esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Homogenization Simulation  
The results of the homogenization simulation performed by DSC measurements are shown in Figure 1. According to the results presented in [24], the highest possible appropriate homogenization temperature was chosen to be 600 \({}^{\circ}\)C, but since the standard homogenization temperature for 8 xxx series alloys is 580 \({}^{\circ}\)C, a simulation of homogenization at 580 \({}^{\circ}\)C was also performed. Out of interest, the K0 alloy was also tested at a temperature of 620 \({}^{\circ}\)C. The results show that when homogenizing the Al-1.4 wt.% Fe alloy (sample K0) at 580 \({}^{\circ}\)C, most of the homogenization process should be completed  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Fe** & **Cu** & **Si** & **Mn** & **Mg** & **Ce** & **La** & **Al** \\ \hline
**K0** & 1.36 & 0.0009 & 0.0062 & 0.0013 & 0.0008 & \textless{}0.001 & \textless{}0.001 & Rest \\
**K1** & 1.43 & 0.0012 & 0.0081 & 0.0023 & 0.0008 & \textless{}0.001 & 0.139 & Rest \\
**K2** & 1.38 & 0.0011 & 0.0087 & 0.0058 & 0.0008 & 0.147 & \textless{}0.001 & Rest \\
**K3** & 1.43 & 0.0012 & 0.0088 & 0.0075 & 0.0009 & 0.0708 & 0.0909 & Rest \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition in wt.% and designations of experimental alloys.  
after 6 h (marked with a green arrow). After this, the chemical composition diffusion process takes its course (the slope of the curve constantly decreases linearly but slightly). In homogenization simulations performed at 600 \({}^{\circ}\)C, homogenization should be completed even earlier, namely after about 5 h (marked with a red arrow). The simulations performed at 620 \({}^{\circ}\)C indicate that the homogenization process is mostly completed after about 4 h (marked with a black arrow). Since the standard homogenization treatment of 8 xxx series alloys is conducted at 580 \({}^{\circ}\)C, further investigations were carried out at homogenization temperatures of 580 \({}^{\circ}\)C and 600 \({}^{\circ}\)C.
## 3 Results and Discussion
### Analysis of VOMs
#### 3.5.2 PCA Analysis of VOMs  
The PCA analysis of the data from Flo, Co4, Co8, S4, and S8 is shown in Figure 5. The first two principal components (PC1 and PC2) accounted for 71.5% of the total variance of the data. The contaminated sourdough and the non-contaminated control sourdough were obviously discriminated even at the culturing time of only 4 h. This indicates that the PCA model developed based on VOMs could be used as a rapid detection method for contamination of _S. aureus_ in sourdough.
## 5 Simulation Results  
To ascertain the accuracy of the oscillation analysis presented herein and to evaluate the efficacy of the proposed oscillation suppression measures, a grid-connected FSC-VSPSU simulation model has been developed within the MATLAB/SIMULINK environment (2023b). This model is constructed in accordance with the structural diagram provided in Figure 1. Table 2 lists the basic simulation parameters. Furthermore, the oscillation analysis results summarized in Table 1 will be verified separately in Section 5.1, Section 5.2, Section 5.3, Section 5.4, and Section 5.5 will verify the proposed oscillation suppression strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(u_{\text{g}}\) & Input voltage of grid-side converter & \(u_{\text{g}}=2800\) V \\ \(u_{\text{d}}\) & DC reference voltage & \(u_{\text{d}}=5200\) V \\ \(L_{\text{g}}\) & Equivalent inductance of the grid & \(L_{\text{g}}=1\) mH \\ \(R_{\text{g}}\) & Equivalent resistance of the grid & \(R_{\text{g}}=0.04\)\(\Omega\) \\ \(C_{\text{d}}\) & DC capacitance & \(C_{\text{d}}=15\) mF \\ \(K_{\text{pv}},K_{\text{iv}}\) & PI parameters of the DC voltage loop & \(K_{\text{pv}}=1\), \(K_{\text{iv}}=80\) \\ \(K_{\text{pc1}},K_{\text{ic1}}\) & PI parameters of the current loop of the & \(K_{\text{pc1}}=2\), \(K_{\text{ic1}}=50\) \\ \(K_{\text{pw}},K_{\text{iw}}\) & PI parameters of the rotation speed loop & \(K_{\text{pw}}=2\), \(K_{\text{iw}}=20\) \\ \(K_{\text{pc2}},K_{\text{ic2}}\) & PI parameters of the current loop of the & \(K_{\text{pc2}}=10\), \(K_{\text{ic2}}=100\) \\ \(K_{\text{pg}},K_{\text{ig}}\) & PI parameters of the turbine governor & \(K_{\text{pg}}=2\), \(K_{\text{ig}}=5\) \\ \(K\) & DC gain of the series correction link of & \(K=200\) \\  & the excitation system & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic parameters of the simulation system.  
Under the parameters listed in Table 2, the system will experience oscillation, which is shown in Figure 17. As it can be seen, when observing the oscillation of the DC-side voltage, the current on the AC side also oscillates at the same frequency. Notably, the three different colors in Figure 17b represent the three phases in the power system. To prevent the article from being too lengthy, only the DC-side voltage is provided to demonstrate the stability of the system in the following content. Furthermore, to verify the correctness of the oscillation analysis results, the following content will analyze the oscillation changes when adjusting the corresponding parameters.
# Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves
## 3 Flux Calculations and Corrections
### Secondary Data Processing
#### 3.2.1 CO\({}_{2}\) Flux Spike Removal  
The computed flux data may have unrealistic spikes though the raw data are filtered using standard corrections. It is assumed that the extreme spikes are due to non-localized advection events or other non-stationary phenomena and not because of the natural phenomena [32].  
The spikes were removed using statistical bounds on the data using a two-stage approach. Firstly, the data was grouped into positive and negative fluxes. Then half-hourly positive flux values which are greater than the respective month average plus two times the standard deviation (\(x>x+2\times\sigma\)) are removed. The opposite was used for removal of absurd data in the negative fluxes (\(x<x-2\times\sigma\)). In the second stage, positive (or negative) flux values greater (or lesser) than the respective monthly diurnal average plus (or minus) two times the standard deviation were removed. After the first stage about 13% of data was removed and an additional 2% was removed after the second stage.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.3 Field Data  
Ground-based field carbon flux data used in this study were derived from the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 of the Heihe Watershed Allied Telemetry Experiment Research (HiWATER-MUSOEXE) [41; 42; 43]. A 30 km \(\times\) 30 km experimental region was set up according to landscape situations, agricultural structures and irrigation status in the study area, in which distributed 21 field observation sites. A core experimental area consists of 17 field observation sites distributed in a 5.5 km \(\times\) 5.5 km region. All the observation sites were distributed in the middle of the study area, as shown in Figure 2[44]. Land use types of the 21 field observation sites, including croplands (mainly maize), orchard, vegetable field, desert, desert steppe, Gobi and wetland. The eddy covariance systems and meteorological tower were set in each observation site. Half-hourly data of carbon flux were obtained from the eddy covariance systems, and meteorological variables were obtained from the meteorological tower. Gap-filling and flux-partitioning were processed using the Edire software package. Then daily carbon flux was obtained by averaging the half-hourly data. Daily ground-based GPP were obtained by partitioning the observed net flux into GPP and ecosystem respiration according to [PERSON] et al. [45], [PERSON] et al. [46],and [PERSON] et al. (2018) from 9 June to 16 September 2012 from the 21 field observation sites; then, eight days mean GPP were calculated by averaging the daily GPP. The ground based eight days GPP from 21 observation sites were used to direct validate the GPP estimates in our study.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Resource Accumulation
#### 4.1.4 KSC/KSC2  
In 2020, researchers at Nazarbayev University in Kazakhstan published a 330 h Kazakh speech corpus (KSC) [114], which was the largest Kazakh database at that time. Motivated by Common Voice, this database was also collected by crowdsourcing. The entire dataset contains more than 150,000 speech utterances from 1612 devices. In the same year, this research group published a Kazakh TTS database, called KazakhTTS [115]. It was recorded by two speakers (one male and one female) and comprises 93 h in total. In 2022, this group expanded KazakhTTS to five speakers and 271 h of speech, called KazakhTTS2 [116]. Recently, KSC and KazakhTTS2 were put into a larger dataset called KSC2 [117], together with additional data including: (1) 238 h of crowdsourcing data; (2) 48 h of high-quality TTS speech; (3) 238 h of speech from TV, self-media, and other sources. Now, KSC2 has reached 1128 h in total. It is the largest Kazakh database at present.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Resource** & **Language** & **Date** & **Contributor** & **Data Size** & **Attributes** \\ \hline THUYG-20 [112]1 & Uyghur & 2017 & THU \& XU & Speech: 20 h & Reading, Office, Mic \\ \hline M2 ASR-Uyghur 2 & Uyghur & 2021 & M2 ASR & Speech: 136 h, Text: 88 M & Reading, Mobile \\ \hline M2 ASR-Kazakh [118]2 & Kazakh & 2021 & M2 ASR & Speech: 78 h, Text: 100 M & Reading, Mobile \\ \hline M2 ASR-Kyrgyz 2 & Kyrgyz & 2021 & M2 ASR & Speech: 166 h, Text: 6 M & Reading, Mobile \\ \hline CommonV-Uyghur [102]3 & Uyghur & 2021 & Mozilla, etc. & Speech: 119 h & Crowdsourced \\ \hline CommonV-Kazakh [102]3 & Kazakh & 2021 & Mozilla, etc. & Speech: 2 h & Crowdsourced \\ \hline CommonV-Kyrgyz [102]3 & Kyrgyz & 2021 & Mozilla, etc. & Speech: 2 h & Crowdsourced \\ \hline IARPA-Kazakh [100]4 & Kazakh & 2021 & IARPA & Speech: 64 h & Mic and Telephone \\ \hline \hline \end{tabular}
\end{table}
Table 3: Active resources for Uyghur, Kazakh, and Kyrgyz.
# Optimal Energy Management within a Microgrid: A Comparative Study
## 4 Explicit Model of OPF for Microgrid
### Equality Constraints  
The set of equality constraints \(h(y)\) in Equation (23) is expressed explicitly as follows: the constraints of active \(\Delta P_{RD\ i}^{t_{z}}\) and reactive power \(\Delta Q_{RD\ i}^{t_{z}}\) balance corresponding to the nodes of the microgrid in AC are written in the first block of Equation (27) where the lower limit \(\forall j\in i\) means each element \(j\) is connected to the node \(i\). For example, the term \(\sum\limits_{\forall j\in i}P_{A\ j}^{t_{z}}\) denotes the sum of the provided active powers (generated) for all wind turbines connected to the node \(i\). It should be clarified that the term \(\sum\limits_{\forall j\in i\}\sum\limits_{j\in N_{\rm{Al}}N_{T}}P_{inv\ j}^{ t_{z}}\) denotes the sum of the active powers injected to the node \(i\) through all terminal feeders and transformers connected to that node. Furthermore, the balance equations of reactive power are formulated only for the \(k\) nodes in AC that have not been connected to power sources (\(\forall k\
otin N_{GEN}\)), that is, not formulated in PCC nodes or nodes where batteries, wind turbines, and photovoltaic modules are connected. From Equation (6), Equations (16) and (17) with the constraints that shape the behavior of the stationary state of each battery and its corresponding DC/AC inverter,as shown in the bottom block of Equation (27), are formulated where \(P_{CD}(V,I)\) represents the term in the second member of Equation (17). Note that Equation (27) is formulated for (\(\forall t_{z}\in T\)).  
\[h^{t_{z}}\left(y^{t_{z}}\right)=\left\{\begin{array}{c}\Delta P_{RD\;i}^{t_{z }}=P_{RP\;i}^{t_{z}}+\sum\limits_{\forall j\in i}P_{BC\;j}^{t_{z}}+\sum\limits_ {\forall j\in i}P_{B\;j}^{t_{z}}+\sum\limits_{\forall j\in i}P_{A\;j}^{t_{z}}+ \\ \sum\limits_{\forall j\in i}P_{CD\;j}^{t_{z}}-\sum\limits_{\forall j\in i}P_{ ij}^{t_{z}}-\sum\limits_{\forall j\in i\in j\in N_{AI},N_{T}}P_{inj\;j}^{t_{z}} (V,\theta)=0,\\ \Delta Q_{RD\;k}^{t_{z}}=-\sum\limits_{\forall j\in k}Q_{lj}^{t_{z}}-\sum \limits_{\forall j\in k}Q_{inj\;j}^{t_{z}}(V,\theta)=0\\ i=1,2,\ldots,N_{bAC};k=1,2,\ldots,N_{bAC}|\forall k\
otin N_{GEN}\\ \Delta I_{CD\;k}^{t_{z}}=I_{CD\;k}^{t_{z}}-f_{CD}\left(V_{CD\;k}^{t_{z}},I_{CD\; k}^{t_{z}}\right)=0,\\ \Delta P_{CD\;k}^{t_{z}}=P_{CA\;k}^{t_{z}}(V,\theta)-\Delta P_{CD\;k}^{t_{z}} \left(V_{CD\;k}^{t_{z}},I_{CD\;k}^{t_{z}}\right)=0,\\ \Delta V_{CD\;k}^{t_{z}}=V_{CD\;k}^{t_{z}}-\left(\frac{\pi}{16}\right)V_{k}^{t_ {z}}=0\\ k=N_{bAC}+1,\ldots,N_{bAC}+N_{bCD}\end{array}\right\}\forall t_{z}\in T \tag{27}\]
# Vibration Isolation of a Surveillance System Equipped in a Drone with Mode Decoupling
## 5 Conclusions  
This study presents vibration isolation through mode decoupling based on system identification with a coupled multi-DOF model. Furthermore, the entire process is presented as a countermeasure to minimize the vibration effects on the surveillance system equipped in a drone. Specifically, modal testing indicated that resonances occurred in the operational frequencies at many coordinates. Therefore, vibration isolators should be installed to improve the VIP of the drone. However, the initial design suggestion--a camera system with a rubber-type mount--suffered from a coupling effect on resonance. Specifically, the locations of natural frequencies were overlapped in all directions; this deteriorated the surveillance performance. This analysis suggests that simply adding vibration isolators without reviewing the target system may have adverse effects in some frequency ranges due to the mode coupling. The equivalent stiffness and vibrational mode of each direction were changed to separate vertical and horizontal natural frequencies by rearranging vibration isolators. Finally, the performances of vibration isolators were verified via experimental modal testing. The VIP of the fore-after, side-by-side, and vertical directions was 98.3%, 94.0%, and 94.5%, respectively, within the operational range of frequency, i.e., in 30-85 Hz, with the proposed vibration isolator compared to the original system without a vibration isolator. This corresponds to a 64.7%, 101.1%, and 37.2% decrement in vibration ratio compared to the initial design suggestion of the vibration isolation system, suggesting that the method presented is effective for an intelligent robotic system. Future work includes performance validation with actual field experiments and application of the present method to the nonlinear design problem, such as dynamic amplitude- and preload-dependence, which originates from the viscoelastic material type mount used in this study.  
Methodology: Y.-H.S.; Validation: D.K.; Writing--original draft: Y.-H.S.; Data Curation: S.S.; Writing--review & editing: K.-Y.O.; Visualization: J.-W.H.; Supervision: K.-Y.O.; All authors have read and agreed to the published version of the manuscript. This work was supported by the Chung-Ang University Research Scholarship Grants in 2020 and by the Korea Electric Power Corporation (Grant number: R20X0O2-5). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data available on request due to restrictions eg privacy or ethical. The authors declare no conflict of interest.
## 4 Problem Equation
### Reward Function  
According to Reference [37], SDN managers of the SIN need to pay for LEO satellite \(l\), GEO data relay satellite \(lg\), cache \(c\), and MEC server \(m\). It is assumed to pay \(\delta_{l}\) to the LEO satellite every Hz, \(\delta_{lg}\) to the GEO data relay satellite per Hz, \(\varsigma_{c}\) to the cache per unit storage space, and \(\eta_{m}\) to the MEC server per joule.  
In addition, the SIN managers need to charge users for information transmission, caching, and computing. Suppose \(\tau_{u}\) is charged per bit of transmission information, \(\kappa_{u}\) is charged per bit of cache information, and \(\phi_{u}\) is charged per bit of calculation information. The reward function is  
\[\begin{array}{l}R_{u}(t)=\sum\limits_{l\in la}R_{u,l}^{comm}(t)+\sum\limits_ {lg\in\{ga_{l}\}}R_{l,lg}^{comm}(t)+\sum\limits_{c\in ca}R_{u,c}^{cache}(t)+ \sum\limits_{m\in ma}R_{u,m}^{comp}(t)\\ =\sum\limits_{l\in la}w_{u}^{l}(t)ComA_{u}^{L}(t)(\tau_{u}ComR_{u}^{u}(t)/ \delta_{l}B_{u}^{l}(t))+\sum\limits_{l\in\in\{ga_{l}\}}w_{u}^{l}(t)ComA_{l}^{ lg}(t)(\tau_{u}ComR_{l}^{lg}(t)/\delta_{lg}B_{l}^{lg}(t))\\ +\sum\limits_{c\in ca}w_{u}^{l}(t)CaA_{u}^{c}(t)(\kappa_{u}CaR_{u}^{c}(t)/ \varsigma_{o})+\sum\limits_{m\in ma}w_{u}^{l}(t)CompA_{u}^{m}(t)(\phi_{u}ComR_{ u}^{m}(t)/\eta_{m}u_{m}u_{m})\\ =\sum\limits_{l\in la}w_{u}^{l}(t)ComA_{u}^{L}(t)(\tau_{u}B_{u}^{l}(t)v_{u}^{l}(t )/\delta_{l}B_{u}^{l}(t))+\sum\limits_{l\in\{ga_{l}\}}w_{u}^{l}(t)ComA_{l}^{ lg}(t)(\tau_{u}B_{l}^{lg}(t)v_{l}^{lg}(t)/\delta_{lg}B_{l}^{lg}(t))\\ +\sum\limits_{c\in ca}w_{u}^{l}(t)CaA_{u}^{c}(t)(\kappa_{u}B_{u}^{l}(t)v_{u}^{l}( t)v_{u}^{c}(t)/\varsigma_{o}o_{u})+\sum\limits_{m\in ma}w_{u}^{l}(t)CompA_{u}^{ m}(t)(\phi_{u}\frac{\Xi_{u}^{c}(t)\phi_{u}}{n_{u}}/\eta_{m}n_{u}c_{m})\end{array} \tag{26}\]  
where \(e_{m}\) represents the energy consumed by the CPU to rotate a circle. We define the reward function \(R_{u}(t)\) as the expected benefit of the unit resource at time \(t\), that is, the ratio of the fee charged to the user and the fee paid to obtain the resource. The higher the value of \(R_{u}(t)\) is, the higher the utilization rate of resources will be.
## 6 Conclusions  
A compound power-split power transmission system model was first established and verified by bench experiments. The engine starting process was analyzed based on an equivalent lever method. Then, the problem of system jerks and vibrations caused by insufficient motor torque compensation under some engine starting conditions was analyzed. To solve this problem, this paper proposed a dynamic torque coordinated control for engine starting conditions (DTCC-ESC). Finally, three control methods (ADCC, ADCC-EAC, and DTCC-ESC) were selected for simulation and comparative analysis on the established hybrid electric vehicle model. The results showed that the proposed DTCC-ESC strategy can reduce system shocks under different engine start-up conditions during the mode switching process that includes engine start-up. Compared with the other two methods, the DTCC-ESC method had a better control effect.  
In future research, the controller signal network delay can be considered to optimize the dynamic coordination control method to further reduce the system shock and vibration.  
Conceptualization, Y.S. and M.H.; methodology, Y.S.; software, C.F.; validation, J.H., D.Q. and Y.S.; formal analysis, D.Q.; investigation, C.F.; resources, M.H.; data curation, Y.Z.; writing--original draft preparation, Y.S.; writing--review and editing, J.H.; visualization, Y.S.; supervision, M.H.; project administration, J.H.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the NSFC Program (No. 61872217, No. U1701262, No. U1801263, No. 51675062), the research is also sponsored in part by the Guangdong Provincial Key Laboratory of Cyber-Physical Systems, as well as be sponsored in part by the Industrial Internet innovation and development project of ministry of industry and information technology.  
Not applicable.  
The authors declare no conflict of interest.
# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 6 Results
### Task Transfer Verification Using Flight Simulator Data  
Barplots in Figures 4 and 5 show the percentages of WL detection as a function of the number of interruptions (0, 1, or 2). The expected pattern was the percentage of WL detection increasing with the number of interruptions. For both projection models, the 3-class problem WL1_WL2_WL3 is the only model that does not follow the expected increasing pattern. For the remaining problems, both architectures seem to behave equally.  
Figures 6 and 7 show the barplots for the number of BL and WL predictions for the four flights. The expected pattern was to have the most detections in Flight 1, Flight 2, and Flight 4 (similar amounts of WL detected) and for Flight 3 to present a decrease in detected WL with respect these flights. Only the feature projector model follows the pattern expected. The most significant differences between flights are evident in the 3-class problem  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **All Population** & **80\% of Population** \\ \hline \multirow{2}{*}{BL-WL2} & BL & \(85.72\pm 7.52\) & \(84.15\pm 7.50\) \\ \cline{2-4}  & WL & \(76.22\pm 17.64\) & \(82.81\pm 11.73\) \\ \hline \multirow{2}{*}{BLs-WL2-WL3} & BL & \(78.16\pm 10.83\) & \(75.5\pm 10.29\) \\ \cline{2-4}  & WL & \(78.62\pm 16.59\) & \(84.35\pm 10.87\) \\ \hline \multirow{2}{*}{WL1-WL2-WL3} & BL & \(72.94\pm 18.08\) & \(70.58\pm 19.29\) \\ \cline{2-4}  & WL & \(77.34\pm 16.72\) & \(82.85\pm 11.48\) \\ \hline \multirow{2}{*}{BLs-WL1-WL2-WL3} & BL & \(80.75\pm 9.87\) & \(79.42\pm 10.07\) \\ \cline{2-4}  & WL & \(76.44\pm 16.81\) & \(80.96\pm 13.16\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input projector model binarized.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **All Population** & **80\% of Population** \\ \hline \multirow{2}{*}{BL-WL2} & BL & \(87.81\pm 7.07\) & \(86.65\pm 7.33\) \\ \cline{2-4}  & WL & \(76.25\pm 19.27\) & \(82.73\pm 14.85\) \\ \hline \multirow{2}{*}{BLs-WL2-WL3} & BL & \(79.00\pm 9.22\) & \(77.11\pm 9.13\) \\ \cline{2-4}  & WL & \(80.94\pm 16.21\) & \(85.96\pm 11.68\) \\ \hline \multirow{2}{*}{WL1-WL2-WL3} & BL & \(81.34\pm 15.76\) & \(81.27\pm 15.21\) \\ \cline{2-4}  & WL & \(82.47\pm 15.78\) & \(86.54\pm 11.81\) \\ \hline \multirow{2}{*}{BLs-WL1-WL2-WL3} & BL & \(84.75\pm 8.88\) & \(83.96\pm 9.24\) \\ \cline{2-4}  & WL & \(76.34\pm 15.78\) & \(80.65\pm 12.49\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature projector model binarized.  
Figure 6: Flight test barplots of WL predictions for the input projector model.  
Figure 7: Flight test barplots of WL predictions for the feature projector model.
## 3 The Proposed Solution
### Detailed Protocol Description
#### 3.3.2 Registration  
We assume that all the communications are encrypted and integrity protected, e.g., using the public keys of the relevant authorities CA and SA. New vehicles can be dynamically added as follows (see Figure 2). Initially \(V\) will sent a join-request to the CA. The CA will choose a unique identifier \(ID\) and send this to \(V\) along with the current registration time \(t_{0}\). Then \(V\) chooses \(r_{0}\in_{R}\mathbb{Z}_{p}\), computes \(AEnc(h,r_{0},ID)\rightarrow(C_{1},C_{2})=(g^{r_{0}},IDh^{r_{0}})\) and also the signature \(\sigma_{V}=sig_{V}(ID,t_{0})\). It will then forward \((C_{1},C_{2}),t_{0},\sigma_{V}\) to the CA.  
The CA decrypts \(C_{1},C_{2}\) to obtain \(ID\) and then verifies \(\sigma_{V}\). On successful verification, the CA sends to \(V\) a signature \(\sigma=sig_{CA}(C_{1},C_{2},t_{0})\). In addition, the CA forwards to the SA \((C_{1},C_{2}),t_{0},\,\sigma\). Both \(V\) and SA will verify \(\sigma\) and the SA will also publish all information (encrypted credentials, time and signature) in a public bulletin board in increasing order wrt the encrypted credentials, i.e., \(\mathcal{B}B_{SA}=[D_{\alpha_{1}},\cdots,D_{\alpha_{N}}]\), so that searching for an encrypted credential can be performed in \(\log N\) time.