esa-sceva/satcom-chunk-collection huggingface dataset:
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 4 Link-Building Planning
### Link-Building Constraints  
The link-building table is represented by a Boolean matrix \(T\), with the size of \(N\times N\)\(\times K\). The element \(t_{i,j,k}=1\) indicates that the constellation's \(i\)th antenna forms a link with the \(j\)th antenna in time segment \(k\), while \(t_{i,j,k}=0\) indicates that no link is built. The visible matrix \(V\) and the link-building matrix \(T\) are subject to the following constraints:  
\[v_{i,j,k}\in\{0,1\},\forall i,j\in A,k\in K, \tag{19}\]  
\[v_{i,j,k}=v_{j,i,k},\forall i,j,k, \tag{20}\]  
\[t_{i,j,k}\in\{0,1\},\forall i,j\in A,k\in K, \tag{21}\]  
\[t_{i,j,k}=t_{j,i,k},\forall i,j,k, \tag{22}\]  
\[v_{i,j,k}-t_{i,j,k}\geq 0,\forall i,j,k, \tag{23}\]  
where \(K\) is the number of time segments in the period \(P\), and \(k=\{1,2,\ldots,K\}\).  
Equations (20) and (22) show that both the visibility matrix and the link-building matrix have symmetry, indicating that the ISL has no directionality, and that the visible and link-building relationships between \(i\) and \(j\) are the same between \(j\) and \(i\). The link between \(i\) and \(j\) must be constructed based on the fact that \(i\) and \(j\) are mutually visible, as shown in (23).  
In addition, we impose some constraints on the ISL-building for the link-building table \(T\); namely,  
\[\sum_{j\in Z(i)}t_{i,j,k}\leq 1,\forall i,k, \tag{24}\]  
\[\sum_{j\in Z(i)}t_{i,j,k}=0,\forall i,k, \tag{25}\]  
where \(Z(i)\) represents the set of antennas in the plane adjacent to the plane where \(i\) is located; (24) indicates that, at the same time, one antenna can only build at most one link with an antenna in the adjacent plane; and (25) indicates that the antenna cannot build a link with antennas in a non-adjacent plane at any time. These two constraints ensure that ISLs are only formed between antennas carried by satellites on adjacent orbital planes.
## 3 Challenges in Big Data for Machine Learning
### Privacy and Security  
The use of ML in big data environments raises significant privacy and security concerns, especially when dealing with sensitive information such as personal health records, financial data, or behavioral data. The integration of ML with big data often requires the collection, storage, and processing of vast amounts of personal information, which can be vulnerable to breaches and misuse. The challenge of ensuring data privacy is exacerbated by the fact that data are often distributed across multiple platforms and jurisdictions, each with its own set of privacy regulations [230; 231; 232].  
Ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the United States, is a complex task. These regulations impose strict requirements on how data can be collected, stored, and used, and they grant individuals certain rights over their personal data, including the right to be forgotten. ML models, particularly those trained on sensitive data, must be designed to comply with these regulations while still delivering accurate and effective results [233; 234; 235].  
Techniques such as differential privacy, which introduces noise to data to obscure individual identities, and federated learning, which allows models to be trained on decentralized data without the need to share raw data across parties, have been developed to address these privacy concerns. However, these techniques also introduce new challenges, such as increased computational complexity and potential trade-offs between privacy and model performance. Additionally, as ML models become more powerful, there is a growing risk of adversarial attacks, where malicious actors manipulate input data to deceive models, further complicating the security landscape [236; 237; 238].
# LDS Realization of High-Q SIW Millimeter Wave Filters with Cyclo-Olefin Polymers
## 3 Filter Design
### Tapered Transition Design  
We broke down the transition into two parts. The first one, considering the same thickness of 300 \(\upmu\)m, connects the microstrip line to a waveguide via a taper line [1]. The second part is a waveguide transition, inspired by Reference [13], on which several regular steps are made from the extremity of the last planar transition to reach a thickness of 1 mm. A good choice of shape of the transition is a key point to ensure correct modal transformation between microstrip TEM and TE10 SIW modes, and vice versa. However, a phase of optimization with an electromagnetic simulator was necessary to correctly adjust all the dimensions in order to obtain a sufficient bandwidth while minimizing insertion losses. The shape of the transition is displayed in Figure 8a, and the dimensions are given in Table 3. The electrical response of the transition, given in Figure 8b, shows a 20 dB relative bandwidth better than 30%, for a central level of insertion losses better than 0.4 dB. It is important to note that, once it is designed, the transition is transparent whatever the filter chosen. This means that the assembly of the whole structure, one filter and two transitions, does not require any modifications.  
Figure 8: (**a**) View of the tapered microstrip to SIW transition; (**b**) simulated response of the transition.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Filter** & **d\({}_{\mathbf{B1}}\)** & **d\({}_{\mathbf{H1}}\)** & **d\({}_{\mathbf{B2}}\)** & **d\({}_{\mathbf{H2}}\)** & **d\({}_{\mathbf{B3}}\)** & **d\({}_{\mathbf{H3}}\)** & **d\({}_{\mathbf{B4}}\)** & **d\({}_{\mathbf{H4}}\)** & **L\({}_{\mathbf{I2}}\)** & **L\({}_{\mathbf{23}}\)** & **L\({}_{\mathbf{34}}\)** & **x\({}_{\mathbf{C1}}\)** \\ \hline
**1** & 0.37 & 1.07 & 0.74 & 1.44 & 0.87 & 1.57 & 0.88 & 1.58 & 3.23 & 3.72 & 3.81 & 1.31 \\
**2** & 0.39 & 1.09 & 0.53 & 1.23 & 0.66 & 1.36 & 0.67 & 1.37 & 3.01 & 3.44 & 3.54 & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions (millimeter) of the two filters designed.  
Figure 7: Electrical response of the two filters before and after modification with relative bandwidths of (**a**) 2.5% and (**b**) 4.5%.
## 3 Background and Methodology
In the end, different thermal conductivity values were eventually generated separately for the heating mode and cooling mode. The data from this test will be analyzed numerically in this study. Figure 1: Potential heat-expansion and cool-contraction of the pile. In this study, the Lambeth College case and the Shell Centre case are analyzed numerically in a 3D model via COMSOL Multiphysics, the details regarding of the methods are as below:  
In industrial applications, it is common that the density of a process fluid varies. These variations can have a number of different sources but the most common is the presence of an inhomogeneous temperature field. The module used in this study includes the Non-Isothermal Flow predefined Multiphysics coupling to simulate systems where density varies with temperature. The model is based on the fully compressible formulation of the continuity equation and momentum equations:  
\[\frac{\partial p}{\partial t}+\
abla\cdot(\rho\mathbf{u})=0 \tag{2}\]  
\[\rho\frac{\partial\mathbf{u}}{\partial\mathbf{t}}+\rho\mathbf{u}\cdot\
abla \mathbf{u}=-\
abla p+\
abla\cdot(\mu(\
abla\mathbf{u}+(\
abla\mathbf{u})^{ \tau}-\frac{2}{3}\mu(\
abla\cdot\mathbf{u})I)+F \tag{3}\]  
where \(\rho\) is the density (kg/m\({}^{3}\)), \(\mathbf{u}\) is the velocity vector (m/s), \(p\) is the pressure (Pa), \(\mu\) is the dynamic viscosity (Pa\(\cdot\)s), and \(F\) is the body force vector (N/m\({}^{3}\)). The heat equation is:  
\[\rho C_{p}(\frac{\partial\mathrm{T}}{\partial\mathbf{t}}+(\mathbf{u}\cdot \
abla)\mathrm{T})=-(\
abla\cdot\mathbf{q})+\tau\cdot\mathbf{S}-\frac{ \mathrm{T}}{\rho}\frac{\partial\rho}{\partial\mathrm{T}}\bigg{(}\frac{ \partial p}{\partial\mathbf{t}}+(\mathbf{u}\cdot\
abla)p\bigg{)}+Q \tag{4}\]  
where in addition to the quantities above, \(C_{p}\) is the specific heat capacity at constant pressure (J/(kgK)), T is the absolute temperature (K), \(\mathbf{q}\) is the heat flux by conduction (W/m\({}^{2}\)), \(\tau\) is the viscous stress tensor (Pa), S is the strain-rate tensor (1/s), and \(Q\) contains heat sources other than viscous heating (W/m\({}^{3}\)). The data were taken from research works concentrated on the heating-cooling circles [21]. The parameter \"heat transfer coefficient at the soil-concrete interface\" is examined. By conducting a series of parametric simulations, the heat transfer coefficient values in the heating or cooling modes are investigated.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 5 Research Limitations and Future Work  
We measured the latency and the loss rate of SP, FMP, and MMP on a global network using SCION. The results show that MMP routing has a low latency and loss rate compared to FMP routing and more MMP paths show lower latency and loss late. Under the traffic congestion, the loss rate of MMP is stable. However, we can think about the limitation of the operation. If all users of the global network use all possible multi-paths, we could not guarantee the performance of MMP as shown above.  
Our suggested future work is to optimize the important factor of MMP operations, such as how many multi-path uses or how many hops of multi-path is the minimum quality for MMP routing. QoS-driven routing is also a possible option for MMP routing policy using QoS metrics, such as latency, loss late, bandwidth, jitter, or other composite metrics. We could make the weight of each multi-path, and some paths could show high latency but stability to communicate.
## 3 Results
### Low-Pressure and O\({}_{2}\) NTP Treatments Effect on the Resin COLOR
#### 3.2.2 Spectrophotometry  
Table 2 summarizes the resin color change (\(\Delta\)E) after each treatment. \(\Delta\)E of group C represented the resin natural aging, and consequently was determined after the evaluation of the other groups (LP, P1 and P6). Then, after three weeks the median value of \(\Delta\)E was 0.70 with a maximum value of 2.07. After a 120-min LP treatment, the median \(\Delta\)E value was 0.63 with a maximum of 2.22. Similarly, after one 120-min O\({}_{2}\) NTP (P1) the \(\Delta\)E was 0.71 with a maximum value of 1.62. No significant modification of \(\Delta\)E was noted in groups LP and P1 compared to group C (\(p>\) 0.05) (Figure 5). Concerning group P6, a significant increase of \(\Delta\)E up to 1.33 with a maximum value of 2.88 was observed (\(p<\) 0.05) (Figure 5). However, all the results remained under the acceptability threshold (\(\Delta\)E \(<\) 4.00) [47].  
Table 3 summarizes the values of the CIELab color coordinates (L* (lightness), a* (redness-greenness) and b* (yellowness-blueness)) evaluated before and after each treatment. Concerning group C, the median lightness value (L*) slightly increased from 34.77 (\(\pm\)0.98) to 35.10 (\(\pm\)1.10) after 3 weeks of natural aging. On the opposite, a slight decrease was observed regarding the color coordinate a* (from 8.36 (\(\pm\)0.65) to 8.25 (\(\pm\)0.66)) and the color coordinate b* (from 1.55 (\(\pm\)0.40) to 1.46 (\(\pm\)0.44)) (\(p>\) 0.05). Similarly, after LP and one O\({}_{2}\) NTP treatments (P1), no significant variation of lightness and of a* and b* color coordinates were noted (\(p>\) 0.05). Regarding group P6, the increase of lightness was significant (from 32.95 (\(\pm\)1.02) to 35.31 (\(\pm\)1.27) (\(p<\) 0.05). In contrast to groups C, LP and P1, a significant increase of the color coordinate a* (from 8.23 (\(\pm\)0.60) to 8.80 (\(\pm\)0.65)) and the color coordinate b* (from 0.98 (\(\pm\)0.30) to 1.45 (\(\pm\)0.43)) were noted (\(p<\) 0.05).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\Delta\)E\({}_{\text{00}}\) & Min & 1 st Qu. & Median & Mean & 3 st Qu. & Max \\ \hline C & 0.13 & 0.49 & 0.70 & 0.75 & 0.97 & 2.07 \\ LP & 0.14 & 0.38 & 0.63 & 0.73 & 0.99 & 2.22 \\ P1 & 0.06 & 0.45 & 0.71 & 0.75 & 0.98 & 1.62 \\ P6 & 0.27 & 1.01 & 1.33 * & 1.40 & 1.77 & 2.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resin color changes (\(\Delta\)E\({}_{\text{00}}\)) in the evaluated groups.  
Figure 5: Box plots. \(\Delta\)E\({}_{\text{00}}\) of groups C, LP, P1 and P6. P6 showed a significant color change compared to the other groups (C, LP, P1); * significant difference according to Kruskal-Wallis and Wilcoxon rank sum tests (\(p<\) 0.05).
## 3 Experimental Evaluation
### Evaluation of Basic Property  
Figure 6 shows the results of the rotational selectivity. The horizontal axis shows the rotation angle of the medium, and vertical axis shows relative diffraction power. A single hologram is recorded, and the diffraction power is measured by rotating the medium. The numerical aperture (NA) of the objective lens for the reference beam was 0.4, 0.25, and 0 (plane wave). We determined the rotational selectivity when NA = 0.4 decreased rapidly for peristrophic multiplexing recording, in which spherical reference wave was used although the crosstalk rotation angle was 10\({}^{\circ}\).  
Next, we evaluated shift multiplexing with rotation. First, shift multiplexing was performed 30 times along \(x\)-direction with 50 \(\mu\)m intervals. After the shift multiplexing, the diffraction power of the shift-multiplexed holograms was measured (0\({}^{\circ}\) readout). The medium was then rotated by 10\({}^{\circ}\), and the diffraction power was measured along same \(x\)-direction (10\({}^{\circ}\) readout). The results, shown in  
\begin{table}
\begin{tabular}{l l} \hline Wavelength \(\lambda\) & 405 nm \\ Focal length \(f\) & 60 mm \\ Medium thickness \(L\) (mm) & 1.5 \\ SLM pixel size \(\Delta\) & 40 \(\mu\)m \\ CCD pixel size \(p\) & 2.7 \(\mu\)m \\ Incident angle of signal \(\theta_{s}\) & 45\({}^{\circ}\) \\ Incident angle of signal \(\theta_{r}\) & 15\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Experimental conditions.  
Figure 5: Experimental setup.  
Figure 7, reveal that crosstalk can be suppressed completely with 10\({}^{\circ}\) rotation. As a result, crosstalk did not become an issue even if a new shift-multiplexed hologram was recorded after rotating by 10\({}^{\circ}\). These results were approximately the same as the rotation selectivity in peristrophic multiplexing previously obtained in this experiment.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
###### Abstract  
The need for a high-speed transmission network has become essential due to the exponential increase in traffic. In this paper, a free-space-optics (FSO) link modelled by integrating two multiplexing techniques, i.e., spectral amplitude coding-optical code division multiple access (SAC-OCDMA) using zero cross correlation (ZCC) codes and polarization division multiplexing (PDM), is proposed. On the X-polarization (\(X_{polar}\)) state, three users with three different ZCC codes are transmitted. In addition, another three users with the same ZCC codes are transmitted on the Y-polarization (\(Y_{polar}\)) state. Each user carries 20 Gbps of information. Weather conditions, such as clear, fog, and snowfall, are considered when assessing the efficacy of our suggested model. The results exhibit 120 Gbps transmission at 10 km under clear weather. For foggy weather, the propagation range varies from 1.6 km to 0.76 km according to the density of the fog. Moreover, the system can transport information up to 1.2 km during wet snowfall, though this range decreases to 0.26 km under dry snowfall showing that the highest attenuation is caused by dry snowfall weather conditions. The achieved ranges are obtained with a bit error rate \(\leq\)10\({}^{-9}\) and Q-factor greater than 6. Consequently, this proposed FSO model is suggested for use in 5G and 6G high speed transmission networks.  
bit error rate; free space optics; polarization division multiplexing spectral amplitude coding-optical code division multiple access; zero cross correlation +
Footnote †: journal: _Applied Sciences_
## 3 Undifferentiated Cells (Stem Cells)
### Adult Stem Cells
#### 3.4.5.2 Applications in BTE  
PDPCs have been proposed as suitable alternatives for BTE applications due to their supreme characteristics such as a high proliferation rate, easy isolation, and expansion as well as the high potential of differentiation into osteogenic lineages [330]. Moreover, it has been shown that PDPCs have greater in vitro osteogenic potential in comparison to MSCs derived from other sources such as adipose tissue or synovium [337]. As previously clarified in the literature, the osteogenic potential of PDPCs is affected by a series of items including the harvesting site, donor conditions, and technical factors. As an illustration, the PDPCs derived from load-bearing bones possess a more osteogenic capacity than those that originated from flat bones [338; 339; 340].  
In 2006, [PERSON] et al. evaluated the osteogenic potential of human PDPCs in 3D PLGA scaffolds using allogeneic serum [341]. They seeded the expanded cells onto the 3D PLGA constructs and cultured them in an osteogenic medium for 28 days. The authors examined the ossification process using molecular and histopathological analyses. The newly formed bone-like nodules and over-expression of osteogenic genes (collagen I, osteoclastin, and osteonectin) were observed, which confirm the potential of this system for the creation of new bone in vivo.
## 5 Experiments and Performance Evaluation
### Energy Efficiency Evaluation  
After confirming the effectiveness of the perception architecture proposed in this article, we can further evaluate its improvement in energy efficiency. As mentioned above, the sensing result of the SOP perception unit can not only help the flexible configuration of the USRP device, but also determine whether it enters the standby state to reduce power consumption. Assume that the access USRP device's running power consumption is P\({}_{1}\), and P\({}_{1}^{\prime}\) in standby mode. The perception USRP device's running power consumption is  
Figure 31: Experiment and results in B1: (**a**) experimenting in parking lot; (**b**) perception result in parking lot B1.  
P\({}_{2}\). The saved power can be calculated as \(\left(\mathrm{P}_{1}-\mathrm{P}_{1}^{\prime}\right)\times\mathrm{N}^{\prime}- \mathrm{P}_{2}\), where \(\mathrm{N}^{\prime}\) is the number of USRP switched to standby mode according to the perception result. The energy efficiency improvement percentage can be further calculated as \(\frac{\left(\mathrm{P}_{1}-\mathrm{P}_{1}^{\prime}\right)\times\mathrm{N}^{ \prime}-\mathrm{P}_{2}}{\mathrm{P}_{1}\times N}\).  
We take a SOP positioning system composed of six X310 and one B210 as an example for power consumption evolution, where X310s are responsible for signal access and B210 is responsible for signal perception. By using a DC power meter, we measured the power consumption of the device in different states: The P\({}_{1}\) and P\({}_{1}^{\prime}\) of the X310 is about 34.7 w and 16.2 w; the P\({}_{2}\) of the B210 is about 0.7 w, which is shown in Figure 33.  
Figure 33: Energy efficiency evaluation experiment: (**a**) a SOP positioning system with six X310 and one B210; (**b**) P\({}_{2}\): the running power of B210; (**c**) P\({}_{1}\): the running power of X3100; (**d**) P\({}_{1}^{\prime}\): the standby power of X310.  
Figure 32: Experiment and results in B2: (**a**) experimenting in parking lot; (**b**) the recognition rate at different distances.  
Based on these data, the reduced power consumption and percentage of energy efficiency improvement at different N\({}^{\prime}\) can be calculated, as shown in Figure 34. It can be seen that the power consumption can be reduced by about 10% to 50% (20 w-110 w) in this system.
## 3 Results and Discussion
### The Relationship between Strength and Damage Parameter  
Due to the compressive actions on the structural concrete, understanding the degradation of the compressive strength under FT cycles is essential for the effective utilization of concrete in cold regions. Moreover, tensile strength is a basic parameter in fracture calculation and simulation of concrete structures. Consequently, determination of strength under FT cycles is vital, which will contribute to the development of concrete performance models and service life tools, supporting a holistic approach for deterioration assessment [43]. For different FT cycles, the strength loss and damage parameters are shown in Figure 9. Damage parameter, compressive strength and split-tensile strength all showed a strong linear relationship with FT cycles. Therefore, for a given concrete subjected to FT cycles, the damage parameter can be used to predict the residual strength.  
Figure 9: The relationship between strength loss and damage parameter for water-exposed (**A**) and sulfate-exposed (**B**) RAC.  
For the specimen subjected to FT cycles, its residual strength can be expressed as;  
\[\sigma_{R}=\sigma_{0}(1-\alpha(1-e^{\beta n})) \tag{6}\]  
where \(\sigma_{R}\) and \(\sigma_{0}\) are the residual and the original strength, respectively, while \(n\) is the number of FT cycles in strength prediction. The coefficients \(\alpha\) and \(\beta\) can be calculated using Equations (7) and (8);  
\[\alpha=(1-\sigma_{R}(N)/\sigma_{0})/(1-E_{dyn,FT-N}/E_{dyn,0}) \tag{7}\]  
And  
\[\beta=\ln(E_{dyn,FT-N}/E_{dyn,0})/N \tag{8}\]  
where \(N\) is the number of FT cycles in the experiment, \(E_{dyn,0}\) and \(E_{dyn,FT-N}\) are the dynamic elastic modulus before and after FT tests with \(N\) cycles. Here, the factor \(\alpha\), which approximately equals to one, reflects the relationship between the deterioration of strength and dynamic elastic modulus, while \(\beta\) indicates the effects of FT cycles on the dynamic elastic modulus. As dynamic elastic modulus has been widely studied for concrete with different mixture properties, it is convenient to determine those coefficients. For the RAC tested in the present study, \(\alpha=1.16\) and \(\beta=0.002\) can be utilized to predict the residual compressive strength of specimens subjected to water-saturated FT condition.
# Effect of Ionic Liquids on the Isobaric Vapor-Liquid Equilibrium Behavior of Acetone-Chloroform
## 1 Introduction  
Acetone and chloroform are extremely important solvents in the chemical industry, especially in the medical field. It is difficult for conventional distillation to separate them because acetone and chloroform attract each other, forming hydrogen bonds and producing a maximum-boiling azeotrope [1]. In various techniques of separating the azeotropic system, extractive distillation is commonly used in industry and a suitable entrainer is crucial to the process of separation [2; 3]. [PERSON] [4] studied several solvents to separate the acetone--chloroform system and found that dimethyl sulfoxide was an appropriate extraction solvent.  
Compared with traditional entrainers or inorganic salts, ILs have some characteristics, e.g., non-volatility, tunable solubility for many organic compounds, good stability, and so on [5]. In this study, [MMIM][DMP] and [EMIM][DEP] were applied to separate acetone + chloroform binary azeotropic system as entrainers. The VLE data for the ternary system containing [MMIM][DMP] or [EMIM][DEP] were measured at 101.3 kPa. Meanwhile, the NRTL model was utilized to correlate the VLE data.  
In order to study the mechanism of ILs as entrainer, it is important to clarify the interactions between ILs and solvent molecules. Quantum chemical calculation is a means to study the interactions between ILs and solvent molecules [6; 7; 8]. In this work, after geometry optimization and frequency analysis calculations, the intermolecular energies were calculated with DFT, basis set superposition error (BSSE) correction, and zero-point energy (ZPE).