esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimal Control Model for PMCU Path Planning
### Kinematic Model of PMCU  
The construction space on the deck of the cruise ship is narrow and complex, so it is difficult for traditional heavy-duty trucks to directly enter the deck space for construction work. At present, PMCU is often transported by workers using special transfer devices. The device shown in Figure 1 is often used to push the cabin during the transportation of prefabricated cabins on board by fixing two devices on opposite sides of the prefabricated cabin. Then, the workers can move the cabin in any direction during the pushing process [6].  
The forces during the pushing process of the PMCU are complicated, including pushing forces of workers, gravity, and device friction. This paper focuses on the path planning problem, focusing on the planning of the movement path and position and pose of the cabin pushing process. Also, because of the low speed of cabin pushing and the flat cruise deck, factors such as lateral sliding as well as inertia forces can be ignored. Combined with the characteristics that the cabin pushing process can move in any direction, the simplified kinematic model of the PMCU pushing (Figure 2) can be expressed as:  
\[\left\{\begin{array}{l}\dot{x}_{i}=v_{i}\cdot cos\theta\\ \dot{y}_{i}=v_{i}\cdot sin\theta\\ \dot{v}_{i}=u_{i1}\\ \theta_{i}=u_{i2}\end{array}\right. \tag{6}\]  
Equation (6) is the state variables of the PMCU pushing process, \(x_{i},y_{i}\) is the position of the \(i\)th PMCU in the 2D space of deck, \(\dot{x}_{i},\dot{y}_{i}\) is the first-order derivatives of \(x_{i},y_{i}\), i.e., the velocity components in \(x,y\) direction, \(\theta_{i}\) is the orientation angle of PMCU pushing process, \(v_{i}\) is the velocity of the PMCU pushing process, and \([u_{i1},u_{i2}]\) are the control variables of the PMCU pushing process.
###### Abstract  
The eddy covariance (EC) technique has been widely used as a micrometeorological tool to measure carbon, water and energy exchanges. When utilizing the EC measurements, it is critical to be aware of the long-term information on source areas. In China, large-scale forest plantations have become a dominant driver of greening and carbon sinks on the planet. However, the spatial representativeness of EC measurements on forest plantations is still not well understood. Here, an EC flux site of a coniferous plantation mixed with cropland in a subtropical monsoon climate was selected to evaluate the spatial representativeness of the two approaches. One is the fraction of target vegetation type (FTVT), which was used to detect to what degree the flux is related to the target vegetation. The other is the sensor location bias calculated from the enhanced vegetation index (EVI), which was used to detect to what spatial extent the flux can be upscaled. The results showed that the monthly footprint climatologies changed intensely throughout the year. The source area is biased toward the southeast in summer and northwest in winter. The study area was mainly a composite of coniferous plantations (70.08%) and double-cropped rice (27.83%). The double-cropped rice, with a higher seasonal variation of EVI than the coniferous plantation, was mainly distributed in the eastern areas of the study site. As a result of spatial heterogeneity and footprint variation, the FTVT was 0.89 when the wind direction was southwest; however, this reduced to 0.65 when the wind direction changed to the northeast and exhibited a single-peak seasonal variation during a year. The sensor location bias of the EVI also showed a significant monthly variation and ranged from \(-\)14.21% to 19.04% in a circular window with an increasing size from 250 to 3000 m. The overlap index between daytime and nighttime (Oday_night) can potentially be a quality flag for the GPP derived from the EC flux data. These findings demonstrate the joint effects of the monsoon climate and underlying surface heterogeneity on the spatial representativeness of the EC measurements. Our study highlights the importance of having footprint awareness in utilizing EC measurements for calibration and validation in monsoon areas.  
eddy covariance; spatial representativeness; footprint climatology; fraction of target vegetation type (FTVT); sensor location bias; subtropical coniferous plantation 2022, 14, 5022. [[https://doi.org/10.3390/rs14195022](https://doi.org/10.3390/rs14195022)]([https://doi.org/10.3390/rs14195022](https://doi.org/10.3390/rs14195022))
## 3 Results
### Long-Term Toxicity Test of Contrast Agents on P. Fluorescens Pf-01 biofilms  
Figure 5 depicts the effect of different contrast agents on the population size of biofilms compared to control batch. In the case of 2% \(w/v\) FeSO\({}_{4}\) and ZnSO\({}_{4}\), the bacterial population size was significantly reduced (\(p\) = 0.0023 and \(p\) = 0.0023, respectively) by around 5 \(log_{10}\) and 4 \(log_{10}\), respectively. Similarly, for 2% \(w/v\) PTA and KI there was a significant impact (\(p\) = 0.0034 and \(p\) = 0.0112, respectively) on the population size with a reduction by 1 \(log_{10}\) and slightly less than 1 \(log_{10}\), respectively. Therefore, no further experiments were conducted using these chemical compounds as contrast agents. At 2% \(w/v\) KBr there was no significant (\(p\geq 0.05\)) reduction in the bacterial population size. However, at 5% \(w/v\) KBr the bacterial population size was reduced significantly (\(p\) = 0.0031) by 1 \(log_{10}\).  
Next to its effect on the number of biofilm cells, the influence of 5% \(w/v\) KBr on the bacterial biomass was assessed by crystal violet staining. The biofilm biomass per cell was found to be increased (Table 2) probably due to the stress induced by the presence of KBr in the growth medium.  
Figure 5: Individual population size of biofilms incubated in each of the contrast agent solutions for 48 h at 28 \({}^{\circ}\)C. Data are represented as the mean with SEM (Standard Error of Mean). Statistical comparisons were made using analysis of variance (ANOVA). In the figure, \({}^{\star}\) and \({}^{\star\star}\) indicates a significant difference \(p<0.05\) and \(p<0.01\), respectively. While ns represents no significant difference (\(p\geq 0.05\)) when compared to control.  
In addition, the effect of 5% \(w/v\) KBr was investigated by confocal microscopy combined with Live/Death staining. Figure 6 indicates that the presence of 5% \(w/v\) KBr affects the phenotype of the bacteria, even if it does not impede the bacterial growth (bacteria still alive). There was cell elongation [51] observed after long term exposure to 5% \(w/v\) KBr suggesting an envelope stress response by the bacteria [52].
# Phase Change Materials and Their Benefits in ETICS
## 2 Materials and Methods
### Constituent Materials
#### 2.1.2 Cement  
Portland cement CEM I 42.5 R, manufactured in Czechia, was used as the binder in the research.  
CEM I 42.5 R Portland cement is manufactured in accordance with EN 197-1 ed 2. It is a hydraulic binder in powder form manufactured by grinding together Portland clinker, calcium sulphate, additional constituent and additives.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Thermo-Physical Properties**} \\ \hline \(\bullet\) & Phase change temperature suitable for desired application, in the desired operating temperature range [30; 32]. \\ \(\bullet\) & High latent heat of fusion per unit volume [30]. \\ \(\bullet\) & High thermal conductivity to assist in charging and discharging of PCM within the limited time frame [30]. \\ \(\bullet\) & High specific heat so that additional energy in the form of sensible heat is available to the thermal energy storage system [30]. \\ \(\bullet\) & PCM should melt completely (i.e., congruent melting) during phase transition so that the solid and liquid phases are homogenous [30]. \\ \(\bullet\) & Thermally reliable (i.e., cycling stability) so that the PCM is stable in terms of phase change temperature and latent heat of fusion and can be used in the long run [30]. \\ \(\bullet\) & Good heat transfer [32]. \\ \(\bullet\) & Small vapor pressure at operating temperature [32]. \\ \(\bullet\) & Favourable phase equilibrium and no segregation [32]. \\ \(\bullet\) & Complete melting [31]. \\ \hline \multicolumn{2}{c}{Kinetic properties} \\ \hline \(\bullet\) & High rate of nucleation so as to avoid super cooling of the PCM in liquid phase [30; 32]. \\ \(\bullet\) & High rate of crystal growth so that heat recovery from the storage system is optimum [30; 32]. \\ \hline \multicolumn{2}{c}{Chemical properties} \\ \hline \(\bullet\) & Chemically compatible with construction/encapsulated materials [30]. \\ \(\bullet\) & No degradation after large number of thermal (freeze/melt) cycles so as to assure long operation life [30]. \\ \(\bullet\) & Non-toxic, non-flammable and non-explosive so as to assure safety [30]. \\ \(\bullet\) & Corrosion resistant to construction/encapsulated materials [30]. \\ \(\bullet\) & Complete reversible melt/freeze cycles [32]. \\ \(\bullet\) & Chemical stability [31]. \\ \(\bullet\) & High freeze/melt stability [31]. \\ \hline \multicolumn{2}{c}{Environmental properties} \\ \hline \(\bullet\) & Low environmental impact and non-polluting during service life [30]. \\ \(\bullet\) & Low embodied energy [32]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main criteria governing the selection of Phase Change Materials (PCMs) [27; 30; 31; 32].
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
###### Abstract  
This study examines how ten different audio features, including MFCC, mel-spectrogram, chroma, and spectral contrast etc., influence speaker change detection (SCD) performance. The analysis is conducted using two unsupervised methods: Bayesian information criterion with Gaussian mixture model (BIC-GMM), a model-based approach, and Kullback-Leibler divergence with Gaussian Mixture Model (KL-GMM), a metric-based approach. Evaluation involved statistical analysis of feature changes in relation to speaker changes (vice versa), supported by comprehensive experimental validation. Experimental results show MFCC as the most effective feature, demonstrating consistently good performance across both methods. Features such as zero crossing rate, chroma, and spectral contrast also showed notable effectiveness within the BIC-GMM framework, while mel-spectrogram consistently ranked as the least influential feature in both approaches. Further analysis revealed that BIC-GMM exhibits greater stability in managing variations in feature performance, whereas KL-GMM is more sensitive to threshold optimization. Nevertheless, KL-GMM achieved competitive results when paired with specific features, such as MFCC and zero crossing rate. These findings offer valuable insights into the impact of feature selection on unsupervised SCD, providing guidance for the development of more robust and accurate algorithms for practical applications.  
speaker change detection; audio features; unsupervised approach +
Footnote †: journal: applied sciences
## 4 Results and Analysis
### Influence of Lateral Pressure Coefficient
#### 4.2.3 Vertical Stress of Roadway Sidewall  
Figure 16 shows the vertical stress distribution of the rock mass of a sidewall under various \(\lambda\) values. With the rise of the \(\lambda\) value of the roadway, the vertical stress concentration range of the rock mass of the roadway sidewall is close to the roadway side, and the stress concentration in the roadway side is higher than that of other parts. Areas with high vertical stress values near the roof and floor and the roadway side also gradually shift into the shallow surrounding rock with the rise of lateral pressure.  
Under varying \(\lambda\) values, with the rise of the distance from the boundary of the roadway sidewall, the evolution pattern of vertical stress inside a roadway is illustrated in Figure 17. Taking the \(\lambda\) value equal to 0.5 as an example, vertical stress at the roof of the roadway at 3 m away from the roadway sidewall reaches 26.16 MPa. The internal vertical stress equals 33.39 MPa at 1.75 m from the boundary of sidewall. When 3.25 m from the roadway sidewall, vertical stress reaches the second peak of 30.7 MPa.  
The relationship between the \(\lambda\) value and the internal stress distribution of a sidewall rock mass is shown in Figure 18. The stress value at the first stress concentration point in the rock mass decreases with the rise of \(\lambda\) value. The position of the stress concentration point gradually shifts to the roadway sidewall with the rise of the \(\lambda\) value. The stress value of the second stress concentration first increases and then decreases with the rise of the \(\lambda\) value. For instance, at a \(\lambda\) value of 0.2, the vertical stress measures 27.01 MPa. Subsequently, as the \(\lambda\) value escalates from 0.2 to 0.5, the vertical stress ascends to 30.7 MPa. However, with a further increase in the \(\lambda\) value from 0.5 to 1.0, the vertical stress gradually declines to 29.3 MPa. In general, the distance between the stress concentration position and the roadway's side decreases slowly and linearly with the augmentation of the \(\lambda\) value.  
Figure 15: Relationship between \(\lambda\) and horizontal displacement of the roadway sidewall.  
Figure 17: Vertical stress monitoring curves inside the sidewall of the roadway under different \(\lambda\).
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 7 Effects of Organic Material-Derived NPs on Medicinal and Aromatic Plants  
Chitosan/tripolypphosphate NPs supplemented to culture media modified the root architecture and differentiation in micropropagated _Capsicum annuum_, and at toxic doses 5-20 mg/L caused interruption of plant growth and development. On the other hand, at a dose of 1 mg/L, it acted as an effective elicitor to trigger organogenesis via micropropagation, causing considerable increases in contents of secondary metabolites, including soluble phenols, proline, and alkaloid [314]. Exposure of seeds of _Capsicum annuum_ to aqueous suspensions of nanoscale chitin (0.001-0.05% \(w/v\)) or hydropriming reduced mean germination time at 25 \({}^{\circ}\)C on blotter paper to 4.9-5.3 days compared to 5.4-6.7 days observed with untreated seeds, seeds treated with 1% acetic acid, or 1% Captain fungicide. Application of 0.05% chitin NPs or hydropriming ameliorated seedling emergence at 19 to 30 \({}^{\circ}\)C and chitin NPs was able to reduce fungal growth as well [315].  
Root meristens of _A. cepa_ obtained from seeds exposed to nanoplastics (50 nm) at doses 0.01-1 g/L showed reduction of MI and caused induction and of cytogenetic anomalies and micronucle suggesting their cytotoxicity and genotoxicity. This adverse impact of nanoplastics is caused by the mechanical surface contact of nanoplastics with root external layers and the internalization of nanoplastics in different cellular compartments. Consequently, nanoplastics internalized into crop plants could enter in different trophic levels of the food chain and, so, they represent a hazard for the health of the human population [316]. Polystyrene NPs applied at doses 25-400 mg/L considerably inhibited growth of _A. cepa_ roots, induced the production of OH and O\({}_{2}\)\({}^{-}\) radicals, and increased DPPH scavenging activity and lipid peroxidation, causing a decrease in MI compared to the control. They also caused various chromosomal and nuclear aberrations and downregulated the expression of the plant cyclin-dependent kinase (CDKA) encoding gene: cdc2, an important cell cycle regulator. The above results suggest the cytotoxic and genotoxic potential of polystyrene NPs [317].
## 2 Dataset
### Rodi  
Many approaches have been developed to distinguish the irregularities from the in situ electron density, and we take a similar method described by [PERSON] et al. [21]. The ionospheric irregularities are detected with the irregularity parameters derived from the satellite in situ Ne measurements: the rate of change of density (ROD) and the rate of change of density index (RODI).  
We calculate the mean electron density with a sliding window of 60 s, and by substracting it from Ne, a high-pass filtered Ne is acquired, which we also call 'detrended Ne'. Then, **ROD** is obtained as the time derivative of the detrended Ne:  
\[\textbf{ROD}(\mathbf{t})=\frac{\textbf{{N}}\mathbf{e}_{d}(\mathbf{t}+\Delta\mathbf{t})- \textbf{{N}}\mathbf{e}_{d}(\mathbf{t})}{\Delta\mathbf{t}} \tag{1}\]  
where \(\textbf{{N}}\mathbf{e}_{d}\) represents the detrended \(\textbf{{N}}\mathbf{e}\), and \(\Delta\mathbf{t}\) is 0.5 s for the Swarm _N_e data (with a sampling frequency of 2 Hz). **RODI** is the standard deviation of **ROD** at a running window of 20 s, corresponding to the irregularities at a spatial scale of approximately 7.5-150 km.  
\[\textbf{RODI}(\mathbf{t})=\sqrt{\frac{1}{\textbf{{N}}-1}\sum_{t_{t}=t-\frac{ \Delta\mathbf{t}}{2}}^{t_{t}=t+\frac{\Delta\mathbf{t}}{2}}\abs{\textbf{ROD}(\mathbf{t}_{ i})-\overline{\textbf{ROD}}(\mathbf{t})}^{2}} \tag{2}\]  
where \(\Delta\mathbf{t}\) is the running window size (20 s), \(N\) is data number in the running window, and \(\overline{\textbf{ROD}}\) is the mean of **ROD**:  
\[\overline{\textbf{ROD}}(\mathbf{t})=\frac{1}{\textbf{{N}}}\sum_{t_{t}=t-\frac{ \Delta\mathbf{t}}{2}}^{t_{t}=t+\frac{\Delta\mathbf{t}}{2}}\textbf{ROD}(\mathbf{t}_{i}) \tag{3}\]
# Synthesis and Characterization of Unsaturated Succin Acid Biobased Polyester Resins
## 2 Materials and Methods
### Characterization Methods  
#### 2.3.1 Nuclear Magnetic Resonance Spectroscopy (\({}^{1}\)H-NMR)  
\({}^{1}\)H-NMR spectra were recorded in deuterated chloroform (CDCl\({}_{3}\)-\(d_{1}\)) on an Agilent 500 spectrometer (Agilent Technologies, Santa Clara, CA, USA), at room temperature. Spectra were internally referenced with tetramethylsilane (TMS) and calibrated using the residual solvent peaks (chloroform-\(d_{1}\), 7.26 ppm for hydrogen atoms).
## 2 Priority-based Coverage-aware Congestion Control (PCC)
### Discussions  
As we described in this section, different components in _PCC_ realize the design considerations in Section 2.1.. First, the separate thresholds for _Event_ and _Non-Event_ packets in queue handler guarantee the high throughput of _Event_ packets during congestion. Second, the optimization algorithm in Equations (5), (6) and (7) provides coverage fidelity of the whole network. Third, the proposed new queue dropping schemes, and corresponding packet admission probabilities \(K_{E}\) and \(K_{N}\) implements flexible queue scheduler. Finally, updating \(P_{i}\) by the probability of network dropping and MAC/PHY link failure efficiently utilize the network resource.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 4 Traffic Performance Analysis  
This section analyzes, evaluates, and compares the network traffic performance of EFA and eEFA. We consider the sample HSR network with data communication sessions between source DANH 1 and destination DANH 10, as shown in Figure 1.  
The following two scenarios are considered:  
* Unidirectional communication between the source and the destination; and
* Bidirectional communication between the source and the destination.  
Figure 8: Process of forwarding frames between the source and the destination.  
Figure 9: Forwarding the \(k\)th (\(k\geq 2\)) frame in eEFA.
## 3 Materials and Methods
### Dataset Preprocessing
#### 3.2.1 Data Cleaning  
Data cleaning is the primary step in data preprocessing, aiming to remove noise, eliminate duplicate content, handle missing values, and standardize data formats. In text data, noise refers to irrelevant information and useless characters that can interfere with the model's training process. Methods for removing noise include deleting HTML tags, removing special characters, and non-text information. Duplicate content can cause the model to overfit specific patterns, reducing its generalization ability. To eliminate duplicate content, text comparison algorithms are used to calculate the similarity between texts and identify and delete duplicates. Suppose there are two text segments \(\alpha\) and \(\beta\), their  
Figure 1: Flowchart of dataset preprocessing.  
similarity \(\sin(\alpha,\beta)\) can be calculated, and a threshold \(\theta\) can be set to determine whether to delete them:  
\[\sin(\alpha,\beta)>\theta\Rightarrow\text{Delete Duplicate Content} \tag{5}\]  
Handling missing values is crucial for ensuring data integrity. Common methods include the interpolation and deletion of records. In interpolation, linear interpolation or other techniques can be used to fill in missing values. For a time series dataset \(\{x_{1},x_{2},\dots,x_{n}\}\), if \(x_{i}\) is missing, it can be filled using the average of the preceding and following values:  
\[x_{i}=\frac{x_{i-1}+x_{i+1}}{2} \tag{6}\]  
If there are many missing values or interpolation is not suitable, records with missing values can be deleted. Additionally, to unify data formats, data from different sources must be converted to a consistent format, such as standardizing date formats and numerical units. For example, different date formats can be standardized using date parsing libraries:  
\[\text{StandardDate}=\text{parse(OriginalDate)} \tag{7}\]  
where OriginalDate represents the original date format, and StandardDate represents the standardized date format.
# Recent Advances in Bacterial Persistence Mechanisms
## 3 Research on the Mechanism of Bacterial Persistence
### SOS Response  
The SOS response plays an important role in DNA repair, which is controlled by two key factors: repressor factor LexA and inducible factor RecA [61]. RecA protein is a recombinase which is important for repairing bacterial recombational DNA [62]. The failure of damage repair by the SOS response usually leads to cell death, while successful DNA repair may eliminate the lysis function of antibiotics [63; 64]. High cell density within biofilms, together with oxidative stress, triggers DNA damage and evokes the SOS response [61]. For instance, _Salmonella_ persisters could retain the function of initiating infection recurrence via RecA-mediated DNA repair [65]. In response to the stress changes, RecA mediates global transcription by stimulating the self-lysis of LexA [66; 62]. In addition, the SOS response is proven to enhance the expression of fibronectin, helping biofilm formation [67]. For resuscitation experiments, it was also observed that after the removal of ofloxacin, the SOS response continuously increased in persistent _E. coli_ cells [68] (Figure 3).  
Of the mechanisms described above, biofilm has been demonstrated in vivo in human pathology. For example, the biofilm formation of _Mycobacterium tuberculosis_ in human lungs has been indicated to contribute to drug tolerance in experiments in animal models of infection and in the lung tissues of patients [69]. However, TA modules, the stringent response, and the SOS response have not been well elucidated in the physiopathology of specific diseases despite their documentation in vitro at the moment.