esa-sceva/satcom-chunk-collection huggingface dataset:
# Biometrics for Internet-of-Things Security: A Review
## 4 Biometric-Based Systems for IoT-Oriented Authentication
### Single-Modal Versus Multi-Modal Biometric Authentication Systems
#### 4.1.5 Biometric Authentication versus Password- and Token-Based Authentication  
For a long time, password-based authentication (according to something you know) has been the standard technique for authentication in the IoT environment due to its simplicity and convenience. The fundamental problem with passwords can be explained succinctly--a short and memorable password can be easily guessed, while a long and complex password is difficult to remember [72]. Token-based authentication (according to something you have) performs authentication with a token, which is a secure storage device that contains passwords or generates one-time passcodes [72]. Unlike traditional password- and token-based authentication, biometric authentication uses features from human biometric traits (e.g., fingerprint, face, and iris) for authentication purposes. A biometric trait is unique to its owner; thus, it cannot be shared or stolen, an attribute passwords and tokens do not have [73]. Moreover, biometrics have the property of nonrepudiation. This property ensures that a user participating in a transaction cannot subsequently reject it as unauthorized or claim not to have participated in the transaction [72].  
The performance of a biometric authentication system in the IoT is usually evaluated by metrics such as the FAR and FRR [74], as discussed in Section 3.2. If the performance of password- and token-based authentication systems is represented in a similar fashion to biometric-based authentication systems using those metrics, then for password-based authentication systems, the FAR corresponds to the percentage of successfully guessing the password and the FRR means the percentage of accidental mistakes made by users at input, while for token-based authentication systems, the FAR and FRR indicate the chances of a token being stolen or lost due to ownership factors [75]. In password- and token-based authentication, users may blame themselves for authentication failure. However, for biometric-based authentication systems, because of biometric uncertainty, although a user is not at any fault, the system can still reject the user [72]. As shown in Table 2, adoption of discriminative biometrics (e.g., using iris rather than voice) or multi-modal biometric systems may improve authentication performance. However, factors such as user convenience, acceptability, and resource constraints of IoT devices should be considered in the design of biometric authentication systems in the IoT.
## 7 Conclusions  
A widespread earthquake insurance scheme is recognized as a powerful tool to share the economic risk resulting from low-probability, high-impact seismic events, at the same time boosting the subsequent recovery stage. The more swift and fair the settling of claims after an earthquake, the quicker the kick-start of the impacted communities, and the greater the overall resilience of the affected country. Unfortunately, claim management is a complex process that does not always scale smoothly with the number of the filed claims, and in the occurrence of a major event in areas with high vulnerability and high policy density, a substantial number of claims can be expected. This occurrence could overwhelm the operational structure of the Turkish Catastrophe Insurance Pool (TCIP), which in Turkey is providing country-wide earthquake insurance with a significant penetration rate.  
A prompt and efficient visual survey of the insured (possibly damaged) building stock would significantly enhance the operational capabilities of the TCIP in the aftermath of a powerful earthquake in Istanbul. An innovative solution has been proposed based on the deployment of an agile mobile mapping system whose field operations are optimized through a geo-statistical approach.  
In order to test the proposed approach, a field test has been carried out in Besiktas, Istanbul. The selected district encompasses different characteristics that are common to other locations in the town, hence providing a significant example of the environmental conditions to be expected on a wider geographic scale.  
This preliminary assessment shows how a careful optimization of the sampling and routing protocols can yield much better results than a simple random survey. In a similar way to pre-eventsurveys, a focus map has to first be evaluated in order to optimize the actual post-event survey. Several datasets and ancillary information can be used, including:  
* distribution or density of buildings;
* distribution or density of policies;
* expected damage (based on pre-computed scenarios);
* damage information based on rapid assessment through remote sensing or aerial/UAV surveys;
* observed damage based on field assessment or reports from civil protection authorities.  
Other information, such as for instance the assessment of road blockage due to debris and/or to search and rescue activities, the probability of fire following earthquake, liquefaction hotspots, and landslide information, can also be included in the focus map, or can be used as additional penalty terms in the routing phase.  
Significant advancement of claim management could be obtained by applying the proposed method on a large scale. In the Istanbul case, any possible means of optimizing claim management must be pursued in order to improve the resilience of the community. Further tests are due, but the preliminary results are promising. We recommend that TCIP implement and embed the proposed approach in the complex operational structure that steers the management of claims.
## 3 Calibration System and Improved Speckle Statistics
### Target Calibration Precision and Optical Fiber Link Test  
An OFLT unit used for calibrating the system is shown in Figure 3. First, the on and off lasers are intensity modulated by two different frequencies and then coupled to a SM filter splitter. Then, the 99% portion is detected by the receiving photodetector and the 1% portion is detected by the monitoring photodetector. A 50/50 SM filter beam splitter is inserted in front of the monitoring photodetector to attenuate the light intensity, in order to prevent detector saturation. In the propagation process of these lights in the OFLT, the CO\({}_{2}\) absorption was set to be negligible. The intensity ratio corresponding to the DAOD was measured using the ratio of the normalized detected intensities of the two wavelengths. The normalized intensity ratio can be written as follows:  
\[I_{R}=\frac{P_{0}(\lambda_{ON})P_{R}(\lambda_{OFF})}{P_{R}(\lambda_{ON})P_{0}( \lambda_{OFF})}. \tag{4}\]  
where \(P_{0}(\lambda_{ON}),P_{R}(\lambda_{ON})\) are the respective powers of the monitoring and receiving portions of the on wavelength, and \(P_{0}(\lambda_{OFF}),P_{R}(\lambda_{OFF})\) are the respective powers of the monitoring and receiving portions of the off wavelength. We set the target precision of the CO\({}_{2}\) measurement as 1 ppm for a 1 km path in the background of 410 ppm. Thus, the fluctuation (\(\Delta I_{R}\)) accuracy budget of \(I_{R}\) is 0.0032 dB [18]. The fixed deviation of \(I_{R}\) from 1 is the bias of the system, and \(\Delta I_{R}\) is the systematic error. As there was almost no CO\({}_{2}\) absorption in the OFLT experiment, \(\Delta I_{R}\) should be 0 dB if the calibration is perfect.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements
### Achievements
#### 2.2.2 Monitoring Element  
We have carried out the design of functional and efficient solutions to collect, process, and maintain a rich and timely view of the network state in KDN-based environments. We started from the optic of the SDN paradigm, which offers data-plane devices with enhanced computing and storage capabilities, and a flexible Southbound Application Programming Interface (API)--OpenFlow, in our case- to retrieve statistics from the data-plane (e.g., traffic measurements). As a result, a novel OpenFlow-based flow-level traffic measurement system with good scalability properties has been proposed. Moreover, we have presented a system that combines DPI and ML to efficiently classify the applications generating traffic in the network. The resulting system has been carefully designed to achieve a good tradeoff between accuracy (both in traffic measurements and application classification), and the cost to deploy and execute it in networks.
## 2 Materials and Methods
### Bridge Assessments Using Health Index
#### 2.3.1. Deterioration-Level HI (DHI)  
Using the observed deterioration data for service year \(t\), the DHI is calculated as follows:  
\[DHI_{(t)}^{(i)}=1-\frac{\Delta A_{scaled}^{(i)}+d_{(t-1)}^{(i)}}{d_{(100)}^{(i)}} \tag{3}\]  
where \(DHI_{(t)}^{(i)}\) denotes the DHI for the \(i\)-th deterioration factor and \(d_{(t-1)}^{(i)}\) denotes the deterioration value derived from the deterioration model for the \(i\)-th deterioration factor in the year immediately preceding the inspection year (\(t-1\)). Additionally, \(d_{(100)}^{(i)}\) represents the threshold deterioration level for the \(i\)-th deterioration factor, defined as the predicted damage for \(t=100\) according to Equation (1). Furthermore, \(\Delta A_{scaled}^{(i)}\) represents the increase in the scaled deterioration area compared to the inspection history data for each bridge component. For example, if the scaled deterioration area of a slab is 0.3 and 0.5 after 20 and 25 years of service life, respectively, then \(\Delta A_{scaled}^{(i)}=0.2\) (\(A_{scaled(25)}^{(i)}-A_{scaled(20)}^{(i)}=0.5-0.3=0.2\)).  
Equation (3) indicates the extent to which the current damage level compares with the threshold service life. Thus, the HI represents the current performance level of the  
Figure 6: HI-based bridge evaluation process.  
structure, which degrades over time owing to deterioration and damage relative to the target performance (safety and durability). A HI of zero indicates that the structure has reached the end of its service life. Moreover, \(\Delta A_{scaled}^{(i)}=0\) in Figure 6 indicates that no new damage was found or considered. An HI curve that incorporates the regional and structural deterioration characteristics learned using the LSTM model can be derived by plotting a graph with t as the domain. Further details are presented in Section 2.4.
# Performances of the Synergy of Silica Fume and Waste Glass Powder in Ternary Blended Concrete
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Glass Waste  
Glass waste was collected from the dumpsite located along Sinaya, Hafr Al-Batin as shown in Figure 1. The glass was crushed first by a Los Angeles grinding machine before being further ground by an electronically controlled blending machine (titanium blade), which had a power rating of 1400 W operating on 220 V, 60 Hz. The glass powder was sieved through sieve No 200 or a 75-micron aperture. The oxide composition, as obtained from X-ray fluorescence, is presented in Table 1, while the X-ray diffractogram (XRD) is shown in Figure 2. The morphology of the particle is shown in Figure 3b. It reveals that glass powder is amorphous, as no phase could be identified except for a diffractive halo within a 2-theta angle, 20-30\({}^{\circ}\).  
Figure 1: Glass waste at dumpsite in Hafr Al-Batin.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Oxides** & **Cement** & **Glass** & **Silica Fume** \\ \hline SiO\({}_{2}\) & 20.17 & 68.1 & 95.85 \\ Al\({}_{2}\)O\({}_{3}\) & 5.58 & 0.9 & 0.26 \\ Fe\({}_{2}\)O\({}_{3}\) & 2.86 & 0.6 & 0.05 \\ CaO & 63.51 & 14.5 & 0.21 \\ MgO & 3.15 & 1.8 & 0.45 \\ Na\({}_{2}\)O & 0.12 & 12.2 & - \\ K\({}_{2}\)O & 0.57 & 0.8 & - \\ SO\({}_{3}\) & 2.56 & 0.4 & 1.00 \\ SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) & 26.89 & 69.6 & 96.16 \\ Specific gravity & 3.14 & 2.48 & \\ Specific surface area (m\({}^{2}\)/kg) & 329.5 & 223.0 & \textgreater{}18,000 \\ LOI (wt.\%) & 2.80 & 0.80 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oxide composition of raw materials.  
Figure 2: XRD diffractogram of the glass waste and cement powder.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 4 Proposed Elliptic Curve Signcryption Based Security Protocol for Smart Card (ECSSP-SC)
### Setup Phase  
In the setup phase, the registration center \(RGC\) chooses the common parameters for the system and publishes them globally. The steps in the setup phase are:  
* \(RGC\) chooses an elliptic curve \(E\) defined on the finite field \(F_{q}\) having parameters \(\{q,\ A,\ B,\ G,\ n\}\). For parameters \(A,\ B\), the condition \(4A^{3}+27B^{2}\
eq 0\) is satisfied.
* \(RGC\) also chooses a hash function \(Hash:\ \{0,1\}^{*}\ \rightarrow\ \{0,1\}^{l}\).
* The registration center \(RGC\) publishes the global system parameters \(\{q,\ A,\ B,\ G,\ n,\ Hash\}\).
## 5 Conclusions  
This paper combines model-based and model-free methods to design a model-based reinforcement learning (Dyna-Q) anti-jamming path selection algorithm framework. For a multi-node wireless communication network, when data are sent from the source to the host under broadband blocking jamming, the anti-jamming decision of intelligent communication is usually modeled as a Markov decision process (MDP). The Dyna-Q algorithm is used to select relay nodes so as to form the optimal communication path. The simulation results show that the shorter the distance between the jammer and the source node or the destination node, the more adverse the impact of the jammer on the whole network. Compared with Q-learning, the Dyna-Q algorithm can find the optimal path faster. The proposed algorithm has only a theoretical value because in a wireless communication network, nodes have interfered. Further, throughput can be degraded along with the traffic load, attenuation loss, fading, and signal-to-noise ratio. Moreover, the algorithm proposed in this paper has good convergence and is beneficial for making fast and correct decisions in a complex electromagnetic environment.  
Methodology, Y.L. and Y.N.; writing--original draft, G.Z.; software, G.Z. and Q.Z.; supervision, Y.N. and Y.L.; writing--review and editing, G.Z.; validation, G.Z. and Q.Z.; funding acquisition, Y.N.; project administration, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (NSFC grants: U19B2014).  
The authors declare no conflict of interest.
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 2 Materials and Methods
### Simulation Specifications  
The performance of the proposed approach was evaluated for a variety of simulation scenarios. With this in view, an optical network simulator has been implemented using Python 3.7 on PyCharm. The simulations were conducted on a PC running Windows 10 Pro with an Intel(R) Core(TM) i5-4590 CPU @ 3.30 GHz and 16 GB RAM. The time complexity of the EAFFB algorithm was calculated to be:  
\[[EAFFB]=O((N+R)(V+L)+2) \tag{9}\]  
This complexity will be referred to as \([EAFFB]\), where \(N\) represents the number of node pairs, \(R\) represents the number of links in a region, \(V\) represents the nodes, and \(L\) represents the links. The complexity arises from the utilization of the Breadth-First Search algorithm to compute the shortest paths, resulting in a complexity of:  
\[O(V+L) \tag{10}\]  
Therefore, the procedure of finding all the shortest paths at the start of the algorithm was estimated at:  
\[O(N\times(V+L)) \tag{11}\]  
while the complexity for calculating the probability of earthquake occurrence was \(O(1)\) due to the use of simple mathematical operations. The algorithmic analysis for determining the backup paths in the virtual topology was denoted as:  
\[O(R\times(V+L)) \tag{12}\]  
while the complexity of assessing the energy consumption was expressed, once again, as \(O(1)\), since it exclusively entails mathematical operations. In the context of the proposed algorithm, its efficiency was evaluated as:  
\[O\Big{(}R+2^{R}+(R+C)\times[EAFFB]\Big{)} \tag{13}\]  
where \(C\) encompasses all the combinations of filtered critical links. Finally, finding all critical links in all regions, filtering critical links, determining all combinations of filtered critical links, and calculating the energy output for the aforementioned combinations have, respectively, a computational complexity of:  
\[O(R) \tag{14}\]  
\[O(R\times[EAFFB])\ [EAFFB]=O((N+R)(V+L)+2) \tag{15}\]  
\[O(C\times[EAFFB]) \tag{16}\]
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 4 Discussion and Conclusions
In addition, according to Table 2 and the following results in Section 3, the most optimal strategy for network design based on the accuracy and quality of the 3D point cloud was the side capture image solution. It also includes fisheye images and self-calibration as data types. Ultimately, uniform illumination remains a key challenge for spherical cameras. The present study does not constitute a comprehensive examination of outdoor structures in 3D reconstruction using low-cost spherical cameras and their associated challenges in reconstruction and data acquisition, which ultimately result in the generation of a 3D point cloud. However, this can be expanded in future works by considering structures that exhibit more complex geometrical characteristics. The calibration of spherical cameras requires consistent definitions and a reliable geometric model for this kind of optical camera. A suitable solution for camera calibration can effectively impact any photogrammetric results, including 3D point clouds and other corresponding products. Further assessments of noise reduction levels could also be insightful for this field. Processing through commercial software restricts researchers to developing and examining detail calculations. Therefore, approaches to modular processing solutions will be more comprehensive and reliable. Conceptualization, H.A. ; methodology, H.A. and S.R. ; data collection, S.R. and A.M.; data analysis, S.R. and A.M.; writing, original draft preparation, S.R. ; writing review and editing, S.R. and A.M.; visualization, S.R. ; supervision, H.A. ; project administration, H.A. and S.R. ; funding acquisition, H.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the Mainz University of Applied Sciences, President's Innovation Fund, project number 1512701. The authors would like to express their sincere thanks for the financial support. Data availability StatementData are contained within the article. Conflicts of InterestThe authors declare no conflict of interest.
## 2 Materials and Methods  
### Description of Scenarios  
Scenarios considered the valorization of cattle manure and cheese whey as a readily degradable co-substrate to improve digestion performance. The energy performance of the system was evaluated, assuming that the process water obtained from the HTC unit is also used as a substrate for the anaerobic reactor. Hydrochar derived from hydrothermal processing of digested manure is used as a fuel to obtain energy from combustion. The use of this char as a supplement in the digestion process and its addition as an active carbon compound during digestate composting was also evaluated.  
Three scenarios were studied. Scenario 1 involves the digestion of manure as a single substrate in the first stage, with the second stage being an HTC process for treating the digested material. The thermally-treated slurry is then separated to obtain hydrochar to be used as fuel in a combustion process. The HTC process water is returned to the anaerobic reactor for treatment and degradation of the organic compounds. In this same scenario, the expected enhancement in performance is analyzed when a fraction of char is added to supplement the digestion process.  
Scenario 2 considers the effect of adding a co-substrate to the digestion system keeping the previous configuration of Scenario 1. It also evaluates the thermal demand associated with the treatment by HTC of a fraction of digestate to obtain the amount of hydrochar needed for the digestion process and for the composting stage to produce an active compost supplemented with carbon-enriched material.  
Scenario 3 considers thermal hydrolysis as a pre-treatment stage followed by anaerobic digestion. The hydrolyzed material obtained is then co-digested in the anaerobic reactor with cheese whey as co-substrate. Biogas is valorized in a combined heat and power system (CHP) for producing heat and electricity. A boiler is also considered in all scenarios for supplying the energy demand for the thermal treatments. Figure 1 shows a simplified representation of the different scenarios.
## 4 Assumptions on the Force Flow  
As stated in Section 3, form diagrams are kept fixed throughout the optimisation algorithm. As their topology and geometry influence the results, they must be chosen carefully to represent the intended force-flow in the system, and be compatible with the observed boundary conditions and with the presence of eventual existing cracks.  
In this work, three different form diagrams are adopted (Figure 3). For each pattern, the four corners of the diagram are defined as restrained nodes (red dots in Figure 3). Therefore, it is assumed that no lateral thrust is transmitted through the unsupported boundary edges.  
The fan-like diagram in Figure 3a is composed of 784 edges. This topology links the ridges of the structure directly to the supports; as a result, this topology represents a series of inclined arches spanning from two of the corner supports. This pattern has 30 independent edges, which are taken as parameters in the optimisation problem (Equation (4)), and represent the degrees of freedom associated with the diagram.  
The orthogonal diagram in Figure 3b has 448 edges. In this arrangement, arches parallel to the boundary edges carry the loads to the diagonals, which redirect those thrusts and channel them to the four restrained supports. This pattern has 12 independent edges.  
The triangulated curved diagram in Figure 3c has 616 edges. This diagram is obtained by curving the straight arches from the orthogonal diagram, and by adding diagonals oriented towards the supports to its quadrilateral faces. This pattern combines the connection to the main diagonals, available in the orthogonal diagram, with the existence of shortestpaths to the supports, as in the fan-like diagram, due to the added modular diagonals. This pattern has 147 degrees of freedom, which considerably increases the number of variables in the optimisation.  
A sensitivity study on the density of meshes has been previously executed in [25] for different levels of discretisation. This level of discretisation refers to the modular divisions of the floor plan applied to the patterns. In the diagrams of Figure 3, 14 modules are considered, a value for which convergence on the values of minimum thickness was observed in [25].  
After a diagram is chosen, the self weight is applied by computing the tributary area of each vertex onto the middle surface. Different form diagrams will pick up the load in different manners within the middle surface of the vault. Figure 4 shows how the self weight is distributed to the nodes for the three diagrams considering \(r/l_{0}=0.5\), and \(\beta=0^{\circ}\). For the third pattern, the addition of the diagonals does not influence the tributary load, which is computed based on an equivalent pattern without the presence of the diagonals.