esa-sceva/satcom-chunk-collection huggingface dataset:
# Reproducibility and Accuracy of Two Facial Scanners: A 3D In Vivo Study
## 3 Results  
### Linear Measurements  
Descriptive analysis of the linear anthropometric measurements is reported in Table 3.  
The intraclass correlation coefficient (ICC) ranges from 0 (no repeatability) to 1 (perfect repeatability). In this case, all ICC values are above 0.90 and significantly different from 0 (\(p\) < 0.05), which indicated that the first and the second measurement showed good repeatability for all measurements (Table 4).  
For these reasons, only the first measurements were considered. The paired-sample Wilcoxon test was conducted to verify any differences between the different linear measurements. The null hypothesis was that the medians of the two variables were equal. It is observed that there are no significant differences (\(p\) > 0.05); therefore, it can be stated that the measurements among the three instruments are similar (Table 5).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **ICC** \\ \hline ANT Tr-Na & 0.999 \\ ANT Na-Prn & 0.995 \\ ANT Prn–Pog & 0.999 \\ ANT Zig–Zig & 0.999 \\ VEC Tr-Na & 0.986 \\ VEC Na-Prn & 0.974 \\ VEC Prn-Pog & 0.993 \\ VEC Zig–Zig & 0.989 \\ EIN Tr-Na & 0.993 \\ EIN Na–Prn & 0.974 \\ EIN Prn–Pog & 0.987 \\ EIN Zig–Zig & 0.995 \\ \hline \hline \end{tabular} ANT: anthropometric measurement; VEC: Vectra measurement; EIN: Einscan H measurement.  
\end{table}
Table 4: Intraclass correlation coefficient (ICC) of linear measurements.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \begin{tabular}{c} **Minimum** \\ **(mm)** \\ \end{tabular} & **Mean (mm)** & **SD (mm)** & \begin{tabular}{c} **Percentile 25** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Median** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Percentile 75** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **(mm)** \\ \end{tabular} \\ \hline ANT Tr-Na & 27.47 & 38.42 & 5.05 & 35.92 & 39.55 & 41.26 & 48.29 \\ ANT Na–Prn & 47.80 & 57.21 & 3.91 & 55.83 & 58.15 & 59.66 & 62.26 \\ ANT & 59.13 & 71.62 & 7.35 & 66.86 & 71.19 & 77.12 & 83.56 \\ Prn–Pog & 94.73 & 103.29 & 6.27 & 97.93 & 102.18 & 105.99 & 119.27 \\ \hline VEC Tr-Na & 28.10 & 38.37 & 5.11 & 35.37 & 39.38 & 41.47 & 47.77 \\ VEC Na–Prn & 45.96 & 56.90 & 4.32 & 55.59 & 57.33 & 59.95 & 62.80 \\ VEC Prn–Pog & 59.32 & 72.14 & 7.61 & 66.69 & 72.19 & 78.88 & 83.47 \\ VEC Zig–Zig & 94.10 & 102.95 & 6.35 & 98.08 & 102.06 & 106.28 & 119.34 \\ \hline EIN Tr-Na & 28.56 & 38.70 & 5.01 & 36.50 & 39.53 & 41.23 & 48.60 \\ EIN Na–Prn & 48.55 & 57.63 & 3.92 & 56.16 & 58.77 & 60.41 & 62.30 \\ EIN Prn–Pog & 59.15 & 72.20 & 7.33 & 66.80 & 72.61 & 78.10 & 83.16 \\ EIN Zig–Zig & 94.23 & 103.18 & 6.67 & 98.07 & 102.34 & 105.88 & 119.88 \\ \hline \hline \end{tabular} ANT: anthropometric measurement; VEC: Vectra measurement; EIN: Einscan H measurement SD: standard deviation.  
\end{table}
Table 3: Descriptive analysis of linear measurements.
## 3 Results and Discussion
### Adsorbent Characterisation
#### 3.1.4 Surface Function Analysis by Fourier Transform Infrared Spectrometry (FTIR)  
The FTIR spectra of ACSCGs, SCGs, and CAC are shown in Figure 5. From these spectra it was possible to identify the functional groups present in the synthesised activated carbon and to compare the spectrum of the coffee grounds with that of its activated carbon. The spectra of the two activated carbons are identical, which confirms that the activation process has taken place. The following functional groups are observed, based on reference spectra [69; 70; 71]:  
* A band at 3440 cm\({}^{-1}\) due to the stretching vibration of OH functional groups, corresponding to carboxylic acids, alcohols, and water molecules in the sample; its intensity decreased after activation.
* Two bands at 2930 cm\({}^{-1}\) and 2852 cm\({}^{-1}\), attributed to the C-H stretching vibration of aliphatic groups and aromatic hydrocarbons, are clear in the SCGs spectra but show a lower intensity for ACSCGs, indicating a decrease in the amount of aliphatic compounds due to carbonisation.
* A band at 1745 cm\({}^{-1}\) is consistent with the presence of carbonyl groups (C=O) in aldehydes, ketones, carboxylic acids, and esters. This band disappears after activation with KOH base.
* The absorption peak at 1644 cm\({}^{-1}\) represents C=C stretching vibrations, indicating the presence of alkenes and aromatic rings.  
\begin{table}
\begin{tabular}{c c c} \hline
**Element** & **\% Weight** & **\% Atomic** \\ \hline C & 84.48 & 91.48 \\ O & 6.37 & 5.17 \\ Mg & 0.99 & 0.53 \\ P & 1.56 & 0.66 \\ S & 0.44 & 0.16 \\ Ca & 6.16 & 2.00 \\ \hline \end{tabular}
\end{table}
Table 2: EDS results of ACSCGs.  
* Two bands around 1386 and 1465 cm\({}^{-1}\) attributed to aliphatic C-H deformation, which disappeared after activation.
* The bands in the spectral region between 1400 and 1000 cm\({}^{-1}\) with maxima at 1246, 1170, and 1030 cm\({}^{-1}\) are commonly attributed to C-O stretching in acid, alcohol, phenol, ether, and ester groups. The intensities of these bands become weak after activation.  
Figure 4: Point zero charge for ACSCGs.  
Figure 5: FTIR spectra of spent coffee grounds (SCGs), activated carbon of spent coffee grounds (ACSCGs), and a commercial activated carbon (CAC).  
These results indicate that activation with KOH leads to a decrease in the concentration of oxygen-containing functional groups on the surface of the activated carbon.
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 2 Methodology for Orbit and Communication Resources Selection
### Determination of Suitable Orbits for Observation  
There are several considerations for the selection of orbits for observation and data collection on the Moon and Mars. First of all, it is important to consider the area of interest  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Celestial Body** &
\begin{tabular}{c} **Minimum Distance** \\ **from Earth (10\({}^{6}\) km)** \\ \end{tabular} & **Mean Radius (km)** & **Mass (10\({}^{24}\) kg)** \\ \hline Mercury & 77.6 & 2440 & 0.3301 \\ Venus & 38.2 & 6052 & 4.8673 \\ Earth & – & 6371 & 5.9722 \\ Moon & 0.38 & 1737 & 0.0735 \\ Mars & 54.6 & 3389 & 0.6417 \\ Jupiter & 588.5 & 69,911 & 1898.1 \\ Saturn & 1205.5 & 58,232 & 568.32 \\ Uranus & 2580.6 & 25,362 & 86.811 \\ Neptune & 24,622 & 4319 & 102.409 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of main celestial bodies of our the solar system.  
to observe in the celestial body, this constraint will help to determine a optimal orbit inclination and altitude. Orbit's height can also be determined based on the payload, e.g., for a camera, a close distance to the surface would be suitable to observe detailed soil and terrain characteristics, but would take more time to complete a large observation campaign than at higher altitudes. In addition, if satellites are being used for sensors data collection on the surface [6], higher altitudes would degrade the signal more than shorter distances. Figure 1 shows with gray color the region covered by a satellite in a circular orbit with inclination, \(i\).  
Inclined orbits offer great advantages for focusing into an area close to the local equator of the Moon and Mars for both the Moon poles. Furthermore, the Moon poles contain some of the coldest places of the solar system [3], where hardware implementation would be difficult as well as in-site activities with rovers or sensors.
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 1 Introduction
Social influence from the perceptions and actions of colleagues, family, and friends can also motivate or derail acceptance [47; 48], especially in tight-knit communities [49] such as Bhutan, where consensus-making is prevalent. Further, establishing the infrastructure required for the implementation, including reliable Internet connections [50], digital education programs, and regulations, is a key enabler for the acceptance and adoption of blockchain-based systems [51; 52]. This paper utilizes the UTAUT model due to its widely recognized effectiveness in measuring technology acceptance across various domains, as it integrates eight key technology adoption models, such as the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), the Motivational Model (MM), the Theory of Planned Behavior (TPB), the Diffusion of Innovation (DOI) and the Task-Technology Fit (TTF) theory [53]. It combines social and environmental conditions with facilitating conditions needed for blockchain adoption, which TAM and TPB overlook by focusing solely on individual perceptions. Although TTF and DOI theory models emphasize aspects of the technology-organization alignment and innovation spread, they overlook important factors like trust and social influence along with regulatory support. UTAUT stands out with its comprehensive and predictive approach because it addresses critical gaps that render it ideal for researching the adoption of blockchain-based digital payment systems. Despite the increasing global interest in blockchain technology, empirical research on its adoption in Bhutan remains scarce. This paper aims to address a critical research gap by pursuing the following objectives: (1) evaluate whether performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influence consumer acceptance of blockchain-enabled digital payment systems in Bhutan and (2) examine the relevance of these factors within the unique cultural and economic context of Bhutan. This paper makes significant theoretical contributions by validating UTAUT in the developing, landlocked economy of Bhutan, enriching the understanding of technology adoption in diverse socio-economic contexts. It provides recommendations for stakeholders to enhance consumer acceptance of blockchain-based digital payments and equips developers with strategic insights for designing solutions aligned with Bhutanese consumer expectations. Using the statistical analysis and partial least squares structural equation modeling (PLS-SEM), the paper determines this correlation and then reveals conclusions and practical recommendations. The structure of this paper is as follows: Section 2 reviews the relevant literature. Section 3 outlines the theoretical framework and hypotheses. Section 4 details the research methodology. Section 5 presents the results. Section 6 discusses the findings. Section 7 presents limitations and future directions. Section 8 provides the conclusion.
# Roman Urdu Sentiment Analysis Using Transfer Learning
## 3 Methodology  
In this paper, we build deep Roman Urdu SA using a DL model for Roman Urdu/Hindi sentiment analysis that integrates Word2 Vec, GloVe, TF-IDF, and FastText word embedding algorithms into a CNN architecture. CNNs have a reputation as promising tools for NLP. Additionally, they enable close-by input elements to extract at lower layers while remote elements interact at higher layers, allowing them to independently regulate the duration of the dependencies. Multiple convolutional layers can thus be used to generate an abstract, hierarchical representation of input text. To get to the root of the matter, we will employ a pooling layer to glean the following info. Local features that can hold useful information are lost due to the pooling layer. To this end, the proposed model incorporates a prior attention layer to the pooling layer to not only identify the significant characteristics but also to dampen the impact of less relevant aspects and aid the pooling layer in discovering the important features considering the context. While it is true that more training data usually results in better CNN performance, we also wanted to investigate how transfer learning might affect the suggested approach.  
As far as we are aware, this is the first time a deep convolutional neural network (CNN) with attention and 4 channel embedding has been proposed for Roman Urdu text classification [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. 4C-CNNAT was also put through a range of transfer learning tests. Figure 1 shows the framework of the proposed model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Classification Algorithm** & **Learning Method** & **Corpus** & **Accuracy** \\ \hline Neural Network [74] & Supervised & 15,000 user reviews & 0.80 \\ \hline NB, DT, KNN [75] & Supervised & Only 300 reviews of two classes & 0.71 \\ \hline SVM, LR, NB, RCNN, Hybrid & & & \\ Multichannel Approach [64] & Supervised & 3241 sentences of three classes & 0.82 \\ \hline RCNN, Rule-based and N-gram [69] & Supervised and Unsupervised & 10,021 user sentences from various websites of three classes & 0.75 \\ \hline KNN, DT, RF, LR, NB, ANN, SVM, & & & \\ AdaBoost, wVoting [76] & Supervised & 11,000 reviews of two classes & 0.82 \\ \hline KNN, DT, RF, LR, NB, ANN, SVM, & & & \\ AdaBoost, wVoting [77] & Supervised & 11,000 sentences of two classes & 0.81 \\ \hline SVM with stemming [62] & Supervised & 26,824 user reviews & 0.68 \\ \hline CNN-LSTM [66] & Supervised & 10,021 Roman Urdu user reviews, UCL contains 20,228 sentences & 0.74 \\ \hline NB, SVM, LR, KNN, ANN, CNN, RNN, & & & \\ ID3 and GB Tree [67] & Supervised & 24,000 reviews & 0.922 LR \\ \hline BiLSTM [68] & Supervised & 26,824 user reviews, & 0.67 \\ \hline DistilBert, XLNet [69] & Supervised & 21,940 Reviews & 1.0, 0.862 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of Existing Literature.  
This section briefly explains the steps of proposed methodology.
## 5 Recent Developments in RF Energy Harvesting
### Metamaterial-Based RF Energy Harvesting  
Metamaterials are engineered materials with customized electromagnetic properties that are widely applied to enhance the efficiency of RFEH systems. By leveraging artificial resonant structures, metamaterial-based energy harvesters can concentrate RF waves and significantly increase the harvested power. A recent breakthrough introduced a metamaterial absorber-enhanced rectenna, which improved power conversion efficiency by over 40% compared to conventional designs [78]. The structure utilized a polarization-insensitive metamaterial, ensuring efficient energy capture across multiple angles of incidence. Another study proposed a wideband metamaterial-integrated rectenna capable of harvesting energy from 900 MHz to 6 GHz, demonstrating a conversion efficiency of 68% across all bands [79]. This broadband operation is attributed to multi-resonant unit cells that overlap spectrally, allowing concurrent energy capture across heterogeneous RF sources such as Wi-Fi, cellular, and radar systems. Moreover, the introduction of reconfigurable metamaterials has enabled tunable RF energy harvesting. A smart metamaterial array was developed to dynamically adjust its resonance frequency based on the surrounding RF spectrum, optimizing energy capture in real time [80]. Such tunability is often achieved using embedded varactors, MEMS switches, or tunable substrates like liquid crystal layers, enabling frequency agility without significant structural modification. From a practical standpoint, metamaterial-based harvesters offer benefits such as miniaturization, angular stability, and selective filtering. However, limitations include the narrow intrinsic bandwidth of unit cells and added fabrication complexity due to active reconfiguration. Thisinnovation presents a compact, wide-angle, polarization-insensitive metamaterial harvester capable of efficiently harvesting electromagnetic energy in the S, C, X, and Ku bands. These advancements have significantly improved the adaptability and performance of RFEH systems in urban and industrial environments.
# Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction
## 2 Related Work
### Human Motion Prediction  
Three main types of methods are proposed to predict future human motion, including recurrent neural network (RNN)-based methods, convolutional neural network (CNN)-based methods, and graph convolutional network (GCN)-based methods.  
For RNN-based methods, human poses are set as states of RNN cells, and the dynamics of pose evolution are modeled via state-state transitions of the cells. [PERSON] et al. [9] proposed an encoder-recurrent-decoder (ERD) model by incorporating a nonlinear encoder and decoder before and after long short-term memory (LSTM) cells. [PERSON] and [PERSON] et al. [10; 11] proposed gated recurrent unit (GRU)-based encoder-decoder models for human motion predictions. [PERSON] et al. [12] proposed a triangular-prism RNN (TP-RNN) for predictions, which models multi-scale features of human motion using a hierarchical framework. However, traditional RNN ignores the spatial modeling, usually resulting in unsatisfying performance. Some recent works incorporated graph representation and quaternion representation for the human body to enhance the spatial modeling of RNN models [13; 14].  
For CNN-based methods, the human motion sequences are usually represented as a 3D tensor. Then, CNN is applied to scan the width, height, and channel of the 3D tensor for modeling the spatiotemporal dynamics of human motion sequences [15; 16]. For example, [PERSON] et al. [17] first represented the human motion sequence as a 3D tensor with the width as joints, the height as time, and the channel as the joint dimension. Then, the authors proposed a convolutional sequence-to-sequence model by taking the 3D tensor as the input, where the convolutional kernels scan the 3D tensor to model the local spatiotemporal features. With the hierarchical representation of multiple convolutional layers, spatiotemporal information with a larger receptive field was extracted for predictions. [PERSON] et al. [8] also represented the motion sequence as a 3D tensor, but unlike [17], the channel is set as time. In this way, the global trajectory information is easily modeled by their CNN model, leading to its state-of-the-art performance.  
For GCN-based methods, the human pose is often represented as a graph with joints as nodes and joint spatial relationship as edges [18]. In these models, the spatial information is modeled by updating the node and edge information of the graph, and the temporal information is usually modeled by discrete cosine transform (DCT). [PERSON] et al. [19] represented the human pose as a fully connected graph where the edges connected every pair of joints. Moreover, the authors transformed the human motion sequence in the trajectory space using DCT and stacked several graph convolutional layers to extract the spatiotemporal features of human motion in the trajectory space. [PERSON] et al. [20] also designed a GCN-based encoder and decoder for predicting human motion. In contrast to [19], the authors dynamically learned inherent and meaningful links of natural connectivity of the human body.
## 5 Conclusions and Future Research Directions  
This study proposed the decision support system (DDS) for crop planning. The optimization and scheduling model is formulated to determine the optimal cropping pattern and the water resource allocation in an irrigation area to maximize the net economic benefit and maximize crop area in the Huay Mae Tho reservoir case study. The constraints consisted of the constrictions of the area's characteristics, water requirements, water availability, cropping area of each irrigation zones, and the cropping pattern alternatives. DDS provides the SA to solve the model. The results show that it can obtain the optimal solution the same as the exact solution obtained. Further analysis of the availability of crop pattern scenarios demonstrated that the current plan implemented in the Huay Mae Tho reservoir has the potential to be improved further.  
Despite several promising results shown in the experiment, the analysis provided in this study consist of only several limiting factors such as the expected crop yield, crop price, land area availability, and water availability. We have not yet tackled many complicated considerations that might be available in the field. Therefore, future research direction may need to consider more factors that might appear in the area, such as the uncertainty of water availability, demand changes, and the risk of crop failures. Furthermore, the implementation of the SA algorithm in larger size instances might show the advantage of the algorithm even more. In addition, a combination of the exact solution approach and approximation approach is nowadays a trend in solving this type of problem.  
Conceptualization, P.J., P.N., M.F.N.M. and A.A.N.P.R.; methodology, P.J., P.N. and A.A.N.P.R.; software, A.A.N.P.R.; validation, P.J., D.G., M.F.N.M. and A.A.N.P.R.; formal analysis, P.J., M.F.N.M. and A.A.N.P.R.; resources, P.J.; data curation, P.J. and A.A.N.P.R.; writing--original draft preparation, P.J. and M.F.N.M.; writing--review and editing, P.J., M.F.N.M. and A.A.N.P.R.; visualization, P.J., M.F.N.M. and A.A.N.P.R.; supervision, P.J.; project administration, P.J. and A.A.N.P.R.; funding acquisition, P.J. and J.D.G. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Comparison of estimated income of the suitable agricultural areas for water utilization areas.  
**Funding:** This research was funded by Mapua University Directed Research for Innovation and Value Enhancement (DRIVE).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in this study.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** This research was partially supported by the National Research Council of Thailand (NRCT). This support is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.