esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of External Rotor Ferrite-Assisted Synchronous
## 2 Challenges and Design Considerations for High Power Density External Rotor Fe-PMaSyn RM
### Demagnetization Concerns with External Rotor Structure  
The risk of irreversible demagnetization is a significant concern for any Fe-PM motor. This can be a more significant concern in external rotor Fe-PMaSynRM with higher torque density and increased bore diameter. Figure 2a,b show a single-pole structure of an internal rotor Fe-PMaSynRM and an external rotor Fe-PMaSynRM, respectively.  
Both motors are assumed to have the same outer dimensions, arc-shaped Fe-PM with the same angular span, and a single flux barrier per pole. However, the external rotor motor's bore diameter is higher to obtain higher average torque (\(D_{external~{}rotor}>D_{internal~{}rotor}\)). Then the PM-thickness will tend to be smaller in the outer rotor motor. This will impact the operating flux density of the Fe-PM, which can be expressed as:  
\[B_{m,j}=B_{rem}-\frac{\Delta f_{r,i}~{}\mu}{t_{m,i}} \tag{4}\]  
where \(t_{m,i}\) is the thickness of magnet in the \(i\)-th flux barrier, \(B_{rem}\) is the magnet remnant flux density, \(\mu\) is the magnet permeability, \(B_{m,i}\) is magnetic flux density in the \(i\)-th flux barrier, and \(\Delta f_{r,i}\) is the rotor magnetic potential difference across \(i\)-th flux barrier. Equation (4) applies to both internal and external rotor PMaSynRM. Here, the \(B_{rem}\) and \(\mu\) are constant for a specific magnet at a given temperature. Also, at a specific temperature, the knee point flux density is constant for a specific magnet. Hence, according to (4), the maximum limit of the applied demagnetizing field is directly related to the magnet thickness (\(t_{m}\)).  
Overall, if the bore diameter (\(D\)) is increased in the external rotor motor for high power density, it might have two significant consequences. The first one is the reduction of reluctance torque, and the second one is the increased susceptibility to demagnetization. The reduction of reluctance torque will reduce the benefit of PMaSynRM, while the increased susceptibility to demagnetization will limit the electric overloading capability and fault tolerance. Usually, any motor is designed to withstand certain electric overloading conditions, while the five-phase motors are designed to withstand open-phase fault operation toward generating a specific average torque. Hence, the primary design challenges and design objectives for high power density external rotor five-phase Fe-PMaSynRM are achieving higher average torque with significant reluctance torque portion and ensuring high anti-demagnetization ability. Detailed discussion on the motor configuration, optimal flux barrier shaping, and parameter optimization are presented.
## 2 System Model and Configuration
### Modulation and Coding Mode Selection  
Higher-order modulation and higher-rate channel coding can increase data transmission efficiency when the channel has sufficient SNR condition. In order to provide flexibility and fully use of different channel scenarios, DVB-S2 supports 28 kinds of MCSs with the span of demodulation threshold up to 21 dB. In order to avoid the frequent switching of MCSs and reduce the complexity of system implementation and simulation analysis appropriately, eight kinds of MCSs are selected in this paper, as shown in Table 1[24].
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 5 Practice
### Exercise 2  
A three-phase induction motor shows the following characteristics: 95 kW, 230/400 V, 50 Hz, and 978 rpm.  
How many pairs of poles does this motor have?  
SolutionThe number of poles is directly proportional to the synchronous speed of the motor. Since the the mains frequency equals 50 Hz, Equation (1) gives the, respective, synchronous frequencies of 3000, 1500, 1000, and 750 rpm for the different values of \(p=1\), 2, 3, and 4. Then, the number of pairs of poles is \(p=3\) since \(\Omega_{rated}=978\) rpm is slightly less than 1000 rpm.
## 2 Preliminaries
### Model-Based CF  
Among various model-based CF techniques, latent factor models are the most popular because they can overcome the weakness of neighborhood-based approaches on extremely sparse data. This type of model aims at uncovering latent features that explain the observed ratings, as proven in Netflix Prize competition [6].  
Let \(R\in\mathbb{R}^{m\times n}\) denote the rating matrix where \(m\) is the number of users, \(n\) is the number of items. By applying SVD factorization, both users and items are mapped into a latent space of dimension \(k\) (\(k\ll m,n\)). Here, each user can be characterized by a user-factorvector \(p_{u}\in\mathbb{R}^{k}\), and each item by an item-factor vector \(q_{i}\in\mathbb{R}^{k}\). The prediction is estimated by taking the following inner product.  
\[\hat{p}_{ui}=q_{i}^{T}p_{u} \tag{3}\]  
Rating value estimated in Equation (3) raises a bias problem in practice. Among users with similar interests, some users tend to give higher ratings than others. Among similar items, some always get lower ratings than others due to irrelevant reasons such as poor video quality. Therefore, user and item-specific biases, \(b_{u}\in\mathbb{R}^{m}\) and \(b_{i}\in\mathbb{R}^{n}\), respectively, are introduced. Specifically, the rating user \(u\) give to the item \(i\) is approximated as  
\[\hat{r}_{ui}=b_{ui}+q_{i}^{T}p_{u} \tag{4}\]  
where \(b_{ui}=\mu+b_{u}+b_{i}\) is the baseline estimate rating of user \(u\) for item \(i\), and \(\mu\) is the mean of ratings.  
Non-negative Matrix Factorization (NMF) is another common model in the study of RS [27; 28; 29]. Different from regular matrix factorization, the objective of NMF is to factorize a non-negative matrix into matrices with no negative element. This constraint is useful in many fields such as computer vision, signal processing or RS in general where non-negative data are explicitly required.  
Even though the idea of mapping the interaction matrix into a lower-dimension latent space provides remarkable accuracy, the latent factors themselves have no explicit meaning. The predicted ratings, therefore, are unpersuasive and untrustworthy, which declines user satisfaction of RSs [30]. This problem also makes it challenging for engineers and researchers to evaluate, diagnose and refine the system in the long term.
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.3 MOD15A2H Data  
This study used MOD15A2H (version 6) data from 2001-2020, which contains two scientific datasets, the FPAR and LAI, with a spatial resolution of 500 m and a temporal resolution of 8 days. Data were obtained from the USGS ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)), accessed on 9 December 2021). The 8 days FPAR and LAI data were combined into monthly FPAR and LAI data using the maximum synthesis method, and the seasonal FPAR and LAI data were obtained using the mean synthesis method. Using FPAR and LAI data, we analyzed the relationship between changes in SO\({}_{2}\) concentration and changes in vegetation FPAR and LAI in different seasons.
## 2 Theoretical Foundations
### Metaheuristic Composition Optimisation Problem  
In the previous sections, crucial concepts of optimisation and heuristics (simple heuristics, metaheuristics, and hyper-heuristics) were introduced. Particularly, metaheuristics essentially comprise an initialiser, search operators (perturbators and selectors), and a finaliser. These components are usually combined using a manual design by a researcher or practitioner.  
However, [PERSON] et al. presented in [30] a taxonomy for the automated design of search algorithms. This taxonomy includes three categories: automated algorithm configuration, automated algorithm selection, and automated algorithm composition. The authors define the General Combinatorial Optimization Problem (GCOP) to involve optimising the combination of components of search algorithms to solve combinatorial optimisation problems. The GCOP is illustrated by applying selection perturbative hyper-heuristics to automate the composition of search algorithms for solving the capacitated vehicle routing and nurse rostering problems. When solving the GCOP, the perturbative heuristics are the components comprising the search algorithms.  
In this work, we focus on the automated algorithm composition of a specific category of search algorithms, namely, metaheuristics. Hence, we define a version of the GCOP, i.e., the Metaheuristic Composition Optimization Problem (MCOP). Furthermore, MCOP applies to both combinatorial and continuous optimisation domains. The research presentedin this paper employs a selection perturbative hyper-heuristic (cf. Definition 9) to solve the MCOP for continuous optimisation. As in [30], the low-level heuristics are the components of the metaheuristic, and the optimisation aims to produce a solution \(\vec{x}_{*}\) by exploring a heuristic space of metaheuristic components instead of the solution space. Details of the proposed hyper-heuristic are presented in the next section.
# Detection of Household Furniture Storage Space in Depth Images
## 4 Evaluation
### Evaluation on the Synthetic Dataset  
A total of 9998 synthetic scenes with 34,727 annotated storage volumes were created. The dataset was split approximately 90:10 for training and testing; 20% of the training dataset was used for validation during training. The distribution of the dataset per furniture class with respect to each split of the dataset is shown in Table 1.  
Table 2 shows the minimal, maximal, and median bounding box (according to the volume) for each furniture class in the synthetic dataset together with the corresponding size (length (\(x\)), height (\(y\)), and width (\(z\))) of a particular bounding box. This table also shows the median length (\(x\)), height (\(y\)), and width (\(z\)) of each bounding box. Distribution of bounding box volumes size per furniture class is shown in Figure 10. The smallest dimension is 7 cm, and the largest is 4.86 m. The average size per axis of the storage volumes in the dataset is 0.31 \(\times\) 0.43 \(\times\) 0.31 m.  
\begin{table}
\begin{tabular}{|l c c c c c c|} \hline \multirow{2}{*}{**Class Name**} & \multirow{2}{*}{**Count of Selected**} & \multicolumn{5}{c|}{**Count of Generated Scenes**} & \multirow{2}{*}{**Count of Label**} \\  & & **\multicolumn{1}{c}{**Training**} & **Validation** & **Evaluation** & **Total** & **Storage Volumes** \\ \hline bookshelf & 41 & 3600 & 901 & 501 & 5002 & 26,841 \\ cabinet & 10 & 725 & 182 & 501 & 1008 & 2702 \\ desk & 18 & 725 & 182 & 101 & 1008 & 2142 \\ table & 34 & 712 & 179 & 99 & 990 & 1512 \\ table empty & 34 & 712 & 179 & 99 & 990 & 1530 \\ negative & 4 & 720 & 180 & 100 & 1000 & 0 \\
**TOTAL** & **141** & **7194** & **1803** & **1001** & **9998** & **34,727** \\ \hline \end{tabular}
\end{table}
Table 1: Distribution of the generated synthetic dataset per furniture class.  
Figure 10: Storage volume per volume size for each furniture class: (**a**) bookshelf, (**b**) cabinet, (**c**) table, (**d**) empty table, and (**e**) desk.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 3 Results and Discussion  
Validation of the Model: Metal/AlN/Metal Integration on a Nanocrystalline Diamond (NCD) Microcantilever  
To validate the numerical model, an initial simulation was conducted based on the characterization of the NCD microcantilever reported in reference [27]. The goal was to compare the simulated results with the experimentally measured resonant frequency (from frequency domain analysis) and quality factor (from time domain analysis) for the first resonant mode.  
The microcantilever beam described in reference [27] is 1 \(\upmu\)m thick, with dimensions of 320 \(\upmu\)m in length and 70 \(\upmu\)m in width. The electrode and AlN films cover the entire surface of the microcantilever. Both the top and bottom electrodes are chromium (Cr) films deposited by sputtering. Since the thickness of the Cr films is not specified in the reference, it was assumed to be 100 nm for the purpose of simulation.  
Using these values, the geometry of the microcantilever was modeled in COMSOL Multiphysics (Figure 4). For both frequency and time domain analyses, a free tetrahedral mesh consisting of 96,434 elements was employed (Figure 4a). The mesh was generated using COMSOL's physics-controlled meshing algorithm with a fine mesh setting to ensure accurate resolution of structural and dynamic features. Figure 4b shows the layer structure of the cantilever.
# Surfactant-Modified Construction Waste Bricks for the Removal of Diclofenac from Aqueous Solutions
## 2 Materials and Methods
### The Adsorption Experiments  
The batch experiments were conducted under controlled conditions, protected from light, and with single variables. Blank controls and parallel samples were also included in the experiment.  
The effectiveness of DCF removal using CCBPs modified with different CTAB concentrations was evaluated by adding 0.06 g of the adsorbent into 30 mL of a DCF solution with an initial concentration of 20 mg/L. The solution and adsorbent were mixed and agitated for 24 h at 150 rpm and 298 K. The pH of the solution was maintained at 7.0 \(\pm\) 0.1 by adding 0.01 mol/L of HCl or NaOH solutions. After the agitation period, the samples were filtered through a 0.22 \(\upmu\)m membrane filter, and the DCF concentration was measured.  
To explore the influence of pH values on the adsorption of DCF onto waste bricks, amounts of 0.06 g of both PRBP and CCBP were added separately into 30 mL solutions containing an initial DCF concentration of 20 mg/L. Using HCl and NaOH solutions, the pH values were adjusted and kept constant at around 4.0-9.0 during the entire adsorption process. The mixture solutions were stirred at a speed of 150 rpm for 24 h at a temperature of 298 K. The experimental conditions were the same as those used in the removal efficiency experiments, and both blank control and parallel samples were included in the experiment.  
In the adsorption kinetics experiments, 1 g of each adsorbent was added to a 500 mL solution with an initial DCF concentration of 20 mg/L. The pH values were controlled at 4.0 \(\pm\) 0.1 using HCl and NaOH solutions. Samples were collected at specific time intervals (0-1440 min), filtered, and analyzed for their DCF concentrations. The experimental conditions were kept consistent with the removal efficiency experiments.  
To investigate the influence of ionic strength on the adsorption of DCF onto waste bricks, different concentrations of NaCl (ranging from 0 to 0.2 mol/L) were added to the solutions. The pH was adjusted to 4.0 \(\pm\) 0.1, and all other experimental parameters were kept constant with the removal efficiency experiments.  
To conduct adsorption isotherms and thermodynamic experiments, 30 mL of DCF solutions with initial concentrations ranging from 1 to 40 mg/L were prepared, and 0.06 g of each adsorbent was added to each solution. The temperature was maintained at 298, 308, and 318 K, while the pH values were kept constant at 4.0 \(\pm\) 0.1 using appropriate HCl or NaOH solutions. Other experimental conditions were consistent with those used in the removal efficiency experiments.  
The DCF concentrations were determined via ultra performance liquid chromatography (UPLC) using a C18 reverse-phase column (100 mm \(\times\) 2.1 mm, 1.7 \(\upmu\)m particle size) and a wavelength of 265 nm. The mobile phase was a mixture of 20% ultrapure water (0.01% CH\({}_{3}\)COOH) and 80% methanol with a flow rate of 0.4 mL/min. The UPLC system used was Acquity from Waters (Pleasanton, CA, USA).
## 6 Side-Channel Timing Attack on Streaming Applications over NDN  
In this work, the attack model was specifically focused on NDN streaming application(s), where the application has a single producer and many consumers.  
The privacy-oriented side-channel timing attacks/measurements can be based on information gathering from the computer implementation systems, rather than exposing software and algorithm weaknesses. The gained information, such as timing responses of the packet, can be used to identify private information by an adversary. In addition, the side-channel timing attack does not require any advanced configuration, because some information is publicly available and can be retrieved by any network consumer [29].  
In NDN, whether or not the content is previously cached by CS, the consumer retrieves the packet in time which is called round trip time (RTT). It is defined as a time difference between the sent interest packet and the received data packet. Therefore, the RTT of cached content is shorter than the RTT of uncached content. In an attack, the adversary takes advantage of the RTT differences to identify the uncached and cached targets (contents) from the NDN router.  
Considering all nodes must cache the data, the privacy of cached contents can be potentially targeted by side-channel timing attacks/measurements in NDN applications. The works [1; 2; 3; 4; 6; 10; 28; 30; 31; 32] discussed that the side-channel timing attack may affect the information privacy in NDN. Depending on the scope of the attack, the adversary is mainly able to determine/identify the content name, cache (e.g., size, content popularity [33]), and the signature (e.g., certificate) through the side-channel timing attack.
## 3 Results and Discussion
### Thermal Analysis of the LDHs  
Figure 5A,B illustrate the TGA and DTG curves for four LDHs, respectively. Upon heating, all LDHs mainly underwent two stages of decomposition. The mass loss of absorbed water and interlayer water in the TGA curve (50 \(\sim\) 200 \({}^{\circ}\)C) for S-NiMgAl-Mn, S-NiMgAl-Co, S-NiMgAl-Cu and S-NiMgAl-Zn are 15.5%, 14.9%, 13.5% and 14.3%, respectively. From the DTG curves, it can be seen that the maximal degradation-rate temperature (\(T_{\rm max}\)) of water molecules for S-NiMgAl-Mn, S-NiMgAl-Co, S-NiMgAl-Cu and S-NiMgAl-Zn are 188 \({}^{\circ}\)C, 178 \({}^{\circ}\)C, 174 \({}^{\circ}\)C and 181 \({}^{\circ}\)C, respectively. The second stage of decomposition, occurring at a higher temperature (200 \(\sim\) 800 \({}^{\circ}\)C), is associated with the dehydroxylation of the metal hydroxide layers and the degradation of carbonates and stearate in the LDHs. From the TGA curves, it can be seen that the mass losses of hydroxyls, carbonates and stearate for S-NiMgAl-Mn, S-NiMgAl-Co, S-NiMgAl-Cu and S-NiMgAl-Zn are 28.0%, 30.1%, 30.4% and 29.9%, respectively. The \(T_{\rm max}\) of hydroxyls and carbonates is about 330 \({}^{\circ}\)C for all LDHs in the DTG curves.  
Figure 4: Contact angle images of the synthesized LDHs. (**a**) S–NiMgAl–Mn; (**b**) S–NiMgAl–Co; (**c**) S–NiMgAl–Cu and (**d**) S–NiMgAl–Zn.  
Figure 3: **A**: SEM (20000\(\times\)) and **B**: TEM images (200000\(\times\)) of the synthesized LDHs. (**a**) S–NiMgAl–Mn; (**b**) S–NiMgAl–Co; (**c**) S–NiMgAl–Cu and (**d**) S–NiMgAl–Zn.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
###### Abstract  
In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial-temporal correlations inherent in transport data. In recent years, many studies have focused on developing various Spatial-Temporal Graph Neural Networks (STGNNs), and researchers have also begun to explore the application of transformers to capture spatial-temporal correlations in traffic data. However, GNN-based methods mainly focus on modeling spatial correlations statically, which significantly limits their capacity to discover dynamic and long-range spatial patterns. Transformer-based methods have not sufficiently extracted the comprehensive representation of traffic data features. To explore dynamic spatial dependencies and comprehensively characterize traffic data, the Spatial-Temporal Fusion Embedding Transformer (STFEformer) is proposed for traffic flow prediction. Specifically, we propose a fusion embedding layer to capture and fuse both native information and spatial-temporal features, aiming to achieve a comprehensive representation of traffic data characteristics. Then, we introduce a spatial self-attention module designed to enhance detection of dynamic and long-range spatial correlations by focusing on interactions between similar nodes. Extensive experiments conducted on three real-world datasets demonstrate that STFEformer significantly outperforms various baseline models, notably achieving up to a 5.6% reduction in Mean Absolute Error (MAE) on the PeMS08 dataset compared to the next-best model. Furthermore, the results of ablation experiments and visualizations are employed to clarify and highlight our model's performance. STFEformer represents a meaningful advancement in traffic flow prediction, potentially influencing future research and applications in ITSs by providing a more robust framework for managing and analyzing traffic data.  
transformer; traffic flow prediction; mask matrix; multi-head self-attention; embedding 2020
# Evaluation of Biosynthetic Pathway and Engineered Biosynthesis of Alkaloids
###### Abstract  
Varieties of alkaloids are known to be produced by various organisms, including bacteria, fungi and plants, as secondary metabolites that exhibit useful bioactivities. However, understanding of how those metabolites are biosynthesized still remains limited, because most of these compounds are isolated from plants and at a trace level of production. In this review, we focus on recent efforts in identifying the genes responsible for the biosynthesis of those nitrogen-containing natural products and elucidating the mechanisms involved in the biosynthetic processes. The alkaloids discussed in this review are ditryptophenaline (dimeric diketopiperazine alkaloid), saframycin (tetrahydroisoquinoline alkaloid), strictosidine (monoterpene indole alkaloid), ergotamine (ergot alkaloid) and opiates (benzylisoquinoline and morphinan alkaloid). This review also discusses the engineered biosynthesis of these compounds, primarily through heterologous reconstitution of target biosynthetic pathways in suitable hosts, such as _Escherichia coli_, _Saccharomyces cerevisiae_ and _Aspergillus nidulans_. Those heterologous biosynthetic systems can be used to confirm the functions of the isolated genes, economically scale up the production of the alkaloids for commercial distributions and engineer the biosynthetic pathways to produce valuable analogs of the alkaloids. In particular, extensive involvement of oxidation reactions catalyzed by oxidoreductase, such as cytochrome P450s, during the secondary metabolite biosynthesis is discussed in details.  
alkaloids; biosynthesis; enzymes; engineered biosynthesis; aromatic amino acids +
Footnote †: journal: _m molecules_