esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, few studies have used additional information, such as the incidence relation and the shared resources between slot-filling and intent-detection modules. However, the above-mentioned studies contain a typical issue: that is, the high-quality and standardized public datasets are highly inadequate because labeling and collecting datasets require more effort and time [12]. Thus, researchers have tried to include an external knowledge base in the prevailing datasets so as to solve the issue of data scarcity. A knowledge base can be engineered into a knowledge cluster, and thus it becomes structured, easy to use and operate, and provides comprehensive and organized knowledge [13]. Moreover, it is a set of interrelated knowledge pieces that are managed, stored, used, and organized in computer memory by some knowledge-representation mode to solve issues in certain domains. Recent research studies have shown that the impact of NLU is enhanced by the presentation of a knowledge base. This article proposes Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for NLU. The CL-DLBIDC technique receives word-embedding as input and uses learned meaningful features to determine the probable intention of the user query. In addition, the CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm has been used in this study. The experimental analysis of the CL-DLBIDC technique took place under a set of simulations, and distinct aspects of the results have been examined.
# A Road Defect Detection System Using Smartphones
## 2 Related Work
### Vibration Sensor-Based Deep Learning Method  
To accurately detect abnormalities on road surfaces, a new approach is proposed, incorporating various deep learning models, including CNN, Long Short-Term Memory (LSTM) networks, and reservoir computing models. This method utilizes crowdsourced data to distinguish potholes from other road instabilities, achieving high accuracy in real experiments [35]. Three deep learning models--the Deep Feedforward Network(DFN), CNN, and Recurrent Neural Network (RNN)--are applied. These models are trained and evaluated using data from various road anomalies collected from vehicles. Additionally, three sets of numerical features are proposed to represent road conditions, and a comparative study of the performance of each deep learning model is conducted [36]. In contrast, there is a paper that compares machine learning and deep learning approaches for detecting anomalies on road surfaces. It focuses on classifying three major road conditions: smooth roads, potholes, and deep transverse cracks. The hypothesis that using features extracted from all sensor axes provides superior classification results compared to using a single axis is tested. The performance of machine learning models, including deep neural networks, is also evaluated, confirming the effective classification of road conditions without manual feature extraction [37]. They demonstrate road surface anomaly detection and classification using crowdsourced smartphone sensor data. This research approaches road condition classification through multi-layered activities that classify road types and anomalies using a Spiking Neural Network (SNN) learning model [38].  
In this paper, the goal is to design an automatic collection system for road defect training data that is more easily collected to solve the difficulty of collecting training data, which is a problem with deep learning methods.
## 2 Network Model and Optimal Cluster Number Calculation  
In this section, we propose a new network structure model and quote the energy consumption model proposed in [12], then suggest a new method to determine the optimal cluster number.
# Influence of Hydrothermal Carbonization on Catalytic Fast Pyrolysis of Agricultural Biomass
## 2 Materials and Methods
### Analyses and Calculations  
Reaction rate constants were obtained by fitting mass loss history to the function described by Equation (1), as follows [72]:  
\[\frac{d\alpha}{dt}=k\cdot(1-\alpha)^{n} \tag{1}\]  
where:  
\(\alpha\)--extent of reaction;  
\(t\)--reaction time, s;  
\(k\)--reaction rate constant, 1/s;  
\(n\)--order of the reaction.  
The extent of the reaction is dimensionless and defined according to the formula [72]:  
\[\alpha(t)=\frac{m_{0}-m}{m_{0}-m_{f}} \tag{2}\]  
where:  
\(m_{0}\)--initial solid mass, kg;  
\(m\)--mass at given time \(t\), kg;  
\(m_{f}\)--solid mass after no more mass loss is observable (mass of char), kg.  
Fitting was performed for \(\alpha\) ranging between 0.25 and 0.7, assuming the order of reaction be equal to one. Subsequently, the kinetic triplet was determined using the Arrhenius plot, assuming that the global kinetic constant follows the Arrhenius rate law [72,75]:  
\[k=A\cdot e^{(-\frac{E_{\alpha}}{k\cdot t})} \tag{3}\]  
where:  
\(k\)--reaction rate constant, 1/s;  
\(E_{\alpha}\)--activation energy, J/mol;  
\(R\)--universal gas constant, J/mol\(\cdot\)K;  
\(T\)--temperature, K.  
The mass yield (\(Y_{m}\)) and energy yield (\(Y_{e}\)) are typically used for the assessment of the performance of thermal valorization processes [76,77]. \(Y_{m}\) was assessed using a direct method according to the following equation:  
\[Y_{m}=\frac{m_{prod-dry}}{m_{feedst-dry}} \tag{4}\]  
where:  
\(m_{prod-dry}\)--dry mass of the product (hydrochar);  
\(m_{feedst-dry}\)--dry mass of the feedstock.  
The energy yield was calculated using following equation [78, 79, 80]:  
\[Y_{e}=Y_{m}\cdot\frac{HHV_{product}}{HHV_{feedstock}} \tag{5}\]  
where:  
\(Y_{e}\)--energy yield, -.;  
\(HHV\)--higher heating value, MJ/kg.  
Ash yield, as defined by [PERSON] et al. [32], has been used for assessing the fraction of inorganics that remained in hydrochars after the HTC process, with respect to the inorganic fraction of the feedstock:  
\[Y_{a}=Y_{m}.\frac{A_{product}}{A_{ feedstock}} \tag{6}\]  
where:  
\(Y_{a}\)--ash yield, -.;  
\(A\)--ash content, dry basis, %\({}_{\text{db}}\).
## 3 System Model  
Consider a multiple-antenna system with \(M_{S}\) transmit antennas and \(M_{D}\) receive antennas as shown in Figure 1. \(h_{m_{D},m_{S}}\) represents the channel coefficient between the \(m_{S}\)-th transmit antenna and the \(m_{D}\)-th receive antenna ( \(m_{S}=1,\ldots,M_{S}\), \(m_{D}=1,\ldots,M_{D}\)). \(s_{n,r}\) represents the \(n\)-th symbol of the \(r\)-th data stream (\(n=1,\ldots,N\), \(r=1,\ldots,R\)), with each data stream being formed of \(N\) information symbols. Each symbol \(s_{n,r}\) is coded by a three-dimensional space-time code \(b_{m_{S},r,p}\) (\(p=1,\ldots,P\)), whose dimensions are the numbers of transmit antennas, data streams, and chips, respectively. We then define the antenna-to-slot allocation factor \(q_{p,m_{S}}\), which is 0 or 1. Both the transmitter and the receiver know these factors \(b_{m_{S},r,p}\) and \(q_{p,m_{S}}\).  
The signal transmitted from \(m_{S}\)-th transmit antenna, during the \(n\)-th symbol period of the \(p\)-th chip, is given by:  
\[x_{m_{S},n,p}=\sum_{r=1}^{R}q_{p,m_{S}}b_{m_{S},r,p}s_{n,r} \tag{6}\]  
where \(s_{n,r}\) and \(q_{p,m_{S}}\) are \((n\), \(r)\)-th and \((p\), \(m_{S})\)-th elements of signal matrix \(\mathbf{S}\in\mathbb{C}^{N\times R}\) and the antenna-to-slot allocation matrix \(\mathbf{Q}\in\mathbb{C}^{P\times M_{S}}\), respectively. \(x_{m_{S},n,p}\) and \(b_{m_{S},r,p}\) are typical elements of the transmitted signal tensor \(\underline{\mathbf{X}}\in\mathbb{C}^{M_{S}\times N\times P}\) and the coding tensor \(\underline{\mathbf{B}}\in\mathbb{C}^{M_{S}\times R\times P}\), respectively. The elements in \(\underline{\mathbf{B}}\) are chosen as \(e^{\sqrt{-\mathrm{T}_{\mathrm{G}}}/2\pi}\), where \(\varsigma\) is taken from random uniformly distributed pseudorandom numbers. In our tensor coding scheme, the number of transmitted data streams is not restricted to be equal to that of transmit antennas, and the data streams can be allocated to an arbitrary set of transmitted antennas. Without considering the allocation of stream-to-slot, the coding scheme in [21] can be regarded as a special case of our tensor coding scheme with a fixed two-dimensional space-time code.  
Figure 1: Block-diagram of the system model.  
Assuming Rayleigh flat fading channels, then the discrete-time baseband signal at the \(m_{D}\)-th receive antenna can be written as:  
\[\begin{split} y_{m_{D},n,p}&=\sum_{m_{S}=1}^{M_{S}}h_{ m_{D},m_{S}}x_{m_{S},n,p}+v_{m_{D},n,p}\\ &=\sum_{m_{S}=1}^{M_{S}}\sum_{r=1}^{R}h_{m_{D},m_{S}}q_{p,m_{S}}b_ {m_{S},r,p}s_{n,r}+v_{m_{D},n,p}\end{split} \tag{7}\]  
where \(h_{m_{D},m_{S}}\) is the \((m_{D},\,m_{S})\)-th element of channel matrix \(\mathbf{H}\in\mathbb{C}^{M_{D}\times M_{S}}\), \(y_{m_{D},n,p}\) and \(v_{m_{D},n,p}\) are typical elements of the received signal tensor \(\underline{\mathbf{Y}}\in\mathbb{C}^{M_{D}\times N\times P}\) and the noise tensor \(\underline{\mathbf{V}}\in\mathbb{C}^{M_{D}\times N\times P}\), respectively.
# Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
###### Abstract  
Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.  
multi-objective optimization; profit scoring; feature selection; credit evaluation +
Footnote †: journal: _Algorithms_
## 4 Discussion
The efficacy of artificial aging can be leveraged to assess the color stability of maxillofacial materials in a prompt, efficient, and proficient manner. There has been a suggestion that the stability of color in silicone, whether artificially aged or not, may be compromised when nanoparticles (NPs) are present [42,50,53]. Ultraviolet (UV) light, which falls within a narrow segment of the electromagnetic spectrum that is not visible to the human eye, is known to primarily induce detrimental effects on colorants and polymers. The excitation of electrons within the nanoparticles of a given medium occurs as a result of their induced vibrational motion upon exposure to ultraviolet radiation. Due to the comparatively smaller sizes of nanoparticles in relation to ultraviolet light wavelengths, a portion of the incident ultraviolet light is scattered while another portion is concurrently absorbed [54]. In accordance with these fundamental principles, UV protection arises as a result of the absorption and scattering of nanoparticles. Due to their nanoparticle nature and ability to scatter and absorb UV radiation, AgNPs have the potential to offer UV protection that is comparable to existing methods. The present study employed a vacuum mixer to achieve uniform dispersion of silver nanoparticles and silicone pigments within the silicone elastomer matrix. This process led to a significant decrease in the extent of color alteration observed in the silicone material. A higher degree of ultraviolet (UV) shielding can be attained with the reduction in the size of nano-oxide particles. Hence, it can be inferred that the utilization of silver nanoparticles has the potential to maintain the color integrity of various materials. There were particular concerns regarding this in vitro study that also need to be considered. The study employed a low concentration of silver nanoparticles (AgNPs) and only one type of silicone elastomer. Rather than relying on outdoor aging to assess color stability, this study used artificial aging over only 500 h. To determine how AgNPs impact the stability of various colored elastomers used in the production of maxillofacial prosthetic silicone in clinical practice requires more investigation. It is necessary to investigate the impact of varying concentrations of AgNPs on the color stability of A-2186 and other maxillofacial silicones. The impact of AgNPs on the color stability of maxillofacial silicone during exposure to natural outdoor weathering must be evaluated. Additionally, the range and duration of artificial aging essential for color change must be assessed.
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 3 Main Factors That Affect the Permeation and Absorption of Solutes in Hair  
### Hair  
#### 3.1.1 Geometry of Hair  
Hair is a biological tissue composed of a variety of biological macromolecules and can be considered to be a heterogeneous porous medium [27]. Some researchers have proposed using porous media theory to describe the permeation properties of solutes in hair [27; 28; 44]. [PERSON] et al. [27] studied the diffusion characteristics of solutes in hair using positron annihilation lifetime spectroscopy, and they found that the free-volume channels in hair fibers are the main pathway for solute diffusion. [PERSON] et al. [28] proposed that in the porous structure of hair, the size, shape, and density of the pores all affect the permeation and absorption properties of solutes in hair. Molecules smaller than the pore size are easily transmitted, whereas molecules larger than the pore size are not transmitted. The permeation distance of solutes in damaged hair is generally higher because the pore size of the damaged hair becomes larger, making it easier for solutes to enter the hair.  
Solutes penetrate into hair mainly through two channels: transcellular channels and intercellular channels [11; 45]. The transcellular channel refers to the permeation of solutes through cuticle cells, which have a dense protein structure and a high degree of crosslinking, thus limiting the permeation of large solutes [1]. The intercellular channel refers to permeation through the glial structures (endocuticle and CMC) between the cuticle cells. As the components of the glial structures are mainly fats and low-sulfur proteins,which are prone to swelling, they are the preferred channel for lipophilic and non-polar solutes [1; 11; 38]. [PERSON] [46] found that hydrophilic substances can enter all of the structures in hair. The CMC can be considered to be a small compartment with the ability to swell and direct the solute from the hair surface to the cortex through capillary forces. It is believed that the outermost part of the cuticle is an open compartment structure, which explains the phenomenon of the rapid loss of dye in the rinsing process after hair dyeing, and it is inferred that the solute enters the cuticle from the external environment through the endocuticle. [PERSON] et al. [38] also found that the endocuticle and CMC are the main permeation pathways for solutes in hair.
## 3 Data and Methods  
### Debris Flow Inventory  
The spatial dataset of debris flow hazards utilized in this study was sourced from the Resources and Environmental Sciences Data Center of the Chinese Academy of Sciences ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)), accessed on 2 May 2025). The data are provided in vector SHAPE file format, with attribute fields encompassing hazard type, geographic coordinates, morphological characteristics, and magnitude classifications. Through integration with field-based investigations, a total of 44 debris flow hazard points were confirmed within the Songpan study area. Concurrently, a multi-source spatial dataset from the same platform was compiled, encompassing the ALOS Digital Elevation Model (DEM), road network vector data, land use raster data, geological fault line vectors, seismic hazard point distributions, annual precipitation raster data, soil erosion type and intensity classification rasters, Normalized Difference Vegetation Index (NDVI) raster data, and population spatial distribution. To ensure spatial consistency for modeling, all raster datasets were resampled to a uniform 12.5 m resolution. For detailed information on the dataset types and resolutions, refer to Table 1.  
Figure 2: Characteristics of debris flow observed in the field investigation. (**a**) Steep slope. (**b**) Upper slope slump. (**c**) Slump at foot of slope. (**d**) Scattered boulders. (**e**) Human engineering activities. (**f**) Waste disposal site. (**g**) Siltation behind the dam. (**h**) Historic debris flow events. (**i**) Local mudslide gully.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 4 The Use of EPR in Passive Tumor Targeting with Nanomedicines
### Biological Barriers Encountered by Nanoparticles  
Due to the unique biological characteristics of the vascular system in rapidly growing solid tumors, the accumulation of nanocariers occurs to a satisfactory extent due to enhanced vascular permeability and defective lymphatic drainage; however, the extent of this accumulation varies between patients and tumor types [125; 128]. Additionally, depending on their route of administration, nanocarriers encounter various biological barriers within the body during their journey from the bloodstream to their extravasation into target tissues, as they interact with the body's biological components and are recognized as foreign materials by the body [129]. These barriers include the interaction of nanocarriers with blood cells and proteins; immunoclearance, primarily in the liver and spleen; extravasation through the endothelium of blood vessels; penetration into tissue; internalization by target cells; and, when necessary, diffusion through the cytoplasm to enter organelles [128; 129]. For example, a key barrier that a nanocarriers in the tumor microenvironment is the extracellular matrix, which determines the plasticity of the tumor and, consequently, its progression, metastasis, and invasion; the richer the matrix components, the more difficult it is to deliver nanomedicines to the tissues [5; 130]. Even if nanomedicines approach the tumor's blood vessels, their penetration into the interior is slow due to poor blood flow in the blood vessels, which results from irregular angiogenesis [5; 8; 82]. Additionally, the tumor microenvironment includes immune cells such as macrophages, which rapidly detect and remove nanoparticles through phagocytosis [5; 131].  
As a result of the obstacles that nanocarriers encounter during their journey within the body, their pharmacokinetics are affected, leading to the ineffective delivery of their payloads to target tissues [128; 129].  
To achieve the region-specific delivery of nanocarriers, it is important to exploit both the pathophysiological conditions of the body and the unique characteristics of tumors, as well as the biophysical and chemical interactions between the carrier and the nanoparticle, including the surface and physicochemical properties of the nanocar [129].
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 2 Materials and Methods
### Including a Noise Component in Gaussian Mixtures  
In the finite mixture framework, noisy data are characterized by the presence of outlying observations that do not belong to any mixture component. A strategy for accommodating noise is to include a uniform component with support in the convex hull of the data [7] resulting in the following mixture log-likelihood:  
\[\ell(\theta,\pi_{0})=\sum_{i=1}^{n}\log\left\{\frac{\pi_{0}}{V}+\sum_{k=1}^{ G}\pi_{k}\phi(\mathbf{x}_{i}\mid\mu_{k},\Sigma_{k})\right\}, \tag{5}\]  
where \(V\) is the hyper-volume of the data region and \(\pi_{0}\) is the mixing weight associated with the noise component, with the mixing weights under the constraint \(\sum_{k=0}^{G}\pi_{k}=1\). An observation contributes \(V^{-1}\) to the likelihood if it belongs to the noise component; otherwise, its contribution comes from the Gaussian components.  
The effectiveness of this approach hinges on obtaining an estimate of the hyper-volume and a good initial specification of the noise to be used for starting the EM algorithm. The estimate of the hyper-volume can be computed using different approaches, such as the following:  
1. The volume of the convex hull, i.e., the smallest convex polygon that contains all the data points;
2. The volume of the ellipsoid hull, i.e., the ellipsoid of minimal volume such that all observed points lie either inside or on the boundary of the ellipsoid;
3. The volume computed as the minimum between the hyper-rectangle containing the observed data and the box obtained from principal components.  
The first option would be the most accurate, but computing the convex hull of high-dimensional data can be challenging, as the number of vertices of the hull grows exponentially with the number of dimensions. The second option is computationally feasible even in high dimensions, but it is often inaccurate, as the volume of the ellipsoid hull can be significantly larger than the volume of the convex hull. A simple and fast approximation to the hyper-volume of the data can be computed using the last option. In fact, the hyper-rectangle is the simplest bounding box that can be used to enclose the data, while the box obtained from the principal components is a more sophisticated method that takes into account the shape and orientation of the data. By taking the minimum between these two volumes, an estimate of the volume of the convex hull can be obtained. Note that the function hypvol() of R package mclust uses this approach by default.  
Regarding the initial denoising, some possible strategies include methods based on Voronoi tessellation [25], nearest neighbors [26], and robust covariance estimation [27]. In this paper we propose the use of data points' contribution to the entropy of the estimated GMM to obtain an initial specification of noisy and outlying data points.