esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Methods and the Use of Location and Activity Data in Forests
## 6 Emerging Research Needs
### Developing Applications to Improve Worker and Recreational Health and Safety in Natural Resources  
Concurrent with the development of activity recognition models to quantify work described in Section 6.1, new, smart methods for quantifying worker health are needed to help monitor and improve health and safety of individuals employed in natural resources. For example, wearable or mobile devices could quantify heat stress, over-exertion, and related factors during wildland firefighting activities. Smart, person-down alerts for logging contractors could improve incident detection and response [88], and similar methods could be developed to create overboard alerts for fisheries workers. Also, methods to detect drowsiness and reduced awareness during equipment operation or other high-risk tasks may help to reduce incident-related injuries. Numerous smartphone-based health apps for achieving health and fitness goals and monitoring long-term conditions already exist [236; 237; 238]; tailoring these kinds of applications to natural resources work and recreational activities can help to improve wellness for forest workers.
## 4 Experimental Results
### Evaluation of Hierarchical Structure Versus Non-Hierarchical Structure  
The current study compared the performance for the validation set between the squashing function in the HSAN and the SoftMax function in the HAN. The results indicate that the squashing function in the attention mechanism can achieve superior classification performance to the SoftMax function in the HAN (e.g., the squashing function had an 11.87% higher \(F1_{overall}\) value than the SoftMax function did). In addition, the paper also applies the squashing-attention to the non-hierarchical model, such as the GRU(Squashing) model, the difference between GRU(Soft) and GRU(Squashing) in the \(F1_{overall}\) is 5.82%, the difference between HSAN and GRU(Soft) in \(F1_{overall}\) is 6.25%, and the different between HSAN and GRU in \(F1_{overall}\) is 26.54%. Table 7 presents a comparison between the hierarchical and nonhierarchical structures when using the squashing-attention and soft-attention networks.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multicolumn{3}{c}{**Validation Set**} & \multicolumn{3}{c}{**Test Set**} \\ \cline{2-7}  & \(PRE_{j}\) & \(REC_{j}\) & \(F1_{j}\) & \(PRE_{j}\) & \(REC_{j}\) & \(F1_{j}\) \\ \hline Slight & 63.43\% & 68.16\% & 65.71\% & 65.60\% & 73.38\% & 69.27\% \\ Medium & 62.55\% & 63.07\% & 62.81\% & 59.42\% & 57.73\% & 58.56\% \\ Serious & 68.29\% & 48.28\% & 56.57\% & 55.56\% & 31.91\% & 40.54\% \\ \hline Average macro & 64.76\% & 59.84\% & 61.70\% & 60.19\% & 54.34\% & 56.12\% \\ Average weighted & 63.56\% & 63.40\% & 63.22\% & 61.82\% & 62.30\% & 61.76\% \\ Overall & 64.16\% & 61.62\% & 62.46\% & 61.01\% & 58.32\% & 58.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the HSAN for the three severity labels.  
Figure 4: The validation set results for the HSAN under different learning rates and hidden layer sizes.
# Study on the Coupling Relationship of Low Temperature Fluidity and Oxidation Stability of Biodiesel
## 3 Experiment Results and Discussion
### Oxidation Stability Experiment Results
#### 3.3.1 Coupling Relationship between Low Temperature Fluidity and Oxidation Stability of Biodiesel  
According to the main influencing factors of low temperature fluidity and oxidation stability of biodiesel, these two properties are associated with the composition and content of FAME of biodiesel. The higher the content of long chain saturated FAME in biodiesel, the worse the low temperature fluidity [20; 21]. The higher the content of unsaturated FAME, the worse the oxidation stability. As shown in Figures 8 and 9, with the increasing content of saturated FAME and unsaturated FAME, the low temperature fluidity and oxidation stability of biodiesel are mutually restricted. When the low temperature fluidity is good, the oxidation stability is poor, and vice versa. Consequently, the low temperature fluidity and oxidation stability of biodiesel have a mutually restrictive coupling relationship. Through adjusting the composition and content of FAME, the low temperature fluidity and oxidation stability of biodiesel can be simultaneously improved.  
Figure 6: Effects of blending ratio of biodiesel on OIT (oxidation induction time).  
Figure 7: Combined effects of blending ratio and antioxidants on OIT.
# GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
## 4 Conclusions  
This paper presents a real-time ZTD/PWV processing solution using the BE-PPP method. Data from 80 IGS stations and 20 EPN stations were processed consecutively for 30 days, employing both the BE-PPP and RT-PPP methods. The feasibility and accuracy of real-time ZTD/PWV data retrieval using the BE-PPP method were thoroughly analyzed and assessed, while the disparities between the BE-PPP and RT-PPP methods were also investigated.  
The results demonstrate the stability of the ZTD estimation obtained from BE-PPP. The mean difference between BE-ZTD and IGS-ZTD was \(-\)1.03 cm, with an average STD of 1.07 cm. On the other hand, RT-PPP yielded a mean bias of \(-\)0.02 cm for ZTD retrieval, accompanied by an average STD of 0.6 cm. Notably, the discrepancy between RT-PPP and BE-PPP is smaller, with only a 4.7 mm STD value.  
In terms of PWV retrieval, the PWVs obtained from both the BE-PPP and RT-PPP solutions exhibited excellent agreement with the ERA5-PWV and RDS-PWV solutions, displaying differences of a magnitude of only a few millimeters. When validated against the ERA5-PWV, the STD for the BE-PWV was 1.69 mm, while for the RT-PWV, it was 0.96 mm. The disparity in the STD between RT-PPP and BE-PPP amounts to merely 0.73 mm. Upon validation with the RDS-PWV, the STD values were determined to be 3.09 mm for the  
Figure 10: Mean value (**a**) and STD (**b**) of the PWV differences for BE-PPP and RT-PPP with respect to RDS at 20 EPN stations during DOY 335-364 of 2022.  
Figure 9: The time series of PWV derived from BE-PPP, RT-PPP, and RDS at station SAS2 during DOY 335-364 of 2022.  
BE-PVV and 1.39 mm for the RT-PWV, indicating an approximate difference of around 2 mm, which satisfies the accuracy requirements.  
Therefore, we can conclude that the real-time retrieval of ZTD/PWV values using the BE-PPP solution is feasible and meets the accuracy requirements. Moreover, the discrepancy between BE-PPP and RT-PPP is minimal, making BE-PPP an effective supplement to the GNSS RT-PPP ZTD/PWV retrieval solution.  
Conceptualization: Z.X. and L.T.; data curation: S.L.; funding acquisition: Z.X.; methodology: Y.X.; resources: Y.X. and G.Z.; software: S.L.; validation: L.T.; writing--original draft: S.L., N.Y. and G.Z.; writing--review and editing: Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant 42030109, and Liaoning Revitalization Talents Program under Grant XLYC2203162.  
Not applicable.  
The data presented in this study are openly available in The International GNSS Service (IGS) for observation files, broadcast ephemeris, and final products. Wuhan University (WHU) for real-time orbit and clock products. The European Centre for Medium-Range Weather Forecasts (ECMWF) for the ERA5 data. Additionally, the University of Wyoming for the radiosonde data.  
The authors declare no conflict of interest.
## 3 Similarity Matrix Based on the Importance of Shared Nearest Neighbors
### The Importance of Node
\begin{table}
\begin{tabular}{l} \hline \hline
**Similarity matrix based on the importance of shared nearest neighbors:** \\ \hline
**Input**: \(n\) data vertexes, \(X=\left\{x_{1},\cdots,x_{n}\right\}\); \\
**Output**: similarity matrix \(SNEW\). \\ \hline
**Step1**. Construct an adjacency matrix \(B\) of graph \(G\) according to Equation (7). The construction of adjacency matrix \(B\) can be similar to the \(\varepsilon\)-neighborhood technique. \\ \(B(x_{i},x_{j})=\begin{cases}1&d(x_{i},x_{j})<TH\\ 0&else\end{cases}\) \\ \hline
**Step3**. Look for shared nearest neighbor vertexes between \(x_{i}\) and \(x_{j}\), and find the maximal importance in shared nearest neighbors; set it as: \(SNN_{\max}\)\(\operatorname{Im}(x_{i},x_{j})\ldots i=1,\cdots,n;j=1,\ldots,n\); \\ \hline
**Step4**. Get a new kind of similarity matrix by Equation (8): \\ \(SNEW_{i}=\begin{cases}\exp(-d(x_{i},x_{j})^{i}/((1+\alpha SNN_{\max}\,\operatorname {Im})\alpha\sigma_{i}))&i\
eq j\\ 1&i=j\end{cases}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The algorithm of similarity matrix based on the importance of shared nearest neighbors.
## 3 Results and Discussion
Figure 4 shows a comparison of the distribution of the Accuracy performance metric. The fill color represents the number of hidden layers, with light blue, blue, and dark blue standing for 1, 2, and 3, respectively. Although the models with a higher quantity of hidden layers have the highest average Accuracy, the amount of neurons in the layers does not significantly affect Accuracy. Finally, the performance of the models in relation to each pattern category, as described in Section 2.2, was evaluated using Balanced Accuracy and F1-score. Figure 5 shows that Pattern Category 1 has the best performance on average (0.63 F1-Score and 0.75 Balanced Accuracy), followed by Categories 2 (0.53 F1-Score and 0.59 Balanced Accuracy) and 4 (0.32 F1-Score and 0.60 Balanced Accuracy). Category 3 is the most difficult to predict (0.07 F1-Score and 0.50 Balanced Accuracy). This observation is inverse correlated with the \(sd_{2019}(L_{\text{den1}d})\) of each pattern category, meaning that its higher the volatility makes this category harder to predict. It is important to note that one-hour time slots are used as a short-term measurement period; thus, improvements in the predictions for Pattern Category 3 can be achieved by combining data from two or more hourly time slots. Figure 4: Statistical distributions of the variable Accuracy for every ANN Model ordered by mean value (red circle) represented though box and whisker plots. The colors group the models by their quantity of hidden layers. Figure 3: Statistical distributions of the Accuracy variable for each hourly time slot, represented though box and whisker plot and sorted by the median value of the Accuracy in decreasing order. To obtain further insights into the prediction ability, an hourly time slot F1-Score performance comparison was carried out for every category. Figure 6 shows similar trends in Categories 1 and 2 regarding median F1-Score for each hourly time slot. Most of these have low variability, meaning that in general any period could be used to predict these environmental pattern categories. On the contrary, Category 3 has low performance at all time slots, improving in the nightly period, but not enough to be confident in the prediction. Therefore, other strategies, for example, increasing the size of the time period or including several hourly time slots as input data, should be considered in future works. Finally, Category 4 presents a wide range of performance values, highlighting the nightly period 21:00-02:00 as the best period. Moreover, the variability of the F1-Score distribution for Category 4 is the highest of all categories. Figure 5: Statistical distributions of F1-Score (**a**) and Balanced Accuracy (**b**) performance metrics for every environmental acoustic pattern, represented as box and whisker plots. Figure 6: Statistical distributions of the F1-Score variable for every hourly time slot, broken down into the four environmental acoustic behaviors (subfigures (**a–d**) for category patterns 1, 2, 3, and 4, respectively) represented though box and whisker plot.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 2 System Model and Problem Formulation
### System Setup  
The number of APs in the D-mMIMO system is equal to \(T\). These APs are used to jointly serve each user in the coverage region. Each AP has a half wavelength-spaced antenna array, hosting \(M\) elements. Two types of antenna elements are considered: patch antennas and isotropic antennas. The radiation patterns for the two types of elements are illustrated in Figure 3.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.11. Impala/ZebraNet  
Impala [37] is the event-based middleware layer of the ZebraNet [37] wireless sensor network. It is designed to allow applications to be updated and adapted dynamically by dispatching events through application adapters. ZebraNet nodes are expected to be inaccessible, and deployed in large numbers, so ZebraNet supports high node mobility, constrained network bandwidth, and a wide range of updates (from bug fixes, through updates, to adding and deleting entire applications). Applications consist of multiple, shareable modules, organized in 2 kB blocks. The Application Updater allows applications to continue running during updates, and can process multiple contemporaneous updates; version numbering is used to ensure compatibility of updates with existing modules. It also handles incomplete updates, and provides a set of simple sanity checks before linking in a new module. Software updates are performed in a three-step process: firstly the nodes exchange an index of modules, then they make unicast requests for updated modules, and finally they respond to requests from other nodes. An exponentially increasing backoff timer reduces management traffic, but can delay updates when separated groups of nodes reconnect. When software reception is complete, then after performing simple sanity checks, the old version application is terminated, the modules in the new version are linked in, and the new application is initialised prior to use.  
Impala provides a reliable update service for ZebraNet. Some of its features, such as compatibility and sanity checks, could be a useful feature for autonomic updates however.
## 2 Materials and Methods  
### Landsat 8 and Sentinel-2 Scenes  
We acquired spatially coincident relatively cloud-free level 2 atmospherically corrected surface reflectance L8 and S2 scenes from 2019. L8 was downloaded from USGS Earth Explorer and S2 was downloaded from Sentinel Hub. L8 scenes have a larger spatial footprint compared to S2; therefore, multiple S2 scenes (with the same acquisition date) were mosaicked to build some of the L8-S2 scene pairs. The path/row of L8 scenes and the corresponding tile IDs S2 scenes are provided in Table 1. In total, 11 S2 scenes and 5 L8 scenes were processed to create 5 L8-S2 scene pairs. Since same-day acquisitions of L8 and S2 are not very common for a given location, we allowed a maximum gap of 3 days between the acquisitions. Consequently, this temporal gap resulted in some land surface changes. There were occurrences of cloud cover and cloud shadow in the image pairs. However, due to the differences in image acquisition times of the sensors, these pixels were present only in one of the L8 and S2 pairs. Figure 1 shows two examples: (a) cloud-free and (b) cloud-covered examples from the training datasets.
# New Physically-Based Mathematical Model and Experiments for a Recently Invented Solar Pot
## 2 System Description
The constant parameters of the system are the following:  
* \(\mathrm{A_{c}}\): surface area of the collector (one side), \(\mathrm{m^{2}}\),
* \(\mathrm{A_{m,e}}\): surface area of the mantle to the environment, \(\mathrm{m^{2}}\),
* \(\mathrm{A_{t,e}}\): surface area of the cooking tank to the environment, \(\mathrm{m^{2}}\),
* \(\mathrm{A_{p,in}}\): surface area between the mantle and the cooking tank, \(\mathrm{m^{2}}\),
* \(\mathrm{U_{c}}\): overall heat loss coefficient of the solar collector, \(\frac{\mathrm{W}}{\mathrm{m^{2}K}}\),
* \(\mathrm{U_{p,e}}\): overall heat loss coefficient of the solar pot, \(\frac{\mathrm{W}}{\mathrm{m^{2}K}}\),
* \(\mathrm{U_{p,in}}\): overall heat transfer coefficient between the mantle and the cooking tank, \(\frac{\mathrm{W}}{\mathrm{m^{2}K}}\),
* \(\mathrm{v}\): volumetric flow rate of the pump, \(\frac{\mathrm{m^{3}}}{\mathrm{s}}\),
* \(\mathrm{V_{c}}\): volume of the collector, \(\mathrm{m^{3}}\),
* \(\mathrm{V_{m}}\): volume of the mantle, \(\mathrm{m^{3}}\),
* \(\mathrm{V_{t}}\): volume of the cooking tank, \(\mathrm{m^{3}}\). During the tests, the cooking tank is filled with water up to the top of the mantle and covered by a stainless steel plate. The stainless steel cover can be removed during the cooking process to check the state of the handled food or drink. Accordingly, there is atmospheric pressure inside the cooking tank. We also circulate water between the solar pot and the solar collector as the working fluid.
## 3 Proposed Scheme: FRBP
### Data Transmission through the Real-Time Broadcast Tree  
The header of each data packet includes the following information: source node information, sequence number, path information, sender ID, delay information of a sender and location information of a sender. The sequence number of the data prevents duplicated reception by comparing the value in the cache. Delay information and location information of a sender are used for delivery speed estimation.  
Fundamentally, when a source node generates a data packet, the data are forwarded by broadcast. Neighbor nodes of a sender listen to the data and match up the ID of the sender with their parents' ID. If the IDs are matched, corresponding neighbor nodes rebroadcast the data. Then, the data packet is spread to all nodes downstream of the pre-constructed real-time broadcast tree. To measure the \((m,k)\)-firm requirement, each node maintains DBP and real-time transmission information at each data transmission for local monitoring as presented in Equation (1).  
\[DBP_{sd}=k_{s}-m_{s}-n(s_{sd}), \tag{1}\]  
where \(DBP_{sd}\) is the measured DBP value of stream \(s\) at destination \(d\), \(k_{s}\) and \(m_{s}\) come from the \((m,k)\)-firm real-time requirement of stream \(s\), \(s_{sd}\) denotes the current \((m,k)\)-firm state of stream \(s\) at destination d and \(n(s_{sd})\) means the number of packets missing their deadline in state \(s_{sd}\). The \((m,k)\)-firm state is mentioned above in Figure 1. A positive DBP value, including zero, stands for meeting the \((m,k)\)-firm requirement, while a negative DBP means the real-time failure state.  
When a child node receives data, the node calculates a delivery speed by a sender's delay and distance between the sender and each child. In real-time broadcast, since every sensor node is a destination, all of them need to maintain their own DBP value. If the measured speed exceeds the real-time delivery constraint, it increases the DBP value of the child node. Otherwise, the node decreases the DBP value. According to the result of the DBP value update, the node determines fault reporting upstream. Furthermore, intermediate nodes have the responsibility to support a series of child nodes downstream, and thus, the node deals with delivery speed for each transmission even if its own DBP status is fine. The detailed concerns about the local monitoring will be discussed in the next subsection.
## 3 Seismic Attributes and Application
### Second Edge Enhancement  
The second method, which follows edge evaluation, was applied to further enhance the discontinuity, and an ant tracking seismic attribute was used. Ant tracking is a patented technology by Schlumberger that performs edge enhancement to identify faults within the seismic data volume [36]. This method is based on the concept of swarm intelligence and is used to introduce a large number of agents (ants) in data volumes and evaluate  
Figure 4: (**a**) Original cube section and the results of edge detection using amplitude contrast attribute with (**b**) nondirectional and (**c**) directional and summation steps.  
swarm collective behavior. The analogy of the habit of ants to follow the shortest route for transporting food to their nests is applied to edge enhancement.  
One of the issues in using ant tracking is that there are six parameters. Therefore, 720 variable combinations are possible, which indicates that different results can be obtained depending on the variable, even when applied to the same data. Another issue is that changing one of these values is difficult and requires a long computation time. Sequential ant tracking was used to avoid these issues. In ant tracking, there are two ant modes: passive and aggressive. Passive ant tracking is a common method for signals to move in a stronger direction and is effective in the identification of the overall trend of faults in large areas. The aggressive mode is more effective for detecting small faults in greater detail compared to the previous mode. In this study, these two modes were applied crosswise rather than individually to the cube to solve the abovementioned problems; the results are shown in Figure 6. The results were compared after cross-applying the passive-aggressive-passive and aggressive-passive-aggressive modes using the ant tracking method in the edge evidence cube. The difference between the two methods did not appear to be significant for this field data. However, the edges of the small faults were found to be slightly more enhanced in the aggressive-passive-aggressive sequence than in the passive-aggressive-passive sequence. In a multiple ant tracking experiment, most features of the first mode were found in the final cross-analysis result.  
The results of fault detection using the conventional workflow and the proposed workflow are shown in Figure 7. The overall pattern of the fault was similar on both sides; however, the conventional workflow generated significantly poor continuity of the fault. On the other hand, the results using the proposed workflow significantly eliminated noise from the cube, and the fault edges appeared sharper and more continuous. Therefore, we demonstrate that the proposed workflow is applicable to field data, and it has the capacity to further improve fault detection.  
Figure 5: First edge enhancement with sequential edge evidence. When first applied along the (**a**) horizontal direction, the edges were sharper, and, when it was subsequently applied along the (**b**) vertical direction, the discontinuity was enhanced.