esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 3 Handshake Performance Measurements  
### Setup  
We consider the setting where a constrained 6 TiSCH device, acting as a (D)TLS client, connects to a powerful (D)TLS server, see Figure 4. The constrained device uses the OpenWSN stack [10]; the powerful host uses the out-of-the-box Linux networking stack. The TCP/IP network stack of the server is unmodified and is unaware it is communicating with a constrained device. The (D)TLS handshake always uses mutual authentication through the exchange of certificates. The certificate chains contain only one X.509 certificate. The received certificate matches with a stored root certificate, acting as a trust anchor for authentication.  
To obtain experimental results on the performance of the (D)TLS handshake on top of 6 TiSCH, we port mbedTLS to the OpenWSN project. We implemented 6 LoWPAN fragmentation and the TCP protocol for the OpenWSN stack. We designed two abstraction layers, called opendtls and opentls, which function as wrappers around the mbedTLS library and allow OpenWSN applications to trigger the handshake protocol.  
We perform several experiments using different sets of configuration parameters for the 6 TiSCH network and (D)TLS stack to assess the impact on the handshake performance. We principally evaluate two performance characteristics: the number of exchanged bytes--measured at the physical layer--and the handshake duration. Unless noted otherwise, we use the configuration depicted in Table 1.  
We use the OpenMote hardware platform to perform our experiments. The OpenMote features the CC2538 SoC [21], which has 32 KiB of SRAM and a 32 MHz ARM Cortex-M3 processor. Additionally, it provides a cryptographic coprocessor for AES and SHA functions and an acceleration engine for several big integer and elliptic curve operations. Figure 4 shows the experimental setup. The root node of the 6 TiSCH network connects through a serial interface to a PC implementing the network gateway.  
The OpenWSN project also provides the gateway software, called OpenVisualizer, to connect the mesh network to the Internet. It implements 6 LoWPAN compression,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Cipher suite & TLS\_ECDE\_ECDSA\_WITH\_AES\_128\_CCM\_SHA256 \\ Curve & SECP192R1 \\ DTLS timeout & 3 s \\ Authentication & Mutual (single certificate in chain) \\ Scheduling func. & Minimal Scheduling Function (MSF) \\ Sloftrame length & 5 \\ Duty cycle & 100\% \\ Cells & 1 Shared TxRx, 1 autonomous Rx, 3 managed cells \\ Topology & Single hop \\ TCP options & MSS, Nagle, SACK, and Delayed ACKs \\ UDP header size & Compressed to 4 B with 6 LoWPAN \\ \hline \hline \end{tabular}
\end{table}
Table 1: 6 TiSCH and (D)TLS configuration.  
Figure 4: Experimental setup.  
decompression, fragmentation, and reassembly of packets. It uses a turn interface to inject packets into the kernel of the PC and route them to their final destination.
## 5 Numerical Examples
\begin{table}
\begin{tabular}{c c c c c c} \hline \(n\) & \(\omega^{\star\star}\) & \(p^{\star\star}\) & \(q^{\star\star}\) & \(TP^{\star\star}_{\text{CT}\eta}\) & \(TP^{\star\star}_{\text{CT}\eta}\) \\ \hline
1 & 43.7111 & 90.0119 & 162.355 & 853.01 & 9307.73 \\
2 & 40.7474 & 90.0137 & 162.376 & 9280.55 & 9308.12 \\
**3** & **39.5397** & **90.0145** & **162.385** & **9375.44** & **9308.23\(\leftarrow\)** \\
4 & 38.9014 & 90.0149 & 162.391 & 9290.00 & 9308.28 \\
5 & 38.5576 & 90.0152 & 162.393 & 9123.64 & 9308.30 \\ \hline \end{tabular} Note: “\(\leftarrow\)” denotes the optimal equilibrium solution generated by the proposed model.  
\end{table} 
Table 2: Solution procedure of Example 1.  
fixed carbon emissions per order or per shipment for the retailer, amount of carbon emission to hold finished goods inventory for the manufacturer, and carbon tax. 5. Increases in the values of carbon emission parameters negatively impact the manufacturer's total profit, except for the amounts of fixed carbon emissions per order or per shipment for the retailer and amount of carbon emission to hold finished goods inventory for the manufacturer. 6. Increases in the amount of carbon emission to hold finished goods inventory for the manufacturer positively affect the retailer's total profit. 7. The optimal amount of carbon emissions produced by the manufacturer increases with increases in the values of the carbon emissions parameters, except for the amount of carbon emission to hold finished goods inventory. Otherwise, it decreases as the values of the retailer's carbon emission parameters increase, except for the amount of carbon emission to hold finished goods inventory. Moreover, a higher carbon tax helps the manufacturer reduce carbon emissions. 8. The optimal amount of carbon emissions produced by the retailer increases with an increase in the values of the carbon emission parameters. Conversely, it decreases with an increase in the manufacturer's carbon emission parameters, except for the amount of carbon emission to hold finished goods inventory. A higher carbon tax also helps the retailer reduce carbon emissions.
## 2 Antenna Array Design
### Array Simulation
The red dashed lines represent the defined beamforming direction.
## 3 Experimental Platform
### Comparison Analysis  
The conventional visual sinkage estimation (VSE) algorithm calculates the amount of wheel sinkage by capturing images from a monocular camera attached to one side of the wheel and utilizing equally spaced concentric circles on a white background. This algorithm detects wheel sinkage with good accuracy and is a superior traditional detection method. The monocular vision sinkage detection (MVSD) method proposed in this paper is compared with the VSE algorithm in the following experiment. The experiments were conducted to detect the amount of wheel sinkage using the VSE algorithm and MVSD method for the same wheel sinkage scenario, respectively, and the specific experimental procedure is shown in Figure 7. The experimental results and detection errors for the eight experiments are demonstrated in Table 4 and Figure 9. According to the experimental results, the errors of the sinkage values measured by the MSVD method are all smaller than the errors of the sinkage values measured by the VSE algorithm. The relative errors measured with the MSVD method do not exceed 4%, while the relative errors measured  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Center Coordinates**} & \multicolumn{2}{c}{**Center Coordinates**} & \multirow{2}{*}{**Pixel Error (pt)**} & \multirow{2}{*}{**Actual Error (mm)**} & \multicolumn{2}{c}{**Error as a Percentage**} \\  & \multicolumn{2}{c}{**(Auxiliary Detection)**} & & & & **of Radius** \\ \hline
1 & 406, 378 & 410, 380 & 4.47 & 0.95 & 1.19\% \\
2 & 408, 379 & 408, 380 & 1.00 & 0.21 & 0.26\% \\
3 & 406, 380 & 408, 380 & 2.00 & 0.42 & 0.53\% \\
4 & 406, 377 & 408, 379 & 2.83 & 0.60 & 0.75\% \\
5 & 410, 379 & 408, 380 & 2.24 & 0.48 & 0.60\% \\
6 & 410, 378 & 406, 380 & 4.47 & 0.94 & 1.18\% \\
7 & 406, 376 & 408, 382 & 6.32 & 1.33 & 1.66\% \\
8 & 410, 382 & 406, 378 & 5.66 & 1.21 & 1.51\% \\  & \multicolumn{2}{c}{Average error} & 3.62 & 0.77 & 0.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Wheel sinkage detection results.  
Figure 8: Error plots of Hough circle detection results.  
with the VSE algorithm reach up to 8.66%. Therefore, the MSVD algorithm in this paper has higher accuracy and can provide more accurate wheel sinkage information for planetary rover passability prediction.
## 4 Results and Discussion
### Performance Evaluation
#### 4.4.2 Detection Rate (DR), Recognition Accuracy (RA) and F-Measure (FM)
The proposed method has the highest Detection Rate of 87.7%, while this value is 58.199% and 26.7% for the A* Path Planning and piecewise painting algorithms, as given in Figure 20. The A* Path Planning and piecewise painting algorithms have low detection rates for the document images in the newly developed LIPI database because these algorithms fail to \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sl. No.**} & **Algorithm for Text** & **No. of Correctly Segmented Text** & \multirow{2}{*}{**Accuracy (\%)**} \\  & **Line Extraction** & & **Lines** & \\ \hline
1 & A* Path Planning & 3258 & 58.19 \\ \hline
2 & Piecewise Painting & 1495 & 26.7 \\ \hline
3 & **Proposed Method** & 4912 & **87.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of accuracy obtained for different existing text line extraction methods and the proposed method. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**No. of GT Lines**} & **No. of Detected Lines** & **DR (\%)** & **RA (\%)** & **FM (\%)** \\ \hline
7535 & 6482 & 85.5 & 99.39 & 91.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of text line segmentation using the detection rate (DR), recognition accuracy (RA) and F-measure (FM). segment some of the text lines. Moreover, the Recognition Accuracy and F-measure values are the highest for the proposed method, as depicted in Figures 21 and 22, respectively. From the detected lines, the number of text lines that are perfectly matched with the ground truth lines are indicated by the Recognition Accuracy. As given in Figure 21, the piecewise painting algorithm has recognition accuracy of only 33.94%. This is because the algorithm fails to segment characters such as 'Chandrakkala' and ascenders and descenders in the Malayalam language correctly. The Recognition Accuracy obtained for A* Path Planning is 86.35%, which indicates that this language-independent algorithm can effectively segment the ascenders, descenders and 'Chandrakkala' correctly. proposed in this work segments 85.507% of the text lines perfectly from the document images. Moreover, a total of 7535 ground truth images are created for the text lines in the document images, which are used to evaluate the method proposed in this paper. From the comparisons performed, it is observed that the proposed technique outperforms the language-independent A* Path Planning and piecewise painting algorithms.
## 2 Materials and Methods  
### Denoising Process  
The traditional denoising methods such as average filtering and Gaussian filtering are using some special template to suppress noise [28]. With the diversity of the original image and noise, the single denoising method reduces the useful signal and blurs the image texture and details. Deep learning denoising necessitates the acquisition of large data sets of annotated infrared images, which are difficult to obtain. Low rank and sparse representation focus on restoring the original image based on the sparsity difference. It is relatively easy to distinguish noise from the original image. To achieve a higher signal-to-clutter ratio (SCR) and a better clarity of the IR image, we utilize the WNNM based on the group sparsity model in IR image denoising. It leads to a better denoising performance in IR images with higher noise and achieves the optimization objectively. Under high noise environments, the adaptive similar block searching is significant with a good restoration effect. Experimental results show that the algorithm can provide a good reconstruction image and high precision. Finally, it allows us to recognize the small target in IR images quickly. The flowchart of the proposed method is displayed in Figure 1.  
The algorithm's flow chart includes four steps. First, we transform the image denoising problem into a mathematical optimization problem based on robust principal component analysis (RPCA). The group sparse representation theory considers the input image to be composed of many groups. These groups are of nonlocal self-similarity. Each group could be transformed into a matrix with a low rank and a matrix with remarkable sparsity. Second, we apply the proposed WNNM algorithm to image denoising by exploiting the image nonlocal self-similarity. Sparse coefficients can be used to recover the original image. The optimization computation is updated by multi-iterations. Third, all groups in each iteration are attained by adaptive patch selection depending on the SSIM. When the iteration result has a higher similarity difference with the last iteration result, we select the pre-filtered image as a group for the iteration computation. Finally, we can evaluate the denoising performance among PSNR and SSIM with different methods in public data sets and real IR images. Our article has completed all of the work based on the process.
## 2 Proposed STPA Configuration for JCAS, Its Design Pipeline, and Performance  
In this section, we present the proposed STPA configuration and its design pipeline. Note that the novelty in this paper lies in the novel combination of known techniques for producing a novel STPA multibeam solution for JCAS scenarios to study communication and sensing performances, and blockage time. First, we present the JCAS architecture and protocol that are used in this paper. Second, we present the proposed STPA array configuration and its design pipeline. Third, we present the communication and sensing performance with corresponding representative channel models.
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
###### Abstract  
In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.  
Internet of Things; multilevel blockchain; lightweight; data reliability; privacy protection +
Footnote †: journal: Sensors  
doi:10.3390/s22218271
## 4 Output Power Modelling for the Switching Process of the DFVSPS Unit
### Pump Mode  
#### 4.2.1 Start-up in Pump Mode  
During start-up of pump mode, since the unit has not been connected to the grid, the output power of the unit to the grid is 0. The specific model is equivalent to the relationship shown in Formula (25), and the duration is the time when the unit speed is loaded to the rated speed:  
\[P_{53}=0,\ 0\leq t<\tau_{1}, \tag{25}\]  
where \(P_{53}\) is the output power of the DFVSPS unit to the grid during the start-up, and \(\tau_{1}\) is the duration of the start-up. The calculation of \(\tau_{1}\) is as follows.  
The control diagram of the whole unit in this stage is shown in Figure 22.  
During the start-up process, the stator side of DFIM is short-circuited through a small resistor. Therefore, the stator-side voltage is different from that of the on-grid operation stage. By controlling the \(d\)-axis current of the rotor-side converter, the corresponding electromagnetic torque can be generated to start the unit. Simplified to Equation (26),  
\[\left\{\begin{array}{l}J\frac{d\omega_{m}}{dt}=T_{e}-B_{m}\omega_{m}\\ T_{e}=-\frac{3p_{m}U_{L}\omega_{3}}{2L_{\omega_{3}}}\frac{k_{PN}}{T_{s}+1}i_{ nref}\end{array}\right.. \tag{26}\]  
It can be seen from the above equation that \(\tau_{1}\) depends on the mechanical parameter, friction factor of DFIM, and the d-axis current. According to the values of the relevant parameters in this paper, \(\tau_{1}=60\) s.
## 3 Classification of Attacks on RPL-Based IoT
### RPL-Specific Attacks
#### 3.2.4 Replay Attacks  
These attacks involve capturing and forwarding legitimate control messages (DIO, DIS, and Destination Advertisement Object (DAO)) within the network. They focus on control messages, setting them apart from similar attacks in WSNs. Replay attacks have a more pronounced impact in dynamic networks, where adversaries can record and replay control messages to force neighboring nodes into updating routing tables with outdated information [45].
## 2 Results
### Chemical Analysis  
The chemical compounds of the _Pelargonium graveolens_ 'Dr. Westerlund' found in the headspace and identified by the GC-MS and the MassHunter Analysis 8.0 Software library are presented in Table 1. Moreover, the retention times, retention indexes, and the Chemical Abstracts Service (CAS) numbers are provided.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name:** & \
\begin{tabular}{c} **Retention Time** \\ **(Minutes)** \\ \end{tabular} & **Retention Index** &
\begin{tabular}{c} **Formula:** \\ **Chemical Compound** \\ \end{tabular} & **CAS Number** \\ \hline alpha-pinene & 14.30 & 1447 & C\({}_{10}\)H\({}_{16}\) & 80-56-8 \\ Camphene & 14.81 & 1476 & C\({}_{10}\)H\({}_{16}\) & 79-92-5 \\ Para-cymene & 16.87 & 1580 & C\({}_{10}\)H\({}_{14}\) & 99-87-6 \\ Alpha -terpineol & 16.98 & 1585 & C\({}_{10}\)H\({}_{18}\)O & 98-55-5 \\ Limonene & 16.99 & 1793 & C\({}_{9}\)H\({}_{13}\)NO\({}_{3}\) & 5989-27-5 \\ Cyclohexanol & 17.92 & 1678 & C\({}_{12}\)H\({}_{22}\)O & 108-93-0 \\
4-Methyl-2-(2-methylprop-1- \\ en-1-yl)tetrahydro-2H-pyran & 19.12 & 1696 & C\({}_{10}\)H\({}_{18}\)O & 3033-23-6 \\ Cyclohexanone & 20.31 & 1757 & C\({}_{10}\)H\({}_{18}\)O & 108-93-0 \\ Citronellol & 21.71 & 1829 & C\({}_{10}\)H\({}_{20}\)O & 106-22-9 \\ Citronellyl formate & 22.80 & 1882 & C\({}_{11}\)H\({}_{20}\)O\({}_{2}\) & 105-85-1 \\ Copenene & 25.49 & 2366 & C\({}_{15}\)H\({}_{24}\) & 3856-25-5 \\ Caryophyllene & 26.50 & 2445 & C\({}_{15}\)H\({}_{24}\) & 13877-93-5 \\ Alpha-Guaiene & 26.75 & 2464 & C\({}_{15}\)H\({}_{24}\) & 3691-12-01 \\ (\(-\))-Aristolene & 26.93 & 2478 & C\({}_{15}\)H\({}_{24}\) & 6831-16-9 \\ Aromadendrene & 27.17 & 2496 & C\({}_{15}\)H\({}_{24}\) & 72747-25-2 \\ Humulene & 27.21 & 2499 & C\({}_{15}\)H\({}_{24}\) & 6753-98-6 \\
3 furopelargone A & 29.67 & 2690 & C\({}_{15}\)H\({}_{22}\)O\({}_{2}\) & 1143-45-9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compounds found in the _Pelargonium graveolens_ 'Dr. Westerlund’ samples.  
Figure 1: Sensory odor profiles of _Pelargonium graveolens_ 'Dr. Westerlund’ measured on the sensory intensity scale. Green reflects the whole potted plant and yellow represents the stem cuttings. The * indicates the statistically significant difference between _Pelargonium graveolens_ 'Dr Westerlund’ in a pot and _Pelargonium graveolens_ 'Dr. Westerlund’ stem cuttings in the attribute soil.
## 3 Experimental Results and Analysis
### Convergence Accuracy Analysis
This indicates that the introduction of the golden sine strategy significantly enhances the global optimality finding ability of the original SSSA. For the unimodal functions \(F_{5}\)\(\sim\)\(F_{7}\), although the optimal values are not sought, their convergence accuracy is higher than that of the remaining four compared algorithms. For the multimodal functions \(F_{8}\), although the mean value of ISSA is the same as Gold-SA, HHO, and GTO, the standard deviation of ISSA is much lower than that of Gold-SA, HHO, and GTO. Among the multimodal functions \(F_{9}\)-\(F_{11}\), the convergence accuracy of ISSA is the same as that of Gold-SA, HHO, GTO, and SSA, and they can all converge to the optimal value. However, the convergence accuracy of ISSA is the best in all cases in the multimodal functions \(F_{12}\) and \(F_{13}\). For the fixed-dimension functions \(F_{14}\)-\(F_{23}\), a total of seven best or tied best convergence accuracies were obtained for ISSA. The ranking results of the six meta-heuristic algorithms are shown in Figure 2. The ranking is determined primarily by the average value of each algorithm; if the average value is identical, the ranking is determined by the standard deviation. The radar plot shown in Figure 2 has 23 polar axes, and each polar axis represents 1 test function. Starting from the center point of the radar plot, there are six circles that expand outward in sequence, and the numbers on the circles represent the ranking of the algorithms. A closed polygon can be obtained by connecting the ranking points of each algorithm on the 23 test functions. The smaller the area of the polygon, the better the convergence performance of the algorithm. According to Figure 2, it can be seen that the area enclosed by ISSA is the smallest, which indicates that the convergence accuracy of ISSA is better than that of PSO, Gold-SA, HHO, GTO, and SSA.