esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 4 Experiments
### Dataset  
The multi-modal dataset for the CNN-SA network was collected using a binocular vision measurement system, a laser tracking system, and a depth camera system, focusing on a wing model made of carbon fiber material. The dataset comprises low-precision point clouds, high-precision control points, RGB images, depth images, and high-precision point clouds. Low-precision point clouds provide approximate shape and structural information about the target object for the network, whereas high-precision control points provide highly accurate positional information. The RGB and depth images provide rich visual information about the target object, and high-precision point clouds serve as the target output for the network, providing more accurate 3D shape information.  
As shown in Figure 6, we performed a series of pre-processing steps to prepare the dataset for network training. First, the RGB and depth images captured using the depth camera system were masked to only extract the parts containing the object, thus reducing the impact of background noise on the network. Second, the low-precision and high-precision point clouds underwent the same denoising and downsampling processes. They were then paired with high-precision control points, RGB images, and depth images to function as network inputs. Finally, the processed high-precision point clouds were used as the target output for the network, enabling an accurate reconstruction of the low-precision point clouds through training.  
The dataset used for the CNN-SA network training was large, consisting of 5000 sets of samples. Each sample included low-precision point clouds, high-precision control points, RGB images, depth images, and high-precision point clouds. Measurements were conducted on different regions and angles of the same target, resulting in diverse datasets.
## 4 Simulation Results and Analysis
### Key Space  
The proposed encryption algorithm utilizes a total of three different keys, which significantly increases the key space and makes it more difficult for unauthorized parties to decipher the encrypted information. The use of three keys in the proposed algorithm not only satisfies the requirements but also surpasses the standards set by IEEE [28], making it an ideal and secure option for encryption. These keys include K, which is the secret key for hill cipher, as well as \(K_{2}\) and \(K_{3}\), generated from the modified sigmoid logistic function. According to the guidelines set forth by the IEEE [28], a key space of at least \(2^{100}\) is necessary for strong encryption. The Hill cipher (K) comprises four different positions due to the 4 \(\times\) 4 square matrix, each of which has 254 possible values (ranging from 0 to 255). This results in a total of \(2^{254}\) combinations or key spaces. For the case of the Sigmoid logistic map (\(K_{2}\) and \(K_{3}\)), the value of the key length varies depending on the size of the image. When using an image of 256 \(\times\) 256, the resulting key length is 65,536. The keys consist of only two positions; each position can have two possible values (0 and 1 when converted to binary numbers), leading to a total combination of \(2^{65536}\). The same approach can be used for the image of sizes 128 \(\times\) 128, 512 \(\times\) 512, and 32 \(\times\) 32 to obtain the key length and number of key spaces. Additionally, as shown in Table 1, the proposed algorithm compares favorably to other studies of a similar nature in terms of averages, further demonstrating its effectiveness as an encryption method. Overall, the use of multiple keys in the proposed algorithm effectively strengthens the security of the encryption and makes it a reliable choice for protecting sensitive information. Equation (13) is used to find the number of key spaces for three keys in general.  
\[keys=\left(10^{15\times 3}\right)\ \approx\ 2^{149} \tag{13}\]
## 2 Data and Methods
### Data  
ROP is the result of engineering factors and formation factors. To make our intelligent ROP prediction model take into account engineering and formation factors, we collected bit data, mud logging data, and well logging data from six wells in the oil field and then merged the data existing in different documents into a data table by depth. The table contains a total of 22,317 pieces of data, and each piece contains 15 features, as shown in Table 2.
## 6 Experimental Evaluation
### Dataset  
The publicly available San Francisco Cabspotting dataset is used in this study. It includes the GPS traces of 536 taxis collected over a period of 23 days. Each trace includes the latitude, longitude, timestamp, and the occupancy status of a taxi. The San Francisco Cabspotting dataset is a commonly used, publicly available dataset. It therefore serves as a benchmark for several anonymization algorithms; see [1; 2; 4; 6; 8]. The dataset is processed to convert the raw GPS data into heat maps. All data used in this study is publicly available for replication [18].  
To simulate the training and reidentification phases effectively, it is necessary to partition the mobility datasets. The splitting process involves dividing the trajectories of each taxi randomly to ensure variability and avoid bias into a 50% training and a 50% reidentification dataset.  
As in [2], we divide the geographical area into an 8 \(\times\) 8 grid of squares of the metro area of San Francisco, each bin roughly 800 m in height and length. Each cell in the heat map shows how often a single taxi visits a specific location; see Figure 1.  
The coordinates used as area are latitude: 37.60-37.85, longitude: \(-\)122.50--122.35.
## 4 Case Study: AQM Implementation
### Verification and Assessment for the CNS
#### 4.3.2 The RVQA Process at the Agent Social Level of the CNS  
Figure 10 shows the RVQA implementation to check the agent data confidentiality disclosure status at the social level. The agent social activities include the stage when the agent account becomes active, the agent establishes connection to the social network, and finally, when the agent account becomes inactive.  
Algorithm 2 shows the RVQA data confidentiality violations detection for the agent social level of the CNS that checks for social information disclosures. As defined by the checking rules for the social level [51], the agent account active status determines the agent critical states, and the agent's connection to online social networking determines the agent's social disclosure status. The agent's  
Figure 10: The RVQA implementation at agent social level of the CNS.  
dynamically established or not established connections to online social networking determine whether the social information are \"disclosed\" or \"protected,\" respectively.  
```
Input: Agent Index: x, Current Time: t
```  
Output:  noDisclosedSocialInformation, noNotDisclosedSocialInformation, noDisclosures  
1 begin  
2 if AccountActive(x,t) then  
3 Critical(x,t) \(\rightarrow\) true  
4 if SocialNetworkingConnected(x,t) then  
5 SocialInformationDisclosed(x,t)  
6 increase(noDisclosedSocialInformation)  
7 increase(x,noDisclosures)  
8 end  
9 if SocialNetworkingNotConnected(x,t) then  
10 SocialInformationNotDisclosed(x,t)  
11 increase(noNotDisclosedSocialInformation)  
12 end  
13 end  
14 end  
15 return noDisclosedSocialInformation, noNotDisclosedSocialInformation, noDisclosures  
**Algorithm 2** Identity and location confidentiality disclosures detection.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 3 Proposed Method
### Comparison with Existing Methods
The proposed method with other algorithms of interest is realized using the sample matrix inversion technique. The main computational costs of processing (including anti-jamming and tracking) a \(K\)-snapshot space-time data matrix, i.e., \(\mathbf{X}=[\mathbf{x}(t-(K-1)T_{\mathbf{s}}),\ldots,\mathbf{x}(t)]\in \mathbb{C}^{N\times K}\), are evaluated, where \(T_{\mathbf{s}}\) is the sampling interval. Some variables are defined as follows: \(M\) denotes the number of satellite signals to be tracked, \(\mathbf{d}=[d(t-(K-1)T_{\mathbf{s}}),\ldots,d(t)]\in\mathbb{C}^{1\times K}\) denotes the reference signal vector, \(\mathbf{l}_{\bullet}=[l_{\bullet}(t-(K-1)T_{\mathbf{s}}),\ldots,l_{\bullet}(t )]\in\mathbb{C}^{1\times K}\) denotes the local replica signal vector corresponding to the early, prompt, or late correlator, and \(r_{\bullet}\) denotes the corresponding correlator output. The computational complexity for key steps of different methods are summarized in Table 1. It can be seen from the table that the complexity of the methods has the following relationship: PI < MMSE < MVDR-like \(\approx\) Proposed. In general, the number of snapshots is much larger than the number of antennas, i.e., \(K\gg N\), so the terms containing \(K\) dominate the computational complexity. Therefore, the computational complexity of the proposed method is two to \(N\) times that of the PI algorithm, depending on \(M\), the number of satellites to be tracked in the receiver. Despite the higher computational complexity, the proposed method can achieve better signal tracking precision and measurement accuracy, as shown in the following simulations.
## 3 Mathematical Model
### Clustering  
Grouping the products into clusters is a problem that has been studied in the past years [46]. Sometimes, some parts are widely used among products, while in other cases, very few parts require a high setup time \(t_{set,k}\). Combinations of the two can also be found.  
Similar parts can be grouped into \"families\". As an example, screws with different thread lengths but the same diameter can be grouped within the same family. In mathematical terms, the membership of a part \(k\) to a family \(F\) is defined via the integer \(f_{k}\):  
\[f_{k}=\begin{cases}1&\text{if part $k$ is in family $F$}\\ 0&\text{otherwise}\end{cases} \tag{8}\]  
As a result, it is possible to evaluate the mean setup time of each family \(t_{F}\) as follows:  
\[t_{F}=\frac{\sum\limits_{k=1}^{N_{c}}t_{set,k}f_{k}}{\sum\limits_{k=1}^{N_{c}} f_{k}} \tag{9}\]  
Different families are described by different mean setup times (\(t_{F}\)). As a result, some families have a greater impact on the overall setup time with respect to others. Due to the Pareto principle [47], \"roughly 80% of consequences come from 20% of the causes\", which translates to the fact that only a few families are responsible for the majority of the setup time.  
By following the Pareto principle, it is possible to cluster the parts into the respective families. The steps of the clustering process can be summarized as follows (Figure 1):  
1. Parts are divided into families (i.e., the parts that are used by a specific machine can be grouped into a family).
2. The families are listed in descending order based on \(t_{F}\) (i.e., at the top of the list are placed the families with higher \(t_{F}\)).
3. The cumulative sum of \(t_{F}\) is performed.
4. Once a certain threshold is reached (e.g., 80% for the Pareto principle), the list of the summed families is retrieved. These families are used for the multi-clustering process.
5. Starting from the first family, the products are divided based on the different parts of the families (thanks to the value of \(m_{ik}\), Equation (1)) to form clusters. Starting from the second family, products with the same part may be placed in different clusters based the previous clustering (Figure 2).  
From Point 5 it is possible to apply TSP to calculate the optimal sequence of products. To do so, the appropriate constraints have to be applied (Equation (7)).
## 6 Linear and Non-Linear Insulin Infusion Control Schemes
### H\(\infty\) Control  
When using linear time-invariant (LTI) models, H\(\infty\) is a practical controller synthesis approach. There is an effective trade-off between the strength of control action, and the tracking error when considering a low-order robust controller characterized by the H\(\infty\). This tradeoff is known as the mixed-sensitivity problem, and the optimal solution in terms of the lowest gain between the input disturbance and the output errors is achieved by this optimal control procedure. The glucose-insulin response obtained by the simulations show that it became stabilized in a reasonable time interval [140]. A Bergman model (see Sections 6.1 and 6.2 for details) along with the H-\(\infty\) control is studied. A self-contained route is used to design H-\(\infty\) control as shown in the Figure 12.  
The symbols given in Figure 12 are: \(P(s)\): transfer matrix of the plant, K(\(s\)): transfer matrix of controller, and \(\Delta\) denotes the system uncertainty model. The plants transfer matrix is related to the input and output matrices, respectively. For Y > 0 an internally stabilized controller exists such that \(\|G_{\mathrm{ZW}}\|<\) Y, \(G_{\mathrm{zw}}\): closed loop transfer matrix from \(w\) to \(z\), Y : is the rate of the pancreatic \(\beta\)-cells release of insulin, \(G_{\mathrm{zw}}\) is given by: \(G_{\mathrm{zw}}\) = F(\(P\),K), Controller K(\(s\)) was found under the conditions given in [123]. It is simulated in the MATLAB to obtain the desired responses. It was observed that  
Figure 12: Schematic overview of the H-\(\infty\) controller (adopted from [125]).  
BG levels were stabilized at basal level of 81 mg/dL using H-\(\infty\) controller. Also, even in the presence of disturbances, it falls to satisfactory level of 67 mg/dL [140]. The simulations results validate the effectiveness of the H-\(\infty\) controller.
# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
## 4 Experimental Results
### False Positives Reduction  
For a given candidate nodule set, we classified positive and false positive nodules by using 3D-ResNet. The results of false positive reduction are shown in Figure 2. With the help of the test set amplification, we obtained an average detection sensitivity of 78% at seven operating points. We found that 3D-ResNet test data augmentation (TDA) significantly improved the network performance. The average detection intensity increased from 72.1% to 78% through the test set amplification. The detection sensitivity of the nodule detection phase was 0.952, and our CAD system achieved 86.5% and 93.3% detection sensitivity at 1 FPs/scan and 4 FPs/scan, respectively.  
As a contrast, we implemented the multifaceted 2D ConvNets of DIAG CONVNET [12] in the false positives detection stage. For each candidate, nine \(50\times 50\times 50\) mm\({}^{3}\) sections were extracted from \(64\times 64\times 64\) mm\({}^{3}\) 3D samples. Each section corresponded to different planes of 3D samples and via 2D ConvNets. Finally, we used a late-fusion method to merge them. The FROC curves of our 3D-ResNet and the late-fusion 9 views network are shown in Figure 2b. 3D-ResNet's CPM = 0.78 was higher than late-fusion 9 views' (CPM = 0.729). At the same time, the detection intensity of 3D-ResNet was also higher than late-fusion 9 views at 1 FPs/scan and 4 FPs/scan.  
Figure 3 gives the candidate 3D samples with positive probability from 0.1 to 0.9 after removal of the false positive nodes. It is obvious from the figure that the more pulmonary nodules the candidate node had, the greater the positive probability.  
Figure 2: (**a**) The line chart of 3D-ResNet test data augmentation (TDA) and free-response operating characteristic (FROC) of noTDA in the false positive reduction stage; (**b**) 3D-ResNet and late-fusion 9 views. The space between the dotted red/blue lines is the 0.95 confidence interval of the data of each method, respectively.
## 4 Simulation Results  
### Tunnel Vertical Displacement  
Figure 7 illustrates the vertical displacement distribution of the existing tunnel vault during the foundation pit excavation, showing that the excavation process disturbs the foundation pit, leading to vertical settlement in the tunnels nearby. A notable increase in the rate of vertical settlement is observed within a specific range of the foundation pit, delineating a significant zone of vertical displacement disturbance.  
Vertical displacement is highly sensitive to disturbances from excavation, even with shallow soil excavation. Throughout the excavation, maximum displacement values are primarily concentrated in the central area of the pit. As the excavation depth increases, the increase in vertical displacement is constrained, peaking at 8.60 mm. Significant disturbances to the tunnel's vertical displacement occur at a depth of 6 m during the excavation of the first soil layer. This disturbance results from the cantilevered distribution of the retaining structure's displacement, with the maximum displacement occurring at the top. The loose soil behind the retaining structure further contributes to the tunnel's vertical displacement; therefore, it is crucial to enhance monitoring and early warning systems for vertical displacement in shallow-buried tunnels during the early stages of pit construction.  
Figure 6: Horizontal displacement of tunnel haunch.  
Figure 7: Vertical displacement distribution of tunnel vault.  
Although the excavation depths of the first three soil layers do not exceed the tunnel's arch bottom, causing no significant changes in tunnel vertical displacement, once the excavation depth surpasses the arch bottom, the vertical displacement notably increases.
## 5 Conclusions  
Regarding the Rrs validation, the results have shown that the best ACs tested in this work were C2 XC and Polymer. The Polymer program estimated most accurately the water-leaving radiance of the visible region, while C2 XC estimated more accurately the Red-edge region and C2X NIR band. However, on the one hand, it should be noted that C2 XC and C2X remove more data, and on the other hand, they are easier to use than the Polymer program for non-professional users due to their integration in the SNAP program.  
Despite the difficulties to obtain the same algorithms as in the previous study after validation with estimated Rrs, the results were coincident for three algorithms: TSS below 20 mg/L, Chl_a below 5 mg/m\({}^{3}\), and PC. Even the validation results for the PC and TSS below 20 mg/L improved. With regard to the bands used, the best validation results for SDD and PC were found using the Rrs of the ACs that obtained the best validation results for the bands used by the specific algorithm.  
The best results after the algorithm validation were obtained using the Rrs of the Polymer and C2 XC; the ACs with the best validation results correlate to the in situ and remote sensing reflectance data for each band.  
From the results obtained, the best water quality algorithm and the most adequate AC can be chosen to carry out the spatial and temporal monitoring of a water body, either for its management or for the performance of applied remote sensing studies using S2-MSI imagery.  
Conceptualization, X.S.-P. and J.D.; methodology, X.S.-P., E.P.U., A.R.-V. and J.M.S.; validation, X.S.-P.; data curation, E.P.U., J.M.S. and A.R.-V.; writing--original draft preparation, X.S.-P.; writing--review and editing, E.P.U., A.R.-V., J.M.S., J.D., E.V. and J.M.; supervision, J.D. and E.V.; funding acquisition, J.M. and X.S.-P. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the ESAQS project, GVPROMETEO2016-132 from Generalitat Valenciana and the European Regional Development Fund; the project RTI2018-098651-BC51 (FLEX L3-L4--Advanced Products L3 and L4 for the FLEX-S3 mission) from the European Union--ERDF and the Ministry of Science and Innovation and the State Research Agency of Spain, and the project SEQUARMON (Sentinel quality reservoirs monitoring), APOSTD/2020/134 by Generalitat Valenciana and the European Social Fund postdoc research grant to X.S.-P.  
The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.  
The authors are very grateful to [PERSON] for her efforts in the laboratory analyses and to [PERSON], [PERSON], and [PERSON] for their efforts in the field campaigns. The authors would also like to thank the anonymous reviewers for their constructive comments and suggestions.  
The authors declare no conflict of interest.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 4 Methodology
### PQC-TLS Using NTRUs  
Since NTRUs are favored for IoT because of their security and efficiency, they have been applied to SSI-PQM in the current study. IoT devices are usually characterized by limited processing power, small storage, and low energy consumption. Thus, NTRUs' lightweight encryption capability is suitable for an IoT system. NTRUs are secure against quantum attacks, protecting against the vulnerabilities common in traditional methods,  
Figure 2: Description of the function of SSI-PQM.  
namely, RSA and ECC. NTRU encryption and decryption processes are faster than many public key methods, which makes them ideal for resource-limited devices [26]. They offer high security with smaller keys, saving valuable storage space. NTRUs consume less power, which is important for battery-dependent IoT devices. Their adaptability to various protocols supports a wide range of applications of IoT. In summary, NTRUs stand out as an effective solution for IoT security, addressing the challenges of quantum computing and resource constraints [14].
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attack Detection Approaches on CC
#### 5.3.1 Signature-Based Detection
Further, the NSL-KDD dataset is used for training and testing purposes. After receiving the notification from Cloud-IDS, the CSP will alert the user and keep a log list of the malicious IP addresses. This approach effectively manages huge data packets, analyses them, and generates reports while detecting anomalies and misuse. The E-CARGO model [111] is used to present a collaborative intrusion detection architecture. The components of an IDS are described by the common intrusion detection frame (CIDF). They also create and clearly outline the agent's behaviors and their relationships. The experiments show that their proposed technique can detect slow-scanning and DDoS attacks, which validates their model. The authors planned to study combining cooperative computing with IDS to deal with real-world problems in future work. Tables 8 and 9 summarize the existing DDoS detection approaches on CC using signature-based detection techniques.