esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data-Driven Model for Aerodynamic Loads on Road Vehicles Exposed to Gusty Bora-Like Winds
## 1 Introduction  
Unsteady aerodynamic loads on vehicles have recently been investigated more closely aiming to improve roadway architecture in order to prevent wind-induced traffic accidents. Along with the development of new experimental and computational techniques, such studies are able to replicate unique flow conditions that were previously beyond the capabilities of traditional wind-tunnel methods. An approach using a conventional wind tunnel capable of generating an impulsive gust from an adjacent shuttered nozzle indicates some practical difficulties ([PERSON] and [PERSON], [1]), while a cross-wind track setup in conjunction with a boundary layer wind tunnel shows promising results ([PERSON] et al. [2]). [PERSON] [3] developed a method for the calculation of the time histories of aerodynamic loads experienced by trains for a time domain and artificial gust cases.  
The scope of the present study is to investigate gusty bora-like cross-wind loads on vehicles, as observed along the Croatian Coast. Downslope winds with characteristics similar to the Croatian bora are commonly present in many world regions (e.g., Grisogono and Belusic, [4]), so the results of the present study are highly relevant. The experiments concerning aerodynamic loads on vehicles exposed to gusty winds are carried out in a novel wind tunnel and a simple data-driven model capable of predicting the time history of unsteady aerodynamic loads on vehicles is developed. Transient wind effects on engineering structures and vehicles have been widely studied in the past, so the present study is important in supporting further developments on this topic.
# A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations
## 1 Introduction  
Risk management is a crucial driver in the successful delivery of construction projects [1; 2; 3; 4; 5; 6]. According to the Project Management Institute (PMI) [2], risk management processes include: planning the risk management, identifying the risks, performing a qualitative risk analysis, performing a quantitative risk analysis, planning risk responses, implementing risk responses and monitoring risks. The first is the most important process of a risk management program, as a risk not properly planned cannot be adequately managed.  
Planning risk management describes the overall risk management strategy and defines how the risk management processes will be integrated with all the other project management activities [2; 3]. To provide the greatest benefit to this process, a risk maturity evaluation study must be carried out at the early stage of this process to investigate the potential strengths and weaknesses of the organization in risk management processes.  
This paper presents a decision support model for the risk maturity evaluation of construction organizations. It employed an analytic network process (ANP) to account for the interdependency between the introduced risk maturity attributes and utilized the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the development of the model. Unlike the maturity models reported in the literature, the developed model accounted for the consideration of authority of the participants and their level of involvement in risk management processes.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem  
Non-TT flows in TSNs are used for soft real-time or non-real-time applications, providing bounded worst-case end-to-end delay (WCD) but with a looser delay constraint than TT flows. Multiple types of data flows, including TT, AVB, and BE, are sent from multiple ESs, as well as presequential SWs, which are connected to ingress ports in a single SW. To solve the composite traffic scheduling problem, the IEEE 802.1 Qbv standard [49] defined the time-aware shaper (TAS) mechanism to achieve traffic scheduling for different priority queues in time windows by establishing gate control lists (GCLs). Under the condition of network clock synchronization, the GCL in TAS periodically controls the opening and closing of the egress gates of the corresponding priority queue. With the adoption of GCLs, the transmission rate can match the egress bandwidth while segregating traffic of different priority levels, thereby reducing the interference of low-priority traffic on high-priority traffic and avoiding the starvation of low-priority traffic as much as possible.  
Typically, the SWs keep a total of eight priority queues, so the sequence of gates is Gate 7 to Gate 0. In the examples in this paper, \"o\" means gate open, \"C\" means gate closed, and each action is based on the time window. Correspondingly, the GCL can tell the time sequence of each type of traffic that is allowed to be sent to the output ports in a scheduling cycle, as shown in Figure 7.  
In this scheduling mechanism, the number of time windows allocated to each priority queue within a common time period is limited. Therefore, as the number of queueing flows increases, it inevitably leads to relative congestion, which eventually causes an increase in the time needed to complete the transmission of each single task. For non-TT flows, ESs connected to each SW may send multiple types of non-TT data with different priorities, such as audio, video or sensor signals. The destination of non-TT flows is basically concentrated on one centralized data server. In this case, when the routing mechanism assigns too many time-consuming non-TT flows to the same data link, it is difficult to meet the soft deadlines of all flows due to severe time slot contention, resulting in a waste of bandwidth resources. When planning routes for low-priority non-TT flows, we considered changing their routing scheme from simply taking the shortest routing approach. When there were already higher priority flows causing queue congestion, we considered \"bypassing\" the congested SWs in exchange for a relative balance of SW loads at the cost of a partial loss of route length.
# A Novel Approach for Software Defect prediction Based on the Power Law Function
## 4 Complexity Analysis  
Authors should discuss the results and how they can be interpreted in perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.  
Algorithm complexities of the other algorithms tested in this study are shown in Table 7. The complexity of k-means, CLA, and CLAMI were obtained from references [4,17]. The proposed approach has a relatively lower complexity than the other algorithms, indicating a low cost in software defect predictions. Therefore, from the perspective of algorithm complexity, our proposed approach possesses some advantages over the other tested algorithms to some extent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & \multicolumn{5}{c}{**Methods**} \\ \cline{2-6}  & **K-Means** & **CLA** & **CLAMI** & **X-Means** & **Power law Function Approach** \\ \hline Camel1.6 & 0.192 & 0.336 & 0.353 & 0.290 & **0.355** \\ Forrest-0.8 & 0.074 & 0.133 & 0.125 & 0.125 & **0.222** \\ Synapse-1.0 & 0.043 & 0.264 & 0.040 & 0.044 & **0.318** \\ Berek & 0.400 & 0.737 & 0.686 & 0.345 & **0.824** \\ Intercafe & - & **0.375** & 0.353 & 0.154 & 0.353 \\ Termo & 0.485 & 0.500 & 0.581 & 0.500 & **0.593** \\ Pbeans2 & 0.410 & 0.462 & 0.348 & 0.410 & **0.500** \\ ar1 & 0.229 & 0.197 & 0.185 & **0.353** & 0.206 \\ ar4 & 0.471 & **0.478** & 0.444 & 0.240 & 0.471 \\ ar5 & 0.737 & 0.640 & 0.615 & **0.740** & 0.615 \\ Apache & 0.409 & 0.715 & 0.725 & 0.529 & **0.725** \\ Safe & 0.595 & 0.681 & **0.694** & 0.619 & 0.680 \\ \hline Average & 0.385 & 0.471 & 0.436 & 0.369 & **0.501** \\ \hline \hline \end{tabular} Boldface typeface indicates the highest value of each dataset.  
\end{table}
Table 6: Comparisons of F-measure results between the proposed approach and other methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **K-Means** & **CLA** & **CLAMI** & **X-means** & **Power-Law Function Approach** \\ \hline Complexity & \(O(nkm)\) & \(O(2n+n^{2})\) & \(O(3n+n^{2})\) & - & \(O(2n)\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Complexity of the tested algorithms.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 5 Experimental Results
### Performance Evaluation  
In Table 3, the execution time of semantic segmentation thread and the whole DS-SLAM algorithm, frame rate, the power consumption and the energy efficiency of the proposed method in this paper are compared with the software implementations on the Intel i7-8750H CPU and the NVIDIA P4000 GPU. In contrast with CPU, our acceleration platform achieves up to 13\(\times\) frame rate improvement and up to 18\(\times\) energy efficiency improvement. The GPU server has more computing resources, thus achieves a higher real-time performance than ours. An important application of mobile robots is to provide indoor services. In order to ensure human safety, the moving speed is relatively slow, so the real-time performance of 5.3 fps can meet the application requirements. In [9], the experimental platform is Intel i7-6800K CPU with NVIDIA P4000 GPU and the overall power consumption is 245 W, which is unacceptable for energy-limited applications. Mobile robots are usually powered by batteries, so it is essential to reduce the power consumption of the processor. The HERO is a low power platform, and the proposed accelerator implemented on it is 2.2 times more energy-efficient than GPU.  
The proposed high energy efficiency accelerator provides the feasibility of deploying the DS-SLAM algorithm on mobile robots. The SegNet network, which is used by the semantic segmentation thread, can be used for 21 kinds of objects including indoor and outdoor scenes, and well supports the semantic segmentation of the datasets used in the SLAM algorithm. The evaluation results on the TUM dataset are shown in Figure 7 and the objects are well segmented. As the dynamic object, people will be eliminated in the final mapping to improve the localization accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ALMS** & **Registers** & **M20 Ks** & **DSPs** \\ \hline
**Utilization** & 24\% & 12\% & 63\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware resource utilization of Arria 10 Field Programmable Gate Array (FPGA).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Software Implementations**} & \multicolumn{2}{c}{**Our Work**} & \multicolumn{2}{c}{**Improvements**} \\  & & & **(vs. CPU)** \\ \hline Platform & i7-8750H CPU &
\begin{tabular}{c} i7-6800K CPU + \\ P4000 GPU \\ \end{tabular} & HERO & - \\ \hline Power & 45W & 245 W & 35 W & 22.2\% \\ \hline Execution time of semantic & 2582 ms & 37.6 ms & 155 ms & 94.0\% \\ \hline Execution time of the whole & 2600 ms & 59.4 ms & 190 ms & 92.7\% \\ DS-SLAM & & & & \\ \hline Frame rate & 0.38 fps & 16.8 fps & 5.3 fps & 13\(\times\) \\ \hline Energy efficiency & 0.008 fps/W & 0.068 fps/W & 0.151 fps/W & 18\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Execution time, frame rate, power consumption, and energy efficiency comparison results.
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 3 Response Surface Analysis
### Stability Study of Geopolymer Stabilized Soft Soil  
#### 3.4.1 Water Stability Studies  
The water stability coefficient (Equation (1)) was used to evaluate the water stability of the geopolymer-stabilized soil in the water stability test [25].  
\[\delta_{\rm cT}=\frac{\rm R_{\rm cRT}}{\rm R_{\rm cT}}\times 100\% \tag{1}\]  
where \(\delta_{\rm cT}\) is the water stability coefficient and \(\rm R_{\rm cRT}\) and \(\rm R_{\rm cT}\) are the UCS (kPa) of the soaked and unsoaked samples at age T, respectively.  
The soft soil without geopolymer could not be shaped, and when the soft soil was mixed with geopolymer, the sample was still intact after 32 days of complete water im  
Figure 4: Compressive strength of the soil stabilized by geopolymer and OPC.  
Figure 5: Shear resistance of the soil stabilized by geopolymer and OPC.  
mersion, reflecting that the stabilization of the soft soil using the geopolymer significantly improved the water stability of the soft soil. The main reason for the improvement of the water stability of the geopolymer-stabilized soft soils was that the gelling substances generated by the geopolymerization reaction bound the soil particles to each other and filled the pores of the soil particles effectively, thus forming a spatial mesh skeleton structure, which enhanced the stability and overall structure of the stabilized soil [20].  
The UCS of the specimens before and after water immersion were the most direct indicator of the water stability of the geopolymer-stabilized soft soils. Figure 6 shows the UCS and water stability coefficients of the geopolymer-stabilized soft soils with geopolymer contents of 10%, 15%, 20%, and 25% under standard maintenance and continuous water immersion for 32 days. It can be seen that the water stability coefficients were all greater than 80%, which reflects the water stability of the geopolymer-reinforced soils. The comparison shows that the softening effect of the water immersion reduced the UCS of the geopolymer-stabilized soil. When the geopolymer content was higher, the loss rate of compressive strength was lower, and the water stability was higher. This is because the more geopolymer was incorporated, the more gel material was generated by the action of the geopolymer, which increased the bonding effect and strengthened the overall structure of the stabilized soil, thus increasing its ability to resist the weakening effect of the water immersion.
## 4 Results
### Gas Geochemical Results: Dissolved and Free Gases, Noble Gas, and Isotope Ratio  
The collected samples refer to both free and dissolved gases depending on the presence/absence of bubbling in the water of selected springs in each survey. Only four samples for free gases were taken in January 2019.  
The results of the performed gas analyses (both free and dissolved gases) are reported in Supplementary File Table S1. Almost all water samples are CO\({}_{2}\) dominated with  
Figure 2: Piper diagram of groundwater samples from the San Vittorino Plain. All samples are predominantly enriched in bicarbonate and calcium ions (fresh waters and mixing waters; Ca-HCO\({}_{3}\) facies). Only springs of the Antrodoco area (i.e., S1 and S2) are included in the calcium-sulphate waters domain (mineralized waters; Ca-SO\({}_{4}\) facies). For spring location see Figure 1.  
concentrations ranging from 3.1 to 663.7 ccSTP/L. Some springs (e.g., S3, S6, and S12) recorded concentrations that were lower than the detection limit. Similarly, CH4 was also measured only in some springs (Supplementary File Table S1) with concentrations between 5.21 \(\times\) 10\({}^{-5}\) and 1.52 \(\times\) 10\({}^{-3}\) ccSTP/L. Helium concentrations vary from 3.19 \(\times\) 10\({}^{-4}\) to 3.92 \(\times\) 10\({}^{-3}\) ccSTP/L. He-isotope composition in the collected fluids ranges from 0.07 Ra to 0.15 Ra. The \({}^{4}\)He/\({}^{20}\)Ne ratios of all investigated fluids are generally higher than 0.318 (i.e., the value of the atmosphere) [86], indicating that air contamination was negligible [73]. The only exception is the sample collected at S11, in which \({}^{4}\)He/\({}^{20}\)Ne is lower than 1, showing that this sample is affected by air contamination. All the samples have \({}^{40}\)Ar/\({}^{36}\)Ar ratios that are indistinguishable from the ratio in the atmosphere.
# Clinical Assessment of Customized 3D-Printed Wrist Orthoses
## 4 Discussion
Even effectiveness of conventional plaster cast and 3D-printed cast is similar, and most of the research concluded that the customized 3D-printed cast was high in comfort, and had lesser side effects [12; 17; 18; 19]. In terms of brace structure, most of the design was two halves fixed by a strap which was stable and safe; only the three pads brace structure resulted worse outcomes [20]. There are a few data regarding the treatment of paediatric upper extremity fracture with 3D-printed casts [17; 18; 19]. Children were successfully treated with the 3D-printed cast, all with high patient satisfaction. An appropriate allowance between wrist and the 3D brace is crucial for immobility, effectiveness, and comfortability which were sometimes in opposite directions in design. In current research, there was no evidence to prove whether or not the tolerance appropriate. Regarding immobility test, [PERSON] et al. assessed volunteers by Jebsen Hand Function Test (JHFT) and Patient-Rated Wrist Evaluation (PRWE), whereas in our study, pressure sensor was used to reveal the appropriate clearance between wrist and 3D brace by considering comfort and immobility. A limitation of this study is that the number of patients was small. This was primarily designed as a safety and proof of concept study. Compared to the experimental group, the number in control group was small in the Phase II trial. Patients in the control group did not undergo wrist correction surgery. Based on patients' status, physician chose the best treatment for patients. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & **DASH** & & & \\ \hline  & **Age: 60** & **F:398 (Age63)** & **M:120 (Age:50)**[25] & **Age: 55** & **F:23 (Age:58.52)** & **M:10 (Age:43.4)** \\ \hline
3 months after injury & N & Mean (95\% CI) & Median (quartiles) & N & Mean (95\% CI) & Median (quartiles) \\ Undisplaced, casted & 145 & 23 (20–27) & 17 (4.4–38) & 9 & 37.41 (25.11–49.7) & 28.33 (25.11–49.7) \\ Reduced, casted & 157 & 26 (23–29) & 22 (11–38) & & & \\ Primary operated & 55 & 22 (16–27) & 15 (8.8–26) & 24 & 24.16 (17.24–31.08) & 25 (11.04–34.17) \\
12 months after injury & & & & & & \\ Undisplaced, casted & 143 & 15 (11–18) & 402 (0.8–22) & 9 & 6.11(\(-\)0.55–12.78) & 2.5 (2.08–6.66) \\ Reduced, casted & 155 & 17 (14–20) & 12 (2.6–28) & & & \\ Primary operated & 62 & 16 (10–21) & 6.0 (2.6–18) & 24 & 0.52 (\(-\)0.48–1.526) & 0 (0–0) \\ \hline \end{tabular}
\end{table} 
Table 8: DASH comparison with a previous study.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
## 4 Overview of Proposed Next-Generation 3D Display Solutions
### Varifocal Displays  
The varifocal approach considers using eye-tracking to determine the gaze direction of the viewer--in essence it determines the vergence angle of both eyes. This information is used to change the focal distance of the image plane so that the vergence is matched with the accommodation, thus eliminating VAC. The means for continuously actuating the focal distance can vary, but typically it is a physically reciprocating display [8; 9] in respect to the eyepiece lens or, alternatively, it can be a varifocal lens--for example, an Alvarez lens [10]. As the eye movements are not extremely rapid, focus-tunable systems can easily adapt in real-time. Nonetheless, as there is still only a single image plane (per eye), the whole content is always in focus, thus inhibiting correct monocular focus cues. The only way how to overcome this is by computationally recalculating the image to include synthetic blur. Nevertheless, this adds computational complexity, if realistic blur is to be simulated, and its true effectiveness and impact on visual perception is still not fully understood [11]. It must be said, that the varifocal approach primarily has been researched in respect to virtual reality applications, where it is more conveniently implementable due to a larger available real-estate, as in comparison to AR displays. Nevertheless, the varifocal principle has also been introduced within AR display products.  
Among commercially-available AR display devices, currently there is one more widely-known case, whereby it has been attempted to mitigate VAC by varifocal design. The company Magic Leap Inc. released, in 2018, its AR display product, which is the first commercially-available AR solution that addresses the pressing issue of VAC and, overall, it lights the way for the general trend. The product is set to use two discrete image focal planes, the switch between which occurs based on the readings from eye-tracking. Though it is not possible to provide a life-like experience with only two focal planes, the addition of the second focal plane at a closer distance brings the boundary for a sharp 3D image to a considerably closer distance [12].
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 3 Experimental Setup
### Neural Network Model  
As the main element for the SAD system we opt for a CRNN based classifier. Particularly, we use the variant using 2D convolutions from the models already presented in our previous work [23]. The schematic representation of the proposed CRNN model is described in Figure 3.  
As it can be observed in the figure, the model is mainly composed of two elements. First, three 2D convolutional blocks are used to process input features. Each of these blocks is integrated by a 2D convolutional layer with 3 \(\times\) 3 kernel size and 64 filters. Then, it is followed by a batch normalisation layer [70] and the application of a rectified linear unit (ReLU) [71] activation function. Finally, a max-pooling mechanism is applied considering a 4 \(\times\) 1 stride, so that only the frequency axis is downsampled. Then the output of the last convolutional block is fed to the RNN block, generated by stacking three bidirectional LSTM layers with 128 neurons each. This block is then followed by a linear layer that generates the speech class score as a single neuron output.