esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Material Platforms for Integrated Photonics
**Bottom**: Band alignment of the black phosphorous/molybdenum ditelluride PN heterojunction in the thermal equilibrium state [141], **(c) Top**: Optical microscope image of the manufactured device, showing the black phosphorous/molybdenum ditelluride heterojunction integrated on one arm of a MZI. **Bottom**: Close-up view of the device highlighted by the black square box in the top panel [141]. Apart from conventional material platforms, the plasmonic platform stands at the forefront of integrated photonics, leveraging the distinct characteristics of surface plasmons to manipulate light at the nanoscale [142; 143; 144; 145]. Through the confinement of light within subwavelength dimensions, plasmonic structures grant unprecedented control over interactions between light and matter, opening pathways for diverse applications in sensing, imaging, and telecommunications [4]. Serving as foundational elements, plasmonic WGs, resonators, and antennas enable the creation of compact photonic circuits capable of efficiently routing and processing optical signals with remarkable speed and efficacy [146]. Also, the capacity to engineer plasmonic materials and structures offers refined tunability, facilitating dynamic adjustments of light properties to accommodate on-demand functionalities. Despite its immense potential, the plasmonic platform for integrated photonics also encounters certain limitations that warrant consideration [147]. One primary challenge lies in the inherent ohmic losses associated with plasmonic materials and structures, stemming from absorption and scattering mechanisms, which can significantly degrade device performance, particularly over longer propagation distances [148]. Likewise, the intricate fabrication processes required for achieving precise control over plasmonic features can be complex and costly, hindering scalability and widespread adoption.
# Tensor Completion Based on Triple Tubal Nuclear Norm
## 3 The Triple Tubal Nuclear Norm
### A Circular Interpretation of TNN and t-TNN  
In this subsection, an illustration of TNN and t-TNN in a circular way [16], which motivates the proposal of TriTNN, will be given. For a tensor \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\), we define the operation called circulant block matricization of \(\mathbf{\mathcal{T}}\)[41] in the following equation:  
\[\text{circ}(\mathbf{\mathcal{T}}):=\begin{bmatrix}\text{circ}(\mathbf{\mathcal{T}}(1, 1,:))&\text{circ}(\mathbf{\mathcal{T}}(1,2,:))&\cdots&\text{circ}(\mathbf{\mathcal{T }}(1,n_{2},:))\\ \text{circ}(\mathbf{\mathcal{T}}(2,1,:))&\text{circ}(\mathbf{\mathcal{T}}(2,2,:))& \cdots&\text{circ}(\mathbf{\mathcal{T}}(2,n_{2},:))\\ \vdots&\vdots&\ddots&\vdots\\ \text{circ}(\mathbf{\mathcal{T}}(n_{1},1,:))&\text{circ}(\mathbf{\mathcal{T}}(n_{1},2, :))&\cdots&\text{circ}(\mathbf{\mathcal{T}}(n_{1},n_{2},:))\\ \end{bmatrix}\in\mathbb{R}^{n_{1}n_{3}\times n_{2}n_{3}}, \tag{11}\]  
where \(\text{circ}(\mathbf{x})\) denotes an \(n_{3}\times n_{3}\) matrix with \(\mathbf{x}\in\mathbb{R}^{n_{3}}\) in the following way:  
\[\text{circ}(\mathbf{x}):=\begin{bmatrix}\text{circ}(\mathbf{x}_{1})&\text{circ}(\mathbf{ x}_{n_{3}})&\cdots&\text{circ}(\mathbf{x}_{2})\\ \text{circ}(\mathbf{x}_{2})&\text{circ}(\mathbf{x}_{1})&\cdots&\text{circ}(\mathbf{x}_{3} )\\ \vdots&\vdots&\ddots&\vdots\\ \text{circ}(\mathbf{x}_{n_{3}})&\text{circ}(\mathbf{x}_{n_{3}-1})&\cdots&\text{circ}( \mathbf{x}_{1})\\ \end{bmatrix}\in\mathbb{R}^{n_{3}\times n_{3}}. \tag{12}\]  
By the permutation operation, there exist two so-called stride permutation matrices [42]\(\mathbf{P}_{1}\) and \(\mathbf{P}_{2}\), such that the following relationship between \(\text{circ}(\mathbf{\mathcal{T}})\) and \(\text{bcirc}(\mathbf{\mathcal{T}})\) holds:  
\[\text{circ}(\mathbf{\mathcal{T}})=\mathbf{P}_{1}\text{bcirc}(\mathbf{\mathcal{T}})\mathbf{P}_ {2}. \tag{13}\]  
Since the matrix nuclear norm is permutation invariant, it holds that [16]:  
\[\|\text{circ}(\mathbf{\mathcal{T}})\|_{\star}=\|\text{bcirc}(\mathbf{\mathcal{T}})\|_ {\star}. \tag{14}\]  
As an example, Figure 3a,b intuitively shows the relationships between the original tensor, the column twist tensor, the block circulant matrix and the circulant block matricization of a tensor \(\mathbf{\mathcal{T}}\in\mathbb{R}^{3\times 3\times 3}\). As illustrated in Subplots (a) and (b) of Figure 3, from the circulant perspective, TNN essentially exploits the tube redundancy in a circulant way while keeping the row and column relationship, and t-TNN essentially exploits the row redundancy in a circulant way while preserving the tube and column relationship [16]. In computer vision applications, the row and column of a data tensor (like color images or videos) often carry spatial information, and the tube often carries temporal  
Figure 2: The column twist and column squeeze operations.  
or channel information. From the computational perspective, FFT is operated along the tube direction to compute TNN, while t-TNN needs FFT along the row direction.
# Smart Roads for Autonomous Accident Detection and Warnings
## 4 Hardware and Experimental Setup
### HC-12 Wireless Communication Module  
The HC-12 is an RF transmitter and receiver used for wireless communications. These modules replace wired communication with a half-duplex wireless serial port. Half duplex means that one module transmits data while another receives it. The HC-12 module is used because of its long range up to 1 km and because its operating frequency ranges from 433 to 437 MHz. Figure 6b shows the pair of HC-12 modules in the proposed system. The frequency range is divided into 100 channels with each having a 400 kHz bandwidth. By selecting different channels, they can communicate with different devices. Modules with the same baud rate and on the same channel can communicate with each other within their communication ranges [62; 63; 64; 65].  
Figure 6: (a) GPS 8M module [62]; (b) HC-12 module [63; 64; 65]; (c) breadboard and jumper wires [56].
# Learning an Efficient Convolution Neural Network for Pansharpening
## 2 Related Work
### Convolution Neural Networks in Pansharpening  
Convolutional neural networks (CNNs) are representative deep learning models that have revolutionized both image processing and computer vision tasks. Given the similarity between single image super-resolution (SISR) and pansharpening, breakthroughs achieved in SISR have made profound influences on pansharpening. For example, PNN [13] and Remote Sensing Image Fusion with Convolutional Neural Network (SRCNN+GS) [16] are pioneering CNN-based methods for pansharpening, while the prototype of them is introduced from SRCNN [17], which is a noted SISR specific network. PNN makes some modifications upon SRCNN to make the three-layer network fit the domain-specific problem of pansharpening. Though impressive performance gains have been achieved, as the network of PNN is relatively simple, there is still plenty of room for improvement. SRCNN+GS also adopts SRCNN to perform pansharpening, and the difference is that SRCNN is employed as an enhancer to improve the spatial information of LRMS, and GS is applied for further improvement.  
Inspired by the success of residual networks [18], the limitation of network capacity has been greatly alleviated, and researchers have begun exploring this avenue for pansharpening. For instance,multiscale and multidepth convolutional neural network (MSDCNN) [12] is a novel residual learning based model that consists of a PNN and a deeper multiscale block. Owing to the deep network architecture, MSDCNN is able to fit more complicated nonlinear mapping and boost the fusion performance. However, the deep architecture of MSDCNN is intractable to be efficiently trained due to gradient vanishing and overfitting. This is extremely important for pansharpening, where the training data are often scarce as opposed to that of other computer vision applications.
## 4 Results and Discussions
### Dynamic Behaviors under Severe Seismic Excitations
The horizontal responses are more amplified because its lowest natural frequency is closer to the dominant frequency range of seismic excitations than that of vertical response. The corresponding tunnel-response spectra show that they have the first small peak at the seismic frequency, the next highest peak at the lowest natural frequency, and the next small peak at the third-lowest natural frequency. Mooring tensions are mostly influenced by the SFT horizontal and vertical motions at their lowest natural frequencies, while there is virtually little contribution near seismic frequencies. The maximum tensions for this earthquake case are much smaller than those caused by extreme wave excitations, as previously considered. However, the earthquake-induced tunnel dynamics can be significantly more amplified when the  
Figure 11: Time histories of vertical displacements of the tunnel at the middle section by respective force components under regular seismic excitations of 3.12 rad/s (**a**), 4.89 rad/s (**b**), and 5.78 rad/s (**c**) (Eq: earthquake only considered; Eq + Sq: both earthquake and seaquake considered; Sq: seaquake only considered; time histories of seismic excitations are multiplied by 10 for better visualization). lowest natural frequencies of the tunnel's elastic responses are closer to dominant seismic frequencies. In the figure, the same dynamic simulation results by OrcaFlex are also given for cross-checking. The two independent computer programs produced almost identical results. In the spectral plots of Figures 12-14, the spectra of tunnel responses and mooring tensions after adding seaquake effects are also given. In Figure 12, there is little change in the case of SFT horizontal motions since the seaquake mainly influences only the vertical responses, as was pointed out earlier. In Figure 13, there is a big reduction in the vertical-response spectrum at its lowest natural frequency (3.12 rad/s) after including the seaquake effect. It is due to the phase-cancellation effects, as discussed in the previous regular-earthquake case of Figure 11a,b. So, this reduction effect directly reflects the reduction in tension i.e., in Figure 14, the tension spectral amplitude is greatly reduced near 3.12 rad/s but remains the same at the lowest natural frequency of horizontal response, 1.92 rad/s. This trend can also be seen in the corresponding time-series comparisons (Figure 15) for the two cases (with and without considering the seaquake effect) regarding vertical tunnel responses and mooring tensions. The relevant statistics obtained from the time series are summarized in Table 4. It is seen that the inclusion of seaquake effect reduces both vertical SFT responses and mooring tensions, as discussed earlier.
## 4 Evacuation Modeling
### Movement Times  
The movement time of each tunnel user depends on: (i) vehicle queuing time; (ii) the pre-movement time of the user (i.e., the sum of the detection and reaction time); (iii) the walking speed of the user.  
The frequency per unit time with which the vehicles enter the tunnel after the start of the fire was considered constant and computed to be equal to 3.27 s for Scenario 0 (VHP = 1100 vehicles/h per lane), 2.12 s for Scenarios Ia and Ib (VHP = 1700 vehicles/h), and 2.25 s for Scenarios IIa and IIb (VHP = 1600 vehicles/h per direction). As a result, the last user escaping from the tube during a fire will be affected by a delay of 1.15 or 1.02 s when the VHP is equal to 1100 vehicles/h per lane rather than 1700 vehicles/h or 1600 vehicles/h per direction, respectively.  
The pre-movement time of each user was set to be 1.5 min for 8, 15, 50, and 100 MW fires, while it was increased by 1 min (i.e., 2.5 min in total) in the event of a 30 MW fire to consider the time needed for all the users to exit the bus [49]. Despite the fact that in the FDS modeling the queued vehicles in the tunnel tube are assumed to be stopped when the fire started, the time with which the vehicles enter the tunnel tube was considered in the evacuation model by attributing an extra pre-movement time to each evacuee.  
The unimpeded walking speed (i.e., the walking speed that users can assume in the absence of any interference) was assumed to be 0.7 m/s. However, the effective walking speed during a fire might be lower than the unimpeded speed due to reduced visibility distance, very high concentrations of smoke, and the presence of obstacles (e.g., queued vehicles) along the escape routes. The effective walking speed is automatically computed by the Evac code based on the FDS output.
## 4 Results and Discussion
### Raw and Heated at 100 \({}^{\circ}\)C Citric Acid Monohydrate Samples
#### 4.1.2 Antioxidant Activity  
As discussed in the previous section, the citric acid monohydrate sample undergoes a structure alteration through esterification at temperatures below 100 \({}^{\circ}\)C. Such temperatures may be used during food or drug processing where citric acid is used as an antioxidant, flavoring, a preserving agent, etc. Thus, we examined the antioxidant activity of aqueous solutions of the raw and the heated at 100 \({}^{\circ}\)C citric acid samples. The antioxidant capacity of the raw sample was found to be equal to 257 \(\upmu\)M TE, while the heated sample exhibited a higher capacity of 312 \(\upmu\)M TE. It should be stressed that citric acid does not have a direct antioxidant activity [36]; that is, it is not a primary antioxidant, but it can act as secondary antioxidant by inhibiting oxidation through chelation [36; 37]. The antioxidant activity/chelation in this assay (which involves iron reduction) is related to the increased number of OH groups present [37], and since esterification leads to the reduction of such groups, the opposite result would be expected. The increase in the antioxidant capacity of the heated sample may be understood in terms of the increase in the molecular weight. More precisely, the esterification may take place between various citric acid molecules, and an oligomer (e.g., dimer, trimer, etc.) may be formed. Per mole of citric acid monohydrate, theoretically there are 4 moles of OH groups. Per mole of citric acid dimer, there are 6 moles of OH groups. This increase may explain the increase in the antioxidant activity. Also, it must be taken into account that inside water, the excess of water, according to Le Chatelier's Principle, may favor the reverse reaction (hydrolysis), and more than six OH groups may be presented per mole of the dimer after hydrolysis. Finally, the different water content of the samples may also affect the observed alteration.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis
### Ablation Experiment  
To validate the prediction performance of the improved ConvLSTM-DSConvGRU hybrid network model, ablation experiments were conducted, and the results are shown in Table 4. The experiments were based on the ConvLSTM network, where the \"\(\vee\)\" in the table indicates the utilization of that network layer in constructing the network model, while \"\(\times\)\" indicates the absence of that network layer. Runtime refers to the time that the prediction model takes to obtain five frames within one prediction. RMSE1 and MAPE1 are for wind speed, while RMSE2 and MAPE2 are for temperature.  
The results of the ablation experiments on the speed component dataset and the temperature variable dataset are shown in Figure 11a,b, and combined with Table 4, the following conclusions can be drawn: (1) The prediction performance of the five network models on the temperature variable dataset was superior to that on the speed component dataset because the temperature variables were more uniformly and stably distributed in time and space compared with the speed components. (2) Compared with the other four models, the ConvLSTM model showed the poorest performance using the two datasets. On the one hand, as seen in Figure 11a,b, the ConvLSTM model performed relatively well in the first two predicted frames, but its performance deteriorated significantly over time, with the highest errors in the fourth and fifth predicted frames among all five models. This indicates that the ConvLSTM model has poor generalization ability. On the other hand, although the prediction performance of the DSConvGRU model was reduced compared with ConvGRU, it achieved a reduction of approximately 88% in the number of parameters. Additionally, although DSConvGRU exhibited the highest error in the first two predicted frames, its error accumulated at a slower speed over time compared with ConvLSTM and ConvGRU, demonstrating a stronger generalization ability. Specifically, for both datasets, the slopes of the blue lines in Figure 11a,b are smaller than those of the red and green lines. (3) The prediction performance of the hybrid models formed by combining ConvLSTM with ConvGRU or DSConvGRU was superior to that of the individual models. Among them, the ConvLSTM-DSConvGRU model proposed in this study performed slightly better than the ConvLSTM-ConvGRU model.
## 3 Undifferentiated Cells (Stem Cells)
### Fetal Stem Cells  
In pregnancy, two fetal membranes, i.e., the amnion and the chorion, surround the human fetus (Figure 5). The amnion is a thin layer that closely covers the human embryo. This membrane is filled with the amniotic fluid (AF), which serves as a protective environment for the developing embryo or fetus. The chorion is the outermost fetal membrane, which exists during pregnancy between the developing fetus and mother. The fetal tissue possesses stem cells in its structures, which are remarkably different from embryonic and adult stem cells [92; 93]. These cells have been defined as a distinct type of stem cells with their own properties [94]. Human fetal stem cells are described as cells from the end of the two months of conception until birth, which is isolated from the fetal tissues and have the capacity to preserve clonality, high proliferative ability, and plasticity [92]. Prior studies have noted the superiority of fetal stem cells compared to embryonic and adult stem cells in terms of safety due to the low risk of teratomas forming and transmitting infection [95]. The growing body of evidence suggests significant heterogeneity within a population of fetal stem cells, which depends on the tissue of origin [96; 97]. Fetal stem cells have been identified in umbilical cord blood (UCB-MSCs, UCB-HSCs, EPC) [98; 99], Wharton's jelly (UCMSCs), amniotic fluid (AF-HSCs, AF-MSCs, and AFSCs), amniotic membrane (AECs, AMSC, and ADSCs), placenta (CMSCs and CTCs), and even maternal circulation (PAPCs) [100; 101; 102; 103; 104]. Fetal-derived stem cells have been shown to exhibit high proliferative ability to resemble embryonic stem cells [105].  
Figure 5: Schematic illustration of the cross-section of human term placenta. All fetal stem cells are isolated from this part of the human fetus. Reproduced with permission from Reference [106].
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Key Findings  
Energy-efficiency is cross-related to the other explained advanced services. For instance, WSNs can include energy scavenging capabilities for monitoring assets. Furthermore, additional energy savings and emission reductions can be achieved by considering the implementation of timetable optimizations (coordination and re-scheduling of trains in real time), the use of wayside devices for the storage of energy, smart metering methods, or energy-efficient driving (optimized train trajectories, enhanced vehicle comfort control and speed profiles).
# A Novel Device-Free Counting Method Based on Channel Status Information
## 3 Crowd Counting Based on CSI
### Leveraging Space Diversity  
Nowadays, multiple antennas are adopted in more and more popular MIMO communication systems, since the signal strength variability can be reduced based on small-scale fading compensation. In an MIMO system, both the transmitter and receiver have multiple antennas, and each combination of receiving and transmitting antennas can be regarded as a stream. CSI consists of these streams. In addition, each stream implies a spatial choice. That is, these streams provide spatial diversity for CSI. Due to different propagation paths of diverse streams in the indoor environment, the CSI received by different antennas are differentiated. Therefore, all streams \(H(f_{i})\) are with \(p\times q\) dimensions and can be represented as:  
\[H(f_{i})=\begin{bmatrix}h_{11}&h_{12}&\cdots&h_{1q}\\ h_{21}&h_{22}&\cdots&h_{2q}\\ \vdots&\cdots&\cdots&\vdots\\ h_{p1}&h_{p2}&\cdots&h_{pq}\\ \end{bmatrix}, \tag{10}\]where \(h_{pq}\) is complex numbers representing the amplitude and phase of each subcarrier for an antenna, and \(p\), \(q\) indicate the number of transmit and receive antennas, respectively.  
As such, without enhancing the bandwidth and transmission power, multiple antennas can improve the reliability of spatial diversity [27; 28]. Figure 6a shows the amplitude characteristic distribution of different numbers of people, while the phase characteristic distribution is displayed in Figure 6b. It can be observed that the features distribution of amplitude and phase do vary over different antennas. In addition, the phase features of all antennas remain relatively stable, but the amplitude features exist significant differences. Meanwhile, the feature variation is also different, whether in amplitude or phase. In this work, we exploit multiple antennas to improve the accuracy and robustness of counting a crowd. Therefore, the antenna with less variation should be selected when counting different people. If the antenna which great variation is selected, that is, the antenna is utilized mistakenly.