esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
However, the \(T_{\mathrm{r}}\) was basically maintained at about 24 \({}^{\circ}\)C and the \(E_{\mathrm{v}}\) increased from 16.2 to 41.3, the expansion properties of copolymer microspheres were obviously improved. At a lower temperature, the shell polymer was not soft enough and retained high mechanical strength, which required higher vapor internal pressure to expand. However, increasing the \(E_{\mathrm{r}}\) of blowing agent provided copolymer microspheres with high vapor internal gas pressure drove to the microspheres began to expand and shrink at a lower temperature. In addition, with the continuous improvement in the expansion temperature, the mechanical strength of the shell polymer decreased, but the vapor internal pressure produced by the blowing agent continued to increase, especially the higher \(E_{\mathrm{r}}\) of blowing agent was, the greater internal gas pressure produced. Up to the \(T_{\mathrm{th}}\) of the microsphere, the equilibrium between the internal gas pressure and the carrying capacity of the shell polymer to resist the internal gas pressure was destroyed. After that, the internal gas pressure was dominant, and resulted in the shell of microspheres cracking, shrinking, or collapsing. As can be seen from Figure 6a,a', only a few number of microspheres expanded when the \(E_{\mathrm{r}}\) of pentane was only 3.4%. The blowing agent encapsulated in the microspheres was too low to provide enough expansion gas pressure for the expansion of the microspheres. When increased the \(E_{\mathrm{r}}\) of blowing agent, the numbers of expanded microsphere and \(E_{\mathrm{v}}\) increased significantly, and the surface of the microspheres became round, as shown in Figure 6b',c'. When the \(E_{\mathrm{r}}\) of pentane continually increased, there was no significant difference in the morphologies of the microspheres, as shown in Figure 6d'. Therefore, from the perspective of expansion properties of microspheres, as long as the \(E_{\mathrm{r}}\) of blowing agent in microspheres is in the range of 10-30%, the microspheres can possess excellent expansion properties. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(E_{\mathrm{r}}\) (\%) of Pentane & \(T_{\mathrm{st}}\) (\({}^{\circ}\)C) & \(T_{\mathrm{sh}}\) (\({}^{\circ}\)C) & \(T_{\mathrm{r}}\) (\({}^{\circ}\)C) & \(E_{\mathrm{v}}\) \\ \hline
3.4 & 87 & 110 & 23 & 16.2 \\
10.0 & 80 & 105 & 25 & 32.2 \\
20.6 & 76 & 100 & 24 & 30.3 \\
28.6 & 70 & 93 & 23 & 41.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Expansion characteristics of copolymer microspheres with different \(E_{\mathrm{r}}\) of pentane.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model
### Kinetic Equations
#### 2.4.1 Inertial Matrix  
The global inertial matrix refers to the sum of the ROV's inertial matrix \(M_{RB}\) and an additional hydrodynamic inertial matrix \(M_{A}\) due to the surrounding fluid's inertial (Equations (6)-(8)).  
\[M=M_{RB}+M_{A}\in R^{6\times 6} \tag{6}\]  
\[M_{RB}=\begin{bmatrix}m&0&0&0&mz_{G}&-my_{G}\\ 0&m&0&-mz_{G}&0&mx_{G}\\ 0&0&m&my_{G}&-mx_{G}&0\\ 0&-mz_{G}&my_{G}&I_{xx}&I_{xy}&I_{xz}\\ mz_{G}&0&-mx_{G}&I_{yx}&I_{yy}&I_{yz}\\ -my_{G}&mx_{G}&0&I_{xx}&I_{zy}&I_{zz}\end{bmatrix} \tag{7}\]  
\[M_{A}=\begin{bmatrix}X_{i\ddot{u}}&0&0&0&0&0\\ 0&Y_{\ddot{v}}&0&0&0&Y_{\ddot{r}}\\ 0&0&Z_{\dot{w}}&0&Z_{\dot{q}}&0\\ 0&0&0&K_{\ddot{p}}&0&0\\ 0&0&M_{\dot{w}}&0&M_{\dot{q}}&0\\ 0&N_{\ddot{v}}&0&0&0^{0}&N_{\ddot{r}}\end{bmatrix} \tag{8}\]  
where \(m\) stands for the mass of the ROV; \(x_{G}\), \(y_{G}\), and \(z_{G}\) denote the mass center of the ROV, respectively; and \(I_{ij}\) is the inertia tensor for each axis of subscripts.
applications in education is increasing, as AI and ML applications and research are being performed in different ES fields to drive education [4; 5]. Updating course content for teachers [6], enabling students to receive personalized education [7; 8], promoting smart course selection in higher education [9], and exam-paper preparation [10] are just a few examples of these applications.  
In recent decades, attempts have been made to predict student performance, both during and at the end of term, employing the classification and regression skills of ML models by using the information obtained from questionnaires, demographic information [11; 12], or stored Massive Open Online Courses (MOOCs), mobile autonomous school (MAS) and blended course information [13; 14; 15]. These studies aimed to increase student achievement levels, provide more effective and individualized teaching strategies for students, and improve their learning skills by predicting the performance of students at the end of term [16], during the active semester [17], or by determining the risk levels [18; 19; 20], drop-out probabilities [21; 22], or the factors that are most influential on student performance [23].  
However, different machine learning models, varied datasets, and different evaluation and validation criteria were reported in these studies, which has led to difficulties in determining the direction of future research, level of success and efficiency, and knowledge provided by the ML models for this field.  
This study aims to provide a reference to researchers for developing more applicable prediction systems for real-life situations by identifying the developments and gaps in the existing student performance prediction studies from a broad perspective and proposing remedies to these gaps. Therefore, a summary of the ML models, validation techniques, and evaluation criteria commonly used in ML studies is presented.  
Secondly, a systematic literature review was conducted in accordance with the PRISMA statement [24], covering the period between 2010 and 2020. The frequency of use of ML methods, evaluation criteria, and validation techniques obtained in the results were determined as percentages, and the general objectives of the research in this field, their effects in directing these objectives, and the datasets used are listed.  
Considering the changing data and developing ML studies, gaps were determined. Possible solutions are presented to carry out student performance prediction studies at a standard level and develop more applicable systems in the future, particularly from the ML perspective.  
The rest of the paper is organized as follows: Section 2 summarizes the basic principles of ML models and common metrics considered in the student performance evaluation studies. Section 3 presents the methodology and the results of a systematic literature review on student performance prediction studies. Section 4 presents a general discussion of the obtained results and potential remedies for the identified gaps. Finally, Section 5 presents the conclusions of the study.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design
#### 4.1.3 Participants and Procedure  
Study 2 recruited 16 participants (9 male; 7 female) from a university campus, with an average age of 23.7 years (\(sd=1.13\)). Among the participants, nine had prior experience with haptic or visual assistive devices, while seven had no previous exposure to such technologies. As in Study 1, the participants were required to simulate a visually impaired state by closing their eyes and making crossing decisions based on the feedback received. After the experiment, the participants also completed two evaluation questionnaires concerning urgency and decision confidence, using the same assessment methods as in Study 1.  
Figure 9: Haptic and Visual Feedback in Study 2. (**a**) Dynamic feedback design (right direction): stimulation transitions from position 1 to position 2, with an equal duration at each position. (**b**) Static feedback design (right direction): using the same stimulation pattern as in Study 1.
# Perspectives and Challenges in Robotic Neurorehabilitation
## 3 Current Trends of Robot-Aided Neurorehabilitation
### Relhabilitative Training
#### 3.1.1 Advances in Motor Control for Robotic Neurorehabilitation
The computational neurorehabilitation paradigm [148] consists of mathematically modelling the mechanisms underlying the rehabilitation process, with the aim of understanding the biological details of recovery and of optimizing the individual treatment of patients. Each model is characterized by three features: (1) It uses, as an input, a quantitative description of the sensorimotor activity obtained by simulations or by the interaction with robots; (2) it is based on the description of computational mechanisms of activity-dependent plasticity; and (3) it produces, as an output, quantitative values of functional outcomes. Some examples of these models have been developed [149; 150; 151] and even if they are limited to specific cases, it could be possible to apply them in conjunction with robotic neurorehabilitation to tailor the treatment to the patient. Despite all these considerations, we must take into account the possibility of the brain to autonomously choose a specific motor control strategy in order to perform a task. For example, when learning to stabilize unstable tools [151], either a strategy that relies more on feedback control [152] or one that is based on stiffness control [153] can be chosen. Therefore, understanding and modelling the strategies and the switching mechanisms between them may give us the possibility to predict more effectively the behavioral consequences of the application of a specific assistance. In this framework, if we could integrate models of sensorimotor control during robotic neurorehabilitation, we might have robots that are fully adaptable to the level of impairment of the patient and able to change their behavior accordingly to the patient's intention. The last aspect that is worth considering is the recent trend of training patients and/or therapists together, in groups or at least in couples (i.e., dyads). This synergy can be collaborative and/orcompetitive with a double goal: In the case of two patients training with the robot jointly, it brings improvement in the efficacy of the treatment through maximization of their motivation [154; 155]; in the case of a therapist who performs a training jointly with a patient, it brings to an improvement of the generalization capabilities of the patient [156; 157].
###### Abstract  
The mechanical properties and structure alteration (e.g., layer morphology and thickness) of the oesophagus wall can reflect its pathological conditions. Hence, quantitative measurement of the above-mentioned properties can play a significant role in aiding the disease diagnosis in clinical application. As a fast and non-invasive imaging modality, Optical coherence tomography (OCT) and vibrational elastography can provide high resolution (<10 \(\upmu\)m) structural and mechanical mapping of soft tissue. This study is a preliminary study to explore the potential of OCT and VOCE to evaluate both structural and mechanical properties of the oesophagus wall. In total, 52 oesophageal tissue samples were acquired from seven human Thiel-embalmed cadavers and were examined by the vibrational OCE. Both the OCT structure image and quantitative elasticity of each sample layer were obtained. In the OCT structure image, the averaged thickness for each sample layer was measured and corresponded with the histological image. Lamina propria has the largest thickness of 158.14 \(\pm\) 8.75 \(\upmu\)m, submuc is the thinest with a thickness of 143.19 \(\pm\) 10.11 \(\upmu\)m, and the thickness of musculara mucosa is 149.49 \(\pm\) 10.85 \(\upmu\)m. Averaged intensity of back-scattered light from each sample layer was evaluated. Intensity of lamina propria layer, musculara mucosa layer, and submuc layer have an average value of 79.27 \(\pm\) 0.51 dB, 69.83 \(\pm\) 0.56 dB, and 76.10 \(\pm\) 0.55 dB, respectively. The quantitative elasticity of each sample layer was evaluated in OCE. Elasticity of the lamina propria layer, musculara mucosa layer, and submuc layer were estimated as 115.64 \(\pm\) 8.80 kPa, 60.28 \(\pm\) 5.27 kPa, and 205.25 \(\pm\) 19.03 kPa, respectively. The quantitative elasticity results obtained by vibrational OCE corresponded with the collagen distribution trend in each sample layer. This study demonstrates the ability of OCT and vibrational OCE in the characterisation and quantitative evaluation of human cadaver oesophagus wall-structure properties and mechanical properties. The feasibility of applying OCT and vibrational OCE in clinical diagnosis of oesophageal disease is also discussed.  
tissue elasticity; optical coherence tomography; optical coherence elastography; human Thiel cadaver oesophagus; lamina propria; musculara mucosa; submucosa 2023
## 4 Description of Different Deployed VPN Solutions
### IPsec IKEv2 X509 Road Warriors Scenario  
This method uses IKEv2 without EAP also called \"Machine Certificate\"-based authentication. Supported clients are:  
* libreswan
* Windows 7 and up
* Windows Phone (requires the latest firmware)
* OS X and iOS
* Android with strongswan client.  
Figure 9 shows the comparison of the average throughput and standard deviation in IKEv2 Cert Road Warriors scenario, wired (on the left) and wireless (on the right). As seen from the figure, Windows 10 as an Ikev2-EAP client turns out to be the best performing, while Mikrotik and Raspberry Pi are almost equivalent. The virtual machine running the demo version of Mikrotik is locked to Ethernet 100 Mbps. Using IKEv2 with certificates, the performance of OpenWRT (client) and the Raspberry Pi 2 are noticeably close. As for the wireless clients, the Android, this time, turns out to be slightly better performing than iPhone 11 and iPhone 8.  
Figure 8: Comparison of the average Throughput in IKEv2-EAP Road Warriors scenario.  
Figure 7: Comparison of the average Throughput in IPsec XAUTH Road Warriors scenario.
## 4 Experimental Results
### Security against RS Steganalysis  
The security of the proposed method against the statistical RS (Regular and Singular groups) steganalysis method [6] is depicted in Figure 7. The RS steganalysis is based on discrimination function (\(DF\)) with \(M\) and \(-M\) as flipping masks. Furthermore, \(R_{M}\), \(R_{-M}\), \(S_{M}\), and \(S_{M}\) parameters are used to find the magnitude of the pixel block using \(DF\) function. If the parameters satisfy \(R_{M}\approx R_{-M}>S_{M}\)\(\approx S_{-M}\), then no hidden data are in the respective image. When an image has hidden data in its least significant bits, \(R_{-M}\) and \(S_{M}\) increases, whereas \(R_{M}\) and \(S_{-M}\) decreases and is exposed by RS analysis. In the RS analysis, we used \(\left[\begin{array}{cc}0&1\\ 1&0\end{array}\right]\) and \(\left[\begin{array}{cc}0&-1\\ -1&0\end{array}\right]\) as \(M\) and \(-M\), respectively. The \(x\)-axis on the RS analysis graph represents the percentage of hidden data in the stego image, and the \(y\)-axis denotes the relative percentages of regular (\(R_{M}\), \(R_{-M}\)) and singular (\(S_{M}\), \(S_{-M}\)) groups with the application of the above masks (\(M\), \(-M\)).  
The RS detection results can be seen in Figure 7 with the proposed method for the Aerial, Airplane, APC, Boat, Car, and Couple images. In Figure 7a, the differences between singular and regular parameters remain constant and close to each other even when the hiding capacity increases. As shown in Figure 7d, the RS detection differences values of the proposed method are very close to each other between \(R_{-M}\) and \(R_{M}\), and between \(S_{-M}\) and \(S_{-M}\) curves. For Figure 7b,c,e,f images, the differences between regular and singular groups are maintained close to each other via the proposed method. These results indicate that the proposed method is secured against RS steganalysis detection attacks.  
Figure 6: Comparison of histograms for images from USC-SIPI [24] dataset. The figures are (**a**) Aerial, (**e**) Airplane, (**i**) Boat, and (**m**) Couple.  
The maximum differences of detection parameters, i.e., \(R_{M}-R_{-M}\), and \(S_{M}-S_{-M}\) for the compared methods [7, 11, 15] are shown in Table 6. From the statistical analysis, it is evident that the proposed method has the smallest average difference in both regular and singular groups, i.e., 0.1853% and 0.1451%, respectively. However, [PERSON] et al.'s method has a slightly higher average difference for both regular singular groups (0.1947%, 0.1542%). Similarly, [PERSON] et al. and [PERSON] et al. methods have higher average difference results for both regular groups (0.2160% and 0.2358%) and singular groups (0.1697% and 0.1934%). In short, the proposed method has the smallest differences against all the compared methods, thus indicating the fewer detection artifacts in stego-images. Moreover, we can say that the proposed method has more capability to resist the RS steganalysis detection attacks.  
Figure 7: Comparison graphs for RS analysis from USC-SIPI [24] dataset. The figures are (a) Aerial, (b) Airplane, (c) APC, (d) Boat, (e) Car, and (f) Couple.
# Privacy Framework for the Development of IoT-Based Systems
## 5 Proposed Methodological Approach: Planning, Requirements, and Design (PRD)
### Foundations of PRD
#### 5.1.2. TpM-Pro Phases  
**Phase 1: Considering the Business**  
In this phase, the business context is thoroughly analyzed to identify the key issues that the proposed solution aims to address. The TpM-Pro methodology places a strong emphasis on understanding the business value that an IoT solution provides, setting it apart from previous IoT approaches. The guiding principle here is that an ill-defined business context will inevitably result in a solution that fails to meet client needs. Therefore, the primary objective of this phase is to comprehensively capture customer requirements, expectations, and concerns, ensuring the development of a successful and effective solution.  
Several critical factors must be considered when defining the problem to be solved:  
Figure 2: Graphical representation of the TpM-Pro methodology (adapted from [PERSON] et al. (2017)), highlighting the roles, iterative phases, and deliverables that serve as the basis for this study.  
First, the _business_ itself must be evaluated, recognizing that even projects within the same industry can have unique requirements. Overlooking these nuances could lead to solutions misaligned with end-user expectations.
* Second, it is essential to identify the _things_ (whether physical or virtual) that the business seeks to quantify, measure, or control. The project manager plays a key role in clearly defining what these \"things\" are.
* Third, the involvement of _specialists_ or domain experts, whether internal or external, is crucial as they provide valuable insights that guide informed decision-making.
* Finally, _business rules_, including any assumptions, constraints, or operational guidelines, must be clearly understood and integrated into the development process to ensure that the solution remains aligned with client needs and complies with regulatory and organizational standards.  
To streamline this phase, the TpM-IoT-Canvas tool is used. This tool facilitates the extraction of essential business and solution requirements while promoting collaborative project planning through a structured visual model. The TpM-IoT-Canvas is organized into eight core blocks: business, justification, benefits, product, things, solution requirements, client, and team. By providing a clear framework for organizing key information, this tool ensures that all stakeholders maintain a shared understanding of project goals and constraints, fostering a more cohesive development process.
# The Task of Post-Editing Machine Translation for the Low-Resource Language
## 1 Introduction
### Overview of Post-Editing Types in Machine Translation  
Post-editing of automated machine translation is carried out by a specialist fluent in the target language. Machine translation post-editing (MTPE) is the process of correcting and improving machine-generated translations to enhance their quality and bring them closer to the standard achieved by human translators [23]. Post-editing can be performed by professional editors or native speakers specialized in the relevant subject matter.  
Two main types of post-editing are commonly used: Partial post-editing involves eliminating grammatical, gross semantic, and stylistic errors in the text; the resulting text is easily comprehensible and entirely understandable. Full post-editing, on the other hand, involves checking and correcting the text until it becomes equivalent to a translation done by a human, reflecting not only the essence of the original text but also the depth of industry-specific terminology. A wide range of expressive language elements is utilized in this process. In-demand websites and modern online editors define four types of post-editing. A detailed description is provided in Table 1.  
* High post-editing: No differences from human translation; the result is an accurate, error-free translation.
* Full post-editing: Emphasizes translation quality, aiming to eliminate errors.
* Light post-editing: Involves editing the machine translation output to make its meaning clear, accurate, and unambiguous, but it may still contain grammar, spelling, and sequence problems. The original style and tone are not emphasized.
* Weak post-editing: Focuses on understanding over language quality and allows for linguistic and stylistic issues [24].  
This table allows for comparing four types of post-editing in machine translation across various aspects, including descriptions, objectives, permissible errors, time investments, and examples of tasks to which they are applied.  
There are several different machine translation systems (MTSs), and some perform translations more effectively than others. However, to determine which translator handles translations for specific language pairs or certain types of texts, it is necessary to conduct an analysis of translation quality based on individual text fragments. As a result of the analysis in this study, various factors were considered.