esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Next Forwarding Node Selection in UWSNs
### Void-Aware
Moreover, this protocol introduces a dynamic zone hop-by-hop method to transmit the data packets. Furthermore, this protocol considers the link quality between nodes and the distance to select the next forwarding nodes, and defines two angles, named current angle and reference angle, as shown in Figure 6. In order to forward the data packet, the sender node broadcasts a hello packet including its position and reference angle to its neighbors. The receiving node then compares its current angle with the reference angle that is included in the hello packet. It discards this packet if its current angle is less than or equal to the reference angle. Otherwise, it can be involved in the forwarding process. Therefore, the forwarder node updates the reference angle that is included in the data packet based on the link quality and rebroadcasts it to its neighbors. DFR addresses the communication void problem utilizing two methods. The first method is to change the dynamic zone's size to be large enough to allow at least one node to enter the forwarding process. The second method will be applied in case none of the nodes have entered the range of the source node. Therefore, a detour path has been discovered towards the sink node. The major advantage of DFR is that it uses the link quality metric. This can improve the packet delivery ratio and the reliability of the network. Moreover, it can handle the communication void efficiently. However, the use of location information is one of the main drawbacks of DFR. Moreover, the energy consumption is increased due to the changing of the dynamic zone and DFR. Furthermore, it did not consider residual energy metric to solve the high energy consumption problem.
## 4 Experimental Results  
### Visual Exploration of Interdependencies  
Figure 4 shows the accumulated sum of performed maintenance actions (component replacements) over time. Each line in the multi-line chart represents the maintenance status of a single component. The results of the multi-line approach indicate that interdependencies between components can be found. As we can see in Figure 4 in sections A1 and  
Figure 3: Framework overview to identify and show the impact of interdependencies in the context of predictive maintenance. (1) visual inspection based on multi-line chart to explore interdependencies, (2) predictive model comparison to show the impact of interdependencies, and (3) SHAP visualization to explain the component interactions.  
A2, component 1 strongly interacts with components 2 and 3. The results indicate that a worn-out state of components 2 and 3 will accelerate the deterioration rate of component 1. To be more accurate, the horizontal line over time shows that a specific component has not been changed over time. In this case, the corresponding component/-s will contain considerable amount of usage time. Simultaneously, over the same period of time other components show an increase of the _accumulated sum of changes_, which indicates that the components have been replaced more often. This is a strong indicator of potential interdependencies as an old worn-out components impacts and accelerate the worn-out process of other components that is interacting with. This can be confirmed by background knowledge and research [2].  
In general, the multi-line approach provides an easy and fast way to understand the existence of interdependencies within the system. The knowledge acquired using visual inspection provides a sound basis for considering interdependencies while building a predictive model.  
Therefore, an evaluation of predictive results and explainability of the interdependencies in a more comprehensive way is shown in the following subsections. This will help to better understand the effect and behavior of interdependencies, thus increasing the plausibility of the results.  
Figure 4: Visual inspection of interdependencies using multi-line charts. Each chart shows the interdependencies between components of a particular machine. In sections A1 and A2 interactions between components can be found. For example, machine 1-section A1 shows that component 1 strongly interacts with other components.
# Interactive Graph Stream Analytics in Arkouda
## 4 Succinct Data Structure  
### Edge Index and Vertex Index  
We propose a Double-Index (DI) data structure to support quick search from a given edge to its incident vertices or from a given vertex to its adjacency list. The two index arrays are called the edge index array and the vertex index array. Furthermore, our DI data structure requires a significantly smaller memory footprint for sparse graphs.  
The edge index array consists of two arrays with the same shape. One is the source vertex array and the other is the destination vertex array. If there are a total of \(M\) edges and \(N\) vertices, we will use the integers from \(0\) to \(M-1\) to identify different edges and the integers from \(0\) to \(N-1\) to identify different vertices. For example, given edge \(e=\langle i,j\rangle\), we will let \(SRC[e]=i\) and \(DST[e]=j\), where \(SRC\) is the source vertex array and \(DST\) is the destination vertex array; \(e\) is the edge ID number. Both \(SRC\) and \(DST\) have the same size \(M\). When all edges are stored in \(SRC\) and \(DST\), we will sort them based on their combined vertex ID value \((SRC[e],DST[e])\), and remap the edge ID from \(0\) to \(M-1\). Based on the sorted edge index array, we can build the vertex index array, which also consists of two of the same shape arrays. For example, in Figure 2, we let edge \(e_{1000}\) have ID \(1000\). If \(e_{1000}=\langle 50,3\rangle\), \(e_{1001}=\langle 50,70\rangle\) and \(e_{1002}=\langle 50,110\rangle\) are all the edges starting from vertex \(50\) (a directed graph). Then we will let one vertex index array \(STR[50]=1000\) and another vertex index array \(NEI[50]=3\). This means that, for given vertex ID \(50\), the edges starting with vertex \(50\) are stored in the edge index array starting at position \(1000\) and there is a total of \(3\) such edges. If there are no edges from vertex \(i\), we will let \(STR[i]=-1\) and \(NEI[i]=0\). In this way, we can directly search the neighbors or adjacency list of any given vertex.  
Figure 1: Overview of Arkouda’s components and working mechanism.  
Figure 2: Double Index data structure for graph stream sketch.  
Our \(DI\) data structure can also support graph weights. If each vertex has a different weight, we use an array, \(V\_WEI\), to express the weight values. If each edge has a weight, we use an array, \(E\_WEI\), to store the different weights.
## 6 Empirical Research  
### Data Sources and Sample Descriptions  
This paper simulates and analyzes the historical operation data of AR05 in the central and southern regions of China, and the route distribution is shown in Figure 2. In Figure 2, the airspace enclosed by the white border is the en route sector. It can be seen that the density of the en routes (blue thick solid line) in the sector is higher; there are more intersections (green English name). The red font represents the name of the flight leg. And this sector is connected to four large busy airport terminals: namely, Guangzhou (ZGGG), Zhuhai (ZGUH), Hong Kong (VHH) and Macao (VMMC).  
Based on the data of AR05 traffic flow from 1 May 2017 to 20 May 2017, we can find the daily periodic variation law of the traffic flow data, as shown in Figure 2. The nonparametric test of the data in Figure 3, using the run-length test method, indicates that the traffic demand sample used is a non-stationary time series [24].  
Figure 2: En route sector AR05 in the central and southern regions.  
Traffic flow prediction can be divided into long-term traffic flow prediction and short-term traffic flow prediction according to time span. Long-term traffic flow prediction is based on the hour, day, month or even year; however, short-term traffic flow prediction generally does not exceed 15 min, the short-term prediction is highly nonlinear and uncertain, and it also has strong correlation.  
Using the traffic demand every 15 min on the sample day as the explained variable and the traffic flow during the same 15-min interval on the previous 20 days as the explanatory variable, we conducted a total of 1920 samples for rolling prediction. Firstly, according to the central limit theorem, it is assumed that the sample space obeys the normal distribution, and the Q-Q distribution of 1920 sets of sample data above is tested by the SPSS (27.0.1) data editor in Figure 4. In this figure, the black circles represent the scatter points plotted based on the quantiles of the sample data and the quantiles of the theoretical distribution, while the black line is the fitted line. It is found that the sample data do not conform to the normal distribution hypothesis. Therefore, the traffic demand is predicted based on the probability density distribution method. The 1920 sets of samples are fed into the quantile regression neural network model, where the model structure is established, and the neural network is trained to stabilize and meet the specified requirements. Subsequently, 100 consecutive conditional quantiles for each 15-min interval from 1 May 2017 to 20 May 2017 are obtained. Finally, these conditional quantiles are input into the kernel density estimation model to determine the probability density curve of traffic demand change in the en route sector every 15 min from 21 May 2017 to 30 May 2017.
# Advancements in Phytoremediation Research in South Africa (1997-2022)
## 1 Introduction
Johannesburg, one of the most developed cities in the African continent, is surrounded by piles of mine waste and abandoned mines. The clouds of dust and leachate, which are contaminated with cyanide, arsenic, lead, copper, and even radioactive uranium from the mine tailings, are having a devastating effect on the health of poor residential communities and ecosystems, including rivers, wetlands, and agricultural lands [19; 20]. There are about 270 tailings facilities that cover approximately 400 km\({}^{2}\) in the Witwatersrand gold fields [21]. Climate change will make matters worse; the hazards of mining wastes are expected to increase in the coming years. However, South Africa's rich biodiversity and exceptional adaptation of indigenous plant species to local conditions offer an invaluable asset in the pursuit of successful phytoremediation projects. Furthermore, several mining-related pollutions, industrial waste, and soil deterioration define South Africa's environmental landscape [22]. These challenges inspired researchers to investigate phytoremediation as a potential fix. For instance, heavy metal contaminants from mining operations plague the Limpopo Province, prompting researchers to look into how native flora may be used to address this problem [23; 24]. In addition, South Africa's commitment to sustainable development, as indicated in the National Development Plan (NDP) and the Green Economy Strategy, aligns seamlessly with the principles of phytoremediation. This will enhance the restoration of balance between human activity and the preservation of the natural environment in South Africa's landscape. However, there are no studies on bibliometric analysis of phytoremediation study in South Africa, and this requires deeper consideration. Bibliometric is the statistical analysis of scientific publications for the identification of research trends, impacts, and comparison of research output of scholars, including scientific collaboration among researchers, institutions, and countries over the years in a specific field of interest [25; 26; 27]. It uses various methods to measure the effect of published scientific articles to offer suggestions and direction for forthcoming projects [28; 29]. Based on bibliometrics, scientific mapping and performance analysis related to authors, institutions, and countries can be identified. Therefore, a bibliometric analysis of phytoremediationrelated research in South Africa was assessed in the Web of Science (WoS) from inception to 2022. This study provides access to essential facts on developments and identifies current snags for future investigation in phytoremediation-related research in South Africa.
## 2 Literature Review
### Data Fusion in Sensor Networks
#### 2.1.3 Typical Routing Protocols
The first is that all nodes are far from the sink nodes; the second is that each node can integrate the received data packets into their own data into a packet of the same size. The PEGASIS algorithm collects data before the first use of a greedy algorithm to connect all the nodes in the network into a single chain, and then randomly selects a node as a special node. The special node sends data collection requests, and data from the single and the endpoints flow to the special node. Intermediate nodes perform a data fusion before passing the data, and finally the data passes from the special node to the sink node. The advantage of this algorithm is that the single-chain structure minimizes the transmission distance of each node, and uses only one node for long-distance data transmission. This makes the algorithm more energy-efficient than the LEACH algorithm, but it does have two shortcomings of greater average delay and poor robustness. Figure 4: Example of single-chain structure. Figure 3: Data fusion in hierarchical routing.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 7 Security Analysis
### Security Against Semi-Honest Cloud Server (CS)  
We first prove security against a semi-honest cloud server.  
**Theorem 1**.: _Let CS be a PPT adversary corrupting the cloud server. Then, there exists a simulator \(\mathcal{S}_{\text{CS}}\) such that_  
\[\text{REAL}_{\Pi_{\text{KMeans}},\text{CS}}(x)\approx_{c}\text{IDEAL}_{\mathcal{ F}_{\text{KMeans}},\mathcal{S}_{\text{CS}}}(x,\mathsf{L}_{\text{CS}})\]  
Proof.: The simulator \(\mathcal{S}_{\text{CS}}\) works as follows:  
1. Simulate encrypted dataset \(\{E(x_{i})\}\) using fresh encryptions of zero: \(\{E(0^{d})\}\) of correct dimension and size \(N\).
2. For each protocol step involving encrypted computations (e.g., distance computations, cluster updates), simulate ciphertexts using the semantic security of the Paillier cryptosystem.
3. Simulate message patterns and ciphertext structures consistent with the expected leakage: number of iterations \(T\), data dimension \(d\), and number of clusters \(K\).  
Since all ciphertexts in the real protocol are semantically secure and indistinguishable from encryptions of random values, \(\mathcal{S}_{\text{CS}}\) produces a view that is computationally indistinguishable from the real execution, using only the leakage \(\mathsf{L}_{\text{CS}}\).
# Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions
## 2 Digital Forensics
### Cloud Forensics
#### 2.4.2 Methods and Frameworks  
[PERSON] et al., (2020) breaks cloud forensics into two sections, agent-based solutions and log-based solutions. Log forensics are more popular and widely used. These can be spread into four kinds of investigations: incident driven, provider driven, consumer driven, and resource driven investigations. Figure 3 illustrates the differences between these two methods.  
Figure 3: Agent-based versus log-based forensics.  
Agent-based cloud forensics relies on an application that collects information and sends it back to another location where it can be forensically analyzed. An example of agent-based cloud forensics is having an application, or agent, in the VM being used by the client that sends forensics reports to the investigators [8].  
Log-based cloud forensics relies on logs created from events that occur in the cloud that can be then forensically analyzed. As [PERSON] et al., (2016) discussed, cloud log forensics can be broken into three subsections: investigation, synchronization, and security [9]. Investigation is focused on analyzing log files for vulnerabilities that could have been caused inadvertently or through malicious intent. Synchronization is focused on creating consistency across different log files from different sources. Security is focused on keeping log files safe from users that may harm the integrity of the data either inadvertently or on purpose.
## 4 Discussion: Identification Data and Algorithm Flow
### Parameter Identification Flow for Voltage Ride-Through Models  
To accurately identify transient control parameters for voltage ride-through and enhance the practicality of the algorithm, a dynamic trajectory feature extraction and PSO-LM hybrid optimization method is proposed. The parameter identification flow is illustrated in Figure 4, with the following steps:  
1. Model Setup and Data Preprocessing: Establish a voltage ride-through model for the energy storage system, define control parameters for identification, and preprocess measured data to extract trajectory features as shown in Table 2.
2. Objective Function Construction: Formulate an objective function linking measured data and control parameters. Initialize particle positions in PSO with parameter candidates.
3. Global Optimization (PSO): Update particle positions and velocities until the global convergence criteria are met. Use dynamic trajectory features to determine whether to switch to local optimization.
4. Local Optimization (LM): Apply the Levenberg-Marquardt (LM) algorithm to refine parameters until convergence. Output the final identified control parameters.  
Figure 3: FRT test diagram.
## 2 System Model  
In RFID systems, both one-way and two-way channels are used to communicate, as shown in Figure 1. These are the so-called forward and backward channels. The \(1\times L\times 1\) notation is used to represent an RFID system with one reader transmitting antenna, \(L\) tag antennas, and one reader receiving antenna. In [16], the system was alternatively called multiple input multiple output (MIMO).  
Let us consider a system in which both channels are Gaussian. The expression to describe the whole channel can be represented as a composition of the forward and backscatter links, i.e., the overall channel is no longer Gaussian but the composition of the two channels. This channel is known as the dyadic backscatter channel (DBC), which was first introduced by [17].  
The mathematical description of a \(1\times L\times 1\) RFID system, considered as a special case of that in [15], is given as:  
\[\mathbf{y}=\sqrt{E}\mathbf{h}^{b}\mathbf{D}^{f}\mathbf{G}+\mathbf{n} \tag{1}\]  
where \(\mathbf{y}\in\mathbb{C}^{1\times f}\) is the received signal vector, \(\mathbf{h}^{b}\in\mathbb{C}^{1\times L}\) is the vector containing the backward channel elements, \(\mathbf{G}\in\mathbb{C}^{L\times f}\) is the impedance modulated codeword drawn from a codebook \(\mathcal{C}\); sent by the tag antennas, \(\mathbf{n}\in\mathbb{C}^{1\times f}\) is the vector containing the additive white Gaussian noise (AWGN) elements, and the matrix \(\mathbf{D}^{f}\in\mathbb{C}^{L\times L}\) is built as follows:  
\[\mathbf{D}^{f}=\text{diag}\{\mathbf{h}^{f}\mathbf{x}_{f}\}=\text{diag}\{ \mathbf{h}^{f}1_{f}\} \tag{2}\]  
In which \(\mathbf{h}^{f}\in\mathbb{C}^{L\times 1}\) is the vector containing the forward channel elements. The vector \(\mathbf{x}_{f}\in\mathbb{C}^{J}\) contains the signal a priori known and sent by the reader transmitting antenna at the \(J\)-th time slot. The transmitting signal \(E\) represents energy and is considered to be constant at all-time instants. In this study, we implement and analyze the \(1\times 2\times 1\) RFID system. Recall that this system has one reader transmitting antenna, two tag antennas, and one reader receiving antenna.
## 3 Comparative Flexural Test of FRP-Concrete Hybrid Beam
### The Influence of the Brittle Failure of Natural Bonding  
Figure 7a shows the load versus slip relationships in different locations in Beam 2. The slip was small (close to zero) for load under 200 kN, which means that full composite action could be obtained by the natural bonding and steel bolts together. This was also supported by Figure 7b, where under 200 kN, the load versus the mid-span deflection curves were almost linear.  
Figure 6: Comparison of Beams 1\(\sim\)2: (**a**) Mid-span deflection; and (**b**) Beam end slip on the left side.  
The strain gauges in the reinforcing bars and steel bolts at the beam end can be seen in Figure 8. Figure 9 shows the load versus strains on a steel bolt in Beam 2. It can be clearly seen that under the load of about 205 kN, the strains were small, and when the load exceeded about 205 kN, the strains all increased dramatically.  
Figure 10 shows the load versus strain at different locations of an upper layer longitudinal rebar and vertical axial strains on stirrups in Beam 2, the details of which can be seen in Figure 8. It can be seen that at about 205 kN, the strains all increased dramatically, which matched well with the results in Figure 8. At about 205 kN, the strains in L13, L9, and W9 increased suddenly. Figure 11 shows that the strain increased with the load in Beam 2 and when the location was closer to the mid-span, the longitudinal strains increased. The results can be explained by the shear and moment diagram where there was more shear stress in the region close to the beam ends and more moment in the area close to the mid-span.  
Figure 8: The locations of the strain gauges in Figures 9–11.  
Figure 7: (**a**) Load versus slip at the beam end curves, and (**b**) Load versus mid-span deflection curves in Beam 2.  
Figure 11: Strain distribution along the right half span of **(a)** the longitudinal bars, and **(b)** the stirrups.  
Figure 10: Load versus the strain of **(a)** the longitudinal bars, and **(b)** the stirrups.  
Figure 9: Load versus strain at the steel bolts.  
Figure 12 shows the load versus strain at different locations in the mid-span section in Beam 2. C4 is the top surface of the concrete, F3 is the bottom of the SIP form, F6 is the lower surface of the upper flange of the FRP I-profile, and F18 is the lower surface of the bottom flange of the FRP I-profile. Theoretically, the longitudinal axial strains of F3 and F6 should be the same when there is no slip, which is supported by the measured data under about 150 kN. After 150 kN, there was a very small difference between F3 and F6. When the load was around 205 kN, a dramatic increase in the strains was found in all four locations. To be specific, the F6 changed from tensile strain to a compressive state, which means that there was a new neutral axis in the FRP web, in other words, a partial composite action was obtained.