esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Limitations  
Our research has several limitations. First, since participants voluntarily joined our sessions, there might be non-response or self-selecting bias. This might affect the research outcomes as it is impossible to determine what differences exist between our sample and the population [64]. However, we mitigated this effect by having a well-balanced mix of participating institutions and people: 26 practitioners who represented 5 different educational institutions that are comparable with other institutions in the Netherlands. This poses another limitation. As we only invited Dutch-speaking participants and thus institutions, outcomes might not be generalizable to institutions in different countries. Further research is necessary to address this shortcoming.  
The quality of survey items is another caveat of our research. In survey research, the validity of a questionnaire is analyzed via factor analysis. However, this requires a large enough sample size. A common rule is to have 10 to 15 respondents per variable [65], which in our case (9 questions) means between 90 and 135 respondents. We only had 23 respondents, so validity could not be statistically established. Moreover, the question whether the capability model is more useful than comparable models might score lower than expected due to participants' limited experience with other models. Two respondents explicitly state that they do not know other models and therefore cannot answer the question. The current questionnaire, however, requires an answer. When we remove this question from the analysis, overall perception of the model raises to 4.1 (SD = 0.3). Although the survey was tested before use, this issue was overlooked until the survey was already distributed. At that point, it could no longer be repaired. This is a known problem when using surveys as research method [47]. Nonetheless, this should be considered in future research.
## 2 Results and Discussion  
### Characterization and In Vitro Diffusion Study of PEs  
#### 2.1.1 Characterization of SNPs  
The size distribution and PDI values for synthesized and surface-modified SNPs were determined by DLS and TEM. Data for mean diameter and PDI values are presented in Table 1. The TEM images showed that SNPs were monodispersed, spherical, and had a smooth surface. It can be clearly seen that the size and morphology did not change significantly during the surface modification (Table 1 and Figure 1). The surface modification of SNPs was confirmed by FTIR spectroscopy; the results were previously published [35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & \(\mathbf{d_{DLS}}\) **(nm)** & \(\mathbf{PDI_{DLS}}\) & \(\mathbf{d_{TEM}}\) **(nm)** & \(\mathbf{PDI_{TEM}}\) \\ \hline
20 HS & 20.1 \(\pm\) 0.2 & 0.008 & 20.0 & 0.011 \\
50 HS & 52.7 \(\pm\) 0.9 & 0.017 & 53.0 & 0.037 \\
100 HS & 105.2 \(\pm\) 3.6 & 0.021 & 103.0 & 0.083 \\
20 ET & 20.1 \(\pm\) 0.8 & 0.158 & 22.0 & 0.210 \\
50 ET & 54.2 \(\pm\) 2.7 & 0.178 & 55.0 & 0.337 \\
100 ET & 110.7 \(\pm\) 4.1 & 0.231 & 112.0 & 0.349 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of SNPs. HS: hydrophilic particle. ET: with ethyl functional group modified particle. Size represented as mean \(\pm\) SD of three parallel syntheses. The numbers refer to the particle sizes.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception of Terrestrial and LEO Satellite Antenna System  
#### 6.2.1 NOAA-19 Reception with Crossed Yagi-Uda VHF  
The NOAA-19 satellite, part of the National Oceanic and Atmospheric Administration, is one of the Polar Operational Environmental Satellites (POESs) from the United States. Its primary mission is to collect meteorological and environmental data globally for weather forecasting, climate studies, and disaster management [29]. NOAA-19 operates in a low Earth orbit (LEO) at an altitude of approximately 870 km in a polar orbit.  
The data received from NOAA-19 from ground station in this research is shown in Table 9 included Automatic Picture Transmission (APT) at a frequency of 137.1 MHz, which provides reduced resolution satellite images captured and downloaded immediately as the satellite passes into visibility. The images received were infrared, consisting of two channels from APT. These images were processed using the WXtoImg program, resulting in the data shown in Figure 20, which depicts the satellite passing over Thailand.  
NOAA-19 completes one orbit around the Earth in approximately 100 min, passing over Thailand three to four times daily. The parameters listed in Table 9 outline the specifications for receiving signals from NOAA-19. Signal reception is achieved using a Yagi-Uda VHF antenna, which must be precisely aligned with the satellite's position during its pass. The antenna's alignment is calculated using the satellite's two-line element (TLE) data.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Satellite & GK-2A \\ Mode & LRIT \\ SNR & \(\sim\)19–20 dB \\ Az/Ele of antenna & 118\({}^{\circ}\), 54.4\({}^{\circ}\) \\ Receiving antenna & 1.9 m dish antenna \\ Decoding software & SatDump \\ Spectrum & 1-channel infrared \\ Duration of receiving & 73 s \\ Image size & 2200 \(\times\) 2200 px \\ Spatial resolution & 5.8 km/px \\ Receiving time & 17:02 UTC \\ Satellite location & 128.3 E \\ \hline \end{tabular}
\end{table}
Table 8: Parameters for the reception of GK-2A.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Satellite & NOAA-19 \\ Mode & APT \\ SNR & \(\geq\) 30 dB \\ Az/Ele of antenna & Tracking during visibility \\ Receiving antenna & VHF \\ Decoding software & WXtoImg \\ Spectrum & 2-channel infrared \\ Duration of receiving & Equal to visibility duration of 3–11 min \\ Image size & Width 1040 px \\ Spatial resolution & - \\ Receiving time & 08:45 UTC \\ Satellite location & Polar LEO \\ \hline \end{tabular}
\end{table}
Table 9: Parameters for the reception of NOAA-19.
## 4 Materials and Methods
### DPPH and the Control Solution Preparation  
The DPPH stock solution was prepared by weighing the appropriate amount of DPPH, and diluted solutions containing 0.625 mM, 1.25 mM, 2.5 mM and 5 mM DPPH free radical solutions with 95% methanol were prepared; the solutions were stored at 4 \({}^{\circ}\)C away from light. The quercetagetin-7-O-glucose, quercetagetin and quercetin control were weighed precisely and dissolved in methanol to a mass concentration of 500 ug/mL, which were stored in a dark refrigerator at 4 \({}^{\circ}\)C.  
Screening of the Potential Antioxidant Components in the Methanol Extract of Marigolds Using Offline DPPH-HPLC-Q-TOF/MS  
Offline DPPH-HPLC-Q-TOF/MS was used to screen the potential antioxidant components in the methanol extract of marigold. Different concentrations of DPPH solution and marigold donors were thoroughly mixed in a 1:1 ratio, and the solution was stored in the dark and was incubated at 37 \({}^{\circ}\)C for 30 min. After that, the mixture was filtered through a 0.22 \(\upmu\)m microporous filter membrane for HPLC analysis. The DPPH group was the mixture of DPPH solution and extract solution, and the DPPH-free group was the mixture of 95% methanol and the extract solution. The peak areas of the compounds with potential antioxidant activities in the HPLC chromatogram of the DPPH group were significantly lower than those of the DPPH-free group. The difference in the reduction in DPPH peak areas (PAs) between the DPPH-free sample and DPPH sample was used for determining the percent radical scavenging activity of the sample according to the following equation [56]:  
\[\text{Radical scavenging }(\%)=\frac{\text{PAs of DPPH }-\text{free sample }-\text{PAs of DPPH sample}}{(\text{PAs of DPPH }-\text{free sample})}\times 100\]  
HPLC-Q-TOF/MS chromatography conditions: Chromatography was performed using an Agilent 1290 HPLC system (Agilent Technologies, Santa Clara, CA, USA) consisting of a rapid resolution binary pump, autosampler, thermostat column compartment, vacuum degasser and tuneable UV detector. Separation was carried out on an X AquaC18 column (150 mm \(\times\) 2.1 mm, 5 \(\upmu\)m, 100 A; Acchrom Technologies Co., Ltd., Beijing, China). The elution system was 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The linear gradient elution program was as follows: 0\(-\)10 min, 5\(-\)25% B; 10\(-\)15 min, 25\(-\)35% B; 15\(-\)25 min, 35\(-\)70% B; 25\(-\)27 min, 70\(-\)90% B; 27\(-\)37 min, 90\(-\)95% B. The sample injection volume was 5 \(\upmu\)L. The rate was set to 0.3 mL/min, and the column temperature was maintained at 30 \({}^{\circ}\)C.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
### Contributions  
In this paper, we propose a new method for identifying critical nodes--LBIA. This algorithm solves the problem of the rough identification strength in the k-shell algorithm, and it can accurately identify important nodes in the restricted area. It has a low time complexity as it only considers information from neighbors. Through experiments on both densely interconnected networks and general networks, the average Kendall coefficient value is 0.7330, the Jaccard coefficient value is 0.6 to 0.8, and the monotonicity value is 1. The recognition ability is more advantageous compared to the comparison algorithms, while it is suitable for most network types.  
The rest of this paper is organized as follows. Section 2 provides an overview of related work, including traditional classical algorithms and recently proposed advanced algorithms. Section 3 introduces key definitions, the SIR model used in this paper, and the k-shell algorithm. Section 4 presents the PKs algorithm and \(LB_{v}\), describes the core idea of the LBIA algorithm, and provides concrete examples to facilitate understanding. Section 5 details the experimental setup, dataset, and evaluation metrics, followed by extensive comparative experiments for analysis and discussion. Finally, Section 6 provides conclusions and a vision for future work.
# An Interference Cancellation Scheme for High Reliability Based on MIMO Systems
## 4 Simulation Results
The proposed scheme does not require complex equalizer because interference symbol is removed at a relay unlike the conventional FIC which removes interference symbol at a destination. For more than three of \(N\), the whole complexity for the proposed scheme is higher than the conventional FIC. However, the increase rate for the complexity of the proposed scheme is linearly increased with respect to the number of antennas and it is slightly large compared to the conventional FIC. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Conventional FIC** & **Proposed Scheme** \\ \hline \hline Addition & 14 & (2\(N\) for (a) in Equation (15) and 2\(N\) for (b) in Equation (15)) \\ \hline \hline Multiplication & 22 & (4\(N\) for composite channel, 4\(N\) for (a) in Equation (15), 4\(N\) for (b) in Equation (15)) \\ \hline Division & 2 & 2\(N\) for division in Equation (15) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The complexity at a destination for the conventional FIC and the proposed scheme. Figure 5: The required value of signal-to-noise ratio (SNR) to obtain targeting BER performance of \(10^{-3}\).
## 3 Methodology
### Data Preparation
#### 3.3.1 Throughput  
This feature represents the total volume of data (in bytes) captured within specific time intervals during the measurement. The collected data includes the volume of individual sent and received traffic aggregated over these intervals. The features are denoted by \(S_{i}\) for the total volume of sent traffic and \(R_{i}\) for the total volume of received traffic within the interval. Here, \(i\) represents the index of the time interval. Thus, for each traffic, the feature 'Throughput' captures the volume of the sent and received data, providing valuable  
Figure 5: Throughput in wavelet domain.  
Figure 6: Ratio in wavelet domain.  
information about the data traffic patterns over time. In Figure 7, the throughput of YouTube traffic in the downlink and uplink is demonstrated.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Communication Protocols
#### 3.1.2 Ubx  
This protocol belongs to U-Blox enterprise, but their specifications are open. Its application is limited to equipment of this manufacturer, so it is not a standard in the industry. The information is sent in binary, which optimizes the size of messages, although a translator is needed to read them.  
In this work, the UBX protocol is used to read the information about the relative position of the GNSS receivers.
###### Abstract  
The time fluctuations of forest fires occurring in Basilicata, a region situated in Southern Italy, between 2004 and 2023 were investigated using various analytical approaches. Analysis revealed a clustering of fire occurrences over time, as indicated by a significantly high coefficient of variation. This suggests that the fire sequence does not follow a Poisson distribution and instead exhibits a clustered structure, largely driven by the heightened frequency of events during the summer seasons. The analysis of monthly forest fire occurrences and total burned area indicates a significant correlation between the two. This correlation is reinforced by shared patterns, notably an annual cycle that appears to be influenced by meteorological factors, aligning with the yearly fluctuations in the region's weather conditions typical of a Mediterranean climate. Furthermore, the relationship between the Standardized Precipitation Evapotranspiration Index (SPEI) and forest fires revealed that the accumulation period of the SPEI corresponds to the cycle length of the fires: longer cycles in fire occurrences align with higher accumulation periods in SPEI data.  
fires; SPEI; statistical methods Article
## 3 Q2: What Are the Challenges of the Current UIoT System
### Technical Challenges of UioT
#### 3.2.2 Unreliable Channel Condition  
In the UIoT Environment, the Cause of Unreliable Communication Channels Refer to the Factors that can Affect Data Transmission Loss Underwater.  
_Limited bandwidth and transmission delay:_ In an acoustic communication channel, the bandwidth is limited, such as from 100 kHz to 500 kHz, from 10 kHz to 100 kHz, and from 500 Hz to 10 kHz for short, medium and long-range communication in the UIoT environment, respectively. Furthermore, the data rate is a maximum of 100 kb/s. This causes a delay in data transmission [21].  
_Attenuation and scattering:_ Approximately \(\leq\)150 MHz and Hz to 10 kHZ can be used for long-range data transmission in an optical and acoustic communication channel. Even though light spreads much more compared to the sound signal in the UIoT environment, both signals suffer the problem of attenuation and scattering in long-range communication. This causes a transmission loss for long-range communication [36].  
_High propagation delay:_ In the UIoT environment, numerous factors such as turbidity, depth, pH level, density, temperature, etc., are the major causes of high propagation delay in optical and acoustic channel communication. This causes transmission loss or delay in transmission [37].  
_Channel noise:_ In the UIoT environment, channel noise refers to the noise factor that affects the underwater communication channel, such as environmental and ambient noise. Environmental noise is the noise generated by human beings such as shipping, fishing, naval activities, etc., and ambient noise is the background sound generated from an unknown source such as wind, underwater objects, sea animals, etc. [38]  
_Node mobility:_ The UIoT environment consists of static and mobile nodes. The static nodes are placed in a fixed position and the mobile nodes move from one place to another for data collection. However, the characteristics of deep seawater such as internal wave, sediment formation and deliberate motion of other particles, force the nodes to move from one to another at any time in the UIoT environment. This term is also defined as external force mobility. Due to external force mobility, the connectivity can be easily broken, which causes data transmission errors [22].
## 4 Discussion
### Concurrent Validity  
In the present study, the maximum quadriceps torque measured with the ID was significantly higher than the maximum quadriceps torque measured with the H-station. These results point towards a similar tendency as previous studies investigating concurrent validity of HHD compared to an ID reported ICC values ranging from 0.42 to 0.85 for isometric quadriceps strength [17; 18; 19; 20] and 0.66 to 0.91 for isometric hamstring strength [17; 19].  
The paired sample \(t\)-test showed that the H-station significantly underestimated the maximum quadriceps torque values with a mean difference of 32.4 Nm (SD = 58.7 Nm). This underestimation can be explained due to the manual calculation of torque values for the H-station. This calculation is only valid on the premise of the foot contact with WBB being perpendicular. In case of dorsiflexion of the ankle joint during the isometric knee extension, the force applied on the WBB would not have acted perpendicularly resulting in an erroneous lever arm. This could introduce a measurement error that may have contributed to the underestimation of the maximum quadriceps torque measured in the H-station.  
Aside from the previous discussed instrumental and/or biological variations causing random error, the current experimental protocol had limitations, which could account for some of the variation within and between participants and the low correlation between devices. The Quad-H test depended on the practitioner's ability to maintain the H-station in place during the tests. [PERSON] et al. highlight that a higher reliability of HHD can be achieved when the practitioner is stronger than the participant [13]. In addition, this aspect is especially important when testing large muscle groups like quadriceps. Movement of the H-station during the tests would result in lower maximum quadriceps or hamstrings torque. The experimenter noticed that the H-station did move during a few tests. This was compensated by simply repeating the specific test. For the Quad-H test, some participants reported discomfort (tip of the toes) during the isometric knee extension. This may have affected the motivation of the participant to produce a maximal and consistent knee extension. Furthermore, all participants wore different types of shoes during testing. It is uncertain if the shoe type influences the torque generation, but it is recommended that further studies should try to control this parameter, by providing shoes for the participants or by modifying the H-station (e.g., adding a foot pad).
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 4 Implementations and Evaluation
### Simulation Analysis
#### 4.2.1 Analysis of Multi-Frequency Ranging Accuracy Improvement  
The carrier phase ranging results of different frequency combinations are given in Figure 9. In the figure above, we can see from the simulation data that the characteristics of the measured values are different under different frequency combinations in the dual-frequency combination mode, the measured values under different combinations show different measurement offsets. The most obvious one is the mode I, and the measurement error is 38 cm. The mode 2 has good ranging precision, ranging error is about 10 cm. The maximum measurement error is 30 cm and the minimum measurement error is 10 cm under the mode 3. The measurement error of the mode 4 is 9 cm.  
The Figure 10 shows the actual ranging data under three combined modes. From the above data, it is found that under three combined modes, the ranging result of mode 5 is about 3.5 m, and the ranging value of mode 6 is about 3.42 m, it can be found that the range error of the three-frequency combination mode is obviously improved compared with the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Frequency Point Combination Mode** \\ \hline Mode 1 & (B1I, B1C) \\ Mode 2 & (B1I, B2A) \\ Mode 3 & (B1I, B2I/B2B) \\ Mode 4 & (B1I, B3D) \\ Mode 5 & (B1I, B1C, B2a) \\ Mode 6 & (B1C, B2a, B3I) \\ Mode 7 & (B1I, B1C, B2I/B2b) \\ Mode 8 & (B1I, B1C, B3I, B2I/B2b) \\ Mode 9 & (B1I, B1C, B2a, B3I, B2I/B2b) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Frequency combinations in different modes.  
Figure 9: Ranging accuracy under different dual-frequency combination modes.  
two-frequency combination, with the maximum error being about 12 cm and the minimum error being in the range of 1 cm.  
Furthermore, Figure 11 analyzes the ranging results of four-frequency combination and five-frequency combination, and it can be found that the ranging error of the two combination modes mainly concentrates within 9 cm.  
Therefore, we further analyze the ranging mean and RMSE values under different combination modes, as shown in the following Table 6.  
Figure 11: Ranging accuracy under different four-frequency combination modes.  
Figure 10: Ranging accuracy under different three-frequency combination modes.  
Based on the above results, the following characteristics can be found: (1) in the process of group phase ranging estimation, the larger the time-frequency difference of the multi-frequency combination mode is, the better the estimation of the carrier phase whole-cycle propagation measurement value is; (2) the introduction of more frequency points can not only improve the ranging accuracy more effectively, but also improve the whole-cycle estimation accuracy to a certain extent, three-frequency combination mode is more suitable for combination. At the same time, the combination of four-frequency and five-frequency can provide a more reliable guarantee for the scene with higher ranging accuracy and ranging stability.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 3 Experimental Results and Analysis
### Instance Segmentation Results  
Figure 7 shows the instance segmentation results produced by TSINet. The model demonstrates strong capability in accurately separating individual leaf instances. Although a small number of misclassified points remain near the interfaces between stems, leaves, and overlapping regions, the majority of both leaf and stem structures are effectively segmented into distinct instances, which is crucial for fine-grained phenotypic analysis.  
To further evaluate the instance segmentation performance of TSINet, we conducted a comparative analysis with two representative dual-task segmentation models, ASIS and JSNet. With a mean precision (mPrec) of 81.54%, mean recall (mRec) of 81.69%, mean coverage (mCov) of 81.60%, and mean weighted coverage (mWCov) of 86.40%, TSINet outperformed all other assessment measures, as indicated in Table 3. Notably, the mRec improved by 13.77% compared to ASIS. These results demonstrate that TSINet is highly effective in handling the complex spatial relationships and overlapping structures present in tomato plant point clouds. Its superior capabilities in feature extraction, instance discrimination, and spatial structure modeling enable accurate and robust instance-level  
Figure 7: Visualization of instance segmentation results by TSINet. The colors of the instance labels are assigned randomly, with each color representing a distinct plant organ instance. Ins.GT refers to the point clouds of plants annotated with instance ground truth labels.  
segmentation, providing strong support for subsequent organ-level growth monitoring and structural analysis of plants.