esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 2 Related Work
### Video Style Transfer  
There are two main branches of approaches proposed for video style transfer, and they are categorised into two types: video style transfer with optical flow estimation and video style transfer without optical flow estimation. Early works [23; 24] firstly introduced optical flow estimation to enhance the temporal consistency between adjacent stylized frames, which were built on original gradient-based optimization methods (e.g., [9]). However, such approaches are less practical as they need minutes to stylize one single frame. To speed up the style transformation process, more works such as [27; 28; 29; 60] proposed feed-forward-based video style transfer methods which use optical flow estimation during the training stage but can stylize videos without them in the test stage. These methods leverage a running performance of up to three orders of magnitude faster speed while obtaining coherent stylized videos.  
However, the methods mentioned above all need optical flow with extra complex computation, which makes it impractical to stylize high-resolution or long videos. To avoid such extra computation, [PERSON] et al. [61] proposed a blind video post-processing technique which is capable of preserving temporal consistency. Their method processes the per-frame stylized results and achieves a real-time execution time as no optical flow is needed in the test stage, but their results still suffer from temporal incoherences as no prior knowledge about style effects is considered. [PERSON] et al. [35] proposed a new linear transformation matrix to minimize the difference between the covariance of content features and style features, which maintains temporal consistency by propagating the computed matrix from the beginning frame to the rest of them. [PERSON] et al. [36] proposed a novel method to make the stylization loss term more robust to motions without optical flow estimation. [PERSON] et al. [62] proposed a multi-channel correlation network which is able to fuse style features and input content features for maintaining the coherence of input videos and stylized videos. More recently, [PERSON] et al. [37] proposed a universal versatile style transfer method with a novel Contrastive Coherent Preserving Loss, which is capable of preserving the coherence of the content source but without degrading the stylization performance. [PERSON] et al. [38] proposeda Unified Style Transfer framework with a domain interaction transformer, which enables temporal information from input videos with rich texture appearance to be integrated with the style reference image for stylized videos. However, these methods without optical flow estimation fail to find the balance between stylization effect and temporal consistency.
## 3 Experimental Procedure  
### Equipment Used and Reconstruction of the Model  
The experiment (Figure 5) was carried out using a fibre laser (manufactured by IPG) with a maximum output power of 4 kW and a six-axis KR30 HA KUKA Robot with a powder feeder (DPSF-2); the laser head was equipped with a lateral nozzle. Argon gas was used to deliver powder and prevent melt-pool from being oxidized during the process of laser cladding. Before the experiment, the workpiece was polished with SiC paper (grade 600) and ultrasonicated in acetone for 5 min, then washed in distilled water and dried in the air.  
A coordinate measuring machine (CMM) was used to collect the data from the curved surface and the results are shown in Table 1.  
For curved parts, model reconstruction is a very important step before the experiment (the nonuniform rational B-spline (NURBS) surface fitting method [21; 22] was used in this experiment). Furthermore, in industrial applications, the accuracy of the double cubic NURBS method can be achieved to G\({}^{2}\) level; thereby, the double cubic NURBS fitting method is able to realize the reconstruction of the model. The process of fitting the curved surface [23] was as follows; firstly, the node vector (\(u\)) was calculated by the accumulative chord length method based on the data in Table 1; then, the node vector U\({}_{i}\)\(=[0,0,0,0,\mathrm{u}_{4},\ldots,\mathrm{u}_{\mathrm{m}},1,1,1,1]\) and its weight \(\omega_{i}\) were calculated, through the parameterisation of the node vector U\({}_{i}\), as follows, \(\mathrm{x}\)\(=\)\(\frac{\mathrm{u}-\mathrm{u}_{i}}{\mathrm{u}_{i}+\mathrm{u}_{i}}\), (\(0\leq\alpha\)); the control point d\({}_{i}\)(i = 0, 1,... m) was obtained based on the matrix operation; then, the cubic nonuniform rational B-spline curve on the \(u\) direction  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**The First Column**} & \multicolumn{3}{c}{**The Second Column**} & \multicolumn{3}{c}{**The Third Column**} \\ \hline
**x** & **y** & **z** & **x** & **y** & **z** & **x** & **y** & **z** \\ \hline
12.00 & \(-\)14.71 & 5.00 & 10.00 & \(-\)19.  
was obtained by using the basic function of the cubic B-spline \(N_{i,3}(u)\), taking the control point \(\mathrm{d_{i}(i=0,1,\ \ldots m)}\) as the data point on the \(v\) direction. Through using the same solving process, as well as the node vector \(\mathrm{U_{j}}\)\(=[0,0,0,0,\mathrm{u_{4}},\ldots,\mathrm{u_{n}},1,1,1,1]\) and its weight \(\omega\), the control point \(\mathrm{d_{j}(j=0,1,\ \ldots n)}\) and the cubic nonuniform rational B-spline curve on the \(v\) direction were obtained. Finally, the double cubic non-uniform rational B-spline surface was obtained based on the Equation (5) (the result is shown in Figure 6) by using MATLAB software.  
\[\mathrm{P(u,v)}=\frac{\sum_{i=0}^{m}\sum_{j=0}^{n}\omega_{ij}\mathrm{d_{ij}N_{ i,3}(u)N_{j,3}(v)}}{\sum_{i=0}^{m}\sum_{j=0}^{n}\omega_{ij}\mathrm{N_{i,3}(u)N_{j,3}(v)}} \tag{5}\]
## 4 Discussion
### Aggregate Analysis
reported that patients with present DPOAEs and TEOAEs in the acute stages revealed the greatest improvement in hearing. Furthermore, the sensitivity and specificity on the seventh day of follow-up in predicting hearing improvement was significant for both DPOAEs and TEOAEs, supporting their role for use in predicting outcome [28]. [PERSON] et al. 's findings suggested that the function of OHCs deteriorated when hearing threshold was elevated, and their activity recovered in correlation with hearing improvement in ISSNHL cases with positive outcomes. The authors also recognized that earswith ISSNHL did not show significant differences in OAEs when compared with ears with other forms of SNHL [35]. The fifth article that analyzed both DPOAE and TEOAE measures determined TEOAEs to be a significant prognostic indicator in cases of ISSNHL, however, their findings regarding the value of DPOAEs were deemed insignificant [31]. Overall, the articles as a whole concluded that DPOAEs and TEOAEs are clinically valuable in predicting the outcome in cases of ISSNHL. The two articles that focused solely on the use of TEOAEs as a diagnostic tool in predicting outcome for cases of ISSNHL had different conclusions. [PERSON] et al. found that present TEOAEs in the acute stages of ISSNHL in patients with hearing thresholds greater than 40 dB HL indicated a positive prognosis, determining this physiological measure may be useful in predicting hearing recovery. However, the authors did not find a specific correlation between TEOAE amplitude and hearing improvement [32]. On the contrary, [PERSON] et al. determined that correlations between TEOAE amplitude and hearing recovery were too weak to support the clinical use of TEOAE measures in predicting outcome in cases of ISSNHL. However, of all seven articles that assessed the use of TEOAEs, only one did not support their clinical use for predicting prognosis [30]. Finally, [PERSON] et al. was the only article that included all three types of OAE measures (DPOAEs, TEOAEs, and SOAEs) and focused solely on ISSNHL affecting low frequency hearing. The authors supported the idea that all three forms of OAEs can be useful in assessing inner ear functional state; however, they concluded that they would not be valuable as a prognostic indicator in cases of LFSNHL in regards to hearing recovery or treatment significance.
## 3 Design and Simulation of a Velocity Loop Controller Based on Discrete SMC
### Basic Theory of Discrete SMC
#### 3.1.3 The Quasi Sliding Mode Domain  
**Definition 1**.: _For the perturbed system, the system will be in a quasi-sliding-mode (QSM) in the \(\Delta\) vicinity of the sliding surface, not at the sliding surface. This specified domain where the QSM occurs is called the quasi-sliding-mode domain (QSMD) \(s_{\Delta}\), the positive constant \(\Delta\) is the QSMD width, and \(s_{\Delta}\) is the QSMD [39; 40; 41],_  
\[s_{\Delta}=\left\{s(k)\big{|}\big{|}s(k)\big{|}\leq\Delta\right\} \tag{14}\]  
The condition in which the system state is stable in the QSMD is defined as:  
\[\left\{\begin{array}{l}-\Delta<s(k+1)<s(k),s(k)>\Delta\\ s(k)<s(k+1)<\Delta,s(k)<-\Delta\\ \big{|}s(k+1)\big{|}\leq\Delta,\big{|}s(k)\big{|}<\Delta\end{array}\right..\]
## 2 Data and Methods
### Analysis  
We compared seasonal and annual WUE derived from MODIS data (WUE_MODIS) with eddy flux data (WUE_EC) at eight ChinaFLUX sites. In this study, WUE was defined as the ratio between GPP and ET (Equation (2)).  
\[\mathrm{WUE}=\mathrm{GPP/ET} \tag{2}\]  
Three agreement measures, including mean bias, root mean square error (RMSE) and Pearson's correlation coefficient (R), between MODIS- and flux-derived WUE were used to evaluate the performance of MODIS-derived WUE over different vegetation types in China. The mean bias, RMSE and R were computed as the following equations:  
\[\mathrm{bias}=\frac{\mathrm{\Sigma(WUE\_MODIS-WUE\_EC)}}{\mathrm{N}} \tag{3}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\mathrm{\Sigma(WUE\_MODIS-WUE\_EC)^{2}}}{\mathrm{N}}} \tag{4}\]  
\[\mathrm{R}=\frac{\mathrm{\Sigma(WUE\_EC-WUE\_EC)(WUE\_MODIS-WUE\_MODIS)}}{ \mathrm{\left(\sqrt{\mathrm{\Sigma(WUE\_EC-WUE\_EC)^{2}}}\right)(\sqrt{\mathrm{ \Sigma(WUE\_MODIS-WUE\_MODIS)^{2}}})}} \tag{5}\]
## 5 Discussion and Open Research Issues
### Emerging I2O WBAN Issues
#### 5.2.2 I2O WBAN Interface Design  
The in-body environment makes I2O WBAN technologies more complex and challenging when compared with other communication networks. I2O WBAN systems can be regarded as a'shared bus', where different kinds of entities that can generate data and transfer the data provided by other entities [43; 44; 45; 46]. Recently, software-based results related to WBAN entity interfaces design have been presented [44]. However, the in-body WBAN interface design should consider both network configuration and network management and there is to date no agreed international standard for the I2O interface technologies [43; 44]. Different layer requirements for in-body device (logical management) entities interface, are reported in [40]. One promising solution reported in [44], is a radio-based I2O system that enables data transmission between the in-body device(s) to the e-health gateway via an external programming device. For on-body WBAN interface technologies, smartphones and other on-body sensor nodes (wearable devices) with data collection, data buffering, information transmission, user authentication, computational and communication capabilities have been proposed to work together with the in-body interface [3; 47].
## 6 Positioning Accuracy
### Discussion
In Experiments 2 and 3, delay is calculated based on signals that arrived via direct line-of-sight, whereas the 2D position of the phone is calculated based on the projection of these direct lines-of-sight onto a 2D plane. A bigger height difference between microphones and the phone generally means reduced difference in the time-of-arrival, causing readings to appear closer to the center of the room. Unfortunately, the relationship between distance to microphones and delay reduction is not linear, making it necessary to know a phone's position before the required compensation can be calculated. Since in our case the phone's position is unknown before trilateration is done, it becomes necessary to calculate the projection of direct lines-of-sight onto a plane and incorporate them into the asynchronous trilateration procedure. This is a complex task and is left for future work, although overall positioning accuracy is still sub-meter without introduction of this correction. In Experiment 3 a simpler constant multiplier (room calibration factor) was used instead. In Experiment 3 each delay was multiplied by a constant value of 1.1 (room calibration factor), which produced significant improvements in accuracy over Experiment 2 from 18 cm to 8 cm. Although the use of a constant multiplier may in some cases result in a decrease in accuracy, it appears to affect accuracy relatively less when compared to other factors. The biggest factor appears to be poor signal reception when the phone is moved to the corners or along the walls. Usually this happens if the microphone is located far away and the direction it is pointing is substantially different from the direction in which the phone is located. Possible solutions to this problem could be to use microphones that are less directional or add more microphones throughout the room. Reception in these extreme areas was also influenced by yaw-orientation of the phone. This was an unexpected factor. Theoretically, since the speaker points directly upwards, yaw-orientation should have no influence on the strength of the signal upon arrival, unless the phone's speaker is also somewhat directional in its manufacture.
## 2 Methodology
### Image Analysis
#### 2.2.2 JMicroVision Software  
In order to obtain more information about particle geometric characteristics and select representative parameters, SEM micrograph examination was also conducted using JMicroVision image processing software.  
Similar to the previous software, spatial calibration was first performed based on a known length from the SEM micrograph, entered into the calibration menu as a known distance (in micrometres).  
Next, object extraction was performed (General description > Object extraction > Colour or grey intensity threshold). Firstly, the area of the image containing particles was selected to prevent accompanying information from affecting the results. The 'all edges' option allowed the software to exclude particles located at the edges from the calculation, in case they were not fully captured in the micrograph. Then, the threshold intensity level was adjusted (Figure 6a), until a satisfactory level of selection was achieved (using the cursor to add colour to the particle with the 'Add' option (Figure 6b). Each particle could be removed from the results if any deviations were observed compared to the original image.  
JMicroVision, through the Image Factory option, provides the capability of classic, non-linear, and gradient filtering. Classic or linear filtering, also known as a mathematical convolution operation, involves multiplying a group of pixels in the input image by a set of pixels in the convolution kernel. The output value produced in the spatial convolution operation is the weighted average of each input pixel and its neighbouring pixels in the convolution kernel. Linear filtering encompasses a wide range of filters for fine processing, sharpening, noise reduction, and edge filters (uniform, triangular, Gaussian, Wiener, etc.). Non-linear filtering is based on the logical separation of filters into a series of relatively simple operations (median, minimum, maximum, Kuwahara filter). Gradient filtering refers to edge detection by computing the magnitude of the gradient vector of the image in two orthogonal directions. After setting all necessary parameters in the software, we took into account quantitative data defining particle size--equivalent circle diameter, length and width--as well as parameters defining particle shape--convexity, elongation, compactness and rectangularity (Figure 7).  
All quantitative parameters describing particle geometry obtained as a result of using the aforementioned two pieces of image analysis software are available in the Supplementary Materials.  
Figure 6: Parameter adjustment in the JMicroVision software: (**a**) threshold adjustment; (**b**) particle selection.
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### IRS Model in VLC Systems  
IRSs are surfaces composed of controllable elements that can modify the characteristics of incident electromagnetic and optical signals, such as phase, amplitude, and direction. In the context of VLC, an IRS is used to improve signal propagation by manipulating light reflection in indoor environments, thus optimizing signal coverage and quality [5].  
Generally, in the literature, there are two types of surfaces widely analyzed and incorporated in indoor communications scenarios: flat mirrors and convex mirrors. Flat mirrors in the IRS are used to reflect incident light in a controlled manner [20]. Since they are flat surfaces, the reflection is specular, which means that the direction of reflected light follows the law of reflection: the angle of incidence equals the angle of reflection [20]. These surfaces can be electronically adjusted to modify the phase of the reflected light, allowing light beams to be directed toward specific receivers.  
However, convex mirrors, unlike flat mirrors, have a curved surface that disperses the reflected light. In an IRS, convex mirrors can be used to extend the coverage of the reflected signal as light reflects in multiple directions [23]. This can be beneficial in environments where a broader signal dispersion is required, although it may result in a potential reduction in the intensity of the received signal.  
Due to the blockage of the LoS channel, communication relies entirely on reflections from the IRS. This can increase the system's sensitivity to the position of the reflective  
Figure 1: The basic schematic of an IRS-aided VLC system.  
elements and the room's geometry. It is crucial to ensure that the IRS is designed to maximize coverage and minimize signal loss due to excessive dispersion or attenuation.  
To develop an optimal indoor VLC system, that is, to maximize the received power from reflections, a process involving mathematical modeling of the environment, the optical properties of the mirrors, and the formulation of an optimization problem is required. This process is described in detail below.
# RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks
## 3 The Proposed Model
### Operation Mode  
The operation mode of RDTIDS system is composed of two steps: training and testing. During the first step, we train the three classifiers that compose our hierarchical model. We start by training the first and the second one and using the results from these two we train the third classifier. Initially,  
Figure 1: General structure of Rules and Decision Tree-based Intrusion Detection System (RDTIDS) system.  
Figure 2: Integration of RDTIDS system in the three-tier fog computing architecture for Internet-of-Things (IoT) networks.  
After that, we perform the training of this classifier and as a result of this sub step, we get model 2.  
In order to train the third classifier, we modify the training data set, where we add two columns, the first one represents the classification results of model 1 for the rows of the training data set and the second one represents the classification results of model 2 for the rows of the training data set. Then, we normalize the different features of the data set. After that, we perform the training of this classifier and as result of this sub step, we get model 3.  
As illustrated in Figure 1, in order to test our hierarchical model, we process each row of the test data set by model 1 and model 2, then we add the outputs of model 1 and model 2 to the features of each row, and finally, we process the result rows by model 3 that classify it as benign or a specific type of attack.  
The whole procedure of transforming the dataset into information that can be read and interpreted from our classifiers is call pre-processing and consists of two steps: the first involves mapping symbolic-valued attributes to numeric-valued attributes and the second is implemented scaling. The main advantage of scaling is to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges. Another advantage is to avoid numerical difficulties during the calculations.
# A Novel Scheme for MIMO-SAR Systems Using Rotational Orbital Angular Momentum
## 3 A Novel RDE-Based Scheme
Compared with the conventional orthogonal waveform schemes, the data processing procedure of our proposed scheme was simpler, which could avoid some complicated demodulation steps. In our scheme, the rotational Doppler shift was implemented by the virtual rotation movement of the antenna, which enabled the frequency division multiplexing technique in the Doppler domain. The translational Doppler shift was still implemented by the movement of the SAR platform, which was used for virtual synthetic aperture in the azimuth. Due to the orthogonality of the vortex EM waves, the intra-modal interference could be suppressed in our proposed OAM-based MIMO-SAR system, and it became applicable for one single system to simultaneously perform high-resolution SAR imaging and wireless communication in the same frequency band. For example, in our scheme, one OAM mode could be used for high-resolution SAR imaging, and because of the added equivalent phase centers and wider beam width, the swath width could be twice that of the SISO mode without range ambiguity. Meanwhile, the other OAM mode could be utilized for wireless communication with better performance [21]. Moreover, because our scheme was implemented by bandpass filtering in the Doppler domain rather than decoding, the performance of our scheme could escape from the impacts of the time-variant channel responses [7].