esa-sceva/satcom-chunk-collection huggingface dataset:
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 5 Anomaly-Based Model  
The variables used for the Normal Behavior model are energy, number of re-transmissions and number of retries of the inner loop of the MAC algorithm. In this section, therefore we are interested in their behaviors in both, normal and jamming conditions and we analyze how discriminant they are. For simulations, the length of traffic packets is 100 bytes. Regarding the propagation model, sensitivity thresholds are between \(-78\) to \(-94\) dBm, and transmission power is \(4.5\) dBm in active mode.
## 3 FORC Tool: Design and Analysis
### Acquisition Requirements  
Acquisition is the crucial first step in the process of extracting data from smartphones. The extracted data is then processed forensically to obtain digital evidence. The FORC tool provides investigators with two options for acquisition: ADB backup and physical acquisition. Before starting the acquisition, investigators must consider the following points:  
* Ensuring the smartphone is turned on.
* Ensuring the smartphone is unlocked.
* Enabling USB debugging.
* Connecting the smartphone to the computer or workstation using the original USB cable.
* Selecting the appropriate option on the smartphone screen to establish a communication between the mobile phone and the workstation.
* For ADB backup, rooting is not required, but for physical acquisition, rooting is essential, and it cannot be performed without rooting. The appropriate tool, such as BusyBox, must be used for rooting the device, depending on the mobile device and the installed Android version.3.2.1. Handling Forensics Image.
* ADB backup:  
After executing the ADB backup command, a file with an.ab extension will be generated. To ensure compatibility with the FORC tool, a vital procedure entails converting the.ab file into a.tar file, as illustrated in Figure 4.  
Figure 3: FORC tool: New Case window.  
* Physical acquisition: For physical acquisition, as previously noted, rooting is imperative, resulting in a.dd file extension. To process forensic images with the.dd extension, the FORC tool seamlessly integrates the Sleuth Kit library through the ptytsk3 module. The extracted data can be handled like any other folder on the computer, akin to data extracted in the case of ADB backup.
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
###### Abstract  
Traditional reversible data hiding (RDH) methods prioritize the exact recovery of the original cover image; however, this rigidity often hinders both capacity and design flexibility. This study introduces a partial reversible data hiding (PRDH) framework that departs from conventional standards by allowing reversibility relative to a generated cover image rather than the original. The proposed system leverages a dual-image structure and an enhanced HC(\(7A\)) Hamming code to synthesize virtual pixels, enabling efficient and low-distortion syndrome-based encoding. Notably, it achieves embedding rates up to 1.5 bpp with PSNR values exceeding 48 dB. While the proposed method avoids auxiliary data, its reliability hinges on paired image availability, which is a consideration for real-world deployment. Demonstrated resilience to RS-based steganalysis suggests viability in sensitive domains such as embedding structured metadata in diagnostic medical imagery. Nonetheless, further evaluation across more diverse image types and attack scenarios is necessary in order to confirm its generalizability.  
Keywords:data hiding (DH); reversible DH (RDH); dual RDH; Hamming coding; exploiting modification direction (EMD) +
Footnote †: journal: Applied sciences
# Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks
## 5 Conclusions  
In this article, we have briefly discussed the critical issue of cyberattacks in the context of LEO satellite networks. Recently the LEO network is under various kind of threats and DDoS specifically poses a huge threat. To address DDoS attacks in LSNs, we have proposed a random routing algorithm k-RAND that improves the average and median cost of attack on an LEO satellite network in addition to increasing uncertainty in the routing algorithm. It was shown that the random routing algorithm enhances the attacker's average and median cost while maintaining the functionality of the network but functions at the cost of a slightly degraded detectability performance. The algorithm is based on the selection of one of four classic algorithms--k-LO, k-DS, k-DG, and k-SP--by introducing randomness to the selection with weighted probability distribution. The optimization of the parameters of the k-RAND algorithm that provides the distribution of our algorithm is performed by formulating a Bayesian optimization problem that maximizes the average cost of the DDoS attacks. The k-RAND algorithm will be an effective and robust defensive measure for the DDoS attacks against the modern LEO satellite communication link. The codes used for the simulation studies are available on [[https://github.com/[PERSON]/icarus-framework](https://github.com/[PERSON]/icarus-framework)]([https://github.com/[PERSON]/icarus-framework](https://github.com/[PERSON]/icarus-framework)), (accessed on 18 January 2020).  
For research articles Conceptualization, R.F., Y.S., R.K. and S.A.; Methodology, R.F., Y.S., R.K. and S.A.; Software, R.F. and Y.S.; Validation, R.F., Y.S., R.K. and S.A.; Formal analysis, R.F., Y.S., R.K. and S.A.; Investigation, R.F., Y.S., R.K. and S.A.; Resources, R.F. and Y.S.; Data curation, R.F. and Y.S.; Writing--original draft, R.F., Y.S., R.K. and S.A.; Writing--review & editing, R.F., Y.S., R.K. and S.A.; Visualization, R.F., Y.S., R.K. and S.A.; Supervision, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank Kreitman School of Advanced Graduate Studies and Ben-Gurion University of the Negev, Israel for providing fellowships to continue the research.  
The authors declare no conflict of interest.  
Figure 5: CDF of the attackers’ detectability/MaxUp of launching a DDoS Attack on an LEO satellite network.
## 4 Discussion
### Limitations  
One limitation of our study is the use of the residency in Bien Hoa of the subjects' mothers to estimate perinatal dioxin exposure. A previous study reported high levels of TCDD in breast milk in Bien Hoa residents (see Materials and Methods, Section 2.1), suggesting that our subjects were exposed to dioxins during the perinatal period. Furthermore, our previous studies reported that dioxin levels in breast milk were associated with neurodegenerativement of children [5; 6; 7; 8]. The present results indicated that the residency in Bien Hoa of the subjects' mothers, which suggests that the subjects were subjected to perinatal exposure to dioxins, was significantly associated with changes in gray matter volumes that were not associated with the adulthood blood dioxin levels of the subjects. These findings suggest possible effects of perinatal exposure to dioxins on brain morphometry although dose-effect relationships are unknown. We previously reported adverse effects of perinatal dioxin exposure on the neurodevelopment of Vietnamese children. However, MRI examination of children is difficult in Vietnam because of the requirement for sedation. In the future, we will perform MRI studies in these subjects when they become adolescents,to investigate the presence of brain structural alterations associated with perinatal dioxin exposure. The small sample size is also another limitation of the present study.  
We also presented regional structural alterations in the brain associated with blood dioxin levels that were different from those associated with perinatal exposure, suggesting the neurotoxic effects of dioxin on the developed brain in adulthood. However, we did not examine the subjects' cognitive functions and emotional behavior in the present study. In future, we will investigate psychiatric symptoms and behavior in residents of Bien Hoa to clarify the effects of dioxins on the mental health of adults without perinatal dioxin exposure.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Outage Probability
\begin{array}{lll}f_{\gamma_{d}}(z)&=&\frac{\gamma_{SD}^{2}[\exp{(-z/\gamma_{SD})} -\exp{(-z/\gamma_{R_{2}})}]}{(\gamma_{SD}-\gamma_{R_{1}})(\gamma_{SD}-\gamma_{R _{2}})(\gamma_{SD}-\gamma_{R_{3}})}\\ &&\frac{\gamma_{SD}\gamma_{R_{2}}[\exp{(-z/\gamma_{R_{2}})}-\exp{(-z/\gamma_{R _{3}})}]}{(\gamma_{SD}-\gamma_{R_{1}})(\gamma_{SD}-\gamma_{R_{2}})(\gamma_{R_{ 2}}-\gamma_{R_{3}})}+\\ &&\frac{\gamma_{R_{1}}[\cup{\tau}]}{(\gamma_{SD}-\gamma_{R_{1}})(\gamma_{R_{ 1}}-\gamma_{R_{2}})}\end{array}  \tag{16}\]  
where,  
\[v=\frac{\tilde{\gamma}_{R_{2}}\left[\exp{(-z/\tilde{\gamma}_{R_{2}})}-\exp{(- z/\tilde{\gamma}_{R_{3}})}\right]}{\tilde{\gamma}_{R_{2}}-\tilde{\gamma}_{R_{3}}} \tag{17}\]  
and  
\[\tau=\frac{\tilde{\gamma}_{R_{1}}\left[\exp{(-z/\tilde{\gamma}_{R_{1}})}-\exp{ (-z/\tilde{\gamma}_{R_{3}})}\right]}{\tilde{\gamma}_{R_{1}}-\tilde{\gamma}_{R _{3}}} \tag{18}\]  
The detailed derivation of \(f_{\gamma_{d}}\) is given in Appendix B. By integrating Equation (B6) and doing some necessary simplification, we get closed-form expression for \(P_{out}\) as:  
\[ \begin{array}{lll}P_{out}&=&1+\frac{\gamma_{SD}^{2}\big{(}\gamma_{R_{2}}exp {(-\gamma_{0}/\gamma_{R_{3}})}-\gamma_{SD}exp{(-\gamma_{0}/\gamma_{SD})}\big{)} }{(\gamma_{SD}-\gamma_{R_{2}})(\gamma_{SD}-\gamma_{R_{3}})(\gamma_{SD}-\gamma _{R_{1}})}+\\ &&\frac{\gamma_{R_{2}}\big{(}\gamma_{R_{2}}exp{(-\gamma_{0}/\gamma_{R_{2}})}- \gamma_{R_{2}}exp{(-\gamma_{0}/\gamma_{R_{3}})}\big{)}}{(\gamma_{SD}-\gamma_{ R_{2}})(\gamma_{R_{2}}-\gamma_{R_{3}})(\gamma_{SD}-\gamma_{R_{1}})}+\\ &&\frac{\gamma_{R_{1}}\gamma_{R_{2}}\big{(}\gamma_{R_{2}}exp{(-\gamma_{0}/ \gamma_{R_{3}})}-\gamma_{R_{2}}exp{(-\gamma_{0}/\gamma_{R_{2}})}\big{)}}{( \gamma_{R_{2}}-\gamma_{R_{3}})(\gamma_{R_{1}}-\gamma_{R_{2}})(\gamma_{SD}- \gamma_{R_{1}})}+\\ &&\frac{\gamma_{R_{1}}^{2}\big{(}\gamma_{R_{1}}exp{(-\gamma_{0}/\gamma_{R_{1}} )}-\gamma_{R_{2}}exp{(-\gamma_{0}/\gamma_{R_{3}})}\big{)}}{(\gamma_{R_{1}}- \gamma_{R_{3}})(\gamma_{R_{1}}-\gamma_{R_{2}})(\gamma_{SD}-\gamma_{R_{1}})}+\\ &&\frac{\gamma_{R_{1}}^{2}\big{(}\gamma_{R_{1}}exp{(-\gamma_{0}/\gamma_{R_{1}} )}-\gamma_{R_{2}}exp{(-\gamma_{0}/\gamma_{R_{3}})}\big{)}}{(\gamma_{R_{1}}- \gamma_{R_{3}})(\gamma_{R_{1}}-\gamma_{R_{2}})(\gamma_{SD}-\gamma_{R_{1}})} \end{array}  \tag{19}\]  
Figure 3 shows the outage probability with one and three retransmissions vs. SNR of the transmitted signal. The SNR is varied from 1 to 40 dB. This simulation is conducted for the case when \(\gamma_{SD}\
eq\gamma_{R_{1}}\
eq\gamma_{R_{2}}\
eq\gamma_{R_{3}}\) and \(\gamma_{0}\) is set to 5 dB. Figure 3 clearly shows that more number of retransmissions reduces the outage probability. With three retransmission, the transmitted signal with low SNR, i.e., 5 dB, outage probability is \(10^{-15}\). This is because, in case of error after first retransmission, second retransmission may help the system to get out of outage and same is the case with third retransmission.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Simulation Study
#### 4.2.6 Results on Average Searching Time  
In this experiment, we varied the number of drones from 5 to 15. Figure 22 shows the results of the simulation running for 20 min. According to the results, the _Centralized_ approach required less time (i.e., 1100 s or 18 min) to search the entire area. In fact, searching time could reduce to almost half when we doubled the number of drones. The _Hybrid_ approach was able to search the entire area.  
Figure 21: Effectiveness of each approach vs. number of drone.  
Figure 20: Number of drones vs. overlapping area.  
approach and the _Distributed_ approach achieved only 80% of the search area after 20 min. The _Random_ approach only searched less than 70% of the entire area after 20 min. Although, as we increased the number of drones, the performance of the _Distributed_ and the _Random_ approaches did not increase as much as the _Centralized_ and _Hybrid_ approaches. Thus, the results showed that an organized searching operation and sharing of the information provided by the Open Collaborative Platform could indeed reduce searching time, increase searching area, and reduce the number of drones needed in SAR operations.
## 4 Results and Discussion
### Change of Ventilation Rate with Heat Flux of Indoor Heat Source  
Figure 8 shows the variation of solar chimney ventilation with the heat flow density of the indoor heat source under different solar radiation intensities. As can be seen from Figure 8, when the outdoor wind speed is 1 m/s, the solar chimney ventilation volume shows an increasing trend with the increase in the heat flow density of the indoor heat source. However, the increase in ventilation volume tends to decrease with the increase in solar radiation intensity. When the indoor heat source heat flow density increases from 0 to 1500 W/m\({}^{2}\) and the solar radiation intensity is 200 W/m\({}^{2}\), the solar chimney ventilation volume increases from 0.393 to 0.519 m\({}^{3}\)/s, with a relative increase of 32.1%; when the solar radiation intensity is 1000 W/m\({}^{2}\), the solar chimney ventilation volume increases by 17.8%. The results show that when the density of heat flow from the indoor heat source increases, the intensity of the heat plume at the surface of the heat source also increases, enhancing the ability to exchange heat with the indoor air, resulting in more air flowing into the solar chimney and increasing its ventilation capacity. When the intensity of solar radiation increases, the air in the chimney absorbs more heat, resulting in higher temperatures, and the increased density difference increases the rate of upward outflow of airflow, weakening the effect of the density of heat flow from the indoor heat source on the ventilation volume. The presence of an indoor heat source increases the ventilation capacity of a solar chimney compared to considering only the effect of solar radiation intensity on the ventilation effectiveness of a solar chimney.  
Figure 8: Change of ventilation rate with heat flux of indoor heat source.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 4 Design of Control Strategies
### Performance Indexes  
The quantitative assessment of controller performance in trajectory tracking utilizes specific Performance Indices (PIs). The selected metrics include: Residual Mean Square, Residual Standard Deviation, and Index of Agreement, as defined in Equations (50)-(52).  
\[\text{RMS}=\sqrt{\frac{\sum_{i=1}^{n}(\text{o}_{i}-\text{p}_{i})^{2}}{n}} \tag{50}\]  
\[\text{RSD}=\sqrt{\frac{\sum_{i=1}^{n}(o_{i}-p_{i})^{2}}{\sum_{i=1}^{n}o_{1}^{2}}} \tag{51}\]  
\[\text{IA}=1-\frac{\sum_{i=1}^{n}(o_{i}-p_{i})^{2}}{\sum_{i=1}^{n}(\left|o_{i} \right|+\left|p_{i^{\prime}}\right|)^{2}} \tag{52}\]  
with  
\[\begin{array}{c}\text{p}_{i^{\prime}}=\text{p}_{i}-\text{o}_{m}\\ \text{o}_{i^{\prime}}=\text{o}_{i}-\text{o}_{m}\end{array}\]  
where \(o_{i}\) represents the observed value and \(p_{i}\) the predictive value.
## 2 Materials and Methods
### Modelling  
In general, the Universal Soil Loss Equation (1) [15] in its national adaptations as operational in 2019 is used in all three regions. Apart from the USLE implementations performed in this study, results are compared to the results derived from the harmonized USLE approach for Europe as presented by [PERSON] et al. [19]. For the Bavarian site, the USLE as originally adapted by [PERSON] et al. [12] is  
Figure 1: Topography and land use of test catchments.  
used, which later on was further refined in the German industry standard DIN 19708 [13]. In the Czech Republic, the USLE, in its original form, has been used linked to distributed GIS-supported approach with individual USLE factors specified by a national Soil Erosion Control Methodology [22]. In Austria, the RUSLE approach is used with some region-specific procedures to determine the USLE factors (see below). All USLE approaches produce potential erosion, so a rigid testing against measured catchment erosion is hardly possible. Hence, it is important to note that there is some underlying assumption that each regional USLE approach performs best in the region of its adaptation. Nevertheless, due to the missing validation against catchment data we purely compare results without judging which model approach performs best.  
\[A=R\cdot K\cdot L\cdot S\cdot C\cdot P, \tag{1}\]  
where \(A\) is long-term mean soil loss [t ha\({}^{-1}\) a\({}^{-1}\)]; \(R\) is the rainfall and surface runoff factor [N h\({}^{-1}\)], \(K\) is the soil erodibility factor [t h ha\({}^{-1}\) N\({}^{-1}\)], \(L\) is the slope length factor [-], \(S\) is slope factor [-], \(C\) [-] is the soil cover and management factor, and \(P\) [-] is the soil protection factor.  
Following the official regulations in the Czech Republic, Austria, and Bavaria, the factors of the USLE are calculated as follows, whereas a focus is set to differences in methods:
# On Finding Optimal (Dynamic) Arborescences
## 4 Implementation Details and Analysis
### Queues  
Heaps are used to implement the priority queues, which track the edges that are incident in each vertex. In this context, three types of heaps were implemented and tested, namely, binary heaps [25], binomial heaps [26], and pairing heaps [27; 28]. The pairing heaps are the alternative that have simultaneously better theoretical and expected experimental results; although binary heaps are faster than all other heap implementations when the decrease key operation is not needed. Pairing heaps are often faster than \(d\)-ary heaps (like binary heaps) and almost always faster than other pointer-based heaps [29]. Our experimental results also consider this comparison (see Section 5). With respect to the theoretical results, using pairing heaps to implement priority queues and assuming that \(n\) is the size of a heap, the common heap operations are as follows: \(\texttt{INIT}(L)\) creates a heap with elements in list \(L\) in \(O(n)\) time; \(\texttt{INSERT}(h,e)\) inserts an element \(e\) in the heap \(h\) in \(\Theta(1)\) time; \(\texttt{GET-MIN}(h)\) obtains the element with minimum weight in \(\Theta(1)\) time; \(\texttt{EXTRACT-MIN}(h)\) returns and removes from the heap \(h\) the element with the minimum weight in \(O(\log n)\) amortized time; \(\texttt{DECREASE-KEY}(h,e)\) decreases the weight of element \(e\) in \(o(\log n)\) amortized time; and \(\texttt{MELD}(h_{1},h_{2})\) merges two heaps \(h_{1}\) and \(h_{2}\) in \(\Theta(1)\) time.  
Our implementation does not rely on the DECREASE-KEY operation, but it relies heavily on the MELD and EXTRACT-MIN operations. In this context, it is important to note that the MELD operation takes \(O(n)\) time for binary heaps and \(O(\log n)\) time for binomial heaps. The EXTRACT-MIN\((h)\) runs in \(O(\log n)\) time for both binary and binomial heaps. On the other hand, both pairing and binomial heaps are pointer-based data structures, while binary heaps are array-based. Hence, it is not clear a priori which heap implementation would be better in practice and, hence, it is a topic of analysis in our experimental evaluation, as mentioned.
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 3 Wettability Control  
The wetting properties of materials have great significance in a wide range of applications and are thus critical for understanding the mechanism of oil-water separation. The wettability of a material comes as the result of the synergy of the surface morphology and chemistry [46]. Inspired by the remarkable superhydrophobic properties of the natural lotus leaf, researchers have mimicked the leaf morphology and surface chemistry to produce artificial surfaces showing extreme water repellency [47]; water droplets do not wet and bounce as a perfect elastic sphere on such surfaces and easily roll off, giving rise to a self-cleaning effect. Lotus leaf microscopic analysis has revealed the presence of hierarchical surface structures, comprising microconical asperities decorated with a layer of waxy nanohairs. Such hierarchical microstructures are behind the self-cleaning features of the lotus leaf, which keep it lush, green, and clean [47]. As a consequence of such remarkable properties, artificially prepared superhydrophobic surfaces have found a wide range of applications in self-cleaning, anti-icing, anti-corrosion, drag reduction, and oil-water separation uses [48; 49]. Superhydrophobic surfaces exhibit a water contact angle (WCA) higher than 150\({}^{\circ}\) and a roll-off angle (sliding angle (SA) in Figure 6b) lower than 10\({}^{\circ}\). Based on the same principles, superoleophobic surfaces exhibit an oil contact angle (OCA) higher than 150\({}^{\circ}\) and a roll-off angle lower than 10\({}^{\circ}\). On the other hand, super hydrophilic and superoleophilic surfaces show contact angles of \(\sim\)0\({}^{\circ}\) for water and oil, respectively.  
Figure 5: (**a–d**) Titanium sample irradiated with F = 0.35 J/cm\({}^{2}\) (**a**) 1 laser shot, (**b**) 40 laser shots, (**c**) 100 laser shots, and (**d**) 200 laser shots [21]. Reproduced with permission from [21] Elsevier 2007. (**e**,**f**) Black silicon surfaces obtained upon FS laser processing in air and SF\({}_{6}\) ambient conditions, respectively [2].
## 3 Experimental Results and Analysis
Training accuracy alone is not sufficient in determining the performance of the models since it is not always representative of each class within the datasets. It is more important to assess the robustness and generalization of a DL model for the new datasets since the ultimate goal of DL is to expand the trained model to other datasets (i.e., new or unseen data) that are not involved with training. On the other hand, IoU is calculated for each class separately, then averaged over all classes to provide the mIoU score of semantic segmentation prediction. The threshold to be considered acceptable prediction is 0.5. If IoU is larger than 0.5, it is normally considered a good prediction [15; 31]. The results show that LiDAR data with two returns and appropriate point density could lead to improved results on the building boundaries. In order to assess the proposed method, we analyzed results from each case for the test and new data on the basis of the visual inspection as well as standard evaluation metrics. Performance of the DL model training is presented in Table 2 in terms of accuracy and loss. The overall evaluation of the trained model is shown in Table 3 with accuracy and IoU. Classification accuracy of each class is shown in Table 4. It was noticed that land cover class of DALES and ISPRS Vaihingen datasets were not the same, as shown in Figures 4c and 7e. Therefore, the classes in test data and new data did not coincide with each other. The major objective of the proposed method was in utilizing multiple returns of the LiDAR data. Specifically, two returns of the laser pulse convey rich information of the building boundaries. In consequence, the accuracy of the building boundaries for the test data in terms of recall were improved by utilizing two returns, as shown in Table 5 and Figure 17:0 However, there was not significant improvement for the new data because of the poor quality of the new data (i.e., 4-7 ppm) caused by low point density compared with the training datasets (i.e., 50 ppm). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Metrics** & **Case 1** & **Case 2** & **Case 3** \\ \hline Accuracy (\%) & 97.08 & 95.55 & 96.45 \\ Loss & 0.20 & 0.23 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training accuracy and loss for each case. Figure 28: Examples of 3D perspective view of DALES data building point clouds: (**a**) test 1; (**b**) test 2. Figure 29: Examples of 3D perspective view of ISPRS Vaihingen building point clouds: (**a**) new 1; (**b**) new 2.