esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
###### Abstract  
The H2020 DEMETRA project provides short latency clock monitoring services to the time users using the Atomium precise point positioning (PPP) software developed by the Royal Observatory of Belgium. In this paper, three recent updates of the current Atomium software are introduced: adding Galileo signals in the PPP computation; the option to constrain the receiver clock; PPP with integer ambiguity resolution. The advantages of these updates are demonstrated: Combining the Galileo and global positioning system (GPS) signals for PPP time transfer will further improve the frequency stability inside the computation batch; PPP with receiver clock constraint is not only used to reduce the short-term noise of the clock measurements but can also be used for some specific applications to a keep continuous clock solution in the computation batch or retrieve correct clock measurements from extremely noisy environments; the integer PPP allows a continuous clock solution, and improves the mid-term and long-term stability of the frequency transfer compared to the current PPP frequency transfer techniques.  
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# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 5 Conclusions  
We have proposed a LoRa signal-based positioning method that uses a fingerprint algorithm instead of the RSSI proximity or TDoA method. We experimentally evaluated this method by applying three different algorithms to interpolated fingerprint RSSI maps. We constructed a private LoRa network for the experiments and developed LoRa gateways, end-devices, and firmware. We also developed positioning server software to manipulate the packets received from the LoRa gateways. We set the test area to be 340 m \(\times\) 340 m with four gateways. In the experiments, we obtained the best mean location error of 24.1 m with 46 validation points. Our results showed relatively high positioning accuracy without Semtech Corporation's Geolocation Capability. We reduced the power consumption significantly compared to that of GPS-based positioning methods. We also reduced the hardware cost by using LoRa for communication and positioning simultaneously.  
The limitations of this study were as follows. It was necessary to create interpolated maps in the offline phase. These interpolated maps should be updated periodically to reflect the changing radio signal environment. The firmware for gateways and end-devices should also be modified to apply our methods to existing private LoRa networks.  
Future work may include the study of a sequential model to analyze continuous RSSI observations. We will investigate sophisticated methods that assign weighting factors to RSSI observation sequences. Fingerprint maps can vary with the surrounding environment, and therefore, we should also ensure that the fingerprint map update process can handle changing signal conditions. Furthermore, we will extend the experimental area and enable more reasonable validation.  
[PERSON] conceived and designed the methodology. [PERSON] wrote the original draft, and [PERSON] reviewed and edited it. [PERSON], [PERSON] and [PERSON] implemented the methodology and developed the software. [PERSON] administrated the project.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Field Application  
### Engineering Survey  
The S1201-II experimental working face of Ning Taota Coal Mine is the first implementation of the pillar-free self-formed roadway construction method. Its inclined length is 280 m, its strike length is 2344 m, the average coal seam thickness is 4.11 m, the burial depth is 90-165 m, the coal seam occurrence is continuous and the coal seam dip angle is 0-2\({}^{\circ}\). It adopts the method of one-time full-height mining, longwall retreat mining, comprehensive mechanized mining and the all-caving method to manage the roof. According to drilling data from near the working face, the roof of this working face is dominated by sandstone and the average strength index of the rock strata is 23 MPa. The basic top is medium-grained sandstone with massive bedding and localized sandy mudstone, 5.4-21.5 m thick. The direct top is thin siltstone and sandy mudstone layers with horizontal bedding; the former is 0.78-4.05 m thick and the latter is about 4 m thick. The direct bottom is a thin layer of siltstone with a thickness of 1.8 to 16.3 m and a thin layer of fine-grained sandstone. The hard floor is fine-grained sandstone 3.2-19.6 m thick.  
The layout of the test working face is shown in Figure 14. Because it is the first test working face to use the pillar-free self-formed roadway method, a rubber transport channel under the working face had been excavated in advance. Therefore, auxiliary haulage along the upper part of the working face will be mined, excavated and retained along with it. The design dimensions of the crossheading are 6.2 m wide and 3.8 m high. The test mine is a low gas mine. After adopting the new mining method, the \"Z\"-type ventilation mode is adopted for the working face; the air flow route is shown in Figure 14.  
In order to study the mine pressure and its manifestation in the test working face, the mine pressure of the automatically formed roadway is monitored using the big data remote online real-time monitoring system outlined in Section 4. Whenever the working face has advanced a certain distance,  
Figure 14: Plan layout of the working face for testing the pillar-free self-forming roadway method.  
the corresponding FBGs are installed in the roadway area behind the working face and connected to the industrial ring network. The layout of the stations is shown in Figure 15.
## 3 Results
### The Impact of Factors on DB15 Adsorption on HNT
#### 3.2.4 Effect of Temperature  
The impact of temperature on DB15 color adsorption onto HNT is introduced in Figure 5d. The information obtained for the traditional thermodynamic boundaries, specifically, \(\Delta\)G\({}^{\circ}\), \(\Delta\)H\({}^{\circ}\), and \(\Delta\)S\({}^{\circ}\) show the kind of response. For instance, the +ve \(\Delta\)H\({}^{\circ}\) values obtained from 303 to 323 K of HNT demonstrate the endothermic interaction. The \(-\)ve benefit of \(\Delta\)G\({}^{\circ}\) for the DB15-HNT framework affirm the suddenness and feasibility of the adsorption interaction. The size of \(\Delta\)G\({}^{\circ}\) esteems is demonstrative of quick and unconstrained adsorption at lower temperatures. Further, it is surmised that +ve upsides of \(\Delta\)S\({}^{\circ}\) (entropy) demonstrate the homogeneity of the surface at the interface of the DB15 color and the HNT [26].  
Figure 5: _Cont._  
Figure 5: Adsorption capacity and percentage of dye removal efficiency: (**a**) Effect of pH,(**b**) Effect of initial concentration of DB15 (adsorbate), (**c**) Effect of HNT (adsorbent) dosage, and (**d**) Effect of temperature.
# Narrowband-Internet of Things Device-to-Device Simulation: An Open-Sourced Framework
## 2 Overview of D2D Communications in Cellular Networks
[33], the expanding number of associated devices is growing significantly and the cellular network structure requires substantial augmentation to absorb that growth. This should be possible by enhancing energy efficiency and proposing a useful and practical framework to use bandwidth and spectrum. Consequently, improved versatility choices for taking care of the expanding number of linked devices ought to be considered. A useful framework is thus required to accomplish this idea to cater for the number of connected devices effectively. The framework should be able to deal with a significant amount of data. Moreover, the whole framework capability should be enhanced w.r.t. current capabilities, by a factor of 100 approximately [33]. Here, D2D communications is an important enabler that is recognized as a paramount technology in developing a beneficial solution for the upcoming sixth-generation wireless network.
## 4 Rice Monitoring Results and Discussion
### Backscattering Coefficient  
Three scattering matrix data; \(S_{LL}\), \(S_{RL}\), and \(S_{RR}\) were collected by assuming \(S_{LR}\) = \(S_{RL}\), since the reciprocity theorem almost holds true for our quasi-monostatic setup. Figure 6 shows the intensity of backscattering coefficients (LL, RL, and RR) as a function of DoY. The backscattering coefficients are normalized to a maximum value of the whole observation period. From Figure 6, we can see that the backscattering coefficients for all three polarizations increase as rice plants grow until DoY 235 because these are related to the leaf area index, rice freshness, and rice height [32], where our rice plants stop to increase those heights from DoY 235, shown in Table 1. Before rice emergence from the soil (non-rice observation), the backscattering intensity exhibits very low values as a result of the specular reflection from the flooded ground.  
The sense of CP-waves is reversed when they are reflected from flat surfaces and smoothly curved spherical reflectors. Therefore, cross- and co-polarization indicate odd- and even-bounce scattering mechanisms respectively. Based on this fact, it is seen from Figure 6 that the surface (cross-polarization) scattering produces relatively higher intensities than the double-bounce (co-polarization) scattering until the end of stage 3 and the opposite is true for later stages. Nevertheless, the maximum difference between polarimetric channels is around 2 dB which is not so significant.
## 1 Introduction
In particular, the Copernicus Sentinel missions, launched by the European Space Agency (ESA), operate a range of instruments [33] which provide the potential for a comprehensive monitoring of volcanic unrest and eruptive dynamics, opening pathways to global, multisensor volcano monitoring. In parallel, due to the sharply-increasing satellite data volume, novel ways of data reduction and analysis by artificial intelligence (AI) are gaining relevance [34; 35; 36; 37; 38]. We here present the first operational volcano monitoring platform, incorporating multisensor satellite-based information (Sentinel-1 SAR, Sentinel-2 SWIR, Sentinel-5P TROPOMI), ground-based earthquakes information (Global Earthquake Catalogues GEOFON and USGS), and AI-assisted monitoring. The system currently monitors 17 volcanoes, located in various climatic and geologic settings across the globe: subduction zones (Colima, PopocatePelt, Fuego, Pacaya, Sangay, Sabancaya, Bezymianny, Klyuchevsky, Udina, Krakatau, Ambrym, Etna), oceanic hotspots (Piton de la Fournaise, Kilauea), and continental rift zones (Nyiragongo, Nyamulagira, Erta Ale). The system is however designed to easily incorporate new targets to monitor, thanks in particular to the global-coverage and free-access of the data. We use freely-available processing toolboxes to perform routine data processing, and allow the user to investigate the multi-parametric results related to a given volcano through an open-access website (www.mounts-project.com). We demonstrate the utility of such an interdisciplinary approach through a number of recent eruptions, describing a range of volcanic processes which can be tracked, from subsurface magma migration, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and aerosol propagation into the atmosphere. In addition, we show how artificial intelligence can play a key role in monitoring tasks. We show how a pre-trained Convolutional Neural Network (CNN) can be incorporated into the processing pipeline to detect large deformation in InSAR interferograms (e.g., related to dyke intrusions), in an automated, timely, and robust fashion.
## 1 Introduction
[33] explored process capability analysis for multivariate nonnormal data using the proportion of nonconforming (PNC). They utilized the covariance distance (CD) to decrease the dimensionality of the multivariate Quality Control (QC) data. While many studies in the literature address nonnormal PCIs [9; 11; 42; 43; 44; 45; 46; 47; 48; 49; 50], only a few studies concentrate on multivariate nonnormal process capabilities. However, the number of these studies has been growing annually [4; 11; 51; 52; 53; 54; 55; 56; 57; 58; 59]. The majority of current multivariate Process Capability Indices (PCIs) rely on normality theory and assume that multivariate Quality Control (QC) data conform to a normal distribution. However, in reality, most QC data do not follow a normal distribution. Additionally, some proposed methods are only applicable in certain situations or are only suitable for a limited number of QCs and distributions [60]. Furthermore, the complexity of statistical calculations can hinder implementation [20]. Therefore, developing a robust multivariate PCI remains a significant research opportunity. The existing methods for estimating the capabilities of nonnormal multivariate processes are limited, and therefore, exploring different approaches is a valuable avenue for research. This study aims to develop a multivariate capability index that considers the correlations among quality characteristics (QCs) and applies it to assess manufacturing processes. The proposed methodology is a general framework for the practitioners to evaluate their process based on multivariate QCs. Regardless of the distribution of the real data, this methodology is effective in estimating a multivariate process capability index (MPCI). The algorithm of this research includes checking normality using the skewness measure and then transforming the data into normality using the same measure. Also, it is worth noting that most products have more than one correlated QC, which should be evaluated together. This study presents an effective methodology for estimating the MPCI for real case data. The proposed methodology has been tested for its accuracy of estimation. MPCI estimation using the proposed methodology in this research is better than the algorithm from the literature in term of estimation accuracy, as presented in the discussion section. To calculate the multivariate capability index, the proposed method initially reduces the multi-dimensional data to a single variable by comparing the process data to the specifications and determining their relative values. Next, the proportion of nonconforming (PNC) data is used to estimate the process capability index. The methodology of this research is presented in detail in Section 2. Section 3 describes the experiment procedures for applying the proposed method. The results of this research are presented in Section 4. Section 5 provides more discussions about the results along with comparing the results with another method from the literature. Finally, Section 6 provides managerial insights for implementing the proposed method for estimating MPI, and the research is concluded in Section 7.
### Traditional Approaches Involving the Use of Organic Solvents  
#### 2.1.1 Meneration  
Maceration, also known as traditional solvent extraction, is an extractive technique that consists of immersing the matrix, typically herbal, in a solvent at room temperature for a variable time (hours or days) depending on the starting material and solvent used. It can also be assisted with stirring [27]. The initial matrix can be previously treated by drying or it can be used fresh, also depending on the desired compounds. Usually, the starting material is cut or milled into finer particle size to facilitate the penetration of the solvent and diffusion of the solutes. However, it is never reduced to too-fine particles or powder, because this can cause the loss of volatile compounds and the excessive absorption of solute in solid, making the separation by filtration of the extract more difficult. The choice of the solvent is very important during the extraction; the solvent must be chosen based upon the chemical nature of the compounds of interest. Generally, alcohols (methanol and ethanol) are the most used solvents, due to their ability to extract a greater part of substances, including hydrophilic molecules and lipophilic ones [28; 29]. A more selective extraction is also possible; in order to isolate only the lipophilic fraction, the extraction is performed using vegetable oils. Meanwhile, water is used to extract only hydrophilic ingredients. At the end, the mixture is strained, the solid material is pressed to recover all of the product, and the combined liquids are filtered or decanted. An interesting study on the effects of long-term frozen storage on the nutraceutical compounds and antioxidant properties of blueberry (_Vaccinium corymbosum_), blackcurrant (_Ribes nigrum_), and raspberry (_Rubus ideus_) was carried out in 2010 by [PERSON] et al. The fruits were extracted by macceration using an acidic ethanol solution. This extraction system allowed them to analyze the total phenolic content, total monomeric anthocyanins, vitamin C, antioxidant activity, and color indices. The results concluded that no significant loss of antioxidant activity happened within 4 months after the freezing of the samples and that blueberries contain
# An Assessment of the Factors Impacting Project Success in the Engineering Sector
## 5 Results
### Regression Analysis  
ANOVA (Analysis of Variance) is especially useful in the context of this study, which has four independent variables. It allows researchers to see if there are any statistically significant differences between the means of the groups [30] defined by these independent variables. By comparing the variance within each group to the variance between groups, ANOVA can determine whether the means of the groups associated with each independent variable differ significantly from one another. This analysis determines whether the independent variables have a significant effect on the dependent variable, which is project success in this case. Understanding these distinctions reveals how each independent variable affects project outcomes. An F-value is calculated, which, if significant enough, indicates a difference between the three or more means. The F-statistic denotes the ratio. It implies that the dependent or outcome variable is influenced by the independent variable or factor [30]. Table 8 below shows the ANOVA data.  
Based on the results in the table above, the regression model is statistically significant, with a significance value of 0.002. This indicates that there are significant differences in the data, which implies the rejection of the null hypothesis. It implies a significant disparity between the means of the groups under consideration. In this situation, the independent variables exhibit a linear solid connection to the dependent variable. Table 9 below shows the coefficients of the independent variables and the intercept coefficient for the dependent variable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & **Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** \\ \hline \multirow{4}{*}{1} & Regression & 11,032 & 4 & 2.758 & 4.635 & 0.002 b \\ \cline{2-6}  & Residual & 40,465 & 68 & 0.595 & & \\ \cline{2-6}  & Total & 51,498 & 72 & & & \\ \hline \hline \end{tabular} a. Dependent variable: PS. b. Predictors: (Constant), EP, EE, EM, CC.  
\end{table}
Table 8: ANOVA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardised Coefficients**} & \multicolumn{2}{c}{**Standardised Coefficients**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} \\ \cline{3-3} \cline{5-6}  & & **B** & **Std. Error** & & **Beta** & \\ \hline \multirow{4}{*}{1} & \multirow{2}{*}{(Constant)} & 0.323 & 0.749 & & 0.431 & 0.668 \\ \cline{2-6}  & CC & 0.314 & 0.140 & 0.253 & 2.251 & 0.028 \\ \cline{2-6}  & EM & 0.250 & 0.109 & 0.250 & 2.286 & 0.025 \\ \cline{2-6}  & EE & 0.151 & 0.087 & 0.193 & 1.731 & 0.088 \\ \cline{2-6}  & EP & 0.222 & 0.095 & 0.256 & 2.346 & 0.022 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Regression coefficients.  
Looking at the \(p\)-value results in the table above, three of the four independent variables are significant. Company culture, effective management, and effective planning have a \(p\)-value less than 0.05, indicating close association.
###### Abstract  
Additive manufacturing has revolutionized the production industry by enabling the fabrication of complex geometries. In recent years, significant advancements have been made in 3D printing using metal filament, particularly with materials such as 316L stainless steel. Known for its high strength, corrosion resistance, and ductility, 316L stainless steel is well suited for demanding applications in the medical, marine, and aerospace industries. However, secondary processes such as debinding and sintering can lead to changes in the dimensions and mechanical properties of the final product. This study investigates the effect of layer thickness on the mechanical properties of 316L stainless steel produced through additive manufacturing. Samples were produced with varying layer thicknesses (100, 200, 300, and 400 \(\upmu\)m) and tested for tensile strength, hardness, and density. The results indicate that tensile strength increases with decreasing layer thickness. The highest tensile strength (432 MPa) and hardness (213 Hv) were observed at a layer thickness of 100 \(\upmu\)m. Additionally, phase analyses and microstructural examinations were conducted. The primary phases identified in the samples were face-centered cubic (FCC) austenite and body-centered cubic (BCC) ferrite (\(\delta\)). In this study, the manufacturing parameters with 316L filament have been optimized, and their impact on the mechanical properties has been examined.  
additive manufacturing; 316L austenitic stainless steel; layer thickness; metal filament; thermal debinding; sintering Article
## 7 Future Research Directions
### Sensor Data Fusion and Transmission  
It is evident that a large part of the processing and decision making should take place on the ship and only fused data should be sent from the ship to the remote operators. It is, however, an open challenge to define how sensor data are most efficiently combined and secured to be transmitted. Local edge-processing servers [62] at the ship are needed and messaging protocols need to be developed. For example, automatic port approach and departure require accurate sensing of the environment and very low delays in the connectivity both inside the ship as well as in ship-to-shore links. This is very challenging to implement and also requires advancements in legislation.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 1 Introduction
Although these cache replacement algorithms are not aiming for ICN, they are excellent references for designing the ICN cache algorithm. For the ICN scenario, the performance of the well-known cache replacement algorithms, e.g., LRU, LFU and FIFO, have been analyzed in [34; 35], and new cache replacement algorithms for ICN have been proposed. A dynamic classification based fast convergence caching replacement algorithm is proposed [36], which develops a dynamic classification method to reduce the time complexity of cache inquiry. The CLOCK mechanism based compact Clock with Adaptive Replacement (CAR) algorithm is introduced to achieve low-complexity approximation and reduce the consumption of  
Figure 1: Default cache management scheme in Named Data Networking (NDN). LCE: Leave Copy Everywhere; LRU: Least Recently Used. cache resources [37]. However, these studies mentioned above concentrate on distributing general independent contents. For a set of organized contents such as video streams, for which requests have particular behavior, the cache scheme has to be reconsidered.