esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Construction of Tyrosine Biosensor  
Expected catalytic activity of TYR enzyme towards Trolox in comparison with 1,2-dihydroxybenzenes was not sufficient to construct a simple bioelectroanalytical device [12]. This means that the amount of Trolox oxidation product formed during enzymatic conversion is low and, therefore, a more sensitive amperometric transducer is required for the detection in the given time frame. Generally, current response increases with larger electrode surface area. For that reason, MWCNTs were used to enlarge the surface of amperometric transducer. Furthermore, MWCNTs provide fast electron transfer, which positively affects the sensitivity of the detection.  
Figure 1: SEM images of bare CPE (**A**) and CPE/MWCNTs (**B**) surfaces.  
Six different amounts (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 mg\(\cdot\)mL\({}^{-1}\)) of MWCNTs in DMF were prepared and a volume of 20 \(\upmu\)L was applied for immobilization of MWCNTs on CPE surface. Obtained CPE/MWCNTs electrodes were compared using cyclic voltammetry of 500 \(\upmu\)mol L\({}^{-1}\) Trolox. As shown in Figure S1 (Supplementary Materials), a significant shift of the cathodic peak potential (\(E_{\mathrm{p}}\)\({}^{\mathrm{c}}\)) to lower values (\"easier reduction\") was observed for the amount of 2.0 mg\(\cdot\)mL\({}^{-1}\) MWCNTs, therefore, this amount was considered as optimum.  
After that, a volume of 5, 10, 15, 20, 25, and 30 \(\upmu\)L, with a constant concentration of 2.0 mg\(\cdot\)mL\({}^{-1}\) MWCNTs of fresh homogenized dispersion was applied on polished surfaces of CPE to immobilize MWCNTs. For volumes from 15 to 25 \(\upmu\)L, a significant increase of current response for cathodic peak (\(I_{\mathrm{p}}\)\({}^{\mathrm{c}}\)) was observed. From Figure S2, it is evident that the measured peak currents decreased for volumes higher than 20 \(\upmu\)L, probably due to the aggregation of MWCNTs and unfavorable morphology at electrode surface. As the optimum volume for immobilization, the value of 20 \(\upmu\)L MWCNTs (2.0 mg\(\cdot\)mL\({}^{-1}\)) dispersion was selected.  
An amount of TYR enzyme in the biorecognition layer was optimised to achieve the highest current response. With increasing content of the TYR in the Nafion\({}^{\textregistered}\) membrane, the height of the anodic peak of Trolox decreased, while the corresponding cathodic peak increased. Higher amounts than 3.0 \(\upmu\)g enzyme did not cause any significant improvement, and therefore it was further used to obtain sufficiently high current responses.
## 4 Numerical Simulation and Analysis
### Effect of the Water-Resisting Curtain
Owing to the restrictions imposed by the steel sheet pile, the seepage path is long, and the seepage flow is limited. The maximum seepage velocity near the elevator shaft is only approximately 2.5 mm/day. After the steel sheet pile is removed, a large amount of groundwater pours into the surrounding area and bottom of the elevator shaft, and the maximum seepage velocity reaches 5.5 mm/day, which is approximately 120% greater than that before the removal of the steel sheet pile. The seepage at the bottom edge of the elevator shaft is the most pronounced, and inrush accidents also occur near the bottom edge of the elevator shaft. Therefore, the main reason for the accident is that the groundwater breaks through the overlying aquiclude and the foundation pit cushion. The bottom sealing curtain separates the internal and external confined water. The confined aquifer in the pit will lose its water supply during the construction and excavation process. The water head height gradually decreases with construction unloading to meet the stability requirements of the foundation pit. However, the water-resisting curtain of the project failed to cut off the confined water, and stopping the pit water could not meet the expected requirements. With increasing excavation depth, the actual hydraulic gradient increased until it exceeded the critical hydraulic gradient. A large amount of water outside the pit poured into the foundation pit through the defect position, resulting in a sudden surge. Owing to the tropical climate, continuous precipitation occurred before the accident, often accompanied by heavy rain. The groundwater level rose rapidly, which affected the accident and aggravated the degree of damage. In the numerical simulation of this study, the influence of surrounding buildings and urban main roads on the foundation pit is not considered. This study analyzes foundation pit inrush accidents and their continuous failure mechanism. The causes of accidents are related mainly to geological conditions, groundwater levels and supporting structures, so these factors do not affect the results of this study. Owing to the complexity, uncertainty, contingency, field data and conditions of underground engineering, it is possible that the local investigation is insufficient, so that further analysis must be performed together with follow-up monitoring of the field situation.
## 2 Materials and Methods  
### Sample Collection  
In this study, a swine wastewater treatment facility that processes manure from approximately 7000 pigs was selected for sample collection. This facility treats an average of 21-25 tons of swine manure daily and employs an ozone oxidation process as part of its overall treatment system. The novel full-scale process was developed for swine wastewater treatment and patented in South Korea with registration number 10-2538468. Samples were collected at each stage of the treatment process to characterize the water quality parameters throughout the treatment system and evaluate its efficiency. The facility's treatment sequence includes a physico-chemical pretreatment stage (coagulation, flocculation, sedimentation, and sand filtration), where 1500 mg/L of aluminum sulfate (alum) is added and gently agitated for 30 min before 180 min of quiescent settling. The clarified overflow is passed through a sand bed with an effective size of 1.5 mm, reducing suspended solids to \(<\)0.5 g/L, and thereby minimizing ozone demand in the subsequent oxidation step.  
Two identical vertical ozonation contacts (internal diameter 1.0 m, liquid height 1.5 m, working volume \(\approx\) 1 m\({}^{3}\) each) are operated in series. Ozone is generated on-site from 95% oxygen by a corona-discharge generator and delivered to the first reactor through 20 \(\upmu\)m ceramic diffusers, creating a fine-bubble blanket. The partially treated effluent then enters a counter-current Venturi injector in the second reactor, where an additional ozone stream is entrained to enhance mass transfer. The ozone dose was optimized in a previous study [37] and preliminary bench-scale trials (1, 3, 5, 7, and 10 mg/L; contact time 12 min). A reaction time of 5 min is consistently achieved for \(\geq\)2-log (99%) removal of the target sulfonamide class. The off-gas is stripped through a catalytic destructor (<0.1 ppm O\({}_{3}\)).  
Following ozonation, granular activated carbon (GAC) filtration (working volume 3 m\({}^{3}\), empty-bed 0.38 m\({}^{3}\)) removes the oxidation by-products and residual dissolved organic carbon (DOC), while a vacuum deaeration tank strips dissolved gases and residual NH\({}_{3}\) before discharge. The overall flow diagram of this treatment system is shown in Figure 1. The approximate pH at each point is as follows: raw wastewater 8.5 \(\pm\) 0.1; after coagulation-sedimentation, 6.5 \(\pm\) 0.2; after sand filtration, 6.2 \(\pm\) 0.1; in first and second ozone reactors, 5.2 \(\pm\) 0.2; after GAC filtration, 6.5 \(\pm\) 0.1; and in the deaeration tank, the pH rises transiently to \(\approx\)12 before being neutralized with hydrochloric acid (HCI) to 7.0 \(\pm\) 0.1 prior to discharge. Samples measuring 2 L were gathered from each stage of the process during periods of consistent operational efficiency. The samples were subsequently sealed, stored at a temperature of 4 \({}^{\circ}\)C, and transferred to the laboratory for analysis. The experiments were conducted at room temperature, 22 \(\pm\) 1 \({}^{\circ}\)C.
## 4 Discussion
### Subgrade Bed Design In Terms of Plastic Deformation  
Since subgrade bed is the uppermost structure that directly bears cyclic train load, plastic deformation that occurs at subgrade bed is worthy of sufficient attention in the design process. As mentioned above, the ballasted track has different adaptive capacity for deformation compared with the unballasted one. Therefore, the limitations of plastic deformation should be different for these two types of rail track. Since unballasted track has a more rigorous requirement for plastic deformation, subgrade fillings should be designed to fall into the rapid stabilization zone, i.e., tendency coefficient \(m>1.5\). For ballasted track adopted in high-speed railway, although plastic deformation can be offset by replenishing new railway ballast, the final magnitude of plastic deformation should also be limited to a constant value given the rapid operating trains. Therefore, subgrade bed should fall into the tardy stabilization zone, i.e., \(1.0<m<1.5\).  
However, for ballasted track used in normal-speed railway with relatively fewer traffic volume, maybe subgrade fillings can be designed to fall into the tardy failure zone given the following two aspects. One is that trains in normal-speed railway operates at a relatively lower speed. The other is that new railway ballast can be added to the ballast bed to offset the plastic deformation for ballasted railway. As revealed in this study, developing tendency coefficient \(m\) decreases quite slowly in tardy failure zone, indicating that plastic deformation rate stays nearly steady with the increasing cyclic amplitude. Therefore, subgrade bed for ballasted track in normal-speed railway can be controlled to stay in tardy failure status to save construction cost.
# Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach
## 5 Results
### Sentiment Classification  
#### 5.2.1 Distribution of Dataset for Sentiment Classification  
Our proposed approach in this study focuses on data balancing to enhance the performance of sentiment classification. Therefore, we conducted sentiment classification for datasets augmented with good-quality synthetic data as well as the original imbalanced dataset. Because our investigation yielded four versions of good-quality synthetic data through the application of various cut-off thresholds to remove similar outputs, we obtained four datasets from this experiment. The dataset balanced with good-quality synthetic data from a cut-off of 1 is named Dataset-100, and similarly, datasets resulting from cut-offs of 0.8, 0.75, and 0.5 are named Dataset-80, Dataset-75, and Dataset-50, respectively. Hence, the outcomes of the sentence-by-sentence data generation in this study provide four distinct datasets for the sentiment classification process.  
In addition to comparing the classification performance of these four datasets with the original imbalanced dataset, we also aimed to benchmark them against the findings from a previous study, which employed a fine-tuning approach to GPT-3 in the synthetic data generation process [11]. Given that the initial preprocessing phase of this study resulted in cleaner, superior original data compared to the preprocessing conducted in the previous study, we elected to use the original data from this study's preprocessing phase alongside the fine-tuning synthetic data to ensure comparable results. Consequently, we performed sentiment classification for six datasets as training data, which are Dataset-100, Dataset-80, Dataset-75, Dataset-50, the fine-tuning dataset, and the original (imbalanced) dataset. The distribution of these six datasets is displayed in Figure 7. From the figure, we can see that only the fine-tuning dataset and Dataset-100 are truly balanced, while Dataset-80, Dataset-75, and Dataset-50 are not entirely balanced. This is because the good-quality synthetic data generated with cut-offs of 0.8, 0.75, and 0.5 did not completely fulfill the synthetic data requirements for balancing, as mentioned in the previous section.  
Figure 7: The frequency distribution of various datasets for sentiment classification.  
In the graph of the original data shown in Figure 7, we also present the testing dataset. We set this testing dataset to be imbalanced, mirroring the inherent characteristics of the original Coursera review dataset. These testing data were utilized to evaluate the overall performance of the model trained on all six datasets employed in this experiment. The decision to use an imbalanced testing set is a deliberate one, aiming to provide a stringent testing environment that closely replicates real-world data scenarios, thus allowing for a robust assessment of the model's ability to generalize across different sentiment classes.
## 2 The Proposed SK Diagnosis Technology  
### The SK and Optimal Denoising Filtering  
The spectral kurtosis (SK) is interpreted in [12] as an adaptive technique used to determine the most suitable frequency band for an extraction of the non-stationary component. It extends the statistical measure of the kurtosis to the function of frequency and indicates how the impulsivity in the signal is distributed in its frequency domain. As defined in [12], the spectral kurtosis \(K(f)\) of the signal \(x(t)\) is the fourth-order spectral moment:  
\[K(f)=\frac{\left<\left|X(t,f)\right|^{4}\right>}{\left<\left|X(t,f)\right|^{2 }\right>^{2}}-2 \tag{1}\]  
where \(\left<\right>\) is the time average operator and \(X(t,f)\) represents the complex envelope of the signal \(x(t)\).  
The \(X(t,f)\) may be estimated by the short-time Fourier transform (STFT) by moving a relatively short window along the signal. The subtraction of 2 in Equation (1) is used to set \(K(f)=0\) in the case \(X(t,f)\) contains only noise.  
The SK can be used to design detection filters since it takes large values at those frequencies where impulsivity is dominant and low values where there is noise only [3]. It is shown in [8] that the SK of the sum \(y(t)=x(t)+n(t)\) where \(x(t)\) is the non-stationarycomponent and \(n(t)\) is the stationary Gaussian noise, can be related to the SK of the non-stationary part \(x(t)\) by  
\[K_{y}(f)=\frac{K_{x}(f)}{[1+\rho(f)]} \tag{2}\]  
where \(\rho(f)=\frac{S_{u}(f)}{S_{x}(f)}\) is the ratio of the power spectral densities of \(n(t)\) and \(x(t)\), reflecting the noise-to-signal ratio with respect to the frequency.  
The signal to noise ratio will be high (i.e.,\(f\approx 0\)) within the resonance bandwidth and low outside, in other words, the \(K_{y}(f)\approx K_{x}(f)\) within the same bandwidth and \(K_{y}(f)\approx 0\) otherwise [13]. Under these assumptions, the Wiener filter  
\[W(f)=\frac{1}{1+\rho(f)} \tag{3}\]  
is proportional to the square root of the SK, which offers a possibility for SK to identify the optimal filter \(W(f)\) for extraction of transients from stationary components [12,13]:  
\[\hat{W}(f)=\sqrt{K_{y}(f)}\;for\;K_{y}(f)>s\;0\;otherwise \tag{4}\]  
The SK should be compared to the significance threshold, indicating the values significantly greater than zero. In [7,8] a statistical significance threshold \(s=s_{\alpha}\) is calculated on behalf of the properties of the Gaussian noise. Supposing that the signal is only Gaussian stationary noise, the STFT-based SK has a normal distribution with zero mean and variance \(\frac{4}{N}\), where N is the number of averages. A statistical threshold given at level of significance is \(s_{\alpha}=u_{1-\alpha}\frac{2}{\sqrt{N}}\), where \(u_{1-\alpha}\) is the percentile of the normal distribution at \(1-\alpha\) and all values below this level will have probability of \(1-\alpha\) of not being a transient [13].
# A Review of Advanced Air Lubrication Strategies for Resistance Reduction in the Naval Sector
## 4 Mathematical Models
### Models on Mbdr  
Yoshida et al. [70] present a mathematical model to study the effects of injecting micro air bubbles into the turbulent boundary layer of a liquid stream.  
This model is used to analyse how these bubbles influence skin friction in the flow. It employs the Lagrangian method to track the motion of individual bubbles in three dimensions, allowing for the estimation of void fraction distributions across flow channel sections and the behaviour of bubbles locally. The model of the liquid phase is based on the mixing length (Equation (4)), and the interaction between the bubble-induced drag and the liquid phase is considered to assess the impact on turbulent shear stress.  
\[l_{m}=\left(\kappa-\frac{\lambda_{m}}{d_{b}}\alpha^{2/3}\right)y \tag{4}\]  
The mixing length \(l_{m}\) depends on the law of wall constant in the absence of bubbles \(\kappa\), the mean turbulence scale \(\lambda_{m}\), the bubble diameter \(d_{b}\), the local void fraction \(\alpha\), and the y-coordinate.  
Equation (4) was developed to represent the reduced skin friction in the presence of bubbles. This equation suggests a decrease in the mean mixing length compared to cases without bubbles, indicating a potential reduction in skin friction due to bubble injection.  
To simplify the calculation, the local void fraction \(\alpha\) is replaced by a mean void fraction \(\alpha_{m}\).  
The skin friction ratio could theoretically be influenced by the ratio of bubble diameter to the mean turbulence scale, highlighting an important parameter in understanding the impact of bubbles on skin friction reduction in turbulent flows.  
A qualitative comparison with experimental results obtained in a cavitation tunnel has been carried out.  
In [18], [PERSON] and [PERSON] explore the optimal rate of air injection for maximum drag reduction in ships using microbubbles. This experimental study uses a catamaran model to analyse the impact of different air injection rates on drag reduction. The turbulent boundary theory is implemented to calculate the water flow rate inside the boundary layer \(Q_{w}\) in order to estimate the injection coefficient \(\alpha\).  
The model, under some simplifications, leads to a \(Q_{w}\) formulation in which the water flow rate depends on the ship length \(L\), width \(W\), and speed \(V\) (Equation (5)).  
\[Q_{w}=0.293\cdot L^{0.8}.\cdot\theta^{0.2}V^{0.8}.W \tag{5}\]  
Different air injection rates have been tested and the results showed that the maximum rate of the drag reduction effect decreases as the speed increases. It was found that if there is an excessive injection of microbubbles, the efficiency of the drag reduction decreases.  
In conclusion, the research finds that there is an optimum range of air injection rates that maximize drag reduction, and exceeding this range can decrease the effectiveness of the drag reduction.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
###### Abstract  
To furnish the network with small cells, it is vital to consider parameters like cell size, interference in the network, and deployment strategies to maximize the network's performance gains expected from small cells. With a small cell network, it is critical to analyze the impact of the uplink power control parameters on the network's performance. In particular, the maximum transmit power (\(P_{\text{max}}\)) for user equipment (UE) needs to be revisited for small cells, since it is a major contributor towards interference. In this work, the network performance was evaluated for different \(P_{\text{max}}\) values for the small cell uplink. Various deployment scenarios for furnishing the existing macro layer in LTE networks with small cells were considered. The \(P_{\text{max}}\) limit for a small cell uplink was evaluated for both homogenous small cell and heterogeneous networks (HetNet). The numerical results showed that it would be appropriate to adopt \(P_{\text{max}}\) = 18 dBm in uniformly distributed small cells rather than \(P_{\text{max}}\) = 23 dBm, as in macro environments. The choice of \(P_{\text{max}}\) = 18 dBm was further validated for three HetNet deployment scenarios. A decrease of 0.52 dBm and an increase of 0.03 dBm and 3.29 dBm in the proposed \(P_{\text{max}}\) = 18 dBm were observed for the three HetNet deployments, respectively. Furthermore, we propose that the fractional power control mode can be employed instead of the full compensation mode in small cell uplinks.  
small cell; maximum transmit power; UE; open-loop power control; interference +
Footnote †: journal: Electronics Letters
## 2 What Is Terrestrial Storage of Biomass (TSB)?
### Enhanced Weathering--Adding Olivine to the Ocean
Ca\({}^{2+}\) and Mg\({}^{2+}\) will compete for the carbonate ions, and it is more likely that the precipitate will be calcium carbonate, thus preserving coral reefs and preventing the dissolution of shellfish shells. One question for olivine addition is how fast does the system approach equilibrium? The time scale for the approach to the equilibrium will depend on the kinetics of the olivine dissolution. The ocean is saturated in both Ca\({}^{2+}\) and Mg\({}^{2+}\) ions ([PERSON] [29]). This will retard the rate of the dissolution of olivine and favor the reverse reaction, the precipitation of olivine. A few other things can retard the olivine dissolution. The dissolution of olivine occurs at the surface. Thus, the rate of dissolution will be faster for smaller particles, and the olivine should be crushed ([PERSON] and [PERSON] [30]). The equipment and energy cost for crushing olivine increases the overall cost of the project. A huge amount of olivine must be crushed to produce a significant amount of CO\({}_{2}\) pull-down. Another issue is that laboratory studies have shown that magnesium silicate can precipitate on the surface of the olivine,  
Figure 1: Relative species concentrations as a function of pH ([PERSON] [28]). Figure 2: The effect of adding Ca\({}^{2+}\) to the carbonic acid buffer system ([PERSON] [28]). forming a crust that can retard further dissolution ([PERSON] and [PERSON] [31]). It has been proposed that the extent of olivine dissolution can be followed by a local increase in the pH. However, ocean currents can quickly move the hydroxyls out to sea, diluting the hydroxyls and eventually leading to difficulty in measuring the ultimate increase in the pH. Thus, monitoring the rate of dissolution of olivine and its effect on CO\({}_{2}\) draw-down remains a significant challenge. The uncertainty of the MRV has dissuaded some investors. Crushing olivine and spreading it on land has been proposed as a NET ([PERSON] [32]). The issue here is that the olivine must first dissolve in water, the solution must capture CO\({}_{2}\) from the air, and then precipitate as magnesium carbonate. Where is the water going to come from? It will only come intermittently from rainwater. What happens to the system as the water begins to evaporate, the hydroxyls concentrate, and the pH spikes remains unknown.
# Analysis of the Development Status of eLoran Time Service System in China
## 5 Construction Content  
The construction of a high-precision ground-based time service system eLoran time service broadcasting station mainly includes the transmission antenna, transmitter, and time frequency systems. The eLoran time service station is shown in Figure 4.