esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### K-Fold Cross Validation
#### 4.8.2 Overfitting Analysis  
The metric values vary slightly between different folds of the cross-validation which is expected due to the nature of the method. The values of the average metrics and standard deviations calculated for each metric across the 10 folds are presented in Table 10 of the article as follows:  
**mAP_0.5:** average: 0.775360; standard deviation: 0.014710.  
**mAP_0.5:0.95:** average: 0.454210; standard deviation: 0.007980.  
**Precision:** average: 0.714740; standard deviation: 0.015290.  
**Recall:** average: 0.748560; standard deviation: 0.013340.  
**Accuracy:** average: 0.505190; standard deviation: 0.009970.  
From this analysis, the following specific comments are listed:  
* **Consistency of Metrics:** The average metrics for mAP_0.5, mAP_0.5:0.95, precision, recall, and accuracy are consistent across the folds, indicating a stable model performance with low standard deviations. This suggests good generalization without overfitting.
* **Comparison between Metrics:** mAP_0.5:0.95 is lower than mAP_0.5. As expected, balanced precision and recall indicate an effective balance between avoiding false positives and capturing true positives.
* **Overfitting Analysis:** A significant difference between the training and validation metrics indicates overfitting. The small variation between folds suggests that the model is not overfitting, showing a consistent performance across data subsets.
* **Conclusion:** The k-fold cross-validation analysis shows no clear evidence of overfitting. The YOLOv586-Hairiness model generalizes well to the validation data, maintaining a good balance between precision and recall. In summary, the results obtained in the k-fold cross-validation are encouraging with the approach showing good performance in the detection of loop fibers and protruding fibers.
# All-Optical Tuning of Fano Resonance for Quasi-BIC and Terahertz Sensing Applications
## 4 Conclusions  
The BIC or quasi-BIC state supports anomalous resonances in the optical or terahertz band with a theoretically infinite quality factor, so it has great potential in sensing, lasing, filters and nonlinear optics. The conventional method of regulating BIC by introducing symmetric breaking elements is static, which means that once the structure is formed, it only supports one kind of state. This undoubtedly limits the application of BIC or quasi-BIC  
Figure 8: Transmission spectra of the proposed structure with different filling and conductivity.  
in practice. This paper presents a novel metasurface consisting of an array rectangular hole structure combined with aluminum and photosensitive silicon. It can realize dynamic regulation by adjusting the pump light power. More importantly, it can achieve bidirectional continuous control from quasi-BIC to BIC-like states with high precision and high response. Then, the mechanism of the modulate process is analyzed in detail through the distribution of electric field and current. And through the optimization of the structure, a terahertz sensing structure with highly sensitive based on optical tuning is finally designed. The narrow-band quasi-BIC resonance is sensitive to medium thickness and refractive index. Due to the high slope of Fano lineshape, sensitivity can reach 9.41 GHz/RIU and 0.65 GHz/\(\,\mu\)m, respectively. And compared with pure metal structure, the sensitivity and dynamic range can be increased by 2.60 times and 2.63 times, respectively. Furthermore, this feasible and practical structure provides an ideal platform for highly sensitive sensing and nonlinear optical control.  
[PERSON]. ([PERSON]) conceived the idea and wrote the paper; [PERSON]. ([PERSON]) designed the models, [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] provided scientific support and conceptual advice. All authors discussed the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 517Z7808, 51922009, 52005457 and 62004179); Applied Basic Research Programs of Science and Technology Commission Foundation of Shanxi Province (201901D211240); Shanxi province key laboratory of quantum sensing and precision measurement (201905D121001); Youth Academic Leader Support Program of North University of China.  
The authors declare no conflict of interest.
## 4 Fault Current Limiting Devices
### Superconducting FCL
#### 4.2.4 Resistive Type FCL: Resistive-Flux-Coupling Type SFCL  
A resistive-flux-coupling type SFCL has been proposed in [38], and is shown in Figure 10b. The SFCL consists of a coupling transformer, an SC, and a semiconductor switch (\(S_{1}\)). Furthermore, an arrestor is employed to overcome the overvoltage in switching instances. In normal operation, \(S_{1}\) is closed and if the coupling coefficient is supposed to be one, then the FCL's impedance is almost zero. When a fault happens, \(S_{1}\) goes to the off state and the overvoltage is restricted by the arrestor. The SC enters into the fault current pass to limit the current level. It should be noted that placing a resistive impedance in the stator side can be much more effective than in the rotor side, due to the voltage sag compensation and the greater active power absorption. The resistive type FCL in the stator side limits both the stator and rotor currents effectively.  
Figure 9: (**a**) The superconducting fault current limiter–magnetic energy storage system (SFCL-MES) [33; 34]; and (**b**) the active superconducting fault current limiter [35].
## 3 Dynamic Flexible Processing of Sampled Signals
### Flexible Processing of Denoised Signal
\[\hat{y}_{i}^{\prime}=\frac{1}{\sum\limits_{n=1}^{N}k_{n}U_{n}}\left[k_{1}U_{1},k_{2}U_{2},\ldots,k_{n}U_{N}\right] \tag{20}\]  
After amplification, the signal information entropy is  
\[H\big{(}\hat{y}_{i}^{\prime}\big{)} = \sum\limits_{n=1}^{N}H(\bar{U}_{i}) \tag{21}\] \[= \sum\limits_{n=1}^{N}\sum\limits_{r=1}^{R}-\frac{k_{n}}{\sum \limits_{n=1}^{N}k_{n}u_{nr}}u_{nr}\ln\left(\frac{k_{n}u_{nr}}{\sum\limits_{n =1}^{N}k_{n}u_{nr}}\right)\] \[= \sum\limits_{n=1}^{N}\sum\limits_{r=1}^{R}-K_{n}u_{nr}\ln(K_{n}u _{nr})\]  
\[K_{n}=\frac{k_{n}}{\sum\limits_{n=1}^{N}k_{n}u_{nr}} \tag{22}\]  
It can be proved that \(H\big{(}\hat{y}_{i}^{\prime}\big{)}<H(y_{i})\) after compensation when \(K_{n}\leq 1\). Proof. : The difference between the spatial information entropy of \(y_{i}\) and \(\hat{y}^{\prime}\) is:  
\[H\big{(}\hat{y}_{i}^{\prime}\big{)}-H\big{(}\hat{y}_{i}^{\prime} \big{)} = \sum\limits_{n=1}^{N}\sum\limits_{r=1}^{R}K_{n}u_{nr}\ln(K_{n}u_ {nr})-u_{nr}\ln(u_{nr}) \tag{23}\] \[= \sum\limits_{n=1}^{N}\sum\limits_{r=1}^{R}u_{nr}[K_{n}\ln(K_{n}u _{nr})-\ln(u_{nr})]\] \[= C\sum\limits_{n=1}^{N}\sum\limits_{r=1}^{R}\left[K_{n}\ln(K_{n}) +(K_{n}-1)\ln(u_{nr})\right]\]  
For convenience of proof, we let \(K_{n}=x\) and \(a=\ln(u_{nr})\). So, \(K_{n}\ln(K_{n})+(K_{n}-1)\ln(u_{nr})\) can be written as \(f(x)=x\ln(x)+a(x-1)\). The derivative of \(f(x)\) is \(f^{\prime}(x)=\ln x+a+1\). From the \(f^{\prime}(x)\), it is possible to calculate the monotonic interval of \(f(x)\). \(f(x)\) monotonically decreases at \((0,e^{-(a+1)})\), and monotonically increases at \([e^{-(a+1)},+\infty)\)  
Because \(a=\ln(u_{nr})\), \(u_{nr}\leq\frac{1}{e}\), so \(e^{-(a+1)}\geq 1\). Therefore, \(H\big{(}\hat{y}_{i}^{\prime}\big{)}-H\big{(}\hat{y}_{i}^{\prime}\big{)}\geq 0\) when \(K_{n}\leq 1\)  
\(k_{n}\) is determined by three main factors of the signal, which are the peak, valley, and power. The change in signal power \(K_{1}\) responds to the increase or decrease in signal amplitude in total terms. The peak and valley changes in the signal (denoted as \(K_{2}\), \(K_{3}\)) are the most intuitive. Based on these factors, the specific formula for determining the final \(k_{n}\) is as follows:  
\[k_{n}=e^{\frac{\left|\ln(\lambda_{1})+\cos(\lambda_{1})\right|}{r}} \tag{24}\]  
\[\lambda_{1}=\alpha_{1}K_{1}+\alpha_{2}K_{2}+\alpha_{3}K_{3} \tag{25}\]  
Considering the spatial information entropy definition, we determine \(k_{n}\) as an exponential function form. As in this paper we deal with IQ signals, the exponential part of \(k_{n}\) is determined
## 3 Results
### Cell Viability after Addition of Cryopreservation Solution  
To examine the effects of different cryopreservation solutions on NP cells during mass cell batch production, we tested extended exposure of NP cells to three types of cryopreservation solution (CB1, SCB, and SCBD-Free). At room temperature, cells exposed to SCB and SCBD-Free showed a rapid decline in cell viability, with \(>\)50% cell loss within 1 h (Figure 4A). In the same process on ice, a high cell viability of \(\geq\)80% was obtained in all solutions for up to 2 h, but the number of viable cells decreased with longer incubations in SCB and SCBD-Free (Figure 4C). Samples left at room temperature for \(\geq\)1 h followed by cryopreservation showed complete loss of viability (Figure 4B). Samples kept on ice before cryopreservation had better viability, although cell viability was 67% in SCB and 57.8% in SCBD-Free conditions after 2 h and decreased further with time (Figure 4D). Cells exposed to CB1 had a survival rate of \(\geq\)90% even after being left for 5 h on ice, although the cell viability decreased with time at room temperature.  
Figure 2: (**A**) The rate of spheroid colonies that formed and (**B**) fold increase of cells on different EZSPHERE® dish types derived from 20,000 human nucleus pulposus cells (\(n=4\)) following 14 days of culture. (**C**) The fold-increase in cell numbers following culture of nucleus pulposus cells (\(n=1\)) on EZSPHERE® dish type 4000-903, without (-) or with 100 ng/mL epidermal growth factor (EGF), 10 ng/mL fibroblast growth factor (FGF), 100 ng/mL platelet derived growth factor (PDGF), and a combination of all three growth factors (Mix). Data is presented as mean values with error bars presenting standard deviation. \({}^{*}\)\(p<0.05\),Figure 3: Relative gene expression levels of (**A**) COL2A1, (**B**) COL1A2, (**C**) ACAN, and (**D**) ANGPT1 for nucleus pulposus cell spheroid colonies cultured on different EZSPHERE(r) dish types. (\(n=4\)) Relative gene expression of (**E**) COL2A1, (**F**) COL1A2, (**G**) ACAN, and (**H**) ANGPT1 of nucleus pulposus cell cultured on EZSPHERE® dish 4000-903 (\(n=1\)) without (-) or with 100 ng/mL epidermal growth factor (EGF), 10 ng/mL fibroblast growth factor (FGF), 100 ng/mL platelet derived growth factor (PDGF), or a combination of all three growth factors (Mix). Data is calculated relative to monolayer cultured nucleus pulposus cells. Values derived using \(2^{-\Delta\text{AC}}\)t method and presented as mean values with error bars presenting standard deviation.
# Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic
## 2 Related Work
### Signature-Based Detection Techniques  
This type of detection techniques contains a repository of attack signatures and compares the network traffic against this repository of signatures. When the match is found, a detection alert is raised. This approach can detect known attacks for which signaturesare stored in the repository, but it cannot detect zero day or new attacks, even if it is not effective against existing attack mutations [12].  
This research, Ref. [13] proposed the use of an artificial immune system (AIS) to overcome the shortcomings of signature-based approaches. This technique created detectors based on attack signature utilizing the immune cell paradigm, which can determine if a packet is legitimate or malicious based on its classification as a self or non-self element. The system has the ability to adopt new patterns as a result of constant system monitoring. However, in a resource constrained IoT environment, the feasibility of such a detection technique is questionable.  
The researchers in [14] solved the resource constraint problem in signature-based IDS by using a separate Linux machine with an adapted version of the Suricata-based signature IDS. On the other hand, the researcher gave no indication of how to keep attack signature up to date. The researchers in [15] expanded the research in [14] by presenting changes to signature matching techniques. Another study by [16] addressed IoT processing power limits by combining auxiliary shift values with a multiple pattern detection technique to reduce the number of matching operations necessary between attack signatures and network traffic packets. The system used signature repositories of the open source IDS (Snort) and the open source antivirus (ClamAV).  
In this research [17] a signature-based IDS proposed to detect DDoS attacks in IoT networks. In a hybrid deployment, it consists of two units: (i) IDS detectors and (ii) IDS routers. The IDS router is a firewall and detection device that is hosted in the border gateway. The sensors monitor the internal traffic was employed by the IDS detectors. The results presented that the scheme identifies version number change and hello flooding attacks.
## 2 Materials and Methods
### Data Collection and Analysis  
#### 2.2.1 Google Earth Engine  
The processing and extraction of satellite data were conducted using Google Earth Engine (GEE), which is a vast repository of satellite imagery and geospatial datasets encompassing several petabytes, equipped with the capability to perform analysis on a planetary scale. GEE seamlessly integrates with a high-performance computing service and is accessible through an internet-based application programming interface (API) written in JavaScript, allowing for efficient data analysis and swift visualization of results [46]. This cloud-based spatial analysis platform is freely available to all users, offering a vast array of datasets and algorithms within a unified online environment, significantly reducing data processing times and enhancing computational efficiency. Thanks to the remarkable features of this platform, simultaneous processing of the Landsat-7, Landsat-8 and Sentinel-2 image collections was conducted.
## 3 Design of Control Strategy
### The Selection of Control Wheel  
Using the same side two-wheel control, adopting the positive and negative yaw rate deviation and the front wheel steering to determine whether to control the left wheel or the right wheel. Through comprehensive analysis, when the Equation (12) is satisfied, the left wheel is controlled to satisfy the Equation (13) when controlling the right wheel.  
\[\Delta\omega=\omega-\omega_{NO}<0 \tag{12}\]  
\[\Delta\omega=\omega-\omega_{NO}>0 \tag{13}\]  
To prevent the brake command from switching back and forth between the wheels on both sides, the following explains the logic when the left wheel is under braking control. When the left wheel is under brake control, \(\Delta\omega<0\), switching from left wheel braking control to right wheel braking control is subject to Equation (14) and Equation (15). The switching logic of the left and right sides wheel braking control is shown in Figure 6.  
\[\Delta\omega>D \tag{14}\]  
\[\Delta\omega/|\omega_{NO}|>P \tag{15}\]  
Among them, \(D\) and \(P\) are the threshold value set. \(D\) and \(P\) represent the absolute error and the relative error of the yaw rate respectively, the two together constitute the switching criterion for the control of left and right-side wheel. When the values of \(D\) and \(P\) are small, the control accuracy is high, and the overshoot of yaw rate is small, but the wheel control on both sides is switched frequently, and the yaw rate jitter is obvious. When the values of \(D\) and \(P\) are large, the control accuracy is low and the overshoot of yaw rate is large, but the wheel control on both sides is not switched frequently, and the yaw rate hardly shakes. In practical applications, the balance between control accuracy and jitter should be weighed and the switching threshold should be set reasonably.
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
### Contribution  
In the 400 Gbps high-speed optical network, nonlinear effects such as four-wave mixing and cross-phase modulation will become one of the main factors limiting the system's transmission performance. A solution based on a novel modulation format, Apol-CRZ-FSK, to implement a 4 \(\times\) 100 Gbps optical communication system is proposed in this paper. This modulation format is based on a dual-light-source scheme, combining the advantages of FSK and Apol modulation; by adding specific chirp amounts to the rising and falling edges of the pulses through a phase modulator, the chirp sign is opposite to the chirp generated by the self-phase modulation effect (SPM) on the optical pulses, further enhancing the ability of the optical signal to resist nonlinear effects in the optical fiber. Compared with traditional 4 \(\times\) 100 Gbps CRZ-FSK and DQPSK systems, it has better transmission performance and higher nonlinearity resistance. The solution for the 4 \(\times\) 100 Gbps Apol-CRZ-FSK modulation signals with high nonlinearity resistance does not require the replacement of optical fiber lines during the upgrading and renovation of traditional networks. Only the optical transmitter module needs to be replaced, and the receiving side does not require complex coherent demodulation. Direct detection can be used to achieve cost reduction and green energy conservation. In addition, in response to the transmission requirements and the signal perception and recognition requirements in future computing optical networks, this study identifies three different modulation formats through the Inception-ResNet-v2 neural network model, which can provide effective solutions for high-speed computing optical networks in the 6G era.
## 3 General Configuration and Assumptions  
Although it is assumed that multiple control loops may be coordinated and scheduled using the technique to be described, for ease of exposition only a single control loop is considered (as depicted in Figure 1). In order to incorporate the possibility of packet re-transmissions or other interference-aware MAC strategies during a transmission round, we also make the simplifying assumption that the sampling and actuation tasks (on the sensor and actuator nodes respectively) take place approximately synchronously, within the level of the global clock accuracy as shown in Figure 2. These synchronized sampling/actuation (SA) events repeat every \(T\) time units, where \(T\) is the loop sampling time. In between these events, data must first be transferred between the sensor and controller node (data transfer DT1) to enable the control calculation (CC) by the latter. Following the control calculation, data must then be transferred between the controller and actuator node (data transfer DT2) to enable the plant control signal to be updated at the next SA event. Such a structure seems particularly suited to many types of industrial wireless networks, including networks using basic or enhanced TDMA variants [8, 10, 11, 21] and simple master-slave polling [9]. It is also suitable for non-specialized architectures such as WiFi employing sNTP (simple Network Time Protocol) for node clock synchronization.  
Although the assumption of synchronized sampling and actuation introduces an overall extra delay of one sample period \(T\) into the control system, it is an effective and commonly used means to limit jitter [15]. In practice, the extra delay is easily dealt with (if the sample time is appropriately chosen) alongside the phase-shift of the Zero-Order-Hold (DAC) by adding an extra time delay of \(\theta=1.5\)\(T\) into the delay of the open-loop plant before controller design [15]. Let the \(k^{th}\) packet from the sensor node to the controller node contain the sampled-data representation of the process measured variable \(y(k)\) obtained from the ADC. Let the \(k^{th}\) packet from the controller node to the actuator node contain the sampled data control signal \(u(k)\) to be applied to the process manipulated variable via the DAC. Let the worst-case duration of a burst be denoted by the parameter \(\beta\in\aleph^{+}\), representing the number of sample periods (of length \(T\)) that a link may be unavailable. The length of \(\beta T\) may be determined to given confidence limits using statistical data. It is assumed that the maximum burst duration time \(\beta T\) does not  
Figure 2: Loop Timing with Approximately Synchronized Sampling/Actuation.  
allow the clocks to drift beyond their stated tolerance. The final assumption made is that only limited re-transmissions of a process packet are attempted, such that packets are \"dropped\" by the network if they cannot be delivered before their (firm) deadline.
## 3 Results  
The proposed method of semi-blind watermarking using deep convolutional generative adversarial networks (DCGANs) showed very good performance at different JPEG quality factors with respect to imperceptibility and fidelity. It scored above 49 dB PSNR, SSIM around 0.95, and BER below 0.008, indicating that the watermark was very resistant to JPEG compression and achieved high fidelity in watermark restoration. Security was ensured with a secret key for unauthorized access or duplication. The proposed method maintained its performance under various quality factor tests (10 to 90), the number of epochs for training, and achieved convergence within 250 epochs. The method outperformed recent deep-learning-based techniques, especially at very low QFs (like 10) with a BER of 0.008. Furthermore, the DSC decreased the number of parameters of the model by 50 percent (from 1.27 MB to 0.69 MB) and was shown to be sufficiently robust with PSNR: \(\sim\)48-49 dB, BER: 0.01-0.014, while a slightly reduced SSIM was noticed (0.95 to \(\sim\)0.90). These results make it clear that it is a good technique in terms of robustness, imperceptibility, and security; it is promising for real-world applications aimed at digital image safeguarding.