esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 1 Introduction
### Related Work on Latency Reduction
For example, both standards employ iterative low-density parity-check (LDPC) decoding, which makes up a substantial part of the packet processing time and is identified as both a key enabler of 5G NR URLLC systems [30] and one of the potential optimization points [29].
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 4 Results  
This section presents the results of the VDCLS technique implementation using the CCRV32 ST processor core. Figure 9 presents the sample trace logged during the PC simulation of VDCLS execution. It can be seen that the delay between the master and slave cores is not constant. In the presented case, the slave core stays three to seven clock cycles behind the master core. As seen in Figure 9, the VDCLS technique is feasible and the processor can execute firmware correctly using this technique. The next subsections show more detailed results concerning performance and resource utilization, as well as the impact that the VDCLS technique may have in safety and security applications.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 5 BGPcoin Architecture for BGP Security  
We firstly design and implement a set of smart contracts that dictate the protocol of the system and act as the interface to the blockchain-based resource management. Secondly, we implement the client that interacts with the smart contracts to trade the Internet number resource and retrieve the resource records.  
We give a view of processing architecture around the BGPcoin contracts in Figure 4, and the set of smart contracts in our system consists of four sub-contracts:  
* BGPcoin_base: This contract is a base contract that IANA is in charge ofregistering IP address resources and allocating to xIRs. It also allows RIRs to register ASN resources.
* BGPcoin_client: IANA builds this contract to provide the operating interface for xIRs allocating IP address resources to its sub-xIRs, then to ISPs and allocating ASN resources to its sub-xIRs or ISPs; for ISPs for allocating IP address resources to its sub-ISPs.
* BGPcoin_ROAserver: IANA builds this contract to provide the operating interface for IPblock holders assigning their IPblock resource to their own ASes.
* BGPcoin_checker: This contract not only serves for all kinds of resource state searching, but also provides an incentive auditing mechanism to report anomalies like policy violation.  
BGPcoin records the resource assignments and authorizations in the form of transactions on the Ethereum blockchain [31]. A corresponding distributed ledger is created and maintained through a network consensus for tracing the state of a resource asset (its ownership and usufruct). Apparently, each resource asset is solely owned or leased on the ledger.  
**Bootstrap setting:** Every operator with its Ethereum account address that owns the Internet number resource in BGPcoin is mapped from a real administrative authority of the Internet. Before IANA establishes the BGPcoin system, all the legitimate resource authority organizations should be mapped to their Ethereum account addresses. All the participating organizations (IANA, xIRs and ISPs) authorize their own Ethereum account, which is collected as a signed profile and publicized by IANA.  
As a result, one transaction between two Ethereum accounts, as an attestation, shows a resource allocation from its present owner (like RIR) to its next owner (like NIR) or a resource assignment from its owner (like LIR) to the leasee (i.e., ISP). After BGPcoin is launched, any change of the Ethereum account address for a resource authority is still allowed. Once a resource authority has changed its  
Figure 4: Architecture of BGPcoin resource management.  
Ethereum account in the case that the account's private key has been stolen, several transactions are submitted to transferall its resource from its former account to its new account. In this way, the resource authority has a new account representing itself, and the safety of its resource is guaranteed.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 4 Experiments
### Analogous Experiment for Very High Frequency
#### 4.2.2 Experimental Results  
As shown in Figure 16, CD-SR successfully separates the overlapping echoes when the degree of overlap is 50% or 60%, and the reconstructed waveforms of \(x_{1}(t)\) and \(x_{2}(t)\) are consistent with the actual waveforms (see Figure 15a). However, SR cannot successfully separate the overlapping echoes. The results are consistent with the results of conventional experiment in Section 4.1. Table 5 shows the amplitude error and position error of CD-SR, indicating that the errors are as small as the simulation results.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Coaxial Cable** \\ \hline Characteristic impedance \(Z_{0}\) (\(\Omega\)) & 50 \\ Electromagnetic wave velocity \(c_{L}\) (m/s) & \(2\times 10^{8}\) \\ Attenuation (dB/cm) & \(\approx 0\) \\ Length (m) & \(d=1.2(1-\delta)\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coaxial cable properties.
## 3 Results and Discussion
### Meteorological Factor
#### 3.1.2 Statistical Analysis and Hypothesis Testing of the Meteorological Factor  
Although the descriptive statistical analysis of the previous section concludes that the region of Lakkomata was different, all possible pairs of subregions have been compared for completeness reasons. Since the initial set of data were five-dimensional, Root Sum Square (RSS) was used as a single metric and the hypothetical distribution of this statistic was estimated using resampling and Monte Carlo simulations ([22], ch. 9). The null hypothesis \(H_{0}\) states that there is no difference between the two regions regarding the RSS mean value. Specifically, the point-to-point difference with respect to the simulated population was computed, and the corresponding probability was estimated. In the case of a common situation, i.e., very central location at the distribution obtained by the Monte Carlo simulation, \(H_{0}\) is not rejected, while a rare/unlike value leads to the rejection of \(H_{0}\).  
An interesting pattern was revealed when all pairs of Kastoria subregions were compared, which is in agreement with the observations of the topological data analysis of the previous section. For instance, when Lithia was compared with the RSS distribution of Argos Orestiko, it was observed that the obtained value of Lithia corresponded to a very common/central value of the distribution (Figure 5), and thus, no statistically significant difference could be concluded. On the contrary, if the RSS values of Lithia, Kalochori, Argos Orestiko, and Polykarpi with the distribution of Lakkomata are compared, their values are out of scale (Figure 6). Extremely rare values correspond to nearly zero probability. From a statistical point of view, this leads to a clear rejection of \(H_{0}\) for all subregions. After visiting the region of Lakkomata and in particular the location of the meteorological station, local farmers helped the research team to investigate if the orientation of this region is such that could be shadowed by neighboring hills/mountains. It was concluded that this is not the case, and from ad hoc measurements of the pyranometer, it revealed that the sensor drifted significantly. When the pyranometer's indicators were removed from the dataset, no statistically significant difference was found.  
Figure 4: Topological data analysis of meteorological data from Kastoria.
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## 3 Optimization Results and Discussion
Omnidirectionality is significant in RF energy harvesting scenarios, as the direction of arrival of the incident field is usually unknown and it varies in recent technologies, i.e., beamforming in 5G NR. As depicted in Figure 6, for the first resonant frequency, \(f_{1}=1.72\) GHz, the 3D radiation pattern of the optimal antenna has omnidirectional characteristics, which are also maintained throughout the operating frequency band, making the optimal antenna a perfect fit for RF harvesting applications. Specifically, while it is higher in the frequency band, the 3D pattern slightly loses its omnidirectionality, as depicted in Figure 7, for the second resonant frequency, \(f_{2}=3.8\) GHz. However, this change is not important and the design can be considered omnidirectional across the whole band. The slight changes in the pattern are also expressed as a difference in the Gain, varying from 2.2 dBi at \(f_{1}=1.72\) GHz to 4.9 dBi at \(f_{2}=3.8\) GHz, as shown below. It must be noted that
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.5 Energy-Based  
Energy-efficient routing protocols are crucial for ensuring reliable communication and extended network lifetime in FANETs. These protocols are designed based on the principle of minimizing energy consumption to reduce network energy usage [49] and extend network lifetime while maintaining communication quality [50; 51].  
In recent years, many researchers have optimized and improved energy-based routing protocols. For example, [PERSON] et al. [52] used fuzzy logic that minimizes the network's energy consumption by locating the UAV and then electing the next cluster head, which increases the network's lifetime. Additionally, [PERSON] et al. [49] proposed an energy-efficient routing protocol based on RPL that supports two transmission modes to meet the needs of UAVs in IoT applications.  
Despite the improvements made to energy-based routing protocols, FANETs still face several challenges and problems. Firstly, most existing routing protocols rely on simplified energy consumption models that may not accurately predict actual energy consumption, leading to reduced routing performance. Secondly, most routing protocols ignore the problem of simultaneously considering multiple objectives (e.g., delay, reliability) in practical applications. Finally, as FANETs continue to grow, existing routing protocols may face issues related to low scalability and high complexity. To address these challenges, future research can focus on exploring more accurate energy consumption models to improve routing performance.
## 2 Formulation of the Effectiveness Criterion
### First Condition: Retrofit Need  
The As-Built acceleration represents the seismic demand on the elementary bridge unit given the stiffness of the pier and the spectral characteristics of the site. Comparing the As-Built with the Elastic Limit acceleration, two scenarios can occur:  
1. the As-Built acceleration is smaller than the Elastic Limit acceleration; this means that the seismic demand is smaller than the capacity of the pier, and retrofit is unnecessary.
2. the As-Built acceleration is larger than (or equal to) the Elastic Resource acceleration; in this case, the pier is unable to withstand the seismic force associated with the supported mass of the deck, and the isolation of the deck can represent a valid solution for reducing its vulnerability.  
The first condition (retrofit need) is hence defined as  
\[a_{EL}\leq a_{As-Built} \tag{7}\]  
The check must be performed in the two principal directions of the bridge, longitudinal and transversal. The retrofit of the bridge is needed if condition (7) is met in at least one direction.  
If the bridge has a regular layout, with uniform spans and cross-sections, and the piers all have the same geometry (cross-section and height) and materials, it is sufficient to verify the inequality (7) for a single elementary bridge unit. Otherwise, it is necessary to define an equivalent SDOF model for each different pier geometry and/or span length and repeat the verification for the corresponding elementary bridge unit.
# Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports
## 4 The Influence of Support Location on the System Inherent Characteristics
### Calculation of the Corresponding Stiffness of Each Support at Different Spatial Locations  
The influence of each support location variation on the stiffness components of each support is studied in this section. Considering the actual installation situation of the simplified rigid structure, the variation of each support stiffness component is analyzed when the corresponding tangential location \(d_{z}\) along the casing changes. Take ten mounting locations at equal intervals along the tangential position \(d_{z}\) of the casing and perform static analysis for each support at different locations. Calculate the local stiffness components of each support at corresponding locations, and then obtain the global stiffness components of each support at corresponding locations through coordinate conversion, as shown in Figure 6. Figure 6a-d show that the normal stiffness component \(k_{y}\) of the top-left support and the bottom-left support are more sensitive to the variation of the support locations.
## 2 Theoretical Background
### Fracture Permeability  
The permeability of an intact rock specimen is usually calculated using the Darcy equation (Equation (3)):  
\[k=\frac{2 QP_{o}\mu L}{A\big{(}P_{i}^{2}-P_{o}^{2}\big{)}} \tag{3}\]  
where \(Q\) is the flow rate, \(\mu\) is the viscosity of the injection fluid, \(P_{o}\) is the downstream pressure, \(P_{i}\) is the upstream pressure, \(A\) is the cross-sectional area of the specimen, \(L\) is the mean specimen length and \(k\) is the permeability of the specimen.  
However, when the specimen is fractured, fluid will pass through the fractures rather than through the rock matrix, greatly increasing the permeability of the specimen, which is the purpose of fracturing. In intact samples, hydrological, mechanical, thermal and chemical processes affect permeability. However, in fractured samples, chemically facilitated mineral dissolution and stress-facilitated mechanical creep (called stress creep) mainly affect the fracture permeability [25; 26]. Moreover, the sample's cross-sectional area cannot be used to calculate the fracture permeability, since the fluid flow takes place through the fracture, which indicates the importance of using the fracture aperture to find the effective area of the sample for fluid flow in fractured samples. According to [PERSON] et al. [27], the modified cubic law given in Equation (4) can be used to calculate the fracture aperture using pore pressure measurements and downstream flow measurements [27]:  
\[Q=\frac{\Delta Pb^{3}d}{12\mu L} \tag{4}\]  
where \(Q\) is the volumetric flow rate, \(\Delta P\) is the pressure difference along the sample, \(d\) is the diameter of the sample, \(\mu\) is the viscosity of the injection fluid, \(L\) is the mean specimen length and \(b\) is the theoretical fracture aperture. However, \(\Delta P\) is equal to the pressure difference between the inlet and outlet. Therefore, Equation (4) can be rewritten as Equation (5):  
\[Q=\frac{(P_{i}-P_{o})b^{3}d}{12\mu L} \tag{5}\]  
Figure 1: Failure patterns in hydraulic fracturing due to different stress conditions (**a**) when \(\sigma_{a}<\sigma_{c}\) and (**b**) when \(\sigma_{a}>\sigma_{c}\).  
By combining Equations (3) and (5), fracture permeability can be calculated using Equation (6):  
\[k=\frac{P_{o}}{\left(P_{i}^{2}-P_{o}^{2}\right)}\sqrt[3]{\frac{2(P_{i}-P_{o})Q^{2 }\mu^{2}L^{2}}{3d^{2}}} \tag{6}\]
## 4 Methodology
### Problem Statement, System Model, and Correlated Failures Model
: The resilience of a data communication network is defined as its ability to provide and maintain an acceptable level of node connectivity in the face of correlated failures triggered by the above specified PSRNGs. In this paper, we will assess the resilience of a communication network after the occurrence of a PSRNG event by means of two metrics, which are mathematically defined in Section 4.4. One metric quantifies whether the network topology remains connected or is partitioned after an event, while the other metric quantifies how well-connected remains a network after the occurrence of a PSRNG event. With this ideas at hand, we can now introduce quantitative definitions for the resilience of a data communication network, which complement Definition 6. **Definition 7**. : A data communication network is defined as resilient to correlated failures triggered by the above specified PSRNGs, if its All Terminal Reliability (ATR) metric is equal to one. In addition, the average degree of resilience of a data communication network, in the face of correlated failures triggered by the above specified PSRNGs, is given by the ATTR metric.
## 2 Background and Related Work
### Cyber Security of Smart Health Domain  
Given their reliance on IoT and similar enabling technologies such as Cloud Computing, Fog computing, and big data, smart health systems are inherently vulnerable to a variety of threats, including data breaches, illegal access, and cyber-attacks [20; 21; 22; 23]. The collection and analysis of massive volumes of data, ranging from patient records to operating information of medical equipment, not only improves healthcare efficiency but also introduces new risks that threaten the entire healthcare ecosystem. These flaws can risk medical procedures, data security, and patient confidentiality [24; 25; 26; 27]. To secure this fragile and interdependent environment, strong security measures must be implemented. Addressing these issues is critical for sustaining trust in smart health systems and guaranteeing the future growth of this technology-driven industry. In general, many healthcare stakeholders may lack knowledge and comprehension of cybersecurity concerns, leaving them more vulnerable to all sorts of cyber-attacks and less likely to implement effective security measures [24; 25; 26; 27]. Healthcare operations are a popular target for hackers, who may exploit system weaknesses, especially during time-sensitive medical procedures or in an emergency. Thus, understanding the risk factors that make smart health ecosystems vulnerable to cyber-attacks is critical. Overall, cybercriminals can exploit entry points created by interconnected smart devices to compromise smart health solutions, targeting flaws at various layers of IoT,including physical, network, middleware, and application layers [27; 28; 29]. While existing research on cybersecurity in IoT has primarily focused on other areas, such as smart cities or on the whole on IoT, there has been limited exploration into smart health systems. Additionally, studies that do exist often fail to explore areas like deception technology and threat intelligence in depth. This research is driven by the need to fill this gap, considering the unique data, critical infrastructure, and specialized security assessments required for smart health systems. Table 1 presents a summarization of the recent related state of the art highlighting whether it is survey or research, whether it employs deception technology or not, and the scope of the study.
# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 6 Performance Evaluation  
### Ray-Tracing-Based Simulation  
The proposed scheme assumes the installation of APs and the use of two frequency bands in an indoor environment. For the simulation, a geometry-based channel was created by using a ray-tracing method with real spatial information [46; 47]. Three sites with different materials and structures were investigated. Figure 5 represents the 3D views and floor plans of the three sites, where (a) and (b) are academic buildings (INMC and ASRI on the Seoul National University campus), and (c) is an apartment building. The locations of the APs on each floor plan are marked with red circles. The height of the APs was set to 1.5 m for each map. The Tx power was assumed to be 0 dBm, and the center frequency was set to 2.4 or 5.2 GHz.  
Figures 6 and 7 represent the RSS distribution according to the center frequency when only one specific AP is considered in the map shown in Figure 5a. Similarly to the theoretical inference, the attenuation in the 5.2 GHz band is larger than that in the 2.4 GHz band, and the difference in attenuation between the two frequency bands is verified to be larger under harsh NLOS conditions. For the evaluation of the ranging and positioning performance, the RSS values from 12 APs were recorded at points on a uniform grid approximately 1 m apart from each other for the three sites. The total number of points evaluated for the three sites was 11,474, and the number of RSS-distance pairs satisfying the noise floor condition was 74,912. All measurement points were split into sets with the  
Figure 5: The 3D maps and the top views of the environments: (**a**) INMC (academic building), (**b**) ASRI (academic building), and (**c**) APT (residential space).  
following proportions: 70% for the training set, 15% for the validation set, and 15% for the test set; then, the deep regression model was trained and evaluated by using the dual-band RSS and distance pairs.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 2 Islanded Smart Microgrid and False Data Injection Attack
### False Data Injection  
The SE is destroyed because of an injection of false or bad data into a physical SG model. It can be assumed that the physical model of the SG with the N bus can be seen in the following AC power model [39] which is given in the Equation (1):  
\[\mathrm{y}=\mathrm{g(x)}+\sigma \tag{1}\]  
where x defines a state vector x with n-dimensional \(\{\mathrm{x_{1},x_{2},\,\dots,\,x_{n}}\}^{\mathrm{T}}(\mathrm{x_{i}\in R})\) for n-th state variables, and y gives a state vector y with m-dimensional for m-th measurements (\(\{\mathrm{y_{1},y_{2},\,\dots,\,\mathrm{zy_{m}}}\}^{\mathrm{T}}(\mathrm{y_{i }\in R})\)), such as the injected power (active or reactive) to every bus, and each transmission line, and \(\sigma\) defines an m-dimensional error vector. The define vector of error gives a Gaussian noise with covariance R and average value of zero. In this way, Equation (2) provides a formula for a power model:  
\[\mathrm{y}=\mathrm{J(x)}+\sigma \tag{2}\]  
where in the Equation (2), J gives an inalterable Jacobi matrix of g(x) which is defined as:  
\[\mathrm{J}=\frac{\partial\mathrm{g(x)}}{\partial\mathrm{x}}|\mathrm{x}= \mathrm{x_{o}} \tag{3}\]  
Behind applying FDIA, the measurement of y is:  
\[\mathrm{y}=\mathrm{g(x)}+\sigma+\delta \tag{4}\]  
where in the Equation (4), \(\delta\) defines the vector of attack. The module of BDD tests difference between real value of y and the estimated value \(\hat{\mathrm{y}}\). If the difference is higher than the threshold value \(\varepsilon\), we will have:  
\[|\hat{\mathrm{y}}-\mathrm{y}|>\varepsilon \tag{5}\]  
that, in this case, the false data is detected.