esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet Video Delivery Improved by Super-Resolution with GAN
## 6 Video Quality Assessment Metrics
### Perceptual Quality Assessment
#### 6.2.2 Video Multimethod Assessment Fusion  
VMAF  
The VMAF [64; 65] correlates subjective evaluations to determine the quality perceived by human vision. It assesses the quality of the distorted video and its ground truth in a two-step procedure. First, it computes elementary metrics for both videos and fuses all calculated values in the first step through an SVM regression. The VMAF final score ranges from zero to 100, with a score of 100 indicating perceptual similarity between the two videos. The video streaming industry has used VMAF, i.e., Netflix ([[https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51](https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51) ee9 ed12]([https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51](https://medium.com/netflix-techblog/vmaf-the-journey-continues-44b51) ee9 ed12), accessed on 19 April 2022), supported by its strong correlation with perceptual evaluations.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Diagnostics Techniques
#### 2.2.4 Thermal Techniques  
These techniques exploit the change in the thermal properties of rail material in the presence of a defect. The presence of an anomaly is inferred from a single frame or from a video sequence of the temperature distribution of the surface of the considered specimen [5; 34]. Some recent approaches are illustrated in Refs. [35; 36].
# Evaluation of the Impact of Various Functional Fillers on Key Properties of Dental Composites
###### Abstract  
The aim of this study was to assess the mechanical properties of experimental flow composites incorporating remineralizing and bactericidal fillers (hydroxyapatite (HAp), calcium fluoride, and nanosilver). The evaluated properties included wear resistance, dynamic properties (impact strength), hardness, and static strength (compressive and bending strength). This work includes SEM analysis. The specimens were prepared using a commercial light-cured composite material based on bisphenol A-glycidyl dimethacrylate (Bis-GMA) (Arkona Flow Art, Niemce, Poland) and composite material modified by adding 2 wt%, 5 wt% of hydroxyapatite powder containing fluoride (calcium fluoride), and nanosilver. For this purpose, hydroxyapatite (HAp) with a grain size of 30 \(\upmu\)m, which was previously synthesized using the wet method, was used. The results were evaluated against the ISO 4049 standard. The results were subjected to statistical analysis using IBM SPSS Statistics, version 29 (IBM Corp., Armonk, NY, USA). A significance level of 5% (\(\upalpha\) = 0.05) was established. Based on the hardness measurements obtained in this study, it can be concluded that the incorporation of hydroxyapatite, calcium fluoride, and nanosilver fillers increased the hardness of the flowable composites (89 ShD). The results revealed that incorporating fillers such as hydroxyapatite, nanosilver, and calcium fluoride led to a decrease in the impact strength of the material (0.0951/cm\({}^{2}\)). The results of the compressive strength tests revealed that the flowable composite containing 2 wt% of HAp, F, and Ag exhibited the highest compressive strength (190 MPa) among all of the tested materials. Among the experimental composites, the highest bending strength was observed in the variant containing 2 wt% of HAp, F, and Ag, with a value of 76.48 MPa. Significantly higher wear was observed for the composite containing nanosilver filler (4.02 \(\times\) 10\({}^{-4}\) Mm\({}^{3}\)/Nm) compared to the other composites. However, the inclusion of nanosilver as a filler in the composite matrix was found to significantly deteriorate the mechanical properties of the material.  
composites; dental; functional fillers; fluoride; hydroxyapatite; silver +
Footnote †: journal: Applied sciences  
Article
# Deep-Learning Based Positron Range Correction of PET Images
## 2 Materials and Methods
### Neural Network  
The CNN was implemented in Python within the Tensorflow framework [43] (v 2.3.0) with Keras [44]. It was based on the U-Net network [35] which has been demonstrated to be useful in many medical imaging applications [45, 46]. We directly used the U-Net model available in Keras with four levels, 64 filters, and a dropout factor of 0.2 (see Figure 4). The source code can be found in Reference [36].  
The inputs to the model were slices of the \({}^{68}\)Ga (PET) and \(\upmu\)-maps (CT) volumes. In this work, we wanted to evaluate the amount of input information needed to perform an accurate PRC with a neural network. Therefore, we considered six different cases (see Table 1). In three of them, only the \({}^{68}\)Ga PET images were considered for the input, and in three of them both the \({}^{68}\)Ga and \(\upmu\)-maps (PET/CT) were considered. In each of them, we evaluated the performance of the method when using one, three, and five input slices. The different slices were used as channels in the input layer (see Figure 4). In all cases, the output was the corresponding central slice from the \({}^{18}\)F-PET image with 160 \(\times\) 160 \(\times\) (1 channel). The sizes of the different input layers are shown in Table 1. In all cases, the total number of parameters was 31.7 million and the size of the model (hdf5 file) was 485 Mb.  
Figure 4: Deep convolutional neural network architecture (U-Net) used in this work.  
In this work, the Swish activation function [47] was used instead of ReLU (except for the final output layer). Swish is a new, self-gated activation function which performs better than ReLU with a similar level of computational efficiency.
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 3 Methodology
### Model Network Structure
The bottleneck layer consists of two swin transformer blocks connected sequentially. In the bottleneck layer, the first swin transformer block connects to the output of the encoder's final patch merging layer through channel normalization and a fully connected layer. The second swin transformer block connects to the input of the lowest-level patch expansion layer in the upsampling stage. This forms a connection between the second swin transformer block of the bottleneck layer and the lowest-level patch expansion layer in the upsampling stage. Feature fusion in the TEBLS model is achieved through skip connections, which fuse the output of a swin block from one dense block with the corresponding upsampling output in the next dense block. The specific process includes first expanding the channel dimensions through a fully connected layer in the patch expansion layer and then using a rearrange operation to reshape adjacent feature map dimensions into larger feature maps and finally applying LayerNorm for channel normalization to complete the upsampling operation. The decoder structure of the TEBLS model takes the feature maps output from the encoder and processes them through the patch expansion layer and then through a linear projection layer to generate pixel-level image predictions. Specifically, the patch expansion layer upsamples the feature maps to restore their resolution to the input resolution \(W\times H\). This patch expansion layer first uses a fully connected layer to increase the channel dimensions, setting the output tensor's dimensions to 16 times that of the input. Next, the rearrange operation reshapes adjacent feature map dimensions into larger feature maps, and LayerNorm normalizes the channels to complete the upsampling. Finally, the upsampled features undergo linear projection to generate pixel-level segmentation predictions with the dimensions \(W\times H\times\text{Class}\), where Class represents the number of pixel-level classification categories and \(H\) and \(W\) represent the height and width of the image, respectively.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 2 Design Methodology
### Human Phantom Modeling  
For the on-body analysis of the proposed dual band wearable antenna, two body parts were modeled in CST MWS. The first one was a flat homogeneous phantom (chest) with three layers used to model muscle, fat and skin. Similarly, the second one was a heterogeneous cylindrical phantom with four layers to model bone, muscle, fat and skin. The flat body phantom had overall dimensions of 160 mm \(\times\) 180 mm \(\times\)53 mm, as shown in Figure 7. The thickness of the muscle layer was 50 mm, while the thickness of the fat layer was 2 mm and the skin was 1 mm.  
In the second case, three heterogeneous cylindrical models of body parts were designed, and the proposed dual band antenna was bent over them, as shown in Figure 8. The first one was a leg having a total radius of 70 mm. The second one was a healthy arm having a 60 mm radius, and the third one, which models the worst condition among all, was a child's arm having a 40 mm radius.  
Figure 6: Antenna over EBG ground planes. (**a**) Mushroom-like EBG; (**b**) EBG with plus-shaped slots.  
Figure 7: Overall flat body phantom used for modeling the typical human chest.  
The average typical electromagnetic properties for the human tissues (bone, muscle, fat and skin) at 2.5 GHz and 5.2 GHz are taken from [38] and are given in Table 3.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on MARS Dataset
#### 5.2.1 Feature-Level Fusion
In cases where gait is not observable, the gait feature vector is replaced by a zero vector of the same dimension (\(d_{\text{gait}}\), where \(d_{\text{gait}}\) is the dimension of the gait feature vector). In our implementation, \(d_{\text{gait}}=128\). This allows the system to function solely with PCA-optimized appearance features. This ensures model robustness in the face of missing data while preserving the advantages of dimensionality reduction, alignment of features obtained by PCA, and flexible weighting offered by \(\lambda\). Table 7 presents the results of applying PCA conditional concatenation to the MARS dataset for different appearance feature extraction architectures. This approach demonstrates more significant improvements than simple concatenation while effectively handling cases where gait information is not available. * OSNet: The OSNet architecture initially achieved an mAP of 81.64% and a rank-1 accuracy of 86.20%. After applying PCA conditional concatenation, we observed a considerable improvement in mAP to 81.89% (+0.25% ) and an improvement in rank-1 accuracy to 86.52% (+0.32%). These results demonstrate the effectiveness of this method in improving OSNet's performance on the MARS dataset. * MGH: The MGH model showed even more marked improvements. Its initial mAP of 85.52% increased to 86.09% (+0.57% ), while its rank-1 accuracy improved from 89.40% to 89.78% (+0.38% ). These larger gains suggest that the MGH model particularly benefits from the integration of gait features via PCA conditional concatenation. Comparing the two fusion methods, PCA conditional concatenation outperforms conditional concatenation for both architectures. For OSNet, PCA conditional concatenation improved mAP by 0.25%, versus 0.03% with conditional concatenation. For MGH, the improvement was 0.57% versus 0.04% in mAP. These results suggest that PCA's dimensionality reduction enables more effective integration of appearance and gait features, especially benefiting complex architectures like MGH. The performance gains achieved through the use of PCA conditional concatenation, coupled with empirically determined \(\lambda\) values for each method and architecture, highlight its potential to enhance Re-ID systems. This approach shows promise for real-world scenarios involving large-scale datasets and varying availability of gait information. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Appearance**} & \multicolumn{5}{c}{**Appearance + Gait**} \\  & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline OSNet [16] & 81.64 & 86.20 & 95.71 & 96.96 & 97.99 & 81.67 & 86.14 & 95.65 & 96.96 & 98.04 \\ MGH [7] & 85.52 & 89.40 & 96.63 & 97.72 & 98.42 & 85.56 & 89.51 & 96.68 & 97.72 & 98.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of feature-level fusion on the MARS dataset (concatenation).
## 4 Empirical Study  
To evaluate the effectiveness of the proposed idea, a number of experiments were conducted, which can be summarized into two categories: (1) objective evaluations of the course degree prediction and (2) subjective evaluations of the whole system. The experiments were performed on a PC with 16 GB RAM and Intel Core i7-4790, running on 64 bits Windows 7. In the experiments, all prediction algorithms and data engineering were coded by the authors in this paper. Note, the course suggestion and course degree prediction can be finished within 1 s because the patterns and user similarities have been completed in the offline stage.
# Investigation of the Effects of Different Working Fluids on Compressor Cascade Performance
## 4 Performance Analysis  
To analyze the influence of physical properties of working fluid on compressor cascade performance, the identical values of Reynolds number and Mach number are set at inlet of compressor cascade using dissimilar fluids in numerical simulation. Figure 4 displays the distribution of total pressure loss coefficient along blade height at the compressor cascade outlet operating with dissimilar working fluids. When heat capacity ratio of working fluid is identical, total pressure loss coefficient after passing through compressor is also identical and when the heat capacity ratio of working fluid is different, working fluid total pressure loss coefficient changes with the change in a specific heat ratio. The results of the numerical simulation prove that the physical property that affects the coefficient of total pressure loss in a cascade is the heat capacity ratio once the Reynolds number and Mach number of different working fluids are identical.  
Figure 5 indicates compressor cascade static pressure ratio using dissimilar working fluids. The difference of static pressure ratio of working fluid with same value of specific heat ratio is very small. Relative error is less than 1%. Then, a specific heat ratio of the working fluid is the main physical property that affects the static pressure ratio of compressor cascade when the Reynolds number and Mach number of working fluid are the same.  
Figure 6 displays static pressure coefficient distribution of compressor cascade with air and carbon monoxide. The static pressure coefficient is as follows:  
\[C_{P}\ =\ \frac{p-p_{1}}{p_{1}^{*}-p_{1}} \tag{16}\]  
Figure 4: Distribution of coefficient of total pressure loss with different working fluids.  
Figure 5: Static pressure ratio using dissimilar fluids.  
As shown in Figure 6, if the specific heat ratio, Reynolds number and Mach number of compressor inlet are equal, the static pressure coefficient distribution of the two types of working fluid will be the same, which also proves the conclusion reflected in Figure 5.
## Appendix A Surrogate Models
### MLP  
A neural network is a network of simple processing elements, also called neurons. The neurons are arranged in layers. In a fully-connected multi-layer network, a neuron in one layer is connected to every neuron in the layer before and after it. The number of neurons in the input layer is the number of input features \(p\) and the number of neurons in the output layer is the number of targets \(q\)[48]. MLPs have several theoretical advantages, compared to other ML algorithms. Due to the universal approximation theorem, an MLP can approximate any function if the activation functions of the network are appropriate [49; 50; 51]. The MLP makes no prior assumptions about the data distribution, and in many cases, can be trained to generalize to new data not yet seen [52]. However, finding the right architecture and finding the setting of training parameters is not straightforward and usually done by trial and error influenced by the literature and guidelines.  
A neural network output \(\hat{y}\) corresponding to an input \(x\) can be represented as a composition of functions, where the output of layer \(L-1\) acts as input to the following layer \(L\). For example, for non-linear activation function \(\sigma_{L}\), weight matrix \(W_{L}\), and bias vector \(b_{L}\) of the respective layer \(L\), we obtain the following:  
\[\hat{y}(x)=t_{L}(x)=\sigma_{L}(W_{L}^{T}t_{L-1}(x)+b_{L}) \tag{105}\]  
With the neural network estimate \(\hat{y}(x)\) and the respective target \(y\) of an input \(x\), we can denote a loss function \(\mathcal{L}\). A very common loss function for MLPs for regression tasks is the mean-squared error:  
\[\mathcal{L}(W,b)=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}(x_{i})-y_{i})^{2} \tag{106}\]  
where \(W\) and \(b\) are the collections of all weight matrices and bias terms, respectively. Optimal weight \(W^{*}\) and bias \(b^{*}\) terms for each layer are identified with minimizing the loss function \(\mathcal{L}\) via back-propagation [53].  
\[W^{*}/b^{*}=\underset{W,b}{\text{argmin}}\ \mathcal{L}(W,b) \tag{107}\]
## 2 System and Channel Model
### System Model
#### 2.1.2 System Model for Scenario 2  
As shown in Figure 1b, the RIS is jammed by the UAV. Assuming that the jammer can effectively align to the RIS unit of the legitimate signal. Thus, the received signal at the destination is denoted as  
\[y_{2}=R\sqrt{P_{s}}h_{i}s+R\sqrt{N_{I}}g_{J}\Lambda s_{J}+n \tag{5}\]  
where \(g_{J}=h_{J}\partial e^{\left|\overline{r}_{P}\right|}h_{q}\).  
From (5), it can be derived that the SJNR is obtained when the jamming is active.  
\[\gamma_{3}=\frac{(h_{i})^{2}P_{s}}{\big{(}g_{I}\big{)}^{2}N_{I}+N_{0}} \tag{6}\]  
When the jamming is idle, the expression of SNR follows (4).
# Cross Sensor Simulation of Tomographic SAR Stacks
## 2 Wavenumbers in SAR Acquisitions
### Wavenumbers in SAR Tomography
The order of magnitude of \(\Delta\theta\) can be appreciated by considering a typical P-band tomographic system whose parameters of interest are summarized in Table 1.  
Figure 3: Geometrical loci associated with the wavenumbers illuminated by a tomographic SAR survey for a single target in the \(\{ky,kz\}\) domain; each segment being associated with a different image of the tomographic stack. The spectral extension shown as \(\Delta k\) is proportional to the resolution in the range direction and is not improved by processing several images together. On the contrary, the slightly different off-nadir angle associated with each acquisition provides bandwidth along the perpendicular direction, here shown as \(\Delta k_{\perp}\). This finite extent is related to the resolution capability of a TomoSAR system along the cross range direction \(\zeta\) shown in Figure 1.  
By combining Equations (3), (5) and (6) a value of about \(1^{\circ}\) for \(\Delta\vartheta\) results. It must be kept in mind that the wavenumber region whose width is \(\Delta k_{\perp}\), is sampled in the transformed domain thus leading to a periodic signal in the direct domain in case of regularly spaced baselines. The period in the cross range direction is related to the sampling step according to Equation (7). \[\zeta_{amb}=\frac{2\pi}{\delta k_{\perp}} \tag{7}\]  
where \(\delta k_{\perp}\) is the separation in the wavenumber domain between subsequent passes, it can be expressed as \(\delta k_{\perp}=\Delta k_{\perp}/\left(N-1\right)\), \(N\) being the total number of passes.
## 8 Security Analysis
### Adversary Model  
In our model, the adversary can be a malicious broker or any malicious party that can access and tamper with the traffic exchanged by the publishers with the interested subscribers. The latter can do so by performing a man-in-the-middle attack. We observe that the communication between a client (either a publisher or a subscriber) and the broker is often protected via well-known security protocols, such as the TLS protocol [1]. However, when the certificate validation in TLS is not properly implemented the whole communication is susceptible to man-in-the-middle attacks [4; 5]. Additionally, we recall that being the communication in MQTT mediated by the broker, the latter can intrinsically perform a man-in-the-middle attack [8].  
In our model, we consider that an attacker can tamper with the traffic in the following ways [9]:  
* the attacker can modify at least one of the exchanged messages;
* the attacker can inject at least one message in the exchanged flow;
* the attacker can delete at least one of the exchanged messages;
* the attacker can change the order of at least one message in the exchanged flow;  
Additionally, in our threat model, we consider that a publisher or a broker can deny having sent a certain flow of messages.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Human Signal Sources
#### 2.1.1 Brain Signals  
The signals that are generated from the brain or related to brain activity are considered brain signals. Brain activity can be defined as the neurons sending signals to each other [16]. Two types of signals fall into this category: Electroencephalogram (EEG) and Electrocoulography (EOG). Measuring brain activities through EEG is a difficult process. Electroencephalograms (EEG) measure the surface potential of the scalp. Although the brain is insulated from the outside by the skull and other tissue, this electrical activity still presents minuscule changes in the electrical potential across the scalp [17]. These changes can be recorded. However, since these voltages fluctuate in the micro-volt range [18], EEG measurements are very prone to noise. However, as EEG is directly measured by the brain's activity, it has some obvious impacts to our mental state.  
Electrooculography (EOG) measures the electrical potential between the front and the back of the eye [19]. Like other electrical potential physiological sensors, this is accomplished through several surface electrodes. This time, the electrodes are placed above and below or left and right of the eye to be measured. Eye position trackers are also often used in this field, although eye position is more voluntarily controlled. Both EOG and eye position have become increasingly relevant in emotion recognition studies [20]. One reason for this is the role eye contact plays in human and primate behavior. Studies have shown that the decoding of facial expressions is significantly impeded without clear eye contact [21].
## 1 Introduction
High-degree cubature rules are applied to numerical integration and a target-tracking problem to demonstrate the system effectiveness and better performance computations. The measured results of target-tracking simulation illustrated that enough accuracy can be obtained employing fifth-degree CKF rather than EKF, therefore combining the use of third-degree CKF and PF; the performance is much better than GHQF [41]. The main contributions of this paper are: (i) creating various scenarios using Orolia Skydel as a space environment involving LEO satellites to receive emitted RFI signals from UAVs; (ii) applying the assumed scenarios as a quasi-real environment to verify the approaches of optimizing geolocation measurement and high-degree nonlinear tracking filters and compare performance with theoretical simulations. The paper is structured as follows. The architecture of geolocation and target-tracking using the Orolia Simulator that is considered and presented in Section 2. The mathematical model is detailed in Section 3. Experimental implementation and actual simulation scenarios are explained in Section 4. Experiment results and performance analysis are illustrated in Section 5. Finally, the conclusion and suggestions for future works are provided in Section 6.