esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Community Detection Based on Small Cliques
## 3 Local Community Detection Algorithms Based on Small Cliques
### Triangle Based Community Expansion
Then the loop is executed, taking the node \(u_{\max}\in S\) with the highest node score (line 4), which is given by Equation (4). This node is then removed from \(S\) (line 5) and we check if it would improve the conductance of \(C\). If it improves the conductance, we add the node to \(C\) and update \(S\) by inserting all neighbors of \(u_{\max}\) that are not in \(C\) (lines 6-8). ```
Input: Graph \(G=(V,E)\), edge weights \(w(\cdot\cdot)\), seed node \(s\). Output: A community \(C\) of the seed node \(s\). 1\(C\leftarrow\{s\}\)
2\(S\gets N(s)\)
3 while\(S\
eq\emptyset\)do
4\(u_{\max}\leftarrow\text{arg max}_{u\in S\text{score}}(u;G,C,w)\)
5\(S\gets S\setminus\{u_{\max}\}\)
6 if\(\textsc{CONductance}(C\cup\{u_{\max}\})<\textsc{CONductance}(C)\)then
7\(C\gets C\cup\{u_{\max}\}\)
8\(S\gets S\cup(N(u_{\max})\setminus C)\)
```  
**Algorithm 1**Triangle Based Community Expansion (TCE) detects a community around a given node. Uses a node scoring function based on triangles to add nodes to the community.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Impact of Contact Information Size on Message Forwarding
#### 5.2.2 Routing Performance of PoiFord
Hence, it would be a better practice to implement summary vector exchange when evaluating the performance of OppNet protocols via simulations. There are also other means through which PoiFord improves the performance of a routing protocol it is incorporated into. Locality awareness allows messages to be forwarded to nodes moving  
Figure 11: Message transmission overhead and average energy consumption of PRoPHET and PoiFord (note that energy consumption in these results is mainly for purpose of comparison, and does not represent realistic energy values). Figure 10: Throughput and average delivery delay of PRoPHET, PoiFord and HERO. closer to the destination's POI. With delivery predictability alone, the transitive property allows more nodes to present higher forwarding utilities, even by slight margins. Hence, nodes receive more messages and message transmission overhead is increased (cf. Figure 11). PoiFord reduces message transmission overhead by about 51% and 53% without and with the implementation of summary vectors, respectively. PoiFord also reduces energy consumption by about 40% and 7% likewise--note that PoiFord's energy reduction tends to increase with increasing network size due to less contact information and fewer message transmissions. It can also be observed from Figure 11 that PRoPHET's message transmission overhead and energy consumption is less when summary vector exchange is implemented. This is because fewer messages are successfully forwarded when summary vectors need to be exchanged beforehand. This is also the case for PoiFord's message transmission overhead. In terms of energy, however, PoiFord records more consumption when summary vector exchange is implemented. This is due to the additional energy consumed in successfully exchanging more number of summary vectors. In the case of PRoPHET, the message transmission overhead when summary vector exchange is not implemented is so high that the energy consumed in the process exceeds this additional energy consumption. PRoPHET's high message transmission overhead due to utilizing the encounter-based delivery predictability alone leads to more number of failed transmissions (cf. Figure 12), as nodes carry more messages and try to forward all of them within limited encounter durations. The resulting increase in transmissions not only expends more energy on nodes (cf. Figure 11), but also leads to increased contention that degrades throughput. Using delivery predictability alone may also degrade throughput since messages are more likely to be dropped due to increased buffer occupancy and overflows. The combination of encounter and location-based knowledge allows PoiFord's forwarding utility to identify a wider range of more suitable relay nodes, thereby reducing failed message transmissions and message drops (cf. Figure 12).
# _Brassica oleraea_ Pevents HCl/Ethanol-Induced Gastric Damages in Mice
## 3 Results
### BOE Inhibits H/E-Mediated Inflammation in the Gastric Tissue  
To explore whether BOE protects the gastric tissues by reducing H/E-mediated acute inflammation, we measured MPO activity in the gastric tissue. Compared to vehicle  
Figure 3: Microscopic effect of BOE on H/E-mediated gastric damage. (**a**) Representative histological image of the fundus/corpus mucosa after staining with hematoxylin and eosin. Dashed squares of left column are enlarged in the middle and right column, and scale bars indicate 80 μm. (**b**) Histomorphometric analyses. Invaded percentage of lesion, mucosa thickness of peri-ulcerative region, and semi-quantitative histological score were calculated as described in the method section. Numerical results are presented as mean \(\pm\) SD of ten mice (**\({}^{*}\)**_p_ \(<\) 0.01 between the vehicle and H/E groups; **\({}^{\#}\)**_p_ \(<\) 0.01, **\({}^{\#}\)**_p_ \(<\) 0.05 versus H/E-administered group). BOE, _B. oleracea_ var. _capitala_ L. extract; ED, edematous change; EP, surface epithelium; H/E, HCl/ethanol; LU, lumen; ML, muscular layer; MM, muscular mucosa; MU, mucosa layer; RA, ranitidine hydrochloride; SM, submuc.  
group, H/E significantly increased the MPO activity. However, pre-administration of three different doses of BOE as well as RA significantly attenuated an H/E-mediated increase in MPO activity (Figure 6a). Next, mRNA and protein levels of pro-inflammatory mediators were determined using real-time PCR and ELISA, respectively. As expected, H/E significantly increased the mRNA and protein levels of NF-kB (Figure 6b), COX-2, iNOS (Figure 6c), TNF-x, IL-19, IL-6, and IL-18 (Figure 6d) compared to vehicle. Pre-administration of three different doses of BOE as well as RA significantly inhibited the increases in mRNA and protein levels of pro-inflammatory mediators induced by H/E (Figure 6b-d). Although the magnitude of the reduction in pro-inflammatory mediators by 100 mg/kg BOE was greater than that by RA (\(p<0.01\)), the inhibitory effect by 50 mg/kg BOE did not differ from that seen in response to RA.
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations on the Effect of Coolant Boiling Caused by Falling Debris  
#### 3.5.1 Experimental Materials and Methods  
In addition to the boiling caused by the accumulated debris, it is also rationally expected that in actual severe SFR accidents, the high-temperature falling debris with decay heat may cause the coolant boiling to surround them during their falling process. Noticing this point, [PERSON] et al. [72] performed a series of experiments by releasing heated particles to elucidate the effect of boiling caused by falling debris on DBF flow regime characteristics. Figure 16c displays the experimental system in which a heating bucket was employed to pre-heat the particles to the target temperature (\(T_{\rm p}\) = 473\(\sim\)673 K). The water temperatures (358 K and 368 K) were selected to study the effect of liquid temperature. Other important parameters, including particle type (Alumina, Al\({}_{2}\)O\({}_{3}\), ZrO\({}_{2}\), and Steel), diameter (0.4\(\sim\)4 mm), and nozzle diameter (30\(\sim\)40 mm), were also considered.
## 4 Results and Discussion
### Dataset Description  
#### 4.1.1. UNSW-NB15  
The IXIA PerfectStorm tool in the UNSW Canberra Cyber Range Lab synthesized the initial network packets of the UNSW-NB15 dataset to create a hybrid of actual modern normal activities and artificial contemporary assault behaviors. Here, 100 GB of raw data were obtained by employing the tcpdump application. Nine varied attacks, namely shellcode, fuzzes, Backdoors, worms, denial-of-service, generic, and exploit attacks, were incorporated into this dataset.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 2 Model Development
### Attention Mechanism  
Traditional models treat temporal and spatial attention independently. In contrast, the HTSA-LSTM model integrates these mechanisms, allowing them to interact and influence one another, resulting in more accurate predictions. The following sections will detail the working principles of the dynamic spatiotemporal attention mechanism and its advantages in real-world applications.
# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 3 Improved White Shark Optimisation Algorithm
### Chaos Elite Pool Strategy
#### 3.1.2 Lens Imaging Reverse Learning for Population Initialization  
Convex lens imaging, an optical principle, involves placing an object at a defocused position to form an inverted real image on the other side of the lens [19]. Reverse learning based on lens imaging helps the golden jackal population obtain more solutions, thereby effectively maintaining diversity. By adjusting the scaling factor k in lens imaging reverse learning, dynamic changes in reverse solutions were achieved, enhancing the optimization accuracy of the algorithm. Since the value of k is inversely related to the range of generated reverse solutions, a smaller k was set to obtain reverse solutions over a larger range, thereby increasing solution diversity.  
The learning process of the current best individual X based on one-dimensional space lens imaging is illustrated in Figure 3. As shown, the search interval for X on the \(x\)-axis is [a,b], and the height of individual P is L, with its projection on the x-axis being X. A convex lens with focal length f was placed at base point O, and individual P was positioned outside the focal length f, forming an inverted real image P* with height L* on the other side of the lens. The projection of P* on the \(x\)-axis is X*. Thus, the reverse point P* of P was obtained based on the lens imaging learning strategy.  
According to the diagram and the principles of convex lens imaging, the following relationship was derived:  
\[\frac{\frac{a+b}{2}-X}{X^{*}-\frac{a+b}{2}}=\frac{L}{L^{*}} \tag{7}\]  
Letting \(\frac{L}{L^{*}}=k\) where k is the scaling factor, the reverse point X* based on lens imaging learning was obtained by transforming the above equation:  
\[X^{*}=\frac{a+b}{2}+\frac{a+b}{2k}-\frac{X}{k} \tag{8}\]  
Figure 3: Convex lens imaging principle.  
Figure 2: Population distribution under three chaotic mappings.  
When k = 1, a special case of lens reverse learning, namely the general reverse learning reverse solution, was obtained as follows:  
\[X^{*}=a+b-X \tag{9}\]  
In summary, during the initialization phase, the prey population size was set to N. Initially, Sine mapping was used to generate N j-dimensional populations P\({}_{1}\). Subsequently, the lens imaging reverse learning strategy was applied to produce the reverse population P\({}_{2}\), resulting in twice the number of candidate solutions. The populations P\({}_{1}\) and P\({}_{2}\) were then merged, and the fitness values of each individual were calculated and ranked. Finally, the top N individuals were selected as the new elite population.
# A Review on Local Failure Probability Sensitivity Analysis
## 5 Conclusions  
In this article, we reviewed different methods to compute the derivatives of the failure probability with respect to the deterministic inputs \(\mathbf{s}\) of the system, for RBDO and local reliability-based sensitivity analysis purposes. For each method, the additional simulation budget needed is underlined, compared to the simulation budget necessary to the estimation of the failure probability.  
If the deterministic inputs are distribution parameters, denoted \(\boldsymbol{\theta}\), two different frameworks are possible. The first framework relies on the score function methods and the estimation is performed in the original input space. The simulation budget is then kept constant and the sensitivity estimates are typically unbiased. However, there are few efficient techniques available to compute the failure probability in the original space. In the second framework, the original inputs are transformed into standard normal inputs with an isoprobabilistic transformation. In this input space, there is a greater number of efficient methods to compute the failure probability. Nevertheless, the sensitivity estimation causes a heavier simulation budget, as the derivatives of the limit state function with respect to \(\boldsymbol{\theta}\) must be evaluated. Some approaches result in unbiased estimates, as in the DS and LS context, but require an even greater simulation budget as evaluations of the gradient of \(g\) with respect to the random inputs are needed. While the other approaches lead to biased estimates, whose bias depends on a scalar parameter (\(\sigma\) or \(h\)), or remains unknown (in the FORM context). The resulting bias cannot be properly controlled.  
If the deterministic parameters are design parameters, denoted \(\delta\), the evaluation of the derivatives of \(g\) with respect to \(\delta\) is mandatory, except in the finite difference schemes context (where the simulation budget is then doubled or tripled). The sensitivity analysis is performed in the standard normal space and the same conclusions can be drawn concerning the simulation budget and the bias than with distribution parameters. Thus, the simulation budget substantially increases with LS or DS and the bias cannot be properly managed with the other approaches.  
M.C., J.M. and S.D.; Methodology, M.C., J.M. and S.D.; Software, M.C.; Validation, M.C.; Formal analysis, M.C., J.M. and S.D.; Investigation, M.C.; Writing--original draft, M.C.; Writing--review & editing, M.C., J.M. and S.D.; Supervision, J.M. and S.D.; Funding acquisition, J.M. and S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Security Analysis
### Impact of the \(T\) Parameter on Security  
The \(T\) parameter introduces an additional dimension to the security analysis, allowing the trade-off between anonymity and transparency to be adjusted. This has the following implications:  
* For a high \(T\)--anonymity is maximized, and the adversary cannot associate the signature \(\sigma\) with the signer or the message \(M\). This setting is ideal for applications such as electronic voting or confidential financial transactions.
* For low \(T\)--the level of anonymity decreases, allowing additional information to be inserted into signatures for auditing. This is useful in applications such as supply chains, where traceability is required.  
Mathematically, \(T\) modulates the signing and verification functions such that  
\[\sigma^{\prime}=(M\cdot T\cdot r)^{K_{\text{priv}}}\text{ mod }n,\]  
and the validation becomes  
\[\sigma^{e}\text{ mod }n=M\cdot T.\]  
This dependency makes the security of the signatures controlled by the value of the parameter \(T\), without compromising the integrity of the message \(M\).
## 4 Discussion
### Winter Wheat (WW)  
The model was subsequently applied to WW crop for three sites over the period 2013-2015--including Crossnacreevy, Carlow and Kileagh. Grain yield is derived by multiplying the daily biomass with the grain filling factor which is assumed to be a constant 0.6 for this study. In our validation for WW crop there are two observations of the grain yield that cause high errors, and if these two observations are removed, then our model shows high correlation with the observed values and becomes statistically significant (R\({}^{2}\) = 0.85, \(p\) < 0.01), As we do not have access to the ground collected raw data, we could not assess the quality of these data points and so cannot simply remove the observation to improve the model accuracy. There are two other possibilities for the low accuracy achieved in estimating grain yield through this approach, first the grain filing factor constant value may not be representative and requires further investigation; the other possible reason may be the lack of available data to more robustly evaluate the model. [23] developed a winter wheat yield potential model (WWYPM) for different sites in Ireland and evaluated against WW crop yield. They obtained an R\({}^{2}\) = 0.37 for biomass and R\({}^{2}\) = 0.19 for yield. The model adapted in this work represents an improvement for biomass, but with a lower R\({}^{2}\) for yield, which was also poorly estimated in [23]. It is clear that the model estimate for WW yield specifically needs to be investigated further and evaluated, subject to alternative in situ data becoming available. In spite of the poorer model performance for yield, the estimated timing of G561 closely matched the available observations (Table 2). The % differential ranges from 0.5-12% with respect to the G561 stage of WW (data not shown). [21] calibrated the Aquacro model parameters for wheat using the canopy cover derived from the Venus satellite images for the crop season 2017-2018 and found R\({}^{2}\) = 0.51; RMSE = 683 g\(\cdot\)m\({}^{-2}\); RRMSE = 68.45% for biomass and R\({}^{2}\) = 0.11; RMSE = 151 g\(\cdot\)m\({}^{-2}\) RRMSE = 26.9% for grain yield on validating Aquacro model for 2018-2019 year on wheat fields in Maccarse, Italy. Ref. [31] estimated wheat yield with R\({}^{2}\) = 0.66 by integrating crop model with the Sentinel-2 remote sensing data. Ref. [13] applied the basic SAFY model to the WW crop in Haouz plain, Marrakech city and found that the model underestimated yield by almost 0.5-0.9 t-ha\({}^{-1}\) when compared to field measurements, which they attributed to differences in growing conditions between the plots used for calibration and evaluation. Ref. [30] integrated in-season remote sensing data into the SAFY model to improve the GAI, biomass and yield estimates. The model was applied to three different years 2013-2015 for WW crop in China, the study found 20%, 17%, and 15% RRMSE on validation with measured yield estimates, averaging to 18% over the three years studied, which is similar to what we found in our work (18.6%).
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Verification of the FE Model  
As shown in Figure 15, the total displacement in the anchoring system includes not only the increased length of the strand, but also the slip displacement between the anchor head and the wedge [28]. The analysis model that has a shorter free length was calibrated for comparison with the displacement measured in the static load test.  
\[\delta_{experiment\ total}=\delta_{s}+\delta_{ht}+\delta_{hb} \tag{12}\]  
\[\delta_{analysis\ total}=\delta_{s}+\delta_{h} \tag{13}\]  
\[\delta_{analysis\ total}^{\prime}=\delta_{s}\times 1500/147+\delta_{h}\times 2 \tag{14}\]  
In the load-displacement curve obtained from the finite element analysis results, the point where the stiffness changes at the beginning of the load in the elastic section is found. This is the effect of 'interference fit' applied to control the analysis error that occurs between the contact between the wedge and the anchor head. It is not a matter to be considered because it is not an area of interest to be obtained through analysis. In the load-displacement relationship obtained from the test and finite element analysis, since the displacement ratio of the strand is dominant compared to the total displacement, the material properties of the strand can be verified. The load displacement from the test and the corrected analysis results are shown in Figure 16, and showed good agreement. In addition, the measured hoop strain was compared to specify the behavioral characteristics of the anchor head. Prior  
Figure 14: Boundary and loading condition.  
Figure 13: Friction coefficient evaluation at wedge-anchor head interface.  
to comparison, the hoop strain of the outer surface of the anchor head obtained from the FE analysis is shown in Figure 17a. When a piece of three-piece-wedge slides into the anchor head, the amount of strain appears differently depending on the contact position with the wedge. Since this strain distribution has unique characteristics depending on the design parameters of the anchor head, it is desirable to verify the analytical model by comparing it with the strain distribution in the test. Figure 17b shows the maximum, minimum, and average values according to the design variables of the hoop strain obtained in the test and FE analysis. The maximum and minimum hoop strains show some differences, but on average, show similar results. This may be attributed to imperfect settlement of the wedge in the anchor head. However, since the overall tendency is similar, the modeling method of the anchor head can be evaluated as well reflecting the actual behavior.  
Figure 16: Comparison of load-displacement curves between the test and the analysis.  
Figure 15: Correction of analysis displacement.
## 3 Measurement Setup
### Experimental Setup on PlanetLab  
The aim of our experimental setup is to provide a long-time and international measurement of the performance of Tor compared to a direct connection. Therefore, we compare the direct access of multiple clients to multiple servers on the Internet with the same connections established via the Tor network. (Note that this is the case of page latency measurements. For the download experiments we used only one target server.) Moreover, we deploy clients internationally with the help of the network research platform PlanetLab ([[http://www.planet-lab.org/](http://www.planet-lab.org/)]([http://www.planet-lab.org/](http://www.planet-lab.org/))) [42]. For this, PlanetLab nodes from most continents were chosen. It was not possible to find reliable PlanetLab nodes in Africa during the time of our experiment.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**PlanetLab Nodes as Web Clients** & **Country** & **Uptime (days)** & **HTTP Requests** & **Downloads** \\ \hline pl2.eng.monash.edu.au & Australia & \(38\) & \(592,773\) & \(43,212\) \\ planetlab2.pop-parnp.br & Brazil & \(38\) & \(525,863\) & \(42,712\) \\ mercury.silicon-valley.ru & Russia & \(38\) & \(605,313\) & \(44,850\) \\ planetlab2.aston.ac.uk & United Kingdom & \(36\) & \(418,217\) & \(37,023\) \\ planetlab2.wiwi.hu-berlin.de & Germany & \(33\) & \(464,604\) & \(37,700\) \\ planet-lab1.cs.ucr.edu & USA & \(30\) & \(325,305\) & \(38,759\) \\  & (West Coast) & & & \\  & USA & & & \\ orbpl1.rutgers.edu & (East Coast) & \(25\) & \(279,339\) & \(24,684\) \\ planetlab02.erin.utoronto.ca & Canada & \(24\) & \(355,481\) & \(32,747\) \\ adam.ee.ntu.edu.tw & Taiwan & \(23\) & \(252,936\) & \(25,598\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental Statistics.  
Each PlanetLab node provides its users with a minimal _Fedora Core 8_ Linux installation [10]. For our experiments, additional software needed to be installed. On all PlanetLab nodes we used _Tor_ in version 0.2.0.32, _Privacy_ in version 3.0.6, _Perl_ in version 5.8.8, _Perl-CPAN_ in version 1.76_02, and _LWP::UserAgent_ in version v5.835.  
Table 3 shows the PlanetLab nodes that were selected for the experiment and experimental statistics. All nodes were capable of executing the Perl scripts for our experiments. Some other nodes we initially selected had to be excluded from the experiment. For example, our supposition why a PlanetLab node in China was not able to connect to the Tor network via public relays is that since September 2009 Chinese authorities started to block the Tor network in China [43,44].