esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
## 4 Simulation Result and Analysis
### Simulation Parameters and Setup  
Traffic data are essential in obtaining power demand prediction from the proposed power management strategy. However, V2V technologies are not widely used yet, which means that obtaining data from other EVs is difficult in experiments [24]. Therefore, the data for this simulation are pre-processed data from the driving cycle profile. The WLTP class 3 driving cycle profile [34] is adopted for this simulation, as shown in Figure 9.  
The simulation for calculated power demand is based on EV model BWM i3s (120 Ah). The parameter for power demand calculation as in Equation (2) is shown in Table 1 while the parameters for HESS are shown in Table 2. The vehicle mass (\(m\)) and road slope (\(\theta\)) significantly impact power demand calculation. In contrast, other parameters such as air density, EV frontal area and wind speed do not significantly impact.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(m\) & 1730 kg \\ \(g\) & 9.81 m/s\({}^{2}\) \\ \(\theta\) & 0 rad \\ \(f_{r}\) & 0.015 \\ \(\rho\) & 1.25 km/m\({}^{3}\) \\ \(A_{f}\) & 2.39 m\({}^{2}\) \\ \(C_{d}\) & 0.3 \\ \(\
u_{w}\) & 0 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter for power demand calculation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Battery Pack** & **SC Pack** \\ \hline Voltage & 352 V & 297 V (2.7 V Cell) \\ Rated Capacity & 120 Ah & 310 F (5.6 F in total, 110 series, 2 parallel) \\ Energy & 42.2 kWh & 68.6 Wh \\ Initial SOC & 80\% & 75\% \\ Type & Lithium-Ion & Generic model \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter for HESS.  
Figure 9: WLTP class 3 driving cycle profile.
## 4 Results and Discussion  
### General Visualization  
The analysis of the drilling parameters provides crucial insights into the relationship between operational conditions and wellbore stability. Figure 4 illustrates the correlation between hole depth and the rate of penetration, which reveals a general decreasing trend as the depth increases. Initially, the penetration rate exhibits significant variability, peaking at approximately 29.76 m/h at a depth of 376.89 m. However, beyond 380 m, the penetration rate stabilizes below 15 m/h, suggesting increased resistance due to lithological changes or operational adjustments.  
In Figure 5, the relationship between weight on bit and standpipe pressure is examined. The data indicate that, as the weight on bit varies within a range of \(-\)0.96 to 1.20 kkgf, the standpipe pressure remains relatively stable, fluctuating around 407-430 kPa. This suggests that, while weight on bit fluctuations might influence torque and drag, they have a limited direct impact on standpipe pressure. These visualizations highlight key trends essential for optimizing the drilling performance and mitigating stuck pipe incidents.  
Figure 4: Rate of penetration vs. hole depth general visualization.  
Figure 5: General visualization of weight on bit vs. average standpipe pressure.
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry
#### 4.1.6 Cross-Sensors Intercalibration and Tandem Phase  
The basic assumption of cross-sensors intercalibration is that two instruments should provide the same TOA signal when viewing the same target on-ground at the same time and with the identical spatial and spectral response [73]. Eventual temporal and spectral mismatches should be accurately accounted for and corrected to properly assess radiometric biases [101]. The vicarious targets introduced in the above paragraphs are largely exploited for intercalibration using the RTM as a transfer reference to compare the radiometric signal of the two systems under analysis.  
Among the various intercalibration methodologies, the simultaneous nadir overpass (SNO) approach has proven very effective, in particular in the thermal infrared domain, as well as for solar reflective bands [73]. The advantages of SNO as compared to other methods is that the same target on Earth is viewed at nearly the same time and in nadir viewing conditions, therefore minimising the impact of residual atmospheric and directional effects in the RTM simulations. SNO approaches can yields to accurate intercalibration coefficients with an uncertainty of about 1% in the VNIR range [102].  
The drawbacks of the SNO techniques are the limited spatial coverage of space and time collocated overlaps, notably for polar-orbiting satellites. Collocation criteria can be relaxed both in space and time domains, and the corresponding natural variability can be modelled through RTMs, albeit at the expense of the degraded accuracy of the inter-comparison analysis. When instruments are placed in the exact same orbit flying at a short time interval, a very accurate intercalibration analysis can be performed with full characterisation of the uncertainty budget. This is the concept of the Tandem Phase, which was successfully implemented during the Commissioning Phase of Sentinel-3 twin satellite constellation with the objective to ensure consistency of the data provided by the two satellites. The Tandem Phase has proven to be the best way to ensure the traceability of the measurements and the in-depth characterisation and validation of their estimated uncertainties. These are critical issues for building a reliable long-term data record as required for climate applications [103]. For S3 optical sensors (OLCI and SLSTR), this phase was essential for advancing in the harmonisation and homogenisation of the data provided by the two satellites (A/B), both at radiometric level and at geophysical products level [104; 105; 106].
## 3 LOS Guidance Algorithms
### Time-Varying Lookahead Distance  
Up to now, adaptive compensation of the sideslip angle is proposed in the case of considering the value of lookahead distance \(\Delta\) as constant. In principle, a smaller \(\Delta\) is selected when the USV is far from the reference path, and this will produce an aggressive behavior to decrease the cross-track error faster; a larger \(\Delta\) is selected when the USV is near the reference path. The fuzzy algorithm is used to optimize the value of \(\Delta\), and it is a dual input and single output system: \(y_{e}\) and \(\dot{y}_{e}\) are input entries; the gain \(\Im\) is the output entry. The final output is \(\Delta=\Delta_{\min}+\Im(\Delta_{\max}-\Delta_{\min})\).  
The method of fuzzy optimization is shown as follows:  
1. \(y_{e}\) and \(\dot{y}_{e}\) are normalized to \([\begin{array}{cc}-1&1\end{array}]\); the data domain of \(\Im\) is \([\begin{array}{cc}0&1\end{array}]\).
2. \(y_{e}\) is equally divided into NB, NS, Z, PS and PB; \(\dot{y}_{e}\) is equally divided into NB, NS, Z, PS and PB; \(\Im\) is equally divided into VS, S, M, B and VB.
3. Zadeh and max-min are used for fuzzy reasoning. Meanwhile, the centroid area center of gravity method is used for defuzzification.  
When the fuzzy algorithm is employed to optimize \(\Delta\), Equation (27) can be rewritten as:  
\[\dot{V}_{1}<-\gamma x_{e}^{2}-\frac{Uy_{e}^{2}}{\sqrt{\Delta_{\max}{}^{2}+(y_{ e}+\Delta_{\max}\hat{\beta})^{2}}}<0 \tag{30}\]  
The corresponding conclusions can be obtained from (30) that when the strategy of changing \(\Delta\) is introduced into the ALOS algorithm, the region where the guidance system is UGAS is constrained by \(\Delta_{\max}\). Therefore, the larger \(\Delta_{\max}\) is, the more limited the region where the system is UGAS becomes.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 3 Results
### Speech and Singing Discrimination
In the MFCC method, file-wise mean and variance normalisation is applied for the calculation of the MFCCs. If there are enough data with high speaker variability in the training database, the MFCC method can learn the characteristics of both classes and generalise to other databases. However, if data are not enough, this generalisation is not good [33]. The pitch-based methods \(\Delta f_{0}\) and DFT of \(f_{0}\) do not get good results compared with our system. We think that this is due to the presence of short voice segments in the database. The  
Figure 9: 5x2 cv test structure. methods are suitable when long voice segments are present. The proposed algorithm and the one that uses notes detected by Tony have similar scores to the MFCC-GMM method in the Bertsolaritza database and all of them generalise well enough and get similar results in the NUS database. Although F-score results are similar, Precision and Recall values are not. MFCC and Tony methods get good recall for singing and therefore good precision for speech, but not so good values for singing precision and speech recall. On the contrary, our algorithm gets more balanced precision and recall values. The algorithm of [PERSON] is designed to analyse singing voice and not speech. Therefore, it is likely that it has a strong bias towards a singing class when identifying notes. In the case of the MFCC method, the bias is due to the unbalance in favour of the singing class in the training data. To assess the statistical significance of the results, we calculated the \(p\)-value for the results of all the alternative systems when compared with our proposed system. Table 6 shows the \(p\)-values for speech and singing detection. Considering these values and a significance level of \(\alpha=0.05\), the differences in performance of the proposed system is statistically not significant comparing to the MFCC system and it is significant comparing it with the rest of the systems.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 4 A Markov Chain Model
### Estimation of Transition Matrix with Aggregated Data  
Let us consider that we only have available a sample \(y_{t}=\sum_{m=1}^{M}x_{t}^{(m)}\) of \(Y_{t}\) defined in (6). Then, following (7) we can construct the following:  
\[\widehat{P}_{t}=\frac{1}{M}y_{t},\ t=0,\ldots,T, \tag{8}\]  
an estimate of \(P_{t}\) using only \(y_{t}\), the single sample of \(Y_{t}\). Hence, according to (5), we can expect \(\Pi\) to satisfy  
\[y_{t+1}\approx\Pi\cdot y_{t},\ t=0,\ldots,T-1, \tag{9}\]  
which leads to the following least squares estimation (LSE) scheme [17] for estimating \(\Pi\):  
\[\widehat{\Pi}=\arg\min_{\Pi}\sum_{t=0}^{T-1}\|y_{t+1}-\Pi\cdot y_{t}\|^{2},\ \ \text{ subject to}\ \mathbf{1}_{N}\cdot\Pi=\mathbf{1}_{N},\ \text{and}\ \Pi\geq 0, \tag{10}\]  
where \(\mathbf{1}_{N}=[1,1,\ldots,1,1]\) is the \(N\)-dimensional vector of all ones. Note that if the data could be broken down or disaggregated by each individual vehicle \(m\), all such available time series \(x_{t}^{(m)}\) could be consecutively stacked into a single one \(z=[x_{t}^{(1)}|\ldots|x_{t}^{(M)}]\); then the application of this LSE estimator given by (10) to \(z\) as a new time series, would lead to the known MLE estimator of \(\Pi\) for disaggregated data (see Appendix A.)  
Concerning the size of the parameter space of this MC model, the parameters to be estimated (i.e., learned) are the coefficients of the corresponding transition matrix \(\Pi\), whose size amounts to \(N^{2}\).  
Problem (10) is a quadratic optimization problem with positivity and linear constraints. This type of problem is denoted as _Quadratic programming_ or more specifically as a _Constrained least squares_ problem. The canonical quadratic form for (10) and the corresponding Karush-Kuhn-Tucker's necessary conditions are derived in Appendix B.  
**Remark 1**.: _The LSE estimate of \(\Pi\) proposed in (10) has been selected among other possible estimates (based on alternative distance measures between vectors), because the quadratic cost in (10) results in an efficiently solvable optimization problem. For instance, alternative costs could have been defined by making use of other norms \(\|y_{t+1}-\Pi\cdot y_{t}\|_{\dot{q}}\); furthermore, considering that \(y_{t+1}\) and \(\Pi\cdot y_{t}\) can be normalized according to (8) to become a discrete distribution, some cost involving the Kullback-Leibler divergences \(\text{KL}(\frac{y_{t+1}}{M},\frac{\Pi\cdot y_{t}}{M})\) and \(\text{KL}(\frac{\Pi\cdot y_{t}}{M},\frac{y_{t+1}}{M})\) could also be a reasonable option._
## 3 Theoretical Comparison of TOF Estimation with Simulation  
To compare the performances of TOF estimation by the proposed NRLM algorithm with the GN model and the DE model, the envelope data extracted from a complete and undistorted echo signal of ultrasonic, such as Figure 1, is used to test the simulation with different signal-to-noise-ratios (SNR). In the simulation, the performances are tested with the root mean square error (RMSE) between the estimated TOF and the real TOF. The RMSE is defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left\|\hat{t}_{0}-t \right\|^{2}} \tag{44}\]where \(\hat{t}_{0}\) is the estimated TOF, \(t\) is the real TOF, and \(N=1000\) is the number of simulation runs at the specified noise level. In the simulation, the noises are added to the envelope signal with different SNRs, which are defined as follows:  
\[\text{SNR}=20\text{log}(\frac{e_{max}}{n_{std}}) \tag{45}\]  
where \(e_{max}\) is the maximum amplitude of the envelope and \(n_{std}\) is the standard deviation of the noise. The simulation results are presented in Figure 7. It is observed that the proposed NRLM algorithm with the DE model has better performance than that with the GN model when SNR is low, and they have similar performances when SNR is high. Therefore, it can be concluded that the proposed NRLM algorithm with the DE model has superior flexibility than that with the GN model.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### System Framework  
As shown in Figure 1, the blind-area assembly guidance method we proposed includes three main modules: (A) assembly pose tracking; (B) assembly step identification; and (C) an AR visual guidance module. Module A is mainly responsible for using RGBD data to track the pose of the dynamic assembly. Module B is in charge of identifying whether the current assembly step is completed and deciding whether to enter the next step. Module C is responsible for visualizing the blind area in the assembly process in AR and guiding the operation and providing the necessary technical information. For different applications, such as training, assembly, and maintenance, we customized the development to make the user experience better. The specific details of each module are explained below.
# An Overview of Key Technologies in Physical Layer Security
## 1 Introduction
### Overview of Cryptography and PLS
In addition, most PLS schemes assume prior knowledge of the eavesdropper's wiretap channel, which is not feasible in practical applications. Furthermore, it is also worth noting that it will be difficult to only use PLS in future wireless systems since it requires a high data rate to ensure security. PLS can be combined with other higher-layer security techniques to achieve security and robustness of wireless communication networks. Authors in [31], proposed cross-layer cooperation as a viable solution for the achievement of reliability and energy efficiency in wireless communication. [PERSON] et al. in [32] also investigated a cross-layer optimization scheme using cooperative diversity for reliable data transfer  
Figure 3: The difference between cryptography and physical layer security approaches [28]. Figure 2: Wiretap channel. in wireless sensor networks to achieve significant energy savings and prolong the network lifetime considerably. Having stated the aforementioned benefits of both technologies, investigations on the concurrent use of PLS and cryptography are worth looking into to provide elevated robustness of the communication network.
###### Abstract  
With increasing commercial success of PCM, the long-term development of properties becomes more important. It has been investigated already for decades in a variety of ways and a wide range of testing conditions. Previous reviews concluded that further work toward standardization of testing is needed. In a desktop study, the current approach to testing was analyzed. It is shown that standardization should not mean to always test with the same methods using the same conditions, e.g., as different applications impose different conditions. Instead, testing should be tailored to the individual case. For this, a new, systematic approach was developed. It identifies first the basic functions and related properties that might be subject to testing, e.g., with a specific application in mind, and then gives an approach to find the degradation effects and underlying mechanisms to allow tailoring and optimizing test procedures. As an example, the approach is applied to the function of heat storage. Here, a new degradation effect was identified, and for degradation by phase separation, it is shown by an example that it could even be reversed at suitable conditions. Therefore, tailoring testing conditions to the individual case is needed. Deeper knowledge of degradation mechanisms is required, so further R&D is suggested.  
PCM; thermal energy; heat storage; property; degradation; ageing; stability; cycling; testing +
Footnote †: journal: Applied Sciences
## 4 Simulation Results and Analyses
### Results and Analyses
#### 4.2.2 Simulation Results and Analyses for 6:00 to 8:00  
Figure 10 is the results of the period 6:00-8:00 taken from Figure 7. During 6:00-8:00, the PV generated power is less than the load demand power, and sub-flowchart a is chosen as the power management strategy; thus, the BS has the higher priority to supply the \(\Delta p\) to keep the power balance of the microgrid. Then, when the BS has no more power, the DG is turned on because the public grid has a low limitation and the PV cannot support 80% of the load demand power, after which the result above repeats twice.  
Figure 11 is the zoom at the key period when the DG is first turned on. Figure 11a shows the curve of the common DC bus voltage and the SOC curves of the SC and BS, and Figure 11b shows the power curves while the DG is turned on at 6:27.  
In Figure 11, the DG is turned on at 6:27 and the system is in case 10. Three steps of case 11 are highlighted: the SC starts discharging to compensate the power of the sluggish dynamic of the DG until the DG can supply stable power in step 1; the DG starts charging the SC and supplying \(\Delta p\) to keep the power balance of the microgrid until the SC finishes charging in step 2; the DG stops charging the SC and starts charging the BS and supplying \(\Delta p\) to keep the power balance of the microgrid until the BS finishes charging in step 3. During these three steps, the microgrid power is well balanced.  
Figure 9: Curves of common DC bus voltage and SOC of storage (**a**) and curves of power (**b**) while the supercapacitor (SC) is recharging.  
For an easy reading of Figure 11a,b the same points are marked in both figures and the given numerical values only reserve the integer part. In the period of step 1, these values show that the DC bus voltage value is 399 V, near to \(v^{*}_{\text{DC}^{*}}\) the PV generator is working in MPPT mode, the load works at its load demand power, the SC is compensating the \(\Delta p\) according to Equation (3), the BS is not working, the DG is turned on with low dynamic power at 0 W, and the public grid is not working. In the period of step 2, the difference is that the DG is working under the \(p_{\text{DC}\_MAX}\) and can recharge the SC. In the period of step 3, the DG is still working under the \(p_{\text{DG}\_MAX}\), and the SC stops charging while the BS is being recharging by the \(p_{\text{DG}}\).  
Figure 11: Curves of common DC Bus voltage and SOC of storage (**a**) and curves of power (**b**) when the diesel generator (DG) is turned on.  
Figure 10: Curves of common DC Bus voltage and SOC of storage (**a**) and curves of power (**b**) during the period 6:00-8:00.  
One notes that during the period 6:00-8:00, the DG is also turned on at 7:10, and 7:56 and shows the same results as those depicted above at 6:27.
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 5 Experiments
### Performance of KRFA  
To evaluate the efficiency of KRFA, we tested its performance on several benchmark datasets from the UCI machine learning repository. The details of these datasets are shown in Table 10. For the corresponding experimental settings, we set the number of the nearest neighbors - \(k\) of these five dataset to 15. To avoid the singular value issue, we adopted KPCA to retain 98% of the variance before formally performing KMFA and KRFA. We used Gaussian kernel in the experiment and for the fairness of the comparison, the subspace dimension of each method was set to \(C-1\). Table 11 shows the comparison results obtained by KRFA, KMFA and RFA.  
As shown in Table 11, the proposed KRFA obtained a comparable and even better result than KMFA. Furthermore, experimental results of KRFA were all better than RFA on the used datasets. That superiority can be especially reflected on the Satimage dataset. The performance of RFA on Satimage was unsatisfactory. However, KRFA conducted effective nonlinear dimensionality reduction and thus obtained good result on the following  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Classes** & **Samples** & **Dimensions** \\ \hline Ecoli & 8 & 336 & 8 \\ Satimage & 7 & 6435 & 36 \\ Vehicle & 4 & 846 & 18 \\ Waveform40 & 3 & 5000 & 40 \\ Wine & 3 & 178 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistics of the UCI datasets.  
Figure 11: Classification accuracy and standard deviation obtained by RFA and PRPCA on the document and webpage classification problems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **KMFA** & **RFA** & **KRFA** \\ \hline Ecoli & 0.4200 \(\pm\) 0.0431 & 0.4230 \(\pm\) 0.0423 & **0.4511 \(\pm\) 0.0648** \\ Satimage & 0.7749 \(\pm\) 0.0345 & 0.3915 \(\pm\) 0.0834 & **0.8182 \(\pm\) 0.0343** \\ Vehicle & 0.7694 \(\pm\) 0.0152 & 0.6808 \(\pm\) 0.0489 & **0.7824 \(\pm\) 0.0187** \\ Waveform40 & 0.7996 \(\pm\) 0.0136 & 0.8216 \(\pm\) 0.0097 & **0.8256 \(\pm\) 0.0062** \\ Wine & 0.9716 \(\pm\) 0.0350 & 0.9716 \(\pm\) 0.0286 & **0.9773 \(\pm\) 0.0239** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Classification accuracy and standard deviation obtained on several UCI datasets.  
classification problem. These two points clearly demonstrate the nonlinear dimensionality reduction ability of KRFA.
## 2 Related Works  
### Selected Hardware and Software Solutions  
Road traffic analysis and modelling is a challenging task due to the complexity of the situation and its stochastic nature. Attempting to avoid inconveniencing the connected driver with attempts to adjust a specific area (e.g., an intersection or a roundabout), road administrators and researchers resort to simulation to determine best road solutions. The literature describes mathematical models and also software solutions and hardware solutions, as well as solutions combining hardware and software. The most numerous are software solutions, for example: MATSim (Multi-Agent Transport Simulation) [11], VISSIM (Verkehr In Statden-SIMulationsmodell) [12], TRANSIMS (TRANportation ANalysis SIMulation System) [13], MITSIM (Massachusetts Institute of Technology SIMulation) [14], AIMSUN (Advanced Interactive Micro-scopic Simulator for Urban Networks) [15; 16], SUMO (Simulation of Urban MObility) [17; 18] or CORSIM (CORridor SIMulation) [19; 20], extensions of existing solutions [21; 22], and multi-agent simulations [23] regarding pollution [24; 25] or driver behaviour [26]. Some authors also focus their attention on solutions close to real-time simulations [27].  
Hardware solutions are broad. They include numerous scale models of selected road junctions [28; 29; 30], hardware-in-the loop solutions [31; 32], solutions presenting a scale model of a real object [33] or GPU(Graphics Processing Unit)-based solutions [34].