esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 2 Overview of Nanomaterials in Crani Tissue Engineering
### Metallic Nanomaterials  
Metal-based nanomaterials are used for their high mechanical strength and include metals such as gold, silver, and titanium [8; 9]. In contrast to bulk metals, metallic nanoparticles, due to quantum effects, can promote osseointegration in addition to promoting many other factors, such as osteoconductivity, mineralization, proliferation, osteoblast and chondrocyte cell adhesion, increased mechanical strength, stimulation of collagen production, stimulation of alkaline phosphatase activity, and stimulation of calcium deposition [8; 9; 10]. For instance, [PERSON] and [PERSON] found that iron oxide nanoparticles with a hydroxyapatite coating could increase osteoblast functions [11]. Due to their antibacterial properties, metallic nanoparticles such as zinc and copper have been incorporated into scaffolds, while silver nanoparticles have been investigated as a replacement for certain dental filling agents [3; 12]. Unfortunately, metallic nanomaterials can accumulate in organs due to their small size and cause potential health effects [7]. These health effects have been hypothesized to be due to oxidative stress and inflammatory reactions [7].
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Attention Mechanism and Transformer  
An attention mechanism is a tool that imitates the human visual or cognitive system and is widely used in the field of deep learning. It was initially widely used in the field of NLP [33] and later in fields such as computer vision. It assigns different weights to each element in the input sequence according to their importance. This weight is dynamically calculated based on the relevance between each element and the target.  
The Transformer is a deep learning framework primarily used for tasks such as natural language processing [34]. Differing from the traditional CNN and RNN architectures, the  
Figure 8: Euclidean and non-Euclidean data structure diagrams.  
Transformer completely abandons these structures and is built on an attention mechanism. The model consists of self-attention and feed-forward neural networks, addressing the sequential computation limitations of RNNs, allowing for any two positions in the sequence to be treated equivalently and thereby overcoming the long-term dependency issue inherent in RNNs [34]. The self-attention mechanism in the Transformer architecture imposes exceptionally high computational costs. However, due to its reliance on matrix operations, the architecture is well suited for efficient parallel computation on GPUs, thereby exhibiting high scalability in large-scale training tasks. A diagram of the Transformer model is shown in Figure 9.  
[PERSON] et al. [35] utilized the multi-head attention mechanism and stacked layers to enable the Transformer to learn dynamic and hierarchical features in traffic data. They proposed the integration of global and local encoders to extract and fuse global and local spatial features, facilitating effective traffic flow prediction and providing a basis for traffic management strategies.  
[PERSON] et al. [36] introduced a novel spatiotemporal graph sandwich Transformer (STGST) for traffic flow prediction. In the STGST, two Transformers equipped with temporal encoding and one spatial Transformer with structural and spatial encoding are designed to model long-range temporal and deep spatial dependencies, respectively. By structuring  
Figure 9: Transformer model diagram.  
these two types of Transformers in a sandwich configuration, the model captures rich spatiotemporal interactions. Experimental studies demonstrate that the STGST outperforms state-of-the-art baseline models.
## 2 Topology Optimization Method
### Optimization Model Setup  
This work focuses on optimizing the core section of the ITPS as presented in Figure 3. Parameters defining the thickness of the ITPS panel, \(t_{T}\), \(t_{B}\) and h, are fixed during optimization. According to the analysis mentioned in the previous section, temperature of the TFS and BFS were fixed at 950 K and 450 K, respectively. In addition, aerodynamic pressure loads with a magnitude of 1 atm were imposed on the TFS. The solid isotropic material with penalization (SIMP) method was adopted to solve the problem. A pseudo-density \(\rho(x)\) was adapted to define the elastic and thermal properties:  
\[E_{SIMP}(x) =E_{0}\rho(x)^{2p} \tag{11}\] \[k_{SIMP}(x) =k_{0}\rho(x)^{p}\] (12) \[\rho_{SIMP}(x) =\rho_{0}\rho(x)^{p} \tag{13}\]  
\(E_{0}\), \(k_{0}\) and \(\rho_{0}\) are the initial elastic modulus, thermal conductivities and densities of materials, respectively. The penalty factor is chosen to be 3 in this problem.  
With the purpose of improving thermal and mechanical performance of the ITPS as far as possible, both equivalent conductivity and strain energy were considered in the objective function formulation. Two critical constraints were adopted in the preliminary design: (1) weight of the optimized design should not be heavier than the initial design to maintain the launch cost; and (2) maximum deflection of the top face sheet (TFS) should be limited to avoid severe local aerodynamic heating. The optimization problem can be denoted by:  
\[\left\{\begin{array}{l}\min:N\frac{W}{W_{\text{int}}}+(1-N)\frac{Q}{Q_{\text {min}}}\\ st:\frac{d_{T\text{max}}}{d_{T\text{max},\text{ini}}}\leq 1\\ s.t.:\frac{d_{T\text{max}}-d_{T\text{min},\text{int}}}{d_{T\text{max},\text{ini }}-d_{T\text{min},\text{int}}}\leq 1\\ st:\frac{V_{\text{max}}}{V_{\text{min}}}\leq 1\\ 0<\rho_{\text{min}}\leq\rho(x)\leq 1\end{array}\right. \tag{14}\]  
The subscript 'ini' represents the value of the reference design shown in Figure 1, T\({}^{\prime}\) represents the TFS, \(d_{T\text{max}}\) and \(d_{T\text{min}}\) are the maximum and minimum deflection of the TFS, respectively, \(V_{\text{in}}\) is the volume fraction of the webs, and \(\rho_{\text{min}}\) is the minimum density employed to avoid numerical singularities. N is the weight factor introduced to balance two objectives. It is assumed that thermal insulation and load-bearing performance are of the same importance for ITPS design, and N is set to 0.5 in this study.  
Figure 3: Design domain of ITPS.
## 2 Methodology
### Model Overview
#### 2.2.3 Parameters  
Parameters are non-varying constants whose values are fixed for simplicity or quick comparison of design architectures. For example, a total of 10 reconfiguration moves from GOM to ROM or vice versa are permitted over a 5-year lifetime, as shown in Table 4. The ground resolution of 0.5 m is also assumed which must be satisfied even when a satellite is tilted the farthest and its distance to the ground is the largest. The Walker phasing parameter of 1 means that the satellites in adjacent orbital planes are separated by \(360/N_{s}\times 1\) degrees where \(N\)s is the total number of satellites in a constellation [41]. The regional latitude of interest is set to be the \(55^{\text{th}}\) parallel of North, assuming a wildfire monitoring mission over Canada and Alaska [42; 43].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline _life_ & Orbit lifetime & 5 & Year \\ \(e\) & Orbit eccentricity & 0 & - \\ _walker\_phase_ & Walker phasing parameter & 1 & - \\ _inc_ & Orbit inclination & 60 & \({}^{\circ}\) \\ _n\_recons_ & \# of reconfigurations over lifetime & 10 & - \\ _gsd_ & Ground sample distance & 0.5 & m \\ _regional\_lat_ & Regional latitude of interest & 55 & \({}^{\circ}\) \\ _global\_lat\_band_ & Global latitude band of interest & [0; 60] & \({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used in ReCon simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variable** & **Description** & **Range** & **Unit** \\ \hline _nk_ & Repeat ground track (RGT) ratio (\(\tau\)) & [31/2, 15/1, 29/2, 14/1] & - \\ _delta\_alt_ & Walker altitude difference from RGT altitude & [ –50, 50] & km \\ _n\_planes_ & \# of planes in Walker constellation & [2, 3, 4, 5, 6, 7, 8, 9] & - \\ _n\_sats_ & \# of satellites per orbit plane & [1, 2, 3, 4, 5] & - \\ _regard_ & Field of regard & [5; 50] & \({}^{\circ}\) \\ _prop_ & Propulsion type & [cold gas, monoprop, biprop] 1 & - \\ \hline \hline \end{tabular} \({}^{1}\) monoproppellant and bpropellant.  
\end{table}
Table 2: Design variables in ReCon simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Internal Variable** & **Description** & **Unit** \\ \hline _aperture_ & Optical telescope aperture diameter & m \\ _fl_ & Optical telescope focal length & m \\ _prop\_dry\_mass_ & Propulsion system dry mass & kg \\ _propellant\_mass_ & Propellant mass & kg \\ _optics\_mass_ & Optical subsystem mass & m \\ _rgt\_alt_ & Repeating groundtrack altitude & km \\ _delta\_v_ & Total lifetime fuel burn & m/s \\ _sat\_dry\_mass_ & Satellite dry mass & kg \\ \hline \hline \end{tabular}
\end{table}
Table 3: Internal variables in ReCon simulation.
# Motion Control and External Force Estimation of a Pneumatically Driven Multi-DOF Robotic Forceps
## 3 Control Scheme
### Control System  
We implemented the abovementioned external force observer in the position controller of the forceps, as presented in Figure 5. The subscripts \"ref\" and \"mea\" indicate the reference and measured values of the corresponding parameters, respectively. This system constitutes a cascade controller including a minor control loop of the pneumatic force controller. Figure 6 shows the block diagram of the pneumatic force controller, where \(\mathbf{u}\) is the input voltage to the servo valve. Cylinder driving forces are determined based on the pressures \(P_{\text{a}}\) and \(P_{\text{b}}\) measured by pressure sensors (PSE510-R06, operating pressure range: 1 MPa, SMC Corp., Tokyo, Japan) to the two sides of the piston, and the cross-sectional areas \(A_{\text{a}}\) and \(A_{\text{b}}\) of the two sides of the piston. Table 2 lists the feedback gains in the controllers.  
reference angles of \(q_{1}\) and \(q_{2}\) were sinusoidal functions with an amplitude of 60\({}^{\circ}\) and frequencies of 0.4 Hz and 0.5 Hz, respectively. These motions cover the average rotational velocities for the commonly used forceps in laparoscopic surgeries. The results presented in Figure 7 reveal that the joint angles of the forceps can exactly follow the references. The maximum errors of the measured joint angles in comparison with the reference angles were both approximately 2\({}^{\circ}\), whereas the mean absolute errors of \(q_{1}\) and \(q_{2}\) were 0.87\({}^{\circ}\) and 0.59\({}^{\circ}\), respectively. Moreover, the performance of the position control of \(q_{2}\) indicates that the motion of the first joint did not interfere with that of the second joint. The interference reduction mechanism introduced in Section 2.2 was validated to work quite effectively.  
Next, we investigated the maximum resolution of the joint position control. In this experiment, the joint positions were controlled where the reference inputs were step functions whose values increase every second. According to the experimental results shown in Figure 8, the maximum positioning resolution was about 1\({}^{\circ}\) for both \(q_{1}\) and \(q_{2}\).
## 5 Reconstruction Methods
### GAN-Based Models  
The principle of GAN [70] is based on a framework known as adversarial training, which consists of two components: a generator designed to mimic the distribution of real data, and a discriminator tasked with distinguishing between generated and authentic data.  
MAD-GAN [45] employs LSTM-RNNs as both the generator and discriminator within a GAN framework to seize the temporal relationships between time series distributions, while also taking into account the complete dataset to uncover the underlying interactions among them. MAD-GAN using standard adversarial loss suffers from gradient explosion and mode collapse. Hence, TadGAN [46] combines wasserstein loss [71] and cycle consistency loss [72] to form the final minmax objective, the former aims to match the distribution of generated time series with the data distribution of the target domain, while the latter prevents conflicting outputs between the two generators. Additionally, MIM-GAN [47] introduces an exponential information metric into the loss function of GANs to avoid local optimal solution and model collapse. It considers a discriminative reconstruction score composed of discriminative and reconstruction loss. MIM-GAN employs LSTM-based generators and discriminators to capture temporal relationships within the data. Soft-DTW [73] is used as a differentiable alternative for the reconstruction loss, and the combination of reconstruction loss and the prior probability distribution of the latent space serves as the anomaly score. DCGANs [17] can adjust the generator to produce samples closer to real data by minimizing the Soft-DTW loss. DCGANs utilize CNNs in both the generator and discriminator networks, while performing parallel computation for the reconstruction of multiple points, effectively alleviating performance bottlenecks (i.e., Mode Collapse). In complex systems, the data generated by devices exhibit complex patterns and a scarcity of labeled data, making anomaly detection a significant challenge. DAEMON [48] employs two discriminators to adversarially train an autoencoder to learn the normal patterns of time series, using reconstruction errors to identify anomalies. Its robustness is ensured through regularization of hidden variables and reconstructed data.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 2 Related Work
In general, OOK-based modulations have been shown to be low-complexity techniques that work correctly with moderate data rates exclusively for short distances. Therefore, this type of OCC modulation can be used for specific IoT applications within the range of 5 m, supporting low data rates. Since polychromatic modulations take advantage of RGB channels for simultaneous transmissions, the data rate may be increased. Nevertheless, the achieved link distances were short, and the system's complexities were moderate for CSK. Nevertheless, the polychromatic version of other modulations showed better results in terms of data rate and should be considered. On the other hand, undersampled techniques provided the fastest transmissions rates with moderate-to-high complexity and medium-range distances. Modulations based on space-time coding reached longer distances with relatively low data rate, but presenting very high complexity. Finally, in all the cases, the modulation schemes that compared consecutive frames reached the most extended distances without a significant impact on complexity. Figure 1 summarizes the analyzed modulations schemes in terms of complexity and distance range.
###### Abstract
Hence, motivated by this, we adopted an RL-based solution to solve the challenge of ABS placement and user association to enable emergency communications. This paper is organized as follows. We summarize the related work in Section 2 of the paper. The detailed system model is presented in Section 3. We formulate the problem in Section 4 and propose the prioritized user association scheme in Section 5. Simulation results are discussed in Section 6 and finally the paper is concluded in Section 7.
## 3 Results  
The ADDA dealiasing scheme described in Section 2 was applied to two typhoon cases, two hurricane cases and three deep convection cases. These seven selected cases were obtained from different radar systems with S- or C-band wavelengths from different radar networks. The typhoon and hurricane cases were collected by three S-band radar systems from China's CINRAD network and the U.S. NEXRAD network. The data of the three deep convection cases were observed using an S-band radar and two C-band radar systems from China's CINRAD network. The radius of the radar radial velocity observation area for an S-band radar station was 230 km and for a C-band radar station was 200 km. The Nyquist radial velocity changed from 22 m s\({}^{-1}\) to 32 m s\({}^{-1}\) for the S-band radar station and from 13 m s\({}^{-1}\) to 15 m s\({}^{-1}\) for the C-band radar, depending on the different PRF from the different PRF from different radars. The ADDA's successive dealiasing covered a stage of more than twelve hours in each of the selected cases. These seven selected cases had 849 volumes and 7463 tilts in total. The total volume and total number of tilts of the seven selected periods are shown in Table 1. The detailed dealiasing results of several cases are presented below and the statistical assessment will be shown in the next section.
## 3 Numerical Analysis Results and Discussion under Static Loading
### Lateral Displacements at Wall Facing
#### 3.1.1 Effects of Wall Facing Type and Construction on Lateral Displacements at Wall Facing
This may be because the wall facing of the IB is 0.90 m thick and has a very high stiffness. It can be implied that the initial movement of the IB wall facing and footing at the start of the construction may have greatly affected the _dx_ behavior showed by the IB models. Hence, the _dx_ at the wall facing in the IB (Case 3A-C) models after the bridge construction and load application is very minimal, with a maximum range of 0.03-0.04% (_dx/H_) near the base of the wall facing. Nevertheless, the _dx_ at the wall facing in the GRS-IB (Case 1A-C), GRS-IBS (Case 2A-C), and IB (Case 3A-C) models are less than the maximum allowable lateral displacement of 1% (_dx/H_). The results inferred that the lateral displacement behavior at the wall facing depends on the type of wall facing system, wall construction method, backfill placement method, and reinforcement-wall facing connection details. In this study, the GRS-IB models performed best compared to the GRS-IBS and IB models.
## 3 System Design and Platform Subsystems
### Flight Software  
The satellite flight operating software consists of several layers, as shown in Figure 11. On the very top of the hierarchy is the Mission Software, which executes MeznSats mission-specific. FreeRTOS [19] has been selected as the main operating system for the mission due to its lightweight, real-time performance and ease of use. The Hardware Abstraction Layer including (HAL) include all of the hardware-specific drivers and also includes a fail-safe FAT32 file system.  
Figure 10: The solar cells distribution around the satellite structure: (**a**) +Z-axis; (**b**) +Y-axis; (**c**) \(-\)Z-axis; (**d**) \(-\)Y-axis; (**e**) +X-axis; (**f**) \(-\)X-axis.  
The software tasks in MeznSat have been categorized into four different classes:  
1. Tasks that run periodically: Include tasks that need to collect data from subsystems every number of minutes. These tasks include the Housekeeping collection task and the beacon transmission task
2. Tasks that run based on a temporal value: Include tasks that need to run at a specific time. Temporal tasks include the ground contact task and the payload operation task.
3. Tasks that run indefinitely: Tasks that are chosen to enter the running state because their delay is over, and they are the highest priority task within the ready pool of tasks. These tasks include the ADCS task and the normal mode task.
4. Tasks that wait for events to happen: include tasks that are waiting on a semaphore or for data to be available on a queue. These tasks include the file management task, the command execution task, the safe mode task, and the initial checksum task.  
The overall architecture of MeznSat software is shown in Figure 12.  
Figure 11: Flight software layers.  
Figure 12: MeznSat software block diagram.
## 3 Results and Discussion
### Characterization of nMt Powder (nC4)
* Between 200 and 500 \({}^{\circ}\)C the mass loss is associated with the decomposition of organic elements (modifiers and surfactants) attached to the MMT platelets usually exhibiting two peaks due to different structural arrangements. This is the area in which the main differences between the OMMT's are identified and, hence, the powder OMMT and the dispersed OMMT of this study are distinctively different, even though similar organic salts were used for the separation of the platelets. * Between 550 and 800 \({}^{\circ}\)C, the mass loss is associated with the loss of structural water from the MMT, known as dihydroxylation. Again, this is the area in which mass changes are expected and observed for the inorganic MMT. The first observation derived from Figure 6 is that the organomodification has taken place for both nC2 and nC4, which became hydrophobic. Because of this, the amount of adsorbed interlayer water is limited and; therefore, the related mass loss is limited too, as can be seen for the temperatures below 200 \({}^{\circ}\)C [47]. Figure 5: Comparison of the Si/Al ratios of the organomodified nanomontmorillonite (MMT) dispersion (nC2), the inorganic MMT dispersion (nC3) [19], and organomodified MMT powder (nC4). In greater detail, as far as nC4 was concerned, the TG analysis showed four endothermic peaks (Figure 6 and Table 4):  
* In the temperature range of 30-130 \({}^{\circ}\)C, with a distinct peak at 67 \({}^{\circ}\)C, attributed to the loss of surface and interlayer water. * In the temperature range of 200-330 \({}^{\circ}\)C, with a distinct peak at 295 \({}^{\circ}\)C, the decomposition of modifier bound to neighboring molecules took place. * In the temperature range of 330-530 \({}^{\circ}\)C, with a distinct peak at 396 \({}^{\circ}\)C, the deconstruction of the modifier bound to the bentonite was completed. * In the temperature range of 570-700 \({}^{\circ}\)C, with a distinct peak at 600 \({}^{\circ}\)C, the remaining OMMT was decomposed.