esa-sceva/satcom-chunk-collection huggingface dataset:
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 6 Simulation Results
### Effect of CQI Threshold-Based Approaches on Network Updates and the Average CQI of the Group  
To reduce the number of network updates generated by the \"optimized tree\" approach, we introduce a CQI threshold, which should be verified before performing the update. Figure 17 shows the progression of the generated updates using different CQI threshold values. Compared with the \"Optimized tree\" approach (CQI = 0), we can notice a gain of almost 30% in network updates when the CQI threshold is equal to 1. This gain continues to progress less significantly with increasing CQI threshold values.  
A set of simulations was carried out to verify the effect that the variation in the CQI threshold can have on the average CQI of the network. Several CQI thresholds were used during the simulations, ranging from 0 (for the optimized approach, as illustrated in Figure 18) to 10. For a better visual comparison, Figure 19 includes the CQI average chart for each CQI threshold value, while the line chart of the two reference approaches, i.e., \"optimized tree\" and \"less cost\", are maintained for better comparison.  
Figure 16: Comparison of “less cost” vs. “optimized tree” approaches in terms of the average CQI with the progression of both the number of updates per second and the total number of updates while the number of users increases.  
Figure 17: Number of network updates generated according to different CQI thresholds.  
Figure 18: _Cont._  
While the CQI threshold varies from 1 to 10, we notice that the resulting average CQI represents a loss compared with the \"optimized tree\" approach but also a gain compared with the \"less cost\" approach. For each number of users grouped into a set of 10, we calculate the gain generated by the average CQI according to simulated CQI threshold values. As a reference, the gain of 100% is obtained with the \"optimized tree\" method. Figures 18 and 19 show excellent results when the CQI threshold is equal to 1 with a 95% gain. When the CQI is equal to 2, the gain becomes slightly less stable depending on the number of users and varies from 88% for 60/69 UEs to 82% for 10/19 UEs. In these  
Figure 19: Average CQI gains according to the number of UEs and CQI threshold values.  
Figure 18: Resulting average CQI for each CQI threshold value (from 1 to 10).  
simulations, the results when the number of users is less than 10 are not represented since the resulting average CQI is extremely unstable.
## 2 Materials and Methods
### Specim Preparation  
Commercially available Co-Cr-Mo alloy powder (MP1, EOS, Krailling, Germany) was utilized to fabricate dumbbell-shaped samples (with longitudinal axes parallel to their build directions, as shown in Figure 2) using two SLM machines (EOSINT M100 and M290, EOS, Krailling, Germany) equipped with fiber lasers (n = 12 for each machine). The dumbbell-shaped samples with conical shoulders were designed using a 3D software program (Rhino 7, Rhinoceros, Seattle, WA, USA) according to the ISO 22674 standard with a 3 mm diameter and an 18 mm parallel section [25]. The chemical composition of the alloy powder provided by the manufacturer is shown in Table 1[7], and its morphology is shown in Figure 3. The particle size distribution of the metal powder follows a normal distribution, with D10, D50, and D90 values of 13, 25, and 42 \(\upmu\)m, respectively [26]. The SLM machines were operated using standard deposition parameters for MP1, with laser powers of 200 and 400 W employed when using the M100 and M290 under Ar and N\({}_{2}\) atmospheres, respectively. The different atmospheres in M100 and M290 are due to the specifications provided by their manufacturer. However, it has been reported that as long as the oxygen level does not exceed a threshold value, the choice between N\({}_{2}\) or Ar atmospheres does not significantly affect the microstructure or mechanical properties [27]. In this study, fabrication was performed at oxygen levels \(\leq\)1000 ppm. The laser scanning pattern was rotated by 67\({}^{\circ}\) around the z-axis between adjacent layers. Half of the samples (n = 6) were heat treated under an Ar atmosphere using the following procedure: (1) heating the furnace from room temperature to 600 \({}^{\circ}\)C, (2) keeping the sample in the furnace at that temperature for 30 min, (3) heating to 1150 \({}^{\circ}\)C, (4) maintaining for one h, (5) turning off the furnace and  
Figure 1: Flowchart of the experiment design.  
opening the furnace door, and (6) waiting until the samples cooled to room temperature (Figure 4). The heat-treated samples were subjected to blasting to remove the surface oxide film using an airborne abrasion (Jet Blast III; J. Morita Corp., Tokyo, Japan) at a pressure of 0.4 MPa using 50 \(\upmu\)m Al\({}_{2}\)O\({}_{3}\). The remaining half of the samples were not subjected to heat treatment; this group was denoted as \"as-built\".  
Figure 4: The heat treatment procedure of the samples.  
Figure 3: Morphology of the powder used in the SLM samples.  
Figure 2: Samples on a base plate. The green plane indicates the direction of the cutting plane for microstructural characterization.
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 3 Panoramic Texture Generation Method
### Panoramic Texture Dataset Generation Method  
First, a face texture dataset was built. Real face texture acquisition always requires expensive scanning equipment, which is time-consuming and labor-intensive, and the face texture contains face information and involves privacy issues. To the best of our knowledge, there is no such dataset that contains both the incomplete texture of the input face image and the corresponding panoramic texture. Furthermore, existing face texture datasets are difficult to obtain owing to privacy issues concerning face information. To address this problem, we introduced a low-cost method for generating a synthetic face texture dataset by pairing the face texture of the unwrapped coarse texture with the panoramic texture to train a 3D texture translation network.  
Figure 2 shows the major steps of texture dataset generation, which corresponds to the data generation part of Figure 1. It includes face data collection, UV texture generation, panoramic texture blending and dataset post-processing. The Poisson blending process was used to obtain the panoramic texture of a face from images captured from various angles under the same lighting and background circumstances, which is similar to the concept of multi-perspective face reconstruction. Each stage was explained thoroughly.
## 2 Materials and Methods
### Volatile Se Compound Analysis  
The supernatant (3 mL) was precisely sampled in a 20 mL headspace bottle, sealed, and placed on the headspace sampler for headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS, TSQ8000, Thermo Fisher Scientific Inc. Austin, TX, USA) equipped with an HP-5 (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column under the following conditions. The extraction head was pretreated at the GC inlet until there was no impurity peak and then extracted at 55 \({}^{\circ}\)C for 30 min. The adsorbed volatile compounds were desorbed at 250 \({}^{\circ}\)C for 5 min at the GC injector and injected into the GC column. At the same time, the instrument was used to collect data. The following parameters were used: extraction head, DVB/CAR/PMDS; carrier gas, helium, 1.2 mL/min; programmed temperature conditions, 40 \({}^{\circ}\)C for 1 min, 4 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C, and 10 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C for 6 min; transfer line temperature, 280 \({}^{\circ}\)C; ion source temperature, 300 \({}^{\circ}\)C; ion potential, 70 eV; ionization mode, EI+; quality scanning range \(m/z\), 33-350 amu. Volatile Se-compounds were identified by comparing the identified mass spectra with the NIST 2017 library using the retention time index and \(m/z\) of the fragments. The relative content of each identified volatile Se compound was expressed as the relative percentile (\(n=3\)) of the dimethyl selenide peak areas (day 4 in the 50 ppm group).
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 4 Results and Discussion
### Quantization of the Channel Output with Minimum Loss of Relevant Information
#### 4.1.2 Genetic Algorithm Quantizer Design Using Exact Nearest Neighbor Search  
Figure 7 shows the quantization regions obtained for a simple exact nearest neighbor search approach described in Section 3.1.1.  
Figure 6(a) uses the Euclidean distance and Figure 6(b) uses the Manhattan distance in Equation (5). The parameters \(\theta_{t}\) were tuned using the genetic algorithm based method from Section 3.2.2.  
Figure 7: _Cont._  
For the genetic algorithm optimization, we have used the configuration consolidated in Table 1. This configuration was determined experimentally and found to yield good results.  
The phenotypes \(\boldsymbol{\theta}^{(I)}\) in this scenario hold \(2\cdot|\mathcal{T}|=2\cdot 16=32\) real valued parameters that represent the real and the imaginary parts of 16 complex numbers. The optimized parameters \(\boldsymbol{\theta}_{t}\) are denoted using \(\times\) markers in Figure 7 in the complex plane. As it can be seen, the genetic algorithm automatically learns favorable positions \(\boldsymbol{\theta}_{t}\) in terms of the maximum preservation of \(\mathrm{I}(\mathsf{X};\mathsf{T})\) under the respective distance \(d(\boldsymbol{y},\boldsymbol{\theta}_{t})\). The quantizers from Figure 7a,b both can be described with \(32<<65,536\) parameters, but have quantization regions with very complicated shapes that allow us to preserve large amounts of relevant information. The preserved relevant mutual information is \(\mathrm{I}(\mathsf{X};\mathsf{T})\approx 1.38286\) bit (i.e., \(92.5\%\) of \(\mathrm{I}(\mathsf{X};\mathsf{Y})\)) for the Euclidean distance and \(\mathrm{I}(\mathsf{X};\mathsf{T})\approx 1.37313\) bit (i.e., \(91.8\%\) of \(\mathrm{I}(\mathsf{X};\mathsf{Y})\)) for the Manhattan distance.  
The conference version of this article [23] also holds a quantitative comparison for different signal-to-noise ratios (SNRs) that we skip here for brevity.
## Materials and Methods
### Structure Characterization of Samples  
Morphology of the samples was analyzed by scanning electron microscopy (Hitachi 3400-N, with accelerating voltage of 20 kV). The chemical composition of the samples was determined by X-ray energy-dispersive spectroscopy (Oxford Instruments INCAx-act X-ray microanalysis spectrometer). Thermal characteristics and temperatures of phase transitions were simultaneously investigated via thermal analysis in the temperature range of 20-370 \({}^{\circ}\)C (Derivatograph, MOM-3 Q-1500 D, in argon atmosphere, and a heating rate of 10 deg./min). Two parallel experiments were performed for each composition. Structural sample analysis was performed by means of in situ XRD (to obtain data on the evolution of PAA phase compositions) in the temperature range 20-300 \({}^{\circ}\)C with steps of 10 \({}^{\circ}\)C (diffractometer, Rigaku \(\iota\)Ultima IV., equipped with a high-temperature camera, Rigaku \(\iota\)SHT-1500., using cobalt K\(\alpha\) irradiation), and by a Raman spectroscopy technique (Triple Raman Spectrometer Horiba SENTERRA T64000, with a laser radiation wavelength of 488 nm). Raman spectra were collected at 2-3 points, both from the volume and surface of at least two samples at each temperature treatment step. Mercury porosimetry was used to determine the porosity distribution of the samples (AutoPore IV, model 9500, Micrometrics, in the range of 0.5 to 31.000 absolute psi, with a stem volume of 0.392). Apparent density of the membranes was measured by a hydrostatic weighing technique (scales RADWAG 220 c/xc, Poland). Sample specimens were weighed in air and in isopropyl alcohol, at least five times each. Relative sample densities were calculated using the crystallographic density data of Sb\({}_{2}\)O\({}_{5}\)-nH\({}_{2}\)O taken from the PDF database (card 01-084-0303).
## 3 MIPs' Applications  
The peculiar properties of MIPs have made them a highly interesting tool for different application areas, including separation sciences and purification, sensors and biosensors, catalysis and drug delivery.  
Molecular imprinting chromatography is one of the most extensively studied application areas of MIPs, which are highly suitable for chromatographic separation, allowing the preparation of tailor-made supports with predetermined selectivity. The increasing demand for optically pure compounds makes MIPs for chiral separations another important application especially to obtain the racemic resolution of drugs [22, 71]. MIPs have also emerged as new selective sorbents for solid phase extraction (SPE) procedures, allowing not only the preconcentration and cleaning of samples but also the selective extraction of target analytes from complex matrices.  
Recently, several studies have demonstrated that MIPs can serve also as artificial binding mimics of natural antibodies and can be used as recognition elements in immunoassay-type analyses. To date MIPs have been successfully used with different types of transducers and several methods have been used to achieve a close integration of the transduction platform with the polymer [24].  
Considerable investigations have also been made on the use of MIPs as active materials for catalyzing some reactions. MIPs with catalytic properties can be considered mimics of natural enzymes and applied in enzyme-like catalysis [25, 72].  
Finally, an area which poses the greatest challenge for MIPs is that of therapeutics and medical therapy. The capability of the MIPs to bind bioactive molecules in specific conditions makes MIT a huge potential for creating suitable dosage forms.  
In the next four paragraphs, present and future prospectives of the most important applications will be discussed.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
## 1 Introduction
[24] analyzed the mechanical mechanism of impact biquette and gas outburst and used the fuzzy comprehensive method to evaluate the risk. [PERSON] [25] studied the impact of gas on coal seam impact risk and proposed an improved comprehensive index evaluation method for coal seam impact risk with gas. Although the above-mentioned dynamic disaster evaluation method considers the impact factors of two disasters, i.e., rock burst and coal and gas outburst, it essentially regards the dynamic disaster as a single disaster. There is a lack of comprehensive evaluation indicators for compound dynamic disasters, the system of evaluation indicators is incomplete, and the weights of the indicators are fuzzy and uncertain, as well as being subjective; thus, making it difficult to determine an index weight that is reasonable. The risk assessment method of compound disasters needs to be improved. In addition, the fuzzy multi-attribute decision-making method for risk analysis has been applied in other fields, such as risk assessment of public institutions [26], risk assessment of open subway construction [27], selection of coal mining subsidence area management model [28], risk assessment of flood disaster in Beijing [29] and risk assessment of coal seam water inrush [30]. Therefore, this paper examines the main causes of compound dynamic disasters in China through an integrated approach. A compound dynamic disaster risk grade evaluation model is developed using the fuzzy comprehensive evaluation method (FCE) and is based on the combination of the analytical hierarchy processes (AHPs) and entropy weight methods (EWMs). In addition, it proposes a compound dynamic disaster grade classification index, forming the AHP-EWM compound dynamic disaster risk evaluation method for predicting and preventing compound dynamic disasters.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 4 Solutions
### Securing the Manufacturing Information Architecture
#### 4.1.1 Network Security and Information Flow
Any change to the OT infrastructure would require extensive planning and thorough testing to ensure that the new equipment functions within the applicable control parameters, and that the process flow remains unaffected. The costs associated with upgrading OT equipment, including for transition redundancy, is also a weighted matter on its own. If OT equipment in a manufacturing plant cannot be easily and regularly upgraded, and traditional IT network security solutions are not viable to OT applications, what viable solution could there then be to the aforementioned problems? The answer lies in a combination of the IT-based security mechanism that is tailored for OT applications, while adhering to the critical requirements of OT networks. A proposed solution to this problem [8] presents the concepts of a comptroller and a manufacturing security enforcement device (MSED) as a combined solution to the problem. A comptroller would be a device running software that performs security tasks on the fly during information exchanges between IT and OT equipment. Such security tasks could be capturing the output data of one system meant as input data to another system, performing cryptographic processes such as authorization in the form of digital signatures and generating output data that can be sent to the destination equipment in encrypted format. An MSED would then be connected directly in-line and in front of any equipment that makes use of information sent over the network so that any information sent to the equipment can be cryptographically validated and the integrity thereof verified. The MSED will essentially perform decryption in real-time as data are received from the comptroller. Furthermore, it will perform any additional cryptographic authentication required to ensure that the data it receives are indeed authentic and valid. The MSED will thus be the final entity that sends commands to the destination equipment. Figure 7 illustrates the concept of a comptroller and MSED in an OT network. Figure 7: Comptroller within an industrial manufacturing environment. MSED: manufacturing security enforcement device  
Any information sent to an MSED for verification will be discarded if its integrity cannot be verified. This would make it nearly impossible for an attacker to inject maliciously altered manufacturing information into the OT network destined for equipment to perform unauthorized actions. Although relatively simple in concept, the comptroller and MSED combination principle could essentially eliminate the problem of the malicious hacking of OT equipment.
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 3 Single Cell Patterning
### Materials  
Cell  
The acute promyelocytic leukemia cells (HL-60) were provided by Professor [PERSON] of the Department of Molecular and Cellular Pharmacology, the Medical School of Peking University, China. Reagents  
1. RPMI-1640 (containing glutamine and HEPES (N-2-hydroxyethylpiperazine-N-2-ethane sulfonic acid) with concentration of 25 mM), Mai Chen Technology, Ltd., Beijing, China.
2. Newborn bovine serum, Gibco, Newyork, USA.
3. Penicillin-streptomycin solution 100\(\times\), Mai Chen Technology, Ltd., Beijing, China.
4. Phosphate buffered saline, PBS, Macgene Technology Ltd., China.
5. Trypan blue solution, Mai Chen Technology, Ltd., Beijing, China.
6. Biotin-BSA, Gelest, Morrisville, PA, USA.
7. Streptavidin Alexa Fluor 488 conjugate, Invitrogen, Carlsbad, CA, USA.
8. Anti-human CD15 Biotin, eBioscience, San Diego, CA, USA.
9. Anti-human/mouse CD45R (B220) Biotin, eBioscience, San Diego, CA, USA.  
Figure 7: Schematic diagram of single cell patterning. Biotin–BSA is employed as the underlying adhesive proteins. Streptavidin is the middle template layer to provide further binding sites for the bonding of the antibodies. Based on the specific immune reaction between antigens and antibodies, the HL 60 cells can be further captured on the surface of the substrate.
## 5 Results and Discussion
In a general way, the wider the effective step size range of an optimization algorithm is, the better the robustness of the algorithm is. As shown in Figure 5, different optimization algorithms have different effective step sizes and ranges. In this experiment, for BGD method, AdaGrad, RMSProp, and Adam algorithms, the optimal step sizes are about 0.013, 0.3, 0.01 and 0.1, respectively. In general, when the step size is too large, they may not converge or even diverge; but when the step size is too small, they require a lot of iteration [29]. The effective ranges of step size of these four algorithms are respectively about 0.01, 0.87, 0.03 and 7.50. It is obvious that the effective step size range of BGD method is significantly narrower than that of the other three algorithms, because they are adaptive step size algorithms, which improve the robustness of the algorithms at the cost of increasing computational complexity. It is worth noting that Adam algorithm not only has the best BER performance, but also has the widest effective step size range, which means that it is more robust. Their effective ranges of step size in ascending order are as follows, \(\text{BGD}<\text{RMSProp}<\text{AdaGrad}\ll\text{Adam}\). Finally, we analyze the computational complexity per iteration of the above optimization algorithms using in tap estimation. As shown in Table 1, BGD method has the lowest computational complexity, i.e., the minimum number of addition (Add. ), multiplication (Mul.) operations and square root (Sqrt.) calculations. Although Adam algorithm has the highest computational complexity compared to the other three algorithms, the difference among them is not obvious. Their complexity in ascending order are as follows, \(\text{BGD}<\text{AdaGrad}<\text{RMSProp}<\text{Adam}\). Figure 4: BER performance of 129-Gbit/s PAM8 system versus ROPs at BTB (**a**) and 2-km transmission (**b**) with FFE, post filter, and MLSD. FFE employs BGD (blue triangle), AdaGrad (green rhombus), RMSProp (orange square) and Adam (purple circle), respectively.
###### Abstract  
Wood, as a natural material that stores carbon, is gaining increasing attention and has potential for use in interior architectural applications. Given the long indoor stay time characteristic of modern society, it is important to scientifically understand the effects of indoor wood application on the occupants. In this study, three residential buildings with an identical area and structure were constructed with different degrees of wood coverage (0%, 45%, 90%) using _Larix kaempfer_. Subsequently, indoor air quality (IAQ) evaluations and relative humidity measurements were conducted to assess the physical and chemical changes in each environment. The IAQ in wooden and non-wooden environments met the recommended IAQ standards established in South Korea. The results of the 8-month observation showed that, the higher the wood coverage ratio, the more the indoor humidity fluctuations were alleviated, and, in the case of the 90% wood coverage ratio condition, the humidity was maintained 5.2% lower in the summer and 10.9% higher in the winter compared to the 0% condition. To further assess the physiological responses induced by the wooden environment, the heart rate variability (HRV) was measured and compared for 26 participants exposed to each environment for two hours. In environments with a 0% and 90% degree of wood coverage, no statistically significant differences were found in the participants' HRV indicators. But, in the group exposed to the 45% wooden environment, the results showed an increase in HRV indicators, natural logarithm of high frequency power (thrF): 4.87 \(\rightarrow\) 5.40 (\(p<\) 0.05), and standard deviation of normal-to-normal intervals (SDNN): 30.57 \(\rightarrow\) 38.48 (\(p<\) 0.05), which are known indicators of parasympathetic nervous system activation.  
L arix kaempfer; wooden interiors; humidity regulation; physiological responses 2020