esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Texture Analysis
At both temperatures, the values of the viscosity index tended to decrease slightly for subsequent storage periods. This may have been caused by a gradual release of water from the tested systems, which caused their dilution as the storage period extended. At the same time, the test temperature had minimal influence on the decrease in the viscosity index. In terms of mechanical properties, the model soups were quite similar to the commercial soups released on the Japanese market [30]. Figure 4: The indices of viscosity of the model soups measured at room temperature and after heating vs. the storage period. (a–g) Different letters indicate a significant difference at \(p<\) 0.05 (n = 8). Both at room temperature and after heating, the values of the texture parameters of the model soups tested in our study were slightly lower than those of commercially available soups. The latter were characterised by higher average density and viscosity values. This effect may have been caused by the addition of thickeners to these products or different amounts or types of vegetables to make the vegetable pulp, which also determined the density and consistency of the soups. Japanese soups can be classified as thick soups. Depending on the type of thickener, thick soups are classified as purees (vegetable soups thickened with starch), bisques (made from shellfish or vegetable puree thickened with cream), cream soups (thickened with bechamel sauce), and veloutes (thickened with eggs, butter, or cream) [33]. As was the case with the model soups, the heating of the commercial soups caused a statistically significant decrease in the values of the texture parameters (except cohesiveness) (\(p<0.05\)). As most soups, especially thick cream soups, are eaten hot, such a change in texture meets the expectations of elderly consumers, who can swallow them more easily. Another noteworthy parameter was the value of the shear force of the meat added to one of the model soups. The average shear force of the beef balls with carrots at 20 \({}^{\circ}\)C was 12.3 \(\pm\) 0.2 N. When heated to about 55 \({}^{\circ}\)C, the shear force decreased to 8.8 \(\pm\) 0.1 N on average (\(p<0.05\)). These values remained at a similar level throughout the entire period of storage for this soup. In earlier analyses, the shear forces of the beef cubes in goulash measured at 20 \({}^{\circ}\)C and 55 \({}^{\circ}\)C were 13.8 N and 10.4 N, respectively [30]. As can be seen, the beef balls from the model soup were not as hard as the meat pieces from the commercial product. It is important to note that the meat pieces in both soups were of a similar size. The structure of minced meat and the balls formed from it fully meet the requirements of consumers who find it difficult to bite and chew solid elements in food. The meat in the soup was easily crushed into smaller pieces with the tongue or gums only.
## 2 Materials and Methods
### Data Processing
#### 2.3.5 Quality Metrics for Models  
In order to assess the quality of the models, Pearson \(r\) correlation coefficient and root mean square error (RMSE) values were employed. The RMSE was computed as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{n}} \tag{7}\]  
where \(y\) is a certified value, \(\hat{y}\) is a predicted value, and \(n\) is the number of samples in the calibration (for RMSE of Calibration or RMSEC) or the test set (for RMSE of Prediction or RMSEP).
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### Inertial Navigation Error Analysis and Modeling  
Because the strapdown INS (SINS) has the advantages of a small volume, low cost, high precision, and vibration resistance in the environment, we can analyze the SINS from the perspective of a low cost, ease of use, and ease of realization in engineering. In SINS, the sources of error include element error, component installation error, interference error, etc. The most important error sources are gyro drift and acceleration bias [27]. As a measuring element, the drift of the gyroscope will cause drift of the mathematical platform, which will lead to measurement error of the carrier angular velocity, and finally enter the system after  
Figure 5: Schematic diagram of the INS+LEO 1 satellite + altimeter tightly integrated navigation algorithm.  
the carrier attitude update. Finally, it will have a great impact on the navigation parameter calculation of the SINS. From now on, INS refers to the SINS, and we do not need to declare this factor.  
To quantitatively describe the error model of the INS system, we provide the following explanation.  
For the error state variables, we use the north, east, and down orders of the axes in the local navigation frame, that is, the NED coordinate system, to express the error state variables. We set the number of dimensions of the error state variables to 9, which are the error angle \(\phi_{N}\), \(\phi_{E}\), and \(\phi_{D}\) (the subscript respectively represents the error along the N, E, and D directions); velocity error \(\delta V_{N}\), \(\delta V_{E}\), and \(\delta V_{D}\); and position error \(\delta L\), \(\delta\lambda\), and \(\delta H\).
## 5 Current Trends
### Overall Observations
Therefore, a number of researchers tend to start off with the static case before coming to the dynamic case. When considering the optimisation objective, as can be observed in Figure 12, one finds that cost minimisation, travel distance minimisation and travel time minimisation have the highest coverage among all other objectives. These objectives can be considered as the most common and general objectives that are widely utilised in different routing-related problems to achieve a \"best\" solution. Additionally, the maximisation of number of matched rides is another objective that has received great attention. It is also important to note that this objective is very closely related to the dynamic matching-related problem. There is an increasing awareness of the extent of damage inflicted to the environment by human activities. This has resulted in the usage of science and technology to be more eco-friendly and beneficial towards the policy of environmental protection. Even though there are fewer studies that are concerned with the minimisation of CO\({}_{2}\) emissions (CE) in shared mobility systems (see Figure 12), soon it will become an inevitable criterion in developing an eco-friendly system or product. This will spur interest in more studies on this topic. In Table 8, heuristic and metaheuristic algorithms have the highest coverage as compared to others due to their black-box optimisation that can solve large instances in low computational time, even though it cannot guarantee optimality. However, the obtained heuristic or metaheuristic solution solely presented cannot validate the obtained solution is a \"best\" solution. The proposed new exact algorithm is capable of finding a high-quality solution for medium and large instances within a reasonable time. Hence, there is a need to have exact solutions as a benchmark to compare with the solution obtained by a heuristic and metaheuristic. The reason why there are a certain number of research papers which are categorised under the third category (exact, heuristic, and metaheuristic) as shown in Table 8. There are some researchers that make use of an exact algorithm to test the proposed model before moving on to using a heuristic or metaheuristic.
## 2 Preprocessing
Potentially cloud contaminated scenes are filtered using MODIS Aqua (moderate-resolution imaging spectroradiometer aboard Aqua) L2 cloud mask data [26] with about 1 km \(\times\) 1 km resolution (collection 6, MYD35, obtained from [[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov))). All MODIS ground pixels which are not flagged as clear or probably clear are considered as potentially cloudy. Aqua is lagging OCO-2 by 15 min and in order to account for the parallax effect and potential cloud movements, we use only OCO-2 data with at least 10 km distance to the nearest MODIS  
Figure 2: Pre-filtering statistics of the 24 days data subset used for the noise model analysis (Section 3.1). The filters are applied in the order: Sounding quality, LAT/SUZ/SAZ/\(\sigma\)ALT, Spike EOF, OMI UV aerosol idx, MODIS clouds, and Radiance level (see main text for a description). The colors represent filter activity and soundings passing all filters are shown in white. Numbers in brackets represent filter throughputs. is not overly conservative, this filter has a throughput of only about 11% and dominates the total pre-filtering throughput of about 4% (Figure 2). Additionally, we filter out very dark or bright scenes, i.e., extreme detector fillings. Specifically, we ensure that the continuum radiance in each band is between 5% and 95% of the maximum band radiance as specified in the OCO-2 L1b ATBD (Algorithm Theoretical Basis Document, [24]). Meteorological profiles come from ECMWF operational analysis data ([[http://apps.ecmwf.int](http://apps.ecmwf.int)]([http://apps.ecmwf.int](http://apps.ecmwf.int))) and have a resolution of six hours, 0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\), and 137 height layers. As part of the preprocessor, these profiles are corrected for the actual surface height of the OCO-2 soundings and split into 20 layers containing the same number of dry-air particles.
###### Abstract  
We propose and demonstrate a new analog-digital combined high-secure optical communication system based on chaotic circuit driving, which achieves encryption in the analog and digital domains. A 3D chaotic system is used for analog domain phase encryption (ADPE) and digital domain time-frequency encryption (DDTFE) simultaneously. The ADPE is carried out by the privately chaotic signal driving the phase modulator (PM), which realizes chaotic phase encryption. The chaotic circuit comprehends highly complex nonlinear dynamics. Its size is 10 cm \(\times\) 5 cm, which has the characteristics of small size and low cost. The DDTFE is performed by the frequency-time encryption of signals in the digital domain. The experimental results show that the optical physical layer encryption scheme based on analog and digital combination can successfully mask the original data. The driving signal of PM is that generated by the chaotic circuit and needs to be privately synchronized, so that the legal receiver may accurately decrypt the encrypted data and the eavesdropper is unable to intercept a valuable message. If the chaotic driving circuit produces a delay of 3 s, the bit error rate (BER) reaches more than 0.3 at the receiver. The results of experiment verify that the scheme can transmit 13.3 Gb/s 16 quadrature amplitude modulation orthogonal frequency division multiplexing (16 QAM-OFDM) signal over 25 km standard single mode fiber (SSMF). This scheme achieves low-cost, high-security communication, making it a suitable foundation for high-speed, secure optical communication at the physical layer.  
high security; optical communication; chaotic encryption 2022 9, 669. [[https://doi.org/10.3390/photonics909669](https://doi.org/10.3390/photonics909669)]([https://doi.org/10.3390/photonics909669](https://doi.org/10.3390/photonics909669))  
3  
A][PERSON], Q][PERSON], A][PERSON], A][PERSON], A][PERSON], A][PERSON], A][PERSON], A][PERSON], A][PERSON], A][PERSON], A][PERSON] [A]
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 4 Reservoir Modeling of the Sangonghe Formation in the Central Junggar Basin
### Model Verification  
The reliability of the reservoir modeling results was assessed by comparing the drilled sand thickness with the predicted thickness using the following two wells: Z111, which participated in the modeling process, and Z12, which did not. For the Z111 well, the cumulative thickness of the sand bodies drilled by \(\mathrm{J_{1}}^{\mathrm{S22}}\), \(\mathrm{J_{1}}^{\mathrm{S212}}\), and \(\mathrm{J_{1}}^{\mathrm{S211}}\) in the Sangonghe Formation were 13.4 m, 39.1 m, and 38.1 m, respectively. The model-predicted thicknesses were 10 m, 26 m, and 33 m, respectively, resulting in an overall agreement rate of 81.6%.  
Similarly, for the Z12 well, the cumulative thickness of the sand bodies drilled by \(\mathrm{J_{1}}\mathrm{S_{2}}^{2}\), \(\mathrm{J_{1}}\mathrm{S_{2}}^{12}\), and \(\mathrm{J_{1}}\mathrm{S_{2}}^{11}\) in the Sangonghe Formation were 29.8 m, 34.9 m, and 42.5 m, respectively. The model-predicted thicknesses were 19 m, 42 m, and 38 m, respectively, resulting in an overall agreement rate of 92.4% (Figure 14 and Table 2). These results indicate that the reservoir modeling results are credible.  
In this real-world project application, compared to traditional geological modeling technologies, the fundamental unit element is defined based on bar development within braided fluvial systems. Within this \"unit element\" (Figure 11), channel fill and individual bars are defined according to the depositional process. The internal heterogeneity of these elements can significantly influence hydrocarbon migration patterns, as discussed in our previous work [71]. However, this new methodology has limitations that stem from the definition of the \"unit elements\" themselves. In real-world exploration projects, particularly when well log data interpretation is limited, it can be difficult to determine the size and parameters of internal bodies, even when the \"unit element\" can be observed in different depositional environments. Nevertheless, this new method provides a framework that allows modelers to generate multiple models spanning the uncertainty space to better match the observed data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Well** & **Thickness (m)** & \(\mathrm{J_{1}}\mathrm{S_{2}}^{2}\) & \(\mathrm{J_{1}}\mathrm{S_{2}}^{12}\) & \(\mathrm{J_{1}}\mathrm{S_{2}}^{11}\) & **Agreement Percentage** \\ \hline \multirow{2}{*}{Z12} & Drilled & 29.8 & 34.9 & 42.5 & \multirow{2}{*}{92.4\%} \\  & Modeled & 19 & 42 & 38 & \\ \hline \multirow{2}{*}{Z111} & Drilled & 13.4 & 39.1 & 38.1 & \multirow{2}{*}{81.6\%} \\  & Modeled & 10 & 26 & 33 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table of the drilled sand body thickness and predicted sandstone thickness of the second member of the Sangonghe Formation in the Z12 and Z111 wells.  
Figure 14: Comparison chart of the reservoir drilling thickness and prediction results of the Sangonghe Formation in the Z12 well in the Zhunzhong area.
## 5 Discussion  
This article proposes a scalable statistical model of wireless optical communication underwater using the Monte Carlo technique. We present the magnitude response of the harbor, coastal, and ocean water by first determining the underwater channel's impulse response. The baseline model has been adopted from our previous study [10] and is upgraded to be more adaptive and to provide a comprehensive knowledge of the trade-offs in UWOC design. In this regard, the most relevant and state-of-the-art models in the literature are [9; 21; 27]. Our model estimates the probability of survival of photons from the transmitter to the receiver that can also be referred to as the expected or average value of the underwater optical field under various water conditions and physical limitations. Therefore, the numerical results have been normalized for greater accuracy. We incorporate a dynamic method for calculating bin size of the discrete histograms to (i) reduce the complexity and (ii) increase the efficiency of our model in the measurement of the impulse response. However, the authors in [9] and [27] manually altered the bin sizes to find the right bin width to accommodate the Time-of-Arrival information of the received photons in the histograms. This process is inefficient and tedious at the same time. Therefore, we use a more efficient way of calculating the exact bin width for the discrete histogram of photon received power, known as Scott's rule. Furthermore, the authors of [9; 21] characterized the underwater channel by applying Monte Carlo together with Fournier-Forand (FF), and Sahu and Shanmugam (SS) phase functions, respectively. Whereas, we used [PERSON]'s measured phase functions along with Monte Carlo to estimate multiple scattering effects. The main findings of this study can be summarized as follows:  
* The increase in transmission distance and scattering events increase multipath attenuation.
* Greater scattering and diffusivity of the optical source spreads the received optical power, thus causing temporal dispersion.
* In harbor water, the temporal dispersion is significantly high, and it also affects the data transmission at longer distances.
* A wider aperture and FoV increases received power gains, especially in the ocean and coastal water; however, the effect of FoV alone on overall BER performance is almost negligible in all water types.
* FoV and aperture of an optical wireless communication system underwater are independent of each other, altering one does not necessarily affect the other.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 4 Implementation Strategy
### LwIP Setup and Userspace Execution  
To set up LwIP for userspace operation, the stack must first be configured and compiled. Customization is performed by editing the configuration file, typically named LwIPopts.h, which defines parameters such as protocol support, buffer sizes, and threading models. For this implementation, LwIP is configured in a single-threaded, event-driven mode to simplify integration with the DTN Module. This configuration ensures support for IPv6, User Datagram Protocol (UDP), and other protocols relevant to DTN operations. Once configured, the stack is compiled into a userspace library using a suitable build system, such as make or cmake. Platform-specific adjustments, such as timing mechanisms and network interface integration, are applied during compilation to ensure compatibility with the target system. On Linux, the netif module is customized to interface with both the TUN device for input and raw sockets for output.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 2 Segmented Telescope Imaging Theory
### Derivation of WFE from Sub-Mirror Position Error
#### 2.2.2 Influence of Z Piston Error on Phase \(\phi_{\rm j}\)  
When the offset of a sub-mirror along the optical axis direction is \(\rm dz\), the Piston error value is twice of the mutual misalignment value between the sub-mirrors along the optical axis direction \(\rm 2 dz\), resulting in a WFE that can be expressed as:  
\[\phi_{\rm j-Piston}=\frac{2\pi}{\lambda}\cdot 2\rm d_{z}=\frac{4\pi}{\lambda} \rm d_{z} \tag{7}\]  
The WFE is linearly related to the Piston error and manifests itself as a translation of the entire wavefront.
## 3 Spectrum Management Issues
### High-Spectrum Access
#### 3.4.1 Below 6 GHz (Sub-6 GHz)  
The current 5G network is compatible with its predecessor's technologies, and the 4G spectrum sub-6 GHz can be easily applied for 5G communication [156]. The management and effective utilization of the current spectrum help avoid the unnecessary usage of mmWave frequency bands. For frequency bands under 6 GHz, the communication band between 3.5 and 4.2 GHz is cleared for use with 5G, and it can offer up to 300 MHz of bandwidth [157]. This novel idea would have some of the following benefits: (1) The sub-6 GHz band can be used in the Line of Sight (LOS) and Non-Line of Sight (NLOS), (2) co-channel interferences between mmWave and small cells are negligible, and (3) the channel state information feedback rate for backhaul connection is smaller than that of 5G NR BS for user equipment fading links because of high mobility [158]. The transmission between macro BS and small cell BS would be a bottleneck due to high user capacity and interference-effect. Therefore, one of the prominent solutions to overcome the issue is to use sub-6 GHz for backhaul communication [159]. The sub-6 GHz band is also advisable to be used by users near macro BS for uplink and downlink transmission. Another author in [160] has focused on the effective implementation of the sub-6 GHz for 5G HetNet for wireless backhauling.
## 3 Flexural Loading Tests of Precast Concrete Slab Joints
### Test Setup  
Figure 7 shows the test setup of the Type A specimen in the flexural loading test. The tests of all specimens were conducted under simple support conditions. The length of all specimens was 2200 mm, and the loading span was 2000 mm, as presented in Figure 5a,b. The loading tests were carried out between 41 days and 45 days after casting joint materials. The load was applied to the center of the span through a loading plate with a thickness of 19 mm and width of 150 mm. The cyclic loading was applied in three stages: flexural cracking, longitudinal reinforcement yielding, and ultimate situation. A load of flexural cracking was determined when the crack opening at the interface between the PCa concrete and the joint increased significantly, and a load of longitudinal reinforcement yielding was determined when one of the longitudinal reinforcements reached the yielding strain.  
During the tests, deflection at the center and quarter of the span was measured by displacement transducers, as shown in Figure 5a. The opening displacement at the interface between the PCa slab member and the joint was measured by a Pi-shape gauge at the position shown by the purple rectangles in Figure 5b,d. In all specimens, the strain at the curved and straight parts of the loop reinforcements and the strain of the concrete or mortar on the side of the joint were measured by strain gauges. The location of these strain gauges is presented in Figure 5c,d,e1,e2, with the red and blue rectangles indicating the reinforcements and concrete, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen Name**} & \multicolumn{2}{c}{**PCa Concrete**} & \multicolumn{4}{c}{**Joint Material**} & \multicolumn{4}{c}{**Main and Transverse Reinforcements**} \\ \cline{2-9}  & \(f_{\ell}\) & \(E_{\ell}\) & \(f_{\ell j}\) & \(f_{\ell j}\) & \(f_{\ell j}\) & \(E_{\ell j}\) & \(f_{\ell s}\) & \(f_{\ell s}\) (D13) & \(f_{\ell s}\) (D19) \\ \hline Type A & 70.0 & 40.6 & 67.7 & 4.91 & 4.9 & 40.6 & & \\ Type B & 68.8 & 41.9 & 64.9 & 7.37 & 7.0 & 33.2 & 404 & 524 & 551 \\ Type C & 72.3 & 41.3 & 64.9 & 7.37 & 7.0 & 33.2 & & \\ Type D & 69.0 & 39.4 & 67.7 & 4.91 & 4.9 & 40.6 & & \\ \hline \hline \end{tabular} Note: \(f_{\ell j}\): compressive strength of PCa concrete (N/mm\({}^{2}\)); \(E_{\ell}\): modulus of elasticity of PCa concrete (kN/mm\({}^{2}\)); \(f_{\ell j}\): compressive strength of joint material (N/mm\({}^{2}\)); \(f_{\ell j}\): tensile strength of joint material (N/mm\({}^{2}\)); \(f_{\ell j}\): flexural strength by 100 \(\times\) 100 \(\times\) 400 mm test pieces of joint material (N/mm\({}^{2}\)); \(E_{\ell j}\): modulus of elasticity of joint material (kN/mm\({}^{2}\)); \(f_{\ell s}\): yield strength of loop and transverse reinforcements; \(f_{\ell s}\): tensile strength of loop and transverse reinforcements.  
\end{table}
Table 4: Material test results for flexural loading test specimens.
## 3 Methodology
### FDS GMF Development
#### 3.3.2 ASCAT Data  
Using the same process, the ASCAT data were collocated with the BF-1 A/B NBRCS data from 1 July to 1 September. The total number is 860,859 with the matchup criteria of 1 h and the nearest longitude/latitude grid. The reason for the lower volume of coincident pairs is that the ASCAT data are limited by their swath and orbit, which are different from the spatially continuous ECMWF models. The matchup maximum wind speed is 33.2 m/s.  
After the same quality control method of the 3-dB antenna beam-width and SNR \(\geq\) 4, 80% of the data were used to train the FDS GMF for the sea surface wind speed. The elevation angle bins remained as 28-90\({}^{\circ}\) with a 1\({}^{\circ}\) bin width. Figure 5 shows examples of the best-fitted results using the ASCAT training dataset.