esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption and Desorption Experiments  
#### 3.2.1 Effect of pH  
The effect of varying pH values on the adsorption efficiency of Cd-I-MA-co-AN for Cd(II) was investigated in the 1-10 pH range (adjusted using 1 mol L\({}^{-1}\) HCl and NaOH). Figure 4 shows that the efficiency of Cd(II) adsorption on the microsphere was increased with increasing pH values up to 6, after which they gradually decreased. This is because, under acidic conditions, the imprinted site was occupied by protons rather than cadmium ions. When the pH increased, the protonation of ligating atoms becomes weak and the coordination between functional groups and Cd(II) at the surface binding sites of Cd-I-MA-co-AN becomes dominant. The maximum adsorption of Cd-I-MA-co-AN was observed at pH 6. After pH 6, Cd(II) began to hydrolyze; with the formation of precipitate, the adsorption rate decreased [32; 33]. Hence, pH 6 was chosen for further experiments.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 3 System Model
### Complexity Analysis  
In this subsection, we analyze the complexity of the proposed algorithm. Let \(N\) denote the number of candidate satellites associated with TSE in the space-time graph, the total number of executions about the loop in line 5 of Algorithm 2 equals \(N\). To update the optimal set \(V_{opt}\) in TSR, the maintenance cost for each loop is \(O(N)\), and the total  
Figure 5: TSHR signaling workflow.  
number of calculations about cumulative transmission time in line 8 of Algorithm 1 is \(E_{ss}\). Consequently, the computation complexity of TSR is \(O(N^{2}+E_{ss})\). The computation complexity of solving the matching problem is \(O(D.|v_{g}|^{3})\), then the total computational complexity is \(O(N^{2}+E_{ss})+O(D.|v_{g}|^{3})\).
## 4 The Quadrature Chaos Shift Keying Communication System's Performance Analysis  
This section delves into the evaluation of noise immunity in the QCSK communication system (Figure 1, Section 2). The communication system is based on the Colpitts chaos oscillator, ensuring security on the physical layer due to the chaos signal properties. The system's model consists of a transmitter, an Additive White Gaussian Noise (AWGN) channel, and a receiver. Within the transmitter, a chaos oscillator operates in drive mode, employing the Chaos Shift Keying (CSK) scheme to form the information-carrying signal. The oscillator's state variables \(X\) and \(Y\) (depicted in Figure 1) distinctly map bit values '1' and '0', respectively. The bit rate of information is 128 kbps for future system employment in IoT systems.  
The information-carrying and synchronization signals undergo modulation before transmission, simultaneously transmitting through the AWGN channel. These signals are demodulated and filtered at the receiver end using a low-pass filter. The filter was carefully adjusted with consideration for signal characteristics. The bandwidth was set to achieve a mean square error of \(-40\) dB between the input and output signals. After filtration, ensuring synchronization of the response chaos oscillator is pivotal and achieved through the substitution method, leveraging the demodulated synchronization signal \(Z^{\prime}\). This synchronization step is fundamental for accurate and reliable signal detection.  
Detection of a transmitted data bit relies on estimating correlation coefficients between the demodulated information-carrying signal and the state variables \(X^{\prime}\) and \(Y^{\prime}\) of the response chaos oscillator. Decision-making regarding the received bit is based on the calculated correlation coefficient.  
This research findings indicate that synchronization within the [PERSON] drive-response system can be established using \(V_{C1}\) or \(V_{C2}\). A communication system was developed for both scenarios to comprehensively study synchronization and the selection of information-forming signals for chaos-based data transmission systems. Table 1 provides a detailed breakdown of how signals were utilized in each study and the correlation coefficient between information-forming signals.  
This study investigates the synchronization dynamics inside the drive-response system, considering the possible effects of employing \(V_{C1}\) or \(V_{C2}\) for synchronization. The correlation coefficient provides insight into the interaction and alignment of information-forming signals, aiding in the knowledge of signal selection for effective chaos-based data transfer. The purpose of an established communication system is to study the complex dynamics of the Colpitts chaotic oscillator in various synchronization setups.
# Classification of Heart Sounds Using Convolutional Neural Network
## 2 Materials and Methods
### Heart Sound Classification Based on the Designed CNN
#### 2.4.3 Addressing the Problem of Class Imbalance  
For binary classification tasks, in the conventional CNN, the feature nodes generated by the fully connected layer are fed into the classification layer to output a probability. When the model calculates the loss value, each category has an equal loss weight, which may lead the model to predict the input as the class with the larger number of samples when performing a classification task with a class imbalance. However, in clinical practice, it is common for the number of normal samples to be much larger than that of abnormal samples. So, in clinical applications of deep learning algorithms, it is essential to deal with the problem of class imbalance.  
To solve the problem of class imbalance for normal and abnormal heart sound samples, in this study, we set different class weights in the loss function, that is, the model applies different penalties to classes with mispredictions. The new weight for each class is defined in Equation (14). It is clear that abnormal samples will acquire a larger weight when they are incorrectly predicted.  
\[\textit{class}_{i}\textit{-weight}=\frac{1}{2}\times\frac{\textit{Number of training samples}}{\textit{Number of class}_{i}},i=0,1. \tag{14}\]
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 1 Introduction  
All terrestrial forms of life have had millions of years to adapt to Earth's gravity (9.8 m/sec\({}^{2}\)). Cytoskeletons constructed with actin, microfilaments and microtubules resist compressive forces and allow internal load bearing [1]. Adhesive molecules such as integrins deal with external compressive forces and allow cells to maintain their positions in space [2]. Mechanosensors shuttling between cytoplasmic membranes and nuclei and matrix clusters of cell extension kinases keep the cell informed of needed changes in gene expression [3, 4]. Gravisensing organelles inform roots and stems which direction in space they should occupy [5]. And cells use thermal convection in which heated fluids rise to the top of the gravity vector and are then exchanged by cooler fluids, establishing a convection current that dissipates heat, renews nutrient supplies, and removes waste materials [6].  
Nature is the ultimate architect and has done well by its creatures on Earth. But man has thrown nature a curve ball and ventured into space where all these adaptations are obsolete.  
The adverse effects of microgravity are particularly severe on the musculoskeletal system, particularly bone. Astronauts at are risk of losing 1.0-1.5% of their bone mass for every month they spend in space despite their adherence to high impact exercise training programs, and diets high in nutrients, potassium, calcium, and vitamin D, all designed to preserve the skeletal system. And the adverse effects of space travel on the skeletal system may persist for years after returning to Earth.  
In this review, I detail the effects of microgravity on bone homeostasis and review the prominent roles that insulin-like growth factor-1 and its receptor, IGF-1R, play in the growth and maintenance this vital tissue.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model
### External Noise  
In this section, the effects of external events, including extreme weather conditions, on wireless signal transmission are considered. First, we modeled the weather conditions as an attenuation model. Subsequently, we calculated the data loss based on the model.
## 4 Improvement of Thermal Performance
### Encapsulation
#### 4.1.3 Nanoencapsulation  
Recent developments in nanotechnology, and specifically in synthesis methods of nanoparticles, allow the encapsulation of PCMs on nanoscale. Systems on nanoscale have essentially different physical and chemical properties to their counterparts on macroscale. The size effects of nanoscale provide outstanding properties to nanoencapsulated PCMs, which can make improvements to the efficiency of existing applications as well as meeting the requirements for new ones. For example, nanoencapsulation is proved to dramatically reduce the supercooling effect. Furthermore, the nanoencapsulated PCMs, due to their size, are able to form stable dispersions in fluids, known as nanofluids, without rapidly increasing their viscosity, which opens the path for new applications. In addition, nanoencapsulated PCMs have proven to be more durable to mechanical stress than larger capsules, which in some applications is very important [22; 32].  
The nanocapsule diameter is expected to be less than 1 \(\upmu\)m, while in microencapsulated PCMs it is most typically 5 to 400 \(\upmu\)m. As a result, not all the fabrication methods of microencapsulation that are presented in the previous section can be adopted for PCM capsules on nanoscale. The methods that are suitable for producing nanocapsules are mainly chemical. Specifically, the efficient synthesis techniques are emulsion polymerization, miniemulsion polymerization, interfacial polymerization, in situ polymerization, and sol-gel technique [22; 32].
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 2 Measurement Principle
### Altimetry Principle
Different from single-platform IRA, whose baseline is usually formed by rigid mast, the baseline of LB-IRA is quite flexible, therefore the baseline inclination error is mostly from baseline length error instead of attitude jitter of the platform [24], as shown in Figure 2.  
According to Figure 2, the baseline inclination error between two satellites can be expressed as [24]:  
\[\sigma_{\alpha}=\frac{\sigma_{B}}{B_{xt}} \tag{7}\]  
where \(B_{xt}\) is the cross-track baseline length, and \(B_{estimated}\) is the baseline length estimated from the orbit track. Apparently, for LB-IRA, large baseline can relatively decrease the altimetric error. Equation (6) indicates that large baseline or small look angle leads to low altimetric error introduced by interferometric phase error. Interferometric error includes phase bias and random phase noise: the phase bias part can usually be calibrated, and therefore, the phase noise ultimately determines the altimetric accuracy level [25]. From the above analysis, we have noticed that large baseline can improve the altimetric accuracy. However, large baseline can also deteriorate the phase noise level, and thus prevent further improving altimetric accuracy from increasing the cross-track baseline length. The contradiction can be solved by cross-interferometry, which will be addressed later in Section 4. Figure 2: Baseline inclination error of Large Baseline Imaging Radar Altimeter (LB-IRA) introduced by baseline length error.
## 3 Results
### Antibacterial Activity of the CH6 Crude Extract  
To verify antimicrobial activity, the MIC and growth assays of the crude CH6 extract against various indicator microorganisms were utilized, and they are presented in Table 1.  
The lowest MIC value (8 mg/mL) was observed for _S. aureus_ ATCC 29213, followed by _P. aeruginosa_ ATCC 9027 (16 mg/mL) and _B. cereus_ ATCC 11778 (16 mg/mL). The highest MIC of 64 mg/mL was observed for the CH6 extract against _M. smegmatis_ MC\({}^{2}\) 155. In addition, the crude extract weakly inhibited the growth of yeast C. _albicans_ ATCC 10231, with an MIC value of 128 mg/mL. In agreement with this result, CH6 exhibited no antifungal activity against phytopathogenic fungi such as _Colletotrichum gloeosporoides_ and _Fusarium solani_, as shown in a dual culture assay (Figure S1).  
Figure 1: Heatmap showing antibacterial activity of metabolites extracted from bacterial isolates against at least one pathogen. ST: _Salmonella enterica_ serovar Typhimurium ATCC 14028; EC: _Escherichia coli_ ATCC 11105; SM: _Mycobacterium smegmatis_ MC\({}^{2}\) 155; PA: _Pseudomonas aeruginosa_ ATCC 9027; SA: _Staphylococcus aureus_ ATCC 29213. (-): no activity. All sorts of color in figure represents the zone of inhibition (mm), red indicates the largest diameter of the inhibition zone, white represents no zone of inhibition.  
The antibacterial effect of CH6 extract on cell growth was further monitored against _M. snegmatis_ MC\({}^{2}\) 155 and _S. aureus_ ATCC 29213, as shown in Figure 2. It was observed that 10 \(\mu\)g/mL CH6 extract did not show growth delay (\(p>0.05\)) in _M. snegmatis_ MC\({}^{2}\) 155. Exposure to 30 \(\mu\)g/mL extract showed significant inhibitory effects on the bacterial growth from 2 to 8 h but not after 24 h (Figure 2A). In contrast, the CH6 extract strongly inhibited the growth of _S. aureus_ ATCC 29213 along the growth curve. About 20 \(\mu\)g/mL of CH6 extract extended the growth lag phase and inhibited bacterial growth until 6 h. After exposure to 30 \(\mu\)g/mL, _S. aureus_ cells were significantly reduced (\(p<0.001\)) and unable to grow (Figure 2B).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tested Pathogen** & **MIC (\(\mu\)g/mL)** \\ \hline _S. Typhimurium ATCC 14028_ & 32 \\ _E. coli_ ATCC 11105_ & 32 \\ _M. snegmatis_ MC\({}^{2}\) 155_ & 64 \\ _P. aeruginosa_ ATCC 9027_ & 16 \\ _S. aureus_ ATCC 29213_ & 8 \\ _E. aerogenes ATCC 13048_ & 32 \\ _B. cereus_ ATCC 11778_ & 16 \\ _C. albicans_ ATCC 10231 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MIC of CH6 crude extract against the tested bacteria and yeast.  
Figure 2: Antibacterial and antioxidant activities of CH6 extract. Growth curve analysis of _M. snegmatis_ MC\({}^{2}\) 155 (**A**) and _S. aureus_ ATCC 29213 (**B**) in response to the different concentrations of CH6 extract. (**C**) Antioxidant potential demonstrated by CH6 extract in the DPPH, hydroxyl, and superoxide anion scavenging assays. _p_-values were calculated via a Student’s unpaired two-tailed _t_-test in Prism 9 (\({}^{ns}\)\(p>0.05\); \({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\); \({}^{***}\)\(p<0.001\)).
## 4 Results and Case Study
### Topic Extractor  
For topic modeling evaluation, we used the UMass topic coherence [28] metrics. The topic coherence (TC) metrics calculate the words' relatedness within the topics; higher coherence values means a good topic. TC is computed as:  
\[C(t;V^{(t)})=\sum_{m=2}^{M}\sum_{l=1}^{m-1}log\frac{D(V_{\text{u}}^{(l)},V_{ \text{l}}^{(l)})+1}{D(V_{\text{l}}^{(l)})} \tag{7}\]  
In this section, we show an example of topics generated by UXWE-LDA, WE-LDA, and LDA to show an improvement by our proposed topic extractor. The red color in each topic in Table 3 show errors, as UXWE-LDA extracted more coherent and meaningful topics compared to the other baseline models.  
We performed parameter tuning of UXWE-LDA such as number of top seed words (n), words similarity (m), and trust score (u). We examined the sensitivity of the three parameters of UXWE-LDA such as top seed words n, most similar words m, and the trust score u. The number of top 15 words gives us more coherent topic with higher TC value with other parameters setting for the given dataset. The experimental results reveals that a top 15 seed word gives higher TC value for electronic data, and for the non-electronic dataset, the top 25 seed words give us higher TC value. Therefore, we can conclude that a few seeds generate the coherent topic.  
Additionally, from experimental results we reveal that TC values increase with more similar words at the initial stage, and gives us a higher TC value at 15 in the electronic dataset; for the non-electronic dataset, the model gives a higher value at 15.  
Figure 10: Mapping of UDXs on Kano Model.  
We examined the electronic products dataset and found TC increases with more similar words at the beginning, then plateaus and gives higher value at m = 15. For the non-electronic product dataset, the UXWE-LDA model gives an almost identical TC value and higher at m = 25. This shows that high quality knowledge is generated by must-links which are produced by the best seed words and word similarity. The similarity computation using TC ensures the quality of a must-link and that proper knowledge is incorporated into UXWE-LDA.  
Figure 11 shows the average TC of each model using different number of topics on two datasets.  
The results show that, with the different number of topics and setting, UXWE-LDA always gives higher TC value than the other models, which shows that the UXWE-LDA is vigorous with a different combination of must-link clusters. Enhancements of UXWE-LDA over other models with \(p\)-value (\(p<0.007\)) were significant in the two-tailed paired \(t\)-test.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results
### Monte-Carlo Simulation
#### 3.1.3 Energy Resolution  
The energy resolution is an important parameter characterizing the detector's ability to distinguish the energy of the detected particles. The detector's energy resolution in this study was determined by counting standard point sources of \({}^{60}\)Co, \({}^{137}\)Cs, \({}^{152}\)Eu, and \({}^{241}\)Am for 1000 s, and the full width at half maximum (FWHM) of each \(\gamma\)-ray are shown in Table 3. The fitted curve for energy resolution \(\eta\) (%) versus energy E (keV) is:  
\[\eta=1.088\times E^{-0.5788} \tag{1}\]  
The typical energy resolution for \({}^{137}\)Cs of NaI detectors, which are widely used in in-situ monitoring in marine nuclear emergency scenarios, is about 6-9% [6; 10; 15; 16]. The energy resolution for \({}^{137}\)Cs of the novel cerium bromide (CeBr\({}_{3}\)) detector is \(\sim\)3.5% [12]. The device developed in this study has an energy resolution for \({}^{137}\)Cs of 2.60%, which is significantly better than that of the NaI detector and comparable with the CeBr\({}_{3}\) detector. Therefore, the application of medium resolution systems such as LaBr\({}_{3}\) and Ce Br\({}_{3}\) detectors will benefit the rapid identification of various radionuclides in nuclear emergency scenarios.
###### Abstract  
In thick section laser welding, filler metal addition is usually required to improve joint fit-up tolerances or to control the chemical composition of the weld metal. With deep and narrow welds produced using an over-alloyed filler metal, it may be challenging to ensure that the filler metal and its elements are homogeneously mixed and evenly distributed throughout the fusion zone. Inhomogeneous filler metal mixing can cause unfavourable changes to weld metal chemistry and microstructure. Filler metal mixing behaviour in laser-arc hybrid and laser cold-wire welding is studied in this work. Welding tests were conducted on 10 mm thick butt-welded joints of AISI 316L austenitic stainless steel. An overmatching type 2205 duplex stainless steel filler wire was used to obtain a composition contrast between the base metal and filler metal. Energy dispersive spectroscopy (EDS) with chromium as the trace element was used for element mapping and stepwise characterization of the weld cross-section samples. Optical metallography was used to observe possible inhomogeneous filler metal mixing behaviour like local acute changes in macro- and microstructural features. The results showed a clear difference in filler metal mixing between the weld surface part (upper half) of the weld and the weld root part (lower half) in 10 mm thick welded cross-sections for closed root gap of I-groove welds or when the gap was only 0.4 mm. In narrow I-groove preparations, inhomogeneous mixing phenomena were more pronounced in laser cold-wire welds than in laser-arc hybrid welds. In both welding processes, a combination of trailing wire feeding and the use of a wider groove enabled filler metal to be introduced deeper into the bottom of the groove and improved mixing in the root portion of the welds.  
Keywords:filler metal mixing; laser-arc hybrid welding; laser cold-wire welding; thick sections; austenitic stainless steel +
Footnote †: journal: Applied Soft. 2019, 9, 1685; doi:10.3390/app9081685
# An Experimental Study of the Leakage Mechanism in Static Seals
## 5 Conclusions  
In this study, contact static seal was studied. A new seal leak rate measurement experimental device was built with a simpler structure. In the 120 min measurement, the leak rate measurement accuracy of the system could reach \(1\times 10^{-10}\) m\({}^{3}\)/s. The device was able to control the following three operation parameters: the fluid pressure (0.01-1.5 MPa), the contact load (50-10,000 N), and the apparent contact area of the circular gasket (up to a radius of 50 mm). Using the device, experiments were carried out to study the effects of the sealing surface characteristics on the leakage, and the effects of the external factors of the sealing device on the leak rate. In addition, the practical application of the fractal surface leak rate prediction model was investigated and verified. The results showed that the model demonstrated a relatively high calculation accuracy for metal fractal surfaces, but was not suitable for use for non-metallic surfaces and surfaces that did not follow fractal properties (with spiral grooves). By combing the calculations from the theoretical model with the experimental verification, the following conclusions are obtained:  
Figure 9: The leak rate versus the outer radius of the gasket.  
* Surface topography plays a decisive role in the order of magnitude of leak rate, which means that the roughness and the leak rate are positively correlated, i.e., the rougher the surface (the larger the fractal dimension), the larger the leak rate.
* The contact load and the leak rate are negatively correlated. In the case of a high contact load (depending on the surface characteristics), its impact on the leak rate is negligible.
* The smaller the leak rate, the smaller the effect of fluid pressure on sealing performance.
* When the requirements for the processing technology and structure are satisfied, in the case of the contact load is constant, the smaller the apparent contact area of the sealing device, the better the sealing performance.  
The conclusions listed above help designers to further understand the leakage mechanism of the sealing interface, and provide theoretical support and experimental evidence for parameter-optimized design of seals.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
This study is funded by the National Natural Science Foundation of China (No. 11702320).  
The authors declare no conflict of interest.