esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Tunnel-Soil-Pipeline Interaction
### Free-Field Movements Induced by Different Force Patterns
#### 2.2.1 Support Pressure at Tunnel Face  
Figure 4a shows that the support pressure is perpendicular to the shield tunnel face during the excavation. An element at the tunnel face is removed to perform the force analysis. The coordinate of the element is (\(r\), 0). The equivalent concentrated force \(dP\), the shadow area of the element in Figure 4a, can be expressed as \(pdrd\theta d\eta\). According to the A-A section, \(d\eta\) is the distance of shield tunnel excavation.  
The coordinate of any point load \(P\) within the tunnel face will be (\(r\)cos(\(\theta\)), 0, \(H\)-\(r\)sin(\(\theta\))). During the shield tunnel excavation, the Cartesian coordinate system will translate \(\eta\) along the \(Y\)-axis. Thus, the coordinate of any point load during the shield tunnel excavation will be (\(r\)cos(\(\theta\)), \(\eta\), \(H\)-\(r\)sin(\(\theta\))). Based on [PERSON]'s solution, the parameters \(R_{1}\) and \(R_{2}\) shown in Figure 4a will be rewritten, respectively:  
\[R_{1}=\sqrt{\left(x-r\cos(\theta)\right)^{2}+\left(y-\eta\right)^{2}+\left(z- H+r\sin(\theta)\right)^{2}} \tag{13}\]  
\[R_{2}=\sqrt{\left(x-r\cos(\theta)\right)^{2}+\left(y-\eta\right)^{2}+\left(z+ H-r\sin(\theta)\right)^{2}} \tag{14}\]  
The support pressure is a horizontal load along \(Y\)-axis. So, the \(x\) in Equation (12) should be replaced with \(y\)-\(\eta\). The displacements and stress fields induced by the support pressure can be obtained using the triple integrals. The domain of the triple integrals is a cylindrical region that the tunnel face passes. According to Equation (12), the vertical displacement and vertical stress with shield tunnel advance are given, respectively.  
\[u_{z}=\int_{0}^{R}\int_{0}^{2\pi}\int_{L_{1}}^{L_{2}}\frac{p(y-\eta)rdrd\theta d \eta}{1\text{e}r\text{e}(1-\eta)}\left[\frac{z-z}{R_{1}^{2}}+\frac{(3-4\mu)(z -c)}{R_{2}^{2}}-\frac{6 cz(z+c)}{R_{2}^{2}}+\frac{4(1-\mu)(1-2\mu)}{R_{2}(R_{2} +z+c)}\right] \tag{15}\]  
\[\sigma_{z}=\int_{0}^{R}\int_{0}^{2\pi}\int_{L_{1}}^{L_{2}}\frac{p(y-\eta)rdrd \theta d\eta}{8\pi(1-\mu)}\left[\begin{array}{c}\frac{1-2\mu}{R_{1}^{4}}- \frac{(1-2\mu)}{R_{2}}-\frac{3(z-c)^{2}}{R_{1}^{5}}-\frac{3(3-4\mu)(z+c)^{2}}{ R_{2}^{2}}\\ +\frac{6c}{R_{2}^{2}}\left(c+(1-2\mu)(z+c)+\frac{5(z+c)^{2}}{R_{2}^{2}}\right) \end{array}\right] \tag{16}\]
# Radio Frequency Compatibility Evaluation of S Band Navigation Signals for Future BeiDou
## 2 Radio Frequency Compatibility Evaluation Methods
### Compatibility Evaluation Method for Code Tracking
The GSO satellites are positioned at 58.75\({}^{\circ}\) E, 80\({}^{\circ}\) E, 110.5\({}^{\circ}\) E, 140\({}^{\circ}\) E and 160\({}^{\circ}\) E respectively. The MEO satellites are operating with an inclination of 55\({}^{\circ}\) relative to the equatorial plane. The three IGSO satellites work at 118\({}^{\circ}\) E using an inclination of 55\({}^{\circ}\).
## 3 Energy Efficiency Problem Model for SONs
### Global Traffic Model  
A realistic global user traffic model is crucial for optimizing satellite optical network energy efficiency, especially for strategies like payload dormancy, because spatiotemporal traffic unevenness significantly impacts resource use and energy consumption. This unevenness, stemming from varied regional request intensities (due to population and development differences) and tidal network access across time zones, informs our spatial traffic distribution model. To create this model, Earth's surface was divided into 288 equal areas. Traffic intensity in each area was primarily set using 2023 Internet user data [29]: one intensity unit per million users, with a one-unit baseline for areas with fewer users. Figure 1 shows this global traffic demand, marking intensities for 155 identified service-generating regions, where higher values mean more frequent network demands.  
To simulate the active periods of Internet users throughout a day, we referred to urban traffic flow models, setting the maximum intensity coefficient to 1, as depicted in Figure 2, which illustrates the daily variations.  
The global traffic demand intensity has been quantitatively analyzed, and the direction of flow still needs to be determined. The gravity model, based on the concept of physical gravitation, was employed to describe the demand for interactions between regions influenced by geographic distance and population density. In satellite optical networks, areas with a higher number of users and shorter distances generate more traffic. The data service density between regions \(i\) and \(j\), denoted by \(T_{ij}\) in Equation (14), depends on the local time-based traffic intensity coefficient \(B_{i}\); the Internet user density \(U_{i}\), similarly for \(B_{j}\) and \(U_{j}\); and the physical distance \(L_{ij}\).  
\[T_{ij}=\frac{B_{i}\cdot U_{i}\cdot B_{j}\cdot U_{j}}{L_{ij}} \tag{14}\]  
Equation (15) calculates the traffic matrix among the 155 global traffic regions; intra-region traffic, which does not pass through the satellite network, has a matrix diagonal value of 0.  
\[T=\left[\begin{array}{cccc}T_{1,1}&T_{1,2}&\dots&T_{1,155}\\ T_{2,1}&T_{2,2}&\dots&T_{2,155}\\ \dots&\dots&\dots&\dots\\ T_{155,1}&T_{155,2}&\dots&T_{155,155}\end{array}\right] \tag{15}\]
## 5 Applications Across Diverse Sectors
### Recent Innovations
#### 5.1.2 Beam-Based Metal AM  
Because of its potential applications, beam-based metal additive manufacturing has attracted a lot of interest from a variety of sectors. However, achieving widespread adoption in industrial settings requires further development, prompting substantial investments in research and development by companies, research institutions, and universities [59]. The rise in patents related to beam-based metal additive manufacturing indicates increased activity in this field, with key players including multidisciplinary companies, AM machine producers, aerospace end users, universities, and research centers [60].  
Several methods have emerged in beam-based additive manufacturing to construct structures using multiple materials or with gradually changing properties. One notable technique involves using blended powders and advanced scanning to produce functionally, structurally, and compositionally graded structures, such as Damascus steel with unique properties [61]. Aerosoint, founded in 2016, introduced \"selective powder deposition\" (SPD), a technology that accurately places multiple powders to form layers comprising two or more materials. Unlike blade recoaters or standard single-material rollers, Aerosint's SPD is suitable for a range of AM processes, such as laser powder bed fusion (L-PBF), binder jetting, and pressure-assisted sintering.  
Aerosint's SPD technology significantly expands the possibilities of additive manufacturing by facilitating selective powder deposition. It makes it possible to construct intricate structures that combine several materials with unique properties all within the same layer. This innovative technique allows for the precise placement of different materials together, enabling the manufacture of parts combining properties like electrical conductivity, strength, flexibility, and corrosion resistance in a single piece [46].
# Onboard Processing in Satellite Communications Using AI Accelerators
## 5 Conclusions and Future Challenges  
Early work in the literature has demonstrated the progress obtained from using AI in SatCom, yet this research is still in its infancy. Due to the multiple possible use cases, the progress is even more limited when focusing on AI onboard use cases. On the other hand, the interest in evaluating the feasibility of using commercial AI-capable chipsets on board satellites continues to grow. In this sense, we consider different use cases and commercial AI-capable chipsets, and present a trade-off and feasibility analysis for implementing onboard processing. In future work, the analysis should be extended to include a more extensive and detailed study of the use cases and commercial AI chipsets, including a radiation tolerance analysis and a chip comparison based on more recent technologies, such as neuromorphic hardware [50].  
Conceptualization, F.O., E.L. and J.Q.; methodology, F.O. and V.M.B.; validation, E.L., J.Q. and S.C.; formal analysis, F.O., V.M.B., L.M.G.-S. and J.A.V.-P.; investigation, F.O., V.M.B., L.M.G.-S. and J.A.V.-P.; data curation, J.L.G. and G.F.; writing--original draft preparation, F.O., V.M.B., L.M.G.-S., J.A.V.-P., J.L.G. and G.F.; writing--review and editing, J.Q., E.L. and S.C.; supervision, E.L., J.Q. and S.C.; project administration, E.L., J.Q. and S.C.; funding acquisition, E.L., J.Q. and S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Space Agency (ESA) funded under Contract No. 4000134522/21/NL/FGL named \"Satellite Signal Processing Techniques using a Commercial Off-The-Shelf AI Chipset (SPAICE)\". Please note that the views of the authors of this paper do not necessarily reflect the views of the ESA. Furthermore, this work was partially supported by the Luxembourg National Research Fund (FNR) under the project SmartSpace (C21/IS/16193290).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Evaluation of Fatigue Life of Asphalt Concrete Mixtures with Reclaimed Asphalt Pavement
## 4 Tests Results  
### Basic Properties of Asphalt Binders and Bituminous Mixtures  
In addition to the mixtures analyzed in this publication (i.e., AC22P and ACWMS16), mixtures for the wearing course and the binder course were also produced and tested. Properties of these mixtures are necessary for the analysis of the pavement structure. In order to determine the stiffness of a bituminous mixture using the empirical method, it is necessary to know the basic properties of the asphalt binder (penetration, softening point) and the selected physical properties of the bituminous mixture (binder and aggregate content by volume). To determine these properties of asphalt binders, laboratory tests were carried out on binders recovered from mixtures containing reclaimed asphalt. Table 1 presents the mixtures, their basic properties, and the properties of the recovered binders. All mixes were designed according to the technical requirements [24] based on PN-EN 13108-1 (asphalt concrete) and PN-EN 13108-5 (SMA). There are two mixtures for base course: AC22P and AC WMS 16. The first one is conventional asphalt concrete, while the second is high modulus asphalt concrete (HMAC). The main differences are due to the composition of these mixtures and volume properties. ACWMS has a lower air voids content, finer grading, and higher binder content. The mixture obtained in this way is characterized by high resistance to fatigue and at the same time good resistance to rutting [25; 26; 27]. In order to increase stiffness, harder binders are used (e.g., 20/30 or 10/40-65). HMAC is often used in Poland for heavily trafficked roads. Mixture AC16W is typical asphalt concrete for binder course, while AC11S and SMA11 are designed for wearing course. Grading curves of all mixtures are presented in Figure 3.
## 3 Related Works
### OpEc-Dynamic Street Light Control  
Street lighting is an essential service provided by municipalities and communities to assure the citizens and goods in public spaces. However, this essential service has non-negligible economic cost economic and also a certain ecological footprint. Regarding the economic cost of street lightening, the European cities are spending up to 60% of their electricity budget to assure this service [33]. Therefore, mastering the energy consumption of street lights is becoming an important priority for European municipalities. Leveraging Information and Communication Technologies (ICT) in managing street lights has been identified as a serious and promising approach. Dynamic street lighting (DSL) refers to allowing the adjustment of the light intensity level to a real and measured need. Different research papers on DSL have been published [34; 35; 36]. Most of the solution proposed in previous works are based on a sensor network to sense relevant ambient-related indicators like luminosity and visibility, which permits the estimation of the need for lighting intensity. Further, other previous works include presence detectors as additional sensors with the objective to consider pedestrian and road traffic activities as complementary cue for the estimation of lighting needs. Further concepts involving the citizens as main stakeholder providing feedback on lighting quality have been also proposed [37].  
As for the control of light intensity, the light-emitting diode (LED) technology has allowed the conception and the implementation of numerous light control and dimming solutions. The most known technologies for dimming solutions are (1) DALI (Digital Addressable Lighting Interface), which is a protocol that permits dynamic control of luminaires; (2) \"0-10 V\" which is an electronic lighting control signaling system where the control signal is a Direct Current (DC) voltage that takes values between zero and ten volts. Our project takes advantage of these evolutions.  
The main objective of the project is to use IoT concepts in order to implement dynamic street light management with the final goal to reduce street light energy consumption while assuring the same level of security.
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 2 Materials and Methods
### Ionospheric Correction  
A common practice for ionospheric correction is to approximate the entire ionosphere as a thin layer located at a height of 450 km, referring to the intersection point of the signal, with the thin layer as the piercing point. The ionospheric delay is calculated using the vertical total electron content (VTEC) at the piercing point (Equation (5)) [22].  
\[|\Delta s_{ion}|=\frac{40.31\times 10^{16}\cdot VTEC}{f_{0}^{2}\sqrt{1-\left( \frac{R}{R+H}sin\theta\right)^{2}}} \tag{5}\]  
The unit of the VTEC is the total electron content unit (TECU), equaling \(\frac{10^{16}}{m^{2}}\); \(f_{0}\) (Hz) represents the signal frequency; \(R\) is the radius of the Earth; \(H\) stands for the height of the ionosphere 450 km; and \(\theta\) denotes the zenith angle on the ground. As most SAR satellites orbit within the ionosphere, the result is larger than the actual ionospheric delay, so theoretically, an adjustment based on the satellite's orbital altitude is needed [18]. [PERSON] et al. proposed that the coefficient is 75% for TerraSAR-X and 90% for Sentinel-1 [18], while [PERSON] et al. considered the coefficient to be around 70% for Sentinel-1 [22]. The adjustment contributes to centimeter-level improvements for Sentinel-1 but may worsen the ionospheric correction accuracy for ALOS-2 [22]. Most of the time, the adjustment is insignificant--less than 5 cm for TerraSAR-X based on our calculations. Therefore, we do not discuss the adjustment for the ionospheric delay further.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 2 System Design
### Design Considerations for Solar Module  
There are two issues to consider in the design of the solar module:  
1. The size of solar panel is large enough to meet both Wi-Fi access points and satellite phone power requirements.
2. The system will also be able to maintain power for a certain period of time at night or on cloudy days.  
To address the issues, we first calculate the initial estimation of power consumption for the system. Suppose that  
* Single-board computer consumption is 5 W (requires a practical investigation on the operating power consumption);
* Satellite phone (Hughes 9201) consumption is 1.36 W;
* Estimated system conversion loss = 40% (loss from the solar controller, cable, voltage converter).  
The power output required to sustain the system in maximum load can be obtained by  
\[\textit{Solar Module Output}=\textit{Solar Panel Output}\times\textit{ Conversion Efficiency} \tag{2}\]  
That is,  
\[\left(1.36\;\mathrm{W}+5\;\mathrm{W}\right)/\left(1\%-40\%\right)=10.6\; \mathrm{W}\]  
Thus, the total power consumption by running the access point 24 h is  
\[10.6\;\mathrm{W}\times 24\;\mathrm{h}=254.4\;\mathrm{Wh}\]  
Based on the total power consumption, we can estimate the surface size of the solar panel, which also has excess power that can charge the discharged battery when powering the access point. The solar panel will have following surface size to meet requirements:  
The data from NASA website [12] shows that, given a 22 degree tilted tile, the average power per day is 3.15 kWh/m\({}^{2}\)/day. Supposing that typical solar panel efficiency is 18%, the required power per day becomes  
\[3.15\;\mathrm{kWh/m^{2}/day}\times 18\%=0.55125\;\mathrm{kWh/m^{2}/day}\]  
Thus, the required solar panel surface is  
\[\left(254.4\;\mathrm{Wh/day}\right)/\left(551.125\;\mathrm{Wh/m^{2}/day} \right)=0.4616\;\mathrm{m^{2}}\]  
In other words, solar panels need a size of at least 0.4616 m\({}^{2}\) to keep the system fully loaded. The battery used to store the energy of the solar panel should be no less than 254.4 Wh to maintain a day of operation.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 5 Numerical Experiments, Results, and Discussion
### Using Nonlinear Optimization Techniques for Global Minimum/Maximum Search  
The localization functions are nonlinear and consequently require the application of nonlinear optimization techniques if simulation time is required. Similar to [31], this study used a Newton-type search algorithm to find the minimum of WSF, DML, and SML localization functions, where the LM (Levenberg-Marquardt) method is employed. Here, instead of LM, we employed a quasi-Newton (QN) technique with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, which performs a Hessian estimate by finite differences. The QN technique was selected because it is available in MATLAB as the fminunc function. The QN technique iteration is terminated if the change in estimated parameters and the function value does not exceed \(1\times 10^{-10}\) between two or more successive iterations. In addition, the allowable maximum iteration number is fixed at 30 for the QN technique.  
The QN technique requires intelligent initial prediction for parameter estimation. Thus, we initialized the parameters so that they were similar to true values by adopting the estimate from the alternating projection (AP) algorithm. In practice, true values may not be available; hence, we propose the approach in [31] as a suggestion. The DML, SML, and WSF initial value was searched using the AP algorithm (i.e., a nonlinear optimization algorithm) [17]. AP always converges to a local minimum and not the global minimum. Moreover, here, we assumed the AP-based local minimum to be the global minimum of the employed search grid. In addition, we used \(U+2\) iterations in the AP algorithm with initialization and a grid of \(5^{0}\) resolution with respect to elevation and azimuth.  
In all of the simulation scenarios, the QN technique and BFGS were used to estimate the maxima of PWD together with MUSIC localization functions, and parameters were initialized close to the true values. PWD and MUSIC can be initialized in practice from individual spatial spectra by estimating the U highest local maxima. From the same grid employed above in the alternating projection algorithm, these spatial spectra can be computed.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 2 State of the Art
### TMA Design under an (Exclusive) Antenna Perspective
#### 2.2.1 Fundamental Mode Pattern
Such a capability of adaptive nulling in time-varying scenarios was evaluated in [32] by [PERSON] et al., and completed in [33]. Further examples are the synthesis of the so-called power-patterns for wide coverage purposes [34; 35], or radar applications [36; 37]. Apart from all of these applications, tools for a more accurate analysis of TMA were also developed. In this sense, and in order to evaluate rigorously some nonlinear performance aspects of TMA as a radiating system (e.g., the effects of mutual coupling between elements), a full-wave computation of the radiated far field was addressed by [PERSON] et al. in [38] by properly developing a multi-domain computer aided design platform. Aligned with this research topic, a full-wave analysis of the instantaneous and average behaviors of TMA is available in [39].
## 4 Experiments and Result Analysis
### Databases  
The concrete compressive strength database, collected by the National Tsing Hua University in Taiwan, consists of 9 variables and 1030 instances. The input variables are cement, lime, fly ash, fine aggregates, blast furnace slag, coarse aggregates, water, and superplasticizer. The output variable is concrete compressive strength. The Boston House Prices database, which is a collection of information about the housing prices in Boston, consists of 14 variables and 506 instances. Input variables are per capita crime rate by town (CRIM), the proportion of residential land zone for lots over 25,000 sq. ft. (ZN), the proportion of non-retail business acres per town (INDUS), Charles River dummy variable (CHAS), nitric oxides concentration per 10 ppm (NOX), the average number of rooms per dwelling (RM), the proportion of owner-occupied units built before 1940 (AGE), weighted distances to five Boston employment centers (DIS), index of accessibility to radial highways (RAD), full-value property tax rate per $10,000 (TAX), the pupil-teacher ratio by town (PTRATIO), the proportion of blacks by town (B), and % lower status of the population (LSTAT). The output variable is the median value of owner-occupied homes (MEDV).