esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Performance across Vegetation Types  
Overall, across all the vegetation types, there was a consistent model improvement with the addition of multiple off-nadir viewing angles in Scenario 4 compared to Scenario 1 (Figure 8). Nadir looking models in Scenario 1 produced consistently higher estimation errors and lower correlation for all the vegetation types individually. The benefits of the off-nadir viewing angles were higher for the savanna and indigenous forest models, where the estimation errors were consistently reduced by 25-35% for both parameters. The H\({}_{\text{mean}}\) for savanna and CC for indigenous forest vegetation yielded the lowest estimation errors with rRMSE of 31.28% and 38.19%, respectively, and with the highest correlation R\({}^{2}\) of 0.67 and 0.57, respectively. The poorest performances were consistently obtained for the forest plantation models for both scenarios (e.g., highest rRMSE = 79% for Scenario 1) (Figure 8b).  
Figure 6: (a) Relative root mean square error for MISR-HR BRF models for the mean tree height (H\({}_{mean}\)) and canopy cover (CC) for Scenario 0 (Nadir 2 bands, red and NIR), Scenario 1 (Nadir 4 bands, blue, green, red, and NIR) and Scenario 4 (all 36 data channels, all view angles and spectral bands). (b) Density scatter plots of the validation models of observed versus predicted for all forest types (Lowveld savanna and SImangaliso Saint-Lucia forest plantations and indigenous forests), Scenario 4, for H\({}_{mean}\) and (c) for CC. The solid blue line is the regression line and the dashed red line indicates the 1:1 relationship.  
Figure 7: (**a**) Relative root mean square error and (**b**) coefficient of determination for Scenario 5 (MISR-HR RPV model parameters) for RPV model parameters \(\rho_{0}\) = Rho parameter, \(k\)= the Minnaert function, \(\Theta\) = Theta parameter and All (referring to the three RPV parameters for all 4 spectral bands combined) for mean tree height (\(\Omega_{\text{mean}}\)) and canopy cover (CC) structural parameters.  
Figure 8: (a) Relative root mean square error (rRMSE) and (b) coefficient of determination (R\({}^{2}\)) of Scenario 1 (Nadir 4 bands, blue, green, red and NIR) and Scenario 4 (all 36 data channels, all view angles and spectral bands) of the estimation of mean tree height (H\({}_{\text{mean}}\)) and canopy cover (CC) across the three forest types (savanna, plantations and indigenous forests).
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Measurement Equipment  
To measure the power consumption of the wireless ECG sensor, we used the measurement circuit presented in Figure 2. The power consumption is measured indirectly by multiplying the measured current and battery voltage. The current is also measured indirectly by measuring the voltage on a shunt resistor with resistance of \(R_{m}=10\,\Omega\)For the analysis of current consumption during individual detectable events, a digital sampling oscilloscope (DSO) Rigol DS1054 is used. With the DSO, we recorded a current consumption envelope with high resolution, which enabled the separation of fine details in the current consumption needed to define the analytical power consumption models. We measured the difference between the start of an event and the end of the event on the current profile in order to assess the duration parameter of the event. The DSO resolution was very high--50 million samples per second, which is equal to a time resolution of 20 nanoseconds.  
For average power consumption analysis, we used the HP3478A benchtop digital multimeter (DMM) with Resistor-Capacitor (RC) low-pass filter (resistance \(R_{f}=10\,\mathrm{k}\Omega\) and capacitance \(C_{f}=1000\,\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro F}\)). The RC filter improves the averaging by suppressing high-frequency components in the measurement signal to prevent aliasing from the limited DMM sampling frequency. According to the Nyquist-Shannon theorem, the sampling frequency should be at least twice as high than the highest frequency component of the measured signal in order to guarantee perfect reconstruction of the sampled (measured) signal. Therefore, perfect signal reconstruction is possible for a signal with a bandwidth \(B\) lower than half of the sampling frequency \(f_{s}\) (\(B<f_{s}/2\)).  
Additionally, to obtain measurements for different TX power settings and sampling rates, a Linux-based single-board computer (SBC) with BLE capability was used. The SBC acts as a central device and exposes the options to set a desired connection interval as well as a desired sampling rate.
# Unit Cell Optimization of Groove Gap Waveguide for High Bandwidth Microwave Applications
## 4 Conclusions and Future Work  
In this paper, we demonstrated a new design of a unit cell and a gap waveguide termed MGGW for applications in the microwave spectrum. A performance comparison of the proposed unit cell design was carried out with a conventional widely used unit cell, that is, a bed of nails. Based on dispersion characteristics, the proposed unit cell achieves an 8% bandwidth enhancement when compared to a bed of nails while keeping a constant air gap between the top metal plate and the top surface of the unit cell. The MGGW was then designed for a straight structure and single right-angle 90\({}^{\circ}\) bend and double right-angle bend transitions, and it was compared with the GGW periodic structure for performance evaluation. The CST simulation results show that the proposed MGGW provides a wider bandwidth towards a lower cut-off frequency spectrum as compared to the GGW, which is always an important aspect for practical design consideration. The fabrication of the MGGW was carried out using CNC milling operation, and the prototype has provided a reflection coefficient of less than \(-\)20 dB for the entire band of interest. The proposed design is suitable for developing ultra-wide bandwidth microwave applications, i.e., filters, transmission lines, resonators, attenuators, etc. The proposed unit cell can be explored for usage in mm-wave spectrum designs, particularly for the sub-bands of 28 GHz and 60 GHz, and further applications, where the fabrication of complex structures is challenging and expensive as well.  
Conceptualization A.H.; methodology and simulations, G.T.; simulation and optimization, G.T. and A.H.; formal analysis, A.H. and S.A.; resources, A.B.; fabrication and analysis of results, G.T., A.H. and S.A.; first draft of manuscript preparation, G.T.; review and editing, A.H. and S.A.; supervision, A.H. and S.A.; project administration, A.B.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the Qatar National Research Fund (a member of Qatar Foundation) under grant number NPRP13S-0212-200345 and ARG01-0522-230274.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors acknowledge the support provided by the College of Science and Engineering, Hamad Bin Khalifa University.  
The authors declare no conflicts of interest.
## 2 Sample Preparation and Testing Steps  
The granite used in the experiment was taken from Jining, Shandong Province, and had a gray-white color, was relatively fresh, and had a low degree of weathering. First, the mineral composition and microstructure of the samples were analyzed under normal temperature conditions to gain a preliminary understanding of the basic physical properties of the granite. An X-ray diffraction (XRD) analysis revealed that the main mineral components of the granite were quartz (24.4%), plagioclase (39.8%), potassium feldspar (30.6%), and biotite (5.2%).  
To reduce errors and external influencing factors in the experiment, all the rock samples used in this experiment were taken from the same granite block. According to the International Society for Rock Mechanics Test Specification (ISRM 2007), all the samples were \(\varphi\) A 50 \(\times\) 100 mm cylinders. To study the anisotropy of the physical and mechanical properties of granite, samples were taken along the three orthogonal directions of granite, according to the sampling scheme in Figure 3. Seventy-two samples were processed in each direction, totaling 216 granite samples. According to the wave speed test results, the three directions are named in a descending order of the wave speed: the Z direction, X direction, and Y direction.  
The heating temperature (T) was set at a temperature gradient of 200 \({}^{\circ}\)C and was set at 25 \({}^{\circ}\)C (room temperature), 200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 800 \({}^{\circ}\)C, and 1000 \({}^{\circ}\)C. Uniaxial and triaxial compression tests with confining pressures of 5 MPa, 10 MPa, and 15 MPa were carried out on rock samples heated at different temperatures.  
The instruments and equipment used in this experiment included electronic balances (with an accuracy of 0.001 g), vernier calipers (with an accuracy of 0.02 mm), a polarizing microscope (OLYMPUS BX53 comes from Beijing, China), an ultrasonic detector (Pundit-PL200 PE, 250 kHz comes from Beijing, China), a high-temperature furnace (SG-XL1200 comes from Beijing, China, with maximum temperature of 1200 \({}^{\circ}\)C and an accuracy of \(\pm 3\)\({}^{\circ}\)C), and a mechanical testing machine (HSW-1000B comes from Beijing, China).  
The experimental procedure was as follows: The rock samples were placed in a well-ventilated place at room temperature for 2 weeks to eliminate the influence of natural moisture content. As shown in Figure 4, the rock samples were placed into a high-temperature furnace according to temperature groups for heat treatment at a heating rate of 3 \({}^{\circ}\)C/min.  
Figure 3: Sampling method and granite samples.  
After heating to the target temperature, the mixture was maintained at a constant temperature for 2 h. After the power was turned off, the samples were cooled naturally in the furnace to room temperature. Microscopic observation, wave velocity testing, and uniaxial and triaxial compression tests were carried out on the rock samples after high-temperature treatment, and the morphology of damaged samples was photographed.
###### Abstract  
This study investigates the valorization of tomato by-products--peels, seeds, and juice--through innovative extraction and bioprocessing techniques. Lycopeneene recovery from tomato peels was optimized using ultrasound- and microwave-assisted extraction (UAE-MAE) with ethyl lactate as the solvent. The optimal conditions (0.03 g/mL, 500 W microwave power, 600 W ultrasound power) yielded a lycopene content of 37.08 mg/100 g of peels and an extraction yield (\(EY\)) of 91.20%. For tomato seeds, oil extraction methods, including conventional stirring, UAE-MAE, Soxhlet extraction, and pressurized liquid extraction (PLE), were evaluated. Conventional stirring achieved the highest oil yield (19.66%), followed closely by UAE-MAE (19.53%). However, PLE produced the highest lycopene content (44.0 mg/100 g oil) and significant levels of linoleic acid (544.7 mg/g oil), though Soxhlet extraction yielded slightly more (608.9 mg/g oil). Tomato juice was processed into high-nutritional value vinegar via a two-stage fermentation process. The final product had 5.42% acidity, a pH of 2.85, and retained a high lycopene content (9.19 mg/100 g). This study underscores the potential of innovative extraction and bioprocessing strategies for the valorization of tomato by-products, promoting waste reduction and the development of high-value functional products in alignment with principles of the circular bioeconomy.  
fatty acids; fermentation; lycopene; pressurized liquid extraction; tomato seed oil; ultrasound-assisted extraction-microwave-assisted extraction; vinegar +
Footnote †: journal: Applied Sciences  
Article
## 4 Path Model Estimation
### Measurement Model  
In order to formulate the measurement model, a measurement frame \(x_{i}\)-o-\(y_{i}\) was constructed in the field of view of the onboard camera, which is the image coordinate system for detecting the guide path, as shown in Figure 8. The image resolution is \(M\)\(\times\)\(N\). The installation orientation error of the camera is \(\varepsilon\). The position of the optical center of the camera is \(J(x_{c},y_{c})\), which is also the vision measurement center of AGV. \(\theta\) is the ideal angular deviation without considering the installation error \(\varepsilon\). _v(k)_ is the velocity of AGV. In this image coordinate system, the linear segment \(EI\) is a straight path. The distance between the optical center \(J\) and the straight path \(EI\) is defined as the lateral position deviation \(e_{d}\).  
If the straight path model is described by the equation \(y=\beta_{1}x+\beta_{0}\) in the image coordinate system, then the position deviation \(e_{d}\) is calculated by:  
\[e_{d}=\frac{\beta_{1}x_{c}+\beta_{0}-y_{c}}{\sqrt{1+{\beta_{1}}^{2}}} \tag{13}\]  
The position deviation can then be converted into the world coordinate system, i.e.:  
\[{e_{d}}^{{}^{\prime}}=\frac{\beta_{1}x_{c}+\beta_{0}-y_{c}}{A_{pix}\sqrt{1+{ \beta_{1}}^{2}}} \tag{14}\]  
where \(A_{pix}\) is the factor to map from image pixel to physical dimension. The factor can be obtained by means of calibrating camera parameters.  
In Figure 8, the actual orientation deviation \(e_{\theta}\) considering \(\varepsilon\) is the angle between \(EI\) and _v(k)_, which is:  
\[e_{\theta}=\arctan(\beta_{1})-\varepsilon \tag{15}\]  
Figure 8: Measurement model of a straight path.  
For the blue center pixels \(\mathrm{S_{C}}\) of the guide path, the mean squared error for parameter estimation is:  
\[\sigma\Big{(}\beta_{0},\beta_{1}\Big{)}=\sum_{i=1}^{n}\Big{(}y_{i}-\beta_{1}x_{i }-\beta_{0}\Big{)}^{2} \tag{16}\]  
How to choose the path parameters \(\beta_{0}\) and \(\beta_{1}\) in order to minimize the value of Equation (16) is essentially a nonlinear parameter estimation problem. It is solved by means of the following IPSO algorithm, which produces a parameter output of a straight path.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 2 Methodology
### Feature Learning  
The basis for this algorithm lies in feature learning. By recognizing common features in objects instead of specific objects, the proposed algorithm possesses the flexibility to deal with a larger variety of situations. Figure 4 shows examples of some of the data used for feature learning. Note that curved corners are also labelled as corners to account for the facts that many real-world objects are deliberately manufactured to have rounded corners for safety purposes (for example, cell phones). Unlike other object recognition algorithms whose training data are labelled with many different classes, the feature learning in this algorithm is labelled with only two classes: edges and corners. It is important to note that the bounding boxes are oriented. It is vital for the algorithm to determine the orientation of these features, as it is used in later steps of the algorithm to determine a grasp position. After the training is complete, the detector is able to identify edges and corners from an image of an object.  
In the proposed method, any oriented object detector based on deep learning can be used. Figure 5 shows the statistics of the training over 1000 epochs for 400 images using YOLOv5 OBB. The training loss decreases significantly over time and starts to plateau around 800 epochs. The precision and recall shows a high percentage of accurate predictions, with 98% correct predictions for both straight edges and corners on a clear background. These training results show the suitability of training the deep learning-based object detector to recognize these features and hence to develop a grasping algorithm from it.
## 3 Results and Discussion
### Single-Factor Analysis of Bond Strength
#### 3.3.2 Effect of Rebar Diameter on Bond Strength  
From Figure 7, it is clear that under the same conditions of concrete types and bonding lengths, the ultimate bond strength (\(\tau_{u}\)) decreased with an increase in the rebar diameter. The rates of decrease were the same for the different bonding lengths (60 mm, 80 mm, and 100 mm). When the diameter was 12-16 mm, the ultimate bond strength (\(\tau_{u}\)) decreased by 3.96-5.94% for every 2 mm increase in the rebar diameter. The rebar diameter had little effect on the overall bond strength.  
The reason for this may be that the bond strength between the ribbed rebar and the concrete is mainly provided by the mechanical occlusion of the concrete in front of the rib of the rebar. According to the Chinese Standard GB 1499.2-2018 [37], the relative rib areas of \(\varphi\)12, \(\varphi\)14, and \(\varphi\)16 mm HRB400 ribbed rebars are 0.055, 0.055, and 0.060, respectively. This shows that the relative rib area does not change significantly as the rebar diameter increases. As the projected area of the transverse rib increases, the area of concrete in front of the rib increases. However, the concrete in front of the rib includes a transverse rib projected area and a partially expanded area of the rib edge. Therefore, the growth of the  
Figure 5: Bond stress–slip curves of each group. (**a**) 14-RBFC. (**b**) 12-CCRC. (**c**) 16-BFC.  
Figure 6: Relationship between ultimate bond strength and bonding length.  
concrete area in front of the rib is less than that of the shaded area with the transverse rib. As a result, the rate of the increase in the bond force (\(F_{u}\)) is lower than that of the diameter (\(d\)). In other words, there is an increase in the bonding diameter as the bond stress (\(\tau\)) subsequently decreases.
## Experiments
### Score-Level Fusion of I-Vector and Latent Variable  
In this subsection, we present the experimental results obtained from a speaker recognition task where the decision was made by fusing the PLDA scores of i-vector features and VAE-based features. Given a set of independently computed PLDA scores \(S_{r}\), \(r=1,\cdots,R\), the fused score \(S_{fused}\) was computed by simply adding them as  
\[S_{fused}=\sum_{r=1}^{R}S_{r}. \tag{21}\]  
We compared the following scoring schemes:  
* _I-vector_: PLDA score obtained by using the standard 200-dimensional i-vector;
* _LM_: PLDA score obtained by using the 200-dimensional latent variable mean;
* _LV_: PLDA score obtained by using the 200-dimensional latent variable log-variance;
* _I-vector+LM_: fusion of the PLDA scores obtained by using the 200-dimensional i-vector and the 200-dimensional latent variable mean;
* _I-vector+LV_: fusion of the PLDA scores obtained by using the 200-dimensional i-vector and the 200-dimensional latent variable log-variance;
* _LM+LV_: fusion of the PLDA scores obtained by using the latent variable mean and log-variance;
* _I-vector+LM+LV_: fusion of the PLDA scores obtained by using the standard 200-dimensional i-vector and the 200-dimensional latent variable mean and log-variance.  
Table 3 and Figure 8 give the results obtained through these scoring schemes. As shown in the results, using the latent variable mean and log-variance vectors as standalone features yielded comparable performance to the conventional i-vector method (i.e., _LM_ and _LV_). Additionally, fusing the latent-variable-based scores with the score provided by the standard i-vector feature further improved the performance (i.e., _i-vector+LM_ and _i-vector+LV_). The best score-level fusion performance was obtained by fusing all the scores obtained by the standard i-vector and the latent variable mean  
Figure 7: DET curves of the speaker verification experiments using 600-dimensional i-vector and combined feature of 200-dimensional i-vector, latent variable mean, and the log-variance of the latent variable.  
and log-variance vector (i.e., _i-vector+LM+LV_), achieving a relative improvement of 25.89% in terms of EER compared to _i-vector_. However, the performance improvement produced by the score-level fusion methods was relatively smaller than the feature-level fusion methods presented in Table 2. This may have been because the score-level fusion methods compute the scores of the i-vector and the latent variable-based features independently, and as a result the final score cannot be considered an optimal way to utilize their joint information.
# Interface Growth and Void Formation in Sn/Cu and Sn0.7 Cu/Cu Systems
## 3 Results and Discussion
### Sn0.7 Cu/EP Cu Joints after Aging at 150 \({}^{\circ}\)C  
Figure 3a shows the interfacial microstructures of as-reflowed Sn0.7 Cu/EP Cu joints. Similar to the as-reflowed Sn/EP Cu joint, a thin layer of Cu\({}_{6}\)Sn\({}_{5}\) was formed at the interface, and no void was observed in the sample. After a 240 h aging treatment at 150 \({}^{\circ}\)C, as shown in Figure 3b, the IMC layer thickness increased obviously. A thin Cu\({}_{3}\)Sn layer emerged between Cu\({}_{6}\)Sn\({}_{5}\) and EP Cu. The thickness of the Cu\({}_{3}\)Sn layer was found to be sensitive to the Cu content. The addition of Cu could suppress the growth of the Cu\({}_{3}\)Sn layer. Also, several voids were found at the Cu\({}_{3}\)Sn/EP Cu interface in the Sn0.7 Cu/EP Cu joint. Figure 3c presents the interfacial microstructures of the solder joints aged at 150 \({}^{\circ}\)C for 480 h. It was observed that the IMC layer continued to grow. However, lesser voids and a thinner Cu\({}_{3}\)Sn layer were found at the Sn0.7 Cu/EP Cu interface than that of the Sn/EP Cu joint.  
Figure 3: Interfacial microstructure of Sn0.7 Cu/EP Cu joints after aging at 150 \({}^{\circ}\)C for (**a**) 0 h, (**b**) 240 h, and (**c**) 480 h.  
Figure 2: Interfacial microstructure of Sn/EP Cu joints after aging at 150 \({}^{\circ}\)C for (**a**) 0 h, (**b**) 240 h, and (**c**) 480 h.