esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results  
To verify the performance of the multi-modal anterior eye imager, experiments were demonstrated on both a healthy subject and a keratitis patient. Before the experiments, informed consent was obtained for the subjects, and the experimental procedures adhered to the tenets of the Declaration of Helsinki. During the imaging procedures, the head of the subject was pressed on the head-rest. The subject was advised to look into the external target with the non-examined eye and avoid blinking within the recording periods. For each subject, a normal SLB examination was performed at first. UHR-OCT imaging and microvascular imaging were then performed sequentially on the central cornea/corneal lesion area and the bulbar conjunctiva area by changing the fixation target location. Fluorescein staining imaging of SLB was performed on the keratitis patient at last. With a well-trained operator and a cooperative subject, the total examination time would be around 15 min, including the imaging time and system adjusting time for all the imaging modes of SLB examination (5 min), UHR-OCT imaging (2 min), microvascular imaging (5 min) and fluorescein staining imaging (3 min).  
In the experiments demonstrated in this paper, 20 UHR-OCT images were recorded at a scan rate of 24,000 A-scans/s, corresponding to 12 frames/s with 2048 A-scans to form a cross-sectional image with a scanning range of 6 mm, costing around 2 s. For microvascular imaging, a bulbar conjunctival vessel network image was taken for vessel fractal analysis under the single-shot mode of the camera by setting the SLB magnification set to 16\(\times\), corresponding to an imaging field of view of \(\approx\)18 \(\times\) 12 mm\({}^{2}\) with an image size of 5184 \(\times\) 3456 pixels. Then, under the Movie Crop function of the camera, bulbar conjunctival blood flow video clips were taken for hemodynamic analysis at a speed of 60 frames/s. With the SLB magnification set to 25 \(\times\) and 60D camera telephoto effect magnification of 7.5\(\times\), the imaging field of view is \(\approx\)1.15 \(\times\) 0.86 mm\({}^{2}\) with an image size of 640 \(\times\) 480 pixels. For averaging purposes, five video clips on the temporal bulbar conjunctiva of one subject were needed to record from multiple locations for 1-2 s on each imaging location. Acquired images and video clips were then processed by our customized software to get the final results.
## 3 Description of the Model and the Accuracy Validation
### Parameters of Numerical Calculation
The temporal term is discretized by a second-order implicit scheme, whereas the pressure-velocity coupling equation is solved using the PISO method. The maximum frequency of the analysis in this research is 2 kHz. According to Equation (22), the time step is determined to be \(2.5\times 10^{-4}\) s, and the number of the sampling in time steps is 800 for later acoustic calculations.
## 1 Introduction
For example, [PERSON] and [PERSON] [25] displayed a simple GPS/GLONASS PPP time transfer. Their study showed that adding GLONASS observations could modify the shape of the curve and improve the short-term stability slightly. Up to now, limited studies focus on international time transfer on the basis of GLONASS-only PPP with considering IFCBs. This contribution seeks to gain insight into the influence of GLONASS IFCBs on international time transfer based on GLONASS-only PPP. PPP strongly depends on the externally final orbit and clock products. Table 1 summarizes theIFCB handling schemes adopted by different IGS analysis centers (ACs) in GLONASS satellite clock estimation. In this contribution, three GLONASS IFCB handling schemes, which are neglect IFCBs, estimate IFCB for each GLONASS frequency number, and estimate IFCB for each GLONASS satellite, are employed into our ionospheric-free PPP model. The remaining paper is conducted as follows. It starts with a representation of GLONASS ionospheric-free PPP model considering IFCBs in detail. The data selection and processing strategies are then introduced. Afterwards, in accordance with the uncertainty indicator, the performance of PPP-base time transfer by using different receiver and antennas, different processing modes (with/without fixing coordinates), and different precise products is evaluated. Finally, it ends with summary and conclusions.
## 3 Proposed Model and Methodology
### Description of the Key Exchange Algorithm and Protocol
#### 3.3.1 Description of the Algorithm
This operation corresponds to a simplification or optimization step, often used in constrained environments like IoT, to reduce computational overhead while maintaining the essential cryptographic properties. 4. Arrows Indicating Contraction (Gray Arrows): The gray arrows illustrate the transformation of the original points into their contracted counterparts. Each arrow represents the magnitude and direction of the contraction, providing a visual interpretation of the mathematical operation. Implications for IoT Devices:
* Efficiency Gains: By contracting the parameters, the computational requirements for key generation and exchange are reduced. This is particularly critical for IoT devices with limited resources, such as memory, processing power, and energy supply. * Secure Communication: Despite the contraction, the mathematical structure of the elliptic curve ensures that the cryptographic strength of the key exchange remains intact. This balance between efficiency and security is essential for enabling lightweight cryptographic protocols in IoT networks. * Scalability: The contraction process also supports scalability in large IoT networks by allowing devices with varying resource capacities to participate in secure communication without significant performance bottlenecks. The Figure 2 highlights a numerical representation of such a model, which meets the requirements of the algorithm. The algorithm includes a series of optimizations adapted to the constraints of IoT devices:  
* Reducing the size of the keys: To limit the consumption of resources, a reduced size of the public key is used, for example 160-256 bits, depending on the capabilities of each device. This offers a level of security equivalent to classic algorithms, but with a more efficient use of resources. * Pre-calculation of points: In order to minimize the computation time during key exchange, IoT devices can pre-calculate and store the common points of the curve,  
Figure 2: Numerical example. frequently used in key exchange operations, so that the session key generation process to be as fast as possible. * Data compression methods: In some cases, the public key can be transmitted in a compressed form, thus saving bandwidth, an essential resource in IoT communications.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
## 3 Solution of Signal Propagation Based on MPS Technique
### Computational Set up of Simulations  
Considering the criteria mentioned above, this paper divides the ionized region into 40 phase screens. For recursion calculation, the input signal amplitude is set to a normalized complex amplitude equal to 1, and the initial PSD of phase and amplitude are set zero. \(\frac{N}{f_{s}}=12\) s is equal to the time period of two sub-frames of BeiDou navigation message. Some more details of the computational set up of the simulations are shown in Table 1.
## 4 Conclusions  
This work described a non-aqueous gel casting process with potential for fabricating porous and complicated TiAl components. Based on the characterization of the raw materials, the rheological properties of the slurries for gel casting, the mechanical and thermal properties of green bodies, and the microstructure and phase composition of the sintered part presented above, the following conclusions can be drawn:  
1. A non-aqueous TiH\({}_{2}\)/Al slurry system for gel casting was developed, which exhibited shear-thinning behavior and was favorable for the gel casting process of TiH\({}_{2}\)/Al green bodies with a complex shape. The optimum amount of PVP addition to the slurry was 1 wt % relative to the TiH\({}_{2}\)/Al powder mixture.
2. The monomer contents have a great effect on the mechanical properties of the TiH\({}_{2}\)/Al green bodies. The flexural strength of the green bodies increased from 28.86 to 62.36 MPa as the monomer contents in the premix solution increased from 15 to 40 vol % relative to the premix solution. The suitable monomer dosage determined was 30 vol % relative to the volume of the solutions.
3. The sintered TiAl alloy exhibited a typical fracture cleavage surface; meanwhile, its pore size distribution was 2 \(\upmu\)m to 8 \(\upmu\)m and its porosity was 23.78%, which indicates that the gel casting route is a suitable way to prepare porous TiAl alloy components. The result of the XRD analysis  
Figure 10: Pore size distribution of the TiAl alloy sintered at 1400 \({}^{\circ}\)C in vacuum for 2 h.  
Figure 9: XRD pattern of TiAl alloy after the sintering process.  
shows that \(\gamma\)-TiAl and \(\alpha_{2}\)-Ti\({}_{3}\)Al were the main phases and no contaminated carbide phase was formed.  
Conceptualization: F.L.; methodology: F.L. and X.Z.; investigation: L.Y., Y.J. and X.Z.; data curation: C.Q.; writing--original draft preparation: F.L.; writing--review and editing: C.Q.; supervision: B.S.  
This research was funded by the National Science and Technology Major Project \"Aero engine and Gas Turbine,\" grant number 2017-VII-0008-0102, the Shanghai Municipal Science and Technology Innovation Action Plan under Project, grant number CXY-2016-004 and the \"13 th Five-Year\" equipment pre research and sharing technology project, grant numbers 41423040203 and 41423040206.  
We thank [PERSON] (Analysis and Testing Center of Shanghai Jiao Tong University) for her help with running SEM pictures. We also gratefully acknowledge helpful discussions with [PERSON] and [PERSON].  
The authors declare no conflict of interest.
###### Abstract  
Concrete has good fire resistance. However, once exposed to rapidly increasing temperatures, concrete may suffer from thermal stress-induced spalling or pore pressure-induced spalling. Compared with normal-weight concrete (NWC), lightweight aggregate concrete (LWAC) has a low thermal conductivity and is more prone to cause a higher temperature gradient under the action of high temperatures. This poses a hidden concern to the fire safety of general LWAC structures. Therefore, this study aimed to investigate the temperature and the pore pressure distribution of LWAC slabs under one-dimensional heating tests and to compare them with NWC slabs. The test variables were the moisture content of the specimen (oven-dried and air-dried), target temperature (600 and 800 \({}^{\circ}\)C), and heating rate (5 and 10 \({}^{\circ}\)C/min). The temperature and the pore pressure distributions were measured separately by thermocouples and pressure gauges embedded in different positions of the specimen. The test results show that the maximum pore pressure (P\({}_{\text{max}}\)) of the LWAC slabs was generally higher than that of the NWC slab at a distance of 10 mm from the heated surface when the specimen was in an oven-dried state. However, at 30 and 50 mm from the heated surface, the P\({}_{\text{max}}\) of the NWC slab tended to be higher. This shows that the P\({}_{\text{max}}\) distribution of the LWAC slab was closer to the heated surface when the specimen was in an oven-dried state, while the P\({}_{\text{max}}\) of the NWC slab occurred further from the heated surface. Further, as the heating rate increased, a higher pore pressure was generated inside the specimen and the pressure rose more rapidly. In particular, at a target temperature of 800 \({}^{\circ}\)C and a heating rate of 10 \({}^{\circ}\)C/min, the corner spalling phenomenon appeared on the air-dried LWAC slab.  
Keywords:lightweight aggregate concrete; pore pressure; elevated temperature +
Footnote †: journal: Applied Sciences
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 3 Supercritical Fluid Extraction  
Supercritical fluid extraction (SFE) is an emerging clean technique that developed in the late 1970s/early 1980s and represents the most studied supercritical carbon dioxide-based technique [139]. Most of the published works focus on the extraction of compounds of interest from plant matrices such as seeds, fruits, or leaves. These studies have mainly been the subject of previous reviews. They are outside the scope of the present review paper, which instead focuses on the extraction of active compounds from food industry waste. The SFE process exploits the advantages of the use of carbon dioxide in the supercritical state: mild critical values of temperature and pressure (Tc = 31.1 \({}^{\circ}\)C; Pc = 73.8 bar); total elimination of the use of organic solvents that could remain present in traces in the extract (with a consequent limitation of the environmental impact of the extraction process); the possibility of simply modulating the solvent power of CO\({}_{2}\) which changes significantlywith pressure and temperature; ease in the scale-up of the process up to the industrial scale [140; 141; 142; 143; 144]. It is important to highlight that carbon dioxide in the supercritical state is a \"hybrid\" between a gas and a liquid; indeed, it has some properties typical of gases (such as high diffusivity, which guarantees a better transport of matter with consequent extraction times drastically reduced than the ones of the classic solvent extraction) and others comparable to those of liquids (such as density, to which the solvent power is directly connected). Moreover, carbon dioxide is non-toxic, non-flammable, available in large quantities, and cheap. The main disadvantage of supercritical carbon dioxide (scCO\({}_{2}\))-based techniques is linked to the investment costs (for the design of equipment that operates at high pressures) and operational costs (for pumping CO\({}_{2}\)). The fundamental elements in an extraction plant are sketched in Figure 4: (a) the carbon dioxide vessel; (b) the pump (with refrigerated head to avoid cavitation phenomena); (c) a heat exchanger to preheat the carbon dioxide; (d) an extraction vessel in which the matrix from which to extract the compounds of interest is loaded; (e) one or more separators at lower pressures than the extractor's one.  
In some cases, due to the low polarity of carbon dioxide, a polar cosolvent can be added to increase the solvent power of the mixture. With this strategy, however, one of the advantages of SFE, which is the complete elimination of organic solvents, is lost.  
In general, homemade experimental apparatus is used for the extraction. However, ready-to-use equipment for PLE is available on the market (for example, Extratex and SFE process).
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 3 System and Noise Model
### Impulsive Noise Models  
The K-component Gaussian mixture model (K-GMM) splits any random variable to a group of K mutually exclusive Gaussian variables [36; 37]. Hence, this model can be effectively applied to any ImpN distribution (Class A, \(S-\alpha-S\) noises..., etc.) either estimated [38; 39; 40] and approximated by a K-GMM [41], or modeled with the actual equation [42; 43]. The K-GMM model can be expressed mathematically with pdf,  
\[f_{\rm{W}}(i)=\Sigma_{k=0}^{K-1}p_{k}.G(i,\sigma_{k}^{2}) \tag{6}\]  
where \(.\{P_{k}\}_{k=0,1,\ldots,K-1}\) with \(\Sigma_{k=0}^{K-1}p_{k}=0\) are the probability of occurrence of each Gaussian component \(k\). With the cleverness and complexity of mathematical manipulation, K-GMM with enough high \(K\) can estimate any realistic distribution as closely as needed. The component which corresponds to \(k=0,i_{0}\approx G(i_{0},\sigma_{0}^{2})\) represents the thermal noise with probability of occurrence \(p_{0}\) and thermal power \(\sigma_{0}^{2}\). The statistical combinations of components from \(k=1\) to \(k=K-1\) characterize the impulse noise with the probability \(p_{I}=1-p_{0}\) and noise power \(\sigma_{I}^{2}\). When \(K=2\), this model will reduce to 2-GMM with a thermal noise component \(i_{0}\approx G(i_{0},\sigma_{0}^{2})\) and an impulse noise component \(i_{\rm{i}}\approx G(i_{1},\sigma_{1}^{2})\) (or in general \(i_{m}\approx G(i_{m},\sigma_{m}^{2})\), where \(\sigma_{i}>>\sigma_{0}\)). The ratio of thermal noise to impulse noise can be expressed as \(\Gamma=\frac{\sigma_{0}^{2}}{\sigma_{1}^{2}}\). This model is simple, widely used and assumes the presence of a strong impulsive noise as the recognition of two mutually exclusive events, with probability \(p_{I}\) and \(1-p_{I}\), respectively.  
The received OFDM signal \(y\) can be expressed as the sum of transmitted signal \(x^{\prime}\) and noise \(i_{k}\), where \(x^{\prime}\) and \(i_{k}\) are two zero-mean independent Gaussian random variables with variances \(\sigma_{x}^{2}\) and noise power \(\sigma_{i}^{2}\). As discussed in the system model, the total noise added to the Gaussian source is the sum of the additive white Gaussian noise and DME noise. This paper put forward new methods to reduce DME noise in LDACS, which utilizes the combined knowledge obtained from K-GMM and the pulse blanking method. The received signal of interest after undergoing CP removal (\(y\)) is modeled using K-GMM distribution. The parameters obtained from K-GMM modeling are effectively utilized to derive instant nonlinearity. The proposed new techniques eliminates the disadvantage of pulse blanking by using this instant nonlinearity to attenuate the noise only to the affected subcarriers in the time domain rather than blanking the whole data.
## 3 Results and Discussion
### Surface Characterization
#### 3.1.3 Water Contact Angle  
Hydrophilicity serves as a crucial indicator for assessing the biocompatibility of materials, making it highly relevant for biomedical applications [33; 34; 35]. This biocompatibility can be effectively evaluated by measuring the water contact angle of the materials. Figure 4A illustrates the water contact angle measurements for PET, PET-COO, and PET-COL at 0 s, 5 s, and 10 s.  
The results reveal that the water contact angles for untreated PET at 0 s, 5 s, and 10 s were 132.97 \(\pm\) 0.6\({}^{\circ}\), 131.49 \(\pm\) 0.2\({}^{\circ}\), and 131.52 \(\pm\) 0.5\({}^{\circ}\), respectively. Following strong alkaline hydrolysis, the water contact angles for PET-COO decreased significantly to 93.46 \(\pm\) 0.4\({}^{\circ}\), 89.45 \(\pm\) 0.3\({}^{\circ}\), and 87.19 \(\pm\) 0.3\({}^{\circ}\) at 0 s, 5 s, and 10 s, respectively, when compared to PET (\(p<\) 0.001) (Figure 4B). These findings suggest that strong alkaline hydrolysis effectively alters the hydrophilicity of PET materials, leading to improved wettability.  
When considered alongside the results obtained from FTIR analysis in Section 3.2.2, it is plausible that the hydrolysis reaction of ester groups on the material's surface gives rise to the formation of hydrophilic -OH groups, thereby enhancing the surface hydrophilicity of the material. Upon coating with type I collagen, the water contact angles for PET-COL  
Figure 4: Water contact angle of PET, PET-COO, and PET-COL scaffolds. **(A)**: The change in water contact angle at three time points (0 s, 5 s, and 10 s); **(B)**: the statistical histogram of water contact angle in different samples at three time points (0 s, 5 s, and 10 s). Data are mean \(\pm\) SD, n = 3; ***: \(p<\) 0.001.  
Figure 3: FTIR infrared spectrum of PET, PET-COO, and PET-COL scaffolds.  
further diminished to 74.74 \(\pm\) 0.3\({}^{\circ}\), 0\({}^{\circ}\), and 0\({}^{\circ}\) at 0 s, 5 s, and 10 s, respectively (Figure 4A). These reductions were statistically significant when compared to both PET and PET-COO (\(p<\) 0.001) (Figure 4B). Evidently, collagen coating further enhances the hydrophilicity of PET materials.
## 2 Theory  
The network architectures of a RBFN and a RBFCCN are given in Figures 1 and 2, respectively. By applying the cascade correlation algorithm, the RBFCCN has a different network topology compared with conventional RBFNs. In RBFCCNs, the transfer function applied in the hidden neuron is the Gaussian function. Unlike a RBFN that usually has only one hidden layer, the RBFCCN has a multi-layered structure. Each hidden layer contains only one neuron. In RBFCCNs, the \(k\)th hidden neuron is connected with \(k+l\) - 1 inputs, where \(l\) denotes the number of input neurons. The output of the \(i\)th object from the \(k\)th hidden neuron \(o_{ik}\) is given by:  
\[o_{ik}=g_{k}\left(\mathbf{x}_{ik}\right)=\exp\left[-\sum_{p=1}^{k+l-1}\left(x_ {ikp}-\mu_{kp}\right)^{2}/\left(2\sigma_{k}^{2}\right)\right] \tag{1}\]  
for which \(g_{k}\) is the notation of the Gaussian function; \(\mathbf{x}_{ik}\) is the input vector; \(x_{ikp}\) is the corresponding \(p\)th element of \(\mathbf{x}_{ik}\). The \(\mu_{kp}\) term denotes the \(p\)th element of centroid \(\mathbf{\mu}_{k}\), and \(\sigma_{k}\) denotes the \(k\)th radius. The \(o_{ik}\) term will depend on two factors: the Euclidean distance between the sample and the centroid and the radius. In the cascade-correlation training architecture, the hidden units are added and trained sequentially during training.  
Figure 1: Network architecture of a RBFN. This network has three input neurons, two hidden neurons, and two output neurons.  
The training process of a RBFCCN includes the following steps:  
1. Initialize the network.
2. Add a hidden neuron to the network. Initialize this hidden neuron by setting initial values of \(\boldsymbol{\upmu}_{k}\) and \(\sigma_{k}\) of the Gaussian function.
3. Train the hidden neuron. Determine the values of \(\boldsymbol{\upmu}_{k}\) and \(\sigma_{k}\).
4. Train the weights \(\mathbf{W}_{k}\) in the output layer.
5. Repeat step 2 to step 4 until a given error threshold is achieved or a given number of hidden units were added.