esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Limitations and Challenges in Passkeys
### Account Revocation
#### 6.1.3 Perception Challenges
Despite FIDO2 providing strong security guarantees, some papers highlight that users think their private information is transmitted and stored off-device, with some users being unsure whether their private information is securely handled at all [11; 48; 73; 88; 90]. Another study highlighted that some users believe that the use of a FIDO2 token alone secures their account entirely, even when weaker fallback options are enabled. Unfortunately, this false sense of security can provide a new attack surface for sophisticated phishing attacks [16; 73; 82]. Users may also be susceptible to sophisticated phishing attacks that trick them into downgrading to a less secure authentication mechanism. That results in a complete disregard for the security that FIDO2 provides [82]. Raising awareness of the possibility of using a passwordless authentication mechanism is also one crucial approach to nudge users in a specific direction. One study shows that only 20% of the participants recognized the possibility of using FIDO2-based authentication on platforms like Microsoft and eBay [42]. Another study shows that many users are unaware of FIDO2 as a technology and its advantages, which hinders its faster adoption rate [11]. Leading companies try to nudge users toward more secure choices, but some users might perceive these nudges as coercive or intrusive, which can reduce trust in the system [89]. Differences in account types have proven to affect users' willingness to adopt FIDO2, where, e.g., users were more inclined to use FIDO2 high-value accounts such as banking and more reluctant for lower-stakes such as social media [48; 88]. It is also crucial not to overlook users with disabilities or non-standard access patterns, who may find it difficult to adopt passkeys. For instance, users with physical limitations that prevent them from using biometrics cannot utilize systems that rely on biometric authentication [89]. Gaining a complete understanding of user perception is a complex and multifaceted challenge. Current research is sparse, and existing studies often involve a limited number of participants, making it difficult to draw firm conclusions. However, to further explore user perception and how to influence it, psychological theories and models of technology acceptance--such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT)--can be employed to better understand how to enhance user perception and, in turn, increase user adoption [95; 96].
## 3 Proposed Method
### ColorNet
#### 3.1.3 Loss Function  
Mapping an NIR image to an RGB image is a regression problem and predicting an RGB image can be expressed by Equation (1) as:  
\[Y^{\prime}=FX \tag{1}\]  
where \(X\) is the input NIR image, \(Y^{\prime}\) represents the predicted RGB image, and \(F\) is our ColorNet.  
Our goal is to minimize the difference in intensity of the pixels between the predicted RGB image and ground truth. The mathematical expression is shown in Equation (2):  
\[F=\text{argmin}\big{(}Y,\;Y^{\prime}\big{)} \tag{2}\]  
where \(Y\) represents the ground truth.  
In the face of different target tasks, it is necessary to choose the most appropriate loss function to achieve the best penalty network effect. The network regression is trained to output an RGB image, which is similar to the ground truth, and the Euclidean distance calculation is more suitable, so \(L2\) regression is used as the loss function. Therefore, Equation (2) can be expressed by the following Equation:  
\[F=\text{argmin}\Big{(}Y^{\prime}-Y^{2}\Big{)} \tag{3}\]  
For a certain pixel, the loss is defined in Equation (4):  
\[loss_{color}=\|Y^{\prime}-Y\|^{2} \tag{4}\]  
Subsequently, for a batch of images, the loss function is defined in Equation (5):  
\[Loss_{color}(F(X;\theta),Y)=\left(\sum_{b}^{B}\sum_{h,w}^{H,W}loss_{color} \Big{(}F(X;\theta)_{b,h,w^{\prime}},Y_{b,h,w}\Big{)}\right) \tag{5}\]  
where \(X\in R^{H\times W\times 1\times B}\) is a set of one channel NIR images. \(Y\in R^{H\times W\times 3\times B}\) represents a set of the three-channel ground truth. \(B\) represents the number of images in a batch. \(F(X;\theta)_{b,h,w}\) represents the output color images of ColorNet. \(\theta\) represents the hyperparameters of the network. The sum of the losses for each batch is taken as the final total loss.  
Figure 5: CLFFNet structure diagram.
## Materials and Methods
### Probe Synthesis  
ClNaph: 2,6-diaminopyridine (3.17 g, 29.1 mmol) in phosphoric acid (50 mL) was added to 3-chloropentane-2,4-dione (3.99 g, 29.6 mmol) and the reaction mixture was stirred at 90 \({}^{\circ}\)C for 24 h. After neutralization with NaOH, the mixture was filtered. The brown residue was extracted with 300 mL of CHCl\({}_{3}\) three times and the organic phase was dried over Na\({}_{2}\)SO\({}_{4}\). The solvent was evaporated _in vacuo_, affording ClNaph as a dark brown solid (3.57 g, 17.2 mmol, 59.2%). \({}^{1}\)H-NMR (CDCl\({}_{3}\)): 8.03 (d, 1H, \(J\) = 8.4 Hz), 6.76 (d, 1H, \(J\) = 9.2 Hz), 2.75 (s, 3H), 2.64 (s, 3H). High resolution ESI-MS calcd ([M + H]\({}^{+}\)) 207.0563; found, 207.0569.  
ClNaph-TO and ClNaph-TO2 conjugates: The conjugates were synthesized according to our previous report [19]. Briefly, aminoethyl group was incorporated into ClNaph and the resulting derivative was attached with the carboxylate-terminated decanyl (C10) spacer-containing TO derivative [26; 27]. ClNaph-TO conjugate, \({}^{1}\)H-NMR (DMSO-\(d_{6}\)) 8.80 (d, 1H, \(J\) = 8.8 Hz), 8.60 (d,1H, \(J\) = 8.8 Hz), 8.13 (d, 1H, \(J\) = 8.8 Hz), 8.05 (d, 1H, \(J\) = 7.6 Hz), 8.00 (t, 1H, \(J\) = 7.6 Hz), 7.80 (d, 1H, \(J\) = 8.4 Hz), 7.76 (t, 1H, \(J\) = 8.0 Hz), 7.63 (t, 1H, \(J\) = 8.4 Hz), 7.41 (t, 1H, \(J\) = 8.0 Hz), 7.36 (d, 1H, \(J\) = 7.6 Hz), 6.93 (s, 1H), 6.78 (d, 1H, \(J\) = 9.2 Hz), 4.58 (t, 2H, \(J\) = 7.2 Hz), 3.44-3.47 (q, 4H), 2.58 (s, 3H), 2.54 (s, 3H), 2.05 (t, 2H, \(J\) = 7.2 Hz), 1.84 (t, 2H), 1.42 (t, 2H), 1.34-1.16 (q, 14H). High resolution ESI-MS calcd ([M + H]\({}^{+}\)) 707.3299; found, 707.3299; ClNaph-TO2 conjugate, \({}^{1}\)H-NMR (DMSO-\(d_{6}\)) 8.72 (d, 1H, \(J\) = 8.8 Hz), 8.61 (d, 1H, \(J\) = 6.8 Hz), 8.07-8.01 (m, 4H), 7.78-7.74 (m, 2H), 7.62-7.58 (t, 1H, \(J\) = 7.6 Hz), 7.43-7.37 (m, 2H), 6.92 (s, 1H), 6.77 (d, 2H, \(J\) = 4.0 Hz), 4.61 (t, 2H), 4.17 (s, 3H), 3.50-3.42 (m, 4H), 2.58 (s, 3H), 2.55 (s, 3H), 2.00 (t, 2H, \(J\) = 7.2 Hz), 1.59-1.12 (m, 18H). High resolution ESI-MS calcd ([M + H]\({}^{+}\)) 707.3299; found, 707.3286.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 5 Experimental Result Analysis
### Example Analysis
Importantly, all SVM-RF models with different station selection methods achieved Pearson correlation coefficients exceeding 80%, collectively showing an average reduction of 31% in RMSE and 35% in MAE. The model in this paper has 94.59% of the predicted values lying within the confidence interval at a confidence level of 95%. Utilizing BEL and HLP as the primary stations yields Pearson correlation coefficients exceeding 90% between the calculated outcomes and the actual values. Among these, the calculation accuracy is highest when HLP is designated as the main station, with a root mean square error of only 1.053 and an average absolute error of merely 0.77 nT. In comparison with the other three algorithms, there is a 21% average increase in the Pearson correlation coefficient, effectively enhancing the accuracy of geomagnetic diurnal variation calculations. This enhancement offers valuable practical insights for marine magnetic measurements by providing dependable geomagnetic diurnal variation data. As depicted in Figure 16, the radar map in (b) illustrates the error distributions of various models, whereas (a) shows the distribution of absolute errors for each daily variation prediction model. The SVM-RF daily variation prediction model introduced in this study demonstrates smaller absolute errors, primarily concentrated toward the lower end of the scale. Specifically, the error distribution of the SVM-RF model is centered within the inner regions of all models, suggesting superior calculation performance and accuracy.
## 4 Interference-Aware Cooperative Anti-Jamming Distributed Channel Selection Algorithm
### A Discussion on the Quick Decision for UAVs  
In the designed anti-jamming transmission slot structure, we have illustrated that users make wideband spectrum sensing and co-channel interference sensing in every time slot, which means that users can sense the states of malicious jamming and co-channel interference precisely. Then, users decide to take joint channel selections via multi-agent Q-learning or take independent channel selections via single Q-learning. After the proposed ICADCSA algorithm converges for one certain scenario, users can make quick decisions via previous learning experience and do not need to learn again when they encounter the same scenario next time. Thus, our proposed approach can realize quick anti-jamming channel selection in high mobility UAV communication networks. This discussion will be investigated and analyzed in the following simulation section.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
###### Abstract  
As a typical pollutant, methylene blue poses a serious threat to the environment and human health. Oily sludge pyrolysis residue loaded with metal oxides could be used to prepare composite materials, which is not only an effective way to treat oily sludge, but also a possible method to treat methylene blue pollutants. In this paper, composite materials (AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\)) were prepared by oily sludge pyrolysis residue-loaded CuO, ZnO, and TiO\({}_{2}\) directly, and characterized by XRD, SEM, EDS, BET, FT-IR, and XPS, and it was shown that the metal oxides were successfully supported on the pyrolysis residue. Then, the composite materials were applied to the removal of methylene blue solution. The removal effect of composite materials on methylene blue with respect to the impregnation time, impregnation ratio and dosage, and the contact time and number of regenerations were investigated, and the removal parameters were optimized by response surface methodology. The removal process for methylene blue was described by applying [PERSON], McKay, Langmuir isotherm, Freundlish isotherm and intraparticle diffusion models. According to the response surface methodology and the main factors affecting the removal effect of methylene blue, the results indicate that the removal effect of 5 mg/L methylene blue could reach 95.28%, 94.95%, and 96.96%, respectively, and the corresponding removal capacities were 4.76, 4.75, and 4.85 mg/g. In addition, kinetic studies showed that the removal process of methylene blue was mainly constituted by chemical adsorption. The intraparticle diffusion showed that the removal of methylene blue may be controlled by both liquid film diffusion and intraparticle diffusion. The isotherms showed that the adsorption sites of composites for methylene blue were uniformly distributed and had the same affinity. Furthermore, regeneration experiments showed that the composite materials were stable and had relatively reusability.  
o 9 April 2022  
Accepted: 5 May 2022  
Published: 7 May 2022  
MMPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 4 AIoT Applications in Aquaculture
### Disease Detection and Classification
For instance, the performance metric-infused weighted ensemble (PMIWE) model, which integrates DenseNet-121, ResNet-50, and EfficientNetB3, has achieved a classification accuracy of 97.53% in identifying freshwater fish diseases [122]. Additionally, hybrid models combining CNNs with feature extraction techniques, such as histogram of oriented gradients (HOGs) and thresholding, have improved disease classification robustness [123]. Adaptive neural fuzzy systems have also been developed to enhance diagnostic precision in aquaculture. An improved U-Net segmentation model combined with multi-head attention has been proposed to precisely identify disease-affected areas in fish photos [59]. Similarly, fuzzy neural network-based expert systems have been implemented for fish health management, showing high accuracy in detecting common diseases in species like grass carp [124]. These intelligent systems contribute to automated disease detection and decision-making in aquaculture. Mobile devices and IoT-based disease monitoring systems offer scalable solutions for real-time disease detection in aquaculture. Convolutional Neural Network (CNN) models, specifically optimised for mobile applications, have been created to deliver disease alerts and treatment suggestions via cloud platforms. This makes AI-powered disease diagnostics accessible to small-scale fish farmers [125]. Furthermore, disease detection models utilising YOLOv5, integrated with intelligent water quality monitoring systems, have achieved accuracy rates of over 97% [126]. Biosensor technology is crucial in pathogen detection, enabling rapid and precise identification of infectious agents. Gold electrode-based biosensors utilising electrochemical impedance spectroscopy (EIS) have been optimised for aquaculture applications. Findings suggest that low surface roughness in gold electrodes enhances impedance response quality, improving pathogen detection sensitivity [127]. Integrating biosensors with AI-driven analytical models offers new opportunities for real-time disease prevention and control in aquaculture environments. These advancements highlight the potential of AI, computer vision, and biosensor technologies in transforming disease detection and management into aquaculture. Future studies should focus on improving the generalizability of AI models across different aquaculture species, enhancing feature extraction techniques, and integrating multimodal data sources to develop more comprehensive disease prediction systems.
## 4 Experimental Design and Results
### Results
#### 4.5.3 Space Saving (SS)  
The SS metric quantifies the reduction in file size as a proportion of the original uncompressed size, serving as an indicator of compression efficiency. Table 6 presents the SS values calculated for all images in the datasets. For encoding algorithms, SS values were computed for HCA, LPHEA, HBILES, and the DEA. The results demonstrated that while HCA provided a baseline level of storage reduction, the DEA achieved significantly greater storage savings across all image types. For compression algorithms, SS values were calculated for the S+DEA compression algorithm and compared against BPG, JPEG-LS, and JPEG2000. The findings indicate that incorporating segmentation as a preprocessing step within the DEA, leading to the S+DEA, further enhanced storage efficiency, achieving even greater space savings.  
To better illustrate these improvements, Table 6 indicates the relative gains of the DEA and S+DEA, emphasizing their performance in storage optimization.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Image** \\ **Name Tag** \\ \end{tabular} } & \multicolumn{5}{c}{**Encoding Algorithms**} & \multicolumn{5}{c}{**Compression Algorithms**} \\ \cline{2-9}  & **HCA**[23] & **LPHEA** & **HBILES** & **DEA** & **BPG** & **LS** & **2000** & **S+DEA** \\ \hline
1.1 & 0.21 & 0.21 & 0.46 & 0.49 & 0.45 & 0.47 & 0.46 & 0.52 \\
1.2 &
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 2 Background on Concepts and Requirements
### Involved Parties in Healthcare Systems
#### 2.5.2 Other Involved Parties  
* Technology Providers: Their responsibility includes designing, developing, and deploying the required technological infrastructure to support smart healthcare systems which enable a seamless flow of data exchange and efficient healthcare services. They design and offer a range of tools and solutions, including electronic health record (EHR) platforms, telemedicine applications, wearable device manufacturing, and health applications.
* Regulators and Government Agencies: Entities like the Data Protection Officer (DPO) and OCR play a critical role in enforcing compliance with the GDPR in the EU and the HIPAA in the US and safeguarding patients' data. These agencies are responsible for overseeing the application of standards and frameworks that control the healthcare sector.
* Researchers and Academia: Collaboration between researchers and academic institutions and smart healthcare systems opens the horizons for innovation in the healthcare sector through collecting and analyzing data to generate novel insights that enhance the quality of the healthcare services provided to patients.
* Caregivers and Family Members: Their responsibility is taking care of and assisting patients at home or in healthcare organizations. They must join the healthcare systems as they have to monitor the health state of the patient.  
In the context of IoT-based healthcare systems, the roles of healthcare providers, patients, and technology providers become even more intertwined. IoT devices generate continuous patient data streams, making real-time data security a primary concern. Therefore, privacy-preserving techniques such as encrypted edge computing, federated learning, and blockchain-based identity management are crucial for ensuring patient confidentiality. These techniques will be explored in later sections, reinforcing the necessity of tailored privacy-preserving strategies for IoT healthcare ecosystems.
## 3 Materials and Methods  
### Experimental Setup  
The system in Figure 1 consists of six independent measurement units, each comprising of a standard LTE modem connected to a microcontroller board, and separate power supplies. Each unit is connected to its own roof antenna as indicated in Figure 2. The antennas are set up on a defined, conductive steel surface with sufficient distance to minimize cross-coupling effects. With this setup, parallel measurement of three providers with two technologies each is possible. For this study, a subset of two providers and LTE (4G) technology was chosen. All measurement units and an independent GNSS module are connected to a mobile personal computer to collect and save data during the measurements. The relevant measurement parameters are summarized in Table 3. Since 3G service has been terminated in Germany in 2021 and the roll-out of 5G is still ongoing, the measurement setup provides a good balance between cost, complexity, and benefit.  
Figure 1: The setup of the measurement system.
###### Abstract  
This study delves into the impact of ship noise on seafarer well-being, emphasizing fatigue--a significant contributor to maritime accidents due to human error. The investigation, centered around the hypothesis that IMO ship construction standards may not adequately minimize noise levels in seafarer cabins, seeks to establish whether these levels are sufficient to ensure seafarer security and prevent sleep disturbances. According to current IMO regulations, noise levels are set at 55 dB for vessels under 10,000 gross tonnage and 60 dB for those over 10,000, yet WHO guidelines recommend a maximum of 40 dB in bedrooms to avoid sleep disruption. A comprehensive survey involving 221 cadets demonstrates that 79.6% of participants experience sleep disturbances, work disruptions, and stress due to noise, indicating that the present noise standards are insufficient. This paper argues that reducing noise levels in individual cabins to below 40 dB is critical for enhancing seafarer health and safety and could significantly reduce human error-related maritime accidents. The findings advocate for more stringent noise control measures and regulatory reforms to bridge the knowledge gaps and improve labor protection in the maritime industry.  
ship noise; seafarer fatigue; sleep; IMO; maritime safety; labor protection +
Footnote †: journal:  
Although there is a 7 dB difference when the ship's engine is on or off, even the lower level does not meet WHO noise standards. Therefore, among the factors exacerbating crew fatigue, as mentioned earlier, excessive workload is significant, but the most crucial are insufficient sleep duration and unsatisfactory sleep quality, which are likely to be among the detrimental factors to the crew's sleep. This paper contains how the three factors of crew fatigue, sleep, and noise are interrelated in Section 2. Section 3 reviews international standards on noise, especially regarding noise standards for ships. Section 4 provided descriptions of the survey respondents, detailed the survey's design, and mentioned the methods used for data analysis. In Section 5, the outcomes obtained from the survey were examined, including frequency analysis and analysis of correlation coefficients based on the questionnaire. Finally, Section 6 concludes with considerations for future research.
# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 3 Development and Excel-Based Implementation of the Simulation Model
### Simulation of Passenger Arrival at the Airport
The function NORM.INV(RAND(), \(\mu,\sigma\)) is a key component in generating random values that follow a normal distribution, as follows:  
* RAND() generates a random number uniformly distributed between 0 and 1;
* NORM.INV(p, \(\mu,\sigma\)) returns the inverse of the normal cumulative distribution function (CDF) for a given probability p, with specified mean \(\mu\) and standard deviation \(\sigma\). When RAND() is used as the input probability p in NORM.INV, the function returns a value that is randomly sampled from a normal distribution defined by the parameters \(\mu\) (mean) and \(\sigma\) (standard deviation). This process transforms a uniformly distributed random number into a normally distributed random variable. In practical terms, it means that each time the formula is recalculated, it produces a new value that follows the bell-shaped curve of the normal distribution. Most generated values will cluster around the mean, while fewer will fall near the tails, closer to early or very late arrivals. This mechanism allows the simulation model to realistically reproduce the variability observed in passenger arrival behavior. As an illustrative example, the following input parameters are selected: a scheduled departure time of 13:50, a total of 205 passengers booked on the flight, an average arrival time of 109 min before departure, and a standard deviation of 20 min. This setup represents an afternoon leisure flight, characterized by relatively homogeneous passenger behavior, as reflected by the lower value of the standard deviation. All time-related inputs are converted into hours using basic Excel functions and subsequently applied within the formula. Additionally, each generated arrival time is assigned a unique passenger index (PAX Index). The PAX Index serves to track individual passengers throughout all handling phases. A passenger who arrives first at the airport may not necessarily be the first to be handled at the check-in desk or security screening. From a development perspective, the PAX Index also ensures transparency and simplifies debugging of the model by maintaining a clear reference for each passenger. The output of this phase of the handling process consists of the arrival times for the specified number of passengers. For a visual demonstration of arrival distribution, Figure 3 presents the input parameters and the output results for the first 10 passengers on the left-hand side, while the right-hand side displays a graph illustrating the variability distribution of all generated arrival times using the specified formula.