esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results of the Foamed Poly(Lactic Acid)
### Foam Strength  
Foam samples were finally tested mechanically. A widely used test of foamed products is the foam strength test. We only performed the test on samples deemed acceptable (void fraction >40%), that is on 2003_3170 and 8052_3170 samples. Figure 12 shows the measurement curves selected as typical. The shape of the curves is characteristically the same as that of other known foam strength curves coincides. At initial loading, in the linear section, deformation is elastic, then a peak follows, which corresponds to \"yield strength.\" In elastic deformation, the cells take up the load, then they are restored to their original state when loading stops. At the inflexion part, however, the cells are deformed and damaged. The straight section (the section after the inflexion point) appeared in the curve of every specimen within the 10% deformation limit, that is, each specimen suffered permanent deformation. In the case of 2003_3170, it is 22.8 \(\pm\) 0.6 MPa, while in the case of 8052_3170, it is 22.4 \(\pm\) 0.7 MPa. There was no significant difference between the compression strength values of foams produced with Tracel3170 and the two different types of PLA; the compression strengths and standard deviations are nearly the same. The aim was to create biodegradable hard foams with the use of commercially available CBAs. Compressive strength is higher, compared physically foamed PLA  
Figure 11: The DSC curves of reference PLA and foamed samples (1 st heat-up, 5 \({}^{\circ}\)C/min).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type of Sample** & \(\mathbf{T_{g}}\) & \(\mathbf{T_{c1}}\) & \(\mathbf{\Delta H_{c}}\) & \(\mathbf{T_{m1}}\) & \(\mathbf{T_{m2}}\) & \(\mathbf{\Delta H_{m}}\) & \(\mathbf{\chi_{c}}\) & \(\mathbf{\chi_{cf}}\) \\ \hline
**[-]** & **[\({}^{\circ}\)C]** & **[\({}^{\circ}\)C]** & **[J/g]** & **[\({}^{\circ}\)C]** & **[\({}^{\circ}\)C]** & **[J/g]** & **[\%]** & **[\%]** \\ \hline
2003\_ref & 59.6 & 116.7 & 23.8 & 151.1 & - & 26.4 & 28.4 & 6.9 \\
8052\_ref & 59.2 & 115.9 & 25.2 & 150.3 & - & 28.8 & 31 & 3.1 \\
2003\_3170 & 57.5 & 108.4 & 26.5 & 147.3 & 153.8 & 28.1 & 30.2 & 1.7 \\
8052\_3170 & 59.6 & 107.5 & 27.4 & 147.6 & 154.8 & 31.4 & 33.8 & 4.3 \\
2003\_4200 & 59.3 & 114.5 & 29.5 & 147.2 & 154.2 & 32.2 & 34.6 & 2.9 \\
8052\_4200 & 59.6 & 107.9 & 29.3 & 143.7 & 154.9 & 30.5 & 32.8 & 1.3 \\
2003\_Luv\_9537 & 57.2 & 105.6 & 28.8 & 146.4 & 154.4 & 30.1 & 32.4 & 1.4 \\
8052\_Luv\_9537 & 55.4 & 105.4 & 33.7 & 147.1 & 154.8 & 34.5 & 37.1 & 0.9 \\
2003\_Hyd\_3168 & 56.2 & 112.3 & 29.6 & 148.5 & 155.2 & 31 & 33.3 & 1.5 \\
8052\_Hyd\_3168 & 57.5 & 108.3 & 27.4 & 148.5 & 155.5 & 28.3 & 30.4 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: DSC results of foamed samples and unfoamed, extruded PLA (1 st heat-up, 5 \({}^{\circ}\)C/min).  
in the literature [21]. The results should be considered a reference value when later modified foam structures are developed later.
## 3 Methods
### Match Cost Aggregation Methods  
In the aggregation operation of matching cost, the algorithm selects the support window with a fixed size and shape. Matching cost aggregation means that the matching costs corresponding to all pixels in the support window are accumulated to obtain the overall matching cost of left and right image regions. After aggregating, the matching cost is between the corresponding pixels in the left and right images. However, this method is often based on the premise that all pixels in the window have similar depth information. In fact, in the edge areas such as depth discontinuity and color difference, it often does not meet this condition, so it is easy to produce false matching.  
To solve this problem, this algorithm uses an adaptive weight method based on color similarity and distance similarity to aggregate the matching cost, that is, the more similar the color of the neighborhood points and the center points, the greater the allocation weight; the closer the space distance between the inner point and the center point, the greater the allocation weight. Among them, color similarity refers to the degree of color similarity between two pixels. To reduce the amount of calculation, Manhattan color distance is used to describe; distance similarity refers to the geometric distance between two pixels, which is described by the Euclidean distance between pixel coordinates, as shown in Equation (10).  
\[d_{e}(p,q)=\sqrt{(u_{q}-u_{p})^{2}+(v_{q}-v_{p})^{2}} \tag{10}\]  
where \((u,v)\) is the pixel coordinate of the pixel, \(d_{e}(p,q)\) is the Euclidean distance between the two points \(p\), \(q\).  
For a central pixel \(p\), the weight of any pixel \(q\) in the support window is determined by the color distance and space distance corresponding to the point \(p\), and the weight is shown in Equation (11)  
\[w(q)=\exp(-d_{e}(p,q)/\lambda_{e}-d_{mc}(p,q)/\lambda_{c}) \tag{11}\]
## 1 Introduction  
With the everyday increase in data traffic requirements ranging from mission-critical to massive machine connectivity, the anticipation of the fifth-generation (5G) is growing at an exponential rate. Although such requirements open new doors to exciting features and business models, the provision of these requirements for such intense traffic and diverse services (Figure 1) remains a challenge for the telecom industry.  
Figure 1: Road to 5G networks and their diverse services.  
These challenges are further enhanced by elements such as expectations of node-to-node or machine-to-machine communications requirements, the uncertainty of topology, diverse application requirements, backward compatibility, user equipment resource limitations, and the rapidly increasing number of devices. These elements exacerbate the technical complications of the implementation of future 5G networks.  
One of the crucial supporting technologies for the implementation of 5G is new radio (NR) [1], a new radio access technology. NR is the new radio interface and access method that is developed to facilitate the growing requirements of 5G in the coming future [2]. As shown in Figure 2, NR provides a flexible frame structure to support all 5G service requirements defined by the International Telecommunication Union (ITU).  
The new NR facilitates communications between the base station and the user/mobile device. With such rapid changes in technology and user demands, NR is designed not only to facilitate the three defined categories of ITU, but also to provide space for future growth in technology, as shown in Figure 3. The three basic categories formulated by ITU for 5G [3] are as follows:  
1. enhanced mobile broadband (eMBB),
2. ultra-reliable low-latency communications (URLLC), and
3. massive machine-type communications (mMTC).  
Figure 3: Features and improvement plan for future 5G [4].  
Figure 2: The flexible frame structure of 5G new radio (NR) [2]. URLLC, ultra-reliable low latency communications; eMBB, enhanced mobile broadband; mMTC, massive machine-type communications; IoT, Internet of Things; NB, Narrowband.
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 3 System Model
### Contention Window Size of Relay Devices Evaluation  
The contention window (CW) is defined as the waiting time frame of the devices for accessing subsequent data transmission after a collision occurrence. The waiting time frame is known as back-off time, which ranges between \([C_{min},\,C_{max}]\), and it increases according to the binary exponential back-off, where \(C_{min}\) and \(C_{max}\) are the minimum and maximum CW size of the devices, respectively. When more than one device transmits data packets simultaneously, there will be a collision between them, and the CW size will increase exponentially. The average channel access to the devices is highly dependent upon the back-off time and the CW size [41]. The device selected in the route, which has high back-off time, will experience a significant delay in the D2D network. Consequently, the CW size of the relay devices plays a vital role in the optimal route computational procedure. Therefore, selecting the relay devices in the optimum route with a smaller CW size is imperative. When the data packet transmission is successful, the CW size goes to a minimum, i.e., \(CW=C_{min}\). In contrast, when a collision occurs during the data packet transmission, the \(CW\) size increases until maximum such that \(CW=C_{max}\) and the binary exponential back-off time becomes two times. \(\Phi\) defines the data packets that are successfully transmitted to the destination, which range varies between 0 and 1. When the data packets are successfully received at the destination, it shows 1, whereas it shows 0 when there is a collision that occurs at the time of data packet transmission. Therefore, the CW size is computed as follows:  
\[CW=\left\{\begin{matrix}\min(CW\times 2,CW_{\max})&\Phi=0\\ CW_{\min}&\Phi=1\end{matrix}\right. \tag{9}\]  
Suppose \(r\) denotes the total number of re-transmissions required for the successful delivery of data packets to the destination device, then the source device randomly selects a back-off time ranging from \([C_{min},CW_{s}^{c}]\) when transmitting the data packets. The \(CW_{s}^{c}\) defines the CW size of device \(c\) at the \(s^{th}\) transmission attempt, and it can be calculated according to the exponential back-off algorithm as follows:  
\[CW_{s}^{c}=\left\{\begin{matrix}\sigma^{s}CW_{\min},&for\,s\in[0,r^{*}-1]&if \,r>r^{*}\\ CW_{\max},&for\,s\in[r^{*},\,r]&if\,r>r^{*}\\ \sigma^{s}CW_{\min},&for\,s\in[0,\,r]&if\,r\leq\,r^{*}\end{matrix}\right. \tag{10}\]  
where \(r^{*}=[\log_{\sigma}\frac{CW_{\max}}{CW_{\min}}]\) and \(\sigma\) are referred to as the back-off increment factor. Therefore, the relay devices are prioritized based on the contention window size. In the situation when the \(CW\) range of a relay device is close to \(C_{min}\), it will get higher priority. In contrast, if the \(CW\) value is high, it will have a lower priority in the route computational procedure.  
In this way, data collisions among the devices decrease, and packet drops reduce during the data packet transmission in the network.
## 3 Monte Carlo Simulation  
Presented framework is tested on three types of networks that are typical for distribution systems in Finland. The networks represent rural, suburban and urban areas and possess different topology, number of customers and line parameters. The networks are depicted in Figure 2. A summary of the regions modelled is shown in Table 1, and an extensive description of the networks and their formation is explained in [24]. The power of the single PV plant is assumed to be equal to a single phase circuit of 16 A, which corresponds to 3.7 kWp at nominal voltage. Such power will be further referred to as the single-phase circuit capacity. PV plants are assumed to be constant power models and their contribution to negative sequence current is \(\
icefrac{{1}}{{3}}\) of the total injected current at nominal voltage.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Transformer** & **Cable** & **Cable Type** & **No. of** & **Buses per** & **Customers** & **Average No.** \\  & **Rating (kVA)** & **Length (m)** & **Cable Type** & **Feeders** & **Feeder** & **Per Bus** & **of PVs** \\ \hline Rural & 50 & 150 & 70 mm\({}^{2}\) & 1 & 8 & 1 & 2.4 \\ Suburban & 200 & 100 & 185 mm\({}^{2}\) & 3 & 4, 3, 3 & 4 & 12 \\ Urban & 1000 & 100 & 2 \(\times\) 185 mm\({}^{2}\) & 3 & 2, 2, 1 & 60 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test network parameters.  
Figure 2: Networks modeled for rural (**a**), suburban (**b**) and urban (**c**) regions.
## 1 Introduction
The problem of satellite communication power allocation under a repeated confrontation scenario belongs to online learning problems, and the communication parties usually need to continuously and dynamically learn and adjust the trade-off between exploiting the current actions and exploring the new actions. The blueprint strategy and the strategy boost in online learning are important factors for obtaining super performance in two-player zero-sum games [14]. The two works in this paper are the common problem forms in the confrontation scenario. Our research and analysis can simulate the confrontation scenario of the actual satellite communication power allocation, which is of great significance to the research of jamming and anti-jamming in satellite communication. This paper is arranged as follows. Section 2 presents the basic system model for power allocation between the communication parties and the jammer. Section 3 introduces the solution to the power allocation problem under the condition of one-shot game and experimental verification. Section 4 introduces the solution to the power allocation problem under the condition of repeated games and experimental verification. Section 5 concludes the article.
# Aerial Mapping of Coseismic Surface Rupture of 2021 M\({}_{\rm w}\) 7.3 Maduo Earthquake, China
## 4 Results  
### Coseismic Surface Rupture  
The surface deformation map shows that the main area impacted by the Maduo earthquake is about 60 \(\times\) 180 km. The rupture zone presents a linear distribution in the SEE direction, and the fault strike becomes smaller at the west and east ends. The rupture is consistent with the preexisting tectonic landform, and mainly passes through the mountain front, indicating that there is a fault with repeated activities. The largest deformation is mainly concentrated on the east of the fault. The maximum displacement is about \(-\)1.0 m in the south side and 1.3 m in the north side of the fault (Figure 2).  
In the field survey, continuous rupture traces with lengths that range from several hundreds of meters to several kilometers were observed. Meanwhile, only a few cracks were found in some areas.  
Based on the branching of the fault strike, the surface rupture of the Maduo earthquake can be roughly divided into four sections.  
The western section is composed of two faults. The north branch, which has a strike of 109\({}^{\circ}\), consists of intermittent small fractures, which are several meters long and cannot  
Figure 9: 3D visualization and measured profiles of push-up structures in the central-eastern section of the rupture with shortening values of (**A**) 0.79 m. \(\theta\) is 52\({}^{\circ}\), and the offset along the fault strike is 1.28 \(\pm\) 0.2 m. and (**B**) 0.95 m. \(\theta\) is 44\({}^{\circ}\), and the offset along the fault strike is 1.32 \(\pm\) 0.2 m.  
measure the offset (Figures 2 and S2A). The south branch, which has a strike of 96\({}^{\circ}\), consists of a fault section that is 28 km long and exhibits a more significant surface rupture.  
The western central fault section strikes 109\({}^{\circ}\) and has a length of \(\sim\)50 km. This section includes the most continuous fault rupture.  
The eastern central fault section strikes 105\({}^{\circ}\) and has a length of \(\sim\)57 km. The rupture of this section is highly discontinuous and intermittent (Figures 2 and S2B); only \(\sim\)2 km rupture in this section is uninterrupted (Figure 2).  
The eastern section of the fault splits into two branches of rupture. One rupture has a strike of 77\({}^{\circ}\) and a length of \(\sim\)26 km, and another rupture strikes southeast (Figure 2).
###### Abstract  
Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method--namely, the Savitzky-Golay (SG) derivative--was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (\(R^{2}\)), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the \(R^{2}\) and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.  
LIBS; sediment analysis; heavy metal; PLSR; data processing +
Footnote †: journal: Applied Sciences  
11 June 2021  
Accepted: 26 July 2021  
Submitted: 3 August 2021
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
###### Abstract  
This study presents a self-testing platform with a foreground digital calibration technique for successive approximation register (SAR) analog-to-digital converters (ADCs). A high-accuracy digital-to-analog converter (DAC) with digital control is used for the proposed self-testing platform to generate the sinusoidal test signal. This signal is then implemented using an Arduino board, and the clock signal is generated to test the ADCs. In addition, fast Fourier transform and recursive discrete Fourier transform (RDFT) processors are adopted for dynamic performance evaluation and calibration of the ADCs. The third harmonic distortion caused by the non-linearity of the track-and-hold circuit, the mismatch of the DAC capacitor array, and the direct current (DC) offset of the comparator can be calculated using the processors to improve the ADC performance. The advantages of the proposed platform include its low cost, high integration, and no need for an extra analogy compensation circuit to deal with calibration. In this work a 12 bit SAR ADC and an RDFT processor are used in the Taiwan Semiconductor Manufacturing Co., Ltd. (TSMC) 0.18 \(\upmu\)m standard complementary metal-oxide-semiconductor (CMOS) process with a sampling rate of 18.75 kS/s to validate the proposed method. The measurement results show that the signal-to-noise and distortion ratio is 55.07 dB before calibration and 61.35 dB after calibration.  
analog-to-digital converters (ADCs); digital calibration method; self-testing; successive approximation register ADC (SAR)  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] 1 The Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; [EMAIL_ADDRESS] (Y.-H.J.); [EMAIL_ADDRESS] (S.-Y.L.); [EMAIL_ADDRESS] (W.-H.J.)  
The Department of Electrical Engineering, National Taipei University, Taipei, 23741, Taiwan; [EMAIL_ADDRESS]  
The Department of Computer Science and Information Engineering, Nanhua University, Chiayi 62249, Taiwan; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +886-6-2757575-62375; Fax: +886-6-234-548  
Academic Editor: [PERSON]  
Received: 29 May 2016; Accepted: 26 July 2016; Published: 29 July 2016
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 2 Background and Related Work  
This section summarizes the background and related work of the paper. We first introduce the background of Ambient Intelligence, Internet of Things, and Vehicular Edge Computing. Then, we summarize the technologies related to graph embedding and their applications. Finally, the recent technologies for traffic incident detection are organized.
## 2 System Model and Signal Processing Methods
### Integrated Navigation Model  
[PERSON] et al. (2019) proposes a direction-finding model for satellite navigation signal sources identified by a 6 + 1 antenna array; however, it is expensive, and the inertial measurement unit (IMU) carried by common carriers remains underutilised. Effective identification and correction of deceptive interference can be realized with the aid of auxiliary information from IMU or wheel sensor ([PERSON] et al., 2019). However, the classic pseudorange consistency detection makes it difficult to support high-accuracy signal source identification and localization. Therefore, this study utilises an integrated navigation model as illustrated in Figure 2, wherein two antennas are arranged along the \(X\)-axis of the MIMU body coordinate system. The proposed method is applicable to any antenna and common-clock receiver that can simultaneously receive at least two navigation frequency bands of the same navigation satellite. The model can be widely applied in vehicles and other carriers that require MIMU/satellite integrated navigation because of its length demand for a single direction only. Additionally, this research proposes a short-baseline integer ambiguity determination method based on an improved ambiguity function method (AFM); therefore, there is no spatial ambiguity between the two antennas. Consequently, the baseline length between the units is extended, providing higher direction-finding accuracy.  
As illustrated in Figure 2, two antennas and an MIMU are solidly connected to the carrier. Antenna 1 serves as the origin of the body coordinate system. The baseline vector between the two antennas aligns with the \(X\)-axis of the carrier coordinate system. The \(X\)-axis of the MIMU body coordinate system is parallel to the \(X\)-axis defined for the carrier coordinate system. The \(o-x_{MIMU}^{b}y_{MIMU}^{b}\) plane of the MIMU body coordinate system coincides with the \(o-x^{b}y^{b}\) plane of the carrier body coordinate system. The \(Y\)-axis of the MIMU body coordinate system is parallel to the \(Y\)-axis defined for the carrier coordinate system.
## 4 Design Implementation
### FPGA Bus Components  
The system was implemented using the Intel Platform Designer [53] on an FPGA chip, as shown in Figure 8. An Intel Nios II micro-processor (\(\upmu\)P) [54], synthesized on the FPGA, was utilized. The software was developed in the C language. Both the FPGA configuration file and micro-processor program were stored in the Serial Configuration Device (EPCS) Read-Only Memory (ROM), also known as bootloaders. A hardware \"Timer\" was integrated into the FPGA, enabling the micro-processor to retrieve precise timestamps for performance evaluations. The hardware timer operates simultaneously, without requiring additional processing in the micro-processor. Additionally, the system had TCP/IP protocol support and a network interface for remote control and data access.  
To benchmark the performance against the \"Key Stream Generator\" in the pipeline data processing flow, the system designed the algorithm serving as the bus controller of Key Stream Generator (KS-Generator) controller. In particular, a Register-based Content-Addressable Memory (Reg-CAM) is used to accelerate the generation of the non-repeating key stream blocks. Lastly, a Scatter-Gather Direct Memory Access (SG-DMA) controller was designed using Verilog and synthesized as a system component connected to the system bus. These controllers allow the micro-processor to access relevant configuration and status registers, facilitating hardware and software cooperation. Specifically, the implemented controllers of Reg-CAM and SG-DMA are introduced as follows:  
Figure 7: Data processing flow from stream signal to network interface.