esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Non-Targeted LC-QTOF-MS Based Metabolomics Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precursor** & **EC Number** &
\begin{tabular}{c} **Is It Included in** \\ **CannGEM?** \\ \end{tabular} \\ \hline BZ1; anthocyanin 3-O-glucoseyltransferase & 2.4.1.115 & No \\ \hline
3 MaT1; anthocyanin 3-O-glucose-6′′-O-malonyltransferase & 2.3.1.171 & No \\ \hline
3 MaT2; anthocyanin 3-O-glucose-3′′,6′′-O-olmalonyltransferase & 2.3.1.- & No \\ \hline
3 GGT; anthocyanin 3-O-glucose 2′′-O-glucoseyltransferase & 2.4.1.297 & No \\ \hline
5 GT; cyanidin 3-O-rutinoside 5-O-glucosyltransferase & 2.4.1.116 & No \\ \hline AA7 GT; cyanidin 3-O-glucose 7-O-glucoseyltransferase (acyl-glucose) & 2.4.1.300 & No \\ \hline UGT79B1; anthocyanin 3-O-glucose 2′′-O-xylosyltransferase & 2.4.2.51 & No \\ \hline
3 AT; anthocyanin 3-O-glucose 6′′-O-acyltransferase & 2.3.1.215 & No \\ \hline
5 MaT1; anthocyanin 5-O-glucose-6′′-O-malonyltransferase & 2.3.1.172 & No \\ \hline
5 MaT2; anthocyanin 5-O-glucose-4′′-O-malonyltransferase & 2.3.1.214 & No \\ \hline UGT75C1; anthocyanin 3-O-glucose 5-O-glucoseyltransferase & 2.4.1.298 & No \\ \hline AA5 GT; cyanidin 3-O-glucose 5-O-glucoseyltransferase (acyl-glucose) & 2.4.1.299 & No \\ \hline
5 AT; anthocyanin 5-aromatic acyltransferase & 2.3.1.153 & No \\ \hline UGAT; cyanidin-3-O-glucose 2′′-O-glucuronosyltransferase & 2.4.1.254 & No \\ \hline GT1; anthocyanin 5,3-O-glucoseyltransferase & 2.4.1.- & Yes \\ \hline
3 GT; anthocyanin 3′-O-beta-glucosidaseyltransferase & 2.4.1.238 & No \\ \hline ACACA; acetyl-CoA carboxylase & 6.4.1.2 & Yes \\ \hline ACSF3; malonyl-CoA/methylmalonyl-CoA synthetase & 6.2.1.- & Yes \\ \hline FASN; fatty acid synthase, animal type & 2.3.1.85 & Yes \\ \hline FAS1; fatty acid synthase subunit beta, fungi type & 2.3.1.86 & Yes \\ \hline fas; fatty acid synthase, bacteria type & 2.3.1.- & No \\ \hline HT2; 3-hydroxyacyl-thioester dehydrogenase, animal type & 4.2.1.- & No \\ \hline FATB; fatty acyl-ACP thioesterase B & 3.1.2.14 & Yes \\ \hline FATA; fatty acyl-ACP thioesterase A & 3.1.2.14 & Yes \\ \hline ACSL, fad; long-chain acyl-CoA synthetase & 6.2.1.3 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Validation of the metabolic reconstruction using precursors of metabolites detected in LC-QTOF-MS data.
## 1 Introduction
[PERSON] [21] found that the corrosion resistance of SSRs embedded in CAC is higher than that of carbon steel rebars with surface coatings, with carbon steel rebars performing the worst. In summary, the SSR exhibits excellent corrosion resistance, effectively addressing the problem of rebar corrosion. However, the mechanical properties of SSRs lead to differences in the structural behavior of stainless-steel rebar concrete (SSRC) structures compared to ordinary reinforced concrete (ORC) structures. The plasticity of the SSR results in greater deformation and wider cracks in SSRC structures during the service stage. [PERSON] [22] discovered through experiments that eccentrically loaded SSR columns develop wider cracks than carbon steel rebar columns with the same eccentricity and reinforcement ratio. The high ductility of SSRs causes differences in the load redistribution and stress redistribution patternsin SSRC structures compared to ORC structures, and the asymmetry in the stress-strain curves of SSRs under tension and compression render existing load-bearing capacity calculation formulas unsuitable [19]. Scholars, such as [PERSON] [23; 24], have pointed out that the load-bearing capacity calculation formulas proposed in Eurocode 2 are not applicable to SSRC beams. In response, scholars have conducted extensive research on the mechanical behavior of SSRC structures. [PERSON] [25] found that the strain distribution in the cross-section of four-point bending SSRC beams adheres to the plane section assumption, and the failure pattern is similar to that of ORC beams. [PERSON] [26] proposed a continuous strength method based on the constitutive relationship of SSRs, which more accurately and reliably predicts the load-bearing capacity of SSRC structures. [PERSON] [27] suggested using equivalent stress to replace the yield strength of duplex and austenitic SSRs to calculate the axial-flexural capacity. Building on this, [PERSON] [22] developed a computational model for predicting the N-M curves of SSRC columns. Additionally, scholars have also explored the fatigue performance [28] and seismic performance [29] of SSRC structures. However, although extensive experimental studies and theoretical design studies have been conducted on SSRC structures, no studies have been reported on the eccentric compression behavior of SCGC columns. In addition, since columns are the basic components of frame structures, it is beneficial to study the mechanical properties of columns in depth, and thus it is crucial to study their eccentric compression behavior [30; 31]. In this study, a novel concrete column structure that integrates geopolymers, CAC, and SSRs is proposed to further address the limitations of carbon steel reinforcement in marine structures. Considering that columns in real projects are usually subjected to eccentric loading, in this paper, eight SCGCs with different eccentricity (\(e_{0}\)) and reinforcement ratios (\(\rho_{\text{s}}\)) were tested with eccentric loading to examine the mechanical behavior of SCGCs under such conditions.
## 1 Introduction
### Related Survey Papers and Contributions of This Survey  
We begin with an appraisal and acknowledgement of the related existing contributions in the literature related to AI/ML-empowered wireless communication networks. Our study and analysis of the field over several months revealed that the existing reviews usually do not investigate or contextualise research works through the lens of the ongoing standardisation effort, industrial take-ups, and collaborative research project frameworks. As a result, the taxonomy of various research contributions uses one of the following three identified methodologies: application-oriented [6; 7; 8; 9], the traditional layering approach of the OSI protocol stack [10; 11; 12; 13; 14; 15; 16; 17], or the learning method, i.e., supervised, unsupervised, DNNs, and reinforcement learning (RL) [18].  
In this survey, we review the existing large-scale research activities on AI-enabled communications and networking from the perspective of standardisation activities to identify significant research gaps and inform and orientate future research directions to solve industrial needs. Even though this review may not be exhaustive, due to the fast development in this field, we believe that the suggested framework in this paper can be useful for future research initiatives, positioning articles, tutorial papers as well as future research efforts. It shall help researchers explain and position the scope of their work as part of a standardised intelligent networking architecture. The contributions of this survey are also summarised below.  
* The O-RAN Alliance and the 3 GPP standardisation body have already made significant progress in recommending how to perform data collection and intelligent control at the RAN and the core network, respectively. In terms of our approach, at the RAN,we categorise ML-based technology components for transceiver design, according to their operational timescales as suggested by the O-RAN Alliance: real-time (less than 10 ms), near real-time (between 10 ms and 1000 ms), and non-real-time (larger than 1 s) intelligent control. For ML models operating at the edge and core networks, we summarize the main salient features of the network data analytics function (NWDAF) developed and standardised by 3 GPP. It is worth noting that only a few research contributions are cognisant of the data collection and analytics architecture suggested by 3 GPP.
* We highlight the use cases for AI-enabled communication networks recommended by ITU, the O-RAN Alliance and ongoing research projects to guide future research activities with high and significant business potentials.
# Changes in Meteorological Elements and Its Impacts on Yunnan Plateau Lakes
## 2 Methodology  
### Study Area  
In this study, we focused on nine plateau lakes larger than 30 km\({}^{2}\) in Yunnan Province, namely, lakes Lugu, Chenghai, and Erhai in northwest Yunnan, and lakes Dianchi, Fuxian, Yangzonghai, Yilong, Xingyun, and Qilu in central Yunnan. The locations of these lakes in study area are shown in Figure 1, and data for associated morphological parameters are presented in Table 1. The total water area of these nine plateau lakes is approximately 1021 km\({}^{2}\), and the total drainage area is 8110 km\({}^{2}\). Among these lakes, Lake Lugu is a temperate climate lake, lakes Chenghai and Erhai are located in a transition area of temperate and subtropical zones, and lakes Dianchi, Fuxian, Yangzonghai, Qilu, Xingyun, and Yilong are subtropical climate lakes. Lake Dianchi is the largest lake in southwest China and the sixth largest freshwater lake in China, whereas Lake Fuxian is the second deepest lake in China and the lake containing the largest volume of water in Yunnan Province. Lake Erhai is the second largest plateau lake in Yunnan Province, and Lake Lugu is the third deepest lake in China. The areas of lakes Dianchi, Erhai, and Fuxian are each greater than 200 km\({}^{2}\), whereas those of the other six lakes are all less than 80 km\({}^{2}\).
## 2 Broadband System
### Worldwide Interoperability for Microwave Access (WiMAX)
#### 4.2.2 TV White Space: Broadcast to Enhanced Mobile Broadband
In this context, spectrum regulators have encouraged a database-assisted TVWS network architecture in which unlicensed WSDs have to acquire the TV channel information from a cloud-based geolocation database [188]. A database-assisted TVWS network architecture that is based on a heterogeneous communication model is illustrated in Figure 8. It comprises a primary network, macrocell network, and femtocell network. The primary network contains the TV tower and TV receivers that are the license holders of TV white spaces that have the entire priority to use it. Furthermore, the macrocell network consists of Macro Secondary Users (MSUs) and Macro Base Station (MBS). The MBS in each macrocell is demanded to serve each MSU. Similarly, the Femto Secondary Users (FSUs) and Femto Base Station (FBS) are entities of the femtocell network in which the FBS is expected to offer service to FSUs. Moreover, the femtocells and macrocells are the secondary networks. The secondary systems are the WSDs that can opportunistically exploit the TVWS [192]. Note that logical entities such as a coexistence discovery and information server (CDIS), coexistence manager (CM), and coexistence enabler (CE) are important for the coexistence of various cognitive radio systems [193]. In this configuration, for the WSDs to access any of the TVWS, they first need to report their immediate locations to a geolocation spectrum database (GLSD). Based on the location information, the database estimates and presents the available TV channels that can be exploited by the WSDs in a certain period. Note that the database-assisted TVWS system represents a standard cloud-enabled virtualized network. In this regard, each of the WSDs denotes an infrastructure-based device such as a base station of a secondary operator. These WSDs offer various cellular-based wireless services to their subscribers using the obtained TV channels. However, certain technical challenges regarding the design, accuracy, and management of geolocation databases demand concerted research effort to ensure an effective database-assisted TVWS network. Besides, efficient means of deploying and optimizing a database-assisted TVWS network are also imperative [188].
## 2 Affine Projection Sign Algorithm Based on Step-size Adjustment
### VSS-APSA and CAPSA  
A variable step size is based on the minimization of mean-square deviation [21], and given as follows:  
\[\mu_{\text{\tiny{VS}}}(n)=\alpha_{\text{\tiny{VS}}}\mu_{\text{\tiny{VS}}}(n- 1)+(1-\alpha_{\text{\tiny{VS}}})\times\min(\frac{\|\ \mathbf{e}(n)\ \|_{1}}{\sqrt{\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}},\mu_{\text{\tiny{VS}}}(n-1)) \tag{9}\]  
where \(\alpha_{\text{\tiny{VS}}}\) (\(0<\alpha_{\text{\tiny{VS}}}<1\)) is the smoothing factor. Although the VSS-APSA has good performance in terms of fast convergence speed and small steady-state misalignment, its tracking performance is very poor when an abrupt change occurs.  
Therefore, to enhance the tracking performance of VSS-APSA, the convex combination method was applied to APSA (CAPSA). For APSA algorithms, two different step sizes, that is, \(\mu_{1}>\mu_{2}>0\), are related to the faster convergence rate and lower steady-state error, respectively. The corresponding weight updates are defined as  
\[\mathbf{w}_{1}(n)=\mathbf{w}_{1}(n-1)+\mu_{1}\frac{\mathbf{U}_{s}(n-1)}{\sqrt {\xi_{1}+\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}}, \tag{10}\]  
and  
\[\mathbf{w}_{2}(n)=\mathbf{w}_{2}(n-1)+\mu_{2}\frac{\mathbf{U}_{s}(n-1)}{\sqrt {\xi_{1}+\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}}. \tag{11}\]  
Combining \(\mathbf{w}_{1}(n)\) with \(\mathbf{w}_{2}(n)\) by a variable mixing factor \(\lambda(n)\in[0,1]\) results in:  
\[\mathbf{w}(n)=\lambda(n)\mathbf{w}_{2}(n)+(1-\lambda(n))\mathbf{w}_{1}(n). \tag{12}\]  
Generally, \(\lambda(n)\) is updated indirectly based on reduction of an overall error \(e(n)\) through a sigmoidal active function (SAF) [27; 29], that is,\[\lambda(n)=\frac{1}{1+\exp(-s(n))}, \tag{13}\]  
where \(s(n)\) is based on a gradient descent scheme by using the \(\ell_{1}\)-norm minimization to ensure robustness in the presence of impulsive noise, as  
\[s(n) =s(n-1)-\mu_{s}\frac{\partial\mid e(n)\mid}{\partial s(n-1)}\] \[=s(n-1)+\mu_{s}\mu_{12}^{\lambda}\frac{\mathbf{u}^{T}(n)\mathbf{ U}_{s}(n-1)sgn(e(n))}{\sqrt{\xi_{1}+\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}}, \tag{14}\]  
where \(\mu_{s}>0\) is a step size, and \(\mu_{12}^{\lambda}=(\mu_{2}-\mu_{1})[\lambda(n-1)(1-\lambda(n-1))+\xi_{2}]\), in which the factor \(\xi_{2}\) is positive and prevents the update of \(s(n)\) from halting when \(\lambda(n-1)\) is equal to \(1\) or \(0\). Generally, \(s(n)\) is restricted in a symmetric interval, that is, \([-4,4]\). (12) is the weight update of CAPSA when \(s(n)<4\) and becomes \(\mathbf{w}(n)=\alpha_{c}\mathbf{w}_{2}(n)+(1-\alpha_{c})\mathbf{w}_{1}(n)+\mu _{2}\frac{\mathbf{U}_{s}(n-1)}{\sqrt{\xi_{1}+\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}}\) only if \(s(n)=4\). \(\alpha_{c}(0\leq\alpha_{c}\leq 1)\) has the same meaning as \(\alpha_{\texttt{PS}}\). However, from [25], we know that there is still an open problem to enhance the convergence performance of CAPSA.
## 1 Introduction
It has been reported that BA2 is mainly activated by pressure sensation, joint position, and complex touch, while BA1 is reported to have a response mainly to vibrating touch [17; 18]. Therefore, in this study, the region of interest (ROI) analysis was performed by selecting BA3, which responds predominantly to vibrational stimuli and can observe a distinct activation pattern. Additionally, in previous studies, when presenting the vibration stimulus the somatosensory area was not divided into sub-areas of BA1, 2, and 3, and the results for the entire area (S1) were extracted. Thus, in this study, for direct comparison with previous studies, the activation area for S1 was extracted. To this aim, we investigated the level of neural activation upon high-frequency vibration stimulation in a number of fingers and phalanges. First, high-frequency vibration (250 Hz), which is known to stimulate the Pacinian corpuscle, was administered to three phalanges (distal, intermediate, and proximal) on four fingers (index, middle, ring, and little) of the right hand. The resulting neuronal activation in the S1 and BA3 regions was measured using fMRI. Second, the activation area of each finger and each phalange was compared.
## 2 Methodology
### Artificial Electric Field Algorithm (AEFA)
In Equation (10), \(\text{best}(t)\) and \(\text{worst}(t)\) denote the ideal and worst fitness values at the \(t\)th iteration, respectively. \(fit_{i}^{t}\) is the fitness value of the \(i\)th particle. Using a greedy method, the algorithm updates the positions particles at each iteration. \[P_{i}^{t+1}= \begin{cases}P_{i}^{t}&\text{when fit }\left(P_{i}^{t}\right)< \text{ fit }\left(P_{i}^{t+1}\right)\\ P_{i}^{t+1}&\text{when fit }\left(P_{i}^{t}\right)\geq\text{ fit }\left(P_{i}^{t+1}\right)\end{cases}  \tag{11}\]  
The position of the individual in the following generation is determined by its fitness, according to Equation (11). The individual's position is unaffected, if it has less fitness value than the fitness value of newly generated individual. If not, its position is re-adjusted. The pseudocode of AEFA can be seen in the following Algorithm 1. ```
Initialize the population of size \(N\) within search range \([P_{\text{min}},P_{\text{max}}]\)  Set the Initial Vel\({}_{i}^{t}\) to 0  Calculate fitness value for all particles  Set iteration \(i\gets 1\) while stopping requirement is not met do  Compute \(K^{t}\), \(\text{best}(t)\) and \(\text{worst}(t)\) for\(i\gets 1\) to N do  Calculate fitness values  Calculate the total force in each direction Equation (5)  Calculate acceleration Equation (3) \(Vel_{i}^{t+1}\leftarrow\tau\times Vel_{i}^{t}+accl_{i}^{t}\) \(P_{i}^{t+1}\gets P_{i}^{t}+Vel_{i}^{t}\) endfor endwhile
```  
**Algorithm 1** Artificial Electric Field Algorithm (AEFA)
###### Abstract
However, as mentioned earlier, to achieve a non-constant bending shape suitable for the certain situation, it is inevitable to connect multiple bending segments [4; 6; 9; 11; 23; 24; 25; 26]. However, this approach not only increases the difficulty of control but is also not ideal in terms of space efficiency and cost-effectiveness. Therefore, a method is needed that enables the creation of desired shapes to the applications where the robot is used while also ensuring a compact design, ease of use, and economic viability. This study proposes a method for creating a desired shape by inserting an additional tendon into a hyper-redundant manipulator with rolling joints, in addition to the main driving tendons that are inserted parallel for general constant curvature bending. To the authors' knowledge, there have been studies on reinforcing the structural stiffness of a  
Figure 1: Description of situation requiring non-constant curvature bending for the overtube for efficient surgical tasks in confined and constrained spaces like gastrointestinal tract. hyper-redundant manipulator through the insertion of additional tendons [15], but no research has focused on creating a desired shape by controlling the additional tendon. The proposed method uses only two steps: it defines the path of the additional tendon and calculates the tension relationship between the additional tendons and the main driving tendons to achieve the desired shape. Tension is applied to the additional tendon and the main driving tendons through load cells to achieve the predefined desired shape. This paper emphasizes two major contributions: (1) It presents a novel method that allows for a hyper-redundant manipulator, equipped with rolling joints, to achieve the desired shape by inserting additional shaping tendons. This method offers a new approach to controlling the configuration of the manipulator. (2) The shape of the hyper-redundant manipulator can be estimated through a simulation, which is based on the principle of minimizing total potential energy. This estimation utilizes the paths of tendon insertion and the applied tensions, providing a comprehensive method to estimate and control the shape of the manipulator. The remainder of this paper is organized as follows: The Analysis of Bending Shape Based on Tendon Insertion Paths and Applied Tensions Section describes a path determination method for the additional tendon. The Comprehensive Discussion Section, along with the Conclusion and Further Work Section, presents the discussion and conclusions of this study, as well as an outline for future research.
## 4 Imaging Experiments
### Experimental Results and Discussion  
After multiple experiments and post-processing, 3D imaging phase RMSE comparison maps of the three reconstruction algorithms were obtained, as shown in Figure 10. Figure 11 shows the 3D image recovered by the three reconstruction algorithms.  
Figure 10: Comparison of 3D imaging phase root mean square error (RMSE) with three reconstruction algorithms.  
Figure 9: Experimental test target.  
Figure 11: _Cont._  
Figure 10 shows that the phase RMSE of the three reconstruction algorithms decreases as the sampling rate increases, which indicates a better 3D imaging effect. The TV reconstruction algorithm has the best reconstruction effect, followed by BP, with OMP being the worst, which is consistent with the simulation results of Section 3.2. From the experimental 3D image in Figure 11, it can be observed that when the TV algorithm has a sampling rate of 0.8, the imaging effect of the square target can maintain the basic contour shape (dashed box). When the sampling rate is reduced to 0.6, the edge information of the 3D imaging is partially missing, and the basic contour shape is not obvious. When the sampling rate is 0.4 or 0.2, the noise is large, and the target shape contour can no longer be discerned at all. BP and OMP cannot display the basic shape of the target well for any sampling rate. The experimental results verify the effectiveness of the single-pixel detector non-scanning 3D imaging system, and they further verify that the TV reconstruction algorithm is far better than the BP and OMP algorithms.  
In the experiment, the imaging results are blurry. Limited by the existing laboratory conditions, we set the DMD resolution to \(8\times 8\), which resulted in a low lateral resolution of the imaging. A weak reflected laser from the diffuse target and a limited laser modulation frequency result in low longitudinal accuracy. In future work, we will optimize the optical path system to reduce the laser power loss in the optical system and increase the laser modulation frequency to improve the longitudinal accuracy. Then, a high imaging resolution can be obtained by setting the DMD to high resolution.
# Tabular Data Models for Predicting Art Auction Results
## 2 Materials and Methods
### Datasets  
From the original dataset, four distinct datasets were generated by further extracting image-related features of the artworks. The core features common to all datasets include the following:  
* ARTIST: Name of the artist.
* TECHNIQUE: Medium or method used (e.g., lithography, etching).
* SIGNATURE: Whether the artwork is hand signed, plate signed or unsigned.
* CONDITION: Physical state of the artwork.
* TOTAL DIMENSIONS: Area of the artwork.
* YEAR: Year of creation.
* PRICE: Final auction price.  
Key Differences across the datasets are as follows:  
1. AuctionResultsNoImg (NoImg) Dataset contains only the core features without any image-related features.
2. AuctionResultsColor (Color) Dataset includes an additional Colorfulness Score [18], a measure of color intensity and variety of the image of the artwork.  
3. AuctionResultsSVD (SVD) Dataset adds SVD Entropy [19] of the image of the artwork to the core features, excluding the Colorfulness Score.
4. AuctionResultsColorSVD (ColorSVD) Dataset adds both Colorfulness Score and SVD Entropy, which quantifies the complexity of the artwork's visual representation.  
The Singular Value Decomposition (SVD Entropy) entropy of an image is a measure of the information content or complexity of an image based on its singular values obtained from the SVD of the image matrix. It can be computed using the following formula:  
\[\mathrm{H_{SVD}}=-\sum_{i=1}^{r}s_{i}\mathrm{log}(s_{i}) \tag{1}\]  
where \(r\) represents the rank (the number of non-zero singular values) of the matrix and \(s_{i}\) is the normalized singular value.  
Additionally, the California Housing (Housing) dataset [20] was used as a baseline to compare the results against a well-known dataset within the research community, providing a point of reference for evaluating the models and methodologies applied.