esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of a Device to Restrain the Horizontal Sliding of U-FREIs
## 3 Regulatory Constraints for the Possible Use of U-FREIs  
Given the general good behavior of U-FREIs as isolating devices, as confirmed by extensive research [11], the next step could be their use in real applications. For these applications, specific standards for the use of U-FREIs are not currently available yet, but there are regulations in different countries regarding the use of isolation systems. Reference to these regulations may be made for using U-FREIs in real applications. In Europe, two standards must be considered: UNI EN 1337-3 [33] and UNI EN 15129 [34].  
UNI EN 1337-3 [33] applies to elastomeric bearings with or without complementary bearing devices, such as flat sliding elements or a sliding surface, as used in bridge structures or any other structure with comparable support conditions. According to this standard, bearings do not need to be connected to the sub-and superstructures, and, to maintain their position, they can resist through friction.  
UNI EN 1337-3 specifies that in the case of structures subjected to dynamic stresses with possible extreme fluctuations in the load, for example, railway bridges and anti-seismic structures, the friction shall be assumed to be equal to zero. In these cases, the standard specifies that it is mandatory to use fastening devices able to resist the maximum horizontal  
Figure 1: Strains under compression load for (**a**) SREIs and (**b**) U-FREIs [30].  
Figure 2: Strains under horizontal load for (**a**) SREIs and (**b**) U-FREIs [31].  
force acting on the bearing. In particular, UNI EN 1337-3 requires that the fastening devices limit the movement between the structure and the supporting surfaces to within 5 mm or less. Moreover, the devices must be such that the support can be removed with the structure raised by no more than 10 mm, unless otherwise agreed upon with the designer of the structure.  
UNI EN 15129 [34] covers the design of devices that are provided in structures with the aim of modifying their response to seismic action. It states that all types of isolators shall be attached to the structure by mechanical anchorages only, and at least 75% of the horizontal load shall be supported by the anchorage, unless the minimum vertical load on the isolators during seismic action has been determined by dynamic analysis.  
According to the two above-mentioned standards, U-FREIs, for their utilization, should have a constraint system against at least horizontal forces, thus becoming Horizontal Sliding Constrained FREIs (HSC-FREIs).
## 4 Numerical Results and Analysis
### Liquid Relative Velocity Distribution  
The distribution diagram of the inter-blade blade relative liquid velocity at 10%, 50%, and 90% heights of the blades in the second stage mixed-transport pump under the IGVF = 20% condition is shown in Figure 11. The liquid velocity distribution at different heights of the blades was different and related to the pressure distribution and IGVF. Given the energy input of the pump shaft, the liquid velocity in the impeller flow passage increased stage-by-stage along the radius orientation, and the liquid velocity distribution was relatively uniform. A small low-velocity region existed on the suction side near the impeller blade exit edge, which was related to the volume distribution of the gas phase. Because of the influence of the centrifugal force, the liquid phase flow separation, and the gas aggregation at the suction surface near the blade outlet, the low-velocity zone of liquid phase was formed. The liquid low-velocity region increased from the front cover to the rear cover, thereby indicating that the gas phase was primarily gathered in the shroud of the impeller. However, the liquid velocity decreased gradually after entering the diffuser due to the action of the diffuser. The kinetic energy was gradually converted into energy pressure. Moreover, after the gas-liquid mixed medium entered the diffuser passage, the velocity of the mixed medium affected by the impeller still existed. Therefore, the velocity of the guide vane pressure surface was greater than the reverse surface. The liquid-phase velocity increased gradually from the front cover to the rear cover within the diffuser. Given the effects of the impeller in the next stage, a low-velocity region of the liquid phase appeared close to the outlet of the fixed vane, which resulted from static and dynamic interference.  
Figure 10: Distribution of GVF on the blade of the second impeller for different gas contents.  
The streamline distribution in the second-stage impeller and diffuser of the mixed-transportation pump under the different inlet gas content is shown in Figure 12. On the whole, the internal flow lines of each impeller were relatively smooth and smooth under diverse gas content conditions. Thus, the impeller exhibited good hydraulic performance under pure water and air and water two-phase conditions. The vortices in the middle part of the diffuser flow channel near the guide vane back surface were obvious. The fixed guide vane diffuser of the multi-stage mixed-transport pump was directly connected to the upper and lower parts, the flow passage was long, and the fixed guide vane wrap angle was too large, bringing about a flow separation and vortex generation. Compared with the streamline distribution in the diffuser with different GVFs, the liquid velocity in the flow passage increased with increasing IGVFs. The vortex position tended to move toward the guide vane inlet of the diffuser with increasing GVFs.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 6 Contiki-NG
### Processes and Scheduling  
Contiki is built on an event-driven kernel, in which all processes run on the same stack [38]. In a purely event-driven OS, a dispatcher oversees process handling with event-handlers. When an internal or external event occurs, the dispatcher hands over control of the processor to the process with the corresponding event-handler [42]. Such events can be caused by for example expired timers, incoming network packets, or user actions [43]. One of the major challenges of event-driven programming is that it can cause processes that perform intensive computations to block all other execution for an undesirable amount of time. For applications that need to perform computationally intensive tasks, Contiki supports preemptive multi-threading as an application library on top of its event-driven kernel [38].  
The purely event-driven model was later refined with the introduction of the protothreads library [44]. With protothreads, each application only consists of one actual process and all sub-processes are implemented as stackless threads. Protothreads allow for application developers to reduce the number of explicit state machines they would need with traditional event-driven programming. The overhead of using protothreads as an abstraction is the amount of memory required for a pointer, which, in practice, means two or three bytes, depending on the embedded system. For example, the size of a pointer in an AVR microcontroller is three bytes and in MSP430 only two bytes [44].  
Contiki-NG follows the event-driven model of the original codebase [43], but it removes support for preemptive multi-threading due to a lack of documented use cases [45]. Consequently, multitasking in applications has to be implemented with events and timers. A process in Contiki-NG is a stackless protothread [44] running on top of the kernel. As was the case in Contiki, Contiki-NG also contains an event-scheduler that is responsible for handing over control of the processor to individual processes. Since the scheduler in Contiki-NG is cooperative, each protothread is expected to restore control back to the scheduler after it has finished executing the task it was assigned. Failing to do so will block other processes and cause a deadlock situation, in which all of the processes are waiting for execution time. After returning control of the processor, a protothread typically becomes idle until the next event is dispatched to it by the scheduler [46].
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
## 1 Introduction
For example, the carrier Doppler may change up to 10.5 Hz over a 1 ms interval under a 2000 m/s\({}^{2}\) acceleration. Therefore, the effects of the traditional aiding are restricted. This paper proposes a sample-wise aiding method, which interpolates the aiding data onto each sampling point before controlling local reference signal generation to further eliminate the high-order dynamic errors with only minor modification of the receiver NCO (numerically controlled oscillator). Section 2 goes deep into the digital realization of the Doppler-aided PLL to explore the influence of the aiding data update rate on the scalar tracking performance. Theoretical derivation of the phase error transfer function reveals that the aiding effect has a lot to do with the product of the frequency of signal dynamics and the aiding update interval. Section 3 presents the sample-wise aiding method to break the performance limitation of the traditionally aided PLL, and simulates the effects of linear and spline interpolations in comparison with the traditional aiding. In Section 4 a testbed for the performance limitation of GPS/INS ultra-tight integration is implemented, which employs a digital IF (intermediate frequency) GPS signal simulator to test the tracking errors of a software GPS receiver ultra-tightly coupled with INS data in various high dynamic scenarios. The test results coincide with theoretical expectations and demonstrate that the sample-wise aiding can eliminate the dynamic errors much more effectively than the traditional aiding.
# An Evidential Reasoning Assessment Method Based on Multidimensional Fault Conclusion
## 3 An Assessment Method of Evidence Reasoning Based on Fault Conclusion
### Objective Function of the ER Model  
The core idea behind the model design is to use monotonicity and trend indicators as evaluation criteria for quantitatively analyzing performance degradation from a statistical perspective. These indicators were chosen because monotonicity captures the irreversible changes in component degradation, while trend indicators effectively reflect the correlation between degradation and time. This approach is crucial for accurately assessing and predicting the health status of high-speed train running gear under varying operating conditions, providing a solid scientific basis for subsequent maintenance and repair strategies. In this way, it is possible to better understand and predict the degradation trends of mechanisms under different conditions, thus providing a scientific basis for subsequent maintenance and repair.  
(1) Monotonicity Indicator  
In actual engineering, the degradation of running gear components is irreversible, characterized by a continuous increasing or decreasing trend in degradation features. This characteristic is known as monotonicity. The monotonicity indicator is defined by the following formula:  
\[\text{Mon}(G_{j})=\left|\frac{\text{Num of d}G_{j}>0}{R_{j}-1}-\frac{\text{Num of d}G_{j}<0}{R_{j}-1}\right| \tag{16}\]  
where \(dG_{j}\) denotes the difference in the feature sequence values, and Num of d\(G_{j}>0\) and Num of d\(G_{j}<0\) represent the number of positive and negative values in the feature sequence, respectively, and \(R_{j}\) is the total number. \(Mon_{j}\) ranges from 0 to 1, with larger values indicating better monotonicity.  
(2) Trend Indicator  
As the service life increases, the running gear gradually deteriorates, a characteristic known as \"trend\". The trend indicator assesses the correlation between the progression of degradation and time. The formula for calculating the trend indicator is as follows:  
\[\text{Corr}(z_{q})=\frac{\left|\sum\limits_{q=1}^{R_{q}}\left(z_{q}-\bar{z} \right)\left(m_{q}-\bar{m}\right)\right|}{\sqrt{\sum\limits_{q=1}^{R_{q}}\left(z _{q}-z\right)^{2}\sum\limits_{q=1}^{R_{q}}\left(m_{q}-\bar{m}\right)^{2}}} \tag{17}\]  
where \(z_{q}\) represents the health feature value in the \(q\)-th cycle, \(\bar{z}\) denotes the average health feature value, \(m_{q}\) denotes the index of the \(q\)-th sampling point, \(\bar{m}\) represents the mean value of the sampling point indices, and \(R_{q}\) indicates the total number of cycles. Using Formulas (16) and (17), the objective function for optimizing the parameters of the ER assessment model is constructed.  
\[\begin{split}\max\{\text{Mon}(\Phi)\}\\ \max\{\text{Corr}(\Phi)\}\\ \text{s.t. }0\leq\vartheta_{j}\leq 1,j=1,2,\cdots,R\\ \Phi=\{\bar{\upsilon}_{1},\bar{\upsilon}_{2},\cdots,\bar{ \upsilon}_{R}\}\end{split} \tag{18}\]
###### Abstract  
Currently, it is a hot research topic to retrieve the wave parameters by using X-band marine radar. However, the rainfall noise usually exists in the collected marine radar images, which seriously interferes with the extraction of the wave parameters. To reduce the influence of rainfall noise, the zero-pixel percentage (ZPP) method is widely used to detect rainfall in radar images, but the detection accuracy is limited, and the selection of the threshold needs to be further studied. Based on the ZPP method, the ratio of zero intensity to echo (RZE) method for rainfall detection is proposed in this paper. The detection threshold is determined by statistical analysis of a large amount of radar data. Additionally, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. In addition, the concept of the occlusion area is proposed. The proposed area and the wave area are used as the rainfall detection area of the radar image, respectively, for experimental research. The data obtained from the Pingtan experimental base in Fujian Province are used to verify the effectiveness of the proposed method. The experimental results show that the detection accuracy of the proposed method is 11.7% higher than that of the ZPP method, and the accuracy of rainfall intensity level retrieval is 84%.  
ratio of zero intensity to echo (RZE); X-band marine radar images; occlusion area; rainfall detection; rainfall intensity level retrieval
# RJA-Star Algorithm for UAV Path Planning Based on Improved RSDOS Model
## 2 Materials and Methods
### Improved Path Planning Algorithm
(2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] UAV Path Planning Model Based on R5 DOS Model Improved A-Star Algorithm. _Appl. Sci._**2022**, _12_, 11338. [CrossRef]* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] The Application of Adaptive A-star Algorithm in Layout of Spatial Pipeline. In Proceedings of the IEEE 9 th Data Driven Control and Learning Systems Conference (DDCLS), Liuzhou, China, 19-21 June 2020; pp. 272-277. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3d environment. _Int. J. Syst. Assur. Eng. Manag._**2021**, _12_, 990-1000. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment. _Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng._**2022**, _236_, 72-81. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Leader-Follower UAV formation model based on R5 DOS-Intersection model. _CMC-Comput. Mater. Contin._**2021**, _69_, 2493-2511. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] A KNN model based on manhattan distance to identify the SNARE proteins. _IEEE Access_**2020**, \(8\), 112922-112931. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Automatic routing study of spacecraft cable based on A-star algorithm. In Proceedings of the IEEE 5 th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 12-14 June 2020; pp. 716-719. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Combined improved A-star and greedy algorithm for path planning of multi-objective mobile robot. _Sci. Rep._**2022**, _12_, 13273. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Geometric A-Star algorithm: An improved A-star algorithm for AGV path planning in a port environment. _IEEE Access_**2021**, \(9\), 59196-59210. [CrossRef]

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# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 3 Framework
### Extracting Content Features  
The main purpose of Step 2 is to extract content features from the header and body information in the joint array. Before the advent of NLP technology, the existing phishing email detection technology relied on email content-based features [33]. [PERSON] et al., divided email content-based features into four categories, namely, body features, syntactic functions, headers features, and URL features. Body features mainly refers to the behavioral features that attackers try to deceive the victim, such as inserting malicious web pages, malicious files, etc., which include HTML code, HTML forms, scripts, attachments and image link, etc. Syntactic features mainly extract semantic information that phishing messages differ from benign messages, such as bad words in the email's body, the absence of \"RE\" in the subject, and the number of characters in the email body. Header features mainly contain information such as the sender, recipient, and encoding method of the message. URL features are obtained from the hyperlinks and domain names of the messages and are used to identify whether they contain malicious hyperlinks and special domain names, such as IP, number of hyperlinks, number of different href, and so on.  
[PERSON] et al. created four sets to store labels for the above four features in each email and then combined these sets into a matrix. The matrix contains labels corresponding to the above features of each email, as well as the label indicating whether the email is phishing or benign.
## 4 Experimental Results
### Two-Stream UDN
#### 4.2.1 \(\beta\) Selection for Caltech and ETH  
In Equation (7), \(\beta\) represents the weights of the two subnetworks. We find the optimal value for the parameter \(\beta\) on the Caltech and ETH dataset. Figure 11 shows the details. For both datasets, the Global network should be given more attention to reach a better LAMR, and the LAMR would rise if the Global network is focused on too much. For the Caltech dataset, the optimal value of \(\beta\) is 0.78, and the according LAMR is 37.66%. For the ETH dataset, the optimal value of \(\beta\) is 0.6, and the according LAMR is 44.58%. This shows that the global features are most important in pedestrian detection, and adding appropriate local features can improve the network's detection performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **VJ [39]** & **HOG [8]** & **HOG + LBP [40]** & **ChnFtrs [10]** & **ACF [41]** & **Improved UDN** \\ \hline Caltech & 94.73\% & 68.46\% & 67.77\% & 56.34\% & 51.04\% & 38.73\% \\ ETH & 89.89\% & 64.23\% & 55.00\% & 57.00\% & 50.04\% & 45.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Log-average Miss Rate (LAMR) of improved UDN and hand-crafted feature-based models.  
Figure 9: Detection results of models based on hand-crafted features and the improved UDN. False positive samples are in the red box. True positive samples are in the green box. False negative samples are in the purple box. (**a**) Ground Truth; (**b**) VJ; (**c**) HOG; (**d**) HOG + LBP; (**e**) ChnFrs; (**f**) ACF; (**g**) Improved UDN. Best viewed in color.
## 1 Introduction
For example, electrodermal activity and electrocardiographic signals can be used to implement a multimodal system for assessing driving stress and driver mentality. In 2023, [PERSON] et al. [11] presented a systematic review of studies that related to analyzing driver mental workload through various in-vehicle physiological sensors, focusing on cardiovascular and respiratory measures. They concluded that a research gap exists in the field of non-contact physiological monitoring in automotive environments that needs to be filled. These scoping and systematic reviews or scientific surveys have analyzed the existing publications in the field, concluding that the selection of proper measuring methods and sensors is crucial to the accuracy and stability of data collection in development of DMSs. Most scoping and systematic reviews in the field of physiological monitoring are related to applications for monitoring specific driver states, such as alertness, drowsiness, fatigue, emotional stress, etc. Others are related to methods for capturing a specific physiological signal or signals related to the function of a driver's circulatory or respiratory system. However, there is still a lack of a structured general framework of methods, techniques, and sensors for acquiring physiological signals used for monitoring physiological parameters that are considered relevant and practically applicable in DMSs. The aim of this paper is, based on recent research on the wide spectrum of methods, techniques, and sensors for measuring physiological signals in DMSs, to propose a taxonomy providing a structured framework for the development of next-generation physiological-based DMSs (PDMSs). Therefore, special attention is paid to methods and sensors for acquisition of physiological signals to monitor the physiological parameters considered important for assessing the driver's state, as well as to discuss their possible applications in real driving scenarios and challenges for further implementation. The rest of the paper is organized as follows. In Section 2, a brief overview of the physiological parameters for driver's state assessment is provided. The methods, techniques, and sensor modalities used for measuring the driver's physiological signals are examined in Section 3. Based on this review, a proposed taxonomy scheme is presented at the end of this section. The potential and challenges of the considered methods for application in real driving scenarios are discussed in Section 4. Finally, conclusions and ideas for future work are presented.