esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Uncertainty Quantification
#### 2.3.5 Characterization  
The sparse PCE methodology is governed by two user-defined parameters: the total degree of multivariate polynomials and the number of training samples. The determination of both these parameters follows the approach proposed by [PERSON] et al. [36]. Initially, the process begins with setting the total degree parameter to \(p=1\) and then gradually increasing the number of training points. For each set of training points, a sparse PCE is systematically constructed, and subsequently, the LOO error is assessed. This iterative procedure continues until the LOO error reaches a point of convergence or until all multivariate polynomials from the pool of basis functions limited by \(p=1\) (as per Equation (2)) are incorporated into the PCE. Should the LOO error exceed a predefined threshold, the total degree parameter is incremented to \(p=2\), thereby introducing additional polynomials into the basis function pool. This iterative process continues until the combination of polynomial order and the number of training samples yields an LOO error that falls below the established threshold.  
In this work, we selected a limiting polynomial degree \(p=3\), in combination with a design of experiment of 5000 training points (\(\chi\)) to reach an LOO error below 1% for all outputs of interest (see Section 2.2.2). This accuracy corresponds to the accuracy of the PCE model achieved in previous work of these authors [30], where a LOO error below 1% is achieved for \(p=3\) on the same power plant simulation model. However, the number of training samples required is significantly higher (5000, as opposed to 20 in [30]), and, as in this work, the number of uncertain parameters is several orders of magnitude higher (243, as opposed to 2). In addition, the gain in computational efficiency by applying sparse PCE instead of the conventional PCE is similar to the one obtained in a recent application to a renewable energy system [46]. To illustrate the evolution of the LOO error with respect to the number of training samples, the evolution of the LOO error and the statistical moments of the coal-fired power plant gross power quantified by the sparse PCE (\(\mu^{\text{PCE}}\) and \(\sigma^{2\text{ PCE}}\)) with respect to the size of the design of experiment are presented in Figure 3 and Figure 4, respectively. The figures with all the LOO errors can be found in the Supplementary Materials.  
Figure 3: The evolution of the LOO error of the PCE for the coal-fired plant gross power with respect to the size of the design of experiment.
# ReDSHIFT: A Global Approach to Space Debris Mitigation
## 3 Additive Manufacturing
### Testing  
A thorough theoretical and numerical analysis was preliminarily performed to identify the state of the art knowledge on the demisability of the materials and components commonly used in the manufacturing of spacecraft. The conclusion was that the current understanding of the phenomenology of the fragmentation and demise processes is limited. Therefore, it is of paramount importance to perform appropriate tests in order to improve the capability of the numerical tools to assist the design process. To this end, a set of destructive tests on spacecraft materials, structures and components (mostly 3D printed) was planned and then performed in the arc-heated supersonic wind tunnel at the German Aerospace Center (DLR). These tests included the first destructive wind tunnel tests ever performed on a complete nano-satellite and on a reaction wheel. The nano-satellite was assembled _ad-hoc_ within the project using standard COTS components, while the reaction wheel was manufactured by Rockwell Collins.  
From these tests, it has been determined that the failure of aluminium structures is highly dependent upon the behaviour of the protective metal oxide layer, and that this can be catastrophic in nature. The tests on the nano-satellite have shown that the structure can be supported by stainless steel spacers between the electronics cards, and that glass fibre reinforced plastic PCBs are more resistant to melting than had been anticipated.  
Finally, the reaction wheel test has shown that the connections between parts are critical to the fragmentation and demise processes, as the glued housing separates quickly, well before melt temperature is reached at the joint. It has also demonstrated the importance of radiative cooling, as the flywheel and ball-bearing unit have survived a test at over 800 kW/m\({}^{2}\) with little damage. Figure 13 shows a few screenshots from the video of the reaction wheel test inside the DLR tunnel.  
Figure 12: **Left panel**: set of five multi-shock panels for 3D-printing. **Right panel**: sample of the 3D printed SCP shield.  
Further testing on the 3D printed material and the ReDSHIFT spacecraft will be performed in the coming months. These tests will include hypervelocity tests at the University of Padova light gas gun. Radiation test will be done in the Padova INFN chamber and standard vibration tests will be performed by EDSS in Spain.
## 1 Introduction
A distributed Bragg reflector (DBR) laser, operating at a wavelength of 1064 nm and power level of approximately 20 mW, was adapted to the space environment of the TianQin-1 mission [19]. In Taiji-1, a high-stability Nd:YAG laser was tested. The main output power was 40 mW and the intensity noise reached \(-74\) dBc/Hz @ 0.1 Hz [20]. TianQin-2 and Taiji-2 are scheduled to launch a pair of satellites in 2023-2025 to make a major technological breakthrough in testing high-precision intersatellite laser interferometry [8]. Ongoing plans would require a laser system with an optical power higher than 2 W and with low-intensity noise down to the millihertz range. To reduce the intensity noise, a low-noise seed source and a fiber amplifier with photoelectric feedback and active temperature control techniques are essential [21]. In this work, we demonstrate a low-intensity noise, single-frequency, all-fiber amplifier oriented to Chinese space-borne GW detectors. First, we present a theoretical noise model in the feedback loop to guide the fiber amplifier design. The intensity noise suppression scheme designed for the amplifier will be discussed with special emphasis. Then, we present a low-intensity noise Yb-doped all-fiber amplifier with an average power of 2.5 W. All polarization-maintaining fibers and fiber components are adopted to improve the long-term mechanical stability and reliability of the fiber amplifier. It produced a strongly linearly polarized output at 1064.35 nm with a linewidth of 1 kHz and a maximum output power of 5 W. By stabilizing the amplifier pump laser diode (LD) current and the temperature of the pump LD and fiber components, the RIN of the amplified laser was suppressed to \(-72.5\) dBc/Hz @ 1 mHz and \(-90\) dBc/Hz @ 0.1 Hz when the output power of the laser is 2.5 W. In addition, we measured the additional linewidth induced by the amplified laser, and the results indicate that no additional linewidth could be measured for the fiber amplifier.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Linearization  
Please note that Equations (2), (3) and (6) are nonlinear and need to be converted to linear form if they are to be solved with branch-and-cut. For Equations (3) and (6), a new variable \(X\) is introduced:  
\[X_{ik}=\left\{\begin{array}{ll}1&\mbox{\it sensor node $i$ covered at time $k$}\\ 0&\mbox{\it otherwise}\end{array}\right. \tag{8}\]  
The relationship between \(Y\) and \(X\) is:  
\[X_{ik}<=\sum_{q=0}^{N}Y_{iqk}, \tag{9}\]  
where \(N\) is the number of available gateways. The objective function becomes:  
\[\max_{C_{q}}\bigg{(}\sum_{k=0}^{n}\sum_{i=0}^{m}X_{ik}\bigg{)}, \tag{10}\]  
while Equation (6) becomes:  
\[\sum_{i=0}^{m}X_{ik}\geq\gamma,\forall k. \tag{11}\]  
For Equation (2), a dependent binary variable \(Z\) is introduced for each \(Y\), defined as:  
\[Y\leq M\cdot(1-Z) \tag{12}\]  
where \(M\) is a large positive number and \(Z\) is:  
\[Z_{iqk}=\left\{\begin{array}{ll}1&\mbox{\it for $\parallel C_{q}-p_{i}^{k} \parallel>d$}\\ 0&\mbox{\it for $\parallel C_{q}-p_{i}^{k}\parallel\leq d$}\end{array}\right. \tag{13}\]  
The following constraint may be added to characterize this relationship:  
\[Z_{iqk}\geq(\parallel C_{q}-p_{i}^{k}\parallel-d)/M \tag{14}\]  
Please note that when \(\parallel C_{q}-p_{i}^{k}\parallel>d\), the right-hand side is decimal-valued in the interval \((0,1)\), and \(Z\) will be fixed at 1. If \(\parallel C_{q}-p_{i}^{k}\parallel=d\) then the right-hand side is 0, whereas \(\parallel C_{q}-p_{i}^{k}\parallel<d\) makes the right-hand side negative. In either of these last two cases, the constraint is redundant because \(Z\) can be either 0 or 1 and is therefore not restricted.  
Based on the constraint in Equation (12), it follows that if \(\parallel C_{q}-p_{i}^{k}\parallel>d\) then \(Z=1\) and \(Y\) is fixed at 0. Similarly, if \(\parallel C_{q}-p_{i}^{k}\parallel\leq d\) then \(Z=0\), so \(Y\) is not fixed. However, maximization will tend to make \(Y=1\). This recharacterization of the optimization procedure renders all the constraints and objective function linear, enabling the solution by the branch-and-cut method.
## 2 Wind Turbine and Control Algorithm
### Control Algorithm
#### 2.2.1 Feed-forward Control Algorithm  
The feed-forward control algorithm adds additional pitch commands as feed-forward terms to the basic closed-loop pitch control of the wind turbine, aimed at reducing the standard deviations of rotor speed and output [8]. Typically, pitch control in wind turbines is used to maintain rated power output, even when wind speeds exceed the rated speed. In other words, pitch control calculates pitch commands based on generator speed to maintain rated power control by adjusting the generator speed. However, conventional pitch control based on generator speed fails to promptly respond to momentary changes in variable wind speeds. The feed-forward control algorithm addresses this limitation by compensating for instantaneous wind changes, rapidly adjusting pitch angles in response to wind variations. Figure 3 illustrates the schematic of the designed feed-forward control algorithm, indicating the use of rotor speed and estimated wind speed as control parameters. Rotor speed uses measured values as inputs, while estimated wind speed utilizes the output from the embedded wind speed estimator.  
As shown in the figure, the feed-forward control algorithm calculates the feed-forward pitch angle command using the rotor speed and estimated wind speed. The feed-forward pitch angle command consists of compensation for pitch angle changes due to variations in wind speed and rotor speed, with each compensation value calculated based on the relationships among pitch angle, wind speed, and rotor speed. The required pitch angle change, \(dB_{FF}\), to maintain rated power output for changes in wind speed, \(dV\), and rotor speed, \(d\Omega_{r}\), in a steady state is obtained with Equation (1) [8]. The feed-forward control algorithm was implemented by adding the feed-forward command to the pitch controller command of the target wind turbine, as shown in Figure 1.  
\[d\beta_{FF}=\bigg{(}\frac{\partial\beta}{\partial V}\bigg{)}dV+\bigg{(}\frac{ \partial\beta}{\partial\Omega_{r}}\bigg{)}d\Omega_{r} \tag{1}\]
###### Abstract  
This study proposes a multi-objective optimization framework for peer-to-peer (P2P) energy trading in South Korea's tiered electricity pricing system. The framework employs the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to optimize three conflicting objectives: minimizing consumer costs, maximizing prosumer benefits, and enhancing energy utilization. Using real microgrid data from a South Korean community, the framework's performance is validated through simulations. The results highlight that MOEA/D achieved an optimal cost of KRW 32,205,0, a benefit of KRW 32,205.0, and an energy utilization rate of 57.46%, outperforming the widely used NSGA-II algorithm. Pareto front analysis demonstrates MOEA/D's ability to generate diverse and balanced solutions, making it well suited for regulated energy markets. These findings underline the framework's potential to improve energy efficiency, lower costs, and foster sustainable energy trading practices. This research offers valuable insights for advancing decentralized energy systems in South Korea and similar environments.  
energy trading; microgrid; optimization; P2P; tiered pricing Article
## 3 Results
### Data Insertion Experiment for the IoT Platform  
The IoTP had to handle multiple concurrent data insertion requests simultaneously, and its performance in data insertion was more critical to the system's reliability than its data reading performance. As the number of central controllers deployed increased, IoTP concurrency also increased, raising the risk of the server encountering issues such as excessive memory usage, inadequate CPU processing power, and blocked IO resources. These problems could eventually result in the service program crashing.  
In order to explore the optimum number of concurrent data insertions that the IoTP could load under the existing server configuration, we developed a Web application using  
Figure 10: The relationship between PDA_RT and RSSI.  
Java programming language and executed it on a Tomcat server. The performance test of the web application running on Tomcat was carried out using the software JMeter 5.6.2. The detailed process was as follows: (1) We established 22 thread groups, each representing a different number of concurrent threads (ranging from 0 to 2200 threads in increments of 100). These thread groups were set up to simulate the task where the Center Controller sent HTTP requests to the IoTP. Each thread simulated a Center Controller. (2) The packet size of each Http request was set as fixed 364 bytes. When IoTP received an HTTP request, it was parsed into a database row structure, and then inserted into the database. The IoTP would respond with a message indicating the successful data insertion. All threads initiated within a span of 5 s. JMeter was used to generate an aggregated report, allowing the analysis of the system's response time and throughput rate.  
The experimental results are shown in Figure 11. For concurrent threads below 1700, there was a gradual increase in both the TR and the average IoTP_RT with the escalation of concurrent threads. However, once the concurrent threads exceed 1700, both the TR and average IoTP_RT stabilized at approximately 254 requests per second (rqs) and 4548 ms, respectively. The cause was the server being configured with one virtual cpu core and 2 GB memory. When the number of concurrent threads reached 1700, the resource usage reached the maximum and the request was blocked. This behavior could be attributed to the server configuration, which consisted of a single virtual CPU core and 2 GB of memory. As the concurrent threads reached 1700, resource usage peaked, resulting in blocked requests. Consequently, it was evident that the IoTP experienced a performance bottleneck when the number of concurrent threads exceeded 1700, highlighting the need to avoid overloading it. On the other hand, when the concurrent threads remained below 1700, the IoTP exhibited improved performance and ensured the availability of additional computational resources to handle unforeseen circumstances.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Urban Applications  
The variety of sensing abilities offered by WSNs also provides an opportunity to gain an unprecedented level of information about a target area, be it a room, a building, or outdoors. WSNs are indeed a tool to measure the spatial and temporal features of any phenomena within an urban environment, providing a limitless number of applications. The most popular applications of WSNs in the urban domain are related to smart homes, smart cities, transportation systems, and structural health monitoring, as depicted in Figure 16 and further described in the rest of this subsection.
## 3 Results and Discussion  
### Environmental Variables: Temperature and Relative Humidity  
Data for environmental variables showed that temperature and relative humidity maintained a relatively consistent diurnal pattern throughout the monitoring period (Figure 2). While separate internal data for temperature and humidity were not measured, it is expected that these were closely related to local meteorological conditions. The agitation hall was not climate controlled but the air was constantly ventilated to maintain a negative pressure situation, ensuring that the internal environment remained similar to prevailing local environmental conditions.  
The average temperature during the monitoring period was 22.3 \({}^{\circ}\)C, with a coldest temperature of 16.1 \({}^{\circ}\)C degrees early in the morning of 17 June and an overall high of 34 \({}^{\circ}\)C on the afternoon of 22 June (Table 1). Daily minimum temperatures (mean = 18.3 \({}^{\circ}\)C, standard deviation = 1.3 \({}^{\circ}\)C) varied slightly less than daily maximum temperatures (mean = 27.1 \({}^{\circ}\)C, standard deviation = 2.9 \({}^{\circ}\)C). As expected, relative humidity showed an inverse pattern compared with temperature, with lower humidity during the day and higher humidity during cooler nights (Figure 2). Relative humidity reached dew point (100% RH) most nights. The relative humidity data attest to two periods of rain during the monitoringperiod from 23-26 June and 12-14 July. The variable environmental data show that the main variation was diurnal, with no discernible trends over the whole monitoring period.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 2 Related Works  
The satellite network topology consists of ISLs whose structure indicates the satellites' location information and interrelationships in orbit. In this section, we will compare the characteristics of static topologies of domestic and foreign satellite networks, introduce two main types of typical dynamic link assignment strategies, and summarize their shortcomings based on this current work.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 3 LP-WAN Solutions for IIoT Services
### LoRaWAN  
This platform is promoted by the LoRa Alliance [19], composed by IBM, Semtech, and Actility, among others. It proposes a star-of-stars topology with dedicated gateways serving as transparent bridges between edge-nodes and the central network, where the data is stored and made available to the subscriber. The edge-nodes connect to the access points via one-hop links by using the LoRa (Long Range) modulation. This is Semtech's proprietary Chirp Spread Spectrum (CSS) radio scheme that employs a wide channel of up to 250/500 kHz (Europe/North America) and provides adaptive data rate capabilities by means of a variable processing gain. Please note that this concept represents the ratio between the chip rate and the baseband information rate, and is usually known as the Spreading Factor (SF). LoRaWAN presents a SF from 7 to 12. Using this last characteristic, edge-nodes can tune the transmission power and bitrate to the real network conditions, allowing a reduction in energy consumption. Moreover, LoRaWAN defines three types of edge-devices depending on their downloading needs: Class A devices have a scheduled downloading window just after each uplink connection (Receiver-Initiated Transmission strategy, low power consumption), Class B devices have additional scheduled downlink windows (Coordinated Sampled Listening strategy, medium power consumption), and Class C devices can receive messages almost at any time (Continuous Listening strategy, large power consumption). In its specification sheets, LoRaWAN claims a Class A edge-node's battery lifetime is over five years.  
Originally, LoRaWAN was designed to work in ISM bands but it can be also adapted for supporting the licensed spectrum. Under these conditions, LoRaWAN claims to demodulate signals 19.5 dB below the noise floor, hence achieving greater ranges than those provided by cellular base stations. In both communication directions, the adaptive data-rate ranges from 0.25 kbps (0.98 kbps in North America due to FCC (Federal Communications Commission) limitations) up to 50 kbps, with a maximum payload length of 256 bytes. Finally, security issues have been thoroughly considered, so that end-to-end AES (Advanced Encryption Standard) encryption security, including the use of a unique network, application, and device keys for encrypting data at different OSI (Open Systems Interconnection) levels, is provided.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 2 The Principle of the Fuzzy-FTA Analysis
### Basic Principles of Fuzzy Mathematics
#### 2.2.3 Fuzzy Numbers and Their Properties  
If you want to blur a crisp number, there are generally two ways, which are to express the crisp number as an interval fuzzy number or a triangular fuzzy number. In fact, the concepts of triangular fuzzy numbers and interval fuzzy numbers are generalizations of the crisp numbers. Moreover, the concept of triangular fuzzy numbers is an extension of the concept of interval fuzzy numbers. However, compared with those of intervals, the operational laws of triangular fuzzy numbers are more mature and easier to use. Therefore, this paper chose the triangular fuzzy number to blur the precise number.  
A triangular fuzzy number consists of three numbers, including the minimum membership degree, the medium membership degree, and the maximum membership degree. In any situation of subjective decision making, this triple number can be respectively regarded as the lower (conservative), the medium, and the upper (optimistic) estimation with regards to a judgment.  
**Definition 1**.: _The fuzzy number \(A_{\lambda}\) is a continuous fuzzy subset of universe \(R\) on \((-\infty,+\infty)\), and its convex membership fuzzy function satisfies_  
\[\underset{x\to R}{max}\mu_{A(x)}=1 \tag{3}\]  
**Definition 2**.: \(L\)_, \(R\) is the reference function of a fuzzy number if_  
\[\mu_{A(x)}=\left\{\begin{array}{ll}L\big{(}\frac{m-x}{a}\big{)}&\text{when }x\leq m,a>0;\\ R\big{(}\frac{m-x}{\beta}\big{)}&\text{when }x>m,\beta>0;\end{array}\right\} \tag{4}\]  
Then, the fuzzy number is renamed as the \(L\)-\(R\) fuzzy number and recorded as \(\underline{A}=(m,\alpha,\beta)\), where m is the average of a and \((\alpha,\beta)\) is the upper and lower confidence limit of \(\underline{A}\). \(\underline{A}\) is not a fuzzy number when \(\alpha,\beta\) are both \(0\). \(\underline{A}\) is fuzzier when the distribution of \(\alpha,\beta\) is large.  
\[\underline{A}=(m,\alpha,\beta)_{\text{LR}} \tag{5}\]  
If a triangular fuzzy number, \(\underline{A}=[(m-\alpha)+\alpha\lambda,(m+\beta)-\beta\lambda]\) is an interval number, according to the classical expansion principle, the following expansion formula holds:  
For \(\forall\lambda\in[0,1]\):  
(1)  
Addition:  
Formula (6) is the addition calculation rule of two triangular fuzzy numbers \(\underset{\sim_{A}}{A}\) and \(\underset{\sim_{\lambda}}{B}\).  
(2)  
Subtraction:  
Formula (7) is the subtraction calculation rule of two triangular fuzzy numbers \(\underset{\sim_{\lambda}}{A}\) and \(\underset{\sim_{\lambda}}{B}\). Multiplication:  
Formula (8) is the multiplication calculation rule of two triangular fuzzy numbers \(\underset{\sim_{\lambda}}{A}\) and \(\underset{\sim_{\lambda}}{B}\)  
Division:  
Formula (9) is the division calculation rule of two triangular fuzzy numbers \(\underset{\sim_{\lambda}}{A}\) and \(\underset{\sim_{\lambda}}{B}\).