esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Analysis
### How the Multi-Scale Affects the Classification  
In order to test the influence of the multi-scale convolutional kernel, we conducted six sets of experiments. In these experiments, the cube size is set to \(5\times 5\times 25\). The first three experiments are convolution kernels with only one scale, whose convolution kernels are 1*1@12, 3*3@12 and 5*5@1 respectively. The fourth and fifth are the combination of two scale convolutional kernels, which are respectively the concatenation of 1*1@6+3*3@6, and the concatenation of 3*3@6+5*5@6. The sixth experiment is a concatenation of three scale convolution kernels: 1*1@4+3*3@4+5*5@4, as shown in Figure 6. Detailed results of the experiments are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Kernels** & **Training Time** & **Testing Time** & **OA** & **AA** & **Kappa** \\ \hline \multirow{8}{*}{Paira} & 1*1@12 & 26.40 & 7.32 & 0.963604 & 0.956682 & 0.951763 \\  & 3*3@12 & 26.15 & 7.23 & 0.978551 & 0.97294 & 0.971562 \\ \cline{1-1}  & 5*5@12 & 26.23 & 7.30 & 0.97834 & 0.966848 & 0.971356 \\ \cline{1-1}  & 1*1@6+3*3@6 & 26.93 & 7.45 & 0.978551 & 0.97294 & 0.971562 \\ \cline{1-1}  & 3*3@6+5*5@6 & 26.84 & 7.48 & 0.978995 & 0.968616 & 0.972227 \\ \cline{1-1}  & 1*1@4+3*3@4+5*5@4 & 27.57 & 7.49 & **0.986153** & **0.983208** & **0.981648** \\ \hline \multirow{8}{*}{Salinas} & 1*1@12 & 25.96 & 8.95 & 0.957255 & 0.982387 & 0.952307 \\  & 3*3@12 & 26.13 & 9.04 & 0.965719 & 0.982829 & 0.961777 \\ \cline{1-1}  & 5*5@12 & 26.12 & 9.27 & 0.964592 & 0.983698 & 0.96056 \\ \cline{1-1}  & 1*1@6+3*3@6 & 26.63 & 9.17 & 0.971393 & 0.986391 & 0.968131 \\ \cline{1-1}  & 3*3@6+5*5@6 & 27.04 & 9.28 & 0.974165 & 0.98662 & 0.971259 \\ \cline{1-1}  & 1*1@4+3*3@4+5*5@4 & 27.61 & 9.53 & **0.975608** & **0.986853** & **0.972731** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy affected by multi-scale kernels. Overall accuracy (OA), average accuracy (AA). The best results are highlighted in bold.  
Figure 5: Effect of the size of cube for the Salinas and Pavia University datasets on the spectral–spatial method.  
As can be seen from Table 3, the best results highlighted in bold were obtained by the last group while combining three scale convolution kernels. This is because multiple scales can get both local and global information.  
We also plotted convergence curves with different kernels, as shown in Figure 7. The multi-scale kernel model can make the training convergence more stable in both the datasets.
## 6 Conclusions  
Three vegetation indices derived from MODIS data were assessed in vegetation dynamic monitoring in the XUAR, China. The annual mean VI images exhibited similar spatial patterns of vegetation conditions with varying magnitudes. NDVI, SAVI and EVI were all related to each other with varying correlation strengths among different vegetation types. The relationship between NDVI, SAVI and EVI were weakest with Pearson's correlation coefficients ranging from 0.29 to 0.66. In general, EVI exhibited high uncertainties in sparsely vegetated land and forest areas due to the disturbance of blue band reflectance. The EVI time-series captured the largest inter-annual variability for the XUAR from 2001 to 2012, with 6.28% of the entire area showing variability higher than 30%. The start of the season and end of the season generated from the three VIs were consistent for most vegetation types. The largest deviations of phenological metrics (37.6 days in SOS and 47.0 days in EOS) were derived from NDVI time series, suggesting the index's sensitivity to soil background and atmospheric effects. Discrepancies of the histogram distributions of phenology metrics from different VIs further revealed their different sensitivities to variation of soil background and physiological development of vegetation.  
The results described here demonstrate the general distinctions of vegetation dynamics assessed with three vegetation indices generated from the 500 m MODIS data, and suggest the suitability of vegetation indices for large area vegetation dynamic monitoring over arid and semi-arid lands. Future research can combine satellite data and climate data to investigate inter-annual variability in vegetation dynamics and provide insights into the impact of changing climate on dryland ecosystems.  
The authors are grateful to [PERSON], Malmo University, and [PERSON], Lund University, for providing the TIMESAT software. This research was supported by the National Natural Science Foundation of China under Grant No. 41471369; the National Key Technology R&D Program under Grant No. 2012 BAC16B01; and the Open Research Fund of State Key Laboratory of Remote Sensing Science under Grant No.OFSLRSS201413.  
[PERSON] and [PERSON] designed the research. [PERSON] and [PERSON] performed the research. All authors analyzed, interpreted and reviewed the data. [PERSON] and [PERSON] authored the manuscript.  
The authors declare no conflict of interest.
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 3 Problem Definition
\(T_{\mathit{collecting}}\) is the time the drone spends hovering over the points in \(P\) to collect all data from the nodes in \(S_{\mathit{subsets}}\). The following equation defines it:  
\[T_{\mathit{collecting}}=T_{\mathit{allnodes}}-T_{\mathit{moving}} \tag{2}\]  
In Equation (2), \(T_{\mathit{moving}}\) is the time the drone spends receiving data when it is moving. In other words, it is the time needed to collect data from the sensor nodes in \(D_{\mathit{move}}\). \(T_{\mathit{allnodes}}\) it the time to receive data from all sensor nodes without considering drone movement during the data gathering. \(T_{\mathit{allnodes}}\) is calculated by finding the shortest path between each sensor node and one of the hovering points in \(P\), summing the size in hops of each path, and multiplying it by the time spent by a sensor node to send all data in its memory on one hop. The heuristics proposed by [PERSON] and [PERSON] [12] do not consider the drone gathering data when it is moving, hence \(T_{\mathit{collection}}=T_{\mathit{allnodes}}\). The main contribution of our work is to consider data gathering during the drone movement. The algorithm proposed here keeps the same \(T_{\mathit{trip}}\) found in the aforementioned work and reduces \(T_{\mathit{collection}}\) by \(T_{\mathit{moving}}\), according to Equation (2). Figure 2 presents an example of output created by the heuristics proposed by [PERSON] and [PERSON] [12]. Figure 1b presents an example of output created by the algorithm proposed here. We can verify that the proposed algorithm reduces the time the drone has to hover to gather data and, consequently, reduces \(T_{\mathit{total}}\). Summarizing, the problem here is how to increase \(T_{\mathit{moving}}\) in order to decrease \(T_{\mathit{total}}\). The GBD problem belongs to the NP-hard class since it has, as a particular case, the Traveling Salesman Problem (TSP), which is NP-hard [22]. In fact, in this analogy, it is enough to consider each city as one point in \(P\) and the traveling salesman as the drone with sufficient capacity to collect all sensor nodes' data. The drone must exit from \(p_{0}\) and go through all the points to collect the information and return to \(p_{0}\) traveling the shortest path. In Table 1, we present the notations for this problem. Figure 2: Example of output created by [PERSON] and [PERSON] [12].
## 4 Experiments
### YOLO Object Detection  
The YOLO detection algorithm was run on the DRISHTI-GS and ORIGA datasets. The purpose of YOLO in this application is to detect the optic disc region in retinal fundus images and crop this region to obtain a new dataset. This approach aims to improve the accuracy of disease classification by focusing on the diseased area using pre-trained CNN models. Before training YOLO, 101 retinal fundus images in this dataset were labeled using the \"labelImg\" graphical image annotation tool ([[https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg)]([https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg)): accessed on 10 May 2024). Subsequently, YOLOv7 was trained for 300 epochs on 68 randomly selected labeled images. The testing process was performed on the remaining 33 images. The model's performance was evaluated based on the mAP and IoU metrics. The performance metrics obtained from the training and testing data are shown in Table 3. When evaluating based on the mean average precision (mAP) metric, bounding boxes above a certain IoU threshold are considered. Taking an IoU threshold of 0.50, the mAP values for the training and testing data are 99.53% and 99.50%, respectively. Both the recall (R) and precision (P) values are 1 for both the training and testing data.  
Figure 4: Preprocessing step.  
Figure 5: Segmentation result.  
After training the YOLOV7 model, all images were given to this model, and the optic disc region was determined. These regions were cropped. A new data set was created with images consisting of cropped optic disc regions. An example of an image in which the YOLO model detects the optic disc region on the test data is presented in Figure 6.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 5 BER Performance Verification Based on Hardware Platform
### BER Performance of the Proposed Decoding Algorithm  
We evaluate the bit error rate (BER) performances of the four cyclic codes with the proposed decoding algorithm using this platform, as shown in Figure 6. The BER performances of the four cyclic codes using the ML decoding are also presented [57]. It can be observed that at a BER of \(10^{-5}\), only a little SNR degradation (<0.2 dB) occurred for the decoding algorithm using the cyclic information set. In addition, Table 3 provides the SNR required at different BERs. For the (63, 30) BCH code, it can achieve a BER of \(10^{-5}\) when the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Decoding Algorithm** & **The Size of the Candidate** \\  & **Codeword List** \\ \hline The order-2 OSD [16] & 232 \\ The order-3 OSD [16] & 1562 \\ Proposed algorithm & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The required candidate codewords for different decoding algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Candidate Sequence** & **Re-Encoding** & **Correlation Discrepancy** \\  & **Generator** & **Unit** & **Calculation Unit** \\ \hline Adder & \(2k\) & 0 & \((n-k)\) \\ Comparators & \(k\) & 0 & \((n-k)\) \\ XOR gates & 0 & _weight_ (\(g\)) & \((n-k)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The required hardware resources for the proposed decoder.  
SNR is only 4.2 dB. The simulation results show that the proposed soft decision decoders can obtain the performance close to that of ML decoding.
## 2 Methodology for Structural Stability Evaluation
### Numerical Analysis Based on the Field Testing Data  
In this work, the beam-spring model was adopted for tunnel modeling in the framework of the finite element method (FEM), which was first proposed by [PERSON] and [PERSON] [25]. The tunnel lining is considered as a beam model, and some special springs are arranged outside the lining of tunnel surface to simulate the influence of resistance induced by surrounding rock. The stiffness of nonlinear springs is related to the geotechnical properties of the surrounding rock. The stress-relative displacement relationship is given in Figure 3a, where \(u\) is the relative displacement of the non-linear spring, describing tensile as positive and compression as negative. \(F_{t}\) and \(F_{c}\) are tensile stress and compressive stress respectively. \(k\) is the stiffness factor of nonlinear springs. It is obvious that the strength of tensile and compressive springs is different, which is consistent with the properties of rock.  
Figure 2: Scheme of nondestructive testing for a grouting hole.  
The stress boundary conditions of the improved model are introduced according to field conditions, as shown in Figure 3b, in which water pressure and soil pressure are calculated respectively.  
\[P_{i}^{w}\ =\ \gamma_{w}h_{i} \tag{3}\]  
where \(\gamma_{w}\) and \(h_{i}\) are the unit weight of water and water level of an individual section \(i\), respectively.  
If the center point of section \(i\) is regarded as the coordinate origin, and \(y\) represents the vertical coordinate, the recommended forms of soil pressures are expressed as follows:  
\[\left\{\begin{array}{c}P_{i}^{up}\ =\ \sum\limits_{j=1}^{n}h_{j}\bigg{(} \gamma_{j}^{s}-\gamma_{w}\bigg{)}\\ P_{i}^{bot}\ =\ P_{i}^{up}+\frac{G-F}{d}\\ P_{i}^{side}\ =\ \sum\limits_{j=1}^{n}\Big{(}h_{j}+\frac{d}{2}-y\bigg{)} \bigg{(}\gamma_{j}^{s}-\gamma_{w}\bigg{)}\lambda_{j}\end{array}\right. \tag{4}\]  
where \(P_{i}^{up}\), \(P_{i}^{bot}\), and \(P_{i}^{side}\) represent the soil pressure located in the top, bottom, and side of the tunnel structure. \(n\) represents the number of geological layers, \(h_{j}\); \(\gamma_{j}^{s}\) is thickness and unit weight of soil layer \(i\). \(d\) is tunnel diameter. \(G\) and \(F\) are gravitational and buoyant forces, respectively.  
Based on the properties of geological layers in the field, the external loads applied on the numerical model are calculated according to Equations (3) and (4). In addition, a hole is used to represent the damaged conditions determined by NDT detection, and it is defined with no mechanical properties.
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
## 3 Fault Detection Based on DGLPP
### Indicators for Monitoring Statistics  
DGLPP utilizes Hotelling's T\({}^{2}\) and SPE statistics as the detection indicators of the model. The system operates normally when the \(T^{2}\) statistic is lower than the control limit \(T_{\alpha}\); otherwise, a failure occurs:  
\[T_{i}^{2}=y^{T}S^{-1}y<T_{\alpha} \tag{18}\]  
where \(S\) is the covariance matrix of matrix \(y\) after the dimensionality reduction of the training set sample \(S=\frac{1}{n-1}Y^{T}Y\).  
\(T_{\alpha}\) is calculated using the following formula:  
\[T_{\alpha}=\frac{\alpha(n+1)(n-1)}{n(n-\alpha)}F_{\alpha}(\alpha,n-\alpha) \tag{19}\]  
where \(\alpha\) is the test level and \(F_{\alpha}(a,n-a)\) is the \(F\) distribution with \(\alpha\) as the first degree of freedom and \(n-\alpha\) as the second degree of freedom.  
The system operates normally when the \(SPE\) statistic is below the control limit \(Q_{\alpha}\); otherwise, a failure occurs:  
\[SPE=\|x-Ay\|^{2}<Q_{\alpha}. \tag{20}\]  
\(Q_{\alpha}\) is calculated as follows:  
\[Q_{\alpha}=\theta_{1}(\frac{c_{\alpha}h_{0}\sqrt{2\theta_{2}}}{\theta_{1}}+1+ \frac{\theta_{2}h_{0}(h_{0}-1)}{\theta_{1}^{2}})^{1/h_{0}} \tag{21}\]  
where \(\theta_{i}=\sum\limits_{j=k+1}^{m}\lambda_{j}^{i}(i=1,2,3)\), \(h_{0}=1-2\theta_{1}\theta_{3}/3\theta_{1}^{2}\), and \(c_{\alpha}\) is the confidence limit of the standard normal distribution.  
In addition, to ensure the reliability of T\({}^{2}\) and SPE, we need to regularly update \(T_{\alpha}\) and \(Q_{\alpha}\). This enables adaptation to changes in data distribution, variable correlation, fault modes, timely detect, and correct model drift. It also ensures the accuracy and reliability of the fault detection model.
# From Ethnographic Research to Big Data Analytics--A Case of Maritime Energy-Efficiency Optimization
## 4 Applying Big Data Analytics for Maritime Energy Efficiency
### Data Sources and Preprocessing  
Sensors collect data continuously on the ships, but only store an averaged value every 10 min. Relevant sensor data sets based on the findings of ethnographic research, e.g., speed over ground (SOG), speed through water (STW), apparent wind speed (AWS), course over ground (COG), fuel consumption, etc., from five different vessels (Ship 1, 2, 3, 4, 5) of the same shipping company and automatic identification system (AIS) data were also obtained. Most attention was paid to the Ship 1's journey between Gothenburg and Kiel, which is longest in distance among all the voyages run by these five vessels. For vessel 1 and 5, the data covers approximately a two-year period and just a one-year period for the remaining vessels. To clarify how the data were pre-processed, the following concepts are provided:  
1. A data point is defined as a 1-dimensional value that represents a 10-min average, starting at a defined point in time. A complete list of data points collected by the on-board ship sensors is provided in Table 1 in the Appendix A. Data points can also carry static values (such as ship identifier) and may not relate to an on-board sensor.
2. A measurement is defined as all data points that belong to the same starting point in time.
3. A data row (or row) is a measurement, extended with values that can be derived from measurements or originate from other data sources than on-board ship sensors.  
Data were provided in Microsoft Excel files (1 file per ship). Columns were renamed or moved to ensure that all files have the same set and order of columns. All the string values are also converted to numerical values. Measurements deliver a continuous stream of information; thus, it is more practicable to divide this stream into sections (separation into journeys). A journey index was assigned to rows of the sensor data. An assumption was that a speed greater than 8 knots marks the beginning or continuation of a journey and a speed below 5 knots marks the end of a journey. The data from different sources had to be integrated, assimilated and normalized and are further described in the following sections. Figure 3 presents an overview of the data pipeline in the pre-processing stage.
## 3 Robust Extended Kalman Filter
### Robust Extended Kalman Filter Model
This matrix is defined in section \"MM Estimation theory\" (Section 3.2) and is calculated in section \"Iterative Reweighted Least Squares Algorithm\" (Section 3.3). The error covariance matrix is updated as follows:  
\[P_{k}=\left(I-KH_{k}\right)P_{k}^{-} \tag{25}\]  
where K the gain of the filter given by:  
\[K=P_{k}^{-}H_{k}^{T}\left(H_{k}P_{k}^{-}H_{k}^{T}+R_{k}\right)^{-1} \tag{26}\]
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 2 Reference Assumption PLL Features
### The Parameter Design of SSV RRAM
#### 2.2.2 The Determination of LNBW  
As discussed above, the errors from oscillator jitters are ignored in SSV applications, and the rule-of-thumb expression of the loop measurement error is expressed as the following equation [13]:  
\[\sigma_{PLL}=\sigma_{tPLL}+\frac{1}{3}\sigma_{d}=\frac{180}{\pi}\sqrt{\frac{B_ {L}}{C/N_{0}}(1+\frac{1}{2T_{coh}C/N_{0}})}+\frac{57.94f_{L1}I_{LOS}}{cB_{L}^{ 3}} \tag{12}\]  
The SSV RRAM chooses a two-quadrant arctangent discriminator, which is the most accurate one among all kinds of phase detectors. Referring to prior discoveries [12], the one-sigma rule threshold for the two-quadrant arctangent discriminator is \(15^{\circ}\). Then we get the maximum bearable jerk of the third-order PLL over the variation of \(B_{L}\):  
\[J_{LOS}=\frac{\left[15^{\circ}-\frac{180}{\pi}\sqrt{\frac{B_{L}}{C/N_{0}}(1+ \frac{1}{2T_{coh}C/N_{0}})}\right]cB_{L}^{3}}{57.94f_{L1}} \tag{13}\]  
In the case that the details of data bit transition are unknown, we set the CIT at 20 ms. Considering the stable condition of the third-order loop is \(B_{L}\cdot T_{coh}<1.0\)[14], \(B_{L}\) changes from 1 Hz to 18 Hz. According to [4], the minimum received C/N\({}_{0}\) is about 14.59 dB-Hz for SSV users. Meanwhile, the normal C/N\({}_{0}\) is about 45 dB-Hz for ground users [13]. Thus, we choose the C/N\({}_{0}\) values ranging from 15 dB-Hz to 45 dB-Hz for weak signal analysis. Then we draw the fluctuation of the maximum bearable jerk in different power levels in Figure 2, where seven different C/N\({}_{0}\) values are selected from 15 dB-Hz to 45 dB-Hz with a uniform spacing of 5 dB-Hz. Without a doubt, a negative jerk is physically meaningless, but the negative jerks in Figure 2 represent the PLL is certainly out of lock under these conditions. The figure shows two important facts:  
1. The third-order PLL is vulnerable to bear any dynamics when the power becomes very weak (20 dB-Hz or lower);
2. The threshold of maximum bearable jerk increases with the growth of LNBW when the received C/N\({}_{0}\) is greater than 30 dB-Hz, but vibrates under a weak signal environment. Thus, there is an optimal LNBW that minimizes the total loop measurement error.  
From the two facts, we can conclude that our SSV RRAM is incompetent to realize both high-sensitivity and high-dynamics simultaneously.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 2 Unmanned Aerial and Surface Vehicle Networks: Applications and Advancement in Their Optimisation
### UAV Network Applications  
UAVs are employed in many domains, including agriculture and agricultural water management, where the UAVs are equipped with thermal, multispectral and hyperspectral cameras to acquire data related to crop health via the vegetation index [24]. In this context, low-altitude UAVs offer better spatial and even temporal resolution than satellite imagery [25]. Another use case of UAVs may be found in mineral explorations [26], cryosphere monitoring [27] and landslide monitoring [28], having essential benefits, especially in the context of climate change and hazardous events.  
UAVs' roles can be further extended in other intelligent applications such as real-time road traffic monitoring, where UAV imagery can be used to detect traffic and speed violations [29].  
Dynamic UAV-based communication led to the development of the so-called Flying Ad Hoc Network (a multi-UAV network) [30] which has many advantages and applications, as well. Thus, it may imply lower costs than the usual infrastructure, especially when a multi-layer ad hoc network approach is preferred [31]. FANET applications include video transmission enhancement [32], wildfire detection, backup communication in the case of disasters [33] and even water quality monitoring for fish or shrimp farmers [34] Among the advantages of UAV networks are flexibility, acquisition and maintenance cost affordability, survivability and reliability [35]. These capabilities were considered when designing our UAV-USV-WSN network.
## 3 Data and Methods
### Stratal Slicing  
For most sedimentary bodies, the thickness is less than their lateral extension range. In other words, the thickness of a sand body is smaller than its length or width. Thin-layer sedimentary bodies that are difficult to distinguish vertically can be easily identified using stratal slices. The effectiveness of stratal slices for interpreting thin layers has been demonstrated through theoretical models and their application ([PERSON], 2001) [58; 59]. Currently, the primary methods for slicing include time slice, horizon slice, and stratal slice. In comparison to time slice and horizon slice, stratal slice considers the variation in deposition rates across the plane, thus facilitating the approximation of isochronous stratigraphic interfaces [59]. In this study, three horizons including the top of S31, the top of S32, and the bottom of S32 are chosen as reference horizons to make stratal slices. Ten slices are made in S3 sand group. Five slices out of the ten are in S31 and the other five slices are in S32. [PERSON]'s method is used to select appropriate slices to represent the representative slices of the objective sublayer [60]. In this study, 2 slices are chosen as representative slices of S31 and S32, respectively, based on the degree of coincidence between the high reflection values of seismic slices and the development of well point sand bodies. Therefore, the plane characteristics of sedimentary bodies can be described [59].