esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principle of the Proposed Eios-TPS
### Modulation Strategy in the Medium and High-Power Range When \(k<1\)  
Under medium- and high-power conditions in the boost mode, mode D is identified as the optimal modulation strategy for achieving efficient operation. Applying the same solution approach, the optimal duty-cycle combination for mode D can be derived as follows:  
\[\left\{\begin{array}{l}D_{1}=1\\ D_{2}=1-(k-1)\sqrt{\frac{1-P}{k^{2}-2k+2}}\\ \Phi=\frac{1}{2}\Big{(}1-k\sqrt{\frac{1-P}{2k^{2}-2k+1}}\Big{)}\end{array}\right. \tag{16}\]  
The intrinsic relationship among \(D_{1}\), \(D_{2}\), and \(\Phi\) can be expressed as:  
\[\Phi=\frac{1+(-2+D_{2})k}{2(1-k)} \tag{17}\]  
For \(k<1\), in mode D, the ZVS conditions are defined as \(i_{L}(t_{1})<0\) and \(i_{L}(t_{2})>0\). As shown in Figure 12, the ZVS region of mode D is enclosed by the planes \(i_{L}(t_{1})=0\) and \(i_{L}(t_{2})=0\), as well as the coordinate planes, forming a closed space. The magenta curve represents the optimal trajectory of mode D, located entirely within the ZVS region. Under these optimized operating conditions, the transmitted power range for mode D is [2k (1 - \(k\)),1].
## 3 Results and Discussion  
### Phasecomposition  
XRD test results for laser cladding the 316L coating and the composite coating are shown in Figure 2. The 316L coating is mainly composed of (Fe, Ni) solid solution and (Fe, Cr) solid solution, which is similar to the finding of previous research [7; 24]. Compared with the 316L coating, some new peaks were detected according to the XRD curve of the composite coating. Diffraction peaks corresponding to TiC were detected at 35.89, 41.70, 60.48, and 73.34\({}^{\circ}\) (JCPDS card: 01-073-0472). Diffraction peaks of NbC were also detected at the corresponding positions (34.70\({}^{\circ}\), 40.25\({}^{\circ}\), 58.26\({}^{\circ}\), 69.65\({}^{\circ}\), and JCPDS card: 01-089-3690). XRD results show that new TiC and NbC ceramic phases were generated in the composite coating.  
Figure 2: XRD results of 316L coating and composite coating.
###### Abstract  
In this study, two years of hourly longwave downward and upward irradiance measurements at Athalassa, an inland location, are used to analyze and compare them. A detailed quality control process was followed according to the suggested tests proposed by the Baseline Surface Radiation Network (BSRN) group. The criteria involved are based on physically possible, extremely rare and climatological limits. Furthermore, comparison tests were also applied between the two longwave components as well as with air and ground surface temperatures. Additionally, time consistency and persistency tests were applied. All the suspect data were excluded from the analysis. The data showed that the frequency distribution of downward longwave irradiances follows a normal distribution function, while the upward longwave follows an almost normal distribution but with a long positive tail. The annual mean daily downward longwave irradiation is 27.3 MJ m\({}^{-2}\) and the annual mean daily upward longwave irradiation is 37.8 MJ m\({}^{-2}\). The net longwave irradiation is always negative ranging from \(-5.9\) to \(-12.1\) MJ/m\({}^{2}\). Various models were tested to estimate daylight and all day downward and upward longwave irradiances under clear-sky and all-sky conditions. For the comparison of measured and estimated values the root-mean-square errors and linear regression correlations have been used. The results of this comparison showed that [PERSON]'s and [PERSON]'s models perform well, and they can be used to estimate downward longwave irradiance under clear-sky conditions. Furthermore, both models were extended to estimate the daylight downward longwave irradiance under all-sky conditions by taking into account the ratio of global to the clear-sky global solar irradiance. In this case, the RMSE of the local calibrated coefficients scheme of [PERSON]'s model was 30.6 W m\({}^{-2}\), while [PERSON]'s model showed slightly lower value (29.0 W m\({}^{-2}\)).  
d 2021 11 719 10.3390/APP11020719
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 2 The Beacon-Less Is-OWC System Description
### Operating Principle of QD  
As shown in Figure 1, QD consists of four identical p-n junction photo-diodes. When QD receives the beacon light, each quadrant can independently output photocurrent whose amplitude corresponding to the energy of the beacon light illuminated in each quadrant, respectively. Therefore, the beacon light spot position can be estimated according to the proportion of the amplitudes of the photocurrents. When the center position of the beacon light spot is \([x,y]\), the dimensionless normalized deviation of Gaussian laser center on the \(x\)-axis and \(y\)-axis are, respectively, expressed in the following equations [13,14]:  
\[\Delta x=\frac{(I_{A}+I_{D})-(I_{B}+I_{C})}{I_{A}+I_{B}+I_{C}+I_{D}}\ \Delta y= \frac{(I_{A}+I_{B})-(I_{C}+I_{D})}{I_{A}+I_{B}+I_{C}+I_{D}} \tag{1}\]  
where \(I_{A}\), \(I_{B}\), \(I_{C}\) and \(I_{D}\) are the photocurrents output from the four quadrants, respectively.  
The photocurrent in the \(i\)-th quadrant is calculated as Equation (2):  
\[I_{i}=\eta\iint\limits_{\sum_{i}}p_{i}(x,y)dxdy\ \ \ (i=A,B,C,D) \tag{2}\]  
where \(p_{i}(x,y)\) is the power density of the light in the i-th quadrant,\(n\) is the response ratio of photoelectric. When there are multiple beams irradiating on QD, the total power density in the i-th quadrant will be equal to the sum of the power of all beams [15]. Then, we can get:  
\[p_{i}(x,y)=\sum_{j=1}^{N}p_{ij}(x,y) \tag{3}\]  
where \(p_{ij}(x,y)\) is the power density of the \(j\)-th light in the \(i\)-th quadrant. Substituting Equation (3) into Equation (2), the total photocurrent in \(i\)-th quadrant is:  
\[I_{i}=\eta\iint\limits_{\sum_{i}}\sum_{j=1}^{N}p_{ij}(x,y)dxdy=\sum_{j=1}^{N} \eta\iint\limits_{\sum_{i}}p_{ij}(x,y)dxdy=\sum_{j=1}^{N}I_{ij} \tag{4}\]  
where \(I_{ij}\) is the photocurrent corresponding to the \(j\)-th light beam irradiating on the \(i\)-th quadrant. Equation (4) is linear superposition which permits the position detection of multiple light beams with one QD by recovering the photocurrent \(I_{ij}\) from \(I_{i}\).
## 8 System Design
### Mass Budget  
The mass budget for the OpenOrbiter spacecraft is presented in this section. The projected mass budget has been created based upon the simplifying assumption of a consistent density, discussed in Section 8.1. Table 7 presents these values, based upon the complete use of the 1.33 kg mass limit and the volume allocations presented in Table 5. Note that, unlike in the previous section, the actual cross-sectional area (11.6 cm x 11.6 cm) is utilized (for a total volume of 1,254.4 cm\({}^{3}\)), as the spacecraft mass cannot exceed the 1.33 kg limit. Projected values from SMAD 4, based on the 1.33 kg mass limit and listed mass utilization percentages for a spacecraft without a propulsion system are also presented.
## 2 Materials and Methods
### Determination of Volatile Compound Composition by SPME-GC\(\times\)GC-TOF-MS Analysis  
In order to determine the volatile compound composition, 0.100 \(\pm\) 0.001 g of SFE-CO\({}_{2}\) extracts was placed in a 20 mL SPME vial and subjected to the solid-phase microextraction (SPME) with a DVB/CAR/PDMS fiber at the following conditions: temperature 40 \({}^{\circ}\)C, equilibration time 15 min, extraction time 30 min. The analysis of SPME-derived samples was conducted on a comprehensive gas chromatography time-of-flight mass spectrometry (GC\(\times\)GC-TOF-MS) LECO Pegasus 4D system, consisting of an Agilent 7890A GC system, a Gerstel multipurpose sampler MPS (Gerstel GmbH, Mulheim an der Ruhr, Germany) coupled with a high-speed TOF-MS detector (LECO, St. Joseph, MI, USA) and a four-jet cryogenic modulator (Zoex, Houston, TX, USA). The chromatographic system was made up of a primary column BPX-5 (30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) (SGE Analytical Science, Australia) linked with a secondary column, BPX-50 (2.0 m, 0.10 mm i.d., 0.1 \(\upmu\)m film thickness). Working conditions were: desorption time 5 min; oven temperature started at 40 \({}^{\circ}\)C (hold 1 min) and ramped to 250 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min rate (hold 1 min); modulator offset temperature 15 \({}^{\circ}\)C; transfer line to MSD 250 \({}^{\circ}\)C; the GC injector port temperature set at 150 \({}^{\circ}\)C then ramped to 250 \({}^{\circ}\)C at 720 \({}^{\circ}\)C/min; carrier gas (He) 1 mL/min; split ratio 1:20; TOF-MS acquisition rate 10 spectra/s, mass range 30-550 m/z units; detector voltage 1550 V; ion source temperature 250 \({}^{\circ}\)C. Data from the GC\(\times\)GC-TOFMS system were collected by ChromaTOF software v.4.22 (LECO) after a solvent peak delay of 360 s. Volatile compounds were identified by comparing their mass spectra with the Adams,NIST, MainLib and Replib mass spectral libraries (acceptable matches: signal-to-noise ratio >50 and similarity >750). The linear retention indexes (LRI) were calculated using the retention times of C\({}_{7}\)-C\({}_{30}\) n-alkanes series and further compared with previously published data in literature [24; 25; 26; 27; 28; 29; 30], when available. The results were expressed as GC peak area arbitrary units \(\times\) 10\({}^{7}\) (further abbreviated as AU) and percentage (%) of the total GC peak area. Experiments were performed in triplicate.
## 4 Performance Analysis  
DMAC divides the channel time into a series of repeated slots, and each of them consists of a CP and a TP. We assume a network with \(n\) nodes and each node sends saturated data to the channel. Wenow only consider one transmission cycle to facilitate the evaluation of DMAC. In each CP, the number of nodes that successfully contend for the channel is calculated as:  
\[E[n]=np_{tr}p_{s} \tag{12}\]  
In the TP, the time that spends on transmitting a packet is given by:  
\[T^{*}=2T_{SIFS}+T_{DATA}+T^{*}_{ACK} \tag{13}\]  
where \(T^{*}_{ACK}\) is the transmission time of an ACK frame. \(T_{SIFS}\) is time of a SIFS, and \(T_{DATA}\) is the time of data transmission. Hence, when combing Equation (1)-(5), (12), and (13), we can obtain the saturated throughput of DMAC, denoted as:  
\[S_{DMAC}=\frac{E[n]E[P]}{(1-P_{tr})\delta+P_{s}P_{tr}T_{succ}+P_{tr}(1-P_{s}) T_{fail}+T_{Bacon}+E[n]T^{*}} \tag{14}\]  
where \(E[P]\) is the average packet payload and \(T_{Bacon}\) is the transmission time of beacon in a transmission cycle.  
For comparison, we also present the system throughput of DCF mechanism that is derived in [4], denoted as \(S_{DCF}\), is given by  
\[S_{DCF}=\frac{P_{s}P_{tr}E[P]}{(1-P_{tr})\delta+P_{s}P_{tr}T^{DCF}_{s}+P_{tr}( 1-P_{s})T^{DCF}_{f}} \tag{15}\]  
where \(E[P]\) is the average packet payload, \(T^{DCF}_{s}\) and \(T^{DCF}_{f}\) are the average time of successful transmission and collision, respectively. With regard to the basic IEEE 802.11 DCF, \(T^{DCF}_{s}\) and \(T^{DCF}_{f}\) are given by:  
\[\left\{\begin{array}{l}T^{DCF}_{s}=T_{DIFS}+T_{DATA}+T_{SIFS}+T_{ACK}\\ T^{DCF}_{f}=T_{DIFS}+T_{DATA}\end{array}\right. \tag{16}\]  
where \(T_{ACK}\) is the transmission time of ACK packet.  
Figure 3 demonstrates how the throughput evolves with the transmission probability \(\tau\) when we set the number of contender as 20. Some parameters, such as \(\sigma\), \(T^{DCF}_{s}\), \(T^{DCF}_{f}\), \(T_{succ}\), \(T_{fail}\), \(T_{Beacon}\), and \(T^{*}\) are considered as invariant constants and Table 1 gives the other parameter configurations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Packet\_Payload & 200,500,1000 bytes & Slot\_Time & 20 us \\ MAC\_Header & 224 bits & DIFS & 50 us \\ PHY\_Header & 192 bits & SIFS & 10 us \\ Data\_Frame\_Rate & 2 Mbps & ACK & 128 bits \\ Simulation\_Time & 1800 s & DTR & 160 bits \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
It is obvious that the throughput of DMAC always increases and then reaches a certain threshold when \(\tau=0.03\). However, the throughput of DCF gradually decreases when \(\tau=0.0175\). Regardless of \(\tau\), the throughput of DMAC is much higher than that of DCF. We also present our simulation results to verify the analytical results in the following section.
## 2 Virtualization in Wireless Sensor Networks
### Network Virtualization  
Network virtualization allows for abstraction and sharing of WSN resources, while providing the impression of lone ownership. Network virtualization allows for the formation of dynamic logical groups of sensor nodes in which each group is set to a distinct domain. This leads to the formation of a  
Figure 5: Comparison of web applications and IoT nodes interfaces.  
VWSN, which is a subset of the WSN limited to a specific domain. Two approaches are discussed for achieving network-level virtualization, namely the cluster based and the overlay based approaches. The cluster-based topology achieves network virtualization by grouping logical instances of several sensor nodes from the underlying WSN infrastructure, while the Overlay networks leverages on the existing infrastructure to create a virtual topology at the application layer of the WSN. The cluster based approach is discussed in more details at this point, while the Overlay networks, being the main focus in this paper, are fully discussed in a later section.  
In traditional WSNs, sensor nodes are arranged into groups called clusters. The sensors in that group communicate through the cluster head. The role of the cluster head is to communicate the aggregated information to the network sink, which in turn saves energy and bandwidth. This follows the analysis that the cost of conveying a bit of data is higher than processing it. Clustering in VWSN entails grouping logical instances of sensor nodes from the underlying network infrastructure and connecting them via virtual links to create sensor networks that are specific to an application task. The nodes are held together by logical links to form a VWSN.  
Clusters are also formed by shared heterogeneous sensor nodes under different application domains. These clusters associate themselves based on their computing, communication, and processing capabilities, as well as based on their energy consumption rates. Rather than deploying private sensor systems in a particular field, these sensor nodes are shared by various end users. An example where clustering finds use is in an industrial surveillance system as part of the IIoT concept. The enterprises, stakeholders, various departments and the OEMs might need to access certain video footages. At the application layer, the relevant stakeholders can have an instance of the service they want to access. Not all end users need to deploy cameras in the respective areas, but they can share the existing infrastructure and each of them have applications running to access the surveillance system as if they are the only one accessing it.  
Clustering can be realized through a static topology that offers local control and a dynamic topology that can change its network parameters on the fly. Following the clustering model, aggregated data can be sent to the sink node to reduce the number of nodes involved in the transmission. Clustering algorithms enable energy efficiency in the network and allow for improved scalability [97]. In this case, communication overheads are reduced for both single and multi-hop topologies.
Recently, post-and-cores with RBC reinforced with a fiber post have been widely adopted [22; 23]. This method has high fracture resistance similar to that of cast post-and-core materials [24]. Additionally, the color of RBC is similar to that of the remaining coronal dentin, so the color mismatch is unlikely to occur even with all-ceramic restorations [25]. Unfortunately, there is no consensus on the optimal surface treatment for abutment tooth built-up with fiber-reinforced RBC post-and-core materials prior to luting, because few studies have investigated the bond strength of luting cement to the RBC build-up material [26; 27; 28]. Additionally, these previous studies only examined the effects of the difference in the luting material, cleaning protocol, and surface treatment on bond strength. Therefore, there is no previous study examining the effect of silanization. It is more difficult to distinguish the boundary between the build-up material and the remaining coronal dentin than the boundary between a cast post-and-core build-up and the remaining coronal dentin. It is therefore possible that the adhesion of the luting cement to the remaining coronal dentin might be inhibited due to contamination by silane. It is desirable to avoid applying different pre-treatment protocols to both materials. This study was therefore conducted to evaluate the effect of additional silane treatment on the immediate and aged microtensile bond strength (uTBS) between the RBC post-and-core build-up material and the RBC CAD/CAM block. The null hypothesis was that additional silane treatment on the surface of the RBC post-and-core material does not improve the immediate and aged uTBS.
## 2 The Composition and Mathematical Model of Doubly-Fed Variable-Speed Pumped Storage Units  
The doubly-fed variable speed pumped storage unit is composed of reversible pump turbine, doubly-fed induction motor (DFIM), converter and control part. The pump turbine provides or consumes power as a prime mover or load. The stator of DFIM is the same as that of a traditional salient-pole synchronous motor. The rotor has symmetrical three-phase windings, which are connected to the grid through the converter to exchange power with the grid. The amplitude, frequency and phase of the voltage or current output by the converter are controlled by an excitation regulator. The structure diagram of the DFVSPS is shown in Figure 1.  
Figure 1: Structure diagram of the DFVSPS.  
GSC is grid side converter; MSC is machine side converter; \(P_{m}\) is the mechanical power; \(P_{s}\), \(Q_{s}\) are the active power and reactive power of DFIG stator side, respectively; \(P_{r}\), \(Q_{r}\) are the active power and reactive power of the DFIG rotor side, respectively; \(P_{c}\), \(Q_{c}\) are the active power and reactive power of the grid flow to the converter, respectively; \(P_{g}\), \(Q_{g}\) are the active power and reactive power of the DFVSPS unit flow to the grid, respectively; \(U_{dc}\) is the direct current bus voltage of converter.
## 5 Results
### Indoor and Outdoor Static Positioning Tests  
The northeast sky coordinate system is used to verify the static positioning accuracy of the algorithm in different regions. Five points are selected indoors and outdoors to test the navigation and positioning accuracy of the algorithm under static conditions, among which point 1 is outdoors, points 2 and 3 are in indoor open areas and points 4 and 5 are in indoor narrow areas. The test results are shown in Table 1.  
Figure 3: Test environment.  
It can be seen from Table 1 that the static positioning accuracy is better than 0.5 m in outdoor areas and indoor open areas and better than 0.7 m in indoor narrow and long areas.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 3 Biocompatible 3D Printing Materials
### Biomaterial Inks
#### 3.2.1 Synthetic Hydrogels  
Synthetic hydrogels are often not suitable for the direct seeding of cells, but have similar non-Newtonian properties to biopolymeric hydrogels, allowing them to be printed by extrusion. Pluronic is a commonly used synthetic ink that can be thermally crosslinked or functionalized with chemical groups for UV crosslinking. It has been used as support material for structures with overhangs, and also as a sacrificial ink to produce hollow structures [48; 68; 69; 55]. Elastomers are also an attractive material for bioprinting, as their mechanical properties mimic the viscoelasticity of native tissues. Polyddimethylsiloxane (PDMS) and silicon have been printed using embedded techniques (freeform reversible embedding (FRE) and printing into microgels, respectively) to produce hollow and bifurcating structures that mimic airways and large vessels [70; 71]. These structures can then be perfused to model flow in large vessels [71].
## Materials and Methods
### A Model for the Uncertainty Analysis of the Measurements
#### 3.3.1 Uncertainty Analysis for OPS Measurement Setup  
Type A, namely \(\delta_{A}\), uncertainty are evaluated by statistical methods (statistical analysis of a series of observations) and have been calculated from standard deviation of the experimental measurements; Type B, \(\delta_{B}\), uncertainty are evaluated by means other than the statistical analysis of series of observations. The main sources of type B uncertainty considered are:  
* Power Supply uncertainty has been evaluated through the data sheet and a Gaussian distribution has been assumed;
* OPS uncertainty has been evaluated considering its maximal lateral resolution, limited by diffraction of the light; a uniform distribution with 0.6 \(\upmu\)m semi-amplitude has been assumed;
* Digital image uncertainty has been evaluated considering the digital resolution of the acquired images (0.6 px/\(\upmu\)m). A uniform distribution with 2 pixels semi-amplitude has been assumed [44; 48; 49];