esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Blackout Analysis
### Initiating Events
#### 4.2.2 Overloads (INIT_2)  
A power device's protections may disconnect it when it is loaded above its capacity, to prevent line lag or device damage. On 31 March 2015, a blackout occurred in Turkey due to the failure of a line on the main corridor connecting the Eastern and Western regions.
## 4 Evaluation Results and Discussion  
In this section, we will first analyze the performance of the models across all the selected databases. Then, the performance comparisons are conducted for individual database settings of codecs, viewing devices, and session durations. Based on the obtained results, some suggestions on how to effectively use the models in different scenarios will be provided.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 3 Hardware Implementation of the Proposed SCbSC and Evaluation of Its Performance
### SPCNG Resilience against Statistical Attacks
#### 3.2.3 NIST Test  
Another important key property of a secure pseudo-chaotic number generator is that the sequences generated should pass the statistical NIST test, which is a package of 188 tests and sub-tests used to evaluate the randomness of long binary sequences. NIST test was applied to 100 pseudo-chaotic sequences of size \(10^{8}\) bits, generated from the initial conditions and the parameters of the chaotic system. For each test, a set of 100 \(p\)-values was calculated to indicate the result of the test. A \(p\)-value larger than \(\alpha=0.01\) (the level of significance of the test) indicates that the sequence would be random and a \(p\)-value less than 0.01 means that the sequence is nonrandom. The proportion of 100 sequences passing a test is equal to the number of \(p\)-values \(\geq\alpha\) divided by 100. The results obtained, given in Table 3, indicate that the sequences generated passed all 15 statistical tests.  
This means that the proposed SPCNG produces indistinguishable sequences of integer random sequences.
## Appendix C  
The performance of the best model as a classifier for the moderate to extreme drought events is presented in Figure A3.  
Further analysis for severe to extreme drought, however returns very poor performance perhaps due to low occurrence of the events in the training data at 4.92% and 10.81% for severe and extreme droughts respectively.  
One possible mitigation to this poor performance in class distribution would be model ensembling. Given that the ANN process realized 21 models that were relatively good performers, we can have all the models participate in the prediction process. A naive approach to model ensembling would be to average the scores from all the models prior to the classification. This approach realizes an overall \(R^{2}\) of 0.81 and an overall accuracy of 74%. At county level, the performance was- Mandera (\(R^{2}\) = 0.70), Marsabit (\(R^{2}\) = 0.82), Turkana (\(R^{2}\) = 0.87) and Wajir (\(R^{2}\) = 0.76). The performance of the classification by county for moderate to extreme drought is provided in Figure A4.  
Figure A4: Performance of the average ensemble classifier for all the vegetation deficit classes for each of the counties showing months of difference in grey and those of agreement in blue. Predictions are done 1 month ahead. The classification accuracies in the severe to extreme vegetation classes are: **(a)** 71% for Mandera county; **(b)** 63% for Marsabit county; **(c)** 80% for Turkana county and; **(d)** 67% for Wajir county.  
From Figure A4, a gain in classification accuracy is realized when the model ensembling approach is used as compared to the use of the single best model. The model ensembling approach, however, has a computational resources and time aspect to it and paired with ANN complicates further interpretability of model outputs.
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 2 FBG Temperature Sensing Principle  
FBGs are composed of an optical fiber inscribed with gratings to selectively reflect light at specific wavelengths. For precision and versatility, the phase mask technique is the common method of fabricating FBG sensors. This is achieved by placing a phase mask with a finely etched diffraction grating of period \(\Lambda\) directly along the length of the optical fiber where gratings are desired. The phase mask, which is aligned with the fiber, diffracts the UV beam emitted from the excimer laser into several orders, creating an interference pattern in the form of fringes along the fiber core. These fringes modulate the refractive index of the fiber core periodically, creating the Bragg grating [28]. For sensing applications, the light beam propagating through the optical fiber is reflected by the inscribed gratings at the central Bragg wavelength \(\lambda_{B}\), which is a function of the effective refractive index of the fiber core \(n_{eff}\). The Bragg wavelength is expressed as  
\[\lambda_{B}=2\;n_{eff}\;\Lambda \tag{1}\]  
when the grating portion is subjected to mechanical and thermal stress. The periodicity and the effective index of the core changes, which causes central wavelength shift \(\Delta\lambda_{B}\) in proportion to the stress experienced, expressed as  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}\;=\;\left(\alpha_{f}\;+\xi\right)\Delta T +(1-p_{eff})\Delta\varepsilon \tag{2}\]where \(\Delta T\) and \(\Delta\varepsilon\) are the change in temperature and mechanical stress, respectively, \(\alpha_{f}\) and \(\xi\) are the fiber thermal expansion and thermo-optic coefficients, while \(p_{eff}\) is the fiber photo-elastic parameter. Given that the effect of strain on the FBG sensor is uniform, the Bragg wavelength shift \(\Delta\lambda_{B}\) is reduced to  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}\ =\ \left(a_{f}\ +\xi\right)\Delta T \tag{3}\]
## 3 Degradation and Fate of PFCs in the Environment
### Bacterial Biodegradation
strain A6 [40; 43]_Pseudomonas parafilva_ strain YAB1 [44], _Pseudomonas aeruginosa_ strain HJ4 [45], and _Pseudomonas plexoglossicida_ 2.4-D [46] are the only microbial species reported to biodegradable PFOA and PFOS. [PERSON] et al. [44] investigated the growth rate of YAB1 which was extracted from soil sediments near a PFC-producing plant. [PERSON] et al. and co-workers conducted their investigation in an aerobic environment and PFOA was added as a carbon source for YAB1. Optimal bacteria growth rate and PFOA degradation were recorded for a PFOA concentration of 500 mg/L. Interestingly, it was reported [43] that a PFOA concentration of 1000 mg/L inhibited the growth rate of YAB1; however, the strain demonstrated considerable tolerance and growth adaptability. The findings by [PERSON] et al. [44] suggests that higher PFOA concentrations hinder microbial activities, consequently inhibiting the bacterial proliferation. This is congruent to the work reported by [PERSON] and [PERSON] [40] and [PERSON] et al. [43] that higher PFOA concentrations become toxic to microbial species, thus hindering microbial activities. [PERSON] et al. [44] reported a PFOA removal efficiency of 48%, which was achieved after adding yeast to promote bacterial growth rate. The growing interest in the investigation of various microbial species to degrade PFCs has demonstrated that PFOS can be degraded by _Pseudomonas aeruginosa_ strain HJ4 under an aerobic environment [45]. PFOS was added as a carbon source at an initial concentration ranging from 1400 ug/L to 1800 ug/L for the growth of strain HJ4. [PERSON] et al. [45] reported an overall PFOS biodegradation of 67%, which was recorded after adding glucose to enhance the growth rate of strain HJ4 for PFOS biodegradation. However, according to [PERSON] et al. [45], no defluorination was observed. Fluoride ion concentration remained constant during biodegradation of PFOS. On the other hand, [PERSON] et al. [46] reported 75% PFOS degradation as a carbon source for the growth of _Pseudomonas plexoglossicida_ 2.4-D. A significant growth rate on the 2.4-D strain was observed after adding NaCl which was maintained at less than 5% to avoid any microbial inhibition. [PERSON] and co-workers reported a fluoride ion concentration of up to 150 mg/L in the culture medium to account for the fact that defluorination indeed took place. Table 3 presents a summary of studies conducted to date aimed at microbial degradation of PFOA and PFOS at the laboratory scale.
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
###### Abstract  
This work describes the flow behavior of the oil recovery obtained by the injection of sulfate-modified/low-salinity water in micromodels with different wettabilities. It provides a detailed microscopic visualization of the displacement taking place during modified water flooding at a pore-scale level, while evaluating the effect of wettability on oil recovery. A comprehensive workflow for the evaluation is proposed that includes fluid-fluid and rock-fluid interactions. The methods studied comprise flooding experiments with micromodels. Artificial and real structure water-wet micromodels are used to understand flow behavior and oil recovery. Subsequently, water-wet, complex-wet, and oil-wet micromodels help understand wettability and rock-fluid interaction. The effect of the sulfate content present in the brine is a key variable in this work. The results of micromodel experiments conducted in this work indicate that sulfate-modified water flooding performs better in mixed-wet/oil-wet (artificial structure) than in water-wet systems. This slightly differs from observations of core flood experiments, where oil-wet conditions provided better process efficiency. As an overall result, sulfate-modified water flooding recovered more oil than SSW injection in oil-wet and complex-wet systems compared to water-wet systems.  
w +
Footnote †: journal: Applied Sciences
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 5 Performance Analysis
### Computation Overhead  
If RSU jurisdiction is divided into \(I\times J\) cells, the number of services is \(U\) and the number of results returned by the LBS server is \(n\).  
In the procedure of an LBS query, the vehicle user needs to generate \(k\) dummy locations. First, as the vehicle user selects \(2k-1\) locations based on service request probability, the computation overhead is \(O(I\!U)\). As the vehicle user selects dummy locations by effective distance through \(k-1\) rounds. In the \(i^{th}\) round, \(i=1,2,\ldots,k-1\), the vehicle user calculates the effective distance of \(2k-1-i\) locations in \(\mathcal{G}^{\prime}\) to \(i\) locations in \(\mathcal{G}^{\prime\prime}\) to update the effective distance of each location, and the location with maximum effective distance of locations in \(\mathcal{G}^{\prime}\) is selected. Hence, the computation overhead is \(O(k^{2})\). Therefore, the computation overhead of dummy location selection algorithm at the vehicle user is \(O(k^{2}+I\!U)\).  
Since RSU does not need additional computation, the computation overhead at RSU is \(O(1)\).  
The LBS server needs to perform service retrieval for \(k-1\) dummy locations and a real location. Hence, the computation overhead at the LBS server is \(O(kn)\).
## 4 Discussion  
### Effect of the Aneurysm Wall Thickness Modification  
Although the hemodynamic parameters did not significantly indicate an effect of modifying the aneurysm wall thickness, the structural parameters did. As seen in Table 3, as the aneurysm wall thinned, in this case, changing from 0.35 to 0.10 mm, the average  
Figure 19: Pressure distribution contours during systole for geometries with a removed aneurysm: (**a**) RG-1, (**b**) UG-1, (**c**) RG-2, and (**d**) UG-2.  
Figure 18: TAWSS distribution contours for geometries with a removed aneurysm: (**a**) RG-1, (**b**) UG-1, (**c**) RG-2, and (**d**) UG-2.  
von Mises stress increased by more than 200%, indicating that the aneurysm had a greater rupture risk due to structural failure of its wall.  
This type of change in the stress magnitude is in accordance with Laplace's law, a principle that relates the pressure, radius, wall thickness, and tension of a sphere. However, it gives only an approximate idea of how the tension evolves with a change in thickness, whereas a numerical simulation can yield results for the entire dome of an aneurysm taking into consideration other factors, such as the geometry. This could give a better idea of how close the aneurysm is to structural failure. Additionally, it would not be realistic to assume that an aneurysm is a sphere, and such an assumption has previously been shown to give different results [36]. A comparison between anisotropic and isotropic models for cerebral aneurysms were reported in [37].  
On the other hand, the displacement exhibited non-intuitive behavior, which contrasted between the geometries. The ruptured geometry, RG-1, displayed the expected trend, where the thinner the wall, the greater was the displacement. Furthermore, this geometry was ten times more sensitive to the change in wall thickness compared with UG-1, according to Table 4.  
The difference in behaviors and sensitivities can be explained by the parent artery's geometry and the blood flow path that it imposes. This can be seen for both cases in Figure 20, where the blood flow of RG-1 points to the aneurysm entering that zone more directly and with more velocity than in UG-1. Once the fluid entered the RG-1 aneurysm, it travelled to its upper zone, where it changed direction and decreased in velocity, exchanging momentum with the aneurysm zone that had the highest contour displacement values, as can be seen in Figure 10. In the other case, almost the entire blood flow followed the artery path with very low deviation into the aneurysm, making its displacement very similar to the surrounding zone in the artery, as shown in Figure 11.
## 4 Conclusions  
The study compared the immobilization of a commercially available _T. versicolor_ increase onto glutaraldehyde-activated, sulfo-NHS/EDC-activated magnetic and non-magnetic nanoparticles by covalent binding and onto magnetic nanoparticles by ion exchange, as well as its use as a nanobiocatalyst for phenol biotransformation. In summary, the most efficient biotransformation and the best activity yield was obtained by using laccase immobilized onto silica-coated magnetic nanoparticles. The magnetic nanobiocatalyst achieved a phenol biotransformation higher than 60%. One major outcome of this study is that the immobilized laccase is magnetically recoverable and can actually be reused in repeated cycles of phenol removal. The easy recovery of the nanobiocatalyst from the reaction media is a remarkable advantage from an operational perspective. Regarding the environmental impacts associated with the production of silica-coated magnetic nanoparticles, the use of energy and chemicals used in the formulations and for re-dispersion are the major contributors. Aiming to reduce the impacts, the use of a lower dose of cyclohexane implied lower environmental impact but negatively affected the immobilization yield of the enzyme, which discouraged this modification in the production process.  
This work was financially supported by the Spanish Ministry of Economy and Competitiveness (CTQ2013-44762-R and CTQ2016-79461-R, program co-funded by FEDER). The authors belong to the Galician Competitive Research Group GRC 2013-032, program co-funded by FEDER. [PERSON] thanks the Spanish Ministry of Economy and Competitiveness for her predoctoral fellowship.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] performed the LCA study, [PERSON] and [PERSON] analyzed the data and revised the different versions of the manuscript; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Results and Discussion  
### Setup of the Testing System  
A testing system was set up for experimental verification after the installation of eight sets of pressure bar sensors, as shown in Figure 6. The testing system consisted of a high-pressure chamber, gas pressure sensors, cylindrical bullet, a laser, a PDV, a serial server, a switch, an oscilloscope, and a computer. The testing process can be described as follows:Firstly, the piston was pulled to release a cylindrical bullet (\(\Phi\) 5.93 \(\times\) 9.92 mm) when the high-pressure gas chamber was pressurized to about 0.4 MPa. As the bullet flew out, the pressure signal, which was recorded by the second channel of the oscilloscope, was measured and used to trigger the oscilloscope.  
Secondly, the elastic stress wave was excited and propagated along the bar to the free end when the cylindrical bullet struck the pressure-bearing end of the bar. Then, the Doppler frequency shift laser reflected from the free end of the bar was gathered by the probe and subsequently transmitted to the PDV.  
Thirdly, the photoelectric signal generated by the interference of the reference laser and frequency shifted laser was recorded by the first channel of the oscilloscope.  
Lastly, the computer adjusted the output power of the laser, controlled the parameters of the oscilloscope, and transmitted the collected signals back to the network for storage and processing through the switch and the serial server.  
Figure 7 is a photograph of the testing system. The sampling rate of the oscilloscope was set to 500 MS/s. The laser was a RIO1174-8-31-1 by RIO company (Santa Clara, USA) with a wavelength of 1552.518 nm. The PDV used in this system was self-developed equipment from the previous research of the project group, which is described in detail in [PERSON]'s thesis for a master's degree [22], and its velocity measurement uncertainty is 0.1 m/s.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 5 Research Directions  
In the previous sections, we have introduced challenges and opportunities. We find that IoT vulnerability discovery, detection, and mitigation technologies continue the trajectory of traditional security research but also have their different research directions.  
* **AI-based vulnerability discovery and detection technology**. Whether function or security, IoT and AI technologies are rapidly converging. The current AI technology is successfully used in vulnerability detection. As research progresses, it will expand to other vulnerability analysis techniques. For example, Generative Adversarial Networks (GANs) [114] have been applied in abnormal detection of IoT system behavior [115]. In the future, GANs may have a potential application in IoT vulnerability discovery because they may learn different attack scenarios to generate samples similar to a zero-day attack and provide algorithms with a set of samples beyond the existing attacks [16].
* **Large-scale vulnerability analysis techniques**. Complexity and heterogeneity of IoT devices hinder automation and large-scale analysis research in Section 4.2. However, this demand has been urgent in the IoT security industry. Security researchers need a cross-platform approach to overcome this problem, which is a long-term research direction.  
* **Automated vulnerability exploiting.** To exploit the vulnerability in IoT devices and protect the device from intrusion, we need to generate PoC in an automated way. It helps to understand the hazards and causes of vulnerabilities better. With the development of the IoT field, the automation attack and defense will also become a hotspot.
* **Vulnerability analysis based on a peripheral system**. Through the above challenges, we found that it is aggravatingly difficult to analyze devices by static and dynamic methods directly. IoT devices are becoming more interactive. Not only will there be more and more vulnerabilities in combination with peripheral systems, but also study on peripheral system analysis methods will increase.
* **Automatic generation patch of multi-platform on binary code.** For some IoT vendors' closed-source and security inaction, device firmware can not be patched in time. To this end, we need an automated repair method for cross-platform binary code vulnerabilities. The automated patch generation on the binary code level requires fully understanding the formation mechanism and the elimination condition. There will be thousands of security vulnerability templates if we rely entirely on expert domain knowledge. Thus, it is difficult to achieve a scaled and feasible solution. At the same time, the variety of operating systems and hardware architecture brings technical challenges. It is a long-term goal of the whole security field to solve the problem of the automatic generation of multi-platform binary code patches.
# Predator-Prey Model Based Asymmetry Resource Allocation in Satellite-Terrestrial Network
###### Abstract  
In the traditional satellite networks, network resources are mainly allocated among all the satellites based on the same allocation algorithm. This kind of symmetry model limits the increase of throughput. In this paper, we study an asymmetry resource allocation method in a satellite-terrestrial network and propose a Lotka-Volterra based predator-prey model to achieve optimal resource allocation among different satellites. In the proposed satellite-terrestrial network, we divide all the satellites into two groups, and we try to achieve load stability between these two satellites groups. Using the predator-prey model, one group is the prey-satellites, which can obtain service requirements from mobile users. The other group is considered as predator-satellites, which can only obtain the loads from the group of the prey-satellites. Once the satellites are divided into two groups using the Lotka-Volterra model, the resource allocation problem among these satellites in two groups would be asymmetry resource. We prove the existence of solutions to the proposed model. Numerical simulation results are given to show the correctness and effectiveness of the proposed model.  
asymmetry resource allocation; load balancing; satellite-terrestrial network; predator-prey model 2021