esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we analyze the advantage of the ICN paradigm in IoT scenarios for seamless global coverage. Thus, the framework of the LEO-SMC-ICN-IoT is proposed first. The framework combines LEO satellite mega-constellation, ICN, and IoT. Based on the LEO-SMC-ICN-IoT, we focus on the in-network caching policy for satellite nodes:  
1. Introducing the concept of \"viscosity\", the proposed CARF caching policy of satellite nodes combines both content popularity prediction and satellite nodes' location prediction, for achieving good cache matching between the satellite nodes and content.
2. Using the RF algorithm which considers the IoT content lifetime and freshness, the policy can accurately predict the matching relationship between the satellite nodes and the content.  
Developing an LEO-SMC-ICN-IoT simulation platform by NS3, the proposed CARF caching policy performs markedly better in terms of the average number of hops, hit rate, and throughput, which also improves the quality of user experience. We conclude that caching performance can be significantly improved by fully analyzing and exploiting the matching relationship between the satellite nodes and the content using RF.  
Conceptualization, methodology, resources, data curation, S.H. and H.L.; software, investigation, writing--original draft preparation, validation, visualization, H.L.; formal analysis, writing--review and editing, S.H. and T.Y.; supervision, project administration, funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Guizhou Province Education Department Projects of China, grant number KY [14]031, KY [2020]007, the Department of Science and Technology of Guizhou Province, grant number QKH [2022] 242, and the National Natural Science Foundation of China, grant number 61561009. (Corresponding author: [PERSON], [PERSON]).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
Figure 15: The trend of the average hop rate with the request rate.
## 3 Results and Discussion
### The Influence of Various Factors on the Dynamic Performance of RFR
#### 3.2.5 The Effect of the Tie Rod Parameters on the Rotor System  
The number of tie rods, the installation radius of the tie rod, and the tie rod radius all have a certain effect on the dynamic characteristics of the RFR. The results are shown in Figures 17-19.  
Figure 17 plots the effect of the number of tie rods on the natural frequencies of the rotor system. With the increasing number of tie rods, the first-order and third-order natural frequencies increase monotonously, while the second-order natural frequency decreases monotonously. The effect of the number of tie rods on the first-order and second-order natural frequencies is larger than the effect on the third-order natural frequency. Moreover, when the preload is relatively small, the effect of the number of tie rods on the natural frequencies is larger.  
Figure 18 shows the effect of the installation radius of the tie rods on the natural frequencies of the rotor system. With the increase of the installation radius of the tie rods, the effect of the installation radius of the tie rods on the natural frequencies of the rotor system is similar to the effect of the number of tie rods on the natural frequencies of the rotor system. The larger installation radius of the tie rods has a larger influence on the natural frequencies of the rotor system.  
Figure 19: The rate of change of natural frequencies of the RFR with different radius of tie rods.  
Figure 18: The rate of change of natural frequencies of the RFR with different installation radius of tie rods.  
Figure 19 plots the effect of the tie rod radius on the natural frequencies of the rotor system. It can be seen that the increase of the tie rod radius makes the natural frequencies of the rotor system increase very slightly. However, the effect is almost negligible.
## 4 Simulation Results
### Environment Settings  
We consider a 400 * 400 square training environment based on the open AI platform, which consists of USVs, targets, and threat areas. The geometric coordinate system is established with the environmental center as the origin of the coordinates. As the target area and static obstacle are regarded as parts of the environment, their settings would not be changed during the training process. In contrast, the dynamic obstacle changes position randomly after each training episode. In our settings, the maximum communication distance \(\rho_{c}\) is set to 0.9 and the width of critical area \(\sigma\) is set to 0.1. In this coordinate system, the size of the threat area is set to 0.25, while the size of the agent and target area are set to 0.04 and 0.20, respectively. We present two indicators to measure the effectiveness of training: the collision rate of USVs' collisions during the current episode and the collision rate between the agent and the critical area during the total episode.
## 2 Materials and Methods
### Input Data
#### 2.2.2 MODIS Data  
MODIS v.5 eight-day composite surface reflectance (\(\rho\)) (products MOD09Q1 and MYD09Q1, Terra and Aqua, respectively) for band 1 (620-670 nm, \(\rho_{\text{red}}\)) and band 2 (841-876 nm, \(\rho_{\text{NIR}}\)) at 250 m spatial resolution were obtained using Google Earth Engine. Reflectances were used to calculate a wide dynamic range vegetation index (WDRVI) time series at eight day intervals as:  
\[\text{WDRVI}=[\alpha(\rho\text{NIR})-\text{pred}]/[\alpha(\rho\text{NIR})+ \text{pred}] \tag{4}\]  
where \(\alpha\) is a weighting parameter (typically 0.1-0.2 and set at 0.1 here). WDRVI is the normalized difference vegetation index (NDVI) when \(\alpha\) equals 1; WDRVI equals zero when \(\alpha\) equals (\(\rho_{\text{red}}/\rho_{\text{NIR}}\)) [19]. WDRVI values from the pixel nearest the geographic center of each field were used for analysis to avoid mixed pixels from adjacent areas due to the 250 m pixel resolution.  
Green leaf area index (gLAI) values were estimated from the WDRVI time series following the approach of [PERSON] et al. [20], with coefficients determined from ground samples at the Nebraska sites:  
\[\text{gLAI}=5.06\cdot(\text{WDRVI value})+(-0.47)\text{ {Maize}} \tag{5}\]  
\[\text{gLAI}=3.68\cdot(\text{WDRVI value})+(-0.24)\text{ {Soybean}} \tag{6}\]  
The gLAI data were interpolated to continuous daily values for the growing season using Curve Expert 1.4 software (Hyams Development, [[https://www.curveexpert.net](https://www.curveexpert.net)]([https://www.curveexpert.net](https://www.curveexpert.net))) and a cubic spline interpolation algorithm.
## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Modal Identification
#### 3.1.1 Natural Frequency Extraction
Additionally, a set of accelerometers was installed at the bridge to derive reference values for the natural frequencies which were extracted by the Stochastic Subspace Identification (SSI) algorithm. Firstly, the NN was trained using only the simulated dataset and the weights of the layers stored; then, the concept of transfer learning was used and the previously trained NN had its last layer weights tuned during the training with experimental data. The results obtained with and without the transfer learning are presented in Figure 7b in the form of a scatter 2D plot in which the vertical axis represents the predicted natural frequency and the horizontal axis represents the \"true natural frequency\". The variation presented in the \"true\" values is due to the nonlinear behavior of the bridge that presents lower frequency values associated with higher excitations provided by higher vehicle speeds. As can be seen in Figure 7b, the model trained with transfer learning can capture the true results very well; however, the same was not accomplished for the model trained with simulation data only. In turn, the authors argue that this is largely due to the extremely simple simulation model used which was unable to capture the torsion modes of the bridge, and better results are expected with the use of more sophisticated models.
# Remote Control Device to Drive the Arm Gestures of an Assistant Humanoid Robot
## 3 APR-02 Humanoid Robot  
The humanoid mobile robot telecontrolled in this work was developed under the Assistant Personal Robot (APR) concept described in [59; 60]. The first prototype developed under the APR concept was the APR-01 mobile robot, which was originally designed as a teleoperated robot [59]. The next APR-02 mobile robot prototype was designed as an autonomous robot with teleoperation capabilities [60]. The APR-02 mobile robot has been applied as a walk-helper [61] and used as a tool for systematic odometry error correction [62; 63]. In the walk-helper application, the height and weight of the robot provided physical support and guidance to people walking, using the arms as a holding support.  
The APR prototypes include communication features such as audio and video connection between the local user and the remote operator combined with omnidirectional mobility. The APR-02 mobile robot includes a set of improvements to increase the anthropomorphism and affinity with the robot. The mobile robot includes a vertical tactile screen to display an iconic face with an animated mouth and eyes that follow the user to enhance the sense of attention from the robot [64]. The robot generates short characteristic sounds to express salutations and provide acceptance or rejection feedback. It has two articulated arms with four degrees of freedom each: three in the shoulder and one in the elbow. The servomotors used in each joint of the arms are digital bus servos (Dynamixel AX-12) that operate in a closed loop to manage arm motion robustly in terms of fault-tolerant control [65]. Figure 1a shows the APR-02 mobile robot used in this paper moving its hands to knock at a door, and Figure 1b shows the same scene from the teleoperator perspective, captured with a panoramic camera located on the head of the robot. Currently, the hands of the APR-02 robot are figurative and fixed because the use of hands for object manipulation is not usually required in social telepresence robots [19].  
In a first stage, mobile robot gestures were controlled with an online imitation system using a finite number of prerecorded gestures. This work expands gesture control options by allowing a human operator to directly control the pose of the arms of the robot.  
Figure 1: APR-02 robot used in this paper moving its hands to knock at a door: (**a**) Side view of the robot; (**b**) teleoperator view from the panoramic camera available on the head of the robot.
## 2 Principle Analysis of the Proposed JSG-DPD Technique
### LEO Satellite-Ground Channel
For the Ka-band 100 MHz signal applied in this paper, the maximum spreading of the bandwidth is 3.3 kHz. Given that the time-varying Doppler effect has been solved by mature techniques [19; 20; 21; 22], so it is not the focus of our study. Consequently, the LEO satellite-ground transmission channel is modeled as an AWGN channel in this paper. The magnitude of white Gaussian noise follows a Gaussian distribution, while the power spectral density (PSD) obeys a uniform distribution.
## 4 Conclusions  
Previous versions of the GTAP-BIO model assume uniform values for the land transformation elasticities for all regions worldwide. They also put forest, pasture and cropland in one nest and assume forest and pasture land can be converted to cropland with identical land transformation elasticities. In prior work there was not much land use change globally that could be used to calibrate GTAP parameters, but in the past decade there has been substantial land cover change corresponding to the period of the biofuels boom. The actual land use changes have varied significantly from one region to another during the past two decades across the world. In addition, in real world converting forest to cropland is more costly than converting pasture to cropland.  
While we recognize that the CET parameter is not the only factor driving the extent and location of land use change, it is one of the important parameters and one that can easily be varied by region. This paper reviews changes in global cropland and indicates that during the past two decades countries around the world have followed different land allocation patterns in the face of changes in markets for crop products. While some regions have expanded their cropland significantly, other regions mainly reallocated their existing cropland among alternative crops. This suggests that the land transformation rates vary across the world. Based on these observations, uniform land transformation elasticities are modified. In addition, the land cover nesting structure of the land supply tree is modified. In the new land supply tree cropland and pasture are in one nest, and the combination of these two types of land cover with forest are in another nest. Then the land use consequences of the USA ethanol mandate are evaluated with these model and parameter changes.  
The implemented experiments show that moving towards regional land transformation elasticities reduces the magnitude of land conversion, and using the two-nest land cover nesting structure decreases the share of forest in land conversion. The combination of these two changes reduces the magnitude of cropland expansion by 45%, and decreases the share of forest in cropland expansion to 7%. The estimated land use emissions due to ethanol production falls by 18%. In addition, about 0.5 MH more cropland pasture will be converted to cropland due to these two modifications.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 5 Energy-Latency-Reliability Trade-Off
### Current Consumption Profile of Different Data Transfer Schemes  
In this section, the current consumption profile of the transmitter and the receiver are considered when using simple transmissions, convolutional FEC coding, blind retransmissions (BR), and stop-and-wait (SAW) ARQ-based retransmissions.  
The same measurement setup in Figure 2 was used and the results are depicted in Figure 14. Figure 14b shows a considerable transmission time and energy overheads when using FEC as the payload is doubled due to coding when compared with Figure 14a. Similarly, transmitting the packet twice as shown if Figure 14c or waiting for an ACK as depicted in Figure 14d has more pronounced energy and time overheads.  
It should be noted that, apart from the transceiver overhead, Figure 15 shows that the MCU energy consumption per measurement does vary when using different data transfer schemes. An energy overhead can be observed when using FEC and the SAW-ARQ protocol due to additional data encoding and packet processing, respectively. However, when compared to the transceiver's contribution which is investigated in two case studies presented in Section 6.2, it is safe to say that the MCU's energy consumption does not make a significant difference.  
Figure 14: Current consumption profile of the transmitter and the receiver when using different data transfer schemes; (**a**) simple transmissions, (**b**) FEC, (**c**) two BR, and (**d**) a SAW-ARQ protocol (also referred to as 1-Rep-ACK in this work).  
Figure 15: MCU energy consumption per measurement for different data transfer schemes.  
All of the previously mentioned techniques (i.e., FEC, BR, and SAW-ARQ) can manage bit errors. Yet, the packet success probability needs to be determined for each technique in order to objectively investigate their usefulness.
# Micro-LEDs, a Manufacturing Perspective
## 3 Assembly Technologies  
Two different approaches have been utilized to assemble displays constructed from individual micro-LED dies. The first one is based on mass transfer, i.e., the so called pick-and-place approach, which includes picking up each individual micro-LED, and precisely positioning it onto the backplane, followed electrically connecting it to the driver IC. This approach allows realization of LED displays in which the active LED area occupies only a small portion of the total area. It can be used for building large-size displays ranging from several inches to tens of inches (such as TV, smartphones, and tablets), which are referred to as direct-view displays [34]. Alternatively, to avoid the complicated pick-and-place mass transfer process, a variety of monolithic fabrication methods have been proposed in order to realize micro-LED displays. Due to the limitation in wafer size used for the growth of LEDs, the monolithic integration approach can only be used for building relatively small-size displays such as near-to-eye projectors and smart watches, which are referred to as micro-LED micro-displays [34].
## 1 Introduction
This phenomenon is very complex because it is governed by a nonlinear dynamic as well as by harmonics and sub-harmonics. However, its ability to measure the speed of fluids in turbulent flows and very narrow spaces is a significant advantage. In theory, to measure the velocity of air in turbulent flows, the bandwidth of the anemometer must be much greater than the frequency of flow fluctuation [22]. After the first micromachined thermal flow sensor reported in 1974 [23], with the rapid development of microfabrication technology, a growing interest in the microthermal flow sensors has been emerged [24; 25; 26; 27; 28; 29]. To date, complementary metal-oxide-semiconductor micro-electro-mechanical systems (CMOS MEMS) technologies are increasingly being used to fabricate thermal flow sensors [30; 31; 32]. All fabricated flow sensors suffer from their low bandwidth and low sensitivity. Although the research in CMOS MEMS or MEMS-based flow sensor has been extensively focused on a variety of topics, few of these sensors were designed and fabricated specifically for detecting flow separation and for wall shear stress measurement. Their bandwidth and sensitivity are still low. Several reported micro thermal flow sensors are still power-hungry devices (>10 mW) with limited flow range and limited bandwidth [23; 33], which cannot fully meet the requirements of flow separation applications. There is a strong motivation to develop such a high-performance MEMS flow sensor that is cost-effective and offers high bandwidth and high sensitivity. The goal of this work was to design a MEMS calorimetric sensor with a high bandwidth and highly sensitive, self-calibrated constant temperature hot-wire anemometer (CTA). The CTA circuit allows injecting a variable current at the hot wire to keep its temperature constant. It provides a better thermal inertia by keeping the temperature constant thus a better cut-off frequency as well as a better sensitivity over a wide speed range [34]. We propose a measurement technique based on the control of a loop to maintain a constant temperature on the heating resistor and a current mirror in series to measure the current. At any value of the flow speed, the power delivered to the hot wire is the maximum available. The power efficiency can be greatly increased as the voltage drop in the current mirror can be kept much lower than the constant voltage drop across the sensing resistor. The described circuit can be used with several control loop arrangements such as constant voltage, thermal sigma-delta modulation, or pulse width modulation (PWM). The circuit allows establishing different voltage values for the configuration of the loop. In addition, offset errors affect the accuracy of the current measurement. Therefore, the circuit includes the technique of offset cancellation and calibration. The paper is organized as follows. Section 2 describes the MEMS calorimetric sensor design Section 3 presents the theoretical analysis of the flow sensor. Section 4 describes the principle of the CTA circuit. A CMOS implementation and experimental results are given in Section 5. Conclusions are drawn in Section 6.
## 2 Results and Discussion
### Imprinting Effects of MMIPs and MMIPs on OFL Adsorption  
As shown in Table 3, the adsorption capacities of \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL on MMIPs were much higher than those on magnetic non-imprinted polymers (MNIPs), which indicated that the MMIPs had high selectivity for OFL. Moreover, the \(\alpha\) values were greater than 1, indicating that the MMIPs had a higher affinity towards the target molecules than the MNIPs. The selectivity of the MMIPs  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Analytes** & **Equations** & **k** & **q\({}_{e}\)** & **q\({}_{e,c}\)** & **R\({}^{2}\)** \\ \hline Pseudo-first- & \(S\)-(\(-\))-OFL & \(\ln(\mathrm{q_{e}-q_{i}})=0.551-0.144\)t & 0.144 & 0.715 & 0.579 & 0.8458 \\ order model & \(R\)-(+)-OFL & \(\ln(\mathrm{q_{e}-q_{i}})=0.517-0.138\)t & 0.138 & 0.712 & 0.598 & 0.8688 \\ Pseudo-second- & \(S\)-(\(-\))-OFL & t/q\({}_{i}=1.399t+7.744\) & 0.253 & 0.715 & 0.701 & 0.9929 \\ order model & \(R\)-(+)-OFL & t/q\({}_{i}=1.404t+7.945\) & 0.248 & 0.712 & 0.708 & 0.9901 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic equations and rate constants for the adsorption of OFL onto MMIPs.  
Figure 8: Adsorption capacity of OFL on MMIPs over six adsorption cycles.  
mainly depends on the binding between the MMIPs and the target molecules, with the binding ability related to the similarities between the functional groups, size and shape of the template, and the target molecules. The results illustrated the success of the imprinting process. After the removal of the template, the imprinting sites on the surface of the MMIPs were accessible to the OFL target molecules.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results
### Fewer Sources than Sensors Case for ULA  
The considered array configuration consists of \(N=10\) ULA elements spaced at \(d=\lambda/2\). The estimation accuracy is measured using the RMSE performance metric, defined as,  
\[e=\sqrt{\frac{1}{K}\sum_{k=1}^{K}(\theta_{k}-\hat{\theta}_{k})^{2}}, \tag{29}\]  
where \(\hat{\theta}_{k}\) indicates the estimated angle parameter of the \(k\)-th source.  
In the following, the performance of the proposed method is evaluated against three state-of-the-art algorithms, including the search-based [19], EPUMA [20], and SS-MUSIC [15] methods. SS-MUSIC is a classical algorithm for DOA estimation. Nevertheless, its applicability is limited to on-grid DOA estimation problems. Although the search-based and EPUMA method can guarantee the accuracy of the off-grid DOAs, the algorithm's exhaustive search grids and numerous snapshot computations result in a high computational burden. The RMSE of four approaches, including the search-based, EPUMA, SS-MUSIC,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Computational Complexity** \\ \hline Search-based [19] & \(O(N_{c}\log_{2}N_{c}+N_{c}+DKN_{c})\) \\ EPUMA [20] & \(O(N_{c}^{2}T+GN_{c}^{3})\) \\ SS-MUSIC [15] & \(O(TN_{c}+N_{c}^{3})\) \\ Proposed method & \(O(TN_{c}+N_{c}^{3}+HKN_{c}^{3})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity of the four methods.  
and the proposed methods, are evaluated against SNR for ULA configurations with fewer sources than sensors, as shown in Figures 1 and 2 for different snapshot quantities. The simulation considers two distinct snapshot scenarios: 10 snapshots and 100 snapshots, respectively. Three source signals impinge on the array from distinct directions specified by \(\theta_{1}=0.5^{\circ},\theta_{2}=30.5^{\circ},\text{ and }\theta_{3}=60.5^{\circ}.\) In the search-based method, we varied the search angle from 0 to \(\pi\) in discrete steps of \(\pi/50N.\) Compared with the other three methods, the proposed method performed the best, regardless of the snapshot number. Moreover, the RMSE of the proposed method attains the CRB when SNR exceeds \(-5\) dB. The reason the search-based and SS-MUSIC methods cannot approach the CRB lies in the finite number of angle-searching grids.