esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 4 Regression Analysis
### Method of Magnitude Grouping
#### 4.1.4 Horizontal PGA Prediction Equation  
The regression coefficients obtained from the fittings described above are then substituted into Model II to derive the horizontal PGA prediction equation for the Loess Plateau region, as shown in Equation (11). This equation generates the PGA prediction curves presented in Figure 12 by inserting various \(R\) and \(M\) value intervals. Figure 12 highlights that the attenuation curves for each magnitude level do not intersect and the higher the magnitude, the higher the curve. As magnitude increases, the attenuation rate accelerates, leading to a more significant difference in PGA near the epicenter than at farther distances. Additionally, Figure 12 reveals that near the epicenter (\(R\) = 1 km), the PGA predictions for \(M_{\rm S}\) 6.5, 6.0, 5.0, and 3.0 earthquakes are 1164, 859, 367, 117, and 28 gal, respectively.  
Figure 13 compares the PGA attenuation curves for \(M_{\rm S}\) 4.5 and \(M_{\rm S}\) 5.0 earthquakes with the stepwise fitting results, suggesting that the developed PGA prediction equation aligns well with the observed values and resolves the issue of intersecting attenuation curves across different magnitudes encountered in the first step of the fitting process. Additionally, the PGA attenuation curve for a \(M_{\rm S}\) 5.0 earthquake generated by Equation (11) is higher than the results from the first fitting step, primarily due to the influence of data from a \(M_{\rm S}\) 6.2 earthquake, which have elevated the overall PGA prediction value for \(M_{\rm S}\) 5.0.  
\[{\rm lg}PGA=-1.399+1.186M+(0.468-0.422M){\rm lg}\Big{(}R+2.290e^{0.373M}\Big{)} \tag{11}\]  
Figure 12: PGA attenuation curves fitted by the magnitude grouping method in the Loess Plateau.
## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN-IFCM with Other Approaches using Wilcoxon Signed-Rank Test
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Classification**} & **Hypotheses** & **Results** \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{IFCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{IFCM}}\) & Reject H\({}_{0}\) \\ \hline Multi-class detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{IFCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{IFCM}}\) & Reject H\({}_{0}\) \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{CGAN-FCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{CGAN-FCM}}\) & Reject H\({}_{0}\) \\ \hline Multi-class detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{SGAN-IFCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{SGAN-IFCM}}\) & Reject H\({}_{0}\) \\ \hline Multi-class detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{SGAN-IFCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{SGMTE-IFCM}}\) & Reject H\({}_{0}\) \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{SGAN-IFCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{SGMTE-IFCM}}\) & Reject H\({}_{0}\) \\ \hline Multi-class detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{SCL-IFCM}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{SCL-IFCM}}\) & Reject H\({}_{0}\) \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{[13]}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{[13]}}\) & Reject H\({}_{0}\) \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{[14]}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{[14]}}\) & Reject H\({}_{0}\) \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{[15]}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{[15]}}\) & Reject H\({}_{0}\) \\ \hline Binary detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{[18]}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{[18]}}\) & Reject H\({}_{0}\) \\ \hline Multi-class detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{BF}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{BF}}\) & Reject H\({}_{0}\) \\ \hline Multi-class detection model & H\({}_{0}\): EQA\({}_{\text{proposed}}\) = EQA\({}_{\text{SVM-RBF}}\) & H\({}_{a}\): EQA\({}_{\text{proposed}}\) \(>\)EQA\({}_{\text{SVM-RBF}}\) & Reject H\({}_{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of Wilcoxon signed-rank test on proposed CGAN-IFCM and various approaches.
## 2 Microfabrication of Suspensions of Polymeric Fibers by Photo-Polymerization
### Control of Fiber Properties  
#### 2.3.1 Fiber Geometry  
The lithography technique offers the possibility of making isolated fibers or a fairly limited number of fibers at precise positions inside the channel as illustrated in Figure 3d-f. Their geometry is entirely controlled by the design of the lithography mask. The fiber length and width are proportional to its dimensions, modulo a reduction factor due to both the microscope objective and tube lens. For an objective of a magnitude of 10, we typically obtain a reduction factor of 4.5, increasing to 18.2 for an objective of 40\(\times\). This needs to be determined for each experimental setup. Low-magnification objectives were preferred because of their larger depth of field allowing an easier focusing. Typically, the width of fibers is a few tens of microns, whereas their length is a few hundreds.  
Figure 3: Examples of photopolymerized fibers. Left panel: (**a**) example of the mask used to fabricate the fibers. The mask shown here is smooth and will allow the fabrication of smooth fibers like the ones shown in the top row of the right panel; (**b,c**) the surface roughness of the fiber can be increased by drawing its borders with saw-tooth lines instead of straight ones ((**b**) mask; (**c**) resulting fiber). Right panel: top, isolated fibers (**d–f**); and suspensions of fibers (**g,h**) fabricated at varying initial orientation. A movie showing the fabrication of the parallel fiber suspension is available in Supplementary Materials Video S1.  
Figure 2: Y-shaped microfluidic channel for in-flow fabrication with a fiber made in the low-flow rate branch. Inset: successive positions of the flowing fiber.  
As explained earlier, the particle height is given by the channel height and the thickness of the inhibition layers along the upper and lower channel walls. In order to measure this thickness, one can fabricate a particle of a width much smaller than the expected height. As it flows downstream, the fiber turns on its side to lower its confinement, its height appearing in the field of view to be precisely measured. In our operating conditions, we found an inhibition layer thickness of around 6 \(\upmu\)m. Note that this value is slightly higher than the 2.5 \(\upmu\)m given in [9], but remains of the same order of magnitude. The fiber height is thus given by the channel height minus twice the thickness of the inhibition layer. For example, for a channel height of 30 \(\upmu\)m, a fiber height of 18 \(\upmu\)m is obtained.  
The produced fibers do not necessarily need to be smooth, but can also be fabricated with a rough surface. The Figure 3 shows a simple mask (a) and a mask drawn with a saw-tooth contour (b) used to fabricate respectively a smooth fiber (d-f) or a rough one (c). We expect such a roughness to modify fiber-fiber or fiber wall interactions.
## 2 Protocol Design and Equipment
### Location and Measurement Point Selection  
To select the appropriate measurement point, it should be ensured that the following contextual factors representative of the site are present in the spatial recording: openness, greenness, presence of landmarks, dominant use (walking, staying), and social presence (related to the dominant use). These are identified as objective metrics often used in urban and landscape research [36; 37; 38; 39; 40], possibly contributing to soundscape assessment [23; 41]. This relies on researcher's opinion-driven assessment--it is advised to observe the location for a moment and then choose the point representative of the context and the first-person user experience. For instance, in a park, it would probably be near a bench in the central area near the fountain; in a busy square, it would be a place where most people gather and have the best view on the landmark. While doing so, the placement too near the prominent vertical objects such as a statue, a wall, or a mast should be avoided as it might cause issues in later handling the visual data (3 m is considered a safe distance from these features). Similar concerns are also true for the audio data and careful attention should be paid to avoid placing the recording equipment near extraneous noisy equipment or in acoustic shadows. Further guidance on this is given in Point 4 of Section 4.1. It is important to avoid placing the recording equipment at a position where no users are expected (i.e., don't put the equipment in the middle of a flower bed or a grass area that nobody uses.  
For the purposes of this protocol, a single location was considered to be an 'environmental unit' wherein the environmental factors are consistent and is typically perceived to constitute a single distinct area. The exact dimensions and delineation of the environmental unit will vary depending on the characteristics of the space, so it is ultimately up to the judgment of the researchers on site to select an appropriate measurement point to best capture the character of the environmental unit.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level of Information** & \multicolumn{3}{c}{**Example Label**} & \multicolumn{3}{c}{**Factors Measured at This Level**} \\ \hline Location & \multicolumn{3}{c}{RegentsParkJapan} & GPS, Architectural typology, visual openness, etc. \\ \hline SessionID & RegentsParkJapan1 & RegentsParkJapan2 & SLM, session notes, ENV \\ \hline GroupID & RPJ101 & RPJ102 &... & RPJ201 &... & BIN, PIC \\ \hline Questionnaire & 1, 2, 3 & 4, 5 &... & 25, 26 &... & QUE, Start \& End time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Labeling system for on site data collection. Regent’s Park Japanese Garden is used as an example location. SLM: Sound Level Meter (acoustical factors); ENV: Environmental factors; BIN: Binaural; QUE: Questionnaires; PIC: Site pictures.
## 4 Numerical Analysis of Case Study
### The Evolution of Global Dimensionless Displacement with the Tunnel Face Advancing  
Due to the lack of the relevant monitoring data, the LDP curve will be obtained by the numerical simulation of step-by-step excavation method. The excavation footage is set as 4 m per day. After each excavation step, the \(\lambda\) of all monitoring objectives are recorded to calculate \(\overline{\lambda}\) in Figure 10a. Then, the mathematical relationship between \(\overline{\lambda}\) and \(x\) is fitted by Equation (8), which was proposed by [PERSON] et al. [33]. The parameters in Equation (8) are \(\lambda_{0}\)= 0.273 and \(\mathbf{X}\) = 3.291, respectively. As shown in Figure 10b, the fitting result is good, and the function of \(\overline{\lambda}\) for \(\mathbf{x}\) could be converted using Equation (12). Once \(\overline{\lambda}\) is certain, the distance away from the tunnel face will be calculated.  
\[x=\left\{\begin{array}{cc}\frac{3.291}{1-0.273}\ln\left(\frac{1-0.273}{1- \overline{\lambda}}\right)&\overline{\lambda}\ \geq 0.273\\ \frac{3.291}{1-0.273}\ln\left(\frac{\overline{\lambda}}{0.273}\right)&\overline {\lambda}<0.273\end{array}\right. \tag{12}\]
## 1 Introduction
This study investigates nine three-month datasets, but primarily one example of a nearly latitudinally located boundary of the Australian and Pacific Plate that is rich in nearby minor plates and is very active. Swarm A/C and B tracks over this boundary are analyzed within a longitude range of around 30\({}^{\circ}\). The dependence of the seismicity level on the season is very often discussed among researchers. Moreover, seasonal periodicities were already estimated in some works [55; 56]. Therefore, at the beginning, we apply three-month-long series of nighttime Swarm data in Papua New Guinea from three different seasons of the year and compare them with the corresponding lists of earthquakes with a M \(\geq\) 5.0. The fourth and fifth data series are from the Aleutian Islands at alternative latitudes and Africa at alternative longitudes. These series are analyzed to better validate Swarm's sensitivity to different seismic activity levels and solar conditions. The remaining four datasets are derived from daytime Swarm passes in Papua New Guinea and analyzed with respect to daytime signal sensitivity. To observe the influence of various factors, Swarm Ne disturbances are analyzed in the time domain together with seismicity, solar radio flux (F10.7), geomagnetic ap and Dst indices, and the ionospheric bubble index (IBI), indicating equatorial plasma bubbles (EPBs) along the satellite tracks [57; 58; 59]. The primary graphical inspection of the correlation of Swarm Ne disturbances with seismicity or with ap, Dst, and F10.7 is summarized by the Pearson correlation coefficients (PCC), calculated between all pairs of parameters and also between their 5, 7, and 10-day averages. PCC validated mutual correlations over time between Swarm Ne variations, seismicity, and solar and geomagnetic parameters. The most pronounced correlation is observed between Swarm Ne irregularities and the cumulative seismic energy at nighttime. This is promising for the future tracking of seismic activity over tectonic plate boundaries using spectrally analyzed LEO data but is at the same time challenging and needs more research employing global data.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 6 Performance Analysis
### PoAh Consensus Algorithm  
We analyzed the PoAh algorithm from several different perspectives. In terms of providing security, PoAh provides a prospective security solution for IoT structure. For decentralized IIoT networks, PoAh provides an adequate level of robustness when used with ECDSA, and addresses the main drawbacks of existing blockchain consensus algorithms, such as instabilities in network connectivity and the 51% attack [10; 11; 22; 24]. In the PoAh algorithm, all participating nodes are qualified to generate new blocks for the blockchain network, but only trusted nodes can authenticate or add new blocks to the chain. Furthermore, 51% attacks that pose a threat in PoW, are mitigated through the dynamic nature of trust values [9; 11].  
From the perspective of execution time, PoAh is a lightweight consensus algorithm that much more effective than other traditional consensus. In addition, the major impact of PoAh is the low latency and energy consumption that designed especially for IoTs applications. From the point of view of resource utilization, PoAh is an ideal selection for resource-constrained devices in IoTs private networks as it consumes minimal energy [10; 11; 26; 30].  
Figure 6: Consumption for each processor.
# Techno-Economic Analysis of Waste Heat Recovery in Automotive Manufacturing Plants
## 4 Results and Discussion
### Economic Analysis of Different Scenarios
In contrast, cases without PV, like Case 2 and Case 4, demonstrate more competitive PBPs, ranging from 6-8 years. Case 4, in particular, stands out with an attractive PBP of approximately 6.2 years, indicating a quicker ROI. This study highlights that, while adding a PV system increases energy production, it may result in a longer PBP and reduced NPV, making cases without PV potentially more attractive from a financial perspective. In this study, all cases demonstrate favourable economic assessment with low LCOE ranging from 0.037 to 0.064 EUR/kWh and ROI above 25%. These low LCOE values suggest that each case is cost-effective in terms of energy production, making them economically attractive options for sustainable power generation and waste heat recovery. It is important to mention that Cases 2 and 4 indicate high ROI above 100%, making them attractive from an economical point of view. The high ROI indicates that the initial investments in each configuration are expected to result in significant financial returns over their operational lifetimes, underscoring the viability of the systems analysed. This study demonstrates that the proposed system is economically competitive, as illustrated in Figure 1 and represented by Case 1. Case 1 achieves an SIC of 1.96 thousand EUR/kW, an NPV of approximately 654,541 EUR, a PBP of 14.06 years, an LCOE of 0.064 EUR/kWh, and an ROI of 25.48%. This system can generate around 1.31 MW, with an estimated annual energy production of 4.30 GWh, indicating both high output and efficient energy costs. As an alternative, Case 4 (the configuration without PV and without a recuperator) demonstrates greater economic attractiveness in terms of SIC, NPV, and PBP. However, this comes at the cost of a significant reduction in power generation, producing approximately 63.59% less energy than the proposed Case 1. The electricity from the grid can power the heat pump and ORC system pump. Therefore, while Case 4 is economically favourable, its lower power output may limit its application where higher energy demands are required. It is also proven that Case 4, which can be referred to as a Carnot battery without a recuperator in ORC, offers competitiveness in waste heat recovery in this study. The proposed system can be designed with a modular approach, allowing the system to scale proportionally with the size of the manufacturing facility. In that situation, the size of the system will influence the area used for installation. Also, Cases 1 and 3 consider PV systems in the installation. The PV system is likely to occupy approximately 5% of the available rooftop area in the automotive manufacturing plant.
## 3 Results and Discussion
### Carbonation Resistance
The accelerated carbonation coefficients (K\({}_{ac}\)) given in Table 6 were calculated from linear regression of the carbonation depth/square root of time curve and [PERSON]'s first law (Equation (7)):  
\[C_{d}=d_{0}+K_{ac}\sqrt{t} \tag{7}\]  
where \(Cd\) is the mean carbonation depth at time \(t\) (mm); \(K_{ac}\), the carbonation rate under the experimental conditions (mm/year\({}^{0.5}\)); \(d_{0}\), the carbonation depth at time = 0 (mm); and \(t\), the exposure time (years). The reliability of the carbonation coefficients calculated was over 98% in all mixes (see R\({}^{2}\) in Table 6). \(K_{ac}\) was 14.5% higher in mix N10/0, 106.6% in N25/0, 54.1% in R0/50, 66.9% in R10/50 and 117.0% in R25/50 than in mix NAC. Those findings denoted a higher rate of carbonation spread in recycled concrete mixes with GRC and/or MRA than in the conventional material. A similar behaviour was reported for mixes with 25% to 50% MRA in combination with 25% fired clay powder processed from CDW and used as a cement substitute [12], and for mixes containing from 50% to 100% RCA, as well as 25% to 55% fly ash [60]. Table 8 lists the carbonation coefficients found for real or natural conditions (\(K_{n}\)) with Equation (8), proposed by [PERSON] and [PERSON] [61] to express the relationship between accelerated and natural carbonation in terms of CO\({}_{2}\) concentration:  
\[\frac{K_{n}}{\sqrt{c_{n}}}=\frac{K_{ac}}{\sqrt{c_{ac}}} \tag{8}\]  
where \(K_{n}\) is the natural carbonation coefficient; \(K_{ac}\) the accelerated carbonation coefficient; \(c_{n}\) the CO\({}_{2}\) concentration under natural conditions (\(\sim\)0.04%) [62]; and \(c_{ac}\) the CO\({}_{2}\) concentration in the accelerated carbonation test (3%). Figure 5: Relationship in 28 d and 90 d mixes between (**a**) carbonation and oxygen permeability coefficient; and (**b**) carbonation and chloride diffusion coefficient. All the mixes studied exhibited values lower than or similar to the 4 mm/year\({}^{0.5}\) indicative of quality concrete [63] and none exceeded the critical value (6 mm/year\({}^{0.5}\)), defined to mixes with low carbonation resistance [59]. Further to the classification proposed by [PERSON] and [PERSON] [64], N10/0 would lie within the same carbonation resistance range (RC3) as NAC, whilst mixes R0/50 and R10/50 would be in class RC4, and mixes N25/0 and R25/50 in class RC5.
## 2 Materials and Methods
### Analysis of Volatile Compounds  
Solid-phase micro-extraction (SPME) was applied to extract volatile compounds of WOF. Five grams of the WOF sample was weighed in a 20 mL headspace vial. The internal standard [5 \(\mu\)L, (-)-borneol, 500 mg/L in methanol] was added into a sealed vial. The samples were maintained at 40 \({}^{\circ}\)C for 30 min to reach the equilibrium state and adsorbed for 30 min using SPME fiber coated with polydimethylsiloxane/divinylbenzene fiber (PDMS/DVB, 65 um, Supelco, Bellefonte, PA, USA).  
The analysis of volatile compounds was carried out using a 7890A gas chromatograph (GC, Agilent Technologies, Santa Clara, CA, USA) with a 5977B mass selective detector (MS, Agilent Technologies). Separations were performed on a fused silica capillary column (DB-5 ms, 30 m length \(\times\) 0.25 mm i.d. \(\times\) 0.25 um film thickness, J&W Scientific, Folsom, CA, USA). Helium (99.999%) was used as the carrier gas at a constant flow rate of 0.8 mL/min. The initial GC oven temperature was maintained at 40 \({}^{\circ}\)C for 5 min, raised to 80 \({}^{\circ}\)C at 1.5 \({}^{\circ}\)C/min and to 130 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, and finally, increased to 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held for 10 min. The temperatures of the injector and transfer line were 230 \({}^{\circ}\)C and 260 \({}^{\circ}\)C, respectively, and the split-less injection mode was applied. The mass spectrometer was operated in the electron impact (EI) ion source mode at 70 eV and a scanning range of 35-350 atomic mass units (a.m.u.).  
Identification of volatile compounds was positively confirmed by comparing retention times and mass spectral data with those of authentic standard compounds. When the authentic compounds were not available, each volatile compound was identified on the base of their mass spectral using NIST.08 and Wiley.9. The retention indices (RIs) of volatile compounds were calculated with n-alkane from C\({}_{7}\) to C\({}_{30}\) as an external standard. The quantification of each peak was performed by comparing its peak area with that of an internal standard compound on GC-MS total ion chromatograms. The experiment was conducted in triplicate.