esa-sceva/satcom-chunk-collection huggingface dataset:
# The Stability Analysis of a Vibrating Auto-Parametric Dynamical System Near Resonance
## 3 The Perturbation Technique
\begin{array}{ll}\xi_{3}=\frac{\widetilde{f}_{1}e^{ip_{1}\tau_{0}}}{2(\omega_{1} ^{2}-p_{1})^{2}}-\frac{[2\widetilde{\beta}\,\Lambda_{2}^{2}(\omega_{2}^{2}-4)+6 \widetilde{\beta}\,\Lambda_{2}^{2}]}{(\omega_{1}^{2}-4)(\omega_{2}^{2}-4)}e^{2i \,\tau_{0}}\\ -[\frac{1}{(\omega_{1}^{2}-\omega_{2}^{2})^{2}}+\frac{\omega_{2}^{4}}{(\omega_ {1}^{2}-\omega_{2}^{2})^{3}}]\tilde{\beta}^{2}\,\omega_{2}^{4}\,\Lambda_{3}e^{ i\,\omega_{2}\,\tau_{0}}+CC\,\end{array}  \tag{37}\]  
\[ \begin{array}{ll}\phi_{3}&=\frac{\widetilde{f}_{1}e^{ip_{2}\tau_{0}}}{2(1-p_ {2})^{2}}+\overline{\Lambda}_{3}\Lambda_{2}\big{[}\frac{d_{1}}{d_{2}}\overline {\Lambda}_{1}e^{i(1-\omega_{1}-\omega_{2})\tau_{0}}+\frac{d_{3}}{d_{4}}\Lambda _{1}e^{i(1+\omega_{1}-\omega_{2})\tau_{0}}\big{]}+\Lambda_{2}\big{\{}\frac{d_ {3}}{d_{4}}\Lambda_{1}^{2}e^{i(1+2\omega_{1})\tau_{0}} \tag{38}\] \[+\Lambda_{3}[\frac{d_{3}^{2}}{d_{8}}\overline{\Lambda}_{1}e^{i(1 -\omega_{1}+\omega_{2})\tau_{0}}+\frac{d_{3}}{d_{10}}\Lambda_{1}e^{i(1+\omega _{1}+\omega_{2})\tau_{0}}]\big{\}}+CC\,\end{array}  \tag{39}\]  
where \(d_{s}\ (s=1,2,3,\ldots,20)\) are functions of \(\omega_{1}\) and \(\omega_{2}\) (see Appendix A). According to the removal conditions of secular terms, Equations (34)-(36), and the following initial conditions, we can estimate the functions \(\Lambda_{j}\ (j=1,\,2,\,3)\):  
\[ \begin{array}{ll}y(0)=B_{01},&\dot{y}(0)=B_{02},&\phi(0)=B_{03},\\ \dot{\phi}(0)=B_{04},&x(0)=B_{05},&\dot{x}(0)=B_{06},\end{array}  \tag{40}\]  
where \(B_{0k}(k=1,2,\ldots,6)\) are known values. Now, we will investigate the arising cases of resonance according to the previous approximate solutions. These cases can be categorized into primary (main) external resonance, which takes place at \(p_{1}=\omega_{1}\), \(p_{2}=1\), \(p_{3}=\omega_{2}\), and internal resonance, which occurs at \(\omega_{1}=\omega_{2}\), \(\omega_{1}=-\omega_{2}\), \(\omega_{1}=2\), \(\omega_{1}=-2\), \(\omega_{2}=2\), \(\omega_{2}=-2\). In this situation, we investigate the three primary external resonances, \(p_{1}\approx\omega_{1}\), \(p_{2}\approx 1\), and \(p_{3}\approx\omega_{2}\), simultaneously. These resonances depict the closeness of \(p_{1}\), \(p_{2}\) and \(p_{3}\) to \(\omega_{1}\), \(1\) and \(\omega_{2}\), respectively. Therefore, detuning parameters \(\sigma_{j}\ (j=1,\,2,\,3)\) can be introduced according to:  
\[p_{1}=\omega_{1}+\sigma_{1},\ \ \ p_{2}=1+\sigma_{2},\ \ \ \ p_{3}=\omega_{2}+\sigma_{3}. \tag{41}\]  
Since the detuning parameters are known as the distances from the oscillations to the strict resonance [35; 36], we can use the small parameter \(\varepsilon\) to express these parameters as follows:  
\[\sigma_{j}=\tilde{\omega}\tilde{\sigma}_{j}\ \ \ \ \ (j=1,2,3). \tag{42}\]  
Substituting parameters (41) and (42) into Equations (8)-(10) and removing the produced secular terms obtains the following conditions of solvability:  
\[
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 7 Summary and Conclusions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & **Parameter** & \multicolumn{4}{c}{**Marginal Effects**} \\ \cline{3-6}  & **Estimates** & **z-Stat** & **No** & **Minor** & **Severe** \\ \hline Constant [M] & \(-0.92\) & \(-7.25\) & & **Injury** & **Injury** & **Injury** \\ Random parameter (normally distributed) & \(-8.17\) & \(-1.77\) & \(-0.0032\) & \(-0.0012\) & \(0.0044\) \\ Town indicator (1 if a crash occurred in town, 0 otherwise) [SI] & (4.58) & (1.86) & & & \\ Heterogeneity in the mean of random parameter & & & & & \\ \hline Low indicator (1 if the crash occurred in town, 0 otherwise) [SI]: & 5.18 & 1.84 & & & \\ Speeding indicator (1 if spreading involved in the crash, 0 otherwise) & \(-1.15\) & \(-3.01\) & \(0.0060\) & \(0.0025\) & \(-0.0085\) \\ Temporal characteristics & & & & & \\ Weechard indicator (1 if a crash occurred during the weekend, 0 otherwise) [SI] & \(-1.15\) & \(-3.01\) & \(0.0060\) & \(0.0025\) & \(-0.0085\) \\ Environmental characteristics & & & & & \\ Daylight indicator (1 if the light condition was daylight, 0 otherwise) [MI] & \(-0.27\) & \(-1.79\) & \(0.0229\) & \(-0.0236\) & \(0.0007\) \\ Daylight indicator (1 if the light condition was daylight, 0 otherwise) [SI] & \(-1.94\) & \(-5.91\) & \(0.0147\) & \(0.0052\) & \(-0.0199\) \\ Dark-Not light indicator (1 if the light condition was dark-not lighted, 0 otherwise) [NI] & 0.62 & 2.05 & \(0.0067\) & \(-0.0047\) & \(-0.0020\) \\ Cloudy indicator (1 if the weather was cloudy, 0 otherwise) [SI] & \(-1.60\) & \(-2.14\) & \(0.0022\) & \(0.0009\) & \(-0.0031\) \\ Rain indicator (1 if the weather was rain, 0 otherwise) [SI] & \(-3.97\) & \(-1.65\) & \(0.0009\) & \(0.0005\) & \(-0.014\) \\ Roadway characteristics & & & & & \\ Payed shoulder indicator (1 if the type of shoulder was paved, 0 otherwise) [MI] & \(-0.35\) & \(-2.21\) & \(0.0178\) & \(-0.0182\) & \(0.0004\) \\ Cure jet indicator (1 if the roadway alignment was curve left, 0 otherwise) [MI] & \(-1.74\) & \(-3.30\) & \(0.0071\) & \(0.0022\) & \(-0.0093\) \\ Vehicle characteristics & & & & & \\ Commercial vehicle indicator (1 if commercial vehicles involved in the crash, 0 otherwise) [MI] & \(-1.10\) & \(-2.51\) & \(0.0674\) & \(-0.0994\) & \(0.0320\) \\ Crash characteristics & & & & & \\ Speeding indicator (1 if speeding involved in the crash, 0 otherwise) [NI] & \(-0.57\) & \(-1.98\) & \(-0.0072\) & \(0.0061\) & \(0.0011\) \\ Human characteristics & & & & & \\ Cystic indicator (1 if cyclists involved in the crash, 0 otherwise) [NI] & \(-2.58\) & \(-5.02\) & \(-0.0089\) & \(0.0086\) & \(0.0003\) \\ Number of observations & & & & & \\ Number of estimated parameters & & & & & \\ Log-likelihood at zero \((LL(0))\) & & & & & \\ \(P^{2}=1-LL(\beta)/LL(0)\) & & & & & \\ BIC & & & & & \\ \hline \hline \end{tabular} Note: N = no injury; MI = minor injury; SI = severe injury.  
\end{table} 
Table 10: Model results of mixed logit with heterogeneity in means for non-angle crashes in Florida 2016-2017.
## 2 Materials and Methods
### Calculation of the Initial Stress, Interpolated Acceleration, Shear Stress, and Shear Strain  
To obtain the initial stresses of the test specimen, finite element analysis (FEA) was conducted using Plaxis 2D utilizing the unit weights of the soil obtained from the oven-dry test. The scale model was first analyzed by activating the embankment. Thereafter, surcharge loads A, which represented the shallow foundations, were activated with a load value of about 1 kPa. The initial effective stress distribution of the test specimen was then obtained, showing a maximum effective stress of about 7 kPa, located directly below the shallow foundations at exactly 55 cm from the crest of the embankment. The stress distribution was confirmed by comparing the measured total stresses and the predicted total stresses at SPV1 before the start of shaking. The measured total stress at SPV1-A and SPV1-B indicated a value of 9.0 kPa and 9.5 kPa, respectively, while the predicted  
Figure 13: Sensor locations and labels.  
total stresses at SPV1-A and SPV1-B were 8.1 kPa and 9.97 kPa, respectively. The stresses obtained using FEM were necessary in the analysis of the measured data.  
Accelerometer A1-C was placed at the bottom of the shaking table to measure the input acceleration in the horizontal and vertical direction. Surface accelerometers (A2-A, A2-B, A2-C, and A2-D) were used to obtain the interpolated accelerations (\(a_{z}\)) at various depths of the soil using Equation (1). From the accelerations and the predicted initial total stresses (\(\sigma_{v0}\)) from FEA, the dynamic shear stresses (\(\tau_{d}\)) and shear strains (\(\gamma\)) can be obtained using Equations (2) and (3), respectively. In Equations (1)-(3), \(a_{surface}\) is the acceleration at the surface (accelerometers A2), \(a_{input}\) is the acceleration at the base of the test tank (accelerometer A1-C), \(z\) is depth, \(h\) is the height of soil from the base of the test tank to the surface, \(g\) is gravity, \(d_{1}\) is the horizontal displacement at the surface, and \(d_{2}\) is the horizontal displacement at the base of the test tank.  
\[a_{z}=a_{surface}+\Big{(}a_{input}-a_{surface}\Big{)}(z/h) \tag{1}\]  
\[\tau_{d}=\frac{1}{2}\sigma_{v0}.\frac{\Big{(}a_{surface}+a_{z}\Big{)}}{g} \tag{2}\]  
\[\gamma=\frac{d_{1}-d_{2}}{h} \tag{3}\]
# Influence of Phosphogypsum Dump on the Soil Ecosystem in the Sumy region (Ukraine)
## 3 Results and Discussion
### The Study of Biochemical Transformations of the Technogenic Array of Phosphogypsum
Cations and anions that are in the absorbed state on colloidal particles of the soil are exchanged for ions adsorbed on other colloidal particles or on the surface of the root cells [41]. Thus, the intake of cations K\({}^{+}\), Ca\({}^{2+}\), and Na\({}^{+}\) are exchanged by micelles for proton H\({}^{+}\), and anions of NO\({}^{3-}\), SO\({}_{4}\)\({}^{2-}\), PO\({}_{4}\)\({}^{3-}\), and others are exchanged for anions of organic acids (malic, citric, and oxalic) directly by the cells of the root. Particularly effective is absorption by contact exchange, in which there is an exchange of ions between colloidal soil particles and plant roots without entering them into the soil solution. Ions are adsorbed on the surface of the cell membranes of the root rhizoderm and are absorbed by the plant [42]. The removal of a certain element from the minerals of the substrate is carried out in the process of biological and physical-chemical (exchange) absorbency. As a result, either the complete dissolution of the mineral or the replacement of the mineral ions by hydrogen ions occurs, releasing the roots in the composition of organic acids without destroying the crystal lattice of the mineral. Thus, organic compounds directly or indirectly interact with mineral particles of phosphogypsum, destroying the crystal lattices, transferring elements from one form to another with a change in valence  
Figure 11: Plant formation occurring naturally in the process of succession. and mobility. The chemical elements that are part of the phosphogypsum, or associated with it, are not necessarily withdrawn in proportion to their content and the relationship in the material. Biological weathering can lead to the transformation of this mineral into another due to a change in chemical composition with selective absorption of elements [39; 43; 44]. The reactions of the soil solution, its concentration, and its ratio of salts have a particularly strong influence on the supply of nutrients [39]. For most plants, a weak acid (pH 5-6) or neutral conditions are most favorable. In slightly acidic soils, which include the substrate of the reclaimed terraces of this dump, nutrients such as phosphorus, iron, zinc, manganese, boron, and others are more easily assimilated by plants, due to the lower pH values of phosphogypsum. We suggest that the large concentration of calcium, due to its presence in phosphogypsum, allows plants to adapt to the conditions and continue to populate the surface of the dump. It should also be taken into account that Ca\({}^{2+}\) in high concentrations can inhibit excess intake of K\({}^{+}\), Na\({}^{+}\), or Mg\({}^{2+}\) by the plant, and vice versa.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 4 Relationships Necessary for the Application of the Method
### Solar EV Charging
#### 4.4.3 Load Curve Reconstruction  
Figure 10 represents the simulated load curves of 11 July 2018, obtained for different strategies and with a 75 kWp PV installation. The PV production is represented in blue. The curve that corresponds to the EV consumption with the PC strategy, in red, shows a large peak of approximately 200 kW at the beginning of the day and a second peak at noon. The curve that corresponds to the MP strategy, in yellow, is smoother and relatively synchronized with the PV production, but we observe that the EVs consume energy during the night (due to EVs that stay connected for more than a day). The curve that corresponds to the Smart Charging strategy, in purple, is in line with the PV production. We observe that there is no power consumption during night-time.  
On this particular day, the Self-Consumption and Self-Production Rates are considerably higher for MP and SC strategies than the basic PC since their charging profiles are distributed throughout the day, in particular, when the PV production is the highest. Thus, the \(SPR\) is equal to 49% with the PC strategy, 89% with the MP and 100% with the PC  
Figure 10: Simulation results on 11 th July 2018 for a PV system of 75 kWp.  
strategy. The \(SCR\) is equal to 31% with the PC strategy, 62% with the MP and 69% with the PC strategy.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 4 Numerical Simulations and Results Discussion  
The mathematical models presented are targeting two deployment scenarios, the first one is to deploy the gNodeB nodes to the area without the existing gNodeB nodes, i.e., new deployment, and the second one is to deploy additional gNodeB nodes to the area with the existing gNodeB nodes, i.e., to increase the overall network capacity.  
For the first scenario, we prepared self-developed datasets to use the mathematical model for the deployment without the existing infrastructure for different scenarios (urban, suburban and rural). For the second one, we use the mathematical model that takes into account the existing infrastructure, here the input values have been obtained from publicly available data (about users, gNodeB nodes, etc.) in the selected district of Central Europe.
## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Voice Trigger for Multiple UAVs Control
#### 3.3.2 Multi-Channel Voice Trigger for Multi-UAV Control
Generally, more confidently recognized speech indicates a higher probability in the top-ranked model, thereby making a large difference from probability in other models. The first criterion (10) observes the ratio between two probabilities, and the second criterion (11) considers the direct difference between these probabilities. In the trigger verification process, the value estimated by the decision criterion is compared to a decision threshold that is determined empirically. If the value is greater than the threshold, the candidate trigger region is accepted and considered a discriminative trigger word. Otherwise, the region is rejected. The proposed consecutive filtering processes are expected to improve confidence in detecting trigger word regions and disregarding non-trigger speech regions. Once a trigger word is finally detected, the next procedures are performed, as shown in Figure 7. The user device attempts to connect to a target UAV corresponding to the trigger word. After connecting to the target UAV, the user speaks a control command. The device extracts acoustic features from the speech command, and then delivers  
Figure 8: Procedure of proposed post-processing for trigger word verification. the features encoded in packet data to the target UAV. After receiving the feature data, the UAV system initiates the speech recognition process and executes the command.
# Facilitating Successful Smart Campus Transitions: A Systems Thinking-SWOT Analysis Approach
## 3 Materials and Methods
### Description of Case Study Context (SAUoT)  
SAUoT is a University of Technology which is situated in the central region of South Africa. It operates from two distinct yet interlinked campuses across two locations. SAUoT has been in operation for nearly four decades, transforming from a Technikon into a full-fledged University of Technology in 2004. Going by SAUoT's vision 2030, which declares its aspiration to be \"a leading African University of Technology, shaping the future through innovation\" [28], the drive for improved productivity through enabling resource efficiency during the delivery of its core mandate and the provision of a digital experience for its students and staff remains critical to this vision. Beyond this immediate focus, SAUoT has always relayed its intention to provide relevant knowledge and competencies to the region within which it is domiciled.  
The SAUoT's host city, Bloemfontein, has been involved in Smart City transitions for the past decade. This aspiration has been reported in several studies [70; 71; 72]. However, limited progress has been made in this direction due to challenges identified by [PERSON] [5].  
SAUoT's campus potential to serve as an ideal living, real-world laboratory to develop and evaluate a range of Smart Campus and potentially Smart City concepts have been reported by [PERSON] and [PERSON] [29]. Accordingly, SAUoT in articulating an institutional Smart Campus strategy is seeking to leverage this potential to provide Smart City solutions to the city of Bloemfontein. However, such contribution will be predicated on the institution's ability to successfully transition into a Smart Campus in itself. To do this, the institution needs to manage the complexities associated with such transitions in manner that is \"people\" oriented rather than solely \"hardware-centered\" [27]. This managerial approach will culminate in the avoidance of simply finding uses for new technologies and data, rather than focusing on the actual needs of those that use and service the city and campus [27; 73].  
This study draws on this imperative to build upon the perspectives of various stakeholders within SAUoT concerning the SWOT factors influencing the institution's Smart Campus transition with adequate consideration of relevant contextual peculiarities as reported by [PERSON] and [PERSON] [29]. It investigates the nature of the causal relationships existing between these SWOT factors. Furthermore, it proposes a framework, which details the nature of these interrelationships, which can be relied upon by university administrators for planning and managing Smart Campus transitions at a strategic level.
## 4 Results  
### Lithospheric Data Analysis  
The acceleration of seismicity prior to the mainshock was analyzed by applying the R-AMR method [48; 49] to the INGV Catalog, in order to highlight a diverging power-law function over time for the cumulative value of Benioff strain. The focal search area was centered on the epicenter of the event, whose responsible fault system has a length of approximately 70 km [41]. Figure 7 shows the result of the R-AMR analysis, excluding the mainshock and its aftershocks. An evident acceleration is observed, characterized by the C value indicating the onset of \"critical\" behavior relative to the background, which is 0.598; however, the estimated critical time t\({}_{\text{f}}\) is 200 days after the mainshock. This accelerated behavior was observed in the fault area within a radius ranging from 0 to 50 km from the epicenter. Additionally, the application of this method provides two expected magnitudes, M(A) = 4.8 and M(B) = 4.7, which, although underestimating the real mainshock magnitude, predict an impending EQ that significantly exceeds the background seismicity. The result of this automatic analysis allows us to place the initial progression of seismic acceleration in the lithosphere around September 2019, i.e., 1155 days before the mainshock, resuming in August 2021, i.e., 455 days before the event. These accelerations are visible from the change in slope in the cumulative curve. Around these dates, it is possible to attempt to identify the establishment of different energy transmission channels towards the ionosphere, as hypothesized in LAIC models.  
Figure 6: Scenario of hypothesized pre-EQ coupling processes between the lithosphere of Central Italy and areas where possible EBs could be detected by LEO satellites.
# Sustainable Energy Solutions: Utilising UGS for Hydrogen Production by Electrolysis
## 4 Simulation Model Results and Discussion  
The UGS gas withdrawal cycle was simulated using an integrated mathematical model. The dependence of hydrogen production on the gas withdrawal capacity and the electrical energy generated is illustrated in Figure 10.  
Figure 10: Hydrogen production as a function of gas withdrawal capacity.  
Figure 9: Model integration in the RESOLVE 13.5 programme.  
During the gas withdrawal process, when the intensity of the reservoir pressure still allowed maintaining a maximum constant gas extraction plateau, the available power at the turboexpander-generator remained constant at approximately 2000 kWh. As the output capacity of the storage decreased, the available electrical energy also decreased, amounting to 760 kWh at the end of the withdrawal cycle at a gas flow rate of 200,000 5m\({}^{3}\)/day. Figure 9 also describes the simultaneous hydrogen production via the PEM electrolysis system, which depends on the current availability of electrical energy at the TE generator. Depending on the gas flow rate through the pressure reduction station, the model results showed a direct dependency between the calculated hydrogen production and the volumetric flow of gas through the pressure reduction station. At 844 kg/day the production was almost continuous for almost two months before dropping to 335 kg/day towards the end of the withdrawal cycle. Since the simulation of the gas withdrawal process ran continuously at the maximum possible output capacities, the simulated withdrawal cycle lasted about 90 days. During this period, it was possible to produce the entire  
prevent the formation of hydrates due to the Joule-Thomson effect in the final phase of the gas withdrawal process.  
The total amount of electrical energy generated by a turboexpander during a single gas withdrawal cycle from the storage was approximately 3.82 TWh. Considering that the production of 1 kg of hydrogen via the PEM electrolysis system requires about 10 L of deionised water with a purity of 5.0 [49], the estimated water consumption for the production of 70 tonnes of hydrogen per gas storage cycle would be about 700 m\({}^{3}\). Historical data on formation water production at the underground gas storage indicate that it varies depending on the withdrawal cycle duration and that between 400 and 850 m\({}^{3}\) of water is extracted per gas withdrawal cycle. If the entire working volume was withdrawn from the storage, the water production was closer to the average values of around 800 m\({}^{3}\) per cycle. From this, it can be concluded that the annual volumes of water produced per storage operation cycle would meet the requirements for hydrogen production.  
Figure 12: Display of cumulative production of natural gas and hydrogen per one gas withdrawal cycle.  
Figure 13: Inlet/outlet gas temperatures at TE and UGS gas withdrawal rate.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 4 From Basic Connectivity to Full Autonomy (Levels 0 to 5)
### Level 3: Advanced Driver Assistance Systems  
Conditional driving automation is the third level of automation that allows a car to take control over steering, acceleration, and braking under particular circumstances [65]. It is this level that depends on a few different driver assistance systems and artificial intelligence (AI) that will decide what to do based on changing driving conditions [66]. Cameras and radar produce data over sensors that detect objects around the vehicle which are used to make decisions to avoid collisions and hazardous situations. An autonomous driving system that allows occupants to do other things in the vehicle without being constantly watching. However, a human driver is always required to be present and ready to take back control if a need arises, such as an emergency or system failure.