esa-sceva/satcom-chunk-collection huggingface dataset:
# Malware Variants Detection Model Based on MFF-HDBA
###### Abstract
In order to solve these problems, this article proposed an effective and intelligent vision-based malware classification framework, referred to as MFF-HDBA; this framework can realize automatic feature extraction and classification without domain expert knowledge, and can also effectively and efficiently identify malware variants from real and daily networks. The main contributions of our research can be summarized as follows:  
1. A malware preprocessing method was implemented, based on bilinear interpolation algorithm and data augmentation. In preprocessing, malware after visualization is normalized via bilinear interpolation algorithm, and data augmentation technique was utilized to solve the issue of malware data set imbalance. 2. A novel malware detection method was developed by combining multi-scale feature fusion and channel attention mechanism for more abundant texture information capture and robust feature extraction. 3. A hyperparameter optimization algorithm was proposed on the basis of the discrete bat algorithm, referred to as HDBA. HDBA was utilized to overcome the disadvantage of the traditional hyperparameter optimization method that lacks a theoretical basis and easily falls into local optima. 4. The proposed method was evaluated through extensive experimentation. The results indicated that the proposed method not only achieves high classification accuracy, but also a low MTTM (Mean Time to Detect) overhead, which are better than state-of-the-art solutions. The remainder of this paper is organized in the following manner. Section 2 reviews the related works of vision-based malware classification. Section 3 introduces the details of the proposed method, including multi-scale feature fusion, channel attention mechanism,and HDBA. Section 4 presents two malware data sets and statistical measures. Experiments are implemented to evaluate the proposed method in Section 5. Section 6 ends the paper with concluding remarks.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Satellite Data Processing
#### 3.3.1 Preprocessing of Sentinel Time Series  
For the time series calculation, we used analysis-ready archive data from the Google Earth Engine, which provides pre-processed Sentinel-1 and Sentinel-2 data. The scenes of the Sentinel-1 data cube are pre-processed to the backscatter coefficient \(\sigma 0\) (sigma0 or sigma0 ught). Preprocessing of the radar time series comprises the following steps: (1) the application of an orbit file, (2) thermal noise removal, (3) radiometric calibration, and (4) orthorectification. In order to limit the effects of look angle and orbit direction for each individual pixel, the Sentinel-1 data was filtered to the orbit direction (ascending or descending) with most observations (majority filter on each pixel). The preprocessing of the Sentinel-2 L2A data included the following steps: (1) cloud filtering (<10 percent cloudy pixel per scene) and (2) cloud masking using the QA60 bitmask band.
# Detection of Tampering by Image Resizing Using Local Tchebichef Moments
## 4 Results
### Experimental Discussions
#### 4.2.2 Identifying Images Obtained by Different Content-Aware Resizing Methods  
In this experiment, the tampered images with scaling ratios from 10% to 50% are obtained by different content-aware resizing methods. They are adopted to test the performance of our proposed algorithm. The average detection accuracies of different content-aware re-targeting methods, where the average detection accuracy is the average value of diagonal elements in the confusion matrix, are summarized in Table 2. Note that \"mixed\" represents the mixed test set of tampered images with the scaling ratios from 10% to 50%. There are three content-aware resizing methods in this paper. Therefore, this is a four-class classification problem (the original images as a special class), according to the ECOC strategy.  
Table 2 shows that the average accuracy is improved with the increment of the scaling ratio. However, the detection accuracy is apparently decreased for highly compressed images with Quality factor (QF) being equal to 75. Through careful analysis of our experimental results, it is found that the main reason for the decrement of the detection accuracy in the compressed condition is that the traces of the tampered images are weakened when images are compressed. Therefore, the detection accuracy is decreased in this case. We have also completed the experiment of the \"mixed\" tampered uncompressed and compressed images and get the confusion matrix. Table 3 shows our experimental results, where CMOMTUI represents the confusion matrix of \"mixed\" tampered uncompressed images, \"CMOMTCI represents the confusion matrix of \"mixed\" tampered compressed images, \"*\" represents the classified accuracy less than 1%, OR represents original images, SNS represents a scale-and-stretch method, SC represents a seam carving method, and SL represents a scaling method. From Table 3, we can see that our proposed method can get a high accuracy for the three content-aware resizing methods mentioned in this paper. However, it can't obtain a good detection accuracy on the tampered images with JPEG compression. In addition, its false positive rate is relatively high for the seam carving method (SC) and the scaling method (SL).
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Collective Contribution to Reliable AI in Healthcare
#### 4.5.1 Hybrid Privacy-Preserving Techniques in Healthcare AI  
In the realm of healthcare AI, the adoption of hybrid privacy-preserving techniques signifies a significant leap in safeguarding data privacy and integrity. These techniques combine various privacy strategies to excel in complex situations in healthcare settings. By integrating a range of methods, they overcome the limitations of single-strategy approaches, resulting in more robust and private models. A prime example of such a hybrid approach is the fusion of Homomorphic Encryption (HE) and Differential Privacy (DP). HE allows for processing encrypted data without decryption, thus maintaining privacy. DP introduces an anonymity layer in tandem, making this combination highly effective in scenarios that demand encryption and data anonymity. Further enhancement in data security is achieved through integrating blockchain technology with either HE or DP. When paired with the privacy capabilities of HE or DP, the immutable ledger of blockchain significantly improves the overall security and confidentiality of data. This is particularly beneficial for securely logging data access and processing records, ensuring the data remain protected and private. Additionally, the integration of Secure Multi-party Computation (SMPC) with blockchain technology harnesses the distributed computation power of SMPC along with the secure, transparent ledger of the blockchain. This combination promotes collaborative data analysis and processing across various entities while safeguarding the privacy and security of each participant's data [9]. These advanced hybrid techniques demonstrate the innovative strides
## 3 Proposed Methods
### Multi-Subsystems Coupling Relationship Analysis of Earth Observation Satellite
We plotted the charging electrical power surface during the sunlight period in one orbital period with different roll angles, as shown in Figure 5.  
Figure 5: Influence diagram of the satellite phases and attitudes on charging power. The \(X\)-axis in Figure 5 is the satellite's roll angle, the \(Y\)-axis represents the phase angle of the satellite's orbit, and the \(Z\)-axis is the charging electrical power. The selected orbit is a Sun-synchronous orbit, and the Sun vector is nearly parallel to the satellite's orbital plane; the normal vector of the satellite solar arrays is along the \(-Z\)-axis of the satellite body coordinate system. From Figure 5, it can be clearly seen that when the normal vector of the solar arrays is nearly parallel to the Sun vector, there is more electrical power that can be gained; this is consistent with the expression of Equation (12). In some situations, some targets may appear in locations where the satellite receives less charging power while observing the targets. When there is sufficient electrical energy for the satellite, the observation of these targets will not cause a shortage in the electrical energy. However, when the supply of electrical energy is insufficient, the satellite probably cannot observe all of the targets and transmit data to the ground stations as a result of the low electrical energy supply. The satellite can obtain more electrical energy if it chooses to observe targets that are located in locations that allow more charging power to be generated. In this way, more observation and data transmission of the targets can be achieved. This reflects the conflicting relationship between the observation subsystem and the electricity subsystem. The state variable that indicates the main constraint between the observation subsystem and the electricity subsystem is the remaining electrical energy of the satellite. When the satellite's initial electrical energy and charging electrical energy are sufficient for the electrical energy consumption of devices and the observation subsystem is the main limiting factor, the path scheduling should allow the satellite to obtain more target observations. In contrast, when the electricity subsystem is the main limiting factor, the path scheduling should allow the satellite to obtain more charging electrical energy.
## 1 Introduction
Representative examples include pulse thermography (PT) and lock-in thermography (LIT) using a light source as an external energy source, as well as static eddy current thermography and  
Figure 1: Structure of light-water reactor-type nuclear power plant. dynamic eddy current thermal imaging-based online scanning using induction heating. However, when examining applicability to actual sites, pulse thermography, which applies heat from a high-power flash lamp in a very short period of time, may have limitations such as a somewhat small inspection area and complex signal processing techniques. Additionally, eddy current thermography (ECT) may have limitations that make it difficult to achieve uniform induction heating due to the complex structure. On the other hand, in the case of lock-in thermography using sinusoidal heating, it is advantageous to evaluate the time dependence between the input and output signals caused by the presence of defects and to control the influence of environmental factors [21; 22]. In addition, it can be advantageous for field application because it is better at detecting, identifying, and quantitatively evaluating defects in complex structures. Therefore, in this study, a study on thinning defect evaluation was conducted by applying lock-in thermography. In this study, in order to evaluate according to the depth of thinning defects, various depth conditions were considered while the defect size was constant. Similar previously published literature mainly focuses on the research of thinning detection and detection performance improvement algorithms and evaluation of defect size. In contrast, the novelty of this study combines signal processing and image processing to conduct defect depth evaluation as well as defect detection capability for CLP thinning. For the study, analysis was performed using phase data and phase images by applying the four-point signal processing technique, which is simple and excellent for improving detectability. The modulation frequency was applied to a low-frequency band that generates enough thermal contrast between the healthy part and the defective part by providing sufficient heat energy. An empirical equation was derived for depth estimation through the fitting of phase data. Additionally, the phase image was binarized using the Otsu algorithm. The defect detection was performed on the binarized image through an optimal threshold, and the results between thinning depth estimation accuracy and thinning detectability was finally presented by comparative analysis.
## 5 Discussion
### AI Techniques in Forecasting TCO  
The findings indicated the growing trend of employing ML and DL techniques, particularly for forecasting energy consumption within HVAC and CWP systems [30,49,50,57,86,110,112], as energy consumption is a major driver of operating costs and TCO. ML and DL tech niques excel in modeling the complex and nonlinear relationships between the aforementioned key parameters. Among ML and DL algorithms, ANNs [30; 86; 111; 112; 113; 113; 127] and LSTM [46; 49; 50; 114; 115] are the most frequently used for prediction in this context. DRL is also popularly used for optimization and control strategies [50; 112; 514]. One potential reason is that DL algorithms have shown advantages over non-deep learning-based ML methods due to their capacity to capture complex nonlinear correlations among unstructured and dynamic parameters or features [122]. However, DL requires large datasets to perform optimally [122], while ML works well with smaller datasets [113]. The variability in dataset sizes and the scarcity of sufficient public data are significant gaps limiting the use of DL, as identified across the literature [3; 6; 30; 52; 86; 113; 122].  
This study highlights substantial cost and energy savings that AI algorithms can achieve by reducing life cycle costs, operating costs, energy consumption, and CO\({}_{2}\) emissions [20; 112]. For instance, [PERSON] et al. [30] successfully applied an ANN model to predict the COP and chiller cooling capacity, estimating up to an 18% reduction in total costs. Similarly, studies by [PERSON] et al. [111; 127] demonstrated that AI-driven model predictive control strategies reduced operating costs from 3.4% to 14.6%. These findings highlight the growing importance of continued research and implementation of AI in this domain.  
Despite the prevalent reliance on traditional ML methods in the literature and their demonstrated effectiveness in efficiently processing small datasets [3; 6; 52; 86; 113; 122], their limitations become apparent when compared to the potential of DL models [122; 124]. [PERSON] et al. [120] noted that DL models offer superior predictive accuracy but require larger datasets to perform optimally. This reliance on smaller datasets and traditional methods highlights the ongoing challenges in data availability, constraining the broader application of advanced predictive techniques [119]. The scarcity of sufficient public data is a significant gap that limits the use of DL [122; 125]. These challenges underscore the need for more comprehensive and publicly available datasets to support developing and validating predictive models across various contexts and applications.  
Therefore, despite these encouraging outcomes, additional research is necessary to broaden the use of AI models, ensuring their adaptability to various chiller systems and environments [7; 46; 124]. Furthermore, continued investigation into their long-term financial and environmental advantages is essential [119].
# A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
## 3 Mobility-Aware Adaptive Duty Cycling Mechanism in Tunnel Excavation
### Energy Saving Strategy  
In tunnel excavation, there are many mobile objects equipped with sensor nodes, such as equipment and workers. Each node sleeps most of the time (\(\tau\)) to conserve energy consumption, periodically wakes up for a very short duration (\(T\)), and checks the medium for radio activity. For effective tracking in both static and mobile scenarios, we present a mobility-aware adaptive duty cycling mechanism, as shown in Figure 6. Each node is defined as in a non-mobility state if and only if its change of RSSI value (\(\Delta RSSI\)) is less than the threshold \(\lambda\); whereas the mobility state implies the opposite. Normally, this is designed to save energy of the static sensor nodes. In order to reduce the latency on high mobility while still maintaining a low power design, it adjusts its duty cycle ratio by decreasing or increasing the sleep time, depending on its mobility state as expressed in Equation (3):  
\[f_{\tau}(\alpha,\beta)=\begin{cases}&\frac{\tau}{\alpha}\text{ if }\Delta RSSI\geq \lambda\\ &\beta\tau\text{ otherwise}\end{cases} \tag{3}\]  
where, \(f_{\tau}(\alpha,\beta)\) is the sleep time adjusting function, \(\alpha\) and \(\beta\) are the factors that are greater than or equal to '1'. When \(\alpha=1\) and \(\beta=1\) are employed, the duty cycle is equivalent to the periodic duty cycle mechanism. Note that these values can be manipulated depending upon the conditions: whether consuming energy reduction is more important or latency reduction is more important.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(\Delta RSSI\) & The change of RSSI value \\ \hline \(p\) & The probability that the \(\Delta RSSI\) of sensor nodes exceeds a threshold \(\lambda\) \\ \hline \(T\) & The wake-up time of duty cycle interval \\ \hline \(\tau\) & The sleep time of duty cycle interval \\ \hline \(D\) & The duty cycle (i.e.,) \(D=T/(T+\tau)\) \\ \hline \(P_{W}\) & The power for wake-up \\ \hline \(\epsilon_{Periodic}\) & The expected energy consumption of periodic duty cycling mechanism with an fixed \\  & sleep time of \(\tau\). \\ \hline \(\epsilon_{Adaptive}\) & The expected energy consumption of adaptive duty cycling mechanism with two adjusted \\  & sleep time, \(\tau/\alpha\) and \(\beta\tau\), according to its change of RSSI value. \\ \hline \(L_{Periodic}\) & The expected latency of periodic duty cycling mechanism with an fixed sleep time of \(\tau\). \\ \hline \(L_{Adaptive}\) & The expected latency of adaptive duty cycling mechanism with two adjusted sleep time, \(\tau/\alpha\) and \(\beta\tau\), according to its change of RSSI value \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation.  
Figure 6: Mobility-aware adaptive duty cycling strategy (\(\alpha=2\), \(\beta=2\)).
## 3 Design of AcPAS
### Design of Antenna  
As shown in Figure 1, the AcPAS antenna structure includes, in addition to the coaxial port, waveguide, and septum polarizer, other components such as a metal base plate, a parabolic metal housing, a dielectric substrate, and metal rings. The metal base plate is mainly used for installing the waveguide and the parabolic metal housing. The top of the metal housing has a circular opening, inside of which the dielectric substrate is installed. The dielectric substrate is made of Rogers 5880 material. Metal rings of the same size are etched on the upper and lower sides of the dielectric substrate.  
When the antenna is in operation, Port 1 is fed, and Port 2 is matched. The septum circular polarizer forms a RHCP wave. On the contrary, when Port 2 is fed and Port 1 is matched, an LHCP wave is produced. The CP wave excites the metal rings to generate radiation. The parabolic metal housing serves to expand the beam width, achieving wide-beam coverage with dual-CP. Furthermore, based on the characteristics of the septum polarizer, by feeding equal amplitude and in-phase or equal amplitude and opposite-phase signals to the two ports, horizontal or vertical polarized waves can also be generated.
# LMI Pole Regions for a Robust Discrete-Time Pole Placement Controller Design
## 2 Preliminaries and Discrete-Time Pole Region Problem Formulation
### Robust Pole Placement for the Defined \(D_{r}\) Region via State Feedback  
In this subsection we recall LMI condition to find the controller gain matrix for control law, Equation (3), introduced in Reference [7].  
**Theorem 2**.: _[_7_]_ _Consider a linear uncertain system, Equation (1), with polytopic uncertainties, Equation (2). If there exist matrices \(H\in R^{m\times n},S\in R^{m\times n}\) and \(N\) symmetric positive definite matrices \(P_{i}\in R^{n\times n}\) such that_  
\[\left[\begin{array}{cc}M_{11i}&M_{12i}\\ M_{12i}^{T}&M_{22i}\end{array}\right]<0\ \ \ \ \ i=1,\ldots,N, \tag{8}\]  
_with_  
\[M_{11i}=R_{11}\otimes P_{i}+R_{12}\otimes\left(A_{i}H+B_{i}S\right)+R_{12}^{T }\otimes\left(A_{i}H+B_{i}S\right)^{T},\]  
\[M_{22i}=R_{22}\otimes\left(P_{i}-H-H^{T}\right),\]  
\[M_{12i}=R_{12}^{T}\otimes\left(P_{i}-H^{T}\right)+R_{22}\otimes\left(A_{i}H+B _{i}S\right),\]  
Figure 4: Inner circle (green) and elliptic (black) approximations to the cardioid region.  
then the closed loop uncertain system, Equation (1), with a state feedback, Equation (3), is robustly \(D_{R}\)-stable in the uncertainty domain, Equation (2), with a state feedback gain matrix given by_  
\[K=SH^{-1}. \tag{9}\]  
Theorem 2 provides the algorithm to calculate the state feedback pole placement controller for the determined matrices \(R_{11},R_{12},R_{22}\), corresponding to the prescribed \(D_{R}\) region where the CLS poles lie. Inequality (8) is in the form of LMI for the unknown matrices \(H,S,P_{i}\) and can be readily solved by any free LMI solver (we used SEDUMI); a nice interface for the MATLAB environment is provided by YALMIP [19]. Therefore, once we determine the \(D_{R}\) region corresponding to the required performance, the respective controller is calculated by solving Equations (8) and (9). The next section is devoted to the crucial task--inner convex approximation to the discrete-time pole region for the prescribed damping and computation of the corresponding matrices \(R_{11},R_{12},R_{22}\) defining the \(D_{R}\) region.
## 2 Proposed Methods
### Patch-Mix Feature Fusion  
In the existing field of audio classification, feature fusion schemes predominantly rely on multi-scale, multi-channel, and multi-spectral approaches to facilitate fusion, thereby implementing adaptive weighting mechanisms and robust recognition for specific scenarios [29]. However, these existing fusion mechanisms are gradually revealing their limitations in urban environmental sound recognition. Unavoidable noise, being a pervasive factor, can obscure audio signals to varying degrees and potentially overshadow the target audio, making current multi-dimensional fusion techniques insufficient in handling noise interference [3]. Additionally, introducing more parameters and complexity increases  
Figure 1: Log-Mel Spectrogram Extraction Process Diagram.  
the model fitness to the training data and, thus, raises the risk of overfitting. This implies that the aforementioned methods are no longer suitable for the application scenarios of this study. Therefore, this paper proposes a novel feature fusion scheme to address the feature fusion problem.  
First, a random value, \(\lambda\), is generated using the Beta distribution to control the mixing process between the two audio features during the feature fusion process [29], determining the weight between the two audio features. The Beta function is defined as follows:  
\[B(\alpha,\beta)=\int_{0}^{1}t^{\alpha-1}(1-t)^{\beta-1}dt \tag{1}\]  
where \(\alpha\) and \(\beta\) are the two shape parameters of the Beta distribution, which determine the shape of the Beta distribution. When both \(\alpha\) and \(\beta\) are 1, the Beta distribution degenerates into a uniform distribution, where all possible values have an equal probability density. This ensures that the generated mixed features effectively reflect the overall information of the feature sets during the feature fusion process, preventing the model from overfitting to a specific spectrogram feature.  
Then, the spectrogram is divided into multiple patches through the Transformer's Patch Embedding module, followed by a strategic random mixing process of these patches using the Patch-Mix method. During the mixing process, patches at the same position are swapped without altering their positional encoding function. Subsequently, related simulation experiments are conducted on the baseline model. The specific experimental results, shown in Table 1, demonstrate that this feature fusion technique significantly reduces GPU capacity and inference time compared to traditional feature fusion schemes, and it can represent environmental sounds.