esa-sceva/satcom-chunk-collection huggingface dataset:
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 7 Performance Results
### Computing Performance  
Computing performance can be viewed as the time a PC takes to launch an algorithm. Here, performance was calculated grounded on the packets exchanged in every protocol. This is due to the fact that the larger the number of packets involved, the higher the number of calculations needed to process them. However, in BLOT, the number of messages (i.e., seven messages) are needed to communicate the location of a friend. Additionally, in BL0K, seven messages are transmitted to query user location. Therefore, BLOT's connection performance is equal while computing performance is comparable, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Computation Time** & **Connection in Time (s)** \\ \hline BMobishare & 1.75 s & 1.5 \\ BLOT & 10 ms & 0 \\ BL0K & 7.8 ms & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computation performance measurement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Information** & **Size of Information (Bits)** \\ \hline J(A) & 64 \\ SNS to the user (X) & 64 \\ User to SNS (e) & 32 \\ SNS to the user (Y) & 64 \\ User to LBS and back (Location) & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information size in bits.  
Figure 9: Performance entropy comparison of BL0K offers has better entropy than BLOT because the BLOT uses a lot of anonymous messages for increasing security, but in BL0K is achieved by using the zero-knowledge proof algorithm. No multiple or redundant information or queries sent on the network.
## 5 Discussion
Compared to third-party and commercial timestamp certification systems, our approach leverages the inherent properties of a public blockchain to ensure that the critical function of proving data integrity remains within the system rather than being outsourced to external entities that may impose arbitrary conditions for service access and utilization. By recording data hashes on a public blockchain, the proof of integrity can be independently verified by authorized entities in the event of disputes. This internal verification process enhances system trust and differentiates our approach from conventional timestamping methods, which rely on external certification providers. We utilized a publicly available dataset of the New York metropolitan area, encompassing approximately eight million residents, and conducted experiments to derive average transaction cost values. These values, derived from analyzing multiple threshold configurations over various time intervals, offer a practical baseline for expense estimation. This baseline, supported by empirical data, allows for the projection of costs over different durations, thereby enabling more informed decisions regarding investment and operational expenses. The results demonstrate that our approach maintains feasible costs even amidst blockchain fee variability. By aggregating data and employing a threshold-triggered strategy, the system avoids unnecessary network congestion and minimizes operational expenses. Consequently, these cost estimates underscore the scalability and applicability of our system in densely populated, data-intensive environments.
## 3 Routing and Wavelength Assignment  
The wavelength requirements in a network depends on both the physical topology, described by the adjacency matrix \(A\), and the logical topology which defines the profile of traffic demands between nodes. This profile is represented by a traffic matrix, where each entry specified the demand between a pair of nodes. As noted previously, in this work we assume a uniform traffic profile where each pair of nodes has one unit of traffic demand, corresponding to a full-mesh logical topology.  
For each traffic demand between a pair of nodes, a path must be found in the physical topology, and a wavelength must be assigned to it. This process is known as routing and wavelength assignment (RWA) problem. Since multiple paths exist between each pair of nodes, the routing objective is to determine the shortest path using a heuristic such as [PERSON]'s algorithm. Furthermore, in the presence of \(1+1\) OCh protection, two node-disjoint paths must be computed. Finding such paths is a well-known problem in graph theory. A common approach to deal with this problem is [PERSON]'s algorithm [19] or [PERSON]'s algorithm [18]. However, these algorithms compute only two shortest node-disjoint paths, and, therefore, cannot be applied to determine the robustness parameter \(R_{G}\), which requires computing the maximum number of node-disjoint paths between each pair of nodes. To address this limitation, this work uses a solution based on the Ford-Fulkerson algorithm, transforming the node-disjoint path problem into a maximum-flow problem [20], and subsequently using Depth-First Search (DFS) to extract the required paths. This solution is implemented using Algorithm 1.  
The wavelength assignment problem consists of assigning one wavelength to each path associated with a traffic demand, subject to two constraints:(i) each path must use the same wavelength on all links along its route from the source to the destination (wavelength continuity constraint); (ii) different paths that traverse the same link must be assigned different wavelengths (wavelength conflict constraint). This problem can be solved using ILP techniques, graph coloring approaches, or heuristics (e.g., first fit, most used, etc.) [5]. ILP techniques provide exact results but are computationally intensive, making them impractical for large networks. Graph coloring is faster than ILP but does not guarantee optimal results. Finally, heuristics are the fastest methods but only provide approximate solutions. As a compromise, between accuracy and computational complexity we used graph coloring in our work.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Mechanical Properties of Mortar
#### 3.2.2 Bulk Density  
The bulk density of mortar, measured using SSD weights for various curing ages, is illustrated in Figure 7. For each mix composition, the average values of three mortar cubes were recorded at curing intervals of 3, 7, 28, 56, and 91 days. As shown in Figure 7, the bulk density increased with MGWP content up to 15% replacement level. The highest bulk density was observed at 10% MGWP replacement, followed by a gradual decline with further increases in MGWP content.  
The enhanced bulk density at lower replacement levels is linked to the micro-filler effect of MGWP, where fine particles infiltrate and plug capillary pores, reducing permeability and densifying the mortar structure [54; 55]. This densification improves the paste-to-aggregate bond, resulting in a more compact and durable matrix compared to conventional mortar [3]. Beyond 15% replacement, however, the reduction in cementitious material leads to a decrease in density, as confirmed by earlier studies [15; 44; 56].  
Figure 6: Effects of MGWP on the compressive strength of mortar.
## 4 Results and Discussion
### End-to-End Delay  
In this section, the end-to-end delay is evaluated of the proposed DR-UAVIoT as compared with the existing AODV and GyTAR solutions. It is observed from the end-to-end results that DR-UAVIoT has better performance and less delay as compared with AODV and GyTAR protocols. The end-to-end delay was measured in the presence of a different numbers of IoT devices in the networks. The proposed DR-UAVIoT protocol was designed for UAVs and IoT devices to collect the data especially when more nodes were present on the IoT network side. Figure 8 clearly shows the better and shorter delay time in the presence of more IoT devices in the network. The delay graph smoothly increases due to there being more load on the network resources and data congestion issues. This is one of the problems in ground IoT networks which is resolved by adopting UAV nodes and designing preferable protocols for data transmission. It was also observed that the performance of AODV degraded and consumed more time for data processing compared with GyTAR as AODV was designed for MANET networks and is not feasible for IoT devices due to its limited processing, and memory capabilities. Figure 8 shows the end-to-end delay results in the presence of different numbers of IoT devices.  
The second results are again on the end-to-end delay with different UAV node velocities in the network. The end-to-end delay is one of the significant factors, especially for routing protocols. The proposed DR-UAVIoT had less delay and better results compared with the existing AODV and GyTAR solutions. It was observed from the end-to-end results that DR-UAVIoT had a better performance and less of a delay compared with the AODV and GyTAR protocols. These results were collected when the UAV nodes were communicating with each other in the sky. The end-to-end delay was measured in the presence of different UAV speeds in the networks. The proposed DR-UAVIoT protocol was designed for UAVs and IoT devices to collect data especially when the UAV nodes are moving at different speeds and providing coverage to IoT networks. Figure 9 clearly shows the better and shorter delay time of the proposed solution when the UAV moves with different velocities. The delay graph smoothly increases due to the higher load on the network resources and data congestion issues. This is one of the problems in ground IoT networks which is resolved by adopting UAV nodes and designing preferable protocols for data transmission. It was also observed that the performance of AODV degraded and consumed more time for data processing compared with GyTAR as AODV was designed for MANET networks and is not feasible for IoT devices due to its limited processing  
Figure 8: End-to-End Delay in the Presence of Different Numbers of IoT Devices.  
and memory capabilities. Figure 9 shows the end-to-end delay results in the presence of different UAV nodes' velocities.
## 2 Full-Scale Experimental Model
### Model Design
#### 2.1.2 Backfill Material  
Currently, most types of locally available backfill material in Danang City, Vietnam, are cohesive soil and they do not meet the requirements of the mechanical, physical, and chemical properties of reinforced backfill materials according to the standards [26; 28].  
In Danang City, the hillside soil from the Hoa Ninh area is the most suitable source of backfill material, based on current standards [3]. Table 1 shows the mechanical, physical, and chemical properties of the local backfill material. The soil chosen was sandy clay soil according to the standards from AASHTO [26], AFNOR [12], and TCVN [14]. The particle size distribution and results of the standard Proctor compaction test are shown  
Figure 1: The test layout of the full-scale MSE model.  
in Figure 2. The grain sizes of D\({}_{60}\), D\({}_{30}\), and D\({}_{10}\) were 0.09, 0.65, and 4.1 mm, respectively. The uniformity coefficient C\({}_{\rm u}\) was determined to be 45.6. The friction angle and the unit cohesion were 34.3\({}^{\circ}\) and 5.1 kN/m\({}^{2}\), respectively. The maximum dry density was 18.16 kN/m\({}^{3}\) when the optimum moisture content was 12.5%. The properties of the selected backfill material met the requirements of reinforced soil according to the standards from AFNOR [12], AASHTO [26], and TCVN [14] for MSE wall constructions.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Saturated density, \(\gamma\) & kN/m\({}^{3}\) & 2.070 \\ Dry density, \(\gamma_{\rm k}\) & kM/m\({}^{3}\) & 1.816 \\ Friction angle, \(\varphi_{\rm soil}\) & Degrees & 34.3 \\ Cohesion, \(\varsigma_{\rm soil}\) & Pa & 5100 \\ Plasticity index, IP & - & 8.55 \\ Uniformity coefficient, C\({}_{\rm u}\) & - & 45.6 \\ pH & - & 5.9 \\ Ion, Cl\({}^{-}\) & mg/g & 0.094 \\ Ion, SO\({}_{4}\)\({}^{2-}\) & mg/g & 0.497 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the local backfill soil.  
Figure 2: Properties of the backfill material: (**a**) local backfill soil; (**b**) grain-size distribution of the backfill soil; (**c**) Proctor compaction results.  
However, the cohesion of 5.1 kN/m\({}^{2}\) in the backfill soil could affect the interaction between the steel reinforcement and the reinforced soil. In addition, this soil contained a remarkable amount of sulfate ion (SO4\({}^{2-}\) = 0.497 mg/g); thus, it could affect the long-term durability of the reinforcement due to corrosion. Therefore, to efficiently utilize the available local backfill material, the reinforcement used in the wall was galvanized to prevent corrosion and the soil-reinforcement interaction was enhanced by arranging steel ribs on the reinforcement mesh.  
The backfill soil was compacted using a light tamping rammer (Niki NK55 from China) to ensure the soil had the same density. Each soil layer of 0.12 m in height was compacted until a relative density of 95% of the maximum dry density was achieved.
## 5 Fuzzy Logic Approaches in Trust Management
### Effective Metrics and Comparative Analysis of Methods Based on These Metrics
#### 5.2.1 Comparative Analysis Based on Metrics
* Availability: The model encourages continuous engagement through rewards and penalties, ensuring that trust evaluations are consistently updated and available for decision making in real-time scenarios, thus improving the availability of trust data in the CEEC network. * Dynamicity: The fuzzy-based trust evaluation model is designed to adapt to the dynamic nature of edge-computing environments. By incorporating fuzzy logic to handle uncertainty and variability in trust factors as well as the incentive negative decay mechanism to address intermittent selfish behavior, the model can effectively respond to changes in trust dynamics and promote continuous cooperation among edge devices, demonstrating dynamicity in trust evaluation. * Context awareness: By incorporating context-specific trust factors and incentives tailored to the CEEC network model, the model demonstrates awareness of the unique characteristics and requirements of trust evaluation in edge computing contexts, thereby enhancing context awareness in trust assessment. Scalability, integration, availability, dynamicity, and context awareness are just a few of the critical requirements addressed in the study cited in [34]:  
* Scalability: By utilizing algorithms that consider user preferences and criteria, the framework can be scaled to handle diverse user requirements and fog service offerings. * Integrity: By ensuring confidentiality and authentication in fog services, the framework contributes to maintaining the integrity of the data and transactions within the fog computing environment. * Availability: The framework aims to provide reliable and available fog services to users by monitoring factors such as the transmission rate, response time, and usability. * Dynamicity: This framework can dynamically match users with trustworthy fog services by adapting to changing user requirements and fog service conditions. * Context awareness: By tailoring fog service selection based on user-specific criteria and priorities, the framework provides an understanding of the context in which fog services are utilized.
## 2 Materials and Methods
### Methods
#### 2.3.3 Health Risk Assessment Procedure  
Evaluations of groundwater quality and environmental management need the help of health risk assessments. In this study, a four-stage risk assessment process comprising hazard identification, dose-response assessment, exposure assessment, and risk characterization was implemented for the health risk evaluation of groundwater nitrate contamination (Figure 2).  
Human health risk assessment due to NO3- contamination in groundwater is based on standards recommended by USEPA [26]. Drinking water contaminated with NO3- can have an impact on human health through ingestion and dermal exposure. The health risk assessment was performed on three exposed groups, namely children, adult females, and adult males. The non-carcinogenic health risk model from ingestion (oral intake/drinking water consumption) and dermal contact routes is computed utilizing the subsequent formulas [24].  
\[\mathrm{CDI}=\frac{\mathrm{CPW}\times\mathrm{IR}\times\mathrm{ED}\times\mathrm{ EF}}{\mathrm{ABW}\times\mathrm{AET}} \tag{1}\]  
\[\mathrm{DAD}=\frac{\mathrm{TC}\times\mathrm{K_{i}}\times\mathrm{EV}\times \mathrm{ED}\times\mathrm{EF}\times\mathrm{SSA}\times\mathrm{CF}}{\mathrm{ABW} \times\mathrm{AET}} \tag{2}\]  
\[\mathrm{HQ_{Oral}}=\frac{\mathrm{CDI}}{\mathrm{RfD}} \tag{3}\]  
\[\mathrm{HQ_{Dermal}}=\frac{\mathrm{DAD}}{\mathrm{RfD}} \tag{4}\]  
\[\mathrm{THQ}=\sum_{i=1}^{n}(\mathrm{HQ_{Oral}}+\mathrm{HQ_{Dermal}}) \tag{5}\]  
The calculated chronic daily intake (CDI), expressed in mg/kg day, is the amount of nitrate that is ingested and dermally absorbed; CPW denotes the nitrate content in drinking water (mg/L); IR represents the water ingestion rate in L/day; ED and EF indicate the exposure length (years) and exposure frequency, respectively, with ED values of 64, 67, and 12 for adult males, adult females, and children, respectively. EF is designated for 365 days annually. ABW and AET denote, respectively, the average body weight (kg)  
Figure 2: Framework of the four-stage HHRA process.  
and average exposure time (days), with ABW values of 65 kg for adult males, 55 kg for adult females, and 15 kg for children. The contact duration (TC) is quantified in hours per day, with a value of 0.4 h per day for both children and adults (females and males). The characteristics of dermal adsorption are represented by EV (times/day: 1 bathing session per day), Ki (cm/h: 0.001 cm/h), and SSA (skin surface area). The SSA values in this study are 12,000 cm\({}^{2}\) for children and 16,600 cm\({}^{2}\) for adults. In the research area, the conversion factor (CF) for adults and children is set at 0.001. RfD stands for reference dosage, and based on the USEPA, the RfD value for NO\({}_{3}\)\({}^{-}\) is 1.6 mg/kg/day. However, because of ingested nitrate pollution, groundwater samples exhibiting total hazard quotient (THQ) values more than 1 (THQ \(>\) 1) may be seriously dangerous to human health [24; 27]. The formulas for calculating the CDI, DAD, and THQ were developed separately for the threats to human health that are evaluated during the wet and dry seasons.
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Observational Data  
The observational meteorological data comes from the SYNOP network, provided by Meteo France [39]. This consists of temperature and precipitation time series, recorded at 3-h intervals from January 1996 to present. The SYNOP data is considered as the reference dataset for the purpose of this study.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 2 GNSS Use in Geodesy and Jamming
In cases of raw observations storage, a post-process quality check can be performed, which allows for a more detailed insight into the quality of the observations from which the final coordinates were obtained. For this purpose, determination of the signal-to-noise (SNR) or carrier-to-noise ratio (C/N\({}_{0}\)) and multipath effects, based on the Melbourne-Wubbena linear combination of code and carrier-phase observations, are the most effective estimates to describe the measurement conditions in the field. The first is related to unintentional disturbances, while the latter is related to both intentional and unintentional disturbances. GNSS instruments for geodetic applications usually do not use inertial measurement units (IMUs) since their aim is the continuous reception of raw GNSS signals, which are used in further processing, in either RTK or post-processing. Reception of raw GNSS observations is essential since positioning is based on the determination of coordinates only from the GNSS, or, in some cases, through a combination of the GNSS and terrestrial geodetic measurements. Knowledge that disruptions can cause positioning problems, either due to the inability of signal reception or wrong ambiguity fixing, is essential. The awareness of mistaken positioning should be taken into consideration since the coordinates are used in further computation.
# E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios
###### Abstract  
With the growing demand for data sharing file formats in financial applications driven by open banking, the use of the OFD (open fixed-layout document) format has become widespread. However, ensuring data security, traceability, and accountability poses significant challenges. To address these concerns, we propose E-SAWM, a dynamic watermarking service framework designed for edge cloud scenarios. This framework incorporates dynamic watermark information at the edge, allowing for precise tracking of data leakage throughout the data-sharing process. By utilizing semantic analysis, E-SAWM generates highly realistic pseudostatements that exploit the structural characteristics of documents within OFD files. These pseudostatements are strategically distributed to embed redundant bits into the structural documents, ensuring that the watermark remains resistant to removal or complete destruction. Experimental results demonstrate that our algorithm has a minimal impact on the original file size, with the watermarked text occupying less than 15%, indicating a high capacity for carrying the watermark. Additionally, compared to existing explicit watermarking schemes for OFD files based on annotation structure, our proposed watermarking scheme is suitable for the technical requirements of complex dynamic watermarking in edge cloud scenario deployment. It effectively overcomes vulnerabilities associated with easy deletion and tampering, providing high concealment and robustness.  
edge cloud; OFD files; semantic analysis; dynamic watermarking +
Footnote †: journal: Future Internet