esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Preparation of Mg Alloy Disks and Coating Procedures  
Specimens were cut from the AZ31 magnesium alloy bars. Small disks with dimensions 13 mm diameter and 2 mm thickness were obtained. Each sample was mechanically ground up with 1200-gri SiC paper, ultrasonically cleaned in acetone, and rinsed with ethanol and distilled water. PHB was dissolved in trichloromethane (analytical grade) at a concentration of 1 wt%. Organic PHB dip coating on Mg AZ31 alloy disk was carried out by a two-step process: first a 2 h hydrothermal treatment at 100 \({}^{\circ}\)C [43], the scope of which was to obtain an uniform surface for all samples with a homogeneous Mg(OH)\({}_{2}\) layer that favors polymer coating adhesion, was carried out followed by dip coating in the PHB solution with subsequent solvent evaporation at 20 \({}^{\circ}\)C for 24 h. Magnesium AZ31 alloy control specimens without polymer coating were also collected after the hydrothermal treatment. Polymer coating thickness was indirectly determined by gravimetric measurements of the samples before and after the coating procedure. Sample weight increase after solvent evaporation of the 1% polymer solution was rationally assumed to be due to the formation of a homogeneous thin polymer film covering the entire sample surface, the thickness of which was extrapolated considering the exposed surface area of the sample (3.47 cm\({}^{2}\)) and PHB polymer density (1.25 g/cm\({}^{3}\)). The gravimetric calculated mean coating thickness was 52 \(\pm\) 6 \(\upmu\)m.
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
In addition, the percentage of selected papers per main aim is depicted in Figure 6. Articles were categorized into two groups based on the presentation or not of novel retracking algorithms for the retrieval of inland water levels. As can be seen, there is a significant number of articles in the category of evaluating satellite mission performances using existing retracking algorithms (77% 137 articles) compared to the category showcasing novel and improved retracking algorithms (23% 11 articles) for inland water level monitoring. More findings regarding this research question are presented in Section 3.2. Figure 2: Modified Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart illustrates the different phases of the scoping review paper and the number of selected articles at each stage of the process. \begin{table}
**Relevant publications** & & & & \\
**classified as Relevant** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion Matrix based on visual inspection analysis, indicating the percentage of predicted publications, with a prioritization score of 0.6 or higher, included in the final dataset. Figure 4: Number of publications per peer-reviewed journal between 2018 and 2022. In terms of simplicity, journals that appeared only once were homogenized in the ‘Other’ class. Figure 3: The ranking performance curve determined by randomly selecting a training-test dataset using a 50/50 split in the prioritization modeling. The yellow line represents the baseline performance we should expect if the ranking score had been generated completely at random. The green line shows the performance based on the test dataset, while the blue line denotes the training set.
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol  
xAAL is a distributed system. Participating entities need to communicate with each other. The xAAL communicating protocol has been designed with the following objectives in mind.  
Objectives:  
* The communicating channel should allow automatic discovering, allowing a new entity to join the group without much job to do, and allowing other entities to know that a new entity arrives.
* The communication stack should be simple and lightweight to implement in constrained devices, in a stateless manner, without having to maintain alive several communication channels in parallel.
* The communication should be lightweight in terms of messages size and number, close to the background noise of a usual home network.
* The communication should address security considerations, regarding users' privacy, or threat to get control.
* The communication should be simple to configure for the end user.  
xAAL messages are carried by UDP multicast packets. xAAL messages are made of two layers: (i) a Security Layer with some clear fields mandatory for transport to receivers, followed by a ciphered payload; and (ii) an Application Layer that consists of the decrypted payload and containing all information for participating applications.
## 3 Methodology
### Production Scheduling Using DES
Phase seven is mainly concerned with the model experimentation, which is trying different solutions to achieve the goal set from phase one. The final phase involves analyzing the results of the simulation model, by comparing the suggested schedule by the simulation to previous executed schedules by the industrial entity to evaluate its feasibility. In this work, FLEXSIM software tool is used to perform the DES to generate the production schedule. Within the experimentation phase, two main criteria are considered: which dispatching rule is used to produce the schedule and when to add the machine breakdown time slots and breakdown times. For the dispatching rule, three main dispatching rules were considered, Earliest Due Date (EDD), Shortest Processing Time (SPT) and Longest Processing Time (LPT). These dispatching rules were considered for the fact that they are the most commonly used and that the case study considered in this paper uses EDD. For the machine breakdown time slots, PdM was explored since there is no guarantee that no extra cost would be inflected on the manufacturing system. First, the data concerning the machines health is collected through an implemented DAQ hardware system. This serves one of the overall objectives of the ongoing research in Industrial Internet of Things (IIoT) and Industry 4.0. The machine health data are processed to be suitable as input for ML software tools to estimate the PdM time slots. The processed data are uploaded to Microsoft Azure software tool (the platform of the ML engine). In the following subsection, the two top levels of the proposed methodology framework is explained.
# Wind Loads on a Solar Panel at High Tilt Angles
## 3 Results and Discussion
### Spanwise Pressure Distributions  
At \(\pi\!/L=0.5\), the spanwise pressure, \(C_{sp}\), and distributions for \(\beta=0^{\circ}\) are shown in Figure 7. There is suction (negative \(C_{sp}\)) on the upper surface for all of the test cases. Inverted U-shaped distributions are observed for small \(\alpha\), particularly for \(\alpha=20^{\circ}\) and \(30^{\circ}\). This produces strong corner vortices, which is in agreement with the results of [PERSON] et al. [10]. For \(\alpha\geq 40^{\circ}\), the \(C_{sp}\) distributions show a small degree of variation. On the lower surface, the footprints of the corner vortices are also visible. The peak value of \(C_{sp}\) (0.29 to 0.99) at \(\psi/W=0.5\) increases as \(\alpha\) increases. The increase in \(C_{sp}\) as \(\alpha\) increases is more significant for \(\alpha\leq 50^{\circ}\).  
Figures 8 and 9 show that \(\beta=30^{\circ}\) and \(45^{\circ}\). There are flattened \(C_{sp}\) distributions in the left half of the panel for \(\alpha=10^{\circ}\) and \(20^{\circ}\). The expansion and compression near the right edge corresponds to the formation of a separation bubble or corner vortices. The variation in \(C_{sp}\) for \(\alpha\geq 40^{\circ}\) is less than 3%. An increase in the value of \(C_{sp}\) (\(-0.738\) to \(-0.693\)) is associated with greater \(\alpha\) (less suction). An increase in the value of \(C_{sp}\) is observed from the left to the right edges, so there is a greater uplift force near the right edge. Figure 10 shows that for \(\beta=135^{\circ}\), the wind blows over the lower surface of the tilted panel. The \(C_{sp}\) distributions on the lower surface show similar patterns to those on the upper surface for \(\beta=45^{\circ}\). This demonstrates that there is an increase in the downward force from the left to right edges.  
Figure 8: Mean spanwise pressure distributions at \(\pi\!/L=0.5\); \(\beta=30^{\circ}\).  
Figure 7: Mean spanwise pressure distributions at \(\pi\!/L=0.5\); \(\beta=0^{\circ}\).
# Continuous Semi-Supervised Nonnegative Matrix Factorization
## 1 Introduction  
Nonnegative matrix factorization (NMF) is a highly versatile data science technique with far-reaching applications. It can identify thematic elements, i.e., groups of words that appear frequently together in a corpus, which together convey a common message [1]. More generally, it can be used to decompose an image into identifiable patterns [2] and as a general-purpose dimensionality reduction or preprocessing method before applying other machine learning methods, as has been done in studying various diseases [3, 4]. Similar to singular value decomposition (SVD) [5], NMF provides a low rank factorization. In NMF, a nonnegative matrix \(X\in\mathbb{R}_{\geq 0}^{n\times m}\) representing a corpus (or other nonnegative dataset) is factored into a low rank approximation \(X\approx WH\) where the inner dimension, \(r\), between \(W\) and \(H\) is such that \(r\ll m\) and \(r\ll n\); however, unlike SVD, there is an additional constraint that both \(W\) and \(H\) are nonnegative, i.e., \(W\in\mathbb{R}_{\geq 0}^{n\times r}\) and \(H\in\mathbb{R}_{\geq 0}^{r\times m}\). This nonnegativity enforces that the data in \(X\) are represented by a nonnegative combination of the dictionary atoms in the factorization, which lends itself to human interpretability. For example, in the foundational work [2], [PERSON] and [PERSON] show that NMF, when applied to facial images, decomposes the images into recognizable parts such as noses, eyes, and mouths.  
When applied to a document-term matrix [6]\(X\), where row \(i\) of \(X\) represents document \(i\) and column \(j\) represents the frequencies of word \(j\) across the documents, the classical NMF method amounts to  
\[(W,H)=\arg\min_{W\in\mathbb{R}_{\geq 0}^{n\times r},H\in\mathbb{R}_{\geq 0}^{r \times m}}||X-WH||_{F}^{2} \tag{1}\]  
where, for \(A\in\mathbb{R}^{n\times m}\), \(||A||_{F}\) denotes the Frobenius norm of \(A\) with \(||A||_{F}^{2}=\text{tr}(A^{\mathsf{T}}A)=\sum_{i=1}^{n}\sum_{j=1}^{m}|A_{ij}|^{2}\). Other variations on the penalty function exist, including the Kullback-Leibler divergence [7]. After computing \(W\) and \(H\), we interpret row \(j\) of \(H\) as the \(j\)thtopic, its components being the weight of each word in that topic, and row \(i\) of \(W\) as the topic-encoding of document \(i\), i.e.,  
\[X_{i,\cdot}\approx\sum_{j=1}^{r}W_{i,j}H_{j,\cdot}.\]  
Throughout this manuscript we make use of \"colon notation\" where \":\" means the full range of indices for a row/column, \"a:b\" indicates a consecutive range of indices from a to b, etc.  
The rest of our paper is organized as follows: the general context to our work and our main contributions are stated in Section 2; in Section 3, we provide the formulation of our method, its algorithmic implementation, its theoretical properties, and their proofs; in Section 4, we provide a proof of concept through synthetic data; in Section 5, we test our method on a real dataset from Rate My Professors; and finally we conclude our work in Section 6.
# Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set
## 6 Test Case
### Partial Model Extraction for the User-Defined Extracted Objects  
Through setting different 'item' elements, the required objects could be extracted. As examples for types of extracted objects, Table 1 shows four examples of partial models extracted from the original model. The first three partial models extract a certain type of object, while two types of objects are extracted in the fourth one. Figure 8 depicts the PMESS-based configuration file for the fourth partial model in the proposed platform. Through the PMESS, physical objects from different disciplines (architecture, structure, MEP, etc.) can be extracted from the original BIM model, such as the door and window for architecture, the beam and column for structure, the equipment and pipeline for MEP.  
Figure 7: IFC model of the shopping mall project in the proposed IFC-based platform.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Extracted Objects**} & \multicolumn{2}{c}{**Original Model**} & \multirow{2}{*}{**Arnount**} & \multirow{2}{*}{**Amount**} & \multirow{2}{*}{**File Size/M**} & \multirow{2}{*}{**Partial Model**} & \multirow{2}{*}{**Visualization**} \\  & **IFC Entity** & & & & & \\ \hline \hline \multirow{2}{*}{Slab} & _IcSlab_ & 5284 & 5284 & 19.9 & 292,912 & \\  & & & (19.7\%) & (15.7\%) & & \\ \hline \multirow{2}{*}{Door} & _IcDov_ & 911 & 911 & 19.2 & 309,332 & \\  & & & (19.0\%) & (16.6\%) & \\ \hline \multirow{2}{*}{Transport element} & _IcTransport_ & 64 & 64 & 15.7 & 253,984 & \\  & _Element_ & & (15.5\%) & (13.6\%) & \\ \hline \multirow{2}{*}{Beam and column} & _IcRam_ and & 6886 & 6886 & 13.8 & 173,763 & \\  & _IcColumn_ & (400262884) & (400262884) & (13.7\%) & (9.3\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some examples of partial model extraction for the required objects by using PMESS.  
By using the IFC File Analyzer [44], IFC model data can be analyzed in detail. As shown in Table 1, all IFC object entities which match the user-defined requirements were correctly extracted. Moreover, through filtering out other objects, the resulting models only contain the required objects and their attributes. For example, the number of IFC instances in the fourth partial model was 173,763, and 90.7% instances were filtered out. Accordingly, the file size decreased to 13.8 M (only 13.7% of the original model). The results show that the proposed method correctly identifies the user-defined requirements and extracts all the semantically required objects from the original model. On the other hand, the decreasing of the partial models in file size is apparent compared with the original model, which avoids filtering redundant information manually and facilitates the fulfillment of downstream business tasks based on useful building information.  
Except the extraction according to the object type, other semantics could be used to extract the required BIM data, such as the relationship and the rules (as shown in the following subsections).
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 2 ICEEMDAN-MPE-AWT Denoising
### Denoising Process of the ICEEMDAN-MPE-AWT  
To address the issue of reduced fault recognition accuracy due to the difficulty in extracting fault characteristics caused by a large amount of noise in the signals captured by sensors in complex work environments; first, a denoising method called ICEEMDAN-MPE-AWT was proposed. Then, the noise-dominated IMF components C\({}_{i}\) were denoised by using an adaptive wavelet thresholding method. Finally, the denoised noise-dominated components and the undenoised signal-dominated components were reconstructed to obtain the denoised signal. The process of fault diagnosis based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 transfer learning model is illustrated in Figure 1.  
Figure 1: The process of fault diagnosis based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 transfer learning model.
## 2 Materials and Methods
### Data Processing Methods
#### 2.3.2 ResNet18 Model and Its Optimization  
A residual network (ResNet) is a classical architecture in deep learning designed to address the challenges of gradient vanishing and degradation [20]. As network depth increases, traditional deep neural networks often struggle to train effectively due to gradient vanishing. To overcome this challenge, ResNet introduces residual blocks (residual blocks), incorporating skip connections in each layer to directly pass the input to subsequent layers. The fundamental formula is as follows:  
\[\mathrm{y}=F(\mathrm{x},\{W_{i}\})+x \tag{3}\]  
where \(\mathrm{F}(\mathrm{x},\{w_{i}\})\) denotes nonlinear transformations (e.g., convolution and ReLU activation), \(\mathrm{x}\) denotes the input, and \(\mathrm{y}\) denotes the output. With this design, the residual block can effectively mitigate the phenomenon of gradient vanishing while improving the feature learning ability and convergence speed of the model.  
ResNet18 contains an 18-layer structure with a 3 \(\times\) 3 convolutional kernel and ReLU activation function, which is particularly suitable for processing high-resolution image data. In this study, parameter optimizations were performed on the model, including the use of batch normalization to reduce the internal covariate bias and speed up the training convergence and data enhancement (e.g., random cropping, rotation) to improve the model generalization ability [21; 22; 23; 24]. Ultimately, the ResNet18 model balances computational efficiency and prediction accuracy while maintaining depth.  
Selection Rationale and Performance Advantages: ResNet18 was selected for its proven ability to efficiently process complex, high-dimensional data under limited computational resources, making it an ideal choice for collapse risk assessment based on multi-source remote sensing data. Its residual structure effectively mitigates the gradient vanishing problem, enabling robust training of deep architectures. In this study, ResNet18 exhibited exceptional capability in extracting spatial and contextual features from diverse inputs, such as slope gradient, lithology, vegetation coverage, and annual precipitation, thereby enhancing the classification accuracy of collapse risk prediction. Furthermore, its integration with batch normalization and data augmentation strengthens model robustness, ensuring reliable performance across diverse remote sensing datasets [16; 25; 26].
## 1 Introduction
With the aim of further improving the measurement accuracy under the condition of severe distortion and enriching the discussion of the influence of closed paths in circulation calculations, in this paper, we compare the performance of several closed path determination methods: perfectly round circles assignation, watershed transformation, maximum average-intensity circle extraction, and the combination of the watershed transformation with maximum average-intensity circle extraction. This paper is organized as follows. In Section 2, we present a brief of the generalized contour-sum method (GCSM), and in Section 3, we describe five closed path determination methods for the GCSM. In Section 4, we show the optical setup for the proof-of-principle experiments. In Section 5, we present some experimental results and discussions. The comparisons were conducted under scenarios of both aberration-free OV beams and OV beams distorted by simulated atmospheric aberrations. A summary and conclusion are given in Section 6.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 2 Transient Model of Annular Pressure Caused by Hydration Heat during Well Cementing
### Transient Temperature Prediction Model during Well Cementing  
(1) Thermal model in casing  
As shown in Figure 1b, the internal energy change to the unit cell includes the heat exchange with the cement in the annulus and the adjacent cells. Given the conservation of energy, the thermal model of the unit cell in the casing can be acquired as:  
\[\begin{array}{c}(\left.T_{c}\right|_{t+\Delta t}-\left.T_{c}\right|_{t})c_{ \ell}\rho_{\ell}A_{c}\Delta z=2\pi r_{ci}\Delta zU_{c}(T_{a}-T_{c})\Delta t\\ \qquad+A_{c}k_{f}\frac{T_{c_{1+\Delta t}-T_{c_{1}}}}{\Delta z}\Delta t-A_{c}k_{ f}\frac{T_{c_{1}|_{z}-T_{c_{1}|_{z}-\Delta z}}}{\Delta z}\Delta t\end{array} \tag{13}\]  
Equation (13) can be written in a differential form:  
\[\frac{\partial T_{c}}{\partial t}=\frac{2\pi r_{ci}U_{c}}{c_{f}\rho A_{c}}(T_{ a}-T_{c})+\frac{k_{f}}{c_{f}\rho}\frac{\partial^{2}T_{c}}{\partial z^{2}} \tag{14}\]  
(2) Thermal model of cement in annulus  
As shown in Figure 1a, the internal energy change to the unit cell includes the heat exchange with the fluid in the casing, the adjacent cells, the formation, and the cement hydration heat. On the basis of the conservation of energy, the thermal model of the unit cell of the cement in the annulus can be acquired:  
\[\begin{array}{c}(\left.T_{a}\right|_{t+\Delta t}-\left.T_{a}\right|_{t})c_{ \ell}\rho_{c}A_{a}\Delta z=(\left.\alpha\right|_{t+\Delta t}-\left.\alpha\right| _{t})Q_{\text{max}}\rho_{c}A_{a}\Delta z-2\pi r_{ci}U_{c}(T_{a}-T_{c})\Delta t \Delta z\\ \qquad+2\pi r_{\text{nU}}A_{t}(T_{c,0}-T_{a})\Delta t\Delta z+A_{a}k_{c}\frac{ T_{a|_{z}+\Delta x}-T_{a|_{z}}}{\Delta z}\Delta t-A_{a}k_{c}\frac{T_{a|_{z}-T_{a|_{z}- \Delta z}}}{\Delta z}\Delta t\end{array} \tag{15}\]  
Equation (15) can be written in a differential form as:  
\[\frac{\partial T_{a}}{\partial t}=\frac{2\pi r_{\text{nU}}L_{a}}{c_{f}\rho A_ {a}}(T_{c,0}-T_{a})-\frac{2\pi r_{ci}U_{c}}{c_{f}\rho A_{p}}(T_{a}-T_{c})+ \frac{Q_{\text{max}}}{c_{f}}\frac{\partial\alpha}{\partial t}+\frac{k_{f}}{c_ {f}\rho}\frac{\partial^{2}T_{c}}{\partial z^{2}} \tag{16}\]  
(3) Thermal model in formation  
As shown in Figure 1c, the internal energy change to the unit cell equals the heat exchange with the adjacent cells. On the basis of the conservation of energy, the thermal model of the unit cell in formation can be acquired:  
\[\frac{T_{|t+\Delta t}-\left.T_{|t}\right|_{t}}{\Delta t}c_{\ell}\rho_{c}\chi \Delta x\Delta\theta\Delta z=\frac{T|_{x+\Delta x}-\left.T\right|_{x}}{\Delta x }k_{\ell}(x+\frac{\Delta x}{2})\Delta\theta\Delta z-\frac{T|_{x}-T|_{x-\Delta x }}{\Delta x}k_{\ell}(x-\frac{\Delta x}{2})\Delta\theta\Delta z \tag{17}\]Equation (22) can be rewritten in a finite difference form as:  
\[\frac{\rho_{e}c_{e}}{k_{e}}\frac{\partial T}{\partial t}=\frac{\partial T^{2}}{ \partial x^{2}}+\frac{1}{x}\frac{\partial T}{\partial x} \tag{18}\]
# The Quanta Image Sensor: Every Photon Counts
## 5 Jot Device
### Background and Motivation
Buried-channel SF and correlated multiple sampling (CMS) techniques [61] were applied to reduce the SF noise, and 35 \(\mu\)V r.m.s. voltage noise was achieved, but with a relatively low CG (46 \(\mu\)V/e\(-\)) yielding 0.76 e\(-\) r.m.s. average read noise [46]. The CMS technique was also explored with low temperature (because of dark current considerations) and achieved limited photoelectron counting capability [44; 47; 51]. However, the CMS technique with a large number of samples is not feasible for QIS application due to its relatively low speed. Our approach to achieve deep sub-electron read noise is to improve CG and reduce SF transistor noise. Since our first report of success with this approach [14; 17] other groups have also reported success at achieving deep sub-electron read noise (and photon counting) without the use of avalanche gain [50; 51]. Improvement of CG was also reported in [62] leading to 0.46 e\(-\) r.m.s. read noise, just short of what is needed to demonstrate photoelectron counting. The photoelectron signal is converted to a voltage signal for readout using the capacitance of the floating diffusion (FD) node. The voltage signal generated by one photoelectron is given by:  
\[CG=\frac{q}{C_{FD}} \tag{15}\]  
where q is the elementary charge of one electron and \(C_{FD}\) is the node capacitance of FD that includes several major components: depletion capacitance between FD and substrate, overlap capacitance between FD and the transfer gate (TG), overlap capacitance between FD and reset gate (RG), SF effective gate capacitance, and inter-metal capacitance. To improve CG, the capacitance of FD needs to be reduced. Note that the reduction of FD capacitance may lead to reduction of FWC in conventional CIS, but it is not a concern for a jet device as the required FWC is very small.