esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
A self-start multi-wavelength laser source based on the OEO structure is proposed and proven in this paper. This proposed multi-wavelength laser source could be packaged in a smaller module using optical waveguide techniques to reduce power loss and simplify the complexity of OEO devices [24]. By employing the double filtering structure, the TNR value of 18 dB and a power fluctuation below 0.18 dB among 10 wavelengths are achieved. To estimate and determine the performance of the proposed scheme, OTF is deployed to select one and two wavelengths to be modulated by the input data through the RSOA. After SMF transmission, the optical signals are converted to electrical signals, which are estimated in terms of bit error rate and eye diagram. According to the experiment  
Figure 8: Comparison of BER curve and eye diagram for BTB and 25 km transmission.  
Figure 9: The measured BER, eye diagram, and electrical and optical spectrum on two selected wavelengths.  
results, both one and two selected wavelengths can achieve error-free transmission and clear eye diagrams.  
Conceptualization, A.-M.D., R.-K.S., R.-B.C. and P.-C.P.; methodology, R.-B.C. and P.-C.P.; data preparation, R.-B.C., J.-W.L. and P.-C.P.; model validation, A.-M.D., R.-K.S. and P.-C.P.; formal analysis, Y.-C.M., H.-C.L. and P.-C.P.; investigation, A.-M.D., R.-K.S., R.-B.C., J.-W.L., Y.-C.M., H.-C.L. and P.-C.P.; and data collection and experimental setup configuration, R.-B.C. and J.-W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 108-2221-E-027 -040-MY2 and by the University System of Taipei Joint Research Program, Taiwan USTP-NTUT-NTOU-105-02.  
Not applicable.  
The authors declare no conflict of interest.
# Tilted Beam Fabry-Perot Antenna with Enhanced Gain and Broadband Low Backscattering
## 5 Conclusions  
In this work, a low RCS high gain F-P cavity antenna is presented, which employs a phase gradient surface to achieve an additional functionality of beam deflection. The design incorporates an absorptive FSS, consisting of lumped resistors soldered in periodic square loops, and a composite PRS. The upper surface of the PRS is a uniform layer of periodic patches. The bottom layer consists of variable length patches to realize the gradient implementation. The patch antenna acts as the excitation source of cavity formed between ground plane and PRS. A high gain beam with 13\({}^{\circ}\) tilt angle in the elevation plane is achieved, along with wideband RCS reduction. The average RCS reduction is 13 dB over a bandwidth of 5.6-16 GHz (96.3%), which also includes in-band frequencies. Future extension of this work can be the further enhancement of the in-band RCS reduction by implementation of in-band metamaterial absorber, or any other strategy. Improvement of the tilt angle by employing multiple gradient layers can also be considered. Having low RCS property, it is feasible to use the antenna with low scattering defense/military platforms, and particularly where communication is desired in a fixed off-the-horizon direction. It is believed that this research will attract further interest in the use of PGM within low scattering F-P antennas.  
Conceptualization, H.U. and T.B.A.L.; methodology, H.U. and T.B.A.L.; software, H.U.; investigation, H.U.; resources, T.B.A.L., Y.Y. and K.K.; writing--original draft preparation, H.U.; writing--review and editing, T.B.A.L.; supervision, T.B.A.L., W.N.L.B.W.M., M.O., M.I.H. and A.I.N.; project administration, T.B.A.L., Y.Y., W.N.L.B.W.M. and K.K.; funding acquisition, T.B.A.L., Y.Y. and M.I.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the University of Malaya (UM), grant number GPF015A-2019.  
The authors declare no conflict of interest.
## 1 Introduction
Many factors determine the performance of the VNS algorithm, i.e., the initialization method, neighborhood structure construction, local search procedures, and update mechanisms. Although there are many determining factors, neighborhood structure is the central concept of the VNS algorithm that significantly influences its performance. One version of the VNS algorithm is based on the dynamic neighborhood model, proposed by [PERSON] and [PERSON] [14]. Changing the neighborhood structure during a search enables the algorithm to escape the local optimum trap [30], as it allows the algorithm to move from one basin of search to another. The construction of a neighborhood is, thus, crucial to the performance of the VNS algorithm. However, in terms of solving the QUBO problem, previous research reports regarding neighborhood construction are difficult to find. The Hamming distance is commonly used in the basic VNS algorithm to construct the neighborhood, when solving a QUBO problem [5; 17; 31]. The VNS algorithm uses a strictly monotonic increasing neighborhood structure, when the local search does not yield a better solution. As a result, the basic VNS algorithm takes quite a long time to yield a good solution. A new version called \"Jump VNS\" was introduced, to speed up the neighborhood construction process. It enables the neighborhood structure to leap ahead, in accordance with parameter \(k_{step}\in\mathbb{N}\)[32]. The basic VNS algorithm, which does not have the jump ability, is obtained by setting \(k_{step}=1\). The performance of the Jump VNS algorithm does not appear to have been previously reported. The VNS algorithm is simple, and the ability to slowly change the neighborhood structure is an advantage of its use. However, this is, also, a weakness of the VNS algorithm. This gradual but slow change may impact the length of computation time. Although it may be sped up with Jump VNS, the thorough search behavior may be lost. For example, setting the maximum distance on a VNS algorithm to 100 results in 100 times neighborhood-structure change, at most, but setting \(k_{step}=2\) on Jump VNS results in only 50 times neighborhood structure change. This value is even less if a larger \(k_{step}\) is used. So, the VNS and Jump VNS algorithms are not flexible. This paper elaborates on the basic VNS algorithm and introduces a new method of improving it by focusing on the neighborhood structure by implementing binomial distribution. Instead of a strictly monotonic increase in neighborhood structure, the neighborhood distance follows a binomial distribution. Although the binomial distribution will cause a non-monotonic increase, the trend of a widening structure will remain the same. We investigated the potential of our proposed algorithm to be used in some QUBO and max-cut problems.
# XLNet-Based Prediction Model for CVSS Metric Values
## 4 Experiments and Results  
### Experimental Data and Experimental Setup  
This paper used data from the US National Security Vulnerability Database [1], which contains all security vulnerabilities released from 1999 to May 2022. The vulnerability description information on the web page was used as the dataset's text item, and the vulnerabilities' metric values were processed into label items. The data sources are shown in Figure 4. If the metric value existed, 1 was assigned to the label. If it did not exist, the value 0 was assigned. Finally, the collected dataset was represented by \(D=\{X,Y\}\). After processing, two datasets were obtained for this study: the CVSS version 2.0 dataset containing 174,838 vulnerabilities and the CVSS version 3.1 dataset containing 101,519 vulnerabilities. The datasets were split into the training and test datasets in the following proportions: 80%:20%. The statistics indicate that 97.72% of CVSSv3.1 vulnerability descriptions have fewer than 128 words, with an average of 43.90 words per sample, and 98.62% of CVSS v2 vulnerability descriptions have fewer than 128 words, with an average of 40.99 words per sample. After tokenization, 99% of the descriptions in CVSS v2 and v3 have less than 256 tokens. The pre-trained XLNet model used in the paper is the XLNet-base model, with 12 transfer layers and 768 hidden layers. All vulnerability descriptions of CVSS v2 and v3.1 were used to fine-tune the pre-trained XLNet model. Two NVIDIA GeForce RTX 3090 GPUs were used for fine-tuning and training.  
Figure 4: Dataset collection process.
# Thermal and Mechanical Behavior of Elastomers Incorporated with Thermoregulating Microcapsules
## 3 Results and Discussion
### Mechanical Properties  
As was commented on in the characterization techniques section, the tensile stress-strain properties were determined following the standard UNE-ISO 37:2013. The tensile strength at break and at a deformation of 150% are important properties for elastomers. The values obtained for the different synthesized materials are shown in Figure 8.  
A reduction of both properties when the mSD-(LDPE-EVA-RT27) microcapsules were added can be observed, especially for E-1.05-20. This decrease could be caused by the elastomer heterogeneity promoted by worse microcapsule distribution, which favors the fractured appearance. On the other hand, elastomer E-1.1 presented better results than E-1.05 in the case of tensile stress at a deformation of 150%, suggesting greater rigidity of E-1.1, while the tensile strength results were very similar.  
Another important mechanical property is the resilience, since it gives an idea of the deformation energy per volume unit that can be recovered after the material's deformation. This refers to the polymer's capacity for recovering after a deformation. The resilience of the synthesized elastomers is shown in Figure 9a.  
According to the experimental results, the presence of the microcapsules improved the resilience of the elastomers, especially for the case of E-1.05. Nevertheless, the resilience of elastomer E-1.1 was always better than that observed for elastomer E-1.05. It is worth mentioning that the maximum resilience was found independently of the [NCO]/[OH] molar ratio for an mSD-(LDPE-EVA-RT27) content of 5 wt.%.  
Figure 8: The tensile strength at break (**a**) and tensile stress at a deformation of 150% (**b**).  
The hardness of the elastomers was tested with a Shore A durometer, and the values are shown in Figure 9b. It can be observed that the hardness was not influenced by the [NCO]/[OH] molar ratio, as the results were very similar for composites E-1.05 and E-1.1. Aside from that, the addition of microcapsules did not change the hardness of the elastomers significantly, with a maximum decrease of 2.98% observed for elastomers containing 20 wt.% MC.
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 4 Results
### Training Results of 1D CNN Based on Data Quantity  
Figures 5 and 6 show the test results according to the amount of training data in the signal region. Each model was tested 10 times according to the proportion of training data. A ratio of 100% on the rightmost x-axis means a total of 4983 data points, with the same ratios in the four classes. The leftmost x-axis proportion of 0.83% also represents a total of 40 data points with a one-to-one ratio in each class.  
In both the time and frequency domains, we can see that the deviation between the maximum and minimum values increases as the amount of training data decreases. At 100% data coverage, the accuracy in the time domain is 95.90% on average, with a minimum of 94.85% and a maximum of 96.88%. In the frequency domain, the average accuracy is 97.19%, with a minimum of 95.77% and a maximum of 97.98%. Each deviation is within about 3%. Gradually, the deviation in accuracy increases from 8.33% of the data in the time domain and 1.67% of the data in the frequency domain. The largest deviations in each domain are 27.02% in the time domain at a rate of 0.83% and 19.12% in the frequency domain at a rate of 0.83%.  
Figure 5: Test data accuracy by training the data ratio in the time domain.  
Figure 6: Test data accuracy by training the data ratio in the frequency domain.  
movements of the user can affect performance. It has been observed that if a large arm movement is detected during the action, the model misclassifies it as an at-risk movement. This is due to the increased diversity of test data that are continuously input as they pass through the logic of the real-time operating application in Figure 4. Although the accuracy decreases in both signal domains, the analysis in the frequency domain still performs better in a real-time inference environment. For the classification of at-risk behavior, which had the largest error margin as shown in Table 4, there were 22 instances where at-risk behavior was misclassified as running in the time domain, but this was reduced to 4 instances in the frequency domain. This shows that frequency domain analysis clearly characterizes certain behaviors.
# A Novel Framework for Cross-Cluster Scaling in Cloud-Native 5G NextGen Core
## 5 Out-of-Band Peering and Network Slicing  
The standard peering approach is called _out-of-band control plane_ (see Figure 3) because two API servers are mutually communicating to each other over the Liqo control plane traffic, and the user plane is completed via the VPN tunnel interconnecting the two clusters.  
Figure 3: Offloaded user plane in the out-of-band peering.  
In the first scenario, we initiate the peering between two K3S clusters that are isolated from each other because they are hosted in different cloud tenants. The Open5 GS control plane is deployed on the local cluster, which is Tenant 2, whereas the user plane is offloaded to the remote cluster on Tenant 1. The authentication is performed using a generated authentication token and the K3S API server URL endpoint along with the cluster ID.  
The Liqo Network Manager represents the control plane of the Liqo network fabric. It is executed as a pod, and it is responsible for the negotiation of the connection parameters with each remote cluster during the peering process. The overlay network is leveraged to forward all traffic originating from local pods/nodes and redirected to a remote cluster via a gateway that serves as a termination/entry point for the VPN tunnel.  
In our Kubernetes clusters, the overlay networking is provided by default CNI (container network interface) for K3S which is Flannel [75]. For each remote cluster, a different instance of the Liqo Virtual Kubelet is started in the local cluster, ensuring the isolation and segregation of the different authentication tokens.  
Liqo extends Kubernetes namespaces across the cluster boundaries by creating twin namespaces in the subset of selected remote clusters whenever a namespace is selected for offloading. Remote namespaces host the actual pods offloaded to the corresponding cluster, as well as the additional resources propagated by the resource reflection process.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 2 Measurement Principle and Error Analysis
### Measurement Error Analysis  
In Equation (7), the roll angle error depends on the \(h_{by}\) and \(h_{bz}\) errors, pitch angle \(\theta\), yaw angle \(\psi\), and incremental errors \(\delta\theta\) and \(\delta\psi\) measured by the magnetometer.  
The measurement errors of the magnetometer mainly include the inconsistency of the sensitivity of each axis, zero drift of the measuring device, interference of external magnetic field sources, interference of internal circuits, the magnetization material of the application object (iron, chromium, nickel and its alloys), and occasional measurement errors and jump points.  
The errors in the pitch angle \(\theta\) and yaw angle \(\psi\) mainly originate from the errors of the satellite measurement equipment, including system positioning errors caused by an insufficient number of satellites, external environment interference errors, and system jump points. There are various random errors in the measurement process of satellite positioning systems, which mainly include ephemeris error and satellite clock error related to the performance of the satellite itself; factors related to the propagation path such as stratospheric delay, ionospheric delay, and multiple path effects; receiver-related factors such as receiver antenna phase center deviation and change, receiver clock deviation, and signal delay deviation between different signal channels.  
The errors in the pitch angle rate \(\dot{\theta}\) and yaw angle rate \(\dot{\psi}\) are high-frequency errors, and the incremental errors \(\delta\theta\) and \(\delta\psi\) of the pitch angle \(\theta\) and yaw angle \(\psi\) are algorithmic errors.  
Generally, errors include the following: external or system interference (external magnetic field source interference, internal circuit interference, and magnetization influence), measurement error (high-frequency error, noise interference, jump points, etc.), system error (different axial sensitivity, zero drift, etc.), and algorithm errors (deviations caused by the assumption of a small angle of attack).  
Different solutions must be adopted for different error characteristics to improve the accuracy of the system. Compensation algorithms can be used to compensate for external interference, electromagnetic and magnetization interference of the system itself, and other external or system interferences. These can be processed by filtering algorithms, and system errors can be corrected by data fitting. After processing by various methods, good measurement accuracy can be achieved to meet the needs of engineering practice. This study focuses on methods to eliminate external or system interference and measurement errors.
# Deep Eutectic Solvents as Extraction Media for
## 2 Factors Affecting the Extraction of Flavonoids Using DES Separation Techniques
### Water Content  
Water content is another crucial factor to the efficiency of NaDES in flavonoid extractions. Water might be found in a DES system unintentionally (during the synthesis, or from remaining in a container) or intentionally through co-solution to create an aqueous system. While some NaDES could be applied as extraction media on their own, their increased viscosity would hamper the speed of extractions. Furthermore, using pure DES as extraction media could increase the costs of extractions, rendering the process cost-inefficient at the laboratory or industrial scale. To tackle this issue, aqueous solutionsof NaDES have been experimentally used instead, attempting the extraction of multiple substances with aqueous NaDES solutions of various concentrations, ranging from 20% all the way up to 80%. According to the literature, particularly in studies in which the water content was a part of optimization, percentages close to 20% water content [20; 44] are the balance between creating a fluid extraction system and maintaining the hydrogen bond mesh of the DES [40]. Higher water contents tend to break the hydrogen bond structure of the DES, decreasing its effectiveness. This, however, does not imply that higher water contents would be ineffective or undesirable, as every extraction, having a great number of variables, could take advantage of higher or lower percentages of water.
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 3 Results
### Simulations for Static Obstacles  
First, we simulate the automatic path planning for the own ship in the presence of irregular static obstacles without designating a prior collision-free path to verify the effectiveness of the proposed algorithm in solving the problems in traditional APF methods.  
1.1 Trap Situations in U-shaped Obstacles and the Goals Non-Reachable with Obstacles Nearby (GNRON) Problem  
This simulation is designed to verify the feasibility of the proposed algorithm for the local minima problem and the GNRON problem of the traditional APF method [10; 26]. The initial state of the own ship and the results are shown in Table 5 and Figure 11.  
These two simulations have a very short total running time of 0.4-0.5 s in 10 repeated trials.  
Figure 11: The static obstacle is a U-shaped slot, a composite potential field composed of a parabola and a rectangle, where the parabola c is expressed as \(3x^{2}-30x+79-4y\). The boundary of this obstacle is indicated by the black lines. (**a**) The own ship is not trapped in the U-shaped slot and can successfully reach the goal, which is very close to the obstacle. (**b**) The goal is located inside the U-shaped slot and is very close to the obstacle; the own ship can still reach the goal.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Situation** & **Start** & **Initial Course [\({}^{\circ}\)]** & **Goal** & **Length of Trajectory [nm]** & **Time Steps** & **Run Time [s]** \\ \hline (a) & (6, 6) & 190 & (5, \(-0.8\)) & 18.22 & 293 & 0.490 \\ (b) & (5, \(-1\)) & 270 & (5, 2.4) & 13.49 & 219 & 0.387 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial inputs and simulation results for U-shaped obstacles.
Many scholars have applied intelligent algorithms to wireless sensor network location algorithms based on distance measurement. The accuracy of positioning is being continuously improved. The combination of the MQSOS algorithm and the DV-hop algorithm to improve the performance of original DV-hop positioning is also proposed in this paper. Section 2 of this paper briefly introduces the DV-hop algorithm and several EC algorithms such as native PSO, SOS, QUATRE, OSOS, and so on. Section 3 is dedicated to the newly proposed MQSOS algorithm and the algorithm MQSOS_DV-hop that is generated in combination with the DV-hop algorithm. Section 4 shows the experimental results under the CEC2013 test suite and compares the proposed method with other EC algorithms. At the same time, the improvement of the DV-hop algorithm by MQSOS and other CI algorithms is also compared. Finally, in Section 5, the corresponding conclusions are drawn based on the experimental results.
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
###### Abstract  
The most recent video coding standard, High Efficiency Video Coding (HEVC), is able to significantly improve the compression performance at the expense of a huge computational complexity increase with respect to its predecessor, H.264/AVC. Parallel versions of the HEVC encoder may help to reduce the overall encoding time in order to make it more suitable for practical applications. In this work, we study two parallelization strategies. One of them follows a coarse-grain approach, where parallelization is based on frames, and the other one follows a fine-grain approach, where parallelization is performed at subpicture level. Two different frame-based approaches have been developed. The first one only uses MPI and the second one is a hybrid MPI/OpenMP algorithm. An exhaustive experimental test was carried out to study the performance of both approaches in order to find out the best setup in terms of parallel efficiency and coding performance. Both frame-based and subpicture-based approaches are compared under the same hardware platform. Although subpicture-based schemes provide an excellent performance with high-resolution video sequences, scalability is limited by resolution, and the coding performance worsens by increasing the number of processes. Conversely, the proposed frame-based approaches provide the best results with respect to both parallel performance (increasing scalability) and coding performance (not degrading the rate/distortion behavior).  
HEVC; video coding; parallel encoding; shared memory; distributed shared memory +
Footnote †: journal: Applied Sciences