esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
## 2 Principles of Detection  
### Oil-Gas Separation Theory  
According to the principle of dissolution equilibrium, when the insulating oil is in a confined container, the dissolved gas molecules gradually diffuse from the oil due to the molecular thermal motion. Therefore, a state of gas-liquid dynamic equilibrium is achieved inside the container. The dissolution equilibrium model is shown in Figure 1.  
As described by [PERSON]'s law, the rate of gas molecule diffusion is proportional to the density difference on both sides of the oil surface. The gas molecule diffusion rate equation is:  
\[\frac{d_{ngi}}{dt}=\left(C_{tai}-K_{i}C_{tgt}\right)D_{i} \tag{1}\]  
where \(n_{gi}\) is the amount of the \(i\)-th gas in the container; \(C_{tai}\) and \(C_{tgt}\) are the concentrations of the \(i\)-th gas in the liquid and gas phases, respectively (at time t); \(K_{i}\) is the Oswald coefficient of the \(i\)-th gas; and \(D_{i}\) is the molecular diffusion factor. For a constant volume of the oil sample, the total amount of the substance is conserved, and the concentration of gas in the container can be described as:  
\[U_{oi}V_{o}=C_{tai}V_{o}+C_{tgt}V_{g} \tag{2}\]  
where \(V_{o}\) represents the volume of the oil sample; \(V_{g}\) indicates the volume of the gas chamber in the container; and \(U_{oi}\) denotes the concentration of the gas in the oil at the time of sampling. According to Equations (1) and (2), the relationship between the time and the amount of substance of gas can be formulated as follows:  
\[n_{\text{gi}}=\frac{U_{oi}}{\left(\frac{1}{V_{o}}+\frac{K_{i}}{V_{g}}\right)}.\left\{1-exp\left[-t\cdot\left(\frac{1}{V_{o}}+\frac{K_{i}}{V_{g}}\right) \cdot D_{i}\right]\right\} \tag{3}\]  
Figure 1: Model of dissolution equilibrium.  
At dynamic equilibrium, the gas phase concentration of the \(i\)-th gas can be expressed as:  
\[C_{gi}=U_{oi}/\left(K_{i}+\frac{V_{g}}{V_{o}}\right) \tag{4}\]  
The equilibrium time \(\tau\) in Figure 2, which can be calculated with Equation (5), is the time required for \(C_{gi}=\left(1-e^{-2.5}\right)C_{gi}\).  
\[\tau=\frac{2.5}{\left(\frac{1}{V_{o}}+\frac{K_{i}}{V_{g}}\right)D_{i}} \tag{5}\]
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 2 Materials and Methods
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 2.4.1 Ultraviolet Vis Spectrometry  
Aliquots (0.2 mL) of the colloidal suspension were taken and diluted in 2 mL of deionized water. The UV-Vis spectra of the resultant diluents were then measured using a double-beam PC scanning spectrophotometer, (LAMBDA 365 UVVis Spectrophotometer, 190-1100 Nm with wavelength range 190-1100 nm, PerkiElmer, Mumbai, Maharashtra, India) carried out at the Scientific Research Center and Measurement-Tanta University. It was possible to identify the bio-reduction of the pure zinc ions in the aqueous solution. The absorption maxima were searched between 200 and 800 nm in wavelength. The absorbance peak for the ZnO nanoparticles was reported to occur at wavelengths of between 310 and 360 nm.
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 3 Proposed Framework
### A Review of Fundamentals in Particle-Based Fluids for Data Acquisition  
In this paper, data are generated using SPH-based fluid particle simulation [10]. This method quickly acquires physical quantities such as density and pressure held by particles using kernels during calculations. Stability is also enhanced by designing kernels that take into account the physical properties of various forces (e.g., pressure, viscosity, surface tension). The equation below describes the basic kernel form of SPH (see Equation (1)).  
\[A_{S}(r_{i})=\sum_{j}m_{j}\frac{A_{j}}{\rho_{j}}W\big{(}r_{i}-r_{j},h\big{)} \tag{1}\]  
where \(A_{j}\) represents the field quantity at position \(r_{j}\), \(r_{i}\) is the position of the target particle, \(m_{j}\) is the mass of the neighboring particles, and \(\rho_{j}\) is the density of the neighboring particles. Additionally, \(r_{j}\) indicates the position of the neighboring particles, \(h\) is the smoothing length, and \(W\) denotes the kernel function. The range within which neighboring particles are found is determined by the size of \(h\), and the influence is calculated through \(W\), a distance-based weight between the target particle and the neighboring particles. As described in Equation (1), the kernel function \(W\) plays a significant role in the movement of particles. Therefore, the design of \(W\) can create simulations with desired characteristics. The following is the SPH kernel used in this paper (see Equations (2)-(4)).  
\[W_{poly}(r,h)=\frac{315}{64\pi h^{9}}\begin{cases}\big{(}h^{2}-r^{2}\big{)}^{ 3}&0\leq r\leq h\\ 0&\text{otherwise}\end{cases} \tag{2}\]\[W_{spiky}(r,h)=\frac{15}{\pi h^{6}}\begin{cases}(h-r)^{3}&0\leq r\leq h\\ 0&\text{otherwise}\end{cases} \tag{3}\]  
\[W_{viscosity}(r,h)=\frac{15}{2\pi h^{3}}\begin{cases}-\frac{r^{3}}{2h^{5}}+\frac{ r^{2}}{h^{2}}+\frac{h}{2r}-1&0\leq r\leq h\\ 0&\text{otherwise}\end{cases} \tag{4}\]  
In the above equation, \(W_{poly}\), \(W_{spiky}\), and \(W_{viscosity}\) are used for calculating density, pressure, and viscosity, respectively, where \(r\) is \(r_{i}-r_{j}\). To train the model proposed in this paper, data acquired from the simulation, such as position, density, the number of neighboring particles, and values of the state equation used in input computation, are utilized. All these values influence the change in density. The level-set values calculated through density serve as a criterion for distinguishing the fluid's free surface and represent the surface through the boundary of classified particles. Learning the changes in density over time allows the model to identify the surface. The state equation used in this paper is as in Equation (5).  
\[p=k(\rho-\rho_{0}) \tag{5}\]  
where \(p\) represents pressure, \(k\) is the gas constant, \(\rho\) is the density, and \(\rho_{0}\) denotes the rest density.
## 8 Reliability Analysis  
It is necessary to set the sequence number in the MHC method because the node values of the message hash chain should be calculated in strict order when constructing the chain. The difference between the sequence number contained in the message hash chain and that contained in IPSec is that the sequence number field is an optional field in IPSec, which is mainly used to provide anti replay services, while the sequence number field of MHC method is a necessary field, and each node of the message hash chain needs to be constructed according to the sequence number. After IPSec establishes a SA for the first time or the SA reaches its life cycle to renegotiate parameters, it will clear the sequence number stored in the SA, and then incrementally count each message. The sequence number of the message hash chain inherits the previous changes and is not cleared, and the verification of each message must verify whether the sequence number changes incrementally in sequence. The reliability of message hash chain is mainly reflected in that the communication receiver should not only compare the sequence number to judge whether it is increased in order, but also verify the integrity, authenticity and non-repudiation of the whole message through the \((MHC,Gen,Sig,Ver)\) scheme, and complete packet loss retransmission, chain synchronization and timely error detection through the sequence number.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 2 Literature Review
### Proactive Approaches  
Proactive methods assess the inherent risks of road design, focusing on characteristics like geometry, traffic flow, and speed to predict potential fatalities and injuries without relying on historical accident data. A key example is the International Road Assessment Programme (iRAP) [13] which combines proactive and reactive approaches for comprehensive road safety analysis. The iRAP methodology includes five protocols [13]:  
1. Crash Risk Mapping, a reactive mapping based on historical accident data;
2. Star Rating, which proactively evaluates road safety using design parameters;
3. Fatalities Estimation Mapping, which estimates potential fatalities and injuries proactively;
4. Safer Road Investment Plans (SRIP), which suggests cost-effective safety interventions;
5. Performance Tracking, which combines proactive and reactive assessments to prioritize interventions.  
Studies have demonstrated the method's effectiveness. For instance, in Algeria, iRAP and GIS tools identified high-risk segments in a new highway project before it opened, suggesting improvements like speed control [14]. In Romania, an integrated proactive-reactive approach classified road segments by risk levels, addressing infrastructure deficiencies and guiding safety investments [15]. This methodology is adaptable to both new and existing infrastructure, offering a robust framework for road safety planning.
## 3 Methods
### Behavior Based Approach  
Behavior-based method is one of the common choices for swarm robotics, since it is typically decentralized and can be realized with less communication compared to the others [1]. It usually defines some simple rules and actions for members in a swarm to guide them to take particular actions when conditions change; finite state machines (FSM) can realize this. For every swarm member, a finite state machine could be defined as a triple \(T=(S,I,F)\) where \(S=\{S_{1},S_{2},\cdots,S_{n}\}\) is a finite non-empty set of states, \(I=\{I_{1},I_{2},\cdots,I_{n}\}\) is a finite non-empty set of inputs, and \(F:I\times S\to S\) is the state-transition function set, which describes how inputs \(I\) affect states \(S\). Since the member has some blind zone in the back, one cannot see any other member in the case of no individual in its visual field. Furthermore, the members need to fly together in V-shape formation without collision with each other or hit the obstacles. The states in \(S\) can be defined as \(S=\{S_{1},S_{2},S_{3}\}\) where \(S_{1}\) is searching team members, \(S_{2}\) is anti-collision with other member or obstacle avoidance, and \(S_{3}\) is forming the  
Figure 2: Visual field limited Time-varying Characteristic Swarm (v-TVCS).  
formation. \(\mathcal{S}_{v}^{t}(r_{i})\) is the TVCS of agent \(i\) at time \(t\); \(l_{c}^{t}\) and \(l_{o}^{t}\) are the measured distance of the nearest member and the closest obstacle, respectively; and \(d_{s}\) is the safe distance. The input set now can be represented as \(I=\{I_{1},I_{2},I_{3}\}\), where:  
\[\begin{cases}I_{1}:\mathcal{S}_{v}^{t}(r_{i})-\{r_{i}\}=\phi\\ I_{2}:l_{c}<d_{s}\lor l_{o}<d_{s}\\ I_{3}:\text{Others}\end{cases} \tag{6}\]  
The state-transition functions could be listed as follows and are represented in Figure 3.
###### Abstract  
Despite the technological progress achieved by our society, natural or man-made disasters continue to pose a challenge and put to the test the organization and preparedness of government apparatuses. The loss of life and property following calamities motivates citizens into action. In response, volunteer Urban Search And Rescue (USAR) organizations are established with the aim of providing help when needed. Just like their government funded and run counterparts, these organizations try to use what technology has to offer to improve their efficiency. However, they face challenges due to their limited funding and inability to access certain resources such as licensed spectrum for their communications. This situation is further deteriorated because civilian communication infrastructure cannot be relied upon to be available in case of disasters. Communications are paramount for any operation, let alone USAR. To address this need, we have designed and built a field deployable communication system that is able to leverage what existing communication infrastructure is available and utilizes 802.11 ax and LoRaWAN that operate in unlicensed spectrum to provide wireless local connectivity. The performance and suitability of the system have been tested in various small and large-scale exercises, while the range and transmission speed achieved by 802.11 ax over its predecessors are determined through a measurement campaign. In this article, we report the results and lessons learned from our approach to fill this communication gap and the suitability of the selected technologies for the role.  
field communications; emergency response; networking system design; Radio Environment Map (REM); 802.11 ax; LoRaWAN; Kriging
# A Review on Solar Energy Utilization and Projects: Development in and around the UAE
## 7 International Solar Projects: Sponsored by the UAE
### Masdar Special Projects, Egypt  
As illustrated in Figure 19a, Masdar has built and installed 7000 off-grid solar household PV systems in houses and commercial buildings in six distant regions, namely Qina, Sohag, Aswan, Matrouh, Al Wadi Al Jadeed, and Luxor. Between December 2015 and April 2016, Masdar installed seven solar-diesel plants totaling 20 MW at 5 MW from Shalateen, 2 MW from Abu Ramad, 6 MW from Marsa Alam, 1 MW from Halayeb, 500 kW from Darb Al Arbaeen, 500 kW from Abu Minqar, and 5 MW from Al Farafra in the governorate of the Red Sea and the Al Wadi Al Jadeed Governorate, Egypt, as shown in Figure 19b,c. This project was financially funded by the Abu Dhabi government, UAE, for rural electrification in Egypt, and it was completed in March 2014. The UAE-Egypt Commission is sponsoring Egypt's New and Renewable Energy Authority (NREA). These four projects, each tailored to the requirements of a unique community, offer solar power to medical clinics/hospitals, mosques, educational institutes/schools, and many houses that previously lacked dependable access to power or were not linked to the national grid.
## 2 Materials and Methods
### Determinations of L-Ascorbic Acid (AA) Content  
L-ascorbic acid (AA) (as the sum of AA and L-dehydroascorbic acid (DHAA) after its reduction to AA) was determined using high-pressure liquid chromatography coupled with a UV-VIS detector (Prominence HPLC system, Shimadzu, Kyoto, Japan) according to the method previously stated in the literature with our own modification [11; 38]. One mL of extract was transferred to a volumetric flask and diluted to 10 mL with 2% of oxalic acid. Then, the obtained solution was diluted in a 1:1 ratio with 1% DTT (DL-dithiothreitol). The solution was left in a dark place for 1 h to reduce DHAA (L-dehydroascorbic acid) to AA (L-ascorbic acid). This reaction solution was filtered through a 0.45 \(\upmu\)m PTFE syringe filter into a chromatographic vial. The sample volume of 20 \(\upmu\)L from each chromatographic vial was injected into the HPLC system. The separation was carried out using an Onyx Monolithic C18, 100 \(\times\) 4.6 mm column (Phenomenex, Torrance, CA, USA), at 25 \({}^{\circ}\)C. 0.1% aqueous solution of m-phosphoric acid was used as the eluent at a flow rate of 1.0 mL/min. The analysis was performed in an isocratic system. L-ascorbic acid was determined at 254 nm.  
Blank samples were prepared in the same way as the analytes. All results were expressed in mg/g of freeze-dried pomace. The test was performed in triplicate.  
The reliability of the HPLC-method was validated for linearity, sensitivity (limit of detection, LOD, and limit of quantification, LOQ), precision, and recovery. Precision was determined by six measurements of an AA standard. The LOD and LOQ were defined as 3 times and 10 times the signal-noise ratio, respectively. Recovery was determined in seven representative extracts (water, methanol, ethanol, acetone, 50% methanol, 50% ethanol, and 50% acetone) from rose fruits pomace. Two levels of AA were added to the tested rose fruits pomace (5 and 20 mg/g), and the recovery level was determined twice for each prepared extract.
# Query Processing in Blockchain Systems: Current State and Future Challenges
## 3 Application Domains Identified through Literature Review
### Food Supply Chains  
A supply chain is a network of entities in such sectors as agriculture and manufacturing ranging from producers, who produce a product or service, to the final consumer. In such supply chains, not only the physical exchange of the goods is important, but also the exchange of information about these goods. This information must be available to the participants of supply chain management in order to be able to ensure comprehensive quality control [73]. In the food industry, for example, meat products must maintain a cold chain in order to avoid endangering consumers' health [74]. This means, the temperature of the meat products has to be permanently monitored and fully documented during transport from the slaughterhouse to a retail store [75]. In order to exclude human errors, IoT technologies can be used for the metering and documentation [76].  
While the use of IoT technologies can prevent unintentional measurement errors, it is also necessary to prevent tampering with the documents retrospectively, e.g., to guarantee that a breach of the cold chain is recognizable. Although the captured data must not be edited subsequently, it has to be possible to modify the accompanying documents to the meat products nevertheless, e.g., if additional entries are made during customs inspections or when the goods are handed over to the next supply chain entity [77]. The use of a blockchain to establish an immutable and decentralized data store for this data therefore makes sense. Besides eliminating the risk of fraud, the transparent data sharing capabilities of the blockchain also increase consumer confidence in the quality assurance of food products, as they can verify it in a tamper-proof manner [78].  
There are multiple examples in literature in which blockchains are used to store proofs and certificates regarding food supply chains, e.g., [PERSON] et al. [79], [PERSON] and [PERSON] [80], [PERSON] et al. [81], [PERSON] et al. [82], and [PERSON] et al. [83].  
Based on this research, we can conclude that there are two types of data in food supply chains:  
* The data generated by IoT devices are events and thus, only valid at a specific point in time (e.g., temperature or location).
* There may exist accompanying documents (i.e., semi-structured data) to the goods that are modified over time (e.g., during customs inspections).  
In the context of food supply chains, queries that provide an aggregated overview of all captured data, as well as comprehensive querying of all documented data related to the transport, are particularly relevant. Typical queries, therefore, include, but are not limited to:  
* Aggregate the events by specific attributes for a given period.
* Retrieve the latest state of an accompanying document for a given transport.
* Retrieve the latest state and a prior state of a specific document to highlight changes in the latest state.
## 3 Results and Discussion
### Nanoparticles Storage Ability  
Figure 14 shows the nanoparticles stability in aqueous media over storage time, revealing that the particle size tends to increase along the time when sulfonate groups are absent in the nanoparticles' structure. Considering that all nanoparticles analyzed in these experiments were washed with deionized water to remove free surfactant molecules, the elimination of OADA excess probably lead to the destabilization of the PSNP systems, causing agglomeration of the smaller nanoparticles. In contrast, the SPSNP, which present sulfonate groups derived from the St-S polymerization, maintained their particle size over all the storage time, even removing the OADA excess. The anionic groups distributed along the polymer chains cause electrostatic repulsion among the nanoparticles, inhibiting their agglomeration. Based on these results, sulfonated polystyrene proved to be more suitable to be applied to transport through reservoirs' pores without causing any clogs by agglomerating.
## 5 Cross-Referencing Bloom-Filter-Based Data Integrity Framework
### False-Positive Rate Analysis
#### 5.4.2 False-Positive Rate Analysis of Data Integrity Solution Based on the CRBF  
This section analyzes the false-positive rate of the data integrity solution based on the CRBF. As described in Section 5.1, the DLBF and DSBF store file and dataset information, respectively. False-positives of the proposed framework only apply when recovering from failed transfers. While DLBF is used to recover already transferred objects of an active file, DSBF is used to recover transferred files. Due to the data and layout aware characteristic of  
\begin{table}
\begin{tabular}{c c} \hline \hline Total elements in DLBF & (\(C\times S\)) \\ \hline Total elements in DSBF & \(N\) \\ \hline Total elements in MDBF & \(N\) \\ \hline Where C = number of active file transfers. & \\ \end{tabular}
\end{table}
Table 3: Entries per Bloom filter in CRBF.  
the DLBF, it is possible to avoid false positives [28]. However, DSBF results in false-positive errors. To efficiently handle the false positives of DSBF, MDBFs at both ends of the data transfer are used as cross-referencing Bloom filters.  
As displayed in Figure 5, file metadata are used to generate the source-end MDBF, and 1's complemented the file metadata to generate the sink-end MDBF. In addition, a different hash functions are used for generating these Bloom filters. Hence, the sink-end bit vector is different from the source-end MDBF. Therefore, the resulting false-positive rate by cross-referencing these Bloom filters is represented as  
\[ln\epsilon=-\frac{m}{n}(ln2)^{2}+-\frac{m}{n}(ln2)^{2} \tag{14}\]  
By simplifying Equation (14),  
\[\epsilon=(\frac{1}{e^{(ln2)^{2}}})^{\frac{m}{n}}\times(\frac{1}{e^{(ln2)^{2}}} )^{\frac{m}{n}} \tag{15}\]  
\[\epsilon\approx(0.38254)^{\frac{m}{n}} \tag{16}\]  
Based on Equations (13) and (16), we can conclude that the proposed integrity framework has a lower false-positive rate than the standard Bloom-filter-based integrity solution.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Case Study
#### 2.2.2 UAS Flight Data and Fusion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Name** & **Data Type** & **Spatial Resolution** & **Acquisition Data** & **Spatial Extent** & **Source** \\ \hline USGS NED DEM & raster & 1/9 arc-second & 2001–2003 & Walnut Creek Watershed & [25; 53] \\ PlanetScope & raster & 3 m & 2018–2020 & Walnut Creek Watershed & [9] \\ UAS DEM & raster & 15 cm & 22 November 2021 & Local & \\ Stormwater Inlets & vector & NA & 29 January 2022 & Town of Cary, NC & [52] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Case Study Data.