esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 RS Systems
### Active
#### 2.2.4 LiDAR  
A typical LiDAR system integrates several different units (e.g., optical, timing, and geo-referencing) to acquire three-dimensional point cloud measurements from the scanned area. LiDAR measures the time it takes for the emitted light to travel to the ground and be received back at the sensor. Each point in LiDAR data presents specific properties of the scanned area (e.g., coordinates and intensity). LiDAR measurements have been used for a variety of ocean and coastal applications, including but not limited to safe marine navigation, inundation and storm surge modelling, hydrodynamic modelling, and coastal vulnerability analysis [51]. In addition to the ocean surface mapping applications, LiDAR enables recovering of the ocean bottom depending on the laser wavelength and quality of ocean water. Thus, underwater target detection and coastal bathymetry have also been investigated using LiDAR data [52; 53; 54].
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 5 Comparison Methods
### Isdsr+ [11]  
This protocol attempts to reduce packet length in secure routing using a signature scheme called identity-based sequential aggregate signatures (IBSAS) [29], which is an ID-based aggregate signature.  
The digital signatures used in this method are based on an ID-based signature [20]. When the source node S establishes a route to the destination node D using this method, S broadcasts the RREQ to its neighboring nodes. The RREQ is allocated a signature by S. When node A, a neighbor node of S, receives the RREQ, A adds its address to the packet, generates a new signature which is an aggregate signature with S, adds the newly generated signature instead of the signature attached to the RREQ from S, and adds itself before forwarding the entire packet. This process is repeated until the RREQ reaches the destination node D. When D receives the RREQ, D verifies the aggregated signature. Subsequently, a signed RREP is generated by D and sent back to S. As in the RREQ phase, each relay node adds its address and aggregate signature to the packet and forwards the RREP until it reaches S. When S receives the RREP and verifies the aggregate signature, a path is established.
## 3 Results and Discussion
### Natural Frequencies  
Natural frequency of the riser is a key characteristic in VIV analysis, because the \"lock-in\" would occur when the natural frequency of a riser is close to the vortex shedding frequency. The \"lock-in\" phenomenon would lead to fatigue failure of the riser because of the large and damaging vibration. The vortex shedding frequency can be calculated by Equation (3) which includes the Strouhal number \(\rm S_{t}\), the outside diameter of the riser D and the flow velocity U. According to the relationship between \(\rm Re\) and \(\rm S_{t}\), the Strouhal number \(\rm S_{t}\) is 0.2 for \(\rm Re\) between 300 and 300,000 and is 0.2-0.3 for \(\rm Re\) between 300,000 and 3,500,000 [50]. More specifically, the \(\rm S_{t}\) are 0.2 for cases 1-3, 0.21 for cases 4-6 and 0.22 for cases  
From the results listed in Table 6, it can be seen the natural frequencies of cases 7 (riser 1) and case 9 (riser 3) are close to their vortex shedding frequencies, and similarly the reduced velocity U\({}_{\rm r}\) of these two cases are close to the range of 4-8. Hence, for these two cases, \"lock-in\" might occur. We have to notice that Equation (3) of vortex shedding frequency and Equation (4) of reduced velocity are calculated based on the rigid cylinder without deformation and fluid structure interaction.
# Collision Detection Method Using Self Interference Cancelation for Random Access Multiuser MIMO
## 5 Conclusions  
In this paper, a collision detection method using asynchronous MU-MIMO called random access MU-MIMO is proposed. This method utilizes the fact that idle antennas, which do not transmit signals, can be employed to receive and detect interference when considering random access MU-MIMO. Hence, idle antennas at the AP can receive preamble signals while transmit antennas at the AP transmits the preamble signals: this procedure is regarded as full-duplex transmission, which cancels self-interference between AP antennas. The interference can be detected by subtracting the short preamble signal, which is multiplied by the estimated channel response using the received signal after the FFT processing. Through the computer simulation, in which IEEE802.11n/ac-based OFDM signal is assumed, the effectiveness of the proposed method is verified by evaluating the characteristics of self-interference reduction and detection of interference from ITS. It is verified that the estimation error on the interference power is less than 1 dB, when the SIR is less than 30 dB. Hence, the proposed method can successfully cancel the self-interference between AP antennas and estimate interference with small power. In addition, we carried out indoor measurement using IEEE802.11n/ac-based OFDM signals, in order to verify how serious the problem of interference is for the DT, when considering the collision detection at the AP. It is shown that the proposed method accurately estimates the interference power even if the interference power is small, and that collision detection at the AP is realized from the measurement.  
As the basic study in the proposed method, interference detection performance is evaluated in this paper. However, to evaluate the total system performance, the bit error rate should be evaluated. As the future study, the evaluation with the BER will be studied when considering the total system performance.  
**Acknowledgments: Part of this work was supported by KAKENHI, Grant-in-Aid for Scientific Research (B) (17H03262, 17H01738).**  
**Author Contributions: This study was led by [PERSON] while [PERSON], [PERSON], T.H., [PERSON] and T.M. assisted with the computer simulations.**  
**Conflicts of Interest: The authors declare no conflicts of interest.**
## 6 Practical Application and Results
### Prototyping and Evaluation
#### 6.4.1 Rapid Prototyping
The spreadsheet was also uploaded to the web platform. 2. _Back-end system._ This organizes, stores, and processes data received from the front-end web platform. Likewise, it retrieves data from the storage services, including a simple storage service (S3), document database (DDB), and relational database (RDB), and formats it for consumption by the front-end mobile application. An application-programming interface (API) facilitates communication between the back-end and front-end, efficiently managing data traffic. 3. _Front-end mobile application._ It is the user interface of the AR and MR prototype solutions, implemented for Android smartphones and Trimble XR10/Microsoft HoloLens headsets, respectively. Visual representations of these interfaces are provided in Figure 6 and Figure S1 of the Supplementary Material. It is noteworthy that the prototypes only display information and do not include user input capabilities. The developed AR and MR prototypes can integrate project documentation and present the SA requirements within an AR/MR environment according to the goal being addressed. This allows for assessing the impact of the proposed design concepts on users' SA using SA evaluation metrics.
# Model-Driven Approach to Cloud-Portability Issue
## 4 Entities of Existing IaaS Platforms
### Example of Code Differences  
As an example, we present a definition of a network and its subnet in Amazon AWS and Microsoft Azure orchestration script syntax. Both examples are shown in YAML format. Not only are there syntactical differences, but also logical ones. In AWS, you have to define in the subnet section which network it belongs to; in Azure, it is vice versa. In the network section, the administrator defines what subnets the network has. All thee documentation of the orchestration syntax used in AWS, Azure and OpenStack can be found in [39; 40; 41].  
================: Amazon AWS test_net:  type: AWS::EC2::VPC  properties:  CidrBlock: 192.168.0.0/16  
test_subnet:  Type: AWS::EC2::Subnet  Properties:  CidrBlock: 192.168.1.0/24  VpcId: { ref : test_net }  
================: Microsoft Azure test_net:  type: Microsoft.Network/virtualNetworks  name: test_network  subnets:  - test_subnet  test_subnet  type: Microsoft.Network/virtualNetworks/subnets  name: test_subnet  properties:  addressPrefix: 192.168.1.0/24
## 3 Experiment and Result
### Obstacle Avoidance Trajectory Planning Simulation Experiment  
In order to evaluate the obstacle avoidance trajectory planning performance of the improved FMT* algorithm in Cartesian space, a three-dimensional path planning simulation environment was built on a computer based on the actual scenario. The length, width, and height of this rectangular space were set to 20 m, 20 m, and 5 m, respectively. A green point was set as the starting point and a red point as the destination. The coordinates of the starting point and destination were (10, 8, 1) and (12, 14, 1), respectively. There was a wall-shaped obstacle with a height of 3 m between the starting point and the destination. In the same simulation environment, the algorithm proposed in this paper was compared with three other path-planning algorithms in a comparative experiment. The results of the path planning are shown in Figure 8, where the green line segments represent the sampled paths and the red line segments represent the optimal path. From Figure 8, it can be seen that the path generated by the algorithm in this paper has a shorter length compared to the other algorithms, and the sampled paths are more concentrated.  
By conducting 20 experiments using each algorithm in the same environment, the experimental results shown in Table 2 were obtained. From the experimental results, it can be seen that the average path length of the improved FMT* algorithm is 18.8 m, which is 13.3% shorter than the standard FMT*, 8.3% shorter than the standard RRT*, and  
Figure 8: Comparison of different path search algorithms with the red path representing the optimal path found by the algorithm.  
22.3% shorter than the standard RRT-connect. This indicates that the path planned by the improved FMT* algorithm has a shorter length compared to the other three algorithms. Moreover, in terms of the variance of path length, the improved FMT* algorithm has better stability, as the path length does not vary significantly in each planning iteration, demonstrating good stability. In terms of the time consumed for path planning, the improved FMT* algorithm ranks second among the four algorithms. It does not have an advantage over the standard FMT* and standard RRT-connect, which may be due to the increased computational requirements of the algorithm. However, the increase in computation time compared to the standard FMT* is only 5.6%, which is acceptable.
# Towards Incidence Management in 5G Based on Situal Awareness
## 4 Information Security Architecture for 5G  
The proposed architecture is mainly focused on autonomous risk management of 5G mobile infrastructures based on SDN/NFV architectures. In order to establish the coverage and limits of this approach, the following addresses the assumptions and requirements of the following design:  
* The elements responsible for monitoring and executing mitigation actions (e.g., virtual functions) are compatible with the SDN/NFV paradigm. If the elements follow the traditional architectures, a compatibility layer is assumed. This additional layer can use the available configuration options to emulate a SDN/NFV enabled element.
* The communication between the different modules of the framework must be performed through secure channels.
* The information provided by the monitoring elements (low level metrics, alerts) are considered reliable.
* The Risk Analysis and the corresponding Situation Awareness procedures are strongly isolated from the data plane forwarding. In other words, the resources (network, storage and computing) used for the operation of the framework belong to administrative domain and, consequently, do not modify the capabilities of the 5G forwarding elements.
* The functional modules are extensible and can be implemented using distributed architectures in function of the available management resources and the size of the managed infrastructure.  
The proposed architecture presents the synergy between 5G risk analysis and the Endsley model, and is depicted in Figure 2. The model describes four functional layers: Virtual Infrastructure and Sensors, Monitoring and Correlation, Analysis, and Decision-Making and Actuators. The Virtual Infrastructure executes the data forwarding engine and Sensors monitors the different metrics of the network. For its part, the perception, comprehension and projection principles of the Situational Awareness are applied in the Monitoring, Correlation and Analysis modules. The Decision-Making and Actuators complete the circle with the execution of proactive and reactive actions to optimize and solve problems located in the virtual infrastructure. The following sections of this chapter focus on the description of the Virtual Infrastructure/Sensors and Monitoring/Correlation.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 2 Materials and Methods
### Inducing Effect of Some Mono- and Polysaccharides on Enzyme Production  
Selected carbon sources at a concentration of 1% (\(w/v\)) were used to supplement the basal medium in order to determine their effectiveness in inducing holocellulase (xylanase and cellulase) production by the _Streptomyces_ strain [29]. The monosaccharides solutions were autoclaved separately and were aseptically mixed with sterile basal salts medium before inoculating with the bacterial suspension. Selection of carbon source was based on monomeric and oligomeric saccharides of lignocellulosic. In addition to CMC and beechwood xylan, the following carbon sources were tested: microcrystalline cellulose (Avicel\({}^{\circledR}\), Sigma-Aldrich, Darmstadt, Germany), cellobiose (ROTH, Cape Town, South Africa), mannose, arabinose (Sigma-Aldrich, Darmstadt, Germany), galactose, D-glucose, D-xylose (Merck, Modderfontein, South Africa), and two types of lignocellulosic wastes biomass: wheat straw and saw dust. The saw dust was locally sourced from a wood factory (Melani village, Alice, Eastern Cape, South Africa). The wheat straw was obtained from farm field (agricultural farm of the University of Fort Hare, Alice, Eastern Cape, South Africa), thoroughly washed with distilled water, oven-dried at 60 \({}^{\circ}\)C for 48 h, pulverized with a laboratory mill (Lasec, Cape Town, South Africa) to 2 mm size particles and stored in air-tight plastic until further use [30]. Fermentation was carried out for 72-96 h; thereafter the broth was centrifuged and assayed for extracellular cellulase and xylanase activity.
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 3 Results
### MN Test  
For the in vivo MN test, rats were treated with SA, mSA, SB, and mSB, and blood was obtained at 48 and 72 h after intratracheal instillation. At 48 h after administration, the RET ratio and the Mn-RET ratio of the SA, mSA, and mSB groups showed no significant difference compared to the negative control. The SB group showed a significantly decreased RET ratio but no significant difference in Mn-RET compared to the negative control. At 72 h after administration, the RET ratio and the Mn-RET ratio of the SA and mSA groups showed no significant difference compared to the negative control. The RET ratio of the SB and mSB groups was significantly decreased by 0.83 and 0.73 times that of the negative control (Table 8). The Mn-RET ratio of the SB group was significantly increased to 1.9 times that of the negative control. However, there was no significant difference between the mSB and negative control groups. The results of the SA, mSA, and mSB groups in the MN test were negative. However, since the Mn-RET ratio of the SB group was significantly increased by 1.9 times, but not a full twofold increase compared to the negative control group, the results of SB in the MN test were weakly positive. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Group** & **Dosage** & **Frequency of** & \multicolumn{4}{c}{**Chromatic Type**} & \multicolumn{2}{c}{**Chromosome Type**} \\ \cline{3-8}  & **(mg/mL)** & **Aberration (\%)** & **Deletion** & **Intrachange** & **Interchange** & **Ring** & **Gap** \\ \hline NC 1 & 0 & 2.7 \(\pm\) 1.5 & 2.7 \(\pm\) 1.5 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 \\ Mitomycin C & 2.5 & 18.0 \(\pm\) 2.6 * & 13.3 \(\pm\) 6.1 * & 0.0 \(\pm\) 0.0 & 3.3 \(\pm\) 0.6 & 0.7 \(\pm\) 0.6 & 5.0 \(\pm\) 3.5 * \\ SA & 1.387 & 2.3 \(\pm\) 1.5 & 1.7 \(\pm\) 0.6 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.7 \(\pm\) 1.2 \\ mSA & 0.778 & 3.0 \(\pm\) 2.0 & 2.7 \(\pm\) 2.1 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.3 \(\pm\) 0.6 \\ SB & 0.353 & 9.7 \(\pm\) 1.5 * & 6.0 \(\pm\) 2.0 * & 0.0 \(\pm\) 0.0 & 0.7 \(\pm\) 1.2 & 0.0 \(\pm\) 0.0 & 4.0 \(\pm\) 1.0 *2 \\ mSB & 1.569 & 2.7 \(\pm\) 1.5 * & 2.0 \(\pm\) 1.0 * & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.7 \(\pm\) 1.2 * \\ \hline \end{tabular}  
* NC: negative control, R.D.W. 10\% _v/v_; SA: sample A, 10\% _v/v_; mSA: modified sample A, 10\% _v/v_; SB, sample B, 10\% _v/v_; mSB, modified sample B, 10\% _v/v_.
* Significant difference in compared with the negative control and treated groups at \(p\) \(<\) 0.05.
* Significant difference in comparison with the SB and mSB groups at \(p\) \(<\) 0.05.  
\end{table} 
Table 6: Frequency of chromosomal aberration without S9 metabolism of unmodified and modified nano-silica in CHO-K1 cells.
## 3 Preliminaries
### Energy Consumption and Charging Model  
In a WRSN, nodes are responsible for sensing and sending and forwarding data to the base station. Therefore, the energy consumption \(r_{si}\) of a node \(s_{i}\) is given by:  
\[r_{si}=\rho\sum_{k=1,k\
eq i}^{n}f_{ki}+Ce_{ij}(\sum_{k=1,k\
eq i}^{n}f_{ki}+f_ {i})+Ev_{i} \tag{1}\]  
where \(\rho\), \(Ce_{ij}\) and \(Ev_{i}\) denote the rates of energy consumption for receiving a unit of data, transmitting a unit of data from \(s_{i}\) to \(s_{j}\) and sensing data for \(s_{i}\), respectively. The data flow rate from node \(s_{k}\) to \(s_{i}\) is denoted by \(f_{ki}\), while \(f_{i}\) represents the time required for one unit of data of \(s_{i}\).  
The wireless energy charging model that we adopt in [7] is shown as follows:  
\[p_{r}=\frac{G_{\mathrm{s}}G_{r}\kappa}{L_{p}}[\frac{\lambda}{4\pi(d+\zeta)}]^{ 2}p_{0}=\frac{\tau}{(d+\zeta)^{2}}p_{0} \tag{2}\]  
where \(\tau=\frac{G_{\mathrm{s}}G_{r}\kappa\lambda^{2}}{16L_{p}\pi^{2}}\), \(p_{0}\) is the source power of charger, \(p_{r}\) is the received power of some node, \(d\) is the distance between charger and the node, \(G_{\mathrm{s}}\) is the source antenna gain, \(G_{r}\) is the receiving antenna's gain, \(L_{p}\) is the polarization loss, \(\lambda\) is the wavelength, \(\kappa\) is the rectifier efficiency and \(\zeta\) is a parameter to adjust Friis' free space equation for short-distance transmission.  
The proposed system utilizes point-to-point energy transfer [34], whereby a wireless charger charges one node at a time by approaching it at the nearest distance to achieving maximum charging efficiency.
## 3 Results and Discussion
### ZnO Nanoparticle Characterization Analysis  
Figure S3 shows the UV-vis absorption spectra of the LPME, ZnO, and LP-ZnO NPs. ZnO had a broad absorption peak around 374 nm, which was the result of the ZnO \(\pi\)-\(\pi^{\text{e}}\) electronic excitation. The UV-vis band of the 50 mg LP-ZnO NPs was similar to the absorption band of the ZnO, but with higher intensity. The absorption band of the 100 mg LP-ZnO NPs was sharper and slightly shifted to a higher wavelength, which could be a result of the enhancement of the optical properties of the ZnO by the LPME [50]. This led to a shift from the initial solution color (white) to a yellowish color (Figure S2), which is additional proof of a nanosized material production. As can be seen, the 100 mg LP-ZnO NPs displayed a stronger UV band intensity (Figure S3) (i.e., a higher photocatalytic activity) when compared with the 50 mg LP-ZnO NPs [51]. Figure S4 shows the XRD pattern spectra of the ZnO and 50 and 100 mg LP-ZnO NPs. The nanoparticles showed similar characteristic diffraction peaks. The ZnO displayed a broad major band at 31.71\({}^{\circ}\) (100), and other minor bands. The nanoparticles showed three major peaks at 20 = 31.50\({}^{\circ}\) (100), 34.19\({}^{\circ}\) (002), and 35.99\({}^{\circ}\) (101) for the 50 mg LP-ZnO NPs, and 31.50\({}^{\circ}\) (100), 34.24\({}^{\circ}\) (002), and 36.03\({}^{\circ}\) (101) for the 100 mg LP-ZnO NPs. Other minor peaks were also found at 28 values of 47.28/47.24\({}^{\circ}\) (102), 56.25/56.36\({}^{\circ}\) (110), 62.68/62.74\({}^{\circ}\) (103), 66.02/66.04\({}^{\circ}\) (200), 67.57/67.75\({}^{\circ}\) (112), 68.89/68.69\({}^{\circ}\) (201), 72.28/72.36\({}^{\circ}\) (004), 76.68/76.72\({}^{\circ}\) (202), 81.09/81.19\({}^{\circ}\) (104), and 89.28/89.42\({}^{\circ}\) (203) for the 50 and 100 mg LP-ZnO NPs. As can be seen, the nanoparticle peaks conformed to those of the ZnO, which indicates a rod-shaped ZnO crystal structure (Joint Committee on Powder Diffraction Standards (JCPDS) card no. 36-1451), thus confirming the formation of LP-ZnO NPs. Our results were in line with previous research on the green fabrication of ZnO nanoparticles [21;52].
## 7 Conclusions  
The development of kilowatt-level lasers at the HiLASE Centre was introduced. The high average power beams are based on diode-pumped solid state laser technology. The thin-disk amplifiers are key parts of the 1-kHz and 100-kHz picosecond beamlines. The 1-kHz beamline was operated at 50 W of average power. The ring cavity for a 500-W amplifier is under development. The 100-kHz beamline achieved about 100 W in pulses compressed to less than 2 ps. The continuous-wave power of more than 500 W of the ring amplifier promises to reach a comparable output for the pulsed regime soon. HiLASE will also operate the cryogenically-cooled 100-J, 10-Hz multi-slab laser, which is currently being developed at the Central Laser Facility, Rutherford Appleton Laboratory (U.K.). We have supported the development of the 10-J and 100-J amplifiers by advanced numerical calculations and wavefront correction by the deformable mirror.  
Figure 20: **(a)** Result of numerical estimation of the output laser beam wavefront; **(b)** result of wavefront correction simulation using uniform square arrays with various numbers of actuators; the sample wavefront to correct is case (a).  
The powerful amplifiers' development is supported by several other activities. The more powerful fiber oscillator will increase the stability and reliability of the laser systems. The repetition rate-stabilized fiber oscillator is intended for applications where precise synchronization between the laser pulses and accelerated electron bunches is needed. The _in situ_ measurement of the thin disk deformations helps to design a proper and stable resonator for a large area fundamental mode. It also helps with the understanding of the improved laser performance under pulsed pumping. The single-shot \(M^{2}\) measurement enables fast evaluation of the key beam parameter.  
The developed high average power lasers will be used for scientific and industrial applications, such as laser-induced damage testing, laser shock peening, laser processing and EUV generation, in the near future.  
This work is co-financed by the European Regional Development Fund, the European Social Fund and the state budget of the Czech Republic (project HiLASE: CZ.1.05/2.1.00/01.0027; project DPSSLasers: CZ.1.07/2.3.00/20.0143; project Postdok: CZ.1.07/2.3.00/30.0057). This work is also supported by the Czech Science Foundation under project GA14-01660S.  
The authors declare no conflict of interest.