esa-sceva/satcom-chunk-collection huggingface dataset:
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
###### Abstract  
This work studied the feasibility of digitalizing the analog Ex-Core Neutron Flux Monitoring System (ENFMS) being used for APR1400 nuclear power plants (NPPs) and as to which strategies and steps must be taken. A fission chamber neutron flux detection and instrumentation model were designed. Its accuracy was evaluated and proven by comparing the model data with data gathered from tests and plant operations. A conceptual design was proposed through a combined structure that digitalizes only part of the system. The detector signal pre-amplification remains in analog form while the other functions such as reactor power calculation as well as signal conditioning and processing will be digitalized. Simulations showed that the true mean squared voltage (MSV) of the digitalized ENFMS maintained a linear relationship between real and estimated reactor power in the wide range compared to averaged magnitude squared value of analog ENFMS. Extended Kalman Filter (EKF) was also utilized for estimating reactor power and reactor period from measurement signals that are contaminated with gamma ray interaction and electric noise. This study proved that the ENFMS can be successfully digitalized as proposed wherein all functional and performance requirements are satisfied. Simulations results demonstrated that the functions and performance can be improved through the use of digital processing algorithms such as EKF and MSV.  
Ex-core Neutron Flux Monitoring System; nuclear power plant; APR1400; extended Kalman filter; mean squared voltage Article
# Compensation of Limited Bandwidth and Nonlinearity for Coherent Transponder
## 5 Conclusions  
Next-generation coherent transponders operate at high baud rate and utilize advanced QAM modulation. For these transponders, the limited bandwidth and the nonlinearity are two severe impairments which need to be mitigated. The limited bandwidth is mitigated by the FIR filter and the nonlinearity is mitigated by the memoryless Volterra filter for its simplicity.  
A novel technique to determine the tap coefficients for the FIR filter and the memoryless Volterra filter is presented. A sinusoidal stimulus is applied during the initial power-up. We then scan the amplitude and frequency of the sinusoidal stimulus and monitor the output from the coherent transmitter. Then, we use the curve fitting method to determine the underlying parameters of the coherent transmitter, such as the limited bandwidth, the nonlinearity, the power imbalance among the tributaries, and the bias point. Accordingly, we determine the tap coefficients of the FIR filter and the tap coefficients of the Volterra filter.  
We apply this technique on a DSP ASIC and a coherent CFP2-ACO transponder. We drive the coherent IQ transponder into the highly nonlinear regime. When the nonlinearity is mitigated by the memoryless Volterra filter, we achieve a noticeable improvement in the constellation diagram. After the compensation of the limited bandwidth and the nonlinearity, we demonstrate a coherent IQ transponder with 300 Gb/s data rate using the 64-QAM modulation format and running at the 30 GBd band rate.  
**Author Contributions**: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization: Q.W. Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization: Y.Y. Resources, writing--review and editing, supervision: J.A.
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors also gratefully acknowledge vigorous encouragement and sturdy support on innovation from [PERSON] at Juniper Networks.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# A Method for Generation of a Sizing System and Representative Models for a Facial Mask Design
## 3 Comparative Selection of RFMs for a Pilot Oxygen Mask for KAF Pilots  
After the sizing system was established, RFMs were identified among KAF pilots based on the weighted sum of Euclidean distance (WSED; Equation (1)), which is the weighted sum of distances between the facial measurements of a pilot and the averages (or medians) of facial measurements of a sizing category. The medians were used as the center of sizing categories drawn based on two key dimensions, SSL and LW; while the  
Figure 4: The final mask sizing system for KAF pilots with four sizing categories, updated by considering economic efficiency (accommodation percentage = 90%). averages were used for the other 16 non-key dimensions as their sizes were not categorized and, in consequence, their medians are unknown. Table 2 shows the averages of facial measurements of KAF pilots by sizing category for 18 face dimensions considered in pilot oxygen mask design. The weight of each facial dimension can be defined according to their relevance (e.g., H: high; M: medium; and L: low) in designing an oxygen mask ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [5]). The WSED of each pilot's face can be calculated under four different analysis conditions by considering types of measurement value (measured value or normalized value) and weight (equal weight or unequal weight). The \(z\)-score normalization was applied to obtain normalized values of facial dimensions for each pilot. For the condition of unequal weights, weight values (e.g., H = 9, M = 3, and L = 1) were assigned to facial dimensions. Table 3 presents the identification numbers of KAF pilots selected as RFM candidates having the smallest WSED in each sizing category under the four analysis conditions. \[WSED_{j}=\sqrt{\sum_{i=1}^{18}\Bigl{\{}w_{i}\times\left(m_{ij}-\overline{m}_{ij} \right)^{2}\Bigr{\}}} \tag{1}\]  
where  
\(w_{i}\) = weight of facial dimension \(i\),  
\(m_{ij}\) = measurement (measured value or normalized value) of facial dimension \(i\) of a single face in sizing category \(j\), and  
\(\overline{m}_{ij}\) = average (or median) of the measurements (measured value or normalized value) of facial dimension \(i\) of all faces in sizing category \(j\).
## 4 Discussion
[39] used hydrodynamic cavitation (HC), a high-pressure technique, combined with alkaline condition (pH 9-12) as second CW treatment. Cultivation of _Cryptococcus curvatus_ KCTC 27583 in this substrate led to 7.2 g/L dry biomass and 4.68 g/L lipid production, with high values of lipid content and productivity (65% of total biomass and 0.195 g/L/h, respectively), indicating that this pretreatment led to accumulation of intra-cellular lipids. _Cryptococcus curvatus_ NRRL Y-1511 produce high quantities of dry biomass (38.5 g/L) [26] but can also accumulate lipids (6.8 g/L, 63% of TDW) [27]. In the case of _Cryptococcus lautreti_ UCD 68-201, the highest lipid content was observed in batch cultivation, both in flasks (70% lipid content) and bioreactor (69% lipid content) [28]. Cultivation of _Debaryomyces hansenii_ strains in second CW substrate, to produce biomass and microbial lipids, according to our knowledge has not been reported yet. On the other hand, _Debaryomyces ethellsii_ BM1 produced low quantities of dry biomass (3.9 g/L) and lipids (0.4 g/L), when cultivated in deproteinized cheese whey [41], in contrast to _Yarrowui lipolytica_ B9, which produced 7.4 g/L dry biomass and 4.3 g/L lipids (58% of TDW) [40]. As far as the microorganism _Cystobasidium oligophagum_ JRC1 was concerned, cultivation on untreated CW led to high biomass production (20.98 g/L) and low lipid content (22% of TDW), but the lipid content increased (44% of TDW) when was cultivated in centrifugated and filtrated SCW, due to low values of nitrogen content [43]. Co-utilization of second CW and other agro-industrial residues and by-products such as wine lees and molasses has also been reported; batch culture of _Cryptococcus curvatus_ ATCC 20509 in ultrafiltrated CW permeate, supplemented with wine lees hydrolysate, led to 33.6 g/L dry biomass production (30.6% lipid content), when cultivated in flasks, but fed-batch cultivation in bioreactor led to even higher lipid content (49.6%), according to [PERSON] et al. [38]. _Cryptococcus laurentii_ 11, was cultivated in centrifugated CW used as sole substrate, and produced 4.6 g/L dry biomass and 1.3 g/L lipids. When the substate was supplemented with molasses, the dry biomass production was, interestingly, increased (16.6 g/L), but not the lipid production [25].
## 1 Introduction
Not all current orders will come in the next delivery week because the lead time is relatively long. If the probability that a certain order will come in the next delivery is high, then the currently available order can wait until the next delivery, and the two shipments can be aggregated together to reduce the shipment costs. Such a probability and a binary decision (come next week, not come) is usually investigated by logistic regression, which is the last and the most important type of forecasting in this study. To prepare for this stage, linear regression is needed to estimate the total number of coming orders in the next week. However, inventory costs, when temporal aggregation is used, will be higher because of the extra waiting. However, such waiting also depends on the period for which the current items available in the warehouse are waiting so far; if this period is not too long, waiting can occur. If the acceptable lead time is, for example, 4 weeks, and the available items have an order preparation time (OPT) of less than 3 weeks, then consolidation is possible. Consolidation can be for all the items from all suppliers for the same customer. However, if some of the current items have an OPT of more than three weeks, then all materials should be shipped immediately to the customer. That means that not all customer demand needs to be forecasted. Such a type of forecasting for temporal consolidation purposes is performed for the first time in this paper. Most of the studies in the literature about forecasting focus on forecasting for other purposes such as inventory management and shop scheduling; the forecasting process is performed by the manufacturing company itself based on large datasets about production processes. However, in this study, the dynamic weekly forecasting process for each customer order is performed from the point of view of the 3 PL, depending on current and historical data of demand and lead time. The forecasting process in this paper is performed for an environment that is overlooked by the previous studies,where the make-to-order supply chain contains four different parties, namely, the design company, many suppliers, the 3 PL, and more than 1000 customers (retailers).
# Towards the Evaluation of Augmented Reality in the Metaverse: Information Presentation Modes
## 5 Discussion
Therefore, in the case of an experiment with true AR conducted in this way, a fiducial marker could be an example of a tracking method that can be used as a reference system for AR information on the LEGO Duplo plate. After establishing the right location, further testing would have to be carried out in the development phase to ensure optimal visual asset registration based on the LEGO bricks to be put in place, resulting in wasted time. Moreover, experimenters should also provide users with all LEGO pieces used in the experiments. However, the main limitation of this solution, compared to ours, is that users would carry out experiments in an uncontrolled environment as regards, e.g., lighting, the relative position of the camera, and LEGO bricks, with the risk of invalidating the results of the study. A second alternative method, already described in the literature, is recording a video prototype illustrating the AR application and gathering data from users through online surveys [43, 44, 45]. However, participants' responses might not reflect their opinions on actually using the interface [43]. Furthermore, it is not possible to ask users to accomplish tasks using the AR application; thus, only subjective data can be collected. The proposed DSTE is aimed only at user studies on information presentation in AR. It can help to test different combinations of visual assets and their properties (e.g., world-fixed/screen-fixed, shading, size, and animation) involving a wide range of participants. Other studies on AR (e.g., interaction, display comparison, and perception) are more affected by real testing conditions. Thus, a tailored simulated testing environment is needed. Nevertheless, our solution does not lack limitations. In this work, we have demonstrated that our DSTE is valid for replacing laboratory experiments with remote ones with controlled variables (lights, camera position, and LEGO pieces). Still, there may be limitations in more in-depth studies on how AR information can be visualized for specific hardware and real industrial constraints. Moreover, despite our efforts to achieve the desired level of visual realism, a user unfamiliar with AR might lose the perception between true and simulated AR, leading to invalid results. This ambiguity could also occur in understanding how true AR works through the proposed video. However, our sample of users had average knowledge of AR, so these problems were not encountered in our user study.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Concrete Structures
On the basis of AE signal measurements, a probabilistic crack classification for cementitious materials was proposed by [PERSON] et al. [88]. Implementing machine learning, a framework was designed and compared to a theoretical approach, yielding a good qualitative prediction of the crack pattern. Recent research work has also combined AE (\"the ear\") with Digital Image Correlation (DIC) (\"the eye\") technique in order to characterise the mechanical performance. [PERSON] et al. [89] studied prestressed concrete sleepers at the standard laboratory level, and it was suggested that this combination could enhance understanding of concrete damage in practical applications. The combination of AE and DIC has also been examined by [PERSON] et al. [90] and the obtained damage mechanisms results were compared with Eurocode expressions. Further to damage assessment, evaluation of the stress state of a concrete structure via AE has also been examined by [PERSON] and [PERSON] [91]. Correlating the results of DIC from structural testing along with AE measurements, they proposed an AE Intensity Distribution that allows stress estimation. Finally, conventional AE sensors are mounted on surfaces of structures. [PERSON] et al. [92] investigated the efficiency of smart aggregates as AE sensors embedded in a concrete beam. Smart aggregates in this study were piezoceramic transducers, and their performance in  
Figure 12: Examples of AE sensors application for crack monitoring. monitoring structural damage was compared with conventional AE sensors, showing that there is potential in this direction.
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
## 1 Introduction
Materials other than Si were also used for nanowire FET biosensors. Carbon nanotubes are the typical material and were used for label-free protein biosensors [18; 19]. However, FET sensors with even higher sensitivity than that of nanowire FET sensors for the detection of target species are preferable and desired, especially in a dilute solution of target ions or molecules. Single-electron transistors (SETs) are a promising candidate for achieving even higher detection sensitivity due to the Coulomb oscillations. There had been no reports on SET-based ion sensors or biosensors, probably due to the difficulty of the room-temperature operation of SETs and incompatibility of biomolecule solution processes with LSI fabrication processes. Recently, using a Si multiple-island channel structure for the room-temperature operation of SETs and the appropriate gate insulator structure for the solution processes, a pH sensor [20] and biomolecule sensors for the detection of streptavidin [21; 22] were developed based on SETs. The immunodetection of PSA has been also demonstrated using an SET [22]. In this review, following this introduction, the mechanism for the ultrahigh-sensitive detection of ions and biomolecules using an SET sensor is first explained, and compared with the detection mechanism using a nanowire FET sensor. Second, there is a description of how to fabricate room-temperature operating SETs and the sensors with them. Then, the experimental results of pH, streptavidin, and PSA detections with SET sensors are introduced with their surface modification methods of the gate insulator. Finally, summaries with future prospective are provided.
## 2 System Models of Integrated Navigation
### Integrated Navigation Measurement Model
#### 2.2.2 Navigation Measurements Model Based on Two-Dimensional Doppler Velocity  
Two-dimensional Doppler velocity measurement means that the two spectrometers on the probe separately measure two radial velocities from the probe relative to Sun and the planetary target object, which are used as the velocity measurement information for the SSBE cruise phase. We adopt the two-dimensional Doppler velocity measurement based on the Sun and the target planetary bodies to compensate for the defect of the velocity estimation accuracy of the XNAV and enhance the state estimation precision for the cruise phase navigation.  
The basic principle of Doppler velocity measurements based on the solar/target planetary object is shown in Figure 2. Assume that two solar photons leave the Sun at \(t_{0}\) and are captured by the spectrometer on board the probe through two paths. One is received by spectrometer 1 facing the Sun at time \(t_{1}\), and the position of the probe at this time is \(\mathbf{r}_{ps}\) (\(t_{1}\)). The other one arrives at Jupiter at time \(t_{2}\), where the position of Jupiter is \(\mathbf{r}_{sj}(t_{2})\). After it is reflected, it is received by the spectrometer 2 facing Jupiter at time \(t\)[33]. The times \(t_{1}\), \(t_{2}\), and \(t\) can be obtained by numerical calculations, corresponding to the velocity \(\mathbf{v}_{ps}\) (\(t_{1}\)), \(\mathbf{v}_{ps}\) (\(t_{2}\)), and \(\mathbf{v}_{ps}\) (\(t\)) of the probe.  
In the cruise phase orbit, the positions \(\mathbf{r}_{ps}\) (\(t_{0}\)) and \(\mathbf{r}_{ps}\) (\(t_{1}\)) of the probe at time \(t_{0}\) and \(t_{1}\) can be approximated as follows:  
\[\mathbf{r}_{ps}(t_{1})=\mathbf{r}_{ps}(t_{0})+\mathbf{v}_{ps}(t_{0})(t_{1}-t_{0})+\frac{1}{ 2}\mathbf{a}_{ps}(t_{1}-t_{0})^{2} \tag{11}\]  
where the acceleration vector for the probe is denoted by \(\mathbf{a}_{ps}\). In the temporal interval \(t_{0}\sim t_{1}\), the distance traveled by the solar photon after departure from Sun is derived as follows:  
\[c(t_{1}-t_{0})=\left|\mathbf{r}_{ps}(t_{1})\right|=\left|\mathbf{r}_{ps}(t_{0})+\mathbf{v} _{ps}(t_{0})(t_{1}-t_{0})+\frac{1}{2}\mathbf{a}_{ps}(t_{1}-t_{0})^{2}\right| \tag{12}\]Similarly, the time t and \(t_{2}\) can be calculated as follows:  
\[\begin{array}{ll}c(t_{1}-t_{0})&=\left|\mathbf{r}_{\text{\emph{sj}}}(t_{2})\right|\\ c(t-t_{0})&=\left|\mathbf{r}_{\text{\emph{sj}}}(t_{2})\right|+\left|\mathbf{r}_{\text{ \emph{ps}}}(t)-\mathbf{r}_{\text{\emph{sj}}}(t_{2})\right|\\ &=\left|\mathbf{r}_{\text{\emph{sj}}}(t_{2})\right|+\left|\mathbf{r}_{\text{\emph{ps}}}( t_{0})+\mathbf{v}_{\text{\emph{ps}}}(t_{0})(t-t_{0})+\frac{1}{2}\mathbf{a}_{\text{\emph{ps}}}(t_ {1}-t_{0})^{2}-\mathbf{r}_{\text{\emph{sj}}}(t_{2})\right|\end{array} \tag{13}\]
## 2 Materials and Methods
### Sampling and Physicochemical Analysis  
Water samples were taken from the influent and effluent of the PSD and AnMBR in the DWTS. The physicochemical characteristics were measured as described previously [22]. In brief, influent and effluent chemical analyses (COD, nitrogen species, phosphorus species and alkalinity) were conducted in accordance with the Standard Methods for the Examination of Water and Wastewater, APHA [23]. The pH of the reactors was determined using a DELTA 320 (Mettler Toledo, Columbus, OH, USA). Gas samples were taken from the reactors via a small sampling port located at the top of the reactor. The biogas production was measured with a wet gas meter (COMBIMASS@ GA-s hybrid premium, Binder Group, Germany), and the CH\({}_{4}\) production was determined using a gas chromatograph (Clarus 580 Arnel, PerkinElmer, Waltham, MA, USA) equipped with a thermal conductivity detector. Standard methods were used to measure TS, VS, TSS, and VSS [23]. Sludge samples were collected from the AnMBR and PSD and centrifuged at 10,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. The remaining pellet of the sludge was transported on ice to the lab, where it was stored at -70 \({}^{\circ}\)C before DNA extraction.  
Figure 1: Schematic diagram of the decentralized wastewater treatment system (DWTS) with a primary sludge digestor (PSD) and anaerobic membrane bioreactor (AnMBR).
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.1 Coco Framework  
CoCo is an NFV framework proposed by [PERSON] et al. [79], which was designed for the deployment of modularized service function chains (MSFCs). One of the major goals of the proposal is the consolidation of processing elements collocated on a VM and handling the placement of the modularized SFC to minimise packet transfer overhead between VMs. The authors designed a placement scheme that selects the right SFC elements for consolidation using performance and resource-aware placement. Fairness is achieved between several NFs tied to a single CPU core using a run-time scheduler implemented in CoCo.  
In terms of scalability, CoCo can utilise a push-aside scheme specifically designed to handle reduction in performance, which might arise due to scaling elements. Unlike most existing NF scalability approaches that start up a VM when there is a need to scale, which leads to more overhead in terms of latency, the push aside algorithm reduces the need for inter-VM hop creation. Rather than creating a new replica (as used by traditional vNF scalability solutions), the CoCo framework adds more resources to elements that are overloaded.  
In terms of performance evaluation, the efficiency of resource utilisation and the reduction in the cost of transferring packets across the service chain are the two major benefits of the CoCo framework. Throughput and CPU utilisation were measured with CoCo implemented using Docker containers to allow for the consolidation of processing elements; thus, Open vSwitch was employed as the virtual switch for VM-VM communication. The results show that CoCo can improve performance by approximately 45.5% and a 2.46X reduction in packet transfer overhead.