esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Prime Technologies for SSC
#### 4.2.1 AI-based Wireless Communication
Furthermore, a DL-based CS algorithm for sparsity detection in the Doppler region was introduced to improve the channel estimation efficiency in high-speed mobile communication scenarios [39]. AI-based channel estimation and prediction algorithms can contribute to future-proofing transportation in SSC by tackling outdated CSIs and optimizing pilot use in high-speed communication scenarios. On top of the outdated CSI issue, the throughput of IoT needs to be improved. The authors in [70] proposed the generated data packet-based throughput maximization (GDPTM) algorithm and the deep deterministic policy gradient (DDPG)-based multi-node resource allocation (DMRA) algorithm to achieve improved throughput and extend the lifetime of IoT nodes. The authors in [71] proposed a two-layer algorithm to set energy causality constraints and address the throughput maximization problem for various scenarios of wireless powered communication networks (WPCN). * _Energy efficiency_: The utilization of AI brings notable advantages in enhancing energy efficiency in SSC [72]. For example, in hospitals and schools, AI technology can leverage sensor systems in smart buildings to reduce energy costs while improving the safety and security of citizens. In [40], it is shown that energy efficiency can be improved via AI-based modules. Management of these AI modules can contribute to increasing the energy efficiency and sustainability of SSCs. Beyond wireless transmission and reception technologies, AI can be applied to smart surveillance cameras across various domains [73; 74]. Firstly, smart building systems canutilize DCNN to assess damage to building structures, as discussed in [75], and pedestrians can be classified from input images based on the CNN method discussed in [76]. Secondly, AI and IoT technologies are employed to implement efficient and convenient healthcare systems. For example, the authors in [77] proposes a GUI that oversees an individual's healthcare using an AI technology called XAI, which is used to explain AI models and their predictions. Lastly, smart energy systems exploit AI technology to manage the excessive use of energy in modern society. For example, the authors in [78] proposed an artificial intelligence-based home energy management system (AI-HEMS) to achieve energy savings through a predictive mechanism. With such diverse applications, AI is expected to provide more innovative and new services in SSCs in the future.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 5 Detailed Elaboration of Each Layer
### Functions of Radio Resource Scheduling Center
Additionally, the dynamic movement of SBSs further complicates spectrum management, as the available spectrum changes with their location. To address these challenges, the RRSC system implements global spectrum management and planning for SBSs' payloads. This is achieved by integrating spectrum sensing data with local radio resource usage policies, ensuring efficient and compliant spectrum utilization across the globe. Service area planning: Based on interference avoidance and management strategies, as well as the available resources of SBSs, the system dynamically plans service areas to meet global service demands. To configure each SBS's payload, the Simple Network Management Protocol (SNMP) is utilized. By combining mobility management, the system achieves efficient scheduling of global resources. This approach optimizes service area planning in multi-satellite overlapping areas and schedules user access to meet user service demands. Additionally, the system supports multi-satellite resource collaborative schedul  
Figure 4: Functional architecture of radio resource scheduling center. ing (MSRCS), which is designed to meet the enhanced service requirements of hotspots and emergency services, such as earthquake relief. Through MSRCS, the system maximizes on-demand service capabilities and realizes space service capability scheduling, ensuring that resources are allocated effectively and efficiently. Payload management of SBSs: The convergence of small base stations (SBSs) over the north/south pole areas results in overlapping interference, including signaling beams, which can affect the scheduling and planning of various resources. To eliminate this intra-system interference, the RRSC should configure regular on-off control for space payloads in these areas.
###### Abstract  
_Dioscorea zingiberensis_ is a perennial herb native to China. The rhizome of \(D\). zingiberensis_ has long been used as a traditional Chinese medicine to treat rheumatic arthritis. Dioscin is the major bioactive ingredient conferring the medicinal property described in Chinese pharmacopoeia. Several previous studies have suggested cholesterol as the intermediate to the biosynthesis of dioscin, however, the biosynthetic steps to dioscin after cholesterol remain unknown. In this study, a comprehensive \(D\). _zingiberensis_ transcriptome derived from its leaf and rhizome was constructed. Based on the annotation using various public databases, all possible enzymes in the biosynthetic steps to cholesterol were identified. In the late steps beyond cholesterol, cholesterol undergoes site-specific oxidation by cytochrome P450s (CYPs) and glycosylation by UDP-glycostransferases (UGTs) to yield dioscin. From the \(D\). _zingiberensis_ transcriptome, a total of 485 unigenes were annotated as CYPs and 195 unigenes with a sequence length above 1000 bp were annotated as UGTs. Transcriptomic comparison revealed 165 CYP annotated unigenes correlating to dioscin biosynthesis in the plant. Further phylogenetic analysis suggested that among those CYP candidates four of them would be the most likely candidates involved in the biosynthetic steps from cholesterol to dioscin. Additionally, from the UGT annotated unigenes, six of them were annotated as 3-O-UGTs and two of them were annotated as rhamnsfarases, which consisted of potential UGT candidates involved in dioscin biosynthesis. To further explore the function of the UGT candidates, two 3-O-UGT candidates, named Dz3 GT1 and Dz3 GT2, were cloned and functionally characterized. Both Dz3 GT1 and Dz3 GT2 were able to catalyze a C3-glucosylation activity on diosgenin. In conclusion, this study will facilitate our understanding of dioscin biosynthesis pathway and provides a basis for further mining the genes involved in dioscin biosynthesis.  
_Dioscorea zingiberensis_; dioscin biosynthesis; transcriptome; cytochrome P450; glycosyltransferase
## 2 Taxonomy of DDoS Attacks
### DDoS Attack Architecture
#### 2.2.2 DDoS Attack Classification and Types in IoT Networks  
DDoS attacks differ greatly in terms of how they are initiated and the impact they have on the target server. Even so, they all share the same goal of interfering with legitimate traffic. IoT networks and devices are heterogeneous in nature. As a result, there will be a variety of threats focused specifically on them. As shown in Figure 5, DDoS attacks can therefore be divided into three categories: volumetric base, protocol base, and application base. Numerous DDoS attack types under each division are also illustrated.
###### Abstract  
The measurement of in-plane displacement in two orthogonal directions is of considerable significance for modern industries. This paper reports on a spatial carrier phase-shift digital speckle pattern interferometry (DSPI) for the simultaneous measurement of in-plane displacement in two orthogonal directions. The object is illuminated from a single direction and observed from four symmetrical directions simultaneously. One pair of the four observation directions is sensitive to in-plane displacement in one direction, and the other pair is sensitive to in-plane displacement in the perpendicular direction, resulting in the displacement in two directions being measured independently. The polarization property of light is used to avoid cross-interference between the two pairs of beams. Spatial carrier frequencies are generated by aperture misalignment, and the displacement in two directions is modulated onto the same interferogram. With a spatial carrier phase-shift technique, the displacement can be separated in the frequency domain and the phase can be evaluated from a single interferogram in real time. The capability of DSPI is described by theoretical discussions and experiments.  
digital speckle pattern interferometry; spatial carrier phase-shift; in-plane displacement Article
# Volatile Composition of Smoked and Non-Smoked Iranian Rice
## Results  
### Identification of Volatile Compounds in Rice  
Table 2 shows the 37 volatile compounds that have been identified by GC-MS. These compounds can be grouped into seven main chemical families: (i) aldehydes (10 compounds); (ii) ketones (four); (iii) phenol derivatives (11); (iv) furans (eight); (v) linear hydrocarbons (two); (vi) esters (one); and (vii) terpen (one). Table 2 also shows the main sensory descriptors of each of the volatiles identified in the rice profile. From this list, it is important to highlight that several compounds have descriptors related to the wood fumes (smoked notes), such as furfural, 2-acetylfuran, 5-methyl-furfural, benzofuran, and guaiaicol.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Sample Code**} & \multirow{2}{*}{**RT \({}^{\dagger}\) (min)**} & \multicolumn{3}{c}{**I\({}^{\dagger}\)�**} & \multirow{2}{*}{**Odor Descriptor \({}^{\ddagger}\)�**} \\ \cline{4-5}  & & & **Exp \({}^{\ddagger}\)** & & **Lit \({}^{\ddagger}\)�** \\ \hline
2-Methylfuran & V1 & 5.55 & 600 & 605 & Ethereal, acetone, chocolate \\ Hexanal & V2 & 7.39 & 804 & 801 & Fatty, green \\
3-Furaldehyde & V3 & 7.69 & 820 & 831 & \\ Furfural & V4 & 8.07 & 839 & 830 & Almond, woody \\
2-Heptanone & V5 & 9.18 & 895 & 891 & Banana, cinnamon, spicy, fruity \\ Heptanal & V6 & 9.50 & 908 & 903 & Oily, fruity, woody, fatty, nutty \\
2-Methyl-2-cyclopenten-1-one & V7 & 9.80 & 918 & 915 & \\
2-Acetyltransferase & V8 & 9.87 & 920 & 921 & Almond, caramel, coffee \\ Anisole & V9 & 10.10 & 928 & 926 & Alcohol, butter, cheese, entheal \\
5-Methyl-furfural & V10 & 11.43 & 972 & 978 & Almond, caramel, spicy \\ Benzaldehyde & V11 & 11.71 & 982 & 978 & Almond, cherry, sweet \\ Phenol & V12 & 11.77 & 984 & 980 & Plastic \\
6-Methyl-5-hepet-2-one & V13 & 11.95 & 990 & 994 & Oily, herbaceous, green \\
2-Amrylfuran & V14 & 12.23 & 999 & 1001 & Fruity, green, earth, bean \\ Octanal & V15 & 12.68 & 1011 & 1006 & Honey, fruity, fatty, citrus \\ Benzofuran & V16 & 12.86 & 1016 & 1015 & Burnt, coffee, woody \\
2-Propionylfuran & V17 & 13.02 & 1020 & 1024 & \\ \(p\)-Methylanisole & V18 & 13.58 & 1034 & 1026 & Floral, earthy, walnut \\ Limonene & V19 & 13.89 & 1042 & 1039 & Herbaceous, minty \\
3-Octen-2-one & V20 & 14.03 & 1046 & 1040 & Berry, nutty, earthy, vegetable \\
2-Methyl-phenol & V21 & 14.75 & 1065 & 1075 & \\
2-Octenal & V22 & 14.89 & 1068 & 1060 & Spicy, herbaceous, green \\ \(p\)-Cresol & V23 & 15.57 & 1086 & 1084 & Medicinal \\ Guaiaicol & V24 & 16.28 & 1104 & 1102 & Woody, smoky \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identification of volatile compounds, by GC-MS, in smoked and non-smoked Iranian rice.
# Dual-Band Light Absorption Enhancement in Hyperbolic Rectangular Array
## 1 Introduction  
With recent advances in nanoscale fabrication of metal-dielectric multilayers and arrays of rods, hyperbolic metamaterials (HMMs) have attracted a great deal of interest because of their unusual optical features [1; 2]. HMMs are characterized by the diagonal permittivity tensor with the principal components being of opposite signs. The corresponding dispersion relation permits wavevectors that lie within a hyperbolic isofrequency surface [3; 4]. This special hyperboloid isofrequency surface allows HMMs to achieve exotic physical properties and functionality unattainable with naturally occurring materials.  
In recent years, HMMs have been widely studied in many fields, such as sub-wavelength imaging [5], negative refraction [6; 7], super-Planckian thermal emission [8], biosensing [9; 10] and nanolasers [11]. From a practical point of view, a strong light absorption of HMMs is essential for various potential applications such as photoelectric detection and solar cells. It has been shown that HMMs can be used to enhance light absorption due to their high-k modes and divergent photonic density of states [4; 12]. To date, many efforts have been made to improve light absorption of HMMs with the absorption wavelengths covering the microwave [13], terahertz [14; 15; 16], infrared [17; 18; 19; 20; 21; 22] and visible wavelength regions [23; 24; 25; 26; 27]. In real applications, selective absorption enhancement is highly desired in fields such as thermal radiation tailoring [28], sensitive detection [29] and sensing [30]. However, previous studies of HMMs mainly focused on light absorption enhancement in broad spectral bands [13; 14; 15; 16; 17; 18; 19; 21; 22; 23; 24; 25; 27], and there are only a few studies on selective absorption enhancement of HMMs.  
In this study, dual-band light absorption enhancement of HMMs is proposed by using a hyperbolic rectangular array (HRA). The enhanced light absorption of the HRA results from the propagating surface plasmon (PSP) resonance, and double absorption bands with peak absorption of \(\sim\)95% in the visible-to-near-infrared region can be obtained. The evolution of the selective absorption properties of the HRA are investigated by using the impedance theory and the effective medium theory (EMT). The dual-band absorption is very robust to the variations of the width and the number of film stack. When these types of HMMs are used in refractive index sensing, the HRA shows good sensor performance due to its stable absorption enhancement.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis  
### Introduction of Public Datasets  
To fully validate the proposed method across diverse appliances, four public datasets are used: PLAID [32], HOUIDI [33], WHITED [34], and COOLL [35]. The PLAID dataset was collected in a laboratory and 55 U.S. households at a sampling frequency of 30 kHz, including 1876 samples from 16 types, totaling 329 appliances. It is the most commonly used dataset for household appliance identification. The HOUIDI dataset was collected in a French laboratory at a sampling frequency of 50 kHz, with 488 samples covering 24 types and 61 appliances. It mainly contains household heating appliances. The WHITED dataset was collected at a sampling frequency of 44.1 kHz across 8 regions in 4 countries (Canada, Germany, Australia, and Indonesia), covering 54 types and 127 appliances, with 1258 samples. It contains light industrial appliances such as drills, air pumps, and sanders, with a wide variation between appliance types. The COOLL dataset was collected in a French laboratory at a sampling frequency of 100 kHz, covering 12 types, 42 appliances, and 840 samples. It also includes light industrial appliances and household appliances.  
This study focuses on class-incremental continual learning, thus requiring merged and preprocessed datasets from four sources to evaluate memory retention and catastrophic forgetting across extended device categories. Notably, public datasets for high-frequency load identification exhibit negligible structural discrepancies, as they predominantly capture authentic operational waveforms from diverse users' appliances. Crucially, real-world households share common appliance types (e.g., hair dryers and air conditioners), resulting in partial overlaps across the datasets. Their primary variations manifest in sampling frequencies and the presence of niche appliance types. Consequently, dataset mergingherein effectively consolidates multi-user measurements, thereby enhancing data richness and operational diversity. To align labeling granularity, the following appliance types are merged: FanHeater with Heater, LEDlight with G4 LED, HandMixer with ElectricMixer, and TVPlasma with LCDTV. After deleting the Blender type, which had insufficient data, 59 types covering 558 appliances with a total of 4463 samples were selected. Due to varying sampling and grid frequencies, the SciPy package [36] was used to downsample the datasets uniformly to 150 points per waveform, equivalent to a 7.5 kHz sampling frequency on a 50 Hz grid. Finally, event waveforms were extracted, as described in Section 2.1, and used as input for the NILM model.
## 3 Determination Method of Optimal Measurement Speed  
### Friction Model of Precision Reducers  
The friction characteristics of precision reducers can be divided into the following four stages according to the Stribeck friction theory: (I) static friction stage, (II) boundary lubrication stage, (III) partial fluid lubrication stage, and (IV) full fluid lubrication stage, as shown in Figure 3. Firstly, the precision reducer is ready to start and it is in the static friction stage when the speed of the precision reducer is zero. The friction is the static friction independent of the speed and it is mainly caused by the elastic deformation of a precision reducer. Secondly, with the increase in external force, there is a low relative speed between the contact surfaces and the lubricating oil film cannot be established between the surfaces. The friction is mainly determined by the impurity characteristics of the boundary layer and the shearing force between solids causes it. At this time, the precision reducer is in the boundary lubrication stage. Thirdly, with the further increase in the speed, the liquid film formed between the contact surfaces is thicker and thicker, but the contact surfaces are not completely separated by the lubricant and there is still a region of solid contact, which indicates the partial fluid lubrication stage. Fourthly, the liquid film between the objects is completely formed when the speed continues to increase, and the region of solid contact does not exist. However, with the increase in the relative speed, the viscous friction, which is proportional to the speed, is the dominant friction. At this time, the friction mainly depends on the speed and the viscosity coefficient of the lubricant and it enters the stage of full fluid lubrication [14; 15].  
In this study, the Tustin empirical model is used to describe the Stribeck friction effect of precision reducers [16; 17]. The clockwise rotation of precision reducers is defined as the forward direction  
Figure 3: Four friction stages of precision reducers: (I) static friction stage, (II) boundary lubrication stage, (III) partial fluid lubrication stage, and (IV) full fluid lubrication stage.  
Figure 2: Optimal measurement speed.  
and the counterclockwise rotation is defined as the reverse direction. The friction model of precision reducers is established, as follows:  
\[\left\{\begin{array}{l}T_{f}^{+}(\omega)=T_{c}^{+}+(T_{s}^{+}-T_{c}^{+})\cdot e ^{-(\omega/\omega_{s}^{+})}+B^{+}\cdot\omega\quad\omega>0\\ T_{f}^{-}(\omega)=-T_{c}^{-}-(T_{s}^{-}-T_{c}^{-})\cdot e^{-(\omega/\omega_{s }^{-})}+B^{-}\cdot\omega\quad\omega<0\end{array}\right. \tag{3}\]  
where \(T_{f}^{+}(\omega)\) and \(T_{f}^{-}(\omega)\), respectively, indicate the forward and reverse friction torques; \(T_{c}^{+}\) and \(T_{c}^{-}\), respectively, denote forward and reverse Coulomb friction torques; \(T_{s}^{+}\) and \(T_{s}^{-}\), respectively, denote forward and reverse static friction torques; \(W_{s}^{+}\) and \(W_{s}^{-}\), respectively, denotes forward and reverse Stribeck speeds; and, \(B^{+}\) and \(B^{-}\), respectively, denote the forward and reverse viscous friction coefficients.
# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
## 3 Results
### Experimental Validation  
#### 3.2.1 Evaluation of the Coefficient of Linear Thermal Expansion  
Dilatometry measurements were performed for three materials: 316L stainless steel, the optimized alloy after casting, and the same optimized alloy produced by LPBF. Figure 4 shows the variation of the expansion coefficient for the different alloys as a function of temperature. Each point was determined by averaging the values within a range of \(-50\)\({}^{\circ}\)C to +50 \({}^{\circ}\)C around the experimental point. The coefficient \(\alpha\) systematically increases by about 30% with temperature and then stabilizes above 500 \({}^{\circ}\)C.  
The average coefficient of expansion over the three measurements was found to be nearly identical for both the cast material (\(\alpha_{CTE}=20.5\times 10^{-6}\) K\({}^{-1}\)) and the LPBF material  
Figure 4: Variation of the thermal expansion coefficient \(\alpha_{\text{CTE}}\) with temperature.  
Figure 3: (**a**) Scheil model and (**b**) equilibrium Thermo-Calc predictions for the selected alloy.  
(\(20.4\times 10^{-6}\) K\({}^{-1}\)). The average value for 316L was slightly lower \(\left(19.7\times 10^{-6}\) K\({}^{-1}\right)\). While the absolute measured values differ from the predictions and are slightly higher for both alloys, the relative variations between them remain consistent with the predictions (Table 4). In all cases, the experimental values exceeded the predicted ones, revealing the limitations of a linear mixture model in accurately predicting \(\alpha_{\text{CTE}}\): while it can quickly estimate the relative variation in \(\alpha_{\text{CTE}}\) between different alloys, the accuracy of the predicted absolute values remains poor and they should not be used directly. Nevertheless, as trends seem correctly captured, such a simple tool can be used for alloy design if the property is to be optimized (e.g., minimized), which is the case here.
## 4 Discussion
### Contributions of Tested Strains in Baijiu Fermentation
#### 4.1.2 S. cerevisiae  
_S. cerevisiae_ generally predominates during the alcoholic fermentations [29; 31; 32] as it has a good capacity to grow in strict anaerobic conditions, whereas it is not an abundant yeast species in Daqu. [PERSON] et al. [33] investigated the microbial diversity in Fen-Daqu, with _S. cerevisiae_ undetected through PCR-DGGE analysis and only one isolate of _S. cerevisiae_ observed using a culture-dependent approach. _S. cerevisiae_ is unable to convert starch to glucose. This was confirmed by our results (Figure 1) showing that most _S. cerevisiae_ strains tested showed extremely low enzyme activities. Starch accounts for 65-81% of the full weight of sorghum grains [34]. The growth of _S. cerevisiae_ is limited at the beginning of Baijiu fermentation with high contents of starch. However, it has the ability to take over the fermentation process in spite of its low concentration in the initial stage, particularly because of the competitive growth with the presence of fermentable sugars as well as higher alcohol tolerance. This was also proved in our results (Figure 1 and Table 1). This species is expected to grow and flourish, becoming predominant in the liquor fermentation stage as observed during various wine fermentation processes [35; 36; 37; 38]. Additionally, it could be the host yeast species that was recycled in fermentation jars. _S. cerevisiae_ is probably considered as the most crucial yeast species during the fermentation process, for its significant contribution to the fermentation rate as well as ethanol production [39]. Thus, the co-culture with the presence of _S. fibuligera_ and _S. cerevisiae_ could promote ethanol production (data not shown). In addition, we found that the presence of S. cerevisiae was linked to the production of some high alcohols and acids (Figure 4).