esa-sceva/satcom-chunk-collection huggingface dataset:
# Acceptance Model for Mobile Building Information Modeling (BIM)
## 4 Proposed Mobile BIM Acceptance Model
### Research Hypotheses  
#### 4.2.1 External Variables for Mobile BIM Acceptance  
The external variables presented in the literature review and factor analysis are as follows (shown in Table 5). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Hypotheses**} & \multicolumn{2}{c}{**Definition**} \\ \hline H1 & a & Tool quality will positively effect ease of use. \\  & b & Tool quality will positively effect usefulness. \\ \hline H2 & a & Personal efficacy will positively effect ease of use. \\  & b & Personal efficacy will positively effect usefulness. \\ \hline H3 & a & Behavior control will positively effect ease of use. \\  & b & Behavior control will positively effect usefulness. \\ \hline H4 & a & Organization innovativeness will positively effect ease of use. \\  & b & Organization innovativeness will positively effect usefulness. \\ \hline H5 & a & Collective efficacy will positively effect ease of use. \\  & b & Collective efficacy will positively effect usefulness. \\ \hline H6 & a & Ease of use will positively effect usefulness. \\  & b & Usefulness will positively effect ease of use. \\ \hline H7 & a & Ease of use will positively effect consensus on appropriation. \\  & b & Ease of use will positively effect individual intention. \\  & c & Ease of use will positively effect organizational intention. \\ \hline H8 & a & Usefulness will positively effect consensus on appropriation. \\  & b & Usefulness will positively effect individual intention. \\ \hline H9 & a & Consensus on appropriation will positively effect individual intention. \\  & b & Consensus on appropriation will positively effect organizational intention. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research hypotheses. Figure 3: Proposed model for the mobile BIM acceptance model. **Tool quality** is defined as the quality of the mobile BIM application. In this study, \"technology\" refers to the mobile BIM application, or the software running on mobile. Assessment items related to technology quality are system quality, information quality, and service quality. System quality is an assessment item from the IS success model [30]. It has been presented as an external variable by many researchers [18; 20; 21; 33; 34; 37]. The IS success model defines system quality as the quality of the measures of the information processing system itself [30], and the performance and convenience of the hardware and software [32]. Moreover, system quality has a similar meaning as job relevance in TAM2 and usability in the literature review. Information quality is also an assessment item that is proposed in the IS success model [31]. It has been presented as an external variable by many researchers [15; 35]. The IS success model defines information quality as measures of the information system output [31] and the quality of the
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Measurement Principles
#### 2.1.1 Pulsed Approach
Thus, pulsed methods need very sensitive detectors working at high frequencies to detect the faint pulses received. As long as, generally speaking, pulsed methods deal with direct energy measurements, they are an incoherent detection case [23; 24; 25]. The advantages of the pulsed approach include a simple measurement principle based on direct measurement of time-of-flight, its long ambiguity distance, and the limited influence of background illumination due to the use of high energy laser pulses. However, it is limited by the signal-to-noise ratio (SNR) of the measurement, where intense light pulses are required while eye-safety limits need be kept, and very sensitive detectors need to be used, which may be expensive depending on the detection range. Large amplification factors in detection, together with high-frequency rates add on significant complexity to the electronics. The pulsed approach is, despite these limitations, the one most frequently selected at present in the different alternatives presented by manufacturers of lidar imaging systems for autonomous vehicles, due to its simplicity and its capability to perform properly outdoors.
# Cable-Driven Mechanism Models for Sensitive and Actuated Minimally Invasive Robotic Instruments
## 1 Introduction
However, problems of communication, sensitivity, and errors are more evident in this case because the zone of the interaction forces is not near the position where the sensor is located. In this paper, we propose the design and control of new sensitive-actuated models for MIRIs. Our objective is to give to the research community of surgical robotics new types of MIRIs that are able to transfer and measure high forces, but also to bypass the problems caused by sterilization. In our sensitive approach, the sterilized forceps, conceived without electronic components, Micro-Electro-Mechanical-Systems (MEMS), or sensors, inside it, will be in contact with the human body. The cable-driven system is used to transfer the measure of the bending motion of the MIRI from the tip of the instrument to its base. The measure of the bending motion in the base gives the measure of forces and displacements in the tip of the MIRI. By using our sensitive approach, the MIRI can be sterilized because it is not built with sensors and electronic components (such as PCBs, MEMS, etc. ), but the cable connection gives the information of the mechanical behavior of the tip directly to the sensors' base. Recently, we published a paper in which we proposed and validated a mechanism model and general formulations, using two test benches for experimentation [5]. Another simplified mechanism model with a new formulation was validated in a virtual multi-body model and tested in a planar test bench for experimentation [12]. In this paper, we perform a critical analysis on our sensitive approach for MIRIs, comparing two models (MM1 and MM2) presented in part in other works ([5]). The results presented in [5] with the implementation of the proposed formula (MM2) in two real test benches is also confirmed and validated in this paper, including a new multi-physics simulation. A comparison of the two mechanism models (MM1 and MM2) gives a general overview on the feasibility of the approach we propose in bypassing the problem of sterilization in surgical robotics. The paper is structured as follows: Section 2 shows formulations used for the definition of the mechanism models; Section 3 shows an overview of the physical implementation of one mechanism model; Section 4 shows the design and control of multi-body models conceived and simulated in this paper; Section 5 shows the results and discussion. The paper ends with the conclusions.
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids
### Multi-Agent System-Based Energy Management Systems  
Multi-agent systems are comprised of agents that collaborate to solve problems that an individual agent finds difficult or ineffective to solve on its own. These agents use their skills and knowledge to work together in a coordinated manner to solve these problems. Multi-agent systems consisting of many agents are applied to microgrids as an energy management strategy [36]. The agent is the fundamental component of an agent-based energy management system. It can be a real or virtual entity. Virtual agents are software algorithms that coordinate system components, whereas physical agents in applications are micro-resources and controllable loads. An agent possesses the capacity to act within the system and alter it through its actions. Depending on how big it is, a microgrid may have a lot of agents.  
Multi-agent systems can be understood as an assembly of intelligent and self-governing entities, referred to as agents, which essentially develop within a perceivable and manipulable environment. Other than the agent itself, this environment can be regarded as anything. Depending on how they are set up, these agents can be somewhat autonomous due to their intelligence. Multi-agent systems in energy management applications are made up of different agents interacting in a particular setting. Agents can perceive changes in their surroundings and use reasoning to determine the best course of action. In the field of Electrical and Electronics Engineering, multi-agent systems find application in diverse problem areas, including but not limited to diagnostics, distributed control, modeling and simulation, protection, and maintenance planning [37].  
Compared to traditional analytical control techniques, multi-agent systems offer many benefits [38; 39]. In today's grid, the classical control techniques used in SCADA systems are not entirely functional [40]. Nonetheless, the control system needs to function well as a large-scale and flawed system in a smart grid with thousands of controllable devices. In multi-agent systems, agents see the world locally and possess a restricted amount of knowledge. Although agents only need to know about their immediate neighbors, more agents need to communicate with each other to work more functionally and cooperate with other agents [41]. In this way, more advantages are provided by choosing multi-agent systems for microgrid energy management. However, if the amount and costs of data to be transmitted in the system are desired, the communication of agents can be limited to the microgrid they belong to and their neighboring agents.
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology
### Fault Pattern Recognition and Crack Size Identification Using SVM  
After generating the residual signals for all conditions, the resampled root means square (RMS) residual signals are determined. The resampled RMS residual signal is presented as a following definition.  
\[R_{MS-HRKF}(k)_{rms}=\sqrt{\frac{1}{M}\sum_{j=1}^{M}R_{MS-HRKF}(k)^{2}} \tag{39}\]  
Here, \(R_{MS-HRKF}(k)_{rms}\) and \(M\) denote the resampled RMS value for the residual signal of unknown signals using the proposed digital twin (MS-HRKF) and the number of windows to determine the resampled RMS residual signal, respectively. For each state, the residual signal is made up of 120,000 samples. It was segmented into 100 windows; each window contains 1200 samples. For signal classification, the RMS feature is used for 100 windows. The support vector machine (SVM) is suggested for the RMS resampled residual signal classification. The training set includes 75% and the testing set includes the remaining 25%. The details of the training and testing datasets for FPRCI using SVM are illustrated in Table 3. In addition, Algorithm 1 illustrates the design steps of the proposed scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Samples**} & \multicolumn{2}{c}{**Testing Samples**} \\ \hline \multirow{3}{*}{Pattern recognition} & NS & 900 & 300 \\ \cline{2-4}  & RFS & 900 & 300 \\ \cline{2-4}  & IFS & 900 & 300 \\ \cline{2-4}  & OFS & 900 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the training and testing resampled RMS residual signals using SVM.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
###### Abstract  
Electrospun nanofibers have become the most promising building blocks for future high-performance electronic devices because of the advantages of larger specific surface area, higher porosity, more flexibility, and stronger mechanical strength over conventional film-based materials. Moreover, along with the properties of ease of fabrication and cost-effectiveness, a broad range of applications based on nanomaterials by electrospinning have sprung up. In this review, we aim to summarize basic principles, influence factors, and advanced methods of electrospinningning to produce hundreds of nanofibers with different structures and arrangements. In addition, electrospun nanofiber based electronics composed of both two-terminal and three-terminal devices and their practical applications are discussed in the fields of sensing, storage, and computing, which give rise to the further integration to realize a comprehensive and brain-like system. Last but not least, the emulation of biological synapses through artificial synaptic transistors and additionally optoelectronics in recent years are included as an important step toward the construction of large-scale, multifunctional systems.  
electrospunning; one-dimensional materials; polymer nanofibers; metal oxide semiconductor; organic field-effect transistor; resistive random-access memory; flexible sensors; brain-inspired electronics; optoelectronic devices +
Footnote †: journal: Applied
## 2 Powertrain Dynamic Model
### Validation of PS-PHEV Powertrain Model  
A PHEV test bench was built to verify the validity of the established model as shown in Figure 4. The engine startup process is tested when the dynamometer is running at 27 rad/s. As shown in Figure 5a, the MT process with the engine startup includes four work stages: (1) pure electric stage with B1 locked, (2) pure electric stage with B1 opened, (3) a motoring stage before engine ignition, and (4) a hybrid driving stage after engine ignition. The engine shaft torque and speed obtained by simulation are basically consistent with the test results. As shown in Figure 5b, the driving shaft torque and speed obtained by simulation are basically consistent with the test results. Figure 5c shows the error between the simulation results and the test results of the drive shaft speed. It can be seen from these results that the maximum error of the drive shaft speed is less than 1.2 rad/s, and the relative error of the driveshaft speed is less than 5%. By comparing and analyzing the simulation results with the test results, the established model can basically reflect response characteristics of the power transmission in the mode switching process. The powertrain system model lays the foundation for the subsequent evaluation of the effectiveness of the proposed dynamic torque-coordinated control method. The main PS-PHEV parameters are listed in Table 1.  
Figure 4: Test bench for a PS-PHEV.  
Figure 5: Model validation of the powertrain system: (**a**) engine output torque; (**b**) drive shaft speed; (**c**) drive shaft speed error.
## 2 System Model
### Queue Length of Devices Estimation
Therefore, the EMBLR routing scheme aims to maximize throughput in the IoT 5G network based on D2D communications with minimizing the backlog queue length size between \(Q_{n}^{c}(t+1)\) and \(Q_{n}^{c}(t)\).
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 4 Proposed Approach  
In this section, we discuss the proposed approach for building a dynamic path for the mobile sink to collect the data from the sensor nodes in the network. The path is built based on selecting a set of RPs that will be used as stopping points where the MS will gather the data from the sensor nodes. First, we introduce the algorithm that determines the initial locations of the RPs, and we then discuss the algorithm that is used to update the RPs' locations in each round of data collection.
# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation  
In this section, we present the performance evaluation of our routing proposal GHR. First, we study the forwarding options and the recovery setup of GHR in order to find the most suitable configuration for this protocol. After that, we compare the best configurations of GHR using different scoring algorithms against an adaptation for VANETs of the classical topological routing protocol Ad-hoc On demand Distance Vector (AODV) routing protocol [21].
###### Abstract  
Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rules without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort's ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.  
Keywords:cybersecurity; intrusion detection; signature-based IDS; SIDS rules filtering; web SIDS +
Footnote †: journal: Applied
## 3 Numerical Study
### Parametric Analysis
#### 3.5.1 Influence of the Soil Friction Angle  
The numerical results for the models with different values of the soil friction angle (19\({}^{\circ}\), 22\({}^{\circ}\), and 25\({}^{\circ}\)) under the same lateral load conditions are shown in Figure 14. It can be observed that the characteristic values of lateral bearing capacity for the models with soil friction angles of 19\({}^{\circ}\) and 25\({}^{\circ}\) are slightly smaller than that of the model with a 22\({}^{\circ}\) soil friction angle. However, the differences among the three cases are minimal, and it can be concluded that the variation in soil friction angle does not significantly affect the lateral bearing performance of the GPF. This indicates that the GPF can be used in bridge construction projects under complex geological conditions, and even in soft soil conditions, its lateral bearing capacity can still be effectively utilized.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 4 Methodology
### Model Evaluation  
The following four established metrics are used for model evaluations. Four terms are used to define the evaluation metrics, which are true positive (_TP_), true negative (_TN_), false positive (_FP_), and false negative (_FN_). TP in simple term is classifying the positive sample as positive (for example, Google Music traffic as Google Music), TN is classifying the negative sample as negative (all traffic other than Google Music), FP is falsely classifying as positive samples (any of all other traffic as Google Music traffic), and FN is falsely classifying as negative samples (Google Music traffic as any of all other traffic).  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 2 Beamforming Imaging Method
### MVDR Method  
Since the weighting vectors of the DAS method are fixed and preset without considering the characteristics of the received data, the performance improvement of imaging is limited. In contrast, the weighting vector is computed with the MVDR method, whose goal is to ensure that the pixel value of the target point in the imaging region is 1 and to minimize the output power. Therefore, the weight vector can be obtained by solving the following constrained optimization problem [25]:  
\[\min_{\mathbf{w}}\mathbf{w}^{\text{T}}\mathbf{R}_{xy}\mathbf{w}\,\,\,s.t.\mathbf{w }^{\text{T}}\mathbf{e}=1 \tag{4}\]where \(\mathbf{R}_{xy}\) denotes the sampling covariance matrix and \(\mathbf{\mathrm{e}}\) is the unit orientation vector. Solving Equation (4) yields the following:  
\[\mathbf{\mathrm{w}}_{\mathrm{MV}}=\frac{\mathbf{R}_{xy}^{-1}\mathbf{\mathrm{e}}}{\mathbf{ \mathrm{e}}^{\intercal}\mathbf{R}_{xy}^{-1}\mathbf{\mathrm{e}}} \tag{5}\]  
Since the sampled data have been previously corrected for delays based on the target pixel point locations, there is \(\mathbf{\mathrm{e}}=1\). In order to avoid possible instability during matrix inversion, regularization is usually performed using the diagonal loading method, i.e.,  
\[\mathbf{R}_{xy}^{-1}=\left(\mathbf{R}_{xy}+\varepsilon\lambda_{1}\mathbf{\mathrm{I}} \right)^{-1} \tag{6}\]  
where \(\lambda_{1}\) is the maximum eigenvalue of \(\mathbf{R}_{xy}\), and \(\varepsilon\) is the regularization factor. Accordingly, the pixel value at position P in the image output from the MVDR method is as follows:  
\[I_{\mathrm{MVDR}}(x,y)=\mathbf{\mathrm{w}}_{\mathrm{MV}}^{\mathrm{T}}\mathbf{\mathrm{z}} \tag{7}\]
## 4 Performance Analysis
### Discussion
However, based on our proposed scheme, it is also difficult for Eve's to wiretap confidential messages, as estimates of the index information of LUs also need to be correct. Moreover, even if one or some (but not all) of the Eves' locations are same as one or some LUs' locations, the confidential messages are still difficult to retrieve for Eve's. Therefore, our proposed method achieves better anti-interception performance and thus improves transmission security.