esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results
### HR Measurement vs. Motion Artifacts  
To compare the performance between distance compensated and non-distance compensated HR measurements against different levels of motion, we plot the percentage of correct measurements versus a motion score (Figure 13). The blue bars are for the HR data with compensation, while the red bars are for the HR data without depth information compensation. In this study, we define motion score as the standard deviation of the mean depth change of the ROI-1 to the optical center of the camera, where depth change is calculated as the difference between depth \(D_{raw}\) of ROI-1 and the mean depth of ROI-1 over the 60 s measurement.  
\[MotionScore=std(Abs((depth_{ROI-1})-mean(depth_{ROI-1})) \tag{2}\]  
Since the motion in real-life situations can sometimes be of an irregular pattern, the motion score defined in this study reflects amplitude and frequency of a participant's motion. The higher the motion score, the larger and more frequent the motion artifacts are. For example, a motion score below 0.3 cm is typical of a participant sitting still, while a motion score above 1 cm is typical of participants adjusting for a comfortable position e.g., sitting restlessly while sitting. It is worth noticing that a motion score of 1 cm does not necessarily mean the participant moves only 1 cm during the measurements. For example, in a measurement with a motion score of 1 cm, the maximum ROI-1 deviation from the mean position can exceed 4 cm. The blue bars are for the HR measurements with compensation, while the red bars are for the HR measurements without depth information compensation. As can be seen from the plot, the performance of the motion-compensated HR measurements outperforms the uncompensated measurements across every motion score category. Even when small motion is introduced (motion score from 0 to 0.3 cm), the distance-compensated HR measurement outperforms the uncompensated measurement by more  
Figure 12: Percentage of ToF–based RR measurements versus deviation from reference oximeter.  
than 75% in terms of success rate. Once the motion becomes larger, the performance of the uncompensated HR measurement plummets, indicating that the raw signal is dominated by motion artifacts. At the same time, the success rate of the motion-compensated HR also drops, but to a lesser extent. For example, when motion score exceeds 0.9 cm, \(\sim\)35% of compensated measurements are correct meanwhile close to none of the uncompensated HR measurements are correct. This is of great significance because motion artifacts are a dominant source of noise in contactless HR measurement, especially with near-infrared wavelength where the absorption of blood is much weaker. We believe the performance of the ToF-based HR measurement system under larger motion could be improved with future development of both the motion compensation model as well as higher performance ToF cameras. We will discuss such future developments in more detail in the Section 5.
# Social Internet of Vehicles for Smart Cities
## 5 SIoV Security Issues
### SloV Threats
#### 5.1.2 False Message Injection  
An insider attacker can sign a false message and broadcast it to the network. The attacker can thus manipulate the traffic flow and affect the decisions of other drivers, causing damage through traffic jams or accidents.  
In vehicular networks, the notion of proof-of-relevance (PoR) has been proposed to address this issue by filtering false data via authentic consensus. PoR is based on the idea that witness vehicles provide authenticated endorsements to information about an event. The event reporter can thus prove that he/she is authentically relevant to the event [93].
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 3 The Mathematical Optimization Model and Two Selected Optimization Problems
### Establishment and Procedure of Solution for Mathematical Optimization Model
#### 3.3.1 Establishment of Approximate Model  
In ISIGHT, model objectives (_freeq1_, \(Nu\) and \(\overline{p}\)) were the input values. Before the optimization work, the relevance between model variables (\(D\) and \(\omega\)) and these model objectives was established by the third-order response surface model (RSM), as shown in Figure 5(a), and RSM was used to establish the approximate model to improve the calculation efficiency. At the same time, in order to make all the test points evenly distributed in the design space, the DOE optimal Latin hypercube (opt. LHD) sampling mode was selected, with the sample number of 50, and the sample number for error analysis was 25.  
The RSM cannot guarantee to pass all the sample points; therefore, there was a certain error compared to the actual model. In order to ensure the calculation accuracy of the approximate model, the fitting accuracy \(R^{2}\) was used to verify the accuracy of the approximate model (Table 2).  
The calculation formula of fitting accuracy \(R^{2}\) is:  
\[R^{2}=\frac{SS_{R}}{SS_{T}} \tag{31}\]  
where \(SS_{R}\) represents the sum of regression squares, \(SS_{R}=\sum_{l=1}^{k}(\hat{y}_{l}-\overline{y})^{2}\); \(SS_{T}\) represents the sum of total squares, \(SS_{T}=\sum_{i=1}^{k}(\hat{y}_{i}-\overline{y})^{2}\); \(\overline{y}\) is the average value of response; \(\hat{y}_{l}\) is the predicted value at the design point; \(y_{i}\) is the real value of response, and \(k\) is the number of sample points.  
It can be observed from Table 2 that the fitting accuracy values for the four optimization objectives were all above 0.97. Therefore, the model can well approximate the actual model, and this model can be used for the following optimization work of the LS channel.
## 4 Conclusions  
This paper presents the AlfaCrux space mission and the main results obtained so far. AlfaCrux was launched on April 1, 2022, and it entered its nominal mode on 27 July 2022, 01:56 UTC. Fundamental aspects of the early orbit phase operation and the development of a digital twin model are discussed, and the lessons learned are presented. A project-based learning methodology has been adopted for developing students professional knowledge and transferable skills. In these first 5 months of operation, and from the engineering point of view, the AlfaCrux satellite is perfectly healthy with a high performance level, and the mission is considered a complete success.  
Conceptualization, R.A.B., A.C.d.S., B.F.-A.G., V.M.d.S.B., J.M.L.A., F.A.A., J.V.Q.S.B., Y.d.C.F.A., A.C.A.d.O., B.T.d.M. and V.F.M.; data curation, M.M.K. and R.B.d.S.; formal analysis, R.A.B., W.R.S., L.A., G.A.B., B.F.-A.G., J.M.F.-C., J.M.L.A., F.A.A., A.C.A.d.O., B.T.d.M., Y.d.C.F.A. and E.C.B.; funding acquisition, R.A.B.; investigation, R.A.B., A.C.d.S., W.R.S., L.A., G.A.B., M.M.K., R.B.d.S., J.V.Q.S.B., A.C.d.O., B.T.d.M., Y.d.C.F.A. and E.C.B.; methodology, R.A.B., A.C.d.S., V.M.d.S.B., J.M.F.-C. and A.C.A.d.O.; project administration, R.A.B., A.C.d.S., B.F.-A.G., V.M.d.S.B. and F.A.A.; resources, R.A.B., A.C.d.S., M.M.K., R.B.d.S. and V.F.M.; software, R.A.B., L.A., M.M.K., R.B.d.S., J.M.F.-C., J.M.L.A., B.T.d.M., E.C.B. and V.F.M.; supervision, R.A.B., A.C.d.S. and V.M.d.S.B.; validation, R.A.B., W.R.S., G.A.B., B.F.-A.G., V.M.d.S.B., J.V.Q.S.B. and B.T.d.M.; writing--original draft, R.A.B. and A.C.A.d.O.; writing--review and editing, R.A.B., A.C.d.S., W.R.S., L.A., G.A.B., M.M.K., R.B.d.S., J.V.Q.S.B., A.C.A.d.O., B.T.d.M. and V.F.M. All authors have read and agreed to the published version of the manuscript.  
The AlfaCrux space mission was funded by the Brazilian agency Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF).  
The AlfaCrux Telemetry Viewer can be accessed at [[https://lodestar.aerospace.unb.br/projects/alfaCrux/radio](https://lodestar.aerospace.unb.br/projects/alfaCrux/radio)]([https://lodestar.aerospace.unb.br/projects/alfaCrux/radio](https://lodestar.aerospace.unb.br/projects/alfaCrux/radio)) (accessed on 20 September 2022).  
The authors would like to thank the Brazilian agencies Higher Education Personnel Improvement Coordination (CAPES), National Council for Scientific and Technological Development (CNPq), and the Federal District Research Support Foundation (FAPDF) that supported this work, and also the Brazilian Space Agency (AEB) and the Brazilian National Telecommunications Agency (ANATEL) for technical support. They would also like to thank the International Amateur Radio Union (IARU) for the frequency coordination of the AlfaCrux satellite.  
The authors declare no conflict of interest.
## 4 Numerical Study
### Design of Experiments  
We designed experiments under two different vessel priority policies, FAFU and LVFU, as shown in Table 2. Columns #LV and #SV mean the number of large and small  
\begin{table}
\begin{tabular}{l} \hline Indices \\ \(i=L\), \(S\) (\(L\): large vessel, \(S\): small vessel) \\ \(k=B\), \(F\) (\(B\): backward, \(F\): forward) \\ Parameters \\ \(CP_{i}\): port dues per trip of type \(i\) vessel \\ \(CR_{i}\): rental cost per year of type \(i\) vessel \\ \(CV_{i}\): voyage cost per one-way trip of type \(i\) vessel at speed S0 \\ \(LT\): minimum target of throughput (transported amount per year) \\ \(MN_{i}\): maximum number of type \(i\) vessel \\ \(S0\): current speed (22.2 km/h (12 knots)) \\ \(SF_{i}\): minimum value of speed factor (0.7) \\ \(SF_{n}\): maximum value of speed factor (1.3) \\ \(VR\): voyage cost change rate per voyage speed (4.3\% per km/h (8\% per knot)) \\ Decision variables \\ \(s_{k}\): speed factor, integer*0.1 \\ \(x_{i}\): the number of type \(i\) vessels, integer \\ Outcomes \\ \(t_{i}\): the number of trips per year by type \(i\) vessel \\ \(y\): throughput (transported amount per year) \\ \(z\): total cost per year \\ \hline \end{tabular}
\end{table}
Table 1: Notations for the optimization model.  
vessels used, respectively. Case C0 represents the current operating condition, in which two large and two small vessels are utilized at the constant voyage speed of 22.2 km/h (12 knots). The first four cases (C0, C1, C2, and C3) used the FAFU policy for vessel priority, and the other four cases (C4, C5, C6, and C7) used the LVFU policy. The simulation model ran for one year with 20 replications. The OptQuest simulation optimization engine was used for the optimization model with the following parameters: default replications: 10, max replications: 30, max scenarios: 3000, confidence level: 0.95, and relative error of the confidence level: 0.05.  
Case C1 uses the same number of vessels as case C0. It investigates the impact of the lower voyage speed determined by the optimization model by examining the forward speed (FS) and backward speed (BS) factors with a decrement of 0.1 from 1.0 to 0.7. Speed factor 0.7 represents 70% (15.6 km/h) of the current speed (22.2 km/h), and speed factor 1.6 represents 160% (35.6 km/h) of the current speed. In case C2, the optimization model determines the number of each type of vessel while the speed factors remain constant at the current operating speed. Our preliminary tests to transport most of the produced products determined the ranges for the number of each type of vessel (i.e., 0-3 for #LV and 0-4 for #SV).  
In particular there may be a trade-off between the number of vessels and voyage speed, such that a higher voyage speed may need a smaller number of vessels and vice versa. Thus, we designed case C3 to find the optimal number of each type of vessel and optimal speed factors simultaneously, considering the FS and BS factors from 0.7 to 1.6. Cases C4-C7 have the same structure as cases C0-C3 except that they follow the vessel priority policy of LVFU.
## 3 Results and Discussion  
### FT-IR Analysis  
FT-IR evaluation of the three oil degradation parameters are of important interest in the current study given that they are used as a reference and validation method for the measurements obtained with the proposed sensor. Furthermore, the critical values for each one can be found in the literature and serve to establish precautionary and critical values. Once the critical limits were reached or surpassed, the lubricant oil had to be replaced to avoid mechanical damage. These values are enlisted in Table 2[26; 27].  
Figure 6 shows the evolution of oxidation, sulfation, and nitration levels of the lubricant at different mileage, obtained according to standard practice ASTM E2412. These measurements represent the main reference to evaluate the quality of the lubricant oil and are useful to correlate and validate the results obtained with the proposed sensor. As expected, all three parameters presented a linear dependence with driving distance given that we were focused on the service life interval of the lubricant recommended by the manufacturer. The linear fit presented good correlation with the experimental data (\(R^{2}\) above 0.9210 for all samples). From the graph it is possible to observe that oxidation level surpassed the critical limits after 6000 km of usage. However, the other two parameters barely reached the critical limits. Considering these results, the oil samples used in this study are useful to evaluate the proposed sensor as they are representative of the different degradation stages of the lubricant. Moreover, its linear behavior with mileage facilitates its future correlation with the obtained results of the plasmonic sensor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test** & **Critical Limits (Abs/0.1 mm)** \\ \hline Oxidation & 23–25 \\ Sulfation & 30–40 \\ Nitration & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Critical limits of the three lubricant oil degradation parameters under study [26; 27].  
Figure 6: Evolution of lubricant oil degradation parameters plotted as function of mileage. Red lines represent linear fits of the experimental data, coefficients of determination (\(R^{2}\)) for each degradation parameter are showed in the inset.
## 2 Background
### Problem Formulation
RGB sequences for the projections in \(B^{t}_{i}\) and RGB sequences for the anchors in \(A^{t}_{i}\) at time instant \(t\) are randomly assigned numerical values in the range of [0,1] as stated in the reference [12] and respectively are defined as:  
\[RGB^{\prime\prime}_{j}:\{R^{\prime\prime}_{j},G^{\prime\prime}_{j},B^{\prime \prime}_{j}\},(n^{\prime}_{j}\in B^{t}_{i})\;\;\text{and}\;\;\;RGB^{\prime \prime}_{j}:\{R^{\prime\prime}_{j},G^{\prime\prime}_{j},B^{\prime\prime}_{j} \},(n_{j}\in A^{t}_{i}) \tag{6}\]  
The task-ring for task projection \(n^{\prime}_{j}\in B^{t}_{i}\) is defined as:  
\[C^{t}_{j}=\{(x,y,z)\big{|}(x-x^{\prime}_{j})+(y-y^{\prime}_{j})\leq(R^{2}- \big{(}k^{t}_{ij}\big{)}^{2}),z=z^{\prime}_{j}\} \tag{7}\]  
where \(k^{t}_{ij}(j=1,...p^{t},i=p+1,...q)\) is the depth difference between the task anchor \(n_{j}\) and the mobile node \(n_{i}\) which can be calculated using the information from their equipped pressure sensors. Note that the minimum communication angle between the task anchor \(n_{j}\) and the mobile node \(n_{i}\) at time instant \(t\) named as \(\alpha^{t}_{ij}(j=1,...p^{t},i=p+1,...q)\) can be measured by the mobile node \(n_{i}\), then the localization issue can be described as follows:  
\[ \begin{split}& Estimate\quad n_{i}\\ & subject\quad\text{to}\quad n_{j}(n_{j}\in A^{t}_{i})\big{(}n^{ \prime}_{j}(n^{\prime}_{j}\in B^{t}_{i}));\alpha^{t}_{ij};k^{t}_{ij};(i=p+1,...q )\end{split}  \tag{8}\]
## 4 Routing Strategies and Hop Distance Distributions
### Furthers Neighbor (FN) Routing  
For multi-hop mm-wave transmission, individual hop distance needs to be maximized for delay-sensitive applications such as video and voice traffic. With \(K\) blockage-free nodes in \(\mathcal{A}\), let the hop distance of the furthest neighbor (FN) routing \(d_{FN}\) be  
\[d_{FN}\triangleq\max\{d_{i}\text{ for }1\leq i\leq k|K=k\}, \tag{12}\]  
where \(d_{FN}=0\) for \(K=0\). The conditional CDF of \(d_{FN}\) given that there is at least one blockage-free node is  
\[F_{d_{FN}|K\geq 1}(x) =\Pr[d_{FN}\leq x|K\geq 1]\] \[\overset{(a)}{=}\sum_{k=1}^{\infty}\Pr[\text{all}\,d_{i}\text{'s }\leq x\text{ for }1\leq i\leq k|K=k]\cdot\frac{\Pr[K=k]}{\Pr[K\geq 1]}\] \[\overset{(b)}{=}\frac{1}{1-P_{out}}\sum_{k=1}^{\infty}\left(F_{ d_{i}}(x)\right)^{k}\frac{Q^{k}e^{-Q}}{k!}\] \[=\frac{e^{-Q}(e^{Q\cdot F_{d_{i}}(x)}-1)}{1-P_{out}}=\frac{1-e^{ \frac{\lambda\Phi e^{-\beta x}\left(-\beta x+\beta x-1\right)}{\beta^{2}}}}{1- e^{\frac{\lambda\Phi\left(1-e^{-\beta R}(\beta R+1)\right)}{\beta^{2}}}}, \tag{13}\]  
where \(0<x\leq R\). In addition, (a) follows from the distribution of the maximum of independent and identically distributed (i.i.d.) random variables as in Equation (12) and Bayes' theorem [51] to calculate the conditional probability associated with the number of nodes following Poission distribution. In addition, (b) follows from \(\Pr[K\geq 1]=1-P_{out}\), \(\Pr[K=k]=\frac{Q^{k}e^{-Q}}{k!}\), and \(\Pr[d_{i}\leq x]=F_{d_{i}}(x)\). The corresponding conditional PDF is  
\[f_{d_{FN}|K\geq 1}(x) =\frac{dF_{d_{FN}|K\geq 1}(x)}{dx}\] \[=\frac{\lambda\Phi x\cdot e^{[\frac{\lambda\Phi e^{-\beta x} \left(e^{\beta x}-\beta x-1\right)}{\beta^{2}}-\beta x]}}{e^{\frac{\lambda\Phi \left[1-e^{-\beta R}(\beta R+1)\right]}{\beta^{2}}}-1}. \tag{14}\]
## 4 Results and Discussion
### Radiation Pattern (2D)  
To evaluate the radiating performance including far-field patterns of the proposed wideband mobile terminal antenna, the simulated and measured radiation patterns in the E-plane and H-plane at 3.5 GHz and 5.1 GHz are depicted in Figure 8. While measuring the radiation patterns of one port, the other ports are terminated with 50 \(\Omega\) load impedance. From Figure 8a, it is easily observed that in the E-plane, Ant-1 and Ant-3 as well as Ant-2 and Ant-4 exhibit the same radiation patterns, while the radiation patterns of Ant-2 are a mirror image of Ant-1 and radiation patterns of Ant-4 are a mirror image of Ant-3. Likewise, from Figure 8b, it is noted that in the H-plane the radiation patterns of Ant-1 and Ant-2 as well as Ant-3 and Ant-4 are the same, while the radiation patterns of Ant-4 are a mirror image of Ant-1 and the radiation patterns of Ant-3 are a mirror image of Ant-2. However, in both the E-plane and H-plane, all the antenna elements exhibit the radiation patterns with minimum cross-polar magnitude of \(-15\) dB.  
Figure 8: _Cont_.  
Figure 8: _Cont._
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space
### Trajectory Optimization Methods
#### 5.2.1 Minimum Trajectory Optimization  
Minimum trajectory optimization only ensures that the generated trajectory is smooth and dynamic, but does not constrain the trajectory itself and is suitable for unobstructed flight between two points. Summary: obstacle avoidance is problematic, but smooth trajectory dynamics are feasible.
## 3 Results and Discussion
### Results of Colour Preference and Lighting Environment Perception
In addition, the fact that each subject rated their own skin tone but the same group of fruit and vegetables, within one experiment, may be significant. As stated earlier, the lighting environment of Exp.2 is perceived to be more comfortable, active, relaxed, and warmer. Note that in Exp.2, the light sources were of higher illuminance (Exp.1: \(\sim\)150 lux; Exp.2: \(\sim\)300 lux) and lower CCT (Exp.1: \(\sim\)6600 K; Exp.2: \(\sim\)5100 K). In related work by [PERSON] et al. [56], similar results were obtained where visual perceptions for indoor lighting with a CCT of 5000 K and relatively high illuminance levels were considered to be more comfortable, energetic, relaxed, and warmer than those with a CCT of 6500 K and relatively low illuminance levels. Obviously, current knowledge could not cover the large variations of practical lighting conditions. Thus, we believe further research focusing on the interactive effect of illuminance and CCT should be encouraged, which would enhance the understanding of human visual perceptions regarding different lighting cases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Visual Attributes** & **Fixed Factors** & **F** & \(p\)**-Value** &
\begin{tabular}{c} **Partial Eta** \\ **Squared, \(\eta^{2}\)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Fruit and vegetables preference} & Lighting environment & 0.480 & 0.489 & 0.003 \\  & Test trial & 1.542 & 0.217 & 0.010 \\  & Gender & 3.531 & 0.062 & 0.023 \\ \hline \multirow{2}{*}{Skin preference} & Lighting environment & 13.165 & \textless{}0.001 & 0.081 \\  & Test trial & 0.173 & 0.678 & 0.001 \\  & Gender & 3.379 & 0.068 & 0.022 \\ \hline \multirow{2}{*}{Uncomfortable/Comfortable} & Lighting environment & 11.325 & 0.001 & 0.070 \\  & Test trial & 0.794 & 0.374 & 0.005 \\  & Gender & 3.135 & 0.079 & 0.020 \\ \hline \multirow{2}{*}{Cool/Warm} & Lighting environment & 19.036 & \textless{}0.001 & 0.113 \\  & Test trial & 2.525 & 0.114 & 0.017 \\  & Gender & 0.003 & 0.954 & 0.000 \\ \hline \multirow{2}{*}{Negative/Active} & Lighting environment & 5.879 & 0.017 & 0.038 \\  & Test trial & 0.633 & 0.427 & 0.004 \\  & Gender & 3.324 & 0.070 & 0.022 \\ \hline \multirow{2}{*}{Tense/Relaxed} & Lighting environment & 4.027 & 0.047 & 0.026 \\  & Test trial & 1.483 & 0.225 & 0.010 \\ \cline{1-1}  & Gender & 0.501 & 0.480 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of MANOVAs for each of the fixed factors on colour preference and atmosphere perception.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 3 Material and Methods  
This section includes the proposed method and introduces theoretical background concerning the proposal. Section 3.1 shows a brief description of the RF fingerprint positioning method. The D2D communication protocol in Section 3.2 explains the feasibility of using the protocol in the proposed method. Clock synchronization is crucial in TOA-based distance calculation, but it is impractical to have all the devices' synchronized clock in a wireless network. Section 3.3 shows the technique to calculate the TOA-based distance despite asynchronous clocks. The proposed method is explained in Section 3.4.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters  
Sensors and devices have been serving agriculture and farming sectors over the years by monitoring various environmental parameters and farm components. Comparing the effectiveness of different sensors, such as temperature, humidity and soil moisture sensors, in real-world farm settings could provide valuable insights for future technology development.  
The invention of IoT (Internet of Things) has made a major revolution regarding the advancements of sensors at field level. The global sensors market has been expanding with an extraordinary growth worldwide of approximately USD 190 billion in 2021 as compared to 2011 (USD 62 billion), where environmental sensors account for more than 13% of the total amount [49]. In this context, a wide range of sensors and devices are currently available for environmental monitoring with various levels of complexities and functionalities, such as pH, temperature, soil moisture, humidity, ultraviolet and passive infrared (PIR), light, airflow, etc. However, temperature, humidity and soil moisture are considered the most critical environmental variables for smart farming practice [50]. Table 4 represents the most common sensors and devices used by IoT in the agricultural sector.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Type** & **Sensor/Devices Operations** \\ \hline pH & Measuring acidity/alkalinity of the soil to determine nutrient availability [51; 52; 53]. \\  & Determination of the location of animals and field machinery, detection of motion of unwanted pests on the farm and generating alerts to farmer [54]. \\ Ultraviolet & Monitoring UV for the effective growth of crops [55]. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Common sensors utilised by IoT systems used in agriculture.