esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## Appendix B Membership Functions and Fuzzy Rules  
In Section 3, the membership functions designed using MATLAB's fuzzy logic toolbox are shown in Figure 11. Figure 11 represents a membership function for wind speed (WS). In the proposed control algorithm, there were three membership functions (Lspd, Mspd, and Hsp) because the non-linear system was represented by three sub-systems for the wind speeds lower than the rated wind speed (WS \(<\) 5.5 m/s), the rated wind speed (WS \(=\) 5.5 m/s), and wind speeds higher than the rated wind speed (WS \(>\) 5.5 m/s). As can be seen from Table 1, the interpolation range of the input wind speed of the membership function was selected as a range between the cut-in speed (3 m/s) and the cut-out speed (9 m/s), which are the operating regions of the target wind turbine. Figure 11 represents the membership function for wind speed derivative (WSD). The values (LSD, MSD, andHSD) of this membership function were selected as trial errors through simulation based on the change (about 0-12%) in turbulence intensity. Since the turbulence strength that could be implemented in the wind tunnel experiment was around 10%, the interpolation range of the input wind speed rate was selected within the corresponding range of about 12 m/s\({}^{2}\). The fuzzy rules for the three sub-systems for each wind speed region and the fuzzy rules for the wind speed derivative are shown in Table 14.
## 5 Conclusions  
In this work, we put forward and demonstrated a bi-direction free-space VLC system supporting multiple moveable Rxs using a LDOF. The proposed LDOF had a silica glass core and acrylate polymer cladding with diameters of 170 \(\upmu\)m and 230 \(\upmu\)m, respectively. The NA of the LDOF was about 0.46. The uniformity of the extracted light around the circumference was adjusted by controlling the number of scattering sights in the fiber core. These nanostructured scattering centers ranged in size from 50 to 500 nm. The LDOF can provide a field of view of 360\({}^{\circ}\) around the fiber circumference and 120\({}^{\circ}\) along the LDOF. In the proposed bi-directional system, the DL signal was launched from a CO away to the LDOF on the client side via a free-space transmission. When the DL signal was at a wavelength of 633 nm and was launched to the LDOF, which acted as an optical antenna to re-transmit the DL signal to different moveable Rxs. The optical signal emitted via the LDOF was safe for human eyes. The UL signal had a wavelength of 520 nm and was sent via the LDOF towards the CO. In a proof-of-concept demonstration, the LDOF was 100 cm long, and the free-space VLC transmission between the CO and the LDOF was 100 cm. In principle, the system can support a large number of Rxs simultaneously as long as there is enough space along the LDOF circumference. Regarding the green UL signal, from a data rate of 100 Mbit/s to 600 Mbit/s, it was error-free. BER started to increase at data rate of 700 Mbit/s, and the BER was 1.14 \(\times\) 10\({}^{-6}\). BER of 2.15 \(\times\) 10\({}^{-3}\) was measured when the UL data rate is 850 Mbit/s, satisfying the 7% pre-FEC (pre-FEC BER = 3.8 \(\times\) 10\({}^{-3}\)) threshold. Regarding the red DL signal, if a low data rate of \(<\) 190 Mbit/s was employed, error-free detection can be achieved for the whole 100 cm-long LDOF. If higher data rates of 200 Mbit/s and 210 Mbit/s were employed, FEC performance (BER = 3.8 \(\times\) 10\({}^{-3}\)) could  
Figure 13: DL BER measurement curves at different data rates and at different positions of the 100 cm LDOF optical antenna.  
be guaranteed. However, when the data rate was increased to 220 Mbit/s, only half the length of the LDOF could satisfy the FEC transmission.  
Data curation, Y.-H.C., Y.-Z.L., Y.-H.J., and C.-C.-W.; Funding acquisition, C.-W.C.; Investigation, Y.-H.C., C.-W.C., Y.L., and C.-H.Y.; Writing--original draft, Y.-H.C.; Writing--review and editing, C.-W.C., Y.L., and C.-H.Y. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available from the first author upon request.  
The authors declare no conflict of interest.
## 2 Related Work
### Anomaly Detection in IoT and Smart Home Environments  
Anomaly detection is a fundamental component of cybersecurity in Internet of Things (IoT) ecosystems, particularly within smart homes where device heterogeneity, data sparsity, and real-time responsiveness are critical challenges. Traditional rule-based and signature-based detection systems often fall short in these environments due to their limited adaptability and inability to recognize novel or evolving threats [6,7].  
As a result, machine learning (ML)-based approaches have become increasingly prominent. A comprehensive survey by [PERSON] and [PERSON] [8] highlights the effectiveness of supervised and unsupervised learning models, including random forests, support vector machines, and neural networks, for IoT anomaly detection. Autoencoders and LSTM networks have been shown to capture temporal dependencies in time-series data, allowing detection of subtle deviations in smart home device behavior [9,10].  
Recent studies have also explored hybrid architectures that combine both supervised and unsupervised methods. For instance, [PERSON] et al. [9] proposed a two-stage detection pipeline integrating an Autoencoder with a random forest classifier, achieving 95.6% detection accuracy in a smart home testbed. Similarly, [PERSON] et al. [1] applied multiple classifiers to IoT network data and demonstrated that ensemble methods, particularly random forests, consistently outperformed single-model baselines on the UNSW BoToT dataset.  
Despite these advances, most existing systems are still centralized, requiring continuous data transmission to the cloud. This setup introduces latency, increases bandwidth usage, and heightens privacy risks--factors that are especially problematic in-home environments where users expect fast and private operation. These limitations underscore the need for distributed and privacy-aware anomaly detection frameworks, such as those leveraging edge computing and federated learning, which this work aims to address.
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 5 Problem Statement and Solution  
### Problem Statement  
Most works [24; 25] have only considered the performance of the task publisher when designing task scheduling, which makes it difficult for these methods to guarantee the profit of the task executors. In contrast, some studies [26; 27] have focused on maximizing the benefit of the task executors when assigning offloading tasks, which may lead to a low task completion rate and a long task execution delay.  
Our goal is to minimize the overhead of UAVs while maximizing the execution utility of SPs. Moreover, QoS requirements for different types of tasks in real-world environments are considered in the modeling of the overhead of the UAVs.  
Our multi-objective optimization problem is given by  
\[P=\begin{cases}\min f_{1}(s)=cos_{u}^{al}=\sum_{i=1}^{I}cos_{u,i}=\sum_{i=1}^{I }\eta_{i1}T_{n,i}^{al}+\eta_{i2}E_{n,i}^{al}\\ \max f_{2}(s)=U_{m}^{al}=\sum_{i=1}^{I}U_{m,i}=\sum_{i=1}^{I}\frac{re_{m,i}}{ cos_{m,i}}=\sum_{i=1}^{I}\frac{re_{m,i}}{E_{m,i}^{al}}\end{cases} \tag{22}\]  
s.t. \[\text{C1}:e_{m}>e_{min},m\in M\] \[\text{C2}:D_{min}=\min\left\{D_{1},D_{2},\cdots,D_{i}\right\},i\in I\] \[\text{C3}:Tr_{m}>D_{min},m\in M\] \[\text{C4}:\forall_{j\in S}\sum_{i\in I}s_{ij}\leq 1,s_{ij}=\{0,1\}\] \[\text{C5}:\forall_{i\in S}\sum_{j\in M}s_{ij}\leq 1,s_{ij}=\{0,1\}\]  
In problem \(P\), constraint C1 denotes the power restriction of the SPs. To ensure the normal use by the user, if the remaining battery of the SP is less than \(e_{min}\), it will not be used for task execution. Constraints C2 and C3 indicate that SPs with remaining residence time lower than the minimum latency requirement of the offloading tasks cannot be used for task execution. \(s\) is a set of solutions for all task assignments in the task queue (\(T\)). Constraints C4 and C5 limit the number of tasks executed by each SP; that is, the execution relationship between an SP and a task is set as a one-to-one relationship to ensure execution efficiency.
# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
## 3 Synthetic Data Analysis
### Cepstrum Analysis of Synthetic Data  
A peak at a navewumber of 3 m can be noticed in the cepstra of all models (Figure 2). It indicates the wheel circumference. Comparing the cepstra of the first three models shows that with increasing wheel-flat depth the amplitude of this peak increases, while the length of the wheel flat has the opposite effect. A longer wheel flat is smoother or less spiky and the resulting harmonics in the spectrum are weaker, which leads to weaker peaks in the cepstrum. The cepstra of models 4 and 5 indicate that an increase in wheel roughness leads to an increase of the peak amplitude. However, comparing the cepstra of models 5 and 6 suggests that decreasing the amplitude of the rail roughness has a similar effect on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Number**} & **Wheel Flat Length in** & **Wheel Flat Depth in** & **Wheel Roughness Std** & **Track Roughness** \\  & **mm** & **mm** & **in mm** & **Std in mm** \\ \hline
1 & 50 & 0.3 & - & 0.01 \\
2 & 50 & 0.6 & - & 0.01 \\
3 & 100 & 0.3 & - & 0.01 \\
4 & 50 & 0.3 & 0.01 & 0.01 \\
5 & 50 & 0.3 & 0.05 & 0.01 \\
6 & 50 & 0.3 & 0.01 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the different relative displacement models.  
Figure 1: Different synthetic data models according to Table 1.  
the peak amplitude than increasing the amplitude of the wheel roughness. This can be explained by applying Equations (2)-(6) to Equation (7):  
\[C\{y(x)\}(\widetilde{x})=C\{u_{r}(x)\}(\widetilde{x})+\left\{\left|\mathcal{F}^{ -1}\left\{\log\left(\left|\frac{U_{\text{z}}(k)}{U_{r}(k)}\right|^{2}+1\right) \right\}\right|^{2}\right\}(\widetilde{x}). \tag{11}\]  
The second term in Equation (11) shows that the power spectrum of the wheel profile is scaled by the power spectrum of the rail profile before the inverse Fourier transform is taken. Thus, the amplitude of the peak in the cepstrum at the wheel circumference can be interpreted as the relation between the contributions of the wheel and track to the relative displacement and hence to the dynamic wheel-rail interaction. If the rail roughness is zero, Equation (11) reduces to Equation (9) and the amplitudes of the wheel irregularities have no influence on the amplitude of the peaks at the wheel circumference.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 5 Current Anti-Phishing Methodologies and Techniques
### Technical Anti-Phishing Techniques
#### 5.2.2 Heuristic Detection  
This is the method of detecting and preventing phishing attacks by extracting features from phishing sites. This method is severely currently limited as heuristic features may not be present in phishing sites, and if the phisher knows the detection features or algorithms used, they can easily bypass detection.
## 4 Empirical Evaluation of Proposed Algorithms
### Experiments with Reference Datasets
#### 4.1.3 Results for Running Time and Convergence  
Running time for the initialization (median of 10 runs) for the parallel experiments is shown in Table 3. Running time for the initialization taken by K-means\(\parallel\) is around 60-80% of the running time of SK-means\(\parallel\). SRPK-means\(\parallel\) runs clearly faster than SK-means\(\parallel\) for datasets with dimensionality more than 100, and for the four highest-dimensional datasets, SRPK-means\(\parallel\) runs clearly faster than K-means\(\parallel\). Please note that differences are small between \(p=5\) and \(p=40\) for SRPK-means\(\parallel\).  
The median number of Lloyd's iterations needed for convergence after the initialization phase are summarized in Table 4, where the statistically significant differences are denoted similarly as in Table 2. The assumption of equal variances was satisfied for all datasets except for FCT. In general, SK-means\(\parallel\) seems to require smaller number of Lloyd's iterations than K-means++ and K-means\(\parallel\), which directly translates to faster running time of the K-means search. Based on the statistical testing, SRPK-means\(\parallel\) is better than or equal compared to the baseline methods in terms of the number of iterations. Therefore, SRPK-means\(\parallel\) can also speed up the search phase of the K-means clustering method. Increasing the RP dimension from 5 to 40 further improved the speed of convergence for SRPK-means\(\parallel\). Out of the parameter values used in the experiments, selecting \(p=40\) gives the best tradeoff between the running time and the clustering accuracy for SRPK-means\(\parallel\). Furthermore, note that there is no statistical difference between K-means++ and K-means\(\parallel\) with respect to the number of Lloyd's iterations.
# Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors
## 4 Experiments
### Verification of the Partitions  
To verify the partitions proposed in Section 3.1 and prepare the datasets to test the algorithms in this paper, we split each domain into training, validation, and test sets with the ratio being 6:2:2. For each domain pair, we trained YOLOv3 with learning rate being 0.0001 and epoch number being 120 on the source training set and used the total loss in the source validation set to choose the final model. All chosen models were tested on the test sets from source and target domains, respectively, to see whether there is a detection precision drop between the domains. The mAP (Mean Average Precision) for each test set is summarized in Table 1. The large drop of the mAP verifies our partition for different domains.
## 4 Discussion
### Genetic Algorithm Improves the Performance of Models  
Previous studies reported that performance of neural network models became worse with limited input variables [57; 58]. However, genetic algorithm (GA) was able to improve the performance of models with fewer input factors [59; 60]. In northern Greece, it was found that ANN models with fewer inputs gave rise to lower accuracy than the GA optimized ANN model [61]. In South Korea, it was reported that GABP models estimated daily ET\({}_{0}\) with acceptable accuracy using only temperature data [29]. In Yangtze River Basin, China, the accuracy of GABP models was also proved higher than that of empirical models with fewer parameter inputs [31]. When choosing r\({}^{2}\) = 0.70 as a threshold value, the number of input variables for BP\({}_{0.7}\) and GABP\({}_{0.7}\) models were only one for Guangzhou (R\({}_{\text{a}}\)) and Sanya (R\({}_{\text{a}}\)) and two for Nanjing (T\({}_{\text{max}}\) and R\({}_{\text{a}}\)) and Jinan (T\({}_{\text{max}}\) and T\({}_{\text{mean}}\)), respectively. With the same input variables, R of GABP models was increased by 12-18% for the above mentioned cities. Moreover, RMSE of GABP models was decreased by 31-55% for Guangzhou and Sanya cities (Figure 8). Our results clearly indicated that GABP models obtained more accuracy than did the BP models with fewer parameters. Specifically, for annual ET\({}_{0}\) estimation, the GABP\({}_{0.7}\) model using less input variables performed well with acceptable accuracy, whereas the GABP\({}_{0.5}\) model was preferable for seasonal ET\({}_{0}\) estimation.
# Periodical Vehicle Inspections with Smart Technology
## 1 Introduction
This may include the mandatory accreditation of PTI under ISO 17020, which has been proven to enhance the quality of PTI activities. Additionally, attention should be given to inspections of PTI. The example of the Slovak Republic is cited, where the introduction of camera monitoring effectively controls the work of inspectors. As a result, the rate of temporary roadworthiness and non-worthiness assessments significantly increased, leading to a reduction in accidents caused by vehicle technical defects. PTIs can also help to reduce emissions from vehicles, which can have positive impacts on air quality and public health, especially in urban areas [12]. According to several studies [13; 14; 15; 16; 17], measuring PN (particle number) during PTI (periodic technical inspection) can be a significant factor in reducing particle emissions and improving air quality. In general, according to [18; 19], road safety in Europe has improved significantly over the past decades due to a combination of factors including improved vehicle safety technologies, changes in traffic laws and enforcement, and improvements in road infrastructure. It is likely that PTIs have contributed to this improvement in road safety to some degree, although it is difficult to quantify the specific impact of PTIs on road safety trends. According to reports by the National Highway Traffic Safety Administration in the USA, the risk of road crashes associated with driving a vehicle manufactured before 2000 is 71% higher than for vehicles manufactured in 2010 and later [20]. The European Union, previously known as the European Community or European Economic Community, has conducted periodical technical vehicle inspections since the late 1970s through directive 77/143/EEC ([21]). As of April 2014, all member states of the European Union are required to conduct periodic safety and emission inspections for most types of motor vehicles according to the EU directive 2014/45 ([22]). Although the specific process for PTI varies among EU countries, inspections generally include the following checks:  
Technical inspections, as per EU directive [23], are concerned with the vehicle's condition and maintenance, focusing on the following aspects:  
Axles, brakes, wheels, tyres, and wheel alignment;* Frame/body, including vehicle identification (such as chassis number and licence plates);
* Exhaust system, with attention to environmental factors such as noise and exhaust emissions;
* Steering;
* Lighting and electrical systems, as well as electrical equipment;
* Windows, mirrors, and visibility;
* Accessories;
* Pedals, seats, and seat belts;
* Electronic safety systems. The tools, devices, and processes used by the inspectors during the inspection process can differ between countries.
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## CE Marking
### United States of America
#### 4.2.2 Market Authorization
They are exempt from Pre-Market Notification (510(k) submission) and only need to comply with basic regulatory requirements, known as general controls [55; 56]. Class II devices require more than general controls to ensure safety and effectiveness. To enter the market, these devices typically go through the 510(k) process, demonstrating substantial equivalence to a legally marketed predicate device. This involves submitting detailed information to the FDA [59]. If no predicate exists, the de novo classification pathway allows for a thorough FDA review to determine the device's classification [58]. Special controls and the 510(k) process together ensure that Class II devices meet all the necessary standards before being marketed [55; 59]. Introducing Class III medical devices into the USA market involves stringent regulatory processes overseen by the FDA. Unlike Class I and Class II devices, which may be cleared through the 510(k)-pathway based on substantial equivalence to predicate devices, Class III devices require PMA [56; 57; 60]. **Unique Device Identification**  
The Unique Device Identification (UDI) system, established by the FDA Amendments Act of 2007 and further defined by the Food and Drug Administration Safety and Innovation Act of 2012, plays a crucial role in identifying and tracking medical devices throughout their lifecycle [61]. Each device has a distinctUDI alphanumeric code containing standardized information, allowing the formal identification of each device and giving access to detailed data in the Global Unique Device Identification Database (GUDID) [34; 44; 61; 62]. Figure 8 provides an example of an UDI in the USA. The UDI Rule, finalized in September 2013, mandates that manufacturers include the UDI on device labels and packages. For devices requiring multiple uses and reprocessing, the UDI must also be marked directly on the device. The information associated with UDIs,  
Figure 8: USA UDI example [61]. such as device attributes, production identifiers and Global Medical Device Nomenclature (GMDN) codes, is submitted to the FDA and enters the GUDID. This database serves as a comprehensive reference system that is accessible to the public, enhancing transparency and facilitating the better integration of device information into healthcare data systems [55; 63]. This UDI system aims to enhance transparency and facilitate better integration into healthcare data systems, with its objectives including reducing medical errors, improving traceability and enabling effective post-market surveillance. By standardizing device identification and providing comprehensive information through the GUDID, the FDA supports patient safety and regulatory oversight [64].
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 3 Description of a Protocol for PKI Enhancing Privacy with MFA
### Signing a Transaction in a PKI Environment  
Within a PKI environment, the user needs to retrieve private key Sk, sign a transaction N, and transmit both the transaction and its signature to the cloud. The transaction can then be openly verified by third parties with the public key. The protocol (see block diagram of Figure 5) is the following:  
\(\bullet\) The user can recover the reference table CT with RN1 by plugging in the physical token, presenting their visual image, and computing the virtual token. Liveness tests can be inserted into the protocol to avoid third parties passing the visual test with an image of the user. The use of biometric images is \"template-less\", which protects the privacy of the user.  
\(\bullet\) The user can recover the ephemeral key K from the reference table CT and RN3 with a CRP mechanism.  
\(\bullet\) The ephemeral key allows the decryption of Sk the private key.  
\(\bullet\) Sk is used to compute S, the signature of transaction T.  
Figure 4: Block diagram showing the public–private key generation [Sk; Pk]. The reference table CT is been generated during the enrollment cycle. A second random number RN2 is used concurrently by the Keystore and the terminal to generate seed a and matrix A from the reference table CT. A randomly picked number generates seed b, then the secret key consisting of vectors V1 and V2, which is used to sign the message M. The rest of the protocol is the DSA from CRYSTALS Dilithium.  
As presented at the end of Section 2, the use of a masking table \(\mathrm{CT}_{\mathrm{mask}}\) can reduce the bit error rates. Additional error management schemes and error correcting schemes can be inserted to ensure that the ephemeral key K is exactly the same as the one generated during the enrollment cycle.