esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 GaN-on-SiC Technology  
GaN-on-SiC technology intrinsically offers excellent endurance properties against high temperatures as well as large power signals. In practice, the major characteristics of semiconductor active devices, specifically in the mmWave frequency range, are chiefly estimated by their parameters of electron mobility, energy bandgap, and thermal conductivity. Table 1 lists the major properties of several microwave semiconductor materials,  
Figure 1: Front-end module configuration using (**a**) GaAs and (**b**) GaN technologies [16].  
including GaN and SiC. Among the parameters, the electron mobility property is an important criterion for evaluating the high-frequency performance of semiconductor devices; it is closely related to the achievable gain and noise performance of the device. According to Table 1, the electron mobility of GaAs is approximately five times higher than that of GaN. However, owing to advancements in technology, the electron mobility of the GaN has been enhanced accordingly and is currently superior to that of Si. The energy bandgap, which refers to the difference between the valence and conduction bands, determines the breakdown voltage of the device where the electrons attain sufficient energy to move to the conduction band and contribute to the drain current. Therefore, a high energy bandgap is highly associated with a high breakdown voltage, which increases the device endurance against a large power signal. GaN and SiC inherently possess superior energy bandgaps and breakdown voltages compared with GaAs, InP, and Si. Thermal conductivity indicates the material's capability to dissipate the heat of the device, which is generated by the device's power consumption. Compared to GaAs, GaN and SiC possess lower thermal resistance and higher thermal conductivity; therefore, GaN and SiC materials are more suitable for high-power PA circuits that need to withstand high temperature conditions.  
The GaN HEMT LNA designed in this work was fabricated using the process design kit of NP15-00, a GaN-on-SiC substrate technology with a gate length of 0.15-\(\upmu\)m, which was provided by the Win Semiconductors Corporation foundry. Considering the stability, minimum NF, and MAG features at the operating frequency, a transistor size of 4 \(\times\) 25 \(\upmu\)m (4F25) GaN HEMT was chosen, which has a breakdown voltage of more than 120 V, a current gain cutoff frequency (\(f_{T}\)) of approximately 34.5 GHz, and a maximum oscillation frequency (\(f_{MAX}\)) of approximately 125 GHz. In addition, this process provides two interconnected metal layers, high-reliability metal-insulator-metal capacitors, and precision tantalum nitride resistors for full MMIC designs.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 1 Introduction
The Sentinel-1 satellite achieved an internal calibration system with minimal dedicated calibration elements, which did not include calibration networks, leading to an almost doubled number of calibration signal types and increased complexity of the internal calibration method [23; 24]. The internal calibration system of the Gaofen-3 (GF-3) satellite adopts a combined scheme of delay calibration and non-delay calibration [25; 26]. It is designed with six calibration loops, wherein the delay reference calibration loop and the full array transmitting and receiving calibration loop utilize optical delay lines to achieve time delays, working together with the other four non-delay loops to complete the radar instrument calibration. For BiSAR systems, Germany's TanDEM-X [27; 28] and China's TH-2 [29; 30] adopt non-delay internal calibration techniques with good amplitude and phase stability of the calibration signals. Due to the shared feed network and calibration network between the inter-satellite synchronization link and the phased array radar in TanDEM-X and TH-2, the synchronization link operation interrupts SAR imaging, causing periodic missing echo data. The LT-1 BiSAR system adopts noninterrupted phase synchronization technology, and performs interferometric measurements in bistatic mode [31; 32], which means the primary satellite emits electromagnetic waves to illuminate the target, and then both the primary satellite and auxiliary satellite receive the echoes simultaneously. Furthermore, a novel BiSAR internal calibration strategy is proposed, combining ground temperature compensation, in-orbit internal calibration, and pulsed alternate synchronization to correct and compensate errors, as well as to meet the requirements for BiSAR interferometric height measurement, multi-track differential interferometry, and multi-polarimetric imaging channel phase (amplitude) consistency. The paper is organized as follows: Section 2 presents the innovative LT-1 internal calibration strategy, including the composition of LT-1 internal calibration system, calibration pulses design, and echo compensation formulas. Section 3 details the hardware implementation of dedicated calibration equipment. Section 4 provides temperature compensation data obtained through ground temperature experiments. Section 5 studies the BiSAR synchronization performance based on the LT-1 ground verification system. Section 6 discusses the experimental verification results and Section 7 summarizes the whole paper.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 3 Design and Implementation of Access Control Method
### Access Control Method Implementation
#### 3.2.3 Content Request Process  
After the content is published to the NDN network, in order to enable the consumer to access the content of interest, two steps are required, first the proxy decryption of the NDN cache node and the user decryption of the consumer client. The content request process needs to execute the following functions:  
1. \(\text{PxDecry}\ (GP,CT,PxK_{wild,aid},\{PK_{sk}\}_{aid\in LA})\to CT^{\prime}\)  
The NDN routing node performs a proxy decryption algorithm. Input \(GP\), \(CT\), \(PxK_{wild,aid}\), and \(\{PK_{sk}\}_{aid\in LA}\), and the partially decrypted ciphertext \(CT^{\prime}\) is output.
2. \(\text{Udecrypt}\ (CT,CT,SK_{wild})\to M)\)  
Consumer user decryption function is executed by the consumer. Input \(CT\), \(CT\), and \(SK_{wild}\), and output plaintext \(M\).
# A Reinforcement Learning Approach to Speech Coding
## 5 Learning (Parameter Adaptation)  
From Equation (4), we see that the parameters to be estimated or identified are \(\widehat{a}_{i}\), \(\widehat{\beta}_{i}\), lag \(d\), and \(\widehat{b}_{i}\), and note that only signals available at the Decoder as well as the Encoder can be used in their calculation. We employ different algorithms and approaches for each parameter. For the parameters of the AR component of Equation (4), \(\widehat{a}_{i}\), we utilize a Recursive Least Squares (RLS) lattice algorithm because of its fast convergence and stability properties [22; 23]. For the parameters of the MA component, \(\widehat{b}_{i}\), we utilize a very simple polarity driven gradient algorithm taken from ITU-T G.726/727 [24; 25]. The calculation of the lag \(d\), uses a combination of a block-based method using only past model values jointly with a recursive, gradient-like update [26]. The coefficients for the long term prediction component, \(\widehat{\beta}_{i}\), are propagated based on gradient algorithms [26]. The details for all of these are given in the following subsections.
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 5 Experiment and Discussion
### Existing Studies-PVA  
The PVA method represents the synchronous reference frame of D-Q transformation as a circular pattern and is considered an excellent method for online fault diagnosis. As shown in Figure 9, when the circle appears as an elliptical shape, it indicates the occurrence of a fault. Thus, the PVA method allows excellent visibility of a fault.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **1–3** & **3–5** & **5–7** & **7–9** & **9–11** \\ \hline Turn short & 2(ITSC) & 4 & 6 & 8 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Turn short fault.  
Figure 8: Method for configuring artificially turn short: (**a**) short of stator winding (**b**) external tab of the turn-short motor.  
Figure 7: Filtering process for noise in the input power: (**a**) before filtering and (**b**) after filtering.  
To determine the degree of distortion of the circle, existing studies defined the distortion rate (DR) using the following equations:  
\[\mathrm{r}=\sqrt{l_{d}^{2}+l_{q}^{2}} \tag{16}\]  
\[\mathrm{DR}=\frac{r_{max}}{r_{min}} \tag{17}\]  
where r is the scalar value for the size of \(I_{d}\) and \(I_{q}\) of PVA, \(r_{max}\) is the largest amplitude, and \(r_{min}\) is the smallest amplitude.  
Figure 10 shows the monitoring result of the fault diagnosis method using DR. It is the result of applying DR in Equation (17) per second.  
As can be seen from Figure 10, linear separation of a 4-turn short and a 6-turn short is definitely possible when compared with the steady state, but ITSC is difficult to diagnose because it has no difference from the steady state.  
Figure 9: [PERSON]’s vector approach (PVA) pattern: (**a**) steady state, (**b**) ITSC, (**c**) 4-turn short, and (**d**) 6-turn short.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 7 Conclusions  
In this paper, we proposed a general calibration method for dual-PTZ cameras under variable focal lengths and postures. For intrinsic parameter calibration, we used [PERSON]'s method to calibrate several intrinsic parameters under discrete zoom values and established different models to fit variations in focal length and radial distortion parameters. Based on the model, we can estimate the intrinsic parameters via the zoom value. For the calibration of extrinsic parameters, we made the cameras stay at specific angles to obtain several rotation and translation matrices between the coordinate system and the checkerboard planar pattern coordinate system while the planar pattern remained in a fixed position. Based on the matrices above, we calculated the actual rotation vectors and rotation radius vectors of the camera. We also determined the zoom-translation vector generated by the optical center motion during zooming into consideration. Based on the parameters we computed above, the extrinsic parameters of current dual-PTZ cameras can be calculated at arbitrary posture and zoom values based on feedback parameters. Our experiments showed that: the average focal length errors estimated by our model are less than 4%; the cosine similarities of rotation matrix R between the left and right cameras are more than 99.8%; the translation vector errors are less than 1%; and the recalculated Euler angle errors are less than 1 degree, in both the horizontal and pitch directions. These results indicate that our method meets the requirements of practical applications in intelligent unmanned systems.  
Conceptualization, K.M. and Y.X.; methodology, R.W.; software, S.P.; writing-original draft preparation, K.M.; validation, K.M., R.W. and S.P.; writing--review and editing, K.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: No applicable.  
**Informed Consent Statement**: No applicable.  
**Data Availability Statement**: No applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 4 Results
### Datasets  
We selected the public datasets SemanticKITTI and NuScenes in the field of autonomous driving as our experimental data.  
SemanticKITTI: The semantic category of this dataset contains 20 objects divided into the categories of moving and nonmoving objects, including traffic participants such as cyclists, motorcyclists, and pedestrians, but also nonmoving objects such as car parks, pavements, and roads. The dataset consists of 43,551 frames of LiDAR scans from 22 sequences. Each sequence is scanned by LiDAR in different street scenes and contains between 200 and 4500 frames of point cloud data. The scene data from sequence 00 to sequence 10 were used as the experimental data. To reduce data repetition and avoid overfitting, the experimental data were selected from one frame from five consecutive frames. The selected data were divided into a training set (about 60%), a validation set (about 20%), and a test set (about 20%).  
NuScenes: This dataset collects information from 1000 driving scenarios in different cities and manually selects scenarios of 20 s duration. The complete dataset contains 390,000 LiDAR scans and 40,000 keyframes. The dataset contains 32 semantic labels in the LiDAR semantic segmentation task. In the experiments, 850 scenes with semantic annotations were used as the experimental data. These selected data were also divided into a training set (about 60%), a validation set (about 20%), and a test set (20%).
## 2 Materials and Methods  
### Surface and Subsurface Vehicles  
The GLIDER project deployed three different surface and subsurface platforms: a Seaglider(r) M1 (Huntington Ingalls Industries Inc.; formerly Kongsberg Maritime), a Wave Glider SV3 (Liquid Robotics), and a Sailbuoy (SB, Offshore Sensing) (Figure 1). The Seaglider(r) is an autonomous underwater vehicle that uses changes in buoyancy to move through the water column in a sawtooth pattern while collecting data from the ocean surface to a programmed depth and back [31]. The vehicle is powered by Lithium Sulfuryl chloride primary batteries 17 MJ allowing operations in the range of several thousand kilometers and several months depending on configuration and payloads. The operational depth range is between 50 and 1000 m. The vehicle is 1.8-2 m long depending on the configuration and has a maximal diameter of 0.3 m. The wingspan is 1 m, and the antenna mast length varies between 0.43 and 1 m. The typical operation speed is 0.5 knots. A 2-way Iridium communication system was used for navigation and transmitting data (for further details [32]). The Wave Glider SV3 is a long-duration unmanned surface vehicle utilizing wave energy for propulsion [33]. The Wave Glider consists of a surface float tethered with an 8 m umbilical cable to a subsurface glider. The surface float houses the command-and-control  
Figure 1: Surface and subsurface platforms employed in the GLIDER project: (**a**) Seaglider® (Huntington Ingalls Industries; formerly Kongsberg Maritime); (**b**) Wave Glider SV3 (Liquid Robotics); and (**c**) Sailbuoy (Offshore Sensing). unit for communication, navigation, and power systems, containing a 980 Wh lithium-ion battery pack charged by solar panels (150 W). The maximum payload weight is 59 kg, and the maximum and average water speed are 3 and 1.8 knots, respectively. Additionally, the Wave Glider has an electric propulsion system to increase mobility in challenging ocean conditions. For the current project, a 2-way Iridium communication system for data transmission and navigational instructions was used (for further details [34]). The Sailbuoy (Offshore Sensing) is a long-duration unmanned surface vehicle using wind for propulsion [35]. The vehicle employed in the GLIDER project was 2 m \(\times\) 0.5 m \(\times\) 1.3 m (length, beam, height), with a displacement of 60 kg and payload up to 10 kg. This vehicle may operate for several months with average speeds of 1-3 knots. Navigable wind speeds are 3-30 m/s (tested up to 30 m/s) and the navigable wave height is up to about 15 m. Autopilot and sensor payloads are powered by separate battery packs charged by solar panels (30 W). Two-way Iridium communication was used for navigational instructions and for transmitting data (for further details [36]). In the GLIDER project, the three vehicles were equipped with current state-of-the-art sensors to measure biogeochemical and biological parameters (Table 1).
## 6 Discussion  
This paper proposes a synergetic software-defined sampling and optimal network utilization mechanism called Sonum. Sonum divides load balancing in data center networks into two parts: Sampling and scheduling. For sampling, Sonum detects long flows throughout consolidating long flow sampling information from multiple switches, with the result of increasing the exchange of the sampling information and reducing the single switch sampling rate. For scheduling, Sonum minimizes the packet loss rate instead of maximizing the link utilization as the optimization target of load balancing. Sonum translates load balancing into arranging minimum packet loss path for long flows. This paper also introduces a heuristic algorithm to solve scheduling problems. The experimental results show that, compared with ECMP and Hedera, Sonum had a better performance on long flow  
Figure 16: Improvement Performance of long flow FCT of Sonum compared to ECMP and Hedera.  
Figure 17: Improvement Performance of long flow FCT of Sonum compared to ECMP and Hedera.  
detection, network throughput and flow completion time. However, the verification of Sonum is based on the simulation environment, and the deployment effect in the real environment is something to be studied.  
[PERSON] proposed software-defined synergetic sampling approach and optimal network utilization mechanism, designed the algorithm, and drafted the manuscript. [PERSON] contributed significantly to manuscript preparation and funding acquisition. [PERSON] performed the analysis with constructive discussions, and revised this manuscript. [PERSON] and [PERSON] designed and performed the experiment and evalution. In addition, we would like to thank [PERSON] and [PERSON]. They participated in the discussion and writing of this manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China under Grant No. 2018 YFB1800305, Fundamental Research Funds for the Central Universities of China under Grant No. 2018 YJS002, CERN Internoation Project under Grant No. NGII20180120 and Jingmen Science and Technology Research and Development Plan Project under Grant No. 2018 YFB055.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ICT & Information Communcaiton Technology \\ DC & Data Center \\ DCN & Data Center Network \\ FCT & Flow Completion Time \\ ECMP & Equal-Cost Multipath \\ ECN & Explicit Congestion Notification \\ SJF & Shortest Job First \\ EWMA & Exponentially Weighted Moving Average \\ LRU & Link Resource Utilization \\ SRU & Switch Resource Utilization \\ NRU & Network Resource Utilization \\ \end{tabular}
## 3 Results and Discussion
### Multifactorial Experiment  
#### 3.2.1 Experimental Design and Results  
Experiments were conducted based on the established experimental matrix, and the obtained experimental results after analysis and calculation are presented in Table 1.  
To evaluate the impact of regression coefficients on Deff, Sp and dE, based on the experimental results, the first analysis of variance (ANOVA) was performed with a second-degree polynomial function. From the analysis results, we eliminated non-reliable regression coefficients and continuously performed the second analysis of variance (ANOVA). From the analysis results, the regression coefficients are all significant (\(p\) < 0.05). Based on the results, it can be seen that the lack-of-fit values of Deff, Sp and dE with \(p\) > 0.05 and adjusted R\({}^{2}\) > 95% ensure reliability  
Therefore, we confirm that the regression model Def, Sp and dE is suitable.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 3 The Proposed Method
### Feature Indexing  
After all the spatio-temporal features of persons are generated by GRU, each person corresponds to a spatio-temporal feature. Due to the large number of pedestrians in surveillance videos, it is difficult to ensure the efficiency of person search by directly calculating the similarity between a large number of spatio-temporal features. Because Locality Sensitive Hashing (LSH) [44] can perform a fast approximate search of the massive high-dimensional data, it is employed to encode spatio-temporal features into short binary codes, and then compute the similarity between different features in the Hamming space.  
All the spatio-temporal features are input into LSH and projected into a low-dimensional Hamming space. For the spatio-temporal feature \(p_{j}\), it is mapped to a b-bit hash code by applying b binary-valued hash functions \(H\), and represented as:  
\[H:p_{j}^{256\times 1}\rightarrow\left\{0,1\right\}^{b} \tag{7}\]  
In the search process, the spatio-temporal feature of the query person is generated and projected into the Hamming space, and the similarity between the query person and persons in the surveillance videos can be computed as:  
\[sim(q,p_{j})=\Pr[H(q)=H(p_{j})] \tag{8}\]where \(q\) is the spatio-temporal feature of the query person.  
After the query person is searched for in the video database through LSH, the persons in the videos are sorted by the similarity of spatio-temporal features, and the top-\(K\) persons with the greatest similarity are taken as the final search results.
# Sound Localization Framework for Construction Site Monitoring
## 2 Research Methods  
The development of the sound-based localization framework suitable for construction sites involved the following steps (Figure 1): First, we examined the existing literature and case studies, and identified the limitations and features of sensor-based monitoring methods currently used at construction sites. We also analyzed sound-based localization methods used in several industrial sectors.  
Second, we analyzed the spatial characteristics of construction sites to determine the factors that should be considered for developing a monitoring system. These factors were then used to perform a comparative analysis of sound-based localization approaches for onsite use and to develop an integrated localization framework.  
Furthermore, pilot tests were performed in a laboratory to verify the developed framework using various microphone arrangements in 2D and 3D environments. The tests were performed at an actual construction site to confirm the usability of the framework.  
Finally, we analyzed the experimental results and evaluated the limitations identified during the framework development process, and identified opportunities for future system development and research directions.