esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 2 Methodology
### Determination of the LSP Metrics Using TIMESAT  
LSP metrics were derived from NDVI data obtained from both the MODIS MOD13Q1 and HLS products using the TIMESAT software. A summarized description of the 13 LSP metrics is presented in Table 1. The NDVI, introduced by [PERSON] et al. [38], is one of the most widely used vegetation indices in remote sensing studies. By utilizing the normalized difference between the reflectance of the NIR and Red bands, NDVI is sensitive to vegetation structure and leaf pigments. Compared to other indices, such as the Enhanced Vegetation Index (EVI), which is also available in the MOD13Q1 product,NDVI is generally less affected by factors like viewing-illumination geometry and canopy-terrain shadows [39; 40; 41]. Additionally, NDVI does not saturate under the sparse vegetation conditions typical of Brazilian savannnas. TIMESAT is one of the most widely used software for retrieving LSP metrics in vegetation phenology studies. Originally designed to handle time series of NDVI data, it now supports other datasets as well [27; 42]. In our study, TIMESAT (version 3.3) was applied to the regularly spaced time series of MODIS NDVI data (16-day spaced intervals) and to the irregularly spaced time series of HLS data, which were converted into regular intervals of 5 days through interpolation and gap filling. In order to ensure data quality, we used the ancillary pixel reliability information from the MOD13Q1 product. We assigned weights of 1.0, 0.5, and 0.0 to pixels classified as good (code 0), marginal (code 1), and cloudy (code 3), respectively. In our study area, good-quality pixels (code 0) generally increased from the rainy season (October to May) to the dry season (June to September), as shown for 2019 in Figure 11 (Appendix A). Cloud-free pixels were considered also in the HLS product. After defining the number of seasons and their approximate timing, a smoothing function was applied to the data using the Savitsky-Golay filter. This filter has been used in many studies of vegetation phenology e.g., [43; 44; 45; 46]. Furthermore, this filter was more efficient in reducing noise than the other options available in TIMESAT.
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 3 The Dispatch of Power Systems Containing VPPs  
### The Construction of the VPP  
The DERs that form the VPP could be the same or diverse, and their layout may be either concentrated or scattered. Information networks and high-level software architecture are employed inside the VPP to connect and control DERs. Therefore, there is no need to change the grid-connected structure and the distributed topology of DERs.  
The construction of a VPP that consists of various DERs is shown in Figure 7.  
As shown in Figure 7, DERs that form the VPP in this paper could be wind power plants (WPP), photovoltaic power plants (PPP), demand side resources (DSR), electric vehicle charging stations (EVCS), distributed energy storage (DES), _etc._ The output of a VPP is the sum of all DER outputs, which can be predicted separately, inside the VPP. The characteristics of different DERs could be complementary, and the uncertainties could be offset to some degree, which gives the VPP better external characteristics, similar to conventional power plants, than scattered DERs.  
Figure 7: The construction of a virtual power plant (VPP). DER, distributed energy resource.
## 2 Related Work
### Relevant Research on Chinese Address Parsing
#### 2.1.2 Rule-Based Address Parsing  
Chinese addresses possess specific rules and distinctive features. By systematically summarizing the attributes and regulations governing Chinese addresses, it becomes feasible to devise address-parsing techniques founded on the characteristics of address components and address structures. This approach facilitates the achievement of a structured address-parsing outcome [17; 18]. [PERSON] et al. [19] undertook the task of consolidating the traits of address elements and dissecting the parsing regulations that govern these attributes. Consequently, they devised a rule-based address-parsing algorithm that integrates feature characters and regulations. [PERSON] et al. [20] introduced innovative mechanisms encompassing rule trees and ambiguity storage, thereby achieving a rule-based approach for address segmentation and matching. This method tangibly enhanced the efficacy of matching addresses that are afflicted by omissions and ambiguities.  
In practical scenarios, rule-based methodologies face challenges akin to those encountered by dictionary-based approaches. Both methodologies are susceptible to complications arising from incomplete dictionaries or deficient address element repositories. Consequently, an established approach is to amalgamate these two strategies [21; 22]. Nevertheless, diverse geographical regions exhibit significant disparities in address nomenclature and usage conventions, rendering the generalized application of dictionaries and regulations across various locales a formidable task.
## 5 Methodology
### Spatial Statistics
#### 5.2.2 Getis-Ord GI* Clustering  
Similarly, we utilized Getis-Ord GI* clustering, also known as hotspot analysis or spatial autocorrelation analysis, as a statistical technique to identify the clusters of high or low values in our mobility data on the same game day. The G statistic, which ranges between \(-1\) and \(1\), is used to measure the spatial autocorrelation [53]. Positive values of the G statistic indicate that high values of the variable tend to cluster together, while negative values indicate that high values tend to be dispersed.  
The Getis-Ord local statistic is given as Equation (3).  
\[G_{i}^{*}=\frac{\sum_{j=1}^{n}w_{i,j}x_{j}-\overline{\chi}\sum_{j=1}^{n}w_{i, j}}{\sqrt[s]{\left[\frac{\mu_{j=1}^{n}w_{i,j}^{2}-\left(\sum_{j=1}^{n}w_{i,j} \right)^{2}}{n-1}\right]}} \tag{3}\]where the weight between feature \(i\) and \(j\) is denoted by \(w_{i,j}\). The attribute value for feature \(j\) is represented by \(x_{j}\), and \(n\) denotes the total number of features in the dataset and the following:  
\[\overline{X}=\frac{\sum_{j=1}^{n}x_{j}}{n} \tag{4}\]  
\[S=\sqrt{\frac{\sum_{j=1}^{n}x_{j}^{2}}{n}-(\overline{X})^{2}} \tag{5}\]  
This method is particularly useful in identifying regions where a certain occurrence is exceptionally concentrated or scattered [54]. The Getis-Ord GI* statistic is a valuable tool for understanding the spatial distribution of a certain phenomenon and how it relates to other variables in the dataset. It allowed us to locate clusters of high to low values in our mobility data and helped us to understand the spatial distribution of mobility patterns in our POI before and after a baseball game.  
We conducted a hotspot analysis to identify significant clusters of people in key locations during the Saint Louis Cardinals vs. Pittsburgh Pirates game on 12 May 2019, with a focus on the Saint Louis City area.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 6 Conclusions  
Through theoretical derivation, on-site monitoring, and numerical simulation, this paper systematically analyzes the tunnel vibration characteristics triggered during shield construction and draws the following main conclusions:  
1. Vibration energy distribution and attenuation law: Vibration energy is mainly concentrated in the low-frequency band of 3-4 Hz, and there is an exponential attenuation trend in the propagation process. This shows that the vibration impact of shield construction on the surrounding structure has obvious spatial limitations, and the vibration energy is rapidly decaying in the area closer to the vibration source.
2. Tunnel axial and radial vibration characteristics: The axial vibration response on the construction tunnel side is the strongest, which gradually decays with increasing distance, while the radial vibration response on the neighboring tunnel side is more significant. This difference is due to the different characteristics of the vibration propagation path and the soil medium.
3. Verification of theoretical model and numerical simulation: Numerical simulation based on PFC3D verifies the correctness of the theoretical formulas, and further reveals the important role of the damping effect of the soil medium and segment structure in the vibration propagation process. The fitting results show that the variation in the damping ratio is in line with the actual situation, which provides a reliable basis for vibration energy prediction.  
Conceptualization, Y.W. and S.Y.; methodology, B.D. and R.W.; software, Y.W. and S.Y.; validation, S.Y. and B.D.; data curation, S.Y., B.D. and R.W.; writing--original draft, Y.W. and S.Y.; writing--review and editing, B.D. and R.W.; visualization, R.W. and S.Y.; supervision, Y.W. and B.D.; funding acquisition, Y.W. and R.W. All authors have read and agreed to the published version of the manuscript.  
The research reported in this paper has been funded by the National Natural Science Foundation of China (51778633); the China Railway Construction Corporation 2022 Annual Scientific and Technological Research and Development Plan and Funding Subjects (2022-C1); and the Science and technology research and development plan topics of China Railway Second Bureau Group Co. (2022-B-4).  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. The data are not publicly available due to the policies adhered to in our laboratory.  
We would like to acknowledge the reviewers and editors for their valuable comments and suggestions.  
The authors declare no conflicts of interest.
# Collaborative Use of a Shared System Interface:
## 2 Theoretical Development
### Eye-Tracking Technology  
The use of eye-tracking (or oculometry) is emerging and informs research in IS and HCI [21]. This trend is illustrated by the fact that for the past decade, eye-tracking has been the most used neurophysiological tool in NeuroIS research [22]. Eye-tracking is a technique permitting measurement of eye movement and gaze location, providing a researcher, at any point of time, with information about what stimulus is being processed by a user [6]. To capture a user's eye movements, eye-tracker systems target physiological characteristics of the eye with infrared technology along with high-resolution cameras [6]. This technique uses image processing software to capture two eye features [4]: the corneal reflection appearing as small bright glint on the eye pupil, and the center of the pupil. The analysis software finds the position of the user's gaze on the screen based on the relative position of the pupil center and the glint, along with trigonometric computation [4].  
Two important eye-tracking elements are saccades and fixation. Saccades are the short duration eye movements (ranging between 30 ms and 80 ms duration) with no information processing [6; 23]. Fixations are short stops between saccades [4] generally lasting a minimum of 50 ms (e.g., for text processing) or 150 ms (e.g., for image processing). Fixations are usually analyzed at specific area of interest (AOI) defined by the researcher [6; 23]. There are five main eye-tracking measures that are usually employed: fixation duration (amount of time a point is fixated by the user), fixation frequency (or fixation counts: number of times a point is fixated), time to first fixation (the time it has taken the user to gaze inside an AOI), visit count (number of times a viewer's gaze entered an AOI), and total visit duration (length of time a user gaze remained in an AOI).  
Additionally, there are two main gaze representations used with eye-trackers: fixation patterns and gaze heatmaps. Fixation patterns are two-dimensional plots of the fixation points for a given user. Gaze heatmaps are heatmaps made of the aggregation of user fixation patterns, with fixation intensities being represented using gradients of a discrete set of colors (red for high, yellow for moderate, and green for low intensity) [24].
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 3 Results and Discussion
### Hardened Properties
#### 3.2.2 Splitting Tensile Strength  
The test results of splitting tensile strength for each RAC at 28 and 56 days are shown in Figure 5.  
Although there are minor discrepancies among the test results with the time, the behavior of tensile strength is nearly similar to that of compressive strength. In the RAC1 series in which the RA quality was relatively low, RAC1-AP-25 and RAC1-AP-50 showed a 3-5% lower tensile strength than the control concrete at 28 and 56 days of age, whereas the RAC1-OA mixes showed 1-3% higher tensile strength to control concrete at 28 days. The RAC2 series, made of high-quality RA, showed similar or slightly higher tensile strength than the control concrete in all RAC mixtures.  
As can be seen in Figure 5, it seems that strength can be improved if the size of OA in RCA rather than RCA is large. The tensile strengths of RAC1-OA-25 and RAC1-OA-50 with a larger size of OA were 2% and 7% higher at 28 and 56 days, respectively, compared to the RAC1-AP series with smaller OA sizes. For RAC2, the RAC2-OA series had up to 10% improvement in tensile strength compared to the RAC2-AP series.  
The correlation between compressive strength and splitting tensile strength of recycled aggregate concretes is shown in Figure 6. A comparison was made between the values calculated by the prediction model given in the ACI code (Equation (2)) and the actual experimental results performed in this study.  
\[f_{ct}=0.56\sqrt{f_{c}} \tag{2}\]  
where \(f_{ct}\) is the splitting tensile strength and \(f_{c}\) is the compressive strength.  
In Figure 6, the RAC1 and RAC2 were integrated into RAC, but were divided into the AP and OA series to evaluate the effect of maximum aggregate size. In the case of the RAC-OA series, in which concrete was manufactured based on the size of OA, a higher value of tensile strength was clearly observed than that of RAC-AP based on the size of RA itself at the same compressive strength level, but the ACI equation clearly overestimated the tensile strength obtained in all RAC mixtures. Although a low correlation coefficient (less than 0.5) between compressive strength and tensile strength has been observed in some studies [58], the RACs used in this study had good correlation coefficients (0.81 and 0.88). In particular, it is worth mentioning that a higher correlation coefficient was observed  
Figure 5: Splitting tensile strength development with age.  
in the RAC-OA series, where the particle size distribution was determined based on OA. Therefore, when concrete is manufactured based on OA particle size, the properties of concrete can be more predictable.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 2 Use Cases and Requirements
### Industrial Requirements
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Use Case** & **Application** & \
\begin{tabular}{c} **End-to-End** \\ **Latency** \\ \end{tabular} & **CSA** &
\begin{tabular}{c} **Message** \\ **Size/Data Rate** \\ \end{tabular} & **\# of Devices** \\ \hline \multirow{3}{*}{Motion control} & Printing machine & \textless{}2 ms & \multirow{3}{*}{\(>\)100} \\  & Machine tool & \textless{}0.5 ms & \(1-10^{-6}\) to \(1-10^{-8}\) & 50 bytes & \textless{}20 \\  & Packaging machine & \textless{}1 ms & & 40 bytes & \textless{}50 \\ \hline \multirow{3}{*}{Control-to-control} & \multirow{3}{*}{Machines coordination} & \textless{}4 ms (cyclic) & \multirow{3}{*}{\(1-10^{-6}\) to \(1-10^{-8}\)} & \multirow{3}{*}{1 kbyte} & \multirow{3}{*}{5-10} \\  & & \textless{}10 ms (acyclic) & & & \\ \hline Mobile control & Periodic monitoring & 4–8 ms (cyclic) & \multirow{3}{*}{\(1-10^{-6}\) to \(1-10^{-8}\)} & 40 to 250 bytes & \multirow{3}{*}{\(<\)5 Mbit/s} \\ panels with safety & User interaction & \textless{}30 ms (acyclic) & & & \\ \hline \multirow{3}{*}{Mobile robots} & Cooperative control & 1 ms & \multirow{3}{*}{\(>\)100} \\  & Machine control & 1–10 ms & & & 40 to 250 bytes \\  & Video operated control & 10–100 ms & & 15 to 150 kbytes & \textless{}100 \\  & Standard mobile robot & 40–500 ms & & & 40 to 250 bytes \\ \hline \multirow{3}{*}{Massive wireless sensor networks} & Condition monitoring for safety & \multirow{3}{*}{\(50\) ms\(-1\) s} & \multirow{3}{*}{\(1-10^{-6}\) to \(1-10^{-8}\)} & \multirow{3}{*}{4 to 24 bytes} & \multirow{3}{*}{\(10^{2}\)-\(10^{3}\)} \\  & Interval-based monitoring & 50 ms\(-1\) s & & \(>\)10\(-10^{-3}\) & 4 to 24 bytes \\  & Event-based condition & \multirow{3}{*}{\(5\)–10 ms} & \multirow{3}{*}{\(>\)10\(-10^{-3}\)} & \multirow{3}{*}{4 to 24 bytes} & \multirow{3}{*}{\(10^{3}\)-\(10^{4}\)} \\  & monitoring & & & & 60 Hz HD (1280 \\ \hline \multirow{3}{*}{Augmented reality} & Process monitoring Step-by-step & \multirow{3}{*}{\(<\)10 ms} & \multirow{3}{*}{\(>\)1 –\(10^{-3}\)} & 60 Hz HD (1280 \\  & instructionsRemote support & & & \(\times\) 720) or FHD (1920 \(\times\) 1080) & 3/BS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Requirements of selected use cases for the factory of the future. Figure 1: Requirements of selected use cases for the factory of the future. Each area represents the range of requirements identified for each use case. Some use cases have two areas to highlight that different applications will require significantly different requirements.
## 6 Statistical Tests
### Discrete Fourier Transform (Spectral) Test  
This test focuses on the peak heights of the Discrete Fourier Transform (DFT) of the generated sequence. It looks in the sequence for periodic features that contradict the assumed randomness of the bit chain.  
The test assigns a value of \(-1\) to each 0 and a value of 1 to each 1 in the n-bit sequence and sums it to produce a value \(X\), then it applies the DFT, \(S=DST(X)\). Afterwards, it computes \(M=\|S(\text{first }n/2\text{ bits})\|\) and the peak height threshold \(T=\sqrt{\left(\log\frac{1}{0.05}\right)n}\). Finally, it computes \(N_{t}=\frac{0.95n}{2}\), the expected theoretical number of peaks under the threshold \(T\), and  
\[d=\frac{N_{o}-N_{t}}{\sqrt{0.95\times 0.05\times(n/4)}};\]  
with \(N_{o}\) the actual number of peaks in \(M\) that are less than the threshold \(T\).  
The test passes when \(P_{value}=erfc\left(\frac{\|d\|}{\sqrt{2}}\right)>0.01\). A low value of \(d\) indicates that there were actually too many peaks in \(M\) above the threshold \(T\).  
\begin{table}
\begin{tabular}{c c c} \hline \(\mathbf{c_{i}}\) & \(\mathbf{M=8}\) & \(\mathbf{M=16}\) \\ \hline \(c_{0}\) & \(k\leq 1\) & \(k\leq 4\) \\ \(c_{1}\) & \(k=2\) & \(k=5\) \\ \(c_{2}\) & \(k=3\) & \(k=6\) \\ \(c_{3}\) & \(k\geq 4\) & \(k=7\) \\ \(c_{4}\) & — & \(k=8\) \\ \(c_{5}\) & — & \(k\geq 9\) \\ \hline \end{tabular}
\end{table}
Table 1: The \(k\) values according to the block lengths.  
\begin{table}
\begin{tabular}{c c c} \hline \(\mathbf{M}\) & \(\mathbf{L}\) & \(\mathbf{N}\) \\ \hline
8 & 3 & 16 \\
128 & 5 & 49 \\ \hline \end{tabular}
\end{table}
Table 2: The pre-set values of \(M\), \(L\) and \(N\) according to NIST.
## 4 Function Design of the Back-End Service Layer
### Access Verification  
When a user attempts to log in, they need to access the database and check the user table for user login permission. To this end, a class needs to be created to handle database connections. If the access path contains \"login.html\" or \"/loginServlet\", the interceptor allows the user to access the login page. Otherwise, the user accesses a non-login page; in this case, it is necessary to check whether the Session object in the cloud server contains information about the accessing user. If the user logs in successfully and the Session object contains the user's login information, the interceptor allows access. If the user has not logged in and the Session contains no user information, the page shows a \"Please log in\" prompt and then redirects the user to the login page.  
Figure 12 shows the flowchart for access verification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource** & **URL** & **HTTP Method** & **Description** \\ \hline Homepage & * & GET & Home page \\ Login & * & POST & Login system \\ Registration & * & POST & Register an account \\ Running & * & POST & Obtain running state parameters \\ status & & & \\ Historical & * & POST & \\ data & & & \\ Fault alarm & * & POST & Obtain fault alarm information \\ User & * & GET & Query user information \\ management & & & \\ Role & * & GET & View user role \\ management & & & \\ Permission & * & GET & View user permission \\ management & & & \\ Operation & * & GET & View user operation logs \\ logs & & & \\ \hline \hline \end{tabular}  
* URL in table, the manufacturer does not agree to publish a valid website.  
\end{table}
Table 6: Commonly data retrieval interfaces used in the monitoring system.
# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 4 Open Issues and Discussions
### Cross-Layer Research Questions  
As discussed in the above section, topics and research trends are focused on different layers of blockchain-enabled trust. For example, decision-making in the social layer; security and privacy issues in the data layer; and mathematical foundation and mechanisms in the technical layer. However, there are some conflicts in the existed literature that cut across these layers. We briefly summarize them into three research questions:  
* _Question 1_: How to design a balanced framework for blockchain-enabled edge intelligence? This is a common issue that exists in most decentralized systems. The [PERSON]'s Triangle [122] points out that it is highly unlikely to have a decentralized system with both security and human-readability. Thus, we could further acknowledge that efficiency, security, and decentralization are three angles in the [PERSON]'s triangle of blockchain-enabled edge intelligence. Researchers should keep this conflict in mind when they use blockchains to enable edge intelligence. For example, either the decentralization level or security level might be sacrificed when they maximize the transaction speed in edge systems. Thus, a trade-off exists among these factors.
* _Question 2_: How to establish standard criteria to verify a high-quality training model for edge intelligence? Deep learning models or parameters are shared and traded in decentralized ML, such as FL. However, there is no general criterion for evaluating and verifying recorded models or parameters. Each paper has its own method that may not be readily compared with that implemented in another article. Furthermore, it might not be a good idea to simply use accuracy or the loss function to verify the quality of the ML model because it could cost a lot of energy and time for training a very accurate model, which might not be preferable for energy-constrained devices in some low-latency cases.
* _Question 3_: How to reduce the complexity of blockchain strategies for edge intelligence? Different from other devices, edge or IoT devices are resource-constrained. Blockchain strategies presented in most papers are not suitable for the blockchain-enabled edge intelligence because most proposed algorithms, such as zero-knowledge proof for privacy preservation, are too complicated for edge nodes to execute. Researchers should keep this in mind when they develop their own strategies in the scenario of blockchain-enabled edge intelligence.