esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 3 Model Improvement
### An Improved Approach to Feature Fusion
This innovation eschews the traditional approach of employing a uniform or fixed sampling strategy on the global feature map. The module predicts a set of offsets for each query location in a specific implementation. It applies these offsets to a predetermined grid of reference coordinates, generating a series of new contextually relevant sampling locations. This mechanism realizes the dynamic focusing of crucial information and its effective extraction, effectively avoids computational redundancy caused by undifferentiated processing of all pixels, and thus improves computational efficiency. Further, the DA2D module adopts a sparse connectivity strategy [21] that incorporates only a small number of parameters into the discrimination process of each forward propagation instead of considering all the parameters, a strategy that may lead to an increase in the number of parameters but reduces the number of floating point operations (FLOPs). With this approach, the study effectively improves the performance of broken region detection while keeping the computational effort low and realizes the goal of substantial performance improvement based on a small computational effort. In summary, the DA2D module offers several advantages that make it particularly suitable for underwater target detection. The module lets the model focus on visual features critical for target detection through a sparse connectivity mechanism, ignoring background noise. Given the wide range of variations in lighting conditions, turbidity, and color in underwater environments, sparse connectivity helps the model to focus on critical structural features of fishing nets, such as mesh patterns and textures, which can be effectively identified even in low visibility conditions. In addition, deformable convolution enhances the model's ability to deal with perspective changes and morphology variations, which is crucial for detecting nets that may have changed shape due to water currents, the influence of marine organisms, or physical damage. By learning the offset, deformable convolution can dynamically adjust the sensory field to capture more accurate target positions, ensuring precise localization in complex underwater environments.
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 3 Performance Degradation Trend Analysis and Fault Early Warning of Marine Diesel Main Engines
### Data Processing
#### 3.1.2 Performance Parameter Selection  
Standard thermodynamic parameters of marine diesel main engines effectively reflect operational health and performance, with most parameters now being automatically monitored, collected, and stored in real-time [27]. Performance degradation manifests as deviations of these parameters from factory-set baselines, where the magnitude of deviation serves as the primary evaluation metric.  
Rational parameter selection forms the foundation of engine performance assessment. This study selected 22 parameters encompassing power output, fuel type, rotational speed, scavenge air temperature, fuel flow rate, specific fuel oil consumption (SFOC), exhaust temperatures of cylinders #1-6, jacket cooling water outlet temperatures of cylinders #1-6, lubricating oil inlet temperature, jacket cooling water inlet temperature, scavenge air pressure, and jacket cooling water inlet pressure. Due to sensor limitations, engine room ambient temperature was approximated using scavenge air temperature, while sea condition influences were excluded through operational condition partitioning. As a critical indicator of fuel efficiency, SFOC was calculated using  
\[SFOC=\frac{Q}{P} \tag{8}\]  
In the equation, \(Q\) denotes fuel flow rate (kg/h), and P represents engine power (kW). The selected parameters comprehensively cover major engine subsystems, ensuring holistic performance characterization. The performance parameter table is as shown in Table 2. Storage specifications for all parameters except SFOC are detailed in Table 3.
## 3 Materials and Methods
### Feature Selection
#### 3.2.2 HOS Features
The difference variance feature, \(f_{10}\), is a measure of the weights that differ from the difference entropy value (the \(f_{11}\) feature below) of the marginal \(\bar{\mathbf{P}}\) matrix, and is given by \[f_{10}=\sum_{i=2}^{2N_{g}}(i-f_{11})^{2}\,\bar{p}_{x-y}(i).\] (16) where \[\bar{p}_{x-y}(k)=\sum_{ \begin{subarray}{c}i=1\\ i-j\end{subarray} }^{N_{g}}\sum_{j=1}^{N_{g}}\bar{p}(i,j),\ \ \ k=0,1,\ldots,N_{g}-1.\]
11. The difference entropy feature, \(f_{11}\), is the weight of the higher difference of the index entropy and is given by \[f_{11}=-\sum_{i=0}^{N_{g}-1}\bar{p}_{x-y}(i)\log\bar{p}_{x-y}(i).\] (17)
12. The information measure of the correlation \(1\) feature, \(f_{12}\), is considered as an entropy measure and is given by \[f_{12}=\frac{f_{9}-\delta}{\max\,(\alpha,\beta)},\] (18)where \(\alpha\), \(\beta\), and \(\delta\) are the entropies of \(\tilde{p}_{x}\), \(\tilde{p}_{y}\), and \(\tilde{p}_{x}\tilde{p}_{y}\), respectively, and are calculated as follows. \[\alpha=-\sum_{i=1}^{N_{g}}\tilde{p}_{x}(i)\log\tilde{p}_{x}(i),\] \[\beta=-\sum_{j=1}^{N_{g}}\tilde{p}_{y}(j)\log\tilde{p}_{y}(j),\]  
\[\delta=-\sum_{i=1}^{N_{g}}\sum_{i=j}^{N_{g}}\tilde{p}(i,j)\log\big{(}\tilde{p} _{x}(i)\tilde{p}_{y}(j)\big{)}.\]  
13. The information measure of the correlation 2 feature, \(f_{13}\), is given as follows. Let \[\eta=-\sum_{i=1}^{N_{g}}\sum_{i=j}^{N_{g}}\tilde{p}_{x}(i)\tilde{p}_{y}(j)\log \big{(}\tilde{p}_{x}(i)\tilde{p}_{y}(j)\big{)}\] be the entropy of \(\tilde{p}_{x}(i)\tilde{p}_{y}(j)\). Then, \(f_{13}\) is given by  
\[f_{13}=\sqrt{1-\exp(-2(\eta-f_{9}))}. \tag{19}\]  
14. The auto-correlation feature, \(f_{14}\), is given by [41]  
\[f_{14}=\sum_{i=1}^{N_{g}}\sum_{j=1}^{N_{g}}ij\,\tilde{p}(i,j). \tag{20}\]  
We content ourselves with only the first four FOS moments as they are usually enough to summarize the underlying data fairly accurately [42]. Higher moments add little value, and can complicate the computations. The compelling evidence of this claim is seen clearly in Table 3 where, out of the five FOS features selected by the GA, four belonged to the first two moments and only one to the fourth moment.
###### Abstract  
The low-earth-orbit synthetic aperture radar (SAR) can achieve enhanced remote-sensing capabilities by exploiting the large-scale and long-duration beam coverage of a geosynchronous (GEO) SAR illuminator. Different bistatic imaging modes can be implemented by the steering of an antenna beam onboard the LEO receiver, such as high-resolution sliding-spotlight mode. In this paper, the accurate focusing of GEO-LEO bistatic SAR (GEO-LEO BiSAR) in sliding-spotlight mode is investigated. First, the two major problems of the accurate bistatic range model, i.e., curved trajectory within long integration time and'stop-and-go' assumption error, for sliding-spotlight GEO-LEO BiSAR are analyzed. Then, a novel bistatic range model based on equivalent circular orbit trajectory is proposed to accurately represent the range history of GEO-LEO BiSAR in sliding-spotlight mode. Based on the proposed range model, a frequency-domain imaging method is put forward. First, a modified two-step preprocessing method is implemented to remove the Doppler aliasing caused by azimuth variance of Doppler centroid and beam steering. Then, an azimuth trajectory scaling is formulated to remove the azimuth variance of motion parameters due to curved trajectory. A modified frequency-domain imaging method is derived to eliminate the 2-D spatial variance and achieve accurate focusing of the echo data. Finally, imaging results and analysis on both simulated data and real data from an equivalent BiSAR experiment validate the effectiveness of the proposed method.  
bistatic synthetic aperture radar (BiSAR); geosynchronous SAR; low-earth orbit; sliding-spotlight mode; equivalent range model; frequency-domain imaging method +
Footnote †: journal: Remote Sens.  
0222, 14, 3178, [[https://doi.org/10.3390/rs14133178](https://doi.org/10.3390/rs14133178)]([https://doi.org/10.3390/rs14133178](https://doi.org/10.3390/rs14133178))
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 2 Conventional 2D Discrete Radon Transform  
In the continuum, the 2D Radon transform allows us to describe a 2D signal (\(f(\cdot)\)), in terms of the integrals along lines parameterized by an angle and a displacement (\((\theta,\rho)\)), instead of single values, accessed by their horizontal and vertical pair of cartesian coordinates (\((x,y)\)). This is:  
Figure 4: From left to right, an image, sheared to accommodate, in a row, the discrete lines of central DRT for minimal, intermediate, and maximal slope of the 0 to 45 degrees quadrant.  
Figure 3: From left to right, an image sheared to accommodate, in a row, the discrete lines of central DRT for minimal, intermediate, and maximal slope of the 0 to 45 degrees quadrant.  
\[\mathfrak{R}f(\theta,\rho)=\iint f(x,y)\ \delta(x\ cos\ \theta+y\ sin\ \theta-\rho)\ \mathrm{d}x\ \mathrm{d}y, \tag{1}\]  
or, equivalently, using the absolute slope and \(d\) displacement form, with \(|s|<1\):  
\[\begin{split}\mathfrak{R}_{|\theta|\leq\frac{\pi}{2}}f(s,d)& =\int f(u,\ u\ s+d)\ \mathrm{d}u,\\ \mathfrak{R}_{|\theta|\geq\frac{\pi}{2}}f(s,d)&= \int f(u\ s+d,\ u)\ \mathrm{d}u.\end{split} \tag{2}\]  
The calculation of the Radon transform on a computer has to deal with the discretization of data. Basically, for regularly spaced discrete data, they will be available only for a finite number of samples, normally accessed with integer indexes. The problem arises when the continuous definition of line integral makes it necessary to evaluate the function at positions where there is no sample available and interpolation is required.  
However, using the pseudo-polar Fourier transform [10] (a variation of FFT that operates on a grid of concentric squares), a discrete Radon transform can be designed that is algebraically exact, invertible, fast [21], and can be generalized to 3D [22; 23]. However, it is based on Fourier transforms, which is something that we want to avoid. Moreover, if, as it is the case in our application domains, the problem to solve is purely discrete, the multi-scale Radon transform performs better than any other discrete Radon transform [24], including those based on pseudo-polar FFT.
# A New Compact Double-Negative Miniaturized Metamaterial for Wideband Operation
## 4 Equivalent Circuit Model of the Proposed Unit-Cell  
Because the proposed metamaterial structure consists of passive inductive-capacitive (LC) circuits, the resonance frequency (\(f\)) is  
\[f=\frac{1}{2\pi\sqrt{LC}} \tag{10}\]  
Figure 3: Experimental setup for measuring the S-parameters.  
where \(L\) and \(C\) are the aggregate inductance and capacitance of the structure. In the proposed formation, inductances are formed by the metal strip and the capacitances are formed by the splits. Electric resonances are produced by coupling between the gaps and electric fields when the applied electromagnetic wave propagates along the structure. Moreover, magnetic resonances are formed by the coupling between the magnetic fields and loops. Typically, from quasi-static theory, the total capacitance between the gaps is,  
\[C=\varepsilon_{0}\varepsilon_{r}\frac{A}{d}\left(F\right) \tag{11}\]  
In addition, the total inductance (\(L\)) for the proposed structure can be estimated from [23; 24; 25] as  
\[L=0.01\times\mu_{0}\left\{\frac{2\left(d+g+h\right)^{2}}{\left(2w+g+h\right)^ {2}}+\frac{\sqrt{\left(2w+g+h\right)^{2}+l^{2}}}{\left(d+g+h\right)}\right\}t \tag{12}\]  
Therefore, the total capacitance (\(C\)) can be calculated by  
\[C=\varepsilon_{0}\left[\frac{\left(2w+g+h\right)}{2\pi\left(d+h\right)^{2}} \text{ln}\left\{\frac{2\left(d+g+h\right)}{\left(a-l\right)}\right\}\right]t \tag{13}\]  
where the free-space permeability (\(\mu_{0}\)) is \(4\pi\times 10^{-7}\) H/m and the free-space permittivity (\(\varepsilon_{0}\)) is \(8.854\times 10^{-12}\) F/m.  
For wide negative refractive index bandwidth, the series and shunt branch of the unit cell circuit form inductance and capacitance. The splits maintain the capacitive effect and are denoted as C1, C2, C3, C4, and C5 in the circuit. Alternately, the metal strips are responsible for the inductance effect and are named L1, L2, and L3. The addition of more splits in the unit cell structure produces a small phase delay and increases the total capacitance of that metamaterial unit cell. The equivalent circuit of the proposed metamaterial structure is given in Figure 4.
## 5 Verification
### Motor Mode  
The S-L analysis of the multi-power system including the PV system (in motor mode) is displayed in Figure 24 where the marking method is the same as Figure 23. It should be pointed out that the input/output bidirectional device (PV storage system) is operating in the motor mode. Its frequency characteristic is in the first quadrant, which absorbs power from the the actual grid (Synchronous Machine Grid Equivalent Model) together with the load.  
Figure 24: S-L analysis of multi-power system including input/output bidirectional devices in the motor mode.  
1 S-L Analysis of Traditional PV Storage System  
After the sudden drop in load, the frequency rises. In Figure 24, the power balance \({P_{\rm G}}^{\prime\rm T}~{}=~{}{P_{\rm L}}^{1}~{}+~{}{P_{\rm G-N}}^{0}\) is reached when the operation line is thick dashed line \(f~{}=~{}f_{1}\). Using the S-L analysis method in the same way as generator mode, \({\Delta{P_{\rm G}}^{\prime\rm T}}\) = 20 kW, \({\Delta{f_{\rm T}}}\) = 0.5 Hz are obtained. It can be found that the results of S-L analysis coincide with the simulation results in Figure 20a.  
2 S-L Analysis of New PV Storage System  
From the initial power balance from Figure 24 corresponding to the operation line \(f~{}=~{}f_{0}\), the equation below can be obtained.  
\[{P_{\rm G}}^{\prime 0}~{}=~{}{P_{\rm L}}^{0}+{P_{\rm G-N}}^{0} \tag{43}\]  
After the frequency regulation is finished, the frequency goes up and the operation line becomes \(f~{}=~{}f_{2}\), corresponding to the final balance state where we obtain the equation below.  
\[{P_{\rm G}}^{\prime 1}~{}=~{}{P_{\rm L}}^{1}+{P_{\rm G-N}}^{1} \tag{44}\]  
Concerning the transient process, for the new PV storage system, it performs frequency regulation by absorbing more active power as a motor after the load decreases.  
\[{P_{\rm G-N}}^{0}+{\Delta{P_{\rm G-N}}}~{}=~{}{P_{\rm G-N}}^{1} \tag{45}\]  
The actual power grid participates in the frequency regulation through less generated power.  
\[{P_{\rm G}}^{\prime 0}-{\Delta{P_{\rm G}}^{\prime}}~{}=~{}{P_{\rm G}}^{\prime 1} \tag{46}\]  
Load does not have its own regulating effect.  
\[{P_{\rm L}}^{0}-{\Delta{P_{\rm L}}^{0}}~{}=~{}{P_{\rm L}}^{1} \tag{47}\]  
The same basic relationship 1 as the Equation (40) can be obtained from Equations (43)-(47). From the geometric relationship in Figure 24, the same basic relationship 2 as in Equation (41) can be easily obtained. It can be seen that the device operating in the motor mode has the same frequency regulation capability and effect as that in the generator mode theoretically. Bring the parameters into above obtained two basic relationship expressions and solve them, we get \({\Delta{P_{\rm G-N}}}\) = 4 kW, \({\Delta{P_{\rm G}}^{\prime}}\) = 16 kW, \({\Delta{f_{\rm N}}}\) = 0.4 Hz, which are consistent with the simulation results in Figure 20b.
###### Abstract  
The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.  
active suspension; machine learning; LQR control; K-means clustering; genetic algorithm +
Footnote †: journal: Applied Sciences
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
###### Abstract  
NAD+ is an important metabolite in cell homeostasis that acts as an essential cofactor in oxidation-reduction (redox) reactions in various energy production processes, such as the Krebs cycle, fatty acid oxidation, glycolysis and serine biosynthesis. Furthermore, high NAD+ levels are required since they also participate in many other nonredox molecular processes, such as DNA repair, posttranslational modifications, cell signalling, senescence, inflammatory responses and apoptosis. In these nonredox reactions, NAD+ is an ADP-ribose donor for enzymes such as sirtuins (SIRTs), poly-(ADP-ribose) polymerases (PARPs) and cyclic ADP-ribose (cADPRs). Therefore, to meet both redox and nonredox NAD+ demands, tumour cells must maintain high NAD+ levels, enhancing their synthesis mainly through the salvage pathway. NAMPT, the rate-limiting enzyme of this pathway, has been identified as an oncogene in some cancer types. Thus, NAMPT has been proposed as a suitable target for cancer therapy. NAMPT inhibition causes the depletion of NAD+ content in the cell, leading to the inhibition of ATP synthesis. This effect can cause a decrease in tumour cell proliferation and cell death, mainly by apoptosis. Therefore, in recent years, many specific inhibitors of NAMPT have been developed, and some of them are currently in clinical trials. Here we review the NAD metabolism as a cancer therapy target.  
nicotinamide adenine dinucleotide; NAD metabolism; therapeutic target; cancer
## 4 Experiments
### Results and Analysis
#### 4.2.4 Scalability Analysis  
To assess the framework's performance under varying loads, we conducted scalability tests by progressively increasing the number of concurrent security events. Figure 3 shows the relationship between system load and response metrics. ARCS maintained a stable performance up to 1000 concurrent events, with response times increasing only logarithmically with load. Beyond this threshold, we observed a gradual degradation in response time, though response effectiveness remained above 85% even at 2000 concurrent events.  
Resource utilization patterns indicate that ARCS effectively manages computational resources, with CPU usage scaling linearly with load while maintaining memory consumption within acceptable bounds. The framework's ability to maintain performance under high load is particularly important for enterprise-scale deployments where multiple security events often occur simultaneously.  
In fact, we want to clarify that the system's scalability metrics shown in Figure 3 are directly influenced by the key components in Algorithm 1. The response time benefits from the TemporalAttention mechanism (Line 9) and TemporalAggregation (Line 10), which efficiently process and prioritize security events based on their temporal relevance. Resource utilization is optimized through the adaptive weighting process (Line 13), which balances computational resources between immediate and strategic responses. The system's effectiveness under increasing load is maintained by the dual-stream architecture's ability  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Variant** & **RE (\%)** & **ART (s)** & **System Disruption (\%)** \\ \hline Full ARCS & \(\mathbf{89.2\pm 1.5}\) & \(\mathbf{23.\pm 2.2}\) & \(\mathbf{2.1\pm 0.3}\) \\ w/o Temporal Attention & \(84.0\pm 1.8\) & \(30.8\pm 2.5\) & \(3.2\pm 0.4\) \\ w/o Dual Stream & \(81.4\pm 1.9\) & \(33.2\pm 2.7\) & \(4.5\pm 0.5\) \\ w/o Adaptive Weighting & \(85.1\pm 1.7\) & \(28.7\pm 2.4\) & \(17.4\pm 1.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results.  
Figure 3: System performance under varying loads. Left: Response time vs. concurrent events. Middle: Resource utilization vs. load. Right: Response effectiveness vs. load.  
to process events hierarchically, where TemporalAggregation prevents computational overhead by focusing on the most relevant temporal patterns.
###### Abstract
The CAR-2-X communication system, employed by the CAR-2-CAR communication consortium (C2C-CC) in Europe, offers a unique design and encompasses in-vehicle, ad hoc, and infrastructure domains [10]. Notably, in-vehicle communication, referred to as the in-vehicle domain, has gained increasing attention due to its implications for driver and public safety. The vehicle-to-vehicle (V2V) communication system enables the exchange of vital information and threat notifications among vehicles, enhancing driver assistance. The vehicle-to-road infrastructure (V2I) communication system provides access to real-time traffic, weather, and environmental data [11]. Figure 1 illustrates VANET communications. The utilization of wireless broadband techniques, such as 3G/4G, in vehicle-to-broadband cloud (V2B) communication systems offers substantial benefits, enabling communication between vehicles, active driver assistance, and vehicle tracking, thanks to the increased data storage capacity provided by broadband cloud [12]. Figure 1: Complete vehicular ad hoc network (VANET) environment, which shows V2V, V2I, and I2I communication [13]. These data can be in different forms, such as real-time weather updates and traffic information, infotainment, and environment data. Figure 1 shows the communication between vehicles [14].
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
###### Abstract  
Predicting the health index of turbofan engines is critical in reducing downtime and ensuring aircraft safety. This study introduces the elite snake optimizer-back propagation (ESO-BP) model to address the challenges of low accuracy and poor stability in predicting the health index of turbofan engines through neural networks. Firstly, the snake optimizer (SO) was improved into the elite snake optimizer (ESO) through an elite-guided strategy and a reverse learning mechanism. The performance improvement was validated using benchmark functions. Additionally, feature importance was introduced as a feature selection method. Finally, the optimization results of the ESO were employed to set the initial weights and biases of the BP neural network, preventing convergence to local optima. The prediction performance of the ESO-BP model was validated using the C-MAPSS datasets. The ESO-BP model was compared with the CNN, RNN, LSTM, baseline BP, and unimproved SO-BP models. The results demonstrated that the ESO-BP model has a superior accuracy with an impressive R-squared (\(R^{2}\)) value of 0.931 and a root mean square error (\(RMSE\)) of 0.060 on the FD001 sub-dataset. Furthermore, the ESO-BP model exhibited lower standard deviations of evaluation metrics on 100 trials. According to the study, ESO-BP demonstrated a greater prediction accuracy and stability when compared to commonly used models such as CNN, RNN, LSTM, and BP.  
turbofan engine; health index prediction; snake optimizer; BP neural network; intelligent optimization algorithms +
Footnote †: journal: Applied  
15 January 2024  
Revised: 16 February 2024  
Accepted: 26 February 2024  
Published: 28 February 2024