esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 2 Different Application and Cooling Requirements for High-Speed Motors
### Turbo-Machinery  
High-speed motors can also provide a driving force for turbo-machinery [14; 15; 16; 17]. Both rotor speed and power requirements are imposed on high-speed motors. The overall cooling of the motors is the main concern. [PERSON] et al. [18] studied the thermal performance of motors in centrifugal blowers. The motor was rated at 225 kW and 34,500 rpm, as presented in Figure 2. The cooling effect was provided by an external fan, which circulated and air-cooled the motor gap. Optimization methods were also investigated, and the results showed that heat dissipation could be improved by changing the shape of the winding slots and selecting a high thermal conductivity liner material.  
Figure 1: Design of bogies for distributed traction high-speed trains [12].  
Figure 2: Turbo-machinery cooling system [18]. (**a**) Prototype structure in 3D cross-section view; (**b**) 2D cross-section view.  
[PERSON] et al. [19] designed and optimized the existing high-speed motors used in oil pumps for the aviation industry. This motor, with natural convection cooling, was designed to operate at 5000 rpm and 10 kW. A highly reliable driving system topology was proposed, which enabled the optimized motor to have better heat dissipation performance. [PERSON] et al. [20] investigated a 30 kW high-speed compressor motor with a rated speed of 30,000 rpm. Water-cooling of the motor housing was chosen for heat evacuation. The thermal analysis of the motor was conducted with different cooling water channel structures and water channels. It was found that an eight-turn spiral water channel with a flow rate of 3 m/s was more favorable for heat dissipation.
###### Abstract  
Plants from the _Aster_ species are known to be a rich source of bioactive chemical compositions and are popularly known for their medicinal properties. To investigate the relationship between the nine species of _Aster_, the floral fragrance and volatile profile patterns were characterized using E-nose and HS-SPME-GC-MS. Initial optimization for fragrance analysis was performed with _Aster yomena_ using E-nose by evaluating the scent patterns in different flowering stages. _Aster yomena_ exhibited varied scent patterns in each flowering stage, with the highest relative aroma intensity (RAI) in the full flowering stage. PCA analysis to compare and analyze the scent characteristics of nine _Aster_ species, showed a species-specific classification. HS-SPME-GC-MS analysis of flowers from nine _Aster_ species revealed 52 volatile compounds including \(\beta\)-mycrene, \(\alpha\)-phellandrene, D-limonene, trans-\(\beta\)-ocimene, caryophyllene, and \(\beta\)-cadience. The terpenoid compounds accounted for the largest proportion. Among the nine _Aster_ species flowers, _Aster koniaresis_ had sesquiterpenes as the major component, and the remaining eight varieties had monoterpenes in abundance. These results could distinguish the species according to the scent patterns and volatile components of the nine _Aster_ species. Additionally, flower extracts from the _Aster_ species' plants exhibited radical scavenging antioxidant activity. Among them, it was confirmed that _Aster pseudoglehnii_, _Aster maackii_, and _Aster arenarius_ had high antioxidant activity. In conclusion, the results of this study provide fundamental data of the volatile compound properties and antioxidant activity of _Aster_ species, offering basic information of valuable natural sources that can be utilized in the pharmaceutical, perfume, and cosmetic industries.  
_Aster_; E-nose; floral scent; GC-MS; secondary metabolites; volatile compounds +
Footnote †: journal: Metabolites
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
## 2 Sparse Coding Algorithm Based on Optimized LWD and FSS  
### Shift Invariant Sparse Coding  
SISC considers that the same atom (also known as the basis function) through any time shift can express every identical event in the signal. It is very suitable for the effective characterization of periodic feature components in vibration signals. Assuming that the input signal \(y_{\left(i\right)}\in R^{p}\), \(i=1,\cdots,m\), \(LD=\left\{\text{ld}_{\left(1\right)},\text{ld}_{\left(2\right)},\cdots,\text{ld} _{\left(n\right)}\right\}\) is a redundant dictionary, and \(s=\left\{s_{\left(1\right)},s_{\left(2\right)},\cdots s_{\left(n\right)}\right\} ^{T}\) is the sparse coefficient, respectively, then \(y_{\left(i\right)}\) can be expressed by a time-shifting atom \(d_{\left(i\right)}\in R^{q},j=1,\cdots,n\) as:  
\[y_{\left(i\right)}=LDs+\varepsilon_{\left(i\right)}=\sum_{j}\text{ld}_{\left( i\right)}*s_{\left(i,j\right)}+\varepsilon_{\left(i\right)} \tag{1}\]  
where \"\(*\)\" is the convolution operation, which is used to represent that each atom can move anywhere in the time domain, \(s_{\left(i\right)}\in R^{p-q+1}\) is the sparse coefficient, and \(\varepsilon_{\left(i\right)}\in R^{p}\) is additive random noise. Here, the dimension of atom \(d_{\left(i\right)}\in R^{q}\) can be lower than that of the input signal. Assuming that \(\beta\) is the sparsity penalty parameter used to control the sparsity degree and c is an iteration error threshold, then the objective function is defined as:  
\[\begin{split}\underset{d,s}{\text{min}}&\sum \limits_{i=1}^{m}\left\|y_{\left(i\right)}-\sum\limits_{j=1}^{m}\text{ld}_{ \left(i\right)}*s_{\left(i,j\right)}\right\|_{2}^{2}+\beta\underset{i,j}{ \text{\sum}}\big{\|}s_{\left(i\right)}\big{\|}_{1}\\ & s.t.\quad\left\|ld_{\left(i\right)}\right\|_{2}^{2}\leq c,1\leq j \leq n\end{split} \tag{2}\]
## 5 Discussion  
As shown in Table 2, this study examines current work on OTTD implementation utilizing optical devices in order to evaluate the uniqueness and effect of such research in the larger area. Due to the challenge of producing ultra-high precision optical time delays using optical chips in current techniques, it is challenging to steer the beam for phased arrays utilizing complete OTTD with high accuracy. In 2018, [PERSON] described the optical chip with the highest precision delay accuracy. With a minimum delay regulation compensation of 0-3.4 ps, it has a construction that is an ultra-low-loss continuously tunable optical real-time delay line device based on a Si3N4 ring resonator (MRR) in a side-coupled integrated spaced sequence resonator (SCISOR) structure [44]. A beam-steering precision of 0 to 3.8991\({}^{\circ}\) is consistent with such a delay accuracy. The MRR, however, has outstanding spectrum properties and is particularly temperature-sensitive; as a result, when the temperature changes, the device's response alters significantly.  
Figure 11: Far-field pattern of small angle steering.  
Figure 12: Far-field pattern of wide and fine beam steering (**a**) Scan details around \(-40^{\circ}\); (**b**) accurate angular scanning over a wide range of \(\pm 40^{\circ}\) (**c**) scan details around \(40^{\circ}\); (**c**) Scan details around \(40^{\circ}\).  
The grating delay line (GDL) is another OTTD option that is simpler to integrate. Its minimum time delay depends on the special design of the grating. The present structure has a minimum beam-steering precision of 5.393\({}^{\circ}\) and uses waveguide gratings as the OTTD for beam synthesis [45]. Nevertheless, because of limitations such as a lack of process precision, the lowest beam gaze accuracy that can be achieved with optical switches and Comb for OTTD is 2.2924\({}^{\circ}\) and 22.0243\({}^{\circ}\), respectively [46; 47].  
In order to accomplish high-precision beam steering while accounting for subarray delays, this work ingeniously integrates a high-precision millimeter beamforming device with an OTTD that is AWGR-based.
## 1 Introduction
Advanced prediction, optimization, and control algorithms are needed to implement DR and DSM in HVAC (heating, ventilation, and air-conditioning) and in specific within-heat pump systems, as well as to increase the system efficiency. This work focuses on the Plus-Energy settlement in Wustenrot, its novel agrothermal heat source combined with a low-temperature DHC network, and 23 residential buildings that are connected to the grid. All buildings have high-energy efficiency standards, are equipped with decentralized heat pumps, PV (photovoltaic) systems, some with battery storages and all relevant electrical and thermal energy flows are monitored in detail. At first, a comprehensive overview is given about the Plus-Energy settlement, including its planning, realization, and construction (costs). Monitored results of the settlement's heat demand and of the novel agrothermal collector system and its operational characteristics are presented. Further, the PV and heat pump concept as well as the approach on economical assessment are explained. Then, the focus is taken to the building level where currently work is still in progress regarding optimized building operation. Thereby, strategies for the application of DR and DSM are presented. The utilization of white box and self-learning black box simulation models for the prediction and application of evolutionary (genetic) algorithms for optimization are explained, and a validation together with an estimation of the prediction inaccuracy is carried out. Based on the knowledge gained and on the models developed, results of the ongoing work are presented, including an assessment of the agrothermal low-temperature DHC systems costs, the building-side end-user costs, results for the optimization parameters, as well as simulation-based estimations on self-consumption, flexible electricity tariffs, and power markets.
# Variations in the Equatorial Ionospheric F Region Current during the 2022 Tonga Volcanic Eruption
## 3 Results
Positive values denote the radially upward FRC, while negative value denote the radially downward FRC. The red arrows and vertical dashed lines denote the radially upward FRC on 15 January, while the blue arrows and vertical dashed lines denote the radially downward FRC on 15 January. The magnetic activity is generally quiet on 13 January (figure not shown). The magnetic storm started at 14:00 UT on 14 January, and Dst attained peak at 22:25 UT on 14 January. After that it was the storm recovery phase. It can be seen from the bottom panel of Figure 2 that the F region current in the daytime (around 11 MLT) was generally weak on 13 and 14 January (black and red curves, no more than 20 nA/m\({}^{2}\)). However, the FRC is greatly enhanced in amplitude during the eruption period (after 12 UT on 15 January 2022), which is much larger than that during quiet or during storm main phase periods. Thus, it seems that the intense FRC is related to the volcanic eruption rather than the magnetic storm. The merging electric field effect on FRC will be discussed in the discussion section.
## 5 Implementation Details
### Health Applications of the System  
The system is a set of subapplications, each of which was considered as a set of microservices. These subapplications are web applications (REST web services) and were developed with Play Framework v2.4.8 [43] and the Scala programming language v2.11.8. Play Framework is an MVC framework and is based on a lightweight, stateless, web-friendly architecture for highly scalable reactive applications. The user interfaces (MVC views) were developed using HTML5, CSS3, JavaScript, Bootstrap, and JSON. Figure 10 shows the microservices of the e-health system.  
Figure 9: System architecture.  
The building of the images of the microservices of the subapplications is part of the CI/CD pipeline that includes the storage of the images in the GitLab container registry. With these facilities, the process of deploying the CI/CD pipeline is performed automatically from the GitLab container registry to the e-Health-System-K8s-cluster nodepool-1. In Kubernetes, the pods of the subapplications were configured to provide scalability based on the increase in requests to the system. External exposure to the services of e-health applications was made through an ingress resource that allows access to multiple services through a single IP address. For additional details of the graphical user interfaces for access to services, see Appendix F.
## 1 Introduction
For this reason, it is required to have a fair energy balancing among the nodes inside the vector and the whole network. Therefore, holding time estimations consider the energy factor to increase the network lifetime. However, the shortest path between the sender and the sink does not guarantee fair distribution of load/energy in the network. Alternatively, better decisions regarding forwardingbased on precise holding time estimations are achieved when each node in the network is updated with the network state information (complete or partial network). Exchange of control packets makes this network state information available; however, this traffic puts additional burden on bandwidth, energy consumption and error rates. Therefore, suggested forwarding schemes for UASNs should address these limitations and offer a balanced tradeoff. Further improvement to reduce the number of duplicate packets can be made if the immediate neighbors estimate their holding times to be greater than the propagation delays. Higher energy consumption, channel errors and long propagation delays of acoustic signals are well-established challenges in aquatic environments [10]. Energy consumption and propagation delays are phenomenally increased when the far away node of the network sends packets to the sink node (fixed at a particular point). For efficient collection of data, sink mobility has been suggested by various schemes in the literature. Mobile Sinks (mobile stations) are nodes that can change their position in two ways: (1) autonomously, and (2) via anchored ropes/vessels. An example of autonomously changing positions is the autonomous underwater vehicles (AUVs) [11]. The mobile sink is assumed to have frequent refueling and recharging made available to ensure roaming in the network. Therefore, the proposed routing schemes for UASNs may also consider the effectiveness of using a mobile sink. In this paper, Extended Energy-Scaled and Expanded Vector-Based Forwarding (EESEVBF) protocol exploits the holding time mechanism to suppress duplicate packets. Moreover, the proposed protocol also tackles the hidden terminal problem due to which a reasonable reduction in duplicate packets initiated by the reproducing nodes occurs. The holding time is calculated based on the following four parameters: (i) the distance from the boundary of the transmission area relative to the PFNs' inverse energy at the 1 st and 2 nd hop, (ii) distance from the virtual pipeline, (iii) distance from the source to the PFN at the second hop, and (iv) distance from the first-hop PFN to its destination. Therefore, the proposed protocol stretches the holding time difference based on two hops, resulting in lower energy consumption, decreased end-to-end delay, and an increased packet delivery ratio.
## 4 Discussion  
### Sample Preparation  
The immunodeficiency strategy involves removing abundant or interfering proteins from the sample to enrich the unbound fraction, thereby enhancing the detection of target proteins or biomarkers. Albumin, the most abundant protein in human plasma, accounts for more than 50% of total plasma protein [32]. A commercially available albumin depletion kit, which effectively removed a significant portion of this abundant protein was used in this study (Figure 1a). Although residual amounts of albumin were still visible on the CBB-stained gel and detected by MS, the number of protein groups, peptide sequences, and peptide spectrum matches increased after depletion. As shown in the Venn diagram (Figure 1a), 199 proteins (43.7%) were common to both pre- and post-depletion samples. The depleted sample, however, yielded a higher number of uniquely identified proteins, with 175 proteins (38.5%) compared to the plasma sample before albumin removal. Additionally, the protein score for albumin decreased from 15,446 in the plasma sample to 5397 in the depleted sample, according to Mascot search results.  
Protein precipitation is a critical step in sample preparation for detailed proteomics analysis. Several precipitation methods are commonly used, including protocols with acetone, ammonium sulfate, trichloroacetic acid (TCA), and chloroform/methanol, which have been applied to human plasma [33], adipose tissue [34], cow milk [35], and rat brain tissue [36]. While these methods generally yield comparable results, their effectiveness depends on the source of the sample, whether tissue, cells, or blood. For instance, TCA and acetone precipitation proved most effective for human plasma [33], while the chloroform/methanol method yielded higher protein recovery from human adipose tissue [34]. A comparison of precipitation methods with Detergent Removal Spin Columns (DRS) and Filter-Aided Sample Preparation (FASP) revealed that the efficiency of each method depends significantly on the initial protein amount [37]. FASP performed best with low protein amounts (around 10 ug), but its efficiency was comparable to other methods at higher starting concentrations (over 100 ug). Thus, it is preferable when limited sample material is available. However, the cost of the filter units used in FASP can increase the overall expense of research. Due to the absence of limitations on sample availability, precipitation methods were opted for over FASP. Among these, the chloroform/methanol protocol was chosen due to its simplicity, lack of refrigeration requirements, and rapid execution (30-40 min), minimizing the risk of protein degradation.
## 4 Simulation Results
### Interference Mitigation Results  
In this section, the simulations of the interference removal using the proposed detector and the notch filter are studied. As discussed before, the detector has the capacity to measure the interference power and the notch bandwidth must be adjusted in a way that maximizes the received SNR. Obtained optimum \(r\) parameters for each input \(\mathrm{SR}\) (Figure 10) are saved as lookup table thus, the \(r\) parameter would be automatically adjusted to the best possible value to completely remove the CWI without damaging the surrounding signal. In a digital receiver, an important criterion for performance measurement is the bit error rate (BER). The BER displays the error in the received message based on the transmitted SNR. If the detector can remove the interference correctly, then the improvement is visible as the amount of error correction. Figure 16 displays the BER of the QPSK digital receiver after the AWGN channel and MCWI with variable power. For comparison, the BER curve while the detector is utilizing the notch filter with the constant \(r\) parameter is also displayed. When the \(r\) parameter is a large constant \(r=0.999\) (a narrow notch), residual interference power deviates the QPSK signals from their original points and causes error increment in received bits. When the notch bandwidth is too wide (constant small \(r=0.99\) ), the CWI has been removed, but a part of the useful signal has been removed as well, which is damaging the overall signal integrity. Figure 16a also represents the filtered signal with the adaptive \(r\) parameter using the LUT. In this case, the CWI has been completely removed and the signal integrity is significantly less affected. Based on this comparison, utilizing adaptive bandwidth for notch filtering can improve the system performance by 2 dB (Figure 16a).  
One of the benefits of the presented design, is its flexibility. This interference mitigation system can suppress MCWI with the help of the several adaptive n  
Figure 16: Bit Error Rate (BER) performance of Quadrature Phase Shift Keying (QPSK) in presence of interference (**a**) CWI with variable power, (**b**) Multiple Carrier Wave Interferences (MCWI) with variable power.  
allows for any amount of CWI to be removed from the signal simply by adding or removing a detector and a filter. For the result in Figure 16b, 3 CWIs with variable power were applied over an AWGN signal. Clearly, without filtering, the system has huge residual errors and is not able to receive any bits. The detector adjusts center frequencies and optimum \(r\) for each notch filter individually and improve the received SNR dramatically.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 3 Methods
### Architecture of the Proposed Approach  
Figure 5 summarizes the flow of the proposed procedure described in this section. Data collection occurs as the movement of the buoys was measured and sent to the computer onshore. Then, these data were filtered out to remove abnormalities before the data were segmented with a properly-sized window. As a result, eight time series data were derived from the window. We calculated two statistical variables, mean and variance, from the data of each time series so that they served asfeatures. These features were standardized, and PCA was applied to these scaled features to reduce their dimension. Before developing prediction models based on ML algorithms, hyperparameter tuning was done with each algorithm. To check the validity of the models, the dataset was split into training and testing sets. We used 70% for training and 30% for testing the models.  
Regression is performed to obtain the forecasting values of power generation. This is done in two ways: with a classifier and without a classifier. Regression with a classifier yields estimates using the classification result in the previous step. Depending on the result, there are three types of models available for regression. Classification has a positive effect on improving the accuracy of regression models [26]. Intuitively, the subsequent regression can narrow the interval of the estimate so that the prediction error could be smaller. Regression without a classifier was employed in only one model. Therefore, the resultant estimate cannot be as accurate as one with a classifier, in general, but it can yield a better result in case an incorrect classification is highly likely.