esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{14}{c}{**Subject Independent 1**} & \multicolumn{14}{c}{**Subject Independent II**} & \multicolumn{14}{c}{**Subject Dependent**} \\ \hline \hline  & & & & **RA** & **RD** & **SA** & **SD** & **LW** & **RA** & **RD** & **SA** & **SD** & **LW** & **RA** & **RD** & **SA** & **SD** & **LW** \\ \hline \multirow{6}{*}{**S**} & & RA & **284** & 0 & 73 & 0 & 16 & **301** & 0 & 26 & 0 & 46 & **365** & 0 & 0 & 0 & 8 \\  & RD & 10 & **299** & 0 & 68 & 40 & 15 & **333** & 0 & 40 & 19 & 3 & **391** & 2 & 4 & 7 \\  & SA & 24 & 0 & **308** & 0 & 1 & 45 & 0 & **269** & 1 & 18 & 14 & 1 & **316** & 0 & 2 \\  & SD & 0 & 9 & 0  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{Subject Independent I} & \multicolumn{6}{c}{Subject Independent II} & \multicolumn{6}{c}{Subject Dependent} \\ \hline  & & & **RA** & **RD** & **SA** & **SD** & **DW** & **RA** & **RD** & **SA** & **SD** & **LW** & **RA** & **RD** & **SA** & **SD** & **LW** \\ \hline \multirow{11}{*}{**Synthetic**} & & RA & **319** & 0 & 41 & 0 & 13 & **307** & 0 & 21 & 0 & 45 & **382** & 0 & 7 & 0 & 14 \\  & & RD & 5 & **277** & 0 & 90 & 35 & 6 & **301** & 0 & 65 & 35 & 8 & **357** & 0 & 15 & 27 \\  & & SA & 75 & 1 & **255** & 0 & 2 & 103 & 1 & **158** & 0 & 71 & 18 & 1 & **312** & 0 & 2 \\  & SD & 0 & 108 & 0 & **176** & 0 & 0 & 104 & 0 & **180** & 0 & 0 & 16 & 0 & **287** & 1 \\  & LWP & 152 & 55 & 10 & 6 & **42** & 16 & 55 & 28 & 5 & **161** & 34 & 29 & 65 & 2 & **135** \\  & LWP & 27 & 64 & 4 & 18 & **705** & 87 & 90 & 20 & 45 & **578** & 57 & 69 & 0 & 31 & **661** \\ \cline{2-15}  & & RA & **349** & 0 & 0 & 0 & 24 & **272** & 2 & 40 & 0 & 59 & **364** & 3 & 0 & 0 & 6 \\  & RD & 0 & **380** & 0 & 2 & 25 & 0 & 382 & 1 & 2 & 22 & 0 & **398** & 0 & 2 & 7 \\  & & zSA & 102 & 0 & **153** & 0 & 78 & 81 & 0 & **194** & 0 & 58 & 0 & 0 & **316** & 0 & 17 \\  & & SD & 0 & 8 & 0 & **272** & 4 & 0 & 64 & 0 & **203** & 17 & 0 & 0 & 0 & **275** & 9 \\  & LWP & 25 & 4 & 0 & 0 & 236 & 26 & 9 & 6 & 0 & **224** & 7 & 15 & 26 & 0 & **217** \\  & LWP & 1 & 7 & 0 & 27 & **783** & 1 & 37 & 10 & 17 & 753 & 0 & 0 & 0 & 1 & **817** \\ \hline \multirow{11}{*}{**Synthetic**} & & RA & **228** & 4 & 27 & 0 & 114 & **228** & 0 & 77 & 0 & 68 & **344** & 0 & 12 & 0 & 17 \\  & RD & 1 & **195** & 0 & 145 & 66 & 17 & **218** & 2 & 113 & 57 & 7 & **373** & 0 & 15 & 12 \\  & SA & 161 & 0 & **166** & 0 & 6 & 143 & 1 & **158** & 0 & 31 & 21 & 2 & **307** & 0 & 3 \\  & SD & 0 & 99 & 0 & **174** & 11 & 0 & 55 & 0 & **225** & 4 & 0 & 16 & 0 & **264** & 4 \\  & LWP & 96 & 100 & 2 & 10 & **57** & 14 & 62 & 20 & 16 & **153** & 42 & 59 & 56 & 1 & **107** \\  & LWP & 173 & 6 & 31 & 203 & **405** & 129 & 71 & 126 & 57 & **435** & 80 & 59 & 8 & 59 & **612** \\ \cline{2-15}  & & RA & **327** & 0 & 14
## Materials and Methods
### Membrane Performance Evaluation  
#### 2.5.1 Time-Lag Measurements  
Single gas permeability and time-lag experiments were performed on a fixed volume/pressure increase instrument constructed by Elektro and Elektronik Service Reuter (Geesthacht, Germany)on circular samples with an effective area of 11.3 cm\({}^{2}\) or 2.14 cm\({}^{2}\). The feed gas was set at 1 bar for all the gases, and measurements at lower pressures (i.e. 0.8 bar, 0.6 bar, 0.4 bar, 0.2 bar and 0.1 bar) were performed only for CO\({}_{2}\) in order to analyze the pressure dependence. The permeate pressure was measured up to 13.3 mbar with a resolution of 0.0001 mbar. The gases were always tested in the following order: H\({}_{2}\), He, O\({}_{2}\), N\({}_{2}\), CH\({}_{4}\) and CO\({}_{2}\), and the effective degassing was guaranteed by a turbo molecular pump (Pfeiffer Vacuum). Permeabilities (\(P_{i}\)) are reported in Barrer (1 Barrer = 10\({}^{-10}\) cm\({}^{3}\) (STP) cm cm cm\({}^{-2}\) s\({}^{-1}\) cmHg\({}^{-1}\)), and the diffusion coefficient was calculated from the so-called permeation time lag, \(\Theta\) (s). The ratio of the permeability over the diffusion coefficient gives the gas solubility coefficient in its approximate form. A more detailed description of the method can be found elsewhere [59; 60].
# Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm
###### Abstract  
Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.  
query optimization; distributed database; artificial bee colony algorithm; dynamic perturbation factor; genetic operators +
Footnote †: journal: Applied Sciences  
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (Z.C.)1
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
[15] proposed a similarity threshold learning-based method, combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) with fastDTW (Fast Dynamic Time Warping) to first identify high-density genesis areas, and then compute similarity between tracks to generate representative paths and classify TCs accordingly. While this approach reduced K-Means randomness and improved classification intelligence, it focused solely on track geometry and ignored the associated physical variables. Similarly, [PERSON] et al. [16] introduced an improved ST-DBSCAN algorithm, combining SS-DBSCAN and DST-DBSCAN, to mitigate DBSCAN's tendency to mislabel valid samples as noise, focusing on high-intensity and high-landfall-probability regions. However, this model emphasized landfall probability over full lifecycle track analysis. [PERSON] et al. [17] classified South China Sea (SCS) TCs using K-Means and verified its efficacy for regional track studies, though the method remained sensitive to initial cluster numbers and outliers, limiting stability. To address the above challenges, this study proposes a novel SD-K-Means clustering model, which it applied to TCs formed in the NWP from 2000 to 2022. The model integrates nine clustering indicators--including track similarity and environmental dynamics--to classify TC tracks. The key contributions of this paper are as follows:  
(1) Beyond track similarity, this study incorporates dynamic changes caused by environmental flow fields as clustering indicators, improving model performance by capturing the coupled spatial-physical evolution of TCs. (2) By integrating Soft-DTW--well suited for handling nonlinear, variable-length time series--with K-Means and introducing a cyclic clustering mechanism, the proposed SD-K-Means model reduces classification randomness. Comparative experiments with standard K-Means and DTW-based K-Means validate its superiority. The remainder of the paper is organized as follows: Section 2 presents the dataset and clustering methodology. Section 3 evaluates the performance of the proposed model against baseline algorithms in terms of accuracy and efficiency. Section 4 analyzes the clustering outcomes and physical characteristics of the resulting TC types, including seasonal trends and potential impacts. Finally, Section 5 summarizes the conclusions of the study.
# Enhancing the Content of Hesperidin and Nobiletin in Citrus Wines through Multi-Strain Fermentation
## 3 Results
### Composition and Content Analysis of Volatile Aroma Components  
Natural fermentation is the most traditional method in fruit wine brewing due to the complex interactions among diverse microbial communities in the fermentation system, which results in wine with rich and complex flavors [29]. This study aimed to simulate the complex fermentation system present in natural fermentation by using multi-strain combination fermentation methods to brew Nanfeng tangerine wine in hopes of achieving a wine with distinctive flavors. Nanfeng tangerine wine, created through fermentation with multiple strains, was examined for its volatile aromatic compounds and concentrations using HS-SPME-GC-MS and an electronic nose, as depicted in Figure 3A. In the fermented Nanfeng tangerine wine with multiple strains, 31 volatile substances were found, which is consistent with earlier studies that found numerous terpen and aldehydes in citrus wine, such as d-limonene, linalool, terpineol, geraniol, and decanal [30; 31; 32]. In a prior investigation, it was discovered that the sequential fermentation of Hg-10-Sc (549.31 mg/L) had a beneficial impact on the aroma of Nanfeng tangerine wine. Compared to these, the multi-strain combination fermentation methods, Sc:(Ce.Hg.Ht)-1:1 (511.64 mg/L) and (Hg.Ht)-10-Sc (500.66 mg/L), had relatively higher richness in aroma compounds (Figure 3A, Table S1). Odor active compound and electronic nose PCA analysis results show that different combinations of multi-strain fermentation methods have distinct flavors and aroma characteristic compounds (Figure 3B,C, Table S2), thus producing Nanfeng tangerine wines with diverse sensory qualities.  
Figure 3: The volatile aroma compounds in Nanfeng tangerine wines. (**A**) is the hierarchical cluster analysis of volatile odor-active aroma compounds; (**B**) is the principal component analysis of volatile aroma compounds (OAV \(\geq\) 1); and (**C**) is the principal component analysis of E-nose datasets.
## 2 Preliminaries
### Principle and System Description of CVQKD
The feature vectors collected by the CVQKD system under the four attacks are highly nonlinear, and same event correlation is high, so it is suitable to apply deep learning models for signal diagnosis. The objective of the deep learning model in CVQKD is to obtain an output vector \(\overrightarrow{v}\) from the input vector \(\overrightarrow{u}\) by meticulously designing function \(f:\overrightarrow{u}\rightarrow\overrightarrow{v}\), which is constructed from a training set  
\[S_{\text{train}}\ =\Big{\{}\Big{(}\overrightarrow{u_{1}},\overrightarrow{v_{1} }\Big{)},\Big{(}\overrightarrow{u_{2}},\overrightarrow{v_{2}}\Big{)}, \Big{(}\overrightarrow{u_{3}},\overrightarrow{v_{3}}\Big{)},\ldots\Big{\}}\]
## 2 Materials and Methods
### Calculation of Fermentation Parameters  
The total yield of erythroid process production (Y), which includes the production of erythroid from glucose and the concentration of erythroid present in post-crystallization erythiritol filtrate, expressed in g/g, was calculated using Equation (1):  
\[\mathrm{Y}=\frac{\mathrm{ERf}+\mathrm{ER}}{\Delta\mathrm{GLU}} \tag{1}\]  
The productivity of total erythroid process production (Q), expressed in g/Lh, was calculated using Equation (2):  
\[\mathrm{Q}=\frac{\mathrm{ERf}+\mathrm{ER}}{\Delta\mathrm{t}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lp.** & **Name** & \multicolumn{1}{c}{**Composition**} & **Purpose** \\ \hline
1. & Inocula & glucose—40.0; peptone—2.0; yeast extract—3.0; distilled water up to 1.0 L & Inoculum preparation \\ \hline
2. & Filtrate originating from & erythiritol—224.0 \(\pm\) 2.9, mannitol—82.6 \(\pm\) 5.1, & Carbon source in the production medium \\  & erythroid crystallization & arabitol—42.8 \(\pm\) 2.1, citric acid—60.2 \(\pm\) 0.6 & Selection of optimal nitrogen source concentration \\ \hline
4. & Production medium 1 & glucose 300.0 (25.0 g/L at the beginning of the process, & Selection of optimal nitrogen source concentration \\  & & 200.0 g/L and 75.0 g/L added after 24 h and 48 h), & \\
5. & Production medium 2 & filtrate (130.0 mL), (NH4)\({}_{2}\)SO\({}_{4}\)—3.1 g/L, & Selection of optimal yeast extract concentration \\  & & 0.25, 0.5, 0.75, 1.0; thiamine 200 ug/L in medium with & \\  & & 0.0, 0.5, 0.75 of yeast extract & \\ \hline
6. & Production medium 3 & Glucose 300.0 (0.0, 5.0, 10.0, 15.0, 25.0 at the beginning of & \\  & & the process and 250.0 g/L and the rest to 300.0 g/L & Analyzing the potential for \\  & & added after 24 h and 48 h), filtrate—250.0 mL, & reducing the initial glucose \\  & & (NH4)\({}_{2}\)SO\({}_{4}\)—3.1 g/L, MgSO\({}_{4}\)—7H\({}_{2}\)O—1; KH\({}_{2}\)PO\({}_{4}\)—0.22; yeast extract—0.0, & \\  & & yeast extract—0.25. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Media composition used in the present study.  
The erythiritol specific production rate (q), expressed in g/gh, was calculated using Equation (3):  
\[\mathrm{q}=\frac{\mathrm{Q}}{\mathrm{Xst}} \tag{3}\]  
The selectivity of erythroid production (S) as the percentage share of erythroid in the total sum of products, expressed in %, was calculated using Equation (4):  
\[\mathrm{S}=\frac{\mathrm{ER}\times 100\%}{\mathrm{SUM}} \tag{4}\]  
In all these equations, ERf indicates the concentration of erythroid in post-crystallization, erythiritol filtrate (g/L); ER indicates the concentration of erythroid produced at the end of the cultivations (g/L); \(\mathrm{AGLU}\) denotes the concentration of glucose consumed (g/L); \(\mathrm{Xst}\) denotes the concentration of biomass in stationary phase (g/L); t denotes the duration of the fermentation process (h); and \(\mathrm{SUM}\) denotes the combination of concentrations of arabitol, mannitol, erythiritol, and citric acid at the end of cultivation (g/L).
# Influence of Magnetic Field on Amplification without Inversion Induced by a Femtosecond Pulse Train
## 1 Introduction  
Amplification without inversion (AWI) is the optical amplification that can be achieved with a smaller population in the upper level than the lower one, which is considered to be counter-intuitive to the traditional laser theory [1, 2]. Up to now, various schemes based on quantum interference effects have been adopted to realize AWI, such as incoherent pumping [3, 4, 5, 6, 7], transient field controlling [8, 9, 10], microwave driving fields [11], etc. Recently, AWI has also been realized in nitrogen molecules using short, high-intensity pulses [12, 13].  
With pulse width on the scale of femtoseconds and repetition rates up to gigahertz, the fs pulse train can be used to manipulate the atomic transition process within its natural coherent time, thus allowing investigation of the transient characteristics and atomic coherent control therein. Many interesting phenomena have been demonstrated, such as electromagnetically induced transparency (EIT) [14], coherent population trapping (CPT) [15], and coherent population accumulation [16, 17].  
In this paper, we propose an AWI system induced by the fs pulse train in the presence of a static magnetic field. The theoretical results show that Zeeman splitting introduces an additional asymmetry and provides additional controllable parameters, which allow the modulation of the gain factor of AWI. The influence of the pulse train parameters, such as pulse number, pulse repetition period, and pulse area, is also analyzed.
## 3 Generalized BSDCD Scheme
It is just based on these _known_ bits that the postcompensation process can be performed at the receiver. This prior preamble field is primitively used for automatic gain control (AGC) convergence, diversity selection, timing acquisition, and coarse frequency acquisition in the IEEE 802.15.4 receivers [1; 2]. The detector given here falls into those that use ad hoc methods [13], such as the decision-feedback-based method [21], the frequency-offset estimation-based method in [14] and the postcompensation-based method in this work. These algorithms can provide a wide range of trade-offs among detection complexity, detection speed, and PER performance. ```
0:\(r_{m,k}\): baseband sample for the \(m\)th bit \(E[m]\); \(L_{1}\): sample number for each bit of the actual data; \(L_{2}\): observation length of the preamble in bit intervals; \(L_{3}\): number of the autocorrelation sample \(m\)th bit of the preamble; \(J\): bit number in preamble field; \(L\): payload length of the PPDU. 0:\(\widehat{E}[m]\): detection of the \(m\)th bit of the actual data. 1: initial \(Y=0\), \(A[m]=0\), and \(J=32\)
2:for\(m=2\); \(m\leq L_{2}\); \(m++\)do
3:for\(k=1\); \(k\leq L_{3}\); \(k++\)do
4:\(A[m]\gets A[m]+r_{m,k}r_{m-1,k}^{*}\)
5:endfor
6:\(Y\gets Y+A[m]\)
7:endfor
8:compute the quantization function \(q(Y)\);
9:for\(m=J+2\); \(m\leq J+L\); \(m++\)do
10:for\(k=1\); \(k\leq L_{1}\); \(k++\)do
11:\(A[m]\gets A[m]+r_{m,k}r_{m-1,k}^{*}\)
12:endfor
13:\(\Xi\gets A[m]q(Y)\)
14:if\(\Xi<0\)then
15:\(\widehat{E}[m]\gets 1\)
16:else
17:\(\widehat{E}[m]\gets 0\)
18:endif
19:endfor
20:return\(\widehat{E}[m]\)
```  
**Algorithm 1** Framework of GBSDCD Algorithm. The role of the differential coding is to facilitate CPO estimation and effectively translate the noncoherent channel to a coherent AWGN channel. Furthermore, the role of the preamble symbols is to facilitate CFOE estimation and effectively wipe out the instabilities associated with the inexpensive carrier oscillators in transmitting node. It is such exact knowledge of these symbols that reduces the degree of randomness and facilitates the estimation of the undesired parameter \(\varphi\) of interest. Note that, in the _postcompensation process_, the undesired parameter \(\varphi\) is now the parameter of interest that we would like to estimate, whereas the \"parameters\" of interest in the _detection process_, namely the data symbols, are now just nuisance (or undesired) parameters (cf. [22], p. 64).
## 4 Possible Mechanism of Action of Ribosome Engineering
### Sublethal Concentrations of Different Antibiotics and Ribosome Engineering  
Sublethal concentrations of different antibiotics, including limomycin, streptomycin, erythromycin, have been used to induce the production of antibiotics. In a 1996 study, tetracycline, streptomycin, and hygromycin were able to induce Act production in _S. lividans_ TK21 [15]. The maximum Act induction was observed when 10 or 15 \(\upmu\)g/mL tetracycline or streptomycin was added to the cultures. At these concentrations, the growth of _S. lividans_ was only slightly affected. In contrast, other tested antibiotics, including chloramphenicol, erythromycin, limomycin, kanamycin, spectinomycin, fusidic acid, thiopeptin, thiostrepton, rifamycin, puromycin, ampicillin, and decoyinine, were unable to induce Act production. Interestingly, [PERSON] and coworkers also showed that limomycin at subinhibitory concentrations (1/10 of the MIC of _S. coelicolor_ A3(2)) increased Act overproduction by overexpression of the pathway-specific regulatory gene actII-ORF4 in _S. coelicolor_ A3(2). Lincomycin (1/2 or 1/3 of theMIC of _S. lividans_ 1326) could also lead to the production of novel congeners of calcium-dependent antibiotics in _S. lividans_ 1326 [98]. At sublethal concentrations, these antibiotics might serve as the language to facilitate communication among the bacterial species [99]. It might be interesting to study the intrinsic relationship between antibiotic language and ribosome engineering. One hypothesis could be that the presence of these antibiotic stimuli in the natural environments might lead to the accumulation of spontaneous mutants, which were subsequently discovered using the ribosome engineering approach.
# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 2 ML Methods in Laryngeal Pathology Assessment  
This study is mainly concentrated on the assessment of laryngeal pathology of organic etiology on estimating the effects of asymmetry in vocal fold vibration, and closure defects, under the acoustic analysis point of view. These effects have been characterized using perturbation features in the time and frequency domain, such as jitter, shimmer, harmonic-to-noise ratio, cepstral peak prominence, long-term spectral tilt, and others similar (see [17]). Recognizing that these correlates based on linear dynamics do not describe the complex non-linear behavior of phonation, other features based on non-linear dynamics have also been proposed [18].  
A critical problem in using acoustical correlates to laryngeal pathology detection and characterization is due to the non-bijective relationship between acoustical correlates and  
Figure 2: Example of a sustained utterance of the vowel [a:] from a patient suffering a larynx carcinoma (male, 61 years old): (**a**) original speech signal, showing strong amplitude and frequency changes through the utterance (rough and unstable phonation); (**b**) spectrogram on the background and two formants in blue (first formant) and red (second formant). The harmonic spectrum (background) shows unstable harmonics, especially from 2.3 s, seen as horizontal yellow parallel bands, showing tremor, and blurring between 2.4 and 3.5 s, which extend from 125 Hz to barely 1200 Hz, indicating a poor voice quality produced by an abnormal larynx function. The first two formants (blue and red lines) show fluctuations, especially apparent from 2.3 s to 3.5 s, indicating possible difficulties in keeping a stable oral opening during phonation, as both lines do not maintain a constant separation.  
pathological syndromes, which is known to be many-to-many indeed [19]. On one hand, a given pathology, such as a polyp, may produce similar acoustic correlates (roughness, breathiness, etc.) as a different pathology, such as a cyst. Besides, there are confounding comorbidities that may express similar acoustical correlates, for instance, an aging voice may share roughness and breathiness with other organic and neurological pathologies. On the other hand, the true added value of acoustic analysis in pathological voice and speech characterization is more focused on functional assessment (evaluating the quality of speech characteristics in terms of respiration, phonation, articulation, and prosody), and monitoring its progress in the timeline (longitudinal analysis) than to automatic diagnosis. Indeed, automatic binary detection of laryngeal pathology from acoustic analysis (if there is pathology present in phonation) is mainly confined to remote screening applications [20], because the main role of computer assessment systems would be to serve as an aid to the specialist in establishing differential diagnostic support tools, in what conceptually is computer-assisted specialist-assessed diagnosis.