esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
###### Abstract  
In this paper, an image recovery problem under the case of salt-and-pepper noise and data missing that degrade image quality is addressed if they are not effectively handled, where the salt-and-pepper noise as the impulsive noise is remodeled as a sparse signal due to its impulsiveness and the data missing pattern, denoted by a sparse vector, contains only zeros and ones to formulate the data missing. In particular, the salt-and-pepper noise and data missing are reformatted by their sparsity, respectively. The wavelet and framelet domains are explored to sparsely represent the image in order to accurately reconstruct the clean image. From the reformulations conducted and to recover the image, under one optimization framework, a joint estimation is developed to perform the image recovery, the salt-and-pepper noise suppression, and the missing patter estimation. To solve the optimization problem, two efficient solvers are developed to obtain the joint estimation solution, and they are based on the alternating direction method of multipliers (ADMM) and accelerated proximal gradient (APG). Finally, numerical studies verify that the joint estimation algorithm outperforms the state-of-the-art approaches in terms of both objective and subjective evaluation standards.  
image recovery; salt-and-pepper noise; data missing; sparsity; joint estimation +
Footnote †: journal: Applied  
_Article_  
Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
## 4 Results
### Validation of Model Credibility and Robustness  
Numerical simulation is performed according to the initial parameters, and the base case is illustrated in Figure 5, where the game model evolves toward (1, 1, 1, 1). The initial parameters satisfy \(\{(X3\cup X4)\cap Y2\cap Z2\cap U2\}\). According to the model analysis, at this point, the system's steady state is \((1,1,1,1)\). This aligns with the model analysis, confirming its validity and the credibility of the simulation experiment. Strategy probabilities \(x\), \(y\), \(z\), and \(u\) converge to 1 in iterations 85, 70, 67, and 46, respectively, with convergence rates of approximately 0.006, 0.007, 0.007, and 0.011. They have means of 0.90, 0.91, 0.92, and 0.96, along with standard deviations of 0.17, 0.19, 0.16, and 0.11, respectively. The game system is stable, and U evolves significantly faster than the other three.  
Thirty repeated experiments were conducted to verify the system's robustness. The results and numerical indicators are presented in Figure 6 and Table 7. The system's final evolution was consistent across experiments, with similar convergence paths for each subject. In each experiment, the mean, median, and standard deviation of strategy probabilities for each agent were not significantly different, confirming the model's robustness. Subsequent simulations were also repeated multiple times, yielding regular results that ensured the reliability of the findings.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Planning and**} & \multicolumn{2}{c}{**Production and**} & \multicolumn{2}{c}{**Operation and**} & \multicolumn{2}{c}{**Dismantling and**} \\
**Designing Party (O)** & **Construction Party (\(\boldsymbol{\gamma}\))** & **Maintenance Party (\(\boldsymbol{Z}\))** & **Recycling Party (U)** \\ \hline \(E_{X}\) & 7 & \(E_{Y}\) & 10 & \(E_{Z}\) & 5 & \(E_{U}\) & 8 \\ \(G_{X}\) & 3 & \(G_{Y}\) & 5 & \(G_{Z}\) & 5 & \(G_{U}\) & 4 \\ \(C_{X}\) & 22 & \(N_{Y}\) & 4 & \(N_{Z}\) & 2.5 & \(N_{U}\) & 1 \\ \(J_{X}\) & 1 & \(C_{Y}\) & 29 & \(C_{Z}\) & 16 & \(C_{U}\) & 15 \\ \(\Delta C_{X
## 5 Discussions and Comparisons  
### Computation Cost  
Table 2 is the computation cost analysis of all stages and roles in this scheme. We analyze the payment verification and browsing phase (entrusted authorization) with the highest computational cost. The CA requires 10 multiplication operations, 3 hash function operations, 4 comparison operations, and 2 signature operations. The Proxy requires 7 multiplication operations, 3 hash function operations, 3 comparison operations, and 2 signature operations. The Licensee requires 7 multiplication operations, 3 hash function operations, 1 comparison operation, and 2 signature operations. The method we proposed has a good computational cost.
## 4 Results and Discussion of the Nasal Cavity Three-Dimensional Structure Reconstruction System  
### Results and Analysis of Nasal Acoustic Signal Acquisition  
In this experiment, the straight tube model and the simulated nasal cavity model from GM Company in the UK were used as the test objects, respectively. The straight tube model is a metal tube with a constant cross-sectional area and made of 304 stainless steel. This tube is used to assess the average detection precision of the nasal cavity three-dimensional structure reconstruction system. The simulated nasal cavity model is a plastic tube with cross-sectional areas similar to those of a typical human nasal cavity and made of polyvinyl chloride. This tube is used to assess the precision of the system in detecting key points, including the starting point, half-way point, endpoint, minimum area point, and maximum area point. During the actual patient examination, a short glass tube of 5 cm length is inserted into the top of the short-source acoustic tube. This allows the patient's nostril to fit snugly against the glass tube, preventing acoustic waves from propagating into the air where they cannot be adequately captured by the microphone. Thus, in order to simulate the actual examination effect, the first 5 cm of both models are not considered as effective measurement depths. As shown in Figure 9a, the straight tube model has a length of 17 cm, an effective measurement depth of 12 cm, and a cross-sectional area of 1.23 cm\({}^{2}\). As shown in Figure 9b, the simulated nasal cavity model is similar to a cone, the length of the simulated nasal cavity model is 22 cm, and the effective measurement depth is 13 cm. A 4 cm thickness of sponge is inserted from 13 cm to the top of the model to absorb the excess pulsed acoustic waves. The cross-sectional area reaches its maximum value of 8 cm\({}^{2}\) at a valid measurement length of 10.4 cm and a minimum value of 0.5 cm\({}^{2}\) at a valid measurement length of 1.8 cm.  
The original nasal acoustic signal in the straight tube model and the simulated nasal cavity model are shown in Figure 10. The horizontal coordinate is the time, and the vertical coordinate is the voltage, which is proportional to the intensity of the acoustic wave. Since the first-stage incident wave has the lowest loss in the short-source acoustic tube and is the earliest pulsed acoustic wave recorded by the microphone. The intensity of the first-stage incident wave should be the maximum. As shown in Figure 10, the maximum peak of the first-stage incident wave is about 0.5 V, both in the straight tube model and the simulated nasal cavity model, and the pulsed acoustic wave oscillates to 0 V gradually with time. As shown in Figure 10, according to the acoustic wave propagation characteristics in a short-source acoustic tube mentioned in Section 3.1.1, it can be seen that the first-stage incident wave is recorded at about 0.8 ms and lasts for 0.8 ms, and the first-stage reflected wave is recorded at about 1.6 ms and lasts for about 1.7 ms.
## 2 Carrier Tracking Loop and the Phase Error
### Dynamic Stress  
The steady state phase tracking error which is induced by dynamic is known as the dynamic stress of a PLL. It can be represented as follows [2]:  
\[e(t)=\frac{2\pi}{\lambda}\frac{1}{\omega_{n}^{N}}\frac{d^{N}R}{dt^{N}}[ \mathrm{rad}] \tag{9}\]  
where \(R\) is the distance between the satellite and the receiver, \(N\) is the loop order, and \(\lambda\) means wavelength of the carrier. It can be seen from the equation that 1 st order loop is sensitive to velocity stress, 2 nd order loop is sensitive to the acceleration stress, and 3 rd order loop is sensitive to the jerk stress. The dynamic stress induced phase error increases as the characteristic frequency, \(\omega_{n}\) decreases. Thus, smaller bandwidth loop can lead to a lager dynamic stress phase error. This means that signal dynamic restricts the reduction of the loop bandwidth.
## 1 Introduction
Global precipitation products (GPPs) successfully offer consistent historical and near real-time precipitation information at a grid level across the globe. However, the validity and reliability of GPPs are not the same across different climatic and topographic regions due to certain limitations, such as topographical effects, the retrieval algorithm, clouds, and insufficient electromagnetic signals [1; 8; 16]. Therefore, global precipitation products are usually validated at the regional and local scales against the available ground observations before application. Pakistan is one of the regions with scarce ground stations and theirunbalanced spatial distribution is unable to represent the entire country's precipitation pattern efficiently [17]. As a result, several studies have been carried out to find the best alternative global precipitation product through the validation process [8; 9; 17; 18; 20; 21]. Global precipitation products are subject to continuous modifications and publish regularly updated versions with more accuracy and reliability. This includes the latest version of CRU TS v4.08 modified in 2024, the ERA5-Land fifth generation, the final run of IMERG v07B published in 2023, and the MSWEP v2.8 released in 2021. Due to the unique combination of climatic variations, topographic diversity, and pronounced seasonal differences in rainfall, Pakistan offers an ideal field for evaluating the performance and reliability of newly modified products across diverse environmental conditions. Therefore, to reveal the strengths and weaknesses of advanced versions of selected GPPs, evaluation against ground observations was performed on a spatial and temporal scale. The first objective of this study addresses the question of which precipitation dataset is most reliable at the provincial scale over Pakistan. The second purpose was to comparatively investigate the performance of GPPs at different time scales. Furthermore, the study area is categorized into four regions based on the elevation gradients and variation in precipitation patterns to elucidate the dependencies of GPPs on the elevation and climatic conditions, which makes this study novel compared to previous studies. Finally, the current study provides comprehensive information about the performance of each selected dataset, which will support researchers and national- and provincial-level government bodies in selecting reliable high-resolution precipitation products for various applications.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 3 Experimental Results
### Load-Longitudinal Strain Curves  
To analyze the effects of the eccentricity and reinforcement ratio on the load-longitudinal reinforcement strain curves, the specimens were divided into two groups: Group A (SSRC3, SSRC4, SSRC5, SSRC6, and HRC9) and Group B (SSRC2, SSRC6, SSRC8, and HRC9). Figure 7 exhibits the load-longitudinal reinforcement strain curves of the specimens.  
The load-reinforcement strain curves under various eccentricity values are depicted in Figure 7a. With the same reinforcement ratio, the ultimate load increases as the initial eccentricity of the specimen decreases. Under the same load, the tensile reinforcement strain of SSRC6 is always greater than that of HRC9. The load-reinforcement strain curves under various reinforcement ratios with the same eccentricity are depicted in Figure 7b. Under the same level of load, the reinforcement strain of large eccentric specimens decreases with an increase in reinforcement ratio.  
Figure 6: Lateral deflection curves.  
Figure 7: Load–longitudinal reinforcement strain curves under various (**a**) eccentricities and (**b**) reinforcement ratios.
## 2 State of the Art
### Technology Roadmapping Approaches  
Nevertheless, the flexibility of a TRM leads to ambiguity and the specific tool often gets confused with other tools and approaches. Several different approaches for a TRM are available in the scientific literature. In fact, the TRM approach is very flexible in terms of objectives, graphical forms, and potential uses. For what concerns the objectives, [26] identify eight purposes: (i) product planning, (ii) service/capability planning, (iii) strategic planning, (iv) long-range planning, (v) knowledge asset planning, (vi) program planning, (vii) process planning, and (viii) integration planning. For what concerns the graphical forms, they identify heterogeneous formats, including the following: bars, single and multiple layers, tables, and flowcharts. For what concerns the potential uses, [28] indicates that about 10% of big organizations applied a TRM at least once but encountered different barriers such as reiterating the TRM in the life cycle, initiating the mapping process and ensuring its robustness. This is mainly due to the fact that companies cannot orient themselves among the different existing roadmaps, tailor-made on the basis of specific needs [26]. Moreover, according to [29], two different application levels exist: (1) the organizational level for defining and communicating the relationships between markets, products and technologies and (2) the industry level for understanding the common needs of multiple organizations. [PERSON] et al. [30] investigated the evolution at the industry level, concluding that even if the most common architecture consists of multiple layers, one linked to the market, product and technology, there are many customized and flexible architectures across industries, consisting of additional layers based on specific topics relevant to a particular industry. The literature review presented in [31] confirms the complexity of a TRM, resulting from the requirements and constraints of the business environment, and therefore structures the TRM into three generations with different focuses: (1) the first one focusing on roadmapping product technologies, (2) the second one focusing on roadmapping emerging technologies, and (3) the third one focusing on roadmapping innovation.  
Building upon the intricate considerations of TRMs in the preceding discussion, our exploration now extends to the critical aspect of gauging technology maturity, particularly through the lens of the Technology Readiness Level (TRL). The adaptability and broad objectives of TRMs necessitate a more nuanced analysis when applied to monitor the evolution of technology within a specific company. This granular analysis involves a thorough evaluation of the specific TRL of each technology within the context of each company. As highlighted in the Technology Readiness Level Section, the TRL serves as a pivotal method for estimating the technical maturity of technology across different domains.
## 2 Methodology
### Assessment of Consistency between Model and Observation Data Sets
#### 2.4.2 Seasonal Cycle  
FPAR seasonal cycle comparisons are made at two spatial scales: global and aggregated by dominant MODIS vegetation types. The MODIS FPAR algorithm uses the MODIS land cover product with the International Geosphere Biosphere Program (IGBP) classes [44, 51]. The JRC generic FPAR algorithm does not consider land cover type [45, 46]. The CLM4 has a subgrid system representingvegetation as patches of PFTs that are derived from various datasets including MODIS Vegetation Continuous Fields data, AVHRR Continuous Fields Tree Cover Project data, MODIS global land cover mapping, Ramankutty and Foley global cropping, and Willmott and Matsuura climate data set [52]. As MODIS and CLM4 use different vegetation classification systems, the dominant vegetation distribution from MODIS is employed for the comparison of this paper. We define the dominant biome type in each half-degree grid as the land cover for the grid [53]. Prior to the analysis, monthly FPAR were averaged for each grid over 2003 to 2005. Monthly FPAR data at global and biome level were calculated by:  
\[FPAR_{a(m,b)}=\frac{\sum_{i,j}FPAR_{a(m,i,j)}\times Area_{i,j}\times frac_{i,j,b}}{\sum_{i,j}Area_{i,j}\times frac_{i,j,b}} \tag{7}\]  
where \(FPAR_{a(m,b)}\) is FPAR in month \(m\) for biome \(b\) (or for the global, if \(b=0\)), \(FPAR_{a(m,i,j)}\) is FPAR at computational grid \((i,j)\) in month \(m\), \(Area_{i,j}\) is the area for the grid \((i,j)\), \(frac_{i,j}\) is the fraction of land unit at computational grid \((i,j)\). Grids that are not dominated by the target biome were defined as \(frac_{i,j,b}=0\). Correlation coefficients (_i.e._, correlation and p-value) are calculated based on \(FPAR_{a(m,b)}\). Monthly FPAR anomalies at global or biome level were calculated by:  
\[FPAR_{ano(m,b)}=FPAR_{a(m,b)}-FPAR_{mean(b)} \tag{8}\]  
where \(FPAR_{ano(m,b)}\) is FPAR anomaly in month \(m\) for biome \(b\) (or for the global, if \(b=0\) ), \(FPAR_{mean\ (i)}\) is averaged FPAR over the whole time period.
## 3 Results and Discussion
### Evaluation Indicators  
To measure the accuracy of different methods for homography estimation, this paper uses the Mean Average Corner Error (MACE) as an evaluation metric, which is the average Euclidean distance between the predicted and the true positions of the corner points. Given a pair of images, \((I_{s},\,I_{t})\), and their corresponding homography matrix, \(H_{st}\), let the coordinates of the four corner points of image \(I_{s}\) be denoted as \(p_{s}^{i}=\big{[}x_{s}^{i},y_{s}^{i},1\big{]}^{T},i\in\{1,2,3,4\}\). The homography matrix predicted by the homography estimation model is \(\overset{\sim}{H_{st}}\). The true positions, \(p_{t}^{i}\), and the predicted positions, \(\overset{\sim i}{P_{t}}\), of the four corner points in the target image, \(I_{t}\), are calculated respectively.  
\[p_{t}^{i}=H_{st}p_{s}^{i},\overset{\sim i}{P_{t}}=\overset{\sim}{H_{st}}p_{s}^ {i},i\in\{1,2,3,4\} \tag{14}\]  
The Mean Average Corner Error here is defined as the average of the four corner errors over \(M\) pairs of images:  
\[\text{MACE}= \frac{1}{4\text{N}}\sum_{\text{j}=1}^{\text{M}}\sum_{\text{i}=1}^{ \text{N}}\bigg{(}\parallel\dot{\text{p}_{\text{t}}^{i}-\overset{\sim i}{P_{t} }}\parallel_{2}\bigg{)}_{j} \tag{15}\]  
Figure 6: Data generation process: (**a**) randomly extract a rectangular image patch PatchA from the image; (**b**) apply random perturbations to the 4 corners of PatchA; (**c**) calculate \(H^{AB}\) based on the perturbations \((\Delta x_{i},\Delta y_{i})\); (**d**) apply the inverse matrix of \(H^{AB}\) to the entire image and extract a corresponding image patch PatchB at the same position and size.
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 4 Results
### Case 1
(2018)) and 600 epochs, respectively, and sought to maximize the performance of the model under these constraints. While [PERSON] et al. (2018)'s DNN naturally showed superior performance given the vast computational resources employed, we found that, at a relatively higher source frequency of 300 Hz (Figures 12 and 13), our model was able to capture the source distribution reasonably well with a limited dataset. The model showed good resolution, less spreading, and was able to detect sparse sources. The limit of the model's performance was identified as 100 Hz since the results at 100 Hz source frequency and below (Figures 14 and 15) with the same amount of training were relatively poor. The model struggled to locate the sources when they were sparsely located (Figure 14) and tended to perform better when they were located in clusters. The increase in spreading is evident from Figure 15 but is still much less than beamforming, wherein the poor resolution renders the results unusable at low frequencies. To resolve low-frequency sources using beamforming, the size of the scanning plane and the distance between the sources would have to be much more than the size of the plane used in this case. The CNN was able to resolve low-frequency sources on the current plane, thus highlighting its superior resolution and detection capabilities. Table 2 reaffirms statistically the frequency-dependent performance of the model. For a source frequency of 300 Hz, the model was able to detect the majority of the sources (four or more) in 35.1% samples (351 out of 1000 samples), and at least half of the sources (three or more) in 74.8% samples (748 out of 1000 samples). At 100 Hz source frequency, the percentages drop to 5% for detecting the majority of the sources (50 out of 1000 samples) and 19.2% for detecting at least half of the sources (192 out of 1000 samples). To obtain better results at 100 Hz and lower frequencies, we have to go beyond the set network architecture and training limits and increase the number of layers/training data samples/epochs. The effectiveness of the shallow CNN in terms of giving promising results with very limited data compared with [PERSON] et al. (2018)'s DNN makes it an avenue worth exploring further.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing
### Data Preprocessing
#### 3.2.2 Handling Outliers and Missing Values  
Abnormal values refer to data that are either incorrectly entered or unreasonable. Typically, abnormal values significantly deviate from the range of most observations. For example, in the passenger status field, a value of 1 indicates the taxi is occupied, while a value of 0 indicates it is empty. Ideally, the passenger status should alternately appear as continuous 0 s followed by continuous 1 s. A typical abnormal situation would be a sudden appearance of a 0 within a sequence of 1 s. Although it is possible that in hotspot areas, the passenger waiting time is so short that an empty interval is too brief to record a complete normal passenger process, such situations are exceedingly rare and are therefore not considered in this study.  
Missing values primarily include the complete absence of data records and the absence of information in certain fields. Based on past experience, the fields most prone to missing values are longitude, latitude, and time. In the process of handling taxi trajectory data in this study, if a sample record has most of its valid data missing and interpolation might change its original meaning, the record is directly deleted.  
In summary, this paper carries out data cleaning under the following circumstances. First, data points with latitudes and longitudes outside the research scope are removed. Chengdu is located between 102\({}^{\circ}\)54\({}^{\prime}\)\(\sim\)104\({}^{\circ}\)53\({}^{\prime}\) E and 30\({}^{\circ}\)05\({}^{\prime}\)\(\sim\)31\({}^{\circ}\)26\({}^{\prime}\) N, and trajectory records outside this range are deleted. Second, some fields may be empty or missing; if these fields affect subsequent analyses, the corresponding records are deleted. Third, there are multiple duplicate records at the same location or from the same vehicle ID within a short period. This could be due to temporary stops or disruptions caused by traffic congestion or road control. Such duplicate trajectory records are removed.
## 2 Materials and Methods
### Statistical Analysis  
Interobserver and intraobserver agreements were determined using the intraclass correlation coefficient (ICC). Binominal and chi-square tests were utilized to compute descriptive statistics for gender and the existence of bruxism in the participants. The Shapiro-Wilk test analyzed variables' conformity to the normal distribution. As a result, it was found that variables did not conform to a normal distribution. Thus, non-parametric tests were applied. The Mann-Whitney U test was employed to compare the FD values within the two groups (patients with bruxism and those without bruxism). The level of significance was set to 0.05. Data were analyzed using Statistical Package for the Social Sciences (SPSS, v.26, IBM Statistics, Armonk, NY, USA).