esa-sceva/satcom-chunk-collection huggingface dataset:
# Allosteric Activation of GDP-Bound Ras Isoforms by Bisphenol Derivative Plasticisers
## 5 Conclusions  
Small GTPases constitute a family of proteins that can govern cellular processes by acting as binary on/off switches. Bisphenols are widely used as industrial plasticisers in polycarbonate plastics and can thus be found in many household products of modern life. Here, we present the first extensive study of how small GTPases, in particular H-Ras, N-Ras, and K-Ras isoforms as well as Rap-1A, interact with bisphenols BPA, BPAF, BPB, BPE, BPF, and BPNH\({}_{2}\). We could show that bisphenols bind selectivity to a subset of small GTPases and induce an active conformation. Not only could we determine their K\({}_{\text{D}}\) values, but we also show that these bisphenolic ligands selectively interact with all Ras isoforms except for Rap-1A. Bisphenols, in particular BPNH\({}_{2}\), can utilise a common site on Ras proteins and allosterically induce their respective active conformations while still bound to GDP. In summary, we show here that Ras isoforms are off-target proteins for bisphenols and that they can act as their agonists. They should thus be considered in future toxicity evaluations of bisphenols and they might also serve as a blueprint for new Ras agonists or even antagonists.  
Supplementary materials can be found at [[http://www.mdpi.com/1422-0067/19/4/1133/sl](http://www.mdpi.com/1422-0067/19/4/1133/sl)]([http://www.mdpi.com/1422-0067/19/4/1133/sl](http://www.mdpi.com/1422-0067/19/4/1133/sl)).  
**Acknowledgments:** We are grateful to the Deutsche Krebshlf e. V. (109776 and 109777), the DFG (SFB 642 A6, INST 213/757-1 FUGG), and the RUB Research SchoolPlus for generous financial support. We acknowledge support by the DFG Open Access Publication Funds of the Ruhr University of Bochum.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], performed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analysed the data; [PERSON], [PERSON], and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
**Abbreviations**  
GDP - Guanosine Diphosphate  
GTP - Guanosine Triphosphate  
SOS - Son of Sevenless  
SAR - Structure-Activity-Relationship  
GDI - Guanine Nucleotide Dissociation  
NMR - Nuclear Magnetic Resonance  
CSP - Chemical Shift Perturbation
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 3 System Architecture
### SpaceX's System  
The Starlink system of the SpaceX company is close to being operational because it has several hundreds of its satellites in orbit. Moreover, the beta test has started too in many countries. This constellation will have 4408 satellites, according to their files of FCC [20; 21; 22; 23], and had its first launch in 2019.  
This system has eight technically identical 1.5 m parabolic antennas in each gateway. These antennas will communicate with satellites visible on the horizon over a minimum elevation angle, which is 25 degrees [24]. For gateway communications, this constellation will use K-band frequencies for: the uplink (27.5-30 GHz), and the downlink (17.8-19.3 GHz) [2].
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 1 Introduction
### Objectives of the Research  
The transition from raw data to actionable insights remains underdeveloped in many air quality monitoring frameworks. Moving up the information hierarchy--from data to information, to insight, and finally to decisions--requires that the data be processed to facilitate meaningful analysis and decision making. The European directive 2008/50/EC [15] on air quality underscores the importance of integrating in situ measurements with modeling approaches to enhance the accuracy of assessments. However, modeling pollutant dispersion is complex and computationally demanding, and the application of models in air quality assessments remains limited. Ongoing research in air quality assessment focuses on enhancing model precision, improving pollutant tracking, and developing novel approaches to assess a wider range of pollutants more accurately and reliably [16; 17].  
This study aims to develop and implement a decision support system (DSS) to enhance urban air quality management through real-time monitoring, high-resolution modeling, and actionable insights. Specific objectives include the following:  
1. Developing a scalable and adaptable DSS for urban air quality assessment.
2. Integrating the AERMOD modeling system to map hourly air pollutant concentrations.
3. Leveraging high-performance computing (HPC) infrastructure to enhance computational efficiency.
4. Providing multi-pollutant analysis with a user-friendly interface for diverse stakeholders.
5. Demonstrating the system's application in analyzing NO\({}_{2}\) dynamics in the Cagliari metropolitan area.
## 1 Introduction
These research products [17; 18; 19; 20] indicated that a combination structure of porous metal and a microperforated metal panel was a promising candidate to develop a practical sound absorber for noise reduction in the low frequency range. In this study, parameter optimization of a combination structure of porous metal and microperforated panel, with a limited total thickness, was conducted with the aim of achieving better low frequency sound absorption performance. Although several combination structures have already been developed [17; 18; 19; 20], some deficiencies and defects have limited their practical application. Firstly, most of the current research has focused on the theoretical modeling of proposed sound absorbers and the actual limits for practical application have not been taken into consideration. One example is the limited total thickness of the sound absorber, which was determined by the space available for installation. Secondly, the influences of the structural parameters of the sound absorber were rarely studied in depth, and the finite experimental validations made it difficult to accurately gauge the absorption principle of the sound absorber. Thirdly, there were few practical methods to obtain optimal parameters for a given sound absorber under certain constraint conditions, especially when the structure of the investigated sound absorber was complex. Thus, in order to overcome these inadequacies, the combination structure of porous metal and microperforated panel was proposed and optimized. A theoretical model of the sound absorption coefficients of the combination structure was constructed through the transfer matrix method [21] based on the Johnson--Champoux--Allard model [22] and [PERSON]'s theory [23], which provided the foundation for further parameter optimization. Afterwards, the optimal structural parameters of the combination structure with a varied total thickness were obtained by a Cuckoo search algorithm [24; 25]. Then, finite element simulation models for the sound absorption performance of the combination structures were constructed in the virtual acoustic laboratory [26] with the aim of preliminarily verifying the reliability of the parameter optimization. After that, samples of porous metal and microperforated panels for the combination structure were prepared according to the obtained optimal structural parameter, and the assembled sound absorbers were measured based on the standing wave method [27; 28]. By comparing the theoretical data, the simulation data, and the experimental data of the obtained optimal sound absorbing structures, a novel method for the development of a low frequency sound absorber is proposed.
###### Abstract
One of the objectives of this study is to evaluate the capability of the proposed structure to handle 50% and 80% losses of actuator effectiveness. The main differences between the proposed control structure and the combination of the optimal control theory with fractional control presented in the literature [51; 52] can be listed as follows:  
1. In the proposed approach, the fractional-order integral controller (FIC) is augmented with an LQR as a pre-compensator to cope with the non-minimum phase problem. 2. The implementation of non-integer order derivatives and integrals in the closed-loop to function as a pre-compensator extends the LQR optimal problem from optimizing just the states of the system to optimizing the new augmented dynamics, whereas, in the literature, for example, the integrator is added to the LQR controller loop as an error tracker. 3. The proposed controller exhibits superior performance even in fault cases that are considered one of the challenges in practical applications. 4. The fractional-order integral control (FIC) is compared with a fractional-order derivative control (FDC) in terms of internal stability and closed-loop stability. 5. The feedback controller and the pre-compensator, i.e., feedforward controller parameters are calculated simultaneously to create a more flexible and robust control system. The article is organized as follows. Section 2 introduces the problem description and dynamics of the aircraft of interest. Then, the methodology with the underlying theoretical explanations is described in Section 3. Following this section for preliminaries, the proposed adaptive linear quadratic regulator fractional integral (A-LQR-FI) control structure is presented in detail in Section 4. The performance of the proposed approach is also compared with the pole placement controller, which is one of the commonly used methods in aircraft control. The results are discussed with plots and tables comparatively. The ultimate remarks derived from this comprehensive work are given in the final section, namely, conclusions.
## 2 Materials and Methods
### System Architecture
#### 2.2.1 Smartwatch Application (SA)  
This component obtains raw format data from the user through a smartwatch. The component is an application that operates in the Fitbit environment and consists of two modules: the application that runs at the smartwatch level and the companion application in the native Fitbit app (Figure 2), which runs at the smartphone level. Both applications were developed using the Fitbit environment.  
Fitbit Device Application  
This module has the role of reading sensor data and forwarding these data. Initially, sensors are initiated and the reading frequency is set for the accelerometer, gyroscope, orientation sensor, and optical sensor. After initialization, the application enters an infinite loop, reading data from the sensors and sending them forward to the companion through peerSocket connection.  
Fitbit Companion Application  
This module acts as an interface between the smartwatch application component and the Frailty Insight Detection System (FIDS). Data are collected from the smartwatch via peerSocket, formatted into JSON, added with the phone's location as longitude and latitude, and sent to FIDS. To optimize the data transmission process, data are sent in batches, where a fixed number of records are stored in a buffer, and when the buffer is full, the data are forwarded to be added to a queue. From this queue, data are transmitted through a secure WebSocket to FIDS. Before transmission, the WebSocket status is checked (it can be OPEN/CLOSED). When the WebSocket is successfully opened, the data are transmitted.
## 2 Methodologies
### Multiscale Sample Entropy  
MSE sheds light on the complicacy and nonlinearity of fluctuations across various time scales, which was put forward to conduct the measurement of the SE on numerous scales. It has extraordinary stability and interference-fighting capability [30; 31], assuming that the time series contain points \(x_{1},x_{2},x_{3},\ldots x_{N}\) sampled every millisecond, for the reason that the initial time scale \(\tau\) is 1 ms. In order to create various scales and resolutions of the signal, coarse graining the data fundamentally refers to conducting the averaging on consecutive points. The following are the specific computation steps for the MSE:  
1. At scale one, the coarse-grained time series are the initial time series.
2. At scale two, the coarse-grained time series can be created by averaging two successive time points as presented in Figure 1a. Defining that \(y_{1}=(x_{1}+x_{2})/2\); \(y_{2}=(x_{3}+x_{4})/2\) and so on.  
Figure 1: The illustration of conventional coarse-graining procedure: (**a**) \(\tau\) is 2; (**b**) \(\tau\) is 3.  
3. At scale three, as illustrated above, the average of three consecutive time points forms the coarse-grained time series shown in the Figure 1b. That is, define \(y_{1}=\left(x_{1}+x_{2}+x_{3}\right)/3\); \(y_{2}=\left(x_{4}+x_{5}+x_{6}\right)/3\) and so on. Mathematically, the above procedures can be transformed into a number of coarse-grained time series \(\{y_{j}^{\left(\tau\right)}\}\): \[y_{j}^{\tau}=\frac{1}{\tau}\sum_{i=\left(j-1\right)\tau+1}^{j\tau}x_{i},j=1,2, \ldots,N/\tau\] (8) where \(\tau\) is the time scale. The coarse-grained series \(y_{j}^{\left(\tau\right)}\), where \(j=1,2,\ldots,N\) are the original series when the time scale \(\tau=\)1. When \(\tau>1\), the coarse-grained time series has a length of \(N/\tau\). The detailed procedures of MSE are displayed in Figure 2.  
The SE of each time series \(y_{j}^{\left(\tau\right)}\) is calculated under the embedded dimension; to choose scale factors to quantify signal nonlinearity, the connection between SE and time scale factor is plotted. The SE value reveals the correlation of the signal under this scale factor. The relationship between SE and MSE is shown below:  
\[E_{MSE}(x,\tau,m,r)=E_{SE}(y^{\tau},m,r)=-\text{In}(n_{r}^{m+1}/n_{r}^{m}) \tag{9}\]  
where \(m\) is the embedding dimension. \(r\) is tolerance. \(E_{SE}(\cdot)\) is sample entropy. The coarse-grained sequence's \(n_{s}^{m}\) and \(n_{s}^{m+1}\) space vectors have dimensions of \(m\) and \((m+1)\), respectively. It should be noted that a larger time scale \(\tau\) contributes to more accurate presentation of a signal's regularity and complexity based on theoretical derivation of MSE, while it results in more computational load. Taking both the presentation of the signal's regularity and the algorithm's efficiency into consideration, and after several experimental trials, the scale factor is chosen as 20 in our application research.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 4 Discussion
### Uncertainty Analysis  
The results obtained from the measurements made it possible to describe the relationship between the apparent permittivity and the moisture content of the material with the second-grade polynomial regression model according to formula (2) and to determine the quality of the model fit to the obtained data using the determination coefficients R\({}^{2}\), as well as the standard errors of measurement expressed as Residual Standard Error (RSE) and Root-Mean-Square Error (RMSE). Additionally, based on the GUM [49], the analysis of the measurement uncertainty and expanded uncertainty in the material moisture function was performed. The calculations were made on the basis of formulas (3) and (4). The obtained results are presented in the form of graphs in Figure 5.
## 2 Materials and Methods
### Preliminaries  
#### 2.2.1 ElGamal Encryption Algorithm  
In 1985, [PERSON] [36] proposed an asymmetric encryption algorithm based on the Diffie-Hellman key exchange. The security of the system primarily relies on the difficulty of solving the discrete logarithm problem in a finite field. The ElGamal encryption algorithm can be divided into three main parts: key generation, encryption, and decryption.  
Key generation: First, randomly select a large prime number, \(p\), such that \(p-1\) has large prime factors. Next, choose a primitive element \(g\) modulo \(p\). Finally, choose \(d\) (\(2\leq d\leq p-2\)) as the private key, then \(y=g^{d}\ mod\ p\) is the public key.  
Encryption: Suppose the plaintext is \(x\). Calculate the ciphertext pair \(C_{1}=g^{r}\ mod\ p\) and \(C_{2}=x\cdot y^{r}\ mod\ p\), where \(r\) is a random number with \(2\leq r\leq p-2\).  
Decryption: Compute the plaintext \(x=\frac{C_{2}}{C_{1}^{2}}=\frac{x\cdot y^{r}}{g^{d}}=\frac{x\cdot y^{d}}{g^{d} +d}\ mod\ p\).  
Multiplicative homomorphism: Assuming that the encrypted plaintexts are \(m_{1}\) and \(m_{2}\), the ciphertext pair generated by encrypting \(m_{1}\) is \((C_{1}^{2},C_{2}^{2})\), and the ciphertext pair generated by encrypting \(m_{2}\) is \((C_{1}^{2},C_{2}^{2})\), where \(C_{1}^{1}=g^{r_{1}}\ mod\ p\), \(C_{2}^{1}=m_{1}\cdot g^{d\cdot r_{1}}\ mod\ p\), \(C_{1}^{2}=g^{r_{2}}\ mod\ p\), \(C_{2}^{2}=m_{2}\cdot g^{d\cdot r_{2}}\ mod\ p\), then:  
\[C_{1}^{1}\times C_{1}^{2} =g^{r_{1}}\times g^{r_{2}}=g^{r_{1}+r_{2}}\ mod\ p \tag{1}\] \[C_{2}^{1}\times C_{2}^{2} =m_{1}\cdot g^{d\cdot r_{1}}\times m_{2}\cdot g^{d\cdot r_{2}}\] \[=m_{1}\cdot m_{2}\cdot g^{d\cdot(r_{1}+r_{2})}\ mod\ p \tag{2}\]  
Therefore, two ciphertexts encrypted by random numbers \(r_{1}\) and \(r_{2}\) are modulo multiplied equivalent to the result of multiplying the corresponding plaintexts of these two ciphertexts choosing the random numbers \(r_{1}+r_{2}\) for encryption, and they decrypt the same result as shown in Equation (3):  
\[Dec(Enc(m_{1}\cdot m_{2}))=Dec(Enc(m_{1})\cdot Enc(m_{2})) \tag{3}\]
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 7 Simulation Analysis
### Experiment Conclusion  
Based on the above experiments and analysis, the performance of the proposed mechanism has been fully verified. Owing to the advantages of large number of D2D multicast receiving users, the utility value of the proposed scheme has greatly improved. And compared with other schemes, even though changing the communication environments, the transmission rate is also better. The efficiency of spectrum and energy has improved dramatically. More importantly, the performance advantage can be more significant as the number of potential requesters increases.
## 2 Materials and Methods
### Methodology  
Developed countries and regions around the world, such as the United States, Japan, and Singapore, are not only economically prosperous but also demonstrate a high degree of professionalism and foresight in the field of urban management and real estate. These regions have developed a sophisticated and complete system for surveying housing vacancy rates, which requires in-depth insights into the property market and a large amount of long-term and continuous field research data. Through this system, the relevant authorities are able to accurately and timely grasp the housing vacancy situation, providing strong data support for policymaking and urban planning.  
However, in countries or regions where there is no official research system in place, such as mainland China, it is particularly difficult to accurately measure the vacancy rate in a given area. Nevertheless, this does not mean that we cannot conduct in-depth research and analysis on this issue. On the contrary, we should strengthen the exploration and investment in the relevant areas in order to gradually establish a housing vacancy rate survey system that meets our own national conditions and provide a strong guarantee for the healthy development of the property market.  
There are already some studies in the academic world that have tried to estimate the housing vacancy rate of a specific region through some direct or indirect data. For example, some studies have used electricity consumption data to set a threshold to determine whether a house is vacant or not [21], while others have combined microblogging check-in data and night-time lighting data to analyze the housing vacancy situation [24]. With the advancement of multi-source big data technology, our research materials have become richer, opening up new avenues for in-depth exploration of housing vacancy rates.  
HVR estimates depend on the correlation of night-time light intensity and vacancy rates, so the statistical analysis of DN values is important and affects all estimates. The heterogeneity of resident habits, 500 m resolution limit, and time change may introduce errors. Therefore, we mixed multi-source data, sampling, and survey sample calibration to minimize errors.  
In this study, rooms that are not used at night are defined as vacant rooms, and the proportion of such rooms in each neighborhood is regarded as the housing vacancy rate in that neighborhood, which is divided into these parts.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 4 Simulation Results
### Evaluation of the Proposed DSA-LSC Method  
Figure 16 shows the simulation results of the proposed DSA-LSC principle for a five-level inverter which is controlled by four level-shifted carriers (see Figure 11a). The system carriers reach their final levels at steady-state quickly (i.e., without iterations) and correctly. The PWM signals at the gate of the high-side switches of the converter cells (\(B_{1}\)\(\dots\)\(B_{4}\)) are shown in Figure 17. The output voltage and the output current waveforms are presented in Figure 18. It can be seen that the control system works properly with fast transient response.  
Figure 16: Simulation result of four level-shifted carriers using DSA-LSC method.  
Figure 15: Multiple reconfigurations of four interleaved carriers using DSA-PSC method, (**a**) carrier phases; (**b**) phase-shifted carriers.  
To validate the reconfiguration capability of the system in the case of removing or inserting a cell, Figure 19 demonstrates the process of reconfigurations for this four-carrier system. At the beginning, the system has four active cells with four perfect level-shifted carriers generated using the DSA-LSC method, as observed from time 0 to 0.02 s. Then, at time 0.02 s, the cell 3 is removed, and the system is reconfigured instantly with three  
Figure 17: PWM control signal at gates of the switches \(\text{B}_{1}\dots\,\text{B}_{4}\).  
Figure 18: Output voltage and output current waveforms.  
level-shifted carriers. Finally, this cell is re-activated at time 0.04 s, and the system returns quickly to the original configuration with four properly level-shifted carriers.  
The output voltage waveform of the investigated inverter during reconfiguration process is shown in Figure 20.  
Figure 19: Reconfiguration of a four level-shifted carriers system.  
Figure 20: Output voltage waveform in a case of reconfiguration.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 3 Hardware Verification Setups
### Outdoor Measurement Setup
#### 3.1.1 Transmitter  
The transmitter (TX) was equipped with two ground plane monopole antennas. For its power supply, the node relied on a heavy-duty battery providing multiple weeks of autonomy. The transmitter node, the antennas and the battery were placed in a waterproof enclosure. As seen in Figure 7, this enclosure was attached to two horizontal iron bars on the outside of a window approximately 20 m above the ground at the south side of a university building, slightly higher than the surrounding houses. To comply with the 1% duty cycle legal restrictions regarding the use of the 434 MHz and 868 MHz ISM-bands, only one packet was transmitted every minute, alternating between both bands. The packet was a 2-byte unsigned integer describing the packet number. A single packet number was, however, used once in each of the two ISM-bands mentioned earlier. Hence, the packet counter increases once every two minutes. The packets were sent at a bitrate of 293 bps and a spreading factor (SF) of 12 was used for the LoRa modulation.  
Figure 6: 3D and satellite views of the outdoor node locations. Map Data: Google, Landsat/Copernicus.  
Figure 7: Location of the transmitter at the Armand Pien observatory.