esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).  
deep learning; imbalanced data; hybrid-sampling; balanced focus loss function +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
It is rarely a simple task, in a given field of study, to find the sequence of processes needed to enhance and segment images. In our case, it has been particularly complex. We consider that the main novelty and contributions of this work are:  
1. The identification and location of the ripe tomatoes and their peduncles;
2. The computing time we achieved for the processing (identification and location) of an image was of the order of milliseconds, while in other works [18; 24; 27], it was of the order of seconds;
3. The use of flash to acquire the images minimized the illumination variations effects;
4. Another very important contribution of this vision system was that it can be used for any tomato-harvesting robot, without having to develop a new vision system for each end-effector prototype, because it locates the needed tomato parts for the different types of harvesting: cutting or embracing/ absorbing.  
Furthermore, as we noted in this paper, this is only a first, yet important step, leading to other tasks that will complete the harvesting automation process (calculating the z-position and cutting or suctioning the tomatoes, improving the detection of tomatoes in poor lighting, etc.).  
Consequently, the objectives proposed in this work were successfully achieved although there are numerous lines of research that could be followed in the future, both to improve the performance of the system that is already implemented and to expand its computer-vision functionality and versatility in detecting commercial fruit, or sorting the tomatoes by quality criteria such as color or size, or using other algorithms (for example, applying CNNs for tomato detection). In addition, the design of the robotic part of the project and the integration of the robot and computer-vision subsystems (the z-coordinate calculation, developing the cut-end effector, exploring pressure systems and picking tomatoes) should be studied.  
**Author Contributions:** M.B.: state of the art of the vision and design and implementation of vision algorithms; M.C.-G.: state of the art of the vision part, design of the vision algorithms, the interpretation of results and the coordination of research work; J.A.S.-M.: image taking, tomato variety selection, agronomic advice and experiment validation; F.R.: state of the art of the robotics part, design of the vision algorithms, interpretation and validation of results. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been developed within the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No. 731884--Project IoF2020-Internet of Food and Farm 2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Monitoring the Capillary Jet Breakage by Vibration Using a Fast-Video Camera
## 2 Materials and Methods
### Droplet Formation Visualization  
The droplet formation was observed using a high-speed camera (HighSpeedStar 6, Lavision, Paris, France), and images were recorded at 5000 to 7000 Hz. The images were analyzed using ImageJ software (National Institutes of Health, Bethesda, MD, USA). The camera was connected to PC Thinkpad X200, Lenovo running on windows XP. The video was recorded with the software Photron FASTCAM Viewer ver. 3.1.8 (Photron, Tokyo, Japan).  
An example of capillary jet breakage recorded with the high-speed camera is displayed in Figure 2. The mean size and size dispersion were obtained by measuring 50 droplets and computing the mean value and the standard deviation. Experiments were repeated a minimum of three times to provide the final value. The length before breakage was defined as the distance between the nozzle before the first droplet had been formed. Droplet velocity was obtained by following one droplet over the image and dividing the droplet traveled distance by the time.
## 3 Results and Discussion
### Cooking Quality of Black Rice  
As shown in Table 1, the gelatinization time (optimal cooking time) gradually decreased with increasing milling degrees, indicating that the destruction of the bran layer shortened the cooking time. This result was consistent with the decreasing trend of gelatinization peak temperature of Differential Scanning Calorimetry (DSC) in a previous publication [10]. In addition, the value and trend of water uptake ratios and expanded volume were similar. The water uptake ratio of whole black rice (A) was significantly lower than those of refined grains (E, F). It was found that fat and wax in bran layers hindered the water uptake into the kernels for a given cooking duration since the hydrophobic waxy cuticle at the outer surface of the whole grain could offer a physical barrier [31; 32; 3]. The insufficient water absorption led to incomplete gelatinization and swelling of the starch in the central part of the grain, resulting in cooked whole black rice with a hard texture. Therefore, the slow gelatinization rate of the starch in whole black rice could be due to the limited water transfer at the kernel surface. The expanded volume and starch-iodine blue value per solid substance weight showed no discernible difference among samples of the same cultivar with different milling degrees.  
Figure 1: Appearance of cooked black rice samples with different milling degrees (A: 0%, B: 0.57%, C: 1.27%, D: 4.16%, E: 6.97%, F: 10.19%) **(a)**; Comparison of uncooked and cooked rice **(b)**; Scanning electron microscopy (SEM) pictures (A(1–4)–F(1–4)) of cooked black rice sample. Surface section: A1–F1 (scale bar: 500 \(\upmu\)m) and A2–F2 (scale bar: 20 \(\upmu\)m), transverse section: A3–F3 (scale bar: 500 \(\upmu\)m) and A4–F4 (scale bar: 20 \(\upmu\)m) **(c)**.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Gelatinization Time (min)** & **Water Uptake Ratios (\%)** & **Expanded Volume (\%)** & **Starch–Iodine Blue Value/Solid Substance Weight** \\ \hline A & 36.21 \(\pm\) 2.15 \({}^{\text{a}}\) & 188.29 \(\pm\) 4.77 \({}^{\text{b}}\) & 187.88 \(\pm\) 5.25 \({}^{\text{b}}\) & 0.81 \(\pm\) 0.26 \({}^{\text{a}}\) \\ B & 36.85 \(\pm\) 1.20 \({}^{\text{a}}\) & 193.53 \(\pm\) 1.79 \({}^{\text{a}}\) & 193.94 \(\pm\) 5.25 \({}^{\text{ab}}\) & 0.72 \(\pm\) 0.06 \({}^{\text{a}}\) \\ C & 32.78 \(\pm\) 1.46 \({}^{\text{b}}\) & 194.57 \(\pm\) 1.62 \({}^{\text{a}}\) & 196.97 \(\pm\) 5.25 \({}^{\text{a}}\) & 0.84 \(\pm\) 0.13 \({}^{\text{a}}\) \\ D & 26.15 \(\pm\) 0.56 \({}^{\text{c}}\) & 192.52 \(\pm\) 3.59 \({}^{\text{ab}}\) & 193.94 \(\pm\) 5.25 \({}^{\text{ab}}\) & 0.66 \(\pm\) 0.07 \({}^{\text{a}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cooking characteristics of black rice with different milling degrees (A–F).
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 4 Results of the Positioning Analyses
### Differential Time Offset  
A receiver-dependent bias is clearly visible in Figure 8, as previously noted by [17; 20; 21]. In order to better investigate this receiver dependent bias, the time series relative to the mean values of Septentrio stations have been differentiated. The choice of Septentrio is due to the observation that these stations show very similar values: for each day the RMS is generally lower than 3 ns. Figure 9 shows the time series of GLGP, GPGA and BDGP obtained for individual Septentrio stations. The QZGP time series is not shown since there is only one station among the six Septentrio stations which tracks this constellation.  
Figure 9: Time series since 1 January 2014 of offsets of the GLONASS (**a**), Galileo (**b**) and BeiDou (**c**) time scales of Septentrio stations relative to GPS.  
The graph of GPGA in Figure 10 starts from 9 October 2015 due to the high scatter observed before this date. From these graphs the receiver dependent bias can be monitored. It is worth noting that for a given receiver type, some stations show similar time offsets, so their time series are quite superimposed, whereas other stations show different values and this difference seems to be constant in time. For example in Figure 10a it is possible to notice that the Trimble and Javad receivers have very similar time offsets, whereas Leica and Topcon show a relative bias up to tens of nanoseconds.  
In Figure 10a a discontinuity of several Leica stations from about 40 ns to about \(-25\) ns in the first half of 2016 is evident. These stations are: CAEN, MOSE, MLVL, PADO, REYK (Table 1). Taking into account the station logsheets it was possible to identify an exact connection between some of the discontinuities in the time offset series and receivers' firmware updates. For example, considering the five Leica stations mentioned above, the correspondence between the discontinuity in the GLGP time offset and the update of receivers firmware for MOSE, PADO and REYK could be observed.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 3 Finite Element Model
### Material Properties  
Temperature changes also influence the properties of metallic materials--the phenomenon of thermal-induced softening. Therefore, the elastic-plastic behavior of the material and the thermal-induced softening must be considered. In the elastic deformation phase, the thermal-induced softening properties are considered to be related to Young's modulus and Poisson's ratio. For metallic materials, without considering the effect of temperature on Poisson's ratio, Young's modulus E(T) versus temperature T can be expressed as [19]  
\[\text{E(T)}=\text{E}_{\text{s}}(\text{T}-\text{T}_{0})+\text{E}_{0} \tag{11}\]  
where E\({}_{0}\) is the Young's modulus at the moment of temperature T\({}_{0}\) and E\({}_{\text{s}}\) is the rate of change of [PERSON]'s modulus with temperature change.  
Similarly, in the plastic deformation phase, the thermal-induced softening properties are related to the yield limit of the material. The yield limit \(\sigma_{y}(\text{T})\) of a metallic material at temperature T is [19]  
\[\sigma_{y}(\text{T})=\sigma_{\text{ys}}(\text{T}-\text{T}_{0})+\sigma_{y0} \tag{12}\]  
where \(\sigma_{y0}\) is the yield limit at the temperature T\({}_{0}\) and \(\sigma_{\text{ys}}\) is the rate of change of the yield limit with temperature change.  
The material parameters for the Ti-6 Al-4V alloy used for the cylindrical and flat specimens are listed in Table 1. The physical quantities of Ti-6 Al-4V, such as thermal conductivity, specific heat capacity, and density with temperature, are shown in Table 2.  
Figure 3: Finite element model of cylinder-on-flat contact.
# Evaluation of the User Adaptation in a BCI Game Environment +
## 3 Materials and Methods
### Materials
#### 3.1.3 Lab Streaming Layer  
LSL [24] is an open-source system used to stream, receive, synchronize, and record time series data streams acquired from numerous acquisition devices available in the network. The LSL protocol provides secure data transmission due to the inheritance of the TCP protocol. It reduces complexity over cross-platform application connectivity and minimizes data loss. For this work, BlueMuse connects with the Muse 2 headband via Bluetooth and automatically creates an LSL stream for transmitting the raw EEG data.  
Figure 2: Muse 2 headband with the corresponding electrodes.  
The LSL stream is connected to OpenViBE's acquisition server in order to record and then process and classify the brain signals.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 5 Error Analysis and Discussion
### Rotating Disk System Error
#### 5.3.1 Roll Angle \(\alpha_{y}\)
\[V_{\rm m2}=S^{\prime}(t)=A_{\rm m}\omega\cos(\omega t) \tag{23}\]  
Combining the velocity components \(V_{\rm m1}\) and \(V_{\rm m2}\), the actual measured velocity \(V_{\rm my}\) of the LDV can be obtained as shown in Equation (24). \[V_{\rm my}=V_{\rm m1}+V_{\rm m2} \tag{24}\]  
Bring Equations (19)-(23) into (24), and calculate them to get  
\[V_{\rm my}=\omega r\bigg{(}\frac{\sin(\beta)}{\cos((\theta+\varphi)\sin(\omega t ))}+\frac{(\tan(\theta+2\varphi)-\tan(\theta))\cos(\omega t)}{2\cos(\beta)} \bigg{)} \tag{25}\]  
This equation shows that the actual measured speed \(V_{\rm my}\) of LDV is time-varying. Considering the averaging process for subsequent signals, the periodic signal terms related to time \(t\) in this equation, such as \(\sin(\omega t)\) and \(\cos(\omega t)\), have approximately zero influence on calculating the incident angle \(\beta\). That is, the average value \(\overline{V_{\rm my}}\) of the actual measured speed of LDV is consistent with the expected value \(V_{\rm m}\), which can be expressed by Equation (26). The following uses a particular case to illustrate the feasibility of simplifying Equation (25) into (26). \[\overline{V_{\rm my}}=\omega\cdot r\cdot\sin(\beta) \tag{26}\]  
Figure 14 is a simulation case based on Equation (25). The values of multiple parameters are as follows: \(\omega=400\) rad/s, \(r=47.50\) mm, \(\theta=0.50^{\circ}\), \(\varphi=0.25^{\circ}\), and \(\beta=\beta_{\rm CAL}=1.9120^{\circ}\)  
The waveforms of the two velocity components \(V_{\rm m1}\) and \(V_{\rm m2}\) changing with time \(t\) are shown by the solid red and the dotted blue lines in the figure, respectively. The figure shows that both curves have prominent periodic characteristics. The mean value \(\overline{V_{\rm my}}\) of coupling velocity \(V_{\rm my}\) equals 633.9679 mm/s. Based on Equation (26), the angle \(\beta_{\rm my}\) under this condition is 1.9121\({}^{\circ}\). In this simulated case, the difference between the measured value \(\beta_{\rm my}\) and the expected value \(\beta_{\rm CAL}\) is 0.0001\({}^{\circ}\). Such results show that the roll angle \(\alpha_{y}\) has little effect on the dynamic measuring system. In a physical sense, although angles \(\theta_{y}\) and \(\varphi_{y}\) will cause the rotating disk to appear at a roll angle \(\alpha_{y}\) and cause the rotating disk to vibrate along the direction of the laser beam, they will not affect the dynamic measurement of the laser beam angle. However, in an engineering sense, vibrations should be avoided as much as possible to prevent the adverse effects of unexpected motion on the entire measuring system.
## 2 Related Work
### Panda Public Auditing (PPA)  
PPA [9] is introduced by the authors as assuring public auditing for secure data storage in the cloud through a third-party auditor. They propose an auditing protocol that uses cipher cryptography while communicating with the TPA. This protocol is focused on data storage and its integrity.  
In this scheme, the third-party auditor executes the edits without needing copies of the CC's cloud data. The auditing process is based on four algorithms comparable to the ones used in [14].  
* \"KeyGen,\" allows generating keys for the TPA and the CC.
* \"SigGen,\" will be run by the TPA for metadata verification.
* \"GenProof,\" the CSP uses this protocol to check on how the data is stored.
* \"VerifyProof\" used to test the confirmation given by the CSP and verify it.  
In their simulation experiments, the authors have measured the performance of the user revocation versus the number of re-signed blocks with or without verification. They are using a computer with 4 GB of RAM, Intel processor i5 (2.5 GHz). They have presented their findings showing better results when using their method. They have also measured the auditing time (ms) versus the number of existing users (0 to 10, increasing by two users) and showed a linear increase. Their last simulation was on measuring the communication cost (KB) versus the number of existing users (0 to 10, increasing by two users) and showed a slight increase.  
In this approach, the third-party auditor completes the auditing without having a copy of the CC's data. The concept of the intermediary server is used by the TPA to send the data. As a result, there is a possibility of hidden server failures that could affect the overall throughput performance.
## 5 Discussion
### Performance and Comparison of Selected Models for Landslide Susceptibility Assessment
The models used correspond to the following: ann, artificial neural networks; lg, logistic regression; rf, random forest; svml, support vector machine with linear model; svmr, support vector machine with radial base model. ANN that uses the an average usually converges better in assigning weights for co-seismic landslide identification. In the case of SVM models, the best-performing models correspond to the radial basis function kernel (RBF) as opposed to the linear kernel. The former achieved AUC values of 90.90% compared to the latter, which reached an AUC of 84.8%. Comparing the accuracy results with those reported in [26], similar values are observed, but the results in this study are higher. This may be because the present data has a higher degree of nonlinearity. Similarly, it is agreed that the best kernel function for modeling landslide susceptibility data corresponds to the radial function (RBF). When comparing the results with those of [25], there is agreement on the need for good data preparation to avoid biases in predictive models. Finally, if we compare the results obtained using LG models with those provided in [19], both studies show good results in terms of the ROC curve. The value obtained by these authors was quite high, which could reflect a possible overfitting of the model to the data. In contrast, the results obtained in [20] are more similar to those obtained in this study in terms of the ROC curve. Both authors agree that the most relevant variables for this algorithm are terrain slope, slope orientation, lithology, and PGA.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 5 Computational Setup
### Simulation Cases  
Simulations are performed for two different wind turbines of the _NREL Phase VI_[34] and the _NREL 5 MW_[35] that represent a small and a large rotor size, respectively. Additionally, the _NREL Phase VI_ rotor has a very blunt blade tip shape as compared with the _NREL 5 MW_ rotor.  
The operational conditions of the two rotors are listed in Table 1. Cases 1, 2 and 3 are set to be consistent with the _NREL UAE_ experiment in axial inflow conditions [34]. Various wind speeds of 7 m/s, 10 m/s and 13 m/s are considered, while the rotating speed remains unchanged. The flow is fully attached on the blade surface at a wind speed of 7 m/s, but is partly separated at 10 m/s and 13 m/s (Higher wind speed leads to heavier flow separation) [8]. Measured pressure distributions (from which  
Figure 3: Mesh configuration for the Actuator Disc/Navier-Stokes (AD/NS) simulations.  
the force can be obtained by pressure integral) at five blade sections (\(r/R=0.30\), 0.47, 0.63, 0.80, 0.95) are available for these cases [50]. Cases 4 and 5 are two typical points on the designed power curve of the _NREL 5 MW_ wind turbine [35]. The wind speed and rotating speed of Case 5 are the rated parameters of this wind turbine. Case 4 represents a condition with a lower wind speed of 8 m/s at which the rotating speed is reduced to 9.22 rpm for tracking the optimum tip speed ratio.  
These cases cover the following multiple situations: Wind turbines with remarkably different rotor sizes and tip shapes; flow with fully attached and separated conditions; operating conditions with various wind speeds and rotating speeds. It is, therefore, interesting to see the performance of the tip loss corrections in these cases.  
Experimental data are preferred as the reference for the computational results of the _NREL Phase VI_ rotor, as well as the full CFD data are also displayed in some results. For the cases of the _NREL 5 MW_ rotor, there is no experimental data available such that the full CFD data are the only reference. The related full CFD simulations were previously conducted by the authors, see [PERSON] et al. [8; 33].
## 3 Methodology
### Kernel Principal Component Analysis  
The principal components of input data X can be obtained by solving the eigenvalue problem of the covariance matrix. With a sample of n observations, \(\mathrm{x_{i}}\) = \(\mathrm{x_{1}}\), \(\mathrm{x_{2}}\)...... \(\mathrm{x_{n}}\), \(\mathrm{x_{i}}\)\(\epsilon\)\(R^{N}\). Given that PCA operates on centred data, that is \(\sum_{i=1}^{N}\mathrm{x_{i}}=0\), PCA diagonalizes the covariance matrix C as:  
\[C=\frac{1}{N}\sum_{i=1}^{N}\mathrm{x_{i}}\mathrm{x_{i}^{T}} \tag{1}\]  
The principal components are then obtained by solving eigenvalue problem:  
\[Cv=\lambda v \tag{2}\]  
In cases where n cannot be linearly separable, PCA is performed on a dot product space F (feature space) instead of the input space R\({}^{N}\) by mapping from R\({}^{N}\) to F. This can be illustrated as \(\phi\): R\({}^{N}\)\(\rightarrow\) F where \(\phi\) is the feature mapping of the input data to a high dimensional mapping. Then, \(\sum_{i=1}^{N}\mathrm{\phi(x_{i})}=0\) PCA; the covariance matrix C in feature space F can be written as:  
\[C=\frac{1}{N}\sum_{i=1}^{N}\mathrm{\phi(x_{i})}\phi(x_{i})^{T} \tag{3}\]The principal components are then obtained by solving the eigenvalue problem in Equation (2) above, where eigenvector \(\mathrm{v}\) is spanned in F as:  
\[v=\sum_{i=1}^{N}\alpha_{i}\ \phi(x_{i}) \tag{4}\]  
Using the inner product between the two points, we can create the kernel:  
\[K(x_{i},x_{i})=\phi(x_{i})^{T}\ \phi(x_{i}) \tag{5}\]  
where \(K_{ij}=K(x_{i},x_{j})\) and \(\alpha=(\alpha_{1}\ \dots\.\ \alpha_{N})^{T}\). For the kernel component extraction, we compute the projection of each data sample \(\mathrm{x}\) onto eigenvector \(\mathrm{V}\):  
\[(\phi(x),V)=\sum_{i=1}^{N}\alpha_{i}\ K(x_{i},\ x) \tag{6}\]  
For any eigenvalue of C, \(\lambda\geq 0\), the formulation of the eigenvalue problem for kernel matrix \(\mathrm{k}\) can thus be defined as:  
\[N\lambda\alpha=k\alpha \tag{7}\]