esa-sceva/satcom-chunk-collection huggingface dataset:
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Channel Coding
#### 5.1.4 Multiplexing and Interleaving  
The last module of the channel coding section includes multiplexing and interleaving procedures. It should be noted that information data UCI is not processed within the given block. Hence, a multiplexing algorithm (Algorithm 1) may be reduced to the following form:  
```
\(i,k=0\) while\(i<G\) \(g_{k}=[e_{in\cdots e_{Q_{m}-1}}]^{T}\) \(i=i+Q_{m}\) \(k=k+1\) end while
```  
**Algorithm 1**Reduced multiplexing algorithm.  
Furthermore, the interleaver is applied in accordance with the 3 GPP standard [21]. It is worth mentioning that at this stage of processing, the initial parameters of the NPUSCH transmission, e.g., the number of slots \(N_{slobs}^{UL}\), are taken into account. Hence, there are plenty of parameters and transmission variants to be tested, which determines the great number of the possible input/output vector sets to be verified. This may be a good opportunity to present the practical use of the unit testing to the students.
## 3 Results and Discussion
### Tribological Results
#### 3.2.3 EPMA Characterization of Wear Scars  
The chemical mapping by EPMA of the wear tracks (40 mm/s, PAO+2H-WS\({}_{2}\)) is reported in Figure 11. The survey reveals the presence of iron, carbon, oxygen, tungsten, sulfur, zinc, and phosphorus on the steel surface. The iron and oxygen elements can clearly show the wear track in Figure 11, so it can be speculated that the iron oxide friction film has formed on rubbed surface. The content of tungsten and sulfur is not very much, and the traces are basically the same, so we can guess that these could be residual WS\({}_{2}\). The image shown in Figure 12a suggests that a small amount of 2H-WS\({}_{2}\) was trapped in the grooves of the flat surface, which can also prove the above conjecture. The phosphorus and zinc elements are evenly distributed throughout the surface, so there is no marked wear track.  
Figure 11: Chemical mapping by EPMA of the wear track obtained when using 2H-WS\({}_{2}\) in oil, to lubricate the rubbed surfaces (40 mm/s, PAO+2H-WS\({}_{2}\)), (**a**) abrasion trace; (**b**) Fe; (**c**) C; (**d**) O; (**e**) W; (**f**) S; (**g**) P; (**h**) Zn.  
Figure 12: (**a**) the enlargement of the SEM images in Figure 11; (**b**) the enlargement of the SEM images in Figure 13.  
The EPMA analysis was also carried out on the rubbed surface lubricated with sample oil containing 2H-WS\({}_{2}\) and ZDDP. The EPMA survey scan is reported in Figure 13. The survey can reveal the presence of iron, carbon, oxygen, tungsten, sulfur, zinc, and phosphorus on the steel surface, proving that a tribofilm has been formed on the steel surface. The scratch width in Figure 13a is relatively narrow compared with Figure 11a. It can be concluded that the presence of ZDDP improves the anti-wear ability of the lubricant under the same experimental conditions. The wear track can be seen from the elemental graphs of zinc and phosphorus. Thus, the worn surface is likely to form a reaction tribofilm, which is mainly composed of zinc phosphate compounds. The amount of oxygen element is obviously lower, and because the ZDDP additive is known for its antioxidant properties, it can be guessed that the ZDDP may play a key role in the protection of the metal surfaces against oxidation. The distribution of tungsten and sulfur elements is less than that in Figure 11, and Figure 12b shows that these may be a small amount of residual WS\({}_{2}\).
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 4 Experimental Results and Discussion
### Dataset and Experimental Settings  
As demonstrated in Section 3.2, the training process includes three phases. Different training sets are employed in different training phases. First, to train the global blur feature extraction branch, we adopted a synthetic global blur dataset. Then, the GoPro dataset [18] is used to train the local blur feature extraction branch. Finally, the GoPro dataset is used to train the overall network. The following are the datasets and parameter settings used in training.  
**Global blur dataset.** We have built a global blur dataset via blur convolution. The blur kernels in Reference [39] are adopted to generate the blur images, which includes 32 motion, 16 Gaussian, and 8 defocus blur kernels. The high-quality images are widely used Berkeley segmentation dataset (BSD68) dataset [40,41]. The global blur dataset contains 3808 blurry and clear image pairs, in which 2536 pairs are used for training and 1272 pairs are used for testing.  
**GoPro dataset.** (GoPro: [[https://github.com/SeungjunNah/DeepDelbur_release](https://github.com/SeungjunNah/DeepDelbur_release)]([https://github.com/SeungjunNah/DeepDelbur_release](https://github.com/SeungjunNah/DeepDelbur_release)) accessed on 8 November 2017). The GoPro dataset [18] includes 3214 pairs of blur and clear images, covering a variety of scenes, and simulating non-uniform blur in dynamic scene. Instead of modeling a kernel to convolve on a sharp image, the blurry images are generated by recording the sharp information with a high-speed camera and integrating frames over time in the GoPro dataset. It is a realistic ground-truth blur dataset. We use the GoPro dataset to train the local blur extraction branch and the overall network. As the same as the settings in Reference [18], it is divided into 2103 training sets and the remaining 1111 images work as the testing set.  
The data argumentation strategies in the training process include random flipping and rotation. The image blocks are as the input of the network, among which 120,000 image blocks are cropped out from the GoPro training set, and 20,000 image blocks are cropped out from the global blur training set.  
**Experimental settings.** In the three network training phases, we adopted the ADAM optimizer [42] network training with default parameters. We adopted NVIDIA GeForce GTX 1080 Ti GPU for model training and testing, and the PyTorch is used to build our network framework.
## 3 Methods
### Automated Geometric Correction Processing Method
#### 3.2.2 Step 2: Automatic CPs Extraction  
The amount and spatial distribution of CPs are key factors for geometric correction because they have direct impacts on the geometric correction accuracy of the corrected images. In this study, we applied the SIFT algorithm based on MATLAB language to extract the CPs automatically from the BNU-1 L1A and the corresponding MODIS image. Due to the lack of texture features of snow and ice surfaces at high latitudes, CPs extracted from the original image pair are usually not sufficient for correcting the geolocation errors. When the number of CPs extracted from the image pair needs to be increased, image division and image enhancement methods are used to enhance the texture features of satellite images [1; 21; 22; 34].  
The combination of an image division method and an image enhancement method was applied to highlight surface features of the BNU-1 and MODIS images in this study. The extraction of CPs was carried out in three steps in Step 2 (Figure 2). Firstly, we extracted the CPs from the original BNU1-1 image and the corresponding MODIS image by using the SIFT algorithm. The Euclidean distances between each pair of CPs from the image pair were calculated. To avoid mismatches of the points, we eliminated the largest 10% points in the Euclidean distance. Secondly, we extracted the CPs from the image pair after processing by different image division schemes. The paired images were divided into 2 \(\times\) 2 = 4 (Scheme 1) and 3 \(\times\) 3 = 9 (Scheme 2) sub-images [22]. Then, an adaptive piecewise linear enhancement consisting of three rules for low, middle, and high reflectance ranges was used to enhance each sub-image [34]. We extracted the CPs from all the pairs of the sub-images again by the SIFT algorithm. The largest 30% of the extracted CPs in Euclidean distance were eliminated in this step. Finally, the CPs extracted in the above two steps were merged and de-duplicated to obtain the CPs with the largest number, which were taken as the final CPs for the geometric correction.
## 3 Chaos Synchronization
Figure 5: Intra-cavity propagation delay \(\tau_{PD}\) map in the injection parameters (frequency detuning \(\Delta f\), injection rate \(\eta\)) plane for keeping \(\kappa=9\) ns\({}^{-1}\), \(\tau_{ext}=10\) ns, and \(\tau_{f}=0\). Figure 6: Transmitter-receiver maximum correlation coefficient \(C_{m}\) (red trace) and the associated intra-cavity propagation delay \(\tau_{PD}\) (blue trace) as a function of injection rate for keeping \(\Delta f=0\), \(\kappa=9\) ns\({}^{-1}\), \(\tau_{ext}=10\) ns, and \(\tau_{f}=0\). The horizontal dashed lines in Figures 4 and 5 indicate the frequency detuning \(\Delta f\) variation at \(\eta=15\) ns\({}^{-1}\). The dependence of \(C_{m}\) and the corresponding \(\tau_{PD}\) on the frequency detuning variation for keeping \(\eta=15\) ns\({}^{-1}\) are shown in Figure 3.0 The simulated results demonstrate that the RL is driven into the chaotic state only for frequency detuning within \(-5\) GHz to \(+5\) GHz. It can be seen that the degree of synchronization \(C_{m}\) (red trace) gradually increases and then decreased when the \(\Delta f\) varied from negative to positive. The associated \(\tau_{PD}\) (blue trace) found lesser near zero detuning and found increasing for higher values of negative detunings. In addition, \(\tau_{PD}\) consistently decreases with increasing positive detuning. Also, a better synchronization and lesser magnitude of \(\tau_{PD}\) can be seen near injection-locking boundaries for moderate injection. Additionally, the mapping of \(C_{m}\) and the associated \(\tau_{PD}\) in the plane of TL's feedback rate \(\kappa\) and injection rate \(\eta\) are shown in Figure 3.1 We kept the other parameters the same as in Figure 3.2 The dark red boundary in Figure 8a indicate that a good synchronization quality (\(C_{m}>0.9\)) can be obtained between TL and RL, which is due to the larger injection rate. And in those regimes, the associated \(\tau_{PD}\) take positive values as well as found to decrease (light green boundary in Figure 8b) as the injection rate increases. It is evident from Figure 8a that the quality of synchronization also depends on the \(\kappa\), wherein which for higher values of \(\kappa\), stronger injection is a necessary condition to achieve good synchronization between TL and RL. Figure 3.3 Mapping of \(C_{m}\) (**a**) and the corresponding \(\tau_{PD}\) (**b**), in the plane of \(\kappa\) and \(\eta\) for keeping other parameters as \(J_{m}=1.10I_{thr}\), \(J_{R}=1.08I_{thr}\), \(\tau_{ext}=10\) ns, \(\tau_{f}=0\), and \(\Delta f=0\). Figure 3.4 Transmitter-receiver maximum correlation coefficient \(C_{m}\) (red trace) and the associated intra-cavity propagation delay \(\tau_{PD}\) (blue trace) as a function of frequency detuning for keeping \(\eta=15\) ns\({}^{-1}\), \(\kappa=9\) ns\({}^{-1}\), \(text=10\) ns, and \(\tau_{f}=0\).
# Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach
## 2 Prediction in Hard Disk Replacement  
An automated and standardized hard disk replacement process can be defined based on the results from automatic testing framework, which is integrated by disk manufacturers in their disk firmware: the SMART system. The hard disk models used in storage centers are often numerous and change over time. Due to the heterogeneity of hard disks of which data-centers are often composed, it is not possible to perform analytical modeling. So modeling by means of machine learning is the best solution in this context. Multiple recent approaches based on SMART attributes have been proposed and show improved prediction performance in general but are usually highly dependent on the specific device they were applied to.  
A Gaussian Mixture based the fault detection approach has been proposed in 2017, which is able to minimize the False Alarm Rate (FAR) of 0%, but its performance drops by a few dozen percentage points in the hours before the last 24 h, and there is no information on the rate of false alarms in the days before the last. In addition, the method has been tested on a single hard disk model [8].  
The authors of [10] propose a technique based on Online Random Forests. Their method reaches an accuracy (defined as the fraction of true positives and true negatives in the whole population) above 93% while maintaining a low rate of false positives (i.e., failure predicted for an actually healthy disk). To achieve this result, this technique considers hard disk metrics over the period of one week. It predicts a disk failure in case any of the smart metrics in the period is positive and is hence sensitive to transient phenomena. Goodresults have been reached using an offline machine learning technique based on a decision forest named Regularized Greedy Forest (RGF) [14]. Only two cases of hard disk models were explored for which a recall (defined as the fraction of correctly detected failures among all positives) value as high as approximately 98% was achieved. The corresponding results for the false positives rate are not mentioned explicitly but can be derived as approximately 2% [14].  
An interesting approach based on the Long Short Term Memory model has been proposed in [15] that achieves good results in terms of the false alarms rate. This paper does not present a labeling method for broken disks; thus, it is not clear how to create the dataset for the machine learning models. Moreover, it does not introduce a solution on how to deal with heterogeneous sets of hard drive models.  
The authors of [11] focus on the wide-spread heterogeneity of data-centers. They addressed the issue of the manufacturer dependencies of the implementation technology, which the SMART monitoring system is integrated with. The authors compare Decision Trees, Neural Networks and Logistic Regression and suggest Decision Tree as the best solution for the problem at hand. The method proposed is able to predict about 52% of all hard disk failures among the truly failed drives.
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 2 Related Work  
### Deeper Architectures  
Convolutional neural networks (CNNs) exhibit excellent performance when dealing with visual tasks owing to their rich characterization capabilities [8; 40]. In visual research, well-designed network architectures can significantly improve performance in a variety of applications. Increasing the depth of the neural network is a simple and effective design method in neural network design. VGGNet [1] and GoogLeNet [2] show that increasing the depth of the network can significantly improve the ability of model learning representation. However, as the network becomes increasingly deeper, gradient propagation becomes more difficult. Batch normalization [3] improves the stability of the network while learning by adjusting the distribution of each layer of input in the network. By using a well-designed multi-branch architecture, inception models [2; 3; 4; 5] enable a more flexible convolutional combination and improve the network's feature learning capabilities. Further improvements have been achieved. Firstly, in order to alleviate the problem of gradient disappearance caused by increasing network depth, ResNet [6] proposes an identity-based skip connection, which makes it possible to achieve a better learning ability in deeper networks. WideResNet [7] shows that using more channels and a wider convolution in the network model can improve performance. ResNeXt [9] and Xception [10] prove that grouped convolutions can improve the accuracy of classification. MobileNet [11] uses depthwise separable convolutions to enable the network to be applied on mobile terminals. Finally, DenseNet [12] is a densely connected network architecture proposed by [PERSON] et al., which provides maximum information transmission between layers in the network.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis
### IEEE 33-Node Distribution Network
#### 5.1.1 Accuracy  
To test the accuracy of the algorithm, it was run 100 times with multiple fault types, such as single faults, multiple faults, and end-of-supply faults. The accuracy of the algorithm is described by the ratio of the number of correct locations to the total number of runs. The test results are shown in Table 6.  
As can be seen in Table 6, all four algorithms have correct result outputs; the difference lies in their ability to maintain consistency in their results. Algorithm 2 and Algorithm 3 cannot guarantee 100% accuracy. The accuracy of Algorithm 2 is around 85%, which is due to the fact that the algorithm applies an intelligent algorithm with stochastic search characteristics to locate faults, and along with the depth of iterations, the population tends to homogenize, leading to possible local convergence; thus, the consistency of results cannot be guaranteed. Algorithm 3 applies a hierarchical strategy to reduce the fault search dimension, and its accuracy is improved, but it still needs to locate the fault branch with the help of an intelligent algorithm; thus, the defect of insufficient convergence stability cannot be eradicated. With the pseudocode DACFL, the algorithm in this paper does not contain random operations, which makes the algorithm have the same execution process for the same fault situation; thus, it can guarantee consistency in its results. The test results confirm that Algorithm 1 achieved 100% accuracy for the fault cases in Table 6.
# Spin-Orbital Transformation in a Tight Focus of an Optical Vortex with Circular Polarization
## 8 Discussion  
In tight focusing, SOC occurs when light passes through a spherical lens. If the optical vortex with the right circular polarization (1) before the spherical lens had a full longitudinal SAM \(\hat{S}_{z}\), equal to beam power \(W\) (2), then the total longitudinal SAM of the beam would decrease immediately after the spherical lens and become equal to (11) \(\hat{S}_{z}=W_{0}-W_{2}\). After the spherical lens, beam (1) has three vortex harmonics [34]: \(\exp(im\theta)\), \(\exp(i(m+2)\theta)\), and \(\exp(i(m+1)\theta)\), instead of one vortex harmonic \(\exp(i\pi\theta)\). Therefore, the total beam power \(W\) after the spherical lens is distributed between these three harmonics \(W=W_{0}+W_{2}+2W_{1}\). The vortex harmonic with the topological charge \(m+1\) has a linear polarization directed along the optical axis \(z\). That is, it propagates perpendicular to the optical axis and does not contribute to the longitudinal projection of the SAM. An optical vortex with topological charge \(m+2\) has a left-hand circular polarization, and therefore its contribution to the longitudinal projection of the SAM will be negative. Therefore, the total longitudinal SAM of the beam (1) after the spherical lens is reduced by the sum of the powers of the indicated harmonics \(\hat{S}_{z,in}-\hat{S}_{zafter}=W-(W_{0}-W_{2})=2(W_{1}+W_{2})\). This difference, by which the spin has decreased due to the SOC, is exactly equal to the amount by which the total longitudinal OAM, which the beam (1) did not have before the spherical lens if \(m=0\), has increased: \(\hat{L}_{z}=2(W_{1}+W_{2})\). On the other hand, this is confirmed by considering the energy flow (the Pointing vector). Before the spherical lens, the beam (1) had only a longitudinal energy flux (19), which was equal to the beam power \(\hat{P}_{z}=W\). After a spherical lens, the longitudinal energy flux decreased and became equal to (18) \(\hat{P}_{z}=W_{0}-W_{2}\). As well as for the spin \(\hat{S}_{z}\) two new vortex harmonics \(\exp(i(m+2)\theta)\) and \(\exp(i(m+1)\theta)\), which are generated after the spherical lens, do not participate in the formation of the longitudinal energy flux. They do not participate in the formation of the longitudinal energy flow because one of these harmonics \(\exp(i(m+1)\theta)\) propagates across the optical axis, while the other harmonic \(\exp(i(m+2)\theta)\) propagates in the opposite direction \(z\). Both of these harmonics form a transverse azimuthal energy flux, which rotates around the optical axis, and which is equal to \(\hat{P}_{\theta}=2(W_{1}+W_{2})\). This azimuthal energy flux is exactly equal to the total longitudinal OAM of the beam at \(m=0\): \(\hat{P}_{\theta}=\hat{L}_{z}=2(W_{1}+W_{2})\).
## 4 CD, GFD, and Nutritional Deficiencies
### In Patients Following GFD  
Many pediatric studies have demonstrated that intakes of folate, magnesium, zinc, and selenium may be lower on a GFD than healthy controls [40, 41, 42]. A recent study, including 101 children, demonstrated that celiac children had lower iron intake than controls, especially at the initiation of GFD [43]. Healthy controls showed a higher intake of folate, iron, magnesium, and selenium than that of children CD-GFD >6 months and a higher intake of folate and iron than that of children initiating GFD <6 months. In contrast, another study found that iron and calcium intakes were higher in CD children than controls [44]. Other evidence highlights that celiac children and adolescents tend to follow a high-lipid, high-protein, low fiber diet compared with healthy subjects [45]. These findings suggest that the diet of celiac children may be potentially less balanced than that of the controls, thus deserving nutritional advice.  
In CD adults, various studies highlighted that the GFD treatment does not fill up mineral and vitamin deficiencies reported at diagnosis, especially iron deficiencies [46; 47; 48; 49]. Calcium deficiencies were also reported [31; 46; 48; 49; 50] in CD patients adhering to a GFD. The intakes of selenium, zinc, and magnesium were found to be lower in CD subjects than in controls [44; 49]. A prospective Australian study showed that more than one in 10 of both newly diagnosed and experienced CD women (adherent to a GFD \(\geq\) 2 years) had inadequate thiamin, folate, vitamin A, magnesium, calcium, and iron intakes [49]. Inadequate intake did not relate to the nutrient density of the GFD. The frequency of inadequacies was similar pre-and post-diagnosis, except for thiamin and vitamin A, where inadequacies were more common after GFD implementation.  
Micronutrient deficiencies in celiac patients following GFD are common and may be associated with habitual poor food choices in addition to inherent deficiencies in the GFD. Dietary education by dietitians is necessary for the management of CD patients, and an adequate micronutrient intake could be achieved through adequate micronutrient supplementation.
# Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment
## 5 Experimental Results
### Analysis Results of Age Classification  
The straightforward CNN model suggested by [PERSON] and [PERSON] [49] was applied to the face age testing data set [79] described in Section 4.3. In this study, we excluded the 0-2 and 4-6 age groups from the evaluation, as individuals in these age ranges are not socially independent and thus not considered relevant in practical scenarios for assistive systems designed for visually impaired users. Six age groups were assessed: 8-13, 15-20, 25-32, 26-32, 38-43, 47-53, and 60+ years. Figure 15 depicts the relevant confusion matrix. The 25-32 age group demonstrated the highest TPR. Some misclassifications across related age groups could be caused by subjective visual cues such as makeup, facial expressions, or variations in lighting and image quality. These features illustrate the challenges in estimating precise ages in practical settings.  
Figure 14: Confusion matrix of the gender classification model.  
Figure 15: Age classification confusion matrix.  
Table 4 presents the precision, recall, and F1 score. According to this table, the age groups of 25-32 and 60+ years exhibited the highest precision of 0.75, whereas the age group of 8-13 years exhibited the highest recall and F1 score of 0.96 and 0.82, respectively. The average values of precision, recall, and F1 score were 0.71, 0.67, and 0.67, respectively.