esa-sceva/satcom-chunk-collection huggingface dataset:
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 4 Analysis of Monitoring Results
### Strain Monitoring Results
The strain of the windward side and the dorsal side is the most dramatic under the influence of sea waves and flows, while the strain of the top is relatively small, and the strain of the measurement point 1 at the bend is greater than that of the measurement point 2 and 3 at the flat section. The strain in the floating transportation of pipeline segment 2 is shown in Figure 9. The maximum tension strain at the top of measuring point 4-1 is 0.2 \(\mu\epsilon\), and the maximum pressure strain is \(-\)167.8 \(\mu\epsilon\); The maximum tension strain at the backwater detection point 4-2 is 278.4 \(\mu\epsilon\), and the maximum pressure strain is \(-\)151.4 \(\mu\epsilon\); the maximum tension strain at the measuring point 4-3 is 26.6 \(\mu\epsilon\), and the maximum pressure strain is 14.8 \(\mu\epsilon\). The  
Figure 8: Pipeline Section 1 floating strain. maximum tension strain at the backwater detection point 5-2 is 337.8 \(\mu\epsilon\), and the maximum pressure strain is \(-\)123.3 \(\mu\epsilon\). The maximum tension strain at the top measuring point 6-1 is 29.9 \(\mu\epsilon\), and the maximum pressure strain is \(-\)53.6 \(\mu\epsilon\); the maximum tension strain at the backwater detection point 6-2 is 150.9 \(\mu\epsilon\), and the maximum pressure strain is -167.5 \(\mu\epsilon\); the measuring point 6-3 at the bottom, the maximum tensile strain is 46.0 \(\mu\epsilon\), and no pressure strain is observed. The maximum tension strain at the top measuring point 7-1 is 25.0 \(\mu\epsilon\), and the maximum pressure strain is \(-\)99.0 \(\mu\epsilon\); the maximum tension strain at the backwater detection point 7-2 is 89.0 \(\mu\epsilon\), and the maximum pressure strain is \(-\)18.6 \(\mu\epsilon\); the maximum tension strain at the measuring point 7-3 at the bottom is 21.4 \(\mu\epsilon\), and the maximum pressure strain is \(-\)5.0 \(\mu\epsilon\). In the early stage of buoyancy movement of pipeline segment 2, the strain value of each measuring point is small, with the maximum strain less than 100 \(\mu\epsilon\). When the buoyancy movement reaches 48 min, a gale of six sprang up off the sea. In general, the strain in the floating transportation of pipeline Section 2 does not exceed the warning value. The steel pipeline is under pressure at the top, under tension at the bottom, and tension at the surface of the back. The strain change on the waterside of the pipeline is the most drastic, followed by the top and the bottom. The strain monitoring curves of test sections 4-1 show good consistency.
## 3 Length Measuring Capability and Three-Dimensional Measurement Capability
### Length Measuring Capability Does Not Define Three-Dimensional Measurement Capability  
While the length measurement capability of a CMM is clearly important, it is also the case that CMM behaviour is not characterised by length measurement capability alone. In general, length measurement capability provides only limited information about other derived features such as cylindricity, etc. In Section 6.3, it is shown how a combination of independent axes scale effects along with squareness effects provide exactly the same length measuring capability as that arising from a single global scale effect. Therefore, it is possible that two CMMs with exactly the same length measurement capability can perform significantly differently on other measurement tasks, such as the measurement of a ball plate.
## 2 Materials and Methods
### Numerical Integration of the Nonlinear Shallow Water Equations
#### 2.2.1 Influx Transparent Boundary Conditions  
Appropriate boundary conditions are needed to let the wave travel in and out of the domain in an undisturbed way. Influx transparent boundary conditions are boundary conditions that make waves on boundaries propagate into the computational domain and are transparent for wave propagation out of the computational domain. In the region of constant depth, the LSWE reduce to two wave equations [2]:  
\[\eta_{tt}-c^{2}\eta_{xx}=0\,, \tag{18}\]  
and  
\[\Phi_{tt}-c^{2}\Phi_{xx}=0\,, \tag{19}\]  
where \(\Phi\) is the time-integral of the displacement past the plane \(x\), up to the time \(t\): \(\Phi(x,t)=\int^{t}u\,\mathrm{d}t\).  
Equations (18) and (19) have the general solutions  
\[\eta(x,t) = \eta_{rg}(\frac{x}{c}-t)+\eta_{lg}(\frac{x}{c}+t)\,, \tag{20}\] \[\Phi(x,t) = \Phi_{rg}(\frac{x}{c}-t)+\Phi_{lg}(\frac{x}{c}+t)\,, \tag{21}\]  
where the subscripts \(lg\) and \(rg\) denote the waves traveling to the left and to the right directions, respectively.  
Differentiating Equation (21) with respect to \(x\) and \(t\) yields  
\[\Phi_{x} = \frac{1}{c}\Phi_{rg}^{\prime}+\frac{1}{c}\Phi_{lg}^{\prime}\,, \tag{22}\] \[\Phi_{t} = -\Phi_{rg}^{\prime}+\Phi_{lg}^{\prime}\,. \tag{23}\]  
To build a left boundary at \(x=x_{l}\) that is transparent for waves propagating to the left but allows waves to propagate to the right, we eliminate \(\Phi_{lg}^{\prime}\):  
\[\Phi_{t}-c\Phi_{x}\mid_{x=x_{l}}=-2\[PERSON]_{rg}^{\prime}\mid_{x=x_{l}}\,. \tag{24}\]  
This is called the left Influx Transparent Boundary Condition (ITBC).  
As \(\Phi_{t}=u\),  
\[u(x_{l},t)=c\[PERSON]_{x}\mid_{x=x_{l}}-2\[PERSON]_{rg}^{\prime}\mid_{x=x_{l}}\,. \tag{25}\]  
Moreover, from (3),  
\[\Phi_{x}=\int^{t}u_{x}\,\mathrm{d}t=-\frac{1}{h_{0}}\eta=-\frac{g}{c^{2}}\eta\,. \tag{26}\]  
Therefore, the velocity at the left boundary can be written as  
\[u(x_{l},t)=-\frac{g\eta(x_{l},t)}{c(x_{l})}+\frac{2g\eta_{rg}(x_{l},t)}{c(x_{ l})}\,. \tag{27}\]  
Similarly, the right ITBC \((x=x_{r})\) that is transparent for waves propagating to the right but makes waves propagate to the left can be written as  
\[\Phi_{t}+c\Phi_{x}\mid_{x=x_{r}}=2\Phi_{lg}^{\prime}\mid_{x=x_{r}}\,. \tag{28}\]  
Therefore, the velocity at the right boundary is  
\[u(x_{r},t)=\frac{g\eta(x_{r},t)}{c(x_{r})}-\frac{2g\eta_{lg}(x_{r},t)}{c(x_{r} )}\,. \tag{29}\]The head-on collision of two single waves on a flat bottom with the use of the left and right influx transparent boundary conditions is shown in Figure 2. Waves of the form \(\eta_{0}(t)=0.1\,\text{sech}^{2}(0.17\,t-2)\) are generated at the boundaries. As the boundaries are transparent to the outgoing waves, no reflection occurs at the boundaries.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 1 Introduction
This is potentially problematic because both Modbus-CAN and Modbus-TCP included such a mechanism. Namely, the former relied on the hardware Controller Area Network (CAN) frame retransmission mechanism at the data-link layer [11], and the latter leveraged TCP segment retransmission at the transport layer [12]. As a consequence, applications designed for them may wrongly assume that the Modbus protocol itself already provides the degree of message delivery reliability they require when they are ported to Modbus-UDP. Application-layer timeouts do exist for Modbus transactions, but their value, typically on the order of 10 ms, may be unsuitable for some classes of real-time systems. This paper proposes a new specification for Modbus-UDP with the aim of addressing the two shortcomings just mentioned. Firstly, it supports _broadcast messages_ by means of a backward-compatible extension of the standard Modbus Application Protocol (MBAP) header of Modbus-TCP. Broadcast messages are used, for instance, by the distributed master election protocol described in [13]--which makes multi-master Modbus systems possible. Secondly, it includes a centralized _UDP datagram retransmission_ mechanism that, in addition to relieving applications from the burden of implementing it themselves, reacts more efficiently than the equivalent Modbus-TCP mechanism, exhibiting 50% of its round-trip standard deviation when subject to a 1% two-way IP datagram loss probability. Last but not least, it achieves a round-trip time of only 38% with respect to Modbus-TCP. Also considering the lower system and network overhead of UDP versus TCP [14], this makes Modbus-UDP better suited for a variety of Industrial Internet of Things (IIoT) systems with limited computing and communication resources. For instance, it could profitably act as a replacement for Modbus-TCP in industrial-grade equipment such as PLCs, industrial PCs, remote I/O aggregators, and SCADA systems. This is made easier by the fact that those systems commonly support Modbus-TCP already. The paper is organized as follows. Section 2 discusses the related work. Then, Sections 3 and 4 define the protocol and compare it with Modbus-TCP, respectively. Section 5 concludes the paper.
## 2 Materials and Methods
### Determination of Cell Cytotoxicity by 2,5-Diphenyl-2H-Tetrazolium Bromide (MTT) Assay  
The anti-cell proliferative activity of NDG was investigated using MCF-7 and HeLa cells by assessing their viability by using an MTT assay [17]. The cultures were grown until the exponential phase and were trypsinized and diluted with DMEM to obtain a cell count of 5 \(\times\) 10\({}^{5}\) cells/mL. The suspended cells were grown overnight in a microplate containing a 100 uL medium, a fresh medium with different concentrations of NDG (1.56-200 ug/mL) was added after aspirating the previously grown culture. Cultures suspended in an FBS-free DMEM served as a negative control. All drug concentrations and controls for the assay were performed in triplicate.  
The cell suspensions were withdrawn, and the wells were cleaned with PBS after 24 h of incubation. Then, 20 uL of MTT solution (at a concentration of 5 mg/mL in PBS) and 80 uL of medium were added and incubated at 37 \({}^{\circ}\)C for 4 h. The culture fluid with MTT was then removed, leaving the formazan crystal to precipitate. The crystals were dissolved in 100 uL of DMSO/acetic acid/sodium lauryl sulfate (99.4 mL/0.6 mL/10 g) at room temperature for 15 min. The microplates were observed, and absorbance was measured at 570 nm using a spectrophotometric microplate reader (Synergy HT, BioTek Inst., Winooski, VT, USA). IC\({}_{50}\) was calculated using GraphPad Prism 5.0 (San Diego, CA 92108, USA).
# Development and Field Testing of a Suspended Mulberry Branch Harvesting and Stubble Cutting Machine
## 3 Key Component Design
### Transmission System Design  
When selecting a 400 mm diameter circular saw blade, the rotational speed of the spindle for the saw blade is calculated using the formula shown in Equation (3). According to the design plan, the linear speed of the circular saw is 90 m/s:  
\[V=\frac{\pi d_{1}n}{60\times 1000} \tag{3}\]  
where \(V\) is the linear speed of the circular saw (m/s), \(d_{1}\) is the diameter of the circular saw blade (mm), and \(n\) is the rotational speed of the spindle of the circular saw blade (r/min).  
Through calculations, the minimum spindle speed of the circular saw required to achieve the desired linear speed of the saw blade is 4297 r/min.  
Figure 9 shows the V-belt transmission system of the cutting device: V-belt pulleys 1 to 6 have different diameters and are driven by the tractor TS250 (2200 r/min). The speed is first reduced by a single-stage reduction (2.20 times) to 1000 r/min, then increased through two stages of speed increase (2.57 times and 2.09 times) to 5371 r/min. Finally, the speed is reduced by a gear reducer and a 1.25-fold reduction, resulting in a theoretical spindle speed of 4297 r/min and a linear speed of 90 m/s for the saw blade.  
The feeding device utilizes the power provided by the tractor's rear output shaft. It connects the rear output shaft to the matching gearbox, which is externally connected to a hydraulic pump. Through hydraulic piping, the hydraulic pump and hydraulic motor are connected, and the spindle of the hydraulic motor is linked to the spindle of the feeding star wheel, thereby enabling the rotational movement of the feeding star wheel and achieving the feeding function. The design plan specifies the rotational speed of the feeding star wheel  
Figure 9: Simplified transmission diagram of the sawing device.  
to be 145 r/min. The corresponding hydraulic pump and hydraulic motor are selected based on the calculation from Equation (4):  
\[n_{m}=\frac{\frac{q_{m}}{1000}\times n_{p}\times\eta_{vm}\times\eta_{mm}}{V_{m}} \tag{4}\]  
where \(n_{m}\) is the rotational speed of the hydraulic motor (r/min), \(q_{m}\) is the flow rate of the hydraulic pump (mL/r), \(n_{p}\) is the rotational speed of the hydraulic pump (r/min), \(\eta_{vm}\) is the volumetric efficiency of the hydraulic motor (%), \(\eta_{mm}\) is the mechanical efficiency of the hydraulic motor (mm), and \(V_{m}\) is the displacement of the hydraulic motor (mL/r).  
Based on the calculations, the BM1-100 four-hole shaft 25 hydraulic motor and the KGP2E2D16Z5F9 hydraulic pump were selected, which are capable of adjusting the rotational speed of the feeding star wheel between 0 and 145 r/min.
# A Systematic Compact Model Parameter Calibration with Adaptive Pattern Search Algorithm
## 3 Configuration of the Experiment  
In this paper, we focus on the Berkeley Short-channel Insulated-gate field-effect transistor Model 4 (BSIM4) [13; 14; 15; 16] as a base model to be calibrated by proposed algorithm. It seems to be well enough to utilize BSIM4 to test our methodology since it is a physics based compact model and considered as an industry standard [16]. Target current vectors, in this study, are generated from the given model using commercial circuit simulator. The type and the physical parameters of the device for the target data is N channel MOSFET and length/width = 0.1 \(\upmu\)m/1 \(\upmu\)m, respectively. Each target current vector is simulated with random value of the model parameters. Each parameter's value is uniformly sample in the certain range that handles the on current (\(\text{\text@V}_{\text{G}}\) = 1.5 V, \(\text{\text@V}_{\text{D}}\) = 1.5 V) from 5-10% reduction to 5-10% increase. The parameter list and the specification we handle for the experiment is summarized in Table 1. Although, we verify the algorithm with BSIM4, a general approach in calibrating the parameters involves fitting the model and target, thus, it can apply to any kind of compact model, such as BSIM Common Multi-Gate (BSIM-CMG) [17; 18], Enz-Krummenacher-Vittoz (EKV) [19], Pennsylvania State university and Philips (PSP) [20; 21] and any kind of behavioral and empirical model composed of analytical equation and parameters. Also, it is possible to calibrate the models to describe emerging devices, such as TFET, NCFET and Feedback FET.
## 5 Results
### Higher-Order Spectra Features  
The results of the higher-order spectra method indicate 11,396 total features. Subject No. 1 performed well, with GSVM at 83.15% and LinearSVM at 82.03%. However, SVM with a polynomial kernel and CART, respectively, at 74.35% and 63.79%, were less effective. Subject No. 2 showed a moderate outcome, with LinearSVM at 68.51% accuracy. Subject No. 3 showed strong performance, with LinearSVM at 88.88%, whilst CART was equally good among other tree-based classifiers, reaching 74.44%. Subject No. 7 had the highest rate for LinearSVM, 89.66%. GSVM was also good, reaching 83.40%. Subject No. 6 had the lowest performance, with the best being LinearSVM, at 46.51%, and the rest of the classifiers performed similarly poorly. Subject No. 9 performed well across classifiers, with LinearSVM reaching 85.21% and GSVM reaching 85.18%, which shows strong results.  
The lowest overall average success rate was attributed to CART, at 58.97%. SVM with a polynomial kernel had better accuracy, 63.41%, followed by GSVM at 72.71%. Still, LinearSVM outperformed all the latter, at 77.20%. The results of applying higher-order spectra methods for feature extraction are presented in Table 4.
## 4 Experimental Results
### Population Size Analysis  
One of the parameters influencing the performance of any evolutionary algorithm is the size of the initial population. Therefore, the behavior of the proposed algorithm for optimal extraction of the parameters of the three PV models was investigated with population sizes (\(n\)) of 10, 20, 40, 50, 80, and 100. For each of these population sizes, the ICO was run 30 times, and the statistical results are summarized in Table 3. As can be seen, for the SD and PVM models, the algorithm could achieve the best solution (Min value) for all population sizes. For the DD model, the algorithm reached the best solution in 9.824849 \(\times\) 10\({}^{-4}\) with \(n\) = 80. In addition, the proposed algorithm showed significant \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{\(I_{p}\)**(A)**} & \multicolumn{2}{c}{\(I_{J_{1}}\)\(J_{2}\)**(\(\mu\)A)**} & \multicolumn{2}{c}{\(u\)**,\(u_{1}\)\(u_{2}\)**} & \multicolumn{2}{c}{\(R_{s}\)**(\(\Omega\))**} & \multicolumn{2}{c}{\(R_{sh}\)**(\(\Omega\))**} \\ \cline{2-10}  & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** \\ \hline SDM & 0 & 1 & 0 & 1 & 1 & 2 & 0 & 0.5 & 0 & 100 \\ DDM & 0 & 1 & 0 & 1 & 1 & 2 & 0 & 0.5 & 0 & 100 \\ PV module & 0 & 2 & 0 & 50 & 1 & 50 & 0 & 2 & 0 & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters’ bounds for three models. robustness with all initial populations, except for the population of 10. The CPU times and Friedman test results through 30 runs are represented in the last three columns of Table 3, and their average values for the three models are shown in Figure 4. Based on these results, it can be seen that the proposed algorithm with \(n=80\) had the best relative performance in the three models, with an average sum rank of 80 and a CPU time of 39.5 s. The population sizes of 40 and 50 ranked second and third among all examined population sizes, respectively. In addition, the convergence characteristics of the algorithm with different population numbers are shown in Figure 5 for three models. When the population number was set above 10, almost the same convergence behavior was seen. However, for the first model, when the population was 40, the speed of convergence was almost better. When the populations of 80 and 100 were considered, the speed of convergence in the second model was the best. For the third model, all populations except 100 almost converged on the same point. For the population of 10, the speed of convergence in the SDM and DDM showed the worst situation among the tested populations, while for the third model, it showed a significant convergence behavior. Figure 4: Average ranks of the utilized population sizes in three models.
## 3 Results and Discussions
### Experiment on Fall Detection with Different Sensor Types  
The data obtained from the three-axis accelerometer and gyroscope are combined to obtain information about the global acceleration and orientation of the body, which in turn are inputs into the algorithm. One of the objectives of this study was to examine and compare the relative effectiveness of accelerometer-based algorithms and accelerometer-plus-gyroscope-based algorithms intended for fall detection as part of our comparative study. In the near-fall dataset, we find that the algorithm that uses gyroscope signals has a markedly better performance than the algorithm that does not use gyroscope signals. As shown in Table 4, using a gyroscope in conjunction with an accelerometer as part of the detection process can potentially lead to incremental benefits compared to using an accelerometer for the detection process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Balancing Techniques** & **F1 Score** & **Balanced Accuracy** \\ \hline Undersampling & 0.86 & 0.84 \\ Oversampling & 0.89 & 0.87 \\ Data Augmentation & 0.91 & 0.92 \\ Customized Loss Function & 0.97 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for various balancing methods of the dataset samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Positions** & **F1 Score** & **Balanced Accuracy** \\ \hline Foot & 0.88 & 0.90 \\ Shank & 0.97 & 0.98 \\ Pelvic & 0.94 & 0.92 \\ Hand & 0.85 & 0.84 \\ Forearm & 0.93 & 0.94 \\ Upperarm & 0.96 & 0.96 \\ Head & 0.90 & 0.91 \\ Hip & 0.93 & 0.92 \\ Thoracic & 0.94 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for various sensor positions.  
Therefore, to detect falls with low energy consumption, we use acceleration along the x, y, and z axes and angular velocity along the x, y, and z axes. The evidence gathered in this study suggests that assessing wearable sensors located on the shins through acceleration and angular velocity features may represent an optimal combination to discriminate falls from non-falls (including near-falls).
# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 3 FedCrow Design
### Problem Analysis  
While federated learning helps to ensure user privacy and data security, there are still potential security risks that could compromise the model and data integrity. Reference [35] delved into these risks and proposed effective preventive measures. Within the threat model of federated learning, insider attacks are the most common challenge, with Byzantine attacks and with attacks being the most effective methods [36]. Potential adversaries in the system can be classified into two types based on their level of proactiveness: semi-honest and malicious. Once semi-honest adversaries turn malicious, they can skillfully manipulate, replay, or delete information to disrupt federated-learning protocols, and even launch destructive attacks.  
Federated learning consists of two stages: training and inference. During the training stage, attackers can potentially target the model through data poisoning or model poisoning. In the inference stage, attackers may interfere with the model, resulting in inaccurate outputs or the collection of false information.  
Data-poisoning attacks can be categorized into clean label and dirty label: clean label assumes that attackers will not modify the labels of the training data; dirty label, on the other hand, allows for the introduction of data samples that do not match the expected labels. These two approaches can be employed in local data collection and partial model training. Dirty-label attacks are diverse, covering techniques such as label flipping and backdooring, which can affect both local and global models, all of which can be utilized to achieve attack objectives.  
During the model-training process, the involvement of malicious end users in federated learning can severely impact the integrity of the global model. They may employ tactics such as data contamination or model poisoning to undermine the reliability of the global model in federated learning. To counter such attacks, it is effective to screen participating client devices, selecting those with high reliability while excluding malicious ones. Typically, the number of malicious users in a system is relatively small, and thus, their contribution to the server's global model is either minimal or negative. Utilizing this principle, our framework calculates the gradient similarity between the user models that participate in the training and the server's global model. Users with a low gradient similarity are deemed unreliable, which enables effective screening of the legitimate users, and thereby reducing the harm caused by malicious users.