esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 1 Introduction
The failure-cause in case A is detected prior to failure occurrence. In case B, the failure-cause is detected long after failure occurrence, and this results in realization of significant failure effects. In general, it is well known that losses incurred after failure occurrence are much greater when compared to those incurred prior to failure occurrence. Thus, the risk involved in case B can be considered the greatest under the assumption that the failure effect per unit time remains the same in case A. In this study, a time-dependent probabilistic approach is proposed considering the real situation that the loss increases as detection of a failure or its causes late, and the risk of the failure-cause is quantitatively evaluated based on this. Assuming a case function for detection time of a failure and its probable causes, an approximate loss function can be deduced for each failure-cause. Based on the value of the expected loss for each failure-cause, the risk priority metric (_RPM_) can be evaluated as the expected loss per unit time by multiplying the expected loss, which is determined by integration of loss function with its probability density function, and the occurrence rate of each failure-cause. Figure 1: Effect of failure-cause detection time on the loss of a failure: If a detection is later than a failure, the loss increases rapidly. This manuscript has been organized as follows. Section 2 describes the proposed TD-FMEA model. In Section 3, a comparative analysis is conducted with the proposed TD-FMEA approach and traditional FMEA based on the design FMEA case of automotive leaf springs. Finally, we will draw conclusions and make suggestions for future research in Section 4.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### Hardware Platform  
The hardware platform represents the ISA100.11a infrastructure and data acquisition equipment comprising one CDS VR950 Gateway and 70 CDS VS210 field devices. Figure 7 shows the hardware used in the experiment.  
Figure 4: The PEAR impact on network topology formation: phase 1. The device highlighted with the green square belongs to a low energy profile whereas the device belonging to a high energy profile is highlighted with a blue square.  
Figure 5: The PEAR impact on network topology evolution: phase 2. The device highlighted with the green square belongs to a low energy profile whereas the device belonging to a high energy profile is highlighted with a blue square.  
Figure 6: The PEAR impact on network topology evolution: phase 3. The device highlighted with the green square belongs to a low energy profile whereas the device belonging to a high energy profile is highlighted with a blue square.  
The VR950 and VS210 employ the VN210 radio module as the IEEE802.15.4 based ISA100.11a 2.4 GHz wireless transceiver unit. The VR950 Gateway implements the ISA100.11a standard management modules such as the system manager, security manager and backbone router on top of an embedded Linux distribution. Moreover, the PEAR solution is integrated within the system and security management modules enabling the energy balancing support. The VR950 board is lined powered and exposes two standard ethernet ports and a serial console for backend integration and redundancy functionalities.  
The VS210 is a development board implementing the ISA100.11a field device functionality including sensor data acquisition and wireless transmission. It has integrated temperature, humidity and pressure sensors whose values are acquired and transmitted every 30 s within a single data packet.
## 3 Service Development Methodology
### Job Posts Data Acquisition  
It was noted from the initial stages of this work that to build education analytics services like the ones presented in this publication, the presence of a sufficient amount of data on the labor market was of utmost importance. Given that such data were not available in the beginning, the priority was to extract them from several sources available online. Therefore, two services were developed to acquire job posts from job posting websites.  
The first service, titled JobCrawler, gets as input a job domain and a Greek job posting website and then crawls this website to gather job post data. Specifically, it performs a search for the given domain and accesses each result (job post), extracting all the relative information from it, including the description, the required skills, and the seniority level, among others. The extracted job posts are stored to an Elasticsearch engine [18] that can facilitate efficient and effective text processing and analytics. The technique of crawling web page data and extracting information out of them is called web scraping. Web scraping is performed through the Scrapy Python framework [19]. At the time of writing this publication, the supported job posting websites included Greek Indeed [20], Skywalker [21], and Kariera [22]. JobCrawler was the main tool utilized for job post acquisition in the context of this study, as the targeted education institution is located in Greece, and hence, mostly Greek job market requirements should be considered. However, it should be noted that at least in the software engineering domain, job market requirements tend to follow technological developments instead of localized geographical criteria, which means that the extracted requirements should represent software-related professions in countries other than Greece.  
JobWatch is another service that was developed to extract job posts from well-known job posting websites. However, it supports only the English language and uses APIs to receive job post data. In the context of this study, it was mostly utilized to reinforce the acquired information regarding job specializations, for which the amounts of data acquired by JobCrawler were insufficient to enable an accurate analysis.  
More than 1500 job posts were gathered for the software job market in Greece through the aforementioned services, to be used for further analysis.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 1 Introduction
However, the current _a priori_ ZTD models mentioned above can only provide ZTD with accuracy not better than 4 cm, which is not accurate enough for the real-time PPP [18]. The Numerical Weather Prediction models (NWP) can also be used for retrieving real-time ZTD. For example, [PERSON] et al. evaluated the accuracy of hourly ZTD derived from the Global Data Assimilation System (GDAS) numerical weather model (NWM) [19]. The average RMS of the GDAS NWM-derived ZTD is about 3 cm by comparing with IGS PPP ZTD at 18 IGS stations over 1.5 years. [PERSON] et al. made comparisons of ZTD between ECMWF analysis and IGS solutions over one month and found an average RMS of about 1.5 to 2.2 cm which depends on the latitudes [20; 21]. However, the NWP products are usually in the time resolution of several hours (e.g., typically from 1 to 6 h), which is not sufficient enough for high-rate real-time applications, especially, under some fast-changing weather conditions. With the development of PPP-RTK technique, the precise ZTD derived from regional Continuously Operating Reference Stations (CORS) network have been used to interpolate ZTD for users, providing more accurate real-time ZTD than _a priori_ empirical models. [PERSON] et al. proposed the modified linear combination model (MLCM) for zero-difference tropospheric interpolation [22]. [PERSON] et al. improved MLCM and addressed the spatial regression model with altitude difference constraint, with interpolated ZTD RMS of about 6-7 mm in flat areas and about 18 mm in undulate areas [23]. However, these models are only valid for users in regions with CORS network available, which is usually at the scale of a city or a province. In recent two years, there are also some publications focusing on wide-area real-time tropospheric product developments, such as [24]. They utilized real-time GNSS stations and IGGtrop ZTD models to generate grid-based ZTD/ZWD products. In this study, we will present a new inverse scale height model and apply it to real-time ZTD solutions at GPS stations at a national scale of China to generate the real-time ZTD grid product for real-time PPP users. The data sources and processing strategy will be described in Section 2, and the construction of the real-time ZTD grid product will be then depictedin Section 3. The real-time ZTD grid product will be evaluated by comparisons with post-processing ZTD and by PPP convergence tests in Section 4. Finally, some discussions and conclusions will be given in Section 5.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Qualitative Analysis of Multi-Object Tracking Performance
#### 3.6.2 Performance Analysis of Multi-Object Tracking  
To visually validate the performance of our multi-object tracking method in handling real-world tracking tasks, we present the test tracking results in MOT17 datasets, as shown in Figure 6.  
Figure 6 presents a qualitative comparison of the performance between our IOF-Tracker method and the two best-performing multi-object tracking methods currently available. As seen in the figure, BYTEtrack assigns the pedestrian labeled as 25 in frame  
Figure 5: Performance analysis of handling occlusion problems (Different colored target boxes represent different ID information).  
88 an ID of 88 after prolonged occlusion. In contrast, both our IOF-Tracker method and BoTSORT correctly identify the pedestrian as number 20 in frame 88 and maintain this ID after the long occlusion. This indicates that BYETrack undergoes multiple ID switches before frame 88 video information arrives and performs poorly in handling occlusions, while our IOF-tracker method and BoTSORT handle occlusion issues better in the tracking process. Upon comparing the newly appearing pedestrian target in frame 382, we find that BYETrack and BoTSORT assign pedestrian IDs of 139 and 98, respectively, whereas our IOF-Tracker method assigns the ID of 91. This suggests that while BoTSORT maintains longer continuous tracking than BYETrack, it still generates a significant number of IDs during the tracking process. In contrast, our IOF-Tracker method clearly addresses the issue of frequent ID switching during long continuous tracking. Therefore, our tracking method demonstrates significant effectiveness in handling occlusion during multi-object tracking in real-time scenarios and managing issues related to frequent ID switching.
## 2 Site Description  
Bartin is located in the Western Black Sea Region of Turkkiye (Figure 1). Rainfall-induced landslides are quite frequently observed in the city. Topography, climate and ground profile provides a suitable environment for rainfall-induced landslides. In the studies conducted in the past, it has been reported that sudden and multiple landslides occurred in the city, especially after the rainfall events in 1985, 1998 and 2016. The landslides in 1985 occurred in April as a result of snowfall in the winter season and the subsequent rains. The landslides in 1998 developed as a result of continuous rain on 20-21 May. The reason for the landslides which occurred in 2016 was heavy rain on 12-13 August. Some singular landslide cases were also observed at various times in other years. According to GIS-based studies carried out for Bartin by the Turkiye Disaster Affairs General Directorate, landslides that occurred in the city were concentrated in the Cretaceous flysch deposits, which are defined as the Ulus formation. According to Reference [44], the Ulus district of Bartin, which gave its name to the Ulus formation, is the district where the most landslide cases occur in Turkiye. As a result of landslides in the past, significant damage to the infrastructure and superstructure occurred, and many agricultural and forest areas became unusable.  
In order to contribute to the understanding of the hydro-mechanical behavior, a representative landslide area was chosen in Hisar village of the Ulus district of Bartin. Hisar village is 42 km from the Bartin city center and 24 km from the Ulus district center. The  
Figure 1: Location and general view of the Hisar landslide area (**a**) Türkiye, (**b**) Bartin, (**c**) Hisar landslide area.  
location and general view of the study area are given in Figure 1. The average slope gradient is 18\({}^{\circ}\). In the location of the examined landslide, some tension cracks, undulations and shear failure surfaces were observed on the topography. The failed mass is approximately 70-75 m wide and 100-110 m long, and it was moved down by translational-type failure. From the observations made in the field, it is estimated that the depth of the slip surface is between 4.5 and 5.0 m. There is no house nearby the landslide area, only a single-story abandoned house located at the foot of the landslide was damaged at various scales due to the landslide.
## 5 Predicted Results Using the Model  
### Results of Feature Importance  
To evaluate the feature importance of each node, the weighted reduction in \(MSE\) (i.e., the sum of reduction in \(MSE\) before and after splitting of features multiplied by the  
Figure 11: Validation results of backbone curve in modified and unmodified numerical models.  
proportion of sample number on each leaf node) was calculated. To compare the relative importance of each feature, the results of feature importance for all nodes were further normalized. The results of feature importance are presented in Figure 12. The detailed values of feature importance and the ranking order are given in Table 7.  
It can be concluded that the shear span ratio \(\lambda\) was the most important feature parameter that affected the three predictive models of \(DMC\), following by the stirrup spacing \(S\) and the concrete strength \(f_{c}^{\prime}\). For the three \(DMC\) models, differences were found as to the order of the 4 th to 6 th important features. Feature importance could be used to measure the contribution of each feature in influencing the results of the predictive models, which optimized the process of feature selection during model training [49]. It should be mentioned that for the random forest method, random seeds or model parameters may lead to different results of feature selection, indicating that the importance of features is dependent on the training set, including the distribution of data, the sample size and the randomness of the model.
## 1 Background  
### The New Urbanization \"Beyond Mere Growth\" in China  
Since 2014, the Chinese central government has promoted new-type urbanization in the _National New-type Urbanization Plan (2014-2020)_[1, 2, 3]. The direction of the plan \"beyond mere growth\" supports green and low-carbon development and requires avoiding urban sprawl, low efficiency in land use, the proliferation of useless urban districts, and waste of urban space in over-dimensioned infrastructures [4, 5]. Consistent with the goals of new-type urbanization, the national guidelines on urban planning released in 2016 prohibit expanding cities beyond what their natural resources can support, enforcing urban growth boundaries [6]. In other words, to be sustainable urban development should not expand beyond a planned threshold, as also required by the environmental protection laws issued since 2015. This limit to expansion is also known as the \"red line of China's cultivated land\", and was first drafted by the National Land Planning Outline issued by the Ministry of Land and Natural Resources in 2009, which also specifies that the impermeabilization of the ground should be diminished [7].  
These policies for preserving agricultural land are combined with the regulations for controlling land supply and the real estate market. Since 2003, the central government established year-by-year construction land development quotas for every local government according to the assessment of the local demand-supply situation [8, 9, 10].  
Together with the sustainability goals, the increase in the urbanization rate is a key national priority in China. It has been one of the most significant drivers of economic, political, and social progress since the reform was promoted at the end of the 1970s [11; 12; 13; 14]. So far, urban population growth has followed economic growth because economic development and city growth are strongly related to Chinese policies. The massive urbanization process has transformed millions of farmers into city dwellers, attracting an essential workforce into the expanding city [15; 16].  
These recent policies promoted by the Chinese government--on one side the goal of a growing economy and on the other side the limits to urbanization to protect fertile land and natural environments--can produce a contradiction and urban concentration: probable newcomers in growing cities and industrial districts versus a not expanding city.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 3 Flexible Packaging and Testing of Sensors
### Flexible Packaging Process for Sensors  
The flexible packaging process of the sensor was as follows:  
(1) A 0.5 \(\upmu\)m thick parylene-C layer was deposited on the surface of the sensor using vapor phase deposition. The specific deposition method was as follows: The parylene precursor material was heated to 150 \({}^{\circ}\)C, causing it to sublimate from a solid state to a gas phase. The resulting sublimated dimer gas was placed in a pyrolysis chamber, where the temperature was further increased to 650 \({}^{\circ}\)C. At this temperature, the bonds of the p-xylene dimer were broken, resulting in the formation of free radical monomers. Finally, the free radical monomers were deposited onto the sensor surface at room temperature, forming a thin parylene film.  
(2) The curing agent and PDMS base were added to plastic cups in mass ratios of 1:30, 1:35, 1:40, and 1:45, separately, to achieve the desired thickness at the bottom of the cups.  
(3) The PDMS mixture was stirred for about five minutes to ensure a uniform mixture.  
(4) The sensor was placed on the surface of the PDMS mixture in the plastic cup, allowed to stand for 15 min, and then placed it in a vacuum drying oven to vacuumize the mixture and remove any air bubbles.  
(5) The prepared PDMS mixture was poured onto the surface of the sensor, and a spin coater was used to spin-coat the mixture to a thickness of 0.3 mm, evenly coating the surface of the sensor.  
(6) The temperature of the drying oven was set to 80 \({}^{\circ}\)C and maintained for 2 h to cure the PDMS mixture.  
(7) The pressure sensor with the PDMS film covering the sensitive area was removed, appropriate adjustments were made, and the production was completed.  
The flexibly packaged sensor can effectively prevent sweat corrosion of the chip and adhere well to the skin, so the subjects do not feel uncomfortable during long-term measurement.  
The sensing mechanism of the sensor is based on the silicon piezoresistive effect. Specifically, when external pressure is applied to the surface of the sensor, the pressure is transmitted from the flexible film composed of PDMS and parylene to the sensor chip. Upon sensing the external pressure, the internal resistance of the sensor changes, resulting in a variation in the sensor output.
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Pre-Processing
#### 2.4.2 Camera Calibration  
Two main solutions for fisheye camera calibration are considered in this step: the chessboard camera calibration method and the self-calibration method. The chessboard camera calibration method, known as [PERSON]'s method (1998), utilizes planar chessboard images in various orientations to determine the camera's geometric model and lens distortion behavior through the calculation of camera calibration parameters. It assumes that the intrinsic parameters are constant during the relative orientation. On the other hand, the extrinsic parameters vary for each image in relative orientation. This process minimizes the re-projection error between the observed and predicted corner locations using a nonlinear optimization algorithm [6]. The accuracy and applicability of the imported calibration parameters depends on the quality of the source project photos, the camera stability, and the distance to the objects of interest [5]. The process of camera calibration, which is performed simultaneously with object identification during photogrammetric evaluation, is referred to as the self-calibration workflow. The images were automatically matched using the SfM algorithm and the calibration parameters were estimated through photogrammetric bundle adjustment during self-calibration [25].  
Thus, 15 images were captured with specific orientations from the chessboard using the X2, X3, and DSLR cameras. The spherical cameras' raw fisheye images were used for capturing. To estimate the unique intrinsic parameters, it was important to capture the chessboard using the separate front-side and rear-side images of each spherical lens. This calibration framework is implemented in the professional commercial Agisoft Metashape software. It is the most commonly used commercial software due to its reliable reconstruction and a speedy and compatible user interface [8]. The front-side chessboard images of the Insta360 One X2 were imported into the software, and during the image processing operation, every single corner in the chessboard image was used for the modeling of the image distortion (Figure 7). There are 11 estimated parameters stored in an.xml file, ordered in a suitable format for the upcoming reconstruction process (listed in Table 4). In the following steps, the calibration file was imported into the Metashape project file, where the relative orientation of the main dataset was performed. The calibration process was repeated for the backside images one at a time, resulting in a different set of estimated parameters (Table 4).  
Figure 6: Laser scanner registration for two capturing stations in Cyclone Register 360 software (V2021.1.2); 73% overlapping area specified between two stations.  
All of the parameters were checked as the consistent intrinsic parameters during the relative orientation calculation in order to obtain the stable relative orientation in the local 3D reference.
# Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key
## 4 Secure Shared Authentication Key (SSAK) Protocol
### Secure Message Sharing Procedure  
It is assumed that [PERSON] and [PERSON] have the same secret angle and authentication key that they shared in the initial stage of authentication in Section 4.1. Suppose that [PERSON]'s secret message \(X\) is a series of classical bit of 0 and 1 in order, \(X(x_{i},\,x_{i+1}\,\ldots\,x_{n})\).  
1. [PERSON] encrypts her secret message \(x_{i}\) by generating a state with a linear polarization using a \(0^{\circ}\) polarizer as bit 0 or using \(90^{\circ}\) polarizer as bit 1 to get \(\theta_{X}\).
2. Quantum state \(\theta_{X}\) is then coupled with initial \(\theta_{A}\) and sent along with the initial authentication key \(\Phi\) by [PERSON]. The combination of \(\theta_{X}\), \(\theta_{A}\) and \(\Phi\) generates \(\psi_{A}\) are sent to [PERSON].
3. [PERSON] receives the quantum state \(\psi_{A}\) and couples it with \((-\theta_{A})\) and \((-\Phi)\) obtains information \(X_{i}\).
4. [PERSON] and [PERSON] frame the received bits and convert the last \(n\) bits to integer value \(N\), given by \[N=\sum_{i=k-n+1}^{k}b_{i}\times\,2^{i}\] (1) where \(k\) is the number of transmitted keys, \(b\) is the sequence of the transmitted bits (i.e., the bits \(b_{k-n+1},\ldots,b_{k}\)) using the last n bits to generate the new angle \(\theta\).
5. [PERSON] and [PERSON] compute new value of \(\theta\) and \(\Phi\), given by \[\theta_{Next}=\frac{N\pi}{2^{k}}+\,\theta_{initial}\] (2)\[\Phi_{Next}=\frac{N\pi}{2^{k}}+\ \Phi_{initial} \tag{3}\]