esa-sceva/satcom-chunk-collection huggingface dataset:
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 4 Experimental Design and Result Analysis
### Wellbore Trajectory Design Under Vertical Obstacle Interference Scenarios
In the horizontal projection (Figure 13b), the trajectory shifts eastward when approaching the red obstacle, and quickly transitions back to the midline after the obstacle avoidance is successful. Before continuing to advance to the blue obstacle area, a flexible deflection occurs again and shifts westward, forming two continuous but compliant and smooth direction correction processes. Between the two obstacles, the trajectory shows the rhythm characteristics of natural return-fine-tuning transition-re-offset, which greatly reduces the additional path length and unnecessary deflection. This path shape avoids an \"S-type sharp turn\" or \"inflection point switching\", but presents a segmented arc combined continuous curve, which is conducive to wellbore construction stability and tool stress control. In the two vertical projection planes (Figure 13c,d), the trajectory quickly enters the near horizontal state after the build-up phase, and maintains a stable wellbore depth during the entire  
Figure 13: Trajectory avoidance optimal trajectory in a double-obstacle environment. obstacle avoidance process without dramatic changes. It shows that the whole obstacle avoidance behavior is mainly completed by horizontal space path planning, without relying on lifting or drilling avoidance, which further guarantees the construction feasibility and mechanical stability of the trajectory.
## 2 System Design of 3D Additive Manufacturing Scheme of SE  
The principle of the SE AM is to print layer-by-layer. When irradiated by a single-pulse laser, the powder on the actual layer is melted. After that, the powder is laid again by the scraper, and the metal powder is melted and formed after being irradiated by the other laser pulse. This step is repeated continuously until the entire sample is formed. In this section, we mainly focus on the construction of the entire optical system used for the SE technology. As shown in Figure 1, the optical system is composed of a short-pulse laser, an optical transmission system, an optically-addressed spatial light modulator (OASLM), and a vacuum tube. The short-pulse laser was provided by Keamtech (China), and it had a wavelength of 1064 nm, full width at half maximum of 8 ns, frequency in the range of 1-10 Hz, and a nearly flat top output laser beam profile. The laser was expanded to 27 mm (1/e\({}^{2}\)) through a pair of concave-convex lenses. Then, it was transmitted through a soft-edge aperture to meet the optical aperture of OASLM, which is 22 mm \(\times\) 22 mm. Finally, the lens pair and mirror relayed the modulated beam to the printing plane. Lenses with different focal lengths can be used to achieve different beam-reduction ratios. In this experiment, the expansion ratios were 3. A vacuum tube without a pinhole filter was used to prevent air breakdown due to the high energy density at the focal point.  
It is widely known that pure metal powders are easily oxidized in air. During the construction process, the oxidation rate of the pure metal powder is accelerated due to the increase in temperature. Therefore, building a low-oxygen construction chamber is required, as shown in Figure 1. The laser was vertically incident on the internal construction platform from the window above the construction chamber. The entire construction chamber was filled with argon having a purity of 99.99% to reduce the oxygen content in the construction chamber. Sn powder was used in the experiment.  
The energy of the incident laser is converted into heat when it reaches the surface of the metal powder. The heat conducted to the adjacent powder results in a temperature rise around the irradiated area, which affects the resolution of the construction samples. For the isotropic material, it can be approximately characterized by the theoretical thermal diffusion length \(L=2\sqrt{D\tau}\)[15] which is a simplified, where D is the thermal diffusion coefficient calculated from thermal conductivity, density, and heat capacity [16], and \(\tau\) is the laser pulse duration. For a pulse with the full width at half maximum of 8 ns, the thermal diffusion in Sn powder is about 1.5 \(\upmu\)m. Compared with the long pulse, the short pulse laser offers the potential to improving the resolution of printing results.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 4 Conclusions  
In this study, the application of the DPRNN approach for echo cancellation was investigated in the ultrasonic through-metal communication system. Firstly, the characterization of the ultrasonic channel was examined through the experiment of impulse response and magnitude response, revealing that owing to the echo cancellation, the coherence bandwidth was only 20.37 kHz and strong frequency selectivity existed. Subsequently, an actual system was constructed based on software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. Two approaches of LMS and DPRNN echo cancelation were comparatively applied to signals with different SNRs at a 2 Mbps transmission rate. Both exhibited a substantial improvement in signal quality, but the DPRNN one evidently achieved much higher efficiency with 20 dB SNR improvements for all situations. Furthermore, the image transmissions through a 50 mm thick aluminum plate were conducted in which the DPRNN also performed well, and achieved a 24.8 dB PSNR and about 95% SSIM. We sufficiently demonstrated that the DPRNN echo cancellation was a suitable choice to improve the ultrasonic through-metal transmission.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Index** & \begin{tabular}{c} **Image without Echo** \\ **Cancellation** \\ \end{tabular} & \
\begin{tabular}{c} **Image with LMS** \\ **Filter** \\ \end{tabular} &
\begin{tabular}{c} **Image with DPRNN** \\ **Model** \\ \end{tabular} \\ \hline PSNR/dB & 11.8911 & 19.1776 & 24.8396 \\ SSIM & 0.4144 & 0.8911 & 0.9471 \\ \(\Delta E\) & 17.1429 & 5.6638 & 3.6256 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of image restoration using signals with different echo cancellation methods.  
**Author Contributions:** Conceptualization, [PERSON] and J.Z. ([PERSON]); methodology, [PERSON] and M.J.; software, M.J.; validation, M.J., [PERSON] ([PERSON]) and [PERSON]; formal analysis, Z.C. and M.J.; investigation, Z.C. and M.J.; resources, Z.C.; data curation, [PERSON] and [PERSON]. ([PERSON]); writing--original draft preparation, M.J.; writing--review and editing, Z.C., M.J., [PERSON] ([PERSON]) and M.G.; visualization, M.J.; supervision, Z.C. and J.Z. ([PERSON]); project administration, Z.C. and J.Z. ([PERSON]); funding acquisition, Z.C. and [PERSON]. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation 62371253.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data included in this study are available upon request by contact with the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## Materials and Methods
### Mathematical Models
#### 2.3.1 Exact Model
Model 1, which is a non-linear 0-1 fractional programming model, can be linearized by introducing the following auxiliary binary variables:  
\[w_{irjkc}=x_{irc}y_{jkc},\ r\in R_{i},i=1,\cdots,m;k\in M_{j},j=1,\cdots,n;c=1, \cdots,p. \tag{11}\]  
To linearize the variable \(w_{irjkc}\), the following extra constraints should be added [57; 58]:  
\[w_{irjkc}-x_{irc}-y_{jkc}\geq-1.5,r\in R_{i},i=1,\cdots,m;k\in M_{j},j=1, \cdots,n;c=1,\cdots,p. \tag{12}\]  
\[1.5w_{irjkc}-x_{irc}-y_{jkc}\leq 0,r\in R_{i},i=1,\cdots,m;k\in M_{j},j=1, \cdots,n;c=1,\cdots,p. \tag{13}\]  
Then, we have the linear 0-1 fractional programming model 2 which is equivalent to model 1 as follows:  
(Model 2)  
\[\text{Maximize}\ \Gamma=\frac{\sum_{c=1}^{p}\sum_{i=1}^{m}\sum_{r\in R_{i}} \sum_{j=1}^{n}\sum_{k\in M_{j}}a_{irj}w_{irjkc}}{\sum_{c=1}^{p}\sum_{i=1}^{m} \sum_{r\in R_{i}}\sum_{j=1}^{n}\sum_{k\in M_{j}}a_{irj}x_{irc}+\sum_{c=1}^{p} \sum_{i=1}^{m}\sum_{r\in R_{i}}\sum_{j=1}^{n}\sum_{k\in M_{j}}\bigl{(}1-a_{irj }\bigr{)}w_{irjkc}} \tag{14}\]  
Subject to Equations (6)-(13) and  
\[w_{irjkc}=0\ \text{or}\ 1,r\in R_{i},i=1,\cdots,m;k\in M_{j},j=1,\cdots,n;c=1, \cdots,p. \tag{15}\]  
Model 2 can then be solved by using a solver such as LINGO adopting the branch-and-bound algorithm. However, the computational burden of optimally solving model 2 seems still to be heavy even if a powerful solver is used due to the presence of too many binary variables and constraints. The total number of binary variables of model 2 is  
\[\sum\limits_{c=1}^{p}\sum\limits_{k\in M_{j}}y_{jkc}=1,\ j=1,\cdots,m \tag{7}\]0  
and the number of constraints is  
\[\sum\limits_{c=1}^{p}\sum\limits_{k\in M_{j}}y_{jkc}=1,\ j=1,\cdots,m \tag{7}\]1  
For example, to solve a large-sized 0-1 GCF problem containing 110 machines, 120 part types, and 248 process plans--which will be tested in Section 4--331,656 binary variables and 663,554 constraints are needed. Therefore, relying on an alternative model with much fewer binary variablesand constraints that leads to the maximization of the GE can be a better strategy. Thus, we use the PMP-type model to solve a large-sized GCF problem by maximizing the GE indirectly.
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
###### Abstract  
Maximum power point tracking (MPPT) is the key to improve the conversion efficiency of wind energy. Concerning the current research on the MPPT control, based on the accurate tracking of rotor speed probability density function (PDF) shape for wind turbines, a novel MPPT algorithm was introduced in detail to improve the power capture and reduce mechanical damage for wind turbines. Considering the influence of wind speed distribution on the wind power generation system performance, this paper expounds a PDF shape control method of a stochastic system based on the Fokker-Planck-Kolmogorov (FPK) equation. Combining the conventional optimal torque (OT) control algorithm with the FPK equation solved by linear least-square (LLS) method, the novel MPPT control law is designed to make the PDF shape of rotor speed track the desired PDF shape as accurately as possible. The simulation verification of the novel MPPT method is carried out in the 1.5 MW wind turbine system. The results reveal that the novel MPPT method can improve the conversion efficiency of wind energy, reduce the frequent fluctuations of system variables, and significantly optimize the performance of wind power generation system.  
wind turbines; MPPT; PDF shape control; wind speed distribution characteristics +
Footnote †: journal: Applied Sciences
## 3 Results  
We evaluate this study firstly by analyzing Gabor filter templates in the simulations, and then using the 2D matching filtering characteristic. Two groups of tests are compared with and without ambient noise to validate the tracking capabilities with jamming targets. A series of quantitative studies are then used to assess the reliability of the target tracking systems under various SNR conditions. Finally, the proposed strategy is tested using real-world sea-trial data.
# Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification
## 5 Conclusions  
The proposed NIDS algorithm is a hybrid method comprising a packet classifier composed of dual LSTM and a session classifier with a single LSTM. The existing machine learning-based NIDS detects intrusion using only some packets and some packets of a session owing to structural limitations, resulting in fatal flaws such as worsening detection performance and a high possibility of bypassing detection. On the other hand, the method proposed to address this problem was designed to detect intrusion by using all the data of a packet and as many packets as possible in a session.  
Consequently, very high detection performance was achieved. In the performance evaluation, the proposed NIDS achieved the highest detection performance of 95.16% and 99.70% when the existing NIDS showed detection rates of up to 93.49% and 98.31% based on F1-score. In addition, the memory-optimized structure significantly improved the amount of memory required per session compared to existing methods, thereby minimizing total memory usage. Consequently, the number of concurrent sessions processed by NIDS can be significantly increased.  
The proposed NIDS consists of a dual LSTM-based packet classifier, an LSTM-based session classifier, and a DNN that performs final classification. This complex model can greatly improve the detection accuracy and provides robust classification performance, but the classification speed decreases compared to other NIDSs. The slow classification speed can be partially improved by using parallel processing, but considering the large increase in traffic volume to be processed by modern NIDSs, high classification speed is very important from the practical point of view of NIDS. To solve this issue, various future work such as feature engineering and optimization of the deep learning model are required.  
Through future work, the proposed NIDS can be extended and improved to be sufficiently used to protect enterprise-class networks. In particular, the proposed NIDS performs classification for each packet, so it is also possible to support real-time intrusion detection and will eventually be of great help in developing deep learning-based network intrusion prevention system.  
[PERSON] and [PERSON] wrote the paper and conducted the research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) NRF202 2R1A2C1011774.  
**Institutional Review Board Statement:** Not applicable.  
Figure 18: Total classification time of each NIDS for each test dataset.
## 2 Materials and Data  
### Seedling Image Acquisition  
To ensure the diversity of seedling varieties and the variability of transplanting environments, the study area includes paddy fields in Jiangsu and gravel fields in the northeast region. The image collection took place in May 2023 in Dongming Town, Shuangliao City, Jilin Province, and Wujiaohu Village, Fanhe Town, Tieling City, Liaoning Province. In June 2023, additional images were captured in Sunjialu Village and Liuchen Village, Xinnan District, Changzhou City, Jiangsu Province, and Sunjialu Village, Liuchen Village, and Taihuang Village, Taixing City, Taizhou City. The subjects of the collection were seedlings of different varieties at each experimental site. The seedlings from the northeast region were characterized by taller plant height, broader sword-shaped leaves, and darker leaf color, while the seedlings from Jiangsu had shorter plant height, narrower strap-shaped leaves, and lighter leaf color.  
Data collection was performed using a Panasonic Lumix V385 digital camera, equipped with optical stabilization, horizontal correction, and autofocus capabilities. This camera could capture high-quality images under the vibration of transplanting machine operation and outdoor natural light conditions. The pixel resolution of the data was 1920 \(\times\) 1080. The image collection involved both static and dynamic approaches. Static data collection involved manually capturing images by holding the camera vertically about 1.5 m above the paddy field. Dynamic data collection involved fixing the camera on the transplanting  
Figure 1: Technology roadmap for seedling planting condition.  
machine's seedling tray frame and capturing images during the machine's operation. In total, 2732 seedling images were collected, including 10,992 qualified seedlings and 2547 floating seedling samples. Figure 2 illustrates the morphology of a single seedling, encompassing images of qualified and floating seedlings under different backgrounds and lighting conditions.
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 5 Results and Discussion
### Adjoint Operators of Central and Periodic DRT  
Both proposed transforms have an adjoint, also called back-projection, operator. In the case of periodic DRT, it will be based on the inverse mapping of Equation (8):  
\[\widetilde{f}^{m}(\boldsymbol{\sigma}|\,0,\mathbf{v}|\,d) +=\widetilde{f}^{m+1}(s_{n-m-1},\,\boldsymbol{\sigma}|\mathbf{v}|\,d) \tag{9}\] \[\widetilde{f}^{m}\bigg{(}\boldsymbol{\sigma}|\,1,\mathbf{v}|\, \big{(}d+s_{n-m-1}+\lambda(\boldsymbol{\sigma})\big{)}\mod N\bigg{)} +=\widetilde{f}^{m+1}(s_{n-m-1},\,\boldsymbol{\sigma}|\mathbf{v}|\,d)\]  
In the case of central DRT, the Equation (6) remains valid. Apart from that, the adjoint operator has to repeat every step on the forward transform but in the reverse order, with the outer loop of algorithm for a quadrant descending from \(m=n-1\) to \(m=0\).  
Figure 17 shows the result of applying the respective adjoint operators on a previously forward transformed image, containing equispaced _deltas_, for each type of transform: conventional, central, and periodic (in that order from left to right).  
There is a sort of _vignetting_ in conventional DRT, due to the different length of discrete lines that are considered in this method: every line traverses \(N\) values, but depending on the slope and displacement, more or less of those values correspond to real pixels on the input image or zeroes in the padded zone.  
The back-projected result, corresponding to central DRT, is equivalent to that of conventional DRT but inside of the rhomboid with vertexes \(\{0,N/2\},\{N/2,N-1\}\), \(\{N-1,N/2\}\), and \(\{N/2,0\}\). The _vignetting_ effect is very notorious outside that zone. Moreover, the shapes around each (originally) black dot are not symmetric: those shapes are lacking the discrete lines discarded by this method.  
The back-projection corresponding to periodic DRT is the more symmetric of the three results, and it is not affected by the vignetting effect; simply all \(N\) values considered for each value of the output have effectively traversed the \(N\) pixels of the input.  
Figure 16: Conventional DRT (**a**), Central DRT (**b**), and Periodic DRT (**c**) of a \(256\times 256\) image of two concentric circles, shown bottom left of first subfigure.
## 6 Conclusions  
This paper mainly studies the deceptive jamming problem of nonlinear-trajectory SAR and proposes a nonlinear-trajectory SAR deceptive jamming algorithm based on hybrid domain efficient modulation. The HDE algorithm calculates the JFR of linear trajectory in the azimuth frequency domain and performs real-time trajectory deviation construction in the azimuth time domain. The characteristic of this algorithm is that it can generate JFR by constructing the trajectory deviation pulse-by-pulse in the azimuth time domain, which makes it especially suitable for deceptive jamming against nonlinear-trajectory SAR. In the initialization and real-time computation stages, the HDE algorithm only uses Fourier transform and complex multiplication operations, balancing computational efficiency and modulation flexibility. Additionally, we have summarized the validity constraints of the HDE algorithm implementation. The simulation and complexity analysis results indicate that, compared to the traditional SA algorithm, the HDE algorithm has a significant computational complexity advantage while ensuring the jamming signals' focusing ability. The HDE algorithm provides a new solution for the deceptive jamming problem of nonlinear-trajectory SAR, with the potential for practical application in SAR jamming. In the future, we will continue to study and extend this algorithm to more SAR imaging modes.  
**Author Contributions:** Conceptualization, J.D., Q.Z. and W.L.; Data curation, J.D.; Formal analysis, J.D. and Q.Z.; Funding acquisition, Q.Z. and W.L.; Investigation, J.D. and W.C.; Methodology, J.D. and W.C.; Project administration, Q.Z. and X.L.; Resources, J.D. and Q.Z.; Software, J.D.; Supervision, Q.Z. and X.L.; Validation, J.D.; Visualization, J.D.; Writing--original draft, J.D.; Writing--review and editing, J.D. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Stable-Support Scientific Project of China Research Institute of Radiowave Propagation (No. A132003W02).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 4 Results  
To evaluate the effectiveness of the developed platoon-based signal-optimization algorithm, two key performance metrics were considered:  
Figure 7: A basic outline for calibrating model parameters.  
* Average HCV delay: The delay experienced by heavy connected vehicles (HCVs), calculated in _VISSIM_ as the difference between actual travel time and the time required to traverse the intersection under free-flow conditions.
* Total number of stops: The average number of times HCVs come to a complete stop before crossing the intersection.  
To assess the effectiveness of the developed algorithm, a non-dimensional Performance Index (PI) was applied, integrating both delay and stop frequency into a single measure. The transformation strategy follows Equation (9):  
\[PI=Average HCV Delay Ratio+Average Number of Stops Ratio \tag{9}\]  
Each metric is normalized based on the maximum observed value across all scenarios, ensuring comparability across different control methods. The performance measure ratio is computed using Equation (10):  
\[Performance\ Measure\ Ratio=\frac{1}{n}\sum_{i=1}^{n}\frac{x_{i}}{X_{\max x_{i}}} \tag{10}\]  
where \(n\) represents the total number of simulation runs, \(x_{i}\) is the average performance measure for each run, and \(X_{\max x_{i}}\) is the highest observed performance measure across all runs. The PI values were calculated for all 24 scenarios tested in VISSIM, with results presented in Figure 8.  
Figure 8: PI values of different signal plans under varying traffic demands.