esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison of Results and Discussion  
It has been observed in the previous section from the 2D and 3D radiation pattern results that at 5.97 GHz, MSFAA gain and directionality are greater as compared to low frequency at 4.17 GHz, but the important factor is that the null is reduced at zero degrees in the E- and H-plane at 4.17 GHz. This leads to a better radiation pattern with fewer side lobes. Moreover, it is evident from the 2D and 3D radiation patterns that the four-element (2 \(\times\) 2 configuration) MSFAA demonstrates reduced back radiation and improved directionality at both resonant frequencies. Therefore, the advantage of high gain and directional radiation patterns in the boresight makes the proposed fractal antenna array design more suitable to work in the currently deployed high-efficiency IEEE 802.11 ax WLANs (Wi-Fi 6E) and the upcoming 5G systems. Additionally, the directional antenna array can be very useful in the intelligent transport system and Internet of Vehicles (IoV) [33; 34] system, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications heavily rely on directional beam-steering antenna array systems. Another potential area of application for the single-element antenna and antenna arrays is flying ad hoc networks (FANETs) [35], where unmanned aerial vehicles (UAVs) are employed for both commercial and military communications [36]. In FANETs, wireless communication links are required for UAV-to-UAV (U2U) and UAV-to-base station (U2B) communications [37]. Table 2 summarizes the comparison of the proposed four-element Minkowski-Sierpinski fractal antenna array simulated and measured results. The data in Table 2 show good agreement between the simulated and measured results of the four-element fractal antenna array. Table 3 gives a detailed comparison of the existing work carried out on fractal antenna arrays with respect to proposed antenna array design techniques in terms of multiband operation, gain, bandwidth, radiation pattern, and efficiency. From Table 3, it is very important to note that in most of the presented designs, the researchers are improving one antenna performance parameter only, such as gain or the bandwidth, although the work presented in this paper shows the overall improvement of all antenna performance parameters, as mentioned in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**MSFAA Simulation Results**} & \multicolumn{2}{c}{**MSFAA Measurement Results**} \\ \cline{2-5}  & **4.17 GHz** & **5.97 GHz** & **4.17 GHz** & **5.97 GHz** \\ \hline Return loss (dB) & 21.2 & 29.1 & 17.1 & 25.8 \\ \hline Bandwidth (MHz) (for VSWR \(<\) 2) & 96 & 165 & 85 & 182 \\ \hline Gain (dB) & 4.9 & 10.5 & 4.19 & 9.6 \\ \hline E-plane Beamwidth (Deg) & 34.8 & 17.5 & 31 & 26.2 \\ \hline H-plane Beamwidth (Deg) & 36.8 & 17.1 & 32 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of simulation and measured results of four-element MSFAA.
## 3 Research Methodology
### Data Source and Pre-Processing  
The data collection period for this paper was selected as 2006-2022. The retrieval date was 31 March 2022. In order to ensure the rigor of the selected documents, the literature was limited to SCI journals, EI journals, core journals, CSCD journals and CSSCI journals [49]. The literature search conditions were set as \"subject = digital technology, article keywords = construction industry\". All recorded literature information was exported. After screening, duplicate and incomplete documents were removed, resulting in a total of 1182 documents. Some examples of the literature used are shown in Table 1.  
The text pre-processing method proposed by [PERSON] et al. was used to pre-process the derived documents [50]. The information from the English abstracts, keywords, graphs and formulas of the exported literature was removed, and the remaining part was selected as the corpus source and saved in Excel format. In this paper, we use the jieba word splitting toolkit in Python for word splitting. To improve the accuracy of word separation, the specialised vocabularies of the construction industry and digital technology were added as a custom dictionary and combined with special deactivation words to remove numbers, English words, meaningless high-frequency words and symbols with no practical meaning before word separation. Since the actual terms used  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Title** & **Literature Content** \\ \hline \multirow{4}{*}{1} & Research on the development trend and & In the process of digital technology covering the world, the \\  & impact of digital technology in & construction industry has also been changed by its development. First, \\  & construction industry & this paper discusses the birth and development of technologies that \\  & & have had an important impact on the construction industry during the \\  & & development of digital technologies from a time perspective... \\ \hline \multirow{4}{*}{2} & Discussion on the application of modern & The rational application of modern digital technology is very important \\  & digital technology in the construction & to the construction and management of water conservancy engineering \\  & management of hydraulic engineering & in the present stage of our country, with the help of the rational \\  & & application of modern digital technology... \\ \hline \multirow{4}{*}{50} & Research on influencing factors of digital &... \\  & transformation of construction industry & Taking the digital transformation of the construction industry as the \\  & based on DEMATEL-AISM & research object, taking Wuhan City as an example, on the basis of \\ \cline{1-1} \cline{2-3}  & & identifying the system of influencing factors of the digital \\ \cline{1-1} \cline{2-3}  & & transformation of the construction industry in Wuhan City... \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representative literature data.  
in different journals vary but have similar meanings, such as digitising government and e-government, such words are retained.
## 4 Discussion
### Misclassification Analysis  
The distribution of the filter misclassification point cloud of the test data is shown in Figure 9. From the graph it can be seen that there are still many misclassification points in this method. Since the original data are all in the forest area, the ground points in the filtering results are almost covered by forest vegetation. From the misclassification of each result map, the misclassified point clouds mainly exist near the ground points, which are very close to the terrain surface.  
According to the terrain characteristics of the forest and the principle of the proposed filtering method, the above misclassified points may be caused by the following:  
1. Vegetation located close to the ground in the forest area is difficult to distinguish. The forest area has many low vegetation, and the shape, range, and location of this vegetation show a great degree of randomness and uncertainty. Moreover, given that the very low vegetation is located very close to the ground, it can be easily misidentified as the ground.
2. The fitting of the filter reference surface is not good enough. The proposed method uses an irregular triangular network to interpolate the ground points, but some errors may be generated in the actual interpolation process, thereby leading to a filtering reference surface that cannot be completely fitted with the actual ground.
3. The decision tree model was used for training, and this model has an accuracy of 87.25%. In the future, a model that is more suitable for point cloud classification can be selected for training to improve the model accuracy.  
In future research, we plan to further optimize and improve the proposed method considering the above aspects.  
Figure 9: Filtering misclassification results of test data.
# Generative Image Steganography via Encoding Pose Keypoints
## 3 The Proposed Method
### Information Extraction Process  
The receiver's local device stores the human pose keypoint sequence generation model proposed in Section 3.1 and the human pose keypoint extraction network proposed in Section 3.3. When the receiver acquires stego-images in the public channel, these two networks recover the secret information. The specific steps are as follows:  
Figure 6: Part of the backbone network module.  
1. Extracting the human pose keypoints: Input a stego-image into the extraction network of human pose keypoints to obtain the keypoint coordinate sequence \(P\). Transform the coordinate sequence \(P\) into the index sequence \(S\) following Formula (1).
2. Restoring the encrypted bitstream: Acquire the initial element candidate set \(Set_{1}\) and establish a mapping relationship between the elements in this set and the binary encoding. Obtain the initial element \(s_{1}\) from the sequence \(S\) and obtain the corresponding binary encoding based on the mapping relationship. Insert this encoding into the encrypted bitstream \(M^{\prime}\). Suppose the sequence \(S\) has \(n\) elements. For each subsequent element starting from the second one, perform the following: Sequentially input the first \(i-1\) elements in \(S\) into the sequence generation model to obtain the probability distribution \(\Pr(s_{i}\,|\,s_{1},\,s_{2},\,\ldots,\,s_{i-1})\). Sort these indexes in descending order of their probabilities and eliminate those indexes that are lower than the threshold \(\tau\) to initially form the candidate set \(Set_{i}\). Subsequently, select the top \(K=2^{Li}\) indexes with the highest probabilities as the final candidate set \(Set_{i}\) and establish the mapping relationship between it and the binary encoding. Obtain the element \(s_{1}\) from the sequence \(S\), obtain the corresponding binary encoding based on the mapping relationship, and insert this encoding into \(M^{\prime}\). Repeating Steps 1 and 2 to extract the bitstreams embedded in multiple stego-images, thereby obtaining the complete encrypted bitstream \(M^{\prime}\).
3. The secret information is obtained through decoding: Based on the encrypted bitstream \(M^{\prime}\) and the _Key_ jointly held by the communicating parties, the secret information bitstream \(M\) is derived through the operation \(M^{\prime}\oplus\textit{Key}\).
# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 3 Methodology
### Use Evaluation Metrics for Performance Analysis of Classifiers  
The performance measurement is essential for evaluating how well a classification model correctly predicts instances and achieves a desired target [17; 49]. The confusion matrix provides a more detailed overview of a predictive model's performance. It represents which classes are being predicted correctly and incorrectly and shows the measurement of type errors. In the confusion matrix, every instance in a given dataset falls into one of the four categories: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) [16]. The above four category data are arranged in a matrix known as the confusion matrix. Table 3 shows the elements of the confusion matrix.  
In this work, we used several evaluation metrics including accuracy, kappa statistics, the F1-Score, and AUROC to assess the performance of each classifier. To calculate these metrics, the confusion matrix is required to generate and gather different types of instances from it. These metrics are described briefly as follows:  
* **Accuracy:** It is a measure of how effective the model is used to predict outcomes [49], in terms of the total number of predictions:  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Target**} & \multicolumn{2}{c}{**Predicted**} \\ \cline{2-3}  & **ASD** & **No ASD** \\ \hline ASD & TP & FN \\ No ASD & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for ASD.  
\[Accuracy=\frac{(TP+TN)}{(TP+FP+FN+TN)} \tag{7}\]
* **Kappa statistics (Kp):** It measures observer agreement for categorical data and expected accuracy and has received considerable attention [50]. \[Kp=\frac{1-(1-p_{0})}{(1-p_{e})}\] (8)
* **Precision:** It is a measure of true-positive predictions against all retrieved positive instances [51]. \[Precision=\frac{TP}{(TP+FP)}\] (9)
* **Recall:** It is a measure of correctly predicted positive observations against all relevant positive classes [51]. \[Recall=\frac{TP}{(TP+FN)}\] (10)
* **F1 score:** It is the harmonic average of precision and recall [52]: \[F1=2\frac{(Recall*Precision)}{(Recall+Precision)}\] (11)
* **AUROC:** It determines how well true-positive values are isolated from false-positive values [15]: \[TRP=\frac{TP}{(TP+FN)}\] (12) \[FRP=\frac{FP}{(FP+TN)}\] (13)
## 4 Experimental Results
### Measurements of the Frequency Discrimination
The results of the experiment of six subjects at the frequencies of the highest hearing thresholds are tabulated in Table 2.  
To improve reliability of the results, the test is usually repeated over a period of time. In audiology research, frequency discrimination threshold of normal adults is about 0.1% to 0.45%, and decreases along with frequency of the probe signal. When the discrimination threshold is worse than the normal range, it may lead to difficulty indistinguishing speech in a noisy environment. This leads to deterioration of speech intelligibility for the patient [5].
## 3 Experiment Results and Analysis  
### Validation of the Half-Symmetric Model and the Numerical Modeling  
The rationality of the half-symmetric model and the numerical modeling are validated by analyzing the test results of the half-symmetric model H2 group, the full-section H1 group, the numerical full-section P1 group, and the numerical half-symmetric model P4 group. Figure 4 compares the relationship between the model test and the numerical model results of the half-symmetric model and full-section model at the final state of the subgrade collapse under \(L=4.0B\). Overall, the curve amounts of the subsidence area of the half-symmetric model H2 group, the full-section H1 group, the numerical full-section P1 group, the numerical half-symmetric model P4 group are similar.  
Figure 4a shows the soil pressure of groups H1, H2, P1, and P4 when \(L=2.0B\). With the increase of the horizontal distance from the subsidence area, the soil pressure increases rapidly in the stable area. The soil pressure of each curve reaches its peak value at the horizontal distance between 1.5 m and 2.0 m from the subsidence area, and then decreases to the initial earth pressure. This shows that the soil within the subsidence area gradually sinks and the soil of the surrounding stable area moves to the subsidence area with the development of a subgrade collapse, so the compactness of the soil near the subgrade collapse interface decreases and the measure of the earth pressure decreases. When \(L=2.0B\), the soil pressure curve trend of the half-symmetric model H2 group is similar with the full-section H1 group, which indicates the soil pressure test results of the half-symmetric model can represent the soil pressure data of the full-section model test. The validity of the numerical model is proved by the approximate similar curve trends obtained from the model test and the numerical models.  
Figure 4b indicates the comparison of the tensile force at the measuring points on the geotextile of the groups H1, H2, P1, and P4 when \(L=2.0B\). The four curves of the tensile force show a similar trend. The values of the subsidence area is much larger than the stable area. With the increase of the horizontal distance from the subsidence area, the values decrease rapidly and finally tend to zero.  
Figure 4c describes the comparison of the settlement amount of the subsidence area of groups H1, H2, P1, and P4 when \(L=2.0B\). All curves have a peak value in the settlement at the center of the subsidence area. The value of the settlement decreases gradually and is accompanied by the horizontal distance increases, and there is a minimum value when the distance from the center of the subsidence area is 0.75 m. The similar variation trend of the four curves means that the settlement is the largest in the center of the subsidence area, and the settlement is inversely proportional to the horizontal distance of the subsidence area.  
Figure 4: _Cont._
## 4 Classification Results and Discussion  
The reference data were generated by visual interpretation of high spatial resolution satellite images obtained from Google Earth. Finally, 46,397 pixels were obtained, and 10% (4641 pixels) of them was randomly selected as training samples, and the remaining 90% (41,756 pixels) was used for testing (the detail number for each class are shown in Table 4). Classification accuracy was evaluated with such metrics as class accuracy, overall accuracy (OA) and the kappa coefficient (Kappa). It should be noted that each experiment was run 10 times, and the averaged accuracy metrics were used to assess the classification result to avoid the biased evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Pixels**} & \multicolumn{2}{c}{**Validation Pixels**} & \multicolumn{2}{c}{**Percentage**} \\ \hline
**Classes** & **Number** & **Number** & **Number** & **\%** & \\ \hline Shrub & 288 & 2587 & 6.2 & \\ Bare land & 183 & 1646 & 3.94 & \\ Agricultural land & 1182 & 10,636 & 25.47 & \\ Forest & 1520 & 13,675 & 32.75 & \\ Built-up area & 425 & 3825 & 9.16 & \\ Water & 344 & 3094 & 7.41 & \\ Road & 699 & 6293 & 15.06 & \\ Total & 4641 & 41,756 & 100 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of training and validation pixels.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **OA** & **Kappa** & **Shrub** & **Bare Land** & **Agriculture Land** & **Forest** & **Built up Area** & **Water** & **Road** \\ \hline R20 & 86.69 & 0.83 & 73.26 & 58.43 & 92.30 & 96.71 & 70.65 & 90.76 & 76.13 \\ R10 & 90.33 & 0.87 & 79.49 & 72.50 & 94.53 & 97.82 & 79.14 & 91.67 & 79.85 \\ G1  
The results obtained from different classification scenarios are distinct but have a significant regularity in spatial resolution and feature sets. In general, for LULC classification of Sentinel-2, from the perspective of spatial resolution, the higher spatial resolution, the better classification accuracy. From the perspective of feature sets, the more features input, the better accuracy can be achieved. Most of the scenarios can meet the requirement of LULC classification, in addition to six data sets solely based on spectral bands. In that case, although all their OA exceeded 85%, some classes accuracy were less than 70%, which cannot satisfy the lowest precision requirements proposed by [PERSON] [64; 65].  
The class accuracy, OA, and Kappa coefficient for each scenario are shown in Table 5. Based on this table, Figures 3-6 are plotted for showing the distinction between different classification scenarios more intuitively. Figures 3 and 4 show the OA and Kappa of all classification scenarios, respectively. Figure 5 presents the feature importance of spectral bands using data sets obtained by all downscaling methods. Figure 6 indicates the differences of GLCM and EAPs in the promotion of class accuracy between all seven data sets.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 4 Discussion
### Comparison of the FDM-PPM with ASK and PPM Method
In sensor networks with limited bandwidth that require subcarrier allocation for multiple nodes, the method proposed in this study demonstrates significant advantages over the other three methods, making it particularly suitable for communication between sensor network nodes with constrained power consumption.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 5 D2D Authentication Process for Cellular IoT Network
### The Standard EPS-AKA Authentication and Key Agreement Protocol  
Various types of authentication protocols are used for IoT networks, but the most common security framework considered for a C-IoT is the evolve packet system authentication and key agreement (EPS-AKA) protocol. The EPS-AKA is based on the cryptography challenge-response protocol, which is similar to the UMTS-AKA protocol, except that it involves the generation of a set identifier (eKSI) and requires a separate indicator process for user equipment (UE) validation [28; 29]. Different secret keys are generated in the LTE framework for RRC communication, non-access stratum communication, and user equipment. The mechanism of the EPS-AKA protocol is described in Figure 3, and the standard steps for this protocol are as follows:  
* The UE sends service requests to the core network.
* In order to generate the EPS authentication vectors, an MME request is sent to HSS and dispensed back to MME.
* MME and HSS mutually authenticate each other and share keys between them.
* The UE dispenses secret data to the serving networks by combining the session key with the integrity key.
## 4 Methodology
### The Benign and Malicious OpenFlow Traffic Collection  
The OpenFlow traffic is collected by utilizing simulated and physical SDN environments. The physical environment comprises five hosts, one PICA8 switch as an OpenFlow switch, one Floodlight 1.2v Master as an SDN controller, and OpenFlow protocol v1.3. The adopted physical environment is limited by the network topology and scale. Therefore, different SDN simulated environments were created using the Mininet tool. By using the simulated environment, we created different network topologies and scales. Table 1 presents the simulation SDN network configurations, which were obtained from the Mininet examples.  
The normal OpenFlow traffic was collected using both simulated and physical SDN environments by utilizing various traffic generation tool that can mimic the load of real-life network traffic and behavior. First, the Cisco TRex realistic traffic generator was employed to mimic the internet traffic via generating concurrent stateless and stateful traffic. It provides the capability to generate L4-L7 traffic via smart replay traffic templates with different rate up to 200 gbps. Second, to generate unidirectional traffic flows for a group of protocols, we utilized the distributed internet traffic generator (D-ITG), which can generate TCP, UDP, Telnet, VoIP, DCCP, DNS, ICMP, and SCTP traffic. Third, the OSTINATO tool was used to generate different traffic flows with different configurations for a variety of protocols such as VLANs, network news transfer protocol (NNTP), real-time streaming protocol (RSTP), internet group management protocol (IGMP), and multicast listener discover (MLD) protocol. As shown in Table 2, the total duration of the collected OpenFlow traffic from the physical environment is equal to 137 h with 250 GB total traffic size and the duration of the simulated OpenFlow traffic is 100 h with 143 GB total traffic size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Default Value** \\ \hline \(N^{c}\) & Number of SDN Controllers & 1 \\ \(N^{s}\) & Number of OpenFlow Switches & 10–200 \\ \(N^{h}\) & Number of Connected Hosts & 50–300 \\ \(N^{t}\) & Network Topology & Star, Ring, Tree, Mesh \\ \hline \hline \end{tabular}
\end{table}
Table 1: SDN simulation environment configurations and specifications.  
Figure 2: The utilization of OpenFlow channel bandwidth under UDP attack.  
To generate malicious OpenFlow traffic, we used Hping3 and LOIC (Low Orbit Ion Cannon) in physical and simulated SDN environments. Using these tools, we were able to launch 31 saturation attacks that cover TCP-SARFU, TCP-SYN, ICMP, UDP, IP-Spoofing, and their combinations. As depicted in Table 2, the total size of the physical malicious OpenFlow traffic is 50 GB with a 30 min duration for each attack session, where the simulated malicious traffic has a total size of about 100 GB and the duration of the attack session is 20 min.