esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 2 Problem Description
### Energy Efficiency Problem in LEO Networks  
Efficient energy management is critical for LEO satellites, which orbit the Earth every 90 min and may experience eclipses lasting several tens of minutes per revolution [19]. During these periods, satellites rely entirely on stored power, and a higher depth-of-discharge (DoD) accelerates battery degradation, thereby shortening both battery and satellite lifespans. Traditional routing protocols designed to minimize path length or latency do not account for such battery-intensive conditions. When applied to LEO networks, they can unevenly increase DoD on certain satellites, leading to imbalanced energy consumption and potential in-flight outages [20]. To address this, we propose an Eclipse-Aware Routing (EAR) algorithm that distributes traffic evenly by considering each satellite's energy consumption rate, eclipse duration, and expected transmission load. This approach balances DoD levels across the constellation, maximizes energy efficiency, and extends operational lifespan, ultimately reducing the substantial operating costs of LEO satellite networks [20]. Moreover, previous studies leveraged GPUs to parallelize and accelerate the EAR and KSP algorithms to achieve high performance; however, a key disadvantage of GPUs is that their high power consumption can actually reduce overall energy efficiency [15].
###### Abstract  
Filtered multitone (FMT) modulation divides the communication band into several subbands to shorten the span of symbols affected by multipath in underwater acoustic (UWA) communications. However, there is still intersymbol interference (ISI) in each subband of FMT modulation degrading communication performance. Therefore, ISI suppression techniques must be applied to FMT modulation UWA communications. The suppression performance of traditional adaptive equalization methods often exploited in FMT modulation UWA communications is limited when the effect of ISI spans tens of symbols or large constellation sizes are used. Turbo equalization consisting of adaptive equalization and channel decoding can improve equalization performance through information exchanging and iterative processes. To overcome the shortcoming of traditional minimum mean square error (MMSE) equalization and effectively suppress the ISI with relatively low computation complexity, an FMT modulation UWA communication using low-complexity channel-estimation-based (CE-based) MMSE turbo equalization is proposed in this paper. In the proposed method, turbo equalization is first exploited to suppress the ISI in FMT modulation UWA communications, and the equalizer coefficients of turbo equalization are adjusted using the low-complexity CE-based MMSE algorithm. The proposed method is analyzed in theory and verified by simulation analysis and real data collected in the experiment carried out in a pool with multipath propagation. The results demonstrate that the proposed method can achieve better communication performance with a higher bit rate than the FMT modulation UWA communication using traditional MMSE adaptive equalization.  
Keywords:underwater acoustic communications; filtered multitone modulation; interference suppression; turbo equalization; low-complexity channel-estimate-based minimum mean square error algorithm +
Footnote †: journal:  
_Article_  
_Article_  
_Article_  
_Filtered Multitone Modulation Underwater Acoustic Communications Using Low-Complexity Channel-Estimation-Based MMSE Turbo Equalization_
# Botanicals in Functional Foods and Food Supplements: Tradition, Efficacy and Regulatory Aspects
## 6 Safety Aspects
### The Case of RYR  
As described previously (see Section 5), in 2011 RYR received a positive opinion for the claim supporting the reduction of LDL-cholesterol plasma level, if the daily intake of monacolin K was 10 mg. In 2018, the EFSA was asked a second time in relation to RYR, with the request being to evaluate the safety of the monacolins contained in it and to provide advice on the daily dose of monacolins that does not give rise to concerns for harmful effects [33]. Since the lactonic form of monacolin K has the same chemical structure of lovastatin (a pharmaceutical drug), the EFSA, after consulting the scientific material provided, concluded that the intake of 10 mg of monacolin K with a food supplement would lead to the intake of a therapeutic dose. Moreover, the evaluation of the data presented showed that several adverse events were reported following the consumption of RYR. These adverse events involved musculoskeletal system (the most severe clinical event associated was rhabdomyolysis) and  
Figure 2: Active amines of _Citrus aurantium_ L. regulated for their content in food supplements.  
liver. The clinical symptoms appeared after consumption of RYR, with an intake of 3-10 mg/day of monacolin K.  
Two problems have therefore been highlighted for RYR:  
1. the dose of monacolin K required to boast the claim about the LDL-cholesterol reduction corresponded to the lower therapeutic dose of the drug lovastatin, chemically identical to the lactonic form of monacolin K. This fact is not acceptable due to the legal definition of a dietary supplement, which must not show pharmacological activity.
2. EFSA \"was unable to identify a dietary intake of monacolins from RYR that does not give rise to concerns about harmful effects to health, for the general population, and as appropriate, for vulnerable subgroups of the population\" [33]. This fact makes uncertain the marketing of RYR as food supplement, products that do not require medical supervision.
## 2 Numerical Simulation
### Mesh Strategy  
The overall grid is divided into stationary and moving grids, as shown in Figure 3. The process of grid generation is carried out using the commercial software ANSYS ICEM 2020 R2 and FLUENT MESHING 2020 R2. A hybrid mesh approach is used to further divide the stationary domain into coarse, fine, and extra-fine regions. The coarse and fine regions further away from the system model have minimal impact on the simulation results. To optimize grid quality and reduce the number of grid cells in these regions, larger structured grids are used in ANSYS ICEM. However, the extra-fine region and moving domain include the wind barrier, bridge, and HST models, which have complex curved surfaces and complex structures. To accurately capture these intricate features, a highly adaptive poly-hexcore mesh was used to discretize these two regions in FLUENT MESHING.  
The extra-fine region and moving domain are crucial for the simulation calculations, with grid densities approximately three times and five times higher than the fine region. To accurately capture the flow field information around the models, 15 boundary layers were applied to the surface of the curved wind barrier, bridge, and HST, as shown in Figure 3a. The grid height of the first layer is set to 2 mm for the wind barrier and train and 6 mm for the bridge. The growth factor is set to 1.1. The surfaces of the system models consist of structured polygonal grids. The surface mesh size of the HST is between approximately 20 and 35 mm, while the mesh size of the curved wind barriers is 30 mm, and the maximum surface mesh size of the bridge is 150 mm, as shown in Figure 3b,c.  
Computational fluid dynamics calculations were performed using ANSYS FLUENT 2020 R2, which is based on the finite volume method in a high-performance computing center. The IDDES with the SST \(k-\omega\) turbulence model is used for the transient solution; the transient formulation uses second-order implicit. The resolution of the pressure and velocity coupling equation utilized the semi-implicit method for the pressure-linked equations (SIMPLE) algorithm. The gradient of the resulting variable at the cell center was determined using the least squares cell-based method. The second-order upwind scheme was used to solve the \(k\) and \(\omega\) equations. A transient calculation model was adopted with a time step size of 0.001 s. To ensure accurate results, a total of 30 iterations were conducted  
Figure 2: Computational domain and boundary conditions. (unit: m).  
for each time step. The convergence criteria were set with a residual tolerance of \(10^{-5}\) for all physical quantities.
# Power Flow Calculation Methods for Power Systems with Novel Structure UPFC
## 2 The General Structure UPFC and Its Equivalent Model
### Power Flow of the UPFC Shunt Side  
The shunt converter of the UPFC is used to control the DC voltage. When the active loss of the UPFC is ignored, the active power exchanged between the shunt converter and the power grid is:  
\[P_{ne}=P_{km}-P_{lm} \tag{11}\]  
When there are multiple series converters, we have:  
\[P_{ne}=\sum_{i}\left(P_{kmi}-P_{lmi}\right) \tag{12}\]  
where \(P_{kmi}\) and \(P_{lmi}\) are the active power flows of the \(i\)th series converter.  
Besides, the shunt converter of the UPFC also controls \(Q_{ne}\) or the voltage magnitude of bus \(n\). When the shunt converter controls \(Q_{ne}\), \(Q_{ne}\) is equal to the set value. When the shunt converter controls the voltage magnitude of bus \(n\), \(Q_{ne}\) is determined after the power flow calculation is solved.
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 2 Materials and Methods  
### Wavelet Transform  
The Mallat wavelet transform provides a powerful framework for signal analysis by decomposing signals into components characterized by diverse frequency spectra and resolution levels. This transformation enables the capture of essential signal features across multiple scales, forming the basis of multi-resolution analysis. Within this paradigm, signal details are represented hierarchically, permitting dynamic adaptation of the analysis scale to accommodate varying local signal characteristics. A critical advantage of this approach lies in its ability to effectively separate signal components from noise across different scales, thereby facilitating robust image denoising strategies.  
Building upon this theoretical foundation, the present study employs two distinct wavelet transform methodologies for image processing: the Discrete Wavelet Transform (DWT) and the Stationary Wavelet Transform (SWT). These transforms are selected for their complementary strengths in decomposing image data into frequency sub-bands, which is crucial for subsequent denoising procedures.
###### Abstract  
To achieve high-precision measurements of target points on long straight tracks, a multi-level measurement method based on length-angle mixed intersection techniques was explored. Firstly, a control network with graded measurement levels was proposed, based on the spatial error characteristics of different measuring devices and the principle of nonlinear least squares, and a method for adjustment calculation based on length-angle mixed intersection was studied. Secondly, numerical simulation was conducted to assess the impact of instrument placement on measurement accuracy, and the results indicated that central positioning within the measurement range can effectively minimize the overall point location errors. Finally, the methodology was validated in a practical setting at a rocket sled test site. Experimental results demonstrated that, within a measurement range of approximately 669 m, when target points were located on one side of the track and distance measurements were used as benchmark values, the measurement control network achieved a distance standard deviation of 0.20 mm. The range of distance deviations was between \(-0.85\) mm and 0.98 mm. This approach offers substantial reference value for high-precision coordinate measurements over extended distances.  
measurement control network; graded measurement; length-angle mixed intersection; layout simulation +
Footnote †: journal: Medical Sciences
## 6 Conclusion  
Wide Swath Altimeters must be part of the future of the overall altimetry observation system. They enable us to get a global coverage of the Earth between -80 and 80 degrees of latitude and an appropriate temporal sampling, required to observe mesoscale phenomena and improve ocean monitoring and forecasting to an unprecedented level. Though their error budget is greater than for a nadir altimeter, and the errors are partially correlated across-track, these new altimeters are the only ones capable of providing us with a global two-dimensional topography of the ocean. Unfortunately, the demonstration of such revolutionary systems could not be incorporated as part of the Jason-2 program. However, studies of such instruments have continued (see [33] and [34]) and hopefully one of them will fly over the oceans to demonstrate their capabilities and add to the scope of nadir altimeter missions.  
The studies performed and presented throughout this paper have led to a better knowledge of the performances of this interferometer and its interactions with all kinds of external factors, finally enabling us to optimize the instrument parameters as well as the definition of the overall mission. Different levels of accuracy associated with different pixel resolutions can be achieved. In addition, integrating overlapping passes over the whole mission lifetime will enable to get even better accuracy.
## 3 Results and Discussion
### Honeycomb  
Recently, 3D-printed honeycomb structures have been investigated in the literature due to the advancement and ease of 3D printing technology. The honeycomb shape offers a good balance of impact resistance performance and strength while having a lower density due to its hollow cells. The results of the simulated drop tests for the two packaging sizes of rubber at 1 m are shown in Figure 9. Figure 9a,c refer to drop impact simulation on 0.22\" honeycomb packaging with stress concentrations for the pear and packaging, respectively. Figure 9b,d refer to drop impact simulation on 0.22\" rubber packaging with stress concentrations for the pear and packaging, respectively.
## 3 Results and Discussion
### Laser-Induced Damage Threshold
Assuming that these ensembles were degenerate (all defects in the corresponding ensemble have the same LIDT), the extraction of their densities was possible by setting their corresponding LIDT values and then fitting each sample's damage probability data. The following points are important to understand the fitting principles and assumptions which were used:  
1. It was assumed that low-LIDT defects were completely removed for the 250 nm etching case. Therefore, the LIDT of the other ensemble of 239 J/cm\({}^{2}\) was obtained by fitting the 250 nm etched sample data (Figure 7d) and was used for the defect density extraction, where the \"high-LIDT\" defect ensemble was also already present (50 nm, 100 nm and 400 nm etched samples)
2. Defect densities of the 50 nm etched sample (Figure 7b) were obtained by fitting data in two ranges separately. A low LIDT of 55 J/cm\({}^{2}\) (marked as a red line) was used for the 0-300 J/cm\({}^{2}\) range fitting (\"Fit 1\"), a high LIDT of 239 J/cm\({}^{2}\) (marked as a blue line) was fixed for the 300-600 J/cm\({}^{2}\) range fitting (\"Fit 2\"--also assuming, that damage probabilities were zero in the 0-300 J/cm\({}^{2}\) range for this high-LIDT defect ensemble). It should be noted that here, the LIDTs of two different defect ensembles are shown, but not the LIDT of the 50 nm etched sample, which was 85 \(\pm\) 11 J/cm\({}^{2}\) (Figure 6);
3. Defect densities for the 100 nm and 400 nm etched samples (Figure 7c,e) were calculated by using a LIDT value of 239 J/cm\({}^{2}\), obtained for the 250 nm etched sample;  
Figure 8 shows the cumulative defect density (sum of both ensemble densities). It is important to understand that the calculated total defect density value is just the lower limit of a possible real density, since deeper than 400 nm etchings might reveal a third ensemble of defects, whose LIDT might still be higher than 239 J/cm\({}^{2}\). The initial defect density was reduced 10 times after the etching to 50 nm. Figure 7: Damage probabilities and fittings for the calculation of defect densities for (**a**) nonetched and (**b–e**) plasma-etched to different depths Co:MALO crystals. The low-LIDT defect ensemble was completely removed after the plasma etching to a 100 nm depth and the remaining high-LIDT defect density dropped to 5.012 \(\times\) 10\({}^{8}\)/m\({}^{2}\). It was slightly reduced after the etching of 400 nm to 3.162 \(\times\) 10\({}^{8}\)/m\({}^{2}\), but the difference was negligible considering the error bars. The data presented in Figures 6-8 suggest that an applied low-energy oxygen plasma etching to depths \(\geq\)100 nm for the investigated spinel crystals is optimal for a huge improvement of their surface LIDT. However, it is worth to note that the optimal depth should also depend on the used chemomechanical polishing procedure and its technological steps, such as the choice of polishing compounds, their fractions, the polishing time of each step, load, temperatures, the pH of the slurries, etc.
## 2 Machine Learning Applications in Big Data
### Energy and Utilities  
The energy and utilities sector is increasingly leveraging ML to optimize operations, improve energy efficiency, and integrate renewable energy sources. One of the primary applications is in predictive maintenance, where ML models analyze data from sensors installed in power plants, wind turbines, and other infrastructure to predict equipment failures before they occur. This enables utilities to perform maintenance proactively, reducing downtime and maintenance costs [74; 75; 76; 77; 78].  
Coupled with predictive maintenance, ML is applied to optimize energy consumption and improve grid stability. Smart grids, which use ML to analyze data from sensors and meters across the grid, can predict energy demand, optimize energy distribution, and integrate renewable energy sources more effectively. This leads to more efficient use of resources, lower energy costs, and reduced environmental impact [79; 80; 81; 82].  
ML also plays a crucial role in the integration of renewable energy sources, such as solar and wind, into the energy grid. By analyzing weather data, ML models can predict the availability of renewable energy, allowing utilities to optimize the use of renewable resources and reduce reliance on fossil fuels. This contributes to the development of a more sustainable and resilient energy system [83; 84; 85].
## 4 Experimental Results  
The theory developed in the previous section assumes that the human operator behaves as a PI controller to provide a desired position for one manipulator (Assumption 3). This assumption is justified in Remark 2, but it is unrealistic to claim that a person can be _tuned_ as required by Theorem 1. Consequently, it might be perceived that this result lacks practical utility.  
To demonstrate its practicality, five experiments have been carried out:  
1. Two PID controllers, as described in (6)-(8), along with a PI control law for the local side, were tuned following the guidelines provided by Theorem 1.
2. The PI control law was subsequently removed, and a human operator manually controlled the robot-first on the local side and then on the remote side. This experiment aimed to show that the gains derived in the previous step maintain the stability of the closed-loop system.
3. The well-known PD controller combined with gravity compensation, as presented in [31], was implemented to serve as a baseline for comparison.
4. To evaluate robustness, the remote end-effector was brought into contact with its environment.
5. Finally, to enhance transparency, a force control term was incorporated into the basic position PID controller.  
The experimental testbed consists of two five-DOF _Geomagic Touch_ haptic devices, depicted in Figure 2. Both robots were simplified to three-DOF manipulators, since actuation is limited to the first three joints [30].  
The remote robot is connected via Ethernet, while the local one is connected via USB to a PC. The sampling time for data acquisition and control was set to \(\text{T}=2\,\text{ms}\) (the fastest possible rate achieved). The software environment employed was VisualStudio 2022 using C++ alongside the _OpenHaptics_ library for communication between the manipulators and the PC to exchange commands and data.  
For this configuration, time delays are insignificant under normal circumstances. However, to create a more challenging scenario, time-varying delays were artificially introduced. Specifically, the mean delays for the local and remote robots were set to \(T_{\text{i}}=0.675\,\text{s}\) and \(T_{\text{r}}=0.625\,\text{s}\), respectively, with a variance of \(0.05\,\text{s}\).  
This resulted in a combined delay of \(1.25\,\mathrm{s}\leq T_{\mathrm{I}}(t)+T_{\mathrm{r}}(t)\leq 1.35\,\mathrm{s}\), as shown in Figure 3. Under these conditions, the round-trip time (RTT) was measured to be approximately \(1.3\,\mathrm{s}\).
## 3 Materials and Methods
### Design of Data Acquisition Module Based on OPC UA Protocol
#### 3.1.2 Data Acquisition Module Based on the Python Language  
With the widespread introduction of intelligent equipment in smart factories, the number of data sources, including CNC machine tools, industrial robots, programmable logic controllers (PLCs), automatic guided vehicles (AGVs), and various sensors, has increased significantly. The diversity of communication protocols and interfaces used by these devices has led to the demand for customized communication solutions, which not  
Figure 2: OPC UA information modeling.  
only increases the complexity of data collection but also poses a significant challenge to the production efficiency and development of smart manufacturing workshops. Therefore, this study proposes a distributed framework that uses different PDC components for devices with different communication protocols to achieve distributed data collection of underlying devices in the workshop, thereby improving the efficiency of the data collection of the entire workshop equipment, as shown in Figure 3.  
This article uses the OPC UA library to design a PDC component, which integrates the OPC UA client function and can communicate with the equipment in the manufacturing workshop through the URL address. The design process of the PDC component mainly includes the following steps:  
1. Start the Kafka service locally. Then, create a new topic named \"query\" and subscribe to it to receive the list of node identifiers provided by the database for monitoring. At this stage, the database side acts as the Producer of Kafka, sending the data information to be monitored pre-written by the workshop, and the PDC side receives this information as the Consumer of Kafka.
2. When PDC can pull data from \"query\", it will process the data, filter out the URL address of the PLC to be detected and all nodes under the address, and integrate the above information into the form of an object in Python. It will then traverse and read all objects, connect to the OPC UA server through the URL, process the node to be detected, obtain the address space index of the node, and then integrate it into a list with NS (NamespaceIndex) and I (Identifier) values.
3. Subscribe to all the node states in the above list through the subscription function. When the value of the node changes, the sending function in the PDC component will be called. At this time, PDC plays the role of Kafka Producer and sends the message to the corresponding topic.