esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 3 Methods
### Point Pyramid Decoder  
The decoder structure is composed of two modules: skip connections and Bidirectional Point Pyramid Attention Network (BiPPAN).  
As shown in Figure 2, we concatenated the input incomplete point clouds with the F\({}_{1}-\)F\({}_{3}\) via the long-range skip connections. We obtained F\({}_{2}\) and F\({}_{3}\) by passing F\({}_{1}\) through fully connected layers. We also employed the mixed attention module to fuse these features and generate F\({}_{1}^{\prime}-\)F\({}_{3}^{\prime}\) as the input of BiPPAN. There are two benefits to such skip connections. One is to provide long-range information compensation, so the raw incomplete point cloud geometry information is still available in the decoder architecture. The other is that residual learning can facilitate gradient backpropagation.  
Figure 4: Structure of the proposed ACMLP.  
BiPPAN is based on the feature pyramid network (FPN) that contains a top-down pathway to aggregate multiscale features from Level 1 to 3 (F\({}_{1}^{\prime}=512\times 3\), F\({}_{2}^{\prime}=256\times 3\) and F\({}_{3}^{\prime}=128\times 3\)), as shown in Figure 5a. Levels 1-3 represent low-to-high levels. The conventional FPN fuses multiscale features in a top-down manner. Formally,  
\[\text{F}_{3}^{\text{out}}=\text{Conv}\big{(}\text{F}_{3}^{\prime}\big{)} \tag{3}\]  
\[\text{F}_{2}^{\text{out}}=\text{Conv}\big{(}\text{F}_{2}^{\prime}+\text{Resize} \big{(}\text{F}_{3}^{\text{out}}\big{)}\big{)} \tag{4}\]  
\[\text{F}_{1}^{\text{out}}=\text{Conv}\big{(}\text{F}_{1}^{\prime}+\text{Resize} \big{(}\text{F}_{2}^{\text{out}}\big{)}\big{)} \tag{5}\]  
where Resize is an upsampling/downsampling or reshape operation for size matching, and Conv is a convolutional operation. F\({}_{1}^{\text{out}}\), F\({}_{2}^{\text{out}}\) and F\({}_{3}^{\text{out}}\) (F\({}_{1}^{\text{out}}=512\times 3\), F\({}_{2}^{\text{out}}=128\times 3\) and F\({}_{3}^{\text{out}}=64\times 3\)) are the predicted missing regions of the incomplete point cloud in different resolutions.  
Following this idea, we designed a bidirectional point pyramid attention network to optimize multiscale feature fusion on top of FPN, as depicted in Figure 5b. First, we added not only a bottom-up path aggregation network, but also cross-level connections. By fully fusing high-level features with rich global information and low-level features with many fine details of local geometry information, we could predict the detailed structure of the missing point cloud. Second, we introduced the mixed attention module to learn the importance of different input features whose purpose is to selectively aggregate the features.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 3 Effective Information Traffic  
### Node Connectivity  
In the general urban network environment, only the road section in the center of the city has high vehicle density most of the time, which can provide better communication conditions for the network. In most cases, the vehicle density is unsaturated. In the sparse urban environment, the number of vehicles is far from enough to make each vehicle can communicate with any node in the network. Thus, for the whole large network, the network topology can be divided into many scattered small topologies. After such an operation, it is convenient for us to analyze the network. As Figure 1 shows an example:  
In Figure 1, the wireless connection between vehicles can be expressed as undirected graph \(G=(V_{N},E_{M})\), where the nodes set \(V_{N}\) composed of N nodes is represented as \(V_{N}=\{v_{1},v_{2},v_{3},\ldots,v_{N}\}\), and \(E_{M}=\{e_{1},e_{2},e_{3},\ldots,e_{M}\}\) denotes the set of wireless links. In this simple network, node connectivity can be defined as the following formula:  
\[C(v)=\sum_{s\
eq r\
eq t}\frac{\sigma_{st}(v)}{\sigma_{st}}, \tag{1}\]  
where \(\sigma_{st}(v)\) denotes the number of shortest path through node \(v\) from \(s\) to \(t\), and \(\sigma_{st}\) is numbers of whole shortest path from \(s\) to \(t\). From (b), it shows a simple topology, each path is the shortest path, which is easy for us to analyze. In the nodes set \(V=\{a,e,d,f\}\), each node is a node at the edge, and there is no shortest path through it, at the same time they have the worst connectivity. In contrast, node c is the node at the center of the topology while also has the highest \(C(v)\). In Figure 1, the absence of nodes in the graph indicates that there is no available route at that time. For node b,  
Figure 1: Topological network: (a) actual environment; (b) micro topology.  
the candidate set of destination nodes is divided into two cases, in the micro topology and not in the micro topology. When the destination node is not in the micro topology, the selection of routing nodes has little effect on whether the destination node can reach the destination node because the topology changes can not be predicted. If the destination node is in micro topology, node \(c\) is the best relay node for node \(b\). Because if node \(c\) is disconnected from \(b\), the number of reachable nodes for node \(b\) will be reduced. Therefore, it is necessary to consider the connectivity of nodes in routing. However, due to the limitation of the actual situation, we can not get all the topological paths in real-time, so we introduce effective information traffic.
## 3 Proposed Model
### AKNN Graph Construction
On this basis, the AKNN method improves the weight function and accuracy calculation function. After calculation, the Euclidean distance between the nodes is distributed between zero and one. A comparison chart of the weight function of the method before and after modification is presented in Figure 2. It can be seen that the weight function (11) causes very small weight differentiation of nodes with different distances such that the weights of most nodes are concentrated in a small interval. In addition, the weight function (16) increases the differentiation of the weights of nodes. Moreover, the calculation coefficient \(\theta\) and weight coefficient \(\gamma\) are introduced to increase the generalization of the method and adjust the calculation cost of adding an edge. Equations (11) and (13) in the above process are replaced with Equations (16) and (17):  
\[W_{ij}=1-\tan\ln\Bigl{(}D_{ij}\Bigr{)}^{\frac{1}{4}}\Bigr{)} \tag{16}\]  
\[p_{i}(t)=\frac{\sum_{m=1}^{t}\Bigl{[}Y_{i}==Y_{node_{i}(m)}\Bigr{]}W_{node_{i }(m)}}{t^{\theta}}\ \ t\in[1,z] \tag{17}\]  
\[\theta=\gamma\overline{W} \tag{18}\]  
where \(\theta\) is the calculation coefficient positively related to the node weight, \(\gamma\) is the weight coefficient, and \(\overline{W}\) is the average value of the node weight. Modification of the calculation of weight and accuracy increases the differentiation of nodes with different distances, reduces the calculation cost of adding edges, and allows the AKNN graph construction method to focus not only on the local information of the few nearest nodes around the central node but also on the global information of additional neighbor nodes. The graph construction process of the HVCB can be summarized as follows: First, the high-voltage circuit breaker signal is converted into nodes in the graph. The signal of the high-voltage circuit breaker is used as the characteristic of the node in the graph. Then, the weight between each node is calculated according to Equation (16) and then according to Equations (15), (17), and (18) to determine the best number of edges K for each node and finally connect each node to its K nodes with the largest weight, according to the value of K, to complete the construction of the graph.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.9 REMFuel  
REMFuel is an analytical solution designed to simulate the remediation of hydrocarbon sources and plumes under dynamic transient conditions. Similar to REMChlor, REMFuel allows users to estimate the timeframe for remediation, specifically the time required to reach a target concentration at a site, while considering various methods of source removal. The release of pollutants is calculated based on a power function for multiple fuel constituents. These pollutants can be removed at any time post-release through natural attenuation and/or enhanced degradation. The model accommodates concentrations within the plume with up to three degradation zones and three degradation times, each with different degradation rates [408]. While this tool remains accessible to users, it is no longer updated or supported.
## 3 Traditional Controllers: PID and MPC Closed-Loop Control Strategies
### Model Predictive Control Based on Centrifugal Chiller MIMO State-Space Representation  
The MPC closed-loop control strategy architecture for MIMO CCS is shown in Figure 7. The model predictive control (MPC) object was generated in MATLAB, as explained is detail in Section 4.2, and the MPC Simulink diagram shown in Figure 7 was obtained using the MPC MATLAB Simulink Toolbox. The MPC object has two manipulated variables (MVs): uCom and u_EXV; one measured disturbance (MD) (temperature Trr set to 48 degrees Fahrenheit); and two measured outputs variables (OV), namely, evaporator temperature Tchw_sp and liquid refrigerant level inside condenser. The MIMO model of the Centrifugal Chiller plant is represented in a discrete-time state space via Equation (6), with the values of the matrices' coefficients given in Equation (7). The MATLAB Simulink simulation results are shown in Section 5.2 and the performance analysis is reported in Section 6.  
Figure 6: Fuzzy logic design application, MATLAB Simulink simulation results: (**a**) Centrifugal Chiller FS temperature within evaporator; (**b**) Centrifugal Chiller FS membership functions; (**c**) Centrifugal Chiller FS temperature within condenser; (**d**) Centrifugal Chiller FS membership functions; (**e**) Chilled water temperature, evaporator training data and ANFIS model output estimation; (**f**) liquid refrigerant level inside condenser training data and ANFIS model output estimation; (**g**) Rule Viewer: fs MISO chilled water temperature inside the evaporator subsystem; (**h**) Rule Viewer: fs MISO liquid refrigerant level within the condenser subsystem.  
Figure 7: Simulink models of MPC using Simulink MPC Toolbox and MPC Design application.  
Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks-MIMO CCS Closed-Loop Control Strategies  
This section focuses on the design and implementation in MATLAB Simulink of two RL DLNN advanced intelligent control strategies for a MIMO CCS simplified state-space model. The first uses the MPC specifications to generate the reward function, and the second uses step response specifications imposed on a MIMO CCS model represented in the simplified state space. The tracking accuracy performance was compared with the MPC integrated in the first controller structure and an improved digital PID based on the MISO CCS ANFIS models.  
Reinforcement Learning Closed-Loop Control Strategy Using Deep Learning Neural Network MIMO Centrifugal Chiller Plant Model Represented in State Space
## 2 Materials and Methods
### Data
#### 2.2.3 Drought Index and Meteorological Data  
The Standardized Precipitation Evapotranspiration Index (SPEI) was first proposed by [PERSON] et al. in 2010 [46], which in turn takes into account the evapotranspiration  
Figure 2: Overview of the study area (**left**) and land use cover (**right**).  
demand (potential evapotranspiration) compared to the Standardized Precipitation Index (SPI) and is able to better respond to different drought types and their impacts in the context of global warming. In this paper, a high-resolution SPEI dataset (SPEI_RF) is used, with highly consistent spatial and temporal distribution characteristics with the currently widely recognized SPEIbase v. 2.6 (developed by [PERSON]) dataset. Compared to the SPEIbase v. 2.6 dataset, the SPEI_RF dataset has the advantage of higher spatial resolution and more accurate identification of localized small-scale droughts [47].  
The drought index is a convenient tool for assessing drought, and relying on meteorological factors to assess drought allows for long-term analysis but ignores the influence of surface factors on drought [48]. It is increasingly recognized that more diverse representations of water demand and supply by different land surface processes should be used to assess the extent of drought [49; 50]. Accordingly, in this paper, we add soil moisture (SM) data and transform it into a standardized soil moisture index (SSMI), which, together with SPEI, indicates drought [51]. We use the Global 1 km resolution surface soil moisture dataset (2000-2020) [52]. This dataset is based on the secondary development of ESA-CCI active-passive fusion product, which integrates the ERA5 reanalysis data and utilizes multi-source remote sensing data to construct a machine learning algorithm to generate a global spatio-temporal continuous 1 km resolution surface soil moisture dataset for the years 2000-2020. Validated by 2346 ground observation stations worldwide, the results show that the product has good accuracy (correlation coefficient is 0.89, and the unbiased root mean square error is 0.045 m\({}^{3}\)/m\({}^{3}\)). _SSMI_ calculation formula:  
\[SSMI=\frac{SM-\overline{SM}}{SM\sigma} \tag{4}\]  
where _SM_ is the value of soil moisture at moment t, \(\overline{SM}\) is the mean value of soil moisture from 2001 to 2020, and _SM\(\sigma\)_ is the standard deviation of _SM_ from 2001 to 2020. SPEI and SSMI drought classifications are shown in Table 1 (SPEI is classified according to fB/T 24081-2017 of China Meteorological Administration [53], and SSMI is classified according to [PERSON] et al. [51]). To be consistent with the SIF data, the SPEI data and SM data were resampled at 0.05\({}^{\circ}\).  
In addition to this, precipitation (UCSB-CHG/CHIRPS/DAILY) and temperature (ECMWF/ERA5/DAILY) data were obtained from Google Earth Engine, and both were converted to monthly scale data.
## 3 System Model and Optimization Formulation
### System Model
We thus assume that transmission power of IoT terminals is selected from the power set of \(N_{p}\) power levels, which can be denoted as \(\mathbf{P}_{set}=\left\{\frac{1}{N_{p}}R_{x},\frac{2}{N_{p}}R_{x},\cdots, \frac{N_{p}-1}{N_{p}}P_{k},P_{k}\right\}\). According to the above definition, the uplink power control problem is also resolved in this paper. An appropriate power level has been selected for \(u_{k}\) from the power set \(\mathbf{P}_{set}\). Such result of power allocation can be denoted in vector manner as \(\mathbf{p}_{k}=\left[p_{k,1},p_{k,2},\cdots p_{k,N_{p}}\right]^{T},p_{k,n_{p}} \in\{0,1\}\), where \(p_{k,n_{p}}=1\) denotes that the transmission power of IoT terminal \(u_{k}\) is \(p_{k,S}=\frac{n_{p}}{N_{p}}P_{k}\). The power allocation result of \(K\) terminals can then be denoted as \(\mathbf{P}=[\mathbf{p}_{1},\mathbf{p}_{2},\cdots,\mathbf{p}_{K}],\mathbf{P}\in \mathbb{R}^{K\times N_{p}}\). In summary, for IoT terminals, the channel and power allocation matrix, \(\mathbf{W}\) and \(\mathbf{P}\), represent their power and channel allocation results, respectively. From IoT terminal \(u_{k}\) to \(b\)th satellite antenna, the entire link gain can be modeled as  
\[G_{k,b}=G_{k}g_{k,b_{n}}(t)G_{b}(\varphi_{k,b_{n}}) \tag{1}\]  
In Equation (1), \(G_{k}\) is the transmit antenna gain of terminal \(u_{k}\), while \(G_{b}(\varphi_{k,S})\) represents the receiving gain of satellite antenna, where \(\varphi_{k,S}\) is the angel between terminal \(u_{k}\) and the antenna center of \(b_{k}\). Furthermore, \(g_{k,b}(t)\) is the channel power gain and can be given by \(g_{k,b}(t)=PL_{k}|h_{k,b}(t)|^{2}\), where \(PL_{k}\) is the large-scale fading component and \(h_{k,b}(t)\) captures all time-varying small-scale fading effects. The small-scale fading is composed of multipath fading and atmospheric attenuation of the satellite link, such as gaseous absorption, cloud attenuation, and rain attenuation Those time-varying components are affected by ever-changing weather and atmospheric conditions. According to the denotations mentioned above, the received Signal to Interference plus Noise Ratio (\(SINR\)) [17] of terminal \(u_{k}\) can be expressed as  
\[SINR_{k}=\frac{p_{k}G_{k,b_{k}}}{\sum\limits_{j=1,j\
eq k}^{K}w_{j,c_{k}}p_{j }G_{j,b_{k}}+\sigma^{2}} \tag{2}\]  
Consequently, following [19], the transmission rate achieved by terminal \(u_{k}\) is given as  
\[ \begin{array}{ll}C_{k}&=B\log_{2}(1+SINR_{k})\\ &=B\log_{2}\left(1+\frac{p_{k}G_{k,b_{k}}}{\sum\limits_{j=1,j\
eq k}^{K}w_{j, c_{k}}p_{j}G_{j,b_{k}}+\sigma^{2}}\right)\end{array}  \tag{3}\]
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 3 Transmission Schemes
### NJ Scheme  
The total transmission is divided into two hops. In the first hop, the source sensor \(S\) sends the signal to the relay sensor while intercepted by \(K\) eavesdroppers \(\{E_{k}\}_{k=1}^{K}\). Hence, the received SNR at \(R_{i}\) and \(E_{k}\) can be, respectively, expressed as  
\[\gamma_{SR_{i}}^{NJ}=\frac{P_{S_{1}}\left|h_{SR_{i}}\right|^{2}}{\sigma^{2}}, \tag{4}\]  
\[\gamma_{SE_{k}}^{NJ}=\frac{P_{S_{1}}\left|h_{SE_{k}}\right|^{2}}{\sigma^{2}}, \tag{5}\]  
where \(P_{S_{1}}=P_{Total}/2\) denotes the transmit power of the source sensor, which obeys the uniform power allocation for ease of analysis. \(\left|h_{SR_{i}}\right|^{2}\) and \(\left|h_{SE_{k}}\right|^{2}\) represent the channel gains of link \(S\to R_{i}\) and \(S\to E_{k}\), \(\sigma^{2}\) is the variance of the additive white Gaussian noise (AWGN).  
Similar to the first hop, the received SNR at \(D\) and \(E_{k}\) can be, respectively, given by  
\[\gamma_{R_{j}D}^{NJ}=\frac{P_{R_{1}}\left|h_{R_{j}D}\right|^{2}}{\sigma^{2}}, \tag{6}\]  
\[\gamma_{R_{j}E_{k}}^{NJ}=\frac{P_{R_{1}}\left|h_{R_{j}E_{k}}\right|^{2}}{\sigma ^{2}}, \tag{7}\]  
where \(P_{R_{1}}=P_{Total}/2\) is the transmit power of the selected relay sensor, and \(\left|h_{R_{j}D}\right|^{2}\) and \(\left|h_{R_{j}E_{k}}\right|^{2}\) denote the channel gains of link \(R_{j}\to D\) and \(R_{j}\to E_{k}\), respectively.  
Due to the presence of multiple eavesdroppers, we take both NCE and CE scenarios into account.  
In the NCE scenario, the eavesdroppers decode information individually without interactions [33]. Hence, the received signal-to-noise ratio (SNR) at the eavesdroppers of the first and second hops can be, respectively, given by  
\[\gamma_{1,NCE}^{NJ}=\underset{1\leq k\leq K}{\max}\gamma_{SE_{k}}^{NJ}=\frac{ P_{S_{1}}\underset{1\leq k\leq K}{\max}\left(\left|h_{SE_{k}}\right|^{2} \right)}{\sigma^{2}}, \tag{8}\]\[\gamma_{2,NCE}^{NI}=\max_{1\leq k\leq K}\gamma_{R_{j}E_{k}}^{NI}=\frac{P_{R_{1}} \max_{1\leq k\leq K}\left(\left|h_{R_{j}E_{k}}\right|^{2}\right)}{\sigma^{2}}. \tag{9}\]  
In the CE scenario, all eavesdroppers can exchange the information with each other and adopt the maximal ratio combining (MRC) for enhancing the intercept ability [34; 35]. Thus, the instantaneous SNR of the eavesdroppers' channels for the first and second hops are expressed as  
\[\gamma_{1,CE}^{NI}=\sum_{1\leq k\leq K}\gamma_{SE_{k}}^{NI}=\frac{P_{S_{1}} \left\|\mathbf{h}_{SE}\right\|^{2}}{\sigma^{2}}, \tag{10}\]  
\[\gamma_{2,CE}^{NI}=\sum_{1\leq k\leq K}\gamma_{R_{j}E_{k}}^{NI}=\frac{P_{R_{1} }\left\|\mathbf{h}_{R_{j}E}\right\|^{2}}{\sigma^{2}}, \tag{11}\]  
where \(\mathbf{h}_{SE}\) denotes the \(K\times 1\) channel vector between the source sensor and the eavesdroppers. Similarly, \(\mathbf{h}_{R_{j}E}\) represents the channel vector between the selected relay sensor and the eavesdroppers.  
The NJ scheme is a benchmark invoked for the purpose of comparison, which can also be applicable for the practical application scenario due to it lower complexity.
## 3 Results
### Comparison of Input Variable Combinations Based on RF  
3.2.1. RF Model Calibration and Input Variable Importance  
Data from the NE1, NE2, and NE3 sites were used to decide two parameters of RF (_nttree_: the number of decision trees; _ntry_: the number of preselected variables for a tree) considering NDVI, SSR, Tmin, Tmax, Tmean, SLN, and NMP as inputs. The gradient of _mtree_ is set to 0-1000, and _ntry_ is set to 1, 2, 3, 4, 5, 6, and 7, respectively. The out-of-bag error was characterized by mean square error (Figure 4).  
From Figure 4a, as _nttree_ increased, the model became more stable, but the computation time also increased. Thus, _ntree_ was set to be 500. MSE had a low value when _ntry_ was set to be 1-3. Considering that seven variables were included in total and based on a previous study [85], _ntry_ was set to be 3. Figure 4b illustrates the importance ranking of input variables in the RF model when _mtree_ = 500 and _ntry_ = 3. SSR played the most critical role, accounting for about 43.3% of the total importance. This is easy to understand since SSR is the energy source for plant photosynthesis. The second was NDVI, whose proportion was 21.8%. The time series of NDVI effectively captures the changes in vegetation growth during various phenological stages [86]. Although temperature affects vegetation growth by controlling processes such as phenological development and enzyme activity [87], three kinds of temperature (i.e., Tmin, Tmax and Tmean) showed the weakest role, only accounting for 8.2%, 5.7%, and 5.5%, respectively. Interestingly, SLN and NMP also were proved to have considerable effects on the RF model, and their proportions were greater than the air temperature. As Figure 4b illustrates, these two factors played key roles in GPP estimation.  
Figure 4: (**a**) Mean square error of RF based on the out of bag error rate with different parameter combinations. (**b**) Importance of input variables in NE1, NE2, and NE3 sites for GPP based on RF.  
Figure 3: SLN function based on the relationship between SLN and NMP with sufficient data from sites NE1, NE2, and NE3. The dots represent the original data. The hollow circle represents the averaged SLN at an interval of 0.03 NMP.
# Real-Time Detection of DDoS Attacks Based on Random Forest in SDN
## 2 Related Work
[18] propose a statistically based DDoS attack detection scheme and proposed a network and statistical signal-based DDoS attack detection method, which eventually achieved a 98% true positive rate. The first of these methods divides the initial dataset by time interval and then uses the K-means algorithm for attack detection. [PERSON] et al. [19] proposed a method for DDoS detection and mitigation in SDN, which consists of a classifier module, a mitigation module, and a collaboration module. Using an integrated classifier to detect DDoS attacks, the effectiveness of the classifier was verified on the NSL-KDD dataset and the proposed method was shown to outperform a single classification method. [PERSON] et al. [20] proposed an automated tool that can detect and mitigate DDoS using machine learning based on the behavioral characteristics of the traffic. [PERSON] et al. [21] analyzed the role of central control in SDNs, but it can become inaccessible due to DDoS attacks, causing a single point of failure. Then, four machine learning algorithms (SVM, MLP, decision tree, and random forest) were used to detect DDoS attacks in an SDN environment. Experimental results show that the best accuracy was obtained with the random forest algorithm and the best processing time was obtained with the decision tree algorithm. [PERSON] et al. [22] used a support vector machine algorithm to implement DDoS attack detection, but the accuracy of the detection needs to be improved. With the development of technology, the scale of network traffic is getting larger, the cost of launching DDoS attacks is getting lower, and the attack methods are becoming more flexible and versatile. The controller, as the core of the SDN, is more vulnerable to DDoS attacks causing a single point of failure.