esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Forecasting
The parameters were selected by testing different architectures and choosing the most promising. The models were compared of an FFNN and an RNN of 128 hidden nodes. The results show that CNN performed similarly to LSTM and better than MLP with an MAE of 114.38. An interesting approach mixing Big Data and Deep Learning is found in [108]. This method was used to next-day-ahead forecast in 30 min intervals. It used a multistep methodology that decomposes the forecasting problem in different subproblems. For the Big Data, Spark Apache was used. The neural Network parameters were searched using the grid search strategy. The best structure was found with 3 hidden layers with between 12 and 32 neurons. The method demonstrated that DL is suitable for big solar data since it has a linear increase in training time and performs better than other methods. The work of [109] makes use of a new kind of Neural Network, the Dendritic Neuron Network [110], in order to forecast PV power. These kinds of neurons have 4 types of layers: synaptic layer, branch layer, membrane layer and cell-body layer. The input data (temperature and irradiance of the actual moment and the last) are transferred to the synaptic layers where they are converted by the sigmoid function and summarized to the branch layer. The results are transported to the cell-body layer for numerical judgment. This layer will transmit the data thought the axon to other neurons when the data exceed a given threshold. This new kind of network provides higher convergence speed and enhanced fitting ability. The network is also aided by WT. The results show that the model outperformed typical Feed-Forward models with an average MAPE of 10.9, with strong fluctuations and 4.55 on weak fluctuations. The experiments were run using MATLAB. In Table 5, a summary of the reviewed models is presented. \begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & **Features** \\ \hline \multirow{7}{*}{Feed-Forward Neural Network} & Nine inputs, 20 hidden nodes on a single layer. [77] \\  & Nine inputs, 2 hidden layers with 6 and 4 nodes, respectively. Trained by a hybrid PSO GA algorithm. [78] \\  & Two inputs, creates ensembles of neural networks. [80] \\  & Two inputs, 1 hidden layer, Conjugate Gradient as learning rule. [81] \\  & Three neural networks, one for each kind of weather. Uses Extreme Learning to optimize the parameters and architecture. [84] \\  & Fuzzy Logic is applied as a filter to the input data. Seven inputs, 2 hidden layers of 9 and 5 nodes, respectively. Trained by a hybrid of PSO and GA. [105] \\  & Uses Big Data. Multistep methodology \\  & decomposes the problems into subproblems. [108] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Models for forecasting.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 2 Propulsion System Design Methodology
### Propeller Positioning and Sizing
#### 2.2.2 Positioning and Sizing  
The second step in the design process is to select the placement of the propellers and assess the available space for the propellers. The outermost propellers of the selected eVTOL configuration are wingtip-mounted: first, this allows one to maximise the radius of the propellers and thus the area for a given wingspan, which results in a lower disk loading; secondly, having outboard rotating propellers at the tips of the wings can help counteract the wingtip vortices due to the propeller-induced swirl, hence reducing the induced drag of the wing [13]. Figure 5 shows a schematic diagram of the positioning of the propellers and of the geometrical parameters that define the sizing and positioning procedure.  
The leftmost vertical dashed line in Figure 5 is the symmetry plane of the aircraft halfway through the fuselage. The distance to the wingtip from this line is the half-span, \(b/2\). Part of this length is taken by half of the fuselage width, \(w_{\mathit{fiss}}/2\). To prevent problems with wing positioning and rotation, it was decided to define the required fuselage-propeller clearance, \(c_{\mathit{fp}}\), with respect to the maximum width of the fuselage, and not the local width. This ensures that, even when the wings are in vertical position, there is sufficient clearance between the inner propellers and the fuselage, without the need for having a detailed design of the fuselage shape at the position of the propellers. The distance between propeller tips is defined by \(c_{\mathit{pp}}\), the propeller-propeller clearance. These two clearances are inputs to the method which may be obtained from sizing constraints or estimated based on reference aircraft. The remaining width is taken by the two inboard propellers and half of the tip propeller. This is generalised in Figure 5 for an arbitrary number of propellers, where \(N_{\mathit{prop}}\) is the number of propellers per half wing:  
\[R=\frac{\frac{b}{2}-\frac{w_{\mathit{fiss}}}{2}-c_{\mathit{fp}}-(N_{\mathit{ prop}}-1)c_{\mathit{pp}}}{2N_{\mathit{prop}}-1} \tag{6}\]
# Development of Path Planning Tool for Unmanned System Considering Energy Consumption
## 7 Conclusions  
In this work, the EPP algorithm is proposed and its performance is compared to the generic SPP algorithm using EKF-filter-based SOC estimation. Using the proposed path planning tool, two different path plans can be obtained and compared in advance. According to the experimental results, the EPP algorithm results in an energy-efficient path.  
Regarding the SOC, 0.96% is saved when using the EEPP algorithm, and this implies that the fuel economy is improved. Regarding the SOP, 11.03 (W) is lowered, and this implies that the internal shock of the battery pack is reduced. Although the rate of improved fuel economy and internal shock of the battery pack is not as good in this study, these two key factors can be enhanced in mission planning with longer trajectories. Here, the difference between simulation and experiment results appears to be caused by factors such as environmental effects and a less mature quadrotor simulation model.  
In the future, instead of 2D path planning, 3D path planning with an enhanced EEPP algorithm are planned to greatly improve the fuel economy of the UAV. Also, rather than the offline method presented here, the development of online real-time energy-efficient path planning based on the probabilistic method is planned.  
This work was supported by the JeonNam TechnoPark (JNTP) through the project (B0080802000131).  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 18: Reference, simulation, and experiment flight trajectories of: (**a**) SPP algorithm (2D), (**b**) EEPP algorithm (2D), (**c**) SPP algorithm (3D), and (**d**) EEPP algorithm (3D).
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 6 Conclusions  
In this study, we propose a multimodal fall detection system that leverages advanced DL techniques to improve accuracy and robustness. By combining the Graph-based Spatial-Temporal Convolution and Attention Network (GSTCAN) for skeleton and motion data and then the Bi-LSTM-CA integration for sensor data, we enhance the system's ability to capture both spatial and temporal features. For the sensor stream, we compared Bi-LSTM  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Author** & **Data Modality** & **Method Name** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** \\ \hline [PERSON] [74] & Keypoints & - & 97.33 & 97.78 & 97.78 \\ \hline Our Proposed & Sensor + & Two-Stream & 99.32 & 99.23 & 99.19 \\ System & Skeleton & DNN & & & \\ \hline \end{tabular}
\end{table}
Table 7: _Cont._CA and its integration, ultimately selecting Bi-LSTM-CA due to its superior performance in capturing long-range dependencies. The GSTCAN model extracts skeleton data using AlphaPose, calculates motion between consecutive frames, builds a graph, applies Graph Convolutional Networks (GCNs), and integrates a CA mechanism to highlight important features. The features from both the skeleton and sensor streams are fused, enabling more accurate and comprehensive fall detection. Experiments on the UP-Fall dataset showed that the multimodal system outperformed individual models, demonstrating that combining both modalities leads to improved fall detection accuracy. Future work will focus on enhancing the skeleton data stream through data augmentation, which will further improve the multimodal system's performance. This will enable more stable and accurate fall detection, even with limited sensor data, ultimately improving safety and reducing the physical and psychological burden on the elderly.  
Conceptualization, R.E., K.H. and A.S.M.M.; methodology, R.E., K.H., A.S.M.M., Y.T. and J.S.; investigation, R.E., A.S.M.M. and J.S.; data curation, R.E., A.S.M.M., Y.T. and J.S.; writing--original draft preparation, R.E., A.S.M.M., N.H. and J.S.; writing--review and editing, A.S.M.M., Y.T. and J.S.; visualization, R.E., A.S.M.M. and N.H.; supervision, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant JP23H03477.  
UP-Fall Dataset: [[http://sites.google.com/up.edu.mx/har-up/](http://sites.google.com/up.edu.mx/har-up/)]([http://sites.google.com/up.edu.mx/har-up/](http://sites.google.com/up.edu.mx/har-up/)) (accessed on 9 April 2025), UR Fall Dataset: [[https://fenix.ur.edu.pl/mkepski/ds/uf.html](https://fenix.ur.edu.pl/mkepski/ds/uf.html)]([https://fenix.ur.edu.pl/mkepski/ds/uf.html](https://fenix.ur.edu.pl/mkepski/ds/uf.html)) (accessed on 9 April 2025).  
The authors declare no conflicts of interest.
###### Abstract  
Fractures and voids are widely distributed in slope rock masses. These defects promote crack initiation and propagation, ultimately leading to rock mass failure. Investigating their damage evolution mechanisms and strength characteristics is of significant importance for slope hazard prevention. A numerical simulation study of Brazilian splitting tests on disk samples containing prefabricated holes and fractures was conducted using the Finite Element Method with Cohesive Zone Modeling (FEM-CZM) in ABAQUS by embedding zero-thickness cohesive elements within the finite element model. This 2021 study analyzed the effects of fracture angle and length on tensile strength and crack propagation characteristics. The results revealed that when the fracture angle is small, cracks initiate near the fracture and propagate and intersect radially as the load increases, ultimately leading to specimen failure, with the crack coalescence pattern exhibiting local closure. As the fracture angle increases, the initiation location of the crack shifts. With an increase in fracture length, the crack initiation position may transfer to other parts of the fracture or near the hole, and longer fractures may result in more complex coalescence patterns and local closure phenomena. During the tensile and stable failure stages, the load-displacement curves of samples with different fracture angles and lengths exhibit similar trends. However, the fracture angle has a notable impact on the curve during the shear failure stage, while the fracture length significantly affects the peak value of the curve. Furthermore, as displacement increases, the proportion of tensile failure undergoes a process of rapid decline, slow rise, and then rapid decline again before stabilizing, with the fracture angle having a significant influence on the proportion of tensile failure. Lastly, as the fracture angle and length increase, the number of damaged cohesive elements shows an upward trend. This study provides novel perspectives on the tensile behavior of fractured rock masses through the FEM-CZM approach, contributing to a fundamental understanding of the strength characteristics and crack initiation mechanism of rocks under tensile loading conditions.  
defective rock mass; Brazilian test; mechanical properties; FEM-CZM method +
Footnote †: journal: Applied sciences  
Article
## 5 MSC-EV and Wound Healing
### Remodeling  
In this stage, overexpression of collagen could produce the formation of large scars. To prevent this process, tissue maturation takes place, involving gradual replacement of collagen III with collagen I, as well as the formation of sweat glands and hair follicles [51]. Skin mechanical properties and reduction of scar formation can be enhanced by MSC-EV. They improve skin elasticity and barrier integrity, increasing some skin barrier proteins such as filaggrin and loricrin, as well as _AQP3_ gene expression [70]. Additionally, another study showed that MSC-EV increase collagen III/collagen I ratios, ameliorate MMP3 expression, via protein kinase/mitogen-activated protein kinase (ERK/MAPK) signaling pathway, and regulate fibroblasts differentiation, to help scarless wound healing [71].  
In the antifi fibrotic effect of MSC-EV application on wounds, an important role of different miRNA carried by these EV has been observed. Thus, MSC-EV could decrease collagen deposition, trans-differentiation of fibroblasts into myofiblasts, and formation of fibrosis scars. MSC-EV transfer miR-192-5p into wounds, and this represses pro-fibrotic _IL-17 RA/SMAD 2_ axis [72]. Other MSC-EV miRNA, such as miR-29c, miR192 and miR-215 have also been involved in this phase [57]. Additionally, MSC-EV can deliver miR-21, miR-23a, miR-125b and miR-145, which inhibit TGF-\(\beta\)/SMAD2 signaling, suppressing myofiblast formation [73] (Figure 2c).  
The ability of MSC-EV to act on different phases of the healing process, through various mechanisms, has made them important candidates for the development of therapies, to promote the healing of difficult-to-head wounds. However, when they are applied in vivo, topics such as clearance, maintenance and preservation of their viability and function must be considered [74]. Thus, currently available information on their pharmacokinetics will be reviewed in the following section. Then, how the use of different biomaterials can facilitate their application, stability and release, for skin-ulcer treatments.
## 5 PSI by Means of TSC: Results and Discussion
### Interferometric Processing Chain Considerations and CPT Overview
#### 5.1.3 The Coherent Pixels Technique
At this stage, low quality arcs are truncated, and those pixels which remain disconnected are removed. Another, the iteration of the minimization process is carried out with the surviving pixels. The process removes the pixels that do not fit the linear model, which may be caused by strong non-linear displacements in some particular cases. Finally, the absolute values of linear displacement for each pixel are obtained through an integration process, using one or multiple seeds with known behavior as tie points [40]. Finally, it must be pointed out that, regardless of the platform (orbital-, airborne- or ground-based), SAR systems only have sensitivity in the LOS direction. When facing landslide monitoring applications, the real displacement produced has an intrinsic topographic dependence, being related to the local slopes of the area under study. With no _a priori_ knowledge, the more realistic kinetic model is based on considering that the surface mostly moves along the steepest gradient of the terrain slope. This information may be obtained from a DEM of the illuminated area. For this reason, results must be down-slope projected prior to its interpretation. Once the displacement vectors are accordingly projected, they are geocoded in map coordinates and may be visualized by using Geographical Information System (GIS) software or a virtual globe viewer.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 2 Portable Sensor System for Registration, Processing and Analysis of PPG Signals
### Preprocessing and Mathematical Methods for PRV Analysis
#### 2.3.2 Module for Determining PP Intervals  
The detection of P peaks in the photoplethysmographic signal is performed while using a mathematical algorithm that is based on the first and second derivatives of the investigated signal [23] and performing additional checks to ensure that a signal peak is detected. The P peaks detection algorithm is programmatically implemented in the web based system, where the received input data for mathematical processing and analysis are sent.  
After determining the consecutive P peaks in the test signal, the time series of consecutive PP intervals are formed--Pulse Rate Variability. A series of adjacent normal-to-normal cardiac cycles are formed (NN) after the successive PP intervals have been determined, which only contains the vertices that are caused by the operation of the sinus node of the heart. Mathematical analysis is performed by calculating the parameters in the time domain, in the frequency domain, and nonlinear PPG based analyzes are performed, based on this normal-to-normal sequence.  
The received input signal is further interpolated while using a cubic splines interpolation procedure that increases the resolution of the samples up to 1 KHz.
## 7 Networks
### Synchronization
#### 7.1.4 Fuzzy Synchronization  
Another kind of synchronization involves using a fuzzy model-based adaptive approach to synchronize two different discrete-time chaotic systems. In [137], Vasegh and Majd considered a Takagi-Sugeno (TS) fuzzy model (see Section 4.1.1) employed to represent a chaotic drive and response system. As the parameters of the drive system are assumed to be unknown, an adaptive law was derived to estimate its unknown parameters. They proposed a control law to stabilize the error dynamics. This design procedure was illustrated using the two examples of the Lozi and Henon maps to demonstrate the effectiveness of the proposed methodology.  
They defined the Lozi fuzzy model as  
\[y(k+1)=-A|y(k)|+By(k-1)+C+Du(k) \tag{104}\]  
and chose two membership functions  
\[\mu_{1}y(k)=\frac{sign(y(k))}{d},\quad\mu_{2}y(k)=1-\frac{sign(y(k))}{d}, \tag{105}\]  
where \(d\) is a large enough constant to cover the range of the output. This model can be expressed as follows:  
* Rule 1: If \(y(k)\) is \(\mu_{1}\), then \(y(k+1)=a_{11}y(k)+a_{12}y(k-1)+b_{1}u(k)+c_{1}\xi(k)\)
* Rule 2: If \(y(k)\) is \(\mu_{2}\), then \(y(k+1)=a_{21}y(k)+a_{22}y(k-1)+b_{2}u(k)+c_{2}\xi(k)\)  
with the following parameters: \(a_{11}=-Ad\), \(a_{21}=0\), \(a_{12}=a_{22}=B\), \(b_{1}=b_{2}=D\), \(c_{1}=c_{2}=C\), and \(\xi(k)=1\).  
For two different chaotic systems where one is used as the drive system and the other as the response system, the task of synchronization is to force the trajectory of the response system to become identical to that of the drive system, where the response system receives the driving signals from the drive system. Vasegh and Majd looked into this problem for uncertain chaotic systems and proved several theorems. As an illustrative example, they considered the Henon fuzzy map as a drive system with the numerical values  
\[y(k+1)=-y(k)^{2}+0.3y(k-1)+1.4, \tag{106}\]  
which was modeled using fuzzy rules similar to the above rules used for the Lozi map, with the membership functions  
\[\mu_{1}y(k)=0.5\bigg{(}1-\frac{y(k)}{d}\bigg{)},\quad\mu_{2}y(k)=0.5\bigg{(}1+ \frac{y(k)}{d}\bigg{)}, \tag{107}\]  
and parameters \(a_{11}=-a_{12}=Ad\), \(a_{12}=a_{22}=B\), \(b_{1}=b_{2}=D\), \(c_{1}=c_{2}=C\), and \(\xi(k)=1\).  
The Lozi fuzzy map was used as the response system with the following fixed parameter values:  
\[x(k+1)=-1.8|x(k)|+0.4x(k-1)+1. \tag{108}\]  
The simulation results showed the effectiveness of the proposed method.
## 4 Performance Evaluation
### Reliability
#### 4.2.2 EPON-based Protection Schemes  
Redundancy is a major way to enhance the reliability of WPF communication network. There are two types of redundancy, device redundancy (OLT and ONU), and fiber redundancy (FF and DF). We assume that the failure rate of the OLT and ONU is small, compared with the number of failures in cables. Therefore, protection of both OLT and ONUs are neglected in our analysis. In feeder fiber protection architecture, only the FF is protected as shown in Figure 10. In this case, the feeder fiber is normally working, while the spare fiber is in standby. In the case of FF failure, the OLT is responsible for switching to the spare fiber. In a full protection architecture, all network components are fully duplicated.  
There are three different protection architectures; category (1) represents architectures with feeder fiber protection; one is working, while the other is in standby. In category (2), DFs are duplicated, and also secondary POS are changed to support the redundancy in the DF, as shown in Figure 11. In the last category (3), all architectures enjoy full protection, with all network components duplicated as shown in Figure 12.  
Figure 11: Reliability block diagram for (distributed fiber) DF protection architecture.  
Figure 10: Reliability block diagram for feeder fiber (FF) protection architecture.  
We calculated the unavailability of 20 different network architectures, using the component reliability data of Table 4 and Equations (6-8). Note that, the MTTR for the OLT is considered as only 4 h, as it is located in the control center. All other network components located on the WT side are considered to take 24 h to repair, in case of a fault.  
\[\mathrm{U}_{EPON(FF)}=\mathrm{U}_{OLT}+\left(\mathrm{U}_{FF}\right)^{2}+ \mathrm{U}_{POS}+\mathrm{U}_{DF}+\mathrm{U}_{ONU} \tag{6}\]  
\[\mathrm{U}_{EPON(DF)}=\mathrm{U}_{OLT}+\mathrm{U}_{FF}+\mathrm{U}_{POS1}+\left( \mathrm{U}_{POS2}+\mathrm{U}_{DF}\right)^{2}+\mathrm{U}_{ONU} \tag{7}\]  
\[\mathrm{U}_{EPON(DF)}=\mathrm{U}_{OLT}+\left(\mathrm{U}_{FF}+\mathrm{U}_{POS1}+ \mathrm{U}_{POS2}+\mathrm{U}_{DF}\right)^{2}+\mathrm{U}_{ONU} \tag{8}\]  
Figure 13 shows the expected downtime for different network architectures. In the case of an unprotected network, architecture (A) has the lowest MDT of about 163 min/year, while architecture (E) has the highest MDT, of about 300 min/year, as shown in Figure 13. In the case of full protection, all three architectures have the same MDT, of about 39 min/year. As we can see, conventional switched-based architecture has the highest downtime of about 320 min/year for an unprotected network configuration, while about 205 min/year downtime for full protection configuration.