esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Histological Analysis--Hematoxylin-Eosin (X-E)
#### 3.4.2 Immunoreactivity for eNOS  
Immunohistochemical staining for eNOS revealed no visualization of eNOS in preparations incubated with 5-HT and in preparations incubated with IQP in SM cells as well as in the myenteric plexus, but, in preparations incubated with IQP +, 5-HT expression was registered, although it was slight with both types of cells (Figure 4).  
The results suggest that IQP has regulatory effects on the serotonergic system, mediated through the enzyme nNOS. In addition, these results, although speculatively, suggest that IQP participates in the regulation and activity of 5-HT and its neuroendocrine functions.  
The present results show that the application of IQP simulates nNOS activity and NO generation as a mediator in the relaxation effects of smooth muscle.  
In agreement with these findings, the in vivo administration of NO inhibitors has been shown to increase the neuronal contents of 5-HT in rat raphe nuclei and 5-HT release from the hippocampus and hypothalamus under normoxic conditions [38].  
Conversely, administration of S-nitroos-N-acetylpenicillamine (SNAP, a NO-donor) markedly decreases 5-HT contents in the hypothalamus, frontal cortex, and raphe nuclei of rat brains under normoxic conditions [39,40].  
The obtained measurement results showed a statistically significant difference between the measured values in the group of preparations treated with 5-HT and IQP, compared to 5-HT + IQP. The analysis of the results of the mean distribution of nNOS in SM cells showed a significantly higher number of nNOS and positive cells per slice when treated with IQP + 5-HT compared to those treated with only 5-HT and IQP (\(p<0.05\)). The myenteric plexus statistical analysis showed a significantly higher number of nNOS-positive slice cells in preparations treated with IQP + 5-HT compared with those treated with only 5-HT and IQP (Figure 5).  
NO is a simple and unique molecule which performs many and various functions, including those of an intracellular second messenger and an intracellular mediator. In this respect, the study of new molecules stimulating the expression of NOS1 or NOS3, which would stimulate an increased local NO production, is especially important. This issue has important clinical and treatment consequences which must be kept in mind when designing new immunomodulating therapies relying on NOS expression to fight viruses, cancer, or other infectious diseases.
## 3 Results and Discussion
### Optimum Detuning between Individual PE Cantilevers  
Frequency tuning of a PEC can be done either by changing the effective mass or the beam stiffness. This can be achieved by variation of the cantilever geometry or material properties. In practical applications, commercially available PECs are likely to be resorted to. Such PECs are usually available in predefined steps of beam stiffness. Therefore, tuning by the tip mass is preferable. At upper frequencies the improvement by adding a further element with the higher resonance frequency to the array diminishes. As an example, Figure 4 shows that the peak power of the PEC 3 (\(f_{0}=41.8\) Hz, \(m_{\text{tip}}=6.76\) g) is lower than the peak power of the PEC 1 (\(f_{0}=40\) Hz, \(m_{\text{tip}}=7.4\) g) and PEC 2 (\(f_{0}=40.9\) Hz, \(m_{\text{tip}}=7.07\) g). The reason for this behavior is the smaller beam deflection with the reduced tip mass.  
Figure 5 presents simulated results for arrays with 2, 3, 4, and 5 PECs, where the detuning \(\Delta f\) is varied. The frequency bandwidth \(B\) (Figure 5a) is defined as the difference between the upper and lower frequencies, where the half of the peak electrical power harvested from the entire PEC array is reached. For a given number of array elements, an abrupt decrease of \(B\) (i.e., the steps seen in the plot) with the increasing detuning \(\Delta f\) is explained by the formation of a deep gap in the frequency dependence of the total electrical power \(P\) in the frequency region between the PEC with the highest resonance frequency and the PEC with the nearest lower resonance frequency (cf. Figure 6). As an example in Figure 6 shows, if the detuning \(\Delta f\) is too large, the resulting bandwidth is determined by two of three PECs. The curve \"SUM\" represents the sum of electrical powers in all three PECs.  
Figure 3: Mechanical set up with a PEC array.  
Figure 5b displays the guaranteed power, which can be harvested from the array at a given value of \(\Delta f\). From the results of Figure 5 the following conclusions can be drawn:  
* The larger the frequency gap between the single PECs, the larger the bandwidth of the PEC array. This improvement works up to the certain maximum detuning \(\Delta f_{\text{max}}\).
* The larger the frequency gap, the smaller the peak power. With the increasing detuning \(\Delta f\), the peak power drops drastically and approaches a value which is practically independent of the number of PECs in the array.  
To the best of our knowledge, the analysis presented in Figure 5 has been performed for the first time.  
Figure 4: Simulated resonance curves of piezoelectric cantilever beam arrays.  
Figure 5: (**a**) Simulated dependence of the bandwidth on the frequency detuning \(\Delta f\); (**b**) Dependence of the power at the half-power frequencies on the frequency detuning \(\Delta f\).
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 3 System Testing  
The main goal of these system tests was to validate the performance of the long-distance signal transmission system within the bistatic RCS testing configuration. The tests were designed to obtain information on the gain and phase stability of the signal transmission system to determine whether its integration into the entire testing system  
Figure 10: DFT of the demodulated and modulating signals.  
Figure 9: Comparison of modulating signal and demodulated signal.  
would affect the system's functionality. Additionally, the tests involved the observation of the clutter and higher-order harmonics introduced by the system to assess whether a filtering device was needed. To prevent the amplitude of the photocurrent detected by the photodetector module from being too small, which could affect subsequent signal processing, the system cascades an amplifier model GWPA010400-35-24, made by Beijing Gwave Technology Co., Ltd. (Beijing, China), after the photodetector. The detailed parameters are shown in Figure 11.
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 2 Background
That is, the components of the signal \(x\) are projected onto the representation matrix that is used to transmit the information expressed as the elements of the vector \(y\) in a compressed manner, i.e., each component of \(y\) is represented as \(y_{k}=<a_{k},x>\). CS guarantees that, with this vector \(y\) with far fewer elements, \(k\), than the dimension of the original signal, \(n\), i.e., \(k<<n\), it is possible to recover with high accuracy the original signal \(x\). Let us explain this projection operation with an example. Given a signal represented as a vector \(x^{T}=(x_{1},x_{2},x_{3},x_{4})\), and a representation basis \(A=(6,2,4,5)\) which for this example contains only one row. The projection is carried out like the next equation details. \[y=Ax= \begin{pmatrix}6&2&4&5\end{pmatrix}\begin{pmatrix}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{pmatrix} =6x_{1}+2x_{2}+4x_{3}+5x_{4}=Z. \tag{2}\]  
So basically we have projected the vector \(x\) onto the projection basis \(A\) obtaining the projected value \(y=Z\). It is worth mentioning that initially, we had a signal with three components \(x_{1},x_{2},\,x_{3}\) and \(x_{4}\) while the projected value comprises only one component \(Z\), reducing therefore the amount of information. The application of this technique to WSNs is described in [5], where the measurements obtained by each sensor of the network are seen as the components of a signal as Figure 2 depicts, applying CS to compress this information and its subsequent recovery. Therefore, knowing the appropriate basis where this signal is sparse, nodes can apply CS generating projection vectors that compress the information as it is sent to the FC without a significant increase in the size of the packet. Until now, we have explained the compression process of the CS technology but we have not dealt with the reconstruction process. This consists in the following minimization of the \(l_{1}\) norm [4]:  
\[\min_{\vec{x}\in\mathbb{R}^{n}}||\vec{x}||_{l_{1}}\text{ subject to }\Phi\vec{x}=y, \tag{3}\]  
where \(||.||_{l_{1}}\) represents the \(l_{1}\) norm of a vector that is \(\sum|x_{i}|\). The reason for minimizing the \(l_{1}\) norm of the vector, instead of minimizing the \(l_{0}\) was proposed by [PERSON] and [PERSON] [6] because of being a good convex approximation to the latter minimization problem. Although the target of this paper is not the analysis of the different techniques to recover the original signal \(x\), i.e., the minimization of the \(l_{1}\) norm of the vector, there are different research works which have successfully addressed this optimization problem. They are, a greedy algorithm called Matching Pursuit [7], as well as different improvements like Orthogonal Matching Pursuit [8] or Fast  
Figure 2: Wireless sensor network (WSN) measurements viewed as the components of a signal represented by a matrix. Bayesian Matching Pursuit [9], another statistical approach named Bayesian Compressive Sensing [10], and a convex optimization approach [11].
## Abbreviations  
CNCs, cellulose nanocrystals; CNFs, cellulose nanofibrils; BNC, bacterial nanocellulose.  
**Vocabulary Section**  
_Cellulose nanomaterials:_ crystallite or cellulose fiber containing at least one dimension in the nanoscale range (1-100 nm).  
_Cellulose nanocrystals:_ cylindrical, elongated, rigid, rod-like nanoparticles with dimensions of 4-70 nm in width, 100-6000 nm in length, usually obtained by hydrolysis.  
_Cellulose nanofibrils:_ commonly obtained by mechanical treatment, are composed of longer and flexible fibers with dimensions of 20-100 nm in width and >10,000 nm in length and a low crystallinity grade.  
_Bacterial nanocellulose:_ consists of ultrafine nanofibers that are 20-100 nm in diameter and micrometers lengths and form an identifying 3D network.  
_Acidification:_ the degradation process that promotes cellulose depolymerization phenomena, resulting in the loss of the typical mechanical properties of the material.  
_Consolidation:_ the restoration work aimed at recovering the compactness of the material, which is obtained by filling the gaps inside the material itself.
## 2 Algorithm Principle
### INS+LEO 3-Satellite Alternate Switching Ranging Integrated Navigation Algorithm
#### 2.1.2 Algorithm Flow  
We assume that the position of PRN1 in the Earth-centered Earth-fixed (ECEF) coordinate system is \((x_{1},y_{1},z_{1})\), the position of PRN2 in the ECEF coordinate system is \((x_{2},y_{2},z_{2})\) and the position of PRN3 in the ECEF coordinate system is \((x_{3},y_{3},z_{3})\). The aircraft's real position in the ECEF coordinate system is \((x_{A},y_{A},z_{A})\) and the aircraft's coordinates in the ECEF coordinate system measured by INS are \((\tilde{x}_{A},\tilde{y}_{A},\tilde{z}_{A})\). The position obtained by the INS solution is \((x_{INS},y_{INS},z_{INS})\) and the alternate interval is \(\Delta T.\rho_{1}\rho_{2}\) and \(\rho_{3}\) are the true ranging values of PRN1, PRN2 and PRN3, respectively, and \(\tilde{\rho}_{1}\), \(\tilde{\rho}_{2}\) and \(\tilde{\rho}_{3}\) signify the virtual values of PRN1, PRN2 and PRN3 measured by INS, respectively. The ranging values \(\rho_{INS1}\),\(\rho_{INS2}\) and \(\rho_{INS3}\) symbolize the virtual ranging values of PRN1, PRN2 and PRN3 obtained by the INS solution, respectively.  
At moment \(t_{1}\), we use the real ranging value \(\rho_{1}\) of PRN1, PRN2 uses the virtual ranging value \(\tilde{\rho}_{2}\) and PRN3 uses the virtual ranging value \(\tilde{\rho}_{3}\). Then, we use the difference \(\dot{\rho}_{1}=\rho_{1}-\rho_{INS1}\) between \(\rho_{1}\) and \(\rho_{INS1}\), the difference \(\dot{\tilde{\rho}}_{2}=\tilde{\rho}_{2}-\rho_{INS2}\) between \(\tilde{\rho}_{2}\) and \(\rho_{INS2}\) and the difference \(\dot{\tilde{\rho}}_{3}=\tilde{\rho}_{3}-\rho_{INS3}\) between \(\tilde{\rho}_{3}\) and \(\rho_{INS3}\), as the UKF filtered observations for filtering. Similarly, at \(t_{2}=t_{1}+\Delta T\) and \(t_{3}=t_{2}+\Delta T=t_{1}+2\Delta T\), we use the real ranging values of \(\rho_{2}\) and \(\rho_{3}\), respectively, and the remaining satellites use virtual values. Finally, we loop this process and switch between the real and virtual ranging value of the satellite at intervals of \(\Delta T\) until the flight time of the aircraft ends.  
Integrated Navigation Algorithm of INS+2-Satellite Alternate Switching Ranging under LEO 3-Satellite
# Effects of Sand-Cement Columns on Primary Consolidation Settlement
###### Abstract
(2016) [17] demonstrated that cement-stabilized sand columns under footing models improved the bearing capacity by up to 62-90%, while also significantly reducing compressibility and settlement. These improvements hold especially in regions with a high moisture content, where stabilization is critical to mitigating pore pressure buildup and swelling. According to [PERSON] et al. (2019) [16], laboratory and field studies have consistently demonstrated substantial bearing capacity gains within composite sand-cement columns, typically in the order of 70-90%, compared to untreated clay foundations. Consolidation settlement is a gradual and continuous process in which the soil beneath a foundation compresses under applied loads, reducing its volume over time due to water expulsion. When the settlement exceeds the allowable limits, it can lead to significant structural issues, including cracks, tilting, and other severe deformations in buildings and infrastructure. The financial impact of repairing structures affected by excessive consolidation settlement is often substantial, making it a critical engineering concern. Addressing this challenge requires innovative stabilization techniques to mitigate settlement and ensure the long-term stability of foundations. This research uses sand-cement columns as a stabilization technique to effectively control consolidation settlement in saturated clayey soils, addressing some limitations of conventional ground improvement methods. This study investigates the combined performance of sand-cement columns, particularly their dual capacity to enhance both drainage and stiffness simultaneously. Additionally, the work provides an innovative analysis of how varying proportions of sand and cement influence consolidation behavior and offers practical guidance for optimized design in soft clay foundations.
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 4 Numerical Results
### IEEE 57-Bus Test System
#### 4.3.3 Case 11: Minimizing the Fuel Cost, Emission, Voltage Deviation and Losses
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Emission**} & \multicolumn{1}{c}{**Fuel Cost**} & \multicolumn{1}{c}{**V.D. (p.u.)**} & \multicolumn{1}{c}{**Power Losses**} \\
**Algorithm** & **(t/h)** & **(USD/h)** & **(MW)** \\ \hline AGTLBO & **1.4328** & **41,929.387** & 0.9526 & 13,2563 \\ TLDB & 1.4357 & 41,923.013 & 0.9528 & 13,6420 \\ MOMICA (Multi-Objective Modified Imperialist Competitive Algorithm) [62] & 1.496 & 41,983.059 & **0.7970** & 13,6969 \\ MOICA (Multi-Objective Imperialist Competitive Algorithm) [62] & 1.7605 & 41,998.566 & 0.8748 & 13,3353 \\ MNSGA-II (Modified Non-dominated Sorting GA-II) [62] & 1.4965 & 42,070.825 & 0.8896 & 14,4557 \\ BB-MOPSO (Bare-Bones Modified Particle Swarm Optimization) [62] & 1.5336 & 41,994.019 & 1.0742 & **12.6090** \\ NKEA (Neighborhood Knowledge-based Evolutionary Algorithm) [62] & 1.5174 & 42,065.996 & 1.0420 & 13,9764 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: \(Cont.\)In addition, the voltage profile is given for all three cases of the 57-bus system in Figure 5. It is clear from the figure that the allowable limits of the bus voltage have been well-observed for the best solutions.
## 3 Results and Discussion
### The Sensitivity of Input Variables  
The input and output variables of the ENN models are used in Equation (22) to detect the impact of admixture contents on SCC characteristics. The calculated sensitivity of each variable in S and CS is presented in Figure 9. From the figure, it can be shown that the main components of the admixture affected SCC characteristics. The S and CS are influenced by the contents of the coarse and fine aggregate; while the FG percentage higher affected CS, the S is affected by the contents of CG. The effect of water content is shown to be high on S values compared to the effect on the CS values of SCC. Here, the SP effect is shown to be low on the CS properties of SCC. Meanwhile, the effect of powdered contents is shown to be high and has a different impact on S and CS values of SCC. The total effect of powdered materials on CS and S is 57.54% and 40.87%, respectively. Thus, the impact of total powdered content is high on the admixture content of SCC compared by other admixture materials. The minimal effect on S and CS from the whole admixture content is the SP percentage. When comparing the powder content, it can be seen that the FA affects the CS of SCC by 35.6%, followed by LP, SF, and C, respectively by 12.36%, 5.34%, 4.34%. While the impact of FA, LP, SF, and C on S of SCC is 12.37%, 8.11%, 11.38%, 9.01%, respectively. These results indicate that the FA can be considered as the main factor that affects the S and CS values of SCC, and the cement has a lower impact.  
Thus, it can be concluded that the sensitivity of powder content of fly ash has a significant impact on the estimated S and CS values of SCC that contains FA, SF, and LP based on its mix design i.e., C, FA, SF, LP, W, SP, CG, FG. Herein, these results are shown coinciding with [PERSON] et al. [4], who concluded that the fresh and hardened characteristics of SCC are affected by the FA percentage. [PERSON] et al. [36] also shown that the FA is the high impact factor for estimating CS of SCC containing C, FA, W, SP, CG, and FG. FA is also seen improved the mechanical performance of SCC that includes C, marble cutting slurry waste, FA, SF, W, SP, FG, and CG [37].  
Figure 8: Performance of models for compressive strength modeling based on the number of data. (a) Violin diagram for the testing stage; (b) Taylor diagram for the validation stage.
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 4 Heterogeneous Graph Structure Learning Model
### Heterogeneous Graph Contrastive Learning  
This section primarily discusses the heterogeneous graph contrastive learning, which is the first part of our proposed model. In our model, we use two kinds of contrastive learning methods to help generate representations of users and POIs. The details can be found in Figure 1(a),b. Figure 1(a) includes the process of generating subgraphs based on the meta-paths, encoding these meta-path-based subgraphs and the first kind of contrastive learning method, which we call meta-path-based graph contrastive learning in this paper. First, we construct a graph including users, POIs, and POI attributes. Since there is more than one type of node, the constructed graph is definitely a heterogeneous graph. For users, we use two meta-paths \(p_{u}^{1}\) and \(p_{u}^{2}\) to construct users' meta-path-based subgraph \(G_{uw1}\) and \(G_{uu2}\), while, for POIs, we obtain \(G_{pp1}\) and \(G_{pp1}\) based on \(p_{p}^{1}\) and \(p_{p}^{2}\). To model the interaction relation between users and POIs, we also extract the interaction graph \(G_{up}\). Then, we use different graph encoders to encode these graphs and obtain the representations for different nodes. Finally, we use the meta-path-based contrastive learning to learn representationsfor users and POIs. This contrastive learning works between different meta-paths of the same node type. Figure 2b illustrates another kind of contrastive learning method, which we call interaction-aware contrastive learning. This contrastive learning considers the representations from the interaction graph. We first fuse the representations of the same node type from the interaction graph with others from the meta-path-based subgraphs. Then, we calculate the contrastive loss in a similar way. We divide the heterogeneous graph contrastive learning component into three subsections: the construction of meta-path-based subgraphs, heterogeneous graph representation learning, and heterogeneous graph contrastive learning. More details can be found in the following subsections.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 2 Problem Description
### Parameter Description  
To facilitate the establishment of constraints for the CubeSat scheduling problem, as shown in Tables 1 and 2, the sets and constants of the problem are first defined.  
The decision variables and auxiliary decision variables of the scheduling model are expressed as follows:  
\(t\) means time \(t\in\mathcal{T}\); \(j\) means job \(j\in\mathcal{J}\); \(x_{j,t}\) is a decision variable that takes the value of 1 for each \(t\) that job \(j\) is executing, otherwise it assumes value 0; \(\phi_{j,t}\) is an auxiliary decision variable that takes the value of 1 at \(t\) when job \(j\) was initiated, otherwise it assumes value 0. It is used to denote the start of job \(j\) at time \(t\), representing this transformation of the task from not working and not consuming power to starting to work and consume power; \(\phi^{\prime}_{j,t}\) is an auxiliary decision variable that takes the value of 1 at \(t\) when job \(j\) was initiated for the first time, otherwise it assumes value 0.