esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 8 Conclusions and Avenues for Future Research
#### Avenues for Future Research  
Below are research areas that could complement the results presented here:  
* Although this paper considered some scenarios involving WiFi interference, further scenarios involving WiFi interference would complement the results presented here. More simulations could also reduce the large variation observed in the scenarios with WiFi interference.
* The U-RR procedure proposed here considers a fixed observation window to estimate the cost of various routes. Since such an estimation depends on the number of unicast packets being transmitted, it would be interesting to study modifications in which the observation window adapts to the amount of unicast traffic generated.
* The U-RR procedure here was designed for the many-to-one routing algorithm of ZigBee. Although the ideas behind the U-RR procedure could also be applied in the other routing algorithms, new simulations and analysis would be necessary to determine whether the U-RR procedure would be useful in other routing algorithms as well.
* In this study, we considered the default parameters of the ZigBee network layer and the IEEE 802.15.4 MAC and physical layers. Given that the performance of these systems can vary with such parameters [29; 30], it would be interesting to study whether the results reported here could be improved by optimizing such parameters.
* It would be interesting to study how the proposed route cost estimation procedure could operate together with clustering procedures [54; 55; 56; 57; 58]. Clustering procedures build a hierarchical topology in which sensors communicate with clusterheads, which forward the message to other clusterheads that relay the message until it reaches the destination. In the context of this paper, such clusterheads would be selecting routes towards the concentrator by using a route cost estimation procedure, such as the U-RR procedure, and it would be interesting to consider clustering algorithms that select clusterheads while taking into consideration the cost variations in the routes that interconnect them.  
Conceptualization: B.F.J.; methodology: B.F.J.; software: S.M., B.F.J.; validation: S.M.; formal analysis: S.M., B.F.J.; investigation: S.M., B.F.J.; resources: B.F.J.; writing--original draft preparation: S.M.; writing--review and editing: B.F.J.; visualization: S.M.; supervision: B.F.J.; project administration: B.F.J.; funding acquisition: B.F.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a confidential sponsor through the NIU grant number G5B70323. The authors thank the confidential sponsor for their support.  
**Acknowledgments:** The authors also thank the reviewers for the useful suggestions that improved the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Analytical Methods
### Zircon Lu-Hf Isotopic Analyses  
Following the completion of LA-ICP-MS U-Pb dating, in-situ zircon Hf isotope analyses were conducted using a Neptune Plus MC-ICP-MS system (Thermo Fisher Scientific Inc., Waltham, MA, USA), equipped with a GeoLas 2005 excimer ArF laser ablation system, located at the State Key Laboratory of Geological Processes and Mineral Resources (SKLGPMR) of China University of Geosciences, Wuhan. Equipped with a \"wire\" signal smoothing device, this laser ablation system is capable of generating smooth signals, even at laser repetition rates down to 1 Hz [42]. The study employed a laser ablation energy density of 5.3 J\({}^{\star}\)cm\({}^{-2}\). Within the ablation chamber, helium was used as the carrier gas and was mixed with argon (serving as a makeup gas) after the ablation process. A straightforward Y-junction was utilized downstream of the sample chamber to introduce a minor quantity of nitrogen (4 mL\({}^{\star}\)min\({}^{-1}\)) into the argon makeup gas stream [38]. All data presented in this study were acquired by analyzing zircon in single-spot ablation mode, utilizing a spot size of 44 um for the collection of all data points. Each measurement comprised a 20-s phase for background signal recording, followed immediately by a 50-s phase dedicated to capturing the ablation signal. The laser ablation system, MC-ICP-MS instrument, and analytical methodology used in this study adhered to the operational parameters outlined in [PERSON] et al. (2012) [43].  
The primary limitation in achieving precise in-situ zircon Hf isotope determination using LA-MC-ICP-MS stems from the substantial isobaric interference posed by \({}^{176}\)Yb, with a lesser contribution from \({}^{176}\)Lu, on the measurement of \({}^{176}\)Hf. In this study, we utilized the real-time 176 Yb value directly measured from the zircon sample. The ratios of \({}^{179}\)Hf/\({}^{177}\)Hf and \({}^{173}\)Yb/\({}^{171}\)Yb were employed to determine the mass bias corrections for Hf (\(\beta_{\rm{Hf}}\)) and Yb (\(\beta_{\rm{Yb}}\)), respectively. These corrections were normalized to the values of \({}^{179}\)Hf/\({}^{177}\)Hf = 0.7325 and \({}^{173}\)Yb/\({}^{171}\)Yb = 1.13017 [44], applying an exponential mass bias correction method. The interference of \({}^{176}\)Yb on \({}^{176}\)Hf was addressed by measuring the non-interfering \({}^{173}\)Yb isotope and employing the ratio \({}^{176}\)Yb/\({}^{173}\)Yb = 0.79381 [44] to compute the corrected value of \({}^{176}\)Yb/\({}^{177}\)Hf. Analogously, the minor interference of \({}^{176}\)Lu on \({}^{176}\)Hf was corrected by measuring the intensity of the non-interfering 175 Lu isotope and applying the recommended ratio \({}^{176}\)Lu/\({}^{175}\)Lu = 0.02656 [45] to derive the corrected value of \({}^{176}\)Lu/\({}^{177}\)Hf. Due to their comparable physicochemical properties, we utilized the mass bias of Yb (\(\beta_{\rm{Yb}}\)) to determine the mass fractionation of Lu. The IC-PMSDataCal v.11.8 software was employed for the offline process of selecting and integrating analyte signals, along with executing mass bias calibrations [46].
## 5 Results
### Overall Comparison: PILLAR vs. DUO vs. TRIbot
#### 5.4.2 Second Choice: PILLAR  
PILLAR was ranked second for robot embodiment across the US and JP participants. Many participants compared the PILLAR as \"the same as a walker\" [PJ44, PU-02,03,35] or cane [PJ-11,16] as it provides passive support [PU-02,03,35] (_min assist_). A participant added that there was flexibility to the PILLAR as you could \"use it for a walker robot and then decide when you wanna use it as a robot\" [PU03]. Participants were able to recognize the familiar rollator walker, which served as an inspiration concept.  
The PILLAR is best suited for more independent CRs who need balance and fall prevention assistance. Participants explained that the PILLAR was best for those who \"were mostly mobile so you could pull yourself up and get up on your feet\" [PU39]. Further indicating that they may only need minimal support [PU31, PJ-32,38] and wanted to continue to use their own strength for as long as they could [PU18, PJ-31,37]. Participants explained that \"PILLAR' just supports the care receiver to use his/her own strength\" [PJ15] and that \"I chose the PILLAR because my father is still able to do things on his own, so I want him to while he can\" [PJ31]. Furthermore, one participant explained that PILLAR \"moves in the way I do when I help my mother\" [PJ32].  
Participants were not fond of the track installation in the ceiling but found it better than the DUO because it needed a smaller amount installed [PU07]. It was also well received that PILLAR could firmly lock in place both in the ceiling and with the wheels [PU-06,07,29,40, PJ40]. Participants also were disappointed in the limited functions that PILLAR had [PJ41], explaining that \"the location and the task are limited by this robot (PILLAR)\" [PJ44] and that \"this robot (PILLAR) also has very limited movements\" [PJ35]. Participants often requested further abilities for PILLAR, such as \"assisting with changing clothes\" or \"handling excrement\" [PJ16].
# Design of a Compact Microstrip Decoupled Array
## 1 Introduction
The antenna unit designed in this paper was miniaturized using the surface slotting technique. The size of the antenna unit is greatly reduced by combining slots from different structures. By means of formula derivation, the suggested decoupling network for a two-element array is generalized, and any two-element antenna array can be decoupled. The miniaturization of the decoupling network is realized using the equivalent circuit. The final-designed mono-pulse antenna is compact while maintaining the shape of the radiation pattern and maximizing the gain.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 2 Data and Methods
### CALIOP Cloud and Aerosol Data  
CALIOP, onboard the CALIPSO satellite, observes the vertical distribution of aerosols and clouds by detecting the laser light backscattered by them. The cloud-aerosol discrimination algorithm is based on statistical differences in the various optical and physical properties of cloud and aerosol layers [15; 16]. The Level 2 Version 4.10 VFM product of CALIOP (CAL_LID_L2_VFM-Standard-V4-10) was used for comparison with the MODIS cloud mask. The VFM product contains the features of clouds, aerosols, clear air, surface, subsurface, invalid, and no signal. The cloud fraction was calculated from the VFM cloud feature. The VFM also has cloud top height (CTH) and cloud base height (CBH) data. The cloud thickness can be derived from CTH and CBH. CALIOP COT and aerosol optical depth (AOD) data from the profile product (CAL_LID_L2_05 kmAPro-Standard-V4-10) were also used to analyze their impacts on MODIS and CALIOP cloud detection. CALIOP can only penetrate to the surface if the total column optical depth is less than 5; once attenuated, lower layers are not visible anymore [15; 16]. In each 5 km horizontal sampling chunk, the horizontal resolution of VFM is 333 m below 8.2 km and 1000 m between 8.2 and 20.2 km, and the vertical resolution is 30 m below 8.2 km and 60 m between 8.2 and 20.2 km [15]. These data were obtained from the NASA Langley Research Center Atmospheric Science Data Center ([[http://eosweb.larc.nasa.gov/](http://eosweb.larc.nasa.gov/)]([http://eosweb.larc.nasa.gov/](http://eosweb.larc.nasa.gov/))).
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### 4.2. Phased FT for AUVs
#### 4.2.4 FT for Battery Charging  
The AUV mounted on the USV sends data collected during the operation and exchanges internal inspection data and mission performance outcomes during the battery charging phase. In order to charge the battery, it also gets power from the USV. The FT is displayed in Figure 2(d), taking into account the communication and battery charging.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Event** & **Meaning** & **Event** & **Meaning** & **Event** & **Meaning** \\ \hline P1 & Motor RPM & P13 & Charging time & P25 & Controller PTM current \\ P2 & Motor horizontal direction control & P14 & CPU usage & P26 & Controller PCM voltage \\ P3 & Motor vertical direction control & P15 & Memory usage & P27 & Controller PCM current \\ P4 & Hydraulic pump motor output current & P16 & HDD Capacity & P28 & Charging current \\ P5 & Throttle valve motor output current & P17 & CPU temperature & P29 & Charging time \\ P6 & Motor battery charging state & P18 & CPU usage & P30 & Depth accuracy w/comms \\ P7 & Discharging current & P19 & Memory usage & P31 & Position accuracy w/comms \\ P8 & Motor PTM voltage & P20 & HDD Capacity & P32 & Underwater sound velocity \\ P9 & Motor PTM current & P21 & CPU temperature & P33 & Depth accuracy w/o comms \\ P10 & Motor PCM voltage & P22 & Controller battery charging state & P34 & Position accuracy w/o comms \\ P11 & Motor PCM current & P23 & Discharging current & P35 & Velocity accuracy \\ P12 & Charging current & P24 & Controller PTM voltage & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic performance event description of the fault tree.  
The components of the FT for charging are a wireless battery charging module and components for data transmission and reception.
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 2 Explicit Formulas to Describe Nonlinear-Hysteretic Response
### Backbone Curve  
The nonlinear-hysteretic soil models follow the basic and extended Masing's rules, which are adopted in conjunction with the backbone curve to express unloading, reloading, and cyclic degradation behavior. It represents trajectory of the extrema of the hysteresis curves. By replacing the strain amplitude (\(\Delta\gamma\)) with the shear strain \(\gamma\) in Equation (1), the backbone curve can be readily obtained for the entire strain range:  
\[\tau_{b}=G_{\text{max}}\gamma\Big{[}1-\text{sgn}(\dot{\gamma})\alpha\gamma \Big{]} \tag{2}\]  
The backbone curves are superimposed on the stress-strain curves for three different strain amplitudes (Figure 1). The results show increasing hysteresis (nonlinear damping) and decreasing slope (the strain-softening effect) with increasing strain amplitude. In addition, the backbone curve is constructed by two quadratic functions joined at the coordinate origin. In fact, similar hysteresis loops are observed in many soils and other granular materials. While the stress-strain model in Equation (1) may not describe all of complex constitutive behaviors of granular materials, it has been shown to capture some essential features in the stress-strain relationship under steady-state cyclic loading.  
Figure 1: Nonlinear stress-strain behavior of sand under 200 kPa for three different shear strain amplitudes \(\Delta\gamma=3\times 10^{-4}\), \(5\times 10^{-4}\), \(8\times 10^{-4}\). Hysteretic loops and backbone curve are obtained using Equations (1) and (2) with G\({}_{0}=146\) GPa and \(\alpha=470\).
## 3 Methodology  
Several challenges have been arisen, key issues based upon economic, technological and social sides are briefly described.  
System Development Cost: Apart from the required medical issues, enabling the population to benefit from the information and communication technologies generates additional costs. Both technologies are core infrastructure and a large amount of budget is required to invest in necessary computer equipment and developing tools. Hardware and software installation and maintenance together with qualified manpower and knowledge transfer demonstrate one-time and periodic investment.  
System Implementation: Telemedicine affects traditional medical care processes and several social concerns may arise. Key concerns including legal, regulatory, security and human resources issues together with successful implementation of the telemedicine services are addressed. Building a telemedicine service is therefore not only about completing the system implementation but also about the impacts of system utilization  
Diagnostic Accuracy: Accuracy of the diagnosis is one of the key concerns especially when the physicians and the patients are at different locations. Face-to-face medical care usually brings confidence to patients, especially the elderly. However, a short period of medical interrogation and vital sign measurement performed at a hospital may not reflect current symptoms due to the white coat syndrome. Physicians may obtain more accurate results by applying continuous health monitoring based upon electronic healthcare approach.  
Telehealth care can be used to deliver medical services regardless of distance and time via information and communication technologies. Feasible opportunities of telehealth care include, among others, cost reduction, preventive medicine promotion, medical education, healthcare equality, service diversity, etc.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Performance of RTK Positioning  
In this last part, we discuss the stability of the ISB between BDS-3/GPS/Galileo in the short and long term. The results show that we can ignore the impact of ISB when the overlapping frequency inter-system DD is applied between identical-type receivers. In this part, we first compare the performance of single-system (SS), dual-frequency (DF) RTK with that of triple-system (TS) DF RTK. Then, the positioning performance of TCM RTK is assessed with respect to LCM RTK. Finally, the positioning accuracy and success rate of the ambiguity resolution under different cutoff elevation angles are analyzed. All RTK positioning is based on instantaneous mode and the SR is computed through dividing the epochs correctly fixed by the total epochs. In RTK processing, we used the Saastamoinen model and Klobuchar model to correct the tropospheric and ionospheric delay, respectively. The broadcast ephemeris is used to compute the position of satellites. The noise of code and phase observations are set to 0.3 m and 0.003 m, respectively. The ambiguity resolution was validated by comparison with the corresponding continuous RTK. The pivot satellites were chosen based on the elevation angle of the satellites. The satellite with highest elevation angle was selected as the pivot satellite for a single constellation (LCM) or mixed constellation (TCM). All the ambiguity resolutions are based on instantaneous RTK and the empirical SR is reported.  
Figure 6: Means for one day, STD for one day, and box plot for one month of both phase and code ISBs between B1C/L1 (**a**), B1C/E1 (**b**), B2a/L5 (**c**), and B2a/E5a (**d**) with the observations from DOY 29 to 70, 2021 between the baseline GODN-GODS of identical-type receivers. The mean value and STD of ISBs were computed within one day, whereas the box plot was computed within one month. In the box plot, the blue line in the box denotes the median of the ISB for one month, while the top and bottom border of the box denote the quartiles of the ISB. The scatters in the box plot denote the outliers.
## 8 Conclusions  
Successfully achieving all the objectives of the hash-chain fog/edge protocol of the distributed identity of key management, using a novel hash-chain algorithm, applying the zero-knowledge proof property for its operations, and the use of a minimum infrastructure with less calculation on IoT devices were demonstrated. The experimental results on different architectures, a workstation, the AWS Cloud, a Raspberry Pi, and the Cooja Simulator, and between their respective inter-connections showed its consistent behavior with similar time performance across different architectures while proving to be efficient. The paper showed that fog/edge operations utilize different types of architectures for their authentication and functioning. The protocol verification proof presented within the paper also provided its correctness. The computational cost comparison showed that the hash-chain performed better in the authentication for a higher number of devices. In future work, we would like to apply range proofs to experiment with different architecture combinations.  
Conceptualization, M.S.P. and S.-M.Y.; methodology, M.S.P. and S.-M.Y.; software, M.S.P. and S.-M.Y.; validation, M.S.P., R.-K.S. and S.-M.Y.; formal analysis, M.S.P. and R.-K.S.; investigation, M.S.P., R.-K.S. and S.-M.Y.; resources, S.-M.Y. and R.-K.S.; data curation, M.S.P. and S.-M.Y.; writing--original draft preparation, M.S.P. and S.-M.Y.; writing--review and editing, M.S.P., R.-K.S. and S.-M.Y.; visualization, M.S.P., R.-K.S. and S.-M.Y.; supervision, S.-M.Y. and R.-K.S.; project administration, M.S.P. and S.-M.Y.; funding acquisition, S.-M.Y. and R.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Tunghai University, Taichung, Taiwan, under the Grant of the Ministry of Science and Technology (MOST) 109-2622-E-029-013 and 110-2221-E-029-001-.  
Not Applicable  
We would like to thank the Distributed Computing System (DCS) Laboratory and Library for allowing us to use their resources at NCTU, Taiwan.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(C\) & Client/edge user \\ \(S/FS\) & Server/fog server \\ \(AS\) & Authentication server \\ \(a_{x}\) & Network IP address of x \\ \(tll\) & Time to live/validity time of the message \\ \(t_{x}\) & Timestamp of x \\ \(PK_{x}\) & Public key of x \\ \(SK_{x}\) & Private key of x \\ \(\lambda_{x}\) & Transition value taken by x \\ \(V_{x}\) & Vertex taken by x \\ \end{tabular}  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Protocol** & **Completion Time (ms)** \\ \hline RSA & 23,500 \\ Hash-Chain Fog/Edge & 20,787 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of standard benchmarks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System** & **Completion Time (s)** \\ \hline WS (server) to Pi (client) & 0.0023081 \\ Pi (server) to Pi (client) & 0.0073781 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ethernet comparison.
## 2 Background
### Comparison with Standard Mobility Management Protocols
The salient feature of CoMP is that it can provide the fast and reliable IP handover of the sensor node without changing the lower layer, with low signaling overhead, and with no packet loss, while taking into account the sleep/wakeup mode of operation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Mobile IP** & **SIP-MM** & **CoMP** \\ \hline Modification of layer & Network & Application & Application \\ Signaling overhead & Large & Large & Small \\ Tunnel used & Bi-directional tunneling & No use tunnel & No use tunnel \\ Application type & Text, Multimedia & Multimedia, VoIP & Resource-based application \\ Power consumption & Very high & High & Very low \\ Resource Provider & Remote Server & Remote server & Mobile sensor \\  & & User Agent & \\ Session initiation agent & Mobile node & (Mobile node) & Web client \\ Mobility management agent & Home agent & Remote server & Remote server \\ Session initiation agent & Mobile node & Mobile node & Web client \\ Retransmission mechanism of & & & Supported \\ handover management & Not supported & Not supported & (Stop-and-wait retransmissions with an exponential back-off mechanism) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of CoMP with Mobile IP and SIP-MM.
## 2 Main 3-HP Biosynthesis Pathways for the Construction of Microbial Cell Factories
### Glycerol Pathway
#### 2.1.2 Glycerol Oxidation via the Coenzyme A-Independent Pathway  
The coenzyme A-independent pathway requires only two reactions to produce 3-HP from glycerol. Similar to the coenzyme A-dependent pathway, glycerol is dehydrated by coenzyme B\({}_{12}\)-dependent glycerol dehydratase (GDHt) to produce 3-HPA, but then it is oxidized into 3-HP by only one enzyme, aldehyde dehydrogenase (ALDH), instead of three enzymes. Due to the short production pathway and few metabolic intermediates, this is the most widely studied pathway for the microbial production of 3-HP from glycerol. However, the low enzyme activity of natural ALDH, together with the imbalance of expression between ALDH and GDHt, are the main limiting factors of the coenzyme A-independent pathway.