esa-sceva/satcom-chunk-collection huggingface dataset:
# Movement Analysis for Health and Biometrics
## 4 Movement Analysis for Human Activity Recognition  
Human activity recognition (HAR) can be referred to as the ability to identify activities using AI techniques from the gathered activity raw data. This field involves the use of various devices, including wearable sensors, electronic device sensors, such as smartphone inertial sensors, and camera devices such as Kinect, closed-circuit television, and some commercial off-the-shelf equipment. The use of different sources makes the HAR an important support for multifaceted application domains, such as healthcare [18].  
Usually, the learning-based techniques for effective human behavior identification focus only on fundamental human activities. Nevertheless, postural transitions can play a critical role in implementing a system for recognizing human activity.  
[PERSON] et al. [19] present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate the recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0), achieving classification accuracies that outperform the state-of-the-art.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 1 Introduction
The experiments were performed using an actual FH dataset to evaluate the reliability of the algorithm. The results confirm that the proposed DIN classifier could improve the emitter ID identification accuracy by more than 1% compared to the baseline (Section 5.1). In addition, the multimode SF ensemble approach proved to be the most effective, achieving the best results with 97.0% identification accuracy for the seven FHSS emitters (Section 5.2). Regarding the detection performance, the classifier output vector of the outliers exhibited a much lower value than those of the training sample. By utilizing these differences, the detector based on the DIN-based ensemble classifier can improve the area under the receiver operating characteristic curve (AUROC) from 0.97 to 0.99 compared to the baseline. This result indicates that the classifier output vectors can effectively be used to detect the attacker signal input (Section 5.4). The remainder of this study is organized as follows. The problem formulation is presented in Section 2. The details of the RFEI method are described in Section 3, and the baseline algorithms are explained in Section 4. The results, a discussion, and other details of the experiments are described in Section 5. The conclusion is presented in Section 6. Figure 1: Non-replicable authentication scenario based on the RFEI method.
## 3 Performance Evaluation
### Error Probability
#### 3.1.3 Monte Carlo Simulations  
Independently from the approaches based on analytical derivation and numerical integration, the BER values are also estimated using Monte Carlo simulations. Monte Carlo simulations are performed according to (12) and (13). The samples of the TWDP fading are generated based on (3) with parameters given by (6) and (7). Samples of a uniformly-distributed random variable are obtained according to the algorithm presented in ([26], p. 340), while a Gaussian random variable is generated using the Box-Muller  
Figure 3: (**a**) Time required for computing BER for a given SNR, showing dependence on the number of Gaussian quadrature points in \(r\) and \(\varphi\) directions; (**b**) Fixed number of points is seven in the \(\varphi\) direction; (**c**) Fixed number of points is seven in the \(r\) direction.  
method ([27], p. 383). The random variables with Tikhonov distribution can be generated by applying the Modified acceptance/rejection method ([27], p. 382).  
The criteria for estimating a single BER value are as follows: the simulation terminates if at least \(10^{4}\) erroneous symbols are detected, or when a maximum of \(2\times 10^{9}\) symbols have been transmitted in total.
# Virtual Pheromone Based Network Flow Control For Modular Robotic Systems
## 3 Virtual Pheromone Based Network Flow Control
### Virtual pheromone-Based Network Flow Control  
The gradient-based routing algorithm [39] is capable of generating one-way directional communication path from _any module to a root_ in a network of physically connected robotic modules. The probability of transmitting data packets from _any module to the root_ by following the gradient decent is guaranteed at the probability of the neighbourhood discovery & awareness and link maintenance process, which is mostly based on the user's behaviours. However, data packets cannot be precisely delivered from the _root_ to any module in the network by following the gradient ascent because the intersection modules may not identify the proper physical communication channel for such packets. The virtual pheromone mechanism can be used as a _probabilistic switching_ for the directional channel selection, especially for the intersection modules responsible for relaying data packets to proper communication channels in the direction from the _root_ to other modules.  
The probability of forwarding data packets in the direction from the _root to any module_ is equal on all the physical directional channels at the initial stage:\[P_{i}=\frac{1}{N-1} \tag{13}\]  
where \(N-1\) channels are available because one channel is used to receive data packets from the _root_.  
The virtual pheromone adjustment mechanism on the intersection modules starts differentiating the level of virtual pheromone concentration, so the concentration level on a channel \(i\) receiving data packets is adjusted by Equation (10) and the probabilistic switching of the channel \(i\) calculated by Equation (12). The virtual pheromone concentration plays a key role as the decision making factor whether this channel is selected for forwarding data packets from the _root to any module_.  
In summary, the gradient-based routing algorithm is used to create _any-to-root_ communication paths while the virtual pheromone-based routing algorithm is applied to form _root-to-any_ communication paths. A combination of the gradient and virtual pheromone-based routing algorithms is a complete solution of generating bi-directional communication paths for the network flow control in modular robotics. Hence, we define this method as the _virtual pheromone based network flow control_.
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 1 Introduction
(2022) combined the knowledge graph with semantic communication to improve the validity of communication [16]. Thus, the knowledge graph can effectively represent semantics; we investigated the semantic communication system based on the knowledge graph (SCKG) for improving communication reliability. The main contributions of this paper are summarized as follows:  
* A semantic extraction method is proposed to extract triplets from transmitted text to represent its core semantic information, reducing the information redundancy of the transmitted text. * A semantic restoration method based on text generation from the knowledge graph is proposed, which completes the semantic restoration process by reconstructing the text structure between entities and relations. * A novel semantic communication system was developed, which can sort triplets based on semantic importance and adaptively adjust the transmitted contents according to the channel quality. The rest of this paper is organized as follows. Section 2 briefly reviews the related work. Section 3 details the proposed system and the semantic extraction and restoration methods used in the model. Experimental results are presented in Section 4 to verify the performance of the proposed model. Finally, Section 5 concludes this paper.
## 4 Theoretical Background and Simulations
Figure 13: Normalized perpendicular (left) and parallel (right) polarized components of the scattering intensity of light from a TiO\({}_{2}\) (rutile) single particle with a diameter from \(10\,\mathrm{nm}\) (blue) to \(10\,\mathrm{\SIUnitSymbolMicro m}\) (orange) with respect to a linearly polarized plane wave with a wavelength \(\lambda=500\,\mathrm{nm}\) coming from the left (marked by an arrow). Top: linear scale, bottom: logarithmic scale. Ideally, a matte, rough surface obeys Lambertian behavior, i.e., the radiance remitted is independent of the viewing angle [1]. The primary assumption relies on particles being small enough that they exhibit negligible gloss, such that their facets do not produce notable specular reflections. Secondly, the (macroscopic) sample itself must not possess glossy surfaces caused by either pressing or polishing. In terms of investigating powder samples experimentally, absorption and scattering coefficients \(\alpha\) and \(\sigma\), respectively, are inaccessible. Yet, these may be calculated from the diffuse reflectance \(R\). Since the Kubelka-Munk function is only correct for infinitely thick samples, i.e., they must not transmit any light, the diffuse reflectance is denoted as \(R_{\infty}\) for clarity. Depending on the material and the interparticle distance, this condition is typically met after a few (sub-)millimeters [143; 144; 145; 21; 33; 62; 15; 146]. In the context of the K-M function (Equation (3)), it is important to note that \(K\) and \(S\) themselves do not relate to any physical quantity due to mutual dependence [1; 147; 15]. Nevertheless, by including additional assumptions or parameters based on evaluation methods described below, further material-dependent properties can be deduced. The general interplay between experimental and theoretical viewpoints using Mie and K-M theory is highlighted in Figure 14.  
Assuming that particle- and material-parameters, especially the size distribution \(q_{0}(d)\) and complex-valued refractive index \(m\) are known, these values can be inserted into the Mie framework in order to obtain the microscopic Mie coefficients \(a_{n}\) and \(b_{n}\). Based on these quantities, the macroscopic Kubelka-Munk absorption and scattering coefficients \(K\) and \(S\) can be extracted, taking the volume filling fraction of the powder pellet into account, which in turn may be used to calculate a theoretical diffuse reflectance \(R_{\infty}^{\text{theo}}\). A comparison with the experimentally determined diffuse reflectance \(R_{\infty}^{\text{exp}}\) helps refining the starting parameters until a satisfactory overlap between both diffuse reflectances is attained. This feedback loop will be expatiati  
Figure 14: Flow chart illustrating the connections between experiment and theory. Macroscopic views are shaded in blue whereas microscopic ones are red. The process from Mie theory towards calculation of \(R_{\infty}^{\text{theo}}\) can be summarized by the simulation of light–matter interaction. Further explanations are found in the accompanying text.
# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 3 Method  
### Dataset  
ODIR-5K dataset--The ODIR-5K dataset is a multi-label classification dataset of fundus images released by a Chinese team, which was released in the \"Wisdom Eye\" competition held by Peking University in 2019, containing a total of 5000 paired fundus image data of the left and right eyes, of which 3500 labeled datasets were released as training data. Compared with other fundus datasets, in addition to the differences in tasks (multi-label classification), the most important difference of ODIR-5K is that it provides paired left and right eye data and related descriptions, which can be used as 7000 individual data or as 3500 pairs of special data for some consistency exploration. In the ODIR-5K, a total of 8 categories of labels are provided, which are normal (N), diabetic retinopathy (D), glaucoma (G), cataract (C), age-related macular degeneration (A), hypertensive retinopathy (H), myopia (M), and other diseases/abnormalities (O). These diseases include many common diseases in ophthalmology, and there are also long-tail problems in the dataset that can be explored.  
Retina dataset--The Retina dataset contains 601 retinal fundus images in 4 categories: 300 normal images, 100 cataract images, 101 glaucoma images, and 100 retina disease images. The data are available from GitHub--yiweichen04/retina_dataset: The Retina dataset contains images of (1) normal, (2) cataract, (3) glaucoma, (4) retina disease.  
Cataract dataset--The Cataract dataset contains 2112 retinal fundus images in 2 categories, including 1074 normal images and 1038 cataract images. The Cataract dataset is available at [[https://www.kaggle.com/datasets/gunavenkatododi/eye-diseases-classification](https://www.kaggle.com/datasets/gunavenkatododi/eye-diseases-classification)]([https://www.kaggle.com/datasets/gunavenkatododi/eye-diseases-classification](https://www.kaggle.com/datasets/gunavenkatododi/eye-diseases-classification)) (accessed on 24 November 2024).  
In this paper, normal fundus images and cataract fundus images were extracted from the three datasets to complete the experiment.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon  
### Radioactive Decay  
Radon is generated from the decay of radioactive elements such as \({}^{238}\)U and thorium (\({}^{232}\)Th), and their descendants [14]. The primary sources of \({}^{222}\)Rn are \({}^{238}\)U, \({}^{235}\)U and \({}^{232}\)Th, which are naturally found in rocks, soils and water [15]. Radioactive decay is a natural process by which an unstable nucleus of an element transforms into a more stable nucleus, releasing particles and energy in the process [16]. \({}^{238}\)U is the most common isotope of uranium, comprising approximately 99.3% of the uranium found in the earth's crust [17; 18]. It decays through a series of radioactive decays, forming a chain of daughter elements. One of the products of this chain is radium (\({}^{226}\)Ra), which through alpha decay (\({}^{226}\)Ra emits an alpha particle composed of two protons and two neutrons) transforms into \({}^{222}\)Rn [19]. \({}^{222}\)Rn is the most common and significant isotope in terms of human exposure and health risk [20]. The decay chain of \({}^{238}\)U includes the main radioactive elements \({}^{234}\)Th, \({}^{234}\)Pa, \({}^{234}\)U, \({}^{230}\)Th and \({}^{226}\)Ra, from which \({}^{222}\)Rn originates [21]. \({}^{235}\)U is another isotope of uranium, although it is much less common than \({}^{238}\)U, representing only about 0.7% of uranium in the earth's crust [22]. It also decays through a series of radioactive decays, forming a chain of daughter elements. However, the amount of \({}^{222}\)Rn generated from the decay of \({}^{235}\)U is less than the amount generated from \({}^{238}\)U, due to its lower abundance and longer decay chain [23]. \({}^{232}\)Th is a radioactive element naturally found in the earth's crust, and also decays through a series of radioactive decays, forming a chain of daughter elements [24]. One of the products of this chain is \({}^{224}\)Ra, which decays to form \({}^{220}\)Rn, also known as thoron [25]. The decay chain of \({}^{232}\)Th includes the main radioactive elements \({}^{228}\)Ra, \({}^{228}\)Ac, \({}^{228}\)Th and \({}^{224}\)Ra, from which \({}^{220}\)Ra originates [26]. However, although \({}^{220}\)Rn is released during the decay of \({}^{232}\)Th, its short half-life (approximately 55 s) limits its contribution to radon exposure compared with \({}^{222}\)Rn [27]. Being a noble gas, \({}^{222}\)Rn moves freely through the pores and fissures of the soil and rocks, in contrast to other decay products, which are solids and remain fixed in place [28]. Once released into the environment, \({}^{222}\)Rn continues to decay into a series of daughter radioactive elements, such as \({}^{218}\)Po, \({}^{214}\)Pb and \({}^{214}\)Bi, which are also radioactive [29]. The decay of \({}^{222}\)Rn and its subsequent decay products emits alpha, beta and gamma radiation [30].
## 5 Results and Discussion
### Feature Importance Analysis  
The GB algorithm allows estimating the importance of input features. In fact, the GB algorithm included many decision trees and for each tree, the feature importance of an input variable was calculated as the fraction of samples that will traverse a node that splits based on that variable. The mean score of all trees then decided the important index of each features. The important index scores will be in the range [0, 1] and the higher scores the more important the feature.  
The result shown on Figure 12: It can be seen that amongst the 14 input variables that used to detect the UCS of cement-stabilized soil, the wet density (We) and the amount of cement (Ac) was the most important features, which score an average sensitive index of 0.7 and 0.212, respectively. From a soil mechanic point of view, We affects the unit weight of soil or decides particle density while the amount of cement (Ac) decides the cohesion between the soil particles, so both of them play an important role in predicting the UCS of cement-stabilized soil. The variables M, Mc and De were ranked as the third to the fifth important predictors with an average sensitive index, ranging from 0.12 to 0.085. The other variables such as D, Cc, A, V and S had a lower sensitive index, ranging from 0.051 to 0.026, indicating that they are not affected much by the regression result. Remain features include T, Cp, Di and L, which had an important index lower than 0.006, showing that they nearly did not affect the prediction result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Model** & **Sample Type** & **Number of Samples** & **R** & **RMSE** \\ \hline \multirow{3}{*}{[PERSON] et al. [18]} & FN & & 0.95 & - \\  & MARS & Cement stabilized soil & 58 & 0.95 & - \\  & MLR & & & 0.73 & - \\ \hline [PERSON] et al. [21] & GPR & UCS of soil & 118 & 0.861 & 0.442 \\ \hline \multirow{3}{*}{[PERSON] et al. [20]} & BRNN & & 0.87 & - \\  & LMNN & Cement stabilized soil & 55 & 0.851 & - \\  & DENN & & & 0.846 & - \\ \hline \multirow{3}{*}{The present study} & RS-ANN & & & 0.925 & 419.82 \\  & RS-GB & Cement stabilized soil & 216 & 0.912 & 446.79 \\ \cline{1-1}  & RS-SVM & & & 0.903 & 446.67 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with previous studies.  
Figure 12: Feature importance index of 14 variables used in this study.
## 3 Results
### Quantitative Synthesis  
The meta-analysis process required the number of True Positives (TP), False Negatives (FN), True Negatives (TN) and False Positives (FP) for each study. Thus, studies that did not report this information, nor supplied data sufficient to retrieve it, were not included in the quantitative synthesis.  
The software R was used with the \"meta\" package to perform univariate analysis and retrieve sensitivity (SN), specificity (SP) and log of diagnostic odds ratio (DOR) forest plots and the \"mada\" package to plot the summary receiver operating characteristic (SROC) curve of all studies [78]. The plots were constructed with the information retrieved from all studies included in the quantitative analyses process, independently of the studied pathology. Thus, a better visual assessment of the distribution of the different metrics among studies is possible.  
The test for heterogeneity retrieved a high chi-square (\(\chi 2\)) value for the analysis of all records, suggesting high heterogeneity between studies (Appendix B). The high values of I2 encountered also support this idea [79]. Thus, a random effects model was used to summarize the effects and different analyses that were made, separating studies focusedon breast cancer and diabetic foot. Still, the values of \(\chi 2\) and I2 remained high for breast cancer studies, while being low for diabetic foot ones (Appendix B).  
The values of sensitivity and specificity differed substantially among all studies with SN ranging in [0.36-1] (Figure 2) and SP in [0.10-1] (Figure 3). The estimator of the random effect model was 3.70 (95%CI, 3.10-4.21) (Figure 4.). The SROC curve presented some scattering, with only few studies inside the 95% confidence interval region. Still, most publications are located on the top left corner of the plot (Figure 5). For breast cancer, SN and SP were in the ranges of [0.36-1] and [0.49-1] with a DerSimonian and Laird (DSL) estimator of 3.74 (95%CI, 3.12-4.36). For diabetic foot, SN and SP were in the ranges of [0.89-1] and [0.79-0.98] with a DSL estimator of 5.23 (95%CI, 4.02-6.45). The separate SN, SP and log (DOR) estimates along with 95% confidence intervals (CIs) for breast cancer and diabetic foot studies are included in Appendices C and D.
# Role of Ion-Exchange Resins in Hydrogen Reactions
###### Abstract  
The role of ion-exchange resins (IERs) as catalysts or catalysts supports, in hydrogenation reactions is revised and their potential application is presented. Both gel-type and macroreticular, basic or acid, IERs have been used for manifold metal-catalyzed hydrogenation processes in gas and liquid phase, including hydrogenation of alkenes, alkynes, carbonyls, arenes, nitrooramatics, and more. When available, qualitative relationships between the morphology and structure of resins and their performance as solid supports for metal catalysts are observed. Noble metals, such as Pt, Au, and Pd, and non-noble metals, such as Fe and Cu, have been introduced into IERs polymeric backbones by simple ion-exchange of a metal salt precursor with the resin, or by a combination of ion-exchange and other protocols, to obtain mono- and bimetallic catalysts supported on IERs. High yields towards target product, as well as the recyclability of metal-doped IERs, have been reported in the literature, with low metal leaching, which makes them highly interesting solid catalysts for a wide array of industrial applications. Multistep reaction processes, involving hydrogenation and hydration/cyclization/aldol condensation/etc., constitute promising applications due to the one-pot synthesis approach and relatively low temperatures required, which adds environmental interest in terms of process integration and optimization.  
Keywords:ion-exchange resin; gel-type; macroreticular; hydrogenation; metal catalyst; bifunctional catalyst; one-pot process +
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