esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Park and Ang Damage Index
#### 1.2.4 Damage Index of Building Component  
The DI value is also suggested for a part of a structure, such as the individual story and the entire structure, by considering a weighting factor that is based on the dissipated hysteretic energy (\(E_{h}\)) of each member or the component as shown below, that are proposed originally by [PERSON] and [PERSON] [12] and developed by [PERSON] et al. [20]. Figure 1 shows the difference between \(DI_{story}\) and \(DI_{component}\). In the component weighting factor calculation, the dissipated energy (\(E_{h}\)) considered is only in the same story, while the calculation of story weighting factor, all energies in all stories are considered.  
\[DI_{story}=\sum_{i=1}^{n_{m}}{(\lambda_{i,component}DI_{i,component})} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Damage Degree** & **Damage Index** & **Physical Appearance** \\ \hline Collapse & DI \(>\) 1.0 & Collapse state \\ Severe & 0.4 \(<\) DI \(<\) 1.0 & Extensive crushing of concrete, \\  & & disclosure of buckled reinforcement. \\ Moderate & 0.25 \(<\) DI \(<\) 0.4 & Extensive large cracks, \\ Minor & 0.1 \(<\) DI \(<\) 0.25 & Smiling of concrete in weaker elements. \\ Slight & DI \(<\) 0.1 & Smiling of damage, light cracking throughout. \\ \hline \end{tabular}
\end{table}
Table 1: Damage Index for RC elements.  
\[\lambda_{i,component}=\left[\frac{E_{h,j}}{\sum_{i=1}^{n_{k}}E_{h,i}} \right]_{component} \tag{6}\] \[DI_{overall}=\sum_{j=1}^{n_{k}}\left(\lambda_{j,story}DI_{j,story}\right)\] (7) \[\lambda_{j,story}=\left[\frac{E_{h,j}}{\sum_{j=1}^{n_{k}}E_{h,j}} \right]_{story} \tag{8}\]  
where:  
\(i\): number of element member, starting at 1,  
\(j\): number of story, starting at 1,  
\(n_{k}\): total number of building's story,  
\(n_{m}\): total element member (beams and columns) in the reviewed story,  
\(DI_{i,component}\): Damage index for each component such as beam and column,  
\(DI_{Story}\): Damage index for each story,  
\(DI_{overall}\): Overall damage index,  
\(\lambda_{i,component}\): Weighting factor of each component,  
\(\lambda_{j,story}\): Weighting factor of each story.
Int. J. Environ. Res. Public Health_**2015**, _12_, 5338-5354; doi:10.3390/ijerph120505338  
International Journal of Environmental Research and Public Health  
**ISSN 1660-4601**  
www.mdpi.com/journal/ijerph  
_Article_  
**Radio Hazard Safety Assessment for Marine Ship Transmitters: Measurements Using a New Data Collection Method and Comparison with ICNIRP and ARPANSA Limits**  
**[PERSON] *  
Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia; E-Mail: [EMAIL_ADDRESS]; Tel.: +61-3-8344-3933  
Academic Editor: [PERSON]  
_Received: 11 March 2015 / Accepted: 6 May 2015 / Published: 19 May 2015_  
**Abstract:** We investigated the levels of radio frequency electromagnetic fields (RF EMFs) emitted from marine ship transmitters. In this study, we recorded the radio frequency (RF) electric field (EF) levels emitted from transmitters from a marine vessel focusing on the areas normally occupied by crew members and passengers. Previous studies considered radiation hazard safety assessment for marine vessels with a limited number of transmitters, such as very high-frequency (VHF) transceivers, radar and communication transmitters. In our investigation, EF levels from seven radio transmitters were measured, including: VHF, medium frequency/high frequency (MF/HF), satellite communication (Sat-Com C), AISnavigation, radar X-band and radar S-band. Measurements were carried out in a 40 m-long, three-level ship (upper deck, bridge deck and bridge roof) at 12 different locations. We developed a new data-collection protocol and performed it under 11 different scenarios to observe and measure the radiation emissions from all of the transmitters. In total, 528 EF field measurements were collected and averaged over all three levels of the marine ship with RF transmitters: the measured electric fields were the lowest on the upper deck (0.82-0.86 V/m), the highest on the bridge roof (2.15-3.70 V/m) and in between on the bridge deck (0.47-1.15 V/m). The measured EF levels were then assessed for compliance with the occupational and general public reference levels of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines and the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) standards. The ICNIRP and the ARPANSA limits for the general public were exceeded on the bridge roof; nevertheless, the occupational limits were respected everywhere. The measured EF levels, hence, compliedwith the ICNIRP guidelines and the ARPANSA standards. In this paper, we provide a new data collection model for future surveys, which could be conducted with larger samples to verify our observations. Furthermore, this new method could be useful as a reference for researchers and industry professionals without direct access to the necessary equipment.  
EMF; electric fields; radio hazard; ICNIRP limits; marine shipboard transmitters; exposure of general public
## 2 Related Work
### Challenges  
State-of-the-art IDSs leveraging deep learning models face several significant challenges. A key issue is achieving high accuracy, which is often hindered by the class imbalance prevalent in benchmark datasets. These datasets typically contain a disproportionate amount of normal traffic compared with attack traffic, making it difficult to detect rare but critical attack types. This imbalance leads to elevated rates of false alarm and reduces overall detection effectiveness. While deep learning holds promise for improving detection capabilities, it also introduces substantial computational complexity and resource demands, raising concerns about scalability and efficiency, particularly in large-scale, real-time operational settings. Another critical challenge is the limited generalizability of these models. They often struggle to adapt to diverse network conditions or to identify novel attack types not included in the training data, reducing their robustness in practical application. Additionally, many studies have prioritized theoretical and experimental advancements in deep learning, often neglecting practical deployment challenges such as data privacy, system latency, and integration with existing security frameworks. A further limitation is the tendency to focus narrowly on accuracy, potentially overlooking other essential performance metrics such as precision, recall, F1 score, and the implications of false positives and negatives. Addressing these interconnected challenges requires a holistic approach that emphasizes balanced data handling, scalability, adaptability to evolving network threats, and practical considerations for deployment in real-world environments. The Transformer-DNN and Autoencoder-CNN models address key limitations in existing intrusion detection systems, demonstrating superior accuracy and other performance metrics compared with traditional methods. These models effectively tackle class imbalance issues through advanced resampling techniques, including enhanced hybrid
## 2 Materials and Methods
### Monte Carlo Simulation  
#### 2.2.1 Tools and Datasets  
The Monte Carlo simulation is a common method for modeling light-skin interactions. Simulated photons are absorbed and/or scattered probabilistically depending on coefficients determined from the skin model provided by the user. Each simulation run typically accounts for a single wavelength, and the results of multiple runs can be combined to generate a full reflectance spectrum.  
One of the most popular Monte Carlo simulation tools is the Monte Carlo for multi-layered tissues (MCML) tool developed by [PERSON] and [PERSON] [27]. MCML has been used extensively in published studies relating to the optical measurement of skin parameters. [PERSON] et al. [28] provide a good description of how the MCML simulation works.  
Unfortunately, MCML processing is relatively slow given the large number of computations that occur for each simulation. Addressing this key weakness, CUDAMCML was developed by [PERSON] et al. [29] to take advantage of the computational acceleration enabled by parallel processing on a graphics processing unit (GPU).  
Two separate simulation datasets were developed for this project. The first was used exclusively by the simulated annealing algorithm for wavelength selection (see Section 2.4). It consisted of 36 simulated spectra from 450-800 nm at 1 nm resolution with parameters as given in Table 1. The parameter values were selected to represent typical values for human skin and melanin concentrations in light-skinned adults, moderately pigmented adults, and darkly pigmented adults [30]. These parameters are discussed further in Section 2.3.  
The second simulation dataset was used for the validation of the wavelength selection and SO\({}_{2}\) estimation algorithms and consisted of 1000 spectra, again from 450 to 800 nm at 1 nm resolution. These spectra were generated using the same parameters as those given in Table 1, except the values for SO\({}_{2}\) and \(f_{mel}\) were selected at random from a uniform distribution bounded by the maximum and minimum values in Table 1.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 4 Discussion and Outlook
### Future Directions  
Future research and technological advancements in Earth Observation (EO) for railway infrastructure monitoring are crucial to overcoming existing limitations. One promising direction involves the integration of EO data with Internet of Things (IoT) sensor net  
Figure 7: IMEO key algorithms for the SAR-based obstacle detection task: (a) telling algorithm; (b) SAR focusing algorithm; (c) change detection algorithm [70].  
works, providing real-time monitoring and enhancing data precision. The combination of high-resolution satellite imagery with ground-based data will enable predictive analytics, allowing infrastructure managers to anticipate and mitigate risks proactively.  
Another key area is the development of hybrid data processing models, incorporating multispectral, thermal, and synthetic aperture radar (SAR) data. This fusion of diverse data types can deliver a comprehensive view of railway systems, improving applications like vegetation management, water level monitoring, and thermal [84] and deformation analysis [85]. The application of advanced artificial intelligence (AI) techniques, including deep learning and explainable AI, is expected to refine anomaly detection and predictive maintenance. It is worth mentioning and considering in future research that recent studies have highlighted the use of advanced technologies like \"cradle-to-cradle\" life cycle assessments and \"cradle-to-gate\" analyses for railway infrastructure, promoting environmental sustainability alongside operational efficiency [86; 87; 88; 89].  
Additionally, efforts should focus on addressing the cost barriers of high-resolution commercial data by fostering collaborations between satellite providers and stakeholders. The use of emerging satellite constellations, like ICEYE and Capella Space, can improve temporal resolution and ensure near-real-time monitoring capabilities. Enhanced algorithms for downscaling data resolution and managing atmospheric interference will also play a critical role in achieving operational efficiency. Such methodologies could significantly benefit the monitoring of railway sub-ballast layers [90; 91] and the assessment of speed distribution heterogeneity on rural road segments, which have been explored using innovative non-parametric similarity measures and other novel approaches [88; 89; 92].  
As railway infrastructure faces increasing challenges from climate change and urbanization, integrating these innovations will position EO technology as an indispensable tool for ensuring the safety, sustainability, and reliability of railway networks.
## 2 Design and Performance of Ultra-Intense \"J-KAREN\" Laser System
### Front-End System  
In the first CPA stage, we use a compact commercial Ti:sapphire CPA laser system (Femtolasers, Femtopower Compact Pro). This system produces submillijoule, sub-30 fs pulses at a repetition rate of 1 kHz. Part of the ASE pedestal on these femtosecond pulses from the first CPA system is initially removed by transmission through a first saturable absorber. We avoid thermally induced damage to the saturable absorber by operating a Pockels cell at a 10 Hz repetition rate in the nine-pass amplifier, which then transmits pulses to the absorber and on to the second CPA stage of J-KAREN at only 10 Hz. The remaining pulses at 990 Hz will be directed to other experimental areas. The pulses are transmitted through the first 3 mm thick saturable absorber with about 25% efficiency (about 75% energy loss) at an incident intensity of 0.2 \(\mathrm{TW/cm}^{2}\). The material of our saturable absorbers is the colored glass filter (RG850, Newport). Because transmission is intensity dependent, the saturable absorber also acts as a spatial filter, and the spatial profile quality is therefore also improved. It is important to note that the main laser pulses must be compressed to femtosecond durations at the input of the saturable absorber in order to remove the unwanted ASE portion (hence the need for a double-CPA architecture). The challenge is to generate the high-energy, cleaned femtosecond laser pulses for the second CPA stage. By injecting high-energy cleaned seed pulses into the second CPA  
Figure 1: Schematics of the J-KAREN laser system.  
stage, the amplification ratio (gain) in the second is kept low and, therefore, the final ASE level can be suppressed.
## 5 SSFXG Architecture and Basic Interactions
### NW Subscription Identity Issuance
#### 5.1.1 NW Subscriber Request to Join the Federated Wireless Communication Network  
As explained in Figure 2, the onboarding process for new NW subscribers within a federated wireless communication NW begins with a crucial preparatory step: the generation of lifetime cryptographic primitives (Step 1 Figure 2). These cryptographic primitives, which typically include private and public keys, are essential for facilitating secure authentication and identification within the NW. By generating these primitives, subscribers lay the foundation for secure interactions within the decentralized environment, ensuring that their identities and communications can be verified without reliance on a centralized authority.  
Once the cryptographic primitives are in place, the new subscribers proceed to create a Global Decentralized Identifier (DID) (Step 2 Figure 2). This DID is a unique digital identity  
Figure 2: Subscribers’ identity issuance and credentials storage and management stages.  
for the subscriber and is recorded on the blockchain. The blockchain ensures that the DID is immutable, traceable, and accessible for verification in a decentralized manner. This approach eliminates the risks associated with centralized identity management systems, such as single points of failure or unauthorized tampering, while promoting transparency and trust among NW participants.  
Following the creation of the DID, the subscribers approach an available NW broker to request subscription identity credentials formally. The NW broker plays a pivotal role as the intermediary, facilitating the onboarding process and managing interactions between the subscriber and the federated NW. During this stage, the NW broker engages with the subscriber to negotiate the terms of their subscription (Step 3 Figure 2). This involves a detailed discussion about the roles, responsibilities, and regulations associated with the subscription. This negotiation aims to ensure that both parties have a clear understanding of the expectations, obligations, and benefits tied to the subscriber's participation in the NW.  
Once the terms are agreed upon, the NW broker finalizes the process by issuing the necessary credentials to the subscriber. These credentials grant the subscriber access to the NW and enable them to interact securely with other participants. To ensure secure storage and efficient management of subscriber information, the federated NW leverages blockchain technology and storage capsules (SCs). The blockchain serves as a decentralized ledger to securely store DIDs and subscription agreements, ensuring immutability and transparency. Meanwhile, storage capsules are utilized to store subscription credentials and associated data, enabling quick and secure retrieval while maintaining privacy and security.
# Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves
## 3 Flux Calculations and Corrections
### Secondary Data Processing
#### 3.2.5 Gap-Filling and Uncertainty Analysis  
In order to form a continuous dataset for annual carbon budgeting, the deleted data is replaced using gap-filled values. Several gap-filling methods have been developed based on interpolation, probabilistic filling, look-up tables, non-linear regression and artificial neural networks [37]. In the present study, a total of 35% of data is deleted after spike removal of energy fluxes and CO\({}_{2}\) fluxes (including the deletion of negative nighttime values and low turbulence fluxes). To form a continuous dataset, we used standard procedures such as the marginal data sampling method (MDS) and mean diurnal variation (MDV) to substitute for the deleted values [36; 38]. Both MDS and MDV methods have proven to be unbiased approaches for gap-filling in comparison with other techniques [37].  
The error and bias in fluxes caused by gap-filling methods (MDS and MDV) were estimated using the random creation of artificial gaps and then refilling these gaps using the valid data periods [37]. During the study period, around 150 half-hourly fluxes were randomly removed to create an artificial gap and then filled with values to compare with the original values. The root mean square error (RMSE) and the bias error (BE) were determined using the flux estimations from 100 simulations. The computed RMSE of CO\({}_{2}\) flux 1.81 \(\pm\) 0.24 umol\(\cdot\) m\({}^{-2}\cdot\) s\({}^{-1}\) was in the reported range of gap-filling errors in six forested types in Europe [37]. The low BE (0.13 \(\pm\) 0.1 umol\(\cdot\) m\({}^{-2}\cdot\) s\({}^{-1}\)) value suggests the minimal bias in the annual NEP estimates. Similarly, the computed RMSE of H\({}_{2}\)O flux was also found to be 0.73 \(\pm\) 0.12 umol\(\cdot\) m\({}^{-2}\cdot\) s\({}^{-1}\).
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 4 Data Acquisition Tool--IT System Design
### Maintainability Analysis Module  
The Maintainability Analysis Module is a key component of the extended Samanta system, designed to improve the efficiency and reliability of maintenance operations. It leverages analytical methods to assess the maintainability of individual tasks, optimize maintenance schedules, and minimize downtime. By integrating predictive algorithms and real-time task tracking, the module enhances decision-making processes, ensuring optimal resource allocation and improved system availability. The following sections outline the mathematical model and schedule strategies employed to achieve these objectives.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 3 System Design  
In this section, the details of the proposed algorithms for sinkhole detection and mitigation in smart cities are explained. The algorithms assume the integration between the application layer and the routing layer of the WSNs. The routing layer provides the routing information to the application layer for intrusion monitoring and detection. Here, two different approaches are proposed for sinkhole attack detection. However, the same algorithms could be used for monitoring other attacks as well.  
Figure 4 shows a sample WSN with 70 nodes and one sink node used for smart cities. The sink node is marked with a red circle on the figure. Certainly, the network could have multiple sink nodes due to the large monitoring field. Other possible scenarios in smart cities are to have more than one WSN with more sink nodes. Our proposed approach is feasible for all of the scenarios.  
If the sensing device is mandated to send its sensed data, it must send a request for connection establishment to its CH first. If there is a route in the routing table of the CH, it puts it on the link to be captured by the next CH device. The CH will otherwise discover an appropriate route. A CH device transmits a route request message (RRM) to other connected CHs to offer the next step route. If the adjacent CH has the route information to the sink node, it returns the route information message (RIM) to the source CH. If not, it will give RRM to the linked CHs.  
Figure 5 shows a typical message routing scenario in which a network of sensors and CHs are deployed in the monitored field and a sink node. If the sensor number 1 wants to send its sensed data, it notifies the CH number 6. CH number 6 receives the message and tries to send it to the sink node. If it has the routing table, it uses it to send the message directly; if not, it sends RREQ to all of its neighbors. One of the neighbors will reply to it, assuming the reply comes based on the CH proximity. Therefore, CH number 5 will reply to CH number 6 with route information message, RREP. After identifying the routes for the sink node to get the routes, it broadcasts a message to all of its neighboring CHs. CHs will continue broadcasting the message to their neighbors by increasing the hop count until it reaches all CHs. The routing information will go back to the base station through the same routes.  
Figure 4: Sample wireless sensor network (WSN) for smart cities (generated by [41]).