esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Objectives  
A careful examination of the abovementioned case studies revealed the substantial research effort that has been made to mitigate vibrations in automobile sunroofs and guarantee their structural integrity. Many research endeavors have been undertaken to reduce vibration and improve the structural soundness of automotive sunroofs. These endeavors have employed experimental and analytical methods, with the latter being supported by test data to ensure the consistency and dependability of the experimental and analytical findings. Nevertheless, limited research has been conducted on the structural rigidity and vibration durability of sunroofs in their individual states, as opposed to while the vehicle is in motion. This suggests that there is a limitation in accurately representing the vibration characteristics of sunroofs in different driving environments. Vehicle sunroofs are a unified system integrated into a vehicle and demonstrate fluctuating vibration properties in response to driving conditions. Therefore, structural enhancements and research to mitigate vibrations must be undertaken by measuring and testing sunroof vibrations while the vehicle is in motion with the sunroof mounted.  
As a result, this study examined the characteristics and magnitude of vibrations in vehicle sunroofs, taking the operational environment of the vehicle into account. Analytical methods were employed to establish the dependability of experimental and analytical data, while analytical simulations were used to propose an optimal design for mitigating vibrations in vehicle sunroofs while in operation. To ascertain the natural frequency of the sunroof systems, the thin plate theory was examined initially, and was then modified appropriately to account for the structure's vibration characteristics. In addition, natural frequencies and vibration characteristics were determined through EMA and operational deflection shape (ODS) tests, and the outcomes of these tests were contrasted with the results obtained from computational structure analysis. Upon demonstrating a satisfactory level of concurrence, it was possible to ascertain that the vibration analysis model pertaining to a given sunroof structure corresponded to the experimental findings and supported the related theoretical conclusions. Subsequently, a parametric study was conducted on the analysis model to ascertain the direction of design modification in accordance with the findings. Figure 1 shows the flowchart depicting the general process of the employed methodologies.  
The remainder of this paper is organized as follows. Section 2 expounds on thin plate theory, concerning the vibration of planar surfaces, such as the glass of vehicle sunroofs. Experimental vibration measurement methods, including modal analysis, EMA, and ODS, are described in Section 3. Section 4 examines structural enhancement via analysis and simulation results. Section 5 concludes with a discussion of future endeavors.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 4 Results and Discussion
#### Adaptive Navigation (Mitigation using RSQM)
This limited satellite availability, coupled with the prevalence of MP and NLOS reception due to the dense urban environment, resulted in the highest PDOP values (above 3) observed during the experiment. These high PDOP values signify a very unfavorable satellite geometry, severely impacting positioning accuracy. Consequently, position errors in the urban environment were substantial, with a mean of 5.3 m and occasional peaks reaching up to 14.3 m. The comparative analysis shows that performance is improved due to the adaptive mitigation through masking and exclusion of faulty measurements. Figure 15: Performance of GNSS after mitigation strategy. (**a**) Satellite availability, (**b**) PDOP and (**c**) Position Error (m).
## 3 Results and Discussion
### UV-Vis Spectrophotometer Analysis  
A higher residual ratio of a reagent in the slurry indicated a lower extent of chemical reaction [35]. In this study, the residual ratio of STG was analyzed under three reaction systems--Cu\({}^{2+}\) + STG, MoS\({}_{2}\) + STG, and MoS\({}_{2}\) + Cu\({}^{2+}\) + STG--at a slurry pH of 8.0, with Cu\({}^{2+}\) at 8 mg/L and STG at 1.0 \(\times\) 10\({}^{-3}\) mol/L. This analysis aimed to elucidate the adsorption behavior of molybden toward STG under different conditions.  
As shown in Figure 8, in the Cu\({}^{2+}\) + STG system, the residual STG ratio was 61.59%, indicating partial consumption of STG due to its interaction with Cu\({}^{2+}\). In contrast, in the MoS\({}_{2}\) + STG system, the residual STG ratio remained as high as 93.98%, suggesting minimal adsorption or reaction on the untreated molybden surface. However, in the MoS\({}_{2}\) + Cu\({}^{2+}\) + STG system, the residual STG ratio significantly decreased to 20.81%, demonstrating that in the STG system with Cu\({}^{2+}\), enhanced STG adsorption onto the molybden surface occurred. This intensified interaction further suppressed the floatability of molybden, aligning well with the flotation results.  
Figure 7: Zeta potential of molybden with different reagent dosage.
# BIM-Based Methodology for the Management of Public Heritage. CASE Study: Algeciras Market Hall
## 3 Case Study: Algeciras Market Hall by the Engineer [PERSON]  
### Phase 1  
[PERSON] was one of the most notable structural engineers of the 20 th century. He was a prominent researcher, designer, scientist and professor, pioneering the use of reinforced concrete structures, especially in laminar structures. Currently, his research and contributions on the behaviour of reinforced and prestressed concrete are internationally acknowledged as well as subject matter to publications, conferences and research projects around the world.  
Algeciras Market Hall is also known as Engineer Torroja Market. Built in 1935, it is one of the most telling examples of [PERSON]'s structural philosophy. The simplicity is integrated over complexity, gathering both technical and art features as well as a deep understanding of the functional aspects. Algeciras is a port city in the Bay of Gibraltar, in southern Spain, whose most important resources until the first half of the 20 th century were the agriculture and fishing industries. The objective of the project was to cover the open-air market with a large open space, absent of interior supports. It was built in the form of an octagon with four streets by defining the contours. The dome was the largest in the history for 30 years (1935-1965) and the structural concepts implied a step forward in the construction of domes [18]. As a result, it was declared an asset of cultural interest by the Regional Government of Andalusia in 2001 as the best example of the Modernist Movement.  
Hence, the essential and main element in the structure is the dome. It is a spherical and thin layer cap of reinforced concrete of 47.76 m span between supports with a thickness of only 10 cm, growing to 50 cm in the eight supports. The support columns are connected with a prestressed ring, forming an octagonal floor plan. The lateral cylindrical vaults together with the supports represent the fundamental elements in the infrastructure. Such supports can be found in the eight vertices of the intersection between the vaults and the dome. The pillars, together with the prestressed ring, absorb horizontal and vertical forces transmitted by the dome. For a better comprehension of the description, see Figure 2.  
Figure 1: BIM methodology applied to public heritage infrastructures (HCIM).  
The structure was in service without the need for any noticeable maintenance work until 1996. At that year, Algeciras Market Hall tendered a rehabilitation project approach to solve noticeable corrosion problems as we can see in Figure 3. The intervention was the result of several inspections that found rust spots and damage in concrete due to the corrosion. The most relevant problems were found in dome, clamping ring and supports. So, the most important rehabilitation works were performed on such elements since they are critical to avoid the collapse of the structure.
## 4 Simulation Results and Discussions
### Many-Objective OPF Problem
#### 4.3.2 IEEE 118-Bus System  
To ensure the effectiveness of the MaMPA in solving the MaOPF problem in the larger system, the IEEE 118-bus was tested. The best-compromised solution between the four objective functions of the MaMPA is compared with those of PSO and SSA as expressed in Table 15 and the hypervolume indicators are computed as in Table 16 while PESA-II and SPEA2 could not find a feasible solution within the given iterations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cost (USD/h)** & **Emission (ton/h)** & **Loss (MW)** & **VSI** \\ \hline PSO & 863.0005 & **0.226071** & 4.9709 & 0.1424 \\ PESA-II & 863.8967 & 0.226163 & 5.0019 & 0.1394 \\ SPEA2 & 861.6285 & 0.226465 & 5.0708 & 0.1390 \\ SSA & **857.4578** & 0.230565 & 5.1405 & 0.1404 \\ MaMPA & 862.1941 & 0.227876 & **4.9211** & **0.1388** \\ \hline \hline \end{tabular}
\end{table}
Table 13: Best compromised solutions for solving many-objective OPF (MaOPF) problems by using all considered objectives as the objective functions in the IEEE 30-bus system (case 19).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Case 19** \\ \hline PSO & 0.03347 \\ PESA-II & 0.02849 \\ SPEA2 & 0.03236 \\ SSA & 0.02876 \\ MaMPA & **0.03367** \\ \hline \hline \end{tabular}
\end{table}
Table 14: Hypervolume comparison of MaOPF problems in the IEEE 30-bus system.  
In the IEEE 118-bus system, MaMPA generated highly efficient results in solving the MaOPF problem. MaMPA could provide the best-compromised solution which is remarkably superior to those of PSO and SSA in all considered objective values as in Table 15 while PESA-II and SPEA2 could not even converge on a feasible solution. The objective values of the best-compromised solution of MaMPA are better than those of PSO at around 8.29, 34.61, 47.94, and 85.23% for cost, emission, loss, and VSI objectives, respectively. They are also greater than those of SSA at about 4.30, 31.73, 20.41, and 4.38% for cost, emission, loss, and VSI objectives, respectively. From Table 16, the hypervolume values calculated by the Pareto fronts of MaMPA for this case are superior to those of PSO and SSA by about 505.15% and 222.97%, respectively. So, the hypervolume values also guarantee that MaMPA provided significantly better Pareto fronts than those of the compared algorithms for this case.  
Hence, MaMPA has a high performance in solving MaOPF problems in the small tested system and in particular in the large tested system. The generated best-compromised solutions and Pareto fronts of MaMPA are competitive to those of the compared algorithms and much better than the compared algorithms when tested in the IEEE 30- and 118-bus systems, respectively. The hypervolume values could also verify the superior Pareto fronts generated by MaMPA to those of the compared algorithms.
## 3 Analysis of Factors Affecting Shear Behavior of Reinforced Concrete Beams
### Effect of the Amount of FRP Grid Reinforcement  
#### 3.1.1 Effect of the Thickness of the FRP Grid  
To investigate the effect of the thickness of the FRP grid on the shear behavior of strengthened beams, taking the strengthened beam B-1 as the reference, keeping the original parameters unchanged, the FRP grid thickness is changed to 1, 2, 3, 4, 6, and 7 mm. The specific parameters are shown in Table 3. Figure 8 is a comparison diagram of the load-midspan deflection curves of the unstrengthened beam and the strengthened beams with different grid thicknesses. The detailed data and comparison are listed in Table 4. As the thickness decreases, the ultimate load of the strengthened beam gradually decreases, and the effective strain of the FRP grid increases significantly, which intuitively reflects that the thickness is positively correlated with the shear behavior capacity and negatively correlated with the grid strain. Compared with the reference beam B-1, for beam B-T3, the grid reinforcement amount decreases from 1.33% to 1.07%, a reduction of 20%, and the ultimate bearing capacity only decreases by 4.43 kN, while the grid effective strain changes from 0.0054 to 0.0083, an increase of 54%, indicating that the grid efficiency of beam B-T3 is more fully exerted. It can be clearly seen from Figure 8 that beams B-T5 and B-T6 have poor overall ductility, and there is no descending section in the load-deflection curve. The author believes that the possible reasons are as follows. First, Section 2.3 determined that the appearance and development of diagonal shear cracks will increase the plastic strain of the concrete in the shear span zone, and the bonding ability with the strengthening layer will be weakened, resulting in a descending section in the load-midspan deflection curve. The presence of the FRP grid will increase the number of cracks, and reduce the crack width and extension height, and the larger the reinforcement amount, the more obvious this effect is, that is, the more obvious the descending section is. As the thickness decreases, the failure characteristics of the strengthened beam and the corresponding failure curves gradually approach those of the unstrengthened beam. This trend provides an explanation for the differences in the curves in Figure 8: when the load reaches about 200 kN, that is, close to the ultimate load level of the unstrengthened beam, the curves show obvious divergence.  
Figure 8: Effect of FRP grid thickness.
## 2 Market Transaction Rules and the Carbon Responsibility Approtioning Method
### Carbon Emission Model Based on Carbon Emission Flow Theory
#### 2.2.2 User Side Carbon Emission Model of Hybrid Power Market  
In the hybrid electricity market, according to the carbon emission flow theory, the carbon emission allocated by users at node \(i\) consists of two parts. The specific expression is as follows:  
\[R_{i}=R_{b,i}+R_{p,i} \tag{3}\]  
where \(R_{b,i}\) is the carbon emission allocated by users at node \(i\) in the bilateral market and \(R_{p,i}\) is the carbon emission allocated by users at node \(i\) in the pool market.  
The carbon emission assigned by users at node \(i\) in a bilateral market can be calculated directly according to the carbon intensity of the power supply, that is:  
\[R_{b,i}=\sum\limits_{j=1}^{M}q_{b,i,j}e_{j} \tag{4}\]  
where \(q_{b,i,j}\) is the bilateral transaction electricity between the user \(i\) and generator \(j\) and \(e_{j}\) represents the carbon intensity of generator \(j\), which is obtained according to the power generation characteristics.  
The carbon emission allocated by users at node \(i\) in the pool market can be calculated via Equations (5) and (6):  
\[e_{i}=\frac{\sum\limits_{i=1}^{I}P_{i}\rho_{i}+P_{j}e_{j}}{\sum\limits_{i=1}^ {I}P_{i}+P_{j}} \tag{5}\]where \(e_{i}\) is the carbon intensity of node \(i\) which represents the equivalent carbon emission caused by the unit electricity consumed by the node. \(P_{i}\) is the power injected into node \(i\), \(P_{j}\) is the generator output power at node \(j\), and \(\rho_{i}\) is the branch carbon flow density injected into node \(i\). I indicates the number of branches that inject power to node \(i\). The branch carbon flow density \(i\) can be replaced by the carbon intensity of the node at the beginning of the branch. After the carbon intensity \(e_{i}\) of the user at node \(i\) is obtained, the carbon responsibility of the user at node \(i\) can be calculated according to the following equation:  
\[R_{p,i}=e_{i}D_{p,i} \tag{6}\]  
where \(D_{p,i}\) is the electricity purchased by user \(i\) in the pool market.  
According to Equations (5) and (6), when sharing carbon responsibility in the pool market, the carbon responsibility of the user is affected by the carbon intensity of the unit and the network topology. Even the user with the same electricity demand will have different carbon emission \(R_{p,i}\). From the perspective of the bilateral market, the users' carbon responsibilities are not constrained by the network topology but are directly determined by the generators. On the basis of Equation (4), when users purchase clean energy to meet part of their electricity demand through bilateral transactions, the carbon intensity \(e_{j}\) of new energy is 0 and its assigned carbon emission \(R_{i}\) will be greatly reduced. Therefore, the introduction of bilateral markets provides an effective way to reduce carbon responsibility for users with large carbon flow rates.  
Hybrid Electricity Market Emission Reduction Model Considering Carbon Emission Flow and Source-Load Coordination
# Joint Stiffness Identification Based on Robot Configuration Optimization Away from Singularities
## 2 Kinematic and Stiffness Modeling
### Stiffness Modeling  
The components of an industrial robot, including links and joints, are nonrigid and are subject to tensile, bending, and torsional deformations in the Cartesian workspace. The stiffness of an industrial robot is derived from the stiffness of the joints, links, and actuators, as well as the dynamic stiffness of the controller. The joints account for more than 70% of the overall deformation, including gear, timing belt, and motor transmission deformations. To simplify the calculations and not lose the generality, this paper assumes that the joint is a torsion spring, and the links are rigid bodies.  
The relationship between the external forces and moments \(\mathbf{F}\) and the robot joint torques \(\mathbf{\tau}\):  
\[\mathbf{\tau}=\mathbf{J}^{\mathsf{T}}\mathbf{F}. \tag{12}\]  
where \(\mathbf{J}^{\mathsf{T}}\) is called the force Jacobian matrix, which is the transpose of the Jacobian matrix \(\mathbf{J}\); the six-dimensional vector \(\mathbf{F}\) represents the external forces and moments acting on the robot EE, and \(\mathbf{\tau}\) is related to the joint angle variation vector \(\Delta\mathbf{q}\):  
\[\mathbf{\tau}=\mathbf{K}_{\theta}\Delta\mathbf{q}. \tag{13}\]  
where \(\mathbf{K}_{\theta}\) is the stiffness matrix in the joint space, \(\mathbf{K}_{\theta}=diag(k_{1},k_{2},\dots,k_{6})\), and \(k_{i}\) is the value of the \(i\)th joint stiffness. The relationship between deformation in the Cartesian space \(\Delta\mathbf{X}\) and \(\mathbf{F}\) is as follows:  
\[\mathbf{F}=\mathbf{K}_{X}\Delta\mathbf{X} \tag{14}\]  
where \(\mathbf{K}_{X}\) is the stiffness matrix in the Cartesian space.  
The differentiation of Equation (12):  
\[\frac{\partial\tau}{\partial q}=\left(\frac{\partial\mathcal{I}^{T}}{\partial q} \right)\mathbf{F}+\mathcal{I}^{T}\frac{\partial\mathbf{F}}{\partial\mathbf{X}} \frac{\partial\mathbf{X}}{\partial q} \tag{15}\]  
\[\mathbf{K}_{\theta}=\mathbf{K}_{\mathrm{C}}+\mathcal{I}^{T}\mathbf{K}_{ \mathrm{X}}\mathbf{J} \tag{16}\]  
where the complementary stiffness matrix (CoSM) \(\mathbf{K}_{\mathrm{C}}\) can be expressed as the robot is subjected to external loading or the Jacobian changes with its configuration, and as long as the robot configurations are chosen appropriately, \(\mathbf{K}_{\mathrm{C}}\) can be negligible. Thus, Equation (16) is simplified as:  
\[\mathbf{K}_{\theta}=\mathcal{I}^{T}\mathbf{K}_{\mathrm{X}}\mathbf{J} \tag{17}\]  
The general form of the robot stiffness is shown in Equation (17). Once the robot configurations are chosen, we can get \(\mathbf{K}_{\mathrm{X}}\) to obtain \(\mathbf{K}_{\theta}\) by Equation (17).
## 3 Simultaneous Three-Axis Control Movements and Mathematical Model  
### Simultaneous Three-Axis Control Movements  
Simultaneous three-axis control movements which include two linear axes and one rotary axis can be conducted by means of the ball bar both in cylindrical coordinate system and in Cartesian coordinate system. The motions are named according to the sensitive direction of the ball bar as radial direction, tangential direction, axial direction in cylindrical coordinate system, illustrated in Figure 4, and X-, Y- and Z-direction in Cartesian coordinate system, illustrated in Figure 5.  
Figure 4: (**a**) B axis radial measurement; (**b**) B axis tangential measurement; (**c**) B axis axial measurement; (**d**) C axis radial measurement; (**e**) C axis tangential measurement; (**f**) C axis axial measurement.  
Figure 5: (**a**) B axis X-direction measurement; (**b**) B axis Y-direction measurement; (**c**) B axis Z-direction measurement; (**d**) C axis X-direction measurement; (**e**) C axis Y-direction measurement; (**f**) C axis Z-direction measurement.
# A Lunar Backup Record of Humanity
## 2 Challenges
### Data Transfer Rate  
New data about ongoing human activity could be continuously transferred to the system through laser communications from Earth. The data rate of sending new data to the system must exceed the rate of new data production (See Figure 1). Let \(r_{U}\) be the data rate of uplink communications to the system. Then, the necessary data rate, in bytes per year, is  
\[r_{U}=\sum_{i=1}^{n}q_{i}r_{i}(a_{i}e^{rd}-a_{i}+1). \tag{2}\]  
Laser communications can be used to transmit data from ground stations to the system at significantly higher rates than radiofrequency waves [25]. However, the bandwidth of ground-to-space laser communications is limited by the scintillation caused by optical turbulence, atmospheric absorption, beam scattering, and the need for a clear line-of-sight path [26, 27]. Maintaining multiple ground stations to transmit data can mitigate the negative effects of cloud cover on transmission. A constant data stream at 622 Mbps from a ground station to the lunar surface--a rate which has been achieved by NASA's Lunar Laser Communications Demonstration (LLCCD) [28, 29]--would be sufficient to transmit over \(10^{15}\) bytes/yr. This is sufficient to transmit all of the aforementioned literature given the current growth rates. Near-term improvements in data rates would enable the transmission of enough data to include satellite imagery produced by the Dove and SkySat constellations operated by Planet Labs (Source: [[https://www.planet.com/faqs/](https://www.planet.com/faqs/)]([https://www.planet.com/faqs/](https://www.planet.com/faqs/)) (accessed on 31 August 2022)). We assume that redundant satellite imagery or higher-resolution imagery from future earth observation (EO) satellites is not necessary for transmission and storage.).  
Laser communication data rates have historically demonstrated exponential growth (See Figure 2), and a previous analysis suggests the U.S. Department of Defense requires an increase in bits per second by an order of magnitude every nine years [30]. NASA's Artemis Program is also utilizing laser communications, and its Orion EM-2 Optical Communications Terminal (O2O) will be the first usage of optical communications for human spaceflight [31]. Future systems can maintain the growth by leveraging new modulation schemes. The existing techniques, such as pulse position modulation (PPM) and differential phase-shift keying (DPSK) modulation [32], have enabled our current capabilities, and the usage of wavelength division multiplexing (WDM) may enable even greater data rates [33]. Terrestrial fibers have already achieved exa-bps (\(10^{18}\) bits per second) using WDM [34], and uplink transmissions to satellites may experience similar increases in capabilities.  
Figure 1: Projected data rate for new content given historical growth rates.