esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of an LED Strip Controller Based on a PWM Driver and a PIC Series Microcontroller
## 3 Results and Discussion
The results of light flux dependence on one channel power are presented in Figure 2:5 As the percentage value of the PWM signal, which determines the on period, changed in the LED strip, the amount of average electricity that powered the LED strip changed. The higher the percentage of on-time, the more time the LED strip was on and the more electricity it received, and therefore the higher the light flux. As can be seen from the presented results, the light flux changed from 100 to 1000 lm when the power of one channel of the LED controller was changed from 1 to 12 W. This dependence was close to linear. After reaching a power of 8 W, the light flux increased a little more slowly, until it reached the maximum value. Such a slowdown in the growth of the light flux could have been influenced by phenomena occurring in the LED diodes, such as changes in the recombination rate of holes (vacancies) and electrons, etc. The non-linearity of this dependence could have been influenced by the fact that LEDs convert part of the electrical energy into heat. LED strip PWM controllers based on a microcontroller and PWM driver are very useful, due to their flexibility, efficiency, and accuracy. Microcontroller-based LED controllers provide great flexibility for programming of LED strip light output. They allow programmers to easily change light intensity, light flux, colors, effects and other functions according to their needs [3; 4; 5; 6]. It is also possible to create complex lighting effects and dynamic models controlled programmatically. PWM signals can be used to regulate the light flux with minimal power dissipation. This allows saving energy and increasing the life of LED strips. Microcontrollers with integrated PWM drivers ensure a high precision of light control. They can generate stable and accurate PWM signals with specific on times, so  
Figure 2:6 The dependence of the power of one channel of the LED controller on the percentage of the PWM signal. the desired light flux can be achieved without large deviations or flicker. Microcontrollers and PWM drivers can be easily integrated with other devices and their control systems, for example with sensors, control panels, or computer interfaces. This makes it possible to create complex and integrated light control systems.
# Feasibility Analysis of Adopting the Hydrogen Hydrostatic Thrust Bearing
## 2 Numerical Model  
### Hydrogen Hydrostatic Thrust Bearing Structure  
The HHTB's structure is presented in Figure 2a,b. The main parameters of the bearings include orifice diameter \(d\), number of orifices \(N\), groove depth \(h\), groove width \(s\) and film clearance \(c_{0}\).
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 2 Materials and Methods
### Performance Evaluation Metrics  
In this study, a range of evaluation metrics was utilized to examine the performance of the proposed machine learning models. These metrics comprised MAE, RMSE, and adj. R\({}^{2}\). These criteria were selected to assess the predictive capability of the XGBoost, RF, DT, and KNN models in determining the minimum methanol quantity required for hydrate inhibition. MAE is a commonly used statistical measurement employed to assess the predictive performance of soft computing algorithms [35]. The three evaluation metrics--adj. R\({}^{2}\), MAE, and RMSE--are widely used metrics for prediction tasks based on machine learning algorithms. In addition, the K-fold cross-validation procedure is important for avoiding the biases that may occur during random sampling in the training phase. Therefore, this phase was used to split the dataset into multiple parts and, at the same time, reduce the overfitting problem usually faced with the machine learning models. The optimal running time is usually determined by a tenfold validation procedure. This work applied the tenfold technique to separate the original training set into ten collections. The final assessment of our model was the average of the evaluation results for 10 training epochs.  
A common approach for testing model validity is to use the Monte Carlo method, which calculates the effect of input dataset variations on the machine learning algorithm performance. This method involves random niche sampling within the input domain, followed by the computation of a simulation model's output [36]. Moreover, the response yields statistical performance that is sensitive to fluctuations in the input variables. Monte Carlo simulations over 50 iterations on the complete dataset were used in this study to automatically and efficiently obtain convergent results.
# Multiellipsoidal Mapping Algorithm
## 2 Mathematical Background
### k-Means Algorithm  
The k-means algorithm is a popular algorithm for clustering and is frequently used in unsupervised techniques. For a set of points \(P=\{p_{1},p_{2},\cdots,p_{n}\}\), the k-means algorithm aims to find the partition \(S=\{S_{1},S_{2},\cdots,S_{k}\}\) with \(k\leq n\), as shown in (6), where \(c_{i}\) is the centroid of the cluster \(S_{i}\).  
\[S=\operatorname*{argmin}_{S}\sum_{i=1}^{k}\sum_{p\in S_{i}}p-c_{i}^{2} \tag{6}\]  
For this paper, this optimization problem is solved using Lloyd's algorithm with a variant of initialization known as k-means++ [12].
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 2 System Overview and Mathematical Modelling
### Thrust Forces  
The external forces acting on the rotorcraft can be summarised as the sum of combinations from different subsystems as follows:  
\[X =X_{M}+X_{T}+X_{F} \tag{8}\] \[Y =Y_{M}+Y_{T}+Y_{F}+Y_{V}\] (9) \[Z =Z_{M}+Z_{T}+Z_{F}+Z_{H} \tag{10}\]  
where the subscripts M, T, H, V and \(F\) are the main rotor, tail rotor, horizontal stabilizer, vertical stabiliser and fuselage, respectively. The simplified thrust produced by the main rotor is given by [28]:  
\[T_{M} =C_{T}\rho A(\Omega R)^{2} \tag{11}\] \[C_{T} =\frac{\sigma a}{2}\bigg{(}(\frac{1}{3}+\frac{\mu_{z}^{2}}{2}) \theta_{0}-\frac{\mu-\lambda}{2}\bigg{)} \tag{12}\]  
where \(T_{M}\), \(C_{T}\), \(A\), \(R\), \(\Omega\) and \(\theta_{0}\) are the developed rotor thrust, the thrust coefficient, the area of the rotor disc, the radius of the rotors, the rotor speed and the collective pitch of the blades, respectively. The other symbols are as defined in Section 2.1. In a similar procedure used for main rotor, the tail rotor thrust, \(T_{T}\), is derived.  
The total forces generated by the main rotor and the tail rotor are described as follows [4]:  
\[X_{M} =-T_{M}\sin a_{lc} \tag{13}\] \[Y_{M} =T_{M}\sin b_{ls}\] (14) \[Z_{M} =-T_{M}\cos a_{lc}\cos b_{ls}\] (15) \[Y_{T} =-T_{T} \tag{16}\]  
where \(a_{lc}\) and \(b_{lc}\) are the flapping angles in the horizontal and lateral direction, respectively.  
Figure 1: Forces and moments acting on the rotorcraft with reference to the body-fixed reference frame.
## 4 Conclusions
### A Test Case for Opportunistic Sensing of the Environment  
CML technology has recently been mentioned as one of the first Internet of Things (IoT) applications [85], which is now getting much attention. While the conditions for CML technology becoming useful seems to be right, and we may see it soon in products as well as in public-good services, it also serves as a pioneering example of the new trend of capitalizing on existing technology by utilizing it for non-intended, opportunistic use [86; 87; 88]. Opportunistic sensing is believed to be the future of environmental monitoring, being a sustainable source of (big) environmental data. The analyses provided in this paper for the case of CML technology can serve as a test case for this emerging trend of opportunistic sensing, demonstrating the need for open bi-directional communication channels within the world of academia, where innovative ideas are initiated and studied, and for contemporary industry, which serves as a source of opportunistic measurements as well as a platform for utilization of new ideas.  
To conclude: The uniqueness of CML technology stems from its special situation, standing between science and technology, between academia and industry. Future development of this technology and its potential use in practice depend on business challenges as well as on science and technology. The focus of this paper is to review the most advanced developments in CML technology and to anticipate its future development, based on an analysis of the opportunities and challenges faced by researchers in this area over the years. Depending on the future evolution of CML technology, it will be important to provide a deep scientific critical review of this technology, including meticulous scientific background on the different sources to the signal's attenuation, comparative analysis of the different algorithms, etc.  
This research is based on integration of works and has received no specific external funding.  
I would like to thank all my students and my collaborators over the years, and especially my co-PI [PERSON], for their contributions to advancing the research on this topic. A special acknowledgment is given to the major cellular providers in Israel, Cellcom, Pelephone and Partner, and to Ericsson AB for sharing know, measurements and data with us.  
The authors declare no conflict of interest.
## 3 Methods
### V-RTI: Data Acquisition  
Various techniques are available for generating RTI, such as using a fixed dome [21] or motorized rotating arcs for light positioning, as well as the adoption of photogrammetry; while extensively applied in the large-scale digitization of museum artifacts [33], the latter is also useful for metrology validation [34; 35] and multiband analysis [4].  
The procedure outlined here comprises four main steps: photogrammetric acquisition, SfM, rendering in a Virtual Reality (VR) environment, and the computation of V-RTI. Unlike traditional RTI setups in which an off-camera strobe is manually handled around the subject, the lighting is exclusively provided by an on-camera flash in V-RTI. To reduce the risk of producing unwanted reflections, the light source is polarized using a custom-shaped polarizing sheet, while a circular polarizer filter is placed in front of the lens to regulate specular light reflection until it is fully blocked (Figure 3). In V-RTI, it is no longer the light that is moving, but the camera.  
Figure 3: Polarization filters on both camera and flash.
## 5 Conclusions  
A bead geometry prediction model, in the LWAM process, was developed using a machine learning regression algorithm and neural network. The proposed algorithm predicted bead height, width and the ratio therebetween, and the results confirmed the profile's trend toward stable deposition. The prediction model is flexible and can be adjusted to follow the trends of various layer-deposition behaviours.  
Figure 13: Regression plot for different samples prediction.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **P** & **V** & **F** \\ \hline Acceptable bead Prediction & Low & Low & High \\ Good layer Deposition & Low & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of the deposition parameters.  
The experimental results revealed that the developed machine learning model is capable of predicting the bead geometry of LWAM-deposited layers with acceptable error rates (i.e., 2\(\sim\)4%). Several data samples have been also collected by a laser scanner to study the influence of different deposition parameters on the bead geometry. The prediction results of low power (P) and travel speed (V) with a high wire feed rate (F) showed better layer deposition and model prediction, as compared with other collected samples.  
In summary, the following conclusions are drawn from this study:  
* Regression algorithms can be used for prediction tasks related to the LWAM process, including for supervision tasks;
* There exists a power decay in correlating process parameters to bead thickness.
* The experimental results showed an increase of width-to-height ratio as a function of layer progression due to the heat conducted from the laser-matter interaction.
* Satisfying prediction models were obtained and could be used to predict the geometry of beads as part of LWAM control programming for horizontal and vertical robot movement.
* In this case, acceptable bead prediction with good layer deposition was obtained when P, V, and F were 1.4 kW, 1.4 m/min, and 0.45 m/min, respectively.  
In the future, the prediction model will be used to develop a robust feedback control system that is able to adjust process parameters automatically for improving the surface quality and geometric accuracy in the LWAM process.  
Conceptualization, methodology, investigation, and original draft preparation N.G.M. and M.A.; review and editing, N.G.M., M.A., P.P., M.E.K. and S.Y.; supervision, P.P.; project administration and funding acquisition, P.P. and M.E.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Interreg V-A Grande Region \"Fabrication Additive par Depot de Fil\" (FAFil) project (Ref. 3477).  
Not applicable.  
This work was supported by the Interreg V-A Grande Region \"Fabrication Additive par Depot de Fil\" (FAFil) project. The authors would like to thank the Institut de Soudure--Yutz, for providing the experimental data.  
The authors declare no conflict of interest.
## 3 Concept  
### Continuous ML in Emulation Environments  
In the following, continuous ML for enabling multi-access routing for moving vehicles in emulation environments is described (Figure 2). The research artifacts refer to the architectural elements, which are illustrated as part of the conceptual view. The emulation process is prepared by creation of a simulated network and vehicle movement data (pre-calculated files) for emulation purposes (step 1.1). Subsequently, emulation is executed within a distributed computing environment (step 1.2), where vehicles will move and transfer data towards a control center. During emulation, network statistics are collected (step 1.3) with measurement tools (i.e., owping [21]), and the results are saved in a database (InfluxDB [22]). Next, the collected measurements (step 2.1) are used for training (sklearn [23]) a ML-based model (step 2.2) for prediction of OWD. Several models may be trained and evaluated (step 2.3), and the model with the best offline performance is selected (step 2.4) for deployment (MLFlow [11]) in the emulation environment (step 2.5). Finally, a second emulation experiment is performed (step 3.2), where the deployed ML-based model is utilized for providing OWD predictions (step 3.3) during the emulation (RedisAI [12]). The measurement data are saved (step 3.1) and used for evaluation of online performance (steps 3.4-3.5) of the prediction model. In the end, the ML-based model, which achieved the best performance in emulation, is selected for deployment into a real mine environment.  
There are several research artifacts (the elements in Figure 2) in this research, which are, in general, related to the emulation environment, and the continuous ML pipeline. The artifacts are evaluated in terms of selected ex-post criteria: feasibility, operationality, fidelity with real world phenomenon, and efficiency.  
Figure 2: Conceptual view of research artifacts for enabling multi-access routing for moving vehicles based on continuous ML in emulation environments. In the view, rectangles illustrate functionalities, ellipsis describes data stores, and arrows depict data flows.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach
### Feature Description and Edge Macro-Pixel Detection  
The specific local features for each macro-pixel \(mp_{i}\) are described by exploiting texture and gradient cues over \(k\)_-ring_ neighboring macro-pixels. Based on the hexagonally packed micro-lens grid, the feature vector is made of 7 micro-lenses in \(1-ring\) neighboring macro-pixels, as shown in Figure 3c.  
\[\mathbf{V}_{mp}=\{C_{i},\
abla_{i}\},i\in\{1,2..7\} \tag{2}\]  
where \(C_{i}\) denotes the color of each pixel in \(mp_{i}\). \(\
abla_{i}\) denotes both the gradients of pixels in \(mp_{i}\), as well as the micro-lens-wise gradients from three directions (\(\
abla_{0^{\circ}}\),\(\
abla_{45^{\circ}}\), and \(\
abla_{135^{\circ}}\)).  
Edge-aware filters, such as the bilateral filter, are typically effective for edge-preserving smoothing and local matching cost aggregation [23]. Micro-lenses containing edges of target objects are considered as edge macro-pixels in \(\mathbf{I}_{MP}(X,Y)\). Edge macro-pixel detection is performed using a rule similar to the Sobel operator. The mean intensity vector \(M\)C\({}^{3\times 3}\) is computed over two kinds of \(3\times 3\) neighboring macro-pixels centered on \(mp_{i}\). Then, a Sobel convolution is executed on \(M\)C\({}^{3\times 3}\). The result of the edge macro-pixel detection is shown in Figure 2d. In the following steps, edge macro-pixels tend to match with high confidence.
###### Abstract  
Cylindrical vector beams are the product of polarization modulation of optical fields, and possess both unique focusing characteristics and excellent properties applicable to machining, imaging, communication and other fields. Mode selection couplers comprise a promising new method to realize the long-term stable output of cylindrical vector beam all-fiber lasers. Mode selection couplers have the advantages of a simple structure, high mode conversion efficiency and high mode purity. However, the production process of conventional asymmetric mode selection couplers is more complicated. Therefore, in this paper, a symmetric two-mode coupler with a 1 \(\upmu\)m band is designed and fabricated using the finite element method, beam propagation method and fused pull-cone method, and a set of all-fiber passive mode-locked lasers based on symmetric dual-mode couplers are constructed. Finally, we obtain cylindrical vector beam outputs with central wavelengths of 1038.97 nm/1067.72 nm, a repetition rate of 8.78 MHz, pulse durations of 660 ps/656 ps, maximum average powers of 5.25 mW/5.2 mW, and the high mode purity of TM (transverse magnetic)\({}_{01}\) mode and TE (transverse electric)\({}_{01}\) mode is 97.18% and 97.07%, respectively.  
symmetric two-mode coupler; cylindrical vector beams; high mode purity; mode-locked lasers
## 3 System Implementation and Performance Evaluation
### Platform Selection
The system is capable of predicting the rest of the execution time to terminate the running specific platform \(e_{j}\), \(\left(f_{e}(u^{\prime}),e_{j},v_{i}\right)\) and calculating the low bound of the execution time \(T_{I}\) to compare with \(\left(f_{e}(u^{\prime}),e_{j},v_{i}\right)\). This study uses to obtain a target server's \"/proc/meminfo\" file. Reading the content of the retrieved meminfo file is to recognize the remaining amount of memory left and can be compared with the least memory amount of \(e_{j}\), \(M_{j}\). The platform selection algorithm is shown as Algorithm 1 below. INPUT: Job \(u\)_from command list \(L_{C}\)_. OUTPUT: Appropriate platform assigned set \(assign(u,e_{j})\). 1. Calculate \(f_{o}(u^{\prime})\)**the remaining amount of memory** for each node \(v_{i}\in V_{C}\) (virtual machines) in the cluster;  
2. _for each platform \(e_{j}\in E_{C}\), each node \(v_{i}\in V_{C}\)do_  
3. Calculate each platform \(\left(f_{e}(u^{\prime}),e_{j},v_{i}\right)\) the rest of execution time to terminate at _each node \(v_{i}\in V_{C}\)_;  
4. _for each \(e_{j}\in E_{C}\)do_  
5. \(assign(u,e_{j})\): \(=\left\{v_{i}\ \middle|\ v_{i}\in V,\left(f_{e}(u^{\prime}),e_{j},v_{i}\right)\leq \text{the low bound of execution time }T_{I}\right\}\), and \(\left(f_{v}\ (u^{\prime}),e_{j},v_{i}\right)\geq\text{the least memory amount of }e_{j},M_{j}\);  
6. _otherwise, \(assign(u,e_{j})\): \(=\left\{v_{i}\ \middle|\ v_{i}\in V,\left(f_{e}(u^{\prime}),e_{j},v_{i}\right)> \text{the low bound of execution time }T_{I}\right\}\), or \(\left(f_{v}\ (u^{\prime}),e_{j},v_{i}\right)\)_ \(<\text{the least memory amount of }e_{j},M_{j}\);  
7. _while_ (\(u\in U\)_with \(assign(u,e_{j})\
eq\varnothing\ \vee\ buffer\ B_{C}\
eq\varnothing\)) _do_  
8. _for each \(e_{j}\in E_{C}\), \(v_{i}\in V_{C}\), allocate the necessary memory amount to \(assign(u,e_{j})\)do_;  
9. _if queue is not full, dequeue \(B_{C}\): \(B_{C}\backslash assign(u^{\prime},e_{j})\ \wedge\ enqueue\ S_{i}\): \(=\ S_{i}\backslash assign(u^{\prime},e_{j})\);_  
10. _elseif queue is not full, enqueue \(S_{i}\): \(=S_{i}\backslash assign(u,e_{j})\);_  
11. _elseif queue is full, enqueue \(B_{C}\): \(=B_{C}\backslash assign(u,e_{j})\);_  
12. _while_ (\(u\in U\)_with \(assign(u,e_{j})\
eq\varnothing\)) _do_  
13. _for each \(e_{j}\in E_{C}\), \(v_{i}\in V_{C}\), \(u\): \(=\)\(assign(u,e_{j})\);_  
14. _enqueue \(L_{C}\): \(=L_{C}\backslash u\);_  
15. _return \(assign(u,e_{j})\)_  
**Algorithm 1**. **Platform selection algorithm (PSA)**
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 4 Results  
The experimental implementation of the RIS-DMRN algorithm in this study was conducted on the following workstation configuration: Processor (CPU): Intel Core i9-13900 HX (2.2 GHz, 6 cores, 12 threads), Memory (RAM): 16 GB DDR4 2400 MHz, Graphics Card (GPU): NVIDIA GeForce GTX 4070 Ti (8 GB GDDR5X). The algorithm was implemented using Python 3.6 + TensorFlow 1.13.1 for program design. Due to the limited availability of publicly accessible datasets for image rectification research, this study conducted validation on the DIR-D dataset [21]. Following the consistent approach outlined in the paper [21], RIS-DMRN set the batch size to 8 during the training process, initialized the learning rate to 1 \(\times\) 10\({}^{-3}\), and performed exponential decay on the learning rate at every 50 epochs. The parameters \(\omega_{local}\) and \(\omega_{global}\) were set to 0.7 and 0.3, respectively, aiming to preserve detailed content while simultaneously focusing on the global shape changes. After the experimentation, this combination was found to be the better choice.
## 2 Materials and Methods
### Spatial Adaptive Feature Modulation Mechanism (SAFMM)  
Due to the low resolution of Multi-spectral images, it usually tends to cause image blurring or distortion when the size is small or the compression is severe. The Spatially Adaptive Feature Modulation Mechanism [22] (SAFMM), on the other hand, is able to dynamically adjust the features at each pixel location to improve the image quality, especially in maintaining the image details and textures with significant advantages. Based on this, the SAFM module is added to the decoder as shown in Figure 1's decoder section.  
The structure of SAFM is shown in Figure 5. SAFM consists of three parts: a shallow convolution, a stacked Feature Mixing Module (FMM), and an up-sampler layer. Specifically, a \(3\times 3\) convolutional layer is first used to transform the input image into the feature space and generate the shallow feature \(F_{0}\). The FMM is then used to generate the shallow features. Feature extraction is then performed using multiple stacked feature mixing modules to generate finer deep features from \(F_{0}\). The FMM consists of a Spatially Adaptive Feature Modulation (SAFM) unit, a Convolutional Channel Mixer (CCM), and two jump connections. Finally, these extracted features are reconstructed using the up-sampler module. The SAFM unit uses independent computations to learn multi-scale features and aggregates these features for dynamic spatial modulation. Since SAFM preferentially exploits non-local feature dependencies, CCM is further introduced to simultaneously fuse local context information and hybrid channels.