esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 4 Experimental Validation
### User Observations Error Elimination and Positioning  
Unlike general NRTK based on DD error correction, the undifferenced NRTK based on undifferenced error correction values in this paper does not directly generate error correction information after the DD ambiguity. Instead, UD ambiguity conversion is performed on the basis of DD ambiguity fixing, and error correction information is independently generated through the original observations of each reference station. Overcoming the correlation of error correction values between reference stations, the positioning is not limited by the number of reference stations and can flexibly select reference stations that need to broadcast error correction values. At the same time, except for the ambiguity parameter, the meaning of all errors is consistent with the original observation, retaining the characteristics of errors on the reference station. Figure 17 shows the error correction values of the UD ionosphere and troposphere generated by the three reference stations.  
From Figure 17, it can be seen that, compared to the traditional NRTK, the undifferenced NRTK in this paper can independently generate error correction values for each reference station, and the correction values for each station are independent of each other. The variation trend of the ionosphere at each station is similar, and there is also consistency among satellites. The troposphere also exhibits similar patterns at various stations. The error correction value generated on each reference station are used to generate a comprehensive error correction value through an error interpolation model to correct the errors of each satellite on the user. Figure 18 shows the variation in atmospheric residual errors on each satellite after correction by the reference station error.  
Figure 17: Sequence of UD ionosphere and troposphere error-correction values.  
Figure 18: The user residual series after the error correction.  
From Figure 18, it can be seen that the variation in residual ionospheric error is relatively stable, and the error always maintains a small value. At the same time, there is also a trend of variation over time, which is caused by the variation in residual ionospheric error after error correction. The fluctuation of tropospheric errors is often large due to the presence of receiver clock errors. The consistent trend of error changes among the three satellites also indicates that this fluctuation trend is caused by receiver errors, so it can be concluded that the variation in residual tropospheric errors is also very stable, After correction, the residuals of each satellite are relatively small. Figure 19 shows the user positioning results for the user in Network C.  
As can be seen from Figure 19, except for individual points, the positioning accuracy in the plane direction is always kept within 0.1 m, and the positioning accuracy in the U direction can also reach the centimeter level, which can meet the needs of real-time dynamic positioning.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 3 Results
### Factors Influencing on the Spatial Consistency
#### 3.4.2 Influence of Climate on the Consistency  
To analyze its influence on the consistency of forest datasets, the Koppen-Geiger climate zones were overlapped with the spatial agreement map of the datasets, as shown in Figure 10. The statistics of spatial agreement in each climate zone were further calculated. As elaborated in Figure 11, the highest percentage of complete agreements is mainly in the Cwb and Cwa zone with 60.62% and 45.03%. These two climates are characterized by a yearly suitable temperature and annual precipitation that is not less than the dry value index. Cwb is mainly distributed in high-altitude areas at low and middle latitudes. The objective conditions of combined climate and terrain are suitable for the growth of forests. The dense distribution and large areas of forests make it easier for forest monitoring with different sources of satellite data. The consistency distribution of Aw and Am climatic zones, respectively, followed with 37.41% and 27.11% of complete agreement. Both have monthly average temperatures of 18 \({}^{\circ}\)C and above throughout the year, with specific differences in precipitation: Annual precipitation is higher in Am than in Aw, yet the degree of forest complete agreement is higher in Aw. This is because Am is mainly distributed in southwestern Myanmar, including Rakhine, Ayeyarwady, Yangon, and Mon. It is near the sea and the landscape is complex and diverse, forests are easily confused by shrubs and vegetation. While Aw is more distributed in central Myanmar, its feature types are more homogeneous. It is possible to identify forests uniformly from different source datasets. The Dwc and ET climatic zones are less distributed in Myanmar. They both have mostly low temperatures throughout the year, and even ET areas have no precipitation indicators. This makes them unsuitable for forest growth, so their consistency ratios are naturally much lower than those of other climate types.  
Figure 9: Consistency distribution among different slope intervals. (**a**) Eight datasets; (**b**) Three datasets in 2015.
# Influence of Drilling Along Pilot Holes on Drilling Performance and Effective Rock Strength +
### Experiment Stage 1: Variation in PDC Bits  
The experiment's first step involves evaluating the drilling performance of a 4-cutter PDC coring bit (6.35 cm diameter) designed to extract core samples and a 2-cutter PDC bit (3.24 cm diameter) for un-drilling.  
Using a staged approach, it was possible to compare the drilling performance achieved with various PDC bit configurations and provide insight into their respective effectiveness in bit-rock interactions. The goal was to compare the un-drilling phase with the 2-cutter PDC bit to the drilling performance and efficiency of the 4-cutter PDC coring bit.  
Un-per-drilling with 2-cutter PDC Bit: As seen in Figure 2, the experiment's initial phase involved pre-drilling operations using a specific 2-cutter PDC bit made just for this purpose. Un-per drilling is the first step in preparing the rock formation for further drilling or coring operations by making a small hole.  
Coring with 4-Cutter PDC Coring Bit: Following the un-per-drilling phase, a 4-cutter PDC coring bit optimized for core sample extraction from hard rock formations was used in the experiment's coring operations. A cylindrical core sample is extracted by drilling further into the rock formation.  
Drilling parameters such as Weight on Bit (WOB), Revolution per Minute (RPM), and Flow Rate (FR) were controlled, regulated and kept constant during Stage 1. This made the experimental conditions consistent and repeatable, enabling a methodical comparison of the drilling performance with various PDC bit configurations.  
Figure 1: Indicates the 2-cutter PDC bit for pre-drilling and the 4-cutter PDC coring bit and represents the gabbro sample used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Number of Samples Tested** & **Mean** & **Standard Deviation** \\ \hline \(\rho b\) (kg/m\({}^{3}\)) & 24 & 2900.63 & 34.46 \\ UCS (MPa) & 7 & 168.4 & 7.54 \\ E (GPa) & 7 & 13.31 & 2.31 \\ \(\rho b\) (m/s) & 5 & 5480.91 & 109.34 \\ \(vs\) (m/s) & 5 & 3433.19 & 53.97 \\ \(E^{\prime}\) (GPa) & 5 & 81.14 & 1.43 \\ N & 5 & 0.18 & 0.03 \\ \(\sigma t\) (MPa) & 14 & 16.27 & 2.56 \\ \(\sigma PLI\) (MPa) & 20 & 14.17 & 0.69 \\ CAI & 5 & 4.16 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical and physical parameters of the gabbro rock [16].  
Assessing the efficacy, suitability, and efficiency of different PDC bit configurations for pre-drilling and coring operations in hard rock formations was the aim of Stage 1. Stage 1 gave important insights into optimizing drilling operations and choosing the best bit configurations for drilling scenarios by evaluating the performance of various PDC bits under controlled conditions.
# Effect of Drying-Wetting Cycle and Vibration on Strength Properties of Granite Residual Soil
## 3 Test Results and Analysis
### Analysis of Unconfined Compression Strength Test Results  
#### 3.2.1 Characteristics of Stress-Strain Curve in UCS Test  
Figure 12 presents the stress-strain curve of the UCS test. Most of the stress-strain curves are strain-softening, i.e., there is a clear downward trend after reaching a peak. Additionally, the DW cycle and vibration have a significant influence on the shape of the stress-strain curve. With an increase in the number of DW cycles and vibration time, the UCS gradually decreases. The most significant change was observed after the first DW cycle or 5 min of vibration for the specimens. Compared to vibration, the DW cycle has a greater impact on the stress-strain curve. After undergoing five DW cycles, the UCS of the specimen without vibration decreases by approximately 60%, while after 15 min of vibration without DW cycles, the UCS decreases by 44%. The combination of the DW cycle and vibration causes the specimen to reach the UCS at a small axial strain. From Figure 12a,b, it can be observed that the UCS is at around 15% axial strain. From Figure 12c,d, it can be observed that after four DW cycles, the UCS shifts to before 7.5% axial strain, indicating that the coupling effect of both factors greatly deteriorates the structure of the soil specimen, leading to rapid failure of the soil specimen under compression.
# A Weighted PageRank-Based Bug Report Summarization Method Using Bug Report Relationships
## 2 Related Work
### Research on Bug Report Summarization  
In 1958, [PERSON] (2007) first proposed a text summarization method. Since then, various summarization methods have been studied, ranging from simple tf-idf (Term Frequency - Inverse Document Frequency) based methods to complex machine learning-based methods ([PERSON] and [PERSON], 2017). In general, text summarization can be classified into extractive summarization and abstract summarization according to the method of generating summary statements ([PERSON], 2010). Extractive summarization is an approach for organizing a summary  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relationship Name** & **Inward Description** & **Outward Description** \\ \hline Blocked & Blocks & Blocked by \\ Bonfire testing & Testing discovered & Discovered while testing \\ Cloners & Is cloned by & Clones \\ Dependent & Depends-on & Is depended on by \\ Derived & Derived from & Derives \\ Design & Has design on & Is design for \\ Duplicate & Duplicates & Is duplicated by \\ Epic & Belongs to Epic & Is the Epic for \\ Relates & Relates to & Relates to \\ User test & Was in test & Participants were \\ Caused by & Causes & Caused by \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relationships between bug reports on the Jira platform.  
by extracting a paragraph, sentence, or word of high importance from the original text. An abstract summarization is a method for generating abstract sentences based on the meaning of the original text. In general, extractive summarization has been used mainly because abstract summarization is much more difficult. In recent years, with the advance of machine-learning technology, research into abstract summarization has increased.  
Bug report summarization methods are mainly developed in the direction of extractive summarization. The methods can be divided into supervised learning and unsupervised learning. Supervised learning is an approach for learning a model with tagged data. [PERSON] et al. [9] first proposed a bug report summarization method using a supervised learning technique. [PERSON] et al. [9] noted that bug reports can be treated as interactive documents. They adopted the [PERSON] and Carenini-supervised [10] learning-based summary method that can be applicable to the minutes and email threads for bug reports. For an experiment, [PERSON] et al. [2] wrote a hand-written summary called the golden summary of 36 bug reports. They then trained the summarizer using the golden summary. Unsupervised learning is an approach for learning a model with untagged data. [PERSON] et al. [4] proposed an unsupervised summarization method. They proposed a method for removing noise from bug reports, and based on this, they applied four kinds of unsupervised learning-based algorithms for bug reports. [PERSON] et al. [3] also proposed an unsupervised summarization method, which applied the PageRank algorithm to take advantage of similarities between sentences in bug reports.
## 4 The Conductors Analyzed and the Experimental Setup
### Experimental Setup for Heating the Conductors  
To obtain the experimental relationship between the AC resistance per unit length \(r_{AC}\) and the temperature \(T\), a 50 Hz AC current was injected into the studied conductors to change their temperature \(T\). The conductors were connected to a controlled high-current transformer (150 kVA, 0-400 Vrms input voltage, 0-10 Vrms output voltage). The current flowing through the conductors was measured with a [PERSON] current sensor (CWT60 LF, \(\pm\)1%, PEM, Nottingham, UK). Low-inertia T-type thermocouples were used in conjunction with a thermocouple data acquisition module (TC08, \(\pm\)1 \({}^{\circ}\)C, Omega, Northbank, UK) to acquire the temperatures at different points on the conductors, which were averaged.  
The DC resistance \(r_{DC}\) was measured using a \(\upmu\Omega\) meter (Centurion II, 10-200 A, \(\pm\)0.1 \(\upmu\Omega\), Raytech, Wohlen, Switzerland), while the AC resistance \(r_{AC}\) = (\(\Delta V/I\))\(\cdot\)cos\(\varphi\) was measured by measuring the voltage drop \(\Delta V\) along the length of one meter of conductor, the current \(I\) flowing through the conductor, and the phase shift between \(\Delta V\) and \(I\). A data acquisition card (USB-6343, 16 bits, 500 kS/s, National Instruments, Austin, TX, USA) was used to acquire the \(\Delta V\) and \(I\) values. This setup allows an accuracy in measuring \(r_{AC}\) better than 1 \(\upmu\Omega\).  
Figure 5 shows a schematic of the indoor experimental setup consisting of a conductor loop, a high-current transformer, and all associated instrumentation. Note that the length of the conductors analyzed is approximately 8 m.  
Figure 4: HTLS (Dhaka ACCC/TW) conductor analyzed in this work.  
Figure 3: Strand configuration of the investigated ACSR conductors. (**a**) Three-layer ACSR conductor. (**b**) Two-layer ACSR conductor. (**c**) Single-layer ACSR conductor.
## 4 Simulation Model  
We consider a case study to simulate the deployment of the backhaul network on Sheil Road in Liverpool, by the Fairfield Medical Centre. This deployment was developed based on a 5G network planning tool, which provides an online copy of Kensington in Liverpool, for the Liverpool 5G network ([[https://www.cgasimulation.com/network-planning-tool/](https://www.cgasimulation.com/network-planning-tool/)]([https://www.cgasimulation.com/network-planning-tool/](https://www.cgasimulation.com/network-planning-tool/)), last accessed: 15 December 2021). From this tool, we were able to build a simulated deployment of nodes and model their performance in an urban environment [32]. Figure 3 illustrates the Fairfield Medical Centre modelled by the planning tool. Moreover, the tool provides information for each of the nodes in the network, such as the site of the node, the kind of installation, the direction, the latitude and the longitude, which we used to build our simulation.  
Using these sites, we were able to obtain details on the link distances and orientations to build a MATLAB-based simulation of this end-to-end deployment that allowed us to carry out the performance evaluation illustrated in the next section. These sites are simulated based on IEEE 802.11 ad, which defines a directional multi-gigabit (DMG) transmission format operating in the unlicensed band around 60 GHz, using single carrier (SC) PHY link [32]. The channels, on the other hand, are modelled as a multipath fading channel using the model environment of'street canyon hotspot' [33].  
We evaluated the individual link performances in terms of their Packet Error Rate (PER) as a function of the signal-to-noise ratio and for different modulation and coding scheme values. Specifically, first, a set of SNR points in dB were selected based on each simulated MCS. Second, for each SNR point, multiple packets were transmitted through a TGay millimeter wave channel [34], then synchronized and demodulated, and the received Physical Layer Convergence Procedure Service Data Units (PSDUs) were recovered. Third, the received PSDUs were compared to those transmitted to determine the number of packet errors and hence the PER. The number of packets considered to compute the PER for each SNR point depends on the following parameters: (1) the maximum number of packet errors simulated at each SNR point. When the packet error number reaches this limit, the simulation at this SNR point is finalized; (2) the maximum number of packets simulated at each SNR point, which limits the length of the simulation if the packet error limit is not reached. In order to obtain meaningful results, we considered 100 and 1000 as the maximum number of packet errors and maximum number of packets, respectively [34].  
Figure 3: Copy of the Fairfield Medical Centre deployment.
## 2 Results  
### Isolated Glandular Trichome Disc Cell Response to In Vitro Conditions for 24 h  
A total of 1400 proteins were quantified via SWATH-MS analysis in glandular trichome disc cells across all timepoints and under all treatment conditions in the _in vitro_ agar plate experimental set up (Supplementary Dataset S1).  
Under control conditions, 24 h plate incubation resulted in changes to the GT disc cell proteome, with 45 decreasing differentially abundant proteins (DAPs) (3.2% of total quantified proteins) and 74 increasing DAPs (5.3% of total quantified proteins) (Figure 1A). Enrichment analysis of these DAPs identified several biological processes that were affected over the course of 24 h (Figure 1B). Biological processes (BP) that were enriched in DAPs that increased included purine metabolism, amino acid metabolism, ribosomal assembly, aerobic respiration, catabolism, and terpenoid biosynthesis. In contrast, BP enriched among decreasing DAPs included responses to desiccation and high light intensity, gluconeogenesis, and monocarboxylic acid metabolism.  
Of the cannabinoids that can be quantified with the HPLC method used, only CBGA, THCA, and CBCA were measured above detectable threshold levels from all isolated GT disc cell samples.  
Metabolite analysis on the isolated disc cells revealed that CBGA significantly decreased by 70% over the course of 24 h under control conditions. While not significant (\(p=0.089\)), THCA also tended to decrease by 37% in content over the same period (Figure 1C).  
Several key proteins involved in stress response and energy production/allocation were differentially abundant after 24 h of incubation (Figure 1D). RESPONSIVE TO DEHYDRATION22 (RD22) and DEGP PROTEASE 7 (DEG7) decreased in abundance while Figure 1: Cannabis (_Cannabis sativa_ L.) glandular trichome disc cells were incubated _in vitro_ on plates (_n_ = 4) under control conditions over 24 h. Proteome was compared at 24 h versus 0 h (**A**), and a gene ontology (GO) enrichment analysis identified top biological processes enriched in amongst differentially abundant proteins (**B**). Trichome cannabinoid content (**C**) and abundance of key regulatory proteins were quantified (**D**). Significance (denoted by _a_) by two-sample Student’s _t_-test. Proteins were deemed differentially abundant when _p_-value < 0.05 and when fold-change <0.67 or >1.5. Cannabinoids were significantly different if _p_-value < 0.05. Boxplots represent median, interquartile range, maximum and minimum, and outliers (closed circles). Abbreviations: CBGA, cannabinoid acid; THCA, tetrahydroocannabinolic acid; CBCA, cannabinoid acid; RD22, responsive to dehydration22; DEG7, degp protease 7; ATPd, mitochondrial ATP synthase subunit d; ACLA-1, ATP citrate lyase A-1. Listed in titles are protein names, closest arabidopsis homolog, and UniProt accession number.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Greedy Wiring  
Greedy wiring is one practical way to cope with the combinatorial nature of (14). It is demonstrated in Sections 5 and 6 to perform well and briefly summarized below:  
1. Start with \(s=0\) and \(\omega=\mathbf{0}_{N\times 1}\).
2. Update \(\omega\) such that it changes in at most a single component.
3. Increment \(s\).
4. If \(s\leq C/K\), go to step 2.  
For quadratic distortion measures, this algorithm is equivalent to matching pursuit [44]. Note that orthogonal matching pursuit as in [45] is not applicable, since restricting the coefficients to signed powers of two results in a generally sub-optimal least-squares solution that does not necessarily satisfy the orthogonality property.
## 5 Baseline 10 MW OWT
### Validation of the FEA  
#### 5.2.1 Modal Analysis  
The modal frequencies of the OWT support structure calculated from the present FEA model are compared against those reported in the literature. The comparison results are presented in Table 6. As can be observed in Table 6, the first two frequencies of both fore-aft and side-to-side bending modes calculated from the present FEA model show good agreement with those reported in References [8; 83], with the maximum percentage difference (11.11%) observed for the second side-to-side bending mode. This confirms the validity of the present FEA model. See Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode Frequencies (Hz)** & **Present** & **References [8; 83]** & **\% Difference** \\ \hline
1 st side-to-side bending & 0.21346 & 0.21320 & 0.12\% \\
1 st fore-aft bending & 0.22297 & 0.21629 & 2.99\% \\
2 nd side-side bending & 1.1602 & 1.0313 & 11.11\% \\
2 nd fore-aft bending & 1.5292 & 1.6561 & \(-\)8.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the support structure mode frequencies with reference values.  
Figure 5: The 3D display of structural model with applied loads and boundary conditions.