esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Background
### Model Descriptions and Grid Sensitivity Analysis
#### 2.2.2 Grid Sensitivity Analysis Results  
Our flow analysis results for channel confluence using the EFDC were evaluated through comparisons to the experimental results presented by [PERSON] [51], which have also been adopted for validating simulation results at confluences in previous studies [21; 23; 52]. In this experimental study, the tributary approached the main channel with a 90\({}^{\circ}\) approach angle. The flow velocity and water depth were each measured using Acoustic Doppler Velocimetry (ADV) with \(\pm 1\%\) accuracy and a point gauge with 1 mm accuracy. We conducted a numerical simulation for the case with a discharge ratio (\(Q_{r}\)) of 0.25, which is a value that develops a prominent recirculation zone, to determine the mixing characteristics in the recirculation zone. For grid sensitivity analysis, the grid size was uniform, and the number of grids for the channel width (\(W/dx\)) is listed in Table 1. The suitability of the grid size was evaluated by calculating the mean absolute percentage error (MAPE) in the water depth and recirculation zone size for depth-averaged flow fields (\(L_{\text{s}}\) and \(b_{\text{s}}\)).  
Figure 2 presents the MAPE calculation results based on the grid size. In the case of \(W/dx=10\), a recirculation zone is not developed in the junction based on the relatively large grid size compared to the other cases, so the error is 100%. In the case of \(W/dx=15\), the length and width of the recirculation zone have MAPE values of 28.5% and 32.3%, respectively. As \(W/dx\) changes to 20 and 25, the recirculation zone length errors are 6.6% and 4.9%, and the width errors are 15.8% and 5.3%, respectively. The changes in water depth decrease significantly in the case of \(W/dx>15\), and in the case of \(W/dx=20\), the error ranges from 1.8% to 2.6%. Based on these results, the numerical simulations were conducted using a grid size of \(W/dx>20\).
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 4 Data Gathering with the Help of Multi-Sink
### Redundancy Reduction on Data Gathering  
As mentioned before, the leader may not be selected from region \(C\) if the density of nodes in the network is too low. On the contrary, high density of nodes will also cause high redundancy on data collection. Therefore, a type of active node selection strategy and a sensing radius adjustment method are described in this section.  
There are two types of sleep scheduling strategies in WSNs, that is centralized and distributed method, respectively [19]. The number of active nodes is relatively small with the help of the centralized sleep scheduling method which could effectively reduce network redundancy. While, time overhead of the centralized method is higher than the distributed one. So, it is not suitable for large-scale sensor networks with high real-time requirement.  
To improve time efficiency in LDGM, the distributed sleep scheduling strategy is executed in each DGA. For example, there are two DGAs in Figure 7 (the white and the gray areas) and the active nodes (black dots) as well as their sensing regions in the white DGA are also shown in this figure. \(S_{10}\) is another active node in the adjacent DGA (the gray one). It is obvious that, both \(S_{8}\) and \(S_{9}\) are active nodes although the sensing areas of them have been completely covered by the sensing area of \(S_{10}\). Therefore, the effect of the distributed sleep scheduling strategy on redundancy reduction may not be the best. Thus, a sensing radius adjustment strategy is proposed as follows.
# Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
## 3 Background
### Publication  
Several aspects need to be taken into consideration when publishing a knowledge graph on the Web. These include aspects around how the knowledge graph will be hosted and what data will be accessible. In the context of the Semantic Web, [PERSON] and [PERSON] [62] describe different methods for publishing linked data on the Web. Linked data may be published directly through static RDF/XML files, RDF embedded into HTML files, a wrapper over existing applications or APIs, relational databases, and triplestores. In addition to having published the knowledge graph, the publisher may also provide tools for accessing the data, such as SPARQL endpoints, data dumps, and search engines. Some publishers also provide documentation pages for concepts [63], or even visualisation tools for the given graph [64].  
FAIR principles (findability, accessibility, interoperability, and reusability) [65], initially proposed for the publication of scientific data, are highly relevant for knowledge graph publication. The FAIR principles ensure that data can be easily found and accessed through the Web and they can easily be explored and reused by others, and these principles include requirements such as links to other datasets, provenance and licensing metadata, and the use of widely deployed vocabularies. Often with the publication of data on the Web, it is preferable to define a licence for the data. The W3C standard vocabulary Open Digital Rights Language (ODRL) [66] allows for defining permission, prohibition, and obligation statements of data, and it can be easily integrated in an RDF serialisation format. The vocabulary allows for modelling common licences such as the Apache [67] or Creative Commons [68] licenses, and it enables a standardised format for licensing linked data on the Web.  
Other aspects relevant for knowledge graph publication are URI strategy and context and versioning [2]. The identity of instances in the model is also of relevance. Having a consistent URI or IRI strategy is important for easily finding the right instance on the Web and keeping it non-changing. Facts contained in the graph may only hold within a certain context, e.g., within a specific time period or domain. There are several ways of applying context to knowledge graphs, such as through reification (adding information directly to the edges) and higher arity representations [69].
# The Development of an Optimized Impact Pad for a Six-Strand Tundish Using CFD Simulations
## 5 Industrial Plant Trials  
Verification tests were performed for the SP-K4 configuration with the \"SPHERIC--K4\"-shaped impact pad developed based on the results of numerical simulations. The casting speed in the verification tests was 1.3 m/min for a casting block of 280 \(\times\) 280 mm. During the tests, structural steel designated as S355, which is used for more demanding construction, was casted. A magnesite gunning mix with a composition of 80% MgO, 6.5% CaO, 2.5% SiO\({}_{2}\), and 7% FeO was used for the working lining. The primary objective was to observe and compare the erosion areas of the working refractory lining caused by incoming molten steel in the upper part of the tundish slag line area. The first verification test was realised over 8-10 h on 12 heats and the second verification test was realised over 18 h on 18 heats. For comparison during the verification tests, the reference configuration K for the shaped impact pad \"KTHE/C\" was used, which is shown in Figure 24. The test configuration \"SP-K4\" with the shaped impact pad \"SPHERIC--K4\" is shown in Figure 25.  
\begin{table}
\begin{tabular}{c c c} \hline
**Configuration** & \(\mathbf{V_{m}/V_{d}}\) & \(\mathbf{V_{p}/V_{d}}\) \\ \hline K & 2.7 & 1.3 \\ SP-K2 & 1.7 & 0.5 \\ SP-K4 & 3.5 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of ratios of different tundish volumes.  
Figure 24: Reference configuration K with the KTHE/C impact pad.  
Plant trials were realized with the same count of heat settings, steel grade, and casting speed. During plant trials, the compactness of the slag layer around the ladle shroud was monitored, and the creation of the red-eye phenomenon was observed in reference configuration K (Figure 26), which can lead to a significant decrease in steel cleanliness.  
Figure 27 shows significant wearing on the working lining and permanent lining after 18 heat cycles in the slag line area using reference configuration K.  
Figure 26: Comparison of the slag layer during plant trials. Reference configuration K (**a**) and experimental configuration SP-K4 (**b**).  
Figure 25: Configuration SP-K4 with the installed impact pad “SPHERC-K4” in the tested tundish.  
Figure 27: Wearing of the working lining after 18 heats in the slag line area: reference configuration K (**a**) and detail image (**b**).  
Figure 28 indicates no visible wear on the working lining after 18 heat cycles in the slag line area using experimental configuration SP-K4 (impact pad SPHERIC K4).
## 2 Nanotechnology and Nanomedicine
### Chitosan Nanoparticles  
Chitosan is a biodegradable, biocompatible and non-toxic polymeric material used in the synthesis of nanoparticles for drug delivery systems [57]. It is a linear polysaccharide mostly obtained from crustacean shells and fungi cells [58]. Chitosan is composed of N-acetylglucosamine and glucosamine derived from N-deacetylation of chitin which is the second most abundant natural bio-polymer after cellulose [59; 60]. The excessive density of amine groups present at the back-bone of deacetylated chitosan promotes strong electrostatic interactions with genes and proteins [61]. The cationic natural polymer for the past few years have being broadly investigated particularly as the main material in synthesizing carriers for therapeutic proteins and as non-viral gene carrying vectors [61; 62; 63]. Chitosan nanoparticles have an advantage of controlling the release of a drug thereby increasing drug stability and decreasing toxicity [58]. They are capable of passing via the biological barriers _in vivo_ to deliver drugs to the lesion site for enhanced therapeutic efficacy [64]. It has a wide range of biological importance such as an antimicrobial property, thus, has the ability to prevent the spread of infections [57], as a wound healing-accelerator which promotes coagulation by amplifying the functions of inflammatory cells [65]. In addition, chitosan can be used as bandages to prevent excessive bleeding during injury [65], as well as an antibacterial agent, that helps to aid the delivery of dermal drugs [64]. Unfortunately, it has poor solubility in water and organic solvents which make it difficult to be modified chemically [66; 67].
## 4 Simulation Experiments and Analysis
### Acceptance Circle Comparison Experiments  
A comparative study is conducted to verify the effectiveness of the adaptive Acceptance circle strategy in improving path-following accuracy. Specifically, FC-LOS (fixed acceptance circle-based LOS), which uses a fixed acceptance circle, is compared with AAC-LOS (adaptive acceptance circle-based LOS). By maintaining a constant following path and applying adaptive heading control, this study compares the following effects of the LOS method using a fixed acceptance circle with the adaptive adjustment acceptance circle strategy. During the simulation experiment, the robot's flight speed is set at \(v=0.5\)m/s. To maintain a consistent height in the following state, the height of following is increased to a fixed height. The path-following paths of the path angles \(\theta_{a}<\pi/2\) at point \(a\) and \(\theta_{c}>\pi/2\) at point \(c\) are selected as the objects of comparison. Figures 13 and 14 show the experimental results. Figure 13 corresponds to the following results at point \(a\), while Figure 14 corresponds to the following results at point \(c\). After analyzing these figures, the following conclusions can be drawn.  
Figure 13: Path following results for point \(a\) under different acceptance circles. (**a**) _xoy_ plane projection; (**b**) _xoz_ plane projection; (**c**) following error of trajectory.  
1. The adaptive acceptance circle strategy effectively solves the issue of switching ahead or lagging and maintains a controllable following error even when the reference path switches ahead. Figure 13 shows that the following trajectory, based on the adaptive acceptance circle, switches to the reference path segment earlier because of the larger acceptance circle radius, resulting in faster convergence to the next path segment. Figure 14 shows that the following trajectory based on the adaptive acceptance circle switches closer to the reference path because of the smaller acceptance circle radius, resulting in a reduction in the maximum following error from 0.156 m to 0.126 m.
2. The convergence of the adaptive acceptance circle strategy is faster. For point \(a\), where \(\theta_{a}<\pi/2\), the overall convergence time using AAC-LOS slightly increases compared with FC-LOS, as shown in Figure 13. However, during the _xoy_ path following, the AAC-LOS can follow the reference path faster compared with FC-LOS. For point \(c\), where \(\theta_{c}>\pi/2\), as shown in Figure 14, the total convergence time is reduced from 1.4 s (from 40.6 s to 42 s) to 1.4 s (from 41.2 s to 42 s), a reduction of about 0.6 s.  
In summary, the adaptive acceptance circle strategy offers more obvious advantages in terms of path-following accuracy and convergence speed.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 4 Experiments  
In this section, the dataset generation and the detailed algorithm implementation of the experiments in this paper are illustrated in detail. The comparative experimental results and experimental analyses are also discussed.
## 5 Choosing the Proper Mesh Element
Too large finite elements cause inaccurate reflection of the spherical wave and blur of its value due to too few number of nodes (Figure 13). It was also checked how the maximum pressure value changes as a function of distance from the epicenter. It was noticed that the pressure drop as a function of distance is also greater than in the analytical solution. Based on the analysis, it was found that the shockwave is most accurately mapped by a 0.005 m AC3D8R (3D acoustic hexagonal 8-nodes) elements. Further reducing the element size significantly increases the calculation time. The problem in the application of hexagonal elements for calculations related to underwater explosions, may be the difficulty in modeling their contact with the fairly complicated ship's hull geometry. Therefore, from the engineer's point of view, it is simpler to use 0.005 m AC3D4 (3D acoustic tetragonal) elements, which represent pressure pulse value is worse, but significantly simplify modeling and ensure optimal calculation time. Figure 13: Spherical shock wave in different mesh elements at the same time step. On the left there is a visible blur of the shockwave character caused by insufficient mesh density.
## 4 Enhancement of Energy Recovery (Biogas/Methane Yield) from the Anaerobic Digestion of Rice Straw
### Co-digestion of RS  
RS is a promising substrate for AD because of its high biogas potential [11]. Untreated RS has a higher amount of total C (e.g., 35-47% TS) and very low content of N (e.g., \(\leq\) 1% TS) with a typical C/N ratio of approximately 47-75 [10; 39]. Optimum C/N ratio ranges from 20 to 30 [59; 60]. As the optimal C/N ratio is crucial for efficient AD, an external source of N is required for the digestion of RS.  
The term co-digestion describes the simultaneous digestion of a mixture of two or more substrates. Co-digestion offers various advantages like improved system stability, adjusted C/N ratio, a better nutrient balance, dilution of toxic compounds, improved buffer capacity, increased biodegradation, adjusted moisture content, a supply of TEs, and increasing OLR. It can therefore, enhance volumetric biogas recovery [61; 62; 63] and improves the fertilizer value of the digestate [64]. These advantages of co-digestion are presented in Figure 6. Co-digestion of RS with different animal manures is considered a more cost-effective method for nutrition regulation than the addition of N-containing chemical reagents like ammonium bicarbonate or urea [40].  
Figure 5: Effect of temperature on methane yield. Values are presented as the mean of three replicates (n = 3). **DM**, Dairy manure; **CM**, Chicken manure; **RS**, Rice straw [26].  
Figure 6: Advantages of anaerobic co-digestion [61; 62; 63].  
[PERSON] et al. [39] attained about 55%, 72%, and 47% higher biogas yield compared to RS mono-digestion when kitchen waste (KW) and pig manure (PM), were co-digested with RS at a mass ratio of 0:2:1, 0.4:1.6:1, and 0.8:1.2:1, respectively. Similar results were obtained by [PERSON] et al. [65], where 70% more biogas was recovered in co-digestion of RS with food waste (FW) as compared to mono-digestion of RS. RS digested with goat manure (GM) performed best, with a mass mixing ratio of 50:50 (vs. 100:0, 90:10, 70:30, 30:70, 10:90 and 0:100) and yielding a 83% higher biogas yield than RS only [66].  
Similarly, co-digestion of RS with various co-substrates such as cow manure (CoM), PM, CM, paper mill sludge (PMS), piggery wastewater (PWW), and municipal sewage sludge (MSS) has been reported by other researchers. The results of these studies (Table 5) show the enhancement in energy recovery from the co-digestion of RS compared to its mono-digestion [22; 28; 41; 67; 68].  
Co-digestion of RS can be realized with several different co-substrates as the results of the above-mentioned studies illustrate. It enhances biogas yield from 26% to 83% and also the CH\({}_{4}\) yield from 2% to 640% relative to RS mono-digestion when optimized for the appropriate C/N ratio. Co-digestion of RS with proper C/N ratio, which can be achieved by mixing one or even more substrates in a suitable amount, is a good approach for the enhancement of energy recovery. This approach can be applied in pilot-scale or farm-scale plants.
## 2 Proposed LED Driver and Analysis of Steady-State Operation
### Analysis of Operation Mode  
The following assumptions are made to simplify the circuit analysis.  
1. In order to ensure ZCS operation for all diodes, the currents of \(L_{p1}\) and \(L_{p2}\) are designed to operate in discontinuous-current mode (DCM), and the winding currents of \(T_{1}\) are all designed to decrease to zero amps.
2. All components are considered ideal except for the parasitic capacitance (\(C_{S1}\) and \(C_{S2}\)) and intrinsic diodes (\(D_{S1}\) and \(D_{S2}\)) of the \(S_{1}\) and \(S_{2}\).
3. The two SCPIC circuits are identical with \(L_{p1}=L_{p2}=L_{p}\), \(C_{B1}=C_{B2}=C_{B}\), \(V_{B1}=V_{B2}=V_{B}\).
4. Capacitors \(C_{B1}\), \(C_{B2}\), and \(C_{o}\) are large enough that the voltages across them (\(V_{B1}\), \(V_{B2}\), and \(V_{O}\)) can be regarded as constant.
5. The winding resistance and iron loss of \(T_{1}\) are neglected. The leakage inductance in the primary and secondary windings are equal (\(L_{l1}=L_{l2}=L_{l}\)), and the mutual inductance \(L_{m}\) is large enough so that the mutual inductance current is regarded as a constant \(I_{Lm}\).
6. \(S_{1}\) and \(S_{2}\) are turned on and off at a high frequency (\(f_{s}\)) well above the input line frequency (\(f_{L}\)). Therefore, the rectified input voltage can be regarded as a constant value in each high-frequency cycle.  
Figure 1: Proposed LED driver.  
Figure 2: Gate-to-source voltages of \(S_{1}\) and \(S_{2}\).  
In steady-state operation, the circuit can be divided into 10 operation modes in each high-frequency cycle according to the status of each component. Figure 3 shows the equivalent circuits for each operation mode, where \(v_{rec}\) denotes the voltage after the bridge rectification and \(R_{o}\) denotes the equivalent resistance of the LEDs. In Figure 3, \(T_{1}\) is represented by a transformer model, where \(L_{l1}\) and \(L_{l2}\) are leakage inductances and \(v_{T}\) is the voltage across the mutual inductance. For a coupled inductor, the following equation can be derived [29].  
\[\mathrm{i}_{L1I1}+\mathrm{i}_{Ll2}=\mathrm{i}_{Lm}, \tag{1}\]  
\[v_{T}=\frac{L_{m}di_{Lm}}{dt}, \tag{2}\]  
Applying [PERSON]'s law to the leakage inductances gives  
\[v_{L1I1}+v_{Ll2}=\frac{L_{l1}di_{L1I}}{dt}+\frac{L_{l2}di_{L2I}}{dt}=\frac{L_{ l}di_{Lm}}{dt}, \tag{3}\]  
Figure 4 shows the schematic waveforms of voltage and current of the main components. The following section describes the details of each operation mode and the derivation of the associated equations.  
Before operation Mode 1, both \(S_{1}\) and \(S_{2}\) are on. Since \(S_{2}\) is turned on before \(S_{1}\), most of the mutual inductance current \(i_{Lm}\) flows in the secondary winding of \(T_{1}\) through \(S_{2}\), that is, \(i_{L1I2}>>i_{L1I}\). When \(v_{GS2}\) changes from high level to low level, \(S_{2}\) is turned off and the circuit enters operation Mode 1.  
Figure 3: _Cont._
## 4 Experimental Setup and Results
### Computational Efficiency  
Regarding computational efficiency, the total number of floating-point operations (FLOPs) required for a forward pass in a neural network consisting of fully connected (dense) layers is calculated below. This calculation is vital for understanding a neural network model's computational complexity and efficiency. For this, Equation (12) is leveraged:  
\[\text{FLOPs}=2\sum_{i=0}^{n}l_{i}l_{i+1} \tag{12}\]  
where \(l_{i}\) denotes the size of the layer \(i\) and systematically assesses the total FLOPs required for a single forward pass through the network. Here, \(l_{0}\) represents the input layer size and \(l_{n+1}\) indicates the size of the output layer, with \(n\) encapsulating the number of hidden layers within the network. This calculation is meticulously applied across all fully connected layers of the SNN, ensuring a robust quantification of the model's computational demands. This method ensures that the SNN application is evaluated for efficiency, laying the groundwork for optimizations that balance computational resource utilization with model performance.  
The results regarding the total FLOPs for each SNN architecture can be seen in Table 3.  
As seen in Table 3, the data indicates that increasing the number of neurons for a given SNN architecture increases the computational complexity measured by FLOPs. However, all the architectures have equivalent computational complexity for a fixed neuron count.