esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Metrics of Proposed Techniques  
There are four different metrics which might affect the performance of the proposed techniques. They are transmitting rate, number of real packets, number of total intervals and delay between real intervals in case of using CAD technique. Using a higher transmitting rate will increase the probability of delivering all real packets within the required delay of the application. In addition, it increases the anonymity of the entire network and decreases the probability of an adversary detecting the realevent. However, the higher rate will cause more fake traffic leading to unnecessary overhead. The real event consists of several real packets; the more real packets there are, the more difficult they are to hide inside the fake traffic. Therefore, more fake traffic rate will be demanded, leading to network overhead. However, this metric cannot be controlled because it depends more on the behavior and type of the real event.  
The total number of intervals of a network's lifetime or the maximum delay required by an application plays a very important role in case of delivery ratio of all real packets. The total number of intervals is calculated using the following equation:  
\[\mathbf{I_{total}}=\frac{\mathbf{t_{s}}}{\mathbf{I_{r}}} \tag{10}\]  
where \(\mathbf{t_{s}}\) is the simulation time and \(\mathbf{I_{r}}\) is the interval rate. More intervals increase the probability of real packets' arrival at the sink, which will improve the overall performance of the network. However, more intervals require more fake packets that will consume resources and energy. The n-intervals is how many intervals the real node will wait before it transmits the following real packet in its buffer without using probability. More intervals mean more security and delay, whereas less intervals means less security and delay. The interval that has a real packet to send is named a real interval. This metric is only valid in the CAD technique.  
Selecting the suitable values of these metrics will rely on the requirements of the application. For example, if the application is tolerant in term of delay, we can select as a low rate as possible that satisfies Equation (9). Assume that the real event consists of three packets, simulation time is 100 s, and interval rate is 0.5. The number of intervals is 200 by using Equation (10). Based on the previous numbers. The selected constant transmission rate can be as small as 0.03. This is because the fake rate is 0.015 by using Equation (7). Then, the average of fake packets becomes three after using Equation (9), which is not less than the number of the real packets. Using a smaller transmission rate for instance, 0.02 is not valid in this situation, because it makes the average number of the fake packets less than the number of real packets, which makes it very easy for the adversary to detect the real event. Eventually, it is considered a tradeoff between delay and overhead at one side and privacy at the other side.
## 3 Results  
The relationship of DER and proton beam energy (0.5-25 MeV) is shown in Figure 2. Figure 2a-c show the variations of DER vs. proton beam energy on the gold nanoparticle size (radius = 15, 25 and 50 nm) and distance between the nanoparticle and DNA (30, 80 and 130 nm). The uncertainty of the DER is less than 2% in the calculation.  
Figure 2: _Cont._  
Figure 2: Dependence of dose enhancement ratio on proton beam energy (0.5–25 MeV). The distance between the gold nanoparticle and DNA was changed to 30, 80 and 130 nm, while the nanoparticle radius was changed to (**a**) 15, (**b**) 25 and (**c**) 50 nm in MC simulations.
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 2 Problem Statement
### Perpetually Increasing Attack Surface  
Recent reports forecast a surge of 12.6% in CAGR by 2027. With the proliferation of IoT devices, predicted to reach 75 billion by 2025 [18] (Figure 1), the attack surface will grow exponentially, increasing security vulnerabilities across the board. This extraordinary growth of a serious problems warrants extraordinary measures. Is our current cybersecurity strategy sufficient? Let us review them.  
\"Complexity is the worst enemy of security, and this is especially true for computers & the Internet.\" [19]. Complexity opens the door to vulnerability. Unfortunately, advancements in computer technology have always been associated with increasing complexity. The more complex a system is, the more vulnerable it is to security threats. It is time we reversed this trend and eliminated all complexities ingrained in legacy systems. With the advent of the IoT and the proliferation of connected devices, attack surfaces and, consequently, vulnerabilities have grown exponentially. Every day, the AV-TEST Institute registers over 450,000 new malicious programs (malware) and potentially unwanted applications (PUA). Over the past decade, the number of malware cases has grown from approximately 100 million in 2012 to a whooping 1.33 billion in 2021 [20]. See Figure 2.  
In the current state-of-the-art, the approach to improving the security of a computer system is to measure the attack surface [21] and minimize it using the following basic strategies:  
Figure 1: The exponential growth of connected devices (Statista.com).  
Figure 2: Every day, the AV-TEST Institute registers over 450,000 new malicious programs (malware) and potentially unwanted applications (PUA). Of total threats examined, 95.30% were malware and 4.70% were PUAs (source: [[https://www.av-test.org](https://www.av-test.org)]([https://www.av-test.org](https://www.av-test.org)), accessed on 27 June 2022).  
* Reducing the amount of code running;
* Reducing entry points available to untrusted users;
* Eliminating services requested by relatively few users [22].  
The zero-trust architecture by NIST also suggests a similar strategy [23]. Although attack surface reduction helps prevent many security failures, it does not mitigate the damage an attacker can inflict once a software vulnerability is found [24].
# Slot Waveguide Enhanced Bloch Surface Waves
## Materials and Methods
### Simulation Methods  
The goal is to excite simultaneously two BSWs at the surface of the rails constituting the polymer slot waveguide. The input waveguide is split into two arms approaching the polymer rails under an angle \(\theta\). This angle must be greater than the critical angle in order to ensure both the propagation of light in the waveguide and the excitation of the BSW.  
Preliminary simulations are performed using a transfer matrix method to estimate the reflection spectrum of the multilayer as a function of the incidence angle, the wavelength, the thicknesses of the layers, and the number of periods. As a starting point, we determine the critical angle which is for the proposed materials and considering TiO\({}_{2}\) as the first deposited layer on the polymer around \(\theta_{\rm c}\) = 41\({}^{\circ}\). Only the angles greater than \(\theta_{\rm c}\) are considered in the further calculations presented in the results section.
# Cooperative Multi-UAV Collision Avoidance Based on a Complex Network
###### Abstract  
The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver--that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.  
Keywords:Multi-UAV; collision avoidance; complex network; key nodes; robustness; connected component +
Footnote †: journal: Applied
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 2 Preliminaries
### Decision-Making for the Position of the Signal Source  
With the movement of robots, the real signal strength can be obtained by  
\[o_{r}(i,t)=f(x_{i}(t),r(t)) \tag{10}\]  
where \(o_{r}(i,t)\) denotes the real measured value for the \(i\)-th robot at \(t\) time; \(f(x_{i}(t),r(t))\) is the signal transmission model depending on the position \(x_{i}(t)\) of the \(i\)-th robot and the real position \(r(t)\) of the signal source. It should be noted that \(o_{r}(i,t)\) can be directly detected by the robot based on the signal measurement sensor.  
In order to estimate the position of the signal source, a particle filter is used in terms of the real signal strength \(o_{r}(i,t)\) and has the following steps.  
1. We first generate \(N\) particles, which are uniformly distributed in the search range.
2. According to Equation (10), the prediction signal strength \(o_{m}(i,t)\) (\(m=1,\ldots,N\)) of the \(m\)-th particle for the \(i\)-th robot at time \(t\) can be described by: \[o_{m}(i,t)=f(x_{i}(t),p_{m}(i,t))+\sqrt{R}\times rand\] (11) where \(p_{m}(i,t)\) is the position of the \(m\)-th particle for the \(i\)-th robot at time \(t\); \(R\) represents the variance of noise; \(rand\) is a random number in [0,1]; \(f(x_{i}(t),p_{m}(t))\) can be obtained according to the real signal transmission model.
3. In terms of (10) and (11), the weight of each particle can be calculated in (12). \[w_{m}(i,t)=\frac{1}{\sqrt{2\pi R}}\exp(-\frac{(o_{r}(i,t)-o_{m}(i,t))^{2}}{2R})\] (12) Further, the normalizing weight is computed by: \[w^{\prime}_{m}(i,t)=\frac{w_{m}(i,t)}{\sum\limits_{m=1}^{N}w_{m}(i,t)}\] (13)4. Based on the normalizing weight \(w^{\prime}_{m}(i,t)\), we conduct a resampling process for particles, that is we remove the low weight particles and copy the high weight particles. These resampled particles \(p^{\prime}_{m}(i,t)\) represent the probability distribution of the real state. Hence, the possible position of the signal source can be estimated by: \[p_{s}(i,t)=\sum_{m=1}^{N}\frac{p^{\prime}_{m}(i,t)}{N}\] (14) where \(p_{s}(i,t)\) is the position of the estimated signal source for the \(i\)-th robot at time \(t\). Further, considering the estimated positions from other robots, we have: \[p^{\prime}_{s}(i,t)=\frac{\sum\limits_{j=1}^{n}a_{ij}p_{s}(j,t)}{\sum\limits_{ j=1}^{n}a_{ij}}\] (15) where \(a_{ij}\) is the element of the adjacency matrix \(A\) and \(p^{\prime}_{s}(i,t)\) as the estimated position of signal source is used in the following simulations and experiments.
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
###### Abstract  
In the Personal Computer (PC) industry, systems with updated configurations, components, and new technologies are introduced to the market each year. Resource arrangement and the prediction of market requirements for products are common challenges in each development phase of these products. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and cloud services influence the PC industry, and product strategies must be examined to fit the requirements of the market. Common designs and market predictions can influence product line resource arrangements, and 5G applications are causing an increasing demand for 5G-enabled products in the market. However, PC systems based on PCI Express, NVMe, USB, and TPM have been introduced into the market with more secure solutions, and common designs and predictable market demand can provide more reliable strategies for navigating these issues. The research reported here is based on the serial bus system, which should simplify protocol transition between PCI Express, CXL, USB 4, and NVMe. Serial bus behavior should also influence performance and power consumption. Product strategies could be based on securing demand with power and performance in AI, the IoT, cloud storage, and high-performance computing. Based on performance and power requirements, application layer devices can use PCIe-based systems to provide secure solutions to extend 5G system reliability.  
5G; IoT; Cloud Computing; PCI Express; CXL; USB; NVME
# Application of the Gait Kinematics Index in Patients with Cerebral Palsy
###### Abstract  
Due to the complexity of the medical issues connected with cerebral palsy (CP), the classification of gait pathologies seems rather difficult. The aim of this study was to asses the usefulness of the Gait Kinematics Index (GKI) from a clinical point of view in the population of patients with CP. The assessment of the possibilities of using the GKI in a group of patients with CP was conducted on the basis of the correlation of its results with the Gilletteit Gait Index (GGI) and Gait Deviation Index (GDI) values. The distribution of the index values was also evaluated with attention paid to the CP types and treatment methods. Analyses were performed on the basis of the gait test results in a group of 56 healthy children and 72 patients with CP. The GKI values for patients with CP were 1.55 \(\pm\) 0.66, as opposed to 0.77 \(\pm\) 0.17 for the reference group. A strong linear correlation was found between the values of the GKI and GGI (r = 0.8 \(\div\) 0.85), as well as between the GKI and GDI (r = -0.89 \(\div\) 0.9), obtained in children with CP. In addition, significant differences were found between the results obtained in all the groups of children with CP divided by treatment method (rehabilitation, botulinum, rhizotomy, \(p\) < 0.05), whereas in the groups of children divided by CP type, significant differences (_p_ < 0.05) were found solely between diplegia and hemiplegia and between hemiplegia and quadriplegia. The results obtained were the same in the case of the GKI, GGI and GDI. To conclude, the results presented in this work confirm the clinical utility of the GKI in the population of patients with CP.  
gait; gait index; gait deviations; cerebral palsy; kinematics +
Footnote †: journal: applied sciences
# Fabrication and Characterization of Modified Graphene Oxide/PAN Hybrid Nanofiber Membrane
###### Abstract  
In this study, a series of novel modified graphene oxide (MGO)/polyacrylonitrile (PAN) hybrid nanofiber membranes were fabricated by electrospinningning a PAN solution containing up to 1.0 wt.% MGO. The GO was initially prepared by a time-saving improved Hummer's method. Subsequently, the formation of GO was confirmed by scanning electron microscopy (SEM), AFM, Fourier-transform infrared spectroscopy (FT-IR), and Raman spectroscopy. This study also prepared the modified GO with polydiallyldimethylammonium chloride (GP) by using a simple surface post-treatment method to improve its dispersion. Varying amounts of GP were incorporated into PAN nanofibers for the better properties of GP/PAN nanofibers, such as hydrophilicity, mechanical properties, and so on. The resulting GP/PAN hybrid nanofiber membranes were characterized by SEM, FTIR, contact angle, and thermal and mechanical properties. These results showed that the hydrophilic and mechanical properties of GP/PAN hybrid nanofiber membranes were dramatically improved, i.e., 50% improvement for hydrophilicity and 3-4 times higher strength for mechanical property, which indicated the possibility for water treatment application. In addition, the notably improved thermal stability results showed that the hybrid nanofiber membranes could also be a potential candidate for the secondary battery separator.  
modified GO; PAN; hybrid nanofiber membrane; polydiallyldimethylammonium chloride (PDDA) +
Footnote †: journal: Applied Materials Science
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles  
Microorganisms are capable of producing many unique nanostructures. This has led scientists to become more interested in utilising these microbes to synthesise nanostructures for various applications. Inorganic molecules can be generated by bacteria and fungi by biologically mediated and induced synthesis [25]. By controlling the biological synthesis, nanostructures of desired geometries and composition can be formed. Despite the accuracy of the nanoparticle physicochemical synthesis, biological synthesis of nanoparticles is still limited in terms of particle geometry controllability and process scalability [26]. Nevertheless, biologically induced synthesis has allowed scientists to synthesise inorganic nanoparticles using common metal precursors [27], while also offers a wide range of composition.  
Microorganisms such as bacteria, fungi and algae are increasingly being used to create nanosized particles. There are a wide variety of microbes, which mostly react differently with metal precursors to produce nanoparticles. For example, bacteria and fungi are capable of intracellular and extracellular production, and both processes have different pathways in different microbes. Intracellular synthesis utilizes the cell wall to transport metal ions, where the ions with a positive charge interact with the negative charge wall. Enzymes in the cells reduce these ions to metal NPs. Figure 1 shows gold nanoparticle synthesis using bacteria _Lactobacillus kimclicus_[28]. Nucleation of HAuCl ions takes place at the beginning of the process, which leads to the creation of nanoclusters through an electrostatic interface. Afterwards, nanoclusters are gradually transferred across the microbe cell wall. The mechanism behind the extracellular synthesis of nanoparticles includes the aggregation of metal ions on the cell surface and the involvement of reducing ions via enzymes. [PERSON] et al. used _Cupriavidas_ sp. for extracellular synthesis of silver NPs [29]. The study concluded that silver ions are trapped on the cell surface and nitrate reductase (an enzyme) reduces them to atomic silver NPs. Huge research efforts went into designing such techniques for generating a wide range of NPs (including gold, palladium, silver, titanium oxide, etc.).
## 4 Comparison with a Design Example and Experimental Results
The pink signal is the output voltage at 20 V  
Figure 11: Signals in the traditional operation, from the bottom to the top, the switch voltage (gold), the inductor current (green), the output voltage (purple), and a zoom in the output voltage ripple (blue). Figure 12: Signals in the proposed interleaved PWM operation, where the voltage ripple at the output port (blue signal) is evidently smaller, from the bottom to the top, the switch voltage (gold), the inductor current (green), the output voltage (purple), and a zoom in the output voltage ripple (blue). per division, which is at approximately 60 V, and the blue signal is the zoom in the output voltage ripple, as the output voltage has ac coupling and a zoom-in at 2 V per division. Compared to the boost converter, the voltage across the switches and capacitors is smaller. The LVC converter has the advantage of requiring less stored energy in capacitors. For example, instead of capacitors of 30 V, it requires capacitors of 22.5 V. Nonetheless, the output voltage ripple in the LVC converter and the boost converter is quite similar and it is a bit smaller in the LVC converter, but its main advantage is the output voltage reduction. However, it can be observed that the LVC converter with the proposed operation has a significantly smaller output voltage ripple than the LVC in the traditional operation, and is also smaller than the boost converter. The ripple reduction factor can be established at approximately 50% since the output voltage ripple in the LVC with the proposed operation is at approximately 0.9 V, while the output voltage ripple of the boost converter and the LVC converter is at approximately 1.8 V.  
The only disadvantage of the proposed operation is that it requires two comparators in the microcontroller, while the traditional operation requires only one. If the comparison is made by software, this will result in two times the operations compared to the traditional version. Figure 13 shows a photograph of the experimental setup, which was made based on the bidirectional boos (half-bridge) evaluation board TDHBG1200 DC100-KIT from the Transphorm brand, and modifications were performed to achieve the proposed topology with two half-bridge modules.
## 4 Conclusions  
Combining the first-principles calculations within density functional theory (DFT) with nonequilibrium Green's function, we investigated the interface magnetism and electronic structures of Mn\({}_{2}\)CoAl/Ag/Mn\({}_{2}\)CoAl CPP-SV. Our calculations reveal that due to the reason that crystal symmetry at the interface is broken, the rehybridization leading to the AMMs of atoms at the interface becomes enhanced. Moreover, analyses of electronic structures reveal that owing to the interface states, the interface half-metallicity is destroyed. The MnCo\({}^{\text{T}}\)-terminated interface preserves the highest ISP of 78%, and the MnMn\({}^{\text{T1}}\)-terminated interface also has a high ISP of 45%. The transmission curves reveal that the transmission coefficient at the Fermi level in the majority spin channel is much higher than that in the minority spin channel. Furthermore, the calculated magnetoresistance (MR) ratio of the MnCo\({}^{\text{T}}\)-terminated interface reaches up to 2886%, while that of the MnMn\({}^{\text{T1}}\)-terminated interface is only 330%. Therefore, Mn\({}_{2}\)CoAl/Ag/Mn\({}_{2}\)CoAl CPP-SV with an MnCo\({}^{\text{T}}\)-terminated interface structure has a better application in a spintronics device.  
Conceptualization, Q.-Y.M.; Methodology, X.-Y.F.; Software, X.R.; Data curation, Z.-Y.L.; Writing-Original Draft Preparation, Z.C. and X.R.; Writing-Review & Editing, M.-S.W.; Supervision, Y.F.  
This research was funded by [National Natural Science Foundation of China] grant number [11747114], and by [Doctor Foundation of Jiangsu Normal University] grant number [17 XLR046 and 16 XLR022], and by [Xuzhou Technology Plan Project] grant number [KC16 SQ179].  
The author declares no conflict of interest.  
Figure 9: The \(k_{//}\)-resolved transmission coefficients of the MnCo\({}^{\text{T}}\)-terminated structure at the Fermi level \(E=E_{//}\). (**a**) Majority spin in PC; (**b**) minority spin in PC; (**c**) majority spin in APC; (**d**) minority spin in APC.