esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Elastic Recovery (ER) Capability  
As shown in Figure 22, the effect of fiber content on the elastic recovery ability of modified asphalt mastic at 20 \({}^{\circ}\)C is a complex issue. Generally, at low fiber content, with the increase in fiber content, the interaction between fiber and asphalt increases, and the dispersion of fibers in the asphalt mastic deteriorates, resulting in a decrease in the elastic recovery ability of the mastic. With the further increase in fiber content, the interaction between fiber and asphalt reaches a certain balance, and the dispersion of fibers in the asphalt mastic gradually improves, leading to an increase in the elastic recovery ability of the mastic. However, when the fiber content continues to increase, the interaction between fibers becomes stronger, resulting in a deterioration of the dispersion of fibers in the asphalt mastic, and the elastic recovery ability of the mastic begins to decrease. Therefore, a better elastic recovery ability can be obtained at an appropriate fiber content. It should be emphasized that in addition to fiber content, other parameters such as fiber length, fiber shape, and asphalt type also influence the elastic recovery of fiber-modified asphalt mastics. Consequently, it is essential to fully consider the combined effects of several aspects of research.
## 4 Conclusions  
We have demonstrated for the first time the possibility of using silicon oxycarbide (SiC) as a core material for guided wave applications. Detailed characterization on morphology, topography, and optical properties of sputtered SiOC film are presented. The channel waveguides are fabricated using UV photolithography and reactive ion etching processes. SiOC optical waveguides with core refractive index n\({}_{\text{e}}\) = 1.578 and SiO\({}_{2}\) cladding provides a much higher index contrast (of about 8.2%) with respect to a conventional glass photonic platform. Moreover, SiOC offers the possibility of tuning the refractive index across a wide range, potentially from 1.45 to ~3.0, which means that index contrast as high as 38% can be achieved. Systematic investigation of SiOC waveguides with different widths (2-4.4 \(\upmu\)m) and lengths (1-3 cm) is reported. The propagation losses of 4 \(\pm\) 0.5 dB/cm for both TE and TM polarizations are presented as estimated from cut-back technique. Results indicate the potential of SiOC as a promising platform for integrated optics.  
In order to reduce the losses of SiOC waveguides, the sputter process may further be optimized to achieve uniform films. Losses are perceived to originate mainly due to scattering from surface roughness and columnar structure present in the films. The annealing step may also be considered to smoothen the film structure. The study provides proof of concept for the SiOC photonic platform which may further be developed with CVD.  
Higher index contrast waveguides and advanced optical devices in silicon oxycarbide are under development and will be demonstrated in future work.  
Figure 9: (**a**) Insertion loss plot of the SiOC waveguides with different chip lengths (1–3 cm), (**b**) SiOC waveguides propagation losses as a function of width (2–4.4 \(\upmu\)m) for both TE and TM polarizations.  
**Acknowledgments:** [PERSON] acknowledges the financial support in his PhD from European Union under Erasmus Mundus 'LEADERS' project. This work was partially performed at Polifab, the micro- and nanofabrication facility of Politecnico di Milano (www.polifab.polimi.it) and the authors thank [PERSON] for the valuable technical support.  
**Author Contributions**: [PERSON] designed and performed the experiments for fabrication and characterizations of silicon oxycarbide thin films and optical waveguides. [PERSON] simulated the optical waveguides in COMSOL educational licensed (PoliMI) version (5.2) software using Wave Optics module based on finite element method (FEM). [PERSON] contributed in measuring the propagation losses of the SiOC waveguides. The results were discussed by [PERSON], [PERSON], and [PERSON]. The manuscript was written by [PERSON] and edited by [PERSON] and [PERSON].  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 Materials and Methods
### Tests and Methods  
Samples cured by microorganisms were immersed in deionized water for 48 h to eliminate the soluble substances on the surface, and then the biological treated and control samples were dried at 60 \({}^{\circ}\)C for 48 h. The corresponding dry mass was denoted as M\({}_{\text{d}}\). Unconfined compressive strength tests were carried out for all the samples at a loading rate of 1 mm/min. After the unconfined compressive strength tests, all fractions of each biological treated sample were collected and mixed with excessive 2 M HCl, and then the final residues were collected by filter paper and were dried at 105 \({}^{\circ}\)C for 24 h. The dry mass of the soil was subsequently measured and denoted as M\({}_{\text{silt}}\). Hence, the mass of calcium carbonate M\({}_{\text{Ca}}\) was calculated through the result of the sample before and after treatment [12; 21]:  
\[\text{M}_{\text{Ca}}=\text{M}_{\text{d}}-\text{M}_{\text{silt}} \tag{1}\]  
The calcium carbonate content, C\({}_{\text{m}}\), was defined as the ratio of the dry mass of calcium carbonate crystals and mixed sands, which can be expressed as:  
\[\text{C}_{\text{m}}\left(\%\right)=\text{M}_{\text{ca}}/\text{M}_{\text{silt }}\times 100\% \tag{2}\]  
The enzyme activity of the bacterial suspension was measured as U\({}_{\text{ini}}\) before grouting, and the enzyme activity of the effluent bacterial suspension was measured as U\({}_{\text{eff}}\) after the bacterial suspension was injected into each sample. The change in bacterial activity can be  
Figure 3: Grouting device.  
assessed by the difference in urease activity between the injected bacterial suspension (U\({}_{\text{ini}}\)) and the effluent suspension (U\({}_{\text{eff}}\)) and determined by the following equation [19]:  
\[\text{Bacterial activity retention (\%)}=(1-\text{U}_{\text{eff}}/\text{U}_{\text{ini}}) \times 100\% \tag{3}\]  
It can be seen that the more retained bacterial cells and urease released in the samples, the higher the retention of bacterial activity.  
Mineralogical composition was examined by X-ray diffraction (XRD) test, and microstructure was examined by the mercury intrusion porosimeter (MIP) tests and scanning electron microscope (SEM) tests. For this MIP and SEM test, trimmed small pieces were all from the central of each sample.
## 4 Analysis of Seepage Locating
For the selection of the heating power of the optical fiber, not only should the sensitivity of the optical fiber be considered, but also the excessive current caused by the excessive heating power on the optical fiber; the measuring instrument should also be considered as the optical fiber is the sensitive element. For application in actual projects, the heating power is chosen as 18 W/m, in which condition the permeability measurement is straightforward and will not have a great impact on the measuring instrument. In order to simulate the seepage of saturated-unsaturated soil, the optical fiber in medium II is used for positioning analysis. The soil in the medium II area is divided into natural water-bearing area and saturated water-bearing area through the control valve on the upper part of the sink. The optical fiber is heated to analyze the temperature change along the optical fiber, as shown in Figure 16. \begin{table}
\begin{tabular}{c c c c} \hline  & **6 W/m** & **12 W/m** & **18 W/m** \\ \hline Saturated soil (\({}^{\circ}\)C) & 2.18 & 4.58 & 6.88 \\ unsaturated soil (\({}^{\circ}\)C) & 0.83 & 2.34 & 3.96 \\ \hline \end{tabular}
\end{table} 
Table 1: Temperature decrease with different heating powers under two conditions. Figure 16: Temperature rise of optical fiber under different heating powers. Figure 15: Temperature rise of optical fiber at concentrated seepage channel 1 in saturated soil. The influence of seepage on the fiber temperature curve is still great, as shown in Figure 16. When seepage occurs, the temperature of the stable fiber after heating is significantly lower than that of the fiber without seepage. When seepage continues, the temperature drop curve tends to be balanced; at the end of seepage, the fiber temperature returns to the stable temperature without seepage; similarly, for different heating powers, the greater the heating power is, the better the permeability measurement and positioning effect is and the more accurate it is. However, compared with the optical fiber temperature drop under natural conditions, the accuracy is reduced. In summary, in three different conditions of the embankment seepage measurement positioning problem, the effect of seepage measurement under saturated seepage condition is lower than that of unsaturated condition and saturated non-seepage condition. With the increase in seepage flow rate, the monitoring effect under the same heating power will decrease, but by increasing the loading heating power, we can still achieve the purpose of seepage monitoring and meet accuracy requirements. Therefore, in the process of practical engineering applications, the heating power of the optical fiber is no less than 6 W/m under unsaturated conditions, the heating power of the optical fiber is no less than 12 W/m under saturated conditions without seepage, and the heating power of the optical fiber is no less than 18 W/m under saturated conditions with seepage.
## 5 Simulation and Mathematical Analysis
### Performance of Orbit Extrapolation
After \(t=10\) h, it is evident that the optimized extrapolation error for the proposed MS-FARIMA decreases by 33.5% compared to the original SGP4. Figure 10 shows the performance comparison between the proposed algorithm and existing extrapolation algorithms, which are exemplified by the two-body extrapolation (TBE) algorithm, SGP4 algorithm, and the high-precision orbit propagator (HPOP) algorithm. As Figure 11 shows, the extrapolation error of the proposed algorithm is generally within 0.5 km after 15 h. In comparison, the extrapolation error of the SGP4 algorithm is generally beyond 0.5 km after 10 h. In the observed time, the extrapolation error of the two-body extrapolation algorithm is higher than that of the SGP4 algorithm and the proposed algorithm, and is also generally greater than 0.5 km. Additionally, the extrapolation error of HPOP generally remains within 0.3 km over 15 h. However, the HPOP algorithm  
Figure 10: Performance comparison of proposed MS-FARIMA and other existing algorithms. Figure 9: PE extraction and extrapolation performance. needs to acquire more ephemeris data through broadcasting. From the experimental results, the prediction errors of all four algorithms exhibit certain fluctuations, but they show an increasing trend overall. This is consistent with the theoretical results. Table 6 shows the mean absolute errors (MAEs) of the four ephemeris optimization algorithm errors. Compared to the traditional SGP4 algorithm, the proposed algorithm's error shows a significant decrease, second only to the HPOP algorithm. In addition, the proposed algorithm has lower complexity.
# A Novel Plug-in Board for Remote Insect Monitoring
###### Abstract  
The conventional approach to monitoring insect swarming is based on traps that are periodically checked by human operators. However, human checking of trap contents is expensive, and in many environments, the pest species most frequently encountered in the traps can be detected and monitored automatically. To achieve this goal, a dedicated data acquisition device is necessary, which makes real-time and online pest monitoring possible from a distant location. In addition, it is beneficial for the device to run machine learning algorithms that count and identify insects automatically from pictures. Thanks to the advantages of integrated circuits, more systems have been designed to improve integrated pest management in the context of precision agriculture. However, in our opinion, all of those systems have one or more disadvantages, such as high cost, low power autonomy, low picture quality, a WIFI coverage requirement, intensive human control, and poor software support. Therefore, the aim of this work is to present a novel plug-in board for automatic pest detection and counting. The plug-in board is dedicated to Raspberry Pi devices, especially the Raspberry Pi Zero. The proposed board, in combination with a Raspberry Pi device and a Pi camera, overcomes the limitations of other prototypes found in the literature. In this paper, a detailed description can be found about the schematic and characteristics of the board with a deep-learning-based insect-counting method.  
embedded system; insect counting; machine learning; precision agriculture; pest monitoring; Raspberry Pi
## 4 Discussion
### SwA and F18 Measurements  
In order to verify TEC behavior, we corroborated by SwA and F18 measurements. The minima with respect to the background values of DVTEC, \(DN_{e}\), and \(DT_{e}\) were \(-52\%\), \(-54\%\) to \(-21\%\), and \(<-20\%\), respectively, under the penumbra of the eclipses of 2018 and 2022. Our DVTEC and \(DN_{e}\) results are consistent with the data provided in prior publications [34; 48; 75]. Also, our \(T_{e}\) decrease values are consistent with the findings of earlier studies. For example, previous work reported drops in temperature (\(T_{e}\sim\)\(-50\) to \(-20\%\)) under the shadow of the 21 August 2017 total solar eclipse [76; 77; 78] and 26 December 2019 annular solar eclipse [28]. The effects on the ionosphere were observed post-eclipse of 2022 with the satellites. We can observe a minimum \(DN_{e}\sim\)\(-30\%\) during the third selected pass of F18 \(\sim\)0.5 h after P4 time. Also, although the pass of SwA for 121\({}^{\circ}\)W was over the Pacific Ocean, somewhat away from the shadow of the eclipse of 2022, it had minima DVTEC, \(DN_{e}\), and \(DT_{e}\) with \(-45\%\), \(-31\%\), and \(-14\%\), respectively. Furthermore, two consecutive passes of SwA showed the notorious increase in the values of the southern EIA crest (DVTEC \(\sim\)65%) between 30\({}^{\circ}\)S and 0\({}^{\circ}\).
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 4 Physical Properties of Foam Concretes
### Air Void Structure and Porosity
With the increase in superplasticizer content, it provides lower water consumption, thus, a better pore structure, thicker porous wall and a stronger matrix are formed. In this way, foam concrete demonstrates higher strength [16]. Porosity is accepted as a main parameter that directly affects the physico-mechanical, thermal and durability properties of foam concrete [51]. The type of aggregate used affects the porosity. The pore structure of the aggregate type used affects the porosity of the foam concrete [51,86]. The use of RCA as fine aggregate was investigated in the study of [PERSON] et al. [51] As a result of the study, an increase in apparent porosity occurred with the use of RCA. This situation was attributed to more porous texture forms than natural sand. By using the RCA as a fine aggregate in geopolymer foam concrete, a more homogeneous pore distribution was achieved. In addition, thinner air pockets occurred in RCA-containing foam concretes. This can be attributed to the greater stability of the sam  
Figure 2: **(a)** Reference mixture containing no foaming agents or foams SEM images, **(b)** foamed concrete mixture SEM image. Reprinted/adapted with permission from Ref. [84]. 2015, Elsevier Science & Technology Journals. ples containing RCA. In stable mixtures, thinner and homogeneous distribution of air voids occurs [19]. In the use of glass and plastic wastes as filling material, glass wastes provide more uniform distribution of voids and a less interconnected void structure. This was attributed to the finer glass waste powder. It was reported that thinner filling materials create uniform air pockets [85]. The use of waste marble dust and RHA in foam concrete reduces porosity. Materials with pozzolanic properties improve the interfacial transition zone, thanks to the filling effect. Thus, it was reported that it provides a filling effect that reduces porosity by providing effective particle packaging [18]. The porosity percentages of different studies are given in Table 7.
# Toward Personalized Vibrotactile Support When Learning Motor Skills
## 1 Introduction  
Society is becoming more and more virtual due to the online capabilities provided by current information and communication technologies in varied ways and flavors (e.g., learning systems, social networks, banking, shopping, entertainment, etc.). Benefits include removing temporal and spatial barriers, but usually entail physical inactivity.  
Nevertheless, technology can foster physicality in virtual reality scenarios, providing a feeling of immersion, presence, engagement and so on. For instance, the field of _exergames_, which entice users to learn motions (e.g., dance moves) or to exercise through gamification techniques, has massively expanded in the past years supported by commercial products such as Nintendo Wii, Playstation Move or Xbox Kinect. These systems rely on motion sensing technology that tracks physical reactions using either inertial sensors (e.g., gyroscopes and accelerometers) in a handheld controller (when available) to detect movement in three dimensions, and/or optical sensing technology that can be used to sense the controller position or to directly track the gestures made by the user's body.  
While the use of these products for exergaming can train the execution of predefined movements and is helpful for people not engaged in physical activity during their leisure time, they do not seem sufficient for achieving moderate intensity health exercise [1]. Nonetheless, these technological advances in the entertainment domain have provided fertile ground to develop applications for other domains, such as physical therapy learning systems [2]. They have also been suggested to support motor skills learning [3]. This paper discusses the need of personalizing the learning of motor skills and the value of vibrotactile feedback to provide this support.
## Appendix A  
Let \(N\), \(W\), \(G\), \(L\), \(O\), \(C\), \(K\), and \(M\) denote the sets of respectively, nodes, wind power units, grid supply points (GSP), power lines, the subset of lines with on-load tap changer (OLTC) transformers, the subset of lines with BTB-VSCs, operating states and time periods. The full optimal operation planning in period \(m\in M\) is formulated as a full AC optimal power flow problem as below:  
\[P_{g}^{min}\leq P_{g}^{m}\leq P_{g}^{max}\quad\forall g\in G \tag{1}\] \[Q_{g}^{min}\leq Q_{g}^{m}\leq Q_{g}^{max}\quad\forall g\in G\] (2) \[0\leq P_{w}^{m}\leq P_{raw}^{m}\quad\forall w\in W\] (3) \[\xi_{b}^{min}\leq\xi_{b}^{m}\leq\xi_{b}^{max}\quad\forall b\in N\] (4) \[r_{o}^{min}\leq r_{o}^{m}\leq r_{o}^{max}\quad\forall o\in O\] (5) \[V_{g}^{m}=1\quad\forall g\in G\] (6) \[\theta_{g}^{m}=0\quad\forall g\in G\] (7) \[V_{min}\leq V_{b}^{m}\leq V_{max}\quad\forall b\in N\] (8) \[\overline{Y}_{l}\overline{Y}_{l}^{*}(\overline{V}_{m\{l\}}^{m}- \overline{V}_{sm\{l\}}^{m}(\overline{V}_{m\{l\}}^{m}-\overline{V}_{sm\{l\}}^{ m})^{*}\leq l_{max}^{2}\quad\forall l\in L\] (9) \[\overline{Y}_{cr}\overline{Y}_{cr}^{*}(\overline{V}_{cr\{c\}}^{m} -\overline{V}_{sm\{c\}}^{m}(\overline{V}_{cr\{c\}}^{m}-\overline{V}_{sm\{c\}} ^{m})^{*}\leq l_{nom}^{2}\quad\forall c\in C\] (10) \[Q_{0\{c\}}^{m}=0\quad\forall c\in C\] (11) \[P_{g\{b\}}^{m}+P_{w\{b\}}^{m}=\xi_{b}^{m}(P_{b}^{m})+\sum_{l\in L }P_{b\{l\}}^{m}+\sum_{c\in C}P_{b\{c\}}^{m}\quad\forall b\in N\] (12) \[Q_{g\{b\}}^{m}=\xi_{b}^{m}(Q_{b}^{m})+\sum_{l\in L}Q_{b\{l\}}^{m} +\sum_{c\in C}Q_{b\{c\}}^{m}\quad\forall b\in N \tag{13}\]  
Equation (1) and (2) represent the limits of the active and reactive power flows through the primary substation transformer (i.e., the grid supply point). Equation (3) represents the operating limits of DGs (wind generators), whereas Equation (4) represents the limits of the demand shifting factor \(\xi\). The demand shifting factor is defined as the proportion of demand that can be shifted at any time. Equation (5)-(7) represent the limits on the tap changer ratio, and the magnitude and phase angle of the voltage at the grid supply point. Equation (8) represents bus-voltage limits. Equation (9) and (10) represent the thermal operating limits on lines and the converters (current limits). In these constraints, \(\{sn,rn,cr\}\in N\) are the sending and receiving ends of a line and the corresponding VSC-connected buses, respectively. It should be pointed out that as per the operating requirements limit Equation (9) may be relaxed for short-term post-fault periods. Equation (11) represents the zero-reactive power limit into the the DC sides of the VSCs. Finally, Equation (12) and (13) represent the total nodal power balance equations at every bus, \(b\) (regardless of AC or DC). For simplicity purposes, only equations for pre-fault state, \(k=0\), have been described but the proposed D-SCOPF algorithm contains the constraints for all states, \(k\in K\).
## 2 Proposed TDoA-Based Method
### Phase Reconstruction Method
The base-band converted IQ digital signal equals to  
\[s_{\text{BBRX}}(t)=s_{\text{RX}}(t)\cdot\mathrm{e}^{-j\,2\pi\cdot f_{0}\cdot t }=\mathrm{G}_{R}\,H_{0}\,\mathrm{e}^{-j\,2\pi\cdot f_{0}\cdot\tau}\sum_{n=- \frac{M}{2}}^{\frac{M}{2}}\,A_{n}\,\mathrm{e}^{i\,j\,2\pi\cdot n\frac{B}{M} \cdot(t-\tau)} \tag{27}\]  
where the \(f_{0}\) frequency related term is removed due to base-band conversion. The resulting Fourier transform for base-band signal is  
\[S_{\text{BBRX}}(f)=S_{\text{RX}}(f+f_{0}) \tag{28}\]  
so its spectrum is exactly the one depicted in Figure 2, shifted to a center frequency \(f_{0}=0\) Hz. To deal with variation in path-loss, a slight modification in transmitted signal must be taken in account to equalize signal alteration due to propagation. Practically, the transmitted signal spectrum is changed to  
\[S_{\text{TX}}(f)=A_{\text{TX}}\cdot\Big{(}\frac{f_{0}}{f_{0}}\Big{)}^{2}\quad \text{with }\,f\in\left[f_{0}-\frac{B}{2},f_{0}+\frac{B}{2}\right] \tag{29}\]  
and this allows to get the received signal  
\[S_{\text{RX}}(f)=S_{\text{TX}}(f)\cdot H(f)=A_{\text{TX}}\,\mathrm{G}_{R}\,H _{0}\,\mathrm{e}^{-j\,2\pi\cdot f\cdot\tau} \tag{30}\]  
Let consider again a multi-antenna structure as the one in Figure 1, with the objective of identifying a steering vector as in (5). The Antenna Switch state is changed by the uC after getting enough samples for each antenna. This leads to get different signal streams \(s_{\text{BBRX}}(t,i)\), one for each antenna, as  
\[s_{\text{BBRX}}(t,i)=\mathrm{G}_{i}(\vartheta,\varphi)\cdot H_{0}\,\mathrm{e }^{-j\,2\pi\cdot f_{0}\cdot\tau}\sum_{n=-\frac{M}{2}}^{\frac{M}{2}}\,A_{n}\, \mathrm{e}^{i\,j\,2\pi\cdot n\frac{B}{M}\cdot(t-\tau_{1})} \tag{31}\]  
where the delay \(\tau_{1}\) and the \(\mathrm{G}_{i}(\vartheta,\varphi)\) antenna gain coefficients take in account the radio-link variations due to the swapping of receiving antenna. The antenna array is not distributed (Figure 1) thus for every antenna the radio-link distance \(R\) is considered to be the same thus \(H_{0}\) is not dependent on antenna index. Note that the Local Oscillator phase misalignment term \(\gamma_{0}\) is supposed to be the same between all the antennas, and this condition can be satisfied implementing a PLL to get the phase-lock with equal phase offset on the \(f_{0}\) carrier signal. In order to define a steering vector as in (5) a single amplitude-phase coefficient for the equivalent wavelength signal must be produced by each different antenna signal observation.