esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 3 Detection Algorithm
### Local Low Gray Region Extraction and Segmentation  
#### 3.2.1 Local Low Gray Region Extraction  
Based on the preprocessing steps, the image is now ready for performing a pixel-wise convolution operation using the template defined by Equation (8), where \(M\times N\) represents the size of the template.  
\[template=\frac{1}{M\times N-1}\begin{bmatrix}1&\cdots&1\\ \vdots&-(M\times N-1)&\vdots\\ 1&\cdots&1\end{bmatrix} \tag{8}\]  
By conducting an analysis of the template, it can be deduced that if the grayscale value of the central pixel surpasses the grayscale values of the surrounding pixels, the resulting convolution will yield a negative value. In situations where the pixel grayscale values within the template area are similar, indicating the presence of large bright or dark regions, the convolution result will be a small positive value. When the grayscale values  
Figure 8: Image preprocessing diagram (**a–b2**).  
are identical, the convolution result becomes zero. Conversely, when the grayscale value of the central pixel is lower than the surrounding pixel values, the convolution result will be a relatively large positive value. Thus, we compute the convolution results by the following formula:  
\[result(i,j)=\left\{\begin{matrix}0,&if\;res(i,j)<0\\ \;res(i,j),&else.\end{matrix}\right. \tag{9}\]  
where _res_ is the convolution result matrix of the original image, and _result_ is the judged result matrix.  
Figure 9 shows the detection results of low grayscale regions in the images. From the local low grayscale detection results, we can see that the proposed convolution template operation achieves grayscale inversion. The original low grayscale regions have transformed into high grayscale regions, while the previously large approximately grayscale regions have become low grayscale values approaching zero, making it easier to separate them from the target. For complex waves, although there are many regions with an uneven grayscale, the presence of irregular sea clutter results in discrete low-grayscale regions with an uneven grayscale distribution. Adaptive threshold segmentation can achieve the separation of the target from most interference.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 4 Combination of Automatic Identification and Separability Improvement
### Combined Methods  
Consider the following combinations of methods. In all methods, assume that they are applied to the leading \(t\) components of Basic SSA. The result is the set \(I\) which consists of the numbers of trend components in the refined decomposition.  
**Algorithm autoFOSSA** with threshold \(T_{1}\) and frequency boundary \(\omega_{0}\).  
1. Apply FOSSA(\(t\)).
2. Apply TAI(\(T_{1}\), \(\omega_{0}\)) to the obtained decomposition; the result is \(I\).
3. **Algorithm autoEOSSA** with threshold \(T_{1}\), frequency boundary \(\omega_{0}\) and the method of clustering the signal roots.  
1. Apply EOSSA(\(t\), method of signal root clustering).
2. Apply TAI(\(T_{1}\), \(\omega_{0}\)) to the obtained decomposition; the result is \(I\). The ways of clustering the signal roots will be described further in Section 5.2.  
**Algorithm autoIOSSA without initial grouping** with threshold \(T_{1}\), frequency boundary \(\omega_{0}\), and maximum number of iterations \(n_{iter}\).  
\begin{table}
\begin{tabular}{c l l} \hline \hline  & **trend\_mse** & **iterations\_num** \\ \hline Basic SSA(3) & mean: 1.1927, med: 1.1751 & mean: 1, med: 1 \\ FOSSA(3) & mean: 0.1125, med: 0.0910 & mean: 1, med: 1 \\ IOSSA(3, 100, \{1\}\cup\{2,3\}) & mean: 0.0422, med: 0.0250 & mean: 7, med: 7 \\ IOSSA(3, 100) & mean: 0.0467, med: 0.0296 & mean: 13, med: 13 \\ EOSSA(3) & **mean: 0.0404, med: 0.0224** & mean: 1, med: 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy (MSE) of trend estimates.  
1. Apply IOSSA(\(t\), \(n_{iter}\)).
2. Apply TAI(\(T_{1}\), \(\omega_{0}\)) to the obtained decomposition, the result is \(I\).  
**Algorithm autoIOSSA + FOSSA** with thresholds \(T_{0}\) and \(T_{1}\), frequency boundary \(\omega_{0}\), maximum number of iterations \(n_{iter}\).  
1. Apply FOSSA(\(t\)).
2. Apply TAI(\(T_{0}\), \(\omega_{0}\)) to the obtained decomposition; the result is \(I_{0}\).
3. Apply IOSSA(\(t\), \(n_{iter}\), \(I_{0}\)).
4. Apply TAI(\(T_{1}\), \(\omega_{0}\)) to the obtained decomposition; the result is \(I\).
# Hardware-Based Architecture for DNN Wireless Communication Models
## 4 Proposed DNN-Based MIMO OFDM Models  
Inspired by recent advances in deep learning technology, we propose three DNN receiver models for the MIMO OFDM systems to improve their performance. The proposed system structures and DNN models are presented in the next subsections. The general DNN model structure with one input layer, three hidden layers, and one output layer is presented in Figure 2.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 7 Performance Evaluation
### Energy Consumption  
In V2V communication, energy consumption is a critical factor determining system endurance, cost-effectiveness, environmental impact, and overall sustainability. The energy consumption generated by V2V authentication includes computational energy costs and transmission energy costs. Computational energy costs primarily depend on intensive computations such as pairing calculations and scalar multiplications. For transmission energy costs, the number of messages sent and received must be considered.  
In this subsection, we conduct an energy analysis for computation and transmission, referring to the 133 MHz SA-1110 Strong ARM microprocessor and the LA-4121 WLAN card, respectively. According to [31; 32], the microprocessor consumes 8.8 mJ for a single scalar multiplication and 47.0 mJ for a bilinear pairing operation. Regarding transmission energy costs, the WLAN card consumes 0.66 ul to transmit 1 bit and 0.31 ul to receive 1 bit. Table 6 summarizes the energy consumption for several basic operations.  
Table 7 compares the energy consumption of different protocols. The results indicate that energy costs increase linearly with the number of users. Figure 8 further illustrates their energy consumption. It is evident that as the number of V2V communications increases from 5 to 100, all energy costs increase linearly. For establishing V2V group sessions, user devices consume more energy on computation than on transmission. As shown in Figure 8, the EAIA protocol demonstrates better performance in both computational and transmission energy costs. This further validates the efficiency of the proposed protocol.  
Figure 7: Comparison of authentication time cost.
# Recent Advances in the Synthesis of Graphene-Based Nanomaterials for Controlled Drug Delivery
## 2 Ways of Controlled Drug Delivery
### Exogenous Stimuli-Responsive Drug Delivery
_Light-stimulated drug delivery._ The most common methods of optically controlled drug delivery are based either on photochemical or photothermal reaction mechanisms. In general, photochemically controlled drug delivery could be achieved through photo-isomerization, photo-dimerization, light-induced fracture, or photo-bonding reactions of light-sensitive groups [66]. By contrast, photothermally controlled drug delivery is based on the light-heat transformation of nanoparticles after they absorb light. The transformation results in the increasement in local temperature and the amount of drugs delivered from the drug carrier. In addition, the thermal energy produced can kill cancer cells and enables photothermal ablation treatment. Nanomaterials that deliver drugs through heat stimulation generally contain gold nanoparticles, up-converted rare earth nanoparticles, and carbon nanotubes. For example, [PERSON] et al. [38] and [PERSON] et al. [40] discovered NIR-controlled Dox release behaviors from GO@Ag nanoparticles and GO/CuS nanocomposites, respectively. _Electric field-stimulated drug delivery._ Electric fields can enable the directional release of drugs by affecting the binding force of drug molecules and nanocarriers. In particular, they cause the movement of charged molecules. Furthermore, additional transmembrane voltage can puncture cell membranes and thus increases the permeability of drugs [67]. For instance, [PERSON] and coworkers developed an electrically controlled drug delivery system that is based on GO nanosheets incorporated into a conducting polymer film, and found that the amount of drug released is linearly related to the time of voltage [42].
## 5 Comparison of the Pseudo-Dynamic Test Results and Nonlinear Dynamic Analysis Results
### Overview of the Nonlinear Dynamic Analysis  
The nonlinear analytical model and method established in Section 5 were found to be effective in simulating the seismic behavior of RC structures retrofitted with NBSD-WSCS. In an attempt to commercialize this NBSD-WSCS developed in the present study, nonlinear dynamic analysis was conducted on the entire RC building with non-seismic details retrofitted with the NBSD-WSCS (See Figure 10), as shown in Section 4.4, based on the analytical model and method established in Section 5.1. The seismic response load, displacement characteristics, and energy dissipation capacity of the test building were then examined before and after the reinforcement, and the energy dissipation capacity and seismic response load and displacement of the used dampers were evaluated to determine the seismic retrofitting performance of the developed system.  
Nonlinear dynamic analysis was conducted using CANNY [50], a three-dimensional analysis program, as in Section 5, and earthquake levels used for seismic design of the target building were applied. The input seismic ground motion was set to 200 cm/s\({}^{2}\) using the Hachinohe wave (EW), under which the reference frame with no reinforcement applied underwent a collapse. Figure 28 shows the RC school building model before and after seismic strengthening, and the amount of seismic strengthening needed for the NBSD seismic control system was calculated by Equations (11)-(13). The equations were derived from the Newmark's equal energy criterion [51; 52], which is the same method used by Japan to evaluate seismic energy absorption of seismic devices [7].  
\[Q_{ydp}=\frac{\left[\left(\frac{E_{Bo}}{E_{o}}\right)^{2}-1\right]\cdot\phi^{2 }\cdot\left(2\mu-1\right)\cdot\mu_{dp}}{2\beta\cdot\left(\mu\cdot\mu_{dp}-1 \right)}\cdot Q_{yst} \tag{11}\]  
\[E_{Ro}=C_{yst}\cdot\sqrt{\phi^{2}(2\mu-1)+2\beta\cdot\alpha_{c}\left(\mu- \frac{1}{\mu_{dp}}\right)} \tag{12}\]  
\[E_{o}=C_{yst}\cdot\sqrt{\phi^{2}(2\mu-1)} \tag{13}\]
## 3 Results  
In total, 311 patients were enrolled in this study. Of these, 39.5% (n = 123) were in the heart failure (HF) group, 19.3% (n = 60) in the STEMI group, 17.0% (n = 53) in the sepsis group and 24.1% (n = 75) constituted the control group. The median age of all patients was 61 years (IQR 53-71) and the majority of patients were male (65.2%,n = 193). Concerning cardiovascular risk-factors, 68.6% of all patients (n = 199) suffered from arterial hypertension, 63.9% (n = 129) had dyslipidemia, 44.9% (n = 102) had a history of smoking and 44.1% (n = 104) were overweight, with a median body mass index (BMI) of 27.6 kg/m\({}^{2}\) (IQR 24.2-31.0) of all patients enrolled. Notably, patients in the HF and the STEMI group had a significantly higher BMI than controls (HF: median 28.2 kg/m\({}^{2}\), STEMI: median 28.0 kg/m\({}^{2}\) vs. 27.4 kg/m\({}^{2}\), \(p\) = 0.030) and chronic kidney disease (CKD) was more prevalent amongst patients in the HF group than patients in the control group (20.0% vs. 5.7%, \(p\) = 0.008). Compared to the controls, patients in the HF, the STEMI and the sepsis group were significantly more often male (HF: 80.7%, STEMI: 71.2%, sepsis: 69.8% vs. control: 34.7%, \(p\) < 0.0001) and diabetes mellitus was more prevalent in these groups than in the control group (HF: 37.0%, STEMI: 29.1%, sepsis: 29.2% vs. control: 17.6%, \(p\) = 0.045) (see Table 1). C-reactive protein (CRP), serum creatinine and serum urea were significantly increased in patients with sepsis when compared to the other groups (CRP: median 235.9 mg/1 (IQR 93.0-336.6), creatinine: 148.0 umol/1 (IQR 107.3-208.3), see Table 1).
## 2 Materials and Methods
### Satellite Image Data and Preprocessing
Rather than using the entire time-series, which can introduce noise and computational inefficiency, we employed a Random Forest (RF) model within the Google Earth Engine (GEE) platform to identify the months yielding the highest classification performance. This data-driven approach pinpointed April, May, August, and October as the most informative periods. The high accuracy achieved by the RF model in these months indicates that they contain the most distinct spectral signatures for differentiating cultivated land quality classes. The selection of April and August corresponds to the critical phenological stages of green-up and peak maturity, respectively. According to Figure 2 of monthly mean NDVI curve, April marks the onset of the growing season. During this green-up phase, the landscape is a heterogeneous mixture of bare black soil and emerging seedlings. This creates strong contrasts in the raw spectral reflectance across multiple bands, providing unique signatures that are crucial for the model to distinguish different early-season growth patterns. Conversely, August represents the period of maximum canopy density and photosynthetic activity, as indicated by the peak mean NDVI. At this stage, the pure and stable spectral signal from the dense vegetation canopy, with minimal soil background influence, serves as a key proxy for land quality. The transitional months of May and October were identified as essential for capturing the dynamic phases of rapid growth and senescence. In May, crops are in a state of rapid development, leading to a swift increase in biomass and changes in canopy structure. The variability in spectral reflectance during this month reflects the diverse growth rates among different plots, offering critical information for classification. October captures the senescence phase, where a decline in chlorophyll and moisture content causes a significant \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Date** & **Grade 1** & **Grade 2** & **Grade 3** & **Grade 4** & **Grade 5** & **Grade 6** & **Overall** & **Kappa** \\  & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline
2022-04-01-2022-04-10 & 0.34 & 0.45 & 0.52 & 0.57 & 0.48 & 0.86 & 0.50 & 0.34 \\
2022-05-202-05-31 & 0.53 & 0.58 & 0.65 & 0.63 & 0.58 & 0.76 & 0.62 & 0.50 \\
2022-08-202-022-03-31 & 0.59 & 0.55 & 0.61 & 0.54 & 0.57 & 0.42 & 0.57 & 0.42 \\
2022-10-10-2022-10-20 & 0.44 & 0.57 & 0.66 & 0.61 & 0.52 & 0.47 & 0.60 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Remote sensing images of typical periods suitable for evaluating cultivated land quality at Shuanghe Farm. Figure 2: Monthly mean NDVI curve. shift in spectral reflectance. This period is also characterized by post-harvest bare soil for some early-maturing crops, further increasing the spectral heterogeneity across the farm.
## 4 Experiments and Results  
### Experimental Setup  
In this paper, the rolling bearing fault data set provided by the Case Western Reserve University Bearing Data Center is used to verify the proposed fault diagnosis method [32; 33; 34]. In the experiment, Svenska Kullager-Fabriken (SKF) rolling bearing was taken as the research object. The data set collected the rolling bearing vibration signals in four states: normal (NM), inner race (IR) fault, outer race (OR) fault and ball elements (BE) fault through the acceleration sensor. The sampling frequency was 12 kHz for the three types of faults, with diameters of 7 mils, 14 mils and 21 mils that were selected for data acquisition.  
The time-domain waveforms of rolling bearing vibration signals with different types and fault severity under 0 load are shown in Figure 3. It can be seen that changes in fault types and fault severity are correlated with changes in amplitude and frequency of vibration signals. Table 1 shows the composition of fault types and fault severity in the experimental sample set, which includes 10 different rolling bearing health states. Each rolling bearing vibration signal is divided into multiple data samples without overlapping, and each sample contains 1024 sampling points to form the experimental data set composed of 50 samples in each healthy state. Among them, 10 samples in each healthy state were used as training set and 40 samples as test set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & \multirow{2}{*}{**Labels**} & \multirow{2}{*}{
\begin{tabular}{c} **Fault Diameter** \\ **(mils)** \\ \end{tabular} } & \multicolumn{4}{c}{**Load (hp)**} \\ \cline{3-6}  & & & **0** & **1** & **2** & **3** \\ \hline Normal & NM & - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Inner race & \multirow{2}{*}{IR07} & 7 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ fault & & IR14 & 14 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & IR21 & 21 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Outer race & \multirow{2}{*}{OR07} & 7 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ fault & & OR14 & 14 & \(\surd\) & \(\surd\) & \(\surd\) \\  & OR21 & 21 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Ball & \multirow{2}{*}{BE07} & 7 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ elements & & & & & & \\ fault & & & & & & \\  & BE14 & 14 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & BE21 & 21 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The composition of fault types and fault severity in the experimental sample set.  
Figure 3: The time-domain waveforms of rolling bearing vibration signals with different types and fault severity under 0 load.
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Zero-Knowledge Proofs for Verifying Model Integrity  
After computations are performed, it is essential to ensure that neither party (the server or client) has modified the model's behavior in an undesirable way. For this purpose, we use Zero-Knowledge Proofs to verify the correctness of the computations.
# Effects of Diverging Nozzle Downstream on Flow Field Parameters of Rotating Detonation Combuster
## 1 Introduction
[PERSON] et al. [25] numerically investigated the unsteady performance of RDCs with different exhaust nozzles and found that using a straight duct nozzle could decrease pressure gain of about 27%. Considering methane is the main component of natural gas, research on RDCs using methane-air is more valuable for engineering applications. However, methane is the hydrocarbon fuel with high activation energy, which means the realization of detonation with methane-air is not as easy as hydrogen-air or ethylene-air. Thus, the combustor size needs to be larger than the others under the same initial conditions. The large scale chamber hampers the design of the experiment rig and imposes a huge burden on the numerical simulations. Therefore, in order to study the RDC with methane, many studies chose to add extra oxygen or hydrogen in the fuel to improve the chemical activity [26; 27; 28; 29]. Besides, the minimum realization size of RDCs can be effectively reduced by increasing the initial pressure, as the detonation cell size is greatly affected by the initial pressure of the mixture [30; 31; 32; 33; 34]. More encouragingly, [PERSON] et al. [35] achieved the methane-air RDC experiment with only a 100 mm chamber diameter by controlling the contraction ratio of the outlet Laval nozzle. This undoubtedly gives us more encouragement and valuable reference for the studies of methane-air RDCs. From the above brief review, it is concluded that the downstream nozzle is one of the main factors that should be considered during the design of the RDC, especially for methane-air RDCs. However, to the best of the authors' knowledge, the present published investigations still cannot fully explain the following questions. (1) Can the downstream nozzle really affect the formation and propagation process of the detonation wave? (2) As the downstream nozzle is adopted, how do the typical parameters at the outlet of the RDC change? (3) Are the changes of parameters beneficial to the downstream component or not? Based on these motivations, the present study performs the three-dimensional numerical simulations to investigate the performance of the RDC under the action of a downstream nozzle (here mainly for a diverging nozzle). The typical wave collisions in a rotating detonation combustor with a diverging nozzle is analyzed to understand the rotating detonation wave formation and propagation process. On this basis, the changing of RDC flow field parameters under a diverging nozzle and different diverging angles are studied in detail.
## 4 Databases and Evaluation Parameters
### Evaluation Parameters
#### 4.2.1 Peak Signal-to-Noise Ratio (PSNR)  
The peak signal-to-noise ratio (PSNR) is a signal value or image-level ratio of the signal or noisy image [23]. Equation (2) demonstrates this scientifically:  
\[PSNR=20 log_{10}\frac{R}{\sigma}. \tag{2}\]  
In Equation (2), the \(\sigma\) is the variance in the intensities of the image, and \(R\) is the maximum value of the image. It can be between 0 and 255, which is the peak of intensity of a digital image. We analyzed the improvement in PSNR named \"PSNR improvement\". The PSNR improvement is the analysis of the difference in the PSNR between the original PSNR image and the PSNR of the output image at each iteration.
###### Abstract  
The precise control of an aerial manipulator presents a formidable challenge due to the inherent mobility of its base, which is subject to both external disturbances and dynamic disturbances due to manipulator motions. In this paper, we introduce two Closed-Loop Inverse Kinematics (CLIK) control algorithms tailored to aerial manipulators. The first algorithm operates at the velocity level and uses the Generalized Jacobian for inverse kinematics, while the second one operates at the acceleration level. We evaluate their performance in a simulated environment, replicating real-world challenges such as the wind effect, sensors noise, uncertainty of the system inertial parameters, and impulsive forces at the end-effector. Trajectory tracking simulated experiments are carried out for a two- and three-degree-of-freedom (DOF) aerial manipulator tracking a circular trajectory with its end-effector. Both algorithms demonstrate promising results in coping with external disturbances and variations in the inertial parameters, enhancing the precision of the trajectory tracking control. The acceleration-level algorithm shows overall better performance compared to the velocity-level one in the face of greater implementation complexity and computational burden.  
aerial manipulation; UAV; robot; inverse kinematics; trajectory tracking control Article