esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Method
### RS Images Processes
#### 3.1.3 Accuracy Assessment  
Note that, owing to subjective factors in the interpretation process, spectrally similar responses from different LULC types and blurred texture features, interpretation error was inevitable. Thus, performing accuracy assessment for LULC classification was essential. Random sample checking by field survey points and checking line were adopted. In 2004, 2005 and 2012, we conducted 3 field surveys in the TRHR to obtain ground truth data, and the field survey materials and records were used to assess the classification accuracy of the LULC maps of 2004 and 2012 (Figure 3).  
339 reference points were extracted randomly from 2068 points recorded by handheld GPS acquired from the field surveys in 2004 and 2005, and the records were used to assess the classification accuracy of the LULC dataset of 2004. The result showed that there were 15 reference points where LULC were misclassified. Thus, the accuracy assessed by survey points was 95.58%. The checking line in Figure 3 involved 1903 patches. 421 patches were extracted randomly and were used to assess the classification accuracy one by one. According to the assessment result, there were 18 patches where LULC were misclassified. Thus, the accuracy assessed by checking line was 95.72%. In general, there were 33 misclassifications among the 760 random checks, and the overall accuracy of the LULC dataset of 2004 was 95.66%. The same method was adopted in the accuracy assessment of the LULC map of 2012, and the overall interpretation accuracy was 96.01%.  
Figure 3: The spatial distribution of the field survey points and checking line.
## 4 Result and Discussion
### Single Dynamic Impact Test
#### 4.1.3 Peak Strain  
The average peak strain of specimens with different fiber contents in the impact compression test is shown in Figure 6. As mentioned above, fiber orientation has little  
Figure 5: The effect of fiber content on strain rate.  
influence on the dynamic peak impact strength of the specimen. However, it can be seen from Figure 7 that its influence on the peak strain was obvious. When the fiber content was 2%, the average peak strain of the fiber-oriented specimen increased by 35.8% compared with the non-oriented specimen. When the fiber content is 4%, the average peak strain of the fiber-oriented specimen was increased by 37.2%.
## 4 Energy Efficiency Optimization
### Density Optimization Given the Transmit Power of Picocell BSs  
In this section, we analyze the optimal density of picocell BSs in order to maximize the EE formulated in Equation (13). The optimization problem can be expressed as  
\[\begin{split}\underset{\lambda_{2}}{\max}&\eta_{EE} \\ \text{s.t.}&\lambda_{2}\geq\lambda_{\text{min}}, \lambda_{2}\leq\lambda_{\text{max}}\end{split} \tag{14}\]  
In Equation (14), \(\eta_{EE}\) is the objective function of the optimization problem, and density is the optimal variable. Here, \(\lambda_{\text{min}}>0\) and \(\lambda_{\text{max}}>0\) are the minimum and maximum density of the BSs, respectively. We assume, without loss of generality, \(\lambda_{\text{min}}\rightarrow\lambda_{1}\) and \(\lambda_{\text{max}}\rightarrow\lambda_{u}\). Through symbolic computation and simulation, we find that the objective function is a convex function and has the global optimum. According to the properties of convex functions, any extreme point of a convex function within a convex set is also its best point. Therefore, we use the one-dimensional optimization algorithm to find the optimal point of the objective function in the effective interval. In this paper, the golden section method is adopted to find the optimal solution of the optimization problem in Equation (14), and the solution is given by  
\[\widehat{\lambda_{2}^{opt}}=\max\{\lambda_{\text{min}},\min\{\lambda_{2}^{*},\lambda_{\text{max}}\}\} \tag{15}\]  
where \(\lambda_{2}^{*}\) is the only stationary point of the objective function.
## 3 Sink-Based Centralized Transmission Scheduling
### Data Collection Procedure  
The sink node stops at each data collection point, computes routes based on the basic geographic routing [14], and decides the transmission order as follows:  
1. The sink node calculates the distance between itself and each sensor node (the location of each sensor node is assumed to be known).  
Figure 2: Conceptual diagram of the proposed method. WuR = wake-up radio; WuC = wake-up call.  
For each sensor node, treat the nodes that are in its transmission range as its neighbors.
* For each sensor node, select from its neighbors the node that is the closest to the sink node as its relay node.
* A tree structure is constructed with the sink node as the root node.
* A depth-first search is executed on the tree. The result of the post-order is the transmission order  
Then, at each data collection point, the sink node collects sensor data from all sensor nodes by repeating the following steps.  
1. In accordance with the transmission order, the sink node transmits a WuC per hop, which carries two addresses, one deciding the sender of data and the other deciding the receiver, where the receiver is actually the relay node for the sender.
2. WuKos at the sender and the receiver, on receiving the WuC, activate their sensor nodes, which communicate during the time (called the communication possible time) assigned by the sink node.
3. After the communication possible time is completed, both the sender and receiver nodes enter the sleep mode again.
4. After the communication possible time, the sink node transmits a WuC to activate the next pair of nodes.  
Whenever the sink node moves to a new data collection point, it recalculates routes and the transmission order based on the position of the data collection point before collecting sensor data.  
The communication possible time is defined in terms of slots. The number of slots in a communication possible time is equal to the number of data frames (including frames to be forwarded) at a sender node. The definition of a slot is given by Equation (1).  
\[\begin{array}{ll}1\ \text{slot}&=\text{data frame transmission time}\\ &+\text{max propagation delay for data frame }(4\ \text{\mu s})\\ &+\text{SIFS }(10\ \text{\mu s})\\ &+\text{ACK frame transmission time}\\ &+\text{max propagation delay for ACK frame }(4\ \text{\mu s})\end{array} \tag{1}\]  
where SIFS (short inter-frame space) is the space separating a data frame and its ACK.  
Figures 3 and 4 show an example of the data collection procedure. First, a WuC is sent to activate A and B, and one frame is transmitted from A to B (first hop). Then, a WuC is sent to activate B and C, and two frames are transmitted from B to C (second hop).
## 3 Results and Discussion  
### Effects of Peptides (M1-M10) on HepG2 Cell Viability  
HepG2 cells have high differentiation and show many characteristics of genotypes of normal human liver hepatocytes [26]. In consequence, HepG2 cells are an ideal model system applied for studying human liver metabolism in vitro, screening the cytotoxicity capacity of novel functional molecules at the lead generation stage, and detecting environmental and dietary cytotoxins and genotoxins [27,28].  
Cell viability determines the response of cells to stress stimuli and usually is used to look at the effects of pharmacological compounds on cells. The viabilities of HepG2 cells treated with peptides YASVV (M1), NFWWP (M2), FWKVV (M3), TWKVV (M4), FMPLH (M5), YFLWP (M6), VIAPW (M7), WVWWW (M8), MWKVW (M9), and IRWWW (M10) were presented in Figure 1, the viability of cells treated with peptide TWKVV (M4) was significantly weaker than that of the normal control group at 100 \(\upmu\)m for 24 h (\(p<0.05\)). Furthermore, the viability of cells treated with peptide YFLWP (M6) was very significantly weaker than that of normal control group at the same concentration (\(p<0.01\)). Therefore, TWKVV (M4) and YFLWP (M6) have stronger cytotoxic activity to HepG2 cells than the other eight peptides and are unfit for serving as the candidate molecules of non-anti-tumor functional food and/or drugs. Therefore, all test peptide except TWKVV (M4) and YFLWP (M6) did not significantly affect the normal proliferation of HepG2 cells and could be applied for the development of non-tumor drugs and functional food.  
Figure 1: Effects of antioxidant peptides (M1-M10) from miuuy croaker (_Michthys miuuy_) muscle on viabilities of HepG2 cells at 100 \(\upmu\)m. Means \(\pm\) SD (n = 3) are used to express the experiment data. \({}^{**}\)\(p<0.01\), \({}^{*}\)\(p<0.05\) versus normal control group.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 4 Simulated Leak Piping System
### Results of Leakage Signal Tests  
In the coupled vibration mode, the 50-60 Hz band was set as the leak frequency detection band through sample measurement because the leak detection frequency range varies depending on the pipe type; here, the simulated leak piping system was a cast iron pipe [14; 23].  
Sampling was done at 1024 Hz, and FFT analysis was conducted by setting a low-pass filter (1000 Hz) for data in a low-frequency band. The results of the leakage signal analysis for each case are given in Figure 7b, referring to the case numbers in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case No.** & **Leak Point** & **Water Flow in the Pipe** & **Water Pressure (MPa)** \\ \hline
1 & None & No & 0.18 \\
2 & None & Yes & 0.17 \\
3 & Valve in branch pipe & No & 0.18 \\
4 & Valve in branch pipe & No & 0.17 \\
5 & Valve in branch pipe & Yes & 0.17 \\
6 & Valve in branch pipe & Yes & 0.14 \\
7 & Main pipe & No & 0.18 \\
8 & Main pipe & No & 0.14 \\
9 & Main pipe & Yes & 0.17 \\
10 & Main pipe & Yes & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Leak testing programs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Measurement Torque** & **RMS of Strain** & **Tensile Stress** & **M16 Bolt Standard Torque** & **M16 Bolt Allowable** \\  & **(N\(\cdot\)m)** & **(\(10^{-6}\))** & **(MPa)** & **(N\(\cdot\)m)** & **Tensile Stress (MPa)** \\ \hline
1 & 10 & 792 & 162.3 & & \\
2 & 20 & 800 & 163.9 & & \\
3 & 30 & 808 & 165.6 & & \\
4 & 40 & 814 & 166.8 & 60 & 217.32 \\
5 & 50 & 821 & 168.3 & & \\
6 & 60 & 830 & 170.2 & & \\
7 & 70 & 836 & 171.4 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Smart bolt strength tests.  
Case 1 in Figure 7b is the FFT result of the acceleration in the state where there was no leakage and no flow, and case 2 is where there was flow in the pipe but no leakage (because the leakage is not present).  
Figure 7: The 1000 Hz low pass filter on acceleration and fast Fourier transform (FFT) results.  
there was no leakage, there was no signal for the dynamic behavior of the pipe). Cases 3-6 are the cases of leakage from the branch pipe, and cases 7-10 are the cases of leakage from the main pipe. In the case of leakage of the main pipe and the branch pipe, it was divided into the case where the water pressure does not change and the case where the water pressure changes according to the amount of leakage.  
When the main pipe leaked rather than the branch pipe, the leak detection signal appeared in the higher-order mode. Conversely, leakage from the branch pipe produced a low-order signal and the leakage signal intervals were wide. In the branch pipe leakage, more leakage signals appeared when there was a large amount of leakage than with a small amount.
## 2 Modeling and Analysis of State Transitions for Energy-Efficient Femto BS  
### Modeling of State Transitions  
Figure 1 shows a proposed state transition model of femto BS considered in this paper, where the state of femto BS is divided into active (data), idle, active (signaling), sleep entering, sleep and waking up states. In the active (data) state, the radio interface of femto BSs is awake, and thus, BSs are sending or receiving data packets with MSs. In the active (signaling) state, the radio interface of femto BSs is awake similar to active (data) state, but this state is different from the active (data) state, since it only deals with location registration signaling, and thus, it has a very short service time compared to that of the active (data) state.  
Figure 1: State transition model of the femto base station.  
After completing service either in the active (data) state and active (signaling) state, the state returns to the idle state, and an inactivity timer is started. If there are no data or signaling transmission until the inactivity timer expires, the state moves to the sleep entering state, and the BSs start to turn off their radio interface and prepare to enter the sleep state. Otherwise, the inactivity timer is reset, and the state returns back to either the active (data) or active (signaling) state, depending on the type of activity, _i.e._, data or signaling transmission. That is, if data arrive, the state moves to the active (data) state. On the other hand, if signaling arrives, the state moves to the active (signaling) state.  
If all of the necessary processing related to sleeping is completed in the sleep entering state, the state moves to the sleep state. In the sleep state, the energy of BSs can be saved significantly. If there is any data packet from MSs when the femto BS is in the sleep state, MSs send a reverse paging signal to wake up the femto BS by using an additional low-power radio interface [21]. As mentioned in [21], the low-power radio interface uses different radio channels with very low power, and thus, it does not consume the battery energy of MSs significantly. Then, the state moves to the waking up state and finally moves to the active (data) state, after completing all of the processing related to waking up. We note that we do not consider the transition from the sleep state to the active (signaling) state using a low-power radio interface, for simplicity, since using an additional low-power radio interface to wake up the femto BSs for location registration is not required necessarily, compared to data session processing.
# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 5 Data Searching and Browsing
### Smart Data Searching and Browsing
Efficient federated querying should support geospatial predicates and functions and should implement distributed query processing techniques that delegate parts of the query plan in the data sources based on their query capabilities. **Challenge 8: keyword-based search over geospatial data sources. **: General purpose dataset discovery is supported with SPARQL queries over metadata catalogs, which may incorporate full-text search capabilities to enable keyword-based search. Similarly, discovering geospatial datasets may be achieved with GeoSPARQL queries over GeoDCAT-AP metadata collections. Beyond dataset discovery, supporting keyword-based queries directly on the contents of the datasets may be of great importance [106]. Thus, for example, scientists researching on Cholera outbreaks are interested in downloading data from areas near the seaside with low elevation (accessible for bathing) and high temperature and rainfall. Such keyword-based queries may not be directly translated to GeoSPARQL and additionally, they cannot be replied using only dataset metadata, as they require accessing vector features and raster coverages. Figure 11: Illustration of challenges towards smart geospatial searching and browsing. An initial basic approximation to the above problem has been proposed in [107], where it is shown that the challenge is complex and that it requires the combination of geospatial and text indexing methods with linguistic values defined with fuzzy sets to incorporate user and expert subjectivity. **Challenge 9: interactive exploratory analysis of very large geospatial datasets. ** Once the required parts of the datasets of interest have been identified, and before advanced data mining and analytic techniques are applied, it is usually required to perform some exploratory analysis of the data. Such descriptive analytical workloads in data warehouses are supported by the evaluation of online analytical processing (OLAP) queries. Integrated geospatial vector-raster OLAP requires the efficient implementation of joins between vector features and raster coverages, and the efficient evaluation of aggregate functions over spatial and temporal dimensions. Achieving interactive response times over very large datasets is an elusive objective, even if parallel processing is implemented. However, given that during browsing the required precision for the results does not have to be high, approximate query processing techniques [108; 109] may be adapted to geospatial data. Those techniques will combine geospatial indexing structures with dataset synopsis such as samplings and/or sketches [110].
## 2 Materials and Methods
To reduce the effect of noise and to get better insights from the trends, we smooth the data with a 7-day-average, similarly to Johns Hopkins, which use the same data source for its COVID-19 Testing Insights Initiative ([[https://coronavirus.jhu.edu/testing/individual-states](https://coronavirus.jhu.edu/testing/individual-states)]([https://coronavirus.jhu.edu/testing/individual-states](https://coronavirus.jhu.edu/testing/individual-states)), access on 1 October 2020). COVID-19 Infection Cases in U.S. The COVID-19 confirmed cases are collected from the New York Times ([[https://www.nytimes.com/](https://www.nytimes.com/)]([https://www.nytimes.com/](https://www.nytimes.com/)), accessed on 1 October 2020) based on reports from state and local health agencies. We exclude Puerto Rico and the District of Columbia since some Internet data for these two regions are unavailable. As a result, 50 U.S. states' daily number of cases are used in this study. We start searching data from 20 January 2020, which is the date when the first U.S. case was reported. The specific study period of each state is from the date of the first confirmed case in this state to 30 May 2020 and is then extended to 30 September 2020 for studying the variation of correlation strength. We smooth the raw data by a 3-days-average to reduce the noise. Correlation analysis. The Spearman correlation is employed in this study using Python's SciPy function. Specifically, we conduct lagged correlation analyses to assess the temporal relationships between Internet data and the COVID-19 pandemic. For each state, we right-shift the daily Internet data from Twitter and Google Trends (with different search terms) by a variable lag and calculate the Spearman correlation to the daily reported COVID-19 cases. The maximum lag is set to 40 days. The Spearman correlation is also used to examine the correlation between the \(c^{*}\) and the state's variables, and between the \(c^{*}/l^{*}\) and the early infection rate, at significance levels from \({}^{*}\)\(p<0.1\) to \({}^{***}\)\(p<0.01\). Proxy of air traffic flow. Using the Air Carrier Activity Information System database ([[https://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/passenger/collection/](https://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/passenger/collection/)]([https://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/passenger/collection/](https://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/passenger/collection/)), accessed on 1 October 2020), we obtain the enplanement data at every commercial service airport in the U.S. for 2017 and 2018. As a proxy of a state's air traffic flow, we calculate the sum of the enplanements of all airports located in a state.
## 4 Theoretical Analysis of Different Detection Schemes  
Consider a MIMO system employing Generalized Quadrature Spatial Modulation (GQSM) combined with Quasi-Orthogonal Space-Time Block Codes (QOSTBC), operating under Nakagami-\(m\) fading conditions. The received signal can be modeled as:  
\[\mathbf{y}=\mathbf{Hx}+\mathbf{n} \tag{33}\]  
where:  
* \(\mathbf{y}\in\mathbb{C}^{N_{N}\times 1}\): received signal vector,
* \(\mathbf{H}\in\mathbb{C}^{N_{N}\times N_{1}}\): MIMO channel matrix,
* \(\mathbf{x}\in\mathbb{C}^{N_{N}\times 1}\): GQSM-QQSTBC encoded transmit signal,
* \(\mathbf{n}\sim\mathcal{CN}(0,\sigma^{2}\mathbf{I})\): additive white Gaussian noise. The entries of \(\mathbf{H}\) follow Nakagami-\(m\) fading with PDF: \[f_{|h|}(x)=\frac{2m^{m}}{\Gamma(m)\Omega^{m}}x^{2m-1}\exp\left(-\frac{mx^{2}}{ \Omega}\right),\quad x\geq 0\] (34)
## 3 Numerical Model and Simulation Scheme of the Strain-Structural Plane  
### 3.1 Numerical Model and Boundary Conditions  
Taking a deep tunnel as an example, the tunnel adopts the section form of a straight wall arch, the specific dimensions, and relative positions of the tunnel and structural plane are shown in Figure 2a. To simplify the calculation process, ensure the reliability of the simulation results, and eliminate the boundary effect caused by calculation, we adopted a plane strain model and set its length in the y direction to 2 m. According to the principle of Saint-Venant and the excavation effect, the model width and height are set to be 60 m and 50 m, respectively. Referring to previous studies, the dimension of a model for simulating underground tunnels mainly ranged from 0.1 m to 0.75 m [28; 29]. In view of the boundary effect and calculation time of the model, the block size is divided into 0.5 m on average by griddle in Rhino, and the edge length of the block is re-divided into 0.1 m after the modeling in 3 DEC. The model is divided into 16,730 blocks, each of which is considered a rock block. The model obtained through Griddle modeling is shown in Figure 2b, where the tunnel model is composed of rock blocks and joints.  
The model's upper boundary and the free surface formed by excavation were free boundaries, and vertical stress was applied to the upper boundary. The remaining boundary conditions were normal displacement constraint boundaries. Assuming the buried depth of the tunnel is 1000 m, both the horizontal stress \(\sigma_{\rm H}\) and \(\sigma_{\rm h}\) are set to 42.72 MPa, while the vertical stress \(\sigma_{\rm v}\) is 28.48 MPa. The initial stress field is applied to the model according to the ratios of \(\sigma_{\rm H}/\sigma_{\rm v}=1.5\) and \(\sigma_{\rm h}/\sigma_{\rm v}=1.5\).  
Figure 1: The 3D discrete element numerical analysis method for rockburst in deep tunnels.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 2 Individual Methods
### Machine Learning Algorithms
#### 2.1.2 Lgb  
LightGBM (LGB) was proposed by Microsoft in 2017 as an improved optimization algorithm based on the GBDT algorithm, aiming at solving machine learning problems on large-scale datasets [33]. The principle of the LGB model is shown in Figure 3, which solves the issues of excessive number of samples and number of features through the use of the unilateral gradient sampling algorithm and the mutual exclusion feature bundle algorithm, further improving the model's operational efficiency. As an efficient and flexible gradient boosting decision tree framework, it has many advantages, especially in large-scale data processing, high efficiency, memory consumption, and prediction performance. LightGBM shows strong performance and low computational overhead when dealing with complex tasks by optimizing the tree construction, histogram algorithm, and leaf-splitting strategy.