esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experiment Settings
#### 3.1.3 Evaluation Index  
This paper adopts the root-mean-square error (RMSE) [37] and [PERSON]'s Coefficient of Correlation (PCC) [38] as performance metrics to evaluate the model. RMSE is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Classification** & **Proportion** \\ \hline \multirow{2}{*}{Gender} & Male & 50\% \\  & Female & 50\% \\ \hline \multirow{4}{*}{SNR} & \(>\)15 dB & 10\% \\  & 10–15 dB & 40\% \\  & 5–10 dB & 40\% \\  & \(<\)5 dB & 10\% \\ \hline \multirow{4}{*}{Speech rate} & Slow & 25\% \\  & Medium & 50\% \\ \cline{1-1}  & Fast & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classifications and proportions of air traffic control speech database.  
used to measure the magnitude of the error between the predicted and true values. PCC is used to assess the strength of the linear relationship between two variables.  
Equation (6) calculates the root-mean-square error between the subjective MOS and predicted MOS. A smaller RMSE indicates the better predictive performance of the model [39].  
\[RMSE=\sqrt{\sum_{i=1}^{N}(a_{i}-b_{i})^{2}/N} \tag{6}\]  
Equation (7) calculates the correlation coefficient between the subjective MOS and predicted MOS, and the closer the absolute value of PCC is to 1, the better the predictive performance of the model is [40].  
\[PCC=\frac{\sum_{i=1}^{N}(a_{i}-a)(b_{i}-b)}{\sqrt{\sum_{i=1}^{N}(a_{i}-a)^{2}(b _{i}-b)^{2}}} \tag{7}\]  
where \(N\) represents the total number of speech samples in the test set, \(a_{i}\) denotes the subjective MOS of the i-th speech sample, \(b_{i}\) represents the predicted MOS of the i-th speech sample, and \(a\) and \(b\) represent the arithmetic mean of the subjective MOS and predicted MOS, respectively.
## 2 Related Research  
In recent years, many new inspection methods have been proposed to reduce economic costs and simplify the process of inspections [16; 17; 18; 19; 20]. Especially, deep learning-based methods have been studied well. For example, research is being conducted on the automatic diagnosis of buildings and the automatic generation of inspection results [21; 22; 23]. Several techniques have been proposed using 3D models for bridge inspection [24; 25]. These existing methods utilize laser survey instruments either installed on the ground or mounted on a drone, conduct surveys from multiple measurement points, acquire point cloud information of a bridge, and create a 3D model of the entire bridge. The point cloud information acquired using these instruments can be used to create a 3D model with an accuracy that provides visual confirmation of concrete delamination and cracking. However, the density of the point cloud information may be inadequate for the quantitative evaluation of the texture.  
Figure 1: Image of model of specimen reproduced with point cloud information (**top**) and partially enlarged image with a sparse point cloud (**bottom**).  
Figure 2: Example of super-resolution of the pair of images of a raw RGB image and its distance image.  
The generated content cannot be easily controlled while generating image data with GAN because model training is conducted using only the authenticity judgment of the generated result as an evaluation index. In this regard, conditional GAN (cGAN) [26; 27] has been proposed as a method for controlling images generated with GAN. In this method, the generated data are adjusted by adding information that controls the output result for the input data used in generating the pseudo-data. A type of cGAN called Pix2 Pix [28] uses annotated images that are color-coded for each region as input signals for pseudo-image generation. Accordingly, the image generation process is controlled by the regions segmented by this annotation, thereby generating images with distinct features.  
As a super-resolution method using deep learning, SRCNN [29] was first proposed by [PERSON] et al. Although this method was a simple model consisting of three convolution layers, it set a state-of-the-art record at the time of publication. [PERSON] et al. proposed VDSR [30], which solves the problems of the training speed and the single resolution of generated images in SRCNN. [PERSON] et al. also proposed FSRNN [31], which solves the computational cost and processing speed problems of SRCNN. As a GAN-based super-resolution method, [PERSON] et al. proposed SRGAN [32] using a ResNet-based generator, which can generate more precise images than previous methods. [PERSON] et al. improved the generator, classifier, and loss function of SRGAN and proposed ESRGAN [33], which produces more precise images with smaller fluctuations. In this paper, we use Real-ESRGAN [34], which improves on ESRGAN's dataset and model structure to better the generalization performance.
# A Damage Model for Concrete under Fatigue Loading
## 3 Thermodynamics Based Continuum Damage Mechanics Model
### Thermodynamics Interpretation
#### 3.1.3 Damage Variable Definition and Its Thermodynamics Interpretation
 \tag{42}\]  
The damage variables \(D_{n}\textsuperscript{E}\) (or \(D_{n}\textsuperscript{e}\)) and \(D_{n}\textsuperscript{i}\) are able to be used to characterize the stiffness degradation and irreversible strain development of concrete under fatigue loading, respectively, as follows (see Figure 7a-c),  
\[E_{n}=\left(1-D_{n}^{\text{E}}\right)E_{0}=\left(1-\frac{D_{n}^{\text{e}}}{1- D_{n}^{\text{i}}}\right)E_{0}=\frac{1-D_{n}}{1-D_{n}^{\text{i}}}E_{0} \tag{43}\]  
\[\varepsilon_{n}^{\text{di}}=D_{n}^{\text{i}}\varepsilon_{n} \tag{44}\]  
Given that in the case of general engineering the value of the minimum stress \(\sigma_{\text{min}}\) is not equal to zero in concrete (Figure 7d), the residual strain \(\varepsilon_{n}\textsuperscript{r}\) related to the fatigue behaviors is distinguished from the irreversible strain \(\varepsilon_{n}\textsuperscript{di}\) by the following definition:  
\[\varepsilon_{n}^{\text{r}}=\ \varepsilon_{n}-\frac{\sigma_{\text{max}}-\sigma_{ \text{min}}}{E_{n}} \tag{45}\]  
\[\varepsilon_{n}^{\text{di}}=\ \varepsilon_{n}-\frac{\sigma_{\text{max}}}{E_{n}} \tag{46}\]  
which are obtained by the equations (Figure 7), respectively, as follows,  
\[E_{n}=\ \frac{\sigma_{\text{max}}-\sigma_{\text{min}}}{\varepsilon_{n}- \varepsilon_{n}^{\text{r}}} \tag{47}\]  
\[E_{n}=\ \frac{\sigma_{\text{max}}}{\varepsilon_{n}-\varepsilon_{n}^{\text{di}}} \tag{48}\]  
Note that little research [10; 11; 12; 41; 42; 43; 44; 45; 46; 47; 48; 49] has considered the difference between the residual strains and the irreversible strains, however, this difference is essential for characterizing the fatigue behaviors of concrete. Specifically, Equations (45) and (46) and Figure 7 illustrate that the value of the residual strains is usually higher than that of the irreversible strains. Additionally, with Equations (9), (10), (14), (15), (17)-(19), (21), (22), (29), (30), (38) and (39) and Figure 7, it is able to correlate the mechanical parameters and damage variables to the thermodynamics parameters as follows:  
\[\frac{\varepsilon_{n}^{\text{de}}}{\varepsilon_{n}^{\text{di}}}=\frac{ \sigma_{n}^{\text{de}}}{\sigma_{n}^{\text{di}}}=\frac{D_{n}^{\text{e}}}{D_{n}^ {\text{i}}}=\frac{\gamma_{n}^{\text{de}}+\psi_{n}^{\text{de}}}{\gamma_{n}^{ \text{di}}}=\frac{\lambda_{n}^{\text{de}}}{\lambda_{n}^{\text{di}}} \tag{49}\]  
Equation (49) shows that the two components of fatigue damage variable (\(D_{n}\textsuperscript{e}\) and \(D_{n}\textsuperscript{i}\)) are able to be correlated to the strain and stress decomposition and the energy dissipation, i.e., the energy (\(\gamma_{n}\textsuperscript{de}\) + \(\psi_{n}\textsuperscript{de}\)) and \(\gamma_{n}\textsuperscript{de}\), \(\lambda_{n}\textsuperscript{de}\) and \(\lambda_{n}\textsuperscript{di}\), respectively. Therefore, Equation (49) illustrates the thermodynamics interpretation of the newly defined damage variables.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 3 Analytical Model  
In this section, the analytical model developed for magnetic force computation is described. The proposed analytical model is developed in four steps by means of a Fourier series. In the first two steps, the definition of the flux density created by magnets and the coils of the stator in the air gap of a slotless machine is addressed. Next, the effect of the slots is included. Finally, in the fourth stage, two flux densities (flux densities of the magnets and armature) are superimposed to obtain the overall flux density in the air gap under load conditions. In addition, to ease the mathematical development, a section to explain the complex Fourier series employed is included.
# Fronthaul Design for Wireless Networks
## 3 Fronthaul Design Methodology
### Network Planning Algorithm  
The network planning algorithm has the goal of finding the optimal number of BBUs to be deployed for a certain environment and where to place them, given the positions of the RRHs and their bit rate needs. Using the previously described link design algorithm and considering the costs associated with a BBU, this algorithm is able to compute fronthaul topologies that minimize the total costs of the network.  
The following subsection details the network planning algorithm, namely the considered economic aspects and the algorithm workflow.
## 3 Proposed System Model
### Selection and Application Methods of Machine Learning Algorithms
#### 3.3.1 Logistic Regression (LR) Algorithm  
Logistic Regression is used for binary classification to predict the probability of class membership, using the sigmoid function to convert data into probabilities between 0 and 1. Parameters are optimized through maximum likelihood estimation or gradient descent, allowing for the interpretation of independent variables' effects on the target variable [54]. It is efficient for small to medium datasets and works well with linearly separable data, though overfitting can occur with nonlinear relationships or many variables. Regularization and hyperparameter tuning are key for improved performance [55].
# CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization
## 2 Related Work
### Cold Start Recommendation  
The cold start problem has been studied in the recommender system community for many years. The main issue of this problem is that there is insufficient information for making recommendations. According to the literature [19], existing research studies can be categorized into two groups, i.e., the explicit solutions and implicit solutions. Next, we present the related works.  
For explicit solutions [20; 21; 22], the recommender system directly interacts with users or experts to collect the information. The user is either asked to fill some questionnaire or rate some products. To select the appropriate questions, active learning-based methods [20; 22; 23] are proposed to collect adequate information without overwhelming users. Apart from the active learning approaches, using an interview is another method for obtaining user preferences. An item set is employed to test the preferences of users. In each round, one item is selected to display and collect the response. There can be three types of responses: like, dislike, and unknown [21]. Although explicit solutions are efficient, users are often reluctant to participate in the query process, which limits their usage.  
Another group of cold start recommendation methods includes implicit methods [6; 24; 25; 26; 27; 28]. These methods try to understand new user preferences via minimum (or only once) interactions. Existing user information such as demographics or social relationships is employed to build the cold start recommender system. Based on the usage of external information, existing solutions in this group can be divided into user-demographic-based approaches [24] and social-relationship-based approaches [25]. Classification methods [26; 27] are usually adopted to identify the character of users based on their demographics. Then, users having similar characteristics are usually recommended equally. To integrate the social relationship, many graph-based techniques [29; 30] are proposed to capture the user's similarity. However, finding useful information is challenging in these methods.  
In loan recommendations, explicit solutions are not appropriate due to the high volume and diversity of applicants. The proposed method CSRLoan falls in the implicit group. To better model the semantic information, CSRLoan employs pre-training techniques and captures the influence of both default risk and conversion rate.
## 3 System Model
### Identifiability Conditions
If the channel matrix \(\mathbf{H}\) does not have a full column or row rank, i.e., \(L<\min\left(M_{S},M_{D}\right)\), where \(L\) is the rank of the channel matrix \(\mathbf{H}\). Thus, identifiability conditions of Theorem 1 are no longer applicable because of the low-rank property of \(\mathbf{H}\). However, we can also deduce identifiability conditions based on Equations (21) and (22), i.e., necessary and sufficient conditions for identifiability of \(\mathbf{S}\) and \(\mathbf{H}\) requires that \(\left(\mathbf{I}_{P}\otimes\mathbf{H}\right)\mathbf{F}_{1}\) and \(\left(\mathbf{I}_{P}\otimes\mathbf{S}\right)\mathbf{F}_{2}\) have full column rank. For this case, we will do further analysis in Section 5._
## 3 Results and Discussion  
### Landscape Evolution for the Glape Catchment Area  
Figure 8 shows the evolution of the landscape structure of the Glape catchment. In 1947, the proportion of cultivated land and grassland was roughly equal (46% cultivated land and 43% grassland). The average size of cultivated plots was 1 ha. Apart from the proximity of inhabited areas (houses and gardens), hedges did not exist in this open field landscape, typical of the silty plateaus of northern France.  
Figure 7: Connectivity indicators applied to the Glape basin.  
In 1994, the area devoted to agricultural activities increased significantly (65% in 1994, +19 points). Among these crops, a few plots were set up on long-established meadows. The average size of the plots was now 1.6 ha. Grassland now represented only 23% of the area of the basin. There was also a slight increase in urbanised areas, especially downstream from the catchment area.  
In 2015, the evolution of land use was still very marked, with 73% of land under cultivation and a net decrease in grassland (13%). The average size of agricultural plots was increasing (equal to 2.8 ha, i.e., three times larger than the 1947 plots).  
As far as urban expansion is concerned, although the surface area of urbanised areas had not increased significantly (1% of the total surface area of the basin, i.e., almost the same proportion as in 1994), the number of dwellings had increased considerably. Moreover, these newly built dwellings were located downstream from agricultural plots likely to produce runoff.  
We are therefore witnessing not only an evolution of the landscape that favours the production of runoff (increase in cultivated areas), but also an increase in the vulnerability of the system by an overexposure of stakeholder infrastructure (construction of new dwellings in areas exposed to hazards, downstream from the agricultural plots).  
The analysis did not take into account the diachronic evolution of linear features (hedges, ditches, etc.) because the basin under study is an open field landscape, occupied by large-scale cultivation and where the density of these features is very low or even absent.
## 2 Materials and Methods
### Experimental Set-Up  
#### 2.2.1 Batch Kinetic Tests  
Batch kinetic tests were conducted to calibrate the model parameters. Artificial seawater (a 3% sea salt solution; this is the same salinity as the West Sea) with different TOC values (TOC 1, 2, and 3 mg L\({}^{-1}\)) was prepared by adding 15 g of sea salt (Sigma-Aldrich, St. Louis, MO, USA) to 500 mL of distilled water. The TOC of the synthesized seawater was adjusted by adding humic acids solution. The solution was prepared by dissolving 116.5 mg of humic acid powder (Sigma-Aldrich, St. Louis, MO, USA) into 1 L of distilled water. To force the humic acids to dissolve in water, the water pH was adjusted to 10 by adding 0.1 M NaOH. Then, the pH was readjusted to 7 using 0.1 M HCl. Then, the solution was sonicated for 30 min and filtered through a 0.2 \(\upmu\)m polytetrafluoroethylene-membrane filter. The synthesized seawater was stored for 12 h at room temperature (18-20 \({}^{\circ}\)C) before use in experiments.  
A real seawater sample was taken from an area on the coast of the West Sea (Incheon, Korea). The collected seawater was filtered through a slow sand filter and subjected to UV irradiation for disinfection. The seawater characteristics were as follows: salinity: 2.83%, pH: 7.89, specific gravity: 1.023, TOC: 1.3 mg L\({}^{-1}\).  
To generate electrochemically-produced chlorine, two cylindrical electrodes were used in this study (Figure 1). A RuO\({}_{2}\) electrode was used as an outer cathode and a Ti electrode was used as an inner anode. The RuO\({}_{2}\) and Ti electrodes had diameters of 100 and 70 mm, respectively. Both electrodes were 5 mm thick and 50 mm long as shown in Figure 1a. To quantify the amount of free chlorine produced by the RuO\({}_{2}\)/Ti electrodes, an experiment was performed using the West Sea water. First, 20 L seawater were placed in a 40 L bucket as shown in Figure 1b. The RuO\({}_{2}\)/Ti electrodes were then installed in the middle of the bucket. Four different electric currents (i.e., 10, 15, 20, and 25 A) supplied by a direct current (DC) power supply (TDP-3030B, Toyotech Co., Incheon, Korea) were tested. While the current was supplied to the electrodes, the seawater was completely mixed using an agitator. Water samples were collected from the bucket at a given time interval.
## 3 Data and Methodology
### Land Cover  
The CORINE (COoRdination of INformation on the Environment) Land Cover inventory, based on satellite imagery classification and photointerpretation, is one of the basic pan-European datasets developed by the Copernicus Land Monitoring Service; it was initiated in 1985 and constantly updated from 1990 to 2018 [110; 111]. The CORINE Coastal Zones Land Cover (CZLC) product used in this study (see the official guidelines  
Figure 2: European Ground Motion data. (**Top left**) Available and released by the Copernicus Land Monitoring Service. (**Top right**) Interpolated EGMS data over the entire study area of Rome using the GRASS r.fillnulls algorithm [108]. (**Bottom**) Comparison of measured and interpolated displacement along a transect.  
\"CZ Nomenclature Guideline, Issue: 1.2\" for details, [112]) covers a 10 km inland buffer zone along the European coasts (a total area of about 715.000 Km2) with high resolution (70 \(\times\) 70 m), providing detailed satellite-derived information about the different types of coastal land cover and usage. The product is available as a vector file in Lambert Azimuthal Equal Area, ETRS89-extended/LAEA Europe--EPSG:3035 geographic projection (m).  
The dataset has changed several times over the decades; here, we use the version resulting from the last major update, in 2018, that now differentiates 71 thematic LC/LU classes. Five levels of category are possible. In particular, we refer to level one (of five) where eight categories of cover can be identified as follows: (1) Urban; (2) Cropland; (3) Woodland and Forest; (4) Grassland; (5) Heathland and Scribs; (6) Open Space with Little or No Vegetation; (7) Wetland; and (8) Water ([PERSON] et al., 2021 and references therein; Figure 3).
# Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities
## 4 Data Entities' Linguistic Patterns and Linguistic Styles
### Data Attribute, DataPrimitiveType and DataEnumeration
#### 4.3.3 Examples  
From the analysis of the BillingSystem example and the general understanding of its domain, the VAT and Product data entities can be represented now in a complete way, by expressing their data constraints (text marked with lightgray), as follows with the linguistic styles defined above.  
With the Style of the CNL-A:  
*_VAT is a Reference DataEntity: VATCode ([]), VATName (NotNull), VATValue (NotNull).
*_Producti s a Master DataEntity: ID ([]), Name ([]), VATCode (NotNull) (e_VAT)), VATValue (NotNull Derived), valueWithoutVAT (NotNull), valueWithVAT (NotNull Derived), size.  
With the Style of the CNL-B:  
DataEntity_VAT (VAT Category) is a Reference  attribute VATCode \"VAT Code\" is a Integer (PrimaryKey),  attribute Rate \"Rate\" (Rate\" ) is a DataEnumeration VATRateKind,  attribute VATName \"VAT Class Name\" is a String(30) (NotNull),  attribute VATValue \"VAT Class Value\" is a Decimal(2.1) (NotNull).  
DataEntity_Product (Product) is a Master  attribute ID \"Product ID\" is a Integer (PrimaryKey)  attribute Name \"Name\" is a String(50) (multiplicity\"1.2\"),  attribute VATCode \"VAT Code\" is a Integer (NotNull ForeignKey (e_VAT)  onDelete PROTECT),  attribute VATValue\" VAT Value\" is a Decimal(2.2) (NotNull Derived) (\"e_VAT.VATValue\")) attribute valueWithoutVAT \"Price Without VAT\" is a Decimal(16.2) (NotNull),  attribute valueWithVAT \"Price With VAT\" is a Decimal(16.2) (NotNull Derived) (\"Self.valueWithoutVAT * (1 + Self.valueValue)\"),  attribute size is a DataEnumeration SizeKind.
## 4 Experimental Studies
### Simulation of the DWTA Problem
#### 4.2.2 Integer Encoding Method  
The allocation scheme obtained by IMOPSO algorithm in solving multiobjective DWTA can be obtained by decision matrix. To complete this process, we need to encode the particles. In order to obtain an efficient WTA scheme, the coding method can not only meet the solution of the model, but also meet the constraint requirements. Moreover, the encoding length should be made as small as possible. Based on the above conditions, this paper adopts integer encoding method. Take the following scenario (\(W=5\), \(T=6\), \(S=1\)) as an example to illustrate the coding method shown in Figure 4, where X is the corresponding decision matrix.  
The decision matrix corresponding to the coding scheme is as follows:  
\[X=\left[\begin{array}{cccccc}0&0&0&0&1&0\\ 1&0&0&0&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&0&1\\ 0&0&1&0&0&0\end{array}\right]\]