esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
All of the aforementioned techniques are nonuniformity correction techniques for infrared focal plane array detectors. In this paper, based on the detector's features, a linear rectification model of an infrared focal plane linear array scan image is proposed using guided filtering. In this paper, we first extract a portion of the infrared linear array scan image and then apply 1D column guided filtering operations to a blurred image and the high-frequency component of the original image. The high-frequency component image is taken as the input image, and the blurred image is taken as the guide image. The 1D row guided filtering is performed to extract the nonuniformity noise of the linear array scan image. By subtracting the estimated nonuniformity noise image from the original image, the partial linear scan array image corrected by the guided filter is obtained. Finally, using the linear regression model, the linear correction coefficients are calculated to complete the nonuniformity correction of the entire frame. Compared to the other correction algorithms [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27], the method in this paper has the following advantages. First of all, this approach employs only a portion of a linear array scan image to calculate the linear model's correction coefficients and complete the frame's correction. The image resolution used for guided filtering calculation is significantly lowered as well as the computational complexity, which is well suited to satisfy the real-time demands of high-resolution infrared focal plane linear array scan images. Secondly, since the same row of pixels in the infrared scan image is determined only by a single detection unit, the algorithm in this study combines the principles of infrared column detector calibration and guided filtering to develop a one-dimensional linear regression coefficient model, which further improves the image's correction effect. The proposed approach corrects the nonuniformity in the spatial domain without degrading the image's high-frequency texture features or introducing ghost artifacts. Thirdly, the approach presented in this paper is a single-frame image algorithm that does not require consideration of the influence of camera jitter and scene heat radiation variations on nonuniformity correction and has high robustness. Finally, the approach presented in this research is superior to the correction algorithm based on deep learning that disregards, without considering the creation of the dataset.
## 3 Signal Analysis Method
### Analog-to-Digital Conversion  
As modern electronic devices and computer systems operate and process based on digital signals, while the signals obtained by the acquisition equipment are analog signals, it is first necessary to perform analog-to-digital conversion (ADC) [31]. This conversion allows the signal to be processed, stored, transmitted, and analyzed within digital systems. The raw audio signals obtained in the experiment were subjected to 16-bit ADC operations, resulting in the outcome shown in Figure 5. It can be observed that the signal after ADC is converted into a series of sample points, preserving the original dynamic range while presenting discretization. The collected acoustic signals had a sound pressure level range from 45 to 75 decibels.
## 3 Results
Spot where laser hits graph paper covering channel entrance shows that the vehicle is tilted toward the right-hand wall of the channel. The results in Table 2 show that we have sufficient accuracy in calculations of the apparent width of the top of the channel in pixels to use this width for tracking the movement of the robot toward a selected feature in the interface of the air slot with the tank bottom. Estimates of the uncertainty in the method were obtained by repeated application of video analysis using the vertical grid lines in Figure 3 as targets for the apparent change in channel width as the robot moved away from the target. Results are shown in Table 3. Mean and standard deviation of absolute error are \(1.466\pm 1.033\) mm. To demonstrate this method under conditions similar to those in air slots, we covered the air-slot mockup in our laboratory and selected a feature in the interface between the channel and cover as a marker to track the movement of our vehicle a short distance down the channel. Figure 4 shows that the laser is hitting the cover on the entrance to the channel to provide a measurement of distance traveled down the channel that can be compared with the results of the image analysis. Video analysis predicts the robot has moved 68.63 mm and laser ranging measurements show that the robot has moved 67 mm. Figure 4: In the top panel, the target is selected, and calculated to be 40.99 mm from camera. In the bottom panel, the robot has moved away from the target and is calculated to be 109.63 mm from the camera. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Estimate (mm)** & **Graph-Paper Standard (mm)** & **Difference (mm)** \\ \hline
17.038 & 20 & \(-2.962\) \\
40.267 & 40 & \(0.267\) \\
31.655 & 30 & \(1.655\) \\
23.31 & 25 & \(-1.69\) \\
35.756 & 35 & \(0.756\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Repeated application of the corrected isosceles-triangle model for distance measurements.
## 1 Introduction
[20] proposed a new sparse atomic decomposition algorithm, called iterative shrinkage threshold algorithm (ISTA), and it gradually became one of the most widely used convex optimization algorithms. The singular value decomposition method is a widely used signal denoising method, but it has encountered many problems in practical application [9]. For example, the cut-off rule in the difference spectrum is difficult to determine, it is easily affected by noise energy and it does not have the ability to identify useful signals. Therefore, a novel signal denoising method called SRSVD is proposed to solve this problem. Through the powerful ability of SR to weaken Gaussian noise, we filter once, find out all components that best match the dictionary, and reconstruct the signal to be measured. Then the improved SVD algorithm is used to remove the irrelevant components to achieve the purpose of secondary denoising. Previously, most methods combining SR and SVD were used for pattern recognition. SVD was generally used for dimensionality reduction, and then SR is used for classification [21, 22]. In our method, SR is first used to remove Gaussian noise, which requires the reconstruction of the original signal. In the second step, improved SVD is used for secondary filtering. As can be seen, the purpose, sequence, and flow of our method is fundamentally different from the previous methods. The major contributions of this paper are as follows:  
1. Combining SR and SVD for weak signal detection for the first time and achieved good results. 2. According to the characteristics of SR and SVD and combined with practical application, different dictionaries are adopted to achieve SRSVD denoising. 3. The PMI index is used for the reconstruction of the signal in SRSVD, and the SRSVD can filter the useful information in the signal. This paper is organized as follows. In Section 2, SR and SVD are briefly described. Section 3 details the proposed method SRSVD. In Section 4, the SRSVD method will be analyzed using experimental test data. Finally, conclusions are drawn in Section 5.
## Appendix A  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Expression**} & \multicolumn{1}{c}{**Function**} \\ \hline Variance threshold & \(\sigma^{2}=\frac{1}{n}\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\) & Calculate the variance of each feature and remove features with variance less than a preset threshold, which are incapable of discriminating samples. \\ \hline Chi-square test & \(\begin{array}{c}\chi^{2}=\frac{1}{\sum\limits_{i=1}^{n}\left(A_{i}-E_{i} \right)^{2}}\\ A_{i}=\text{Observed value}\\ E_{i}=\text{Theoretical value}\\ k=\text{Number of observed value}\end{array}\) & Calculate the difference between observed value and theoretical value, larger result implies stronger correlation between features and classification. \\ \hline MIB & \(\begin{array}{c}M1(X,Y)=\sum\limits_{x\in X}\sum\limits_{y\in Y}p(x,y)\log \frac{p(x,y)}{p(x)p(y)}\\ X,Y=\text{Random variables}\\ p(x,y)=\text{Joint probability distribution}\\ p(x),p(y)=\text{Marginal distributions}\end{array}\) & Assess correlations between random variables, larger mutual information value referring to feature and label implies stronger correlation, accordingly more important features. \\ \hline Fisher score & \(\begin{array}{c}I_{Fisher}(k)=\frac{S_{k}^{k}}{S_{k}^{k}}\\ S_{k}^{k}=\sum\limits_{i=1}^{C}\frac{n_{i}}{n}\left(m_{i}^{T}-m^{T}\right)^{2} \\ S_{k}^{k}=\sum\limits_{i=1}^{C}\sum\limits_{x\in i}\left(x^{k}-m_{i}^{T}\right) ^{2}\\ \end{array}\) \\ [PERSON] score & \(\begin{array}{c}S_{k}^{k}=\text{Between-class variance of the $k$-th feature}\\ S_{k}^{k}=\text{Intra-class variance of the $k$-th feature}\\ n=\text{Number of samples for a given dataset}\\ C=\text{Number of classes based on $n$, each class involves $n_{i}$ samples}\\ x^{k}=\text{The value of sample $x$ for the $k$-th feature}\\ m_{i}^{T}=\text{The average value of sample in class $i$ for the $k$-th feature}\\ m^{T}=\text{The average value of all classes of sample for the $k$-th feature}\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mathematical expression and function of the seven algorithms for feature selection.
## 2 CWP Model  
The details of the CWP model (Figure 1) have been described in detail in a previous paper [24]. Briefly, in this model, adsorbed water is present on the surface of the metal particle [28], and, during friction between the film and particle powder, this adsorbed water is also subject to friction. The adsorbed water is then assumed to dissociate into positively charged water and negatively charged species by gaining frictional energy (Step 1). A reasonable assumption is that the positively charged water contains H\({}^{+}\) ions and the negatively charged species contain OH\({}^{-}\) ions; however, the identification of these species is a subject for future work. The positively charged water overcomes electrostatic attraction and penetrates the film with the lapse of time (Step 2). [PERSON] et al. reported that during frictional charging of the toner and metal particle powder the local temperature at their contact point can reach or exceed 100 \({}^{\circ}\)C [29]. Such an increase in temperature increases the mobility of the polymer chains, which may draw the positively charged water into the film. The penetrating positively charged water then creates a film bulk charge that reduces the film Fermi level. On the other hand, the negatively charged species are assumed to remain on the film surface. Assuming that in the frictional charging of insulating polymers the charge transfer carrier is an electron and the electron transfer driving force is the difference between the metal particle and the film Fermi levels (electron transfer model in frictional electrification) [30], then the driving force increases more when positively charged water species penetrate into the film than when they do not. Electron transfer occurs from the metal particle powder to the film in such a way as to equalize the Fermi levels (Step 3), and the amount of electron transfer is greater when the charged water penetrates into the film. Because the amount of adsorbed water on the metal powder is expected to increase with increasing ambient humidity [28], the amount of positively charged water generated by friction also increases with increasing humidity. This induces further enhancement of the Fermi level difference and increases the amount of electron transfer from the metal particles. Thus, the negative charge of the film surface should increase with increasing humidity.  
However, when the humidity exceeds approximately 60%RH, the effect of water-induced charge leakage may prevail, and the amount of negative charge may decrease.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 7 Conclusions  
The intermittent and random nature of EH-WSN in connectivity motivates us in designing loop-aware routing protocol for real-world EH-WSN applications. Through preliminary simulation experiments, we dissected the structure of the prominent CTP and carefully analyzed its behaviors, and we found that unwise parent updating and unconscious beaconing scheme are the main culprit for CTP to fail in handling loops in EH-WSN. Based on the above observations and analyses, we proposed La-CTP, a loop-aware routing protocol for practical EH-WSNs and conducted extensive testbed-based  
Figure 10: Comparisons in beaconing overhead under two protocols running in EH2. (**a**) beacon sent; (**b**) beacon received.  
experiments to evaluate its performance. The experiment results showed that the new parent updating metric of La-CTP can considerably lower the chances of loop occurrence, and that the proactive adaptive beaconing scheme of La-CTP can effectively and efficiently remove the difficult-to-avoid loops. La-CTP can run in a fully distributed way, without any centralized control. Another advantage of La-CTP lies in its strong practicability in engineering because it retains the framework and low-level components of CTP. Some prior works considered the loop issue in their energy efficiency-oriented routings, or focused on designing loop-free routing algorithms with only theoretic performance guarantee. La-CTP is complementary to these works and it can also help fill the gap between the academic evaluation and practical use.  
In the future we will evaluate La-CTP in more tangible scenarios that have power-different or heterogeneous environmental energy sources. In addition, we will attempt to employ the transmit power adjustment policy to unlock the loops in the EH-WSNs that are prone to partitioned network topology.
## 2 Materials and Methods
### Heavy Metal Content Measurements  
All samples were air dried at room temperature after sampling. After removing obvious gravel and roots, the samples were ground into a powder by sieving through a 74 \(\upmu\)m mesh for element content measurements.  
Heavy metal content measurements were performed at the ALS Minerals-ALS Chemex Laboratory, Guangzhou. An aliquot of a dry burning sample without organic matter was dissolved in aqua regia. Contents of heavy metals Copper (Cu), Nickel (Ni), and Cadmium (Cd) were determined using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700x). Zinc (Zn), Lead (Pb), and Chromium (Cr) contents were measured by an X-ray fluorescence spectrometry. Arsenic (As) and Mercury (Hg) contents were analyzed using atomic fluorescence spectrometry. The detection limits were the following (as mg/kg): 0.2 Cu, 0.2 Ni, 0.001 Cd, 2.0 Zn, 0.5 Pb, 1.0 Cr, 0.2 As, and 0.004 Hg. During the measurements, quality controlling samples including standard samples, duplicate samples, and blank samples were added among the detected samples according to the regulations stated in ISO17025.
## 2 Theoretical Foundations
### Technical Task Matching Model (TTF)  
The TTF model is widely utilized to explain how new technologies can lead to performance improvements. It assesses the impact of technology adoption and evaluates the alignment between task requirements and technology characteristics [22]. Developed by [PERSON] and [PERSON] in 1995, the TTF model explains how the alignment between task characteristics and technology attributes affects the degree of fit, influencing end-user performance and technology utilization. According to this model, users are more likely to adopt and consistently use information technology when it is perceived to be well suited to their tasks and significantly enhances their productivity or performance [13]. This implies that if a technology effectively supports communication and educational tasks related to cultural heritage while enhancing user experience and learning outcomes, it is more likely to gain widespread acceptance and usage. For instance, [PERSON] et al. [22] proposed a TTF integration model to examine the ongoing intention to use MOOCs; [PERSON] et al. [23] applied a TTF model to evaluate the utility of gamification in sustaining the use of gamified training in higher education; and [PERSON] et al. [24] explored visitors' intention to use digital museums by integrating a new TTF model within the UTAUT2 framework, considering social distance. This underscores the importance of successfully applying new technologies in cultural heritage contexts. When combined, the UTAUT and TTF models create an integrated framework for elucidating and predicting end-user behaviors and intentionsregarding the acceptance and adoption of new technologies [19]. This study aims to identify the influential factors for learning using metaverse platforms in cultural heritage education based on the combination of the UTAUT and TTF models. By providing a clear and logical explanation of the current situation, the study seeks to pinpoint the factors that significantly influence users' engagement with metaverse technologies for educational purposes in cultural heritage.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 2 Damage Detection Using the Quasi-static Displacement Influence Lines of Bridges  
In this section, we first discuss the method of generating the quasi-static displacement ILs of actual bridges. Then, we discuss some key issues, such as establishing the damage feature, defining the metric used to evaluate the damage feature and determining the threshold of damage detection.
# Role of Restored Underwater Images in Underwater Imaging Applications
## 2 Proposed Restoration Method
### Transmission Map Estimation  
The proposed transmission map estimation involves the use of depth estimation in Equation (3). Reference [17] estimates transmission map using only the direct signal, with the effects of backscattered signals neglected. In contrast, the proposed transmission map estimation involves the use of both direct and backscattered signals in estimating the transmission map, as shown in Equation (14).  
\[\widetilde{t^{c}}(x)=t^{c}_{D}(x)+t^{c}_{B}(x),c\in\{R,G,B\} \tag{14}\]  
where \(\beta^{c}_{D}\) is the spectral attenuation coefficient of the direct signal and \(\beta^{c}_{B}\) is the spectral attenuation coefficient of the backscattered signal.
# Real-Time Communication Aid System for Korean Dysarthrie Speech
###### Abstract  
Dysarthria is a speech disorder characterized by difficulties in articulation and vocalization due to impaired control of the articulatory system. Around 30% of individuals with speech disorders have dysarthria, facing significant communication challenges. Existing assistive tools for dysarthria either require additional manipulation or only provide word-level speech support, limiting their ability to support effective communication in real-world situations. Thus, this paper proposes a real-time communication aid system that converts sentence-level Korean dysarthrie speech to non-dysarthric normal speech. The proposed system consists of two main parts in cascading form. Specifically, a Korean Automatic Speech Recognition (ASR) model is trained with dysarthric utterances using a conformer-based architecture and the graph transducer network-connectionist temporal classification algorithm, significantly enhancing recognition performance over previous models. Subsequently, a Korean Text-To-Speech (TTS) model based on Jointly Training FastSpeech2 and HiFi-GAN for end-to-end Text-to-Speech (JETS) is pipelined to synthesize high-quality non-dysarthric normal speech. These models are integrated into a single system on an app server, which receives 5-10 s of dysarthric speech and converts it to normal speech after 2-3 s. This can provide a practical communication aid for people with dysarthria.  
dysarthria; communication aid system; conformer-based automatic speech recognition; JETS-based text-to-speech; Korean 2025151416
# Android-Based Audio Video Navigation System Forensics: A Case Study
## 4 Android-Based AVN System Forensics
### Android 4.2.2-Based AVN System Forensics  
First, the AVN systems are removed from the vehicle, and their manufacturer and model number are identified. Then, we discover their Android OS version and other features utilizing the manufacturer's documents and navigation update service [29; 30]. For example, the AVN system removed from a Kia K5(2015) was physically identified as LG Electronics' LAN5020 KKJF. We searched for the related documents and navigation update program. Finally the AVN system's OS was identified as Android 4.2.2 jellybean and we obtained its detailed specification.  
Applying the results from Section 3.3, we entered the engineering mode and found the hidden menu to activate ADB. We connected a PC to the target AVN system through a USB interface, activated ADB, and configured the ADB-related settings. Then, we successfully entered the ADB shell. However, to explore the file systems of the target system, the root privilege is required. To obtain root privilege, we exploited the dirty COW (copy-on-write) vulnerability--a privilege escalation vulnerability--of the Linux kernel [31]. Now the data in the storage devices could be accessed and identified. This procedure is applicable to the two Android 4.2.2 Jellybean-based AVN systems.  
Both AVN systems have block device files mmcblk0 and mmcblk1. File mmcblk0 represents the storage device eMMC chip and has partitions such as /data, /system, /ivilog and /ivibackup. Particularly, in the subdirectories of /data, many files that seem to be user data are found. File mmcblk1 represents the storage device SD card and contains the files for the map data of the navigation app. Since we are interested in user behaviour, we created an image of mmcblk0 using command dd. Figure 2 illustrates the above procedure.  
We collected directories and files from the partitions in the created image utilizing Autopsy. In /system, APK (Android application package) files installed on the system are  
Figure 2: Data acquisition from Android 4.2.2-based AVN systems.  
found. They are related to the functions provided by the AVN system such as Bluetooth communication, navigation, telematics and radio. These apps create user data in partition /data while running. In the following sections, Bluetooth data, navigation data and Android system logs are analysed and many artefacts are identified.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Requirements on Tools in MLOps
#### MM-2: Model Registry  
The goal of the model registry is to store every model along with all of its versions and metadata. Without a model registry, the model serving tool cannot process automatic requests of querying the models. The registration of models within a central registry helps in coordinating the selection and deployment of specific versions. It helps in governance and offers better visibility while decreasing the time to production for already saved models.