esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The values correspond to the solutions shown in Figures 3 and 4, i.e., \(V_{A}=9.968\,\mathrm{V}\), \(V_{B}=6.806\,\mathrm{V}\), and \(V_{A}=6.815\,\mathrm{V}\), \(V_{B}=9.452\,\mathrm{V}\), respectively. Thus, the solution determined at the default settings of the DC analysis, \(V_{A}=8.063\,\mathrm{V}\), \(V_{B}=8.556\,\mathrm{V}\), shown in Figure 1, corresponds to the unstable equilibrium point. The instability is due to the presence of capacitors and inductors, usually nonlinear, present in the semiconductor devices, passive elements, and the parasitic capacitance and inductance of the connections. It follows that the designer, unaware of the problem, obtains a numerical solution that meets the mathematical description, but is unstable. Therefore, it is essential to use procedures that allow the determination of multiple DC solutions, such as the one proposed in this work. For the circuit shown in Figure 6, the measured voltages corresponding to the two solutions are \(V_{A}^{(1)}=1.345\,\mathrm{V}\), \(V_{B}^{(1)}=0.061\,\mathrm{V}\) and \(V_{A}^{(2)}=2.481\,\mathrm{V}\), \(V_{B}^{(2)}=3.641\,\mathrm{V}\), respectively. Three DC solutions \(V_{A}^{(1)}=1.345\,\mathrm{V}\), \(V_{B}^{(1)}=0.100\,\mathrm{V}\), \(V_{A}^{(2)}=2.493\,\mathrm{V}\), \(V_{B}^{(2)}=3.597\,\mathrm{V}\), and \(V_{A}^{(3)}=2.009\,\mathrm{V}\), \(V_{B}^{(3)}=2.087\,\mathrm{V}\), were determined using the proposed method. The default DC analysis found the solution corresponding to the stable equilibrium point. Prototyping was performed only to determine which solutions were stable equilibrium points. It was not important to bring the voltages into a perfect agreement since the influence of the accuracy of the available models, component tolerances, temperature, parasitic parameters, and measurement uncertainty causes the voltages in the actual systems to vary from the simulation. However, statistical analyses taking tolerance into account showed that parameter scatter within the assumed limits for both prototyped circuits did not change the number of solutions. Thus, even taking these phenomena into account, the measurements qualitatively reflect the specifics of the circuit, and the (quantitative) measurement results differed slightly from the simulation results. The solutions, copied from the output file, of the circuits shown in Figures 7 and 8 are placed under the corresponding schematics. Summarizing the results shown in Table 1, we see that, in all 35 circuits, the existence of multiple solutions was confirmed. For 33 circuits, all DC solutions were determined. For the circuits labeled 28 and 31, only part of the solutions was determined. For the circuit labeled 31, changing the node to which the BC source is attached from the default to another allowed all 11 solutions to be determined. The analysis time, determined by the time of file operations, was short and did not exceed 6.0 s.
## 3 Results and Discussion
### Workflow from Data Acquisition to Location Tracking
#### Shortest Route Calculation Using GNSS/GIS Algorithms  
Calculating the shortest distance between two points is a very common spatial problem, which was solved using the Network Analysis tool in QGIS. The Road Graph plugin of QGIS with network analysis algorithms was used to calculate the shortest path distance or time, by calculating the cumulative cost between two points in a network. The plugin provides a measure of the cumulative cost based on the length between two nodes of a network. The measurements took into consideration the first case, when the speed limit was the same for all the roads (the edges of our network) or the second case, when the speed limit differs for some selected roads. The \"Roads\" and \"Routes\" information was downloaded from Geofabrik [52] repository in the form of shapefiles. The shortest path algorithm in Figure 6 showed that a distance of 4.15 km could be covered in 0.6 hr at 40 km/hr.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 5 Conclusions  
In this paper, a mathematical framework for downlink mmwave cellular network has been proposed where the NLOS path is modeled by SEPLM and a 3 GPP LOS probability function. The SEPLM was first validated for mmwave NLOS channel using data from an open source measurement-based simulator NYUSIM, and then a stochastic geometry-based mathematical model for mmwave communication in an outdoor scenario with obstructions has been developed. Using this developed model, the impact of cell radii, BS densities, and obstructions/ blockages on SINR coverage probability and ASE has been investigated. Our analysis shows that the SINR coverage probability is improved as the cell radius is increased. The coverage probability decreases as the number of obstructions in the environment increases because of increased BS blockage. It is also determined that, as network density increases, the coverage probability falls exponentially to zero due to increased network interference. The ASE is observed to increase linearly with increasing BS densities reaching a maximum value and then dropping to zero. It is also found that the achievable ASE is low for a highly obstructive environment and cannot be increased by deploying more BSs. As a more realistic path loss model for mmwave, SEPLM shows a strong dependence on cell radius and BS compared to the existing path loss model resulting in different coverage performance.  
HMThe coverage probability derived in this work utilizes a sectored antenna model for simplified analysis. In future, this work can be extended to include realistic antenna  
Figure 8: SINR coverage probability \(P_{c}(T)\) comparison of conventional path loss model for mmwave and SEPLM. (**a**) SINR vs. SINR threshold (T) (**b**) SINR vs. BS density (\(\lambda\)).  
patterns with the main lobe and observe the impact of beamforming in a dense network. Furthermore, the path loss parameters used and validated in this work mainly assume static blockages (such as buildings) in an urban microcellular scenario. In future, more investigation is required to determine the impact of blockage on the path loss parameters in different propagation environments.  
Conceptualization, H.M., I.A. and M.I.A.; methodology, H.M. and S.A.; software, H.M.; validation, H.M., M.I.A. and I.A.; formal analysis, H.M.; investigation, H.M., I.A. and I.U.R.; writing--original draft preparation, H.M.; writing--review and editing, H.M., I.A., S.A., M.I.A. and I.U.R.; supervision, I.A. and M.I.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data set supporting reported results in Figure 2 is generated using [[https://wireless.engineering.nyu.edu/nyusim/](https://wireless.engineering.nyu.edu/nyusim/)]([https://wireless.engineering.nyu.edu/nyusim/](https://wireless.engineering.nyu.edu/nyusim/)) (accessed on 31 October 2022).  
The authors declare no conflict of interest.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 2 Related Work
#### Limitations of Existing Mechanisms  
In HexDD [18], the flooding of the query within the lines causes a set of sensor nodes to deplete their energy earlier than other nodes and introduces the energy hole problem. Furthermore, there is no mechanism provided to increase the width of primary lines. In RDDM [19], the rendezvous cluster head is responsible for transmitting the query and data packets. These tasks increase the load on rendezvous cluster heads and exhaust their energy very quickly. It triggers frequent re-election of rendezvous cluster heads and adjustments in the rendezvous-based data dissemination tree. It affects energy consumption and data delivery delays. In QDVGDD [20], the mobile sink travels around the boundary of the sensor network. However, in this routing strategy, there is no provision for providing the location information of the sink to the sensor nodes. Due to this, the routing path does not need to be the shortest all the time. Apart from this, the sensor nodes lying at the boundary carry so much burden that they will lose their energy earlier than other nodes. The construction of multiple rings in the large-scale sensor field is a time- and energy-consuming process in QRRP [23]. In QBR [24], query and data packets travel through backbone nodes to reach the intended sensor nodes and sinks, respectively. In this way, backbone nodes carry most of the burden, and there is no provision for exchanging the responsibilities of backbone nodes with remaining nodes, which makes routing challenging.  
VGRQ restricts the broadcast of query messages by enabling the sharing of query messages exclusively among cluster heads through chains, which is an improvement over HexDD [18]. The participation of multiple cluster heads in query and data routing ensures an equitable distribution of the workload among them. This is different from RDDM [19], where only the rendezvous cluster head is responsible for transmitting the query and data. The random movement feature of VGRQ makes it well-suited for a wide range of applications, which is an advancement compared to QDVGDD [20], where the sink follows a fixed mobility pattern. Moreover, in VGRQ, the sink periodically shares its location information with the sensor nodes, enabling the identification of the shortest routing path. This functionality is absent in QDVGDD [20]. The process of constructing a grid in VGRQ is significantly easier and more efficient compared to constructing rings in QRRP [23] and establishing a backbone structure in QBR [24].
## 3 Results
### Light Use Efficiency Models  
SRS-sPRI and \(\alpha\) values were used to derive light use efficiency models and compared to GPP to assess each model's accuracy. APAR values were also compared to GPP (Figure 11a) to determine its contributing proportion to the overall model. APAR closely tracked temporal changes in GPP throughout maturity, however, during leaf fall GPP and APAR show differing paths. The LUE model constructed from \(\alpha\) values (Figure 11b) was significantly correlated to GPP (\(R^{2}\) = 0.79, \(p\) < 0.05). The model's closest association to GPP occurred during leaf senescence, as GPP started to decline. The light use efficiency model driven by the SRS-sPRI dataset (Figure 11c) had very similar results to that of the \(\alpha\) based model and showed significant correlations to GPP (\(R^{2}\) = 0.77, \(p\) < 0.05). Again, the GPP and models' paths were most closely associated during senescence, as leaf fall initiated.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Traffic Optimization
#### 4.3.2 Load Optimization  
In order to improve effectiveness, increase accessibility, and shorten reaction times, traffic from the network is distributed across several pathways or pieces of network equipment through the technique of load optimization. The flexible load optimization technique is well suited to be implemented in a KDN situation due to consideration of both network conditions and application regulations for optimization.  
Work in [274] further expands the blockchain system to allow Load Balancing utilizing Deep Reinforcement Learning (LB-DRL) in a ledger-based strategy for distributing loads in the blockchain network. Furthermore, reinforcement learning is used for traffic congestion prediction to balance the traffic load using an edge computing platform where transactions are secured by access control using the Hyperledger Fabric blockchain [275]. Similarly, a framework known as Blockchain-based Controller Load Balance (BCLB) has been utilized in the distributed control architecture of KDN, where blockchain is utilized as a decentralized data sharing model to provide inter-domain links and a global view for global controller load balancing, which also aids deep reinforcement learning analysis to offload load from the overloaded controllers [276].  
For automotive KDNs, a multiple-stage blockchain design for multi-controller load optimization has been examined using a fuzzy approach to generate knowledge [277]. This architecture makes use of a fuzzy inference-based technique to perform actual-time adaptive reconfiguration of the blockchain in situations like changes in traffic, changes in controllers, etc. Likewise, a framework known as Intelligent Vehicular Edge Computing (IVEC) integrates the centralized control power of KDN along with the smart contract mechanisms of distributed blockchain to create a resource management controller for load balancing in IVEC that uses artificial intelligence techniques for computations [278].
## 2 Materials and Methods
### Mix Design  
The mix design is shown in Table 2. Firstly, the AMF content considered was 0%, 5%, 10%, 15%, and 20% by weight of machine-made sand. Secondly, based on the 10% AMF (optimum content based on initial trails), the addition of aluminum sulfate was based on the weight percentage of cementitious materials (OPC and superplasticizer) by 0%, 0.5%, 1.0% and 2.0%. Lastly, based on the 10% AMF and 1% aluminum sulfate (optimum content), the content of PPF considered was 0, 0.6, 0.9, and 1.35 kg/m\({}^{3}\). Fresh concrete was cast into different molds and covered with a plastic sheet to prevent any water evaporation. The specimens were demolded after 24 h and cured under a standard environment (95% RH and \(20\pm 2\)\({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & **AS \({}^{1}\)** & **PPF \({}^{2}\)** & **AMF Content** & \multicolumn{4}{c}{**Raw Material (kg/m\({}^{3}\))**} \\  & **(wt\%)** & **(kg/m\({}^{3}\))** & **(wt\%)** & **OPC** & **Gravel** & **MS \({}^{3}\)** & **Water Superplasticizer** \\ \hline NO.1 & & & & 0 & & & & \\ NO.2 & & & & 5 & & & & \\ NO.3 & \(-\) & \(-\) & 10 & & & & & \\ NO.4 & & & & 15 & & & & \\ NO.5 & & & & 20 & & & & \\ \hline NO.6 & 0.5 & & & & & & & \\ NO.7 & 1.0 & \(-\) & & 10 & & & & & \\ NO.8 & 2.0 & & & & & & & \\ \hline NO.9 & & & 0.6 & & & & & & \\ NO.10 & 1.0 & 0.9 & 10 & & & & & \\ NO.11 & & & 1.35 & & & & & \\ \hline \hline \end{tabular}  
* AS—aluminum sulfate. \({}^{2}\) PPF—polypropylene fiber. \({}^{3}\) MS—machine-made sand.  
\end{table}
Table 2: Mix design.  
Figure 1: Particle size distribution and XRD: (**a**) particle size distribution of gravel conforming to [48]; (**b**) particle size distribution of machine-made sand (MS) conforming to [49]; (**c**) particle size distribution of aggregate micro fines (AMF), and (**d**) XRD result of AMF.
## 4 Results  
Figure 6 shows the period-independent maps of \(\hat{M}\) and \(\hat{R}\) associated with a return period of 475 years resulting from the application of the criteria described in the previous section. Maps are shown for disrupted slides and falls (Figure 6a,b), coherent slides (Figure 6c,d), and lateral spreads and flows (Figure 6e,f) separately. The maps show that for a given magnitude, disrupted slides and falls may occur (at a given site) farther from the epicenter than coherent slides. The latter, in turn, may occur as far as or farther from the epicenter than lateral spreads and flows. For the same return period of 475 years, Figure 7 shows the period-independent triggering maps for each failure mechanism.  
For each landslide type, the entire procedure described in Section 3.3 was repeated for return periods of 975 and 2475 years. The results, which are not reported here for the sake of brevity, show that the number of sites for which the triggering of landslides cannot be excluded (red points) increases with increases in the return period. In other words, the longer the return period, the higher the triggering potential.  
For each return period considered, the period-independent triggering maps obtained for each category of landslides can be merged into a single comprehensive map. These maps classify sites based on their potential to trigger different types of earthquake-induced landslides. The maps are shown in Figure 8 together with the location of landslides that occurred in Italy in the past (black and blue dots) [5; 6; 7; 9; 10]. Sites in red are those where all three types of landslides can potentially be triggered by seismic activity and consequently have a higher triggering potential. Sites displayed in orange are those where two types of landslides (disrupted slides and falls and coherent slides) can potentially be triggered, suggesting a moderate triggering potential. Yellow points indicate sites where seismic activity may potentially trigger disrupted slides and falls exclusively, indicating a relatively lower triggering potential. Finally, green areas denote sites where none of the aforementioned types of landslides are expected to occur, indicating a negligible triggering potential.  
Figure 6: Maps of reference period-independent magnitude (left column) and distance (right column) scenarios associated with a 475-year return period for different failure mechanisms: (**a,b**) disrupted slides and falls, (**c,d**) coherent slides, and (**e,f**) lateral spreads and flows.  
Analyzing the triggering maps in conjunction with the geographic distribution of past landslide events shows that most historical observations fall in red or yellow areas, thus indicating the congruence of our results with the data collected in the CEDIT and CFTI5 Med catalogs. Only a handful of observations (eight points for return periods of 475 and 975 years, and five points for a return period of 2475 years) fall in areas where the seismic triggering of landslides is not expected (areas in green).
## 2 Methodology
### Collection of Dataset and Data Normalization: EMI Method  
The proposed scheme assumes that EMI responses of an FRP-strengthened reinforced concrete beam under different damage conditions are captured using PZT sensors and an impedance analyzer (in this work, the Agilent HP 4192A analyzer, Santa Clara, CA, USA). For this, different sensors are bonded at different beam locations to measure a range of electromechanical impedance responses in the frequency domain.  
[PERSON] et al. [36] first introduced the EMI method to a one-dimensional model. This is shown in Equation (1), which gives the relation between the electrical admittance (inverse of the impedance, Z) of the PZT, Y(\(\omega\)), and the mechanical impedance of the structure. Mechanical variations in the structure due to damage might be identified by means of admittance measurements.  
\[\mathrm{Y}(\omega)=\mathrm{j}\omega\frac{\mathrm{w}\mathrm{l}}{\mathrm{h}} \left(\vec{\varepsilon}_{33}^{\mathrm{T}}-\frac{\mathrm{Z}_{\mathrm{s}}( \omega)}{\mathrm{Z}_{\mathrm{s}}(\omega)+\mathrm{Z}_{\mathrm{a}}(\omega)} \mathrm{d}_{33}^{2}\vec{\chi}_{\mathrm{xx}}^{\mathrm{E}}\right) \tag{1}\]  
where \(\mathrm{Z}_{\mathrm{s}}\) and \(\mathrm{Z}_{\mathrm{a}}\) are the mechanical impedance of the host structure and the PZT sensor, respectively; w, l, and h are the width, length, and height of the PZT sensor, respectively; \(\vec{\varepsilon}_{33}^{\mathrm{T}}=\vec{\varepsilon}_{33}^{\mathrm{T}}(1- \delta\mathrm{j})\) and \(\vec{\chi}_{\mathrm{xx}}^{\mathrm{E}}\) are the electrical permittivity at the zero electric field and the complex Young's modulus of the PZT at zero stress, respectively; \(\delta\) denotes the dielectric loss factor of the PZT; and d31 is a coupling piezoelectric coefficient [37].  
Additionally, with the purpose of accelerating and improving the training of the neural networks used in the procedure, impedance data are preprocessed by means of a min-max normalization, as follows:  
\[Z_{normalized}=\frac{Z-\mathrm{min}(Z)}{\mathrm{max}(Z)-\mathrm{min}(Z)} \tag{2}\]  
where \(Z_{normalized}\) represents the computed normalized impedance spectrum.  
Figure 2 shows an example of an impedance spectrum before and after normalization.
# Energy Management Scheme for an EV Smart
## 4 EMS for a Smart Charger
### Active Power Ride-Through  
Consider the power system shown in Figure 10. The grid is transferring power \(P_{g}=100\) kW to the PCC. A constant power load \(P_{load}=250\) kW with the unity power factor is connected to the PCC. A DG supplying \(P_{dg}=150\) kW to the load is located in close vicinity to the PCC. When plugged in, the EVs connected to the smart charger are idle, charging or discharging based on the rate governed by the DSO. The EV power during such ongoing schemes is represented by \(P_{LTS}\), and this scenario is given by the following equation:  
\[P_{g}+P_{dg}=P_{load}\pm P_{LTS} \tag{18}\]  
A smart charger is authorized by the DSO to provide grid support whenever required. The primary objective of this power ride-through management subsystem is to designate the EV charging and discharging power to mitigate the short-term power imbalances caused by the DG. In the real world, the grids have power transfer limits due to line losses and thermal limitations. Therefore, the DSO defines the power transfer limit depending on the distribution line ampacity and distribution transformer rating.  
During renewables' power variance, the EMS calculates the active power reference \(P_{sc}^{*}\) for the smart charger. The low output from the DG corresponds to an increased power transfer from the grid to the PCC, and the EMS calls for V2G operation. In contrast, during surplus generation by the DG, the power transfer from the grid decreases, and the EVs are charged to absorb the surplus power, while the G2V improves the load factor. The total amount of active power required or to be consumed by EVs is calculated as follows:  
Figure 10: Energy management scheme (EMS) control functioning within the smart charger system.  
\[P_{sc}^{*}=P_{load}-(P_{g}+P_{dg}) \tag{19}\]  
where \(P_{g}\) is fixed by the DSO (in this study: 100 kW). When \(P_{sc}^{*}\) is negative, EVs are charging, whereas they are discharging when the reference power is positive. The calculated power is subjected to a saturation block according to the kVA rating of the charger. The power reference \(P_{(inv)}^{*}\) fed to the smart charger inverter is given by:  
\[P_{(inv)}^{*}=\begin{cases}P_{sc}^{*}\,&\forall\quad P_{dg}\
eq 150\;\text{kW}\\ 0\,&\text{otherwise}\end{cases} \tag{20}\]  
The power angle \(\delta^{0}\) indicates the power angle of preferred power transfer from the grid to the PCC, and the deviation of power angle from this value (\(\Delta\delta\)) is calculated as:  
\[\Delta\delta=\delta-\delta^{0} \tag{21}\]  
The DSO also defines the magnitude of the maximum deviation from the preferred power angle \(|\Delta\delta|_{max}\). In this study, \(|\Delta\delta|_{max}=0.0125\) rad is considered corresponding to a 100-kW power transfer.