esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Different from traditional push-broom imaging modes, the degraded image of dynamic scanning mirror imaging, caused by satellite vibration, is analyzed in this paper. In this dynamic imaging model, the image quality will be influenced by the linkage between the satellite and the payload. Therefore, the vibration mechanism of the frame-by-frame imaging model is more complex. In order to simplify the complex relationship of the model, the disturbance of the satellite is displayed by the random vibration factors, and the disturbance of the payload is displayed by the periodically changing attitude angle factors. Then, the frequency domain-filtered template of random vibration is constructed. The following results can be obtained by simulating the dynamic imaging process with the orbital height 500 km and the scanning velocity \(10^{\circ}\)/s of the mirrors in front of the space camera:  
1. In the process of dynamic imaging, the ground image information becomes larger, and the distortion degree of the output image on the focal plane grows larger with the increasing angle between the optical axis of the camera and the position of the satellite nadir.
2. When the attitude angle is a certain value, the MTF drops rapidly with the increasing error of velocity.
3. The camera exposure time is determined by the periodically changing attitude angle. When the scanning mirrors scan at the satellite nadir, the camera exposure time is the shortest. With the increasing angle between the optical axis and the satellite nadir, the camera exposure time is extended. When the error of velocity is a certain value, the MTF drops with the axis of symmetry of the satellite nadir.  
Figure 11: The simulation process of remote sensing image degradation.  
In a group of wide, single-frame output images, the error of velocity is caused by the random vibration of the payload mechanism, resulting in an error image shift on the focal plane of the camera. In a set of single-frame images that constitute wide-format imaging, the distortion effect and degradation degree of each single-frame image are different. The single-frame image far away from the satellite nadir has the largest distortion, and the image degradation phenomenon is obvious.
## 3 Results
The accuracy rates presented in Table 7 demonstrate a strong correlation between the model-predicted intensity values and the actual measurements. The majority of accuracy rates exceed 98%, confirming that the model exhibits stable and reliable performance across different distances, angles, and surface types. The highest accuracy among the analyzed scenarios was achieved on the SB-Matte (Black) surface with 98.891%. The low reflectivity and homogeneous texture of matte surfaces contribute to a more consistent detection of laser light. Since specular reflections do not occur on matte surfaces, the discrepancy between the model-predicted intensity values and actual measurements remained minimal. Conversely, the lowest accuracy was observed on the SMRTG-Gloss (Green) surface with 98.195%. On glossy and highly reflective surfaces, the scattering of laser light in multiple directions introduces measurement uncertainty, thereby reducing the model's prediction accuracy. Additionally, factors such as vehicle glass surfaces, logos, inscriptions, and dust accumulation directly influence the measured intensity values. Glass surfaces, rather than reflecting laser light, refract it, causing dispersion in different directions. This disrupts the homogeneity of laser backscatter, thereby impacting the model's predictive accuracy. Similarly, logos and color transitions on vehicles alter the optical properties of the surface, leading to localized prediction errors. Furthermore, discrepancies between the reflectivity coefficient values used in the model training process and the actual paint-coating properties of real vehicles introduce variability in measurement accuracy. The model in this study estimates intensity based on standardized reflectivity coefficients. However, real-world vehicle surfaces exhibit different reflectivity characteristics due to variations in paint material, coating type (e.g., matte, glossy, metallic), and surface condition. These factors contribute to fluctuations in accuracy across different scenarios.
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 4 Discussion
In general, the catheter width only occupied 6-10 pixels, which means even a one-pixel error in the results of segmentation will lead to large errors in millimeters. In fact, the segmentation of particularly fine objects inherently has some deviation, especially at the edge positions where pixel values are relatively low, which can easily be neglected by the model, and result in an overestimated scale. DS is a percentage that is unrelated to the scales, while the scale conversion of MLD, RVD, and LL may amplify the errors. Therefore, a larger reference for scale calculation is supposed to be taken into account to reduce the error in the future. Additionally, the value of LL derives from the merging of small stenotic regions, which requires improving the rationality of the parameters in interval merging and boundary setting. Additionally, the LL of manual QCA may be smaller because people may choose a boundary position of the stenosis interval with very small stenosis lesions by mistake, similar to the proximal location in Figure 12c. In fact, there are also limitations to AI-QCA. Firstly, the current algorithm is heavily dependent on the quality of the image and the performance of the segmentation model. In order to improve its accuracy, it is important to include more high-quality training images in the training process. Secondly, only one frame of CAG is involved, while incorporating dynamic frames from DICOM can provide more information for better image analysis. Furthermore, a single view of CAG may cause incorrect estimation of diameters if the stenosis lesion is an eccentric stenosis. Incorporating results from different views may provide more detailed and accurate information in 3D space for stenosis lesion analysis. In the future, AI-QCA has the potential to be used to help cardiologists rapidly locate the stenosis area and acquire quantitative results for reference. To achieve this, further assessment in external clinical contexts is necessary to test its practicality. In addition, AI-QCA has the potential to be integrated into the SYNTAX scoring system [45], a commonly used tool to evaluate the complexity of CAD and determine the optimal course of treatment. By incorporating AI technology into the SYNTAX score, the accuracy and efficiency of this system could be significantly improved.
## 2 Methodology
### Event Filtering and Expert Knowledge Extraction Modules: Structure and Operation
#### 2.2.1 Event Filtering Module  
The objective of this module is to process raw sensor data and identify significant deviations that may indicate a potential drop in product quality. This is critical because even slight deviations (when sustained) can signal underlying issues that affect the final quality of the steel product.  
The module is architected as a multi-stage pipeline with clear separation of responsibilities.  
* File Loading Submodule  
Reads raw CSV data and a complementary criteria file, which defines the expected data types, valid ranges, and grouping intervals. This ensures that all incoming data meet predefined standards.  
* Content Transformation Submodule  
Corrects any formatting inconsistencies and applies basic corrections as needed. This step ensures the data's integrity, preserving the raw relationships for later processing.  
Figure 6: The EDDYeyes system of ISEND S.A.  
* Data Grouping and Windowing Submodule The data are segmented into fixed time windows (typically 4-h windows, limited to the most recent 100 points) to capture temporal behavior while smoothing out transient fluctuations.
* Statistical Processing and Anomaly Detection Submodule Within each window, statistical metrics (e.g., minimum value, mean, and standard deviation) are computed. A logarithmic transformation is applied for smoothing, and an anomaly is flagged when the window's value exceeds the mean plus three standard deviations (this is the criterion adopted to obtain satisfactory results). A threshold of at least 10 anomalous points is enforced to eliminate spurious spikes.
* Output Generation Submodule The final output is two-fold: a CSV file that adds to the original dataset Boolean flags (indicating the presence of anomalies) and graphical representations for quick visual analysis. This modular design, whose operation is illustrated in Figure 7, was chosen to isolate each processing step, thereby increasing maintainability and allowing for independent tuning or replacement of submodules. The clear delineation between file handling, transformation, windowing, and statistical detection ensures robustness and scalability. It also facilitates debugging and further enhancements as new types of sensor data or criteria emerge.
# Adaptive Multi-Level Search for Global Optimization:
## 5 Benchmark Problem
### Mathematical Problem G10
A comparison of results by ASIM, MAM, and other algorithms is given in Table 12. Although all methods listed are very competitive and has the ability to find global or near global optimum, ASIM demonstrates superiority over the others in terms of computational efficiency. Evolutionary algorithms usually need over 150,000 simulations to find the global optimum, while ASIM could reduce the number of function evaluations to 19,522 by more than 80%. Furthermore, the optimum (7049.2481) achieved by ASIM is in a good agreement with the global optimum (7049.2480). Although HCS [23] proposed a best optimum (7049.237), the fourth constraint is slightly violated and, therefore, is not a feasible design. Summarily, ASIM outperforms other methods in seeking the global optimal solutions of complex black-box optimization problems in terms of efficiency and accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Worst** & **Mean** & **Best** & **S.D.** & **NFEs** \\ \hline
1 & 142,392.7156 & 17,882.82055 & 7069.390888 & 30,251.49815 & 18,436 \\
2 & 68,065.07371 & 11,619.11583 & 7049.296446 & 11,320.46339 & 19,388 \\
3 & 43,458.18348 & 10,426.99053 & 7049.249948 & 6247.162418 & 19,584 \\
4 & 76,953.38912 & 12,669.27407 & 7052.442664 & 12,331.32358 & 18,478 \\
5 & 53,751.55465 & 11,938.01326 & 7064.48503 & 8513.607171 & 19,122 \\
6 & 38,601.51929 & 11,216.42827 & 7049.304236 & 5669.873504 & 17,274 \\
7 & 133,020.3445 & 12,395.33809 & 7062.698763 & 19,714.98684 & 19,640 \\
8 & 50,195.68872 & 12,527.61721 & 7061.831868 & 10,079.5634 & 19,668 \\
9 & 86,553.78422 & 12,382.57366 & 7053.509331 & 13,257.22511 & 20,270 \\ \hline Average & 7069.390888 & 7056.46585 & 7049.249948 & 7.334903947 & 19,095 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical results for G10 by MAM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Worst** & **Mean** & **Best** & **S.D.** & **NFEs** \\ \hline
1 & 7071.746167 & 7054.064714 & 7049.24996 & 9.89925027 & 19,374 \\
2 & 7058.554639 & 7051.206052 & 7049.248851 & 4.112416516 & 20,452 \\
3 & 7151.048877 & 7070.606275 & 7049.248909 & 45.01472868 & 19,612 \\
4 & 7361.05089 & 7112.256788 & 7049.275307 & 139,0818573 & 18,450 \\
5 & 7063.107951 & 7053.22295 & 7049.318392 & 5.982378418 & 19,094 \\
6 & 7049.802007 & 7049.418117 & 7049.248849 & 0.23813293 & 19,318 \\
7 & 7361.648467 & 7111.850254 & 7049.248177 & 139.6416717 & 20,102 \\
8 & 7206.603344 & 7081.369993 & 7049.26296 & 70.01358744 & 19,780 \\
9 & 7052.697369 & 7049.999679 & 7049.251224 & 1.512797685 & 19,962 \\
10 & 7105.192042 & 7060.643287 & 7049.261412 & 24.90483007 & 19,794 \\ \hline Average & 7049.318392 & 7049.262676 & 7049.248177 & 0.024517331 & 19,522 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistical results for G10 by ASIM.
# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 4 An Illustrative Example
### Effect Evaluation
#### 4.3.1 Merging Bottleneck Control Effect Analysis  
In order to further analyze the effect of the control strategy in improving the operation efficiency, the traffic flow state parameters (flow, density, speed) under uncontrolled state and cooperative control state are output, as shown in Figure 18.  
It can be seen that when there is no control, the sudden drop in traffic capacity in the two merging areas near 36 min occurs. At this time, the traffic density in the merging area rises rapidly, and the speed drops sharply, which becomes the merging bottleneck restricting the operation of expressway traffic flow. After the cooperative control is opened, the traffic flow of the main line and ramp into the region is limited under the variable speed limit and ramp merging control. The maximum value of the traffic flow in the two merging areas is smaller than that in the uncontrolled state. Although the traffic capacity decreases, the magnitude of the decrease is significantly reduced, and the change is more moderate. The traffic flow is maintained at a high level during the peak period, and the traffic flow operation state is significantly improved compared with the uncontrolled state.  
Figure 18: Flow, density, and speed curves under uncontrolled and cooperative control. (**a**) Flow curve; (**b**) Density curve; (**c**) Speed curve.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
###### Abstract  
Precise measurements of electrostatic fields in harsh environments are required in fields ranging from particle accelerators to industrial installations. Many techniques disrupt the field distribution due to the presence of conductors. We present a fully dielectric sensor for very harsh environmental conditions, that exploits the Pockels effect manifested by electro-optic crystals. This system is designed to allow DC measurements to be performed in the frequency domain. The paper discusses an analytical model and simulations of the system, validated with experimental results. The working principle of the measurement technique is explained in detail along the known limitations and possible solutions to further increase the performance.  
electro-optic systems; electro-optic crystals; electrostatic field measurements Article  
Electro-Optic Sensor for Measurement Electro-Optic Sensor for  
biasing electric field. The bias field sets the working point of the system, and can be used to compensate for errors caused by space charge, temperature drift, laser fluctuations and other similar phenomena.  
Additionally, modulation of the bias field makes it possible to measure a DC field in the frequency domain. This novel approach for compensating the space charge extends the applicability of measurements with EO crystals to frequencies below 20 Hz.  
This work is being developed primarily for physics experiments requiring DC or quasi-DC particle beams, e.g., the future Search for Hidden Particle (SHiP) at CERN [17]. Specifically, two pairs of sensors arranged symmetrically around the beam pipe would form a BPM for DC beams. Replacing beam-coupling antennas of traditional BPMs with EO crystals introduces several attractive advantages:  
* non-destructive coupling to the beam's DC field;
* precise and continuous field measurements;
* compatibility with Ultra-High Vacuum (UHV) and high levels of ionizing radiation;
* optical-fibre-based signal transmission.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Method** & **Strengths** & **Limitations** \\ \hline
[32] & Fusion Entropy & Detect high-rate DDoS attacks & - & Cannot detect low-rate DDoS attacks \\  & & & - & Rely on a static threshold \\
[33] & Machine-learning algorithm & Cooperative between control and data plane & - & Inefficient detection for high-rate network traffic \\  & & & - & Rely on a static threshold \\
[34] & Theory-based information & Detect high-rate DDoS attacks & - & Low detection rate for multi-victim attacks \\
[35] & Factorization machine & Classify low-rate DDoS attacks & - & Rely on a static threshold \\  & & & - & Cannot detect low-rate DDoS attacks \\
[36] & Safe-Guard Scheme & Detect high-rate DDoS attacks against the controller & - & Delay in detecting DDoS attacks \\  & & & - & Rely on a static threshold \\
[28] & Entropy & Early detect DDoS attacks & - & Low detection rate for multi-victim attacks \\  & & & - & Rely on a static threshold \\
[37] & ID & Detect DDoS attacks targeting a single victim & - & Low detection rate for multi-victim attacks \\  & & & - & Rely on a static threshold \\
[38] & FADM & Classifies traffic flow-Detect DDoS attacks & - & High false positive rate for multi-victim attacks \\  & & & - & Rely on a static threshold \\
[39] & EDDM & Prevent legitimate packets from being lost & - & Low detection rate for multi-victim attacks \\  & & & - & Rely on a static threshold \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of DDoS attack on SDN controller detection techniques. As shown in Table 1, all existing approaches rely on a static threshold. The fixed threshold will be ineffective when the attack traffic rate varies. In addition, the existing approaches can only detect either low-rate or high-rate DDoS attacks, but not both, unlike the current proposed approach, which relies on a dynamic threshold that adopted EWMA [40]. The usage of a dynamic threshold highly contributes to the detection of DDoS attacks regardless of DDoS attack type (low-rate or high-rate).
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## Materials and Methods
### Cell Survival after Permeabilization  
For cell survival experiments, 200 \(\upmu\)L of cells in suspension in growth medium at concentration 2 \(\times\) 10\({}^{6}\) cells/mL was pipetted in a 2 mm gap electroporation cuvette (VWR International, Radnor, PA, USA) and exposed to electric pulses. After pulse application, 50 \(\upmu\)L of fetal bovine serum (Sigma Aldrich, Darmstadt, Germany) was added to cell suspension. Cells were incubated for 5 min at 37 \({}^{\circ}\)C [61]; afterwards, 1 \(\times\) 10\({}^{4}\) C2C12 myoblasts or 2 \(\times\) 10\({}^{4}\) 1306 fibroblasts from cuvette were seeded in 100 uL of growth medium in a 96-well plate (TPP, Trasadingen, Switzerland). Cells were seeded in triplicates. The plate was then incubated for 24 h (37 \({}^{\circ}\)C, 10% CO\({}_{2}\) for C2C12 and 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) for 1306). Cell survival was determined with the MTS-based Cell Titer 96 AQqueous One Solution Cell Proliferation Assay (Promega, Madison, WI, USA). After incubation, 20 uL of MTS reagent were added to each well and cells were incubated for additional 2 h in the incubator (37 \({}^{\circ}\)C, 10% CO\({}_{2}\) for C2C12 and 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) for 1306). Absorption at 490 nm wavelength was measured with a Tecan Infinite M200 spectrophotometer (Tecan, Mannedorf, Switzerland). An average absorption obtained in the samples containing only growth medium was subtracted from the absorption measured in cell samples. To calculate the percentage of viable cells the absorption of each cell sample was divided by an average absorption of the control cell samples, where cells were treated as described but not exposed to electric pulses.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 3 Experiment and Analysis
### Experiment on Transmission Tower Object Detection
#### 3.2.2 Model Evaluation and Result Analysis  
The models were evaluated on Server 1 and Server 2 using the test dataset, with the results detailed in Table 5. The results indicate that the YOLOv8s achieved the highest accuracy on the test dataset, while the YOLOv5n-conv head demonstrated the fastest inference speed on the dataset. The results show that, on Server 1, the inference speed of YOLOv5n-conv head improved by 1.7 ms compared to the YOLOv5n. On Server 2, compared to the YOLOv5n, its speed improved by 3.3 ms. Despite a reduction of approximately 0.7 in GFLOPs, there was no significant decrease in [EMAIL_ADDRESS] and [EMAIL_ADDRESS]. In comparison to YOLOv8s and YOLOv8n, the [EMAIL_ADDRESS] for YOLOv5n-conv head only decreased by 0.003 and 0.006, respectively, while [EMAIL_ADDRESS] declined by 0.028 and 0.03. Moreover, when the batchsize was set to 1, there was a notable reduction in inference time. Therefore, on the current dataset, the YOLOv5n-conv head proposed in this study excels in both speed and accuracy, offering a viable option for practical applications in mobile transmission tower detection. YOLOv8s exhibits the highest accuracy and is suitable for the establishment of a template library.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 5 Conclusions  
This paper presents a 1-bit RTA with out-of-band RCS reduction, consisting of AFST and RTA layers. The AFST layer uses the first three resonant modes of a bent metallic strip with a centrally loaded resistor to achieve out-of-band scattering suppression and in-band transmission. Meanwhile, the RTA layer adopts a receiver-transmitter structure with an active receiving dipole and a passive orthogonal transmitting dipole to achieve 1-bit transmission phase reconfiguration. Subsequently, a 16 \(\times\) 16-element prototype was designed and measured, revealing in-band high-gain radiation and beam scanning capabilities, along with out-of-band wideband RCS reduction. For radiation, the measured gain was 20.1 dBi at 7.5 GHz, corresponding to an aperture efficiency of 12.7%. The gain loss of beam scans to \(\pm\)60\({}^{\circ}\) was about 5 dB in both principal planes. For scattering, the bandwidth of 10 dB RCS reduction was about 52.5% and 43.8%, respectively.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l} RTA & reconfigurable transmitarray antenna \\ RRA & reconfigurable refelctarray antenna \\ RCS & radar cross-section \\ AFST & absorptive frequency selective transmission \\ FSS & frequency selective surface \\ HPBW & half-power beamwidth \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & \(f_{0}\) **(GHz)** & **Gain ( dBi)** & **AE (\%)** & **Beam Scanning** & **RCS Reduction** \\ \hline
[16] & 12.5 & 17 & 14 &
\begin{tabular}{c} E: 50\({}^{\circ}\)/5.7 dB \\ H: 50\({}^{\circ}\)/5.8 dB \\ \end{tabular} & No \\ \hline
[17] & 9.8 & 22.7 & 14.8 &
\begin{tabular}{c} E: 60\({}^{\circ}\)/4.8 dB \\ H: 40\({}^{\circ}\)/3.1 dB \\ \end{tabular} & No \\ \hline
[19] & 13.5 & 21.4 & 20.2 &
\begin{tabular}{c} E: 50\({}^{\circ}\)/4.2 dB \\ H: 40\({}^{\circ}\)/3.4 dB \\ \end{tabular} & No \\ \hline
[21] & 12.8 & 18.4 & 14.7 & \
\begin{tabular}{c} E: 60\({}^{\circ}\)/3.6 dB \\ H: / \\ \end{tabular} & No \\ \hline This Work & 7.5 & 20.1 & 12.7 &
\begin{tabular}{c} E: 60\({}^{\circ}\)/5.1 dB \\ H: 60\({}^{\circ}\)/5.0 dB \\ \end{tabular} & Yes \\ \hline \end{tabular}
\end{table}
Table 2: Radiation performance comparison for 1-bit RTA.