esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applicability of TR and Its Approximate Combination Rules  
The TR1 and TR2 combination rules are approximations of the TR combination rules. To verify the applicability of the TR and its approximate combination rules, the Monte Carlo simulation method was used to analyze the applicability of the TR combination rules first, and then the accuracy of TR1 and TR2 combination rules was analyzed under the applicable conditions of the TR combination rule. The distribution of random numbers generated by the Monte Carlo simulation method is a Gaussian process, but the effect components of building structures in practice may have strong or weak non-Gaussian characteristics. Therefore, the Monte Carlo simulation method used in this paper did not verify the combination of non-Gaussian effect components. However, the combined methods proposed in the paper are based on the condition that the effect components' distributions are Gaussian process. Due to the high precision of CQC or SRSS rules for the scalar sum of the effect components, this section analyzes the relative errors of the TR, TR1, and TR2 combination extremes, and determines the application range of TR and its approximate combination rules. Considering that the simplified combination coefficient of the combination rule proposed above is related to the correlation coefficient and the standard deviation ratio, the applicability of TR, TR1, and TR2 rules is approached from the perspective of the correlation coefficient and standard deviation ratio. When determining the combination coefficients, this paper and EN 1990 (2002) [26] are both based on the probability and statistics theory. However, the effect of wind action is not considered in EN 1990 (2002) when seismic action is involved in the combination of load effect. The probability distribution of extreme wind speed in EN 1991-1-4 is the extreme value Type I distribution. In accordance with EN 1990 the basic wind speed are characteristic values having annual probabilities of exceedance of 0.02. They are both the same as the assumption in GB 50009-2012, which is adopted in this paper. Furthermore, the probability of the calculated wind actions does not exceed the probability of the basic wind speed.
## 2 Security Requirement and S-Box Design Methods
### S-Box Properties
#### 2.1.3 Algebraic Degree (AD)  
\(deg(f)\) is denoted from the number of variables in the largest monomial for the component function \(f\) of an S-box. Therefore, the AD of an S-box is determined by the maximum degree of all component functions, as follows:  
\[\text{AD}(F)=\max\{deg(f_{1}),deg(f_{2}),\ldots,deg(f_{n})\}\]  
The value of the AD of an S-box must be high [14] in order to resist higher order differential cryptanalysis [5]. In our study, we set \(\text{AD}\geq 4\).
## 4 Discussion
### Uncertainties of the SWH Model
This may be an additional reason why the SWH model performed better in biomes with a more uniform vegetation distribution. Another uncertainty associated with the SWH model is its lack of consideration of intercepted canopy water evaporation, which may introduce biased estimations of ET in forest ecosystems with high and frequent rainfall events and, consequently, potentially reduce the model performance at EBF sites. However, the MODIS algorithm, which estimates wet canopy evaporation independently, also clearly exhibited poorer predictive ability in EBF (Figure 3). This result suggests that canopy interception is not an important source of the inconsistencies between the SWH model and observations at EBF sites. Soil water content is a necessary driving variable in the SWH model and was estimated using a one-layer bucket model. We expected that inaccurate estimates of soil water content would introduce large uncertainty into the modeled ET. However, the sensitivity analysis indicated that the modeled ET does not depend on soil water as strongly as anticipated. A 10% change in soil water content introduced a less than 1% change in ET (Table 2). This result is reasonable considering the time step of the model. It is acknowledged that ET would depend highly on soil water content at diurnal and daily time scales. At daily and lower time scales, the effect of soil water content is largely through impacts on plant stomatal conductance. In our study, ET was estimated at an eight-day time step, in which the effect of soil water content would largely ne via impacts on Leaf Area Index (which can be quantified with NDVI), instead of stomata. This might be the reason why NDVI is the most sensitive variable in the model. In addition, soil moisture was only used to estimate the soil surface resistance of evaporation, \(r_{\text{ss}}\), in the SWH model (Equation (1)), which makes the ET simulation depend on soil water content very little. The SWH model simulates ET, as well as the major components of soil water evaporation and plant transpiration. However, we failed to quantify the model performance for estimating each component in this study. We recently analyzed eddy flux tower measurements from three Chinaflux forest sites at which flux towers were installed both above and below the canopy, allowing us to partition ET with direct measurements. The results indicated that the model outputs were generally consistent with the observations [53]. However, due to the scarcity of such observations, and the possible overestimation of E/ET mentioned above, we acknowledge that further work is needed to fully evaluate the partitioning performance of the model.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 2 Related Work  
### Monocular Depth Estimation  
Supervised monocular depth estimation requires ground truth data for training. Early supervised approaches [7; 8; 9; 10; 11; 12; 13] tended to define depth estimation as pixel-wise regression. A recent method called IEBins [14] used the idea of classification-regression with iterative elastic bins to predict depth. As a research focus of generative learning, diffusion models were also introduced to monocular depth estimation [15; 16]. To fully utilize the geometric regularities of indoor scenes, many works added proper geometry-related loss functions to improve their performance. Some approaches [17; 18] designed surface normal loss since surface normal is an important local feature. There are also some classic works such as NDDepth [19; 20], P3 Depth [21], PlaneReg [22] and PlaneNet [23] leveraging plane-aware supervision to achieve significantly better accuracy.  
Without the ground truth as supervision, self-supervised depth estimation usually utilizes image synthesis loss as the main supervision. Inspired by structure-from-motion, SfMLearner [24] first proposed image synthesis loss for self-supervised depth learning. A classic work Monodepth2 [25] focused on the issues of occlusion and moving objects in outdoor driving scenes. Although Monodepth2 behaved well on KITTI [26], it still could not deal with the challenge of low-textured regions in indoor scenes. The original image synthesis loss can cause serious matching errors in low-textured regions. In order to enhance the estimation results in indoor scenes, P\({}^{2}\)Net [1] designed a patch-based photometric consistency constraint to avoid nondiscriminative matchings. Furthermore, planar consistency loss was also designed to predict better in planar low-textured regions. Other effective geometric-aware loss terms were also proposed, such as the co-planar loss and the Manhattan normal loss in StructDepth [2].  
The current geometry-based losses cannot provide sufficiently dense supervision. Not all the pixels are covered in the supervision. To handle the issue, F\({}^{2}\)Depth [3] introduced a self-supervised optical flow network to provide extra pixel motion supervision for depth learning. Yet it was still hard to calculate high-accuracy optical flow for all pixels, since there are some unmatched pixels occluded or beyond the image boundaries. DistDepth [27] transferred structure knowledge from a supervised expert network DPT [28; 29] to the student network DepthNet. Although DistDepth learnt densely supervised structure depth, it has not been able to completely eradicate its reliance on ground truth.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 3 Measurement System
Should AGC be used, a description of which will follow, \(R\geq 3\). This setting accounts for the final symbol's potential distortion, as hardware adjustments may affect the receive gain during this period. Increasing the value of \(M\) can improve the received SNR through symbol averaging. However, this improvement comes at the cost of extended transmission time and a reduced maximum resolvable Doppler frequency. Furthermore, the structure includes \(H_{\text{a}}\) TDMA slots, where \(H_{\text{a}}\) corresponds to the number of antennas (see Figure 2). Following the activation and recording of all elements, the system can enter a quiet state for a duration of \(B/120\,\mathrm{MHz}\) seconds, where \(B\) represents the number of FPGA ticks and \(120\,\mathrm{MHz}\) is the FPGA clock rate. Figure 2: During one TDMA slot, only one antenna is transmitting while all others are receiving. In the next TDMA slot, the next antenna is transmitting while all other are listening. By saving all channels, even the reciprocal ones, one can use the information for over-the-air calibration.
## 4 Discussion
A pixel may be composed of both low- and high-reflectance surfaces, leading to the higher radiances of BNU-1 and Landsat 8 images over low-reflectance surfaces compared to ground observations. Secondly, although the BNU-1 image corrected by the relative sensor normalization method has significantly improved in visual performances, and the radiances are consistent with the Landsat8 images, the corrected BNU-1 images may still have random errors. These errors should be corrected using on-orbit absolute radiometric calibration. In addition, the BNU-1 images corrected by the relative sensor normalization still have some strip noises, which is mainly caused by sensor random errors. Removing the strip noises of the BNU-1 images is another challenge we will face in the future. Currently, deep learning methods are being widely used for image denoising; this may be a new direction for future research.
# An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0
## 2 Conventional Works
### AEA Protocol
The element of AEA<wakeup> is set to 'true' in Table 1 in the case that the AEA message corresponds to the wake-up field. However, it is unlikely that the wake-up service is available to smart phones. Note that most smart phones adopt internal antennas due to size limitations. The ATSC 3.0 receiver usually renders very poor receiver performance in the internal antenna case, since the ATSC 3.0 system operates at the carrier frequency of about 600 MHz [25]. This prevents a reliable decoding of the wake-up field in the internal  
\begin{table}
\begin{tabular}{c c} \hline
**Element** & **Description** \\ \hline AEA & AEA message format \\ \textless{}aeald\textgreater{} & The identifier of AEA message \\ \textless{}audience\textgreater{} & The intended distribution of the AEA message \\ \textless{}wakeup\textgreater{} & A true/false value for whether the wake-up field was activated for the emergency that the \textless{}aeald\textgreater{} \\ \textless{}measure & message describes \\ \hline Header & The container for the basic alert envelope \\ \textless{}effective\textgreater{} & The effective time of the AEA message \\ \textless{}expires\textgreater{} & The expires time of the AEA message \\ \textless{}EventCode\textgreater{} & A code identifying the event type of the AEA message \\ \textless{}EventDesc\textgreater{} & A short plain text description of the emergency event \\ \textless{}Location\textgreater{} & The geographic code that identifies the affected area of the AEA message \\ \text{AEAText} & The specific informative text of the AEA message \\ \textgreater{} \textbackslash{} \textantenna situation. Some complicated decoding algorithms were suggested for a reliable decoding of the wake-up field at low SNR levels [26; 27]. However, the adoption of the algorithms may lead to an excessive consumption of smart-phone battery. Therefore, the wake-up service is more suitable for fixed TV stations than smart phones. As stated in Section 2.1, the 5G CBS protocol adopts the paging service, which is similar to the wake-up service of ATSC 3.0. The paging service is available even in the internal antenna case, since 5G cellular systems operate at the carrier frequency of about 3.5 GHz. Therefore, it is required to converge the 5G and the ATSC 3.0 networks for better emergency alert service in mobile environments, which effectively takes the advantages of the 5G and the ATSC 3.0 systems. The convergence offers more reliable emergency alert service as well as rich alert information, which will be verified in Section 4.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
### Novelty  
This research introduces innovative techniques and methodologies that address limitations in prior transformer fault detection studies.  
* Employs advanced machine learning models (e.g., Decision Trees, Subspace KNN) to achieve 80-100% fault classification accuracy.
* Expands fault detection to include complex types like axial displacement and contamination, which are underexplored in prior studies.
* Introduces refined feature extraction techniques to enhance sensitivity and reduce false negatives.
* Establishes a comprehensive annotated FRA dataset for diverse fault classification and future research.  
The article is organized into distinct sections with Abstract summarizing the study's focus on transformer fault detection using FRA and machine learning, Introduction (Section 1) discussing the importance of transformers, challenges like aging infrastructure, and the need for advanced diagnostics; Section 2 focuses on FRA and Frequency Bands explaining their role in fault detection and machine learning integration; Section 3: Methodology detailing data preparation, transformer simulation, and classifier training; Section 4: Results and Discussion presenting performance of various machine learning models in detecting faults and analyzing their accuracy; Sections 5 and 6: Conclusion summarizing key findings, highlighting model accuracy (80-100%), and suggesting future improvements.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 3 RRC-Divided Chaotic Filter
### Hybrid Dynamical System
#### 3.2.2 Dynamic Behavior  
The Lyapunov exponent is an important index to judge whether a dynamical system is chaotic. Figure 5 shows the calculation of the maximum Lyapunov exponents of the system of Equations (10) and (11), which are 4.8 and 4.5648, respectively. The Lyapunov exponent remains unchanged when parameter \(B\) is invariant. Furthermore, the Lyapunov dimension of the chaotic signal is unchanged by the wireless channel [11], which means that the baseband waveform can be transmitted in a physical media that linearly filters signals.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum MIMO Transmission and Quantum Channel
#### 3.4.6 Combined Noise Model  
To simulate more realistic quantum environments, the individual noise channels are aggregated into a composite model. The overall effect on a quantum state is given by Equation (23).  
\[\mathcal{N}(\rho)=p_{X}\mathcal{N}_{X}(\rho)+p_{Z}\mathcal{N}_{Z}(\rho)+p_{D} \mathcal{N}_{D}(\rho)+p_{A}\mathcal{N}_{A}(\rho)+p_{P}\mathcal{N}_{P}(\rho) \tag{23}\]  
In all equations, \(\mathcal{N}(\rho)\) denotes the output noisy state, \(\rho\) is the initial quantum density matrix, and \(X\), \(Y\), \(Z\) are standard Pauli operators. The dagger symbol \((^{\dagger})\) indicates the Hermitian conjugate. By randomly adjusting individual noise probabilities based on the channel SNR, the performance of the system can be rigorously tested under a variety of realistic channel conditions.
## 5 Empirical Validation and Practical Application
### Educational Impact  
The intervention significantly improved learning outcomes. Students demonstrated improved knowledge retention, particularly in their understanding of complex concepts. The levels of participation were also notable, and many participants actively participated in the Metaverse environment and reported that the immersive experience improved their academic achievements. Figure 8 on perceived educational impact depicted this positive change, reinforcing the value of Metaverse as a transformative educational tool. Figure 8 reveals students' perceptions of the impact of Metaverse on their learning outcomes, with significant improvements observed in engagement, retention, and comprehension of complex concepts. The immersive nature of the Metaverse allowed students to simulate real-world scenarios, fostering a deeper understanding of theoretical knowledge. These results validate the transformative potential of virtual environments in enriching traditional educational models. The intervention significantly changed students' expectations of the Metaverse's educational impact (Z = \(-1.48\), \(p\) < \(0.05\)). The effect sizes were small (r = \(-0.18\) for negative ranks and r = \(-0.26\) for positive ranks), indicating that while the changes were statistically significant, the practical influence of the intervention on shaping students' expectations was modest.  
Figure 7: Distribution of challenges faced.
## 5 Conclusions  
Large-scale wireless sensor networks are expected to play an increasingly important role in data collection in hazardous areas. However, the physical fragility of sensor nodes makes reliable routing in hazardous areas a challenging problem. Because several sensor nodes in a hazardous area could be damaged simultaneously, a network should be able to recover routing after node failures over large areas. Many routing protocols take into account recovery after the failure of a single node, but it is difficult for these protocols to recover the routing after large-scale failures. In this paper, we propose a routing protocol, referred to as ARF, which allows a network to recover quickly from failures over large areas. ARF detects failures by counting the packet losses from a parent node, and upon failure detection, ARF decreases the routing interval to notify the neighbor nodes of the failure. Our experimental results indicate clearly that ARF could recover from large-area failures quickly with less packets than previous protocols.
# Sharpness-Based Distance Detection
## 3 Sharpness Assessment
### Image Sharpness Assessment  
In previous experiments, there were many ways to assess the sharpness of objects. For example, the LIVE database [39] is a colour image database developed by the University of Texas at Austin in 2006. It includes 779 distorted images with different average opinion scores (DMOS) from 161 observers, resulting in an image quality score ranging from 1 to 100. The AVA dataset [40] contains more than 250,000 images, each containing a semantic label, a style label, and an aesthetic score. There are 66 categories of semantic labels and 14 categories of style labels, with 10 scores ranging from 1 to 10. The TID2013 database [41; 42] is a colour image database developed by Tampere University of Technology in Finland in 2013. It consists of 25 reference images in 'BMP' format, 3000 distorted images and 24 distortion types, subjectively evaluated by 985 observers, resulting in an image quality score ranging from 0 to 9. The NITS-IQA database [43] is composed of four hundred and five distorted images and nine original images. Subjective evaluations were made by 162 observers, resulting in an image quality score ranging from 1 to 100.  
The image sharpness predictor used in this paper is implemented based on an improved Inception V4 network [44], replacing the last layer of the CNN with a fully connected layer with 10 neurons, which aims to generate a score ranging from 1-10 for any given image, which is then activated and outputted with softmax. The network structure is shown in Figure 7. In addition, the initial weight of the CNN is obtained by training on the ImageNet dataset [45]. In order to obtain the scores of image quality assessment, this paper conducts training on a large number of annotated datasets, including the LIVE dataset, TID2013 dataset, etc. It is worth mentioning that the model is not only based on a single scale prediction, but can make multi-scale predictions and obtain the final score by combining these predictions. In order to prove the reliability of the definition predictor, this paper also verifies the existing dataset by predicting the evaluation score distribution of the AVA dataset and generating histograms to compare with the original evaluation score distribution. As shown in Figure 8, the predicted mean value distribution is very close to the real mean value distribution.  
Figure 6: Plot of predicted sharpness as a function of distance.  
Figure 7: Convolutional neural network structure.  
To this end, the same image was tested with different resolution scores, and the experimental results are shown in Figure 9. It can be seen that when the image is the clearest, the score is 4.84678, while the fuzzy image score is only 2.68806. Even with a very clear image, it is extremely difficult to reach a high score.
## 2 Materials and Methods
### Least Mean Square (LMS)  
For a random signal, the main objective of the AR modelling technique is to find recursively the optimal coefficients that minimize the mean square error (MSE) [34]. The LMS algorithm was developed by [PERSON] and [PERSON] in 1960 [35]. It employs a stochastic gradient algorithm to solve the least square problem. This method is obtained from steepest-descent implementation by replacing the required gradient vectors and Hessian matrices by more suitable approximations. The equations that constitute the adaptive LMS algorithm are as follows [36]:  
\[\mathbf{x}(n)=\left[x(n),\ \ \ x(n-1),\ \ \ x(n-2),\ldots x(n-M+1)\right]^{T} \tag{2}\]  
\[\mathbf{w}(n)=\left[w_{0}(n),\ \ \ w_{1}(n),\ldots\ \ w_{M-1}\ (n)\right]^{T} \tag{3}\]  
\[y(n)=\mathbf{w}^{T}(n)\ \mathbf{x}(n) \tag{4}\]  
\[e(n)=x(n+1)-y(n) \tag{5}\]  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+2\mu\ e(n)\mathbf{x}(n) \tag{6}\]  
where \(\mathbf{x}(n)\) is an input data at sample \(n\), \(y(n)\) is output, \(e(n)\) is error, \(\mathbf{w}(n)\) is filter weight, \(\mu\) is step size, and \(M\) is filter length.  
In the current study, two methods were implemented and compared for time-series forward prediction; first Yule-Walker based AR model and second LMS-based AR model. The AR model computes AR coefficients once, whereas the LMS-based AR model computes them at each instant. The LMS-based AR model algorithm starts from an initial condition without having the desired information and then updates the filter weights based on the input data sequence. The filter length for the LMS-based AR model is the same as for the AR model order.