esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DNA Extraction and 16S rRNA Gene Amplicon Analysis  
We collected three samples from PSD and AnMBR, respectively. For each sample, three replicates of DNA extraction were conducted individually, and combined as a composition sample, before the PCR reaction. A PowersoilTM DNA Isolation Kit (Qiagen, Hilden, Germany) was used to extract microbial DNA from PSD and AnMBR according to the manufacturer's protocol. The yield and quality of DNA were analyzed electrophoretically on 1% agarose gel. Prokaryotic universal primers 515F and 806R [24], where the barcode was an 12-base sequence unique to each sample, were used in PCR to amplify the V4 region of the bacterial 16S ribosomal RNA gene. The PCRs were carried out in 50 \(\mu\)L volume with 10 ng of template DNA, 25 \(\mu\)L of 2 \(\times\)_ExTaq_ PCR Master Mix (Takara Biotechnology, Dalian, China) and 1 \(\mu\)L of each 10 mM primer. The PCR amplification was run under the following conditions: (1) pre-denature at 95 \({}^{\circ}\)C for 2 min; (2) 28 cycles of denaturing at 95 \({}^{\circ}\)C for 30 s, annealing at 55 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 30 s; and (3) a final extension at 72 \({}^{\circ}\)C for 5 min. The PCR products were detected by 2% denaturing agarose gels, after which the 16S rRNA gene amplicons were extracted from the gels and purified with a QIAquick PCR purification kit (Qiagen, Dusseldorf, Germany) and quantified with Fluorometer (Qubit\({}^{\circledotimes}\) 3.0, ThermoFisher Scientific, Waltham, MA, USA). The purified 16S rRNA gene amplicons were paired-end sequenced on an Illumina Hiseq platform at Novogene Bioinformatics Technology, Beijing, China.  
Analyses of the sequence of 16S rRNA genes were performed using QIIME2 software [25]. The following parameters were used for quality filtering: minimum/maximum length = 250/600; maximum 2 ambiguous bases; maximum 2 primer mismatches; average quality score > 25; and homopolymer < 6 nucleotides. OTUs with 97% sequence identity were clustered using the UCLUST software and the Greengenes, used as a reference 16S rRNA gene sequence database [26]. A representative sequence was chosen for each phylotype and aligned against the Greengenes using PyNAST [27]. The UCHIME algorithm using USEARCH (v7.0) was used to remove potentially chimeric sequences from the aligned representative sequences [28]. The taxonomic assignment of representative sequences from each OTUs was performed using the UCLUST consensus taxonomy assigner [29]. Finally, to estimate alpha diversity, a random sub-sampling method for each sequence library was used for microbial community diversity index calculations to control for the effects of library size. Alpha diversity indices were calculated for all samples with 1000 repetitions using a size of 16,126 sequences per sample. For principal coordinate analysis (PCoA), all samples were also subsampled to 16,126 sequences per sample to remove sample-size effects.
## 3 Multipath Multimedia Dynamic Source Routing (MMDSR)
### Multipath Routing Scheme in MMDSR  
MMDSR is a multipath routing protocol that uses the standard DSR as the engine to search for available paths. MMDSR uses up to three paths through which the three types of video frames will be sent. As Figure 2 shows, traditionally the most important video frames (I frames) would be sent through the best available path, P frames through the second best path and B frames through the third best path (worst one). In our results, we obtained that there are notable benefits in arranging two or three paths to transmit the video frames. However, arranging more than three paths in a multipath scheme does not provide a big improvement, while increases unnecessarily the management. Similar results were obtained in [30].  
The user requirements are negotiated using QoS parameters to provide the required image quality. We use the following parameters: minimum expected bandwidth (\(BW_{min}\)), maximum percentage of packet losses (\(L_{max}\)), maximum delay (\(D_{max}\)) and maximum delay jitter (\(J_{max}\)).  
Figure 1: MPEG-2 GoP structure.  
Figure 2: Multipath routing scheme using three paths.  
\[user\_req\equiv\{BW_{min},L_{max},D_{max},J_{max}\} \tag{1}\]
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 5 Discussion  
The results presented in this study demonstrate the effectiveness of using MT-InSAR data in detecting settlements induced by tunneling processes.  
The applied methodology has several advantages over traditional monitoring methods. First, it provides continuous monitoring over the entire AoI, reducing the risk of missing any significant deformations. Second, it provides high spatial resolution and can detect even millimetric displacements. Third, it is a non-invasive method, which eliminates the need for physical access to the monitoring area.  
However, it may present some limitations assuming the pure vertical ground displacement field when converting LOS displacements to ground settlements. Nevertheless, in  
Figure 14: Superimposition of effects (solid red line) vs. monitored settlements dv (black dots). Blue circles indicate the location of the tunnels. Sections A to I refer respectively to those in Figures 10 and 11a,b.  
greenfield conditions, the influence of the expected distribution of horizontal components of displacements could be considered when computing the settlement profiles.  
By applying the procedure to the first excavation, it was noted that, in most cases, the monitored data fell within the expected settlement range. Furthermore, the volume loss could be assessed, and the results were found to be in the expected range of 0.5% to 1% for TBM excavation.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods
### Data  
In this study, the Global Land Surface Satellite (GLASS) Gross Primary Production (GPP) and Evapotranspiration (ET) datasets from 2001 to 2017 with a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days were adopted to calculate WUE. The GLASS GPP product was produced with a Bayesian multialgorithm integration method combining light use efficiency models and has been validated worldwide with high accuracy [43]. The GLASS ET product was produced by integrating five latent heat flux algorithms and AVHRR, MODIS, and MERRA reanalysis data with Bayesian algorithms [44]. To classify the underlying land cover types in the Three-North region, MODIS land cover data (MCD12C1) with a spatial resolution of 0.05\({}^{\circ}\) were selected. The MCD12C1 product has divided land cover into 17 classes, as shown in Figure 2. The major land cover types in this region are barren land (41.01%) and grasslands (36.70%). To quantify the influences of nature and human activities, meteorological data (temperature and precipitation) developed by [PERSON] et al. [45] with a spatial resolution of 0.5' were used to calculate natural contributions.  
Figure 1: Location of the Three-North region of China.  
Figure 2: Land cover map and statistics of the Three-North region of China.
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Proposed Radio Power Adjustment (RPA) Routing Protocol for Energy Savings
A safety margin of 2% was considered as well, and was added to the value calculated by Equation (10) to provide more reliability during data packet transmission. Figure 2 shows the process used to compute the minimum level of \(P_{TX}\) needed to establish reliable communications. The first step is to identify the address of the node that sent the data and calculate the RSSI. This procedure is illustrated in Figure 2a, and the corresponding pseudocode is detailed in Algorithm 1. This RSSI value is then used as an input parameter in Equation (10) to obtain the value of \(P_{TX}\), which is added to the safety margin value and saved in the column of the routing table that stores the level information power relative to that destination node. ```
1 for each data packet receiveddo
2 - Check the destination node;
3 - Calculate RSSI;
4 - Get the appropriate level for transmit power using the fit equation;
5 - Adds the safety margin to the calculated power level;
6 - Changes the transmit power value for the destination in routing table;
7
8 end for
```  
**Algorithm 1**Identification of the \(P_{TX}\) level of a destination node. When it is needed to carry out a transmission, the source node consults the power level value in its routing table, updates it, and sends the data packet. This procedure is illustrated in Figure 2b, and the corresponding pseudocode is detailed in Algorithm 2.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 3 Results and Discussion  
### Dry Measurement of Paint Sample without Contact Patch  
As a first step, we used the THz-CCS system to measure the paint thickness in a dry environment without the contact patch. This was done using the reflection setup shown in Figure 2A with an incident angle of 20\({}^{\circ}\). The resulting sample time trace is shown in Figure 4A. It is clear that two reflections were separated by 3.9 ps. Applying Equation (1) and using tabulated values n \(\in\)[1.7-2] for the refractive index of the paint [20], the calculated thickness was in the range 273 um-319 um, which was far from the actual thickness. This was assumed to be because oscillations following a reflection overlap and the assumed refractive index were not exactly those of the sample. Fitting Equation (6) on a three-layer model with constant indices of refraction instead, the best fit shown in Figure 4C, with a coefficient of determination of \(R^{2}=0.960\), determined the paint thickness to be 235 um and the refractive index to be 2.34. The measured thickness deviated by 5 um from the Alicona 3D scan, which was within the mean surface height deviation. Therefore, we concluded that the two measurements were in agreement. When using the refractive index of 2.34 of paint from the best fit together with Equation (1), it resulted in a calculated thickness of 234.6 um. This result was also in agreement with the Alicona 3D scan. The difference in calculated thickness between Equation (6) and the best fit was only 2.2%. This high degree of agreement between the methods suggested that the THz-CCS device could be used to extract the thicknesses and refractive indices of multilayer films correctly.  
Figure 4: _Cont._
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
Nevertheless, few studies focus on the damage evolution during the water-weakening process considering the immersion time. On the other hand, to accurately determine the variation degree of rock mechanical parameters in the process of water-rock interaction, a damage model combining two damage stages induced by unloading and water-weakening should be established. As for the application of the damage model, [PERSON] et al. [35] established a numerical analysis model of unloading relaxation of rock mass and applied it to the simulation of slope excavation to dynamically simulate the excavation process of a slope. Some articles studied the relationship between post-peak mechanical properties of rock and strain-softening phenomenon, derived the damage evolution equation with softening parameters as variables, and proposed some methods to consider unloading damage effect in finite element numerical calculation [36; 37; 38]. [PERSON] [39] studied the progressive water inrush caused by particle loss and seepage failure, established a new evolution model of particle loss rate and permeability, and applied it to fluid-solid coupling calculation. [PERSON] et al. [40; 41] used the proposed damage model to simulate of simple loading conditions such as uniaxial tensile and compressive tests and practical and complicated loading conditions such as tunnel excavation. [PERSON] et al. [42] investigated the effect of strain rate on the damage and failure of tuff through finite element method, which found the tensile strength increases with the increase of strain rate. [PERSON] et al. [43] embedded the finite element into the discontinuous algorithm, established the tensile strength criterion with the maximum normal stress, and analyzed the rock damage and failure process, which is in good agreement with the test. [PERSON] et al. [44] used an explicit finite element to simulate the unstable rock failures under different unloading conditions. However, most researches about damage simulation focused on the unloading effect; few are based on the unloading and water-weakening coupling damage models. Not to mention considering the time effect in the softening process. In this paper, the softening stages and softening mechanism of surrounding rock of tunnel engineering in water-rich weak geology are analyzed considering the effects of excavation unloading and underground water. Based on the principle of equivalent strain, dual-softening coupling models of compression-shear damage softening, and tensile damage softening are derived, and the deterioration equations of rock parameters are also derived considering the time effect of water-softening. The applicable conditions of each softening model are summarized in combination with different engineering conditions. A hydro-mechanical-damage multi-field coupling simulation method is proposed based on the dual softening model, which is applied to tunnel engineering to verify its accuracy and applicability.
# Applying Selective Laser Melting to Join Al and Fe: An Investigation of Dissimilar Materials
## 1 Introduction  
The joining of aluminum, a low-density material with good corrosion resistance, and steel, a high-strength material with good formability, has great potential to meet the increasing demands of industries such as automotive manufacturing. The combination of aluminum and steel has unique physical and mechanical properties, such as a reduction of product weight, which is a significant advantage of aluminum-steel bimetallic parts. However, large differences in the melting temperatures, thermal conductivities, and thermal expansions of steel and aluminum, as well as low solubility, makes it quite difficult to join them. A variety of methods have been applied to join dissimilar materials, such as mechanical assembling (e.g., riveting, clinching, screwing) and heat joining (e.g., friction stir welding [1; 2], arc welding [3; 4], laser welding [5; 6; 7; 8; 9], hot-dip aluminizing [10]).  
Selective laser melting (SLM) is an additive manufacturing process that is widely used in many industrial areas. The SLM printing process includes of a series of steps, ranging from computer-aided design data preparation to the removal of a fabricated component from the building platform. A typical configuration of an SLM printer is shown in Figure 1[11]. In the beginning, the piston head is raised to lift the material powder. At the same time, the substrate in the build cylinder is dropped to a distance that is equal to the layer thickness. The scrapper travels from the feed container to the overflow container to create a layer of powder on the substrate and then comes back to the initial position. The laser scans the surface of fabricated bed area basing on each slide data. The processes are repeated until the part has been finished. The SLM allows fabrication of complex-shaped parts that cannot be manufactured by other traditional methods, with high density and accuracy of the printed product. Therefore, SLM is attractive for manufacture of automotive, aerospace, and medical components. However, most SLM printers that are currently commercially available have been designed to produce parts from a single material. Hybrid parts manufactured by selective laser meltingwere investigated [12]. The concept of multi-material fabrication by SLM has been reported in the literature [8; 9]; however, these products were printed by physically mixing powders before layering.
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## 3 Low-Complexity Online Calibration Method  
In this section, we first propose an online calibration method that can eliminate the strong interference of communication signal. Then we derive closed-form calibration accuracy expressions of the proposed method. We also discuss the complexity of the proposed online calibration method compared with previous methods.
## 3 Methodology
### Evaluation of Phenological Stages and GPP Estimation  
In order to evaluate the performance of the DLF and HGAM in phenology stages extraction, we compared modeled SOS and EOS with the digital images acquired on the same day, as well as 8 days ahead of and lagging behind the date, by visual inspection. The phenology cameras were able to capture the seasonal variations in foliage pigments of most PFTs, which result in an increase in canopy greenness in the spring and a decrease in canopy greenness in the fall. The agreement between the 8-day estimated GPP and observed GPP was evaluated by the coefficient of determination (\(\mathrm{R}^{2}\)) and the root-mean-square error (RMSE). Furthermore, GPP estimates from the VPM model were used as a reference to evaluate phenology-based modeled GPP. \(\varepsilon_{0}\) and \(\mathrm{T}_{\mathrm{scalar}}\) were set according to site-specific references [3; 6; 39; 53], and others were default parameters.  
\[\mathrm{RMSE}=\sqrt{\frac{1}{n}\times\sum_{i=1}^{n}(\mathrm{Y}_{\mathrm{pre\_ t}}-\mathrm{Y}_{\mathrm{obs\_t}})^{2}} \tag{18}\]\[\mathrm{R}^{2}=\left(\frac{\sum\limits_{i=1}^{n}\big{(}\mathrm{Y_{pre\_t}-Y_{pre\_ mean}}\big{)}(\mathrm{Y_{obs\_t}-Y_{obs\_mean}})}{\sqrt{\sum\limits_{i=1}^{n}\big{(} \mathrm{Y_{pre\_t}-Y_{pre\_mean}}\big{)}^{2}\times\sum\limits_{i=1}^{n}( \mathrm{Y_{obs\_t}-Y_{obs\_mean}})^{2}}}\right)^{2} \tag{19}\]  
In the equation, n represents the total number of samples, \(\mathrm{Y_{pre\_t}}\) is the predicted value for the current period, \(\mathrm{Y_{obs\_t}}\) is the observed value for the current period, \(\mathrm{Y_{pre\_mean}}\) is the mean value of the predicted values, and \(\mathrm{Y_{obs\_mean}}\) is the mean value of the observed values.
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 1 Introduction  
In the field of fluid mechanics, drag related problems are fascinating to examine. With a small modification of complex geometry, flow aerodynamics can be changed; thus, less energy will be needed for the propulsion system for a vehicle.  
Drag related problems were first examined in the aeronautical industry after the automotive industry also realised that it could affect fuel consumption reduction. For ground vehicles, drag related problems are specific due to external effects, such as road inclination and wind. However, the examined velocity regime is tighter than the airplanes because the speed is limited to 50-130 km h\({}^{-1}\). This limit also gives us several more simplifications, as the air can be incompressible and isothermal due to low velocity. In addition, road vehicles suffer more from pressure drag compared to airplanes and ships, where skin friction is more relevant.  
Ground vehicles have many small additions attached to their surfaces, e.g., mirrors, sunroof, antennas. These small shapes are not intended to have large impacts on aerodynamics and fuel consumption, yet could generate some noise [1]. Small objects (e.g. wings, winglets, flaps) on the surface of the vehicles can be categorized as vortex generators (VGs). Firstly, the VG was introduced by [PERSON] [2] in 1950. It had two purposes: delay flow separations and increase pressure inside the wake region for aircraft wings. With these properties, regardless of creating some drag locally, the overall altered pressure distribution could reduce the total drag coefficient (C\({}_{\text{D}}\)). Similarly, special active devices or shape modifications could also alter flow around vehicles to reduce drag.  
Studies related to drag reduction and vortex generators were made recently. For example, examining the connection between heat transfer and VGs by [PERSON] et al. [3] and, also, topology optimization in the field of fluid mechanics by [PERSON] et al. [4]. Notable reviews connected to road vehicles and drag were made in 1993 [5], continued by a review in 2014 for heavy vehicles [6].  
This review covers papers from the beginning of 2000 to April 2020, intended to provide a guidance for drag reduction method application on ground vehicles. Thus, the quantitative analysis mainly focuses on research results between 2008 and 2020. The assessed research papers attained their results by wind tunnel measurements or calculating validated numerical models. The study mainly focuses on hatchback and notchback shaped ground vehicle drag reduction methods, such as active and passive systems. The presented results could be applied to ground vehicles below subsonic regions where air density is considered constant; thus, methods can be applied for low tip speed wind turbines. Nonetheless, lift coefficients are not discussed. In addition to the previous reviews, this work provides a detailed quantifiable comparison between drag reduction methods.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 2 Methodology
### The 5G Signal Structure and the Role of SRS and PRS for Positioning
In the case of lower numerologies, specifically when (\(\mu=0\)), a subframe is comprised of a single slot. Conversely, higher numerologies allow the allocation of up to 16 slots, with all subframes employing a CP to ensure reliable communication. Higher numerologies enable shorter slot durations and finer time granularity. Accordingly, this contributes to better positioning resolution, particularly in latency-sensitive or high-mobility applications. These flexible structures enable 5G to adapt to various use cases, ensuring efficient spectrum utilization and minimizing interference. The optimization of SCS and CP usage by 5G enables the achievement of high data rates and low latency. The 5G resource grid is a two-dimensional framework in which one axis denotes time and the other represents frequency, forming a matrix of OFDM symbols and subcarriers. It serves as the foundation for allocating transmission resources between the gNBs and the UE. The resource grid (RG) is segmented into multiple resource blocks (RBs), each consisting of a group of subcarriers. Each RB is comprised of a predetermined number of consecutive subcarriers. The number of subcarriers within an RB is contingent upon the subcarrier spacing, a parameter determined by the numerology \(\mu\) defined in 3 GPP NR standards. To illustrate, in a configuration with a 15 kHz spacing, each RB includes 12 subcarriers. As the numerology increases, the duration of OFDM symbols decreases, thereby enhancing time resolution at the expense of frequency resolution. In the time-frequency domain, an OFDM symbol for each numerology is defined over an RG, spanning one subframe in time and the entire allocated carrier bandwidth in frequency. Within this structure, the Resource Element (RE), the smallest unit, is uniquely identified by its frequency index (\(k\)) and symbol position (\(\ell\)). The total number of subcarriers in the RG is given by \(N_{RB}^{h}\cdot N_{SC}^{RB}\), where \(N_{RB}^{h}\) represents the number of RBs for a given numerology \(\mu\), and \(N_{SC}^{RB}\) denotes the number of subcarriers per RB. The highest subcarrier index denoted by \(k\), is determined by the following relationship \(k=N_{RB}^{h}\cdot N_{SC}^{RB}-1\). This indicates that subcarrier indexing begins  
Figure 4: OFDM symbols and CP arrangements. at \(k=0\) and ends at this maximum value. As illustrated in Figure 5, the RG, RBs, and RE are depicted.