esa-sceva/satcom-chunk-collection huggingface dataset:
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
## 4 Results and Discussion
### N-Alhanes  
Saturated n-alkanes are one of the most compounds in source rocks, and their carbon composition and distribution is mainly affected by sedimentary environment and thermal evolution [12]. Odd-Even Predominance (OEP) is 1.01-1.16, consistent with odd carbon  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Horizon** & **Density (g/cm\({}^{3}\))** & **Viscosity (Pa.s-20 \({}^{\circ}\)C)** \\ \hline
1 & P\({}_{2}w\) & 0.8513 & 18.77 \\
2 & P\({}_{2}w\) & 0.8809 & 63.10 \\
3 & P\({}_{3}w\) & 0.8517 & 19.69 \\
4 & P\({}_{3}w\) & 0.8542 & 20.17 \\
5 & T\({}_{1}b\) & 0.8400 & 9.74 \\
6 & T\({}_{1}b\) & 0.8654 & 28.25 \\
7 & J\({}_{3}q\) & 0.9270 & 2779.70 \\
8 & J\({}_{3}q\) & 0.9556 & 4847.00 \\
9 & J\({}_{3}q\) & 0.9564 & 4712.00 \\
10 & K\({}_{1}tg\) & 1.1300 & - \\
11 & K\({}_{1}tg\) & 1.1190 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Density and viscosity data of crude oil samples in Wuerhe area.  
number predominance. It indicates that the source rocks and crude oil in the study area were highly mature.  
Pristane/Phytane (Pr/Ph) is an important index for judging the redox conditions of the depositional environment [2]. The ratios of the samples are 0.36-0.96, all less than 1 (Table 3). The values of Pr/ n-C17 and Ph/ n-C18 can also reflect redox conditions and their oil source correlation. The ratios of Ph/nC18 and Pr/nC17 in source rocks, crude oil, and bitumen are deposited in the reducing environment and may be related to each other (Figure 6).
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Multi-Tasks and Joint Learning
#### 3.3.2 Task 2: AE-Based Latent Representation Learning and Data Reconstruction  
Task 2 uses a temporal convolutional AE neural network to generate latent representation based on the spatial patterns of the data and to reconstruct the data. The AE learns the latent representation of the data in an unsupervised manner. For data samples with \(M\)-dimensional \(X^{(i)}\in\Re^{M},1\leq i\leq N\), the AE reduces the data dimensionality to \(K\) dimensions, with \(Z^{(i)}\in\Re^{k},1\leq i\leq N\), where \(Z^{(i)}\) is the compressed representation of dimension \(i\). The optimization goal is to minimize the error between the reconstructed and original data.  
The model is expressed in Equation (13), where \(Encoder_{\theta 2}\) is the encoder network, \(Decoder_{\varphi 2}\) is the decoder network, and \(\theta_{2}\) and \(\varphi_{2}\) are the parameters to be learned. \(Encoder_{\theta 2}\) maps the original data \(X\in\Re^{M\times K}\) to a latent representation \(z_{ae}\), as shown in Equation (14).  
\[\sigma_{\text{Task2}}=Decoder_{\varphi 2}(Encoder_{\theta 2}(X)), \tag{13}\]  
Figure 4: Schematic of the sliding window.  
\[z_{ae}=Encoder_{\theta 2}(X) \tag{14}\]  
The errors of Task 2 are derived from the difference between the reconstructed values of the AE network \(\sigma_{\text{Task2}}\) and the real values \(\sigma_{\text{Task2}}\), and are calculated using the _logcosh_ function such that  
\[Loss2=L(\theta 2,\phi 2;X)=\sum\limits_{i=1}^{N}\log(\cosh(\sigma_{\text{Task2}} {}^{(i)}-X^{(i)})). \tag{15}\]
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
## 3 Energy Harvesting Application
### Principles of the Energy Harvesting Process  
The estimation of the harvestable energy is based on Ericsson-like thermodynamic cycles depicted in Figure 8, with iso-field (D-A, B-C) and iso-stress (A-B, C-D) steps. For an Ericsson cycle [42] considering compressive stress, in initial state A, the specimen is left in an initial state with no magnetic excitation field and no stress, then the magnetic field is increased to state B (without stress). Afterwards, the stress is reduced to state C at constant excitation field, and finally, the magnetic field is decreased back to state D. Then, the removal of the stress at the zero-excitation field leads back to state A. For an Ericsson cycle considering tensile stress, the same process is performed in the order of A-E-F-B-A. During the tensile process, the tensile stress is first increased (A-E), then the magnetic field  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Separated Model**} & \multicolumn{2}{c}{**Separated Model**} & \multirow{2}{*}{**Unified Model**} \\  & \multicolumn{2}{c}{**(Tensile)**} & & \multicolumn{2}{c}{**(Compressive)**} & \\ \hline
**Phase** & \(i=1\) & \(i=2\) & \(i=1\) & \(i=2\) & \(i=1\) & \(i=2\) \\ \hline \(\alpha\) (T) & 0.4997 & 0.9375 & 0.1741 & 1.091 & 0.4464 & 0.9991 \\ \hline \(\beta\) (m\(\cdot\)A\({}^{-1}\)) & \(24.9\times 10^{-3}\) & \(2.6\times 10^{-3}\) & \(10.6\times 10^{-3}\) & \(6.8\times 10^{-3}\) & \(1.227\times 10^{-3}\) & \(11.04\times 10^{-3}\) \\ \hline \(\gamma\) (Pa\({}^{-1}\)) & \(3\times 10^{-8}\) & \(4.99\times 10^{-8}\) & \(27.95\times 10^{-8}\) & \(3.765\times 10^{-8}\) & \(2.940\times 10^{-8}\) & \(4.329\times 10^{-8}\) \\ \hline \(\mu\) (T\(\cdot\)m\(\cdot\)A\({}^{-1}\)) & \(7.253\times 10^{-5}\) & \multicolumn{2}{c}{\(11.33\times 10^{-5}\)} & \multicolumn{2}{c}{\(7.817\times 10^{-5}\)} \\ \hline \(\kappa\) (T\(\cdot\)m\(\cdot\)A\({}^{-1}\cdot\)Pa\({}^{-1}\)) & \(7.305\times 10^{-15}\) & \multicolumn{2}{c}{\(1.879\times 10^{-12}\)} & \multicolumn{2}{c}{\(1.566\times 10^{-13}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the model separating tensile and compressive stress and the common model.  
is increased to its maximum value at the constant stress level (E-F), after that, the stress comes back to the initial level (F-B), and finally the magnetic field is decreased to the initial state A. Both processes lead to cycles in a clockwise direction, denoting the mechanical-to-magnetic energy conversion process. The theoretical ultimate converted energy from the material produced in this process could be described mathematically by the area enclosed in the cycle (shaded part in Figure 8). Therefore, during the process of energy harvesting, the energy could be maximized by combining the tension and compressive stresses, as shown in Figure 8; an Ericsson cycle from \(-\)30 MPa to 30 MPa could be performed in the sequence of D-E-F-C-D.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Selection of Combination Patterns  
Theoretically, our strategy is extensible in terms of the number and type of preprocessing denoisers. Therefore, our first problem is to establish the type and number of denoised methods involved in our combination, which will directly affect the performance of our method. We first conducted combination experiments for two types of denoised images to achieve a trade-off between effect and efficiency. As there are various methods in the field of image denoising, we only selected six representative and high-performing denoisers of various denoising methods to combine. We used the PSNR as our image quality measure to objectively evaluate the performance of the denoising methods. PSNR measures the intensity similarity between the clean image and the denoised image. The higher the PSNR value, the more similar are the intensities of the clean image and the denoised image. Table 1 lists all PSNRs for each method on the common image set. The denoising effect of the combination of FFDNet and BM3D is better than that of the combination of other state-of-the-art denoisers. As we know, BM3D is a typical model-based denoiser, whereas FFDNet is a typical learning-based denoiser. Their combination can explore both the internal and external information of the initial denoised images because of their strong complementarity.  
Figure 5: A range of 10 commonly used test images in the references.  
Figure 6: Some representative images in the BSD.  
After this, we utilized many combinations of denoising methods, and the results are shown in Table 2. It can be observed that when the number of denoisers is increased by three or more, the improvement in the processing effect of our method is not very significant, and there is even a slight decrease. Therefore, since the preprocessing of the generation of the initial denoised images will take some time and the improvement in the quality of the combined image is also lower, we selected BM3D and FFDNet as our initial denoisers because there is a good trade-off between effect and efficiency. In the experiments, the average processing time for this combination pattern is 4.3 min for each image, which is acceptable. In the future, we can select other state-of-the-art complementary denoisers to generate initial denoised images with specific demands on the computational complexity, denoising effect or a trade-off between the two goals. In our study, we aim to further boost the denoising performance.
###### Abstract  
The measurement of shaft bending (whirling) moment can be performed via a telemetry system including strain gauges which can obtain the bending stress. By using a single bridge of strain gauge, it was possible to obtain only the nominal bending moment. However, in case of the propeller shaft vibration measurement, the true dynamic bending moment is needed to evaluate the effect of propeller forces on the stability of the bearings. To deal with this, typically two bridges of strain gauge at 90 degrees are needed. This research presents a novel reliability assessment method in establishing the true dynamic bending moment using only one bridge of strain gauge. This is achieved by using the actual bending stress measured by strain gauge combined with its own phase lag stress at an angle of 90 degrees. To validate this technique, the experiments were performed under the rapid turning transient states during a sea trial of a 50,000 DWT oil/chemical tanker. As a result, great fluctuations in propeller force were detected, resulting in a non-uniform oil film in the bearings. The displacement sensor was also installed and confirmed the established true dynamic bending moment.  
true dynamic bending moment; strain gauge; propeller force; bearing stability; oil film +
Footnote †: journal: Applied  
Acticle  
Establishing the True Dynamic Bending Moment of Propeller Shaft Using a Single Bridge of Strain Gauge
## 7 Performance Evaluation  
### Simulation Setup  
In this section, we present the experimental results obtained by comparing two different simulations: binary ant colony algorithm with hill climbing (BACA-HC) and binary ant colony algorithm with simulated annealing (BACA-SA). The experiments were conducted to evaluate the performance of these algorithms in solving the sensor placement problem. Specifically, the two algorithms were systemically tested to discern their relative performance and determine which one demonstrated superior results in the problem of optimizing sensor placement and obtaining an optimal coverage of a 2D field. The implementation and evaluation of these algorithms were conducted using the Python programming language.  
We conducted a series of tests with varying parameters to evaluate the performance of our system. Some parameters were kept constant across all tests. Specifically, we set the number of iterations for the BACA (binary ant colony algorithm), SA (simulated annealing), and hill climbing algorithms at 100 iterations each. This decision aimed to strike a balance between computational efficiency and thorough exploration of solution spaces. Furthermore, a consistent ant population of 100 ants was employed for all experiments, contributing to the reliability and consistency of our comparative analyses. The last parameter that was kept constant was the sensing range. A standardized sensing range of 4 units was maintained for the sensors throughout all tests. This consistency served as a foundational element in our experimental design, providing a constant baseline for comparison.  
In order to explore different scenarios and assess the system under various conditions, we strategically adjusted other parameters across 3 distinct experiments, each representing a unique combination of these parameters. The parameters that were modified include the sensing area and the number of sensors deployed. These modifications allowed us to explore different search spaces and enabled a thorough examination of our system's performance under different spatial and sensor density conditions. The three experiments can be described by the following:  
Experiment 1: We conducted experiments in sensing fields with an area of 300 (20 units by 15 units) and a deployment of 12 sensors.  
Experiment 2: For a larger sensing field with an area of 600 (30 units by 20 units), we deployed 22 sensors.  
Experiment 3: To further investigate the system's performance in larger environments, we conducted experiments in a sensing field of an area of 1000 (40 units by 25 units) with a total of 40 sensors.  
To enhance the statistical reliability of our findings, each experiment underwent 100 repetitions. This approach provided a comprehensive view of algorithmic performance, contributing to a more thorough assessment of our results. The coverage percentages obtained for each iteration of the simulations were recorded and analyzed. The results are presented in the following figures and tables.
## 4 Conclusions  
In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm named CGAN-IFCM were proposed. CGAN demonstrated its effectiveness in generating new training data to reduce the effect of the imbalanced class and thus the deviation between the true positive rate and true negative rate of the detection model. CGAN improved the TNR and TPR by 10-12.6% and 5.8-16.2%, respectively. IFCM addresses the limitation of the existing FCM, where the IFCM uses multi-class voice disorder detection for three common types of voice disorders. IFCM introduces the interaction between data (feature space) and its neighboring data into the fuzzy membership function. The results show that the IFCM improves the TNR and TPR by 7.3-9% and 3.1-12%, respectively. We also discussed the advantages of CGAN for managing an imbalanced dataset with conditional information. This model could increase performance by 4-6% compared to traditional SMOTE and CSL methods. In addition, the performance between the proposed CGAN-IFCM was compared with the results of existing works that demonstrated TNR and TPR enhancements of 12.9-43.5% and 9.1-44.8%, respectively.  
Future research directions are suggested as follows: (i) merging databases SVD and VOICED to enlarge the number of samples in each class, thereby allowing data heterogeneity to be properly handled. (ii) Investigating the possibility of extending the multi-class detection model to incorporate more types of voice disorders. It is expected that the performance of detection model will be lowered when the number of classes (the complexity of the model) increases. Therefore, further studies on the aspects of feature extraction and model construction should be carried out.  
Formal analysis, K.T.C., M.D.L. and P.V.; investigation, K.T.C., M.D.L. and P.V.; methodology, K.T.C.; validation, K.T.C., M.D.L. and P.V.; visualization, K.T.C. and M.D.L.; writing--original draft, K.T.C., M.D.L. and P.V.; writing--review and editing, K.T.C., M.D.L. and P.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Secure Internal Data Markets
## 5 Open Research Questions
### Automated Deployment  
Many institutions that could benefit from sharing data internally do not have the capabilities to model and implement such internal data markets. Thus, mechanisms for the automation of both the modelling phase as well as the deployment phase are required, including the issue of providing a secure environment that still allows for the required flexibility.
## 4 Influence of QACs on Thermo-Mechanical Properties of Polymer Blends and Polymer Composites
### Influence of QASs and QAILs on Polymer Blends
However, the mechanical properties, such as tensile strength and tensile modulus of the blends increased by up to 14% and 24%, respectively, compared to the neat PET/PE blend. This is attributed to the vital role of the BDAC, which acted as a compatibilization agent between PET and PE [11]. Nevertheless, the tensile extension of the blends decreased because of their lower ductility compared to the blend without BDAC [11]. Moreover, the polymer chains of PET/PE blends containing BDAC are stiffer in comparison with the neat blend that reduced the ductility and deformability as well. This effectively decreased the tensile extension of the BDAC-compatibilized PET/PE blends. On the other hand, QAIL like BmimCl (chemical structure is shown in Figure 6) has been applied for compatibilization of NW/Cellulose blends [10]. The application of BmimCl influenced the degradation temperature, glass transition temperature, tensile strength, tensile modulus, and tensile extension of the blends strength, tensile modulus, and tensile extension of the blends (Table 7). The thermal properties, such as degradation temperature and glass transition temperature of the blends increased by up to 25% and 33%, respectively, compared to the neat NW. This is attributed to the miscibility between NW and cellulose as well as the formation of strong interaction between these biopolymers generated by BmimCl [10]. Moreover, the mechanical properties, for instance, tensile strength, tensile modulus, and tensile extension of the blends increased by up to 66%, 100%, and 56%, respectively, in comparison with the NW. This is due to the increase of the cellulose content in the blends and also good interfacial adhesion between them [10]. QAIL like DmimNTf\({}_{2}\) (chemical structure is shown in Figure 6) has been applied for compatibilization of PBS/RS blends [12]. The application of DmimNTf\({}_{2}\) has influenced the degradation temperature, melting temperature, tensile strength, tensile modulus, tensile
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
###### Abstract  
Recently, there has been an explosive growth in wireless devices capable of connecting to the Internet and utilizing various services anytime, anywhere, often while on the move. In the realm of the Internet, such devices are called mobile nodes. When these devices are in motion or traverse different domains while communicating, effective mobility management becomes essential to ensure the continuity of their services. Software-defined networking (SDN), a new paradigm in networking, offers numerous possibilities for addressing the challenges of mobility management. By decoupling the control and data planes, SDN enables greater flexibility and adaptability, making them a powerful framework for solving mobility-related issues. However, communication can still be momentarily disrupted due to frequent changes in IP addresses, a drop in radio signals, or configuration issues associated with gateways. Therefore, this paper introduces Routage Inter-domains in SDN (RI-SDN), a novel anchor-based routing method designed for inter-domain mobility in SDN architectures. The method identifies a suitable anchor domain, a critical intermediary domain that contributes to reducing delays during data transfer because it is the closest domain (i.e., node) to the destination. Once the anchor domain is identified, the best routing path is determined as the route with the smallest metric, incorporating elements such as bandwidth, flow operations, and the number of domain hops. Simulation results demonstrate significant improvements in data transfer delay and handover latency compared to existing methods. By leveraging SDN's potential, RI-SDN presents a robust and innovative solution for real-world scenarios requiring reliable mobility management.  
IP mobile; software-defined networking (SDN); mobility management; handover; Proxy Mobile IPv6 (PMIPve); mobility anchor +
Footnote †: journal:  
0000-0001-9566-1000]A.A.J.R.K.
## 4 Passive Anti-Biofouling Nanoc Coatings
### Fouling Release Strategies
#### 4.2.1 Hydrophobic Polymers  
Using the SIATRP technique (Surface-initiated atom transfer radical polymerization), [PERSON] et al. recently prepared superhydrophobic surfaces by grafting fluorinated polymethacrylates from previously chemically-etched polyethylene terephthalate (PET) fabrics [114]. The authors reported the excellent antifouling abilities and robustness for their superhydrophobic fabrics, which preserved these properties even after 2500 abrasion cycles, 1000 laundry cycles, and prolonged exposure to UV irradiation. Since PET is one of the most largely used plastic materials in medical applications in the form of membranes, filaments, and meshes including surgical meshes, vascular grafts, sewing cuffs for heart valves, and scaffolds for ligament and tendon repair [115], the improvement of the antifouling properties of PET-based medical devices is of course of great interest.
## 2 Literature Review  
The author in [5] introduced a novel integration scheme for the discriminator-based multipath mitigation technique and the VT loop. The proposed method was evaluated using real intermediate frequency (IF) signals collected in dynamic and static scenarios. The augmented receiver outperformed standalone VT and standalone ST concerning position estimation accuracy. Compared to the integration of ST with the multipath discriminator correlator, the minimum and maximum enhancements of the introduced integration scheme are 1.13 dB and 2.26 dB in terms of the tracking threshold, as noted in four-satellite and seven-satellite scenarios, respectively. The vector delay lock loop (VDLL) performance in multipath mitigation in an urban environment was assessed in [6]. Based on the findings of the paper, the VT loop identified the presence of a multipath based on the value of the variance of the measurement noise.  
Moreover, VDLL exhibited an enhanced performance with respect to position accuracy compared to ST loops, even in the presence of severe multipath conditions. [PERSON] et al., in [7], introduced an adaptive VT loop to minimize the effect of poor quality channels on the performance of VT. They proposed a rescaling method that rescales the measurement covariance matrix of the navigation filter. This adaptive implementation of VT has shown better positioning accuracy without any additional computational complexity. The VT loops in [8] were able to track blocked satellites' signals without the need for re-acquisition, which, in turn, resulted in a significant enhancement in the computational demands of the software receiver. A performance comparison between vector delay frequency lock loop (VDFLL), VDLL, and ST was carried out in [9]. Based on the results, VDFLL was more robust to transmission disturbances such as multipath and high dynamics.  
Furthermore, it outperformed other architectures in both tracking and navigation domains. The authors in [10] introduced two multi-pole complex adaptive infinite impulse response (IIR) and finite impulse response (FIR) notch filtering techniques in a vector-based global navigation satellite system (GNSS) receiver to suppress a continuous wave (CW) jamming signal. The proposed method was able to suppress the jamming signal based on the carrier phase measurements. In [11], a performance assessment of VT loops in the presence of radiofrequency (RF) interference was carried out. Their interference signals were examined in terms of additive white Gaussian noise (AWGN), CW,and ultra-wideband (UWB). VT loops outperformed ST in terms of position accuracy and tracking performance and position accuracy. Moreover, VT loops provided a gain of 5 dB in the carrier-to-noise ratio (C/N\({}_{0}\)) compared to ST loops. In [12], VDFLL showed an outstanding performance in terms of carrier tracking, C/N\({}_{0}\) and position accuracy in dense foliage and extreme urban canyon environments.