esa-sceva/satcom-chunk-collection huggingface dataset:
# Metalens Antenna Based on an Ultrathin Dual-Polarized Double-Layer Huygens' Metasurface
## 2 Theoretical Analysis and Design of Huygens' Meta-Atoms
Due to \(g_{y}\) controlling the response to \(y\)-polarized waves, the phase distribution exhibits significant variation along the \(y\)-axis, while remaining slightly varying along the \(x\)-axis. A similar phenomenon can also be observed under \(x\)-polarized excitation, which is not shown for simplicity. Within acceptable errors, it can be concluded that the \(g_{x}\) and \(g_{y}\) can independently control the responses to the corresponding polarization, indicating the polarization excitation independence of the meta-atom. The  
Figure 2: Structure configuration of the proposed meta-atom. (**a**) 3-D schematic view. (**b**) Top view. investigation demonstrates that the devices implemented based on the proposed Huygens' metasurface have the potential for two-channel or multi-channel operation. Figure 4: Transmission coefficient of the proposed meta-atom operating in two-channel mode as a function of \(g_{x}\) and \(g_{y}\) at 25 GHz under \(y\)-polarized excitation. (**a**) Amplitude; (**b**) Phase. Figure 3: Numerical investigation of the proposed dual-polarized meta-atom with \(g=g_{x}=g_{y}=0.2\) mm under the \(y\)-polarized wave exciting. \(g\) is the gap between adjacent corner-ring-shaped patches. (**a**) Transmission amplitude and phase. (**b**) Normalized electric surface admittance and magnetic surface impedance. (**c**) Electric current distribution and magnetic field distribution on the top and bottom surfaces at 25.2 GHz, in which a transmission peak is observed. (**d**) Transmission amplitudes and phase as a function of \(g\) at 25 GHz.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 5 Experimental Results and Analysis
### Statistical Comparison of Deep Learning Methods in Aspect Level Sentiment Classification
If no statistically significant differences are observed in the performance of two methods for the F1 score criterion, then the Wilcoxon test is carried for Accuracy and then for time. The process is repeated until a clear winner is found or until all the criteria are exhausted. The results of the Wilcoxon test for pair-wise comparison of ASGCN and GAT-BERT methods are shown in Table 6. It can be observed from Table 6 that there are no significant performance differences in Accuracy and Macro F1 score of GAT-BERT and ASGCN. However, the Wilcoxon test for time testifies that ASGCN performs significantly better than GAT-BERT in terms of Time.
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
###### Abstract
However, the extraction accuracy of this method is greatly affected by the segmentation results. [PERSON] [22] introduced a roundness threshold and obtained label information through the centroid, which significantly improved leak detection, but the results were affected by constraint conditions. In summary, existing studies generally rely on preprocessing results, it is impossible to accurately extract feature points without the effective preprocessing. During the lifting operation of prefabricated beams, the extraction of feature points faces various challenges and difficulties due to factors such as the operating environment and manufacturing processes. In this paper, multiple approaches are employed to mitigate the impact of interference factors and the contributions are as follows:  
* The lightweight YOLOv5s model is used for rough positioning of the lifting holes, reducing the impact of sample imbalance on pixel classification results. * The ADD model is developed by improving the ResNet50 backbone network and introducing multi-stage attention mechanism and additional shallow channels. This model alleviates the influence of lighting conditions, ensures the integrity of complex image segmentation, and achieves higher accuracy. * In order to mitigate the reliance on segmentation results, VLFGM is proposed to evaluate the coordinates of alignment points based on line features, and has better robustness and accuracy.
sensor drift, high sensitivity to the environment, and poor repeatability [11]. GC\(\times\)GC is an analytical method with a powerful separation function, which is often used by the sample analysis of complex ingredients. However, GC\(\times\)GC is not as widely used as GC-MS due to its high cost and complex experimental operation.  
With the development of science and technology, more and more precision analytical instruments are used for the detection of VOCs, which provides more technical support for the research of TCM [12]. Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) is a new type of analysis method with the advantages of being fast, having high sensitivity, and no sample pre-treatment. In addition, the HS-GC-IMS has intuitive visualization of data, and is especially suitable for the variance analysis [13]. On the basis of GC-MS, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) integrates the extraction and concentration technology of headspace solid phase microextraction and simplifies the sample pretreatment method [14]. At the same time, GC-MS has a high ability to isolate volatile compounds and provide detailed information of compounds [15]. Therefore, in this study, HS-GC-IMS and HS-SPME-GC-MS were used to comprehensively characterize the VOCs of SS. HS-GC-IMS usually detects small molecules of volatile components, which makes up for the deficiency of HS-SPME-GC-MS that can only capture medium molecules of volatile components [16]. The combination of these two analytical methods could easily, quickly, and accurately characterize the VOCs of SS.  
It is well known that different production areas are often one of the important factors that led to the quality difference between the TCM. Hence, it is of great significance for the quality evaluation of TCM in different production regions. SS is one of the most popular traditional Chinese herbal medicines and distributed widely in China. In this study, SS from six regions were studied, including Hebei (B), Henan (N), Jiangsu (J), Shandong (S), Zhejiang (Z), and Anhui (A). In addition, a comprehensive strategy that integrated HS-SPME-GC-MS and HS-GC-IMS was proposed for the first time for rapid identification of chemical components of SS, with further treatment of data by different multivariate statistical analyses. In addition, this study also uses the same data processing program to compare two different hardware methods to distinguish the applicability of SS in different regions. Hopefully, by this example, new technical support can be provided for more convenient comparison of VOCs in different TCM.
## 3 Results and Discussion
### Bacterial Growth and Degradation of Beta-Cypermethrin  
The average recovery of fortified beta-cypermethrin in LB medium was 105.3% with 4.0% of relatively standard deviation (RSD), showing the HPLC method applied was acceptable to quantify the concentration of beta-cypermethrin for following experiments. After optimization, the bacterial growth was monitored by measuring the OD\({}_{600}\) value of BCC01 in LB medium. In Figure 2, BCC01 showed normal growth and entered the logarithmic phase after inoculation without an obvious lag phase. Its growth reached a stationary phase at 40 h after inoculation and began to decline after 70 h. In beta-cypermethrin spiked cultures, the growth of BCC01 was slightly slower at the beginning of incubation than it was in the pure LB medium (Figure 2A). After a certain period of adjustment, the conditions could be recovered to be favorable enough for bacterial growth. By monitoring the growth of strain BCC01 and quantifying the residual beta-cypermethrin concentrations at different intervals, we found that the added pyrethroid was rapidly degraded within logarithmic phase. When the growth of BCC01 reached at the stationary period, the degradation of beta-cypermethrin slowed down (Figure 2B), suggesting the pyrethroid-degrading ability of BCC01 was closely related to the tendency of bacterial proliferation.  
Figure 1: Three-dimensional response of the effects of pH (\(X_{1}\)) and inoculum volume (\(X_{3}\)) on the degradation (\(Y\)) of beta-cypermethrin by BCC01.
## 2 Study Area and Data Sources  
### Overview of the Study Area  
The Ming Great Wall in Shanxi Province extends for a total length of 896.53 km and encompasses 6 cities and 25 counties. It consists of three sections: the Outer Great Wall, Inner Great Wall, and Riverside Great Wall, as shown in Figure 1. The Outer GreatWall (segment k1) originates from Huai'an in Hebei Province and stretches westward to Tianzhen, passing through Yanggao, Xinrong, Zuoyun, and Youyu, before reaching Piankou, spanning approximately 380 km. The Inner Great Wall (segment k2) commences in Pingshan, Hebei Province, and enters Fanzhi in Shanxi Province. It traverses Hunyuan, Ningwu, and Shencheng, before turning towards Piankou, covering a distance of about 400 km. The Riverside Great Wall (segment k3) is constructed along the Yellow River. The westernmost segment, at the junction between Shanxi Province and Shaanxi Province, spans around 70 km from Piankou to Hequ.  
Figure 2 illustrates the coverage area of the Sentinel-1A images used in the study. The study area is outlined in yellow, while the red markings indicate the ascending orbit image range of Sentinel-1A.
## 4 PD Measurement System  
The major online PD measurement systems of generators include neutral detection methods and high voltage terminal detection methods [5]. From the perspective to improve the SNR of measured signal, high voltage terminal detection methods outperform neutral detection methods. However, the sensors installed on the high voltage terminals introduce an extra security risk and threaten the reliability of the generators, since the breakdown of the sensors may lead to single phase to ground short circuit fault. In the last decade, Huazhong University of Science and Technology has been devoted to the research of generator PD on-line measurement and fault diagnosis. The PD measurement system consists of a sensor unit, a conditioning unit, a synchronization unit, an acquisition and analysis unit, as shown in Figure 3. Taking the operation safety into account, the sensor unit is mounted at the neutral point of the generator. When propagating from discharge site to detector location, the PD pulses suffer a severe loss of frequency components, especially the high frequency components. To guarantee the fault coverage of high windings, a capacitor with a bandwidth of 100 kHz-20 MHz is adopted as the PD sensor. The homemade signal conditioning unit is used to amplify the signal, of which the output voltage is between \(-\)5 V and +5 V. The synchronization unit is used to get the phase of the signal; it is composed of an isolating transformer and conditioning device. The isolating transformer is installed in the PT cabinet, and takes the voltage of a phase from PT secondary windings and transforms the voltage from 100 V to about 10 V. The conditioning device is installed in the PD monitoring cabinet and used to condition the sinusoidal signal to square signal, the rising edge is synchronized to the phase \(0^{\circ}\). The acquisition unit is mainly composed of a high-speed analog/digital converter, which is installed in the industrial control computer in the PD monitoring cabinet. When the acquisition unit is idle and the rising edge of square signal is detected, the signal acquisition is triggered and starts to sample PD signal with a resolution of 12 bits, and every acquisition lasts for 100 ms. The analysis unit consists of the aforementioned computer and analysis software. The proposed wavelet shrinkage scheme in this paper is applied to the analysis unit and the sampled signal is analyzed. The above described PD measurement system has been applied to more than 40 large generators and plays an important role in the condition monitoring and fault diagnosis of stator windings.
## 4 Methodology and Setup
### Base Experiment  
The independent variable of each base experiment is the SIR (Signal-to-Interference Ratio), defined as the ratio between the power of the transmitter signal and the power of the interferer signal; thermal noise is not taken into account since both signals are well-above its value (\(P_{N}=-107\) dBm) considering the experiment conditions, i.e., ambient temperature (T = 25 \({}^{\circ}\)C) and signal bandwidth (B \(\leq\) 5 MHz). In contrast, the dependent variable of each experiment is the PDR (Packet Delivery Ratio), defined as the percentage of packets that are successfully received at the receiver under the presence of the interference signal generated by the interferer device.  
For all the experiments, the value of the SIR ranges from \(-12\) dB to \(+6\) dB, meaning that the transmitter power is between \(1/16\times\) and \(4\times\) the interferer power. We use such a wide SIR range to ensure that the PDR transitions from 100% to 0% regardless of the interferer and transmitter modulation under test. In addition, to validate the results obtained from each base experiment, the first test is conducted without interference signal (the interferer device does not transmit during that test). In that case, the PDR is expected to be 100%. This can be observed with the \(Inf\) mark on the \(x\)-axis of the figures presented in Section 5.  
To determine the PDR for each SIR for a given transmit modulation, the transmitter device sends 1000 frames to the receiver device. Frames are transmitted with a 5 ms inter-packet delay. The payload is composed of the \(\{0x00,0x01,\ldots,0x13\}\) sequence for the 20-bytes payload and the \(\{0x00,0x01,\ldots,0x77\}\) sequence for the 120-bytes payload. Each frame is completed with the FCS (Frame Check Sequence) field of the PSDU depending on the selected modulation (2 bytes for OQPSK-DSSS, 4 bytes for SUN-OFDM).  
During the transmission, the interferer device injects an inteferent signal using the selected modulation for that particular base experiment. In contrast to data packets, the payload of the interference packets is composed of a 123-bytes \(\{0x55,\ldots,0x55\}\) sequence. That payload is transmitted continuously using the continuous transmission feature of the radio transceiver. That is, the transceiver operates in packet mode, but the packet header (SHR + PHR) is only transmitted once and the payload (PSDU + FCS) is transmitted indefinitely until the experiment is finished.  
There are two conditions that need to be met for a packet to be considered successfully received. First, the FCS calculated by the receiver radio transceiver based on the received bytes has to match the FCS value attached by the transmitter device at the end of the payload. Second, the received bytes have to match the transmitted bytes depending on the packet length for that base experiment. This ensures that the PDR results are correct even in the unexpected case where the calculated FCS sequence matches the received FCS sequence despite one or multiple bit errors caused by the interferer signal.
# Compression Behavior of Rubber-Bentonite Mixture under Different Salinities
## 2 Results and Discussions
### Modulus of Compression Change of MX80 Bentonite/Rubber Mixture
#### 2.2.2 Influence of Different Mass Ratios on Compression Modulus  
From Figure 7a, it can be observed that in the external environment of a 0.1 mol/L NaCl solution, except for the B10 sample, the compressive modulus decreased with increasing rubber content under different vertical loads.  
Within the load range of 100 kPa to 200 kPa, the compressive modulus of **the** samples with different mass ratios decreased. Under the action of vertical load, the compressive modulus showed an increasing trend. When the ion concentration in the salt solution increased to 0.5 mol/L, the compressive modulus of samples with different mass ratios gradually increased with the increase in load. These results indicated that under high-concentration solution environments, the compressive modulus of samples with different mass ratios gradually increased with the increase in vertical load. However, compared to the 0.1 mol/L salt solution environment, the compressive modulus in the high-concentration salt solution environment decreased significantly, indicating that high-concentration salt solution environments are not favorable for samples with different mass ratios.  
However, among the samples with different mass ratios, it can be clearly seen that at 20% and 40% mass ratios, increasing rubber content effectively alleviated the influence of salt solution ion concentration. This indicates the possibility of improving performance and stability under these conditions.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
###### Abstract  
Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure of the thruster valve. This paper aims at developing a thruster fault detection method that can deal with the thruster fault caused by the failure of the thruster valve and play an emergency role in the cases of hardware sensor failure. Firstly, the failure mechanism of the thruster was analyzed and modeled. Then, thruster fault detection was employed by introducing an angular velocity signal, using a blended filter, and determining an isolation threshold. In addition, to support the redundancy management of the thruster, an evaluation method of the nonlinear model-based numerical control prediction was proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under the failure of the thruster valve. The simulation results showed that the method is stable and allowed for the effective detection of thruster faults and timely evaluation of recovery performance.  
reusable launch vehicle; thruster valve failure; thruster fault detection; Kalman filter
# International Airline Alliance Network Design with Uncertainty
## 3 Airline Alliance Network  
In this section, we present mathematical formulations for the alliance route network design problem with consideration of the uncertainty of cost parameters. Three objectives are considered in the model: (1) determining the optimal locations of international gateways; (2) determining the network configuration, including transport paths for demands and the flow volume on each path; (3) making the international gateway location decisions risk-resistant in terms of the unit transportation cost.
## 6 Conclusions  
As 5G network deployments are growing in popularity, utilizing reliable wireless communication for smart grid operations increases data visibility and network configurability with limited additional infrastructure investment. Utilizing an open-source package, OpenAirInterface, we tested the response latency of two production IEDs by comparing the end-to-end latency of GOOSE, DNP3, and Modbus packets sent over a standard wired Ethernet setup to the latency achieved over a 5G network. Here, the end-to-end latency was measured for different test cases consisting of TCP-encapsulated packets, TCP encapsulation with multiple hops, and TLS encryption, both with and without a 5G communication session. Although the results presented in this paper indicate that in its current form, we would fail to meet GOOSE's strict latency requirements, they nevertheless validate the merit of this approach for OT WAN monitoring applications and other applications using Modbus or DNP3, where latency is of lower criticality compared to factors such as ease of centralized monitoring or secure communications. This was confirmed by evaluating their end-to-end latency. Furthermore, our results indicate the potential to meet GOOSE's latency requirements through optimization of the network segments and operations, which is the focus of our ongoing efforts.  
Investigation, M.B., P.S., M.H. and H.S.; writing--original draft preparation, M.B., P.S. and M.H.; writing--review and editing, M.B., P.S., M.H., H.S. and J.L.J.; supervision, H.S. and M.H.; project administration, H.S., M.H. and J.L.J.; funding acquisition, H.S. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the University of Nebraska-Lincoln's Nebraska Center for Energy Sciences Research (NCESR) under Cycle 16 Grant# 20-706.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 2 Background Knowledge
### Sensitivity Approach on HVDC Allocation and Operation
As the DERs penetration level increases, estimated line loading is calculated based on expression as follow:  
\[f_{l,k\ (estimated)}=\sum\limits_{1}^{n}(\ DER_{i}\psi_{l,k}^{i})+\textit{ Generation Effect}+\textit{Average Loading}_{l,k} \tag{10}\]  
where \(f_{l,k\ (estimated)}\) is the resultant branch flow of the expected DER penetration at bus \(i\), \(\ DER_{i}\). It reflects the effect of the generator change owing to the penetrated capacity, \(\textit{Generation Effect}\), and the average load of branch between bus \(l\) and \(k\), _Average Loading_\(l\), on the existing lines, as well as the effect of the DERs. The final goal of this optimization process is to increase the acceptable capacity of the DERs. To this end, HVDC power flow controllability was used to search for an optimal location to lower the overload rate and increase the utilization rate of the lines. The problem was configured as a minimax problem. It involves finding an optimization point that minimizes the maximum overload rate among the lines, which can be expressed as follows:  
\[|V_{i}|=1\ p.u.\]0  
\[ \begin{array}{c}\text{s.t }\textit{P}_{gen}+\textit{P}_{ DERs}=\textit{P}_{Load}+\textit{P}_{Loss},\\ \sum\limits_{1}^{n}X_{i}=0,\ \sum\limits_{1}^{n}Y_{i}=Number\ of\ Stations,\\ |X_{i}|\ \leq HVDC\ Station\ Rating\ of\ i,\\ 0\ \leq\ Y_{i}\ \leq 1\end{array}  \tag{12}\]  
where \(f_{l,k\ (Future)}\) is the result of the HVDC operation effect on the branch flows. \(Y_{i}\) is a binary variable indicating when a converter station exists at bus \(i\), and \(X_{i}\) is the operating  
Figure 2: Sensitivity analysis result of test system. point of this station. Each of \(P_{gen},\ P_{DERs},\ P_{Load},\) and \(P_{Loss}\) represents the total real power of synchronous generators, DERs, loads, and losses. When this problem is solved, the position of the converter is determined. Based on this position, a DER input simulation was performed, and the proposed methodology of the hosting limit evaluation procedure was conducted assuming that the installed HVDC operates through an optimal operating (OP) strategy to decrease the maximum overload rate of the lines. During this process, the DER penetration was used to predict future power flow based on the assumption that the power generation is directly proportional to the amount of the installed capacity.