esa-sceva/satcom-chunk-collection huggingface dataset:
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## Vehicle Model and Its Coupling with Road and Environment
### Longitudinal and Vertical Dynamics of Half-Car Vehicle Model  
The proposed half-car model exhibits seven degrees of freedom (DoFs), where the first DoF is related to the longitudinal motion of the vehicle model, the next two DoFs describe heave and  
Figure 5: Force-velocity characteristics of the MR damper based on hyperbolic function.  
pitch motion of the body, respectively, and a further four DoFs are related to the wheel vertical and rotational motion.  
Longitudinal dynamics of the vehicle, as well as heave and pitch dynamics of the body, were described, as follows, when assuming that pitch angles of the body are so small that trigonometric relationships can be neglected:  
\[(m_{uf}+m_{ur}+m_{s})\ddot{x}_{s}=F_{nx}+F_{rx}+F_{\text{roll},x}+F_{\text{ drag},x}, \tag{19}\]  
\[m_{s}\ddot{z}_{s}=F_{sf}+F_{sr}+F_{\text{drag},z}-m_{s}\cdot g, \tag{20}\]  
\[I_{sp}\ddot{\varphi}_{sp}=-l_{f}\cdot F_{sf}+l_{r}\cdot F_{sr}-h_{s}\cdot m_{s} \ddot{x}_{s}, \tag{21}\]  
where \(g=9.81\) ms\({}^{-2}\) is the gravitational acceleration. The expression \(h_{s}\cdot m_{s}\ddot{x}_{s}\) is related to the pitching torque caused by accelerating or braking. Forces that are generated in the front and rear suspension parts, denoted as \(F_{sf}\) and \(F_{sr}\), respectively, are evaluated, as follows:  
\[\begin{split} F_{sf}&=-k_{sf}\cdot(z_{sf}-z_{uf})- c_{sf}\cdot(\dot{z}_{sf}-\dot{z}_{uf})+F_{mr,f},\\ F_{sr}&=-k_{sr}\cdot(z_{sr}-z_{ur})-c_{sr}\cdot( \dot{z}_{sr}-\dot{z}_{ur})+F_{mr,r},\end{split} \tag{22}\]  
where vertical displacements and velocities of front and rear parts of the vehicle body are calculated, as follows:  
\[\begin{split} z_{sf}&=z_{s}-l_{f}\cdot\varphi_{sp} \qquad\dot{z}_{sf}=\dot{z}_{s}-l_{f}\cdot\dot{\varphi}_{sp}\\ z_{sr}&=z_{s}+l_{r}\cdot\varphi_{sp}\qquad\dot{z}_ {sr}=\dot{z}_{s}+l_{r}\cdot\dot{\varphi}_{sp}\end{split} \tag{23}\]  
Vertical dynamics of each wheel is described by the following differential equation:  
\[m_{u}\ddot{z}_{u}=F_{nz}+F_{rz}+F_{\text{roll},z}-F_{s}-m_{u}\cdot g. \tag{24}\]  
In the case of rotational dynamics, the equations are distinguished according to the braking and drive system for the front and rear wheel, respectively:  
\[\begin{split} I_{uf}\ddot{\varphi}_{uf}&=T_{nf}+T_{ rf}+T_{bf},\\ I_{ur}\dot{\varphi}_{ur}&=T_{nr}+T_{rr}+T_{er}, \end{split} \tag{25}\]  
where the additional index \(f\) or \(r\) included in notations \(T_{n}\) and \(T_{r}\) indicates the front or rear wheel, respectively. Notations \(v_{x}\), \(a_{x}\), or \(v_{z}\), \(a_{z}\) used in the reminder of the manuscript denote the first and second derivatives of \(x\) or \(z\) (i.e., \(\dot{x}\), \(\dot{x}\) or \(\dot{z}\), \(\ddot{z}\)), respectively, which are numerically calculated based on the vehicle model.
# Evaluation of Patients with Hallux Valgus Wearing a 3D-Printed Orthosis during Walking
###### Abstract  
Hallux valgus (HV) is a foot deformity most commonly found in female and elderly patients. Its symptoms include foot pain, impaired gait patterns, poor balance, and falls in older adults. Recently, various HV orthoses have been introduced in the market; however, they have many shortcomings, such as high costs, unclear therapeutic effects, and effects on push-off of the foot during walking. The present study employs 3D printing technology to develop an HV orthosis and uses motion analysis to investigate the effects of wearing it. This study recruited 12 individuals with HV, who were asked to first perform a static HV measurement without orthosis, followed by a dynamic HV measurement using a Vicon motion analysis system in three trials. The results indicated that wearing the 3D-printed orthosis significantly corrected the HV angle by approximately \(11^{\circ}\) during static standing and by approximately \(9^{\circ}\) during dynamic walking. However, no significant difference was observed during use of the orthosis in terms of the ground reaction force. The obtained results demonstrate that the 3D-printed HV orthosis is an effective device for correcting the HV angle during static standing and dynamic walking, especially during the push-off phase of gait.  
h +
Footnote †: journal: Applied Sciences  
1  
ALLux valgus; 3D printing; orthosis; motion analysis; orthopaedic
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 2 Related Works
### Fast Haze Removal with Median Filters  
[PERSON] et al. [16] proposed a fast filtering-based algorithm for visibility restoration whose complexity is a linear function of input image pixels. Using this method, the airlight marked \(\mathbf{V}\) is estimated in lieu of the transmission map. The authors proposed the inference that the airlight can be approximated by a percentage of the difference between the local average and the local standard deviation of the input pixel minimum channel \(\mathbf{W}(x,y)=min_{c\in[R,G,B]}[\mathbf{\Gamma}(x,y)]\). Additionally, the constraint that \(\mathbf{V}\) must be positive and less than \(\mathbf{W}\) is applied to estimate the airlight [16]:  
\[\mathbf{m}\mathbf{W}(x,y)=median_{i,j\in\Omega(x,y)}[\mathbf{W}(i,j)], \tag{7}\]  
\[\mathbf{std}\mathbf{W}(x,y)=median_{i,j\in\Omega(x,y)}\Big{[}|\mathbf{W}(i,j)-\mathbf{m}\mathbf{W }(i,j)|\Big{]}, \tag{8}\]  
\[\mathbf{V}(x,y)=max\{min[p\times[\mathbf{m}\mathbf{W}(x,y)-\mathbf{std}\mathbf{W}(x,y)],\mathbf{W}(x,y )],0\}, \tag{9}\]  
where \(\mathbf{m}\mathbf{W}\) and \(\mathbf{std}\mathbf{W}\) denote the local average and local standard deviation of \(\mathbf{W}\) respectively, and \(0<p\leq 1\) is a constant to control the amount of haze to be removed. The nested median filtering operation in Equation (8) is named median of median along lines [16]. Accompanying the estimated airlight, Equation (1) can be re-written to find the haze-free image \(\mathbf{J}\) as follows:  
\[\mathbf{I}(x,y)=\mathbf{J}(x,y)\bigg{[}1-\frac{\mathbf{V}(x,y)}{A}\bigg{]}+\mathbf{V}(x,y), \tag{10}\]\[\mathbf{J}(x,y)=\frac{\mathbf{I}(x,y)-\mathbf{V}(x,y)}{1-\frac{\mathbf{V}(x,y)}{A}} \tag{11}\]  
To recover the scene radiance of \(\mathbf{J}\), the atmospheric light \(A\) must be given. The authors stated that the atmospheric light is pure white and can be set to (1, 1, 1) if the white balance in the input image is performed correctly. Accordingly, \(\mathbf{J}\) is computed as:  
\[\mathbf{J}(x,y)=\frac{\mathbf{I}(x,y)-\mathbf{V}(x,y)}{1-\mathbf{V}(x,y)}. \tag{12}\]  
This method suffers from halo artifacts using a large median filter, however. The median filter tends to blur the image edges with depth discontinuities. To solve this problem, [PERSON] et al. (2018) proposed using a modified hybrid median filter in place of the standard median filter to provide better performance at image edges when estimating the airlight. This method will be discussed in Section 2.4.
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 3 Outage Probability  
Outage probability is a very important factor in the wireless communication system, which is defined as when the system's instantaneous SNR drops below a certain threshold \(\gamma_{th}\). We investigate the OP of asymmetric dual-hop MIMO mixed RF/FSO DF relaying system. The end-to-end SNR outage probability of the system can be expressed as [18]  
\[\begin{split} P_{out}&=\Pr\big{[}\gamma_{eq}<\gamma_{ th}\big{]}=\Pr[\min(\gamma_{RF},\gamma_{FSO})<\gamma_{th}]\\ &=F_{\gamma_{RF}}(\gamma_{th})+F_{FSO}(\gamma_{th})-F_{\gamma_{RF}}( \gamma_{th})F_{FSO}(\gamma_{th}).\end{split} \tag{13}\]Then, substituting the CDF of \(\gamma_{RF}\) and \(\gamma_{FSO}\) from Equations (1) and (12), respectively, after some mathematical simplification, the closed form of OP can be obtained as  
\[\begin{split} P_{out}& =1+\exp\left(-\frac{\gamma_{th}}{\hat{\gamma}_{RF}}\right)^{N_{ th}N_{tr}-1}\frac{1}{u!}\left(\frac{\gamma_{th}}{\hat{\gamma}_{RF}}\right)^{u}\\ &\times\left(\frac{2^{\alpha_{s}+\beta_{s}-2}}{\pi\Gamma(\alpha_{ s})\Gamma(\beta_{s})}G_{1,5}^{4,1}\left[\frac{(\alpha_{s}\beta_{s})^{2}}{16} \frac{\gamma_{th}}{\hat{\gamma}_{FSO}}\bigg{|}\frac{1}{\Delta 1,0}\right]-1\right). \end{split} \tag{14}\]  
Similarly, we can get the OP of the system considering the pointing error by replacing \(F_{FSO}(\gamma_{th})\) with \(F_{FSO}^{p}(\gamma_{th})\).
## 3 Materials and Methods
### Evaporative Cooling (EC) Systems
#### 3.1.1 Direct EC (DEC) System  
Direct evaporative cooling (DEC) system is the simplest and old type of EC system in which air comes directly in contact with the water surface to reduce its temperature [30; 31]. Continuous evaporation of water (adiabatic process) vapors causes a cooling effect up to a saturation point of air [1; 3; 32]. Enthalpy (kJ/kg) of air at inlet and outlet conditions remains the same, whereas the humidity ratio (gram of water per kg of dry air i.e., g/kgDA) increases. The DEC system can potentially reduce the temperature of the inlet air up to its wet-bulb temperature with wet-bulb effectiveness (WBE) of 75-95% [3; 33]. Schematic and psychrometric representation of the DEC system is shown in Figure 4a.  
Figure 3: A pictorial representation of the experimental apparatus for MEC and IEC EC systems.  
Cooling effect in the DEC system is produced by utilizing latent heat of evaporation governed by Equation (1) given in the literature [3].  
\[h_{outlet}=1.006\ T_{db}+w(2501+1.86T_{db}) \tag{1}\]  
where \(h_{outlet}\) represents enthalpy of the outlet air (kJ/kg), \(T_{db}\) denotes dry-bulb temperature of the air (\({}^{\circ}\)C), and \(w\) represents the humidity ratio of the outlet air (kg/kg). For further study on DEC, Equations (2) and (3) provide more insight [3].  
\[\left(DBT\right)_{outlet}\geq\left(WBT\right)_{inlet} \tag{2}\]  
\[\left(RH\right)_{outlet}>\left(RH\right)_{inlet} \tag{3}\]  
where \(DBT\) denotes dry-bulb temperature air (\({}^{\circ}\)C), \(RH\) denotes relative humidity of air (%), and subscripts outlet and inlet represent outlet and inlet air conditions, respectively.
## 4 Stability Analysis of VSC-HVDC with Improved Virtual Impedance Control Strategy
### Impact of Virtual Impedance by Frequency Responses  
Figure 9 shows the impedance frequency responses of \(Z_{dc1}\) and \(Z_{dc2}\) when the active power is set as \(-500\) MW and the length of DC cable is \(50\) km. The proposed strategy in this paper is adopted for a DC-voltage-controlled converter, and the controller parameters are set as \(k_{pcdc}=15\), \(k_{intc}=100\), and \(k_{i}=10\). Figure 9a shows the impedance frequency responses of \(Z_{dc1}\) under a different \(R_{eq}\)s. The pink line represents \(Z_{dc2}\) and the blue, red, and yellow lines represents \(Z_{dc1}\) when \(R_{eq}=0.5\), \(2\) and \(5\), respectively. The blue line shows that when the phase of \(Z_{dc1}\) impedance in the mid-frequency band is below \(-90^{\circ}\), the system exhibits negative damping. The phase difference at the intersection of the pink and blue lines in the mid-frequency band is approximately \(180^{\circ}\), and the DC voltage is predicted to oscillate. The red and yellow lines show that when the phase of \(Z_{dc1}\) impedance in the mid-frequency band is above -\(90^{\circ}\), and the phase difference is less than \(160\) degrees, the DC voltage is predicted to stable. However, with the increase of \(R_{eq}\), the phase difference at the intersection of the pink and blue lines in low-frequency bands increases, and the system tends to lose stability.  
Figure 9b shows impedance frequency responses of \(Z_{dc1}\) under a different \(L_{eq}\)s. The pink line represents \(Z_{dc2}\) and the blue, red, and yellow lines represent \(Z_{dc1}\) when \(L_{eq}=0.002\), \(0.02\), and \(0.05\), respectively. Blue line shows that phase of \(Z_{dc1}\) impedance in mid-frequency band is below \(-90^{\circ}\), the system exhibits negative damping. The phase difference at the intersection of the pink and blue lines in the mid-frequency band is approximately \(180^{\circ}\), and the DC voltage is predicted to oscillate. Red and yellow lines show that when the phase of \(Z_{dc1}\) impedance in the mid-frequency band is above \(-90^{\circ}\), and the phase difference is less than \(160\) degrees, the DC voltage is predicted to be stable.  
Figure 8: (**a**) Nichols plots of \(T_{m}\) when transmission power is \(\pm 500\) MW. (**b**) Impedance frequency responses of \(Z_{dc1}\) and \(Z_{dc2}\) when transmission power is \(\pm 500\) MW.  
However, with the increase of \(L_{eq}\), the phase difference at the intersection of the pink and blue lines in the low-frequency band increases, and the system tends to lose stability.  
The conclusion is that if the virtual impedance is too small, it will not be enough to suppress the oscillation. If the virtual impedance is too large, a new oscillation may occur in the low frequency band.
## 6 Conclusions
This study is limited to energy consumption during the heating season, excluding cooling demand, which is crucial given Italy's high summer temperatures. Future research should assess cooling energy demand to provide a more comprehensive analysis. Additionally, this study only focuses on residential buildings, leaving out non-residential sectors such as offices and schools, which could benefit from similar measures. Expanding to include these sectors is essential for future studies. Moreover, while the analysis centers on the Superbonus 110% incentive, future research should explore a wider array of incentives and funding mechanisms. The integration of photovoltaic systems and Renewable Energy Communities presents an opportunity to further enhance energy savings and sustainability, which should be explored in subsequent work. Conceptualization, F.C. and M.V. ; methodology, F.C. ; software, M.V. and L.C. ; validation, M.V., F.L.C. and L.C. ; formal analysis, F.L.C., L.C. and M.V. ; investigation, F.C., F.L.C., L.C. and M.V. ; resources, F.C. ; data curation, F.C., F.L.C., L.C. and M.V. ; writing--original draft preparation, M.V. ; writing--review and editing, L.C. ; visualization, F.C., F.L.C., L.C. and M.V. ; supervision, F.C. ; project administration, F.C. ; funding acquisition, F.C. All authors have read and agreed to the published version of this manuscript. Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3-Call for tender No. 1561 of 11.10.2022 of Ministero dell'Universita e della Ricerca (MUR); funded by the European Union-NextGenerationEU\(\bullet\) Award Number: Project code PE0000021, Concession Decree No. 1561 of 11.10.2022 adopted by Ministero dell'Universita e della Ricerca (MUR), CUP E63C22002160007, Project title: \"Network 4 Energy Sustainable Transition-NEST\". Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare
## 2 Related Works
### Customer-Driven Design and Manufacture at the Core of MC Systems  
Customers' preferences are critical to product development [18]. To enable and fully support the development of a successful MC system, the main elements of a manufacturing strategy along with the user/customer preferences need to be considered [11]. These are included within the following:  
* Agile manufacturing is the ability to thrive and prosper in a competitive environment of continuous and unanticipated change in order to respond quickly to rapidly changing markets driven by customer-based valuing of products [19]. Here, it is important to truly capture user preferences and requirements based on attitudes and emotions, which may imply further challenges [17].
* Supply-chain management concerns the coordination of resources and the optimisation of activities across the value chain to obtain competitive advantages [20; 21]
* Customer-driven design and manufacture at the core of MC systems [22] \"actively considers the market trends and individual customer requirements during the design, manufacturing and delivery of the products\". Some authors call this practice \"One-of-a-Kind Production\" (OKP). The application of customer-driven practices in MC systems aims at providing the conditions for the customer to take part in the design process, and for the company to build an infrastructure to develop such customer-driven products, hence the relevance of defining customer preferences though a robust design requirement specification [15].
* Lean manufacturing is an efficient way to satisfy customer needs while giving producers a competitive edge [23]. MC production addresses four elements of lean production: product development, the chain of supply, shop floor management, and after-sales services [24].  
The main enabling technologies supporting MC can include additive manufacturing (AM) [25], computer numeric control (CNC) [11], flexible manufacturing systems (FMS) [26], computer-aided design (CAD) [27], computer-aided manufacturing (CAM), computer-integrated manufacturing (CIM), and electronic data interchange (EDI) [28]. The use of these technologies is justified by their inherent flexibility, and their capability to alter the economies of manufacturing and remove barriers to product variety. Additionally, these technologies allow full exploitation of the fundamental MC attributes such as agility and flexibility.
# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 2 Theoretical Basis of Acoustic Information Monitoring for Cabin Structures
### Analysis of the Influence on Information Completeness of Acoustic and Vibration Monitoring Points  
In this section, the completeness of the cabin structure information monitoring for different sampling points is discussed, providing a theoretical basis for the selection of the number of monitoring points. Equal intervals of the interior and exterior surfaces of the cabin structure were divided into M \(\times\) N face elements (axial N and circumferential M), as shown in Figure 5. The information collected at the center position of each panel approximately represents the information of the whole panel, the collected information of the internal sound field is the sound pressure near the inner cabin surface, the collected information of the vibration is the vibration velocity on the inner cabin surface, and the collected information of the external sound field is the sound pressure near the outer cabin surface. Finally, the sound and vibration information of the whole cabin structure is obtained.  
In order to ensure the complete monitoring information in the full frequency band, the monitoring points should be arranged according to the spatial sampling theorem, which requires half of the wavelength corresponding to the highest frequency as the monitoring point spacing. However, this scheme requires a large number of monitoring points and brings in more redundant information in the low-frequency band. We calculated the  
Figure 4: Sound and vibration monitoring information signal-to-interference ratio: (**a**) PSIR; (**b**) VSIR.  
Figure 5: Cabin structure information monitoring diagram.  
average error of the full frequency band of the information collected by each physical field under different numbers of monitoring points, as shown in Figure 6. To ensure that the sampling information can effectually reflect the real acoustic and vibration characteristics of the cabin structure, the number of internal acoustic field collection points should be greater than 9 in the circumference and 5 in the axial direction; the number of collection points on the cabin surface should be more than 15 in the circumference and 6 in the axial direction. As the external sound field is affected by both acoustic and force excitations, the distribution of the sound field is complex; therefore, the number of measurement points is large. Regarding the number of external sound field acquisition points, the numbers of circumferential and axial points should be greater than 15 and 9, respectively. When the number of sampling points is greater than that in this scheme, the effect of improving the completeness of the collected information is limited. To reduce monitoring costs and improve the prediction speed, the monitoring scheme should be selected based on the following conclusions of this section in the radiation noise prediction.
## 4 Results and Discussions
### Performance of Models after Adding CMAQ Data at All Locations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **XGB** & **LGBM** & **GRU** & **CNNLSTM** & **BiULSTM** & **LSTM** \\ \hline F\({}_{\mathrm{P}}\) + F\({}_{\mathrm{m}}\) & 0.9014 & 0.8959 & 0.8829 & 0.9030 & 0.9268 & 0.9339 \\ F\({}_{\mathrm{P}}\) + F\({}_{\mathrm{m}}\) + F\({}_{\mathrm{c}}\) & 0.9041 & 0.8992 & 0.8905 & 0.9084 & 0.9334 & 0.9368 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Models’ average \(IA\) values with/without CMAQ features. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **XGB** & **LGBM** & **GRU** & **CNNLSTM** & **BiULSTM** & **LSTM** \\ \hline F\({}_{\mathrm{P}}\) + F\({}_{\mathrm{m}}\) & 0.8307 & 0.8273 & 0.8587 & 0.8620 & 0.8887 & 0.8947 \\ F\({}_{\mathrm{P}}\) + F\({}_{\mathrm{m}}\) + F\({}_{\mathrm{c}}\) & 0.8350 & 0.8304 & 0.8640 & 0.8668 & 0.8927 & 0.8989 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models’ average \(R\) values with/without CMAQ features. Figure 8: The predicted results of CNNLSTM. Figure 7: The predicted results of GRU. Figure 9: The predicted results of BiULSTM. Figure 11: The predicted results of all models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **XGB** & **LGBM** & **GRU** & **CNNLSTM** & **BiULSTM** & **LSTM** \\ \hline S1 & 3.3872 & 5.0846 & 3.4858 & 3.2584 & 2.6409 & 2.6484 \\ S2 & 4.2806 & 5.4585 & 4.2922 & 4.1063 & 4.0045 & 4.0995 \\ S3 & 3.7949 & 3.7964 & 2.9551 & 3.1759 & 3.0464 & 2.8623 \\ S4 & 4.7489 & 5.0793 & 6.1284 & 5.0941 & 4.4651 & 4.2987 \\ S5 & 4.3686 & 4.4039 & 3.6297 & 3.9163 & 3.6814 & 3.5057 \\ S6 & 4.7882 & 5.1493 & 3.5640 & 4.1717 & 3.4768 & 3.3964 \\ S7 & 4.9267 & 4.8347 & 4.0547 & 4.3727 & 4.2379 & 4.5891 \\ S8 & 4.4136 & 4.4271 & 3.5166 & 3.7900 & 3.4439 & 3.2771 \\ Mean & 4.3386 & 4.7792 & 3.9533 & 3.9857 & 3.6246 & 3.5847 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Models results for _MAE_ (\(\rm\mu g/m^{3}\)). Figure 10: The predicted results of LSTM.