esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity
## 5 Results and Discussion
\begin{array}{l}C_{n}^{2}=3.8\times 10^{-14}w+2.0\times 10^{-15}T-2.8\times 10^{-15}W \\ \qquad\qquad+2.9\times 10^{-17}W^{2}-1.1\times 10^{-19}W^{3}-2.5\times 10^{-15}S \\ \qquad\qquad+1.2\times 10^{-15}S^{2}-8.5\times 10^{-17}S^{3}-5.3\times 10^{-13} \end{array}  \tag{16}\]  
where \(T\) is the average air temperature, \(W\) is the average relative humidity, \(S\) is the average wind speed from the local weather station, and \(w\) is a scaling coefficient. The average values of \(C_{n}^{2}\) using [67] were \(3\times 10^{-15}\) on a clear day, \(1\times 10^{-16}\) on a clear night, \(6\times 10^{-16}\) under light rain, \(8\times 10^{-16}\) under moderate rain, and \(2\times 10^{-15}\) under heavy rain. This concurred with earlier experimental values computed using the scintillation index within \(1\times 10^{-15}\). The refractive index structure parameter \(C_{n}^{2}\) was in the range of \(1\times 10^{-15}\) to \(1\times 10^{-13}\). Based on the turbulence strength classification in [62], this indicated weak to modern turbulence. A probability distribution function of the \(C_{n}^{2}\) values from the experiment could be computed to determine if this adhered to relevant channel probability distribution functions for FSO spatial mode diversity and FSO aerial communications such as Malaga, log normal, Gamma-Gamma, Nagakami, and others [68; 69; 70; 71; 72]. Log normal and Gamma-Gamma channel models are considered as special cases of the Malaga channel model, for weak turbulence and moderate-strong turbulence, respectively [72]. Nevertheless, comparison of the time-varying SINR plots from the experiment with existing channel models do not provide accurate time-varying error analysis, as existing channel models are probabilistic [73]. Existing channel models only provide the statistical averages for the random variations and do not describe the evolution of the channel characteristics with time. In simulations modelling the FSO channel, the time-varying atmospheric turbulence is typically emulated by continuously upgrading the random phase patterns to model random irradiance fluctuations, based on a known channel probability distribution function. This is only valid when the atmospheric turbulence is stationery during the laser pulse width [74]. Expanding on the previous work, we performed the false-nearest-neighbors algorithm on the measured time-varying channel matrix, which revealed that the fraction of false nearest neighbors converged to 0. This indicated the  
Figure 11: BER for all channels under varying rain levels. (a) Composite Channel 1,2. (b) Composite Channels 3,4.  
presence of nonlinear deterministic chaos. This opens up new possibilities for research on state-space reconstruction and the dynamic attractor for modeling the nonlinear FSO-UAV channel matrix.
# Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks
###### Abstract  
Cyberattacks against the elements of technological data transmission networks represent a rather significant threat of disrupting the management of regional electric power complexes. Therefore, evaluating the functioning quality of data transmission networks in the context of cyberattacks is an important task that helps to make the right decisions on the telecommunication support of electric power systems. The known models and methods for solving this problem have limited application areas determined by the admissible packet distribution laws. The paper proposes a new method for evaluating the quality of the functioning of data transmission networks, based on modeling the process of functioning of data transmission networks in the form of a stochastic network. The proposed method removes restrictions on the form of the initial distributions and makes the assumptions about the exponential distribution of the expected time and packet servicing in modern technological data transmission networks unnecessary. The method gives the possibility to evaluate the quality of the network functioning in the context of cyberattacks for stationary Poisson transmission and self-similar traffic, represented by Pareto and Weibull flows models. The obtained evaluation results are in good agreement with the data represented in previously published papers.  
cyberattack; data transmission network; stochastic network; equivalent function; Laplace-Stieltjes transform; distribution function; functioning quality; delivery time of data packets; jitter; packet loss probability  
30 June 2021  
29 July 2021  
5 August 2021  
###### Abstract  
Cyberattacks against the elements of technological data transmission networks represent a rather significant threat of disrupting the management of regional electric power complexes. Therefore, evaluating the functioning quality of data transmission networks in the context of cyberattacks is an important task that helps to make the right decisions on the telecommunication support of electric power systems. The known models and methods for solving this problem have limited application areas determined by the admissible packet distribution laws. The paper proposes a new method for evaluating the quality of the functioning of data transmission networks, based on modeling the process of functioning of data transmission networks in the form of a stochastic network. The proposed method removes restrictions on the form of the initial distributions and makes the assumptions about the exponential distribution of the expected time and packet servicing in modern technological data transmission networks unnecessary. The method gives the possibility to evaluate the quality of the network functioning in the context of cyberattacks for stationary Poisson transmission and self-similar traffic, represented by Pareto and Weibull flows models. The obtained evaluation results are in good agreement with the data represented in previously published papers.
## 2 Optical Data Information Encoding
### Data Encoding Based on Azimuthal Variation  
Currently, 3D laser lithography enables the custom processing of almost arbitrary structures. Thus, using the change in azimuth angles of the structures (i.e., different orientations of the structures) is a reliable method to achieve optical storage encoding (Figure 4a). The simulating results of different structures with different azimuth angles revealed different wavelength-based reflectance spectra, with spectra processed by a 3 \(\times\) 3 high-pass filter (Figure 4b).  
Figure 5 shows the reflectance spectra under _TE_/_TM_ polarizations for the structures when the azimuth angle is increased. Eighteen different spectral responses were obtained from different structures.  
Figure 4: The encoding strategy. (**a**) Variation in structure according to azimuth, (**b**) wavelength-dependent spectra of different azimuths.  
Figure 3: Simulated wavelength-dependent spectra for 12 data sequences (Azimuth fixed to 0\({}^{\circ}\)).
## 5 Conclusions  
To the best of our knowledge, this is the first detailed study to use IT-AFM to determine spatial and temporal biomechanical changes at the nano-scale during murine PT-OA. Our study shows that nano-stiffness of the interterritorial matrix of the articular cartilage increases in a time- and depth-dependent manner during DMM-induced PT-OA. The Young's moduli for collagen and proteoglycans showed increasing stiffness gradients from the superficial zone to the middle and deep zones in both Sham and DMM cartilage. The increase in stiffness 2 weeks after DMM was more pronounced than in Sham, likely due to proteoglycan loss associated with the early status of OA. The age-related increase in nano-scale AC stiffness was partially abolished 8 weeks after DMM due to structural deterioration of the collagen network. Interestingly, the tendency for stiffness to increase was greater in the lateral compartment, even when little or no OA-like degeneration was observed histologically. Last but not least, with this study, we further support the importance of nano-scaled IT-AFM as a complementary methodology for understanding OA pathophysiology and diagnosing the early phase of the disease.  
Conceptualization, L.F., D.M., S.G., A.A. and H.C.-S.; methodology, L.F., D.M., Z.F. and P.A.; software, L.F. and P.A.; validation, L.F., P.A., A.A. and H.C.-S.; formal analysis, L.F., P.A., A.A. and H.C.-S.; resources, S.G., A.A. and H.C.-S.; data curation, L.F., D.M., P.A., A.A. and H.C.-S.; writing--original draft preparation, L.F.; writing--review and editing, L.F., D.M., S.G., P.A., A.A. and H.C.-S.; visualization, L.F., Z.F. and P.A.; supervision, S.G., A.A., P.A. and H.C.-S.; project administration, S.G., A.A. and H.C.-S.; funding acquisition, S.G., A.A. and H.C.-S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Research Foundation (DFG) as part of sub-project 1 (AA 150/11-1/2 and CL 409/4-1/2) and 4 (GR 1301/19-1/2) of the Research Consortium ExCarBon/FOR2407-1/2. LF and HCS acknowledge additional funding from the Bavarian State Ministry for Science and Art through the Research Focus \"Herstellung und biophysikalische Charakterisierung von dreidimensionalen Geweben--CANTER\" and the Bavarian Academic Forum (BayWISS)--Doctoral Consortium \"Health Research\".  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the District Government of Lower Franconia (approval number: 55.2-2531-2-289; date of approval: 27 July 2016).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We would like to thank [PERSON] for the fruitful discussion and for carefully reading the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
applied sciences  
A Fast Method for Determination of Ethylene Oxide Using Gas Chromatography Coupled with Mass Spectrometry GC-MS/MS  
[PERSON] 1,2R, [PERSON] 1, [PERSON] 2,*,[PERSON] 1 and [PERSON] 1  
Toxicology studies conducted by the Agency for Toxic Substances and Disease Registry (ATSDR) and the Environmental Protection Agency (EPA) regarding oral exposure to food sterilized with ethylene oxide have confirmed the information on the toxicity, genotoxicity, and carcinogenicity of ethylene oxide. [4]. In particular, the studies focused on the inhalation exposure to EtO, which can lead to cancer because of ethylene oxide's toxic effects on respiratory, hematologic, neurological, endocrine, and reproductive systems [5]. According to the International Agency for Research on Cancer (IARC) classification, it is included in Group 1 of carcinogenic substances. Cytotoxicity of EtO results from the occurrence of alkylating reactions with the nucleic acid, which can cause the coagulation of blood proteins and the deactivation of cellular enzymes [6].  
Directive 86/355 of 21 July 1986 is one of the first documents to have mentioned the need to ban ethylene oxide usage for sterilization purposes across the European Union due to its toxicity. According to legislative documents, pursuant to the Regulations of the Council of the European Union (EC) 528/2012 and 1107/2009, this substance does not serve as a means to preserve the nutritional value of food products. The Regulation of the European Union 231/2012 prohibited the use of ethylene oxide for the fumigation of food products [7]. However, taking into consideration that several non-EU countries still permit the use of ethylene oxide as a food fumigant, this substance must be constantly monitored in imported food products [8].  
Subsequent directives and regulations of the European Union imposed increasingly stringent requirements with regard to the maximum amount of ethylene oxide and 2-chloroethanol in plant products and food additives. A recent regulation, number (EU) 2022/1396, severely limited the level of these pesticides to 0.1 mg/kg in food additives. Implementing Regulation (EU) 2020/1540 increased the number of mandatory checks on sesame seed samples originating from India to 50% [9].  
In 2020, the CVUA Stuttgart (Chemisches und Veterinaruntersuchungsamt Stuttgart) published an analytical method for the quantification of ethylene oxide and 2-chloroethanole in food commodities by GC-MS/MS based on the standards for the quick, easy, cheap, effective, rugged, and safe (QuECHERS) method or the multimethod commodities of high lipid and low water content (QuOil) extraction. The analytical method presented in this paper draws upon a modification of the QuOil CNT/TS 17062:2019 method and allows for the quick determination of the full MRL definition of ethylene oxide in food products [10].  
The aim of the study is to present an easy and quick analytical GC-MS/MS method for detecting a residue called ethylene oxide, which is defined as the sum of ethylene oxide and 2-chloroethanol.
# Aggregate and Particle Size Distribution of the Soil Sediment
## 3 Results
### Comparison of Three Tested Methods  
The PSD curves of five randomly selected samples (Samples 1-5) obtained by all three methods are shown in Figure 3. In Appendix A, Tables 13-13 show the representation of the particles smaller than 2, 10, and 50 \(\mu\)m for all particle size curves obtained by the compared methods. The representation of particles smaller than 2, 10, and 50 \(\mu\)m were averaged and compared in Table 1. Overall, the least differences are between the results from the laser diffractometer and the PARIO, especially the results for the fraction content smaller than 2 \(\mu\)m are very similar (difference 6%). Sedimentation methods are close to each other in fraction content smaller than 50 \(\mu\)m (difference 4%), but comparing both of them to the laser diffractometer there were large differences (18% and 14%) for this fraction. Results for the fraction content smaller than 2 \(\mu\)m obtained by the hydrometer differ significantly compared to those from the laser diffractometer (by 22%) and PARIO (by 16%). The comparison showed that the volume representation of the fraction smaller than 2 \(\mu\)m measured by the laser diffractometer was generally lower than the volume representation analyzed by hydrometer (by 22%) and PARIO (by 6%). On the other hand, the fraction content smaller than 50 \(\mu\)m s lower for the sedimentation methods (by 18% and 14%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **\textless{}2**\(\mu\)m & **\textless{}10**\(\mu\)m & **\textless{}50**\(\mu\)m & **Method** \\ \hline LD & \(-22\pm 9\%\) & \(-4\pm 7\%\) & \(18\pm 2\%\) & Hydrometer \\ LD & \(-6\pm 9\%\) & \(11\pm 10\%\) & \(14\pm 5\%\) & PARIO \\ PARIO & \(16\pm 8\%\) & \(15\pm 9\%\) & \(-4\pm 4\%\) & Hydrometer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of average differences of representation of particles smaller than 2, 10 and 50 \(\mu\)m between three tested methods (\(\pm\)SD), differences between the results for methods in the first and the last column.
## 4 Experimental Verification and Discussion
### Wavefront Aberrations Evaluation
Scalar approximation fails to accurately describe and evaluate CGHs with small periods in optical testing due to the imperfections. If the rigorous methods can be promoted and developed, the reconstruction accuracy of CGH can be greatly improved. However, it is difficult to design CGHs with rigorous vector method today due to the complex description of light propagation and lack of no effective vector optimization methods. Based on the latest research results [20], we are now developing a more effective and flexible method to design perfect CGHs to solve the limitations of imperfections.
## 4 Description of Proposed Protocol
### Relay Operation  
In this paper, the UAV-Assisted VANETs can be regarded as the UAV-assisted VANETs can be regarded as the delay tolerant networks (DTNs). Therefore, UAV-assisted VANETs can use the SCF method for information transmission. Further, the UAV mobility is exploited to let mobile network nodes physically carry the message as the RN, and forward the message opportunistically upon contacts. Thus, how to determine an appropriate relay selection strategy and the message forwarding criteria is the key problem. Epidemic routing is one of the first message forwarding schemes, which copies the message to any newly encountered nodes in the network [33].  
Motivated by the above \"network-centric\" idea, we adopt the flooding technique to make the message delivery timelier to minimize \(T_{a,b}\). In other words, the SN adopts the multi-copy transmission scheme, which selects the neighbor nodes in \(x_{j}^{*}\) as the RN. However, increasing the number of copies can improve the network performance, but this leads to the increment of the overhead. Therefore, we conceive a utility function \(\psi\left(x_{j}^{*},N_{b}\right)\) by jointly considering the NEF and the message TTL to limit the overhead, which is defined as  
\[\psi\left(x_{j}^{*},N_{b}\right)=\frac{T_{a,j}}{M_{th}}F_{j,b}\left(\tau_{m} \right)=\frac{T_{a,j}E_{j,b}\left(\tau_{m}\right)}{M_{th}\sum\limits_{k=1}^{N} E_{j,k}\left(\tau_{m}\right)}, \tag{16}\]  
where \(\psi\left(x_{j}^{*},N_{b}\right)\) represents the utility value of the message at the RN \(N_{j}\) (the DN of message is \(N_{b}\)), and \(T_{a,j}\) represents the time taken for the message to be forwarded from \(N_{a}\) to \(N_{j}\).  
After receiving the message, \(N_{j}\) calculates the utility value according to (16). If (17) is satisfied, \(N_{j}\) stores the message, otherwise the message is discarded.  
\[\psi\left(x_{j}^{*},N_{b}\right)\geq D_{j}\left(\tau_{m}\right)E\left(\psi \right), \tag{17}\]  
where \(E\left(\psi\right)\) represents the average utility value of the cache messages, which is given by  
\[E\left(\psi\right)=\sum\limits_{i=1}^{\varphi}\psi\left(x_{j}^{*},N_{b}\right) _{i}\Bigg{/}\varphi, \tag{18}\]  
where \(\varphi\) represents the number of messages cached at \(N_{j}\).
## 4 UAN Classes
### DTN UANs  
Finally, DTN UANs constitute a UAN class currently in experimental status [15]. DTN UANs exploit urban vehicles, such as public transportation buses or garbage trucks, equipped with a gateway that collects the data obtained by sensor nodes. Communication between the sensor nodes and the gateway occurs only when the gateway is within the coverage range of the sensor nodes and vice versa. The gateway on the vehicle can subsequently transmit the data, typically by means of a cellular connection. The temporary and infrequent connectivity between the sensor nodes and the gateway is a characteristic of DTNs, and the vehicle with the gateway plays the role of a _data mule_[38].  
When sensor nodes are not mains-powered, the DTN UAN can only be feasible in terms of energy consumption if the radio of the sensor nodes is in sleep state by default. In order to efficiently collect data in such conditions, a radio-triggered wake-up system is used [39]. In this scheme, both the vehicle and the sensor nodes are equipped with two radio interfaces. The primary interface is a common wireless, low-power interface for data communication (e.g., IEEE 802.15.4). The secondary interface is a component of the wake-up system. At the vehicle side, the secondary interface transmits a special radio signal called wake-up signal. At the sensor node side, the secondary interface is a receiver designed to detect the wake-up signal. Although the wake-up signal receiver is always active, it consumes only a few microwatt. Upon detection of the wake-up signal, a sensor node activates its primary radio for data communication, otherwise it remains in sleep mode. The vehicle can thus collect the data from sensor nodes only when they are in its vicinity.  
Connectivity of sensor nodes with the data mule only happens for reduced time periods which depend on the speed and/or stop time of the vehicle, and may have a duration of up to tens of seconds in the best case. On the other hand, the wake-up range that can be achieved with current systems is typically of around 30 m, although it can be increased by using directive antennas [40]. Furthermore, regulations may impose duty cycle constraints in the frequency bands used for the wake-up signals. Therefore, the connectivity time and the volume of data that can be exchanged is limited.  
Note that whereas garbage trucks may typically delay data collection up to one day, they provide wide coverage since their routes cover the whole city. On the other hand, public transportation buses offer greater data collection frequency, but may cover a smaller area of the city.  
Whereas the IRTF has produced protocol specifications for DTNs [41], these are overwhelmingly complex for constrained node networks. DTN UANs use more simple communication mechanisms. In order to enable communication between the gateway and the sensor nodes, IEEE 802.15.4 is generally used as the radio link technology. The data collection application operates on top of the link layer (Figure 3e). The gateway implements an IP-based protocol stack on top of the cellular link. Nevertheless, solutions for DTN UANs have not yet been standardized.
## 2 Study Area and Earthquake Catalog  
The study area is located at 24\({}^{\circ}\)-36\({}^{\circ}\) N, 92\({}^{\circ}\)-106\({}^{\circ}\) E on the southeastern margin of the Tibetan Plateau. It covers mainly western Sichuan, north-central Yunnan, eastern Tibet, and southern Qinghai. According to the division scheme of tectonically active blocks in China [16; 17], the study area mainly includes the Sichuan-Yunnan block, the Qiangtang block, and the Bayan Har block. Fault zones, such as the Karakorum-Jiali fault zone, the Jinshajiang-Honghe fault zone, the Mani-Yushu-Xianshuihe fault zone, the Zemuhe-Anninghe-Xiaojiang fault zone, the Longmenshan fault zone, and the Kunlun-Maqin fault zone, are present in the study area. There are complex geological structures, vigorous modern tectonic activity, and frequent strong earthquakes in the region. In particular, the Sichuan-Yunnan block is divided into two: the western Sichuan block and the central Yunnan block [18; 19]. The slip rate of the former is 5.4-7.6 mm/a, while that of the latter is 3 mm/a approximately. The modern tectonic movement of the western Sichuan block is approximately twice as rapid as that of the central Yunnan block. The distributions of tectonic blocks and \(M\geq 6.7\) earthquakes in the study area in 1970-2022 are illustrated in Figure 1. Since 1970, strong earthquakes (\(M\geq 6.7\)) have mainly occurred in the fault zones along the boundary of the western Sichuan block (the Xianshuihe fault zone and the Jinshajiang fault zone), as well as the Longmenshan fault zone. The earthquake catalog adopted is the National Small Earthquake Catalog published by the China Earthquake Networks Center (it includes all \(M_{\rm L}\geq 1.0\) earthquakes in China since 1970). The Richter local magnitude scale is used in the catalog (in \(M_{\rm L}\)); thereafter, it is uniformly represented by \(M\) in this paper. A complete and reliable earthquake catalog is important for seismicity analysis. Previous research [20; 21; 22] has indicated that the records for \(M\geq 3.0\) earthquakes in the Sichuan-Yunnan region since 1970 and those in the Tibetan Plateau since 1980 are complete. Hence, since 1970 \(M\geq 4.0\) earthquakes in the study area can ensure their completeness. Figure 2a-c presents the epicenter distribution, magnitude-frequency graph, and M-t curve of \(M\geq 4.0\) earthquakes in the study area in 1970-2022.  
Figure 1: Distribution of tectonic blocks and \(M\geq 6.7\) earthquakes in the study area in 1970–2022. Red circles denote \(M\geq 6.7\) earthquakes in 1970–2022, while black lines are the boundaries of the tectonic blocks. I: Sichuan–Yunnan block (I\({}_{1}\): western Sichuan block, I\({}_{2}\): central Yunnan block), II: Qiangtang block, III: Bayan Har block. F1: Karakorum-Jiali fault zone, F2: Jinshajiang–Honghe fault zone, F3: Mani–Yushu–Xianshuihe fault zone, F4: Zemuhe–Anninghe–Xiaojiang fault zone, F5: Longmenshan fault zone, F6: Kunlun–Maqin fault zone.