esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Question 14 Distribution of Respondents  
Question 14 drives to the heart of the concern with spacecraft that are a significant distance from Earth (e.g., Voyager 1 and 2) and future prospective interstellar missions. At a certain point, the spacecraft must transition from a data collection tool to a decision making entity: it must make an assertion (or finding) and defend it. Failing to do this may result in data being sent back that is insufficient to draw any conclusion (causing an expensive, multi-decade mission to result in scientific failure). For these long-distance missions, there may be little choice. However, this same sort of technology could, prospectively be used on Earth or on closer proximity planetary science missions.  
In responding to Question 14, 41% of participants indicated some form of agreement (between agree-no preference and strongly agree), 18% indicated no preference and 39% indicated some form of disagreement (between disagree-no preference and strongly disagree). Note that the percentages do not add up to 100% due to rounding, five surveys where this question was not responded to and one survey where an invalid (an indication of a response between two categories) response was provided. This data is presented in Table 13 and Figure 14.
## 3 Results and Discussion
### Electronic Properties of MgBi\({}_{2}\)O\({}_{6}\)
In addition, the values of \(m_{h}\)\({}^{*}\)/\(m_{e}\)\({}^{*}\) of MgBi\({}_{2}\)O\({}_{6}\) are in the range of 6.2-10.9, being larger than the corresponding value (2.1) of anatase TiO\({}_{2}\)[30] widely investigated as an important photocatalytic material. Thus, semiconducting MgBi\({}_{2}\)O\({}_{6}\) can be considered as a photocatalyst with highly efficient separation of photoinduced electrons and holes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**Conduction Band**} & \multicolumn{3}{c}{**Valence Band**} \\ \hline \(\Gamma\) & \(\left(\begin{array}{ccc}0.221&0&0\\ 0&0.206&0\\ 0&0&0.171\end{array}\right)\) & \(\left(\begin{array}{ccc}-1.572&0&0\\ 0&-1.370&0\\ 0&0&-1.871\end{array}\right)\) \\ \end{tabular}
\end{table} 
Table 2: Calculated effective electron mass (\(m_{e}\)\({}^{*}\)) and hole mass (\(m_{h}\)\({}^{*}\)) along the three principal directions at the \(\Gamma\) point. All values are in units of free electron mass (\(m_{0}\)). Figure 2: Calculated electronic band structure of MgBi\({}_{2}\)O\({}_{6}\) using hybrid functional PBE0 method. Band structure from conventional DFT calculation (dotted line) is also presented for comparison. The calculated electronic total and partial density of states (DOS) of MgBi\({}_{2}\)O\({}_{6}\) is shown in Figure 3. It can be clearly seen that the DOS near the top of the valence band is derived from the Bi-5\(d\), Mg-2\(p\) and O-2\(p\) states. The bottom of the conduction band is mainly composed of O-2\(p\), Bi-6\(s\), and Mg-3\(s\) states. The strong hybridizations between Bi-5\(d\) and O-2\(p\) states in BiO\({}_{6}\) octahedrons and bonding interactions between Mg-2\(p\) or Mg-3\(s\) and O-2\(p\) states in MgO\({}_{6}\) octahedrons should be directly responsible for the structural stability of MgBi\({}_{2}\)O\({}_{6}\). To get a deeper understanding of the bonding nature of Bi, Mg, and O atoms, the electron localization function (ELF) was further calculated. According to the original definition, the ELF values are scaled in the range from 0 to 1. The high ELF means strong covalent bonding interaction between atoms and the very low ELF close to 0 corresponds to the ionic bonding. Figure 4 shows the calculated ELF for (110) plane of MgBi\({}_{2}\)O\({}_{6}\) passing through Bi, Mg, and O atoms. The ELF values between Mg and O atoms are extremely low, indicating the ionic bonding in MgO\({}_{6}\) octahedrons. This can be well understood on the basis of the previously obtained recognition on MgO, which is usually considered as one kind of classical ionically bonded compounds. The maximal value of ELF between Bi and O atoms reaches about 0.61, which clearly identifies the partially Bi-O covalent bonding interaction in BiO\({}_{6}\) octahedrons. Figure 3: Calculated electronic density of states of MgBi\({}_{2}\)O\({}_{6}\) using PBE0 method.
## 4 Discussion  
The biomechanics of clear aligner therapy is complex and not directly comparable to that of fixed appliance therapy. Unlike brackets and archwires, which have discrete points of force application, clear aligners depend on complex and dynamic contact points between the aligner and the dentition. Additionally, aligner design and material characteristics play important roles in determining the force systems generated during orthodontic tooth movement.  
Direct printing of aligners allows for precise design and manufacturing, reducing errors associated with the conventional thermoforming method [14]. With DPA, the thickness is more uniform, and the offset between the aligner and dentition can be precisely controlled. This may contribute to improved fit and more predictable force systems [9; 10; 11]. Recently, a photopolymer resin called TC-85 has become available, with mechanical properties beneficial for CAT, including favorable strength, elasticity, and shape memory effect [14]. Therefore, the aim of the present study was to evaluate the forces and moments produced by DPA and TFM using ATMOS and Zendura FLX foils during lingual bodily movement.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Activation (mm)** & **Material** & **Angular Deviation (\({}^{\circ}\))** & **Off-Axis Force (N)** \\ \hline
0.25 & ATMOS & 47.3 & 4.14 \\ \cline{2-4}  & Zendura FLX & 66.3 & 6.83 \\ \cline{2-4}  & TC-85 & 33.2 & 0.65 \\ \hline
0.50 & ATMOS & 68.2 & 19.27 \\ \cline{2-4}  & Zendura FLX & 60.2 & 14.39 \\ \cline{2-4}  & TC-85 & 12.0 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Angular deviation and off-axis force at the maxillary left central incisor (21) for each aligner material and activation amount.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Activation (mm)** & **Material** & **M/F Ratio (mm)** \\ \hline
0.25 & ATMOS & \(-11.93\) \\ \cline{2-3}  & Zendura FLX & \(-11.26\) \\ \cline{2-3}  & TC-85 & \(-4.31\) \\ \hline
0.50 & ATMOS & 8.61 \\ \cline{2-3}  & Zendura FLX & 4.84 \\ \cline{2-3}  & TC-85 & \(-6.49\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Initial moment-to-force ratios generated on the maxillary left central incisor (21) for each aligner material and activation amount.
## 4 Experiments
### Comparison of Image Segmentation Effectiveness
#### 4.2.2 Effectiveness of the Improved Image Center Point Selection Method  
When processing 8052 images, the original algorithm, which determined the center point based on image dimensions, led to excessive segmentation (Figure 13b) when the center point was located outside the foreground region (Figure 13a). To address this, the proposed method introduces multiple reference center points, binarizes them based on grayscale values, and identifies the intersection with the highest number of points having a grayscale value of 255 as the foreground center. This approach enables precise segmentation (Figure 13c).  
Figure 10: (**a**) Segmented image when the threshold is 200, (**b**) Segmented image using our algorithm.  
Figure 9: (**a**) The original image before segmentation, (**b**) Segmented image when the threshold is 40.  
Figure 11: (**a**) The original image before segmentation, (**b**) The image segmented by Magefreehome.  
Figure 12: (**a**) The image segmented without shadow removal, (**b**) The image segmented by our algorithm.  
Figure 13: (**a**) The original image before segmentation, (**b**) The image segmented by Magefreehome, (**c**) The image segmented by ours.  
2.3 Effectiveness of the Continuity Detection-Based Segmentation Start/End Point Determination Method  
During the segmentation of 1491 noisy images (Figure 14a), the original algorithm erroneously included noise in the foreground region, leading to suboptimal performance (Figure 14b). The proposed continuity detection method redefines the segmentation start and end points, effectively excluding noise from the foreground region and achieving complete segmentation (Figure 14c).
## 9 Results and Discussion
### Results of the SWORD Algorithm
During rainfall condition, the microcontroller measures soil moisture based on the soil humidity sensor to control the operation of the _SWORD_ algorithm. In case of rainfall, the soil is wet, irrigation is unnecessary, and no data transmission occurs from the sensor node to the router node. Consequently, the sensors and XBee module of the sensor node enter sleep mode to conserve energy. In addition, power consumption and data transmission vary from summer to winter. However, soil moisture changes according to temperature and humidity. In our work, we set the constant value of the soil moisture (i.e., threshold) to trigger the sensor to alert the users if intervention is necessary, such as a switch on the water irrigation system or based on an automated irrigation system. The percentage values of 40-50% are considered acceptable soil moisture values in plant growing environments as proven in [58]. Figure 16: Measured and transmitted data regarding climate conditions from the sensor node to the router node by applying the _SWORD_ algorithm. Figure 17: Estimated battery life versus battery capacity in the sensor node based on the _SWORD_ algorithm, sleep/wake scheme, and traditional operation. The output power of the solar panel is adequate to supply the sensor node because the power consumption of the sensor node varies from 0.75 (_SWORD_ algorithm) and 5.409 (sleep/wake scheme) to 143.19 mW (traditional operation). This result is based on the recommendation in [26], which indicates that the capacity of a solar cell should be at least six times the average power consumption of the load. The power consumption of the router and main router nodes is 7.155 mW and 13.764 mW for the sleep/wake scheme, whereas the value is 128.76 mW for traditional operation. The energy of the adopted solar cell can successively supply the sensor, router, and main router nodes. Consequently, when the solar cell (12 V/5 W) is used with the WAS hardware to supply power to the nodes alongside the _SWORD_ algorithm, an infinite energy supply for the WAS node can be expected.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 4 Simulation Environment
Concerning the physical layer, the data rate set for packet broadcasting is 6 Mbit/s, as this is the maximum rate for broadcasting under the IEEE 802.11p. In terms of the Medium Access Control (MAC) layer, channel access priorities were implemented: four different Access Categories (ACs), where AC0 has the lowest and AC3 the highest priority, providing a different priority for application messages. In order to keep the simulations close to real-world behavior and features, we have modified the simulator to make use of our Real Attenuation and Visibility (RAV) propagation model [32], which increases the level of realism of the vehicular network simulations by taking into account real urban roadmap layers and obstacles that have strong effects on the wireless impairments, hence the signal propagation. The RAV model is based on real-world measurements and accounts for attenuation and fading due to radio obstacles. Regarding data traffic, vehicles operate in two modes: (a) warning mode and (b) normal mode. Warning-mode vehicles inform other vehicles about their status by sending periodically, every \(T_{w}\) seconds, warning messages. Such messages have the highest priority at the MAC layer (similarly to the Decentralized Environmental Notification Messages (DENMs) defined by the European Telecommunications Standards Institute (ETSI) [33]). Normal-mode vehicles enable the forwarding of the warning packets. They also send periodically (every \(T_{b}\) seconds) beacons with information such as their positions, speed, etc. (similar to the Cooperative Awareness Messages (CAMs) also defined by ETSI). These periodic beacons are not propagated by other vehicles, and they have a lower priority than warning messages. Finally, as the desired RSU deployment should be suitable for a wide range of vehicle densities, we tested different number of vehicles involved in the simulations: 50 (12.5 vehicles/km\({}^{2}\)), 100 (25 vehicles/km\({}^{2}\)), 200 (50 vehicles/km\({}^{2}\)), 300 (75 vehicles/km\({}^{2}\)) and 400 (100 vehicles/km\({}^{2}\)). Table 2 presents the parameter set in our simulations.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Shading Probability  
The shading influence is complicated and varies by the position and the relative orientation (to the sun position) of the panel. In the case of the car roof PV, the position (orientation) of the panel cannot be predicted. One practical approach is to rely on the probability model, supposing that some statistical model randomly distributes the distribution of height and density of the shading objects, and the orientation of the car is independent and random as well.  
Assuming that the segmented annular region, and the building or other shading objects are randomly distributed in this region with random height (Figure 1), the probability of the shading can be approximated as a linear relationship [26], because the height of the shading object is assumed  
The area of the segmented annular is convenient to model such a situation (Figure 1) [24]. Note that the annular in place of the disk should be considered to avoid the situation of \"division by zero\" in angle calculation [26]. In addition, note that the line slope varies in two directions, one is along the road, and the other is orthogonal to the road [24]. A single parameter can represent this trend, namely, average shading height, corresponding to 50% probability of shading [26].
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 1 Introduction
Our proposed MIHE-Net can be regarded as an extension of existing multi-oriented target detectors that demonstrates remarkable generality and enables compatibility with various detectors. This paper makes the following main contributions:  
1. This paper systematically analyzes and reveals the deep coupling relationship between target scale variation and direction perception ability in remote sensing images for the first time. When the target scale is large, local feature extraction is limited by the receptive field range, making it difficult for local features to effectively express the overall direction information of the target, which affects the accuracy of target positioning and recognition. To address this phenomenon, in this paper we begin with scale modeling, then integrate direction modeling strategies to achieve collaborative perception and enhancement of scale and direction, thereby providing a new idea that can improve comprehensive scale-direction modeling capability in remote sensing target detection. 2. A multi-kernel contextual interaction module is constructed to effectively address the problem of gradual target scale changes. Based on existing multi-scale feature extraction methods, this paper adopts a parallel arrangement structure of non-dilated deep convolution kernels with different sizes to achieve efficient extraction of multi-scale features and modeling of local contextual information. This design not only effectively alleviates the feature sparsity problem caused by dilated convolutions but also improves the density and integrity of feature representation, thereby enhancing the detector's modeling capability for targets of different scales in complex remote sensing scenes. 3. Through a collaborative optimization framework of the multi-kernel contextual interaction module and hierarchical expansion attention mechanism, the proposed method can establish an effective complementary relationship between feature extraction at different scales and global contextual modeling, thereby significantly improving the performance of remote sensing target detection. 4. Finally, an anchor box convergence method is proposed; by generating boundary center offset amounts for external oriented rectangular anchor boxes using pre-extracted fine-scale feature information, this offset regression method compensates for the problem of drastic changes in loss functions caused by angle regression while making the anchor boxes converge compactly and accurately.
## 2 Results  
### Nutritional Facts  
The present study evaluated the moisture, ash, protein, lipid, carbohydrate, reducing sugars, non-reducing sugars, and total caloric value of the honey samples (detailed results in Table 1).  
The honey MSH presented a higher moisture content (27.11 g/100 g) compared to MIH (20.11 g/100 g).  
The ash content was also higher in MSH (0.31 g/100 g), which was double the amount found in MIH (0.15 g/100 g). Regarding protein content, MIH showed a slight difference compared to MSH. The values found were 0.19 and 0.16 g/100 g, respectively. There are no reference values for this parameter; however, studies indicate that the protein content found in stingless bee honey is generally low [28].  
The lipid content found in the two samples was very similar: 0.20 g/100 g in MIH and 0.19 g/100 g in MSH.  
The _M. interrupta_ honey showed a carbohydrate content of 79.35 g/100 g, higher when compared to _M. semitigra_, which presented a value of 72.24 g/100 g. Found values ranging from 48.59% to 69.36% in stingless bee honey samples [29], slightly lower than those found in the present study.  
The percentage of reducing sugars showed a difference between the analyzed samples: 76.95 g/100 g in MIH and 68.75 g/100 g in MSH. Therefore, MSH is more in line with the established limit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **MIH (_M. interrupta_)** & **MSH (_M. semitigra_)** \\ \hline Moisture (g/100 g) & 20.11 \(\pm\) 0.61 & 27.10 \(\pm\) 0.13 \\ Ash (g/100 g) & 0.15 \(\pm\) 0.06 & 0.31 \(\pm\) 0.05 \\ Protein (g/100 g) & 0.19 \(\pm\) 0.09 & 0.16 \(\pm\) 0.01 \\ Lipid (g/100 g) & 0.20 \(\pm\) 0.12 & 0.19 \(\pm\) 0.08 \\ Carbohydrate (g/100 g) & 79.35 \(\pm\) 0.62 & 72.24 \(\pm\) 0.32 \\ Reducing sugar (\%) & 76.95 \(\pm\) 0.51 & 68.75 \(\pm\) 0.17 \\ Non-reducing sugar (\%) & 1.43 \(\pm\) 0.69 & 3.14 \(\pm\) 0.13 \\ Total caloric value (kcal/100 g) & 305.71 \(\pm\) 2.34 & 278.34 \(\pm\) 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical parameters in honey samples of _M. interrupta_ and _M. semitigra_.  
Regarding non-reducing sugars, MSH presented a value twice as high as that of MIH, with values of 3.14 g/100 g (3.1%) and 1.43 g/100 g (1.4%), respectively. Therefore, the samples analyzed here are within the established standard. Values ranging from 7.2% to 8.5% have been reported for honey from _M. fasciculata_[30]. The total caloric value was higher in MIH (305.71 kcal/100 g) than in MSH (278.34 kcal/100 g). The reducing sugars influence the energy value of honey, characterizing it as an immediate source of energy [31].
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 1 Introduction
### Background
In addition, the demand for natural gas by users fluctuates greatly with the change of temperature, which has a strong randomness [12]. As different users have different gas consumption characteristics, the gas consumption law has no obvious cycle or growth law, and the gas load data have strong nonlinearity, which brings great difficulties and challenges to the operation and scheduling of gas pipe network [13; 14; 15]. Therefore, this paper carries out in-depth and systematic research on the forecast of city gas daily load based on the multiple weather parameter (MWP) concept, so as to provide decision support and technical guidance for engineering practice.
# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 3 Method
### Corpus Collection  
Our study attempted to complete the corpus collection through three LLMs: the iFLTTEK, DeepSeek and ERNIE Bot. This paper attempts to guide the LLMs to output the expected text through prompt engineering to ensure alignment with real-world events. To maintain corpus authenticity, the prompt templates explicitly require verifiable sources (e.g., news articles) and mandate the inclusion of UAV aerial photography task contexts with entity-specific objectives. Table 1 shows the prompt word template and the large model output results.  
Table 1 reveals critical challenges in LLM-driven corpus collection. ERNIE Bot and iFLTTEK exhibited task misinterpretation, producing non-compliant outputs resembling instructional guidelines rather than authentic journalistic content. DeepSeek generated new articles, but its outputs contained divergent elements unrelated to its core objectives (e.g., \"drone no-fly zone management\" discussions). The experimental results indicate two limitations:  
1. Specifying a task directly to the LLMs may cause the LLMs to misinterpret the task, such as in the output of ERNIE Bot and iFLTTEK.
2. Specifying a task directly to the LLMs may cause the output of the LLMs not to be as expected, such as the output of DeepSeek.  
To address these limitations, we used a few-shot learning approach. This paper implemented a structured few-shot learning protocol with DeepSeek by providing three domain-specific news articles as contextual exemplars. As evidenced in Table 1, this approach successfully achieved effective corpus collection.  
However, due to length constraints, DeepSeek could only output approximately 15 corpus entries per interaction. Therefore, it needs multiple rounds of interaction to collect the corpus. To ensure comprehensive coverage and factual accuracy in the constructed knowledge base, we present an iterative saturation protocol to govern corpus collection. This operational standard requires continuing data acquisition until three consecutive interaction cycles yield no novel task-relevant content from verified sources (news reports and official announcements), thereby guaranteeing two critical metrics:  
1. Information Completeness:  
Full coverage of all publicly available task-related data that existed before the experimental time.
2. Content Authenticity:  
Strict adherence to source verification protocols for collected materials  
Finally, 50 valid corpus entries meeting the requirements were collected through five rounds of iterative querying. It should be noted that completeness in this paper means that the final constructed knowledge base covers all task-related information on the internet at the time of the experiment, which is also the reason why the iterative saturation principle was adopted.