esa-sceva/satcom-chunk-collection huggingface dataset:
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 4 Clutter Statistics and Detection
### K-Distribution
#### 4.1.2 Non-Linear Least Squares Method (NLLSQ)  
Another way to estimate the known parameters of K-distribution is to use the NLLSQ method. This method uses the Levenberg-Marquardt algorithm [43] to find the local minima of a function which is expressed as a sum of the squares of the difference between the proposed PDF and the data PDF. The minimization function is expressed as  
\[\operatorname*{argmin}_{\alpha}\sum_{m=1}^{M}(f_{\mathrm{T}}(I,L,\alpha)-h_{ \mathrm{m}})^{2}, \tag{21}\]  
where \(f_{\mathrm{T}}(I,L,\alpha)\) is the distribution function having \(\alpha\) unknown parameters and \(M\) histogram bins \(h_{\mathrm{m}}\).  
Regardless of being a widely used model, it is found that the K-distribution works well only in the case of high-resolution homogeneous ocean environments. Cases where the ocean is highly heterogeneous or the signal energy recorded by the radar system is extremely low, the estimated shape parameter values are not acceptable. Using the MoM for estimation yielded either to too high or too negative values [44].  
An example of the ship detection using the K-distribution applied on the RC F-SAR X-band HH polarized data is shown in Figure 18. The NLLSQ method was used to estimate the unknown parameters of the K-distribution. For a desired \(P_{\mathrm{fa}}=10^{-6}\), the computed CFAR threshold is applied in range-Doppler domain as per the methodology described in previous sections. For visualization purposes, the detections obtained in Doppler domain are shown in time domain.  
In Figure 18, it can be observed that along with the ship (left) the heterogeneous ocean clutter (i.e., the high sea spikes) at lower incidence angles are also detected. Therefore, in the next section other models are discussed as an alternative to the K-distribution.
## 3 Pressure Data Analysis  
### Mean Pressure Analysis  
The goal of the discharge cone is to convert the kinetic energy into potential energy with minimum hydraulic losses. This energy conversion is expressed by the wall pressure recovery coefficient \(\mathrm{c_{p}}\), which it is given in dimensionless form by Equation (2),  
\[\mathrm{c_{p}}=\frac{\overline{\mathrm{p}}-\overline{\mathrm{p}}_{L0}}{\rho \mathrm{V_{t}^{2}/2}}\ \ where\ \ \mathrm{V_{t}}=\frac{Q}{\pi\mathrm{D_{t}^{2}/4}} \tag{2}\]  
where \(\overline{\mathrm{p}}_{L0}\) is the mean pressure on the wall at L0 level--Figure 3a, \(\overline{\mathrm{p}}\) is the mean pressure measured downstream on the cone wall, \(\varrho=998\) kg/m\({}^{3}\) is the water density, \(\mathrm{V_{t}}\) is the bulk velocity in the throat and \(\mathrm{D_{t}}=2\mathrm{R_{t}}=0.1\) m is the throat diameter corresponding to L0 level.  
The distribution of the pressure coefficient \(\mathrm{c_{p}}\) along the cone wall against the dimensionless axial coordinate is plotted in Figure 3b. We consider the dimensionless axial coordinate regarding the throat radius \(\mathrm{Rt}\) displaced at L0 level of the test section--Figure 3a. The wall pressure recovery in the first part of the cone up to L1 level is negligible if it uses the diaphragm. In contrast, a significant improvement of the wall pressure recovery is obtained downstream to L1 level delaying the separation flow on the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Diaphragm Inner** & **Diaphragm Inner** & **Cone Outlet Area** & **Shutter Area** & **Shutter Area Ratio** \\
**Diameter** & **Area** & \(A_{d}=\pi\mathrm{d^{2}/4\ [m^{2}]}\) & \(A_{0}=\pi\mathrm{D^{2}/4\ [m^{2}]}\) & \(A_{r}=A_{D}-A_{d}\ [m^{2}]\) & **Shutter Area Ratio** \\ \hline
0.134 & 0.0140 & & 0.0061 & 0.3 \\ \hline
0.113 & 0.0100 & & 0.0101 & 0.5 \\ \hline
0.100 & 0.0078 & & 0.0121 & 0.6 \\ \hline
0.088 & 0.0060 & & 0.0141 & 0.7 \\ \hline \end{tabular}
\end{table}
Table 1: Data for all investigated diaphragm openings.  
Figure 3: Detail of test section and swirl generator (**a**). Pressure coefficient measured on the cone wall for swirling flow with vortex rope (solid line) and the cases with diaphragm method (**b**).  
wall. For instance, the pressure recovery coefficient (at the wall) is practically twice in the middle of the conical diffuser (on L2 level).  
It is expected when the pressure recovery on the cone wall is improve it will have diminished additional hydraulic losses associated to the vortex rope. However, the hydraulic losses increase on the cone once the diaphragm continues to obstruct the flow. The hydraulic losses cannot be correctly quantified based on the available experimental data.  
[PERSON] et al. [34] revealed that the hydraulic losses are less than half of the value received for the case with the vortex rope up to the relative shutter area of 50% (d = 0.113 m), and no larger than twice up to the relative shutter area ratio of 70% (d = 0.088 m), respectively.
## 6 Equivalent Circuit of the Proposed Metamaterial Absorber Unit Cell  
The projected metamaterial absorber consists of both inductive and capacitive constituents. The metal bars of the of the octagonal close ring resonator (OCRR) and other metal strips involved in the unit cell acted as inductors, whereas the opening ends within the metal bar created capacitors. Hence, an \(LC\) resonance circuit is formed in this perfect metamaterial absorber (PMA) unit cell. Therefore, the resonance frequency (\(n\)) can be extracted from Equation (6) [42].  
\[\begin{split} 2\pi n\sqrt{LC}=1\\ \text{or, }\ n=\frac{1}{2\pi\sqrt{LC}}\end{split} \tag{6}\]  
where \(C\) and \(L\) are the capacitance and inductance of the capacitors and the inductors respectively. The capacitance formed by the splits can be derived by Equation (7)  
\[\frac{C}{\in_{r}}=\in_{0}\frac{A}{d}(F) \tag{7}\]  
where \(\in_{0}\) and \(\in_{r}\) are the permittivity in free space and the relative permittivity respectively and \(A\) is the cross-sectional area of the conducting strip, \(d\) is the split gap.  
Figure 10: Absorption at (**a**) varying octagonal width (**b**) varying tuning fork split gap.  
\[L(nH)=2\times 10^{-4}l\left[\ln\left(\frac{l}{w+t}\right)+1.193+0.02235\left(\frac{ w+t}{l}\right)\right]C_{g} \tag{8}\]  
Here, \(Cg\) = Correction factor, \(w\) = width, \(l\) = length and \(t\) = thickness of the strip.  
Figure 11a denotes a predictable equivalent circuit for the considered metamaterial absorber unit cell. \(L1\), \(L2\), \(L3\), \(L4\), \(L5\), and \(L6\) of the circuit model refer to the inductors, while \(C1\), \(C2\), \(C3\), and \(C4\) represent the capacitors. The circuit was boosted and simulated using a simulator advanced design system (ADS). As such, inductor \(L1\) and \(L2\) are used for copper back and \(C1\) is the coupling capacitor. The capacitors \(C2\) and \(C3\) are used for the gaps between two tuning forks which are responsible for the first resonance (6.45 GHz). While \(C4\) is the coupling capacitor, \(L5\) and \(L6\) refer to two parallel inductors replacing the dumbbell shape metal strip, which enhances the second resonance (14.89 GHz). Figure 11b represents a comparative assessment of the CST-simulated S11 and Advanced design software (ADS) that describes the nearby resemblances between these two S11 values.
## 1 Introduction
Sometimes the re-optimization design is needed in the presence of contradictory results between different required performances. In the meanwhile, the variables for the general optimization design only depend on the structural dimensions or process parameters, while the material properties or loads are deemed to be the deterministic values. However, the material parameters are related with artificial manufacturing procedures, and they should be random due to uncertain technological conditions, as well as loads in the utilizations. Therefore, this paper presents a fatigue life and weight co-optimization method to obtain the compatible optimal results for a welded A-type frame in a mining dump truck. Moreover, it considers that there are some deviations in structural geometry in the design stage, and material properties are also varied under different manufacturing processes, as well as loads in different usage conditions. The multi-source uncertainty variables including structural geometry dimensions, material properties, and loads were all considered when conducting multi-objective optimization design in this paper. Then, the monotonic tensile and fatigue tests of welded joints were studied to obtain the mechanical material parameters for the both base metal and weld metal and cyclic properties for the weld metal. The strain-life curve was considered as the calculation basis in the following lifetime prediction. The micro properties were also observed to understand the fatigue characteristics of this -veld metal. A multi-body dynamic model was built to obtain the load history and its accuracy was verified by comparing the simulated results with the experimental ones. The finite element model of welded A-type frame was also constructed and validated. The elastic and plastic finite element analysis of this frame was performed and its fatigue life was calculated as per the Manson-Coffin equation. The multi-objective optimization function was built by considering the multi-source uncertainties in different stages. The 50 sample points were obtained from the Latin hypercube sampling method. Based on the Kriging approximation model, the optimization mathematical model was constructed and validated by 10 other sample points. To obtain the optimal result, the NSGA-II was utilized to determine the Pareto solution sets.
## 4 Experimental Verification  
### Construction of the Experimental Platform for Image Acquisition  
The purpose of identifying the pressure relief hole of drilling robot in coal mine is to optimize the hole layout, so as to improve the pressure relief effect. In this process, the formation time of the pressure relief hole is relatively short, and the pressure relief hole does not show significant deformation. In the underground pressure relief area of the coal mine, it is difficult to collect images in time after the formation of the pressure relief hole in the previous stage. Most collected pressure relief holes have a relatively long formation time, and will inevitably show deformation under the high ground stress conditions. The images of pressure relief holes with abnormal structural features will inevitably have an impact on the training model. Therefore, in order to obtain the original relief hole images, a drilling experiment platform was built, as shown in Figure 8. The coal and rock specimens used in this experiment were customized by referring to the actual composition of coal and rock. The drilling robot was operated to drill the specimens many times, and then the images containing the characteristics of the pressure relief hole were collected through an industrial camera.  
In order to simulate the actual working environment of the drilling robot as much as possible, the industrial camera considered different conditions of brightness, shooting angles, blurring degree, and shooting distance when collecting pictures. Partial images of pressure relief holes acquired under the above conditions are shown in Figure 9.  
Figure 7: Improved Faster R-CNN model for pressure relief hole target recognition.
## 2 Literature Review
### Pure Flow Shop Lot Streaming (PFS-LS)  
The majority of early publications on lot streaming are for pure flow shops. However, recent literature indicates that PFS-LS still continues to attract the attention of the research community. A tabu-search based three-stage algorithm for PFS-LS was developed in [6]. The three stages of the algorithm involve (i) predetermining sublot sizes, (ii) developing a schedule based on the predetermined sublot sizes, and (iii) varying the sizes of the sublots to improve the solution quality. [PERSON] and [PERSON] [7] developed a linear programming hybridized genetic algorithm with variable sublots to minimize the makespan. The authors demonstrated that, in the presence of setup time, variable sublot could bring substantial improvement in makespan compared to consistent or equal-sized sublots.  
A genetic algorithm for PFS-LS with limited buffer capacities and equal-sized sublots was developed in [8] to minimize earliness and tardiness. [PERSON] et al. [9] developed a multi-objective genetic algorithm (given the number and size sublots) to minimize the makespan, total flow time, machine idle time, and earliness time. [PERSON] et al. [10] developed an improved migrant birds optimization for minimizing the makespan with equal sublots and sequence-dependent setup time. A bee colony algorithm was proposed in [11] to minimize the makespan and earliness in a blocking PFS-LS with no intermediate buffer between adjacent stages. The author combined setup time with processing time and assumed that the number and size of sublots are determined before scheduling.  
An exact heuristic based on dynamic programming and Lagrangian relaxation was developed in [12] for a two-machine PFS-LS to minimize total flowtime. A convex programming technique for a single-job two-machine PFS-LS was developed in [13] with due date criterion and minimization of the total energy consumption. The energy consumption was optimized by varying the processing speed of the sublots. [PERSON] et al. [14] developed an algorithm for PFS-LS with intermingling and variable sublots having detached setups. The authors demonstrated that the assumption of a detached setup could reduce the makespan.
## 4 Technologies for Smart Grid
### Telecommunication Systems  
Traditionally, SCADA systems have been used for distant monitoring and controlling of the conventional power systems. The backbone of a SCADA system is the communication network. Such network structure generally utilizes wireless radio connection, dial-up lines, Ethernet, and IP protocol to support the communication requirements in a SCADA system. However, such communication mediumy may face challenge of data congestion due to high volume of data information in SG systems [58]. Therefore, a communication structure/system that would handle big data flow would be required for future smart grid application. The communication system in a SG is required to provide high-level quality of services such as reliability, availability, wide area coverage, highest level of security and privacy [59]. The wireless data communications are more advantageous in compare to the wired communication. Such advantages are cost effective for installation, speedy placement, maneuverability and remote applicability [60]. Wireless technology based on IEEE 802.15.4 protocol is recommended by the reference [61]. Such protocols are ZigBee, Wireless HART, and ISA100.11a. Furthermore, satellite technology offers effective solution in remote monitoring and controlling because of its comprehensive coverage ability and speedy placement [62]. It has also realized that microwave communication technologies are reliably utilized for point-to-point communications because of its secured data exchange capability at a large bandwidth [38]. However, wire-based communication networks such as fiber-optic and power line communications are also  
Figure 6: Power output of renewable energy sources with and without energy storage system.  
expected to integrate into SG system [36]. The power and telecommunication network of smart grid is represented in Figure 7[63].
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 4 Experiment and Result
### Simulation
Scenario 2 also uses planning time, trajectory running time, trajectory length, and trajectory smoothness as evaluation indicators. The results are shown in Table 4. In scenario 2, compared with Fast-Planner, the proposed method still has obvious advantages in various indicators. The planning time of the two algorithms is approximately the same, which can meet the real-time requirements. However, the trajectory length and smoothness of the proposed method, are significantly less than Fast-Planner, so it is more suitable for multi-rotor UAV execution.
## 4 Hardware Design of Dual-Phase Sensitive Lock-in Amplifier Circuits
### Circuit of Phase Shift  
After the reference signal passes through the amplifier, it has two different paths: one goes directly to the phase-sensitive detector that participates in the multiplication operation; the other passes first through a 90\({}^{\circ}\) phase shifter and then to another phase-sensitive detector for multiplication. The phase shifter here is a special type of all-pass filter whose gain frequency characteristics are fixed and do not filter out noise, but whose phase frequency characteristics are changed. Therefore, it is usually used in situations where the phase of the signal needs to be changed without changing the amplitude of the  
Figure 5: Schematic diagram of bandpass filter.  
signal. In this paper, we used the all-pass filter technique to design this 90\({}^{\circ}\) phase shifter, the structure of which is shown in Figure 6.  
When \(R_{1}=R_{2}\), \(R_{3}=R\), \(C_{1}=C\) in this circuit module, the transfer function T(s) is Equation (16)  
\[T(s)=\frac{-1+sRC}{1+sRC} \tag{16}\]  
From (16) can be introduced gain A = 1, and the phase change relationship \(\psi\) is Equation (17).  
\[\psi=\arctan(\frac{2\omega RC}{\left(\omega RC\right)^{2}-1}) \tag{17}\]  
Bringing \(\omega=\frac{1}{RC}\) into Equation (17) gives Equation (18).  
\[\psi=\arctan(\frac{2\omega RC}{\left(\omega RC\right)^{2}-1})\approx arctan \infty=\frac{1}{2}\pi \tag{18}\]  
Based on Equations (15) and (17), the phase shifter module can shift the phase angle of signals by 90\({}^{\circ}\) without altering the amplitude of the reference signals. This allows for the acquisition of a set of reference sub-signals that are orthogonal to each other. These two sets of signals are fed into two different phase-sensitive detectors together with the target signal.
## 2 State of the Art  
Most of the research works on spam filtering [11] are based on analyzing the content of the message body. However, some researchers have focused on the use of message header characteristics for email filtering with good results. A few have even made use of both body and header, as proposed in the present study. Furthermore, it is very important to properly select the input features [12; 13] ensuring that they provide enough information for classification and can be extracted/computed quickly (extraction and classification processes must be executed during the SMTP transaction). In the review by [PERSON] et al., it is suggested that the future of Spam detection models can be considered to evaluate email headers, URLs, and domain characteristics.  
Most proposals take advantage of ML techniques to classify messages. Among the ML techniques that have performed best in spam classification are Naive Bayes (NB), Support Vector Machines (SVM), Decision Tree (DT), and Random Forest (RF) [11]. ML techniques have long been applied to spam filtering [14; 15], but not in a proactive, real-time way. We have also found some examples of real-time anti-spam techniques but based on continuous training of the ML models [8]. However, the aim of this work is for the filter to be executed during the SMTP transaction, so retraining the model after each successful classification seems to be a major limitation.  
To our knowledge, there are no relevant advances in the context of classifying message classification techniques during SMTP transactions. Therefore, although the time to accept/reject the delivery of the message is quite flexible, it is reasonable to think that the shorter the time the email takes to be classified and accepted for delivery, the more emails the server will be able to handle. In addition, it is mandatory to reduce the time needed to accept/reject the delivery of a message in order to avoid delays in the delivery of legitimate emails.  
Many approaches to spam detection have been developed and tested, but they are not accurate enough or cannot be applied during the SMTP transaction. Thus, given the lack of solutions for proactive spam filtering, we have developed a framework that allows email administrators to apply a wide variety of checks during SMTP transactions. The proposal is introduced in the next subsection.
## 3 Materials and Methods
### Hand-Crafted Feature Extraction
#### 3.6.2 Gray Level Co-Occurrence Matrix (GLCM)  
The Gray Level Co-occurrence Matrix (GLCM) [27] is a second-order statistical method that quantifies the spatial relationships between pixel intensities in an image. It provides useful texture descriptors such as:  
* Contrast: Measures the intensity variation between adjacent pixels.
* Correlation: Quantifies the linear dependency of gray levels.
* Energy: Represents image uniformity.
* Homogeneity: Evaluates the closeness of distributed gray levels.  
These GLCM-derived texture descriptors were computed for maize leaf images to capture subtle structural changes caused by pest infestation. By analyzing these statistical dependencies, GLCM aids in distinguishing healthy and infected leaves, as well as identifying different severity levels of pest damage.
## 5 System Environment Temperature Analysis  
We use Code V software to simulate the effect of temperature on the image quality of the two subsystems in a vacuum environment. The camera uses silicon carbide (SiC) as the mirror substrate, the primary and secondary mirror structures are made of carbon fiber, and the rear optical system structure is made of titanium alloy.  
The result of the system MTF varying with the ambient temperature is shown in Figure 9. It can be seen from the figure that the system MTF value of the visible light band subsystem is higher than 0.4 at 50 lp/mm between 18 \({}^{\circ}\)C and 22 \({}^{\circ}\)C, and reaches the highest  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tolerance Type** & **Value** & **Tolerance Type** & **Value** \\ \hline Third Mirror radius & \(\pm\)1 mm & Mirror tilt & \(\pm\)10\({}^{\prime\prime}\) \\
4 th Mirror radius & \(\pm\)0.7 mm & Mirror decenter & \(\pm\)0.02 mm \\
5 th Mirror radius & \(\pm\)0.6 mm & RMS figure error & \(\lambda\)/20 \\ Mirror conic & \(\pm\)0.001 & Air spacing & \(\pm\)0.02 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Long wave infrared band subsystem tolerance allocation.  
Figure 8: Three-dimensional schematic diagram of the integrated system.  
point of 0.45 at 20 \({}^{\circ}\)C. The system MTF value of the long-wave infrared band subsystem is higher than 0.354 at 20 lp/mm between \(-\)20 \({}^{\circ}\)C and \(-\)60 \({}^{\circ}\)C, and reaches the highest point of 0.364 at 20 \({}^{\circ}\)C. The two subsystems have certain adaptability to environmental temperature changes, and the simulation results achieve the expected results [16; 17].