esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 5 Results and Discussion
### Thickness Detection Results  
The test piece was an aluminum block, which is shown in Figure 13. The two sides of the test piece could be viewed as 17 mm high steps. One side of the test piece was covered with a piece of paper and tape. Please note that the ultrasonic transducer used here was the 1P20. The test piece was placed at the bottom of the water tank. The transducer was placed on the surface of the test piece at a distance of 15 cm. Therefore, the direction of ultrasound propagation was along the \(x\)-axis, which is shown in Figure 14. The thickness of the aluminum block is calculated by the range difference of the surface of the aluminum block and the subsurface of the water tank.  
The thickness of the test piece was measured using the micrometer. Four different points of the test piece were measured, as shown in Figure 13. The results are listed in Table 3. The model of micrometer used was a Micromar 40 ER, with a resolution of 0.001 mm. The main measuring error was due to the measurement mode. The proposed ranging system was a noncontact measurement method. The height of the test piece was calculated by the range difference of the surface of the test piece and the subsurface of the water tank, whereas the micrometer was a contact measurement. The relative error was less than 1.3%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Point** & **Results/mm** & **Results by Micrometer/mm** & **Relative Error** \\ \hline A & 17.158 & 16.983 & 1.03\% \\ B & 17.181 & 16.977 & 1.20\% \\ C & 17.222 & 17.022 & 1.17\% \\ D & 17.303 & 17.102 & 1.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurement results of the test piece.  
Figure 14: Measurement of the thickness of the aluminum block; the thickness is the difference between H1 and H2.  
Figure 13: Image of the test piece and measuring points.
# Recent Advances in Muconic Acid Extraction Process
## 4 Muconic Acid Extraction
### Stripping Process for MA Recovery from Organic Phase  
However, in addition to the MA reactive extraction, a vital but understudied step towards a useful downstream process is re-extraction (stripping) of the acid from the organic phase that produces pure acid in an aqueous phase for further processing. This second step consists of reversing the reaction to recover the acid and turn it into a product phase and an extractant that is free of acid and is, hence, reusable. Various regeneration techniques can be used to recover the acid from a loaded organic phase. These need the use of stoichiometric excesses of trimethyl amine, HCl, or NaOH/NaHCO\({}_{3}\), or they can be achieved by temperature and diluent swing (using a fresh aqueous stream at the highest temperature).  
[PERSON] et al. (2017) studied MA re-extraction from the organic phase consisting of ethyl oleate, tri-n-octylamine 0.015 mol/kg, and 1-decanol as phase modifier (weight fraction 0.1) using two methods for recovery [59]:  
* A pH-shift, which implies the use of an aqueous solution (citrate buffer, with a pH interval of 3-7.8) as the re-extraction phase, results in a yield of 99% at a pH higher than 7 due to the dissociated form of MA at pH values higher than its pKa.
* The use of amines that are soluble in water as an extra reactive element (propylamine (M-C3), butylamine (M-C4), hexylamine (M-C6), and tri-n-propylamine (T-C3)). The acid is again extracted into an aqueous phase along with a complex of amines that are water-soluble. The efficiency of different concentrations of amines used for re-extraction was analyzed through the partition coefficient, \(\mathrm{K_{D}}\). Using M-C3, after re-extraction, \(\mathrm{K_{D}}\) yields infinity, indicating the complete recovery of MA from the organic phase at high amine concentration in the organic phase. Additionally, similar results were obtained when using M-C4. These results indicated that the complex stoichiometry is a 2:1 ratio due to the binding of one molecule of M-C3 or M-C4 per MA carboxylic group [59].
* Analyzing these results, the use of water-soluble amines can be considered the best stripping strategy, as it provides 100% re-extraction efficiency.
## 4 Conclusions  
In this study, we propose a modified steering kernel algorithm based on image matching to improve the blurring phenomenon that occurs in the edges of the image as a result of AWGN removal. As a typical steering kernel only contains gradient information for a local area of the image, blurring occurs while filtering high-frequency components, such as edges. To solve this problem, the proposed algorithm proposes a modified steering kernel algorithm based on image matching.  
The proposed algorithm determined the modified steering kernel weight by utilizing the weights derived from image matching applied to the original steering kernel. The image matching was processed by utilizing the similarity between center and matching windows, and the final images were computed using the modified steering kernel weights applied to the pixel values of the matching areas. The performance of the proposed algorithm was evaluated using the resulting and enlarged images. According to the simulation, the proposed algorithm displayed better edge components, a higher contrast in the high frequency areas, and a larger reduction of noise by suppressing the blurring effect. The PSNR and SSIM were used for more quantitative evaluation, with the proposed algorithm displaying superior results compared to existing methods as a result of comparing different types of images and various levels of noise.  
The proposed algorithm has excellent noise removal functions but requires a relatively long time to eliminate noise owing to the increased computational complexity. In the future, we intend to conduct research aiming to simplify and optimize the algorithm to solve these shortcomings and enhance the overall performance.  
Conceptualization, B.-W.C. and N.-H.K.; Software, B.-W.C.; Validation, N.-H.K.; Formal analysis, B.-W.C.; Investigation, B.-W.C.; Data curation, B.-W.C.; Writing--original draft preparation, B.-W.C.; Writing--review and editing, B.-W.C. and N.-H.K.; Visualization, B.-W.C.; project administration, N.-H.K.; All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 The SciFlow Approach to Scientific Writing
### Four Guiding Principles
#### 3.2.3 Safety and Recoverability  
In our survey, 82.2% of users performed backups rather often or very often (see Section 2.3). SciFlow provides two ways to support the user with document backups. For one thing, it incorporates revision management by taking snapshots of the document every two minutes. For another, these snapshots are pushed to the server, whenever a change is made and the user is connected to the internet. To keep the used bandwidth and disk space to a minimum the snapshots and their backups are made incrementally, meaning that only those parts of the document that have changed, are changed on disk. For a detailed description of the snapshot features please see [24].  
However, since all SciFlow documents are also saved within the user's document directory, he may make manual backups at all times. Documents are saved in the DocBook XML format which may be read by a number of clients. In case of a hard drive failure the SciFlow client automatically retrieves backed up documents from the server. In the case of a software failure, the SciFlow client retrieves the last made snapshot. It may check the integrity of a document by comparing the files that are contained within a snapshot with the structure file it saves alongside the parts of the document. Should a file be missing from the snapshot, SciFlow may retrieve a version from an older snapshot and thus repair the file.  
Implementation To perform backups, any web service that can store and retrieve files may be considered. In our implementation we have chosen Amazon S3 for several reasons. Amazon S3 files are mirrored in several data centers in multiple geographic locations. Furthermore, load tests conducted by [25] show that even when a single file is accessed excessively, it is still well accessible.  
In order to maintain revisions of a document, SciFlow uses a copy-on-write snapshot implementation [27]. Copy-on-write, in contrast to redirect-on-write, describes a technique, where changes on data are always written on the original data. To keep a copy of earlier revisions, the original data is copied to a new location before the changes are saved.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 7 Literature Review of Problem Solution Techniques
### DRL-Based Scheduling Algorithms
To tackle this problem, the authors proposed using either machine learning-based reinforcement learning algorithms or rule-based heuristic algorithms. They suggested that deep reinforcement learning could improve decision-making in resource-constrained environments and formulated a solution using the Markov Decision Process framework. Their proposed solution was a Double Deep Q-Learning (DDQL)-based scheduling algorithm that determines whether to assign a task to a fog node or to a cloud data center. While the DDQL-based scheduling algorithm yielded significant results in simulations, there is still a need to enhance the efficiency and effectiveness of the agents' learning process for this novel idea.
## 3 Results
### Comparative Performance Analysis  
A comprehensive comparison of the three stud welding methods--ARC CF (ceramic ferrule), ARC SG (shielding gas), and ARC SRM (radially symmetric magnetic field)--highlights distinct performance trade-offs relevant to both structural and stainless-steel applications.  
From the macrostructural results presented in Section 3.1, the ARC CF method consistently achieved the deepest weld penetration and the widest fusion zone. This greater heat input ensures robust metallurgical bonding and higher mechanical strength but requires careful control of ferrule integrity and post-weld cleanup due to possible residue or ferrule breakage.  
The ARC SG technique demonstrated moderate penetration depths with good fusion continuity and minimal surface contamination. The absence of ferrules simplifies the welding process and reduces waste, making SG more convenient for applications where moderate joint strength and cleaner post-weld surfaces are acceptable. However, the observed \"cold welds\" in some samples underscore the need for precise alignment and parameter calibration to prevent incomplete fusion.  
The ARC SRM method produced the most uniform fusion zones with the narrowest heat-affected areas. The magnetic field effectively stabilized the arc, resulting in highly repeatable weld collars with minimal spatter and excellent surface quality. Although the penetration depth was shallower than CF and SG, the controlled heat input minimizes distortion and residual stresses, which is advantageous for thin plates or parts with strict dimensional tolerances.  
Hardness measurements confirmed that deeper penetration correlates with higher weld metal hardness for ARC CF, while ARC SG and SRM provided smoother hardness transitions and narrower thermal gradients, reducing the risk of brittle microstructures.  
Defect analyses through visual, macrostructural, and non-destructive testing verified that all joints met the acceptance criteria for Quality Level C under ISO 5817:2014. Minor defects, such as isolated gas pores in SRM or arc blow in SG, were within allowable limits and did not compromise overall weld performance.  
Overall, the selection of an optimal stud welding process depends on specific application requirements. ARC CF is suitable when maximum joint strength and deep fusion are priorities. ARC SG balances reasonable strength with cleaner surfaces and simpler processing. ARC SRM is recommended when high repeatability, minimal heat input, and excellent surface finish are critical, particularly for automated production and thin-walled assemblies.
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## Appendix A  
### Estimation of Parameters  
1. Initialization: Suppose \(\mathbf{Y}_{1},\cdots,\mathbf{Y}_{K}\) are the respective MVs, and are scaled such that \(\mathbf{E}(\mathbf{Y}_{i})=0\) and \(\mathbf{V}(\mathbf{Y}_{i})=1\). We are interested in expressing each LV as a linear combination of MVs, represented in compact form: \[\mathbf{\hat{X}} =\mathbf{YM}\] \[\mathbf{\hat{x}}_{g} =\frac{\hat{x}_{g}}{\sqrt{VAR(\hat{x}_{g})}},g=1,\cdots,G\] (1) Hence, the LVs are initialized as: \(\mathbf{\hat{X}}=\mathbf{\hat{x}}_{1},\cdots,\mathbf{\hat{x}}_{G}\).
2. Inner approximation Within the inner model domain, the estimation of the path parameter of each LV can be mathematically represented as the weighted sum of its neighbouring LVs. \[\mathbf{\tilde{X}} =\mathbf{\hat{X}E}\] \[\mathbf{\tilde{x}}_{g} =\frac{\tilde{x}_{g}}{\sqrt{(VAR(\mathbf{\tilde{x}}_{g}))}},g=1,\cdots,G\] (2) The approximate estimation of the inner model path parameter takes: \(\mathbf{\tilde{X}}=(\mathbf{\tilde{x}_{1}},\cdots,\mathbf{\tilde{x}_{G}})\).
3. Outer approximation The outer approximation is computed based on the weight of the LV loads from the inner approximation. This comes in two forms, Mode A and Mode B. For Mode A, a multivariate regression coefficient with the block of MVs as the response and the LV as the regressor: \[\mathbf{\hat{w}}_{g}^{\top}=(\mathbf{\tilde{x}}_{g}^{\top}\mathbf{\tilde{x}}_ {g})^{-1}\mathbf{\tilde{x}}_{g}^{\top}\mathbf{Y}_{g}\] (3) Mode B is a multiple regression coefficient with the block of MVs as the response and its block of MVs as the regressor: \[\mathbf{\hat{w}}_{g}=(\mathbf{Y}_{g}^{\top}\mathbf{Y}_{g})^{-1}\mathbf{Y}_{g}^ {t}\mathbf{\tilde{x}}_{g}\] (4)
4. Outer weight vector Let \(k_{g}=\{k\in\{1,\cdots,K\}|y_{k}\;x_{g}\}\) be a set of indices for MVs related to LV \(x_{g}\); then, \(w_{g}\  
resulting in the outer estimation: \(\mathbf{X}=(\hat{\mathbf{x}}_{1},\cdots,\hat{\mathbf{x}}_{G})\).
* Iteration If the relative change of all the outer weights from one iteration to the next are smaller than a predefined tolerance, \[\left|\frac{\hat{w}_{kg}^{old}-\hat{w}_{kg}^{new}}{\hat{w}_{kg}^{new}}\right|<e,\forall,k=1,\cdots,K\wedge g=1,\cdots,G,\] (10) the estimation of factor scores done in (11) is taken to be final. Otherwise, go back to (12).
Then, a dual-reflectarray system and a 1.8 m parabolic reflectarray will be shown in Sections 4 and 5, respectively, to generate half the required spots (two colors) simultaneously in transmission (Tx) and reception (Rx). The main characteristics of the four reflectarrays will be compared in Section 6, proving the great potential of reflectarrays for multibeam satellite applications and their capability to halve the number of antennas and feed-chains required onboard the satellite to generate a four-color coverage.
## 4 Discussion and Conclusions
### Comparison of Tolerance to Sodium
In this study, results from both PMA-qPCR (Figure 2) and pyrosequencing techniques indicated the most abundant methanogen in the second-stage reactor was _Methanomicrobiales_ (hydrogenotrophic methanogen). Although aceticlastic methanogenesis had been regarded as the main pathway of methane generation for a long time [44], recent observations [45; 46; 47; 48] suggest that _Methanomicrobiales_ is mainly responsible for biogas generation in anaerobic digestion of sludge, especially under harsh conditions. With the long-term exposure of methanogens to a high sodium concentration derived from the first-stage reactor in this study's system, the microbial shift of _Methanomicrobiales_, in the second-stage reactor differentiated from that in the single-stage reactor (Figure 3). The significant difference was the predominance of _Methanoculleus_, which was characterized with the S-layer cell envelope. The latter functioned as a protective coat against environmental stresses, such as high salinity [49]. HAc degradation can be carried out by either the aceticlastic methanogens or the acetate-oxidizing reaction [50]. _Methanomicrobiales_ can be metabolically connected with SAOB to drive the final methanogenesis of HAc [51]. The strain AOR (nick-named _\"Reverobacter\"_) [52], _Thermotoga letttingae_[53], _Clostidium ultunense_[54], _Thermacetogeniumphaum_[55] and _Syntropicticus schinkii_[56] were isolated as the major SAOB in anaerobic digestion of different substrates [50]. The microbial shift towards a higher abundance of SAOB (_Thermotoga_ and _Clostidium_) at the genus level, in association with the hydrogenotrophic methanogens, might also contribute to the higher degradation rate of HAc and then alleviate the inhibition (indicated as decreased HAc degradation rate) caused by sodium in the second-stage reactor. It was reported that higher abundance of SAOB associated with hydrogenotrophic methanogens were observed under stress conditions (i.e., high salinity) [57].
## 4 Analysis of Influencing Factors
### Effect of the Support Structure Insertion Ratio
#### 4.4.1 Tunnel Horizontal Displacement  
Figure (a)a depicts the tunnel's maximum horizontal displacement upon completion of foundation pit excavation under varying insertion ratios. It is evident from the figure that the horizontal displacement decreases by 17.42%, 25.05%, 28.77%, and 29.55% as the insertion ratio increases from 0.2 to 1.0. The tunnel's horizontal displacement exhibits an exponential decrease with increasing insertion ratio. When the insertion ratio is 0.2, the horizontal displacement exceeds the control value specified in the existing specifications. When the insertion ratio exceeds 0.8, increasing it further has minimal effect on reducing the tunnel's horizontal displacement.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 5 Implementations in Graphics Processing Unit (GPU)
### Proposed CUDA Block and Thread Mapping Strategies
#### 5.2.4 Shell-Thread Mapping (STM)  
Shell-Thread Mapping (STM) assigns each CUDA thread to one shell-based ERI in Algorithm 3. Although this strategy of GPU thread allocation appeared in [32; 47], our implementations use the different shell-based ERI calculation algorithms. To exemplify the basic idea of thread assignment in STM, Figure 10 shows an example of the parallel thread assignment of CUDA blocks and CUDA threads to the shell-based ERI computation in STM. The reader may note the relevant roles of Algorithm 2 and 3 in the construction of the components. Each thread is assigned to one element in line 1 of Algorithm 3. Namely, the computation of lines 2-5 of Algorithm 3 as well as the computation of Algorithm 2 are performed sequentially. Therefore, the computation cost of each thread becomes larger than the above mappings. Following the same motivations in SBM, we capitalize on the merits of the _atomicAdd_ function to enable the cumulative computations in memory exclusively.
## 4 ML Applications for RIS-Assisted Communications  
This section presents the research frameworks for RIS-assisted wireless communication systems based on ML. Our comparative analysis comprises the type of ML algorithm applied, the network architecture, the type of database used (if applicable), and whether or not the source code is available. Figure 12 shows the principal issues related to the application of ML to RIS-assisted wireless systems. Most reported frameworks are based on channel estimation and beamforming for optimal phase-shift selection. Signal detection approaches can be based on sensing the spectrum, classifying the modulation, or demodulating the transmission signal. Finally, we review the FL methods (Figure 11), where the RIS is used to distribute the parameters coming from the UE.  
Figure 11: Federated learning scheme application on an RIS-aided communication system.  
Figure 12: Applications of ML for enhancing RIS-aided wireless systems.  
In this section, we also describe the source databases available to the research community for wireless applications, focusing on RIS, MIMO, and mmWave systems. Finally, we shed light on the main frameworks based on RIS-assisted wireless communication systems as a research problem.
## 5 Result Analysis and Discussion
### Analytical Insights and Discussion (Unveiling the Findings)  
In the initial phase, a thorough analysis was conducted to determine the most effective placement of BSSs and dynamic reconfiguration in the IEEE 118-, 69-, and 33-bus distribution networks and their interconnected subnetworks. This evaluation, utilizing the proposed methodology, considered both technical and economic considerations. Figures 13-15 illustrate the probability density functions of various desired objective functions throughout the optimization process. Figure 11: Temperature fluctuations corresponding to load changes at each step. Figure 12: Alterations in wind speed corresponding to changes at each step. 118, 69, and 33 for each analyzed load percentage. These visuals offer valuable insights into the efficient management of power generation and storage within the network under varying load conditions. Figure 16: Optimal generation allocation for each unit at different load factors across various case studies (IEEE 118, 69, and 33 bus). Figure 15: Probability density function of each objective function for IEEE 33-bus case study. The precise findings detailing the optimal placement and operational configurations of BSSs for the IEEE 118\(-\), 69\(-\), and 33-bus systems are presented in Tables 2-4, correspondingly. These tables provide comprehensive information on the most effective locations and optimal charging/discharging states of BSSs within each network, offering valuable insights into the enhancement of power system performance and efficiency. Moreover, the proposed framework delineates the ideal configurations of dynamic switches across varying load factors. Figures 20-22 illustrate the diverse states of interconnected switch breakers to ascertain the most efficient setup for the primary distribution network of IEEE 118, 69, and 33, respectively. This visualization aids in identifying the optimal configuration that maximizes the network's performance and efficiency. Figure 17: The optimal placement and optimal dis/charging of BSSs across various load percentages within the IEEE 118-bus network. Figure 18: The optimal placement and optimal dis/charging of BSSs across various load percentages within the IEEE 69-bus network. Figure 19: The optimal placement and optimal dis/charging of BSSs across various load percentages within the IEEE 33-bus network.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results
### Real-World Open Sky and Urban Scenario
The plot content is analogical to Figure 12.1 For this satellite, it is assumed that the signal is less affected by the environment because there are fewer variations of the signal amplitude. Only in the case of the first position is the probability function biased. It should be considered that the latest PVT estimate might be already biased and thus the herein presented offset \(\mu\) might not show the true bias. Dedicated analysis is planned for future studies using in one case a GNSS simulator and in another case a surveyed static reference position together with precise orbits. Nevertheless, the presented figures give an impression of the effects occurring (deformations and shifts) on the probability function. This is clearly visible when comparing the open sky case to the urban scenario, even if the biases can not be quantified with absolute values.