esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## Materials and Methods
### Preparation of Nanostructures  
Chemical materials including zinc nitrate hexahydrate Zn(NO3)26H2O, aluminum nitrate hexahydrate Al(NO3)39H2O, and urea CO(NH2)2 were purchased from Sigma-Aldrich. Series of Zn-Al-GO LDHs consisting of different concentrations of graphene oxide (1.3%, 2.3% and 3.5%), and Zn-Al layered double hydroxides were synthesized through adding GO during the growth of Zn-Al LDHs. The prepared graphene sheets were treated with hydrogen peroxide for 2 h in the ultrasonic bath to oxidize and become suspended in the solution. Appropriate amounts of the treated graphene (40 mg, 70 mg and 110 mg) were added to aqueous solution of zinc nitrate (0.05 mol/L) and aluminum nitrate (0.02 mol/L) in addition to urea (0.05 mol/L). By thermal treatment at 80 \({}^{\circ}\)C for 24 h in the water bath, grey-white material was precipitated. Pure Zn-Al LDHs was prepared by the same method for comparison. The samples were called Zn-Al-GO1, Zn-Al-GO2 and Zn-Al-GO3 agreeing with the content of the treated graphene 40 mg, 70 mg and 110 mg; respectively.
## 5 Conclusions  
Despite the number of analyzed acquisitions, this was not high enough to derive general rules. The proposed methodology seems to effectively allow the evaluation and comparison of the performance of the demodulation process itself, as well as the goodness of the band indicators traditionally used for bearing diagnostics. In fact, the paper proposes two novel a posteriori indicators, which were used for the computation of both a demodulation effectiveness metric and an indicator effectiveness metric.  
From the results in Section 4, derived from a portion of the CWRU database, which, although reduced, considers many relevant signal peculiarities, it was possible to find a ranking of the considered band indicators. According to Table 3, by averaging the \(iE\)s in the four different cases, it was easy to notice how the autogram indicator \(AK\) proved to be very good for the selection of the optimal center frequency for the demodulation, as  
Figure 6: Traditional band indicators as a function of the center frequency \(f\), compared to the proposed \(NA\) and \(SNR\) a posteriori indicators. The maximum of each indicator is highlighted by a circle, and the value of \(NA\) and \(SNR\) at that point are reported in the legend. The average of such two values produces the \(iE\) which is summarized in Table 3. **(a)** IR014_1, **(b)** IR014_3, **(c)** B021_0.  
well as the averaged negentropy I, taken from the infogram. All of the other indicators, on the contrary, although only in some cases, appeared to behave better than the kurtogram indicator \(K\), but not on average. This probably explains why the spectral kurtosis, the milestone for band selection, is still the reference algorithm for the bearing diagnostics.  
Future improvements and applications may involve the definition of confidence intervals for the indicators [34], the application on different machineries and datasets such as in [35, 36], the compensation of non-stationary rotational speeds [37, 38, 39], and the analysis of the effect of the sampling rate on the results.  
The main limitation of this sort of \"brute force\" approach to the optimization is that it requires the computation of the SES in all the sub-bands, leading to longer computational times which prevent the use of such an algorithm in real time. Hence, further studies on the computational complexity and optimization of the algorithm could be conducted in the future.  
Conceptualization, D.A.P., F.A., G.L.; methodology, D.A.P.; software, D.A.P.; validation, F.A., G.L., and M.S.; formal analysis, D.A.P.; investigation, D.A.P.; writing, D.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 5 Findings
### Detection Architecture
The results across 100 randomized attack sessions showed that the Proximal Policy Optimization (PPO) algorithm demonstrated the fastest convergence and highest average rewards, making it the preferred choice for HoneyIoT. Testing results indicate that reinforcement learning-driven approaches significantly improve interaction quality, with rewards structured to reflect progress towards capturing detailed attack information [35]. The HoneyShell testing experiment evolved through two phases, as [PERSON] [61] shows. In Phase 1, the initial deployment minimally modified the original Cowrie code, collecting data and identifying responses that could reveal the honeypot's nature. It accepted all username-password combinations to maximize attacker interaction. Three instances were deployed: two on-premises and one in the cloud. In Phase 2, launched nine months later for one on-premises instance (with the other remaining in Phase 1 for comparison), HoneyShell refined its operations. It restricted valid credentials to the top 30 combinations observed in Phase 1 and enhanced attacker environments by adding new users, file systems, and commands. Logs from Phase 1 informed improvements, such as emulating file command responses, ensuring more realistic interactions and attracting further attacker activity [61]. The experiments demonstrated that adaptive honeypots, powered by reinforcement learning like the PPO algorithm and iterative refinements, significantly enhance interaction quality and realism, ensuring more effective data collection on attacker behavior. Dynamic honeypots represent an advanced layer of cybersecurity by leveraging constant changes in their configurations, services, or system appearances to confuse and mislead attackers. Unlike static honeypots, which remain fixed and easier to profile over time, dynamic honeypots thrive on unpredictability, increasing the attackers' uncertainty and discouraging them from exploiting vulnerabilities. This dynamism helps honeypots evade detection and sustain their effectiveness in hostile environments. An evolution of this concept is found in evolutionary dynamic systems, where the honeypot is treated as a dynamic ecosystem involving defenders, attackers, and legitimate users. These honeypots not only change configurations but also cycle between real and fake services, creating a security equilibrium that further enhances their deceptive capabilities. Unlike conventional dynamic honeypots, which primarily focus on altering appearances or configurations, evolutionary honeypots adopt a broader strategy by actively simulating real-life network behavior and integrating legitimate-like interactions [67].
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 3 Efficiency Optimization Control Based on Current Angle Look-Up Table
### Principle of EDS Efficiency Optimization by Current Angle  
The salient rate \(\rho\) of PMSM in this paper is 1, so its current limit circle and voltage limit circle can be drawn as Figure 5. MTPA and flux weakening (FW) control are most widely used in motor control. Take MTPA and FW control as examples to analyze the change of motor working state. Generally, MTPA (\(i_{d}=0\)) control is applied to the working area before the base speed of the motor, in which the stator current is controlled to move on the q-axis like \(i_{s1}\) and \(\beta\) is \(\beta_{1}\). At this time, the d-axis current of the motor is 0 A, so the copper loss of the motor can be minimized while the iron loss of the motor is  
Figure 5: Schematic of PMSM stator current action.  
not considered. After exceeding the base speed, the stator current will not meet the limit of the voltage limit circle if it was still controlled by MTPA, so it is necessary to increase the component of d-axis current to deflect \(i_{s}\) to the left like \(i_{s2}\) and \(\beta\) is \(\beta_{2}\), making motor controlled by FW control.  
It can be found that with the change of EDS working state, \(\beta\) of the motor is constantly changing as well. From the relationship between the stator current \(i_{s}\) and the dq-axis current, it can be seen that the change of \(\beta\) will bring the change of \(i_{d}/i_{q}\) at the same time. By adjusting \(\beta\), the direction and amplitude of \(i_{s}\) can be changed, so as to change \(i_{d}/i_{q}\) and optimize the efficiency of electric drive system.  
From the mathematical relationship, \(i_{d}/i_{q}\) can be written as:  
\[\left\{\begin{array}{l}i_{d}=-i_{s}\cos\beta\\ i_{q}=i_{s}\sin\beta\end{array}\right. \tag{26}\]  
In brief, the direction and amplitude of \(i_{s}\) can be changed by adjusting the angle between stator current and d axis called \(\beta\) so as to change the dq-axis current and optimize the efficiency of electric drive system. At this time, the \(i_{s}\) is directly determined by \(\beta\), and it is unnecessary to judge whether it exceeds the current limit \(i_{lim}\), which will reduce the logic judgment and save more computing resources.
## 5 Experimental Results
### Result Comparison
Figure 23 shows that the training curve of each model using 200 training data had converged before 50 iterations. The result indicates that 200 training data were enough for each model, and the benefit of increasing the training data was insignificant. Figure 24 presents the recognition result of using 200 training data for training in the confusion matrix. The x-axis is the actual fault type, and the y-axis is the identified fault type. The red grids in the confusion matrix represent the number of misidentified types. The green grids represent the number of correctly identified types. Each fault type's recognition accuracy and error rate were the green and red values in the x-axis light gray grids. The overall fault type recognition accuracy and error rate were the green and red values in the dark gray grids in the lowest right portion of Figure 24. The overall fault type recognition accuracy was calculated by dividing the total value of green grids by the total value of green and red grids. In Figure 24, 97 out of 100 test data of Type 2 were identified correctly; the recognition rate was 97%. Similarly, the recognition accuracy rates of Type 1,  
Figure 23: Training curve of each model using 200 SDP training data for CNNs. Figure 22: Recognition curves of each model using 200, 300, and 400 training data. Figure 24: Confusion matrix of 200 SDP training data for CNN. This study compared the Lorenz e1e2 chaos dynamic error scatter diagram and SDP feature extraction methods, where 10% and 20% noise were added to the test data, respectively. Figure 25 shows the recognition variation curves of the methods. The decreasing amplitude of the recognition rate was observed to be smaller after the noise was added when the SDP was used for feature extraction. The decreasing amplitude of the recognition rate was larger after the noise was added in when the Lorenz e1e2 chaos dynamic error scatter diagram was used as a feature map. The data comparison showed that the chaos dynamic error scatter diagram was likely to be influenced by noise, and the SDP had better resistance to noise.
## 4 Parametric Analysis on Creep Deformation of PMMA Frustum Observation Windows
### Effect of Loading Rate on Creep of Frustum Observation Window  
Utilizing the parametric analysis of the aforementioned observation window, finite element modeling is conducted based on the structural parameters of the optimized observation window model (with a mesh size of 5 mm for the observation window, a mesh size of 15 mm for the window seat, and a temperature of 30 \({}^{\circ}\)C). Through a substantial number of finite element calculations, it has been observed that different loading rates influence the displacement, strain, stress, and other output values of the observation window. This also has a certain influence on the creep behavior of the observation window. By employing four different loading rates, respectively, 2.3 MPa/min, 4.5 MPa/min, 6 MPa/min, and 8 MPa/min, the pressure of the observation window model was ultimately loaded to 115 MPa and maintained for 5 h. The finite element calculation results depicting the center position of the lower surface of the observation window at different loading rates are presented in Figures 16-18. Due to the variations in the four loading rates, the time spent in the loading stage differs, with lower loading rates requiring more time. It is apparent from Figures 16-18 that the differences in the values of displacement, stress, and strain among the four different loading rates during the loading stage are minimal. Nevertheless, in the holding load stage, the differences become more pronounced. Although the holding load time is consistent, the amplitude of strain and stress in the holding load stage decreases with an increase in the loading rate. During the stage of holding the load, the displacement at the center of the inner surface of the observation window increases with the rise in the loading rate. Under loading rates of 8 MPa/min and 2.3 MPa/min, the observation window exhibits its maximum and minimum axial displacements, respectively. Specifically, the axial displacement at 8 MPa/min is 7.2213 mm, while at 2.3 MPa/min, it is 6.9279 mm. The axial displacement of 4.5 MPa/min is 7.1107 mm, and the axial displacement of 6 MPa/min is 7.1554 mm. Therefore, in the finite element simulation calculation of the observation window, the loading rate should not be too fast during the pressurization process, otherwise it is not conducive to the creep strain and displacement release of the observation window in the high-pressure environment.  
Figure 15: Loading stage (30 \({}^{\circ}\)C), maintaining stage (30–2 \({}^{\circ}\)C): (**a**) Stress cloud before creep; (**b**) Stress cloud after creep; (**c**) Displacement (mm) cloud before creep; (**d**) Displacement (mm) cloud after creep; (**e**) Strain cloud before creep; (**f**) Strain cloud after creep.  
Figure 16: The stress change curve at the center of the inner surface at different stress rates.  
Figure 17: The displacement curves at the center of the inner surface under different stress rates.  
Figure 18: The strain curves at the center of the inner surface at different stress rates.
## 3 Results  
### The Impact of Vibration Amplitude on the Performance of Seeder Drill  
As shown in Figure 6, under the influence of vibrations with a frequency of 6 Hz and an amplitude of 0-3 mm, the seed metering device exhibited gradual changes in the seed discharge rate per unit time and in the variation coefficient of seed metering uniformity, maintaining relative stability. This indicates that under these conditions, the seeds within the seed metering device remained relatively calm without detaching from the hole-wheel. The simulation and experimental results are basically stable, with their curves intersecting, indicating that the simulation and experimental results are largely consistent. Thus, within this amplitude range, the vibration magnitude has little effect on the performance of the seed metering performance of the device.
# New Insights on Mechanical Stimulation of Mesenchymal Stem Cells for Cartilage Regeneration
## 2 Chondrogenesis In Vitro  
The essence of regenerative therapy lies in the use of stem cells, bioactive molecules, biomaterials, and their combinations [15]. Physiological chondrogenesis is regulated by specific cytokines and transcription factors, such as the transforming growth factor beta (TGF-\(\beta\)), fibroblast growth factor (FGF), and insulin-like growth factor 1 (IGF-1) [16]. As another example, SRY-related high-mobility group box gene 9 (SOX-9) is a transcription factor essential for chondrogenesis processes [17] and also to avoid dysregulated chondrocyte hypertrophy [18]. Commonly, MSCs undergo chondrogenic differentiation, recognizable by increasing in specific ECM components, when in the presence of TGF-\(\beta\). This pathway consists of the proper formation of the receptor complex that requires type I and type II serine/threonine kinase receptors and intracellular effectors. The type I receptor family counts seven members which phosphorylate different small mother against decapatplegic (SMAD) proteins. Transcriptional activation of chondrogenic genes, like those encoding type II collagen and aggrecan, is mediated by the involvement of the type I receptor 5 (ALK 5) and subsequent phosphorylation of SMAD 2/3. Otherwise, TGF-\(\beta\) could bind ALK 1 and activate the SMAD 1/5/8 pathways, resulting in hypertrophy-related gene expression [19]. Nevertheless, one of the clinical limits of this kind of chondrogenic differentiation, based on the TGF-\(\beta\) pathway, relies on the risk of incurring undesired and premature hypertrophy entrance of MSCs [20]. Considering this scenario, a more sophisticated and authentic approach to differentiation is offered by understanding the mechanical forces naturally experienced by chondrogenic progenitors and applying them, even in the absence of exogenous growth factors, to stem cells in order to mimic the same environment within the joint.
###### Abstract  
This paper presents a forward kinematic model of a wire-driven surgical robot arm with an articulated joint structure and path generation algorithms with solutions of inverse kinematics. The proposed methods were applied to a wire-driven surgical robot for single-port surgery. This robot has a snake-like robotic arm with double segments to fit the working space in a single port and a joint structure to secure stiffness. The accuracy of the model is highly important because small surgical robot arms are usually controlled by open-loop control. A curvature model is widely used to describe and control a continuum robotic body. However, the model is quite different from a continuum robotic arm with a joint structure and can lead to slack of the driving wires or decreased stiffness of the joints. An accurate forward kinematic model was derived to fit the actual hardware structure via the frame transformation method. An inverse kinematic model from the joint space to the wire-length space was determined from an asymmetric model for the joint structure as opposed to a symmetric curvature model. The path generation algorithm has to generate a command to send to each actuator in open-loop control. Two real-time path generation algorithms that solve for inverse kinematics from the task space to the joint space were designed and compared using simulations and experiments. One of the algorithms is an optimization method with sequential quadratic programming (SQP), and the other uses differential kinematics with a PID (Proportional-Integral-Derivative) control algorithm. The strengths and weaknesses of each algorithm are discussed.  
surgical robot; kinematic model; real-time path generation; inverse kinematics; snake-like robotic arm +
Footnote †: journal: Applied Mechanics and Physics Letters
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.2 Gleam  
The Global Land Evaporation Amsterdam Model version 3 (GLEAM v3b; [PERSON] et al. (2017), [PERSON] et al. (2017)) is used to evaluate the impact of the vegetation updates on evapotranspiration. GLEAM is a semi-empirical-process-based model that computes evapotranspiration and its components globally over land at daily scale using a set of algorithms driven by satellite-based observations. Potential evaporation is derived over four land cover tiles (low vegetation, tall vegetation, bare soil and open water) using the Priestley-Taylor formulation. Then actual transpiration is calculated by multiplying the potential evaporation by a stress function based on root-zone soil moisture and vegetation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Development** & **Experiment Type** & **Name** & **Comment** \\ \hline \multirow{3}{*}{Surface water representation} & Offline Surface simulation & OSW & Updated lake parameters \\  & Coupled Forecast + Data & DASW & Updated Glacier, Land water \\  & assimilation & & mask and lake \\ \hline \multirow{3}{*}{Updated LU/LC and LAI} & Offline Surface simulation & CLAI & ESA-CCI land cover and LAI \\  & Coupled Forecast simulation & FCLAI & conservative disaggregation \\ \hline \multirow{3}{*}{Seasonal vegetation cover} & Offline Surface simulation & OSCV & LAI based Seasonal \\  & Coupled Forecast simulation & FSCV & vegetation cover \\ \hline \multirow{3}{*}{Prognostic LAI} & Offline Surface simulation & OPLAI & Prognostic LAI based on daily \\  & & & CO\({}_{2}\) balance \\ \hline \multirow{2}{*}{New snow multilayers} & Offline Surface simulation & OSnML5 & 5 Layers snow scheme \\  & Coupled Forecast & FSnML5 & 5 layers scheme \\ \hline \multirow{3}{*}{Increased soil depth and discretization} & \multirow{3}{*}{Offline Surface simulation} & OSML10\_TW & 10 Layers soil 8 m for \\  & & & temperature and water \\ \cline{1-1}  & & OSML10\_T & 10 Layers soil 8 m for \\ \hline \multirow{3}{*}{Coupling with hydrodynamic} & Offline Surface simulation & \multirow{3}{*}{-} & \multirow{3}{*}{details in Section 4.7} \\  & coupled with routing model & & \\ \hline \multirow{3}{*}{Urban parametrization} & Offline Surface simulation & OSMU & offline 1D and 2D \\  & & & urban parametrization \\ \cline{1-1}  & Single Column simulation & SCMU & coupled 1D forecast with \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental configurations for the evaluation of model developments.  
optical depth. Soil moisture is the result of a soil-water balance model that computes the evaporative flux and is driven by observation-based precipitation. The last component of GLEAM is the interception loss which follows the Gash analytical model driven by precipitation and vegetation properties. Further details of GLEAM are documented in [PERSON] et al. [78] and [PERSON] et al. [77].