esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Case Study Analysis
### Analysis of Power Grid Equipment Abnormalities Under Tropical Cyclones  
Figure 7 illustrates the following trends in wind power output at bus 2: during \(t\in[1,10]\), the output slowly increases as the typhoon approaches; during \(t\in[11,19]\), as the wind farm nears and exits the risk wind circle, wind speed rises rapidly, resulting in fluctuations at high speeds that cause frequent start-stop operations and abnormal ramping of turbines; during \(t\in[20,24]\), as the typhoon moves away, the wind speed decreases gradually from \(v_{\text{out}}\) to \(v_{\text{rate}}\), maintaining rated power output. The wind farms at buses 3 and 8 are impacted by the typhoon earlier than at bus 2, showing a similar wind power development trend but with an \"advance\" phenomenon.  
The time-varying fault probabilities of the branches are illustrated in Figure 8. As shown in Figure 5, the branches affected by the typhoon risk circle include branches 1-6, 8, 14, and 15. Branches 8, 14, and 15 are the first to be impacted by the typhoon, being close to the wind circle's edge. In the period \(t\in[1,10]\), these branches experience high  
Figure 6: Each bus load of IEEE 14 system.  
Figure 7: Wind power curve of IEEE 14 system during typhoon.  
wind speeds, causing a sharp increase in cumulative fatigue damage, which results in fault probabilities of 0.1971, 0.8937, and 0.2699, respectively. In the interval \(t\in[11,24]\), as the typhoon moves away, the cumulative fatigue damage no longer increases, and the fault probabilities for branches 8, 14, and 15 stabilize. Compared to branch 14, branches 1-6 are further inland, where the typhoon's intensity has decreased, resulting in lower wind speeds and, consequently, lower cumulative fatigue damage, leading to reduced fault probabilities. The remaining branches are far from the typhoon and outside the risk wind circle, resulting in a fault probability of 0.
###### Abstract  
An accelerated least-squares approach is introduced in this work by incorporating a greedy point selection method with randomized singular value decomposition (rSVD) to reduce the computational complexity of missing data reconstruction. The rSVD is used to speed up the computation of a low-dimensional basis that is required for the least-squares projection by employing randomness to generate a small matrix instead of a large matrix from high-dimensional data. A greedy point selection algorithm, based on the discrete empirical interpolation method, is then used to speed up the reconstruction process in the least-squares approximation. The accuracy and computational time reduction of the proposed method are demonstrated through three numerical experiments. The first two experiments consider standard testing images with missing pixels uniformly distributed on them, and the last numerical experiment considers a sequence of many incomplete two-dimensional miscible flow images. The proposed method is shown to accelerate the reconstruction process while maintaining roughly the same order of accuracy when compared to the standard least-squares approach.  
singular value decomposition; missing data approximation; low-rank approximation; least-squares method; randomized algorithm; discrete empirical interpolation method
## 4 System Design and Goals
### Modification to Message Intervals and New Message Variables
#### 4.4.2 LI Function  
This module captures all incoming packets and either discards them (if the packet's validity time (VTime) has expired) or it extracts and stores key information from the packet into arrays for further processing (that is, saving array information into Neighbor and Link Sets where applicable). The module terminates if either the HELLO packet is received or if the LI timer expires. The algorithm broadcasts a HELLO message at the end of the LISTEN period, only if EH = 1 or if EH = \(-1\) and no HELLO message was heard. The procedure for LI is explained in Algorithm 2. The algorithm is designed to enhance routing efficiency in our D2D network by leveraging the LISTEN state. This state allows a node to monitor network traffic and update its Link and Neighbor Sets based on received HELLO (HI) and Topology Control (TC) messages. The algorithm dynamically adjusts the duration of the LISTEN state or the subsequent DB_CHORE state based on the observed network traffic. Each node enters the LISTEN state, where it passively listens to network traffic by receiving HI and TC messages from neighboring nodes. During the LISTEN state, the node computes the level of network traffic based on the frequency and density of the received HI and TC messages. After completing the LISTEN state, the node may transition to the DB_CHORE state, where it performs tasks such as route calculation, database maintenance, or message generation based on the updated Link and Neighbor Sets.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 3 Results
### The Antimicrobial Effect of TiHA against Planktonic Bacteria  
The antimicrobial effect of TiHA against planktonic bacteria, including _S. sanguinis_, _A. naeslundii_, _S. aureus_, and _E. coli_, was evaluated (Figure 2). In the absence of UVA irradiation, the CFU values of planktonic bacterial cultures placed on specimens containing different concentrations of TiHA were not significantly different from those on the control (data not shown).  
Figure 2: The antimicrobial effect of TiHA against planktonic bacteria. The specimens containing 0% (control), 3%, 6%, or 9% TiHA were used. An aliquot (100 \(\upmu\)L) of _S. sanguinis_, _A. naeslundii_, _S. aureus_, or _E. coli_ cultures was placed on the specimen surface, and the specimens were irradiated for 2 or 4 h. The bacteria were then plated to determine the CFU values. Data are presented as the mean \(\pm\) standard deviation for three specimens. The asterisk indicates a significant difference between the CFU values of the control and TiHA-containing specimens (\(p\) < 0.05).  
Upon 2 h of UVA irradiation, the numbers of CFUs of planktonic _S. sanguinis_ on specimens containing 6% and 9% TiHA were significantly lower than those on the control (\(p\) < 0.05). Under the same conditions, the CFU values of _A. naeslundii_, _S. aureus_, and _E. coli_ on specimens containing 3%, 6%, and 9% TiHA were not significantly different from those of the control. When _S. sanguinis_, _A. naeslundii_, and _S. aureus_ placed on the specimens were irradiated for 4 h, the CFU values were significantly reduced with an increasing TiHA content of the specimens (\(p\) < 0.05). By contrast, the CFU values of planktonic _E. coli_ on TiHA-containing specimens were not significantly different from those of the control even after a 4 h irradiation. Thus, TiHA exerted an antimicrobial effect against planktonic _S. sanguinis_, _A. naeslundii_, and _S. aureus_ upon a 4 h UVA irradiation but not against planktonic _E. coli_.
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 2 Materials and Methods
### Edge Detection  
In image-processing technology, commonly used edge detection operators include the Roberts operator, Sobel operator, Prewitt operator, Laplace operator [34], and Canny operator [35]. These operators have different edge detection capabilities for images with different characteristics. In an image, shallow gullies appear as linear features with weak feature information. The Canny operator has the characteristics of good anti-interference ability and accurate positioning and can effectively identify and locate edges on slope data. Compared with other edge detection operators, the Canny operator can seek the best solutions for anti-noise solutions and precise positioning. Therefore, this study selected the Canny operator to complete detection of the experimental sample area. To make the shoulder line features more obvious and to avoid image noise interference, the images needed to be preprocessed.
## 1 Introduction
The distance value of all pixels in 2-D or voxels in 3-D is utilized as the feature vector. The remaining work is to align the position and orientation of the target organ, and the alignment method is different for each organ and for each purpose. As of now, there are no studies which investigate the procedure of constructing the SSM of a humeral head. This study aims to propose a method of constructing the SSM of a humeral head towards the design of patient-specific artificial humeral heads. The proposed method consists of three steps: segmentation of humerus, humeral head extraction, pose alignment of humeral head, and finally, construction of the SSM by applying PCA. This study particularly focuses on the shape of the humeral head, whereas our previous study [5] focused on the shape of the whole humerus. Furthermore, this experiment demonstrates the effectiveness of SSMs by studying the individual shape variability using the SSM. The rest of the paper is structured as follows: Section 2 discusses the images used in this study and Section 3 describes the proposed methods. Section 4 presents the experimental results. Finally, the paper is concluded by summarizing the experimental results and the achievements of the research, as well as future research directions.
## 3 Methodology  
### Time Series InSAR Method  
In this paper, we used the SBAS-InSAR (Small Baseline Subset InSAR) technique to obtain time-series displacement. Figure 2 shows the processing workflow, and Table 2 shows the detailed data parameters.  
Firstly, the general processing of SBAS-InSAR starts from SAR image co-registration [17; 18], and the co-registration accuracy reaches 0.1 pixels. Precise orbit files were downloaded from ESA (European Space Agency) and preprocessed. For the selection of differential interference pairs, 36 days and 150 m were used as the time and perpendicular baseline thresholds of Sentinel-1 data. Due to the small amount of PALSAR-2 data, no time baseline thresholds were set. The temporal and perpendicular baselines are shown in Figure 3. After determining the interference pairs, a total of 262 interference pairs were generated. Then, we used the SRTM DEM data to remove the topographic and flattened phases and geocode the InSAR products.  
All possible interferometric pairs interfered, and the interferograms' coherence threshold was set to 0.7. Then, we used an adaptive filtering function of 128 window size based on the power spectral density [25]. We used the minimum cost flow (MCF) algorithm on a triangular mesh for phase unwrapping [26]. After atmospheric errors associated with elevation were removed, high-quality corrected unwrapped interferograms were retained. Finally, time-series deformation through singular value decomposition (SVD) was inverted [27].  
Figure 2: Workflow of SBAS-InSAR technology.
## 5 Simulation Experiment and Analysis
### The Third Simulation Environment  
To assess the viability of our algorithm (IEHA) in complex, large-scale scenarios akin to real-world settings, we conducted experiments using a 50 \(\times\) 50 grid map with intricate obstacle arrangements, as shown in Figure 13. Following a thorough analysis of the initial three sets of experiments, our team identified limitations in the ant colony algorithm's performance relative to the problem scope defined in this study. Consequently, we decided to discontinue its testing from the fourth experiment onward. Instead, we focused on algorithms that demonstrated superior security and operability to enhance the efficiency and practicality of our experiments. In the fourth simulation experiment, after changing the location of the start and target points in Figure 14, running the experiment multiple times, the results in Table 4 show that the IEHA algorithm significantly outperforms both the traditional A* and IEA algorithms in path planning. This algorithm can effectively ensure safety and reduce operational costs. Compared to the traditional A* algorithm, while the IEHA algorithm does not have a particularly significant advantage in terms of the shortest path length, it does have clear advantages in terms of Average Turning Count and Average Collision Count. Specifically, compared to the A* and IEA algorithms, the IEHA algorithm reduces the Average Turning Count by 44.4% and 75%, respectively, and eliminates the Average Collision Count entirely.  
In this study, the robustness of the proposed IEHA algorithm is systematically verified through multiple sets of simulation experiments carried out in three different experimental environments. The experimental design encompasses simulation environments under two distinct starting conditions, assessing the algorithm's performance and adaptability under diverse operating scenarios. The findings consistently demonstrate that this algorithm exhibits a high degree of feasibility and efficiency in UAV path planning tasks for carbon emission monitoring applications that involve vehicles in port areas. This result underscores the algorithm's reliability and effectiveness in addressing complex real-world problems and provides a solid theoretical and empirical foundation for its further application in related fields.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 4 Calculating the Natural Frequency of a Decaying Oscillator  
Equations (29) or (30) contain five unknowns, the four parameters of \(\chi\) and \(\omega\). Three of them, \(\bar{\varphi}\), \(\bar{b}\) and \(\omega\), can be determined from Equation (30) using the maximum likelihood method once \(\bar{\omega}\) and \(\alpha\) are known. However, \(\hat{\omega}\) and \(\alpha\) must be determined numerically. Equations (29), (30), or (34) are completely unsuitable for numerical optimization.  
The fastest and computationally most effective methods for accurately estimating the frequency of decaying exponentials in noise operate in the frequency domain and use the fast Fourier transformation (FFT). [PERSON] and [PERSON] showed that the maximum likelihood frequency estimator for the decaying resonator signal in noise is given, just as in the case of an undamped sine wave, using the argument of the periodogram maximizer, i.e., looking for the parameter \(\lambda\) which provides the maximum in the discrete Fourier transform [50; 51]:  
\[f_{ML}=\operatorname*{arg\,max}_{\lambda}[Y(\lambda)],\text{ where }\ Y(\lambda)=\left|\sum_{n=0}^{N-1}w_{n}e^{- \beta mn\lambda}\right| \tag{47}\]  
To determine the carrier frequency and damping constant of a decaying oscillator, a two-stage procedure is proposed in the literature, which is analogous to the case of a constant oscillator oscillation. In a first search, the rough frequency position is narrowed down and, based on this, the exact frequency is determined in the second step.
# Impact of CoAP and MQTT on NB-IoT System Performance +
###### Abstract  
The IoT protocols used for data transfer in the application layer, namely the Constraint Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) have dependencies to the transport layer. The choice of transport, Transmission Control Protocol (TCP) or the User Datagram Protocol (UDP), on the other hand, has an impact on the Internet of Things (IoT) application level performance, especially over a wireless medium. Furthermore, we touch upon the impact of different security solutions. The motivation of this work is to look at the impact of the protocol stack on performance over a narrowband IoT (NB-IoT) link. The use case studied is infrequent small reports sent from the sensor device to a central cloud storage over a last mile radio access link. We find that while CoAP/UDP based transport performs consistently better both in terms of latency, coverage, and system capacity, MQTT/TCP also works when the system is less loaded.  
CoAP; IoT; MQTT; NB-IoT; TCP; UDP Article Impact of CoAP and MQTT on NB-IoT System Performance
## 2 Related Works
### Atrous Spatial Pyramid Pooling  
As the number of network layers deepens, the resolution of the images decreases, and the generated semantic features become less effective in dense prediction tasks. To tackle this issue, various solutions have been proposed [15; 16; 17; 18].  
DeepLab V3 [18] is a semantic segmentation model based on atrous convolution, incorporating the atrous spatial pyramid pooling (ASPP) method to effectively fuse features at different scales. As depicted in Figure 1, ASPP comprises five parallel branches: a \(1\times 1\) convolutional branch; three atrous convolutional branches with varying expansion rates (6, 12, 18); and a global average pooling branch. The global average pooling branch downsamples the feature maps to a \(1\times 1\) size and subsequently upsamples them to the original size using \(1\times 1\) convolution and bilinear interpolation. The outputs of these five branches are concatenated to create a richer feature representation. Finally, a \(1\times 1\) convolution layer is employed to reduce the number of channels in the feature map to the desired level.  
The inclusion of the ASPP structure during liver and liver tumor segmentation can combine the advantages of atrous convolution to expand the receptive field of the convolution kernel without losing resolution. This assists the network in learning semantic information from the multi-scale receptive field, ultimately enhancing the model's segmentation performance.
## 3 Results and Discussion
### CO Diffusion Law
#### 3.2.1 Effect of \(L_{0}\) on the CO Diffusion  
Figure 12 shows the variation in CO concentration under the influence of \(L_{0}\). From the longitudinal height of each curve, it can be seen that the CO concentration at the same measurement point decreases with the increase in \(L_{0}\). In general, the peak CO concentration shows a pattern of \(C_{L_{0}=1\text{ m}}>C_{L_{0}=2\text{ m}}>C_{L_{0}=3\text{ m}}\). However, there is an exception, such as the curve \"13.6 m from the closed end\" in Figure 12, which shows a pattern of increasing and then decreasing with the increase in \(L_{0}\). Similarly, in terms of the horizontal width of the curves, there is a time lag in the appearance of the CO concentration curves at each measurement point at different \(L_{0}\) values. With little difference between the CO concentration profiles at \(L_{0}=1\text{ m}\) and \(L_{0}=2\text{ m}\), the CO concentration profiles at \(L_{0}=3\text{ m}\) appear later. This means that it takes longer for an equal amount of CO to be transported to a given point when \(L_{0}\) is particularly large and inappropriate. This also affects the total time required to remove all quantitative CO from the model. It can be seen from Figure 12 that the ventilation time (\(t\)) required to remove all the CO from the model has the pattern of \(t_{L_{0}=3\text{ m}}>t_{L_{0}=2\text{ m}}\approx t_{L_{0}=1\text{ m}}\). Therefore, it can be summarized that there exists a suitable range for \(L_{0}\) in practical engineering. Within the suitable range, \(L_{0}\) has little effect on pollutant transportation, but beyond it, the total time of pollutant discharge will be greatly increased. This increase in the total duration of ventilation further supports the conclusion that the wider vortex zone slows down the process of releasing harmful  
Figure 11: Comparison of CO transport laws between a flat tunnel and a V-shaped tunnel: (**a**) CO concentration of a flat tunnel [31]; (**b**) CO concentration of the V-shaped tunnel model.  
gases near the working face [21], and this conclusion remains applicable to the ventilation of V-shaped tunnels.
## 2 Materials and Methods
### Data Preprocessing  
Two-dimensional slices of the fracture geometry were selected perpendicular to one of the major axes and fracture directions, in the \(xy\) and \(zy\) planes. This plane was chosen to obtain as much fracture geometry shape as possible. A slice of this geometry can show the roughness pattern of the fracture as well as the aperture size of the fracture, which has the geometry information of the fracture while reducing the information that needs to be processed by the model later. These slices have dimensions of 200 \(\times\) 200 pixels and were stacked to form a synthetic composite image and saved in lossless image file format. This image was used as the input feature map for the model. An illustration of input preprocessing is shown in Figure 7.  
This approach was applied to convert the input data into a format commonly used in the CNN while making it easier to store and read. This also reduced computational load when processing input data while still retaining its geometry information needed for predicting its parameters, although using the entire three-dimensional geometry as an input could provide more information and improve the model capabilities.
## 2 Geological Background  
The study of the HPHT zone is conducted using the data from Sepat Field located in Malay Basin (Figure 2). Malay Basin had experienced three major structure movements, involving extension, thermal subsidence, and basin inversion (Figure 3). This basin originated by extension during Late Eocene to the Early Oligocene and subsequently by thermal subsidence during Miocene to recent [13]. Sedimentation during Miocene was accompanied by structural inversion that caused the growth of major east-trending anticlines in the axial part of the basin. Exploration activities in the Malay Basin had started in the late 1960s, which resulted in discoveries of several fields. Most of the explorations targeted the conventional play type in Upper Miocene clastic of Group D, E, and Top F. The play in these groups is commonly situated within the hydrostatic to slightly overpressured zone; usually at the upper region of the pressure ramp up. Depositional environment for Group A, B, and D is interpreted as fluvial and delta front prostaglandin sand, Group E as lower coastal plain, Group F as delta front to shallow marine. According to [PERSON] and [PERSON], the sediments of Group H are described to be marine delta owing to their general correlation to the global sea-level curve [14]. The hydrocarbon accumulations in this basin are mostly trapped within the east to west faulted anticlines in Group E, characterised by siliciclasticsprone. The previously discovered hydrocarbon forms are generally gas with condensates, and some oil rims have been observed.  
Figure 3: Regional stratigraphic column of the Malay Basin and the reservoir target which is highlighted by a red rectangle. The study is focused on a reservoir target in Group H (modified from [13]).  
Figure 2: Geographical map of Malay Basin.