esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Configuration  
The schematic diagram in Figure 2 illustrates the GFMC APF system. The system is powered by a DC link, represented by \(v_{\text{gdc}}\), which could be a battery or another converter. The power electronics section consists of a GFMC controlled by pulse-width modulation (PWM) signals to generate the desired output. The interface between the converter and the grid is achieved through an \(LCL\) filter, comprising the converter-side inductor \(L_{\text{ggi}}\), the filter capacitor \(C_{\text{ggi}}\), and the grid-side inductor \(L_{\text{gg}}\). The primary control objective is to regulate the capacitor voltages \(v_{\text{gdbc}}\) to control the phase and magnitude of power injected into the grid. The nonlinear load, connected at the PCC, generates the harmonic currents \(i_{\text{labc}}\), which the GFMC APF compensates for, ensuring harmonic-free grid-injected currents \(i_{\text{ggabc}}\). The nonlinear load is modeled with an inductor \(L_{\text{load}}\) and a resistance \(R_{\text{load}}\), representing its dynamic characteristics, and is configured as a three-phase diode bridge. The grid is defined as a series connection of grid inductors \(L_{\text{s}}\) and grid voltage sources \(v_{\text{sabc}}\). Voltages at the PCC, \(v_{\text{gabc}}\) are measured for grid synchronization, and the injected power is regulated through the control of \(v_{\text{gdabc}}\). This setup ensures effective power transfer, grid synchronization, and harmonic suppression.
## 3 LSTM Network
### LSTM Training  
To design an LSTM network for the simulation of nonlinear aeroelastic forces, it is crucial to select the proper variables as its inputs. Conventionally, the deck displacements and velocities are utilized to represent the bridge motion state. To take the mass effects on bridge aerodynamics into account, the accelerations are also often added into the inputs. Considering that the major purpose of the present study was to predict the post-flutter LCO performance of the bridge decks with various leading edges, both the leading-edge angle and wind speed were part of the inputs. Accordingly, the inputs of the LSTM network contain the leading-edge angle \(\alpha_{d}\), the wind speed \(U\), the displacements \((h,\alpha)\), velocities \((\dot{h},\dot{\alpha})\) and accelerations \((\ddot{h},\ddot{\alpha})\). Due to the fluid memory effects, the responses of the bridge decks under the nonlinear self-excited forces not only depend on the current motion state but also on the previous motion states. Previous research has pointed out that the fluid memory fades over time, which means that the responses of the bridge deck are only impacted by limited time steps in the past [14]. In the LSTM network, the forget gate is designed to achieve this. Several studies have discussed the input length of the LSTM network to balance the accuracy and efficiency in the consideration of fading fluid memory effects [30]. In this study, the length of the training data was determined to be 0.3 s (i.e., 300 time steps with a time interval of 0.001 s).  
The CFD simulation responses of the bridge decks with the leading-edge angles of 120\({}^{\circ}\), 130\({}^{\circ}\), 140\({}^{\circ}\), 150\({}^{\circ}\) and 165\({}^{\circ}\) were utilized as the training datasets, while the CFD simulation data with \(\alpha_{d}\) of 126\({}^{\circ}\) and 134\({}^{\circ}\) were used as test datasets to verify the effectiveness and accuracy of the trained LSTM network. It was noted that all the data \(D_{T}\) used in the training were normalized to improve the training efficiency (using \(\widetilde{D}_{T}=\frac{(D_{T}-R_{T})}{\sigma_{T}}\) where \(\mu_{T}\) and \(\sigma_{T}\) were the mean value and standard deviation of the CFD simulations).
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
###### Abstract  
Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.  
Keywords:dual-branch; disentangling network; branch attention +
Footnote †: journal: Applied sciences
# An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
## 4 Results and Discussion
### Recognizing the Existing Obstacles in the Development of SCs
#### 4.1.5 Waste Management  
Waste collection is one of the most important sectors of SCs [48]. In this regard, IoT can reduce a lot of problems in this regard [49]. To achieve this, a sensor will be placed on each waste container, which will gather data regarding the level of waste in the container; then, after the container is filled, a notification will be sent to truck drivers via a mobile app. By following this useful and effective plan, truck drivers will expend time and energy to only empty full containers instead of half-full ones [50]. A study in China of recycling and household waste segregation between 2018 and 2019 showed that integration of the Internet of Things (IoT) was effective in household waste management. During the study, collections of recyclable waste and biodegradable food waste were elevated by 431.8% and 88.8%, respectively, which had good environment effects and meant that this macro policy increased the recyclable waste collection by 431.8% in Shanghai [51].
## 1 Introduction and Problem Formulation
### Modeling of the Petri Net-Based Concurrent Control System
Once the system reaches a state with marked places \(p_{2},\ p_{3},\ p_{4}\), transition \(t_{3}\) is fired. Note that there are no input signals assigned to \(t_{3}\), thus it is fired once all its input places contain a token. As the consequence, tokens from all input places of \(t_{3}\) (that is, from places \(p_{2},\ p_{3},\ p_{4}\)) are consumed and added to each of its output places: \(p_{5},\ p_{6},\) and \(p_{7}\). In such a resulting state, three tasks are executed concurrently. Two of them refer to operations \(y_{2}\) and \(y_{3}\), as it was already described (during the initial marking). The remaining process is related to place \(p_{7}\). Depending on the combination of input signals \(x_{1},\ x_{2},\ x_{3}\), three possible places can be reached by this particular process: \(p_{1}\) (upon firing of \(t_{5}\)), \(p_{8}\) (when \(t_{6}\) is fired), or \(p_{9}\) (upon firing of \(t_{7}\)). Note that the input signals here play an essential role and allow for enabling the particular transition. In general, Petri nets are not deterministic, and such a situation is called a _conflict_[41; 43; 82]. Such a problem can be easily resolved by application of the input binary signals--this is one of the additional (and huge) benefits of interpreted Petri nets. Finally, the system executes operation \(y_{1}\) (if \(t_{5}\) is fired), \(y_{4}\) (\(t_{6}\)), or \(\overline{y_{1}}\) (\(t_{7}\)). The first possibility returns to \(p_{7}\) (once input signal \(s\) becomes active and transition \(t_{1}\) is fired), while the remaining two eventually lead to the initial state, where place \(p_{3}\) is marked. The concurrent control system presented in Figure 2 illustrates the power of Petri net-based specification. There are several tools that support the modeling of Petri net-based systems. A graphical specification of a model can be prepared for instance within Input Output Place Transition-Tools v 1.2 (IOPT-Tools) [83] or Platform Independent Petri net Editor 2.5 (PIPE) [84]. Moreover, the model of the system can be easily examined [20] (initial verification) and even validated (simulated). The concurrent control system described by interpreted Petri net \(\mathcal{N}_{1}\) is live and safe. This means that each transition of the net can be reached (by a sequence of firing of other transitions). Moreover, there is at most one token at any place (in each reachable state) in the system. Such a verification (of liveness and safeness) can be performed within the modeling tools (e.g., PIPE). However, in the case of more complex concurrent control systems, advanced methods and tools can be applied, such as Hippo [19; 20; 29; 31], GreatSPN [85], and IOPT-Tools [19; 83; 86; 87].
###### Abstract  
Today, the most common way of laser sealing is using a glass frit paste and screen printer. Laser sealing using glass frit paste has some problems, such as pores, nonuniform height, imperfect hermetic sealing, etc. In order to overcome these problems, sealing using fiber types of sealant is attractive for packaging devices. In this work, (70-x)V\({}_{2}\)O\({}_{5}\)-5 ZnO-22 BaO-3B\({}_{2}\)O\({}_{5}\)-\(\lambda\)(PO\({}_{3}\))\({}_{\rm n}\) Glass (mol%) incorporated with xM(PO\({}_{3}\))\({}_{\rm n}\) concentration (where M = Mg, Al, = 2, 3, respectively) were fabricated and their thermal, thermomechanical, and structural properties were investigated. Most importantly, for this type of sealing, the glass should have a thermal stability (\(\Delta\)T) of \(\geq\)80 \({}^{\circ}\)C and the coefficient of thermal expansion (CTE) between the glass and panel should be 1.0 ppm/\({}^{\circ}\)C. The highest thermal stability \(\Delta\)T of the order of 93.2 \({}^{\circ}\)C and 112.9 \({}^{\circ}\)C was obtained for the 15 mol% of Mg(PO\({}_{3}\))\({}_{2}\) and Al(PO\({}_{3}\))\({}_{3}\) doped glasses, respectively. This reveals that the bond strength and connectivity is more strongly improved by trivalent Al(PO\({}_{3}\))\({}_{3}\). The CTE of a (70-x)V\({}_{2}\)O\({}_{5}\)-5 ZnO-22 BaO-3B\({}_{2}\)O\({}_{3}\)-xAl(PO\({}_{3}\))\({}_{3}\) glass system (mol%) (where x = 5-15, mol%) is in the range of 9.5-15.5 (\(\times 10^{-6}\)/K), which is comparable with the CTE (9-10 (\(\times 10^{-6}\)/K)) of commercial DSSC glass panels. Based on the results, the studied glass systems are considered to be suitable for laser sealing using fiber types of sealant.  
V\({}_{2}\)O\({}_{5}\) glass; metaphosphate; thermal stability; laser sealant  
Received: 25 March 2021  
Accepted: 6 May 2021  
Submitted: 18 May 2021  
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**Copyright:** **© 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 2 System Model
### Space-Frequency Analog Fronthaul Channel
We now detail the signal model for fronthaul transmission. To this end, let us define the \(l_{F}\times l_{S}\) matrix \(\mathbf{\tilde{Y}}_{k}\) containing the signal to be transmitted by the EN \(k\)-th to the BBU over the copper cable as  
\[\mathbf{\tilde{Y}}_{k}=[\mathbf{\tilde{y}}_{k}^{1}(t),\mathbf{\tilde{y}}_{k}^ {2}(t),...,\mathbf{\tilde{y}}_{k}^{l_{S}}(t)], \tag{9}\]  
where the \(k\)-th column \(\mathbf{\tilde{y}}_{k}^{c}(t)\in\mathbb{C}^{l_{F}\times 1}\) denotes the signal transmitted on twisted-pair \(c\) across all the \(l_{F}\) cable frequency resources. The signal \(\mathbf{\tilde{R}}_{k}\in\mathbb{C}^{l_{F}\times l_{S}}\) received at the BBU from the \(k\)-th EN across all the cable space-frequency resources is then computed as  
\[\mathbf{\tilde{R}}_{k}(t)=\mathbf{\tilde{Y}}_{k}(t)\mathbf{H}_{c}+\mathbf{ \tilde{W}}_{k}(t), \tag{10}\]  
where \(\mathbf{\tilde{W}}_{k}(t)=[\mathbf{\tilde{w}}_{k}^{1}(t),\mathbf{\tilde{w}}_{ k}^{2}(t),...,\mathbf{\tilde{w}}_{k}^{l_{S}}(t)]\in\mathbb{C}^{l_{F} \times l_{S}}\) is the additive white Gaussian cable noise uncorrelated over cable pairs and frequencies, i.e., \(\mathbf{\tilde{w}}_{k}^{c}(t)\sim\mathcal{CN}(\mathbf{0},\mathbf{I}_{l_{F}})\) for all pairs \(c=1,\ 2,\...,\ l_{S}\). As commonly assumed in wireline communications to control cable radiations [37], the power of the cable symbol \([\mathbf{\tilde{y}}_{k}^{c}(t)]_{f^{\prime}}\) transmitted from EN\({}_{k}\) over twisted-pair \(c\) at frequency \(f^{\prime}\) is constrained to \(P_{c}\) by the (short-term: One can also consider the \"long-term\" power constraint  
Figure 5: Mapping of radio resources over cable resources: (**a**) maximum normalized cable bandwidth (full redundancy), \(\mu=1\), or \(\eta=l_{S}\); (**b**) minimal normalized cable bandwidth (no redundancy), \(\mu=1/l_{S}\), or \(\eta=1\). \(n_{T}^{-1}\sum_{t}\mathbb{E}[|\tilde{\mathbf{y}}_{k}^{c}(t)|_{f^{\prime}}|^{2}] \leq P_{c},\,\forall c\in[1,I_{S}],\,f^{\prime}\in[1,I_{F}]\) with minor modifications to the analysis and final results) power constraint  
\[\mathbb{E}\left[\left|\left|\tilde{\mathbf{y}}_{k}^{c}(t)\right|_{f^{\prime}} \right|^{2}\right]\leq P_{c}\quad\forall c\in[1,I_{S}],f^{\prime}\in[1,I_{F}], t\in[1,n_{T}]. \tag{11}\]  
In the following, we will omit the time index \(t\), when no confusion arises.
###### Abstract  
Using coral aggregates instead of natural aggregates can save both raw material transportation costs and time in island construction projects. However, coral aggregates are characterized by high brittleness, low strength, and a high chloride content, which result in the brittleness and durability of coral aggregate concrete (CAC). On the other hand, carbon emissions are prominent in the production of Portland cement. A reinforced concrete structure composed of a stainless-steel rebar (SSR), geopolymer concrete, and coral aggregate was employed to address these challenges. Considering that columns are commonly used structural elements, this paper investigates the eccentric compression behavior of stainless-steel rebar-reinforced coral aggregate geopolymer concrete columns (SCGCs) by varying eccentricity (\(\epsilon_{0}\) = 70, 140, 210, and 280 mm) and reinforcement ratio (\(\rho_{\text{s}}\) = 0.302%, 0.536%, and 0.838%) values. The failure patterns, load-deformation curves, and crack development of SCGCs were obtained. These results indicate that the failure pattern of SCGCs under eccentric compression is similar to that of conventional reinforced concrete columns. However, SCGCs exhibited a greater lateral deformation. A refined load-bearing capacity prediction model for SCGCs was developed by integrating the constitutive model of coral aggregate geopolymer concrete (CAGC). Furthermore, a crack width prediction model specifically suited for SCGCs was developed, considering the differences in the bond performance between CAGCs and the SSR compared to ordinary concrete and carbon steel and the characteristic of the SSR lacking a yield plateau. The calculations using the refined load-bearing capacity prediction model and the crack width prediction model fit well with the experimental results, indicating that these two models have good application prospects.  
c 2025 15 3570 +
Footnote †: journal: Applied Sciences  
Article
# Artificial Intelligence-Driven Eye Disease Classification Model
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods/Metrics** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** & **Sensitivity** & **Specificity** \\ \hline Proposed EDC & 99.1 & 98.9 & 99.0 & 98.9 & 96.4 & 98.9 & 96.3 \\ [PERSON] et al., model [39] & 96.5 & 94.5 & 95.1 & 94.8 & 94.2 & 94.5 & 92.4 \\ [PERSON] et al., model [35] & 97.8 & 96.1 & 95.8 & 95.9 & 93.1 & 96.1 & 93.1 \\ [PERSON] et al., model [37] & 96.1 & 95.6 & 95.7 & 95.6 & 94.5 & 95.6 & 90.5 \\ [PERSON] et al., model [38] & 97.6 & 96.3 & 96.8 & 96.5 & 95.1 & 96.3 & 92.6 \\ [PERSON] et al., model [40] & 98.7 & 96.9 & 97.2 & 97.0 & 94.8 & 96.9 & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Findings of generalization applied to the ODIR dataset. Figure 7: Findings of comparative analysis—ODIR dataset [35; 37; 38; 39; 40]. Lastly, Table 8 highlights the CI and SD of the EDC. The outcome highlights the significance of the proposed model in dealing with uncertainties. It indicates the reliability of the proposed model's decisions. In addition, it shows that the model can deliver a helpful outcome in the real-time environment. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods/Metrics** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** & **Sensitivity** & **Specificity** \\ \hline Proposed EDC & 99.4 & 98.7 & 98.7 & 98.7 & 96.5 & 98.7 & 96.3 \\ [PERSON] et al., model [39] & 98.1 & 97.5 & 97.8 & 97.6 & 96.4 & 97.5 & 93.1 \\ [PERSON] et al., model [35] & 97.6 & 97.2 & 97.6 & 97.4 & 95.1 & 97.2 & 93.7 \\ [PERSON] et al., model [37] & 98.4 & 98.5 & 98.2 & 98.3 & 96.3 & 98.5 & 93.5 \\ [PERSON] et al., model [38] & 97.7 & 98.3 & 98.8 & 98.5 & 96.5 & 98.3 & 93.8 \\ [PERSON] et al., model [40] & 99.3 & 99.1 & 98.7 & 98.9 & 96.1 & 99.1 & 92.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Findings of generalization applied to the EDC dataset. Figure 8: Findings of comparative analysis—EDC dataset [35; 37; 38; 39; 40]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ODIR**} & \multicolumn{3}{c}{**EDC**} \\  & **MAD** & **RMSE** & \
\begin{tabular}{c} **Testing Time** \\ **(Seconds)** \\ \end{tabular} & **MAD** & **RMSE** &
\begin{tabular}{c} **Testing Time** \\ **(Seconds)** \\ \end{tabular} \\ \hline Proposed EDC & 0.289 & 0.341 & 1.08 & 0.223 & 0.342 & 1.15 \\ [PERSON] et al., model [39] & 0.314 & 0.414 & 2.05 & 0.249 & 0.351 & 1.89 \\ [PERSON] et al., model [35] & 0.412 & 0.356 & 1.89 & 0.315 & 0.364 & 2.03 \\ [PERSON] et al., model [37] & 0.318 & 0.348 & 2.12 & 0.247 & 0.402 & 1.76 \\ [PERSON] et al., model [38] & 0.298 & 0.342 & 1.57 & 0.265 & 0.371 & 1.85 \\ [PERSON] et al., model [40] & 0.285 & 0.324 & 1.32 & 0.253 & 0.357 & 1.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Findings of loss function analysis.
# A Numerical Study of the Vibration Characteristics of a Haptic Actuator for a Dial Gear Shifter
## 4 Conclusions  
This study investigated the design optimization of a dial gear shifter using a haptic actuator. The numerical model of the haptic actuator exhibited a reliable agreement (within 1% of the resonance frequency and vibration level) with the actual model. Following the validation of the actuator model performance, we conducted an FEA to ensure that the product could provide a sufficient level of haptic feeling to users. Because the baseline model was not able to generate sufficiently high acceleration at the hand grip, we performed a design optimization process. Four design parameters were considered, and the optimal design of the dial gear shifter was determined via a sensitivity analysis such that it exhibited a relatively high level of vibration (\(\gtrsim\)25%). It was noticeable that an enhancement of over 27% in the vibration level could be achieved with a marginal change of the design parameters. It is thus meaningful to determine the optimal design out of the nine design candidates using numerical analyses without fabricating an actual product. In future work, we will demonstrate the performance of haptic actuation in a dial gear shifter using a real prototype and human assessments.  
Conceptualization, H.K.; Methodology, H.E. and C.K.; Software, J.C.; Formal analysis, J.W. and J.C.; Investigation, H.K; Resources, K.K. and H.K.; Data curation, J.W., H.E. and C.K.; Writing--original draft, J.W.; Writing--review & editing, K.K. and H.K.; Project administration, H.K.; Funding acquisition, K.K. and H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program-the Technology Innovation Program) (20026372, Smart safety-based SCU and e-Shift module system integration technology and demonstration technology development) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). This work was supported in part by an INHA UNIVERSITY research grant (No. 71354-1).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data is not publicly available due to privacy.  
The authors would like to acknowledge [PERSON] at Sanil Co., Ltd. (Incheon, S. Korea) for providing test samples of the haptic actuators and sharing the information on the frequency response of the actuator.  
Authors [PERSON] and [PERSON] were employed by the company LS Automotive Technologies Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.