esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SP-Rife Algorithm
### SP-Rife Algorithm  
The SP-Rife algorithm, as presented in Algorithm 1, is proposed based on the comprehensive content from Sections 3.1-3.3. The computation process of the algorithm involves spectral refinement and phase angle interpolation estimation, aiming to optimize the issues of the spectral leakage, direction correction determination, and interpolation method.  
```
Input: A sinusoidal signal \(s(n)\) Output: Frequency estimation \(\hat{f}_{0}\) Initialize: Windowing \(x(n)=s(n)h(n)\) Step 1: Obtain the position of the maximum spectral \(k_{1}=\max(|FFT\{x(n)\}|)\) Step 2: Refine the spectrum at \([k_{1}-1,k_{1}+1]\) by a factor of 100 and update \(k_{1}\) Step 2: Calculate \(FFT\{x(k_{1})\}\) and \(FFT\{x(k_{1}\pm 1)\}\) Step 3: Calculate the phase angles \(P_{1}\), \(P_{2}\) and \(P_{3}\) at \(k_{1}\), \(k_{1}+1\) and \(k_{1}-1\) with phase angle function \(angle(\cdot)\) Step 4: Calculate correction direction and frequency deviation \(P_{2}>P_{3}\) \(r_{1}=1\) \(\delta_{1}=\frac{P_{0}}{P_{1}+P_{2}}\) Else \(r_{1}=-1\) \(\delta_{1}=\frac{P_{1}}{P_{1}+P_{2}}\) End of if \(\hat{f}_{0}=\frac{(k_{1}+P_{3})\hat{f}_{0}}{N}\) Return \(\hat{f}_{0}\)
```  
**Algorithm 1**SP-Rife Algorithm  
The SP-Rife algorithm can be summarized as follows:  
1. The signal is windowed, and the position \(k_{1}\) corresponding to the maximum value in the spectrum is detected.
2. The spectrum between \(k_{1}-1\) and \(k_{1}+1\) is refined 100 times, and the maximum and second-largest values in the refined spectrum are found.
3. The phase function \(angle[\cdot]\) is used to calculate the phase angle \(P_{1}\) corresponding to the maximum value in the spectrum and the phase angles \(P_{2}\) and \(P_{3}\) corresponding to the adjacent two frequencies.
4. By comparing the values of \(P_{2}\) and \(P_{3}\), the correction direction \(r_{1}\) and the frequency deviation factor \(\delta_{1}\) are determined. Assuming \(P_{2}=angle[X(k_{1}-1)]\), if \(P_{2}>P_{3}\), interpolation is performed on the left side of the maximum spectrum, and \(r_{1}=-1\) ; if \(P_{2}<P_{3}\), it is concluded that the actual frequency point is on the right side of the maximum spectrum, and \(r_{1}=1\).
5. The final frequency estimation value is computed using Equation (16).
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
## 3 Experiments and Analysis
Figure 7:4 Results of Aerial 2. **(a)** EDLines. **(b)** MSEDLines. **(c)** LSD. **(d)** MSLSD. It can be seen from the visualization results in Figures 8-16 that the proposed methods MSLSD and MSEDLines alleviated the segmentation fracture effect of line segments. Also, it can be seen from the local area marked by the yellow frames in the nine figures that the line extraction results of conventional LSD and EDLines method were affected by image factors such as grayscale distortion, causing a relatively severe fracture phenomenon. MSLSD and MSEDLines had the effect of optimization and could merge multiple broken short line segments into one complete long segment. Additionally, we can clearly see from Figure 9, Figure 10, and Figure 13 that the proposed methods, MSLSD and MSEDLines, played a role in removing redundancy, eliminating the redundant lines while retaining the key geometric contour lines. From the comparison of Figure 10a,b, it was found that the proposed method MSEDLines could remove some pseudo-lines extracted by algorithm mistakes. Table 1 shows the quantitative results of line extraction with nine sets of experimental data of indoor, outdoor, and aerial images. Regardless of indoor or outdoor environments or aerial scenes, MSLSD and MSEDLines extract longer lines than the corresponding traditional methods, LSD and EDLines. The longer the average length of line segments, the higher the integrity of extracted line segments on the images. For Indoor 1, the line segment average length of MSEDLines and MSLSD was 85.3 pixels and 85.2 pixels respectively, which is much higher than that of EDLines (63.1 pixels) and LSD (51.3 pixels). Since our methods had the effect of removing line segment redundancy, the number
## 1 Introduction
### Limitations of Previous Studies  
Previous research primarily focused on determining the optimal assembly sequences for individual components to minimize construction challenges using BIM and GA. However, these studies were confined to generating assembly sequences based on physical characteristics, such as component weight and spatial occupancy, overlooking critical considerations for on-site assembly planning, such as the working radius and task interference. Consequently, their practical implementation faces challenges, and experiments typically involve a limited number of components. Moreover, most studies have focused on creating initial assembly sequences and have overlooked the necessity of adapting these sequences to dynamic on-site conditions. In this study, a combination of BIM and GA was employed to develop a system that incorporates on-site assembly requirements, going beyond the scope of planning initial assembly sequences during the construction phase. The objective was to create a system capable of adjusting to evolving on-site conditions and visualizing the final application in the field. It is assumed that all the components required for assembly are readily available, and the specific details regarding hoisting, except for the tower crane's location information, are not addressed. This research focused on generating assembly plans, emphasizing the sequence of assembly rather than determining the initial components of the hoist.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 2 Related Works
### Timing-Dependent Task Offloading in MEC
The authors in [20] proposed an enhanced multi-objective version of the orangutan algorithm for offloading interdependent tasks in edge cloud environments, in which multi-objective optimization reduced offloading latency and improves resource utilization. The authors in [21] jointly optimized dependent task offloading, computational resource allocation, user-transmission power control, and channel resource allocation to maximize the quality of user experience by building a weighted aggregate model for reducing latency and energy saving and optimizing a multi-cellular, multi-user task offloading and resource allocation model based on the multi-agent proximity policy. The authors in [22] addressed the triangular dependencies of multiple subtasks and the routing of interdependent subtasks to maximize the total offline revenue. The authors in [23] investigated a lightweight and efficient edge computing task migration scheme for low-power IoT systems. By taking into account the topology/schedule of IoT tasks, heterogeneous resources on the edge servers, and wireless interference, the authors proposed a distributed consensus algorithm to support multi-user migration, which effectively reduced the task execution time and improves resource utilization. In addition, to ensure the quality of experience, achieve low latency, and provide highly reliable services, the authors in [24] proposed a hybrid hierarchical task offloading strategy. On the other hand, the authors in [25] introduced an optimization scheme by implementing a map-based algorithm. The authors in [26] established a system-cost evaluation metric to minimize this metric as an objective for solving the multi-service task MEC problem. However, the above research work on task offloading considering computational resources in a multi-user multi-server environment has not considered the problem of task-timing dependency constraints that exist for user requests in real-world environments, making the formulation of offloading strategies more difficult. Therefore, subsequent research works need to comprehensively consider the timing dependency constraints among tasks and the task scheduling and resource allocation issues in multi-user concurrent scenarios to better fit actual scenarios and realize more efficient task offloading and scheduling strategies.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 2 Current Landslide Monitoring Methods
### Remote Sensing Methods
This integration can supplement remote sensing data and observations with on-site geophysical datasets capable of detecting precursory deformations. Table 4 provides advantages and limitations of remote sensing methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Remote Sensing Methods** & **Advantages** & **Disadvantages** \\ \hline SAR and DInSAR & \begin{tabular}{c} Active microwave remote sensing, capable \\ of imaging around the clock under all \\ weather conditions; DInSAR can measure \\ deformation with millimeter-level \\ accuracy and conduct dynamic \\ monitoring of slopes. \\ \end{tabular} & \begin{tabular}{c} Difficulties in image registration and \\ atmospheric correction can \\ complicate analysis; frequent \\ imaging is required, leading to high \\ operational costs and significant \\ demands on data processing. \\ \end{tabular} \\ \hline Passive Optical Sensors & \
\begin{tabular}{c} Characterizes terrain morphology using \\ multispectral data; high-resolution \\ satellite imagery allows for detailed \\ feature identification through stereoscopic \\ compositions; detects changes to identify \\ areas with slope variations. \\ \end{tabular} &
\begin{tabular}{c} Difficult to achieve real-time \\ monitoring; data acquisition is \\ affected by satellites and weather \\ conditions; unable to directly \\ measure pre-deformation of slopes; \\ surface features hardly reflect the \\ subsurface instability mechanisms. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of remote sensing methods.
###### Abstract  
This study aims to determine if a dose-response relationship exists between the pressure applied during deep friction massage (DFM) and the time to the onset of analgesia in an asymptomatic patellar tendon. For this purpose, pressures applied by physiotherapists during DFM (study 1) were characterized and then, based on these pressures, the effects of different DFM pressures on the time to the onset of analgesia were assessed (study 2). First, the mean pressure applied by 40 physiotherapists during a DFM session was assessed with a pressure sensor through an observational, cross-sectional and analytical study. Next, the effects of different pressure intensities (the median, the percentile 25 (P25), and the percentile 75 (P75) of the mean pressure obtained in study 1) were studied in a crossover trial enrolling 30 participants with an asymptomatic patellar tendon. A pressure sensor was used to register the pressures applied during DFM. Our main results indicated that the physiotherapists applied pressures with a wide variation ((mean pressure: 2.317 kg/cm\({}^{2}\) (P25: 1.022 kg/cm\({}^{2}\); P75: 4.161 kg/cm\({}^{2}\))). It was also shown that higher pressures had shorter times to the onset of analgesia (pressure: 1 kg/m\({}^{2}\), time to the onset of analgesia: 67.0 s (P25: 84.5 s; P75: 113.5 s); pressure: 2.3 kg/m\({}^{2}\), time to the onset of analgesia: 59.0 s (P25: 73.5 s; P75: 87.3 s); pressure: 4.2 kg/m\({}^{2}\), time to the onset of analgesia: 37.8 s (P25: 54.0 s; P75: 62.0 s)) (\(p\leq 0.001\)). In conclusion, the mean DFM pressure obtained by the physiotherapists was 2.3 kg/cm\({}^{2}\) (P25: 1.02 kg/cm\({}^{2}\); P75: 4.16 kg/cm\({}^{2}\)). Higher pressures of DFM resulted in shorter times to the onset of analgesia.  
Keywords:Cyriax; pain; patellar tendon; manual therapy; physical therapists +
Footnote †: journal: Applied Soft. 2020, 10, 2705; doi:10.3390/app10082705
## 3 Act  
### Problem  
There are mainly two types of congestion in WSNs. The first type is the node-level congestion which occurs due to queue overflow inside the node. Queue overflow might lead to packet drop and this leads to retransmission if required and therefore consumes additional energy. Wireless channels are shared by several nodes using Carrier Sense Multiple Access (CSMA)-like protocols and thus collisions among sensor nodes can occur when multiple sensor nodes try to occupy the channel concurrently. This is the second type of congestion-link-level congestion. Link-level congestion increases the packet service time and decreases the link utilization. Both the node level and the link-level congestions have direct impact on energy efficiency and Quality of Data (QoD). Therefore congestion must be efficiently controlled. Congestion control protocol efficiency depends on how much it can achieve the following objectives: first, energy-efficiency should be improved in order to extend system lifetime. Therefore congestion control protocols need to avoid or reduce packet loss due to buffer overflow, and remain lower control overhead that consumes less energy; second, it is also necessary to support traditional QoS metrics such as packet loss ratio, packet delay, and throughput; third, fairness needs to be guaranteed so that each node can achieve fair throughput [15].  
The main purpose of the proposed ACT scheme is to guarantee a high quality of data by reducing dropped packets due to the congestion. First ACT reduces the amount of generated packets on the source node with compression scheme-DWT, ADPCM, and RLC. Second ACT reduces the amount of transmitting rate on the relaying node with compression scheme under the congestion by adjusting the quantization step on the ADPCM. Third ACT assigns the priority based on the result of DWT to guarantee the reconstruction of data with packet loss. Last, for the fast propagation of congestion notification, the queue is operated adaptively according to the congestion state and queue state.
## 3 Proposed Lagrange Multiplier Selection Algorithm
### Summary of the Proposed Algorithm  
The detailed procedure of the proposed Lagrange multiplier selection algorithm is outlined in the following Algorithm 1.  
In order to access the accuracy of the proposed \(\mathrm{R}-\lambda\) model, \(\mathrm{R}^{2}\) is utilized as the quantitative metric which can measure the degree of data variation from a given model [50]:  
\[\mathrm{R}^{2}=1-\frac{\sum\limits_{i}\bigl{(}\mathrm{X}_{i}-\hat{\mathrm{X}} _{i}\bigr{)}^{2}}{\sum\limits_{i}\bigl{(}\mathrm{X}_{i}-\overline{\mathrm{X}} _{i}\bigr{)}^{2}}, \tag{35}\]  
where \(\mathrm{X}_{i}\) and \(\hat{\mathrm{X}}_{i}\), respectively, are the actual and the estimated values of the \(i\)th data point. \(\overline{\mathrm{X}}\) is the mean of all the data points. The maximum \(\mathrm{R}^{2}\) value is 1, which occurs when \(\mathrm{X}_{i}=\hat{\mathrm{X}}_{i}\) for any \(i\). The closer the value of \(\mathrm{R}^{2}\) is to 1, the more accurate the model is. In our experiment, for each test sequence, the \(\lambda\) value of each MCTF level is fitted at the target bitrate by the proposed \(\mathrm{R}-\lambda\) model. The \(\mathrm{R}^{2}\) statistics for four-level MCTF are tabulated in Table 2. From this table, we can notice that the \(\mathrm{R}^{2}\) values are all close to 1. Consequently, it can be concluded that the analytical \(\mathrm{R}-\lambda\) model works well for sequences at the different temporal decomposition levels. This is a crucial point of our approach.
###### Abstract  
This paper proposes using direct adaptation (DA)-based turbo equalization in multiple-input-multiple-output (MIMO) filtered multitone reversal (TR) acoustic communications to jointly suppress noise, residual co-channel interference (CCI) and intersymbol interference (ISI) after the TR process. Soft information-based adaptive decision feedback equalization (ADFE) adjusted according to the recursive expected least squares (RELS) algorithm, including interference cancellation and decoding, is used to construct the DA-based turbo equalization. In the proposed method, soft information is exchanged between soft symbols with soft decisions of decoding iteratively, and interference suppression is proceeded successively and iteratively until the performance is stable. The principle of the proposed method is analyzed, and based on the acoustic channel responses measured in a real experiment, the performance is assessed in relation to that of anther two methods. Compared with the MIMO-FMT TR underwater acoustic communication using interference suppression without error control coding (ECC), the proposed method performs better, benefitting from the ECC included in turbo equalization. Additionally, compared with the MIMO-FMT TR underwater acoustic communication using interference suppression based on hard decision equalization and decoding, the proposed method exhibits superior performance by exploiting soft information.  
underwater acoustic communications; filtered multitone; time reversal; adaptive turbo equalization; soft information-based adaptive decision feedback equalization
## 3 Results and Discussion
### Molecular Size Analysis of GlycosJ Anti-Wrinkle Finishing Agent  
The anti-wrinkle finishing agent has a difference in molecular size due to the mutual attraction and repulsion between the functional groups in the molecular structure. The molecular size of the finishing agent determined the diffusion speed in solution, the adsorption performance of the cellulose, and the diffusion speed into the fiber [17; 18; 19; 21]. Therefore, we calculated the CSEV of OFr, OSu, OTr, openSu, openTr, and BTCA, GA, and DMDHEU according to the method in Section 2.2.5. We calculated the spherical model radius of different finishing agent molecules according to formula one (due to the  
Figure 4: Comparison of anti-wrinkle performance treated with different finishing agents.  
Figure 3: Structure of glycosyl polyaldehyde and glycosyll polyuronic acid finishing agents.  
larger molecular structure of raffinose and stachyose, the molecules cannot be regarded as spherical at the lowest energy; therefore, they have not been analyzed), and the CSEV of different anti-wrinkle agents are shown in Figure 5.  
As shown in Figure 5, the CSEV values of glucose and OFr were 111.25 and 138.728, respectively. The CSEV of monosaccharides was similar to the CSEV of GA, and their molecular radius was smaller, which made it easier to diffuse into the fiber. However, the structure of monosaccharides has fewer reactive groups and cannot form more effective cross-links with cotton fibers. The wrinkle resistance of the finished fabric was poor. As an example, compared with GA, OFr also contained two aldehyde groups, but the aldehyde groups in its structure were prone to condensation, which reduced the content of aldehyde groups of OFr. Therefore, the anti-wrinkle properties of OFr- and GA-finished fabrics were different.  
The CSEV difference between OSu and openSu was relatively small, 273.942 and 273.885, respectively, indicated that their diffusion performance was similar in the cotton fabrics, but the wrinkle resistance performance of their finished fabrics was different, which was related to the higher aldehyde group content of openSu and had the carboxyl groups in their structure. The same CSEV difference between OTr and openTr was also small, 265.769 and 270.955, respectively, but the wrinkle resistance of their finished fabrics was also quite different. The molecular sizes of ORa, OSt, openRa, and openSt, which cannot be regarded as spherical, were much larger than that of disaccharide anti-wrinkle finishing agents, which made them difficult to diffuse into the cellulose and form effective cross-linking with cellulose. Therefore, the anti-wrinkle performance of their finished fabric was poor.  
It was speculated that although the molecular radius of the finishing agent affected its absorption and diffusion into the cellulose, the final anti-wrinkle performance of the finished fabric was mainly determined by the cross-linking performance and the type and number of reactive groups of the anti-wrinkle finishing agent.
###### Abstract  
This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator's night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.  
autofocus; night vision goggles; image fusion; sparse and low-rank matrix decomposition
## 1 Introduction
The temperature gradient method can viably chirp each grating section in a reconfigurable way and in this manner, introduces continuously adjustable abilities to the grating in contrast to fixed chirped grating in PPLN. This method offers an advantage over multi-grating QPM structures for some applications where lateral translation of the crystal is undesirable. In this work, we demonstrate the generation of CW orange light using single-pass SFG phase-matching in a step-chirped magnesium oxide doped periodically poled lithium niobate (MgO:PPLN) crystal. The technologically important orange light is specifically generated using a 974 nm laser diode (LD) mixed with a C-band amplified spontaneous emission (ASE) laser source (1525-1565 nm). Multiple wavelengths in the range of 593-599 nm were obtained by applying a temperature gradient to the 21-mm crystal, which could find many potential applications in biomedicine and spectroscopy. To control the local heating of distinctive areas, two temperature-controlled thermoelectric ovens were utilized, therefore, controlling the fundamental wavelength acceptance bandwidth which adds a unique advantage of continuous reconfigurability and adjustable abilities to the step-chirped QPM structure. Therefore, this technique has the potential to expand the existing capabilities of step-chirped structures and is expected to additionally broaden the wavelength coverage of solid-state lasers in the visible spectrum. Up to 9.3 mW CW triple-wavelength output power was realized corresponding to \(\sim\)20.3%/W normalized conversion efficiency.