esa-sceva/satcom-chunk-collection huggingface dataset:
# DDC Control Techniques for Three-Phase BLDC Motor Position Control
## 3 DDC Control System Design
### Design of MFAC Response  
Consider the general discrete time single input, single output (SISO) nonlinear system in Equation (8). The system should satisfy the assumptions in Section 3.1 for the CFDL method.  
Figure 14: Step response of BLDC Motor using MFAC.  
Figure 13: General Block diagram of Model Free Adaptive Control.  
The basic theory of and stability criteria for MFLAC are present in reference [21]. By using the CFDL scheme, the control algorithm for MFLAC based on a weighted one-step control input criterion function is as follows:  
\[J\big{(}u(k)\big{)}=\big{|}y^{*}(\kappa+1)-y(\kappa+1)\big{|}^{2}+\lambda\big{|} u(\kappa)-u(\kappa-1)\big{|}^{2} \tag{16}\]  
where the weighting function is \(\lambda\), and \(\lambda\big{|}u(\kappa)-u(\kappa-1)\big{|}^{2}\) is the criterion function. The introduction of \(\lambda\big{|}u(\kappa)-u(\kappa-1)\big{|}^{2}\) will overcome the deficiencies of the controller.  
The general MFAC control law is obtained as follows:  
\[u(\kappa)=\mathrm{u}(\kappa-1)=\frac{\rho_{x}\phi(\kappa)}{\lambda+\phi( \kappa)^{2}}(y^{*}(\kappa+1)-y(\kappa)). \tag{17}\]  
The present approaches for PPD estimation introduce a new parameter estimation criterion as follows:  
\[J\big{(}\phi(\kappa)\big{)}=\bigg{[}\Big{(}y^{0}(\kappa)-y(\kappa-1)-\phi( \kappa)\Delta u(\kappa-1)\Big{)}^{2}+\mu\big{(}\phi(\kappa)-\hat{\phi}(\kappa- 1)\big{)}^{2}\bigg{]}. \tag{18}\]  
In Equation (18), the parameter \(\mu\big{(}\phi(\kappa)-\hat{\phi}(\kappa-1)\big{)}^{2}\) is used to adjust the rate of change of the parameter estimation. From Equation (18), we can get the parameter estimation algorithm in Equation (19):  
\[\hat{\phi}(\kappa)=\Bigg{[}\hat{\phi}(\kappa-1)+\Bigg{(}\frac{\eta_{x}\Delta u (\kappa-1)}{\mu+\big{|}\Delta u(\kappa-1)\big{|}^{2}}\Bigg{)}\cdot\big{(} \Delta y(\kappa)-\hat{\phi}(\kappa-1)\Delta u(\kappa-1)\big{)}\Bigg{]} \tag{19}\]  
\[\hat{\phi}(\kappa)=\hat{\phi}(1)\text{, If }\big{|}\hat{\phi}(\kappa)\big{|} \leq\varepsilon\text{, or }\big{|}\Delta u(\kappa-1)\big{|}\leq\varepsilon \tag{20}\]  
where \(\mu\) is a positive weighting constant, \(\eta_{x}\) is a step size constant series, \(\eta_{y}\subset(0,2)\), \(\hat{\phi}(1)\) is the initial estimation information of \(\hat{\phi}(\kappa)\), and \(\varepsilon\) is a positive constant.  
By selecting appropriate parameter, the following result is obtained using the MFLAC scheme.  
Figure 15 shows the response of the BLDC Motor using MFLAC. It can be seen that the result is better than the traditional PID and MFAC methods. The overshoot value and settling time is reduced.
## 3 Minimum-Time Attitude Maneuver of Small Satellite Mounted with DRC Antenna and RW  
### Converting the Optimization Problem into an NLP  
The time-optimal rest-to-rest maneuvering control problem of a rigid spacecraft was studied. [PERSON] et al. [18] presented a novel method to solve the time-optimal rest-to-rest maneuvering problem of a rigid spacecraft by transforming the problem into an NLP with the help of an iterative procedure. An NLP method does not utilize [PERSON]'s minimum principle which one needs to solve a set of difficult differential equations [19]. In this study, we considered only the control input to be discrete in order to convert the optimal problem into an NLP. The procedure used in converting the optimal problem into an NLP, is indicated as follows [20]:  
1. We divide the time axis \(t\in[0,t_{f}]\) into elements of \(N\), and defined a dimensionless time at each node as follows: \[{t_{i}}^{*}={t_{f}}i/N,\ (i=0,1,\cdots,N)\] (17)
2. We fix the control input for each element as \(\mathbf{u}(t)={\mathbf{u}_{i}}({t_{i}}^{*}),\ (t\in[t_{i},\ t_{i+1}])\). The state vector \(\mathbf{x}(t)\) can be calculated using Runge-Kutta 4 th-order method, after substituting the control input \(t_{f}\) into the state equation. 3. We define the time-independent decision variable \(\mathbf{X}\) as follows: \[\mathbf{X}=\left[{t_{f}}\ {\mathbf{u}_{1}^{T}}\ \cdots\ {\mathbf{u}_{N}^{T}}\right]^{T}\] (18)  
We use \(\mathbf{X}\) to convert the optimization problem into an NLP as follows:  
\[ \begin{array}{ll}\text{minimize}&\Phi\left(\mathbf{X}\right)\\ \text{subject to}&G_{E}(\mathbf{X})=\left[{\mathbf{C}_{E}^{T}(\mathbf{x}_{i},\mathbf{u}_{i},t_{ f})\ \mathbf{\varphi}_{f}^{T}}\right]^{T}=0\end{array}  \tag{19}\]  
where \(i=0,1,\cdots,N\), \(\Phi\) represents an objective function which is a scalar value, and \(G_{E}\) represents equality restriction conditions. Formulation of the Problem of the Minimum-Time Attitude Maneuver of the Satellite Mounted with DRC Antenna and RW  
In this study, we have assumed that antenna joint motor can create the output of an ideal torque. We defined control input vector and state vector as follows:  
\[\mathbf{x}=\left[{\mathbf{q}^{T}}\ \mathbf{\omega}_{s}^{T}\ \mathbf{\phi}^{T}\ \mathbf{\dot{\phi}}^{T}\right]^{T},\ \mathbf{u}=\left[{\mathbf{\dot{ \Omega}}^{T}}\ \mathbf{\tau}_{\mathbf{\phi}}^{T}\right]^{T} \tag{20}\]  
where \(\mathbf{q}\) represents quaternion vector of satellite attitude. \[\begin{array}{ll}\text{minimize}&J=\int_{0}^{t_{f}}\text{d}t=t_{f}\\ \text{subject to}&\left\{
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 5 Conclusions  
In this paper, we proposed a new change detection approach using feature points for detecting small object changes from high-resolution satellite images. We showed that feature points could be pre-classified as changed and unchanged objects through a feature point matching process to decrease false alarm rates. We also showed that feature point-based change analysis could improve change detection performance in terms of changed pixel, changed feature point, and changed object detection. Our test results with three Kompsat-3A image sets confirmed that our approach outperformed a pixel-based approach by producing higher precision and recall at a fixed false alarm rate. For a false alarm rate of 10%, our approach achieved an averaged precision rate of 88.7% and an averaged accuracy of 86.1%, while a pixel-based approach achieved 77.7% and 82.6%, respectively. The time of the research processing was less than 5 min, excluding the pre-processing.  
One can classify traditional change detection from satellite images based on the unit of change determination: pixel-based, feature-based, and object-based. Our approach offers a new feature-based approach, which is less noisy than pixel-based approaches and faster than object-based approaches. This may produce clues to small object changes without extensive texture or object analysis compared to existing feature-based or object-based approaches. One may also classify traditional change detection as supervised or unsupervised based on the usage of prior knowledge. Our approach is an unsupervised one, which can be applied as a pre-processor for a sophisticated and time-consuming supervised approach such as deep learning [11; 14; 27; 32] and neural networks [4; 6; 16]. Major contribution of this paper is that a unique approach of using feature points was proposed for unsupervised change detection.  
We also observed the limitation of our approach that it cannot handle featureless objects, because we use features as the only unit of change. We might overcome this problem by implementing more intelligent schemes for merging pixel-based and feature-based change detection results, and by using both merged features and merged pixels as units of change determination. This will be left to future research.  
Investigation, J.S.; Methodology, J.S., W.P. and T.K.; Validation, J.S., W.P. and T.K.; Formal analysis, J.S., W.P. and T.K., Resources, J.S. and T.K., Writing--original draft, J.S. and T.K.; Writing--review & editing, J.S. and T.K. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Enlarged feature-point extraction results.  
**Funding:** This research was funded by the Satrec Initiative.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Mechanical Properties
### Heat Treatment  
It is well-known that as-built AM samples and components are characterized by the presence of high residual stresses due to the complex thermal history to which the material is subjected while being processed. Because of this reason, AM components usually undergo specific post-heat treatments that allow the reduction of internal stresses and the homogenization of microstructures.  
However, to date, only a small number of studies have been carried out on the effect of the stress-relieving/annealing heat treatments on DED AISI 316L properties [78; 90]. The main tensile tests results are summarized in Table 5. The data show that, typically, the YS and US of DED AISI 316L parts are reduced as a consequence of heat treatments. This reduction was mainly attributed to the decrease in the \(\delta\)-ferrite content [78] and to the reduction of the dislocation density [85]. Furthermore, it is also interesting to underline that a higher strain hardening was observed in heat-treated samples; this can also be due to different dislocation contents.  
Despite the differences in the mechanical properties, the fracture surfaces of as-built and heat-treated samples are usually very similar (Figure 15). [PERSON] et al. reported, for example, a ductile fracture surface characterized by the presence of micrometric dimples for both as-built and heat-treated samples [90]. Moreover, in both samples, extremely fine Mn/Si oxide particles can be detected in the dimples.  
Figure 15: Fracture surfaces of AISI 316L stainless steel: (**a**) as-built and (**b**) heat-treated. Arrows mark a few of the many examples of fine particles observed resting inside ductile dimples throughout the fracture surface [90].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**P (W)** & **V (mm/s)** & **Conditions** & **YS (MPa)** & **US (MPa)** & \(\epsilon\) (\%)** & \(\mathbf{H_{c}}\) & **Ref.** \\ \hline
360 & 8.5 & As-built & 405–415 & 620–660 & 32–40 & 0.49–0.63 & [78] \\  & & 1150 \({}^{\circ}\)C 2 h Air quenched & 325–355 & 600–620 & 42–43 & 0.69–0.91 & [78] \\ \hline
380 & - & As-built & - & 720 & 56 & & [90] \\  & & 1060 \({}^{\circ}\)C 1 h Vacuum treated & & 605 & 78 & & [90] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tensile properties of DED AISI 316L samples in the as-built and heat-treated conditions.  
Other more specific mechanical properties of DED 316L were also recently investigated by several authors. [PERSON] et al. and [PERSON] et al. studied, for example, the DED 316L impact and fatigue performance of as-built parts [91,92]. Their main finding is that DED samples have a Charpy impact energy and fatigue crack growth rate similar to conventionally manufactured samples. Furthermore, crack propagation in DED samples is transgranular and accompanied by a strain-induced martensite formation.
# Prediction of Shallow Failure on a Slope Using Volumetric Water Content Gradient Characteristics
## 2 Materials and Methods
### Flume Experiment
#### 2.2.2 VWC Gradient Analysis in Flume Experiments  
The VWC gradient at each installation depth was examined (Figure 4). At almost all measurement depths, the VWC gradient was 0.1 or higher, but it was less than 0.1 in some cases. No correlation was found between ECR and the VWC gradient. Even at the same ECR value, the VWC gradient could exist in various ranges. This appears to be because the penetration rate of the created slope was not constant in the repeated experiments, and the difference in the measurement depth was not large.  
Figure 4: VWC at different measurement depths: (**a**) 0.05 m; (**b**) 0.1 m; (**c**) 0.15 m; (**d**) 0.2 m; (**e**) 0.25 m; (**f**) 0.3 m.  
Figure 3: Schematic representation of the flume test equipment used for measurements at a slope angle of 35\({}^{\circ}\).  
For the utilization of sensors, the sensor installation position is also very important. The characteristics of the VWC gradient were examined according to the installation position, but no characteristic was observed according to the slope angle and the installation position on the slope (crest, slope, and toe parts) in this study (Figure 5).  
In general, soil slopes with a high slope angle are vulnerable to frequent failure in the berm part of a slope. These tendencies, however, were not observed in the experimental results. This may be attributed to the fact that the scale of the flume experiments was small, and different types of failure from actual soil slopes occurred. Therefore, to identify characteristics according to the installation position, it was inferred that large-scale flume experiments are required considering the scale of an actual site.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 3 LoRa, Channel and Noise
### Channel and Noise  
Industrial environments are characterized by high noise levels and more frequent interference than other environments, such as domestic areas and offices. This is due to high operating temperatures, intense vibrations, and excessive electromagnetic noise [37, 38]. Also, wireless communication performance can be degraded by the attenuation effect of obstacles and the random movements of objects or people encountered in the propagation path [39].
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Blurring the Images  
Blurring the images is another effective method of image augmentation, particularly useful in medical imaging where fine details can vary significantly, due to differences in scan quality or machine calibration. By applying a blur filter, such as a Gaussian blur, the images simulate the effects of lower resolution or focus that might occur in real-world clinical environments. This type of augmentation is crucial because it trains the model to perform well even when images are not perfectly sharp, mirroring conditions that might be encountered in clinical settings with varying equipment qualities. Moreover, blurring helps to reduce model overfitting to high-frequency noise that is not clinically relevant, thus promoting a focus on more significant, larger patterns within the data. This generalization ensures that the model remains robust and consistent in its diagnostic capabilities, even when presented with images that are less than ideal in terms of clarity and detail resolution.  
Geometric techniques are indispensable when images from diverse patient populations are used, where variations in anatomy and scan orientations are common. Photometric techniques are vital when deploying models across various hospitals or clinics with different imaging equipment and settings, ensuring consistent performance regardless of technological disparities. Both augmentation types are often used in tandem, to prepare robust models capable of handling the range of variations seen in clinical practice. Their combined use ensures comprehensive training, covering both anatomical variations and imaging conditions, which is essential for deploying AI in diverse medical environments. In conclusion, both geometric and photometric augmentation techniques offer distinct and complementary benefits. Their judicious application can significantly enhance the effectiveness and generalizability of deep learning models in medical image processing, ultimately leading to better diagnostic tools and improved patient outcomes.
## 1 Introduction
[13] proposed their adaptive sampling criterions to convert an arbitrary mesh into the Delaunay mesh by splitting non-Delaunay edges. Their methods tended to increase the number of vertices while the original mesh was converted to the Delaunay mesh. Therefore, the computational complexity including space complexity of data storage and time complexity of model processing will be inevitably increased, which may limit the application and development of Delaunay triangulation, especially for the case of original meshes with narrow triangles. Although [PERSON] et al. [14] proved a hybrid mechanism operation of edge splitting and collapsing based on differential equations, which were able to reduce the complexity of the computational processing, the basic principle of algorithms has not been optimized. In this paper, we propose two algorithms for the construction and simplification of Delaunay meshes. The Delaunay construction algorithm can transform arbitrary triangular meshes into the Delaunay meshes with the same topological type. Because the positions of the inserted vertices are calculated according to the geometric information of the mesh, and the identification criteria of the narrow triangles are designed to terminate the algorithm loop, both the space complexity and the time complexity of the Delaunay mesh construction algorithm can be optimized. In addition, a simplification algorithm is designed to simplify the inserted vertices during the construction process, and then it will decrease the data storage of the constructed meshes. The hybrid algorithms can preserve the geometric features of the original CAD model with the principle of minimal volume destruction. The experiments show that the proposed algorithms were effective for optimizing the non-Delaunay meshes, and especially for the improvement of the quality and quantity of the Delaunay meshes.
## 5 Simulation and Analysis
### INCM Beamforming Performance
#### 5.3.3 Relationship between Output SINR and DOA Estimation Error  
To verify the impact of DOA estimation accuracy on each beamforming algorithm, simulations are conducted to compare the output SINR under different error conditions. Figure 9 below compares the performance of various algorithms with the OFDM signal DOA estimation errors within \([-4^{\circ},\,4^{\circ}]\) conditions, where the SNR is set to 20 dB and each group undergoes 100 Monte Carlo experiments. The figure shows that algorithms like INCM-subspace, INCM-volume, INCM-linear, INCM-projection2, and the algorithm proposed in this paper maintain a linear relationship between SINR and DOA error, which indicates that these algorithms can effectively resist the DOA estimation error in a certain range. In contrast, the INCM-projection1 algorithm has a sharp decrease in performance after the DOA estimation error, in the absolute value, exceeds \(2^{\circ}\).
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 5 Results and Discussion
### Local Scour and Bed Topographic Change
#### 5.1.2 Quantitative Analysis  
To test the results of the experiment and simulation intuitively, the local scour depth at the spur dike head is sketched in Figure 6 and listed in Table 3. According to the data in Figure 6 and Table 3, we can see that with the increase in spur dike spacing, the shielding effect of the first spur dike is weakened. This conclusion is consistent with the previous experimentally obtained conclusion. Although there are some differences between the simulation results and the experimental results, the conclusions about the shielding effect of the first spur dike can thus be considered reliable. The calculation needs can be satisfied in terms of local scouring morphology and bed deformation characteristics. On the whole, the numerical calculation reasonably reflected the experimental conclusion. Both experimental and numerical results indicated that the maximum scour depth formed in the vicinity of the first spur dike head (x = 0, y = 12.5 cm for \(2l\) and \(3l\) spacing, and x = \(-40\), y = 12.5 cm for \(4l\) spacing). The maximum local scour depths were 11.45, 11.16, and 11.01 cm in the 3 experiments. The simulated local scour depths were 10.48, 10.12, and 11 cm, corresponding to 92%, 91%, and 100% of those observed in the experiments, respectively. The relative error of local scour depths of other spur dikes is within 30%, mainly because the experiment involved non-uniform sediment and the simulation involved uniform sediment. At the same time, due to the different riverbed deformations of spur dikes I and II, the water flow structure changed, and eventually changed the scouring patterns of spur dikes III and IV. In summary, the different local scouring depths of spur dikes in different locations were mainly due to the difference in the shielding effect of the first spur dike in each case, which differed because the flow field structure around the spur dikes changed as the spacing of the grouped spur dikes was varied.  
Figure 5: Final bed configuration with spur dikes spacing \(2l\), \(3l\), \(4l\). (**a**) experiment for \(2l\) spacing; (**b**) experiment for \(3l\) spacing; (**c**) experiment for \(4l\) spacing; (**d**) simulation for \(2l\) spacing; (**e**) simulation for \(3l\) spacing; (**f**) simulation for \(4l\) spacing.
###### Abstract  
Wireless localization and target sensing both rely on precise extraction of parameters such as signal amplitude, propagation delay, and Doppler shift from the received signals. Due to the high multi-path resolution and strong penetration, both localization and sensing can be achieved through identical UWB waveforms. In this paper, we try to properly allocate resources for localization and sensing to fully exploit the potential of UWB systems. Considering the complexity of the multi-slot networks, we derive the Fisher Information Matrix (FIM) expressions for single-slot and dual-slot integrated sensing and localization (ISAL) networks, respectively, and propose two resource optimization schemes, namely the step-by-stepscheme and the integrated scheme, respectively. The numerical results show that: (i) for the sensing-resource-limited networks with relatively uniform node distribution, the energy allocated to each step in the step-by-step scheme satisfies the relationship energy for clock offset \(<\) energy for radar localization \(<\) energy for target sensing: (ii) in the multi-slot ISAL networks, more energy will be allocated to the time slots where the networks are relatively sensing-resource-limited; (iii) the step-by-step scheme is more suitable for the sensing-resource-abundant networks, while the integrated scheme is more suitable for the sensing-resource-limited networks.  
Keywords:integrated sensing and localization; clock offset; spatiotemporal cooperation; Fisher information; resource optimization allocation +
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