esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study proposed an enhanced information retrieval method based on ontology to enhance the efficiency of bridge inspection. The automatic mapping technology was introduced into the seven-step method to quickly build the Blontology. A bridge inspection prototype system was developed to realize the proposed IR method. The effectiveness of the proposed method was validated by two case studies. The conclusions can be summarized as follows:  
1. Compared with the keyword-based IR method, the proposed ontology-based IR method can be used to correctly predict the target element to be inspected. The bridge inspection case demonstrated that the performance of our prediction algorithm based on the proposed IR method is better than that of the context-aware computing method [21].
2. The improved seven-step method based on automatic mapping technology can make full use of the relational data in a BMS and can promote the construction of the Blontology from manual to semi-automatic. The construction efficiency of the Blontology can be greatly improved compared with the traditional manual method. The proposed method has a wider application scope than the existing semi-automatic method [13].
3. The proposed software architecture can be used to integrate ontology, and the developed prototype system based on this architecture can be utilized to realize the ontology-based IR method. The software architecture and the prototype system deepen ontology application in bridge engineering and promote the relevant research from a conceptual stage to a practical stage.  
It is noteworthy that due to the lack of the spatial relationships in the relational database of our BMS, the spatial relationships were manually added to the Blontology, which is time-consuming. This problem is expected to be solved by ontology generation methods [13; 43] from IFC models. Automatic extraction of spatial relationships from an IFC model will be left as part of our future work. Recently, the ontology building method based on the knowledge-type-aware framework has attracted wide attention. We will try to adopt the method to improve the build process of bridge ontology in the future.  
Conceptualization, Y.Z. and P.L.; funding acquisition, P.L.; methodology, Y.Z. and P.L.; software, Y.L.; validation, Y.Z. and S.L.; visualization, G.L. and S.L.; writing--original draft, Y.Z.; writing--review and editing, Y.Z. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant number: 51878059).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 2 Related Works  
### Osteoporosis  
Osteoporosis is a silent disease that may not cause pain before fractures occur, but as a person ages, their bone mass continually decreases in the body. Osteoporosis reduces the body's bone mineral density (BMD) and causes fragility of the bones in the hip, spine, and wrist. It also leads to fractures and other complications (e.g., intervertebral compression fracture, hip and wrist joint fracture, etc.), which is a disease of bone metabolism [8]. Bone is a self-renewing, active tissue. In the process of maintaining bone health, the body continuously breaks down old bone and replaces it with new bone tissue. During childhood and adolescence, new bone formation occurs rapidly, resulting in larger BMD values, which reach their peak around the age of 20. In the following 7 to 10 years, the rate of new bone production is about the same as the rate of decomposition of old bone, and the adult skeleton reaches complete renewal [9]. However, when people reach about 40 years old, the rate of bone mineral increase slows down. At this point, the rate of decomposition of bone is greater than the rate of new bone production, and the shell of the bone becomes thinner, making it more fragile.  
In bone density examination, DXA calculates BMD as a direct reference indicator, and the lower the value of BMD, the more likely you are to get osteoporosis. Another commonly used indicator is the T-score, which is regarded as an extension of the BMD calculation as a basis for assessing the presence or absence of osteoporosis. The T-score is calculated by dividing the difference between the BMD calculated by a specialized instrument such as DXA and the expected young normal (YN) by the standard deviation (SD) of the BMD in young people [10]. The T-score's formula is defined as the following Equation (1):  
\[\text{T-Score}=(\text{BMD}-\text{YN})/\text{SD} \tag{1}\]  
According to the standard definition of the World Health Organization [11], a T-score greater than \(-1\) indicates normal bone mass, less than or equal to \(-1\) or greater than \(-2.5\) indicates low bone mass, and less than or equal to \(-2.5\) indicates osteoporosis. As humans age, bone loss is inevitable, so when the age gradually increases, the two numerical indicators of bone density and T-score will be lower than those of young people. In the clinical diagnosis of osteoporosis in the hip, the diagnosis of osteoporosis is based on a DXA report of the femoral neck and the total hip.
## 3 Results and Discussion
### Auger Electron Microscopy (AES) Surface Characterization  
#### 3.1.1 Auger Electron Microscopy (AES) Spectra Analysis  
To obtain Auger spectra information about the FDTS SAM film pattern, a high spatial resolution secondary electron SEM image of an area 50 \(\upmu\)m by 50 \(\upmu\)m was taken as shown in Figure 2a. Three areas on the silicon wafer were selected: two dark areas, Area1 (red rectangle) and Area3 (blue rectangle) within the FDTS SAM film pattern, and one light area, Areas2 (green rectangle) within the silicon dioxide pattern. As shown in Figure 2b, the four elements C, O, Si, and F were selected to perform Auger electron detection. In the two dark areas, conspicuous fluorine KLL Auger electrons (F1) were detected at 648 eV. For oxygen elements, O1 and O2 KLL Auger electrons were detected at 479 eV and 502 eV. For carbon element, C1 KLL Auger electrons were detected at 271 eV. For silicon elements, Si1, Si2, Si3, and Si4 KLL Auger electrons were detected at 59 eV, 1501 eV, 72 eV, and 1602 eV, respectively. We selected C1, O1, F1, and Si2 as the typical element states to characterize the FDTS SAM patterns. As shown in Figure 2c about atomic concentration (AC%) of the four elements, concentrations of fluorine KLL Auger electrons on Area1 (11.07%) and Area3 (10.03%), containing FDTS SAM patterns, were much higher than that of the fluorine KLL Auger electrons (0.36%) on Area2 containing silicon dioxide patterns. As for carbon element, concentrations of carbon KLL Auger electrons on Area1 (27.91%) and Area3 (26.37%), containing FDTS SAM patterns, were higher than that of the carbon KLL Auger electrons (14.11%) on Area2 containing silicon dioxide patterns. As for oxygen element, concentrations of oxygen KLL Auger electrons on Area1 (37.60%) and Area3 (35.81%), containing FDTS SAM patterns, were lower than that of the oxygen KLL Auger electrons (52.68%) on Area2 containing silicon dioxide patterns. As for silicon elements, concentrations of silicon KLL Auger electrons on Area1 (23.42%) and Area3 (27.78%), containing FDTS SAM patterns, were lower than that of the silicon KLL Auger electrons (32.85%) on Area2 containing silicon dioxide patterns.  
Carbon and oxygen KLL Auger electrons were detected on all three areas, which usually happens when there are carbon and oxygen contaminants on the sample surface from the ambient environment, even on the silicon dioxide surface. Silicon KLL Auger electrons were detected in all three areas because the detection depth of AES is about 7.0 nm for this tool (ULVAC-PHI 710, Japan), which is much larger than the FDTS film thickness of 1 nm [30]. Therefore, the silicon element information underneath the FDTS SAM film was also collected in the two dark areas within the FDTS SAM film patterns, as well as the light area within the silicon dioxide pattern.
## 2 Materials and Methods
### Decision Trees, Proxy Geometry and Semantic Scene Segmentation
Figure 4: In this Figure, in each row, we present the triplet of the input image corresponding to semantic segmentation and visualization of the Agent’s position (white capsule shape) towards the blue platform. (**a**) Input image. (**b**) Semantic segmentation of the input image. (**c**) Bird-eye view of the scene. (**d**) Input image. (**e**) Semantic segmentation of the input image. (**f**) Bird-eye view of the scene. ```
Data: Input: \(I\)--image, \(NN\)--neural network model computed with PPO (see Section 2.1), \(S\) definition of all semantic classes that are possible to find in input images. Result: F, C--a matrix of features and classification results of NN for image \(I\). // Perform classification of input image with NN \(C\gets NN(I)\); // Perform semantic segmentation of \(I\), \(I\) and \(I^{s}\) have same resolution. \(I^{s}\leftarrow\textit{SemanticSegmentation}(I)\); // Initialize empty list of features \(F\) gets\(\varnothing\); // For each output of the network for\(c\in\mathbb{C}\)do // Compute GradCAM of input image \(m^{c}\gets M^{c}(I)\); // Interpolate values of \(m^{c}\) to match the resolution of \(I_{s}\) // (we use nearest neighbour interpolation). \(m^{c}_{resized}\gets Interpolate(m^{c},I_{s})\); // Initialize matrix of features \(F_{c}\) \(F_{c},borders_{n},borders_{m}\gets Algorithm\ 1(n,m)\); // Split \(I^{s}\) into regions according to \(borders_{n},borders_{m}\) // \(I^{s}_{splitted}\) has exactly the same number of segments as \(F_{c}\) \(I^{s}_{splitted}\gets Split(I^{s},borders_{n},borders_{m})\) // Split \(m^{c}_{resized}\) into regions according to \(borders_{n},borders_{m}\) // \(m^{c}_{splitted}\) has exactly the same number of segments as \(F_{c}\) \(m^{c}_{splitted}\gets Split(I^{s},borders_{n},m^{c}_{resized})\); // For each segment in \(I^{s}_{splitted}\) \(i=1\); // Iterate through all indexes of \(I^{s}_{splitted}\) and \(m^{c}_{splitted}\) while\(i\leq\#I^{s}_{splitted}\)do // For each semantic class in S for\(s\in S\)do // Count all pixels in image segment that belong // to class s, the sum is weighted by \((m^{c}_{splitted})^{\alpha}\) // with threshold \(t\) for\(j,k\in I^{s}_{splitted}\)do \(F_{c}[i,s]+=\left\{ \begin{array}{ll}if&(m^{c}_{splitted}[j,k])^{ \alpha}<t,I^{s}_{splitted}[j,k]\in s&then&(m^{c}_{splitted}[j,k])^{ \alpha}\\ else&0\end{array} \right.\)  end if // Scale features among i, to be at range [0,1] for\(s\in S\)do \(F_{c}[i,s]\leftarrow\frac{F_{c}[i,s]}{\sum_{j}F_{c}[i,s]}\)  end for // Append features to list of all features \(F\gets F\cup F_{c}\);  end for return\(F_{c}\)
```  
**Algorithm 2**Generate image features using proxy geometry
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 5 Spacecraft-Relative Navigation Model
### Relative Dynamics  
The chief and deputy spacecraft move along in Earth-centered orbits, as shown in Figure 5. The relative position vector of the deputy spacecraft with respect to the chief spacecraft is \(\boldsymbol{\rho}=\boldsymbol{r}_{d}-\boldsymbol{r}_{c}=\left[x,y,z\right]^{T}\) in the LVLH frame based on the chief spacecraft. The nonlinear dynamics of the relative motion between them are described with Tschauner-Hempel (T-H) equations [37]. Then, the relative dynamics are calculated using the following equations:  
\[\ddot{x}=2\dot{v}\dot{y}+\dot{v}y+\dot{v}^{2}x-\frac{\mu\left(r_{c}+x\right)}{ \left[\left(r_{c}+x\right)^{2}+y^{2}+z^{2}\right]^{3/2}}+\frac{\mu}{r_{c}^{2}} \tag{44}\]  
Figure 5: Definition of related coordinate frames.  
\[\ddot{y}=-2\dot{v}\dot{x}-\ddot{v}x+\dot{v}^{2}y-\frac{\mu y}{\left[\left(r_{c}+x \right)^{2}+y^{2}+z^{2}\right]^{3/2}} \tag{45}\]  
\[\ddot{z}=-\frac{\mu z}{\left[\left(r_{c}+x\right)^{2}+y^{2}+z^{2}\right]^{3/2}} \tag{46}\]  
where \(\mu\) is the gravitational parameter of Earth, \(v\) denotes the true anomaly, and \(r_{c}\) is the orbit's radius. They are described by \(\ddot{v}=\frac{-2\dot{r}_{c}\dot{v}}{r_{c}}\) and \(\ddot{r}_{c}=r_{c}\dot{v}^{2}-\frac{\mu}{r_{c}^{2}}\), respectively.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 3 Model Introduction and Problem Analysis
### Task Model  
This section discusses a dependent task model. We assume that in the satellite working stage, a satellite node can achieve a remote sensing image online reasoning task [14]. The image reasoning task can be divided into several dependent subtasks. Each subtask can be assigned to different satellites for AI online analysis according to the satellite channel conditions and computing power. The division method for dependent subtasks [26] and AI on-orbit analysis [27; 28] have been investigated to some extent, but they are not the topic of this paper. Hence, they are not discussed in detail.  
Figure 2: Satellite Communication Model.  
As shown in Figure 3, we use a typical DAG to represent the subsequent research's subtask dependence model. The DAG can be expressed as \(\varphi=(O,E)\). \(O=\big{\{}o_{1},\cdots,o_{p}\big{\}}\) represents \(p\) subtask nodes, and \(E=\big{\{}e_{ij}|(i,j)\in p\big{\}}\) denotes the set of directed edges in DAG. Furthermore, we define any subtask \(o_{i}(i\in p)\) as \(o_{i}=\{D_{i},\zeta_{i}\}\). \(D_{i}(Mb)\) and \(\zeta_{i}(CPUcycle/Mb)\) indicate the quantity of computation and computational complexity of subtasks, respectively. When the subtask \(o_{i}\) calculation is finished, the calculated result data quantity \(RDo_{i}\) must be transferred to the subtask \(o_{j}\), as indicated by the weighted directed edge \(e_{ij}=RDo_{i}\).  
At the same time, we represent the precursor subtasks set of the subtask \(o_{i}\) as \(PRE_{i}\). The subtask \(o_{i}\) is authorized to begin computation when all the subtasks in \(PRE_{i}\) have been completed and the appropriate calculation results have been successfully sent to the satellite node where the subtask \(o_{i}\) is situated.  
According to the above analysis, the task scheduling strategy in the LEO satellite network is the mapping scheme from task graph DAG to the \(SatTEG\).
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 5 Experiment
### Results  
The results are presented in Figure 13. As shown, the measured self-noise values exhibit excellent agreement with the theoretical and simulated results, particularly above 0.05 Hz, where the alignment is especially strong.  
However, in the low-frequency range, the agreement is less precise. This discrepancy may be attributed to several factors: (1) potential deviations between the instrument response parameters used in the model and their actual values in the low-frequency range, and (2) external influences such as pressure and temperature variations affecting each instrument differently, making it challenging to completely eliminate environmental noise using the three-channel coherence method.  
The measurement uncertainty of the self-noise results was primarily estimated based on the standard deviation of repeated measurements. Table 3 presents the uncertainties and confidence intervals for the self-noise values at selected representative frequencies. The confidence intervals were computed at a 95% confidence level.  
Figure 12: Experimental setup.  
In this experiment, we analyzed the total noise of the seismometer, providing indirect validation of our noise transmission model. However, this approach does not directly verify the transmission characteristics of individual noise sources. A more rigorous validation would require isolating and testing each module within the system. At present, practical limitations--primarily the potential damage to the instrument from disassembly, which could compromise measurement accuracy--make such experiments infeasible.  
Here, we outline a strategy for future experimental validation. Under ideal conditions with minimal environmental interference, noise injection techniques can be used to isolate and study the transmission characteristics of individual modules. For each module, noise would be introduced at the input or output of the module itself using a low-noise signal generator while maintaining the rest of the system unchanged. By observing the system's output and converting it to an EIN, the change in EIN before and after injection can reveal the module's noise transmission characteristics. This approach offers a precise method for assessing each module's noise transmission characteristics.
## 1 Introduction  
Digital pathology has been actively implemented in recent years at many hospitals in Korea as well as in other countries around the world. Implementation of these systems has been facilitated due to technological developments in whole-slide imaging, file compression, data streaming, fair storage server costs, and connection of worldwide Internet networks [1, 2, 3]. Many software programs for computer-aided diagnosis (CAD) have been developed, and clinical use of such software has been made feasible by the advent of deep learning technologies in the image processing domain [4, 5].  
There have been many discussions about the importance and effectiveness of digital pathology and CAD during the process of implementing digital pathology. However, during the implementation process, the order of implementation or the parts that are focused on may differ by country or region since each location has different medical systems, hospital systems, and degrees of expertise among pathologists [6; 7]. Thus, a variety of strengths and concerns about digital pathology should be considered and carefully designed location-specific approaches used when implementing strategies to satisfy unmet needs. Nevertheless, practical surveys of pathologists regarding these unmet needs are limited.  
Although white papers and reviews have been published by hospital sites utilizing digital pathology, information on pathologists' recognition of and demand for digital pathology remains sparse. Furthermore, most pathologists lack the opportunity and experience to discuss or comment on CAD because there is little research on the development of CAD systems that are actually being used in the diagnostic process.  
Recent research has demonstrated the viability of CAD software for prostate cancer diagnosis, which is one of the few medical fields where practical technologies for digital pathology are being introduced at an increased pace [8; 9]. Indeed, a prostate needle biopsy is one of the areas where CAD is being developed at the highest level; however, there are significant discrepancies among pathologists, and prostatic cancer is a subject that requires considerable effort for diagnosis [10; 11]. Thus, CAD targeting prostate needle biopsy is a good example to provide pathologists with an opportunity to experience CAD software and to evaluate their perceptions of CAD in detail. In addition, almost all Korean pathologists share the same experiences with regard to prostate cancer diagnosis, and we have developed an artificial intelligence (AI)-based diagnostic assistant software for a prostate needle biopsy that is currently undergoing a clinical trial called PROMISE-P.  
We conducted an online survey of Korean pathologists to investigate their perceptions of and demand for digital pathology and CAD. At the time of the survey, a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD.
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 3 Design
### Design Goals  
The design goals of NetAP include improving deployability and obtaining the optimal polling interval for each VM, which are explained, as follows:  
First, to improve deployability, NetAP only modifies the vHost thread of the host system. If we modify the front-end device driver of each guest OS, the user may experience an inconvenience from re-compiling the front-end driver and re-installing the driver module into the guest OS. Therefore, by modifying only the vHost thread of the host OS, we preserve the interface with an unmodified front-end driver.  
Second, NetAP tries to obtain the optimal interval for each VM to reflect its individual workload characteristics. A vHost thread is dedicated to its own VM and it fetches network requests from its dedicated shared queue. Because each VM generates and consumes packets at a different speed, each vHost thread needs to have different polling intervals. Therefore, we apply a novel algorithm to determine the polling interval for each vHost thread.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Technologies
#### 3.1.6 Satellite  
A satellite is a communications system that can pick up signals from the Earth and retransmit them using a transmitter and receiver. Satellite is positioned high above the Earth's atmosphere. Depending on the use, satellites circle the planet in a variety of ways. Nowadays, satellites are used in weather forecasting, telecommunication, and geo-positioning. Satellites are used to analyze the region where normal access is impossible; in the near past, satellites sent images of the solar system.
## 3 Test Results and Analysis
### Mechanical Properties  
For the qualitative analysis of the degradation of the mechanical properties of RAC and Steel-RAC members after exposure to high temperatures, according to the axial load-displacement curves of prism specimens and short columns, the load-span deflection curves of bending and shear beams, the corresponding mechanical properties of various types of members, such as bearing capacity \(N_{\text{P}}\), peak deformation \(\Delta_{\text{P}}\), displacement ductility coefficient \(\mu\), and energy dissipation coefficient \(\eta\) are determined. The variation rules of various mechanical properties are shown in Tables 2-6 and Figures 13-16. In order to compare and analyze the performance degradation of different members, the performance indexes of all specimens with different replacement percentage at the same temperature were averaged, and normalized according to performance indexes at room temperature. The degradation coefficients (\(\beta_{N}\), \(\beta_{\Delta}\), \(\beta_{K}\), \(\beta_{\mu}\), and \(\beta_{\eta}\)) were obtained. Similarly, the replacement percentage influence coefficients (\(\alpha_{N}\), \(\alpha_{\Delta}\), \(\alpha_{K}\), \(\alpha_{\mu}\) and \(\alpha_{\eta}\)) of each performance are defined based on the performance index of the replacement percentage of 0%.
## 2 Modeling Fault Detection and Fault Correction Processes
### Considering the Fault-Detection Process and Fault-Correction Process Together  
Most existing SRGMs only focus on describing the behavior of the fault detection process and assume that faults will be fixed instantaneously upon detection, but actually most latent software faults may remain uncorrected for a long time due to the complexity of software systems and incomplete comprehension of software by the testers and learning process even after they are detected.  
Suppose \(m_{c}(t)\) denotes the mean value of corrected faults by time \(t\), and assume that the mean number of faults corrected in the time interval (\(t\), \(t+\Delta t\)) is proportional to the mean number of detected but not yet corrected faults remaining in the software system. The MVF \(m_{c}(t)\) can be expressed in terms of the following equations:  
\[\frac{\mathrm{d}m_{c}(t)}{\mathrm{d}t}=\mu(t)[m(t)-m_{c}(t)] \tag{4}\]  
where \(\mu(t)\) is the fault correction rate, and \(m(t)\) is shown in Equation (3).