esa-sceva/satcom-chunk-collection huggingface dataset:
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
## 2 GaN-on-Si Process  
In this study, the power device and their drivers were implemented using E/D-mode transistors on the same low-resistance (<2 ohm-cm) silicon substrate. Figure 1a shows the device structure, where the AlGaN/GaN epitaxy consists of an AlGaN buffer layer, a 2 um GaN buffer and channel layer, a 15 nm AlGaN barrier layer, and a 70 nm p-GaN stack on a Si substrate. AlN is grown on the top and bottom of p-GaN as buffer layers, and metal is grown on the top as a contact layer. Figure 1b shows an SEM photograph of part of the device structures. Figure 1c,d present the E/D-mode transistors, respectively, while Figure 1e,f show their masking layouts. From Figure 1f, the D-mode transistor can be obtained by directly etching away the p-GaN layer under the same process. Table 1 summarizes the performance of p-GaN/AlGaN/GaN HEMTs with gate lengths of LG = 1.5 \(\upmu\)m, LGS = 5 \(\upmu\)m, LGD = 8 \(\upmu\)m, and gate width of 100 \(\upmu\)m. The reasons for the variation of device parameters in the table are mainly due to the growth temperature of AlGaN and p-GaN between samples, as well as slight differences in the etching process, resulting in different surface roughness and defects, which, in turn, affect the threshold voltage and conduction current of the devices.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 2 Experiment Design
### Methodology of Data Exploration
#### 2.2.1 Statistical Analysis  
A correlation coefficient stands for the linear estimation of dependence between two random variables. The calculation is defined as their covariation divided by multiplication of their standard deviations (see Equation (1)) [13].  
\[corr(X,Y)=\frac{cov(X,Y)}{\sigma_{X}\sigma_{Y}} \tag{1}\]  
Result can vary between \(-1\), \(0\) and \(1\), which stands for anti-correlation (the progress of variables possess opposite moves), no correlation (no statistical dependence between variables) and strong correlation (when variables have a similar progress) respectively.  
Analysis of variance (ANOVA), developed by [PERSON], is a set of tools aimed to test a variation in the means of several independent variables. The most important point in traditional ANOVA is a test of significance of the difference among the means \(\mu_{i}\) of variables. This test permits us to conclude whether the differences among the means of several variables are too deviated to be attributed as a sampling error or a significant difference [14; 15]. Based on that, the design of the adjusted hypothesis is defined as follows: \(H0:\mu_{1}=\mu_{2}=\cdots=\mu_{r}\), which means that under the zero hypothesis, there is no significant difference among the given means. The rejection of the zero-hypothesis will imply a presence of at least one variable with a significantly different mean from the rest of the variables. The additionally applied post-hoc analysis reveals which variable is the one with the confirmed difference.
## 3 Experimental Section
### Prokaryotic Expression of VvRiLin and Purification of Recombinant Protein  
To obtain prokaryotes-expressed protein, we subcloned the fragment containing _VvRiLin_ into a pET32a expression vector (Novagen, Madison, WI, USA). Besides, the corresponding ORF was amplified from the reconstructive plasmid pMD19T-_VvRiLin_ by PCR using Pfu DNA polymerase. Forward primer used was 5'-CGGAATTCATGGGATTCTCTTGCCTTTTACG-3' containing an _EcoR_ I site (underlined) at the initiating ATG codon. Reverse primer was 5'-CTTGACGGTCGACCT ACAGGGGAATGCTTCAAG-3' including a _Sal I_ site (underlined). The encoding region of _VvRiLin_ amplified by PCR was subcloned into the pET32a expression vector downstream of the (His)\({}_{6}\) tag. The reconstructive plasmid pET32a-_VvRiLin_ was transformed into _E. coli_ Rosetta (DE3). A plasmid DNA was purified and verified by sequencing. Single colonies of _E. coli_ Rosetta (DE3) with pET32a-_VvRiLin_ were incubated overnight at 37 \({}^{\circ}\)C and 180 rpm in a 5-mL Luria-Bertani medium supplemented with ampicillin (100 \(\mu\)g/mL). After that, the medium of 500 \(\mu\)L was transferred into a 500-mL volume of fresh Lysogeny broth (LB) medium containing ampicillin (100 \(\mu\)g/mL). The bacterial culture was kept shaking in a water bath at 37 \({}^{\circ}\)C until reaching an optical density of 0.8 at a wave length of 600 nm. Isopropyl-\(\beta\)-d-thiogalactoside (IPTG) was then added into this medium to a final concentration of 1 mM. The medium was incubated at 16 \({}^{\circ}\)C and 150 rpm for another 16 h to reach an optimal protein concentration. The _E. coli_ cells containing target protein were collected by centrifugation andfurther subjected to a purification procedure using Ni-NTA His Band Resin (Novagen, Madison, WI, USA). The purified protein was dialyzed against a buffer (30 mM HEPES, pH 7.2, 7.5 mM MgCl2, 20 mM MnCl2, 5% glycerol, 5 mM DTT) [25] as described by [PERSON] _et al._[43]. The analysis of SDS-Polyacrylamide gel electrophoresis showed that there existed a single band corresponding to the target protein on a 10% polyacrylamide gel (Biodee Biotechnology Co., Ltd., Beijing, China), which indicated that the prokaryotes-expressed protein obtained could meet the requirement of identification of biochemical function.
# Semantic Microservice Framework for Digital Twins
## 4 Proof-of-Concept: Automatic Sensor Data Evaluation
### Smart Data Service and Communication Infrastructure  
Figure 5 shows more details of the Smart Data Service as well as the needed communication infrastructure. The separated processes that build the Smart Data Service, their encapsulation into Docker containers, and the communication between them are shown.  
The Smart Data Service provides access to the knowledge graph formed by interlinking various SPARQL endpoints. Essential plant information, such as equipment and topology information, is directly stored at the Smart Data Service. For the presented proof-of-concept, the Plant, Equipment, Topology and Instrumentation Ontology (PETIont) [48] was used to describe the heating process use-case. Information provided by other services is stored and managed by the services themselves but included in the knowledge graph by the federated SPARQL engine FedX. Thus, this information is interlinked and accessible through the Smart Data Service.  
The time-series data from the sensors are stored in a PostgreSQL [49] database. To make these data accessible through the Smart Data Service and interlink them with context information, OBDA is utilized. The Ontop Framework [50] is used to map the data into the Sensor, Observation, Sample, and Actuator (SOSA) ontology [51]. Therefore, mappings are defined to populate the SOSA ontology with individuals based on the data stored in the PostgreSQL database. As an advantage of this approach, the data remain in the relational database and are only loaded in a virtual knowledge graph if needed by a SPARQL query request.  
The communication between the services is handled by the stream-based MOM Apache Kafka. Kafka is designed as a distributed system, running on a server cluster. Apache Zookeeper provides a centralized service to manage the nodes inside this cluster. Kafka topics are used to send data to services inside the DT. The request-reply communication pattern is implemented on top of the Kafka infrastructure. The requesting service can add a reply topic to its request, to which the reply will be sent. Such a pattern is implemented to communicate with the Smart Data Service.  
As the Smart Data Service communicates with SPARQL internally, a wrapper is implemented to enable requests from other services over Kafka. Services can send queries to the Kafka topic, to which the Smart Data Service is listing to. The answer is sent to the reply-topic specified in the request formatted in JSON-LD  
Figure 5: Smart Data Service and Communication Infrastructure.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 2 Requirements of Calibration Target for Radiometric Calibration
### The SCR Requirement  
To work as a calibration target, the target must be distinguished from the surrounding background scatterers (often termed clutter), which means that the target must have a large enough RCS to ensure sufficient visibility. The measure of visibility in a SAR image is the target SCR, and is defined as [13]:  
\[\text{SCR}=\frac{\sigma_{pq}^{T}}{\langle\sigma_{pq}^{C}\rangle}=\frac{\sigma_{ pq}^{T}\sin\theta_{i}}{\langle\sigma_{pq}^{o}\rangle p_{a}p_{r}} \tag{3}\]  
where \(\sigma_{pq}^{T}\) is the RCS of the calibration target, \(\sigma_{pq}^{C}\) is the background clutter RCS, \(\sigma_{pq}^{o}\) is the normalized average clutter RCS (i.e., the clutter backgrounding coefficient), the subscript \({}_{pq}\) stands for the transmitted and received polarization of the radar signal, \(\theta_{i}\) is the radar incident angle, and \(p_{a},p_{r}\) are the azimuth and range sampling interval of the SAR images, respectively.  
Generally, the SCR is directly related to radiometric calibration accuracy [21], as shown in Figure 2. A SCR of at least 25 dB is desired to guarantee that the radiometric calibration error is less than 0.5 dB, which is normally used as a fundamental requirement for the SAR radiometric calibration experiment. The bistatic backscattering coefficient of dry grass, when the radar incident angle is 20\({}^{\circ}\), is likely to be \(-10\) dB at the X-band [22], and we can obtain a calibration target RCS of at least about 11 dBm\({}^{2}\) when the SCR is 25 dB, and the azimuth and range sampling interval are both 0.5 m. In practice, the background reflections should be as small as possible. It is important to carefully consider the deployment of calibration targets to restrict the influence of background clutter [23].  
Figure 1: Radar cross-section (RCS) of a perfectly conducting sphere normalized to the optics value \(\pi a^{2}\).  
Figure 2: Upper (blue) and lower (red) limits of the radiometric calibration errors vs. signal-to-clutter ratio (SCR).
# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
## 3 Design of Distributed Unequal Error Protection Rateless Codes
### Analysis of System Throughput  
The system throughput is defined as the ratio of the total number of recovered information symbols \(k\) and the sum of received output symbols \(n\) in one transmission session period. Since \(y_{l}\) is the asymptotic DER, there exists a unique ideal throughput upper bound for a certain system model that is related to \(y_{l}\). For the LT code, the ideal DER tends to 0 when \(\gamma\geq 1\), which means that the upper bound is only affected by the overhead \(\gamma\). In this way, we can derive the throughput upper bound for a typical BEC model as shown in Figure 1, which is given by  
\[thr\_up=\min\Big{\{}\frac{k}{n},\frac{n(1-\varepsilon)}{n}\Big{\}}. \tag{9}\]In (9), \(\varepsilon\) is the channel erasure probability, which affects the overhead \(\gamma\). Moreover, by considering the periodic motion of relay \(R\) in the dynamic SIN scenario, we can derive the throughput upper bound of system model in this paper as  
\[thr\_up=\min\Big{\{}\frac{k_{1}+k_{2}}{N_{1}+N_{2}},\frac{N_{1} \varrho\_-\varepsilon_{1D}-\varepsilon_{2D})+N_{2}(1-\varepsilon_{RD})}{N_{1} +N_{2}}\Big{\}}. \tag{10}\]
## 3 End-to-End Modeling
### Lidar Signal Distortion
#### 3.3.2 Multiple Scattering Effects  
The multiple scattering (MS) contributions are taken into account through the MS coefficient \(\eta\)[29], and depend on the wavelengths [66]. As previously explained (Section 2.2.3), this effect is negligible in UV (355 nm, \(\eta\approx 1\)), but not in NIR (1064 nm, \(\eta\approx 0.96\)). An example of simulations in UV is given in Figure 12a; there is no much difference between the single scattering signal (green curve) and the total simulated signal (single and multiple scattering, cyan curve). Simulations in NIR for the same conditions are given in Figure 12b; the multiple scattering contributions (red curve) significantly affect the lidar signal. By comparing the single scattering signal and the total simulated signal, bias of \(\sim\)2-3 m can be observed on the location of the tree crown and relative errors of \(\sim\)5% were found on the _QMCH_ and _FOT_ estimations. This result also confirms the study of [PERSON] _et al._[67], who highlighted that multiply scattered photons magnify the amplitude of the reflected signal, especially that originating from the lower portions of the canopy. Similar uncertainty may affect the location of the ground echo if there is undergrowth. Such bias can be partially corrected after the retrieval of the vertical profile of the extinction coefficient and will be ignored hereafter. Note that the multiple scattering effects can be corrected at the first order considering the FOT. It will not be taken into account hereafter.  
Figure 11: **(a)** Illustration of the simulation of a forest site with a slope 0; **(b)** Example of different lidar signal simulated through the canopy of Fontainebleau for different slopes between 0 and 45\({}^{\circ}\) for a footprint of 20 m; **(c)** Example of different lidar signal simulated for different footprint sizes with slope of 30\({}^{\circ}\).
## 4 Results and Discussion
The LCCA highlighted the use of ammonia in ICE as the most cost-efficient option among those investigated, but the LCA comparison showed that this power system configuration increases the impact on climate change while reducing the impact on acidification and human toxicity. Although battery power systems onboard Croatian ferries represent the most environmentally and economically appropriate alternative to conventional marine fuel, the full electrification of the Croatian purse seiner would result in higher costs than the existing powering option. The main reason for this is trip duration. Ferries operate frequently, follow schedules on fixed routes, and the onboard battery can be charged with shore power while passengers and vehicles are boarding. However, purse seiners cruise to the open sea and do not cruise back until the fishing trip is over, which can last over 10 h. Due to that, battery capacity needs to be enough to support the purse seiner in each fishing activity. The battery capacity of the considered ship is 4.8 MWh (weighted 22 tons), which results in major investment costs that make up 64% of the total costs. The all-electric ship results in a 40.2% lower carbon footprint than the existing ship. However, high investment and maintenance costs are limitations for the widespread use of fully electrified purse seiners. The alternative solutions that satisfy both the environmental and economic criteria of the alternative fuel comparison are the LPG-powered ship and methanol-powered ship. Their use onboard reduces the environmental impact of the ship at a reasonable price. However, the use of LPG as marine fuel needs to be further developed and methanol is a more suitable alternative. The technology behind its use onboard is familiar and commercially available, and it has been implemented in many ship types. Although methanol use onboard cannot reach a GHG reduction of 50%, a hybrid power system with methanol in a dual-fuel engine and a battery can achieve the required reduction at reasonable costs. In order to highlight the uncertainty regarding fuel prices, a sensitivity analysis was conducted, in which fuel prices varied by \(\pm\)30% with an increment of 10%. The analysis was performed for several fuels and the results are presented in Figure 22.  
The results presented in Figure 22 show that fuel price fluctuations can influence the total cost of a ship's power system. Among the investigated fuels, LBG is sensitive to fuel  
Figure 22: Sensitivity analysis. price fluctuations, since more than 80% of the total costs related to ship power systems is fuel cost (due to great fuel consumption and high fuel price).
## Data
### In Situ Reference Evaporranspiration Data  
One effective method for quantifying the ET\({}_{0}\) is to use the pan evaporation (E\({}_{\text{p}}\)) and the pan coefficient (K\({}_{\text{p}}\)). The E\({}_{\text{p}}\) is the in situ observation using an evaporation pan, and the K\({}_{\text{p}}\) is defined as the ratio of ET\({}_{0}\) to E\({}_{\text{p}}\). Given an E\({}_{\text{p}}\) and a K\({}_{\text{p}}\), one can directly obtain the ET\({}_{0}\) even without meteorological observations. In the case of KMA, the K\({}_{\text{p}}\) was prepared in advance by fitting the linear relationship between the observed E\({}_{\text{p}}\) and the ET\({}_{0}\) derived by the PM equation using in situ meteorological data. Through this procedure, KMA provides the ET\({}_{0}\) by the multiplication of E\({}_{\text{p}}\) and K\({}_{\text{p}}\) for the nine points in South Korea (Figure 2). They are part of the Automated Surface Observing System (ASOS) stations on the croplands having the equipment of evaporation pan. The ASOS meteorological data and the E data of the nine stations have almost no missing values during 2013-2019. We aggregated the KMA ET\({}_{0}\) data on a daily basis for the calibration/validation for the ML-based daily ET\({}_{0}\) model.  
\[\text{ET}_{0}\ =\ \frac{0.408\Delta(R_{n}-G)+\gamma\frac{900}{\tau+273.15}u_{2}(e _{\text{s}}-e_{a})}{\Delta+\gamma(1+0.34u_{2})} \tag{1}\]  
where \(\Delta\) is the slope of the vapor pressure curve; \(R_{n}\) is the net radiation; \(G\) is the soil heat flux; \(\gamma\) is the psychrometric constant; \(T\) is the near-surface air temperature; \(u_{2}\) is the two-dimensional wind speed; \(e_{\text{s}}\) is the saturation vapor pressure; \(e_{a}\) is the actual vapor pressure. The constant 0.408 is for the conversion of the unit from MJ/m\({}^{2}\)/day to mm/day, and the constant 273.15 is for the temperature conversion from Kelvin to Celsius. The constants 900 and 0.34 are the empirical coefficients for the daily ET\({}_{0}\) proposed by FAO [1].
## 4 Day-Ahead Coordinated Preheating Control
### Single Household Demand Response Regulation  
The objective of single household demand response regulation (SDR) is to reduce user bills within the scheduling period while meeting the user's temperature demands. The optimization problem is formulated as Equations (16)-(18).  
\[\underset{P_{AC}}{\min}\sum_{t\in T}P_{AC}(t)\Delta t\cdot pr_{t}+\rho_{e}e_{t} \tag{18}\]  
\[s.t.\text{ Eq.(\ref{eq:12})}\sim\text{Eq.(\ref{eq:15})}\]  
\[Q_{AC}(t)=f_{PQ}(P_{AC}(t),T_{a}^{*}(t),T_{out}(t)) \tag{19}\]  
\[T_{lb,t}+e_{t}\leq T_{a}(t)\leq T_{ub,t}+e_{t} \tag{20}\]  
Equation (18) is the objective function, where \(T\) is the scheduling interval; \(\Delta t\) is the scheduling time step; \(pr_{t}\) is the electricity price at time \(t\); \(e_{t}\geq 0\) is a slack variable, used to avoid failure to solve optimization problems under tight constraints, and \(\rho_{e}\) is the corresponding penalty factor. Equations (12)-(15) are the discretized equivalent heat balance constraints, and each time step corresponds to four heat balance equations. Equation (19) is the _COP_ mapping relationship of IACs, which is discretized into a piecewise linear constraint of \(P_{AC}\to Q_{AC}\). The addGenConstrPWL function in Gurobi makes it easy to add to constraints. Equation (20) represents the user's demand constraints at different times, that is, the room temperature should be within the upper and lower limits set by the user. The upper and lower temperature limits at time \(t\) are respectively denoted as \(T_{lb,t}\) and \(T_{ub,t}\). There are different settings for working and sleeping. In addition, considering the threshold of IAC in actual operation, \(P_{AC}\) is set as a semi continuous variable, that is, it should meet \(P_{AC}(t)=0\) or \(P_{lb,t}\leq P_{AC}(t)\leq P_{ub,t}\).
## 1 Introduction
The accuracy of robotic positioning could be improved by reductions in complexity and uncertainty. These can be achieved by matching the actual controlled procedures involved and their virtual procedures. The contribution examines considerations for staff training and/or the planning of surgical interventions using real and virtual phantoms, and the use of augmented matched digital twins (DTs) for real interventions. The contents of this article are summarized as follows: In the second section, open, laparoscopic, and robotic surgeries are discussed and analyzed. The third section is dedicated to the details of image-guided robotic interventions and their performances in the case of nonionizing scanners and particularly magnetic resonance imaging (MRI). The fourth section introduces the possibility of a self-sufficient scheme for controlling MRI-assisted interventions and its monitoring for managing complexities and uncertainties via DT tools. Section 5 presents MRI radiofrequency field ruling equations and their implication in the scanner as well as the verification of MRI compatibility of inserted robotic tools in scanner scaffold. Section 6 details DT monitoring involvements in surgical interventions including projecting, tutoring, and implementation with human involvement in increased DT. Section 7 discusses some notions revealed in the preceding analyses. The last section summarizes the main conclusions of this paper.
# Haskap Juicing Method Effects on Haskap Juice Quality
## 1 Introduction
The evaluation system of juice quality in publications is typically focused on (1) basic physiological qualities, which include juice yield, soluble sugar, pH, acidity, color, cloud value, and viscosity; (2) sensory evaluation, which include human-based evaluation of appearance, texture, aroma, and flavors; (3) health qualities and safety concerns, which might include assays of sugar content, vitamin C, antioxidants, phenolics, flavonoids, and microorganisms; and (4) identification of enzymatic activities and by-products [24; 25; 26; 27]. Colloidal stability and enzymatic browning are also part of the evaluation for apple juice and orange juice [28]. Meanwhile, blending juices is an emerging market trend and been evaluated often, including aspects such as the blended juice ratios and their potential storage length [11]. Finally, different brands of juices are typically evaluated to ensure they meet the requirement of customers [29]. Since haskap is a new commercial berry, only few publications are available for haskap juice extraction. A lab-scale wine press extraction method has been evaluated by comparing thawing and osmotic treatment on haskap juice quality at varying times during the pressing process [30]. Juicing techniques have not been widely applied to haskap fruits in both research and commercial industry. Due to the recent interest in haskap production, processing, and marketing, this work evaluates three haskap juicing methods (steam juicer, centrifugal juicer, and wine press), comparing these techniques for multiple characteristics, including basic physiochemical and nutraceutical properties. These lab-scale operations will provide some useful information for expanding haskap processing methods.