esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 4 Static Loading Experiment
### Results of the Static Loading Experiment  
The results shown in Table 2 refer to two different load cases: Load case 1 (\(F_{x}=6.47\) N; \(F_{y}=86.59\) N; \(F_{z}=86.84\) N) and Load case 2 (\(F_{x}=45.97\) N; \(F_{y}=17.17\) N; \(F_{z}=47.34\) N). These load cases were selected since they showed the largest coupling errors that need to be recovered. In the two load cases, the measurement error is relevant with both the locknut and necked collar links if no decoupling is performed, while it decreases significantly (less than 2% in the worst case), after the application of the decoupling matrix, which is defined in Equation (2). On top of that, it is observed, as expected, that the maximum relative errors (35.52% and 17.33%) occur in the direction of the lowest force component (\(x\) in load case 1 and \(y\) in load case 2).  
For the sake of comparison, the measured forces and the decoupled forces, obtained after application of Equation (2), are shown in Figure 7 (locknut collar link) and Figure 8 (necked collar link), where each point coordinates are the applied load (\(x\)-axis) and the measured force (\(y\)-axis). It is clear that full symbols, representing the decoupled forces, lie closer to the bi-sector line of the plot, demonstrating the need for a decoupling process of the measured quantities.  
Figure 6: Simultaneous application of static loads in three orthogonal directions.  
Figure 8: Decoupling method applied to necked collar links (Load case 1: Circles; Load case 2: Triangles).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Percentage** & \multicolumn{3}{c}{**Measured Force Outputs (\%)**} & \multicolumn{3}{c}{**Decoupled Applied Forces (\%)**} \\
**Error** & \(\mathbf{F}_{1}\) & \(\mathbf{F}_{2}\) & \(\mathbf{F}_{3}\) & \(\mathbf{\widetilde{F}_{x}}\) & \(\mathbf{\widetilde{F}_{y}}\) & \(\mathbf{\widetilde{F}_{z}}\) \\ \hline Lock Nut Collar & & & & & & \\ Link & & & & & & \\ Load Case 1 & 35.52 & 1.45 & \(-\)3.07 & \(-\)0.75 & \(-\)1.04 & \(-\)0.041 \\ Load Case 2 & 0.27 & 17.33 & \(-\)5.62 & \(-\)0.22 & \(-\)1.65 & 0.25 \\ \hline Necked Collar & & & & & & \\ Link & & & & & & \\ Load Case 1 & \(-\)10.51 & 0.54 & \(-\)1.45 & 1.16 & \(-\)0.22 & \(-\)0.32 \\ Load Case 2 & \(-\)1.29 & 9.81 & \(-\)4.39 & \(-\)0.02 & \(-\)0.26 & \(-\)0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage error of the measured forces and decoupled forces for simultaneous loading experiment.  
Figure 7: Decoupling method applied to locknut collar links (Load case 1: Circles; Load case 2: Triangles).
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System
### Adaptive Controller  
Apart from the above control methods which were discussed above. Several factors may influence the system stability and performances such as the mass variation of the driver, system modelling and nonlinearity of the plant. To cope up with these drawbacks, adaptive control methods were proposed. It has been most widely used in the recent control fields [93]. In the active control area, the adaptive controller is superior to the classical controller in terms of reducing the noise and suppressing the vibration practically if the ideal model reference is available. The adaptive controller method consists of three main parts: a plant which contains the system parameters, a reference model which represents the desired performance of the system and the adaptation mechanism which updates the system parameters instantly [94]. The key feature of the adaptive controller is to find the proper parameters which guarantee the system stability and ensure zero tracking error. Figure 15 represents the adaptive controller scheme.  
Figure 14: System trajectory of sliding mode controller (SMC) with sliding surface [90].  
Figure 15: Adaptive block diagram representations [95].  
Filtered-x least mean squares (FXLMS) algorithm is one of the adaptive control methods, which was proposed in 1963 [96]. It has been extensively used in reduction of noise and vibration due to its robustness and vibration attenuation capacity. FXLMS algorithm integrated with the on-line Fast-block LMS (FBLMS) identification process to suppress the vibration of the active seat suspension in the vertical direction under low frequency periodic excitation were reported in [70]. The effectiveness of the controller was examined through an experimental test with different road profiles. The results of the switching frequency test have validated the stability of the control.  
In addition, the FXLMS algorithm was proposed to decrease the undesired vertical vibration of the vehicle seat in [71]. The FXLMS algorithm was combined with a robust controller to achieve good performance. The source frequency was set at 10 Hz, 20 Hz and 30 Hz. Therefore, the FXLMS was found to be able to attenuate the vibration at 20 Hz where the low frequency was considered in the range of 0-30 Hz. Additionally, the adaptive controller mechanism was proposed to compensate the seat suspension system in terms of the driver mass variation [69].
## 3 Results  
Figure 3 shows the experimental pyroelectric signal amplitude for the AO/MO mixture, in which the symbols represent experimental points and the solid lines correspond to the best fitting of Equation (1) to the data. Equation (1) was also fitted to RO/MO and SO/MO signals to obtain the corresponding thermal effusivity. Equation (3) was used to obtain thermal diffusivity values for the studied vegetable-motor oil mixtures. The obtained thermal diffusivity values as concentration functions are presented in Figure 4. In the range of interest (5% to 20%), SO/MO and AO/MO mixtures decreased linearly, while the thermal diffusivity of RO/MO increased. Table 1 shows linear coefficients for sunflower, almond, and rapeseed mixtures. It can be observed that the linear adjustment was more approximate in the values found for almond and rapeseed oils. Although the measurement errors were low, the linear fitting of the sunflower mixture had a low R\({}^{2}\) value, indicating a non-linear behavior. In Table 2, the obtained values of thermal diffusivity and effusivity for the studied mixtures are shown; also, the values for the pure oils are presented. Thermal conductivity values were calculated by using the relationship \(\kappa~{}=~{}e\sqrt{\alpha}\). The presented error for thermal conductivity was calculated by error propagation by using the uncertainty values from experimental measurements. It can be seen that the obtained uncertainty values were lower than 2%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Slope** & **Intercept** & \\  & \(\times\)**10\({}^{-10}\)** & \(\times\)**10\({}^{-8}\)** & **R\({}^{2}\)** \\ \hline Sunflower & \(-1.08\) & 7.25 & 0.32 \\ Almond & \(-4.54\) & 8.02 & 0.93 \\ Rapeseed & 3 & 7.12 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The linear coefficient for thermal diffusivity behavior for the studied samples. Figure 3: Experimental IPPE signal amplitude with AO/MO mixtures as the sample; triangles represent the experimental points, and solid lines are the best fitting of Equation (1) to the experimental points. The thermal diffusivity of RO/MO mixtures increased linearly as the concentration increased, similar to the Jathoppha mixtures already reported in the literature [7]. However, SO/MO and AO/MO thermal diffusivity values decreased when the vegetable oil concentration increased; a similar behavior was found in castor oil [27]. The increase in the thermal diffusivity of RO/MO mixture could be related to the difference in the relationship of monounsaturated/polyunsaturated fatty acids, as Table 3 shows. In sunflower and almond oils, the content of monounsaturated fatty acids was lower than the content of polyunsaturated fatty acids. Furthermore, the linear relationship between thermal diffusivity and mixture concentrations has already been established [30], and the calculated values for thermal conductivity were within the range for vegetable oils reported in the literature [8; 31; 32; 33].
# Picoammeters Based on Gradiometric Superconducting Quantum Interference Devices
###### Abstract  
High-sensitivity ac current sensors based on a superconducting quantum interference device have been designed, fabricated and characterized. In particular, double-washer schemes in either parallel or series configurations have been considered. The advantages and the drawbacks of both configurations have been examined by measuring the main features and parameters, such as the flux-to-voltage characteristic, the magnetic field spectral noise and flux-to-current transfer factor. The devices are designed to have similar flux-to-current transfer factors and are fabricated on the same chip to avoid differences in parameters due to the fabrication process. Both devices exhibited a current sensitivity as low as 1-2 pA per bandwidth unit, allowing for their use in ultrahigh-sensitivity applications.  
picoammeter; SQUID; magnetic flux noise; gradiometer +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: email: _Applied Sciences_  
Footnote †: journal: Applied Physics Letters  
operation of a SQUID sensor, we can say that it is a magnetic flux-voltage transducer. In fact, if a SQUID is placed in a magnetic field and biased with a direct current just above twice the individual Josephson junction critical current, an oscillating voltage is observed across the sensor, whose period of oscillation is equal to \(\Phi_{0}\). Therefore, by measuring the voltage across the sensor and knowing the voltage responsivity \(V_{\Phi}=\partial V/\partial\Phi\), it is possible to obtain the value of the magnetic field. Obviously, a magnetic sensor, in addition to the flux and the magnetic field, can measure everything that can be converted into a magnetic field or flux, such as a current, a voltage, a displacement, etc.  
The design of a SQUID sensor strongly depends on the type of measurement it will have to carry out [10]. Due to their great sensitivity, SQUID sensors typically operate in a shielded environment; however, it is possible to use particular configurations called gradiometric configurations that allow the use of these sensors even in moderately or poorly shielded environments [11].  
In this article, we report experimental results concerning the development of SQUID current sensors in which the superconducting ring has a gradiometric configuration of both series and parallel type. These devices can be used to develop ultrasensitive ammeters or magnetometers and gradiometers by connecting them via superconducting soldering to external superconducting coils.  
In the second paragraph, the adopted criteria for the device design together with the fabrication process and experimental setup will be described. In the third paragraph, we will show the experimental data concerning the characterization at low temperatures and the relative comparisons between the two different configurations.
## 5 Case Study: An AHP/TOPSIS-Based Optimal Gas Station Site Selection
### Dataset Analysis and Prediction Criteria
#### 5.1.1 Gas stations Dataset and the Competition Criterion  
The NYC Open Data [39] provides the New York City gas stations data. Detailed addresses of the 416 gas stations present in New York City are provided in this dataset and shown in Figure 2. In this study, the competitors that are near the candidate gas stations are computed with the help of the reference dataset. According to [7], one of the most significant factors in identifying the ideal location for opening a gas station is the competitors in that area. The best location for a new gas station is the location with the least number of competitors.  
Figure 1: Flow of the important steps in the AHP/TOPSIS approach.  
Let us suppose that \(G\) represents the set of all gas stations in New York City, while \(g\) refers to the geographical location of a gas station. The competition criterion can be computed with the help of the number of gas stations within radius \(r_{g}\) of each candidate location \(l\in L\). Equation (1) explains the abovementioned appraoch.  
\[|\{g\in G:dist(g,l)<r_{g}\}| \tag{1}\]  
where the Euclidean distance between locations \(a\) and \(b\) is represented by \(dist(a,b)\).
## 5 Coning and Projecting  
Given a graph \(G=(V,E)\), the _coned graph_\(G^{c}\) of \(G\) is obtained by adding a new vertex \(v_{0}\) to \(V\) and joining \(v_{0}\) to every vertex of \(V\). For a framework \((G,p)\) in \(\mathbb{R}^{d}\), any realisation of the coned graph \(G^{c}\) in \(\mathbb{R}^{d+1}\) is called a _coned framework_ of \((G,p)\). Coning a framework arose in engineering folklore [76] and is now a fundamental technique in rigidity theory. In particular, coning a framework from \(\mathbb{R}^{d}\) to \(\mathbb{R}^{d+1}\) preserves static and infinitesimal rigidity and is hence a powerful tool for transferring results on the infinitesimal rigidity of frameworks between dimensions (see [71], for example). The converse operation, projecting from a cone-vertex to any hyperplane not containing the cone vertex, is also significant as a tool. In particular, coning and projecting is a tool for confirming the projective invariance of properties such as infinitesimal rigidity. Coning and projecting applies in all projective metrics we have encountered. We will see in Section 11 and in our companion paper [27] that coning is a widely applicable technique wherever the concepts are projectively invariant.  
In Section 4.1, we have shown that infinitesimal and static rigidity can be transferred between \(\mathbb{R}^{d}\) and \(\mathbb{S}^{d}\). We did this by first transferring the Euclidean framework to the affine space \(\mathbb{A}^{d}\) and then interpreting the final \(|V|\) rows of the affine rigidity matrix as rows corresponding to edges joining a fixed cone point at the origin with all the other vertices. The vertices of the graph (except for the pinned cone vertex) can then be pulled back to the unit sphere without changing the rank of the matrix, resulting in the equivalent spherical rigidity matrix.  
Note that if we start with the spherical rigidity matrix of an infinitesimally rigid spherical framework (modeled as a coned framework with fixed cone point) and then release the cone point, then we add \(d+1\) columns to the matrix. This increases the dimension of the kernel by \(d+1\), so that the kernel of the extended matrix has dimension \((\frac{d+1}{2})+(d+1)=(\frac{d+2}{2})\). This is the dimension of the space of trivial infinitesimal motions in \(\mathbb{R}^{d+1}\), so this shows that the coning procedure transfers infinitesimal and static rigidity between \(\mathbb{R}^{d}\) and \(\mathbb{R}^{d+1}\). A simple, but often useful, observation here is that moving individual vertices along their cone rays does not change the rank of the rigidity matrix, and hence preserves infinitesimal and static rigidity. See Figure 11 for an illustration.
## 2 Modeling of Dynamic Rotational Error for Spindle-Rolling Bearing System
### Nonlinear Bearing Model
#### 2.1.1 Modeling of Dynamic Supporting Stiffness
The resultant force vector of the inner ring due to all of the contacting rolling elements is  
\[\mathrm{F}_{\mathrm{i}}=\left[\mathrm{F}_{\mathrm{x}},\mathrm{F}_{\mathrm{y}},\mathrm{F}_{\mathrm{z}},\mathrm{M}_{\mathrm{x}},\mathrm{M}_{\mathrm{y}} \right]^{\mathrm{T}} \tag{15}\]  
where  
\[ \begin{split}\mathrm{F}_{\mathrm{x}}&=\sum\limits_{ \mathrm{k}=1}^{\mathrm{N}_{\mathrm{b}}}\left(\mathrm{Q}_{\mathrm{ik}}\mathrm{ cos}\alpha_{\mathrm{ik}}+\frac{\lambda_{\mathrm{ik}}\mathrm{M}_{\delta k}}{ \mathrm{D}}\mathrm{sin}\alpha_{\mathrm{ik}}\right)\mathrm{sin}f_{\mathrm{bk} }\\ \mathrm{F}_{\mathrm{y}}&=\sum\limits_{\mathrm{k}=1}^{ \mathrm{N}_{\mathrm{b}}}\left(\mathrm{Q}_{\mathrm{ik}}\mathrm{cos}\alpha_{ \mathrm{ik}}+\frac{\lambda_{\mathrm{ik}}\mathrm{M}_{\delta k}}{\mathrm{D}} \mathrm{sin}\alpha_{\mathrm{ik}}\right)\mathrm{cos}f_{\mathrm{bk}}\\ \mathrm{F}_{\mathrm{z}}&=\sum\limits_{\mathrm{k}=1}^{ \mathrm{N}_{\mathrm{b}}}\left(\mathrm{Q}_{\mathrm{ik}}\mathrm{sin}\alpha_{ \mathrm{ik}}-\frac{\lambda_{\mathrm{ik}}\mathrm{M}_{\delta k}}{\mathrm{D}} \mathrm{cos}\alpha_{\mathrm{ik}}\right)\\ \mathrm{M}_{\mathrm{x}}&=\sum\limits_{\mathrm{k}=1}^{ \mathrm{N}_{\mathrm{b}}}\left(\mathrm{Q}_{\mathrm{ik}}\mathrm{sin}\alpha_{ \mathrm{ik}}-\frac{\lambda_{\mathrm{ik}}\mathrm{M}_{\delta k}}{\mathrm{D}} \mathrm{cos}\alpha_{\mathrm{ik}}\right)\mathrm{I}_{\mathrm{i}}\mathrm{cos}f_{ \mathrm{bk}}\\ \mathrm{M}_{\mathrm{y}}&=-\sum\limits_{\mathrm{k}=1}^{ \mathrm{N}_{\mathrm{b}}}\left(\mathrm{Q}_{\mathrm{ik}}\mathrm{sin}\alpha_{ \mathrm{ik}}-\frac{\lambda_{\mathrm{ik}}\mathrm{M}_{\delta k}}{\mathrm{D}} \mathrm{cos}\alpha_{\mathrm{ik}}\right)\mathrm{I}_{\mathrm{i}}\mathrm{sin}f_{ \mathrm{bk}}\end{split}  \tag{16}\]  
According to the relationship between the resultant forces and displacements of the inner ring, the dynamic supporting stiffness matrix of the bearing can be obtained  
\[\begin{split}\left[\mathrm{K}^{\mathrm{b}}\right]&= \left[\begin{array}{ccc}\frac{\partial\mathrm{F}_{\mathrm{x}}}{\partial \mathrm{S}_{\mathrm{x}}}&\frac{\partial\mathrm{F}_{\mathrm{x}}}{\partial \mathrm{S}_{\mathrm{y}}}&\frac{\partial\mathrm{F}_{\mathrm{x}}}{\partial \mathrm{S}_{\mathrm{z}}}&\frac{\partial\mathrm{F}_{\mathrm{x}}}{\partial \mathrm{S}_{\mathrm{x}}}&\frac{\partial\mathrm{F}_{\mathrm{x}}}{\partial \mathrm{S}_{\mathrm{y}}}\\ \frac{\partial\mathrm{F}_{\mathrm{y}}}{\partial\mathrm{S}_{\mathrm{x
## 3 Methodology and Application to a Case Study
### Influence of Barriers and Incentives at the Territorial Level  
In Part B of the interview, a five-point Likert scale ranging from 0--\"not relevant\" to 5--\"highly relevant\" was used so that the experts rated the relevance of a set of barriers and incentives on PtG deployment. For each item, the average and the frequency of the answers were calculated. Table 2; Table 3 include the percentages of respondents who provided ratings of 4 and 5 and the average value of all the answers collected. The perception of the interviewees about the barriers is summarized in Table 2.  
The barriers raised by the survey questions can be classified into five typologies: economic (B.03, B.06, and B.09), financial (B.05 and B.10), regulatory (B.01 and B.04), technical (B.02 and B.07), and other (B.08). The results show that most of the experts surveyed (92%) consider that the payback period of the investment is the element that restrains the deployment of PtG. The initial investment, which relates mainly to the price of the electrolyzers, appears to be the most limiting factor for the development of PtG, as noted by nearly 75% of respondents. In addition, 53% report the lack of equipment as a relevant barrier, even though the technical complexity of these kinds of installations is only mentioned by less than half of respondents. Finally, barriers related to the access to financing and regulation are of intermediate importance, which may be due to the initial level of development of PtG technology. The incentives raised by the survey questions can be classified into 5 typologies: economic (I.03, I.06, and I.09), financial (I.05), regulatory (I.01 and I.04), technical (I.02 and I.07), and other (I.08 and I.10). The perception of interviewees regarding incentives is summarized in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Type of Organization** & **Profile of the Experts** \\ \hline Public administration, R\&D institutes, and other organizations & 5 engineers, 2 scientists, 1 economist \\ \hline Industries, private companies that operate in the energy sector & 5 engineers, 1 lawyer, 1 other \\ and professionals & \\ \hline \end{tabular}
\end{table} 
Table 1: Profile of the experts selected as key informants. Information campaigns and collaboration with R&D centers are considered to be the incentives with the lowest impact, as shown in the lower percentage of higher scores (46% in both cases). Conversely, more than three-quarters of the interviewees note that giving subsides or legislating in favor of these technologies, lowering technology prices, and reducing the technical complexity of the installation process would be the best way to foster PtG. The average results for barriers and incentives were aggregated in each of the five typologies and are represented in the radar chart of Figure 1. The same scale from 0 to 5 indicates where the score is proportional to the relevance of each group of determinants of PtG deployment.
## 2 Battery Models
### Dual Polarity (DP) Model  
The DP model is an ECM that uses a voltage source, a resistor, and two RC branches. The voltage source represents the battery's output voltage, the resistor represents the internal resistance of the battery, and the two RC branches represent the short-term and long-term transient behaviors of the battery [20]. In the DP model, one RC branch represents the battery's concentration polarization, while the second RC branch represents the electrochemical polarization of the battery. These polarizations are responsible for a quick rise in voltage followed by a slow recovery after the battery is subject to a discharge period and the current is cut off [21]. This model was selected because it provides a good trade-off between accuracy and computational efficiency. Figure 1 depicts the circuit diagram of the DP model.  
In the circuit diagram, the voltage source represents the open circuit voltage of the battery (\(OCV\)). The internal resistance is represented by \(R_{0}\), the electrochemical polarization resistance and capacitance are represented by \(R_{pa}\) and \(C_{pa}\), respectively, and the concentration polarization resistance and capacitance are represented by \(R_{pc}\) and \(C_{pc}\), respectively [9].  
Lastly, the system's state space representation may be described by the following equations:  
\[\begin{bmatrix}U_{pa}\\ U_{pc}\end{bmatrix}=\begin{bmatrix}\frac{1}{R_{pa}C_{pa}}&0\\ 0&\frac{1}{R_{pc}C_{pc}}\end{bmatrix}\begin{bmatrix}U_{pa}\\ U_{pc}\end{bmatrix}+\begin{bmatrix}\frac{1}{C_{pa}}\\ \frac{1}{C_{pc}}\end{bmatrix}I_{s} \tag{1}\]  
\[U_{L}=U_{ocr}(SOC)-U_{pa}-U_{pc}-I_{L}R_{0} \tag{2}\]