esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The MPPT for Photovoltaic System
### Design of Boost Converter Circuit  
The circuit architecture of boost converter [8; 9] is shown in Figure 3. It adjusts duty cycle \(D\) of the converter, which allows the PVMA to operate on the MPP.  
The boost converter in this paper is designed with a duty cycle \(D\) of the switch for 0-1, which enables the inductor current to work in a continuous conduction mode (CCM). It can be deduced from reference [10] that the inductance value of the boost converter must satisfy the condition of Equation (1). However, the function \(D(1-D)^{2}\) in Equation (1) has a maximum value when \(D=1/3\). Therefore, it can be known from Equation (1) that if the inductance current is to operate in current CCM when the duty cycle \(D\) is between 0 and 1, the required inductance value \(L\) is the maximum when \(D=1/3\). Therefore, the inductance value is the required value when \(D=1/3\). The converter can be operated in current CCM under all duty cycles. The boost converter used for switching frequency \(f\) is 20 kHz, rated power \(P\) is 1800 W, and the maximum output voltage \(V_{o}\) is 400 V, so the resistance value of the load \(R\) can be calculated from \(R={V_{o}}^{2}/P\) is 89 \(\Omega\). Therefore, according to Equation (1), when \(D=1/3\), the required \(L\) value can be calculated as 330 \(\mu\)H, and then multiplied by 1.25 times the margin to ensure that the inductor current can be operated in CCM. Therefore, the inductor value chosen in this paper is 0.5 mH.  
\[L\geq\frac{D(1-D)^{2}R}{2f} \tag{1}\]  
Since the change in electrical charge of the filter capacitor during active conduction of the switch was [10]  
\[|\Delta Q|=\frac{V_{o}}{R}DT=C\Delta V_{o} \tag{2}\]  
The filter capacitance derived from Equation (2) was  
\[C=\frac{D}{Rf(\Delta V_{o}/V_{o})} \tag{3}\]  
where \(\Delta V_{o}/V_{o}\) was the ripple ratio of output voltage. To usefully reduce the ripple of output voltage, the ripple ratio of the output voltage was set at \(\Delta V_{o}/V_{o}=1\%\), and this value was substituted into Equation (3) to obtain the capacitance value of 18.71 \(\mu\)F. Since the withstand voltage of the capacitor should be greater than the output voltage of 400 V and considering the convenience of parts acquisition, a capacitor with a specification of 470 \(\mu\)F/450 V was selected. For the component selection of the switch and the diode, considering that both withstand voltages should be greater than the output voltage of 400 V  
Figure 3: Circuit architecture of boost converter.  
cutoff and the withstand current of switch component calculated according to [10] should be greater than 4.53 A, as well as the convenience of laboratory acquisition, IRFP460 was selected as the switching component specified at 500 V/20 A. FMP5 FS was selected as the diode specified at 1500 V/10 A.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 4 Augmented MIMO Throughput Evaluation  
### Line-of-Sight Environment  
The throughput in the Augmented MIMO prototype system was measured with IPerf2 [18] installed on a pair of Windows PCs, as shown in Figure 7. Although IPerf2 measures transmission control protocol (TCP) throughput but not physical layer bandwidth, it is appropriate for this experiment to demonstrate the practical throughput improvement with Augmented MIMO. IPerf2 is an open-source tool that has been reported to be the most widely used network traffic generation tool over a 13-year period from 2006 to 2018 [19]. To the best of the author's knowledge, such a universal and non-proprietary tool trusted by the research community for measuring throughput in the lower physical/network layers is not provided.  
In Figure 7, one of the PCs is the IPerf server (receiver), and the other is the client (transmitter). The server is connected to one of the gigabit Ethernet ports of an IEEE 802.11 ac-compatible WLAN router using a category-6 Ethernet cable. On the client side, an IEEE 802.11 ac-compatible WLAN transceiver dongle is connected directly to a universal serial bus (USB) 3.1 port of the PC. Two detachable whip antennas are removed from the dongle, and two chip antennas are connected to the dongle with coaxial cables.  
The experiments were conducted in a conference room in a university building. In this scenario, the channel includes the LOS path, as shown in Figure 7b. The WLAN connection was configured to operate in the 2.4 GHz band only, not in the 5 GHz band. The AP was configured to use channel 6 in the 2.4 GHz band, centered at 2.437 GHz, and the bandwidth  
Figure 6: (**a**) Setup to measure transmittance dependence on PCB distance. One of the PCBs is rotated to avoid the mechanical interference of coaxial connectors. The minimum PCB distance is 0.6 mm, due to the height of the chip antenna. (**b**) Measured \(|S_{11}|\) and \(|S_{31}|\) at 2.45 GHz.  
was fixed at 20 MHz to avoid throughput fluctuations due to automatic channel width selection. With a channel width of 40 MHz, the throughput fluctuates significantly due to interference from other WLAN links in the vicinity of the experimental system.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 3 Decellularization Methods  
Both academic and industrial researchers have developed protocols to decellularize various native tissues, including those mentioned above. They are classically sorted into three categories of chemical, enzymatic, and physical methods. Almost all protocols, however, consist of combinations of these three approaches. To investigate these candidates in as many trials as possible, decellularization studies furthermore apply and compare very different protocols combining various agents instead of varying only one substance to assess its effects.  
We here review decellularization methods specifically used for the production of acellular matrices for skin repair and evaluate the properties and effects of each substance commonly used in literature. The results of this analysis led us to propose another classification of these substances into main decellularizing agents that can disrupt cells and eliminate all DNA content when used alone (detergents, acids and bases, physical-based methods) and complementary agents that are added to improve these effects. Particularly, enzymes such as proteases are reviewed as common complementary agents because their exclusive use, which has been tested several times, never demonstrated total decellularization or satisfying in vitro cell repopulation of the matrices obtained.
## 5 Case Study
### Feature Selection  
Features are selected according to the methodology outlined in Section 4.2, treating the inertia tank, the laboratory, and the exterior as three distinct blocks with varying temperatures in contact.  
The first step is to identify heat sources, and a clear difference in temperature dynamics is observed between weekdays, when the laboratory is occupied with investigators working, and weekends when the laboratory is empty. As an example, Figure 7a shows how during working hours, the temperature increases due to the presence of investigators, going above the set point; in contrast, Figure 7b presents a more gradual approximation and stabilization around the set point on a weekend day when no-one entered the laboratory.  
This observation highlights the number of people as a heat source with a substantial influence on the laboratory's temperature.  
Referring to Equation (2), the temperature of the inertia tank, the outdoor temperature, and the number of people are identified as crucial features for predicting the laboratory's temperature. These features have a significant impact on the system and will be used to train the laboratory's model.  
Figure 6: Temperatures over 24 h of a weekday. (**a**) Temperature and set points of the laboratory. (**b**) Temperature and set points of the inertia tank.  
Figure 7: Temperatures through a working day and a weekend day presenting differences in behavior due to the heat flux caused by human presence or absence in the room. (**a**) Temperature and set points of the laboratory. Data collected on a working day. (**b**) Temperature and set points of the laboratory. Data collected on a weekend day.  
The features that affect the inertia tank are straightforward to identify since this element is more isolated than the laboratory. The heat flux of the inertia tank will be through the machine room, the laboratory if the fan coil is ON or OFF, and its own set point heating the tank when it gets below the target temperature.  
Table 2 displays the features selected to build the models with their types and short descriptions.  
Table 2 collects the features that describe the systems according to their thermodynamic properties and those will be the variables used to train the models. It is essential to note that the primary objective of the models is to predict the temperatures of both the laboratory and the inertia tank. Therefore, all features used to train the models must be available for each time instant at which a forecast is to be made in the future.
## 4 Results and Discussion
### Separation Angles  
The separation angles of three cylinders are collected in Figure 22, and they are determined by \(Cf\). Because the flow separation points of cylinder A are symmetrical on the upper and lower sides, only one is shown here for convenience. As shown, when \(L/D=1.1\), the separation points on the upstream cylinder, with black square symbol, is lower than that of a single cylinder. No separation point on the gap-side of the downstream cylinders (red triangle symbol and blue diamond symbol) is identified, because there is no regular vortex shedding at the gap. At \(L/D=1.2\), there is an extremely sharp growth in the separation point of cylinder A, because the increase of gap space leads to the shear layers of the upstream cylinder flow through the gap. The separation point of the upstream cylinder is quite backward because the gap is still small, which limits the separation of shear layers. As \(L/D\) goes up, the separation point of the upstream cylinder moves forward rapidly, starting to gradually adhere to the value at the single cylinder when \(L/D=1.7\). For the separation points on the free-stream-side of cylinders B and C, which are shown as red circle symbol and blue star symbol, respectively, they are smaller than in the case of a single cylinder when \(L/D\) is small, indicating that the separation position is advanced. As \(L/D\) increases, the separation position gradually leans forward, and when \(L/D=1.4\), the separation angle is close to that of single cylinder. For the separation points on the gap-side of downstream cylinders, they are special in the deflected gap flow regime. When  
Figure 21: Sketch diagram of special parameters analyzing the pressure coefficient distribution.  
Figure 20: Time-averaged pressure coefficient distributions on surfaces of the cylinders at different \(L/D\).  
\(L/D=1.2\) and 1.4, the gap-side separation angle of cylinder C is conspicuously large in contrast to that of cylinder B. This is because at these \(L/D\), the gap flow is deflected towards cylinder C, resulting in the delayed separation of the shear layers of cylinder C. The situation is reversed at \(L/D=1.3\).
## 4 Proposed DEIDV-Hop Algorithm  
### Goal  
Given that the distance between the unknown node and the anchor node is an estimated value, the error is significant in the DV-Hop localization algorithm. In the original localization algorithm, it uses the unbiased estimation criterion to calculate the average distance per hop, and the estimation error of the average distance per hop achieved through this method is zero. However, the error complies with the Gaussian distribution. According to the parameter estimation theory, as the cost function of estimating the sub-error, it is more reasonable to use the mean square error than use only the variance or deviation. Therefore, the calculation formula of the average distance per hop is improved in this investigation.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 2 Related Work
### Deep Convolutional Network Reconstruction Method  
In view of the excellent feature learning capabilities of deep networks, researchers have started using deep networks to directly learn the reconstruction mapping from snapshot measurements to original hyperspectral images [26; 27; 28; 29; 30; 31]. Under this framework, [PERSON] et al. [32] used convolutional neural networks to learn hyperspectral compressed snapshot reconstruction. [PERSON] et al. [30] proposed a two-stage hyperspectral image reconstruction model called \(\lambda\)-Net. The first stage of \(\lambda\)-Net employs a generative adversarial network (GAN) to obtain an initial reconstruction. The second stage is to refine each spectral band of the initial reconstruction. [PERSON] et al. [33] expanded the iterative steps of the GAP optimization algorithm into different stages of the network model, and its denoising sub-steps were implemented using a pre-trained denoiser. TSA-Net [29] introduces a self-attention mechanism to capture the dependence between spatial and spectral dimensions of hyperspectral images. These methods based on deep networks can directly output the reconstruction results through a feed-forward network calculation without iteration. Therefore, they have high reconstruction efficiency. However, although the above-mentioned convolutional networks can effectively represent local details, they still have certain limitations in capturing long-term spatial-spectral correlation.
# Introducing Cement-Enhanced Clay-Sand Columns under Footings Placed on Expansive Soils
## 3 Experimental Program
### Oedometer Testing  
In accordance with ASTM D4546 [28], a series of one-dimensional odometer tests were conducted to assess the compressibility and swelling behavior of the mixtures. Four basic steps were engaged in the process for all tests: dry loading, wet loading, unloading, and free swell (swelling under a seating load). For a brief period of time (15 min), dry loading was provided with a seating load of 7 kPa to ensure that the air was released and that the sample's cover cap and rod made contact. In order to measure free swell, samples were submerged at the same 7 kPa seating load while vertical strain (heave) was continuously monitored. When there is no evidence of further significant deformation, equilibrium heave is assumed to be reached. The time it took to reach equilibrium heave was typically between 24 and 36 h, dependent on the amount of clay in the mixture. Samples were gradually loaded with a standard load increment ratio of 1 up to 400 kPa after reaching equilibrium. Until initial consolidation was finished, each stress was sustained (24 h). An LVDT was used to track the sample's vertical deformation throughout the test. At the conclusion of every loading phase, the vertical strain was assessed using these vertical deformation data. The samples were unloaded (rebound) at a load increment ratio of 25% after reaching their maximum compression stress. At the end of the test, the water content of the sample was determined. The entire test takes eight to ten days to complete. The consolidation and unloading curves were plotted to illustrate the relationship between the applied stress (on a logarithmic scale) and the void ratio. A schematic representation of these curves is displayed in Figure 3, which includes the steps of the test and definitions for the key parameters: the compression index (C\({}_{\text{c}}\)), the rebound index (C\({}_{\text{r}}\)), and the swelling value (SP).  
\begin{table}
\begin{tabular}{c c c} \hline
**Clay** & **OMC** & \({}^{\gamma}\)**dmax** \\
**Content (\%)** & **(\%)** & **(kN/m\({}^{3}\))** \\ \hline
10 & 11 & 17.85 \\
20 & 13 & 18.15 \\
30 & 13.5 & 18.03 \\
40 & 19 & 19 \\
60 & 25 & 25 \\ \hline \end{tabular}
\end{table}
Table 4: Compaction data for all mixtures with different clay contents.
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 2 Materials and Methods
### Overview of the Selected Electric Energy Storage Technologies
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Type of** \\ **Battery** \\ \end{tabular} } & \begin{tabular}{c} **Range of** \\ **Temperatures** \\ **for Charging** \\ \end{tabular} & \begin{tabular}{c} **Range of** \\ **Temperatures** \\ **for Discharging** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Operational** \\ **Cycles** \\ \end{tabular} & \
\begin{tabular}{c} **Density** \\ **Energy** \\ **Energy** \\ \end{tabular} &
\begin{tabular}{c} **Charging** \\ **Possibilities** \\ \end{tabular} \\ \cline{2-6}  & [\({}^{\circ}\)C] & [\({}^{\circ}\)C] & [-] & [Wh/kg] & [C] \\ \hline LiFePO4 & \(0\div 45\) & \(-20\div 60\) & \(\geq\)2000 @ 100\% & \(90\div 160\) & \(0.5\div 1.3\) \\ \hline NMC & \(0\div 45\) & \(-20\div 55\) & \(\geq\)3000 @ 75\% DOD & \(180\div 240\) & \(0.3\div 1.0\) \\ \hline NiMH & \(0\div 45\) & \(-20\div 50\) & \(\geq\)1000 @ 50\% DOD & \(60\div 120\) & \(0.1\div 1.0\) \\ \hline GEL Deep-cycle & \(0\div 50\) & \(-20\div 60\) & \(\geq\)600 @ 50\% DOD & \(30\div 50\) & \(0.1\div 0.3\) \\ \hline AGM & \(0\div 50\) & \(-20\div 60\) & \(\geq\)600 @ 50\% DOD & \(30\div 50\) & \(0.1\div 0.3\) \\ \hline Ni–Cd & \(-20\div 50\) & \(-20\div 50\) & \(\geq\)1000 @ 20\% DOD & \(12\div 30\) & \(0.2\div 0.4\) \\ \hline Lead–Carbon & \(-40\div 65\) & \(-40\div 65\) & \(\geq\)2000 @ 50\% DOD & \(30\div 50\) & \(0.1\div 0.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of batteries available on the market (LiFePO4–lithium ferrophosphate battery, NMC—nickel-mangan-cobalt battery, NiMH—nickel-metal hydrate, GEL—gel battery, AGM—absorbent glass mat battery, NiCD—nickel-cadium battery, Lead Carbon—lead-carbon battery). \(I_{10}\)--Nominal battery current given by the manufacturer [A]. The voltage value at the battery terminals (Equation (3)) can be determined from the following relationship given in [46]. \[V_{bat}=n_{b}E_{b}+n_{b}R_{bat}I_{bat} \tag{3}\]  
where:  
\(V_{bat}\)--Battery voltage [V];  
\(n_{b}\)--Number of cells in series [pcs. ];  
\(E_{b}\)--Electromotive force of the battery cell [V];  
\(R_{bat}\)--Battery internal resistance [\(\Omega\)];  
\(I_{bat}\)--Battery current [A]. The proposed solution is a lead-carbon battery, which has parameters that do not limit the operation of the lighting system, despite harsh working conditions. It allows for charging and discharging processes to be carried out at temperatures from \(-20\)\({}^{\circ}\)C to \(65\)\({}^{\circ}\)C and to achieve 2000 duty cycles with a discharge limit of 50% capacity, enabling a minimum of 5.5 years of operation. These assumptions were placed in simulation model.
# Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning
## 2 Literature Review
### Ensemble Learning Techniques  
In general, one model is used to predict a value based on data in deep learning. However, more accurate predicted values can be obtained by ensemble learning, which combines multiple models [32]. The key to ensemble learning is to combine multiple models to create a robust model. Depending on the combination method, it can be divided into Bootstrap Aggregation (bagging), boosting, and stacking.  
Bagging is a method of aggregating results by drawing samples multiple times and training each model connected in parallel. The output of each model was aggregated by voting in the case of categorical data, and the final result was obtained by averaging the continuous data. Bagging prevents overfitting by improving the stability of the algorithm because it adjusts the results from each sample to a median value.  
Boosting is a method of creating a robust predictive model by applying a weight to the learning error of each serially connected model and sequentially reflecting it on the next learning model. First, the raw data are assigned the same weight, but a high weight is assigned to the incorrectly predicted data by the model, and a low weight is assigned to the correctly predicted data such that the incorrectly predicted data are predicted.  
Bagging and boosting are methods of arranging multiple identical models; however, stacking, unlike the above two methods, combines different models to create a model with the best performance. Therefore, although a large amount of computation is required, high performance can be obtained by taking the strengths of each model and supplementing their weaknesses.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 5 Simulation Results  
The IEEE 802.16e standard defines six code rates (1/2, 2/3A, 2/3B, 3/4A, 3/4B, and 5/6) of the LDPC codes. In this paper, density evolution and Gaussian approximation simulation of LDPC codes with six code rates under 802.16 standard were carried out under the BIAWGN channel. Then, from the perspective of density evolution and Gaussian approximation, the convergence of BP algorithm and TDMP algorithm for 1/2 code rate LDPC codes under 802.16 and DTMB standards was compared. In the simulation, the maximum number of iterations of BP algorithm and TDMP algorithm were set as 20 and 10, respectively. Finally, the calculation of the normalization factor and the performance comparison of the TDMP algorithm were simulated.
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 6 Conclusions  
This paper proposes a new Dynamic Spatial-Temporal Self-Attention Network model (DSTSAN) to solve the traffic flow prediction problem. Compared to current approaches that focus only on local dependencies in traffic flow data based on the topology of traffic networks, this approach, based on the spatial self-attention module, achieves effective modelling of local spatial correlations and global spatial correlations in traffic flow data through the introduction of spatial masking matrices and semantic masking matrices. As for some current approaches that focus only on spatial dependencies, this approach not only models the features such as daily periodicity and weekly periodicity that traffic flow exhibits in real life, but it also fuses the dynamic temporal dependencies in traffic flow data through the temporal self-attention module. The effectiveness of the proposed method is verified through comparison tests with 11 representative baseline methods on four real traffic flow datasets.  
However, there are some limitations in this approach: (1) There are many conditions that affect the traffic flow conditions, such as weather, temporary activities, etc., for which the approach has not been able to model such features effectively. (2) The computational cost in this model is high due to the computation of multiple multi-head self-attention mechanisms, and the model is inefficiently executed when the amount of data is large. As a result, subsequent work will also focus on addressing the issues of improving model performance and execution efficiency, as well as multimodal feature fusion for traffic flow.  
Conceptualization, H.Z.; Methodology, H.Y.; Software, D.W. and H.Z.; Validation, D.W. and H.Z.; Formal Analysis, H.Y. and H.Z.; Writing, D.W.; Review and Editing, H.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the project \"Research on Key Common Technology of Digital Industry Innovation Platform\": 01108/110822150.  
All datasets in this paper are publicly available datasets.  
The authors declare no conflicts of interest.