esa-sceva/satcom-chunk-collection huggingface dataset:
# The Efficacy of MOOC to Support Students in Pedagogical Research
## 2 Theoretical Background--Mooc  
In recent years, MOOCs are becoming a very popular form of teaching for people of all ages. In most cases, MOOCs are made available through specialized systems that help course providers spread them among students while helping them to find courses they are interested in. The year 2017 represents the sixth year since the MOOC trend was established and for the first time according to statistics, this year marks a slowdown of inflow of new students [14]. In 2017, for the first time, approximately 23 million new students enrolled in their first MOOC, with the total number of students increasing to 81 million. These numbers are similar to the statistics from 2016, which confirms the stagnation of the inflow of new students. However, although user growth has been relatively flat over the past period, new courses are still being created. [PERSON] [14] states that at the end of 2018, more than 900 universities were offering 11,400 MOOCs and the number of people who were studying online was 101 million.  
In present days, universities around the world use MOOC platforms such as Coursera, Udacity, and edX. Coursera, one of the most important MOOC platforms, has 185 partners (including universities and organizations) in 28 countries and it offered 3449 courses in March 2019 [15].  
In terms of the number of registered users, the top five MOOCs in 2018 included Courser with 37 million users, edX with 18 million, XuetangX with 14 million, Udacity with 10 million, and FutureLearn with 8.7 million users [14].  
[PERSON], a founding member of Google Brain, registered more than 1.7 million students in his first machine learning course by the end of February 2019 [16].  
According to [PERSON] and [PERSON], MOOCs can be primarily divided into cMOOCs and xMOOCs [17]. cMOOC (connectivist MOOC, based on connectivist principles, openness, and participatory teaching) and xMOOC (extended MOOC, aims to transfer knowledge to the student, teacher-centered, and based on the behaviorist learning theory [18].
## 2 Theoretical Analysis
\right., \tag{9}\]  
where \(A=(\sigma-1),B=(2-\sigma),C=(\gamma^{2}+\varphi^{2})^{\frac{1}{4}},D=(\omega \tau+\varphi)^{2}+\gamma^{2}\), and the variance of the vacuum mode introduced by the coupling mirror of signal and other intra-cavity losses is equal to 1, i.e., \(Var(\delta X_{\alpha_{s,in}})=Var(\delta X_{\beta_{s,in}})=Var(\delta Y_{ \alpha_{s,in}})=Var(\delta Y_{\beta_{s,in}})=1\). \(Var(\delta X_{p})\) is the variance for the amplitude quadrature of the pump. The noise power spectrum of the intensity difference between the signal and idler can be written as  
\[ \begin{array}{lcl}S_{out}(\delta X_{s,out},\delta X_{s,out})&=&\left\langle \Delta^{2}(X_{s,out}-X_{i,out})\right\rangle\\ &=&1-\frac{2\gamma BC(\sigma\sqrt{2\gamma_{i}})^{3}-\gamma_{i}BC)}{\sigma^{2}D} \\ &-&\frac{8\gamma AC\sqrt{2\gamma_{i}}\left(2\gamma_{s}BC^{2}+\sigma^{2}D\right)- \gamma_{i}BC(\sigma D+\gamma BC^{2})-2\sigma^{2}\gamma_{s})C\left[-2\gamma_{i} \sigma^{2}AD^{2}\right.}Var(\delta X_{p})\end{array}  \tag{10}\]  
If the signal and idler are coherent states without quantum correlation, the intensity difference fluctuations of two beams is equal to the sum of their intensities; that is \(\left\langle\Delta^{2}(X_{s,out}-X_{i,out})\right\rangle=\left\langle X_{s,out} \right\rangle+\left\langle X_{i,out}\right\rangle\), which corresponds to the SNL. \(\left\langle X_{s,out}\right\rangle\), \(\left\langle X_{i,out}\right\rangle\) are the intensity of the signal and idler, respectively. If the intensity difference fluctuations of the two beams satisfy the following relation of \(\left\langle\Delta^{2}(X_{s,out}-X_{i,out})\right\rangle\)\(<\)\(\left\langle X_{s,out}\right\rangle+\left\langle X_{i,out}\right\rangle\), the two beams are quantum correlated. According to Equation (10), the impact factors of spectrum of quantum correlation between the signal and idler include the pump parameter, the intra-cavity losses, the detuning, the analysis frequency, the cavity round-trip time of the signal, and the variance for the amplitude quadrature of the pump.
###### Abstract  
This study explores pollution detection and classification in the Shatt al-Arab River using advanced image processing techniques. Our proposed system integrates Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The Shatt al-Arab River in Basra, Iraq, faces increasing pollution from human activities, including oil spills, debris, and wastewater. We conducted extensive surveys of the river and its tributaries using a DJI Mavic drone, amassing over 1000 images to train machine learning models. The results indicate that RF excels with 94% accuracy for oil spills, 92% for wastewater, and 95% for debris. SVM also performs well, achieving 92%, 88%, and 94% accuracy for the respective pollutants. KNN, though insightful, lags with 85%, 89%, and 86% accuracy. Trained on this novel image dataset, these models show promising accuracy in detecting various pollution types from drone footage.  
pollution detection; image processing; Shatt al-Arab River; Support Vector Machines; Random Forest; environmental monitoring; Basra; Iraq
# Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files
## 2 Background and Preliminaries
### Portable Document Format  
PDF has become the industry standard for document exchange since its inception by Adobe Systems in 1993. The format is light, easy to use, and most importantly, independent of hardware, software, and operating systems. In 2008, it became an open standard released as ISO 32000-1 [25]. PDF allows for the embedding of text, images, JavaScript, and Flash, as well as the opening of external resources from a local computer or the Internet [26]. Although these factors, among many others, contributed to the popularity of the format, yet they enabled attackers to exploit vulnerabilities in PDF document viewers. To understand how this can be done, we must look at the structure of a PDF file.  
A PDF file is mainly composed of four parts as shown in Figure 2: the header, the main body, the cross-reference table, and the trailer [27].  
* The Header: it is the first line of a PDF. It specifies the version of the PDF used when the document was produced. The format of the header is given by %PDF-a.b where a.b represents the version.
* The Body: it holds the main content of the PDF file represented as objects, which are the basic building blocks of a PDF, with multiple types depending on the element they hold. Note that any type of object can be obfuscated in the file structure and only de-obfuscated at runtime. This makes obfuscation an option that attackers could exploit to evade classic signature-based malware detection software.
* The cross-reference table (xref table): it lists the byte offsets of all the objects in the main body. The xref table is used to quickly access a certain object without having to search.
* The trailer: it gives the location of the xref table and the root object in the body. The trailer ends with the format %%EOF, which marks the end of the document as well.  
When a reader parses a PDF file, it starts by parsing the trailer to get the reference of the root object in the file structure. Then, it uses the xref table to traverse the objects in the PDF body and render them.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 3 Deep-CNN Shape Sensing
### Data Collection and Processing  
Nonlinear regression models can achieve more accurate predictions by increasing the training dataset size, although this requires significant computational resources. Our research utilizes a dataset generated from experimental studies of a cable-driven continuum robot equipped with an e-textile sensor. For each data sample, we sequentially applied tension to the three servo motors actuating the robot and recorded data from the 16 sensing points on the e-textile sensor, which serve as the model features. The curvature \(\kappa\) and the angle of the curvature plane \(\phi\) are the targets of our CNN model. We estimated these parameters using an Inertial Measurement Unit (IMU) sensor attached to the robot's tip, based on the constant curvature assumption [1].  
It can take a while to investigate every potential set of tensions for the robot's actuation. In order to practically address this, we created an extensive dataset of 1330 data points. These data points were gathered inside the anticipated range of the 18 cm-long continuum robot being tensioned by the servo motors. The sampling procedure was carefully planned to provide complete coverage of the robot's working range while respecting allowable servo restrictions. Thirty percent of the dataset was set aside for testing, fifteen percent for validation, and seventy percent for training in order to guarantee consistent model performance and generalization. The purpose of implementing this partitioning technique was to enable thorough model training and assessment.  
To utilize CNNs for shape sensing of soft robots, each data sample, represented as the sensor readings \(p_{i}\in\mathds{R}^{16}\), must first be converted into a 2D feature image, denoted as \(\mathbf{I}\in\mathds{R}^{1330\times 4\times 4}\). This transformation is not merely a reshaping of data but a critical step in enabling the CNN to interpret the spatial correlations between different sensing points on the robot's surface.  
It is worth mentioning that if the sensor input falls outside the range of data that the CNN model was trained on, particularly in the presence of noise, it could affect performance. However, during dataset generation, the actuation was performed rapidly, causing disturbances in the sensor readings. These disturbances helped to broaden the range of fluctuations included in the training data, improving the model's ability to handle such variations.  
To improve the stability of the model during training, the training and testing datasets are standardized using the Z-score normalization method. This process ensures that our dataset has a mean (\(\mu\)) of 0 and a standard deviation (\(\sigma\)) of 1. The normalization of each data point \(\mathbf{p}\) is conducted as follows:  
\[\mathbf{p}^{\prime}=(\mathbf{p}-\mu)/\sigma\]  
It is essential to highlight that the values of \(\mu\) (mean) and \(\sigma\) (standard deviation) used for normalization are computed exclusively from the training dataset.
## 2 Materials and Methods  
### Power Consumption Modeling  
During the machining process, all the various forms of energy generated by machine tools are derived from electrical energy, including mechanical energy, sound energy, and thermal energy. Therefore, in this paper, when measuring the power consumption of various energy-consuming subsystems of machine tools, only the electrical energy consumption was measured.  
In this paper, the machine tool power consumption is split into several components, and their sum is the total power consumption of the machining process, which can be illustrated with Equation (1).  
\[P_{\text{total}}=\sum_{i=1}^{I}P_{\text{fix-i}}+\sum_{j=1}^{J}P_{\text{var-j}} \big{(}n,f_{Z},a_{\text{p}}\big{)} \tag{1}\]  
where \(P_{\text{total}}\) represents the total power consumption of the machine tool; \(P_{\text{fix-i}}\) represents the power consumption of the \(i\)-th fixed energy consumption subsystem; \(P_{\text{var-j}}\) represents the power consumption of the \(j\)-th variable energy consumption subsystem; \(j\) represents the number of fixed energy consumption subsystems; \(n\) represents the spindle speed;\(f_{z}\) represents the feed per tooth; and \(a_{p}\) represents the cutting depth.  
The power consumed by each fixed energy consumption subsystem can be considered as a constant value that does not vary with machining states. The power consumed by each variable energy consumption subsystem is influenced by process parameters, cutting tools, and workpiece materials. Therefore, this paper focused on analyzing and establishing power consumption models for variable energy consumption subsystems. The Kern EVO micro-milling machining center was the research object, as shown in Figure 1.  
On this basis, the total power consumed by the machine tool can be further decomposed as Equation (2).  
\[P_{\text{total}}=P_{\text{i}}+P_{\text{m}}=P_{\text{b}}+P_{\text{a}}+P_{\text{ s}}+P_{\text{f}}+P_{\text{m}} \tag{2}\]  
where \(P_{\text{b}}\) represents the basic power of the control systems, lighting, and air pumps; \(P_{\text{i}}\) represents idle power consumption; \(P_{\text{a}}\) represents the auxiliary power of the cooling, lubrication, chip collection, and tool-changing systems; \(P_{\text{s}}\) represents the non-machining spindle power; \(P_{\text{f}}\) represents the non-machining feed power; and \(P_{\text{m}}\) represents the material removal power.  
Figure 1: Energy consumption boundary of Kern EVO micro-milling machine tool.  
The machine tool power varies with different machining process stages, as shown in Figure 2. It can be observed that the tool change process has a short duration and low power consumption, which can be neglected if the tool change frequency is not high. Upon activating the cutting lubrication system, there is a significant increase in the machine tool power. Furthermore, it can be seen that different process stages exhibit distinct variations in cutting power, indicating that changes in process parameters lead to different power consumptions.
# Alternative Fiber-Based Paperboard Adhesion Evaluation with T- and Y-Peel Testing
## 2 Materials and Methods
### Adhesion Strength Testing  
The testing samples were prepared according to the modified ASTM D1876-08 standard for the T-peel testing and glued together with water-based adhesive EUKALIN 6550-VL-80 (Eukalin, Eschweiler, Germany) with a Brookfield viscosity of 1000 mPas. The adhesive was applied evenly on the surface with a roller over the mask (the quantity of adhesive applied was 0.5 g per testing sample area (2.5 \(\times\) 24 cm)) and two paperboard samples were pressed together to provide good adhesion. To ensure an even pressure on the whole surface, the samples were pressed with a standardized FINAT 2 kg roller. Before further testing, all prepared samples were conditioned in accordance with the ISO 187 standard.  
Testing of the T-peel and Y-peel was performed on a multi-testing machine (ZwickRoell, Ulm, Germany) using a 10 kN measuring head and adjusted specimen holders. The samples were clamped in two different geometries. Firstly, a well-known T-peel test was performed in accordance with the ASTM D1876 standard, where the unbonded part of the sample is clamped in a T position. Additionally, the Y-peel test was carried out with the same settings as the T-peel test, but with modified clamping geometry.  
Additionally, after bonding the samples together, an SEM analysis of the joints after testing was performed using a Jeol JSM-6060 LV (Jeol, Tokio, Japan) scanning electron microscope.
## 2 Materials and Methods
### Methods
#### 2.2.3 Distances in the Latent Space  
Once a model is trained, the latent representation \(z\) of any input image \(x\) can be obtained. Then, for any practical applications, a distance (or similarity function) needs to be defined which should satisfy the metric axioms and, hopefully, will capture the intended distance between the corresponding inputs \(x_{1},x_{2}\). The stochastic process involved in obtaining z violates the first axiom, therefore we use a distance over the probability distributions.  
Since the latent space representation of an input image \(x\) is given by a multivariate Gaussian distribution, the distances between these representations cannot be computed as Euclidean distances. Let \(P=N(\mu_{P},\Sigma_{P})\) and \(Q=N\Big{(}\mu_{Q},\Sigma_{Q}\Big{)}\) be the two multivariate Gaussians corresponding to the latent vectors \(z_{1}\) and \(z_{2}\). In our case, we tested two distances:  
* Bhattacharyya distance (BD): \[D_{B}(P,Q)=\frac{1}{8}\Big{(}\mu_{P}-\mu_{Q}\Big{)}^{\prime}\Bigg{(}\frac{\Sigma _{P}+\Sigma_{Q}}{2}\Bigg{)}^{-1}\Big{(}\mu_{P}-\mu_{Q}\Big{)}+\frac{1}{2}\ln \frac{\text{det}\Big{(}\frac{\Sigma_{P}+\Sigma_{Q}}{2}\Big{)}}{\sqrt{\text{ det}\Sigma_{P}\text{det}\Sigma_{Q}}}\] (10)
* Symmetrized Kullback-Leibler divergence (SKLD): \[D_{KL}^{*}(P,Q)=D_{KL}(P\Big{\|}Q)+D_{KL}(Q\Big{\|}P)\] (11) where \(D_{KL}\) is Kullback-Leibler divergence as used in the latent loss from Equation (3). The Equation (11) further simplifies in the case of diagonal covariance matrices for the two distributions.  
To measure the similarity of two patches, we therefore compute the Bhattacharyya distance given in Equation (10) or the symmetrized KL divergence from Equation (11) of the probability distributions defined by the encoder output for given input patches. We leverage the disentanglement properties of the model by choosing only the dimensions which capture the morphological information instead of the whole latent space, in order to measure the similarity in a stain independent manner. Therefore, only the probability space over the dimensions defining \(z_{m}\) are used for the distance computation. Visualizations of the whole latent space and the subspaces using principal component analysis (PCA) and t-stochastic neighbour embedding (tSNE) are shown in Figures 4 and 5 respectively.  
Figure 3: Layout of the decoder without skip connections to reconstruct the input.
## 6 Discussion  
In this two-year longitudinal retrospective study, we assessed the changes of retinal morphology through a multimodal approach, including OCT, BCVA, LCLA and VF, in 46 RR-MS patients treated with fingolimod. Fingolimod phase two and three randomized clinical trials reported dose-dependent and time-dependent risks (with higher risk within the first 3-4 months) of the development of ME, ranging from 0.3% to 1.2% [11, 12]. Fingolimod-associated ME has also been observed to be more frequent in the case of coexistence with systemic pathologies such as diabetes or inflammatory ocular pathologies (e.g., uveitis) [35]. The present study has a longer follow-up compared to previous reported real-world studies, but similar to the latter, it reports no cases of ME [16, 36]. Regarding the BCVA in the whole sample, in accordance with previous studies [15, 36], we observed a stability and an improvement rate that was not clinically significant, being less than 10 ETDRS letters (2 lines) on average, which is considered the cutoff for this test. According to previous studies, we observed a stability and a non-clinical improvement in the entire cohort, relative to the LCLA and VF-MD. In our study, the OCT assessment was not only focused on ME screening, but also on the variation of both macular (CRT and TMV) and optic nerve fiber parameters (pRNFL-T, NRR-T and RNFL-T 4 quadrants), analyzing their progression rates during the 2-year follow-up. Finally, we also assessed the differences in annual progression rates between ON+ and ON\(-\) patients. The TMV and CRT have also been analyzed in some previous real-life studies conducted on patients with RR-MS and on therapy with fingolimod. In one of these papers, with a study conducted on 23 patients, [PERSON] et al. did not identify statistically significant changes in terms of TMV or CRT, both during the study and at the end of the 12-month follow-up, regardless of the presence of previous ON [32]. In our study, considering the whole sample, we recorded a statistically significant reduction rate of both the CRT and TMV during the follow-up. By comparing the progression rate between the two subgroups considered, we did not detect significant differences in relation to the CRT and the TMV during the 2-year follow-up. However, we observed an average thinning rate of the CRT and the TMV, which was more accentuated in ON+ patients than in ON\(-\) patients. This assumes that, although in therapy, these patients are subject to a more rapid deterioration of macular cell populations. Furthermore, regarding optic disk parameters, there is only one short-term study (with a 5-month follow-up) conducted in RR-MS patients treated with fingolimod that showed a stability of RNFL parameters during the follow-up [15]. Contrarwise, when analyzing our longer follow-up data, we observed a statistically significant reduction rate of all RNFL parameters. Moreover, our data demonstrate an annual
# Numerical Simulation of Co-Barrier Efficiency of Air Curtains in Mine Refuge Chambers
## 2 Model Development
### Description of Air Curtain System Model  
Figure 2 illustrates that the mine refuge chamber includes a transition room and survival room, with the blast door of the transition room being closed during regular mine operations. In the event of a gas explosion or fire in the underground tunnel, evacuees reach the transition room door firstly from the outside environment, then open the blast door into the transition room, and then get into the survival room. Installed air curtains between the outside environment and the transition room can produce an airflow barrier to CO gas from entering the survival room [35; 36; 37].  
Figure 3 illustrates that the dimensions of the outside environment in XYZ-direction are respectively 12 \(\times\) 3.6 \(\times\) 3 m, the dimensions of the transition room in XYZ-direction are, respectively, 4 \(\times\) 3.6 \(\times\) 3 m, the dimensions of the door frame wall of the refuge room in XYZ-direction are respectively 0.8 \(\times\) 1.6 \(\times\) 0.72 m. The transition room model used here is the same as in the previous publication, namely Ref. [34]. To more accurately study the air curtain barrier performance on CO gas, the size of the outside environment is modified in this paper, making the volume of the outside environment three times larger than the volume of the transition room, which ensures the accuracy of the simulation results [38; 39].  
Figure 2: Refuge space division.  
The CO concentration above the transition room is higher than that below because the mass fraction of CO is smaller than air. The measurement point is arranged at 1\(\#\) (14.72, 1.805, 2 m) to ensure the accuracy of the concentration of the measurement points in the transition room. Figure 4 illustrates that the air curtain is a row of round holes in a circular duct as outlets for airflow. The pipeline air curtain is installed close to the refuge door wall. Therefore, the air outlets on the air curtain pipeline are directly simplified as the air outlets on the wall, and the size of the hole diameter and hole distance is determined by the simulated working conditions.
## 4 Performance Evaluation
### Performance Metrics  
The network is evaluated on the basis of the following metrics: Throughput of the network, energy consumption of the network, energy efficiency of the network, collisions/data packet, delay time, communication time, and data PDR.