esa-sceva/satcom-chunk-collection huggingface dataset:
# Heat Transfer in Thermoelectric Generators for Waste Energy Recovery in Piston Engines
## 4 Results
### Influence of Internal Geometry  
Figure 11 shows the temperature distribution of the heat exchanger wall for the finned geometry compared to the base case without fins. Fins manage a 50 \({}^{\circ}\)C increase in the entrance and middle regions but do not greatly increase the temperature near the outlet; this is due to the higher heat dissipated upstream. It can be observed that the hot inlet gas loses temperature across the TEG, and as a result, the upstream wall temperature is higher than the downstream wall temperature near the end of the heat exchanger. The fins manage to keep a high temperature near the inlet (approximately 380 \({}^{\circ}\)C) and at the end of the central squared body (around 340 \({}^{\circ}\)C).  
In Figure 12, the internal temperature distribution is shown. The cross-shaped temperature distribution (Figure 10a) in the inlet of the central body is due to the recirculation regions at the corners of the heat exchanger. In the middle part, the recirculation region takes up less cross-sectional area because the recirculation bubbles are ending. In the rear part, the recirculation is not present, and the temperature profile is more homogeneous. In fact, the fins confine the recirculation region, diminishing its effects. This is also reflected in the pressure drop (Table 6). Despite being oriented with the flow stream, the fins should cause a slight increase in the pressure drop. Nevertheless, their effect of confining the recirculation region results in an overall slight reduction in the pressure drop.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Pressure Drop (Pa)**} & \multicolumn{2}{c}{**Wall Heat Transfer (W)**} \\ \hline Base case & 324.3 & - & 841.8 & - \\ \hline Finsned case & 292.7 & \(-9.74\%\) & 865.4 & +2.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pressure drop comparison.  
Figure 11: Temperature (\({}^{\circ}\)C) distribution in the central body (**a**) without fins and (**b**) with fins.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 4 Evolution Challenges
### Engineering Tool Challenges  
In TSN, the promised performance relies on traffic engineering and scheduling, which further rely on two TSN entities: (i) Centralized User Configuration (CUC) and (ii) Centralized Network Configuration (CNC), as specified in IEE 802.1 Qcc. CUC is responsible for discovering the network's physical topology, collecting requirements and properties of every TSN flow from the end devices, e.g., the packet size, cycle time, end-to-end latency, and sending the collected information to the CNC. The CNC executes the scheduling and returns the decision of whether a TSN flow can be accepted. For an accepted TSN flow, the corresponding end-to-end path will be sent to CUC together with the scheduling along the path. The scheduling is also sent out from CNC to the bridges along the end-to-end path via network management protocols, e.g., NETCONF.  
The first challenge is the lack of a standardized northbound interface of CUC, i.e., the parameters to characterize each TSN flow, which are necessary to reduce the manual input from the operator and enable the plug-and-play functionality of end devices. Another challenge arises from online diagnosis and configuration. After the initial offline scheduling and configuration, the process is expected to operate with very short and even zero downtime. This requires an online diagnosis to detect potential problems beforehand and may further result in some reconfiguration of the related TSN flows. Moreover, online configuration is needed when adding new devices or new applications to the operation network. In this case, CUC will send requests for the new TSN flows to CNC responsible for calculating schedules and updating the configuration. However, when the network is unable to handle new TSN flows due to the lack of available resources, it is not appreciated to simply decline the request and return a notice to upgrade the network. Instead, the CNC should provide a good scheduling strategy to accommodate all the new critical TSN flows while making a negligible effect on the existing TSN flows to limit the configuration changes on the bridges.  
From an implementation perspective, the support of YANG models and NETCONF is not entirely in place. Despite the fact that the YANG data model has been included in the TSN standard and it is agreed that NETCONF will serve as the network management protocol between CNC and the bridges, the development of YANG models and NETCONF in many switches progress rather slow compared to other TSN functionalities. The lack of efficient configuration presents a gap to enable an automated engineering workflow.
## 2 Materials and Methods
### Defect Detection Using Convolutional Neural Network (CNN)
#### 2.3.1 Dataset Preparation  
Datasets for Sample No.1 and Sample No.2 were prepared for the training, validation, and generalization of the CNN model. Two dataset classes, representing defective and non-defective areas of Sample No.1, were used for CNN training and validation. Sample No.2 was used as \"unseen\" data to evaluate the generalization capabilities of the model, and also had two classes of datasets. The CNN model was designed to detect defects based on ultrasound C-scans at each time point within a selected time interval, which captured a sufficient amount of data for training. This interval extended from the first interface reflection to the point where the reflections were attenuated. It included eleven multiple reflections for Sample No.1, and five for Sample No.2.  
To extract the required images, C-scan generation was performed for each time moment. In total, 1906 images were generated, equally divided between two classes: defective and non-defective. Then, the dataset was divided into training and validation sets such that 70% of the data was used for CNN model training, while the remaining 30% was allocated for validation. In the case of the \"unseen\" data, all generated C-scans were used for validation and generalization testing. As a result, 667 images with defects and 667 without defects were used for training, while 286 defective and 286 non-defective images were set aside for validation and generalization. Examples of the C-scan images for both samples, which were used as inputs for the neural network, are presented in Figure 6.
## 2 Methodology
### Hyperspectral Data Formulation  
The symbols used in this work has been illustrated in Table 1. Let us assume a Hyperspectral dataset can be expressed as \(X=[x_{1},x_{2},x_{3},...,x_{L}]^{T}\in R^{L\times(M\times N)}\) consisting of \(M\times N\) samples associated with \(C\) classes per band with total \(L\) bands, in which each sample is represented as \((x_{i},y_{j})\), where \(y_{j}\) is the class label of \(x_{i}\) sample. In a nutshell \(i\)th sample belongs to \(j\)th class.  
Furthermore, suppose that \(n=50\) number of initial labeled training samples (given equal representation to each class) are chosen from \(X\) to form the training set \(X_{T}=\{(x_{i},y_{j})\}\) where \(i=1,2,3,\ldots,n\) and \(j=1,2,3,\ldots,C\). The remaining samples make the validation set \(X_{V}=\{(x_{i},y_{i})\}_{i=1}^{m}\). It should be noted that \(n<<m\), and \((X_{T}\cap X_{V})=\emptyset\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Explanations** \\ \hline \(X\) & Hyperspectral Data Cube, where \(X=[x_{1},x_{2},x_{3},...,x_{L}]^{T}\in R^{L\times(M\times N)}\) \\ \(L\) & Number of Bands in Hyperspectral Image Cube. \\ \(M\times N\) & Number of Samples in each Band of Hyperspectral cube. \\ \(C\) & Total Number of Classes in Hyperspectral Cube. \\ \(n\) & Total Number of Initial Training Samples. \\ \(m\) & Total Number of Initial Test Samples. \\ \(h\) & Total Number of Queried Sample \\ \(\hat{m}\) & Number of Samples Selected in each Iteration where \(\hat{m}\ll m\) also \(n+\hat{m}\ll m\) \\ \(X\) & Index of Selected Samples. \\ \(x_{i}\) & \(i\)th Spectral Sample and \(i=1,2,3,\ldots,n\). \\ \(y_{j}\) & Class label of \(i\)th Spectral Sample, where \(j=1,2,3,\ldots,C\) \\ \(X_{T}\) & Training Set consist of \(n\) Samples. \\ \(X_{V}\) & Initial Test Set consist of \(m\) Samples, where \(n<<m\) and \((X_{T}\cap X_{V})=\emptyset\) \\ \(\mu\) & Membership Matrix, i.e., output of classifier \\ \(u_{ij}\) & Membership of \(i\)th sample for \(j\)th class \\ \(F(\mu_{ij})\) & Fuzziness computed through \(u_{ij}\) \\ \(\alpha_{j}\) & Selected Reference Training Samples \\ \(\beta_{ij}\) & Spectral Angular Distance among the New and Existing Training Samples \\ \(OA\) & Overall Accuracy \\ \(\kappa\) & Kappa metric \\ TP & True Positive \\ FP & False Positive \\ TN & True Negative \\ FN & False Negative \\ MI & Mutual Information \\ BT & Breaking Ties \\ MBT & Modified Breaking Ties \\ \hline \hline \end{tabular}
\end{table}
Table 1: Explanation of used Symbols.
# High-Power Broadband Frequency Chirped Intensity-Modulated Single-Frequency 1064-nm Laser
## 4 Experimental Setup  
Figure 2 shows the experimental setup. The system used a non-planar ring oscillator (NPRO) single-frequency laser as the seed source [19]. The output laser was modulated via an EOM (CONQUER: KG-AM-1064-10G, Beijing, China). The maximum optical power input to the EOM was 50 mW.  
A frequency chirping signal was loaded into the EOM driver. The modulation frequency was tuned from 10 MHz to 2.1 GHz. A bias was used to ensure the Mach-Zehnder modulator operated. A low-frequency low-amplitude dither signal together with a bias voltage was injected into the modulator to ensure it operated stably. The modulated light was separated via a fiber splitter. Here, 1% of the output was directed to a photodetector (PD). The output from the PD was fed back to the DC bias controller to make sure that an appropriate bias voltage was loaded into the EOM to compensate the phase drift. It kept reading the output from the modulator and determined the condition of the bias voltage and the related error. A compensate bias voltage was applied afterwards according to the previous measurements. The bias voltage kept the work point of the modulator transfer function at a fixed position (phase shift) to minimize the second harmonic distortion. Here, 99% of the modulated light was directed to the fiber amplifier.  
Figure 1: Numerical calculation of the dependence of the amplified output power on the fiber length at different pump and signal powers.  
The modulated low-power laser beam was amplified by a two-stage optical fiber power amplifier. The first stage used a 5-m gain fiber (Nuferm: LMA-YDF-10/125, Granby, CT, USA), and the available pump power of the first stage amplifier was 4 W. For the second-stage amplifier, a 12-m fiber (Nuferm: LMA-YDF-25/250, Granby, CT, USA) was used. The pumping source was two 20-W laser diodes. A mode field adapter was used to match the different core diameters of the two gain fibers to reduce transmission loss. A pump stripper was used to filter the residual pump light in the inner cladding of the second gain fiber. Photoelectric feedback was a common method to suppress laser intensity noise. Here, 0.1% of the output was directed to a PD. After the electric signal of laser power fluctuation was processed by the feedback control circuit, the driving current of the pump light in the optical fiber amplification system was adjusted to achieve stable laser output.
## Appendix A State Space Equations for FG-GPLRC Arch with Different End Conditions
### C-F Arch  
The corresponding end conditions are:  
\[\begin{array}{ll}U_{1}=W_{1}=0,\quad\text{at }\theta=0\\ \Gamma_{N}=0,\ Y_{N}=-\frac{c_{33}}{c_{13}}\frac{k_{11}}{c_{0}}\frac{\lambda} {\eta}\Big{(}\sum\limits_{k=2}^{N}g_{Nk}^{(1)}U_{k}+W_{N}\Big{)},&\text{ at }\theta=\theta_{0}\end{array} \tag{18}\]  
The state equations are:  
\[\begin{array}{ll}\frac{\partial U_{i}}{\partial\zeta}=\frac{\lambda}{\eta}U_ {i}-\frac{\lambda}{\eta}\sum\limits_{k=2}^{N}g_{ik}^{(1)}W_{k}+\frac{c_{0}}{ c_{55}}\Gamma_{i}&(i=2,3,\cdots,N)\\ \frac{\partial Y_{i}}{\partial\zeta}=\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11 }}{c_{0}}\sum\limits_{k=2}^{N}g_{ik}^{(1)}U_{k}+\frac{\lambda}{\eta}\Big{(} \frac{c_{13}}{c_{33}}-1\Big{)}Y_{i}-\frac{\rho}{\rho_{0}}\Omega^{2}W_{i}+\frac {\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}W_{i}&(i=1,2,\cdots,N-1)\\ -\frac{\lambda}{\eta}\sum\limits_{k=1}^{N-1}g_{ik}^{(1)}\Gamma_{k}\\ \frac{\partial W_{i}}{\partial\zeta}=-\frac{c_{13}}{c_{33}}\frac{\lambda}{\eta} \sum\limits_{k=2}^{N}g_{ik}^{(1)}U_{k}+\frac{c_{0}}{c_{33}}Y_{i}-\frac{c_{13}} {c_{33}}\frac{\lambda}{\eta}W_{i}&(i=2,3,\cdots,N)\\ \frac{\partial\Gamma_{i}}{\partial\zeta}=-\frac{\rho}{\rho_{0}}\Omega^{2}U_{i }+\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}\sum\limits_{k=2}^{N}\Big{(} g_{iN}^{(1)}S_{Nk}^{(1)}-g_{ik}^{(2)}\Big{)}U_{k}&\\ -\frac{c_{13}}{c_{33}}\frac{\lambda}{\eta}\sum\limits_{k=1}^{N-1}g_{ik}^{(1)}Y_ {k}-\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}\sum\limits_{k=2}^{N-1}g_{ ik}^{(1)}W_{k}-\frac{2\lambda}{\eta}\Gamma_{i}&(i=1,2,\cdots,N-1)\end{array} \tag{19}\]
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
## 6 Conclusions  
Time-domain anti-jamming technology is one of the most effective ways to suppress narrowband interference. Traditional time-domain anti-jamming methods are usually used in continuous signal processing, but for T/R time-sharing reception technology the information loss caused by these methods cannot be ignored, because their long convergence time will have a great impact on the performance of the navigation receiver. This paper presents a robust anti-jamming algorithm based on T/R time-sharing technology, which can solve the information loss caused by repeated convergence of the time-domain anti-jamming filter. Based on the stability characteristics of interference, the prior weight vector is stored in memory to provide the initial value of the anti-jamming filter for the next transceiver period. The experimental results show that the CNR loss of the robust anti-jamming algorithm has been greatly improved compared with the traditional anti-jamming algorithm, and the information loss is generally 20 dB less than the traditional one. Regardless of the characteristic changes of interference and signal, the robust anti-jamming algorithm can achieve good performance improvement. In addition, the performance advantages of the robust anti-jamming algorithm may be more evident for other BeiDou link communications which are still under construction.  
[PERSON]. ([PERSON]) performed the theoretical study, conducted the experiments, processed the data and wrote the manuscript. [PERSON] and [PERSON] designed the system, provided research suggestions and revised the manuscript together. [PERSON], [PERSON] and [PERSON] helped in performing the experiments. [PERSON] and [PERSON]. ([PERSON]) provided the experiment equipment and suggestions for the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.62003354).  
Not applicable.  
The authors would like to thank the editors and reviewers for their efforts in the publication of this paper.  
The authors declare no conflict of interest.  
Figure 16: The data loss under different signal characteristics. (**a**) The performance of the traditional anti-jamming algorithm. (**b**) The performance of the robust anti-jamming algorithm.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 6 Development and Experiment of a High-Resolution Polarization Imaging Instrument
### Analysis of Verification Results  
Under the condition of natural light exposure during the day, the infrared image and the intensity image are not much different. After the polarization image and the intensity image are fused, the contrast is greatly improved; The image contrast is improved again by fusing the polarization image, infrared image and intensity image.  
When illuminated by LED light at night, the imaging effect of the intensity image is very poor, the infrared image is much clearer, and the polarization image is better than the intensity image. After the polarization image and the intensity image are fused, the image contrast is significantly improved.
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
###### Abstract  
Under the current trend of power electronics in energy systems, a high percentage of renewable energy transports clean energy to the grid through grid-connected inverters. The pulse-width modulation (PWM) technique brings high-order harmonics near to the switching frequency, and LCL filters with low-pass characteristics become the common choice for grid-connected inverters. However, the low-order harmonics caused by nonideal switching characteristics are difficult to filter out, and the new resonance point introduced by the LCL filter causes a security problem for the energy systems. Firstly, the generation mechanism of the \(6\,k\pm 1\) order harmonic and high-frequency resonance from a PV grid-connected inverter is analyzed. Then, a virtual resistor is constructed by the active damping method to absorb the resonant component. Meanwhile, this paper also presents an adaptive modulation voltage compensation method to decrease the low-order harmonics. Finally, the actual measured data of user photovoltaic (PV) and multiple comparative simulations verify these theories. Simulation results show that the proposed coordinated control algorithm reduces the peak of the resonance point, and the rate of low-order harmonics mitigation is more than 50%. The proposed method is suitable for various operating conditions.  
photovoltaic grid-connected inverter; power quality; wideband harmonic mitigation; coordinated control
## 5 Methodology for Flood Analysis  
### Estimation of River Overflow Pattern from Water Level Data  
Figure 20 schematically illustrates the river channel and floodplain flow fields in 2019. The blue arrow in the center indicates the river channel, the yellow areas on the two sides indicate the floodplains, and the light blue section indicates the HDP. The arrows (\(q_{A},\ q_{B}\), and \(q_{C}\)) from the river channel to the floodplain indicate the river overflow, and the double-headed arrow (\(q_{H}\)) indicates the water exchange between the river channel and the HDP. Two arrows (\(q_{D}\) and \(q_{E}\)) in different directions are plotted separately on the second drainage gate near the downstream end, the former indicating the flooding when the gate was open and the latter indicating the levee overtopping from the land side after the gate was closed.  
It is not easy to simulate a complex flow field containing such a large number of branches with a single numerical model. Therefore, prior to the analysis of the entire flow field, some variables were estimated in advance from the measurement data.
## 3 Results and Discussion  
During the spontaneous fermentation process, non-_Saccharomyces_ yeasts usually dominate and start the AF, while the conversion of sugars into ethanol is often completed by _S. cerevisiae_ strains [27; 28]. However, all the 18 non-_Saccharomyces_ yeasts that were investigated in this survey, presented in Table 1, are isolated from spontaneous fermented wines which exhibited no off-odor or off-flavor characteristics, as previously described [2]. The aforementioned species, which have rarely been isolated from the wine environment, are often characterized as spoilage yeasts. More precisely, _T. californica_ is characterized by the literature as a contaminant of Californian wines and genetically closely related to _Trigonopsis cantarelli_, _Trigonopsis variabilis_, and _Trigonopsis vinaria_[29; 30]. Recent research suggests that _T. californica_ is considered a high acetic acid producer and has been isolated from wines at the end of AF and during aging, conferring a \"Brett\" character to wine [31; 32]. Additionally, _P. manshurica_ is found in several fermented foods and alcoholic drinks [33; 34; 35; 36]. The spoilage capacity of _P. manshurica_ regional isolates was evaluated in red wine, and the production of volatile phenols and cadaverine was evidenced [34; 35; 37]. However, only a few researchers have investigated their contribution to a wine's microbial environment. For instance, _Z. bailii_ stands out as a spoilage yeast, mainly in acidified food products, various non-carbonated fruit drinks, and in wine with fermentative metabolism [38; 39; 40]. Despite the higher levels of produced sorbic, benzoic, and especially acetic acid that are produced by _Z. bailii_ compared to those produced by _S. cerevisiae_, no other off-flavor observations have been reported to indicate spoilage behavior in wines [40; 41]. Finally, according to [PERSON] et al. [42], _P. carsonii_ was reported to influence the volatile composition of musts from grape berries and was not able to catabolize all residual sugars on must. _P. carsonii_ has also been isolated from oils, fermented sausages, and lambic beer [43; 44; 45]. The main objective of the present survey was to examine the performance of these indigenous and rarely isolated non-_Saccharomyces_ strains in monocultures' fermentation schemes as potential starter cultures. Strain differentiation was achieved using repetitive extragenic palindromic PCR (rep PCR) using the primer GTG5 (Table 1). For isolates from the same species, which were also identified within the same wine samples, a lack of strain diversity was predominantly observed. Consequently, only one strain per species of _T. californica_, _P. carsonii_, and _P. manshurica_ was identified. On the contrary, when investigating _Z. bailii_, nine isolates originating from various samples were analyzed, and molecular fingerprinting revealed the presence of three distinct strains. It is noteworthy that Zb-Z3 is detected in both red and white wines from two different wineries. A single representative strain for each species was subsequently selected for further investigation of its enological potential.