esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Non-Conventional TMD Design  
The non-conventional TMD has been designed according to the well-established literature design procedure described in [3].  
Given the four-degrees-of-freedom (DOF) frame structure, we model by taking the masses lumped at floor level and assuming purely translational motion. The isolation system located below the fourth floor segments the four-story frame structure into a lower (L) portion, also denominated as substructure, and an isolated upper (U) portion, also denominated as superstructure, corresponding to a number of DOF indicated as N\({}_{\text{L}}\) = 3 and N\({}_{\text{U}}\) = 1 DOF, respectively (see in Figure 5a). With reference to this physical model, it is possible to utilize a reduced-order 2-DOF model for design purposes (Figure 5b): the first DOF with mass \(m_{1}\), stiffness \(k_{1}\), and damping \(c_{1}\), represents the generalized primary structure, F3S configuration (Table 1), and the second one represents the non-conventional TMD with mass \(m_{2}\), stiffness \(k_{2}\), and damping \(c_{2}\).  
Figure 4: HDRB isolator: (**a**) plant view, (**b**) section A-A.  
Figure 3: Three-story frame structure with support plane and non-conventional TMD, configuration FTMD: (**a**) physical model, (**b**) detail of the non-conventional TMD, (**c**) vertical sections, dimensions, in millimeters.  
The response quantities and dynamic properties of the reduced 2-DOF model are evaluated as reported in [3]. The design parameters of the isolation system (\(k_{2}\), \(c_{2}\)) that has a TMD mass \(M_{\text{U}}=m_{2}\)= 100 kg were determined according to the optimization problem proposed in [3], where an energy performance index was defined and maximized in the space of the system parameters.  
Based on the three-story physical model defined in Section 2, the dynamic properties of the generalized primary structure obtained through a finite element model are: the natural frequency and damping ratio, evaluated as \(\omega_{1}=\sqrt{k_{1}/m_{1}}=25.06\) rad/s and \(\zeta_{1}=c_{1}/(2\sqrt{k_{1}m_{1}})=0.01\), respectively, with \(m_{1}=253\) kg being the first mode modal mass. For the TMD the following non-dimensional parameters were evaluated: the mass ratio \(\mu=m_{2}/m_{1}\) and the frequency ratio \(\alpha=\omega_{2}/\omega_{1}\), where \(\omega_{2}=\sqrt{k_{2}/m_{2}}\) represents the uncoupled frequency of the secondary oscillator and the secondary oscillator uncoupled damping ratio \(\zeta_{2}=c_{2}/(2\sqrt{k_{2}m_{2}})\). In the study, the mass ratio was valued as \(\mu=0.39\), whereas the obtained design parameters were \(\alpha_{opt}=0.7\) for a \(\zeta_{2 opt}\approx 0.10\), which correspond to the TMD stiffness and damping coefficient, respectively, of \(k_{2}\cong 30\) kN/m \(c_{2}\cong 0.35\) kNs/m, furnished by two HDRBs placed in series, as depicted in Figure 2(b).
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 3 Results and Discussion  
### The Forecasting Performance  
Due to data missing, the ionospheric foF2 data from 10 ionosonde stations shown in Table 3, each for the year 2015 (solar maximum) and 2019 (solar minimum) except SHY (missing data in 2019), are selected to verify the forecasting ability and performance from 4 models in the prediction of the hourly foF2, including two neural network BPNN and GABP models, a deep learning LSTM model, and the IRI2016 model. The forecasting results of all these models are compared with that of the observed data. In this work, three evaluation criteria are utilized to evaluate the forecasting ability and performance of the models: minimum root mean square error (RMSE), percentage deviation (PD), and correlation coefficient \(\rho\). The specific definitions are expressed in Formulas (3)-(5). The comparison results between the BPNN model, the GABP model, the LSTM model, and the IRI2016 model are shown in Table 3, listed the predicted RMSE, PD, and \(\rho\) of the 4 prediction models for the year 2015 and 2019. Table 3 illustrates that the deep learning LSTM model in each station has significantly lower RMSE values, PD values, and a higher correlation coefficient in both 2015 and 2019, which means the LSTM model has better forecasting performance than the other models for the high and low solar activity years. The RMSE of the BPNN model and GABP model is between the IRI2016 model and the LSTM model, which reflects that the prediction performance of the two neural network models is better than the IRI2016 empirical model. For instance, at Brisbane (BRI) ionosonde station, the LSTM model gives an RMSE as 0.676 MHz, a \(\rho\) as 0.757, and a PD as 11.44% for the year 2019, while those of the BPNN, GABP, and IRI2016 models are 0.768 MHz, 0.728, 13.18%, 0.785 MHz, 0.722, 13.80% and 0.98 MHz, 0.757, 16.59%, respectively.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 5 EIT Clinical-Based Applications
### Osteography  
In the majority of the scenarios, the healing of fractures is a simple procedure. However, in a few fractures, the union can be absent or even delayed. Plain film X-ray is usually used for the process of the assessment for the healing of the fractures but it is difficult. To envisage these fractures that are predisposed to vibration, non-union, ultrasound and mechanical stressing of the fracture, several techniques have already been employed. Some methods such as plain films indicate these fractures but they do it late in the process. The bone-scintigraphy technique is used to confirm an established non-union but their reports are based on the prognostic values and they are contradictory [98]. EIT indicates the electrical properties of the fractured and the normal limbs. In 1983, the very first human forearm image 'in vivo' was presented. [PERSON] and [PERSON] [99] took the use of the EIT in the clinical side in six adult subjects. Across the fractured humerus, they have obtained the static images and compared it with the normal contra-lateral limb. Results from the images of the fresh fracture highlighted a drastic difference as compared to the contralateral normal limb. In addition, these images possibly showed edema represented by a huge area of low resistivity. In another case in a patient, a large area highlighted an increased resistivity, probably representing callus formation having a solidly united fracture. Another patient having an un-united fracture and it showed an area of low resistivity, most likely indicating fibrosis and edema. As union progresses, impedance osteography helps to indicate the existence of a fracture and variation in resistivity. Although the properties of a recognized non-union are presented, still it needs to highlight whether, at an early stage, impedance osteography gives precise information for the non-union of the fractures.
## 2 Materials and Methods
### Robust Extended Kalman Filter Algorithm
After determining the equivalent variance-covariance array consisting of equivalent variance and covariance \(\overline{Q}\), the measurement update process of the extended Kalman filter is obtained by replacing the measurement variance-covariance matrix in the extended Kalman filter with the equivalent array \(\overline{Q}\), which can be expressed as follows:  
\[\overline{K}_{k}=P_{k}^{-}H^{T}\Big{(}HP_{k}^{-}H^{T}+\overline{Q}\Big{)}^{-1} \tag{16}\]  
\[\hat{X}_{k}=\hat{X}_{k}^{-}+\overline{K}_{k}\big{(}y_{k}-H\big{(}\hat{X}_{k}^{ -}\big{)}\big{)} \tag{17}\]  
\[\overline{P}_{k}=\big{(}I-\overline{K}_{k}H\big{)}P_{k}^{-} \tag{18}\]  
where \(\overline{K}_{k}\) represents the Kalman gain matrix; \(P_{k}^{-}\) represents the prediction covariance matrix; \(\hat{X}_{k}\) represents the optimal estimation matrix; \(\hat{X}_{k}^{-}\) represents the state prediction matrix; \(H\) represents the state observation matrix; \(I\) represents the identity matrix; \(y_{k}\) represents the observations of the state matrix.
## 3 Experiment Setup and Data Acquisition
### Data Acquisition  
Fluctuating pressure was measured using the CY301 high-precision digital fluctuating pressure sensor by Chengdu TST in China, along with its accompanying acquisition software Smartsensor 4.30. The sensor is equipped with features, such as filtering and correction, which are applied to the pressure signal captured by its sensitive components. It directly outputs digital signals that can be displayed and stored without the need for additional data acquisition equipment. Moreover, it can be easily connected to a computer, enabling direct reading of the pressure value on the computer. Following the sampling theorem, the fluctuating pressure sampling frequency was set to 50 Hz, with a sampling duration of 20.48 s, resulting in 1024 discrete points.  
To examine the distribution of fluctuating pressure on the gate panel, seven pressure measurement points are arranged on the gate panel, as depicted in Figure 4. The convenient installation of the pressure sensor and the structural arrangement of the main beam of the gate allow for the positioning of measuring points P1, P3, P5, and P7 along the centerline. Points P2, P4, and P6 are located 0.8 m away from the centerline.  
\begin{table}
\begin{tabular}{c c c c} \hline The dimensions of the radial gate & \
\begin{tabular}{c} 5.5 m \(\times\) 8 m \\ (width \(\times\) height) \\ \end{tabular} & The density of water & 1000 kg/m\({}^{3}\) \\ \hline The cross-sectional size &
\begin{tabular}{c} From 5.5 m \(\times\) 12 m \\ to 5.5 m \(\times\) 8 m \\ \end{tabular} & The temperature of water & 20 \({}^{\circ}\)C \\ \hline The length of the pressurized slope section & 20.0 m & The model Reynolds number & Surpasses 1 \(\times\) 10\({}^{5}\) \\ \hline The slope ratio of the pressurized slope section & 1:5 & The model flow velocity & Exceeding 6 m/s \\ \hline \end{tabular}
\end{table}
Table 1: The input experimental parameters.  
Figure 3: Partial opening diagram. (**a**) Gate opening diagram; (**b**) gate opening schematic diagram.
## 2 Failure Modes of Lithium Ion Batteries
### Power Fade  
While capacity fade refers to the reduction of cell or battery capacity as it ages, power fade is related to the inability of the battery to supply the power required by the system or application. Both failure modes, capacity and power fade, are closely correlated because of the previously-stated relationship between capacity and impedance.  
As can be deduced from Equation (1), an increase in the impedance will cause a reduction in the power that the battery can supply. Thus, cell impedance should be monitored in order to evaluate the degradation of the battery.  
\[P=I\cdot V=I^{2}\cdot Z=\frac{V^{2}}{Z} \tag{1}\]  
Table 1 shows a summary of the battery failure modes describing their primary causes, symptoms and parameters required monitoring in the failure mode.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Failure Mode** & **Level** & **Primary Causes** & **Symptons** & **Comments** & **Parameter to Monitor** \\ \hline
**Shelf** & Cell & -High ambient temperatures both during usage and storage & -Loss of capacity -Increase in the cell & -SOC -Temperature \\ \hline  & & & -Internal or external short circuit of charging & -Cell heating during charging, of charging & -The failure originates at cell level but it can propagate to the other cells composing the system. & -Temperature -Cell Voltage -SOC \\
**Thermal** & Cell & -Internal or external short circuit a cell -High temperature operation & -High voltage drop during rest periods & -Decurs when the SOC of different cells in a battery pack deviates from each other & -Cell voltages -SOC \\
**Thermal** & & -High temperature operation (overheating) & -High voltage drop during rest periods & -Decurs when the SOC of different cells in a battery pack deviates from each other & -Cell voltages -SOC \\
**Thermal** & & -Different behaviour of cells within the same module & -Different cell voltages & -Occurs when the SOC of different cells in a battery pack deviates from each other & -Cell voltages -SOC \\
**Thermal** & & -Aging (time and use) & -Capacity decreases with time & -Occurs with each other-discharge cycle of the battery & -Temperature -SOC \\
**Thermal** & Cell/Module/System & -Cell over-discharge -Low temperature operation & -Increase in cell’s & -Occurs when the SOC of different cells in a battery pack deviates from each other & -Temperature -SOC \\
**Thermal** & & -Low temperature operation & -Internal resistance & -The relative originates at cell level but it can propagate to the other cells composing the system. & -Temperature -Cell Voltage -SOC -SOC -SOC \\
**Thermal** & & -Aging (cycle aging) & -High battery impedance & -Usually coupled to capacity fade & -Impedance \\
**Thermal** & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of battery failure modes.
# Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis
## 3 Measurements  
### Transfer Function Measurements  
The measured transfer function is the most important part of interior sound synthesis. The noise transfer function is an inherent characteristic of the system, describing the attenuation of noise or vibration when unit excitations of the system structure are transmitted along a certain transfer path. In practical measurements, the transfer function is commonly represented by the FRF. Because the accuracy of the FRF determines the accuracy of the interior sound synthesis, the measurement accuracy of the FRF must be improved as far as possible by an appropriate method, which should be selected according to the test situation. Transfer functions are usually measured by direct or reciprocity methods.  
(1) In the direct method, the coupling point is excited by a force hammer or exciter, and the excitations are measured first at the coupling point and then at the response point. Force hammer excitation requires no installation, but its direction is difficult to control. The imprecise excitation direction leads to inaccurate measurement of the transfer function. An exciter avoids the drawback of the force hammer. Exciter installation is simple and convenient, and the excitation signal is controllable and repeatable. The exciter can be installed in the direction of the desired excitation, providing strong directional control.  
(2) In the reciprocity method, the response at the coupling point is measured by the excitation at the response point. Briefly, reciprocity means that the path in one direction equals the path in the opposite direction. The underlying theory is the reciprocity of linear systems. The reciprocity method is widely used for measuring structure-acoustic and acoustic transfer functions.
## 3 Relaying
### Pushing with NB-IoT  
Integration of the NB-IoT into our Relay system underscores a strategic embrace of cellular technology for the IoT. Pycom provides PyBytes as the easiest, most direct connection of their own branded development boards with the cloud using, among other technologies, the NB-IoT. Nonetheless, hassle-free NB-IoT bridging is especially remarkable, as current utilization of the NB-IoT within the end-user scope is still cumbersome. Integration tends to be carrier-dependent, and documentation is scarce. In fact, carriers or vendors from whom rights to use NB-IoT communications must be acquired--usually (e)SIM cards--are hard to find.  
Thus, having a carrier-agnostic middleware which masks carrier-related issues and avoids specific settings tuning is something worth highlighting. With this, once PyBytes is properly configured and the modem effectively connects to the NB-IoT's network (Figure 4), sending data to the middleware using the NB-IoT just implies using a library function in the fashion of the following:  
...  
pybytes.send(_chunk_)  
...  
For the cycle to be completed and NB-IoT messages properly forwarded from PyBytes to the HA instance, rules must be defined in the middleware. These rules are evaluated every time a new message is received--messages are called Signals within the PyBytes middleware, as depicted in Figure 5.  
Figure 4: AT commands (finite state machines’ states) printed by the LTE modem in console when the FiPy module is authenticating in the NB-IoT network.  
For this project's specific case, we just checked if new messages had an identifier embedded in one specific field of the Relay's forwarded messages. If so, a POST REST request was sent to the HA API--with the proper authorization token--which would handle it accordingly and hopefully store the information received in the designated sensor states.
# Suitability of Active Noise Barriers for Construction Sites
## 2 Theory
### Active Noise Barriers  
The overall objective of adding active noise control sources to the barriers is to extend the efficiency of the acoustic barriers at low-frequency spectra, typical of construction noise [8]. An effective design of an active noise barrier defines the location of the error microphones and secondary sources properly. Installing the transducers close to the barrier can be advantageous in terms of supporting them on the barrier itself. The optimized position of transducers highly depends on the position of the noise source and the target area where it is aimed to control the sound level.  
In this study, the locations of transducers are optimized for different positions of the noise sources and two different target areas, namely, the street area and the facade of the neighboring building. The optimization procedure is followed by a two-step method [34; 35; 36; 37; 38; 39]. The first step is to determine the positions of the control sources, which ensures the maximum noise level reduction in the target area. Once the suitable location of the control source has been found, the position of the error microphones is defined. It bears noting that the objective in the second step is not to maximize the attenuation in the error sensors, but to minimize the sound level in the target area.
# Rapid Characterization Method for SMC Materials for a Preliminary Selection
## 4 Experimental Results
### Comparison between SST and Toroidal Magnetic Test Methods  
As the comparison between the SST parallelepiped sample and toroidal magnetic tests, specific iron losses and magnetic permeability have been considered at different frequencies. Two SMC materials have been selected based on previous experimental tests (Sections 4.1-4.4). Figure 16 shows the comparisons between the SST method and toroidal magnetic test methods.  
Figure 14: Coercivity values in SMCs with nano-ferrites layers (0.2 wt% and 800 MPa) in green state and after heat treatment in different environments (400 \({}^{\circ}\)C 15 min).  
Figure 13: Coercivity values in SMCs with phenolic and epoxy layers prepared by turbula mixing (0.2 wt% and 800 MPa).  
The phenolic SMC with 0.2 wt% of resin content was chosen for high resistivity values. After the parallelepiped-shaped sample characterization, a toroidal-shaped sample was prepared to adopt the same production process (0.2 wt% of resin, 800 MPa). In the case of specific iron losses, the differences are 1% for 200 Hz and 8% at 500 Hz. Instead, the differences are about 12% for magnetic permeability for both frequencies.  
The selected material for the low coercivity is ferrite SMC, treated in a vacuum atmosphere at 600 \({}^{\circ}\)C and compacted at 800 MPa with 0.2 wt% of ferrite content. The  
Figure 16: Comparisons between SST method and toroidal magnetic test methods: specific iron losses and magnetic permeability for 200 Hz and 500 Hz.  
Figure 15: Coercivity values in SMCs with nano-ferrites layers (0.2 wt% and 800 MPa) for different heat treatments temperatures and environments.  
comparison shows a slight overestimation of 5% and 14% of the specific iron losses for 200 Hz and 500 Hz, respectively. On the other hand, the differences remain almost constant for the magnetic permeability: 6% and 8%, respectively.  
The SST method shows constant differences for magnetic permeability values in the proposed frequencies range, while for specific iron losses, the differences were limited at low frequencies and incremented with the increase in the frequency.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### LEO Satellite Network Model  
#### 2.3.1 Communication Routing Model  
Suppose \(\mathcal{VP}^{c}\)= \(\{vp_{1}^{c},\cdots,vp_{NS_{c}}^{c}\}\) is the set of communication tasks in \(\mathcal{G}(c)\), and each task contains a start node \(v_{k}^{cstart}\) and a end node \(v_{k}^{c,end}\), which denoted as \(vp_{k}^{c}\)=\(\{v_{k}^{cstart},v_{k}^{c,end}\}\), \(v_{k}^{cstart},v_{k}^{c,end}\in\mathcal{V}\). We make the following assumptions:  
* Assumption 1: For \(vp_{k}^{c}\), there are \(NM_{k}^{c}\) feasible paths \(path_{r}^{c,k}\), \(r=1,\cdots,NM_{k}^{c}\) (\(NM_{k}^{c}\geq 1\) means that there is at least one feasible path in \(vp_{k}^{c}\)). All feasible paths form the set \(\mathcal{P}_{k}^{c}=\{path_{1}^{c,k},\cdots,path_{NM_{k}^{c}}^{c,k}\}\).
* Assumption 2: An arbitrary feasible path \(path_{r}^{c,k}\) consists of non-repeating edges. \(\delta_{(i,j)}^{path_{r}^{c,k}}\)=1, \(i,j=1,\cdots,n\), \(r=1,\cdots,NM_{k}^{c}\) is used to represent \((v_{i},v_{j})\in path_{r}^{c,k}\), and vice versa.  
The routing selection of \(vp_{k}^{c}\) is equivalent to selecting a path with the least cost from the \(NM_{k}^{c}\). Let \(po_{k}^{c}\) be the optimal path in \(\mathcal{P}_{k}^{c}\), and all optimal paths \(po_{k}^{c},k=1,\cdots,NS_{c}\) form the optimal path set \(\mathcal{PO}^{c}\)= \(\{po_{1}^{c},\cdots,po_{NS_{c}}^{c}\}\). The optimal connection matrix of \(\mathcal{G}(c)\) is represented by \(\mathcal{H}^{c}\)=\([\)\(l_{ij}^{c}]\in\mathbb{R}^{n\times n}\)\(h_{ij}^{c}\in\{0,\,1\}\) with the property that if \((v_{i}^{c},v_{j}^{c})\in\mathcal{PO}^{c}\), \(h_{ij}^{c}=1\); otherwise, \(h_{ij}^{c}=0(i,j=1,\cdots,n)\).