esa-sceva/satcom-chunk-collection huggingface dataset:
# TraceGuard: Fine-Tuning Pre-Trained Model by Using Stego Images to Trace Its User
## 3 Proposed Approach
### Design Overview  
In this section, the pipeline of the proposed TraceGuard is illustrated in Figure 2. All notations used are defined in Table 2. Firstly, Figure 1(a) shows the learning of the stenography network, composed of an encoder and decoder pair, by using \(D_{f}\) (\(D_{f}\in D_{s}\)). The encoder leans to embed the secret keys into cover images, and the decoder learns how to extract the secret keys from the embedding provided by a base pre-trained model. During the learning of the stenography network, the base pre-trained model is frozen. Secondly, Figure 1(b) shows how to fingerprint each base pre-trained model using \(D_{s}\) and secret keys. Before fingerprinting, each authorized user is designated a unique secret key.  
Then, the steganography network in the previous step is frozen, and the base pre-trained model is fine-tuned by using stego images such that one secret key among all the secret keys can be extracted with a lowest error rate. Such fine-tuning is accomplished for each secret key so that the base pre-trained model is fingerprinted for each authorized user and can be traced to the corresponding user. Finally, Figure 2c shows how to trace the suspect model by querying stego images. The owner of the pre-trained model embeds the secret key designated to each authorized user into \(D_{t}\) and feeds the stego images into the suspect pre-trained model. By comparing the error rate between the secret key extracted from the embedding provided by the suspect model and the embedded secret key, the owner can trace the responsibility of the authorized user holding the secret key with the minimum error rate, provided that the error rate is lower than the pre-set threshold \(T\). Otherwise, the suspect model is determined to be an independent model.  
Figure 2: Overview of TraceGuard. (**a**) The steganography network composed of encoder and decoder is learned by using a base pre-trained model, \(D_{s}\), and a set of secret keys. (**b**) The base pre-trained model is fine-tuned to be individually fingerprinted by using the steganography network, \(D_{f}\), and secret key designated to each authorized user. (**c**) The suspect pre-trained model is traced by querying stego-images, which are generated by using \(D_{t}\) and steganography network. The authorized user allocated for the secret key with the lowest error rate between the embedded and extracted and below the pre-set threshold \(T\) is responsible for the suspect pre-trained model. For a concise description, we only show a single extracted key. In effect, we use the query set to obtain a set of extracted keys so as to obtain a stable error rate. See details in Section 3.5.
## 5 Simulation  
The proposed system is evaluated in the two aspects, the feasibility of simultaneous transmission and localization accuracy. We demonstrate that the HFM-based multiple access system supports simultaneous transmission, even in the Doppler-channel. In addition, we prove that the MAC delay should be removed in the AUV localization to enhance the localization accuracy.  
The simulation parameters used for the evaluation are as follows. The network size is 300 m for short range communication and 1500 m for long range communication respectively, and the ANs are deployed on the surface as shown in Figure 6. The AN is supposed to be equipped with GPS, thus the time between all the ANs is automatically synchronized. The AUV can measure the depth using the pressure sensor, consequently the z-coordinate on the ranging is replaced with the measured depth. The sampling frequency used in the acoustic modem and the symbol duration of the HFM signal is set up to 44 kHz and 20 ms respectively. In addition, the total and sub-channel bandwidth is set up to [10; 30] kHz and 2 kHz respectively, thus ten unique chirp rates can be generated, which are allocated to each user. The simulation parameters are summarized in Table 1.
## 4 The Physical Layer Security Performance of the Relay Transmission Model
### The Influence of the Relay Power for P(Cs > 0)  
#### 4.1.1 Simulation Results with Various Relay Power  
Figure 5 is a typical cooperative relay communication model adopted from [7], with source, destination and eavesdropper nodes fixed and relay node's location changing between the source and destination. The difference in this paper is that the relay has different relay behaviors. We simulate the \(P(C_{S}>0)\) verses \(P_{r}/P_{s}\) under different relay behaviors, where \(P_{r}\) and \(P_{s}\) are the power in relay and source, respectively, and the SNR of the source to destination (S-D) channel is 5 dB. The path loss in the simulation is \(1/d^{2}\), where \(d\) is the distance between two nodes.  
The relay's normalized location is fixed at (0.5; 0) (the middle of S and D) and source power \(P_{S}\) is fixed at \(-40\) dBm. Relay power \(P_{r}\) is changed from \(-70\) dBm to \(-20\) dBm. We simulate the \(P(C_{S}>0)\) under three different relay behavior according to (11), (12) (\(P_{off}=50\%\)) and (13) as shown in Figure 6. We find that there exists an optimal relay power \(P_{r}=P_{s}\), where \(P(C_{S}>0)\) would reach a peak value in cooperative and selfish scenario. When the relay power \(P_{r}\) increases beyond \(P_{s}\), the \(P(C_{S}>0)\) decreases. We also notice that increasing the malicious relay power always decreases the \(P(C_{S}>0)\).
## 4 Conclusions  
In this study, a simulation analysis model was developed in order to rapidly analyze the system characteristics of a wind turbine generator, using powertrain combination. Before the system construction, the system power generation efficiency and annual power generation were predicted using simulation. The results of this study could be summarized as follows:  
1. The overall efficiency was examined by testing the performances of a hydraulic pump and a hydraulic motor with hydrostatic transmission, which were the key components of wind turbine generators. In particular, it was confirmed that the overall efficiency decreased with the capacity of the hydraulic motor.
2. The serial combination of generators exhibited a higher power generation efficiency when the wind speed was higher than 7 m/s, while the parallel combination showed a higher power generation efficiency when the wind speed was lower than 7 m/s.
3. The analysis of e annual power generation energy, which considered the regional characteristics, showed that step 2, which was a combination of the parallel hydraulic motor and serial generator, was the optimal combination and step 5, which was the combination of the large-capacity pump and motor, was the worst combination.
4. It was confirmed that power generation efficiency declined sharply when the wind speed exceeded 10-11 m/s, because the maximum power generation capacity of the induction motor was limited. This indicated that the maximum power generation capacity needed to be determined, considering the regional characteristics for designing a wind turbine generator.
5. It was possible to predict system performance before the system construction and to systematically and specifically approach problems that may have arisen after the system construction using a simulation model.
6. The system performance could be analyzed using an analysis model for designing the controller and blade of a wind turbine generator. This would save the time and cost that were required for research and development.
7. The major disadvantage of using a simulation in the concept design stage, was that it could not perform the performance test on all of the components. Therefore, the performance tests on all of the components are required.
8. The system will be assembled based on the results of this study, and a full system performance test will be performed. In addition, the verification of the full analytical model will be required before applying it to the various fields that use analytical models.  
Drafting of manuscript: D.K.; acquisition of design parameters: B.P.; model and analysis: D.K.; planning of research: B.P.; and supervision of the research: J.J.  
**Acknowledgments:** The author thanks [PERSON] at the Korea Electric Power Research Institute for supplying the research materials used in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Numerical Results and Discussion
### Effect of Random Cumulative Pitch Deviations on Unmodified HGRS
#### 3.1.2 Dynamic Responses Analysis  
Figure 11 shows the dynamic transmission errors (DTEs) of the helical gear pair with random cumulative pitch deviations under different load conditions. The load torques are also 200 N\(\cdot\)m, 350 N\(\cdot\)m, 600 N\(\cdot\)m, and 1000 N\(\cdot\)m, respectively. The input speed is 4000 r/min. It can be seen that the frequency spectrum becomes complex considering random cumulative pitch deviations. In frequency domain of DTEs, the amplitude of shaft frequency is the largest and the sideband frequencies around mesh frequency and its harmonics (MFIHs) appear. When the load torque is smaller than 600 N\(\cdot\)m, the amplitudes of MFIHs are less than that of sideband frequencies \(f_{m}\pm if_{s}(i=1,2,\ldots,n)\). With the increase of load torque, the amplitudes of MFIHs will be enhanced. The mesh frequency amplitude become larger than that of sideband frequencies when load torque is higher than 1000 N\(\cdot\)m.  
Figure 10: Load distributions in the twelfth mesh cycle considering random cumulative pitch deviations.  
For bearing 1, the vibration acceleration along \(y\) direction at different load torques are shown in Figure 12. As similar as DTE, the frequency components of bearing vibration acceleration also become complex. The sideband frequencies \(f_{m}\pm if_{s}(i=1,2,\ldots,n)\) appear in the two sides of MFIHs. Some sideband frequencies are larger than mesh frequency when load torque is smaller than 600 N\(\cdot\)m. As the load torque increases, mesh frequency amplitude will increase correspondingly. When the load torque is larger than 1000 N\(\cdot\)m, the mesh frequency becomes the most predominant. Unlike DTEs, the shaft frequency is not the most predominant frequency in bearing vibration acceleration.  
Figure 11: DTEs at different load torques considering random cumulative pitch deviations.
## 4 Hierarchical Cooperative MIMO Clustering
### Design Considerations
#### 4.5.1 Factors Affecting Clustering  
There are three main factors which affect the clustering process. The first is the transmission range of a sensory node. A node is able to communicate with any other node within a given transmission range. As we increase transmission range, there is also an increase in the radius of a cluster. For sparse networks, we may have to increase the transmission range but for dense networks the transmission range is kept less so as not to burden the clusterheads. The second parameter is the residual energies of nodes. A node with a higher amount of energy is a better candidate for becoming a clusterhead. The third factor is link quality.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### System Loss  
System losses in SAR systems are critical factors that directly impact the performance and effectiveness of the radar, particularly in terms of the radar equation and the NESZ equation. System losses in a SAR system encompass all losses in the radar signal path, from transmission to reception. This includes losses in the antenna, transmission line, radar processing chain, and any other components that the signal traverses through. These losses can be caused by a variety of factors like absorption and scattering in the atmosphere, impedance mismatches in the system, inefficiencies in the antenna design, and losses in the electronic components such as amplifiers and filters. In the radar equation, losses reduce the effective power of the transmitted signal and the received signal. This directly impacts the SNR and the NESZ, which is a measure of the radar's sensitivity. A higher system loss means that the radar needs to emit more power to achieve the same level of performance, or conversely, it will have a reduced ability to detect and image weak targets. In practical terms, engineers and designers of SAR systems aim to minimize losses to maximize the efficiency and effectiveness of the radar. This is achieved through careful design of the system's components, optimizing the signal path, and selecting materials and components with lower losses [30; 31; 46]. Though it depends on the engineering capability, in the present work it is assumed that the losses vary between 2 and 8 dB.
## 2 Results and Discussion
### Identification and Quantitation of Compounds and OAV Analysis
According to results obtained by [PERSON], those with OAVs greater than 1 were considered to contribute to the aroma of samples [19]. In addition to ethyl acetate, the OAVs of the other compounds were greater than 10 in the samples and are therefore considered as aroma-active compounds in RRT fruits. Among these compounds, ethyl 2-methylpropanoate (0.12 mg/kg), ethyl butanoate (0.59 mg/kg), ethyl 2-methylbutyrate (0.24 mg/kg), 3-methylbutyl acetate (0.19 mg/kg), E-2-hexenal (1.78 mg/kg), ethyl hexanoate (2.21 mg/kg), 3,7-dimethyl-1,3,6-Octatriene (0.49 mg/kg), 2-Heptanol (1.06 mg/kg), ethyl heptanoate (0.02 mg/kg), ethyl E-2-hexenoate (0.04 mg/kg), and ethyl octanoat (0.54 mg/kg) were present at relatively low concentrations (<1 mg/kg). Despite their low levels, the OAVs of these compounds were above 10, so these compounds might significantly contribute to the aroma of RRT fruits [20]. Ethyl 2-methylpropanoate (OAV of 1167), ethyl butanoate (OAV of 3279), ethyl 2-methylbutyrate (OAV of 811), and ethyl hexanoate (OAV of 2205) were present with much higher OAVs than other compounds. However, volatile compounds such as 2-heptanone, ethyl tglate, ethyl 3-hexenoate, 2-nonanone, nonanal, benzaldehyde, 2-undecanone, caryophyllene, butyric acid, heptanoic acid, octanoic acid, and eugenol had high AIs, but they do have a lower OAV, which could be related to the difference of thresholds in air and water. According to previous studies, OAV might be a more effective method for the verification of the aroma-active compounds [21]. The GC-O method simply verifies the contribution of each compound to the overall aroma, but the OAV method considers the interaction for the aromatic compound and the food matrix. Thus, the use of OAVs may provide a better assessment of the OAVs of compounds as long as the quantitative data and the odor thresholds detected are accurate.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 4 Purification of Silicon  
Production of silicon from several agricultural wastes is not usually obtained directly without passing through the refining process. Thus, purification of silicon is an essential part, especially in industrial solar-grade (SoG)-silicon. Due to the shortage of semiconductor-grade, which is expensive, it has been a great challenge for its production [49; 50].  
This later led to the new ways of upgrading the metallurgical-grade silicon at a lower cost though at the expense of loss of some of silicon. The upgraded metallurgical-grade silicon (UMG-Si) generally contains impurities, which affects the photovoltaic performance, namely, short-circuit current (JSC), open-circuit voltage (VOC), fill factor (FF), and power conversion efficiency in silicon-based solar cells [51; 52; 53; 54].  
Furthermore, the phosphorus diffusion getting process has been widely utilized to improve the performance of Si solar cells. Moreover, the enhanced performance in the photovoltaic technology has been mainly due to improving the electrical properties of UMG  
Figure 6: Flow chart of existing bulk silicon synthesis routes, including magnesium reduction [45].  
Si wafers and solar cells. The minority-carrier recombination lifetime and photovoltaic performance degradation of p-type silicon solar cells, as well as the effects of metallic contaminant type and concentration thereof (Al, Cu, Ni, and Fe), have been reported [55].  
Therefore, the stable SoG-Si has led to the quest to find suitable purification methods and to reduce the production cost minimally without affecting the desired properties of the built solar cells. It is worth noting that these several purification methods are usually the simple preparation of SoG-Si from lower grades, mostly the MG-Si. Consequently, it has been shown that heat treatment could be used to essentially modify the morphology of purified silicon.  
Presently, the solvent refining process has been proved to be an important purification method that is beneficiation to the cost due to its high purification efficiency and low processing temperature. It usually involves alloying silicon with another element to enable the solvation and solidification with recrystallization of silicon from the solvent at a much lower temperature than the melting point of silicon, subsequently followed by separation of the crystallized silicon [56; 57]. Several other refining methods include directional solidification, fractional melting, leaching, slag treatment/gas blowing, electron beam melting, etc., [57].
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### UAV Handover Scenarios
#### 3.5.1 HO Scenarios with Flying Base Stations  
When a UAV is used as a fly BS, three scenarios can exist. In the first possible scenario, the UAV experiences HO when it changes its ground BS. In the second type of HO scenario, the UE changes from connecting to the serving UAV BS to connecting to another serving BS. In the third type of HO scenario, the UAV experiences HO when it changes its serving satellite node to another one. These scenarios are illustrated in Figure 7.  
Figure 7: HO for UAVs as base stations in a future ultra–dense heterogenous network.
## 4 Multi-Objective Optimization of Fan Blade Based on RBF Model with CST Parameterization as Inputs
### RBF Model  
The approximate model is a mathematical method approaching a set of input variables and output variables.  
\[y(x)=\widetilde{y}(x)+\varepsilon \tag{6}\]  
where \(y(x)\) is the actual response values; \(\widetilde{y}(x)\) is the response approximation, \(\varepsilon\) is the random error, respectively.  
The Radial Basis Function network can approximate arbitrary non-linear functions and has good generalization ability [27; 28]. Therefore, the Radial Basis Function network (RBF) is selected in this paper. Figure 9 shows the RBF model used in this optimization  
Figure 8: Opt LHD test design.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(v_{i}\) & **min** & **max** \\ \hline \(v_{1}\) & 0.3 & 0.7 \\ \(v_{2}\) & 2 & 4 \\ \(v_{3}\) & 1 & 3 \\ \(v_{4}\) & 0.4 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Value range of design variables \(v_{i}\).  
process. The model is a three-layer forward network structure divided into an input layer, hidden layer and output layer.  
The data in Figure 8 were normalized and imported into Isight software to establish an approximate model and perform multi-objective optimization. Isight software is a software framework to integrate and automate the tedious and repetitive analytical design process. This data (sample set) is generally composed of design and optimized variables (\(Qv\), \(\eta\)). The first 40 samples were selected as training samples, and the remaining five were used as test samples. Generally speaking, the training samples were used to build an approximate model, and the test samples were used to examine the accuracy of the approximate model. The \(R^{2}\) term is used to evaluate the accuracy of the approximate model:  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-\overline{y})^{2}}}{\sum \limits_{i=1}^{n}{(y_{i}-\overline{y})^{2}}} \tag{7}\]  
where \(i\) is the number of training samples, \(n\) is the total number of samples, \(\overline{y}\) is the average of the samples and \(\hat{y}\) is the predicted value of the test sample. The closer the \(R^{2}\) is to 1, the better the fitting effect. Table 4 shows that the \(R^{2}\) terms of each parameter is less than 0.95, so the approximate model is considered reliable.  
The value \(R^{2}\) of this method shows that the prediction is reliable. The optimal sample was selected among 40 groups of samples for the purpose of determining the weight.
## 2 Materials and Methods
### Kinematic Data Analysis
#### 2.3.2 Cycle-Averaged Kinematic Parameters Analysis  
First, repeatability within participants was checked for the 12 cycle-averaged kinematic parameters (two related to trajectory and ten related to kinematic synergies) using the most appropriate tests depending on data normality, including the Intraclass Correlation Coefficient and Concordance Correlation Coefficient, and Cohen's Weighted Kappa for ordinal data. Repeatability was confirmed for nearly all parameters. Based on graphical inspection and the limited sample size, each participant's values were averaged across the five movement cycles.  
Then, due to the reduced number of participants, non-parametric statistical analyses were performed. To examine differences between test conditions in each health condition group, the [PERSON] test was applied to each cycle-averaged kinematic parameter with factor test condition (RBBT/VBBT) for each health condition group (healthy and patient). Finally, to examine differences between health conditions in each test condition group, the Kruskal-Wallis test (the non-parametric equivalent to Analysis of Variance (ANOVA)) was applied to each cycle-averaged kinematic parameter with factor health condition (healthy/patient) for each test condition group (RBBT and VBBT).
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 3 Congestion-Aware Routing Algorithm
### Route Parameter Updates  
The object of this paper is an active wireless sensor network, so nodes need not consider energy consumption. After the node finishes forwarding the data, it will update its network parameters and broadcast its routing parameters to the neighbor node through the HELLO message. Each node maintains a neighbor node information table that records parameters such as the load state of the neighbor node, cache queue status, and node distance. When each neighbor node receives the HELLO message, it will update its node information table to monitor the congestion status of the adjacent area in real-time. Finally, the node calculates the next-hop route for each packet, effectively alleviating the network congestion situation.The HELLO message is a kind of control packet. The size of the control packet is small, so this paper does not consider the traffic generated by the control packet.
# Weighted Matrix Factorization Recommendation Model Incorporating Social Trust
## 5 Experimental Evaluation  
### Experimental Environment and Datasets  
All experimental evaluations presented in this research paper were performed within a consistent experimental environment, employing Ubuntu 20.04 as the operating system, an Intel(R) Xeon(R) Silver 4214R CPU operating at 2.40 GHz, manufactured by Intel Corporation (Santa Clara, CA, USA), and an NVIDIA Tesla T4 GPU with 16 GB of memory manufactured by NVIDIA Corporation (Santa Clara, CA, USA).  
To validate the performance of the Trust-WMF model, experiments were conducted using two public datasets of different sizes and levels of sparsity: Epinions and Ciao.  
Epinions is derived from a consumer review website (www.epinions.com) accessed on 1 December 2003, where users can write reviews and rate products. Additionally, users can establish trust networks by indicating which other users' reviews they trust. Ciao is from an online shopping guide website (www.ciao.co.uk), accessed on 1 May 2011. It not only offers user ratings and review functionalities but also allows users to establish friendships, thereby creating a social network system.  
The rating range for both datasets is 1-5, and the trust relationships between users are unidirectional. Table 1 describes the basic characteristics of these two datasets.