esa-sceva/satcom-chunk-collection huggingface dataset:
# Inhibition of _Tp53_ Mutant Oral Cancer by Reactivating p53
## 3 Results
### Increased Acetylation of p53 upon Treatment with 4 HR  
Treatment of YD-15 cells with 4 HR increased the expression levels of Ac-p53 (Lys319) (Figure 2). The relative expression level of Ac-p53 in the untreated control was 0.022 \(\pm\) 0.012 (Figure 2). Upon 1 \(\upmu\)M 4 HR treatment, the relative expression levels were 0.024 \(\pm\) 0.011, 0.038 \(\pm\) 0.009, and 0.542 \(\pm\) 0.090 at 2, 8, and 24 h after treatment, respectively; for 10 \(\upmu\)M 4 HR, this was 0.027 \(\pm\) 0.011, 0.746 \(\pm\) 0.064, and 1.051 \(\pm\) 0.073 at 2, 8, and 24 h, respectively; and for 100 \(\upmu\)M 4 HR, this was 0.032 \(\pm\) 0.009, 1.103 \(\pm\) 0.131, and 1.278 \(\pm\) 0.122 at 2, 8, and 24 h, respectively. The difference between groups was statistically significant (\(p\) < 0.001). In the post hoc comparison test, the difference between the untreated control and 4 HR-treated groups was statistically significant (\(p\) < 0.001 for 10 and 100 \(\upmu\)M 4 HR treatment at 8 h and \(p\) < 0.001 for all groups at 24 h).  
The expression levels of HDAC4 decreased after 4 HR treatment (Figure 3A). The differences were statistically significant (\(p\) < 0.05). The relative expression level of HDAC4 in the untreated control was 1.270 \(\pm\) 0.219. Upon 1 \(\upmu\)M 4 HR treatment, the relative expression levels were 0.893 \(\pm\) 0.095, 0.487 \(\pm\) 0.100, and 0.394 \(\pm\) 0.024 at 2, 8, and 24 h after treatment, respectively; for 10 \(\upmu\)M 4 HR, this was 0.667 \(\pm\) 0.096, 0.444 \(\pm\) 0.038, and 0.175 \(\pm\) 0.042 at 2, 8, and 24 h, respectively; for 100 \(\upmu\)M 4 HR, this was 0.568 \(\pm\) 0.150, 0.583 \(\pm\) 0.063, and 0.043 \(\pm\) 0.007 at 2, 8, and 24 h, respectively. The difference between groups was statistically significant (\(p\) < 0.001). In the post hoc comparison test, the difference between the untreated control and 4 HR-treated groups was statistically significant (\(p\) = 0.010 for 1 \(\upmu\)M 4 HR treatment at 2 h and \(p\) < 0.001 for the others). A similar expression pattern was observed in YD-9 cells (Supplementary Figure S2). HDAC activity was inhibited upon treatment with 4 HR (Figure 3B). The differences were statistically significant (\(p\) < 0.05). In the post hoc comparison test, the difference between the untreated control and 4 HR-treated groups was statistically significant (\(p\) = 0.003 for 10 \(\upmu\)M 4 HR treatment at 24 h and \(p\) < 0.001 for 100 \(\upmu\)M 4 HR treatment).  
Figure 1: The change in p-p53 expression levels upon 4 HR treatment in YD-15 cells. When compared to the untreated control, 4 HR treatment increased p-p53 expression levels significantly (\({}^{\star}\)\(p\) < 0.05 by Bonferroni test).
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 1 Introduction
In the STD structure, the DSSS technique can optimize the transmit signal with the RAKE technique to simplify the equalization operation. However, it still leads to MAI. We propose an STD method based on a noise-reduction learning model to solve the above problems, which performs noise-reduction and diversity combining in a phased manner. The main work of this paper can be summarized as follows:  
* To effectively suppress the nonlinear components contained in the VURN, we propose a CGAN-based residual cancellation learning model by referring to the structure of the KBM-ANC. The proposed model puts the received signal \(\mathbf{y}(t)\) and the reference noise \(\mathbf{\hat{n}}(t)\) into the CGAN. A nonlinear mapping is created between them by performing \"generative-adversarial\"-based learning in CGAN to estimate noise \(\mathbf{\hat{n}}(t)\). Then, the residual cancellation learning is performed by the RCL module to obtain the final noise-reduction estimate \(\mathbf{\hat{y}}(t)\). * To efficiently handle the complex-type characteristics of UWA signals, we propose a pre-processing structure for complex-type orthogonal compression. The proposed structure performs a complex convolution operation on the UWA signal to obtain separated real and imaginary features. The separated features are trained separately for noise-reduction to avoid the loss of complex-type data features. The separation of real and imaginary parts can lead to orthogonality corruption. To minimize the corruption, we normalize the orthogonal scale by compressing the real and imaginary features orthogonally. The proposed pre-processing method effectively fits the structure of the DM in the complex-CGAN, together with CReLU, and effectively improves the performance of the noise-reduction learning model for complex-type data. * To effectively reduce the MAI due to CDMA signals, we propose an improved STD model. The model matches a weight matrix for each array element at the receiver. The model allows the main path signal to be enhanced and effectively reduces the influence of other multipath signals. Thus, the model reduces the correlation between signals and suppresses MAI in the system. The rest of this paper is organized as follows. In Section 2, we compile typical literature and methods in noise-reduction and STD processing and summarize the advantages and disadvantages of each method to complete the summary of related works. Section 3 discusses the transmit-receive model of the underwater acoustic array, specifying the mathematical model of the transmit signal and noise. In Section 4, the structure of the proposed joint model is described in detail. It mainly includes the pre-processing data methods, the main components of the noise-reduction learning model, and the STD model based on the weight update. Finally, the evaluation and result analysis are shown in Section 5. In Section 6, conclusions are given.
# Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
## 3 Simulation and Measurement Results
### Broadband FR2-Band PA Design
#### 3.2.2 Measurement and Simulation of the CS-Driven Two-Stack GaN PAs  
The design of the medium-power common-source (CS)-driven two-stack GaN PA is described in [12], with the measurement and post-layout PEX EM simulation results summarized in Figure 5. The measurement data appear to match up recently well with the PEX EM simulations for this PA as well. In measurement, the medium-power GaN PA reaches \(P_{OUT,SAT}\)\(\sim\)22-24 dBm across its BW from 24 to 39 GHz, with \(\sim\)20 dB gain. A Watt-level version of the medium-power CS/2-stack PA was also designed, and the post-layout PEX EM simulations are summarized in Figure 6. The Watt-level CS/2-stack PA shows \(P_{OUT,SAT}\)\(\sim\)1 W from 24 to 28 GHz, with \(\sim\)20 dB of small signal gain from 24 to 44 GHz.  
Figure 4: This figure shows the differential cascode/stacked Si CMOS PA’s (**a**) schematic and die micrograph along with its measured vs. PEX EM-simulated (**b**) S-parameters and (**c**) summarized large-signal performance using \(V_{DD}\) = 1.8 V for \(\sim\)0.22 mA/\(\upmu\)m current density on each of the devices.  
Figure 5: This figure shows the medium-power CS/2-stack GaN PA’s (**a**) schematic and die micrograph along with its measured vs. PEX EM-simulated (**b**) S-parameters and (**c**) summarized large-signal performance using \(V_{D1}\) = 6 V and \(V_{D2}\) = 20 V for \(\sim\)0.22 mA/\(\upmu\)m current density on the device M1 and \(\sim\)0.36 mA/\(\upmu\)m current density for devices M2 and M3.  
Figure 6: This figure shows the Watt-level power CS/2-stack GaN PA’s (**a**) schematic and die micrograph along with its PEX EM-simulated (**b**) S-parameters and (**c**) summarized large-signal performance using \(V_{D1}\)/\(V_{D2}\) = 10/20 for \(\sim\)0.19 mA/\(\upmu\)m current density on the device M1 and \(\sim\)0.08 mA/\(\upmu\)m current density for devices M2 and M3.
## 2 Materials and Methods
### InSAR Analysis  
#### 2.2.1 Data  
Regarding InSAR processing, free SAR scenes from the Sentinel-1 satellite constellation were used (Table 1). Sentinel-1 is designed to operate in four modes: strip map (SM), interferometric wide (IW) swath, extra wide (EW), and wave (WV) with IW the standard image mode product following the novel Terrain Observation with Progressive Scans (TOPS) acquisition mode. Each original TOPSAR image is composed of three sub swaths (IW1, IW2, and IW3). Every sub swath consists of a series of bursts, which in total can provide a large coverage of 250 km with pixel resolutions of 5 m and 20 m along the range and azimuth directions, respectively [18]. For interferometric processing, in this study the next tracks of Sentinel-1A IW swath mode Single Look Complex (SLC) images covering the pre- and post-seismic periods were downloaded from European Space Agency's (ESA) Sentinel Hub site ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)) accessed on 21 January 2021): (i) ascending track 131 (slice number: 7) dated 6 October 2020/30 October 2020; and (ii) descending track 36 (slice number: 13) dated 24 October 2020/5 November 2020. In this way, an interferometric pair was created for each ascending and descending orbit.
## 2 Design Parameters of the Metamaterial and Simulation Setup
### Extraction Process of Medium Parameters  
To extract the various properties of the material, the S-parameters model of the post-processing module of CST can be deployed [32]. This software is applied to obtain information associated with the three important characteristics of permittivity (\(\varepsilon_{r}\)), permeability (\(\mu_{r}\)) and refractive index (\(n_{r}\)) of the proposed unit cell of metamaterial to realize EM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Symbol** & **Size (mm)** & **Parameter Symbol** & **Size (mm)** \\ \hline L & 11 & g & 0.40 \\ B & 11 & R\({}_{1}\) & 4.30 \\ c & 10.40 & R\({}_{2}\) & 3.80 \\ d & 10.40 & r\({}_{1}\) & 3.3 \\ G & 0.50 & r\({}_{2}\) & 2.8 \\ e & 0.75 & r\({}_{3}\) & 1.5 \\ t & 0.50 & r\({}_{4}\) & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric values of the unit cell configuration.  
Figure 1: (**a**) Unit cell (**b**) perspective view of the unit cell (**c**) simulation setup.  
properties [33]. Moreover, the refractive index, S parameters (reflection and transmission coefficient) and impedance can be correlated with the help of Equations (1)-(5) of the robust retrieval method described in [34].  
\[|S_{11}|=\frac{R_{01}-R_{01}e^{2 nkd}}{1-R_{01}^{2}e^{d2 nkd}} \tag{1}\]  
\[|S_{21}|=\frac{\left(1-R_{01}^{2}\right)\,e^{2 nkd}}{1-R_{01}^{2}e^{d2 nkd}} \tag{2}\]  
\[R_{01}=\frac{Z^{2}-1}{\left(Z+1\right)^{2}} \tag{3}\]  
Here, impedance is expressed as:  
\[Z=\pm\,\sqrt{\left\{\frac{\left(1+S_{11}\right)^{2}-S_{12}^{2}}{\left(1-S_{11 }\right)^{2}-S_{12}^{2}}\right\}} \tag{4}\]  
where \(S_{11}\) = reflection coefficient, and \(S_{21}\) = transmission coefficient. Then,  
\[\text{Refractive Index},\;n_{r}=\frac{1}{kd}\left[\left\{\ln\left(e^{inkd} \right)\right\}^{n_{r}}+2m\pi-i\left\{\ln\left(e^{inkd}\right)\right\}^{r}\right] \tag{5}\]  
The electromagnetic wave is fixed to propagate along the z direction, together with the perfect electric and the magnetic fields, which are applied as boundary conditions along the x and y directions, respectively. Additionally, the relative permittivity (\(\epsilon_{r}\)) and relative permeability (\(\mu_{r}\)) can be derived from Equations (6) and (7), respectively, using the Nicolson-Ross-Weir (NRW) technique [35].  
\[\text{Permittivity},\;\epsilon_{\text{r}}=\frac{\text{c}}{\text{j}\pi\text{fd}} \cdot\left[\frac{1}{1+\left(\text{S}_{21}+\text{S}_{11}\right)}-\frac{\left( \text{S}_{21}+\text{S}_{11}\right)}{1+\left(\text{S}_{21}+\text{S}_{11} \right)}\right] \tag{6}\]  
\[\text{Permeability},\;\mu_{r}=\frac{\text{c}}{\text{j}\pi\text{fd}}\cdot\left[ \frac{1}{1+\left(\text{S}_{21}+\text{S}_{11}\right)}-\frac{\left(\text{S}_{2 1}-\text{S}_{11}\right)}{1+\left(\text{S}_{21}+\text{S}_{11}\right)}\right] \tag{7}\]  
where \(c\) = speed of light, \(f\) = frequency and d = the thickness of the substrate. MATLAB codes are written based on Equations (6) and (7). The values of the material parameters extracted through the NRW technique are verified and compared with the results of numerical simulation.
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 1 Introduction
### Definition and Development History of Digital Twin
In urban planning, digital twin technology may provide researchers and planners with more information regarding the growth of cities or help predict the effectiveness of using urban facilities. Though digital twin technology offers significant potential, several challenges remain to overcome, including data processing on a large scale, data security and privacy, cost control, technical limitations, and seamless integration of the physical and digital worlds. Several additional challenges and complexity arise in the architectural field, particularly in data collection and processing, real-time monitoring, data updates, data access, personalization of user interface design, cybersecurity, and integration with intelligent solutions [7]. Professionals in related industries are actively engaged in research and development to overcome these challenges. As a result of these initiatives, several industry leaders have made significant investments in digital twin technology. At the same time, the increased attention and increasing number of publications have led to a strong impetus for advancing the technology [8]. The application of digital twin technology has achieved significant growth in recent years, a trend closely related to the rapid development of Industry 4.0. Along with providing a wide array of application scenarios, the development of Industry 4.0 also provides the necessary support for the technology's development. With the continuous development of Industry 4.0, digital twin technology has more significant potential for solving current problems and advancing the industry further.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 1 Introduction
### Problem Identification and Motivation
Consequently, a process engine must be able to cope with IoT device characteristics [9]. On one hand, tools are needed to control and monitor the process, on the other, mechanisms are required to execute the IoT actions (i.e., sensing and actuating). There are many efforts to extend BP modeling languages with IoT specific aspects; most existing approaches, however, suffer from four drawbacks:  
1. IoT-specific extensions do not cover all IoT characteristics, but only support common behaviors (i.e., sensing and/or actuating). 2. IoT-specific extensions increase the complexity of the modeling language. 3. The proposed solutions focus on the modeling phase, but either neglect execution or depend on specific IoT technologies or application domains. 4. Existing approaches either do not support the (real-time) monitoring of IoT-driven BPs or the monitoring approach depends on specific IoT technologies. Overall, the management of IoT-driven BPs is challenging due to a high complexity and heterogeneity. To remedy the above drawbacks, we consider the following research questions:  
1. In what way should BPMN be extended to enable the modeling of IoT-driven BPs in a user-friendly way? 2. How to design an IoT-driven BPMS that covers the modeling, execution and monitoring of IoT-driven BPs? 3. How to support process engineers in modeling, executing and monitoring IoT-driven BPs?
## 3 Methodology
### System Model
#### 3.1.6 Neighboring Node Identification (NNI)  
The core method of NNI is given in Equations (1)-(3), respectively. Now, each equation has its unique characteristics. The discovery of nodes takes place in this step, but the nodes must find the best nodes among all the nodes, that is, the nodes with the highest energy level, and the highest and most stable communication path.  
Each node transmits welcome packets to identify other nodes in its transmission range. The nodes maintain the isolation line between contiguous nodes below the depth threshold, which enables the identification of the most appropriate and optimal transponder nodes for data transmission. By identifying neighboring nodes, analyzing routing paths, and exchanging depth information, the neighboring node identification lays the groundwork for efficient communication in UWSNs. This procedure ensures that subsequent phases of data transmission and routing are both efficient and effective.  
\[W_{g}=\frac{\min(PL_{SiRi},PL_{Ri,Di})+\min((SNR_{Si,Ri},SNR_{RiDi})}{\max((R.E_{ Ri},R.E_{Di})} \tag{1}\]  
\[\mathrm{PL}=20~{}n~{}Log_{10}(d)+c \tag{2}\]  
\[\mathrm{SNR}=\frac{P_{signal}}{P_{power}},SNR_{db}=10 log_{10}(\mathrm{SNR}) \tag{3}\]  
In the equations, PL represents the path loss between the communicating nodes (_Si_, _Ri_, and _Di_). _Si_ stands for the sender node, _Ri_ for the relay node, and _Di_ for the destination/receiver node. The signal-to-noise ratio (SNR) quantifies the quality of the communication link between two terminals. For the links between the source and the receiver nodes and between the sender and destination nodes, the SNR values (SNR (_Si_, _Ri_) and SNR (_Ri_, _Di_)) are calculated. Renewable energy, also known as residual energy, is the energy remaining in the nodes after several cycles of communication. This is an essential consideration when selecting NHNs, as it ensures that only nodes with sufficient energy are chosen to anticipate in the communication procedure, thereby extending the network's lifetime. The weighted gain (\(W,g\)) identifies the most efficient NHN (_Ri_) for data transmission among the sender (_Si_) and receiver (_Di_) nodes by considering path loss, SNR, and residual energy. This strategy maximizes the overall performance of the submerged wireless sensor network via ensuring that the transmission process is both energy-efficient and reliable.
## 3 Laboratory Experiment and Measurement  
### Sensor Experiment  
#### 3.1.1 Sensor Calibration  
Standard gas samples preparation is crucial for gas detection, due to the fact that the accuracy of curve fitting and calibration depends on the uncertainty of the distributed gas samples. In the following experiment, compared with the static injection distribution, including operation error, a dynamic gas distribution using a mass flow meter was adopted. A 5000 ppm CO\({}_{2}\) sample with 2% uncertainty and a 99.999% pure N\({}_{2}\) were used as the gas sources to distribute desired gas samples.  
In consideration of the actual circumstances in a greenhouse, the measurement calibration range was set to 0\(-\)4000 ppm. A series of gas samples distributed with a desired concentration was kept flushing the chamber and the data was recorded for >10 min until the chamber was completely filled with the gas sample, as shown in Figure 5a. According to the Lambert-Beer law, the relationship between the measured CO\({}_{2}\) concentration (C) and the differential-ratio value between the two amplitudes of the output voltage signals (defined as \(U_{1}\) and \(U_{2}\)) is  
\[C=43.43-1779.38\times\ln(\frac{0.2-\Delta U}{1.37}) \tag{5}\]  
Figure 4: The diagram of the FLC (Fuzzy logic controller) for CO\({}_{2}\) concentration regulation.  
Figure 5: (**a**) Experimental data dots of the differential voltage (\(\Delta U\)) versus the standard CO\({}_{2}\) concentration. The inset shows the measured \(\Delta U\) under 100 ppm for 10 min; (**b**) The measured detection errors for the prepared samples with the calibrated CO\({}_{2}\) sensor.  
Based on the measurement data shown in Figure 5a, the relative detection errors were calculated and plotted in Figure 5b. The maximum detection error occurs when the measured gas sample is near 0 ppm which is 7.2%, and the detection error became smaller with the increasing of the concentration.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 2 Data and Methodology
### Numerical Model
#### 2.3.2 Boundary Conditions  
The space domain of the model was as follows: upstream to the Jiangyin Station in Jiangsu Province, at approximately 200 km away from the Xu River in the YRE. The offshore had three open boundaries in the south, north, and east, respectively. The spatial extent of the model was from 29.5\({}^{\circ}\) N to 32.5\({}^{\circ}\) N in the S-N direction, and from 120\({}^{\circ}\) E to 124.5\({}^{\circ}\) E in the W-E direction, with a total area of 93,185 km\({}^{2}\). The topographical river data were measured. Then, the sea area data were digitalized to an underwater topographical sea chart. The computational domain was composed of an unstructured triangular mesh with 54,243 nodes and 92,771 elements. The largest mesh was located at the eastern ocean boundary, and the smallest was located in the North Passage. Generally speaking, the mesh size decreased from the open sea to the near shore (Figure 4). Then, based on the results of previous tests of mesh sensitivity, the differences in the physical factors were computed using mesh lengths from 200 to 8000 m.  
The land boundary of the model required the conditions of \"non-importable and slippery\". That is to say, the flow velocity was zero along the normal direction of the closed boundary. In the open sea areas, a tidal wave model was used to calculate the tide levels of the three outer boundaries of the outer sea. In this study, the global tidal wave model TPXO6.2 [21,22] was selected. The open boundary varies with time. The initial tidal level and velocity are defined as 1 m and 0 m/s, respectively.  
According to the flow correlation analysis between Datong and Jiangyin stations [23], the runoff processes of the two stations were found to be similar. Therefore, the discharge data of the Datong station was selected as the flow boundary of Jiangyin. The flow boundary of Cangqian was determined to be 1000 m\({}^{3}\)/s. During the experimental process, dry and wet grids were used to discriminate the model shoals. The values of hdry = 0.005 m, hflood = 0.05 m, and hwet = 0.1 m were utilized for each parameter [20].  
The salinity value of the upper reaches of the model was 0, and salinity levels of 15% to 30% were linear interpolation from west to east in the southern boundary; a 35% salinity linear interpolation was determined in the northern boundary from west to east, using a 25% to 35% salt content as the  
Figure 4: The computational mesh of Yangtze River Estuary.  
linear interpolation. The salinity field obtained from the initial salinity 0 (continuous for 3 months) was used as the initial salinity field of the model [24].
## 3 Results and Discussion
### Analysis of Surface Microtexture and Roughness
It can be concluded that the proposed method for predicting the geometric dimensions of textures in the form of parallel microchannels formed by nanosecond laser pulses based on the use of the ablation crater diameter in calculations has a fairly good accuracy (the deviations did not exceed 10%).