esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Analysis of Trade-Off According to Patch Coverage Change
#### 4.2.1 Patch-Count Increase and Aggregation Benefits  
As previously observed in Section 3.3, upscaling allows more patches to be extracted per parcel, thereby increasing the total number of patches for parcel-level inference. This configuration makes parcel-level aggregation more informative, providing a strong rationale for tiling-based approaches.  
Table 5 indicates that under the 1\(\times\) condition, the gain from patch level to parcel level was modest (accuracy up to +0.0238, macro F1 score up to +0.0293, and Kappa up to +0.0296), whereas under the 2\(\times\) condition, the same aggregation offered more pronounced improvements (accuracy up to +0.0518, macro F1 score up to +0.0542, and Kappa up to +0.0669). Thus, increasing the patch count is not simply a matter of having more predictions but also of combining different patch-level outputs for more stable parcel-level decisions.  
However, FPE + CBS showed a slight accuracy drop of 0.0019 after aggregation. Product-based aggregation can yield robust performance when patches exhibit uniformly high confidence, effectively capturing a consensus among similar texture patterns, yet it also demands consistent outputs across all patches, so a single outlier patch that assigns extremely low confidence to the correct class can substantially diminish the final aggregated score. This trade-off helps explain why, in most configurations, aggregation provided noticeable gains--particularly for classes with relatively uniform texture--while FPE + CBS encountered a drop in this scenario due to one or more outlier patches. Despite this limitation, under upscaling, tiling-based parcel-level prediction continued to demonstrate its structural advantage overall, compensating for potential instabilities at the patch level in most cases.
## 4 Experiment and Results
### Results
#### 4.2.3 Result of 3D Reconstruction  
As shown in Figure 5, the visualization results of different 3D reconstruction methods are displayed in CloudCompare [36]. As can be seen from the figures, all the methods exhibited relatively good results and were able to reflect the multidimensional details of the damaged building. Compared to the higher part of the building, the UAV obtained more detailed building information and could obtain more detailed stereo semantic information in the modeling process to complete stereo matching. Therefore, all of the methods performed well in this type of building. The Intra-view AA added in the feature extraction part of AA-RMVSNet maintained the correlation between the original geometric features of the image and the Inter-view AA added in the cost aggregation part on the basis of fusing multi-scale features. Compared to \(D^{2}\)HC-MVSNet, both improved the accuracy of the 3D model. Unlike AA-RMVSNet and \(D^{2}\)HC-MVSNet, CasMVSNet uses a multi-layer cascaded and gradually refined depth calculation method, which can better reflect the advantages of finer division and calculation of depth information when dealing with UAV image reconstruction. The building wall information in the 3D model reconstructed by this method was complete and delicate.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 3 Principle and Analysis  
In this design, our main target is to obtain a wide impedance bandwidth with good radiation patterns within the whole band. Figure 2 shows the design process of the proposed antenna. It can be seen that there are two main design steps, which are the loading of Slots 1 (from Antenna I to Antenna II) and the loading of Slots 2 (from Antenna II to Antenna III), respectively.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 6 The Structure of Proposed Model for Probabilistic Wind Power Forecasting
\begin{tabular}{c c c} \hline \hline
**Used Data** & **Input Data** & **Time** \\ \hline Historical data & & Every hour from \\ from wind farm & & 7/2019 to 3/2020 \\  & RWRF (m/s) & Every hour from \\ Data from NWPs & & 7/2019 to 3/2020 \\  & WEPS ensemble wind speed & & Every 3 h from \\  & (m/s) & 7/2019 to 3/2020 \\ \hline \hline \end{tabular}   
Step 3: Since the value of wind speeds varies dramatically, the normalization must be used for calibrating them into the range of zero to unity. Step 4: The preprocessing process for the original data includes a dimension reduction using autoencoder and wind power curve calibration. The detail about the calibration of a wind power curve was proposed in our previous work [21]. In real operations, wind power curtailment is commonly implemented by wind farm operators because of some emergency conditions, resulting in deviations from a normal power curve. For instance, the manual operation of wind turbines by operators sometimes reduces wind power generation. However, in the process of model training, these abnormal operating points should be removed to avoid training errors. Step 5: Deterministic forecasting is implemented every 3 h by XGBoost, as shown in Figure 9.  
Step 6: Tuning parameters after training model by XGBoost. Step 7: Obtain all of the errors from previous generated power. Then, construct the probability density function (PDF) of the errors. The lower bound and upper bound are obtained from the forecasting errors, which are associated with the highest probability. Next, compute the lower and upper bounds of the produced power as follows:  
\[WP_{up}=WP_{forecast}+E_{up}WP_{low}=WP_{forecast}+E_{low} \tag{18}\]  
where \(E_{up}\) and \(E_{low}\) are the upper bound and lower bound of the forecasting errors, respectively; \(WP_{up}\) and \(WP_{low}\) are the upper bound and lower bound outputs of the LSTM model; and \(WP_{forecast}\) is the deterministic forecasting obtained from the XGBoost model. Step 8: Train the model with the above lower and upper bounds of wind power as the output. Different inputs are shown in Figure 10. The data in February were used for prediction, and the data in January were used as validation data. The historical data from July 2019 to December 2020 were used as the training data. Step 9: The performance of the model is assessed by using the mentioned criteria, i.e., PICP, PINAW, and CWC.
## 2 Materials and Methods
### Simulation  
The model was implemented using Engineering Equation Solver software, EES [36]. This software has integrated the thermophysical properties of various substances and can be programmed in a user-friendly environment. The simulation procedure is illustrated in Figure 3. The properties of the base fluid were obtained from the data provided by [37], while the properties of the nanoparticles were assigned as shown in Table 4. The various parameters used in the simulation are listed in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Collector area, Ac, [m\({}^{2}\)] & 1.13 \\ Number of covers, \(N\) & 1 \\ Number of riser pipes, \(N_{t}\) & 6 \\ Length of collector, \(L_{col}\) [m] & 1.13 \\ Width of collector, \(W_{col}\) [m] & 1 \\ Internal pipe diameter, \(D_{lnr}\) [m] & 0.013 \\ External pipe diameter, \(D_{out}\), [m] & 0.016 \\ Width between pipes, \(w\) [m] & 0.166 \\ Plate thickness, \(\delta\) [m] & 0.0002 \\ TC of the plate, \(K_{p}\)[W/mK] & 384 \\ Glass emittance, \(\varepsilon_{g}\) & 0.84 \\ Plate emittance, \(\varepsilon_{p}\) & 0.05 \\ TC of the insulation, \(K_{i}\) [W/mK] & 0.038 \\ Thickness of the insulation, \(L_{ins}\) [m] & 0.0254 \\ Transmittance, Absorbance, \(\alpha\), taken from [34] & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Solar collector specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & [35] & **This Work** & **Error, [\%]** \\ \hline Useful heat, W & 2055 & 2010 & 2.1 \\ Efficiency, \% & 44.9 & 45 & \(-\)0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model validation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nanoparticles** & **Density (kg/m3)** & **Specific Heat (J/kg K)** & **TC, (W/m K)** & **Reference** \\ \hline Fe\({}_{3}\)O\({}_{4}\) & 4950 & 670 & 6 & [17] \\ Al\({}_{2}\)O\({}_{3}\) & 3600 & 880 & 30 & [24] \\ \hline CuO & 6000 & 551 & 33 & [24] \\ MgO & 3560 & 955 & 45 & [38] \\ \hline MWCNT & 1350 & 650 & 1500 & [17] \\ SWCNT & 2100 & 600 & 3500 & [1] \\ \hline Graphite, G & 2210 & 709 & 1950 & [39] \\ Fullerene, C\({}_{60}\) & 1720 & 506.7 & 0.4 & [23] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermophysical properties of nanoparticles at 20 \({}^{\circ}\)C.  
Figure 3: Flowchart followed in the simulation.  
Assumptions  
The assumptions made in the simulation are as follows:  
* Fully developed flow and stable conditions.
* The ambient temperature at the top and bottom of the collector are the same.
* Negligible addition to the collector surface by the header.
* In all pipes, the fluid is in uniform flow.
## 4 Performance Evaluation
### Performance Evaluation Environment and Factors  
The performance evaluation of both the existing research and the proposed scheme was conducted using the NS-3 network simulator [22]. A network comprising 100 devices arranged in a grid structure within a 100 m \(\times\) 100 m area was simulated. In this scenario, five devices for each pattern--linear, non-linear, and random--move at a speed of 10 m/s. To represent different mobility scenarios, the following movement patterns were defined:  
Linear patterns: Devices move along straightforward and predictable trajectories, representing scenarios with consistent and uniform mobility.  
Non-linear patterns: Devices follow trajectories with moderate variability, introducing complexity to their movement paths while still retaining some level of predictability.  
Figure 11: An example of resource allocation by the proposed scheme.  
Figure 9: An example of resource allocation by the existing scheme [21].  
Random patterns: Devices exhibit highly irregular and unpredictable movements, reflecting scenarios with significant uncertainty and complexity in mobility.  
Each device has a transmission range of 50 m and transmits data to a gateway at one-second intervals. To evaluate the impact of network changes due to device mobility on transmission success ratio, we assume an ideal transmission success ratio of 100%. Performance evaluations were conducted for 6400 s. The results presented are the average values derived from ten separate simulation runs to ensure reliability and accuracy. The performance evaluation factors and attributes exploited to learn the decision tree are as follows:  
The transmission success ratio is defined as the ratio of packets successfully delivered to the gateway to the total number of packets sent by the devices.  
The resource occupancy ratio refers to the proportion of resources utilized for network configuration compared to the total available resources.  
The \(R^{2}\) (coefficient of determination) measures the linearity of trajectories. Values closer to 1 indicate a highly linear trajectory, while lower values represent less linear paths.  
The \(dangle\_std\) captures the variability of angular changes across a trajectory. A lower value represents smoother and more predictable paths, while a higher value corresponds to more erratic movements.  
The dataset used for classifier development consists of a total of 60 samples, distributed equally across three mobility patterns: 20 samples for linear trajectories, 20 for non-linear trajectories, and 20 for random trajectories. Each sample includes the computed values for \(R^{2}\) and \(dangle\_std\), which are used as inputs for the decision tree model.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 4 Discussion
Otherwise, the TSR stitching algorithm should be preferred over the NR algorithm, as the optimization of the reflectance map, adapted to any elementary map, facilitates the in-plane registration, which in turn can be more precise, especially for the registration of the smaller roughness scales. An optimization problem can be formulated to find the best algorithm for each test case. It is a multi-objective optimization problem, respecting the following sub-objectives:  
* Minimization of run time;
* Minimization of memory usage;
* Minimization of mean repositioning error;
* Minimization of stitching error estimator. A novel plot is proposed to compare stitching algorithms and to solve this multi-objective optimization problem graphically: the stitching gain lift plot or, shorter, the SGL plot. Memory usage is plotted as a function of the stitching run time. The size of each marker is inversely proportional to the mean repositioning error, so that a bigger marker indicates a lower mean repositioning error. The marker color depends on the value of the stitching error estimator: it is green if the stitching error estimator value is the best, red if the value is the lowest, and black if the stitching has failed. With the SGL plot, the best stitching algorithm to use for each test case would be closer to 0 in run time and memory usage, has a larger marker size and, preferably, a green color. The SGL plot is a simple tool allowing users to select the best stitching algorithm for a given elementary topographic dataset. In addition, all results from the five criteria for each test case are contained in this single plot, making it a good sum up plot for the comparison of 3D topography stitching. Figure 17 shows the SGL plot for each test case, and the stitching algorithm is indicated by the marker shape. The qualitative and quantitative results show the interest in the use of the novel reflectance-based multimap algorithm, the rising sun algorithm, and its variants (NR, EER and TSR) for the stitching of 3D topography, for all scales and for any 3D optical profilometers, including large stitching. This could be especially useful for the stitching of microscopic or sub-microscopic elementary topographic maps, which are still a challenge for existing 3D topography stitching algorithms to this day.
## 8 Numerical Simulation and Experimental Testing
### Experimental Testing and Analysis
#### 8.2.2 Unmanned-Ship Positioning Test Based on Multi-Sensor Data Fusion
Figure 7: Filtering results of the unmanned-ship navigation trajectory based on five filtering algorithms: (**a**) positioning trajectories on section A; (**b**) positioning trajectories on section B. From the performances of Std and RMSE in the A/B section, compared with the other five algorithms, the value of the TCPF algorithm was also the smallest, which shows that the estimation performance of the TCPF algorithm is also the best. This is mainly due to the fact that the proposed TCPF algorithm integrated the latest multi-sensor positioning data collected by the unmanned-ship positioning system into the proposal distribution of the particle set in the importance sampling process, which made the proposal distribution closer to the real posterior probability density and improved the estimation performance of the algorithm. In addition, the integration operation in Bayesian estimation was solved according to the Monte Carlo method adopted by the basic particle-filter algorithm, which reduced the interference of environmental noise on the information collected by multi-sensors, and improved the credibility of multi-sensor positioning data samples. The calculation shows that, compared with the other five algorithms, when the unmanned ship navigated based on TCPF in section A, the average positioning error, RMSE and, Std performances of its trajectory decreased by 35.0%, 36.0%, and 35.0%, respectively. In addition, the average positioning error, RMSE and, Std performance of its trajectory in section B decreased by 37.0%, 40.0%, and 39.0%, respectively. In the experiment involving the whole river, the three performances of TCPF decreased by an average of 36.0%, 38.0%, and 37.0%, respectively, which fully shows that the proposed TCPF algorithm had a high estimation performance and fault-tolerant performance, and the positioning accuracy of the unmanned ship's navigation trajectory was improved.
## 4 Modeling and Results
### Results
#### 4.3.2 Experiment 1: Handwritten Kanji Recognition (150 Classes)  
The first experiment evaluated the performances of the four proposed training approaches (Vanilla Preact ResNet-18, Preact ResNet-18 with data augmentation, Algorithm 1, and Algorithm 2) in the multiclass classification task involving the 150 most-sampled handwritten kanji characters from the Kuzushiji-Kanji dataset. It should be noted that this experiment corresponds to the research conducted in [12]. Figures A2-A5 in Appendix A illustrate the accuracies and losses achieved using the proposed approaches. These figures generally show rapid initial convergences for all the approaches within the first 2-4 epochs, characterized by sharp increases in accuracy and decreases in loss. Subsequently, the models exhibit more gradual refinements. Notably, Algorithm 2 consistently converges to the lowest loss values among the methods. The results, based on various performance metrics for all the described approaches, are presented in Table 5. This table highlights that Algorithm 2 achieved the highest performance metrics for the 150-class task, notably 97.94% accuracy.  
An in-depth evaluation of the separate kanji recognition performances showed the highest and lowest accuracy rates for the different training strategies, which are presented below:  
* Vanilla Preact ResNet-18: The lowest accuracy rate for % (77.14%) and the highest for % and some others (100%);
* Preact ResNet-18 with data augmentation: The lowest accuracy rate for % (70%) and the highest for % and some others (100%);
* Algorithm 1: The lowest accuracy rate for % (70.51%) and the highest for % and some others (100%);
* Algorithm 2: The lowest accuracy rate for % (76.92%) and the highest for % and some others (100%).
# Impact of Silicon Substrate with Low Resistivity on Vertical Leakage Current in AlGaN/GaN HEMTs
## 2 Experiments  
Our study was carried out on AlGaN/GaN epitaxial layers grown on p-Si (111) and n-Si (111) substrates by metal organic chemical vapor deposition (MOCVD). The schematic of the epitaxial structure with p-type substrate is illustrated in Figure 1a. The epi-stacks consists of a 250 nm AlN nucleation layer on a p-Si substrate with a resistivity of 0.004 \(\Omega\)-cm, a 4.36 \(\upmu\)m graded Al\({}_{\mathrm{x}}\)Ga\({}_{1\mathrm{-x}}\)N (x = 0.01-0.68) transition layer, a 400 nm undoped GaN channel layer, and finally a 2 nm GaN cap on a 25 nm Al\({}_{0.2}\)Ga\({}_{0.8}\)N barrier layer. The epitaxial structure for n-type is the same as that of p-type structure [Figure 1a] except the Si substrate with n-type doped (0.004 \(\Omega\)-cm). The fabricated structure has two Ohmic contacts with one above the AlGaN layer and the other under the Si substrate. The top contact is formed by depositing Ti/Al/Ni/Au metal stacks, followed by rapid thermal annealing at 850 \({}^{\circ}\)C for 35 s in nitrogen atmosphere. The bottom contact is formed by Al deposition on the Si substrate. The current-voltage (I-V) characteristics were obtained in the dark using an Agilent's B1500A semiconductor device analyzer.
Sensors_**2009**, \(9\), 8624-8657; doi:10.3390/s91108624  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics--An Overview  
[PERSON] 1,2,* and [PERSON] 2  
1 LREIS Institute of Geographic Sciences & Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China  
2 Department of Forest Resources Management, Faculty of Forestry, University of British Columbia 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]S]; Tel.: +86-10-64889283; Fax: +1-604-822-9106.  
_Received: 20 August 2009; in revised form: 12 October 2009 / Accepted: 26 October 2009 / Published: 29 October 2009_  
**Abstract:** Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO\({}_{2}\) mixing ratio towers and chambers.  
**Keywords:** terrestrial carbon and water dynamics; ecohydrological modeling; remote sensing; eddy-covariance flux tower; scaling
## 5 Experiments and Results
### Multi-Person Pose Estimation and Tracking
#### 5.1.3 Results on the PoseTrack Benchmark
\begin{table}
\begin{tabular}{c c c c c} \hline latency (batch) & 2 & 4 & 8 & 16 \\ \hline ST-baseline & \(1\times\) & \(1\times\) & \(1\times\) & \(1\times\) \\ \hline ST (ours) & \(1.46\times\) & \(1.39\times\) & \(1.28\times\) & \(1.17\times\) \\ \hline Lite-Pose & \(1.01\times\) & \(0.98\times\) & \(0.96\times\) & \(0.95\times\) \\ \hline VoxelTrack & \(1.24\times\) & \(1.19\times\) & \(1.15\times\) & \(1.09\times\) \\ \hline AOP & \(1.19\times\) & \(1.13\times\) & \(1.11\times\) & \(1.05\times\) \\ \hline Location-free & \(1.12\times\) & \(1.08\times\) & \(1.05\times\) & \(1.01\times\) \\ \hline Location-Global & \(1.15\times\) & \(1.13\times\) & \(1.11\times\) & \(1.07\times\) \\ \hline \end{tabular}
\end{table} 
Table 6: Results of inference latency on single GPU. \begin{table}
\begin{tabular}{c c c c c} \hline
**Detector** & **Backbone** & **Sampling?** & **mAP** & **MOTA** \\ \hline R-FCN & ResNet-50 & \(\times\)1  & 67.2 & 57.8 \\ \hline R-FCN & ResNet-50 & \(\surd\) & 71.1 & 61.8 \\ \hline Cascade R-CNN & ResNet-101 & \(\times\) & 73,1 & 58.3 \\ \hline Cascade R-CNN & ResNet-101 & \(\surd\) & 73.5 & 62.8 \\ \hline Faster R-CNN & ResNet-50 & \(\times\) & 69.8 & 58.9 \\ \hline Faster R-CNN & ResNet-50 & \(\surd\) & 72.7 & 60.4 \\ \hline Faster R-CNN & ResNet-50 DCN + FPN & \(\times\) & 71.8 & 60.3 \\ \hline Faster R-CNN & ResNet-50 DCN + FPN & \(\surd\) & 73.7 & 63.1 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparing different person detection models integrated with spatiotemporal sampling module.