esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 8 Conclusions  
This paper investigated how important reliability is in the context of vehicles and safety, especially in the context of stopping distances. We analyzed real world field test data and showed how highly reliable communication could assist and even shorten the stopping distance via the significantly reduced reaction time. We observed that autonomous or automatic braking systems will be a lot more efficient in reducing stopping distances, which in turn will allow for more efficient traffic. A model for calculating the reduction to stopping distance when subjected to consecutive losses has also been produced and quantified, and this helps to highlight the importance of reliability when a system is based solely on communication. Our results also showed that almost perfect packet reception must be guaranteed for safety when CAVs become more prevalent on the road, whilst also showing that lower data rates are not suitable for all safety needs.  
We also proved that a method of reduced safety distance with the use of our system can reduce the effect of tailgating. We showed that our safety distance algorithm is viable in our density-based simulations.  
We showed that our system can improve safety for vehicles, using the DSRC and adequate mathematical interpretation to maintain safe distances when driving and equipped in conjunction with ADAS systems. We also prove that the main detriment to DSRC is the collisions that can occur between vehicles and that distance is a much smaller factor in comparison. With this said, we show that with the use of both DSRC and ADAS the safety zone can be reduced, which would in turn reduce the number of density collisions and, by proxy, increase road efficiency.  
Conceptualization, S.K.F.; methodology, S.K.F.; software, S.K.F., Z.T; validation, S.K.F., I.Y.; formal analysis, S.K.F.; investigation, S.K.F.; resources, S.K.F.; data curation, S.K.F.; writing--original draft preparation, S.KF.; writing--review and editing, S.K.F., Z.T., J.H.; visualization, S.K.F. and Z.T.; supervision, Z.T.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EPSRC and European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 824019 and grant agreement No 101022280.  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 2 Theoretical Background
### Uncertain Simulation Algorithm for Calculating the URI  
In this section, the uncertain simulation (US) algorithm for URI is explored by combining the philosophy of uncertain simulation [41] and Definition 6.  
Let LSF \(g(\mathbf{\xi})\) of an engineering structure contain independent uncertain variables \(\mathbf{\xi}=(\xi_{1},\xi_{2},\cdots,\xi_{n})^{\text{T}}\) with uncertainty distributions \(\Phi_{1}\), \(\Phi_{2},\cdots\), \(\Phi_{n}\), respectively. Then, the implementation of the US algorithm (Algorithm 1) is briefly summarized as follows.  
1. [label=Step 0.0]
2. Set \(m_{1}(i)=0\) and \(m_{2}(i)=0\), \(i=1,2,\cdots,n\).
3. Generate a set of uncertain variables \(\xi_{k}=(\xi_{k}^{(1)},\xi_{k}^{(2)},\cdots,\xi_{k}^{(n)})^{\text{T}}\), \(k=1,\cdots,N_{\text{u}}\) from the uncertainty space based on the inverse uncertainty distributions \(\Phi_{1}^{-1}\), \(\Phi_{2}^{-1}\), \(\cdots\), \(\Phi_{n}^{-1}\).
4. Rank uncertain variables \(\xi_{k}^{(i)}\) from small to large: \(\xi_{1}^{(i)},\cdots,\xi_{N_{\text{u}}}^{(i)}\), \(i=1,\cdots,n\).
5. From \(k=1\) to \(k=N_{\text{u}}\), if \(g(\xi_{k})>0\), \(m_{1}(i)=m_{1}(i)+1\), then denote as \(x_{m_{1}(i)}^{(i)}=\xi_{k}^{(i)}\); otherwise, \(m_{2}(i)=m_{2}(i)+1\), denote \(y_{m_{2}(i)}^{(i)}=\xi_{k}^{(i)}\), \(i=1,2,\cdots,n\).
6. If \(m_{1}>0\) and \(m_{2}>0\), calculate \(a^{(i)}=\[PERSON]_{i}(x_{m_{1}(i)}^{(i)})\wedge(1-\[PERSON]_{i}(x_{1}^{(i)}))\wedge(\[PERSON] _{i}(x_{1}^{(i)})+1-\[PERSON]_{i}(x_{2}^{(i)}))\wedge,\cdots,\wedge(\[PERSON]_{i}(x_{m_{ 1}(i)-1}^{(i)})+1-\[PERSON]_{i}(x_{m_{1}(i)}^{(i)}))\); and \(b^{(i)}=\[PERSON]_{i}(y_{m_{2}(i)}^{(i)})\wedge(1-\[PERSON]_{i}(y_{1}^{(i)}))\wedge(\[PERSON] _{i}(y_{1}^{(i)})+1-\[PERSON]_{i}(y_{2}^{(i)}))\wedge\cdots\wedge(\[PERSON]_{i}(y_{m_{ 2}(i)-1}^{(i)})+1-\[PERSON]_{i}(y_{m_{2}(i)}^{(i)})),i=1,\cdots,n\), else if \(m_{1}>0\), return \(L=1\), otherwise, return \(L=0\).
7. If \(a^{(i)}<0.5\), return \(L_{1}^{(i)}=a^{(i)}\), \(L_{2}^{(i)}=1-a^{(i)}\); if \(b^{(i)}<0.5\), return \(L_{1}^{(i)}=1-b^{(i)}\), \(L_{2}^{(i)}=b^{(i)}\); otherwise, return \(L_{1}^{(i)}=0.5\), \(L_{2}^{(i)}=0.5\), \(i=1,\cdots,n\).
8. If \(a=L_{1}^{(1)}\wedge L_{1}^{(2)}\wedge\cdots L_{1}^{(q)}>0.5\), then \(L=a\); if \(b=L_{2}^{(1)}\wedge L_{2}^{(2)}\wedge\cdots L_{2}^{(q)}>0.5\), then \(L=1-b\); otherwise, return \(L=0.5\).
9. Return the estimated value \(URI=L\).  
**Algorithm 1** Uncertain simulation for URI  
The US algorithm does not depend on the condition that LSF is a strictly monotone function for uncertain variables. Moreover, this algorithm avoids solving complex equations when the LSF is highly nonlinear or high-dimensional are present.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 3 Antenna Measurements
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref. Antennas**} & **Antenna Size** & **Controllable** & **Controllable** & **Design** \\  & **(mm\({}^{3}\))** & **Notch-Bandwidth** & **Notch Frequency** & **Complexity** \\ \hline
[19] & \(44\times 44.4\times 0.1\) & No & No & simple \\
[20] & \(48\times 50\times 1\) & Yes & Yes & simple \\
[21] & \(50\times 50\times 1.575\) & No & No & complex \\
[22] & \(28\times 18\times 0.8\) & Yes & Yes & very complex \\
[23] & \(38\times 42\times 0.5\) & Yes & No & very complex \\
[24] & \(8.5\times 22\times 0.8\) & Yes & No & complex \\ This work & \(16\times 25\times 1.52\) & Yes & Yes & simple \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of the proposed antenna with existing rectangular notched antenna in literature. Figure 16: Simulated measure radiation pattern in E-and H-plane radiation pattern at 3, 7, and 10 GHz.
## 3 Materials, Methods, and Findings
### Random Forest (RF)  
The Random Forest algorithm is configured with the parameter n_estimators = 100. A more stable and generalizable model was obtained by combining multiple decision trees. The model made the final prediction by a majority vote of the output of each tree. This method was preferred especially to better model the complexity of the dataset and to increase the generalization capability. Figure 7 shows the complexity matrix of the Random Forest algorithm.  
Figure 6: Decision Tree confusion matrix.  
Figure 7: Random Forest confusion matrix.  
While the model made 90 correct predictions in the \"no verbal violence\" class, it incorrectly labelled 12 instances as \"verbal violence present\" (false positive). In the \"verbal violence present\" class, the model made 98 correct predictions and incorrectly labelled only 10 instances as \"no verbal violence\" (false negative). These results show that the Random Forest algorithm is highly successful in detecting verbal violence (89.52% recall) and keeps the false negative rate very low. At the same time, the limited number of false positives (11.76%) shows that the model also performs well in the \"no verbal violence\" class.
## 4 Failure Mechanisms and Design Recommendations
### Effect of the Steel Diagonal Braces  
This section aims to reveal the mechanism of the cracks distribution patterns observed from the experiments and to discuss the efficiency of steel diagonal braces. For this kind of hybrid structure, due to the technological requirements in TPPs, steel truss and A-shaped brackets bear the important equipment and facilities directly, are expected to suffer negligible damage subjected to the earthquake. Accordingly, RC columns are expected to dissipate most of the earthquake energy. Figure 17a,b show the simplified analysis models of the structure without and with diagonal braces, respectively. As shown in Figure 17, the equivalent lateral loads to the experimental loading actions can be represented by \(F_{1}\) and \(F_{2}\). The bending moment distribution patterns of the column without and with diagonal braces under the lateral loads are presented in Figure 17a,b, respectively. \(M_{\text{b}}\), \(M_{\text{c}}\) and \(M_{\text{t}}\) represent the bending moment of the base column, corbel and top column, respectively. It can be found that the moment distribution of the column was changed after setting the diagonal braces due to the change of action transfer mode. For the structure with diagonal braces, the moment mainly distributed in three areas including the bottom of column, lower part of the corbel and upper part of the corbel, which were same with the cracks distribution areas observed from the experiments. However, for the structure without diagonal braces, the moment mainly distributed in the bottom of column and the upper part of column. Accordingly, the increase of energy dissipating areas for the column by setting diagonal braces might improve the energy dissipation capacity of this hybrid structure. Furthermore, in order to avoid the sudden decrease of the overall stiffness and loss of the structural integrity might be caused by the failure of steel diagonal braces, the braces should be designed strong enough. On this basis, the seismic performance of this kind of hybrid structure can be improved effectively by the steel diagonal braces.
## 2 Methodology
### Time-Series Analysis
#### 2.4.3 Determination of the Number of Iteration Steps  
The third step in the time-series analysis is to determine the number of steps when terminating the iteration.  
According to Sections 2.4.1 and 2.4.2, the CV (i.e., the SD/mean) corresponding to each iteration step has been achieved. At this time, the convergence of the SD/mean values is investigated by drawing a curve that the abscissa is the number of iterations, and the ordinate is SD/mean, as shown in Figure 4a. Figure 4a shows that when the iterative process reaches the 8000 th step, the curve tends to converge. To make the result more accurate, the difference of SD/mean corresponding to \(T_{1}-T_{0}\), \(T_{2}-T_{1}\), \(T_{3}-T_{2}\),..., \(T_{n}-T_{n-1}\) are first calculated, and then a curve is plotted as shown in Figure 4b. The difference between the 9000 th step and 8000 th step is almost zero. With the increase in the number of iterations, the difference is basically equal to zero. In addition, the value of SD/mean does not change. At this time, it is thought that the iterative process could be terminated when iterating to the 8000 th step.  
The aforementioned method of determining the number of iterations is based on the statistics, i.e., the SD/mean, for the energy of all rock blocks of a jointed rock slope. The calculation of energy considers the displacements and weights of all blocks. The proposed energy-based method is a generalized form of the displacement-based method, and it is much more applicable than the displacement-based method. In the proposed energy-based method, there is no need to determine the potential sliding surface before conducting the numerical modeling. The proposed energy-based method can be applied in the cases where the sliding surface is easy or difficult to be determined.
_Applied_  
_sciences_  
_Article_  
**Optimization of the Antioxidant Activities of Mixtures of Melastomataceae Leaves Species**  
_(M. malabathricum [PERSON], M. decemfidum,_  
**and M. hirta) Using a Simplex Centroid Design**  
**and Their Anti-Collagenase and Elastase Properties**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 5**  
1 Halal Products Research Institute, Universiti Putra Malaysia, Putra Infoport, Serdang 43400 UPM, Malaysia; [EMAIL_ADDRESS]  
2 Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400 UPM, Malaysia  
3 Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400 UPM, Malaysia; [EMAIL_ADDRESS]  
4 Department of Forest Production, Faculty of Forstry, Universiti Putra Malaysia, Serdang 43400 UPM, Malaysia; [EMAIL_ADDRESS]  
5 Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400 UPM, Selangor, Malaysia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +60-389-474-945; Fax: +60-389-381-015  
Received: 19 August 2020; Accepted: 15 September 2020; Published: 8 October 2020  
In this study, a simplex-centroid mixture design (SCMD) approach was used to select the optimal proportions of three different Melastomataceae leaves species (_M. malabathricum_, _M. decemfidum_, and _M. hirta_) extracts to determine the optimum antioxidant activities of total phenolic compound (TPC) and ABTS (2, 2'-azino-bis (ethylbenzthiazoline-6-sulfonic acid)) radical-scavenging activities. Twelve experimental designs were set up, consisting of points that were equally weighted mixtures of 0 to 1 components, which were pure blends (1, 0,..., 0), binary blends (1/2, 1/2, 0,..., 0), tertiary blends (1/3, 1/3, 1/3, 0,..., 0), and a control point. The in vitro anti-elastase and collagenase activities were evaluated in order to determine the anti-aging efficacy of the optimized mixture extracts. A high-performance liquid chromatography (HPLC) analysis was employed to identify the flavonoid content (rutin and quercetin) present in the optimized mixture extracts. The results showed that the best proportions of the optimum phenolic compounds and ABTS activity corresponded to 30%, 40%, and 30% of _M. malabathricum_, _M. hirta_, and _M. decemfidum_ leaf extracts, respectively. The in vitro anti-collagenase and elastase activity evaluation of the optimized mixture extracts showed 70% inhibition against both elastase and collagenase enzymes. The HPLC analysis revealed two flavonoids (rutin and quercetin) at retention time 7.770 and 8.769 min, respectively, in the mixture extracts. This study suggests the potential exploitation of mixtures of Melastomataceae leaves (_M. malabathricum_, _M. decemfidum_, and _M. hirta_) as cosmetic ingredients for antioxidant and anti-wrinkles applications.  
_Melastoma malabathricum_; _M. hirta_; _M. decemfidum_; simplex centroid; rutin; quercetin; antioxidant; collagenase; elastase
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Topology Aware Routing Protocols
#### 4.1.3 Continuous Hopfield Neural Network Optimization (CHNN-DSR)  
[PERSON] et al. optimized the DSR protocol on the basis of continuous Hopfield neural network and named it CHNN-DSR [39]. DSR [40] is a reactive routing protocol based on topology, it uses the active caching strategy and extracts topology information from source routing, as shown in Figure 5, where \(U_{i}\) is the input voltage of electronic amplification element, \(V_{i}\) is the output voltage, and operational amplifier \(i\) is the \(i_{th}\) neuron. The key of this paper is to map the routing problem to the neural network and find the optimal path through the dynamic evolution of the neural network. Therefore, the author maps the FANET network nodes to the CHNN network to find the route when the network state becomes stable. The dynamic equation for calculating the input state increment is  
\[\begin{split} dU_{mi}=-S\sum_{j\
eq i}V_{mj}-Q\sum_{m\
eq n}V_{nj }-T(\sum_{m}\sum_{j}V_{mj}-16)^{2}\\ -P\sum_{n}s_{mn}(V_{n(i+1)}+V_{n(i-1)})-\frac{U_{mi}}{R_{mi}C_{mi}}.\end{split} \tag{2}\]  
where \(S,Q,T,P\) are penalty parameters, and they all are fixed values. Then the output state of the neural network is updated according to the sigmoid function.  
**Advantages:** Using this method, a more stable route is obtained, the stability of FANET is improved, and the communication efficiency of the network is improved. Simulation results show that in FANET system using CHNN-DSR, packet delivery rate, end-to-end average delay, throughput and other key indicators are better than FANET system using DSR directly.  
Figure 5: CHNN circuit topology.  
**Disadvantages:** (1) Once the path selection process fails, the whole evolution process will start again, so it has poor fault tolerance. (2) The speed of UAV has a great impact on routing performance. Experiments show that when the speed of UAV reaches 50 m/s, the delay of CHNN-DSR is as high as 7.5 s.
## 4 PFAC Calibration Results
### Sentinel-3A
#### 4.1.1 Sentinel-3A Pass No. 14 Cal/Val Results  
The ascending Sentinel-3A Pass No. 14 approaches the Gavdos island from the south. Calibration over sea surface is carried out using the Gavdos Cal/Val facility. About 9 s later, on its ascending pass, the satellite reaches the CDN1 transponder Cal/Val site where it is also calibrated (Figure 15). The bias in sea-surface height and range for Sentinel-3A along its Pass No. 14 are given in Figure 16 for all available passes at the time of writing.  
Figure 14: The Sentinel-3A sea-surface calibration regions for its Pass No. 14, No. 278 and No. 335 over the PFAC Cal/Val facility in west Crete, Greece. Sea-surface calibration is carried out at three different sea regions based upon the Gavdos, CRS1 and RDK1 Cal/Val sites. The CDN1 transponder Cal/Val site is also illustrated with a red star.  
The sea-surface bias for Sentinel-3A Pass No. 14 is calculated as \(-\)11.98 mm, whereas the range bias with the transponder is determined as +4.90 mm. These results indicate that the altimeter measures larger ranges than the \"ground reference\" by +4.90 mm with the present settings. The negative value of \(-\)11.98 mm in the SSH bias seems to support both Cal/Val results. The absolute difference between the two Cal/Val results is less than 1 cm, whereas the FRM uncertainty of each independent technique is of the order of \(\pm\)3 cm.  
Figure 16: The latest calibration results for the ascending Pass No. 14 of Sentinel-3A using the sea-surface (upper) and transponder (lower) calibrations.  
Figure 15: The Sentinel-3A Pass No. 14 is calibrated using both sea-surface and transponder calibration techniques at the Gavdos and CDN1 Cal/Val facilities, respectively.
## 1 Introduction
[41] proposed an algorithm of extracting color features from the first-order histogram of the RGB planes. Because the characteristic color of blood belongs to the red plane, the RGB color-based method is widely applied in bleeding detection. However, the RGB method only involves the color information, not the color intensity and saturation, resulting in difficulty to individually analyze the RGB color. In the HSV color space, three components, that is, hue, saturation, and intensity, are used to separately represent the identification color, color purity, and light intensity, which solves the problem of RGB color space. Moreover, the HSV color space is more appropriate for classification and description [42]; thus, several researchers applied the HSV color space analysis method to bleeding detection. [PERSON] et al. [43] utilized the histogram analysis of the mean and variance in the HSV color space to detect the wireless capsule endoscopy bleeding images. [PERSON] et al. [38] used the RGB, normalized RGB, HSV, Lab, and YIQ color planes to extract the bleeding region and applied the semantic segmentation approach to detect the bleeding zone in capsule endoscopy images. The result has shown that the HSV color space achieved the higher performance in extracting the bleeding features. Another reason why several researchers used the HSV color space is because of the high saturation value of bleeding images. Given the previous discussion, this study applied the HSV approach to detect the bleeding of the extra prostate tissue. However, a red light, which has the same color as blood, arising from the cutting loop tool would greatly affect the detection in processing bleeding images. It was difficult to distinguish the bleeding color from the red light color of the cutting loop. Thus, the ResNet-50 model was used to classify red light and non-red light images of the cutting loop. Furthermore, the HSV color plane was used to detect and calculate the bleeding areas in the TURP surgery. This paper is organized as follows: Section 2 introduces the methods of bleeding volume detection. Section 3 discusses the detailed experimental results with a comparison of the three classical segmentation algorithms, U-Net, Autoencoder and Segnet models. Section 4 concludes this article.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 1 Introduction
As a summary, the followings are the main contributions of this research:  
1. Developed a culvert blockage visual dataset using multiple sources, including real culvert images from WCC records, simulated lab-scale hydrology experiments and computer-generated synthetic images;
2. Explored the potential of existing deep learning CNN and conventional machine learning models for classifying blocked culvert images as a potential solution towards automating the manual visual classification process of culverts for making blockage maintenance-related decisions;
3. Highlighted the challenges of culvert blockage visual dataset and inferred important insights to help improving the classification performance in future;4. Proposed a detection-classification pipeline to achieve higher blockage classification accuracy for practical implementation. Furthermore, a partial automation framework based on the class prediction probability is introduced using a single deep learning model to assist the visual inspection process. The rest of the paper is organized as follows: Section 2 presents the theoretical background of the implemented CNN models in this investigation. Section 3 provides information about the dataset used in this study for culvert blockage classification. Section 4 outlines the experimental protocols adopted to perform the experiments. Furthermore, this section provides information about the evaluation measures used to assess the classification performance of the implemented models. Section 5 presents the results of the experiments and also reports the critical insights from the investigation. Section 6 presents a brief introduction to the detection-classification pipeline towards improving the blockage classification performance. Section 7 concludes the study and reports the highlighted outcomes from the experiments. Furthermore, the section lists potential future applications of the presented research.