esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Analysis  
### Effectiveness of Malware Image  
Malware images often tend to have special visual textures for reusing core code, repeatedly executing a same code block or carrying malicious data. Although there were a number of researches that used malware images as model input for malware analysis and proved to work well, directly converting the whole code to an image where malicious code get mixed up with the regular code seems to be rough intuitively. However, CNN structure, which is a powerful image feature extractor, is used in this research to embed the malware images and it obtains discriminative class features of malware while eliminates these irrelevant features. To prove it, we visualize the gradient backpropagated from cosine similarity in the embedding space to the original input image, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measures**} & \multicolumn{4}{c}{**Datasets**} \\ \cline{2-5}  & **Filtered** & **Unfiltered** & **VirusShare** & **Drebin** \\  & **LargePE** & **LargePE** & & \\ \hline Accuracy & 0.8334 & 0.6807 & 0.7559 & 0.6858 \\ Precision & 0.8349 & 0.6893 & 0.7666 & 0.6966 \\ Recall & 0.8225 & 0.6778 & 0.7545 & 0.6623 \\ F1 Score & 0.8158 & 0.6684 & 0.7460 & 0.6494 \\ AUC & 0.9568 & 0.8978 & 0.9349 & 0.8831 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance assessment measures for 5-shot 5-way problem.  
Figure 6: Confusion matrix of first 20 classes in meta-testing set of LargePE dataset. Weights less than 0.01 are omitted.  
From the visualization, we can see that the visual texture areas which are often related to the malware characteristics have higher response sensitivity to the cosine similarity than these unstructured areas. This indicates that even when small changes occur in these texture areas, they will have significant influence on the final result whereas these plain areas will not. Also it proves that ConvProtoNet can automatically extract discriminative features from the mixed image representation and complete the classification task while ignore these useless features. Besides, malware image has many advantages over traditional malware features: Conversion from malware to image requires no expert knowledge; Images can be easily handled by modern CNN structures; Images can be visualized for interpretability; Conversion process is fast etc.
## 2 Materials and Methods
### Acdc: A Method for the Automatic Cell Detection and Counting  
Advances in optics and imaging systems have enabled biologists to visualize live-cell dynamic processes by time-lapse microscopy images. However, the imaging data recorded during even a single experiment may consist of hundreds of objects over thousands of images, which makes manual inspection a tedious, time-consuming and inaccurate option. Traditional segmentation techniques proposed in the literature generally exhibit low performance on live unstained cell images. These limitations are mainly due to low contrast, intensity-variant, and non-uniform illuminated images [1]. Therefore, novel automated computational tools tailored to quantitative system-level biology are required.  
ACDC is a method designed for time-lapse microscopy that aims at overcoming the main limitations of the literature methods [10; 19; 47], especially in terms of efficiency and execution time, by means of a fully automatic strategy that allows for reproducible measurements [48]. Each step of the pipeline underlying ACDC has been carefully optimized to reduce the running time required by large size images. We also provide two distributed versions of ACDC that allow the users to analyse a stack of images in real-time.  
The processing pipeline of ACDC exploits classic image processing techniques in a smart fashion, enabling feasible analyses in real laboratory environments. Figure 4 outlines the overall flow diagram of the ACDC segmentation pipeline, as described hereafter. It is worth noting that the number of parameters that need to be set in the underlying image processing operations involves only the kernel size of spatial filters and the structuring element sizes in morphological operations, providing a reliable yet simple solution. Nevertheless, these parameters allow the user to have control over the achieved cell segmentation results. Unlike DCNN-based black- or opaque-boxes, ACDC offers an interpretable model for biologists that may conveniently adjust the parameters values according to the cell lines under investigations. Differently from supervised Machine Learning approaches [11; 18; 22], ACDC does not require any training phase, thus representing a reliable and practical solution even without the availability of large-scale annotated datasets.  
Figure 4: Flow diagram of the ACDC pipeline. The gray, black and light-blue data blocks denote gray-scale images, binary masks and information extracted from the images, respectively. The three macro-blocks represent the three main processing phases, namely: pre-processing, seed selection, and watershed-based segmentation.
# XPS on Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) Nanoparticles with R = Gd or Ho
## 2 Materials and Methods
### Characterization  
The crystal structure of the prepared Co\({}_{1-x}\)R\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles was studied by measurements performed at room temperature, using a Bruker D8 Advance diffractometer with CuK\({}_{\alpha}\) radiation. The FullProf Suite Software (Windows (64 bits) for Windows 7-11 Intel) was employed to obtain the lattice parameters by Rietveld refinement of XRD patterns [55]. The average crystallite sizes of the nanoparticles were estimated according to the Debye-Scherrer equation:  
\[D=\frac{\text{k}\lambda}{\beta\cos\theta}\]  
where \(\beta\) represents the peak full width at half maximum (in radians) at the observed peak angle \(\theta\), k is the crystallite shape factor (was considered 0.94), and \(\lambda\) = 1.5405 A is the X-ray wavelength.  
The morphology of the Co\({}_{1-x}\)R\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles was examined using transmission electron microscopy (TEM) and scanning electron microscopy (SEM), specifically with a Hitachi HD2700 CFEG STEM operating at 200 kV, which is capable of secondary electron imaging. The chemical composition of the synthesized nanoparticles was investigated by energy dispersive X-ray spectroscopy (EDS).  
X-ray photoelectron spectroscopy (XPS) spectra were recorded at room temperature using a SPECS PHOIBOS 150 MCD system equipped with a monochromatic Al K\(\alpha\) source (250 W, hv = 1486.6 eV), hemispherical analyzer, and multichannel detector. The pressure was around 10\({}^{-10}\) mbar inside the chamber during the measurements. Charge neutralization was used in all measurements. The binding energy (BE) of the obtained spectra was charge referenced to the C 1s photoelectron peak at 284.6 eV. The high-resolution spectra were recorded using an analyzer pass energy of 30.  
Magnetic properties were studied using a vibrating sample magnetometer, 12 T VSM from Cryogenic Limited, London, in the 4.2-300 K temperature range, and external magnetic fields up to 10 T. The saturation magnetizations, \(M_{s}\), were determined from magnetization isotherms according to the approach to the saturation law:  
\[M=M_{s}(1-b/H)+\chi_{o}H\]  
where, by \(b\), we denote the coefficient of magnetic hardness and by \(\chi_{o}\) a Pauli-type contribution.
# Innovative Approaches for Minimizing Disinfection
## 5 Machine Learning Approaches
The primary reason for this is the capacity of random forest regression and SVR to accurately represent the intricate and nonlinear connections between water quality parameters that impact the creation of DBPs. UV254 was the most influential water quality measure in predicting the target DBPs. [PERSON] et al. [54] focused on developing multiple ML models to forecast the occurrence of DBPs in water distribution networks accurately. Eleven ML and model regularization techniques were evaluated and categorized into four groups: (a) the first group consisted of two advanced non-parametric regression methods: support vector regression (SVR) and GPR; (b) the second group comprised three regression tree-based methods: boosting, bagging, and random forest (RF); (c) the third group consisted of three linear regression based methods: MLR, Lasso, and principal components regression (PCR); and (d) the fourth group included three neural network (NN) based methods: Bayesian regularized NN (BRNN), resilient back-propagation (RPROP) NN, and GRNN. The study conducted by [PERSON] et al. [54] utilized eleven ML algorithms, categorized into four groups, to create prediction models for three often-seen emergent disinfection byproducts in surface water distribution networks. The models were constructed and optimized using the identical dataset, and their predictive efficacy was evaluated and compared. The results indicate that the SVR and GPR models showed superior performance compared to other models in terms of both prediction accuracy and stability. SVR demonstrated superior accuracy and stability in predicting DCAN and TCP, whereas GPR proved to be the most effective model for CPK prediction. The GRNN model in the NN category had highly favorable prediction performance, with the BRNN and standard RPROP NN models following closely behind. The random forest (RF) technique is considered the most effective method for predicting emergent DBPs within the category of regression trees. Linear regression-based models provide the lowest level of prediction accuracy, suggesting that basic multivariate linear models are inadequate for capturing the intricate patterns observed in the water quality data. It is worthwhile to investigate the performance of other advanced machine learning approaches, such as extreme learning machines, Bayesian additive regression trees, and extreme gradient boosting, in predicting new DBPs in the future. Furthermore, future modeling work can incorporate other influential water quality indicators, operational factors, and DBP species in addition to the variables already considered in the study performed by [PERSON] et al. [54].
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 4 Simulation Model
### Metrics  
The simulator calculates several metrics to evaluate performance. Each node collects the following information:  
* number of successful and failed transmissions;
* airtime--the time spent on successful transmissions;
* channel access delay--the time elapsed between the start of two consecutive successful transmissions.  
These per-node metrics are merged after each simulation to calculate the following overall network performance metrics:  
* normalized airtime--ratio of all nodes' aggregated airtime to the simulation time (this channel efficiency measure is an equivalent of throughput);
* [PERSON]'s fairness index--calculated based on per-node airtime to describe how well the channel (as a resource) is shared between nodes. It takes values from the range \(\left[\frac{1}{n},1\right]\), where \(n\) indicates the number of nodes and 1 indicates perfect fairness.
# A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform
## 3 Algorithm Process
### Copyright Information Extraction Algorithm  
Because the feature matrix construction algorithm required for copyright information extraction is the same as that shown Algorithm 1, we will not re-introduce the algorithm framework here. We will only provide a textual description and a flowchart.  
1. Preprocess the attacked copyright image that is to be verified and obtain the unencrypted SIFT binary feature matrix using steps 1-2 in Section 3.1. Combine the encryption algorithm and key to perform chaotic encryption on the SIFT binary feature matrix, facilitating subsequent XOR operations;
2. Retrieve the corresponding zero-watermarking image information from a third-party intellectual property database. XOR this information with the SIFT binary feature image encrypted with the cipher to generate the watermarking image that requires secondary decryption and a restoration of scrambling order;
3. Decipher the watermarking image in ciphertext format obtained in the previous step to obtain an image containing copyright information symbols. Ensure that both subjective perception and objective indicators allow for the identification and differentiation of copyright information.
4. In the case of multiple copyright disputes, apply for a timestamp from a timestamp authority to bind the date. This provides legal copyright protection.  
The timestamp mechanism in this algorithm prevents copyright disputes caused by the illegal binding of watermarking information to copyright images for a second time. Since zero-watermarking does not have embedding capacity limitations, a single image can be embedded with multiple public keys using one private key. The original author and various copyright holders can generate their corresponding chaotic watermarking using their own keys to confirm exclusive copyright ownership, as shown in Figure 5.  
Figure 4: Zero-watermarking generation algorithm process.  
To verify the security of the algorithm proposed in this paper, let us consider an extreme scenario where all key sequences, except for the chaotic initial value key, K1, used for watermarking information encryption, have been stolen. To crack the K1 sequence, attackers would need to determine the length of the key. Here, we assume the key length is 7, and the correct key is set as K1 = 0.2124650. In the security test, the experimenter uses 0.2124601 as the first test value and 0.2124700 as the last test value. These 100 keys are numbered, and the NC values for the 100 deciphered images and the correctly decrypted image are calculated within each interval. From Figure 8, we can observe that, excluding the NC value for the correct key, the NC values for the other 99 incorrect keys are in the range [0.4898, 0.5106]. The similarity of the images remains stable at around 0.5, as shown in Figure 8.
## 1 Introduction  
A Mobile _Ad hoc_ Network (MANET) is a collection of wireless mobile nodes that can be deployed rapidly as a multi-hop packet radio network without the presence of any or existing infrastructure [1]. Such networks can be used to enable next generation network applications like battlefield operations, emergency rescue, commercial or personal usage in terms of resource or information sharing. As the MANETs belong to a category of infrastructure-free networks, they have some special characteristics,like limited bandwidth and unreliable wireless medium for communication, dynamic network topologies and constantly changing member nodes. While these characteristics provide flexibility in terms of deployment of MANETs, they also introduce certain security concerns that are absent or less prevalent in infrastructure-based networks. Thus, traditional routing and security schemes cannot be adapted as such for MANET scenarios. The main security issues for any network can be classified as Authentication, Authorization, Non-Repudiation, Integrity and Confidentiality, but, in the case of an _ad hoc_ network, there exists another aspect that is quite important, _i.e.,_ Availability. The attacks that happen in MANETs for various reasons can be broadly classified as Passive and Active attacks [2].  
The security-related aspects in MANETs are interesting and an emerging area of research among researchers. One particular area of focus is secure transmission of data in MANETs. In order to have secure transmission, a critical and basic aspect is route selection, which depends upon the routing protocol being used. The routing protocols in MANETs are classified as Proactive, Reactive and Hybrid routing protocols [1]. In proactive routing protocols, each node maintains predetermined routing information to the destination. The Optimized Link State Routing (OLSR) and Destination Sequenced Distance Vector (DSDV) belong to this category. The reactive routing protocols, however, start the route established whenever it is required, such as _Ad hoc_ On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR). The hybrid protocols contain the features of both proactive and reactive routing protocols like Zone Routing Protocol (ZRP).  
In this paper, a solution is proposed for secure data transmission by extending the AODV routing protocol and making use of trusted multiple routes and asymmetric key encryption. In order to provide loop free routing, special control packets are introduced. Section 2 of the paper provides an insight into some of the existing security solutions. Section 3 highlights the problem and security needs. In Section 4, the details of the proposed solution are described. For analysis purposes, the simulation setup details and results are provided in Sections 5 and 6. In Section 7, a comparative analysis is presented between the reviewed and proposed solution. Section 8 highlights certain limitations of the proposal along with future scope. Finally, in Section 9, the conclusion is presented.
# An Analysis of Bubble Migration in Horizontal Thermo-Capillarity Using the VOF Modeling
## 5 Conclusions  
In this work, the thermo-capillary effect and related dynamics of vapor bubble migration under different temperature gradients, bubble sizes, and bubble shapes have been studied using VOF numerical modeling.  
1. The simulation based on the volume-of-fluid method and temperature-dependent surface tension has been carried out. The results have been compared with theoretical and experimental studies, which demonstrates qualitative agreement. The corresponding dynamics have been qualitatively captured.
2. The bubble shape deforms with the sizes larger than the capillary length, and the bubble shapes remain spherical with the sizes smaller than the capillary length.
3. For the first group of bubble sizes from \(R=0.01\) m to \(0.05\) m, the bubble with radius \(R=0.05\) m remains near the center of the domain even under the temperature gradient \(\Delta T=100\) K. The bubble with size \(R=0.01\) m can reach the left wall in a shorter time.
4. For the second group of bubble sizes from \(R=0.0045\) m to \(0.01\) m, the larger size bubble can arrive at the left wall faster compared with the smaller size bubble.
5. Coefficient of thermo-capillary force (\(C_{Th}\)) is defined. It is found that as the bubble radius increases, the Weber number increases since a larger bubble size induces a higher drag force and the surface tension effect is not significant. Also, the thermo-capillary coefficient increases due to a large thermo-capillary force.  
It is verified that this approach can be used to investigate the thermo-capillary effect and provide more physical insights into bubble migration dynamics. In addition, it can provide sound guidance for precise critical heat flux estimation and direction of accurate manipulation of bubble migration which is of great importance in various engineering  
Figure 13: Thermo-capillary coefficient (\(C_{Th}\)) vs. the Weber number (\(We\)) for \(R=0.03\) m.  
applications. In the future, we will extend our work to study thermo-capillary bubble migration, including solid surface wettability [25].  
R.K.: Investigation, Methodology, Software, Validation and Writing--original draft. C.-W.L.: Methodology. Y.-C.L.: Conceptualization and Visualization. M.-C.L.: Conceptualization and Writing--review. H.-Y.H.: Conceptualization, Methodology, Investigation, Data curation, Project administration and Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education of Taiwan (MOE), Ministry of Science and Technology of Taiwan (MOST), National Taipei University of Technology, Hanyang University (HY-20140000002393), the National Research Foundation of Korea (2015R1D1A1A01061017), and the BK21 FOUR (Fostering Outstanding Universities for Research) program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea. And The APC was funded by National Taipei University of Technology.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Evaluation
### Experimental Setting  
To achieve better classification results in the CMU-MOSI and CMU-MOSEI datasets, this experiment has carried out some hyperparameter settings, as shown in Table 2. The hyperparameters are determined in the valid set. Batch size represents the number of training samples in each batch, Learning_rate_bert represents the learning rate of BERT word embedding, Epochs represents the number of training iterations, and hidden sizes of the Bi-GRU network layer are used to transmit input information. The kernel size is used to process input sequences of text, video, and cross-modal fusion features before the fully connected layer. The model is trained under Pytorch1.10.2 architecture. For a fair comparison, the average performance after five runs is selected as the final result in model training. The experiment adopts the Dropout method to prevent overfitting.  
Under the CMU-MOSI and CMU-MOSEI datasets, the experiment optimizer was Adam, and the Batch size value was set to 32. After running the experiment five times, we set Epochs to 100 and 120, respectively. The hidden sizes of the Bi-GRU network layer are set to 16, 32, and 64, respectively, during the experiment process. When the value is 32, the classification effect is the best. Hence, the hidden sizes of the Bi-GRU network layer are set to 32. The value of the Learning_rate_bert parameter was determined by a similar experimental method. The kernel size is 1\(\times\)1 convolution kernel, and one-dimensional global vectors of text features, video features, and cross-modal fusion features are obtained and sent into the fully connected layer. The value analysis of parameter Dropout is shown in Section 4.4.2.