esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion of Findings  
Figure 2 illustrates the outcome effect validation of the model. The overall fitness indices for the IoTAM indicated an acceptable fit. It showed that facilitated appropriation can be explained to the tune of 21.6% by user character and cyber resilience. It appears that PU can be explained to the tune of 50.8% by FA, CI, SI, trust and PEOU. PEOU can be explained to the tune of 51.7% by SI and trust. Attitude can be explained to the tune of 60.4% by PU and PEOU. BI can be explained to the tune of 71.8% by attitude. It also apparent that user character explains the strongest FA as the concerned path coefficient (\(\beta=1.000\)) is strongest. FA was found to be the mediator in the series of constructs starting from user character, to FA, to PU, to attitude and finally to the outcome BI. This translates to an upswing of behavioral intention towards IoT products and applications. Since personal character influences attitude formation through facilitated appropriation, it is proven that the facilitation degree is affected more by consumers' technology stance than their age. Implicitly, it is presumed that consumers will have the technological knowledge and skills to deal with this \"digital environment\". However, without any formal technology prerequisites, individuals are equipped with differing technological skills, stratified by prior experience. Beyond skills, individuals vary in their history with technology, which results in a range of technology identities, influencing their relationship with technology. Since technology expertise is not just a set of technical skills but a bundle of interactions, experiences and understandings of a new technology, knowledge about technology affects an individual's technology stance [55, 56] and according to our results, technology stance strongly influences facilitation. Therefore, it is concluded that previous knowledge about technology affects facilitation. Accordingly, [PERSON] and [PERSON] [57] proved that in the context of internet use particular beliefs about technology adoption rates differently affect consumers representing diverse segments of the population. Trust explains PEOU stronger than social influence, since the concerned path coefficient (\(\beta=0.411\)) is higher. It is observed that trust of consumers, as a focal construct in social psychology, strongly affects PU (trust \(\rightarrow\) PEOU), probably because, especially in an online environment, consumers feel free to explore every aspect of the new IoT product or application only if they believe that there will be no privacy breaches and infringements. This trust stimulated \"freedom of movements\", which probably countersigns a predisposed ease of use. Rooted in [PERSON] and [PERSON]'s integrated framework [58], this model showed how PU and PEOU mediate the effect of trust on individual intention towards IoT technologies. E-commerce research and evidence supports this proposal. [PERSON] [35] found that
## 5 Conclusions  
This paper presents a comprehensive analysis of the system error rate of the configuration memory in the SRAM-based FPGA, with a focus on soft errors. To achieve this goal, we created test vectors with more than 90% fault coverage using ATALANTA, an open-source ATPG tool. Based on the generated test vectors, we conducted a statistical fault injection test with a confidence interval of 95% (error 1%) using the developed ACMFI tool for four types of target security circuits. The AVF was calculated based on the results, and the \(SER_{est}\) for each security circuit was determined using \(SER_{bit}\) data obtained from the literature.  
Utilizing the ACMFI tool, we accurately identified and assessed soft errors in the programmable logic of the AES circuit. This tool enabled us to pinpoint and analyze soft errors at specific target locations within the device under test (DUT), specifically within the configuration memory synthesizing the security circuit. Our approach effectively eliminates the problem of underestimating the architectural vulnerability factor (AVF) values due to side effects, as demonstrated by the strong linear correlation between the DUT coverage and the AVF values, accompanied by a high coefficient of determination (\(R^{2}=0.9757\)).  
In this study, we conducted a reliability analysis on a target board using exponential distribution reliability modeling based on the estimated soft error rate. Previous studies have suggested that the architectural vulnerability factor (AVF) may be underestimated due  
Figure 13: Reliability of the Zynq-7000 SER.  
to side effect problems, which can lead to overestimated reliability compared to actual beam test results. However, while the ACMFI tool excels at identifying and analyzing soft errors within an SRAM configuration memory, it is limited in its ability to cover the remaining components of the FPGA, such as the processor core, Block RAM, DSP blocks, and IO blocks. For future research, it is necessary to enhance the functionality of SEM IP and to inject faults into the processor core or BRAM in order to estimate AVF and SER in a manner that is more aligned with actual testing conditions than those that are currently available.  
The experiment, data analysis, and writing of the paper were conducted by [PERSON]; the experimental methodology and data review were completed by [PERSON], D.P. and Y.K.; and the validation, methodology, and paper editing were handled by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the GRRC program of Gyeonggi province (GRRC Korea Aerospace University 2023-B02) and the National Research Foundation of Korea (NRF), grant funded by the Korean government (MSIT) (No. 2022K1A3A1A2001493).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters
## 3 Numerical Modeling  
When the natural gas transported in the pipeline contains impurities such as water vapor, CO\({}_{2}\), and H\({}_{2}\)S, under specific conditions, water vapor may condense into a liquid state. Impurity gases can dissolve in the liquid water, leading to the corrosion of the inner wall of the pipeline (26; 27; 33; 34).  
This study primarily addresses the internal corrosion of long-distance pipelines caused by impurity gases. The modelled corrosion defect was assumed to be rectangular as it a common assumption for pipeline corrosion (27; 36; 37). The soil-pipeline interaction in a landslide context is intricate and nonlinear. All modeling tasks are executed using the ABAQUS software, and this work is based on the following assumptions:  
* Both the soil and the pipeline are composed of uniform materials.
* The soil thickness in the landslide area is consistent, with cracks penetrating the landslide wall.
* The soil displacement in the landslide area follows a fourth-power parabolic distribution.
* Temperature effects on pipeline stress are neglected.
* Landslide displacement is applied from the rear of the model.
* The corrosion defects are considered as regular metal losses with uniform depth.  
To better simulate the mechanical response of pipelines under landslides, field investigations were conducted. The landslide depth was determined to be 3 m, with 3 m of soil beneath the landslide area and a width of 25 m for the landslide zone. Based on study (38), the impact of rear soil on the pipeline stress becomes negligible when its length exceeds 5D. Therefore, a starting rear soil length of 6 m is used. The pipeline has a burial depth of 1 m, an internal pressure of 10 MPa, a non-landslide soil width of 25 m, and an initial crack located at 3 m. The detailed properties of the pipeline and soil are presented in Tables 1 and 2. The soil's constitutive model utilizes the Coulomb-Mohr model (19), while the pipeline material is based on stress-strain data from relevant research (39). To ensure nonlinear contact between the pipeline and soil, a face-to-face contact approach is adopted (40), a hard contact is used in the normal direction, while a penalty method is applied in the tangential direction, and the friction coefficient between the soil and the pipeline can be set at 0.4. The bottom of the model is fixed, and except for the landslide area, vertical surface constraints are applied to the four sides of the model. The initial mesh has four layers aligned with the pipeline thickness, with two layers designated for defects in corroded regions and another two for non-defective areas. Figure 2 depicts the on-site conditions, model details, and mesh layout.  
Figure 2: Overview of working conditions and model: (**a**) on-site conditions; (**b**) model description; and (c) model meshing.
## 4 Systems Implementation and Evaluation
### Experimental Setup  
The method that we proposed was implemented in the Python programming language. Our networks were trained on NVIDIA GTX 1080 in a 64-bit computer with an Intel(R) Core(TM) i7-6700 CPU @3.4 GHz, 16 GB RAM and Ubuntu 16.04 operating system.
# Edge Federated Optimization for Heterogeneous Data
## 3 Methodology
### Dataset Description  
The datasets utilized in this study are sourced from PhysioNet [46], a publicly available resource for ECG data. We apply two distinct arrhythmia databases: the MIT-BIH Arrhythmia Database (MIT-BIH database) [47] and the St Petersburg INCART 12-lead Arrhythmia Database (St database) [48]. Note that the MIT-BIH database comprises ECG recordings from 48 patients, each lasting 30 min. Each recording includes 2 standard ECG leads, sampled at a rate of 360 Hz to ensure high-quality data collection. The St database, encompassing ECG recordings from 32 patients, comprises 75 half-hour ECG recordings, with each record lasting 30 min. Each recording includes 12 standard ECG leads, sampled at a rate of 257 Hz to ensure high-quality data collection.  
The choice of these datasets is based on their wide availability and significance in ECG research. The MIT-BIH database is highly regarded as it provides extensive, long-duration ECG records from multiple patients, making it suitable for a wide range of arrhythmia studies.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 3 System Preview  
### Architecture  
This scheme combines consortium blockchain, attribute-based encryption, and IPFS technology to propose a new distributed VANET data storage and sharing system, as shown in Figure 1.  
This system consists of six parts:
* The vehicle data owner (VDO) is the producer and sharer of data in telematics. VDO represents vehicles and corresponding onboard devices, which usually do not have the performance of storing and sharing data on a large scale. In addition, it has dynamic characteristics, so it only connects to CBN through RSU as a user.
* The roadside unit (RSU) is a communication unit distributed along both sides of the road at a certain distance. RSU has strong processing performance, sufficient storage space, and a good network connection. In this system, all RSUs form a consortium chain network as nodes. RSUs perform user data upload and access operations within their coverage area while using their high performance to do most data encryption and decryption work.
* The consortium blockchain network (CBN) is an intermediate party ensuring data sharing security and trustworthiness. In this system, CBN is composed of all RSU nodes together. The information in the system that involves user privacy and data encryption is recorded securely on the blocks of CBN.
* The smart contract (SC) is a complete set of operation methods defined on CBN, automatically performing different operations in different phases. For example, in the system initialization phase, SC is responsible for generating system keys. In the user registration phase, RSU can write user registration information to BN by calling SC.
* The data user (DU) is the user of data. The DU requests data by calling SC through RSU. In the physical layer, DU and VDO may be the same entity, and the vehicle can share its data while requesting data.
* The InterPlanetary File System (IPFS) is the data service provider. All shared source data in the system are stored in IPFS. VDO uploads metadata to IPFS via RSU.
## 3 Experimental and Numerical Setup
### Influence of Key Parameters
#### 4.2.2 Diameter of Particle \(\mathrm{d_{P}}\)  
For quasi-static penetration (low velocities), the drag force behaves independently of the penetration velocity. \(V\) = 10 mm/s (less than 1/10 of \(V_{c}\)) is chosen as the base value for later simulations. To take into account the impact of sensitive parameters on the penetration process, the drag force is scaled as follows:  
\[F^{*}=\frac{F_{a}}{\mu\rho_{\mathrm{P}}gd_{1}^{2}Z_{a}} \tag{14}\]  
To minimize the impact of the boundary effect, the diameter ratio of container to intruder \(d_{\mathrm{c}}\) : \(d_{\mathrm{i}}\) is 5 and the penetration depth should be 1/2 of the total height of penetration. Meanwhile, five different particle diameters are used in this part, shown in Table 1. The penetration forces of different particle diameters are shown in Figure 10. It can be observed that the drag force of penetration decreases with the particle diameter in the bed, and the effect of particle diameter on boundary effects is negligible when the particle diameter decreases to 6 mm (\(d_{c}\) : \(d_{\mathrm{i}}\) : \(d_{\mathrm{p}}\) = 100:20:6). \(d_{\mathrm{p}}\) = 6 mm is chosen as the base value for subsequent simulations.
# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### Application Domains and Examples
#### 4.1.1 Health Care  
Concerning an application in the health care domain, [PERSON] et al. [14] describe a multi-user dialogue game for achieving behaviour change in a patient by applying a goal-setting strategy. Here, two or more virtual health coaches have a conversation with a patient to reach agreement on a behaviour-change goal. For example, the agents and a patient may engage in a discussion to nudge the patient towards more healthy nourishment and apply different conversational strategies on how to achieve this. Although the author's work presents an interesting use case for applications in health care, they only provide a conceptual framework of the dialogue game without the implementation of an actual prototype.
## 3 Reliability Models of WSNs
### Impact of Routing on the WSN Reliability
#### 3.5.3 LEACH Protocol  
While previous protocols consider that the WSN is flat, the LEACH protocol [26, 27] periodically creates clusters by electing a leader of the cluster (CH) to receive packets from sensor nodes and forwards them to the sink node. In this way, any node (_Node 7_ and _Node 13_) is one or two hops away from the sink node (_Node 1_) as shown in Figure 15.  
Figure 14: Region Model when the WSN uses the FLOODING protocol.  
Figure 13: Region Model when the WSN uses the DIRECT protocol.  
In this case, the Region model has three node blocks as _Node 13_ is not a CH (see Figure 15). The first block (_Node 13_) represents the node belonging to the region, the second block (_Node 7_) is the CH, and the last block (_Node 1_) is the sink node.  
Table 1 briefly presents some differences between the three aforementioned protocols. The FLOODING protocol uses multipath to deliver a packet, which means that even if one of the paths fails, the packet is successfully transmitted. On the other hand, this protocol uses more sensor nodes and, therefore, consumes more energy.  
By adopting the DIRECT protocol, packets are directly sent to the sink node, which usually decreases the WSN power consumption. However, nodes that are distant from the sink node consume more energy and usually die first. Finally, LEACH creates clusters in WSNs. This protocol helps to decrease the WSN power consumption due to the use of a cluster header (CH), whilst the CH can become a central point of failure.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 5 Experimental Analysis under Forwarding Spoofing Attack  
In order to verify the effect of the PTC-SLM method, we simulated two GNSS spoofing scenes of forwarding spoofing attack and induced spoofing attack. In this section, only the experimental analysis under the forwarding spoofing attack is analyzed. We try to compare PTC-SLM method with the GNSS method and VINS-Fusion method [7]. The GNSS method is the results of GNSS receiver and it will be directly affected after GNSS spoofing occurs. The VINS-Fusion method is a coupled algorithm of GNSS and visual SLAM. After GNSS spoofing occurs, the obtained spoofed GNSS results will enter the back-end optimization process, which will affect the final localization result of VINS-Fusion method. In the following passage, we compare the three algorithms in rural and urban scenes.
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## Data and Methodology
### Methods  
In order to perform the evaluation of IMERG and 3B43V7 products relative to the reference rain gauges data, several indices including Pearson Correlation Coefficient (R), mean error (Bias), relative Bias (rBias), Root Mean Square Error (RMSE) and mean absolute error (MAE) were computed (see Table 1). Pearson correlation coefficient (R) is a dimensionless statistical index used to assess the linear correlation between the reference ground-based data and the satellite precipitation estimates. Mean error (Bias) represents the systematic error of satellite precipitation estimates, a measure of the overestimation or underestimation of the gauge data. Relative Bias (rBias) estimates the relative difference (in percentage) between the two data sources (satellite estimates and rain gauges). RMSE quantifies the average error magnitude (mm/time) between the satellite estimates and the rain gauge data. Mean absolute error (MAE) reflects the magnitude and extent of the mean error of satellite precipitation estimates. For seasonal analysis, the year was divided into four seasons: winter (December to February); spring (March to May); summer (June to September); and autumn (October to November).  
Figure 2 shows the number of stations that are distributed within the grid cells of each Satellite Precipitation (SP) dataset. The number of grid cells for the study area was 19 for the TRMM 3B43 data and 61 for IMERG data, respectively. On the one hand, the TRMM 3B43 grids show a notable variation of the available number of gauge stations residing (e.g., from 1 to 23 stations per grid cell). It should be also noted that 42% of the available gauge stations (57 of 136) were located within only three cells, although these 3B43 cells are not the ones with the maximum correlation with the corresponding gauge  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Unit** & **Equation** \\ \hline R (correlation coefficient) & - & \(\frac{\sum_{i=1}^{n}(\text{S}_{i}-\text{O}_{i})}{\sum_{i=1}^{n}(\text{O}_{i} )}\) \\ \hline Mean Error (Bias) & mm & \(\frac{\sum_{i=1}^{n}(\text{S}_{i}-\text{O}_{i})}{n}\) \\ \hline Relative Bias & - & \(\frac{\sum_{i=1}^{n}(\text{S}_{i}-\text{O}_{i})}{\sum_{i=1}^{n}\text{O}_{i}}(100)\) \\ \hline RMSE & mm & \(\sqrt{\frac{\sum_{i=1}^{n}(\text{S}_{i}-\text{O}_{i})^{2}}{n}}\) \\ \hline Mean Absolute Error & mm & \(\frac{\sum_{i=1}^{n}|\text{S}_{i}-\text{O}_{i}|}{n}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of statistical indices used to evaluate the satellite precipitation products (S\({}_{1}\): satellite estimates, O\(i\): observations).  
values. We notice that the distribution of the available gauge station within each IMERG grid cell was more balanced (1 to 5 stations per grid cell).
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 7 Discussions and Conclusions  
### Discussion  
There is complex structural morphology, crisscross of deep and large faults, and massive earthquakes in the Xichang area, which have produced rich seismic scientific research and structural models. Those results provide high-quality datasets for crustal medium structure modeling and are conducive to studying the automatic modeling technique. By means of automatic modeling, the results can well reflect the features of the regional crustal medium structure.  
The crustal medium structure in the Xichang area shows that with the continuous enhancement of tectonic stress, the high-speed body is not easy to fracture due to its high degree of crystallization and strong mechanical properties of rock. Once it is fractured, the earthquake will focus on the fault section. However, the low-velocity body has potentially good ductility or rheology due to the participation of underground fluid or partial melting, which is mainly plastic deformation. The folded basement between the two is brittle and easy to release energy and cause earthquakes, and the penetration of the seismic fracture surface further forms a fault. It further explains why the fault plane is greatly affected by the shape of the high-speed body in the folded basement.  
Combined with the fault morphology and source structure, it can be seen that the location of the earthquake in the study area is usually concentrated in three areas: (1) At the intersection of faults, the rocks in this area are broken. It is easy to be squeezed and deformed when the surrounding stress is strengthened, forming the mid-small events with high frequency and dense distribution. (2) The region with large fault bending amplitude, which is mainly influenced by the shape of a high-speed body, is prone to the locked status and stress concentration release and then forms a moderate earthquake. (3) The low-velocity zone, which is greatly affected by temperature and fluid, has certain rheological properties and ductility. The unloading area is formed around it, which can cause small and medium earthquakes. In summary, the crustal structure and geological mechanics background can control the stress distribution and energy release. Therefore, we can further calculate the stress distribution state and concentration area through numerical simulation and analyze the area where earthquakes may occur combined with the mechanical background of geological bodies. It can helpfully estimate the earthquake risk source and verify the reliability of the model based on the observation datasets.
###### Abstract  
Large rainfall-induced landslides are among the most dangerous natural hazards in Taiwan, posing a risk for people and infrastructure. Thus, better knowledge about the evolution of landslides and their impact on the downstream area is of high importance for disaster mitigation. The aim of this study is twofold: (1) to semi-automatically map the evolution of the Butangbunasi landslide in south-central Taiwan using satellite remote sensing data, and (2) to investigate the potential correlation between changes in landslide area and heavy rainfall during typhoon events. Landslide area, as well as temporary landslide-dammed lakes, were semi-automatically identified using object-based image analysis (OBIA), based on 20 Landsat images from 1984 to 2018. Hourly rainfall data from the Taiwan Central Weather Bureau (CWB) was complemented with rainfall data from Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to examine the potential relationship between landslide area changes and rainfall as a triggering factor. The OBIA mapping results revealed that the most significant landslide extension happened after typhoon Morakot in 2009. We found a moderate positive relationship between the landslide area change and the duration of the heavy rainfall event, whereas daily precipitation, cumulative rainfall and mean intensity did not present strong significant correlations.  
Keywords:landslide; remote sensing; Landsat; object-based image analysis (OBIA); time series; heavy rainfall; rainfall data; typhoon; landslide-dammed lake; Taiwan +
Footnote †: journal: Applied Sciences