esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Present day GNSS offers a variety of signals from different satellite constellations and at various frequencies. This paper is based on the work conducted in the project SiL 2.0 and will focus on the study of multi-constellation/multi-frequency GNSS signals as received on top of construction equipment as part of the SiL 2.0 dissemination solution. This paper aims to study the impact of foliage and reflective environments on the various signals of multi-constellation GNSS with a focus on GPS and Galileo. Signal strength indicators (SSI) have been used as a measure to understand the shadowing environment around a stationary GNSS antenna mounted on an excavator. It is quite clear from the analysis that traditional GPS L2 signals, C2W and L2W, are weaker in strength than the L2C and L1 C/A signals, and this is found to be consistent for all of the GPS satellites. The effect of signal degradation due to bending over sharp metallic edges is also discussed.  
GNSS; multi-constellation; signal bending or diffraction; reflective environment; foliage signal degradation +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 5 Commercial Potential  
According to FAO, worldwide algal output (including cultivation and wild collection) rose by more than 60 times between 1950 and 2019, from 0.56 million (wet) tons to 35.82 million tons. In 2019, 54 countries/territories contributed 35.8 million tons of global algal output (including seaweeds and microalgae), with cultivation accounting for 97% of the total. Green algae contributed with 32,926 tons; the highest production countries were the Republic of Korea through cultivation and India through wild collection [161].  
Ulva species have been farmed in many regions of the world in experimental commercial systems due to their potential utility as food, animal feed, biofuel, and bioremediation, as detailed more below. The initial trials of commercial _Ulva_ culture, according to [PERSON] 1983 [156], documented the inaugural commercial _Ulva_ culture trials in America, coinciding with the initiation of the US Ocean Food and Energy Farm project in 1973, where _Ulva_ was employed as biomass for biomethane conversion. However, due to the challenges associated with offshore development methods, Ulva production for biofuels was subsequently discontinued [156]. In the meantime, [PERSON] et al., 2002 [162] and [PERSON] and [PERSON], 2008 [163] showed there is a folklore in Japan to eat _Ulva_ as food, and if we look to the statistics [93], it is sold as \"aonori\" and \"aosa,\" reaching around 1500 tons of dry weight per year. In 2019, according to FAO, 2021, _Ulva_ sp. contributed 2155 tons to different industries [161].  
In addition, _Ulva_ has also been cultivated for other sectors, for instance as a biofililter in integrated multi-trophic systems where _Ulva_ culture is combined with aquaculture of marine animals reported by [PERSON] et al., 2000 [164] in Israel and [PERSON] et al., 2014 [165] in Saudi Arabia. [PERSON] et al., 2008 [166] reported that it has been intensively studied in South Africa as animal feed; also, _Ulva_ was cultivated in outdoor tanks to investigate its biomass production potential for bioenergy in Denmark according to [PERSON] al., 2011 [167] and [PERSON] et al., 2016 [168] referred to the cultivation in Australia to explore its potential in nutraceuticals and cosmeceuticals [93].
## 3 Materials and Methods
### Water Demand Estimation
#### 3.5.3 Water Demand for Agriculture and Forest Uses  
Under normal and new normal conditions, the water demand for agriculture and forest uses was estimated based on the evapotranspiration coefficient and reference evapotranspiration [50], as shown in the following equation.  
\[\text{ET}_{\text{c}}\text{= K}_{\text{c}}\text{\times}\text{ET}_{\text{o}} \tag{5}\]  
where \(\text{ET}_{\text{c}}\) is the water requirement (mm/day), \(\text{K}_{\text{c}}\) is the evapotranspiration coefficient, and \(\text{ET}_{\text{o}}\) is reference evapotranspiration (mm/day).  
In particular, the water demand for agriculture and forest use was calculated based on the area of each agriculture and forest type, the evapotranspiration coefficient (\(\text{K}_{\text{c}}\)) (see Appendix A Table A1), and reference evapotranspiration using the Penman-Monteith method (see Appendix A Table A2).  
In this analysis, the water balance between 2020 and 2029 was evaluated with and without the consideration of ecological water requirements based on the estimated water  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Community Types** & **Water Consumption Rate (Liters/Person/Day)** \\ \hline
1 & City municipality & 250 \\
2 & Town municipality & 200 \\
3 & Sub-district municipality & 120 \\
4 & Outside the municipality & 50 \\ \hline \hline \end{tabular} Source: Royal Irrigation Department [48].  
\end{table}
Table 4: Water consumption rates in different community types.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Tourist Types** & **Water Consumption Rate (Liters/Person/Day)** \\ \hline
1 & Tourists & 300 \\
2 & Excursionists & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The water consumption rates of tourists and excursionists.  
supply and demand under two different scenarios (dry year and wet year) and two different conditions (normal and new normal).  
In particular, the annual and monthly water balance without the consideration of ecological water requirements was evaluated based on the annual and average monthly water supply derived from the SWAT model. Additionally, the annual and monthly water balance with the consideration of ecological water requirements was evaluated based on water supply, which was derived from the SWAT model for 70% of the time and 0.5 m\({}^{3}\) per second of flow (standard criteria) under the criteria of minimum water from the flow duration curve at the outlet at X.191 station of Kholong Bang Yai watershed, as suggested by Southern Region Irrigation Hydrology Center, Royal Irrigation Department [51] (see Figure 5 and Table 6). On this basis, Phuket Island's annual ecological water requirement was 147.64 million m\({}^{3}\) per year, while the monthly ecological water requirement was 12.30 million m\({}^{3}\) per month.
###### Abstract  
The multi observations from the China Seismo-Electromagnetic Satellite (CSES) were presented and analyzed during the biggest magnetic storm on 25-26 August in the quiet solar activity year of 2018, together with the Swarm satellite and GNSS TEC (Global Navigation Satellite System, Total Electron Content). The whole tempo-spatial evolutional process was demonstrated in electromagnetic fields and in-situ plasma parameters within the whole magnetic storm time period of three phases, the main phase with quick decrease in SYM-H, the quick recovery phase, and the slow recovery phase. Strong correlations were revealed in time and space between electric fields and electron density. During the main phase, the penetrated electric field was the major factor to induce the injection of electric fields to low latitudes even to the equator and contribute to constructing the double peaks of Ne at altitudes above 500 km of CSES in daytime. In the quick recovery phase, Ne depletion was found in low middle and low latitudes in the daytime, associated with a quick decrease in solar wind dynamic pressure, but in the nightside Ne maintained or increased. Due to the high solar wind speed following the quick recovery phase, it controlled the enhancements in an electric field below 1125 Hz at medium and low latitudes in daytime and produced similar structures in a 225 Hz electric field with the mid-latitude trough of Ne in local nighttime and maintained their equator-ward movements in this time period. Ne/TEC showed typical local time-dependence in this magnetic storm, which illustrated that although the electron density in the ionosphere was mainly caused by this solar activity event, local background environments must also not be ignored for their final evolutional modes.
## 3 Results and Discussion
### Characterization of Volatile Compounds of FAA Tea in Four Harvest Times  
#### 3.4.1 Analysis of Volatile Compounds by HS-GC-IMS  
HS-GC-IMS is distinguished by its simplicity, speed, and high sensitivity. Figure 5a,b show the three-dimensional and two-dimensional spectra of FAA tea, illustrating the differences in volatile compounds across various harvest periods. To compare this difference more obviously, the spectrum of FAA-3 was selected as a reference, with spectra from other samples subtracted from it. In this differential visualization, white indicates no difference, red signifies a higher concentration of a compound relative to the reference, and blue indicates a lower concentration. The two-dimensional difference spectrum (Figure 5c) clearly shows significant variations in volatile compounds between FAA tea samples from different harvest periods. To further investigate, all peaks were selected for fingerprint analysis.  
Figure 5: GC-IMS analysis of volatile components of FAA tea under different harvest times. (**a**) Three-dimensional HS-GC-IMS spectra of volatile organic compounds in FAA tea samples; (**b**) HS-GC-IMS two-dimensional spectra of volatile organic compounds in FAA tea samples; (**c**) differential HS-GC-IMS spectra of volatile organic compounds in FAA tea samples.  
Based on the identified compounds, PCA analysis was performed to explore the diversity among FAA samples, as shown in Figure 6a. The principal components PC1 and PC2 accounted for 51% and 18% of the total variance, respectively. Therefore, the distinct separation of FAA-6 from other FAA samples based on PC1 and PC2 reflects the significant differences among them. As can be seen in Figure 6b, the fingerprints of volatile flavor compounds in the FAA tea samples identified 44 known flavor compounds, including 20 aldehydes, 6 alkenes, 9 alcohols, 6 ketones, 2 ethers, and 1 ester. Aldehydes and alcohols were significantly higher in FAA-3 than in other harvest periods, while ketones peaked in FAA-5, and alkenes, ethers, and esters were most prevalent in FAA-6. Specific compounds like benzaldehyde, with its special almond aroma and (Z)-4-heptanal with grassy aroma, were more prominent in the early harvest period. With the prolongation of the harvest period, compounds, such as 6-methyl-5-hepten-2-one, pentanol, and nonanal, increased and then decreased, peaking during the middle to late harvest stages. Certain compounds, including hexanal, 2,3-butanedione, linalool, rose ether, \(\alpha\)-terpinadiene, \(\gamma\)-terpinene, \(\beta\)-pinene, heptanal, \(\alpha\)-pinene, (E)-2-hexenol, (E)-2-pentenen-3-one, penten-3-ol, octanal, \(\alpha\)-salicylic acid alkene, gibberell acid, camphor, gibberell acetate, and 1,8-eucalyptus brain, showed a gradual increase in content, with high levels observed during the late harvest stage. Others, such as (Z)-2-heptenal and pentanal, associated with fruity and bready aromas, were more abundant in the early and mid-late harvest periods. These findings highlight the dynamic changes in FAA tea's volatile flavor compounds across harvest stages, directly influencing its aromatic profile.
## 1 Introduction
The simulation results show that compared with NCR, in the network having many nodes, ANCR can reduce the network delay by about 50% and improve the network throughput by 66.67%. Although ANCR mainly deals with the problem in WSN applications, it can also be a good reference in other wireless networks. The main contribution of this paper is that it proposes an ANCR model which can work efficiently in real networks with different transmission success rates on links and different transmission success rates in both directions of a link.
## 4 Results and Evaluation
### Preparation
#### 4.1.3 Datasets  
It is important to note that our dataset comprises solely textual input sequences; it does not include images for validation purposes. This is because our methodology does not necessitate any form of retraining of the model.  
We directly used the large language model to generate a series of text data in various forms as the dataset, including the following:  
* Simple sentence text descriptions (easy), e.g., \"A plain with flat terrain\", \"A soft island\".
* Text descriptions containing descriptions of the map and some geographical concepts (medium), e.g., \"A mountain in the center of the map\", \"four wonderful mountains, on the four corners of the map\".
* Text descriptions containing a large number of geographical concepts and descriptions (hard), e.g., \"an island with a central plateau. Include a gentle slope on the eastern side leading down to a flat area, and a sharper incline on the western side rising towards a small peak\".
* Text descriptions directly specifying geographical features in numerical form (digit).  
The datasets mentioned above have been made public at our project homepage. Due to the limited space in this paper, we do not describe in detail the specific methods for generating them and their properties here. We have placed this content in the link mentioned above.
## 2 Numerical Model Establishment and Validation  
### Case Overview  
The national highway transportation hub near Furujima Station in Naha City spans the Aja River, with a box culvert for drainage embedded along the river. With the increasing demand for drainage capacity, a new drainage pipeline adjacent to the existing culvert was planned to enhance the serviceability. The new pipeline is situated to the left of the existing one, with a jacking distance of 60 m, as shown in Figure 1. Given that pipelines cross beneath the transportation hub and that the jacking zone is mainly composed of soft clay, the \"rectangular pipe jacking + PPJM\" method was employed to alleviate surface settlement during the construction of both the existing and new pipelines.  
The detailed case parameters are illustrated in Figure 2. The main underground structures were constructed by box culverts, both measuring 6.4 m in height and 7.4 m in width. The existing and new pipe roofs, installed via the PPJM method, comprise 28 and 19 steel pipelines, respectively, each with a diameter of 800 mm and a total length of 60 m. Each steel pipeline consists of multiple steel pipe strings, each 900 mm long, with a tail void of 20 mm, and the center-to-center distance between adjacent steel pipe strings is also 900 mm. The existing pipe roof follows a conventional layout, commonly known as a 'gate-shaped' system. In contrast, the new pipe roof is designed based on the existing structure, featuring steel pipelines only along its upper section and left side--an arrangement typically referred to as the 'L-shaped' system [12]. In addition, the tunnels are buried at a depth of 7.5 m and spaced 1.85 m apart, while the upper boundaries of the pipe roofs are positioned 5.7 m below the surface. Therefore, the spacing between the pipe roof and the tunnel is 1.16 m at the upper boundary and 0.505 m along the side.  
According to geological surveys, the soil layers encountered during the construction process are dominated by soft soils--namely, gravel clay and clay layers--with their mechanical properties presented in Table 1.  
Figure 1: The case location and detection plan layout.
## 2 Optimization Methods and Design Procedure
### Principle of Particle Swarm Optimization Algorithm
#### 2.2.2 Algorithm Test  
The classic multiobjective benchmark function test set ZDT test function was selected to test the M-MOPSO algorithm. The classical algorithm NSGA-II was used for comparative analysis. The selected ZDT test functions include ZDT1, ZDT2, ZDT3, and ZDT6 [19]. Each test function represents the characteristic of different kinds of multiobjective functions, including convex front solution, concave front solution, and discontinuous front solution set. The decision variables of test function ZDT1, ZDT2, and ZDT3 were set to 30 dimensions. The decision variable for the test function ZDT6 was set to 10 dimensions. M-MOPSO algorithm parameters were set at population size of 200, external file size of 100, and maximum iteration number of 500, and all comparator-algorithm-related parameters were set according to references. The NSGA-II algorithm was set to the same population size, external file size, and maximum number of iterations. Test results were paired with classical NSGA-II algorithm solutions and real Pareto curves, as shown in Figure 4.  
In Figure 4, the optimal results of the M-MOPSO algorithm for test functions are consistent with the real noninferior forward solution sets. The solution obtained for the ZDT6 function is better than that obtained by the NSGA-II algorithm. In terms of distribution, the M-MOPSO algorithm also performed well, and optimized results were evenly distributed. Whether convergent or distributive, multiobjective particle swarm algorithm with variable operation has obvious advantages in multiobjective problem and is an effective algorithm for multiobjective optimization.  
Figure 3: Flowchart of M-MOPSO algorithm.
## 2 Results
### HS-GC-IMS Analysis
#### 2.1.2 Differences in the Characteristic Volatile Fingerprints of SS  
In order to more obviously compare the differences of samples from different regions, the chemical composition in the samples was classified and presented as a fingerprint in Figure 1B. The main VOCs in SS are terpenoid substances, as shown in Figure 1B-a. In each region, the highest concentration of substances is 1-menthol and d-camphor. Other terpenoids have a relatively high concentration in the two kinds of A and B. In general, A contains a high concentration of terpenoids. The alcohol substances in the SS samples are summarized in Figure 1B-b. The content of 2-hexanol has the highest content in the sample, but 3-heptanol and 3-furanmethanol have significant differences in the sample, and the content of these two substances is higher in the three kinds of J, A, and S. 1-Octen-3-ol content is generally low in Z. Interestingly, as can be seen from the Figure 1B, there are also differences among different batches within the same region, which may contribute to their different storage methods--for example, different storage times and temperature [21; 22]. As shown in Figure 1B-c, the samples varied significantly according to the proportion of aldehyde substances in each. Trans-2-pentenal was higher in the J; (E)-hept-2-enal was higher in the N; benzaldehyde was higher in the J, A, and S. As shown in Figure 1B-d, there is no significant difference between samples according to the proportion of ester substances in each sample. There were relatively high levels of ethyl butanoate in J. The contents of ketone substances are summarized in Figure 1B-e. Acetophenone and cyclohexanone were present in high concentrations in all samples, but the 6-methyl-5-hepten-2-one had a relatively high concentration in B and nonan-2-one was higher in Z. In addition, there was a relatively high amount of p-cresol in A.  
However, the limitations of the currently available library for HS-GC-IMS hinder the qualitative analysis of the VOCs of SS. As shown in Figure 1C, twenty-nine species were not identified. In these undefined compounds, Figure 1C-a summarize substances with high content in the sample. As shown in Figure 1C-b, the samples varied significantly in the undefined compounds.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.5 Collision Avoidance II  
In [63], the authors developed a decentralized sensor-level collision avoidance policy for multirobot systems that can operate safely and efficiently in complex scenarios. This policy directly maps raw sensor data to steering commands, eliminating the need for perfect sensing of other robots or obstacles and avoiding tedious parameter tuning for different scenarios. The authors seek to bridge the gap between decentralized and centralized approaches, ultimately deploying their policy on physical robots in diverse scenarios, including navigation in dense human crowds.  
The authors in [63] used a multiscenario to train their collision avoidance policy. Their approach relies on the PPO RL algorithm that minimizes the average arrival time of all robots in a given scenario. The RL was performed for large-scale and distributed policy optimization on multiple robots. As shown in Figure 5, a robot used perceptions to classify the world around it into three instances: \"_simple, complex, and emergent_\" [63]. It employed many controllers in particular, including a PID policy for straightforward scenarios, an RL policy for complex scenarios, and a safe policy for emergent conditions. To optimize and move through barriers carefully, each robot independently learned through interactions with its environment, receiving rewards for reaching goals, penalties for collisions, and small penalties for large rotational velocities.  
The above-mentioned approach enhances the policy's robustness and generalization by exposing robots to diverse environments. The hybrid framework combines learned policy with traditional control methods, demonstrating practical safety and effectiveness in both simple and complex situations. The authors' thorough tests show that the hybrid  
Figure 5: Hybrid control system architecture for collision avoidance. For more information, please refer to Table 4.  
approach is useful and could be used in the real-world scenarios. It combines the flexibility of DRL with the reliability of traditional model-based control methods.
## 4 Implementation and Experimental Results
### The Platform of the Data Stream Classification  
In this work, four main tools were used: scikit-multiflow, Sklearn, Weka, and RapidMiner. Scikit-multiflow is an open source package that is used in Python programming language for data stream mining tasks, including classification and clustering. It was developed with ECOSYSTEM project by the University of Waikato, which includes a Massive Online Analysis (MOA) platform for stream mining and the Waikato Environment for Knowledge Analysis (Weka) for batch mining. Scikit-multiflow provides many popular mining techniques, flow generators, and concept drift detection techniques. In our implementation, it has been used to perform the data streaming and classification techniques.  
Sklearn is a free Python library for machine learning tasks. It contains many classification techniques, such as random forest and boosting. In this work, Sklearn was used for feature extraction from EEG data and also for data row aggregation. RapidMiner is another software that is used in this implementation for calculating the correlation among the features of the CHB-MIT dataset. It is developed for data science, including deep learning, machine learning, and text mining. Amazon AWS cloud service has been utilized in this implementation as an on-demand computational resource. The EC2 virtual machine VM was used as a master node which has 8 CPU cores, 32 GB memory, and a 300 GB SSD hard drive. Ubuntu Linux operating system, Python, scikit-multiflow, and Sklearn have been installed in the VM to perform the stream classification. On the other hand, a Raspberry Pi3 Model A+ unit is used as an edge node with a 1.4 GHz quad-core processor, 512 MB memory, and Raspberry Pi operating system with Python and the same packages in the master node. Pickle python object has been used for transmitting the classifier object from the master node to the edge node byte by byte across the network.