esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Fiber Coating Analysis  
The fiber fabrication is described in detail in Section 3.3 and is depicted in Figure 3. Basically, a mixture (M1) of monomer, photoinitiator, and crosslinker was inserted in a silica tube (ST), and polymerized under UV--light for 60 s. Afterwards, the silica tube was destroyed by immersion in hydrofluoric acid (HF) during 1 h. The whole procedure takes about 2 h to finish.  
In Figure 4, the SEM images of the bare steel wire (Figure 4a) and the coated steel wire (Figure 4b) show a very homogeneous surface coating with a regular thickness of 67 \(\upmu\)m, with a total volume of 0.561 mm\({}^{3}\) of coating material, which is more than PDMS7 (0.026 mm\({}^{3}\)) and similar to PA85 (0.521 mm\({}^{3}\)) [32]. This length and thickness can be changed by using silica tubes with different sizes and different internal diameters, maintaining the homogeneity of the overall fiber that will result in a better reproducibility. Moreover, this method allows for the production of almost identical fibers from batch to batch that result in a high repeatability between different fibers.
### Contributions  
In this article, I propose a cross-server E2E patient authenticated key agreement protocol for DNA-based U-healthcare services in IoLT networks. Specifically, my work allows two patients to mutually authenticate each other through the assistance of respective servers, so that they can securely establish a reliable shared session key for E2E communications. The efficiency of the communications in the proposed protocol is also considered in the design. The contributions of this work can be summarized as follows.  
1. I introduce a DNA-based U-healthcare application constructed in CS-E2E communication environments. In the proposed model, multiple servers provide U-healthcare services based on real-time DNA sequencing data produced by smart tiny sequencerswith TGS technology in the IoLT network. Patients are allowed to share healthcare data with each other directly.
2. The protocol allows the patients to store single registered credentials on a smart card and enter the credentials once per session only. They are allowed to choose specific servers of a multi-server system from a list in the device to enjoy multiple registered services. I call this solution \"smart-card-based single sign-on (SC-SSO)\". Furthermore, the proposed SC-SSO is designed without a centerless solution to alleviate communication cost and reduce the security risk of third-party authority compromise.
3. The authentication protocol is designed using three factors, combining password, smart card and biometrics. It can guarantee higher security for communications compared to the single-factor or two-factor solutions. In the protocol, a perfect forward secrecy of shared E2E session keys is assured. Patient anonymity and untraceability are provided in the protocol. Patients can also update their passwords and biometrics to ensure higher security.
4. The security proof of my proposed protocol is presented using formal verification tools, including the real-or-random (RoR) model and Burrows-Abadi-Needham (BAN) logic. In addition, an informal analysis is provided to further discuss the resistance to various security attacks, e.g., replay attacks, impersonation attacks, etc.
## 2 Dataset, Models and Method
### Dataset
#### 2.1.3 In Situ Data  
_In situ_ data were obtained from the African Monsoon Multidisciplinary Analysis database. Atmospheric profile observations were retrieved from radiosondes (Vaisala radiosonde RS92) launched four times daily from the Niamey (2.17\({}^{\circ}\)E, 13.48\({}^{\circ}\)N) station in June 2006. The associated soil conditions were either missing or unsuitable for validation during the simulation period. Consequently, comparisons were performed using satellite-derived soil moisture products.
# NMR Evaluation of Shale Oil Mobility: Combined Pyrolysis and CO\({}_{2}\) [PERSON]
## 2 Materials and Methods
### Design of CO\({}_{2}\) Huff-N-Puff-NMR Experiment  
Supercritical CO\({}_{2}\) exhibits superior injectivity and oil displacement capabilities within shale oil reservoirs. Supercritical CO\({}_{2}\) interacts with oil, reducing viscosity, promoting diffusion, enhancing dissolution, facilitating extraction, and improving miscibility. This effectively enhances shale oil mobility during the huff-n-puff process [36; 37]. First, shale  
Figure 1: Workflow of pyrolysis and CO\({}_{2}\) huff-n-puff experiments.  
samples were selected and processed into plug samples for the original state NMR experiment. The NMR instrument was adjusted with suitable parameters, and 1D \(T_{2}\) spectra and 2D \(T_{1}\)-\(T_{2}\) spectra were acquired. Second, NMR testing was performed on oil-saturated samples. It was inevitable to cause some loss of oil and gas during the sample collection, so the samples were vacuumed and pressurized to saturate with oil. Then they were weighed, and 1D \(T_{2}\) spectra and 2D \(T_{1}\)-\(T_{2}\) spectra measurements were conducted on the plug samples. Through weighing before and after oil saturation and NMR experiments, experimental support was provided for the calculation of the plug sample's recovery rate and movable oil. Third, a combined CO\({}_{2}\) huff-n-puff-NMR measurement was executed, as shown in Figure 2. The oil-saturated samples were placed into the CO\({}_{2}\) huff-n-puff experimental setup, and supercritical CO\({}_{2}\) was injected into the high-pressure container, entering the soaking stage. After the soaking stage, the container was depressurized, and 1D spectra were measured, followed by 2D spectra once stabilized. The sample was then removed for weighing. The huff-n-puff was repeated for four rounds, and the recovery rate was calculated based on mass changes and NMR signal intensities, providing a basic reference for subsequent analysis. The experimental parameters for CO\({}_{2}\) huff-n-puff were set at an injection pressure and soaking pressure of 30 MPa, a soaking duration of 4 h, and a static period of 2 h. The primary purpose of setting the pressure is to facilitate the dissolution of CO\({}_{2}\) into the oil, increasing its volume. This process is accomplished through a compression-release cycle within the pores, which enhances the fluid mobility. The NMR testing was conducted at a frequency of 23 MHz, with an echo interval of 0.06 ms, a number of echoes of 5000, and a number of accumulations of 16.
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 5 Simulation Results
### Performance under CA-SCL Decoders  
For the short or moderate code-length, the performance of polar codes under SC decoder is dissatisfied. To improve the performance, [PERSON] and [PERSON] proposed CRC-aid SC list (CA-SCL) decoder for polar codes [22]. Next, we investigate the BER performance of the proposed scheme together with two referenced schemes under the CA-SCL decoder. For QSPCs and punctured QSPCs, systematic bits contain 16 bits CRC, i.e., the length of source message is \(MK-16\). At the decoding stage, LLRs of these 16 bits are initialized as 0 and the list size is set as 32. Taking CRC into consideration, the rates of the DJSCC scheme become \(\bar{R}=(2.256,1.128)\) in channel-use/source-symbol. In DSSCC schemes, information bits of both DSC and CC contain 16 bits CRC and the list size is also set as 32.  
Figure 8 shows the BER performance under the CA-SCL decoder. We can see that compared with the SC decoder, the performances of all schemes are improved significantly. Due to the reasonable rate allocation, the performance of \"DSSCC 1\" is the best and very close the performance of the proposed scheme. However, the performances of other two DSSCC schemes are dissatisfied. It can be observed that the proposed scheme outperforms \"DSSCC 1\" and the DJSCC scheme with classic SPCs by approximately 0.2\(\sim\)1 dB.
# Broadcast Ephemeris with Centimetric Accuracy: Test Results for GPS, Galileo, Beidou and Glonass
## 3 Mathematical Model and Results for Glonass
To tailor the broadcast parameters on a precise ephemeris and clock, we formulate the minimum variance algorithm as follows:  
\[\sum_{i=1}^{96}[\Delta(XYZT)]^{2}=f(a0,a1,a2,X_{0},\dot{X_{0}},\dot{X_{LS}}, \dot{Y_{0}},\dot{Y_{0}},\dot{Y_{LS}},Z_{0},\dot{Z_{0}},\dot{Z_{LS}})=min \tag{2}\]  
We minimize the sum of the 96 \(\times\) 4 = 384 square discrepancies \(\Delta(XYZT)\) between the SP3 precise values and those computed with the broadcast message, using as adjustable parameters the three clock terms \(a0\), \(a1\) and \(a2\), and a 9D vector of state containing position and velocities at epoch Toe, and three constant accelerations. If the 24-h interval is broken into 24 consecutive arcs each of one hour, then we have to adjust 288 parameters. These become 144 if we test arcs of a 2-h duration. The Time of clock in the clock polynomial and the Time of ephemeris are taken coincident. Contrary to the GPS-like approach, where the arc terms were coupled by the global terms, i.e., the Helmert parameters (Figure 1), the partial derivative matrix set up to linearize Equation (2) is strictly block diagonal, with no correlation between arc parameters of different arcs (Figure 10). We consider on 2 January 2020, satellite R01 and precise ephemeris and clock computed by CNES ([[https://cddis.nasa.gov/archive/grss/products/mgex/2086/GRG0](https://cddis.nasa.gov/archive/grss/products/mgex/2086/GRG0) MGXFIN_200020000_01D_15M_OR.SP3.gz]([https://cddis.nasa.gov/archive/grss/products/mgex/2086/GRG0](https://cddis.nasa.gov/archive/grss/products/mgex/2086/GRG0) MGXFIN_200020000_01D_15M_OR.SP3.gz), accessed on 10 October 2021). The results of the parameter optimization for this test case are shown in Figures 11 and 12. Figure 11 suggests that also in the example of Glonass it is possible to fine tune the broadcast model parameters so that the positions and clock corrections computed with the adjusted parameters very closely fit the reference CNES final orbit of R01. The corrections to the broadcast parameters are shown  
Figure 10: Structure of the partial derivative matrix H of the pre-fit discrepancies relative to the arc parameters. The arc parameters are indexed with the time Toe (Toe is equivalent) and the nominal values are dependent on the individual arcs being 2 h or 1 h long. graphically in Figure 12. Besides the rms of 0.024 m as an indication of the final accuracy, it should be mentioned as an additional benefit that the interval of validity of the corrected broadcast ephemeris has been doubled to 1 h. Tests with 2-h arcs resulted in rms spreads considerably larger. We propose 1 h as a reasonable compromise between accuracy and refresh rate. Figure 11: Cont. Figure 11: Pre-fit (dots) and post-fit (diamonds) residuals of **(a)** X, **(b)** Y, **(c)** Z, and **(d)** T for R01. Length of arc for fit is 1 h. Different colours denote different ephemeris blocks. Figure 12: _Cont._
# Structural Response of Double-Layer Steel Cylinders under Inside-Explosion Loading
## 4 Analysis of Experimental Results  
### Results of PDV Measurement  
The relationship between the time and the deformation of the double-layer steel cylindrical shells under a 120 gTNT explosion was obtained by PDV measurement, and the velocity curves of the inner and outer cylinders are shown in Figure 5. It can be found that the maximum velocity of the inner cylinder is 151.2 m/s. After the collision of the inner and outer cylinders, some part of the outer wall of the inner cylinder was embedded into the \(\phi\) 3 mm through-hole of the outer cylinder, causing an instant increase in inner cylinder velocity, which led to a peak in the curve. After the collision, the velocity of the inner cylinder decreased while the velocity of the outer cylinder increased to the maximum value of 85.6 m/s. It can be seen that it takes 35.7 \(\upmu\)s for the outer wall of the inner shell from the start of deformation to contact the inner wall of the outer shell. The gap between the inner shell and outer shell is 4 mm, and the average velocity of the radial movement of inner shell before the impact of the outer shell was calculated to be about 112 m/s.  
As is shown in Figure 6, the relationship between radial displacement and time of the inner cylinder and outer steel cylinder measured by PDV can be obtained by integrating the curves of the velocities. The maximum radial displacement of the inner cylinder is 5.41 mm and that of the outer cylinder is 2.45 mm.  
Figure 4: Experimental device of PDV measurement double-layer cylindrical shells.  
Figure 5: Velocity of double-layer shells measured by PDV.  
The maximum radial displacement of the outer cylinder was measured to be 2.54 mm after the experiment, compared to which the error of PDV measurement was about 2.8%. After incising the double-layer cylinder, the maximum radial displacement of the inner cylinder was measured to be 5.72 mm after the experiment, compared to which the error of PDV measurement was about 5.4%. It can be concluded that the radial displacement measured by PDV was in accordance with that measured after the experiment.  
The maximum velocities of the three double-layer cylindrical shells under 120 gTNT explosive yield were measured by PDV. The maximum velocities of the three experiments are shown in Table 2. The maximum velocities of the inner shell are reduced with the increase in the thickness of the inner cylindrical shell. The maximum velocities of the outer shell increase at first and then decrease with the increase in the thickness of the inner shell.
## 2 Proposed \"Hybrid BEM-p-y Curve\" Method
### Soil Modelling  
The \"far-field\" soil (soil at small strain level) is modeled as a multi-layered elastic half-space. BEM analysis requires an appropriate elementary singular solution to be integrated on the surface of the problem domain. In the case of piles subjected to horizontal loading, the elastic Mindlin [13] solution is generally used. This solution, which evaluates the pile-soil interactions, is valid and rigorous only in the case of a homogeneous elastic half-space, however it can still be considered valid in the case of a multi-layered elastic half-space as reported by [PERSON] and [PERSON] [14]. The horizontal displacement \(s_{ij}\) at a point \(i\) belonging to the half space by a horizontal load \(P_{j}\) applied at point \(j\) can be expressed as in Equation (4) (Figure 4). Where the term \(b_{ij}\) represents the general expression for each \"far-field soil\" flexibility matrix coefficient.  
\[s_{ij}=\frac{P_{j}(1+v)}{8E_{p}(1-v)}\left[\frac{(3-4v)}{R_{1}}+\frac{1}{R_{2 }}+\frac{x^{2}}{R_{1}^{3}}+\frac{3-4v}{R_{2}^{2}}x^{2}+\frac{2 cz}{R_{2}^{2}} \left(1-\frac{3x^{2}}{R_{2}^{2}}\right)+\frac{4(1-v)\cdot(1-2v)}{R_{2}+z+z+c} \left(1-\frac{x^{2}}{R_{2}(R_{2}+z+c)}\right)\right]=b_{ij}P_{j} \tag{4}\]  
Figure 3: Scheme for the flexibility matrix of a pile modeled as a beam with variable flexural rigidity (see Equation (3)).  
The \"near-field\" soil (soil subjected to high stress and strain variations) and its non-linear response is introduced by placing non-linear springs (zero-length elements) in series to each pile-half-space node (Figure 5), with a shape dependent on the soil type. In the present study, the \"p-y\" curves proposed in [31; 32; 33] are used. The input data required for the soil are: the elastic modulus \(E_{max}\) at small strain levels (which can be estimated starting with the maximum shear modulus \(G_{max}\)), the Poisson ratio, and the angle of internal friction (and the relative density \(D_{R}\)) or the undrained shear resistance if the soil is cohesionless or cohesive, respectively.  
In order to simply the modeling, the development of additional shear stresses along the pile shaft interface is neglected.  
Currently, it is only possible to analyze free-head or fixed-head single-piles, however a different restraint can be added. The computation process ends with the calculation of the unknowns, which in this case are the soil pressures acting at the pile-soil interfaces, the rotation, and the horizontal displacement at the pile-head section. The analyses are performed in an incremental manner, using an adaptive step-size control.
## 3 Carbon-Based NPs in Water Treatment
### Carbon Nanofibers (CNFs)  
Carbon nanofibers (CNFs) are nanometer-sized filaments with diameters ranging from 3 to 100 nm, composed of stacked graphene layers oriented to the fiber axis. This material is commonly categorized into three types based on the angle between the graphene layers and the growth axis: parallel (angle: 0\({}^{\circ}\)), fishbone (0\({}^{\circ}\) < angle \(<\) 90\({}^{\circ}\)), and platelet (angle: 90\({}^{\circ}\)). CNFs exhibit a turbostatic nature, characterized by an average spacing between graphene layers of approximately 0.34 nm, closely resembling the interlayer spacing in graphite (0.335 nm) (Figure 6).  
CNFs are primarily synthesized through two main methods: CVD and electrospinning/carbonization. The synthesis processes are influenced by several parameters that provide flexibility in tailoring the properties of the CNFs. The final characteristics of the CNFs are significantly influenced by the choice of carbon precursor and the reaction conditions. Generally, high temperatures exceeding 500 \({}^{\circ}\)C are required for the synthesis [85].  
CNFs exhibit promising potential for water treatment applications due to their large surface area and unique properties. They can be functionalized for enhanced adsorption, effectively removing pollutants like heavy metals and organic compounds from water. CNFs can serve as a catalyst support, contributing to electrocatalytic processes for advanced  
Figure 6: (**a**–**c**) Formation of a cup-stacked CNF structure and a (**d**) platelet CNF structure. Source: adapted from [84].  
oxidation. Their integration into membranes enhances filtration capabilities, offering high permeability and selectivity [86]. Additionally, CNFs can be utilized in electrochemical processes, such as capacitive deionization for ion removal. Some types of CNFs possess antibacterial properties, aiding in water disinfection. Despite their potential, ongoing research is crucial to optimize their performance and scalability and address environmental and health considerations associated with nanomaterials [87; 88].
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 6 Conclusions and Future Work  
This study proposes a fatigue driving detection system based on the combination of 3D convolution and attention mechanism. First, we locate the key areas of the driver's face based on the Dlib library, then use 3D-SE-Net to extract the features of the eyes and mouth and finally output the fatigue state through a full binary tree SVM classifier. The experimental results show that the model can accurately extract the features of eyes and mouth and achieve satisfactory accuracy on the four datasets. At the same time, we have done a lot of control experiments on the attention mechanism module. The results show that the addition of the attention mechanism module can optimize the feature weight and improve the detection accuracy. We also use Grad-CAM to visualize the influence of different lighting conditions and whether wearing glasses on the model detection accuracy and the results show that 3D-SE-Net can still achieve better detection results in dark environments. On the YawDD dataset, the detection accuracy of the proposed model reaches a satisfactory 99.03%, which is more accurate and robust than other methods. Last but not least, we use a template fusion approach to design a facial privacy protection software that can record changes in the driver's facial status in real time while keeping the driver's privacy safe.  
Our proposed method still has some shortcomings, the model is complex and the 3D convolution operation consumes a lot of computing power. Therefore, in the future, we consider using a residual network to simplify our model. In addition, we will expand our detection range to add more perspective driver behavior datasets to take into account the detection of driver distracted behavior in the car.  
Conceptualization, W.X. and W.Z.; methodology, W.X.; software, W.X.; validation, W.X. and C.L.; formal analysis, W.X.; investigation, W.X.; resources, W.X.; data curation, F.L.; writing--original draft preparation, W.X.; writing--review and editing, W.X.; project administration, W.Z.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No. 51805312).  
Not applicable.  
The authors would like to express their appreciation to the developers of Pytorch and OpenCV, the authors of Grad-CAM and dataset provider School of Information Science, Beijing Language and Culture University.  
The authors declare that they have no conflict of interest.
# Nano-/Microcapsules, Liposomes, and Micelles in
## 2 Spherical Nanoparticles
### Liposomes  
In recent years, liposomes have become increasingly popular and the subject of numerous research papers due to their distinctive properties. Liposomes have gained attention due to their potential applications in drug delivery, cosme, and food industries. Their unique properties make them an attractive option for targeted drug delivery due to their ability to encapsulate hydrophilic and hydrophobic drugs, improve drug solubility, and protect drugs from degradation. The term 'liposome' is derived from the Greek words 'Lipos' meaning fat and 'Soma' meaning body [96]. In 1965, [PERSON], a British biophysicist, discovered them [97]. The scientific literature has introduced the concept of nanoliposomes, defined as lipid bilayer vesicles which possess and maintain a nanometric size that fluctuates during storage and use, are recognised as a novel technology that offers health benefits by encapsulating bioactive ingredients with enhanced functional properties [98]. Overall, these structures display comparable characteristics; nevertheless, nanoliposomes present superior properties such as increased surface area, enhanced solubility, and precise release in contrast to liposomes [99]. Liposomes are defined as spherical vesicular structures made up of phospholipids, where a lipophilic bilayer is sandwiched between two hydrophilic layers. The efficacy and potential of liposomes as a drug delivery system for diverse active agents have been thoroughly researched and verified in the peer-reviewed scientific literature [100; 101]. The purpose of liposomal encapsulation is to safeguard delicate bioactive elements and improve their effectiveness by lessening the influence of negative environmental elements and hindering the contact of enclosed ingredients with detrimental external agents [102].  
The characteristics of liposomes are impacted by their ultimate organisation, and their structure and physicochemical properties are determined by elements such as lipid type, morphology, size, concentration, and charge [103]. There is a plethora of classification systems for liposomes described in the literature. These objects can be classified according to the number of layers that compose them, which include monolayer, multilayer, oligolamellar, and multilamellar vesicles [104; 105]. On the other hand, membrane size and structure form the basis for another widely used classification system. An alternative system is in use, which classifies compounds based on their intracellular mechanism of action and lipid composition, as well as their mode of preparation [98]. Liposomes present a promising approach to encapsulating different types of essential oils and fatty acids [106].