esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 5 Results
### Component Selection Validation  
Component selection validation accuracy for both experienced and inexperienced subjects was measured, and the results are hereby presented. Results include whether subjects selected the correct component or not, and then how the application validated the selection (correct/incorrect selection). Figure 13 shows the results for the component selection validation. All participants selected the correct component at every repetition. For the expert user, the application was able to validate that the correct component had been selected 79 out of 80 times. The accuracy for the inexperienced subjects was lower: the application validated correct component selection 56 times, 4 times it mistakenly interpreted as the incorrect component had been selected, and 20 times it was not able to validate the component selection (for various reasons including bad positioning of the camera, bad lighting, and so on).
## 4 Discussion
### Influence of the Input Length  
In the GRUD-WGAN model, the GRUD, which is the core component of both the generator and discriminator, receives sequences of length \(l\) and mines the temporal dependencies in the inputs. Hence, the parameter \(l\) determines the width of the receptive field of the GRUD model. Figure 20 shows the influence of different input length \(l\) on the GRUD-WGAN model at various missing rates. Obviously, when the missing rate is low (\(r=0.1\)), the filling error decreases with the increase in \(l\), because longer input sequences contain more valid information. However, when the missing rate increases (\(r=0.3,\ 0.5,\ 0.7\)), the imputation accuracy of the GRUD-WGAN increases first and then decreases with the increase in \(l\), reaching the minimum value at \(l=20\). This is because the input of too many missing values affects the learning of potential patterns. It is also worth noting that the training time cost rises with the increase in \(l\). Hence, in order to improve the accuracy and efficiency of the model, it is critical to select the appropriate input length \(l\).
## 4 Discussion
### Combination
#### 4.4.3 Synergistic Effect of Combination  
A combination of paclitaxel + benztropine demonstrated synergism (CI < 1) in both combinations tested (Figure 19). These drugs work together to produce an effect more potent on MCF-7 cells. On the contrary, the combination of paclitaxel + thioridazine resulted in a synergistic (CI < 1) and antagonist effect (CI > 1). The combination with the lowest thioridazine concentration, corresponding to its IC50, resulted in a synergistic effect (Figure 19). Using thioridazine at 100 nM concentration resulted in an antagonist  
Figure 22: Schematic representation of the combination model used to assess the efficacies of paclitaxel (Drug A) and the repurposed drugs tested (Drug B).  
effect. It is interesting to see this duality with thioridazine since it indicates that at lower doses, the mechanism of paclitaxel is potentiated, without the need to overly increase thioridazine's concentration.  
This study suggests that the selected repurposed drugs have anticancer activity, by decreasing cell proliferation. Synergism was evaluated using the Chou-Talalay method based on the median-effect equation derived from the mass-action law principle [41]. This unified theory encompasses the Michaelis-Menten, Hill, Henderson-Hasselbalch and Scatchard equations in biochemistry and biophysics. It provides a quantitative definition for additive effect (CI = 1), synergism (CI < 1) and antagonism (CI > 1) in drug combinations. Our results demonstrated that benztropine and thioridazine as single agents can decrease cell viability in a concentration-dependent manner, and their combination can improve the anticancer activity of paclitaxel in MCF-7 cells. These results imply that these drugs may be a promising chemosensitizing compound for enhancing paclitaxel's cytotoxic effects. Since benztropine and thioridazine are already on the market, they can be used in clinical practice for anticancer treatment. More research into other breast cancer cell lines is needed because different breast cancer cell lines have different metabolic profiles, phenotypic and genotypic characteristics and drug resistance patterns (such as MDA-MB-231). According to previous research, benztropine could inhibit CSC functions via acetylcholine receptors, dopamine transporters/receptors and/or histamine receptors, which in combination with the paclitaxel capacity of multipolar divisions can be a promising therapeutic option. In the case of thioridazine, it is known to interfere in critical oncogenic pathways, such as the PI3-K/Akt/mTOR pathway, and induce G1 cell cycle arrest, which sensitizes cancer cells by enhancing antineoplastic drugs' activity. Nevertheless, to better understand the anticancer mechanisms underlying these combinations, more mechanistic research is needed. These combinations should also be investigated for other types of cancer, such as prostate cancer.
# Cross-Entropy Method in Application to the SIRC Model
## 1 Introduction
### Importance Sampling  
Therefore, it was decided to apply the importance sampling (IS), which is a technique used for reducing the variance. The system is simulated using other parameter sets (more precisely, a different probability distribution) that helps increase the likelihood of this occurrence. Optimal parameters, however, are very often difficult to obtain. This is where the important advantage of the CE method comes in handy, which is a simple procedure for estimating these optimal parameters using an advanced simulation theory. [PERSON] and [PERSON] [PERSON] and [PERSON] (2008) have used the cross-entropy method as the support of the importance sampling to solve the problem of the optimal epidemic intervention of HIV spread. The idea of his paper will be adopted to treat the Susceptible-Infectious-Recovered-Cross-immune (SIRC) models, which has a wide application to modeling the division of population to four groups of society members based on the resistance to infections (v. modeling of bacterial Meningitis transmission dynamics analyzed by [PERSON] et al. [PERSON] et al. (2018), [PERSON] (2018)). The result will be compared with an analytical solution of [PERSON] et al. [PERSON] et al. (2019).
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 2 Related Work and Methods  
### Related Work  
#### 2.1.1 End-to-End Module for Binocular Stereo Matching  
Conventional stereo matching can be roughly divided into global and local methods [15]. The global method usually solves an optimization problem by minimizing a global objective function that includes data and smoothness terms [16; 17]; the local method only considers neighbor information [18; 19], which is faster than the global method [15; 20]. Although traditional methods have made great progress, they are still affected by challenging situations [21].  
Inspired by FlowNet [22], the DispNet approach proposed by [PERSON] et al. [5] was the first end-to-end convolutional neural stereo matching network. The input binocular stereo are rectified image pairs, and the predicted disparity map is directly output through the designed convolution neural network model. Compared with the traditional methods, DispNet runs faster and has higher accuracy. Compared with the real-time algorithm in [23], the error is reduced by 30%. PSM-Net proposes an end-to-end learning framework for stereo matching without any post-processing, which improves the accuracy by 20% compared with GC-Net. Although end-to-end binocular stereo matching is the focus of the current research, there are still many defects in this method. Because binocular stereo matching aims to obtain depth information by calculating the disparity of two similar pixels [24], the intuitive performance is that the more obvious the feature changes are, the easier it is to find the similarity, so that the accuracy is higher in the region with obvious feature changes. This inherent characteristic also causes the end-to-end stereo matching method to have weak textures, deformation, and object edges prone to error matching [25]. Therefore, this paper explores and proposes solutions to these deficiencies.  
To solve the above issue, this paper proposes fusing multi-resolution features in the feature extraction stage to enrich the feature information of complex regions and to improve the existing matching cost volume model. The matching cost volume is composed of multi-scale features and scale-based inner products to describe the similarity between features. In the cost aggregation part, multi-scale cost aggregation [26] is designed to further the fusion of contextual information for rich details [21].
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
### Related Works
#### 1.1.3 Land Use Mix (LUM)  
Land use mix has been studied in different fields, from transportation to public health and housing market analysis to urban economics. Fundamentally, LUM refers to the mix of varying land-use types present in an area or a building. Having an appropriate level of LUM in an area has many benefits. It can help lower the commuting times and shorten the commute distance [19]. In urban economics, a place with different land types has more potential to raise land values [20]. Additionally, it can encourage people to travel more within an area to discover recreational land-use types such as community centers or parks. These studies demonstrate the importance of LUM in urban areas and the need for effective methods for measuring and characterizing it.  
To measure LUM, a number of metrics have been proposed, such as adjacency, intensity, and proximity, to understand the spatial distribution of land use types in an area [7]. [PERSON] et al. [21] used navigational POIs to develop a series of land use mix indicators to characterize neighborhoods along with users' mobile phone activity in 24 h as a proxy of neighborhood vibrancy. [PERSON] and [PERSON] [22] focused on describing temporal aspects of land use mix and guided land use policies and innovative transportation.  
Furthermore, [PERSON] and [PERSON] [23] presented a study on influence parameters for predicting mixed land use in urban areas. They developed a mathematical model to analyze the relationship between various factors that impact land use patterns, such as zoning regulations, population density, transportation networks, and others. The results showed that this approach could accurately predict the mixture of land uses in a given urban area.  
However, to the best of our knowledge, no study focuses on identifying LUM in urban areas in an efficient and automated way using publicly available data sources. This gap in the literature motivated the current study, which proposes an efficient method utilizing POI-based data and Voronoi triangulation to identify LUM in urban areas.
###### Abstract  
Owing to the rapid development and broad adoption of multiple antenna communication systems over the past few years, space-time block coding (STBC) identification has emerged as a crucial responsibility for smart radios. The majority of previous analysis of STBC identification assumed that the utilized modulation schemes for single-user and uncoded broadcasts were known. This paper investigates the challenge of joint STBC and modulation identification for uplink transmissions with numerous users in single-carrier frequency division multiple access (SC-FDMA) systems using coded transmissions. Multi-user channel estimation brings us one step closer to implementing the proposed design in real-world systems. We additionally employ the channel decoder's deliverables, which are common in many real-world systems, to enhance the identifying and estimating procedures. Mathematical findings prove that a recursive approach can be utilized to tackle the maximum likelihood (ML) problem of simultaneous STBC and modulation identification with channel estimation. Distinguishing the superimposed signals that originate at the base-station (BS) is accomplished with the use of the space-alternating generalized expectation-maximization (SAGE) algorithm. After that, an expectation-maximization (EM) engine is deployed to make the necessary adjustments to the parameters being considered for each user. The success of the above-mentioned architecture for usage in practical applications is demonstrated by the simulation results obtained under various conditions.  
Keywords:signal identification; multiuser transmissions; SAGE procedure +
Footnote †: journal: Applied
## 4 Experimental Work  
### Datasets  
In this study, four distinct datasets were used for the assessment.  
* The WOS-5736 dataset [41] consists of three higher-level classes containing 5736 documents. We converted the dataset into two balanced classes for the empirical evaluation of our proposed approach. The class with the highest number of documents, comprising 2847 abstracts, was chosen as the positive class. The remaining two classes, with 1597 and 1292 abstracts, were combined to create the negative class.
* The LitCovid dataset [42] consists of 16,127 multi-label records across five categories. For our study, we focused on single-label records, resulting in the following distribution: forecasting (119), case-report (1334), prevention (6513), treatment (1632), and mechanism (429). To apply fastNT, we transformed the dataset into a binary class format, considering prevention as the positive class and the remaining categories as the negative class. After that, we balanced the dataset by downsampling the positive class to 3500 records, resulting in a final dataset size of 7014.
* The arXiv paper abstract dataset [43], a multi-label text dataset from Kaggle, was used to create a binary balanced class dataset. The positive class comprised papers in the Computer Vision and Pattern Recognition category, totaling 8822 samples. The negative class was formed by merging the remaining fields, resulting in 8341 samples. To preserve the distribution, we applied stratified sampling for the non-relevant fields.
* The Multi-Label dataset [44] includes 20,972 documents (abstracts and titles) categorized into Quantitative Finance, Quantitative Biology, Physics, Mathematics, Computer Science, and Statistics. For evaluation purposes, we chose 3500 documents labeled only as Computer Science to represent the positive class. We randomly selected 3500 documents without the Computer Science label to form the negative class.  
Figure 8: The working mechanism of the M component (second iteration). The red border covers the topic clusters and their corresponding word lists utilized for the training and testing processes in the specified iteration.
## 4 Experiment
### Comparative Experiments  
To validate the effectiveness of the algorithm, we compared it with several classical and advanced dehazing methods, including DCP, CycleDehaze, FFANet, MSBDN, C2 PNet, and our method. DCP is a classic prior knowledge-based method. CycleDehaze is an improved algorithm based on CycleGAN. A new feature fusion attention module is proposed with FFANet, which combines channel attention and pixel attention for feature weighting. A multi-scale dense feature fusion module is proposed with MSBDN, which is based on combining U-Net with the back-projection feedback scheme in image super-resolution. A physical-aware dual-branch unit is proposed with C2 PNet, which assigns the estimation tasks of transmittance and atmospheric light value to two parallel branches for learning based on the atmospheric scattering model. To qualitatively compare the strengths and weaknesses of these algorithms, a unified dataset and training scheme are used to train the networks.
## 3 Method
### Modeling Land-Use Changes and Assessing Landscape Characteristics
Independent variables that affect land use include soil characteristics, topography (altitude and slope), climate (annual rainfall and mean temperature), and proximity to water (distance to stream), distance from road, total population density (individuals / km2), and agricultural population density (households / km2). The logistic regression was used because it relates the predicted probabilities of the locations of land use (dependent variable) and a set of location characteristics (independent variables) in which the dependent variable is dichotomous or binary (e.g., paddy or not paddy). Spatial policies and restrictions have a direct effect on human use of the environment. In this study, national parks and the WSC 1 are defined as restricted areas under the market-based and conservation scenarios, where additional agricultural development and settlement are prohibited. LU/LC specific conversion rules indicate the annual dynamics of the land-use predictions. The relative elasticity values range from 0 (easy conversion) to 1 (irreversible change). LU/LC conversions were defined in a conversion matrix and cost for investment was included. For instance, human settlement is not likely converted to other classes due to permanent features, but cash crops are easily converted to maize or rubber plantations based on market demand. The local knowledge gathered during the consultation workshop revealed that abandoned areas of shifting cultivation take at least 5 years before regenerating to close crown-cover condition, while newly planted forest and miscellaneous land use will take at least 7 and 10 years. Based on visual image classification by the Royal Forest Department [9], closed crown-cover is categorized as forest although its quality is considerably far from the natural condition [49]. This study also assessed landscape structure and fragmentation using FRAGSTATS (version 4) [50]. The following indices were calculated (1) total area, (2) number of patches, (3) mean patch size, (4) largest patch index, (5) mean core area (area within a fragment located beyond a specified edge distance, 1 km for this study), (6) total core area, and (7) mean nearest neighbor distance index to measure patch isolation.
## 3 Numerical Results
### Values for w(a/2,b/2), \(\sigma_{x}\) vs. \(T\) and \(R_{n}\)  
Figure 3 presents the w(\(a/2\),\(b/2\)) values at \(t=0.1\) s versus \(T\) for 100 K, 600 K, 1000 K, and \(R_{n}\) for 0.1-10 under TSDT with \(c_{1}=0.925925/\)mm\({}^{2}\), \(k_{x}c(t)=0\), \(L/h^{*}=5\), \(\overline{T}_{1}=100\) K, \(\theta_{\rm L}=30^{\circ}\), and \(60^{\circ}\), respectively. The w(\(a/2\),\(b/2\)) values of \(\theta_{\rm L}=30^{\circ}\) are presented, the greatest value on \(T=1000\) K, \(R_{n}=0.2\) for w(\(a/2\),\(b/2\)), is \(-28.84869\) mm. Values of w(\(a/2\),\(b/2\)) decrease vs. \(T\) from 600 K to 1000 K under \(R_{n}=0.5\) and 10; also, the w(\(a/2\),\(b/2\)) can withstand heating deformation under \(T=1000\) K. The w(\(a/2\),\(b/2\)) values of \(\theta_{\rm L}=60^{\circ}\) are also presented, the greatest value on \(T=1000\) K, \(R_{n}=0.2\) for w(\(a/2\),\(b/2\)) is \(-106.298721\) mm. Values of w(\(a/2\),\(b/2\)) decrease vs. \(T\) from 600 K to 1000 K under \(R_{n}=0.1\); also, the w(\(a/2\),\(b/2\)) can withstand heating deformation under higher \(T=1000\) K.  
Figure 4 shows \(\sigma_{x}\) on \(z=0.5h^{*}\) vs. \(T\), \(R_{n}\) at \(t=0.1\) s under the TSDT with \(c_{1}=0.925925/\text{mm}^{2}\), \(k_{c}c(t)=0\), \(\overline{T}_{1}=100\) K, \(L/h^{*}=5\), \(\theta_{\text{L}}=30^{\circ}\), and \(60^{\circ}\), respectively. The \(\sigma_{x}\) values of \(\theta_{\text{L}}=30^{\circ}\) are presented, the greatest value on \(T=1000\) K, \(R_{n}=0.2\) for \(\sigma_{x}\), is \(-2.9435\times 10^{-3}\) GPa. Values of \(\sigma_{x}\) decrease vs. \(T\) from 600 K to 1000 K under \(R_{n}=0.1\), 0.5, 5; also, the values of \(\sigma_{x}\) can withstand heating deformation under \(T=1000\) K. The \(\sigma_{x}\) values of \(\theta_{\text{L}}=60^{\circ}\) are also presented, and the greatest value on \(T=1000\) K, \(R_{n}=0.2\) for \(\sigma_{x}\), is \(-6.4179\times 10^{-3}\) GPa. Values of \(\sigma_{x}\) decrease vs. \(T\) from 600 K to 1000 K under \(R_{n}=0.1\); also, the values of \(\sigma_{x}\) can withstand heating deformation under higher \(T=1000\) K.  
Figure 4: _Cont._
## 2 Channel Measurement and Simulation
### Two-Path Theoretical Model  
Based on the two-path communication model for HSR proposed in [8], the field strength equation for the signal arriving at the seat in a two-path communication scenario can be derived. The two-path model assumes that there are two paths for the signal: one is the direct path, and the other is the path reflected through the ground or obstacles. In this scenario, the expression for the received power can be expressed as:  
\[\begin{array}{c}Pr=Pt+20n\text{log}_{10}\Big{(}\Big{(}\big{(}\frac{\lambda}{4 \pi d}\Big{)}\times\Big{|}1+R_{//}e^{jkr^{\prime}}+(1-R_{//})R_{//}e^{jkr^{ \prime\prime}}\Big{|}\Big{)}\\ \hskip 142.26378 pt+G_{r}+G_{t}-L(\theta_{LOS})-L(\theta_{R})\end{array} \tag{1}\]  
where \(Pt\) is the known to transmit power, \(n\) is the path loss exponent, \(G_{t}\) is the directional gain of the transmit antenna, and \(G_{r}\) is the directional gain of the receive antenna; \(d\) is the distance between the transmitting and receiving antennas; \(r^{\prime}\) is the length of the reflection path; \(r^{\prime\prime}\) is the length of the reflection path in the presence of a second reflection length; \(k=2\pi/\lambda\) is the wave number; \(\lambda\) is the wavelength; \(R_{//}\) is the Fresnel reflection coefficient under parallel polarization with the following equation:  
\[R_{//}=\frac{\frac{\varepsilon^{2}}{\varepsilon^{1}}\cos\theta_{i}-\sqrt{\frac {\varepsilon^{2}}{\varepsilon^{1}}-\text{sin}^{2}\theta_{i}}}{\frac{\varepsilon ^{2}}{\varepsilon^{1}}\cos\theta_{i}+\sqrt{\frac{\varepsilon^{2}}{ \varepsilon^{1}}-\text{sin}^{2}\theta_{i}}} \tag{2}\]  
where \(\theta i\) is the reflection angle of the ground, \(\varepsilon 1\) is the dielectric constant of air, and \(\varepsilon 2\) is the dielectric constant of the ground. In addition, the penetration losses of the direct and reflected diameters in the HSR glass are expressed by \(L(\theta_{LOS})\) and \(L(\theta_{R})\), respectively; \(\theta\) is the angle of the signal incident on the glass and the HSR glass penetration loss formula \(L(\theta)\) is derived based on the measured data of the HSR compartment glass penetration loss [8], which is given by the following equation:  
\[L(\theta)=-0.0001\times\theta^{3}+0.0111\times\theta^{2}-0.6903\times\theta+ 31.3069 \tag{3}\]  
As shown in Figure 2, the direct and reflected diameter HSR glass incidence angle formula can be calculated separately for the angles when the direct and reflected diameters are incident on the glass under the seats of different compartments. Then, the angle of incidence \(\theta_{LOS}\) of the direct path transmission into the glass is:  
Figure 2: Schematic diagram of the base station-train two-path communication scenario.  
\[\theta_{LOS}=\arctan(\frac{h_{T}-h_{R}}{d_{y}}) \tag{4}\]  
The angle of incidence \(\theta_{R}\) of the reflection path transmission into the glass is:  
\[\theta_{R}=\frac{\pi}{2}-\arctan\left(\frac{d_{y}}{h_{T}+h_{R}}\right) \tag{5}\]