esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 4 Experiments and Results
### Results  
#### 4.4.1 Results of the Main Datasets  
We compare our method with current metric-based methods [7; 8; 9; 10; 11; 19; 20; 39; 40; 41] as well as some meta-learning based methods [22; 23; 24; 26; 27; 28]. All the images of our experiments are resized to \(84\times 84\) dimensions, and a feature extractor with four convolutional layers is employed. The experimental results of our model on the main datasets are showed in Table 2 and Table 3, respectively. It can be observed from the table that the proposed dual-branch multi-scale relation networks with tutoring learning outperform other metric-based methods and similar multi-scale approaches in both 5-way 1-shot and 5-shot scenarios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Class-All** & **Class-Train** & **Class-Val** & **Class-Test** \\ \hline miniImageNet [8] & 100 & 64 & 16 & 20 \\ tieredImageNet [27] & 608 & 351 & 97 & 100 \\ StanfordCars [59] & 196 & 130 & 17 & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The splits of the datasets. Our proposed method makes fuller use of image information at hierarchical layers, such as deep multi-scale information in Branch 1 and feature information at different layers in Branch 2. The rich knowledge learned makes the model have robust recognition and classification ability. Compared with MSDN [16] with only different depth features, our method improves by approximately 6% and 5.7% in 5-way 1-shot and 5-way 5-shot scenarios, respectively, on the miniImageNet dataset. At the same time, compared with the method MsKPRN [11], which also adopts multi-scale and relation network metrics, our method improves by 1.8% in 5-way 1-shot and 2.13% in 5-shot. Compared with ABNet [49], which splits the image into significant patches and combines the attention mechanism, our method does not process original images too much, nor does it introduce the attention mechanism, but improves by 0.7% and 2.17% in 1-shot and 5-shot settings, respectively. On the tieredImageNet dataset, our method achieves a performance improvement of 2.37% and 6.58% for 1-shot and 5-shot scenarios, respectively, compared with DCN [14], which employs multi-scale measurements at different depths. In comparison with MRN [54], which also utilizes relation networks for similarity metrics and incorporates external memory units to store information and class relationships, our model demonstrates only a marginal enhancement of 0.3% but improved by 4.8% in 5-shot scenarios. This is because our method can conduct tutoring learning of the dual-branch relation networks module, which uses the relation network containing more high-dimensional and global information to tutor the relation networks containing more low-dimensional and local information, transfers the learned knowledge between different layers of relation networks, and further improves the task adaptive measurement ability of relation networks, which is more robust and has higher classification accuracy than a single measurement method.
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 8 Conclusions  
With the development of information technology, digital images have been widely used in every corner of our life and work, the research of image quality assessment algorithm also has very important practical significance. In this letter, we describe a framework for constructing an objective no-reference image quality assessment measure. The framework is unique, since it absorbs the features of human visual system. On the basis of human visual attention mechanism and BRISQUE method, this paper proposes a new NR IQA method based on visual perception, which we named as ROI-BRISQUE. First, we detailed the algorithm and the extraction model of ROI, and demonstrated the ROI consistent quite well with human perception. Then, we used BRISQUE method to extract image features and evaluated the ROI-BRISQUE index in terms of correlation with subjective quality score, and demonstrated that algorithm is statistically better than some state of the art IQA indices. At last, we demonstrated that ROI-BRISQUE performs consistently across different databases with similar distortions.  
Further, the experimental results show that the new ROI-BRISQUE algorithm can be easily trained for a LIVE IQA database. In addition, it may be easily extended beyond the distortions considered here making it suitable for general-purpose blind IQA problems. The method correlates highly with visual perception of quality, and outperforms the full-reference PSNR and SSIM measure and the recent no-reference BLIINDS-II, DIIVINE indexes, and excels the performance of the no-reference BRISQUE index.  
In the future, we will make further efforts to improve ROI-BRISQUE, such as further exploring human visual system and extracting the region of interest accurately.  
Both authors contributed equally to this work. [PERSON] and [PERSON] conceived this novel algorithm. [PERSON] performed the experiments and analyzed the data. [PERSON] tested the novel algorithm. [PERSON] wrote the paper, and [PERSON] revised the paper. Both authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
## 3 Emerging Patterns from Existing Studies
### Spatial resolution requirements
#### Temporal data requirements  
The mapping of irrigated lands with remote sensing is strongly affected by the timing of image acquisition and the number of images used [84]. The image-based monitoring relies heavily on the temporal monitoring of crop evolution during the growing season by means of vegetation indices. Thus, precise timing of image capture is necessary to effectively distinguish irrigated crops from each other and from other land cover types [37]. This requires knowledge of the growth schedules of crop types common to the area. For example, [PERSON] _et al._[35] concluded that in addition to the number of images needed, timing of those images within the agarian calendar proved to be more important than expected for lands under continuous cultivation in the Nile delta. In that study, nonproductive lands could not be identified with adequate accuracy by using images from low-growth seasons and their areas were overestimated by at least 400 percent with data sets consisting of two, three, or four low-growth-season images. Thus, even large multi-temporal datasets do not entirely mitigate the effects of timing interactions between images. Timing and image combinations restrict the accuracy of the identification of irrigated lands even with large image data sets. In some locations this is unfortunate, because low vegetation imagery is often the only cloud-free imagery available.  
Most studies at local scales show that irrigated area - overestimation problems decrease as more peak-season images are incorporated into the data sets. However, given data sets of equal numbers of images, peak-growth imagery may not always perform as well as combinations of peak and low growth-season imagery. The result is that in some cases, incorporating the variability or range of the NDVI values across seasons delineates irrigated from non-irrigated lands more accurately than maximum NDVI data alone can do [35].  
These studies also suggest that the number of images needed to achieve a predetermined level of accuracy will differ between geographic regions. This emphasizes the fact that the number of images needed to monitor irrigated lands will vary by location, regional agricultural practices, and the feature of interest.  
As stated, the quality of irrigation maps derived from remote sensing partly depends on the number and timing of images used. While there is a need for multi-temporal imagery in distinguishing summer crops, this analysis shows that in the study area, methods such as NDVI thresholding based on single images produces useful results, especially if guided by high frequency remote sensing observationslike MODIS. This is particularly important for incorporating remote sensing technology into the future irrigation management practices in many of the world's semi arid areas. While remote detection of summer irrigated lands by this means may be simplistic, it provides an excellent example of the practical utility of remote sensing for summer irrigation monitoring.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 5 5G Capacity Analysis for V2X Communications
As the spectrum needs of a mobile communication network are in principle the sum of the spectrum needs of each individual supported UC, those that demand the exchange of a significant amount of information (e.g., high-definition videos and maps) will create significant challenges. In conclusion, a critical factor that needs to be addressed is the significant improvement of the spectral efficiency for the UL and SL. This means that the vehicles should be equipped with more advanced modems capitalizing the benefits of Multiple Input-Multiple Output (MIMO) systems.
## 9 Performance Validation and Analysis
### Multipath Scenario in Urban Canyons  
This scenario is designed to analyze the performance of the proposed algorithm in severe urban environments. Multipath and line of sight problem are the primary source of errors in urban environments. Experiment location is surrounded by high skyscrapers as shown in the Figure 1b. In Figure 8 CNo of visible satellite is shown from GPS and BeiDou satellites.  
Figure 6: Flowchart of observation error simulation in true navigation data.  
Figure 7: Error plot for emulated scenario 2.  
Figure 8 demonstrations that \(CNo\) of both BeiDou and GPS is unstable. And the disruption in line represents the loss of satellite visibility. Moreover the multipath signal interferences introduce additional errors in observations.  
Positioning results are provided in the Figure 9 in terms of RCEP and PMD. As RCEP represents the accuracy of estimated position during most of time, it can be found in the figure that the proposed estimation technique significantly improved the positioning accuracy.
## 1 Introduction  
### Background  
Figure 1 describes that, since 2010, the transportation sector accounts for about 14.3% of total greenhouse gas emissions (GHGs) (Figure 1a) and that approximately 95.2% or more of the transportation-related GHGs (Figure 1b) is attributable to the vehicles on the road among all transport systems in Korea [1]. Particularly, it is reported that a huge part of them originates from idling vehicles waiting at signalized intersections in the urban network [2, 3]. To tackle this problem, recently manufactured vehicles can automatically control vehicle idling with a stop-and-go function when waiting for a desired traffic signal. Besides this, autonomous vehicles can be another promising technology to control vehicle idling; however, most studies regarding autonomous vehicles have been mainly focused on the safety and mobility and not on the environmental impact.  
The safety and mobility performance of autonomous vehicles could be secured and reinforced with the aid of communication capability in the fashion of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications (i.e., cooperative intelligent transportation system, C-ITS), so it is necessary to investigate the environmental benefits of autonomous vehicles under C-ITS, particularly vehicle idling control at signalized intersections in urban areas.
# Shear Mechanical Properties of Bolt-Grout Interface under Different Bolt Surface Profiles
## 3 Test Results
### Interface Shear Failure Characteristics
#### 3.4.2 Influence of Rib Angle on Interface Failure Modes  
For ease of exploring the impact of varying rib angles on interface failure modes, we conducted a specific analysis on the failure morphology of the interface under normal stress \(\sigma_{n}=2\) MPa for five different rib spacings, as illustrated in Figure 17.  
Figure 16: The failure patterns of interfaces under different rib spacing.  
Figure 17: _Cont_.  
When the rib surface angle \(\theta=15^{\circ}\), according to the top view, most of the gypsum debris is located near the root of the ribs. The failure is mostly abrasion failure occurring in climbing, and there is no obvious shear-break or compression-shear crushing, so the interface failure mode is mainly shear-slip failure. Because the rib angle is small at this time, the gypsum protrusion and the inclined surface of the rib are gentle, indicating that the interface roughness and undulation are small. With the increase in shear displacement, the gypsum protrusion is prone to climbing along the inclined surface of the rib, and in the process of climbing, the gypsum contact interface with the rib due to the restriction of the normal stresses. Part of the gypsum debris is left in the vicinity of the rib, which generates the shear-slip failure.  
When the rib angle \(\theta=30^{\circ}\), according to the top view, most of the gypsum debris is located near the root of the ribs, and there are large pieces of debris and wider debris bands. The proportion of shear-break failure has increased, but the failure mode is still dominated by the shear-slip failure. This is because, as the rib angle increases, the gypsum bulge and the inclined surface of the ribs become steeper, indicating that the interface roughness and undulation increase, and with the increase in shear displacement, the difficulty of climbing increases. The climbing phenomenon is rarer in occurrence, and shearing occurs at the edge part of the bulge, making the percentage of shear-failure increase.  
When the rib angle is \(45^{\circ}\), \(60^{\circ}\) and \(75^{\circ}\), the interface failure morphology is similar to that of the interface failure morphology when the rib angle is \(30^{\circ}\). Also, the failure modes are all combined failure, and the shear-slip damages are dominant; however, due to the difference of the rib angle, the proportion of shear-break failure has changed.
# Streamlining Haptic Design with Micro-Collision Haptic Map Generated by Stable Diffusion +
## 2 Related Works  
### Haptic Texture  
In haptic research, texture is a key attribute, referring to the sensation experienced when touching the fine structural irregularities on a surface. Vision and touch are the primary modalities for texture perception, with haptic texture generally consisting of multiple dimensions, such as roughness-smoothness, warmth-coldness, hardness-softness, and stickiness-slipperiness [13]. Rendering texture through vibrotactile feedback has long been a central focus in haptic interaction research [14]. Vibrotactile feedback is one of the main methods of haptic feedback [15]. When using vibrotactile feedback to render texture, it effectively portrays the texture distribution and roughness [16]. Furthermore, vibrotactile feedback demonstrates good experience when rendering collisions [17]. The haptic perception of textures can be regarded as collisions with different shapes and distributions.  
Texture perception requires users to engage through intermediaries or direct skin contact, exploring via motion. [PERSON] et al. [7] examined motion-coupled vibrotactile feedback during interaction and found that amplitude influences the perceived texture intensity, while force and timbre create different experiences, emphasizing the significance of motion in haptic texture perception. [PERSON] et al. [18] introduced a learned action-conditional model that uses the force and speed applied by the user as input to generate haptic texture signals. Meanwhile, due to the complexity and variety of natural textures and the uncertainty of user actions during interaction, some studies focus on improving the experience of freely exploring complex textures. [PERSON] et al. [19] proposed a method using linear predictive coding to render isotropic textures. [PERSON] et al. [14] proposed a hybrid framework combining force and vibrational feedback, using linear predictive coding to render highly realistic uniform or non-uniform textures. [PERSON] et al. [10] utilized height maps of virtual textures to extract height profiles from the movement trajectories on the texture, and then synthesized haptic signals based on these profiles to drive actuators. These studies are dedicated to allowing users to freely explore virtual materials and obtain as realistic vibrotactile feedback as possible.  
While these studies have significantly improved user experience, they may rely on high-performance actuators, additional auxiliary devices, or more complex computational and development workflows. Currently, the most widely used haptic devices include game controllers, smartphones, and wearables. Haptic designers also seek to achieve haptic experience with simpler development, easier usability, and lower costs [20].
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
###### Abstract  
It is essential to extract high-fidelity surface waves in surface-wave surveys. Because reflections usually interfere with surface waves on X components in multicomponent seismic exploration, it is difficult to extract dispersion curves of surface waves. To make matters worse, the frequencies and velocities of higher-mode surface waves are close to those of PS-waves. A method for surface-wave extraction is proposed based on the morphological differences between surface waves and reflections. Frequency-domain high-resolution linear Radon transform (LRT) and time-domain high-resolution hyperbolic Radon transform (HRT) are used to represent surface waves and reflections, respectively. Then, a sparse representation problem based on morphological component analysis (MCA) is built and optimally solved to obtain high-fidelity surface waves. An advantage of our method is its ability to extract surface waves when their frequencies and velocities are close to those of reflections. Furthermore, the results of synthetic and field examples confirm that the proposed method can attenuate the distortion of surface-wave dispersive energy caused by reflections, which contributes to extraction of accurate dispersion curves.  
higher-mode surface waves; dispersion curves; morphological component analysis; Radon transform +
Footnote †: journal: Applied Physics Letters
## 5 CT and Left Atrial Appendage
### Left Atrial Appendage Closure (LAAC)  
In atrial fibrillation (AF), the LAA loses its contraction capability and undergoes a remodeling process that transforms it into a static pouch, with an increased risk of stagnation and thrombosis [120]. The risk of thrombosis is also higher in patients with left ventricular dysfunction and elevated left ventricular end-diastolic pressures, even without AF [121]. Traditionally, oral anticoagulation therapy has been the cornerstone of therapy for stroke prevention in patients with nonvalvular AF [122]. However, in some frailty patients, the increased bleeding risk associated with anticoagulation therapy might overcome the benefit of stroke prevention. Percutaneous left atrial appendage closure (LAAC) is a validated procedure that excludes the appendage with the use of dedicated devices. Two main devices are currently used in clinical practice: the Watchman (Boston Scientific), a parachute-shaped nitinol device with a permeable membrane over the atrial side and fixation barbs to secure it between the LAA walls, and the Amulet device, a self-expanding flexible nitinol mesh with a distal lobe, keeping hooks, and a proximal disk for sealing the LAA orifice. Both these devices have reported high post-procedural and long-term success rates of LAAC [123; 124; 125].
## 2 Literature Review
### Audio MFCCS Generation  
A technique for generating speech from filter bank Mel-Frequency Cepstral Coefficients (MFCCs), which are normally useless for speech synthesis, is presented in this study. It introduces a generative adversarial network-based noise model, predicts fundamental frequency and voicing information, and transforms spectral wrapping information into all-pole filters [16].  
AI-generated or modified deepfake content can look authentic in text, audio, video, and images. The Mel-Frequency Cepstral Coefficient (MFCC) technique is used to examine habits and machine learning-based and deep learning-based tactics to detect deepfake audio. The Fake-or-Real dataset, divided into four sub-datasets, was used as a benchmark. On the for-rece and for-2-sec datasets, the support vector machine (SVM) outperformed other machine learning prototypes in terms of accuracy, whereas the gradient boosting model performed well on the for-norm dataset [17]. Convolutional and recurrent neural networks were used to develop a novel approach to sound classification. To simulate the human auditory system, features were extracted from the audio using the Mel-Frequency Cepstral Coefficient (MFCC) method. Due to its impressive accuracy, the model showed promise as a flexible model for audio classification, with potential applications in healthcare, music production, and speech recognition. More studies may result in advances in the categorization and analysis of audio data [18].
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 2 Materials and Methods
### Methods
#### 2.2.2 Classification  
To reduce the complexity of the algorithm and to enforce a higher prediction, classification is performed in this study [28]. In other words, each class is obtained, and learning is performed independently for each class. Here, classification is performed by stacking two images to consider the characteristics of both SAR and panchromatic images using fuzzy C-means (FCM), which is an unsupervised classification algorithm [35]. FCM is based on the optimization of the objective function based on the similarity measure considering the distance between data and the center of the cluster as shown by Equation (2):  
\[J(U,V)=\sum_{n=1}^{N}\sum_{c=1}^{C}u_{kn}{}^{m}d^{2}(y_{n},\ v_{k}) \tag{2}\]  
where \(N\) is the number of data; \(c\) is the number of clusters; \(u_{kn}\) is the membership function and satisfied the condition \(0\leq u_{kn}\leq 1\), \(\sum_{k=1}^{c}u_{kn}=1\); \(m\) is a weighting exponent that control the degree of fuzziness in the resulting membership functions and is set to 2 for simplicity [36]; \(d^{2}(y_{n},\ v_{k})=\|y_{n}-v_{k}\|^{2}\) is squared distance between \(y_{n}\) and \(v_{k}\), in which \(Y=[y_{1},\ y_{2},\ \cdots y_{n}]\) is a dataset to be grouped and \(v_{k}\) is the cluster center. To minimize the objective function, the FCM algorithm performs an iterative process, and the membership functions and cluster centers are defined as Equations (3) and (4):  
\[u_{kn}=\frac{1}{\sum_{j=1}^{c}\left(\frac{\ell^{2}(y_{n},\ v_{j})}{\ell^{2} \left(y_{n},\ v_{j}\right)}\right)^{\frac{1}{(m-1)}}} \tag{3}\]  
\[v_{k}=\frac{\sum_{n=1}^{N}u_{kn}{}^{m}y_{n}}{\sum_{n=1}^{N}u_{kn}{}^{m}} \tag{4}\]  
\(U\) and \(V\) are iteratively updated to obtain an optimal solution, and the iterative process ends when \(\|U^{(r)}-U^{(r-1)}\|<\varepsilon\), where \(U^{(r)}\) and \(U^{(r-1)}\) are the membership functions in the \(r\)th and \(r-1\)th iterations and \(\varepsilon\) is a predefined small positive threshold [37]. Furthermore, the number of clusters is a key parameter in the proposed method as it determines the number of training models in which land-cover distribution characteristics as well as performance and training time should be considered. If there are not enough clusters, the land-cover distribution characteristics will be neglected, and if there are too many clusters, the training time will increase, complex computations will be necessary, and overtraining may occur. Therefore, in this study, the number of clusters is set to 6 to not only obtain appropriate performance and training times but also to consider the land cover distribution characteristics [28].