esa-sceva/satcom-chunk-collection huggingface dataset:
# Prognostics of Electromechanical Actuator with Partial Time Scaling Invariant Temporal Alignment
## Appendix A Detailed Algorithms
### Advection from Source to Target Time Series, Section 5  
```
0:\(\hat{X}_{j}^{l}\) et \(\hat{Y}_{j}^{h}\) are the source and target points nnAssignmentArray is the array of the closest neighbors from \(X\) to \(Y\) projSrcWIndex is the array of the projected source with the associated indices projTargetWIndex is the array of the projected target with the associated indices sigmoid contains two hyperparameters for the displacement, one for each dimension
0:\(\hat{X}_{\mathcal{T}}\) is the cloud of source points transported on the target advectCost represents the movement cost, i.e., movement from source to target
1:functionAdvection(\(\hat{X}_{j}^{l}\), \(\hat{Y}_{j}^{h}\), nnAssignmentArray, projSrcWIndex, projTargetWIndex, sigmoid)
2: advectCost \(\leftarrow\) 0
3: cumXDisplacement \(\leftarrow\) 0
4: distArray integer array of length(\(\hat{X}_{j}^{l}\))
5:for\(i\gets 0\) to length(projSrcWIndex) do
6: xtarget \(\leftarrow\)\(\hat{Y}_{j}^{h}\)[projTargetWIndex[nnAssignmentArray[i]]]_{x}\)
7: ytarget \(\leftarrow\)\(\hat{Y}_{j}^{h}\)[projTargetWIndex[nnAssignmentArray[i]]]y\)
8: xsrc \(\leftarrow\)\(\hat{X}_{j}^{l}\)[projSrcWIndex[i]]_{x}\)
9: ysrc \(\leftarrow\)\(\hat{X}_{j}^{l}\)[projSrcWIndex[i]]_{y}\)
10:
11: /* Compute Euclidean distance between source and target points */
12: distArray[\(i\)] \(\leftarrow\)\(\sqrt{\left(\text{y}_{\text{target}}-\text{y}_{\text{src}}\right)^{2}+\left( \text{x}_{\text{target}}-\text{x}_{\text{src}}\right)^{2}}\)
13: /* NB: Sigmoid function defines the distance ratio to keep */
15: xdisplacement \(\leftarrow\)\(\text{sigmoid}[0]\) \(\times\) (xtarget \(-\) xsrc)
16: ydisplacement \(\leftarrow\)\(\text{sigmoid}[1]\) \(\times\) (ytarget \(-\) ysrc)
17: cumXDisplacement \(\leftarrow\)\(\text{cumXDisplacement}+\text{x}_{\text{displacement}}\)
18: \(\hat{X}_{j}^{l}\)[projSrcWIndex[\(i\)]]_{x}\) \(\leftarrow\)\(\hat{X}_{j}^{l}\)[projSrcWIndex[\(i\)]]_{x}+\text{x}_{\text{displacement}}\)
19: \(\hat{X}_{j}^{l}\)[projSrcWIndex[\(i\)]]_{y}\) \(\leftarrow\)\(\hat{X}_{j}^{l}\)[projSrcWIndex[\(i\)]]_{y}+\text{y}_{\text{displacement}}\)
20: endfor
21: advectCost \(\leftarrow\) advectCost \(+\left(\sum\limits_{i}\text{distArray}[i]\right)/\text{length}(\hat{X}_{j}^{l})\)
22: \(\hat{X}_{\mathcal{T}}\)\(\leftarrow\)\(\hat{X}_{j}^{l}\)
23: return advectCost, \(\hat{X}_{\mathcal{T}}\)
24:endfunction
```  
**Algorithm 3** Advection from the source to the target
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 7 Conclusions  
In this paper, the cooperative content services in an integrated satellite-terrestrial network are studied, where the inter-satellite and satellite-terrestrial cooperative caching is considered. The optimization problem is formulated to minimize the total deployment costs of each user service, which is viewed as the weighted sum of storage resource usage and network bandwidth consumption. The CCRA algorithm based on a neighborhood search is proposed to find a feasible strategy, as well as simplifying the system complexity.  
The experiments are conducted for three different scenarios of content services, where it can be shown that the content services based on cooperative caching can outperform the content services based on edge caching and the cloud. Furthermore, the performance is evaluated for three different algorithms of CCRA, Greedy, and BFS, where the performance for CCRA is better than that of Greedy and BFS. Additionally, considering that the content services for users may be provided by the satellites that are far from the access satellite as the content sharing approach is given priority for all the satellite in a neighboring sub-network, the average bandwidth costs of each user service for CCRA may be more than that of Greedy and BFS. However, it is shown from the performance evaluation that the proposed CCRA algorithm is effective and efficient in providing the cooperative content services for users in an integrated satellite-terrestrial network.  
**Author Contributions:** Methodology, X.G. and Y.S.; Software, H.Z. and Y.L.; Resources, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**M**} & \multirow{2}{*}{**Q**} & \multicolumn{4}{c}{**Storage Costs per User (Mbits)**} & \multicolumn{4}{c}{**Bandwidth Controls per User (Mbits)**} & \multicolumn{4}{c}{**Total Deployment Costs per User**} \\  & & \multicolumn{4}{c}{**Greedy**} & \multicolumn{4}{c}{**BFS**} & \multicolumn{4}{c}{**CCRA**} & \multicolumn{4}{c}{**BFS**} & \multicolumn{4}{c}{**CCRA**}
## 11 An Overview of the Operational Performance  
A map and full list of events that necessitated dissemination of tsunami bulletins by KOERI-RETMC since its operationalization as a candidate tsunami service provider in 2012 are provided in Figure 9 and Table 2, respectively. In 34 out of the total of 38 events, tsunami bulletins were disseminated successfully. From those successfully sent bulletins, 31 bulletins were issues at the correct alert level with respect to the decision matrix. From those, 23 bulletins were at the 'information' level, 6 at the 'advisory' level and 5 at the 'watch' level. In 4 events, 3 messages in 2012 were not disseminated because of exceedance of the target message dissemination time of 15 min in accordance with the SOPs defined at that time, while 1 message (18 May 2020 Crete 5.7 Mw) was not disseminated due to a duty officer error. The average message dissemination latency was 9.6 min. The average magnitude difference between the magnitude issued by KOERI-RETMC in the tsunami bulletin and the USGS final magnitude was 0.2. The average difference in the epicentral location, hypocentral depth and hypocentral distance between KOERI and USGS solutions remained satisfactory at 14 km, 7 km and 18 km, respectively.  
Figure 8: **(left)** Distribution of maximum water surface elevation due to 3 h simulation of the submarine landslide generated tsunami triggered by a Mw 7.4 scenario earthquake (corresponding fault line is shown as a white bar) in NEAMWave17 [27]. **(right)** Initial tsunami conditions, distribution of maximum wave height and wave travel times for the KOERI–NOA NEAMWave21 scenario [28].  
As in all systems in the operational environment, KOERI-TSP has also experienced some erroneous actions and outages during its history. In 2014, two erroneous earthquake notifications were sent to KOERI's subscribers for events outside its service area, namely in the Southern Indian Ocean and Vanuatu Islands, respectively. A similar problem was again encountered in 2015 for an earthquake in the South Sandwich Islands. A monthly communication test was sent erroneously on the wrong day in October 2015. A scenario-based exercise message concerning an earthquake in the Jordan-Syria region was sent erroneously during daily system tests in January 2018. Due to an infrastructure problem effecting primary and backup communication channels, KOERI-TSP services were interrupted for 14 h during November 2019. A similar problem was experienced in July 2021, resulting in a service outage of 6 h. In July 2021, a 'cancellation' message was sent out erroneously as a real message by the duty officer during a regular internal tsunami exercise.
## 2 Materials and Methods
### Pilot System
Process control was managed using ProCoDa (version 3.0.0.12), an open-source software developed by the AC group, built on the National Instruments LabVIEW platform (LabVIEW 2019 SP1, 64-bit). It supports signal acquisition, basic computation, and actuator communication. Data acquisition was performed using a National Instruments NI 6008 card (National Instruments, Austin, TX, USA), modified in-house to produce 4-20 mA output signals on two analog channels and collect voltage inputs on eight analog channels. Turbidity was continuously monitored at three stages--raw, clarified, and filtered water--using HF Scientific MTOL + online turbidimeters. Sampling was driven by a Cole Parmer 7523-70 Masterflex L/S peristaltic pump (Cole Parmer, Chicago, IL, USA) with three stacked Easy-Load 900-1315 pump heads,ensuring constant and uniform flow through all turbidimeters. Each channel was sampled at 1-s intervals.
# Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment
## 3 Experiment and Analysis
### Dynamic Environment  
In this section, we compare the proposed DIAPF method to the ROS [26] path plan package which employs the DWA and A* algorithm. The experimental environment includes four static obstacles and a moving obstacle. As can be seen from Figure 13, the ROS uses A* algorithm to plan a global path and uses the DWA algorithm to handle nearby obstacles in the local map. If the speed of the moving obstacle is slow, the robot can avoid the obstacle directly as shown in Figure 13a. However, if the obstacle moves fast, the robot needs to re-plan the path according to the position of the obstacle. The global path also needs to replan according to the local path changes as shown in Figure 13b. In contrast to the ROS method, the proposed DIAPF can make decisions in advance according to the relative velocity between the moving obstacle and robot as shown in Figures 14 and 15. The planned path using the DIAPF is more efficient than the one using the ROS module as seen in Figure 16, which shows the changes of the distance between the robot and the target point. The DIAPF shows a linear decrease of the distance while the ROS shows a curve that decreases first and then increases due to a re-planned path.  
Figure 14: The robot uses the DIAPF approach to avoid a fast moving obstacle in the dynamic environment: (a) before avoiding; (b) avoiding the dynamic obstacle; and (c) finish avoiding.  
Figure 13: The robot uses path planner in robot operating system (ROS) to avoid a (a) slow and (b) fast moving obstacle in the dynamic environment.  
Figure 15: The robot uses the DIAPF approach to avoid a slow moving obstacle in the dynamic environment: (a) before avoiding, (b) avoiding the dynamic obstacle; and (c) finish avoiding.
## 3 Proposed Vehicle Detection and Tracking Scheme
### Methodological Flow of Proposed Work  
Figure 1 shows the proposed scheme of vehicle detection and vehicle tracking, which basically works in two phases: first, the vehicle detection with YOLOv5 by generating the bounding box for each vehicle, and second, vehicle tracking with Deep SORT to track the detected vehicles by YOLOv5. The YOLOv5 algorithm detects vehicles in each frame of the video or image sequence (Algorithm 4). YOLOv5 is an object detection algorithm that directly generates predictions for the object's bounding boxes and their corresponding class labels in the input [56; 57]. It extracts appearance features from the detected vehicles using a pre-trained deep neural network, such as ResNet or VGG. These features will be used for matching and association in the tracking phase. The proposed method performs online tracking by associating the detected vehicles across frames using the Deep SORT algorithm. Deep SORT combines the appearance features extracted in the previous step with the motion information to associate and track the vehicles over time. The tracker updates the vehicle tracks and estimates their positions and velocities using a Kalman filter.  
The proposed method for vehicle detection and tracking operates in two phases. Firstly, it utilizes the YOLOv5 algorithm to identify the bounding boxes of vehicles in the target image. These perform the feature extraction and data aggregation using CSPNet and PANet, respectively, to enhance the object detection results. The output of the YOLOv5 is the bounding boxes, which will be the input to the Deep SORT for predicting the trajectories of the vehicles, where \(x_{i},y_{j}\) are the coordinates of the input image; p is the class probability; c is the confidence score; \(b_{box}\) is the bounding box; r is the region; \(z_{i}\) is the input image; and n is the number of images in the set.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 4 Experiments and Results
### Experimental Evaluation
#### 4.2.2 Qualitative Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(W_{R}=50\) m} & \multicolumn{4}{c}{\(W_{R}=100\) m} & \multicolumn{4}{c}{\(W_{R}=200\) m} \\ \hline  & **0.85/5** & **0.85/10** & **0.9/10** & **0.85/5** & **0.85/5** & **0.85/5** & **0.9/10** & **0.9/20** & **0.85/5** & **0.85/5** & **0.85/5** & **0.85/10** \\  & **(T)** & **(T)** & **(T)** & **(T)** & **(U)** & **(TU)** & **(T)** & **(T)** & **(T)** & **(U)** & **(TU)** & **(T)** \\ \hline median & 3 & 2 & 4 & 3 & 3 & 3 & 4 & 4 & 5 & 4 & 4 & 4 \\ mode & 4 & 3 & 4 & 3 & 3 & 3 & 4 & 5 & 5 & 4 & 4 & 4 \\ q1 & 3 & 2 & 3 & 3 & 3 & 3 & 3 & 4 & 4 & 4 & 4 & 3 \\ q2 & 3 & 2 & 4 & 3 & 3 & 3 & 4 & 4 & 5 & 4 & 4 & 4 \\ q3 & 4 & 3 & 4 & 4 & 4 & 4 & 4 & 5 & 5 & 5 & 5 & 4 \\ q4 & 4 & 4 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Extracted statistics from users’ responses in Q1 of the qualitative user evaluation, for the results depicted in Figure 2. q\({}_{\text{i}}\) denotes the \(i\)-th inter-quartile range. Figure 8: Tag clouds extracted from merged tiles that correspond to the extracted area that may has been characterized as _Koukaki_. Top-10 (most important) tags have been colored in shades of green, while less important ones in shades of red. The font size is proportional to tag frequency. Figure 7: Tag clouds extracted from merged tiles that correspond to the extracted area that has been characterized as _Filopapou_. Top-10 (most important) tags have been colored in shades of green, while less important ones in shades of red. The font size is proportional to tag frequency. Figure 10: Top-3 tags per geo-cluster resulting upon the application of the TF-IDF scheme described in Section 3.2, on the set of geo-clusters produced for \(W_{\mathrm{R}}=200\) m, \(S=0.85,L=5\), (T). Figure 9: Tag clouds extracted from the vernacular regions of: (**a**) Filoppapou (see Figure 7) and (**b**) Koukaki (see Figure 8). To facilitate readability, only top-50 tags are displayed (top-10 in shades of green, remaining in shades of red). The font size is proportional to tag frequency.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 3 The Proposed HMIPS
### Indoor Map Registration and Modeling  
#### 3.1.1 Indoor Map Preparing and Registration  
Indoor maps are the most fundamental material for developing a marker-based indoor positioning system. Because we use AR when servicing marker-based indoor navigation, we do not display an indoor map on the user UI. However, because activities such as the marker installation location are performed based on it, it is essential to prepare the indoor map. However, it may be practically impossible to prepare all indoor maps of the service area desired by users who want to use the proposed system as a system administrator. To solve this problem, we made it possible to replace the indoor map simply by registering an image. In other words, if a user cannot obtain an indoor map for a service area, if the user can simply draw an indoor layout this can be used to play the role of an indoor map. The indoor map is registered by assigning an ID for each floor of the building, and the movement between floors is displayed on the routing using link information between markers. Of course, the positioning accuracy in such a case cannot be expected to match that of an accurate indoor map; however it is adequate since the proposed system calculates the pixels of the registered image for positioning.  
After the indoor map registration has been completed, as shown in Figure 2, the user needs to use the planning tool to determine and mark the location of markers to place them on the registered indoor map. In the planning tool, a marker is registered as a node and has a unique ID. After the marker is registered, each node is connected using the link menu of the planning tool, and the link connecting a node is given a unique ID. When the node and link registration for each indoor map has been completed, the basic activity of the marker-based positioning is complete. In addition, the HMIPS provides interfaces to register the POI, such as elevators, escalators, toilets, and information centers. Users can easily register a desired POI and set it as a destination using this function.
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
###### Abstract  
High-dynamic-range (HDR) images are widely regarded as the ideal format for digital images due to their ability to accurately render a wider range of luminance values. Recently, research has focused on introducing data-hiding techniques to HDR images, but these studies often suffer from a low hiding capacity. In 2011, a steganography scheme was proposed, which utilizes homogeneity in RGBE (red, green, blue, and exponent) format, a popular HDR format, and results in cover images with only slight and ignorable distortions after embedding. However, the capacity of the scheme is limited, and their steganography process may raise suspicions due to the abnormal distribution of pixel values caused by the multiplication and division in the embedding process. There is no denying that security is always the main concern for steganography. A major potential problem became clear after a careful revisiting of the scheme. This paper presents an enhanced steganography scheme that improves embedding capacity by modifying non-embeddable pixels to become embeddable in cover images and avoids potential security weaknesses by using additional random numbers to alter pixel values. The proposed scheme improves the embedding capacity of HDR images while maintaining their visual quality and security against statistical analysis attacks. The experimental result shows that the capacity increases 10 times without visual distortion.  
high dynamic range (HDR); steganography; RGBE; steganalysis Article
## 2 Test Design and Scheme
### Test Equipment and Model Building  
#### 2.3.1 Test Equipment  
The test equipment includes a soil moisture sensor, a custom settlement measuring rod, a seepage channel, a total station, and an acquisition instrument.  
The soil moisture sensor model is RS485. The settlement measuring rod and seepage pipe were custom-made. The settlement measuring rod consists of a steel rod with a diameter of 0.8 cm and a 2 cm diameter disc welded at the bottom. The lengths of the rods were 100 cm (4 rods), 80 cm (4 rods), and 60 cm (4 rods), which were buried at the corresponding positions of each layer sensor. A reflection patch was attached to the top of the settlement measuring rod. The total station was used to monitor the settlement changes at each point during the experiment in real time. Figure 5 shows the schematic of the custom-made settlement measuring rod, seepage pipe, and total station. The pore water pressure data acquisition instrument used in the model test is JC-YBY-2001, produced by the Tokyo Institute of Measurement, Japan.  
Tables 2 and 3 show the technical indicators of the soil moisture sensor and JC-YBY-2001 data acquisition instrument, respectively.  
Figure 5: Test equipment. (**a**) Moisture sensor; (**b**) settlement measuring rod; (**c**) seepage pipe; (**d**) total station; (**e**) acquisition instrument.
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
## 4 Numerical Results  
In this section, we compare the proposed step-flipping algorithm and the existing flipping algorithm in terms of \(\beta\), the average number of iterations, and the BLER with different code lengths. Simulations are performed with additive white Gaussian noise (AWGN) channel and binary-phase shift keying (BPSK) modulation. The additional simulation parameters are listed in Table 4. The simulations for the (256, 128), (512, 256), and (1024, 512) polar codes are concatenated with 24-bit CRC and for (64, 32) polar codes with 11-bit CRC. The \(m\) CRC bits are attached to the information block, where \(m\) is the CRC remainder length, and all the \(K\) bits are sent into the error-correcting encoders.
# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 2 Proposed Method
### MRI-Net Structure
#### 2.2.1 Encoder-Decoder Structure  
In the traditional U-Net structure, each block in the encoder and decoder contains two convolution layers and a pooling layer, but frequent convolution and pooling will cause the image to lose more semantic information. As the depth of the network deepens, the phenomenon of gradient disappearance may occur. In order to avoid the gradient disappearing and speeding up network convergence, the traditional convolution module is replaced by the Resnet module. In addition, the convolution operation with a step size of 2 is used to replace the traditional pooling operation in order to reduce the loss of image information as much as possible. The feature map obtained by the convolution operation has the same dimensions as the feature map obtained by the traditional pooling method.  
Figure 4: Samples of random segmentation: (**a**) randomly extract 48 \(\times\) 48 patch image blocks from the preprocessed image and (**b**) the corresponding label from the gold standard image.  
Figure 6 shows the proposed Res-Block structure. Among them, dropout is used to randomly inactivate some neurons to prevent overfitting during model training; Batch Normalization (BN) is used to normalize the activation value of hidden layer neurons, which can prevent the gradient vanishing due to noise in the retina and can improve the expression ability of the model; each convolutional layer in the paper uses linear correction units for feature extraction. Relu can effectively reduce the complexity of the network and increase the convergence rate of the network. Its formula is as follows:  
\[F(x)=\left\{\begin{array}{ll}0&x\leq 0\\ x&x>0\end{array}\right. \tag{2}\]