esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Relationships between Fractal Dimensions and Free Oil Content  
There was an obvious linear negative correlation between S1, OSI, and the fractal dimensions of the shale of K\({}_{2}\)n\({}^{1+2}\). The correlation between S1 and D2 (R\({}^{2}\) = 0.9464, T \(>\) 2.58, \(p\) < 0.01) was better than that of S1 and D1 (R\({}^{2}\) = 0.6603, T \(>\) 2.58, \(p\) < 0.01), indicating that D2 can be used to characterize the oil content of shale (Figure 11). This is consistent with the negative correlation shown between the Qingshankou Formation S1 and the fractal dimensions (Figure 11, R\({}^{2}\)D2q \(>\) R\({}^{2}\)D1q). Shale samples with higher free oil levels always have a lower fractal dimension D2 value, which may be due to the fact that free oil is mainly stored in large pores, and these pores have lower D2 values. Contrary to shale gas adsorption, the molecular diameter of shale oil is much larger than that of shale gas.  
Figure 10: (**a**) Relationship between TOC content and fractal dimensions D1 and D2 of K\({}_{2}\)n\({}^{1+2}\) and K\({}_{2}\)q shale samples. (**b**) Relationship between TOC content and average pore diameter of K\({}_{2}\)n\({}^{1+2}\) and K\({}_{2}\)q shale samples. The K\({}_{2}\)q compared to [78].  
Therefore, shale oil is intelligently stored in pores with larger diameters, which causes the phenomenon that shale with higher free oil content usually has lower D2 [51]. As a result, combining the influence of the TOC content on the fractal dimensions and the relationship between the free oil and fractal dimensions, we conclude that the best area for exploitation in the low maturity shales of the Nengjiang Formation in the Songliao Basin would be the area with lower pore fractal dimensions and higher TOC content.
# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
## 2 Methodology
### Network Performance Evaluation
When evaluating the performance of the inversion network, the inversion quality is given by the following equations [30]:  
\[\text{RAscore}=\varphi\times\text{ACC}+(1-\varphi)\times\text{SSIM} \tag{6}\]  
\[\text{ACC}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}, \tag{7}\]  
where RAscore measures the recognition ability of the inversion network and ACC computes the recognition accuracy on the test dataset. TP refers to the true-positive samples representing the recognized samples with correct target locations; FP denotes the false-positive samples with wrong target positions; and FN refers to the false-negative samples that fail to identify the targets. \(\varphi\) is a weighting coefficient used to balance the weights of ACC and SSIM in Equation (6), which was set to 0.75 in this work. We consider that the maximum error limit for defect position recognition should be less than 5% of the side length of the defect reconstruction image, which is 12.8 pixels for a 256 \(\times\) 256 image. Therefore, we used 10 pixels as the threshold value in this work. If the distance between the centroid position of the inverted target and the true value is within the threshold value, the inversion result is regarded as a TP. Otherwise, an FP label is assigned. In addition, if no corresponding target information is inverted within 10 pixels around the actual target position, the inverted image is classified as an FN. It needs to be noted that direct inversion with high resolution introduces a huge number of weights in the fully connected layer, resulting in graphics memory explosion. Thus, we first use the low-resolution GAN to invert low-resolution crosshole GPR images (\(64\times 64\)) to the same-size defect reconstruction image with noise and false-positive samples, then use the high-resolution GAN to enlarge the size of the defect reconstruction image to \(256\times 256\), and refine it by removing noise and small false-positive samples. Figure 2(a) depicts the training losses of the low- and high-resolution GANs, in which the MSE value of the low-resolution GAN reaches approximately 1400, while the high-resolution GAN further improves the MSE value to around 400. This indicates that the high-resolution GAN greatly optimizes the low-resolution results. Figure 2(b) demonstrates the inverted defect reconstruction images of the low- and high-resolution GANs, respectively. In the first step, the low-resolution GAN translates the crosshole GPR images to a low-resolution defect reconstruction image with considerable noise. In the second step, the high-resolution GAN enhances the resolution of the defect reconstruction image and reduces the noise tremendously.
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
## 2 Modeling and Measuring Bathymetric Uncertainties  
### Modeling Approach  
The predictions of the depth uncertainty induced by varying sources were derived from the model presented in [6; 7]. This model was developed under the assumption of independent contributors (total uncertainty is the square root of sum of the squares of the individual uncertainty sources) and a flat bottom. The bathymetric uncertainty sources can be categorized as [25]:  
1. Echosounder contribution, \(\sigma_{\text{des}}\), due to the non-zero beam opening angle in the along-track direction, uncertainties in the measurements of the range (between the transducer and a point on the seafloor), and the angle of impact of the incoming sound at the transducer. The latter depends on the method used within the MBES processing chain for bottom detection, i.e., using the amplitude and phase of the received signal. The former is obtained either from the maximum amplitude of the received signal or the center of gravity of the signal envelope after beamforming [26]. As for the phase detection, the full MBES receiving array is divided into two sub-arrays [4]. First, the received signals on both sub-arrays are beamformed focusing the two sub-arrays in a desired direction. The time at which the two signals are in phase (zero phase difference between the two beams formed in a chosen direction) is then taken as the arrival time [4].
2. Angular motion sensor contribution, \(\sigma_{d_{\text{AngMot}}}\), due to the uncertainties in roll and pitch measurements and the imperfectness of their corrections.
3. Motion sensor and echosounder alignment contribution, \(\sigma_{d_{\text{Align}}}\), due to the discrepancies between roll and pitch angle measurements at the motion sensor and the transducer.
4. Sound speed contribution, \(\sigma_{d_{\text{SS}}}\), due to the sound speed uncertainties at the transducer array and in the water column.
5. Heave contribution, \(\sigma_{H}\), due to the uncertainties in the heave measurements and those induced due to the vertical motion of the transducer with respect to the vertical reference unit caused by the angular motions of the vessel. For uncorrelated contributors, the total depth uncertainty is then expressed as: \[\sigma_{d}=\sqrt{\sigma_{d_{\text{ES}}}^{2}+\sigma_{d_{\text{AngMot}}}^{2}+ \sigma_{d_{\text{Align}}}^{2}+\sigma_{d_{\text{SS}}}^{2}+\sigma_{H}^{2}}\;.\] (1)  
The equations for quantifying the contributions of the uncertainty sources are not presented here, and an interested reader can refer to [6; 7].
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 6 Simulation Results
### Simulation Tools and Settings  
The code was written in Python 3.9.0. Traffic simulations are used to Simulate Urban MObility (SUMO) version 0.3.0. More details in simulation parameters are marked in Table 3. Each packet that needs to be transmitted is considered as be an agent, and the agent learns the VANETs environment. Then, the agent selects an action according to Equation (10). Subsequently, based on received reward, the agent adjusts the action. Ultimately, the optimal action is obtained to improve the routing performance.  
In addition, Table 4 lists the simulated values of RL hyperparameters. A fully connected neural network is adopted in the network, and there are one input layer, two  
Figure 6: Learning rate for OneCycleLR policy: (**a**) from episode 1 to 20,000; (**b**) from episode 12,000 to 20,000.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Default Value** \\ \hline Simulation area & 6 km \(\times\) 6 km \\ \hline Communication protocol & IEEE 802.11p \\ \hline Packet Size & 512 bytes \\ \hline Number of transmitting packets in each second & 10 \\ \hline Vehicle density & 10–50 vehicles/km \(\times\) km \\ \hline Number of direction & 2 \\ \hline Number of lanes & 2 in each direction \\ \hline Traffic type & Constant bit rate \\ \hline Vehicle velocity & 5–25 m/s \\ \hline Transmission range & 250 m \\ \hline Mobility model of vehicles & Random-Way point \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
hidden layers, and one output layers. The input layer has five neurons that are consistent with the dimensions of the state. The influence of some key parameters, such as learning rate and exploration factor on the performance of the DRL, are analyzed. Moreover, numerical results are reported with an average of 100 runs in order to avoid errors. The environment and state are reset at the beginning of each episode, and each episode contains 100 time steps.
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 6 Experimental Results
### Test Results  
For testing and evaluation, 150 images are used from three scenes, including a parking lot, road, and construction site. Due to the differences between the three scenes, models performance is varied and highly related to the complexity of images and categories of objects. The test results are shown in Table 3.  
A precision-recall curve is also drawn to evaluate the two models. Figure 9 shows the relation between precision and recall while changing the thresholding value of the binary classification. As it is shown in Figure 9, Tables 2 and 3, the LNSNet model has almost similar accuracy, precision, and recall rates rates compare to those of ENet. The difference was around 2% and the precision-recall curves are approximately overlapped, indicating no significant difference in accuracy.  
Figure 8: Training visualization for LNSNet and ENet models: (**bottom**) training accuracy vs. epochs, (**top**) loss variation vs epochs
## 3 PAH Formation--Mechanistic Features
Although less likely to occur, another alternative to HACA is the mechanism involving the ethynyl radical, which could represent a potential reaction route at low temperatures [65]. The formation of phenylacetylene from benzene and the ethynyl radical was predicted by considering the ethynyl addition mechanism (EAM) [66]. More recently, the formation of benzo(a)pyrene from chrysene was predicted by using the hydrogen abstraction ethynyl radical addition (HAERA) [67]. Vinyl radical addition (HAVA*). Similar to the HAVA reaction, hydrogen abstraction vinyl radical addition (HAVA*) can also lead to the formation of PAHs. It was assessed through pyrolysis studies, and its suitability for the formation of cyclopentafused PAHs was pointed out, as well as its significance in the formation of pyrogenic PAHs at moderate temperatures [68,69]. Phenyl addition cyclization (PAC). During toluene pyrolysis, the proposed route involves the addition of the phenyl radical, hydrogen abstraction, dehydrocyclization, and subsequent conversion into thermally stable condensed PAHs [70]. It is considered not competitive with HACA due to the low prevalence of benzene in flames, as opposed to acetylene [71]. Its most important features are attributed to the formation of asymmetric PAHs, with high efficiency if the phenyl radical is present, and the potential for continuous growth by enabling multiple fusion sites during each reaction step [52,72,73]. Resonantly stabilized radicals (RSR). The combustion environment is a suitable setting for recombination reactions (radical-radical, radical-neutral) to occur involving resonantly stabilized radicals (RSR), being relatively stable and in high concentrations in flames. Computational studies involving RSR pathways pointed out three important mechanisms: the propargyl radical [74, 75, 76], cyclopentadienyl radical [77, 78, 79, 80], and indenyl radical [81, 82, 83].
# Design of Low-Power ECG Sampling and Compression Circuit
## 3 Proposed Scheme
### Adc
At the beginning of calibration, the auxiliary transistors are reset first, connecting the gates of \(\text{M}_{\text{OCP}}\) and \(\text{M}_{\text{OCN}}\) to GND, and the differential inputs of the comparator input the same common-mode voltage. The comparator compares and outputs the results \(V_{\text{O+}}\) and \(V_{\text{O-}}\) when the clock is switched to high. The calibration logic determines the connection \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Capacitor (fF)** & **SNR (dB)** & **SFDR (dB)** & **ENOB (bit)** & **ENOB’s Std Dev (bit)** \\ \hline
7.02 & 72.83 & 81.14 & 11.80 & 0.19 \\
12.09 & 73.35 & 81.85 & 11.89 & 0.16 \\
15.59 & 73.50 & 82.06 & 11.92 & 0.15 \\
18.14 & 73.58 & 82.16 & 11.93 & 0.14 \\
21.53 & 73.65 & 82.25 & 11.94 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The simulation results of the influence of capacitor mismatch. position of the calibration voltage based on the first comparison output. Taking the first comparison results as \(V_{\text{O+}}=1\) and \(V_{\text{O-}}=0\) as an example, the calibration logic connects the gate of M\({}_{\text{OCN}}\) to the calibration voltage \(V_{\text{C}}\). As the step calibration voltage increases, the equivalent input offset voltage decreases. Until the outputs become \(V_{\text{O+}}=0\) and \(V_{\text{O-}}=1\), the calibration logic disconnects the calibration voltage \(V_{\text{C}}\) and the gate of M\({}_{\text{OCN}}\) to end the calibration, and the calibration voltage is maintained in the capacitor \(C_{\text{CN}}\).
## 4 Discussion
Therefore, the number of oxygen moles needed to burn each of these compounds has been calculated by taking into account the density values. If the product of combustion of all compounds is carbon dioxide then the number of oxygen moles needed to burn 1 dm\({}^{3}\) of the compound is 80.34, 80.25, 80.43, 80.43, and 72.75, respectively, for oleic acid, linoleic acid, linoleic acid, and n-hexane. This comparison shows that the number of oxygen moles necessary to burn the same amount of n-hexane is significantly lower than for the three acids mentioned above. These differences should be reflected in the values of heat of combustion of n-hexane and tested acids, and thus canola oil. The heat value of canola oil combustion per 1 dm\({}^{3}\) is 35284.32 kJ. It turned out that this value did not differ much from the value for oleic acid (35147.60 kJ). However, the combustion heat of n-hexane, as expected from the amount of oxygen needed for its combustion, is lower and amounts to 31911 kJ. Taking these values into consideration, the heat of combustion of the canola oil/n-hexane mixture as a function of its composition was calculated. The calculations show that in the range of concentrations (from 0 to 20% of n-hexane in the mixture) the heat of combustion slightly decreases from 35284.32 to 34609.66 kJ/dm\({}^{3}\) (Figure 11). Changes in the combustion heat of a canola oil and n-hexane mixture are related to the amount of oxygen needed to burn the mixture. Considering that more than 90% of the oil composition is made up of the acids tested, the average value of oxygen mole needed to burn 1 dm\({}^{3}\) of oil was used to calculate the number of oxygen needed to burn 1 dm\({}^{3}\) of oil. The calculated amount of oxygen mole needed to burn 1 dm\({}^{3}\) of the mixture, similarly to the heat of combustion, changes slightly (from 80.34 to 78.82). On the basis of the conducted considerations, it can be stated that the addition of n-hexane to canola oil slightly changed the heat of combustion and significantly improved the physicochemical properties such as the surface tension and viscosity. The addition of n-hexane to canola oil caused a significant decrease in its viscosity and surface tension. Changes in the surface tension of canola oil with the addition of n-hexane as a function of composition are not linear, and synergy in the reduction of surface tension is observed. It was found that the mixture of canola oil and n-hexane is not ideal. A significant decrease in the viscosity and surface tension of oleic acid, the main component of canola oil, by adding n-hexane, indicates that such a mixture can be used in diesel engines.
## 2 SC-FDE Communication System Model
\[\eta(dB)=10\text{log}_{10}\left(\frac{E\left[\left\|\mathbf{s}^{T}(n)\mathbf{h}\right\| _{2}^{2}\right]}{\left|\alpha\right|^{2}}\right) \tag{8}\]  
If \(\eta\) is greater than 0, it means that the signal power is greater than the interference power, and if it is less than 0, it indicates that the interference power is greater. When interference is present, the reception performance of the signal deteriorates, and the impact becomes more severe as the interference becomes stronger. When strong interference exists, accurate CIR estimation may be difficult, so interference cancellation must be performed beforehand.
###### Abstract  
In this paper, we present the extensive load scheduling problem with intermittent and uncertain wind power availability. A chance-constrained bilateral tradeoff decision model is established to solve the problem. Our model aims at maximizing the wind power utilization and minimizing the system operation cost simultaneously by means of responsive loads, which are precisely divided into shiftable loads and high-energy loads. The chance constraint is applied to restrict the system imbalance with a small probability. Then, a revised sample average approximation (SAA) algorithm is developed to transform the chance constraint into sample average reformulations. Furthermore, the multi-objective differential evolution (MODE) method combined with SAA is proposed to solve the problem. Experiments enabling an effectiveness analysis of the two kinds of responsive loads are performed on the power system in Yancheng. The research of parameters of MODE, the sensitivity of different risk levels and the influence of iteration numbers are discussed. Finally, computational results prove that the combination of shiftable loads and high-energy loads have a better effect than adopting shiftable loads and high-energy loads separately, and the proposed method is convergent and valid in solving the problem.  
chance constraint; dual objectives; high-energy loads; multi-objective differential evolution; sample average approximation; shiftable loads Article
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 1 Introduction
However, fault characteristics of DFIG-based wind farms and the performance of distance protection when unbalanced faults occur on high voltage transmission lines should be further studied. According to analyses in the literature [17; 18; 19], the DFIG short-circuit current involves a transient alternating current (AC) component decay at near rotor frequency. As a result, the conventional distance protection based on nominal frequency may fail to measure the correct impedance. To fill the gap, this paper analyzes real fault cases where unbalanced faults occurred at the transmission lines connected to DFIG-based wind farms in China. The fault characteristics and distance protection applicability of the transmission lines connected to DFIG-based wind farms are obtained. Furthermore, a time-domain distance protection method combined with the least-squares algorithm is proposed. The proposed method can overcome the influence of frequency variation as a result that fault distances can be identified accurately and quickly when faults occur on the transmission line connected to a DFIG-based wind farm. With the help of the proposed method, Relay Protection Equipment Manufacturers can improve the equipment level of technology, and the grid companies can improve the safe operation level of power system. The rest of this work is structured as follows. Section 2 illustrates the composition of DFIG short circuit current and fault characteristics of DFIG-based wind farms connected to transmission lines. Section 3 analyzes the distance protection applicability, and a time-domain distance protection method is proposed for the transmission lines connected to DFIG-based wind farms. The reliability and correctness of the method are validated through real fault cases and simulation tests. Section 4 presents the conclusions.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 3 Models and Goals
### Vehicle Registration  
Before, initial road network deployment vehicles must register with a Trusted Authority (TA). The TA is a governmental authority that provides certificates to the vehicles at the time of registration. The vehicle registration is shown in Algorithm 1. First, the vehicle will request from TA the registration that binds the vehicle to vehicle identity, License Plate Number (LPN), and other necessary items. Upon successfully verifying LPN, the TA issues certificates to the requesting vehicle; otherwise, the invalid LPN vehicle request is rejected. The TA provides a set of \(P\) pseudonyms \(PUI_{i,k}\) to the vehicles, where \(k\) belongs to (1...P) that are public-key certificates. The beacon message is signed with a private key of the sender's vehicle in connection with pseudonyms for proper authentication. When a message is broadcast by a vehicle in a region to disseminate road status information, upon reception of the message, the receiver vehicles in the vicinity can verify a sender's vehicle authenticity with its public key [46; 49]. The pseudonyms are used for a short period to protect the identity of vehicles. Pseudonyms have an expiry time, and after the expiration, the vehicle may request another pseudonym pool. In our case, we set a pseudonym pool for long periods, i.e., for a number of days (week/month).  
```
1:\(Vehicle(i)\): Any vehicle \(i\) request for registration, \(V_{ID}\): Vehicle identity, \(LPN\): License Plate Number, \(PseudoID\): Pseudonym identity of a vehicle \(Expiry(PseudoID)\): The vehicle monitors pseudonyms pool expiry, \(Valid(LPN_{i})\): Validity of vehicle LNP is checked, \(Issuence(PU_{i,k},P)\): issue certificate with pseudonym pool Input:\(V_{ID}\), \(LPN\) Output: Issuance of Certificate to vehicle
2:for\(Vehicle(i)=1\to n\)do
3:\(Request_{i}(V_{ID},LPN)\ \to\ TA\)
4: TA Verify \((LPN_{i})\)
5:if\(Valid(LPN_{i})\)then
6:\(Issuence(PU_{i,k},P)\)
7:else
8: Discard \((Request_{i})\)
9:endif
10: Check \(Expiry(PseudoID)\)
11:if\(Expire(PseudoID)\)then
12: Go to step 2
13:else
14: Continue Usage of certificate
15:endif
16:endfor
```  
**Algorithm 1** Vehicle Registration.