esa-sceva/satcom-chunk-collection huggingface dataset:
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Reconfigurable Intelligent Surfaces  
The RIS or Intelligent Reconfigurable Surfaces (IRS) paradigm is also connected to some extent to the ELAA, XLMIMO, LIS, mMIMO, and umMIMO concepts addressed above. RIS is a more generic term than the above-mentioned antenna types and it refers to antennas which can dynamically control the propagation of the electromagnetic waves; this can be used to enhance the radio-based communications, sensing, and/or positioning tasks at the receivers by reflecting, refracting, or absorbing/blocking signals as to improve coverage, capacity, and energy efficiency. RIS has gained a lot of attention in the context of JPSAC in the past few years due to its unique potential to dynamically control the propagation environment. For example, such dynamical adjustments can improve the signal coverage and capacity, especially in challenging environments such as urban areas with many obstacles, and they can help in managing interference and in redirecting signals on additional NLOS paths when LOS is blocked. A comprehensive survey on RIS in ISAC can be found in [60]. Some of the unsolved research questions pertaining to RIS applicability in JPSAC context are: how to best model the reflection losses and the specular (i.e., similar to a mirror's reflections, with the incident angle equal to the reflected angle) and non-specular reflections on RIS, how to distinguish the signals coming from different emitters through multiple bounces or reflections on RIS, and how to build realistic RIS channel models and perform accurate channel estimations.
## 4 Simulation Results  
To verify the theoretical prediction of the proposed method, PSPICE simulations were performed for the obtained linear oscillators in Figures 5b and 7b. The CCII+ and CCII- were realized by current feedback operational amplifier (AD844) ICs [26,27]. The macromodels of AD844 were employed for the simulations with a DC supply of \(\pm 15\) V. For the circuit in Figure 5b, we chose \(C_{1}=C_{2}=C=0.1\)\(\mu\)F and \(R_{2}=R_{3}=1.6\) k\(\Omega\). As shown in Figure 8, the oscillation eventually died out with
## 4 Conclusions  
In order to develop a composite main reflector, which is a core component of a satellite deployable reflector antenna, the structural performance according to the laminated design and material properties of the composite main reflector was analyzed to design the main reflector. Afterwards, based on the designed main reflector, the manufacturing method, the application of the adhesive film, and the venting path inside of the sandwich were selected as design variables during the manufacturing process, and four types of composite main reflectors were manufactured. For each main reflector, weight measurement, non-destructive inspection, surface error measurement, and stiffness measurement during deployment were performed. Through the performance analysis of the composite main reflector according to the manufacturing variables, a process that can achieve additional weight reduction and secure high stiffness during deployment was selected. Through this study, the design, analysis, manufacturing, and verification of the composite main reflector were performed, and a composite main reflector that can be applied to a deployable reflector antenna was developed through the process.  
In the future, we plan to conduct environmental tests (e.g., antenna performance evaluation for vibration and thermal loads) to verify the structural performance of the developed deployable reflector antenna, as well as deployment and RF characteristic verification to verify the antenna's functionality.  
Conceptualization, Y.-J.Y.; methodology, D.-G.K.; software, D.-G.K. and H.-G.K.; validation, D.-G.K. and H.-G.K.; formal analysis, D.-G.K.; investigation, D.-G.K.; resources,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Mass** & **Surface Error** & \multirow{2}{*}{**NDT**} & **1 st Frequency @** \\  & **[kg]** & **[mm, RMS]** & & **Deployed Condition [Hz]** \\ \hline
1 & 1.75 & 0.387 & N/A & 2.99 \\ \hline
2 & 1.78 & 0.393 & N/A & 2.98 \\ \hline
3 & 1.61 & 0.290 & N/A & 3.04 \\ \hline
4 & 1.66 & 0.325 & N/A & 3.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Qualification results of the composite main reflector.  
Y.-J.Y.; data curation, D.-G.K., D.-Y.K. and R.-H.-D.; writing--original draft preparation, D.-G.K.; writing--review and editing, K.-R.K.; visualization, D.-G.K. and H.-G.K.; supervision, K.-R.K.; project administration K.-R.K.; funding acquisition, K.-R.K. All authors have read and agreed to the published version of the manuscript.  
This study was conducted with support from the Korea Research Institute for defense Technology planning and advancement and the Defense Acquisition Program Administration in 2021 (20-207-B00-011-001).  
Not applicable.  
All data are in the paper; there are no additional data.  
The authors declare no conflicts of interest.
## 3 Results
### Exposure Assessment  
The maximum ozone concentrations measured in the test room, placing the 60 and 110 mg/h ozone generator outside the appliance, were 100 and 180 ppb reached after 15 and 27 min. Both experiments gave the same concentration decay profile and the ozone half-life in the test room was 16.01 min (Figure 1). This was consistent with previous data showing a half-life range of 6-30 min, due to surface removal, with no air exchange and no main reactant chemicals [27] and 15-60 min [28] in a 1 m\({}^{3}\) experimental chamber.  
Ozone concentrations inside and outside the dryer, when the dryer was working either empty or loaded with fabrics, showed that reactions with fabrics were very important for the ozone decay, and the concentrations dropped quickly inside the drum when it was working loaded.  
Ozone concentrations, using the low-level 5 mg/h ozone generator, reached a maximum of 100 ppb inside the empty dryer (Figure 2). Outside the dryer, in the room, ozone concentrations were always below our instrumental limit of detection (LOD) of 10 ppb.  
The results of the mass-balance model, using the Monte Carlo simulation, are illustrated in the cumulative probability chart in Figure 3. The median and the 90 th percentile of ozone concentration in the room when the dryer was working with an ozone emission rate of 5 mg/h as continuous source, were 47 and 85 ppb, respectively. The acute exposure scenario is illustrated in Figure 4. The maximum ozone concentration inside the drum during RC, with the 5 mg/h ozone generator, was 100 ppb. Using this figure, maximum ozone concentrations in the room just in front of the dryer, after accidentally opening the empty, fully working appliance, and taking into account the outdoor ozone, were 115 and 143 ppb for the 50 th and 90 th percentiles, respectively.  
Figure 1: Half-life curve of ozone decay in the test room. A least squares regression method was used to fit experimental data.  
Such exposure would occur only for a short time because of the fast ozone decay due to removal reactions with the room surfaces and the other atmospheric gases.  
Figure 3: Monte Carlo analysis results reached after 20,000 simulation trials, showing the cumulative probability plot for ozone concentrations in a small room (15 m\({}^{3}\)), with a penetration factor of ozone through the building shell of 100% and assuming the dryer was working as a continuous source of 5 mg/h.  
Figure 2: Ozone concentrations inside the empty dryer with a 5 mg/h ozone generator. Outside ozone was undetectable (<10 ppb) under this experimental setup.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
###### Abstract  
This paper deals with the problem of geodetic monitoring of structures by means of permanent GNSS stations, with a focus on a specific project of monitoring a bridge by a small network of three stations. What is peculiar about this paper is that the stations used are endowed with low-cost GNSS receivers, and the data treated continuously cover a time-span of more than 4 years. The monitoring service GeoGuard, at work on the project, has proved to be reliable in terms of both hardware and software. The results display almost uniform accuracy at less than the 1 mm level for daily adjusted coordinates and at the level of \(\sim\)1-2 mm for hourly solutions. After a short review on the basics of positioning by GNSS phase observations, the error of the estimated coordinates is discussed in detail, and a procedure of warning/alarm is described. The experience in terms of hardware and software employed is then presented together with the results, which are mostly displayed in graphical form and with a few tables.  
GNSS; structural health monitoring; bridge; GeoGuard +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
20221212468. [[https://doi.org/10.3390/app122312468](https://doi.org/10.3390/app122312468)]([https://doi.org/10.3390/app122312468](https://doi.org/10.3390/app122312468))
## 2 Data Quality Characteristic Analysis
### Signal Power
As mentioned in the Introduction section, there is substantial research indicating a strong connection between smartphone GNSS observation quality and the CNR. Hence, it can be inferred that satellite elevation does not significantly affect smartphone GNSS observation quality.
## 3 Recommended Architecture for Industrial Software Service Components
### FM-Based Models for Service Recommendation  
#### 4.2.1 Introducing Hidden Vectors  
A factorization machine (FM) is based on logistic regression. By adding the second-order feature crossover part, introducing the concept of hidden vectors for each feature, and using the inner product of the hidden vectors as the weight of each combination of features, the introduction of hidden vectors solves the problem of data sparsity and provides second-order crossover ability. This enhances the model's expression capability compared to logistic regression; however, because of the combinatorial complexity, extending the FM model to higher-order feature interactions presents challenges. The FM is defined as follows:  
\[\hat{y}_{FM}(x)=w_{0}+\sum_{i=1}^{n}w_{i}x_{i}+\sum_{i=1}^{n}\sum_{j=i+1}^{n} \left\langle v_{i},v_{j}\right\rangle x_{i}x_{j}. \tag{3}\]  
With the parameters \(w_{0}\in\mathbb{R}\), \(W\in\mathbb{R}^{n}\), and \(V\in\mathbb{R}^{n\times k}\), where \(w_{0}\in\mathbb{R}\) represents the global bias term, \(w_{i}\) signifies the strength of the impact that the \(i\)th feature has on the outcome variable, \(\left\langle.,.\right\rangle\) denotes two vectors making up an inner product, \(v_{i}\) is the \(i\)th row variable of \(V\) having \(k\) factors, denoted as the embedding vector of the \(i\)th feature, i.e., the hidden vector, and \(k\in\mathbb{R}_{0}^{+}\) is a hyperparameter defining the dimension of the factorization.
## 3 Framework Development
### Integrating Corrosion Effects into Seismic Fragility Curves
\begin{tabular}{l l} \(\theta^{\prime}_{\text{ds}}(\mathfrak{t})\) & The adjusted median capacity of the structure to resist a specific damage state i at \\ \(\mathfrak{t}\), accounting for the effects of corrosion over time; & \\ \(\theta_{\text{ds}_{\text{i}}}\) & The baseline median capacity of the structure to resist the damage state i without the \\ \(\mathfrak{f}_{\text{CL}}(\mathfrak{t})\) & corrosion consideration; \\ \(\mathfrak{f}_{\text{CL}}(\mathfrak{t})\) & The degradation factor, a function of time t, quantifying the reduction in structural capacity due to corrosion, based on corrosion level (CL) and elapsed time; \\ \(\mathfrak{t}\) & The time in years since the beginning of the structure's exposure to corrosive conditions; \\ CL & The corrosion level, which represents the severity of the corrosion affecting the \\  & structure. The corrosion level can be categorized (e.g., minor, moderate, intensive). \\ \end{tabular}   
To enhance the reliability of our research and substantiate the simulation outcomes, the findings regarding the degradation rate were juxtaposed with the results presented by [PERSON] et al. [78]. Their study delineated deterioration rates for RC bridges' fragility curves over five distinct time spans within a marine environment, which is typically considered a setting of intensive corrosion. The fragility curves reported by [PERSON] et al. [78] encompassed durations of 0, 25, 50, 75, and 100 years, providing a robust framework for comparison given the similar environmental conditions. The comparative analysis revealed that for all damage states across the time spans of 25, 50, and 75 years, our results aligned closely with those of [PERSON] et al., exhibiting a deviation ranging between 3% and 14%. This similarity indicates a significant validation of our simulation techniques and methodologies, underscoring the accuracy of our degradation rate estimations within the expected margins. However, it should be noted that for the 100-year time span, our results were consistently more conservative. This discrepancy suggests that the actual vulnerability of the structures might be higher than our models predict. Such an underestimation is attributed to the simplifications considered in our modeling approach, as mentioned in Section 3.1. Acknowledging this potential underestimation is crucial for refining future models and enhancing the predictive accuracy of our research in assessing the long-term impacts of corrosion on structural integrity. This awareness allows for the continuous improvement of our methodologies, ensuring more reliable and accurate predictions in future studies.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 4 Experimental Results and Analysis
### Polarization Imaging Experiment of Underwater Targets with Different Materials  
The polarization characteristics of targets made of different materials are quite different. The degree of polarization image contains a lot of information that is difficult to detect in the light intensity image. In the underwater target imaging experiment with different materials, in order to explore the polarization characteristics of metal and non-metal targets, four different materials of targets are tested in this paper. The materials of the targets are, respectively, brass, red copper, ceramics and plastics, and the size of the targets is 100 mm \(\times\) 20 mm \(\times\) 2 mm. Polarization imaging was performed in clear water with a depth of 30 cm. The light intensity image and degree of polarization image obtained are shown in Figure 9. By observing the degree of polarization image, it can be seen that some colors of red copper and brass metal materials are white, while some colors of ceramic and plastic nonmetallic materials are black. According to the relationship between the gray value of the digital image and color, it can be concluded that the degree of polarization of metal materials is higher, while that of the nonmetallic materials is lower. It is proved that polarization imaging technology can be used to effectively distinguish metal and nonmetal targets.  
Figure 8: Curve fitting of pixel response gray value.  
Figure 9: Image I and image DOP of different materials: (**a**) image I; (**b**) image DOP.  
In Figure 9, the targets made of ceramics, red copper, brass and plastics are arranged from top to bottom. A single target is extracted by clipping the polarization degree images of different materials. The quality of polarization degree image is evaluated, respectively. The data distribution ladder diagram of evaluation results is shown in Figure 10.  
According to the results shown in Figure 10, it can be concluded that the information entropy values of all polarization degree images are high. The values of EME and average gradient of ceramic and plastic materials are far lower than those of red copper and brass. It is further verified that the polarization characteristics of metal and nonmetal materials are quite different.
# Numerical Study of Geotextile-Reinforced Flexible Pavement Overlying Low-Strength Subgrade
## 3 Results and Discussion
### Effect of the Geotextile's Elastic Modulus on Pavement Response  
In this parametric study, three types of geosynthetics with variable elastic modulus values (as outlined in Table 3, i.e., soft, medium, and stiff) are evaluated, and their effect on pavement behavior is discussed. This parametric analysis was conducted on a paved section with geotextile reinforcement maintained at the same position, i.e., the base-subgrade interface. Figure 7 shows the effect of elastic modulus on vertical displacement on the subgrade. The reduction in vertical deformation was 43.1%, 48.7%, and 51.1% for geotextiles with an elastic modulus of 42 MPa, 1575 MPa, and 3150 MPa, respectively. Figure 8 presents the impact of the geotextile's elastic modulus on stresses transferred to the subgrade. A reduction in stresses of 86.9%, 90.9%, and 92.1% was observed for the elastic modulus values of 42 MPa, 1575 MPa, and 3150 MPa, respectively.  
All the reinforced sections with varying elastic modulus values experienced less stress transferred to the subgrade and, consequently, less deformation than the unreinforced section. Notably, stiff geotextiles yield more reinforcement benefits. This observation agrees with that of [PERSON] et al. (2018), although the reinforcement material was a geogrid.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geotextile** & **Strength at 5\% Strain** & **E (MPa) Assuming** & **Poisson Ratio** \\
**Description** & **(kN/m)** & **t = 0.00254 m** & **Poisson Ratio** \\ \hline Soft & 5.3 & 42 & 0.3 \\ Medium & 200 & 1575 & 0.3 \\ Stiff & 400 & 3150 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material properties for geotextiles.  
Figure 7: Comparison of vertical displacement on top of the subgrade for geotextiles of varying elastic modulus.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Routing of Other Methods in WBAN  
#### 3.6.1 Multipath-Based Routing  
In addition to the above routing methods, some researchers have introduced the WSN multipath routing method into the routing design of WBAN, by generally, using the node-independent multipath routing method [34; 35]. Multipath refers to the simultaneous establishment of multiple transmission  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Goal** & **Characteristic** & **Priority** & **Delay** & **Reliability** & \
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} \\ \hline LRPD (2017) & optimization delay & Modularization & \(\surd\) & low & N/A & medium \\ \hline HDPR (2017) &
\begin{tabular}{c} Optimizing Delay, \\ Reliability and Node \\ Temperature \\ \end{tabular} & Modularization & \(\surd\) & low & high & high \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the Qos-based routing protocols.  
Figure 13: Modular diagram of the HDPR protocol.  
paths, between the source node and the destination node, to transmit data in groups, in parallel. While node-independent multipath routing refers to the independent paths between the source node and the destination node, and there is no common node or path between them, as shown in Figure 14. The advantage of this method is that the multipath can be used to ensure a stable transmission, balance energy consumption of nodes, and prolong network lifetime, but there might be shortcomings when data failure in a packet might affect the accuracy of the whole data.
## 3 Quasi-Static and Inverse Kinematic Characteristics of Single Cable
If \(\varepsilon_{\max}\to 0\) then \(\Pi\subset\Delta\) is the affine plane that contains \(\Psi\) and its associated vector space that is the orthogonal complement to \(\Upsilon\). Thus, the projection of \(\Psi\) onto \(\Upsilon\) is the affine line \(\pi\subset\Upsilon\). Moreover, for each given \(L\) value, if \(\varepsilon_{\max}\to 0\) then there exists only one feasible tension. Therefore, \(\Psi\) becomes the spatial curve in \(\Delta\). \(\pi\) can be expressed as the relationship between \(T_{z}\) and \(T_{x}\) in (21)_  
\[(\pi):T_{z}=a_{\pi}T_{x}+b_{\pi} \tag{21}\]  
_where \(a_{\pi}\) and \(b_{\pi}\) denote the parameters that depend on \(\mathbf{B}^{d}\) and \(\varsigma\)._  
The quasi-static model is established when these parameters are formed in explicit function of \(\mathbf{B}^{d}\). A certain case of \(\pi\) is obtained by using linear regression to fit the projected solution on \(\Upsilon\) and it is illustrated in Figure 5.  
On the other hand, [PERSON], [PERSON], et al. pointed out the nonlinear relationship between \(T_{z}\) and \(T_{x}\) in [21]. In case of CDPM, the conditions of \(\rho gL<w_{\max}\) and \(T_{x}>T_{x_{\min}}\) must be satisfied in order to maintain the stability of the system where \(w_{\max}\), \(T_{x_{\min}}\) are the maximum self-weight of cable and minimum tension along the \(X\)-axis. Moreover, the nonlinear segment of the relationship between \(T_{z}\) and \(T_{x}\) is located at the low part of \(T_{x}\). Hence, we can ignore the nonlinear part of it and linearize the relationship between \(T_{z}\) and \(T_{x}\) when \(\rho gL<w_{\max}\), by employing (21). Moreover, on the simplification of cable model in [13], [PERSON], [PERSON] et al. proved that if \(T\ll EA\), then the elongation of cable caused by elasticity has an ignorable impact on the relationship between \(T_{z}\) and \(T_{x}\). This is also proved in [20]. In fact, the above assumption is always satisfied since cables used in CDPM often have very high stiffness to withstand the load of the mobile platform. Therefore, \(a_{\pi}\) and \(b_{\pi}\) now just depend on \(\mathbf{B}^{d}\) and \(\rho\). In Section 4, we will figure out the quasi-static model of a single cable by combining an analytical approach and empirical approach.
# An Overview of CubeSat Missions and Applications
## 2 The Success of CubeSats and Its Enabling Factors
### Support from Space Agencies and Research Institutions
In India, although the Indian Space Research Organisation (ISRO) does not operate a named CubeSat initiative akin to CLSI or Fly Your Satellite, it has long supported student and academic CubeSat missions by offering launch services aboard its PSLV rockets. Successful examples include STUDSAT, Jugnu, SATHYABAMASAT, and INSPIRESat-1, developed by Indian universities and launched as secondary payloads. Moreover, through the establishment of IN-SPACe (Indian National Space Promotion and Authorization Center), ISRO now actively promotes private-sector and university participation in satellite development, providing technical guidance and facilitating integration and launch opportunities [40]. Up until now, China does not have a centralized CubeSat aid program like NASA's CLSI, but the CNSA supports CubeSat development through university collaborations and international partnerships. Chinese universities like Harbin Institute of Technology and Tsinghua University frequently launch CubeSats, and projects such as ICUBE-Q show China's commitment to educational and cooperative space missions [41].