esa-sceva/satcom-chunk-collection huggingface dataset:
# The Attachment of Carbon Nanotubes to Atomic Force Microscopy Tips Using the Pick-Up Method
###### Abstract  
In the last 30 years research has shown that the resolution and reproducibility of data acquired using the atomic force microscope (AFM) can be improved through the development of new imaging modes or by modifying the AFM tip. One method that has been explored since the 1990s is to attach carbon nanotubes (CNT) to AFM tips. CNTs possess a small diameter, high aspect ratio, high strength and demonstrate a high degree of wear resistance. While early indications suggested the widespread use of these types of probes would be routine this has not been the case. A number of methods for CNT attachment have been proposed and explored including chemical vapor deposition (CVD), dielectrophoresis and manual attachment inside a scanning electron microscope (SEM). One of the earliest techniques developed is known as the pick-up method and involves adhering CNTs to AFM tips by simply scanning the AFM tip, in tapping mode, across a CNT-covered surface until a CNT attaches to the AFM tip. In this work we will further investigate how, for example, high force tapping mode imaging can improve the stability and success rate of the pick-up method. We will also discuss methods to determine CNT attachment to AFM probes including changes in AFM image resolution, amplitude versus distance curves and SEM imaging. We demonstrate that the pick-up method can be applied to a range of AFM probes, including contact mode probes with relatively soft spring constants (0.28 N/m). Finally, we demonstrate that the pick-up method can be used to attach CNTs to two AFM tips simultaneously. This is significant as it demonstrates the techniques potential for attaching CNTs to multiple AFM tips which could have applications in AFM-based data storage, devices such as the Snomipede, or making CNT-AFM tips more commercially viable.  
carbon nanotubes; atomic force microscope tips; tapping mode +
Footnote †: journal: Applied Physics Letters  
1
## 3 Problem Descriptions and Optimization
 \tag{18}\]  
Equation (16) can be rewritten as:  
\[T_{\mathbf{\eta}}^{\left(u\right)}=\mathbb{E}\left[G(\eta_{u})\binom{U}{M}\prod \limits_{i\
eq u,j}P(\eta_{i})\prod\limits_{j\
eq u,j}\left(1-P(\eta_{j}) \right)\right]. \tag{19}\]  
In Equation (19), \(G(\eta_{u})\) is the average long-term throughput of EH-ST \(u\), assuming there are \(M\) EH-STs co-transmitting packets in the same frame. \(\prod_{i\
eq u,j}P(\eta_{i})\) is the steady-state probability of other \(M-1\). EH-STs are also transmitting packets, and \(\prod_{j\
eq u,i}\left(1-P(\eta_{j})\right)\) represents the steady-state probability of the rest of the \(U-M\) EH-STs being in an idle state. According to Equation (11), the network overall throughput under the packet transmission policy \(\eta\) becomes:  
\[T_{\eta}=\mathbb{E}\left[\sum_{u=1}^{M}G(\eta_{u})\binom{U}{M}\prod_{i\
eq u,j} P(\eta_{i})\prod_{j\
eq u,i}\left(1-P(\eta_{j})\right)\right]. \tag{20}\]  
In order to keep the packet transmission procedure fair, this paper adopts symmetric control policies [28], i.e., all EH-STs employ the same RA policy \(\eta_{u}=\eta,\forall u\). Therefore, Equation (20) can be rewritten as:  
\[T_{\eta}=\mathbb{E}\left[MG(\eta)\binom{U}{M}P(\eta)^{M-1}\left(1-P(\eta) \right)^{U-M}\right]. \tag{21}\]  
The total number of EH-STs (\(U\)) registered in the network can be acquired from the satellite broadcast information. The optimization problem (Equation (12)), under the admissible symmetric policies, can be represented as:  
\[\eta^{\star}=\underset{\eta\in\Omega}{\arg\max}\mathbb{E}\left[MG(\eta) \binom{U}{M}P(\eta)^{M-1}\left(1-P(\eta)\right)^{U-M}\right]. \tag{22}\]
## 3 Results and Discussion
### Effects of Anisotropic Wettability and Roughness on Boiling Heat Transfer  
Figure 13a,b illustrate the mechanisms of bubble nucleation and liquid rewetting on microgrooved surfaces. As depicted in the figure, more bubble nucleation sites were activated when the wettability in the parallel direction was lower. In addition, higher wettability in the normal direction provided good rewetting because it facilitated liquid spreading [41]. Therefore, a superior heat transfer performance was achieved with a larger CA difference between the parallel and normal directions, which is denoted as higher anisotropy. The experimental results showed that the S2, S3, and S4 surfaces demonstrated greater heat transfer performance than the S1 surface owing to their increased anisotropy, as observed in Table 4. Supported by slightly increased anisotropy, the S2 surface with the smallest groove spacing exhibited a higher HTC than and a CHF similar to those of the S3 and S4 surfaces.  
The effects of the anisotropic roughness can be observed in Table 3 and Figure 13c,d. In the normal direction, the S2, S3, and S4 surfaces exhibited more significant surface roughness compared with that of the plain surface (S1). For the S1 condition, the surface was relatively smooth, leading to limited space for nucleation. On the contrary, the microgrooved surfaces with sufficient space inside the grooves could provide more nucleation sites. The strengthened nucleation ability achieved a superior HTC on the microgrooved surfaces. Similar boiling experiment results can be observed in previous studies [24; 58]. The average roughness factors are shown in Figure 13d. These roughness factors were mainly affected by the groove spacing, suggesting that the smaller the groove spacing that was selected, the higher the roughness factor that was derived. This is because more microgrooves could be presented on the test surface with a smaller spacing. With a larger space for bubble nucleation, the S2 surface could exhibit an HTC superior to those of the S3 and S4 surfaces.
## 1 Introduction
[29] proposed a hybrid approach combining Variational Mode Decomposition (VMD) to decompose complex data into Intrinsic Mode Functions (IMFs) and an RVFL network to predict each IMF, thereby improving load forecasting accuracy and robustness. The SCN, a stochastic learning method, was proposed by [PERSON] et al. [30] in 2017. Common stochastic learning algorithms include Radial Basis Function (RBF) networks [31] and RVFL networks [32]. The SCN learning algorithm outperforms other methods due to its supervision mechanism [33; 34; 35; 36; 37]. However, the lack of supervision mechanisms and the challenge of setting appropriate parameter ranges can lead to poor model performance [38], as demonstrated mathematically in [39]. The SCN exhibits faster convergence and superior forecasting ability compared to other stochastic networks [31]. The SCN has achieved great success in various fields, including software implementation, computer vision, medical data analysis, fault detection and diagnosis, and the modeling and forecasting of various systems. In the realm of power load forecasting, [PERSON] et al. [40] developed a recurrent version of the SCN for time series problems. However, when dealing with power load data, comparing it with only a single model does not sufficiently demonstrate its superiority in handling such data. Our aim is to contribute an effective and accurate hybrid load forecasting model to the field. The proposed LSTM-SCN approach offers distinct advantages over traditional statistical methods. Conventional statistical models typically assume linearity or stationarity in the data and focus on capturing dependencies and seasonal variations in time series, making them more suitable for simpler load forecasting scenarios. In contrast, LSTM-SCN, as a hybrid model, is capable of handling complex nonlinear relationships and data heterogeneity, enabling it to deliver more accurate predictions in challenging power load forecasting tasks. Compared to existing hybrid models, the LSTM-SCN approach introduces the SCN model innovatively to enhance predictive performance. The main contributions of this paper are as follows: (1) We propose an innovative fusion method for STLF by combining LSTM and the SCN. We extract features using LSTM, which are then fed into the SCN for forecasting. (2) We introduce the SCN as an alternative to traditional forecasting methods, leveraging its Universal Approximation Property to enhance forecasting accuracy in STLF. (3) We contribute to the literature by comparing the LSTM-SCN model with other baseline models, particularly examining its performance in high-volatility regions, and thereby highlighting its superior predictive accuracy. The rest of the paper is organized as follows: Section 2 introduces the principles of the SCN, LSTM, and the proposed LSTM-SCN hybrid model. Section 3 validates the Universal Approximation Property of the SCN using two regression datasets. Section 4 applies the LSTM-SCN model to the Australian electricity load dataset and analyzes its performance in comparison with other baseline models. Section 5 concludes the paper.
# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
## 2 Failure Mechanism and Vibration Characteristics  
### Failure Mechanism  
In the centrifugal pump system, due to the sudden closing of the pressure pipeline valve, the sudden stop of the centrifugal pump unit, etc., the flow rate of water suddenly changes, causing water hammer. This hydraulic phenomenon is called water hammer [15]. Water hammer belongs to the phenomenon of unsteady fluid flow. When water hammer happens, the instantaneous pressure generated can reach dozens or even hundreds of times the normal pressure in the pipeline, resulting in changes in the speed and torque of the centrifugal pump and other characteristic parameters, and even some serious pipeline damage [16; 17]. Industrial water supply sites are often unattended. Only after monitoring and recording the occurrence of water hammer failures can timely measures be taken to avoid serious accidents.
## 2 Materials and Methods
### Wavelet Transform
#### 2.1.2 Meaning and Application of the Algorithm  
The Mallat wavelet decomposition algorithm is an efficient and widely used method for analyzing signals in many fields. Decomposing a signal into approximating and detailing coefficients at different levels allows the signal to be analyzed at different frequency ranges and resolution levels. This is useful for identifying various signal characteristics, such as trends, details, noise, and features. The resulting coefficients can be used for a variety of tasks, including noise reduction (by thresholding the detailing coefficients), data compression (by discarding small coefficients), and feature extraction for classification and pattern recognition.
## 7 Practical Applications of NCS
### UAVs  
Today, unmanned aerial vehicles (UAVs) have become more and more well-liked in a wide field of applications. Though principally for military functions within the earlier period, it is noticed that there are various areas which may prove helpful [163]. For example, in the agriculture domain, they are applied in field observations or for chemical distributions. They patrol as a fireguard for forests or are used for traffic observation within the cities. They may also be used for automatic landscape photographing. They are terribly attention-grabbing, like in educational analysis, e.g., as flying laboratories, a workplace for control algorithms, or as an education gear for college scholars [63].  
Figure 19 shows the diagram of an autonomous UAV. The block diagram consists of various control modules, sensors, actuators (valves, motors), communication modules, etc., which acts as a bridge between the remote access system and the UAV system. The remote control system can access the information, as well as send the useful commands to the UAV. The power available in the vehicle is calculated by the computing power module.  
Figure 18: Industrial Manufacturing Control System.
## 2 Related Work  
### WSN Routing Algorithms  
Typical routing algorithms in WSNs can be a guide for designing EH-WSNs routing protocols, as EH-WSNs are WSNs-based [11; 12]. So far, numerous routing protocols for WSNs have been introduced. Greedy Perimeter Stateless Routing (GPSR) algorithm [19] is a typical geographic-aware routing protocol. It includes two types of data packet forwarding strategies, namely greedy forwarding strategy and perimeter forwarding strategy. By taking a greedy forwarding strategy, nodes select the next hop closest to the destination. At same time, in areas where greedy forwarding fails (i.e., routing hole region), the GPSR algorithm adopts the perimeter forwarding strategy (a void recovery method) to forward the packets bypassing this area, thus ensuring the stable transmission of packets. Through a void recovery method, it solves the routing hole problem where greedy forwarding fails. Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm [20] is an adaptive clustering topology algorithm. It consists of two phases. In the set-up phase, the cluster head node is randomly selected using a probability function, and other nodes join the corresponding cluster following the proximity principle. In the data transmission phase, non-cluster head nodes send data to the cluster head, and then the cluster head fuses the collected data and forwards it to the sink. Ant Colony Optimization in combination with Hop Count Minimization (ACOHCM) algorithm [21] comprehensively considers the energy constraint, network load balancing and dynamic network topology, combines the ant colony algorithm and the minimum hop algorithm and selects the next hop according to the residual energy and distance. This algorithm can find the optimal routing path with low energy consumption and balanced energy consumption on each node. Optimizing routing based on a congestion control (CCOR) algorithm [17] is a congestion control algorithm. In this method, a queuing network model is established to detect the congestion degree of nodes. Moreover, it constructs a hybrid formula that considers the position, average packet service rate and congestion degree to determine the optimal next hop.  
Reviewing the above algorithms, it is easy to find that WSN algorithms mainly concentrate on how to minimize energy consumption and increase the lifetime of a network. However, as inspiring as these works for traditional WSN routing protocols can be for our research, the EH-WSN routing algorithms design is still very different from these works.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
###### Abstract  
This paper addresses beam scheduling for tracking multiple smart targets in phased array radar networks, aiming to mitigate the performance degradation in previous myopic scheduling methods and enhance the tracking performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem, where each bandit process is associated with a target and its TEC states evolve with different transition rules for different actions, i.e., either the target is tracked or not. However, non-linear measurement functions necessitate the inclusion of dynamic state information for updating future multi-step TEC states. To compute the marginal productivity (MP) index, the unscented sampling method is employed to predict dynamic and TEC states. Consequently, an unscented sampling-based MP (US-MP) index policy is proposed for selecting targets to track at each time step, which can be applicable to large networks with a realistic number of targets. Numerical evidence presents that the bandit model with the scalar Kalman filter satisfies sufficient conditions for indexability based upon partial conservation laws and extensive simulations validate the effectiveness of the proposed US-MP policy in practical scenarios with TEC states.  
target tracking; beam scheduling; restless bandits; Whittle index +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Characterization
#### 3.1.3 UV-Visible Study  
Electronic and optical properties of materials can be determined by UV-Vis spectroscopy. Figure 3a,d,g shows the UV-Visible spectra of Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) before adsorption in N-methyl pyrrolidone (NMP). In the spectrum of iron oxide, a peak at 386.89 nm is due to the interaction of incident radiation with the outer most shell electrons of Fe in iron oxide. As a result, these electrons start to oscillate with the frequency of incident radiations showing surface plasmon resonance phenomenon (SPR) [46]. Another peak at 535.42 nm shows the presence of the DBSA moiety which was used as an emulsifying agent as reported elsewhere [47].  
The spectrum of PANI showed a peak at 316.11 nm which corresponds to the \(\pi\)-\(\pi^{\star}\) transition of benzooid while another peak at 611.29 nm can be attributed to quinoid ring excitation as shown  
Figure 2: Energy Dispersive X-ray (EDX) spectra of Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/ Fe\({}_{3}\)O\({}_{4}\) before adsorption (**a–c**), adsorption of Cr (VI) (**d–f**) and Ni (II) (**g–i**).  
in Figure 3d. Both these peaks are the characteristics absorption bands of the emaraldine base [48]. Deprotonation of emaraldine salt may occur due to formation of hydrogen bonds between hydrogens attached to amine group of PANI and the C=O group of NMP [49].  
Formation of PANI/Fe\({}_{3}\)O\({}_{4}\) composites can be characterized with two characteristics bands observed at 329.15 and 643.42 nm adsorption as shown in Figure 3g. Band at 643.42 nm can be assigned to the transfer of charge from the benzenoid to quinoid ring while the other band observed at 329.15 nm is attributed to the excitation of \(\pi\) electrons in the benzenoid ring. In the early research work, red shift is observed in the bipolaron band due to the doping of the amine group of benzenoid with Fe\({}_{3}\)O\({}_{4}\)[50].  
Removal of Cr(VI) through Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites was conducted by the UV-visible spectrum as shown in Figure 3b,e,h, while Figure 3c,f,i shows the same for Ni(II). The typical bands of the adsorbent materials were shifted to longer wavelengths, suggesting some physical interactions of these metals with active sites present on the surfaces of adsorbent materials. Cr(VI) cause more red shift as compared to Ni(II). Enhancement in Cr(VI) adsorption can be attributed to its larger size which can exist as HC\({}_{r}\)O\({}_{4}\)\({}^{-}\) in aqueous solution, thus causing greater London or dispersion forces [51]. The intensity of the peak at 378.11 nm increased when Cr(VI) came in contact with the surface of composite materials as shown in Figure 3h. In our already published work, it was explained that doping of PANI with Fe\({}_{3}\)O\({}_{4}\) caused an increase in the surface area of the composite resulting in the enhancement of adsorption [42].
# Evaluation of the Prospects for the Use of Microalgae in Functional Break Production
## 3 Results
### Physicochemical Properties of bread with microalgae  
The physicochemical properties of bread samples with microalgae are presented in Table 7. In terms of physicochemical properties, all samples (Table 7) did not contradict the generally accepted standards for wheat bread.  
The results of studying the volume yield of bread with the addition of various amounts of microalgae are shown in Figure 1. The data (Figure 1) indicate that bread samples with the addition of 3% microalgae had the highest volumetric yield.  
The texture of bread samples with microalgae are presented in Table 8. Bread samples (Table 8) with 3% and 5% microalgae mixture additions had slightly better textural properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Value**} & \multicolumn{4}{c}{**Samples**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline Weight, g & 49.4 \(\pm\) 0.2 & 47.8 \(\pm\) 0.1 & 47.1 \(\pm\) 0.1 & 50.2 \(\pm\) 0.2 \\ Maximum height, mm & 58.1 \(\pm\) 0.2 & 56.6 \(\pm\) 0.2 & 55.1 \(\pm\) 0.2 & 52.1 \(\pm\) 0.2 \\ Specific crumb volume, mL/g & 3.5 \(\pm\) 0.01 & 3.5 \(\pm\) 0.01 & 3.7 \(\pm\) 0.01 & 3.2 \(\pm\) 0.01 \\ Crumb density, g/mL & 0.27 \(\pm\) 0.0008 & 0.29 \(\pm\) 0.0009 & 0.27 \(\pm\) 0.0008 & 0.34 \(\pm\) 0.0010 \\ Crumb moisture content, \% & 28.0 \(\pm\) 0.08 & 29.8 \(\pm\) 0.09 & 32.4 \(\pm\) 0.10 & 39.2 \(\pm\) 0.10 \\ Crumb porosity, \% & 71.8 \(\pm\) 0.2 & 69.7 \(\pm\) 0.2 & 68.4 \(\pm\) 0.2 & 55.6 \(\pm\) 0.2 \\ pH & 6.6 \(\pm\) 0.02 & 6.6 \(\pm\) 0.02 & 6.6 \(\pm\) 0.02 & 6.2 \(\pm\) 0.02 \\ a\({}_{\text{w}}\) & 0.975 \(\pm\) 0.003 & 0.953 \(\pm\) 0.003 & 0.938 \(\pm\) 0.003 & 0.918 \(\pm\) 0.003 \\ \hline \hline \end{tabular}  
* 1—control sample, without added microalgae; 2—bread with a microalgae content of 1.0%; 3—bread with a microalgae content of 3.0%; 4—bread with a microalgae content of 5.0%, a\({}_{\text{w}}\)—water activity. Data presented as a mean \(\pm\) SD (\(n=3\)). The results shown in the table are obtained on the 1 st day after baking.  
\end{table}
Table 7: Physicochemical properties of bread samples with microalgae.  
Figure 1: The results of studying the volume yield of bread with the addition of various amounts of microalgae: 1—control sample (no added microalgae); 2—bread with a microalgae content of 1.0%; 3—bread with a microalgae content of 3.0%; 4—bread with a microalgae content of 5.0%.
# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 4 Discussion
These phenomena, in itself, cause observable differences between WTs of the same WF. However, one of the most significant things in this context is that the estimates are made from data provided by the SCADA system. Apart from the fact that the sensors are subject to noise, what is also very relevant is that these data are obtained from temporal averages of usually 10 min (although, in our case, they are 5 min). Temporal averages combined with wind variations in this time interval explain these differences between the estimated and manufacturer curves.