esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2012**, _12_, 16194-16210; doi:10.3390/s121216194  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**TDMA-Based Dual-Mode Communication for Mobile Wireless Sensor Networks**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA 94720,  
USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA 94720, USA;  
E-Mails: [EMAIL_ADDRESS] (B.K.); [EMAIL_ADDRESS] (S.D.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-510-643-6690.  
_Received: 3 September 2012; in revised form: 2 November 2012 / Accepted: 9 November 2012 / Published: 22 November 2012_  
Small highly mobile robots, and in particular micro air vehicles (MAVs), are well suited to the task of exploring unknown indoor environments such as buildings and caves. Such a task imposes a number of requirements on the underlying communication infrastructure, with differing goals during various stages of the mission. This work addresses those requirements with a hybrid communications infrastructure consisting of a stationary mesh network along with the mobile nodes. The combined network operates in two independent modes, coupling a highly efficient, low duty cycle, low throughput mode for routing and persistent sensing with a burst mode for high data rate communication. By strategically distributing available frequency channels between the mobile agents and the stationary nodes, the overall network provides reliable long-term communication paths while maximizing data throughput when needed.  
mobile wireless sensor networks; WSN; MAV; TDMA; IEEE 802.15.4
## 4 Seismic Vulnerability and Sensitivity Analysis  
### Development of Fragility Curve and Sensitivity Analysis  
This research evaluates the seismic vulnerability of the old-school type RC structure, and analyzes the sensitivity to uncertain material and sectional properties. The seismic vulnerability analysis is defined as the conditional probability that the response of the structure exceeds the limit state function. The reliable seismic vulnerability curve is generated using the Probabilistic Seismic Demand Model (PSDM), which performs a regression analysis of the relationship between the Engineering Demand Parameter (_EDP_) and Intensity Measure (_IM_) as shown in Equation (1):  
\[S_{D}=alM^{b} \tag{1}\]  
where \(S_{D}\): median value of structural demand corresponding to _EDP_, \(a\) and _b_: regression coefficients of structural demands calculated from a nonlinear dynamic analysis. In this research, the ground motion intensity, IM, is defined as spectral acceleration (\(S_{a}\)), and the seismic response of the RC structure, _EDP_, is defined as an interstory drift ratio at the global response level and shear demand of column at the local response level. The PSDM performs linear regression by converting to the logarithmic function as shown in Equation (2):  
\[\ln(S_{D})=\ln(a)+b\cdot\ln(IM) \tag{2}\]  
The defined structural demand and limit state function are assumed to be log-normally distributed. Thus, seismic vulnerability is expressed as the lognormal distribution as shown in Equation (3):  
\[\mathrm{P}[D>\mathrm{C}|IM]=\Phi\left[\frac{\ln(aIM^{b}/S_{c})}{\sqrt{\beta_{ d|IM}^{2}+\beta_{c}^{2}}}\right]=\Phi\left[\frac{\ln(IM)-\lambda}{\xi}\right] \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
where \(\Phi[\cdot]\): the standard normal cumulative distribution function, \(S_{c}\): median values of limit states, \(\beta_{c}\): the dispersion of limit states, \(\beta_{d|IM}\): the standard deviations of demand for \(IM\), \(\lambda\): the logarithmic median values of vulnerability, \(\xi\): the dispersion of vulnerability. \(\beta_{c}\) is generally 0.25-0.35.  
The sensitivity analysis was performed using the Tornado Diagram Analysis (TDA) method to evaluate the effect of uncertain variables on the structural response of the constructed analytical models. The TDA method can be used if the uncertainty range of the input parameters is known. It is a deterministic sensitivity analysis tool that evaluates the impact of uncertain variables. Many researchers have adopted the TDA method to assess the effect of uncertain models and to determine important variables [23; 24]. Therefore, the sensitivity index of this research has been investigated using lower and upper bounds, named the swing, in the exceedance probability of seismic vulnerability corresponding to the analytical result of each uncertain model. The relative sensitivity of each variable, (\(f_{c}\),\(f_{y}\)\(\rho_{l}\), and \(\rho_{w}\)), has been evaluated by comparing the seismic vulnerability curves considering all uncertain parameters using the collected swing for each damage level.
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 5 Performance Overview
### Effects of Sense Resistor \(R_{\text{sense}}\) Phase Error on Measurement Accuracy  
We also modelled the sense resistor R\({}_{\text{sense}}\) using an equivalent circuit with data from the datasheet to determine the effect of its parasitic components on the measured signal phase at the system's maximum bandwidth. Rsense is a high-precision 1 K\(\Omega\) Foil Resistor with a TCR of \(\pm\)2.0 ppm/\({}^{\circ}\)C and a tolerance of \(\pm\)0.005% according to the datasheet [34]. It has a maximum parasitic capacitance and inductance of 1 pF and 0.1 uH, respectively. The equivalent circuit is illustrated in Figure 15.  
Figure 14: SD noise analysis with and without phase compensation.  
Figure 15 illustrates the lumped impedance model of Resense, which was used to determine phase error contribution to the overall measured phase. The model was tested at the highest bandwidth specified in this paper. The corresponding phase contribution of these values is below 0.03\({}^{\circ}\), which is negligible.  
Another source of error relates to the common mode signal component of the differential voltage across Resense. This error type can alter the comparator response and, thus, phase detection. Using a high CMRR instrumentation amplifier eliminates that error across the spectrum.  
Table 3 further shows a summary of measured performance of PECSD.
## 2 Materials and Methods
### Methods

Table 1: Source and macroscopic features of the analyzed samples (the question marks indicate doubtful interpretations requiring in-depth validation). The analytical techniques applied for each sample (“t.s.” = thin section; “f.” = fragment with no prior preparation) are also specified (details follow in Section 2.2). The samples investigated at the Department of Geosciences were prepared as polished stratigraphic thin sections and analyzed by cathodoluminescence microscopy (CL), \(\upmu\)-Raman, and SEM after C coating. The CL analyses were performed with an optical microscope Nikon Labphot2 Pol (with long-working-distance objectives providing 4\(\times\), 10\(\times\), and 40\(\times\) magnification) equipped with a CCL (Cold Cathode Luminescence) 8200 mK3; in standard analytical conditions, voltage is 20 kV and beam current is about 200 \(\upmu\)A. For the \(\upmu\)-Raman analyses, the microscope used was a Raman Thermo Scientific DXR with 532 nm laser, 50\(\times\) long-working-distance objective, 3 mW power, 25 \(\upmu\)m pinhole, and frequency range between 100 and 3500 cm\({}^{-1}\). The SEM analyses were performed with a microscope CamScan MX2500 with W source, detector of secondary and back-scattered electrons, and EDAX EDS (energy-dispersive spectroscopy) microanalysis; the analytical conditions were 25 kV voltage and 15 to 25 mm working distance. The samples investigated at the Department of Chemical Sciences were analyzed by X-Band CW-EPR spectroscopy. The fragments were placed in a quartz tube as sample holder (3 mm inner diameter, 4 mm outer diameter) and examined at room temperature with a Bruker ECS106 instrument, equipped with a TE102 cavity. Spectra were acquired with 10 scan, modulation amplitude of 0.5 mT, field sweep of 160 mT, and center field of 320 mT. Microwave power was set to 20 mW and microwave frequency was 9.538 GHz. A short \(\upmu\)-Raman characterization of the back and front of the two samples was carried out using a Renishaw InVia Spectrometer with laser at 514 nm, objectives 10\(\times\) or 20\(\times\), 10 s integration time, 50% total output power.
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 1 Introduction  
Robotic Exploration involves the systematic exploration of initially unknown environments, typically achieved by creating a map--either 2D or 3D--using autonomous robots. The process requires three essential components: a mechanism for mapping the environment, knowledge of the robot's relative position within the map, and a strategy for the robot to autonomously plan its trajectory within the explored area, in order to expand it. The integration of the first two components constitutes the Simultaneous Localization And Mapping (SLAM) problem, a field that has witnessed considerable advancements in recent decades and remains an active area of study [1]. While a straightforward solution for exploration involves teleoperating (remote operation) a robot performing SLAM, the necessity for autonomous exploration arises when direct control becomes impractical due to dangerous environments or substantial time delays. This is particularly relevant in scenarios like search and rescue missions conducted in hazardous settings and space exploration.  
While a single robot can effectively explore a designated area of interest, employing multiple robots to cooperatively explore the same setting offers significant advantages. Distributing the exploration task among multiple agents can notably reduce exploration time, computational load, and distance traveled by an individual robot, thereby enhancing the overall operational efficiency. In the realm of multi-robot exploration, two primary architectures are considered: a centralized approach, where a central node possesses all relevant information and coordinates each robot, and a distributed approach, where robots can only periodically exchange information under specific conditions, such as being within communication range.
## 3 Results and Discussion
### Performance Evaluation Results
#### 3.2.7 Chemical Resistance Safety  
The result of the chemical resistance safety test was calculated by the formula in Equation (3):  
\[\text{Mass change rate}(\%)=\frac{(b-c)-(a-c)}{(a-c)}\times 100 \tag{3}\]  
where \(a\) is the pretest sample mass (g); \(b\) is the post-test sample mass (g); and \(c\) id the mass in the dry condition.  
As shown in Figure 12, the result of the chemical resistance safety test showed that samples A, B, and C had little change in mass under acid, sodium chloride, and alkali treatment conditions. In the case of sample B, one specimen showed a reduced rate of change that finely exceeded the quality standard under hydrochloric acid treatment, but it was confirmed that the performance secured an average value.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 3 Results
### Validated Synchronized Floodwater Index
#### 3.1.2 In Situ Field Survey  
In order to confirm and validate to the MLSWI's behavior, the probability density function (PDF) histogram method was employed to directly compare the difference of pixel-based distributed MLSWI between flood and flood-free areas in the case of the 2015 flood. Figure 4a shows the distributed histogram of MLSWI's pixel values both before flood (black bar) and during flood (blue bar) in the entirety of the Sirajganj district. After the flooding, Figure 4b shows remarkable change from increased MLSWI closed to 1. To understand the flood situation and micro-geographical information, a small Unmanned Aerial Vehicle (UAV: Phantom2 vision+ quadcopter with a flight control system, DJI Technology Co. Ltd., Shenzhen, China), equipped with a small high-vision camera for aerial photos and video shooting, was used to collect ground truth data from three representative inariganj district, including those in the flood on 2-7 September 2015 (circle of H in Figure 4c). The UAV (Phantom2 vision+) captured images of river water and damaged rice fields covered with floodwaters about two meters deep at the peak of the flood. Extensive field surveys were also conducted to obtain total ground truth samples using a FieldSpec(r) HandField 2 spectroradiometer by the Analytical Spectral Devices Inc. (ASD, Colorado, USA), at seven homogeneous training sites (black points, 1-7, in Figure 4c), in which MLSWI's pixel values at 500 m spatial resolution were confirmed. The field-portable HandHeld 2 provides spectral features with a wavelength range of 325-1075 nm, at an accuracy of 1 nm. These sites were selected by the pixel-based classification of MLSWI between flood-prone and agriculture-dominant areas located in the area of 60%-100% damage levels that represent the whole of the Brahmaputra flood plain of Bangladesh [35].
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 5 Conclusions  
In the presented work, a novel methodology is proposed based on FRCNN with FKM clustering for automated localization and recognition of diabetes-based eye diseases, i.e., glaucoma, DR and DME in retinal images. The proposed technique is composed of two phases: a disease detection and localization phase and the other is the segmentation of the localized regions through the FKM clustering. The FRCNN technique can extract the deep features with an optimal representation of eye diseases and increases the performance of segmentation in contrast with the latest solutions. The results demonstrate that the proposed solution achieved the mean IoU of 0.95 and mAP value above 0.94 into three diseases. Moreover, our proposed approach can also be utilized to resolve the different segmentation complexities of medical imaging as well. The research work will be extended by addressing other retinal image diseases i.e., cataracts, age-related macular edema degeneration, etc. in the future.  
Conceptualization, T.N. and A.I.; methodology, T.N. and A.I.; software, A.I., and A.J.; Experimental design, T.N., A.I., A.J.; validation, T.N., A.I., H.M., and A.J., H.M.; Writing--original draft, T.N.; Writing--Review and Editing, A.I.; H.M.; Funding Acquisition, D.H., R.A.N.; Project Administration, D.H., R.A.N.; Supervision, D.H., R.A. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the KIAS (Research Number: CG076601) and in part by Sejong University Faculty Research Fund.  
The authors declare that they have no conflict of interest.
# Optimization of Pico-eNB Tx Power and the Effects of Picocell Range Expansion in Multiband HetNet
## 5 Effects of CRE
### User Throughput versus CSO  
Here, we investigate the effects of CRE under the condition of a pico-eNB Tx power close to the optimal value, according to the results obtained in Figure 5. The parameters used for system-level computer simulations are the same as those listed in Table 1. Figures 13 and 14 show the average and 5-percentile user throughput as a function of CSO for two types of pico-eNB Tx power.  
Figure 11: A CRE using a positive CSO in HetNet.  
Figure 14: Average and 5-percentile user throughput as a function of CSO for pico-eNB Tx power of 31 dBm.  
Figure 12: Cell selection procedure using CRE operation in HetNet.  
Figure 13: Average and 5-percentile user throughput as a function of CSO for pico-eNB Tx power of 37 dBm.  
Figure 13 shows the average and 5-percentile user throughput as a function of CSO when the pico-eNB Tx power is 37 dBm. The average user throughput gradually decreases as the CSO increases, whereas the 5-percentile user throughput increases up to a CSO of approximately 30 dB. The reason for the decrease in average user throughput is that the increased UE connected with pico-eNBs by the CRE share the radio resources of pico-eNBs. At the same time, the reason for the increase in 5-percentile user throughput is that the 5-percentile UE is changed from the UE connected with macro-eNBs to the UE connected with pico-eNBs.  
Figure 14 shows the average and 5-percentile user throughput as a function of CSO when the pico-eNB Tx power is 31 dBm. The average user throughput here increases as the CSO increases up to 6 dB, and then decreases as the CSO passes 9 dB. The average user throughput for a pico-eNB Tx power of 31 dBm and CSO of 6 dB is almost the same as that obtained with a pico-eNB Tx power of 37 dBm and a CSO of 0 dB. The performance behavior of 5-percentile user throughput is similar to the result obtained in Figure 13. From an average user throughput perspective, the CSO of 6 dB can provide the best performance when the pico-eNB Tx power is 31 dBm. On the basis of these results, it was determined that CRE did not work when the pico-eNB Tx power was 37 dBm, and a CSO of 6 dB in the CRE could improve the average user throughput when the pico-eNB Tx power was 31 dBm. Therefore, for the operational procedure of HetNet, the pico-eNB Tx power, which is typically fixed to 37 dBm, should be reduced to 31 dBm for CRE activation. This will lead to savings in power consumption while maintaining the average user throughput.
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 2 Cell Manipulation Based on Holographic Optical Tweezers
### Holographic Optical Tweezer System  
Holographic optical tweezers load the hologram onto the SLM (Space Light Modulator) to modulate the phase. The laser's single incident light is divided into numerous separate beams by SLM, which is utilized to generate any type of light field. The process of catching, controlling, and transferring the cells is then accomplished by creating many optical traps through the microscopic objective lens. Image processing determines each cell's location beneath the microscope and sends that information to the camera for imaging [33].
## 4 Feeder Selection with Reinforcement Learning
### Routing Using Hierarchical SLA (HSLA)  
We explored the use of hierarchical SLAs besides the above two methods as a means to improve the efficacy of the routing decisions. To illustrate, consider that four feeder links \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\) are available to route packets. Two LAs, LA1 and LA2, located at level-1 of the hierarchy handle half of the links each, i.e., LA1 selects among \(s_{1}\) and \(s_{2}\) whereas, LA2 handles the other two links \(s_{3},s_{4}\). At level-2 of the hierarchy, LA3 selects the optimal link among the outcomes of LA1 and LA2. Figure 2 depicts the general concept behind hierarchical SLAs.
# Calibration Method of PFC3D Micro-Parameters under Impact Load
###### Abstract  
Micro-parameter calibration is essential in constructing an accurate and reliable numerical model of particle discrete element PFC3D 6.0 software. Micro-parameter calibration is mainly accomplished according to the macro-parameters obtained from static or quasi-static laboratory tests such as UCS. However, there is little current research concerning the calibration method under impact load. An SJM micro-parameter calibration method, based on the SHPB rock test and the FLAC3D/PFC3D coupling method, is proposed to solve this problem. Firstly, UCS, SHPB, and other laboratory rock tests were carried out to determine the rock sample's macroscopic physical and mechanical parameters. Secondly, the FLAC3D/PFC3D numerical coupling model of the SHPB test was established, and the single-factor and double-factor orthogonal numerical simulation was carried out. Then, the main micro-parameters that affect the macroscopic physical and mechanical parameters of the SJM particle discrete element model were proposed. Finally, the quantitative relationship between the model's macro-parameters and micro-parameters was established through multiple linear regression. A set of PFC3D micro-parameter calibration processes under impact load was established. The relative errors of the macro-parameters obtained from laboratory and numerical tests totaled less than 5%, which further verifies the rationality of the calibration method. This method provides some reference values for PFC3D micro-parameter calibration under impact load.  
impact load; SJM micro-parameters; SHPB rock tests; FLAC3D/PFC3D coupling method
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 2 Model and Analysis
### Simulation Results and Analysis
The simulation results of the S parameters of the gold wire bonding network model built in this experiment are relatively satisfactory, on this basis, we need to establish the data set, in order to make the study more feasible, we need to carry out boundary constraints on the geometric parameters, and then carry out parameter scanning on the S parameters to establish the data set to provide data support for the artificial neural network model built subsequently, the range of variation in each design parameter is shown in Table 1 below:  
In the X-band, each design parameter is restricted to the parameter range, and the parameter simulation is performed within this range. The S-parameters obtained from the simulation are sampled at equal intervals with an interval of 0.1 GHz, and the sampling points of each data set are 41, and the sampling points of the three data sets are 123 in total, and a total of 39375 pairs of data are obtained, and the data set is established. The three sets of data are S11, S12, and VSWR. S11 [29] in the literature indicates the return loss, S12 [30] in the literature indicates the insertion loss, and VSWR [31] in the literature indicates the standing wave ratio.