esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CFD Model Validation by Experimental Comparisons and Time Domain Analysis  
We validated the simulation results by comparing them with experimental measurements. To investigate the acoustic characteristics, an FFT was applied. Figure 6 presents the FFT analysis of the simulated noise, recorded by a virtual microphone placed 10 cm in front of the ATH head at a rotational speed of 320,000 rpm. Figure 6 illustrates the FFT analysis result (sampling points \(n=2048\), Hanning window) from the actual experiment at 320,000 rpm.  
Several prominent frequency components appear in both results. The significant peaks in the simulation (5555, 10,555, 16,111, and 21,111 Hz) closely match the experimental results (5200, 10,350, 16,500, and 20,750 Hz). Despite some differences in sound pressure, the predicted characteristic frequencies closely approximate those obtained from actual measurements.  
The frequency deviation between the simulated and experimental results was within 7%, with the largest deviation being 6.8% at the first harmonic. These discrepancies are considered reasonable given the high-speed rotational conditions and the complexity of the airflow field. Minor differences may stem from discrepancies between simulation and experimental conditions, such as fluctuations in the experimental rotational speed, as well as geometric simplifications and mesh resolution limitations near the impeller. Despite these factors, the simulation results successfully captured the major spectral features observed in the experiment. Overall, these results indicate that the current framework accurately captures peak frequencies.  
In addition to spectral validation, the transient behavior of the pressure field was analyzed to further verify the consistency of the simulated aeroacoustic response. Figure 7 shows the time-resolved contours of the pressure fluctuation field from 5 us to 60 us. These snapshots clearly illustrate the outward propagation of concentric acoustic wavefronts originating from the front of the ATH region. The propagation distance of approximately 20 mm over 60 us corresponds to a frequency of \(\sim\)17 kHz, which closely matches the third harmonic (16,111 Hz) observed in the simulated FFT spectrum. This time domain validation strongly supports the physical accuracy of the aeroacoustic source modeling and confirms that the dominant noise arises primarily from rotor-stator interaction.  
Figure 6: Frequency spectrum analysis of aeroacoustic noise emitted by the air turbine handpiece. SPL = sound pressure level. (**a**) Simulated spectrum at 320,000 rpm; (**b**) experimental spectrum at 320,000 rpm.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 2 Control Design
### ESO-Based PIDSMC  
The ESO estimates system states and perturbation. The estimated-state information is used in the control design. The perturbation is combined with PIDSMC input and utilized as a feedback term to compensate the actual perturbation (Figure 2). The estimated PID sliding surface (\(\hat{s}\)) is designed as:  
\[\hat{s}=\dot{\hat{e}}+c_{1}\cdot\hat{e}+c_{2}\cdot\int\hat{e}, \tag{22}\]  
where \(\hat{e}\) is the estimated error (\(\hat{e}=\hat{x}_{1}-x_{d}\)), and \(c_{1}\) and \(c_{2}\) are constant. The actual PID sliding surface (\(s\)) is defined as:  
\[s=\dot{\hat{e}}+c_{1}\cdot e+c_{2}\cdot\int e, \tag{23}\]  
where \(e\) is the error (\(e=x-x_{d}\)), and \(c_{1}\) and \(c_{2}\) are constant (similar to Equation (22)). Equation (22) can be written as:  
\[\hat{s}=\dot{\hat{x}}_{1}-\dot{x}_{d}+c_{1}(\hat{x}_{1}-x_{d})+c_{2}\cdot\int \hat{e}, \tag{24}\]  
by using Equation (6) in (24):  
\[\hat{s}=\hat{x}_{2}+l_{1}\text{sat}\Big{(}\widetilde{x}_{1}\Big{)}-\dot{x}_{ d}+c_{1}(\hat{x}_{1}-x_{d})+c_{2}\int\hat{e}, \tag{25}\]based on Assumption 2, Equation (25) can be updated as:  
\[\hat{s}=\hat{x}_{2}+l_{1}\bigg{(}\frac{\overset{\sim}{x}_{1}}{\varepsilon_{o}} \bigg{)}-\dot{x}_{d}+c_{1}(\hat{x}_{1}-x_{d})+c_{2}\cdot\int\hat{e}, \tag{26}\]  
to satisfy the stability condition (\(\hat{s}\cdot\hat{s}\leq 0.\)), the control input is calculated as follows:  
\[\dot{\hat{s}}=-K^{\prime}\cdot\text{sat}(\hat{s}), \tag{27}\]  
where \(K^{\prime}\) is the gain and sat is the saturation function, defined as:  
\[\text{sat}(\hat{s})= \begin{cases}\frac{\hat{s}}{|\hat{s}|},|\hat{s}|\geq \varepsilon_{c}\\ \frac{\hat{s}}{\varepsilon_{c}},|\hat{s}|<\varepsilon_{c}\end{cases} , \tag{28}\]  
where \(\varepsilon_{c}\) is the boundary-layer thickness. The derivative of Equation (26) can be written as  
\[\dot{\hat{s}}=\hat{x}_{2}+\bigg{(}\frac{l_{1}}{\varepsilon_{0}}\bigg{)}\overset {\sim}{x}_{1}-\ddot{x}_{d}+c_{1}\Big{(}\dot{\hat{x}}_{1}-\dot{x}_{d}\Big{)}+c_ {2}\cdot\hat{e}, \tag{29}\]  
by using Equations (6) and (7) in (29):  
\[\dot{\hat{s}}=\hat{x}_{3}+\bigg{(}\frac{l_{2}}{\varepsilon_{0}}\bigg{)} \overset{\sim}{x}_{1}+u+\bigg{(}\frac{l_{1}}{\varepsilon_{0}}\bigg{)}\overset {\sim}{x}_{1}-\ddot{x}_{d}+c_{1}\bigg{(}\dot{\varepsilon}_{2}+\bigg{(}\frac{l_ {1}}{\varepsilon_{0}}\bigg{)}\overset{\sim}{x}_{1}-\dot{x}_{d}\bigg{)}+c_{2} \cdot\hat{e}, \tag{30}\]  
Equation (30) can be written as:  
\[\dot{\hat{s}}=\hat{x}_{3}+\bigg{(}\frac{l_{2}}{\varepsilon_{o}}+\frac{c_{1} \cdot l_{1}}{\varepsilon_{o}}\bigg{)}\overset{\sim}{x}_{1}+u+\bigg{(}\frac{l_ {1}}{\varepsilon_{o}}\bigg{)}\overset{\sim}{x}_{2}-\ddot{x}_{d}+c_{1}\big{(} \dot{x}_{2}-\dot{x}_{d}\big{)}+c_{2}\cdot\hat{e}, \tag{31}\]  
Figure 2: Detailed block diagram of proposed algorithm. **Assumption 3**.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 3 Experiments
### Experimental Results  
#### 3.4.1 Performance Analysis of Stereo Matching  
Due to the diversity and complexity of road environment and weather environment, it is easy to be affected by many uncertain factors in the modeling process of autonomous driving. Therefore, these problems should be paid attention to in the process of stereo matching. We compare with the state-of-the-art network on scene flow. As shown in Table 2.  
As can be seen from the Table 2, DenseGASPP had the smallest EPE and 1-pixel error with values of 0.67 and 7.2, respectively, which was better than the state-of-the-art networks.  
In order to achieve the best performance of the network, we set up six groups of experiments on the dilation rate and dilation rate interval of the DenseGASPP module when the cost volume scale was fixed at {(1/2), (1/4), (1/8)}. When the dilation was certain, we conducted three groups of experiments in the cost volume stage. The experimental results are shown in Tables 3 and 4, respectively.  
As can be seen from Table 3, when the dilation rate interval was one and the dilation rate combination was {(2,3), (4,5), (6,7)}, the EPE on scene flow was 0.86 and the EPE on KITTI 2015 was 0.69, and the network could achieve the best performance. As can be seen from Table 4, when the cost volume scale was {1/3, 1/6, 1/12}, the EPE on scene flow was 0.67 and the EPE on KITTI 2015 was 0.44, and the network could achieve the best performance.  
\begin{table}
\begin{tabular}{c c} \hline
**Software/Hardware** & **Configuration** \\ \hline CPU & Intel Core i9-10920x \\ GPU & RTX 6000 \\ Operating system & Ubuntu 20.04 \\ CUDA version & CUDA 10.0 \\ Language & Python 3.7 \\ Deep learning framework & PyTorch 1.3.0 \\ \hline \end{tabular}
\end{table}
Table 1: The parameter configuration of the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline
**Network Model** & **EPE** & **>1 px** \\ \hline PSM-Net & 1.09 & 12.1 \\ GA-Net & 0.87 & 9.9 \\ AANet & 0.86 & 9.3 \\ DenseGASPP & 0.68 & 7.2 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of our DenseGASPP network with the state-of-the-art stereo matching networks under the scene flow dataset.  
Based on the experimental results in Table 3 and 4, we selected the dilation rate ((2,3), (4,5), (6,7)), cost volume scale (1/3,1/6,1/12) that should be used as the parameters of the DenseGASPP and multi-scale cost volume, respectively.
## 1 Introduction  
Industrial controls, and in particular, PLC controllers currently form an important technological basis for the automation of industrial processes. Even in the age of industry 4.0 (I40) and industrial internet, it can be assumed that these controllers will continue to be required to a considerable extent for the production of tomorrow. However, the controllers must fulfill a range of additional requirements, resulting from the new production conditions.  
When applying Industry 4.0 principles [1], high-quality networked production systems result, based on cyber physical systems (CPS), also referred to as cyber physical production systems (CPPS). A series of I40 requirements are placed on the future controllers used in these systems. These include:  
* Introduction of the service paradigm in production automation (production services);
* Autonomy, reconfigurability and agility (plug and work);
* Overcoming the strict information encapsulation of controllers;* Networking in local and global networks;
* Interoperability between heterogeneous control systems;
* Dependencies are to be changeable dynamically at runtime;
* Use of models for the development of \"higher-quality\" control approaches;
* Orchestration of heterogeneous controllers.  
Current PLC controllers cannot yet fulfill the majority of these requirements or can only do so on a rudimentary basis or at extremely high expense.  
The paper describes the concept and two prototype implementations for a new type of a PLC controller in which the controller functions (control programs) will be implemented as smart control services in a cloud. The programming of this new PLC occurs as is usual in industry, pursuant to the standard IEC 61131-3.
# Effect of Inense Ash on the Engineering Properties of Cement-Based Composite Material
## 3 Results and Discussion
### Durability of Incense Ash Paste  
The number of pores is closely associated with the strength and durability of concrete. One method of assessing the durability of a specimen is estimating its water permeability based on its water absorption ratio. As Figure 6 indicates, at various incense ash replacement ratios, the water requirement decreased with the W/C. The number of pores is primarily determined by the amount of water used when mixing; therefore, the water absorption ratio increases with the water used. At a water/binder ratio (W/C) = 0.3, the water absorption test results of specimens cured for 3 days revealed that when the incense ash replacement ratio was 0%, 5%, 10%, 20%, and 30%, the water absorption ratio was 6.91%, 6.84%, 6.93%, 7.01%, and 7.01%, respectively. Because incense ash is finer than cement, more voids are created. Therefore, at a high replacement ratio, the increase in the number of pores results in a higher water absorption ratio. This is particularly evident at the 30% replacement ratio. Additionally, the water absorption ratio dropped as the curing time lengthened. This is because the pozzolanic reaction of Ca(OH)\({}_{2}\) increased the cohesion of the cement mortar, and the hydration products also helped in filling the pores [39], causing the water absorption ratio to drop.
# Autonomous Environment Generator for UAV-Based Simulation
## 3 Experimental Setup
### YOLO v4 (Python)  
The image and coordinate data get sent to our custom python script for YOLO v4 processing. The purpose of using YOLO v4 was to locate particular items in the given satellite image stored in the backend. Our YOLO v4 algorithm was specifically trained to detect buildings, roundabouts, and trees to create the basic models of the world in simulation (see Figure 3). Once YOLO v4 detects these objects, they can be properly generated in their appropriate world position and scale in the Gazebo simulator. A more detailed description of YOLO v4 and the following Mask R-CNN description will be provided later.  
Figure 3: Sample YOLO v4 detection in location provided by China Center for Resources Satellite Data and Application (CRESDA). Showcases ability to segment buildings in a monochromatic image.  
Figure 2: The Beyond Skyline Cloud Platform, including the satellite view of the map and the EasyPrint button in the top left corner.
## 1 Introduction  
Nowadays, virtualized environments have gained much popularity thanks to ease of deployment and installation and are being used in various contexts such as: server/cloud deployment of applications provided by various parties possibly taking into account resource preferences of different applications [1]; IoT systems at various levels, i.e., server/cloud [2], middleware [3], gateway/fog [4] and sensor/edge [5] and, finally, in high-performance computing (HPC) systems potentially as a way to deploy applications in the cloud [6]. In the latter, it is of special interest to investigate potential performance impacts as HPC systems have been developed primarily with performance in mind and both execution times as well as scalability measured with speed-ups are of key importance. It can be noticed that virtualized environments can be used to run various workloads in parallel, from, e.g., multiple Cassandra clusters [7], through multiple DeviceHive IoT middleware containers [3] to compute intensive HPC applications [8].  
Contribution of this work is investigation of performance and overhead of a virtualized Docker versus native multi-node environment built using commodity hardware (multi-core CPUs and 1 Gbs network) for running parallel MPI applications that are representative of most common parallel programming paradigms, such as: dynamic master-slave (Master-Slave), geometric Single Program Multiple Data (SPMD), as well as divide-and-conquer (DAC) [9]. We perform detailed analysis of execution times and speed-ups for all 1 to n tested numbers of processes and our specific contribution is consideration of best configurations taking into account various compilation flags and numbers of processes which turn out to be different for various applications.
# A New Smoke Segmentation Method Based on Improved Adaptive Density Peak Clustering
## 4 Results  
### Experimental Design  
The accuracy rate (Acc), precision rate (\(P\)), recall rate (\(R\)), and F-measure (\(F_{1}\)) are used as the evaluation indicators.  
\[\text{Acc}=(N_{TP}+N_{TN})/(N_{TP}+N_{TN}+N_{FP}+N_{FN}) \tag{10}\]  
\[P=N_{TP}/(N_{TP}+N_{FP}) \tag{11}\]  
\[R=N_{TP}/(N_{TP}+N_{FN}) \tag{12}\]  
Figure 3: Automatic selection of clustering centers via tangent value calculation.  
\[F_{1}=(2\times P\times R)/(P+R) \tag{13}\]  
where the \(N_{TP}\) represents the number of pixels that are correctly segmented into the smoke area; the \(N_{FP}\) is the number of pixels incorrectly segmented into the smoke area; the \(N_{FN}\) is the number of pixels incorrectly segmented into the background area; the \(N_{TN}\) is the number of pixels that are correctly segmented into the background.  
In order to better verify the accuracy of TSDPC for fire smoke image segmentation, this paper also uses the mean intersection over union (mIoU) and volume overlap error (VOE) to evaluate the performance of the model. The larger the mIoU or the smaller the VOE, the better the segmentation effect. The mIoU is calculated by Equation (14).  
\[\text{mIoU}=\frac{1}{k+1}\underset{i=0}{\overset{k}{\sum}}\frac{p_{ii}}{\sum_{ j=0}^{k}p_{ij}+\sum_{j=0}^{k}p_{ji}-p_{ii}} \tag{14}\]  
where, since each pixel in the image has a category label, we assume that the total number of categories is \(k+1\), including \(k\) object categories and 1 background. \(p_{ij}\) represents the number of pixels of category \(i\) predicted to be category \(j\).  
The VOE is calculated by Equation (15).  
\[\text{VOE}=1-\frac{\text{V}_{\text{seg}}\cap\text{V}_{\text{gt}}}{\text{V}_{ \text{seg}}\cup\text{V}_{\text{gt}}} \tag{15}\]  
where \(\text{V}_{\text{seg}}\) indicates the predicted smoke area; \(\text{V}_{\text{gt}}\) indicates the real smoke area.
###### Abstract  
The Magallanes region, in southern Chile, is positioned as a strategic hub for the production of green hydrogen (GH2), green ammonia, and synthetic fuels, thanks to its exceptional wind potential and commitment to sustainability. This article analyzes the opportunities and challenges of these energy vectors in the context of global decarbonization, highlighting the key role of the Magallanes region in the energy transition. Green hydrogen production, through wind-powered electrolysis, takes advantage of the region's constant, high-speed winds, enabling competitive, low-emission generation. In turn, green ammonia, derived from GH2, emerges as a sustainable alternative for the agricultural industry and maritime transport, while synthetic fuels (e-fuels) offer a solution for sectors that are difficult to electrify, such as aviation. The sustainability approach addresses not only emissions reduction but also the responsible use of water resources, the protection of biodiversity, and integration with local communities. The article presents the following structure: (i) introduction, (ii) wind resource potential, (iii) water resource potential, (iv) different forms of hydrogen and its derivatives production (green hydrogen, green ammonia, and synthetic fuels), (v) pilot-scale demonstration plant for Haru Oni GH2 production, (vi) future industrial-scale GH2 production projects, (vii) discussion, and (viii) conclusions. In addition, the article discusses public policies, economic incentives, and international collaborations that promote these projects, positioning Magallanes as a clean energy export hub. Finally, the article concludes that the region can lead the production of green fuels, contributing to global energy security and the fulfillment of the Sustainable Development Goals (SDGs). However, advances in infrastructure, regulation, and social acceptance are required to guarantee a balanced development between technological innovation and environmental conservation.  
on-shore wind energy; desalinated water; green hydrogen; green ammonia; green e-fuels; sustainability
## 2 Materials and Methods
### Groundwater Numerical Simulation
#### 2.2.3 Groundwater Flow Numerical Model
By adjusting the permeability and storage coefficients, the simulated water levels were made to closely correspond to the actual observation well water levels, ensuring that the flow field's dynamic changes aligned with observed data. Four observation wells (Well 2020-Taiguan2, Well 2022-Taiguan3, Well 2022-Taiguan4, and Well 2022-Taiguan5) were selected for identification and validation. The hydrogeological parameters in different zones were continuously refined to ensure a close match between the observed and calculated water levels. The changes in both observed and calculated water levels for the four wells are consistent, demonstrating a good fit. Although the water level for Well 2022-Taiguan5 showed a minor discrepancy over the first 4 months, the overall trend remained consistent, indicating a satisfactory fit overall (Figure 9). By making appropriate adjustments to the boundary conditions and dewatering rates in the simulation area and through multiple simulation iterations, the results were refined to better align with actual conditions. This process led to the determination of optimal hydrogeological parameters (Table 2). In the established model, the average hydraulic conductivity was 0.0342. Among the partitions, Zone 5 exhibited the lowest hydraulic conductivity of 0.0021, while Zone 2 showed the highest value of 0.09. The average storage coefficient was 6.51 \(\times\) 10\({}^{-6}\), with Zone 7 having the maximum storage coefficient of 2.0 \(\times\) 10\({}^{-5}\) and Zone 3 possessing the minimum value of 1.0 \(\times\) 10\({}^{-12}\). Figure 9: Observed and calculated water level changes for wells: (**a**) Well 2020-Taiguan2; (**b**) Well 2022-Taiguan3; (**c**) Well 2022-Taiguan4; and (**d**) Well 2022-Taiguan5.
## 5 Discussion
### Depositional Palaeenvironment
#### 5.2.2 Palaeosalinity  
Strontium (Sr) is easily dissolved in water and can migrate with water, whereas barium (Ba) can be adsorbed and precipitated by clay minerals or other ions, reducing its content in water [81]. Thus, the Sr/Ba ratio can indicate whether the depositional environment is terrestrial or marine. Sr/Ba < 0.6 indicates slightly brackish water deposition; Sr/Ba = 0.6-1.0 indicates brackish water deposition; Sr/Ba > 1.0 indicates salt lake or marine deposition [54]. The Sr/Ba ratios of the No. 15 coal samples are greater than 1, except for the roof sample, with an average value of 7.20, indicating that the coal is deposited in a marine environment.  
The Th/U ratio can also determine marine and terrestrial depositional environments. Th/U > 7.0 indicates a terrestrial freshwater environment; Th/U = 2.0-7.0 indicates slightly brackish water to the semi-saltwater environment; Th/U < 2.0 indicates a marine saltwater environment [82]. The average Th/U ratio of the No. 15 coal is 1.78, which is less than 2.0, indicating a marine saltwater environment.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 3 Simultaneous Target Localization and Unmanned Aerial Vehicle (UAV) Trajectory Optimization
### Simultaneous Target Localization and UAV Trajectory Optimization
#### 3.2.3 Generalization to Other Motion Models  
Note that the above description of the simultaneous target localization and UAV trajectory optimization was based on the assumption that the target has a constant velocity motion mod0 el. However, the above algorithm is also applicable to other motion modelssuch as the constant acceleration model and constant turn motion model, which are the most widely used motion models [28].  
As different motion models of the moving target have different state transition functions with the different dimensionalities of the target state vector, the target estimation procedures are also different for different motion models. For instance, for the constant acceleration motion model, the state transition function has the same form as (9), except that the transition matrix and noise input matrix have different forms:  
\[\mathbf{x}_{k}=\mathbf{\Phi}_{k|k-1}^{a}\mathbf{x}_{k-1}+\mathbf{\Gamma}_{k|k-1}^{a} \mathbf{v}_{k-1}^{a} \tag{32}\]  
where  
\[\mathbf{\Phi}_{k|k-1}^{a}=\left(\begin{array}{ccc}\mathbf{I}_{2\times 2}&\Delta t \mathbf{I}_{2\times 2}&\frac{1}{2}\Delta t^{2}\mathbf{I}_{2\times 2}\\ \mathbf{0}_{2\times 2}&\mathbf{I}_{2\times 2}&\Delta t\mathbf{I}_{2\times 2}\\ \mathbf{0}_{2\times 2}&\mathbf{0}_{2\times 2}&\mathbf{I}_{2\times 2}\end{array}\right) \tag{33}\]  
and  
\[\mathbf{\Gamma}_{k|k-1}^{a}=\left(\begin{array}{c}\frac{1}{2}\Delta t^{2}\mathbf{I} _{2\times 2}\\ \Delta t\mathbf{I}_{2\times 2}\\ \mathbf{I}_{2\times 2}\end{array}\right) \tag{34}\]  
denote the transition matrix and noise input matrix, respectively. Additionally, note that the target state vector is of six dimensions, as \(\mathbf{x}_{k}=[x_{g,k},y_{g,k},v_{x,k},v_{y,k},a_{x,k},a_{y,k}]^{\top}\), where the last two elements denote the acceleration variables; and \(\mathbf{v}_{k-1}^{a}\) is noise vector of the acceleration. Corresponding to this target motion model, the predicted information matrix involved with the position variables can be obtained by dropping the columns and rows of order from 3 to 6 of the full information matrix.  
On the other hand, the transition matrix will be replaced with the Jacobian matrix obtained at the previous state if the target has a constant turn motion model because the state transition function is nonlinear. The target state also contains the turn rate of the target compared with the constant velocity model. Hence, the position-related predicted information matrix can be acquired by dropping the columns and rows of order from 3 to 5. The corresponding noise input matrix and noise vector is also changed according to the constant turn model. For more details, we refer the reader to [28].