esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Analysis in Maritime IoT Applications
### Case Studies of Successful Data Analysis in Maritime IoT
#### 4.1.3 Case Study 3  
Navigation and Safety: Rolls-Royce Marine has developed an Intelligent Awareness System that uses a combination of IoT and data analysis for safer navigation. The system employs a network of sensors to collect data regarding the vessel's surroundings. This data is analyzed and presented in an augmented reality format to the ship's crew, helping them make informed navigation decisions and enhancing safety.  
Rolls-Royce Marine's development of an Intelligent Awareness System (IAS) showcases how IoT and data analysis can significantly enhance maritime navigation and safety. The system represents a significant step towards the realization of autonomous vessels.  
Intelligent Awareness System--IoT Integration  
The IAS is a comprehensive solution designed to augment the situational awareness of a vessel's crew. It employs an extensive network of IoT sensors, including LiDAR (Light Detection and Ranging), radar, thermal imaging, HD cameras, and satellite data to continuously collect data about the vessel's surroundings. This data encompasses everything, from weather conditions, nearby ships, and possible obstacles to the depth and condition of the water.  
Data Analysis and Augmented Reality  
The raw sensor data collected is analyzed using machine learning algorithms and artificial intelligence. The system can identify and classify objects, predict their future positions and movements, and assess potential collision risks.  
Moreover, the processed data is presented in an augmented reality format on the bridge, giving the crew a comprehensive and intuitive view of the vessel's surroundings. The system can highlight potential hazards and propose optimal navigation paths, allowing the crew to make informed decisions and take appropriate actions promptly.  
Enhanced Safety and Navigation  
The implementation of the IAS has significantly improved maritime safety. By providing the crew with a complete understanding of the vessel's environment, it helps prevent collisions and other navigational accidents. The system also allows for safer operations in challenging weather conditions or congested waterways, where traditional navigation systems might fall short.  
Additionally, the system enhances navigation efficiency. By suggesting the best paths based on real-time conditions and predictive analytics, it helps optimize fuel consumption, reduce travel time, and improve overall voyage efficiency.  
Future Implications  
The IAS has implications far beyond improving navigation and safety. It is a key step towards fully autonomous vessels, as it addresses one of the primary challenges in autonomous navigation--understanding and responding to the maritime environment.  
Rolls-Royce Marine's Intelligent Awareness System illustrates the profound impact IoT and data analysis can have on maritime safety and navigation. It paves the way for safer, more efficient voyages, and potentially autonomous shipping in the future. It serves as a testament to the transformative potential of these technologies in the maritime industry.
## 4 Performance Comparison
### Handover Latency  
Handover, or handoff, is a method of mobile communication in which cellular transmission (voice or data) is transferred from one base station (cell site) to another without compromising the cellular transmission link. Handover is an important part of mobile transmission because it sets up data sessions or phone calls between always-mobile devices. Handover latency and failure are major problems in mobile data networks that slow down and stop service. We need lower latency to achieve better performance. Equation (5) refers to [66] to measure the speed of latency by calculating the average hop count of content transmission with a variety of mobile producer speeds.  
**Handover latency equation for KITE operation**  
Equation (5) describes the handover latency for the KITE operation, while Table 6 shows the parameter setup.  
\[HL_{litc}=((T_{p}T_{p}+(l/v))+(1-(T_{p}T_{p}+(l/v))))\times(l_{pn}+a\times Lvlup +2d\times LvrcgInt+a\times LvltrcdInt+2d\times LvltrdIntA2) \tag{5}\]  
**Handover latency equation for PMSS operation**  
Equation (6) describes the handover latency for the PMSS operation, while Table 6 shows the parameter setup.  
\[HL_{pmss}=((T_{p}T_{p}+(l/v))+(1-(T_{p}T_{p}+(l/v))))\times(l_{pn}+a\times Lvl _{mobilett}+2c\times Lvl_{mobilett}+a\times Lvl_{int}+c\times Lw_{int}A_{2}) \tag{6}\]  
**Handover latency equation for hybrid NeMO operation**  
Equation (7) describes the handover latency for hybrid NeMO operation, while Table 6 shows the parameter setup.  
\[HL_{\text{Hybrid}}=((T_{p}T_{p}+(l/v))+(1-(T_{p}T_{p}+(l/v))))\times(L_{pn}+a \times Lvl_{hybrid}+2c\times Lvl_{hybrid}+a\times Lvl_{int}+c\times Lw_{int}A_{ 2}) \tag{7}\]  
**Average handover latency equation for each mobility operation**  
Equation (8) describes the average handover latency for each method.  
\[HL_{\text{AVERAGE}}=(HL_{10}+HL_{20}+HL_{30}+HL_{40}+HL_{50}+HL_{60}+HL_{70}+ HL_{80}+HL_{90}+HL_{100})/10 \tag{8}\]
# A Bio-Inspired Control Strategy for Locomotion of a Quadruped Robot
## 5 Results and Discussion
### Experiment with a Real Quadruped Robot  
After the virtual prototype simulation, the CPG-NN-WT control model with the same parameters is applied to a physical prototype whose basic parameters are shown in Table 4. Besides, an accelerometer is added in its body geometric center to measure the accelerations in three directions, the \(x\)-axis (lengthways), the \(y\)-axis (vertical) and the \(z\)-axis (lateral).  
In order to validate the presented control strategy, the experiment results of the trot, walk gaits and their mutual conversions are shown in Figure 20. The robot's actual stride length \(S\) is about 0.2 m, the gait period \(T\) is about 1 s, and the maximum height of leg raise \(H\) is about 0.03 m, which is basically consistent with the parameters set in CPG-NN-WT and preplanned foot WT. Besides, the average move speed in 10 seconds is about 0.4 m/s and the biggest lateral shifting is 0.1 m, which is about 0.5 \(S\).  
The acceleration curves of the robot when moving are shown in Figure 21. In the vertical direction (\(y\)-axis), the acceleration is stable at around 9.8 m/s\({}^{2}\) (gravitational acceleration), which shows the predesigned WT reduce the mechanical shock well. In the lengths direction (\(x\)-axis), the fluctuation of the acceleration is bigger in trot but smaller in walk due to the walk gait being more stable than trot. In the lateral direction (\(z\)-axis), except for the gaits transition period, the acceleration range is very small.  
To sum up, from the view of the whole moving process, the locomotion of the physical prototype is fluent and the body shakes are in an acceptable range. In addition, from the hip joint coordinate system, the foot trajectory is still in accordance with the preplanned WT well, which verifies that the presented control strategy is correct and effective.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline size: \(L\times W\times H\) (mm) & 400 \(\times\) 220 \(\times\) 420 \\ mass (kg) & 3.6 \\ \(l_{1}\) (mm) & 150 \\ \(l_{2}\) (mm) & 150 \\ hip joint range & (\(-\pi\), \(\pi\)) \\ knee joint range & (\(-\pi\), \(\pi\)) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical prototype parameters.  
Figure 19: The simulation video capture, where (**a**) the typical trot gait; (**b**) starting the transition from trot to walk gait; (**c**) the gait transition process; (**d**) transition finished; (**e**), (**f**), (**g**) and (**h**) the typical walk gait, robot’s leg lift sequence is 1-3-2-4; (**i**) starting the transition from walk to trot gait; (**j**) and (**k**) the gait transition process; (**l**) transition finished; (**m**) the typical trot gait.
## 5 Evaluation
### Address Generation Evaluation  
In this section, we will evaluate the address generation algorithm, divided into the performance of randomly generated addresses and the scattering of randomly generated addresses.  
First, we evaluate the performance of randomly generated addresses. We assume that \(D_{min}\) is 16, i.e., only the number of consecutive bits preceding the generated address that are 0 is greater than or equal to 16 can be used as a node that can be used for normal communication in the network. We implemented the algorithm using Java and completed the experimental framework. To reduce experimental error and for statistical purposes, our experiments were divided into five groups, each of which generated addresses that were not limited by \(D_{min}\) 1000 times, and then we counted the metrics in each group of address generation experiments.  
We conducted our experiments on a computer with an Inter i5-3470 CPU, 4 GB of RAM and Ubuntu 18.04 as the operating system. We counted the total time consumed by each set of experiments, the average time consumed per address generated, and the number of valid addresses and the number of addresses with BinLen of 8 or more under the above assumptions. The results of the experiments are shown in Table 1.  
Among the five groups of experiments, we observed that the shortest total time spent was 589 s in group 3, where the average time taken to generate each address was 589 ms. The longest total time spent was 660 s in group 5, where the average time taken to generate each address was 660 ms. The highest number of valid addresses generated among the five groups was in group 5, with five valid addresses. The lowest number was group 3, with no valid addresses. The highest number of addresses with BinLen of 8 or more generated in the five experimental groups was group 5, with 62 addresses. The least was group 3 with 43 addresses.  
Next, we evaluate the dispersion of randomly generated addresses, i.e., the logical distance dispersion of node addresses. We assume that \(D_{min}\) is 0, i.e., all generated node addresses can be used as nodes in the network that can be used for normal communication. We generated 1000 addresses using the same method as above and then calculated the maximum, minimum, average and variance of these 1000 addresses, as shown in Table 2.
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 4 Discussion
The review of previous studies shows that the accumulation of ions in quinoa organs, particularly when irrigated with graywater, is significantly influenced by salinity levels and irrigation methods. Ref. [49] demonstrated that increasing salinity EC\({}_{i}\) from 0.3 to 25 dS m\({}^{-1}\) resulted in significant accumulation of sodium and chloride ions in quinoa shoots, with increases of 82.2% and 75.8%, respectively. In seeds, Na and Cl also showed notable increases (43.3% and 50%, respectively) under higher salinity, while calcium levels remained stable [49]. These findings highlight that salinity stress affects both the vegetative and reproductive stages of quinoa growth, as evidenced by the significant increases in Na+ and Cl+ concentrations in the seeds [50]. Additionally, Ref. [50] emphasized that seeds play a crucial role in nutrient content, with nitrogen and potassium being more concentrated in the shoots, which are essential for supporting overall plant growth. When comparing the findings of this study to previous research, such as [24], which reported significantly higher phosphorus content in quinoa compared to other crops like wheat and maize, it suggests that quinoa may possess a specialized nutrient uptake mechanism. This mechanism allows quinoa to efficiently manage and accumulate essential nutrients, particularly in saline environments. Additionally, [PERSON]. [51] highlighted quinoa's unique capacity for phosphorus acquisition, with genotypic variations showing phosphorus accumulation ranging from 1.2 to 11.8 mg. This ability to accumulate and compartmentalize ions may provide quinoa with a competitive advantage under conditions of nutrient stress or suboptimal soil quality, further reinforcing its reputation for salt tolerance and resilience. Finally, the study identified a geometric progression in the quality of graywater through the cyclic treatment stages, which could be used to predict the quality of graywater beyond the three treatment levels tested. The observed significant correlations between the parameters analyzed provide a foundation for modeling the evolution of graywater quality. This predictive framework is valuable for optimizing treatment systems and ensuring that graywater can be safely reused in agricultural irrigation, particularly in regions facing water scarcity. The cyclic treatment system demonstrated significant potential in improving the quality of graywater, challenges such as salinity control and pathogen removal remain. Future research should focus on refining the treatment process to address these issues and ensure the sustainable use of treated graywater in agriculture.
# Mitigating Electrochemical Effects in Resistive Frost-Detection Sensors
## 2 Materials and Methods  
To investigate the effects of a pulsed DC excitation control method, an experiment was designed using two identical resistive sensors. One sensor served as the control, using the same continuously powered measurement method as in previous studies [16; 18], while the other implemented a pulsed DC excitation method. To ensure consistency with prior research, the sensor acquisition rate was maintained at 3 s intervals, preserving the same measurement conditions.
## 4 Experimental Validation of Predicted Feed Rate
Notably, the 162 mm/min condition, predicted by our XGBoost model, yielded the narrowest interquartile range in both metrics, indicating high consistency and reduced variability in surface finish. These trends align with the 95% confidence intervals summarized in Table 4, further supporting the statistical significance and repeatability of the improvements achieved by the predicted optimal feed rate. These findings demonstrate that the predicted feed rate not only improves quantitative surface metrics such as Pt but also visually suppresses chatter effects, as confirmed by the smoother, more uniform surface textures observed in the profilometer data. The successful validation of the predicted feed rate highlights the practical value of combining genetic algorithm-calibrated cutting force modeling with XGBoost regression. By learning the nonlinear relationships between process parameters and chatter-prone regions, the model provides data-driven, actionable recommendations that reduce the need for trial-and-error tuning in robotic machining systems. Figure 12: Surface profiles measured by contact-type profilometer under different feed rates. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feed Rate**} & **162** & **156** & **180** & **120** \\  & **(mm/min)** & **(mm/min)** & **(mm/min)** & **(mm/min)** \\ \hline Pt (\(\upmu\)m) & 19.49 & 24.34 & 21.64 & 24.84 \\ Ra (\(\upmu\)m) & 0.94 & 1.09 & 0.85 & 2.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Surface finish metrics (Pt and Ra) under different feed rates. Furthermore, the results confirm that machine learning models trained on theoretically simulated data can achieve high fidelity when applied to real experimental settings. This method is beneficial in robotic machining, as it saves time and resources by avoiding extensive empirical data collection. The integration of simulation-based data generation, vibration analysis, and supervised learning thus provides a robust framework for intelligent, adaptive manufacturing.
# Verification Methodology for Simulation Models of the Synchronous Generator on Transients Analysis
## 2 Modeling Methods for Transients Calculation
### Sudden Short-Circuit by a Dynamic Model
The presented algorithm is suitable for simulation of transients that are symmetric, such as a three-phase short-circuit, fault synchronization, or asynchronous start-up. In case of asymmetric states in the network, such as a two-phase short-circuit, other assumptions should be introduced. In the case of a two-phase short-circuit in the network, the system can be used as determined by the corresponding values of the phase voltages, known as direct and inverse components. In the case of a two-phase short-circuit without a network, on an open phase terminal the system is simulated by adding a fictitious resistance to the open phase and subsequently performing the transformations described in [2]. The algorithm for calculating the initial inductances, the transient inductances, the initial time constants, and all linkage factors, is described in [2]. Differential Equations (5)-(9) are used to calculate the linked fluxes, and (10) presents the equation of motion. Figures 5 and 6 present the equivalent diagrams for transverse and longitudinal axes, as the result of this system of equations. The following equations have been used to calculate initial conditions:  
\[\psi_{\mathrm{d0}}=(\mathrm{L}_{\mathrm{md}}+\mathrm{L}_{\mathrm{os}})\cdot\mathrm{ i}_{\mathrm{d0}}+\mathrm{L}_{\mathrm{md}}\cdot\mathrm{i}_{\mathrm{d0}} \tag{11}\]  
\[\psi_{\mathrm{q0}}=(\mathrm{L}_{\mathrm{md}}+\mathrm{L}_{\mathrm{os}})\cdot \mathrm{i}_{\mathrm{q0}} \tag{12}\]  
\[\psi_{\mathrm{f0}}=(\mathrm{L}_{\mathrm{md}}+\mathrm{L}_{\mathrm{of}})\cdot \mathrm{i}_{\mathrm{f0}}+\mathrm{L}_{\mathrm{md}}\cdot\mathrm{i}_{\mathrm{d0}} \tag{13}\]  
\[\frac{\text{d}\psi_{\text{d}}}{\text{dt}}=\text{u}_{\text{d}}-\frac{1}{\text {T}^{\prime\prime}_{\text{ds}}}\bigg{(}\psi_{\text{d}}-\frac{\text{k}_{\text {D}}\text{L}_{\text{\sigma D}}}{\text{L}^{\prime}_{\text{f}}}\psi_{\text{f}} -\frac{\text{k}_{\text{f}}\text{L}_{\text{\sigma f}}}{\text{L}^{\prime}_{ \text{Df}}}\psi_{\text{D}}\bigg{)}+\omega_{\text{el}}\psi_{\text{q}} \tag{5}\]0  
\[\frac{\text{d}\psi_{\text{d}}}{\text{dt}}=\text{u}_{\text{d}}-\frac{1}{\text {T}^{\prime\prime}_{\text{ds}}}\bigg{(}\psi_{\text{d}}-\frac{\text{k}_{\text {D}}\text{L}_{\text{\sigma D}}}{\text{L}^{\prime}_{\text{f}}}\psi_{\text{f}} -\frac{\text{k}_{\text{f}}\text{L}_{\text{\sigma f}}}{\text{L}^{\prime}_{ \text{Df}}}\psi_{\text{D}}\bigg{)}+\omega_{\text{el}}\psi_{\text{q}} \tag{5}\]1  
Under the no-load operation, the only current flowing through the generator is the excitation current \(\mathrm{i}_{\mathrm{d0}}\), which influences only the magnetic fluxes in d-axis. Therefore, the initial values of parameters in the q-axis equal zero. In the windings of armature and damping, the magnetic flux in the p. u. system is \(\psi_{\mathrm{d0}}=\psi_{\mathrm{D0}}=1\). The magnetic flux in the winding of excitation is slightly higher, because it contains the component of leakage flux between the poles.
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
## 3 Results
### HAS2 Knockdown Partially Inhibits CD44-Related Genes and Responses  
Since KO of CD44 affected HAS2 expression concomitantly with hyaluron synthesis, we sought to investigate whether downregulation of HAS2 would have an impact on CD44-mediated gene transcriptional responses and related phenotypes. By lentiviral delivery, two distinct shRNAs targeting _HAS2_ (#3 and #4) were used to create stable shHAS2 U251 MG knockdown cells. Interestingly, HAS2 knockdown decreased _VEGFA_, _ LOX_, and _HAS2-ASI_, which have been correlated to hypoxia [33] (Figure 5a). Furthermore, we confirmed these results by transiently transfecting another GBM cell line, U2990, with a siRNA targeting CD44. Downregulation of _CD44_ led to decreased expression of _HAS2_, _HAS2-ASI_, and _LOX_ mRNAs, as was also observed in CD44 KO cells, further consolidating that expression of these genes depends on CD44 (Figure S4). We evaluated the self-renewal capacity of shHAS2 U251 MG cells by performing ELDA and observed a reduction in stem cell frequency of HAS2 knockdown cells compared to control cells. In addition, treatment with the hyaluron synthesis inhibitor 4-MU, or the \(\gamma\)-secretase inhibitor DAPT, dramatically  
Figure 4: CD44 promotes hyaluron production and adhesiveness of U251 MG cells. **(a)**_HAS2-ASI_, _HAS2_, _TMEM2_, and _VCAN_ mRNAs were determined by real-time qPCR analysis after normalization to _TBP_. **(b)** The hyaluron levels in conditioned medium were measured and normalized to total RNA extracted from cells cultured in the indicated conditions. **(c)** An equal number of suspended parental or CD44 KO U251 MG cells (Clone #3) was subjected to adhesion assay to uncoated plates or plates coated with hyaluron (HA), fibronectin (FN), or collagen type I (COL I); after 30 min, adherent cells were stained with crystal violet. Results are depicted as arbitrary absorbance units. All graphs show the average \(\pm\) SEM values of at least three independent experiments. Asterisks show significant differences compared to the respective control condition: \({}^{\star}\)\(p<0.05\), \({}^{\star\ast}\)\(p<0.01\), \({}^{\star\ast\ast}\)\(p<0.001\).  
diminished the stem cell numbers, further consolidating the importance of HAS2-mediated hyaluron synthesis and CD44 cleavage, respectively, in self-renewal capacity (Figure 5b).  
Interestingly, HAS2 knockdown led to a significant reduction in the phosphorylation of CREB and ERK1/2, as witnessed using two different shRNAs, phenocopying the result of CD44 genetic depletion, when cells were grown in sphere-like conditions (Figure S5a,b). Additionally, HAS2 knockdown caused a decrease in the CD44 protein levels, implying a positive feedback loop mechanism involving CD44 and HAS2 in GBM (Figure S5b).
## 5 Discussion
### Using the Approach in Practice  
We initially analyzed existing distributed SDN management platforms, such as HyperFlow [70] application, Onix [71], Kandoo controller [72], OpenFlow protocol [73], ElasticCon [74], ONOS controller [75], High Availability Controller [76] and others.  
The analysis showed that all the above platforms do not implement SDN recovery after CAs. When control is transferred from the main to the backup controller after its failure as a result of a large CA, the backup controller will also be attacked. Thus, the development of such approaches to ensure SDN resilience is required, which are able not only to ensure uninterrupted network management by providing redundancy, but also to detect and eliminate the causes of a controller failure.  
The proposed approach helps to choose the most reasonable countermeasure. Knowing the place where the attack was detected and its type, the administrator, in accordance with the proposed approach, evaluates the SDN resilience under the conditions of a possible attack implementation, and the justified LSTM neural network, through the OvS Agents, monitors the state of the controllers and automatically rebuilds them.  
In this case, this estimate can be refined. By evaluating, on the one hand, the results of calculating the resilience and possible losses caused by a CA, and, on the other hand, the costs of implementing countermeasures, the administrator can either manually select the most appropriate countermeasure or it can do so automatically using a reasonable SDN protection system under CA conditions. The trained LSTM determines the most probable attack scenario in real time and offers the network administrator to launch a countermeasure (recovery) scenario. Further, after the decision is made, the selected scenario is executed by the official. The procedure is loaded from the configuration database, automatic settings are made and connection to the backup controller is performed when the full reconfiguration mode is selected.  
In addition, in order to reduce the time for performing mathematical calculations during a detected attack, it is possible to prepare stochastic networks in advance for the most known types of attacks and build families of dependencies of the average time of their implementation on the parameters characterizing the attack scenario and SDN configuration. This will reduce the time to develop and justify a countermeasure and, ultimately, will contribute to a higher level of SDN security in general.