esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 2 Overview and Theoretical Foundations
### The Convergence of HPC and DISC in Industrial IoT Systems  
The integration of High-Performance Computing and data-intensive scalable computing paradigms is reshaping industrial IoT architectures, enabling previously unattainable synergies between real-time analytics and high-fidelity simulations. This convergence addresses a fundamental challenge in smart manufacturing: the need to reconcile the data-driven nature of distributed sensor networks with the computational demands of physical process modeling.  
Traditional HPC systems, while unparalleled for compute-intensive tasks like computational fluid dynamics or materials science simulations, often struggle with the data-locality requirements of industrial IoT deployments. The inherent latency in transferring terabyte-scale sensor data from edge devices to centralized HPC resources can undermine time-sensitive applications. Conversely, DISC frameworks like Apache Spark excel at distributed ETL and stream processing for equipment condition monitoring or supply chain optimization, but lack the numerical precision required for complex physical simulations.  
Modern industrial systems are overcoming these limitations through innovative hybrid architectures that strategically distribute workloads across the cloud-edge continuum. In automotive battery manufacturing, for example, Spark-based edge clusters preprocess and compress real-time test data from assembly lines before selectively triggering HPC-resident electrolyte flow simulations. This workflow optimization reduces data movement overhead while maintaining simulation accuracy. The programming model divide between HPC's MPI/OpenMP and DISC's data-parallel abstractions persists, but emerging tools like Spark-MPI are bridging this gap by enabling HPC simulations to execute as managed stages within Spark pipelines. Storage architectures are similarly converging, with Lustre-over-HDFS hybrids allowing seamless data sharing between paradigms.  
This technological synthesis supports Industry 5.0's vision of sustainable, human-centric manufacturing by enabling real-time edge analytics to inform high-precision simulations, and vice versa. As ARM-based edge computing matures and 5G networks reduce communication latency, the boundaries between HPC and DISC in industrial settings will continue to blur, creating new opportunities for optimizing manufacturing processes through integrated analytics and simulation [11; 12; 13].
# Dimensional Tolerances in Mechanical Assemblies: A Cost-Based Optimization Approach
## 2 Applicable Concepts and References Using Similar Proposal Approaches
### General Approaches Used for Tolerance Optimization  
Optimizing the component tolerances of an assembly becomes relevant only when at least one dimensional constraint exists; otherwise, unrestricted variation would be possible. In this context, optimization represents a tolerance synthesis process that allows for the allocation of tolerances based on one or more variables. Cost is typically considered the primary variable, given its economic importance, as discussed earlier, followed by quality considerations such as functional performance and reliability. However, in situations where dimensional constraints must adhere to KCs related to safety or critical damage, they take precedence over cost and quality requirements.  
The Lagrange multiplier (LM) method is a classical approach used to solve constrained optimization problems. Its concept involves transforming the constrained optimization problem into an unconstrained one. This transformation is achieved by introducing undetermined multipliers and incorporating the constraint to create a new unconstrained objective function, known as the Lagrangian function. The transformed constrained problem can then be solved using numerical optimization methods designed for unconstrained problems, such as utilizing [PERSON]'s expansion.  
[PERSON] and [PERSON] proposed a tolerance allocation approach for the reciprocal exponential cost-tolerance function, which integrates the Lambert W function, a complex multivalue function [31]. Similarly, [PERSON] et al. developed a least cost-tolerance allocation method based on the Lagrange multiplier technique and the Lambert W function. To obtain the desired result, they simultaneously derived a reciprocal power cost function and a quality loss function [32]. Furthermore, [PERSON] applied the Lagrange multiplier method using an extended formulation of the reciprocal power cost-tolerance function [33].  
In addition to the works mentioned above, a wide range of papers can be found in the literature that explore various approaches to optimizing cost to tolerance [34; 35; 36; 37; 38; 39; 40; 41; 42]. These papers contribute to the understanding and advancement of the field by presenting different methodologies and techniques. A recent comprehensive review by [1] provides a detailed examination of tolerance versus cost optimization. The review includes up to 290 articles published since 1970 that are relevant to the topic. The authors elucidate important concepts related to tolerancing, including tolerance-cost optimization, tolerance analysis, and tolerance synthesis, among others. Despite the well-known benefits of these approaches, their full potential is often underutilized due to their inherent complexity and the limited understanding of the proposed methods.
# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 2 Digital Simulation Prediction and Analysis Method of Assembly Deviation  
Figure 1 illustrates the digital prediction and analysis method developed in this paper for detecting the occurrence of assembly deviation, which involves the digital simulation of assembly deviation based on design tolerance, the in-machine 3D digital measurement of components and the digital simulation of assembly deviation based on actual measurement data.  
Digital simulation of assembly deviation based on design tolerance.  
Based on design tolerance, the digital simulation model of assembly deviation is established in 3 DCS, and the Monte Carlo method is used to simulate the assembly at least 2000 times to obtain the prediction results of the assembly deviation. The simulation results are used to analyze the key influencing factors for assembly deviation, determine the cause of assembly deviation and evaluate the rationality of the design before actual production. If the design is reasonable, the parts of the machine can be produced, and if it is not reasonable, the design needs to be changed until the design meets the requirements.  
Figure 1: Digital assembly process.  
By adopting advanced 3D tolerance analysis, cost and dimensional effectiveness can be controlled, improving design robustness.  
Three-dimensional digital measurement of components.  
By identifying the key influencing factors for assembly deviation and the causes of error through design tolerance analysis, the key dimensions, form errors and 3D contours of key features related to the assembly of the actual machined components are subjected to highly accurate measurement, 3D scanning. In this paper, the MetraSCAN 750 TM I Elite handheld laser 3D scanning system is applied in combination with Hexagon's GLOBAL CMM for the measurement of key components of the core machine. High-precision dimensional measurement of parts is mainly carried out via CMM, and part contour scanning and part deformation measurement is carried out via a handheld scanner. The scanned data were processed in a standardized manner and extracted using Polyworks. Then, the part measurement data and the design model were fused in 3 DCS to create a fusion model of the part.  
The digital simulation of assembly deviation based on actual measurement data.  
Based on the actual measurement data of the assembled parts, the assembly-deviation-simulation model was established in 3 DCS to predict and analyze the impact of machining errors on assembly deviation. If the simulation results of assembly deviation meet the design requirements, the parts can be assembled into a machine, and if they do not meet the design requirements, the parts need to be repaired. By taking into account the extent and direction of assembly deviation as measured, the relevant parts with excessive deviation can be reworked in advance, which reduces the scrap rate and improves the efficiency of assembly. Moreover, the accuracy of on-site assembly can be ensured and the visual guidance and optimization of on-site assembly can be achieved.
## 4 Experimental Results
### Reconstruction of a Character String  
The above proposed signal processing was applied to reconstruct the character string \"EMC\", which was written with Tahoma font and was displayed on the monitor, as shown in Figure 8. The character string was displayed monochromatically with black dots on a white background. Each character was composed of, at most, \(9\times 9\) dots in which one dot corresponded to one pixel of the \(800\times 600\) pixels resolution of the monitor. The common-mode noise waveform for the top horizontal line of the characters is shown in Figure 9. By using the proposed signal processing, the waveform shown in Figure 9a was reformed to the image in Figure 9b, displaying shaped pulse responses without any beat. Figure 10 shows the reproduced image expressed in 8-bit gradation after the whole processing. We confirmed that the characters were reproduced clearly from the measured common-mode noise emitting from the power cable. The small ripples on high and low levels in the waveform shown in Figure 9b produced the slight gray parts shown in Figure 10. A clearer image could be obtained by using an additionally simple binary signal processing with a threshold level.  
Figure 8: Character string displayed on monitor (\(800\times 600\) pixels).  
Figure 9: Common-mode noise waveforms for \(800\times 600\) pixels monitor specification: (**a**) before processing, (**b**) after processing.  
For further testing, the proposed reconstruction method was tested for an image with the resolution of \(1280\times 1024\) pixels and the dot clock frequency of 110 MHz. The same character string, \"EMC\", as shown in Figure 11, was selected as the target image. The common-mode noise waveforms before and after the signal processing for the top horizontal line of the character string are shown in Figure 12a,b, respectively. Shaped pulse responses similar to those in Figure 9b could be derived, despite the fact that the dot clock frequency of 110 MHz was higher than the measurement bandwidth of 50 MHz. Figure 13 shows the reconstructed image, where the character string composed of (at most) \(9\times 9\) dots displayed in the \(1280\times 1024\) pixels monitor specification was reconstructed successfully.  
Figure 11: Character string displayed on monitor (\(1280\times 1024\) pixels).  
Figure 10: Reconstructed image from common-mode noise \(800\times 600\) pixels monitor specification.  
**Remark 2**: _In the proposed signal processing, the dot clock frequency of the monitor was assumed to be known and was used for synchronizing the measured data to the monitor image line by line. In the case where this frequency is unknown, the proposed processing algorithm needs to be tuned until it meets the appropriate frequency._  
**Remark 3**: _It may require a more complex signal processing to reconstruct a multicolor image due to separated responses of red, green, and blue signals._
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 7 Conclusions  
This paper discusses the problem of maximizing the lifetime of the WSN. Through studying the characteristic of the energy consumption in WSNs, an efficient solution combining sensor node scheduling with routing protocol optimization is proposed. Firstly, in order to avoid the redundant coverage, the MCCA algorithm is proposed to find the minimum number of sensor nodes to monitor all MTPs. To keep monitoring MTPs continuously, a wake-up scheme is proposed in order to activate appropriate redundant node to replace the dead node. Based on the minimum coverage set, to optimize the energy consumed on the data communication, I-DEEC routing protocol is also proposed from three aspects. In CHs selection, by considering the energy and distance to the sink, the probability of nodes becoming CHs is adjusted. In cluster construction, some uneven clusters are also constructed to balance the energy consumption. Meanwhile, through comparing distances between the sensor node and the sink, and the CH, sensor nodes around the sink choose the communication way flexibly. The simulation results show that the proposed algorithms can improve the energy efficiency and the network lifetime, which is meaningful for the research on self-sustainable of the WSN. In this paper,  
Figure 14: The lifetime of the network.  
Figure 13: Comparison of network energy consumption.  
we consider the ideal channel model. In the practical scenario, the current algorithm should make some changes to adapt to the unreliability of the wireless channel which will lead to transmission failure and sensing data missing. To guarantee the transmission of the sensing data, the proposed algorithm should combine with a certain efficient channel coding or retransmission scheme when it is applied to a real unreliable network.  
Conceptualization,Y.X., M.T. and W.J.; methodology,Y.X.; software, M.T.; validation, formal analysis, and investigation, Y.X., M.T. and W.J.; resources, Y.X.; data curation, M.T.; writing--original draft preparation, Y.X.; writing--review and editing, visualization, supervision, project administration, and funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NO.61701241) and Natural Science Foundation of Jiang su Province (NO.BK20170935).  
The authors declare no conflict of interest.
# Age- and Sex-Adjusted Reference Intervals in Tear Cytokine Levels in Healthy Subjects
## 2 Materials and Methods
### Data Analysis
#### 2.3.1 Reference Intervals Determination  
We calculated percentile curves for each one of the considered cytokines as a function of age stratified by sex using generalized additive models for location, scale and shape (GAMLSS) [17]. GAMLSS are very general and flexible methods for modeling a response variable, in our case a cytokine level, as a function of explanatory variables: age and sex. Any parametric distribution, including highly skewed and kurtotic distributions, can be fitted to the response variable by modeling four parameters: a location parameter, denoted by \(\upmu\); a scale parameter (\(\sigma\)); and two shape parameters: \(\upnu\) and \(\tau\) modeling skewness and kurtosis, respectively. Each of the parameters can be a parametric or a non-parametric smooth function of the explanatory variables. In this way, GAMLSS allow to overcome some of the generalized linear models' limitations. First, the distributional assumption for the response variable is relaxed and a general distribution family can be used. In addition, we can model as a function of explanatory variables, not only the location parameter but also other parameters of the distribution.  
Different distributions were fitted to the observed distribution of each molecule concentration on log2 scale. Four distribution families were considered: normal; Box-Cox Cole and Green to explore the existence of skewness in the data; power exponential to explore the possibility of kurtosis; and Box-Cox power exponential distribution, to show if both skewness and kurtosis are present in the data. The age influence on parameters of the considered distributions was modeled as a constant, as a linear function or as a cubic spline. The sex was added to the model as qualitative variable. For a particular cytokine, Akaike Information Criterion (AIC) [24] was used in a stepwise algorithm to choose the best model. In the framework of each distribution family, the strategy for selecting relevant explanatory variables for each of the parameters that characterize the distribution is summarized in Figure 1. Finally, the best model was chosen by selecting the distribution whose final model was better fitted to the data.  
Figure 1: Stepwise algorithm for selecting relevant explanatory variables given a distribution family. Forward selection stage starts with the simplest model, the model with all the parameters fitted as constants, and tests the addition of sex and age, as a constant, a linear function or a cubic spline, using the Akaike Information Criterion (AIC). Backward elimination stage starts with the best model fitted in the forward stage, and tests the deletion of each explanatory variable using the AIC. The distributional parameters are: \(\mu\) location; \(\sigma\) scale; \(\
u\) skewness; and \(\tau\) kurtosis.
## 2 Basic Physical and Chemical Properties of Gangue
### Chemical Property  
The primary mineral composition of gangue was determined using X-ray diffraction (XRD). Dried gangue samples were pulverized to 200-mesh fineness, and 5 g of powder was uniformly compressed into discs to ensure crack-free, flat surfaces. Diffraction peak intensity data were collected using a Bruker D8 Advance (Bruker AXS GmbH, Made in Karlsruhe, Gemany) XRD instrument [30]. Chemical composition analysis was performed via X-ray fluorescence (XRF) spectroscopy. Samples were ground to 325-mesh fineness, with 3 g of powder mixed with lithium tetraborate flux. The mixture was fused at 1000 \({}^{\circ}\)C for 15 min in a muffle furnace. Scanning was conducted under vacuum using a Thermo ARL PERFORM'X (Thermo Fisher Scientific, Made in Shanghai, China) XRF spectrometer. Relative elemental concentrations were calculated via the fundamental parameters method, with instrument calibration established using certified reference materials [31]. The resultant analytical data are presented in Figures 2 and 3.  
Based on the analytical results presented in Figures 2 and 3, the mineral composition of gangue samples exhibited fundamental consistency across tested specimens. The dominant mineral phases were identified as quartz and kaolinite, with orthoclase demonstrating minimal presence. Quartz content peaked at a diffraction angle of 27\({}^{\circ}\), indicating its crystalline predominance. Chemically, the samples were primarily composed of SiO\({}_{2}\) (mean concentration: 64.74%) and Al\({}_{2}\)O\({}_{3}\) (mean concentration: 20.84%), with subordinate levels of Fe\({}_{2}\)O\({}_{3}\), CaO, and MgO as depicted in the elemental distribution profiles.
## 11 **The amount of half-rate capacity usage**
### Fault Detection
#### 2.5.2 Indicators' Analysis  
As discussed in the previous section, we considered parts of the network as having faults and unacceptable status using the CCC index. We investigated the parts of the network that needed quick reaction and action to improve the conditions. Therefore, ourdata was divided into fault and non-fault categories. As a result, we could use classification algorithms, including a decision tree, to analyze the network status and check indicators. The questions we sought to answer in this section included the following [10]:  
1. Which indicators play a more effective role in fault detection?
2. Which combination of values of indicators leads to faults in the system?
3. The value of which indicators have a higher impact on creating faults in the network?
4. In general, what are the indicators of network faults caused by violations?
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
## 3 Development of the Proposed Analytical Model
### Structural Characteristics of CFS Partition Walls
#### 3.2.1 Screw Connections
The force-deformation relation of each screw connection can be divided into three important phases: (1) elastic, (2) tilting, and (3) pull-through phases which are idealized by a tri-linear relation using experimental results. The screw tilting is assumingly initiated at about 75% of the maximum load obtained from experimental results [27]. The bearing capacity of a GB corresponding to the maximum is defined as the initiation of pull-though of the screw. During imposed larger deformations, the load-bearing capacity of the screw connection is degraded and finally the GB completely detaches from the connection where it is remarkably reduced up to 50% of the maximum load. The monotonic behavior and deformed shapes corresponding to phases of each screw connection can be found in the right top figure in Figure 3c. When imposing the specific value of \(\delta_{h}\) on a partition wall, the relative deformation, \(\Delta_{t,i}\) of the \(i\)th screw connection occurs and varies over its location along the stud, as presented in Figure 3c. With the internal shear force,\(f_{i}\) corresponding to \(\Delta_{t,i}\) obtained from the tri-linear force-deformation relation of each screw connection, the force, \(F_{v}\) acting along the axial direction of a stud is obtained by summing\(f_{i}\) of all connections. Considering the force equilibrium condition at the point O in the figure, the lateral force, \(F_{SC}\) of all the screw connections of a partition wall is calculated for the imposed specific horizontal displacement. The multi-linear lateral force-displacement relation of the screw connections of the partition wall is finally computed by repeating this series of calculations subjected to the increasing value of the \(\delta_{h}\), as shown in the blackline in the bottom right-hand corner of Figure 3c. For the application of an analytical model, the trilinear lateral force-displacement relation of the screw connections can be simplified, as shown in the red line in the same figure.
## 3 Results and Discussion
### WA Characteristics and Fluoride Removal Mechanism
\[\text{CaCO}_{3(\text{s})}+\text{H}_{2}\text{O}\rightarrow\text{Ca}^{2+}+\text {HCO}_{3}^{-}+\text{OH}^{-} \tag{1}\]  
\[\text{Ca}(\text{OH})_{2(\text{s})}\rightarrow\text{Ca}^{2+}+2\text{OH}^{-} \tag{2}\]  
\[\text{Ca}^{2+}+2\text{F}^{-}\rightarrow\text{CaF}_{2} \tag{3}\]  
Based on the Cls XPS spectrum of WA (Figure 5), four peaks were deconvoluted. The peaks at 284.6 eV and 289.1 eV were attributed to C-C/C=C and C=O [60], respectively. Meanwhile, the peak at 282.2 eV appeared to correspond to either metal-C or Si-C [61], while the peak at 292.7 eV corresponded to potassium [62] rather than carbon. Notably, these two peaks disappeared in the F-WA spectrum because of the dissolution of impurities. In the Ca2p spectrum, the Ca2p3/2 peak shifted upon fluoride adsorption and overlapped with the Ca2p1/2 peak, which also shifted. This shift was likely attributed to the strengthening of the peak at 348 eV due to CaF\({}_{2}\) formation, while the peak near 346.5 eV, associated with Ca(OH)\({}_{2}\) and CaCO\({}_{3}\), decreased upon fluoride adsorption [58,62,63]. The presence of a peak at F1s for F-WA further confirmed the adsorption of fluoride. Figure 6 shows a schematic diagram of fluoride removal using WA, based on the results of this study. Fluoride was removed from the solution via chemisorption onto the surface of WA, facilitated by surface precipitation with the Ca present in WA. Figure 5: _Cont._