esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Throughput  
The throughput illustrates the total data packets delivered to the total simulation time of the scenario, as given in Equation (5) [63]:  
\[Throughput=\frac{\sum\textit{All received Packets}}{\sum\textit{Complete Simulation Time}}(\textit{Size of Packet}) \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attributes** & **Values** \\ \hline Maximum Vehicles & 45 \\ Vehicles per km & 5–20 \\ VANET Range & 300–1000 m \\ Advertisement Interval & 5 s \\ IEEE Protocol & IEEE 802.11p \\ Phy-Modulation & OFDM \\ Channel Bandwidth & 10 MHz \\ IEEE 802.11p Data Rate & 3–27 Mbps \\ Frequency IEEE 802.11p & 5.850–5.925 GHz \\ Transmission Mechanism (MAC) & Carrier Sense MA/CA \\ Maximum Speed & 60 km/h \\ NS Connection category & Transmission Control Protocol \\ Application Category in NS & File Transfer Protocol \\ NS Radio Propagation & Two Ray Ground \\ NS Radio Propagation Aerial & Free Space Model \\ \hline \hline \end{tabular}
\end{table}
Table 2: UGAVs-MDVR simulation parameters.  
The throughput of the proposed protocol has been recorded as above 86% at the vehicular mobility of 60 m/s because of strong IVC links, stable cluster formation, and a multicast communication approach. On the other hand, the throughput of MAODV and DSR decreases continuously upon high mobility. The throughput of the proposed protocol is observed to be more than 80%, even at 31 CM per cluster, as depicted in Figure 5.  
The throughput of the MAODV and DSR is declining continuously because of a rise in mobility. Hence, the nodes cannot maintain the inter-vehicular connection in dynamic movements and conditions, resulting in network partitioning. Hence, the MDM dissemination is affected by varying vehicle mobility in MAODV and DSR. However, the MDM dissemination is found efficient with stable inter-vehicular links in the proposed protocol because of its ability to cater to vehicles' dynamic and sudden mobility.
## 3 Data Analysis
### The Perturbations in Electron Density and Temperature from CSES
The regional peak Te was enhanced on 26 August in the northern hemisphere, with its top boundary further extending to the equator, when the Te peak boundary in the southern hemisphere also moved to the equator about 10\({}^{\circ}\). From 27-29 August, the whole Te peak structure at middle latitude maintained the same equatorward movement as those of Ne trough. To illustrate the interaction of electric field and Ne during this magnetic storm, the first orbits were selected in the dayside and nightside, two on 26 August to represent the main phase and two on 28 August for recovery phase, respectively. The correlation between electric field spectra at 225, 725, 1125 Hz and Ne was computed at different latitude scales, with the interval of 15\({}^{\circ}\) along the same orbit. As seen in Table 2, in the dayside of the main phase on 26 August, the positive correlation values were detected at the equatorial area at a latitude of 0-30\({}^{\circ}\) N with r \(>\) 0.9 at all three frequency bands, but during the recovery phase on 28 August, high positive correlations were revealed at 45-60\({}^{\circ}\) S with r \(>\) 0.8, and r \(=\) 0.79 at 0-15\({}^{\circ}\) S at 225 Hz. As for the nightside, r decreased totally relative to those in the dayside but with high negative values of r \(<\) \(-\)0.5 occurring at 45-60\({}^{\circ}\) N and 45-60\({}^{\circ}\) S. Large positive correlations with r\(>\)0.6 were shown at 15-30\({}^{\circ}\) S at 225 Hz and 30-45\({}^{\circ}\) S at 1125 Hz on 26 August, which demonstrates the strong penetration of an electric field during the main phase, with the increasing Ne in middle and low latitudes, especially in the southern hemisphere. Nightside data exhibited the enhanced electric field mainly at middle latitudes of 30-45\({}^{\circ}\) S while partially contributing to those at 0-15\({}^{\circ}\) S. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Day of August** & **24** & **25** & **26** & **27** & **28** & **29** \\ \hline Latitude in northern hemisphere at UT 06–08/\({}^{\circ}\) N & 53.544 & 48.65 & 33.24 & 42.53 & 47.11 & 45.24 \\ Latitude in southern hemisphere at UT 06–09/\({}^{\circ}\) S & / & / & 55.60 & 64.11 & / & / \\ Latitude in northern hemisphere at UT 18–20/\({}^{\circ}\) N & 59.41 & 57.20 & 57.46 & 49.54 & 58.15 & 58.45 \\ Latitude in southern hemisphere at UT 18–20/\({}^{\circ}\) S & 49.80 & 46.04 & 40.71 & 41.60 & 41.66 & 42.83 \\ \hline \end{tabular}
\end{table} 
Table 1: The first minimum position of midlatitude trough of Ne from CSES during 24 and 29 August.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 2 Parameterized Representation of Distributed Energy Storage (DES) Characteristics  
In general, DES normally exhibits different economic and technical characteristics [27]. The common characteristics of energy storage are the capital cost of DES, lifetime in cycles and years, power/energy density, and maximum depth of discharge (DoD). The investment cost of DES includes the cost of energy storage and power inverters, which is the most important factor affecting the economic benefits of DES. The cycle efficiency indicates the round-trip efficiency in one cycle operation. Low cycle efficiency will increase the cost of effective output power, further increasing the total energy purchase cost from the external grid. Maximum depth of discharge is the ability to discharge the total energy of DES in a discharge duration. High DoD means a higher total energy output at the same energy capacity. Meanwhile, DES with high DoD is more economical on the same capacity of total energy output. The lifetime demonstrates the durability of DES. Low cycle life will result in quickly life decreasing during the frequent switching of discharge-charge state and add to the total cost owing to high-frequency equipment updates. Overall, the characteristics of DES largely affect the results of DES planning. Therefore, all these characteristics of DES need to be taken into account and parameterized while modeling.  
In this paper, three representative types of DES are considered, and the parameters are shown in Table 1[7; 10]. The three types of DES have their own features: a lead-acid battery is the most widely used rechargeable battery owing to its low investment cost; a Li-ion battery has the highest efficiency; and a vanadium redox flow battery (VRB) has an exceptionally long lifetime. As a consequence, parametrized modeling is needed to search their applicability in different demand scenarios.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Photoplethysmography (PPG)
#### 3.2.3 Camera-Based PPG
In 2015, [PERSON] [62] proposed integrating an NIR camera with active LED illumination into a rearview mirror to monitor HR while driving. The presented results for heart rate measurement had a correlation coefficient of 0.95 with the reference signal. In 2018, [PERSON] et al. [63] tested the practical applicability of an NIR camera with active 940 nm illumination in a vehicle to capture iPPG signals under controlled and variable ambient light. In parallel, they also evaluated the performance of an RGB camera. Comparing the results from the NIR and RGB cameras, they reported better performance of the NIR camera. However, despite the applied noise reduction algorithm in the raw iPPG images, the recordings from both types of cameras were significantly affected by the changes in ambient light, which considerably hindered iPPG measurements. Vibrations from a moving vehicle also negatively affected the performance of the cameras. The same team in 2020 [64] implemented a new algorithm and hardware for monochrome imaging with a narrower NIR bandwidth of active illumination to overcome the described shortcomings. Despite the better results, they noted that further improvements will be needed for integration into a real DMS. In 2022, [PERSON] et al. [65] demonstrated a driver monitoring system with a NIR time-of-flight (ToF) camera. They measured HR and RR parameters by simultaneously capturing grayscale images and the distance to the driver by the ToF camera. This approach was chosen to compensate for the interference of variable light from NIR active illumination in HR monitoring. Motion artefacts in RR monitoring were compensated by directly capturing chest movements from the ToF camera, measuring the distance to the driver's chest wall. The authors reported a mean bias for RR measurements of 1.4 brpm, and a success rate of 71.9% for HR measurements. In 2023, [PERSON] et al. [66] measured HR from a series of facial images using a camera with two different optical channels: RGB and NIR. The contribution of the study is the innovative application of several algorithms as noise reduction techniques.
## 4 Numerical Results
### Localized Behaviour: \(L\)-Shape Structure  
In this section, we consider a L-shaped structure illustrated in Figure 13. The structure is fully clamped on its upper boundary, free on the other boundaries and subjected to a uniform force distribution on its right side \(\mathbf{T}=(0,20,0)\) Pa. The plate's behaviour is assumed to be linearly elastic, with a Young's modulus of \(E=200\) Pa and a Poison coefficient of \(\
u=0.3\).  
This configuration corresponds to the nominal structural system, whereas the real system is assumed to contain a damaged area. The structure is equipped with a finite element mesh, depicted in Figure 14, that serves as support for the approximation of the different mechanical fields: displacement, strains and stresses. The elements located in the damaged region are highlighted in red in Figure 14; here, the Young's modulus is reduced to \(E^{\prime}=0.1\cdot E\). The sensors location is assumed, for the sake of simplicity, to coincide with some nodes of the mesh, particularly the blue ones in Figure 14.  
Figure 11: Top view of \(x\) (**left**), \(y\) (**center**) and \(z\) (**right**) components of the displacement field associated with the nominal solution (**top**); reference solution that takes into account the real damaged region (**middle**) and corrected model solution (**bottom**).  
Figure 12: Top view of the difference in absolute value between the corrected and the reference displacement field, for \(x\) (**left**), \(y\) (**middle**) and \(z\) (**right**) components.  
After solving the minimization problem (16), the elements in which \(|a_{e}|>\epsilon=0.19\texttt{ min}\{a_{e}\}\) are identified and highlighted in red in Figure 15. It can be observed that the region in which the damage locates is well identified.  
With both the identified damaged elements and the identified reduced mechanical properties (reduced element stiffness), the solution of the mechanical problem was solved and compared with the reference solution as depicted in Figures 16 and 17. The proposed procedure exhibits an excellent accuracy for correcting the model while completing the data.  
Figure 14: Damaged region (red) and location of the 22 sensors (nodes highlighted in blue). (a) Top view; (b) bottom view.  
Figure 15: Identified elements affected by the damage (in red) at the first iteration (\(q=1\)) of the algorithm proposed in Section 3.1.  
The errors between the corrected and the reference (the nominal and the reference) displacements field, for \(x\), \(y\) and \(z\) components, are as follows:  
\[\left\|U_{x}^{Corr}-U_{x}^{Ref}\right\|_{2} =9.36\ (35.6)\ \ \,\ \ err_{\%}^{x}=\frac{\left\|U_{x}^{Corr}-U_{x}^{Ref} \right\|_{2}}{\left\|U_{x}^{Ref}\right\|_{2}}\times 100=2.0\%\ (7.9\%);\] \[\left\|U_{y}^{Corr}-U_{y}^{Ref}\right\|_{2} =4.72\ (63.3)\ \,\ \ \ err_{\%}^{y}=\frac{\left\|U_{x}^{Corr}-U_{y}^{Ref} \right\|_{2}}{\left\|U_{x}^{Ref}\right\|_{2}}\times 100=1.3\%\ (18.6\%);\] \[\left\|U_{z}^{Corr}-U_{z}^{Ref}\right\|_{2} =1.75\ (3.62)\ \,\ \ \ err_{\%}^{z}=\frac{\left\|U_{z}^{Corr}-U_{z}^{Ref} \right\|_{2}}{\left\|U_{z}^{Ref}\right\|_{2}}\times 100=29.7\%\ (61.4\%).\]
# Chemo-Mechanical Model for the Expansion of Concrete due to Alkali Silica Reaction
## 5 Summary of Model Parameters and Sensitivity Analysis  
The reference input parameters are summarized in Table 1. In order to minimize the number of model parameters, a sensitivity analysis was carried out. The one-way sensitivity analysis was adopted for the simplicity. The verification example at Section 6.1 is for the effect of temperature on ASR expansion. It has all the key features of the developed model, such as formation of basic gel driven by alkali ions, swelling of basic gel driven by water imbibition, infiltration of gel, etc. All the model parameters play a role in this example. Therefore it is suitable for sensitivity analysis. By taking the expansion at 38 \({}^{\circ}\)C as a reference, the influence of each parameter was investigated as shown in Figure 4. Each parameter was changed by 10%.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(w/c\) * & 0.47 & \(k_{i}\) & 0.22 \\ \(a/c\) * & 2.25 & \(C_{\phi}\) & 2 \\ \(D_{f}^{ref}\) & 1.0 \(\times\) 10\({}^{-11}\) m\({}^{2}\)/s & \(\varepsilon_{0}^{\prime}\) & 0.0001 \\ \(D_{mf}^{ref}\) & 3.0 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s & \(\beta\) & 4.5 \(\times\) 10\({}^{6}\) \\ \(D_{af}^{ref}\) & 8.3 \(\times\) 10\({}^{-16}\) m\({}^{2}\)/s & \(v_{0}\) & 0.00001 \\ \(D_{mf}^{ref}\) & 2.0 \(\times\) 10\({}^{-16}\) m\({}^{2}\)/s & \(K\) & 2.15 \(\times\) 10\({}^{9}\) Pa \\ \(D_{af}^{ref}\) & 1.1 \(\times\) 10\({}^{-21}\) m\({}^{2}\)/Pa/s & \(c_{q}\) & 1000 J/kg/K \\ \(D_{ci}^{ref}\) & 1.0 \(\times\) 10\({}^{-16}\) m\({}^{2}\)/s & \(\lambda\) & 1.74 W/m/K \\ \(\beta_{as}\) & 0.5 & \(E\) & 2.5 \(\times\) 10\({}^{9}\) Pa \\ \(\beta_{ca}\) & 0.5 & \(v\) & 0.2 \\ \(a_{ift}\) & 25\(\pi\)\(\mu\)m\({}^{2}\) & \(f_{t}\) & 2.0 \(\times\) 10\({}^{6}\) Pa \\ \(n_{ift}\) & 1 & \(G_{t}\) & 120 J/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the reference input parameters for the sensitivity analysis.  
Figure 3: Schematic description of the proposed model.  
According to the sensitivity analysis, the parameters can be grouped into two sets as shown in Figure 4. Because the influence of group A is marginal, parameters belonging to group A may be fixed for any of furthers simulations. Group B are significant parameters. Among them, \(k_{i}\), \(\beta_{as}\), \(a_{ift}\), and \(n_{ift}\) can be determined independently from the mix proportion and the chemical information in advance before analysis. \(C_{\phi}\) and \(\beta\) can be determined separately, too, by referring to the literature information [51]. Therefore, there are only three parameters, i.e., \(D_{af}^{ref}\), \(D_{wi}^{ref}\), and \(D_{g}^{ref}\), to be calibrated in the following verification examples.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 6 Simulation Results
### Impact of \(M\) on Protocol Performance
from each other and the nodes with more residual energy are more likely to take part in cooperative transmissions. Namely, more energy is used for cooperative transmissions and thus more cooperative gain is obtained, which leads to less energy consumption of each data packet transmission. The results in Figure 18 validate the above viewpoint. Figure 19 shows the network throughput with varying \(M\). It can be seen that in any case, the throughput decreases with the increase of \(M\) because more HTS packet transmissions lead to more transmission delay, thus reducing the throughput to some extent. Therefore, comprehensively considering Figures 15, 16 and 19, if the network lifetime and the number of data packets transmitted by each node already achieve the maximum, it should not increase \(M\) anymore to avoid reducing the throughput.
## 3 Chromogenic: Definition, Materials and Basics
### General Sensing Mechanism of Gasochromic WO\({}_{3}\)  
Currently, there are four models related to the gasochromic coloration of WO\({}_{3}\) sensors, namely charge transfer of electrons between (i) W\({}^{6+}\)/W\({}^{5+}\) states, (ii) W\({}^{5+}\)/W\({}^{4+}\) states, (iii) W\({}^{6+}\)/W\({}^{5+}\) and W\({}^{5+}\)/W\({}^{4+}\) states, and (iv) the oxygen vacancy model. In the first three models, the gasochromic coloration of WO\({}_{3}\) is explained by charge transfer-electron transitions between different states and the composition of the colored state changes to tungsten bronze (H\({}_{\mathrm{x}}\)WO\({}_{3}\)) due to the double injection of hydrogen ions and electrons into WO\({}_{3}\) lattice as follows [67]:  
\[\mathrm{xH^{+}+xe^{-}+WO_{3}\to H_{x}WO_{3}} \tag{1}\]  
The above reaction is comprised of following steps (i) absorption and dissociation of the H\({}_{2}\) on the surface of noble metal, (ii) spillover of the H from the noble metal onto the WO\({}_{3}\) surface, (iii) diffusion of H along the grain surface of WO\({}_{3}\), (iv) injection of H into the outer surfaces of WO\({}_{3}\) (v) diffusion of H into the interior of WO\({}_{3}\) lattice and formation of H\({}_{\mathrm{x}}\)WO\({}_{3}\)[68]. The magnitude of coloration in H\({}_{\mathrm{x}}\)WO\({}_{3}\) is closely related to the amount of hydrogen insertion (\(x\)), which usually varies with the structure, morphology, and crystallinity of the WO\({}_{3}\) sensing layer [67; 69]. The oxygen vacancy model supposes the formation of a sub-stoichiometric WO\({}_{3-\mathrm{x}}\) structure due to the formation of oxygen vacancies [67].  
In fact, in the oxygen vacancy model, H\({}_{2}\) is dissociated on the noble metal and then transferred to WO\({}_{3}\), where it reacts with oxygen on the surface of WO\({}_{3}\), leading to the formation of water and oxygen vacancies and a blue color as follows [70]:  
\[\mathrm{xH^{+}+xe^{-}+WO_{3}\to H_{x}WO_{3}} \tag{2}\]  
The oxygen vacancies diffuse into the interior of the WO\({}_{3}\) and lead to the coloration, while the H\({}_{2}\)O slowly desorbs from the WO\({}_{3}\)[68].  
A challenging task in gasochromic sensor science lies in improving sensor dynamics, which is important for the rapid detection of hydrogen leakage. Reduction of sensor size from micro to nano-scale increases its surface area significantly, which means an increase in the number of available surface sites for the adsorption of atomic hydrogen, leading to faster sensor dynamics. Furthermore, nanostructured gas sensors are expected to exhibit an improved response to H\({}_{2}\) gas even at low concentrations [30]. In the following sections, different WO\({}_{3}\) nanostructured gasochromic sensors are presented and discussed.
# Individual Differences in Signal Perception for Takeover Request in Autonomous Driving
###### Abstract  
In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. For this objective, the research examined the correlations between age,  
In circumstances involving partial autonomy, the driver and vehicle must work together to complete the primary driving task. Different ages, levels of expertise, and personal histories result in distinct driving strategies and visual attention levels. In scenarios involving partially autonomous driving, it is crucial that the vehicle signal the human driver for a TOR and that the human driver recognize the signal promptly and accurately. To investigate whether an individual's background affects signal perception, signaling guidelines in a traditional TOR scenario were reviewed, and an experiment was conducted to determine how individuals respond to and prefer various modalities. The research inquiries focused on determining the impact of gender, age, and other personal characteristics on responses to distinct signals. Specifically, one of the key research questions was whether reaction times and preferences change linearly or cascade as the detailed characteristics of each modality's signal (e.g., pitch for auditory or amplitude for tactile) change.  
In this study, existing signaling guidelines for traditional TOR scenarios were reviewed, and an experiment was conducted to examine individuals' responses and preferences across different modalities. The focus was specifically placed on the influence of gender, age, and other personal characteristics on participants' responses to distinct signals. By exploring the relationships between personal characteristics and TOR responses, the aim was to enhance the understanding of how these factors affect signal perception and provide guidance for the design of effective TOR systems. By addressing the research questions concerning the impact of personal characteristics on TOR responses and investigating the changes in reaction times and preferences with varying signal characteristics, valuable contributions were made to the existing knowledge in the field. These findings have the potential to inform the design and implementation of TOR systems in autonomous driving, leading to enhanced safety, performance, and user satisfaction.
## 7 Discussion of the Filtering Impact on Security and Privacy
### Influence of Filtering on LINDDUN Impacts  
Just like in the assessments before, influences on the threat impacts are calculated by always taking the minimum influence that was identified for any of the vectors that enable it. The summarised results are shown in Tables 15-18.  
When looking at the tables, it can be seen that there is a higher coverage of entries with at least _some_ influence in comparison with the results of the STRIDE assessment. A _medium_ influence has been found for linking data on the _Smart Home Device_ (_LDD_) for all scenarios except the last one. This can be explained by the fact that for \(S_{s}\), the impact of obtaining status information about the _Smart Home Device_ from the _Smart Hub_ (_SID_), which has a _no or only low_ rating, is also considered in the STRIDE assessment for _Information Disclosure_, which in turn determines the _IDS_ vector on which the _LDD_ impact is based. A _high_ influence has been identified for linking data on control devices (_LDC_). Like in the STRIDE assessment, the scenario-specific adjustments lead to higher influence levels between the _Essential Server_ and the _Unessential server_ in scenario \(S_{a}\). More precisely, a _medium_ influence was found for linking data on the server (_LDS(US)_) or identifying whom these data belong to (_IUS(US)_), and even a _high_ influence on the threat, that the server collects or passes on more information than the user agreed on (_MDS(US)_, _MDT(US)_), while _no or only low_ influence was found for all of them regarding the _Essential Server_.