esa-sceva/satcom-chunk-collection huggingface dataset:
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 2 Related Work
[21] determined the dynamic correlation between brain nodes based on phase synchrony coefficients. Exciting new work has shown that viewing the brain FC as dynamic over time and frequency can successfully reveal the disruptions of the normal human brain in a disordered state [24; 25]. At present, the accuracy of ASD classification models based on multiple ABIDE training datasets ranges from 70-93% over the full ABIDE database. However, despite the increasing number of automated classification models, it remains a challenge to find discriminative models that provide superior accuracy with low false prediction in FC-based ASD diagnosis. Clearly, inaccurate predictions can have a negative impact on a patient's life and even cause financial costs for healthcare institutions [26; 27]. For example, if a model misclassifies an ASD patient as a normal case or misidentifies ASD subtypes, the disorder may go untreated and may worsen the patient's impairment symptoms. In this case, there can be serious consequences for the reputation and performance of health facilities. On the premise of ensuring a robust and efficient ASD diagnostic model, this study proposes a new dynamic FC as an ASD biomarker for more accurate classification of ASD from NC. Evidence suggests that the temporal dynamics of FC is a key feature in identifying brain disorders [20; 21]. Therefore, we proposed a new metric called wavelet coherence fluctuations (WCF), which represents the amplitude of coherence between brain regions during low-frequency fluctuations using the wavelet coherence transform. The functional brain network is constructed based on WCF, and biological ASD markers are identified by using a variance analysis-support vector machine (ANOVA-SVM) method. Then, the wavelet coherence plus singular value decomposition is implemented to generate a 2D matrix representing the coherence of the ASD biomarkers based on the pure time-frequency components. This matrix is known as the principal wavelet coherence (PWC) connectivity. By using SVD, the useful properties of the WC matrix in classifying ASD vs. NC can be extracted. Then, the PWC matrix is converted into a 2D image. Finally, a three-layer CNN (3L-CNN) is used as an AI algorithm to examine the performance of the proposed framework in identifying ASD patients using PWC images. Three main sections of the paper are presented as follows: the methodology in Section 3 explains the complete ASD classification methods based on the principal subspace of dynamic functional connectivity, including the WCF calculation, the ANOVA-SVM algorithm, the PWC generation and the proposed CNN models. Section 4 presents the results and discussion, and lastly, Section 5 provides the conclusion and future works.
## 2 Materials and Methods
### Encapsulation Performance  
Total eugenol was determined using the method of [PERSON] et al. [11] with modifications. The spray-dried powder (40 mg) was dissolved in 10 mL of 60% methanol by sonication for 30 min. The samples were filtered through a membrane with a 0.45 \(\upmu\)m pore size and the injection volume was 20 \(\upmu\)L. Analyses were performed using HPLC equipped with an RS diode array detector at 280 nm. The mobile phase consisted of 60 mL/100 mL aqueous methanol using an isocratic mode at a flow rate of 1 mL/min in a C-18 column (Agilent, Zorbax Eclipse Plus; 4.6 mm \(\times\) 25 cm, 5 \(\upmu\)m, 100 A). The eugenol concentration was determined from the sample peak area and used in the calculation of the loading capacity (LC). Encapsulation efficiency (EE) is defined as the percentage of total eugenol mass in spray-dried product with reference to the corresponding mass of eugenol in feed. The calculation of mass of eugenol in feed involved non-solvent mass in emulsion and mass of collected product.  
\[\text{Loading (g/100g)}=\frac{\text{Mass of eugenol}}{\text{Mass of collected product}}\times 100 \tag{1}\]  
\[\text{Mass of eugenol in feed (g)}=\frac{\text{mass of eugenol in emulsion}}{\text{ non solvent mass in emulsion}}\times\text{mass of collected product} \tag{2}\]  
\[\text{Encapsulation efficiency (g/100g)}=\frac{\text{Mass of eugenol}}{\text{mass of eugenol in feed }}\times 100 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & \begin{tabular}{c} **Eugenol Concentration in** \\ **Primary Emission (\%)** \\ \end{tabular} & \begin{tabular}{c} **Chitosan Concentration** \\ **in Chitosan Solution (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Eugenol Concentration in** \\ **Secondary Emission (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Chitosan Concentration in** \\ **Secondary Emission (\%)** \\ \end{tabular} \\ \hline F1-chi 0.0\% & 3.0 & 0.0 & 2.0 & 0.067 \\ F1-chi 0.0\% & 3.0 & 0.2 & 2.0 & 0.067 \\ F1-chi 0.2\% & 3.0 & 0.6 & 2.0 & 0.2 \\ F1-chi 0.33\% & 3.0 & 1.0 & 2.0 & 0.33 \\ F2-chi 0.0\% & 1.5 & 0.0 & 1.0 & 0.067 \\ F2-chi 0.0\% & 1.5 & 0.6 & 1.0 & 0.2 \\ F2-chi 0.33\% & 1.5 & 1.0 & 1.0 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The formulation of emulsion and name of the samples obtained.
## 3 Results and Discussion  
### Direct Carbon Emissions Assessed by Three Different Methods  
#### 3.1.1 Comparison of N2O Emission Calculations  
The annual direct N2O emissions from 49 WWTPs in 2022 calculated using the IPCC 2019, T/CAEPI 49-2022, and T/CUWA 50055-2023 methods were 601,287.0 kg, 468,870.9 kg, and 293,044.3 kg, respectively (Figure 1a). Among them, the N2O emission calculated using the IPCC 2019 method was the highest, while that calculated using the T/CUWA 50055-2023 method was the lowest.  
N2O emissions from wastewater treatment plants (WWTPs) primarily stem from incomplete nitrification and denitrification during the biological removal of nitrogen [1]. This occurrence is closely linked to the concentration of total nitrogen in the effluent. The IPCC 2019 method only considers TN in the influent and does not take into account the amount of total nitrogen (TN) removed, resulting in an overestimated value. In contrast, T/CAEPI 49-2022 and T/CUWA 50055-2023 consider the TN removal, yielding more accurate results. The N2O emission factors in T/CAEPI 49-2022 and T/CUWA 50055-2023 display a substantial disparity. The former is 0.016 kgN2O-N/kgN, while the latter is 0.01 kgN2O-N/kgN, causing the results of T/CUWA 50055-2023 to be significantly lower than those of T/CAEPI 49-2022.  
Further calculations of N2O emissions per unit mass of total nitrogen removed by different treatment processes using the IPCC 2019 method are shown in Figure 1b. The results indicate that the UCT process has the highest N2O emissions per unit of total nitrogen removed, while the anaerobic-anoxic-oxic (AAO), anaerobic-oxic (AO), and oxidation ditch process (OD) processes have relatively lower emissions. This suggests that the choice of nitrogen removal process is crucial for controlling N2O emissions. Although the IPCC 2019 method can reflect the differences in emissions among different processes, its overall tendency to overestimate N2O emissions needs to be noted.  
Figure 1: N2O emissions and emission intensity. **(a)** Comparison of N2O emissions among the three standards (IPCC 2019 vs. T/CAEPI 49-2022/2023). **(b)** N2O emission intensity per unit mass of total nitrogen removed calculated using IPCC for different treatment processes.
## 2 Materials and Methods
### Calculation of Fatigue Performance  
The fatigue life evaluation criterion of the restoration material and dentin tissue compares the maximum principal stress generated during loading with the stress-life (S-N) diagram. The S-N curve is a function of the number of cycles (N) to failure and represents the stress amplitude (\(\sigma_{a}\)). The fatigue values for the crown material were determined by the three-point bending tests as in previous studies. The fatigue behavior of the materials was calculated using the non-linear Basquin's Equation (1) developed by [PERSON] et al. (2017).  
\[\sigma=\text{A(N)}^{\text{B}} \tag{1}\]  
The A and B values for the crown material are demonstrated in Table 3. Wohler curves for enamel and dentin were taken from the literature and plotted for precise inverse loading (\(\sigma_{\text{m}}=0\)). Mathematically, the Equation (2) to be applied for any other mean stress (\(\sigma_{\text{m}}\
eq 0\)) has been demonstrated in previous studies (22; 23).  
\[\sigma_{a}=(\sigma_{f}-\sigma_{m})\text{ (2N)}^{\text{b}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Young’s Modulus** & \multirow{2}{*}{**Poisson’s Ratio**} & **Shear Modulus** & **Tensile Failure** & **Compressive** & \multirow{2}{*}{**Reference**} \\  & **(MPa)** & & **(MPa)** & **Limit (MPa)** & **Limit (MPa)** & **Failure Limit** \\ \hline Glass & X, 37.0 & XY, 0.27 & XY, 3.1 & & & \\ fiber-reinforced & Y, 9.5 & XZ, 0.34 & XZ, 3.5 & & & \\ post & Z, 9.5 & YZ, 0.27 & YZ, 3.1 & & & \\ \hline \end{tabular}  
MPa: megapacals, X,Y,Z: axis in the Cartesian coordinate system.  
\end{table}
Table 2: The orthotropic properties of fiber-reinforced composite resin posts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A (MPa)** & **B** & \(\sigma_{\text{f}}\) (MPa)** & **b** & **References** \\ \hline LS & 95.845 & \(-\)0.012 & & & [19] \\ PICN & 61.40 & \(-\)0.043 & & & [23] \\ Dentin & & & 247 & \(-\)0.111 & [23] \\ \hline \end{tabular}  
\end{table}
Table 3: Coefficient and exponent constants of the fatigue curves of dental ceramics and dentin.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 5 Discussion
### Typical Region Analysis  
Correlation analysis was conducted between the prediction results of this study and the three-dimensional landscapes in the real world, aiming to achieve two purposes. The first is to verify the validity, reliability, and usability of the research outcomes in the real world. The second is to observe the dynamic changes in landslide susceptibility by comparing data from different time periods in the same region.
# Provoking Actual Mobile Payment Use in the Middle East
## 3 Research Methodology
### Pre-Test  
In the end, an ideal survey questionnaire is unattainable. However, researchers can still design valuable surveys. It is vital to pre-test the survey questionnaire before deploying it to assess its usefulness. Pre-testing can help determine the survey's strengths and weaknesses in question type, wording, and sequence. The pre-test's importance lies in addressing any potential issues that may arise during data collection. As explained by [25], a pre-test is vital to ensuring any ambiguities in the questions are addressed to ensure participants understand the question is in accordance with what they are designed and intended to assess. Although this study is aided by questionnaires from past literature, pre-testing can also help ensure that the instrument tests the desired variables. Therefore, two approaches were employed to pre-test the questionnaire (participating and undeclared).  
_Participating_: The participants are informed of the purpose of the questionnaire before filling in the questionnaire. This approach was facilitated by an interview with the researcher. During the interview, the participant was asked to explain their reactions to the question forms, wording, and order of questions. This approach was helpful in determining whether the questionnaire was clear and understandable. Four participants were involved in this approach; the first was a professor of economics (associate dean of research and innovation) from Middle East College in the Sultanate of Oman. The second one was a doctorate lecturer at the same university. The other two were experts in the field: the first was a senior employee in the e-channel department at Bank Muscat, and the second was the assistant general manager at the Bank al Izz--Sultanate of Oman.  
_Undeclared_: The participants in this approach were asked to fill out the questionnaire without knowing that it is just a pre-test. It was similar to answer an actual questionnaire. The participant was informed that this practice is voluntary and that his/her response will be used only for the study purpose. No names or emails were requested. As we designed the questionnaire in two languages, two participants chose the Arabic version and the other two chose the English version.  
The participating group commented on the sequence of questions and their questionnaire included more instruments to measure the variable, accordingly emphasising the sequence and assessing the applicability of the new instrument after evaluating the length of the questionnaire. While the undeclared group suggested some wording in the Arabic version, they suggested arranging the multiple-choice options horizontally to reduce the amount of scrolling needed. Most of the questions needed scrolling to see the complete list of answers. This phase highlighted valuable feedback and comments on improving the questionnaire before deploying it to an actual study sample.
## 3 Results and Discussion
### Cell Interactions with Surfaces
The procedure to prepare SHS2 substrate allows us to establish significant differences (\(p<0.001\)) between the uncoated PES substrate for all the cell lines. Moreover, in the case of A431 cell line, significant differences with SHS1 were also found. These results demonstrated that the A431 seems to be more sensitive to the coating procedure of the fabrics, independently of the coating type, when compared with native PES. However, in the case of non-tumoral cell lines, the modification of PES using SHS2 conditions results in being more favorable than that obtained with SHS1. The effect of different coating (either SHS1 or SHS2) demonstrated to induce differences (\(p<0.001\)) in cell responses when the two non-tumoral cell lines (3T3 and HaCaT) were considered. When the coated SHS1 substrate was considered, differences between fibroblast (3T3 cell line) and the two keratinocyte cells lines (HaCaT and A431 cell lines) were found. In the case of SHS2 substrate, differences between all cell lines were found. The obtained results are in agreement with our working hypothesis through which SH surfaces could be effective in decreasing cell adhesion.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 2 Materials and Methods
### Methods
#### 2.2.2 Measurement of the Contact Angle  
The advancing and receding contact angle (denoted CA) was determined for each material with a method based on a sessile drop technique [17]. This method, along with others, typically calls for samples to be a polished block of intact material [18; 19]. However, in this study where the samples were granular, a material preparation process specific to soils had to be adopted [20]. A brief description of the adopted method is provided here, and the reader is directed to [17; 20] for further details. The granular materials in this study were adhered with a consistent weight per unit area for 5 s to double-sided tape that was attached to a flat, rigid surface to create a bed of adhered grains. Excess material was removed by gently tapping near the grains, and the adhering process was repeated once to increase the number of grains adhered. The samples were tested within a laboratory where the air was continually conditioned to a relatively consistent temperature. Before undertaking any tests, the room temperature was required to be 22 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C for consistency. Deionized water droplets around 5-7 mm or larger in base diameter were deposited on the surface of the grain bed with a syringe. The droplets used to measure the advancing angles required no special treatment. However, the droplets used to measure the receding angle required removing water from the droplet until its base diameter started reducing [17]. The images of the droplets were captured with a digital camera (Panasonic DMC-FZ20 with a standard lens) positioned at the level of the tape approximately 40 mm from the droplets. The image analysis software ImageJ was used to manually measure the angle between the grain bed surface and a tangent drawn to the droplet meniscus at its contact with the grain bed. Nine or more droplets were measured to observe the variance. The particle sizes adhered to the tape were kept between 20 and 50 \(\upmu\)m for the crushed materials to avoid a possible effect of variable surface roughness on the CA. However, the sand was not crushed to 20-50 \(\upmu\)m as it was unclear if the surface characteristics would remain representative of the uncrushed sand. Thus, the CAs of the crushed rock and coal materials were not strictly comparable with those of the sand, as the CA measured for the sand had an extra source of variability.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 1 Introduction  
There is a worldwide industry trend towards Industry 4.0, smart-cities, smart- agriculture, and a connected future. The main goals are to reduce operational costs by optimising resource consumption, reducing waste, and automating processes through the use of a network of intelligent sensors and actuators in the Internet of Things (IoT) and Machine to Machine (M2M) networks. To achieve this vision, Low Power Wide-Area Networks (LPWANs) have allowed nodes that require ultra-low power consumption, low data throughput, low-cost, and deep coverage to be deployed in a variety of applications.  
Amongst the various types of IoT-network, the IoT industry worldwide has taken a keen interest in Long-Range Wide-Area Networks (LoRaWANs) due to the LPWAN's robust physical layer LoRa modulation scheme. LoRaWAN [1] appears particularly suitable for research applications due to (i) the open protocol, (ii) the availability and low cost ofhardware components, and (iii) the possibility of establishing small, stand-alone private networks on unlicensed Industrial, Scientific, and Medical (ISM) frequency bands (US: 902-928 MHz, EU: 863-870 MHz). LoRaWAN is a protocol that defines the data-link and network layer in the Open System Interconnection (OSI) model. The main advantages of LoRa are the low cost of transceivers and gateways, the low power consumption and high link budget of transceivers, and the ease of deployment attributable to the licence-free sub-GHz ISM frequency bands usage of LoRa.  
In a typical LoRaWAN, specified in [2], nodes transmit messages based on a pure-Aloha Medium Access Control (MAC) protocol, followed by opening two reception slots to receive acknowledge messages from the gateway (if an acknowledgement was requested by the node), or possibly down-link messages. In this up-link-centric network, with no collision avoidance, the network suffers from significant packet loss (or re-transmissions in the case of packet acknowledgement) in high-node-density networks.  
In this paper, we propose a novel mesh-network based on the LoRa physical layer and compare it to a traditional LoRaWAN implementation. We have developed a simulation model in ns-3 that simulates the behaviour of the proposed mesh-network based on LoRa in an accurate way and use it to simulate comparable results to LoRaWAN.
## 2 Background
### Overview of Recurrent Neural Networks
#### 2.2.2 LSTM Model  
LSTM network is a modified version of recurrent neural networks, which makes it easier to remember past data in memory. The vanishing gradient problem of RNN is resolved here. LSTM is well-suited to classification process and time series prediction given time lags of unknown duration. It trains the model using back-propagation. LSTM architecture consists of five main parts (Figure 2) [34; 35]:  
* Cell state (\(c_{t}\))--1D vector of fixed shape with random value initialization. It contains the information that was present in the memory after the previous time step.
* Forget gate (\(f_{t}\))--changes the cell state, intending to eliminate non-important values from previous time steps. This helps the LSTM network to forget the irrelevant information that does not have any impact on the future price prediction.
* Input gate (\(i_{t}\))--changes the cell state with the aim of adding new information about the current time step. It adds new information that may affect the stock price movement.
* Output gate (\(o_{t}\))--decides what the next hidden state should be. The new cell state and the new hidden is then carried over to the next time step. Returns the final relevant information, which will be used for stock price prediction.
* Hidden state (\(h_{t}\))--it is calculated by multiplying output gate vector by cell state vector.  
The values of these vectors are calculated by the Equations (3)-(7).  
\[i_{t}=\sigma(W^{(i)}x_{t}+U^{(i)}h_{t-1}+b^{(i)}) \tag{3}\]  
\[f_{t}=\sigma(W^{(f)}x_{t}+U^{(f)}h_{t-1}+b^{(f)}) \tag{4}\]  
\[o_{t}=\sigma(W^{(o)}x_{t}+U^{(o)}h_{t-1}+b^{(o)}) \tag{5}\]  
\[c_{t}=i_{t}\odot u_{t}+f_{t}\odot c_{t-1} \tag{6}\]  
\[h_{t}=o_{t}\odot tanh(c_{t}) \tag{7}\]  
where \(x_{t}\) is input vector, \(c_{t-1}\) is previous cell state, \(h_{t-1}\) is previous hidden state, W and U are input-to-hidden and hidden-to-hidden weight matrices, \(\sigma\) is the logistic sigmoid function, and \(\odot\) denotes the element-wise multiplication.  
Figure 2: A single cell LSTM architecture.