esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Electrochemistry of Nickel Electrodeposition
### Watts Bath Nickel Electroplating Reactions  
Figure 2 shows a generic arrangement and the reactions that take place during the electrodeposition of nickel from an additive-free Watts bath. The DC power source drives the deposition process by applying a current to the electrochemical cell. Two main processes occur once there is an effective potential or overpotential in the cell: oxidation, and reduction. These processes are governed by the principles of thermodynamics and dynamic electrochemistry [4].  
Oxidation occurs at the anode as nickel atoms dissolve into the electrolyte, forming nickel ions (\(Ni^{2+}\)) while releasing two electrons for each ion. This reaction is given by  
\[Ni\;(s)\to Ni^{2+}+2e^{-}. \tag{1}\]  
This is a thermodynamically favorable process under the influence of applied potential [4]. Simultaneously, at the cathode, a complimentary reaction, i.e., reduction of nickel ions, takes place. Reduction of nickel ions from the electrolyte occurs due to the acceptance of two electrons, depositing the nickel onto the substrate as solid nickel metal. The reduction reaction is  
\[Ni^{2+}+2e^{-}\to Ni\;(s). \tag{2}\]  
Figure 2: Schematic of nickel electroplating process [11].  
This process is governed by the availability of electrons supplied by the DC power source. The electrolyte facilitates the migration of ions during these processes. Positively charged ions \(Ni^{2+}\) and \(H^{+}\) atoms, also known as cations, move toward the negatively charged cathode, while negatively charged \(Cl^{-}\), \(OH^{-}\), and \(SO_{4}^{2-}\) ions, also known as anions, migrate to the positively charged anode. The exchange of these cations and anions establishes a dynamic equilibrium, where ion fluxes and charge transport maintain overall electrical neutrality. At the electrode-electrolyte interface, the balance between the applied potential, overpotential, and concentration gradients of the elements governs the rate of nickel electrodeposition [4; 15; 30]. The quantitative relationship between electron flow and material deposition is governed by [PERSON]'s law given as [31]  
\[m=\frac{I\cdot t\cdot M}{n\cdot F}, \tag{3}\]  
where \(m\) is the mass of the deposited metal, \(I\) is the applied current, \(t\) is the time of deposition, \(M\) is the molar mass of the metal, \(n\) is the number of electrons involved in the reduction of one atom, and \(F\) is [PERSON]'s constant (96,485 C/mol). The overall effect of these processes is the transfer of nickel from the anode to the cathode, forming a dense, adherent nickel microstructure on the substrate.  
The electrodeposition of nickel involves a careful balance of electrochemical principles, electrolyte composition, and current control to ensure efficient ion transport and quality deposition. The decisions made on process parameter selection and deposition types are specific to certain requirements and each needs to be thoroughly tailored. More information on the fundamentals of electrodeposition can be found in [5; 15; 32].
## 3 Results and Discussion
### Detection of LM Canola Using the Newly Developed Multiplex PCR Method  
The novel multiplex PCR method was utilized to detect the seven LM canola in 260 monitoring samples, from 2014 to 2017 in South Korea. As a result, 22 samples were identified as being LM canola (Figure 4). All the LM canola identified were GT73. To confirm the detection of the GT73, nucleotide sequencing analysis was performed and all the results indicated that the target region was detected correctly (data not shown). In 2014, there were no LM signals detected in any of the samples, indicating that they were non-LM canola. Positive bands were identified from 2015 to 2017, 1 in 2015, 1 in 2016, and 20 in 2017 (Figure 5). In particular, the LM canola was identified nationwide and dramatically increased in 2017. The increase is possibly because LM canola, unapproved as a cultivation crop, was mixed with non-LM canola and grown in 56 places nationwide in 2017. The canola plants identified as LMO were eradicated and the LMO sites were monitored for the next five years.  
Figure 4: Application of the multiplex PCR method for living modified organism (LMO) identification with the collected suspicious canola samples. The numbers indicate the collection year of the suspicious canola samples (red) and are identified as LM canola (yellow).  
Figure 5: Living modified organism (LMO) identification in the samples collected from 2014 to 2017. The maps show the collection sites of the suspicious canola samples (yellow spot) and the sites of the LM canola (red spot) in 2014 (**A**), 2015 (**B**), 2016 (**C**), and 2017 (**D**). The bar graph presents the number of collection sites for the suspicious samples (yellow) and the LM canola samples (red). The green line shows the ratio of the LM canola identified sites per sample collection site (**E**).
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Artificial Time Series  
All of the time series considered have the model \(\mathsf{X}_{N}=\mathsf{T}_{N}+\mathsf{S}_{N}+\mathbf{\varepsilon}_{N}\), where \(\mathbf{\varepsilon}_{N}\) is white Gaussian noise with zero mean. The variance of the noise is given in the example descriptions. The time series length is 100, and the window length is 50.  
**Harmonic trend of rank 2. Good approximate separability.** The components of the signal have the form  
\[t_{n}=8\cos\biggl{(}\frac{2\pi n}{50}\biggr{)},\qquad s_{n}=\cos\biggl{(}\frac {2\pi n}{3}\biggr{)}. \tag{6}\]  
In this example, the trend has the form of a harmonic with a large period 50; this trend is well separable from the sinusoidal component with period 3. The signal rank is \(r=4\). The noise standard deviation is \(\sigma=1\).  
**Complex trend of rank 3. No weak separability.** The components of the signal have the form  
\[t_{n}=0.2e^{0.05n}+2\cos\biggl{(}\frac{2\pi n}{60}\biggr{)},\qquad s_{n}=4.12 \cos\biggl{(}\frac{2\pi n}{30}\biggr{)}. \tag{7}\]  
The trend in this example consists of an exponential series and a harmonic with a large period. The example lacks weak separability of the trend and the periodic component. The component contributions are different. The signal rank \(r=5\). The noise standard deviation is \(\sigma=1\).  
**Trend of infinite rank. No contribution separability.** The components of the signal have the form  
\[t_{n}=\ln(n),\qquad s_{n}=0.4\cos\biggl{(}\frac{2\pi n}{12}\biggr{)}. \tag{8}\]  
The signal in this example has no finite rank. The example has a small lack of weak separability of the trend and periodic component. The component contributions are close. The noise standard deviation is \(\sigma=0.2\).  
**Polynomial trend of rank 3. No weak and contribution separabilities.** The components of the signal have the form  
\[t_{n}=0.001n^{2}-0.2n+15,\qquad s_{n}=12\cos\biggl{(}\frac{2\pi n}{30}\biggr{)}. \tag{9}\]  
The trend of this time series is polynomial and produces three equal roots \(\mu_{i}=1,i=1,\ldots,3\). In the example, there is a lack of weak and contribution separabilities. The signal rank \(r=5\). The noise standard deviation is \(\sigma=1\).
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 2 Wellsite Diagnostic Method
### Decline Curve Analysis Method of Production  
In 1945, [PERSON] proposed the Arps decline analysis method based on a summary of previous work on production dynamics data statistics and concluded that the capacity decline curves of different oil and gas formations and different development stages follow one of three production decline laws: exponential, hyperbolic, or harmonic [25]. The Arps decline curve model is widely used and is suitable for flow regimes that reach the proposed steady state. However, in tight low-permeability oil and gas wells, production does not reach the forecasted steady state, which makes the Arps model inapplicable to these kinds of reservoirs. To address this problem, some scholars have proposed diverse methods to modify the decline exponent in the Arps model, such as the SEPD model [26; 27] and the Duong model [28; 29]. The exponential decline rate in the Arps model is constant but becomes variable in the SEPD model. The Duong model, on the other hand, is based on the existence of a certain relationship between flow rate and time when the fluid is in the fracture linear flow regime. This method is more accurate in predicting the results when the gas well production decrement is in the stable stage of slow decrement. However, the influence of fluid backdraft or working regime in the pre-production period can lead to discrete fluctuations in production data and cause multiple solution problems. The three models mentioned above can only be used to analyze the data from boundary-controlled flows. [PERSON] established the Fetkovich-Arps analysis method [30], based on the theory of unsteady seepage in bounded homogeneous formations, by introducing the unsteady flow equation from the well test analysis into the decline analysis, which applies to closed systems with constant flow pressure.  
\[q=\frac{q_{i}}{(1+bD_{i}t)^{1/b}} \tag{2}\]  
\[N_{\text{P}}=\frac{q_{i}}{(1-b)D_{i}}\big{[}1-(1+bD_{i}t)^{1-1/b}\big{]} \tag{3}\]  
The decline curve in Figure 1 analysis method for oil and gas wells is mainly used to determine whether the drop in production is caused by formation damage, formation energy decay, or water flooding. Therefore, it can only give a qualitative interpretation of whether the formation is damaged or not. Moreover, [PERSON] et al. [PERSON] et al. [31; 32] proposed a new method based on dynamic measurements such as pressure and production data, obtained during the production process. Furthermore, the more advanced Blasingamemethod of decline curve analysis was based on the classical oil and gas seepage theory. Nonetheless, this method can only evaluate specific parameters, such as the formation permeability and skin coefficient, and cannot achieve a single-factor damage evaluation.
# Nucleotides as Bacterial Second Messengers
## 5 The c-di-AMP Nucleotide
### Cellular Processes Regulated by c-di-AMP  
#### 5.2.1 Cell Wall Homeostasis  
Changes in c-di-AMP levels compromise bacterial cell wall integrity. In _S. aureus_, DaCA and GdpP enzymes synthesize and degrade c-di-AMP, respectively. The involvement of c-di-AMP on the cell wall has been studied with GdpP-deficient strains, with a consequent an increase in the intracellular levels of c-di-AMP. The resulting phenotype have bacterial growth retardation and increased resistance to acid stress and beta-lactam antibiotics in _S. aureus_[89]. These mutants have increased peptidoglycan cross-linking. By microscopy, the GdpP-deficient _S. aureus_ strain significantly reduces cell size compared to the wild type [76].  
In contrast, attempts to obtain _dacA_ gene mutants in _S. aureus_ have failed, suggesting that c-di-AMP production is vital for growth in this bacterium. Strains of _B. subtilis_ and _L. monocytogenes_ that have a mutated _gdpP_ gene display increased resistance against beta-lactam antibiotics. In the case of _Lactococcus lactis_ and _L. monocytogenes_. GdpP-mutant strains are more resistant to heat stress [90] and acid [91], all indicating that cell wall changes occur in strains with high levels of c-di-AMP [76]. In contrast to GdpP-mutant strains, strains overexpressing this protein have decreases in the level of c-di-AMP, and in _B. subtilis_ and _L. monocytogenes_, it has been observed that they are susceptible to antibiotics targeting the cell wall [91].
## 2 Materials and Methods
### Determination of Total Phenolic Content and Total Antioxidant Capacity  
Total phenolic content (TPC) was measured using the Folin-Ciocalteu colorimetric method and expressed as mg of gallic acid equivalent (GAE)/g of dry extract. Total antioxidant capacity was determined by 2,2-diphenyl-1-picrylhydrazaryl (DPPH) radical scavenging assay and expressed in umol of Trolox equivalent (TE)/g dry extract.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 2 Related Work
### Deep Learning for Road-Marking Analysis and Alignment  
The demand for robust and autonomous navigation systems has accelerated advancements in deep-learning-based road-marking analysis and alignment. Unlike traditional methods, deep-learning models can learn complex patterns directly from data, making them effective in managing the diverse appearances and environmental variations of road markings.  
A key application of deep learning in this area is the detection of road markings, using CNNs [16; 17] for semantic segmentation, allowing for pixel-level classification of lane lines, arrows, and crosswalks. Many studies have employed encoder-decoder CNN architectures to effectively detect and outline lane markings and road signs in real time, frequently using classical post-processing techniques for precise geometric fitting. These models can accurately identify and localize road markings across a range of lighting and weather conditions.  
In addition to detection, deep learning is increasingly used for extracting structured geometric representations that aid in alignment and mapping. Advanced systems, such as the Vectorized-Map-Annotation (VMA) [18] framework, utilize transformer-based architectures to represent road markings as ordered \"corner point sequences\". This output provides detailed geometric information suitable for dense mapping tasks. Deep models also enhance our understanding of the relationships between road elements; for example, networks, like TopoNet [19], model the connections among lanes, intersections, and traffic signs, supporting the global alignment and interpretation of road layouts.  
These developments underscore the crucial role of deep learning in enhancing road-marking analysis, providing effective solutions for traditionally challenging tasks. While many techniques focus on detection, the results they produce often serve as the foundation for subsequent alignment processes. This manuscript presents a novel self-supervised method for learning consistent keypoints on structured, feature-poor templates, thereby distinguishing it from the broader CNN-based detection and segmentation tasks.
###### Abstract  
The influence of the front facet reflectivity on the spectral linewidth of high power DFB (distributed feedback) diode lasers emitting at 780 nm has been investigated theoretically and experimentally. Characterization of lasers at various front facet reflections showed substantial reduction of the linewidth. This behavior is in reasonable agreement with simulation results. A minimum linewidth of 8 kHz was achieved at an output power of 85 mW with the laser featuring a front facet reflectivity of 30%. The device with a front facet reflectivity of 5% reached the same linewidth value at an output power of 290 mW.  
semiconductor laser; narrow linewidth; high power diode laser; front facet reflectivity +
Footnote †: journal: Applied Sciences
# Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification
## 3 Algorithms
### Chi-Square Statistical Test  
The Chi-square (\(\chi^{2}\)) statistical test has been widely accepted as a statistical hypothesis test to evaluate the dependency among two variables [37]. In natural language processing, the chi-square test is often applied to test the independence between the occurrence of the term and the occurrence of the class. It is often used as a feature selection method in NLP. Formula (1) is used to rank the terms that appear in the corpus [38].  
\[\chi^{2}(\mathbb{D},t,c)=\sum_{e_{t}\in\{0,1\}}\sum_{e_{t}\in\{0,1\}}\frac{(N _{e_{t}e_{c}}-E_{e_{t}e_{c}})^{2}}{E_{e_{t}e_{c}}} \tag{1}\]where \(e_{t}\) and \(e_{c}\) are binary variables defined in a contingency table; \(e_{t}=1\) means the document contains term \(t\) and \(e_{t}=0\) means the document does not contain term \(t\); \(e_{c}=1\) means the the document is in class \(c\) and \(e_{c}=0\) means the document is not in class \(c\); \(N\) is the observed frequency in \(\mathbb{D}\) and \(E\) is the expected frequency. For example, \(N_{11}\) is the observed frequency of documents appearing in class \(c\) containing term \(t\); \(E_{11}\) is the expected frequency of \(t\) and \(c\) occurring together in a document assuming the term and class are independent. A higher value of \(\chi^{2}\) indicates that term \(t\) and class \(c\) are dependent, thus making term \(t\) a useful feature since the occurrence of \(t\) means the document is more likely to be seen in class \(c\).  
Utilizing the property of \(\chi^{2}\) that higher \(\chi^{2}\) values of term \(t\) indicate higher likelihood of occurrence in the class \(c\), we use \(\chi^{2}\) to weight the context words in the CBOW model. The key aspect of our discovery is that words with higher \(\chi^{2}\) statistics tend to be keywords for class identification. Thus, we are using the chi-square statistical test to select the lexicon that particularly caters to the specific class identification task of short sentences such as tweets. Our rationale is that in our modified CBOW model, words are weighted according to their \(\chi^{2}\) statistics; words that are likely to be valuable for the classification task are more heavily weighted thus reducing the disturbance of the noise words which are not helpful comparatively to the later task.
# Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm
## 3 Results
### Normal Incidence  
#### 3.1.1 Design-1  
A five-layer microwave absorber was designed in the FR of 2-8 GHz for normal incidence. The materials and thicknesses of layers were optimized to minimize the maximum RC in this FR. The maximum total thickness was set to 5 mm in the optimization process. The parametric comparison of the optimized design with the existing designs obtained by DE [16], CFO [10], and LSA [12] methods is shown in Table 2. The maximum RC of the proposed design was \(-\)25.765 dB. Although its total thickness was slightly larger than the design in [PERSON] (2010) [10], it was less than the design in [PERSON] et al. (2010) [10] and [PERSON] (2017) [12]. Visual comparisons of the RC curves versus frequency are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**LAYER**} & \multicolumn{6}{c}{**2-8 GHz**} \\ \cline{2-9}  & \multicolumn{2}{c}{**BESOA**} & \multicolumn{2}{c}{**DE**} & \multicolumn{2}{c}{**CFO**} & \multicolumn{2}{c}{**LSA**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Design-1**} & \multicolumn{2}{c}{[16]} & \multicolumn{2}{c}{[10]} & \multicolumn{2}{c}{[12]} \\ \cline{2-9}  & \multicolumn{2}{c}{**Material**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**d**} \\ \hline
1 & 16 & 0.41701 & 16 & 0.384 & 16 & 0.377 & 14 & 0.4626 \\ \hline
2 & 6 & 1.10903 & 6 & 0.433 & 6 & 1.572 & 6 & 1.7694 \\ \hline
3 & 6 & 1.78825 & 6 & 1.143 & 6 & 0.991 & 6 & 0.6101 \\ \hline
4 & 3 & 0.21456
## 4 Methods for Improved Reliability and Robustness
### Quality Control Techniques
#### 4.1.2 Post-Analysis QC Tools
When dealing with post-analysis, they attempt to determine inconsistencies by making comparisons between long and short-axis views, LV and RV volumes, end-diastole and end-systole phases. They implement two support vector machine (SVM) classification algorithms to detect abnormalities in the obtained volume and strain curves. Table 2 summarizes the main characteristics of the reported post-analysis QC tools. In addition to the distinction among classifiers and regressors (_Regression_), we highlight whether a proposed method formulates the problem in a traditional supervised manner, thus requiring QC labels (_no QC labels_). Given the cost of data labelling, it can be disadvantageous to require QC labels on top of the labels required to train the segmentation algorithm. Classification methods typically exploit qualitative (e.g., correct/incorrect) labels, whereas regressors require quantitative labels (e.g., DSC), which can be difficult to obtain [85]. To avoid these, a final set of methods avoid the use of QC labels by considering alternative self-supervised techniques or registration-based approaches as the RCA. Finally, Table 2 also highlights whether a given method allows the identification of the specific areas of segmentation failure, or it just gives an estimation of the general quality (_detection_).
# Wildfires in the Siberian Arctic
## 3 Results  
### Changes in Fire Frequency and Burnt Area Extent  
The total annual number of fires in the Siberian Arctic increased by a factor of 3.0 from the first (3792) to the second (11,547) decade of the 21 st century (Figure 2a). This increase in decadal frequency was accompanied by a 2.6 times increase of the total area burnt (Table 1). Annual fire frequency and the extent of the burnt area for the whole Siberian Arctic were positively related to variation in summer air temperature and negatively related to variation in precipitation and ground cover moisture content (Table 2). The response of fire frequency and burnt area extent to climate was uneven within the Arctic. In the WS and ES, decadal fire frequency increased from the 2001-2010 to the 2011-2021 period by factors of 2.3 and 2.1, respectively. Burnt area increased in the WS and in the ES by factors of 3.5 and 2.5, respectively. No significant changes in fire frequency or burnt area extent were observed for the FE sector (Table 1).  
Figure 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Whole Siberian** & **Western Siberia** & **Eastern Siberia** & **Far East Sector** \\  & **Arctic** & **Sector** & **Sector** & **Far East Sector** \\ \hline Fire frequency ratio & 2.8 & 2.3 * & 2.1 & 1.1 \\ Burnt area extent ratio & 2.6 & 3.5 ** & 2.5 & 0.5 \\ Fire season duration (days) & **18.0 ** & **36.5 ** & 8.5 & **-19.0 * \\ Onset of fire season (days) & \(-\)6.0 & \(-\)23.0 ** & \(-\)5.0 & 6.5 \\ Conclusion of fire season (days) & 11.5 & 18.5 * & 1.0 & \(-\)15.5 \\ Northern fire boundary shift & 0.0 & **2.6 ** & \(-\)0.4 & 0.1 \\ \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & Kruskal-Wallis test significance at \(p<0.10\) (*) and \(p<0.05\) (**). Mann-Whitney U-test significance at \(p<0.10\) (italic) and \(p<0.05\) (bold).  
\end{table}
Table 1: Wildfire parameters for the Siberian Arctic and its regional sectors: a median values comparison of two time intervals (2001–2010 vs. 2011–2021).  
Figure 2: For the Siberian Arctic as whole, (**a**) decadal mean fire frequency and burned area (broken lines) increased 2.8 and 2.6 times from 2001–2010 to 2011–2021. (**b**) The mean timing of the wildfire season has a unimodal pattern, whereas the 2019 extreme fire year is bimodal, atypical of the Siberian Arctic. (**c**) Duration of the wildfire season differs across the Arctic, with an increase for Western Siberia and decrease for the Far East sector. Trends significant at \(p<0.05\).