esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 3 Design Parameters
### Determining the Inductance: \(L\)  
In a discontinuous current mode(DCM) single-stage buck-boost converter, the switching cycle time \((T_{SW}=1/f_{SW})\) can be expressed as [5]:  
\[T_{SW}=T_{ON\_S}+T_{ON\_D}+T_{IDLE} \tag{11}\]  
where \(T_{ON\_S}\) is the switch turn-on time, \(T_{ON\_D}\) is the diode on-time and \(T_{IDLE}\) is the time, in which no current flows in the inductor. Since \(T_{IDLE}\) cannot be zero to ensure DCM operation under all conditions, \(D_{S}\) and \(D_{D}\) should satisfy \(D_{S}+D_{D}<1\). Therefore, from the volt-second balance law, the inequality for \(D_{S}\) becomes:  
\[D_{S}<\frac{V_{O}}{V_{IN}+V_{O}} \tag{12}\]  
where \(D_{S}\) is the duty ratio of the switch turn-on time (\(=T_{SW}/T_{ON\_S}\)) and \(D_{D}\) is the duty ratio of the diode turn-on time (\(=T_{SW}/T_{ON\_D}\)). The input current is induced as \(D_{S}^{2}\cdot T_{sw}\cdot V_{IN}/2L\) from the buck-boost relationship, which is substituted into the power conversion relationship (\(V_{O}\cdot I_{O}=\eta_{eff}\cdot V_{IN}\cdot I_{IN}\)) and rearranged for the inductance L:  
\[L<\frac{\eta_{eff}}{2f_{sw}}\cdot\frac{V_{O}}{I_{O}}\cdot\left(\frac{V_{IN}}{ V_{IN}+V_{O}}\right)^{2} \tag{13}\]  
where \(f_{SW}\) is the switching frequency. From Equation (13), the minimum inductance can be determined at \(V_{IN\_MIN}\), \(V_{O\_MAX}\) and \(f_{SW\_MAX}=70\ kHz\).  
\[L<\frac{0.9\times 112}{2\times 70\ k\times 92.2\ m}\cdot\left(\frac{90}{90+112} \right)^{2}=1.55\ mH \tag{14}\]  
Considering the size of inductor and margin, an inductance of 1.38 mH should be chosen and manufactured with an EE1616 core.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 3 Satellite Formation and Interferometry Baseline  
### Helix Orbit Configuration  
The main objective of LB-IRA from satellite formation is to extend the baseline length, which is essential for improving altimetric accuracy. Different from single-platform interferometry, the baseline formed by satellite formation is flexible without any mast, thus the baseline length is determined by the orbit configuration. In order to obtain a cross-track baseline, a tiny angle is introduced between two orbit planes, as shown in Figure 4a. To provide passive safety in case of a vanishing along-track separation, the suggested safety formation distance should be larger than 150 m, hence a small eccentricity is introduced which yields the radial separation at the poles, as shown in Figure 4b. This type of orbit configuration is referred to as helix orbit by the TSX/TDX team [24].  
Figure 3: Schematic diagram of relative and absolute altimetric error.  
Under the helix orbit configuration, the variation cycle of cross-track/along-track baseline length could be adjusted by thrusters at the initial stage, and the cross-track baseline reaches its maximum length while the along-track baseline is about zero at the equator; after that, along-track baseline length starts to increase, and reaches its maximum near the poles. Since ocean surface is randomly changing, OST altimetry near the high latitude region will suffer from worse time decorrelation due to larger along-track separation. Therefore, the optional rotation of the argument of perigee can be adjusted to achieve proper baseline configuration [24], as shown in Figure 4c; however, at the same time, the low-latitude region will no longer be suitable for OST altimetry.
## 5 Conclusions  
This paper presents a methodology to optimally size and dispatch a battery energy storage unit for electricity cost minimization for large consumers in Ontario, Canada. The algorithm proposed in this paper particularly minimizes the charges associated with the global adjustment costs for class-A customers. Moreover, other utility charges, such as demand charges, are considered and explicitly minimized by the algorithm. Moreover, energy price arbitrage and participation in the IESO demand response programs are considered as revenue streams. A single comprehensive mathematical formulation is developed to encompass all these revenue streams in one optimization problem abiding by the regulations of the jurisdiction. For quality and performance assurance, the proposed method is applied to real class-A customer data and the corresponding market spot prices in Ontario. The contributions of this work are twofold. Firstly, the developed methodology attains the optimal size of BESS units to minimize total energy costs, including GAC for class-A customers in Ontario. Various revenue streams are stacked to maximize the total gain. The proposed technique can be applied to different class-A facilities with different load profiles and operational features. Secondly, the straightforward optimization model and its results open the doors wide for a viable investment opportunity with obvious technical and financial merits. The results demonstrate that a class-A customer employing a BESS with the optimal battery size and dispatch schedule, can achieve significant savings in energy bills. The results show the efficacy of the developed method in attaining a favorable investment ROI in a short-term financing plane.  
Furthermore, the proposed algorithm takes into consideration the jurisdiction regulations, which can be flexibly modified and updated to match the regulations of other jurisdictions. Various sensitivity analyses were conducted to explore the impact of some solution parameters on the results. Another sensitivity analysis based on C-rate changes was also performed. For future work, benefits to the grid reliability, resiliency, stability and profitability may be important to consider.  
Conceptualization, A.K., F.M. and M.A.; methodology, A.K., F.M. and M.A.; software, A.K. and M.A.; validation, A.K. and F.M.; formal analysis, A.K.; investigation, A.K. and F.M.; resources, A.K., F.M. and M.A.; data curation, A.K. and M.A.; writing--original draft preparation, A.K.; writing--review and editing, F.M. and M.A.; visualization, A.K.; supervision, F.M. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 14: Results of the C-rate sensitivity case study in terms of the revenue components.
## 4 Results and Discussion
### Sensitivity Experiments of Radiation Schemes  
Solar radiation is one of the most critical drivers to planetary boundary layer dynamics. There are a couple of short- and longwave schemes to handle the surface downward short- and longwave radiation for the ground heat budget in the current WRF model. The impact of different schemes on surface solar radiation prediction in winter and summer is needed to understand given a target region. The setup of the numerical experiments for inter-comparison of shortwave parameterization schemes is summarized in Table 1. The longwave scheme we adopt in this test is the Rapid Radiative Transfer Model (RRTM) [35]. Two-week in January and July is chosen to represent winter and summer, respectively, with consideration of relatively limited computation resources.  
Figure 4 illustrates the variations of the predicted solar radiation from the five sets of experiments with different shortwave parameterization schemes, referred to as Dudhia, Goddard, CAM_SW, RRTMG_SW, and New_Goddard. It is easy to find that outputs of the WRF model underestimate the solar radiation in winter while overestimate it in summer for the Maizuru site. Opposite phenomena can be found for the Qinghai site. Among five shortwave schemes, the case of RRTMG_SW has the smallest normalized standard deviation (NSD) in both winter and summer for the Maizuru site. For the Qinghai site, the case of Dudhia has the smallest NSD and the most considerable correlation. In addition, we have also examined the impact of longwave parameterization schemes; however, no significant difference has been found. Thus, the RRTM longwave scheme is adopted for both two sites; the schemes of the RRTMG shortwave and Dudhia shortwave are chosen for Maizuru and Qinghai sites, respectively.  
\begin{table}
\begin{tabular}{l l} \hline
**Experiment** & **Short Wave Scheme** \\ \hline Dudhia & Dudhia scheme [36] \\ Goddard & Goddard shortwave scheme [37] \\ CAM\_SW & Community Atmospheric Model [38] \\ RRTMG\_SW & Rapid Radiative Transfer Model for general circulation model [39] \\ New\_Goddard & New Goddard shortwave scheme [40] \\ \hline \end{tabular}
\end{table}
Table 1: Shortwave schemes included in the sensitivity analysis.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 2 KREONET-S: Wide-Area SDN Infrastructure Development and Deployment
However, the proposed architecture distinctly applies VDN and VDN in its own mechanism for network resource utilization and end-to-end latency optimization. Figure 3: The hierarchical multi-access edge computing (MEC) service model based on virtual dedicated network (VDN) and VDN orchestrator (VDNO). Figure 2: Layered architecture and development status of KREONET-S. The detailed framework and procedure of VDN is described in Section 3. Likewise, the location- and load-aware VDN orchestration architecture is introduced in the same section to indicate automated and intelligent orchestration, allowing the coordination of virtual network slices and computing and storage resources on demand in an integrated manner.
## 5 Studies on Using other Types of PMC in Concrete for Thermal Energy Storage  
While organic PCMs are the most often used in concrete, there are other varieties available for specific uses. Due to their low cost and high heat of fusion, organic PCMs are the material of choice for concrete. References [81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101] provide an overview of related research (within this inquiry) concerning PCMs that was not included in the preceding sections. Workability, mechanical strength, early age hydration temperature increase, and thermal characteristics of cement concrete were investigated as a result of the addition of macro-encapsulated PCM aggregate by [PERSON] et al. (2018) [81]. Diatomite- and ceramics-based thermal energy storage aggregates were created utilising a direct impregnation technique, with PCM provided by a ternary fatty acid eutectic comprising lauric acid, myristic acid, and palmitic acid. A 166.6 \(\mathrm{J/g}\) latent heat as well as a 31.1 \({}^{\circ}\)C melting point were the measured properties of PCM. For ceramics, the PCM adsorption volume in the pore system reaches 28.1%, while for diatomite, it reaches 89.8% of the total porosity. Slump, compressive strength, and thermal conductivity are all reduced when thermal energy storage aggregates are mixed into new concrete. The compressive strength of concrete with thermal energy storage aggregate at 80% by volume is more than 18 MPa. Early-age hydration temperature increases can be better suppressed by using a diatomite-based thermal energy storage aggregate as opposed to a ceramics-based one. The use of PCMs to prevent freeze-thaw damage to concrete roads was studied by [PERSON] et al. (2018) [82]. Utilising the latent heat of fusion of a paraffin-based organic PCM (N-Tetradecane) with a phase transition temperature of 4.5 \({}^{\circ}\)C was used to cut down on the predicted number of freeze-thaw cycles. Preliminary research confirmed that PCM microencapsulated (mPCM) with a melamine-formaldehyde resin was effective at heat storage and release. Even though its effect became minimal with prolonged exposure to an ambient temperature far below the transition temperature. Figure 9 shows that the low transition temperature of PCM has promising potential to extend the service life of concrete pavements against freeze-thaw deterioration. Incorporating PCM that has been microencapsulated was shown to reduce the mortar's compressive and flexural strengths but improve its volume stability at younger ages.
# Underwater Optical Path Loss after Passage of a Tropical Storm
## 2 Selection of Typhoon-Influenced Area  
Typhoons hit the western North Pacific Ocean from early June to early December in 2012 and early March to early December in 2014 [10]. To study the typhoons' effect on UWOC, ideally the IOP data should be collected from measurements that are taken at the same geographic location directly before and after a storm event since the influence of hydrography, land, and seasonal variability cannot be ignored. However, such a practice is difficult due to the basic features of tropical storms and the difficulty in IOP observation. Tropical storms typically form over large bodies of relatively warm water and span a large range of sizes, from 100 to 2000 km as measured by the radius of vanishing wind [10]. The average life of a hurricane, determined by time and place of origin and rate of forward movement, is nine days. The longest life cycle of a hurricane ever recorded was Tropical Cyclone Ginger, which lasted 31 days (September 5-October 5, 1971) [11].  
To overcome such a difficulty, we chose a typhoon-influenced area with high ocean transparency and low seasonal variability. The Secchi disk depth (SDD) is a key index of water transparency. The satellite observations by the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) show the global climatological annual mean and absolute seasonal amplitude (maximum minus minimum) of SDD [12]. The area 124\({}^{\circ}\) E-134\({}^{\circ}\) E, 15\({}^{\circ}\) N-27\({}^{\circ}\) N is characterized as high transparency with low seasonal variability from Figure 2a,b [6].  
In 2012, a tropical disturbance formed south-southeast of Pohnpei on June 7, and was upgraded to a tropical depression on June 10 in the area 124\({}^{\circ}\) E-134\({}^{\circ}\) E, 15\({}^{\circ}\) N-27\({}^{\circ}\) N. The system later intensified in favorable conditions, and reached typhoon intensity (i.e., Typhoon Guchol) on June 15. It reached super typhoon status on June 16-17, before making landfall over Japan as a typhoon on June 19 (Figure 1). Guchol brought heavy rain and strong winds in the Western Pacific Ocean near Japan on June 19 and 20. On the contrary, there was no typhoon activity in the Western Pacific Ocean from January 6 to February 28, 2014 [6]. The IOPs were measured from Seagliders of the NAVOCEANO during two different periods: (a) June 25-30, 2012, which is seven days after super Typhoon Guchol's passage, and (b) January 9-February 28, 2014 with no typhoon activity.
# Adaptive Robust Admittance Control of Robots Using Duality Principle-Based Impedance Selection
## 2 Problem Statement  
Consider the following robotic Cartesian dynamics:  
\[M(x)\dot{x}+C(x,\dot{x})\dot{x}+G(x)+F(\dot{x})=\tau+f_{\epsilon} \tag{1}\]  
where \(x,\dot{x},\dot{x}\in R^{n}\) denote the Cartesian position, velocity, and acceleration at the robot-environment interaction point, respectively; \(M(x)\in R^{n\times n}\) and \(C(x,\dot{x})\in R^{n\times n}\) are the inertia matrix and the Coriolis/centrifugal matrix, respectively; \(G(x)\in R^{n}\) is the gravitational vector; \(F(\dot{x})\in R^{n}\) denotes the friction force; \(f_{e}\in R^{n}\) denotes the robot-environment interactive force; and the control input \(\tau\in R^{n}\) satisfies \(\tau=J^{-T}u\), with \(J\) being the Jacobian matrix and \(u\) being the torques applied by the motors.  
The inertia matrix \(M(x)\) is symmetric and positive definite and satisfies  
\[\sigma_{1}\xi^{T}\xi\leq\xi^{T}M(x)\xi\leq\sigma_{2}\xi^{T}\xi,\ \forall\xi\in R^{n} \tag{2}\]  
where \(\sigma_{1},\sigma_{2}>0\).  
The environment dynamics can be modeled by  
\[-f_{e}(t)=K_{e}x+D_{e}\dot{x} \tag{3}\]  
where \(K_{e}\) and \(D_{e}\) denote the environmental stiffness and damping, respectively. In a robot-environment interaction, the environmental stiffness and damping are usually assumed as diagonal matrices and can be defined as \(K_{e}=\text{diag}\{k_{ei}\}\) and \(D_{e}=\text{diag}\{d_{ei}\}\), constrained by  
\[k_{d1}\leq k_{ei}\leq k_{d2},\ d_{d1}\leq d_{ei}\leq d_{d2} \tag{4}\]  
with \(k_{d1},k_{d2},\ d_{d1}\), and \(d_{d2}\) being positive constants.  
From (3), the desired interactive force can be modeled as  
\[f_{d}=-K_{e}x_{d}-D_{e}\dot{x}_{d} \tag{5}\]  
where \(x_{d}\) denotes the desired trajectory for the robot corresponding to \(f_{d}\).  
Consider the following desired impedance model:  
\[M_{d}\ddot{e}_{d}+D_{d}\dot{e}_{d}+K_{d}e_{d}=-(f_{e}-f_{d}) \tag{6}\]  
where \(e_{d}=x_{d}-x\) denotes the desired trajectory tracking error, and \(M_{d}\), \(K_{d}\), and \(D_{d}\) are diagonal positive definite matrices and denote the desired inertia matrix, stiffness matrix, and damping matrix, respectively.  
The objectives of this study are: (i) to estimate environmental stiffness and damping using composite adaptation and determine robot stiffness and damping based on the duality principle; (ii) to design adaptive robust admittance control based on time-delay estimation to obtain the prescribed admittance trajectory tracking.
## 3 Results  
### Research Hot Regions Mapping  
Figure 4 shows the spatial distribution of rocky desertification research hot regions in China based on the literature from the 1950s to 2016. The research hot regions on rocky desertification are mainly concentrated in three provinces in southwest China, that is, Guizhou, Guangxi and Yunnan. There are also a few rocky desertification studies in Sichuan, Hunan, Hubei, Chongqing and other provinces. Among them, the hot regions of rocky desertification with a moderate degree or greater of research are mainly distributed in western and southern Guizhou, eastern Yunnan and northern and western Guangxi, including 108 counties and a total area of 245.7 thousand km\({}^{2}\).  
Figure 4: The Map of Hot Regions of Rocky Desertification Research in China.  
Figure 3: The Grid Map of the Rocky Desertification Sensitivity Index in China.  
Specifically, in terms of rocky desertification, the most literature was found in relation to Bijie, western Guizhou Province, which belong to the area with very high research hotness of rocky desertification. The rocky desertification phenomenon is also serious with high research hotness in Guiyang in central Guizhou Province, Southwest Guizhou Autonomous Prefecture in southwestern Guizhou Province and in parts of northwestern Guangxi (e.g., Fengshan County, Pingguo County and Mashan County). In Liupanshui, Anshun of western Guizhou, Buyi and Miao Autonomous Prefecture of QianNan of southern Guizhou, Hechi, Baise, Nanning and Guilin in western and northern Guangxi and Wenshan Zhuang and Miao Autonomous Prefecture in southeastern Yunnan, the research hotness of rocky desertification is lower than those of the previous two types and these regions belong to moderate research hot regions of rocky desertification.
## 5 IETF ROLL Working Group
### Research Challenges
#### 6.1.1 Base CoAP
Instead of specifying the Option Number directly, the instances must appear in order of their Option Numbers and a delta encoding is used between them (The Option Number for each instance is calculated as the sum of its delta and the Option Number of the preceding instance in the message. For the first instance in a message, a preceding option instance with Option Number zero is assumed. Multiple instances of the same option can be included by using a delta of zero). The fields in an option are:  
* **Option Delta**: 4-bit unsigned integer. A value between 0 and 12 indicates the Option Delta. A value of 13 indicates that an 8-bit unsigned integer follows the initial byte and indicates the Option Delta minus 13. A value of 14 indicates that a 16-bit unsigned integer in network byte order follows the initial byte and indicates the Option Delta minus 269. The value 15 is reserved for the Payload Marker and cannot be used here. The resulting Option Delta is used as the difference between the Option Number of this option and that of the previous option (or zero for the first option). * **Option Length**: 4-bit unsigned integer. A value between 0 and 12 indicates the length of the Option Value, in bytes. A value of 13 indicates that an 8-bit unsigned integer precedes the Option Value and indicates the Option Length minus 13. A value of 14 indicates that a 16-bit unsigned integer in network byte order precedes the Option Value and indicates the Option Length minus 269. The value 15 is reserved for future use. * **Value**: A sequence of exactly Option Length bytes. The length and format of the Option Value depend on the respective option, which may define variable length values. As an example of a simple CoAP option consider the Content-Format option. This option indicates the representation format of the message payload. This option has the Option Number 12 and its Option Length is between zero and two bytes. The Option Value itself is an unsigned integer that is defined in the CoAP Content Format registry (Section 12 of the draft). The IETF CoRE working group considers the constrained restful environments as an extension of the current web architecture. The group envisions that CoAP will complement HTTP and that CoAP will be used not only between constrained devices and between servers and devices in the constrained environment, but also between servers and devices across the Internet [86]. An important requirement of the CoRE working group is to ensure a simple mapping between HTTP and CoAP so that the protocols can be proxied transparently. Thus proxies and/or gateways play a central role in the constrained environments architecture. These proxies have to be able to communicate between the Internet protocol stack and the constrained environments protocol stack and to translate between them as needed. Figure 14: CoAP Option Format.