esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## Materials and Methods
### The Multi-GNSS Precise Point Positioning and Its Strategy
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Item** & **Models** \\ \hline Observations & G: L1 and L2; R: L1 and L2; E: L1 and L5; C: L2 and L7 \\ \hline Models & Traditional IF combination \\ \hline Estimator & Robust Kalman filter \\ \hline Sampling rate & 30 s \\ \hline Cut-off angle & 7\({}^{\circ}\) \\ \hline Observation weight & Elevation-dependent weight \\ \hline Satellite orbit and clock & The products of GBM are used for GPS/GLONASS/GALILEO, while the products of WUM are used for BDS \\ \hline Eclipse strategy & S1:deleted eclipsing satellites; S2: adopted nominal attitude; S3: adopted modeled attitude \\ \hline A Priori troposphere & GPT2 model and VMF mapping function were applied [33]; Not considering the tropospheric gradient \\ \hline Ionosphere & The first order of the ionospheric delays is eliminated by IF combination; Not considering the higher-order delays [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The GNSS PPP model and its strategy.
## 3 Results and Analysis
### Bond Strength
The continued high temperatures cause cumulative temperature damage to the matrix concrete, which results in the change in concrete strength properties [25]. At temperatures between 20 and 200 \({}^{\circ}\)C, hairline cracks and voids form inside the specimen due to the evaporation of free water inside the concrete. The water and water vapor in the crevice are pressurized due to temperature rise, which creates tension on the surrounding concrete. Stress is concentrated at the seam tip after the specimen is loaded. Stress concentration contributes to crack expansion and a slow decrease in bond strength. The free water inside the specimen evaporates when the temperature is between 200 and 300 \({}^{\circ}\)C. Coarse aggregates and cement paste within the concrete have unequal temperature expansion coefficients and the temperature deformation difference causes cracks to form at the aggregate interface. This causes a slight reduction in the tensile strength of the concrete. These factors of the reduction in bond strength are noticeable. However, after 400 \({}^{\circ}\)C, the temperature deformation difference between the aggregate and cement paste continues to grow and interface cracks develop and spread. Cement hydration products, such as Ca(OH)\({}_{2}\), dehydrate and expand in volume, which causes  
Figure 12: \(\beta_{\rm T}\)–\(T\) curves. the cracks to expand. When the temperature rises above 600 \({}^{\circ}\)C, the quartz component of the unhydrated particles and aggregates in the cement decomposes and crystallizes, which results in the development of large-scale expansion. As a result, cumulative damage at high temperatures appears as follows: cracks and voids are formed inside the concrete by water evaporation; the difference between the thermal properties of coarse aggregates and cement paste produces deformation gaps and internal stresses and interface cracks are formed; coarse aggregates are ruptured by thermal expansion. Eventually, these microcracks in turn reduce the tensile strength of concrete more severely than the compressive strength of concrete, leading to the different failure patterns of the NC specimens at different temperatures. However, steel fibers in the heated specimens are able to control these cracks. With the increase in \(V_{f}\), the ultimate bond strength of the SFRC specimens increases.
# Experimental Investigation of the Fatigue Life of a Bridge Crane Girder Using S-N Method
### Comparison of Fatigue Life  
According to STN 270103, the number of cycles \(N\) per technical lifetime is:  
\[N=6\cdot 10^{5}\text{ to }2\cdot 10^{6}.\]  
According to the experimental-computational procedure, the number of cycles per technical lifetime is:  
\(H_{A}=886,978\cong 8.9\cdot 10^{5}\)--for girder A without any welded rail,  
\(H_{B}=417,921\cong 4.2\cdot 10^{5}\)--for girder B with the welded rail.  
Figure 12: View of the rail welded to girder B.  
Figure 13: Comparison of the normal stress courses obtained at the same instant of time when the crab passed through the center of the girders: (**a**) girder A (locations 1, 2, 8), (**b**) girder B (locations 3, 4, 7).  
Number of cycles (\(N_{op}\)) operated in 51 years of operation  
\[N_{op}=2.53\cdot 10^{5}.\]  
The number of cycles operated so far meets both of the above conditions  
\[N_{op}=2.53\cdot 10^{5}\ <\ N=6\cdot 10^{5}\ \text{to}\ 2\cdot 10^{6}N_{op}=2.53 \cdot 10^{5}\ <\ H_{B}=4.2\cdot 10^{5}\]  
thus the girders still have a sufficient fatigue life reserve.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.6 Model Training Time Analysis  
An essential factor to consider when evaluating the performance of ML models is their training time, particularly given the significant computational costs associated with some models. The training time directly impacts the feasibility of deploying these models in real-world applications where computational efficiency is often critical. Of the surveyed papers, ten studies [4, 20, 29, 43, 45, 51, 55, 66, 67, 74] provide training time comparisons for the models evaluated. In these papers, the training time of the best-performing TBML model and DL model is analyzed.  
The results demonstrate that TBML models significantly outperform DL models in terms of their training efficiency (Table 2). Notably, there is only one instance [29] where a DL model, an MLP, trained faster than its TBML counterpart (XGBoost). In this specific case, the MLP achieved a 22.55% reduction in its training time compared to XGBoost. However, across all ten studies, TBML models demonstrated a marked advantage. On average, TBML models were 126,934.94% faster than DL models, with the median training time advantage  
Figure 14: FPA score for each model class based on the focus of the paper.  
Figure 15: WRA score for each model class based on the focus of the paper.  
being 5603.43% faster. This stark difference underscores the efficiency of TBML models in scenarios where computational resources and time constraints are limiting factors.  
In order to assess the impact of data characteristics and computational efficiency differences between models, it is important to analyze how these model performances are quantified in the first place.
## 2 Materials and Methods
### Automation of Processes
#### 2.4.2 Edge Detection Algorithms  
The Canny edge algorithm follows a multistep process that starts with a Gaussian filter to smooth the image, followed by finding the intensity gradient [28]. Next, non-maximum suppression is applied, which reduces the edge width to a single pixel; finally, hysteresis thresholding is used to track and join edge segments by using two thresholds.  
In our implementation, we decided to use a dynamic thresholding strategy based on the median intensity of the image, adjusting the lower and upper thresholds proportionally using the sigma value. This approach allows the function to adapt to different lighting conditions and contrast levels in the input image.  
The Sobel operator is a gradient-based method that approximates the first derivative of image intensity. It uses convolutional kernels, typically two 3 \(\times\) 3 kernels (Equation (1)), to compute gradients in the vertical and horizontal directions [29]. This means that this method can detect changes in the intensity of each direction that combine to obtain the gradient magnitude (Equation (2)).  
\[G_{x}=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix}\hskip 56.905512 ptG_{y}=\begin{bmatrix}-1&-2&1\\ 0&0&0\\ 1&2&1\end{bmatrix} \tag{1}\]  
\[\text{Magnitude}=\sqrt{\left(G_{x}\right)^{2}+\left(G_{y}\right)^{2}} \tag{2}\]The last step of this method is to apply a threshold that will isolate the significant edges from the ones that are not important.  
The Laplacian of the Gaussian filter, also known as the Laplacian, uses the second derivative of the image to detect the zero-crossing of the image intensity [30].
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Data Caching  
Internet data traffic is exploding at a rapid pace with the increasing popularity and demand of different smart city applications such as infotainment, AV, VR, interactive gaming, and XR. Consequently, it becomes challenging to accommodate these data in terms of storage and transmission for applications that require an ultra-low latency such as autonomous vehicles and smart healthcare. To address this challenge, edge caching [52] is seen to be a potential solution that provides storage facilities to the IoT data at the edge of the network,  
Figure 7: Deep Learning (DL)-based time series prediction of network traffic data.  
i.e., in proximity to the mobile devices. This enables IoT applications to retrieve data in real time from the edge resources, eliminating backhaul link communication. Consequently, edge caching reduces data transmission time and energy. However, determining the optimal cache content and cache placement strategies in a dynamic network is challenging [53]. DL can be effective in designing optimal caching strategies, as shown in Figure 8. [PERSON] et al. [54] proposed a distributed deep Q-learning-based caching mechanism to improve the edge caching efficiency in terms of cache hit rate. The mechanism involves the prediction of users' preferences offline followed by the online prediction of content popularity. However, due to the limited caching resources of the edge nodes and spatial-temporal content demands from the mobile users, cooperative edge caching schemes are required. [PERSON] et al. [55] proposed a DRL-based cooperative edge caching approach that enables the communication between distributed edge servers to enlarge the size of the cache data. However, cooperative schemes often collect and analyze the data at a centralized server. The sharing of sensitive data, such as users' preferences and content popularity, among different edge and cloud servers, raises privacy concerns. To tackle this challenge, federated learning (FL) can be a promising solution in which learning models to predict content popularity are trained locally at the IoT devices for cooperative caching [55].
# Wide-Area Protection System for Radial Smart Distribution Networks
## 5 Conclusions  
This paper proposes a wide-area protection system for modern distribution networks that effectively addresses the challenges posed by DER integration. By incorporating ANNs for fault detection and a Fault Zone Relay Condition (FZRC) matrix for fault location, the system streamlines the fault location and relay coordination processes.  
The simulation results demonstrate the efficiency and accuracy of the proposed approach in fault detection and isolation where it achieves a total accuracy of 99.26%, with only two malfunctions and no missed faults. The 100% fault detection accuracy highlights the robustness and reliability of the proposed approach. Utilizing both the mean and superimposed values of the network signals significantly enhances the performance of the ANN.  
The findings of the simulation study showed the potential of the proposed system to enhance the applicability of the DFIG-integrated test network and is most probably an efficient tool for utilizing renewable energy resources and reducing the carbon footprint. Further research and real-world implementation considering noise can lead to even more refined protection strategies, improving the applicability of the proposed system.  
Conceptualization, H.R.; Methodology, H.R., S.J. and P.S.; Software, H.R.; Validation, S.J. and P.S.; Formal analysis, S.J.; Investigation, H.R.; Data curation, H.R.; Writing--original draft, H.R.; Writing--review & editing, S.J. and P.S.; Supervision, S.J. and P.S.; Project administration, P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
## 4 Data Sets  
The observations from 14 MGEX stations in the service areas of IRNSS on seven consecutive days from 7-13 July 2019, are used for analysis, and the distribution of the employed MGEX stations is shown in Figure 2. The details of each station are provided in Table 3. All stations equipped with geodetic receivers can support the IRNSS L5 signal, and are able to simultaneously track at least four IRNSS satellites for more than 12 h in a single day. The sampling rate of observations is 30 s, and the cut-off elevation is set to 10\({}^{\circ}\). Single-frequency (GPS L1 and IRNSS L5) SPP and RTK are carried out to investigate the positioning performance.  
\begin{table}
\begin{tabular}{r r r r r r} \hline \hline
**Station** & **Latitude** & **Longitude** & **Receiver Type** & **Antenna Type and Radome** & **Firmware Version** \\ \hline DARW & \(-12^{\circ}50^{\prime}37.3^{\prime\prime}\) & \(131^{\circ}757.9^{\prime\prime}\) & SEPT POLAR5 & JAVRINGANT\_DM & NONE & 5.2.0 \\ DGAR & \(-7^{\circ}16^{\prime}10.9^{\prime\prime}\) & \(72^{\circ}22^{\prime}12.9^{\prime\prime}\) & SEPT POLAR5 & ASH701945E\_M & NONE & 0.0.0-181002 \\ KAT1 & \(-14^{\circ}22^{\prime}33.6^{\prime\prime}\) & \(132^{\circ}91^{\prime\prime}11.8^{\prime\prime}\) & SEPT POLAR5 & LEIAR25.R3 & LET & 5.2.0 \\ KITG & \(39^{\circ}80.2^{\prime\prime}\) & \(66^{\circ}35^{\prime}12.3^{\prime\prime}\) & SEPT POLAR5 & TRMB59800.00 & SCIS & 5.2.0 \\ MAW1 & \(-67^{\circ}36^{\prime}17.2^{\prime\prime}\) & \(62^{\circ}52^{\prime}14.6^{\prime\prime}\) & SEPT POLAR5 & AOAD/M.T & AUST & 5.2.0 \\ MBAR & \(-0^{\circ}36^{\prime}53.3^{\prime\prime}\) & \(30^{\circ}44^{\prime}16.4^{\prime\prime}\) & JAVAD TRE\_3 & DEITA & ASH701945B\_M & SCIS & 3.7.4 \\ MIZU & \(39^{\circ}86.6^{\prime\prime}\) & \(141^{\circ}75^{\prime\prime}8.3^{\prime\prime}\) & JAVAD TRE\_3 & JAVRINGANT\_GST & NONE & 3.7.6 \\ SUTM & \(-32^{\circ}25^{\prime}53.2^{\prime\prime}\) & \(20^{\circ}48^{\prime}39.3^{\prime\prime}\) & JAVAD TRE\_3 & JAVRINGANT\_GST & NONE & 3.7.6 \\ ULAB & \(47^{\circ}51^{\prime\prime}54.2^{\prime\prime}\) & \(107^{\circ}78.8^{\prime\prime}\) & JAVAD TRE\_3 & JAVRINGANT\_GST & NONE & 3.7.6 \\ URUM & \(43^{\circ}48^{\prime}28.6^{\prime\prime}\) & \(87^{\circ}36^{\prime}2.4^{\prime\prime}\) & JAVAD TRE\_3 & JAVRINGANT\_GST & NONE & 3.7.6 \\ VACS & \(-20^{\circ}17^{\prime\prime}49.5^{\prime\prime}\) & \(57^{\circ}29.49^{\prime\prime}\) & SEPT POLARCS & JAVRINGANT\_DM & NONE & 0.0.0-181002 \\ WIND & \(-22^{\circ}34^{\prime}29.7^{\prime\prime}\) & \(17^{\circ}52^{\prime\prime}22.0^{\prime\prime}\) & JAVAD TRE\_3 & JAVRINGANT\_GST & NONE & 3.7.6 \\ YAR3 & \(-29^{\circ}24^{\prime}47.4^{\prime\prime}\) & \(115^{\circ}20^{\prime}49.8^{\prime\prime}\) & SEPT POLAR5 & LEIAR25 & NONE & 5.2.0 \\ YARR & \(-29^{\circ}24^{\prime}7.7^{\prime\prime}\) & \(115^{\circ}20^{\prime}49.1^{\prime\prime}\) & SEPT POLARX5 & LEIAT504 & NONE & 5.2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of each station.  
Figure 2: Distribution of 14 MGEX stations.
## 4 Full-Scale Blasting Experiment  
### Bench Blasting  
The aggregate production step comprises blasting, a vibration feeder, a primary crusher, a secondary crusher, belt conveyors, and vibrating screens. The feed size of the jaw crusher operating in the A-aggregate mine requires a rock fragment size of at least 1000 mm or less. Therefore, to input the jaw crusher for primary crushing, it is necessary to perform secondary work on fragments by hydraulic rock breakers after blasting the rock, which incurs additional costs. Approximately $25,000 US dollars per month are required to operate large hydraulic rock breakers, and mine A has ten of such equipment. Figure 10 shows the bench blasting design applied to aggregate mine A, and the blasting results are compared by applying sand and STF-based stemming materials to each different bench part. Table 3 lists the main parameters of the full-scale bench blasting experiment. In the blasting experiment, the STF-based stemming was applied to the left part, and general sand stemming was applied to the right part.  
Figure 9: Time-pressure hysteresis curve inside the blast hole.  
The properties of the rock mass condition can have a significant influence on the fragmentation outcomes of the blast. Rock properties, such as compressive strength, porosity, Young's modulus, Poisson's ratio, and rock fracturing and jointing, can all influence fragmentation. In this experiment, by performing blasting at the same experimental area, the variation in results due to the rock mass condition difference was reduced as much as possible. The type of rock in this quarry mine is speiss, and the uniaxial compressive strength is approximately 130-160 MPa. The rock density is approximately 2.6 g/cm\({}^{3}\), and the porosity is less than 0.15%. As shown in Figure 10(a), a bench slope with a discontinuity direction and similar region was selected as the experimental site. The spacing of the discontinuities was observed to be approximately 1.2-1.5 m each, and they were under completely dry conditions. Figure 10(b),c show the resulting image after the blasting. Under the same blasting conditions, the rock fragments in the STF-applied part are generally smaller than in the sand stemming part.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Hole diameter & mm & 75 \\ Hole length & m & 9.0 \\ Burden & m & 2.8 \\ Hole spacing & m & 3.2 \\ Charge per hole & kg/hole & 24.0 \\ Charge per delay & kg/delay & 72.0 \\ Charge type & - & Emulsion 1.0 kg \\ Specific charge & kg/m\({}^{3}\) & 0.308 \\ Rock fracture per hole & m\({}^{3}\)/hole & 77.95 \\ Number of holes & ea & 30 \\ Stemming length & M & 2.5 \\ Stemming type & - & STF or Sand \\ Total charge & kg & 720 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameters of the full-scale bench blasting experiment.  
Figure 10: Bench blasting design of full-scale blasting experiment.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 5 Diamond-Based Electric Biosensors
### Biosensors for Influenza  
#### 5.3.1 Detecting M1 Protein as a Universal Influenza Biomarker  
Influenza A virus matrix protein M1 is a widely abundant membrane-associated protein that lies under the envelope of the virus particles, helping to maintain structural integrity and participating in several essential processes of the viral life cycle. When released into the serum, M1 proteins can agglomerate and help the various components of the virus assemble to form a new viral particle [56]. M1 can be monitored as a biomarker of influenza infection with an important advantage over other antigen proteins because it is transversal to all serotypes of influenza virus.  
A biosensor for the M1 protein of influenza was built from a nanocrystalline BDD diamond thin film functionalised with polyclonal antibodies via diazonium salts electrodeposition [31; 33]. The biosensor was able to detect the M1 protein in the H1N1 influenza strain by impedance measurements in PBS supplemented with 0.5% Triton X, a non-ionic surfactant. Under these conditions, the sensor response stabilised within 5 min, presenting a linear range of 10 to 100 fg/mL and a limit of detection as low as 0.7 fg/mL [31], which corresponds to approximately 40 viral particles [33]. The biosensor was also able to detect the H3N2 strain under the same conditions with almost no change in the impedimetric response [31].
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 3 Experimental Setup
### Algorithm Setup
#### 3.1.2 Algorithms  
The following three approaches employ a penalty function (refer to Equation (3), Section 2) to penalize violating decisions which do not satisfy the DM's preferences. Each of these approaches were incorporated into the hybrid DE and are the three constraint managing approaches evaluated in this study:  
1. Proportionate Penalty: With this approach, the penalty is proportional to the violation, and violating decisions are penalized during function evaluation. Algorithm 5 presents this approach, referred to as PPA for the rest of the article.  
```
1:procedureFuncEvaluate(F, x, t)
2:\(\triangleright\) F: multi-objective function to be evaluated
3:\(\triangleright\) x: decision vector
4:\(\triangleright\) p: as defined in Equation (2), page 3
5:\(\triangleright\) x: as defined in Equation (2), page 3
6:\(\triangleright\)\(\lambda\): a random number between 100 and 1000
7:\(\triangleright\)\(I_{1}\): as defined in Equation (2), page 3
8:\(\triangleright\) calculate objective value of x
9:\(\triangleright\)\(\text{aculate violation of z}\)
10:if\(\text{aculate violation of z}\)then\(\triangleright\) x is a non-violating decision, no penalty applied
11:\(\triangleright\) x is a preference violating decision, proceed to penalize it for violation
12:\(\text{penalty}\leftarrow\lambda\cdot\text{d}\)\(\triangleright\) calculate penalty
13:\(\text{penalty}\leftarrow\text{I}_{1}\cdot\text{penalty}\)\(\triangleright\) vectorize penalty
14:\(\text{z}\leftarrow\text{z}\) + penalty\(\triangleright\) impose proportionate penalty to objective value of x
15:return z\(\triangleright\) return new penalized objective value of x
16:
17:\(\text{penalty}\leftarrow\text{z}\)
```  
**Algorithm 5** Proportionate Penalty Algorithm
# Applications of Plant Polymer-Based Solid Foams: Current Trends in the Food Industry
## 1 Introduction  
Foams are a type of material of great importance with an extensive variety of applications due to their combining several characteristics, such as ultra-low density and tunable porous architecture and mechanical properties. Industrial applications include their being used in kinetic energy absorbers, biomedical scaffolds, thermal insulation, construction, separations, and devices for the storage and generation of energy [1].  
The production of polymer foams worldwide is currently dominated by those based on synthetic polymers. They are mostly inexpensive and tailor-made to serve several purposes, having a wide range of applications where mechanical function is important, both in the food and non-food industries [2; 3]. Synthetic polymers might be biodegradable (e.g., poly(glycolic acid) (PGA), poly(lactic acid) (PLA) and their copolymers) or non-biodegradable [4]. The latter is a great environmental concern, since non-biodegradable polymer based-foams have become a significant part of countries' solid waste, posing a major waste management challenge [5; 6]. Commonly, non-biodegradable synthetic polymer-based foams are petroleum-derived, such as polyethylene (PE), polystyrene (PS), polyurethane (PU), and polypropylene (PP) [6; 7]. Biodegradable polymers decompose into CO\({}_{2}\), water, inorganic compounds, and biomasses that finally form humans [8], which recommends them as alternatives to non-biodegradable polymer foams [8; 9].  
In this context of increased environmental concern, many researchers have developed foams from renewable resources, such as cellulosic materials, starch, proteins, and other biopolymers, for food, environmental, medical, and other applications [5; 6]. Some of these have shown great short-term promise, particularly in medical and environmental contexts, and research into this area has been making great advances in the last decade. Due to the high number of them, not all the applications can be considered in this systematic review, which will restrict its focus to the development of solid foams produced from plant polymers with potential or direct applications in the food industry over the period from 2015 to 2021. A systematic literature search was performed across the Web of Science, Science Direct, and MDPI databases. The information is organized according to the main sources of the foaming agents, with a special emphasis on the foam structure-property relationships.