esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Machine Learning Estimation of the PL
#### 3.1.1 WHIPP Indoor Dominant Path Loss Model  
Although the WHIPP network planning tool also features common and simple path loss models like one-slope models or the Motley-Keenan multi-wall model [35], the Indoor Dominant Path Loss (IDP) model [12; 33; 34] was specifically designed for more accurate path loss estimations. This model forms a compromise between semi-empirical models, only considering the direct ray between transmitter and receiver, and ray-tracing models, where hundreds of rays and their interactions with the environment are investigated. In the IDP model, propagation is based on the dominant path between the transmitter and receiver. It considers the path distance, the properties of the materials along the path (e.g., brick, drywall, concrete, wood, glass, and metal), and the number and types of interactions. The use of the IDP model on the WHIPP tool is justified by the fact that more than 95% of the energy is received in 2 or 3 rays and its accuracy is equal to, or higher than, the ray-tracing models [33]. Using Equation (1) [12] to calculate the \(PL\) [\(dB\)] in the IDP model, it was demonstrated that it performs better than ray-tracing and multi-wall models.  
\[PL\left[dB\right]=PLo+10 nlog\bigg{(}\frac{d}{do}\bigg{)}+\sum_{i}Lw_{i}+\sum_{ j}L_{B_{j}} \tag{1}\]  
Figure 1: Flow graph of the network optimization algorithm and its validation with the WHIPP tool.  
PL [dB]_ is the path loss along the path. _PLo_ [dB] is the path loss at a distance of _do_, \(d\) [m] is the distance between the 5G source and the given point, _do_ [m] is a reference distance, and \(n\) is the path loss exponent. \(\sum\limits_{i}{{Lw_{i}}}\) is the cumulated wall loss along the path (i.e., sum of the wall losses \({Lw_{i}}\) of all \({w_{i}}\)), \(i=1,\ldots,w\), where \(w\) is the total number of walls along the path. \(\sum\limits_{j}{{L_{B_{j}}}}\) is the interaction loss, i.e., the cumulated loss \({L_{B_{j}}}\) caused by all propagation direction changes \({B_{j}}\) of the propagation path from access point to receiver, with \(j=1,\ldots,B\), where \(B\) is the number of times the propagation path changes its direction. It was shown in [3] that such a complex model, accounting for physical properties of the signal propagation, was able to yield path loss estimations with an accuracy of 2 dB.
## 4 Experimental Results
### Evaluation Metrics  
There are several metrics that are used by the research community for medical image analysis to measure the performance of classification and segmentation models, including precision, recall, dice coefficient and intersection over union (IoU). To calculate these metrics, the following four measures are required:  
_True Positive (TP)_: represents the number of pixels being correctly identified in the segmentation tasks and the number of correctly predicted infected CTs in the classification task.  
_True Negative (TN)_: denotes the number of non-lung/infection pixels being correctly identified as non-lung infection in the segmentation tasks and the number of correctly predicted healthy CTs in the classification task.  
_False Positive (FP)_: represents the number of non-lung/infection pixels being wrongly classified as lung/infection pixels in the segmentation tasks and the number of mistakenly predicted infected CTs in the classification task.  
_False Negative (FN)_: denotes the lung/infection pixels being wrongly classified as non-lung/infection pixels in the segmentation tasks and the number of mistakenly predicted healthy CTs in the classification task.  
_Accuracy_: this metric measures the ratio of correctly identified predictions divided by the entire predictions and it is defined in Equation (1).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
_Precision_: this metric is a measure of exactness calculated as the ratio of true positive predictions divided by the number of predicted positives and defined in Equation (2).  
\[\text{Precision}\ =\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
_Recall_: this metric is a measure of completeness calculated as the ratio of true positive predictions divided by the number of actual positives and defined in Equation (3).  
\[\text{Recall}\ =\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
_Area Under the Receiver Operating Characteristic Curve (AUROC)_: this metric is a measure of separability that summarizes the ROC curve which plots the rate of true positive predictions versus the false positive ones for all possible thresholds.  
_Area Under the Precision Recall Curve (AUPRC)_: this metric is another measure of separability that summarizes the PR curve which plots the precision versus the recall for all thresholds.  
_Dice Coefficient (also known as Dice score)_: it is the overlap ratio between the prediction and the ground truth, giving more weight to the intersection between the two. Its value ranges between 0 and 1, and the higher the value is, the better the segmentation result. It is defined in Equation (4) as follows:  
\[\text{Dice}=\frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{FN}} \tag{4}\]  
_Intersection over Union IoU (also known as Jaccard index/F1 score)_: it is another popular metric that measures the overlap between the prediction and the ground truth and is defined in Equation (5).  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{5}\]
## 6 Analysis of Experimental Results
### Time Performance Comparison  
In addition to the detection performance, the time performance of the system is also a very important measure. Therefore, the detection time performance of the Mythril method and Oyente before and after the improvement were compared through experiments.  
The experimental data is still 2000 smart contracts, and these data are used to simulate the real detection environment of users using the three tools. In the experiment, three tools were used to test the test contracts one by one, and the detection time was recorded. Finally, the time performance comparison chart in Figure 7 was obtained. It can be seen from the figure that the detection time of Mythril after improvement is within 50 s. It can be completed. The detection is relatively fast and can reach a contract in more than ten seconds. The detection time before the improvement is obviously longer than that of the improved and Oyente, and it is basically about twice the detection time after the improvement. The slowest detection requires time and may take about 90 s, and the detection time trends of the three tools are basically consistent.  
Table 4 shows the average elapsed time of the types of detection contract vulnerabilities. From the results, we can see that the efficiency of the improved Mythril method has improved significantly and does not require the above-mentioned unit detection elapsed time of up to 59.9 s, which has a huge advantage in terms of efficiency.
## 3 Trust-Optimized 6G-IoT Framework
### System Architecture
#### 3.1.1 Mobile Devices  
The state of the \(i\)-th mobile device \(\mathbf{M}_{i}(x,t)\in\mathbb{R}^{n}\) is governed by a system of nonlinear stochastic partial differential equations (SPDEs) [29] (Appendix C). The dynamics incorporate spatial and temporal interactions:  
\[\frac{\partial\mathbf{M}_{i}(x,t)}{\partial t} = \mathcal{L}_{i}\mathbf{M}_{i}(x,t)+\mathbf{B}_{i}(\mathbf{M}_{i}( x,t),\mathbf{u}_{i}(t)) \tag{1}\] \[+\int_{\Omega}\mathbf{K}_{i}(x,\xi,t)\mathbf{H}_{i}(\mathbf{M}_ {i}(\xi,t))d\xi\] \[+\mathbf{G}_{i}(\mathbf{M}_{i}(x,t))\frac{\partial\mathbf{W}_{i} (x,t)}{\partial t},\]  
where \(\mathcal{L}_{i}\) is a differential operator that captures spatial derivatives, and \(\mathbf{K}_{i}(x,\xi,t)\) is a kernel modeling non-local influences. The stochastic input \(\mathbf{W}_{i}(x,t)\) is modeled by a space-time noise process with covariance structure:  
\[\mathbb{E}[\mathbf{W}_{i}(x,t)\mathbf{W}_{i}^{\top}(x^{\prime},t^{\prime})]= \mathbf{Q}_{i}(x,x^{\prime})\delta(t-t^{\prime}), \tag{2}\]  
where \(\mathbf{Q}_{i}(x,x^{\prime})\) is a covariance matrix reflecting the spatial correlation of the noise. The output from the mobile device \(\mathbf{y}_{i}(x,t)\), representing processed data, is derived through a nonlinear transformation:  
\[\mathbf{y}_{i}(x,t) = \mathcal{H}_{i}(\mathbf{M}_{i}(x,t)) \tag{3}\] \[= \int_{\Omega}\mathbf{J}_{i}(x,\xi)\mathbf{M}_{i}(\xi,t)d\xi+ \mathbf{R}_{i}(\mathbf{M}_{i}(x,t)),\]  
Figure 1: Proposed architecture of the TEO-IoT, illustrating the integration of mobile devices, edge nodes, and central servers.  
with \(\mathbf{J}_{i}(x,\xi)\) representing an integral kernel mapping the state space to output space. The control input \(\mathbf{u}_{i}(t)\) is optimized by solving the stochastic optimal control problem:  
\[\mathbf{u}_{i}^{*}(t) =\arg\min_{\mathbf{u}_{i}(t)}\mathbb{E}\Bigg{[}\int_{t}^{T}\ell_{i }(\mathbf{M}_{i}(x,\tau),\mathbf{u}_{i}(\tau))d\tau\] \[\qquad\qquad\qquad+\phi_{i}(\mathbf{M}_{i}(x,T))\Bigg{]}, \tag{4}\]  
where \(\ell_{i}(\cdot)\) is the running cost and \(\phi_{i}(\cdot)\) is the terminal cost.
_Int. J. Mol. Sci._**2014**, _15_, 5970-5987; doi:10.3390/ijms15045970  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
Tritternoid Saponin Biosynthetic Pathway Profiling and Candidate Gene Mining of the _Ilex asprella_ Root Using RNA-Seq  
**[PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON] ***  
Research Center of Chinese Herbal Resource Science and Engineering,  
Key Laboratory of Chinese Medicinal Resource from _Lingnan_,  
Guangzhou University of Chinese Medicine, Guangzhou 510006, China;  
E-Mails: [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (X.M.);  
[EMAIL_ADDRESS] (R.Z.)  
* Authors to whom correspondence should be addressed;  
E-Mails: [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (W.C.);  
Tel.: +86-20-3935-8331 (H.X.); +86-20-3935-8268 (W.C.).  
_Received: 26 January 2014; in revised form: 23 March 2014 / Accepted: 26 March 2014 / Published: 9 April 2014_  
**Abstract:**_Ilex asprella_, which contains abundant \(\alpha\)-amyrin type triterpenaponions, is an anti-influenza herbal drug widely used in south China. In this work, we first analysed the transcriptome of the _I. asprella_ root using RNA-Seq, which provided a dataset for functional gene mining. mRNA was isolated from the total RNA of the _I. asprella_ root and reverse-transcribed into cDNA. Then, the cDNA library was sequenced using an Illumina HiSeq(tm) 2000, which generated 55,028,452 clean reads. _De novo_ assembly of these reads generated 51,865 unigenes, in which 39,269 unigenes were annotated (75.71% yield). According to the structures of the triterpenaponions of _I. asprella_, a putative biosynthetic pathway downstream of 2,3-oxidosqualene was proposed and candidate unigenes in the transcriptome data that were potentially involved in the pathway were screened using homology-based BLAST and phylogenetic analysis. Further amplification and functional analysis of these putative unigenes will provide insight into the biosynthesis of _Ilex_ triterpenaponions.  
**Keywords:**_Ilex asprella_; triterpenaponions; biosynthesis; \(\alpha\)-amyrin; RNA-Seq
## 5 Discussion
### Communication & Latency  
Third party and energy reduction schemes that are dependent on fast and reliable communication will be limited by the high latency and unreliable connections of DSRC and LTE-V2X networks. Services which require geolocation data for optimal route finding, smart intersections or other navigational third-party services are limited by the inability to send and receive traffic or environmental information reliably and efficiently. Limitations in these communication technologies are most notable in network congested areas such as intersections or traffic jams. Technologies which can alleviate the effects of poor networking speeds such as 5G, Adaptive Relay-Node Selection, edge caching, and vehicle fog technologies are currently in development and can be utilized for networking communications [73; 74; 75]. 5G can achieve 1 ms latency for vehicle communications. Third party services and energy reduction schemes will have a short wait for efficient and reliable networking capabilities. These enhanced networking paradigms are summarized along with their benefits in Table 2.
## 2 Methods
### Initial and Boundary Conditions  
As mentioned above, the model uses three domains, namely, the blood, the artery and the cardiac muscle. The numerical simulation considers the solution of the 3D Navier-Stokes equations and the fluid-structure interactions. The initial and boundary conditions required to solve the equations of the model are given hereafter.  
The human heart beat is cyclically time-varying. Thus, the velocity at the inlet of the aorta is not set to be constant. It has a periodic profile as a function of time. During a cycle, the velocity varies between a minimal and a maximal value. The duration of each period is 0.8 s. We introduced the following function [31]:  
\[f(t)=\left\{\begin{array}{ll}0.8\ \sin(2.857\pi t)&0\leq t\leq 0.35\\ 0.6\ \sin(2.22\pi(t-0.35))&0.35\leq t\leq 0.8\end{array}\right. \tag{6}\]  
The inlet velocity for the artery in this simulation was fixed to 0.3 [m/s]*f(t). The temporal evolution of the inlet velocity is represented in Figure 2. The outlet condition pressure was considered to be 125 [mmHg]*f(t). These conditions are similar to those adopted by [PERSON] and [PERSON] [31]. This function has two peaks which have different amplitudes and different periods.  
On the aortic walls, a non-slip boundary condition is applied, and the wall is assumed to be impermeable since the fluid is supposed not to penetrate through the walls as it flows along the vessel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Density & \(\rho_{artery}=960\) kg/m\({}^{3}\) \\ Shear modulus & \(\mu^{s}=62.04\times 10^{5}\) N/m\({}^{2}\) \\ Bulk modulus & \(\kappa^{s}=124\times 10^{5}\) N/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the artery.
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
###### Abstract  
Uncoated weathering steel (UWS) bridges have been extensively used to reduce the lifecycle cost since they are maintenance-free and eco-friendly. However, the fatigue issue becomes significant in UWS bridges due to the intended corrosion process utilized to form the corrodent-proof rust layer instead of the coating process. In this paper, an innovative model is proposed to simulate the corrosion-fatigue (C-F) process in UWS bridges. Generally, the C-F process could be considered as two relatively independent stages in a time series, including the pitting process of flaw-initiation and the fatigue crack propagation of the critical pitting flaw. In the proposed C-F model, [PERSON]'s law has been employed at the critical flaw-initiation stage to describe the pitting process, in which the pitting current is applied to reflect the pitting rate in different corrosive environments. At the crack propagation stage, the influence of pitting corrosion is so small that it can be safely ignored. In simulating the crack propagation stage, the advanced NASGRO equation proposed by the NASA is employed instead of the classic Paris' law, in which a modified fatigue limit is adopted. The fatigue limit is then used to determine the critical size of pitting flaws, above which the fatigue effect joins as a parallel driving force in crack propagation. The model is then validated through the experimental data from published articles at the initiation stage as well as the whole C-F process. Two types of structural steel, i.e., HPS 70W and 14 MnNbq steel, have been selected to carry out a case study. The result shows that the C-F life can be notably prolonged in the HPS 70W due to the enhancement in fatigue strength and corrosion resistance. Besides, a sensitivity analysis has been made on the crucial parameters, including the stress range, stress ratio, corrosive environment and average daily truck traffic (ADTT). The result has revealed the different influence of the above parameters on the initiation life and propagation life.  
corrosion-fatigue; pitting corrosion; critical pitting size; fracture mechanics; uncoated weathering steel +
Footnote †: journal: Applied Soft. 2019  
19 July 2019 August 2019
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 2 Link Relationship Graph--The Time Continuity Model of Network Topology
### The Geometric Linkable Factor  
As shown in Figure 1, two satellites \(S_{i}\) and \(S_{j}\) are used to explain the link relationship in the space information system. Since \(S_{i}\), \(S_{j}\), and geocentric \(O_{e}\) are coplanar, it is advisable to introduce virtual instantaneous circular orbits for \(S_{i}\) and \(S_{j}\) in this plane. Then, the link relationship of \(S_{i}\) and \(S_{j}\) is equivalent to the link relationship of two points on two virtual circular orbits. Due to the occlusion of the earth and the effect on signal translation of the atmosphere, there is a maximum visible link distance written as \(d_{\max}(t)\) between \(S_{i}\) and \(S_{j}\)[33]:  
\[d_{\max}(t)=d_{S_{i}A}(t)=\left[\left(r_{e}+h_{S_{i}}(t)\right)^{2}-\left(r_{e }+h_{atm}\right)^{2}\right]^{1/2}+\left[\left(r_{e}+h_{S_{i}}(t)\right)^{2}- \left(r_{e}+h_{atm}\right)^{2}\right]^{1/2}\]  
Let \(d_{\text{Link}}\) denote the effective distance of the inter-satellite link, and \(d_{S_{i}S_{j}}(t)\) represents the distance between \(S_{i}\) and \(S_{j}\). Then,  
\[d_{S_{i}S_{j}}(t)=\left|\overset{\rightarrow}{r}_{S_{i}}(t)-\overset{ \rightarrow}{r}_{S_{i}}(t)\right|=\left\{\left[x_{S_{i}}(t)-x_{S_{j}}(t) \right]^{2}+\left[y_{S_{i}}(t)-y_{S_{i}}(t)\right]^{2}+\left[z_{S_{i}}(t)-z_{S_ {i}}(t)\right]^{2}\right\}^{1/2} \tag{1}\]  
Wherein the satellite position vector can be calculated according to the following formula [38] when ignoring the orbit change caused by perturbation and other factors.  
\[\left[\begin{array}{c}x(t)\\ y(t)\\ z(t)\end{array}\right]=\frac{a\big{(}1-e^{2}\big{)}}{1+e\cos f}\left\{\begin{array}[ ]{c}\cos[f+\omega]\cdot\cos\Omega-\cos i\cdot\sin[f+\omega]\cdot\sin\Omega\\ \cos i\cdot\cos\Omega\cdot\sin[f+\omega]+\cos[f+\omega]\cdot\sin\Omega\\ \sin i\cdot\sin[f+\omega]\end{array}\right\}\]  
It is obvious that \(d_{S_{i}S_{j}}(t)\leq\min(d_{\text{Link}},d_{\max}(t))\) means \(S_{i}\) and \(S_{j}\) can be linked, and \(\sigma_{1}(t)=1\); otherwise, \(\sigma_{1}(t)=0\).
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 5 Evaluation
### Evaluation
This is because the _Port-Status_ message verification needs to store a copy mapping information temporarily, but the impact of this consumption is negligible. In conclusion, the experimental results demonstrate that the defense overhead of PIEDefender is acceptable. From the above results, we can conclude that the PIEDefender can prevent the packet injection exploiting attack on the control plane and data plane of the SDN network with limited overhead. **Further discussion:** We demonstrate that the PIEDefender is effective for mitigating the packet injection exploiting attack. However, it is questionable whether the PIEDefender is useful against other common attacks in SDN. Therefore, we further discuss its applicability. For a certain kind of attack, we think the PIEDefender is applicable if it can, or after extending, defend. Otherwise, it is not applicable. The result is shown in Table 4.  
PIEDefender constructs and maintains the mapping table between switches and hosts connected. Since having the latest global topology status, in addition to detecting malicious injection behaviors, PIEDefender can quickly identify malicious hosts and topology attacks with legitimate network configuration information. That is to say that the PIEDefender is beneficial for preventing topology poisoning attacks and packet injection attacks. Besides, PIEDefender utilizes OpenFlow message and flows features to detect hosts which use injected hosts to carry out a DoS attack. These characteristics are compatible with general high-rate DoS attacks. They will increase the messages rate and generate many flows quickly, like the saturation attack, the flow table overflow attack, and the new flow-based DoS attack. However, the PIEDefender has limitations in defending against other attacks, such as fingerprinting and low-rate DoS attacks. We will focus on these attacks in the future. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attack** & **Related Works** & **Applicable** & **Not Applicable** \\ \hline Flow table overflow attack & [11; 36; 37; 38] & ✓ & \\ Flow rule conflict & [39; 40; 41; 42] & & ✓ \\ Packet injection attack & [16; 17] & ✓ & \\ Fingerprinting attack & [43; 44; 45; 46; 47] & & ✓ \\ Topology poisoning attack & [7; 23; 48] & ✓ & \\ Saturation attack & [10; 11; 29; 50; 51] & ✓ & \\ New-flow based DoS attack & [16; 1, 16] & ✓ & \\ Low-rate DoS attack & [20; 52; 53; 54; 55] & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Applicability of PIEDefender. Figure 11: Defence overhead of PIEDefender. (**a**) CPU. (**b**) Memory.
## 3 Results and Discussion  
### Chemical Composition of Olive Cakes  
Table 2 presents the chemical composition of different olive cakes, expressed as a percentage of dry matter (DM). Several parameters are evaluated, including OM, NDF, ADF, ADL, cellulose (CEL), CP and crude fat (CF). Observing the data, it is evident that the composition varies significantly among the different types of olive cakes. For instance, the dehydrated TPOC and COC 1 exhibit the highest DM content, surpassing 90%, indicating a relatively low moisture content. On the other hand, TPOC (pitted) has the lowest DM content, indicating a higher moisture content compared to the other samples. Regarding OM, COC 2 shows the highest value, suggesting a higher concentration of organic matter compared to other samples. This could be attributed to factors such as the olive variety and processing method. NDF and ADF are indicators of the fibre content in the olive cakes. Generally, TPOC (dehydrated) shows the highest NDF content, while EOC 2 displays the highest ADF content. These differences could be due to variations in the olive fruit composition and processing techniques. ADL and CEL are components of fibre associated with the structural integrity of plant cell walls. EOC 2 and COC 2 exhibit the highest ADL and CEL content, indicating differences in the lignin and cellulose composition among the samples. CP content reflects the protein content in the olive cakes. Notably, EOC 2 and COC 2 show the highest CP content, suggesting a higher protein concentration compared to other samples. Finally, CF represents the ether extract content, which includes lipids and other organic compounds. COC 1 has the highest CF content, followed by EOC 1, indicating a higher lipid concentration in these samples compared to others. Overall, the data illustrate the diverse chemical composition of olive cakes, influenced by various factors such as olive variety, processing methods and environmental conditions. Understanding these differences is crucial for potential applications in industries such as agriculture, food and bioenergy. The chemical composition of olive cake can vary according to the climate and the extraction method used. These factors directly impact its constituents--pericarp, mesocarp, endocarp and stone each with distinct chemical compositions. Notably, olive cake is distinguished by its high fibre content, often highly lignified, and significant protein content, typically around 10% [8; 40]. Consistent with previous studies, our findings align with reported chemical composition values [41; 42]. Notably, there is a significant difference in moisture content (\(p\) < 0.001) between TPOC pitted (64.59 \(\pm\) 0.074) and TPOC dehydrated (3.24 \(\pm\) 0.54), attributable to the drying process applied. Traditional or three-stage mills yield olive cakes with approxi