esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion  
### SnO\({}_{2}\) Powder Characterization  
The crystalline microstructure of the prepared SnO\({}_{2}\) was studied by XRD analysis, as shown in Figure 2a. The main characteristic peaks are centered at 26.7\({}^{\circ}\), 34.2\({}^{\circ}\), 37.8\({}^{\circ}\), and 51.9\({}^{\circ}\) and correspond to the (110), (101), (200), and (211) SnO\({}_{2}\) crystal planes, respectively. The peaks appeared broad and with weak intensity, thus resulting with small average crystallite size and low crystallinity. This latter one was ascribed to the presence of defects on the material surface, which greatly affect the reactive sensing sites and electronic structure of gas-sensing material, influencing the gas sensing properties [35]. XRD spectra acquired at increasing temperatures, from 25 \({}^{\circ}\)C up to 500 \({}^{\circ}\)C, proved the high stability of the sample, being all the registered patterns superimposable.  
The morphology of the powder was studied by SEM analysis. Figure 2b shows that sample is composed of homogeneous small particles of spherical form with size distribution ranging from 0.8 to 2.1 \(\upmu\)m. A compact oxide thin film was used as the sensing layer.
## 4 Results and Discussion
### Geophysical Logging Response Characteristics of Coal Quality
#### 4.3.5 Logging Response Characteristics of Total Sulfur  
Total sulfur content in coal exhibits an initial decrease followed by an increase with rising apparent resistivity (Figure 10a), a trend linked to coal's micro-pore structure and the type and abundance of conductive mineral elements [83]. Pyrite's polarizability and thermoelectric properties induce pronounced resistivity anomalies in coal seams [68; 71; 82; 84]. In the study area, deep burial depths and active tectonics amplify crystal defects like pores and fractures, extending ion and electron migration distances while reducing micro-pore proportions and ion-conductive pathways, thereby elevating resistivity [4; 70; 82]. Total sulfur and density similarly show an initial increase before declining (Figure 10c), reflecting the dominance of lower-density organic sulfur in medium- to high-sulfur coals compared to pyrite sulfur in low-sulfur varieties [17].  
Total sulfur content correlates positively with lateral resistivity, short-spaced gamma-gamma (CGS), and acoustic transit time (AC) (Figure 10b,e,f). CGS values directly reflect inorganic mineral content [14; 85], with sulfide-rich seams exhibiting lower resistivity than organic sulfur-dominated coals [4]. Increased inorganic minerals reduce micro-pore  
Figure 9: Relation between fixed carbon content in coal and logging parameters. (**a**) The relationship between fixed carbon content and apparent resistivity; (**b**) The relationship between fixed carbon content and lateral resistivity; (**c**) The relationship between fixed carbon content and bulk density; (**d**) The relationship between fixed carbon content and natural gamma; (**e**) The relationship between fixed carbon content and short-spaced gamma-gamma; (**f**) The relationship between fixed carbon content and acoustic transit time.  
abundance [70], prolonging free electron presence in fractures and elevating lateral resistivity and AC [81,83].  
A positive correlation between total sulfur and natural gamma (GR) (Figure 10d) arises from Th and U enrichment during peat accumulation or early diagenesis [86,87]. Carboniferous-Permian coal seams, formed in alkaline oxidation-reduction transition environments, further promote U, K, and Th migration alongside pyrite and kaolinite formation [78, 79, 80, 88, 89].
## 2 Si CMOS Detector
The dimension of such a defined quantity is given in W/\(\sqrt{\text{Hz}}\). Later in Section 4, we will present experimental noise measurements that validate the applicability of Equation (2). The maximum responsivity and NEP values appear at different gate voltages (compare both curves in Figure 2a). This is due to the nonlinear dependency of the DC drain-source resistance \(R_{\text{DC}}\) on the gate voltage. The maximum voltage response, and corresponding responsivity, were at the gate voltage bias 180 mV, and the minimum NEP was at 475 mV. In the following experiments, the detector was biased with the gate voltage of 475 mV, which represents the device's working regime with the best NEP values. In the system with a VDI multiplier as a source, the TeraFET exhibited an optical voltage responsivity of 193 V/W (current responsivity of 38 mA/W) and a minimum optical NEP as low as 47 \(\text{pW}/\sqrt{\text{Hz}}\) at 609 GHz. It should be pointed out that the presented values are the optical ones for the detector module, i.e., in the calculation, we used the total available THz beam power at the position of the detector (measured after all OAP  
Figure 1: (**a**) The THz homodyne measurements’ optical setup. (**b**) Simplified schematic image of the THz detector. The dimensions of the antenna metallization (yellow) and of the Si lens (grey) are not to scale in order to demonstrate the device structure clearly. mirrors, attenuators, and other optical elements). Hence, the optical losses introduced by the detector module itself, such as in a silicon lens or radiation coupling to the integrated antenna, are not deducted. We also measured our detector's broadband performance in the Toptica system, where an original photomixer detector was replaced with the TeraFET (Figure 2b). The used sample exhibited a nearly flat frequency response characteristic from 0.4 up to 1.4 THz. The achieved minimum optical NEP was as low as 50 \(\mathrm{pW}/\sqrt{\mathrm{Hz}}\) at 0.6 THz and 81 \(\mathrm{pW}/\sqrt{\mathrm{Hz}}\) at 1.4 THz. The slightly worse detector characteristics in the Toptica system can be explained by a different beam profile and optical setup differences.
## 4 Results and Discussion
### Interpretation
#### 4.2.4 Abandoned Channel  
Description  
In this study area, the exposed lower part of the formation, which is also the top of the lower period of the channel belt deposit, has many abandoned channel deposits (Figures 3 and 7). It is mainly composed of weakly stratified mudstone (Mh), trough cross-bedded (St), and laminated fine siltstone/sandstone (Sh). The sedimentary structure is relatively simple, and there is basically no interior erosion interface.  
Interpretation  
As a product of low energy, an abandoned channel filling usually tends to be the finest-grained part of the whole sediment load, which represents the complete abandonment of channel deposition. Fine-grain deposition in abandoned channels is common and often found in laminated and ripple strata, which may be caused by migration to the other side of the valley as the fluvial system silts up and the slope decreases.
## 2 The Proposed Approach
### Reduction of Grid Values  
Each of these grids of \(P_{t}\) are reduced into a single value through a pooling operation, so that \(P_{t}\) is converted into a compressed traffic congestion matrix \(C_{t}\) with R rows and C columns, as shown below:  
\[C_{t}=\begin{bmatrix}c_{1,1}^{t}&c_{1,2}^{t}&\cdots&c_{1,C}^{t}\\ c_{2,1}^{t}&c_{2,2}^{t}&\cdots&c_{2,C}^{t}\\ \vdots&\vdots&\ddots&\vdots\\ c_{R,1}^{t}&c_{R,2}^{t}&\cdots&c_{R,C}^{t}\\ \end{bmatrix}\]  
where an element \(C_{i,j}^{t}\) of \(C_{t}\) will be calculated by a pooling function applied upon a set \(\{\ p_{t,i,j}^{k}\ |1\leq k\leq g^{2}\ \}\) which contains all values in a grid index by \((i,\ j)\) of \(P_{t}\).  
Figure 2: Transformation from a special raw snapshot to a derived image mask based on it. (**a**) A special raw snapshot captured at late night. (**b**) A derived image mask.  
Through this process of grid-based partition and reduction, each element of \(C_{t}\) is a derived value representing one corresponding grid of \(P_{t}\).Thus, \(P_{t}\) is now down-sampled and compressed into \(C_{t}\) by a ratio of \(1/g^{2}\), yet the relative spatial relationships between roads are mostly kept, as shown in Figure 1d.  
Pooling operations used by the reduction process above are requisite to our proposed approach because the effectiveness of grid-based representation of road network traffic congestion data is determined by such operations, which act as a kind of feature extraction filter. We propose a pooling function which retrieves the maximum of all values (MAV) in a grid of \(P_{t}\), which is rarely used when representing traffic congestion data. MAV is described by Equation (1):  
\[c_{i,j}^{t}=max(\ \left\{p_{t,i,j}^{k}\ |\ k=1,2,\ldots,g^{2}\right\}) \tag{1}\]
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 4 Sample Implementation
### Trusted Application (TA)  
The BioShare Trusted Application is implemented as a trusted module deployed in the Trusted Executive Environment on the terminal side. The key challenge is the choice of the appropriate biometric algorithms and implementations for facial recognition and comparison, due to the limitations of TEE capabilities. We attempt to choose open-source implementations with complete functionality, good performance, low computing and memory overheads, embedded-environment support, and appropriate licensing agreements.  
**Facial Recognition and Comparison:** Currently, many open-source projects for facial recognition are available from GitHub, and Table 4 lists some popular projects. In our implementation, we chose SeetaFace2 because it is built with C++, is independent of third-party libraries, and is compatible with X86, iOS, and Android systems. We used pretrained models from the SeetaFace2 project. According to the SeetaFace2 document, Cascaded-CNN is used as a face-detection algorithm, achieving 92% in the FDDB public dataset. FEC-CNN is used as a face landmarker algorithm, achieving a 0.069 average  
\begin{table}
\begin{tabular}{l l l} \hline  & **Terminal** & **Server** \\ \hline CPU & Mediatek MT8788 (ARM Cortex-A) & Intel Xeon E3 \\ \hline RAM & 1.3 GB & 64 GB \\ \hline TEE & ARM TrustZone & Intel SGX \\ \hline OS & Android 9.0 & CentOS Linux 7.4 \\ \hline TEE OS & Nebula TEE1 1.1.0 & \\ \hline \end{tabular}
\end{table}
Table 3: The Configurations of Mobile Terminals and the Server.  
positioning error on the 300-W Challenge public dataset. ResNet50 is used for facial feature extraction/comparison with 25 million parameters, and the model has been pretrained with 33 million photos with an accuracy rate of more than 98% in the general 1:N scenario on a 1000-person dataset when the error acceptance rate is 1%. Figure 2 shows the flow chart of the processes.  
**Liveness Detection:** We chose the open-source project FeatherNets for liveness detection in the implementation, because several models provided by the project achieve the goal of low computing cost (about 80 M FLOPs), small model size (0.35 M parameters), and complete functionality (applicable to infrared images and depth images, etc.). Table 5 shows the test results of the models within the FeatherNets project.
## 3 Methodology
### Streamflow Simulation Schemes  
In this study, the entire Chindwin River basin was separated into 204 grid cells on a 0.25\({}^{\circ}\) resolution. Meanwhile, five precipitation data sets were constructed, and they are the gauge-based gridded precipitation data, the original and corrected 3B42V7, and the original and corrected IMERG data sets on a 0.25\({}^{\circ}\) resolution. These data sets were used to drive the XAJ model to perform historical daily hydrological simulations at each grid cell in the Chindwin River basin from 1 April 2014 to 31 December 2015, for a total of five simulation runs. For each simulation run, the XAJ model parameters were independently calibrated by fitting the calculated historical daily streamflow time series against the observed data at the five streamflow stations (Figure 1). The calibration period was 1 April 2014-31 December 2014, and the validation period was 1 January 2015-31 December 2015. Model calibration was achieved with the aid of the Shuffled Complex Evolution (SCE-UA) automatic optimization method [50; 51]. To derive the model parameters that can comprehensively characterize both high- and low-flow processes, model calibration was performed using the objective function given by  
\[f=max(NSE)+max(LogNSE) \tag{9}\]  
\[NSE=1-\frac{\sum_{i=1}^{n}\left(Q_{i}^{s}-Q_{i}^{o}\right)^{2}}{\sum_{i=1}^{n} \left(Q_{i}^{o}-\overline{Q^{o}}\right)^{2}} \tag{10}\]  
\[LogNSE=1-\frac{\sum_{i=1}^{n}\left[log\left(Q_{i}^{s}\right)-log\left(Q_{i}^{o }\right)\right]^{2}}{\sum_{i=1}^{n}\left[log\left(Q_{i}^{o}\right)-\overline{ log\left(Q^{o}\right)}\right]^{2}} \tag{11}\]  
where \(n\) is the sample size of the observed or calculated streamflow time series; \(Q_{i}^{o}\) and \(Q_{i}^{s}\) denote the observed and calculated daily streamflow at the \(i\)th time step (m\({}^{3}\)/s); and \(\overline{Q^{o}}\) and \(\overline{log(Q^{o})}\) represent the mean observed streamflow and mean log-transformed observed streamflow (m\({}^{3}\)/s), respectively. The five sets of simulated streamflow time series using different precipitation inputs were compared against the observed streamflow to assess the feasibility of 3B42V7 and IMERG precipitation products in streamflow simulations in the study area. The accuracy of streamflow simulations were evaluated using four statistical indices, namely, BIAS, CC, NSE, and LogNSE. Detailed description of streamflow simulation schemes can be found in Supplementary Material S1.
## 4 Apertureless-Type SNOM
### Surface Plasmon Imaging  
Surface plasmon resonance is a phenomenon arising from the oscillation of free electrons near a material surface under illumination by a light source. Surface plasmons may be localized (within 10's of nanometers) on a surface due to nanostructures, or may propagate over larger areas depending on the material properties. Using a suitable excitation source, imaging of a surface using s-SNOM can reveal details about the surface plasmon distribution, as shown in Figure 3a for a wide piece graphene ribbon on an SiC substrate, and Figure 3b for a graphene ribbon tapering to a point [33]. AFM images of the same flake before and after are shown on the leftmost and rightmost edges, and the colored images show SNOM images taken with 9200 nm (left), 9681 nm (middle) and 10,152 nm (right) light wavelengths. The plasmon resonance wavelength may be tuned over a wide spectral range by adjusting the excitation wavelength used for s-SNOM in the IR spectrum. This is made possible by the dependence of the dielectric constant of the SiC substrate on the excitation wavelength. In this way, the propagation of plasmon waves over the graphene surface may be studied. In Figure 3a, fringes in the wider part of the ribbon are due to plasmon interference caused by plasmon reflections at the graphene edges. Examining the tapered ribbons shows interference fringes with a spacing that decreases with increasing SiC dielectric constant. This is due to a larger substrate permittivity yielding a smaller graphene plasmon wavelength [38]. This type of plasmon imaging can lead to improved s-SNOM performance through the design of enhanced tips. A novel application that has been proposed using graphene plasmonics is the fabrication of graphene-plasmon enhanced s-SNOM tips [39]. In this unusual approach, localized plasmons on graphene are mingled with surface plasmon polaritons to confine plasmon waves into the apex of a nearfield scanning optical microscopy tip. The tip is fabricated as a traditional metallic s-SNOM tip coated with a layer of graphene, resulting in very low loss, high resolution tips [39].  
In addition to graphene, metal nanostructures have been investigated to study surface plasmon polaritons (SPP). SPPs are a type of surface wave that can be guided along a surface with applications in light generation or photonic data storage. This type of surface effect can be induced through the introduction of cross grooves fabricated in an Au thin film [40]. SPP excitation is this case was found to depend on the direction of the electric field of the incident light relative to the cross-sections of the grooves [41]. s-SNOM is uniquely suited to study plasmon activity in nano structures such as graphene because of the extremely high resolution and the requirement that the sample be illuminated by a light field to produce plasmons.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 4 Methodology  
Forecasting the future trajectory of a ship entails numerous unknown factors. To address this ambiguity, it would be suitable to employ a technique that involves acquiring a comprehensive understanding of the trajectory pattern through the utilization of a well-established model based on previously recorded trajectory data. The AIS data collected from vessels within a specific maritime region were utilized. Preprocessing is necessary for the AIS data due to errors in the trajectory data and irregular time intervals for the location information. This preprocessing involves reduction, filtering, grouping, and interpolation. The AIS data underwent preprocessing and were organized into \(N\) trajectory sequences in the following manner:  
\[T_{n}^{d}=\left\{Lat_{n}^{t},\;Long_{n}^{t}\right\}_{n=1}^{N} \tag{27}\]  
In Equation (27), \(T_{r}\) is an individual trajectory dataset, the subscript \(n\) is the index of \(T_{r}\), \(d\) is the direction index, \(Lat\) and \(Long\) denote the latitude and longitude, respectively. Superscript \(t\) represents the time index. The spectral clustering technique [28] is used on preprocessed AIS data to isolate trajectories focused on the traffic route. This is because vessels are generally obligated to travel along specified traffic routes. To predict the trajectory of a vessel, observation of its trajectory patterns was conducted using a motion model [6; 40]. A model was trained using the observed trajectories to predict the trajectory, based on the SGP. This process included optimization of hyperparameters using GA. Inthis procedure, both short-term and long-term predictions are considered. Short-term prediction relies on the open-loop prediction technique, which forecasts the next time step in a trajectory using only the available data. In contrast, long-term prediction relies on a closed-loop prediction methodology. By iteratively forecasting the trajectory for the next time step based on the predicted outcome of the previous time step, it eliminates the need for observational data. Figure 1 generally illustrates the flowchart of the methodology mentioned above.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 6 Conclusions  
In this paper, we reviewed the existing regional and global forest AGB maps, including five maps obtained through GIS-based modelling; sixteen maps produced by integrating field biomass data directly with optical and/or radar data; five high-resolution maps retrieved from field biomass data and LiDAR data; fourteen maps generated from a combination of field data, LiDAR data, and optical and/or radar data; and seven maps generated or regenerated using existing biomass datasets. In spite of numerous efforts to map the spatial distribution of forest AGB with a variety of datasets and advanced methods, current AGB maps still contain large uncertainties. Comparison of biomass datasets across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America, suggested that they were almost entirely inconsistent, particularly in woody savannas and savannas. The uncertainties in AGB maps could be from the allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but have not been fully quantified. We suggest the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41801347), the National Key Research and Development Program of China (No. 2016 YFA0600103), and the Fundamental Research Funds for the Central Universities (FRF-TP-17-041A1).  
The authors declare no conflicts of interest.
## 4 Evaluating the Indoor Luminous Environment and Visual Comfort Perception
### Visual Comfort Evaluation Metrics  
To estimate the comfort degree of occupants, or to understand if they are comfortable with its surrounding lighting environment, many indicators have been proposed and numerous have been established within the design regulations and guidelines.  
Visual comfort is mainly evaluated based on the intensity of light falling on a surface (i.e., illuminance, \(E\)), either vertical (\(E_{p}\)) or horizontal (\(E_{h}\)), measured in lux. Thus, the visual comfort metrics are generally based on the light intensity, distribution, source and directionality. The quantity and quality of light should be enough to allow building users to perform the assigned area's correspondent activities safely and without induced visual fatigue. Artificial lighting should only provide the missing light to reach the minimum requirement (specific for each task) only when natural light is not intense enough, aiming to avoid the development of eye sight diseases.  
Depending on the regulations governing the design and the building's intended use, the lighting requirements can be evaluated by different indicators and acceptable thresholds or ranges of such. Nevertheless, regardless their time domain, they can be summarized in two main groups: (i) quantity and uniformity of light (Section 4.1.1) and (ii) direct light discomfort (Section 4.1.2).
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 4 The Proposed Scheduling
### Experiments
Obviously, the delay of CEM-RM is generally lower than that of M-RM. Moreover, as the routing hop increased, the average delay of our proposed scheduling was much lower than that of M-RM. For the packets of four-hop nodes, the average delay of our scheduling is about 32 ms lower than that of M-RM. The use of CES and multiple sinks effectively decreases the end-to-end delay, and CES can also reduce the impact of the DL-ACK loss phenomenon. The experimental results roughly indicate that our proposed scheduling not only improves the efficiency of scheduling but also decreases the end-to-end delay.