esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Volume of Irregular Shape Pellets Estimation Using 2D Imaging Measurement
## 4 Discussion  
The received examination results showed that not only the shape of approximation has a great effect on the precision of evaluation, but also the position of the pellet in relation to the video camera. When a less characteristic image of a pellet is scanned, it does not provide sufficient valuable geometric information which could be interpreted correctly. After evaluating measurement results, it was noted that a similar level of discrepancy is found with both approximation of layers using circle and approximation using ellipse. The average absolute error when layers are evaluated as circles was 3.116 cm\({}^{3}\), while the relative error is up to 17.40%. When layers are evaluated using the ellipse shape average the absolute error is 2.954 cm\({}^{3}\), while the relative error is up to 17.20%. Therefore, the assumption  
Figure 16: Distribution of pellets according to volume.  
Figure 17: Distribution of pellets according to roundness.  
Figure 18: Average diameter of pellet from different samples (D\({}_{50}\)).  
is made that the area and volume of a pellet at a place of notch should be evaluated as a geometric shape of ellipse, because it provides more precise results of measurements. These results can later be used for management of other, more complex processes [41]. The proposed more accurate method of evaluating irregularly shaped pellets can be ensured by implementing a computer vision quality monitoring system in the fertilizer production line. Monitoring the results allows operators to adjust the line parameters that determine the shape of the particles. Ensuring the quality control of production processes determines the quality of the final product. High-quality fertilizer production influences better absorption of useful substances for plants, and more even fertilization.
## 2 Simulation Method
### Numerical Analysis Model  
According to a large number of measured water temperature data of different reservoirs, the isothermal surface is basically parallel to the horizontal plane [26]. To meet the needs of the program for the temperature control simulation of concrete dams, the model only considers the vertical change of water temperature at the front of the dam, so the three-dimensional water temperature problem can be simplified to a vertical 1D water temperature problem [27]. Figure 1 is a schematic diagram of the simulation model.  
Per unit time, the heat entering in the vertical direction is \(\frac{\partial}{\partial y}(c\rho q_{y}T)dy\); the heat entering in the horizontal direction is \((c\rho q_{i}T_{i}-c\rho q_{0}T)dy\); the solar radiation is \(\frac{\partial}{\partial y}\Big{[}(1-\beta)\varphi q_{0}e^{k(y-H)}A\Big{]}dy\); the effect of diffusion is \(\frac{\partial}{\partial y}\Big{(}Aac\rho\frac{\partial T}{\partial y}\Big{)}dy\); and heat absorbed by water temperature rise is \(-c\rho\frac{\partial T}{\partial t}Ady\). The processed 1D model was:  
\[\frac{\partial T}{\partial t}+\frac{1}{A}\frac{\partial}{\partial y}(q_{i}T)= \frac{1}{A}\frac{\partial}{\partial y}\Big{(}Aa\frac{\partial T}{\partial y} \Big{)}+\frac{q_{i}T_{i}-q_{0}T}{A}+\frac{1}{c\rho A}\frac{\partial}{\partial y }(RA) \tag{1}\]  
where \(c\) is the specific heat capacity of water, \(\rho\) is the density of water, \(T\) is the temperature, \(A\) is the area of the reservoir, \(a\) is the thermal conductivity of water, \(q_{i}\) is the inflow rate, \(q_{0}\) is the outflow rate, \(T_{i}\) is the temperature of inflow water, \(R\) is the solar radiation, \(\beta\) is the surface absorption coefficient, \(\varphi_{0}\) is the solar radiation on the water surface, and \(k\) is the latitude correction factor.
# Antenna on Chip (AoC) Design Using Metasurface and SIW Technologies for THz Wireless Applications
###### Abstract  
This paper presents the design of a high-performance 0.45-0.50 THz antenna on chip (AoC) for fabrication on a 100-micron GaAs substrate. The antenna is based on metasurface and substrate-integrated waveguide (SIW) technologies. It is constituted from seven stacked layers consisting of copper patch-silicon oxide-feedline-silicon oxide-aluminium-GaAs-copper ground. The top layer consists of a 2 \(\times\) 4 array of rectangular metallic patches with a row of subwavelength circular slots to transform the array into a metasurface. This essentially enlarges the effective aperture area of the antenna. The antenna is excited using a coplanar waveguide feedline that is sandwiched between the two silicon oxide layers below the patch layer. The proposed antenna structure reduces substrate loss and surface waves. The AoC has dimensions of 0.8 \(\times\) 0.8 \(\times\) 0.13 mm\({}^{3}\). The results show that the proposed structure greatly enhances the antenna's gain and radiation efficiency, and this is achieved without compromising its physical size. The antenna exhibits an average gain and efficiency of 6.5 dBi and 65%, respectively, which makes it a promising candidate for emerging terahertz applications.  
Antenna on chip (AoC); metasurface; terahertz (THz); substrate integrated waveguide (SIW); gallium arsenide (GaAs) 2021  
19 March 2021  
1
## 2 Methods
### Building Heights  
A Digital Surface Model (DSM) was derived from the tri-stereoscopic Pleiades data using the approach described in [17]. As dense matching requires a relative orientation at sub-pixel level, bundle block adjustment was used estimate RPC (rational polynomial coefficient) bias corrections for each Pleiades image. Pairwise dense image matching was performed using Semi-Global Matching (SGM), producing a point cloud for each of the three stereo pairs. The point clouds were reprojected into a digital surface model with a grid spacing of 0.5 m and merged using pixel-wise median. After removing small, inconsistent regions, remaining no-data areas caused by occlusions and dark shadows were filled by propagating the ground height. A digital terrain model (DTM) was generated from the DSM using an adapted version of the method reported in [18], which analyzes height steps and slopes along multi-directional trajectories at each DSM pixel. Holes in the DTM were closed using interpolation, and a normalized DSM (nDSM) was generated for obtaining relative heights (DSM minus DTM).  
It was straightforward to determine building heights for objects in flat areas (such as the CBD). For other areas, Kigali's concave and convex slopes, as well as high building density in some areas, presented a challenging setting for stereoscopic analyses and resulted in positive and negative variances. A building class-dependent approach was chosen to assign building heights to the respective footprints of 2015. The mean value of the nDSM of each object was derived, which had lower deviations than the median or maximum value. In case the value exceeded the minimum or maximum threshold of the reference data (see Table 1), the height of the building was assigned to the respective threshold.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Change Code** & **Type of Change** \\ \hline
1 & No change \\ \hline
2 & Newly built (on a previously unbuilt area) \\ \hline
3 & Upgrading/ renovation (roof, structure) of an existing building or newly built building \\  & without a significant change of the building class or footprint area \\ \hline
4 & Upgrading/ renovation (roof, structure) of an existing building or newly built building \\  & with a change of the building class. No change in the number of buildings and the footprint area \\ \hline
5 & Building replaced by one or more newly built buildings with a change of the building \\  & class OR a significant change of the footprint area \\ \hline
6 & Building demolished \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of change types.
## 2 Results and Discussion
### Optimization of HS-SPME Procedure
#### 2.1.1 Effect of Extraction Time  
Extraction time is a critical factor for equilibrium techniques such as SPME. The HS-SPME extraction time was examined at 10, 20, 40, and 60 min. SPME is an equilibrium technique, therefore the maximum amount of analyte that can be extracted is reached at equilibrium time. Figure 1 shows the intensity of heptane, benzaldehyde and hexenal/4-methyl-3-penten-2-one/1-octen-3-ol at various extraction times. As it can be observed, at 40 min several analytes can be detected and the signal for most of them is higher. Only for the ketone does the intensity continues to increase with the increasing of time. This can be attributed to lower diffusion coefficient which leads to slower mass transfer and longer equilibrium time compared to the rest analytes [22]. However, for heptane and benzaldehyde, the intensity is slightly reduced with the increasing of time to more than 40 min. As a compromise, 40 min was finally chosen as extraction time.
## 5 Experimental Evaluation
### Effect of Packet Loss on Prediction Accuracy  
With the jitter, delay, and burst-loss kept constant, the effect of packet loss on the performance of the noise-trained model and noise-network-trained model was compared, as shown in Figure 8. The performance of the MiniatureVQNet-Noise-Network and MiniatureVQNet-Noise models decreases with the increase in the packet loss; the MSE increases with an increase in the packet loss. Meanwhile, at all loss measurements, the MiniatureVQNet-Noise-Network model outperforms the MiniatureVQNet-Noise model. However, the performance difference is not very high, except for a 10% and 20% packet loss. Training the model on the noise-network dataset offers a better MOS prediction accuracy compared to models trained on the noisy dataset under all packet loss conditions.  
Figure 6: Effect of noise type on prediction accuracy.  
Figure 7: The effect of jitter on prediction accuracy.
# Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines
## 4 Smart Ultrasound Sensor Design
### Excitation Signal  
The conventional method is using a square wave as the excitation signal. The frequency response of the V111 peaks at 8.44 MHz; therefore, the frequency of the pulse must be similar in order to resonate and optimize the received signal's strength. Adding more pulses increases the signal's strength but at the expense of a larger pulse duration that deteriorates the time resolution of the ToF estimation. In our system, the period and the rise and fall times of the square wave can be controlled using a 125 MHz clock. The square wave pulse is bipolar (\(\pm 15\) V), and the generated frequency is obtained by dividing the clock frequency by an even integer \(125/16=7.8\) MHz to guarantee a 50% duty cycle for positive and negative pulses.
## 6 Discussion
### Summary of Work
#### 6.1.1 Inspection Performance
However, since this was not the case, the sensitivity of both software must be improved to be comparable to the human operator. The strength of the inspection software lies in the computational power and fast processing speed [98,99]. Results showed that both algorithms (IP and AI) required a fraction of the time of the human, excluding the setup time. However, as mentioned above, the defect detection rates were not comparable. Conversely, the inspection time of the 3D scanner for a full scan and analysis of the scan data was twice as long as the human operator. [PERSON] et al. [77] stated that the image processing speed should not limit the throughput. However, there is also the time required for part preparation and image acquisition in software inspection. For 3D scanning, there is also a setup time, which can take up to four times the processing time. If not fully automated, the setup time can account for the greatest proportion of the overall time. This must be considered from an operational perspective when making the decision to implement advanced technologies and selecting the most appropriate one. All three technological systems showed perfect repeatability for the present research sample, i.e., the systems consistently made the same serviceability decision when presented with the same part twice. The human operator in contrast was, on average, consistent only 82.5% of the time. However, five operators (10%) showed a consistency of 100%. No previous study was found that applied repeated inspection to measure the repeatability of the inspection performance, possibly because it is expected that any technology would perform highly repeatably. 6.1.2 Benefits and Limitations of Humans and Advanced Technologies  
The strengths, weaknesses, opportunities and threats of each technology were identified and compared with the human operator. In summary, both humans and advanced technology were not perfect and came with their own benefits and limitations. An overview of the generic pros and cons is provided in Table 7. Generally, the strength of one system (human) is the weakness of the other (technology), and vice versa. Thus, some factors may be listed only once to avoid repetition.
## 4 Results Analysis and Discussion
### Scenario 1
These findings indicate that the models' efficacy varies, with SVM and J48 performing moderately and KNN displaying the lowest accuracy in this job. Overall, the accuracy study emphasizes the need to choose the best machine learning model adapted to the dataset's features to attain optimal predictive performance in renal illness prediction.
## 5 Conclusions and Hypothetical Mechanisms
Our data suggest that, whereas SPARC enhances collagen expression and muscle strength, the lactate increases mitochondrial enzyme expression in a SPARC-dependant manner (probably via mitochondrial biogenesis induction) and, therefore, the oxidative phosphorylation capacity. **[B]** The similarities between exercise benefits and effects shown to be regulated or modulated by SPARC, such as inflammation, cancer growth, metabolic and structural remodeling of the skeletal muscle, and even neurite outgrowth and neurogenesis, all suggest that part of exercise benefits would be mediated by or dependent on SPARC expression. Abbreviation: SPARC, secreted protein acidic and rich in cysteine. These conclusions are based on the fact that, as per Table 1, there is no effect of the genotype for LT speed, exercise speed, exercise time, exercise distance and lactate concentrations during exercise. This means that mice of two different genotypes (_Sparc_ KO and WT) had equal amounts of training (similar speed, distance, time and frequency). Therefore, genotype effects seen for MT-CO1, COL1A1 (Figure 1) and grip power (Table 4) are, indeed, due to the genotype itself (_Sparc_ KO) rather than the difference in the exercise amount; suggesting that these exercise-induced changes are SPARC-dependent/mediated. Such exercise-induced effects in the muscle represent a part of the adaptation via increasing the respiratory capacity, mitochondrial content [52] and contractile properties of the SM [53]. In addition to these SPARC-mediated effects, the similarity of exercise benefits and the effects shown to be regulated or modulated by SPARC, such as inflammation [22; 47], cancer growth [8; 46], metabolic and structural remodeling of the SM [53], and even neurite outgrowth [117; 118] and neurogenesis [85; 119], all suggest that some of the exercise benefits would indeed be mediated by or dependent on SPARC expression (Figure 3B).
# Student Dropout Prediction for University with High Precision and Recall
## 4 SDP (Dropout Student Prediction System)
### Dropout Predictor  
The SDP system uses XGBoost [20] and CatBoost for high precision and recall, respectively. XGBoost is based on the gradient boosting framework and provides a parallel tree boosting (also known as GBDT and GBM), which solves many data science problems quickly and accurately. We set values with a high metric selected using GridSearchCV for other hyperparameters in the scikit-learn package. The tree method parameter is set to gpu_hist to use GPU using distributed training. CatBoost [21] is also a gradient-boosting framework that attempts to solve problems by making permutations of the categorical features. We set values with high metric selections using GridSearchCV in the scikit-learn package.  
Once the two models produce the output, we combine the prediction results using the combiner depicted in Figure 3. We prioritize the model producing a high prediction rate over a high recall rate for efficient counseling of the prospective candidates.  
We defined the combiner's rules as \"High-Risk\" dropouts when the high-precision model predicted \"True\". Next, we defined \"Low-Risk\" dropouts when even one model predicted \"True\". Finally, we defined non-dropout as when all models predicted \"False\". Table 7 describes the summary of the decision.
## 3 Results and Discussion
### Design and Optimization Results  
The NSGA-II algorithm implemented in the design process provided four optimized geometry solutions, which are detailed in Table 7 and plotted in the Pareto front shown in Figure 14. These optimal solutions show convergence into the same values of B, L, \(\beta_{1}\), and \(\beta_{2}\), while there is not a remarkable difference between the hydraulic efficiency calculations and the fiction and windage losses shown in the inferior left front of the Pareto graph. Thus, only a geometry of B = 24 [cm], L = 24 [cm], \(\beta_{1}=4^{\circ}\), and \(\beta_{2}=170^{\circ}\) was chosen for the following analyses.  
Figure 12: Simulated torque vs. time of the original bucket geometry.  
Figure 13: Pressure distribution on the original bucket surface at different time instants.  
The three-bucket simulation revealed a periodic pattern (Figure 15), and the optimized bucket design exhibited a maximum torque in the middle bucket of 16,611 [Nm] at t = 0.032 [s] and a hydraulic efficiency of 82.21%. According to Figure 16, the greatest pressure zone is situated at the bottom of the bucket, and has a value of \(1.6\times 10^{6}\) [kPa] at t = 0.032 [s], while the minimum pressure zone is situated close to the bucket cutout, and has a value of \(-2.7\times 10^{6}\) [kPa] at t = 0.032 [s].  
\begin{table}
\begin{tabular}{c c c c} \hline \(\beta_{2}\) [\({}^{\circ}\)] & \(\beta_{1}\) [\({}^{\circ}\)] & \(L\) [cm] & \(B\) [cm] \\ \hline
169.9936 & 4 & 24 & 24.00013 \\ \hline
169.9587 & 4.000001 & 24 & 24.0084 \\ \hline
169.998 & 4.000221 & 24 & 24.00008 \\ \hline
169.9937 & 4.033618 & 24 & 24.00001 \\ \hline
170 & 4 & 24 & 24.15218 \\ \hline \end{tabular}
\end{table}
Table 7: Optimal solutions of the NSGA-II algorithm in the last generation.  
Figure 14: Pareto front of the optimized geometries in the 99 th generation.  
Figure 15: Simulated torque vs. time of the optimized bucket geometry.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
### LEO Satellite Communications for Internet of Remote Things  
For IoT applications, LEO satellites are practical for the most remote regions where terrestrial infrastructure is out of reach [7; 12]. LEO communications are categorized by:  
* Communication directness;
* LEO orbit configuration;
* By satellite service type.  
Regarding communication directness, individual sensor nodes can communicate directly to a satellite (known fittingly as \"direct-to-satellite\") or indirectly via a local network (often an LPWAN such as Bluetooth) centered around a satellite _gateway_[8; 10]. The latter case is impractical in our case, as a gateway adds another independent hardware part not under the IoRT node control, which needs to be designed and dimensioned for multiple unknown nodes, and requires a critical mass of nodes to be useful.  
Figure 1: Schematic showing a GNSS-IR water-level sensor with integrated LEO satellite connectivity.  
Among satellite services, there are those provided by companies from the pre-IoT era (who often offer satellite internet and phone coverage as well), and the emerging sparse constellation [13] networks, such as Swarm, Lacuna, Enxaneta, Kepler or Astrocast provided by independent LEO satellite companies [8; 10], which are more suited for IoT. The independent LEO satellite services use CubeSats, which are small and modular picostatellites [8]. Since IoT can tolerate intermittent connectivity better than satellite phones, their satellites can use polar orbits to provide global, but intermittent, coverage [10] by sparse constellations. In contrast, traditional LEO providers deliver continuous or near-continuous coverage using a combination of polar and non-polar orbits [10].  
Using emerging _sparse LEO constellations_ for IoRT has the primary benefit of requiring fewer satellites and less cost [10], but requires waiting until a satellite passes overhead [10]. To achieve low-energy LEO networking, a suitable algorithm must be devised to schedule sensing and transmissions at appropriate times such that the data is transmitted at (near-)minimal cost in energy [10].
## 4 Experiment and Analysis
### Metrics  
We use the Average Displacement Error (ADE) and the Final Displacement Error (FDE) as evaluation metrics.  
(1) Average Displacement Error (ADE) refers to the mean square error of all predicted trajectory points and real trajectory points. ADE was defined as  
\[ADE(i)=\frac{1}{T}\sum_{t=1}^{T}\left[\left(x_{i}^{t}-\widetilde{x}_{i}^{t} \right)^{2}+\left(y_{i}^{t}-\widetilde{y}_{i}^{t}\right)^{2}\right]^{1/2},ADE= \frac{1}{N}\sum_{t=1}^{T}ADE(i) \tag{26}\]  
(2) Final Displacement Error (FDE) refers to the distance between the predicted trajectory of the endpoint and the actual trajectory of the endpoint. FDE was defined as  
\[FDE(i)=\left[\left(x_{i}^{T}-\widetilde{x}_{i}^{T}\right)^{2}+\left(y_{i}^{T} -\widetilde{y}_{i}^{T}\right)^{2}\right]^{1/2},FDE=\frac{1}{N}\sum_{i=1}^{N} FDE(i) \tag{27}\]
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 4 Design of Energy-Efficient and Obstacle-Avoiding Routing Protocol  
To improve packet delivery ratio and reduce energy consumption, the EOAR protocol uses geo-opportunistic routing. It assumes that sensor nodes know the geographical location of themselves and their neighbor nodes. Besides, EOAR uses three constraints which contained signal-to-noise ratio (SNR), the distance between the marine animal and the neighbor nodes of the current node, and the included angle between the current node and its neighbor nodes, to determine the candidate forwarding relay set, and then uses the fuzzy logic-based forwarding relay selection scheme to calculate the forwarding probability of the nodes in the candidate forwarding relay set. Then, EOAR uses a priority-based forwarding method to set different waiting time for the candidate forwarding relays based on their forwarding probability. The overall procedure of the proposed EOAR protocol is detailed in Figure 2.