esa-sceva/satcom-chunk-collection huggingface dataset:
# Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
## 2 Image and Video Processing  
[PERSON] et al. [1] proposed GP-Net, combining Transformer and CNN in parallel, to address the challenge of image manipulation localization by efficiently building global context and capturing low-level details, with an effective fusion module named TcFusion, outperforming existing methods in manipulation detection and localization.  
[PERSON] et al. [2] introduced a novel cycle generative adversarial network (CGAN) method with gradient normalization for generating high-quality infrared images from visible images. By employing a residual network in the generator and integrating channel and spatial attention mechanisms, the method enhances feature perception and detail generation in infrared images. Furthermore, the introduction of gradient normalization in the discriminator stabilizes the training process, reducing model collapse.  
[PERSON] et al. [3] proposed a model designed to address model collapse in generative adversarial networks (GANs). Inspired by the relationship between Hessian and Jacobian matrices, the proposed framework focuses on disentanglement and mitigating model collapse.  
[PERSON] et al. [4] developed an image processing protocol using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each representing a different aspect of the embryo and describing a different characteristic of the blastocyst.  
[PERSON] et al. [5] simplified the existing Siamese convolutional network by reducing the number of network parameters and proposing an efficient CNN-based structure, namely, adaptive deconvolution-based disparity matching net (ADSM net), by adding deconvolution layers to learn how to enlarge the size of input feature map for the following convolution layers.  
[PERSON] et al. [6] addressed the challenge of predicting video memorability by analyzing and experimentally verifying the most impactful factors influencing memorability. The proposed Adaptive Multi-modal Ensemble Network framework integrates temporal 3D information, spatial information, and semantics derived from video, image, and caption. It utilizes three individual base learners (ResNet3D, Deep Random Forest, and Multi-Layer Perception) within a weighted ensemble framework to predict video memorability.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 4 Malware and DoS Attacks in the P2P Network  
In this section, we describe in detail the attacks that we mathematically model using Markov chains. It is important to note that our work theoretically models both the DoS and worm-based attacks similarly to how the Erlang B formula and the basic P2P system (also derived from continuous-time Markov chains) model the blocking probability in telephone systems, i.e., assuming exponentially distributed times (interarrival and service duration). In this regard, our analysis only considers the communication links available or disabled under these types of attacks, as well as the rate of propagation of worms in P2P networks under different conditions. As in the Erlang B model and P2P basic system, there is no consideration of the electronics, computing capabilities, interference, noise, or many other components and variables involved in these systems. The main reason for this is that a mathematical model that considers all of these details may be very hard to produce and even more computationally difficult to solve.  
Considering the basic P2P system presented in [44,45], in which exponentially distributed times were considered as well, we aim to extend this model by introducing these two types of cyber attacks, which, given the distributed nature of P2P networks, are very harmful. These attacks are modeled using exponential assumptions, which may differ from practical attacks but give a first approximation to the theoretical performance of these networks. The results are similar to those of the basic model, in which the user interarrivals or dwelling times may not follow an exponential distribution, but the theoretical performance presents a good approximation to real systems (see the results in [45]). Following this, we assume that the DoS attack follows an exponential distribution. Specifically, we assume that the period between attacks and the duration of each attack are random variables with exponential distributions. This may differ from a real attack pattern where botnets are generating such disruptions in the system. In this regard, the exponential model presented in this work has the advantage of presenting a base model that can be easily extended to cover different attack patterns. Indeed, the exponential distribution assumption implies that both the attack interarrival times and attack duration have a coefficient of variation equal to one (\(CoV=\sigma_{x}/E[x]\), where \(\sigma_{x}\) is the standard deviation, and \(E[x]\) is the mean of these times). In the case that the attack pattern has a different \(CoV\), it can be modeled using an Erlang distribution (\(ifCoV<1\)), a hyper-exponential distribution (\(ifCoV>1\)), or even a Markov modulated poisson process (MMPP) in the case that there are bursts of such attacks. All of these different attack patterns use the basic exponential distribution chain that we developed in this work as the base.
# Generating Job Recommendations Based on User Personality and Gallup Tests
## 3 Proposed Approach
Figure 3: Comparison of cumulative weights before and after normalization. The third step of the process is to employ the CliftonStrengths questionnaire and analyze all the candidate profiles. The result of this process is that every candidate is associated with a vector of 34 weights that correspond to the importance of each trait in the candidate's personality. In this step, we take advantage of the power of the Gallup test methodology in capturing the importance of each trait in a person's character. The final step of the recommendation process is to compare the job description traits with the candidate traits and rank jobs accordingly for each candidate. This step employs a vector similarity metric to compute the similarity (or distance) between the job and the candidate vector. Any of the common metrics (e.g., cosine similarity, manhattan, or Euclidean distance) can be applied at this step. Alternatively, we could rank the traits by their importance for the candidate and the position and compare the correlation of the two rankings using a correlation metric. When the process finishes, each candidate gets as an output a ranked list of positions that match their personality traits. Algorithm 1 summarizes the main steps of our approach. ```
0: Set of job descriptions \(J\), Set of candidate profiles \(C\)
0: Ranked job list for each candidate
1: Initialize trait matrix \(T_{jobs}\leftarrow\{\}\)\(\triangleright\) Store trait weights per job
2: Initialize global trait bias matrix \(M\in\mathbb{R}^{34\times 34}\gets 0\)
3:for all job \(j\) in \(J\)do
4: Construct GPT-4 prompt with job title, sector, and industry
5:\(R_{j}\leftarrow\) GPT4_RankCliftonStrengths(\(j\))\(\triangleright\) Return ranking of 34 traits
6:\(W_{j}\leftarrow\) ConvertToWeights(\(R_{j}\))\(\triangleright\) Assign weights based on ranking
7:\(T_{jobs}[j]\gets W_{j}\)
8:endfor
9:\(T_{jobs}\leftarrow\) NormalizeBias(\(T_{jobs}\))\(\triangleright\) Apply normalization if bias threshold is exceeded
10:for all candidate \(c\) in \(C\)do
11:\(V_{c}\leftarrow\) GallupTestVector(\(c\))\(\triangleright\) Obtain vector of 34 weighted traits
12: Initialize \(R_{c}\leftarrow\{\}\)\(\triangleright\) Recommended job scores
13:for all job \(j\) in \(J\)do
14:\(V_{j}\gets T_{jobs}[j]\)
15:\(D\leftarrow\) DistanceMetric(\(V_{c},V_{j}\))\(\triangleright\) Use weighted Manhattan, Euclidean or cosine
16:\(R_{c}[j]\gets D\)
17:endfor
18: RankJobs[\(c\)]\(\leftarrow\) SortBySimilarity(\(R_{c}\))
19:endfor
20:return RankJobs
21:
```  
**Algorithm 1** Job Recommendation Based on Gallup Test Scores
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 5 Experimental Result  
### Effectiveness of the LFP Module  
We compare the LFP-TCN with baselines, including the MLP, LSTM, CNN, and TCN. The output layer for each model is a fully-connected layer with a single neuron, which renders anomaly scores. These baselines have almost the same parameter size as our LFP-TCN. Moreover, we compare our LFP module with the LFE module [34] to handle the local information loss. The details of each model are as follows:  
* MLP: 3-layer MLP with neurons of \((400,200,128)\) on each layer. We use the ReLU activation function.
* LSTM: 5 stacked LSTM blocks with hidden states of 128. The hidden states at the last time step are fed into the output layer.
* CNN: 10 convolution layers with a kernel size of three. ReLU is used for the activation function. The final feature map is flattened and fed into the output layer. This is equivalent to the below TCN, in which we replace the dilated causal convolution layers with ordinary convolution layers and remove the skip connection.
* TCN: 5 stacked residual blocks in Figure 3 without the LFP module.
* LFE-TCN: the above TCN with the LFE module [34]. The LFE module puts an additional five residual blocks on top of the TCN with the decreasing dilation rate, resulting in 10 stacked residual blocks with the dilation rate \((1,2,4,8,16,16,8,4,2,1)\) in each block.  
Table 3 demonstrates the averaged EERs and the number of parameters for different models. As depicted in Table 3, the TCN outperforms the MLP, LSTM, and CNN with an EER of 12.76% and 14.02% in the HMOG and BBMAS dataset, respectively. The TCN can learn noise-robust features from sensor data, exploiting its sizeable receptive field.  
On top of that, our LFP-TCN achieved the lowest EERs in both datasets, showing values of 12.25% and 13.70%, respectively. This result shows the effectiveness of our LFP module, which improved the EER by 0.51% and 0.32% in each dataset. This improvement is contributed by aggregating local feature information, yielding fine-grained impostor representations.  
Additionally, we implemented the LFE module with the TCN, even though it was originally designed for image segmentation. We confirmed that the LFE module doubledthe parameters and did not improve the EER in our task. On the other hand, our LFP module kept the parameter size, and the LFP-TCN outperformed the baselines.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 3 How EIT Works  
The EIT works by using different processes starting from electrical properties of tissues to highlight the tissue state to characterize diagnostically valuable information. The following are the steps involved to represent how EIT works.  
1. Contrasting the electrical properties of the tissues.
2. EIT measuring devices and electrodes positioning.
3. Data collection and solving the forward problem.
4. Image reconstruction (inverse problem).
5. Image processing on EIT image.
6. Taking valuable information for the diagnosis of disease from EIT image.  
In this paper, the focus will be on the steps of solving the forward and the inverse problem.
###### Abstract  
The fabrication of components involves the deposition of multiple beads in multiple layers for wire-arc additive manufacturing (WAAM). WAAM performed using gas metal arc welding (GMAW) allows for the manufacturing of parts through multiple-bead multi-layer deposition, which depends on the process variables. Thus, the selection of process parameters along with their required levels is mandatory to deposit multiple layers for WAAM. To obtain the desired levels of parameters, bead-on-plate trials were taken on the base plate of low alloy steel by following an experimental matrix produced through the Box-Behnken design (BBD) on GMAW-based WAAM. Wire feed speed, travel speed, and voltage were chosen as the input parameters and bead width and bead height were chosen as the output parameters. Furthermore, the robustness and adequacy of the obtained regression equations were analyzed by using analysis of variance (ANOVA). For both responses of BW and BH, values of R\({}^{2}\) and adj. R\({}^{2}\) were found to be near unity, which has shown the fitness of the model. Teaching-learning-based optimization (TLBO) technique was then employed for optimization. Within the selected range of process variables, the single-objective optimization result showed a maximum bead height (BH) of 7.81 mm, and a minimum bead width (BW) of 4.73 mm. To tackle the contradicting nature of responses, Pareto fronts were also generated, which provides a unique non-dominated solution. Validation trials were also conducted to reveal the ability and suitability of the TLBO algorithm. The discrepancy between the anticipated and measured values was observed to be negligible, with a deviation of less than 5% for all the validation trials. This demonstrates the success of the established model and TLBO algorithm. The optimum feasible settings for multi-layer metal deposition were determined after further tuning. A multi-layer structure free from any disbonding was successfully manufactured at the optimized variables. The authors suggest that the optimum parametric settings would be beneficial for the deposition of layer-by-layer weld beads for additive manufacturing of components.  
wire-arc additive manufacturing (WAAM); low alloy steel; 2.25 Cr-1.0 Mo steel; metal-cored wire; optimization; teaching-learning-based optimization (TLBO) +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Effects of Heating Mode  
Figure 16 depicts the effects of the heating mode on the \(V_{g}\) and \(R_{gw}\) within 1080 days with LWH&SHIA (480 d). When the heating mode changes from CH to intermittent heating (IH) and the heating interval is 30 days, the \(V_{g}\) decreases from 3.931 \(\times\) 10\({}^{6}\) to 3.869 \(\times\) 10\({}^{6}\) ST m\({}^{3}\). The result is consistent with the findings of [PERSON] et al., who experimentally studied the dissociation and gas production behavior of hydrates under two hot brine injection modes. Their results indicate that continuous thermal injection produces more gas than intermittent thermal injection [52]. Compared with the LWH&CH&SHIA (480 d) production, the \(V_{g}\) of LWH&IH&SHIA (480 d) just decreases by 1.58% during the 1080-day production cycle, and the cumulative heat input is only 50.00% of the LWH&CH&SHIA (480 d) production. Compared with the WHH&CH production, the \(V_{g}\) only decreases by 4.59%, but the cumulative heat input is only 4.76% of the WWH&CH production. After 1080 days of production, the \(R_{gw}\) in the above cases remains around 200, and there is no significant difference.  
Figure 14: Gas and water production curve with different heating times within 1080 days. (**a**) Gas production rate and cumulative gas production. (**b**) Water production rate and gas-to-water ratio.  
Figure 15: \(EROI_{custom}\) curve with different heating times within 1080 days.  
As shown in Figure 17, the \(EROI_{custom}\) curve of intermittent heating mode is higher than that of the continuous heating mode, which indicates that the intermittent heating mode has a better energy output-to-input ratio.
## Proposed Proportionate NMCC Algorithms
### Proportionate NMCC with a CIM
 \tag{29}\]where \(\chi_{1}=\xi\beta\), and \(\rho_{\text{CIM}}=\beta\gamma_{\text{CIM}}\) is a regularization parameter used for controlling the zero attracting strength. Similar to the early reported zero attracting algorithms, there is an additional term \(-\rho_{\text{CIM}}\dfrac{1}{N\sigma_{1}^{3}\sqrt{2\pi}}\widehat{\mathbf{w}}(n) \exp\left(-\dfrac{\left\|\widehat{\mathbf{w}}(n)\right\|^{2}}{2\sigma_{1}^{2} }\right)\) in the CIM-PNMCC algorithm, which is called a CIM zero-attractor. In the devised CIM-PNMCC, the constructed CIM zero-attractor attracts small coefficients to zero with a high probability. Furthermore, our proposed CIM-PNMCC algorithm employs a gain controlling matrix to assign a large step-size for the dominant coefficients, while the zero attractor mainly gives a strong zero-attraction to the inactive coefficients. Therefore, our CIM-PNMCC algorithm has the advantages of the proportionate-type algorithms, normalized adaptive filtering algorithms and zero attraction adaptive filters. From the derivation of the CIM-PNMCC, we can put the remarks as follows:  
1. A PNMCC algorithm is devised by using a generalized Gaussian distribution function to utilize the prior-sparse-structure information in the in-nature systems. 2. A CIM constraint is adopted and incorporated into the proposed PNMCC's cost function to create a modified cost function. 3. The derivation of the devised CIM-PNMCC algorithm is presented by the use of the LM method to further take the advantages of the prior-sparse-structure information. 4. The convergence of the CIM-PNMCC is analyzed and its performance is discussed for identifying sparse systems, which is compared with the previous MCC algorithms. 5. Our developed CIM-PNMCC outperforms the previous MCC algorithms in terms of the convergence and MSD.
# Spam Classification Based on Signed Network Analysis
###### Abstract  
Online social networking services have become the most important information-sharing medium of modern society due to several merits, such as creating opportunities to broaden social relations, easy and instant communication, and fast data propagation. These advantages, however, are being abused by malicious users to disseminate unsolicited spam messages, causing great harm to both users and service providers. To address this problem, numerous spam detection methods utilizing various spam characteristics have been proposed, but most of them suffer from several limitations. Using individual behaviors and the content of messages for spam classification has been revealed to have bounded performance, since attackers can easily fake them. Instead, exploitation of social-network-related features has been highlighted as an alternative solution, but recent spam attacks can adroitly avoid these methods by controlling their ranking through various forms of attack. In this paper, we delineate a signed-network-analysis-based spam classification method. Our key hypothesis is that the edge signs are highly likely to be determined by considering users' social relationships, so there will be a substantial difference between the edge sign patterns of spammers and that of non-spammers. To identify our hypothesis, we employ two social psychological theories for signed networks--structural balance theory and social status theory--and the concept of surprise is adopted to quantitatively analyze the given network according to these theories. These surprise measurements are then used as the main features for spam classification. In addition, we develop a graph-converting method for applying our scheme to unsigned networks. Extensive experimental results with Twitter and Epinions datasets show that the proposed scheme obtains significant classification performance improvement compared to conventional schemes.  
online social network; security; spam; signed network; status theory; balance theory +
Footnote †: journal: Accepted: 11 December 2020; Published: 15 December 2020
###### Abstract  
Thermal energy storage is known as a key element to optimize the use of renewable energies and to improve building performances. Phase change materials (PCMs) derived from wastes or by-products of plant or animal oil origins are low-cost biosourced PCMs and are composed of more than 75% of fatty acids. They present paraffin-like storage properties and melting temperatures ranging from \(-23\,\mathrm{\SIUnitSymbolCelsius  
technical evolution of phase change materials, including inorganic systems (salt and salt hydrates and even clathrate hydrates) and organic compounds such as paraffin waves, esters, and polymeric materials or fatty acids (FA) [4; 5; 6; 7; 8; 9]. These latter are promising PCM candidates. Indeed, not only do they represent more than 75% of PCMs derived from wastes or by-products of plant or animal oils, but they also have advantageous thermal properties and energy densities for a limited cost [10]. Therefore, the applicability of FA as low-cost biosourced PCMs is studied in the perspective of limiting CO\({}_{2}\) emissions and favoring renewable energy sources.  
A screening step is required in order to select the most suitable fatty acids as well as their related binary systems. The most common methods used to establish the materials phase diagrams are differential scanning calorimetry (DSC) and differential thermal analysis (DTA), but the determination of a reliable phase diagram via these standard methods is very time-consuming and poorly adapted to screening procedures.  
Our objective is to use the innovative method based on infrared thermography (IRT method) in the frame of a faster preliminary estimation of the phase diagrams, in a few hours only. This method was developed in the framework of the European FP7 Research Project SAM.SSA (2012-2015) and has been validated for the assessment and the study of simple phase diagrams of sugar alcohols (eutectic transitions) [11]. Our goal is now to adapt this method to fatty acids systems presenting more complex phase diagrams. To do so, we chose the palmitic acid (PA) and stearic acid (SA) binary system as an example because its transition temperature fit the aimed applications and its phase diagram includes eutectic, peritectic, and metatectic transitions. The results received with the IRT method for this binary system have been compared with the experimental data obtained using standard methods and with those collected from literature. The results have also been compared with a numerical thermodynamic model especially adapted in this work to PA, SA, and their polymorphisms.
## 2 Terrain Model
### Initial Geometry
#### 2.1.1 Circular Edge of Fault  
The fault divides the geometric layer (\(G\)) into two separate subsets of points. Let \(p_{o}=(x_{o},y_{o})\) be a random point existing in \(G\), so \(p_{o}\in G\). If \(F\) is a designated circle with radius \(r\) and centre in the point \(p_{o}\), then \(F\) determines the edge of the fault that divides \(G\) into subsets \(A\) and \(B\), where \(A\cup B=G\) and \(A\cap B=\varnothing\). The distance (\(s\)) of any point \(p=(x,y)\in G\) from the fault edge is determined by the circle with centre in the point \(p_{o}\) and radius \(r\):  
\[s=r^{2}-(x-x_{o})^{2}-(y-y_{o})^{2} \tag{1}\]  
Based on the distance factor, it is possible to evaluate which sides of the fault a point of height-field is located. Let \(p\) be any point existing in \(G\), so \(p\in G\) and let \(s\) be its distance from fault edge (\(F\)). Let us assume that \(A\subset G\) is a subset of points, where \(s>0\) and \(B\subset G\) is a subset of points where \(s\leqslant 0\). Therefore, we can specify properties of the fault collected in Equation (2) and visualised in Figure 6:  
Figure 4: Model generated by circular function of the fault. (Grid resolution: \(100\times 100\); fault radius (each): \(r=25\); fault height (max in centre): \(0\leqslant\Delta h\leqslant 1\)). (**a**) \(1\) fault. (**b**) \(10\) faults. (**c**) \(50\) faults.  
Figure 5: Model generated by linear/step function of the fault. (Grid resolution: \(100\times 100\); fault height (each): \(\Delta h=1\)). (**a**) \(1\) fault. (**b**) \(10\) faults. (**c**) \(50\) faults.