esa-sceva/satcom-chunk-collection huggingface dataset:
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 3 One-Class Classifiers
### Local Outlier Factor  
LOF estimates a score, named Outlier Factor, which reflects the level of abnormality of each observation from a dataset [27]. It works based on the idea of a local density. The \(k\)-Nearest Neighbours algorithm is applied in the data, and each data instance is given a locality, which is used to estimate the clusters' density. First, the number of neighbours must be decided using a hyperparameter \(k\). Choosing an appropriate value for \(k\) is very important, since a small \(k\) has a tighter focus, but has more errors when dealing with noisy data. On the other hand, a large \(k\) can include all the instances that are outliers.  
The algorithm uses a function called \(k\)-_distance_, which is the distance of an instance to its \(k\)-th neighbour. This \(k\)-_distance_ is used for the calculation of the reachability distance (RD), which is the largest value out of the distance between two instances and the \(k\)-_distance_ of the second instance,as showed on Equation (2). If an instance \(a\) is in the \(k\) neighbours of \(b\), \(RD(a,b)\) is equal to the \(k\)-_distance_ of \(b\), otherwise it will be the real distance between \(a\) and \(b\).  
\[RD(a,b)=max\{k\_distance(b),distance(a,b)\} \tag{2}\]  
This RD function is used to calculate another function, the local reachability density (LRD). To calculate the LRD of an instance \(a\), the RD of \(a\) to all its \(k\) neighbours (\(N_{k}(a)\)) is calculated at first, followed by the average of the resulting values as presented on Equation (3).  
\[LRD(a)=\left(\begin{array}{c}N_{k}(a)\end{array}\frac{RD(a,n)}{k}\right)^{-1} \tag{3}\]  
Next, the LRD of each point in the dataset is calculated. Each point compares its LRD to their \(k\) neighbours'. The LOF is the average ratio of the LRD of a point \(a\) to the LRDs of its closest points, \(k\) neighbours. As shown on Equation (4), if the LOF is greater than 1, the LRD of a point \(a\) is on average greater than the LRD of its \(k\) neighbours.  
\[LOF(a,N_{k}(a))=\begin{cases}\approx 1&\text{Similar density as neighbours}\\ >1&\text{Lower density than neighbours (Outlier)}\\ <1&\text{Higher density than neighbours (Inlier)}\end{cases} \tag{4}\]
# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 4 Authentication  
Authentication is critical to ensure that the sensors, base station, and cluster heads are tested and authorized prior to providing or revealing information. These messages and information should be coming from the correct original source [16]. Several authentication procedures are outlined in Table 2.  
Some authentication issues would be based on static and dynamic node deployment. In general, the authentication process starts with the mobile node to verify which of the sensor nodes are on the same body. After the authentication, the mobile node records the sensor data from the node. Until that time, all the data will be ignored [20]. In the personal health system, users will be able to connect to and read data from these sensors by using their cell phone and expecting the system to operate flawlessly. However, these sensors might not only be connected to the user's personal cell phones, but also unauthorized devices [20].  
We consider a motivational example of two users living in the same house using identical sensor devices. User 1 should be able to put on either device and have the cell phone recognize which device is attached to the user. This will automatically create the phone-device association without an explicit paring step. For this to happen, there needs to be two problems addressed first. User 1's device must be able to determine which sensors are attached to user 1. They must ignore other sensors that may be close but not attached to user 1. The next step would be to have the phone and sensor device agree on a shared encryption key to ensure their communications are secure. The cell phone would analyze the data coming from the sensors to verify the wearer by some biometric measure.  
We can compose a solution by including an accelerometer sensor to every device, in addition to the primary sensors. Accelerometers are inexpensive additions, and can be used for biometric authentication (see [24; 25]). First, the user wears the sensor on their body, and then turns it on. Next, the sensor will detect that it was utilized, and then it will transmit the accelerometer signal. Finally, the mobile receives the transmission, authenticates the device, and then connects with the sensor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Procedure** & **Description** \\ \hline One-Way & A single message is sent from the sender to the receiver node. \\ \hline Two-way & A communication link between two parties is certified. \\ \hline Three-way & When clock synchronization fails, a third \\  & message is sent from the sender to the receiver \\ \hline Implicit & Authentication is performed as a subset of another process. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Available authentication procedures.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results
### Performance Evaluation
#### 4.3.2 Experiments with the KSC Data Set  
The second experiment was based on the KSC data set. It was again repeated 10 times over the randomly split training and test data, where 1% per class were selected as training samples. Table 2 reports the average classification results of each method. Table 2 shows that SPCM and LCEM obtain the best and second results of 94.51% and 93.83%, respectively. These results prove again that correntropy matrix-based methods are fit for HSI classification. We can also find that LCMR, LCEM, and SPCM perform better than other compared methods. This phenomenon also verified the effectiveness of the spectral-spatial correlation learning.  
Figure 8 shows the full classification maps, where only 1% of samples in each class are used for comparison. Figure 8 shows that in the classification results of SVM, the SCMK and CEGCN methods lose too much image structure. PRNet and LCEM produce severe salt-and-pepper noise. Although LCMR is very obvious in suppressing salt-and-pepper noise, it also has a oversmooth phenomenon. The proposed SPCM can effectively solve this problem. SPCM emphasizes the correlation of the spectrum and also makes use of the spectral and spatial information. As can be seen from Figure 8 and Table 2, compared with other methods, a good trade-off is achieved with SPCM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline No. & LCMR & SCMK & CEGCN & RPNet & LCEM & SPCM \\ \hline
1 & 75.54 & 88.09 & **96.93** & 85.58 & 87.69 & 88.41 \\
2 & 75.48 & 83.91 & **97.06** & 95.79 & 95.13 & 93.15 \\
3 & 64.41 & 64.50 & **85.72** & 95.99 & 71.82 & 67.72 \\
4 & **95.58** & 82.96 & 67.12 & 45.25 & 85.98 & 87.12 \\
5 & 92.47 & 96.53 & **100.00** & 12.05 & 91.74 & 99.78 \\
6 & 77.50 & 63.02 & 65.61 & 42.85 & 93.32 & **95.53** \\
7 & 88.83 & 84.62 & 95.99 & 79.30 & 92.81 & **97.45** \\
8 & 71.77 & **77.16** & 53.64 & 75.86 & 63.62 & 75.75 \\
9 & **91.70** & 70.73 & 65.09 & 24.95 & 48.17 & 49.70 \\ \hline OA & 77.61 & 80.63 & 85.13 & 73.96 & 88.04 & **90.97** \\ AA & 81.47 & 79.06 & 76.75 & 50.02 & 81.14 & **83.52** \\ Kappa & 71.48 & 74.63 & 79.89 & 62.78 & 84.20 & **86.97** \\ \hline \end{tabular}
\end{table}
Table 1: Classification results obtained by LCMR, SCMK, CEGCN, RPNet, LCEM, and SPCM using 0.1% of the available labelled data for training with the Pavia University data set. The best results are expressed in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline No. & LCMR & SCMK & CEGCN & RPNet & LCEM & SPCM \\ \hline
1 & 92.69 & 86.97 & **99.53** & 85.13 & 97.33 & 94.65 \\
2 & **91.51** & 73.79 & 70.46 & 63.07 & 80.88 & 88.46 \\
3 & 92
## 2 Related Work  
[PERSON] et al. [7] introduced enhancements to the ElGamal signature system, presenting a novel blind signature technique grounded in number theory operations and modular arithmetic operations. This approach offers enhanced participant anonymity by ensuring that multiple signatures of the same message yield distinct results.  
The authors in [8] proposed the Shortened Complex Digital Signature Algorithm (SCDSA), leveraging short complex numbers for efficient signature and verification operations. SCDSA targets user-centric IoT devices [3], aiming to reduce communication and computational overhead on networks while ensuring secure communication.  
In [9], [PERSON] and [PERSON] designed the Lightweight Digital Signature Algorithm (LWDSA) based on ECDSA [10] and cryptographic hash functions tailored for Wireless Sensor Networks (WSNs). However, the computational efficiency of the signature generation and verification processes remains unspecified, and validating the scheme's efficacy in large-scale, dynamic WSN networks presents significant challenges.  
The authors of [11] developed the Enhanced Digital Signature Algorithm (EDSA) to safeguard data integrity and secure access in cloud storage environments.  
The authors of [12] introduced an attribute-based proxy signature scheme on lattices that is resistant to quantum attacks. This scheme combines attributes and proxy features, offering fine-grained access control. However, lattice-based methods often involve large matrix operations and storage, which can limit practical usability.  
[PERSON] et al. [13] proposed a mixed private- and consortium-based blockchain system for secure storage of medical cyber-physical data. Their approach includes a blockchain-based threshold signature system for joint consultation, although it lacks support for multiparty signatures.  
The authors of [14] introduce a solution that integrates functional signatures and multiparty ECDSA signatures to establish a multiparty functional signature tailored for private blockchains. This approach ensures verification of each transaction component, and in cases where the aggregate signature of the entire transaction cannot be verified, it identifies the specific part of the transaction that failed authentication without rejecting the entire transaction. However, its applicability remains limited to private blockchains.  
In [15], the authors presented two practical protocols for multiparty EdDSA signatures, designed for both semi-honest and malicious settings. These protocols streamline operations by securely maintaining a global state, thereby eliminating the need for distributed hashing in EdDSA-based blockchains. Additionally, they enhanced the malicious protocol to withstand DoS attacks. Nonetheless, these protocols are specifically tailored for blockchain environments.  
The aforementioned works suffer from many problems, such as high computational complexity, limited work environment, large data size, etc. Therefore, the proposed work aims to address these shortcomings.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
###### Abstract
They then carried out static tests after high temperature exposure, comparing and analyzing the residual load bearing capacity of prestressed concrete beams, and proposed a simplified calculation method for the residual load bearing capacity of prestressed concrete flexural members after high temperature exposure. [PERSON] et al. [10] designed prestressed concrete beams with different cross-sectional forms, including T-sections, and carried out fire resistance tests under simulated fire heating conditions. It was found that the cross-sectional form has an important influence on the fire resistance performance, and for the structure's safety, the fire resistance design needs to be carried out for different cross-sectional forms of prestressed concrete beams. [PERSON] et al. [11] found that the fire resistance design code for concrete beams was not applicable to the fire resistance design of prestressed concrete beams by carrying out a full-scale simulation analysis of eight prestressed concrete beams with different prestressing levels and that a method for assessing the fire resistance of prestressed concrete beams needed to be established. [PERSON] et al. [12] conducted fire resistance tests on prestressed concrete beams under different prestress levels and concluded that the prestress level affects the failure mode, and thus exhibits different fire resistance properties. Few studies have been carried out on the influencing parameters in the fire resistance of prestressed concrete structures, while the form of the cross-section has an important influence on the fire resistance. The T-section is widely used in existing prestressed concrete beams, and attention needs to be paid to the fire resistance of prestressed concrete T-beams. Therefore, ABAQUS finite element software is used to investigate the fire resistance of prestressed concrete T-beams under different conditions. This is to investigate the influence of fire mode, concrete strength, flange plate thickness, ratio of prestressing bar, concrete protective layer thickness, and load rate for the fire resistance, and to propose a calculation formula applicable to the fire resistance of prestressed concrete T-beams, which will provide a reference for subsequent fire resistance design and post-fire repair and strengthening.
## 5 Conclusions  
In summary, this article proposes an algorithm based on component prior knowledge and an improved version of YOLOv8, which achieves the identification of parabolic antennas on satellites. With reference to the specified imaging geometry and standard imaging techniques, the CPICs of the parabolic antenna in the THz band were analyzed, and the corresponding dataset was established. By effectively using the special CPICs, we transformed this component identification problem into three different types of object detection problems. Subsequently, by incorporating Dyhead and WIoU, the multi-scale target challenges and sample imbalance in the satellite ISAR images were addressed. Trained on simulated data, the Improved-YOLOv8 model achieved 0.935 and 0.52 in AP50 and AP50-95, respectively, surpassing the baseline YOLOv8 model. Compared with five other detection algorithms, YOLOv3, YOLOv5, YOLOv6, YOLOv8, and YOLOv11, our method notably improved about by 0.33, 0.10, 0.21, 0.19, and 0.20 in mAP50 and improved by about 0.16, 0.04, 0.02, 0.08, and 0.11 in mAP50-95. And for the anechoic chamber measurement data, the effectiveness and reliability of the proposed method in identifying parabolic antennas were also verified.  
Recognizing the parabolic antenna, which is a major component of satellite communication systems on satellites in space, possesses immense application potential. It plays a significant role in ensuring the success of satellite missions and maintaining satellite's safety and repair. In the future, we will consider this method to apply to other types of satellites as well as the detection of parabolic antennas and recognition under low signal-to-noise ratios. And the discussion about the improvement of the backbone of the network and different training strategies will be taken into account.  
Theoretical study, experiments, and writing L.Y; experimental environment and software W.L. and R.W.; review and editing, H.W., Y.Z. and B.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62301573.  
Data Availability Statement: Not applicable.
## 5 Engineering Validation
### Assessment of Cable Tension  
The identified time-varying tension can be used to assess the service performance of cables. The two indices are introduced to evaluate the operating condition of the measured cable, the 40% ultimate bearing tension, and the initial cable tension. The 40% ultimate bearing tension is the maximum allowable tension in the design stage to ensure  
Figure 16: (**a**) TFR spectrogram of measured acceleration responses and (**b**) identified cable tension using 15 th frequency.  
Figure 17: Comparison of cable tension to (**a**) stress and (**b**) GPS displacement.  
a safety coefficient within 2.5. The initial cable tension was measured when the bridge was completed in 2008. It is found in Figure 18a that the measured tension in the field experiment is between the middle of these two indices, 5732 kN and 4284 kN. There is still a large allowable range of cable tension under such a loading case.  
The identified cable tension can also be used to reflect the deterioration or abnormal conditions of cables. Firstly, the time-varying tension is identified at 12 o'clock on March 31 from 2017 to 2019, as shown in Figure 18b. The identified tensions in 2017, 2018, and 2019 are marked with orange, pink and blue colors, respectively. The average values of these three years are all about 4500 \(\pm\) 150 kN. The cable tension is mainly controlled by the moving loads. The operating condition of the cable is pretty healthy, and no obvious deterioration is found. Then, an abnormal condition is chosen to show the applications of the proposed algorithm during a strong wind in the summer of 2018, as marked with purple color in Figure 18b. The maximum wind speed at the height of the tower is more than 50 m/s during this server tropical storm. The cable tension increases to about 5100 \(\pm\) 50 kN, much greater than the initial cable tension of 4284 kN. Fortunately, the cable tension remains within the designed 40% ultimate bearing tension of 5732 kN, showing that the service performance of the cable is under safety conditions.
# Multiscale Sieve for Smart Prime Generation and Application in Info-Security, IoT and Blockchain
## 4 Multiscale Prime Candidate Sets  
For the algorithm to work properly and to employ further optimization, it is essential to analyze and understand how to handle sets. From [20], we can understand the relations between \(\Lambda\), \(\Psi\), and \(\Gamma\) given in terms of others and in terms of themselves at different scales.  
Starting from  
\[\Lambda_{i}=p_{i}\Gamma_{i-1},\quad\forall i\in\mathbb{N} \tag{13}\]  
and imposing a substitution through the following expression  
\[\Gamma_{i}=p_{i}\Gamma_{i-1}-\Gamma_{i-1}=(p_{i}-1)\Gamma_{i-1},\quad\forall i \in\mathbb{N} \tag{14}\]  
we can generalize the relation between \(\prod p_{i}\) and the classes \(\Lambda\).  
Figure 1: Multiscale prime candidate set generated up to the third level, where, in dark grey, we have marked the eliminated composite numbers, while, in light grey, we have marked the composite numbers not eliminated at the current level as they are useful for the generation of subsequent sets.  
Now, let us show the evaluation step by step.  
First, substituting the expression of \(\Gamma_{i}\) into \(\Lambda_{i}\),  
\[\Lambda_{i}=p_{i}\Gamma_{i-1} \tag{15}\] \[=p_{i}((p_{i-1}-1)\Gamma_{i-2})\] (16) \[=p_{i}(p_{i-1}-1)\Gamma_{i-2} \tag{17}\]  
Next, continue substituting iteratively:  
\[\Lambda_{i}=p_{i}(p_{i-1}-1)((p_{i-2}-1)\Gamma_{i-3}) \tag{18}\] \[=p_{i}(p_{i-1}-1)(p_{i-2}-1)\Gamma_{i-3} \tag{19}\]  
After \(n\) steps, the generalized form is  
\[\Lambda_{i}=p_{i}(p_{i-1}-1)(p_{i-2}-1)\cdots(p_{1}-1)\Gamma_{0} \tag{20}\]  
Assuming \(\Gamma_{0}=1\) for simplicity, we have  
\[\Lambda_{i}=p_{i}(p_{i-1}-1)(p_{i-2}-1)\cdots(p_{1}-1) \tag{21}\]  
Rewriting this in terms of the \(m\) level,  
\[\Lambda_{i}=p_{m}(p_{m-1}-1)(p_{m-2}-1)\cdots(p_{1}-1) \tag{22}\]  
Then, we obtain the following result.  
Theorem 1: \(\Lambda_{m}\) _indicates the multiscale prime candidate sets generated at each \(m\) level before the reduction process:_  
\[\Lambda_{m}=\left(\prod_{i=1}^{m-1}(p_{i}-1)\right)p_{m} \tag{23}\]  
Proof: The thesis is directly obtained by applying the induction principle, as the property holds for a base case and is preserved for subsequent steps.  
The analysis in Figure 2 reveals that \(\Lambda_{m}\) increases almost linearly with \(\prod p_{i}\) on a log-log scale, confirming the exponential growth model. The high coefficient of determination (\(R^{2}=0.99991\)) indicates an excellent fit for the model.
## 8 Regulations and Codes Regarding Steel Liner Design  
The most known codes are:  
* The CECT issued a regulation [6] that explicitly makes reference to the calculation of steel liners. It includes equations and nomograms derived from the Amstutz formulation and requires a safety factor greater than 1.6.
* The USACE Code [12] recommends the methods provided by [PERSON], [PERSON], and [PERSON] with regard to overall instability. For analysis of local buckling of the span between stiffeners, it recommends the formulations of [PERSON] and [PERSON]. Indeed, design is considered to be acceptable when the calculated stresses do not exceed 80% of the yield stress. Other related codes are the following:
* The Boiler and Pressure Vessel Code (2001), provided by the American Society of Mechanical Engineers (ASME).
* The Technical Guide on Pipelines for the Transport of Pressurised Water, provided by the Centre for Studies and Experimentation in Public Works: (Centro de Estudios y Experimentacion de Obras Publicas, CEDEX, Spain), that gathers an extensive range of Standards associated with calculation methods in pressurised water tunnels (based on the American Water Works Association Manual M11).
* ASTM F1216 (1998), published by the American Society for Testing and Materials (ASTM) [34].
* The Recommended Practice DNV-RP-C202 for the Buckling Strength of Shells, published by Det Norske Veritas [8].
* Buckling of Steel Shells: European Recommendations, provided by the European Convention for Constructional Steelwork (ECCS) [44].
* Design Guidelines for Pressure Tunnels and Shafts AP-5273, provided by the Electric Power Research Institute (EPRI) [1].  
However, design guidelines for the application of high-strength steel for steel liners are still missing [11]. For that, [PERSON] et al. [45; 46; 47] proposed methodology for a safetyassessment of the steel liners of pressure tunnels and shafts, particularly for the weakness that welding imperfections may affect the liners.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 6 Conclusions and Future Work  
In this paper, we proposed a peer selection algorithm for D2D communications in dense small-cell networks. We exploited both the social and physical parameters of the UEs to improve the willingness of the users and enhance the performance of the overall D2D communications in terms of throughput and energy efficiency. We exploited a modified TOPSIS algorithm to determine the most suitable D2D peer. The simulation results for the proposed scheme are plotted to demonstrate its efficiency in terms of the computation time, selection of UEs with the best values of \(\delta_{s}\), \(\delta_{p}\), and \(\Delta\), \(\Im\) and \(\varepsilon\varepsilon\) of the D2D communications. The selection of UE with the best value of \(\Delta\) ultimately achieved the best throughput for D2D communications between the requesting and selected DUEs. Similarly, the proposed small-cell paradigm was more energy-efficient as compared to the macro-cell system. In the future, we aim to consider realistic mobility traces and analyze the impact of various social and physical parameters on the D2D peer selection process. Similarly, we can further improve the performance by incorporating user preferences and dynamically assigning weights based on user application requirements to select a desired D2D peer. Moreover, the proposed work can be extended to scenarios where the DUEs were connected to different SCBSs.  
[PERSON] performed computer simulations and wrote the paper, [PERSON] supervised the research and participated in problem formulation, data analysis and revision process.  
This research was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011).  
The authors declare no conflict of interest.