esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 2 Surface Defect Detection Method
### Defect Detection Model Based on MobileNet-SSD Network
#### 2.2.3 Surface Defect Detection Algorithm Based on MobileNet-SSD Model  
In the filling line, the sealing surface is easily damaged by friction, collision and extrusion in the recycling and transport of pressure vessels. The common defects include breaches, dents, burrs and abrasions on the sealing surface. In this paper, the MobileNet-SSD model can greatly reduce the number of parameters, and achieve higher accuracy under the limited hardware conditions. The complete model contains four parts: the input layer for importing the target image, the MobileNet base net for extracting image features, the SSD for classification regression and bounded box regression and the output layer for exporting the detection results.  
This model supports fast and accurate detection because the structure of MobileNet reduces the complexity of computing. However, the structure of Pw changes the distribution of output data of Dw. This may cause a loss of precision. To solve this problem, the fully-connected layers were abandoned, and eight standard convolutional layers were added with the aim to widen the receptive field of the feature image, adjust the data distribution and enhance the translation invariance of the classification task. To prevent the disappearance of the gradient, the BN layer and activation function ReLU6 were introduced to each layer of the added structure. In addition, the two-layer feature image in MobileNet and the four-layer feature image in the added standard convolutional layers constituted a feature image pyramid (Figure 5). Kernels 3 \(\times\) 3 in size were adopted as the convolutional kernels to convolve selected feature maps. The convolutional results were taken as the final feature for classification regression and bounded box regression.  
A 300 \(\times\) 300 image was taken as the input. The six layers of the pyramid respectively contain 4, 6, 6, 6, 6 and 6 default boxes. Besides, different 3 \(\times\) 3 kernels were adopted for classification and location with the step length of one. The numbers in brackets were the amount of 3 \(\times\) 3 filters that are applied around each location in the feature map. Its number was the amount of default Box \(\times\) number of categories (classification) and the amount of default Box \(\times\) 4(location), respectively. The general structure of MobileNet-SSD is shown in Table 2.  
Figure 5: MobileNet-Single Shot MultiBox Detector (SSD) network feature pyramid.  
In Table 2, Conv_BN_ReLU6 is a standard convolutional layer, while Conv1_Dw_Pw is a deep and separable convolutional layer. Besides, the sign \(\%\) represents the feature image to be used in classification regression and bounded box regression. Considering the small size of the target defects, the feature image of the shallow Conv7_Dw_Pw output was adopted for further analysis.
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 2 Materials and Methods
### Simulation Analysis of Fine Tracking System Performance  
Based on the structure of the fine tracking control system, a simulation system for dynamic target tracking is constructed. The P-I rate-dependent hysteresis model represents the PZT FSM, and its inverse model represents the feedforward controller; the feedback controller is the PID control method.  
To explore the impact of hysteresis compensation on the performance of the fine tracking control system, this paper simulates and compares three different control methods. The PID controller parameters are adjusted under the same conditions for all control methods.  
1. Single-PID feedback control: The PID controller parameters are \(K_{p}\): 0.15; \(T_{i}\): 170.2; and \(T_{d}\): 0.00026.
2. The compound control which combines P-I model feedforward control and PID feedback control: The PID controller parameters are \(K_{p}\): 0.11; \(T_{i}\): 301.3; and \(T_{d}\): 0.00051.
3. The compound control which combines P-I rate-dependent model feedforward control and PID feedback control: The PID controller parameters are \(K_{p}\): 0.18; \(T_{i}\): 351.7; and \(T_{d}\): 0.00048.  
A sinusoidal signal whose frequencies increase from 1 Hz to 400 Hz and amplitudes decrease exponentially was utilized to simulate vibration with a rapid frequency change. The error suppression capabilities of the above three control methods were tested. Figure 10 shows the error between the system output and input signals and Figure 11 shows the error suppression bandwidth of the system.  
Figure 10: Comparison of control effect of three control methods in the time domain. (**a**) PI + PID compared with PID. (**b**) (R-D)PI + PID compared with PID.  
Figure 9: Compound control method block diagram.  
According to the simulation results, the error suppression bandwidth of the single-PID feedback control system is approximately 48.7 Hz. When adding feedforward compensation based on the P-I model, the error suppression bandwidth is approximately 57.5 Hz. When adding feedforward compensation based on the P-I rate-dependent model, the error suppression bandwidth is approximately 69.6 Hz. This represents an improvement of about 43.75%. It is proven that feedforward compensation for hysteresis can effectively enhance the closed-loop tracking performance of the fine tracking control system. Additionally, the effect of using feedforward compensation based on a P-I rate correlation model is better.
### Main Contributions  
To comprehensively tackle the challenges discussed above, this paper proposes a **D**ecentralized **D**igital **W**u**r**t**r**t**r**t**r**r**t**r**  
solutions for IoV data management [15], we introduce digital watermarking technology to ensure the authenticity of raw video data rather than simply using hash values or digital signatures to verify data integrity. To the best of our knowledge, D2 WaVe is the first scheme that provides assurance and resilience-oriented data services for IoV networks through the organic synergy of digital watermarking and blockchain technology.  
In summary, the main contributions of this paper are highlighted as follows:  
* We propose the system architecture of D2 WaVe, a decentralized digital watermarking framework consisting of (i) a novel FIAE-GAN model for watermark embedding and extraction and (ii) BloVA, a blockchain-based authentication scheme to support secure video data storage and sharing.
* We introduce a system structure for the FIAE-GAN-based digital watermarking model and provide details on the encoder, decoder, noisier, and discriminator.
* We evaluate the performance of the watermarking model and overheads incurred by the video authentication framework. Experimental results demonstrate that both the AEM and FIM are crucial for the watermarking model's performance, surpassing existing models in watermarked image quality and resilience against diverse attacks.  
The remainder of this paper is organized as follows: Section 2 provides background knowledge regarding feature integration in DenseNet and attention module and reviews the state-of-the-art digital watermarking technologies and blockchain-based security solutions to IoV networks. Section 3 introduces the design rationale and system architecture of D2 WaVe, as secure and auditable video data framework for IoV networks. Details of the FIAE-GAN model and core components are explained in Section 4. Section 5 reports the prototype implementation and performance evaluation. Finally, Section 6 summarizes this paper, briefly discussing current limitations and future directions.
## 4 Specific Prenylquinones
### Thermoplasmaquinone and Chlorobiumquinone
#### 4.4.2 Thermoplasmaquinone and Chlorobiumquinone in Parasitic Protozoa  
[PERSON] et al. [66] in 2011 found TpQ-7 in trophozoite plasma membranes from _E. histolytica_, suggesting that this compound could be one of the main quinones involved in TPMET. The reduction of 1,2-naphthquinone-4-sulphonic acid (NQSA), ferricyanide and \(\alpha\)-lipoic acid (ALA) is evident, which suggests that TPMET redox chains are present in _E. histolytica_. Due to these quinone-mediated redox chains, it is possible to transfer electrons from the intracellular NADH or NADPH oxidation reactions to other compounds located in the extracellular space [67]. In _E. histolytica_ amoebic liver abscesses, this redox chain could be essential for parasitic survival [67]. Similarly, in promastigotes of _L. donovani_, ChQ was found in fractions containing purified plasma membranes. In addition, in this case, prenylquinone was suggested to be involved in ALA, NQSA and ferricyanide reduction [65].  
In both _E. histolytica_ and _L. donovani_, the two prenylquinones can be destroyed by UV light irradiation, which leads to TPMET loss [65; 67]. However, this TPMET loss can be recovered by exogenous addition of TpQ or ChQ analogs [65; 67]. Both works discuss the phylogenetic origin of prenylquinone. First, _E. histolytica_ most likely acquired genes responsible for MQ biosynthesis [65; 67]. On the other hand, ChQ was detected in only _L. donovani_ and green sulfur bacteria, which is consistent with the endosymbiosis theory for trypanosomatid ancestors [65]. Both studies focused on TPMET, so it remains unknown how prenylquinones are biosynthesized and whether these compounds could be acting on other metabolic points [65; 67]. Regardless, the absence of TpQ-7 and ChQ in animals makes these compounds interesting subjects for the discovery of novel etiological treatments [65; 67].
## 4 Case Study
### Performance Evaluation
#### 4.2.2 Calculation of the Weights of the Evaluation Indicators  
(1)  AHP to determine subjective weights  
(1)  Constructing judgment matrices  
By consulting experts and university professors associated with the construction machinery industry, the judgment matrix was constructed using expert survey methods based on understanding the significance of each metric. The values of each judgment matrix are recorded in Tables 2-6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Economic Cost** & **B\({}_{41}\)** & **B\({}_{42}\)** & **B\({}_{43}\)** & **B\({}_{44}\)** \\ \hline Selection of friction sub-materials & 1 & 2 & 3 & 4 \\ Selection of lubricants & 1/2 & 1 & 5 & 3 \\ Payback period & 1/3 & 1/5 & 1 & 1 \\ Profitability & 1/4 & 1/3 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Economic cost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level 1 Indicators** & **Weighting at Level 1** & **Secondary Indicators** & **Secondary Weights** & **Subjective Weights** \\ \hline \multirow{4}{*}{Lubrication effect} & \multirow{4}{*}{0.5384} & Equipment load & 0.1074 & 0.0578 \\  & & Operating temperature & 0.3818 & 0.2056 \\  & & Viscosity of the lubricant & 0.2206 & 0.1188 \\  & & Anti-wear properties of lubricants & 0.0967 & 0.0521 \\  & & Thickness of oil film & 0.1935 & 0.1042 \\ \hline \multirow{4}{*}{Fault maintenance} & \multirow{4}{*}{0.2326} & Failure rate & 0.6008 & 0.1397 \\  & & MTBF & 0.2290 & 0.0533 \\  & & MTTR & 0.1702 & 0.0396 \\ \hline \multirow{2}{*}{Energy saving and reduction of consumption} & \multirow{2}{*}{0.1019} & Lubricant replacement intervals & 0.6768 & 0.0690 \\  & & Lubricant consumption & 0.1925 & 0.0196 \\  & & Electricity consumption & 0.1307 & 0.0133 \\ \hline \multirow{4}{*}{Economic cost} & \multirow{4}{*}{0.1270} & Selection of friction sub-materials & 0.4476 & 0.0568 \\  & & Selection of lubricants & 0.3391 & 0.0431 \\ \cline{1-1}  & & Payback period & 0.1069 & 0.0136 \\ \cline{1-1}  & & Profitability & 0.1064 & 0.0135 \\ \hline \hline \end{tabular}
\end{table}
Table 7: AHP subjective weighting results.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 2 Materials and Methods
### Imerg  
IMERG (Integrated Multi-satellite Retrievals for GPM) is a global precipitation dataset produced as part of the GPM (Global Precipitation Measurement) mission [27]. Precipitation rates are retrieved using intercalibrated passive microwave measurements from a constellation of satellite radiometers in low-Earth orbit (LEO). Geostationary infrared (IR) measurements are used to morph and advect the precipitation in a physically consistent way between LEO passive microwave overpasses.  
IMERG data are available every half hour on a global 0.1\({}^{\circ}\) grid. Each CYGNSS track was matched to the temporally closest IMERG file (with the caveat that the IMERG time could not be later than the midpoint time of the track), and then the precipitation at the nearest neighbor IMERG gridpoint was matched to each specular reflection within the CYGNSS track. Due to the timing of when the track-based processing occurred, CYGNSS track files prior to 2019 were matched to the version 5 Late IMERG products, while 2019 data were matched to version 6 Final IMERG products. Based on a sensitivity analysis using a subset of data (not shown), these version differences did not significantly impact any of this study's results.
# Anonymous Access System with Limited Number of Uses in a Trustless Environment
## 4 The Proposal
### General Overview  
In this proposal, blockchain technology is used through Smart Contracts to provide a secure implementation of the multiple authentication mechanism that protects \(\mathcal{U}\) in front of a malicious \(\mathcal{SP}\). The idea is that the Smart Contract manages the life cycle of every authentication in order to properly restrict the total number of authentications to the predefined maximum \(n\).  
Figure 1 illustrates the overall protocol, outlining the three defined roles and highlighting the blockchain's new role within the system. A detailed explanation of each protocol step follows.  
1. First and only once, \(\mathcal{SP}\) deploys the Smart Contract initialised with the public key of a trusted \(\mathcal{AP}\).
2. \(\mathcal{U}\) begins the protocol by requesting \(\mathcal{AP}\) for a credential for a given scope \(S\).
3. After user identity and attribute verification, \(\mathcal{AP}\) provides \(\mathcal{U}\) with a credential. This credential is valid only for the requested scope \(S\).
4. \(\mathcal{U}\) stores the credential and computes the cryptography data and proofs required to continue with the protocol.
5. \(\mathcal{U}\) presents the credentials, proof of possession of the private key, and knowledge of the maximum value of the counter to obtain an access counter from \(\mathcal{SP}\).
6. \(\mathcal{SP}\) calls the Smart Contract that verifies the credentials and the ZKPs, and all of them are valid, it stores a counter on the chain, identified by the hash of credentials, with a starting value of 0, the maximum value \(n\), and the blinded public key of \(\mathcal{U}\). If verification fails, the protocol is aborted.
7. \(\mathcal{SP}\) provides or refuses the counter.
8. To use the counter, \(\mathcal{U}\) presents their unique ID and proof of possession of the private key and knowledge of the next value.
9. \(\mathcal{SP}\) calls the Smart Contract that retrieves the actual value, the maximum value, and the blinded public key of \(\mathcal{U}\). Then, it checks the proof of possession of the private key and the knowledge of the next value. The Smart Contract also checks that the number of iterations is less than the maximum allowed counter value. If the verification holds, it provides the service; otherwise, the service is refused.
10. \(\mathcal{SP}\) provides or refuses the service.
# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 4 Experiments with Data from the SNS Pesedia
### Metric for the Experiments  
In this section, the metric that is used in the experiments will be explained using a formula. This metric is propagation of detected value (PDV). The following terms are later used for the formula of the metric:* _messages_with_replies_: Total amount of messages that generated replies.
* _messages_with_propagated_state_: Aggregated value of messages with the propagated state, which are messages with the same detected state as is present in most replies.  
The following formula describes the calculation of the metric used in the experiments conducted:  
\[PDV=\frac{messages\_with\_propagated\_state}{messages\_with\_replies}\]  
For the sake of example, we propose the following scenario: There are three messages posted in the social network, two of them with replies and one without. All the messages and their replies have been analyzed and a state associated with them by the system. This can be seen in Figure 3. As can be seen, the first message generated three replies and two of them were positive (making the most present value in the replies positive), so there has been propagation from the original message to the replies since this original message also resulted in positive state. For the case of the third message, there is no propagation since there are three replies detected as positive and only one negative, and the original message was detected as negative. In this scenario, messages_with_replies would be computed as two, since only two messages have replies. For computing Messages_with_propagated_state, we summed the number of messages that generated replies and propagated the state detected in them to those replies. As has been said, only one of the messages propagated the state to the replies; therefore, the value of Messages_with_propagated_state in this example is one. Finally, the value of PDV for this example would be computed as 0.5.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 4 Performance Evaluation
### Varying Carrier Frequency Error Scene
Compared with the coherent pre-filter, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, in test scene C. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tracking Error**} & \multicolumn{3}{c}{**Root Mean Square of Estimation Error for Tracking Error**} \\ \cline{2-4}  & **Carrier Phase Error (cycle)** & **Carrier Frequency Error (Hz)** & **Code Phase Error (chip)** \\ \hline Discriminator & 0.30656 & 27.81829 & 0.08758 \\ Coherent pre-filter & 0.26179 & 2.00818 & 0.07386 \\ Non-coherent pre-filter & 0.15270 & 1.04078 & 0.03666 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of tracking error estimation methods in test scene C.  
Figure 13: (**a**) The carrier phase error estimation in test scene C; (**b**) The carrier frequency error estimation in test scene C.  
Figure 16 shows the STD values of estimation error for code phase error. Similarly, the pre-filter outperforms NC-EMLE discriminator in different \(C/N_{0}\). When \(C/N_{0}\) is greater than 28.8 dB-Hz, the estimation accuracy of the coherent pre-filter for code phase error is higher than that of the non-coherent pre-filter. When \(C/N_{0}\) is less than 28.8 dB-Hz, the estimation accuracy of the coherent pre-filter for code phase error reduces seriously and the non-coherent pre-filter outperforms the coherent pre-filter. Overall, the tracking error estimated by coherent/non-coherent pre-filter is less noisy than that estimated by the ATAN/ATAN2/NC-EMLE discriminator. The non-coherent pre-filter outperforms the coherent pre-filter for tracking error estimation in weak signal or highly dynamic scene. The non-coherent pre-filter also outperforms the coherent pre-filter for carrier phase error estimation in strong signal condition.
## 2 Mathematical and Physical Models
### Governing Equations  
The system of the N-S equations for the hypersonic thermochemical nonequilibrium flow are given as follows:  
The species continuum equations:  
\[\frac{\partial\rho_{s}}{\partial t}+\frac{\partial\rho_{s}u_{j}}{\partial x_{j}}= \frac{\partial}{\partial x_{j}}\left(\rho D_{s}\frac{\partial Y_{s}}{\partial x _{j}}\right)+\omega_{s},s=1,2,\cdots,N_{s}, \tag{1}\]  
and the total density is  
\[\rho=\sum_{s=1}^{N_{s}}\rho_{s}=\sum_{s=1}^{N_{s}}\rho\cdot Y_{s}, \tag{2}\]  
Total momentum equation:  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\rho_{i}u_{i}u_{j}}{ \partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial\tau_{ij}}{ \partial x_{j}}, \tag{3}\]  
The total energy equation for the one-temperature model:  
\[\frac{\partial\rho e}{\partial t}+\frac{\partial\rho hu_{j}}{\partial x_{j}}= \frac{\partial\tau_{ij}u_{i}}{\partial x_{j}}+\frac{\partial}{\partial x_{j}} \left(k\frac{\partial T}{\partial x_{j}}\right)+\frac{\partial}{\partial x_{ j}}\left(\sum_{s=1}^{N_{s}}\rho D_{s}\frac{h_{s}}{M_{s}}\frac{\partial Y_{s}}{ \partial x_{j}}\right), \tag{4}\]  
The total energy equation for the two-temperature model:  
\[\frac{\partial\rho e}{\partial t}+\frac{\partial\rho hu_{j}}{\partial x_{j}}= \frac{\partial\tau_{ij}u_{i}}{\partial x_{j}}+\frac{\partial}{\partial x_{j}} \left(k_{tr}\frac{\partial T_{tr}}{\partial x_{j}}+k_{ve}\frac{\partial T_{ve }}{\partial x_{j}}\right)+\frac{\partial}{\partial x_{j}}\left(\sum_{s=1}^{N_ {s}}\rho D_{s}\frac{h_{s}}{M_{s}}\frac{\partial Y_{s}}{\partial x_{j}}\right), \tag{5}\]  
The vibrational-electronic energy equation for the two-temperature model:  
\[\frac{\partial\rho e_{ve}}{\partial t}+\frac{\partial\rho e_{ve}u_{j}}{ \partial x_{j}}=\frac{\partial}{\partial x_{j}}\left(k_{ve}\frac{\partial T_{ ve}}{\partial x_{j}}\right)+\frac{\partial}{\partial x_{j}}\left(\sum_{s= dia.}\rho D_{s}\frac{e_{ves}}{M_{s}}\frac{\partial Y_{s}}{\partial x_{j}} \right)+\omega_{ve}, \tag{6}\]  
where \(N_{s}\) is the total number of gas mixture components; \(\rho_{s}\), \(e_{ves}\), \(h_{s}\), \(Y_{s}\) and \(D_{s}\) are the density, vibrational-electronic energy, specific enthalpy, mass fraction and mass diffusion coefficient of the \(s\)-th component, respectively; \(u_{j}\) denotes the \(j\) directional flow rate; \(e_{ve}\), \(e\) and \(h\) denote the vibrational-electronic energy, total specific energy and total specific enthalpy, \(T\) is the temperature, \(\rho\) is the total density, \(T_{tr}\) and \(T_{ve}\) are translational-rotational temperature and vibrational-electronic temperature, \(P\) and \(\tau_{ij}\) are the pressure and molecular viscous stress respectively; \(\omega_{ve}\), \(\omega_{s}\) are the vibrational-electronic energy source term and the \(s\) component chemical reaction generation source term respectively, and the radiation source term can be neglected in this paper; \(k\), \(k_{tr}\) and \(k_{ve}\) correspond to the total thermal conductivity, the thermal conductivity of the translational-rotational temperature and the vibrational-electronic temperature respectively.