esa-sceva/satcom-chunk-collection huggingface dataset:
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 3 Energy of Acceleration Signals
### Peak Based Decomposition  
The instantaneous amplitude of a signal is sampled at the same rate as the original signal, which means that they have same length. When the dataset for the entire track length is being processed, it is necessary to reduce the size of the data to avoid time consuming and computationally expensive signal processing. At the same time, the energy information in the signal needs to be retained. In this section, a simple process, called peak based decomposition (PBD), is proposed to represent the signal energy in a much more compact function. PBD employs a simple multi-step process. In each step, the local maxima of the Hilbert amplitudes are taken and stored as a new representation of the same signal. In this way, small peaks in the original amplitude signal are removed, but the main peaks are kept. The output of this process constitutes a shorter length version of the original signal, but it contains the significant energy levels of the signal in the space domain. The output of the first step is called Peak Function 1 (PF1). If the process is repeated on PF1, then a new shorter version of the signal is obtained, which is called PF2. The process can be repeated several times until the desired combination of signal energy and length is obtained. Figure 5 shows how PBD works by approximating the original signal in each step and decomposing it to reduced sizes while the main amplitudes are kept.  
The PBD process is applied to a 400 m sample of measurement in Figure 6. It can be observed how PF4 and PF5 represent the energy level of the raw signal. The average signal energy that was proposed by [PERSON] et al. [23] using a 25 m window is also shown. As this algorithm averages the energy of the signal in a pre-specified window, it might not accurately find the locations in which the signal contains high levels of energy. In addition, if the width of the window is not properly chosen, the algorithm will sort the energy of part of signal into two windows and reduce the real amplitude. This might cause some parts of the signal with high energy levels not to be discovered. Fortunately, there is no need to select a window band with PBD.  
Accurately finding the peaks is important when multiple train runs are studied where the alignment of multiple measurements in the space domain is required. The proposed PD process produces a peak at the right location with good accuracy. This allows for the peaks to be aligned from several runs. However, there are several factors contributing to the accuracy of peak locations, e.g., the performance of the GNSS under different train forward speeds. These factors may cause some levels of mismatch between the responses in different passes.  
Figure 5: The peak decomposition (PD) process.
# Preparation of Nanoparticle-Loaded Microbubbles via an Electrohydrodynamic Atomization Process
## 3 Results and Discussion  
### Microbubbles with and without Nanoparticles Loaded  
Figures 2 and 3 show the representative optical micrographs of the produced microbubbles with nanoparticles loaded and without nanoparticles loaded at different time points. The operating conditions were as follows: flow rate of liquid (Q\({}_{l}\)) 10 mL/h, flow rate of gas (Q\({}_{g}\)) 50 mL/h, and applied voltage 12 kV. As shown in Figures 2 and 3, both microbubbles with and without nanoparticles loaded could be obtained successfully by coaxial electrohydrodynamic atomization. However, less microbubbles were produced under the same conditions when loading nanoparticles into microbubbles, indicating that the optimal operating conditions for obtaining microbubbles with and without nanoparticles were different. It can also be observed that smaller bubbles tended to disappear more easily for both types of microbubbles. This may be caused by the Oswald ripening effect [35; 36], in which small-sized microbubbles dissolve into the surrounding larger sized microbubbles. Figure 4 shows the dimensionless bubble number varying with time for cases with nanoparticles loaded and without nanoparticles loaded. It is obvious that with the loading of nanoparticles, the reduction trend of microbubble number becomes less, suggesting that the microbubbles loaded with nanoparticles have better stability and longer survival time.  
Figure 2: Representative optical micrographs of microbubbles without nanoparticles. (**A**) \(t\) = 0 min. (**B**) \(t\) = 5 min. (**C**) \(t\) = 10 min. (**D**) \(t\) = 15 min.
###### Abstract  
With the successful proliferation of data outsourcing services, security and privacy issues have drawn significant attention. Data authentication in particular plays an essential role in the storage of outsourced digital content and keeping it safe from modifications by inside or outside adversaries. In this paper, we focus on online data authentication using a Merkle (hash) tree to guarantee data integrity. By conducting in-depth diagnostics of the side channels of the Merkle tree-based approach, we explore novel solutions to improve the security and reliability of the maintenance of outsourced data. Based on a thorough review of previous solutions, we present a new method of inserting auxiliary random sources into the integrity verification proof on the prover side. This prevents the exposure of partial information within the tree structure and consequently releases restrictions on the number of verification execution, while maintaining desirable security and reliability of authentication for the long run. Based on a rigorous proof, we show that the proposed scheme maintains consistent reliability without being affected by continuous information leakage caused by repetitions of the authentication process. In addition, experimental results comparing with the proposed scheme with other state-of-the-art studies demonstrate its efficiency and practicality.  
data outsourcing; integrity; online authentication; Merkle (hash) tree; data loss; information leakage; reliability +
Footnote †: journal: Applied Sciences
## 3 System Modeling with Petri Nets
### Device Modeling
Thus, a charging tank should be modeled such that the behavior of both modes is described; the obtained PN model is shown in Figure 3. In this model, \(p_{s}\), \(p_{c}\), \(t_{1}\), \(t_{2}\), and \(t_{3}\) are the same as for the storage tank shown in Figure 2. Transitions \(t_{4}\) and \(t_{5}\), and place \(p_{5}\) are added to model the behavior of the SCF mode. Enforcing place \(p_{5}\) that can hold at most one token at a time is a control place that decides the operation mode of a tank. If a charging tank starts to adopt the SCF mode, one token is created and put into \(p_{5}\) such that \(t_{2}\) and \(t_{3}\) are disabled, and \(t_{5}\) is forced to fire to move the token in \(p_{c}\) back to \(p_{s}\) immediately if \(p_{c}\) is not empty. At the same time, \(t_{4}\) is forced to fire for feeding a distiller and \(t_{1}\) can fire to charge the tank, i.e., the SCF mode is applied. When an instruction is received to end the SCF mode and switch to the normal mode, the token in \(p_{5}\) is removed away. Then, \(t_{4}\) is not enabled and cannot fire, and the tank goes back to the normal mode. Note that a charging tank can start to operate under the SCF mode, but the crude oil in it is no less than the safety stock, i.e., one token can be put into \(p_{5}\) only if the sum of the volume associated with the token in \(p_{s}\) and \(p_{c}\) is greater than or equal to the safety stock. This can be decided by a schedule. In this way, the behavior of a charging tank is well modeled. Figure 3: PN model for charging tanks. The PN model for a pipeline that can hold three different segments of crude oil is shown in Figure 4 and is the same as that in [21,22]. Places \(p_{1}\)-\(p_{3}\) represent different oil segments in the pipeline. When discrete transitions \(t_{1}\) and \(t_{2}\) are enabled and fire, one token is moved from an input place into an output place immediately. Let \(T_{I}=\{t_{I1},\,\ldots,\,t_{Ik}\}\) and \(T_{O}=\{t_{O1},\,\ldots,\,t_{Ok}\}\). As crude oil mixing is not considered here, the pipeline receives crude oil from only one storage tank and feeds only one charging tank with the same rate at a time. Hence, only one transition in \(T_{I}\) and one in \(T_{O}\) can fire with the same rate at a time. To simplify the PN model for the entire system, the pipeline is modeled in a hierarchical way and a macro transition \(y\) is used to describe the behavior of the pipeline when there is no confusion. In other words, when \(y\) fires, it implies that one transition in \(T_{I}\) and one in \(T_{O}\) fires at the same rate at the same time, which models the crude oil's transport from storage tank to charging tank via a pipeline.
_Appl. Sci._**2015**, \(5\), 566-574; doi:10.3390/app5030566  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Q-Switched Operation with Carbon-Based Saturable Absorbers in a Nd:YLF Laser**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Departamento de Optica, Facultad de Ciencias Fisicas, Universidad Complutense de Madrid,  
Avda. Complutense s/n, 28040 Madrid, Spain; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Departamento de Fisica Aplicada III, Facultad de Informatica, Universidad Complutense de Madrid,  
Avda. [PERSON] s/n, 28040 Madrid, Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-91-394-4508; Fax: +34-91-394-4683.  
Academic Editor: [PERSON]  
_Received: 17 August 2015 / Accepted: 1 September 2015 / Published: 11 September 2015_  
**Abstract:** We have numerically studied the influence of the absorption modulation depth of carbon-based saturable absorbers (graphene and carbon nanotubes (CNTs)) on the Q-switched regime of a diode-pumped Nd:YLF laser. A short-length cavity was used with an end mirror on which CNTs or mono- or bi-layer graphene were deposited, forming a saturable absorber mirror (SAM). Using a standard model, the generated energy per pulse was calculated, as well as the pulse duration and repetition rate. The results show that absorbers with higher modulation depths, _i.e._, graphene, deliver higher energy pulses at lower repetition rates. However, the pulse duration did not have a monotonic behavior and reaches a minimum for a given low value of the modulation depth typical of CNTs.  
**Keywords:** diode-pumped lasers; Q-switched Nd:YLF laser; mode-locked Nd:YLF; graphene saturable absorber; carbon nanotube saturable absorber
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 2 Methodology
### Statistical Analysis Using SPSS  
To estimate landslide triggering factors through soil test results, correlation and regression analysis were used as statistical analysis using SPSS [57] in this study. Correlation analysis is widely used as a traditional statistical method to analyze the correlations between independent and dependent variables [58]. It evaluates the presence or absence of a linear relationship between two variables. Therefore, the main purpose of correlation analysis is to verify if a relationship exists, not to find out what functional relations there are between the variables. However, there are often cases where one wants to predict the value of a variable from other variables. In this case, regression analysis can be used, which is a statistical analysis method that estimates a mathematical model from the data or makes a correlation through statistical inference.  
Correlation analysis predicts the change of scale and direction of other variables as one variable increase or decrease. As such, the correlation is determined by how much of the variance (variate) of one variable changes as other variables' variance change, which is the covariance (covariate). Another important factor in correlation analysis is the correlation coefficient, which is an index that summarizes the degree and direction of a relationship between variables with a single numerical value, an absolute value between 0 and 1. The closer the correlation coefficient is to 0, the lower the correlation, and the closer to the absolute value 1, the higher the correlation.  
Regression analysis is a method used to predict values, which is often a way to understand the extent and direction of one or more variables affecting another variable and analyze how the dependent variable (Y) changes as the independent variables (X) change. Once the values of the slope B and the Y-intercept A are determined, and the regression equation is obtained, it can predict the value of Y with the value of X.  
Here, RMSE is the square root of the mean of the squared error between the value predicted from an estimate or model equation and the value observed in a real soil environment. It is a method to check the difference between the predicted value and the real soil experiment and observation results. Furthermore, the correlation and suitability of regression model results and direct experiment results were evaluated.
# Advancing in RGB-D Salient Object Detection: A Survey
## 2 Classification and Comparison of Cross-Modal Fusion Strategies
### Comparison of Fusion Strategies  
The performance of fusion strategies in RGB-D SOD varies according to the specific requirements and constraints of the detection tasks. In image-processing tasks where spatial alignment is crucial, early fusion is often an effective strategy. This method maximizes the interaction between RGB and depth data at the initial stage of feature extraction, thereby fully leveraging spatial consistency. However, as noted by [PERSON] et al. [27], early fusion, despite its advantages, may exhibit lower robustness when dealing with noisy or incomplete depth data. Mid-level fusion typically achieves superior performance in terms of object detection accuracy and detail preservation. For example, [PERSON] et al. [28] introduced an attention-guided fusion model that dynamically adjusts the focus on the salient features of each modality, effectively enhancing the overall detection performance in complex scenes. In contrast, late fusion generally offers greater robustness by allowing separate modality paths to mitigate the impact of noisy data before fusion. [PERSON] et al. [29] exemplify this approach, demonstrating how late fusion enhances detection reliability when dealing with varying quality across modalities in RGB-D human pose estimation. Hybrid fusion strategies combine elements of early, mid, or late fusion, integrating RGB and depth data more intricately to maximize the strengths of each fusion type while compensating for their weaknesses. This approach is seen in the work of [PERSON] et al. [30], who utilized hybrid fusion to improve detection accuracy while managing computational efficiency. A comparison of these methods is shown in Table 1.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
###### Abstract  
Consumer depth cameras bring about cheap and fast acquisition of 3D models. However, the precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. In this paper, we present a super-resolution method for reconstructing a high resolution 3D face model from a low resolution 3D face model acquired from a consumer depth camera. We used a group of radial curves to represent a 3D face. For a given low resolution 3D face model, we first extracted radial curves on it, and then estimated their corresponding high resolution ones by radial curve matching, for which Dynamic Time Warping (DTW) was used. Finally, a reference high resolution 3D face model was deformed to generate a high resolution face model by using the radial curves as the constraining feature. We evaluated our method both qualitatively and quantitatively, and the experimental results validated our method.  
3D face model; super-resolution; radial curve; Dynamic Time Warping Article
## 4 Robotic Arm Position Detection
### Experimental Setup  
The experiment took place in a commercial office space where there were regular communication signals and interference from other WiFi devices.  
In Figure 5, we depict the experimental setup for our robotic arm position detection experiment. For all experiments shown in this paper, we used a single antenna. On both sides of the robotic arm, there were Tx and Rx devices set up with a distance of 1.02 m between them. These devices were Raspberry Pi 4B units that operated on channel 2 of the 2.4 GHz WiFi spectrum. We selected the 2.4 GHz spectrum according to the 802.11n WiFi standard for all our experiments in this work to ensure a fair comparison with previous studies [11]. Due to the natural property of CSI, it is very sensitive to changes in the environment and the layout of Tx-Rx. Hence, the robotic arm and devices were controlled  
\begin{table}
\begin{tabular}{c c c} \hline  & **Shoulder** & **Elbow** \\ \hline \(K_{p}\) & 15 & 15 \\ \hline \(K_{I}\) & 2.5 & 0.5 \\ \hline \(K_{D}\) & 5 & 0.55 \\ \hline \end{tabular}
\end{table}
Table 1: Robotic arm control system’s PID parameters.  
Figure 4: System architecture for robotic arm position control.  
Figure 3: 2-DOF [PERSON] robotic arm.  
remotely while all other setups stayed in a constant position throughout the experiment in order to maintain minimal interference in the channel.  
The Tx propagated packets into the channel at a rate of 1300 Hz via one antenna with a 2 dBi gain. We chose this high packet rate to maximise the CSI sampling rate and allow for better sensing resolution. Each sample contained CSI values for 52 independent subcarriers. We collected CSI for 10 s with the robotic hand programmed to hover over each alphabetic key (A-Z) on a standard keyboard. This experiment involved all 26 alphabetic keys. For each key, 3 independent trials were conducted, each lasting 10 s. In total, CSI data were collected for 780 s across all trials and keys, corresponding to over 1 million CSI frames sampled at 1300 Hz.  
We repeated this 3 times, with two sets of data used for training and the third for validation.
# DVCW-YOLO for Printed Circuit Board Surface Defect Detection
###### Abstract
[11] proposed an improved PCB component detection model based on YOLOv8, which utilizes the C2 Focal module as the core of the backbone network, combining fine-grained local features with coarse-grained global features. [PERSON] et al. [12] introduced the W-YOLOv8 model, which uses a gradient gain allocation strategy with a dynamic non-monotonic focusing mechanism to direct the model's attention to anchor frames of ordinary mass, improving overall performance. While these studies have enhanced detection accuracy, a balance between detection performance and model size remains elusive. In response to the challenges posed by small target sizes, missed detections, and excessive model parameters in PCB surface detection, this study introduces an enhanced detection model based on YOLOv8, called DVCW-YOLO. This model significantly mitigates parameter redundancy and model complexity through the integration of depthwise convolution (DWConv), VOVGSCSP modules, a self-constructed C2 fCBAM module, and a weighted intersection over union (WIoU) loss function. The specific contributions of this work are outlined as follows:  
1. The C2 fCBAM module is developed by synergistically combining the C2f architecture with the convolutional block attention module (CBAM) mechanism. This integration enables the detection model to effectively prioritize critical regions by enhancing salient features while attenuating less relevant ones. Consequently, a novel backbone network is established to facilitate the extraction of key features. 2. A neck network is designed by incorporating the VOVGSCSP module and DWConv. The implementation of the WIoU loss function during training serves to alleviate the adverse effects of low-quality and extreme samples, thereby improving model accuracy, accelerating convergence, and reducing the high rate of missed detections commonly associated with PCB defect identification. 3. We augmented the dataset and executed comparative experiments involving nine detection algorithms, which substantiate that the proposed DVCW-YOLO consistently outperforms its counterparts in most cases. The main contributions of this paper are outlined as follows. Section 2 introduces the architecture of the proposed DVCW-YOLO defect detection model. Section 3 presents the dataset construction, including data presentation, augmentation, and defect distribution characteristics. Section 4 details the experimental setup, parameter settings, detection results, and analysis. Finally, Section 5 concludes the paper with a summary of key findings.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 3 Proposed Encoding Method for DNA Data Storage  
DNA storage faces challenges associated with complex error types and high error rates, including base insertions, deletions, and substitutions introduced during synthesis and sequencing, as well as sequence loss. Given the low error rate required for DNA storage, a key challenge is designing highly reliable coding schemes tailored to DNA storage to effectively address these diverse error types [33; 34].  
To address this challenge, we propose an encoding method optimized for hardware implementation. As shown in Figure 2, this scheme consists of the following steps. First, the binary data to be stored are divided into fixed-length blocks, and each block is encoded with LDPC codes for error correction. This step enhances the robustness of the data against errors introduced during storage and readout. Then, an interleaving operation is applied to the encoded codewords to enhance resistance against burst errors caused by sequence loss during storage. By redistributing bits across different positions, this step improves the error correction capability of the code in handling consecutive errors. Then, the interleaved codewords are segmented and mapped into DNA sequences using the following base mapping rules: (00) \(\rightarrow\) A, (01) \(\rightarrow\) T, (10) \(\rightarrow\) G, (11) \(\rightarrow\) C. Furthermore, to facilitate the identification of sequencing reads during the recovery process, unique index sequences are added to both ends of each DNA sequence. Finally, primer pairs are added to both ends of the sequence.  
Our proposed encoding scheme employs LDPC error correction, interleaving, and index optimization, providing an efficient and reliable encoding strategy for DNA data storage. Specifically, we employed the LDPC (64,800, 54,000) code and used a 360 \(\times\) 180 interleaving matrix to interleave the encoded data. After interleaving, each LDPC codeword was grouped into 120 bits, which were mapped to 60 nucleotides, resulting in 540 setsof payload sequences. Using this encoding method, we encoded a total of 9240 LDPC codewords, resulting in 4,989,600 DNA sequences with a total storage capacity of 59.4 MB.  
We designed a method that uses both primer and index sequences to distinguish different molecules. These large-scale DNA sequences were divided into 12 oligonucleotide pools corresponding to 12 primer pairs, with each oligonucleotide pool containing 415,800 oligonucleotide molecules. For each pool, a set of index sequences of 12 nucleotides was designed. The shortened Hamming code (\(24,19\)) was used to encode a 19-bit binary number into 24 bits, which was then mapped to a 12 nt base sequence. In the generated index set, the first 415,800 index sequences with homopolymer lengths of less than or equal to 3 were used, and each DNA molecule was assigned a unique index.