esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
All anatomical parameters are normalized based on the characteristic dimensions native to each personal hip. Those were based on the convention set by [69], which deems each of these dimensions to be representative of the overall pelvis size along each axis. In detail, parameters are reported in terms of mean \(\pm\) standard deviation, along with observed minimum and maximum values when appropriate.  
The variables, both measured and calculated, were all assessed using a Shapiro-Wilk test for normality. Normally distributed data were statistically analyzed using Student's \(t\)-test. However, the variances in each pair of data sets were first analyzed to determine if  
Figure 6: Best-fit planes along the acetabular ridges and their normal vectors.  
they had equal variances; this was performed using the two sample F-test. Non-normally distributed data were statistically analyzed using a non-parametric Mann-Whitney U-Test. Categorical data were analyzed using a Chi-squared test.  
In all of the statistical analyses, a \(p\)-value of less than 0.05 was taken to reveal statistically significant differences; otherwise, non-significance was indicated.  
An intra-observer reliability test was carried out by calculating the intra-class correlation coefficient (ICC) with a confidence interval of 95%. Four random patients were selected from each group, and an anatomical landmark used in the construction of the bone embedded anatomical reference frame was palpated a second time. The time between the first and second time landmark selections was no less than 6 weeks.  
Calculations and graph productions were carried out using Matlab (Matlab(r), R2022a, The MathWorks, Inc., Natick, MA, USA) and were supplemented with the use of Excel (Microsoft Corporation, Redmond, WA, USA) for preliminary analysis and data visualization.  
Power Analysis  
Power analyses were carried out at a power of 80% with a 0.05 probability confidence to select the proper patients' samples. A difference in terms of linear and rotational evaluations between the affected and contra-lateral healthy side within each group, as well as between the two groups, equal to 3 mm or degrees, both with a standard deviation of 2 mm or degrees, was hypothesized.
# Identification of a Human-Structure Interaction Model on an Ultra-Lightweight FRP Footbridge
## 5 Discussion
### Variation of the Step Frequency  
An assessment of the identified HSI model is presented hereby considering a single pedestrian crossing the footbridge at three different step frequencies: 1.8 Hz, 1.9 Hz, and 2.0 Hz. Test subject 1, whose mass is 71.8 kg (Table 2), walked over the structure at the mentioned step frequencies, and a new test subject (No. 7) with a mass of 73.4 kg was asked to cross the FRP bridge three times. For both pedestrians, the step frequency in each test was controlled with a metronome, and the measured mean speeds during the experiments were 1.2 m/s, 1.3 m/s, and 1.5 m/s, respectively.  
From the recorded acceleration response at midspan of the footbridge, experimental MTVVs were obtained. Whilst the numerical values were obtained using the the mass of each test subject and the parameters of the identified MSDA system. Results accounting for both pedestrians are displayed in Figure 14. When the fourth harmonic of the walking human (\(f_{a}=1.9\) Hz) was synchronized with the structure's fundamental frequency, the highest structural response was obtained. For the tests with both humans, experimental and numerical results show a good agreement, obtaining a relative error between the MTVVs less than 5%.  
At step frequencies of 1.8 Hz and 2.0 Hz, results from the experiment with the person of 73.4 kg present more differences than the outcomes obtained with the test subject 1. However, these cases are not the worst in terms of the dynamic behaviour of the footbridge as the MTVVs were less than 0.5 m/s\({}^{2}\). For (near-) resonant structural response of the FRP footbridge, which is the worst case scenario, the identified HSI model led to an accurate estimation of the vertical vibrations due to the action a single pedestrian.  
Figure 13: Comparison of the numerical acceleration response at midspan of the FRP footbridge using two HSI models.
## 6 Discussion  
Anti-collision protocols are a critical part of any RFID system. This section offers a critical analysis of the different protocols presented in the previous sections. The tag collision problem results in the  
Figure 24: Flow chart of Adaptive BTSA.  
wasting of bandwidth and energy, and an increased latency. Thus, an optimized anti-collision protocol is essential for a competitive RFID system.  
The breadth of the literature reveals that there has been a great amount of research conducted in this area. There are two main types of anti-collision protocols: deterministic and probabilistic. In probabilistic protocols, the tags transmit their own ID in randomly selected slots in a frame in order to reduce the possibility of a collision. The tag answers are distributed into the slots and all of them have a chance of being identified. These type of protocols are highly adaptable to the appearances and disappearances of tags from the interrogation area. Deterministic protocols, on the other hand, are ideally designed to identify the whole set of tags in the interrogation area during each cycle. These protocols usually have a simple tag design and can work very well with uniform sets of tags. However, these protocols do not admit unexpected appearances and disappearances as easily as Aloha-based protocols. Tree-based protocols must restart their reading process if a new tag appears in a reader's interrogation area while the tags are being read.  
And finally, these types can be combined to form hybrid protocols, which provide very competitive protocols. Hybrid protocols have been created in order to avoid the problems of the Aloha and tree-based protocols, but this comes at the expense of complex reader and tag designs. Table 6 shows observations regarding Aloha, tree-based and hybrid protocols.  
From these explanations of the protocols, it cannot be concluded that certain protocol types stand out from the rest. However it should be noted that the newest protocols have become more sophisticated and attain better results in simulations. This also contrasts with the ability to implement these solutions in real hardware. RFID systems are very constrained systems, and their hardware needs to be very simple in order to comply with tags' needs. That is why many of these solutions have yet to be tested under real hardware conditions.
## 4 Experimental Results and Performance Evaluation
### Average Path Setup Latency  
Path setup time defined the installation time to set up the full path to route flows from source to destination. The time of path installation increased as the path length augment and decreased when the path is short. It can be observed initially from Figure 2, FSL is having a slightly higher time compared to RPSO while RPF time is high. Since the first ping usually takes a longer time than the subsequent ones because of the need to consult the controller to compute the first path of each and upload the corresponding switch flow table to guide the routing process. The study focuses on the first path setup involving a set of critical switches. In other words, higher time was observed on the set of such switches because of the roles they played. As such, RPSO has reduced the setup time by 73% and 93% compared to FSL and RPF, respectively. This implies that RPSO results in a lower number in path installation time, and the merit is attributed to the better load-balancing along with the switches on the path. Because faster installation flow rules are proportional to the number of rules in the set of switches. The benchmarking works FSL and RPF overlook critical switches and treat all switches equally, which is not always the case.
# Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby-Mori-Tanaka Theory
## 3 Study on Cement-Soil Mix Proportions
### Experimental Results
#### 3.2.2 Unconfined Compressive Strength of Cement-Soil  
After 7 days of curing in a constant temperature and humidity chamber, unconfined compressive strength tests were conducted on specimens compacted at the maximum dry density for various cement mixing ratios. The test results indicate that the unconfined compressive strength increases with the increase in cement content and shows a significant linear correlation (see Figure 6). Linear fitting of the experimental data reveals a functional relationship between the unconfined compressive strength of cement-soil and the cement content.  
\[[\sigma_{\text{cement-soil}}]=[\sigma_{\text{soil}}]+k\mathsf{S}=0.613+11.963 \mathsf{S}, \tag{1}\]  
In the equation:  
\([\sigma_{\text{soil}}]\) represents the unconfined compressive strength of maximum dry density pure clay.  
\([\sigma_{\text{cement-soil}}]\) represents the unconfined compressive strength of maximum dry density cement-soil specimens.  
\(k\) is a constant closely related to cement strength.  
S is the cement content in the cement-soil.  
Figure 4: Compaction test of cement-soil with different cement content.  
Figure 5: Optimum moisture content and maximum dry density of cement-soil.  
By nondimensionalizing the constant k in Equation (1) as \(\gamma\), the strength of cement-soil can be expressed as:  
\[[\sigma_{\text{cement-soil}}]=[\sigma_{\text{soil}}](1+\gamma\text{S}), \tag{2}\]  
In the equation, \(\gamma=\frac{k}{[\sigma_{\text{soil}}]}=19.51\)  
From Formula (2), it can be seen that the strength of cement-soil is closely related to the strength of clay and the content of cement. The main reason is that cement, as a hydraulic inorganic gel material, cements loose particles into a whole to improve its strength, but the lack of strength of soil itself will also affect the overall strength.
# Imaging of Cell Structures Using Optimized Soft X-ray Contact Microscopy
## 2 Materials and Methods  
### Characterization of Soft X-ray Contact Microscope (SXCM) System  
The system is based on a laser-plasma soft X-ray source with an argon/helium gas puff target, and it is shown in Figure 1. A small cylindrical chamber was used to house a double stream valve with two colinear nozzles, injecting argon through the central round (0.4 mm in diameter) nozzle and helium through the outer, ring-shaped nozzle (diameter of 0.7-1.5 mm). A commercial compact 10 Hz, 740 mJ, 4 ns, 1064 nm Nd:YAG laser (NL 303 HT, from EKSPLA, Lithuania) was focused using a 25 mm focal length lens (CVI, Norfolk, VA, USA), to a spot \(\sim\)100 microns in diameter, reaching the power density of \(\sim\)\(2\times 10^{12}\) W/cm\({}^{2}\).  
Due to a laser-matter interaction, argon plasma is produced with a sufficient ionization degree to emit radiation in the 2.3-4.4 nm spectral band (Figure 2, blue line). The plasma emission spectrum was obtained using a transmission grating spectrometer equipped with a 30 \(\upmu\)m-wide entrance slit and 5000 lines per mm gold free-standing transmission grating. The resolving power of the spectrometer was estimated to be \(\lambda\)/\(\Delta\lambda\)\(\sim\)50. The 200 nm-thick Si\({}_{3}\)N\({}_{4}\) membrane \(0.5\times 0.5\) mm\({}^{2}\) in size was placed \(\sim\)16 mm from the plasma. This membrane separates the source chamber's vacuum environment from the He environment inside a cylindrical-shaped smaller sample chamber. A specially designed sample holder allowed loading the PMMA-coated Si wafers and locating them \(\sim\)21 mm from the plasma, so only a few mm gap filled with He gas at slightly higher than 1 bar pressure was between the sample and the separating membrane. The use of He gas allowed the removal of the air from the sample chamber, which efficiently attenuates the SXR radiation. A 5 mm gap filled with He at 770 Torr gas pressure had, on average, \(\sim\)85% transmission in the wavelength range from 3-5 nm, while air at 760 Torr pressure \(\sim\)12-15%. This allowed obtaining \(\sim\)\(8\times 10^{3}\) ph/\(\upmu\)m\({}^{2}\)/pulse at the surface of the PMMA in the wavelength band from 3 nm to 4 nm, narrowed by the nitrogen N-K edge of the Si\({}_{3}\)N\({}_{4}\) membrane (Figure 2, green line), taking into account the attenuation of He gas and Si\({}_{3}\)N\({}_{4}\) separating membrane.  
Figure 1: (**a**) Soft X-ray contact microscopy (SXCM) system and (**b**) the flowchart of the SXCM sample-processing procedure.  
Figure 2: The Ar spectrum (the normalized intensity plotted versus wavelength) directly from the plasma () and at the sample plane ().  
The photon yield was measured using AXUV 100 (Opto Diode Corporation, Camarillo, CA, USA) photodiode. More details about the source and the system were reported in [12].
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 1 Introduction  
Graphene-based nanomaterials are composed of sp\({}^{2}\) and sp\({}^{3}\) hybridized carbons by the introduction of functionality, which provides an optical bandgap that leads to fluorescence [1; 2; 3; 4; 5; 6; 7; 8; 9]. In particular, GO is rich in oxygen functionality and can lead to the partial reduction of GO-containing functional groups to produce rGO. GQDs are obtained by cutting layered graphene sheets to a nanometer size [10; 11; 12; 13; 14]. They contain a size-dependent optical bandgap via the quantum confinement effect. GO and its derivatives are water-soluble, non-toxic, cost-effective, biocompatible, eco-friendly, have a high photostability, and display excitation-wavelength-dependent fluorescence [15; 16; 17; 18; 19; 20; 21]. In particular, 2D materials have strong interactions with adsorbed conjugated molecules through various interactions, such as \(\pi\)-\(\pi\) stacking, hydrogen bonding, and electrostatic interactions, which could offer a significant platform for conjugated systems. Therefore, graphene-based nanomaterials have been utilized extensively in fluorescent sensing and imaging applications to date. These materials not only act as a tunable fluorophore, but are also sometimes employed as an efficient quencher in fluorescence.  
Over the years, graphene-based nanomaterials have been utilized for various applications, such as in energy, photoelectronic, catalyst, biomedicine, and sensing fields [22; 23; 24; 25]. To date, various review articles in the field of sensing utilizing graphene-based materials have been reported [26; 27; 28; 29]. Despite this, the role of the FRET process in graphene-based nanomaterials and its various modes of target sensing applications have not been discussed. Therefore, the present review aims to discuss the nature of the fluorescence originating from graphene derivatives, and shows the design principles of graphene-based fluorescentchemosensors. In addition, we reviewed the sensing of various target analytes through utilizing graphene nanomaterials as donors/acceptors via the FRET process. Furthermore, future directions, challenges, and outlooks for the sensing of graphene-based nanomaterials are also highlighted.
## 3 Multi-Ship Collision Avoidance Decision Making Based on Full Communication
### The Intention-Based Real-Time Route Planning Method  
In this paper, the CA intention is represented in two ways: on the one hand, the CA intention is expressed as the ship's compliance with COLREGs, which is the compliance field (CF); on the other hand, the CA intention is expressed as how the ship tends to pass through the encounter situation, which is the determination process of the path-planning target point (PT). All the decision-making is made based on the artificial potential field (APF) method; the planned trajectory is guided to PT through an attractive potential field \(U_{att}(p)\).  
The attractive field \(U_{att}\) is a function of the linear distance between the ship and the destination. In order to guarantee the ship can be attracted by the destination anywhere, the attractive field is designed to work globally. And the further away the ship is, the bigger the attractive field is, which can be written as follows:  
\[U_{att}(p)=\alpha\|p_{d}-p(t)\|^{\beta} \tag{2}\]  
where \(p_{d}\) and \(p(t)\) are the position of destination and the current position of the OS, respectively; \(\|p_{d}-p(t)\|\) is the Euclidean distance between \(p_{d}\) and \(p(t)\); \(\alpha\) is the scalar field parameter, which can influence the strength of the attractive field; \(\beta\) is a positive constant,which can influence the shape of attractive field. Then, the virtual attractive force \(\overset{\rightarrow}{F}_{att}(p)\) can be defined as the negative gradient of \(U_{att}(p)\).  
The attractive field \(U_{att}\) and virtual force \(\overset{\rightarrow}{F}_{att}(p)\) are both globally used functions that, where there is a collision risk with other ships, have an impact on the CA path planning process. This is also the main difference between the attractive field and the repulsive field in the APF method. Based on the limited reference to the ship CA intention research, [PERSON] et al. (2021) proposed an inference method for COLREGs compliance intention based on the dynamic Bayesian network. However, this method only considers the two-ship encounter situation and directly maps the compliance behavior to the corresponding target region. In our previous research [18], the CA intention in multi-ship encounter situations is summarized as CAL. In the modeling of the attractive field, the determination of the path target is the representation of the CAL.
## 1 Introduction
(1984) monitored the vegetation biomass of Eastern Colorado in 1991 through Landsat TM data, and found that red band was more sensitive to green vegetation and more suitable for biomass estimation. [PERSON] et al. (2016) analyzed the relationship between MODIS NDVI, EVI, and crop yields in northeastern Australia, and found a good correlation between EVI and crop yields--this result can be used for biomass estimation studies in the region. [PERSON] (2017) used SPOT NDVI data to establish a grassland biomass estimation model suitable for the Madagascar region, which showed that NDVI can be used for the dynamic monitoring of pasture. [PERSON] (2018) used MODIS NDVI and EVI data to study the grassland biomass in northern Xinjiang from 2004 to 2006, and found that EVI is more suitable for grassland biomass estimation in this region. In general, research on grassland production in arid and semi-arid areas is mainly concentrated in Inner Mongolia and the Qinghai provinces of China, with relatively little research on Mongolian grassland production. Most studies focus on the analysis of vegetation coverage changes in the Mongolian Plateau, or on the grassland production in a year or a few years in the same region, and there is a lack of continuous monitoring. This study is based on ground survey data of the grassland along the China-Mongolia Railway, combined with the MODIS remote-sensing image and meteorological data. The aim of this work is to define an estimation model of grassland production suitable for the high-altitude and arid environment of the region, to study the spatial and temporal distribution and change characteristics of grassland production from 2006 to 2015.
## 3 Material and Methods
### AMT Data  
AMT data were acquired with a 3-5 km station interval. A KMS-820 recording system was used with 4 non-polarized electrodes (LEMI-701; Ag-AgCl + Pb-PbCl) and 2 high-frequency magnetic induction coils (LEMI-118; 1-70,000 Hz). The non-polarized electrodes were spread out on the ground at the recording site at a 50 m distance from the center in north-south and east-west directions. The recording unit and induction-coil magnetometers were located at the center in north-south and east-west directions (10 m away from the data logger). A total of 3 frequency bands were recorded at 80 kHz, 20 kHz, and 4 kHz for 3 min 57 s, 5 min 16 s, and 13 min 10 s, respectively.  
By using the above configuration, the AMT data at 80 sites were measured during the fieldwork using the time-varying of natural electrical and magnetic fields. The relationship  
Figure 2: Yellow-filled triangles show the location of the AMT stations; black-filled red squares are the gravity stations. P1–P15 are the selected profiles for modelling; DEM is the background.  
between the EM field components was represented in the frequency domain as a four-element impedance tensor. The AMT data were acquired in a passive mode using a combination of electrodes and induction-coil magnetometers. The electrodes were used to determine the electric field, which was derived from the measurements of the voltage difference between electrode pairs Ex and Ey. The induction coils were used to measure the magnetic field components (Hx and Hy) in orthogonal directions. The ratio of the recorded electric and magnetic fields in the frequency domain (Ex/Hy) provided an estimate of the apparent resistivity of the subsurface at depth.  
The AMT data were recorded in a binary format, and then KMS pro software [40] was used to display, edit, filter, and sample the time series. Finally, a statistical means tool was used to estimate the impedance tensor from the spectra. The data were then ready to be imported into WinGLink software for the inversion [41].  
The inversion algorithm strategy was based on analyzing the AMT data by the comparison of different geothermal benchmark modelling and inversion try-out results using a variety of inversion methods. Before that, the AMT stations were rotated by 45\({}^{\circ}\) to be a normal to regional geological strike (northwest). To image the subsurface resistivity structure in two dimensions, the AMT data were inverted using the algorithm of [PERSON] [42] within the WinGLink software. This routine finds a regularized solution to the 2D inverse problem by using the non-linear conjugate gradients method [42].