esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Impact Energy Principles  
The following are some energy considerations to better understand the theory underlying the impact method. Consider the impact of a mass \(m\) with a semispherical surface and having a velocity \(v_{0}\) on the flat surface of a semi-finished space. The direction of impact is perpendicular to this surface. Moreover, the velocity \(v_{0}\) of all points of the mass is equal and coinciding with the velocity \(v_{0}\) of its center of gravity. In this case the kinetic energy of the mass at the moment of impact is given by Equation (1):  
\[\varepsilon_{c1}=\frac{1}{2}mv_{0}^{2} \tag{1}\]  
and the momentum is:  
\[Q_{1}=mv_{0}. \tag{2}\]  
Considering the instant \(t_{0}\) in which the mass touches the surface and the instant \(t_{1}\) in which the maximum contact deformation \(\delta\) occurs and in which the velocity \(v_{0}\) is canceled (Figure 6a), the corresponding momentum variation results:  
\[|mv_{0}-mv_{1}|=\int_{t_{0}}^{t_{1}}Fdt \tag{3}\]  
and therefore:  
\[mv_{0}=\int_{t_{0}}^{t_{1}}Fdt \tag{4}\]  
The force impulse is given by the area \(A_{1}\) subtended to the curve (\(F\), \(t\)) obtained experimentally as shown in the Figure 6b.  
The kinetic energy provided by the mass is:  
\[\varepsilon_{c1}=\frac{1}{2}mv_{0}^{2}=\frac{\left(\int_{t_{0}}^{t_{1}}Fdt \right)^{2}}{2m} \tag{5}\]  
Subsequently from the instant \(t_{1}\), in which the vector displacement of the mass changes direction, at the instant \(t_{2}\), in which the contact between mass and flat surface ceases, the change in momentum of the mass results:  
\[mv_{2}=\int_{t_{1}}^{t_{2}}Fdt \tag{6}\]  
Figure 6: (a) Geometry of the mobile mass; (b) force–time curve obtained from an impact test.  
wherein \(v_{2}\) is the velocity of displacement of the mass from the surface, and results \(v_{2}<v_{0}\). The value of the integral (6) is given by the area \(A_{2}\). The ratio between initial and final mass momentum provides the return coefficient \(e\) that measures the elasticity of the impact:  
\[\frac{mv_{2}}{mv_{0}}=\frac{v_{2}}{v_{0}}=e \tag{7}\]  
In the perfectly elastic collision \(e=1\), in the perfectly inelastic collision \(e=0\).  
Figure 6b shows that the return coefficient is given by the following equation:  
\[\frac{\int_{t_{1}}^{t_{2}}Fdt}{\int_{t_{0}}^{1}Fdt}=\frac{A_{2}}{A_{1}}=e \tag{8}\]  
The energy returned in the collision is given by:  
\[\varepsilon_{c2}=\frac{1}{2}mv_{2}^{2}=\frac{\left(\int_{t_{1}}^{t_{2}}Fdt \right)^{2}}{2m} \tag{9}\]  
The ratio between the energy supplied and returned is given by:  
\[\frac{\varepsilon_{c2}}{\varepsilon_{c1}}=e^{2} \tag{10}\]  
The energy dissipated \(\varepsilon_{d}\) in the impact due to the elasticity of the materials is given by:  
\[\varepsilon_{d}=\left(1-e^{2}\right)\varepsilon_{c1} \tag{11}\]  
Therefore, in the case of a perfectly elastic impact:  
\[e=1\]  
, i.e.,  
\[A_{2}=A_{1} \tag{12}\]
###### Abstract  
Fourier-transform infrared (FTIR) spectroscopy is a fast and simple technique for functional group identification. This work provides a review and insight into the application and interpretation of FTIR spectroscopy for cementitious binders that comprise ordinary Portland cement, alkaline-activated binders, geopolymers, and material characterization for civil engineering material applications. This technique can be used to identify different compounds and a moiety of bond vibrations in inorganic molecules such as Si-O, -OH, H-O-H (water), C-O (carbonate or carbonation), aluminosilicate (Si-O-T, where T is Al or Si), and S-O (sulfate or gypsum) found in hydrated cement, alkaline binders, and geopolymers. The prominent bands include those representing carbonation (CO\({}_{3}\)\({}^{2}\)-1390-1475 cm\({}^{-1}\)), calcium carbonate (871, 1792-2516 cm\({}^{-1}\)), hydroxylation and water molecules (1607, 3400-3650 cm\({}^{-1}\)), strength skeletal framework compositions or Al-Si substitutions, silicate organization (C-A-S-H, N-A-S-H, or C-S-H (950-1055 cm\({}^{-1}\)), and sulfate (600-680, 1080-1100 cm\({}^{-1}\)). Some of the factors that could affect the spectra bands include elemental displacement due to changes in molar mass, activated temperature, pH, activator concentration, w/b ratio, Ca/Si ratio, Si/Al ratio, and the silica modulus (SiO\({}_{2}\)/Na\({}_{2}\)O) of the activators used in the binder synthesis. The method could be used for destructive and non-destructive testing on paste sample by using transmission and attenuated total reflectance methods, respectively.  
Fourier-transform infrared spectroscopy; geopolymers; alkaline-activated binder; material characterization; aluminosilicate materials +
Footnote †: journal: Applied
## 3 Fresnel Zones  
Multipath effects are the accuracy-limiting factor in positioning applications. In this case, one can be more critical to avoid regions where significant multipaths can be expected under a particular satellite constellation. In this context, the focus is on determining active scattering regions called Fresnel zones. In radio wave propagation, the Fresnel zones concept is commonly employed. It is used to calculate diffraction and reflection losses between the transmitter and receiver. The first Fresnel zone serves as a guideline for establishing the minimum object size required to yield significant multipath signals. The term Fresnel zone is frequently used to describe the ellipsoid's cross-section along the line of sight. The ellipsoid depicts the volume of space surrounding the line of sight that must be devoid of obstructions for clean transmission to occur. Recently, [PERSON] et al. [21] used georeferenced 3D point clouds to investigate how obstructions are identified by applying adaptive elevation masks in the antenna environment.  
For a reflector with a horizontal plane and carrier wavelength \(\lambda\) and with satellite elevation angel, \(\theta\), and the distance of the antenna from the reflector plane, \(h\), with semi-major and semi-minor axes \(a\) and \(b\), respectively, the first Fresnel zone is given by  
\[b=\sqrt{\frac{\lambda h}{\sin\left(\theta\right)}+\left(\frac{\lambda}{2\sin \left(\theta\right)}\right)^{2}};\qquad a=\frac{b}{\sin\left(\theta\right)} \tag{6}\]  
In general, for a flat reflector surface, the size of the Fresnel zones decreases as the satellite elevations increase independent of the satellite's azimuth angle. The principal path from transmitting to receiving antennas is a straight line. However, it is conceivable for the signal to be reflected off objects in the antennas' beamwidth but not in the direct route. Dueto the increased length involved, whatever fraction of the transmitted signal is reflected towards the receiving antenna will arrive after the direct signal.  
If the direct signal path is not obstructed and the direct and indirect signals are superimposed, checking the satellite visibility alone is not adequate. [PERSON] et al. [19; 21] demonstrated a sufficient minimizing strategy to attain accuracy in the millimetre to centimetre range, especially in kinematic applications, by identifying Fresnel ellipsoids--active reflecting surfaces. In effect, the intersection of the ellipsoid with a reflecting surface produces Fresnel zones, which are active scattering regions that generate signal reflection, and, as a result, multipath effects are superimposed on the signal received on the direct signal path. For our two colocated GNSS stations that operate continuously, KBG1 and KBG2, the Fresnel zones for all azimuths, elevation angles (5, 10, 15, 30 degrees), and all satellites available within a 24 h period are colour coded and shown in Figure 2, following [52]. The experimental setups for KBG1 and KBG2 GNSS stations are discussed in greater detail in Section 4.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### System Description
#### 3.2.4 BS Load System  
The optimal size of the hybrid supply system depends mostly on the cellular network's power demand. The overall energy consumption of a base station can be expressed as follows [53; 54]:  
\[\begin{array}{ll}P_{in}=M_{\text{sec}}(P_{1}+\Delta_{p}P_{\max}(\chi-1))& \text{If }0<\chi\leq 1\\ =M_{\text{sec}}P_{Sleep}&\text{If }\chi=0\end{array} \tag{6}\]  
where \(P_{1}=P_{0}+\Delta_{p}P_{\max}\) represents the peak power consumed by the BS, \(\Delta_{p}\) is the power slope depends upon the load, \(P_{0}\) is the power consumed at the idle state, \(P_{sleep}\) is the power drawn at sleep mode, and \(\chi\) represents traffic load. The base station parameters along with the power consumed by the major components of the BSs from different system bandwidths are obtained from references [53; 54].
## 2 Mathematical Models of the WT and of the Doubly Fed Induction Generator
### Mathematical Model of the Slip Ring Induction Generator
At variable frequency, the asynchronous generator works at the constant stator flux value  
\[\Psi=\frac{U_{N}}{\omega}=\frac{U_{N}}{2\pi f}=\frac{690}{2\pi 50}=2.1963\ [ \mathrm{Wb}], \tag{10}\]  
at which the \"\(U/f\)\" ratio in the stator becomes \(\frac{U_{s}}{f}=\Psi\times 2\pi=13.8\ [\mathrm{Vs}]\), leading to the final generator power influenced by \(R_{ad}\) to be obtained as  
\[P_{\mathrm{GE}}=\frac{3\times 13.8^{2}\times\left(2.3805\times 10^{-2}+R_{ad} \right)/\left(1-2n/f\right)}{\left(\frac{2.3805\times 10^{-2}+(2.3805\times 10^{-2} +R_{ad})/\left(1-2n/f\right)}{f}\right)^{2}+\left(2.0618\times 10^{-3} \right)^{2}}, \tag{11}\]  
**Remark 4**: _With the generator operating at variable frequency, two limitations are imposed:_  
1. _The controlled stator flux has maximal value of_ \(\Psi=2.1963\ [\mathrm{Wb}]\)_._
2. _The stator current_ \(I_{s}\) _is saturated at nominal value_ \(I_{N}=724.64\ [A]\)_._  
The wind speed value used to discriminate between the two operating regions being analyzed is \(V_{MAX}=10.788\ [\mathrm{m/s}]\) and was obtained by the nonlinear equation system formed with the conditions \(P_{TV-MAX}=2442.3V^{3}=P_{GE}\), plus \(I_{S}=I_{N}=724.64\ [\mathrm{A}]\), plus the nominal slip condition \(1-2n/f=-1.2231\times 10^{-2}\). Solving the above nonlinear equation leads to \(f=101.55\ [\mathrm{Hz}]\), \(n=51.394\ [\mathrm{rps}]\) and \(V_{MAX}=10.788\ [\mathrm{m/s}]\). The value \(V_{MAX}\) is a threshold between the two operating regions in our work. Notice that the power converters and the generator have their own internal transient dynamics; however, the time scale is much faster than that of the WT control, so static relationships in terms of current, voltage and power are acceptable. We briefly underline some critical analysis between induction generators and permanent magnet synchronous ones. Induction generators (with slip rings) have the following advantages. (1) They do not have permanent magnets (PMs), so there will not be failures when the PM demagnetizes. (2) They are generally cheaper compared to synchronous generators with PM. (3) They can very easily switch to motor mode when a quick start of the turbine is desired. Several disadvantages of the induction generators: (1) They cannot provide reactive power. (2) They absorb reactive power from the grid in order to operate. Synchronous generators with PM have the following advantages. (1) They can provide reactive power. (2) They can operate without voltage from the grid. Some disadvantages include the following. (1) When the PM demagnetizes, they can no longer operate. (2) They require more complex power electronics converters, which may increase the cost and complexity of the system.
# Online Group Student Peer-Communication as an Element of Open Education
## 3 Results
### The First Part of the Study: Qualitative Analysis of Student Group Conversation
The popularity of this topic was associated with the selected review period (December is the last month before certification). The next most popular topics were how others completed the task (14%) and clarification of information on the task (13%). Less popular topics were questions about the given task and how to do the task (10% each) and messages related to the organization of joint group work (collection of necessary information, direct work on the task) (9%). We identified scored categories of requests for sharing information given by the teacher (7%) and requests for solving the task (5%). Fewer messages were associated with sharing additional sources, completed tasks, requests and consent to do tasks for another. Among organizational issues, there were more questions about certification (what to do, when and where to go for getting certification on a subject, retake, etc.) (30%). Next were organizational issues related to specific classes (for example, who will attend/do something, the format, cancellations, rescheduled classes and the name of the teacher) (21%), followed by topics not related to academic subjects (about the things left, addresses of buildings, requests to pick up educational documents,  
Figure 3: Examples of visual representation of the features of groups with the longest (**a**) and shortest (**b**) average message size. management schedule, library schedule, etc.) (16%). Questions about class schedules represented 13% of all organizational messages. It is interesting that this information was in most cases presented on the site, but students preferred to ask rather than look there. In one of the groups, a special bot was created that answered similar questions. Eight percent of messages were devoted to the topic of communication methods with the teacher. There were also messages about lateness (5%), as well as discussions about scholarships, skipping classes, etc. The main content of everyday communication was discussion of diverse common interests (49%), jokes and answers to them (43%). There were also congratulations, words of support, organization of joint extra-university events, cues of courtesy, etc. We identified the 30 most common words in the student polylogues (Figure 4). The most popular word was \"thank you\", followed by forms of appeal to groupmates. In Figure 4, the words related to the obtaining of information are in violet, the names of specific subjects and forms of classes are in yellow, and the words that describe other elements of the educational process are in blue.
## 3 Results and Discussion
### Three-Point Bending Test  
#### 3.2.1 Fabricated Specimens  
As shown in Figure 10, a total of 5 types of specimens were prepared according to the material and printing method. Since the manufacturer did not provide the mechanical properties of the base resins of materials A and B, test specimens for a single material were also prepared. In addition, since materials could also be changed through complicated processes with the existing equipment, half-and-half structures were fabricated by changing the material only once, as shown in Figure 10,d. In general, when conducting a bending test, compressive force is generated on the upper surface under load and tensile force is generated on the opposite surface. Accordingly, since the flexural stress behavior varies depending on the upper and lower positions of materials A and B, two types of  
Figure 8: Layer-by-layer additive manufacturing for laminar composite structures.  
Figure 9: Schematic diagram of the suggested process: (**a**) photo-polymerization of material A; (**b**) moving the separator for material switching; (**c**) photo-polymerization of material B.  
specimens were prepared in the order of material A-B and B-A, respectively. Finally, the laminar composite structure described in Section 3.1 was fabricated while continuously changing the material for each layer. As can be seen in Figure 10, the printed specimen was observed to have almost the same color in an indoor space without ultraviolet rays, although there was little difference in contrast; however, it was confirmed that different colors were shown due to the influence of the photochromic pigment present inside the material when irradiated with ultraviolet light. When material A with red pigment and material B with yellow pigment were printed with only a single composite material, this was observed according to the color of the pigment mixed in each material. For the half-and-half structures printed with different materials at half of the total thickness, the color of the material located on the top was observed. For the laminar composite structure in which the material was changed for every printed layer, a mixed color of red and yellow was observed. Through this, it was confirmed that the structures printed with a composite material mixed with photochromic pigments were able to show the differences between materials through ultraviolet irradiation.
## 4 Results and Discussion  
### Effectiveness of PPF Control on Time History  
We quantitatively investigated the system's results using MATLAB's (R2023b) \"Ode 45\" module. (MTSM) was also used to explain how different factors affected the behavior of the controlled system. This method has allowed investigation into how changes in certain parameters can affect the overall dynamics or performance of the system under control; Figure 2 depicts a schematic depiction and a simplified two-mass model of a series hybrid electric system [6]. In this section, a study is conducted to examine the steady-state responses of the primary system and its related controller. The examination focuses on several controller parameters under the situation of (SHR). This experimentation study resolves to establish the modifications in controller parameters that affect the system's stability and performance in the 1:2 IR operational zone. The primary system's results are plotted as steady-state amplitudes versus detuning parameters. Plotting figures requires the usage of the following system parameters.  
\(\omega_{2}=\omega_{1}\)\(=10\); \(\mu\)\(=0.01\); f \(=40\); \(\alpha_{1}\)\(=0.5\); \(\alpha_{2}\)\(=0.1\); \(\omega\)\(=5\); \(\gamma\)\(=0.005\); \(\beta\)\(=0.005\); \(\eta\)\(=0.0003\); \(\lambda_{1}\)\(=25\); \(\lambda_{2}\)\(=25\).  
In Figure 3, the initial steady-state amplitudes of the basic system, before integrating PPF controllers, are depicted at approximately 5.41, representing the worst resonance case. Upon the incorporation of PPF controllers, the amplitudes of the primary system have notably decreased to a value of 0. 0635. This indicates that the efficacy of the PPF controllers is approximately 90%, resulting in a reduction in vibrations by roughly 99% compared to their levels without control.
# Experimental Study on the Shear Strength of Silt Treated by Xanthan Gum during the Wetting Process
## 3 Results and Discussion
### Direct Shear Test
#### 3.4.4 Variation Rules of Shear Strength Parameters  
Figure 18 shows the relationship curves of water content, cohesion \(c\), and internal friction angle \(\phi\) of silt. Table 3 summarizes the parameters of the direct shear strength tests before and after silt treatment during the wetting process. Table 3 and Figure 18 show that at low water content (2-4%), the \(c\) values of XG-silt increased by more than a factor of 2.3, and \(\phi\) increased by more than a factor of 1.5. However, when the water content was lower than 8%, the sample strength was enhanced both before and after treatment, whereas \(c\) and \(\phi\) only slightly increased. At high water contents (16% and 22%), \(c\) increased by factors of 1.4 and 1.8 times, respectively, and \(\phi\) increased by factors of 1.4 and 1.9. The experimental results are consistent with those of [PERSON] et al. [35; 36] and [PERSON] et al. [55]. Additionally, the test results showed that the addition of XG can greatly improve the strength and stability of silt during the wetting process.
## 2 Hybrid Model Updating Method
### Hybrid Method  
The hybrid method is a combination of the aforementioned techniques that involves following processes illustrated in Figure 2.  
1. Applying LHS method to select reasonable samples of random parameters as input datasets;
2. Performing a series of the FE analyses with the input parameters to obtain the interested mode frequencies of the structure as the output datasets;
3. Determining the architecture of the Kriging meta-model, yielding its correlation coefficients with the input and output datasets with Equations (6)-(10) and extracting the explicit expression based on Equation (11);
4. Applying GMPSO as an optimization technique with an objective function which is the sum of natural frequencies residuals between experimental value and Kriging model according to Figure 1;
5. Yielding the updated values of parameters after several iterations.  
To apply this hybrid method to solve model updating problem, a combined program based on both MATLAB-code and ANSYS-APDL is developed. The program is performed in modular form so that it can be easily used in other model updating of structures, which obviously facilitate practical application.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
###### Abstract  
Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.  
logging constraint; CS; seismic exploration; low-frequency expansion Article
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring  
Recent research breakthroughs have revealed an abundance of new knowledge and methodologies that are transforming the investigation and development of equipment and tools. These new developments significantly sharpen our capacity for monitoring shoreline activity in an efficient and effective manner, on budget and on schedule. Using recently developed methodologies [11, 32], scientists can now acquire and interpret data with higher accuracy, gaining enhanced insights into coastal processes and making shoreline environments more sustainably managed.
## 3 Methods
### Assessment Metrics and Methods
#### 3.2.3 Economic Metrics  
Financial sustainability was measured through performance indicators that fall under the economic metric and was also relevant in decision-making. Capital costs (AUD/kW) and annual O&M costs (AUD/kW/year) were the selected typical performance indicators in this study [3; 49]. \"Capital costs\" refers to the estimated total of the initial one-time expenses that are necessary for buying and setting up the system or technology. The estimation of capital costs may vary depending on the type of technology, geographical location, system size and project lifetime [50]. The typical calculation can be expressed as Equation (2):  
\[Capital\ Costs=\frac{Cost\ of\ technology\ (AUD)}{Capacity\ of\ technology\ (kW)} \tag{2}\]  
\"Annual O&M costs\", on the other hand, refers to the estimated total annual expenses for the energy that is required to operate and maintain the service (i.e., system or technology). It can be generally calculated from Equation (3):  
\[Annual\ Energy\ Cost=\frac{Cost\ of\ electricity\ \left(\frac{AUD}{year}\right)}{ Capacity\ of\ technology\ (kW)} \tag{3}\]