esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 2 Materials and Methods
### Experimental Setting  
In this study, four sets of experiments were conducted. The first set of experiments was implemented to test the sulfonamide-degrading ability of milkfish pond sediments. For each experiment, 125-mL bottles with 45 mL of medium and 5 g of milkfish pond sediments were used. The sulfonamide concentrations used in the experiments were 2 and 20 mg L\({}^{-1}\) each of SMX, SDM, and SMZ. Samples for sterile controls were autoclaved at 121 \({}^{\circ}\)C for 30 min [9]. Aerobic experiments were incubated with shaking at 25 \({}^{\circ}\)C in the dark. Anaerobic experiments were performed in an anaerobic glove box (Forma Scientific, model 1025 S/N, USA) filled with N\({}_{2}\) (85%), H\({}_{2}\) (10%), and CO\({}_{2}\) (5%) gases. Bottles were capped with rubber stoppers and incubated static at 25 \({}^{\circ}\)C in the dark. Each treatment of the experiments was repeated in triplicate. Samples were collected to check residual sulfonamides by HPLC. Bacterial communities were analyzed by next-generation sequencing (NGS).  
The second set of experiments was designed to isolate and identify SMX-degrading bacteria and test SMX degradation abilities. Marine agar plates containing 2 mg L\({}^{-1}\) SMX under aerobic and anaerobic conditions were used to isolate SMX-degrading bacteria. Individual colonies were collected, and DNA were amplified by PCR with the F8 and R1510 primers. The 16S rRNA gene amplicons were sequenced by the ABI Prism automatic sequencer, and the database was searched using the BLAST of the National Center for Biotechnology. Phylogenetic analysis was performed using MEGA 7[10]. To confirm the ability of SMX degradation, experiments were performed using the isolated bacterial strains (10\({}^{5}\) CFU mL\({}^{-1}\)), 50 mL of medium, and 2 mg L\({}^{-1}\) SMX under aerobic and anaerobic conditions. Samples were collected to analyze residual SMX by HPLC.  
The goal of the third and fourth sets of experiments was to test the aerobic and anaerobic SMX degradation efficiency in milkfish pond sediments. For the third set of experiments, aerobic and anaerobic batch experiments each containing an SMX-degrading bacteria strain (10\({}^{5}\) CFU mL\({}^{-1}\)), 5 g of sediment, 45 mL of medium, and 2 mg L\({}^{-1}\) of SMX were conducted. Samples were collected to analyze residual SMX. The fourth set of experiments were conducted using 1000-mL bottles with 450 mL of medium and 50 g of sediment, with or without degrading bacteria (10\({}^{5}\) CFU mL\({}^{-1}\)) under aerobic and anaerobic conditions. Then, 2 mg L\({}^{-1}\) of SMX was used for the first and second additions, and 10 mg L\({}^{-1}\) of SMX was used for the third and fourth additions. SMX was re-added, while SMX decreased to ND (not detected). The process was repeated by adding SMX four times. The experiments lasted for 80 days. Samples were collected to survey residual SMX and to analyze the bacterial community by NGS.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Data Preparation and Generation Module
#### 4.3.4 Hamming Distance Calculation and Result Upload  
Finally, node \(N_{k}\) calculates the partial Hamming distance and uploads the result. It locally sums the arithmetic shares for all \(m\) bits to generate the partial Hamming distance share:  
\[[D_{i,j}]_{A}^{k}=\left(\sum_{l=1}^{m}\ [d_{i,j,l}]_{A}^{k}\right)\text{mod }p \tag{14}\]  
Subsequently, the node uploads \(\left[D_{i,j}\right]_{A}^{k}\) along with the obfuscated index \(\pi_{\text{gid}}(k)\) to IPFS, generating a CID and recording it via the smart contract. Batch interaction reduces communication frequency [29], and the random permutation mechanism enhances privacy protection, ensuring the computation process is both efficient and secure.  
The algorithm for masked difference conversion and Hamming distance calculation is described in pseudocode as shown in Algorithm 3.  
```
Input: full_masked_diff: Fully reconstructed masked difference; mask_arithm_share: Arithmetic mask share; const_one_share: Arithmetic share of constant one; m: Length of Bloom filter Output: partial_hamming_share: Partial Hamming distance share
1: \(\text{arith\_diff}\leftarrow\) []
2:for i \(\gets\) 1 to m do
3:if full_masked_diff[i] = 0 then
4: \(\text{arith\_diff.append(mask\_arith\_share[i])}\)
5:else
6: \(\text{arith\_diff.append(const\_one\_share[i]\text{ - mask\_arith\_share[i])}}\)
7:endif
8:endfor
9: \(\text{partial\_hamming\_share}\gets 0\)
10:for i \(\gets\) 1 to m do
11: \(\text{partial\_hamming\_share}\leftarrow\text{partial\_hamming\_share + \text{arith\_diff[i]}}\)
12:endfor
13:return partial_hamming_share
```  
**Algorithm 3** B2A conversion And Hamming Distance Algorithm  
In Algorithm 3, lines 1-8 convert each bit of the fully reconstructed masked difference into an arithmetic share vector using the arithmetic mask share and the arithmetic share of constant one, achieving a Boolean-to-arithmetic conversion. Lines 9-13 sum the arithmetic share vector to generate the partial Hamming distance share, which is uploaded to IPFS, submitted to the smart contract, and returned for final result aggregation.
## 4 SAR System Requirements Effect on Satellite Design
### Power Consideration  
Designing a power system for a small or micro-SAR satellite involves understanding the power requirements of the SAR system, particularly the power amplifier, and then calculating the necessary battery and solar panel capacities. The SAR system's transmitter, especially the power amplifier, is the most power-hungry component. It requires high peak power to generate the radar pulses. The exact power depends on the desired resolution and swath width. SAR systems do not continuously transmit; they operate in pulses. The duty cycle (ratio of on-time to total time) significantly affects power requirements. Receivers and processing units typically consume less power than the transmitter but must be accounted for in the overall electric power budget. Microsatellites generally have lower electric power capabilities compared to larger satellites. This limitation often results in a trade-off between resolution, coverage area, and revisit time. Here, it is aimed to show the recent small or micro-SAR satellites instead of the prominent larger ones. The Japan Aerospace Exploration Agency (JAXA) has some publications stating that around a 1 kW peak transmitting power for desired resolutions should be provided for small satellites. According to their claims, around \(-\)15 dB NESZ value is enough, and the duty cycle can be raised up to 25% thanks to the GaN HEMT power amplifier for the X-band [64; 65; 66; 67]. TechSAR which is an Israeli SAR satellite has a 260 kg launch mass with a 100 kg SAR payload generating 1.6 kW solar panel power for a payload peak power of 850 W [68]. The mass of ICEYE satellites is around 92 kg and the peak transmitting power is 3.2 kW, while Iceye-X3 offers around a 3 kW transmit peak power [69; 70] with solar panel power that is much less when compared to  
Figure 9: Antenna area–transmitting power–range and azimuth resolutions.  
RF power. The Capella micro-SAR satellite weighs below 100 kg in mass and has 600 W of transmitting power [71].
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Indoor Search and Rescue (SAR) Operations for Swarms of Drones  
There is a fast-growing body of literature dedicated to various aspects of deploying robots during--or in the aftermath of--a disaster. Until recently, practical considerations ensured that the majority of applications assumed autonomously operating _ground_-based robots. In these 2D environments, solutions to problems such as the collision-free movement of a team of robots [26] in--or through--the environment, or the movement of a team while maintaining some formation [27], are being proposed. This article targets drones (airborne cyber-physical systems) but simplifies the environment to be 2D (we discuss extending this work to 3D in Section 6.2 on future work). We argue that in-flight collision in 3D space could be avoided by simply assigning a different flight altitude to every drone. Since the simulated environment/our model for the environment does not contain vertical obstacles (that can be avoided by moving in the z-axis) this practically allows us to partition the 3D space into horizontal _slices_, providing one for each drone to operate in. The performance results we obtained are therefore expected to translate well into true 3D operations.  
The use of UAVs for SAR is proposed in the literature [20] for a number of reasons. These include the handling of materials that are dangerous for humans [17] or located in environments that are dangerous or inaccessible for humans that need to be inspected first [18]. The flexibility [13] of drones makes them well suited to serving as mobile/aerial urban sensing platforms [23] for SAR operations or for aerial tracking [17], especially in inaccessible environments and for the detection of victims or victims' life signs [14].
# Michelson Interferometric Methods for Full Optical Complex Convolution
###### Abstract  
Optical real-time data processing is advancing fields like tensor algebra acceleration, cryptography, and digital holography. This technology offers advantages such as reduced complexity through optical fast Fourier transform and passive dot-product multiplication. In this study, the proposed Reconfigurable Complex Convolution Module (RCCM) is capable of independently modulating both phase and amplitude over two million pixels. This research is relevant for applications in optical computing, hardware acceleration, encryption, and machine learning, where precise signal modulation is crucial. We demonstrate simultaneous amplitude and phase modulation of an optical two-dimensional signal in a thin lens's Fourier plane. Utilizing two spatial light modulators (SLMs) in a Michelson interferometer placed in the focal plane of two Fourier lenses, our system enables full modulation in a 4F system's Fourier domain. This setup addresses challenges like SLMs' non-linear inter-pixel crosstalk and variable modulation efficiency. The integration of these technologies in the RCCM contributes to the advancement of optical computing and related fields.  
H acmcopy 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
freespace optics; Fourier optics; 4F system; optical convolution
## 2 Frequencies and Mode Shapes of the Double-Beam System
### Mathematical Formulation
#### 2.1.3 Fixed-Pinned Boundary Conditions  
The boundary stiffness is  
\[\left\{\begin{array}{c}k_{\
u,1}=k_{\
u,2}=\infty\\ k_{\
u,1}=\infty\\ k_{\tau,2}=0\end{array}\right. \tag{31}\]  
The boundary conditions can be described as  
\[\left\{\begin{array}{c}\varphi_{1}(0)=0\\ \varphi_{1}^{\prime}(0)=0\\ \varphi_{1}(l)=0\end{array}\right.^{\prime},\left\{\begin{array}{c}\varphi_ {2}(0)=0\\ \varphi_{2}^{\prime}(0)=0\\ \varphi_{2}(l)=0\\ \varphi_{2}^{\prime}(l)=0\end{array}\right. \tag{32}\]  
Equation (18) becomes  
\[\mathbf{\Phi}=\left(\begin{array}{cccccccc}1&1&1&1&0&0&0&0\\ 0&0&0&0&1&1&1&1\\ 1&-1&i&-i&0&0&0&0\\ 0&0&0&0&1&-1&i&-i\\ e^{\prime_{1}l}&e^{-\tau_{1}l}&e^{i\tau_{1}l}&e^{-i\tau_{1}l}&0&0&0\\ 0&0&0&e^{\prime_{2}l}&e^{-\tau_{2}l}&e^{i\tau_{2}l}&e^{-i\tau_{2}l}\\ e^{\prime_{1}l}&e^{-\tau_{1}l}&-e^{i\tau_{1}l}&-e^{-i\tau_{1}l}&0&0&0\\ 0&0&0&0&e^{\prime_{2}l}&e^{-\tau_{2}l}&-e^{i\tau_{2}l}&-e^{-i\tau_{2}l}\\ \end{array}\right) \tag{33}\]  
Det \([\mathbf{\Phi}]=0\) results in  
\[\left\{\begin{array}{c}\tan(r_{1}l)-\tanh(r_{1}l)=0\\ r_{5}=0\end{array}\right. \tag{34}\]  
or  
\[\left\{\begin{array}{c}r_{1}=0\\ \tan(r_{5}l)-\tanh(r_{5}l)=0\end{array}\right. \tag{35}\]Equations (34) and (35) result in the same solution for Equation (13). If Equation (34) is chosen, the solutions of \(r_{1}l\) in the first six modes are as in Table 2.  
It should be noticed that the wavenumber equation for \(r_{1}l\) in Equation (34) or for \(r_{5}l\) in Equation (35) are the same as that for a single beam with fixed-pinned boundary conditions.  
By solving Equation (16), the mode shapes of the secondary beam are obtained from Equation (13) to be  
\[\varphi_{2,n}(x)=C\Bigg{[}\Big{(}\cos r_{1}^{(n)}x-\cosh r_{1}^{(n)}x\Big{)}- \frac{\cos r_{1}^{(n)}l-\cosh r_{1}^{(n)}l}{\sin r_{1}^{(n)}l-\sinh r_{1}^{(n)} l}\Big{(}\sin r_{1}^{(n)}x-\sinh r_{1}^{(n)}x\Big{)}\Bigg{]} \tag{36}\]  
It is noticed that Equation (36) is the same as Equation (21) except that the values of \(r_{1}l\) are different. The mode shapes of the primary beam are obtained by substituting Equation (36) into Equation (4), which results in the same as Equation (22) except the values of \(r_{1}l\).  
The frequencies of the fixed-pinned double-beam systems can be worked out by substituting the values of \(r_{1}\) in Table 2 into Equation (9).
# Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review
## 4 Results
### Artificial Intelligence/Machine Learning
#### 4.1.3 Neural Networks  
[PERSON] et al. [34] proposed a method for integrating quantitative and qualitative analyses to provide an information security risk assessment for CPS. They used a Petri net for the description of the system and its relationship with big data analysis (performed by experts) in order to provide the right indexes for the neural network, so that the latter could provide risk evaluation.  
[PERSON] et al. [35] presented a model according to which risk value in an organisation's network depends on the following variables: the quantity of workstations, typical user roles, super user roles (users with increased awareness), daily work hours, open ports, third-party users, data volume, and frequency of daily briefings to strengthen security. The model receives additional enhancement through the utilisation of a balanced dataset (network traffic) which is used as input for an external neural network. These are combined to calculate the risk value. In addition, the model takes into account the applied defence mechanism. Simulations showed that the risk value is linked to the annual incident cost and it depends on the aforementioned variables and the defence mechanism in place.  
[PERSON] et al. [36] analysed all possible risk assessment methods and suggested that the most efficient methods to conduct risk assessments in smart city environments are those which are based on AI, such as ANN, because they can deal with big data, and they are quick and accurate. The authors used synthetic datasets and NS-3 to construct a potential dynamic network of a smart city. They specified five potential attack types (grey hole, black hole, DoS, DDoS, and wormhole) along with the probability of occurrence. In the next step, device types were identified, based on the supposition that they performed the same function in the system and interacted with the same number of devices of another type. Device types could be traffic lights, medical sensors, vehicles, etc. In addition, they took into consideration the interaction between devices. The authors established a threshold for unacceptable risk, guided by the principle that assets impacting people's lives and health should have the lowest acceptable failure probability. In their simulated environment, they created one training dataset and one test. Their neural network model was built with the help of CORAS and TensorFlow, and it included:  
* a 38-neuron input layer;
* one covert layer containing 20 neurons;
* one output layer.  
They achieved 97% maximum classification accuracy.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 3 System Architecture, Scenarios of Multi-Hop LoRa, and Results
### Flat Topology Calculations
#### 3.1.2 The Energy Consumption of LoRa Nodes Calculations
This can be observed in Figure 6, where the y-axis shows the number of the scenarios, and the x-axis shows the total amount of energy consumed by the nodes of the corresponding scenario. When the number of hops decreases, the total required energy decreases, as shown in Figure 6. We observed that the second scenario improve energy by 88% and the third scenario by 98%. The three scenarios have different results because of the number of nodes involved in the communication (number of hops). In scenario 1, all nodes cooperate, which leads to more energy consumption in each node, whereas in the second scenario, less than half of the nodes are involved in forwarding other nodes' packets, so the average energy consumption is less. Furthermore, the smaller number of hops leads to lower end-end delay and higher throughput. This is via either appropriate dimensioning of their transceivers and processors or via appropriate scheduling of the transmitted packets by different cluster nodes to avoid packet collisions and packet queuing. Moreover, the distance between sensors is related to the application type and requirements. For example, in LoRa underground monitoring applications, the maximum distance is 200 m and, in some cases, is 150 m, as in [48], whereas the distances are larger in aboveground applications. Calculations in Table 4 show that the communication range can reach 500 m, which is within the achievable range defined for LoRa-WAN via link budget calculations, such as those mentioned in [49].
## 8 Experimental Results
### Over-the-Air Demonstrations  
We performed over-the-air flight demonstrations at the general aviation airport in Nordlingen, Germany. We assembled a 4-antenna ground station using telescoping antenna mounts and a UAV user by mounting the hardware enclosure to a DJI S1000+, depicted in Figure 20. The following experiments were performed at a carrier frequency of 783 MHz using 10 MHz bandwidth. This demonstration only uses the simple ToF estimation technique defined in Section 4, not the Kalman filter included in the cabled results.  
Figure 19: Ranging precision for 1 of the 16 antenna pairs in a cabled configuration using three different ToF estimation techniques. To highlight the estimator variance, we have subtracted the mean for clarity. The interpolated correlator estimator (gray) achieves a ranging precision (standard deviation) below 1.7 cm. The adaptive extended Kalman filter techniques (black, red) drive this precision (standard deviation) as low as 0.3 cm but require more settling time.  
Figure 20: Flight test experimental configuration. One user consists of 4 patch antennas mounted on telescoping mounts (**top left**) and the hardware enclosed in a ruggedized case (**left**). The second user is mounted to a DJI S1000+ UAV for flight testing (**top right**). These experiments were performed on a runway about 700 m long (**bottom left**) using a simple antenna geometry (**bottom right**).
## 4 Results and Discussion
### Seismic Sounding and Profiles  
The Figure 4 shows the overlay of the bathymetric surveys and the layout of the 25 profiles listed in Tables 2 and 3 showing that, the study was carried out according to 25 seismic refraction profiles, 11 of which were transverse and 14 longitudinal. The geodetic parameters and transformations used for the implementation of the profiles are summarised in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**GEOPHYSICAL SURVE**} \\ \hline \multicolumn{4}{c}{**Coordinates of the Realised Profiles**} \\ \hline \multicolumn{4}{c}{**Transversal Lines**} \\ \hline
**Lines/Profiles** & **Start of Profile** & \multicolumn{2}{c}{**End of Profile**} \\ \hline T1 & 910,940.95 & 1,449,419.4 & 910,710.53 & 1,449,421.7 \\ \hline T2 & 910,656.41 & 1,449,279.5 & 910,929.63 & 1,449,275.59 \\ \hline T3 & 910,944.06 & 1,449,471.7 & 910,717.15 & 1,449,474.93 \\ \hline T4 & 910,949.94 & 1,449,383.2 & 910,702.68 & 1,449,379.69 \\ \hline T5 & 910,901.23 & 1,449,217.3 & 910,665.33 & 1,449,222.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coordinates of the transverse seismic profiles. Figure 3: Bathymetric map. (**a**) Side Scan Sonar Survey of the Douala’s Estuary area from [35], modified; (**b**) Bathymetric map of the Douala’s Estuary area from [35]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Local Datum Geodetic Parameters**} \\ \hline Ellipsoid & International 1924 \\ \hline Semi-major axis: & a = 6,378,388.000 m \\ \hline Inverse Flattening: & 1/f = 297.00 \\ \hline \multicolumn{3}{c}{Datum Transformation Parameters from international 1924 to Local Datum} \\ \hline Shift dX: \(-\)28.209999 m & Rotation rX: 0 arcsec \\ \hline Shift dX: 132.589996 m & Rotation rY: 0 arcsec \\ \hline \multicolumn{3}{c}{Dy:} \\ \hline Shift dZ: 77.730003 m & Rotation Rz: 0 arcsec \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Geodetic parameters and transformations [36; 37; 38]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**GeOPHYSICAL SURVE**} \\ \hline \multicolumn{3}{c}{**Coordinates of the Realised Profiles**} \\ \hline \multicolumn{3}{c}{**Transversal Lines**} \\ \hline \multicolumn{3}{c}{**Lines/Profiles**} & \multicolumn{2}{c}{**Start of Profile**} & \multicolumn{2}{c}{**End of Profile**} \\ \hline T6 & 910,935.68 & 1,449,319.9 & 910,704.72 & 1,449,317.14 \\ \hline T7 & 910,942.96 & 1,449,650.5 & 910,709.98 & 1,449,651 \\ \hline T8 & 910,938.23 & 1,449,600.8 & 910,716.13 & 1,449,599.7 \\ \hline TA1 & 910,944.6 & 1,449,554.8 & 910,713.04 & 1,449,552.84 \\ \hline TB2 & 910,910.11 & 1,449,079.7 & 910,677.55 & 1,449,089.42 \\ \hline TB3 & 910,887.71 & 1,448,979.9 & 910,662.64 & 1,448,984.86 \\ \hline \multicolumn{3}{c}{Number of profiles realized} & \multicolumn{2}{c}{11} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._