esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 2 Materials and Methods
### AFR-EMD: Image Registration Algorithm Based on Average EMD Feature-Maps  
We propose a second registration algorithm based on the average of EMD-based features. This algorithm is called _Average Feature-map Registration using EMD or AFR-EMD_. Algorithm 2 presents the details of AFR-EMD. In this case, we first construct a single feature-map for each of the floating and reference images by taking average of IMFs from all EMD levels (scales). Then, we register the average feature maps based on the chosen similarity measure. To take advantage of hierarchical registration, we downsample the feature-map and perform a level-by-level registration. Similar to LR-EMD, various similarity measures including SSD, CC, RC, and MI are used to perform AFR-EMD. Free form deformation [44] is used here to estimate the transform.  
The average of the IMFs is a denoised and normalized feature map. Averaging the IMFs provides a feature map with reduced size. Therefore, the features are easier to interpret for a human observer. This reduction in the feature size could also reduce the computational cost in a single-scale registration algorithm. In our case, we designed a multi-scale hierarchical registration based on downsampling in AFR-EMD, therefore the gain in computational cost is in the same order as LR-EMD. In the next section, we will explore the ability of LR-EMD and AFR-EMD in robust registration of images in the presence of bias field noise.  
```
Input:\(I_{f\_l}\) floating image, \(I_{ref}\), reference image, \(n\) number of IMFs or EMD levels, \(\mathbb{SIM}\) Similarity measure Extract \(n\) IMFs for both \(I_{f\_l}\) and \(I_{ref}\), Find \(\mathbb{F}_{f\_l}\) and \(\mathbb{F}_{ref}\), the average of IMFs for both \(I_{f\_l}\) and \(I_{ref}\) Initialize registration with unity transform \(f(x)=x\) for\(i=1\)to\(n\)do\(\triangleright\) where \(i=1\) is the coarse-grained level and \(i=n\) is the fine-grained level  Compute \(\mathbb{F}_{f\_l}^{i}\) and \(\mathbb{F}_{ref}^{i}\), the downsampled versions of \(I_{f\_l}\) and \(I_{ref}\) with respect to scale level \(i\)  Register \(\mathbb{F}_{f\_l}^{i}\) to \(\mathbb{F}_{ref}^{i}\) based on \(\mathbb{SIM}\) Similarity measure and find transform \(f(x)\)  Initialize transform for next level of registration with \(f(x)\) endfor
```  
**Algorithm 2** AFR-EMD: Image registration based on Average EMD feature-maps
# Musical Mix Clarity Prediction Using Decomposition and Perceptual Masking Thresholds
## 3 Test Stimuli
### Independent Dataset  
An independent dataset was synthesised to investigate the effect of specific signal degradation and how it would impact the predicted clarity score. For this, the stimuli used in the subjective listening test (Section 3.1) were processed in 3 different ways, with 7 levels of severity, creating 3 test datasets. The processing was intended to gradually decrease the level of perceptual clarity at each level of severity by gradually introducing masking from noise-like signals common in music production. By forming the dataset from the stimuli which had be rated subjectively by listeners, the effect of the degradation on the proposed model could be evaluated relative to the MCSs the stimuli received.  
The 3 sets included:  
* Set 1--Addition of broadband pink noise in 6 dB steps (\(-\)36 dBFS, \(-\)30 dBFS, \(-\)24 dBFS, \(-\)18 dBFS, \(-\)12 dBFS, \(-\)6 dBFS, 0 dBFS). This degradation simulates a gradually raising noise floor or an extremely 'busy' mix where lots of conflicting signals become noise-like.
* Set 2--Addition of reverberation in 6 dB steps of wet mix level (\(-\)36 dBFS, \(-\)30 dBFS, \(-\)24 dBFS, \(-\)18 dBFS, \(-\)12 dBFS, \(-\)6 dBFS, 0 dBFS). In this case, the reverberation was meant to simulate a large hall, and thus had a diffuse tail with a decay time of 3 s, with decay times more pronounced for low frequency energy rather than high frequency energy. Artificial reverberation is commonly added in music production to enhance the sense of space and listener envelopment. However, it also disperses diffuse energy over time adding to the signal's noise floor and can smear transient energy over time making them less clearly defined.
* Set 3--Clipping applied at a ceiling calculated as a percentile range of amplitude values about the 50 th percentile (90%, 80%, 70%, 60%, 50%, 40%, 30%), such that clipping applied is uniform regardless of signal amplitude. Clipping is often used as a creative effect, though also occurs in signals recorded without an appropriate level of headroom. This can cause a reduction in dynamic range, decreasing the energy difference between the signal peaks and the noise floor. In addition, clipping can produce additional harmonic content making the signal more spectrally dense, thus increasing the potential for masking to occur. However, increasing spectral density and brightness of transient and steady-state components may be beneficial to the perception of clarity in some cases, whereby the masking potential of the transients and steady-state components in the signal are increased greater than the masking potential of the residual component.  
Each of the test signals generated were loudness normalised to \(-\)23 LU after the signal degradation had been applied.
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 3 Results and Discussion
### Compressive Strength  
A compressive strength test was conducted for different compositions containing epoxy as a binder replacing cement. Figure 4 shows the effect of different fillers used with epoxy resin on the compressive strength of the specimens. The compressive strengths of all four specimens (EP, ES, EM, and ESS) and C were tested after 28 days of composition. After 28 days, the cement mortar (C) compressive strength was found to be 33 MPa [34], and the compressive strength of the other four specimens, ESS, EM, ES, and EP, was found 40 MPa, 35 MPa, 33 MPa, and 24 MPa respectively. It is determined that ESS has a maximum compressive strength of 40 MPa and EP has a low compressive strength of 24 MPa due to the PVC waste material. So, the polyvinyl chloride (PVC) waste is more porous, which would decrease the density and compressive strength [35]. After the comparison of C with  
Figure 3: SEM analysis of cement mortar (C), epoxy PVC waste (EP), epoxy sand (ES), epoxy marble dust (EM), and epoxy steel slag (ESS).  
all four specimens, the results showed that the compressive strength of epoxy mortar was affected by the type of fillers.
# Solving Integer Linear Programs by Exploiting Variable-Constraint Interactions: A Survey
## 2 Preliminaries
### Graphical Representations  
Here, we overview some natural graphical representations which have been used to capture the variable-constraint interactions of ILP instances. We remark that such representations are not unique to the ILP setting: indeed, they have been used and studied extensively also in settings such as, e.g., constraint programming [28; 29] and Boolean satisfiability [11; 30].  
Let \(A\) be an \(m\times n\) integer matrix that is provided as part of an ILP instance \(\mathcal{I}\). The _signed incidence graph_ of \(A\) (or, equivalently, of \(\mathcal{I}\)) is the edge-labeled bipartite graph \(G_{SI}(\mathcal{I})=(R\cup C,E,\lambda)\), where \(R=\{r_{1},\ldots,r_{m}\}\) contains one vertex for each row of \(A\) and \(C=\{c_{1},\ldots,c_{n}\}\) contains one vertex for each column of \(A\). There is an edge \(\{r,c\}\) with label \(\lambda(\{r,c\})=A_{r,c}\) between the vertex \(r\in R\) and \(c\in C\) if \(A_{r,c}\
eq 0\), that is, if row \(r\) contains a nonzero coefficient in column \(c\). In other words, the vertex set of \(G_{SI}\) is \(\operatorname{con}(\mathcal{I})\cup\operatorname{var}(\mathcal{I})\), a variable is adjacent to a constraint if and only if it occurs in that constraint with a non-zero coefficient, and the labels on edges encode this coefficient.  
The _incidence graph_ of \(A\) (or \(\mathcal{I}\)), denoted \(G_{I}(\mathcal{I})\), is equal to the signed incidence graph without the edge-labels. The _primal graph_ of \(A\) (or \(\mathcal{I}\)) is the graph \(G_{P}(\mathcal{I})=(C,E)\), where \(C\) is the set of columns of \(A\) and \(\{c,c^{\prime}\}\in E\) whenever there exists a row of \(A\) with a nonzero coefficient in both columns  
Figure 1: An example of a graph with clique-width 2 and signed clique-width 4.  
and \(c^{\prime}\). This graph is also sometimes called the _Gaifman graph_ in the literature. The _dual graph_ of \(A\) (or \(\mathcal{I}\)) is the graph \(G_{D}(\mathcal{I})=(R,E)\), where \(R\) is the set of rows of \(A\) and \(\{r,r^{\prime}\}\in E\) whenever there exists a column of \(A\) with a nonzero coefficient in both rows \(r\) and \(r^{\prime}\). In other words, the vertex sets of these graphs are \(\mathrm{var}(\mathcal{I})\) and \(\mathrm{con}(\mathcal{I})\), respectively; an edge then signifies that two variables directly interact via a constraint or that two constraints directly interact via a variable, respectively. For all graph representations introduced above, we drop the \(\mathcal{I}\) in the parenthesis when the instance is clear from context. Figure 2 illustrates the four graphical representations of a constraint matrix.  
For a decompositional width measure \(\mathrm{dw}\in\{\mathrm{td},\mathrm{tw},\mathrm{cw},\mathrm{scw}\}\), we denote by \(\mathrm{dw}_{SI}(\mathcal{I})\), \(\mathrm{dw}_{I}(\mathcal{I})\), \(\mathrm{dw}_{P}(\mathcal{I})\), \(\mathrm{dw}_{D}(\mathcal{I})\), the width of the signed incidence graph, the incidence graph, the primal graph, and the dual graph of \(\mathcal{I}\), respectively.
# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 4 Evaluation and Results
### RSU Pedestrian Detection  
This section presents the tested scenario for pedestrians near and on the crosswalk that may correspond to a trigger message situation event. The experimental layout is explained and the results of the computer vision processing are depicted in Figures 9 and 10. Figure 9 represents the layout with a single crosswalk.  
The test scenario includes the following outcomes: Figure 10a, in which all pedestrians are outside the crosswalk ROI; Figure 10b, in which there are pedestrians inside and outside the crosswalk ROI; Figure 10c, in which there are non-pedestrian objects on the crosswalk ROI; and Figure 10d, in which there are pedestrians outside and on both sides of the crosswalk ROI. The pedestrians detected out of the crosswalk ROI are marked with a green bounding box and the ones detected on the crosswalk ROI are highlighted with a red bounding box.  
Figure 10: Test scenario with one ROI: (**a**) all pedestrians out of the crosswalk; (**b**) pedestrians on the crosswalk and facing the camera; (**c**) pedestrians out of the crosswalk and a car passing over the crosswalk, and (**d**) pedestrians on both sides and out of crosswalk.  
Figure 9: Test scenario with single region of interest (ROI) (crosswalk).  
When a pedestrian is detected on the crosswalk (within a red bounding box), it triggers the RSU-ITS-G5 module to send the DENM messages that alert nearby drivers via the in-vehicle OBU station.  
The test implemented for pedestrian detection using the combination of motion sensors and the YOLO software reveals itself to perform well in typical geometric applications, i.e., one or two cameras pointing to a crosswalk and with the standard YOLO trained dataset.
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 6 Simulation Results and Discussions
### Random-Walk Mobility Model for mPC  
The mobile mPCs are modeled in simulations by considering the random-walk mobility model. In Figure 5, we plotted mPC moving path by using random-walk mobility model. We considered this model because it is suitable for low speed mobile networks. The other reason for opting for the random-based mobility model is because here the mPC can move randomly without any restrictions. To be more specific, each mPC speed, destination, and direction is chosen randomly without relating other mPC. Hence, the random-walk mobility model is considered to model the movement in the public safety scenario.  
Effect of Varying SINR Target Control \((P_{0})\) and Path Loss Compensation Factors (\(\alpha\)) on User Transmit Power  
The performance of the PS-CARA scheme is considerably improved as compared to the C-SRA scheme because of implementing fraction power control scheme. The selection of power control parameter is very important to provide the better performance, and its selection varies for each environment. The main parameters of concern in fractional power control are; SINR target control \((P_{0})\) parameter and path loss compensation factor (\(\alpha\)). The improper selection of these parameters can result in high interference and coverage degradation which in turn will degrade the system sum-rate. Thus, we need to select the value which gives us maximum coverage but at the cost of less interference.  
To select a proper value of \(\{P_{0},\alpha\}\), firstly we checked the user transmit power at lower values \(\{P_{0},\alpha\}=\{-80\text{ dBm},0.7\}\) for both MUE and mPUE. Secondly, we keep the \(P_{0}\) constant and changed \(\alpha=0.8\). After comparing these two cases, we found that by increasing \(\alpha=0.7\rightarrow\alpha=0.8\), the users need to transmit with more power to compensate the path loss. This will in turn increase interference as more users will start transmitting with relatively high power as shown in Figure 6, and hence degrades the system sum-rate. A similar trend is noticed when user transmit power is plotted for the values \(\{P_{0},\alpha\}=\{-60\text{ dBm},0.7\}\) and \(\{P_{0},\alpha\}=\{-60\text{ dBm},0.8\}\). Thus, based on these results we selected \(\{P_{0},\alpha\}=\{-80\text{ dBm},0.7\}\) for the simulations as it will result in the best sum-rate performance due to less interference.  
Figure 5: mPC traveling pattern in public safety deployed area.
# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 2 Methods
### Graph Construction  
In the methods of [9,11], an initial graph constructed from the vessel segmentation result is modified using heuristic methods to correct typical error types like a false link between parallel vessels. We can see from Figure 4 that using the vessel segmentation result for the graph construction is inherently prone to making false links between arteries and veins. Therefore, we construct a graph from the pixelwise AV classification result instead.  
Before the graph construction, the AV classification result is simply refined by multiplying with the binary vessel segmentation. It reduces false positives, since the vessel segmentation is better in separating vessels from background. We also remove vessels within the segmented optic disc, as mentioned in Section 2.1.1. Then, the following are performed on the refined AV classification map: (1) centerline extraction by morphological thinning for arteries and veins, (2) logical OR operation on the two centerline maps of (1) for generating a single map, (3) junction detection by locating intersection points on the centerlines, and (4) vessel segment separation by removing the detected junction points.  
An initial graph \(G(V,E)\) is constructed as an undirected graph. \(V=\{v_{j}\}_{j=1}^{N}\) is a set of vertices and is defined by setting each vessel segment as a vertex. Thus, the number of vertices, \(N\), is equal to the number of vessel segments. We will call \(v_{j}\) as the \(j\)-th vertex or the \(j\)-th segment according to the context. Since each segment is composed of many pixels, we denote the \(j\)-th segment as a set \(v_{j}=\{v_{jm}\}_{m=1}^{N_{j}}\), where \(N_{j}\) is the number of centerline pixels composing the \(j\)-th segment. \(E\) is a set of edges, and an edge is constructed for every possible segment pair at every junction. For a junction where four segments join, we have \(4C_{2}\) numbers of edges. All of those pairs are considered in the connectivity prediction.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Device-to-Device (D2D)
#### 4.2.3 Interference in D2D
Finally, both co- and cross-tier interference from the D2D transmitter can be reduced at a D2D receiver using a suitable power allocation strategy, spectrum allocation strategy, or both.
# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
###### Abstract
This method entails utilizing a Dungan text analyzer to extract prosodic labels from Dungan sentences for model integration, employing a Tacotron2-based Mandarin acoustic model, and fine-tuning the Dungan language acoustic model with a limited Dungan speech corpus. The primary contributions are delineated below:  
* Front-end: We have implemented a complete text analyzer for the Dungan language, encompassing modules for text normalization, word segmentation, prosodic boundary prediction, and unit generation based on transformer technology. This analyzer can produce initials and finals as speech synthesis units with prosodic labels from Dungan sentences. * Back-end: We have achieved seq2 seq Dungan language speech synthesis by adapting a pre-trained Mandarin acoustic model within the Tacotron2+WaveRNN framework. This was accomplished by replacing Tacotron2's location-sensitive attention with forward attention, enhancing convergence speed and stability. The rest of the article is organized as follows. We first present our transfer learning-based Dungan speech synthesis framework under Tacotron2+WaveRNN in Section 2. The experimental setup and results are presented in Section 3, while the results are discussed in Section 4. Finally, a brief conclusion and outline for future work are provided in Section 5.
# New Magnetstrictive Transducer Designs for Emerging Application Areas of NDE
## 1 Introduction  
The use of guided waves for the long-range inspection of components is a rapidly growing segment of the nondestructive evaluation (NDE) service business [1; 2; 3]. Magnetostrictive sensors have been shown to be very effective transduction tools for the guided wave testing of pipes and plates [4; 5]. However, there is still a great demand for enhanced sensor characteristics or specific requirements for sensor design compared to what is possible with most conventional guided wave sensor offerings. For example, some applications require especially low profile or small sensors [6]. Another example is the need for sensors for the structural health monitoring (SHM) of components operating at elevated and extreme temperatures. A very robust sensor design is needed that is capable of surviving the high temperatures as well as the stresses induced by thermal cycling [7; 8; 9]. A third example is the guided wave testing of components with high attenuation, such as buried or coated pipes, for which higher transmitted signal amplitudes would be very beneficial [10; 11]. There is also interest in utilizinghigher frequency guided waves for estimations of flaw depth using multi-mode testing [12; 13] or multi-frequency guided waves for the characterization of notch/crack type of anomalies. These types of anomalies demonstrate pronounced variations in signal amplitude as a function of the used frequency range [14; 15].  
A comprehensive review of magnetostrictive patch transducers (MPTs) and different methods of magnetostrictive transduction was given by [PERSON] and [PERSON] [16]. In this paper, the authors used the abbreviation MsT (magnetostrictive transducer) for magnetostrictive sensors that are used for the generation of both longitudinal and torsional vibrations using Joule and Wiedemann effects and their inverse effects, sometimes known as the Villari and Mateucci effects, for receiving the signals.  
This paper will describe a novel transducer that was invented in 2008 [17]. The authors consider the principle of transducer operation to be classified specifically as a reversed Wiedemann effect. The transducer was given the abbreviation MsT in an earlier publication on this topic by [PERSON] in [18]. This is why in this paper, the abbreviation 'MsT' will be explicitly used for transducers utilizing the reversed Wiedemann effect that are used for the generation of transversal vibrations (torsional in a cylindrical structure and shear horizontal in plate structures).  
Reversed Wiedemann effect transducers were found to address the high challenge areas described earlier very well. Note that not all of the characteristics of magnetostrictive transducers of the MsT-type have been completely understood in terms of numerical models, but some experimental data representing the transducer performance were presented by [PERSON], [PERSON] and [PERSON] [19]. This paper discusses the results that were obtained earlier, and also the results of additional research that has been conducted on the same topic.
# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 3 Improved Information Maximizing Generative Adversarial Nets Extreme Scene Generation
### The Model Training Process  
In this paper, historical power output profiles of wind power, solar, and load, as well as historical extreme weather data, are used as real samples to train the improved InfoGAN network. The proposed InfoGAN model can learn the latent distribution patterns of interpretable features from the data. During model training, a control parameter sequence (\(c\)) is generated, and by adjusting this sequence, the statistical characteristics of the generated scenarios can be dynamically altered, thus enhancing the model's flexibility. The training steps are as follows [(29)]:  
1. Data preprocessing: the sample data is uniformly normalized according to Equations (1) to (4).
2. Generator G training: random noise \(z\) is generated, and the initial control parameter sequence \(c\) is fed into the generator G.
3. Discriminator D training: The generated data and real data are fed into the discriminator D, and the loss function of D is computed.  
Figure 1: Improver information maximizing generative adversarial networks framework.  
4. Control parameter update: The generated results are input into the subsidiary network Q, where the control parameter (_c_) is trained. Both the control parameters \(c\) and the generator G's parameters are updated. The detailed training process of the control parameters \(c\) will be discussed in the next section.
5. Iterative process: this process is repeated until the set number of iterations is reached or the model converges.  
By adjusting the control parameter (_c_) during training, the statistical characteristics of the generated scenarios can be effectively controlled, thereby enhancing the model's flexibility.