esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Analysis
### Lumped Element Circuit  
The equivalent lumped element circuit of a simple rectangular patch antenna can be designed as a parallel RC resonator which is based on the cavity model as shown in Figure 11a [22]. The loading of the insets means the insertion of two parallel slots into the patch increases the current path. The increment of the current path results in an additional series inductance (\(\Delta\)Lp1). An additional series capacitance (\(\Delta\)Cp1) has also been added to the circuit due to the slots. So, the final equivalent circuit is a combination of two RLC (A circuit combination of resistive(R), capacitive (C) and inductive (L) elements) resonators connected by a coupling capacitor Cc1 as shown in Figure 11b, and this design gives a dual-band frequency response [30]. The addition of a single stub, whose length is not equal to a quarter of the wavelength offers inductive and capacitive impedances to the higher and lower resonance frequencies, respectively. So, the loading of a single stub can result in an equivalent circuit with the addition of the capacitive impedance \(\Delta\)Cp2 and inductive impedance \(\Delta\)Lp2 to the circuit (Figure 11c). After the two stubs at each side of the center stub have been placed, another RLC resonator forms as shown in Figure 11d with a coupling capacitor (Cc2). Therefore, a triple resonance frequency has been observed. To ignore this resonance, the notches are inserted and the distance between the side stubs and the center stub has been increased. For this reason, the notches increase the current path which in turn increases the series inductance and the coupling capacitance. The value of the coupling capacitance can be ignored because of its low value with respect to the inductance of the third RLC circuit. Therefore, the simplified equivalent circuit will be the same circuit as that shown in Figure 11b with different parameter values.  
The complete lumped element circuit of the antenna can be found by connecting the coupling parameters of the dielectric substrate and the equivalent circuit of the DGS. As in the patch antennas, the dielectric material is used to separate two metallic layers of parallel combinations of capacitance, Cs and conductance Gs which can be assumed in the shunt between these two layers, as Gs is considered due to dielectric loss. Finally, the DGS section is connected in series with this parallel combination to form the complete lumped equivalent circuit of the antenna which is shown in Figure 12.  
Figure 11: The equivalent circuit of various configurations of the antenna. (**a**) Simple patch antenna; (**b**) Patch antenna with inset; (**c**) Patch antenna with inset and single stub; (**d**) Patch antenna with inset and all stubs.  
Figure 12: The complete lumped element circuit of the proposed antenna.
# Hybrid-Margin Softmax for the Detection of Trademark Image Similarity
## 3 Results
### Experimental Setup  
The details of the detection process are given in this section. The detection based on the loss-driven methods includes feature extracting and classifying.  
The process of feature extracting is as follows: the backbone of the feature extractor is an SNN consisting of two identical CNNs that have the same structure and weight, as shown in Figure 4. Images input through the SNN can be encoded to vectors in the same feature space. Several CNN structures are implemented, including a simple self-defined six-layer CNN and resnet18, 34, 50, 101, and 152 [25]. The fully connected layer in resnet is removed, and the six-layer CNN has a similar structure to resnet, including a batchnorm (BN) layer and pooling layer, as shown in Figure 5. There is no residual module in the six-layer CNN.  
Figure 4: The procedure of TM image similarity detection.  
The process of classifying is as follows: output vectors of the feature extractor are concatenated, activated by the transformation of softmax, and then sent to the fully connected layer, i.e., the classifier with the sigmoid activation function. Output judgment of similarity, the same as the input label, is one-hot encoded.  
The detection processes of the data-driven methods are as follows:  
1. Fine-tuning method: An SNN to be transferred is trained on the LFW training dataset. The backbone of the SNN is composed of an original series of resnet. When the SNN reaches 95% or more accuracy on the LFW test dataset, the fully connected layer is removed and the rest of the weights are frozen. The trained and frozen SNN and a new fully connected layer compose the TM detection model. Then, the model is further trained on the TM training dataset and tested on the TM test dataset.
2. Triplet model: Each input consists of two similar TM images, a dissimilar one, and corresponding labels. The triplet is built from the TM training dataset by attaching a random TM image to the pairs of similar samples.  
The six-layer CNN is excluded from the data-driven methods since a shallow-depth CNN is not able to meet the demand of fitting in the triplet network model and fine-tuning method.  
Other experiment setups are as follows:  
The scale factor s in softmax was set to 90, the angular margin in SphereFace was 4, the additive margin of cosine term in CosFace was 0.006, the additive margin of angular term in ArcFace was 0.003, the additive margins of cosine term and angular term in HMS were 0.006 and 0.003, respectively. All experiments were conducted in the pytorch framework. Cuda was used to accelerate training. The learning rate was set to 0.001, the batch size was 16, the optimizer in the triplet network was Adam, and the optimizer in other methods was SGD (momentum was 0.9).
## 6 Simulation and Analysis
### Performance of the Optimal Procedure  
The optimization of the mean file round-trip delay \(EC_{K}\) and the corresponding energy consumption \(ED_{K}\) was related to weight parameter \(\alpha\) (\(\alpha\in[0,1)\)). In Figures 5-7, we plot the optimal values of the number of coded packets \(M_{r}\), the spreading time \(T_{s\  
As is shown in Figures 5-7, the optimal values of \(M_{r}\) and \(T_{s,r}\) decreased with the increase of the index of a cycle, \(r\), which represented the number of independent packets the destination node still needed to decode the file. Hence, a higher \(r\) meant that the source had to add more redundancy (\(M_{r}\)) to the network, and packets needed a longer diffusion time (\(T_{s,r}\)) to reach the destination. Note that the maximization of \(NDM\) was equivalent to the minimization of the round-trip delay \(ED_{K}\) when \(\alpha=0\). Thus, the optimal values of \(M_{r}\) and \(T_{s,r}\) in the first cycle, i.e., \(M_{5}\) and \(T_{s,5}\), were significantly larger than the cases when \(\alpha\) got other values. As a result, the delivery of a file accomplished in the first cycle, and the round-trip delay of file was reduced significantly at the cost of energy. The optimal value of  
Figure 8: Optimal values of the mean round-trip time \(EC_{K}\) under different \(\alpha\).  
Figure 7: Optimal values of \(T_{w,r}\).  
Figure 9: Optimal values of the energy consumption \(ED_{K}\) under different \(\alpha\).  
\(T_{w\_r}\) seemed to be independent of \(r\). Since \(T_{w\_r}\) controls the time period that the source waits for ACKs in cycle \(r\), it was less affected by r than \(M_{r}\) and \(T_{s\_r}\).  
In Figures 8 and 9, we compare the optimal values of the \(EC_{K}\) and the \(ED_{K}\) with the simulation results, respectively. It can be observed that the simulation results were in good agreement with the analytical results, under different values of \(\alpha\). This validated our overall optimization procedure based on our model.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 3 Methods
### Customized Gaze Tracking System
The definition of each virtual camera coordinate system is shown in Figure 4, and the No. 1 camera coordinate system is defined as the device coordinate system. Figure 3: The left graph is the eye image captured when the near infrared light emitting panel is closed, while the right graph is when the light emitting panel is turned on. When the light emitting panel is turned on, the eye image has obvious dark pupil effect, and the image contrast is also enhanced. Although the structure of the proposed gaze tracking device is more complicated than the devices based on single or binocular cameras, the proposed device still has a compact structure, and only needs to calibrate stereo cameras. The semi-transparent mirrors can be erected, and it could make the device thinner. In addition, pinhole cameras are miniature devices suitable for wearable devices. The proposed method has the potential applying to the glasses-type device.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Intelligent Content Creation
#### 3.3.2 Technology Used: MLLMs in Intelligent Content Creation  
The implementation of MLLMs in intelligent content creation involves several key technologies. Transformer-based multimodal models, such as GPT-4 [9] and BLIP-2 [5], process and generate content across multiple modalities. GPT-4, for example, accepts both text and image inputs, enabling the generation of rich, contextually relevant content that combines textual explanations with visual elements.  
Image generation models like DALL-E and DALL-E 2 [15; 16] can generate high-quality images from textual descriptions. These models facilitate the creation of visual content such as diagrams and illustrations, enhancing comprehension of complex concepts.  
Natural language generation (NLG) is another crucial technology. Advanced language models like GPT-4 [9] and T5 [56] generate coherent and contextually appropriate textual content, including explanations, summaries, and questions.  
MLLMs also enable multimodal content synthesis by integrating various data types to create interactive and multimedia educational materials. Models that combine audio and visual data can generate instructional videos or interactive simulations [13].  
Adaptive content personalization is achieved by analyzing learner data, allowing MLLMs to generate content personalized to the learner's proficiency level, interests, and learning style [57]. This involves adapting the difficulty, presentation style, and content focus to suit individual needs.
## 1 Introduction
LET refers to the energy that charged particles deposit per unit length as they move through a material or tissue. LET increases progressively as the particles approach the Bragg peak and reaches a sharp peak near the distal end due to energy loss from successive nuclear and electromagnetic interactions. This high LET region creates dense ionization clusters that effectively damage DNA and are less influenced by oxygen levels. This characteristic allows it to overcome the radioresistance of hypoxic tumor cells, which is a limitation of conventional photon therapy [10]. In radiobiological studies, LET is typically averaged based on dose deposition (\(\mathrm{LET_{d}}\)) rather than track length (\(\mathrm{LET_{I}}\)). A systematic review of 354 proton therapy publications revealed that 69% reported \(\mathrm{LET_{d}}\)[11]. However, calculating LET accurately is challenging because primary protons continuously lose energy and produce secondary particles. Additionally, researchers have different methods for averaging, specifying mediums, normalizing densities, and including secondary particles. These variations can result in significantly different LET values, hindering the development of reliable relative biological effectiveness (RBE) models. RBE evaluates therapeutic efficacy by comparing clinical outcomes with those achieved using standard radiation, usually Co-60 and X-ray irradiation. However, the RBE of particle therapy remains poorly quantified due to its dependence on multiple factors, including LET [12; 13; 14; 15]. Since LET is strongly correlated with RBE--and directly correlates under constant radiobiological conditions--accurate LET characterization is crucial for conducting RBE-weighted dose calculations in treatment planning systems (TPSs) [16; 17]. However, most clinical TPSs rely on a fixed RBE value of 1.1 [18], which may underestimate the true effectiveness of high-LET areas at the distal Bragg peak and increase the risk of damage to surrounding healthy tissues. This limitation presents a critical gap in ensuring that the tumor receives the optimal dose and the appropriate LET distribution. Addressing this gap requires the implementation of MC simulations, which offer the precision necessary to correlate the Bragg peak with LET positioning. Further investigation is required to refine these correlations and enhance TPS accuracy in clinical applications. The purpose of this study is to characterize the proton beam energy and energy spread obtained from the proton cyclotron accelerator at King Chulalongkorn Memorial Hospital in Bangkok, Thailand. The objective parameters include the Bragg peak position, R\({}_{80}\), distal fall-off, and \(\mathrm{LET_{d}}\) values.
# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 2 Background Literature  
Low-resource natural language understanding (NLU) aims to develop models and systems that can process and understand human language in situations with limited available data. This field is particularly important for languages and tasks that lack large-scale annotated datasets like English. The scarcity of data poses significant challenges in training large neural network models, which typically require substantial amounts of labeled data to function effectively. Tasks such as machine translation, sentiment analysis, and Named Entity Recognition (NER) are especially challenging in low-resource settings due to a lack of parallel data or annotated examples. To address these issues, transfer learning utilizing pre-trained models like BERT, GPT, and XLM-R has emerged as an effective strategy. These models are initially trained on large-scale multilingual or cross-lingual data and then fine-tuned on small datasets of low-resource languages or tasks. Furthermore, cross-lingual transfer allows the transfer of knowledge from high-resource languages to low-resource tasks, facilitated by joint training or fine-tuning on small datasets.  
Beyond transfer learning, data augmentation, and meta-learning techniques also provide important solutions in low-resource NLU. Synthetic data generation through back-translation or other representational techniques helps mitigate the data scarcity problem and expand the available datasets. Additionally, active learning techniques support effective model training by selectively annotating the most informative examples from unlabeled  
Figure 1: Comparison between different few-shot text classification methodologies.  
data. Meta-learning techniques like few-shot and zero-shot learning endow models with the ability to generalize to new tasks or languages with only a minimal number of labeled examples. These techniques simulate low-resource conditions to train models to learn from limited data. The ultimate goal of low-resource NLU is to build robust models that perform well in data-scarce environments through innovative technologies such as transfer learning, data augmentation, meta-learning, and multilingual models.
## 5 Algorithm Performance Evaluation and Comparison
### Simulation Setup  
The simulation experiment in this section uses the PyCharm 2024.3.1 and PyTorch 2.7.1 frameworks to implement the proposed MADDPG algorithm. It primarily studies a cloud-edge collaborative system composed of one LEO satellite, multiple UAVs, multiple MEC servers, and multiple M-IoT devices. It is assumed that each UAV is equipped with an MEC server, and the M-IoT devices can offload tasks to the MEC servers via wireless channels for computation. The maritime domain is assumed to be 500 \(\times\) 500 m\({}^{2}\), and the positions of the maritime IoT devices are randomly distributed within this domain. The input data size of the computation tasks is uniformly distributed between [1; 5] Mbits, with the required CPU cycles ranging from [10\({}^{8}\), 10\({}^{9}\)]. The specific simulation parameters are shown in Table 1.  
In the MADDPG algorithm, the Actor network and Critic network are neural networks with two hidden layers, each containing 400 and 300 neurons. The discount factor \(\gamma\) is set to 0.99, the experience replay buffer \(\mathcal{D}\) size is 1000, the minimum batch size \(B_{b}\) is 100, and the soft update rate \(\xi\) is 0.001. Additionally, the learning rates for updating the Critic and Actor networks for all the intelligent agents using the ADAM optimizer are 0.001 and 0.002, respectively. To evaluate the performance of the proposed algorithm, this section introduces four baseline algorithms for comparison:  
1. All-local algorithm: Each M-IoT device executes computational tasks independently with its maximum computing capability, without offloading any tasks to the UAV or LEO satellite.
2. All-offload algorithm: Each M-IoT device completely offloads its task data to the UAV, where the MEC server on the UAV performs assisted computing.
3. Random algorithm: Each M-IoT device randomly allocates computational tasks between local execution and execution on the UAV.  
D3 QN algorithm: This algorithm uses the D3 QN algorithm to optimize the MEC server resource allocation and offloading strategies. Specifically, a single D3 QN network is used to decide the offloading strategy for intelligent agents, with the action space, state space, and reward space combining the actions, states, and rewards of all the maritime IoT devices and UAVs.
# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 1 Introduction
Accordingly, using a logistic regression model, it was analyzed that accident time, road surface condition, accident type, and vehicle type had a significant influence on the severity of traffic accidents. However, the severity method using the logistic regression model is used when the category is in binary format. Traffic accidents need to be predicted from multi-categorical data because analyzing only in binary format creates ambiguity. Therefore, this study proposes a weighted feedback-based harmonic MDG (Mean Decrease in Gini)-ensemble model for the prediction of traffic accident severity by using traffic accident data. It considers objective criteria for determining the severity of traffic accidents and the characteristics of various data and solves the over-fitting problem to reflect traffic accident severity. For performance evaluation, the proposed ensemble algorithm is compared with Random-Forest, KNN (K-Nearest Neighbor Algorithm), and XGbTree (eXtreme Gradient Boosting Tree) as single algorithms in terms of classification accuracy. In addition, an accuracy comparison based on the application of the mean decrease in Gini, coefficient harmonic-mean conformity, AUC of proposed model and comparison with conventional models are analyzed. This paper is comprised of the following: Section 2 describes the variable selection using the MDG coefficient and the classification methods that use ensembling. Section 3 describes the weight feedback-based harmonic MDG-ensemble model for the prediction of traffic accident severity; Section 4 describes the performance evaluation; and Section 5 presents the conclusion of the study.
# Fault Investigation of Circulation Pumps to Detect Impeller Clogging
## 3 Results and Analysis
### Static Values in Healthy and Faulty Condition  
First the static values are considered. Especially the influence of the faults on the efficiency of the pump is in focus. Table 1 summarizes the mean values of the measured values. All data, except the right column, were from the second test run (see Section 2.3). The flow was calculated using the process curve determined in the first test run. The right-hand column was from the third test run and applies to the healthy pump at the same electrical power consumption as in the case of a fault. This value was important for the interpretation of the results.  
Figure 4: Implementation of the fault impeller clogging for the example of two clogged impeller channels.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Flow (m\({}^{2}\)/h)** & **Head (m)** & **Hydraulic Power** & **Proportion of Active** & **Electric Power** & **Efficiency (\%)** & **Hailby Pump** \\  & & & **(W)** & **Channels (\%)** & **(W)** & & **(Same Power) (\%)** \\ \hline Healthy & 17.9 & 6.6 & 321 & 100 & 580 & 55 & - \\
1 clogged channel & 16.6 & 5.7 & 257 & 86 & 556 & 46 & 55 \\
2 clogged channels & 15.7 & 5.2 & 222 & 71 & 494 & 45 & 54 \\
3 clogged channels & 14.5 & 4.6 & 181 & 57 & 454 & 40 & 51 \\
4 clogged channels & 12.6 & 3.6 & 123 & 43 & 396 & 31 & 46 \\
5 clogged channels & 11.0 & 2.7 & 81 & 29 & 343 & 24 & 37 \\
6 clogged channels & \(-^{1}\) & 1.1 & \(-\)1 & 14 & 333 & \(-^{1}\) & 35 \\
7 clogged channels & \(-^{1}\) & 0 & \(-^{1}\) & 0 & 214 & \(-^{1}\) & 0 \\ \hline \hline \end{tabular}  
\({}^{1}\) Too low head to calculate the flow with the help of the reference curve (see Section 2.3).  
\end{table}
Table 1: Values of healthy and faulty circulation pump (2500 rpm) for measurement at constant process curve.  
In a healthy condition, the pump consumed a power of 580 W and produced a hydraulic power of 321 W resulting in an overall efficiency of 55%. In a faulty condition, both electric power and hydraulic power decreased. However, the hydraulic power decreased faster than electric power. This results in the decreasing efficiency of the pump. The hydraulic power decreases in a similar ratio as the proportion of active channels. For example, in the case of three clogged channels (four of seven active channels) the hydraulic power decreased by 44% and the proportion of active channels is reduced by 43%. The head and the flow is decreased in dependence on the process curve. Two effects are responsible for efficiency reduction. First is the faulty and uneven operation of the impeller and second the higher losses of the pump unit resulting in lower efficiency. The second effect is a known behavior for pumps and motors under partial load [24]. To overcome this effect, the efficiencies of the healthy variant under the same load condition were shown in the right column. This makes it easier to estimate how much the pump efficiency is affected by the fault. It can be seen that the efficiency is always 9 to 15% lower than in a healthy condition.
# Polyacrylonitrile Nanofibers Containing Viroblock as Promising Material for Protective Clothing
###### Abstract  
Antimicrobial viroblock/polyacrylonitrile nanofiber webs fabricated using the electrospinning method were assessed in terms of the antimicrobial activity against infectious agents as a potential material used in mask production. Viroblock (VB) is an amalgam of lipid vesicle and silver. Lipid vesicle depletes the virus outer membrane, which contains cholesterol, while silver ions penetrate the virus, interact with sulfur-bearing moieties, and possess the virus bactericidal property. VB, having anti-coronavirus and anti-influenza properties, was prepared in four different concentrations, 0.5 wt%, 1 wt%, 1.5 wt%, and 2 wt%, in regard to nanofiber weight. The resultant nanofibers were characterized by scanning electron microscope (SEM), Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), water contact angle, water content, and thermogravimetric analysis (TGA). Moreover, all nanofibrous samples were evaluated for cell proliferation assay and ATCC antibacterial tests. Based on characterization results and cytotoxicity, the developed composite nanofibers-based webs showed good promise for utilization in anti-viral masks. Particularly, 2 wt% VB/PAN nanofibers have the highest antibacterial properties against negative and positive bacteria along with excellent cell viability.  
antimicrobial; antiviral; viroblock; nanofibers; cell viability +
Footnote †: journal: Applied Sciences