esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Some researchers [29; 30] introduced the concept of cone angle and, on this basis, developed an improved model to account for stress diffusion in the bottom soil. The corresponding studies involving the two-phase medium [31], three-phase medium [32], pipe pile [33] and incomplete bonding effect of the pile-soil interface [34] have further expanded the application range of the fictitious soil pile model. The vibration characteristics of large-diameter-bored piles are further complicated by two factors in addition to sediment. The first factor is radial unloading of the soil, which weakens the soil around the pile during or after the hole'sformation, making it radially inhomogeneous. This phenomenon, which is positively related to pile diameter, has an adverse effect on both the bearing capacity [35; 36; 37] and dynamic response of the pile [38; 39; 40; 41]. Therefore, it deserves extra attention when dealing with the vibration of large-diameter-bored piles. The second factor to consider is the 3D effect of the pile. Historically, pile vibration studies have primarily relied on one-dimensional (1D) theory [42], which is suitable for slender rods. However, for large-diameter-bored piles, the 1D theory is not strictly applicable due to the 3D effect of the pile [43]. A notable discrepancy has been observed between measured and calculated results derived usingthe 1D theory [44; 45; 46], and this phenomenon is ascribed to the wave dispersion effect when propagating in a large-diameter-bored pile [47]. Given the challenge of solving the rigorous 3D wave equation for the pile, an alternative approach was proposed. In this method, the pile is treated as a Rayleigh-Love rod, and its governing equation can be easily established by considering the pile's transverse inertia on the basis of the 1D wave equation for the pile. This approach significantly reduces the computational requirements while maintaining precision. It has been adopted by a series of researchers, including [PERSON] et al. [48], [PERSON] et al. [49], [PERSON] et al. [50], [PERSON] et al. [51], and [PERSON] et al. [52]. For large-diameter-bored piles, both the radial unloading of soil and the 3D effect of wave propagation should be considered simultaneously, when accounting for the action of bottom sediment. However, the correlative research has not yet been reported to the authors' knowledge. Therefore, this paper presents a rigorous solution to address this gap. The bottom sediment is modeled as a fictitious soil pile and is simulated by the Rayleigh-Love rod model in conjunction with the large-diameter-bored pile. The radial unloadingof the surrounding soil is considered by the gradual variation in thesoil properties in the radial direction. On the basis of the solution, the coupling effect of the sediment, pile parameters, and radial unloading of the soil on the longitudinal dynamic response of the large-diameter-bored pile is analyzed.
## 5 Data Processing and Results  
To demonstrate our research contribution, it is necessary to compare some of the previous studies that used a different set of predictors. We choose to compare with the first study that adopted the same approach [3], which has 7 predictors for each subsystem. Hence, in each of the following subsections, we present the findings of each dataset side by side. The original study [3] only includes data between years 2002 and 2012. To facilitate direct comparison, we extend the data to 2002-2023 based on the data source.  
More precisely, our dataset \(O\) consists of the data for all 30 regions between years 2002 and 2023 for each predictor. Recall that we have 12 predictors (\(X_{11}\), \(X_{12}\), \(X_{13}\), \(X_{14}\), \(X_{21}\), \(X_{22}\), \(X_{23}\), \(X_{24}\), \(X_{31}\), \(X_{32}\), and \(X_{33}\)) for the technological innovation subsystem, and 12 predictors (\(Y_{11}\), \(Y_{12}\), \(Y_{13}\), \(Y_{14}\), \(Y_{21}\), \(Y_{22}\), \(Y_{23}\), \(Y_{24}\), \(Y_{31}\), \(Y_{32}\), and \(Y_{33}\)) for the financial development subsystem.  
The reference dataset \(R\) consists of the data for all 30 regions between years 2002 and 2023 for each predictor. There are seven predictors (\(X_{11}\), \(X_{12}\), \(X_{13}\), \(X_{14}\), \(X_{15}\), \(X_{16}\), and \(X_{17}\)for the technological innovation subsystem. There are also seven predictors (\(Y_{11}\), \(Y_{12}\), \(Y_{13}\), \(Y_{14}\), \(Y_{15}\), \(Y_{16}\), and \(Y_{17}\)) for the financial development subsystem.  
The compiled datasets \(O\) and \(R\) are saved as two separate Excel sheets. All data pre-processing and processing are carried out using the Python 3.9 programming language on an iMac computer with 64 GB memory. The datasets and all the Python scripts are made available as a GitHub project ([[https://github.com/wenbingcsu/ccd](https://github.com/wenbingcsu/ccd)]([https://github.com/wenbingcsu/ccd](https://github.com/wenbingcsu/ccd))).
## 1 Introduction  
Zirconia has attracted a great deal of attention due to its unique physical and chemical properties, such as a high melting point and strength, excellent chemical stability and corrosion resistance, the presence of weak acid and basic sites on its surface [1; 2; 3]. In order to improve the properties of such a material, a great deal of work has focused on the adjustment of its crystal phase, pore structure, and surface properties [4; 5; 6]. Morphology is also a key factor determining the properties of zirconia. [PERSON] et al. [7] successfully prepared flower-like zirconia nanostructures, which exhibited much better photocatalytic activity for the photodegradation of Rhodamine B in contrast to ordinary zirconia nanoparticles. Unique zirconia nanoshells were synthesized by [PERSON] and co-workers [8]. The nanoshells showed a value of photoluminescence properties 100-times higher than that of irregularly shaped zirconia nanoparticles. Morphology-oriented zirconia (mesoporous, sphere, star, rod) supported vanadium oxides were fabricated by [PERSON] et al. [9] for the NH\({}_{3}\)-SCR process. The morphology of zirconia showed a great influence on the catalytic performance of the catalysts. Therefore, it is of great importance to tailor the morphology of zirconia.  
Various strategies have been employed for controlling the morphology of zirconia, including hydrothermal treatment, sol-gel processes, spray pyrolysis, magnetron sputtering and electrospinning methods [10; 11; 12; 13; 14]. Among them, hydrothermal treatment provides a relatively simple process. Until now, hydrothermal synthesis approaches were developed to prepare zirconia with different morphologies (needle, sphere, rod, spindle, star, flower) [7; 8; 15; 16; 17; 18]. As far as we know, the morphology-controllable synthesis of zirconia through hydrothermal treatment assisted by rosin-based surfactants has not been reported. Different from traditional surfactants derived from fossil resources, rosin-based surfactants can be synthesized with raw resin acid, which is a kind of renewable resource from pine trees. The unique tricyclic phenanthrene-like structure endows the rosin-based surfactant with excellent surface activity, making it a good candidate for the morphology control of inorganic materials. In this thesis, by using the hydrothermal treatment of ZrOCl\({}_{2}\) aqueous solutions with the assistance of a rosin-based surfactant, dehydrodietyltrimethyl ammonium bromine (DTAB), hollow cube-like zirconia was firstly synthesized. In order to effectively control the microstructure of the material, the effects of surfactant addition and calcination process on the material were investigated. The introduction of DTAB could improve the dispersion of the sample. As the calcination temperature increased, the morphology of the material progressively transformed into a hollow sphere.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 2 System Model
### Asynchronous Random Access Channel Model
The useful received power is assumed to be constant over the entire replica \(r\), i.e., \(\mathsf{P}_{i}^{(r)}=\mathsf{P}^{(r)}\) for \(i=0,\ldots,n_{s}\) and for \(\forall r\in\mathcal{R}\). For simplicity, we further assume that users are received with the same power thanks to perfect power control, i.e., \(\mathsf{P}^{(r)}=\mathsf{P}\) for \(\forall r\in\mathcal{R}\). This is a typical assumption for fixed satellite communication systems. As a result, \(\mathsf{P}_{i}^{(r)}=\mathsf{P}\). The aggregate interference power for symbol \(i\) is \(\mathbb{Z}_{i}^{(r)}\triangleq\mathbb{E}\left[|z_{i}^{(r)}|^{2}\right]\). Finally, the noise power is \(2\sigma_{n}^{2}\), and we define the signal-to-noise ratio, \(E_{s}/N_{0}=\mathsf{P}/2\sigma_{n}^{2}\). We also define the signal-to-interference ratio \(\gamma_{i}=\mathsf{P}/\mathbb{Z}_{i}\).
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm  
The proposed sensor-scheduling algorithm for multi-target tracking is composed of three mechanisms: dynamic clustering, target prioritizing, and task allocation. In what follows, we elaborate on each of the three component mechanisms.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 2 Time Reversal Approaches for Structures
### Time Reversal Refocusing as Outcome of Reciprocity
#### 2.2.2 Time Reversed Switched Boundary Conditions  
Here, we choose only the boundary conditions state to be the reversed in time switched in space quantity. The swapping of boundary conditions is meant by means of energetically corresponding pairs, that is:  
\[v(x,t)\leftrightarrow\frac{\partial^{3}v(x,t)}{\partial x^{3}}\] \[\frac{\partial v(x,t)}{\partial x}\leftrightarrow\frac{\partial^ {2}v(x,t)}{\partial x^{2}}\]  
therefore:  
\[\int\limits_{0}^{L}w(x,t)*u(x,t)\,dx-\int\limits_{0}^{L}q(x,t)*v(x,t)\,dx=\] \[EI\bigg{[}\frac{\partial^{3}u(x,t)}{\partial x^{3}}*\frac{ \partial^{3}u(x,t^{*})}{\partial x^{3}}-\frac{\partial^{2}u(x,t)}{\partial x^ {2}}*\frac{\partial^{2}u(x,t^{*})}{\partial x^{2}}+\frac{\partial u(x,t)}{ \partial x}*\frac{\partial u(x,t^{*})}{\partial x}-u(x,t)*u(x,t^{*})\bigg{]}_{0} ^{L}\] \[+\rho A\int\limits_{0}^{L}\dot{v}_{0}(x)u(x,t)+v_{0}(x)\dot{u}(x,t )-\dot{u}_{0}(x)v(x,t)-u_{0}(x)\dot{v}(x,t)\,dx. \tag{15}\]The proposed procedure can be seen as a generalization of [31, 34], where only free surfaces have been studied. A more specific choice of this reciprocal statement would be that of concentrated impulsive load on some point \(x_{s}\) for the forward configuration while for the backward one to assume to be free of excitation loading. Both states are considered to be under silent initial conditions and therefore the reciprocity relation can be written as:  
\[v(x_{s},t)=\] \[EI\left[\frac{\partial^{3}u(x,t)}{\partial x^{3}}*\frac{ \partial^{3}u(x,t^{*})}{\partial x^{3}}-\frac{\partial^{2}u(x,t)}{\partial x ^{2}}*\frac{\partial^{2}u(x,t^{*})}{\partial x^{2}}+\frac{\partial u(x,t)}{ \partial x}*\frac{\partial u(x,t^{*})}{\partial x}-u(x,t)*u(x,t^{*})\right]_{0} ^{L} \tag{16}\]  
In this case, we can not take advantage of modes' orthogonality; however, the terms of the form of Equation (13) existing here are also dominating resulting in a maximum value of \(v(x_{s},t)\) at time \(t{=}T\) with a refocusing of the wave field on the point \(x_{s}\).
## 3 Results
### Vegetation Indices  
The temporal trends of the considered spectral vegetation indices are reported in Figure 3. All the vegetation indices that represent vegetation greenness (NDVI, EVI, SAVI and GNDVI) have quite similar trends. In addition, NDVI from the two different sources (Landsat and Proba-V) match quite well, with the difference that NDVI\({}_{\text{PV}}\) has slightly higher values than NDVI. All the vegetation indices increase from the beginning of the growing season, to reach a peak around end of May or beginning of June. After that, they start decreasing slowly for about 3 months. Around September, there is again a slight increase in the vegetation index. This last increase is better seen with NDVI\({}_{\text{PV}}\) especially in years 2016 and 2017.  
Similar patterns in fluxes were visible every year: GPP shows some seasonality and has two peaks during the growing season, a high peak around end of May-beginning of June and a low peak in October. In between the two peaks, there is a period of low C uptake (lower GPP) values in July and August. Similar trends were visible in NEE and R\({}_{\text{eco}}\). In fact, a strong correlation was observed between NEE and GPP during the growing season, both for midday and daily averages (r = 0.96 and 0.89 respectively).  
The maximum values of VPD and Tair match the low GPP period. Rg, however, reaches its yearly peak earlier than VPD and Tair, somewhere between May and June. Precipitation data shows no real pattern of seasonality, as the distribution over the year seems quite random. However, less precipitation was observed in July and August in years 2016 and 2017.  
Figure 2: Daily average of NEE (**a**) (the red line represents the moving average of daily NEE with a 20 days window); R\({}_{\rm{eco}}\) and GPP (**b**) (the green and red lines represent the moving average of daily GPP and R\({}_{\rm{eco}}\), respectively; 30 min average of VPD (**c**), Tair (**d**), Rg (**e**) and total daily precipitation (**f**) recorded between 2014 and 2017 at the study site.  
The year 2016 shows an unusual trend, with a very low value recorded for some vegetation indices in August, due to a fire that occurred at that period. This unexpected disturbance was visible in GNDVI, NDVI, EVI, SAVI, LSWI and MNDWI, but less visible in NDVI\({}_{\mathrm{PV}}\) and NDSVI.  
LSWI is a water-related vegetation index, but it has a trend that is similar to the greenness-related vegetation indices. NDSVI, which is also a water-related vegetation index, does not decrease during June, July and August as MNDWI.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 3 Proposed Algorithm
### Specific Steps of TOPSIS-MPR Algorithm  
\(N(A)\) represents the set of 1-hop neighbors of node \(A\); \(N_{2}(A)\) represents the set of 2-hop neighbors of node \(A\); \(M(A)\) represents the MPR set of node \(A\); \(S_{2}(A)\) represents the judgment flag for \(M(A)\) to fully cover \(N_{2}(A)\). As shown in Figure 5, this is the flowchart of the TOPSIS-MPR algorithm: To facilitate understanding of the algorithm flowchart, the specific steps are described as follows:  
**Step 1.** Initialize \(M(A)=\varnothing\), \(S_{2}(A)=N_{2}(A)\).  
**Step 2.** By traversing \(N(A)\), calculate the distance \(d\) between node \(A\) and its neighbors, and judge whether \(d\) is greater than the communication distance \(R\). If so, remove the neighbor from \(N(A)\); otherwise, keep the neighbor.  
**Step 3.** By traversing \(N(A)\), calculate link state evaluation indicators, namely: LD, SDL, ANSCR, LL, and LNS.  
**Step 4.**\(\exists i\in N(A)\), so that node \(i\) is the only reachable relay of a node in \(S_{2}(A)\), then add node \(i\) to \(M(A)\), that is: \(M(A)=M(A)\cup\{i\}\), and remove node \(i\) in \(N(A)\) and the 2-top neighbors in \(S_{2}(A)\) reachable through node \(i\), then proceed to the Step 6.  
**Step 5.**\(\forall i\in N(A)\), \(i\
otin M(A)\), calculate the proximity factor \(c_{i}\) of all nodes \(i\) based on the evaluation algorithm of TOPSIS-MPR, select the node \(i\) with the highest \(c_{i}\) value, then add node \(i\) to \(M(A)\), that is: \(M(A)=M(A)\cup\{i\}\), and remove node \(i\) in \(N(A)\) and the 2-top neighbors in \(S_{2}(A)\) reachable through node \(i\), then proceed to the Step 6.  
**Step 6.**Judge \(S_{2}(A)=\varnothing\)? If so, proceed to the Step 7; otherwise, proceed to the Step 5.  
**Step 7.**The algorithm ends and \(M(A)\) is obtained, which is the MPR set of node \(A\).
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 3 Fault Detection  
Protection system is essential for the safe and effective operation of electrical systems. Its task is to eliminate faults in the shortest possible time to avoid danger to people and damage to equipment. At the core of any protection system is a fault detection system that determines whether there is a fault in the system. However, as described in Section 2, not all fault types are easy to detect, nor do they cause the same damage. LIFs, which are characterized by high currents and significant voltage drops, are easily detected by the protection system. Conventional protection schemes such as overcurrent are effective, but new methods such as those presented in [45] are often proposed to further increase the level of safety.  
As far as HIFs are concerned, there is no fully effective system of fault detection other than differential protection. However, the cost of differential protection is high and depends heavily on the communication link, so it is not an option for microgrid protection. However, various HIF detection methods have been proposed, many of which are based on intelligent classification, since fault detection is essentially a classification task. As mentioned in the introduction, this work focuses on HIF detection with NNs trained in a way that considers  
Figure 1: Fault current of the HIF model. The fault buildup starts at \(t=0.3\) s and lasts until the current reaches its maximum magnitude and is followed by the shoulder. The nonlinear stage lasts until the fault is cleared at \(t=0.8\) s.  
an open-set recognition problem. Therefore, this section describes classification with NNs, the OSR problem and how the NNs are adapted to solve this problem.
## 1 Introduction
Furthermore, in the judgment of the damage state, the sentry function (SF), which has been used to specify the behavior of composites under tensile test [31], is used an aided validation method to describe the damage in composites. [PERSON] et al. [32] used AE technology to monitor the buckling process of multi-delaminated composites and analyzed AE signals based on the k-means algorithm and PCA. The results showed that AE signals of the compression process can be divided into three clusters, which represent three kinds of damage modes such as matrix cracking, fiber/matrix debonding, delamination and fiber breakage, respectively. In this study, mechanical characterization and failure processes of carbon fiber woven composites were conducted under a tensile loading condition. For a better understanding of the damage and evolution of the composites, AE characteristic parameters such as AE count, energy, and peak amplitude and frequency were used to analyze the damage behavior in composites. This paper addresses a new method based on the mechanical behavior and AE information. Compared with other literature, this method not only uses the k-means algorithm cluster analysis to identify the tensile progressive damage and failure mechanism but also combines the peak frequency distribution with the SF of composite materials during tensile load. This method can determine the behavior and mechanism of the material in different damage stages.
# A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks
## 4 The Proposed Method
### Hierarchical Generative Embedding Model
#### 4.1.3 Node Attribute Properties  
Given a node \(i\) with an attribute \(a\), the logistic model is used with a mean value of the multidimensional Gaussian distribution of node \(i\), i.e., \(\mu_{i}\), as input features to predict the probability of node \(i\) associating with attribute \(a\):  
\[R_{i,a}=\sigma(W_{a}^{\intercal}\mu_{i}+b), \tag{8}\]  
where \(W_{a}\) is the logistic weight factor, \(b\) is the bias and \(\sigma(.)\) is a logistic function defined as \(\sigma(x)=(1+e^{-x})^{-1}\).
## 1 Introduction  
Dental implants are designed to restore partial function of a replaced tooth. Generally, only the surface of the implant is in contact with the host tissue, so its material is vital in determining the biological properties [1; 2; 3; 4; 5; 6]. Thus, improving the surface-modification technique of implants, which can enhance the interface healing between the implant and the bone, is always the key point in dental implant development. In 1969, Dr. [PERSON] designed a titanium (Ti) metal cylinder with an optical chamber inside and screwed it into a rabbit's thighbone. Several months later, it was found that the Ti cylinder was fused to the bone. This phenomenon was named as osseointegration [7]. With rapid development in biomedical research such as cell isolation, molecular biology, cell biology, and biochemistry, cellular and tissue engineering has been recognized as a new field [8]. To better understand the mechanisms of the regulation of bone, more research on long-term observations with osteoblasts and appropriate precursor cells as well as implants made with proactive biomaterial is desired [9]. New technological developments including new medical materials, products, and surgical techniques have greatly promoted clinical technologies. As a new product, dental implants, especially the surface part made with polymeric material, have to be biocompatible with the physiological environment. A previous research indicated that discharge plasma has the potential to be used to sterilize Ti dental implants [10]. Moreover, plasma modification techniques have also been applied to alter Ti surface properties (e.g., topography, chemical composition, and microstructure) to improve its biocompatibility and hemocompatibility [5; 11; 12; 13].  
Currently, various polymeric surface modification techniques are widely used in biomaterial research to produce thin polymer coatings [14; 15]. It was found that the surface modification technique that deposits a thin plasma-polymerized layer (such as amino-groups) on the material surface can potentially enhance the biocompatibility and became a crucial approach [16; 17]. Moreover, different bioactive biomolecules such as growth factors, peptides, proteins, and enzymes can be bonded by amino-groups on the material surface to facilitate cell adhesion and proliferation, and thus the surface biocompatibility of the material can be significantly promoted [18; 19; 20]. Though studies focusing on the application of plasma techniques were broadly performed, limited information between the plasma polymerization parameters and surface properties is available. As a result, the present study was to develop a promising plasma modification approach (low-temperature plasma polymerization with allylamine) to fabricate a functional coating (amino-groups) on the surface of Ti and investigate its surface characteristics and optimal plasma modification parameters.
# SARS-CoV-2 Detection Using Optical Fiber Based Sensor Method
## 4 Fiber-Optic Sensing for COVID-19  
The utilization of fiber-optic sensors has found applications in chemical, civil, physical and health sectors. Since they are immune to electromagnetic interference, they offer higher sensitivity and detection limits. These characteristics make them advantageous over conventional schemes. One of the other advantages with optical fiber sensing is the provision of flexibility in configuration of fiber probes (D shaped, U shaped and tapered). Out of these possibilities, the U-shaped fiber probe configuration is the most efficient configuration for the localized plasmon response as it provides the quickest response compared to other probes [29]. The proposed U-shaped fiber probe method has excellent sensitivity in the range of parts per billion (ppb), with a response time ranging from 25 to 60 s [30; 31]. The steps followed for the fiber-optic-based sensing is summarized step by step in sections below according to the recent work shown in [32; 33]. Figure 4 also shows the schematic overview of steps involved.  
Figure 3: Block diagram of latest COVID-19 detection technologies structures: surface plasmon resonance (SPR) configuration (**A–D**); (**E**) bioreceptor fiber optic sensing method; (**F**) surface enhancement Raman scattering, and (**G**) laser-induced fluorescence (LIF) detector.