esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 4 Fido2 cap: FIDO2 Captive-Portal Authentication Protocol
### Architecture Design  
RFC 8952 [8] describes the general architecture of a captive portal in a network. In this work, we adapt and extend this architecture for enabling network authentication using FIDO2 Authenticators. This solution can be implemented to provide access control for users in an access-level network, such a Wi-Fi network or a cabled network of end devices.  
Figure 2 shows the final architecture diagram, highlighting our proposed changes. In our architecture, we include several elements.  
* **FIDO2 Authenticator**. The user FIDO2 Authenticator can be a hardware security key, a platform authenticator, or a FIDO2 multi-device credential (passkey). The user interacts with the FIDO2 Authenticator to prove their identity when accessing a network. A platform authenticator may be included in the User Equipment. For describing the general architecture, we consider it a separate device communicating via the FIDO CTAP protocol.
* **Web browser with WebAuth API**. The User Equipment must have a compatible client for performing authentication through the W3C WebAuth API.
* **WebAuth Authentication Web Application (WAWA)**. A WebAuth Relying Party is implemented as a web-based application, which works as the User Portal displayed by the captive-portal system. This includes a web interface displayed at the compatible web browser, and the authentication server.
* **User Database**. A database that stores the FIDO2 credentials linked to a user identity.
* **Session database (optional)**. A database of opened sessions, which can allow the user or the administrator to list all open sessions linked with a user identity.
* **Registration portal**. The registration system allows a registrar to register the FIDO2 Authenticator of the user. This portal may also show the active sessions linked with a user and an authenticator. Alternatively, the registration portal may provide a self-registration flow for a user to register themselves. For instance, this self-registration can be controlled by a temporal access token, shared as a QR code.
* **Registration Equipment**. The Registration Equipment and the User Equipment may be different, depending on the registration flow, for example, if the registration process is performed by an administrator, or there is a setup equipment for self-registration.  
The proposed system architecture works similarly to the RFC 8952 [8] for network authentication. After joining the network, the captive portal intercepts the traffic and provisions the WAWA URI. Then, the operating system of the User Equipment displays the WAWA user interface to the user. The WebAuthn Authentication Web Application now starts the FIDO2 authentication process, involving the user and the FIDO2 Authenticator via the WebAuthn API and the FIDO CTAP protocol. If the authentication is successful, the Enforcement Device is instructed to allow access to the External Network for the User Equipment. This authorises the user to access the protected network.
## 5 Challenges
### Security Issues of DL Applications in IoMT  
Data privacy and security: Smart healthcare faces significant data privacy and security challenges due to sensitive personal health information from IoT devices and wearables. These sources are prime targets for cyber attacks. With more connected devices, healthcare networks are increasingly vulnerable to hacking and data breaches. Many IoT devices lack proper security, risking unauthorized access. Healthcare providers must navigate strict regulatory environments like HIPAA and GDPR [211] to ensure compliance while maintaining robust security. Breaches can lead to severe penalties and loss of patient trust, highlighting the need for innovative security measures in smart healthcare systems to protect sensitive data. Federated learning, combined with DL, offers a solution to solve these challenges. Raw patient data remain on IoMT devices locally, and only the necessary data are shared with a central server. This decentralized paradigm reduces the exposure of sensitive health information to potential attackers. Attack on service: This attack aims to disrupt the accessibility of systems managing medical data or connected IoMT devices. IoMT devices often have limited specifications, including constrained memory, network bandwidth, and battery life, as well as data storage capacity, making them particularly susceptible to denial-of-service (DoS) threats. Hackers frequently target hospital networks with DoS attacks to affect patient care. These attacks can delay or block access to critical treatments, including life-saving medications, and hinder clinicians from retrieving essential patient records, significantly impacting healthcare delivery [212]. DL can be utilized as an intrusion detection system (IDS) to identify and analyze network attacks, supporting the timely detection of malicious activities and mitigating potential threats to the system. Hole attacks: In this type of attack, malicious nodes, known as sinkhole attackers (SHAs), lure legitimate nodes by presenting themselves as the most efficient route to a target destination. Once the legitimate nodes start routing their packets through the malicious nodes, the attackers disrupt network traffic in several ways: they may selectively drop packets to avoid detection by an IDS, fail to send critical information to the destination, or transmit incomplete or altered data. The consequences of such attacks include a significant decline in network performance, reduced communication reliability, and overall inefficiency in data transmission [213]. DL models, such as CNN and RNNs, could be trained to recognize patterns of normal network behavior. Any deviation from expected routing
## 6 Conclusions  
In this paper, a method was suggested to determine the FT of REBs using vibration data. In this method, PCA was used to produce PC1 as a degradation growth indicator from vibration features. Moreover, copula models were applied to reveal the relationship between the vibration features and PC1. With the best-fitted copula model in hand, the FT distribution was defined as a probability distribution based on a 25% vibration fluctuation criterion and the mode of this distribution was used as a constant value for the FT. The proposed method was applied to one accelerated life test dataset as well as an industrial dataset. Applying the method to the accelerated life tests led to the development of three different models for the failure mode. The third model, which used the separated data of each failure mode for PCA and copula analysis, provided more accurate FTs that took the type of failure into account. It was shown that the estimated FT for the rolling element was less than that for the inner race. this was because of prompt growth of degradation after the initiation of the defect in the rolling element. Additionally, using model 1 to estimate the FT regardless of the failure mode led to a more conservative FT. Applying the method to the industrial data showed that the proposed method provided reasonable FTs for actual field data too. Due to the limitations of industrial data, only model 1 was used to estimate the FTs for this dataset.  
FT selection plays a significant role in the RUL prediction of REBs. Since the method proposed in this paper estimates FTs using the high fluctuations in the degradation trend, it can provide a more accurate RUL prediction. Also, the provided FT distribution can be used in the reliability analysis of rotary machines. Furthermore, the method proposed in this paper can be used to estimate FTs under different machine operating conditions, which is what we plan to investigate in our future work.  
Conceptualization, S.F. and H.A.A.; investigation and methodology, S.F.; data curation, A.D.; writing--original draft preparation, S.F. and H.A.A.; writing--review and editing, M.B. and D.M.; visualization, S.F.; supervision, M.B.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.  
The authors gratefully thank Behravesh Vibration Engineering Co. and Takhte Feshorde Shomal Co. for their help in providing the industrial machine vibration condition monitoring data.  
The authors declare no conflict of interest.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 3 Methodology
### Multi-Domain Feature Extractor (MDFE)
The notation \(\alpha_{\text{c}}\) denotes the \(c\)-th channel of \(\alpha\), and the same notation applies to \(\beta^{\text{c}}\), \(f_{\text{s}}^{\text{c}}\), and \(f_{\text{f}}^{\text{c}}\). The symbol \(\otimes\) represents element-wise matrix multiplication.
# Production of Sustainable Postbiotics from Sugarcane Straw for Potential Food Applications
## 3 Results and Discussion
### Antioxidant Activity  
The antioxidant capacity of the extracts was determined by ABTS and DPPH through the IC\({}_{50}\) (mg/mL) and ORAC results were expressed in umol Trolox equivalents/g dry weight (Table 4).  
According ABTS, DPPH, and ORAC methods, the capacity of the extract to neutralize free radicals increased after _S. cerevisiae_ fermentation of sugarcane straw extract, since a significant decrease (_p_ < 0.05) in IC\({}_{50}\) was observed for ABTS and DPPH and an increase in (_p_ < 0.05) Trolox equivalents for ORAC (Table 4), whose reaction mechanism may be through hydrogen atom transfer.  
Different antioxidant activities have been reported for different _S. cerevisiae_ strains. A study between PY _S. cerevisiae_ var. _boulardii_ and _S. cerevisiae_ BY4742 showed that var. _boulardii_ had 6-10-fold higher scavenging activity (DPPH), and this was due to higher content of total phenolics and flavonoids [68]. Another work described an increase in the antioxidant capacity of apple juice fermented sequentially with _S. cerevisiae_, dealcoholization, and _L. plantarum_[69]. It was also reported that autolysates of baker's yeast, _Pichia fermentatus_ BY5 and _Issatchenka orientals_ BY10, showed antioxidant activity in scavenging of DPPH radical, nitric oxide, hydroxyl radical, and metal ion chelating [70; 71; 72].  
The bioactive peptides in yeast hydrolysates and other antioxidant substances such glutathione, Maillard reaction products, sulfur-containing amino acids, and polysaccharides are likely what give them their antioxidative properties [73].  
Besides the contribution of yeast autolysates to the antioxidant capacity, it was also reported in the literature that _S. cerevisiae_ had the capacity to increase the bioaccessibility of phenolic content, thus increasing the radical scavenging properties [33]. The fermentation of 4 cereals (wheat, buckwheat, wheat germ, barley, and rye) by _L. rhamnus_ and _S. cerevisiae_ showed an increase in the antioxidant activity [74]. Additionally, an increased antioxidant capacity was observed in a _Lactobacillus paracasei_ postbiotic extract tested in liver of male albino rats [75]. Antioxidant activity in butter samples increased after the addition of _ferulago angulata_ extract when combined with postbiotic compounds produced by _Lactiplantitibacillus plantarum_ subsp. _plantarum_[76].  
As previously described in the present work, the total phenolic content increase observed in straw extract obtained after saccharification and yeast fermentation may be contributing to antioxidant activity increase.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 5 Experimental Design  
### Dataset  
To evaluate the performance of the implemented measures, images from the database with 25 color images published in [6] were used and seven decolorization methods were applied to each image: Rec. 601 [17], 709 [18], 2020 [19], LAB [20], GD [21], CDF [9], and DL [30]. Images from the database are shown in Figure 3. The selected image dataset encompasses diverse images of various scenes represented with distinct color ranges and tones. The dataset includes content such as plants, people, nature, artistic images, and computer-generated images designed for testing colors. It also reflects diversity in terms of origin, composition, and other visual attributes, ensuring a broad spectrum of representation [6]. Images 1, 4, 5, 10, 11, 12, 14, 15, 16, 17, 20, 23, 24, and 25 are natural images, Images 3, 7, 19, and 21 were drawn by artists, while other images are artificial images with generated content (2, 6, 8, 9, 13, 18, and 22). For each of the 175 decolorized images, the results of the RMS, NRMS, GRR, and FSIM measures described in Section 4 were calculated. An x64 architecture with an Intel i5 1.60 GHz processor (Intel, Santa Clara, CA, USA) and 16 GB of RAM was used.
## 4 Experiments
### Evaluation Metrics
#### 4.3.1 Performance Metrics  
The ability of the system to accurately distinguish panic-induced behaviors from normal pedestrian activities is quantified using five key classification metrics: Accuracy, Precision, Recall, F1-score, and AUC-ROC. These metrics collectively assess the model's effectiveness in minimizing both false negatives (missed panic events) and false positives (erroneous panic alerts) while ensuring robustness under varying crowd densities and pedestrian dynamics.  
The accuracy of the model is defined as the ratio of correctly classified samples to the total number of samples, as expressed in Equation (13).  
\[Acu=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
where \(TP\) (True Positives) and \(TN\) (True Negatives) represent correctly classified panic and non-panic events, respectively, while \(FP\) (False Positives) and \(FN\) (False Negatives) denote incorrect classifications. Although accuracy provides an overall performance measure, it may be biased in imbalanced datasets, where panic events occur less frequently.  
Precision quantifies the proportion of correctly predicted panic events among all instances classified as panic, as given in Equation (14).  
\[Pre=\frac{TP}{TP+FP} \tag{19}\]  
A high precision score indicates a lower occurrence of false alarms, which is critical for ensuring system reliability in real-world deployment scenarios.  
Recall, also referred to as sensitivity, measures the proportion of actual panic events that were successfully detected by the system, as formulated in Equation (15).  
\[Rec=\frac{TP}{TP+FN} \tag{20}\]  
Since precision and recall present a trade-off, F1-score is employed as a harmonic mean of the two, ensuring a balanced assessment of the detection performance. The F1-score is computed using Equation (16).  
\[F=2\times\frac{Pre\times Rec}{Pre+Rec} \tag{21}\]  
A higher F1-score reflects both a reduced false alarm rate and a strong sensitivity to panic events, making it a crucial metric in safety-critical applications.  
The Area Under the Curve--Receiver Operating Characteristic (AUC-ROC) is used to evaluate the model's discrimination ability between panic and non-panic cases across different decision thresholds. The AUC is calculated using Equation (17).  
\[AUC=\int_{0}^{1}TPR(FPR)\ d(FPR) \tag{22}\]  
where TPR (True-Positive Rate) and FPR (False-Positive Rate) are plotted at varying thresholds to measure the model's classification robustness. A model with \(AUC>0.9\) is considered highly effective in distinguishing between panic and non-panic behaviors. As Table 5 shows, our framework attains 91.7% accuracy, 0.892 precision, 0.873 recall, and an F1 of 0.882, confirm ing balanced performance on the Itaewon set. The AUC of 0.92 indicates strong separability, and the 71 ms mean latency (batch = 1, RTX 3080) meets real-time requirements.
# The Life-Cycle Environmental Impact of Recycling of Restaurant Food Waste in Lanzhou, China
## 1 Introduction
It has special characteristics, such as a large production scale, complex composition, and high water, oil, carbon, and nitrogen content [29]. Therefore, it is important to conduct a comprehensive evaluation of the Chinese restaurant food waste treatment process in combination with China's actual conditions. China produced a total of approximately 91.1 million tons of restaurant food waste in 2015. The average daily production was 250,000 tons. In particular, the restaurant food waste produced in large cities was no less than 60 million tons. However, the capacity to properly treat and dispose of this waste is less than 14,000 tons/day. The daily treatment rate was only 5.25% [30]. Common treatment techniques in China and abroad include incineration, landfill disposal, turning food waste into feedstuff or biogas, composting, and anaerobic digestion [31]. The three food waste treatment techniques in China (turning food waste into feedstuff, turning food waste into fertilizer, and anaerobic digestion) make up 6%, 9%, and 85% of the overall food waste disposal in China, respectively. Anaerobic digestion is the most widely used [32]. The city of Lanzhou was the site of the first experimental project in China addressing food waste. Lanzhou received approval for the resource treatment of restaurant food waste in 2016, and the city gradually built a network of food waste collection points covering the main city area. A total of 16,915 restaurants and 80 restaurant food waste shipping routes were established as of 31 December 2016. A total of 424,108.32 tons of restaurant food waste was disposed of and shipped as of the same date [33]. The reclamation experience of restaurant food waste in Lanzhou is called the \"Lanzhou pattern\", which became a national pilot and was promoted throughout the country. Here, we used the life-cycle theory to assess the environmental impact of the anaerobic digestion-aerobic composing technique. The study also compared the results to those of landfill and incineration techniques, analyzed the energy consumption and environmental impact of the life cycle, explored the principal environmental impacts, and also conducted a sensitivity analysis. As a life-cycle assessment case of restaurant food waste in China, the study is expected to enhance the green and cyclic development of businesses and provide useful suggestions for the reclamation of restaurant food waste in other cities.
## 2 Materials and Methods
### Results Validation  
The time step, grid generation, and mesh independence were selected in Sections 2.2 and 2.3. The numerical algorithm can be considered to be reliable if results of the numerical simulations were similar to those of wind tunnel tests. Due to the lack of experimental data for comparison with the computational model used in the current work, a previously reported wind tunnel test for a 18 scale high-speed train under crosswinds was used for validation [40]. To make the verification more reliable, the same train model and wind tunnel were used in the numerical simulation. The CRH 2 train model was used in this study, so validation was conducted based on the CRH 2 train. Yaw angles from 0\({}^{\circ}\) to 19.8\({}^{\circ}\) and aerodynamic forces were investigated in an 8 m \(\times\) 6 m wind tunnel [40]. In the simulation, a resultant wind speed was used to achieve the yaw angle of the test, so the width of the computational domain was wider than that of the experiment, and the length was also longer to allow improved development of the inlet wind. However, the height was identical to that of the wind tunnel test. The numerical method, mesh strategy, and other relative settings were the same as those described above.  
Table 2 shows a comparison of aerodynamic forces between the simulation and the test. The maximum deviation between the simulation and the test was less than 5% for the tail car, possibly as a result the complicated flow underneath the tail car. Overall, the comparison results indicate that the relevant numerical settings in the present work are feasible for further CFD analysis. The train's aerodynamic forces are dominated by the flow structures, indirectly proving that the analysis of average flow structures presented in this paper is acceptable; this indirect approach was also used in previous studies [41].  
Figure 6: Comparison of different mesh resolutions.
# Si Photonics for Practical LiDAR Solutions
## 4 Si Photonics Optical Phased Array
### OPA System Design
#### 4.1.3 2D Beam Steering  
In an OPA design, the antennae can be arranged in two dimensions and in principle steer optical beam in 2D space. However, as analyzed earlier, the requirement on beam width and steering range may demand a large number (\(N\)) of antennae in each dimension, i.e., a total of \(N^{2}\) antennae may be impractical to implement. Therefore, the largest 2D OPA demonstrated so far is \(8\times 8\)[54; 62] which does not meet the requirement on either beamwidth or steering range.  
As it can be seen in Table 5, in a 1D OPA, wavelength tuning is a common way to steer the beam for the second FOV dimension. The highest efficiency of wavelength steering is about 0.3 deg/nm [68] while most are around 0.15 deg/nm [50; 51; 55; 59; 60; 61; 62] or slightly higher [63; 64]. It means for the requirement of 20-30\({}^{\circ}\) vertical FOV mentioned earlier, it demands a laser source with tuning range of 100-200 nm. A tunable laser with reliable and fast operation at such wide range is generally very expensive. For example, a high-volume mature product called integrated tunable laser assembly (iTLA) is widely used in telecom and supports 100 nm tuning range. However, it may be lack of sufficient tuning speed for high scanning rate required by LiDAR, and it costs more than what a mass market LiDAR desires. In addition, a grating coupler used as an antenna in wavelength steering may not provide good upward diffraction efficiency over full scanning range especially at angles near 0\({}^{\circ}\) due to high back reflection. The unavailability of low-cost automotive qualified tunable lasers and the nonoptimal diffraction efficiency may pose great challenges for OPA based LiDAR products despite research works show impressive results. The efficiency of wavelength steering is related to antenna design and more details are discussed in Section 4.2.1.
## 7 Current Challenges and Future Research Directions
### Feature Selection  
One obvious drawback of traditional machine learning techniques is that they rely heavily on feature engineering. LDDoS attacks can disguise themselves with regular traffic, making them difficult to detect. The first problem with the current solution is that the feature selection is not straightforward. Since most of the current detection methods are associated with an LDDoS attack of the SDN controller and OpenFlow switches, the features are sampled using the entire flow table as a sample. There is an urgent need to find apparent features of LDDoS attacks disguised with flow traffic in SDN architecture.  
Feature selection is quite tricky. Thus, we expect deep learning models to be proposed in the future as the feature selections can be automatic in deep learning models.