] (29) \[\theta_{\text{s}}(\tau,\omega) =\text{arccos}(I_{uv}(\tau,\omega)-\varepsilon_{3}(\tau,\omega)) \tag{30}\]  
From Equations (28)-(30), we can see that arccot, arctan, and arccos functions are all monotone functions, which are what we expected. Besides, we also note that (\(\theta_{\text{s}}\),\(\phi_{\text{s}}\)) is given, and \(I_{uv}\), \(I_{uv}\) and \(I_{uv}\) can be computed by Equations (14)-(16). However, \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\) are unknown, which reflect the impact of noise and interferences. According to the assumptions made in Section 2.1, if we are able to select the TF components (\(\theta_{\text{s}}\),\(\phi_{\text{s}}\)) dominated by the target speech, and the local SNR at this (\(\tau\),\(\omega\)) is high, then \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\) can be ignored, since they will have values approaching zero at these (\(\tau\),\(\omega\)) points. In such conditions, we obtain the desired formulas to compute (\(\theta_{\text{s}}\),\(\phi_{\text{s}}\)):  
\[I_{uv}(\tau,\omega)\approx\frac{\sin\theta_{s}\cos\phi_{s}}{\sin\theta_{s}\sin \phi_{s}}+\varepsilon_{1}(\tau,\omega)\ =\cot\phi_{s}+\varepsilon_{1}(\tau,\omega) \tag{23}\]0
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AES & Advanced Encryption Standard \\ AI & Artificial Intelligence \\ AXI & Advanced eXtensible Interface \\ BSI & Bundesamt fur Sicherheit in der Informationstechnik \\ CAVP & Cryptographic Algorithm Validation Program \\ CBC & Cipher Block Chaining \\ CFB & Cipher FeedBack \\ CCM & Counter with CBC-MAC \\ CMAC & Cipher-based Message Authentication Code \\ CMRT & CryptoManager Root-of-Trust \\ CoT & Chain-of-Trust \\ CPA & Correlation Power Analysis \\ CPU & Central Processing Unit \\ CTR & CounTeR \\ CSPRNG & Cryptographically Secure Pseudo-Random Number Generator \\ DoS & Denial-of-Service \\ DPA & Differential Power Analysis \\ ECB & Electronic CodeBook \\ ECC & Elliptic Curve Cryptography \\ ECIES & Elliptic Curve Integrated Encryption Scheme \\ ECDH & Elliptic Curve Diffie-Hellman \\ ECDSA & Elliptic Curve Digital Signature Algorithm \\ ECMQV & Elliptic Curve Menezes-Qu-Vanstone \\ EM & Electromagnetic Emission \\ EMA & Electromagnetic Emission Analysis \\ EPC & Enclave Page Cache \\ EPCM & Enclave Page Cache Map \\ EPI & European Processor Initiative \\ FSM & Finite State Machine \\ GCM & Galois Counter Mode \\ GE & Gate Equivalent \\ GPIO & General Purpose Input/Output \\ GPP & General Purpose Processor \\ HIEE & Hardware-assisted Isolated Execution Environment \\ HMAC & Hash-based Message Authentication Code \\ HPC & High-Performance Computing \\ HSM & Hardware Security Module \\ HW & Hardware \\ IEEE & Institute of Electrical and Electronics Engineers \\ IoT & Internet-of-Things \\ LUT & Look-Up Table \\ MAC & Message Authentication Code \\ MCU & Master Control Unit \\ NIST & National Institute of Standard and Technology \\ OFB & Output FeedBack \\ OTP & One-Time Programmable \\ PQC & Post-Quantum Cryptography \\ PSP & Platform Security Processor \\ RISC & Reduced Instruction Set Computer \\ RNG & Random Number Generator \\ RoT & Root-of-Trust \\ SCA & Side-Channel Attack \\ SGX & Software Guard eXtensions \\ SoC & System-on-Chip \\ SHA & Secure Hash Algorithm \\ \end{tabular}
## 5 An Iterative Calibration Method
According to the induction method, the results of the \(n\) times estimation is \(\widetilde{\mathbf{D}}_{n-1}\):  
\[\widetilde{\mathbf{D}}_{n-1}=(\mathbf{I}+\mathbf{M}_{n})\mathbf{D}_{n-1}=(\mathbf{I}+\mathbf{ M}_{n})\mathbf{M}_{n-1}\cdot\cdots\mathbf{M}_{2}\mathbf{M}_{1}\cdot\mathbf{E}_{t} \tag{34}\]  
Then after \(n\) times estimation, the calibration residual is:  
\[\mathbf{D}_{n}=\widetilde{\mathbf{D}}_{n-1}-\mathbf{D}_{n-1}=\mathbf{M}_{n}\cdot\cdots\mathbf{M}_{ 2}\cdot\mathbf{M}_{1}\cdot\mathbf{E}_{t} \tag{35}\]  
Except for the first-time calibration, the rest of the calibrations are all aiming to estimate the residual error. Therefore, after \(n\) times iterative calibrations, the state vector as well as the error coefficient could be obtained by Equation (36):  
\[\mathbf{E}=\mathbf{E}_{1}+\sum_{i=2}^{n}\widetilde{\mathbf{D}}_{i} \tag{36}\]  
The more times the error is estimated, the more accurate the error coefficients could be obtained. But it is impossible to iterate forever. The iteration is stopped when the spectral radius \(\gamma\) of the error matrix \(\mathbf{M}=\mathbf{M}_{n}\cdots\mathbf{M}_{2}\cdot\mathbf{M}_{1}\) is less than \(1\). When \(\gamma<1\), it indicates that the error matrix \(M\) converges to a stable value [6]. Although we can continue to estimate the error coefficients, the effect is not obvious.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 4 Simulation and Analysis  
In this section, we use simulation data to evaluate the performance of the proposed method, and compare it with the ESPRIT and RELAX algorithms under different SNRs and limited measurement conditions.  
Suppose the illuminator of opportunity A, represented by IO(A), locates at (35 km, 80\({}^{\circ}\)) and operates at the carrier frequency of 200 MHz, while the illuminator of opportunity B, denoted by IO(B), locates at (30 km, 20\({}^{\circ}\)) and works at 280 MHz. The receiver is located at (20 km, 0\({}^{\circ}\)). Additionally, the rotation angle \(\Theta\) is assumed to be 4\({}^{\circ}\) during the observation time.
## 5 Numeric Simulation
### Simulation Study
model information to achieve the model-based compensation and the additional estimated disturbance signal to reduce the uncompensated effects. Case 2: This case is to demonstrate the superiority of the proposed PP-NFTSMC-ESO controller for the trajectory tracking. In this case, the proposed controller was compared with three other controllers (i.e., PI, SMC-ESO, and NFTSMC-ESO), which are described in Section 5.2. The physical parameters of the joint actuator, the dynamic uncertainties, and unknown external disturbances were set the same as in Case 1, and the design parameters of the four controllers and the ESO were set as the values shown in Table 4. As the fast motion with deep-sea working condition is more challenging, only the tracking performance of the fast motion trajectory \(y_{d}=\frac{2}{9}\pi[0.5-\cos(4\pi t)][1-\exp(-  
Figure 7a illustrates the position response. On the surface, it seems that almost all the four controllers manifested the same position-tracking performance, but actually they exhibited significant differences from a micro perspective, which can be clearly shown from the position tracking errors in Figure 7b. As seen, the proposed PP-NFTSMC-ESO and NFTSMC-ESO controllers have better tracking performance than PID and SMC-ESO controllers in the overall dynamic response process. This is attributed to the fact that the proposed PP-NFTSMC-ESO controller and NFTSMC-ESO controller not only employ both the system model information to achieve the model-based compensation and the additional estimated disturbance signal to reduce the uncompensated effects, as explained previously, but also take advantage of the property of the fast convergence speed and high stability precision of the NFTSMC controller. In addition, we can also see from Figure 7b that NFTSMC-ESO controller has better tracking performance than the SMC-ESO controller, which is mainly due to the fact that the NFTSMC controller is the improvement of the SMC controller, thus has a faster convergence speed and higher tracking accuracy [30]. Furthermore, it can be seen from Figure 7b that the PP-NFTSMC-ESO controller has superior tracking performance relative to the NFTSMC-ESO controller, of which the maximum steady state tracking error reduces almost 10 times compared with the NFTSMC-ESO controller shown in Table 6. This is mainly due to the fact that introducing the prescribed performance function into the controller makes it more efficient to improve the control action, which can be observed from the control input signal presented in Figure 7c. Figure 6: Position-tracking performance of fast motion with the proposed PP-NFTSMC-ESO controller. **(a)** Position response. **(b)** Position-tracking error. **(c)** Velocity estimation. **(d)** General disturbance estimation.
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods
### Dataset
#### 2.1.3 Train, Validation and Test Sets  
The ratio of train, validation, and test set sizes do not have a perfect balance and cannot be estimated beforehand; instead, it needs to be determined based on the abundance of data supplied [39]. For a small dataset, the validation and testing ratio will be considerably higher than the amount used for training. On the contrary, as the volume of data increases, the validation and testing ratio will decrease [39]. Ideally, the test set should be a dataset acquired independently. Having a large number of independent test sets strengthens the generalization argument [39]. The EuroSAT dataset was divided into three groups: the train set (80%), the validation set (10%), and the test set (10%). These EuroSAT dataset subsets were randomly chosen; nonetheless, the same subsets were utilized for training and evaluating all models. All images were rescaled to 200 by 200 pixels, 128-photo mini-batches were used, and only images from the train set were subjected to data augmentation methods.
# A Two-Stage Anomaly Detection Method Based on User Preference Features and the Deep Fusion Model
## 5 Conclusions  
This paper proposes a two-stage anomaly detection method based on user preference features and a deep fusion model. Based on the distributed and parallel execution of detection tasks, the stability and detection accuracy of DDoS attacks are improved. It provides auxiliary decision-making for network security operations and maintenance personnel. In the framework, the detection tasks are divided into two stages. A deep fusion classifier is trained using the features extracted from the input traffic data. The experimental results illustrate the superiority of the proposed method over typical methods in terms of detection accuracy and its higher time efficiency over the stand-alone detection mode. In the future, the effectiveness of the method will be further explored with larger real-world network traffic.  
The methods proposed in this paper need to be improved in the following aspects: first, the training data used are all public data sets rather than large-scale real network data, and the training effect needs to be further improved; second, model training requires a large number of label data sets, and there is a certain amount of manual classification and feature summary workload.  
Conceptualization, S.-L.Z.; methodology, B.Z.; software, Y.-T.Z.; validation, Y.-X.G.; formal analysis, J.-L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study does not require ethical approval and we recommend excluding this statement.  
Not involved.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.