esa-sceva/satcom-chunk-collection huggingface dataset:
The integration of information and communication technologies, compatibility of hardware and software, standardized interfaces, modular system design, consistent information storage, and inter-operable hardware and software play crucial roles in automating material flow processes within logistics systems. Furthermore, an intelligent, automatic, and autonomous SCM system is a softwarized SCM. This article examines the concept of Cyber-Physical Systems (CPS), exploring their applications and significance within emerging industrial frameworks aimed at achieving Industry 4.0 goals. It delves into the integration of new business models, emphasizing software and system architecture orchestration based on the concept of service-oriented architecture (SOA), and the evolving industrial architecture of smart factories, with a specific focus on Supply Chain Management (SCM) environments. This emphasis is due to the unique applications and disruptive potential of CPSs, intending to identify both their impact and challenges. As a case study, in this paper, a CPS platform is presented that enables real-time data acquisition, from logistics vehicles, using CPS devices, with a special emphasis on the seamless integration of these data into a Transportation Management System. The integration process of data sources and data sinks, in a data acquisition system, can be a very time- and resource-consuming phase. Even if data communication standards and data representation standards are used, in real-life scenarios we will find cases where sensors from different developers use different data formats and communication protocols. This happens even in the case that those sensors provide similar information (e.g., the GPS coordinates of a vehicle). After the data are acquired, the data must be sent to the corresponding sinks, which on the other hand may also require data format adaptation or even transmission using different methods and protocols. A traditional approach for this integration, using ad-hoc integration solutions, may end up with a quadratic number of integration solutions. In the solution that is presented in this paper, that number has a linear growth as the number of data sources and sinks grow. The rest of this paper is as follows: the second section introduces the concepts involved in Industry 4.0 and describes how CPS can revolutionize the way companies conceive, improve, and disseminate their products. The third section presents the characteristics of CPS and their potential in several applications, with a special focus on Industry 4.0. The next section describes orchestration software and architectures in the context of CPS environments, and related work. Section five presents the case study, which is related to the integration of real-time data for logistics platforms. The last section is the conclusion of this paper.
## 1 Introduction
Therefore, the estimated target area could be narrowed down gradually, with the weighted mean of the last MWLE being the point estimate. In this paper, IMWLE is used to improve the accuracy for 3-D localization. The problems introduced by ToF measurement errors briefly addressed. The resulting spatial distribution of pseudo-targets has analyzed. The IMWLE method used to take into account the spatial distribution of pseudo-targets to narrow down the target area and give a point estimate of target location. The results of our proposed method and probability-based ToF method have been compared. It is shown that the IMWLE method has a higher degree of accuracy than the probability-based ToF method.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description  
This research will use the Butterworth filter and Chebyshev filter as prototypes, conduct particle swarm optimization and Pareto efficiency to achieve multi-objective optimized filter design parameters, and design the filter design parameters through the obtainedoptimal design parameters. Low-pass, high-pass, and band-pass filters complying with Butterworth and Chebyshev Type I theorems are presented below.
## 1 Introduction
A reduction of the median collapse intensity when the modeling uncertainty was accounted for in the collapse assessment was noticed, and the dispersion due to epistemic uncertainty was seen to be higher for bare frames with respect to infilled frames. In another study, [PERSON] and [PERSON] [13] investigated the effects of uncertainties in seismic fragility assessment of Indian RC infilled frames, noticing a higher dispersion instead in the structural response of infilled frames with respect to the bare-frame counterparts. The main shortcomings of the abovementioned research studies and available literature in the field can be attributed to how the variability surrounding the mechanical properties of the infills is accounted for and to the type of numerical model that is implemented. Specifically, despite the high level of uncertainty surrounding the masonry infill properties, constant mechanical and geometrical properties have been typically assumed. Regarding the formulations to define the hysteretic response of masonry infills adopted in previous studies, the three main needed parameters (formulation to define the backbone curve, the relationships to calculate the width of the equivalent strut, and the strength model of the infill) were often selected a priori without any consideration of the specificities of the considered masonry infill. Moreover, the numerical model and subsequent collapse assessment did not account for the possible shear failure of poorly-detailed RC columns, which could occur due to frame-infill interaction. This is also a consequence of modeling the masonry infills through a single-strut modeling approach [14; 15]. With the above in mind, this study presents the seismic risk assessment of one of the aforementioned masonry-infilled RC school buildings, with a focus on the importance of the uncertainty surrounding the definition of the infill panels and the effects of modeling uncertainty on the expected seismic losses. The variability in masonry infill properties is accounted for through a macro-classification comprising five masonry infill typologies. Their impact on both capacity curves and the selected engineering demand parameters (Interstorey Drift and Peak Floor Acceleration) is scrutinized, showing how a sound characterization of the mechanical properties of the masonry infills is needed to properly assess the building seismic response. The effect of a more comprehensive modeling characterization of the uncertainty on the loss assessment is also investigated by means of two sets of dispersion values. The impact on the expected annual loss ratio is finally quantified in terms of both structural and non-structural components, depending on the engineering demand parameter to which they are sensitive.
## 3 Result and Discussion
### Catalyst Morphology
However, the BET analysis results provide an adsorption-desorption isotherm graphic form of each catalyst shown in Figure 5, with the presence of a hysterical loop observed at a relative pressure of 0.45-0.99. This graphic form has been reported as basement C type graphs on Brunauer-Emmett-Teller (BET) calcification [15; 21]. The presence of hysterical loops and high graph rises at P/Po characterize that each catalyst, Z, Z3, Z5, Z7, Ni-Z3, Ni-5, and ni-Z7, corresponds to mesoporous material. Furthermore, Figure 5 also shows that the Ni-Z7 catalyst produced a greater amount of N2 gas adsorption compared to other catalysts. This shows that this graph is strongly influenced by the pore shape and catalyst surface area. Generally, Z7 has got bigger surface area than Z and Ni-Z catalysts. This is caused by contaminants that cover the pores of the Z catalyst. The activation and calcination processes were able to remove contaminants and dissolve Al of zeolite structures: consequently, the Z7 surface area increases. Then, the decrease of the surface area in Ni-Z catalyst with Ni metal impregnation was caused by the metal impregnated to Z, covering the surface of the pores [25]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Surface Area (m\({}^{2}\)/g)** & **Total Pore Volume (cc/g)** & **Average Pore Radius (nm)** \\ \hline Z & 59.60 & 0.15 & 1.722 \\ Z3 & 111.66 & 0.14 & 1.597 \\ Z5 & 132.44 & 0.20 & 1.593 \\ Z7 & 132.38 & 0.28 & 2.609 \\ Ni-Z3 & 72.32 & 0.15 & 1.720 \\ Ni-Z5 & 53.96 & 0.14 & 1.600 \\ Ni-Z7 & 107.70 & 0.21 & 1.870 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specific surface area, pore volume, and average pore radius of Z, Z3, Z5, and Z7.
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 4 Model Setup and Validation  
### Model Setup  
#### 4.1.1 Initial and Boundary Conditions  
The SWAN model has land and water boundaries. According to its nature, the land boundary is further subdivided into three types: complete absorption, partial absorption, and total reflection. The expression is as follows:  
\[\frac{\partial\varphi}{\partial n}=ik\frac{1-K_{r}}{1+K_{r}}\varphi \tag{5}\]  
where \(K_{r}\) is the reflection coefficient. The total reflection value is 1, total absorption is 0, and partial reflection ranges from 0 to 1. The \"water boundary\" mainly refers to the area where the incident wave is. The boundary condition expression in SWAN is as follows:  
\[\frac{\partial\varphi}{\partial n}=-p\varphi-q\frac{\partial^{2}\varphi}{ \partial\theta^{2}}+f \tag{6}\]  
where \(f=\partial\varphi_{0}/\partial r+p\varphi_{0}+q\partial^{2}\varphi_{0}/ \partial\theta^{2}\), \(p=\left[k^{2}r^{2}+k_{0}^{2}r^{2}+ik_{0}r+0.25\right]/\left[2 ik_{0}r^{2}\right]\), \(q=1/\left[2 ik_{0}r^{2}\right]\), and \(\varphi_{0}=\varphi_{1}+\varphi_{R}\). \(\varphi_{1}\) and \(\varphi_{R}f=\partial\varphi_{0}/\partial r+p\varphi_{0}+q\partial^{2}\varphi _{0}/\partial\theta^{2}\), \(p=\left[k^{2}r^{2}+k_{0}^{2}r^{2}+ik_{0}r+0.25\right]/\left[2 ik_{0}r^{2}\right]\), \(q=1/\left[2 ik_{0}r^{2}\right]\), \(\varphi_{0}=\varphi_{1}+\varphi_{R}\), where \(\varphi_{1}\) and \(\varphi_{R}\) represent the incident wave potential and the reflected wave potential, respectively. \(k_{0}\) is the wave number on the open boundary.
## 4 Experimental Results
### Experimental Result Evaluation
#### 4.2.2 Image Sensor Communication Performance  
Distance estimation performance evaluation depends on successful communication between a transmitters' multiple LEDs on the ceiling and the receiver CMOS image sensor. The receiver included the camera rolling shutter mechanism and an Android application that could manually adjust the shutter speed of the camera before implementing the experiment. To observe our proposed system communication performance between LED transmitter and receiver image sensor, we conducted an experiment with the data rate of the proposed FSOOK modulation technique at different distances and configurations of the camera frame rate, as shown in Figure 11a. It is clear that the data rate increases with camera frame rate, i.e., the data rate is directly proportional to the camera frame rate. For the practical implementation of the image sensor, the communication with high frame  
Figure 10: (**a**) Light intensity vs. communication distance; (**b**) flow chart of Android application for luminance measurement; (**c**) luxmeter vs. image sensor performance.  
rates creates difficulty during the decoding process on the receiver side. In our system, the experiment was performed using a current Android smartphone (Model: Samsung Galaxy S8) with a camera frame rate of 20 fps. Owing to the loss of cache memory of the smartphone, we could not configure the camera frame rate to more than 20 fps in our system. Table 2 shows the configuration of camera parameters used in the experiment to decode data successfully. In our experiment, we observed the performance of the image sensor during the image computation time in terms of the image format and image resolution setup, as shown in Figure 11b, where two types of image formats, JPEG and YUV, were tested. As shown in Figure 11b, the computational time of image processing with respect to the image resolution of the YUV format requires less time compared with that of the JPEG format. Moreover, in our system, we set the image resolution to 600 \(\times\) 800, which consumed a total computational time of 27.19 ms. Meanwhile, the JPEG format consumed 40 ms, which was comparatively higher than that of the YUV image format.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
###### Abstract  
Self-adaptive software (SAS) is gaining in popularity as it can handle dynamic changes in the operational context or in itself. Time behaviors are of vital importance for SAS systems, as the self-adaptation loops bring in additional overhead time. However, early modeling and quantitative analysis of time behaviors for the SAS systems is challenging, especially under uncertainty environments. To tackle this problem, this paper proposed an approach called Timed-SAS to define, describe, analyze, and optimize the time behaviors within the SAS systems. Concretely, Timed-SAS: (1) provides a systematic definition on the deterministic time constraints, the uncertainty delay time constraints, and the time-based evaluation metrics for the SAS systems; (2) creates a set of formal modeling templates for the self-adaptation processes, the time behaviors and the uncertainty environment to consolidate design knowledge for reuse; and (3) provides a set of statistical model checking-based quantitative analysis templates to analyze and verify the self-adaptation properties and the time properties under uncertainty. To validate its effectiveness, we presented an example application and a subject-based experiment. The results demonstrated that the Timed-SAS approach can effectively reduce modeling and verification difficulties of the time behaviors, and can help to optimize the self-adaptation logic.  
self-adaptive software; time behavior; formal modeling; quantitative analysis; formal templates 2023132018
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 2 System Composition
### Laser Emission System  
The laser emission system is composed of a cross laser and an adapter. The cross laser is composed of two line lasers with a wavelength of 635 nm. The maximum output power  
Figure 2: Schematic diagram of system composition.  
of the two lasers is 50 mw. The two line lasers are fixed into a cross laser by the adapter and installed near the main INS as the measuring datum.
# Selection of a PCM for a Vehicle's Rooftop by Multicriteria Decision Methods and Simulation
## 2 Materials and Methods  
### Materials Determination  
[PERSON] et al. did a research where 35 PCMs passed through a MCDM selection, where the candidates were considered for heating, ventilation and air-conditioning application in buildings; also in this investigation, the author takes in consideration the Figures of Merit (FOMs), for the performance of the heat extraction per unit volume (FOM1) and the response time of the material (FOM2). Thus, these criteria will be used as the base, where 15 with the best specific heat capacity will be considered for the MCDM proposed for automotive rooftop applications. However, in this selection, some materials from the same family have the same specific heat capacity; for these materials, the one with the best heat extracted per unit volume (FOM1) calculated is considered. In this way, the materials PlusICE PCM A22, PlusICE PCM A23, PlusICE PCM S19, PlusICE PCM S21, PlusICE PCM S25, Rubitherm GmbH PCM SP21E2, Rubitherm GmbH PCM SP25E2, Rubitherm GmbH PCM RT21, Rubitherm GmbH PCM RT24, Rubitherm GmbH PCM RT25, Rubitherm GmbH PCM RT27, Rubitherm GmbH PCM RT21 HC and Rubitherm GmbH PCM RT22 HC are rejected and, in the case of the materials Rubitherm GmbH PCM SP24E and Rubitherm GmbH PCM SP26E that have the same values, the series SP24E stands for its wide range of phase-change temperatures [25]. In this sense, Table 1 displays the materials considered with its thermal properties. Furthermore, it is important to point out that this investigation will use the lowest thermal phase-change temperature and characteristics in the liquid state; also, an M index was added to the materials as a label to be used in the MCDM to be used instead of the full name. On the other hand, Table 2 shows the results of the calculations made for the figures of merit [25].
[23] proposed using Artificial Intelligence (AI) techniques to optimize Space-to-Air-to-Ground Integrated Networks (SAGINs). First, the authors discuss several main challenges of SAGINs and explained how AI can solve these problems. They consider satellite traffic balancing as an example and proposed a deep learning (DL)-based method to improve traffic control performance. The simulation resultsconclude that the DL technique can be an efficient tool for enhancing the performance of SAGINs. However, the authors mention that implementing AI techniques in SAGINs is still a new issue and it requires more effort in order to improve its performance.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 3 Results
### DFN Simulations  
The simulations showed systematically more fractures and more connected fractures in granite compared to gneiss. Fractures in the gneiss were predominantly short and mostly arranged into one preferred orientation (Figure 9a). Fractures in pegmatoid granite had two preferred orientations, and they were longer (Figure 9b). Both the number of large fracture clusters (>100 fractures), and the maximum number of fractures in one cluster increased linearly as the volumetric content of PGR increased. However, the total number of connected microfracture clusters was negligible in both rock types. The results are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & **Rock Type** & \begin{tabular}{c} **Density** \\ **[kg/m\({}^{3}\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Porosity** \\ **[\%]** \\ \end{tabular} &
\begin{tabular}{c} **Depth** \\ **[m]** \\ \end{tabular} & \(D_{f}\) & \(D\) & \(E^{*}\) & \(F\) \\ \hline EDZ109 & VGN & 2710 & 0.40 & 0.10 & 1.62 & 1.51 & 2.24 & 18.18 \\ EDZ110 & VGN & 2737 & 0.27 & 0.16 & 1.78 & 1.49 & 2.23 & 18.54 \\ EDZ112 & VGN & 2748 & 0.70 & 0.34 & 1.72 & 1.48 & 1.85 & 16.77 \\ EDZ114 & VGN & 2747 & 0.53 & 0.45 & 1.61 & 1.48 & 2.07 & 16.95 \\ EDZ180 & VGN & 2727 & 0.39 & 0.56 & 1.59 & 1.51 & 2.82 & 21.06 \\ EDZ181 & VGN & 2721 & 0.38 & 0.74 & 1.64 & 1.49 & 2.51 & 20.00 \\ EDZ182 & VGN & 2732 & 0.40 & 0.80 & 1.67 & 1.54 & 1.90 & 15.90 \\ EDZ155 & PGR & 2612 & 0.75 & 0.09 & 1.66 & 1.55 & 2.17 & 16.40 \\ EDZ156 & PGR & 2608 & 0.61 & 0.15 & 1.63 & 1.55 & 1.92 & 15.47 \\ EDZ157 & PGR & 2616 & 0.43 & 0.29 & 1.63 & 1.61 & 1.93 & 15.91 \\ EDZ158 & PGR & 2630 & 0.37 & 0.35 & 1.65 & 1.57 & 2.35 & 18.24 \\ EDZ159 & PGR & 2629 & 0.41 & 0.40 & 1.63 & 1.56 & 1.65 & 14.69 \\ EDZ161 & PGR & 2620 & 0.45 & 0.62 & 1.61 & 1.62 & 2.24 & 17.68 \\ EDZ162 & PGR & 2631 & 0.45 & 0.68 & 1.62 & 1.58 & 2.25 & 17.81 \\ EDZ163 & PGR & 2626 & 0.48 & 0.73 & 1.69 & 1.51 & 1.96 & 16.80 \\ EDZ164 & PGR & 2621 & 0.37 & 0.79 & 1.66 & 1.57 & 1.99 & 16.80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Determined fractal dimensions and fracture length distribution parameters of the microfracture networks. \(D_{f}\) is the fractal dimension of the fracture network, \(D\) is the fractal dimension of the fracture-midpoints, \(E^{*}\) is the fracture length index, and \(F\) is a constant of the fracture length distribution model. Depth is measured from the excavated surface.  
Figure 9: Example of the resulting fracture networks: **(a)** gneiss (VGN); **(b)** pegmatoid granite (PGR). Size of the images is \(10\) m \(\times\) 10 m.
## 4 Results and Analysis
### Satellite Spread-Spectrum Transponder Fault Case  
#### 4.2.1 The Phenomena and Causes of the Fault  
On 3 October 2018, the downlink telemetry fault of a satellite spread spectrum transponder occurred. The ground telemetry, track and command (TT&C) station could not accept the satellite's downlink telemetry signal, and the satellite telemetry remote communication returned to normal after enabling the backup spread spectrum transponder. After the incident, the data transmitted about the spread spectrum transponder telemetry showed that the spread spectrum transponder was in a shutdown state; the 5 V power telemetry jumped to 0 V; the solid-state amplifier current jumped from 0.715 A to 0.13 A; the solid-state amplifier power jumped from 11.19 w to 0.13 w; the temperature data showed the occurrence of the fault in the high-temperature region; the 12 V power telemetry slightly decreased; and other telemetry parameters were in the normal range. The spread spectrum transponder 5 V power telemetry performance before the jump to 0 is illustrated in Figure 10a. A similar exponential increase in the 5 V voltage before the fault and the eventual power failure is illustrated in Figure 10a.  
The post-fault analysis located the fault as an anomaly in the DC-DC module of the spread spectrum transponder power module. This module is responsible for converting the voltage of the 42 V primary power supply to generate 5 V and 12 V power and then supplying power to the spread spectrum receiver and the spread spectrum transmitter. The analysis of the telemetry data of the occurrence of the fault shows that both the 5V and 12 V voltage fluctuations of the spread spectrum transponder are correlated with the temperature fluctuations of the spread spectrum transponder. As shown in Figure 10b, periodic fluctuations in the spread spectrum transponder temperature occurred because the satellite-Z surface where the stand-alone transmitter is located fluctuates periodically due to the influence of an external heat source. The 5 V voltage telemetry also fluctuated with the temperature before the fault. The 5 V voltage fluctuation to higher values mainly occurred in the high temperature region, while the 5 V voltage fluctuation to lower values mainly occurred in the low temperature region.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 3 Concepts of Functional and Stochastic Atmospheric Constraints  
Atmospheric products such as Global Ionospheric Maps (GIM) and zenith path delays might play an important role with regards to significant improvement in multi-GNSS PPP solution quality and accuracy. Using the a priori vertical total electron content (VTEC) and accuracies provided in GIM products, PPP convergence can be significantly improved. However, the positional accuracy can be affected given different approaches used in generating the GIM models, as well as the density of stations used.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Processing Parameters** & \multicolumn{2}{c}{**YorkU GNSS PPP Engine Settings**} \\ \hline Processing technique & Uncombined mode using raw strength of observations \\ \hline Antenna corrections & IGS ANTEX \\ \hline Satellite orbits and clocks & GBM (GFZ) \\ \hline Elevation mask & Minimal 10\({}^{\circ}\) \\ \hline GNSS system & GPS, GLONASS, Galileo and BeiDou \\ \hline Observation processing mode & Dual-frequency, Triple-frequency, GPS ambiguity resolution, static processing \\ \hline Data format & RINEX 2.\(\times\) or 3.\(\times\) \\ \hline Ionospheric mitigation & Slant ionospheric delays: estimated \\  & GIM delays: used to constrain only the first epoch \\ \hline \multirow{3}{*}{Troposphere modelling} & Hydrostatic delay: Davis (GPT) \\  & Wet delay: estimated \\ \cline{1-1}  & Mapping function \\ \hline \hline \end{tabular}
\end{table}
Table 1: Processing strategy and parameters used in YorkU GNSS PPP engine for data analysis.  
Figure 1: Map of global distribution of selected multi-GNSS stations.