esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 2 Review and Security Analysis of [PERSON]'s Scheme
### Registration Phase
#### 2.1.1 User Registration Sub-Phase  
In this sub-phase, the user (\(U_{i}\)) is registered by the control server (\(CS\)). The communication between \(U_{i}\) and \(CS\) is through a secure channel. The steps involved in this sub-phase are as follows:  
Step 1: \(U_{i}\) selects an identity (\(ID_{i}\)), pseudo-identity (\(PID_{i}\)), password (\(PW_{i}\)), and nonce (\(b_{i}\)). Then, \(U_{i}\) computes \(HP_{i}=h(PW_{i}\parallel b_{i})\) and sends the registration request message \(M_{1}=\left\{ID_{i},PID_{i}\right\}\) to \(CS\) were \(h(\cdot)\) is a one-way hash function, \(\parallel\) represents concatenation operation and \(\oplus\) represents an exclusive-or operation.  
Step 2: Upon receiving the registration request message \(M_{1}\), \(CS\) verifies if \(ID_{i}\) is valid or not. In case that \(ID_{i}\) is invalid, the registration process will be closed. On the other hand, \(CS\) computes \(C_{1}^{\star}=h(PID_{i}\parallel ID_{CS}\parallel x)\) and \(C_{2}^{\star}=h(ID_{i}\parallel x)\). Then, \(CS\) stores \(ID_{i}\) in a database and sends the registration response message \(M_{2}=\left\{C_{1},C_{2},ID_{CS}\right\}\) to \(U_{i}\).  
Step 3: After receiving the registration response message \(M_{2}\), \(U_{i}\) computes \(C_{1}=C_{1}^{\star}\oplus HP_{i}\), \(C_{2}=C_{2}^{\star}\oplus h(ID_{i}\parallel HP_{i})\) and \(C_{3}=b_{i}\oplus h(ID_{i}\parallel PW_{i})\), and stores \((C_{1},C_{2},C_{3},PID_{i},ID_{CS})\) in his smart card.  
At this point, the user registration sub-phase is over and \(U_{i}\) was registered by \(CS\).
## 3 ITS-Assisted Road Weather Services for VNs  
For pilot and field measurements, a test-track featuring an advanced state-of-the-art 5G test network and ITS-G5 was used. The network works as a testing platform to study, develop and pilot an advanced Intelligent Transport System (ITS) and real-time service architecture. The initial work on the 5G assisted road weather services was carried out in a previous initiative, namely the 5G Safe project, funded by Business Finland [5]. Providing an extra robustness for the information exchange between RWS and vehicles, 5G offers more refined road traffic climatic facilities. The test track infrastructure at the FMI supports different road weather services particularly designed to benefit VNs, as illustrated in Figure 2. Table 1 summarizes the considered road weather pilot services, including the real-time collection of weather information and alerts by using different IoT sensors. State-of-the-art equipment was used, including road friction instruments like Teconer RCM 411 (Teconer Oy, Helsinki, Finland) and WCM 411 (Teconer Oy, Helsinki, Finland) installed in vehicles and road weather station sensors including the Vaisala PWD-22, Vaisala DSC-111 (Vaisala Oyj, Vantaa, Finland), 2D Ultrasonic Anemometer, DST-111, 2*PT100, DRS-511, HMP45D, and a Zavio B7210 Full HD camera installed on the test-track. Ultimately, this real-time data is distributed to the nearby vehicles from RWSs for road traffic safety. Furthermore, the V2V and V2I communication in the 5G test network and the ITS-G5 were tested with a special \"see-through\" application, tailored to deliver vehicle camera data from the front of a vehicle queue during poor visibility conditions, making it possible to take precautionary measures for unexpected traffic anomalies. However, the pilot services were conducted using 5G and ITS-G5 on the test-track, which were performed and verified during the field measurements on the test-track at a conceptual level. In the near future, the pilot measurements will be further extended [10]. The vehicles receive the road traffic and weather services and gather observational information directly from the RWS and other vehicles. This information needs a high level of security and encryption. We also considered the security aspect of data. Indeed, data security was ensured before data broadcast and secure information management practices in the RWS, vehicles, and data clouds were implemented. We conducted an analysis to ensure that the generation of different weather service procedures were not corrupted or transferred without authorization. For data security, we are currently developing and contributing to different projects, offering security practices and methodologies for the VANETs use cases. One of the European union Electronic Components and Systems for European Leadership (EU-ECSEL JU) SafeCOP project at FMI designed and developed an extra safety layer for VANETs with an explicit run-time manager providing the validity and security of an individual communication object [11].
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 4 Experimental Results
### Liquefaction-Resistance Ratio  
The aforementioned test results were presented herein in terms of the normalized liquefaction resistance--or simply the liquefaction resistance ratio (\(LRR\))--which will form the basis of the empirical relations developed in the subsequent sections. The liquefaction-resistance ratio was calculated as below:  
\[LRR=\frac{(CSR_{N=20})_{part}}{(CSR_{N=20})_{sat}} \tag{8}\]  
where \((CSR_{N=20})_{sat}\) and \((CSR_{N=20})_{part}\) are the cyclic-stress ratios that correspond to 20 cycles for the nearly/fully saturated and partially saturated sands, respectively.  
Figure 12 shows that for a given loading cycle (\(N=20\)), the liquefaction-resistance ratio (\(LRR\)) showed a tendency to increase with a decreasing degree of saturation. Similarly, as the effective confining stress increased, the \(LRR\) markedly increased in the partially saturated sand specimens and slightly decreased in the nearly/fully saturated sand specimens.  
The effective confining stress plays a significant role in the stress-strain-strength behavior of saturated sands. Depending on the effective confining stress value, saturated sands may exhibit strain-softening or strain-hardening behavior during shear, and the sand behavior may change from contractive to dilative or vice versa [64]. Loose sand specimens at high effective confining stresses are expected to show a highly contractive behavior and suffer a larger build-up of excess pore pressures during undrained cyclic loading. The slight decrease observed in the liquefaction resistance of fully/nearly saturated sand specimens  
Figure 12: Variation in liquefaction resistance and liquefaction-resistance ratio with the degree of saturation and effective confining stress.  
with increasing confining stress could be ascribed to the enhanced contractive tendency of the sand. On the other hand, the liquefaction behavior of the partially saturated sand specimens with occluded gas/air bubbles was controlled by the pore-fluid compressibility or potential volumetric strains. The occluded gas/air bubbles in the pore spaces played a role in absorbing the excess pore pressures and eventually increased the resistance of the sand to liquefaction. The compressibility of the pore fluid and hence the liquefaction resistance were enhanced by lowering the degree of saturation and/or increasing the effective confining stress.
## 3 Results and Discussion
### Headspace Volatile Analysis Using SPME-GC-MS of Mallab Samples (Roasted/Unroasted)
** & **Volatiles** & **KI** & **Rt (min)** & **WG** & **WE** & **RE** & **RRE** \\ \hline
59 & Pentadecane & 1482.7 & 11.96 & 0.05 \(\pm\) 0.03 & 0.10 \(\pm\) 0.02 & 0.01 \(\pm\) 0.00 & 0.10 \(\pm\) 0.00 \\
60 & 2,24,6,6-Pentamethylheptane & 1023.9 & 7.355 & 0.01 \(\pm\) 0.00 & 0.01 \(\pm\) 0.01 & 0.01 \(\pm\) 0.00 & 0.01 \(\pm\) 0.00 \\
61 & 2,27,7-Tetramethyloctane & 1031.8 & 7.492 & 0.07 \(\pm\) 0.01 & 0.04 \(\pm\) 0.01 & 0.11 \(\pm\) 0.02 & 0.03 \(\pm\) 0.02 \\
62 & Tetadecane * & 1384.9 & 11.028 & 0.16 \(\pm\) 0.07 & 0.13 \(\pm\) 0.05 & 0.04 \(\pm\) 0.01 & 0.04 \(\pm\) 0.00 \\
63 & Hexadecane * & 1599 & 13.199 & 0.01 \(\pm\) 0.00 & 0.18 \(\pm\) 0.06 & 0.23 \(\pm\) 0.06 & 0.14 \(\pm\) 0.13 \\
64 & Heptadecane * & 1709.8 & 14.229 & 0.00 \(\pm\) 0.01 & 0.02 \(\pm\) 0.02 & 0.02 \(\pm\) 0.01 & 0.12 \(\pm\) 0.14 \\
65 & Octadecane * & 1797.4 & 15.003 & - & - & - & 0.04 \(\pm\) 0.05 \\
66 & Nonadecane * & 1888.9 & 16.022 & - & - & - & 0.01 \(\pm\) 0.01 \\ \hline \multicolumn{6}{c}{Total aliphatic hydrocarbons} & \multicolumn{6}{c}{0.48} & 0.65 & 0.50 & 0.92 \\ \hline \multicolumn{6}{c}{**Aromatics**} \\
67 & Unknown & 1326.2 & 10.473 & 2.46 \(\pm\) 2.17 & 0.97 \(\pm\) 0.54 & 3.32 \(\pm\) 1.00 & 8.05 \(\pm\) 8.15 \\
68 & Naphthalene * & 1044.1 & 7.607 & - & - & - & 2.71 \(\pm\) 3.83 \\
69 & Unknown & 1207.9 & 9.335 & 0.10 \(\pm\) 0.02 & 0.10 \(\pm\) 0.03 & 0.05 \(\pm\) 0.02 & 0.59 \(\pm\) 0.17 \\ \hline \multicolumn{6}{c}{Total aromatics} & \multicolumn{6}{c}{2.56} & 1.07 & 3.37 & 11.35 \\ \hline \multicolumn{6}{c}{**Esters**} \\
70 & Butyl formate & 653.4 & 3.464 & 0.15 \(\pm\) 0.07 & 0.01 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & - \\
71 & _n_-Butyl acetate & 768.2 & 4.524 & 0.87 \(\pm\) 0.28 & 0.05 \(\pm\) 0.01 & 0.13 \(\pm\) 0.09 & 0.06 \(\pm\) 0.04 \\  
ethyl palmitate & 1915.6 & 16.137 & 0.01 \(\pm\) 0.00 & 0.02 \(\pm\) 0.01 & 0.05 \(\pm\) 0.03 & 0.02 \(\pm\) 0.02 \\ \hline \multicolumn{6}{c}{Total esters} & \multicolumn{6}{c}{7.14} & 3.76 & 5.94 & 8.85 \\ \hline \multicolumn{6}{c}{**Ethers**} \\
90 & Estragole * & 1207.4 & 9.319 & 0.01 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & 0.05 \(\pm\) 0.01 & 0.03 \(\pm\) 0.03 \\
91
## 2 Requirements of 5G-Based V2X Use Cases
### Typical Traffic Format  
The V2X ecosystem assumes a distributed system due to various requirements. Thus, the messaging formats used are standardized. Those messages are encoded efficiently into relatively small packets. The size of the packets depends on the encoded information. Not all of the information is encoded in all packets; some segments are periodically included. The security certificates are also only encoded in some of the packets (typically in every 10 th). This results in frequently changing packet sizes. Various studies define the packet size or the data volume for a certain period of time and the sending frequency for different scenarios depending on the traffic situation, vehicle density, and transport environment [1; 28; 2].
## 4 Conclusions
higher precision and RMS-residual values than the BoVW-based approach, indicating its potential for enhanced accuracy in estimating image positions. Overall, the proposed method for determining overlapping image sets is expected to enhance the accuracy and efficiency of image localization, particularly for UAV image datasets that lack prior location information, such as GNSS data. In future research, we will focus on evaluating the proposed method across diverse environmental conditions and seasons to assess its robustness in various scenarios. We aim to incorporate deep learning-based feature extraction techniques to improve performance under challenging conditions. Ultimately, by developing high-efficiency and high-precision image-set determination methodologies, we seek to further enhance the accuracy and efficiency of localization, thereby increasing the applicability of this study. Conceptualization, J.L. and K.C. ; methodology, J.L. and K.C. ; software, J.L. and K.C. ; validation, J.L. and K.C. ; formal analysis, J.L. and K.C. ; investigation, J.L. and K.C. ; resources, J.L. and K.C. ; writing--original draft preparation, J.L. ; writing--review and editing, K.C. ; visualization, J.L. ; supervision, K.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00210493). Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the following reasons: The datasets are part of an ongoing multi-institutional collaborative study supported by national funding. The image data are jointly owned by the participating institutions, and sharing with external parties is restricted until the project's conclusion. The authors declare no conflict of interest.
# Respiratory Muscle Strength as a Predictor of VO2 max and Aerobic Endurance in Competitive Athletes
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Characteristics** & **X** & **SD** & **B** & **SE** & **B** & **T** & \(p\) & **Zero-Order** & **Partial** \\ \hline (Constant) & & & 1716.990 & 1173.112 & & 1.464 & 0.154 & & \\ MIP (cm\(/\)H\({}_{2}\)O) & 99.08 & 29.70 & \(-\)7.307 & 4.376 & \(-\)0.250 & \(-\)1.670 & 0.105 & 0.347 & \(-\)0.292 \\ MEP (cm\(/\)H\({}_{2}\)O) & 130.95 & 42.82 & 11.606 & 3.210 & 0.572 & 3.616 & **0.001** & 0.624 & 0.551 \\ MAC (cm) & 91.40 & 10.720 & \(-\)187.015 & 110.081 & \(-\)2.308 & \(-\)1.699 & 0.100 & 0.597 & \(-\)0.296 \\ ACI (cm) & 95.20 & 10.216 & 44.433 & 62.518 & 0.523 & 0.711 & 0.483 & 0.607 & 0.129 \\ ACE (cm) & 89.13 & 10.827 & 129.642 & 88.048 & 10.616 & 1.472 & 0.151 & 0.602 & 0.260 \\ MSC (cm) & 78.82 & 10.454 & 76.020 & 71.878 & 0.915 & 1.058 & 0.299 & 0.621 & 0.190 \\ SCI (cm) & 82.95 & 9.462 & \(-\)81.135 & 45.639 & \(-\)0.884 & \(-\)1.778 & 0.086 & 0.562 & \(-\)0.309 \\ SCE (cm) & 76.63 & 10.505 & 34.126 & 62.958 & 0.413 & 0.542 & 0.592 & 0.625 & 0.098 \\ MVV & 135.63 & 38.39 & 5.857 & 3.350 & 0.259 & 1.748 & 0.091 & 0.584 & 0.304 \\ \hline F\({}_{(9-30)}\) = 6.562 & & & P = 0.000 & & & & & \\ R = 0.814 & & & R\({}^{2}\) = 0.663 & & & & & \\ \hline \hline \end{tabular} MIP: Maximum inspiratory pressure, MEP: Maximum expiratory pressure, MVV: Maximum voluntary ventilation, MAC: Mid-Avilla Circumference, ACI: Avilla Circumference On Inspiration, ACE: Avilla Circumference On Expiration, MSC: Mid-Subcostal Circumference, SCI: Subcostal Circumference On Inspiration, SCE: Subcostal Circumference On Expiration; X: Mean, SD: Standard Deviation, SE: Standard Error, B: Regression Coefficient, 6: Standardized Regression Coefficient, T: t-Statistic, \(p\): \(p\):Value, Zero-order: Bivariate Correlation Values, Partial: Partial Correlation Values.  
\end{table} 
Table 4: Regression analysis results regarding VO2 (Mean \(\pm\) S.D. = 3877.52 \(\pm\) 868.47 mL) prediction of the parameters. Figure 2: Regression analysis results predicting VE from various parameters. In Table 5, there is a strong and significant relationship with VCO2 (R = 0.802, R\({}^{2}\) = 0.643, \(p\) < 0.05). These variables combined account for 64% of the total variance in VCO2 (Figure 4). According to the standardized regression coefficient (\(\beta\)), the values of MEP and inspiratory subcost circumference measurement have a significant effect on VCO\({}_{2}\), while other predictor variables do not seem to have a significant effect on VCO\({}_{2}\). When the bilateral and partial correlations between the predictor variables and VCO\({}_{2}\) were examined, low-level correlations were found between MEP and VCO\({}_{2}\), and moderate and positive correlations with other parameters.
## 2 Background and Related Work
### Background Concepts  
* Ontology. Ontologies offer a formal model of concepts of interest (classes), features and attributes of each concept (properties) and property restrictions, involving a specific knowledge domain in the real world [9; 10]. Ontologies are a layer of the W3C standard stack ([[https://www.w3.org/standards/semanticweb/](https://www.w3.org/standards/semanticweb/)]([https://www.w3.org/standards/semanticweb/](https://www.w3.org/standards/semanticweb/)) (visited on 24 October 2019)). A knowledge base is made up of an ontology and its instances (set of class and property individuals). A knowledge base provides services to make heterogeneous systems and databases interoperability easier.
* RDF. Resource Description Framework [11] is a W3C recommendation for describing resources in terms of triples. An RDF triple comprises a subject, a predicate and an object. RDFS (RDF Schema) [12] provides a language for describing vocabularies that are used in RDF statements.
* OWL. The Ontology Web Language (OWL) is an extension of RDF and RFFS for defining machine understandable ontologies on the Web. From a formal point of view, an OWL ontology corresponds to a TBox in the context of a very expressive DL (description logic) [13]. Thanks to the equivalence of OWL with DL, OWL-DL provides maximum expressiveness while keeping computational completeness and decidability [14].
* SPARQL [15] is the W3C query language for querying RDF repositories. SPARQL works with RDF graphs [16] where data sources are identified by URIs.
## 2 Related Works
In [29], a distributed cluster head selection algorithm was proposed, where each node computes its priority based on its ID, current communication round, energy level, and speed. In this algorithm, the nodes with the highest priority become cluster head. In [16], a Topology Adaptive Clustering Algorithm (TACA) was proposed, where two major node parameters, like its mobility and battery power, were considered for achievingnode suitability and cluster head. This improved the network life time and reduced maintenance overhead. In [3], a weighted-based adaptive clustering algorithm optimized for mobile hybrid networks (WACA) was proposed, where investigations focused on the problem of minimizing cluster head re-elections by considering stability criteria. These criteria were based on topological characteristics as well as on device parameters. This was to avoid needless cluster head re-elections for stable clusters in mobile _ad hoc_ networks. However, the existing algorithms did not consider reliability, scalability, automatic awareness among cluster heads, clusterhead candidate and cluster member, dynamic change due to mobility, and the fuzziness of cluster head formation when the network size increased in proportion to the node's number in flat structure or distributed network structure. Thus, this paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems, such as energy consumption, transmission rate reduction, decrease in throughput, and incorrect cluster head election. The proposed FRCA constructs clusters more efficiently by reducing the incorrectness and ambiguity in the selection of cluster heads.
# Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
## 6 Conclusions  
In this paper, we introduced two designs of automatic energy consumption measuring platforms for embedded systems, one using a standalone device and the other using an auxiliary peripheral device. We used the GPIO level signal as a link to establish a connection between power consumption data and runtime status using the target board. By using GPIO, we solved the problems related to obtaining high-precision timestamps and the synchronization between timestamps and power consumption data. Our designs simplified and eliminated many complicated manual steps in embedded system energy consumption measurement tasks, allowing energy consumption measurement to be unattended and carried out automatically. Our standalone design is suitable for occasions requiring a higher sampling frequency and more GPIO signals, and our auxiliary design is suitable for occasions where only a single GPIO signal is required with more concise wiring. Although the hardware and software of these two designs were different, they demonstrated similar accuracy and automated measurement capabilities. We designed groups of test sets to verify our designs' capabilities, with the test results showing that our designs measured energy consumption automatically, in real time, and accurately. We also used our standalone design to measure the energy consumption of the \(\mu\)C/OS system functions. Finally, our designs are easily \"upgraded\". For the standalone design, the adoption of an intermediate device with a higher performance or using a power-sensing module with a higher sampling frequency could improve its measurement precision and accuracy, and for the auxiliary design, a more compact design could be achieved by using Raspberry Pi or a similar kit-board with a high performance and plenty of GPIO.  
Methodology, H.W.; software, H.W.; investigation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, K.W.; supervision, C.C. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Temperature Fiber Sensor Based on Tapered Fiber Bragg Grating Fabricated by Femtosecond Laser
## 1 Introduction
The temperature sensitivity for the higher-order mode is 9.6 \(\pm\) 0.1 pm/\({}^{\circ}\)C and that for the fundamental mode is 10.0 \(\pm\) 0.1 pm/\({}^{\circ}\)C. However, in the literature, the point-by-point direct-written FBG by femtosecond laser in a fiber-tapered region has not yet been reported. Here we propose a FBG fabricated by femtosecond laser inscribed in a fiber taper. The temperature-sensing experiment is performed by monitoring the characterized wavelengths under different temperatures. Such a tapered FBG will have applications in sensing physical, chemical and biomedical parameters.
## 2 PFAC Infrastructure & Instrumentation
### The Regional GNSS Network  
To determine precise geodetic coordinates for the reference marks of the PFAC instrumentation, continuously operating reference GNSS stations have been setup at each Cal/Val site (Gavdos, CRS1, RDK1, CDN1). Additional GNSS stations have also been installed in west Crete, in support of PFAC operations and to provide better understanding of the tectonic deformations in this earthquake prone region (Figure 6). The same instrumentation has also been used to derive the atmospheric delays (wet and dry troposphere, ionosphere) for the altimeter's signals.