esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The proposed DGP model has the advantages of both the Gaussian process and DNN block, which improves the prediction accuracy and lays the foundation for probabilistic conflict detection. 3. Trajectory prediction for different flight operations are considered in the proposed framework, which simplifies the TP task in air traffic study. The proposed approaches for regulating flight altitude and speed provide a solution of predicting the flight trajectory with high maneuverability, which is able to improve the TP performance for the whole flight process. Figure 1: The framework of the proposed trajectory prediction approach. DPG: deep Gaussian process (DGP); NFT: nominal flight trajectory (NFT); CI: confidence interval (CI). 4. The proposed approach not only predicts a deterministic flight trajectory (NFT), but also estimates its CIs at different predicting instants, which greatly improves the applicability of the prediction results. Based on the GP characteristics, the positional distribution of flight trajectory at a different instant subjects to a Gaussian distribution. 5. A probabilistic conflict detection algorithm is implemented based on the proposed DGP trajectory prediction approach. The Gaussian distribution provides required probabilistic elements of trajectory positions and further supports the conflict detection, in which the Monte Carlo sampling is applied to simplify the integral solution.
## Appendix A  
Augmented Reality: One technique that can link/combine paper-based and digital-based environments is augmented reality (AR). AR is \"a technology that superimposes a computer-generated image (model) on a user's view of the real world, thus providing a composite view\" [58]. AR is a part of the reality-virtuality continuum [59] (Figure A1). According to [PERSON] [60], AR \"allows the user to see the real world, with virtual objects superimposed upon or composited with the real world. Therefore, AR supplements reality, rather than completely replacing it.\" In other words, AR is a combination of real-world and digital information through a single interface [21]. Thus, AR is an appropriate technology that can be used to access detailed information.  
There are two types of AR techniques, i.e., marker-based and markerless. The following paragraphs explain the differences between these two techniques:  
**Marker-based AR (feature-based, artificial markers)** In this approach, an artificial marker needs to be located in the scene or environment as a reference. Then, information about the marker is interpreted by a handheld computing device (smartphone/tablet) application. Artificial markers are printed and attached to the locations [1]. Some examples of artificial markers are dot-based markers [61], QR code markers [62; 63], circular markers [64], square markers [65], and alphabetic combination markers [65]. Due to fiducial marker use in the environment, and the fact that these markers are distinguishable in the environment (physical world), the marker-based tracking approach is very robust with high accuracy [66; 67; 68].  
**Markerless AR (feature-based, natural features)** This type of AR system uses the natural features of the environment as references [24]. Depending on the algorithm used for this system, these features could be edges, corners, segments, or points [23]. In this online approach, features extracted from current video frames taken from the scene are compared with features extracted from an initial key frame. Then, correspondence between feature pairs is created. This loop continues until the best match between features has been computed [1]. If enough numbers of matches are identified, the virtual data stored in repository is queried and appears on the screen of the computing device, such as a smartphone or tablet.
## 3 Results
### Comparison with Field Measurements
#### 3.2.2 Evaluation of Seasonal Variability
While the MODIS CIs generally follow the same trend as the field-measured CIs, there are some deviations in values, especially for the Yatir and Tonzi sites which have relatively sparse canopy distributions. The MODIS CIs mainly respond to the top structure of vegetation, while the field-measured CIs are usually influenced by understory vegetation [25; 99]. At the Yatir sites, the field-measured CIs are higher (\(\sim\)0.7) in the middle of the year and lower (\(\sim\)0.5) at the beginning and end of the year, while the C5 and C6 CIs undulate at approximately 0.6. This site is located between arid and semiarid climatic zones, and the forest usually consists of individual canopies for the limited available water [83]. The relatively low field-measured CI values at the beginning and end of the year may be associated with the vegetation developed during the rainy season. At the Tonzi site, both versions of the MODIS CI data and field-measured CIs are lower from April to June and higher at the beginning and end of the year. This site generally consists of two layers of vegetation, a tree overstory and grass understory, that operate in and out of phase with each other over the course of a year [80; 110]. The CI values decrease during DOYs 1-120, keeping pace with the growth of tree leaves, and the values increase slowly from the dry season until November (beginning of the rainy season). While the MODIS-derived CIs capture the overall trend of the seasonal variation caused by vegetation phenology, the satellite CI retrievals somewhat overestimate field-measured CIs [47; 89]. The inconsistency between field-measured CIs and MODIS-derived CIs may be caused by the large difference in spatial scale between field measurements and satellite observations, as well as the influence of the change in background reflectance.
## 4 Case Study
### Result of the Multi-Objective Model  
As shown in Table 2, this network is composed of eight lines, and each line is optimized by PE. The optimization process for the \"four east-west and four north-south\" railway network is shown in Figure 6.  
Figure 6: The optimization process for the “four north-south and four east-west” railway lines. (**a**) The 1 st north–south railway line; (**b**) the 2 nd north–south railway line; (**c**) the 3 rd north–south railway line; (**d**) the 4 th east-west railway line; (**e**) the 1 st east–west railway line; (**f**) The 3 rd east–west railway line.  
The optimized \"four east-west and four north-south\" railway network is shown in Table 4. The second east-west and the fourth east-west lines are unchanged, while the others (the first, second, third, and fourth north-south and the first and third east-west lines) are changed. Specifically, for the first north-south line, Bengbu station is not a stop; for the second north-south line, Shijiazhuang is not a stop; for the third north-south line, the Tieling and Siping stations are not stops; for the fourth north-south line, the Taizhou station is not a stop; for the first east-west line, the Shangqiu and Luomen stations are not stops; for the third east-west line, the Dezhou station is not a stop. Thus, the network becomes optimal after optimizing the transportation hubs of Beijing, Shanghai, Wuhan, Guangzhou, and Zhengzhou using the tri-objective model.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.10 Electroencephalogram (EEG)  
EEG sensors [22] are utilized to capture and analyze the electrical activity of the brain. These sensors come in various forms, from portable consumer-grade headsets to sophisticated clinical systems with multiple electrodes, enabling the monitoring and assessmentof brain function in diverse applications. From diagnosing neurological disorders such as epilepsy and sleep disorders to advancing brain-computer interface technologies for assistive communication and rehabilitation, EEG sensors offer invaluable insights into the intricacies of brain activity, fostering innovation and progress in both research and healthcare realms. Examples include the NeuroSky MindWave Mobile EEG Headset, among others.
## 2 Materials and Methods
### Computational Parameters and Load Conditions  
The cage is subjected to the combined loads of external waves and currents under practical operational conditions. In the numerical simulation, wave spectra are commonly employed from the perspective of energy distribution. There are different types of wave spectra, including the PM spectrum, JONSWAP spectrum, ISSC spectrum, and so on. It should be noted that the JONSWAP spectrum, as a three-parameter spectrum, is the most widely used to simulate the impact of ocean waves on marine structures. and adopted in this work. The corresponding expression is as follows:  
\[S(f)=\frac{\alpha g^{2}}{16\pi^{4}}f^{-5}\mathrm{exp}\left[-\frac{5}{4}\left( \frac{f}{f_{m}}\right)^{-4}\right]\gamma^{b} \tag{5}\]  
\[b=\mathrm{exp}\left[-\frac{1}{2\sigma^{2}}\left(\frac{f}{f_{m}}-1\right)^{2}\right] \tag{6}\]  
In the expression, \(\sigma\) is the spectral shape parameter. When \(f\ \leq\ f_{m}\), \(\sigma\) takes the value of 0.07; while, when \(f\ >\ f_{m}\), \(\sigma\) takes the value of 0.09. \(\gamma\) is the spectral peak enhancement factor, which reflects the degree of concentration of wave energy at the spectral peak frequency. A larger value of \(\gamma\) indicates a more concentrated energy, while a smaller value indicates a more dispersed energy. In this study, a value of 3.3 is adopted. \(f_{m}\) is the spectral peak frequency. \(\alpha\) is the spectral energy parameter; the value can be calculated in OrcaFlex once the significant wave height \(H_{s}\) is given.  
Figure 6: Motion response comparison of the novel SPM-GDW net cage between numerical model and experiment. (**a**) Maximum heavy values under different wave heights; (**b**) Maximum pitch values under different wave heights.  
Figure 7: Mooring tension comparison of the novel SPM net cage between numerical model and experiment under different conditions. (**a**) Wave height is 16 cm, wave period is 1.64 s, and current velocity is 10.3 cm/s; (**b**) Wave height is 24 cm, wave period is 2.01 s, and current velocity is 12.9 cm/s.  
The SPM-GDW cage studied in this research is intended to be deployed in the Wanshan waters of Zhuhai, China. The water depth there is 20 m. The designed environmental load conditions are based on a 25-year service period. The significant wave height is set as 7.66 m, and the spectral peak period is set as 14.17 s. And the statistical value of maximum current velocity in the proposed operating water area is 1.24 m/s. Thus, 3 cases of current velocities (pure wave, half of maximum current velocity, maximum current velocity) were considered. The detailed sea state parameters are listed in Table 2 below.
# Anti-Collision System for Small Civil Aircraft
## 4 Accuracy of Determining the Position of Flying Objects by Anti-Collision System
### Discussion and Results of Anti-Collision System Accuracy Evaluation
When selecting the initial coordinates of the FOs, we placed the individual users due to the geographical course of the LOT5 MF flight, on which the ACS is located. The geographical course of the flight LOT5 MF was equal to 345\({}^{\circ}\). The geographical course of RJA39K was 74\({}^{\circ}\), the geographical course of FHM6112 was 255\({}^{\circ}\), the geographical course of LOT653 was 173\({}^{\circ}\), and the geographical course of WZZ3007 was 160\({}^{\circ}\). We gradually changed the distances of individual FOs from the ACS in the range of 10 to 50 km. Selected simulation results are shown in Figure 10.  
Figure 10 shows that as the distances of FOs RJA39K, FHM6112, LOT653, and WZZ3007 from the ACS increase, the ACS positioning errors increase. On this basis, the accuracy of ACS positioning depends on the relative position of network users. The simulation results confirmed that the proposed ACS determines its position concerning other network users with the required accuracy to a distance of at least 50.0 km.
# Effect of Mechanical Damage in Green-Making Process on Aroma of Rougui Tea
## 3 Results
### Analysis of Transcriptomic Profiles of Tea Samples  
A total of 33,932 genes were obtained via the transcriptome sequencing of all samples, including 30,458 known genes, accounting for 89.76%, and 5076 unknown new genes. The results of the PCA showed (Figure 1A) that two principal components could distinguish all samples, and the variance of the two principal components was 73.6% and 11%, respectively, collectively accounting for 84.6% of the total variability of the volatile compounds, which was sufficient to interpret the information of the volatile components in each tea sample. Figure 1B reveals that the distribution patterns of different samples were similar, in which the distribution density of the LY group was lower than that of the CK group. The transcriptional data further showed the number of significantly different genes in CK at different stages and SY gradually increased, and the number of downregulated genes was greater (Figure 1C,D) (\(p<0.05\)). Compared with SY, LY7 had the largest number of significant differences (3849 genes were upregulated and 9299 genes were downregulated in expression). Then, the expression of most genes in the fresh tea leaves decreased after long-term mechanical injury and dehydration, and the effect of the green-making process on the gene expression in the fresh tea leaves was more obvious. These results were similar to the previously obtained transcriptome data of RGT that was treated with different degrees of green-making [36].  
Figure 1: The profiles of the RNA-seq dataset from RGT samples during different processing stages. (**A**) The PCA score plot of different leaf samples based on the transcriptomic dataset. (**B**) The violin plot of different leaf samples. (**C**,**D**) The number of significant differential genes in various paired comparisons with the criteria of log2 \(\mid\)(fold change) \(\mid\geq\) 1 and false discovery rate \(<\) 0.05. The numbers labeled on each comparison represent the number of up- or downregulated genes. (**E**) The Venn diagram of the genes quantified in the transcriptome. (**F**) The enrichment analysis of the differentially expressed genes between the comparison groups.  
The Venn diagram (Figure 1E) showed that LY5 vs. CK5 and LY7 vs. CK7 had the largest number of DEGs (3923 genes), and the four comparison groups shared 263 DEGs. The KEGG pathway enrichment results (Figure 1F) showed that there were significant differences in glucosinolate biosynthesis, phenylpropanol biosynthesis and glutathione metabolism. The metabolic pathways related to aroma formation are enriched by DEGs, including monoterenoid biosynthesis, alpha-linolenic acid metabolism and terpenoid backbone biosynthesis. These metabolic pathways are important in the formation of RGT's aroma and should be the focus of further study.
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 4 Case Study
### Implementation  
#### 4.2.1 Identifying Potential Products Using Association Rule  
The association rule was used to determine the relationships between product categories and data were separated into two groups--shipper and consignee datasets--to investigate (a) which products are often delivered together and (b) customer behaviors seen when sending/receiving products (Table 3). Association diagrams for hidden product categories are shown in Figure 4.  
Sequential association rules were applied and Rules 1 through 11 (Table 3) are those with the highest support rates (>0.005). Rule 1 indicates that more than 9% of consignees who receive garment products (in group of handicrafts souvenirs and gifts) and also receive clothes (in group of clothes, garment and accessories) and the data show a confidence level of 80.81%, indicating is a strong relationship between these two items. Rule 2 indicates that 1.9% of consignees who receive garments and related products also receive clothes. Rule 3 indicates that 1.8% of consignees who receive bags and handbags also receive clothes. In this study, Rule 1 is selected as an example rule.  
Using Rule 1, a list of garment products consignees can be provided to shippers who are clothes suppliers. There can be many shippers and consignees who have not traded with each other. However,  
Figure 4: Association diagram of hidden product items.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**No.** & **Rule** & **Support** & **Confidence** \\ \hline
1 & Garment products ==> Clothes & 0.090 & 80.81 \\
2 & Garments and related products ==> Clothes & 0.019 & 60.79 \\
3 & Bags and handbags ==> Clothes & 0.018 & 61.93 \\
4 & Car accessories ==> Spare parts & 0.010 & 56.49 \\
5 & Garment products ==> Bags and handbags & 0.008 & 87.76 \\
6 & Meatballs ==> Perishable foods & 0.008 & 64.21 \\
7 & Underwear ==> Clothes & 0.007 & 52.53 \\
8 & Computers and spare parts ==> Electronics equipment & 0.007 & 50.00 \\
9 & Garment products ==> Garments and related products ==> Clothes & 0.006 & 89.81 \\
10 & Sewing equipment ==> Clothes & 0.006 & 75.61 \\
11 & Steaks ==> Seasoning ==> Perishable foods & 0.006 & 67.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Product association rules.  
not all garment products consignees may want to have clothes delivered. There are 1435 consignees who receive garment products and 14,818 clothes shippers; therefore, investment marketing costs may be high. This necessitates a screening process to identify possible matches. The clustering technique is used for this purpose.
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Contention-Based
#### 3.3.2 Random Access-Based  
Random access MAC protocols are designed to send and receive data between multiple nodes by using the medium randomly without any control or HS mechanism. This increases the likelihood of high collisions and massive energy consumption [70]. Researchers developed a new FD-random access based MAC protocol for multi-hop ad-hoc networks to address this problem, as discussed below.  
Figure 5: Throughput comparison of FS-MACA, HD-MACA and IBFD-MAC protocols.  
* **FDCP-MAC**. A self-organized FDCP-MAC protocol for multi-hop ad-hoc UWAN network has been presented in [39]. It has the ability to utilize the channel and avoid huge collision more efficiently at high network load with the help of fixed time HS and time stamped conflict resolution mechanism. This system randomly generate the data packets to random destination and all the sending nodes are completely aware of the all the static destination of the which are randomly distributed in the UW environment. This FDCP-MAC achieves enhanced throughput and lowest rate of collisions [39] as compared to the conventional HD-MAC protocols [7; 71].
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 3 Problem Definition and Modeling
### Comprehensive Satisfaction of Tour Itinerary
#### 3.2.2 Scoring Based on the Attractiveness of Recommended Scenic Spots  
Furthermore, people typically prefer to visit scenic spots that are more popular and interesting. Firstly, we define the scenic spot attractiveness score \((SAS)\) of scenic spots \(a_{i}\) as follows:  
\[SAS_{a_{i}}=Pop_{a_{i}}*Int_{a_{i}} \tag{2}\]  
where \(Pop_{a_{i}}\) represents the popularity of scenic spot \(a_{i}\) and \(Int_{a_{i}}\) represents the level of interest tourists have in scenic spot \(a_{i}\). We then express the scenic spot attractiveness score of the itinerary \((ISAS)\) based on the average \(SAS\) of the scenic spots recommended in itinerary \(I\), as defined using Equation (3):  
\[ISAS(I)=\frac{\sum_{a_{i}\in I_{a_{i}}\in R}SAS_{a_{i}}}{RecNum} \tag{3}\]  
where \(R\) represents the set of recommended scenic spots, and \(RecNum\) represents their total number.
# Principles of Product Design in Developing Countries
## Results  
### Project Context and Background  
There were many small villages in the valley, all of which depended primarily on agriculture for their livelihoods. Few trees existed in Spiti and connection with the outside world was via a single road running through the central village, Kaza. Natural resources were scarce and availability of materials and expertise limited. The Spiti Valley was opened up to the western world a little over two decades ago and is still early on in the process of adopting more western educational and technology systems.  
The Spitian community lacked reliable electricity. Hence, they had a reliance on dung fires or Liquid Petroleum Gas (LPG) for cooking and heating. Dung is foraged from the high pasturelands during the summer and bottled LPG is transported approximately 580 km by road from Ambala. Negative health and environmental implications exist around burning dung [27] and LPG poses a high emission cost and supply volatility risks.  
The region is isolated and access to global technology, in the form of hardware, knowledge and skills, is limited. Even the possibility of having electricity all day is still in the remote future. Consequently, some of the innovations that might benefit the region, such as improved home construction (perhaps even extending to 3D printing of building elements) are unrealistic at the current time. Even something as ubiquitous in the developed world as cooking apparatus, was not possessed by all inhabitants. Hence the scope of the project, that of developing simple solar cookers, was relevant to this community whereas it might not have been elsewhere. The isolation also meant that it was necessary to adapt the design to be achievable using local construction materials and local knowledge and skills. This is important if the product is to be repairable and replicable by the community themselves. Hence the innovation in this context was not so much the technology per se, but ensuring the product was technologically sustainable within the constraints faced by the community.
## 4 Establishing the Data Fusion Kalman Filter Equations
### Calculation Process

Table 3: Comparison of Fusion Accuracy. ror during pure inertial navigation for \(SRINS_{1}\), \(SRINS_{2}\), and \(SRINS_{3}\). \(\min\{Er_{SRINS1}\), \(Er_{SRINS2},Er_{SRINS3}\}\) represents the minimum value among \(Er_{SRINS1}\), \(Er_{SRINS2}\), and \(Er_{SRINS3}\). From Table 3, it can be seen that the longitude error decreases by approximately 7.9785%, the latitude error decreases by approximately 6.0212%, and the overall positioning accuracy improves by approximately 6.3645%. Therefore, the method proposed in this paper can effectively estimate the arm lengths between multiple SRINSs, thereby constraining the positions of multiple SRINSs and improving the overall positioning accuracy.