esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 4 Results and Discussions
### Control Effects on Flow Structures and Pressure Fields
#### 4.2.3 Spanwise Coherence  
To clarify further the nature of the vortical structures, the coherence between two points separated by spanwise distances \(\Delta z\) at \(x/L=0.5\) and \(y=0\) was examined. Figure 18 shows the spanwise coherence of the streamwise velocity with and without control. Although the coherence shows locally-high values, depending on the pitch \(s/L=0.5\) of the actuators, overall it is decreased.  
Figure 19 shows the phase-averaged fluctuation pressure with and without control at the phase where the vortex collides with the downstream edge of the cavity. In the baseline flow, the distribution of the pressure is coherent in the spanwise direction. At the collision of the large-scale vortical structures  
Figure 16: Iso-surfaces of the component of the second invariant related to vortices with a spanwise axis \(q_{z}/(U_{0}/L)^{2}=-20\) color-coded by the spanwise vorticity, together with contours of the streamwise vorticity in the shear layer (\(y/\theta=2.9\)) at the phase where the vortex collides with the downstream edge of the cavity: (**a**) baseline flow; and (**b**) controlled flow.  
with the downstream edge of the cavity, the radiated sound is reinforced in the spanwise direction by the coherent pressure fluctuations. In the controlled flow, however, the fluctuation pressure varies in the spanwise direction. This variation also contributes to the weakening of the cavity tone.  
Figure 19: Contours of the fluctuation pressure at \(U_{0}=30\) m/s for phase-averaged flow fields at the phase where the vortex collides with the downstream edge of the cavity: (**a**) baseline flow; and (**b**) controlled flow.  
Figure 17: Predicted power spectra of velocity, \(u_{\rm h}\), with and without control at the peak of maximum power for \(y/\theta=6.4\) and \(3.6\) at \(x/L=0.5\) and \(z=z_{\rm pa}\), with and without control, respectively. The measured spectra at the peak of maximum power for \(y/\theta=5.7\) and \(2.8\) at the same streamwise and spanwise positions of \(x/L=0.5\) and \(z=z_{\rm pa}\) with and without control, respectively, are also shown.  
Figure 18: Spanwise coherence of streamwise velocity, \(u\), at \(x/L=0.5\) and \(y=0\) for baseline and controlled flows.
## 4 Simulation Results
### Performance Comparison with the Algorithm of Chakraborty (Energies, 2019)  
For a head-to-head comparison of SJaya with the Java variant developed in [24], 100 independent runs of SJaya are executed and the results summarized in Table 12. A comparison of the mean of 100 best-of-run costs (Table 12 in this paper and Table 14 in [24]) shows that the present approach's mean value is lower in five of the 13 cases and higher in the remaining eight. The difference, however, is not statistically significant, as seen from the results (Table 13) of the Wilcoxon signed rank test which shows no clear advantage for either algorithm on this metric (the one-tailed \(p\)-value is much closer to 0.5 than to zero). On the success count metric (Table 15 in [24]), [PERSON] outperforms the method of [24] in six cases and is outperformed in four cases, with three cases being ties. On the mean FirstHitEvals metric (Table 15 in [24]), [PERSON] wins in 11 out of the 13 cases, with the difference seen to be statistically significant at the 5% level (the \(p\)-value in Table 13 is 0.0044). Thus we conclude that [PERSON] is quite competitive with the method of [24]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PopSize**} & \multirow{2}{*}{**Gens**} & \multicolumn{2}{c}{**Best-of-Run Fitness**} & \multicolumn{2}{c}{**FirstHitEvals**} \\ \cline{3-6}  & & \(\mathbf{t}\)**-Statistic** & \(\mathbf{p}\)**-Value** & \(\mathbf{t}\)**-Statistic** & \(\mathbf{p}\)**-Value** \\ \hline
20 & \(10\) & 0.7429 & 0.2303 & — & — \\
15 & 20 & 1.6673 & 0.0512 & \(-0.7872\) & 0.2191 \\
20 & 20 & \(-0.0627\) & 0.4751 & 3.1175 & 0.0021 \\
20 & 25 & 0.0865 & 0.4657 & 2.2976 & 0.0137 \\
25 & 40 & 1.4068 & 0.0850 & 0.7256 & 0.2356 \\
40 & 25 & 1.0202 & 0.1578 & 1.5742 & 0.0612 \\
20 & 100 & 1.0461 & 0.1521 & 0.2620 & 0.3971 \\
100 & 20 & 1.0279 & 0.1562 & 0.7564 & 0.2276 \\
30 & 100 & 1.0172 & 0.1587 & 1.4129 & 0.0830 \\
100 & 30 & 1.0147 & 0.1593 & 1.6751 & 0.0503 \\
40 & 100 & 0.9838 & 0.1667 & 0.1056 & 0.4582 \\
100 & 40 & 1.0158 & 0.1591 & 1.4530 & 0.0763 \\
100 & 100 & 1.0190 & 0.1583 & \(-0.1178\) & 0.4534 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: [PERSON]–[PERSON] tests: [PERSON] vs. [PERSON] on the fuel cell problem. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Metric** & **\#zero Diff.** & \(n\) & \(W+\) & \(W-\) & \(W\) & \(\alpha\) & **Critical \(W\)** & **Mean of \(W\)** & **Std. Dev. of \(W\)** & **\(z\)-Statistic** & **Left Tail \(p\)** \\ \hline Mean of Best-of-Run Fitnesses & 0 & 13 & 90 & 1 & 1 & 0.05 & 21 & 45.5 & 14.3091 & \(-\)3.1099 & 0.00009 \\ Mean of FirstHiteFvals & 0 & 12 & 71 & 7 & 7 & 0.05 & 17 & 39 & 12.7475 & \(-\)2.5103 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Wilcoxon signed rank tests: [PERSON] vs. [PERSON] on the fuel cell problem.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 3 Model and Method
### Fitting Correction Methods  
A fitting correction is conducted in this study based on the relationships between historical CMPA and historical GPM (or SMP). The relationships are established by using the 2015-2016 dataset. Three methods, including linear fitting correction, nonlinear fitting correction, and CDF fitting correction, are used in this study to examine which one is effective in improving the GPM (or SMP) precipitation.
# Investigating the Effects of Fish Effluents as Organic Fertilisers on Basil (_Ocimum basilicum_)
## 2 Materials and Methods  
### Outline  
The experiments took place inside a 7.2 \(\times\) 3.4 m (24 m\({}^{2}\)) greenhouse located on the second floor of the School of Design of the University of Greenwich in London, UK. The experiment started on July 13 th and ended on 26 August 2021, lasting a total of 44 days.  
Basil plants were grown in plastic pots (one plant per pot) placed within plastic trays under grow lights. Two types of potting substrates, one with compost and one without, and two fish effluent types (fish water and fish sludge) were used in combination.  
Tap water was sourced from the University's potable water system, whilst fish effluents were obtained from the University of Greenwich aquaponics greenhouse. The greenhouse accommodated a floating raft aquaponic system stocked with approximately 300 Nile tilapia (_Oreochromis niloticus_), alongside a variety of plant species such as beans, squash, mint, tomato, cucumber, okra, fern, turmeric, cape gooseberry, melon, and various ornamentals. The Nile tilapia, weighing between 100 and 500 g, were fed twice daily, once in the morning and once in the afternoon, with Aller Aqua Primo 6 mm sinking pellet feed (37% Crude Protein, 12% Crude Fat, 32.5% Nitrogen-free Extracts, 7% Ash, 3.5% Fibre, 1% Phosphorus, 19.6 MJ Gross Energy, 16 MJ Digestible Energy), totalling 500 g of feed per day.  
Most of the fish sludge was removed from the water before it entered the plant (hydroponic) compartments. The first step of filtration involved the clarifier, where the water from the fish tanks was drawn from the bottom, and as it flowed into the clarifier, the settleable solids dropped. To ensure that almost all large solids were removed before the water entered the hydroponic section, single or multiple tanks with orchard netting were employed, achieving almost total removal of solids. The fish sludge was collected from the bottom of the clarifiers, while the fish water was taken from the sump, a compartment where the filtered water flowed and was further aerated before heading to the plant units.
## 3 System Analysis for a Two-User System
### Computation of Secrecy Outage Probability  
For the derivation of proposed system's Secrecy Outage Probability (SOP), the min-max approached is used. The SOP for R-IBFD cooperative communication for the relay selection scheme is given as:  
\[\begin{split} S_{op}&=Pr[C_{sec}^{R_{s}}<C_{th}]\\ &=Pr\left[\log_{2}\left(\frac{1+\min\{\frac{\zeta_{s11}}{\zeta_{ s11}+1},\zeta_{12}\}}{1+\zeta_{1e}}\right)<C_{th}\right],\\ &=\prod_{k=1}^{K}\int_{0}^{\infty}Pr\Big{[}\log_{2}\left(1+\min \biggl{\{}\frac{\zeta_{s11}}{\zeta_{11}+1},\zeta_{12}\biggr{\}}\right)<a+by \Big{]}\\ f_{\zeta_{1e}}(y)dy,\\ &=\prod_{k=1}^{K}\int_{0}^{\infty}F_{Z}(a+by)f_{\zeta_{1e}}(y)dy.\end{split} \tag{11}\]  
where \(Pr[.]\), \(f_{X}(.)\) and \(F_{X}(.)\) are the notation for probability, Probability Density Function (PDF) and Cumulative Distributive Function (CDF). \(a=2^{C_{th}}-1\), \(b=2^{C_{th}}\), \(y=\gamma_{1e}\geq 0\) and \(f_{\zeta_{1e}}=e^{-\frac{y}{\gamma_{1e}}}/\lambda_{1e}\). The CDF \(F_{Z}(z)\) of the random variable \(Z\) is derived in Appendix A and is given as  
\[F_{Z}(z)=1-\frac{\lambda_{a1}}{\lambda_{a1}+\lambda_{11}z}e^{-z(\frac{1}{ \lambda_{a1}}+\frac{1}{\lambda_{12}})}. \tag{12}\]  
Substituting the required parameter and considering \(\zeta_{a1}=\zeta_{12}\) the SOP is given as  
\[\begin{split} S_{op}&=\prod_{k=1}^{K}\int_{0}^{ \infty}1-\frac{\lambda_{a1}}{\lambda_{a1}+\lambda_{11}z}e^{-z(\frac{1}{ \lambda_{a1}}+\frac{1}{\lambda_{12}})}e^{-\frac{y}{\lambda_{1e}}}dy.\\ &=\left(\frac{e^{-\frac{2e}{\zeta_{a1}}}}{b\zeta_{1e}\zeta_{11}} \left(be^{\frac{2e}{\zeta_{a1}}}\zeta_{1e}\zeta_{11}+e^{L}\Gamma\zeta_{a1}+e^ {L}\zeta_{a1}\log\right.\\ &\left.\left(\frac{1}{\zeta_{1e}}+\frac{2b}{\zeta_{a1}}\right)-e ^{L}\zeta_{a1}\log\left(\frac{b\zeta_{11}}{a\zeta_{11}+\zeta_{a1}}\right)- \zeta_{a1}\right.\\ &\left.1F1^{(1,0,0)}[1,1,L]\right)\right)^{K}.\end{split} \tag{13}\]  
The above expression is the conditional expression with \(\text{Re}[p]>0\) and \(\text{Re}[1/\zeta_{1e}+2b/\zeta_{a1}]\geq 0\) where \(L=\frac{(2b\zeta_{1e}+\zeta_{a1})(a\zeta_{11}+\zeta_{a1})}{b\zeta_{11}\zeta_{1 1}\zeta_{11}}\), \(\Gamma=\)EulerGamma and \(1F1[1,0,0]\) is the Kummer confluent Hypergeometric function. Wolfram Mathematica software is used for the derivations of this paper.
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 3 Results
### The 316L Stainless-Steel Tensile Test Results  
As shown in Figure 8, the instrument used for the tensile test was the German Zwick electronic universal testing machine. Tensile tests were performed on nine samples in turn. The speed used in this test was 2 mm/min.  
The measured stress-strain curve is shown in Figure 9:  
Figure 8: Schematic diagram of tensile test: (**a**) Zwick electronic universal testing machine; (**b**) sample clamping state.  
Figure 7: Isotropic topology optimization results: (**a**) stress contour; (**b**) iterative curve.  
It can be seen from Figure 9a-c that the tensile properties of the samples were similar in the same printing direction. This shows that the additive manufacturing technology had good stability, in addition to providing stable support for the research of anisotropic topology optimization based on its process.  
The obtained data in each direction are shown in Figure 10.  
According to Figure 10, the yield strength, elastic modulus, tensile strength, and elongation of flat printing were stronger compared to other layo  
Figure 10: Anisotropy of additively manufactured sample (including yield strength, elastic modulus, tensile strength, and elongation).  
Figure 9: Stress–strain curve: (**a**) flat; (**b**) side; (**c**) vertical; (**d**) three directions.  
vertical printing were the lowest. This result is consistent with the previous conclusions of other researchers [26; 27], suggesting that the experimental data are reasonable.
## 4 Results and Discussion
### The Impact of SLADS on Traffic Efficiency and CO\({}_{2}\) Emissions  
#### 4.1.1 The Impact of SLADS on Traffic Delay  
Under the real distribution of Chinese driving style, the impacts of SLADS on traffic delay on the expressway and the secondary motorway were analyzed with the ICV penetration rate changing from 0 to 100%. As for the expressway, it is shown in Figure 1a that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Road Type**} & \multicolumn{3}{c}{**Traffic Volume Input (pcu/h)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Free Condition**} & \multicolumn{2}{c}{**Congested Condition**} & \multicolumn{2}{c}{**Forced Condition**} \\ \cline{2-6}  & **First Service** & **Third Service** & **Saturated Service** & **Oversaturated** & **Oversaturated** \\  & **Level** & **Level** & **Level** & **20\% Service Level** & **40\% Service Level** \\ \hline Expressway & 2003 & 4406 & 5874 & 7049 & 8224 \\ Secondary motorway & 420 & 1120 & 2800 & 3360 & 3920 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Traffic volume on the expressway and the secondary motorway.  
at the first and the third service level, no matter how the ICV penetration rate changes, SLADS does not have a significant impact on traffic delay. At the saturated service level, however, SLADS has the most significant impact on traffic delay. The average delay per vehicle can be reduced by 3.30 s, which is 26.81%, if the ICV penetration rate reaches 100%. As the volume input meets the service level, which is oversaturated by 20% and 40%, it declines The maximum benefits are both obtained when the ICV penetration rate reaches 80%, reducing the delay by 2.56 s and 2.58 s, which are 14.57% and 13.86%.  
In terms of the secondary motorway, a similar tendency can be found. It is shown in Figure 1b that at the first and the third service level, no matter how the ICV penetration rate changes, SLADS does not exert an apparent impact on traffic delay. At the saturated service level, however, SLADS has the most significant impact on traffic delay. The average delay per vehicle can be reduced by 3.30 s, which is 29.26%, if the ICV penetration rate reaches 100%. As the volume input increases to the 20% and the 40% oversaturated service level, the impacts of SLADS on traffic delay declines. The maximum benefits are both realized when the ICV penetration rate reaches 80% that it can be reduced by 2.56 s and 2.58 s, which are 20.31% and 20.09%.  
It is interesting that although SLADS has different ratios of impacts on the expressway and secondary motorway traffic delay, the absolute values of the impacts are almost the same. It is deduced that the difference in driving behaviors of ICVs reflected in traffic delay with SLADS and HVs may not change with road scenarios, but more experiments are needed to verify this.
# Expanding Ground Vehicle Autonomy into Unstructured, Off-Road Environments: Dataset Challenges
## 1 Introduction  
Rapid advancements are being made in the area of autonomy for the application of autonomous driving by unmanned ground vehicles (UGVs) [1, 2, 3, 4, 5, 6, 7]. In the commercial sector, autonomous vehicles are commonly referred to as'self-driving' cars. Tesla and Waymo are two entities that have seen recent successes to varying degrees in their autonomous navigation aspirations in the public domain [8, 9]. To the best of our knowledge, both employ deep learning (DL) methodologies for computer vision (CV) tasks to understand their sensed environment [10, 11, 12, 13]. This scene understanding is required to enable the development of autonomous behaviors and navigation strategies for a smooth passenger experience. While there are many factors and components that go into the development of a fully autonomous UGV software stack, this paper focuses on computer vision-related tasks.  
In this paper, we address a significant research gap in the field of autonomy for unstructured environments [14]. These environments have numerous potential applications, such as search and rescue missions and natural disaster responses. However, creating and using robust and diverse datasets that capture the complexity and dynamics of these environments poses significant challenges for computer vision-based autonomous navigation. We present some of the key difficulties involved in creating and utilizing such datasets, and propose a framework for developing a comprehensive and high-quality off-road dataset. By doing so, we aim to contribute to the advancement of the field and enable more effective and reliable autonomy in highly complex and unstructured environments.  
The remainder of this paper is organized as follows. This section will conclude with a quick look into the most popular publicly available datasets for unstructured environments. In Section 2, challenges associated with the development of a dataset for CV-based autonomy applications are presented, along with deeper philosophical questions that must be considered when creating such a dataset. A framework for a new off-road dataset that is being developed by the Engineer Research and Development Center and Mississippi State University's Center for Advanced Vehicular Systems will be presented in Section 3. Finally, this paper will be concluded in Section 4.
# A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base
## 4 Case Study
BRB uses expert knowledge to set the initial values of the parameters so that only a small number of samples are needed to complete the training, solving the small sample problem. BRB utilizes the membership function to deal with the randomness of the APT attack time. The SVM and MLP models are fit-based, while the BRB model is rule-based, and its interpretation is better than the fitted model. The BRB model is white box and has good interpretability, which facilitates the analysis and traceability of BRB results.
# Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networks
## 5 Experiments  
### Experiment Design  
To demonstrate the superior performance of the proposed trustworthy transaction spreading method, we constructed a test blockchain network in an InterPlanetary File System (IPFS) testbed. IPFS is a protocol for storing data in a distributed file system and sharing it over the internet. IPFS libp2p (peer-to-peer library of the IPFS software suite) supports peer-to-peer networks over IPFS. The IPFS has a significant advantage in producing blockchain simulations. A blockchain network can be constructed based on an IPFS peer-to-peer network, and IoT nodes can easily store data such as blocks and smart contracts in the IPFS testbed [42; 43]. We used IPFS libp2p FloodSub (flood subscription module of the peer-to-peer library of IPFS) to build an experimental environment for the proposed trustworthy transaction spreading. The Amazon Elastic Compute Cloud (AWS EC2) virtual server was used to create sufficient nodes. A private blockchain module is installed on the IPFS network. In our previous study [1; 2], we customized the Hyperledger sawtooth [44] blockchain software for IoT devices. For small-scale IoT devices, we downsized the consensus engine, validator, database, and data-serialization functions. We used Docker [45] to install a blockchain software module for IoT devices. Docker is an  
Figure 6: Reliable area identification under the malicious node attacks. (**a**) Single malicious node: 17; (**b**) Double malicious nodes: 6 and 29; (**c**) triple malicious nodes: 14, 38, and 56.  
open-source virtualization platform. Docker guarantees identical execution in various computing environments without complex deployment procedures [46]. The transferable Docker image of the developed blockchain software can facilitate distribution and maintenance. A Python simulator was developed in the IPFS test bed to implement the proposed trustworthy spreading. In the Python simulator, node features are defined as: node classification (i.e., source/target/border node for each transaction traverse), connected edges, number of stored transactions, number of created transactions, remaining battery power, computing power, network connection status (transmission speed, bandwidth), number of duplicate transactions, and number of border node executions. In addition, to measure the transaction-sharing rate, the number of duplicated transactions is added to the node features.
## 3 Results  
Descriptive data, group means, standard deviations (\(\pm\)SD), 95% confidence intervals (CI), F-statistics, range, effect sizes (\(\eta^{2}\)), \(p\)-values of the one-way ANOVA and statistical power (\(1-\beta\)), for all variables are depicted in Tables 2 and 3. No significant differences between the different playing positions was found. However, there was a large effect size (\(\eta^{2}\geq 0.14\)) in the body mass as well as in the VO\({}_{2}\)-peak, HR, jump height and 20-m fast break time in the GBPT. A medium effect size (\(\eta^{2}\geq 0.06\)) was found in age, and in total running distance and HR in the Yo-Yo IR1 test, 15-m and 30-m sprint time in the SSPT as well as in offence time, 10-m fast break time and fast retreat time in the GBPT. In the Pearson Product-moment correlations (see Table 4), a significant within the test correlation was found between 15-m and 30-m sprint time in the SSPT (R = 0.944, \(p\) < 0.001) and in the GBPT between VO\({}_{2}\)-peak and HR (R = 0.437, \(p\) = 0.042) and also the fast retreat time (\(R\) = \(-\)0.482, \(p\) = 0.020), between blood lactate concentration and throwing velocity in the jump shot (R = 0.445, \(p\) = 0.033), defence and offence time (R = 0.803, \(p\) < 0.001), 10-m and 20-m fast break time (R = 0.831, \(p\) < 0.001) and between fast retreat and 10-m fast break time (R = 0.626, \(p\) = 0.001) and also the 20-m fast break time (R = 0.668, \(p\) = 0.001). Between the different tests (see Table 4), a significant correlation was found between Yo-Yo IRI running distance and 10-m fast break time (R = \(-\)0.513, \(p\) = 0.012) and fast retreat time in the GBPT (R = \(-\)0.603, \(p\) = 0.002) as well as with 15-m (R = \(-\)0.557, \(p\) = 0.006) and 30-m sprint time in the SSPT (R = \(-\)0.458, \(p\) = 0.028), respectively. In addition, significant correlations were also found between VO\({}_{2}\)-peak in the GBPT and Yo-Yo IR1 running distance (R = 0.563, \(p\) = 0.005) and Yo-Yo IR1 HR (R = 0.515, \(p\) = 0.014), respectively, between HR in the GBPT and HR in the Yo-Yo IR1 test (R = 0.833, \(p\) < 0.001), between 10-m fast break time in the GBPT and 15-m (R = 0.426, \(p\) = 0.043) and 30-m sprint time in the SSPT (R = 0.438, \(p\) = 0.036), between 20-m fast break time in the GBPT and 15-m sprint time in the SSPT (R = 0.445, \(p\) = 0.038) and between fast retreat time in the GBPT and 15-m sprint time in the SSPT (R = 0.445, \(p\) = 0.034). To further analysing the results in the between test correlations, linear regression plots were made between Yo-Yo IR1 running distance and VO\({}_{2}\)-peak in the GBPT (A), between Yo-Yo IR1 HR and HR in the GBPT (B), between 30-m sprint time in the SSPT and GBPT defence time (C) as well as between Yo-Yo IR1 running distance and fast retreat time in the GBPT (D), which are shown in Figure 2.