esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Intelligent Content Creation
#### 3.3.4 Case Study: Automated Quiz Generation Using GPT-3  
Assessments are crucial for evaluating learner understanding and progress. Creating high-quality assessment items is resource intensive and requires subject matter expertise. Automated quiz generation using MLLMs like GPT-3 offers a solution by generating diverse and contextually appropriate questions based on educational content [58].  
GPT-3 is leveraged for its advanced language generation capabilities to create questions and answers. It can generate various types of questions, including multiple-choice, true/false, and open-ended formats. _Prompt engineering_ involves crafting specific prompts to guide GPT-3 in generating questions that align with learning objectives and appropriate difficulty levels. _Content filtering and quality assurance_ algorithms evaluate the generated questions for correctness, relevance, and potential biases, ensuring the quality of assessments.  
Educational platforms integrate automated quiz generation to provide immediate assessments after lessons, enhancing engagement and retention. Adaptive learning systems generate quizzes tailored to learner performance, allowing the system to adjust the difficulty and focus of subsequent content. Teacher support tools enable educators to use AI-generated quizzes as a starting point, saving time in test preparation and allowing them to focus on instructional design.  
A study by [PERSON] et al. (2022) investigated the use of GPT-3 for automated question generation in educational settings [58]. The findings indicated that GPT-3 generated questions of acceptable quality, covering key concepts with varying difficulty levels. Automation significantly reduced the time required to create assessments, and the model allowed for generating questions tailored to specific topics and learning outcomes.  
Challenges included content accuracy, as some generated questions contained inaccuracies or ambiguities, necessitating human review. Bias and fairness were also concerns, as the model occasionally produced content reflecting biases present in the training data, highlighting the need for oversight. Ensuring questions are directly relevant to specific instructional content remains a challenge, emphasizing the importance of contextual alignment.  
MLLMs like GPT-3 have the potential to automate educational assessment creation. While offering significant efficiencies, human oversight is essential to ensure accuracy, fairness, and alignment with educational objectives. Ongoing advancements in MLLMs are expected to improve the quality and reliability of automated content creation.
## 3 Experiment and Result
### Singular Point Avoidance Simulation Experiment
In Figure 11(b), the joint angular velocity curves are shown. In experimental group 1, significant angular velocity changes occur in joints 1, 4, 5, and 6, which can be attributed to passing through the shoulder singularity point. This result is consistent with theoretical analysis. In contrast, in  
Figure 10: Comparison of joint configurations of the robotic arm with and without the application of the singular point avoidance algorithm. experimental group 2, the trajectory does not pass through the shoulder singularity point, resulting in overall smoother changes in joint angular velocities.
## 3 DWA Algorithm and Improvements
### Optimization of the Evaluation Function  
A series of trajectories are simulated by employing a motion model and sampling approach. Subsequently, the optimal trajectory is identified among these simulations to serve as the operational path for the AGV. This selection process is facilitated by the utilization of an evaluation function. In the context of the traditional dynamic window approach (DWA) algorithm, the evaluation function is mathematically represented by Equation (9).  
\[G(v,\omega)=\varepsilon[\alpha\times head(v,\omega)+\beta\times dist(v,\omega) +\gamma\times vel(v,\omega)] \tag{9}\]  
In the above formula, \(head(v,\omega)\) is the azimuth deviation between the end of the simulated trajectory and the target point; \(vel\) is the evaluation function of the predicted velocity; \(\varepsilon\) is the normalized parameter of the three sub-functions; \(\alpha,\beta,\gamma\) is the weight coefficient, and \(\alpha,\beta,\gamma\in(0,1)\).  
The DWA algorithm's vulnerability to local optima, especially when confronted with concave obstacles, stems from its reliance on a single target point. To mitigate this limitation, the improved A* algorithm's key nodes are leveraged as local target points within the DWA algorithm. This enhancement aims to provide a more comprehensive navigation strategy. The resulting optimized evaluation function is mathematically depicted by Equation (10).  
\[G(v,\omega)=\varepsilon[\alpha\cdot xhead(v,\omega)+\beta\cdot dist(v,\omega) +\gamma\cdot vel(v,\omega)] \tag{10}\]  
In the above formula, \(xhead(v,\omega)\) is the azimuth deviation between the endpoint of the simulated trajectory and the local target point.
## 3 Results
### Mechanical Properties  
#### 3.3.1 General Observations  
The mechanical properties are presented in Figure 6. The hot-rolled variant showed poor performance when compared to the quenched and tempered variants. The strength and hardness were inferior to the other tested samples that had a martensitic microstructure. Yield strength remained under 500 MPa at 471 \(\pm\) 24 MPa while the tensile strength was 711 \(\pm\) 23 MPa for the hot-rolled state (Figure 6a). The measured hardness was 195 \(\pm\) 4 HV10 (Figure 6b). Also, the ferritic-pearlitic steel had very poor tapothness as the tested Charpy-V samples showed only a few joules of impact energy. Only the uniform elongation exceeded that of the martensitic variants. Nevertheless, the steel has not been designed to be used in the hot-rolled condition, but the HR variant was included for comparison to understand the effect of both quenching and tempering on the mechanical properties of the steel.  
The water quenched version without any tempering exhibited the highest tensile strength exceeding 2200 MPa (Figure 6c). However, the highest hardness (668 \(\pm\) 15 HV10) was measured for the variant tempered at 150 \({}^{\circ}\)C. The highest yield strength (1616 \(\pm\) 13 MPa) was measured for the sample tempered at 200 \({}^{\circ}\)C, which surpassed the WQ (1528 \(\pm\) 13 MPa) and 150 \({}^{\circ}\)C (1606 \(\pm\) 22 MPa) samples. Both yield and tensile strength decreased rapidly with tempering temperatures of 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, and hardness decreased as well. Uniform elongation also followed the same decreasing trend with the tensile strength and hardness. Impact toughness was improved with the low-temperature tempering up to 200 \({}^{\circ}\)C but tempering at 300 \({}^{\circ}\)C or 400 \({}^{\circ}\)C did not produce any further improvement in the low-temperature Charpy-V energies (Figure 6d). Impact energies were measured in joules and converted directly proportional to J/cm\({}^{2}\).  
Figure 5: **(a)** Pricricic acid etched sample (300 \({}^{\circ}\)C tempered) revealing the prior austenite grain boundaries (normal-to-rolling direction) and **(b)** the prior austenite grain size distribution.
# Attention-Guided Wireless Channel Modeling and Generating
## 3 Modeling and Generating
### Se-Gan
#### 3.2.1 Global Spatial Information Compression  
SEL starts by utilizing AdaptiveAvgPool2D to compress the spatial dimensions of input feature maps, thereby extracting global spatial features for each channel. The AdaptiveAvgPool2D is a module defined in the PyTorch library. It is used to perform adaptive average pooling, which automatically adjusts the output feature map size based on the input dimensions. This step aids in capturing the global statistical characteristics of each channel, providing a foundation for subsequent importance evaluations. The concrete description is given an input tensor x of shape, the layer applies a global average pooling operation using AdaptiveAvgPool2D. This operation reduces each spatial dimension of the feature map to 1, effectively obtaining a scalar value for each channel. This scalar is an aggregated representation of the global information for each feature channel. The result is a tensor y of shape, which contains the global average of each channel's feature map. This step compresses spatial information and distills each channel's most important features for further processing, eliminating the need for spatial positioning information in the recalibration process.
# Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems
## 3 Problem Statement
Notations:  
\(b\in B:\) number of batches  
\(k\in K:\) number of orders  
\(i,j\in V:\) number of storages  
\(S\subset V:\) subset of storage  
\(C:\) picker capacity  
\(w_{k}:\) weight of order \(k\)  
\(d_{ij}:\) distance from storage \(i\) to storage \(j\)  
\(s_{ik}=\left\{ \begin{array}{cl}1,\text{ item in the order $k$ located at storage $i$}\\ 0,\text{ otherwise}\end{array} \right.\)  
\(X_{k}^{b}=\left\{ \begin{array}{cl}1,\text{ order $k$ assigned to batch $b$}\\ 0,\text{ otherwise}\end{array} \right.\)  
\(Y_{ij}^{b}=\left\{ \begin{array}{cl}1,\text{ batch $b$ traveled from storage $j$ to storage $i$}\\ 0,\text{ otherwise}\end{array} \right.\)  
\(Z_{i}^{b}=\left\{ \begin{array}{cl}1,\text{ batch $b$ visit storage $i$}\\ 0,\text{ otherwise}\end{array} \right.\)  
\(\text{min}Z=\sum_{b\in B}\sum_{i\
eq j\in V}d_{ij}Y_{ij}^{b}\) (1)  
Subject to:  
\[\sum_{j\in V,j\
eq i}Y_{ij}^{b}=Z_{i}^{b}\ \forall b\in B,\ i\in V \tag{2}\]  
\[\sum_{i\in V,j\
eq j}Y_{ij}^{b}=Z_{j}^{b}\ \forall b\in B,\ j\in V \tag{3}\]  
\[\sum_{i\in S,j\in V\setminus S}Y_{ij}^{b}\geq Z_{i}^{b}\ \forall b\in B,\ S\subset V \tag{4}\]  
\[Z_{i}^{b}\geq s_{ik}\cdot X_{k}^{b}\ \forall b\in B,\ i\in V\ k\in K \tag{5}\]\[\sum_{b\in B}X_{k}^{b}=1\;\forall k\in K \tag{6}\]  
\[\sum_{j\in V,j\
eq i}w_{k}\cdot X_{k}^{b}\leq C\;\forall b\in B \tag{7}\]  
\[X_{k}^{b},\;Y_{ij}^{b},\;Z_{i}^{b}\in 0,1\;,\;\;\forall i,j\in V,\;b\in B,k\in K \tag{8}\]  
Objective (1) represents the objective function of the total picker route distance of all batches. Constraints (2) and (3) indicate that in order to avoid repetitive calculations, storage \(i\) can serve as the depot and destination only once. In addition, the constraints can be used to express whether the batches in storage have been picked. Constraint (4) represents a subcycle-eliminating constraint that prevents the formation of subcycles. The limiting function ensures that all picking points are picked and that each storage unit that requires picking is picked only once. Constraint (5) forces the selected orders and items in a batch to transform into binary variables. Constraint (6) reveals that an order can be assigned to only one batch to prevent such an order from being repetitively assigned to various batches. Constraint (7) indicates the capacity of the picking vehicle, signifying that the batch weight after order consolidation cannot exceed the maximum vehicle load. Constraint (8) indicates that all variables are binary.
## 2 Materials and Methods
### Deep Learning Model  
To denoise the sparse-sampling CT images, a deep learning model was employed in the current study. The outline of the training phase and deployment (denoising) phase using a deep learning model is represented in Figure 1. In the training phase, pairs of original and noisy CT images were used for constructing a deep learning model. In the deployment phase, we used the deep learning model for denoising noisy CT images. We used a workstation with GPU (GeForce RTX 2080 Ti with 11 GB memory, NVIDIA Corporation, Santa Clara, California, USA) for training and denoising.  
Two types of deep learning models were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) [7] and deeper U-net with skip connection [6]. RED-CNN combines autoencoder, deconvolution network, and shortcut connections into its network structure, and it performed well in denoising low-dose CT images. RED-CNN used image patches extracted from the CT image (size \(55\times 55\) pixels) for training [7]. [PERSON] et al. developed deeper U-net for denoising sparse-sampling chest CT images and showed that deeper U-net was superior to conventional U-net with skip connection [6]. Contrary to RED-CNN, deeper U-net made it possible to use entire CT images (size \(512\times 512\) pixels) as training data. In the current study, the usefulness of deeper U-net was evaluated and compared to RED-CNN.  
Figure 1: Outline of the training phase and deployment phase of the deep learning model.  
We implemented deeper U-net using Keras (version 2.2.2, [[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/))) with TensorFlow (version 1.10.1, [[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/))) backend. The major differences of network structure between our deeper U-net and [PERSON]'s deeper U-net were as follows: (i) the number of maxpooling and upsampling was 9; (ii) the numbers of feature maps in the first convolution layer of our U-net was 104. After the maxpooling layer, the number of feature maps in the convolution layer was doubled. However, if the numbers of feature maps were 832, the number of feature maps was not increased even after the maxpooling layer. The changes in the network structure of our deeper U-net including (i) and (ii) are shown in Appendix A in more detail. To train deeper U-net, pairs of original CT images and sparse-sampling CT images were prepared. Mean squared error (MSE) between the original and denoised CT images represented the loss function of deeper U-net. Adam was used as an optimizer, and its learning rate was 0.0001. The number of training epochs was 100. 4000 seconds were required for training deeper U-net per one epoch.  
RED-CNN was trained using its PyTorch implementation ([[https://github.com/SSinyu/RED_CNN](https://github.com/SSinyu/RED_CNN)]([https://github.com/SSinyu/RED_CNN](https://github.com/SSinyu/RED_CNN))). RED-CNN was trained on an image patch size of \(55\times 55\) pixels. Network-related parameters of RED-CNN were retained as described previously [7].
# Inverse Design of Distributed Bragg Reflectors Using Deep Learning
## 4 Conclusions  
In this study, we used Deep Learning to design a network that can accurately produce a Distributed Bragg Reflector (DBR) on demand. We successfully developed a Simulation Neural Network (SNN) with a promising mean absolute error of 0.0036 and a percentage accuracy of 97% for the test dataset. This was combined with an Inverse Design Neural Network (IDNN) to form a tandem network, which was able to produce predictions with a test mean absolute error of 0.00791, and an average accuracy of 94% for a range of inputs. This proves the capability of Deep Learning in the inverse design of multilayer systems, and could be applied to the photonic device industry while keeping the design and processing costs low. To demonstrate the model's versatility, reflectance predictions for the test dataset were then converted into CIE colour space values. These were plotted on a chromaticity diagram to demonstrate the possible colours which were within the scope of the design constraints. It was shown that a broad colour variation was achieved, and that marginal changes to the material structure allow for the generation of vivid colours at the corners of the RGB colour space.  
Conceptualization, supervision, M.K.H.; methodology, writing--original draft preparation, formal analysis, data curation, S.H.; writing--review and editing, M.K.H. and S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this research are available to the public at [[https://github.com/sblHead/Supplementary-code](https://github.com/sblHead/Supplementary-code)]([https://github.com/sblHead/Supplementary-code](https://github.com/sblHead/Supplementary-code)), accessed on 8 April 2022.  
The authors declare no conflict of interest.
## 1 Introduction
If the drug is attached to the CNTs through a degradable linkage, the released drug's activity and functionality need to be maintained, which can be a challenge. On the other hand, non-covalent approaches do not cause changes in the chemistry of drugs. The non-covalent approach to drug loading is to load the molecule onto the CNT surface by simple adsorption, \(\pi\)-stacking, hydrophobic interaction, or capillarity-induced filling [48; 49; 50]. Both pure CNTs and functionalized CNTs have been used in drug delivery, and in the case of noncovalent bonding, the advantages of functionalized CNTs can still be utilized. Among functionalized CNTs, carboxylated CNTs (f-CNTs) are highly water dispersible and our studies have demonstrated their potential to enhance dissolution rates [51]. F-CNTs can be synthesized so that the carbon to oxygen atomic ratio can be varied to give different levels of hydrophilicity and it is conceivable that by varying the degree of functionalization, the drug can be released at different rates. Therefore, the level of functionalization is expected to be an important factor. The objective of this work was to study the effect of the degree of functionalization of the incorporated f-CNTs on hydrophobic drugs during anti-solvent synthesis of micron-scale drug particles as well as the dissolution rates. Of particular interest to this study were the antifungal agent griseofulvin (GF) and the antibiotic sulfamethoxazole (SMZ).
## 3 Results
### Reliability Assessment  
#### 3.1.1 Intra-Rater Reliability  
Intra-rater reliability was checked by the same examiner, recording the performance of the LEMOCOT in 12 people with MS, with the results presented in detail in Table 2A. Excellent intra-rater reliability was found with ICC\({}_{2,1}\) (95% CI) = 0.96-0.97 (0.85-0.99), with acceptable error level SEM: 3.01-3.13 targets, and MDC\({}_{95\%}\): 8.32-9.58 targets.
# Spin Dependent Transport through Driven Magnetic System with Aubry-Andre-Harper Modulation
## 4 Closing Remarks  
A possible route of engineering spin polarization by means of light irradiation is proposed considering a tight-binding magnetic chain with cosine modulation in site energies. Each site of the magnetic system possesses a finite magnetic moment which interacts with the itinerent electrons. Because of this interaction, up and down spin channels get misaligned. Simulating the nanojunction, formed by placing the magnetic chain between two non-magnetic contacts, within a tight-binding framework, we determine spin dependent transmission probabilities using the Green's function formalism. From the transmission function we evaluate spin polarization co-efficient. The interplay between the irradiation, included into the Hamiltonian following the standard Floquet prescription, and the AAH potential has an important role and we investigate it critically on SP. Apart from achieving a high degree of spin polarization we can also selectively tune its phase with the help of irradiation. The peculiar gapped nature of up and down spin energy channels in presence of cosine modulation allows us to get higher filtration efficiency at multiple energy zones, and most importantly, it is also possible near the energy band centre together with other energy regions. Our analysis can be utilized to investigate spin dependent transport phenomena in different driven magnetic systems with correlated impurities.  
This paper was accomplished based on a collaborative work of the authors. [PERSON] conceived the project. [PERSON] and [PERSON] performed numerical calculations. All the authors have analyzed the data and co-wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FONDECYT 1180905 and Centers of Excellence with BASAL/ ANID financing, Grant AFB180001, CEDENNA.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
AK thanks DST-SERB, Government of India (Project No. EMR/2017/000504). JHOS recognizes Universidad Pedagogica y Tecnologica de Colombia (Project No. SG1 2834).  
Figure 8: Spin dependent transmission probabilities (\(T_{\uparrow}\rightarrow\) light green color and \(T_{\uparrow}\rightarrow\) black color) and corresponding spin polarization co-efficient \(P\) as a function of system size \(N\) at two typical energies. Here we set \(A_{x}=0.25\), \(\phi_{v}=0\) and \(\theta_{i}=0\)\(\forall\)\(i\).
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 3 Existing Contention-Based Random-Access Mechanism
### Preamble Transmission (UE \(\to\) eNB)  
By choosing a randomly generated one of the accessible CB preambles from a uniform distribution, a UE initiates CB-RA and sends it to the eNB at the next available RACH slot. The eNB regularly broadcasts SIB messages that encourage the UEs to select a good preamble from them. The physical properties of RA preamble in a PRACH contain the RA radio network temporary identification (RNTI) and the preamble data configuration. The UE waits for an RA response (RAR) window once the preamble is submitted.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Datasets
#### 4.1.3 AZH Dataset  
The AZH dataset was collected from the Wound care center in Milwaukee, Wisconsin, over two years. This dataset contains 730 wound images, having four different wound types, namely: Diabetic, Pressure, Venous, and Surgical wound. All the images are in the jpeg format, having varied sizes. The width of the images varies from 320 to 700 pixels, whereas the height varies from 240 to 525 pixels.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Performance Aspects
#### 3.4.2 Electrostatic Accelerometer
In the measurement bandwidth [1-100 mHz], the main contributor is due to the contact potential difference, which is the worst case based on the estimation from previous missions. Below 1 mHz, the main contributor is the thermal sensitivity of the bias, considering the same stability as in the GOCE mission. A better thermal stability will decrease this noise. In this configuration, the measurement range is \(\pm\)7.5 10\({}^{-6}\) m/s\({}^{2}\) and the control range is \(\pm\)9.4 10\({}^{-5}\) m/s\({}^{2}\) (the control range is the maximal acceleration before losing control of the proof-mass). Figure 29b shows the performance on the radial axis. The performance is degraded with respect to the other axes due to the control of the rotation with the same electrodes that control also the translation:  
Figure 28: (**a**) HybridSTAR electrostatic accelerometer. (**b**) HybridSTAR electrode plates with the cubic proof-mass. Above 0.1 Hz, due to the increased detector noise, a consequence of the decrease of the detector gain in order to be able to measure the proof-mass rotation when it compensates the satellite rotation of the Raman laser beam;
* In the measurement bandwidth, the performance is degraded, first, due to the higher voltage to be measured, a consequence of the control of the rotation, and second, due to the increase of the electrostatic stiffness caused by the higher voltage on the electrodes (impact on the detector noise contribution);
* Below 1 mHz, the thermal sensitivity of the bias is increased due to the thermal sensitivity of the wire stiffness. Note that an improvement of the radial axis performance is feasible by changing the design of the EA electrodes in a way to separate the angular control of the PM from its linear control. We can also notice that the levels of noise estimated in Figure 29 are compliant with the results obtained with the simulator (cf. Figure 21).