esa-sceva/satcom-chunk-collection huggingface dataset:
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
###### Abstract  
The Synthetic Aperture Radar (SAR) target recognition model usually needs to be retrained with all the samples when there are new-coming samples of new targets. Incremental learning emerges to continuously obtain new knowledge from new data while preserving most previously learned knowledge, saving both time and storage. There are still three problems in the existing incremental learning methods: (1) the recognition performance of old target classes degrades significantly during the incremental process; (2) the target classes are easily confused when similar target classes increase; (3) the model is inclined to new target classes due to class imbalance. Regarding the three problems, firstly, the old sample preservation and knowledge distillation were introduced to preserve both old representative knowledge and knowledge structure. Secondly, a class separation loss function was designed to reduce the intra-class distance and increase the inter-class distance, effectively avoiding the confusion between old and new classes. Thirdly, a bias correction layer and a linear model was designed, which enabled the model to treat the old and new target classes more fairly and eliminate the bias. The experimental results on the MSTAR dataset verified the superior performance compared with the other incremental learning methods.  
incremental learning: SAR target recognition; knowledge distillation; old sample preservation; class imbalance +
Footnote †: journal: Applied Sciences
## 3 Background and Previous Related Work
### Dlt
#### 3.3.2 Hash Functions  
These are mathematical algorithms that take an input and produce a fixed-size output, which is called a hash. In DLT, hash functions are used for data integrity, tamper resistance, and generating unique identifiers. Examples of hash functions used in DLT include SHA-256 (used in Bitcoin) and Keccak-256 (used in Ethereum).
## 4 Optimization Design Algorithm  
### Optimization Model  
According to the analysis of the simulation results, the optimization range of the structural parameters to be optimized is determined. The optimization range is shown in Table 2. The other structural parameters are fixed to the initial value set in Section 3.3, and the eccentricity of the gas bearing is fixed to 0.4 during the optimization process. The optimization goal is to improve the bearing capacity while minimizing gas consumption.  
According to the derivation of bearing capacity and gas consumption in the engineering calculation method, the following optimization model can be obtained:  
\[\min F(X)=[f_{1}(X)=-W,f_{2}(X)=Q]\]  
\[s.t.\ \ x_{i}^{\min}\leq x_{i}\leq x_{i}^{\max},i=1,2,3,4\]  
\[X=[x_{1},x_{2},x_{3},x_{4}]=[d,l,h_{av},n]\]  
where \(f_{1}\) is the objective function of the bearing capacity, and \(f_{2}\) is the objective function of gas consumption. The \(X\) vector consists of four structural parameters, and the boundary condition is the optimization range of the four structural parameters.
## 5 Experimental Results and Discussion
Consequently, by considering the structure of all four architectures, it will be demonstrated that more convolutional layers can help to produce more features that lead to the satisfying accuracy rate, for example, 3D-GoogleNet obtained a higher accuracy rate than 3D-AlexNet. More convolutional layers, however, can make a more complicated model to extract features. Therefore, the complex model of 3D-GoogleNet achieved a lower accuracy rate than the proposed straight 3D-CNN and hybrid 3D-CNN architectures. Hence, creating balanced CNN layers is the most significant issue in classification problems. Although 3D-GoogleNet achieved a higher sensitivity (prediction of samples with cancer) of 83.17%, specificity (prediction of samples without cancer) of 91.61% and precision (prediction of samples with cancer among all samples with the cancer label) of 88.36% compared to 3D-AlexNet (82.74% sensitivity, 88.04% specificity and 83.66% precision), the proposed two 3D-CNN architectures have obtained higher sensitivity, specificity and precision in the diagnosis of lung cancer. Therefore, according to the sensitivity, hybrid 3D-CNN using RBF-based SVM classifier and straight 3D-CNN by using softmax classifier could classify 88.53% and 86.40% samples with cancer among all samples respectively. However, 11.47% and 13.60% of samples with cancer among all samples were misclassified and are determined as without cancer samples by hybrid 3D-CNN and straight 3D-CNN as well. Furthermore, according to the specificity of both proposed architectures, only 5.77% and 6.91% of samples without cancer among all samples have been misclassified by hybrid 3D-CNN and straight 3D-CNN respectively. Both two proposed 3D-CNN architectures have surpassed the other two architectures in terms of classifying samples with cancer among all samples with the cancer label (specificity) and, additionally, hybrid 3D-CNN has obtained the highest specificity (94.23%). Figure 6 illustrates Roc curves of all four architectures based on sensitivity and 1-specificity. According to the results, it can be proved that not only did hybrid 3D-CNN by using RBF-based SVM achieve the highest results among all three architectures, but it also could acquire satisfying results by using the RBF-based SVM classifier than softmax classifier in straight 3D-CNN. Thus, the RBF-based SVM classifier could improve the performance of CNN architecture in the classification of 3D lung CT scan images. Figure 5: Accuracy of the four 3D-CNN architectures over epochs. Figure 6: ROC curve of the four 3D-CNN architectures.
## 4 Review of Existing Methods and Capabilities
### Safety Controls  
Safe control of UASs is a critical area of concern. Manned aircraft have the unique advantage of having a pilot in the loop directly at the vehicle with the ability to react to safety concerns immediately. UASs require either automated response capabilities or the ability to quickly provide critical information to an operator for response. The notion of run-time assurance to provide confidence in autonomous decisions is a critical area of concern. Collision control is a huge concern for UASs, which has resulted in work on air, ground, and object avoidance. Boundary and population control work has focused on keeping UASs in controlled areas and out of the area of risk to populations. Weather avoidance enables a vehicle to evaluate the weather and determine whether the flight path should be modified autonomously. Fault tolerance, isolation, and prognostics is a large research area focused on enabling vehicles to continue to operate under less than ideal functionality. Flight termination is a major concern for how to manage vehicles that are not operating properly and must immediately cease flight operations. Test safety is a unique area of concern focused on how to properly test vehicles prior to release to normal operations where the likelihood of failure is higher and risks can be increased.
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
## 2 Leakage Protection Method Based on 3 AC Detection Circuit
### Mathematical Model Based on 3 AC Detection Method
#### 2.2.2 Two-Phase Leakage  
When a two-phase leakage fault occurs in the \(A\) and \(B\) phases, the insulation of \(R_{A}\) and \(R_{B}\) decreases, and the insulation resistance of \(R_{C}\) can be equivalent to infinity. Therefore, the formula of \(\dot{U}_{0}\) changes as follows:  
\[\dot{U}_{0}=\frac{-\dot{U}_{A}\frac{1}{R_{A}^{\prime}}-\dot{U}_{B}\frac{1}{R_ {B}^{\prime}}-\dot{\mathrm{j}}\omega C_{2}\dot{U}_{A}}{\frac{1}{R_{A}^{ \prime}}+\frac{1}{R_{B}^{\prime}}+\dot{\mathrm{j}}\omega 3C+\dot{\mathrm{j}} \omega 4C_{2}} \tag{9}\]  
When a two-phase leakage occurs, the insulation resistance is the parallel resistance of the two phases, which is set to \(R_{A}^{\prime}/2\), simplified to obtain  
\[\dot{U}_{0}=\frac{-(\dot{U}_{A}+\dot{U}_{B})\frac{1}{R_{A}^{\prime}}-\dot{ \mathrm{j}}\omega C_{2}\dot{U}_{A}}{\frac{2}{R_{A}^{\prime}}+\dot{\mathrm{j}} \omega 3C+\dot{\mathrm{j}}\omega 4C_{2}} \tag{10}\]
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
###### Abstract  
Maximum likelihood sequence estimation (MLSE) and maximum a posteriori probability (MAP) equalizers are optimum receivers for dealing with intersymbol interference (ISI) in time-dispersive channels. However, their high complexity and latency limit their widespread implementation; therefore, research into reducing their complexity is an open topic. This paper proposes a novel modification to reduce the computational complexity of the aforementioned algorithms, which exploits the representation of the communication channels in a time-delay-domain basis expansion model (BEM). It is shown that an appropriate basis is a set of modified prolate functions, in which the transmitter and receiver filters are considered in the kernel construction. Simulation results show that a reduction in sums and multiplications on the order of 55% can be obtained, maintaining the same bit error rate performance as in the traditional implementation.  
Keywords:basis expansion modeling (BEM); doubly selective channels; maximum a posteriori probability (MAP); maximum likelihood sequence estimation (MLSE); single-carrier transmission +
Footnote †: journal:
## 2 Materials and Methods
### LIDAR Performance Metrics  
In LIDAR systems, NPC and intensity are the key metrics used for performance evaluation. NPC represents the number of valid points obtained during a single scan cycle, providing insights into the spatial resolution and data density of the scanned area. Environmental factors and scanning parameters can significantly influence NPC values [18].  
In this study, laser intensity is considered a key parameter for performance evaluation. Intensity represents the optical power of the laser light that is reflected from the target surface and detected by the LIDAR sensor. The returning laser signal was captured by photodetectors and converted into an electric current. This current was subsequently transformed into voltage by a transimpedance circuit, amplified, and transmitted to the LIDAR central control unit. Within the control unit, a digitization process normalized the intensity value within the 0-255 range for each laser pulse. This value serves as a direct indicator of both the surface reflectivity and the power of the laser light.  
Laser intensity varies as a function of the optical properties of the target surface, laser geometry, and environmental conditions. Among these, surface reflectivity plays a critical role in determining laser intensity. Reflectivity quantifies how efficiently a surface reflects incident laser light and is influenced by physical factors such as material composition, color, and surface roughness. The color of a vehicle's surface is one of the most crucial optical parameters affecting the accuracy of laser intensity measurements [19].  
This study conducts a comprehensive analysis of the impact of vehicle surface color and reflectivity on laser intensity. Understanding the interplay between vehicle surface characteristics and laser intensity is essential for enhancing LIDAR sensor performance and optimizing environmental perception capabilities.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **EV** & **EA** & **Positive Control** & **Negative Control** \\ \hline \multirow{4}{*}{Coronal third} & \(n\) & 10 & 10 & 10 & 10 \\  & Mean & 3.72 & 7.63 & 7.94 & 10.31 \\  & Median & 2.41 & 8.50 & 8.30 & 9.22 \\  & SD & 3.35 & 3.38 & 4.17 & 7.90 \\  & Minimum & 0.00 & 2.23 & 0.92 & 2.70 \\  & Maximum & 10.06 & 11.59 & 14.20 & 27.75 \\ \hline \multirow{4}{*}{Middle third} & \(n\) & 10 & 10 & 10 & 10 \\  & Mean & 7.97 & 9.11 & 13.40 & 17.70 \\  & Median & 5.56 & 9.66 & 16.97 & 16.13 \\  & SD & 8.62 & 5.44 & 9.56 & 12.89 \\  & Minimum & 0.31 & 0.00 & 0.54 & 4.52 \\  & Maximum & 23.87 & 17.21 & 30.43 & 47.54 \\ \hline \multirow{4}{*}{Apical third} & \(n\) & 10 & 10 & 10 & 10 \\  & Mean & 7.41 & 5.18 & 8.29 & 11.84 \\  & Median & 6.94 & 5.26 & 5.04 & 6.22 \\  & SD & 5.55 & 3.23 & 7.78 & 12.78 \\  & Minimum & 0.35 & 0.43 & 1.37 & 0.34 \\  & Maximum & 14.49 & 10.28 & 26.23 & 40.54 \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive analysis of the relationship between the residual endodontic smear layer volume and the root canal system volume at coronal, middle, and apical third of the RCS and the isthmus (%) of the study groups. Figure 5: Box plot of the relationship between the residual endodontic smear layer volume and RCS volume in the coronal (**A**), middle (**B**), and apical third (**C**) of the RCS and the isthmus (mm\({}^{3}\)) of the study groups. but a higher ratio related to the EA study group at the middle third. In conclusion, the EV endodontic irrigation system provided higher smear layer removal at coronal and middle root thirds, compared to EA endodontic irrigation system; however, the EA endodontic irrigation system provided higher smear layer removal at the apical root third, compared to EV endodontic irrigation system.
# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
## 2 Methods  
OCT is an imaging technology using the principle of low coherence interference. OCT technology measures the interference signals of reflected light from the reference arm and backscattered light from the sample arm in the Michelson interference light path to obtain the tomographic image of the sample. For retinal OCT images damaged by speckle noise, the wave algorithm has outstanding layer segmentation ability, so it can also extract CME boundaries from images containing CME. However, due to its unidirectional characteristics, the wave algorithm can only extract local contours. To solve this problem, this paper describes an omnidirectional wave operator, which endows the wave algorithm with multidirectional capability through the direction adjustment function, so that it can achieve complete segmentation of objects with closed curves.
## 7 5G Radio Propagation Mechanisms
### Propagation Characteristics  
The identified bands for the deployment and execution of IMT-2020 and beyond systems are the microwave and millimeter wave bands (i.e., 0.5-100 GHz). The bands are divided into FR1 and FR2 frequency ranges, with each band having unique benefits and applicability. While the FR2 (24.25 GHz-52.6 GHz) band is ideal for short-range communications and data rate delivery, the FR1 (410 MHz-7.125 GHz) bands are suitable for communications via conventional cellular mobile communication lines. FR 2 band transmissions cannot travel more than a few miles or easily pass through structures, in contrast to FR 1 band signals. Instead, FR2 bands can pass through extremely crowded communication lines, ensuring effective spectrum usage [160; 161]. Since mmWave will use higher gain antennas to avoid route loss, it will be necessary to take advantage of and quickly react to the spatial dynamics of the wireless channel [72]. As they have a crucial impact on the architecture of the 5G system, their propagation characteristics need to be properly taken into account for effective propagation at these bands [162; 163]. However, some additional loss variables, including rain, vapor, and gaseous losses, are unavoidable in mmWave networks.  
* Reflection: When an electromagnetic wave in motion collides with an impediment greater than its wavelength, reflection occurs. A traveling wave may be hampered by natural obstacles, such as the reflection of the earth's surface, structures, and walls.
* Diffraction: This happens when the wave's tip encounters a solid barrier or a sharp object. As a result, the traveling wave will bend around the obstacle's tip, thus altering its course of propagation.
* Scattering: This can be seen when a radio wave that is propagating encounters a rough surface or a very small body whose size is of the order of the radio signal wavelength. Similar to the physical concept of diffraction, energy from a transmitter is emitted in numerous directions when scattering takes place [164; 165].
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Performance Evaluation
#### 4.3.1 Comparison with CFA Counterparts
Figure 5a shows the online performance analysis with 95% confidence interval for the Rosenbrock (30) function. It is seen that the 95% confidence interval of the best function value obtained by the three competing algorithms (GSO, FA, and CFA) converges with various speeds. Our CFA converges at a much faster speed and reach towards a better function value than its two counterparts. The FA is the second-best performer followed by the GSO. To investigate the detailed performance during the second half duration of the execution, we enlarge the plot for this period as shown in Figure 5b. It can be seen that the CFA significantly outperforms the other two algorithms with 95% confidence level during the second half execution duration. We also found that the GSO performs better than the FA after 80,000 FEs, although the GSO may not beat the FA at the early stage of the execution as previously noted. The online performance comparison with 95% confidence level for the Rastrigin (30) function is shown in Figure 6. We observe that during the whole execution period the CFA significantly outperforms the GSO and the FA. The FA performs better than the GSO when the allowed number of consumed FEs is less than 20,000, but the FA is far surpassed by the GSO if more FEs are allowed. Figure 7 shows the online performance variation with 95% confidence level for the Griewank (30) function. Again, the CFA is the best performer among the three algorithms throughout the whole execution duration. However, we see a phenomenon differing from those for the two previous test functions with the GSO and the FA. The GSO and the FA performs about equally well before consuming 50,000 FEs, although the former is a more stable performer because it has a shorter confidence interval. However, after this critical execution period, the FA becomes very effective both in the convergence speed and the function value. As shown in Figure 7b, the FA significantly surpasses the GSO and reaches a comparative performance with the CFA.
# Development of an Energy-Efficient Smart Socket Based on STM32F103
## 4 Experimental Validation and Discussion
Assessment context was established with the following assumptions: Commonly occupied residential appliances, including TV, TV receiver box, and stereo system were included in assessment with their operational power consumption, standby power and daily operating hours are listed in Table 4. The assessment results indicate that application of the developed smart socket can contribute to 6.40%, 77.76% and 38.76% of energy saving for TV, TV receiver box and stereo system respectively. Given the electricity tariff of 60.00 cents/kWh (which is the current market rate applied nationally in China) and the expected lifespan of 20 years for the smarter socket (identified based on the service life of major electronic components), the embodied cost of the smarter socket calculated based on the prices of the components comes to 37.00 Chinese yuan and the lifecycle costs were obtained as shown in Table 4. The assessment results indicate that an investment of 125.60 Chinese yuan (\(\forall\)37.00 and \(\forall\)88.60 as the embodied and operational costs of the smarter socket respectively) might contribute to a standby cost saving up to 1725.60 Chinese yuan in an expected 20 years' lifecycle. The payback is about 14 times to the investment. The results also show that the standby energy waste, taking 32.63% of the total energy consumption of electrical appliances, is a significant component over their lifecycle operations. On the other hand, the investment payback period was obtained as about 159 days based on the embodied and daily operational costs of the smart socket, as well as the daily standby cost saving. It then can be concluded that the developed smart socket is an economically attractive solution for standby energy and cost reduction. Figure 8: The monitoring current values displayed on the LCD module (Power off). Figure 7: The monitoring current values displayed on the LCD module (Power on).
## 4 Time-Frequency Analysis for Feature Extraction
### Wigner-Ville Distribution (WVD)  
The WVD is a sophisticated TFA method that fully characterizes signals in both time and frequency. As a bilinear time-frequency tool, it evaluates signal energy distribution by integrating temporal and spectral information. For a continuous-time signal \(x(t)\), the WVD is expressed as:  
\[W_{x}(t,f)=\int_{-\infty}^{\infty}x\bigg{(}t+\frac{\tau}{2}\bigg{)}x^{*} \bigg{(}t-\frac{\tau}{2}\bigg{)}e^{-j2\pi/\tau}d\tau \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline STFT & CWT & HHT & WVD & FWT & SWT \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time–frequency representations.  
Here, \(W_{x}(t,f)\) represents the WVD of \(x(t)\) at time \(t\) and frequency \(f\); \(\tau\) is the delay variable, and \(x^{*}(t)\) denotes the complex conjugate of the signal.
###### Abstract  
Humic substances (HS) are often used as feed additives in livestock feeding. The long-term effects of different concentrations of HS additives in aquafeed on growth and production performance, antioxidant status, stress resistance, gut microbiome, overall health condition, final product yield, sensory properties of fresh and cooked meat, and composition of fatty acids of market size in African catfish _(Clarias gariepinus)_ originating from aquaculture were compared in this study. _C. gariepinus_ were exposed to dietary inclusions of HS (0, 1, and 3, and 6% \(w/w\)) in a long-term experiment (six months in total) until fish reached market size. The growth parameters, condition factor, and selected somatic indices did not differ (\(p\) > 0.05) between the tested groups (HS0-HS6). Biochemical parameters were not different between the tested HS groups by the end of experiment, and only glucose (GLC) levels significantly increased (\(p\) < 0.05) with increasing concentrations of HS fed as an additive. The levels of cortisol (COR) and GLC did not differ between the tested groups (\(p\) > 0.05) after the stress challenge, but the results of GLC levels before and after the stress challenge showed an increasing tendency with increasing levels of HS addition in the diet. The levels of COR were slightly lower in groups HS3 and HS6 than in HS1 and the control group. In the case of protein and fat contents, differences between groups (HS0-HS6) were statistically insignificant. Significant differences were found, however, in water and ash content. In some cases, statistically significant differences were found in fatty acid profiles and in nutritional indices assessing fatty acids between samples. Sensory characteristics of fresh fillets did not differ between the tested groups HS0-HS6. The total content of polyphenols increased depending on the addition of humates. The main gut microbiota of samples analyzed (HS0-HS3 group) comprised the following three genera: _Ralstonia, Pseudomonas_ and _Cetobacterium_; other genera were present in all samples at a low relative abundance: _Staphylococcus_, _Bradyrhizobium, Bacillus_, and _Anarouchacillus_. The relative abundance of _Pseudomonas_ decreased while the presence of _Cetobacterium_ increased in samples fed with 3% of HS. The results of our study yielded a comprehensive set of experimental results about African catfish fed with HS as additives. Although a significant effect of HS on overall performance of _C. gariepinus_ was not proven, a positive effect on antioxidant status was seen as well as a decrease in gut microorganisms that can be present as pathogenic contaminants in aquatic environments.  
**Keywords: humic substances; health effect; antioxidant status; microbiome; sensory analysis**