esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 7 Conclusions  
In this paper, the coordinates of monitoring points obtained by static relative positioning of the continuously operating reference station and monitoring station were taken as reference values. The GPS system was taken as the reference system, and G/R/E, G/R/C and G/R/E/C combined PPP calculation modes were used to carry out multi-system combined PPP calculation experiments on the data of monitoring points. The results show that:  
(1) G/R/C, G/R/E, and G/R/E/C combined PPP converged to within 0.1 m after 12.5, 14 and 30.5; 12.5, 13.5 and 33; and 12.5, 14 and 30.5 minutes in NEU directions, respectively. The PPP convergence time of the three combinations is basically the same, and the positioning accuracy of all the three directions of NEU can reach centimeter level after about 30 min.  
(2) The accuracy of the PPP solution results of G/R/C, G/R/E and G/R/E/C combinations is basically the same. After the three PPP combinations converge in the NEU directions, the internal coincidence accuracy of the positioning results is better than 1 mm on average, and the external coincidence accuracy is better than 5 cm, which is relatively stable and suitable for landslide monitoring with the slope movement of a centimeter.  
[PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] wrote the manuscript; [PERSON] and [PERSON] reviewed the manuscript; [PERSON], [PERSON] and [PERSON]. ([PERSON]) ran the experiments; [PERSON] and [PERSON] performed the verification analysis; [PERSON], [PERSON] ([PERSON]) and [PERSON] contributed to the design of the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Nature Science Foundation of Fujian, China, grant number 2019J01649, Opening Fund of Key Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources (Fujian Key Laboratory of Geohazard Prevention), China, grant number FJKLGH2021K002, the innovation and entrepreneurship training program for college students of Fujian, grant number 201910386016, and Nation-al Natural Science Foundation of China, grant number 41861134011.  
Not applicable.  
Data Availability Statement: The multi-GNSS precise orbit and clock products issued provided by IGS are available from [[ftp://igs.ensg.ign.fr/pub/igs/products/](ftp://igs.ensg.ign.fr/pub/igs/products/)]([ftp://igs.ensg.ign.fr/pub/igs/products/](ftp://igs.ensg.ign.fr/pub/igs/products/)) (accessed on 30 June 2021).  
We thank the editor and referees for their useful comments and criticisms. The authors gratefully acknowledge IGS for providing multi-GNSS precise orbit and clock products. We thank [PERSON] for her valuable contributions and suggestions during the revision of the paper.  
The authors reported no potential conflict of interest.
## 1 Introduction
They examined the effect of nozzle diameter, liquefier temperature, extrusion velocity, filling velocity, and layer thickness, each having three levels, on surface roughness, tensile strength, and production time. Using a central composite design for the experiment, they validated the previously developed regression model and showed that by increasing nozzle diameter, filling velocity, and layer thickness both surface roughness and tensile strength increased but production time was reduced. Generally, liquefier temperature and extrusion velocity had less impact on the product than expected. [PERSON] et al. [33] examined the mechanical properties, surface quality, and microstructure of FFF 3D-printed ABS samples by varying four process parameters, i.e., layer thickness, and thickness. thickness, printing speed, raster angle, and build orientation, and found the most important factor to be layer thickness, for all three dependents followed by the build orientation. This literature review shows there exists a number of statistical-based experimental studies to optimize the FFF process. However, the design of experiment (DoE) in previous studies is mostly limited to three input factors with three or less levels for each factor, and therefore, a smaller number of experimental runs. Furthermore, most of the reported works in the literature solely examine either quality or efficiency, rather than the correlation of both. Hence, there is a gap concerning a broad analysis, which includes a wide range of factors and their levels and which provides a detailed insight into the interactions and effects of varying these parameters on the quality and resource efficiency of printed parts. In an attempt to fill this gap, this paper reports a comprehensive investigation aimed at optimizing FFF printing parameters through experiment-based data analysis to fulfil the requirement of low-energy consumption coupled with high productivity and with high-quality printed parts in terms of surface quality and dimensional accuracy. Following this introduction, the remainder of the paper is organized as follows. First, the experimental work, entailing material and methods, is described. Here, the applied processing parameters, measurement instrumentations, and procedures are presented. Next, the results are analyzed and discussed. Finally, conclusions are drawn and future work is suggested.
# Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks
## 2 Methods
### Numerical Simulation and CNN Samples  
A numerical model of the steel frame (Figure 5) with a length of 10.62 m, a width of 0.354 m and a height of 0.354 m was developed by using the finite element software package ABAQUS (SIMULIA Inc., Providence, RI, USA). Each rod had a hollow circular cross section with a radius of 0.005 m and thickness of 0.002 m; additional weight (0.45 kg) was added at each node to simulate the steel ball of the real structure. The elastic modulus, Poisson's ratio, density, and modal damping ratio were 212 GPa, 0.288, 7800 kg/m\({}^{3}\), and 0.02, respectively. All rods were meshed with beam element (B31 type).  
The performance of the structure could be changed by both damage in the elements and damage in the connections. A bridge case showed that the damage in the cables had greater effect on the structural characteristics than that in the connections [38]. In this paper, the damage of a rod was simulated by the change of its elastic modulus. It was assumed that the damage level of a rod was proportional to the reduction of its elastic modulus value. Only 1 level of damage was considered, i.e., the elastic modulus of the rod was reduced by 50%.  
In this paper, the training samples of CNNs were derived from the numerical simulation data. Firstly, the single damage was studied, which means that the damage in one rod was considered for each damage scenario. The structure consisted of 61 rods (Figure 5); thus, there were 62 CNN samples including 61 damage scenarios plus the intact structure.  
For the scenario with damage simultaneously in 2 rods, two of rods 1-17 were randomly selected; the damage level was 50%. There were 136 (\(C_{17}^{2}\)) damage scenarios, i.e., 136 training samples.  
Figure 4: Intact rod (Rods 1 and 2) and damaged rod (Rods 3 and 4, with 50% cut-off of the cross section).  
Figure 5: Numerical model of the steel frame with 381 rods.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 1 Introduction
Well logging curves contain a large amount of repetitive, redundant, and anomalous feature information, and each logging curve represents a comprehensive characterization of adjacent formations. Directly using traditional methods for point-by-point predictions while ignoring the influence of adjacent formations may lead to poor predictive performance and weak model generalization capability [13; 14; 15]. To address these issues, this paper proposes a porosity prediction model that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and Transformer networks (CNN-BiLSTM-Transformer). The method utilizes CNN to extract local features from well logging curves, BiLSTM to capture sequential neighborhood information, and the self-attention mechanism of the Transformer to focus on temporal dependencies and input features, effectively modeling global relationships. To further optimize model performance, the Adam optimization algorithm is employed to adjust network weights, and hyperparameters are fine-tuned based on feedback from prediction accuracy. Finally, the effectiveness of the proposed method is validated through experiments and comparative analysis with traditional approaches, providing both theoretical and practical guidance for porosity prediction in highly heterogeneous carbonate reservoirs.
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 3 Proposed Studies  
In WSNs, identifying compromised or malicious nodes that can disrupt data flow or infuse harm is a sensor network, and in order to evaluate the trust, the trust mechanism can be used. In this paper, we propose a Trusted Energy-Aware Hierarchical Routing (TEAHR) protocol, which is capable of addressing the following issues that WSNs have to encounter, such as limited energy, network security, and scalability. Sensor nodes are equipped with tiny batteries, so energy management must be taken care of cautiously. WSNs are threatened by many attacks, and trust mechanisms can resist malicious behavior. TEAHR protocols can efficiently cope with large-scale WSNs. Table 1 summarizes the above various types of trust management in WSN.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feature/** & **Probability** & **Weighted** & **Fuzzy Logic** & **Game** & **Directed** & **Undirected** & **TEAHR** \\
**Aspect** & **Trust Model** & **Trust Model** & **Trust Model** & **Theory-Based** & **Graph-Based** & **Graph-Based** & **Graph-Based** \\  & Updates trust based on & Aggregates & logic to & **Trust Model** & **Trust Model** & **Trust Model** & **Trust Model** \\
**Trust** & Bayesian & & various trust & combine & game where & & \\
**Calculation** & inference; uses & metrics with & various & nodes interact & weights of & & \\
**Method** & past behavior & weighted & metrics; Trust & and adjust & incoming & of connected \\  & to adjust trust & importance & is determined & strategies based & edges from & edges from \\  & scores. & factors & by fuzzy & inference rules & other nodes & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of trust models.
## 4 Reference Scenarios
### Roles of the Nodes  
From a use-case point of view, the WHAN setup represents wireless nodes associated with one or several electronic devices of everyday use. The nodes thus are commonly mains-powered [1]. The devices connected to a node in a WHAN may be sensors, actuators, controllers and other kinds of appliances. The wireless nodes are possible sources and destinations of information transmitted in the network. We define three roles for the nodes that cover most of the typical WHAN use cases:  
* Nodes that have only a one-to-one relationships with other nodes of the network, for example, a light switch and a light bulb.
* Nodes that have one-to-one relationships with certain nodes of the network and that also produce data for a destination of many-to-one traffic. An example is a node with a motion sensor that causes an alarm for a security center upon noticing movement, while the node also periodically sends the measurements of a temperature sensor to a heating, ventilation and air conditioning (HVAC) system.
* Nodes that have one-to-one relationships with certain nodes of the network and are the destinations of many-to-one traffic. An example is an HVAC controller that collects temperature information and that is able to remotely control heaters, ventilators, _etc_.
## 3 Results and Discussion
### Phase 5: Attribute Selection Processes
#### 3.5.1 Unweighted Pair Group Method with Arithmetic Mean (UPGMA)
SAR Information Descriptors: Green [measures of central tendency, i.e., average (AVG), median (MED), mode (MOD), and mid-mean (MDM); an exception is the maximum pixel value (MAX)], Blue [dispersion measures, i.e., range (RNG), coefficient of dispersion (COD), standard deviation (STD), variance (VAR); average absolute deviation (AAD), and median absolute deviation (MAD)], and Grey [metrics of the pixel distribution: skewness (SKW) and kurtosis (KUR)]. Selected variables are indicated (+); see also Table 3. * Same outcome: NLT2 = NLT3. The phenon line, represented by the horizontal red dashed line in Figure 3 (i.e., 0.3 Pearson's r correlation coefficient) defines the actual groups from which we select one variable of each--groups are formed when this cut-off line crosses a vertical line (i.e., branch or edge) [49; 50]. In fact, the groups formed in this manner match the preliminary visual analysis of the dendrograms:  
* Three groups are observed (Green + Blue, Yellow, and one Red) when 16 variables are analyzed, i.e., NLT.1, NLT.2, and NLT.3--NLT.4 is an exception;
* Four other groups are also formed (Green + Blue, Grey + Yellow, and two Red ones) when 19 variables are accounted for, i.e., NLT.0, NLT.5, NLT.6--NLT.7 is an exception;
* Three other groups are formed in NLT.4 (16 variables) in which VAR, RNG, and MAX cluster together forming an extra assemblage (Light Blue)--Light Blue + Green + Blue, Yellow, and one Red;
* Six groups are formed in NLT.7 (19 variables): Green, Grey, Blue, Yellow, and two Red ones. One should pay close attention to the two Red groups, as from them, three variables are selected--e.g., NLT.0 (Fractal, PtoA, and Compact)--because such variables have no correlation. The number of selected variables ranges between 4 and 7 variables, depending on the NLT (Table 3), such that:  
* **AVG** is selected from the Green + Blue group to maintain the simplest possible analysis;
* **VAR** is selected when the Blue group is alone (only in NLT.7) to keep it simple as possible;
* **SKW** is preferable from the Grey group as it measures asymmetry;
* **LEN** is selected from the Yellow group as Area and Perimeter are both present in the ratios;
* The three morphological ratios (Red group: **PtoA, Compact,** and **Fractal**) are always selected when present.
# Design of a Digital Array Signal Processing System with Full Array Element
## 4 Conclusions  
Based on the on-chip RF integrated system chip, this paper adopted the HIF architecture to design an all-digital, low-power, multi-channel, high-speed broadband digital array signal processing system with 64 receive channels and 64 transmit channels in a small size, forming a hardware platform for multi-channel radar system applications. Through hardware implementation and verification of the broadband MIMO phased array digital multi-beam formation algorithm, the designed system can achieve simultaneous multi-channel sampling and amplitude and phase calibration with a high sampling rate and large bandwidth and has a high performance of broadband digital array signal processing, which verifies the feasibility and engineering value of the design described in this paper.  
Conceptualization, G.X. and Z.S.; methodology, G.X. and Z.S.; software, G.X. and Z.S.; validation, G.X. and Z.S.; formal analysis, G.X.; investigation, Z.S.; resources, G.X.; data curation, Z.S.; writing--original draft preparation, G.X. and Z.S.; writing--review and editing, Y.Q. and B.W.; visualization, G.X.; supervision, G.L.; project administration, G.X.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science Funds for Distinguished Young Scholars of Shannxi Province under Grant 2021 JC-23.  
Figure 19: Testing environment.  
Figure 20: Transmitting beam spectrum: (**a**) Spectrum pointing to \(-45\) degrees; (**b**) Spectrum pointing to \(15\) degrees.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 5 Experimental Analysis
### Data Utility
#### 5.3.1 Data Utility Comparison  
There are many methods [46] for calculating the information loss in the published data. Here we describe a simple method to illustrate the basic information loss metric.  
We estimate that each attribute value of the microdata table is correlated with a generalized taxonomy tree. The cost is calculated from the published dataset and is called the distortion ratio. If the attribute value of a tuple is at the leaf node of the taxonomy tree, then the value is not generalized, and the distortion of that value is 0. Consequently, if the attribute value is generalized and does not represent the leaf node, then the distortion is defined by the position of the generalized attribute value and the height of the taxonomy tree. For instance, the age 22 is not generalized, and it stands at the leaf node; therefore, the height is 0, and the distortion is likewise equal to 0. While the attribute value is generalized one level up in the taxonomy tree, the distortion is equal to 1/\(H\). Here \(H\) represents the height of the taxonomy tree. Let \(d_{j,k}\) is the distortion of the attribute \(A_{j}\) of tuple \(t_{k}\). The distortion of the entire published microdata table is equal to the sum of the distortions of all values in the generalized dataset. In addition, the distortion is defined as discussed in [46]:  
\[\sum_{j=1,k=1}^{n,m}d_{j,k}\]  
The distortion ratio is \(D_{R}=\frac{D_{P}}{D_{G}}\), where \(D_{R}\) is the distortion ratio, \(D_{P}\) is the distortion of the published table, and \(D_{G}\) is the distortion of the fully generalized (i.e., all attribute values are generalized by the root of the taxonomy trees) dataset.  
Figure 5 illustrates the experimental result for data utility, based on data loss in the published datasets. For the data loss experiment, we selected a 4.5K dataset with _6-anonymity_ for the Hybrid [2], \(\epsilon-DP\)[41], Probabilistic [23], Composition [24] and Mondrian [44] methods, and _6-diversity_ for the Merging method. The results show that the Merging method yields less data loss than all the other methods. We know the full generalization of the attribute values reduces the published data  
Figure 4: Privacy risk on Education dataset (\(k=6\), \(l=6\)).  
utility [25]. The Merging method employs selective generalization in the cell if it is necessary to meet the privacy requirements. Therefore, it preserves more data utility than the other methods.
## 3 Results and Discussion
### Fragrance Retention Test  
The primary goal of this work is to determine the correlation between capsule thickness and changes in porosity after the addition of TEOS, and their effect on fragrance retention. The fragrance retention of the silica microcapsule was assessed at a high temperature, representing harsher conditions than those in the previous experiment (Section 3.2). The concentration of the released fragrance compound was measured for up to four months at room temperature and 60 \({}^{\circ}\)C to determine the fragrance retention.  
After 133 d, more than 60% of the fragrance compound remained within the T5 microcapsules, whereas 50% remained within the T3 microcapsules and only 25% remained within the T1 microcapsules at room temperature (Figure 4A). The fragrance encapsulated within the T0 microcapsules was completely released after 42 d. The fragrance leaked more rapidly at 60 \({}^{\circ}\)C (Figure 4B). Approximately 20% of the fragrance compound remained in the T5 microcapsules after 84 d. In contrast, all of the fragrance compound leaked from the T3, T1, and T0 microcapsules after 84, 40, and 7 d, respectively. Silica fragrance capsules with thicker shells exhibited more effective fragrance retention because of the additional silica at the surface of the microcapsule; however, this was only observed after the additional TEOS was adequately emulsified. A mechanical overhead stirrer speed of less than 200 rpm, or a short stirring time, resulted in lower fragrance retention (Table S1), which indicates that seeded growth occurs consistently when TEOS emulsions are uniformly distributed throughout the phase.  
Silica microcapsules for fragrance retention should ideally be devoid of pores; however, because silica has mesoporous properties, pores should be minimized to ensure fragrance retention for long periods in harsh environments. Despite the low number of pores in the synthesized microcapsules, hexyl cinnamaldehyde molecules can leak from the capsule  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Additional** & **Total Surface Area** & **Micropore Area** & **Total Pore** & **Micropore** \\
**TEOS** & **(BET) (m\({}^{2}\)/g)** & **(t-Plot) (m\({}^{2}\)/g)** & **Volume (m\({}^{3}\)/g)** & **Volume (t-Plot)** \\ \hline
0\% & 155.16 & 30.08 & 0.2456 & 0.0134 \\ \hline
1\% & 130.42 & 20.71 & 0.2128 & 0.0089 \\ \hline
3\% & 92.28 & 5.36 & 0.1821 & 0.0015 \\ \hline
5\% & 145.94 & 31.93 & 0.2272 & 0.0144 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface areas and porosities of fragrance-containing silica microcapsules synthesized following the addition of various amounts of TEOS.  
Figure 4: Fragrance retention in 15% SDS solution (**A**) at room temperature and (**B**) at 60 \({}^{\circ}\)C.  
through the few slit-like mesopores in the shell structure. As schematically represented in Figure 5, the addition of TEOS to the microcapsules at 25\({}^{\circ}\)C under weakly acidic conditions significantly reduces the internal pore area because of the low rate of hydrolysis, while the silica formed by seeded growth is gradually deposited in the pores.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 4 Biofouling Mitigation and Advanced Cleaning Technologies  
Biofouling, the accumulation of marine organisms on humanmade surfaces, poses a significant challenge for the shipping industry as traditional antifouling coatings, such as foul release coatings, only partially address the issue, particularly with persistent biofilms [81]. Fouling and biofilm formation on stainless steel surfaces pose serious risks to public health and food quality in the dairy industry. Fouling in the food-processing industry presents significant challenges that affect production costs and food safety, particularly in ultra-high-temperature systems [82]. NBs can penetrate organic materials and disrupt biochemical structures, including microbial biofilms. They reduce surface tension and promote bacterial detachment. When NBs burst, they release significant energy that generates hydroxyl and oxygen radicals, effectively inactivating bacteria. This technology enhances microbial inactivation, particularly when combined with methods like ultrasonication [83].  
In recent years, biomimetic materials capable of forming gaseous plastrons on their surfaces have gained significant attention, particularly for antifouling applications [84]. Gaseous plastrons, found in natural organisms like plants, insects, and animals, provide inherent resistance to biofouling by creating a physical barrier that reduces fluid-surface contact. This has inspired the development of novel antifouling strategies. The presence of air bubbles, as seen in the lotus leaf, flower petals, and Salvinia, helps form and maintain a gaseous plastron, offering self-cleaning and antifouling benefits. Researchers have utilized these biomimetic materials for anti-adhesion and antifouling purposes, leveraging their simple, nontoxic, and environmentally friendly nature. The antifouling effect based on gaseous plastrons holds great potential for future engineering applications.