esa-sceva/satcom-chunk-collection huggingface dataset:
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Man-in-the-Middle Attack  
No intruder can perform a man-in-the-middle attack, because the session key cannot be forged with the use of only the parameters exchanged on the insecure communication channel. The session key calculation uses the entities' real identities, which is a secret value not disclosed in the insecure channel.  
Table 3 data are derived from the analysis of the data presented in [6] and [7]. The protocol designed by [PERSON] et al. [7] does not guarantee anonymity, non-traceability, and resistance to patient's mobile device loss/stealing, which are three critical failures. First, as detected by [PERSON] et al. [6], in the protocol of [PERSON] et al. [7], the patient's real identity is sent in plain text through a public channel, which compromises its anonymity. We observed it also affects the patient's non-traceability. Second, as detected by [PERSON] et al. [6], the proposal of [PERSON] et al. [7] fails to be resistant to the patient's mobile device loss/stealing, because it does not perform access control and requests login and password to the user, which makes the system vulnerable to the access of non-authorized people and hampers its confidentiality.  
The protocol of [PERSON] et al. [6] fails to prevent DoS attack. No initial verification parameter is generated (timestamp, nonce, sequence number) to be sent with the parameters exchanged. Then, the validity of a simpler parameter is not verified before the recipient processes more complex calculations. Therefore, the protocol is vulnerable to DoS attacks, because the system of D2D devices is not robust enough to deal with message flooding. Our protocol accomplished all security objectives analyzed and can, therefore, be considered safer than those designed by [PERSON] et al. [7] and [PERSON] et al. [6].
## 5 Taxonomy of Soft Computing Techniques Aware Clustering-Based Routing Protocols in VANETs
### Particle Swarm Optimization  
PSO is replicated by the social behavior of bird flocking. It includes a swarm of particles in search space, in which a particle captures a position and a velocity in a global search area. Figure 5 shows how a particle achieves the best optimal solution. Throughout the search time, each particle displays its own individual best named as \(pBest_{i}\) and a global best named as \(gBest\). Later obtaining the \(pBest_{i}\), \(gBest\), and particle \(P_{i}\) revises its velocity and position in every iteration by utilizing the following equations:  
\[V_{i,d}(t+1)=w\times V_{i,d}(t)+c_{1}r_{1}(pBest_{i,d}-X_{i,d}(t))+c_{2}r_{2}(g Best-X_{i,d}(t)) \tag{1}\]  
\[X_{i,d}(t+1)=X_{i,d}(t)+V_{i,d}(t+1) \tag{2}\]  
Figure 4: Soft Computing Techniques in VANETs.  
Figure 5: Particle representation in PSO.  
Where w suggests the inertial weight, r\({}_{1}\), r\({}_{2}\) show two evenly dispersed random digits and c\({}_{1}\), c\({}_{2}\) suggest two non-negative factors called acceleration factor, generally set to 2.0. After finding a new location in every iteration, the \(pBest_{i}\) and \(gBest\) of particle \(P_{i}\) is updated by calculating the fitness function. This method is repeated until a static amount of iterations \(I_{\text{max}}\) is achieved.  
[PERSON] et al. [24] proposed a PSO-based efficient clustering V2V routing (CRBP) scheme in VANETs to increase the execution of V2V. The protocol consists of cluster formation, particle coding of the path, and cluster routing. Initially, vehicles are identified with identical changing routes, and the CHs are chosen. Second, the route particle and its velocity, iteration procedures, and fitness functions are intended for optimal routing. Thirdly, methods are suggested that can considerably enhance the effectiveness of routing. In order to create a stable cluster, the position, velocity, and neighbors of the nodes are gathered, and the link fitness is determined so that the optimal route can be found immediately. The results of the simulation signify that node density, node contact radius, and maximum hops among the CH and any member node have a significant impact on CRBP performance; CRBP has a 20% increase in PDR compared to CBVRP and QoS-OLSR and a 47% decline in delay.  
In summary, the PSO-based clustering algorithm improves the stability of the clusters, reduces the delay, improves the efficiency of transmission, and finds the best optimal route from source to destination. However, the no infrastructure traffic environment is assumed with all vehicle nodes having GPS devices and an identical communication radius only.
## 4 Applications of Intelligent Acoustic Monitoring
### Health Monitoring and Ambient Intelligence
#### 4.3.1 Speech Monitoring and Disorder Detection  
In [119], [PERSON] et al. developed an intelligent system for the automatic detection of voice disorders, which could be used in healthcare in smart cities. The system extracted speech signal features based on linear prediction analysis (LP). It assessed the distribution of acoustic energy, identifying phonation disorders, especially in the low-frequency range (1-1562 Hz), which proved crucial in detecting irregularities. The solution achieved very high efficiency--almost 10%--for both steady sounds and continuous speech, making it useful in everyday use. The system could support prevention in individuals particularly exposed to occupational hazards (for example, teachers, lawyers, and singers) and among seniors, whose number is growing worldwide. The work presented a valuable proposition for e-health--a simple, effective, and ready-to-implement technology for monitoring voice health in urban environments.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Geochemical Analysis of Water and Sediments  
Lake water and sediments were sampled in different seasons during the period 2009-2011. Water samples were collected in Teflon jars previously treated with HCl (5%) and rinsed several times with Milli-Q water. Redox potential (Eh), pH and electrical conductivity (EC) were determined in situ with a multiparameter probe (Hanna Instruments, Ann Arbor, MI, USA). Salinity was determined with a Hanna refractometer. In the laboratory, the samples were filtered (0.45 microns). Nutrient concentrations (NH4\({}^{+}\), NO3\({}^{-}\), and PO4\({}^{3+}\)) were determined by ion chromatography using a Dionex 4500i system (Dionex Inc., Sunnyvale, CA, USA). DBO5 was determined by incubation in an OxiTop Box (WTW at 20 \({}^{\circ}\)C).  
Sediment samples were obtained from the center of each lake with PVC tubes of length 150 cm and inner diameter 80 mm. In the laboratory, sediment samples were cut into 3 to 5 cm thick segments. The sediment samples were used to determine the influence (both present and past) of seawater in each lake, using the concentration of pyrite (S pyrite) in sediments relative to organic C content as a proxy measure [22]. The organic C/pyritic S ratio is a proxy that distinguishes anoxic freshwater sediments (with low concentrations of pyrite, FeS\({}_{2}\)) from anoxic marine sediments (with high pyrite contents) [22; 23; 24]. The total organic C (TOC) content was determined in ground samples in a Leco TruSpec CHN analyzer (after removal of carbonates with 1M HCl), and the total S (TS) content was determined in a Leco SC144 DR /C element analyzer. Fe oxyhydroxides and Fe pyrite (FeS\({}_{2}\)) were determined by sequential Fe extraction (for more details see [25; 26]). The pyrite S content was determined stoichiometrically from the pyrite Fe content.
## 2 Principle  
### Background and Spectral Characteristics in Laser Additive Manufacturing Process  
The technology used in this paper in the additive manufacturing for metal parts is direct energy deposition (DED), as shown in Figure 1a. During the printing process, the laser and metal powder converge at the appropriate position on the substrate and form a molten pool. With the continuous movement of the printer nozzle in the X and Y direction, the molten pool cools and solidifies rapidly. Through the in-plane scanning path and layer-by-layer superposition of each layer, the printer nozzle scans above the substrate and rapidly melts and solidifies the metal powder to finally realize the rapid prototyping of metal parts.  
Therefore, the interaction between laser and materials is often accompanied by strong light and splashing of metal powder. For this paper, the output wavelength of the printing laser is 1064 nm, the printing power is 1.8-2 kW, and the maximum temperature in the printing is about 2000 \({}^{\circ}\)C. The image during printing is shown in Figure 1b. It can be seen that the image of metal parts acquired in visible light is overexposed due to the interference of strong light and the thermal effect of the printed parts. Consequently, the traditional structured light 3D shape measurement system cannot meet the measurement requirement in this environment.  
The laser additive manufacturing process produces a strong glow, and it cannot be directly observed by the naked eye. The light radiation is relatively complex and mainly comes from three types of components:  
Partial refection of the laser by the molten metal surface, and its wavelength is the same as that of laser; that is, 1064 nm.  
Figure 1: Additive manufacturing scenario: (**a**) metal additive manufacturing printer nozzle system; (**b**) image when 3D printing.  
Forming metal vapor plasma [23]. During the interaction between the laser and the metal powder, the metal material with high laser energy density will melt and evaporate rapidly, and the accumulation of high temperature in the vicinity will aggravate the metal gasification and metal vapor ionization around the molten pool. The spectrum produced by the ionizing radiation process is generally between 400-600 nm [24].  
The thermal effect is considerable in the process of laser melting metal powder. The wavelength of metal melting and thermal radiation covers the range of visible light wavelength (380-780 nm) to infrared wavelength (\(\sim\)14 \(\upmu\)m).  
Therefore, spectral characteristics in the laser additive manufacturing process will be considered in the construction of a measurement system, and a UV light source below 380 nm is selected as the measurement light source in this paper to reduce the influence of light radiation.
## 6 Analysis of Results and Discussion
### Analysis of Results
Additionally, Git was used to establish versions of automation scripts, ensuring traceability and collaboration between development and operations teams. This approach allowed for the creation of an environment where updates to Salesforce or RPA processes could be implemented quickly, with a reduced risk of errors and greater consistency. The integration between RPA and DevOps was not limited to the automation of repetitive tasks, but also included the application of principles such as continuous monitoring and feedback loops. For example, logs generated by UiPath have been consolidated into dashboards in Jenkins, enabling the proactive identification of failures and continuous process optimization. This synergy between RPA and DevOps has not only accelerated loan application processing but also created a foundation for future evolution, where automation can be expanded to other workflows, such as credit analysis and financial reporting. This approach allowed for a clear analysis of the gaps between manual and automated processes, highlighting future opportunities for greater integration between systems and the adoption of more advanced automation practices. The incorporation of DevOps in its strictest sense, with the use of tools such as Jenkins and Git, demonstrates how the organization can evolve towards a more integrated software development model, where the automation of business processes and the continuous delivery of technological solutions go hand in hand. * Compliance rate: Compliance data were obtained from the financial compliance verification system, which tracks the regulatory requirements for each application. The rate of compliance was calculated based on successful completions of the automated and manual processes. * System scalability: This was measured based on the volume of applications processed per day. Data were obtained from both manual and automated systems, showing the system's capacity to handle increased loads after automation. The results presented here demonstrate significant improvements in key areas such as processing time, error reduction, and scalability. These outcomes are reflective of a balanced and realistic performance boost, aligning with the moderate expectations of the framework implemented. The bar chart, Figure 2, illustrates the significant improvements in key variables following the implementation of the FRIDA. It compares the performance of the manual process before implementation with the automated process afterward. The data reveal an impressive 81.25% reduction in the error rate, dropping from 8% in the manual process to 1.50% after automation. Furthermore, the automation resulted in a drastic 80% decrease in manual steps, reducing the reliance on human intervention from 100% to 20%. Although the improvement in the compliance rate was more modest, it still showed a notable 6.5% increase, rising from 92% to 98%. These results highlight the substantial efficiency gains and enhanced accuracy achieved through the automation process.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
However, temporally continuous field measurements over large spatial areas cannot be easily obtained using these traditional optical instruments. Wireless sensor networks (WSN) are a new type of information acquisition technology, capable of providing novel opportunities to achieve continuous LAI measurement over spatially distributed samples [31,32]. Among the existing WSN-based LAI observation systems, LAINet has been widely used in LAI validation activities [33, 34, 35]. The goal of the study was to simultaneously validate multiple decametric, hetometric and kilometric LAI products derived from the sensors Sentinel-2, MODIS, PROBA-V and AVHRR. We proposed a direct approach to validate coarse resolution LAI products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric resolution Sentinel-2 LAI product. It is based on the use of temporally continuous LAINet measurements and the recalibration of Sentinel-2 LAI product, and it may constitute an alternative to the standard CEOS LPV upscaling validation strategy. Maize is widely cultivated all over the world, and the annual output ranks first in cereal production [36]. The study was conducted over maize croplands in northwestern China. Section 2 briefly describes the study area, field measurements and the multi-resolution satellite LAI products. Section 3 describes the upscaling validation method. Section 4 displays and discusses the uncertainty of field measurements, the performance of the proposed up-scaling approach and the validation results of multi-scale LAI products. Finally, Section 5 provides the main conclusions.
## 4 Results
### Experiment 2
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**AUC (Numerical Datasets)**} & \multicolumn{8}{c}{**AUC (Discretized Datasets)**} \\ \hline
**Course** & **ICS1** & **ICS2** & **ICS3** & **ICS4** & **avg** & **ICS1** & **ICS2** & **ICS3** & **ICS4** & **avg** \\ \hline
**ICS1** & 0.958 & 0.477 & 0.464 & 0.569 & 0.62 & 0.742 & 0.535 & 0.554 & 0.474 & 0.58 \\
**ICS2** & **0.576** & 0.789 & 0.504 & 0.557 & 0.61 & **0.636** & 0.761 & 0.523 & 0.402 & 0.58 \\
**ICS3** & 0.544 & 0.547 & 0.739 & 0.525 & 0.59 & 0.446 & 0.506 & 0.739 & 0.514 & 0.55 \\
**ICS4** & 0.410 & 0.477 & 0.542 & 0.790 & 0.55 & 0.428 & 0.455 & 0.483 & 0.685 & 0.51 \\  & \multicolumn{8}{c}{avg mean} & 0.59 & \multicolumn{8}{c}{avg mean} & 0.56 \\ \hline \multicolumn{8}{c}{**AUC LOSS (Numerical Datasets)**} & \multicolumn{8}{c}{**AUC LOSS (Discretized Datasets)**} \\ \hline
**Course** & **ICS1** & **ICS2** & **ICS3** & **ICS4** & **avg** & **ICS1** & **ICS2** & **ICS3** & **ICS4** & **avg** \\ \hline
**ICS1** & - & 0.480 & 0.494 & 0.389 & 0.45 & - & 0.206 & 0.187 & 0.268 & **0.22** \\
**ICS2** & 0.213 & - & 0.285 & 0.231 & 0.24 & **0.126** & - & 0.238 & 0.359 & 0.24 \\
**ICS3** & 0.195 & **0.192** & - & 0.214 & **0.20** & 0.293 & 0.233 & - & 0.225 & 0.25 \\
**ICS4** & 0.380 & 0.314 & 0.248 & - & 0.31 & 0.257 & 0.230 & 0.202 & - & 0.23 \\  & \multicolumn{8}{c}{avg mean} & 0.30 & \multicolumn{8}{c}{avg mean} & 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AUC Results and Loss in Portability in Engineering degree group. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{8}{c}{**AUC (Numerical Datasets)**} & \multicolumn{8}{c}{**AUC (Discretized Datasets)**} \\ \hline
**Course** & **MM** & **MA1** & **MA2** & **avg** & **MM** & **MA1** & **MA2** & **avg** \\ \hline
**MM** & 0.807 & 0.559 & **0.641** & 0.67 & 0.639 & **0.630** & 0.563 & 0.61 \\
**MA1** & 0.542 & 0.880 & 0.591 & 0.67 & 0.578 & 0.697 & 0.603 & 0.63 \\
**MA2** & 0.574 & 0.592 & 0.905 & 0.69 & 0.546 & 0.525 & 0.642 & 0.57 \\  & \multicolumn{8}{c}{avg mean} & 0.68 & \multicolumn{8}{c}{avg mean} & 0.60 \\ \hline
**AUC LOSS (Numerical Datasets)** & \multicolumn{8}{c}{**AUC LOSS (Discretized Datasets)**} \\ \hline
**Course** & **MM** & **MA1** & **MA2** & **avg** & **MM** & **MA1** & **MA2** & **avg** \\ \hline
**MM** & - & 0.249 & **0.166** & **0.21** & - & **0.009** & 0.076 & **0.04** \\
**MA1** & 0.337 & - & 0.288 & 0.31 & 0.119 & - & 0.094 & 0.11 \\
**MA2** & 0.331 & 0.313 & - & 0.32 & 0.096 & 0.117 & - & 0.11 \\  & \multicolumn{8}{c}{avg mean} & 0.28 & \multicolumn{8}{c}{avg mean} & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: AUC Results and Loss in Portability in Physics degree group.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 3 Results
### Comparison of the Accuracy of Slopes Derived from Digital Terrain Models
* Slopes above 30\({}^{\circ}\)--The comparative graphs deviated from the trend of the curves due to a smaller number of values, especially above 40\({}^{\circ}\) (see Figure 4). Evaluation of digital terrain models (a comparison with DTM 5--1 m \(\times\) 1 m):
* The less detailed the model, the bigger the deviation of the average slope and the bigger the variance in the standard deviation. * The 5 m \(\times\) 5 m models (DTM 4)--Achieved small deviations below 1\({}^{\circ}\) for slopes up to 20\({}^{\circ}\). For bigger slopes, especially above 40\({}^{\circ}\), the slope was almost less than one-third lower than in DTM 5. DTM 4 was suitable for slopes up to 20\({}^{\circ}\), the use is not recommended for slopes above 40\({}^{\circ}\). * The 10 m \(\times\) 10 m models (DTM 3)--Achieved small deviations below 1\({}^{\circ}\) for slopes up to 15\({}^{\circ}\), and at 30\({}^{\circ}\), the slope exceeded the limit of one-third of the slope difference compared to DTM 5. DTM 3 was suitable for slopes up to 15\({}^{\circ}\); for slopes above 30\({}^{\circ}\), it is not recommended. * The 30 m \(\times\) 30 m models (DTED 2)--They differed from DTM 5 by 1\({}^{\circ}\) already after 10\({}^{\circ}\) of slope inclination; from this value, the difference in inclination compared to DTM 5 was lower by more than one-third. Areas with inclinations above 50\({}^{\circ}\) in DTM 5 had less than 10\({}^{\circ}\) in DTED 2. The detail of the DTED 2 network of 30 m \(\times\) 30 m points did not allow for identifying a more fragmented terrain relief. DTED 2 could only be used to determine slopes up to 10\({}^{\circ}\).
# Comparing New Wireless Sensor Network Protocols through Simulation and Data Analysis +
## 4 Results and Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Environment size & 1000 \(\times\) 1000 m \\ Number of nodes & 25 and 50 \\ Traffic type & Constant bit rate \\ Nodes type & Mobile \\ Packet rate & 4 Packets/s \\ Packet size & 512 byte \\ Number of Flows & 25 and 50 \\ Simulation time & 3600 s \\ Number of submission hosts & 25 and 50 \\ Number of receivers & One \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter vs. Value for 2 nd case. ### End-to-End Delay  
We will examine each scenario and compare both protocols with regard to the number of nodes and the kind of scenario in order to study the findings for end-to-end delay of the selected protocols in both scenarios with varied numbers of nodes. When we consider the scenario in Figure 4a, in which there is no failure of any of the nodes, we find that the DSR performs quite similarly whether there are 25 or 50 nodes present. The availability of fewer alternate routes causes a lower increase in time in this case, but node mobility has no impact on the pattern of delay. The existence of numerous routes is the cause of this because it is evident from looking at the 50-node scenario that it provides a smaller delay with a constant rate. There are few ROUTE ERROR messages and ROUTE REQUEST messages (route requests do not need to spread around the network). However, when a network expands, the proportion of ROUTE ERROR messages rises, which has an impact on the throughput rate, as evidenced by an unstable curve over time. When compared to DSR, OLSR performs better in the case of 25 nodes, but as the network gets larger, it drastically reduces its rate. This is because of the way it operates. All paths are calculated in advance; however, because the nodes move around, its routing table entries are ineffective in larger networks. While it is possible to compute pathways at runtime in smaller networks, this is not viable in bigger networks, as Figure 7.  
Figure 6: (**a**) Mobile nodes without failure. (**b**) Mobile nodes with failure. Figure 7: (**a**) Mobile nodes without failure. (**b**) Mobile nodes with failure. When examining the node failure scenario shown in Figure 5b, we can observe the throughput behavior of DSR in the 25-node example.
The proposed scheme compresses graph streams incrementally, considering changes in vertices and edges over time, and efficiently records and compresses these changes along with patterns in an in-memory environment using provenance data. By managing patterns based on provenance and maintaining their size, the proposed scheme effectively handles important patterns and increases the space efficiency of pattern management, retaining only essential patterns. Furthermore, the proposed scheme assigns scores to the latest patterns to respond to real-time changes in the graph stream while maintaining a certain compression rate. To support this novel approach, we developed efficient algorithms for maintaining a dynamic dictionary of patterns and optimizing compression based on the relationships among patterns. This enables our method to achieve high compression rates while support ing fast update and query processing. Extensive experiments on real-world graph stream datasets demonstrate the superiority of the proposed scheme compared with existing graph compression methods that utilize pattern mining techniques. The results confirm that the proposed pattern dictionary management technique effectively improves upon existing pattern dictionary-based graph compression methods and outperforms graph compression techniques in a stream environment, in terms of compression ratio, memory usage, and processing time. This paper is organized as follows: Section 2 analyzes the existing schemes and enumerates the limitations of existing methods. Section 3 describes the proposed graph compression scheme. Section 4 presents a performance evaluation of the proposed scheme to confirm its superiority over the existing methods.
## 2 Materials and Methods
### Data and Data Preparation
#### 2.2.3 Topographic Data  
We used the freely available ArcticDEM (spatial resolution of 2 m), provided by the Polar Geospatial Center, for the validation of the generated DEMs. The ArcticDEM dataset is generated based on overlapping very high-resolution (VHR) optical satellite images with sub-meter resolution and stereoscopic imagery [63; 64].