esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Parameters for PLA Through Additive Manufacturing: Taking Mixed Shapes as an Example
## 4 Conclusions  
1. The number of experiments can be effectively reduced using the Taguchi method. The factors influencing the printing quality can be found with ANOVA. The factors' influence can be obtained from the S/N ratio. Based on the actual printing and analysis, the nozzle gap height is the most important factor influencing dimensional accuracy. It is followed by the printing platform temperature and the nozzle temperature; the contribution values are 91.34%, 3.79%, and 2.50%, respectively. The Infill pattern is the most important factor influencing the tensile strength, followed by the nozzle gap height and the fill spacing; the contribution values are 62.93%, 19.60%, and 12.50%, respectively.
2. Comparing the results of the Taguchi and the response surface methods, the Taguchi method is better. The results show that the Dimensional accuracy increased to 82.20% and the tensile strength is increased by 5.731 MPa. The experimental S/N ratios of the optimized parameters of the experiment fall in the prediction confidence interval, meaning the parameters are reliable.
3. The research results indicate that nozzle gap height has a significant impact on both dimensional accuracy and tensile strength, while other processing parameters need to be adjusted according to the specific application. Further investigation will be conducted to explore whether there is an interaction between the two sets of processing parameters.
4. This research can help manufacturers using additive manufacturing to reduce material waste, save time, and reduce costs when printing PLA materials.
5. As part of future work, it is recommended to conduct additional tests, such as bending and impact tests, as well as tests on the morphology of the parts, to highlight the  
Figure 12: Stereogram of the tensile test model.  
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Development of material extrusion 3D printable ABS/PC polymer blends: Influence of styrene-isoprene-styrene copolymer on printability and mechanical properties. _Polym.-Plast. Technol. Mater._**2023**, _62_, 419-432. [CrossRef]
* [PERSON] (2023) [PERSON] ASTM D638-10 Type I Tensile Specimen. Available online: [[https://www.researchgate.net/figure/Schematic-representation-of7](https://www.researchgate.net/figure/Schematic-representation-of7) standard-specimens-for-mechanical-testing-ASTM-D638-10-Type_fig1_341357867/]([https://www.researchgate.net/figure/Schematic-representation-of7](https://www.researchgate.net/figure/Schematic-representation-of7) standard-specimens-for-mechanical-testing-ASTM-D638-10-Type_fig1_341357867/) (accessed on 24 December 2023).  
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# Prediction of Soil-Water Retention Curves of Road Base Aggregate with Various Clay Fine Contents
## 5 Validation of the Proposed SWRC Model  
To verify the validation of the proposed model, SWRC with \(FC\) = 0%, 4%, and 8% were used to calculate the fitting parameters in Equation (20). The values of the fitting parameters are shown in Table 1.  
Figure 6 shows the comparison between the measured SWRC and predicted SWRC. It can be observed from Figure 6c that there is good agreement between the measured and predicted SWRCs, since the correlation coefficient (\(R^{2}\)) is 0.9869 for \(FC\) = 0%, 0.9974 for \(FC\) = 4% and 0.9966 for \(FC\) = 8%, respectively. The large \(R^{2}\) implies that the values of the fitting parameters are reasonable.  
Figure 7 shows the predicted and measured SWRCs at \(FC\) = 2% and 6% by using the fitting parameters in Table 1. The correlation coefficient is 0.9926 for \(FC\) = 2% and 0.9986 for \(FC\) = 6%, respectively. The reasonably good agreement between the predicted and measured SWRCs indicates that the proposed model can predict SWRC satisfactorily at different \(FC\)s.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\mathbf{\kappa_{c}}\) & \(\mathbf{n_{c}}\) & \(\mathbf{m_{c}}\) & \(\mathbf{\kappa_{f}}\) & \(\mathbf{n_{f}}\) & \(\mathbf{m_{f}}\) & \(\mathbf{\chi}\) & \(\mathbf{k}\) \\ \hline \(1.1\times 10^{-5}\) & 1.174 & 2,781,837.05 & 0.015 & 3.48 & 0.054 & 2.36 & 0.0567 \\ \hline \end{tabular}
\end{table}
Table 1: The fitting parameters for the model.  
Figure 6: The tested and predicted data for (**a**) the SWRCs of inter-aggregate pores and (**b**) the SWRCs of intra-aggregate pores; (**c**) \(FC\) = 4% and 8%.
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Application in Biomedicine
#### 3.2.5 Veterinary Medicine
The Plasma Care NIPP device from Terraplasma Medical (Garchingen, Germany) was used for NIPP generation. The therapy lasted 12 days until complete wound closure. The plasma treatment was carried out every for 3-4 days with durations of 1 to 3 min. The authors concluded that the combination of common wound care and NIPP showed much better results than each of these approaches separately. This combined technique can also be applied for wound and tissue healing in dogs [237]. The optimisation of wound healing in sheep with the use of low-temperature atmospheric plasma was recognised as an innovative therapeutic method for extensive and chronic wound treatment [238]. [PERSON] et al. studied square wounds (4 \(\times\) 4 cm) on sheep backs under 2 min of daily indirect plasma treatment, using the radiofrequency plasma source described in [239]. They reported an increase in cell proliferation, a reduction in inflammation, a drastic reduction in bacterial load, and stimulation of blood vessel formation, after the completed plasma therapy.
## 5 Experimental Data Analysis
### Uncertainties and Sensitivity Analysis  
In the process of analyzing experimental data, we noticed that there is a certain degree of uncertainty in the experimental data, which mainly comes from factors such as experimental conditions, measuring equipment, and human operation. Analysis shows that changes in certain parameters (such as support material strength and model size) have a significant impact on experimental results, while the effects of other parameters (such as environmental temperature and humidity) are relatively small. During the experiment, in order to reduce the uncertainty of experimental data, we also took various measures, including using high-precision measuring equipment, conducting multiple repeated experiments, and strictly controlling experimental conditions. At the same time, in the process of data analysis, we also fully considered the impact of measurement errors and adopted appropriate data processing methods to reduce errors.
## 3 Results
### Mineral Content
#### 3.2.2 Micronutrient Content  
The source of iodine (I\({}^{-}\) or IO\({}_{3}\)\({}^{-}\)) had a significant effect on the concentrations of microelements in lettuce. As observed with the macroelements, there were no antagonistic effects between the application of iodine and the concentrations of microelements (Table 3). Apart from that, there was a significant increase in Mn when Cs-KI is applied (single application, 25 mg I kg\({}^{-1}\)). On the other hand, it appears that the interaction between iodine concentrations and Cs did not exert any significant effects on microelement concentrations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**Concentration (mg I kg\({}^{-1}\) Substrate)**} & \multicolumn{4}{c}{**g plant-1 (n = 6)**} \\ \cline{3-6}  & & **LFW** & **HFW** & **LDW** & **HDW** \\ \hline CsC & 0 & 212.00 b–d & 667.63 ab & 17.62 c–e & 28.48 a–d \\ Cs-KIO\({}_{3}\) & 5 & 294.32 a & 695.52 a & 24.27 a & 34.05 a \\ Cs-KIO\({}_{3}\) & 25 & 264.95 ab & 634.78 ab & 22.50 ab & 33.80 a \\ Cs-KIO\({}_{3}\) SDA & 25 & 238.58 a–c & 582.67 ab & 19.58 a–c & 29.28 a–c \\ AC & 0 & 146.72 e–f & 534.50 ab & 14.63 de & 22.37 d-e \\ KIO\({}_{3}\) & 5 & 201.42 c–f & 579.57 ab & 18.50 b–d & 19.13 ef \\ KIO\({}_{3}\) & 25 & 201.17 c–f & 595.68 ab & 16.97 c–e & 24.65 b–e \\ KIO\({}_{3}\) SDA & 25 & 178.15 c–f & 499.05 b & 16.65 c–e & 19.95 ef \\ CsC & 0 & 196.97 c–f & 596.07 ab & 17.57 e–e & 24.20 b–e \\ Cs-KI & 5 & 190.12 c–f & 577.57 ab & 16.47 c–e & 24.92 b–e \\ Cs-KI & 25 & 155.77 d–f & 526.18 ab & 13.67 e– & 22.70 c–e \\ Cs-KI SDA & 25 & 143.04 f & 280.17 c & 14.73 a–c & 15.03 f \\ AC & 0 & 194.55 c–f & 499.48 b & 17.37 c–e & 25.87 b–e \\ KI & 5 & 214.85 b–d & 587.67 ab & 19.13 b–d & 29.57 ab \\ KI & 25 & 205.10 b–e & 581.43 ab & 18.90 b–d & 27.48 a–d \\ KI SDA & 25 & 177.78 c–f & 495.32 b & 17.43 c–e & 24.95 b–e \\ \hline Concentration (C) & & 0.0311 & 0.0077 & ns & 0.019 \\ Chitosan (Cs) & & 0.0494 & ns & ns & ns \\ Chemical species (CSp) & & 0.0041 & 0.0116 & 0.0229 & ns \\ C C C C & & ns & ns & ns & ns \\ C C C C C & & ns & ns & ns & ns \\ Cs \(\times\) CSp & & \(<\)0.0001 & ns & 0.0001 & \(<\)0.0001 \\ C C C C C C C C C C & & ns & ns & ns & ns \\ \hline \hline \end{tabular}  
* Means with the same letter are statistically identical (least significant difference (LSD), \(p\leq 0.05\)); sample size, 6 (n = 6), ns, not significant. SDA, split dose application; LFW, leaf fresh weight; HFW, head fresh weight; LDW, leaf dry weight; HDW, head dry weight.  
\end{table}
Table 1: Comparison of lettuce biomass means.
# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 4 Experiments  
In this work, five actions are chosen from Table 1 as daily actions that are performed by an occupant. The selected actions are eating, laying down on a sofa, sitting down, standing up, and using a laptop.  
Video sequences of these actions are selected from the Microsoft Research (MSR) 3D daily activity database [36]. Some of the frames from some exemplar sequences can be seen in Figure 6.  
Figure 7 shows the confusion matrices of the action recognition system in terms of the relevant actions from the MSR 3D daily activity dataset [36]. The classification performance of these sequences are used to simulate the classification of actions of interest and then, consequently, the relabelling of those actions. The actions are a subset of the available actions in the MSR 3D daily activity dataset.  
The selected actions (\"Eating\", \"Using laptop\", \"Standing\", \"Sitting\", and \"Laying\") are simulated to run over different periods of time with: 1.2, 2, 0.8, 2, and 2 h, respectively (totalling 480 min).  
Three cases (fixed, ground-truth, and classified or adaptive) are used to control the indoor temperature based on the proposed thermal system. A comparison between fixed, ground-truth, and classified states are provided by considering the following:  
* Twelve action classifications assumed for every 2 min, distributed based on the selected actions and their periods (assumes a classification rate of 0.1 Hz).
* Metabolic rate averaging is performed over a window of 12 actions (i.e., 2 min). If the two minutes covers two actions (i.e. at the boundary), then the metabolic rate will be a mixture of the two.
* For the fixed state: a constant metabolic rate is used for the selected actions over the whole duration with \(M=58\) (sitting) and \(M=70\) (standing).  
Figure 6: Frames from exemplar sequences of the Microsoft Research (MSR) 3D daily activity database [36].  
* For the ground-truth state: ground-truth data of the selected actions is used to extract the metabolic rate of each one within its respective period.
* Classified state: the results of the action recognition model for the selected actions are used to extract the metabolic rate. The classified state can include actions other than the five selected actions. Metabolic rates for the misclassified states are therefore selected.
* The start-point temperature is 20 degrees for all experiments.
* Two minutes period is considered here for temperature change rate.  
By doing this, the average metabolic rate can be extracted every two minutes based on the number of actions (in this case 12) while using the aforementioned methodology.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 4 Results and Discussion
### Comparison with IOEM Method

Table 3: Registration accuracy between non-adjacent bands. Figure 12: Registration accuracy of adjacent bands using different methods. Figure 13: _Cont._  
The registration results can help facilitate the application of Zhuhai-1 hyperspectral imagery to various fields. Hyperspectral imagery can capture the reflectance at hundreds of wavelengths and enable the analysis of the spectra of materials on the Earth's surface. Registered Zhuhai-1 hyperspectral imagery can be used for urban land cover mapping, mineral mapping, and forest and plant species identification [36]. The full-spectrum registration approach can also be compared with other approaches (e.g., deep-learning based methods) for further improvement [37; 38]. With the capability of capturing increasingly complex image characteristics, deep convolutional neural networks have been applied to feature extraction for image registration and outperformed traditional feature detectors with fewer outliers [39; 40]. The proposed method can be adjusted and applied to hyperspectral images acquired by other satellite or airborne hyperspectral sensors. It also has great potentials for registration of satellite images captured at different times, from different viewpoints or from different sensors, to facilitate applications such as change detection and urban sprawl monitoring [41].
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 2 Analysis of the Autonomous Navigation Process
### Autonomous Collision Avoidance Under the Risk of Collision  
#### 2.2.1 Navigation Rules for Inland Tributary Waterways  
When making collision avoidance decisions, inland ships shall fully comply with the RPRCIRCA. Rule 15 of the RPRCIRCA specifically governs navigation scenarios at the confluence of mainstream and tributary rivers. It stipulates the following:  
1. Same-direction navigation: If a mainstream ship and a tributary ship are sailing in the same direction, the mainstream vessel shall give way to the tributary vessel.
2. Opposite-direction navigation: If the mainstream ship and the tributary ship are sailing in opposite directions, the upstream vessel (regardless of mainstream/tributary status) shall give way to the downstream vessel.  
Based on these RPRCIRCA-defined navigation rules for mainstream and tributary waterways, the priority of right-of-way in different encounter situations within inland tributary waterways is determined, as shown in Figure 3.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 4 Solution Approach
### Simulated Annealing Chimp Optimization Algorithm with Sine-Cosine Operator
#### 4.2.1 Initial Solution Construction and Variable-Dimension Matrix Encode  
The process of generating the initial solution is divided into two stages: the generation of a truck route and the generation of a drone route.  
Truck route generation method: Use the \"sorting first, then grouping\" approach to construct truck routes. That is, first randomly sort all customer nodes, and then divide and determine the specific customer nodes to be served based on truck capacity.  
Drone route generation method: First, all customer nodes are divided into two categories based on the maximum flight distance and the maximum load of the drone: One is served by a drone, \(N^{u}=\big{\{}i|q_{i}\leq Q_{u}\&d_{ij}\leq D_{\max}/2,\forall i,j\in V\big{\}}\); and the other is served by trucks, \(N^{k}=V-N^{u}\). Then, the algorithm needs to remove the drone service node and its front and rear nodes from the truck route and add them to the drone route. Next, duplicate nodes are removed from the drone route, and the remaining nodes are served by the truck in the original order. Finally, the algorithm judges the loading capacity and the maximum flight distance of the drone route and only keeps the legal drone route. This forms a complete truck-drone solution.  
The variable-dimension matrix encode is a \(p*q\) dimension matrix structure where \(p=l+k+1\) represents the total number of truck customer nodes, and \(l\) represents the number of trucks used. \(q=\text{max}(i)+1\), where \(r(i)\) represents the number of drone customers corresponding to the \(i\)-th retrieving node. To display the variable-dimensional matrix encode more intuitively, this paper provides the route structure in Figure 3; the corresponding coding is shown in Figure 4.  
The encode in Figure 4 is a multi-layer structure. The first layer is the truck layer, which represents the access node sequences for all trucks. The other layers, except the first layer, are the drone layers that represent docking and access node sequences selected by all drones. The first row of the drone column is the launch node and the first row of the subsequent drone column is the retrieval node. The decoded routes in Figure 4 are truck route 1 (0\(\rightarrow\)10\(\rightarrow\)2\(\rightarrow\)10), truck route 2 (0\(\rightarrow\)5\(\rightarrow\)7\(\rightarrow\)8\(\rightarrow\)12\(\rightarrow\)0), drone trip 1 carried by truck 2 (5\(\rightarrow\)6\(\rightarrow\)7), drone trip 2 (8\(\rightarrow\)9\(\rightarrow\)11\(\rightarrow\)12), drone trip 3 (12\(\rightarrow\)13\(\rightarrow\)0), truck route 3 (0\(\rightarrow\)3\(\rightarrow\)1\(\rightarrow\)0), and drone trip 4 carried by truck 3 (3\(\rightarrow\)4\(\rightarrow\)1). The variable-dimension matrix encode can clearly show the collaborative delivery route of multiple trucks and multiple drones. This encoding method can also be extended to other combinatorial optimization problems with branch structures.
## 2 Mission Concept
### Mission Concept of Operations
#### 2.3.1 Initial Mode  
After the first power up, Light-1 operation will begin from initial mode. It is essential as it has a safety period of 45 min after ejection without performing any operation to ensure that it is away from the deployer. This mode's sole responsibility is to probe the system's health and ensure all components coincide and are working correctly. Thus, the attitude determination and control subsystem (ADCS) will operate in case of a needed de-tumbling operation. After the initial 45 min safety period, if the Light-1's electrical power subsystem (EPS) power is above the 50% threshold, the UHF antenna deployment process will commence. The antenna deployment is a critical process since the transmitted beacons will include all the information related to the CubeSat's state. In order to mitigate the risk of an unsuccessful antenna deployment, the satellite requires the angular velocity rate to be within 20 deg/s. If the required rate is not achieved, the detumbling process will automatically start. After the antenna deployment is successful, Light-1 will begin the  
Figure 1: Modes of operation.  
beacon transmission process with an interval of 30 s, and then continue with the solar panel deployment process. Once again, the solar panel deployment is conditional to a specific limit of angular velocity. After deploying the antenna and solar panels, Light-1 will enter the detumbling mode if the angular velocity is higher than 5 deg/s in each axis. After the detumbling process is completed, Light-1 will wait for a command from the ground station to switch into normal mode and begin its operations.
## 1 Introduction
[PERSON] applied remote sensing data and field surveys to analyze surface-water amounts for water management [15]. [PERSON] applied GIS and the SCS-CN model to assess the water budget in Phuket Island [16]. Similarly, [PERSON] et al. applied GIS and the SCS-CN model to assess the water balance of Phuket Island [17]. Recently, Prince of Songkla University, Phuket Campus, applied SCADA (supervisory control and data acquisition) to explore surface water and created a database of current water sources. The tool is reliable for Phuket Island and has an online display that shows the water level in real-time, and it applies the information to enable water management in Phuket Island [18]. Nevertheless, the integration of remote sensing, land-use change modeling (CLUE-S model), and a distributed hydrological model (SWAT model) for water balancing has not been conducted in Phuket Island. Consequently, increasing water demand, and the real threat of water scarcity to needed economic tourism and livelihoods on Phuket Island, requires an integrated modeling framework to help manage water resources. Our objectives were therefore to (1) to assess LULC status and its change, (2) to simulate LULC data using the CLUE-S model, (3) to estimate water yield using the SWAT model, (4) to estimate water demand based on the water footprint, and (5) to evaluate the water balance (surplus or deficit).
## 5 Conclusions  
The results obtained in this work indicate that soilborne saprophyytes were the dominant group of isolates that colonized the roots, leaves, and fruits of American cranberry plants grown in a commercial plantation in Poland, while the number of fungi described in the literature as typical pathogens did not exceed 10% of all isolates tested.  
Fungi of the genus _Trichoderma_ were isolated mainly from root samples. Among them, isolates were found that showed the greatest similarity of the DNA fragments tested to those described only in China strains of species _T. amoenum_ and _T. dorothopsis_, as well as _T. hamatum_ and _T. koningii_, which can be found in different climatic zones and are known plant growth stimulators. Furthermore, fungi closely related to strains of _P. sporulosa_, _P. murcianum_, _P. ilacinum_, and _P. radicina_ were also discovered.  
Leaf-colonized fungi were identified as more similar to cosmopolitan species with a wide range of host plants, such as _P. scoparia_, _P. unicolor_, _P. radicina_, _A. geophila_, and _Neopestalotiopsis_ spp. Additionally, members of the _D. eres_ species complex, which are known to be quarantine fungi in American cranberry plants, as well as isolates most similar to _A. flavus_ and _A. fumigatus_, which are known producers of toxic secondary metabolites, were found.  
The isolates that contaminated the fruits were dominated by fungi with the greatest similarity to _P. scoparia, P. unicolor_, the toxicigenic _A. versicolor_, and _P. vacciii_, which is a serious pathogen of the American cranberry.  
Current knowledge of the fungi that inhabit the tissues of American cranberry plants is derived from studies of isolates obtained from plants that exhibit disease symptoms. The identification of filamentous fungi presented in this work, for which colonization of leaves, roots, and fruits was not associated with the appearance of the disease, can contribute to the detection of latent infections and the evaluation of potential risks that may occur in a given plantation, depending on the environmental conditions and the plant cultivars grown. Additionally, an understanding of the relationships between the tested plant and fungi expands our knowledge of the host range and distribution limits of some species around the world.  
Conceptualization, M.P.O. and E.G.M.; methodology, M.P.O. and A.K.; formal analysis, M.P.O.; data curation, M.P.O.; writing--original draft, M.P.O.; writing--review & editing, E.G.M.; supervision, S.J.P. All authors have read and agreed to the published version of the manuscript.  
The APC is financed by the Wroclaw University of Environmental and Life Sciences.  
Not applicable.  
Data Availability StatementData supporting the reported results are collected at the Laboratory of Biogeochemistry and Environmental Microbiology at the Wroclaw University of Environmental and Life Sciences in Wroclaw. The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.