esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the slow-changing channel case, the CSI feedback will be much less than that of the fast-changing channel, and the channel acquisition overhead is acceptable. If cell-free massive MIMO is deployed on a campus, in a conference venue or in other walking-oriented scenarios, where users move slowly and the channel coherence time is long [19], then the feedback of downlink CSI is lower, and there is sufficient time for estimation and feedback. This paper proposes that in a slowly time-varying channel environment, both the CPU and user equipment (UE) have sufficient time to obtain instantaneous CSI to use for power control. When the system is deployed in a location where people are walking or sitting, such as on a university campus, for large gatherings, or in large conference venues, the slowly time-varying fading channel condition can be realized when the UE moves slowly [20]. The main results of this work are as follows. We elaborate on the channel hardening and favorable propagation of the cell-free massive MIMO channel and the centralized massive MIMO channel [11]. We note that the favorable conditions in the cell-free massive MIMO channel still exist. The channels between users are orthogonal; however, the channel hardening phenomenon is not as evident as that in centralized massive MIMO [16]. Based on this fact, we propose to use accurate instantaneous CSI instead of statistical CSI for downlink transmission and power control. First, we use the instantaneous CSI to analyze the achievable rate of downlink transmission considering the channel estimation and apply the result to power control. Second, to reduce the feedback overhead, we quantize the estimated CSI with low precision and consider the effect of quantization error in the power control. Finally, we compare the performance of FDD cell-free massive MIMO with instantaneous CSI to that of other schemes under the urban microcell (UMi) scenario. The rest of this paper is organized as follows. In Section 2, we describe the FDD cell-free massive MIMO system model. In Section 3, we present the CB and power control with instantaneous CSI for downlink transmission. The quantization for feedback and power control with different quantized bits is developed in Section 4. We provide numerical results and discussions in Section 5 and finally conclude the paper in Section 6. _Notations_: Boldface letters \(\mathbf{G}\) and \(\mathbf{g}\) denote matrix and column vectors. The superscripts \(()^{*}\), \(()^{*}T\), and \(()^{H}\) stand for the conjugate, transpose, and conjugate-transpose, respectively. \(\lVert.\rVert\) and \(\mathbb{E}\{.\}\) denote the Euclidean norm and the expectation operators, respectively. \(a\sim\mathcal{CN}(0,\sigma^{2})\) denotes a circularly symmetric complex Gaussian random variable \(a\) with zero mean and variance \(\sigma^{2}\). \(\Re(. )\) and \(\Im(. )\) are the real and imaginary partsof a complex matrix, respectively. \(\circ\) stands for the Hadamard product, and \(vec(. )\) indicates vectorization of a matrix.
## 4 Results and Analysis  
### Landforms and Sand Sources  
The landform units passed by the railway include the piedmont alluvial-proluvial inclined plain and the alluvial-proluvial inclined plain in the southern margin of the Tarim Basin. The overall terrain is high in the south and low in the north, with flat and open topography and sparse vegetation. The section from Ruoqiang (DK0+000) to Washixia (DK80+500) has a surface elevation of 890-1000 m and is dominated by Gobi landform (Figure 2a-c) and distributed with isolated residual dunes locally. Here, the strata are mainly alluvial-proluvial fine sand and fine round gravel soil. The railway mainly passes through the front margin of the alluvial-proluvial inclined plain. By contrast, the section from Washixia (DK80+500) to Qiemo (DK261+000) has a surface elevation of 970-1420 m. The strata here are dominated by silty fine sand. The surface is largely distributed with aeolian sand, which forms dunes locally. The railway mainly passes through the middle of the alluvial-proluvial inclined plain, and oases, wetlands, and aeolian landforms are developed along the railway. Roughly 52.5% of the railway in the study area is exposed to dunes and Gobi areas. The areas affected by sand hazards along the railway were studied by sections according to the mileage pile numbers. Table 1 provides the starting and ending mileage pile numbers. Figure 2 shows the typical landforms along the railway in the study area. To be specific, DK0-DK80 are dominated by Gobi areas (a-c) and threaten railway safety mainly in the form of wind-sand flows under the action of strong high-frequency winds. DK113-DK261 are mainly mobile and semi-mobile dunes (d-f) with abundant sand sources. Under the action of sand-moving wind, sand materials are transported and accumulated to the railway, and the sand drift rate here is significantly higher than that in Gobi areas (Table 1). Mobile and semi-mobile dunes constitute the primary sand source of sand hazards in DK113-DK261.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Starting**} & \multirow{2}{*}{**Ending**} & \
\begin{tabular}{c} **Landform** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Vegetation** \\ **Coverage** \\ \end{tabular} & \multirow{2}{*}{**Sand Drift Rate (m\({}^{3}\)/m.a)**} \\ \hline
1 & DK0 & DK50 & Gobi & 0–5\% & 3–5 \\ \hline
2 & DK50 & DK55 & Gobi & 0–5\% &
\begin{tabular}{c} 10–15 (insufficient sand sources, \\ recommended to be 5 m\({}^{3}\)) \\ \end{tabular} \\ \hline
3 & DK55 & DK80 & Gobi & 0–5\% &
\begin{tabular}{c} 5–10 (insufficient sand sources, \\ recommended to be 5 m\({}^{3}\)) \\ \end{tabular} \\ \hline
4 & DK113 & DK149 & Mobile dune & 0–5\% & \textgreater{}20 \\ \hline
5 & DK149 & DK161 &
\begin{tabular}{c} Semi-fixed \\ dune \\ \end{tabular} & 10–30\% & 3–5 \\ \hline
6 & DK252 & DK261 & Mobile dune & 0–15\% &
\begin{tabular}{c} \textgreater{}20 (artificial afforestation, \\ recommended to be 5–10 m\({}^{3}\)) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landform types and sand drift rates along the railway.
## 5 Monitored and Controlled Indicators Critical to Microalgae Cultivation  
### The Concentration of Nutrients and Microelements  
Nutrient elements such as nitrogen, phosphorus, and iron play a decisive role in the effective growth of microalgae biomass [117]. High nitrogen concentrations in the cultivation medium generally stimulate and accelerate the growth of microalgal biomass, although nitrogen can become a limiting factor under certain conditions, such as the presence of elevated ammonium ion concentrations [118]. Nitrogen deficiency has been shown to significantly reduce microalgal growth rates and overall cultivation efficiency [119]. Similarly, the availability of phosphorus has a significant effect on biomass productivity, and a lack of phosphorus can inhibit the process [120]. The results show that 1 kg of phosphorus can enable the production of about 50 kg of dry microalgae biomass [121].  
In contrast to nitrogen and phosphorus, the availability of organic carbon is generally not a limiting factor for the autotrophic growth of microalgae. In heterotrophic or mixotrophic culture systems, however, it can be a decisive limiting factor [122]. In PBRs with high concentrations of organic carbon, the proliferation of competing microorganisms such as bacteria can lead to medium turbidity and reduced light penetration, which directly suppresses the growth of the microalgal biomass [123].  
Many microalgae species require external sources of vitamins for intensive growth, including thiamine, biotin, vitamin B12, and riboflavin, as well as purines, pyrimidines, silicon, and other growth-promoting factors [124]. The nutrient ratio in cultivation media such as wastewater is usually C:N:P = 20:8:1, while the optimal ratio for the production of microalgae biomass should be about 106:15:1 [125]. Supplementation with carbon--mainly in inorganic form--is therefore required to ensure adequate quality of the cultivation medium [126]. This can be achieved by increasing CO\({}_{2}\) saturation or by introducing leachate from fermentation chambers into the culture system.
# MUXER--A New Equipment for Energy Saving in Ethernet
## 1 Introduction
Energy saving is reached when the devices spends a significant fraction in the low power mode. Other alternatives, like Rapid physical layer device (PHY) Selection (RPS) [18], have been also proposed to accelerate the speed change. However, these approaches required the large changes in the Ethernet infrastructure, which are impossible with wide deployment and implementation of Ethernet in the Internet. To overcome the disadvantages of current approaches, this paper proposes a different method to reduce the wasted power consumption in low-traffic conditions of Ethernet. The method doesn't change any feature of the current Ethernet infrastructure. It proposes a new device named MUXER (a set of modified multiplexer) that is put behind end-terminals and before network devices, switches in this paper, to control the power (ON/OFF) of network devices, or to switch data packet transaction to other devices. MUXER must be guaranteed the simplicity, plug-and-play property, and cheap price. The overall process model of this device can be summarized in threefold, as shown in Figure 1. First, it tries to determine the satisfied conditions to generate controlling signals to turn off its network devices. During this process, MUXER has to answer these questions: \begin{table}
\begin{tabular}{c c c} \hline
**Device** & **Approximate Number Deployed (millions)** & **Total AEC (kWh/year)** \\ \hline Hubs/Switch & 0.6 & 29.7 \\ Wireless router & 46.2 & 42.4 \\ Wired-only router & 1.7 & 78.3 \\ Total/Avg & 48.5 & 43.5 \\ \hline \end{tabular}
\end{table} 
Table 1: Breakdown of energy draw of various networking devices (kWh refers to kilo-Watt hours and AEC refers to Annual Electricity Consumption). 1. What conditions does it consider to generate a controlling signal to turn off its network devices? 2. Which devices of its neighbor MUXERs can contain the more data traffic transferred from its network devices? 3. Which actions does MUXER determine to do at the end of this process? Second, MUXER makes decisions to send turn-off signals to its devices, and switches the data signals to other devices which are determined in the previous process. Final, MUXER tries to contact with its devices and its neighbor MUXERs if it can turn on its devices to increase the performance of networks.
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 5 Case Study of Green Construction Code Checking  
In this paper, some types of rules and the corresponding clauses in GB/T50905-2014 are shown as examples.  
Class 1: Code checking based on simple defined attribute value in IFC schema.  
9.3.5 Extra item: staff dormitory must meet the requirements of at least two square meters per person.  
mvdXML rule:  
IfcPhysicalQuantity.IfcPhysicalSimpleQuantity.IfcQuantityArea.AreaValue>2*4 (there are four people in every room in this case).  
SQWRL rule:  
Room(?r) ~ hasWidth(?r,?width) ~ hasHeight(?r,?height) ~  
swrlm:eval(?area, \"width * height\",?width,?height)  
-> sqwrl:select(?area).  
Class 2: Code checking based on the extension of IFC entities.  
6.2.1 General item: fly ash, slag admixture, and other new materials should be used during the construction period.  
The fly ash and slag admixture must be inserted into IFC baseline, because they are not defined in existing IFC (see Figure 15).  
mvdXML rule:  
IfcObject.IsDefinedBy.IfcRelDefinesByProperties.RelatingPropertyDefinition.IfcMaterialDefinition.HasProperties.IfcMaterialProperties.Name=Flyash  
IfcObject.HasAssocialtions.IfcRelAssociatesMaterial.RelatingMaterial.IfcMaterial.Material.Name= slag.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**EXPRESS** & **OWL** & **JESS** \\ \hline real, integer, number, etc. & \
\begin{tabular}{l} owl: real, xsd: integer, xsd: \\ decimal, owl: rational, etc \\ \end{tabular} &
\begin{tabular}{l} jess.ru.integer, jess.ru.float, \\ jess.ru.long \\ \end{tabular} \\ \hline string & xsd: string & jess.ru.string \\ \hline boolean & xsd: boolean & The atoms “TRUE” and “FALSE” \\ \hline \hline \end{tabular}
\end{table}
Table 3: The data type mapping relationships between EXPRESS, OWL, and JESS.  
After the checking is executed, the mvdXML checker captures each generated issue in a BCF report which mainly includes a markup file and a viewpoint file. The markup file is where most of the information about an issue resides. In a markup file, information about the model, about the issue, and about each comment on the issue can be obtained. The generated issue comments in the markup file contain the description of the \"concept\" defined in the mvdXML file to make domain users understand the requirement that is violated. The viewpoint file contains all the information about the camera, the elements in the view, and the software used. BIM software can be used to find and analyze the generated issues from the mvdXML checker. The BCF report can be opened with BCFier in Revit (see Figure 16).  
Figure 16: The BCF report of the code checking.  
Figure 15: Insert the new properties in IFC.
## 3 System Model  
An MCGV is introduced for charging battery and gathering data. According to our assumption, the MCGV is expected to sojourn anywhere to wirelessly charge nodes. However, too many possible locations may greatly increase the computational complexity. Hence, a logical cellular structure is laid out to simplify this problem, as shown Figure 1. We use many adjacent hexagonal cells with a side length \(D\) to represent a two-dimensional plane. The cells that the MCGV needs to visit are determined by their discharge rates. The MCGV only works at their centers, and we call these points anchor points. We assume that the base station is on the edge of the network in this paper. The MCGV travels from the base station to each selected cell, i.e., the charging cell, at a speed of \(S\). The MCGV recharges nodes in these charging cells and gathers data from nodes which are in or near charging cells. The distance between a node and the anchor point in its nearest charging cell is defined as the upload distance. Due to the low power reception efficiency caused by long distances, we stipulate that the sensor can only be charged when the MCGV sojourns at the cell in which it is located. After visiting all charging cells, the MCGV goes back to the base station. When the MCGV completes the above process, we assume that the network completes a round and the time \(T\) taken for each round is fixed.  
The graph G(C,V,E) defines the network. \(c_{m}\in C\) represents one cell. Each vertex \(v_{m}\in V\) denotes a node. Each edge \(l_{ij}\in E\) is the upload link between the node \(v_{i}\) and its next hop. \(v_{j}\) falling into the transmission range of \(v_{i}\) is necessary for the existence of the communication link. The two-dimensional plane is dotted with \(N\) random fixed homogeneous nodes. They have the same battery capacity of \(E_{o}\) and the same maximum communication distance of \(R\). Most energy is consumed in the process of collecting, receiving, and sending data. Hence, we do not consider the remaining energy consumption, and all packets in the study are the same dimensionally [26].
###### Abstract  
Silicon nanocrystals in silicon nitride matrix are fabricated by thermal annealing of SiN\({}_{x}\)/Si\({}_{3}\)N\({}_{4}\) multilayered thin films, and characterized by transmission electron microscopy, X-ray reflectivity and diffraction analysis, photoluminescence and X-ray photoelectron spectroscopy techniques. Si nanocrystals with a mean size of about 4 nm are obtained, and their properties are studied as a function of SiN\({}_{x}\) layer thickness (1.6-2 nm) and annealing temperature (900-1250 \({}^{\circ}\)C). The effect of coalescence of adjacent nanocrystals throughout the Si\({}_{3}\)N\({}_{4}\) barrier layers is observed, which results in formation of distinct ellipsoidal-shaped nanocrystals. Complete intermixing of multilayered film accompanied by an increase of nanocrystal mean size for annealing temperature as high as 1250 \({}^{\circ}\)C is shown. Near-IR photoluminescence with the peak at around 1.3-1.4 eV is detected and associated with quantum confined excitons in Si nanocrystals: Photoluminescence maximum is red shifted upon an increase of nanocrystal mean size, while the measured decay time is of order of microsecond. The position of photoluminescence peak as compared to the one for Si nanocrystals in SiO\({}_{2}\) matrix is discussed.  
nanocrystals; silicon; superlattice; photoluminescence; X-ray diffraction; HRTEM; EFTEM; XPS +
Footnote †: journal: Applied Physics Letters  
1 October 2019; Accepted: 2 November 2019; Published: 6 November 2019
## 1 Introduction
Some of the results are presented in [24] and are explained here in detail, together with the measurement uncertainty analysis and the complete description of the construction methodology of the CR. Descriptions of the geometry and the model of the CRs have been described in previous works [1; 2; 3; 25; 26]. However, none of those describes details of the construction process. The geometry of the wire supports, the wire strain, and the terminal-wire joining method strongly affect the stability of a CR resistance value. The wire heat treatment of the CR is fundamental to reduce their TC below 1 u\(\Omega\)/\(\Omega\)/\(\circ\)C. Even if the CR is in a temperature-controlled environment within \(\pm\)0.01 \(\circ\)C, to get variations of less than 0.01 u\(\Omega\)/\(\Omega\) in their reference value, its TC needs to be under 1 u\(\Omega\)/\(\Omega\)/\(\circ\)C.  
High-resistive wires with diameters around 20 u\(\mathrm{m}\) need to be employed to achieve nominal resistance values of 10 k\(\Omega\). The use of this type of thin wire makes each test time-consuming and challenging to perform. Therefore, obtaining the correct levels of TC and stability mentioned above without proper guidance, can take several months or even years. The purpose of this work is to serve as a guide and help to obtain the correct levels of temperature and stability of CRs in less time by using detailed procedures described in this paper and obtain CRs with an excellent performance that allows them to be used to measure the frequency dependence of impedance standards with traceability to QHR. Section 2 of this research describes the geometries and models of the CR type bifilar, quadrifilar and octofilar resistors developed. Likewise, a detailed procedure to perform heat treatment to NiCr alloy wires, methods to perform wires to terminal connections, and geometry considerations in the wire supports are presented. Section 3 exposes the results of the stability related to the terminal-wire joining techniques, the thermal treatment of two types of NiCr wire (Isaohm and Evanohm-S), and the stability related to the CR support geometry; at the end of the section, the measurement results between the CRs of CENAM and the METAS are presented. Finally, in Section 4, the discussion of this research is given.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 3 Results
### Spatial Variations of the Response of Terrestrial Carbon Fluxes to Drought  
Global maps with maximum correlation coefficients between the monthly terrestrial carbon fluxes and the SPEI were generated to assess the spatial patterns of carbon fluxes' responses to drought (Figure 2). The significant positive correlations of carbon fluxes with SPEI (red colored areas) mainly occurred in the southern hemisphere and at low to middle latitudes of the northern hemisphere. This indicated that carbon fluxes decreased significantly in drought months in these areas. At high latitudes of the northern hemisphere, correlations of carbon fluxes with SPEI were positive, but weaker. In abnormal wet months, terrestrial carbon fluxes might decrease correspondingly in these regions. These findings revealed differential responses of terrestrial carbon fluxes to drought in different regions.  
The maximum correlation coefficients analysis in this study showed that terrestrial GPP, NPP, and ER showed similar correlograms with drought throughout many regions across the globe, with the exception of the high latitudes of the northern hemisphere, where ER had a different spatial pattern compared to GPP and NPP (Figure 2a-c). The areas with  
Figure 1: Comparison of simulated monthly GPP (**a**), ER (**b**) and NEP (**c**) with data compiled from FLUXNET2015 database. The black solid line is the regression line for all data while the dash line is the 1:1 line. The red dotted line is the regression line for data in drought months (monthly SPEI-1 < 0), and the blue dotted line is the regression line for data in wet months (monthly SPEI-1 > 0).  
Figure 2: Spatial distributions of the correlations between detrended SPEI and monthly GPP (**a**), NPP (**b**), ER (**c**), and NEP (**d**) for the period 1981–2016. The values represent the maximum correlation recorded for each pixel, independent of the month of the year and the SPEI time scale. The maximum correlation coefficient was selected from the calculated 288 (= 24 \(\times\) 12) correlation coefficients. Desert, sea, and ice areas are depicted in white.  
low values for the maximum correlation coefficients of monthly GPP, NPP and ER with SPEI were mainly located in northern North America, northern Eurasia, the Qinghai-Tibet Plateau, the Amazon Basin, and Southeast Asia. As for the other regions of the globe, the maximum correlation coefficients were generally above 0.75, indicating that the higher the degree of wetness, the stronger the terrestrial photosynthesis and respiration; and, in contrast, the higher the degree of drought, the lower the terrestrial photosynthesis and respiration of terrestrial ecosystems.  
Compared to those for GPP, NPP, and ER, the maximum correlation coefficients between NEP and SPEI were generally lower and had higher degrees of spatial heterogeneity. NEP was jointly controlled by GPP and ER, which could decrease concurrently in drought conditions with similar magnitude. However, the relatively higher reductions in ER or GPP would indicate a weaker or stronger response of NEP to drought.