esa-sceva/satcom-chunk-collection huggingface dataset:
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Ethers  
Three ethers were tested: one of low molecular weight, diethyl ether, CH\({}_{3}\)-CH\({}_{2}\)-O-CH\({}_{2}\)-CH\({}_{3}\), and two of high molecular weight, polyethylene glycol 400 (PEG 400), HO-(CH\({}_{2}\)-CH\({}_{2}\)-O)\({}_{\rm n}\)-H and polypropylene glycol 2000 (PPG 2000), HO-(CH\({}_{2}\)-CH(CH\({}_{3}\))-O)\({}_{\rm n}\)-H. Their efficacy at oil recovery is medium to high, with diethyl ether performing slightly better (up to 90%) due to its small molecule size (Figure 5).  
Surprisingly, the other two ethers, which are polymers, show acceptable performance, even though polymers usually tend to stabilize emulsions. Diethyl ether has a boiling point of 34.6 \({}^{\circ}\)C, making it unsuitable for industrial use, as the produced water typically exceeds 45 \({}^{\circ}\)C and the environment in oil fields can have high temperatures. If other alternatives do not require cooling of the produced water or special storage conditions, it is better not to consider it as a candidate. Moreover, it is toxic due to its narcotic effects and dangerous because it can form explosive mixtures. PEG 400 and PPG 2000, on the other hand, are completely harmless and not dangerous. However, their performance is not sufficient to recommend them for industrial use.  
Figure 4: Percentage of residual oil removed from produced water: (**a**) ketones and (**b**) influence of the number of carbons in ketones.  
Figure 5: Percentage of residual oil removed from produced water versus various volumes of ethers.
## 3 Results  
### General Characteristics of Identified Studies  
#### 3.1.1 Distribution of Studies in Time  
Before we examine the contents of the identified studies, we shall first look at their general characteristics, starting with their form of publication and distribution in time. Figure 3 shows how the number of relevant publications evolved in the subsequent years.  
The two earliest identified relevant works were published in 2014 [14; 15]. Both were conference papers, the first journal papers appearing only two years later [16; 17; 18]. The number of published conference papers surpassed the number of journal articles in every considered year. In the entire period, conference publications constituted almost 2/3 of the relevant papers analyzed.  
The largest growth in the scientific output has been recorded in 2018. Since that year on, the number of publications steadily kept growing, but at a somewhat lesser pace (note that 2023 is the year in progress, and complete data for it are not available at the moment of writing these words).
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 4 Experiments and Results  
### Basic Settings  
#### 4.1.1 Datasets  
In this section, we conducted experiments and evaluations on classic datasets commonly used in the field of ICS intrusion detection, including the NSL-KDD dataset, the recently released CIC-IDS2017 dataset, and the CSE-CIC-IDS2018 dataset. Although relatively older, the NSL-KDD dataset has been extensively validated by numerous methods, making it suitable for comparative studies. It contains labels for five attack types and one normal traffic type. The specific attack types are DoS (Denial of Service), Probe, R2L (Remote-to-Local), U2R (User-to-Root), and Normal traffic. The CIC-IDS2017 dataset, developed by the cybersecurity research group at the Royal Military College of Canada, consists of real-world network traffic data. Samples in CIC-IDS2017 are labeled as normal traffic or various network attack types, such as Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attacks, Infiltration, Botnet, and DDoS attacks. Due to similarities among some attack types, we grouped them into seven specific classes. The dataset is split in a 4:1 ratio, excluding data from four days of traffic for training, except Monday. The CSE-CIC-IDS2018 dataset offers a more complex, real-world data environment compared to CIC-IDS2017, with additional challenges such as missing values and redundant features. For testing, we utilized 10% of this dataset. All three datasets exhibit characteristics of class imbalance, as shown in Figure 7, where the dark green represents the distribution of various types of data in the training set, and the light green represents the test set. For clarity in the diagrams, CIC-IDS2017 is labeled as IDS2017, while CSE-CIC-IDS2018 is labeled as IDS2018.  
Although the datasets used in this study are not ICS-specific, they are selected due to their representation of common network attack patterns, such as Denial of Service, scanning, and unauthorized access, which are relevant to the security concerns in ICS. The diverse range of attacks captured in these datasets provides valuable insights into the robustness of intrusion detection systems in both traditional and industrial network environments.
# Hybrid Consensus Algorithm Based on Modified Proof-of-Probability and DPoS
## 3 System Model and Algorithm
### Main Algorithm  
The algorithm flow of the proposed consensus algorithm is shown in Figure 6.  
When the transaction occurs, the whole network publishes a true hash and many false hashes.  
Each PoP node uses its own hash sorting algorithm to give the mining order.  
The PoP node creates the block header and exhausts the nonce value for mining.  
If a hash is decrypted, the nonce value is compared with the expected value given by the supernode of DPoS. If they do not match, the PoP node needs to enter the waiting time (the waiting time depends on the number of PoP nodes. If the number of nodes is less than 10, the waiting time is 1 min. If the number of nodes is greater than or equal to 10, the node needs to wait until a block is successfully verified, i.e., at most wait for ten minutes) and mining should be continued by other PoP nodes. When the next hash is decrypted, the current supernode can specify one more expected value, until the tenth time, when ten expected values are specified; it must be consistent. If it is consistent, it shall be submitted to other supernodes for further verification. If more than half of the supernodes pass the verification, it shall be deemed as successful mining (it should be noted that the nodes that generate blocks and pass all validation should also enter the waiting time to prevent the same high computing power node from generating blocks continuously). Otherwise, this PoP node will enter the waiting time, and other PoP nodes will continue mining. After a block is generated and verified successfully, the next supernode will verify when the next hash is decrypted.  
DPoS supernodes are elected through calculating the corresponding votes of each DPoS node by using the sublinear function according to the number of tokens they own (not one vote for one person, which increases the cost of controlling votes, and is not linearly related to the number of tokens, which limits the voting weight of nodes with many tokens). If more than half of the tokens have voted, 72 nodes with the most votes will be selected as supernodes from the candidate pool.  
After randomly disrupting the order of supernodes, each supernode validates the block by giving the expected value in turn. If the node does something bad, it is removed from the system by voting, and a node is selected from the candidate pool to replace it. After ten rounds, they vote again to select the supernodes.  
Figure 6: Main algorithm.
## 5 Detection Methods: Modulation Spectroscopy for Wideband Absorption
### Multicomponent Spectral Fitting  
Multicomponent spectral fitting is mainly used to eliminate spectral interference, which commonly occurs in MIR detection. Furthermore, benefitting from the use of multivariate regression and nonlinear least square fitting, it can calculate the multi-component concentration in a gas mixture, i.e., achieving multiple component detection simultaneously using a single DFB ICL [165; 164]. The Levenberg-Marquardt (LM) algorithm, also known as the damped least-squares method, was applied for the data fitting. Reference WMS-2f signals of all the components were obtained beforehand. The concentrations of all components in the mixture comply with the constraint condition of non-negative parameters. In each iteration of the fitting routine, the WMS-2f signal of the mixture was simulated with the updated parameters. Once the routine converged, the best fitting parameters were determined as the concentrations of the components.  
Multicomponent spectral detection could benefit from the redundancy of the multiple spectra, not only in the magnitude of the absorption but also in the line shape related to temperature and pressure broadening. To make full use of the information buried in the detected spectral lines, we presented an improved multicomponent spectral fitting algorithm for the sensor. We also applied the method of normalizedWMS-2f by 1f for the sensor immunity of laser energy fluctuation [162].
## 3 Results
### Statistical Analysis
In Greece, the 3G network was abolished by all providers before mid-2023. The 900 MHz band was retained for the 2G technology, primarily for long-distance voice communication. In addition to the band around 3.5 GHz, a band in the 700 MHz range was allocated for 5G connectivity. All other frequency bands are used for the 4G cellular system. Isolating the total electric field from cellular bands and using Equation (3) to find the percentage contribution from each cellular generation (2G, 4G, and 5G) revealed that, during the period when spot measurements were performed, the highest contribution was from 4G cellular networks in all the environments. The 5G network in the 3.5 GHz band had a small contribution in all cases. This can be explained by the relatively low usage of 5G FR1 mobile terminals, the small number of BS installed and the fact that only broadcasting signals are active, without directing a beam towards the measurement spot. Although no 3.5 GHz 5G base stations were deployed in the suburban region of Central Macedonia during the measurement period, a small contribution in this band appears in Figure 4b. This can be attributed to two main factors. First, the overall recorded electric field levels from cellular networks in the area were low, which makes even small contributions in the 3.5 GHz band more prominent in relative terms. Second, even without local 3.5 GHz transmitters, weak emissions from distant 5G base stations--potentially reaching the area under favorable propagation conditions such as LOS--may have contributed marginally to the measured field. Together, these factors may explain the minor, yet detectable, presence of 5G signals in this band.
## 3 High-Dimensional Mapping Entropy
### High-Dimensional Mapping Theory
#### 3.1.3 Determination of Similarity Tolerance for High-Dimensional Mapping Entropy  
When selecting the similarity tolerance in a sample entropy plane space, it is important to strike a balance between preserving time series detail information and filtering out noise.  
A common approach is to choose a value for the similarity tolerance, denoted as r, based on the standard deviation (SD) of the original sequence. In the case of using Euclidean vector distances in high-dimensional mapping entropy, the selection of similarity tolerance involves considering the characteristic scale of the data or the specific requirements of the problem. The similarity tolerance can be determined based on the standard deviation of the primary sample, which reflects the fluctuation and complexity of the data. Choosing an appropriate similarity tolerance has an impact on the calculation of high-dimensional mapping entropy. A larger similarity tolerance may result in the loss of fine-grained information, while a smaller tolerance makes the calculation more sensitive to noise, potentially reducing the accuracy of entropy estimation.  
The similarity tolerance 0.15 SD-0.25 SD is taken as the range of parameter r, which is the value adopted by the empirical mechanical system [22]. In this paper, when \(r=0.15\) SD, the empirical test shows that the algorithm can work normally. Therefore, it shows that \(r\) is set in the range of 0.15 SD-0.25 SD to obtain satisfactory results, which provides a valuable reference for researchers. This range can meet the requirements of reliability and practical applications.
## 3 Findings
### Feedback on App Usability and Workflow Modifications
#### 3.3.3 Verifying Data  
When an image is processed, the record is stored into the ODK 2.0 database. The user then launces the ODK Survey app by pressing the \"Transcribe\" button to verify the data. This allows the field worker to move field-by-field checking for any necessary edits. Most of the field workers considered the data verification process time-consuming, which may have contributed to the relatively lower data quality in comparison to the manual data management process. One field worker explained this process as follows: \"I performed verification by pressing next option to move from one field to another. I checked all data points of one form and verify each field to confirm that data written on form is properly scanned and all mistakes are rectified. The information is easily viewable.\"  
Moreover, one field worker explained the positive aspect of this verification step and said, \"Using this app feature, I feel more confident about the quality of data as I view and verify each data fields one-by-one. It feels like I am taking complete charge of the data quality.\"
## Appendix F Description of Mechanisms for High Performance
### High Performance and Scalable Byzantine Fault Tolerance [464]  
Jiang and Lian [464] introduce High Performance and Scalable Byzantine Fault Tolerance (HSBFT) with the goal of improving on the communication complexity of PBFT to achieve higher scalability and better performance. Modifications to PBFT include sub-protocols to add and remove nodes at runtime. The proposed mechanism provides no Sybil attack resistance.
## 2 Equivalent Thermodynamic Model
### Equivalence of Heat Transfer  
The coupling of material mechanical characteristics and heat transfer subjected to laser irradiation constitutes the thermoelastic model, which ignores the inertial force and applies the quasi-static condition for solving the temperature field. It assumes that the laser energy is absorbed by the surface of the target, and the absorption of the substrate is negligible [23; 24; 25; 26]. Therefore, the absorption of the laser energy is treated as a heat source on the target surface to solve the heat conduction equation inside the target. From the above conditions, the control equation of the thermal response and the solution conditions of the target under laser irradiation are as follows:  
Boundary condition  
\[-k\left.\frac{\partial T}{\partial n}\right|_{\Sigma_{1}}=\alpha l-hT \tag{9}\]\[-k\frac{\partial T}{\partial n}\Big{|}_{\Sigma_{2}}=-hT \tag{10}\]  
where \(\alpha\) is the laser absorptivity of materials; \(h\) is the convective heat transfer coefficient; \(k\) is the thermal conductivity; \(I\) is the laser power density; \(\Sigma_{1}\) is the laser irradiation surface; \(\Sigma_{2}\) is the non-laser irradiation surface; \(n\) is the normal coordinate of laser incident plane.  
If the prototype structure's variable \(y\) is \(y_{1}\), the similitude model variable \(y\) is \(y_{2}\), and assuming the similarity multiple is \(C_{y}\), then  
\[c_{y}y_{1}=y_{2} \tag{11}\]  
Similar transformation of the heat conduction equation can be obtained  
\[\rho C_{p}\frac{c_{T}\partial T_{1}}{c_{t}\partial t_{1}}-k\frac{c_{T}}{c_{L} ^{2}}\
abla^{2}T_{1}=0 \tag{12}\]  
Taking similar indicators  
\[\frac{c_{t}}{c_{L}^{2}}=1 \tag{13}\]  
Equation (9) becomes  
\[-k\frac{c_{T}}{c_{L}}\frac{\partial T}{\partial n}\Big{|}_{\Sigma_{1}}=ac_{l} I-hc_{T}T \tag{14}\]  
Get similar indicators  
\[\frac{c_{T}}{c_{L}}=c_{l}\text{ and }\frac{c_{T}}{c_{L}}=c_{T} \tag{15}\]  
Similar transformation of Gaussian distributed heat source power density model  
\[I_{2}=\frac{c_{x}P_{1}}{\pi c_{L}^{2}R_{0}^{2}}\exp(-\frac{c_{L}^{2}x_{1}^{2}+ c_{L}^{2}y_{1}^{2}}{c_{L}^{2}R_{0}^{2}})=\frac{c_{x}}{c_{L}^{2}}I_{1} \tag{16}\]  
where \(I\) is the laser power density; \(P\) is the laser power; \(R_{0}\) is the laser spot radius.  
Combined with Equation (15)  
\[c_{x}=c_{T}\cdot c_{L} \tag{17}\]  
Since the focus of the present paper is on the thermal response of the cylinder, the temperature field similarity coefficient is set to 1. The scale rate of the thermal response of the laser irradiated cylinder is shown in Table 1. It can be seen that the prototype cylinder test data that exceeds existing test conditions can be obtained by scaling parameters such as laser power, spot size, effector size thickness, and irradiation time.
## 1 Introduction
In addition, computation offloading will cause additional delay [16], such as the delay of transmitting tasks on the uplink, which does not exist in local computing. Therefore, reasonable task offloading decision and computing resource allocation are very important in satellite-terrestrial cooperation, i.e., the cooperation between the LEO satellite in the local area and the terrestrial BS in the adjacent area, which assists UEs in computing tasks. In this paper, the STN consists of UEs, an LEO satellite, and a BS. Considering tasks of UEs in a rural area. The LEO satellite and the BS work as computing nodes, which can assist UEs computing tasks through satellite-terrestrial cooperation. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs' indivisible tasks. The main contributions of this paper are as follows. * The system model of MEC-enabled STN is designed, which utilizes the satellite-terrestrial cooperation to provide MEC services for UEs in the rural area without coverage of terrestrial networks. The joint offloading decision and resource allocation problem to minimize the task completion delay of all UEs is formulated, and then decomposed into the task offloading decision problem and computing resource allocation problem. * A joint offloading decision and resource allocation scheme is proposed. The computing resource allocation problem is first decomposed into two sub-problems, which is according to the LEO satellite and the BS, respectively. Both sub-problems are convex and can be solved by Lagrange multiplier method. The task offloading decision problem is considered as a multi-UEs task offloading game, which is proved to be an exact potential game (EPG) and reach a Nash equilibrium (NE) within a finite number of iterations. * The performance of the proposed scheme is evaluated by extensive simulations. The simulation results denote that the proposed scheme can achieve better performance gain compared with other baseline schemes. The rest of this paper is structured as follows. The related work is presented in Section 2. Section 3 describes the system model. The UEs' task completion delay minimization problem is formulated in Section 4. The solving process and the proposed scheme are exhibited in Section 5. In Section 6, the simulation results and discussion are presented. The conclusion is provided in Section 7.
# Construction Method of Probabilistic Boolean Networks Based on Imperfect Information
## 2 Preliminaries
### Probabilistic Boolean Networks  
First, we explain a (deterministic) Boolean network. A BN is defined by  
\[\left\{\begin{array}{l}x_{1}(k+1)=f^{(1)}([x_{j}(k)]_{j\in\mathcal{N}^{(1)} }),\\ x_{2}(k+1)=f^{(2)}([x_{j}(k)]_{j\in\mathcal{N}^{(2)}}),\\ \vdots\\ x_{n}(k+1)=f^{(n)}([x_{j}(k)]_{j\in\mathcal{N}^{(n)}}),\end{array}\right. \tag{1}\]  
where \(x:=[\ x_{1}\ \ x_{2}\ \cdots\ \ x_{n}\ ]^{\top}\in\{0,1\}^{n}\) is the state, and \(k=0,1,2,\ldots\) is the discrete time. The set \(\mathcal{N}^{(i)}\subseteq\{1,2,\ldots,n\}\) is a given index set, and the function \(f_{i}:\{0,1\}^{|\mathcal{N}^{(i)}|}\rightarrow\{0,1\}^{1}\) is a given Boolean function consisting of logical operators such as AND (\(\wedge\)), OR (\(\vee\)), and NOT (\(\dasheddot\)). If \(\mathcal{N}^{(i)}=\emptyset\) holds, then \(x_{i}(k+1)\) is uniquely determined as 0 or 1.  
Next, we explain a probabilistic Boolean network (see [3] for further details). In a PBN, the candidates of \(f^{(i)}\) are given, and for each \(x_{i}\), selecting one Boolean function is probabilistically independent at each time. Let  
\[f_{l}^{(i)}\left([x_{j}(k)]_{j\in\mathcal{N}_{l}^{(i)}}\right),\ \ l=1,2, \ldots,q(i)\]  
denote the candidates of \(f^{(i)}\). The probability that \(f_{l}^{(i)}\) is selected is defined by  
\[c_{l}^{(i)}:=\text{Prob}\left(f^{(i)}=f_{l}^{(i)}\right).\]  
Then, the following relation  
\[\sum_{l=1}^{q(i)}c_{l}^{(i)}=1 \tag{2}\]  
must be satisfied. Probabilistic distributions are derived from experimental results. Finally, \(\mathcal{N}_{i}\), \(i=1,2,\ldots,n\) are defined by  
\[\mathcal{N}_{i}:=\bigcup_{l=1}^{q(i)}\mathcal{N}_{l}^{(i)}.\]
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## 4 Experiment Results and Discussions  
### Measurement of Laser Linewidth  
The length of the delay fiber can be settled to 25 km for measuring the nominal linewidth from 3 kHz to 1.0 MHz [30], and can be settled to 2950 m for less than 100 Hz [31]. The beating spectrums and fitting results are shown in Figure 8. In Figure 8a-d, the FWHM of the fitted spectrum was twice  
Figure 7: FS-BOTDR setup for temperature sensing.  
the laser linewidth, hence the measured linewidths of five lasers were, respectively, 1.05 MHz, 101 kHz, 10.2 kHz, and 3.1k Hz, which were close to the nominal specifications. For the linewidth less than 100 Hz, the accurate measured linewidth was calculated by utilizing the value of \(\Delta S\) by the amplitude difference comparison of coherent envelope (ADCCE) method (See Appendix B) [31], because the length of the 2950 m delay fiber was much shorter than the coherence length of the measured laser. The accurate linewidth was 98 Hz, as shown in Figure 8e, according to the ADCCE method.  
Figure 8: Linewidth measurement results of five lasers, of which the nominal linewidth are respectively: (a). 1.1 MHz; (b). 100 kHz; (c). 10 kHz; (d). 3 kHz; (e). <100 Hz.
## 2 Methodology
### Test Materials
#### 2.3.2 Test Particles  
The wind tunnel tests employed simulated material instead of actual snow particles. Prior to the test, several types of particles with characteristics resembling snow grains were selected based on similarity criteria, including snow grain density, particle size, angle of repose, and settling velocity. The physical properties of materials such as artificial snow, salt, and silicon sand of various grades were compared. Based on the results of air tests, fine silicon sand with a particle size of 0.2 mm was ultimately chosen as the substitute for snow particles. Table 2 shows a comparison of the physical and mechanical parameters of the selected fine silica sand particles used in the indoor wind tunnel test. The silica sand was sourced commercially as abrasive media for sandblasting.