esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Variography
#### 3.3.1 Analysis of Grade Variography  
Considering geographical variation and randomness, the variogram function may depict the spatial variable structure of regional variables. Variography was conducted in multiple directions and dips to examine gold anisotropy. Most variograms showed various ranges and sills, indicating anisotropy in the gold deposit. An omnidirectional model is shown in Figure 5. The analytical model featured a nugget effect of 0.41, an exponential model with a sill value of 0.48, and a range of 29.2 m. According to the spherical model with a range of 29.2 m, the spatial correlation appears to decrease with increasing distance, but it may still be observed up to 29.2 m.  
Table 4 displays an exponential variogram-based directional model. The relatively small nugget effect and large sill value in the directional model indicate a significant spatial dependency in the data. With a range of 23.4 m, this model's spatial correlation appears to cover a relatively short distance, and the exponential variogram model indicates that the spatial correlation drops quickly with distance. Because most of the drilling composite assay values are of a relatively low grade, a normalized nugget of roughly 0.31 is not surprising.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Direction Model** & **Type** & **Nugget (ppm\({}^{2}\))** & **Sill (ppm\({}^{2}\))** & **Range (m)** \\ \hline Omnidirectional & & 0.41 & 0.48 & 29.2 \\ Downhole & Exponential & 0.31 & 0.88 & 15.6 \\ Directional & & 0.37 & 0.51 & 23.4 \\ \hline \end{tabular}
\end{table}
Table 4: Characteristics of Variogram Models for Estimation.  
Figure 5: Variography for gold content three types of variograms: (**a**) omnidirectional, (**b**) downhole, and (**c**,**d**) directional variograms. The black line shows an experimental variogram and the red line represents the fitting model.  
The inspection of the variogram revealed the presence of anisotropy. The Directional model accounts for the anisotropy of the spatial correlation, which can potentially increase the accuracy of interpolation and prediction in regions with directional variability [62].
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 4 Discussion
### Computational Efficiency  
Computational efficiency is a crucial metric in machine learning, primarily influenced by computer setup and model selection. This study employs CPU time as an evaluation metric to compare model efficiencies. During training in a CPU i5-7400 and GPU GTX 745 environment, the ILB model exhibited the shortest CPU time of 0.4 s, as shown in Figure 9. SVM and MLP followed with CPU times of 10.8 s and 83.1 s, respectively. In contrast, GBM and RF required over 400 s, showcasing comparatively longer CPU times. The significant CPU time difference between the ILB and batch learning models is noteworthy. The ILB model's CPU time is approximately 1/1000 that of the RF model, highlighting a substantial difference. Batch learning models often involve tuning hyperparameters through grid search methods, a time-consuming step due to the numerous hyperparameters involved. In contrast, the ILB model does not require hyperparameter adjustments, contributing to its significantly higher computational efficiency than batch learning models. The high computational efficiency of the ILB model provides it with unique advantages in real-world applications. On one hand, this ensures that the model is always up-to-date when predicting landslide disasters. As researchers collect new slope cases, the model can be immediately updated, thereby enhancing its performance. On the other hand, this facilitates the development of an online slope prediction platform. Different researchers can upload newly collected slope cases to the platform via the internet, enabling real-time updates to the prediction model, thereby creating a slope prediction platform with improved forecasting capabilities.  
Figure 9: Computing cost of each classification model.
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A
### Accelerometer Inversion of SWARM-A Atmospheric Density  
The GPS-derived non-conservative force acceleration data are used as the calibration standard, which is projected in the direction of the calibrated accelerometer data components and then combined with Equation (10) to find the calibration parameters \(B\), \(S\), and \(Q\) by the least-squares method. After obtaining the estimated values of the variables, the corrected acceleration values can be calculated by Equation (10).  
For the SWARM-A satellite, the non-conservative acceleration signal is the strongest in the satellite along the orbital direction, where the acceleration due to atmospheric drag dominates. Although the accelerometers of the SWARM-A satellite measure the acceleration in the three axes of the satellite's reference system, this paper only corrects and discusses the acceleration data along the satellite's along-orbit direction, and the acceleration data in the other directions can be obtained in the same way.
# Visual MAV Tracker with Adaptive Search Region
## 2 Model  
The proposed tracking algorithm comprises four steps, as shown in Figure 1. First, the location and size of the SR are obtained from the tracking results of previous frames. Second, the size of the SR is adjusted to a constant size (\(100\times 100\)). Next, the location and size of the object in the SR are estimated using FCNN. Finally, the tracking results of the SR are transformed, and the full image is obtained.  
This SR-based tracker can accurately track objects with fast computation. First, the variations in the size and location of the target objects (in images) can increase the tracking error. The error is large if the object appears very small in the images. If the aspect ratio is extremely large or extremely small, accurate tracking becomes challenging. Second, tracking with full images requires a long computation time if the size of the image is large. Thus, an SR-based tracking model was developed and used instead of considering the full image.  
The compact SR (CSR) tracker determines the current SR using the last tracking result only. Specifically, the center of the current SR was determined as the center of the object in the previous frame. The width and height of the current SR change depending on the object size in the previous frame. If the size of a target object (in images) remains constant over time, tracking can be very accurate and robust. In this study, the ratio of the SR width to the object width remained constant across several frames. However, this approach does not guarantee accurate and robust tracking. Thus, various strategies and methods have been developed to improve the quality of the SR.
## 5 Results
### Performance Evaluation of DVB-T2 Using BER
#### 5.1.4 NUC-Based DVB-T2 with LDPC Coder  
Previously, the native DVB-T2 system performance with LDPC was presented when only Gaussian noise was considered and also when a TU6 channel was considered. Here, we show NUCs' performance in DVB-T2 and the CFO impact in the NUC-based DVB-T2 system in both AWGN and TU6 cases.  
Figure 18 gives a view of the results gathered with only Gaussian noise. The SNR values of 6.02 and 6.04 were obtained at a BER of \(10^{-4}\) and \(10^{-5}\), respectively. By comparing those values to those obtained in the native DVB-T2 case without CFO (Figure 16), we can conclude that the 2D-NUCs allowed gains of 0.15 dB and 0.14 dB at a BER of \(10^{-4}\) and \(10^{-5}\), respectively. Additionally, when a CFO was applied in that system, it engendered losses of 0.04 dB, 0.11 dB, 0.25 dB, and 0.54 dB at a BER of \(10^{-4}\) for CFO values of 0.01, 0.02, 0.03, and 0.04, respectively. This loss increased up to 0.85 dB for 0.05 CFO value. By comparing the maximum CFO loss to that obtained with the native DVB-T2 (Figure 16), one can conclude that the difference was 0.011 dB and the losses of both systems (native DVB-T2 and 2D-NUC-based) were under 0.1 dB.  
Figure 19 illustrates the BER performance as a function of SNR when QAM was substituted for NUCs in the DVB-T2 system using a TU6 channel instead of only Gaussian noise. The same behavior of BER curves as in Figure 17 is demonstrated here. These results mean that the CFO performance losses of NUC-based DVB-T2 were comparable to those obtained with the native DVB-T2 using TU6 channel.  
Figure 19: BER vs. SNR in the NUC-based DVB-T2 system with coding: CFO evaluation in the TU6 and AWGN case.  
Figure 18: BER vs. SNR in the NUC-based DVB-T2 system with coding: CFO evaluation in the AWGN-only case.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### Selection of Structural Material of RF MEMS Switch with Capacitive Switching Principle
However, capacitive RF MEMS switches with a low coefficient of rigidity of the elastic suspension elements and low values of the control voltage are subject to deformation, curvature or deformation of the edges of the elastic elements, which prevents the formation of an air gap. The solution to this problem is to use the sandwich structure as a material of elastic suspension elements and the membrane. It embodies two materials of a given geometry and scale, as shown in Figure 4, formed in order to provide high strength and bending rigidity at low mass. The separated surfaces on both sides of the base material increase the moment of inertia of the structure and form a structure that is well resistant to various kinds of deformations and warping [59]. Another major advantage of sandwich structure is that the balanced structure minimizes the thermal-stress-induced upward wurpage in the release process [60; 61]. As shown in Figure 4, surfaces with a thickness of \(t\) take up most of the load, so they must be tough and durable. In this case, it is proposed to use TiN the advantages of which are high hardness, good adhesion and plasticity, high chemical resistance, high wear resistance, resistance to the formation of oxides and high temperature. In addition, TiN layers prevent the formation of warps, deformations of the edges of elastic suspension elements, which may be present after deposition in the process of manufacturing the structure. In other words, the TiN layers balance the residual stresses in the Al layer that have arisen after the technological processes. Also, TiN is characterized by high-resolution in the process of photolithography, due to good light adsorption. In the end, the composite material is proposed to use the sandwich structure TiN/Al/ TiN, as a material of elastic suspension elements and the membrane.
## 4 Results and Discussion
### Experiment  
#### 4.2.1 Experimental Results  
The experiment uses the accuracy as the performance index. To better check the generalization ability of each algorithm and avoid overfitting of the training caused by the cutting of the dataset, which affects the experimental results, the accuracy of each algorithm in the dataset is tested by 10-fold cross-validation repeated ten times (i.e., 10\(\times\)10 cross-validations), and then the average is taken. The accuracy results of the seven algorithms on the 30 sets UCI datasets are shown in Table 3, and the box plots of the nine selected datasets are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset ID** & **AdaBoost** & **Bagging** & **DECORATE** & **ERT** & **GBDT** & **RF** & **WELAD** \\ \hline
1 & 0.6481 & 0.6760 & 0.6831 & 0.6864 & **0.6934** & 0.6906 & 0.6836 \\
2 & 0.8681 & 0.8654 & 0.8459 & 0  
The bold values in Table 3 indicate the best performance of a line. The WELAD achieved the highest mean accuracy of 0.8123 on the 30 datasets, being superior to the six algorithms of the control group on nine of the sets. The standard deviation of the mean value of the WELAD is also the lowest, showing that the algorithm has good stability. Clearly, ERT, GBDT and RF all achieve good and similar performances. The experimental results also reflect the instability of AdaBoost. However, it reached the highest accuracy on 4 of the 30 datasets. It had the lowest accuracy on 11 datasets, resulting in its mean accuracy being the worst among all algorithms. The box plots of the nine datasets also reflect the advantages of the WELAD.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 4 Design of the PAUM System
### Events Classification  
It is significant to ensure the data transmission of the healthcare system by making prioritization for various data from different sensors and to guarantee the diverse quality of services to those events [28]. In this paper, the prioritized scheduling of data packets generated from different UEs is inspired by the fact that different diagnostic reports are related to each other, and the importance of an event is determined by the degree of its relevance with one or more other parameters. Initially, the eNB allocates time slots to uplink users, assuming that they are producing data within the normal range. Once it receives data from different users, it examines and finds out the relationship among the events, including the selection of the priority of the concerned event. Table 2 gives some examples of the event classification using diseases, probable locations to collect data, and the corresponding diagnosis. First of all, we attempt to reference the syndromes and analogous diagnoses to categorize the events. The instantaneous identification of any disease inside a smart hospital is reflected as a Critical Event (CE), which is required to be handled as crisis/emergency. The diagnoses and treatments that are applied as a direct intervention or in a supportive role for a critical event are termed as Relevant Events (RE). Then, all unvarying procedures are named Normal Events (NE). There are numerous diseases with their own symptoms and diagnoses. Still, some of the diseases  
Figure 2: Functional Block Diagram of the Proposed PAUM System. and their diagnoses that can be applied with smart-sensor-based equipment are mentioned in the proposed Table 2. By all means, CE must get a chance first to transmit data with the highest priority, followed by RE and NE. Applying the Bayes Theorem [29], the sensed information is categorized into different types of events, based on its vital sign for effective data transmission. This proposed system uses the concept of a candidate model that approximates a target function for mapping inputs to outputs known as a hypothesis. The learner considers these inputs as some set of candidate hypothesis \(s\) and is interested in finding the most probable hypothesis \(s\in S\) given observed data \(R\), which returns the output as the categorized event described in Algorithm 1 in the following subsection. According to Algorithm 1, event classification depends on sensory data and their co-relation. We use sensory data to determine event-wise training data based on its threshold value. This training data act as prior knowledge to find the likelihood of events in our proposed application. The Bayes Theorem expresses the likelihood of an event based on prior knowledge of the conditions that may be associated with it. Therefore, the Bayes Theorem can be relied on to identify the likelihood and categorize the occurrences with specific conditions to determine the more accurate probability of the emerging event.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 1 Introduction
### Previous Work on DO Monitoring  
Much advancement has been made toward the use of AI and drones to help manage production and maintenance on aquaculture farms. Besides DO monitoring, methods have been proposed to automate activities such as fish counting, fish length estimation, and facility surveillance [6; 7]. Two distinct approaches have been used to tackle the problem of automated DO monitoring. One approach employs DO sensors mounted on the pond buoy, powered by a solar panel and connected to the central office via wireless communications. Such a sensor pod is installed in each pond to constantly monitor DO [8; 9; 10]. While this type of solution does reduce labor costs, equipment, and installation costs do not scale well for commercial-sized farms which have thousands of ponds. Additionally, sensors which remain in water will biofoul if they are not regularly maintained. These hardware and maintenance requirements will detract from the goal of reducing overall costs. Another issue is that it provides limited spatial coverage of the pond where the DO level may vary extensively in different locations, especially in a large pond.  
The other type of approach to automatically monitor DO levels involves the design of sensor packages [11; 12] and vehicles [13; 14] to travel between ponds on a regular basis to collect their DO data. This reduces the maintenance burden and eliminates the need to install sensors for each pond. It also enables the measurement of DO levels at different locations in the pond, providing more reliable data. Remote sensing techniques have also been proposed to predict DO levels using multispectral images of the pond surface [15]. While remote sensing would greatly simplify the task for the drones, it introduces the risk of poorly predicting the DO due to the unknown level of oxygenation beneath the surface. Remote sensing, in particular satellite remote sensing, is subject to the weather conditions (i.e., cloud coverage). On the farm, the catastrophic fish loss may occur within an hour if DO depletion is not detected and mitigated in time.  
While studies of autonomous aquaculture systems are relatively common, work regarding robotic motion planning for autonomous aquaculture management has been sparse. Motion planning for oyster farm maintenance [16], offshore farm net pen cleaning [17], and mesh networks [18] are some of the few examples. The rising demand for aquaculture products, the short supply of laborers, and so far limited research interest makes this a prime topic for further investigation.
###### Abstract  
Roads are a type of typical artificial terrain, and are key components of urban terrain. Road networks formed by connections between different roads not only form the skeleton of urban terrain, but also plays an important role in transmitting energy and matter on the urban surface. Therefore, how to consider characteristics when constructing the digital road elevation model (DEM) has become an important research topic in the field of geographic information and mapping. Using high-definition unmanned aerial vehicle (UAV) images as the basic data source, this study proposes a new method for constructing the road DEM by analyzing semantic features such as road shape and function. This method first takes the sideline and centreline of the road as the macroscopic undulation morphological constraints. It uses the shape control equation of the local domain to constrain the morphological change characteristics of the road surface in the transverse and longitudinal directions, in order to construct the road DEM with high fidelity to the surface shape characteristics. Then, in terms of the water catchment function of the road surface, a road DEM correction method considering surface flow direction characteristics is designed to ensure that the water catchment path of the road surface conforms to the actual situation. For this paper, several typical roads in Chuzhou University in Anhui Province, China, were selected as the experimental objects to carry out a DEM construction experiment. The results indicate the following: (1) compared with the traditional construction method, the DEM shape of the road constructed by this research method is more consistent with the actual road shape, and the smoothness of the road surface is better; (2) due to the high density and high elevation accuracy of the point cloud used in modeling, the elevation adjustment strategy of the sideline and centerline of the road implemented in this study does not reduce elevation accuracy, indicating that an adjustment to the elevation information is necessary for constructing the DEM of special artificial terrain; and (3) the DEM correction method proposed in this paper to find the correct catchment path can ensure that the processed DEM can accurately simulate the surface catchment process, and the correction of the elevation of the road DEM is also controlled within a small range without affecting the elevation accuracy of the regional DEM. This study has reference value for implementing projects such as urban terrain expression in the construction of 3D China.  
digital elevation model; surface modeling; road; morphological constraints 2023
## 3 Time-Optimal Trajectory Planning Based on the MPE and MPC
### Numerical Results  
A point-to-point task (start position \(\mathbf{p}_{4}\) and end position \(\mathbf{p}_{6}\) in Figure 6) was set with no predetermined path. To solve (6) for the DAM-3R and JAM-3R, a simulation framework was constructed using MATLAB Simulink for time-optimal trajectory planning and RecurDyn for multibody dynamics analysis (Figure 7). The time-optimal MPC was solved using the Model Predictive Control Toolbox in MATLAB 2020a [27]. The prediction horizon length \(N_{p}\) was 10, and the control period \(T_{s}\) was 1 ms.  
Figure 8 shows the time-optimal trajectory results for the end-effector reference position (dashed line) and actually controlled position through the controller (solid line). The arrival time of the JAM-3R was 1.57 s, whereas that of the DAM-3R was 1.27 s (19.1% faster). It is interesting to note that the optimal trajectory in the \(y\)-axis for the DAM-3R and JAM-3R slightly descended by 6.2 mm and 6.1 mm, respectively, at the center of the path, \(\mathbf{p}_{5}=(150\text{ mm},\text{ }-150\text{ mm})\). This is because the direction of the time-optimal trajectory was  
well aligned with that of the maximum allowable velocities at \(\mathbf{p_{4}}\), \(\mathbf{p_{5}}\), and \(\mathbf{p_{6}}\) (yellow highlighted arrows in Figure 6).  
Figure 9 shows the detailed simulation results regarding the actuating forces and output motion in the joint space. Figure 9,\(c\) represents the resultant actuating force and slider position, respectively, for the DAM-3R. Figure 9,\(d\) also represents the resultant actuating torque and joint angle, respectively, for the JAM-3R. The total energy consumption of the DAM-3R was reduced by 45.0% compared with that of the JAM-3R (316.5 mJ for the DAM vs. 575.3 mJ for the JAM in Table 4). The proposed MPE-augmented MPC can fully and optimally utilize a high level of redundancy (additional design variables or control degrees of freedom) so as to move in the quickest manner. Particularly, for the DAM-3R, which has higher redundancy than the JAM-3R, the maximum velocity was achieved by less actuating of Joints 1 and 2, which have larger inertia and therefore require larger thrusting torque. This led to faster motion and less energy consumption for the DAM-3R compared with the JAM-3R. Thus, it is clear that, by virtue of conducting MPC augmented by MPE results (explicit and accurate performance models), a redundant manipulator can achieve higher performance in terms of working time and energy consumption for trajectory planning.  
Figure 8: Time-optimal trajectory results for the end-effector reference position (dotted line) and actually controlled position through the controller (solid line).  
Figure 7: Proposed time-optimal trajectory planning using the MPE results and MPC method.