esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Training and Validating/Testing the Dataset
### Feature Extraction and Data Processing Description Using ML and DLMIDPSM Approach  
To further emphasize the performance of our proposed DLMIDPSM, we employed an ML (machine learning) approach, using decision tree (DT) and support vector machine (SVM) methods. Previous researchers used the DT and SVM methods for deep learning in [31, 32]; however, we instead dwelled on the DT and SVM evaluation metrics. Based upon this, we captured the CICIoT203 dataset in two different forms: the csv and pcap file formats. We used the CSV file format to load the data in our proposed IDPST. We also generated the dataset based on the different scenarios which we captured. The files included the features extracted using the pcap files based on a fixed-size packet window. We used a sequence of packets containing information between hosts to extract the features. Figure 16a below depicts the method used to produce the dataset. Initially, we captured the data generated, then extracted and labeled the data. Furthermore, the initial stage was to execute intrusion attacks against IoT devices. Thereafter, we processed the data so that other researchers could have access to and generate the data with ease. Consequently, we conducted the deep learning multilayer perceptron and the ML evaluation using classification, training, and validation with Algorithm 2 to leverage the dataset generated. In Figure 16b below, we show how we generated, extracted, labeled, and conducted each intrusion attack scenario, including benign traffic scenarios, for the deep learning and the other machine learning methods used for analysis. We used different tools to execute intrusion attacks against the IoT devices in our proposed IDPST. Thereafter, we captured the network traffic in pcap format using Wireshark. Consequently, we captured all the labeled data based on the executed intrusion attack. Figure 17 below depicts the data processing steps. In the data processing steps, network traffic data included all intrusion attacks captured and benign traffic. Since the network traffic was enormous, above 520 GB, we needed to split the data into small packet chunks of over 8 MB in size for parallel conversion. We used tcpdump [60] connected to our proposed IDPST to attract all intrusion packets from IoT devices and analyzed them effectively for any intrusion attacks generated through the CICIoT203. Consequently, we executed a parallel procedure to extract all dataset features using the DPKT package [61] and stored them in independent csv files. Examples of these features are shown in Table 2 below. Figure 16: (**a**). ML method to produce intrusion attacks in the CICIoT2023 dataset, (**b**) ML method to produce the intrusion attacks in the CICIoT2023 dataset.
# Multiband and Lossless Compression of Hyperspectral Images
## 3 Lossless Multiband Compression for Hyperspectral Images (LMBHI)
### Computational Complexity  
The main computational costs of our approach are due to the resolution of the linear system Equation (6), used to generate the optimal coefficients, which need the computation of the predicted pixel. By using the normal equation method, the linear system can be solved with \(\left(N+\frac{B}{3}\right)\cdot B^{2}\) floating-point operations [24]. Figure 3 shows the trend of the computational complexity of our predictive model, in terms of number of operations (\(Y\)-axis) that are required for the solving of the linear system, by using configurations with different parameters (\(X\)-axis).  
If we use only the previous band as a reference (\(B=\ 1\)), only about 20 operations are needed to solve the system. Instead, four or nine times more operations are required if we use two previous bands (\(B=2\)) or three previous bands (\(B=3\)). A linear system can have three kinds of solutions: no solutions, one solution, and infinity solutions. In the first and the third scenarios, the proposed predictive structure cannot perform the prediction. In these scenarios, it is desirable to use another  
Figure 3: The number of operations (\(Y\)-axis) required to solve the linear system Equation (6), by using different parameters (\(X\)-axis).  
low-complexity predictive structure and we have used the _3-D Distances-based Linearized Median Predictor_ (_3D-DLMP_) [21].
## 4 Descriptions of Different Topologies of WSNs
### Tree-Based Topology  
In this topology, all the deployed sensors construct a logical tree. Data are passed from a leaf node to its parent nodes. In turn, a receiver node receiving data from the child node sends data to receiver's parent node after aggregating data with its own data. In this fashion, data flow from leaf nodes to the root node, which generally acts as the sink. The idea behind constructing logical tree is that it avoids flooding and data can be sent using unicast instead of broadcast. This way the topology can save energy. Figure 5 shows a typical formation of logical tree. The arrows show the data flow from a leaf node to the root node/sink.  
Tree topology is used to design various protocols for WSNs, such data collection scheme (TBDCS [34]), routing protocols ([72; 73]), data dissemination protocols ([56; 57]) _etc_.  
One of the advantages of this topology is that it consumes less power than flat topology, as flooding is not necessary for data communication. Furthermore, it can save a bit more energy than some protocols based on cluster topology. In [74], [PERSON] _et al_. prove that for data acquisition, tree-based topology saves more energy than cluster-based topology.  
Figure 5: Tree-Based topology architecture.  
The disadvantages of tree-based topology are:  
* Formation of tree is time consuming and costly.
* It is not resilient to node failures. If a parent node fails, then its entire sub-tree is cut off from the base station during the current epoch.
* Power consumption is uneven across network nodes. The nodes closer to the base station consume a lot of power in forwarding packets from all the nodes in their sub-tree, whereas the leaf nodes in the spanning tree do not have to perform any forwarding at all and consume the least power.
* Long delay for sending data from leaf to root node.
* Tree maintenance overhead is high.
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 2 Design Process  
### In-Phase Three-Way Power Divider  
The SIW right-angled corner dividers as proposed in [20] are redesigned in order to include the effect and accommodate the size of the antipodal Vivaldi array elements. The configuration includes a single-mode SIW as input, port 1, and an over-moded SIW section which is split into the three output ports numbered 2 to 4. Figure 1 shows the three-way power divider structure with several additional via holes whose sizes and locations are optimized for best performance. Their dimensions are reported in Table 1.  
As shown in Figure 2, the dominant TE\({}_{10}\) mode appears at port 1 and enters the over-moded waveguide section. Contrary to the TE\({}_{10}\)-to-TE\({}_{30}\) mode transducer in [19] where there is a 180\({}^{\circ}\) phase difference between the three separated output ports, the sizes and via holes in this application are optimized to produce the same amplitude in the three output ports with maximum phase variation of below 50 degrees (Figure 2).  
The SIW port parameters are designed by following the equivalent-width principle in [21]. Using RT/duroid 6002 with \(\varepsilon_{r}=2.94\) and height \(h=508\) um, the width of the input SIW waveguide is defined as \(a_{SIW}=5.4\) mm, the via diameter is \(d=1/64^{\prime\prime}\approx 0.3969\) mm, which is a standard drill size, and the pitch distance is \(p=0.6\) mm. The cutoff frequency according to [21] is 17.15 GHz.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **mm** & **Parameter** & **mm** & **Parameter** & **mm** \\ \hline \(X_{1}\) & 5.46 & \(y_{1}\) & 7.1 & \(r_{1}\) & 0.5 \\ \(X_{2}\) & 5.7 & \(y_{2}\) & 9.7 & \(r_{2}\) & 0.25 \\ \(X_{3}\) & 10.7 & \(y_{3}\) & 7.3 & \(r_{3}\) & 0.5 \\ \(X_{4}\) & 13.3 & \(y_{4}\) & 3.3 & \(r_{4}\) & 0.25 \\ \(X_{5}\) & 9.4 & \(y_{5}\) & 3.2 & \(r_{5}\) & 0.15 \\ \hline \end{tabular}
\end{table}
Table 1: Three-way H-plane SIW power divider dimensions.  
Figure 1: Three-way H-plane SIW power divider structure and parameters.  
A fine-optimization using Powell's method is applied. By optimizing the metallic via locations in \(x\) and \(y\) directions, and the radii of the circular vias in the right-angled region (considering standard drill sizes), a good match with reflection coefficient better than 13 dB from 22 GHz to 25 GHz, and insertion loss better than 0.8 dB (on top of the 4.77 dB power division) from 23 GHz to 25 GHz are achieved which are presented in Figure 3. All simulations are performed in the full-wave simulator CST Studio Suite 2017 (CST of America, Inc, Framingham, MA, USA).
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Service Life Expectancy and Performance Level  
Predicting service life expectancy and performance in standard conditions involves defining a building component's service life as the age when it fails to meet performance criteria. Life expectancy is, in standard deterioration, the service duration until refurbishment becomes impractical or replacement is necessary.  
By utilizing the typical deterioration path (TDP) for given wear conditions, it is possible to forecast the continued decline in component performance (CP) over time (see Figure 1), and predict the service life expectancy (LE), up to the point where rehabilitation or replacement is needed, which is established by referring to the point where the typical deterioration path (TDP) and the minimum required component performance (MRCP) line intersect. An upper and lower limit for the predicted service life interval (PSLI) of building components at a given performance level can be determined by referring to the intersection points of the prediction interval (PI) boundaries, in which the line represents the MRCP. This defined range of service years represents a given statistical significance. Conversely, the intersection points of the prediction interval (PI) boundaries, in which the line corresponds to a given component's age, support the predicted component performance interval (PCPI) at that point in time.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 2 Related Works
### Synchronous and Semisynchronous BFT Protocols  
The exploration of BFT consensus protocols traces back to the foundational work by [PERSON] et al [16]. Over the years, synchronous [17; 18] and semisynchronous [19] BFT protocols came into existence, the majority of which were characterized by deterministic protocols. Among them, PBFT [3] stands as a classic example, establishing fundamental  
Figure 1: The overview of FlexBFT.  
concepts like the view-change mechanism and voting with message exchange. Subsequent work aimed to enhance PBFT for lower latency and higher throughput. HotStuff [6], an advanced BFT-like consensus algorithm, introduced a three-phase consensus incorporating threshold signatures for quorum certificates (QC) to replace multicast-based voting, reducing message complexity. Additionally, optimistic consensus protocols such as Zyzzyva [4], CheapBFT [20], SBFT [5], FastBFT [21], Flexico [22], etc., integrated optimistic fast paths, showcasing excellent performance in a fault-free scenario where all nodes operate as intended. Zyzzyva enhances performance through speculation, bypassing explicit consensus by executing requests as ordered by the primary. CheapBFT utilizes TEEs in its optimistic BFT protocol, requiring participation from only \(f+1\) replicas for execution and consensus. FastBFT, another TEE-based BFT method, secures consensus with \(2f+1\) replicas using secret sharing for security. SBFT combines fast, optimistic execution with linear PBFT for efficient dual-mode consensus. These protocols typically follow a fixed path order, primarily relying on the fast lane as an entry point, while semisynchronous BFT protocols are resorted to if and only if the fast path encounters failure. These synchronous and semisynchronous BFT protocols often exhibit lower latency but tend to lose liveness when confronted with an asynchronous network.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 7 Simulation Experiments and Analysis of Results
### Consensus Latency  
First, we will assess the consensus latency of the four algorithms. Consensus latency is defined as the time interval between when a client issues a request and when it receives a reply. As a critical performance indicator for blockchain systems, consensus latency directly impacts user experience, real-time system performance, throughput, and security. Low-latency blockchain systems offer significant advantages in various application scenarios, providing more efficient and reliable services. We fixed the number of requests at 200 and conducted 100 transaction tests across a range of 0 to 100 nodes in increments of 10, calculating the average as the result. This outcome will serve as the basis for subsequent analysis. The consensus delay is calculated as follows:  
\[T_{D}=t_{s}-t_{r}, \tag{7}\]  
where \(t_{s}\) is the time when the client sends a request, and \(t_{r}\) is the time when a reply is received. The experimental results are shown in Figure 8.  
As the number of participating clients increases, the consensus delay of all four algorithms correspondingly rises. However, the increase in delay for the AE-PBFT algorithm is the least pronounced. The delay times presented in the data of Table 6 are significantly lower compared to the other schemes. On average, it is reduced by about 98.02% compared with PBFT, GPBFT, and CBFT. This improvement is attributed to the new approach, which incorporates only one broadcast stage in the process, thereby reducing the volume of information exchanged. Additionally, by utilizing a small-scale consensus group that is updated rapidly in real-time, the number of nodes participating in the consensus is minimized, enhancing adaptability in real-world environments.  
Figure 8: Consensus latency.
## 4 Evaluation
### Evaluation Results
Therefore, it avoids forwarding packets to high occupancy nodes. In the model using OSPF, some nodes are on the shortest path of multiple source-destination nodes pairs. These nodes will bear more forwarding than edge nodes. The situation is more serious with the increase of traffic intensity. In our experiments, the new received packets will be dropped when the buffer is full. PDR is the most direct index to reflect packet loss. We designed a new experiment to verify the relationship between PDR and buffer size. The traffic intensity is fixed at 50% and the buffer size ranges from 24 to 48. Figure 9 shows the PDR curves of each model. As we can see from the figure, buffer size has little effect on DGRL. When the buffer size is 32, as set in the previous experiments, the PDR of DGRL is 77.9%. When the buffer size is reduced to 28 and 24, the PDR decreased by 0.7% and 1.4%, respectively. When the buffer size increases to 48, the increase of PDR is little. This can be understood as DGRL has a better utilization rate for the network buffers. A buffer of size 32 already meets the requirements of DGRL. The PDR of the other three models are closely related to buffer size. As buffer size decreases from 32 to 24, the PDR of OSPF decreases by 4.2%, DRL-OR by 4.4%, and DRL-FC by 7.1%. Increasing buffer size to 48 gives a improvement, ranging from 3.9% to 11% for the three models. It is obviously due to the increase in buffer size, which reduces packet loss caused by full buffer. Figure 9: Comparison between DGRL and other algorithms when buffer size changes from 24 to 48. Figure 10 shows the experiment about _reward_, which aims to reflect the influence of different parameters in the _reward_ on the training model. In this experiment, the traffic intensity is fixed at 50%. In addition to the trained DGRL model, there are three other variants of DGRL. The _reward_ function of DGRL-I considers \(delay_{pre}\) and \(buffer\). DGRL-II considers \(buffer\) and \(hop\). DGRL-III considers \(delay_{pre}\) and \(hop\). It is clear that the performance of DGRL is better than the other three models. Figure 10a shows that the missing parameter \(buffer\) has the greatest impact on the PDR. It results in a 23.6% drop in PDR than DGRL. Compared with DGRL, DGRL-I and DGRL-II also have a similar decrease in PDR. Figure 10b shows that the loss of all the three parameters each lead to an increase in mean transmission delay. Figure 10c shows that the impact of \(buffer\) and \(delay_{pre}\) on \(Dispersion\) is greater than that of \(hop\). In conclusion, all the three parameters are indispensable to _reward_.
## 4 Feature Extraction and Recognition of Obstacles
### Parking Space Identification  
The recognition of the parking space mainly involves the acquisition of the type of parking space and judging whether the identified parking space can meet the parking conditions. The types of parking spaces are shown in Table 1. We can classify them according to the parking modes, the existence of parking lines, and the existence of vehicles around. Because most research is mainly about parking spaces with parking lines and surrounding vehicles, this part only includes the situation without parking lines and vehicles on both sides. Because the parallel parking mode and the vertical parking mode are similar in terms of the identification methods, this part takes the latter as an example.  
As shown in Figures 14 and 15, the laser emitting devices are illuminated on the ground to present the shape of the checkerboard grid. We divide the region of vehicles and parking space by acquiring the contours of vehicles when processing the captured image of the parking space. According to the contours of the A, B, D, and E cars, we can judge the posture of the vehicle body, thereby we can acquire the type of paring space, such as a vertical parking space and an oblique parking space. The laser grid area between two cars' contours can be considered as the area of the parking space. If there are no obstacles in this area, then the parking space can be considered as valid.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parking Modes** & **Parking Lines** & **Vehicles Around** \\ \hline Vertical parking & Standard parking lines & One side \\ Parallel parking & Only parking angles & Both sides \\ Oblique parking & No parking line & None \\ \hline \end{tabular}
\end{table}
Table 1: Different types of parking spaces.  
Figure 14: Vertical parking mode.  
Figure 13: Division of obstacle areas. (**a**) The area of the obstacle; (**b**) the area of the ground.
## 5 Discussion  
Several existing FCL schemes were compared in terms of FCL ability, cost, power loss, and influence on recovery and restart operations. The energy dissipation resistance-based FCL method in Reference [14], the inductance-based FCL scheme [15; 35], and the proposed EFCLC were simulated under controlled conditions in this section.
## 1 Introduction
The trajectory classification ability of GMM-based features consisting of the sparse trajectory and the spatial-temporal sparsity of the user trajectory are examined in terms of their effectiveness for classification accuracy via a comparison with IDNN-based features. As a result, this study demonstrates that GMM-based features exhibit greater robustness to trajectory sparsity and higher generalization performance than the features extracted by the IDNN. These findings highlight the superior capability of GMM-based features in effectively classifying user age groups from sparse trajectories, compared to existing trajectory-based features, making them a promising approach for scenarios where only spatial-temporal information is available.
## 1 Introduction
Evolving from our earlier investigations, which empirically reached almost 70% of overall accuracy to separate seeps from spills in Campeche Bay [29; 30; 31; 32; 33; 34], we set out an exploratory data analysis to try, with more rigor, accuracy improvements in relation to our previous approach. The investigative nature of the research reported here aims to answer four scientific questions:  
1. Which SAR backscatter coefficient (i.e., sigma-naught (\(\sigma^{o}\)), beta-naught (\(\beta^{o}\)), and gamma-naught (\(\gamma^{o}\))) provides the most accurate seep-spill discrimination? 2. Which SAR calibrated product (i.e., measures of the received radar beam given in amplitude or decibel, with or without a despeckle filter) leads to the best seep-spill discrimination? 3. Which of the three tested data transformations (i.e., none, cube root, and \(\log_{10}\)) leads to more effective discrimination between seeped and spilled oil? 4. Which combination of attributes describing the oil-slicks' signature (e.g., size information and SAR basic qualitative-quantitative statistics) better discriminates between the two oil-slick categories? Our study contributes both to academic ocean remote sensing research and to practical applications in the oil and gas industry and elsewhere. We expect that approaches other than our LDA-based algorithms may also lead to improved seep-spill discrimination; however, these require further attention from the scientific community. This manuscript adopts a customary writing structure: introduction (Section 1), methods (Section 2), results (Section 3), discussion (Section 4), and concluding remarks (Section 5). While Section 1 dwells upon our research motivations, justifications, and contributions, Section 2 presents information about the explored dataset (Section 2.1) and about a specific ongoing operational petroleum industry application currently applying our proven oil-slick remote sensing technique (Section 2.2), as well as it discloses the concepts for discriminating the oil-slick category (Section 2.3) and sets forth a description of our exploratory data analysis (Section 2.4).