esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Consumption of Type A Nodes  
Node type A had a standby consumption of 1.1 mA. Figure 5 shows the current required in each of the states through which the node passes in operating mode (i). The measured current and the duration of each of these states are shown in Table 3. With these values, taking into account the standby current, the total consumption of the mode has been calculated with Equation (1).  
\[\overline{I}_{A\_total\_i}=\frac{\sum_{A\_state=4,3}^{A\_state=4,3}\left( \overline{I}_{A\_state}-\overline{I}_{A\_standby}\right)\cdot t\_state}{24\cdot 60 \cdot 60}=0.084\text{ mA} \tag{1}\]  
Figures 6 and 7, Tables 4 and 5, and Equations (2) and (3) show similar calculations for the other two modes of operation of the node: modes (ii) and (iii). Note that mode (i) occurs once a day, i.e., every 86,400 s (24\(\cdot\)60\(\cdot\)60) and modes ii and iii every 30 min, i.e., every 1800 s (30 \(\times\) 60).  
Figure 5: Node A—mode (i): NTP sync, sensor reading and data sending (once every 24 h).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**State** & 1 & 2 & 3 & 2 & 4 & 5 & 4, 3 \\ \hline \multirow{2}{*}{**Description**} & Time synchronization & Task & Access to & Task & Data sending & & Sensors & Access to \\  & & & signaling & microSD & signaling & & reading & & microSD and \\ \hline \multirow{2}{*}{**I\({}_{\text{average}}\) (mA)**} & 61 & 4.6 & 21 & 7.1 & 74 & 282 & 75 \\ \hline \multirow{2}{*}{**Duration (s)**} & 13 & 6 & 0.5 & 1 & 10 & 16.5 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of functional states of node A—mode (i).  
Figure 6: Node A—mode (ii): Sensor reading with data sending (every 30 min).  
Figure 7: Node A—mode (iii): Sensor reading without data sending (every 30 min).  
\[\overline{I}_{A\_total\_iii}=\frac{\sum_{A\_state=2}^{A\_state=3}\left(\overline{I}_ {A\_state}-\overline{I}_{A\_standy}\right)\cdot f_{A\_state}}{30\cdot 60}=1.86\;\text{mA} \tag{3}\]  
Once the total consumption current of each of the operating modes has been determined, taking into account the standby current and the capacity of the battery installed in the nodes, it is possible to calculate (Equation (4)) the autonomy (in days) provided that the battery is not charged by any means.  
\[\text{Number of days}=\frac{3000\;\text{mA}\cdot\text{h}}{\left(\overline{I}_ {A\_total\_i}+\overline{I}_{A\_total\_ii}+\overline{I}_{A\_total\_iii}+ \overline{I}_{A\_standy}\right)\cdot 24\;\text{h}}=22.38\;\text{days} \tag{4}\]
## 3 Simulation Result Analysis  
### Accuracy Validation  
To validate the correctness of the proposed model in this study, field measurements of the maximum frost depth in the soil layer above the pipeline were conducted during the first freeze-thaw cycle, and the results were compared with numerical simulations, as shown in Figure 6. A slight discrepancy between the simulated and measured data is observed, which arises from the simplified temperature load and some assumptions applied in the simulation--an approximate curve fitted based on annual average air temperature--deviating from actual temperature variations. However, the error between the simulated and measured data remains within 20%, and the trends of the two curves are generally consistent. Therefore, the methodology presented in this study is deemed applicable for practical engineering analyses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **d (m)** & **R (m)** & **S0** & \
\begin{tabular}{c} \(C_{\text{s2}}\) \\ (k/kg.\({}^{\circ}\)C) \\ \end{tabular} &
\begin{tabular}{c} \(\lambda_{2}\) \\ (W/(m.\({}^{\circ}\)C)) \\ \end{tabular} & \(k_{\text{s2}}\) (m/s) \\ \hline Case 1 & 1.0 & 0.4 & 0.2 & 0.42 & 0.5625 & \(1\times 10^{-8}\) \\ \hline Case 2 & 1.5 & 0.5 & 0.4 & 1.125 & 1.125 & \(5\times 10^{-8}\) \\ \hline Case 3 & 2.0 & 0.6 & 0.6 & 1.68 & 2.25 & \(1\times 10^{-7}\) \\ \hline \hline \end{tabular}  
* Nomenclature: d— burial depth of pipeline; R—pipeline radius; S0—initial saturation; Cs2—heat capacity of layer2; \(\lambda_{2}\)—thermal conductivity of layer2; \(k_{\text{d2}}\)—permeability coefficient of layer2.  
\end{table}
Table 2: Variation of parameter values.  
Figure 6: Temporal variation of the maximum frost depth.
# Structure-Aware Trail Bundling for Large DTI Datasets
## 3 Method
### Seeding and Tracing DTI Trails  
To start with, we need to construct the actual 3D trails representing streamlines in the major DTI eigenvector \(\mathbf{e}_{1}\). The standard method to seed such streamlines is to look at regions \(M\subset V\) of so-called _high anisotropy_, i.e., points \(\mathbf{x}\in V\) for which the diffusion is far stronger in one direction than in all others. \(M\) can be computed by finding high-anisotropy voxels in \(V\), i.e., points where the eigenvalues \(\lambda_{1},\ldots,\lambda_{3}\) strongly differ [46]; alternatively, \(M\) is readily provided by so-called _masks_, suitably segmented from \(V\) by medical professionals based on their knowledge of fiber-rich versions [47]. The masks \(M\) we use in all our work are of the latter type, and they are provided with the public DTI datasets we use in our experiments [48; 49; 50]. Figure 1 shows such a mask for one of the datasets used in our work.  
We use such regions \(M\) for two purposes. First, we densely seed \(M\) to _start_ tracing streamlines in the vector field \(\mathbf{e}_{1}\) from points inside it. Secondly, we _stop_ tracing such streamlines when they exit \(M\), therefore avoiding that they enter low-anisotropy regions. Given a dense seed-set of points \(S\subset M\), we trace a streamline \(\mathbf{t}_{i}\) in the vector field \(\mathbf{e}_{1}\) for each seed point \(\mathbf{x}_{i}\in S\) until \(\mathbf{t}_{i}\) exists \(M\). The set of streamlines \(T=\{\mathbf{t}_{i}\}\) represents our dense 3D tract set we aim next to simplify (Section 3.2) and render (Section 3.3).
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 4 Optimizations of the Coil Position Parameters  
In this section, we optimize the distance (\(d_{13}\)) between the two external coils to improve the SIR. We select proper skew distance \(d_{12}\) of the implant coil to avoid the substantial decrease of \(k_{23}\) when the coil separation (\(d\)) is small. In addition, we optimize the turn number (\(N_{3}\)) of the external data coil to further improve the SIR. In order to guide the optimizations of the coil parameters, the formulas of mutual inductance, self-inductance and coupling coefficient are derived in Appendix A.
## 3 Discussion
Studies have demonstrated that _LcDXS3_ and _LcTPS42_ overexpression significantly enhances monoterpene accumulation in _L. cubeba_[11], suggesting that light regulates monoterpene biosynthesis in _L. cubeba_ through a dual mechanism--the provision of biosynthetic precursors/cofactors and transcriptional control via light-responsive promoter elements, which is consistent with previous research [39]. In conclusion, this study provides a reference for the effect of light on terpene synthesis and the production of essential oil in _L. cubeba_. Darkness treatment significantly reduced the content of monoterpenes in _L. cubeba_ fruits, as well as the expression levels of genes in the terpene synthetic pathway, including _LcACOT1_, _LcHMGS1_, _LcDXR_, _LcDXS3_, and _LcMDC_. The expression of TPS-encoding genes, particularly _LcTPS1_, _LcTPS9_, _LcTPS19_, and _LcTPS22_ was also significantly downregulated following darkness treatment. This work elucidated the transcriptional mechanisms underlying terpenoid biosynthesis in _L. cubeba_, and the identified DEGs provide valuable targets for enhancing the production of essential oil in this species through metabolic engineering.
# A Comparative Analysis of 3D Software for Modeling Fatigue Crack Growth: A Review
## 2 Software
### Analytical-Based Approaches
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Software** & **Application** \\ \hline
1. & ABAQUS & \begin{tabular}{c} Automotive industry: crashworthiness analysis, vehicle dynamics, railway axle, railway wheel, and structural integrity [115; 116; 117; 118]. \\ \end{tabular} \\ \cline{2-4}  & \begin{tabular}{c} Aerospace industry: aircraft structural analysis, composite material analysis, and fatigue life prediction [119; 120; 121]. \\ \end{tabular} \\ \cline{2-4}  & \
\begin{tabular}{c} Civil engineering: structural analysis of buildings, bridges, and dams [122; 123; 124]. \\ \end{tabular} \\ \cline{2-4}  &
\begin{tabular}{c} Biomechanics: analysis of orthopedic implants and human body mechanics [125; 126; 127]. \\ \end{tabular} \\ \hline
2. & FRANC3D & \begin{tabular}{c} Aerospace industry: evaluation of fatigue life and crack propagation in aircraft components [128; 129]. \\ \end{tabular} \\ \cline{2-4}  & \
\begin{tabular}{c} Power generation: assessment of crack growth in turbine blades and power plant components [130; 129; 131]. \\ \end{tabular} \\ \cline{2-4}  &
\begin{tabular}{c} Structural engineering: analysis of cracks in bridges, pipelines, and offshore structures [132; 133]. \\ \end{tabular} \\ \hline
3. & ZENCRACK & \begin{tabular}{c} Aerospace industry: assessing fatigue crack growth in aircraft components [134; 135]. \\ \end{tabular} \\ \cline{2-4}  & \begin{tabular}{c} Automotive industry: predicting crack propagation and estimating remaining fatigue life in automotive parts and railway axles [136]. \\ \end{tabular} \\ \cline{2-4}  & \
\begin{tabular}{c} Power generation: evaluating fatigue crack growth in turbine blades and power plant structures [137; 138] \\ \end{tabular} \\ \cline{2-4}  &
\begin{tabular}{c} Structural engineering: analyzing fatigue crack growth in bridges, offshore structures, and pipelines [139]. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Some real-life applications of the specified software.
# Design General Cam Profiles Based on Finite Element Method
## 8 Conclusions  
This paper presents a new and general computational formulation for the synthesis of cam mechanisms using the finite element method. The formulation is based on a general theoretical description of the cam curve in curvilinear coordinates. The governing equation for the synthesis of cam profile is proposed. The penalty-type functional is proposed for the weak form. The stabilization functional is further added for convexity of the total potential. The discretization of the weak form is derived in the framework of the finite element. The Gaussian quadrature is used to integrate the finite element computation. The cam profile is obtained by solving a nonlinear system of equations resulting from minimizing the total functional.  
The case study of the flat-face cam mechanism demonstrates that the formulation is able to handle all aspects accurately, robustly, and efficiently. The proposed framework can be applied for more general cam mechanisms, which is a subject of future work.  
Data curation, V.-S.N.; Formal analysis, T.X.D. and V.-S.N.; Investigation, T.T.N.N.; Methodology, T.T.N.N. and T.X.D.; Resources, T.X.D.; Writing--original draft, T.T.N.N.; Writing--review & editing, T.T.N.N. and T.X.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data are available from the authors upon request.  
Figure 6: Influence of parameters \(\epsilon\) and \(\mu\): (**a**) penalty parameter \(\epsilon\); (**b**) parameter \(\mu\).
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 2 Related Work
The last prototype was further evaluated in smartphone-based GPS navigation test by normally sighted users not wearing a blindfold. This short review shows that GPS-haptics navigation has been considered for assisting blind pedestrians for quite some time. However, most of the haptic user interfaces reviewed require constant hand interaction either for receiving the system's feedback or for carrying the device. This has two shortcomings: (1) users get quickly tired of hand-holding a device during walking and (2) busy hands limit the use of the primary aids. In this paper, we propose a novel, wearable user interface for the foot that addresses this issue by supporting hands-free interaction.
## 5 Conclusions  
The current research presents a BMI system based on MI and attention paradigms that has been tested to control a lower-limb exoskeleton. Participants performed 2-5 sessions to assess the effect of practice on the performance. Each session was divided into two parts: the training and test phases. First, participants completed trials in which they had to perform certain brain tasks and the exoskeleton was controlled remotely by the laptop with predefined commands. During half of the trials, the exoskeleton was walking, and during the other half, it was completely static. Therefore, contrary to previous works, brain tasks to discriminate happened under the same conditions. Moreover, this setup can reduce the effect of artifacts on the predictions. The average performance in the last session was 68.44 \(\pm\) 8.46% for the MI paradigm and 65.45 \(\pm\) 5.53% for the attention paradigm. The second part of the each session consisted of closed-loop controlled trials in which the exoskeleton was commanded by the predictions of the BMI. The BMI worked as a state machine that used different classifiers depending on whether the exoskeleton was static or moving. Training trials were used to train the classifiers corresponding to each state of the state machine. The BMI took a decision every 0.5 s and the average percentage of correct commands chosen was 64.50 \(\pm\) 10.66% for the last session of both subjects.  
Participants did not have any motor impairment, but since the main of objective of the system is to promote neurorehabilitation and neuroplasticity, future research will focus on people with motor disabilities.  
Conceptualization, L.F. and M.O.; methodology, L.F., V.Q. and M.O.; software, L.F. and V.Q.; validation, M.O. and E.I.; formal analysis, L.F. and V.Q.; investigation, L.F. and V.Q.; resources, M.O., E.I. and J.M.A.; data curation, E.I.; writing--original draft preparation, L.F.; writing--review and editing, L.F. and M.O.; visualization, L.F.; supervision, M.O., E.I. and J.M.A.; project administration, J.M.A.; funding acquisition, J.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science and Innovation, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund in the framework of the project Walk--Controlling lower-limb exoskeletons by means of brain-machine interfaces to assist people with walking disabilities (RTI2018-096677-B-100); and by the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana), and the European Social Fund in the framework of the project Desarrollo de nuevas interfaces cerebro-maquina para la rehabilitation de miembro inferior (GV/2019/009).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Miguel Hernandez University of Elche (DISJAP03.18 and 22/01/2019).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Polymer Materials
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Categories** & **Applications** \\ \hline Predictive modeling & Mechanical properties: Predicting tensile strength, elasticity, and impact resistance of polymers. \\ \hline Material design and discovery & Polymer synthesis: Discovering new polymer structures with desired properties. \\ \hline Process optimization & Polymerization processes: Optimizing conditions for polymer synthesis such as temperature, pressure, and catalysts. \\  & Extrusion and molding: Improving process parameters to enhance the quality and performance of polymer products. \\ \hline Failure analysis and prevention & Degradation and aging: Predicting the lifespan and degradation behavior of polymers under various environmental conditions. \\ \hline Surface engineering & Surface modification: Designing surface treatments to improve properties like hydrophobicity, adhesion, and biocompatibility. \\  & Coating applications: Developing advanced polymer coatings for protection and functionalization. \\ \hline Subfields in biomaterials & Tissue engineering scaffolds: Creating biodegradable polymer scaffolds for cell growth and tissue regeneration. Biodegradable polymers: Designing environmentally friendly polymers for medical implants and drug delivery systems. \\  & Smart polymers: Developing stimuli-responsive polymers for applications in drug delivery, tissue engineering, and diagnostics. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Application of AI and ML in the field of polymers. Figure 9: LSTM model for prediction of the thermal stability. The figure is reproduced from [68].
## 1 Introduction
We assume that the receiver is a battery-operated device and tries to minimize the amount of time with a radio interface operating in listening mode. As has been shown e.g., in [7], the radio receiving mode constitutes a big chunk of energy utilization in wireless sensor nodes due to the much longer listening time than transmission time. The receiver can calculate the expected time of data transmission but does not know the clock skew of the transmitter. The transmitter sends a pack on a pre-defined interval (e.g., once per day) and has the clock synchronized during the production process. After some time (e.g., a few months) the devices have been installed and start the transmission using a radio interface. We analyze the problem regarding how to (1) receive the first packet from the transmitter with a minimum amount of energy used and (2) select the listening time window for the consecutive packets to balance between the maximization of the probability of packet reception and the energy utilization. While the second problem is widely documented, the problem of finding a long inactive network node still has room for analysis. We propose the algorithms for listening window time selection and evaluate it using simulation. Our propositions solve an existing and important problem of reconnection of the device disconnected from the system for a longer period of time, and increase the efficiency of current methods implementing in the system in case of a follow-up clock synchronization and optimization of a listening window's size. The proposed algorithm using listening windows of growing size or shifted in time allows decreasing the amount of energy used for listening by between 10 and 30%, compared to a commonly used repetition of the same window size. The rest of the paper is organized as follows: Section 2 contains the literature review, Section 3 formulates the problem and describes the model, Section 4 shows the results of the performance evaluation of the proposed algorithm. We finish with a short conclusion.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 7 Discussion
Further experiments will be also carried out in the BROADBAND\({}^{\text{LIGHT}}\) testing polygon situated next to the Faculty of Electrical Engineering and Computer Science at the Technical University of Ostrava. It consists of a full-mesh network solution, covering 10 lamp posts, which altogether carry 18 LED public lighting sources from various manufacturers. Each lamp can be individually and remotely controlled from the lab. The setup is prepared for native deployment of VLC and serves as a real-life showroom of smart technologies. Another set of similar tests will be carried out on the Octavia III taillight. For this different light source, an approach previously used in an earlier article will be used. A special box used for the simulation of various natural phenomena will be used to simulate the ever-changing conditions of weather throughout the year. Figure 18: Proposed concept of the hybrid VLC-4G/5G network for vehicular data transmissions. The development of new custom parts is currently ongoing. Apart from the photodetector mentioned earlier, a new and less expensive bias tee was constructed. This new design should push manufacturing costs lower, while maintaining a certain level of quality. Moreover, it is much easier to replace damaged parts on site, as it is possible to avoid external manufacturing. A new custom light source with interchangeable LED matrices is also under development to test the various influences of LED quality on VLC [66].
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 5 Detailed Analysis of Predictability Performance and Model Validation
### Forecasting Model
#### 5.1.2 LSTM  
Long short-term memory (LSTM) is an extension of recurrent neural networks (RNNs). It addresses the short-term memory limitations of RNNs by incorporating three specialized gate structures and memory cells. These additions mitigate the vanishing gradient problem, allowing for distant information to have a greater impact on the current time step. The structure of an LSTM cell is illustrated in Figure 14.  
Figure 14: LSTM cell structure.  
Figure 12: Wind speed prediction process.  
Figure 13: TCN structure.  
Where \(x_{t}\) is the input at time \(t\), \(h_{t}\) is the hidden layer output at time \(t\), \(C_{t}\) is the memory unit at time \(t\), \(C_{t}\) is the temporary memory unit, \(\otimes\) represents element-by-element multiplication, \(\oplus\) represents element-by-element addition, the forget gate determines the information added to the current memory unit \(c_{t}\), and the output gate controls the output result of the memory unit \(C_{t}\). The calculation process is as follows:  
\[\left\{\begin{array}{l}f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f})\\ i_{t}=\sigma(W_{t}\cdot[h_{t-1},x_{t}]+b_{i})\\ c_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c})\\ C_{t}=f_{t}\otimes C_{t-1}\oplus i_{t}\otimes c_{t}\\ o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}=o_{t}\otimes\tanh C_{t}\end{array}\right., \tag{7}\]  
where \(W_{f}\), \(W_{i}\), \(W_{c}\), and \(W_{o}\) are the weight matrices corresponding to each module; \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are bias terms; \(\sigma\) is the sigmoid activation function; and \(\tanh\) is the hyperbolic tangent activation function.
## 3 Postulated Models Describing the Mechanism of Very High Cycle Crack Initiation and FGA-Formation
### Dispersive Decolhesion of Spherical Carbide Model  
[PERSON] et al. [10; 23; 115] postulated a mechanism based on carbide detachment in the immediate vicinity of non-metallic inclusions. Within the FGA, an increased carbon content around the non-metallic inclusion was determined using electron probe microanalysis (EPMA). While the investigation of fracture surfaces without the FGA shows a homogeneous carbon distribution with a lower carbon density. On the basis of the results, the increased carbon content is attributed to carbide particles remaining in the fracture surface. The formation of the FGA is described by the \"dispersive decohesion of spherical carbide\" model. Based on the examinations and computer simulation, this model assumed that individual micro cracks occur as a result of the cyclic stress caused by carbide detachment. These micro cracks grow separately and then merge into a long crack that can propagate during continuous cyclical stress. As a result, the crack runs along the detached carbides, creating the rough surface observed inside the FGA [23; 115].