esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 4 Results
### Waiting Interface Strategies in Page-Level Transitions
#### 4.1.3 Half-Screen Page Hierarchical Navigation Scenario  
In the half-screen page scenario, repeated measures of ANOVA were conducted to analyze participants' responses across different interface strategies. The results indicated that in this context of intra-level navigation within the same page, there were significant differences in time perception for different waiting interface strategies (F = 3.657, \(p\) < 0.01). The average reproduced durations, ranked from shortest to longest, were as follows: Interface 5 (blank screen + spinner) < Interface 3 (skeleton screen) < Interface 4 (spinner + skeleton screen) < Interface 1 (blank screen) < Interface 2 (spinner). This suggests that Interface 5 resulted in the shortest perceived wait time. There were no significant differences in loading speed, although Interface 5 yielded the highest mean rating. Significant differences were found in user satisfaction (F = 2.551, \(p\) < 0.05). Ranked from highest to lowest, the satisfaction scores were Interface 5 > Interface 4 > Interface 3 = Interface 2 > Interface 1. There were no significant differences in emotional valence, although Interface 5 had the highest mean value. Similarly, the arousal level showed no significant difference, with Interface 5 having the lowest mean arousal level. LSD post hoc tests were performed on all within-subject variables that showed significant effects. The results are illustrated in Figure 10.  
Time Perception: The time perception for Interface 5 was significantly lower than that of Interfaces 1 and 2, indicating that users perceived a significantly shorter wait duration using Interface 5. Interface 2 showed a significantly higher time perception than Interfaces 3, 4, and 5, suggesting that, among spinner-based interfaces, adding a secondary screen (such as a blank or skeleton screen) helped reduce the perceived waiting time.  
Satisfaction: Interface 5 yielded significantly higher satisfaction than Interfaces 1, 2, and 3, demonstrating that users preferred the dual-stage feedback design (blank screen + spinner) over single-element strategies.  
Figure 10: Results of interface strategy for the half-screen page scenario (Experiment 1). Vertical lines: standard deviations (SDs). **: \(p\) < 0.01, *: \(p\) < 0.05.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 4 Discussion  
The qualification test campaign on the filter has been performed successfully and has proven that the design is robust against the space environment. Currently, a demonstrator is being built that is planned for in-orbit verification on the Heinrich Hertz Satellite [10], which is scheduled for launch in 2021 [11]. This demonstrator unit will carry four tunable filters, similar to the one described here. They will be tested in orbit in regular intervals during an overall period of many years. This will allow for collecting further important data on long-term behavior in a real satellite environment and can open the door to applications.  
Individual important aspects of the test campaign performed were:  
Figure 10: Power handling test (see Table 2 for meaning of the labels).  
1. The stability of the RF response curve in Figure 9 over the qualification test sequence was good. Especially, here, the in-orbit verification will provide valuable further test data gained over longer periods of time.
2. The result of the power handling test in Figure 10 was good. The application of overdrive power of 20 dBm is a failure condition and limited in time, and it is allowed that the filter response shows some graceful degradation under this condition. When limiting power again to a maximum nominal value of 0 dBm, the filter response drifts back to the original performance after some time, as it should. Given the timescale, the degradation is most likely a thermal effect caused by dissipation of RF power in the liquid crystal.  
Future work in the field may also be directed to aspects concerned with improvements in the filter design:  
1. While the filter presented in this article can be shifted in center frequency with bandwidth remaining constant, it would be a valuable extension to enlarge the filter design to include variable bandwidths.
2. For the two extreme tuning states, a difference in insertion loss at center frequency of 1.9 dB was observed (Section 2.5). This can be mitigated by including tunable couplings in the filter and artificial increase of insertion loss for filter states with initial lower insertion loss [12].  
Conceptualization, validation, investigation, writing--original draft preparation, T.K.; validation, investigation, writing--review and editing, C.F.; validation, investigation, writing--review and editing, M.F. All authors have read and agreed to the published version of the manuscript.  
Test acknowledges that this work has been funded by the German Federal Ministry of Economic Affairs and Energy (BMWi) under the project management of the German Aerospace Center (DLR, contract no. 50 YB1501).  
Tesat would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for design and test of the control electronic, [PERSON] for mechanical design, [PERSON] for materials and processes, [PERSON] and [PERSON] for manufacturing, [PERSON] for filter tuning, [PERSON] and [PERSON] for quality assurance, and [PERSON] and [PERSON] for testing.  
The authors declare no conflict of interest.
# Zero Energy IoT Devices in Smart Cities Using RF Energy Harvesting
## 3 Materials and Methods
### Testbeds Studies
#### 3.3.3 Problem Identification  
1. Powercast energy transmitters disseminate energy in a directional form up to 60 degrees, while the dipole and patch antennae receive this energy at 360 and 120 degrees. In our previous experiment, we used static charger position and dynamic sensing device position. We have used 1 to 5 feet distance for devices to check the energy harvesting and data sending processes. Moreover, we have conducted the 1 to 3-m distance experiment and observed that when the sensor device goes away from the charger, it degrades the energy harvesting and consequently the data sending process. Thisphenomenon is called the nearest-farthest charger problem, and this is the proof that received signal strength is inversely proportional to the square of transmission distance [30].
2. The next problem is related to Powercast devices. The energy transmitter transmits in a directional form and not in an omnidirectional form. Next, the dipole antenna has a 360-degree receiving angle, but the patch antenna has a 122-degree receiving angle. So, we cannot shrink the distance to a point where all devices can perform their specific task efficiently. For example, if the charger was omnidirectional, then the usage of distance was nil, and we would put the charging devices near the charger.
3. Powercast evaluation board/charger devices are zero-battery devices, and during our previous experimental setup, it has been observed that these zero-battery devices required continuous energy for life. If at any stage or at any time the energy transmitter direction changes, then the evaluation board goes to die. So, we need such a technique that can overcome the nearest farthest problem.
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
## 3 Semantic Segmentation Algorithm Based on Track Fasteners--Mask FRCN
### FRCN--A Method to Improve Image Segmentation Boundary  
In this section, we propose the Fully Refined Convolutional Network (FRCN), a deep learning-based image segmentation module. Traditional segmentation methods tend to result in blurry and imprecise boundaries at the edges of rail fasteners, which leads to a decrease in segmentation accuracy and precision. To address this issue, the present study proposes the use of FRCN to optimize the boundaries of image segmentation. Although the data have already undergone segmentation, the boundaries of the segmentation are not precise enough, and hence, further optimization using FRCN is necessary to improve the accuracy and precision of the segmentation.  
The FRCN architecture consists of six convolutional layers and six deconvolutional layers as Table 1 shows, each of which includes convolution, batch normalization (BN), and ReLU activation function. The detailed architecture is shown in the equation below, where conv denotes convolution operation, deconv denotes deconvolutional operation, BN denotes batch normalization operation, and ReLU denotes ReLU activation function. Input and output dimensions of each convolutional and deconvolutional layer are given in parentheses.  
The FRCN architecture uses various convolution kernel sizes and strides to adapt to images of different sizes. The kernel size of the first layer is 3 \(\times\) 3, with a stride of 1, whereas the kernel size and stride of subsequent convolutional layers are 3 \(\times\) 3 and 2, respectively. The deconvolutional layers use upsampling techniques to restore smaller image dimensions, with the kernel size and stride of the deconvolutional layer both being 3 \(\times\) 3.  
In addition to the convolutional and deconvolutional layers, FRCN includes multiple fully connected layers for further feature processing and optimization. FRCN uses two fully connected layers to map features to 128 dimensional and 64 dimensional spaces, respectively, to improve feature representation.  
In summary, FRCN uses multiple convolutional and deconvolutional layers, fully connected layers, and a global context attention mechanism that effectively optimizes image segmentation boundaries and improves segmentation accuracy and precision.
## 1 Introduction
Although discrete techniques enable the precise modelling of cracks, they also lead to increased computational cost and complexity. Continuum damage models preserve the FE mesh continuity by decreasing local stiffness to account for damage in a distributed manner. Continuum damage mechanics (CDM) takes into account progressive degradation, where the loss of stiffness can be attributed to a series of microcracks. Early CDM-based simulations of FRPs were conducted in the 1990s [9; 10]. Due to its ease of implementation and computational efficiency, CDM has become a popular framework for simulating progressive damage in FRPs, such as in axial crushing [11], impact analysis [12], compression after impact tests [13], bi-axial load cases [14], and the prediction of fibre kinking [15]. Compared to studies on FRPs, research on wood and wood-based materials using numerical methods is relatively scarce. Although several 3D constitutive CDM-based material models have been developed (e.g., [16; 17; 18; 19; 20]), many of these models are implemented in user-defined subroutines, which limits their general use. In addition, 3D material models typically require a large number of input parameters, and hence a comprehensive calibration procedure is needed [16]. [PERSON] et al. [21] investigated built-in CDM-based material models in the commercial FE software LS-DYNA and found that most FRP models are applicable to wood when considering ply-based input parameters. Irrespective of the applied material model, it is important to consider how to obtain the corresponding FE input data, particularly when simulating progressive damage where some parameters cannot be measured in isolated experimental tests. When computationally efficient FE models are considered, data-driven calibration techniques have shown significant potential in providing a transparent and objective means of determining optimal input parameters. Examples include the use of machine learning methods [22; 23] and the application of genetic algorithms (GA) [24; 25]. This paper presents a ply-based material model in combination with a GA-based calibration procedure for the efficient FE simulation of progressive damage in thin wood laminates, including the consideration of uncertainty. Section 2 describes the material model and its incorporation into an efficient FEA in the commercial software LS-DYNA. The input parameters are calibrated in Section 3 by means of a data-driven calibration algorithm. Section 4 validates the FE framework against experimental results obtained from a range of open-hole tension tests. The benefits and limitations are discussed in Section 6 before concluding remarks in Section 7.
###### Abstract
This, in turn, adversely affects any of the corresponding decision-making or analytical schemes, such as fault detection and fault localization, which are extremely valuable to system operators. The aim of this paper is to propose a hybrid edge-cloud framework that satisfies the delay requirements of time-critical applications, such as demand response, synchro-check, adaptive relaying, out-of-step protection and fault detection, by moving them to the edge whilst leaving the monitoring applications, i.e., WAMS, and computationally demanding applications centralized. Such an approach provides several advantages over a strictly centralized approach: (a) response-critical applications are performed at the edge nodes, thus bypassing delay issues related to sending measurements from the edge device to the control center and the corresponding response back to the edge, (b) potential packet losses are eliminated by storing data at the edge nodes. Ensuring that no data are missing is of paramount importance for heavily data-reliant schemes, (c) applications that are not time-critical, such as the post-mortem investigation of faults, are executed in the cloud. This provides the ability to execute computationally demanding analysis that would have otherwise exceeded the computational capability of the edge device, (d) since the detection of events is no longer centralized, but rather takes place locally at the edge, the amount of data transferred to the control center during the steady-state conditions of the network can be significantly reduced. However, in case of an event, the full resolution of the measurements is still provided to the control center. The structure of this paper is as follows: In Section 2, we evaluate the quality of service (QoS) for PMU data transmission over LTE and, in contrast to the strictly centralized approach used in TNs, a hybrid edge-cloud computing framework is proposed for DNs with LTE connectivity. Next, as the fault detection and localization is one of the compelling potential applications for the proposed edge-cloud-based computing framework, we provide a brief overview of the fault localization methods in Section 3. The method from [11], which served as the basis for the fault localization algorithm (FLA) applied on the proposedframework, is also presented in more detail in this Section. The distribution network where the proposed framework was implemented and the installed measuring devices required for the FLA are discussed in Section 4. Section 5 provides an in-depth explanation for each component of the proposed framework from the perspective of communication and system integration. Lastly, conclusions are drawn in Section 6.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Vector Tracking Loops
\right] \tag{18}\]  
This simplifies to:  
\[X_{k+1}=FX_{k}+W_{k} \tag{19}\]  
where \(T\) is the KF update time, and \(W_{k}\) is the process noise with mean 0 and covariance \(Q\). Figure 5: Methods of the vector tracking process. In VT, the EKF takes the pseudo-range residual \(\delta\rho\) and the pseudo-range rate residual \(\delta_{\rho}\) as the observation quantities. The relationship between the linearized pseudo-range and pseudo-range rate residuals and the state variables is expressed as:  
\[\mathbf{Z}_{k} =\left[ \begin{array}{cccccc}\delta\rho_{1}&\cdots&\delta\rho_{i}& \delta\rho_{1}&\cdots&\delta\dot{\rho}_{i}\end{array} \right]^{T} \tag{20}\] \[=\left[ \begin{array}{cccccc}-a_{x,1}&-a_{y,1}&-a_{z,1}&0_{1\times 3 }&0_{1\times 3}&1&0\\ -a_{x,i}&-a_{y,i}&-a_{z,i}&0_{1\times 3}&0_{1\times 3}&1&0\\ 0_{1\times 3}&a_{x,1}&a_{y,1}&a_{z,1}&0_{1\times 3}&0&-1\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0_{1\times 3}&a_{x,i}&a_{y,i}&a_{z,i}&0_{1\times 3}&0&-1\end{array} \right]\mathbf{X}_{e,k}+ \left[ \begin{array}{cccccc}\eta_{1}&\cdots&\eta_{i}&\xi_{1}&\cdots&\xi_{i} \end{array} \right]^{T}\] \[=\mathbf{H}\mathbf{X}_{e,k}+\mathbf{V}_{k}\]  
where \(\mathbf{V}_{k}\) is the noise term with a mean of 0 and covariance \(R\). In VT, internal tracking improvements are similar to those in scalar tracking and can be categorized into four approaches: extending coherent integration time, improving discriminator estimation accuracy, enhancing the navigation filter, and hybrid vector/scalar tracking. Since the methods for extending coherent integration time and improving discriminator accuracy are applicable to both scalar and vector loops and have been well discussed in scalar tracking, they will not be revisited here. This section focuses on the evolution of VT, recent advancements in filter optimization, hybrid tracking strategies, and combined applications with IMUs.
## 2 System Synthesis
### Position Detection Subsystem  
A **Position Detection Subsystem** is required for a SailREC System to localise itself, primarily for the safety of its own and other vessels. The subsystem will assist on a local level in detecting nearby or approaching vessels and on a global level in tracking them in the high seas. Thus, the Position Detection Subsystem plays a crucial part in making the SailREC System follow the desired trajectory. The positional information is also required for enabling the transfer of fuel in the case of a tanker docking to the SailREC System, or also making the decision of docking to a port if the system is navigating to one.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 5 Performance and Evaluation
### The Computation Cost of Network Coding  
Network coding is essentially a mechanism that trades computation time for network throughput. As long as the time spent on encoding or decoding one packet is shorter than that of transmitting one packet (in other words, the coding bandwidth is larger than the effective link bandwidth), the coding operations will not hurt the link throughput, because they can be pipelined with each other and performed in parallel.  
To quantify the computation cost of network coding, we test the average bandwidth of \(GF\) (\(2^{8}\)) random linear encoding, on a 3.5 GHz desktop PC with 16 GB memory. Figure 6 plots the encoding bandwidth as a function of batch size and block size. Since the encoding always operates over a dense matrix, it tends to dominate the decoding time. Therefore, we focus on encoding bandwidth only. When the batch size is small, the coding bandwidth can be larger than the effective bandwidth of the 802.11 links [34]. Such a result is consistent with existing literature [19]. However, the actual coding bandwidth in our experiment is lower than [19]. This is mainly because our implementation is based on less optimized code. In fact, existing and ongoing work has already exploited fast network encoding/decoding methods, such as hardware accelerated approach [15].  
Considering the high computing power of existing clients, such as handheld mobile devices and laptops, the computation load of decoding and encoding operations of the clients usually do not affect the bandwidth of MAC protocols. In our experimental evaluation of CoFi, we also assumed the cost of network coding could be ignored.  
However, for low-end access points, such as sensors, computation resources may be limited, and the complexity of network coding may make a considerable difference. In our ongoing work, we implement and test network coding over a pair of programmable wireless nodes (the USRP testbed [28]), and dynamically adjust the batch size to satisfy the real-time QoS constraint of the link. We found that when the CPU is heavily loaded with other concurrent tasks, even with batch size 10, and packet size 1.5 KB, the computation cost of network coding is considerable. This work will be complementary to our CoFi protocol.  
Figure 6: The computation bandwidth of random linear encoding.
# Effects of BIM-Based Construction of Prefabricated Steel Framework from the Perspective of SMEs
## 2 Literature Review
### BIM for Prefabricated Steel Frame Construction
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Researcher/s (Year)** & **Research Content** \\ \hline \multirow{8}{*}{Design} & [PERSON] and [PERSON] [37] & Development of an automation module for modeling steel frame joints that can be used in structural detail design and modeling stages \\ \cline{2-3}  & [PERSON] et al. [38] & Development of an automatic design system for steel connections based on set-based Design with structural building information modeling (S-BIM) \\ \cline{2-3}  & [PERSON] and [PERSON] [39] & Development of an interface module that can exchange information between structural analysis software supporting structural design work and BIM software supporting detailed modeling and drawing work \\ \cline{2-3}  & [PERSON] et al. [40] & Description of a modeling system for steel structure joints based of BIM at the design stage \\ \cline{2-3}  & [PERSON] and [PERSON] [41] & Introduction of a BIM-based structural sustainability appraisal system \\ \hline \multirow{8}{*}{Construction} & [PERSON] and [PERSON] [42] & Development of a 4D simulation system prototype through automatic-process production \\ \cline{2-3}  & [PERSON] et al. [43] & Development of a tracking method for lifting paths of a steel frame tower \\ \cline{1-1} \cline{2-3}  & [PERSON] and [PERSON] [44] & Development of a smart creation process for shop drawings depending on drawing types \\ \cline{1-1} \cline{2-3}  & [PERSON] [45] & Application of real-time locating system (RTLS) technology for automating spray-applied fire-resistive covering work \\ \cline{1-1} \cline{2-3}  & [PERSON] et al. [46] & Using radio-frequency identification and real-time virtual reality simulation for optimization in steel construction \\ \cline{1-1} \cline{2-3}  & [PERSON] et al. [47] & Using BIM to improve the design and construction of bridge projects \\ \hline \end{tabular}
\end{table} 
Table 2: Research highlights of previous studies that studied BIM for steel frame construction. Previous studies focused on improving the productivity of the unit tasks of steel frame construction (e.g., automatic modeling of joints and drawings) and managing the process. However, the SMEs need to derive the effects of BIM for tasks that are related to the design, fabrication, and construction, rather than applying the technology to the unit tasks. This study presents a case study on steel frame construction to verify the main BIM-based construction management function to be installed in the system for an SME.