esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 4 The Proposed Approach  
The proposed approach detects ICMPv6 flooding DDoS attacks in IPv6 networks by combining IGR, chi-square, and LSTM algorithms. First, IGR and chi-square algorithms are employed to select features that significantly contribute to detecting ICMPv6 flooding DDoS attacks. Then, the features selected by IGR and chi-square are used to train a detection model by the LSTM algorithm. Finally, we choose the LSTM algorithm after comparing three DL algorithms: RNN and its variants (LSTM and GRU), as shown in Section 5.3. Figure 3 illustrates the three successive phases of the proposed approach, namely, data preprocessing, ensemble feature selection, and LSTM-based ICMPv6 flooding DDoS attacks detection. The following subsections go over these three phases in detail.
## 3 Results  
A total of 400 carcasses were assessed over the three-year period considered; of these, 107 samples were referred to the SMS and 298 to the MDS. The distribution according to the season was 222 samples in SS and 178 in AW. The carcasses sampled in 2017, 2018, and 2019 numbered 133, 134, and 133, respectively.  
The overall prevalence recorded in the carcasses was 13% (CI = 9.7-16.3%), with 52 samples positive for _Salmonella_ spp. The results according to season and slaughterhouse capacity are reported in Figure 1. No difference was recorded in terms of the prevalence of _Salmonella_ in relation to either the slaughtering capacity or season.  
The prevalence results for each of the selected years are reported in Figure 2. A significant increase was recorded between 2017 and 2018 (\(p<0.05\)) with an OR of 2.36, but not in 2019.  
Figure 1: Overall prevalence (%) and 95% CI (bars) of pig carcasses positive for _Salmonella_ according to season and slaughterhouse capacity. SS = Spring-Summer; AW = Autumn-Winter; SDS = small-sized slaughterhouse (<5000 pigs/year); MDS = medium-sized slaughterhouse (5000–50,000 pigs/year).  
Figure 2: Prevalence (%, CIs) of pig carcasses positive for _Salmonella_ according to the year of sampling. a, b = different letters indicate differences between years.  
Among the 52 positive samples, an identification was obtained for 45 isolates (7 remained unidentified). The most diffused serovars were _S. enterica_ serovar Derby and _S. enterica_ serovar 4,[5],12:i-, together representing over 70% of the isolates. A similar prevalence was registered for these two serotypes according to abattoir capacity and sampling season. The prevalence of the two major _Salmonella_ serovars was similar across the three years of observation.  
The minor serovars detected were Infants, Bredeney, Typhimurium, Give, London, Rissen, Brandeburg, and Goldcoast. Typhimurium and Give were detected only in small slaughterhouses and the other serovars were detected only in the medium-sized abattoirs. The distribution according to the seasons was Infants, Give, and London only in AW and Typhimurium only in SS. Infants was isolated only in 2017; Bredeney, Typhimurium, and Give only in 2018; and London, Rissen, Brandeburg, and Goldcoast in 2019.  
The distribution of _Salmonella_ serovar according to the year of sampling is reported in Table 2.
## 2 Materials and Methods
### Data
#### 2.3.2 Flood Conditioning Factors  
The accuracy of the flood susceptibility maps greatly depends on the precision of the flood inventory data and the selected flood conditioning factors. In the current study, previous reports, research, literature, and experiences of the authors were considered to determine the flood conditioning factors that have an important impact on flash floods. So, nine flood conditioning factors, namely elevation, slope, curvature, aspect, Topographic Wetness Index (TWI), Stream Power Index (SPI), soil groups, drainage density (D\({}_{\text{d}}\)), and distance from the stream, were selected to generate the flood susceptibility map as shown in Figure 3.  
Apart from soil groups, all flood conditioning factors were processed in ArcGIS using Digital Elevation Model (DEM) with a resolution of 10 m obtained from 1: 25,000-scaled topographic maps. Studies showed that a flood susceptibility map generated by using a DEM with finer cell sizes may provide limited improvement in the predictions when compared with coarser cell sizes, but cannot significantly affect the accuracy of predictions [74; 75]. Only DEM of the Gokirmak sub-basin was used to obtain slope, aspect, and curvature grids, while after filling sinks' operation of DEM, flow direction and accumulation cells were determined. The great soil groups that are present in the basin are alluvial (A), gray-brown podzolic (G), alluvial (K), brown forest (M), non-calcareous brown forest (N), and red yellow podzolic (P), which were considered. Then, the TWI and SPI were determined as a function of flow accumulation and slope grids. The stream network of the watershed was extracted based on the threshold value of the flow accumulation cell to determine the flood conditioning factor of distance from the stream and D\({}_{\text{d}}\).  
In the current study, conditioning factors such as elevation, slope, distance from the stream, TWI, SPI, and D\({}_{\text{d}}\) were categorized using the natural break method in five sub-classes, whereas the supervised classification method was used to categorize the rest of the factors such as aspect, curvature, and soil groups. Aspect was classified based on the cardinal and inter-cardinal directions. Curvature is classified based on the curvature properties of the surface, which are flat (\(-\)0.05-0.05), convex (\(>\)0.5), and concave (\(<\)\(-\)0.05). Soil groups were classified based on the six great soil groups in the catchment since they do not contain any numerical values. This supervised classification enabled the significant interpretation of the flash floods based on the curvature classes, directions, and soil groups.  
Figure 3: _Cont_.
## 3 Method
### Model Overview
#### 3.2.2 Semantic Decoder Module  
To create visual features (synthesized by CVAE) that better express their semantics, a semantic decoder \(Dec\) module, \(\mathcal{X}\longrightarrow\mathcal{A}\), is introduced here to reconstruct the synthesized visual features relative to their corresponding semantic embeddings, and a cycle-consistent loss is used for the reconstructed semantic embeddings to ensure that the synthesized visual features can be reconstructed relative to their corresponding semantic embeddings. This module can ensure that the visual features and their semantics can be modally aligned, reducing semantic bias and enhancing semantic-consistent features. The cycle-consistent loss of the semantic embedding is achieved via the \(\mathcal{L}_{1}\) reconstruction loss as follows.  
\[\mathcal{L}_{R}=\mathbb{E}[\|Dec(x)-a\|_{1}]+\mathbb{E}[\|Dec(\hat{x})-a\|_{1}] \tag{2}\]
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 3 AEWT Based on Optimized Component Screening
### Component Screening Based on KSES and CN  
As the kurtosis index is more sensitive to single transient impact than to multiple periodic transient impact if the kurtosis index is adopted to judge the largest component of a periodic transient errors can easily occur. If the component of the fault impulse is reflected in the envelope spectrum, it will have obvious regularity, while the single impulse caused by a random fluctuation is irregular and unordered. Because of this property, the kurtosis index of the envelope spectrum (KES) approach is employed in component screening. However, because of noise, the envelope spectrum structure of the features of a fault can be submerged or contaminated with noise, so that its value is not different from that of other components, which is not conducive to screening. In this paper, we calculate the kurtosis index of the squared envelope spectrum (KSES) to highlight the fault characteristics and suppress other components. The squared envelope spectrum kurtosis can further highlight the difference between the periodic transient impact component and other components, which is easy to screen and has stronger robustness and adaptability.  
In addition, the fault impulse is often modulated to more than one frequency band. There is a main resonance band and some number of sub-resonance bands, that is, there is a gap between the shock sequence and energy. As the correlation number (CN) can reflect the correlation and energy of each component and the original signal, we use the CN of each component and the corresponding original signal to screen components. The Pearson correlation coefficient is used here.  
As mentioned in the previous section on EWT theory, each empirical mode component corresponds to a filter function. If the selected component is used directly, it may cause signal distortion. Therefore, this paper uses the inverse empirical wavelet transform (IEWT) to reconstruct the components to minimize signal distortion.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 4 Experimental Evaluation
### Diagnosis of a Bearing with an Inner Race Fault
#### 4.3.3 Experimental Study #6  
Similar to experiment three, the vibration signal and rotational speed are collected as the speed increases from 18 Hz to 28 Hz with a equal to 3 Hz/s to verify that the proposed method is still effective in the case of rapidly changing speed and wild fluctuations. These signals are shown in Figure 36a,b, respectively. The frequency spectrum and the TFR of the vibration signal are shown in Figure 36c,d, respectively.  
Figure 35: The envelope order spectra of the signal obtained by different methods: (**a**) FK, (**b**) WPTK, (**c**) Protrugram and (**d**) FOSCK.  
Figure 36: The signal measured from an inner race fault bearing: (**a**) time-domain signal, (**b**) the shaft rotational frequency from 18 Hz to 28 Hz, (**c**) the frequency spectrum of (**a,d**) TFR by using STFT.  
Figure 37 shows the signal analysis results of the inner race fault case. The FK is paved in Figure 37a, in which the optimal frequency band is [625, 1250] Hz. Figure 37b shows the WPTK, in which the maximum kurtosis is calculated at the 4 th decomposition level, and its corresponding optimal frequency band is [1875, 2500] Hz. The Protrugram is shown in Figure 37c and the center frequency is 543.5 Hz. Figure 37d shows the FOSCK, and its optimal frequency band is [0, 1250] Hz. The envelope order spectra of the filtered signals are shown in Figure 38a-d. It can be seen that only the envelope order spectrum obtained by the FOSCK can extract the first three octaves in Figure 38d. Therefore, the FOSCK is better than other methods in bearing inner race fault diagnosis in this case.  
Figure 37: The optimal frequency band obtained by different methods: (**a**) FK, (**b**) WPTK, (**c**) Protrugram and (**d**) FOSCK.  
Figure 38: _Cont._
# 5G Positioning: An Analysis of Early Datasets
###### Abstract  
Global Navigation Satellite Systems (GNSSs) are nowadays the prevailing technology for positioning and navigation. However, with the roll-out of 5G technology, there is a shift towards 'hybrid positioning': indeed, 5G time-of-arrival (_ToA_) measurements can provide additional ranging for positioning, especially in environments where few GNSS satellites are visible. This work reports a preliminary analysis, the processing, and the results of field measurements collected as part of the GINTO5G project funded by ESA's EGEP programme. The data used in this project were shared by the European Space Agency (ESA) with the DICA of Politecnico di Milano as part of a collaboration within the ESALab@PoliMi research framework established in 2022 between the two organizations. The _ToA_ data were collected during a real-world measurement campaign and they cover a wide range of user environments, such as indoor areas, outdoor open sky, and outdoor obstructed scenarios. Within the test area, eleven self-made replica 5G base stations were set up. A trolley, carrying a self-made 5G receiver and a data storage unit, was moved along predefined trajectories; the trolley's accurate trajectories were determined by a total station, which provided benchmark positions. In the present work, the 5G data are processed using the least squares method, testing and comparing different strategies. Therefore, the primary goal is to evaluate algorithms for position determination of a user based on 5G observations, and to empirically assess their accuracy. The results obtained are promising, with positional accuracy ranging from decimeters to a few meters in the worst cases.  
5G; new radio; GNSS; network-based positioning; hybrid positioning; time of arrival 2023  
1  
[[https://doi.org/10.3390/s2322922](https://doi.org/10.3390/s2322922)]([https://doi.org/10.3390/s2322922](https://doi.org/10.3390/s2322922))
## 6 Conclusions  
To better implement the power allocation of HESS, this study conducts the offline optimization for the power allocation of HESS by means of DP and through the analysis of offline data from DP, and a general law of the power allocation of HESS is discovered. On the basis of the law, an adaptive rule-based controller is proposed. Then, to further enhance the real-time performance of the controller, the traffic information, including traffic condition and road grade, is taken into account. Moreover, the future trend prediction of vehicle speed is obtained by combining a K-means clustering algorithm and the traffic condition. By comprehensively considering the traffic information and the future trend of vehicle speed, a fuzzy logic controller is developed to correct the coefficient of power allocation in the adaptive rule-based controller. Thus, a comprehensive controller is established by selecting the adaptive rule-based controller as the main controller and the fuzzy logic controller as the auxiliary controller. In conclusion, the following key findings are acquired:  
1. A general law exists in the optimal power allocation of HESS under various types of driving cycles, and the controllers based on the law can achieve good economic performance of HESS.
2. Considering traffic information in a controller is beneficial to the performance promotion of HESS. In future works, the vehicle experiment for the comprehensive controller will be conducted.  
[PERSON] wrote the first draft of the manuscript, completed the offline optimization of dynamic programming, and developed the fuzzy logic controller considering traffic information. [PERSON] fitted the offline data and designed the adaptive rule-based controller. [PERSON] and [PERSON] provided insights and additional ideas on presentation. All of the authors revised and approved the manuscript.  
This research received no external funding.  
This work was supported by the Chongqing Natural Science Foundation (Project No. cstc2015 jcyiA60005), the Fundamental Research Funds for the Central Universities (Project No. 106112016 CDJX2338825), and the National Key Research and Development Program of China (Project No. 2016 YFB0101402). These projects offered all of the costs of this series of research. The authors appreciate for their supports on this research.  
The authors declare no conflict of interest.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
###### Abstract
Methods such as convolutional neural network(CNN) [16] and self-organizing map neural network (SOMNN) [17] are introduced to improve the accuracy of the extraction results; however, the computation complexity has not been decreased due to the redundant information in high-resolution images. Although high-resolution images can provide high resolution object information in the extraction process, the following problems still exist: (1) High cost: the high-resolution images are not free for the public. The extraction of roads in a large area or long-term monitoring of road network changing requires numerous images, which will increase the cost substantially. (2) Too much redundant information: road information is just a small part of the high-resolution images, which means the images also contain a lot of texture information and spectral information of other ground objects, such as buildings, trees, water and mountains. The residential information largely weakens the major characteristics of roads. Therefore, the application of a new data source with low cost and distinguishing characteristics of road networks is required in the extraction. Satellite-derived nighttime light data, as one of the new generation remote sensing data, has been proven existing in high correlation with human activities and has been widely adopted in researching human activities [18; 19], including GDP spatialization [20], fossil fuel combustion [21], material stock estimation [22], disaster damage assessment [23] and energy consumption prediction [24]. Satellite-derived nighttime light data are the electromagnetic wave information of an overnight light source in a near-visible infrared band through earth surface reflection. Compared with census data, which are collected by administrative boundaries, nighttime light data are much timelier with lower cost and flexible spatial scale. Hence, this study attempted to prove the capability of the Luojia 1-01 data source in the extraction of city roads, through the establishment of a hybrid model. The extraction is based on the following few steps: firstly, the image is enhanced (sharpened) using Laplace-filtering with an elimination of the diffusion, then urban areas are detected using the threshold value in the light intensity. Afterwards, the PCNN is established for pattern recognition. The final step, before road extraction itself, includes optimization methods such as clean, edge loss filling and close. The results of the road extraction in representative parts of Wuhan city are compared with Open Street Map data.
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 4 Comparison between Calculation and Test Results
### Dnv (2013)  
Figure 16 shows a comparison between the shear strength calculated by the DNV code and the test results. The difference between the DNV code and experimental results is still quite discrete. The shear strength of some specimens is seriously overvalued, and a few calculated values are nearly three times higher than the test results. Overestimated strength results in 12 specimens with a deviation ratio exceeding 10%, and there are also 33 specimens whose strength is undervalued with a deviation ratio exceeding 10%. For the most part, the DNV code specification may lead to unsafe design results.
## 5 Managing Obesity and Type 2 Diabetes  
Obesity and T2D are intricately linked conditions that often coexist, creating a significant clinical challenge. Excess weight, particularly around the waist, impairs the body's ability to utilize insulin effectively, leading to insulin resistance--a hallmark of T2D. Conversely, chronically elevated blood sugar levels in T2D further contribute to weight gain by creating an environment where the body prioritizes fat storage over burning it for energy. This vicious cycle underscores the importance of a comprehensive approach to managing both conditions simultaneously.  
Fortunately, the advancements discussed in this review hold immense promise for their comprehensive care. Here is how these novel approaches can be specifically applied to comorbidity management:  
**Hydrogels:** These versatile materials can play a crucial role. Implantable hydrogels loaded with medications like insulin could offer targeted and sustained release, improving glycemic control while promoting weight loss through glucagon-like peptide-1 (GLP-1) analogs [127; 128; 129; 130]. Additionally, self-healing hydrogels could enhance patient comfort and reduce the need for frequent replacements.  
**Ultrasound Neuromodulation (pFUS):** This non-invasive technique shows promise for tackling the underlying causes of both conditions. By targeting the liver with focused ultrasound waves, pFUS may reduce inflammation and improve insulin sensitivity, leading to better blood sugar control and potentially aiding in weight management [135; 136; 137]. Further research is needed to explore the long-term efficacy and optimal treatment protocols for patients with this comorbidity.  
**AI and Machine Learning (ML):** These technologies can personalize treatment plans by analyzing a patient's CGM data, weight trends, and other factors. AI-powered systems can predict blood sugar fluctuations and adjust insulin delivery accordingly in closed-loop systems. ML algorithms can also identify high-risk individuals for early intervention, allowing for preventative measures to manage both obesity and T2D.  
By combining these advancements with traditional therapies like dietary modifications and exercise programs, healthcare professionals can create a more comprehensive and effective treatment approach for patients with obesity and T2D. This integrated approach has the potential to significantly improve their quality of life and overall health outcomes.
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 3 Methodology
### Generation of Multi-Scale and Multi-Layer Features  
The support image \(x_{si}\) and query image \(x_{qj}\) are fed into the feature extractor \(F_{\theta}\) to obtain two groups of feature representations with different layers: \(\Phi_{s}^{d}=\left\{\Gamma_{\theta}^{d}(x_{s})\right\}_{d=1}^{2}\) and \(\Phi_{q}^{d}=\left\{F_{\theta}^{d}(x_{q})\right\}_{d=1}^{2}\), which serve as the feature representations for different depths in Branch 2, while the deeper features \(\Phi^{2}\) are utilized as input for the multi-scale feature generator.  
Generating multiple scale features helps to obtain multiple unique representations of features from original images to mitigate the problem of limited data in few-shot situations. This paper proposes a multi-scale feature generator inspired by [19]. Additionally, to learn deeper features while reducing the size of the feature maps, a \(3\times 3\) convolutional layer is added before the \(2\times 2\) max-pooling layer in the fifth subbranch. As shown in Figure 3, the multi-scale feature generator consists of five subbranches: the first subbranch does nothing; the second subbranch contains a \(3\times 3\) convolutional layer; the third subbranch contains a \(5\times 5\) convolutional layer; the fourth subbranch contains a \(1\times 7\) convolutional layer before a \(7\times 1\) convolutional layer; and the fifth subbranch contains a \(3\times 3\) convolutional layer and a \(2\times 2\) max-pooling layer.  
With the help of the multi-scale feature generator, we obtain deep multi-scale features for both support and query images \(\Phi_{s}^{z}=\left\{G_{\phi}^{z}(F_{\theta}^{2}(x_{s}))\right\}_{z=1}^{5}\) and \(\Phi_{q}^{z}=\left\{G_{\phi}^{z}(F_{\theta}^{2}(x_{q}))\right\}_{z=1}^{5}\) and then feed them into the relation network module in Branch 1. Simultaneously, the outputs from the fifth part of the multi-scale feature generator, \(\[PERSON]_{s}^{5}=G_{\phi}^{5}\left(F_{\theta}^{2}(x_{s})\right)\) and \(\[PERSON]_{q}^{5}=G_{\phi}^{5}\left(F_{\theta}^{2}(x_{q})\right)\), will serve as the deepest features \(\Phi^{3}\) in Branch 2, alongside the features \(\[PERSON]_{s}^{d}=\left\{F_{\theta}^{d}(x_{s})\right\}_{d=1}^{2}\) and \(\[PERSON]_{q}^{d}=\left\{F_{\theta}^{d}(x_{q})\right\}_{d=1}^{2}\) extracted by the feature extractor at the different layers. This combination enables pairwise similarity measurements at hierarchy layers within Branch 2.