esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Bench Press and Squat Velocity Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **BP + S** & **S + BP** & **Difference (95\% CI)** & \(p\)** & \(g\)**-Value** \\ \hline MPV-S1 (m\(\cdot\)s\({}^{-1}\)) & 0.62 \(\pm\) 0.09 & 0.65 \(\pm\) 0.10 & \(-\)0.03 (\(-\)0.08 to 0.02) & 0.22 & \(-\)0.29 \\ MPV-S2 (m\(\cdot\)s\({}^{-1}\)) & 0.63 \(\pm\) 0.09 & 0.63 \(\pm\) 0.12 & \(-\)0.01 (\(-\)0.07 to 0.06) & 0.86 & \(-\)0.05 \\ MPV-S3 (m\(\cdot\)s\({}^{-1}\)) & 0.61 \(\pm\) 0.07 & 0.61 \(\pm\) 0.10 & 0.00 (\(-\)0.04 to 0.04) & 1.00 & 0.00 \\ PV-S1 (m\(\cdot\)s\({}^{-1}\)) & 1.14 \(\pm\) 0.14 & 1.21 \(\pm\) 0.13 & \(-\)0.07 (\(-\)0.19 to 0.04) & 0.18 & \(-\)0.49 \\ PV-S2 (m\(\cdot\)s\({}^{-1}\)) & 1.15 \(\pm\) 0.11 & 1.16 \(\pm\) 0.18 & \(-\)0.01 (\(-\)0.13 to 0.11) & 0.86 & \(-\)0.06 \\ PV-S3 (m\(\cdot\)s\({}^{-1}\)) & 1.12 \(\pm\) 0.10 & 1.11 \(\pm\) 0.17 & 0.00 (\(-\)0.10 to 0.11) & 0.95 & 0.02 \\ TPV-S1 (m\(\cdot\)s\({}^{-1}\)) & 735.50 \(\pm\) 93.44 & 849.30 \(\pm\) 311.05 & \(-\)113.80 (\(-\)324.17 to 96.57) & 0.25 & \(-\)0.45 \\ TPV-S2 (m\(\cdot\)s\({}^{-1}\)) & 802.90 \(\pm\) 149.38 & 810.00 \(\pm\) 164.12 & \(-\)7.10 (\(-\)83.25 to 69.05) & 0.84 & \(-\)0.04 \\ TPV-S3 (m\(\cdot\)s\({}^{-1}\)) & 806.20 \(\pm\) 195.80 & 881.80 \(\pm\) 216.52 & \(-\)75.60 (\(-\)191.78 to 40.58) & 0.18 & \(-\)0.34 \\ \hline \hline \end{tabular} \({}^{*}\)_p \(<\) 0.05; \(p\)_ in bold denotes statistical significance; data are presented as mean \(\pm\) standard deviation unless otherwise stated. BP + S, bench press followed by squat; S + BP, squat followed by bench press; CI: confidence interval; g: Hedge\({}^{*}\)s g effect size; MPV: mean propulsive velocity; PV: peak velocity; TPV: time to peak velocity; S, set.  
\end{table} 
Table 3: Comparison between training protocols on squat velocity performance after each set.
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 3 Numerical Simulation
### LM Model  
The LM model of article [13] is shown in Figure 3.  
In the LM model, the tether is discrete into n + 1 mass points and n massless springs. Each mass point is connected by two springs at the front and back to form a line. For each mass point, the force diagram is shown in Figure 4, where \(m_{i}\), \(F_{Ci}\), \(F_{Gi}\) and \(\mathbf{r}_{i}\) are the mass, centrifugal force, the gravitational force and the position vector of the \(i\)-th discrete mass point, respectively. \(l_{i}\) is the length of the \(i\)-th spring, \(T_{i}\) is the force of the \(i\)-th spring, \(\alpha_{i}\) is the angle between the \(i\)-th spring and the \(X\)-axis and \(\theta_{i}\) is the angle between the direction of \(F_{Gi}\) and the \(X\)-axis.  
According to the force balance, the balance equation of the mass point can be obtained:  
\[\begin{split} 0&=T_{i}\cos\alpha_{i}-T_{i-1}\cos\alpha_{i-1} +F_{Ci}-F_{Gi}\cos\theta_{i}\\ 0&=-T_{i}\sin\alpha_{i}+T_{i-1}\sin\alpha_{i-1}-F _{Gi}\cos\theta_{i}\end{split} \tag{26}\]  
where \(F_{Ci}\), \(F_{Gi}\) and \(\theta_{i}\) can easily be obtained from the position vector \(\mathbf{r}_{i}\) of the mass point. The position vector can be obtained from the anchor point, the angle \(\alpha_{i}\) and the length of the each spring:  
\[\mathbf{r}_{i+1}=\mathbf{r}_{i}+\big{[}l_{i}\cos\alpha_{i}\quad l_{i}\sin \alpha_{i}\big{]}^{\top} \tag{27}\]  
where \(l_{i}=(\frac{T_{i}}{EA}+1)\times l_{0i}\), \(EA\) and \(l_{0i}\) are the axial stiffness and the original length of the spring, respectively.  
Add the boundary conditions at the end of the tether:  
\[\begin{split} 0&=-T_{n}\cos\alpha_{n}+F_{Cc}-F_{Gc} \cos\theta_{c}\\ 0&=T_{n}\sin\alpha_{n}-F_{Gc}\cos\theta_{c}\end{split} \tag{28}\]  
From Equations (26) and (28), the spring force \(T\) and the angle \(\alpha\) can be solved by numerical methods.
## 1 Introduction
Mangrove biomass assessment and mapping can be done using LAI as an index [27; 28]. There have been numerous studies mapping the spatial extent of mangroves and their spatio-temporal changes, and several studies on mangrove ecosystem functions and processes in the past decade [29; 30; 31; 32; 33]. However, research focusing on fragmentation and its impact on mangrove biodiversity, biophysical characteristics, and ecosystem processes and functions within the fragmented landscape are scarce. Mangrove fragmentation is assumed to distress ecological processes and ecosystem functioning; thus, establishing a relationship between mangrove cover change, fragmentation, and ecosystem processes will ultimately provide important baseline data for the formation of a stronger policy to protect the remaining mangroves and achieve Malaysia's Sustainable Development Goals, particularly goals 13, on climate action; 14, on life below water; and 15, on life on land. Remote sensing and Geographic Information System (GIS) are cost- and time-effective technologies that can be used to support large scale mangrove mapping, monitoring its changes [34] and analyzing the landscape's fragmentation [1; 2; 29]. The launch of the Landsat mission promoted the creation of global and regional mangrove maps at 30 m spatial resolution. These maps can be further improved using freely available satellite images with better spatial resolution, such as that of Sentinel-2. Digital image classification algorithms have been widely developed for obtaining land use and land cover using remotely sensed images. Pixel-based classification involving the unsupervised or supervised method are very common and useful. In the last decade, object-oriented, neural network, decision tree, and machine learning based classification algorithms, with advantages of improved accuracy and reduced computational time, have become more popular. Reviews by [PERSON] et al. [35] and [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [25] concluded that most of the classification algorithms can give overall accuracy above 80%, while, with improvements such as training samples selection, satellite data quality etc., a higher classification accuracy (~90%) can be achieved. Better classification results can improve the mangrove extent mapping and the change detection analysis to accurately detect the gain and loss of mangroves. This study aims to describe the mangrove loss and fragmentation in the Iskandar Malaysia region over the 2000-2019 period and analyze the loss and fragmentation effects on mangrove LAI and GPP values. More specifically, we intend to (1) document the loss and gain of mangroves over a period of two decades (2000-2019), (2) analyze mangrove fragmentation, and (3) relate and analyze the loss/fragmentation impacts on the LAI and GPP values in order to define the function of mangroves to assimilate CO\({}_{2}\) in relation to mangrove cover changes and fragmentation in the study area. It is hypothesized that a higher loss of mangrove canopy cover and fragmented areas will decrease the LAI and GPP of the ecosystem.
## 3 Results and Discussion
### Chemical Composition of Essential Oil
_flavovirens_ collected in the central-east part of the country. The volatile compound \(\alpha\)-pinene, which was quantified at a relative amount of 17.04% in the EO of _T. polium_ collected from Ref, is less abundant in _T. luteum_ subsp. _flavovirens_ (6%). Moreover, \(\alpha\)-bisabolol, (+)-\(\gamma\)-gurjunene, zierone, and \(\tau\)-muvrolol are not present in the essential oil of _T. polium_ from Ref at all [33]. On the other hand, the EO of _T. polium_ subsp. _gabesium_ from Gafsa (southwest Tunisia) [20] is characterized by a high abundance of \(\beta\)-pinene (35.97%), which was not even detected in the EO from _T. luteum_ subsp. _flavovirens_. Furthermore, large discrepancies were observed for other abundant compounds since the main constituents of the EO from _T. luteum_ subsp. _flavovirens_ have not been detected in the EO from the aerial parts of _T. polium_ subsp. _gabesianum_. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Compounds** & **R.T. (min)** & **Obs. LRI \({}^{*}\)** & **Lit. LRI \({}^{*}\)** & **Rel. \%** & **Class** \\ \hline Oleic acid & 39.1 & 2141 & 2144 & 0.5 & Other \\ Total identified & & & & 83.9 & \\ Monoterpene hydrocarbons (MH) & & & & 10.5 & \\ Oxygenated monoterpenes (OM) & & & & 8.9 & \\ Sesquirepene hydrocarbons (SH) & & & & 16.7 & \\ Oxygenated sesquiterpenes (OS) & & & & 43.8 & \\ Oxygenated diterpene (OD) & & & & 2.5 & \\ Others & & & & 1.5 & \\ \hline \hline \end{tabular} \({}^{*}\)Identification by comparison with NIST 2014 MS spectra database. R.T.: retention time; LRI: linear retention index; Rel. %: relative percentage; UNK: unknown; MH: monoterpene hydrocarbon; OM: oxygenated monoterpene; SH: sesquiterpene hydrocarbon; OS: oxygenated sesquiterpene; OD: oxygenated diterpene.  
\end{table} 
Table 1: _Cont._  
Figure 2: Chemical structures of the most abundant constituents of the essential oil from the aerial parts of Tunisian _Teucrium luteum_ subsp. _flavovirens_. Overall, these data show high chemical variation in the species within the Teucrium genus originating from Tunisia and other North African Mediterranean regions. Variations in the chemical composition of EOs might be due to several factors: environmental factors (temperature, humidity and altitude), geographical origin, plant organ, time of picking, storage of plant material, and individual genetic variability [27; 28; 33]. Finally, different extraction protocols can also contribute to the observed variations, as already reported by other authors [34; 35].
# A Simplified Pointing Model for Alt-Az Telescopes
###### Abstract  
The telescope pointing model plays a crucial role in characterizing and explaining the inaccuracies in the telescope's pointing. It uses mathematical models to express these errors and suggests methods to calibrate and rectify them. By employing the pointing model, one can gain a deeper understanding of the discrepancies in the telescope's pointing accuracy at various positions and orientations. This article introduces the establishment and validation of a simplified pointing model for correcting the pointing errors of an alt-az telescope. The traditional telescope pointing model, with its complex formulas, makes it difficult to quickly apply for the calibration of the telescope's pointing. This method utilizes two-dimensional surface fitting to obtain the telescope's pointing error function, enabling a rapid determination of the telescope's pointing model, which is then validated on the Planetary Atmospheric Spectroscopic Telescope (PAST). Using this method, the azimuth axis and altitude axis errors (root mean square error--RMSE) of the PAST telescope are 6.8 arcs and 3.8 arcs, respectively.  
pointing models; alt-az telescope; mounts 2023
# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 3 Proposed Methods  
### Overall Network Framework  
The overall network framework of the self-supervised-learning-based color document show-through-removal network, CDSR-CycleGAN, is illustrated in Figures 1 and 2. CDSR-CycleGAN transforms the unpaired image show-through-removal problem into an image-to-image generation problem, generating non-show-through images through a self-supervised-learning cycle-consistency network. This section provides a detailed description of the network composition, which consists of two parts: a show-through-removal framework and a show-through-generation framework.  
In the diagram, \(X\) represents the input show-through image. \(De\) represents the show-through-removal generator, and \(De(X)\) represents the show-through-removal image. \(Y\) represents the clear image. \(Re\) represents the show-through generator, and \(Re(Y)\) represents the synthetic show-through image. \(S\) represents the similarity network; \(S\_De\) and \(S\_Re\) are, respectively, used to generate similarity non-show-through images and show-through images. \(D_{\text{show-through}}\) is the adversarial discriminator for classifying between the real show-through image and the generated show-through image. \(D_{\text{non-show-through}}\) is the adversarial discriminator for distinguishing between the real non-show-through image and the show-through-removal image. The light blue lines in the figure represent pseudo-similarity loss and cycle-consistency loss, respectively.  
Since Figures 1 and 2 share a similar framework structure, we will use Figure 1 as an example to illustrate the procedural steps of our method. As shown in Figure 1, the input image \(X\) with show-through is fed into the show-through-removal-generator \(De\), resulting in the bleeding-free image \(De(X)\). Then, \(De(X)\) is passed through both the show-through generator \(Re\) and the similarity network \(S\_De\), producing the bleeding-added image \(Re(De(X))\) and the similarity image \(S(De(X))\), respectively. Subsequently, the cycle consistency loss\(\_1\) and the pseudo-similarity loss are computed. Both \(De(X)\) and \(S(De(X))\) are then fed into the discriminator \(D_{\text{non-show-through}}\) to determine whether they are bleeding-removed images. Finally, \(S(De(X))\) is input into the show-through generator \(Re\) to generate the bleeding-added image \(Re(S(De(X)))\) and the cycle consistency loss\(\_2\) is computed.
## 5 Performance Evaluation
### Experiment 6  
For a more thorough understanding of the performance of BLJCAR with longer communication path in multiple rate case, the rate of one of both flows is fixed. In this experiment, the rate of flow 1 is fixed to 400 kbps and the rate of flow 2 varies from 400 kbps to 550 kbps with step of 10 kbps. Figure 12a-d shows the jitter performance of flow 1, network throughput, average end-to-end delay and packet loss ratio, respectively.  
Figure 11: (**a**) Average packet jitter _versus_ offered load; (**b**) Network throughput _versus_ offered load; (**c**) Average end-to-end delay _versus_ offered load; (**d**) Packet loss ratio _versus_ offered load.  
Figure 12: _Cont._  
From Figure 12, it is seen that BLJCAR can improve the jitter, network throughput, average end-to-end delay and packet loss ratio as compared with the ONC policy.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 4 Discussion
### Sociological Impact of Flooding  
Flooding also unequally affects local communities throughout the ROIs in the Mozambique Channel. According to the INGD, 227 latrines and 24 schools were damaged in the Zambezia region alone [29]. This poses an immediate water, sanitation, and hygiene (WASH) problem, whereby impacted communities' basic needs are not met and are more vulnerable to the appearance of water-borne disease [10]. In addition, as communities become scattered, flooding places women and girls at increasing risk of organized trafficking, gender-based violence, lack of access to menstrual health management [38] and reduced access to food and resources [39]. In the future, an integrative approach to cyclone impact assessment should look at the effects of TCs on women and girls' vulnerability and gender-based violence. This is, however, currently limited by the scarcity of survey data available and being conducted in the Mozambique Channel. This has also been emphasized by COVID-19, whereby international NGOs and UN bodies did not conduct health and gender surveys for two to three years, leaving many women and girls unrepresented in data.
## 3 Results and Discussion  
### Analysis of GC-MS  
#### 3.1.1 Volatile Compounds in Different Processing Stages  
Volatile compounds in Daokou braised chicken at different processing stages were detected by GC-MS, as shown in Table 1. The relative contents of alcohols changed significantly during processing; these relative contents were second only to aldehydes (\(p\) < 0.05). In particular, the content of enols was the highest in frying stage, but this significantly decreased after 2 h of braising, which may have been due to conversion into aldehydes, ketones and other substances [22]. It has been reported that the oxidation of polyunsaturated fatty acids leads to the production of alcohols during heating processes. Saturated alcohol has little effect on the flavor of the braised chicken because of its high odor threshold (500-20,000 \(\mathrm{\SIUnitSymbolMicro g}\)/kg), while unsaturated alcohol has a certain effect on flavor because of its low odor threshold. Additionally, 1-Octene-3-ol and (E)-2-Octenol contributed significantly to the flavor of the Daokou braised chicken due to their high concentrations and low odor thresholds. The former compound could confer a mushroom aroma upon Daokou braised chicken [23]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NO.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 4 Discussion  
### NDVI, EVI, GOSIF, and GPP Loss at Different Drought Levels  
NDVI, EVI, GOSIF, and GPP showed an increasing trend from 2002 to 2020, indicating an improvement in vegetation growth (Figure 3). This can be explained by the increased rainfall and ecological protection in the study area [26; 58]. Lower values of NDVI, EVI, GOSIF, and GPP were found in 2007, 2009, 2016, and 2017, which may be correlated with the large-scale droughts in these years [59]. The results indicate that droughts are the key drivers of vegetation change in Inner Mongolia, which has been reported in previous research [26; 60]. In this study, an overall decrease in grassland vegetation growth during the 2007 and 2017 drought events was found in Inner Mongolia (Figures 8 and 10). This finding aligns with similar observations in other regions, such as southwest or northern China, Midwestern U.S., the tropical Amazon, and temperate Europe [2; 61; 62]. Moreover, we found that the decrease in grassland GOSIF was much more substantial than that observed in NDVI and EVI during the 2007 and 2017 drought events (Figures 9 and 11). This heightened sensitivity of GOSIF to drought events in Inner Mongolia's grassland is significant. SIF captures information about vegetation photosynthesis, such as Absorbed Photosynthetically Active Radiation (FPAR) and light use efficiency [63]. As a sensitive indicator of vegetation photosynthesis, SIF can more realistically and sensitively reflect variations in canopy photosynthesis compared to traditional vegetation indices [19]. For example, studies conducted in Midwestern U.S. and Hulun Buir grassland have demonstrated that GOSIF is more sensitive to drought than NDVI and EVI [20; 62]. Previous studies have also highlighted the superior performance of SIF in capturing variations in GPP during drought events [37; 62]. Our findings further support the viewpoint, as the trend of GPP closely aligns with that of GOSIF for the 2007 and 2017 drought events (Figures 9 and 11). The significant correlation between droughts and GOSIF also supports previous studies, suggesting that GOSIF has the advantage in assessing drought-induced stress on vegetation in the Yellow River Basin [64], the three-River Headwaters Region, China [65], and Europe [66].
# Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
###### Abstract  
This work proposes a fast and straightforward method, called natural point correspondences (NaPoCo), for the extraction of road segment shapes from trajectories of vehicles. The algorithm can be expressed with 20 lines of code in Python and can be used as a baseline for further extensions or as a heuristic initialization for more complex algorithms. In this paper, we evaluate the performance of the proposed method. We show that (1) the order of the points in a trajectory can be used to cluster points among the trajectories for road segment shape extraction and (2) that preprocessing using polygonal approximation improves the results of the approach. Furthermore, we show based on \"averaging GPS segments\" competition results, that the algorithm despite its simplicity and low computational complexity achieves state-of-the-art performance on the challenge dataset, which is composed of data from several cities and countries.  
GPS; trajectory; segments; averaging; road network Article
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 3 Overview of Observational Methods and Platforms
### Volunteer Monitoring  
Volunteer monitoring often referred to as citizen science, is the collection of scientific samples and data by members of the general public [100]. It is often carried out in collaboration with professional scientists to record biodiversity or monitor the environment. It can provide scientifically robust data at large spatial coverage and high frequency (e.g., CoCo-RAHs, [[https://www.cocorahs.org/](https://www.cocorahs.org/)]([https://www.cocorahs.org/](https://www.cocorahs.org/)) accessed on July 2021 Weather Observation Website [WOW] [[https://www.metoffice.gov.uk/](https://www.metoffice.gov.uk/)]([https://www.metoffice.gov.uk/](https://www.metoffice.gov.uk/)), accessed on July 2021). Volunteer monitoring is also an effective approach for engaging the public in science and environmental monitoring, empowering individuals and communities to understand and manage the quality of their local environment [101]. Volunteer monitoring data can be complementary to remote sensing data, as they can be used together to provide independent cross-validation [34], although data quality of specific citizen science applications are not quite on par with scientific spectrometers [102].  
The number of citizen science projects relevant to water quality monitoring is growing globally (e.g., see [[https://scistarter.org/](https://scistarter.org/)]([https://scistarter.org/](https://scistarter.org/)), accessed on July 2021), and the development of mobile phone apps to gather geo-located data (e.g., Lake Observer, www.lakeobserver.org, accessed on July 2021) electronically makes integration with satellite data relatively easy. Some relevant examples of citizen science projects focused on water quality are listed in Table 1. Key challenges still exist in relation to quantifying the uncertainty of citizen science data, incorporating both measurement and sampling bias (locations and time/date of sampling), and also in recruitment and retention of citizen scientists [103; 104], but integration with remote sensing and other in situ measurements could help overcome these challenges.
## 4 Conclusions
As a follow-up to this study, future related studies may be carried out in three directions. First, because the study performed a static IO analysis using the 2017 IO table, it is necessary to collect IO tables for a number of years and perform multi-period IO analyses using them. For example, a dynamic IO model may be considered [41]. Second, although the study used the national IO table, multi-regional IO analysis can be carried out by employing a multi-regional IO table. This would allow quantitative analysis of inter-regional effects as well as intra-regional effects [42; 43]. Third, various further implications can be obtained if comparative IO analysis is performed using IO tables for other countries with economic structures similar to that of South Korea, considering that this article utilized the IO table only for South Korea. The advantages and disadvantages of the country's IHS and DHS sectors in terms of the economic effects could be clarified through a comparative analysis. All three authors played their own significant roles in planning and writing this note. ([PERSON]-H.K.) proposed ideas for the paper, laid out the basic framework for the study, and wrote half of the paper; (S.-Y.K.) analyzed the data and wrote Sections 2 and 3; and (S.-H.Y.) supervised the entire course of the research, wrote part of the paper, and refined the entire paper. All authors have read and agreed to the published version of the manuscript. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20184030202230). The authors declare no conflict of interest.