esa-sceva/satcom-chunk-collection huggingface dataset:
# On Invertibility of an Interconnected System Composed of Two Dynamic Subsystems
## 2 Inversion of Nonlinear Interconnected System
### Inverse of Interconnected System
#### 2.2.1 Inverse of Nonlinear System  
This section describes the inverse of a dynamic system. The classical definition of invertibility for non-interconnected systems is given first. For the high-level subsystem depicted in (2), the input-output map is defined as \(\mathcal{M}_{\rm H}:\mathcal{U}_{\rm L}\to\mathcal{Y}\), where the input function space is \(\mathcal{U}_{\rm L}\) and the corresponding output function space is \(\mathcal{Y}\). \(\mathcal{M}_{\rm H}\) maps an input \(u_{\rm L}(.)\) to the output \(y(.)\) generated by the system driven by \(u_{\rm L}(.)\) with an initial condition \(x_{H0}\). The definition of invertibility of a nonlinear dynamical system is given below, as shown in [5].  
**Definition 1**.: _Fix an output set \(\mathcal{Y}\) and consider an arbitrary interval \([t_{0},\ T)\), the system (2) is invertible at a point \(x_{H0}\ :=\ x_{H}(t_{0})\in\mathcal{X}_{H}\) over \(\mathcal{Y}\), if for every \(y_{[t_{0},\ T)}\in\mathcal{Y}\), the equality \(\mathcal{M}_{H(x_{H0})}\Big{(}u_{\rm L1[t_{0},\ T)}\Big{)}=\mathcal{M}_{H(x_ {0})}\Big{(}u_{\rm L2[t_{0},\ T)}\Big{)}=y\) implies that \(\exists\varepsilon>0\), such that \(u_{\rm L1[t_{0},\ t_{0}+\varepsilon)}=u_{\rm L2[t_{0},\ t_{0}+\varepsilon)}\). The system is strongly invertible at a point \(x_{H0}\) if it is invertible for each \(x_{H}\in\mathcal{N}(x_{H0})\), where \(\mathcal{N}\) is some open neighborhood of \(x_{H0}\). The system is strongly invertible if there exists an open and dense sub-manifold \(\mathcal{M}\) (called inverse sub-manifold) such that \(\forall x_{H0}\in\mathcal{X}\), the system is strongly invertible at \(x_{H0}\)._  
In fact, by Definition 1, invertibility at \(x_{\rm H0}\) is equivalent to saying that \(u_{\rm L1[t_{0},\ t_{0}+\varepsilon)}\
eq u_{\rm L2[t_{0},\ t_{0}+\varepsilon)}\) for all \(\varepsilon\in(0,\ T\ -\ t_{0})\) implies that \(\mathcal{M}_{\rm H(x_{0})}\Big{(}u_{\rm L1[t_{0},\ T)}\Big{)}=\mathcal{M}_{ \rm H(x_{H0})}\Big{(}u_{\rm L2[t_{0},\ T)}\Big{)}\). This notion was captured by [PERSON] in [18]. The concept of the inverse of nonlinear systems can now be extended to interconnected systems.
## 2 Methodology
### Color Similarity
#### 2.3.3 Distance in Lab Color Space as Similarity Measure  
It was previously mentioned that distances in Lab color space obey Euclidian distance equation. This allows one to judge how similar two colors are depending on how close their corresponding Lab vectors lie. As we deal with histograms rather than actual colors, we first propose to calculate the average intensity for the histograms in every channel, as well as to treat such triplets as the coordinates of a point in Lab space. In this case, the number of pixels \(h_{oij}\) in bins \(j\) of the original histogram \(h_{o}\) act as weights so that the average intensity in color channel \(i\) is calculated as:  
\[I_{i}=\frac{\sum_{j=0}^{255}jh_{oij}}{\sum_{j=0}^{255}h_{oij}}. \tag{5}\]  
For the image segments of a uniform color triplet (\(I_{L}\), \(I_{a}\), \(I_{b}\)), it is expected to lie close to the main color that is perceived by the human eye. In the case of multiple subregions with different colors, it may not correspond to any real color that is present in the image. However, this is sufficient for distance calculation purposes. As such, for two segments of the same class:  
\[d=\sqrt{\left(I_{L1}-I_{L2}\right)^{2}+\left(I_{a1}-I_{a2}\right)^{2}+\left(I_ {b1}-I_{b2}\right)^{2}}. \tag{6}\]  
We then propose a normalized similarity measure \(S_{d}\) with values in [0, 1] range, which is related to \(d\) as:  
\[S_{d}=\left\{\begin{array}{c}1-\frac{d}{K_{d}}\;if\;d<k_{d},\\ 0\;otherwise,\end{array}\right. \tag{7}\]  
where \(k_{d}\) is an empirical distance coefficient and, in this work, \(k_{d}=35\).
## 3 Automated Operational Modal Analysis Based on Modal Uncertainty
### Discussion  
A number of parameters are used in the proposed AOMA. However, the parameters adopted in the pre-cleaning stage can be considered as being nearly constant because they were set to prevent the omission of real modes. The other parameters are the cumulative probability \(p\), used for determining whether two modes are identical and \(n_{min}\), the minimum number of modes for a cluster of identical modes. Among them, if the cumulative probability \(p\) is set to a small value, the cluster will comparatively include only accurate modes. On the other hand, when \(p\) is set to a large value, the cluster will include a larger number of modes but \(p\) will have a limited effect on the identification of identical modes if \(p\) is set within a range of 95-99%. Consequently, the parameter that should be set in reality is the minimum number of modes, but this condition is rarely applied.
## 4 V2H and V2G
### Technological Developments
#### 4.3.4 Japan  
Japan takes the lead globally in the EV industry, and the involved departments have developed a V2H standard called the EV Power Supply System Association (EVPOSSA) to promote the industry and standardize product specifications. In addition, numerous companies are deeply involved in V2H technology, including Nissan, which has created commercial products based on the technology. In 2016, Nissan and multinational Enel launched a joint V2G pilot project in the UK, which equipped 100 V2G systems for Nissan LEAF and e-NV200 models, enabling their owners to profit by selling electricity from their onboard batteries to the grid [75]. Moreover, Nissan launched the Leaf model with V2G capability (Figure 6c) in 2017.
## Appendix A
It is notable that strategy (1) yields a higher weekly profit (1.65 million USD), but its transport volume is lower (3000 TEUs). In contrast, strategy (2) results in a lower weekly profit (1.3 million USD), yet it fully satisfies the demand, achieving a transport volume of 3500 TEUs. These two solutions represent trade-offs between the two conflicting objectives of the model. However, if we relax the integer variable \(n_{sr}\) to be continuous, as in \(n_{s_{1}r_{1}}=0.5\), then \(n_{s_{2}r_{1}}=2.5\) will also satisfy the weekly operation requirement. Since \(Q_{s_{1}}\cdot n_{s_{1}r_{1}}/N_{r_{1}}+Q_{s_{2}}\cdot n_{s_{2}r_{1}}/N_{r_{1} }=6000\times 0.5/3+3000\times 2.5/3=3500\) TEUs \(=D_{od}\), the demand is fully satisfied, i.e., \(z_{odr_{1}}=3500\) TEUs. In this case, the company needs to charter in one ship of type \(s_{2}\), i.e., \(n_{s_{2}}^{\text{in}}=1\), \(n_{s_{1}}^{\text{in}}=n_{s_{1}}^{\text{out}}=n_{s_{2}}^{\text{out}}=0\). Then, the weekly profit in this case can be calculated as follows:  
\[ \begin{array}{l}p_{od}z_{odr_{1}}-n_{s_{1}r_{1}}/N_{r_{1}}(F_{s_{1}r_{1}}+B_{s _{1}r_{1}})-n_{s_{2}r_{1}}/N_{r_{1}}(F_{s_{2}r_{1}}+B_{s_{2}r_{1}})\\ -n_{s_{1}r_{1}}/N_{r_{1}}\cdot P_{s_{1}r_{1}}^{\text{extra}}-n_{s_{2}r_{1}}/N_{ r_{1}}\cdot P_{s_{2}r_{1}}^{\text{extra}}-c_{s_{2}}^{\text{in}}n_{s_{2}}^{\text{in}}\\ =800/10^{\circ}\times 3500-\frac{05}{3}\times 2.7-\frac{2}{3}\times 0.9-0-0-0.25 \times 1=1.33\text{ million USD}.\end{array} \]  
This relaxed LP solution achieves a higher value in the first objective while keeping the second objective unchanged compared to strategy (2). Critically, \(n_{s_{1}r_{1}}=0.5\), \(n_{s_{2}r_{1}}=2.5\) is practically invalid as we cannot assign a ship by half. Hence, the LP relaxation of \(n_{sr}\) inherently produces non-integer solutions conflicting with the real-world integer operation requirement, proving \(n_{sr}\) cannot be freely relaxed. \(\square\)
## 2 Proposed Bearing Fault Diagnosis Method using Acoustic Emission Signals
### Short-Time Fourier Transform (STFT)  
For diagnosing bearing faults in variable rotational speeds, most fault frequencies are not determined because these frequency bands directly depend on rotational speeds, where STFT can provide the information to analyze. STFT provides time-frequency analysis which can solve two notable problems that time or frequency domain analysis face: (1) single domain analysis has difficulty in illustrating time-dependency of the spectrum for nonstationary signals; (2) Fourier Transform (FT) is not compatible with representing transient signals due to its non-periodic characteristics. For the appropriate analysis and synthesis of nonstationary signals, Short-Time Fourier Transform (STFT) is based on a series of overlapped Fast Fourier Transforms (FFTs) applied for signal segmentation [14].  
In transient states such as acceleration periods, start-up, and shutdown, the acquired AE signals can contain more suitable information about bearing conditions compared to steady stages of machines. For this reason, the STFT, which can represent the transient information from AE signals, is adopted in our proposed method. The spectrogram is used as a visual representation method of the AE signals in both time and frequency domains, using a color scale of the image to indicate the frequency's amplitude.
## Materials and Methods
### Validation Approach  
The study used K-fold cross validation method to evaluate the performance of the best prediction model derived from PALSAR-2, Sentinel-1A, and combination of both data. This method provided better indication on the prediction performance than the common residual method. Residual method does not provide an indication as to how well the model makes new predictions over new sample data, but this method does. In this study, 10-fold cross validation method [34] was used where all sample plots data were randomly grouped into 10 groups. One group was used as a testing set while the other nine groups were used in developing the model. The root mean square error (RMSE) was calculated using the testing set. This process was iterated 10 times where each group was used as a testing set once. Then, the average of all RMSEs was calculated to get the overall RMSE of that model.
## 5 Multimodal Route Planning and Guidance  
The route recommender agent uses collective information about different regions (macroscopic) in the urban environment, such as available public transports in the region, as well as required information about road segments such as travel time and CO\({}_{2}\) emissions to calculate the fastest and eco-friendliest routes. Each route-finding agent deals with one commuter request at a time, so that a cluster of route-recommender agents enables the multimodal recommender system to handle multiple commuter requests in parallel. Once the commuter submits a route request indicating the type of route he favors (i.e., the fastest or eco-friendliest), the route recommender agent searches the transport layers and computes a multimodal route that satisfies the commuter preferences (e.g., departure time). In this research, we used the Dijkstra algorithm Dijkstra (1996) to find the route between two segments with the least cost, where cost is estimated as the total journey time (in the case of the fastest route) or CO\({}_{2}\) emissions (in the case of the eco-friendly route), as summarized in the formula below. The cost of occupying a route solution is the aggregation of travel time and carbon footprint of all segments making up the route. Our recommendations aimed at reducing carbon emissions by inducing travel behavior change rather than improving the execution time of route planning and calculation.  
\[Rout\;cost(x,\;y)=\sum_{i=1}^{\alpha}(C_{\alpha})+\sum_{j=1}^{\beta}(PT_{ \beta})+\sum_{k=1}^{\gamma}(Bk_{\gamma})+\sum_{l=1}^{\delta}(W_{\delta})\]  
where \(x\) is the origin segment, \(y\) is the destination segment, \(\alpha\) is the number of road (\(C\)) segments in the solution, \(\beta\) is the number of public transport (\(PT\)) segments, \(\gamma\) is the number of bike (\(Bk\)) segments, and \(\delta\) is the number of pedestrian (\(W\)) paths. It is worthwhile to point out here that we applied some heuristics to limit the number of switches between the different modes of transport based on the user preferences. Eventually, the shortest path finding algorithm achieves the objective of the simple formula by minimizing the cost of the suggested routes with respect to estimated travel time and CO\({}_{2}\) emissions.  
In an ideal scenario, the fastest route should include the use of cars with minimum idle time, resulting in fewer carbon emissions. Travel time is estimated in miles per second. However, the eco-friendliest route strives to combine public transport, cycling, and walking to promote sustainable travel behavior. Carbon emission is estimated in grams per kilometer. Whenever possible, the route recommender agent suggests the eco-friendliest route instead of the fastest route.
## 4 Thermomechanical Performance
Overall, we found that the high localized stress was acceptable for the sandwich structures, and the permanent deformation and maximum deflection should be considered along with the stress failure in designing sandwich structures. In the next paragraph, we present a discussion on the deflections of the sandwich structures in terms of deflection limits. To discuss the effectiveness of the corrugated core sandwich structures compared with the dense structure used in our previous research [6], we also performed a thermomechanical analysis of the dense structure made of Inconel material. The maximum deflection was just 0.14 mm, which satisfied the deflection limit for the outer surface of the vehicle; the weight of the solid structure was 0.62 kg, while that of the corrugated structure was 0.21 kg. To prevent boundary layer transition at high Mach numbers (M \(>\) 5), [PERSON] et al. [1] and [PERSON] et al. [8] proposed a criterion for the deflection limit of the outer surface at the windward forebody and leeward forebody; that is, it should be less than 1% of the main diagonal span of the TPS panel at the windward forebody and 1.5% at the leeward forebody. Moreover, [PERSON] et al. [4] added a deflection limit, which was imposed to prevent permanent compaction in the insulation layer; that is, the deflection limit should not exceed 10% of the total TPS panel thickness. As shown in Figure 19, a typical TPS panel for the BCS with a 240 mm nominal diagonal length was fabricated with a total thickness of 33.6 mm. We calculated the ratios of the maximum deflections to the diagonal length and total thickness of the TPS panel and concluded that all maximum deflections from the UCS and BCS made of Inconel and stainless steel satisfied the deflection limits. Thus, our results confirmed that the two bio-inspired corrugated-core sandwich structures are suitable for the future TPS panel under the heating rates applied in this study. In addition, their weights are more compatible than that of the previous design of the dense structure. Therefore, in terms of weight and deflection limits, we concluded that the developed bio-inspired corrugated-core structure is an excellent candidate structure for the skin structure of the TPS panel. For the optimal design of the TPS panel, we also examined the effect of emittance of the outer face sheet and the core thickness on the thermomechanical performance. We further investigated whether improving the surface coating of the outer face sheet and changing the weight of the corrugated core could affect the localized failures and deflection limits of the sandwich structures. The details of the investigation are presented in Section 5.
# Fabrication of Partially Etched Polystyrene Nanoparticles
## 3 Results
### Evaluation of Pickering Emulsion Stability  
The stability of oil-in-water Pickering emulsions, which were stabilized with partially etched PS NPs, was compared to those stabilized with intact spherical PS NPs. Note that Pickering emulsions formed with solid particles have a wide range of applications in various fields such as food, cosmetics, pharmaceuticals, oil recovery, and phase-transfer catalysis due to their high stability, biocompatibility, and tunable properties [24; 32; 33; 34; 35]. As shown in Figure 5, the emulsions stabilized with the intact spherical PS NPs (\(t_{DVB}=0\) min sample in Figure 3c) almost disappeared after 2 months, whereas those stabilized with partially etched PS NPs (\(t_{DVB}=70\) min sample in Figure 3c) remained the same after 2 months, demonstrating their extremely robust and long-lasting stability. This enhanced stability can be attributed to the surface roughness of the partially etched particles. The surface roughness of the partially etched particles increases the surface area exposed to each fluid phase (i.e., oil and water), leading to stronger attachment energy of the particles to the interface [19; 20]. This increased attachment property can result in more robust and stable emulsions.  
Figure 4: SEM images of partially etched PS NPs after chemically etching them using different solvents: THF (**a,d**), chloroform (**b,e**), and toluene (**c,f**). The top and bottom are the PS NPs polymerized in the presence of SDS and CTAB surfactants, respectively, where \(t_{DVB}=70\) min for all the cases.  
Figure 5: Microscopic images showing the Pickering emulsion stability formed with spherical PS NPs (\(t_{DVB}=0\) min sample shown in Figure 3c) (**a**,**c**) and partially etched PS NPs (\(t_{DVB}=70\) min sample shown in Figure 3c) (**b**,**d**).
## 2 Materials and Methods  
### Studied Plants  
The experimental trials were conducted at Cantine Paolo Leo, located in San Donaci (BR) (Figure 1), on a red winemaking line processing Syrah grape.  
Two stainless-steel fermenters, made of AISI 304 and 316 for parts in contact with the product, were used. These fermenters were identical in model and capacity, both equipped with the same heat exchange surface for circulating the cooling solution produced in the chiller (Table 1; Figures 2 and 3). However, they were equipped with different cap-breaking  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine** & **Technical Parameters** & **Installed Power** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Fermenter 11A \\ (Traditional System) \\ \end{tabular} } & Capacity = 800 hL & 11.35 kW \\  & No. 1 Cooling jacket: h = 2.4 m; & (Pumping-over pump: 3.6 kW \\  & position from the top: 3.8 m & Diffuser: 0.75 kW \\  & & Pomace extraction unit: 7.0 kW) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Fermenter 21A \\ (Air-Modulated Injection System) \\ \end{tabular} } & Capacity = 800 hL & \\  & No. 2 Cooling jackets: h = 1.2 m; & \\ \cline{1-1}  & position from the top: 2.5 m; distance & - \\ \cline{1-1}  & between jackets: 1.5 m & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Compressor (Air-Modulated \\ Injection System) \\ \end{tabular} } & \multirow{2}{*}{Flow rate = 3427 L/min} & \multirow{2}{*}{30.0 kW} \\  & & \\ \end{tabular}
\end{table}
Table 1: Main characteristics of the vinicitation line related to tanks 11A and 21A.  
Figure 1: Paololeo Winery, San Donaci (BR), site of the experiment.  
systems: Fermenter 11A was fitted with a traditional cap irrigation system for pumping-over and a single cooling jacket (Table 1; Figure 2). Fermenter 21A had two cooling jackets and was equipped with the AIRMIXING M.I.\({}^{\lx@sectionsign}\) system, patented by Parsec S.r.l. (Sesto Fiorentino, FI, Italy), for cap-breaking (Table 1; Figure 3).  
In Fermenter 21A, a circular conduit was installed around the external lateral surface, at 2.0 m from the base and connected to the centralized compressed air network. From this conduit, four radial pipes, each 50.0 cm long, extended into the tank, terminating in four injection nozzles (Figures 3 and 4; Table 1).  
Each nozzle was equipped with a fast-opening valve, designed to open and close sequentially relative to the others. The opening times corresponded to a few hundred milliseconds, following predetermined combinations.  
Compressed air (4-6 bar) was injected through each nozzle, following a specific pulse sequence that generated waves. This, in turn, caused the must to agitate and the cap to break up. The program allowed for 15 extraction levels, each characterized by a precise pulse sequence and a defined volume of injected air.  
The pumping-over system of Fermenter 11A consisted of a recessed impeller electric pump, an external pipe, and a diffuser equipped with a gear motor and an inverter, which irrigated the cap (Figures 2-5; Table 1).
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 2 Materials and Methods  
There are three main phenomena that need to be modeled in thermoacoustics. The first is the propagation of the initial energy source, in this study near infrared optical or radio frequency electromagnetic waves, and calculation of the total fluence/intensity distribution inside the tissue medium. The second phenomenon is the acoustic propagation, after calculating an initial pressure from the intensity distribution. The third phenomenon is the reconstruction of the tissue parameters, which depend on the type of energy source used for tissue excitation. A flowchart showing the similar steps between RACT and PACT is shown in Figure 1. This study is focused on using a time reversal [25; 26] reconstruction method to recover the initial pressure, followed by a simple division to find the tissue parameters. There are several alternative approaches for reconstructing the tissue parameters that can be integrated with our study in the future. Back-projection methods may be used when using specific geometries [27] and parametrix methods for more general geometries [28]. The back-projection methods are analytically exact, but only for geometries with a special symmetry, and with a constant speed of sound. Parametric methods do not provide analytically exact methods, but instead give approximations with error bounds for general geometries, and can even be developed for regions with varying speeds of sound. A survey of several reconstruction methods is given in Reference [29].  
Figure 1: The similarities between key steps of the photoacoustic computed tomography (PACT, blue) and radio frequency (RF)-induced acoustic computed tomography (RACT, red) workflows, with methods that are shared represented in yellow. Step A represents the finite element mesh in Section 2.5, used for both types of simulation. Step B\({}_{1}\) is represented in Figure 9a, while step B\({}_{2}\) is represented in Figure 7a. Step C\({}_{2}\) uses the optical diffusion Equation (1). Step C\({}_{1}\) uses the electromagnetic wave Equation (5). The resulting pressure rise for the optical (D\({}_{2}\)) and RF (D\({}_{1}\)) radiations are represented by Figures 7b and 9b, respectively. The initial pressure propagates out to the boundary (E) via the scalar wave Equation (7). The data received by acoustic sensors at the boundary (F) can then be used in reconstruction algorithms (G) to recover the initial pressure. In this paper, a time-reversal algorithm is applied for reconstruction (Section 2.4). The reconstructed pressure is then divided by the intensity (I\({}_{2}\), I\({}_{1}\)) to recover the recover the absorption or conductivity for the optical and RF cases, shown in Figures 7c and 9c, respectively.