esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 4 Results and Discussion
### Microgrid Test-Bed  
The proposed system was tested using the updated configuration of a DC microgrid test bench introduced in [27]. The test bed microgrid consists of two RESs including a 400 W micro-wind turbine and a 400 W PV system, each with its respective charge controller. The generated energy is stored in a 12 V deep-cycle battery bank. A 400 W inverter and a DC-to-DC converter were required to supply the loads. Arduino off-the-shelf modules are used to measure currents and voltages at different points in the microgrid, as presented in Figure 1. The WSANCS communication was implemented using the XBee modules. The mosfet switches are connected to the Arduino micro-controller to control the load state (turn-on or turn-off) through a digital signal, as shown in Section 3.
## 4 Experimental Results
### Basic Property Analysis
#### 4.4.1 Randomness Analysis  
For a key to be secure, it needs to guarantee randomness, which can increase the computational complexity of brute force attacks. To validate this point, we test the randomness of the generated bio-keys by the standard NIST statistical test suite. Firstly, we randomly select 1000 face images to generate the corresponding bio-keys, and then these generated bio-keys can be sequentially formed into 1000 \(\times\) 512 binary bits as a source file, which is larger than 10,000 bits. Next, the source file is fed into NIST statistical test suite for evaluating randomness. Finally, according to the test benchmark, 16 tests of randomness are generated. According to NIST recommendations, if the \(p\)-value of 16 tests is larger than 0.01, the binary bit of the bio-key is random. The computed \(p\)-values of 16 tests are all greater than 0.01 in our scheme. This is because that generated bio-key is derived from random binary code. Overall, our generated bio-keys pass the randomness test and meet the randomness property.
# A Semi-Explicit Multi-Step Method for Solving Incompressible Navier-Stokes Equations
## 3 Numerical Example
### Accuracy of the Scheme
[PERSON]'s equation was solved using the biconjugate gradient stabilized algorithm with a diagonal preconditioning and a fixed tolerance of \(10^{-3}\). The computational times corresponding to the solution of the fractional momentum equation (first step) and the pressure Poisson equation are displayed. The computational cost of the momentum equation correction is not displayed, being negligible. Computational times were calculated as the average over all time steps. Figure 5 shows the computational times corresponding to the tests performed on the rough mesh. One can see that the costs of the solution of [PERSON]'s equation using both the proposed and the implicit schemes are nearly identical (around 0.02 s) for all considered time step sizes. The computational cost of the momentum equation solution equals 0.015 s in the case of the semi-explicit scheme and 0.035 s in the case of the implicit scheme for \(\Delta t=0.001\) s. Increasing the time step to 0.01 s, the computational cost of the first step nearly does not change for the semi-explicit scheme, while it increases to 0.075 s in the case of the implicit scheme. This occurs due to the increased number of non-linear iterations necessary for the convergence of the implicit scheme. Figure 6 shows the computational times obtained in the simulation carried out on a fine mesh. Similar to the results shown in the previous figure, the time required for the solution of [PERSON]'s equation is similar in both methods, while there is a considerable difference in the time required for the solution of the fractional momentum equation (first step). One can see again that for larger time steps, several non-linear iterations are required in the case of the implicit scheme. In the considered case, for \(\Delta t=0.01\) s, the overall computational cost of the implicit method is more than three-times larger than that of the semi-explicit scheme (the cost of the first step is more than five-times higher in the case of using the implicit scheme). We also note that Figure 4 indicates that for achieving a given accuracy, the semi-explicit scheme allows using much larger time steps. For instance, a relative error of approximately \(10^{-4}\) requires using \(\Delta t<0.001\) s in the case of the implicit scheme, while the semi-explicit scheme leads to this accuracy already for \(\Delta t=0.01\) s. Thus, the overall computational cost corresponding to the time accuracy is much smaller in the case of using the semi-explicit scheme. We note that the one can estimate the restrictions of the semi-explicit scheme for a given problem knowing Courant and Fourier numbers. The latter strongly restricts the time step in the case of high viscosities. However, such engineering fluids as air or water are characterized by low viscosity. Thus, the present methodology can be successfully used for a wide range of practical applications. Figure 5: Computational time of different solution steps. Semi-explicit vs. implicit method. Mesh of 6500 nodes.
## 3 Results and Discussion
### Results of the Analysis of HiperFer-17 Cr2
#### 3.2.1 Low-Cycle Fatigue (LCF)
As concluded from Figure 7, a correlation of the 3 MA-X8 features and the mechanical measures \(\varepsilon_{a\textit{-}\textit{inter. }}\) and \(HV10\) are given. Since the changes in mechanical and micromagnetic properties are related to the same microstructural effects, the next step is to investigate whether 3 MA-X8 measurements can be directly correlated with microstructural features. Thus, the dependency of the 3 MA-X8 feature \(W10Z\) on the precipitation density (which is given in area-%, see [12]) is shown in Figure 8a as a representative example for this correlation analysis. Although some reference values exhibit a high standard deviation, a rough correlation can be observed. By using a multidimensional approach, specifically Ridge regression, the precipitation density can be predicted based on the correlation between the 3 MA-X8 features and the microscopically determined precipitation density (Figure 8b). Figure 8: Correlation of the 3 MA-X8 feature W10Z with the precipitation density (**a**) and the prediction of the precipitation density based on micromagnetic feature (**b**) for HiperFer-17 Cr2 loaded under LCF conditions. Figure 7: Prediction of (**a**) \(\mid\!e_{\Pi}\!\mid\), (**b**) \(\varepsilon_{a\textit{-}\textit{inter. }}\) and (**c**) \(HV10\) for HiperFer-17 Cr2 loaded under LCF conditions based on micromagnetic measurements. However, it has been observed that the Vickers hardness and the \(\epsilon_{a,p\text{-}intor. }\) can be predicted more accurately than the precipitation density. One possible explanation for this phenomenon is that the regression approach for the prediction of Vickers hardness uses a large number of 3 MA-X8 features, which already individually show a clear dependence on Vickers hardness. The individual 3 MA-X8 features correlate better with the Vickers hardness than with the precipitation density, because the micromagnetic features have built up a combination of microstructural effects (see Section 2.4), which cannot be built up separately. One potential explanation for this phenomenon is that for the prediction of the Vickers hardness, the regression approach uses a multitude of 3 MA-X8 features that show a clear dependency on the Vickers hardness, influenced by both microstructural features, i.e., dislocation density and precipitation density, which are reflected together in the mechanical hardness. This might be caused by the dependency of \(HV\), \(\epsilon_{a,p\text{-}intor. }\) and the micromagnetic measures on multiple microstructural features (e.g., precipitation density, dislocation density and structure, etc.). Consequently, these measures contain the integral response of all microstructural features and, hence, correlate well witch each other. In contrast, the precipitation density is only one microstructural feature and hence, a correlation with the integral measures derived from micromagnetic measurements must lead to deviations, even if the precipitation density is the dominating microstructural feature [12; 13].
# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 3 Vehicular Traffic Congestion Avoidance Methodology
Based on this, in the selected region from Barcelona, the default SUMO can simulate at most 10 K+ vehicles and still remain in free flow mode, whereas the proposed solution can simulate at most 30 K vehicles and still remain in free flow mode. This is an improvement of 200% in the number of vehicles that can run in free flow. ### Conclusions on Experimental Results

As listed in Table 1, vehicular traffic congestion was simulated by running a large number of vehicles on specific road networks multiple times. To the best of our knowledge, our solution simulates vehicular traffic congestion by covering the most comprehensive set of road network topologies (four different urban regions). We generated moderate and high traffic congestion, reaching 49 K (the most) concurrent vehicles running simultaneously on a road network. In addition, we generated free-flow scenarios for the evaluation. Figure 11: Speed distribution in clusters for 100 K+ simulated vehicles in Bucharest with \(\delta=4\). Table 14 shows the maximum percentage improvement provided by the aforementioned vehicular traffic congestion reduction or avoidance solutions in urban areas. The percentage improvement was compared with the original approach, in which each congestion reduction solution was applied. Based on the above, to the best of our knowledge, our solution tested the most comprehensive set of vehicular traffic congestion scenarios and compared it with the existing solutions, obtaining the best percentage improvement for time spent in traffic, fuel consumption, and CO\({}_{2}\) emissions reduction. \begin{table}
\begin{tabular}{r r r r} \hline \hline
**Metric** & **SUMO** & **CASUMO\({}_{\text{8}}\)** & **\% Improvement** \\ \hline s\({}_{\text{av}}\) & 3.62 & 7.40 & 104.59 \\ d\({}_{\text{total}}\) & 529,354.16 & 727,528.96 & \(-\)37.44 \\ t\({}_{\text{total}}\) & 146,400.54 & 98,348.14 & 32.82 \\ t\({}_{\text{simulation}}\) & 10.35 & 8.71 & 15.89 \\ fuel\({}_{\text{total}}\) & 490,976.24 & 340,683.68 & 30.61 \\ CO\({}_{\text{2}_{\text{total}}}\) & 1,140,770.46 & 791,859.42 & 30.59 \\ countV\({}_{\text{peak}}\) & 40,895 & 35,742 & 12.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results and improvement of the proposed solution for 100 K+ simulated vehicles in Tokyo. Figure 12: Speed distribution in clusters for 100 K+ simulated vehicles in Tokyo with \(\delta\) = 8. ## 5 Conclusions and Future Research

Considering the vehicular transportation challenges in urban areas due to congestion, in this paper, we proposed a novel, effective, scalable and reliable vehicular traffic congestion avoidance solution (CAVTTM). We introduced a new vehicular traffic thresholding mechanism that is used to reduce and avoid traffic congestion on a comprehensive set of road network topologies. In the proposed solution, traffic modelling on the road network was performed using the KEP tree data structure we proposed in [8].
## 3 Results and Discussion  
Prior to applying GP tools, linear regression models were developed and tested according to the 10-fold cross-validation scheme. Standard lm(0) function of R environment was applied, yet obtained results were far from being satisfactory. In the case of FPF prediction, RMSE was 12.03 (NRMSE = 20.44%), and in the case of ED predictions, RMSE was 11.30 (NRMSE = 32.2%).
###### Abstract  
The AQUASENSE project is a multi-site Innovative Training Network (ITN) that focuses on water and food quality monitoring by using Internet of Things (IoT) technologies. This paper presents the communication system suitable for supporting the pollution scenarios examined in the AQUASENSE project. The proposed system is designed and developed in the SimulLTE/OMNeT++ simulation for simulating an LTE network infrastructure connecting the Wireless Sensors Network (WSN) with a remote server, where data are collected. In this frame, two network topologies are studied: Scenario A, a single-hop (one-tier) network, which represents a multi-cell network where multiple sensors are associated with different base stations, sending water measurements to the remote server through them, and Scenario B, a two-tier network, which is again a multi-cell network, but this time, multiple sensors are associated to local aggregators, which first collect and aggregate the measurements and then send them to the remote server through the LTE base stations. For these topologies, from the network perspective, delay and goodput parameters are studied as representative performance indices in two conditions: (i) periodic monitoring, where the data are transmitted to the server at larger intervals (every 1 or 2 s), and (ii) alarm monitoring, where the data are transmitted more often (every 0.5 or 1 s); and by varying the number of sensors to demonstrate the scalability of the different approaches.  
wireless sensor network; water quality monitoring; simulation 2  
are associated with local aggregators and communicate using short-range technology (e.g., Wi-Fi or ZigBee). The aggregators coordinate their local network of sensors, collect and aggregate their water measurements, and transmit them to the remote server through LTE base stations (eNBs), with which every aggregator is associated. For the above scenarios, we provide valuable performance results regarding the network's Quality of Service (QoS) under different conditions.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### Programming Models  
#### 2.1.1 Message Passing Interface (MPI)  
The MPI standard [5] defines the interface for message passing libraries. Made publicly available in May of 1994, the MPI method exchanges messages between processes to enable parallel programming and facilitate the transportation of data. The purpose of MPI is to achieve portability, efficiency, and flexibility in message passing applications. It is noteworthy that MPI is a specification, not an implementation or programming language, and its operations are expressed using Fortran, C, or C++ functions, subroutines, or methods. The MPI offers numerous options, including Open MPI [17] and message passing interface chameleon (MPICH) [18], which are open-source and free, as well as commercial options, such as IBM(r) Spectrum MPI [19] and Intel(r) MPI [20]. As the MPI provides exceptional features [19; 20], it is highly advantageous for the following.  
* Standardisation, as it is an exclusive message passing library that can, satisfactorily, be considered a standard. It works on almost any high-performance computing system and, in most cases, can replace any existing message passing library.
* Portability, as it requires minimal or no source code to port an application to a supported platform.
* Vendors, as they can to use its native capabilities to examine performance opportunities and achieve optimal performance, wherever possible. Optimal algorithms may also be developed for any implementation.
* Functionality, as it has over 430 functions, with MPI-2 and MPI-1 included in MPI-3. However, a dozen or so routines are required to create a typical MPI application.
* Availability, as it has a wide selection of vendor and open-source options available.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 1 Introduction
### Entry Points for Attacks to 5G Networks
#### 1.1.2 The Access Network  
The second entry point can reside in the access network, particularly concerning the S1/NG interface in 4G/5G (see Figure 1) and this poses significant risks to mobile networks. Vulnerabilities related to the access network include using gNodeBs (gNBs) to attack the core service and inject fake traffic into applications. Weaknesses in data and signaling encryption further exacerbate these risks. During initial authentication procedures, the messages exchanged lack encryption and integrity protection, potentially exposing sensitive data like International Mobile Subscriber Identity (IMSI) [16]. The absence of authentication between Serving and Home Networks, along with inadequate cryptosystems, leaves networks vulnerable to redirection attacks and message modification. Seamless interoperation between access technologies, such as Global System for Mobile communications (GSM), Universal Mobile Telecommunications System (UMTS), and LTE, also presents security challenges [17; 18; 19], especially with the combination of downgrade attacks, as reported in [20]. The GSM Authentication and Key Agreement (AKA) protocol suffers from weaknesses, including a lack of mutual authentication and integrity protection, which can be exploited [17]. The storage of authentication triplets in the Visitor Location Register (VLR) further exposes vulnerabilities [17]. Additionally, signaling overload, limited bandwidth, and heavy processing in Radio Resource Control (RRC) procedures create opportunities for attacks in the Core Network (CN) [21].
## 3 Methodology
### Kernel Normalized Difference Vegetation Index Calculation  
kNDVI can offer substantial support in investigating regional carbon sinks. To incorporate the impacts of carbon sequestration in our ES identification methods, we take kNDVI into our framework. In previous studies [65], it has been demonstrated that kNDVI can effectively alleviate the saturation problem and showcase high robustness in performance compared to NDVI. It is defined by adopting the RBF kernel function, recovers all higher order differences between the NIR and red reflectance bands, and when an appropriate kernel function is used, the index comes in a relatively simple and practical expression [40; 66]. The simplified equation is as follows:  
\[\text{kNDVI}=\text{tanh}((\frac{\text{NIR}-R}{2\sigma})^{2}) \tag{7}\]  
where NIR and \(R\) are the reflectance in the NIR and the red bands, respectively, and \(\sigma\) is a kernel length scale parameter that must be specified in each application. The application of the kernel function and parameter allows kNDVI to deal with the seasonal saturation effects and reflect complex vegetation phenology [40].  
In this study, kNDVI was calculated on the GEE platform using the MODIS dataset. Year-by-year kNDVI values from 2000 to 2020 at a location (\(\kappa,\text{y}\)) were used to fit this series with the linear regression model, and the slope was used to reflect the trend of kNDVI at that location [67].
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Perpendicular Distance from a Node to the Trajectory
#### 3.3.1 Establishing the Reference RSSI Curve Library  
As shown in Figure 7a, there are \(n\) parallel and identical trajectories with \(m\) broadcast points on each trajectory. The spacing between adjacent trajectories is \(\Delta d\), and the interval between adjacent broadcast points is \(\Delta L\). The projection of the reference node on all the trajectories are their respective midpoints. The perpendicular distance from the reference node to trajectory 1, trajectory 2,..., trajectory n are \(d_{1}\), \(d_{2}\),..., \(d_{n}\), respectively. When the random noise \(\zeta\) in Equation (2) is 0, we name the RSSI value received by the reference node as the reference RSSI value. Each trajectory corresponds to a reference RSSI curve, as shown in Figure 7b. For the \(i\)th trajectory, a reference RSSI vector \(\mathbf{R_{i}}\) can be constructed using the reference RSSI value of the broadcast point at \(x_{1}\), \(x_{2}\),..., \(x_{m}\),  
\[\mathbf{R_{i}}=[r_{i1},r_{i2},\ldots,r_{im}] \tag{25}\]  
We use \(\mathbf{R_{i}}\) to represent the \(i\)th reference RSSI curve. All reference RSSI curves can establishing a reference RSSI curve library, which can be expressed as  
\[\mathbf{L_{c}}=\begin{cases}\mathbf{R_{1}}=[r_{11},r_{12},\ldots,r_{1m}]\\ \mathbf{R_{2}}=[r_{21},r_{22},\ldots,r_{2m}]\\ \vdots\\ \mathbf{R_{n}}=[r_{n1},r_{n2},\ldots,r_{nm}]\end{cases} \tag{26}\]  
Figure 7: The establishment of the reference RSSI curve library: (**a**) the positional relationship between the reference node and \(n\) trajectories; and (**b**) the reference RSSI curve library.  
Since the perpendicular distance from the node to each trajectory is different, the transmission distance from the same broadcast point of each trajectory to the reference node are different, the reference RSSI values of the respective trajectories at the same broadcast point are different, and the reference RSSI vector of each trajectory are different. Therefore, there is a mapping relationship between the perpendicular distance from the reference node to the trajectory and the reference RSSI curve, which can be expressed as  
\[d_{i}=F\left(\mathbf{R_{i}}\right) \tag{27}\]  
where \(i=1,2,...,n\), \(F\) represents the mapping relationship. All mapping relationships between the reference RSSI vector and its perpendicular distance can establish a mapping relationship library, which can be expressed as  
\[\mathbf{L_{r}}=\begin{cases}d_{1}=F(\mathbf{R_{1}})\\ d_{2}=F(\mathbf{R_{2}})\\ \vdots\\ d_{n}=F(\mathbf{R_{n}})\end{cases} \tag{28}\]
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
###### Abstract  
To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately.  
Keywords:damage detection; linear model; ARX model; temperature effects; vibration-based; RC slab +
Footnote †: journal: Applied Sciences