esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Wear Characterization and Durability Enhancement of an Aeronautic Braking System
## 2 Materials and Methods
### Test Bench and Monitoring Parameters  
The brake was tested on a test bench with a slope that was determined considering the range of positions of the brake in a real working environment. A set of monitoring parameters were measured through the test rig, allowing the conditions of the brake to be checked in terms of wear, and the wear progress was correlated with changes in these parameters. Measurements were performed at the beginning, in the middle and at the end of each test.  
The monitoring parameters were:  
Engagement threshold voltage \(V_{e}\), which represents the minimum supply voltage for the solenoid to be able to engage the anchor. Variations in this parameter give an indication of the wear conditions of the surfaces. In particular, when the wear increases, the disk pack volume is reduced and the air gap increases, resulting in an increase in the threshold voltage. The wear progress gradually increases the air gap until the solenoid is no longer able to engage the anchor and the system fails. In the experimental characterization, this parameter was measured by manually supplying the brake and gradually increasing the voltage.  
Release threshold voltage \(V_{r}\), which represents the minimum supply voltage for the solenoid to keep the anchor engaged. Variations in this voltage also provide information about the wear progress. In fact, if the air gap is wider due to wear, the initial release voltage slowly becomes insufficient to keep the anchor engaged with respect to the elastic force exerted by the springs. When this condition is reached, the solenoid partially or totally releases the anchor, even at voltages compatible with engagement conditions. The measurement of this voltage was performed by gradually reducing the voltage to the solenoid while the anchor was engaged until the anchor was released and checking eventual partial releases.  
Inductance \(H\), which provides information about variations in the air gap and therefore about wear. In fact, if the air gap becomes wider due to wear, the reluctance increases with a consequent inductance decrease. During the middle check, the inductance was measured through an LCR tester to check that the value was between the established minimum and maximum.  
Braking torque \(T_{b}\), which represents the fundamental performance parameter of the brake and which is strongly influenced by the tribological conditions of the surfaces. This parameter was measured by using a torque wrench and evaluating the maximum torque that can be applied without the occurrence of slips between rotors and stators.
## 4 Results
### 3D Full Head/Face Scanning and Parametric Head Model
#### 4.1.1 Photogrammetry  
Photogrammetry presented quality and consistency issues. The main drawbacks this technology poses are its reliance on the steadiness of the operator, the space limitations for its operation, and the zenithal image capturing. Figure 10 shows the 3D model of the human head where the lack of cranial images leads to a heavily distorted head model that has little detail and use for customized product development. But recent drone-based and automated image capturing systems could help improve these results to allow access to a more reliable and low-cost 3D capturing method.
## 1 Introduction
Nevertheless, both standalone and add-in tools routinely exhibit limited capacity to represent other geospatial systems and are often not updated for use in newer GIS software environments. Additionally, fundamental components of geographic CA models like neighborhood functions or transition rules are repeatedly disconnected from theoretical terminology, lack flexibility, are entirely hard-coded, or are replaced with black-box mechanisms that impede transparency necessary for supporting real-world decision making. Consequently, the rigidity and black-box nature of many specialized GUI-based CA modeling tools alienate multi-disciplinary researchers who may have benefited from even basic model-building functionality. To address the hyper-specialized, fragmented, and programmer-centric nature of modern geospatial technologies and workflows supporting CA, the main goal of this study is to develop a general-purpose, user-friendly geosimulation modeling tool accessible in a contemporary GIS software environment. The proposed framework and add-in functions should support end-to-end CA model-building activities, which include data pre-processing, model parameterization, model execution, model evaluation, and visualization of simulation outcomes. To meet the primary objective, this research describes the design and development of the _Geographic Automata_ add-in. The add-in is compatible with ArcGIS Pro 3.1 and newer versions. The proposed framework and features aim to (1) provide generic functions for creating binary or multi-class representations of various geospatial systems and (2) facilitate end-to-end model-building workflows that can be entwined with ArcGIS Pro functionality. The utility and capabilities of the _Geographic Automata_ add-in are demonstrated via two case studies involving hypothetical and real-world geospatial datasets. The first case study focuses on multi-class insect infestation modeling encodedwith previously reported behaviors, while the second case study introduces the inaugural use of automated machine learning (AutoML) with CA to inform urban growth allocations.
# Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS
## 4 Results
### Comparison Between FY-3F Retrieval Results and METOP-C Products
The red and yellow bars represent the probability density distributions of IWP values equal to zero from FY-3F and METOP-C, respectively, while the orange bars indicate their overlapping areas.
## 2 Materials and Methods
### Data Sets
#### 2.2.3 SPEI Data  
The SPEI data set for the period from 1901 to 2015, with a monthly frequency temporal resolution and a 0.5\({}^{\circ}\) spatial resolution at timescales from 1 to 48 months, was obtained from the Consejo Superior de Investigaciones Cientificas (CSIC; [[http://spei.csic.es/database.html](http://spei.csic.es/database.html)]([http://spei.csic.es/database.html](http://spei.csic.es/database.html))). The SPEI value at each pixel was calculated based on the difference between precipitation and potential evapotranspiration (PET) to describe the drought conditions with respect to normal conditions for a given period. This process revealed that a decrease in the SPEI may be caused by an abnormal increase in the PET or a decrease in precipitation. In this SPEI data set, the PET was calculated based on the FAO-56 (Food and Agriculture Organization of the United Nations, Irrigation and Drainage Paper 56) Penmann-Monteith equation [36], which integrates the mass transfer and energy balance and has been widely used around the world [5; 7]. In addition, the SPEI has different implications at different timescales. For example, the SPEI for longer timescales, such as 12 or 24 months, can reflect medium-term trends in precipitation patterns and may provide an annual estimation of the reservoir levels, stream flows, and even groundwater levels. While for shorter timescales, such as 1 to 3 months, the SPEI can be used to mirror prompt changes in soil moisture, which is particularly important for biomass production. Because the aim of the study was to investigate the drought variations and their impacts on vegetation areas, which are sensitive to soil moisture, a 3-month timescale SPEI for the period of 1982 to 2015 was  
Figure 1: Study area and the distribution of biome types. The land cover classification map was obtained from the [PERSON] et al. [33].  
selected to reveal the variations in drought characteristics, including the drought duration, intensity, and frequency, during the growing season in the northern hemisphere.
# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
## 4 Proposed Work
By performing limited sampling and putting constraints over the responses, LLMs are made capable enough to perform zero-shot learning in varying computing scenarios of the fog environment. The actor-critic Q-learning algorithm usually finds it difficult to derive the policies through the implicit exploration process. Just by applying sample efficiency, proper reward reshaping is not possible and often ends up with the wrong heuristic value. Hence, the use of LLMs represents a promising approach for reward reshaping through autonomous state space exploration for Q-learning agents. Any possible inaccuracies in the learning steps are handled through fine tuning of the learning steps, which enhances the speed of inference and minimizes the impact of hallucination. Figure 1: The high-level architecture of LLM-guided Q-learning framework for resource provisioning. ```
1:Start
2:Input: IoT device request set: \(\textbf{IoT}(\textbf{REQ})=\{\textbf{Req}_{1},\textbf{Req}_{2},\textbf{Req}_{3}, \textbf{Req}_{4},\ldots,\textbf{Req}_{n}\}\), Markov Decision Process MDP, Large Language Model G, Prompt P
3:Output: Resource provisioning policies i.e., \(\textbf{RPP}=\{\textbf{Rpp}_{1},\textbf{Rpp}_{2},\textbf{Rpp}_{3},\textbf{Rpp}_{4 },\ldots,\textbf{Rpp}_{n}\}\)
4:Training phase of LLM_QL
5: Initialize the Heuristic Q buffer: MDP(G(P)), actor-critic \((\mu_{\emptyset},\textbf{Q}_{\theta 1},\textbf{Q}_{\theta 2})=\textbf{NULL}\), and target actor-critic \((\mu_{\emptyset^{\prime}},\textbf{Q}_{\theta 1^{\prime}},\textbf{Q}_{ \theta 2^{\prime}})=\textbf{NULL}\)
6: Generate Q buffer: \(\textbf{D}_{g}\leftarrow\{(\textbf{S}_{i},\textbf{a}_{i},\textbf{Q}_{i})|( \textbf{S}_{i},\textbf{a}_{i},\textbf{Q}_{i})\in\textbf{MDP}(G(P)),\textbf{i}=1,2,\ldots,\textbf{n}\}\), where \(\textbf{S}_{i}\) = state of the agent at time step \(i\), \(\textbf{a}_{i}\) = action performed at time step i, and \(\textbf{Q}_{i}\) = Q value for action \(\textbf{a}_{i}\) in state \(\textbf{S}_{i}\).
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 2 Principle of 3D Reconstruction of RGB-D Camera  
The three-dimensional reconstruction technology of the RGB-D camera mainly includes the acquisition of RGB image and depth image, image data preprocessing, point cloud registration and fusion, and its ability to transform a real scene into a mathematical model that conforms to the logical expression of computer. At present, the commonly used RGB-D cameras mainly include the structured-light [17] depth camera (Figure 1a), which emits light with feature points to objects with smooth surfaces and no features, and assists in extracting the depth information of objects according to the three-dimensional information in the light source; the time of flight (TOF) [18; 19] depth camera (Figure 1b), which obtains the distance by measuring the interval of flight time between the transmitted signal and the received signal on the premise of a certain speed of light and sound; and the binocular stereo [20] depth camera (Figure 1c), a passive depth perception method that simulates the principles of human vision.  
The RGB-D camera can capture color images and depth images. As shown in Figure 2, RGB images are obtained by varying the three color channels of red (R), green (G), and blue (B) and superimposing them on each other to obtain a variety of colors. RGB is the color system representing the three channels of red, green, and blue. This standard includes almost all the colors perceived by human vision and is one of the most widely used color systems at present. In 3D computer graphics, a depth image is an image or image channel that contains information related to the distance of the surface to the scene object at the point of view. In this case, the depth image is similar to a grayscale image, except that each of its pixel values is the actual distance of the sensor from the object. According to the colors shown in Figure 2b, the blue part and the red part of the figure represent different ranging depths, increasing in depth from blue to red. Usually, RGB images and depth images are aligned and thus have a one-to-one correspondence between pixel points. In addition, the color image provides the (x, y) coordinate values in the image pixel coordinate system, and the depth image provides the distance value from the camera to each pixel in the scene, that is, the (z) coordinate value in the camera coordinate system.
## 5 Field Application of Active Bearing of the FSP  
### Implementation Process of Active Bearing  
After the FSP is installed in the tunnel, a simple loading device needs to be applied to preload the end of the steel pipe so as to achieve the active bearing effect of the FSP. At the same time, before the steel pipe preloading, the appropriate preloading amount shall be calculated according to the surrounding rock conditions, and the steel pipe preloading shall be carried out quickly on the premise of basically not interfering with the construction so as to better conduct the tunnel site test and application.  
The tunnel construction environment is complex, and the setting position of the FSP and the included angle with the arch are different. What is worse, there is no stable loading platform in the tunnel. Therefore, it is the key factor to realize the active load-bearing of the FSP to preload the end of the FSP more conveniently in the tunnel.  
According to the structural form of the arch frame and the loading test of the FSP, the loading support suitable for the steel arch frame is designed. The loading support is easy to install and can adapt to the vertical loading of the FSP at different positions of the arch frame. Details of the loading device can be seen in Figure 19.  
To ensure that the active bearing design scheme and loading device of FSP can be used stably in the tunnel, the specific construction steps are as follows:  
(1) After the excavation of the upper bench of the tunnel is completed, the steel arch shall be erected, and the FSP shall be driven. The end of the steel pipe shall be sealed before driving to prevent the soil from entering;  
(2) A self-designed loading support is installed at a position 30 cm upward from the end of the steel pipe. One hand holds the handle of the loading base, hooks one end of the  
Figure 19: Detail drawing of loading device.  
\"7\"-shaped screw to the arch or grating steel frame, and the other hand rotates the nut to temporarily fix it and then uses a wrench to tighten it [17]. The device can bear several tons of weight;  
(3) A loading jack device and a connecting member between the steel pipe and the loading device are installed under the loading support;  
(4) To ensure the safety of the loading process, adjust the angle adjusting screw at the bottom of the loading support to keep the loading device vertical or perpendicular to the steel pipe. The installation diagram can be seen in Figure 20;  
(5) Start loading, and stop loading when the preload amount reaches the design requirements;  
(6) If active loading of steel pipes is to be carried out in the middle of tunnel deformation, foam filling shall be carried out within 30 cm of the connection between steel pipes and steel arches to reserve the loading space. After the concrete spraying is completed and reaches a certain strength, the steel pipe can be loaded with the help of concrete as the loading surface;  
(7) To achieve the active bearing effect, after loading, the steel arch is overlapped or welded with the steel pipe, and then the loading device is removed.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 3 Results
### The Coupled Tank Control Results
#### 3.2.2 The Dahlin Algorithm  
Let the chosen sampling period be \(T=1\) s, and the time constant of the desired aperiodic response be \(1/\lambda=10\) s. Then, the transport delay for the CT system is \(\tau=1\) s.  
Based on the system parameters presented in Table 6 and relation (23), the transfer function of the Dahlin controller is:  
\[D(z)=\frac{4.3027\cdot 10^{5}\cdot(1-0.97/79\cdot z^{-1})\cdot(1-0.9949\cdot z^{ -1})}{(1-0.9048\cdot z^{-1}-0.09516\cdot z^{-2})\cdot(1+0.9909\cdot z^{-1})}. \tag{35}\]  
The Simulink model and the output of the CT system with the Dahlin controller are shown in Figures 19 and 20, respectively.  
Figure 17: The CT linearized SISO model.  
Figure 18: The linearized CT model response.  
The transfer function of the modified Dahlin controller with the \"no ringing\" effect of the control signal, based on (25), is:  
\[D(z)=\frac{1.9734\cdot 10^{5}\cdot(1-0.9906\cdot z^{-1})\cdot(1-0.9926\cdot z^{-1 })}{1-z^{-1}}. \tag{36}\]  
Control signals from the Dahlin controller with the effect of \"ringing\" and from the modified Dahlin controller with \"non-ringing\" behavior are shown in Figure 21.  
Figure 21: Control signals from: (**a**) The Dahlin controller with ringing; (**b**) The modified Dahlin controller with non-ringing, for CT system.  
Figure 19: The Simulink model of the CT system with the Dahlin controller.  
Figure 20: The output of the CT system with the Dahlin controller.  
The transfer function of the Dahlin PID controller, which is the variant of the modified Dahlin controller, based on (26) and (27), is:  
\[D(z)=5.3115\cdot 10^{3}\bigg{[}1+\frac{0.0042}{1-z^{-1}}+36.148\cdot(1-z^{-1}) \bigg{]}. \tag{37}\]  
The outputs of the CT system with the different Dahlin controllers are presented in Figure 22.  
The obtained output of the coupled tank system with the Dahlin controller has a given response time of 10 s and an expected delay time of 1 s. The expected aperiodic output with no oscillation and no overshoot was achieved, with a rise time of \(T_{s}=21\) s and setting time of \(T_{s}=41\) s as shown in Table 7.  
Control signals from the modified Dahlin controller and the Dahlin PID controller are shown in Figure 23.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Dahlin Algorithm** \\ \hline Rise time & 21 s \\ \hline Overshoot & 0 \\ \hline Setting time & 41 s \\ \hline Steady-state error & 0 \\ \hline \end{tabular}
\end{table}
Table 7: The parameters of the transient response with the Dahlin controller.  
Figure 23: Control signals from the modified Dahlin controller and the Dahlin PID controller.  
Figure 22: The outputs of the CT system with the different Dahlin controllers.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 2 Materials and Methods
### Front-End Feature Extraction Module  
The front-end feature extraction module is based on an improved PointNet architecture that first resolves the unordered nature of the point clouds. Let a single point cloud be \(X\)\(\in\)\(R^{N\times D}\). A T-Net learns an alignment matrix \(T_{\text{in}}\)\(\in\)\(R^{D\times D}\) to ensure invariance to rotation and translation, mapping the raw point cloud as:  
\[\mathbf{X^{\prime}}=\mathbf{X}\mathbf{T}_{in},\ \mathbf{X^{\prime}}\in\mathbb{R}^{N \times D} \tag{1}\]  
where, \(\mathbf{X^{\prime}}\) is then fed into three MLP layers that successively map it to 64-, 128-, and 128-dimensional spaces, yielding local features \(\mathbf{F}_{1}\in\mathbb{R}^{N\times 128}\); \(T_{\text{in}}\) is A T-Net learns an alignment matrix.  
Next, a second T-Net learns an alignment matrix \(\mathbf{T}_{f}\in\mathbb{R}^{128\times 128}\), which aligns \(\mathbf{F}_{1}\):  
\[\mathbf{F}_{1}^{\prime}=\mathbf{F}_{1}\mathbf{T}_{f} \tag{2}\]  
where, the aligned \(\mathbf{F}_{1}^{\prime}\) is then passed through two additional MLP layers to produce \(\mathbf{F}_{2}\in\mathbb{R}^{N\times 1024}\), after mapping to 512 and then 1024 dimensions; \(\mathbf{T}_{\text{f}}\) is a T-Net learns an alignment matrix.  
To extract global context, a max-pool operation over the \(N\) points is applied to \(\mathbf{F}_{2}\), yielding a global feature vector \(g\in\mathbb{R}^{1\times 1024}\):  
\[g=\max_{1\leq i\leq N}(\mathbf{F}_{2}[i,:])\in\mathbb{R}^{1\times 1024} \tag{3}\]  
The local feature \(\mathbf{F}_{1}\), the high-dimensional local feature \(\mathbf{F}_{2}\), and the global feature \(g\) are concatenated along the feature channel dimension to form a multiscale fused feature:  
\[\mathbf{H}[i]=[\mathbf{F}_{1}[i]\|\mathbf{F}_{2}[i]\|g]\in\mathbb{R}^{1\times (128+1024+1024)},i=1,\ldots,N \tag{4}\]  
An MLP then maps \(\mathbf{F}_{\text{concat}}\) down to dimension \(\mathbf{F}\), producing an initial high-dimensional feature \(\mathbf{F}_{high}\in\mathbb{R}^{N\times F}\). Since relying solely on high-level features can weaken  
Figure 1: Schematic of the overall framework of the FFN.  
shallow geometric information, FFN introduces a residual connection by concatenating the original shallow geometric feature \(\mathbf{X}\) with \(\mathbf{F}_{high}\):  
\[\mathbf{Z}[i]=[\mathbf{X}[i]\|\mathbf{F}_{high}[i]]\in\mathbb{R}^{1\times(D+F)} \tag{5}\]  
Finally, \(\mathbf{F}_{res}\) is passed through one more MLP to reduce its dimension back to \(F\), yielding the front-end output feature \(\mathbf{F}_{out}\in\mathbb{R}^{N\times F}\). In this way, \(\mathbf{F}_{out}\) preserves both shallow geometric detail and global semantic context, providing high-quality descriptors for the back-end clustering stage.
## 3 Results
### Antibiofilm Activity  
The effect of the two solvent mixtures MEK/TCE and MEK/OOil was evaluated on _C. albicans_ SC 5314 biofilms formed during 24 h (Figure 1) by comparison with the solvents alone and the standard protocol. Washing with NaOCl and EDTA (standard procedure in clinical practice) resulted by itself in a reduction of about 1 Log (CFU/cm\({}^{2}\)) (Figure 1A). After contact of the biofilm with both solvent mixtures, a further significant reduction in biomass was observed. Interestingly, the MEK/OOil association caused a total elimination of the preformed biofilm. MEK/TCE achieved a 3-log reduction compared with the use of NaOCl and EDTA and a 4-log reduction compared with the control (Figure 1A). The subsequent contact of the NaOCl-EDTA-treated _C. albicans_ biofilms with the isolated solvents showed different efficacies, with complete cell eradication after TCE and OOil exposures, and a significant reduction (CFU/cm\({}^{2}\)) with MEK, showing good efficacy of these compounds by themselves.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solvent** & **Inhibition Zones (mm)** \\ \hline MEK & 8.7 \(\pm\) 1.2 \\ TCE & 9.0 \(\pm\) 1.4 \\ OOil & 50.8 \(\pm\) 7.2 * \\ MEK/TCE (1:1) & 13.8 \(\pm\) 1.8 \\ MEK/OOil (1:1) & 45.0 \(\pm\) 5.0 * \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antimicrobial activity (disk diffusion method) of different solvents against _C. albicans_ SC 5314 (* means \(p<0.05\)).  
Figure 1: **(A)** Viable cell counts of _Candida albicans_ SC 5314 biofilms performed after 24 h of aerobic incubation at 37 \({}^{\circ}\)C. Values of cultivable sessile cells were expressed as Log CFU per area (cm\({}^{2}\)). **(B)** SEM analysis of the development of 24-h biofilms of _C. albicans_ SC 5314 after exposure to the conventional final irrigation protocol (3%NaOCl + 17%EDTA) followed by the solvent mixtures (MEK/TCE or MEK/OOil).  
The SEM results (Figure 1B) show the benefit of applying the suggested mixtures (MEK/TCE or MEK/OOil) after treatment with NaOCl and EDTA.