esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Results
#### 5.1.2 Slooped Road Dataset  
First, the supervoxel segmentation method was used to over-segment an MLS point cloud data with slopes. Then, based on the super-voxels, the region growing algorithm was applied for iterative clustering to extract the road surface point cloud. Finally, the extracted road surface results were compared with those obtained using the other three segmentation algorithms, as shown in Figure 15. It should be noted that the blank holes in the road parts of figures are errors caused by occlusion during the data acquisition conducted by the vehicle-mounted laser scanning system.  
From the comparison of the extraction results in Figure 15, it is evident that for a road scene with slopes, the extracted road surface region in Figure 15d is more complete and clearer compared with the other three segmentation algorithms. Figure 15a shows under-segmentation and discontinuity issues in the boundary parts of the extracted road surface, whereas Figure 15b shows under-segmentation and rough edges. The extraction result in Figure 15c shows a large number of over-segmentation phenomena on the road surface. Furthermore, to verify the time efficiency differences between the proposed method and comparative methods, the authors conducted experiments under the same conditions and recorded the time required for target information segmentation and clustering using different algorithms. The results are presented in Tables 5 and 6, respectively. Compared to the other three algorithms, the proposed method also demonstrates improved computational efficiency.
# DC Converter with Wide Soft Switching Operation, Wide Input Voltage and Low Current Ripple
## 2 Circuit Diagram  
The circuit configuration of the studied circuit is shown in Figure 1 with the abilities of wide voltage operation capability, wide zero-voltage switching load range and less input ripple current. In the presented circuit, \(S_{1}\)-\(S_{4}\) are MOSFET devices, \(S_{ac}\) is an ac switch. \(L_{r}\) is resonant inductor, \(C_{r}\) is resonant capacitor, \(L_{1}\) and \(L_{2}\) are input inductors, \(T\) is the isolated transformer with the magnetizing inductance \(L_{m}\), \(D_{o1}\) and \(D_{o2}\) are rectifier diodes, \(C_{o1}\) and \(C_{o2}\) are the output capacitors and \(R_{o}\) is a load resistor. The circuits (\(L_{1}\), \(S_{1}\), \(S_{2}\), \(C\)) and (\(L_{2}\), \(S_{3}\), \(S_{4}\), \(C\)) are two boost converters. The gating signals of \(S_{2}\) and \(S_{4}\) are interleaved by half of switching cycle. Thus, the resultant input current ripple \(\Delta_{in}\) is reduced to zero. \(S_{1}\)\(\sim\)\(S_{4}\) have the same duty cycle and equal to 0.5. Therefore, the boost voltage \(V_{C}\) is equal to \(2V_{in}\). In order to realize wide voltage input capability, there are two operated modes (Figure 1,c) in the studied circuit. When input voltage is under a low input voltage mode \(V_{in,\ min}\)-\(2V_{in,\ min}\), switch \(S_{ac}\) is off, as shown in Figure 1. Circuit components \(C\), \(S_{1}\)\(\sim\)\(S_{4}\), \(C_{r}\), \(L_{r}\), \(T\), \(D_{o1}\), \(D_{o2}\), \(C_{o1}\) and \(C_{o2}\) are operated as a full-bridge structure _LLC_ converter. The current ripple \(\Delta_{in}\) is equal to zero. When \(2V_{in,\ min}<V_{in}<4V_{in,\ min}\) (high voltage mode), active devices \(S_{ac}\) and \(S_{3}\) are turned off and \(S_{4}\) is on (Figure 1). Circuit components \(C\), \(S_{1}\), \(S_{2}\), \(S_{4}\), \(C_{r}\), \(L_{r}\), \(T\), \(D_{o1}\), \(D_{o2}\), \(C_{o1}\) and \(C_{o2}\) are operated as a half-bridge structure _LLC_ converter to obtain lower voltage gain. Therefore, a current-fed and wide input voltage _LLC_ converter is achieved. Owing to resonant behavior of the _LLC_ converter, power switches and rectifier diodes have soft switching operation capability.  
Figure 1: \(Cont.\)
## 3 Results
### Model Results  
#### 3.2.1 XGBoost Model Results  
The optimal XGBoost hyperparameter values chosen for the study are summarized in Table 4. The number of iterations was 100, and the maximum number of tree splits was 6. Of the total, 80% of the samples were used for training, with learning rate of 0.3. To prevent overfitting of the model, 'colsample_bytree', 'alpha', and 'lambda' value were tuned as 1, 0, and 1, respectively, based on the cross-validation.  
Overall accuracy of the XGBoost model developed in the study was 82.54%, where 89,930 raster cells among 108,953 cells were correctly modeled (Table 5). In detail, the ratio that predicted urbanized cell as urbanized, and non-urbanized cell as non-urbanized were 76.99% and 85.53%, respectively. The model better predicts the non-urbanized area compared to the urbanized area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of iterations & 100 \\ \hline Max depth & 6 \\ \hline Subsample ratio & 0.8 \\ \hline Learning rate & 0.3 \\ \hline Colsample\_bytree & 1 \\ \hline Alpha & 0 \\ \hline Lambda & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter tuning of XGBoost model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{1}{c}{**Total**} & **Urbanized** & **Non-Urbanized** \\ \hline Number of samples & 544,765 & 190,977 & 353,788 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Socioeconomic \\ features \\ \end{tabular} } & Population density & 505.99 & 1176.58 & 144.00 \\ \cline{2-5}  & GRDP per capita & 23.28 & 27.30 & 21.11 \\ \cline{2-5}  & (KRW 1,000,000/person) & 23.28 & 27.30 & 21.11 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Topographic \\ features \\ \end{tabular} } & Elevation (m) & 200.51 & 82.55 & 264.19 \\ \cline{2-5}  & Slope (\({}^{\circ}\)C) & 13.79 & 5.90 & 18.05 \\ \hline \multirow{9}{*}{\
\begin{tabular}{c} Land-cover features \\ (distance to nearest) \\ \end{tabular} } & Residential area (m) & 528.44 & 269.65 & 668.14 \\ \cline{2-5}  & Commercial area (m) & 1881.78 & 1268.82 & 2212.67 \\ \cline{2-5}  & Industrial area (m) & 2335.20 & 1263.24 & 2913.85 \\ \cline{2-5}  & Transportation area (m) & 687.68 & 340.89 & 874.87 \\ \cline{2-5}  & Agricultural area (m) & 229.90 & 123.97 & 287.09 \\ \cline{2-5}  & Forest area (m) & 118.06 & 197.20 & 75.34 \\ \cline{2-5}  & Grassland area (m) & 74.14 & 51.07 & 86.59 \\ \cline{2-5}  & Wetland area (m) & 79.61 & 48.85 & 96.21 \\ \cline{2-5}  & Bare land area (m) & 166.56 & 130.73 & 185.91 \\ \cline{2-5}  & Water area (m) & 807.71 & 602.59 & 918.43 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Environmental \\ features \\ \end{tabular} } & Ecological ECVAM grade & 2.81 & 3.84 & 2.25 \\ \cline{2-5}  & Legislative ECVAM grade & 2.13 & 2.46 & 1.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between urbanized and non-urbanized area.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 3 Experiments and Results  
### Data Sets  
One of the biggest challenges facing intrusion detection research is the availability of realistic data sets. Due to various privacy concerns, organizations are often unwilling to provide real network traffic data for research. Consequently, researchers often use synthetic data sets or data sets created through simulations. The survey by [31] lists and analyzes the various publicly available data sets for intrusion detection research. We used the CICIDS-2017 data set, which is one of the most recent data sets to have emerged for intrusion detection research. The CICIDS-2017 data sets contain labeled flows as an Excel spreadsheet in.csv format. Each record in the spreadsheet is a result of parsing the corresponding PCAP file and extracting about 83 statistical features for each flow. We used data from the afternoon of Friday, 7 July 2017. The afternoon data contain a mixture of labeled benign network traffic flows, various forms of port scans, as well as DDoS LOIT attacks. This experiment extracted one hour's worth of traffic from the data set.
# Coastal Sargassum Level Estimation from Smartphone Pictures
## 3 Materials and Methods
### Deep Convolutional Neural Networks
#### 3.1.4 ResNet  
ResNet [29] is a well-established architecture in the deep learning community, known for winning the ImageNet competition (ILSVRC, 2015) for classification tasks. This model solved the problem of vanishing gradients for learning models with many layers of convolutional network. One of the observations that was made with AlexNet networks was that the deeper the network, the better the performance. However, it was found that beyond a certain depth, performance deteriorates. The error gradient through the back propagation tends to cancel it out. To ensure that the information is not lost during the backpropagation, the authors invented a particular architecture that implement residual connections. The structure of the net is organized into several blocks, each one composed of two or three layers of convolutions, associated with a batch normalization layer and a ReLU activation. Thus, the stacking of these blocks allows the construction of deeper convolutional learning models by reducing the effect of the vanishing gradient. Several ResNet architectures have been proposed where each block uses two convolutions for shallower models, such as ResNet18 or ResNet34, or three convolutions for a deeper one, such as ResNet50, ResNet101, and ResNet152.
## 3 Fundamentals of the ED Process
\begin{array}{c}\mathbf{w}_{\mathbf{r}}(n)\\ \mathbf{h}_{\mathbf{r}}\left(n\right)s_{\mathbf{r}}(n)+\mathbf{w}_{\mathbf{r}}(n)\end{array} \right., \tag{1}\]  
where \(\mathbf{h}_{\mathbf{r}}\left(n\right)\) and \(s_{\mathbf{r}}(n)\) are complex vectors of \(\mathbb{C}^{1 XM}\) and \(\mathbb{C}^{MX1}\), respectively. The \(\mathbf{h}_{\mathbf{r}}\left(n\right)\) is the communication channel gain among the \(M\) Tx and _r-th_ Rx antenna, \(\mathbf{s}_{\mathbf{r}}(n)\) is the received signal by SU at the _r-th_ Rx antennae during the _n-th_ sampling moment. Additionally, and \(\mathbf{w}_{\mathbf{r}}(n)\) represents additive white Gaussian noise (AWGN) associated with the received signal at the _r-th_ Rx antennae of SU. It is assumed that the noise can be represented as a Gaussian random variable at the SU with a mean equal to zero and a variance equal to \(\sigma_{w}^{2}\). Moreover, assuming that the received signal distribution is circularly symmetric, the ratio of signal and noise (SNR) at _r-th_ antenna of the SU branch can be expressed as \(SNR_{r}(n)=\gamma_{r}(n)=\frac{\left|h_{\mathbf{r}}(n)\right|^{2}\frac{1}{N}\sum_{ \mathbf{r}=1}^{N}\left|s_{\mathbf{r}}(n)\right|^{2}}{2\sigma_{w}^{2}(n)}\). For the case of all \(R\) Rx antennas in the _n-th_ SS period, the total SNR is \(\gamma_{SLC}=\sum_{\mathbf{r}=1}^{R}\gamma_{\mathbf{r}}(n)\) and the average SNR observed at the SU is \(\overline{\gamma_{SLC}}=\frac{1}{R}\sum_{\mathbf{r}=1}^{R}\gamma_{\mathbf{r}}(n)= \frac{1}{R}\gamma_{SLC}\). An essential process for CRN is SS, which is dedicated to monitoring the occupancy of the frequency band by the SU in terms of the PU signal transmission or non-transmission. Thus, the current status of the sensing process can be described as testing the binary hypothesis with the variables \(H_{0}\) and \(H_{1}\) as formulated in Equation (2):  
\[\mathbf{Y}(n)=\left\{ \begin{array}{cc}\sum_{\mathbf{r}=1}^{R}\mathbf{w}_{\mathbf{r}}(n):&H_{ 0}\\ \sum_{\mathbf{r}=1}^{R}\mathbf{h}_{\mathbf{r}}\left(n\right)\mathbf{s}_{\mathbf{r}}(n)+\sum_{ \mathbf{r}=1}^{R}\mathbf{w}_{\mathbf{r}}(n):&H_{1}\end{array} \right., \tag{2}\]  
where hypothesis \(H_{0}\) indicates that the licensed spectrum is not occupied and hypothesis \(H_{1}\) implies that the licensed spectrum is not available for SU transmission. In the _n-th_ ED interval, \(\mathbf{Y}(n)\) reflects the total signal received by the SU over the \(R\) Rx branches. The goal of the ED process is to assess whether the observed signal \(\mathbf{Y}(n)\) conforms to hypothesis \(H_{0}\) or \(H_{1}\). As a result, the existence or absence of a PU transmission in every moment is identified by comparing the energy of the PU signal sensed by all \(R\) Rx branches with those set by the DT. When the sensed energy of the PU signal detected at all \(R\) Rx chains of the SU exceeds the set DT, hypothesis \(H_{1}\) is proven and the spectrum is occupied with the PU transmission. Hypothesis \(H_{0}\) will be confirmed if the measured energy of the PU signal detected by all \(R\) Rx chains at the SU site is less than the set DT which suggests that the frequency spectrum is free.
For these data sources, Landsat TM/ETM+ has a high spatial resolution of 30 m, but has a long revisit cycle of 16 days. It may be difficult to identify crop phenology accurately with a high possibility of images influenced by cloud. Other data sources generally have coarse or medium spatial resolution, which will lead to mixed pixel problems for areas with complex planting structure. To solve the problem of lacking data sources with both high spatial and temporal resolutions, downscaling of moderate spatial resolution MODIS data using Landsat data [40] and fusion of Landsat data and other satellite-sensor data [41] has been tested. However, these re-processing methods can further increase the uncertainty of the derived indexes on the basis of error between satellite-derived NDVI and ground-based NDVI [42]. Therefore, remote sensing data sources with both high spatial resolution and frequent revisit time are desired for crop identification. On 6 September 2008, China launched the HJ-1A/1B satellite constellation for environment and disaster monitoring. The satellite constellation has achieved high temporal resolution (four days revisit period of a single satellite) and mid-high spatial resolution (30 m) [43]. Previous studies have indicated that the NDVI series derived from HJ-1A/1B could present a complete phenological cycle of crops and the retrieved phenological metrics were comparable with local agro-metrological observation [29]. Monthly NDVI series from HJ-1A/1B is also used to identify the species of salt marshes in coastal zones [44]. However, few studies had applied HJ-1A/1B data to crop identification in arid and semi-arid areas. The main objective of this study is to identify the multi-year distribution of main crops (maize and sunflower) in Hetao Irrigation District of China with complex planting structure using the NDVI and phenological characteristics derived from HJ-1A/1B images.
# Solving Integer Linear Programs by Exploiting Variable-Constraint Interactions: A Survey
## 1 Introduction  
Integer Linear Programming (ILP) is among the most successful and general paradigms for solving computationally intractable optimization problems in computer science. In particular, a wide variety of problems in areas such as process scheduling [1], planning [2, 3], vehicle routing [4], packing [5], and network hub location [6], to name a few, are efficiently solved in practice via a translation into an Integer Linear Program.  
ILP is **NP**-complete, and a significant amount of research has been carried out on tractable fragments of ILP defined in terms of the algebraic properties of the instance (see, e.g., the work of [PERSON] and [PERSON] exploiting total unimodularity (Section 13.2, [7])) or in terms of restrictions on the number of constraints or variables (see [PERSON]'s algorithm [8] together with its subsequent improvements by [PERSON] [9], [PERSON] and [PERSON] [10]).  
On the other hand, until recently we have lacked a systematic study of the complexity of ILP through the lens of variable-constraint interactions. This represented a stark contrast to our understanding of other fundamental problems such as Boolean Satisfiability and Constraint Satisfaction, where we have classical results that explore and showcase how interactions between variables and constraints (formalized via graphical representations) can be used to define natural tractable fragments of the problem--consider, e.g., the early work of [PERSON] [11], [PERSON] and [PERSON] [12]. The situation for ILP changed drastically in recent years thanks to a flurry of results that together lay out a detailed complexity landscape for the problem centered around variable-constraint interactions, captured in terms of graphical representations of instances. The aim of this survey is to summarize these recent developments, put them into context and a unified format, and make them more approachable for experts from many diverse backgrounds. We will also call attention to prominent open problems in the area.  
Survey Organization.After introducing some basic preliminaries for ILPs, graphical representations and structural parameters in Section 2, Section 3 proceeds to a brief overview of classical algorithms for ILP that rely on explicit restrictions such as bounds on the number of variables and/or constraints. In Section 4 we focus on algorithms and lower bounds for ILP that target instances whose variable-constraint interactions give rise to graphical representations of bounded treewidth and/or treedepth. Section 5 then covers results utilizing other structural parameters related to variable-constraint interactions, and the final Section 6 provides an outlook to future work in the area.
# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 2 Materials and Methods
### Lidar Scan Filtering, Classification and Quality Control  
Hub-height wind speed and direction during the scans are estimated as the mean values from supervisory control and data acquisition (SCADA) measurements obtained with cup anemometers and wind vanes on the nacelles of three of the target wind turbines. The data are conditionally sampled to select periods when the lidar was operating in arc scan configuration, and the HH wind speed (\(U\)) and direction are consistent with wake generation and propagation from the wind turbines deployed:  
\[3\;\text{m s}^{-1}\leq U\leq 25\;\text{m s}^{-1}\]  
\[229^{\circ}\leq Wind\;dir.\;\leq 346^{\circ}\]  
A total of 3307 scans meet these criteria for wind direction, wind speed and arc scan classification (around 25% of scans). Much of the rotational speed for the arc scans was kept constant at \(1^{\circ}/\text{s}\), meaning that the lidar rotated one degree per second while moving through its scanning arc. Further, angular resolutions were kept consistent at \(1^{\circ}\).  
Radial velocity values are then filtered to remove values with high measurement uncertainty and include only those where:  
\[-5\;\text{dB}\;\leq CNR\leq 25\;\text{dB}\]
# Hamming Code Strategy for Medical Image Sharing
## 6 Conclusions  
In this paper, we proposed a novel method that can be used to construct and securely store shadows of medical images. The best results can be achieved by manipulating the most significant bits in pixels. The shadows are completely blurred out and it is impossible to obtain the important details of medical images from them. Experimental results demonstrate that using (7,4) Hamming code gives a more desirable blurring effect than using (15,11) Hamming. Hamming code employs simple and fast matrix operations. As a result, our scheme runs fast at low computational costs. Thus, the scheme is suitable for mobile devices like tablets and phablets, which are used prevalently in medical practice today. Our scheme currently is designed for a false safe database system with two databases. It would be sufficient for small size hospitals or clinics. However, we foresee that the scheme can be made scalable for systems with more than two databases. Therefore, in future, we would expand this scheme to accommodate an arbitrary number of original images and shadows.  
**Author Contributions:** Data curation, C.-C.C. ([PERSON]), J.B. and H.-D.L.; Formal analysis, J.B. and H.-D.L.; Funding acquisition, C.-C.C. ([PERSON]); Investigation, C.-C.C. ([PERSON]) and C.-C.C. ([PERSON]); Methodology, L.L. and C.-C.C. ([PERSON]); Project administration, L.L., C.-C.C. ([PERSON]) and T.-H.M.; Resources, C.-C.C. ([PERSON]) and T.-H.M.; Software, C.-C.C. ([PERSON]); Visualization, H.-D.L. and [PERSON]; Writing--original draft, H.-D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by National Natural Science Foundation of China (No. 61370218), and Public Welfare Technology and Industry Project of Zhejiang Provincial Science Technology Department (No. LGG19F020016).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.3.1. Generated Temperature Vegetation Dryness Index (TVDI)  
The calculation of the TVDI imposes two requirements on the underlying surface: the ground must exhibit both bare soil and dense vegetation coverage characteristics, and the soil moisture must have two extreme conditions. Once these conditions are met, the 2D scatter plot of the NDVI and LST for each pixel in the region displays a trapezoidal distribution. In this study, we utilize the TVDI proposed by [PERSON] [44] based on the LST-NDVI feature space to represent surface SM levels. Its calculation formula is as follows:  
\[TVDI=\frac{LST-LST_{\min}}{LST_{\max}-LST_{\min}} \tag{6}\]  
\[LST_{\min}=a_{1}+b_{1}NDVI \tag{7}\]  
\[LST_{\max}=a_{2}+b_{2}NDVI \tag{8}\]  
where, in (6), \(LST_{min}\) represents the minimum land surface temperature for a given NDVI value, \(LST_{max}\) indicates the maximum land surface temperature for the same NDVI value, and LST refers to the land surface temperature for any pixel with the corresponding NDVI value. Equations (7) and (8) describe the wet and dry edge equations in the LST-NDVI feature space, and \(a_{1}\), \(a_{2}\), \(b_{1}\), and \(b_{2}\) are the fitting coefficients for each equation.
_applied sciences_  
_Gluconacetobacter diazotrophicus_ Inoculation of Two Lettuce Cultivars Affects Leaf and Root Growth under Hydroponic Conditions  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1 (c) and [PERSON] 3,* (c)  
1 College of Agricultural Sciences, The Pennsylvania State University, University Park, PA 16802, USA; [EMAIL_ADDRESS] (R.L.S.); [EMAIL_ADDRESS] (S.W.D.); [EMAIL_ADDRESS] (R.D.B.); [EMAIL_ADDRESS] (J.D.L.)  
2 Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA; [EMAIL_ADDRESS]  
3 United States Department of Agriculture-Agricultural Research Service (USDA-ARS) Pasture Systems & Watershed Management Research Unit, University Park, PA 16802, USA  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +1-814-280-2152  
**Abstract:** The growth-promoting effects of _Gluconacetobacter diazotrophicus_ inoculation on the leaf lettuce (_Lactuca sativa_ L.) cultivars \"Black Seeded Simpson\" and \"Bibb/L limestone\" were investigated. Plants of each cultivar were grown hydroponically in Kratky jars in a growth chamber-controlled environment in a completely randomized factorial design with three or four replications. Each experiment was repeated once. Factors were (1) with or without inoculation and (2) seven levels of nitrogen (N) fertilization ranging from deficient (37.5 mg L\({}^{-1}\) N) to excessive (172.5 mg L\({}^{-1}\) N). The shoot, root, and total biomass accumulation, nitrogen density, and carbon/nitrogen (C/N) ratios were measured for each variety. Black Seeded Simpson demonstrated a shifting of production towards aerial tissues, with significantly greater shoot production and reduced root production. The observed increase in shoot biomass was greatest at the slightly deficient N rate of 105 mg L\({}^{-1}\) N where inoculated plants produced 14.8% more than uninoculated plants. Lower N density and higher C/N ratios in inoculated shoot tissues indicate greater N use efficiency. Bibb/Limestone responded to inoculation with an average increase of 10.9% in shoot production and with greater root biomass. Bibb/Limestone also exhibited lower N density in inoculated shoot tissues with a corresponding increase in the C/N ratio. For growers looking to maximize lettuce yields, G. diaz inoculation may present a beneficial additive to the growing system by increasing leaf yields while not increasing N fertilizer requirements.  
**Keywords:** lettuce; _Lactuca sativa_ L.; _Gluconacetobacter diazotrophicus_; hydroponic