esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nature and Characteristics of _A. altissima_
### Mechanical Properties of A. altissima  
According to the literature, tree-of-heaven wood is characterized by satisfying mechanical properties, especially for its weight [50]. [PERSON] et al. [57] marked relatively low density and strength and considered this species less suitable for certain applications, those of high strength requirements. It presents a very high increase in strength after drying. [PERSON] and [PERSON] [58] recorded lower modulus of elasticity (MOE) and modulus of rupture (MOR) than other hardwood species, such as oak and beech, as well as a lower density than these hardwoods. [PERSON] et al. [59] attributed the quite low MOE and MOR to the presence of extractives, such as tannins, although the presence of these extractives could improve the durability and resistance of wood to decay, as well as to the presence of lignin. In general, the MOE has been reported to range from 7.2 to 17.1 GPa, while its MOR wood has been reported to range from 50.5 to 91.2 MPa (Table 2). A more recent study by [PERSON] et al. [60] evaluated the mechanical properties of this wood species untreated and treated with a soybean oil-based resin. The results showed that the treated wood demonstrated improved MOE and MOR compared to untreated wood, indicating that the resin treatment had reinforced the wood. Of course, the stress and strain values of _A. altissima_ wood vary depending on the specific growth conditions (age, growth rate, etc.), origin, and environmental conditions, as well as the testing methods used, and other factors such as density, structure, extractives and treatment applied [61]. Although its wood may not have similar or superior mechanical properties compared to other valuable hardwoods, it could still be considered suitable for certain applications where the weight, biological durability, or acoustic properties are of important consideration.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Ref.** & **Density r10** & **Shrinkage (\%)** & **Axial** & **MOR(Bend.** & **MOE(Bend.** & **Mergh** & **Bend.** & **Hardness** & **Hardness** & **Tangential** \\
**(g/cm\({}^{3}\))** & & & **Strength** & **(N/mm\({}^{3}\))** & **(N/mm\({}^{3}\))** & **(N/mm\({}^{3}\))** & **(J/cm\({}^{3}\))** & **(J/cm\({}^{3}\))** & **(J/cm\({}^{3}\))** & **(N)** \\ \hline \hline [PERSON] et al. [62] & 0.658 & - & - & - & - & - & - & - \\ \hline \hline [PERSON] & & & & & & & & \\ [PERSON] [63] & 0.531 & 10.81 & 36.3 & 81.5 & 10,492.41 & 3.94 & 7702 & 6839 \\ \hline [PERSON] and [PERSON] [52] & 0.545 & 16.00 & - & - & - & - & - & - \\ \hline [PERSON] et al. [64] & 0.62 & 14.85 & 55.83 & 71.11 & 11,906 & - & 4407 & 5407 \\ \hline [PERSON] et al. [65] & 0.638 & 16.2 & 110.9 axial & 148.3 & - & - & - & - \\ \hline [PERSON] and & & & & & & & \\ [PERSON] [66] & 0.6 & - & 64.47 & 101.29 & 10,259.31 & 3.811 & 5687.8 & 5141.5 \\ \hline [PERSON] et al. [67] & 0.555 & - & - & - & - & - & - & - \\ \hline [PERSON], and [PERSON] [68] & 0.653 & - & 53.4 & 111.07 & 11,665 & 6.11 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical and physical properties of _A. altissima_ wood.
## 2 Materials and Methods
### Estimation Procedure for YPLL-Based Estimands from Administratively Interval Censored Ages at Death  
As previously described in Section 1, we characterized disparities in COVID-19-attributable YPLL by sex through the estimation of percentages of total YPLL by sex-- contrasting them with their respective percent population shares--and age-adjusted male-to-female YPLL RRs. Additionally, to provide context on the magnitude of COVID-19-attributable YPLL experienced by males and females, we also estimated total YPLL and age-adjusted YPLL rates by sex. As explained in Section 2.2, estimation uncertainty pertaining to the above YPLL-based estimands of interest can be attributed to two sources: (a) administrative interval censoring of ages at death and (b) suppression of low death counts within individual age groups, which we elaborate upon here.  
We first focus on the issue of administrative interval censoring of ages at death, assuming momentarily there are no suppressed death counts for purposes of illustration. As the exact ages at death for each individual are unknown, exact YPLL values for each individual are also unknown. In such settings, the standard approach to calculate aggregate YPLL is to operationally assume the age at death for each individual in a given age group is equal to the midpoint, also referred to as the \"midpoint method\", which implicitly assumes that ages at death within each age group are uniformly distributed [49; 50]. However, applied epidemiological studies using the midpoint method to estimate YPLL-based quantities typically do not quantify the uncertainty attributable to the administrative interval censoring of ages at death (e.g., [51; 52; 53; 54; 55]).  
[PERSON] et al. (2021a) [48] proposed a MC simulation procedure to quantify the uncertainty associated with YPLL-based estimates obtained from mortality data summarized as death counts within age intervals, which has been used in other applied research [40]. The full details of the procedure can be found in their paper, but to summarize it briefly, [PERSON] et al.'s MC simulation procedure consists of stochastic simulation of ages at death for each individual in the data from continuous uniform distributions defined over their respective age intervals at each MC iteration. A point estimate of the YPLL-based estimand of interest is then calculated from the collection of simulated ages at death at each MC iteration, and the overall point estimate is taken to be the mean of the collection of MC point estimates, while the lower and upper endpoints of a \((1-\alpha)\times 100\%\) interval estimate (which can be conceptualized as a \"range interval\" per [PERSON] and [PERSON] (2010) [57]) are defined as the \(\frac{\alpha}{2}\) and \(1-\frac{\alpha}{2}\) quantiles of the collection of MC point estimates, respectively.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 4 Results
### Relationship between Parameters and Position
#### 4.1.2 Relationship between Time-Spread and Position  
For a specific sampling location, we denote the 95% statistical value of delay as \(D_{first,95\%}\) for the first effective MPC and \(D_{last,95\%}\) for the last effective MPC. The time-spread, which is denoted as \(TS\) for this location, is defined as:  
\[TS=D_{last,95\%}-D_{first,95\%} \tag{6}\]  
Using (6), the time-spread of every sampling position on \(T_{1}\) is calculated, and the relationship between time-domain parameters and position is shown in Figures 17 and 18.  
Figure 17 shows the relationship between the delay of the first effective path and position. The nonlinear curve fitting method is used to fit the curve in Figure 17; the fitting curve is the red line in the figure, and the fitting equation is:  
\[y=a\times x^{b} \tag{7}\]  
where \(a=18.04322\pm 3.86076\) and \(b=0.74836\pm 0.0436\).  
Figure 18 shows the relationship between time-spread and position; at the positions of 152.4 m and 202.4 m, there is a large time-spread sample in each of these two positions, which will be further analyzed in the section on the impact of typical objects. If we exclude these two samples for now, we get Figure 19.  
The nonlinear curve fitting method is used to fit the curve in Figure 19; the fitting curve is the red line in the figure, and the fitting equation is:  
\[y=\frac{a}{1+e^{(-k\times(x-xc))}} \tag{8}\]  
Figure 16: Relationship between number of MPCs and position.  
where \(a=2212.6593\pm 778.51346\), \(k=0.02413\pm 0.0063\) and \(xc=143.79056\pm 36.49551\).  
Figure 17: The relationship between delay of the first effective path and position.  
Figure 18: The relationship between time-spread and position.
## 1 Introduction
For the past four years, many researchers have worked within the framework of the European Cooperation in Science and Technology (COST) Action ES1309 to meet the aforementioned challenges by developing innovative optical tools for proximal sensing of ecophysiological processes (OPTIMISE; [[https://optimise.dcs.aber.ac.uk/](https://optimise.dcs.aber.ac.uk/)]([https://optimise.dcs.aber.ac.uk/](https://optimise.dcs.aber.ac.uk/))). The OPTIMISE community has compiled three papers on instrument characterization [38], measurement setups and protocols (this paper), and retrieval techniques [39] to make the gathered information available to the community. The set of papers aims to summarize the state-of-the-art for highly precise radiance measurements with a focus on F to complement other review papers on the emerging topic of F spectroscopy ([40; 41], [PERSON] et al., in press). The aim of this paper is to provide an overview of the subject and discuss proximal and airborne measurement setups and protocols currently used to retrieve F from spectrally resolved surface radiance (L) and incoming downwelling radiance (E) measurements. We differentiate between the leaf and canopy level. After this introduction (Section 1), we first provide some brief background information on passive F measurements (Section 2) before reviewing ground and airborne measurement setups and protocols for F measurements on the leaf and canopy level (Section 3). Then, we review current approaches to address open challenges regarding atmospheric influences, quality checks, spatial scaling effects, and metadata and ancillary data (Section 4). In the end, we discuss the current approaches and offer an outlook.
## 3 Description of the Launcher System Modules
### Voltage-Measuring Module  
Measuring the capacitor bank voltage and reporting it without direct contact with the microcontroller are a challenge. Therefore, the circuit diagram shown in Figure 7 is modified to measure voltage and convert it to a period. This circuit is very convenient for measuring high DC voltage. It completely isolates the high-voltage and low-voltage sides (control circuits). This module comprises three optoisolators, comparators, capacitors, and voltage regulators. In detail, a calibrated capacitor is charged through a calibrated resistor by the measured voltage. As the estimated voltage increases, the capacitor is charged faster. During the charging process, the voltage across the capacitor is compared with a reference voltage to obtain the time pulse as shown in Figure 7. As demonstrated, this circuit converts any input voltage into a time pulse with a different duration according to the input voltage's level.  
Figure 5: Capacitor bank of 5000 \(\upmu\)F with a power thyristor.  
Figure 6: The velocity measurement module.
# Rapid In Situ Biomonitoring of Subsoil Contamination by Applying an Algae-Soaked Disc Seeding Assay
## 2 Materials and Methods
### Analysis of Algal Toxicity Effects  
To extract _C. reinhardtii_ grown on each disc, we removed the soil particles adhered to the disc after each six-day exposure. The discs were placed in in 24-well microplates (height 15.6 mm, volume 3.4 mL per well) containing 1 mL [PERSON]'s basal medium and shaken, under the same conditions as those used to sub-culture the algae. The algal suspension was extracted from the supernatant of each well.  
To analyze the biomass of the _C. reinhardtii_, algal chlorophyll a was extracted after mixing 0.05 mL algal suspension and 0.2 mL ethanol in dark conditions (3 h, 24 \(\pm\) 2 \({}^{\circ}\)C, 100 r/min). The fluorescence of chlorophyll a was measured using a fluorescence microplate reader (Gemini Molecular Devices, Orleans Drive Sunnyvale, CA, USA), with 420 nm as the excitation wavelength and 671 nm as the emission wavelength [21].  
To analyze the cell morphology of the _C. reinhardtii_, algal suspensions were measured using a flow cytometer (FACScalibur, BD Biosciences, Franklin Lakes, NJ, USA), with forward-scattered light (FSC) and side-scattered light (SSC). FSC and SSC parameters indicate cell size and granularity, respectively. The gating region in the dot plots was adjusted to divide algal populations and the background (e.g., soil fine particles), and data were acquired from 10,000 events in the region. The geometric means of the FSC and SSC intensities were analyzed using FlowJo V10 (FlowJo LLC, Ashland, OR, USA).  
To analyze esterase activity, oxidative stress, and membrane permeability of the _C. reinhardtii_, the algal suspensions were centrifuged at 2500 r/min for 5 min and resuspended in 2.7 mL of Bold's basal medium. Algae were stained with fluorescent dye calcein-acetoxymethyl ester for esterase activity, 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorofluorescin diacetate (DCFH-DA) for oxidative stress or fluorescein diacetate for membrane permeability, according to [PERSON] et al. [20], [PERSON] et al. [22], and [PERSON] et al. [23]. FL1 (500-560 nm band pass filter, excitation at 488 nm blue laser, 15 mW, argon ion laser) was used to measure esterase activity, oxidative stress, and membrane permeability. In addition, unstained algae were measured using FL1 to verify autofluorescence. The gating region, number of events, statistics of FL1 intensity, and FlowJo V10 were determined by cell morphology analysis.  
Figure 2: A diagrammatic sketch showing how the periodic evaluation of silver nanoparticles could be used as a biomonitoring technique to evaluate subsoil quality.
## 2 CFD Analysis
### Analysis of Lift and Drag  
Lift coefficients \(c_{l}\) calculated for the high-lift profiles are plotted as functions of the angle of attack (AoA) in Figure 4's left hand side. In general, the \(c_{l}\) increases with the AoA. At a certain angle, stall occurs and the \(c_{l}\) suddenly drops. As the LE is lowered from S4 HL to S5 HL, the stall is delayed to higher stall angles. The maximum values of lift coefficient \(c_{l,max}\) and stall angles of the high-lift profiles are listed in Table 1. By changing the profile from S4 HL to S5 HL, \(c_{l,max}\) is increased by 14% from 2.80 to 3.06. The stall angle is also increased from 14.2 to 16.4 degrees.  
Figure 3: 3D mesh generated around profile S5 HL. (**a**) CFD mesh including far field. (**b**) Detail view of CFD mesh around the wing. (**c**) Channel between the wing main part and the flap.  
The stall can also be found in the plots of the drag coefficient \(c_{d}\) in Figure 4's right hand side; these suddenly increase at the respective stall angles. The difference in \(c_{d}\) among the profiles before the stall is minor. For the AoA larger than 4 degrees, \(c_{d}\) is reduced as the LE is lowered. For the AoA between 0 and 4 degrees, \(c_{d}\) of S5 HL is slightly higher than \(c_{d}\) of S4 HL (2.4% at AoA = 2 degrees).
## 3 Experimental Results and Analysis
### Ductility Analysis  
The ductility coefficient was used as a parameter to measure the ductility to facilitate the observation of the final deformation capacity of each specimen under seismic loading, and the calculation Equation (1) was as follows [28]:  
\[\mu_{\text{d}}=\frac{\Delta\mu}{\Delta y} \tag{1}\]  
Notes: \(\mu_{\text{d}}\)--the ductility coefficient.  
The W-1 specimen was used as the control group. The ductility coefficients of the five specimens were compared, as shown in Table 5. (1) W-2 had the best ductility and W-3 had the second-best ductility. This implied that the fiber volume doping amount within 0.4% could improve the ductility of the members and enhance the plastic deformation ability of the members. (2) The ductility coefficient of W-5 was the smallest, which indicated that the ductility of the sandwich wall panel was better. (3) The ductility coefficient of W-1 was higher than that of W-4, which indicated that the use of ceramic concrete as the load-bearing material could improve the ductility of the wall panels.
## 4 Results
: _Suppose an unauthorized user can access the medical record management system without the authorization of the network administrator, such an attacker may find it very difficult to retrieve and read medical records._  
Test: In our design, all entities, administrators, doctors, and patients participating in the network are encrypted with a public key, and to retrieve the data, any applicant must be in access lists where it is essential to know the private key of the patient to decrypt the packet of information. Considering that this private key is unique and only the assigned user knows it, the probability that the attacker can obtain or guess the private key to decrypt and obtain data in the network is almost zero. Moreover, within this study, it is suggested to employ an H64 algorithm for the computation of the hash and the verification of the transactions associated with each block. Consequently, an experiment was conducted to compare the duration required for computing the hash of three blocks, utilizing distinct algorithm types (SHA256, MD5, SHA1, H128, and H64). The resultant temporal curves are visually presented in Figure 2. The fluctuations observed in each curve are attributable to the random variability in block sizes generated during each cycle. The obtained outcomes reveal that the transaction validation period using the hash64 method has exhibited an average acceleration of 22% when compared to SHA256. Consequently, in our implementation of smart contracts, there exists a substantially diminished necessity for computational resources when employing the proposed hash64 approach. The execution time data of the smart contracts (SC) developed for the proposed system is compared with the data extracted from smart contracts mentioned in the literature review (SC_LIT) [46; 50; 51]. The implementation of SC_LIT considers the metrics investigated by [PERSON] [52], which include the following:  
SLOC: source lines of code;  
LLOC: logical lines of code;  
CLOC: comment-only lines of code;  
NF: number of functions;  
McCC: [PERSON]'s cyclomatic complexity of the functions. Figure 2: Consensus performance test. To analyze the SCs and extract metrics from the object-oriented (OO) programming paradigm, we utilized the SolMet tool implemented in Java, as provided by [PERSON] [52]. The obtained results are illustrated in Figure 3, displaying two curves. The orange line represents the time required to execute the SCs using the proposed method, while the other curve represents the SC_LIT method. It is evident that the execution times increase with an increase in the number of source code lines. This growth is nearly linear, with an execution time observed in the SC_LIT being 7.5 s for a contract with 15 SLOC, as compared to our proposed method which achieved 6.0 s. On the other hand, the maximum execution time with the SC_LIT method is 23.5 s for a contract with 71 SLOC, whereas the proposed method achieved 18.33 s. Overall, our proposal demonstrates an average reduction of 14% in the execution time of smart contracts.
## 2 Requirements Specifications and Measurements Gaps
### Wind Speed
The use of novel techniques using Signals of Opportunity (SoOp), such as those from Direct Broadcast Satellite (DBS) television signals at the Ku- or X-band [33], can be potentially exploited in the future to measure precipitation and winds over the sea surface at higher revisit times. These signals are potentially sensitive to detecting fluctuations of the sea surface roughness and light precipitation. As compared to GNSS-R systems, the spatial resolution will be better (higher frequency) and the Signal-to-Noise Ratio (SNR) higher as transmitters transmit more power. In this regard, a receiver of signals of opportunity at the Ku- or X-band can provide cost benefits and high quality data, but these techniques have yet to be developed.
## 3 An Approach to Generate the MEP Logic Chain from BIMs  
This section proposes a three-step approach to generate the MEP logic chain as shown in Figure 3.  
The basis of the approach is that the components spatially connected to each other are usually located in the same system. Within this system, it can be further determined that whether they are at the same logic level or closely connected as upstream or downstream components, through prior knowledge or user-defined identification rules. The following three processes utilize the MEP information and some pre-defined knowledge to identify the logic relations among various MEP components.  
The first step discussed in Section 3.1 analyses the geometric information of MEP components stored in either IFC or other 3D formats. If the information contained in the existing model is sufficient to assess spatial relations, the relevant parameters in the IFC file are extracted for analysis. If the solid model does not contain the required spatial information, B-rep representations could also be used for topological analysis. B-rep representations will be transformed into triangular facet models for spatial triangle intersection judgments. These two methods will both output a connection table to be used in the following steps. A connection table here refers to a table structure that stores the spatial relation in forms of MEP component pair records indicating the spatial connections in corresponding subsystems.  
The second step discussed in Section 3.2 involves the transformation of typical MEP systems and custom information requirements to generate the identification rules. To generate the MEP logic chain, engineers must figure out some logic identification rules to specify which kind of components is upstream or downstream of a specific component. These rules are further divided into two parts. One part is based on the prior experience knowledge, and the other is based on the user-defined logic. The former extracts the upstream and downstream relationships from typical compositions of various MEP systems as a template, while the latter integrates user-defined rules of logic relations according to the characteristics of MEP components as the extended rules for automatic generation. The above two rules utilize a query language to represent their information requirements in formal statements, which are then parsed in the third step.  
Figure 3: Flow diagram of the automatic completion mechanism.  
The third step discussed in Section 3.3 is the retrieval of MEP logics, and completion of the logic chains. The upstream component of a given MEP subsystem will first be determined by identification rules. After creating a graphic structure based on the upstream terminal component and connection table, automatic completion algorithms will perform a traversal searching for a component that meets the requirements of inspections and add upstream or downstream components as extended attributes to represent their logics, an algorithm for selective traversal which uses a tag for each node in the MEP logic tree. [37]