esa-sceva/satcom-chunk-collection huggingface dataset:
# Determination of Cellulose Degree of Polymerization in Historical Papers with High Lignin Content
## 3 Results and Discussion
### Determination of Kappa Number and Solubility in CED
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample No.** & **Time of Delignification (h)** & **Kappa Number** & **Solubility in CED** \\ \hline  & 0 & above 100 & \\
1 & 3 & 44 & \\  & 6 & 38 & \\  & 9 & 19 & + \\ \hline  & 0 & 97 & \\
2 & 6 & 19 & + \\ \hline  & 0 & 97 & \\
3 & 6 & 18 & + \\ \hline  & 0 & 72 & \\
4 & 6 & 8 & + \\ \hline  & 0 & above 100 & \\
5 & 6 & 42 & \\  & 9 & 18 & + \\ \hline  & 0 & 75 & \\
6 & 6 & 16 & + \\ \hline  & 0 & 76 & \\
7 & 6 & 20 & + \\ \hline  & 0 & 92 & \\
8 & 6 & 32 & \\  & 9 & 11 & + \\ \hline  & 0 & 81 & \\
9 & 6 & 25 & \\  & 9 & 7 & + \\ \hline  & 0 & 62 & \\
10 & 6 & 10 & + \\ \hline  & 0 & 57 & \\
11 & 6 & 16 & + \\ \hline  & 0 & 81 & \\
12 & 6 & 20 & + \\ \hline  & 0 & 19 & + \\
13 & 6 & 3 & + \\ \hline  & 0 & 24 & \\
14 & 6 & 3 & + \\ \hline  & 0 & 82 & \\
15 & 6 & 23 & + \\ \hline \end{tabular}
\end{table} 
Table 2: Kappa number of paper samples before and after 3, 6, or 9 h of delignification and their solubility in CED (+ indicating completely soluble in CED). The Kappa number is calculated from three measurements with an average RSD of 4%.
###### Abstract  
Post-harvest strawberries are hard to store and can easily rot during cold chain transportation (CCT). This leads to considerable economic losses. This paper proposes a strawberry quality perception method used in CCT, based on the correlation between environmental parameters and strawberry quality parameters. The proposed method constructs a shelf-life prediction model based on a back propagation (BP) neural network, using four kinds of environmental parameters, including temperature, humidity, oxygen, and carbon dioxide, to perceive the quality of post-harvest strawberries, and builds a cold chain transportation quality perception system (CCT-QPS) with the help of LabVIEW software for monitoring the cold chain environment and commodity quality constantly. The results showed that the proposed method could precisely predict the remaining shelf-life of post-harvest strawberries. In addition, the proposed system could reflect the vehicle operation in real time, such as commodity quality and the internal environment of transport carriages. Moreover, the quality perception approach can inform decision making for managers and effectively improve the related regulatory measures in the strawberry supply chain.  
cold chain transportation; quality perception; correlation analysis; BP neural network; shelf-life prediction Article Research on Strawberry Cold Chain Transportation Quality Perception Method Based on BP Neural Network  
2022
# Strength Characteristics of Iron Tailings Blended Soil as a Road Base Material
## 3 Results and Discussion
### Mineral Compositions Affected by Additive Agents  
X-ray diffraction (XRD) analyses were deployed to evaluate the microscopic mechanism of strength behavior during various phase changes. Based on the best mix proportion of ITBS, these samples were prepared as follows: ITBS with 50% pure ITS, ITBS with 50% ITS and 2% curing agent, ITBS with 50% ITS and 5% cement, ITBS with 50% ITS and 5% cement and 2% curing agent were labeled as A, B, C and D respectively.  
XRD tests were performed after standard curing for 28 days and the results are depicted in Figure 9. It can be seen that the position and number of diffraction peaks of the four types of samples are approximately identical in nature, but the intensity of diffraction peaks is different, which suggests that the mineral composition is similar but the crystallinity is different. The 1 st peak is the diffraction peak of silica (quartz), the 2 nd peak is that of albite, the 3 rd one represents the peak of calcium carbonate (CaCO\({}_{3}\)), while the 4 th diffraction peak is that of calcium silicate gel. From the comparison of group A and group B, C, D, it can be seen that there is no new diffraction peak when only the curing agent is added, the diffraction peak intensity of quartz (SiO\({}_{2}\)) and albite (Na(AlSi\({}_{3}\)O\({}_{8}\))) increases  
Figure 8: Flexural-tensile strength of ITBS with 5% cement, 2% curing agent and different ITS content.  
Figure 7: Splitting strength of ITBS with 5% cement, 2% curing agent and different ITS content.  
to a certain extent, and other diffraction peaks experience no significant changes. The diffraction peaks of newly formed calcium carbonate (CaCO\({}_{3}\)) and calcium silicate hydrate (C-S-H) gels appear when cement is added alone. The diffraction peaks of newly produced calcium carbonate (CaCO\({}_{3}\)) and calcium silicate hydrate (C-S-H) gels also appeared when the solidifying agent and cement are mixed together, and the diffraction peak strength is improved to a certain extent compared with that of only addition of cement. Therefore, the following conclusions can be drawn: (i) the properties of the mixed soil of pure iron tailings after reinforcement by curing agent do not change, however, there is ion exchange [21] between the curing agent itself and the soil particle surface, (ii) calcium silicate hydrate (C-S-H gel) was consolidated by cement hydration to impart strength, and it was also accompanied by formation of small amount of CaCO\({}_{3}\) crystals, (iii) the agglomerated particles of the curing agent and the filling action of cement hydration product, i.e., C-S-H gel jointly contribute in strength development.
## 3 Results and Discussion
### Structure Elucidation of Degradation Byproducts DP1--DP16  
AMO chlorination-produced degradation byproducts DP1--DP16 (Figure 3) were isolated by chromatographic processes and identified on the basis of their physical features (Scheme 1).  
In AMO treatment with an unbuffered pH value, the changes of the drug were monitored with HPLC. Its main degradation byproducts (DP1--DP16, Figure 3) were identified by comparing their retention times with those of the standard compounds and by employing NMR and MS analyses. The concentrations of DP1--DP16 were at a maximum after 5 min and were in the range of 3.01-0.89%.  
The first three DPs (DP1--DP3) were C6C2 skeletal compounds obtained from the hydrolysis of the amide bond of the phenylethanoic acid residue and the subsequent oxidation of the alkyl chain. The DP4--DP6 had a C6C1 skeleton and, thus, it was easy to hypothesize that they were products derived from the decarboxylation of the previous three. Moreover, the DP7--DP10 had a C6C1 skeleton with an oxidized or chlorinated aromatic ring. DP11--DP15 products were di- or mono-carboxylic acids, which were final oxidation products. A separate discussion should be had for DP16, which is a phenylbenzoic ester chlorinated on the alcoholic part and clearly obtained from the esterification of two oxidation byproducts. The plausible mechanism of the DP formation from AMO is shown in Figure 4.  
The reaction could start by a single-electron transfer from the lone electron pair of the amino group to HClO, which formed the corresponding radical cation and chloride. This aminyl radical cation (\(II\)) could undergo a \(\beta\)-lactam cycle when two fragments, \(I2\) and \(I3\), formed. The first fragment formed via hydrolysis and gave DP11. The \(I3\) fragment first hydrolyzed the ketene function to a carboxylic function, giving the intermediate \(I4\) that led to DP1 and DP14. The second fragment oxidized to DP12 and CO2. From the product DP1, it was possible to obtain DP2--DP3 with a C6C2 skeleton, DP4--DP6 with a C6C1 skeleton, and DP7--DP10 with a C6C0 skeleton through a series of oxidations, decarboxylations, and chlorations. Finally, DP13 and DP15 was obtained by opening the aromatic ring. A slightly different argument to justify DP16, the synthesis of which could come from the chlorination and subsequent oxidation of intermediate \(I8\), in turn was obtainable from the esterification of two DP5 molecules.
# Design and Construction of a Low-Cost Test Bench for Testing Agricultural Spray Nozzles
## 3 Results and Discussion  
### The Whole System Test  
Figure 7 shows the main components of the entire system: the water tank with the electric motor pump and the manual pressure regulators; the mobile platform with the nozzle holder and the sensor pressure moving along the two rails; the cabinet with the electric/electronic components; the DSLR camera for drop image acquisition; and the wood table supporting the Petri dishes, which is mechanically insulated from the other parts of the frame.  
At the beginning of the experiment, the electric motor driving the pump is switched on, and all the flow is recirculated to the tank (the electro valve of the hydraulic circuit is closed, Figure 1). After the working pressure is chosen and the manual pressure regulator is adjusted accordingly, the whole experiment is managed by the software. The user sets the desired platform speed (acceleration and deceleration are internally computed in the motor drive, according to Equation (1)), the initial delay necessary to reach steady-stay conditions, and the CSV file name to log the pressure, flow rate, velocity and position data. Then, the experiment starts: the mobile platform is carried at the home position (at one end of the rails), the electro valve is switched on, the nozzle starts spraying, and after the delay has elapsed, the platform moves and the test liquid is sprayed over the three Petri dishes containing silicone oil. After one pass, the platform stops at the opposite end of the rails. The distance between the spraying nozzle and Petri dishes is 0.5 m. The images of the drops trapped into the oil are immediately acquired in situ by using the camera.
## 1 Introduction
Furthermore, there are limited studies exploring hybrid models that combine the strengths of both CNNs and transformers to achieve higher accuracies with lower computational costs. For example, the WetMapFormer classifier introduced by [PERSON] et al. [19] demonstrated the potential of hybrid architectures by integrating CNN and transformer models, achieving superior classification accuracy. WetMapFormer achieves this with a significantly lower number of parameters and reduced computational cost compared to traditional CNN or transformer models. This efficiency makes it particularly suitable for large-scale wetland mapping, where computational resources are often a limiting factor. However, the exploration of such hybrid models remains sparse. The advantageous design of a hybrid architecture specifically for wetland classification arises from the unique characteristics of wetlands, which require both local detail and long-range spatial dependencies for accurate classification. Wetlands exhibit complex patterns that are influenced by various environmental factors and surrounding land cover types. Developing a hybrid architecture specifically designated for wetland mapping could be beneficial in achieving even higher accuracy at a reasonable computational cost. A main challenge in using transformer models for wetland classification is their high computational complexity and requirement for a substantial amount of training samples. Particularly, the computational complexity of transformer models increases quadratically in relation to spatial dimensions [46]. Therefore, considering their possible high cost, using transformer models for practical mapping cases could be challenging and infeasible. This study mitigates the computational cost of transformers by introducing a novel hybrid CNN-transformer model, namely Wet-ConViT, designed for precise wetland classification with optimal computational complexity. Levering Sentinel-1 and Sentinel-2 data sources, the hybrid Wet-ConViT model consists of a sequence of convolutional and transformer blocks. By using multi-head convolutional attention rather than self-attention, these blocks considerably reduce the computational complexity of vanilla ViT models. Furthermore, the model uses a local feed-forward network (LFFN) that incorporates local CNN-based insights into the feed-forward stage of transformer architectures. This cohesive fusion of convolutional and transformer components not only overcomes computational challenges but also improves the model's capacity to accurately classify wetlands using remote sensing imagery data.
## 2 Related Works
### LLM-Assisted Automatic Heuristic Design  
In recent years, significant advancements have been made, especially with the integration of Large Language Models (LLMs) and Evolutionary Computation (EC) methods. One of the notable developments is searching in the function space using the LLM capability (FunSearch) [7]. It is an evolutionary framework that pairs a pre-trained LLM with automated evaluation to search program space instead of direct solutions. Candidate programs are iteratively sampled, improved via best-shot prompting, and scored by an evaluator, enabling the emergence of correct and novel code. Interpretable program outputs facilitate human-machine feedback loops and real-world deployment, which distinguishes FunSearch from raw-output discovery methods. Based on that, [PERSON] et al. [8] introduced Quality-Uncertainty Balanced Evolution (QUBE), which redefines the priority criterion within the FunSearch framework by employing the quality-uncertainty trade-off criterion.  
Furthermore, [PERSON] et al. [9] proposed EoH, which represents heuristic ideas as natural-language thoughts generated by LLMs. These thoughts are then translated into executable codes. The co-evolution of thoughts and codes within an evolutionary search framework has shown remarkable efficiency in generating high-performance heuristics. Experiments on combinatorial optimization benchmark problems demonstrated that EoH outperforms commonly used hand-crafted heuristics and previous AHD methods. Moreover, unlike traditional hyper-heuristics limited to curated heuristic components or neural combinatorial optimization requiring extensive training, ReEvo [10] leverages a dual-feedback reflective evolution loop where LLMs iteratively generate and reflect on heuristics, enabling gradient-like guidance across black-box problem settings. In another related work, [PERSON] et al. [11] pointed out that there is a gap in understanding the properties of heuristic search spaces and achieving a balance between exploration and exploitation. To address that, they proposed two diversity measurement metrics and introduced an adaptive LLM-based framework called HSEvo that uses a harmony search algorithm to balance diversity and convergence. Experiments showed that HSEvo achieved high diversity indices and good objective scores while being cost-effective.  
In summary, recent research in Automatic Heuristic Design has focused on leveraging the power of LLMs and EC, as well as addressing key challenges such as the balance between exploration and exploitation, and improving the performance of heuristics in various optimization problems. These studies have not only advanced the theoretical understanding of AHD but also provided practical solutions for real-world applications.
# Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
## 2 Limitation of Previously Used Dead-Reckoning Approach
### Lie Theory Application for Three-Dimensional Dead-Reckoning of Vehicles  
The \(SE_{2}(3)\) group is a group extended from \(SE(3)\) for rigid body transformations related to kinematic changes. The DR process in the Lie group \(SE_{2}(3)\) is as follows:  
\[\begin{split}\mathbf{X}_{t_{k+1}}&=\mathbf{X}_{t_{k} }\oplus(\mathbf{U}_{t_{k}}\cdot\triangle t)\\ &=\mathbf{X}_{t_{k}}\cdot\exp(\mathbf{U}_{t_{k}}\cdot\triangle t). \end{split} \tag{16}\]  
In Equation (16), \(\oplus\) is an operator that combines Lie group elements and Lie algebra elements, and the result of the operation belongs to the Lie group. The Lie group \(SE_{2}(3)\) is a transformation matrix that constitutes the position, velocity, and attitude of a vehicle in 3D space, and is expressed as follows:  
\[SE_{2}(3):=\mathbf{X}_{t_{k}}=\left\{\begin{bmatrix}\mathbb{R}(\mathbf{\theta}_{t _{k}})&\hat{\mathbf{p}}_{t_{k}}&\mathbf{\rho}_{t_{k}}\\ \mathbf{0}_{1\times 3}&1&0\\ \mathbf{0}_{1\times 3}&0&1\end{bmatrix}\ |\ \mathbb{R}(\mathbf{\theta}_{t_{k}})\in SO(3),\hat{ \mathbf{p}}_{t_{k}}\in\mathbb{R}^{3},\mathbf{p}_{t_{k}}\in\mathbb{R}^{3}\right\}. \tag{17}\]  
The expressions \(\mathbf{p}_{t_{k}}\) and \(\hat{\mathbf{p}}_{t_{k}}\) also represent the position and velocity of the vehicle in the ENU coordinate frame. The measured input \(\mathbf{U}_{t_{k}}\) is the same as Equation (6) in Section 2. The  
Figure 1: Lie theory.  
expression \(\exp(\mathbf{U}_{k}\hat{\cdot}\ \triangle t)\) is calculated from Equation (15). The comprehensive overview of the entire algorithm is presented as a block diagram, as shown in Figure 2.
## 3 Background and Related Concepts  
This section overviews the fundamental concepts necessary to comprehend the proposed model's architecture and components.
# Direct Selective Oxidation of Hydrogen Sulfide: Laboratory, Pilot and Industrial Tests
## 15 Carbon Nanofiber-Based Catalysts
The catalyst was activated carbon with supported iron and cerium oxides. The introduction of ceria was a positive factor, increasing catalytic activity due to the improved oxidation of Fe\({}^{2+}\) to Fe\({}^{3+}\) by redox-pair Ce\({}^{4+}\)/Ce\({}^{3+}\). Also, the sorption capacity increased significantly. The adsorption-catalytic parameters of the system were investigated at a relative humidity of 80%, an oxygen content of 10 vol.%, a temperature of 30 \({}^{\circ}\)C and a space velocity of 7440 h\({}^{-1}\). The time of continuous stable operation with the sulfide conversion close to 100% was 71 h, and the value of the adsorption capacity was 820 mg S/g catalyst, which significantly exceeded this indicator for KNa/AC systems. It was found that the obtained sulfur is mainly precipitated inside the pore volume of the AC, but that some also formed on the AC surface. Note that, depending on the nature of the process occurring on activated carbon during gas purification, the requirements for its porous structure may be different. For an adsorption process, carbons with narrow pores are required, the surface of which should have minimal catalytic activity. For catalytic oxidation of hydrogen sulfide, a wide-pore carbon is needed with a large total pore volume and, naturally, high catalytic activity. Large pores are needed to accumulate the resulting sulfur, in which up to 120% sulfur relative to the mass of the carbon can be adsorbed [100]. In connection with the development of technologies for the production of nanoscale carbon fibers, recently, the use of these materials and catalysts based on them in the reaction of partial oxidation of hydrogen sulfide to sulfur has attracted much interest [87; 94; 101]. It has been shown that carbon nanofibers make it possible to increase both the catalyst activity and sulfur resistance to deposition on the catalyst surface at low temperatures compared to conventional catalysts. The issue of the use or regeneration of spent AC sorbents/catalysts deserves special consideration. Standard (industrial) processes for solving this problem are:  
1. burning out sulfur at elevated temperatures
2. treatment of catalyst/sorbent with steam, with resulting hydrogen sulfide formation
3. washing catalyst with an organic solvent, effectively dissolving sulfur. Obviously, the second and third options are the most acceptable for carbon materials, because, when exposed to oxygen at high temperatures, destruction (combustion) of the carbon matrix will inevitably occur.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 3 Reptile Search Optimization (RSA) Based MPPT Algorithm
### RSA Algorithm for MPPT
#### 3.1.1 Initialization  
The initial phase of the RSA-MPPT based algorithm involves the selection of a random set of candidate solutions in the form of duty cycles.  
\[\mathbf{D_{i}}=\mathbf{rand}(\mathbf{D_{\text{max}}}-\mathbf{D_{\text{min}}})+\mathbf{D_{\text{min}}} \tag{1}\]  
\(\mathbf{D_{\text{max}}}\) and \(\mathbf{D_{\text{min}}}\) are maximum and minimum values of the duty ratio.  
The RSA algorithm operates in two phases: firstly, it performs the encircling mechanism for the first half of the total number of iterations and then switches to the exploitation (hunting) mechanism for the second half.
## 3 Results
### Signal from M. Extensor Carpi Ulinaris
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Gait**} & \multirow{2}{*}{**Signal Variation**} & \multicolumn{2}{c}{**Signal Loss (Residual Signal) per Horse**} & \multicolumn{2}{c}{**Common**} \\ \cline{3-6}  & & **Horse 1** & **Horse 2** & **Horse 3** & **(Residual Signal)** \\ \hline Walk & Low-pass 10 Hz & 27.0\% (73.0\%) & 27.7\% (72.3\%) & 26.6\% (73.4\%) & 27.1\% (72.9\%) a  \\ Walk & Bandpass 40–450 Hz & 7.7\% (92.3\%) & 7.6\% (92.4\%) & 6.1\% (93.9\%) & 7.2\% (92.8\%) b  \\ Walk & Bandpass 7–200 Hz & 27.8\% (72.2\%) & 27.8\% (72.2\%) & 28.3\% (71.7\%) & 27.5\% (72.5\%) a  \\ \cline{2-6}  & \(p\)-value & & & \(<\) 0.0001 \\ \hline Trot & Low-pass 10 Hz & 26.4\% (73.6\%) & 22.9\% (77.1\%) & 23.6\% (76.4\%) & 24.3\% (75.7\%) a  \\ Trot & Bandpass 40–450 Hz & 11.1\% (88.9\%) & 9.6\% (90.4\%) & 11.2\% (88.8\%) & 10.7\% (89.3\%) b  \\ Trot & Bandpass 7–200 Hz & 25.9\% (74.1\%) & 28.8\% (71.2\%) & 23.1\% (76.9\%) & 25.9\% (74.1\%) a  \\ \cline{2-6}  & \(p\)-value & & & \(<\) 0.0001 \\ \hline \end{tabular}
\end{table} 
Table 8: Mean signal loss (%) and residual signal (%) data compared between signal variants (low-pass filtered with a 10 Hz cut-off frequency, bandpass filtered with a 40–450 Hz cut-off frequency, bandpass filtered with a 7–200 Hz cut-off frequency) for signals obtained from the m. extensor carpi ulnaris during walking and trotting. Figure 5: Representation of sEMG signals obtained from the m. extensor carpi ulnaris during walk (**A–D**) and trot (**E–H**). (**A,E**) Signal loss (%) and residual signal (%) data. (**B,F**) Segment of burst after a low-pass 10 Hz filtering (red line) on the background of the raw signal (black line). (**C,G**) Segment of burst after a bandpass 40–450 Hz filtering (blue line) on the background of the raw signal (black line). (**D,H**) Segment of burst after a bandpass 7–200 Hz filtering (green line) on the background of the raw signal (black line).
## 5 Case Study
### Input Data for Calculations  
A comprehensive model for calculating the technical condition of HV SB devices was developed and supported by a newly designed data model, enabling automated condition assessment. Using this computerized procedure, practical evaluations were conducted based on extensive input data for HV SB devices, including CBs, Ds, and ITs, installed in switchyards operated by TSO ELES. The calculations were based on data from the device population listed in Table 5, which were collected over an extended period through IBM Maximo IS.
More complete description and comparisons are presented in our paper, (e.g., the physical layer technologies used in WMSN, MAC layer protocols proposed for WMSN, methodologies used in designing the routing protocols in the routing layer). Finally, we tried to stress on open issues for new researchers in this field and to give a view of what we foresee are going to be the future trends. In particular, we describe the different network architectures for WMSNs and their characteristics in Section 2. In Sections 3-7, we discuss existing solutions and open research issues at the physical, MAC, routing, transport, and application layers of the communication stack respectively. Recent works in cross-layer design are covered in Section 8. Section 9 outlines the coverage issues in WMSN and the proposed algorithms for solving them, while in Section 10 we discuss the research challenges in designing security solutions in WMSN. In Section 11, we survey and classify the existing hardware and prototype implementations for WMSN. Finally, we conclude the paper in Section 12.