esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Analysis and Design for General VSM with Energy Recovery Control
### Control Design and Coordination of General ESS-Based VSM(s)  
Based on the theoretical analysis above, the complete procedure of the ESS-based VSM control design is illustrated by Figure 12 and explained below. The control design is independent of the specific energy storage types and power converter topologies, making it general enough to cover VSM implementation in various occasions, without violating the control and physical constraints.  
First, the virtual inertia, damping and governor parameters will be designed based on frequency regulation and stability requirement. The ESS will then be sized based on the inertia and damping parameters [1]. Depending on the occasions where VSM is installed and the ESS types selected, proper power electronic interface and control will be designed, for example the single ESS, hybrid DC and AC-coupled ESS discussed above. The steady-state energy variation of ESS will be analyzed and verified.  
Afterwards, additional energy recovery control loops will be designed, based on the bandwidth separation rule and stability requirement. Finally, simulation and hardware test will be carried out to verify the VSM control and modify the parameters if necessary.  
Notice that it is relatively easy to regulate a limited number of generators in the conventional power system so as to achieve system stability. However, in a future grid with a massive number of distributed grid-forming converters, maintaining system stability will be more challenging. Aggregating coherent VSM as groups will be a promising way to  
Figure 11: Root locus as the governor control gain \(k_{\text{p,sa}}\) increases from 5 p.u. to 30 p.u., with (blue) and without (red) FAESS adopting VSM control.  
Figure 10: Illustration of the bandwidth of frequency and the SoC control loops.  
manage these new power system players in the frequency regulation market [32]. Prioritizing multiple ESS in inertia and damping emulation as per the SoC level, or aggregating them based on SoC balancing control, are two options to coordinate the large numbers of VSMs in the power system.
# Characteristics and Processing of Hydrogen-Treated Copper Powders for EB-PBF Additive Manufacturing
## 2 Materials and Methods
### Simulation and Validation of Variable Spacing Single Melt Track Width Wafers  
We hypothesized that during melting, H\({}_{2}\)O vapor escapes from both the liquid melt pool and from powder in the heat affected zone. For the powder adjacent to the melt pool, the internal pressure of the H\({}_{2}\)O vapor overcomes the mechanical strength of the grain boundary, and the pore begins to expand in size. This leads to the fracturing of the particle along the grain boundaries and a release of H\({}_{2}\)O vapor into the vacuum system. To explore this, single melt track width scenarios were simulated at various hatch spacings, utilizing the 3D transient heat transfer model developed by [PERSON] et al. [41] at the Oak Ridge National Laboratory. Calculations were made using FEniCS software (2019.1.0, FEniCS), utilizing python scripts, and the thermal model output was analyzed using ParaView visualization software (v5.7, Kitware Inc., Clifton Park, NY, USA). The simulation used variables including heat input, beam speed, thermal conductivity, and so on, to model the EB-PBF process and the temperature profile across a solid plate with the thermophysical properties of a comparable copper powder bed during different operating conditions. The simulation variables used in this study are listed in Appendix A.  
Based on the model results, single melt track width wafers 13 mm long and 10 mm tall were fabricated using hydrogen-treated HO-Cu powder with 2.0 mm on center spacings, as shown in Figure 3. The build substrate was a machined OFE copper substrate that measured 90 mm in diameter and 30 mm in thickness. During the EB-PBF processing, the build temperature was maintained at 270 \({}^{\circ}\)C, measured by a thermocouple attached to the bottom of the plate, and the partial pressures of H\({}_{2}\)O vapor and other gas species were monitored using the RGA. Each wafer was melted with a line energy of 0.8 J/mm (600 mm/s, 8 mA, 0 mA focus offset) and layer thickness of 40 \(\upmu\)m, with the power analyze function (Arcam specific thermal controls) disabled. The beam scan direction was constant (right to left) for each wafer and each layer.  
Upon the build completion, each block of wafers was removed from the surrounding sintered powder and build surface using a razor blade. The sintered powder cake between the solid wafers was serially sectioned with a razor blade as a function of the distance from the melt pool, and placed on carbon tape for the SEM analysis. Additionally, the top surface of the intact sintered cake was analyzed on a JOEL 6010 LA, in order to observe sintered powder characteristics at specific distances from the melt track.
# Reliability-Aware SPICE Compatible Compact Modeling of IGZO Inverters on a Flexible Substrate
## 3 Result and Discussion
### SPICE Compatible Compact Modeling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bending Condition** & **Bias Condition** & \(\boldsymbol{\tau(s)}\) & \(\boldsymbol{\Delta V_{\text{T0}}(V)}\) & \(\boldsymbol{\beta}\) \\ \hline \multirow{4}{*}{Flat} & Driver positive stress & \(4\times 10^{2}e^{(\frac{-\frac{\text{iv}}{25}}{25})}\) & \(1.8\times 10^{-1}e^{(\frac{\text{iv}}{30})}\) & 0.2 \\ \cline{2-5}  & Driver negative stress & \(2.5\times 10^{4}e^{(\frac{-\frac{\text{iv}}{20}}{25})}\) & \(4.8\times 10^{-2}e^{(\frac{\text{iv}}{30})}\) & 0.3 \\ \cline{2-5}  & Driver recovery & \(3\times 10^{2}e^{(\frac{-\text{iv}}{125})}\) & \(1.7\times 10^{1}e^{(\frac{\text{iv}}{35})}\) & 0.2 \\ \cline{2-5}  & Load positive stress & \(1.4\times 10^{3}e^{(\frac{-\text{iv}}{45})}\) & \(2.2\times 10^{1}e^{(\frac{\text{iv}}{35})}\) & 0.45 \\ \cline{2-5}  & Load negative stress & \(4.5\times 10^{1}e^{(\frac{-\text{iv}}{125})}\) & \(0.12\times 10^{1}e^{(\frac{\text{iv}}{125})}\) & 0.3 \\ \cline{2-5}  & Load recovery & \(5\times 10^{1}e^{(\frac{-\text{iv}}{125})}\) & \(1.6\times 10^{1}e^{(\frac{\text{iv}}{35})}\) & 0.6 \\ \hline \multirow{4}{*}{Bending} & Driver positive stress & \(1.5\times 10^{4}e^{(\frac{-\text{iv}}{15})}\) & \(3.0\times 10^{-1}e^{(\frac{\text{iv}}{45})}\) & 0.2 \\ \cline{2-5}  & Driver negative stress & \(1.6\times 10^{4}e^{(\frac{-\text{iv}}{22})}\) & \(5.5\times 10^{-2}e^{(\frac{\text{iv}}{158})}\) & 0.3 \\ \cline{2-5}  & Driver recovery & \(9\times 10^{2}e^{(\frac{-\text{iv}}{22})}\) & \(2.3\times 10^{1}e^{(\frac{\text{iv}}{25})}\) & 0.2 \\ \cline{2-5}  & Load positive stress & \(3\times 10^{3}e^{(\frac{-\text{iv}}{45})}\) & \(3.5\times 10^{1}e^{(\frac{\text{iv}}{45})}\) & 0.45 \\ \cline{2-5}  & Load negative stress & \(4.5\times 10^{4}e^{(\frac{-\text{iv}}{125})}\) & \(0.003\times 10^{1}e^{(\frac{\text{iv}}{124})}\) & 0.12 \\ \cline{2-5}  & Load recovery & \(5\times 10^{1}e^{(\frac{-\text{iv}}{125})}\) & \(4.5\times 10^{1}e^{(\frac{\text{iv}}{05})}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extracted parameters for compact SPICE modeling. Figure 6: Measured values and simulation results of (**a**) a load transistor, (**b**) a driver transistor, and (**c**) a voltage transfer curve (VTC) of an inverter in a flat condition. Similar results for (**d**) a bending load transistor, (**e**) a bending driver transistor, and (**f**) a VTC of a bending inverter.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
###### Abstract  
To address the continuous growth in high-speed ubiquitous access required by residential users and enterprises, Telecommunication operators must upgrade their networks to higher data rates. For optical fiber access networks that directly connect end users to metro/regional network, capacity upgrade must be done in a cost- and energy-efficient manner. 40 Gb/s is the possible lane rate for the next generation passive optical networks (NG-PONs). Ideally, existing 10 G PON components could be reused to support 40 Gb/s lane-rate NG-PON transceiver, which requires efficient modulation format and digital signal processing (DSP) to alleviate the bandwidth limitation and fiber dispersion. The major contribution of this work is to offer insight performance comparisons of 40 Gb/s lane rate electrical three level Duobinary, optical Duobinary, and four-level pulse amplitude modulation (PAM-4) for incorporating low complex DSPs, including linear and nonlinear Volterra equalization, as well as maximum likelihood sequence estimation. Detailed analysis and comparison of the complexity of various DSP algorithms are performed. Transceiver bandwidth optimization is also undertaken. The results show that the choices of proper modulation format and DSP configuration depend on the transmission distances of interest.  
fiber optics communications; next generation passive optical network; nonlinear equalization; maximum likelihood sequence estimation; link power budget
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Encryption and Confirmation Process  
Step 1: BMiX, the designated miner for DeFi X, initiates the transfer by generating an asset hash \(H\) for Ass, termed Ass Hash X = \(H\)(Ass X). BMiX also generates a nonce from a true random number generator (TRNG) and captures the hash of the preceding block \(PH\). Using these values, BMiX constructs a block defined as \(B=B_{X}\)(Ass H, \(T,PH\), nonce) and securely transmits it using ECC encryption and a digital signature, denoted as \(\text{send}(\sigma_{B_{X}}\), \(\text{Enc}_{{{\text{\scriptsize{${}_{\text{\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \texttexttexttexttexttexttext   }}}}}}}}}}}}}}}}} (B_{X}))\).  
Step 2: At the destination, BMiY mirrors the actions performed by BMiX. BMiY decrypts and verifies the received block \(B_{X}=\text{Dec}_{sK_{BMiY}}(B_{X})\) and verifies \((pK_{BMiX},B_{X},\sigma_{B_{X}})\). Post verification, BMiY recalculates the asset hash Ass H = \(H\)(Ass X) and reconstructs the block as \(B_{X}^{*}=B_{X}^{*}\)(Ass H, \(T,PH\), nonce). A successful verification confirming the accuracy of the transferred asset is established if \(B_{X}\) equals \(B_{X}^{*}\). Algorithm 2 provides a detailed procedure for the improved transfer confirmation (ITC) process.  
Moreover, ITC enables quick and reliable transfer confirmations and ensures precise finality, making CrossDeFi more effective and capable of managing massive asset transaction volumes with a higher level of user satisfaction. This process not only improves the entire user experience against double-spending but is also highly beneficial for time-sensitive DeFi operations.  
Figure 5: Asset transfer communication scenario.
## 3 Methodology  
The proposed hybrid model has two components that are connected sequentially. The first one is called the basic model and includes ARIMA forecasting to capture the linear relationship of the input data. The output of the ARIMA is fed into the second component. A neural network model capable of capturing both linear and nonlinear patterns is used in the second component. As most data sets are composed of linear and nonlinear elements, two separate models that work in tandem will be more effective in capturing different portions of the data. Before moving into details of the hybrid model, brief descriptions of ARIMA, and ANN models are given below.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 3 SC-CNN-BiLSTM-Based Signal Detection for UWA-OTFS  
Figure 2 shows the proposed deep learning-based OTFS system, where the transmitter is the same as the typical OTFS system and the detection module is replaced by SC-CNN-BiLSTM. We assume that the CIR is known in the detection module.  
SC-CNN-BiLSTM training is performed using a set of training sets known at the transmitter and receiver. The training data are pseudo-randomly generated by the transmitter and sent to the receiver through the DD channel. The received signal vector \(y\) and the transmitted signal vector \(x\) can be used for training the neural network. After being trained, the real and imaginary parts of \(y\) in the validation set are used as input to the SC-CNN-BiLSTM to recover the unknown transmitted data.
## 4 Experiments and Analysis
### Comparative Analysis of Single-Source Domain and Multi-Source Domain Transfer Learning Task Results  
In order to verify the performance of the proposed method more objectively [37], single-source domain and multi-source domain learning were performed on three groups of transfer learning tasks. The model structure and parameters remained unchanged. The experimental results are shown in Table 5.
# Wheat Germ Agglutinin--From Toxicity to Biomedical Applications
## 4 WGA Biological Activity  
### Antifungal Activity  
The main fungal cell wall component is chitin, which is a \(\beta\)(1,4)-homopolymer of GlcNAc. WGA specifically binds to fungal cell walls and disrupts the cell's structural integrity [27]. It demonstrates WGA function in plant immunity against pathogenic fungi. However, there are not many results published regarding native WGA antifungal activity to human pathogenic fungi [28; 29]. Recombinant WGA linked to the effector Fc region of murine IgG2a was designed for mycosis treatment. WGA-Fc antibodies were successfully bound to chitin standard fungal cultures and inhibited the growth of _Histoplasma capsulatus_, _Cryptococcus neoformans_, _Candida albicans_, and _Saccharomyces cerevisiae_. It also augmented antifungal macrophage functions and fully protected mice against _H. capsulatus_ infections [29]. Other herein class lectins demonstrate strong antifungal activity against _Candida albicans_ (URM 5901), minimal inhibitory concentration (MIC) 25 \(\upmu\)g/mL, against _Candida krusei_ (URM 6391)-MIC 12.5 \(\upmu\)g/mL [30] and _Candida tropicalis_ (ATCC 750)-MIC 11.9 \(\upmu\)g/mL [31].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defatting Agent** & **Purification Strategy** & \begin{tabular}{c} **Number** \\ **of Steps** \\ \end{tabular} & \begin{tabular}{c} **Yield of WGA (mg Per** \\ **1 g Raw Wheat Germ)** \\ \end{tabular} & **Reference** \\ \hline \multirow{4}{*}{Light petroleum} & Extraction in water & & & \\  & Protein precipitation 55\% (NH4)2 SO4 & & & \\  & \
\begin{tabular}{c} Dialysis in water \\ Ion exchange chromatography using a DEAE \\ 1-cellulose volume, two SE 2-Sephadex \\ columns, and QAE 3-Sephadex (\(\times\)4) \\ \end{tabular} & 7 & 0.61 & [22] \\ \hline \multirow{4}{*}{Light petroleum} & Extraction in water & & & \\  & Protein precipitation 55\% (NH4)2 SO4 & & & \\  &
\begin{tabular}{c} Dialysis in water \\ Affinity chromatography CNAG 4-Sepharose \\ (\(\times\)1) \\ \end{tabular} & 4 & 0.3 & [25] \\ \hline \multirow{4}{*}{Acetone} & Extraction in 0.05 N HC1 buffer & & & \\  & Protein precipitation 35\% (NH4)2 SO4 & & & \\  & n-Butanol treatment & & & \\  & Dialysis in 0.05 N HC1 and second protein & 6 & 0.48 & [17] \\  & precipitation 35\% (NH4)2 SO4 & & & \\  & Ion exchange chromatography on & & & \\  & DEAE-cellulose column (\(\times\)1) & & & \\ \hline \multirow{4}{*}{n-Hexane} & Extraction in 0.05 N HC1 buffer & & & \\  & Tangential flow filtration MWCO 5 [FOOTNOTE:5]Footnote 5:
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 4 Scan-To-Model Matching with POU Implicit Surface Representation
### POU Implicit Surface Representation for Planar Features
#### 4.2.2 POU Implicit Surface Representation  
The implicit surface is defined as the set of zeros of a function \(I(x)\), which also behaves as a distance function from \(x\) to the implicit surface. In this work, we use the POU implicit surface representation.  
Similar to the POU implicit surface framework in [32], we define the POU implicit function \(I^{M^{k}}(x)\) as an approximate distance of planar feature point \(x\in\mathbb{R}^{3}\) at time \(k\) to the POU implicit surface defined by the map \(M^{k}\):  
\[I^{M^{k}}(x)=\frac{\sum_{s_{i}\in P_{c}}W_{s_{i}}(x)(x-p_{s_{i}})\cdot \overrightarrow{n}_{s_{i}}}{\sum_{s_{j}\in P_{c}}W_{s_{j}}(x)} \tag{9}\]  
where \(p_{s_{i}}\) is the projected points on the corresponding planar patch \(s_{i}\), \(\overrightarrow{n}_{s_{i}}\) is the normal at point \(p_{s_{i}}\). For approximation, we use the quadratic B-spline \(b(t)\) to generate weight functions for planar patches.  
\[W_{s_{i}}(x)=b(\frac{3\left|x-c_{s_{i}}\right|}{2R_{s_{i}}}) \tag{10}\]  
where \(c_{s_{i}}\) is the geometric center of planar patch \(s_{i}\) and \(R_{s_{i}}=\max\limits_{x\in s_{i}}\left\|x-c_{s_{i}}\right\|\) is a spherical support radius of the planar patch \(s_{i}\). The function is proportional to the distance between \(x\) and \(c_{s_{i}}\) and inversely proportional to \(R_{s_{i}}\). It means that the closer \(c_{s_{i}}\) to the \(x\), the higher weight will be allocated. And the more scattered the points that form the planar patch are, the higher the weight. In addition to computing the distance from the feature point to the model surface, the surface normal at the projected point of the feature point \(x\) should also be computed. In this work, we use the normal of the closestpoint \(p_{s_{cloud}}\) to \(x\) as the approximation of the surface normal at the projected point. Compared with the method using only one plane to represent a surface (could be cylinder or sphere, etc.), we adopt multiple local planes with various planarities to represent a surface which can achieve higher accuracy and obtain more accurate distances from feature points to their correspondences. Figure 3 shows the illustration of POU implicit surface.
## 2 Effect of CO\({}_{2}\) on Crude Oil Viscosity Reduction
### Changes in Expansion Coefficient  
Figure 3 illustrates the relationship between the coefficient of volume expansion and pressure and the amount of CO\({}_{2}\) injection. The volume expansion coefficient refers to the ratio of the crude oil volume under formation pressure after adding CO\({}_{2}\) to the volume without adding CO\({}_{2}\) (still under formation pressure). This coefficient reflects the capacity of CO\({}_{2}\) to expand crude oil after gas injection. Figure 4 shows the curve of the coefficient for the CO\({}_{2}\)-ground oil system against the amount of CO\({}_{2}\) injected. The experimental results show that the volume of crude oil significantly expands after CO\({}_{2}\) injection, and the volume expansion coefficient increases with the increase in the amount of CO\({}_{2}\) injected. Because the solubility of CO\({}_{2}\) in crude oil increases with rising pressure, the ability of CO\({}_{2}\) expansion volume is enhanced by increasing the injection pressure, which is conducive to improving the oil displacement efficiency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**CO\({}_{2}\)****Molar**} & \multirow{2}{*}{**Saturation**} & \multirow{2}{*}{**Gas-Oil Ratio/**} & \multicolumn{2}{c}{**Coefficient of**} & \multirow{2}{*}{**Density/g/cm\({}^{3}\)**} & \multirow{2}{*}{**Viscosity/mPa-s**} \\  & & & & & \\ \hline
0 & 0.00 & 5.69 & 20.0 & 1.0000 & 0.8526 & 19.03 \\
1 & 18.85 & 8.55 & 40.9 & 1.0465 & 0.8536 & 12.35 \\
2 & 40.00 & 15.45 & 82.5 & 1.1145 & 0.8566 & 7.85 \\
3 & 52.10 & 22.12 & 121.2 & 1.1918 & 0.8645 & 6.56 \\
4 & 62.00 & 33.05 & 175.5 & 1.2895 & 0.8751 & 5.76 \\
5 & 69.00 & 47.26 & 225.4 & 1.3805 & 0.8935 & 5.49 \\
6 & 72.45 & 57.76 & 271.6 & 1.4315 & 0.9048 & 5.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of experimental results.  
Figure 2: The relationship between the saturated pressure of crude oil and the amount of CO\({}_{2}\) injected.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 6 Mathematical Models  
To assess the feasibility of previously proposed designs, especially for long distances, it is essential to perform a link budget calculation for every wireless link between two points. This calculation involves several key parameters of wireless modules, including transmit (TX) power, antenna gain, receiver sensitivity, operating frequency, and the distance between the two points. These factors collectively determine the ability of the communication link to maintain a reliable connection under specified conditions [29].
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 2 Methods
### Attentional Mechanisms  
The attention mechanism is a popular neural network technique used in natural language processing, computer vision, and speech recognition.  
The objective of the attention mechanism is to concentrate the incoming data on the information that is most pertinent to the subsequent processing step. The attention mechanism in neural networks is typically employed to choose input features or hidden states in order to efficiently complete various tasks. To enable the model to concentrate on the most relevant areas of the image and produce better descriptions, [PERSON] et al. [19] presented two attention techniques. A graph neural network model based on attention mechanisms was proposed by [PERSON] et al. [17] and employs a self-attentive mechanism to analyze graph structure data. In order to better understand the spatial and temporal information in geo-aware time series data, A multi-level attention network model for geo-aware time series prediction was put forth by [PERSON] et al. [20]. This model uses multiple levels of attention mechanisms to capture various temporal dependencies.  
We apply graph convolution and attention methods for modeling, accounting for the same high spatio-temporal correlation in crowd flow data, as inspired by the aforementioned.