esa-sceva/satcom-chunk-collection huggingface dataset:
# A 20 W, Less-Than-1-kHz linewidth linearly Polarized All-Fiber Laser
## 4 Conclusions  
In conclusion, we demonstrated an all-fiber high-power narrow-linewidth polarization-maintaining continuous laser system, whose output power was 20 W and linewidth was 0.88 kHz at the wavelength of 1550 nm. The laser was comprised of a single-frequency fiber laser seed source that was amplified through a three-amplification-stage MOPA system up to a power of 20 W. The power amplifier consisted of a cladding-pumped large-core EYDF with a 25 \(\upmu\)m core and a 300 \(\upmu\)m clad, which helped to increase nonlinear thresholds. Neither obvious SBS nor other new wavelengths were observed in the proposed cascaded MOPA system. We are looking forward to achieving a higher power output and maintaining a narrow linewidth at the same time by increasing pump power and cooling the EYDF. The demonstrated high-power narrow-linewidth laser could have applications in laser remote sensing, synthetic aperture lidar, or range finding.  
Conceptualization, M.X. and C.G.; Data curation, M.X.; Formal analysis, M.X.; Funding acquisition, C.G. and S.Z.; Investigation, L.N., J.Z., and H.H.; Methodology, M.X.; Project administration, S.Z. and C.S.; Validation, M.X.; Writing--original draft, M.X.; Writing--review & editing, M.X. and C.G.  
National Natural Science Foundation of China\" grant number \"61875227\" and \"National Key Scientific Instrument and Equipment Development\" grant number \"2013 YQ310633\".  
This works was supported by the \"National Natural Science Foundation of China\" (Nos.61875227) and the \"National Key Scientific Instrument and Equipment Development\" Project (Grant No. 2013 YQ310633).  
The authors declare no conflict of interest.  
Figure 9: Beam quality M\({}^{2}\) measurement results.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 3 Optimum Composition Ratios
### Optimum Proportion Based on Storage Stability  
#### 3.3.1 Air-Drying Test Analysis  
To compare the effective retention rate after air-drying, we prepared 5 samples within the range of the ratio determined in Section 3.2, put them into an empty disk and then tested their percentage of remaining (_POR_) after 24 h air-drying using Rhinophilat as the contrast group. The calculation method is shown in Equation (1) and the _POR_ results are shown in Table 4.  
\[POR=\frac{G_{2}{-}G_{P}}{G_{1}{-}G_{P}}\times 100\% \tag{1}\]  
_POR_--Percentage of remaining after air-drying, %;  
\(G_{P}\)--The weight of the plate, g;  
\(G_{1}\)--The weight of plate and wet material, g;  
\(G_{2}\)--The weight of plate and dry material, g.  
From Table 4, the 5 samples' _POR_ results were all larger than Rhinophilat. The highest _POR_ is No.1, which was 1.5 times than Rhinophilat. The _POR_ values of No. 1-5 were all greater than the total ratio of the rock asphalt, reducing agent and penetrant, proving that the three components were all remaining after the air-drying. It showed that the proportion based on the quick-drying analysis was reasonable; in this range the agent had good quick-drying and storage performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Components** & & & & & & & & & & & \\ \hline Number & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 \\ Rock asphalt/\% & 40 & 40 & 30 & 30 & 30 & 25 & 20 & 15 & 20 & 35 & 40 \\ Reducing agent/\% & 20 & 15 & 15 & 10 & 20 & 25 & 30 & 35 & 40 & 15 & 10 \\ Penetrant/\% & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\ Solvent oil/\% & 20 & 25 & 35 & 40 & 30 & 30 & 30 & 30 & 20 & 20 & 20 \\ Surface dried/h & 2 & 2 & 1.5 & 1.5 & 1.5 & 2.5 & 2.5 & 3.5 & \textgreater{}5 & 2.5 & 1 \\ Fully consolidation/h & 4 & 5 & 2.5 & 2.5 & 2.5 & 4.5 & 4.5 & 5 & \textgreater{}5 & 3 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: 11 proportions and quick-drying test results.
### Previous Research
These divide into two obvious groups; materials derived from nature, such as calcined clays, limestone flour and natural pozzolans, and industrial SCMs, such as blast furnace slag and combustion ashes (largely from coal--Pulverized Fuel Ash (PFA), waste and wood/plant product incineration), along with many other by-product materials. Of the former group, calcined clays have seen substantial global interest, following the development of LC\({}^{3}\) formulations which show considerable promise (see [[https://lc3.ch/](https://lc3.ch/)]([https://lc3.ch/](https://lc3.ch/)), accessed on 1 June 2025). Ground limestone is an important component of blended cements containing a source of reactive alumina, as this may be consumed during hydration ([PERSON], 2016). The use of more traditional SCMs (slag and ash) continues apace, however, reflecting their desirable technical properties, familiarity and wide availability. Despite the success of cement manufacturers in reducing the embedded CO\({}_{2}\) per tonne of cement, global economic growth has increased our consumption of cement, increasing the need for a dramatic reduction in greenhouse gas emissions. This need is immediate, and the role of blast furnace slag cement in achieving a step change is the subject of this paper. Blast furnace slag is a by-product of iron-ore reduction, comprising glassy silicates which are recovered and granulated by rapidly quenching the slag with water. The granules are ground to produce cementitious slag _'ground granulated blast furnace slag'_ (GGBS), which is used to replace Portland cement by up to 70%, or a little more by mass. Concretes made with blended Portland-slag cement have a high durability, especially in marine applications, and remain in great demand. Around 330 Mt/year of cementitious slag is produced worldwide, and around 90% is used as a blending component in cements ([PERSON], 2016). Changes in the iron and steel industry are likely to decrease the availability of slag in the medium term as the fraction of steel produced from scrap using electric arc furnaces increases, relative to primary iron production. The decision by Tata Steel to close the two remaining blast furnaces in Port Talbot, South Wales, has put some strain on GGBS supplies in the British Isles, so reliance on imported slag (with its associated 'carbon miles') will soon increase. Consequently, there is an urgent demand to conserve slag consumption, which has prompted the company Ecoem Ireland Ltd. to investigate blending cementitious slag with recycled glass powder (RCGP) as an industrial pozzolan, or supplementary cementitious material.
## 3 Hierarchical Control System
### Structure and Its Main Parts  
It was decided to implement a hierarchical control structure which allows to control variable parameters with mixed dynamics as well as the proper management of the SBR work cycle. The general structure of the proposed control system is shown in Figure 3.  
Figure 2: Structure of the Swarzewo aeration system.  
The air flow controller (lower level of the presented control system) is responsible for controlling fast-changing variables (seconds), while the adaptive DO controller manages the slowly changing ones. Airflow is controlled through the internal model control (IMC) [25], which uses the inverted model of the aeration system to calculate the control signal for blowers. In order to create such signals, three different variables have to be monitored: reference and real airflow, as well as, the volume of wastewater in SBR. In general, two blowers are considered as independent equipment, which means that one is used as the main blower, and the other as an auxiliary. This action helps to control the usage of both blowers and extends the functionality of the presented air flow system [21].  
Slower changing variables are managed through the controller with a reference model (direct model references adaptive control--DMRAC) [26]. In general, this solution allows the control system to adapt to the changing work conditions--complicated dynamics: nonlinearity and non-stationary plant and disturbance (respiration) by modifying the control law parameters. The implemented controller was modified because of the high level of complexity of the investigated plant to minimise the impact of constantly changing work conditions. Major changes affect the values of learning rates, anti-windup filter parameters and DMRAC starting points, which can be now adjusted during the optimization process to meet various conditions. In addition, the anti-windup filter reduces the impact of the control signal constraints, such as maximum rotational speed of blowers [27; 28; 29]. This part of the control system uses the control error \(e_{DO}\) and \(Q_{air}\) to calculate the \(Q_{air}\)_ref_ for the lower-level controller mentioned above.  
Finally, the supervisory controller generates the DO reference trajectory to meet the trajectory as much as it is possible in various internal conditions during the work cycle. Details of this part of the control system are described in further sections of this paper.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 2 Computational Modeling  
Plasma modeling is inherently complex due to the multi-species nature of the fluid, which includes electrons, ions, and neutral particles. The modeling approach depends strongly on the specific type and application of the plasma [25]. In thermal plasmas, electrons and ions are typically assumed to be in thermal equilibrium. Key parameters for thermal plasma modeling include [26; 27; 28; 29]:  
1. Plasma composition: Plasma properties depend on electrons and ions. The behavior of an ionized plasma is different for different materials.
2. Temperature: In thermal plasmas, ions, electrons, and neutrals are assumed to share the same temperature.
3. Plasma density: The number of particles per unit volume can determine the plasma characteristics and the interaction rate of the particles.
4. Pressure: Influences the density and behavior of plasma particles.
5. Collisional effects: In a thermal plasma, collisions are frequent and lead to equalization of the temperature of ions and electrons.
6. Radiation: Plays a crucial role in energy balance.
7. Boundary conditions: Materials, the shape of electrodes, heat flux to walls, and inflow and outflow conditions impact plasma operation and heat and mass transfer.
8. Chemical reactions: Ionization and recombination are to be defined accurately.
9. Transport properties: Properties such as thermal conductivity, viscosity, and electrical conductivity are significantly affected by temperature and plasma composition.  
In this study, thermal plasma in a non-transferred plasma torch is modeled using the magnetohydrodynamic (MHD) equations within COMSOL Multiphysics v6.1. The Equilibrium Discharge Interface (EDI) model is derived from the single-fluid MHD formulation, incorporating static electric fields while disregarding induction current effects [30]. The coupled equations combine the Navier-Stokes, heat, and Maxwell equations to describe the motion of the conducting fluid in an electromagnetic field. Solving the EDI involves the following assumptions:  
1. The modeled plasma comprises ions and electrons and satisfies charge neutrality.
2. Plasma is typically modeled as a locally neutral mixture that behaves as a Newtonian fluid.
3. A steady-state analysis is conducted under the assumption that the arc operates under steady conditions.
4. Laminar and quasi-incompressible flow conditions are assumed in the modeling.  
5. The plasma is assumed to be in local thermal equilibrium (LTE), indicating that the temperature difference between electrons and heavy particles can be considered negligible under a low electric field at an atmospheric pressure.
6. The plasma is assumed to be optically thin, resulting in a simplified set of MHD equations.
### Formulation and Preparation of Insulin Containing Microparticles
#### 2.5.2 Emulsion-Solvent Evaporation Method of Microparticle Preparation  
An emulsion-solvent evaporation method previously described for preparation of insulin microparticles [34] has been used with slight modification to prepare two insulin-containing microparticle formulations investigated in this study. One insulin microparticle formulation contained Eudragit(r) L100 together with _A. vera_ polysaccharides, while the other insulin microparticle formulation contained _A. vera_ polysaccharides only.  
The internal phase (38 mL) of the emulsion for the first microparticle formulations consisted of a mixture of 0.01 M HCl (10.98 mL), ethanol (20.8 mL), Tween (r) 80 (0.3 mL), _A. vera_ polysaccharides (0.303 g), insulin (156 mg), and Eudragit(r) L100 (1.5 g). The internal phase (38 mL) of the emulsion for the second microparticle formulation consisted of a mixture of 0.01 M HCl (10.98 mL), ethanol (20.8 mL), Tween 80 (0.3 mL), _A. vera_ polysaccharides (0.1 g), and insulin (159 mg); insulin amount was varied due to weighing differences; however, the end concentration in the permeation studies were worked out on basis of amount of microparticles. The continuous phase consisted of 98.5 mL mineral oil, to which a sufficient quantity of Span(r) 80 was added to make up a volume of 100 mL.  
The internal phase was added dropwise using a peristaltic pump through an 18-gauge needle into the continuous phase while stirring at 2000 rpm. After the total volume of the internal phase was completely dropped into the continuous phase, the mixture was stirred (2000 rpm) for 24 h at room temperature to allow the ethanol to evaporate. The resultant microparticle suspension was washed using n-hexane to remove the mineral oil, and the microparticles were separated by filtration and then transferred to a container. This microparticle formulation prepared by the emulsion-solvent evaporation method containing Eudragit(r) L100 in addition to _A. vera_ polysaccharides was referred to as \"E + AVP emulsion\", while the microparticle formulation prepared using emulsion-solvent evaporation consisting of _A. vera_ polysaccharides only was referred to as \"AVP emulsion\".
# Design of a Lightweight Cryptographic Scheme for Resource-Constrained Internet of Things Devices
## 3 Block Cipher SLA  
To ensure difficulty in differential and linear cryptanalysis, SLA uses a substitution-permutation network [24], having 16 rounds with 16 keys. The block size is 64 bits with an 80- and 128-bit key size. The block diagram of the SLA cipher is shown in Figure 1, and Figure 2 shows the detailed SLA block cipher.  
Each of the sixteen rounds includes an eXclusive OR ('XOR') logic operation to obtain new keys \(\mathrm{K_{r}}\) for \(1\leq\ r\leq 16\). They are produced by the 80/128-bit key register. Finally, one additional key will then be produced and XOR-ed to obtain the ultimate ciphertext. The confusion layer represents a non-linear substitution (box) table. The diffusion layer represents one of the durable layers between extant LWC schemes. Figure 2 depicts the block cipher, including pseudocode, with each phase.
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
## 2 Methodology of the Random-Speckle Optical Detection and Processing Algorithm
### Normalized Cross-Correlation (NCC) for Edge Detection
Figure 6 shows the two normalized cross-correlation coefficient curves acquired when both blue and red subset images are matched with their corresponding reference templates. It is apparent that the red subset image scanned past an edge did not possess the clear peak that is essential for accurate depth detection. To resolve this problem, two new subsets, namely boundary and corner subsets, were proposed as described in the following section. To achieve precise contour measurements for an object with discontinuous surface features, a novel 3D edge detection approach was proposed. The related procedures are presented in the flowchart shown in Figure 7. Algorithm 1 describes the steps and conditions required. First, a digital image-normalized cross-correlation was performed on the full-field detected speckle image to generate an initial 3D point cloud. In Step 2, object segmentation was applied to the initial 3D point cloud for the detection of object surface boundaries and contour edges. Following this, the mean and standard deviation of the correlation coefficient of object points were calculated and used as indexes for edge searching. Furthermore, the edge point was estimated using the geometry relationship of  
Figure 5: Typical subsets formed when the template scans on a surface with an edge. Figure 6: Two normalized cross-correlation coefficient curves acquired when both the blue and red subset images were matched with their corresponding reference templates. each measured point as the detection center with its eight neighboring points. In Step 5, the boundary search point was defined as a non-edge point located adjacent to an edge point. The subset type of search NCC was determined by the geometry relationship of the inspection point and its eight neighboring points. A new edge point was detected and updated when the correlation coefficient of the search portion exceeded the pre-defined edge point threshold. The algorithm is detailed in Sections 2.3-2.5. Figure 7: Flowchart of the developed approach.
# Multi-Label Classification for Predicting Antimicrobial Resistance on _E. coli_
## 3 Experimentation
### Results and Discussion
The performance evaluation revealed that XGBoost consistently outperformed the other models across all five metrics, effectively predicting drug resistance. XGBoost, unlike other ML models, effectively manages model complexity through sparsity-aware split algorithm and regularization techniques (L1 and L2). Its gradient-boosting framework and use of multiple trees allow XGBoost to capture intricate data patterns while mitigating potential overfitting, resulting in the better generalization and accuracy of unseen data. Moreover, XGBoost's ability to build trees sequentially and allow for the parallelization of branch creation within each tree, exploiting modern CPUs' multi-threading capabilities, makes it a practical choice for real-world applications. Compared to models like DT and k-NN, XGBoost is less susceptible to overfitting. While models like CatBoost and GBC are similar to XGBoost, XGBoost performs better due to its optimization and regularization techniques. However, CatBoost may handle categorical features more effectively. Though k-NN and SVM are faster and simpler to train, their performance was lower than that of XGBoost because they do not capture non-linear relationships as effectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Models** & **Accuracy** & **Precision** & **Recall** & **F1-score** & **HAM** \\ \hline [PERSON] et al. [15] & Deep TL & 0.915 & 0.890 & 0.930 & 0.830 & - \\ \hline [PERSON] et al. [16] & ECC (RF) & 0.72 & 0.890 & 0.94 & 0.98 & 0.11 \\ \hline [PERSON] et al. [21] & CNN & 0.870 & 0.710 & 0.890 & - & - \\ \hline Proposed Approach & XGBoost & 0.890 & **0.891** & 0.890 & 0.890 & **0.110** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance quantitative comparison with previous related research. Despite its advantages, XGBoost requires more computational power and memory than models like MNB or k-NN. Additionally, XGBoost can be sensitive to noisy data without proper regularization or early stopping, potentially leading to overfitting. XGBoost is widely used for predicting the likelihood of diseases, including chronic conditions and infectious diseases, and analyzing patient data and biomarkers. Furthermore, XGBoost can be used to analyze the genetic variations that contribute to diseases. For example, [39] evaluated the performance of ML models in predicting AMR in _E. coli_ using datasets from England and several African countries. XGBoost's versatility makes it suitable for various machine learning tasks, including classification, regression, and ranking, across multiple domains [40; 41; 42].
## 4 Materials and Methods
### Subcellular Localization  
To investigate the subcellular localization of DZHDZ32, the coding sequence of _DZHDZ32_ was cloned into the PQG110-EGFP vector, resulting in the construct PQG110-_DZHDZ32_-EGFP. This construct was introduced into _A. tunefaciens_ strain EHA105. The transformed _Agrobacteriumium_ was cultured in LB medium containing 50 mg/L kanamycin and 20 \(\upmu\)M AS (acetylingone) at 28 \({}^{\circ}\)C with shaking at 200 rpm for 8 h. The overnight culture was then resuspended in infiltration buffer containing 10 mM MES, 10 mM MgCl\({}_{2}\), and 200 \(\upmu\)M AS and adjusted to an OD600 of 0.8. The suspension was incubated in the dark at 22 \({}^{\circ}\)C for 2 h before being infiltrated into the abaxial side of young _D. zingiabrenensis_ leaves.  
Following infiltration, plants were kept in the dark for 12-16 h and then exposed to light for 17-21 h. The empty PQG110-EGFP vector was used as a negative control. Subcellular localization of the EGFP-tagged DZHDZ32 protein was examined 30-48 h post-infiltration using a laser scanning confocal microscope (Leica, Wetzlar, Hessen, Germany). DAPI staining was used to visualize the nucleus. All experiments were conducted with three biological replicates.
## 2 Materials and Methods
### Datasets
#### 2.2.5 Mswep2.8  
Multi-Source Weighted-Ensemble Precipitation (MSWEP) is a global precipitation dataset introduced for the first time in 2016, with a 3-hourly temporal resolution and 0.25\({}^{\circ}\) spatial resolution [37]. Since the release of version 1, significant modifications have been adopted to enhance the accuracy and reliability of the product. The latest version (MSWEP2.8), released on February 4, 2021, provides a high spatial resolution (0.1\({}^{\circ}\)) and temporal resolution (3 h) precipitation dataset from 1979 to the near present. MSWEP2.8 integrates gauges, satellites, and reanalysis dataset estimates to produce high-quality precipitation products. The full global coverage, including ocean, high resolution, optimal merging of precipitation estimates based on gauges, satellites, and reanalysis, distributional bias corrections mainly to improve the precipitation frequency, worldwide 76,747-gauge observation incorporation, and consideration of regional differences during the gauge reporting time, are the unique features of the MSWEP2.8 product, which make it distinct from other GPPs [15]. This dataset can be acquired for non-commercial research purposes freely on special request from [[https://www.glob2o.org/mswep/](https://www.glob2o.org/mswep/)]([https://www.glob2o.org/mswep/](https://www.glob2o.org/mswep/)) (accessed on 22 July 2024).