esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Needs and Requirements Arising from Connected and Automated Driving
## 3 5 GCAR Use Cases
### High Definition (HD) Local Map Acquisition  
The high definition local map-acquisition use case belongs to the class of _intelligent autonomous navigation_, as shown in Figure 4. The goal is to have a high definition, real-time, and precise local dynamic map that is updated on the move to allow optimal route selection. More precisely, the optimal route selection for an autonomous vehicle relies on long term planning, i.e., learning about the possible routes, traffic, and road conditions, and the short-term planning, such as lane changes, overtaking, and collision avoidance. The long-term planning requires static and dynamic map information on a large scale to learn about the possible routes, traffic, and road conditions. In its turn, the short-term planning requires information from a precise local dynamic map. An off-board system that collects data from different sources builds an optimal route map. The data is structured into different layers, also called layers of dynamics, including the map provider layer (static layer) and the layer of cooperative sensing of the different vehicles (temporary/dynamic layer). The collected data are processed and divided into polygons before being distributed by push/pull methods to the vehicles. Poll methods are used on a regular basis (periodically). The push methods are based on updates that are triggered by major changes or hazardous events.
## 3 Methods
### Step 2: Time Series Analysis
#### 3.2.2 Land Surface Phenology for Vegetation Dynamics 2.0
Thus, in a first step, we focused on the value of peak of season (VPS) metric, which is the maximum fractional cover for each year \(f_{VPS,y}\) (see Figure 3) representing the fractional cover of all photosynthetically active vegetation types (PV). On Crete, vegetation growth below the alpine zone is not limited by temperature, but by water availability: woody vegetation is predominantly evergreen, and thus a fairly constant photosynthetically active fractional cover persists throughout the year, while the seasonal growth in woody vegetation is relatively small. In contrast, herbaceous vegetation has a strong seasonal component, where photosynthetically active fractional cover rapidly increases in response to winter rainfall and then quickly turns into NPV at the beginning of the hot and dry summer. Thus, in the second step, we additionally selected LSP metrics that approximate the seasonal growth of these vegetation components, and consequently offer a  
Figure 3: Fractional cover time series for two selected pixels. (**a,b**) Mixture of rock, grass, and shrub surfaces (35.36\({}^{\circ}\)N, 24.92\({}^{\circ}\)E); (**c,d**) mixture of rock and grass surfaces, with a higher share of woody vegetation before disturbance (35.29\({}^{\circ}\)N, 24.96\({}^{\circ}\)E). (**a,c**) Time-series plotted as a temporal sequence; (**b,d**) time series plotted in polar space; the grey isolines match the 10% fraction increments shown as horizontal lines in (**a,c**). The color-coding is consistent between related plots and shows the interpolated time series. Green circles represent the value of peak of season (VPS) metric; white circles represent the beginning/end of the phenological years \(\overline{\theta_{s}}\); the lines in between represent the value of base-level (VBL) metric; the arrows represent the value of seasonal amplitude (VSA) metric. decomposition of the total PV cover into woody and herbaceous components. The value of base-level (VBL) is defined as the minimum fractional cover of each year \(f_{VBL,y}\), computed as the average of the first fractional cover value after \(\overline{\theta_{s}}\) and \(\overline{\theta_{s+1}}\), and hence approximates the fairly stable fractional cover of woody vegetation. The value of seasonal amplitude (VSA) is the difference between VPS and VBL: \(f_{VSA,y}\), thus \(f_{VPS,y}=f_{VBL,y}+f_{VSA,y}\). It approximates the seasonal growth of PV cover and is thus well aligned with the seasonal dynamics of herbaceous vegetation. It further follows that the remainder represents other cover types (soil, rock, or non-photosynthetically active vegetation), i.e., \(F_{NS}=1.0-\left<f_{VBL,y}+f_{VSA,y}\right>\).
## Results
### LP Plasma Deposition of Differently Cell-Repulsive Coatings
In fact, in this case, to avoid electrical instabilities of the discharge, the frequency remained the same during the three steps, and this could have led to a PE-like coating (step 1), and not enough cross-linked for optimizing the best adhesion of the coatings to be deposited in the next steps. When the process is carried out in higher fragmentation conditions of the feed (high Va or high f values), the resulting PE-like film, more cross-linked, remains attached to the substrate underneath and grants better adhesion of the layer to be deposited on top. An evaluation of the cell-adhesive and repelling character was carried out (Figure 13d-f). A clear non-fouling effect was tailored on these stable surfaces, due to their high PEO-character (~70%). Very few cells were found adhering to these coatings, all characterized by the globular morphology, assumed to minimize cell-substrate interactions. On the contrary, osteoblasts appear numerous and spread on native PC (Figure 13a) and PEOB coatings (Figure 14c). Figure 2:7 Osteoblast cells were grown after 24 h on different substrates: (**a**) PC native, (**b**) PEOB deposited at 26 kHz and 6.5 kV\({}_{\text{P}-\text{P}}\) (**c**) PEOB deposited at 26 kHz and 8.5 kV\({}_{\text{P}-\text{P}}\). Figure 2:8 Cells fixed after 24 h of culture and colored with Comassie blue growing on different substrates: (**a**) Native PC; (**b**) PEO-like with 70% PEO-character, deposited at LP (PEO_LP, 5 W, 0.5 sccm DEGDME, 5 sccm Ar, 53 Pa, 60 min); (**c**) PEOA with 71% PEO-character deposited at AP (16 kHz, 6.5 kV\({}_{\text{P}-\text{P}}\), the arrow indicates the detachment of the film); (**d**) PEOA with 69% PEO-character deposited at AP (16 kHz, 8.5 kV\({}_{\text{P}-\text{P}}\)); (**e**) PEOA coatings with 70% PEO-character deposited at AP (26 kHz, 6.5 kV\({}_{\text{P}-\text{P}}\)); (**f**) PEOA coatings with 66% PEO-character deposited at AP (26 kHz, 8.5 kV\({}_{\text{P}-\text{P}}\)). The cells' behavior on PEO-like coatings deposited at AP PE-CVD in low monomer fragmentation conditions (i.e., low \(\phi\)He-carrier, PEOA) could thus definitively be assumed comparable with those deposited in low fragmentation conditions at LP (PEO_LP, Figure 13b). After 24 h of culture (Figure 14), many osteoblast cells could be found spread across PEOB surfaces (PEO-character lower than 70%) obtained at a higher carrier flow rate (\(\phi\)He-carrier: 6.85 slm) indicating cell adhesion affinity similar to the control (not treated PC). Additionally, in this case, the strategy of three deposition steps in continuo resulted in successfully obtaining a three layer gradient coating stable in cell culture conditions. The results in terms of cell adhesion clearly show that no difference can be found in cell behavior on PEO-like coatings obtained with LP and AP plasma depositions, provided that at least 70% of PEO-character is obtained.
# Plastic Behavior of Laser-Deposited Inconel 718 Superalloy at High Strain Rate and Temperature
## 3 Experimental Procedure  
The plastic flow behavior of Inconel 718 was investigated by performing tensile tests on cylindrical dog-bone specimens with a gauge diameter of 3 mm and a gauge length of 5 mm with threaded ends. The dog-bone specimens for the tensile tests were machined from the raw bars with the longitudinal axis (i.e., the loading direction), which was the deposition direction (i.e., the direction of height increase during manufacturing). The same geometry was used both for quasi-static and high strain rate tests to avoid introducing geometrical effects and make possible a direct comparison of the results.  
Quasi-static and high strain rate tests were carried out at the DYNLab of Politecnico di Torino in Italy. The nominal strain rates were 0.001 and 1500 s\({}^{-1}\), respectively.  
The quasi-static tests were performed at room temperature on a standard electromechanical testing machine--a Zwick Z100. The testing speed applied was 0.005 mm/s. The deformation applied to the specimen was evaluated by digitally analyzing the sequence of images recorded using a DINO-LITE digital microscope with a high resolution (2592 \(\times\) 1944 pixels) at 2 fps. More specifically, the deformation was obtained from the displacement time history of some specific points on the samples (see Figure 1), where the motion was tracked using the DICE software program [21].  
Figure 1: Scheme of the digital image analysis for the evaluation of deformation.
## 2 Related Work  
[PERSON] _et al._ proposed a memetic algorithm (MA)-based solution of energy-aware topology control (ToCMA) for WSNs using a combination of problem-specific light-weighted local searches and genetic algorithms [11], which can provide a better performance than the classical minimum spanning tree solutions. In ToCMA, the composing entities of a chromosome or genes are the power of sensor nodes; the fitness function of a solution is the sum of the power assigned to each gene, and repair and improvement methods are employed to refine solutions.  
[PERSON] _et al._ used a memetic algorithm to dynamically optimize the design of WSNs and considered different constraints such as application-specific requirements, communication constraints and energy consumption [14]. Their work showed that the hybridization of the original GA with the local search operations presented in memetic algorithms brought some improvement on the performance of the design process.  
[PERSON] _et al._ used memetic algorithms to implement energy-efficient coverage control in cluster-based WSNs (CoCMA) [15], which contains a memetic algorithm-based schedule for sensor nodes and a wake-up scheme. In CoCMA, the coverage solutions are represented by binary strings, and the status of a node is represented by an allele of a chromosome. CoCMA can prolong the network lifetime while maintaining coverage preservation of WSNs with no sensing error, and has a significantly improved performance compared with the LEACH [16], LEACH-Coverage-U [17], _etc._  
[PERSON] _et al._ proposed a memetic algorithm to solve the set k-cover problem of WSNs, which has the effectiveness and efficiency of extending the network lifetime [18]. In MA, a chromosome represents the sequence in which all sensor nodes are collected to form covers, and the fitness value of a chromosome is the sum of coverage contributions of all sensor nodes and can enhance the differentiation of promising chromosomes.  
[PERSON] _et al._ proposed a knowledge added improved memetic algorithm (iMA) for target coverage in WSNs, which is concerned with exploiting all available knowledge and demonstrated the effectiveness in extending the lifetime of WSNs [19]. iMA encodes the candidate solution as the chromosome represented by a matrix in which row represents the sensor nodes, and column represents the targets.  
In this paper, our proposed MOCOAMA uses the coverage optimization framework of CoCMA [15], MA [18] and, iMA [19], but it deals with the coverage problem of WSNs with the sensing error, divides the target area with the virtual cells as the basis of chromosome representation, and randomly and alternately selects some local search algorithms to achieve the dynamic local search.
# Numerical Study of Endwall Modification with Micro-Scale Ribs in a Turbine Cascade
## 4 Conclusions  
In this paper, the original endwall and ribbed endwall were numerically investigated, and the effect of the rib on the cooling and heat transfer characteristics was analyzed. In addition, the phantom cooling, flow pattern, and aerodynamics were also studied to present a comprehensive evaluation for the ribbed endwall. The conclusions are as follows:  
1. The rib-like vortex changed the flow of the coolant and had various impacts on the cooling characteristic. The cooling characteristic of the ribbed endwall performed generally better than the original endwall (excluding the horizontal rib). The difference between the ribs is caused by the layout of the rib, which is related to the direction  
Figure 16: Laterally averaged \(C_{\text{pt}}\) of plane in \(x\)/\(C_{\text{ax}}\) = 1 for all cases.  
Figure 17: Area-averaged values of \(\eta\) and \(h\) for endwall cases. (**a**) Film cooling; (**b**) Heat transfer.  
of the local mainstream. Specifically, when the rib is parallel to the mainstream, it benefits the cooling characteristic, and vice versa.
2. The reason for the difference in phantom cooling among different ribbed endwalls is due to the flow guiding effect of ribs on the coolant and whether the coolant dissipated before it reached the SS of the blade. So, the vertical ribs exhibit the best phantom cooling performance compared with horizontal and tilted ribs. For the tilted ribs, the phantom cooling would not perform well, since the rib direction guided the coolant to the opposite direction. Horizontal ribs exhibited the worst phantom cooling performance for the dissipated coolant before reaching the SS of the blade. The case of vertical \(s=8\) mm had the best cooling performance on the endwall surface (an increase of 26.9%, compared with original endwall) and the SS of the blade.
3. Compared with the cooling characteristic, the heat transfer of the ribbed endwall mainly depends on the combination of the secondary flow and rib-like vortex. The horizontal and tilted rib interfered most with the mainstream and thus caused a negative impact on the heat transfer. The vertical rib had the smallest influence on the heat transfer characteristic, because the high-\(h\) region, caused by the PV, was inhibited by the vertical rib layout. In addition, the ribbed endwall increased the aerodynamic loss, which was present in different \(z/H\) positions.  
Conceptualization, Y.S. and W.Z.; methodology, Z.L. and Y.S.; software, Z.L. and Y.S.; validation, Y.L. and W.Z.; formal analysis, Y.S.; investigation, Y.L. and Z.L.; resources, Z.F.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; visualization, Z.F. and Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Major Project (J2019-III-0007-0050).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
## 3 Data Filtering for Short-Term Forecasting of Photovoltaic Power Plants Generation  
### Case Study of the PVPP Generation Short-Term Forecast  
The study object was a real PVPP located in the Astrakhan region of Russia, with an installed capacity of 15 MW. In total, 6076 observations are being examined for the period from 26 September 2017 to 05 February 2019. To verify the forecasting models, data for various characteristic periods were considered:  
\begin{table}
\begin{tabular}{c c c} \hline  & **Total Absolute Error, kW-h** & **Mean Absolute Percentage Error, \%** \\ \hline Error of stages 3–6 & 11,590 & 8.7 \\ Error of stages 1–6 & 43,112 & 32.3 \\ \hline \end{tabular}
\end{table}
Table 1: Errors for the daily forecast.  
Figure 2: Actual versus Forecasted values of PVPP generation for day-ahead time horizon (stages 1–6).  
* spring weather period: 26 February 2018-11 March 2018, 14 days, 164 observations;
* summer weather period: 21 May 2018-1 June 2018, 12 days, 199 observations;
* autumn weather period: 11 September 2018-20 September 2018, 10 days, 130 observations;
* winter weather period: 28 January 2019-03 February 2019, 7 days, 81 observations.  
The division into periods given in the upper list is intended to characterize the weather conditions corresponding to a particular season, not the calendar seasons. Therefore, those periods were chosen that corresponded to certain season from the point of view of weather conditions. In total, there are 574 observations in the studied data for a period of 43 days. Each observation includes the following information:  
* _measured directly on site:_
* actual PVPP generation, kWh;
* actual global horizontal irradiance, W/m\({}^{2}\);
* actual ambient temperature, \({}^{\circ}\)C;
* actual wind speed, m/s;
* formed by the meteorological service:
* actual and forecasted cloudiness, p.u.;
* forecasted air temperature, \({}^{\circ}\)C;
* forecasted wind speed, m/s;
* actual and forecasted air humidity, p.u.  
In addition, the calculations used passport and operational data of the PV panels and inverters.
# ICARUS--Very Low Power Satellite-Based IoT
## 2 ICARUS System Overview  
ICARUS implements an uplink (UL) and a downlink (DL). The uplink is used by the tags to transmit sensor data to the ISS. After tag data reception, the downlink is used to optionally sent re-configuration commands to the tags. The downlink signal is a constant carrier which contains spread QPSK symbols. This continuous carrier is used by the tag as a beacon signal to coordinate wake-up cycles and for timing synchronization. The following Table 1 summarizes the basic signal and data rate properties of both uplink and downlink.  
ISS overflights are used by the tags to transmit one single uplink burst of 223 bytes payload data. Prior to transmission a complex wake-up procedure is realized, which saves battery lifetime. This procedure consists of six individual steps which are explained in Figure 2. During the wake-up and data transmission phases, the tag performs an ISS orbit propagation based on ephemeris data received in the downlink. Based on the tag GPS position and the ISS orbit data, the exact data transmission time instant is calculated to meet the uplink time window of 8 s duration only. This appears quite challenging when considering an uplink burst duration of almost 3.5 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Uplink (UL)** & **Downlink (DL)** \\ \hline \multirow{4}{*}{Carrier} & burst traffic, one single burst & Constant carrier used to \\  & per tag per ISS overflight, & synchronize tags and to broadcast \\  & multiple access channel & tag-individual commands, \\  & & broadcast channel \\ \hline \multirow{2}{*}{Modulation} & QPSK modulated CDMA, & QPSK modulated DS-Spread \\  & Spreading factor 1023 & Spectrum, Spreading Factor 23 \\ \hline Chip rate & 900 kHz & 33.75 kHz \\ \hline Roll-Off & 30\% & 30\% \\ \hline Center frequency & 402.25 MHz & 468.1 MHz \\ \hline Net data rate after FEC decoding and overhead & 560 bit/s, 223 byte/1784 Bit & 668 bit/s \\ \hline \multirow{2}{*}{FEC} & irregular-repeat-accumulate & irregular-repeat-accumulate \\  & LDPC rate 0.4 & LDPC rate 0.4667 \\ \hline Number of Rake fingers & 10 & 5 \\ \hline \multirow{2}{*}{Tolerable multi-path} & 11 \(\mu\)s (3.3 km) & 148 \(\mu\)s (44 km) \\ \hline Typical SNR & \(-\)30 dB up to \(-\)20 dB & \(-\)5 dB up to 5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: ICARUS Uplink and Downlink System Parameters.
## 3 Results and Discussion
### K-Means Clustering  
The normalised features were fed to the k-means clustering algorithm. In general, the k value should be decided by applying the elbow method. The elbow method was tested. However, the method suggested the optimal cluster number should be nine. The reason behind it is that the data set is small. On the other hand, using silhouette scores as measurements implies that four clusters is optimal. Since it is known that there were three different categories, the k value was set to 3. The results of the clustering are shown in Figure 7. By checking the label, all the data points with the same label were clustered together. From bottom left to top right, the cluster regions represent the no-squat, 1 mm deep squats and 4 mm deep squats cases. It is also possible to see from the figure that the 4 mm data are well-separated from the other two cases while the 1 mm data are close to the no-squat case. The corresponding confusion matrix achieved using the actual class labels is shown in Table 4. The achieved accuracy is 100%.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 6 Performance Evaluation
### Resilience to Selective Forwarding Attacks
Figure 7a,b shows the energy expenditures of forwarding a report at each hop affected by _fn_ and _ps_ in Scenario 1 and Scenario 2, respectively. It can be observed that, for both PKAEF_NTV and PKAEF_TV, the energy expenditure of forwarding a report at each hop increases with the increase of _fn_ and _ps_. This is because when _fn_ and _ps_ are larger, the sender needs to choose other upstream nodes to forward the report more times, resulting in higher energy expenditure. Happening in both scenarios, PKAEF_NTV requires a higher energy expenditure of forwarding a report at each hop than PKAEF_TV, regardless of whether _ps_ is 0.2, 0.6, or 1.0. This is because, in PKAEF_TV, the sender prefers forwarding nodes with high trust values which are less likely to be compromised nodes, and thus the report can be successfully forwarded earlier. However, in PKAEF_NTV, the randomly selected forwarding nodes are more likely to be compromise nodes, and the sender needs to try more forwarding nodes for successful forwarding, resulting in higher energy consumption. It can be seen that, compared with the method of randomly selecting the forwarding nodes, the method of selecting the forwarding nodes based on trust values is a better choice for PKAEF. Figure 7: Proportion of available reports as a function of the probability of dropping reports. (**a**) in Scenario 1; (**b**) in Scenario 2.