esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis of the Thermal Performance  
### Description of the Experiment Setup  
To investigate the heat transfer performance of the proposed internal cooling structure, a turning experiment was performed on a classical type of horizontal lathe. The type of the lathe is CA6236 and it is produced by the Shenyang machine tools company, Shenyang, Liaoning, China. The temperature field on the tool-chip interface was captured by an infrared thermal imager (Ti32, Fluke Corporation, Everett, WA, USA), and three thermocouples were used to measure the temperatures within the tiny grooves. An NI PXI data acquisition platform was utilized to measure the temperature. The sampling frequency was set as 80 Hz. The purified water was pumped with a peristaltic pump through the V-shaped channel and formed a closed internal cooling circuitry in the tool-holder. Considering the simplicity in this study, the coolant speed was kept constantly at 80 mL/min using a flowmeter. A workpiece in the form of hollow bar (the material is 1045 and its diameter is 60 mm) was chosen as the test specimen of the turning experiment. The machining parameters of the turning test are listed in Table 3. A photo of the experimental setup for turning trials is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline rotation speed of the spindle & 410 r/min \\ feed speed & 0.1 mm/r \\ cutting depth & 1 mm \\ \hline \end{tabular}
\end{table}
Table 3: Machining parameters of the turning test.  
Figure 11: The temperature filed between the insert and tool-holder.
## Nomenclature  
\begin{tabular}{l l} \(AE\) & Average efficiency (dimensionless) \\ \(AEG\) & Average efficiency gain (dimensionless) \\ \(b\) & Face width (m) \\ \(d\) & Pitch diameter (m) \\ \(E\) & Electrical energy (kWh) \\ \(EG\) & Energy gain (dimensionless) \\ \(F_{t}\) & Transmitted load (N) \\ \(g\) & Acceleration of gravity (m/s\({}^{2}\)) \\ \(h_{a}\) & Available head (m) \\ \(L\) & Total length of screw (m) \\ \(m\) & Module (m) \\ \(N\) & Number of blades (dimensionless) \\ \(P_{e}\) & Power output of the electric generator (W) \\ \(P_{g}\) & Power output of the electric generator (W) \\ \(P_{h}\) & Hydraulic power (W) \\ \(P_{m}\) & Mechanical power (W) \\ \(p\) & Pitch of one blade (m) \\ \(R_{i}\) & Radius of screw's inner cylinder (m) \\ \(R_{o}\) & Radius of screw's outer cylinder (m) \\ \(q\) & Flow rate (m\({}^{3}\)/s) \\ \(T\) & Torque (Nm) \\ \(\gamma\) & Lewis form factor (dimensionless) \\ \(\theta\) & Angle of the screw turbine (\({}^{\circ}\)) \\ \(\eta\) & Micro-hydropower plant efficiency (\%) \\ \(\eta_{e}\) & Electric generator efficiency (\%) \\ \(\eta_{g}\) & Gearbox efficiency (\%) \\ \(\eta_{t}\) & \(AST\) efficiency (\%) \\ \(\rho\) & Water density (kg/m\({}^{3}\)) \\ \(\omega\) & Rotational speed (rad/s) \\ \(\sigma\) & Stress (kg/m\({}^{2}\)) \\ \(\sigma_{c}\) & Critical cavitation factor (dimensionless) \\ \end{tabular}
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 2 Background Review and Related Works
### Inverse Distance Weighted (IDW)  
IDW is a deterministic multivariate interpolation method that uses a known distributed set of points. A weighted average of the values available at the known points is used to assign values to unknown points. The range of the interpolated values calculates the specified weight. The concept is an unknown value point that is inversely proportional to the value distance of the known point around it; the further the distance, the lower the degree of impact. The degree of impact between the unknown point and the established point is the force of the distance. The formula is presented as follows:  
\[f(x,y)=\left[\sum_{N}^{i=1}w(d_{i})z_{i}\right]\div\left[\sum_{N}^{i=1}w(d_{i})\right] \tag{1}\]  
where \(w(d_{i})\) is the weight equation, where \(z_{i}\) is \(i\) of the value's first known point, and \(d_{i}\) is \(i\) of the distance from the point to the unknown point. The inverse ratio of the power determines the size.
# Surface Electromyography-Based Action Recognition and Manipulator Control
###### Abstract  
To improve the quality of lives of disabled people, the application of intelligent prosthesis was presented and investigated. In particular, surface Electromyography (sEMG) signals succeeded in controlling the manipulator in human-machine interface, due to the fact that EMG activity belongs to one of the most widely utilized biosignals and can reflect the straightforward motion intention of humans. However, the accuracy of real-time action recognition is usually low and there is usually obvious delay in a controlling manipulator, as a result of which the task of tracking human movement precisely, cannot be guaranteed. Therefore, this study proposes a method of action recognition and manipulator control. We built a multifunctional sEMG detection and action recognition system that integrated all discrete components. A biopotential measurement analog-to-digital converter with a high signal-noise rate (SNR) was chosen to ensure the high quality of the acquired sEMG signals. The acquired data were divided into sliding windows for processing in a shorter time. Mean Absolute Value (MAV), Waveform Length (WL), and Root Mean Square (RMS) were finally extracted and we found that compared to the Genetic-Algorithm-based Support Vector Machine (GA-SVM), the back propagation (BP) neural network performed better in joint action classification. The results showed that the average accuracy of judging the 5 actions (first clenching, hand opening, wrist flexion, wrist extension, and calling me) was up to 93.2% and the response time was within 200 ms, which achieved a simultaneous control of the manipulator. Our work took into account the action recognition accuracy and real-time performance, and realized the sEMG-based manipulator control eventually, which made it easier for people with arm disabilities to communicate better with the outside world.  
sEMG; feature extraction; action classification; BP neural network; GA-SVM +
Footnote †: journal: Applied
## 3 Results and Discussion
### Prediction of Fatigue Parameter (G*.sin\(\delta\)) Using Short-Term Aged Parameters  
Further, in the previous section, the essence of using the unaged input variables is to reduce the prediction cost for the prediction of the fatigue parameter. However, in this case study, the RTFO conditioned dataset was used to evaluate the progress of G*.sin\(\delta\) parameter in the asphalt binder. Although the modelling cost is higher, this research aimed to investigate the reliability of modelling G*.sin\(\delta\) parameter using the GPR algorithm.  
The results of the four algorithms show higher training accuracy for the modelling of G*.sin\(\delta\) parameter using the short-term aged input variables as shown in Table 5 and Figure 4. As observed in Section 3.2, the simulation performance were lower than the training performance. The poor performance of the models especially, with 3% latex modification imply that the training dataset is insufficient for extracting the complex chemical and physical changes features that take place during binder ageing. The complexity of binder ageing can be seen in the bahaviours of the model with 3% latex modification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Training Results**} & \multicolumn{6}{c}{**Simulation Results**} \\ \hline  & & & & & **3\% Late Dataset** & \multicolumn{3}{c}{**6\% Late Dataset**} \\ \hline  & R\({}^{2}\) & RMSE & MAE & R\({}^{2}\) & RMSE & MAE & R\({}_{2}\) & RMSE & MAE \\ \hline ANN & 0.98 & 0.027 & 0.019 & 0.50 & 0.225 & 0.214 & **0.68** & **0.044** & **0.035** \\ \hline GPR & 0.96 & 0.036 & 0.025 & **0.60** & **0.136** & **0.116** & 0.64 & 0.050 & 0.040 \\ \hline RNN & 0.97 & 0.029 & 0.021 & 0.52 & 0.132 & 0.110 & 0.71 & 0.077 & 0.063 \\ \hline SVM & 0.96 & 0.035 & 0.025 & 0.50 & 0.201 & 0.187 & 0.66 & 0.045 & 0.036 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance results for the prediction of fatigue parameter (G*.sin\(\delta\)) parameters using short-term aged dataset.  
Figure 4: The comparison of predicted vs. measured G*.sin\(\delta\) parameters using the short-term aged dataset.
## 2 Modeling of Subsystem
### Probability Density Function Fitting
#### 2.3.3 Normal Distribution  
The probability density function of the normal distribution has two parameters, \(\mu\) and \(\sigma\). The accumulative distribution function meets the relation shown in Equation (11) if the function is converted to standard normal distribution.  
\[F(t_{i})=\Phi\biggl{(}\frac{t_{i}-\mu}{\sigma}\biggr{)}=\Phi(z_{i}) \tag{11}\]  
The linear equation can be constructed for the normal distribution as shown in Equation (12).  
\[t_{i}=\sigma z_{i}+\mu \tag{12}\]  
In Equations (11) and (12), \(z_{i}\) is the lower fractile of standard normal distribution and can be calculated by the inverse function of the standard normal distribution. If the sample data meet normal distribution, \(z_{i}\) and \(t_{i}\) are linear and the observed value of parameters \(\mu\) and \(\sigma\) can be calculated by Equation (13).  
\[\left\{\begin{array}{c}\hat{\mu}=b\\ \hat{\sigma}=a\end{array}\right. \tag{13}\]
## 3 System Configurations  
For determining valid delivery plans for the last mile, typical instances of the VRP need to be solved, especially if vehicles with limited capacity are considered. Different system configurations are investigated with only one central depot or several depots, from where all tours to the final customers start and end (Figure 1a). Furthermore, hubs can be introduced to the distribution system with central depots serving all hubs, from where the customers are served. In this case, VRP problems may also occur for serving the hubs from the depot.  
A customer within such a system could also be represented as an APL, where parcels are buffered to be picked up by the final recipients. Figure 1b) reflects such an approach, where the service provider plans routes to multiple recipients, including APLs. In extreme cases, all recipients choose to utilize APLs (Figure 1c).  
The key question is which approach is more efficient in terms of distance traveled and CO\({}_{2}\) emissions. A comparison of the different approaches can provide insight into the respective advantages and disadvantages in optimizing delivery operations.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 4 System Models
### Analysis of SVO Logic
#### 5.2.4 Security Proof
\mid\!N_{4}\}\). From S16, S17, and NV1, we can obtain the following: S18: 6 MAG\({}^{\rm new}\) believes AAA believes \(\{T\!I\!D_{LMA}\!\mid\!\mid\!T\!I\!D_{GL}\!\mid\!\mid\!T\!D_{6G}\!\mid\!\mid\! N_{4}\}\). From S18, P12, and J1, we can obtain the following: S19: 6 MAG\({}^{\rm new}\) believes \(N_{4}\). From S17 and F1, we can obtain the following: S20: 6 MAG\({}^{\rm new}\) believes fresh \({}_{KGL}+_{G}\). From S14, S19, P9, and P12, we can obtain the following: S21: 6 MAG\({}^{\rm new}\) believes fresh \({}_{KGL}+_{G}\). **Step S10-S21:** The new Mobile Access Gateway, 6 MAG\({}^{\rm new}\), verifies the legitimacy of the identifiers, timestamps, and MAC values provided during the handover process. It ensures the following:  
The freshness of the key \(K_{GL}+_{G}\) is validated through nonce and cryptographic principles. The integrity and authenticity of the key exchange process. This step ensures that 6 MAG\({}^{\rm new}\) and the group leader securely establish the session key, \(K_{GL}+_{G}\), which will be used for secure communication in the new environment. S22: 6L\({}_{\rm MNGL}\) believes \(\{(T\!I\!D_{A}\!\mid\!\mid\!T_{A}\!\mid\!\mid\!N_{4}\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! **Step S22-S30:** The group leader verifies that the 6 MAG\({}^{\text{new}}\) has received and authenticated the MAC value, ensuring the shared key is genuine and fresh. This is achieved by the following:  
* Verifying freshness using nonce N4 and timestamp \(T_{\text{A}}\). * Ensuring integrity through the MAC value and hash computations. * Through this process, both the group leader and MAG are assured of mutual authentication and commitment to the shared key. Through this verification, the group leader and 6L\({}_{\text{MAG}}\)\({}^{\text{new}}\) are assured that the shared key is genuine and that the MAC value sent by the 6L\({}_{\text{MAG}}\)\({}^{\text{new}}\) is accurate. Based on (21) and (30), it may be concluded that the group leader and the 6L\({}_{\text{MAG}}\)\({}^{\text{new}}\) are committed to the key \(K_{\text{GL}}\)\(+_{\text{NG}}\).
# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 4 Results and Discussion
When the active AOI pointing is carried out using the AOCS on-board the satellite, the SWAP is significantly increased and provides almost real-time or near-real-time coverage, i.e., 75% to 98%, which will facilitate the near-real-time disaster response. This is evident from the results that were reported, and it is clear that when the nadir pointing is used, the FOV is relatively low for most AOI. Figure 14 shows the list of spacecrafts that will access the AOI as part of the planned constellation. It also displays the amount of time that these satellites will have access to Australia, and it presents the orbits that correspond to these satellites throughout the duration of the simulation time. These findings suggest that astrionics, i.e., hardware accelerators for on-board edge computing, could be considered for future space missions. This would allow for the improvement of the framework, the efficient organisation of space-to-ground dataflow, and the provision of real-time or near-real-time information, both of which could be very helpful in the management of extreme events and humanitarian emergencies. It was discovered that the outcomes were influenced by the direction in which the sensors were pointed when the measurements were taken. These results can also be affected by the orbits of the satellite, the minimum EA of the AOI, the camera mounting position, and location in respect to the FOV of the satellite if the satellite is not continually pointed at the AOI. The orbits of the satellites can be altered by employing Keplerian parameters and by modifying to the appropriate AOI in accordance with the needs of the owner or operator. In the future, cameras will be able to be mounted on gimbals that can rotate freely on the satellite, and the many sensors that are distributed across the constellation will be able to be used to improve the results. This not only makes it possible for the satellites to point directly downward, also known as nadir pointing, but it also makes it possible for \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Location** & **Latitude (deg)** & **Longitude (deg)** & **n** & **SWAD (sec)** & **SWAP (\%)** & **N** & **SWAD\({}_{\text{T}}\)** & **SWAP\({}_{\text{T}}\)** \\ \hline
**Africa** & 11.2027 & 17.8739 & 10 & 300 & 1.3889 & 545 & 16,350 & 75.6944 \\ \hline
**Australia** & \(-31.25\) & 146.92 & 5 & 150 & 0.6944 & 691 & 20,730 & 95.9722 \\ \hline
**Europe** & 40.1209 & 9.0129 & 10 & 300 & 1.3889 & 712 & 21,360 & 98.8889 \\ \hline
**North America** & 44 & \(-120.3\) & 14 & 420 & 1.9444 & 699 & 20,970 & 97.0833 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: System-wide coverage parameters with respect to a scenario duration of 6 h, i.e., 21,600 s.  
Figure 14: Australia satellite access duration with reconfiguration and its orbit. the gimbals to be adjusted so that they can track the AOI independently, and it makes it possible for heterogeneous sensors to provide useful data at a diverse array of wavelengths.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 3 Method
### Joint Selection  
In the selection of human body joints, 15 joints are used in the proposed method. Although Kinect 2.0 can obtain the coordinates of 25 joints, not all joints have to be used for recognizing human actions. As mentioned in [23; 24], human actions are mainly accomplished by the cooperation of the limbs and trunk, which means that some joints are informative for recognizing human actions, whereas the rest are redundant. For example, except for some subtle actions such as sign language or hand poses, the motion information of hand joints is redundant. Moreover, those redundant joints may cause noise and lead to low recognition accuracy.  
Therefore, for the purpose of eliminating redundant information and ensuring the accuracy of classification, the 15 joints labelled in Figure 2 are adopted in the proposed method.  
Figure 1: The overall flowchart of our method for action recognition. (**a**) Extract 15 joints for each original skeleton sequence, and a data augmentation strategy is proposed to enlarge training samples and prevent overfitting. (**b**) Calculate three relative features to represent human actions and then encode these features into a color image. (**c**) Input the generated motion images into a shallow CNN model for HAR.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 5 Simulation Results
### Energy Efficiency Analysis  
In order to calculate the EE of the proposed scheme, if the underwater node requires to transmit data over the underwater channel with a data rate of 10 bpcu. Unlike the conventional M-ary signal constellation which achieves the rate by modulation order of 1024, the SMTs convey the information bits in the signal constellation, antenna index, and index of spreading codes. Hence, the SMTs physically transmit only the M-ary signal constellation. By this concept, as discussed in Section 4, we can calculate the energy saving achieved by mapping part of the transmitted bits into antennas index or spreading codes. The energy-saving percentage of SM, GSM, QSM, FGSM, EFGSM, GCIM, and SS-FGSM methods are given by (30)-(36), respectively. Figure 9, shows the energy-saving percentage at different number of the antenna transmit \(N_{t}\) and different spreading code \(\mathcal{N}\). When \(N_{t}=\mathcal{N}=2\)  
Figure 8: The performance of ABER of proposed SS-FGSM and conventional SMTs at 12 bpcu (measured).  
Figure 7: The performance of ABER of proposed SS-FGSM and conventional SMTs at 10 bpcu (measured).  
the proposed DLSS-FGSM scheme achieves energy saving with a percentage of 30% compared to 10%, 10%, 20%, 10%, 20%, and 20% for SM, GSM, QSM, FGSM, EFGSM, and GCIM, respectively. In case of \(\text{N}_{t}=\mathcal{N}=4\), the proposed SS-FGSM can achieve energy saving with a percentage of 70% compare to 20%, 20%, 40%, 30%, 50%, and 40% for SM, GSM, QSM, FGSM, EFGSM, and GCIM, respectively. In the case of \(N_{t}=\mathcal{N}=8\), the proposed SS-FGSM can achieve energy saving with a percentage of 130% means that 13 bpcu compared to 30%, 40%, 60%, 70%, 60%, and 60% for SM, GSM, QSM, FGSM, EFGSM, and GCIM, respectively. This high performance of the proposed scheme in EE is due to a low number of bits transmit physically, where it uses two axes of the spatial domain to convey additional information bits while other SMTs use only one axis for index information.
# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
## 3 Results and Discussion
### Accuracy Analysis of the Selected Model
For example, in Reference [47], ETo was estimated using ANN and climatic data (solar radiation, maximum and minimum temperature, maximum and minimum relative humidity, and wind speed) obtaining a weighted standard error of 0.3 mm/day. The Penman-Monteith method was considered as the ground truth, finding that the error of a lysimeter-based method was 0.74 mm/day. Although they used a different accuracy metric, the obtained value was far from the 0.0899 mm/day MAE error achieved with our proposal. Another technique using minimal climatic data (maximum and minimum air temperatures, extraterrestrial radiation, and daylight hours) and ANN was presented in Reference [48], reporting a mean squared error in the daily estimation of 0.356 (mm/day)\({}^{2}\), which corresponds to an RMSE of 0.597 mm/day. Figure 8: Projection of the samples in dataset (iii) into the subspace of the two first principal components, PC1 and PC2. The size of the points is proportional to the corresponding ETo values. Figure 7: Error histogram of the RFC + K\({}^{*}\) regression model in the estimation of ETo. Moreover, [PERSON] et al. [49] explored several methods of computing ETo using satellite imagery, ground measurements, and meteorological data, observing values of relative root-mean-squared error in the range 10%-30%; again, this is worse than our obtained MRE of 6.5%. Nevertheless, we consider that more experiments are necessary to verify the excellent results achieved by the proposed method. For example, since the data acquisition was only done in a single configuration, it would be advisable to perform more experiments in different environmental settings, with different climates, crops, ground types, etc. These factors could translate into significant evapotranspiration changes, and this could affect the effectiveness of the generated models. However, even in this case, the same methodology proposed in this article could be followed to obtain and prepare the data, as well as to generate and compare the models. Finally, it is interesting to observe that the presented model does not consider any prior expert knowledge about the evapotranspiration computation, and it only uses generic machine learning algorithms. Therefore, the application of the proposed methodology to a different environment would be straightforward. It is likely that the results could be further improved in the future upon introducing changes in the model to take into account this expert knowledge.