esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Frost Heave and Thaw Settlement Characteristics of Sanya Estuary Channel Soil Layer
## 3 Frost Heave and Thaw Settlement Experiment Content and Methods
### Design of Frost Heave and Thaw Settlement Experiment  
Soil samples were taken from the working shaft of the Hexi port of the Sanya River Estuary Tunnel, from the ground surface to a depth of 39 m. The four types of soil samples included fine sand, silty clay, gravel sand, and clay. For each group of moisture content determinations, two additional sets of parallel experiments were conducted, resulting in a total of three repetitions per group. Specifically, the moisture contents for fine sand were recorded as 17.11%, 17.69%, and 17.71%; for silty clay, they were 29.56%, 30.28%, and 30.15%; for gravel sand, the values were 11.83%, 12.01%, and 12.46%; and for clay, they were 34.61%, 35.19%, and 34.54%. To comply with geotechnical testing standards, the average values of these measurements were adopted as the standard values. The basic physical and mechanical parameters of the soil samples are shown in Table 1.  
The water content of the soil is an important parameter that affects the frost heaving and thawing characteristics of the Sanya Estuary Channel Project. Therefore, the main research contents of this chapter are as follows:  
1. Investigate the variation patterns of frost heaving and thawing characteristics of fine sand, silty clay, gravel sand, and clay layers under natural moisture content.
2. Study the variation patterns of frost heaving and thawing characteristics of the most adverse soil layer under different moisture contents (24%, 27%, 30%, 33%, and 36%).
## Abbreviations  
\begin{tabular}{l l} \(\text{CLC}_{\text{ground}}\) & Crown leaf cover derived from ground observation \\ EOD & End of the decreasing phase \\ EOR & End of the rising phase \\ GEI & Green excess index \\ GRVI & Green-red vegetation index \\ g\({}_{\text{s}}\) & Stomatal conductance \\ LAI & Leaf area index \\ MOD & Mid of the decreasing phase \\ \end{tabular}  
Figure 14: Comparison between effective plant area index (PAI) derived from digital hemispherical photographs (DHP) and crown leaf cover based on the ground observation (CLC\({}_{\text{ground}}\)) for eight canopy trees measured in 2021. To quantify effective PAI from DHP at individual tree level, we chose samples with a large crown but small influence from neighboring trees. The DHP images were acquired using a Nikon D5100 camera (Nikon, Tokyo, Japan) fitted with a 4.5-mm F2.8 EX DC circular fisheye converter (SIGMA, Kanagawa, Japan) and were imported into ImageJ software for post-processing. We defined the processing range for DHP using the circle of interest adjusted according to the crown edges of the individual trees, and calculated effective PAI using CIMES software [66]. All values on panel (**I**) are normalized to a scale of 0 to 1. Dotted lines are 1:1 lines, and solid lines are linear regression equations as follows: (**A**) \(y=0.766\)x + 0.142, \(r^{2}=0.886\), root mean square error (RMSE) = 0.140; (**B**) \(y=0.806\)x + 0.003, \(r^{2}=0.903\), RMSE =  
Figure 14: Seasonal dynamics of daytime and nighttime sap flux density (SFD) for four representative trees measured in 2019. Nighttime SFD gradually increased from spring to autumn and was found in 19 of the 33 individuals. Abbreviation: DOY, day of the year.  
Figure 6: Monthly mean air temperature (T) and solar radiation (R\({}_{\rm s}\)), and monthly total precipitation in 2019 and 2020. Precipitation data were taken from the data of the Kawanehoncho meteorological station (the Japan Meteorological Agency, 2021). Error bars indicate the standard deviation.  
Figure A7: _Cont._  
Figure A7: Comparison of the daytime total sap flux density (SFD\({}_{\text{day}}\)) values estimated by leaf phenological metrics and the measured values; individual trees basis (**A**), all pooled data (**B**), and comparison of seasonal trajectories (**C**). Measured SFD\({}_{\text{day}}\) values are normalized to the scale from 0 to 1, and the scale of the estimated SFD\({}_{\text{day}}\) was adjusted accordingly. The solid lines in panels (**A**,**B**) are 1:1 lines. Root-mean-square error and coefficient of determination of linear regression analysis for the data in panels (**A**,**B**) are shown in Figure 5. Abbreviations: \(\text{CLC}_{\text{ground}}\), crown leaf cover based on the ground observation; GCC, green chromatic coordinate; GEI, green excess index; GRVI, green-red vegetation index; DOY, day of the year.
## 3 Numerical Analysis
### Boundary Conditions  
Based on the real sizes of the collectors that were tested in the experiments, the numerical simulation was created. Figure 3 depicts the intended model for absorber surface C1. The collector entry is shown as the inlet and the exit is shown as the outlet. The surface-area-increasing elements on the absorber surface are evaluated as walls. The inlet air temperature is considered to be 36 \({}^{\circ}\)C, and the flow rate is 0.05 kg/s. Those two values were obtained from the experimental work. The upper surface, with a constant heat flux of 800 W/m\({}^{2}\), is thought to be made of glass. Table 1 lists the characteristics of the glass and air.  
In order to achieve high efficiency, the scourers were placed above and below the absorbing surfaces of the collectors. In this case, double air flow was provided.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(\rho\) (kg/m\({}^{3}\)) & \(C_{p}\) (J/kgK) & \(k\) (W/mK) & \(\
u\) (m\({}^{2}\)/s) \\ \hline Glass surface & 3000 & 500 & 1.8 & - \\ \hline Air & 1.136 & 1007 & 0.026 & \(1.655\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the glass surface and air.  
Figure 3: Collector dimensions and boundary conditions with C1 case.
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 9 Emerging Mycoremediation Applications
### Mycoremediation of Dye  
The white-rot fungus _Phanerochaete chrysosponum_ mineralizes various primary aromatic pollutants, including nitrotoluenes, PAHs, dioxin, and chlorophenols, in addition to lignin [277]. Three possibilities exist for the fate of adsorbed dye: (1) association with hyphal systems, (2) physical desorption to the solution, and (3) enzymatic destruction via residing hyphal structures. Dye-saturated mycelium can be rebuilt for subsequent adsorption through physical desorption and enzymatic breakdown. Utilizing active mycelium and extracellular enzymes results in the quickest regeneration. This method could be applied to the continuous treatment of wastewater using _Trametes versicolor_ by sequentially adsorbing and degrading dyes through the resident fungal mycelium. _Aspergillus foetidus_ rapidly settles around the interior of biomass pellets, suggesting strong bios adsorption rather than the biotransformation of colours. By utilizing the dead macrofungi _Phellinus ignarius_ and _Fomes fomentarius_, Rhodamine B's carboxylic and amino compounds help to increase sorption. Rarely do colours that are being deteriorated by fungus become mineralised. After adding _P. chrysosporium_ for 12 days, mineralisation (23-48%) of radiolabeled azo dyes was achieved.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 1 Introduction
This article uses hundreds of these coincident acquisitions, from a large area of the Australian continent, to perform a direct comparison of the surface reflectances from these instruments, and quantify the differences between Sentinel-2A and the two Landsat instruments. It also presents some analysis showing the impact of these differences on two models used to estimate biophysical quantities. It should be noted that the differences in response are dependent on the nature of the target being observed, and so this study is first and foremost applicable to the Australian landscape. The techniques used here closely follow those used by [PERSON] [7] when comparing Landsat-8 Operational Land Imager (OLI) with Landsat-7 Enhanced Thematic Mapper+ (ETM+).
## 1 Introduction
The main contributions of this paper include the following:  
* A proposal for calculating the information integrity, validity, and redundancy based on coverage. * The determination of a node deployment strategy based on twofold coverage. * The construction of the CH selection mechanism through partition based on the uniformity of the CH distribution, the residual energy of the nodes, and the distances from the nodes to the BS. * The optimization of Dijkstra to achieve [PERSON]'s computational complexity reduction. The remainder of this paper is organized as follows: Section 2 introduces a node deployment strategy based on twofold coverage. Section 3 describes the details of the protocol in the paper. The simulation study is conducted in Section 4. Finally, Section 5 summarizes the research and prospects for the future.
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
###### Abstract  
The network real-time differential positioning technique is a good choice for meter and sub-meter level's navigation. More attention has been paid to the Global Positioning System (GPS) and GPS + GLONASS (GLObal Navigation Satellite System) network real-time differential positioning, but less on the GPS + BDS (BeiDou Navigation Satellite System) combination. This paper focuses on the GPS + BDS network real-time differential positioning. Since the noise of pseudorange observation is large, carrier-phase-smoothed pseudorange is usually used in the network real-time differential positioning to improve the positioning accuracy, while it will be interrupted once the satellite signal is lost or a cycle slip occurs. An improved algorithm in the position domain based on position variation information is proposed. The improved method is immune to the smoothing window and only depends on the number of available satellites. The performance of the network real-time differential positioning using the improved method is evaluated. The performance of GPS + BDS combination is compared with GPS-only solution as well. The results show that the positioning accuracy can be increased by around 10%-40% using the improved method compared with the traditional one. The improved method is less affected by the satellite constellation. The positioning accuracy of GPS + BDS solution is better than that of GPS-only solution, and can reach up to 0.217 m, 0.159 m and 0.330 m in the north, east and up components for the static user station, and 0.122 m, 0.133 m and 0.432 m for the dynamic user station. The positioning accuracy variation does not only depend on whether the user is inside or outside the network, but also on the position relation between the user and network.  
GPS + BDS; Network real-time differential positioning; range domain; position domain +
Footnote †: journal:  
Article GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
# Solving Multi-Document Summarization as an Orienteering Problem
## 5 The Proposed Solution
### Preprocessing  
Four preprocessing steps are applied including text segmentation, tokenization, stemming, and stop word removal. Text segmentation and tokenization divide the text into sentences and words, respectively, by using the Stanford CoreNLP tools [65]. Stop words are removed to filter out common words with low semantic weight [66]. Examples of these words are \"and\" \"the\" and \"to\". An English stop word list ([[http://jmlr.csail.mit.edu/papersvolume5/lewis04a/a11-smart-stop-list/english.stop](http://jmlr.csail.mit.edu/papersvolume5/lewis04a/a11-smart-stop-list/english.stop)]([http://jmlr.csail.mit.edu/papersvolume5/lewis04a/a11-smart-stop-list/english.stop](http://jmlr.csail.mit.edu/papersvolume5/lewis04a/a11-smart-stop-list/english.stop))) from the SMART information retrieval system is used. Word stemming is performed by using Porter stemmer ([[https://tartarus.org/martin/PorterStemmer/](https://tartarus.org/martin/PorterStemmer/)]([https://tartarus.org/martin/PorterStemmer/](https://tartarus.org/martin/PorterStemmer/))). This step enables an equal treatment of the different variants of terms.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 3 Method  
### Overall Framework  
The overall framework of our method is depicted in Figure 1. In the Deeplab v2 [3]-based 2D segmentation branch, we discard the last Argmax layer of the network. We save pixel-wise semantic probability maps for every image instead. With the help of COLMAP-SfM [25], we simultaneously estimate the camera parameters and depth ranges for the source images. In order to acquire 3D geometry for large scale scenes, we utilize learning-based MVS method R-MVSNet [6] to estimate depth maps for multiple images. After 2D segmentation and depth estimation, we obtain a dense semantic point cloud by the semantic fusion method according to multi-view consistency. Finally, we propose a graph-based point cloud refinement algorithm integrating both local and global information as the last step of our pipeline.
## 4 Computational Experiments and Results Analysis
### Comparison Between Models
#### 4.4.1 Single Instance Comparison with \(\delta\) Variation  
Firstly, all the models are compared using a predefined instance in the city of Providencia. Table 4 displays the input parameters for this particular instance, detailing the number of generators and batteries for each type (_len_).  
Table 5 displays the algorithm parameters, including the number of iterations performed in the ILS and the selected number of scenarios for the stochastic model with cross-validation.  
Each model was run with two instances of data from Providencia, one with \(\delta\) equal to 1, and another with \(\delta\) equal to 24. The objective of varying the \(\delta\) values is to provide an  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data** & **Value** \\ \hline \(a_{max}\) & 110 \\ \(i_{r}\) & 8.08\% \\ ENS & 12\% \\ years & 20 \\ Time horizon & 1 day, 24 h \\ \(s^{+}_{cost}\) & 0.2 \\ \(fuel_{cost}\) & 0.556 \\ \(\Delta\) & 90.38\% \\ \(lent_{diesel}\) & 9 \\ \(lent_{solar}\) & 42 \\ \(lent_{wind}\) & 21 \\ \(lent_{batteries}\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input data model.  
Figure 12: Comparison of different \(\delta\) values for different levels of allowed ENS: 1% Allowed ENS; 10% Allowed ENS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Iterations & 200 \\ Scenarios & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Input data algorithm.  
option with a flexible LPSP and another with a more constrained LPSP. The results obtained are shown in Table 6.  
In Table 6, it can be observed that due to changes in the way LCOE is calculated, direct comparisons between one-year and multi-year models are not straightforward. However, it is worth noting that the stochastic model exhibits a better LCOE compared to the deterministic one. This is attributed to the stochastic model's ability to explore more options.  
Regarding the \"Gen\" generation variable, there are various alternatives, without a distinct pattern. However, it can be observed that for this specific instance, the deterministic model utilizes less diesel. Another noteworthy factor is the reduction in surplus energy and ENS in multi-year configurations.  
In terms of algorithm execution time, multi-year configurations require the longest time, as indicated in Table 7, the use of longest computing time is due to the fact that the multiyear model evaluates the scenarios in a specific time horizon, for this work considered 20 years.
# A Data Processing and Distribution System Based on Apache NiFi
## 4 Commercial Deployment of NiFi to Monitor a 5G Optical Network
The value would be taken from the JSON field specified by the JSON path in the _Value_ column. Later, these newly created attributes would be utilized by the _Prepare insert statement_ FlowFile Processor to compose an appropriate SQL statement. An example of the FlowFile attributes can be found in Figure 5.  
Once the required data were extracted, the contents of the original JSON were no longer needed and could be overwritten with the SQL statement, which was necessary in order to upload new data into the reference database. This task was performed by _Prepare insert statement_. Part of its configuration is shown in Figure 6. It stipulates that NiFi will overwrite the original JSON with the replacement value \(\$\{attribute\_name\}\) in the SQL template (the \(\$\{\}\) notation is a part of NiFi Expression Language ([[https://nifi.apache.org/docs/nifi-docs/html/expression-language-guide.html](https://nifi.apache.org/docs/nifi-docs/html/expression-language-guide.html)]([https://nifi.apache.org/docs/nifi-docs/html/expression-language-guide.html](https://nifi.apache.org/docs/nifi-docs/html/expression-language-guide.html)), accessed on 13 February 2023), which is a simple scripting language; here, it is used to fetch the value stored in a particular attribute). Afterward, the previously prepared SQL statements were merged in batches of 1000 to avoid overloading the database with too many transactions and to improve performance. A deployment scale can be observed in Table 1 (this system is still in the development stage as of 05.10.2022; thus, not every physical object has been uploaded to the system, and the final object count will be larger). With so many optical network components, the monitoring system has to be greatly optimized to stay responsive, robust, and consistent. It is especially important for demanding 5G applications and will be even more critical in the context of 6G. This might become a challenge to a great extent when there are heterogeneous optical devices in a physical network. Figure 5: Attributes’ creation. Figure 6: Preparation of the SQL statement. The data concerning optical transceivers were stored in a single Kafka topic in the form of 1,536,536 JSON files. It took around 4 min to load the data, process them in Apache NiFi, and save the results in the reference database. The execution time was estimated due to the constantly increasing size of the data (each change of state of a given network element generated a new Kafka message). The typical time delay between publishing a new JSON file to the Kafka topic and saving it in the Apache NiFi database was less than 1 min. This is fully sufficient for operators to reflect changes in the optical topology when serving 5G deployments and will also be acceptable in the case of 6G.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 2 Logistics Space
### The Influencing Factors of the Evolution of Logistics Space
#### 2.2.5. Government Policy  
With the rising status of logistics in social and economic development, countries have begun to use logistics as the artery and basic industry for the development of the national economy. The government can inspire and guide the distribution intention of logistics enterprises and guide the relocation of logistics enterprises through pre-launching of city plans and public government information [43]. In forming a logistics space location, the government can control rent and availability by changing traffic conditions, such as re-planning transportation routes and cargo terminal locations, and by increasing or decreasing logistics land use [44]. Land use planning and pricing can be used by the government to control the sprawl of logistics [2], and can also be used to stimulate spatial concentration of logistics enterprises [45]. For example, China has increased its support for land and other policies, implemented and improved land-use policies that support the development of the logistics industry, and supply logistics land according to law. It also identified 29 first-level logistics park layout cities and 70 second-level logistics park layout cities. Through the advantages of facilities and agglomeration effects of logistics parks, logistics enterprises are guided to focus on parks, resources are shared, and logistics organization efficiency is improved. The government can also improve the effectiveness of logistics clusters by providing comprehensive infrastructure, effective administrative procedures, and increased training of employees [3]. In addition, the government promotes energy conservation and emission reduction in the transportation industry by establishing a logistics information platform, formulating tax policies and fuel economy standards, and ensuring the sustainable development of logistics [46]. The logistics industry has made great contributions to promoting circular economy [47]. The government can rationally allocate logistics resources through macro regulation and control, and avoid the uncoordinated regional logistics and economic development. The distribution of logistics parks and logistics centers is often guided by the government. The positioning of logistics enterprises is also affected by local policies. In addition, some places tend to adopt preferential policies to attract foreign logistics enterprises [48].
## 3 Results and Discussion
### Dosimetric Characterization
#### 3.2.2 Composition Optimization  
Optimal glass composition was identified based on the dosimetric characteristics, i.e., TL response, maximum peak temperature and the effective atomic number of the synthesized composition. TL measurements were obtained using the optimal heating rate, 7\({}^{\circ}\)C s\({}^{-1}\). The glow peak intensity was observed to increase with the increase in the dopant concentration up to a nominal ratio of 0.4 mol%, which exhibited a decreasing trend afterwards. The effect of dopant concentration on the TL response of magnesium borate glass is evidenced in Figure 6. The inconsistency in the glow peak intensity can be attributed to the irregular sample shapes. The composition was optimized by normalizing the TL response and eliminating random errors by averaging multiple TL measurements for the individual data point. Figure 7 shows the normalized TL response of the synthesized MgB\({}_{4}\)O\({}_{7}\):Zn glass series. The nominal 0.4 mol% ZnO was identified as the optimal concentration that produces a significant TL response with minimum standard deviation.  
Figure 6: Glow curves of MgB\({}_{4}\)O\({}_{7}\):Zn glass with different dopant concentrations.  
Figure 7: Effect of dopant concentration on TL response of MgB\({}_{4}\)O\({}_{7}\):Zn glass.