esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Background
#### 3.1.2 K-Nearest Neighbors (kNNs)  
The KNN is an instance-based classification algorithm. It classifies data points based on the majority class of their K-Nearest Neighbors. To classify a data point, it first identifies the closest K neighbors from the training data using a distance metric such as the Euclidean distance [52]. Later, the class is determined based on the majority class using a classification rule such as a majority rule or a weighted rule. Due to its ease of implementation and flexibility for different types, it is a popular classifier.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Distinguishing Attack Traffic from Benign Traffic
#### 5.1.1 Similarity vs. Time Window
For network layer DDoS, attack tools specify the length of the data packet, the number of data packets per second, and the port number of the target host. These parameters will make the distribution of flow features consistent. The similarity will increase over time. Figure 4: Distance of MFPK method between benign vs. attack traffic. The DRDoS attacker sends forged requests to the server, and the victim receives responses from the server. As time goes on, the response packets number gradually increases, so the distance value decreases and the similarity become greater. However, from 120 s to 180 s, the background traffic can disturb the distance value in the experimental platform, so the distance value increases. In general, the DRDoS distance value is lower than that of benign traffic, and the similarity is higher than that of benign traffic. In summary, we choose the time window 90 s. This results in an apparent deviation of the similarity value between attack traffic and benign traffic in the shortest time.
# A Phase Correction Model for Fourier Transform Spectroscopy
## 4 All-Pass Filter and PCM-APF Model  
### Digital All-Pass Filter  
Filters with amplitude responses that remain constant across the entire frequency band are called all-pass filters. An all-pass filter is an infinite impulse response (IIR) filter, and its system transfer function can be written in fractional form as follows:  
\[H(z)=\frac{a_{N}+a_{N-1}z^{-1}+a_{N-2}z^{-2}+\cdots+1z^{-N}}{1+a_{1}z^{-1}+a_{ 2}z^{-2}+\cdots+a_{N}z^{-N}}=\frac{z^{-N}Q(z^{-1})}{Q(z)} \tag{5}\]  
where \(Q(z)=1+a_{1}z^{-1}+a_{2}z^{-2}+\cdots+a_{N}z^{-N}\). We can further determine the zeros and poles of the all-pass filter:  
Figure 3: (**a**) ZPD offset calculation process in proposed PCM-APF model. (**b**) The line shape changes in Hilbert difference method and peak detection.  
\[H(z)=\frac{z^{-N}Q(z^{-1})}{Q(z)}=\prod_{i=1}^{N}\frac{z^{-1}-p_{i}^{*}}{1-p_{i}z^ {-1}}=\prod_{i=1}^{N}\frac{-p_{i}^{*}\Big{[}z-\Big{(}\frac{1}{p_{i}}\Big{)}^{*} \Big{]}}{z-p_{i}} \tag{6}\]  
Obviously, \(p_{i}\) and \((1/p_{i})^{*}\) are a set of zeros and poles of the filter, which are conjugated to each other.  
Based on previous research, we can infer that the phase response of an all-pass filter is  
\[\theta(\omega)=-N\omega-2\arg Q\Big{(}e^{i\omega}\Big{)} \tag{7}\]  
where the arg function represents the phase angle calculation using the arctan method, similar to Equation (2) [12; 13].
# A Study of High-Efficiency Laser Headlight Design Using Gradient-Index Lens and Liquid Lens
## 1 Introduction
### Background  
Automotive illumination technology improved fast over the last decade and a half. As we have seen, incandescent halogen bulbs give way to high intensity discharge (HID) and light emitting diode (LED) and its related technologies. Even Audi and BMW have started to ship vehicles that use a laser-based illumination system [8; 9] in recent days.  
Currently, BMW has the most mature technology for the mass production of laser light modules (Figure 1), wherein the laser source moves toward the mirror, which reflects the light on the freeform reflector lamp cup. This then diffuses and projects the laser beam over the area to be illuminated [10]. Audi has its design performed by ultra-precise aspherical reflection surface [11].  
One of the major problems is optical design and simulation for newer lighting sources due to their optical characteristics. Furthermore, new developments in adaptive headlamp technologies and laser diode as light source become more complicated from the point view of optical system design. For example, the Audi Pixelated Laser Headlights Light employed a laser lighting system and a micro mirror device (DMD), which is very close to DLP projectors from the point view of optical design. This system can vary its lighting pattern directionally, then have its image projected on the road. In this proposed research, we have incorporated an adaptive lighting capability into our simulation of automotive Illumination with laser source [12].
## 4 Results
### Validation of the S-GWR Models
#### 4.2.2 Validation of the Accuracy  
In order to validate the accuracy and effectiveness of the proposed S-GWR model, we compare the prediction results with actual units free of or containing landslides. Table 4 shows the prediction result of the proposed S-GWR model. Among the 291 actual landslide points, 36 points are classified as stable points with a missing rate of 12.37%. ROC curve and success rate curve are used for further evaluation. Figure 6 shows that the AUC value of ROC curve is 0.859, and the AUC value of success rate curve is 0.850.  
Figure 5: Spatial distribution of the principal component coefficient function estimate. (**a**) Regression coefficient of geological factor (PC1); (**b**) Regression coefficient of slope shape factor (PC2); (**c**) Regression coefficient of hydrographic factor (PC3); (**d**) Regression coefficient of aspect factor (PC4)).
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 2 WEC Device  
### WaveBot Device Specifications  
The WaveBot is a point absorber type of WEC device that operates as a heaving buoy using a PTO mechanism that translates vertical oscillations into rotational energy. This design enables effective energy extraction by directly converting wave motion into electrical power. WaveBot employs a rack-and-pinion mechanism with a high gear ratio, enhancing the rotational velocity transferred to the PTO and maximizing power generation. Essential device specifications are listed in Table 1, which shows the WaveBot's primary components and energy conversion stages. An illustration of the device is shown in Figure 1.  
Figure 1: CAD diagram of the WaveBot WEC device [18].
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 3 Results
### Denoising Performance under Different Noise Levels  
The denoising performances under different noise levels were evaluated in Figure 6. The AMF is inferior to other compared methods. The TV-L\({}_{0}\) outperforms the PANO-L\({}_{1}\) although PANO provides adaptive sparse representation. This observation implies that the L\({}_{0}\) norm is more robust than the L\({}_{1}\) norm to the outliers introduced by the impulse noise. The proposed method not only makes use of adaptive sparsity in the representation but also incorporates the robust L\({}_{0}\) norm to better remove outliers in the data fidelity term. Therefore, improved denoising performance by the proposed method are consistently observed in all experiments with different noise levels.  
Figure 4: Denoised boat images at the noise level of 0.5. (**a**) is the noise-free image, (**b**) is the noisy image, (**c**)–(**f**) are denoised images using AMF, PANO-L\({}_{1}\), TV-L\({}_{0}\), and the proposed FDLCP-L\({}_{0}\), respectively.  
Figure 5: Denoised Lena images at the noise level of 0.5. (**a**) is the noise-free image, (**b**) is the noisy image, (**c**)–(**f**) are denoised images using AMF, PANO-L\({}_{1}\), TV-L\({}_{0}\), and the proposed FDLCP-L\({}_{0}\), respectively.
# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 5 Conclusions  
In this study, we propose a bi-level routing attention tailhook recognition model based on YOLOv5. By building upon YOLOv5s, this method integrates the advantages of attention mechanisms and deformable convolutions. It significantly improves the detection capability for small objects, especially tailhooks, through the meticulous optimization of the loss function. This method improves accuracy and reduces the rates of false positives and missed detections, thereby improving overall detection efficiency. Experiments carried out on the CATHR-DET and the VOC dataset validate the effectiveness of our method and demonstrate its potential for practical applications. In future research, we will continue to improve the existing model and apply it to the newer YOLO version for testing and validation. Additionally, carrier-based aircraft typically rely on the arresting wire system on the deck and the tailhook to rapidly decelerate during landing. The state of the tailhook is a key factor in ensuring the safety and success of the landing. Therefore, we also plan to use the carrier-based aircraft tailhook recognition results as one of the inputs for the landing signal officer (LSO) landing decision-making model, enabling human-machine collaborative guidance during landing.  
Conceptualization, K.W.; methodology, A.L., P.L., J.Y. and K.W.; software, J.Y.; validation, J.Y.; investigation, Z.L.; writing--original draft preparation, A.L.; writing--review and editing, P.L., Z.L. and K.W.; supervision, K.W.; project administration, K.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the China Postdoctoral Science Foundation (No. 2020M682348) and the Innovation Foundation of Ocean Defense Technology (JJ-2022-709-01).  
The data will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 6 Results and Discussion: Forecasting Performance
Rnk-RF also provides an accurate result in most of the predictive situations, which can indicate that the use of RF as a predictor in each attribute evaluator is an interesting choice. MOES-LR closely follows the performance in this particular situation under analysis. But, as stated in the results, MOES-RF seems to be the better dataset for utilization. According to Table 5, with all the four different predictive techniques, the best accuracy is achieved with this subset. When using this FS combination, the best predictive algorithm is RF, as depicted in Figure 3, where it can be seen that RF offers the best average accuracy because of the low standard deviation in the RMSE in each step--which results in performance stability. SVM is able to achieve a better performance in short prediction, as well as LR, but they soon increase errors in future steps--which can be inferred by their bigger standard deviation. Figure 3: Comparative evolution of GBV complaints’ RMSE evolution obtained with MOES-RF feature selection technique for different predictive algorithms. Figure 2: Comparative evolution of GBV complaints’ RMSE evolution obtained via an RF predictive algorithm with different FS techniques. The better forecasting results with RF can be also inferred by averaging the averaged RMSE (\(\overline{\overline{\text{RMSE}}}\)), expressed by \(\overline{\overline{\text{RMSE}}}\), and then by making a comparison between prediction methods. RF then offers an \(\overline{\overline{\overline{\text{RMSE}}}}=0.2046\) u/10,000 pop, followed by GP with \(\overline{\overline{\overline{\text{RMSE}}}}=0.2680\) u/10,000 pop. SVM results in variable performance (as shown in Figure 3), averaging 0.2963 u/10,000 pop. LR cannot follow the accuracy of the other algorithms even closely. We need to bear in mind that, for a population of 47,329,981 inhabitants, the achieved \(\overline{\overline{\overline{\text{RMSE}}}}\) for RF is equal to a RMSE of 968.37 complaints for all the country (per month). Taking into account that the average of complaints in 2019 per month in Spain was 14,014 petitions, the error is around 6% and good enough for our purposes. Taking into account these results, it is possible to make a practical check of the prediction evolution in the months from October 2019 to March 2020 using MOES-RF with RF as a forecasting technique, before comparing the results with the real evolution. As can be observed, the prediction is accurate enough to identify trends, while logically being more separated from the real curve as we progressively expand the predictive horizon (Figure 4). Following the same procedure with the selected Spanish provinces of different populations (Madrid, Alicante, and Segovia), we can confirm that similar results can be found. Table 6 summarizes the \(\overline{\overline{\text{RMSE}}}\) of the 6-step predictions carried out with the four predictive techniques and with each of the eight data subsets. Figure 4: Prediction for the months October 2019 to March 2020 and real data comparison. Feature selection: MOES-RF. Forecasting technique: RF.
## 2 Regulation of CTPS Activity by GTP
### Structure-Activity Studies  
[PERSON] et al. [72] examined the ability of a variety of GTP analogues to act as allosteric activators of _Ec_CTPS. The structural requirements for activation proved to be quite stringent, with only a few analogues of GTP capable of activating Gln-dependent CTP formation: GTP \(\approx\) 6-thio-GTP \(>\) inosine-5\({}^{\prime}\)-triphosphate \(\approx\) guanosine-5\({}^{\prime}\)-tetraphosphate \(>\) 0\({}^{6}\)-methyl-GTP \(>\) 2\({}^{\prime}\)-deoxy-GTP. Overall, the 5\({}^{\prime}\)-triphosphate, 2\({}^{\prime}\)-OH, and 3\({}^{\prime}\)-OH groups were required for full activation, while the 2-NH\({}_{2}\) group was required for binding recognition, and substituents at the 6-position of the purine ring played an important role in activation. On the other hand, the structural requirements for inhibition appeared to be quite lax, with all GTP analogues able to weakly inhibit the utilization of exogenous NH\({}_{3}\) as a substrate by the enzyme. Nucleotide and nucleoside analogues of GTP and guanosine, respectively, inhibited both NH\({}_{3}\)- and Gln-dependent CTP formation to a similar extent. The inhibition appeared to be due solely to the purine base and, with the exceptions of inosine, ITP, and adenosine, was insensitive to the identity of the purine. 8-Oxoguanosine (IC\({}_{50}=80\)\(\upmu\)M) was initially identified as a good inhibitor, binding 4-fold tighter than guanosine [72]. Subsequent studies supported the notion that only the purine heterocycle was required for inhibition, with xanthines and uric acids also exhibiting inhibition [80]. For example, 1,3,7-trimethyluric acid was identified as a particularly good inhibitor of _Ec_CTPS-catalyzed NH\({}_{3}\)- and Gln-dependent CTP synthesis (IC\({}_{50}=70\)\(\upmu\)M). Indeed, when this inhibition strategy was applied to CTPS from _T. brucei_, uric acid (IC\({}_{50}=100\)\(\upmu\)M) was shown to be a reasonably effective inhibitor of that enzyme [14]. Overall, these observations suggested that the bound purine base was sufficient to inhibit the use of exogenous NH\({}_{3}\) and weakly impede GTP binding and activation.  
Site-Directed Mutagenesis and Kinetics Studies: Mapping Regions of CTPS Contributing to GTP-Dependent Effects  
Kinetics studies have provided invaluable insights into the role of GTP as an allosteric activator and have implicated several specific residues and regions of CTPS that participate in GTP-dependent activation of the enzyme. For the following discussion, the nomenclature used for structural elements (i.e., loops and lids) is that introduced by [PERSON] and co-workers for _Ec_CTPS [32].
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### GEO-LEO Satellite Image Fusion Algorithm
#### 2.3.2 Observed Object Matching  
Only if the study region is near the equator can the optical path difference caused by the different observation zenith angles and azimuth angles of the VISSR and AMSU-A be ignored. The remaining regions require the intercalibration of geostationary and polar-orbiting satellite sensors [19], as expressed in (6), to ensure the consistency of the observation objects:  
\[\left|\frac{\cos\theta_{LEO}}{\cos\theta_{GEO}}-1\right|<\alpha \tag{6}\]  
where \(\theta_{LEO}\) is the zenith angle of a polar-orbiting satellite, \(\theta_{GEO}\) represents the zenith angle of a geostationary satellite, and \(\alpha\) is an empirically determined threshold. If the difference between the zenith angles of the two satellites is less than the empirical threshold \(\alpha=0.08\), the objects observed by the two sensors onboard can be considered the same. Thus, when the difference between the zenith angles of the VISSR and AMSU-A satisfies (6), a matching sample is selected.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Control Channel Saturation Problem  
In CR-WSNs, one dedicated channel (or one dedicated control time duration) is necessary to communicate with other sensors. A problem occurs in a dense network environment when the network load increases. The control channel becomes the bottleneck preventing the data channels from being utilized efficiently. This problem is also called the control channel bottleneck problem [58].
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 3 Design
### Membership Cancellation  
Membership to a group can be cancelled either at the endpoint owner level or at the endpoint device level. In the former case, the corresponding _membership_ VC is _revoked_, and all IoT endpoints of the affected provider are removed from the group. In the latter case, the corresponding DID document is deleted from the IoT endpoint, and the affected endpoint is removed from the group (since it cannot generate any more valid singed responses).  
Membership VC Revocation  
As discussed in Section 3.1, a _membership_ VC has an expiration time. Therefore, a trivial approach for removing an endpoint owner from a group is to not update the corresponding _membership_ VC. Of course, on the other hand, this creates a security-performance tradeoff.  
An alternative solution is to use the revocation scheme described in [19] for verifying the status of a _membership_ VC. Based on this scheme, the group administrator maintains a revocation list that concerns all non-expired VCs it has issued. This list is a bitstring, and each VC is associated with a position in the list. Revoking a VC means setting the bit corresponding to the VC to 1. Furthermore, each issued VC includes a field named \"revocationListIndex\" that specifies the position of the VC in the revocation list. Therefore, given a revocation list and the revocationListIndex of a non-expired VC, a client can learn the status of that VC.  
This revocation scheme implies that clients can access the revocation list. This problem is solved in [19] by requiring from issuers (i.e., the group administrators in our system) to \"publish\" the revocation list under a URL and include this URL in the VCs. In our solution, we follow an alternative approach: since the size of a revocation list will be small, we include it in a _TXT_ DNS record of the group FQDN with the name \"RevocationList\". Therefore, clients can easily retrieve it with the DNS resolutions that take place before sending a CoAP message (described in Section 3.4).
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.4 Energy Consumption Evaluation  
The experimental results are summarized in Table 3, showcasing the worst, best, mean, and standard deviation of the average energy consumption for all algorithms. The task involves drones flying from their starting location to the destination while avoiding collision with terrain obstacles. These results represent the average of 30 independent runs, with the best value (minimum energy consumption) highlighted in bold. Observing Table 3, it is evident that IPSO outperforms other competitors across various metrics (best, mean, average, SD), demonstrating superior results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Drone** & **Algorithm** & **Best** & **Worst** & **Mean** & **STD** \\ \hline \multirow{4}{*}{**UAV1**} & **PSO** & 2017.13 & 2026.48 & 2017.82 & 1.80282 \\ \cline{2-6}  & **IPSO** & **2014.5** & **2018.32** & **2014.81** & **1.0032** \\ \cline{2-6}  & **CIPSO** & 2015.29 & 2020.77 & 2015.71 & 1.24308 \\ \cline{2-6}  & **ABC** & 2016.41 & 2056.93 & 2032.06 & 13.1891 \\ \cline{2-6}  & **GA** & **2045.672** & 2288.015 & **2109.42** & 69.81839 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The worst, best, average, and standard deviation of the four heuristic approaches.  
Figure 5: Comparison of the IoD path optimality and convergence curves.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 4 Methodology
### Ensemble Techniques
#### 4.1.2 Extra Trees  
It is an ensemble technique where different sub-samples of data are fit using a randomized decision tree and uses an averaging technique to calculate accuracy and avoid over-learning. In Extra Trees, the trees are built based on a random subset of features without replacement, and a split also is made based on random subsets of the feature at every node. These are the two points that make it different from the random forest that builds random trees with the replacement of features and splits based on the optimum (best) split rather than random split.
## 3 Temperature Prediction Model for Subgrade  
### Thermal Interactions of Soil Temperature, Water Content, and Frost Heave  
In seasonally frozen soils, temperature dynamics are closely connected to the interactions between water content and frost heave. Temperature directly drives phase changes in soil moisture, causing cycles of freezing and thawing that in turn impact both water content and frost heave. High water content significantly influences soil thermal behavior due to water's high specific heat capacity, which slows temperature decreases under comparable heat loss conditions, delaying the onset of frost heave. Conversely, increased frost heave often indicates substantial ice accumulation, suggesting that the affected soil is both colder and moisture-rich.  
These variables--temperature, water content, and frost heave--are interdependent, with their combined effects driving shifts in soil temperature through latent heat exchange during freeze-thaw cycles. Therefore, an effective prediction model must integrate these features to accurately capture the nonlinear thermal response of subgrade soils to environmental fluctuations.  
Figure 4: Subgrade monitoring data time–history curves of the right shoulder of the section: (**a**) temperatures at different subgrade depths; (**b**) temperature, water content, and frost heave in the partial time period.