esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Scenario  
In order to compare the performance of two protocols, we observe three performance metrics: the packet delivery ratio, the end-to-end delay, and the hot spot ratio. We simulate these protocols under two different scenarios in order to study the efficiency of the MCDM method. Each scenario is described below.  
* Scenario 1 (case 1): In this scenario, the network consists of common medical sensors such as temperature and blood pressure sensors. Therefore, it is most important to prevent the temperature rise of the nodes rather than guarantee the low end-to-end delay and the high reliability. Given this fact, the pairwise comparison matrix can be determined as below. \[\begin{array}{c}H\\ temp\\ link\end{array}\begin{array}{c}H\\ link\end{array}\begin{array}{c}Temp\\ link\end{array}\begin{array}{c}Link\\ 1/4\\ 1/3\\ 2\end{array}\begin{array}{c}Result\\ 1/3\\ 1/3\end{array}\begin{array}{c}0.137\\ 0.623\\ 0.240\end{array}\] (14) In the above matrix, we determined the scales using Table 1 in order to set the weight of _Temp_ to be highest. Note that, in Table 1, number 4 denotes \"moderately to strong\" and number 3 denotes \"moderate importance\".
* Scenario 2 (case 2): In this scenario, the network consists of critical medical sensors such as Electromyography, Electroencephalography, and Electrocardiography sensors. Thus, it is necessary to prevent the delay and loss of data in the forwarding process. Based on this, we set the pairwise comparison matrix as follows. \[\begin{array}{c}H\\ temp\\ link\end{array}\begin{array}{c}Temp\\ link\end{array}\begin{array}{c}Link\\ 1/3\\ 2\end{array}\begin{array}{c}Result\\ 1/3\\ 1\end{array}\begin{array}{c}0.170\\ 0.387\\ 0.443\end{array}\] (15) Unlike scenario 1, we set the scale of \(link\) to be highest in order to increase the reliability of the transmission. It may be noted that number 2 denotes \"equally to moderately\" and number 1 denotes \"equal importance\" as shown in Table 1. In addition, we set the scale of \(link\) and \(temp\) as \"equal importance\" so as not to neglect preventing the temperature rise.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Simulation time & 1800 s \\ Network size & 2 m \(\times\) 2 m \\ Number of nodes & 30 \\ Number of mobile nodes & 10 \\ Number of traffic source & 5 \\ Number of sink & 1 \\ Data processing rate & 15,000 bits/s \\ Transmission range & 30 cm \\ Traffic model & IP traffic flow \\ MAC & 802.15.4 \\ Mobility model & Random waypoint \\ Pause time & 3 s \\ HELLO packet timer & 5 s \\ Temperature threshold & 43 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 9: Simulation Parameters.
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
## 4 Statistical Model Analysis
### Multipath Power Attenuation  
In this section, the multipath power attenuation will be studied. Figure 9 gives the mean and maximum of multipath power with different multipath time delays. The mean and maximum of multipath power are a linearly decreasing function of time delay. There are two reasons for this phenomenon. One reason is that the power of the electromagnetic wave propagation attenuates in the atmosphere. The other important reason is that the multipath signal is shadowed with a higher probability when the multipath delay is longer. The fitted curve is expressed as follows:  
\[f(x;a,b)=a\cdot x+b \tag{10}\]  
where \(x\) is the time delay, and the parameters \(a\) and \(b\) are given in Table 6. The table indicates that most of the multipath power within one chip delay is more than 30 dB and that the maximum could be more than 40 dB. It is also observed that the multipath power is always less than 45 dB but that the LOS signal power may exceed 50 dB.  
Figure 10 gives the probability distribution of multipath power attenuation. According to the left panel, the attenuation is mainly distributed between \(-10\) dB and \(-17\) dB, and the multipath power may occasionally be higher than LOS. The right panel gives the mean distribution of power attenuation as a function of time delay, and it is a linearly decreasing function of time delay.  
Figure 10: Probability of multipath power attenuation as a function of time delay.  
Figure 9: Maximum and mean of multipath power as a function of time delay.  
All of the fitted parameters are shown in Table 7.  
During our experiments, the statistical characteristics of time delay and power attenuation are not distinct for different orbit satellites. This is reasonable because the time delay and power attenuation are largely related to the distribution and materials of surrounding reflecting/scattering objects other than the satellite orbits. However, when the antenna is static, the multipath fading frequency mainly depend on the satellite velocity relative to earth. Since the satellite velocities to earth of GEO, MEO, and IGSO are different, the different fading frequency behaviors for different orbits are discussed.
###### Abstract  
Medical wearable devices monitor health data and, coupled with data analytics, cloud computing, and artificial intelligence (AI), enable early detection of disease. Privacy issues arise when personal health information is sent or processed outside the device. We propose a framework that ensures the privacy and integrity of personal medical data while performing AI-based homomorphically encrypted data analytics in the cloud. The main contributions are: (i) a privacy-preserving cloud-based machine learning framework for wearable devices, (ii) CipherML--a library for fast implementation and deployment of deep learning-based solutions on homomorphically encrypted data, and (iii) a proof-of-concept study for atrial fibrillation (AF) detection from electrocardiograms recorded on a wearable device. In the context of AF detection, two approaches are considered: a multi-layer perceptron (MLP) which receives as input the ECG features computed and encrypted on the wearable device, and an end-to-end deep convolutional neural network (1D-CNN), which receives as input the encrypted raw ECG data. The CNN model achieves a lower mean F1-score than the hand-crafted feature-based model. This illustrates the benefit of hand-crafted features over deep convolutional neural networks, especially in a setting with a small training data. Compared to state-of-the-art results, the two privacy-preserving approaches lead, with reasonable computational overhead, to slightly lower, but still similar results: the small performance drop is caused by limitations related to the use of homomorphically encrypted data instead of plaintext data. The findings highlight the potential of the proposed framework to enhance the functionality of wearables through privacy-preserving AI, by providing, within a reasonable amount of time, results equivalent to those achieved without privacy enhancing mechanisms. While the chosen homomorphic encryption scheme prioritizes performance and utility, certain security shortcomings remain open for future development.  
privacy preservation; homomorphic encryption; electrocardiogram; deep learning; cloud framework; wearable sensor data; atrial fibrillation +
Footnote †: journal: Applied Sciences  
Article  
Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 6 Performance Evaluation and Discussion
### Anonymous Entropy  
Figure 8 shows the anonymous entropy of four different dummy location selection algorithms, the proposed RR-DLS algorithm, random dummy location selection algorithm, E-DLS algorithm in [18], and Dest-ex algorithm in [25]. From Figure 8, we observe that the anonymous entropy of proposed RR-DLS algorithm is the largest. This is because the proposed RR-DLS algorithm can ensure the validity of dummy locations. Since Dest-ex algorithm only considers the road information, the anonymous entropy of Dest-ex algorithm is smaller than that of proposed RR-DLS algorithm, and larger than that of random dummy location selection algorithm and E-DLS algorithm. Since E-DLS algorithm selects dummy locations according to service request probability and CR, some dummy locations can be filtered using auxiliary knowledge. The anonymous entropy of E-DLS  
Figure 6: Impact of privacy parameter on execution time.  
Figure 7: Impact of privacy parameter on data traffic.  
algorithm is low. Since random selection algorithm selects dummy locations randomly, the anonymous entropy of random dummy location selection algorithm is the lowest.
## 2 Test Materials and Methods
### Test System  
The triaxial static compression test and triaxial fatigue compression test were conducted using the DTAW-8000 rock high-pressure dynamic test system(made in China), as shown in Figure 2a. It consists of a loading system, a control system, and a data acquisition system. It can be applied for uniaxial compression, triaxial compression, and cyclic loading and unloading tests on rocks under various stress conditions. And, it can achieve flexible conversion and control between different loading modes. The main technical indicators are shown in Table 2.  
Freeze-thaw cycle tests were conducted using the JCD-25 freeze-thaw cycle machine(made in China) shown in Figure 2b. Box temperature range: \(-40\)\({}^{\circ}\)C\(\rightarrow\)\(+25\)\({}^{\circ}\)C. Fluctu-  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Sample**} & \multirow{2}{*}{\begin{tabular}{c} **Longitudinal** \\ **Wave** \\ **Velocity** \\ **(km/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Confining** \\ **Pressure** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Loading** \\ **Rate** \\ **(mm/min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Loading** \\ **Frequency** \\ **(Hz)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Amplitude** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Cycle** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{**Loading Method**} \\ \cline{1-2} \cline{6-11}  & & I-30 & 2.66 & & & & & & \\  & I-45 & 2.59 & 4 & 0.06 & — & — & — & Triaxial static loading \\  & I-60 & 2.57 & 2.64 & & & & & & \\ \hline \multirow{6}{*}{II} & II-30 & 2.62 & & & & & & & Static and dynamic \\  & II-45 & 2.62 & & & & & & & & initial stress upper \\  & & & 4 & 0.06 & 1 & 0.5 & 1000 & 1000 & 10000 & 10000 \\ \cline{1-2} \cline{6-11}  & II-60 & 2.61 & & & & & & & & Then, the static load \\ \cline{1-2} \cline{6-11}  & II-90 & 2.60 & & & & & & & & was continuously \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fractured sandstone specimen loading test program.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Test System Type**} & **DTAW-8000** \\ \hline Static loading maximum load & 8000 kN \\ Dynamic loading maximum load & 2000 kN \\ Maximum confining pressure & 50 MPa \\ Displacement loading rate & 0.001–7 mm/s \\ Stress loading rate & 0.01–300 kN/s \\ Host stiffness & 6 GN/m \\ Dynamic loading frequency & 0.1–10 Hz \\ Output waveform & \multicolumn{2}{c}{Sine wave, square wave,} \\  & & sawtooth wave, etc. \\ Control method & \multicolumn{2}{c}{Load control, axial displacement control, and} \\  & & axial strain control \\ \hline \hline \end{tabular}
\end{table}
Table 2: DTAW-8000 rock high-pressure dynamic test system.  
The main magnetic field was 0.3 \(\pm\) 0.05 Tesla and the magnet temperature control was \(-\)25-25 \({}^{\circ}\)C (control accuracy of \(\pm\)0.1 \({}^{\circ}\)C). It can be used for testing and analyzing the changes in the internal microstructure of porous media materials under different temperature conditions.
## 2 Theoretical Choice of SCs Arrangement  
Former studies [23; 24; 25] considered the airflow forced convection for the thermal management and showed that for the SCs placed in aligned or staggered arrangement the last two rows are the most disadvantaged. As mentioned before, the staggered arrangement has better heat transfer for maximum three rows [23; 25]. If the number of rows is greater, the aligned configuration is the most efficient [25]. This result is explained by the local dynamics of the air flow. Indeed, the staggered configuration creates a more complex vortex dynamics, increases the local turbulence intensity and makes it possible a more intense heat dissipation for the first rows. However, this flow dynamics will increase the air temperature of the cooling flow faster and this will affect the last rows which will lead to an increase in their temperature. Until this time, the possibility of combining aligned and staggered arrangements has not yet been explored. For the above reasons, we consider that the proposed new configuration (Figure 1) takes advantage of the both classic arrangements by proposing a mix between them.  
This paper proposes an experimental study completed by a numerical investigation. The experimental study investigates a mixed configuration with five rows (Figure 1). The first three rows are aligned. The last two rows, where the highest temperatures have been found in previous studies [23; 24; 25; 26], are staggered. By going to a staggered configuration for the last two rows, we expect that the thermal transfer coefficient at this location to increase. Thus, the temperature at this place may be lower than if we had kept an aligned arrangement.  
Figure 1: Mixed configuration proposed in the current study.
## 3 Numerical Simulations and Discussion
### Affect of Sensor Resolution
#### 3.4.1 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disc** & **Unbalance** & **Value** & **Value** & **Value** \\ \hline
**\#1** & Amplitude & (695.26\%, 4 Hz) & (19.45\%, 268 Hz) & - & - \\  & Angle & (140.04, 4 Hz) & (10.86\%, 268 Hz) & - & - \\  & Amplitude & (597.53\%, 4 Hz) & (22.77\%, 270 Hz) & - & - \\  & Angle & (94.44\%, 4 Hz) & - & - & - \\  & Amplitude & (1722.04\%, 8 Hz) & (262.49\%, 268 Hz) & - & (11.76\%, 1268 Hz) \\  & Angle & (120.31\%, 16 Hz) & (20.6\%, 270 Hz) & - & (11.3\({}^{\circ}\), 1268 Hz) \\  & Amplitude & (853.26\%, 6 Hz) & - & - & - \\  & Angle & (165.1\({}^{\circ}\), 10 Hz) & (10.04\({}^{\circ}\), 270 Hz) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical results of peak values outside the assumed allowable range when the resolution is 1 nm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disc** & **Unbalance** & **Value** & **Value** & **Value** \\ \hline
**\#1** & Amplitude & (695.26\%, 4 Hz) & (19.45\%, 268 Hz) & - & - \\  & Angle & (140.04, 4 Hz) & (10.86\%, 268 Hz) & - & - \\  & Amplitude & (597.53\%, 4 Hz) & (22.77\%, 270 Hz) & - & - \\  & Angle & (94.44\%, 4 Hz) & - & - & - \\  & Amplitude & (1722.04\%, 8 Hz) & (262.49\%, 268 Hz) & - & (11.76\%, 1268 Hz) \\  & Angle & (120.31\%, 16 Hz) & (20.6\%, 270 Hz) & - & (11.3\({}^{\circ}\), 1268 Hz) \\  & Amplitude & (853.26\%, 6 Hz) & - & - & - \\  & Angle & (165.1\({}^{\circ}\), 10 Hz) & (10.04\({}^{\circ}\), 270 Hz) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical results of peak values outside the assumed allowable range when the resolution is 0.1 nm. Moreover, similar results can also be obtained in simulations of g4.4, h1.4, and h4.4.
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 8 Application of the Template Based Solution to the WLTP Driving Cycle  
To demonstrate the benefits of the methods discussed in this paper, the template adaptation algorithm was tested using the WLTP Class 3 driving cycle for light weight vehicles [30]. Using this cycle, a standardized comparison basis can be established, which models a real-world scenario. The cycle defines a speed trajectory with a sample time of 1 s. The values in between are generated by linear interpolation. This speed trajectory was used as a reference signal for the simulation. As illustrated in Section 4.2, the template adaptation algorithm is based on speed profiles. Thus, it can be directly applied to the speed profiles given by the WLTP cycle.  
The simulation used the motor given in Table 11. In order to use most of the nominal speed range of the motor, the speed trajectory was multiplied by a factor of 11 rpm/kph, thus simulating a transmission and wheels. A total inertia of \(J_{\mathrm{T}}=0.3405\,\mathrm{kgm}^{2}\) was assumed to require most of the nominal torque range of the motor. A 370 W motor was not ideal to represent an electric vehicle operation, which typically requires multiple 10 kW of rated power. This motor was chosen for consistency with respect to the other simulations and measurements presented in this paper and because it is currently the only available test bench. The power losses were integrated over the duration of the driving cycle and compared to a simulation model with no anticipation like [16], which only adjusted the optimal steady state flux according to the speed controller output.  
Figure 19 shows the saved energy, when comparing the template adaptation algorithm to the optimal steady state flux control [16] over time. It is clearly visible that this approach will result in lower energy consumption and thus higher energy efficiency. Depending on the transient slope, there is more energy saved for large changes in flux and less energy saved for little changes in flux. During the periods of standstill, there is neither energy saved nor lost, which proves that for non-transient states, the steady state optimal flux is applied. The template adaptation algorithm is able to save 0.25% of the energy used by the optimal steady state flux control and 65% when compared to constant rated flux operation.  
It must be considered that the WLTP driving cycle has a relatively low dynamic characteristic compared to the capabilities of electric motors. Ramps and speeds often remain nearly constant for multiple seconds, and there are many segments of standstill. The efficiency improvement will be higher with more dynamic applications.  
Figure 18: Measurements comparing fmincon based solution without anticipation to model predictive solution.
## 3 Numerical Simulations
### Case Investigations Using Numerical Simulation
#### 3.2.3 Fault Detection Using the Present Method
The final classification result is listed in Table 3; from the table, we can see that the accuracy rates of unbalance, misalignment and rub-impact  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Unbalance**} & \multicolumn{6}{c}{**The Calculated Results in 8 Frequency Bands of Unbalance Fault**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SD & \(6.74\times 10^{-6}\) & \(1.16\times 10^{-5}\) & \(6.48\times 10^{-6}\) & \(1.08\times 10^{-5}\) & \(5.07\times 10^{-6}\) & \(5.41\times 10^{-6}\) & \(5.05\times 10^{-6}\) & \(5.59\times 10^{-6}\) \\ Peak & \(2.04\times 10^{-5}\) & \(30.5\times 10^{-5}\) & \(2.06\times 10^{-5}\) & \(3.07\times 10^{-5}\) & \(1.27\times 10^{-5}\) & \(1.20\times 10^{-5}\) & \(2.04\times 10^{-5}\) & \(1.89\times 10^{-5}\) \\ Kurtosis & \(2.6652\) & \(2.4046\) & \(3.1223\) & \(2.9342\) & \(2.5410\) & \(2.8829\) & \(4.2142\) & \(3.9455\) \\ CLF & \(4.2875\) & \(3.6660\) & \(4.6801\) & \(4.1462\) & \(3.9915\) & \(4.5466\) & \(6.2779\) & \(5.5409\) \\ IF & \(3.7053\) & \(3.1932\) & \(3.3981\) & \(3.5342\) & \(3.8083\) & \(3.8888\) & \(5.2489\) & \(4.5311\) \\ \hline \multicolumn{9}{c}{**The Calculated Results in Frequency Bands of Misalignment Fault**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SD & \(5.91\times 10^{-6}\) & \(7.51\times 10^{-6}\) & \(6.35\times 10^{-6}\) & \(6.26\times 10^{-6}\) & \(6.53\times 10^{-6}\) & \(6.75\times 10^{-6}\) & \(8.08\times 10^{-6}\) & \(5.61\times 10^{-6}\) \\ Peak & \(2.10\times 10^{-5}\) & \(1.86\times 10^{-5}\) & \(2.11\times 10^{-5}\) & \(1.67\times 10^{-5}\) & \(2.02\times 10^{-5}\) & \(2.25\times 10^{-5}\) & \(2.26\times 10^{-5}\) & \(1.81\times 10^{-5}\) \\ Kurtosis & \(3.2595\) & \(2.4692\) & \(3.5937\) & \(2.5028\) & \(2.9993\) & \(3.8173\) & \(2.2704\) & \(2.9021\) \\ CLF & \(5.2483\) & \(3.5136\) & \(5.3666\) & \(3.9021\) & \(4.4516\) & \(5.6814\) & \(4.1518\) & \(4.5716\) \\ IF & \(4.4209\) & \(3.0262\) & \(4.3949\) & \(3.3024\) & \(3.8290\) & \(4.8527\) & \(3.5028\) & \(3.9603\) \\ \hline \multicolumn{9}{c}{**The Calculated Results in 8 Frequency Bands of Rub-Impact Fault**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline SD & \(7.96\times 10^{-6}\) & \(8.59\times 10^{-6}\) & \(2.16\times 10^{-5}\) & \(7.83\times 10^{-6}\) & \(6.76\times 10^{-6}\) & \(5.84\times 10^{-6}\) & \(7.70\times 10^{-6}\) & \(6.98\times 10^{-6}\) \\ Peak & \(3.51\times 10^{-5}\) & \(2.21\times 10^{-5}\) & \(5.19\times 10^{-5}\) & \(2.54\are 93%, 95% and 89%. For the combination of rub-impact and unbalance fault, the result attains 91%, which verifies the proposed personalized diagnosis methodology. It is noted that the accuracy rates are given automatically by the SVM toolkit. More details about the multi-classification using the SVM toolkit for fault detection can be seen in [41].
# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## Data and Metadata Representation and Storage
### Geospatial Smart Representations
The RDF Cube Vocabulary [55] enables the representation of multidimensional data cubes in RDF, however it does not support the sampling spatial and temporal dimensions required to achieve efficient representations for spatio-temporal coverages. Therefore, research is still needed to reach efficient integrated smart data representation and storage technologies for vector and raster geospatial data warehouses. Furthermore, in spite of the existence of general purpose ontologies like SSN [13; 44], a complete ontology that supports the representation of any kind of environmental observation (including in-situ and remote sensing) and modeling processes has not still been designed. Based on the above, the following research challenges, which are graphically illustrated in the information architecture of Figure 7, should be overcame to achieve effective smart geospatial and environmental data representations and efficient relevant storage technologies. **Challenge 1: geospatial RDF representation. **: An effective RDF representation that enables the integrated modeling of vector features and raster coverages in heterogeneous geospatial data warehouses has to be defined. Such representation should treat vector features and raster coverage cells as first class citizens, to ease the integrated querying of both types of structures. **Challenge 2: efficient integrated semantic vector-raster storage. **: Separate data storage and access technologies already exist for vector features and raster coverages, and many datasets are currently stored with those technologies. However, the efficient combined access to vector and raster geospatial data may only be achieved if new integrated data storage and access technologies are developed. **Challenge 3: environmental observation and modeling ontology. **: A general top-level ontology must be designed to support the representation of all the data and metadata involved in any type of environmental data generation process, which include in-situ and remote environmental sensing and also environmental modeling. That ontology should be aligned to top-level ontologies like SSN [13; 44] and should enable the incorporation of existing vocabularies such as the climate and forecast (CF) conventions. Figure 7: Illustration of challenges towards smart environmental data and metadata representation and storage.