esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 1 Introduction
### Research Problem and Gap  
Despite various technological advancements, public transportation systems often struggle with real-time challenges such as traffic congestion, vehicle malfunctions, and fluctuating passenger demand [11]. These challenges lead to delays, overcrowding, higher operational costs, and reduced user satisfaction. Most traditional public transportation systems operate on outdated schedules and infrastructure. These factors limit the systems' ability to adapt dynamically to disruptions or optimize operations [12; 13].  
Digital twins can use real-time data from GPS, sensors, and passenger feedback, and they provide feedback to enable planners to predict issues like vehicle breakdowns or overcrowding [13]. Planners can also optimize operations and simulate future scenarios. Furthermore, there are also possibilities of adding new routes or adjusting schedules in real time. This adaptability can help improve decision-making, enhance the passenger experience, and make public transit systems more resilient to urban challenges. This can also help alleviate environmental concerns.  
Although digital twin technology has been successful in many fields like manufacturing and smart city infrastructure [14], its potential for public transportation has not been fully explored. While there has been a lot of research focusing on traffic management and smart cities, more comprehensive and integrated public transit systems have not been studied in detail. Hence, there are limited studies that investigate digital twins and their usage of real-time data, advanced simulations, and predictive analytics to enhance urban public transit networks. As urban mobility is becoming more complex with a growing demand for efficient, data-driven solutions, it is important to explore how digital twins can improve public transportation through real-time data, simulations, and predictive analytics. This literature review aims to bridge this gap by compiling and analyzing existing knowledge to support the effective adoption of digital twins in public transit networks. Other initiatives include further identification of key challenges and outlining future research directions.  
This research will explore how digital twin technology can optimize multimodal public transportation systems. The paper will identify challenges in integrating data sources such as real-time GPS, traffic conditions, vehicle diagnostics, and passenger feedback into a singular digital twin model, which can predict and mitigate issues in real time. Addressing this gap can produce efficient and adaptive public transit systems.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 5 Implementation
### Schema Design
#### 5.2.2 Relational Design  
Figure 11 shows the design of the Yelp dataset modelled in a relational database, specifically PostgreSQL. The _location_ attribute is indexed with an R-tree using the PostGIS extension. The decision not to extend _city_ and _state_ attributes into separate tables was taken, otherwise more joins would be required for an attribute that is never queried. _Location_ is the only purely spatial attribute--the main attribute in the benchmarking. It may be faster to simply index state as an attribute due to the low cardinality of city and state paired in a single table. City is indexed and clustered such that records in the same city are physically stored together which, alongside location, should help retrieval speeds from a spatial query perspective. PostgreSQL makes use of B-trees and hash indexes for native data types [26]. The review table is commonly used in queries and holds the most interesting attribute in terms of temporal insight. Since temporal information is one-dimensional and ordinal, the _date_ attribute is indexed and clustered. A business category and a user's friends are many-to-many relationships. The linking tables bus_2_cat and friends tables handle these relationships.  
Regarding the medical response dataset (Figure 12), one major feature is that responses may result in either the patient being transported or treated on site. Due to this, two one-to-zero-or-one relations are created with the transfer and on_scene tables and the presence of a relation to either will deduce whether the response resulted in a transport or not. The priority of a response and the resource number of a response are low cardinality attributes which are then separated into tables with a one-to-many relation.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 3 Preliminaries
### Algorithms  
Our scheme consists of the following algorithm, which is based on [17] and that we modified:  
* \(\mathrm{SysParm}(1^{s})\rightarrow\lambda\): The parameter generating algorithm \(\mathit{SysParm}\) takes a security parameter s as input and generates a set of system parameters \(\lambda\).
* \(\mathrm{KGen}(\lambda)\to pk\), \(sk\), \(k\): Given \(\lambda\) as input, the key generating algorithm \(\mathit{KGen}\) generates a public key \(pk\), a private key \(sk\) and a secret key \(k\) with a control center CC for a symmetric cryptographic system(pseudorandom function).
* \(\mathrm{EnC}(pk\_m)\rightarrow\mathrm{C}\): Given a public key pk and a message m as input, the encryption algorithm EnC produces a ciphertext C.
* \(\mathrm{Ord}\_P(R)\to O_{p}\): CC analyses the report R, produces an Order \(O_{p}\) and transmits it to a participant P.
* \(\mathrm{Rpt\_CC}(O_{p})\rightarrow\mathrm{Act}\): According to the received order \(O_{p}\), P reacts the Act and reports it to CC.
* \(\mathrm{RKeyGen}(sk_{1},\ sk_{2})\to rk_{1\to 2}^{1},\ rk_{1\to 2}^{2}\): Given secret keys \(sk_{1}\) and \(sk_{2}\) as input, the re-encryption key generation algorithm RKeyGen generates unidirectional re-encryption keys \(rk_{1\to 2}^{1},\ rk_{1\to 2}^{2}\).
* \(\mathrm{PrxyRE}(f_{k_{STP}}(rk_{1\to 2}^{1}),C)\rightarrow\mathrm{RE}\): STP (semi-trusted party) decrypts the encryption of the re-encryption key \(f_{k_{STP}}(rk_{1\to 2}^{1})\). With this, STP re-encrypts C, then produces the re-encryption RE.
* \(\mathrm{Dec}(f_{k_{x}}(rk_{1\to 2}^{2}),\ RE)\rightarrow\mathrm{m}\): Given a re-encryption key \(f_{k_{x}}\left(rk_{1\to 2}^{2}\right)\) and \(RE\) as input, the decryption algorithm DeC outputs the original message m.
* \(\mathrm{Trt\_Str}(m_{P})\to C_{p}\): Upon input of a treatment \(m_{P}\) by the participant P, this treatment and storage algorithm outputs the encryption \(C_{P}\) for \(m_{P}\). Then, \(C_{P}\) is stored in CS.
## 2 Materials and Methods
### Headspace Solid-Phase Microextraction  
The coffee samples were prepared for headspace solid-phase microextraction (HS-SPME) according to three states (beans, powder, and brew). Compared with other extraction methods, solid-phase microextraction can save 70% of sample pretreatment time and meet the needs of the rapid detection of large quantities of coffee samples in a shorter time [20; 21]. The coffee bean and coffee powder weights were 1.5 g for GC-TOF/MS and 0.75 g for GC\(\times\)GC-TOF/MS. The coffee brew was measured to a volume of 4 mL for GC-TOF/MS and 2 mL for GC\(\times\)GC-TOF/MS. The samples, according to the brand and state, were immediately placed and sealed into 20 mL headspace vials. The internal standard (IS) was prepared by dissolving 2-methyl-3-heptanone (purity 95%, GC standard, liquid, Mobile Biotechnology Co., Ltd., Shanghai, China) in methyl alcohol (purity \(\geq\) 99.9%, GC standard, liquid, Aladdin, Shanghai, China) to the final concentration of 0.41 mg/mL. The IS solution (1 \(\upmu\)L) was then manually added to vials using a 10 \(\upmu\)L syringe before the SPME process [22].  
Volatile compounds in the prepared samples were extracted using the 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Stabelflex, Supelco, Bellefonte, PA, USA) [9; 23]. The samples were equilibrated at 40 \({}^{\circ}\)C for 10 min. The volatiles were extracted at 40 \({}^{\circ}\)C for 20 min and subjected to desorption for 5 min [12].
# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
## 2 Materials and Methods
### AI Algorithm Development  
The deep-learning algorithm used in this study was developed using native and contrast-enhanced CT studies of the spine, abdomen, and thorax. It is meant to be used as a medical device in a clinical setting. The DLA consists of two deep-learning models that work together. One model uses a U-Net-like segmentation approach coupled with volumetric analysis to determine the presence of potentially malignant lesions in the spinal vertebrae. The second model employs a vertebral localization component that enables the proper selection of the region of interest. It is trained using a training set of 224 cases and tested on 735 cases. Additionally, the DLA is evaluated on an external dataset of 420 cases. Statistical analysis of the datasets was performed to verify the demographic distribution of the data.
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## Materials and Methods
### Analysis of SOS and EOS Fluctuations for the Validation of Thresholds  
We did not have any in situ observations of SOS and EOS to validate the dates derived using the best thresholds in TIMESAT. Therefore, we further attempted to validate our thresholds for SOS and EOS through analyzing the interannual variations of SOS and EOS, and investigating whether the major observed deviations from the normal trends in SOS and EOS can be explained by occurrences of environmental disturbances/anomalies (such as temperature anomalies, hurricanes, drought, or other natural/anthropogenic disasters) that might have influenced those deviations. Here, we derive the SOS and EOS for the salt marshes of southeast and southwest LA using the best thresholds in TIMESAT. We analyzed southeast and southwest LA separately, as the nature and effect of environmental disturbances on SOS and EOS may vary across climatic zones. Further, the southeast and the southwest climatic zones of LA comprise entirely salt marsh habitats; hence, the effect(s) of any localized environmental disturbance(s) within the climatic zones will be manifested entirely in the salt marsh phenology. The analysis attempts to provide further justification of the SOS and EOS thresholds chosen for deriving the seasonality parameters of the LA salt marshes.
# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
## 3 Results
### Clustering Analysis
A good correlation between the cumulative hits of surficial oxide film rupture and the corrosion current density in carbon corrosion was reported [35]. However, to the emission of AE is not only affected by corrosion phenomena, but also by the mechanical condition of steel structure. It is very difficult to determine the corrosion rate by AE in practical settings. In addition, though the recognition and identification of AE signals could offer useful information regarding the  
Figure 10: Waveforms from top to bottom: (**a**) and (**b**) low duration with low frequency and low counts, (**c**) high duration with low frequency and low counts, (**d**) high duration with high frequency and high counts. corrosion mechanism, identification of the group of signals that represents the corrosion rate requires further investigation.
###### Abstract
[37] confirmed that there was no significant difference between the actual vegetation height estimated using the 3D point cloud generated by UAV photogrammetry and the results measured using UAV RTK (SP 60) and UAV LiDAR. They also verified that UAV photogrammetry could effectively be used to monitor forest growth from stock volume, even without actual high-resolution digital terrain model (DTM) data [38]. However, the 3D point cloud generated based on UAV photogrammetry for vegetation volume estimation in river is highly sensitive to factors such as drone operation and image quality which can be influenced by wind, sunlight, and weather. Along with measurement precision, anomalies in the point cloud are generated due to errors stemming from environmental and mechanical factors, like drone altitude and camera angle, and inherent error rates of the measurement equipment. Therefore, it is necessary to selectively extract and carve out the desired objects from the huge volume of cloud data randomly measured on-site, reproduced by reducing outliers and noise, and then regenerated with optimal data. For rivers, even when they are in very close proximity, different flows occur depending on the environment, necessitating precise 3D information on the volume and height of vegetation at each location. Therefore, this study began by generating a 3D point cloud for riparian vegetation by employing the efficient UAV photogrammetry method and targeting rivers with clustered vegetation. Second, this study explored methods that could determine vegetation volume using the alpha shape technique, which is primarily used to secure 3D topographical features, and to evaluate their applicability. Third, an analysis of the changes in vegetation volume was conducted that considered the degree of outlier and noise removal from the 3D point cloud of riparian vegetation. Finally, a spatial estimation method for patch-shaped riparian vegetation was proposed based on previously attempted 3D estimation techniques for individual objects. Such techniques can serve as foundational technology to represent the spatial attributes of riparian vegetation when related to 3D spatial information of rivers that is based on UAV photogrammetry.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Precision Requirements  
Precision requirements are defined here to ensure that the measurements provided are of sufficient quality for the analysis required to meet FMF-3 and -4. Table 6 describes instrument band performance requirements, as well as band co-alignment requirements for multi-spectral analysis and geo-location requirements to enable users to locate ground targets.  
Although it is clear that the goal is to achieve maximum precision, threshold values for these requirements were defined to support flexibility in design at the mission level as well as overall mission feasibility and affordability. Throughout this analysis the threshold was not intended to represent the optimal design but rather the minimum performance at which the intended analyses and production of the intended data products would be possible (although potentially limited in scope compared with the optimal design).
# On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks
## 3 Classification for Predicting Ticket Reopening
### Different Approaches to Classification  
Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data and make reasonable decisions with minimal human intervention. In contrast with many statistical modeling approaches, which generally value inference over prediction, the focus of machine learning is predictive accuracy (see [30]). High predictive accuracy is usually achieved by training complex predictive models, often involving advanced numerical optimization routines, on a very large number of training examples.  
According to the survey provided in [31], in this paper, the following supervised classification techniques are considered: decision tree, random forest, boosting, logistic regression, Artificial Neural Network (ANN) and SVM.  
The chosen architecture for the decision tree based classifier is inspired by [32]. The random forest based approach to classification follows [33]. The setup for boosting resembles [34], whereas the logistic regression one is inspired by [35].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Ticket Close Code** & **Relative Frequency** \\ \hline Known network outage & 42.99\% \\ Network outage—monitoring & 22.35\% \\ Access network degradation—backbone provider & 13.14\% \\ Network infrastructure macroproblem & 12.48\% \\ No trouble found & 4.10\% \\ Line/device problem—reset device & 1.74\% \\ LNI-minimum bitrate & 1.71\% \\ Known problem pending for resolution & 0.44\% \\ OLO2 OLO problem & 0.41\% \\ Activity on the OCA protocol & 0.30\% \\ Access network degradation—network operator & 0.10\% \\ Maximum obtainable performance & 0.10\% \\ Network infrastructure access degradation—monitoring & 0.04\% \\ Performance degradation—monitoring & 0.04\% \\ Unexploited link/GNP & 0.03\% \\ Line/device problem—reboot device & 0.02\% \\ Wrong assignment & 0.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative frequencies of the values exhibited by the ticket close code variable.  
Instead, in the case of the ANN, we consider a two-layer fully connected network. For the hidden layer, we resort to ReLU nonlinearity, whereas, for the output layer, we have a Softmax loss function. The size of the neural network for the input and output layers is dependent on the input dataset (\(X\) and \(X_{real}\), alternatively) and classes respectively, while the hidden layer is arbitrarily set.  
Finally, the SVM classifier follows the classical approach from [36].
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Plug and Play
#### 2.1.2 Wireless Communication Technologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Data Rate** & **Coverage** & **Disadvantages** & **References** \\ \hline \multirow{4}{*}{PLC} & NB & 10–500 kbps & 150 km & Difficult to achieve high bit rates. \\  & BB & 10–200 Mbps & 1.5 km & Signal attenuation. \\  & & 10–200 Mbps & 1.5 km & Interference from electric component connected to the line. \\ \hline Fiber & PON & 100 Mbps–2.5 Gbps & 10–60 km & High capital costs. \\ optics & AON & 100 Mbps & 10 km & Difficult to upgrade. \\ \hline \multirow{3}{*}{DSL} & HDSL & 2 Mbps & 3.6 km & Possible data quality degradation. \\  & ADSL & 1.3–8 Mbps & 5 km & High operational costs. \\ \cline{1-1}  & VDSL & 16–85 Mbps & 1200 m & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different wired technologies for M2M communication. although it may reduce the quality and security of the protocol at the same time [49; 62]. Figure 3 represents a comparison of different wireless technologies based on range and data rate. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Technology**} & \multicolumn{1}{c}{**Data Rate**} & **Coverage** & **Disadvantages** & **References** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Cellular \\ network \\ communication \\ \end{tabular} } & GSM & Max 14.4 Kbps & 1–10 Km & Data rates low & \multirow{3}{*}{[35]} \\  & GPRS & Max 170 Kbps & 1–10 Km & Data rates low & & \\  & 3G & Max 2 Mbps & 1–10 Km & High cost & & \\  & WMAAX & Max 75 Mbps & Max 50 Km & Availability limited & & \\ \hline \multirow{6}{*}{Short range} & ZigBee & 250 Kbps & Approre 50 m & Short range and low data rate & [35; 49; 57] \\  & 6 LoWPAN & 250 Kbps & 10–100 m & Short range and low data rate & [58] \\  & Bluetooth & 1–2 Mbps & 15–30 m & Short range & [49; 59] \\  & Wi-Fi & 54 Mb/s & 100 m & Short range & [49; 57; 59] \\  & UWB & 110 Mb/s & 10 m & Very short range & [49; 57; 59] \\ \hline \multirow{3}{*}{LPWAN} & LoRa & 0.3–37.5 Kbps & 3–5 Km (Urban) & Low data range & \multirow{3}{*}{[49; 60; 61]} \\  & SIGFOX & 0.1 Kbps & 3–10 Km (Urban) & & \\  & eMTC & Less than 1 Mbps & 30–50 Km (Rural) & Low data range & \\ \cline{1-1}  & eMTC & Less than 1 Mbps & 5 km (urban) & & \\ \cline{1-1}  & & 17 km (rural) & & Licensed network & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different wireless technologies for M2M communication. Figure 3: Comparison of different wireless technologies based on range and data rate. Figure 2: A schematic illustration of (**a**) the mesh topology of ZigBee and (**b**) the start topology of LoRa communication protocols.
## 2 Related Work
The work reported in [PERSON] _et al._ (2016) also provided a secure data aggregation routing protocol, but unfortunately, it is also based on MACs; in addition, the digital signature and the remaining supporting parameters cause the packets to be very large, making them difficult to process and leading to the consumption of enormous amounts of energy. The physical and MAC layers of a WSN are based on IEEE standard 802.15.4, which was specifically developed for low-rate data transfer. Table 1 briefly compares the most recent network layer protocols for security in WSNs. The detailed literature review presented above yields the conclusion that secure data routing in WSNs is complicated by WSN resource constraints, such as limitations on power, signal strength, reliability of wireless communication, and memory storage. The shortcomings of the reviewed security mechanisms, as summarized in Table 1, prevent them from successfully securing WSNs against most network-layer attacks. Therefore, in the design of a security mechanism specifically for WSNs, the abovementioned limitations should be considered.
## 1 Introduction  
System identification is the theory or art of constructing mathematical models of dynamic systems based on observed inputs and outputs. It involves creating models for unknown systems, simulating real-world behavior in cases where there is limited prior knowledge of the system's structure. In this context, the identification of nonlinear systems is considered a challenging problem because it involves two main stages: selecting the model structure with a certain number of parameters and selecting an algorithm that estimates these parameters [1].  
Given the complexity of modeling nonlinear systems, techniques like Takagi-Sugeno (T-S) are employed [2], providing a powerful tool for the identification and modeling of fuzzy systems due to their versatility, interpretability, and adaptability to a wide range of applications. These advantages make them valuable in control engineering, decision making, and other areas where the modeling and analysis of complex systems are required.