esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The findings demonstrated that the joint exhibited a favorable energy dissipation capacity and could be efficiently restored by replacing damaged components, such as high-strength bolts and precast beams. [PERSON] et al. [20] conducted a comprehensive study aimed at assessing the seismic performance of hybrid structures comprising prefabricated reinforced concrete columns and steel beams (RCS). The investigation involved experiments and finite element analysis. The results indicated that the utilization of semi-rigid connections in these structures resulted in a decrease in the overall stiffness and an increase in the self-oscillation period compared with that of rigid RCS structures. [PERSON] et al. [21] investigated the seismic performance of semi-rigid joints in PEC composite column-beam frames. Through a combination of experimental testing and finite element simulations, they discovered that equipping joints with pads allowed them to achieve the seismic design objective of \"strong columns and weak beams\". This design approach facilitated the full utilization of the performance capabilities of the semi-rigid joints in composite members, resulting in a good energy dissipation capacity. In this research, a novel type of prefabricated semi-rigid joint is proposed, taking into account the respective advantages of steel and concrete structures [22]. The joint uses steel sections and bolts to connect the concrete beam-column structures. Three-dimensional analysis models of the new joint and a traditional cast-in-place concrete joint were created using the ABAQUS 2020 finite element simulation software. Low circumferential reciprocal displacement loads were applied to both joints, and the ultimate bearing capacity and ductility of the joints were investigated by analyzing the differences between the hysteresis curves and the displacement reverse-skeleton curves. The damage characteristics and force mechanisms of the joints were evaluated by the analysis of the force nephograms. In addition, finite element software was used to analyze the performance variations of the novel joint under different conditions.
## 2 Nanocomposite Materials
### Synthetic approaches to nanocomposite materials
Alternative procedure to overcome the phase separation and aggregation phenomena that can occur in _ex situ_ formed NP polymer nanocomposites consists of introducing chemical functionalities in the organic components, _i.e._ in the polymeric chains, which enhance the specific interactions and the affinity between NC surface and surrounding media. Moreover, the proper choice of a common solvent able to well disperse both pre-formed organic capped inorganic NPs and polymers is fundamental to obtain uniformly dispersed NP based hybrids and to preserve physical-chemical and mechanical properties of the material [106]. In this perspective, PMMA based copolymers functionalized with either carboxylic acid or amine groups, namely poly(methyl methacrylate-co-acrylic acid) (PMMA-co-MA) and poly(methyl methacrylate-co-dimethylaminoethyl methacrylate) (PMMA-co-DMAEMA) have been exploited to prepare colloidal TOPO capped high emitting CdSe(\(\varnothing\)ZnS NCs in order to study the influence of the chemical functionalization of the polymeric chains on the optical and morphological properties of nanocomposite thin films. Indeed, chemical groups bounded to the polymeric chains can directly interact with the NC surface and provide a right interplay by replacing or intercalating the original NC surfactants and thus provide the uniform dispersion of the inorganic nanostructures in the polymeric matrix and an improvement of the NC photoluminescence quantum efficiency [107]. Nonlinear optical properties of high optically transparent TiO\({}_{2}\) nanorod modified acrylic acid functionalized PMMA co-polymers have been recently reported, highlighting the influence of carboxylic groups in stabilizing NPs and preventing aggregation. [108] Spectroscopic and structural investigations performed on the acrylic acid modified PMMA based nanocomposite show highly homogeneous dispersion of TiO\({}_{2}\) NCs in the functionalized matrix, compared with that carried out on PMMA homo-polymer based nanocomposites. In particular, the refraction index of the nanocomposites linearly increases while enhancing NC content in the functionalized co-polymer, and for high titania nanorod loading the optical transparency is preserved. The tunability of the linear refractive index of the nanocomposites as a function of nanorod concentration, the negligible two-photon absorption coefficient and the negative value of the nonlinear refractive index point out the potential application of such nanocomposite materials as optical devices.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 3 Results
### Response Characteristics of Vegetation to Water Deficit
#### 3.2.3 Response of Different Vegetation Types to Water Deficit  
The dominant vegetation types in the study area are mainly cultivated vegetation and grasses, which accounted for 45.03% and 32.96% of the total vegetation coverage, respectively. Shrubs, broadleaved forests, coniferous forests, and alpine vegetation accounted for 7.71%, 5.43%, 3.09%, and 0.07%, respectively. Cultivated vegetation is mainly grown in the semi-arid and semi-humid climate zones, accounting for 61.81% and 32.54% of the total cultivated area, respectively. Grasses are predominantly distributed in the semi-arid climate zone and the arid climate zone, accounting for 76.28% and 16.74% of the total cultivated area, respectively.  
As depicted in Figure 8, the degree of response and the timescale of vegetation growth in response to the water deficit varied across different growth stages and vegetation types. In the growing season, grasses were the most affected by the water deficit, followed by cultivated forests and shrubs, while alpine vegetation showed the least sensitivity to the water deficit. During the late growing season (August to October), grasses and cultivated forests were the plants most affected by the water deficit, whereas coniferous forests and broadleaved forests, as well as alpine forests, showed maximum sensitivity in the mid growing season (June to August). Moreover, the response time of VWD during the growing season also varied among different vegetation types, with grasses exhibiting the highest response time, followed by alpine forests, shrubs, cultivated forests, broadleaved forests, and coniferous forests. Although grasses were the least sensitive vegetation regarding the response time to water deficit during the growing season (Figure 8), their sensitivity varied across different growing seasons. In the early growing season, broadleaved forests were the most sensitive vegetation to water deficit, while grasses were the least sensitive. In the middle of the growing season, alpine forests showed the least sensitivity to water deficit, while grasses were the most sensitive. During the late growing season, grasses remained the vegetation most sensitive to water deficit, while coniferous forests were the least sensitive. These differences in vegetation water requirements, timescales, and critical growth periods should be considered when assessing the impact of water deficit on vegetation [3; 5]. The response characteristics and sensitivity of different vegetation types to water availability vary, leading to spatial and temporal heterogeneity in the response of vegetation growth to water availability.
## 3 The Working Scenario and Problems Statement
### Problems Statement  
The problems we discuss in this article can be split into two main sub-problems. Firstly, since each sensor node has a limited communication ability, relay nodes are a great need to keep the whole network connected. However, different criteria will lead to different sensor node placement strategies. In this paper, we study the optimal relay nodes' placement with minimum overall energy usage as a criterion. The procedure of solving this problem is elaborated in Section 4. Secondly, as mentioned in previous sections, unmanned locomotives will charge sensor nodes to make sure that no sensor node will suffer from the shortage of energy supply. However, the design of charging strategies of locomotives is not trivial. Many factors, such as the traveling path, the charging power and charging durations for different sensor nodes, should be taken into consideration. In Section 5, in order to deal with this problem, we formulate an optimization problem and reshape it into a linear programming problem, which can be solved efficiently.
## 1 Introduction
This method has also been used to non-destructively evaluate the curing mechanics of epoxy resins at different temperatures [22; 31; 32]. However, a deeper understanding of the use of ultrasonics as a non-destructive method is needed to effectively understand the curing mechanics of resins and adhesives under different parameters that can affect the quality of the final product. As an example, in the aerospace industry, epoxy and film adhesives are used to substitute mechanical fasteners for joining carbon fiber composites. This methodology has been proposed as a result of an adhesive bond's potential for increased strength, weight reduction, and improved stress distribution [33; 34; 35]. On the other hand, the Federal Aviation Administration (FAA) is concerned about the performance of adhesive bonds after aging during their life cycle [36]. In addition, failures associated with weak bonds, bondline voids, and contamination remain critical issues in today's adhesion community [37]. Manufacturing parameters, such as temperature and pressure, are difficult to precisely control and unwanted fluctuations can have an impact on the curing process and final product [38]. In addition, the usage of a mold release or other chemicals throughout the manufacturing process can lead to potential contamination and reduction in performance [39; 40]. As men tioned, the presence of residual solvent on an epoxy adhesive can considerably decrease its adhesive strength [6]. The focus of this paper is to detect changes in sound speed in an aerospace epoxy adhesive during the curing process as a result of residual solvent concentration. In this study, varying amounts of isopropyl alcohol (IPA) were added into the adhesive to simulate trapped solvent. Since the addition of IPA affects the crosslinking density and mobility of the adhesive, TGA was performed to analyze the decomposition of each of the sample types and to confirm the presence of different weight percentages of IPA in the adhesive samples. Fourier transform infrared (FTIR) spectroscopy was performed to investigate the affects in the chemical structure of the samples as a function of solvent concentration. Lap shear testing was used to evaluate the effects of IPA on the mechanical and adhesion properties of the adhesive under a shear load. We hypothesize that the sending and receiving of ultrasonic waves has the ability to detect the presence of this solvent and to determine how it influences the curing process of the adhesive. This curing process was modeled to better understand how variations in trapped solvent affect the curing mechanics of the material. Comprehending these effects can improve future techniques designed to use ultrasonics as an in situ, non-destructive quality control method during the manufacturing process. In addition, measuring the sound speed of fully cured samples that have been properly manufactured can serve as an in-field quality control method during the lifetime of polymeric materials.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Rotor Fault Feature Extraction Based on VMD Symmetrical Polar Image  
#### 3.1.1 Variational Mode Decomposition  
VMD method is an adaptive signal decomposition method, which decomposes the original rotor vibration signal \(f(t)\) into a series of modal functions \(u_{1}\), \(u_{2}\),..., \(u_{k}\), with different center frequencies. The sum of modal functions is equal to the input signal under the constraint, and the constrained variational problem is constructed as follows:  
\[\begin{array}{c}\min\limits_{u_{k},\omega_{k}}\left(\sum\limits_{k=1}^{K}{ \left\|{\partial_{t}\left[{\left({\delta(t)+\frac{j}{\pi t}}\right)u_{k}(t)} \right]}e^{-j\omega_{k}t}\right\|_{2}^{2}}\right)\\ \sum\limits_{k=1}^{K}{u_{k}}=f(t)\end{array} \tag{1}\]  
where, \(\{u_{k}\}\) = \(\{u_{1}\), \(u_{2}\),..., \(u_{K}\}\) and \(\{\omega k\}\) = \(\{\omega_{1}\), \(\omega_{2}\),..., \(\omega_{K}\}\) are modal functions, and each mode function's central frequencies.  
To solve the problem, the quadratic penalty factor \(\alpha\) and Lagrange multiplication operator \(\lambda\) are used to ensure the reconstruction precision of the signal and the constraint strictness in the presence of Gaussian noise, respectively.  
\[L(u_{k},\omega_{k},\lambda)=\alpha\sum\limits_{k=1}^{K}{\left\|{\partial_{t} \left[{\left({\delta(t)+\frac{j}{\pi t}}\right)u_{k}(t)}\right]}e^{-j\omega_{ k}t}\right\|_{2}^{2}}+{\left\|{f(t)-\sum\limits_{k=1}^{K}{u_{k}(t)}}\right\|_{2}^{2}}+ {\left\langle{\lambda(t),f(t)-\sum\limits_{k=1}^{K}{u_{k}(t)}}\right\rangle} \tag{2}\]  
Initialize \(u_{k}\), \(\omega_{k}\) and \(\lambda\). The solution to the constrained variational problem is using the alternate direction method of multipliers to find the saddle point of the augmented Lagrangian in a sequence of iterative sub-optimization. Then modal component \(u_{k}\) and the center frequency \(\omega_{k}\) can be expressed as follows:  
\[\begin{array}{c}\tilde{u}_{k}^{n+1}(\omega)=\frac{{\hat{f}(\omega)-\sum \limits_{i\
eq k}{\hat{u}_{i}(\omega)+\frac{\lambda(\omega)}{2}}}}{{1+2\alpha {{{\left({\omega-\omega_{k}}\right)}^{2}}}}}\\ \omega_{k}^{n+1}=\frac{{\int_{0}^{\infty}{\omega{{{\left|{\hat{u}_{k}(\omega)} \right|}^{2}}}d\omega}}}{{\int_{0}^{\infty}{{{{\left|{\hat{u}_{k}(\omega)} \right|}^{2}}}d\omega}}}\end{array} \tag{3}\]  
In the VMD algorithm, modal decomposition parameter \(K\) and quadratic penalty factor \(\alpha\) directly affect the precision of signal decomposition. At present, the parameter selection method based on the default parameter setting method is difficult to highlight the advantages of this signal decomposition method and limits its performance. Although intelligent search algorithms can optimize the parameter values given above, such methods take a long time and are difficult to achieve the purpose of actual detection. Therefore, an efficient method of the VMD decomposition key parameter selection is necessary.
# Investigation on the Application of Artificial Intelligence in Prostodontics
## 1 Introduction
Artificial intelligence (AI) might help with precise color matching in difficult aesthetic circumstances involving a single central incisor or several front teeth. In implant prosthodonics, implant locations may be identified with the use of intraoral detectors, and this information can then be inputted into the CAD program in real-time. Artificial intelligence (AI) has the capacity to improve dental implant design and fabrication [32]. AI (artificial intelligence) applications are used in many parts of our digital lives [16; 17; 18; 19], such as online assistants like \"Alexa\" and \"Siri\", and in many technical fields. In the medical field, AI algorithms are often used to analyze images by pulling out features and doing target studies [20; 21]. In the past five years, there have been more studies on how AI can be used in radiology to improve workflow and make the radiologist's job easier. Dental technology is getting better, but it is still not as good as medical technology [22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Standardized digital dental methods are used in traditional dental care. In labs, CAD/CAM fabrication is now the norm. AI application is a new idea that is starting to take shape as digital dentistry gets better [32]. Dentures for people with no teeth are technique sensitive because and they have to meet strict aesthetic and functional standards. Machine learning in the CAD/CAM software can realign the teeth to fabricate complete dentures with accuracy. Artificial intelligence (AI) could help match colours exactly in case of missing central incisors or adjust the colour with remaining natural teeth. In implant prosthodontics, an intraoral detector can be used to find the location of implants. This information can then be put into a CAD program in real-time. AI has the potential to make it easier to design and make better dental implant prosthetics [32]. The present review is novel and very specific to the field of dentistry, specifically prosthetic dentistry. It fills the gap in the literature in a way that there are various reviews pertaining to the use of AI in dentistry, endodontics, and oral medicine, but there is a need to provide a review in prosthetic dentistry which is lacking in the literature. This lacuna is filled by this review, as it explains the use and enhances the value of AI in field of dentistry and, more specifically, to prosthetic dentistry. This review will help in explaining the use and working of AI in different fields of prosthodontics, which would ultimately help the patients and improve satisfaction with the prosthesis. Humans are increasingly demanding, and always ready for more detailed and thorough medical/dental care; therefore, relying on artificial intelligence can improve the standard of care for patients. AI provides cutting-edge decision-supporting technologies in the discipline of prosthodonics. Therefore, this review aimed to explore and identify the various filed in prosthodontics where AI is used and how it is useful in providing prosthodontic care.
## 2 Related Works  
Researchers used SVM [9] and linear regression [15] to classify the songs based on the audio features of the songs [16; 17]. They used traditional approaches such as the mel-frequency cepstral coefficients (MFCCs) for extracting audio features from the songs. [PERSON] et al. [16] proposed the text mining approach to calculate the artist similarities to classify the songs. [PERSON], [PERSON], and [PERSON] [17] proposed that by extracting latent features from the audio signals presented in the songs provides a better classification. Traditional approaches such as MFCCs do not include the metadata (such as artists_familiarity, artist_location, duration, mode, year, tempo, song_id, etc.) presented in the music to classify the songs. The music tracks were classified into positive and negative classes based on the latent features extracted from the songs in [18]. Based on these classifications, they extracted the relationship between the artist and the music track. Social tagging services, such as Last.fm [19] allow users to provide tags describing the genres, moods, instrumentation and locations for classifying the songs. [PERSON] et al. [20] proposed a method that correlates the social tags and the keywords mined from the artist profiles to calculate artist relationship scores. These approaches use traditional approaches to extract latent music features to understand users to music relationship. However, these approaches are time-consuming and involves a lot of user interference.  
Researchers proved that the using the DCNN for extracting latent music features gives better performance when compared to traditional approaches [12; 13]. They proved that the DCNN approaches outperforms the traditional machine learning techniques such as the SVM [9] and the linear regression [15] in terms of classifying songs. The deep neural networks (DNN) approach such as the DCNN [12], the gated recurrent unit (GRU) [21] and the long short-term memory (LSTM) [21] have the capability to work on the huge amount of data in a distributed manner. [PERSON], [PERSON], and [PERSON] [12] proposed usage of the DCNN approach for classifying the songs by identifying the latent music features presented in the songs. [PERSON] and [PERSON] [13] proposed usage of DCNN for environmental sound classifications. The existing PMRS algorithms [12; 13] are limited to recommending songs based on latent music features presented in the user's music listening history. The latent music features for each song are obtained from the audio signal presented in that song. In the proposed EPMRS, we extract the latent features presented in the user's data (containing the user's information and the user's music listening history) and the music data. The EPMRS uses these latent features to recommend songs to the user. In Section 3, we discuss the system architecture and the data flow of the EPMRS.
## 2 Experimental Analysis  
### Test Description  
Acoustic emission data were obtained from a standard axial compression test of a concrete sample, using a static hydraulic test machine [47] with a capacity of 500 kN. The concrete specimen, presented in Figure 4, was positioned directly between the press's steel plates, without Teflon layers. The compressive force steadily increased at a constant load rate of 1.5 kN/s until reaching the peak load. After that, the load was first reduced by 80% and then totally lifted.  
The acquisition of AE signals was made through two independent multichannel systems in parallel: \"AEmission\" by Lunitek [2] with 8 piezoelectric sensors (model LT18  
Figure 4: (**a**) Compression test scheme with sample dimensions 0.3 \(\times\) 0.3 \(\times\) 0.3 m and, (**b**) AE sensors’ locations. The coordinate system’s origin is at the cube’s geometric center.  
003-PRD-00-R0 with a frequency range of 15-625 kHz) and \"MicroSHM\" by Mistras [3] with 2 piezoelectric sensors (model R15A-150 KHZ general-purpose AE sensor with a frequency range of 20-600 kHz). The piezoelectric transducers were fixed to the free faces of the cube with epoxy resin (cold glue X60) and aluminum adhesive tape. Both acquisition systems use a 5 MHz sampling frequency and store data in parametric form. The recorded parameters for each detected signal are:  
* Signal start time = instant of the first reading that exceeds the detection threshold: 40 dB (corresponding to 100 \(\mu\)V) for the Mistras system, and 49 dB (280 \(\mu\)V) for Lunitek;
* Peak amplitude, expressed in dB (\(A_{\text{dB}}=20\text{log}10(V_{\text{max}}/1\ \mu\)V));
* Number of oscillations (counts) = the number of intermediate crossings of the threshold by the signal. This measure is also commonly used to estimate the AE signal's average frequency (\(AF\)) through the counts/duration relationship (signal duration \(\equiv\) end time-start time);
* AE energy (\(E_{AE}\)) = integral of the waveform. As indicated by RILEM [48], it can be approximated by the envelope area of the triangle formed by the signal peak amplitude and its duration.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 4 Results
### Simulation Experiments
#### 4.1.4 Robustness and Runtime Analysis
Four frames were chosen from the subsequent six frames to obtain the reconstruction results, as shown in Figure 11b. Although the training dataset did not include the last six frames, the test results are highly consistent with the true concentration distribution. Even an extremely small area, such as ROI3, can be reconstructed clearly, as indicated by the pink rectangular boxes in Figure 11b. Tables 3 and 4 listed the reconstruction results of three methods. As a classic traditional iterative method, MLEM cost about 0.2 s for reconstruction of each image. TV mixed a postprocessing of iterative method to improve the image quality, it takes almost twice as long as MLEM. As for cGANs, it only cost 0.007 s of each image because the model was trained in advance and the test time is very short which even can be ignored. Figure 11: Robustness verification graphs of [PERSON] head phantom for different frames. (**a**) Concentration distribution of training images (the first 12 frames). (**b**) Reconstruction results for [PERSON] head phantom with a 70 min scan and 5 \(\times\) 10\({}^{7}\) counts. From top to bottom: MLEM results, TV results, cGAN results, and ground truth. From left to right: the 13 th, 14 th, 16 th, and 17 th frames.