esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 4 Experimental Results and Analysis
### Experiment of Parallel Connection of Thermoelectric Plate  
#### 4.3.1 Analysis of Parallel Output Characteristics  
The output characteristics of the TEG module in series have been analyzed previously, so the parallel mode of the TEG module is studied and analyzed here. The experiment is carried out on the basis of 20 thermoelectric plates coated with thermal grease and cooled by water. Parallel combination method uses 2 groups (each group of 10 plates in series) thermoelectric plates in parallel. When the temperature difference between the hot and cold ends is 120 \({}^{\circ}\)C and remains stable, the load resistance is changed to obtain the measured data, as shown in Table 3.  
As can be seen from Figure 17, the volt-ampere characteristic curve of the parallel circuit is approximately a straight line. As the load resistance increases, the current decreases and the output voltage increases. During the experiment, the combination mode's total voltage is lower than the theoretical sum of a single thermoelectric plate. This could be attributed to the increase in thermal radiation during the temperature rise process. The thermal radiation reduces the temperature of hot end to a certain extent, and the change of internal resistance when the temperature rises.  
Figure 18 shows the voltage and power characteristic curves of the thermoelectric plate in series and parallel modes with varying load resistance. Under the same conditions, the maximum power obtained by series combination is 8.71 W. When the parallel combination mode is adopted, the maximum power is 8.39 W. Considering the uncertainty of temperature distribution, the current and voltage of the maximum power point are lower than theoretical value. The situation may be the result of a number of factors, includes errors due to uneven temperature distribution across the TEG.  
Figure 16: Diagram of load and power.  
\begin{table}
\begin{tabular}{c c c} \hline
**Load Voltage/V** & **Load Current/A** & **Power/W** \\ \hline
4.72 & 1.29 & 6.09 \\
5.29 & 1.2 & 6.35 \\
6.23 & 1.1 & 6.85 \\
6.97 & 1.03 & 7.17 \\
8.31 & 0.92 & 7.65 \\
8.67 & 0.9 & 7.8 \\
9.17 & 0.87 & 7.98 \\
10.23 & 0.82 & 8.39 \\
11.23 & 0.7 & 7.86 \\
12.16 & 0.61 & 7.42 \\
13.25 & 0.51 & 6.76 \\
14.39 & 0.42 & 6.05 \\ \hline \end{tabular}
\end{table}
Table 3: Output characteristic data of TEG module.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 3 Optimization Model  
### Objective Function  
According to the cost-benefit analysis method, an objective function is built as the following, which is the minimum cost containing the generation cost, reserve cost, and expected outage cost.  
\[\min F=f(p_{i,t})+g(r_{i,t})+O_{t}, \tag{10}\]  
\[f(p_{i,t})=\sum_{i=1}^{N}\sum_{t=1}^{T}\big{[}a_{i}P_{i,t}^{2}+b_{i}P_{i,t}+c_{ i}+S_{i,t}\big{]}, \tag{11}\]  
\[g(r_{i,t})=\sum_{i=1}^{N}\sum_{t=1}^{T}\big{(}\alpha_{i,u}r_{u,i,t}+\alpha_{i, d}r_{d,i,t}\big{)}, \tag{12}\]  
\[O_{t}=\sum_{t=1}^{T}\gamma\cdot E_{t}, \tag{13}\]  
where \(N\) is the number of thermal power units; \(T\) is the number of the scheduling interval; \(f(p_{i,t})\) is the cost of thermal power generator unit \(i\) at the time \(t\), which includes the operating cost and start-up fee \(S_{i,t}\); \(a_{i}\), \(b_{i}\), and \(c_{i}\) are the cost coefficients of the generator unit \(i\); \(P_{i,t}\) is the output of thermal power unit \(i\) at the time \(t\); \(g(r_{i,t})\)is the reserve cost; \(\alpha_{i,u}\) is the up spinning reserve price of the unit; \(\alpha_{i,d}\) is the down spinning reserve price of the unit \(i\); \(r_{u,j,t}\) and \(r_{d,j,t}\) are the up and down spinning reserve capacities, respectively; \(O_{t}\) is the expected outage cost; \(\gamma\) is the power loss cost, which can be obtained from statistical results; \(E_{t}\) is the expected energy not serve (EENS) of the power system at the time \(t\), which can be calculated by  
\[E_{t}=\int\limits_{M_{t}}^{+\infty}(y-M_{r}+\Delta P_{\rm g})f(y)dy, \tag{14}\]  
where \(y\) is the electricity demand deviation of the whole power system, \(M_{r}\) is the up spinning reserve capacity at the time \(t\), \(\Delta P_{\rm g}\) is the dispatching difference of the thermal power units due to outage, and \(f(y)\) is PDF of the electricity demand deviation \(y\).
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 4 Results and Discussion  
### Aggregate Gradation  
The void content within the aggregate structure is determined by coarse aggregate gradation. The cement paste is required to fill void space in order to obtain the adequate workability for concrete. In addition, the coarse aggregate gradation decreases bleeding, plastic shrinkage cracking, and aggregate segregation. In particular, concrete containing coarse aggregate with poor gradation would increase the segregation in the plastic state before hardening. When there is a loss of moisture from the concrete due to surface evaporation and/or internal consumption at cement hydration, the filled cement paste in void space between the aggregate tends to shrink. Hence, in order to attain the maximum particle interlock and solid volume density in concrete, effective coarse aggregate grading is required.  
In this study, a sieve analysis test was conducted for RCA and Basalt coarse aggregate in order to determine the particle size distribution, Figure 1. The grading curves obtained for both coarse aggregates were matched, and the size and quantity of the Basalt aggregate to be used in the RCA was determined accordingly. This provided a similar grading curve for the combined RCA. This means, the combined RCA-25 (i.e., RCA made with 25 MPa concrete) contains 72% of RCA and 28% of Basalt aggregates by volume, and combined RCA-40 (i.e., RCA made with 40 MPa concrete) contains 91% of RCA and 9% of Basalt aggregate. The compressive strength of the three concretes produced with specific coarse aggregate is illustrated in Table 4. Basalt aggregate concrete obtained 26.47 MPa and 42.15 MPa compressive strength at 28 days, which satisfied the design strength of 25 MPa and 40 MPa, respectively. Both RCA-25 and RCA-40 concretes achieved lower compressive strength at 28 days and did not achieve their design strength. However, combined RCA-25 and combined RCA-40 concretes achieved the required design strength at 28 days, Table 4. This demonstrates that aggregate grading plays a vital role in the compressive strength determination of RCA concrete. Thus, both combined RCA-25 and combined RCA-40 concretes were used to prepare concrete specimens for testing mechanical properties.
## 2 Eruption Forecasting Algorithm
### Algorithm Implementation  
Block diagrams highlighting the functionality of the forecasting algorithm are shown in Figures 8 and 9. Once the process is initiated at a selected volcano, it searches all available monitoring and modeling data for outliers on a day-by-day basis. As data are fed into the algorithm, the user has the option to use the information to model the source of an event. Once the source has been determined, the modeling input parameter (\(X_{MM}\)) is set to 1 if the unrest is the result of a magmatic intrusion or 0 otherwise. In addition, the algorithm provides a mechanism for a human analyst to override any of the automated results. This allows the analyst to introduce information that may not have been accounted for in the algorithm's decision making process (e.g., personal experience or information not included in the training data).  
Upon the detection of unrest, the algorithm's trigger state vector transitions from 0 to 1. While in the trigger state, data corresponding to the appropriate explanatory variables is routed directly to nodes 2-5 for a user specified number of days. During this time the algorithm produces probability estimates for each node, a daily color coded hazard declaration, an unrest severity estimate, and a vent location  
Figure 8: Schematic diagram showing the data flow to and from the forecasting algorithm.  
probability map. Once the specified number of days has been reached, the trigger states returns to 0, and outlier detection is reinitialized.  
The logistic models can be recalibrated upon the introduction of new samples into the training dataset. Once the models have been redefined and validated, the old models are replaced, and the new functionality is instantly available at all sites being monitored. This guarantees that all forecasting results are derived from the most recent set of logistic models.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Dataset  
In this section, the simulated and real datasets for the algorithm are introduced. For training, a large number of data are needed; however, in practice, it is time-consuming and less cost-effective to sample the data. Thus, simulating data can be taken into consideration for comprehensive training. A variety of simulated environments can be obtained by using  
Figure 5: The process for DOA estimation task and architecture in detail for the segmentation strategy.  
SMA response (SMIR) [36]. The SMIR is decided by the size of the room, the locations of array microphones and sound sources and the reverberation time (\(RT_{60}\)). Figure 6 shows the flow chart of the process of the simulated dataset generation.  
In terms of the room size, six rooms are considered. The six rooms have different sizes except for a fixed height of 3 m. Four rooms are used for training, and all the rooms are utilized for testing. \(RT_{60}\) can be chosen between 0.2 and 1 s. Eigenmike [37], a rigid SMA of radius of 4.2 cm with \(L=32\), \(N=4\), is used as the array for generating the signals. The distance between each source and the center of the SMA is at least 1 m. Moreover, the source and SMA positions are at a minimum distance of 50 cm away from all the walls in the room. Clean speeches are chosen for generating the dataset. Then, all the SMIRs and the source signals are convolved to obtain new signals. In terms of noises, white Gaussian noise is considered. Moreover, voice activity detection (VAD) [38] is performed on the clean speeches to filter out the silent frames and only maintain the speech frames. The method utilized for generating class labels is the same as that in [39] to treat DOA estimation as a classification task.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 5 Applications of Quantum Baths to Realistic Systems
### Structural Properties: Hydrogen Bonds
#### 5.2.1 High-Pressure VII-X Transition in Ice  
In the crystal phase VII of ice under high pressure, hydrogen atoms are connected to their nearest neighbour oxygen via a covalent bond and to their next-nearest neighbour via a hydrogen bond: O\(-\)H \(\cdots\) O. As pressure increases, the oxygen atoms become closer and the proton tends to delocalize over two equivalent positions (resonating O\(-\)H\(\cdots\)O and O\(\cdots\) H\(-\)O configurations) [57]. Beyond a critical pressure \(P_{c}\), the asymmetry vanishes: there is no more a (longer) hydrogen bond and a (shorter) ionic-covalent bond; the proton forms two equivalent bonds to the neighboring O atoms. This proton-centered phase (ice X) is cubic with _Pn3m_ symmetry (space group 224). The order parameter \(x\) for this transition can be taken as the difference of the proton distances from the two neighboring O atoms [57]. By first-principle constrained minimization at fixed \(x\) and excluding all nuclear quantum effects, an expression can be obtained fitting the proton effective potential energy [1]:  
\[V(x;P)=ax^{4}+b(P-P_{0})x^{2}+\frac{b^{2}(P-P_{0})^{2}}{4a}\qquad\text{ for }\quad P\leq P_{0} \tag{25}\]  
with \(a=7.2\) eV/A\({}^{4}\), \(b=0.04\) eV/(GPa A\({}^{2}\)), and \(P_{0}=100\) GPa. Under increasing pressure \(P\), the O-O distance shrinks; the potential wells come closer and the barrier lowers (see Figure 9). In the classical framework, the proton centering occurs when the barrier disappears, so the critical pressure is \(P_{c}=P_{0}\); in the quantum framework, the proton is delocalized, so that proton centering occurs at much lower pressure, when the zero-point energy equals the barrier height so that \(P_{c}\simeq 70\) GPa \(<P_{0}\). Experimentally, the VII tophase transition in ice at room temperature under pressure takes place at approximately 65 GPa, while classical simulations, that do not take NQEs into account, mostly predict a transition pressure between 90 and 100 GPa. In QTB simulations at different pressures [1], the onset of hydrogen-bond symmetrization occurs at \(P\simeq 65\)-70 GPa, in agreement with previous results obtained via a path-integral based method [57].
# Review of sEMG for Robot Control: Techniques and Applications
## 4 Application of sEMG Interface in Robot Control  
The unique advantage of EMG in driving robots is its ability to capture bioelectric signals released during muscle contraction. The practicality, convenience, and earlier-than-movement characteristics of sEMG sensors also enable real-time control of robots. In the existing research, controlled robots are not limited to popular mechanical arms and exoskeletons. Wearable mechanical dexterous hands and gloves are also common in the rehabilitation field, and some scholars have studied the control of mobile robots, such as wheelchairs and wheeled robots. The control methods and difficulty levels vary for the different types of devices used in applications. In Section 4.1, we will discuss the differences in control strategies used in current research when the similarity between human motion and robot motion is different. In Section 4.2, we distinguish two methods for extracting human motion information from sEMG. These two different methods, combined with the three control strategies in Section 4.1, will form different robot control schemes.
# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
## 3 Materials and Methods  
The experiment we designed has three phases, namely, Data Collection, Data Analysis and Preparation, and Applying Machine Learning Models. Figure 1 explains our entire experimentation process. The main reason behind designing the three phases of experimentation is to work with the real data that a rider receives and/or gives. Moreover, it is imperative towards our goal, which is to provide each specific rider with a generic platform that can relay useful information with as few input data as possible without depending on the model or manufacturer of the e-mobility.  
An application is employed to gather raw data, which is subsequently stored in the cloud. This application may obtain information from a variety of sources, such as user interactions or through the use of APIs. Once the raw data are collected, they undergo analysis to identify patterns, followed by data preparation for the machine learning process, which involves cleaning and transforming the data. In order to evaluate the performance of various ML algorithms, 10-fold cross-validation is employed, utilizing methods such as K-nearest neighbors (K*), random forest (RF), support vector machine (SVM), and additive regression (AR). After applying these ML algorithms using 10-fold cross-validation, model evaluation is conducted using log performance metrics, including mean absolute ratio (MAR), root mean squared error (RMSE), and relative root squared error (RRSE). More details will be provided in the subsequent sections.  
We have selected an e-scooter for the experiment called \"Road Gear CT\" [25], which is depicted in Figure 2. This e-scooter operates on a 36 V battery that has a capacity of 6 Ah. Additionally, it is equipped with a motor that can generate up to 300 W of power when operating at 36 V. The detailed technical description provided in Table 2 further highlights the specific capabilities of the e-scooter.  
Figure 1: Experiment process design. See the nomenclature section for a detailed description of the variable.  
Figure 2: Road Gear CT electric scooter.
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 2 Materials and Methods
### Non-Linear Temperature Distribution (NLTD)  
It is presumed that the temperature is varied only along the radial direction of the shaft. The temperature variation in a hollow cylinder at any radial distance \(r\) is given by a 1-D Fourier heat conduction equation, as shown in Equation (3). \(T_{m}\) and \(T_{c}\) are inner and outer temperatures of a cylinder, while \(R_{i}\) and \(R_{o}\) are the innermost and outermost radii of the cylinder, respectively, \(K\) is thermal conductivity at radius \(r\).  
\[\frac{d}{dr}\bigg{[}rK\frac{dT}{dr}\bigg{]}=0 \tag{3}\]  
Considering that there is no heat generation and, for boundary conditions, \(T=T_{m}\)_when \(r=R_{i}\)_and \(T=T_{c}\)_when \(r=R_{o}\)_gives the non-linear temperature distribution \(T(r)\), Equation (4) is the solution of Equation (3). Only the first seven terms of a polynomial series are considered as the solution [45]. \(K_{cm}=K_{c}-K_{m}\) and \(R_{i}\leq r\leq R_{o}\)  
\[T(r)=T_{m}+(T_{c}-T_{m})\frac{\left[\sum_{j=0}^{5}\Big{(}\frac{(-1)^{j}}{j+1} \Big{(}\frac{K_{cm}}{K_{m}}\Big{)}^{j}\Big{(}\frac{r-R_{i}}{R_{o}-R_{i}}\Big{)} ^{j+1}\Big{)}\right]}{\left[\sum_{j=0}^{5}\Big{(}\frac{(-1)^{j}}{j+1}\Big{(} \frac{K_{cm}}{K_{m}}\Big{)}^{j}\Big{)}\right]} \tag{4}\]
## 3 Background
### Iot Protocol Stack Development
#### 3.1.1 Application Layer Protocols Comparison  
CoAP, MQTT, MQTT-SN, and XMPP are the most often used application layer protocols in IoT. The following paragraphs offer a comparative review to choose an ideal application layer protocol for smart agriculture based on their advantages and limitations.  
Constrained Application protocol (CoAP) is a web transfer protocol that is designed to be a lightweight version of HTTP based on Representational State Transfer (REST). CoAP runs over UDP, eliminating most of the TCP overhead of HTTP, decreasing the bandwidth requirements, providing more simplicity, and makes it more appropriate for smart agriculture applications. CoAP is built upon the request/response model and supports methods such as GET, PUT, POST, and DELETE as well as unicast and multicast transmission. CoAP is efficient in terms of infrastructure, bandwidth and power usage [33].  
Message Queuing Telemetry Transport (MQTT) is another protocol designed for resource constraint devices by IBM. It is based on an asynchronous publish/subscribe protocol that runs over TCP. The message pattern in MQTT includes a broker, publisher and subscriber. The broker is tasked with controlling and distributing of data packets between subscribers and publishers. The addition of TCP makes it much more reliable, however it adds more latency and causes a higher bandwidth and power consumption. Hence, it is not recommended for real time applications.  
MQTT-SN is a modified version of MQTT, designed especially for sensor networks. It uses UDP instead of TCP to minimize the drawbacks of MQTT but its core infrastructure is the same as MQTT. The main difference between the two is that, MQTT-SN requires a gateway to translate all MQTT-SN messages over UDP to MQTT messages over TCP. Currently, this functionality is integrated within the brokers [34]. This extra step for the interpretation of messages, adds complexity to the overall framework hence increases power consumption.  
The Extensible Messaging and Presence Protocol (XMPP) is designed to allow short messages and low latency, making it ideal for IoT communications. The XMPP protocol may handle both request/response and publish/subscribe formats, which allow for bidirectional and multidirectional communication, respectively. The decentralised nature of XMPP allows for high scalability [35]. The XMPP protocol use XML for communication, which increases network traffic. This results in high bandwidth use, high CPU usage, and no guarantee of QoS.  
The comparative analysis of various application layer protocols show that CoAP generates less overhead than the other protocols. In terms of power consumption, bandwidth and infrastructure requirements and is most suitable for the proposed CSFSA framework.
## 4 Performance Analysis
### Transaction Information Privacy Analysis  
The BTPP scheme ensures the privacy of transaction information, including the transaction relationship and transaction amount of the parties involved. Apart from the transaction participants, no one, including the transaction verification party, can know the specific details of the transaction.  
In terms of transaction relationship, the sender uses the recipient's dual public key information to generate the stealth address T for the transaction. This address is bound to the corresponding note as the recipient's temporary address, effectively hiding the recipient's actual address. Only the corresponding recipient possesses the confirmation key for the stealth address, which proves their ownership of the note. This process severs the transaction relationship between the trading parties, achieving unlinkability of the transaction relationship.  
In terms of transaction amounts, the BTPP scheme employs commitments for amounts. For example, the commitment form of an amount v, where r is a random blinding factor chosen by the proving party, relies on the elliptic curve discrete logarithm problem, making it difficult for an attacker to compute the amount v, even if they obtain r. This ensures the protection of the transaction amount. The Pedersen commitment for the amount hides the transaction amount while completing the verification of transaction legality.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 7 Discussion
[PERSON] and [PERSON] [76] proposed a client selection method based on the client's radio resources and hardware to reduce communication costs. The authors in [82, 83] discovered that selecting clients with higher local losses can sharply increase the rate of convergence. Our experimental results in Section 6.2 do not deviate from the observation that biased selection affects convergence. An error in vanilla FL almost fails to converge, whereas the curves in mobile FL look stabilized and show a similar convergence rate to that in Section 6.1.