esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
But when a certain degree is reached, the error increases and eventually stabilizes. However, the arc length of objects in LEO in real scenarios is often less than 60 s, or even only a dozen seconds, so there are still technical bottlenecks in the IOD with the VSA observations. There are several algorithms for the initial orbit-correlation problem, and [14] introduced a geometric algorithm for initial orbit correlation for LEO object angle observation data, which were tested using real data, and the results showed that the correct rate of initial orbit correlation for the same object was higher than 80%. [PERSON]. [15] proposed a new method to locate space objects using noncoherent covisual observation techniques, which were validated using optical data from the Changchun, Delingha, and Xuyi stations in the Space Object and Debris Observation Network of the Chinese Academy of Sciences and debris laser-ranging (DLR) data from Changchun station. The results showed that the SMA error of the fixing accuracy was about 1 km. Ref. [16] proposed a new algorithm that does not require initialization and finds a set of observations with minimum residuals using the method of the edge value problem, which can provide complete state and covariance information. Ref. [17] analyzed the influence of the observation geometry on the short-arc angles only IOD and concluded that the optical sensor has the worst surveillance capability for space debris in the same orbital plane. Ref. [18] analyzed the application of algebraic geometry in IOD problems and tested its performance with various scenarios of observations. The detection of space debris clouds from space events is very significant. With the construction of optical telescopes, more and more optical telescopes are coming into use. This provides a good opportunity to detect events with ground-based telescopes. It is a good idea, but it is difficult to realize and there are few related public studies now. The difficulty lies in obtaining the exact orbit of space debris. To get the exact orbit elements of space debris, the IOD with one short arc and the correlation between the IOD elements are the basis for the detection of space debris. In this paper, the very short arc IOD and the orbit-correlation study are carried out based on the ground-based optical array of Changchun LEO EA and SAO FoucusGEO. The paper is organized as follows. The first part introduces the background of the paper research. The second part introduces the observation equipment, data, and methods. The third part presents the data-processing results. Finally, the conclusion and outlook are presented.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 1 Introduction
Among the different superconducting photon and particle detectors developed so far, Microwave Kinetic Inductance Detectors (MKIDs, or sometimes just Kinetic Inductance Detectors, KIDs) [1] have the most straightforward and most efficient way of multiplexing (please see chapter 2), promising scalability up to mega-pixel low temperature detector arrays. Therefore, MKIDs allow to exploit the significant advantages of superconducting low temperature detectors in arrays with (currently) thousands to tens of thousands of pixels, allowing them to become one of the most promising novel detector technologies. They are being developed and adapted for more and more modern scientific applications in astronomy, particle physics, and many other areas. The goal of this paper is not to give an exhaustive and complete overview of all MKID literature published so far as this would easily lead to an overwhelming, impracticable, and not very helpful review. Instead we intend to give an as complete as possible overview of how and where MKIDs are being used so far or are suggested to be used soon and to highlight several of the most important publications on these applications. The goal is to provide an easily usable, audited, and complete list of possible starting points for further literature research for anyone who wants to read deeper into the topic.
## 3 Statistical Properties
### Quantile Function and Stochastic Ordering
#### 3.5.1 Quantile Function  
Let \(X\sim f_{PZD}(x;\beta,\theta)\) given in (4). The quantile function, say \(Q(p)\), \(0<p<1\) defined by \(F(Q(p))=p\), is the root of the equation  
\[\ln\left(\theta+2+\theta Q(p)^{\beta}\left(\theta+2+\theta Q(p)^{\beta} \right)\right)-\theta Q(p)^{\beta}=\ln(1-p)+\ln(\theta+2) \tag{17}\]  
Table 2 presents some quantile values for some choices of \(p\) where the real and positive roots are assigned. It can be noted that the \(Q(p)\) values are increasing in \(p\).
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 4 Experiments and Models
### Models  
We selected the most vital segmentation models to test our dataset, as: U-Net, PSPNet and HRNet.  
U-net was used successfully in the biomedical field at first and become one of the most-applied models for the SAR image segmentation task. The reason why it is preferred for SAR images is that it can obtain more successful results with less training data. U-Net's name comes from its architecture, which resembles the letter U, as presented in the figure. This model has two parts that are symmetric. The first has convolutional layers which are transposed and 2D, and, on the other hand, the other one consists of known convolutional layers.  
A pyramid scene separation network (PSPNet) [40] is designed to better acquire the broad contextual representation of the scene. Patterns are derived from the input images using an extended mesh strategy and with the help of a feature extractor. Feature maps are then fed into a pyramid pooling module to separate models at divergent scales. Then, they are summarized on four different ranges, each corresponding to a pyramid level, and modified with a 1 \(\times\) 1 layer to decrease their size. By this method, each pyramid level analyzes the lower regions of the image with different positions. The outputs of the pyramid levels are eventually upgraded and combined into initial feature maps to include local and general context information. Then, these outputs are processed by a last layer to output pixel-based predictions.  
The high resolution net (HRNet) is proposed at first for estimation of human-readable data. This model processes high resolution images into the process. Then, the model connects resolution convolutions high-to-low in parallel and generates very high-resolution images by repetitively managing fusions across parallel convolutions [41]. HRNet is used for a wide range of visualization tasks, such as semantic segmentation and object detection. Experimental results shown in [41] prove that HRNet is one of the recent and popular models.
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 2 EET Mechanisms  
In general, microbial cells cannot exchange electrons with their extracellular environments because their electron transport chains are closed by nonconductive lipid membranes, peptidoglycans, and lipopolysaccharides [24]. Exceptionally, due to the evolution of special EET pathways across the cellular envelope, EAMs can exchange electrons with extracellular electrodes or insoluble minerals to enable energy flow between living organisms and the external environments [1]. Some of them (termed as exoelectrogens) can export electrons generated from the biological oxidation of organic substrates to extracellular insoluble electron acceptors, such as microbial anodes and iron and manganese oxides, through outward EET pathways, while others, referred to as electrotrophs, can take up electrons from extracellular electron donors, such as the microbial cathode, using inward EET pathways [1]. Meanwhile, the EET pathways between EAMs and electrodes are usually divided into a direct process and an indirect process based on the contact between EAM cells and the electrodes (Figure 1) [25]. The former depends on electron transport carriers on the cell surface, such as \(c\)-cytochromes (Cyts), vesicles, and nanowires, which can make direct contact with the electrodes, while the latter generally uses diffused electron shuttles and extracellular enzymes.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 4 Evaluating Existing Networks
### NapoNet  
At the turn of the millennium, the 450 km of the middle and upper Napo River of Peru was without almost without health services or schools. There had previously been no communication infrastructure along the river. A satellite company (GILAT) had installed communication centers in some of the villages. The centers never became popular. The communication network that was championed by the Enlace Hispano Americano de Salud (EHAS) was funded in part by the Global Fund and in part by the City of Madrid. The network was designed to link the 15 Napo River health posts with the Peruvian Health service in Iquitos. As Iquitos is linked with the rest of the world by microwave links carrying internet, a head-end in Iquitos then could have the additional benefit of linking the Napo to the World Wide Web. Without existing infrastructure, there was no choice but to build a broadband private network. The most cost-effective transmission medium per bandwidth at the time was WiLDNet, as implemented over IEEE 802.11g.  
NapoNet was constructed in 2006 and 2009. It has been continuously upgraded since that time from 802.11b to g, n to ac. A problem with the network has been the strict rules under which it was built. The network was designed for transmitting health data with no stipulation for generating revenue. Ownership and operation of the network then passed from the Global Fund to the Health service of Peru at the completion of the PAMAFRO (Control de la Malaria en las Zonas Fronterizas de la Region Andina: Un Enfoque Comunitario or the Pan Andean program for Malaria Control in frontier areas) project in 2014, without a revenue source other than the state, and the network has struggled to keep operations going. The network has been used a testbed for new technologies as funded by the European Union among other means of preservation.  
The main problem of this network is that there is no value proposition, no less a validated value proposition. In the case of NapoNet, EHAS saw an opportunity. The PAMAFRO project offered an opportunity to EHAS to be involved in a near state of the art network in a region where there was no other communication. Stipulations in the original contract were that control of the network must revert to the Health Service of Peru after completion of the PAMAFRO project.  
In order to build the network, EHAS obtained funds from the Global Fund and from the Town Hall of Madrid. There is some provision for maintenance of the network in the annual budget of the Health Service, but no provision was made for the network to engage in any revenue-producing activity. The subsequent (to 2014) operation of the network has been characterized by down times due to maintenance issues. In the light of the proposed framework, this project is an excellent example of a situation with no business approach and no paying customers.
# On the Properties of WC/SiC Multilayers
## 2 Experimental Section
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**W/SiC**} \\ \hline
**Period (nm)** & **Stress (MPa)** & **Roughness (nm)** \\ \hline
2.15 & \(-345\) & - \\
2.98 & \(-300\) & 0.14 \\
3.86 & \(-361\) & 0.12 \\
4.69 & \(-489\) & 0.13 \\
5.79 & \(-596\) & - \\
6.10 & \(-592\) & - \\
9.15 & \(-142\) & 0.13 \\
14.68 & \(-200\) & 0.15 \\
26.66 & \(-389\) & - \\ \hline \hline  & **WC/SiC** & \\ \hline
3.81 & \(-324\) & 0.14 \\
28.63 & \(-631\) & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stress and surface roughness in as-deposited W/SiC and WC/SiC multilayers of different periods. A dash “-” indicates points where a surface roughness measurement was not performed. of only one pair of SiC and WC layers, each 40 nm thick. After deposition the sample was cut in half. One half was annealed at 700 \({}^{\circ}\)C for 24 h while the other was kept as a control. Both samples were exposed to 2.275 MeV He\({}^{++}\) ion beam at normal incidence angle and RBS spectra were collected with a normal angle detector at 160\({}^{\circ}\) and a grazing (movable) detector at \(\sim\)100\({}^{\circ}\). Samples for Transmission Electron Microscopy (TEM) were prepared using in situ FIB lift-out technique on an FEI Dual Beam FIB/SEM (Evans Analytical Group, San Diego, CA, USA). TEM images were obtained with an FEI Tecnai TF-20 FEG/TEM operated at 200 kV in bright-field (BF), dark-field (DF) and high-resolution (HR) TEM mode.
# Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis
## 2 Related Work
In [23], [PERSON] and [PERSON] introduced a memory dump approach that triggered based on the three events: Performance, API and instrumentation based. The approach dumps the memory based on the events rather than dumping the memory at the end of the execution process. [PERSON] and [PERSON] [24] surveyed the main memory acquisition and analysis techniques. [PERSON] and [PERSON] [25] tested 200 malicious files using static analysis and memory forensics. They observed that successful malware infection leaves a footprint in the memory. [PERSON] and [PERSON] [26] examined the artifacts left by rootkits in the memory kernel-level such as the drivers, modules, SSDT hooks, IDT hooks and callback functions. The experiment noticed that activities like modified drivers, callback functions and attached devices, are the major activities related to malware in the kernel-level. In [27], [PERSON] et al. introduced an analysis method for extracting features like DLLs, function calls and the registry from the memory. The method aimed to increase the information accuracy by comparing it with the information available in other memory structures. The features were then classified based on their frequency in the training data set, and the detection rate of 98% was achieved through Naive Bayes. A significant drawback of this method is the high false positive rate. In the [PERSON] et al study, it exceeded 16%. Similarly, [PERSON] et al. [4] introduced an approach to detect malware based on three features extracted from the memory images, registry activities, API function calls, and imported libraries. The authors tested each feature individually, and the maximum accuracy equaled to 96% which was reached by applying SVM on the data from the registry activities. Afterward, in the next research, [PERSON] et al. [28]introduced an approach that uses process handles to detect malicious files with accuracy slightly higher than 91.4%. The experiment observed that the main types of handles for the malicious files used were section handles, process handles and mutants. Likewise, [PERSON] et al. [29] introduced a method for malware classification based on a memory dump. A file is extracted from the memory dump and then converted into an image with a gray scale. After that, the image is transformed into a fixed size. The API call features were extracted from the images and used to classify malware. The highest accuracy equaled to 93.9% which was achieved by applying the random forest. Despite the favorable results achieved by the above techniques, no other works attempt to integrate dynamic analysis with memory analysis to get the advantages of both analyses and overcome their limitations.
# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
###### Abstract
In this way, the difference in the LiDAR response for the different types of weather can be understood. An algorithm is developed to improve the signals received during adverse weather. The experiments show that the LiDAR results can be improved to be comparable to the signals received during normal weather. The results verify the effectiveness of the algorithm for making improvements in LiDAR systems for autonomous vehicles.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 3 Typical Flow Table Saturation Attack
### Case Studies--Fixed Timeout  
In the first case, we consider the case where all flow rules are set with fixed timeouts. The idle timeout is 10 s. Figure 2 demonstrates the process of probing timeouts using the binary search and the incremental search methods. The blue arrow indicates that the packet can hit flow rules. The red one indicates a table-miss. The range of binary search is 1 s to 30 s.  
Figure 2a applies incremental search. Since the probing packet with a 10 s interval can hit while the one with an 11 s interval triggers a table-miss, the idle timeout is inferred as 10 s. Figure 2b applies binary search to narrow the search space of timeouts. After several rounds of probing, it can obtain the same results, which is also consistent with the controller setting. It is evident that typical attack methods are effective under fixed timeout settings.
## 3 Results
### Equilibrium and Kinetics Studies of The Adsorption of BF on RPNS
#### 3.4.1 Equilibrium Isotherms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Isotherm** & **Equations (Generalized/Linearized Forms)** & **Parameters** & **Value** \\ \hline Langmuir & \(q_{e}=\frac{q_{m}K_{L}\ C_{e}}{1-K_{L}\ C_{e}}\) & \(q_{m}\) (mg/g) & 58.82 \\  & \(\frac{C_{e}}{\dot{q}_{e}}=\ \frac{1}{q_{m}\ K_{L}}+\frac{C_{e}}{\dot{q}_{m}}\) & \(K_{L}\) (L.mole\({}^{-1}\)) & 0.076 \\  & \(\frac{C_{e}}{\dot{q}_{e}}=\ \frac{1}{q_{m}\ K_{L}}+\frac{C_{e}}{\dot{q}_{m}}\) & \(\mathrm{R}^{2}\) & 0.968 \\ \hline \multirow{3}{*}{Freundlich} & \(q_{e}=\ K_{F}\mathrm{C}_{e}^{\frac{1}{2}}\) & 1/n & 0.637 \\  & \(q_{e}=\ K_{F}\mathrm{C}_{e}^{\frac{1}{2}}\) & \(K_{F}\) (mole/g)(L/mole)\({}^{1/n}\) & 5.011 \\  & \(\log(q_{e})=\log(K_{F})+\left(\frac{1}{n}\right)\log(C_{e})\) & \(\mathrm{R}^{2}\) & 0.932 \\ \hline \multirow{3}{*}{Temkin} & \(q_{e}=\ \frac{RT}{b_{T}}\ \ln(A_{T}\ C_{e})\) & \(b_{T}\) (J/mole) & 200.6 \\  & \(A_{T}\) (L/mole) & 0.867 \\  & \(q_{e}=\ \frac{RT}{b_{T}}\ln(A_{T})+\frac{RT}{b_{T}}\ln(C_{e})\) & \(\mathrm{R}^{2}\) & 0.976 \\ \hline \multirow{3}{*}{DR} & \(\ln(q_{e})=\ \ln(q_{m})-\beta\epsilon^{2}\) & \(\beta\) & \(9\times 10^{-9}\) \\  & \(\epsilon=RT\Big{(}1+\ \frac{1}{C_{e}}\Big{)}\) & \(E\) (kJ/mole) & 7.45 \\ \cline{1-1}  & \(\sigma_{\mathrm{s}}\)(mg.g) & 43.12 \\ \cline{1-1}  & \(E=\frac{1}{\sqrt{2\beta}}\) & \(\mathrm{R}^{2}\) & 0.918 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: General and linearized equation of Langmuir, Freudlich, Temkin, and Dubinin-Radushkevich isotherms, beside their parameters for the adsorption of BF on RPNS. Figure 8: Adsorption isotherms of BF on raw pistachio nutshells including, (**A**) Langmuir, (**B**) Freundlich, (**C**) Temkin, and (**D**) Dubinin-Radushkevich (DR). The following conditions were used to obtain the isotherms; DC: 40–400 ppm, AD: 0.100 \(\pm\) 0.005 g, pH: 12.00 \(\pm\) 0.20, and ST: 2 h.  
Temkin isotherm, Figure 8C, gives an idea about the adsorbate/adsorbent interaction by the factor that describes the fact that the heat of adsorption of all the molecules in the layer decreases linearly with the adsorbent-adsorbate interactions. Data shown in Table 8 show that the R\({}^{2}\) value = 0.967 signifying that this isotherm is applicable for studying the adsorption of BF on the RPNS. Finally, the DR isotherm, Figure 8D was applied to differentiate between the adsorption mechanisms on a heterogeneous surface [31]. The physical adsorption occurs when free energy is \(<\) 8.0 kJ/mol, while the chemical adsorption occurs when free energy is \(>\) 8.0 kJ/mol. The free energy for adsorption of BF on RPNS is physical because the free energy = 7.45 kJ/mol as shown in Table 8. On the other hand, this isotherm is not applicable for the adsorption of BF on RPNS because the R\({}^{2}\) value = 0.913.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
###### Abstract  
Fog-based MANET (Mobile Ad hoc networks) is a novel paradigm of a mobile ad hoc network with the advantages of both mobility and fog computing. Meanwhile, as traditional routing protocol, ad hoc on-demand distance vector (AODV) routing protocol has been applied widely in fog-based MANET. Currently, how to improve the transmission performance and enhance security are the two major aspects in AODV's research field. However, the researches on joint energy efficiency and security seem to be seldom considered. In this paper, we propose a source anonymity-based lightweight secure AODV (SAL-SAODV) routing protocol to meet the above requirements. In SAL-SAODV protocol, source anonymous and secure transmitting schemes are proposed and applied. The scheme involves the following three parts: the source anonymity algorithm is employed to achieve the source node, without being tracked and located; the improved secure scheme based on the polynomial of CRC-4 is applied to substitute the RSA digital signature of SAODV and guarantee the data integrity, in addition to reducing the computation and energy consumption; the random delayed transmitting scheme (RDTM) is implemented to separate the check code and transmitted data, and achieve tamper-proof results. The simulation results show that the comprehensive performance of the proposed SAL-SAODV is a trade-off of the transmission performance, energy efficiency, and security, and better than AODV and SAODV.  
mobile ad hoc network; fog computing; security; AODV; energy efficiency +
Footnote †: journal: Sensors
## 4 Results and Discussion
### Analysis of the Computational Complexity
#### 4.5.2 Empirical Performance and Computational Complexity Discussion  
Figure 8 plots the actual computational complexity for each framework and validates the orders of the computational complexity in the theoretical analysis. This common graph allows for a test of how good this view of computational complexity was voted.  
Figure 7: Latency comparison of ACRLCP with benchmark techniques.  
It is clear from the plot that the following observations can be made:
* The **ACRLPC framework** (solid blue line) has a computational complexity of \(O(N\log N)\). This rate of growth is slower than that of a complexity with \(O(N^{2})\), thus computationally establishing the greater efficiency of the ACRLPC framework (where the system is expected to be expanded on larger datasets).
* The **FDU-MAC** (red dashed line) and **TCH-MAC** (green dashdot line) frameworks present a quadratic order (\(O(N^{2})\)) behavior regarding computational complexity. This indicates that they require growing computational resources from the system, and, when a system scales up, they are less applicable in such large-scale applications where calculation speed is the main concern.
* The **UW-ALOHA-QM** framework (as indicated by the solid orange line) is linear in its growth of computational complexity (\(O(N)\)). The ACRLPC framework outperforms this \(O(N^{2})\) system both in terms of throughput and accuracy metrics, even though the adopted structure is relatively more efficient.  
It is evident that the ACRLPC framework is scalable (both theoretically and practically) and computationally cheaper than the other three methods. The ACRLPC framework has a slow growth rate, which allows it to have a very low resource consumption for larger datasets.