esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Flow Duration Curve  
The flow duration curve (FDC) is a cumulative frequency curve that shows the percentage of time specified discharges were equaled or exceeded during a given period. The \(Y\)-axis represents the discharge and the \(X\)-axis represents the frequency (percentage exceedance) at which discharge larger than the \(Y\)-axis discharge occurs, which can be calculated using Equation (2). Since the \(X\)-axis represents flow data over a period of time in order of magnitude (0-100%), it is easy to visually understand fluctuations in the magnitude of the stream flow [22].  
\[\textit{Percentage}\ \text{exceedence}\ (\%)=\frac{n}{N}*100 \tag{2}\]  
where \(n\) is the rank of discharge data in descending order and \(N\) is the number of data.  
For daily flow data, data from eight-day intervals were used, provided by the TMDL monitoring network. In general, it is preferable to use daily flow data for the entire period to create FDC. It is difficult to secure measurement data due to realistic conditions or if calculation results with a margin of safety (MOS) less than 10% (compared to the reference flow) are required; however, the total flow frequency of the stream can be represented through the flow data from eight-day intervals, accumulated over three to six years. Therefore, measurement data from eight-day intervals are used for FDC and LDC analysis in Korea [22; 23].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Location** & **Year** & **BOD (mg/L)** & **COD (mg/L)** & **SS (mg/L)** & **T-N (mg/L)** & **T-P (mg/L)** & **TOC (mg/L)** & **Discharge** (m\({}^{3}\)/s) \\ \hline \multirow{4}{*}{Upper} & 2016 & 5.22 & 9.44 & 15.82 & 3.37 & 0.2 & 7.34 & 0.25 \\  & 2017 & 5.23 & 8.8 & 9.38 & 3.3  
The magnitude of flow is divided into five stages according to hydrological conditions. In Korea, it is divided into: High flows (HFs) with an exceedance probability of 0-10%, moist conditions (MCs) with 10-40%, mid-range conditions with 40-60%, dry conditions (DCs) with 60-90%, and low flows (LFs) with 90-100%, according to the flow duration [24].
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 3 Results
### Tracked Satellites  
Using only one GNSS, a receiver needs to track at least four satellites to derive the first fix. In this sense, the number of operational satellites, i.e., those providing useful signals, contributes to the quantity of satellites that a receiver is able to track. At the time of the acquisition of the recordings, BeiDou had 46 operational satellites in orbit, but only 34 of them were visible from the test location, 27 were following medium-Earth orbits (MEOs), 6 were following inclined geosynchronous orbits (IGSOs), and 1 had a geostationary orbit (GEO). Furthermore, Galileo had 23 operational satellites, GLONASS had 24, and GPS had 31. In addition, the robustness of the emitted signals, in terms of both power and modulation, facilitates the tracking of the satellites. In order to study this effect, the quantity of tracked satellites is obtained from the GSV (satellites in view) NMEA sentences provided by the receiver at the time of achieving its first fix.  
In Figure 3, the quantity of satellites of each constellation that the receiver tracks in nominal conditions, i.e., without applying any attenuation, along the 4 days of the test, is shown. In addition, Table 1 provides statistical results of the data depicted in Figure 3. It can be observed that GPS is the system that, on average, allows the receiver to track the highest number of satellites. On the other hand, Galileo is the one with the lowest number of tracked satellites. In addition, BeiDou is the GNSS that shows the greatest variability over time in terms of tracked satellites.  
The evolution of the average number of tracked satellites as a function of attenuation is depicted in Figure 4. These data show that GPS and GLONASS are affected in a similar way, losing two satellites until 20 dB of attenuation. Galileo exhibits a similar behaviour, as its tracked satellites are lessened from 5 to 3 at 18 dB. On the other hand, for BeiDou, the reduction in tracked satellites with attenuation is larger, with a steeper drop starting from 16 dB.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**GNSS**} & \multicolumn{4}{c}{**Tracked Satellites**} \\ \cline{2-5}  & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Minimum** & **Maximum** \\ \hline BeiDou & 6.6 & 1.3 & 4.0 & 10.0 \\ Galileo & 5.0 & 0.6 & 4.0 & 6.8 \\ GLONASS & 6.5 & 1.1 & 5.0 & 9.0 \\ GPS & 8.1 & 1.0 & 5.2 & 10.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of tracked satellites without attenuation.
## 5 The Security of QBC
### Concealing of QBC  
We first analyze the ideal protocol without error and loss to prove that QBC in ideal conditions is information-theoretically concealing. Then, further consider the practical conditions.  
**Theorem 1**: _Protocol 3 in ideal conditions without imperfect facilities and errors is information-theoretically concealing._  
**Proof.** According to the description of Protocol 3, it is easy to see that the relation of \(r_{i}\), ciphertext \(c_{0}\), \(c_{1}\) and the commit value \(b\) is  
\[\bigoplus_{i=1}^{2k}r_{i}=c_{0}\oplus c_{1}\oplus b. \tag{26}\]  
Suppose \(\rho_{\psi}^{(2k)}\) is the density operator of the whole state received by [PERSON] when [PERSON] commits \(b\), where \(b^{\prime}=c_{0}\oplus c_{1}\oplus b\). Then  
\[\rho_{b^{\prime}}^{(2k)}=\frac{1}{2^{2k-1}}\sum_{\bigoplus_{i=1}^{2k-1}r_{i}=b ^{\prime}}|\Psi_{r_{i}}\rangle\langle\Psi_{r_{i}}|. \tag{27}\]  
As \(\langle\Psi_{0}|\Psi_{1}\rangle=\cos\varphi\), define  
\[|\Psi_{0}\rangle=\begin{bmatrix}\cos\frac{\varphi}{2}\\ \sin\frac{\varphi}{2}\end{bmatrix},\qquad|\Psi_{1}\rangle=\begin{bmatrix}\cos \frac{\varphi}{2}\\ -\sin\frac{\varphi}{2}\end{bmatrix}\]  
According to the process of analysis in [62], the density operators \(\rho_{0}^{(2k)}\) and \(\rho_{1}^{(2k)}\) satisfy  
\[\rho_{0}^{(2k)}-\rho_{1}^{(2k)}=2\times\begin{bmatrix}0&\sin\frac{\varphi}{2} \cos\frac{\varphi}{2}\\ \sin\frac{\varphi}{2}\cos\frac{\varphi}{2}&0\end{bmatrix}^{\otimes 2k}. \tag{28}\]Then, trace distance is  
\[D(\rho_{0}^{(2k)},\rho_{1}^{(2k)})=\frac{1}{2}\mathit{Tr}\left|\rho_{0}^{(2k)}- \rho_{1}^{(2k)}\right|=(\sin\varphi)^{2k}. \tag{29}\]  
For any positive polynomial \(p(\cdot)\) and every sufficiently large \(n\),  
\[D(\rho_{0}^{(2k)},\rho_{1}^{(2k)})<\frac{1}{p(n)} \tag{30}\]  
holds. The theorem is proved.  
In practical QBC protocol, the commit value is \(b=b_{0}^{(i)}\oplus b_{1}^{(i)}\), where \(i=1,2,...,l\). The \(OT_{1}^{2}\) protocol is executed \(l\) times. When [PERSON] breaks [PERSON]'s privacy just once in \(OT_{1}^{2}\) protocol, he knows the commit value. Some security parameters of \(OT_{1}^{2}\) protocol are given is Table 1 and the probability that [PERSON] breaks [PERSON]'s privacy \(p_{2}\) is controlled to be a magnitude of \(10^{-6}\). Suppose the times of executing \(OT_{1}^{2}\) protocol in bit commitment protocol is \(l=40\), a malicious [PERSON] can obtain what [PERSON] has committed before opening phase with a probability of  
\[p_{br}=1-(1-10^{-6})^{40}\approx 4.0\times 10^{-5}. \tag{31}\]  
In practical protocol, the probability of breaking the concealing of bit commitment around \(4.0\times 10^{-5}\) is allowed.
# Seismic Hazard Evaluation and Strain Dynamics in the
## 3 Methodology and Implementation
### Spatiotemporal Distribution of Earthquake Energy
Figure 8: Spatial energy density results of earthquakes in the study area (the black dashed line represents the main zone examined in this study, the SFZ, while the red lines indicate other active faults in the region). Examining the temporal density of earthquakes for seismic hazard analysis provides valuable understanding into determining the frequency of seismic activity over time. The way earthquakes concentrate over time, changes in their magnitudes, and particularly variations in the recurrence intervals of large earthquakes offer significant insights into the accumulated energy in the region. Similar to spatial density analysis, temporal density analysis that focuses solely on the number of earthquakes provides information about the timing of increases and decreases in seismic activity but does not provide sufficient insight into the destructiveness of earthquakes. However, the temporal analysis of the cumulative energy released by these earthquakes is more closely related to their destructiveness. With this goal in mind, the temporal densities of the earthquakes in the dataset  
have been analyzed both in terms of the number of events and the energy released (Figure 9). The results show that during the instrumental period, the energy density was primarily around the GFZ located to the west of SFZ, but after 1960, it was concentrated around the EF located to the north of the Simav segment of SFZ. The findings indicate that in the study area, particularly before 1970, earthquakes releasing high energy predominantly occurred at the intersection of the GFZ and SFZ. However, after this period, seismic activity shifted towards the central segments of the SFZ and its intersection with the EF located to the north, possibly driven by the 1970 Gediz earthquake (Mw: 7.2). Between the 1980s and the end of 2025, the significant energy release from the Gediz earthquake, combined with the occurrence of the 2011 Mw: 5.8 and 2012 MI = 5.4 Simav earthquakes in the approximately same region, has resulted in an intensified release of seismic energy in the central segment of the SFZ.
## 3 Data and Methods
### Data Sources and Pretreatment
#### 3.1.2 Data Pretreatment  
The software MODIS Reprojection Tool (MRT) was used to reproject the MODIS data into Albers projection. ArcGIS was used to clip the grid files by the Xuzhou central area boundary and China boundary. MOD17A3 data were processed to obtain the NPP value and MCD12Q1 data were processed to reclass the land use cover into cultivated land, forestland, grassland, water area and built-up land. Finally, MOD17A3 data and MCD12Q1 data were analyzed by the overlay analysis function in ArcGIS to obtain the spatial distribution of the ecological footprint.
## 6 Recent Developments and Innovative Approaches
### AI and Machine Learning in Photocatalyst Design  
Artificial intelligence (AI) and machine learning (ML) are being used to accelerate the discovery and optimization of new photocatalysts. These tools enable the following:  
* Prediction of band structures and material properties.
* Identification of optimal synthesis conditions.
* Optimization of operational parameters for specific contaminants.  
Data-driven approaches can significantly reduce the experimental costs and time, while revealing novel catalyst combinations with high performance.  
Researchers developed a data-driven approach integrating ML with experimental validation to design high-performance nitrogen-rich covalent triazine frameworks (CTFs) for photocatalytic applications. Utilizing graph neural networks, the study achieved high accuracy in predicting the photocatalytic properties, leading to the identification and synthesis of a novel CTF structure with an ultrahigh hydrogen evolution rate [172]. A machine learning-based module was developed by [PERSON] and [PERSON] [173] to expedite the design of Bi2 WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity. By constructing a dataset and employing support vector regression, the study identified key synthesis parameters influencing degradation rates of rhodamine B dye, facilitating the discovery of nanocomposites with improved performance.  
Figure 13e provides a conceptual overview of how artificial intelligence (AI) and machine learning (ML) contribute to the development of advanced photocatalysts. It presents a streamlined and symmetrical layout, by linking AI to three core tasks, including predicting material properties, identifying synthesis parameters and optimizing operational conditions, and emphasizing the integration of data-driven tools in accelerating photocatalytic research and design.
## 1 Introduction
### Significance in Lunar Small-Satellite Missions (Artemis and Beyond)  
In this research, the importance of implementing the major technologies executed in LEO to be utilized in Lunar missions was emphasized. The operations of Artemis are crucial for all of humanity as a whole. Hence, the smooth communication and operation of the mission is key for the successful lifetime operations of the same. The communications and operations support will remain extremely important for the astronauts and other systems to cooperate and be implemented effectively. Figure 5 presents the process of establishing the lunar orbits utilizing the technologies utilized for LEO and supporting the Artemis mission for its operations.  
Figure 4: Disaster management implementation process.  
In this process, the costs involved in the logistics, the planning to make small-satellites better utilized contributing to the main goal of the Artemis and beyond, and strategies of trajectory alignment for continuous payload/necessities transport will be crucial to be analyzed for optimal resource consumption and overall significance of the project.  
Overall, this paper focused on providing insight on a severe environment scenario through a simulation which will be effective in ground communication and operations on lunar surface utilizing the lessons learnt in LEO significantly. A major portion of this paper was also published in Ref. [3], which is only visible to its specific audience. The main goal of this work was to emphasize constellation operation using small satellite configurations under a severe space environment while still being able to optimize the coverage and pace up the data relay and delivery with the best possible solution. Some of the key benefits include fast data reception to the end users, quick relief provision at the affected areas of disaster, increased and well-aligned revisits to a particular location/ground station, autonomous in-orbit decision-making for the trajectory to be taken while maintaining their positions, and an optimized solution in a harsh LEO environment. Simultaneously, the Artemis mission can also be supported with these techniques to enhance the ground capabilities and the mission itself.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Power-Related Characteristics  
From a power perspective, the wireless channels are characterized by the fluctuations in the received power, i.e., the signal's strength at the receiver. The received power decreases as the signal travels for a longer distance, typically as a result of reflected paths vs. the direct path. An example of single measured PDP is shown in Figure 11. Based on the measurements, the noise floor for the TSMA6 was estimated to be about \(-123\) dBm for 5G NR. As already mentioned, the TSMA6 is able to operate with low SNR values (\(-5\) dB) because it performs coherent demodulation of the System Information Block (SIB) signal for each path. In this section, we discuss the power-related characteristics of the wireless channel, such as pathloss, K-factor, and power decay.
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
## 3 Calculation Method and Verification  
### Method of Calculation  
The turbulent development in the near-wall region of the small low-speed diagonal flow fan studied in this paper is not sufficient, and the influence of turbulent pulsation on the fluid motion may be less than that of molecular viscosity. Considering the internal curved streamline of the diagonal flow fan, the turbulence is anisotropic, and the viscosity coefficient \(\mu_{t}\) should be an anisotropic scalar value. Therefore, the realizable \(k\)-\(\epsilon\) model is selected to modify this point. The mathematical expression of the model for the flow problem of the diagonal flow fan is:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{ i}}=\frac{\partial}{\partial x_{i}}\bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{ \sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{i}}\bigg{]}+G_{k}-\rho\epsilon \tag{2}\]  
\[\frac{\partial(\rho\epsilon)}{\partial t}+\frac{\partial(\rho\epsilon u_{i}) }{\partial x_{i}}=\frac{\partial}{\partial x_{i}}\bigg{[}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{k}}\bigg{)}\frac{\partial\epsilon}{\partial x_{i}}\bigg{]}+ \rho C_{1}E\epsilon-\rho C_{2}\frac{\epsilon^{2}}{k+\sqrt{v\epsilon}} \tag{3}\]  
In the formula:  
\[\begin{array}{l}\sigma_{k}=1.0\,\sigma_{s}=1.2\,C_{2}=1.9\\ \\ C_{1}=\max\Big{(}0.43,\frac{\eta}{\eta+5}\Big{)}\\ \\ \eta=\big{(}2E_{ij}\cdot E_{ij}\big{)}^{0.5}\frac{k}{\epsilon}\\ \\ E_{ij}=\frac{1}{2}\Big{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\Big{)}\end{array} \tag{4}\]  
The flow medium selected was 25 \({}^{\circ}\)C air, and the rotating domain is the impeller basin. The multiple reference frame model (MRF) is used for calculation. The rotating speed of the impeller is 2000 rpm, and the inlet and outlet boundary conditions are pressure-inlet and pressure-outlet, respectively. The solution is based on the implicit solution of pressure. We solved the three-dimensional Reynolds average N-S equation using the realizable \(k\)-\(\epsilon\) model. The SIMPLE algorithm was selected to couple the velocity and pressure. A second-order upwind scheme was adopted for all spatial discrete schemes. When the root mean square (RMS) of the residuals of each control equation was less than \(1\times 10^{-5}\), the calculation was considered to be convergent.
# Efficient Power Control Framework for Small-Cell Heterogeneous Networks
###### Abstract  
Heterogeneous networks are rapidly emerging as one of the key enablers of beyond fifth-generation (5G) wireless networks. It is gradually becoming clear to the network operators that existing cellular networks may not be able to support the traffic demands of the future. Thus, there is an upsurge in the interest of efficiently deploying small-cell networks for accommodating a growing number of user equipment (UEs). This work further extends the state-of-the-art by proposing an optimization framework for reducing the power consumption of small-cell base stations (BSs). Specifically, a novel algorithm has been proposed which dynamically switches off the redundant small-cell BSs based on the traffic demands of the network. Due to the dynamicity of the formulated problem, a new UE admission control policy has been presented when the problem becomes infeasible to solve. To validate the effectiveness of the proposed solution, the simulation results are compared with conventional techniques. It is shown that the proposed power control solution outperforms the conventional approaches both in terms of accommodating more UEs and reducing power consumption.  
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is to deploy a huge number of small cells in our environment [4]. But telecommunication companies are feeling reluctant on bearing the huge cost of buying, installing and maintenance of these small cells. By sending the user's traffic over efficient small-cells BSs, telecommunication companies will need to install fewer small cells in our environment and it will also cause less traffic at the macro-cell.  
However, deploying a large number of small-cell BS may not be the best solution and different users (vehicles, and pedestrians) may have different demands [5; 6]. It is because increasing the number of small-cell BSs may result in a high amount of intercell interference. Moreover, the traffic demands within the cells may vary in practice due to the number of admitted users and their quality of service requirements. Thus, it is not feasible to simultaneously activate all the small-cell BSs at any particular time. The key theme of this paper is to use this intuitive fact to the advantage of network operators by reducing the amount of power consumed for operating small-cell BSs.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 3 Bacterial Biofilms
### Bacterial Communication within Biofilms  
Quorum sensing (QS) is a bacterial communication system which allows the bacteria to coordinate their behavior using chemical molecules as a communication signal. It is important to understand that QS communication system aims at orchestrating a grouped bacterial behavior which results in maximum benefits for the population within the biofilm including optimum use of nutrients, increased pathogenicity, and extension of survival rates [18; 51]. That being said, it is logical to expect a certain threshold for activation of the QS system by reaching a minimum population level in order to trigger the onset of the system [41; 45]. The mechanisms of QS and its role in controlling group bacterial behavior have been extensively studied, revealing that QS system utilizes an inducer/receptor mechanism to control bacterial gene expression [18; 43; 51; 52; 53]. The same system is used in both Gram-negative and Gram-positive bacteria. However, the signaling pathway is different. Gram-negative bacteria recruit a system based on N-acyl-homoserine lactones (AHLs) inducers that are synthesized via AHL synthase genes, such as different _lux1_ genes, and sensed by their cognate receptors Luxk-type QS receptors [54; 55]. The AHLs molecules are secreted in minute amounts that are undetected at low bacterial population. However, at high population levels, the amount of secreted AHLs is enough to bind to specific bacterial receptors, forming QS receptor-AHL complex. After hitting a certain threshold concentration, the QS receptor-AHL complexes control the transcription of genes involved in biofilm formation [56; 57; 58; 59; 60]. On a parallel basis, QS systems in Gram-positive bacteria employ autoinducing peptides (AIPs) that are detected by two-component membrane-bound signal transduction systems [55; 61; 62; 63]. The different QS systems in Gram-negative and Gram-positive bacteria are represented in Figure 3. QS plays a crucial role in biofilm formation through controlling the production of extracellular polymer matrix, upregulation of bacterial virulence genes, increasing secretion of exoenzymes, siderophores, antibiotics and bioluminescence [50; 64].  
Figure 2: Schematic representation of the stages involved in biofilm formation.