esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact FEM Implementation for Parabolic Integro-Differential Equations in 2D
## 3 Galerkin Discretizations
### Backward-Euler Scheme  
Let \(I_{h}\) be the Lagrange interpolant corresponding to \(\mathbb{V}\). Then, the fully discrete backward-Euler scheme may be stated as follows: given \(U^{0}\), where \(U^{0}=I_{h}u_{0}\), find \(U^{n}\in\mathbb{V},\ n\in[1:N]\) such that  
\[\int_{\Omega}\partial U^{n}\phi\mathrm{d}\mathbf{x}^{\prime}+a(U^{n},\phi)=\sigma^ {n}(b(t_{n};U,\phi))+\int_{\Omega}f^{n}\phi\mathrm{d}\mathbf{x}^{\prime},\ \ \forall\phi\in\mathbb{V}, \tag{6}\]  
where  
\[\sigma^{n}(b(t_{n};v,\phi))=\big{\langle}\sum_{j=0}^{n-1}\tau_{j+1}B(t_{n},t_{ j})\
abla v(t_{j}),\
abla\phi\big{\rangle}, \tag{7}\]  
if the left rectangular rule is used to discretize the integral term, or  
\[\sigma^{n}(b(t_{n};v,\phi))=\big{\langle}\sum_{j=1}^{n}\tau_{j}B(t_{n},t_{j}) \
abla v(t_{j}),\
abla\phi\big{\rangle}, \tag{8}\]  
if the right rectangular rule is used. In any case, both are consistent with the backward-Euler scheme and the order of accuracy is same.  
The discrete problem (6) becomes: find \(\mathbf{U}^{n}=\sum_{j=1}^{M}u(z_{j},t_{n})\phi_{j}(\mathbf{x})\in\mathbb{R}^{m}\) such that  
\[(\mathbf{M}+\tau\mathbf{A})\mathbf{U}^{n}=\mathbf{M}\mathbf{U}^{n-1}+\tau^{2} \mathbf{A}(B(t_{n},t_{0})\mathbf{U}^{0}+\ldots+B(t_{n},t_{n-1})\mathbf{U}^{n- 1})+\tau\mathbf{b}^{n}, \tag{9}\]  
where the mass matrix \(\mathbf{M}\), the stiffness matrix \(\mathbf{A}\) and the load vector \(\mathbf{b}^{n}\) are given by  
\[\mathbf{M} =\{M_{i,j}\}=\int_{\Omega}\phi_{j}\phi_{i}\mathrm{d}\mathbf{x}^{ \prime}, \tag{10}\] \[\mathbf{A} =\{A_{i,j}\}=\int_{\Omega}\
abla\phi_{j}\cdot\
abla\phi_{i} \mathrm{d}\mathbf{x}^{\prime},\] (11) \[\mathbf{b}^{n} =\{b_{i}^{n}\}=\int_{\Omega}f^{n}\phi_{i}\mathrm{d}\mathbf{x}^{\prime}, \tag{12}\]  
respectively.
## 2 Design Aspects
### House Profile and Cooling Loads  
The proposed methodology was applied to a residential house located in Baghdad, Iraq. The house layout, wall layer details, and various construction components are given in Appendix A. As for the design of the house: the windows are on the north, east, and west walls; overhangs have a projection factor (overhang depth/window height) of 0.6. Two doors are on the north and east sides. Windows and doors are not specified on the south wall (to minimize heat gain through radiation). The window-to-gross-wall area is kept at 29%. The zone temperature is specified as 25 \({}^{\circ}\)C; the new design envelope specifications are as follows:  
* The window-to-gross-wall area should not be greater than 35%.
* Overhangs should be placed on the east, west, and south windows of the building with a projection factor (overhang depth/window height) of greater than 0.5.
* Lighting devices should have an efficiency of 60 lumens/W.
* Specific lighting, 15 W/m\({}^{2}\).
* Specific gain (equipment and people), 15 W/m\({}^{2}\).
* Occupation rate 0.05 occupants/m\({}^{2}\).  
Concerning the house under study, the monthly cooling demand of the house is variable during the summer season. An enormous portion of that variable is involved in the cooling load configuration due to the (transient) storage nature inherent in the cooling load. The sum of the components of the cooling load gives the total load of the house building. The calculation of the cooling demand was carried out using CARRIER software, version 4.04 (Carrier Software Systems, Syracuse, NY, USA, 2015), based on weather data for Baghdad. The inside conditions: temperature 25 \({}^{\circ}\)C and relative humidity 50%, ambient summer design dry-bulb temperature 48 \({}^{\circ}\)C, coincident wet-bulb 26 \({}^{\circ}\)C and 18.9 \({}^{\circ}\)C (daily range). Solar insolation varies according to the time of year, especially from April to October. The house's peak load occurs in August at 4 p.m., where the maximum outside temperature is about 49 \({}^{\circ}\)C. This value should be adopted for design purposes. Figure 3 illustrates the design temperature profiles for August; the maximum total cooling load according to CARRIER software is 25 kW was in August. Figure 4 shows the percentage of the various peak cooling load components.  
Figure 3: Design temperature profiles for August.
# Define a function to generate the graph representation of the CNN architecture  
def generate_graph(input_dim, output_dim):  
nodes = [ ('conv1', torch.tensor([1, 1, 3, 3])),  
('pool1', torch.tensor([2, 2, 2, 2])),  
('conv2', torch.tensor([1, 16, 3, 3])),  
('pool2', torch.tensor([2, 2, 2, 2])),  
('linear1', torch.tensor([32 * 7 * 7, 256])),  
('linear2', torch.tensor([256, output_dim]))  
edges = [ ('conv1', 'pool1'),  
('pool1', 'conv2'),  
('conv2', 'pool2'),  
('pool2', 'linear1'),  
('linear1', 'linear2')  
node_names = [node [0] for node in nodes]  
node_shapes = [node [1] for node in nodes]  
node_indices = [sum(node_shapes[:i]) for i in range(len(node_shapes))]  
edge_indices = [(node_names.index(edge [0]), node_names.index(edge [1])) for edge in edges]  
x = torch.tensor([[1.], [0.], [0.], [0.], [0.]])  
edge_index = torch.tensor(edge_indices, dtype = torch.long).t().contiguous()return Data(x = x, edge_index = edge_index)  
In this example, the CNN architecture is defined using PyTorch, and a graph representation of the architecture is generated using the generate_graph function. The nodes of the graph represent the different layers of the CNN (e.g., conv1, pool1, etc.), and the edges represent the connections between the layers. The x tensor contains the initial node features, which in this case is set to [1., 0., 0., 0., 0., 0.], indicating that the first node (corresponding to the first layer) is the starting point for traversing the graph. The edge_index tensor contains the indices of the nodes that are connected by each edge.
# Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
## 3 Hydrogels
### Tuning Hydrogel Properties  
The properties of the cellular microenvironment are critical for cellular behavior and are dynamically regulated in living tissues. Cells sense the mechanical properties of the matrix through the linkage between membrane receptors and ECM proteins and, therefore, not only the matrix stiffness but also the type and concentration of anchoring proteins available for the cell to build up adhesion complexes and the way they are linked to the matrix or biomaterial, influence key cellular functions such as cell differentiation [72]. Furthermore, migrating cells are strongly affected by the steric hindrance constraints imposed by the surrounding ECM (e.g., pore size and porosity) [73] as well as other structural characteristics (e.g., fiber density and orientation) [66], that dictate the way in which they apply contractile forces, which in turn will modify the local mechanical properties of the surrounding matrix (Figure 2D). That is why the successful use of hydrogels in these applications relies on the ability to tune some properties, such as the degradation rate or the mechanical properties (e.g., cross-linking nature, density or dynamics) [74].  
In order to increase the mechanical properties of hydrogels, often researchers tend to change the polymer or crosslinker concentration [75], thereby addressing the exploration of interesting cellular responses such as the effect of spatio-temporal gradients in mechanical properties on cell growth and migration, by using microfluidics techniques or photopolymerization [76]. However, changes in the crosslinker concentration modifies as well the porosity of the gel, which in turns affects the degradation dynamic and cell behavior (an increase of the cross-linker density causes a decreased degradation). Consequently, a trade-off exists between the macroporosity (10-400 \(\upmu\)m) and the mechanical strength of the scaffold. As an alternative to improve hydrogel mechanical stability, covalent crosslinking of hydrogels has been used, but this is not an ultimate solution given the fact that chemical crosslinking is irreversible and may lead to the formation of toxic by-products [77,78].  
A common method to control the degradation rate of hydrogels is to incorporate peptides into the network that are targets for proteases. Several studies have demonstrated that changing either the concentration of these peptides or their amino acid target sequences (i.e., RGD) can be used to control the rate of gel degradation [79,80]. However, the hydrogel can result in the release of polymer fragments depending on the initial network connectivity or cleavage of chemical bonds, and little is known about how these degradation strategies, which is the loss of polymer content, may affect the spreading of cells, migration and proliferation.
## 4 Hardware Implementation  
Based on the method proposed in Section 3, this paper proposes a dual-channel data reading and writing controller, through which a two-dimensional read / write operation can be carried out from any position in the data matrix, and the bandwidth equilibrium of two-dimensional data access can be guaranteed to meet the efficient matrix transposition needs of SAR imaging. The overall structure of the controller, as shown in Figure 14, consists of a register manager, an address generator, a DDR3 read/write module, and an input/output First-In-First-Out (FIFFO) module. The controller invokes the Memory Interface Generator (MIG) IP core to control the dual-channel DDR3.  
The data read or written from the controller are often input to the buffer RAM, so they work under the system working clock sys_clk. The read or write operation between the controller and DDR3 is performed under the user clock ui_clk provided by the MIG IP core. Under normal circumstances, there is a difference between sys_clk and ui_clk, so it is necessary to use FIFO for cross-clock domain processing.
# Model-Based Design and Simulation of Paraxial Ray Optics Systems
## 4 Results and Discussion
### Fabry-Perot Interferometer  
A typical Fabry-Perot interferometer (FPI) is comprised of a pair of planar mirrors with reflectivity \(r\) that traps the incident ray (orientation \(\theta_{0}\)) inside a cavity of length \(d\) and refractive index \(n\), promoting successive reflections as shown in Figure 5a. The light transmitted by the mirror is collected with a thin lens and focused on a photodetector for assessing the output light intensity \(I\). Interference cannot be evaluated merely by geometrical optics; alternatively, one may determine the light confinement using ray tracing and calculate \(I\) by wave optics approach,  
\[I=\frac{{{I_{0}}/{{(1-r)}^{2}}}}{{1+{{(2F/\pi)}^{2}}\sin(\phi/2)}} \tag{2}\]  
\(I_{0}\) is the input intensity, \(F=\pi\)\(r^{1/2}/(1-r)\) is the resonator finesse, and \(\varphi=4\pi nd/\lambda\) is the phase difference, where \(\lambda\) is the wavelength [22]. The high sensitivity to \(n\), \(d\), and \(\lambda\) makes the FPI applicable for chemical and displacement sensing [28], as well as an optical filter or spectrum analyzer [29].  
The FPI surrounded by the air (\(n_{0}=1.00\)) is modeled as depicted in Figure 5b, neglecting the thickness of mirrors and using a thin lens with \(f=20\) mm. Distances are set to \(d_{1}=10\) mm and \(d_{2}=20\) mm. To investigate that the effect of the cavity refractive index \(n\), simulations are carried out with \(r=0.9\), \(\lambda=1\)\(\upmu\)m, \(d=10\) mm, and \(1.00\leq n\leq 1.40\). Ray tracing results with \(y_{0}=5\) mm and \(\theta_{0}=5\)  
Figure 4: Transmission through multimode optical fiber: (**a**) ray optics representation; (**b**) matrix optics model, S is refraction and R is reflection component; (**c**) ray tracing obtained by simulation for \(y_{0}=0\) and \(2^{\circ}\leq\theta_{0}\leq 5^{\circ}\).  
\(-5^{\circ}\), Figure 5c, indicate that the incident light undergoes multiple reflections inside the cavity while the output paraxial rays are appropriately focused on the observation plane of the photodetector, as predicted by the analytical model. Moreover, scanning the FPI response for different \(n\) reveals the expected normalized intensity profile. Analogous tests could be performed to examine the contribution of \(d\) or \(\lambda\). Furthermore, this study suggests that optics models belonging to different approaches can be coupled using the modular strategy and run into a unique simulation environment.
## 2 Measurement of Complete Geometric Errors
### Complete Geometric Errors of Multi-Axis Machine Tools  
Complete geometric errors of multi-axis machine tools include geometric size, shape and position errors, which are displacement and angle errors or their coupling, in the final analysis. Although the orientation error caused by the inaccurate positioning of a workpiece and fixture is also one of the main errors in machined parts, it is mainly considered in the process of machining. Therefore, the orientation error is not taken into account in this paper. There are 6 items of geometric errors for each axis (translating or rotating axis): 3 items of geometric displacement errors and 3 items of geometric angle errors [35], as shown in Figure 2. Complete geometric errors of multi-axis machine tools include 36 items, as shown in Table 1. Among them, items of geometric errors are coupled with each other, except axial displacement errors.  
Figure 1: Flow chart of the coupling and decoupling measurement method.  
Figure 2: Complete geometric errors of the X-axis and the A-axis.
## 3 Results and Discussion  
### Variation in Melting Point and Latent Heat  
Table 3 presents the melting point (T) and latent heat of fusion (\(\Delta\)H) of composite PCMs with different mass ratios. Figure 5 presents the DSC curve of the sample prepared in experimental group A\({}_{2}\), which illustrates the variation in the sample's heat flow (H) with temperature.  
As seen in Figure 7, the composite PCM exhibits a single thermal peak during both the melting and solidification processes. This indicates that the composite PCM stores or releases all the heat, demonstrating excellent thermal response performance. As shown in Table 3, with the increase in the mass fraction of butyl stearate, both the melting point and latent heat of fusion of the composite PCMs decrease. This is because the melting point and latent heat of fusion of butyl stearate are lower than those of paraffin.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experimental Groups** & **m\({}_{1}\):m\({}_{2}\)** & **T (\({}^{\circ}\)C)** & **\(\Delta\)H (J/g)** \\ \hline A\({}_{1}\) & 0:1 & 30.94 & 142.12 \\ A\({}_{2}\) & 3:7 & 28.30 & 128.26 \\ A\({}_{3}\) & 4:6 & 26.15 & 123.98 \\ A\({}_{4}\) & 5:5 & 23.49 & 120.13 \\ A\({}_{5}\) & 6:4 & 21.70 & 116.29 \\ A\({}_{6}\) & 7:3 & 19.28 & 111.20 \\ \hline \end{tabular}
\end{table}
Table 3: Melting point and latent heat of composite PCMs with different mass ratios of stearic acid n-butyl ester (m\({}_{1}\)) to paraffin (m\({}_{2}\)).  
Figure 7: The DSC curve of the sample prepared in experimental group A\({}_{2}\).  
The sample prepared in experimental group A\({}_{2}\) has a melting point of 28.30 \({}^{\circ}\)C and a latent heat of fusion of 128.26 J/g. The PCM begins to melt at 28.30 \({}^{\circ}\)C, fully absorbing and storing heat, thereby maintaining the indoor temperature to around 28 \({}^{\circ}\)C, which meets the requirements for an appropriate phase change temperature in Hainan. Therefore, when the mass ratio of butyl is 3:7, a composite PCM with a suitable phase change temperature can be prepared by mixing these two organic PCMs.
# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 2 Materials and Methods
### VOC-Sensing Characterization  
The gas-sensing properties of the RGO-metalloporphyrin sensors were investigated by subjecting sensors to three VOCs (ammonia, acetone, and isopropanol) and carbon monoxide gas, while simultaneously measuring the change in electrical resistance of the sensors. In this study, a static gas-sensing testing method was used (Figure 2). First, a sensor was positioned inside a test chamber (4 L in volume). When the electrical resistances of the sensors were stabilized, a pre-determined amount of gas analyte was then introduced into the test chamber using a gas-tight micro-syringe through a laboratory septum. Next, the gas concentration was determined by the volume ratio of the injected gas to air under standard atmospheric pressure [40]. Throughout the entire test, an Agilent 34401A digital multimeter was used to record the electrical resistance of the RGO-metalloporphyrin sensors. In addition, an electric fan was installed in the test chamber to distribute the gas analyte and have a homogeneous diffusion of gas inside the chamber. RGO-metalloporphyrin sensors were then subjected to four different VOCs to simulate breath with disease states. It should be noted that the temperature inside the chamber was maintained at \(\sim\)21 \({}^{\circ}\)C. The gas-sensing performance of the RGO sensors was investigated using the normalized change in resistance as follows:  
\[R_{norm}[\%]=\frac{\Delta R}{R_{i}}\times 100 \tag{1}\]  
where \(R_{i}\) is the initial baseline resistance under ambient air condition, and \(\Delta R\) is the change in resistance between the bulk film resistance of the RGO-metalloporphyrin film at a certain gas analyte condition with respect to its baseline resistance (\(R_{i}\)).
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 2 RNN and LSTM Models for Structural Response Prediction  
### RNN Model  
RNNs are a kind of neural network with short-term memory ability for processing and predicting sequence data. Different from the traditional deep neural network (DNN), the RNN adds a hidden state \(h_{t}\) to connect the neurons in the hidden layers and has the ability to record information. As shown in Figure 1, the hidden unit \(h_{t}\) is determined by the previous hidden unit \(h_{t-1}\) and the current input \(x_{t}\). The forward propagation process of RNNs can be expressed as:  
\[\left\{\begin{array}{l}h_{t}=\sigma(w_{xh}x_{t}+w_{hh}h_{t-1}+b_{h})\\ \hat{y}_{t}=\sigma(w_{hy}h_{t}+b_{y})\end{array}\right. \tag{1}\]  
where \(w_{xh}\), \(w_{hh}\), and \(w_{hy}\) are the weights, \(b_{h}\) and \(b_{y}\) are the biased terms, and \(\sigma(\bullet)\) is the activation function. The back propagation algorithm is adopted to iteratively update parameters \(\theta(w,b)\) in the model. The updated equation of the parameters is as follows:  
\[\theta^{\text{new}}=\theta^{\text{old}}-\alpha\
abla_{\theta}f(\theta) \tag{2}\]  
where \(\alpha\) is the learning rate and \(\
abla_{\theta}f(\theta)\) is the gradient.  
The linear and nonlinear characteristics of the structural system and different types of excitation inputs will affect the dynamic behavior of the structure. The response prediction models for linear and nonlinear structures during earthquakes were established based on the RNN model. The input of the model is the seismic wave sequence \(\tilde{a}_{g}=[\tilde{a}_{g^{\prime}}^{1}\tilde{a}_{g^{\prime}}^{2}\tilde{a}_ {g^{\prime}}^{3}\ldots,\tilde{a}_{g}^{\tilde{u}}]\) and the output is the structural response \([u,\dot{u},\ddot{u}]\), as shown in Figure 2.  
Figure 1: Schematic diagram of an RNN unit.  
Figure 2: RNN model for structural response prediction.
## 3 System Concept
### Interface Board  
In order to further process the raw measurement signals, the RF frontend is connected to an interface board, which contains an ADC (ADS1298 from Texas Instruments, Dallas, TX, USA) with a maximum resolution of 24 bits to digitize the raw data. Using its serial peripheral interface (SPI), the data are handed over to the Infineon XMC4500 microcontroller, arranged into UDP packets and sent to a computer via the Ethernet interface. Alternatively, the data can be directly saved on a memory card. Moreover, the microcontroller is used to address all further components of the RF frontend such as VCO and amplifiers. Two external status lights indicate whether the system is transmitting RF power (green led) or an error has occurred (red led), which facilitates the use of the system as a medical instrumentation device in a clinical environment. To ease the alignment of the system, the interface board controls a class 1 laser, which is placed in the center between Tx and Rx antenna and projects the shape of a cross onto the target, indicating the focus of the antennas. To mark the occurrence of specific events in the data, such as interventions during a proband study, a pushbutton can be connected to the interface board and is digitized simultaneously with the radar signals. The system is powered by an AC/DC-converter, which guarantees an isolation of \(4\,\mathrm{k}\mathrm{V}\), meeting the requirements for medical devices as given by the common standards of IEC 60601-1-2. To avoid distortions caused by the grid, multiple stages of low-dropout regulators (LDOs) filter the supply and suppress unwanted noise.
## 7 Issues and Challenges
### DSM with Waste-to-Energy Sources  
In recent times, waste-to-energy has gained significant potential in the renewable and biologically eco-friendly energy market owing to its nature-oriented disposal and pollution-free generation. DSM can be implemented at the prosumer level, with the prosumers being active power generation sources putting greater emphasis on waste-to-energy potential as their preferred source of energy to defer them from using conventional grid-supplied electricity. However, the present scenario is plagued with many issues, some of which affect DSM integration with waste-to-energy potential as a potent source of power generation. Some of these issues are:  
* Waste segregation and management on the ground level are the primary tasks that need to be focused on. In developed countries, this is not a major issue, as thegeneral population is aware at a high level in comparison to countries with developing economies. Better awareness among the general masses can be a solution to the segregation and management of waste.
* Complex techniques are involved in waste-to-energy-based generation, such as pyrolysis in controlled environments and the use of a specific mixture of substances to keep the entire process pollution-free.
* High investment costs are required for setting up the incineration and biogas plants.