esa-sceva/satcom-chunk-collection huggingface dataset:
# [[https://github.com/opscq/exploit-CVE-2016-10033](https://github.com/opscq/exploit-CVE-2016-10033)]([https://github.com/opscq/exploit-CVE-2016-10033](https://github.com/opscq/exploit-CVE-2016-10033))
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Num. of** & **Reference Host** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Random \\ Forest \\ \end{tabular} } & All host & 9226 & 83.08\% & 55.90\% & 80.54\% & 66.00\% \\  & PoC host & 1712 & 83.40\% & 56.59\% & 79.96\% & 66.27\% \\  & No-PoC host & 4868 & 82.88\% & 55.73\% & 78.09\% & 65.04\% \\  & No host & 0 & 83.06\% & 56.10\% & 77.81\% & 65.19\% \\ \hline \multirow{4}{*}{\begin{tabular}{c} Linear \\ SVC \\ \end{tabular} } & All host & 9226 & 81.61\% & 53.16\% & 82.60\% & 64.69\% \\  & PoC host & 1712 & 81.38\% & 52.77\% & 82.57\% & 64.39\% \\  & No-PoC host & 4868 & 80.20\% & 50.92\% & 80.94\% & 62.51\% \\  & No host & 0 & 79.84\% & 50.36\% & 80.40\% & 61.93\% \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Logistic \\ Regression \\ \end{tabular} } & All host & 9226 & 81.59\% & 53.11\% & 82.76\% & 64.70\% \\  & PoC host & 1712 & 81.52\% & 53.00\% & 82.71\% & 64.60\% \\  & No-PoC host & 4868 & 80.51\% & 51.40\% & 81.57\% & 63.06\% \\  & No host & 0 & 80.44\% & 51.29\% & 81.38\% & 62.92\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Decision \\ Tree \\ \end{tabular} } & All host & 9226 & 77.46\% & 46.82\% & 77.72\% & 58.44\% \\  & PoC host & 1712 & 76.75\% & 45.88\% & 78.02\% & 57.78\% \\  & No-PoC host & 4868 & 75.65\% & 44.33\% & 75.82\% & 55.95\% \\  & No host & 0 & 75.57\% & 44.16\% & 74.84\% & 55.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test set 4 results of the exploit prediction experiment based on Reference Feature. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Num. of** & **Reference Host** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Linear \\ SVC \\ \end{tabular} } & All host & 9226 & 81.81\% & 53.53\% & 81.90\% & 64.75\% \\  & PoC host & 1712 & 81.62\% & 53.22\% & 81.73\% & 64.46\% \\  & No-PoC host & 4868 & 80.64\% & 51.61\% & 80.85\% & 63.00\% \\  & No host & 0 & 80.54\% & 51.46\% & 80.33\% & 62.73\% \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Logistic \\ Regression \\ \end{tabular} } & All host & 9226 & 81.93\% & 53.72\% & 82.27\% & 65.00\% \\  & PoC host & 1712 & 81.60\% & 53.19\% & 81.52\% & 64.37\% \\  & No-PoC host & 4868 & 80.94\% & 52.11\% & 80.61\% & 63.30\% \\  & No host & 0 & 80.59\% & 51.55\% & 80.33\% & 62.80\% \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Decision \\ Tree \\ \end{tabular} } & All host & 9226 & 77.57\% & 46.96\% & 77.39\% & 58.45\% \\  & PoC host & 1712 & 76.65\% & 45.71\% & 77.32\% & 57.46\% \\  & No-PoC host & 4868 & 76.26\% & 45.09\% & 75.57\% & 56.48\% \\  & No host & 0 & 75.32\% & 43.78\% & 74.09\% & 55.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test set 3 results of the exploit prediction experiment based on Reference Feature.
This latter step is crucial as it allows the researcher not only to understand if the innovation meets the real needs of those who will be its users (the millers), but also to have useful opinions for optimization directly from the insiders of a specific sector [14]. End-user validation is the best strategy to transform a technology into something that really meets the needs of the olive oil sector, determining the desired changes in the value of the product (i.e., a hydroxytyrosol content coherent with the application of health claims approved by the European Food Safety Authority (EFSA)) and in the income of the producers (due to the premium price gained by the high quality market segment) [15; 16]. Technology validation and technology transfer are indispensable factors for ferrying modern mills towards greater competitiveness in the global market based on two fundamental principles: producing higher quantities of oil and obtaining products of superior quality [17; 18]. The achievement of the TRL 9, i.e., the technology in its final form, still requires some efforts that are only possible with the collaboration of the millers, who are able, from the height of their experience, to suggest ways of continuous improvement also in relation to the organoleptic characteristics desired in the product. The aim of the work was to validate the application of ultrasound in the EVOO extraction process in order to ascertain if, when the system prototype is placed in the operational environment, the innovative method is able to increase oil yields, reduce the process time, and guarantee oil quality preservation. Several batches of olives of several cultivars, grown in different geographical areas from the Apulia region in 2017 and harvested at different maturation indexes, were selected to produce the 23 oils analyzed in this work. The analytical investigations were applied to all the EVOOs produced from the same batches of olives, with and without the use of ultrasounds. In particular, for the analysis of phenolic profiles, we used the official method of the International Olive Council (IOC) [19] improved by acid hydrolysis of the hydroalcoholic extract [20; 21] and recently validated after these modifications [22; 23]. The analysis of volatile compounds by HS-SPME-GC-MS [24] has been performed in order to assess if the introduction of ultrasound can induce a damage of the enzymatic complex of the lipoxygenase (LOX) pathway with a detrimental effect on the sensory characteristics of the product.
# Development of the Virtual Reality Application:
## 2 Methodology
### Polygon Mesh Optimization
#### 2.2.4 UV Unwrapping  
A UV map is the representation of the surface of a 3D model onto a 2D map that is later used for texturing the specific surface. The process of creating a UV map is called UV unwrapping. For 3D texturing it is necessary to unwrap all of an object's surfaces to the UV space. To further explain, the process works as follows: the unwrapped surface is projected onto a specific place in the UV coordinates, which enables the surface to display the texture information on the 3D model. An example of a UV-unwrapped texture is shown in Figure 10.  
The way the UV maps are organized is crucial for the texturing process. For example, if two or more surfaces are overlapping in the UV map, these surfaces will keep rendering  
Figure 10: Example of UV unwrapping. Texture created with Adobe Substance 3D Painter.  
Figure 9: High poly to low poly workflow example. Screenshot from Blender and Adobe Substance 3D Painter.  
the same textured coordinates. In addition, the UV maps have to be distortion-free and maintain their proportion ratio, otherwise the texture appears stretched and disproportionate. UV unwrapping is also critical with respect to the texture quality. In fact, the larger the item on the UV space, the greater the density, meaning that there is more space available for painting details.  
Another thing to take into account when unwrapping is the seams. A seam is the edge where the mesh surface is \"cut\" in order for the 3D model to be mapped onto the UV map. Seams can create visual discontinuation of the texture if they are misplaced, so when possible, they should be hidden in places not easily visible, or where the material changes.  
All known 3D-modeling software include tools for UV unwrapping the 3D model, as this procedure is mandatory and texturing depends on that. \"The Ships of Navarro\", were unwrapped into Blender and their UV maps organized hierarchically by size, in a way that most space was held by the surfaces that were most likely to be seen closely by the user.
## 7 Conclusions  
In wireless sensor networks, an attacker can inject false data to damage the network data integrity by utilizing a compromised node. Existing researches did not combine data aggregation, data confidentiality, data integrity and false data detection together well. This paper proposes FESA, secure data aggregation with fully homomorphic encryption in large-scale wireless sensor networks. FESA can effectively reduce the network overhead while satisfying the above requirements. Compared to the existing technologies, our scheme can ensure the data confidentiality and integrity during data aggregation process and forwarding process, and also detect the false data as early as possible, leading to reduction of communication overhead and hence less energy consumption, thus prolong the life of the  
Figure 8: The total data transmission for FESA, DAA and SDA-PH, where in FESA and DAA detect false data during data aggregation and forwarding while SDA-PH detects only in BS.  
sensor nodes and networks. In our future work, we plan on investigating the further study of the performance of WSNs and the multiple applications of FHE such as in a VANET environment [31], and then lead to more secure and efficient data processing.  
This work is supported by the National Natural Science Foundation of China under Grant No. 61272074 and No. U1405255, and the Industrial Science and Technology Foundation of Zhenjiang City under Grant No. GY2013030.  
All authors have contributed to the presented work in various degrees. [PERSON] proposed the protocol, designed the research and drafted the manuscript. [PERSON] and [PERSON] joined the discussion of the proposed model as the consultative opinion. [PERSON] supervised the proposed model and technical contents and revised the paper according to reviewer's comments.  
The authors declare no conflict of interest.
## 3 Influence Factors of Higher-Order CSI Effect
### Influence Analysis of Control Gains  
The control force of a SDOF system is:  
\[u=-\big{(}g_{x}x+g_{\dot{x}}\dot{x}\big{)} \tag{26}\]  
where \(g_{x}\) and \(g_{\dot{x}}\) are the control gain according to displacement and velocity, respectively.  
The force equilibrium of a SDOF system can be described by substituting Equation (26) into Equation (22).  
\[m\ddot{x}(t)+c\dot{x}(t)+kx(t)=-\big{[}g_{x}x(t)+g_{\dot{x}}\dot{x}(t)\big{]}+ \ddot{x}_{g}(t) \tag{27}\]  
Negative feedback control is commonly used in civil engineering, so two cases are analyzed for a SDOF system, namely:  
\[g_{x}=\gamma k,\ g_{\dot{x}}=\varepsilon c\ (\gamma,\varepsilon=1,2,\cdots,5) \tag{28}\]  
When a higher-order CSI effect is considered, the state equation of the SDOF system is:  
\[\dot{\dot{Z}}=\left[\begin{array}{ccc}0&I&0\\ -\dfrac{k}{m}&-\dfrac{c}{m}&\dfrac{1}{m}\\ \dfrac{R_{a}}{L_{a}}g_{x}&\dfrac{R_{a}}{L_{a}}g_{\dot{x}}+\dfrac{K_{b}K_{i}K_ {\dot{x}}^{2}}{L_{a}r_{m}^{2}}B_{s}^{T}&0\end{array}\right]\dot{Z}+\left[ \begin{array}{ccc}0&0\\ \dfrac{1}{m}&0\\ 0&\dfrac{K_{i}K_{g}}{L_{a}r_{m}}\end{array}\right]\left[\begin{array}{c} \ddot{x}_{g}\\ V\end{array}\right] \tag{29}\]  
Its observation equation is:  
\[Y=\left[\begin{array}{ccc}I&0&0\\ 0&I&0\\ -\dfrac{k}{m}&-\dfrac{c}{m}&\dfrac{1}{m}\\ 0&0&1\end{array}\right]\dot{Z}+\left[\begin{array}{ccc}0&0\\ 0&0\\ \dfrac{1}{m}&0\\ 0&0\end{array}\right]\left[\begin{array}{c}\ddot{x}_{g}\\ V\end{array}\right] \tag{30}\]  
The changes of the phase transfer functions from the control voltage to the control force of the AMD system with different displacement and velocity gains are shown in Figures 11 and 12. From Figure 11, with the increase of the displacement gain, the absolute value of the phase  
Figure 10: Transfer functions from control voltage to control force with different dampings, (**a**) Magnitude; (**b**) Phase.  
transfer function becomes smaller, which means that the time-delay of the system becomes shorter. From Figure 12, with the increase of the velocity gain (the equivalent damping of the system becomes larger), the absolute value of the phase transfer function becomes bigger, and indicating that the time-delay of the unstable system becomes longer.  
In sum, system time-delay is inversely proportional to displacement gain, and is proportional to velocity gain. Therefore, for considering the stability of the system, when an algorithm is used in the design of a controller, it is necessary to guarantee that the weight coefficient according to displacement in the \(Q\) matrix is large and that the weight coefficient according to the velocity is small.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 2 Overview of Ant Colony Algorithm and Improvements
### Improvements on ACA
#### 2.2.3 Adaptive ACA  
To address the problem of stagnation that conventional ACAs are susceptible to, we introduce an adaptive ACA. Compared with conventional ACAs, the adaptive ACA shows the following improvements:  
1. Introducing the pheromone-smoothing mechanism to improve the algorithm's performance.  
In conventional ACAs, the pheromone volatility coefficient is a constant. In the initial stage of algorithm implementation, to avoid the excessive impacts of positive feedback on the ant colony and the risk of a local optimum, the value of \(\rho\) should be kept to a minimum. In the later stage of algorithm implementation, a larger \(\rho\) is required to ensure a low residual pheromone concentration on the route, and the global positive feedback needs to be strengthened to improve the running efficiency of the algorithm.  
The pheromone-smoothing mechanism employs a real-time adjustment of the pheromone volatility coefficient so that the algorithm can search as many solutions as possible in the initial stage. In addition, the running efficiency to output the global optimum in the later stage is improved, thereby balancing the algorithm's search and utility capacity. The value of \(\rho\) in the present work is calculated by Equation (9), where \(N_{c}\) is the current number of iterations and \(N_{c\max}\) is the total number of iterations.  
\[\rho\ =\ \rho_{0}+(1-\rho_{0})\frac{N_{c}}{N_{c\max}} \tag{9}\]
2. Setting the pheromone concentration of routes within a fixed range of [\(\tau_{\min}\), \(\tau_{\max}\)].  
A crucial idea of the adaptive ACA is to confine the pheromone concentration on the route within a certain range to avoid large discrepancies between sections and hence to preclude stagnation of the algorithm. The value setting of \(\tau_{i,j}(t)\) is shown in Equation (10):  
\[\tau_{i,j}(t)\ =\ \begin{cases}\tau_{\min},\tau_{i,j}(t)\leq\tau_{\min}\\ \tau_{\max},\tau_{i,j}(t)\geq\tau_{\max}\end{cases} \tag{10}\]  
Figure 2: Moving routes of ants.  
where \(\tau_{\max}\) is defined as follows:  
\[\tau_{\max}\ =\ \frac{Q}{\rho L_{\min}} \tag{11}\]
## 3 Improved Inter-Node Load Balancing for Multithreaded Applications
### Enhancements for Selecting Tasks for Inter-Node Migration for Multithreaded Applications
#### 3.2.1 Eliminating the Thread Group Leader for Migration  
The thread group leader is not selected because the Linux kernel uses the first-touch method [16] for physical memory allocation. A physical memory page is allocated the first time a thread accesses it and on the same node as the requesting thread. We observe the memory consumption for multithreaded applications, the first thread that touches the memory page is usually the thread group leader. The PARSEC 3.0 [6] benchmark suite contains 30 multithreaded applications, including 13 programs from PARSEC 2.0, 14 programs from the SPLASH benchmark suite, and three network programs. Table 2 shows these benchmark programs [4; 6].  
Each benchmark program's memory consumption was measured during its execution on the AMD server [18]. In Linux, the _free_ command is used to obtain the current status for memory usage. A script that executes the free command every 0.1 s is used to record the memory usage footprint for the entire system. The increased memory usage during the benchmark program's execution is then attributed to the benchmark program. Each benchmark program is run with different threads, ranging from 1, 2, 4, 8, 16, and 32.  
The results show that memory consumption is independent of the number of created threads for some benchmark programs. Other benchmark programs consume more memory as the number of created threads increases. Figure 5(a) shows the former situation for the benchmark program parsec.canneal. For this type of benchmark program, the memory pages of a multithreaded process are first touched by the first thread. For the benchmark program splash2x.fmm in Figure 5(b), the individual threads first touch the memory pages of a multithreaded process. The results in Table 2 show that 22 of 30 applications allocate and initialize the allocated memory pages in the initializing thread (i.e., the thread group leader). The thread group leader for each multithreaded process is not migrated to reduce the scattering of memory pages on different nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Benchmark Suite** & **Benchmarks** & **Memory Allocated \& Initialized** \\ \hline \multirow{3}{*}{PARSEC 2.0} & blackscholes, bodytrack, canneal, dedup, raytrace, & by thread group leader \\  & streamcluster, swaptions & by individual threads \\  & facesim, ferret, fluidanimate, freqmine, vips, x264 & by individual threads \\ \hline \multirow{3}{*}{SPLASH-2x} & barnes, cholesky, fft, lu\_cb, lu\_ncb, ocean\_cp, ocean\_ncp, & by thread group leader \\  & radiosity, radix, raytrace, volrend, water\_spatial & by individual threads \\ \cline{1-1}  & fmm, water\_nsquared & by thread group leader \\ \hline Network & netdedup, netferret, netstreamcluster & by thread group leader \\ \hline \hline \end{tabular}
\end{table}
Table 2: The characteristics of benchmark programs in PARSEC 3.0.  
Figure 5: The modified flow for the efficient selection of tasks for inter-node migration.
## 5 Conclusions  
In this paper, the effect of air leaking into the working fluid on the steam ejector performance was investigated using numerical simulations. The effects of air on the internal flow structure and working fluid physical parameters was studied. The cause of the change in the steam ejector performance was also analyzed. Furthermore, the species component distribution of air and steam was drawn. The conclusions are summarized as follows:  
1. The air leaking into the primary fluid (\(Y_{1}\)) increases the entrainment ratio but decreases the critical back pressure. On the contrary, the entrainment ratio reduces due to air leaking into the secondary fluid (\(Y_{2}\)). Moreover, \(Y_{1}\) has a more significant effect on the entrainment ratio than \(Y_{2}\) at the same air mass fraction.
2. By calculating the mass flux and mixing layer growth rate, we found that the main reason for the entrainment ratio variation is a change in the working fluid physical parameters caused by the leaked air.
3. In experimental operations or industrial production, the air mass fraction leaking into the working fluid must be minimized to obtain a real entrainment ratio of the steam ejector.
4. Where possible, using a mixture of the steam and air as the secondary fluid could be an effective way to improve the steam ejector performance.
5. By comparing the experimentally measured \(Er\) with its standard experimental value and calculating the \(Er\) coefficient of distortion rate, we determined whether air leaked into the steam ejector refrigeration experimental system, into which fluid the air leaked, and the amount of leaked air.  
In this study, the perfect gas model was adopted, and the steam phase change inside the nozzle was neglected. Therefore, future research should focus on the effect of air leaking into the working fluid on the ejector performance in the two-phase flow state.  
**Author Contributions:** Conceptualization, H.L. and X.W.; methodology, H.L. and J.N.; software, H.L.; validation, J.N., P.Z. and H.H.; formal analysis, H.L. and H.H.; investigation, J.N.; resources, X.W.; data curation, H.L. and P.Z.; writing--original draft preparation, H.L.; writing--review and editing, J.N. and H.H.; visualization, H.H.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, (grant no. 51775098).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Experimental
### Characterization Techniques  
The microstructure of the Mixed SSPCMs were analyzed through scanning electron microscopy (SEM, JSM-6360A, JEOL, Tokyo, Japan) at room temperature. The SEM images of the samples were obtained with an accelerating voltage of 12 kV and a working distance of 12 mm, and the samples were gold coated at a few nanometers in thickness to increase their electrical conductivity [19]. Fourier transform infrared spectroscopy (FT-IR, Nicolet 6700, Thermo Scientific, Waltham, MA, USA) was used to confirm the change of chemical groups of the Mixed SSPCMs at room temperature. Clear potassium bromide (KBr) disks were made from powder and used as backgrounds. The Mixed SSPCMs were analyzed over the range of 650-4000 cm\({}^{-1}\), with a spectrum resolution of 8 cm\({}^{-1}\). All spectra were averaged over 32 scans. This analysis of the Mixed SSPCMs was performed by point-to-point contact with a pressure device [25]. Thermal properties of the Mixed SSPCMs, such as melting and freezing point and latent heat capacities, were analyzed by the differential scanning calorimetry (DSC, DSC-Q1000, TA instrument, New Castle, PA, USA). The melting and freezing point were measured by drawing a line at the point of maximum slope of the leading edge of the peak and extrapolating to the base line, and the latent heats of the Mixed SSPCMs were analyzed as total by numerical integration of the area under the peaks that represents the solid-solid and solid-liquid phase transition [17]. The DSC measurements were performed at a 3 \({}^{\circ}\)C/min heating and cooling rate and a temperature range of \(-\)20 to 80 \({}^{\circ}\)C. Thermal stability of the Mixed SSPCMs was analyzed by thermogravimetric analysis (TGA, Q-5000 IR, TA Instruments, New Castle, PA, USA) on approximately 11-14 mg of the samples within the range from 20 to 600 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min under a nitrogen flow of 20 mL/min. The TGA was measured with the composites placed in a high quality nitrogen (99.5% nitrogen, 0.5% oxygen content) atmosphere to prevent unwanted oxidation [26]. The thermal conductivity of the Mixed SSPCMs were measured by TGi thermal conductivity analyzer (C-Therm Technologies Ltd., Fredericton, NB, Canada) at the temperature of 10 \({}^{\circ}\)C. The TGi is a device for conveniently measuring the thermal conductivity of a small sample by using the Modified Transient Plane Source (MTPS) method. It is the advantage of the TGi that can measure the thermal conductivity of materials in the states of solid, liquid, powder, and mixed [27].