esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Magnetic Scanned Image before Filtering  
Figure 4 shows the magnetic scanned image with the mean subtraction of the MFLT, ECT and PSECT methods. The results were obtained with 100-mA and 2000-Hz current supply of the bobbin coil current and 120 mT of the magnet. The mean subtraction is aimed to remove the bias among the  
Figure 3: The prototype of the electromagnetic testing (ET) systems: the MFLT probe has the Hall sensor array and a magnet; the ECT probe has a Hall sensor array and a bobbin coil; the PSECT probe has a Hall sensor array, magnet and a bobbin coil. The PSECT probe could be the MFLT probe if the bobbin coil is disconnected (open circuit).  
methods, as expressed in Equation (17). The results show challenging to observe the corrosion signal because of the high variation of the background signal. Only a few large corrosions having a diameter of 2.5 mm could be observed. In the MFLT result, the deformation and material degradation of the MSR tube could be observed, which have strong intensity in large areas. For better observation of corrosion, differential data in the scan direction is usually carried out [18; 19; 20], as expressed in Equation (18). The differential data of the three methods are shown in Figure 5. The first row of corrosion has the same diameter of 1.3 mm with decreasing depth from 100% to 10% of the wall thickness (1.2 mm), and the second row of corrosion have the same diameter of 2.5 mm and depth of 100%, 87% and 58% of the wall thickness (indicated in the ECT result). The fins effect could be observed in the MFLT result with the slight vertical lines along to the fins' profile. The fins' effect was less in the ECT method because the eddy current was mainly concentrated on the inner surface of the MSR tube. However, the fins effect was a little clearer in the PSECT method because the eddy current could deeper penetrate the outer surface of the tube. The corrosions were better detected in the ECT and PSECT method, and the PSECT method provides a higher intensity of corrosion than the ECT method.  
\[\overline{\mathbf{V}}_{RMS}(i,j)=\mathbf{V}_{RMS}(i,j)-\frac{1}{L}\sum\
olimits_{j=1} ^{L}\mathbf{V}_{RMS}(i,j) \tag{17}\]  
\[\Delta\mathbf{V}_{RMS}(i,j)=\mathbf{V}_{RMS}(i,j+1)-\mathbf{V}_{RMS}(i,j) \tag{18}\]  
Figure 4: Mean subtraction of magnetic scanned image of the MFLT, ECT and PSECT methods on the MSR finned tube and the section view at the holes of 2.5 mm diameter (sensor #5).
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
###### Abstract  
Interferometric radiometers operating at L-band, such as ESA's SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors' fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FFGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS.  
radio frequency interferece; RFI; FPGA; Earth observation; interferometric radiometers; polarimetry 2024248001  
1
## 3 Results and Discussion
### Random Forest Performance
Figure 8: Correlation plots visualising the relationship between the in situ and predicted SSS and SST of RF for October 2022, October 2023, and June 2024. Moreover, comparing the scatter plots and PCCs from 2022 and 2023 to those of 2024 clearly demonstrates that the RF algorithm requires a substantial amount of data to effectively learn the underlying patterns and relationships within the dataset. This need for sufficient data is evidenced by the relatively lower PCCs observed in 2024 compared to the preceding years. The strength of the RF algorithm lies in the diversity and independence of its individual trees. A larger number of data points ensures that the trees are sufficiently diverse from one another, thereby enhancing the overall performance of the ensemble and providing more reliable statistical estimates. As the dataset size increases, RF is able to identify a broader range of patterns and relationships, thereby enhancing the robustness and accuracy of the ensemble model. In contrast, smaller datasets may lack the diversity required for the trees to learn effectively, resulting in reduced prediction performance. Furthermore, a larger dataset enables the RF to discern more generalised patterns rather than overfitting to noise or irrelevant details, helping to avoid overfitting and better capture the subtle interactions and dependencies between features. In comparison, smaller datasets may fail to represent the full complexity of these relationships. Nevertheless, the strong performance for both parameters demonstrates the robustness and adaptability of the RF algorithm in handling various types of oceanographic data.
## 2 Materials and Methods
### MP-SAWS Principle
The fabrication process involves surface photolithography and magnetron sputtering to draw patterns on the piezoelectric substrate and deposit interdigital electrodes. Using surface photolithography technology, IDT patterns are created on the surface of LiNbO\({}_{3}\). Aluminum electrodes are deposited onto the piezoelectric substrate through magnetron sputtering. The pre-patterned design ensures that the deposited aluminum forms an alternating interdigitated structure. Aluminum serves as both the transmitter and receiver of electrical signals, and its thickness should not exceed 1% of the wavelength \(\lambda\) to avoid excessive mass loads. Subsequently, the connection between the magnetostrictive material and the piezoelectric substrate is completed using suitable wafer bonding techniques such  
Figure 1: The MP-SAWS schematic diagram. as anodic bonding. Finally, the PDMS shell is coated using surface-charge lithography techniques [53; 54; 55; 56; 57]. Additionally, during the packaging stage, to protect the interdigital electrodes on the upper surface, Al\({}_{2}\)O\({}_{3}\) can be deposited using the atomic layer deposition (ALD) technique. This can potentially protect the piezoelectric layer and the interdigital electrodes in medical application environments [58; 59]. The application of an external magnetic field causes the magnetostrictive material to expand, thereby exerting pressure on the MP-SAWS through changes in its volume. Since MP-SAWS exhibits excellent sensing characteristics for pressure, it can be used to sense magnetic field data. By considering parameters such as Young's modulus and Poisson's ratio of the material involved in the compression process, specific magnetic field sensing characteristics can be calculated.
# Paint Removal with Pulsed Laser: Theory Simulation and Mechanism Analysis
###### Abstract  
This paper studies paint removal using laser technology. A finite element model was created using COMSOL Multiphysics software, and the temperature field generated during the cleaning process was analyzed and verified. Laser paint removal behavior was investigated using a fiber laser, and its mechanism studied by combining Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. In-depth analysis of this relatively new technology could provide the theoretical basis for industrial application. The results of this study show that, when compared to the original paint layer, the infrared absorption spectrum of the cleaned surface had two additional two peaks--1333.36 cm\({}^{-1}\) and 678.82 cm\({}^{-1}\). In addition, there was a decrease in C element content on the treated surface and an increase in O content. In addition, new organic and complex compounds were formed on the cleaned surface as a result of bond cleavage and rearrangement. Furthermore, paint particles of varying sizes and shapes were produced by the impact of plasma shock. Under high-energy laser irradiation, the paint layer underwent combustion, resulting in spherical nanoparticles of uniform shape.  
pulsed laser; paint removal; mechanism; chemical bond; plasma; combustion Article
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 4 The Mathematical Model of Dispatch under F-H Weather
### Objective Functions
\[\varphi(L_{AQI})=1+\alpha\cdot(L_{AQI}-1),L_{AQI}=1,2,...,6 \tag{18}\]  
where \(\alpha\) is the empirical value of the proportionality coefficient of \(\varphi(L_{\text{AQI}})\), obtained from the comprehensive analysis of the policies, F-H condition and emission characteristic of the units. (3) Minimizing carbon emission of TPPs: \(f_{3}\). Carbon emission is namely greenhouse gas emission, which mainly includes CO\({}_{2}\), N\({}_{2}\)O, CH\({}_{4}\) and O\({}_{3}\). Only CO\({}_{2}\) is discussed in this paper, because the other greenhouse gases could be converted into CO\({}_{2}\) equivalent. \[ \begin{array}{c}\min f_{3}=\sum_{i=1}^{M}\left(C_{Xi}\cdot c_{ei}\cdot e_{Gi }\cdot\sum_{t=1}^{T}P_{Gi,t}\right)\end{array}  \tag{19}\]  
where \(C_{Xi}\), \(c_{ei}\) and \(e_{Gi}\) are the cost coefficient of carbon emission, the coal consumption rate and the carbon discharge coefficient of unit \(i\). (4) Minimizing generation cost of VPPs: \(f_{4}\). It is assumed that the generation cost of a DER and that of its output have a linear relationship to simplify the dispatch model, based on a comprehensive consideration of the basic construction cost,operation and maintenance costs and control cost of DERs. Similarly, the generation cost and output of a VPP have a linear relationship. \[\text{min}f_{4}=\sum_{k=1}^{n}\left(w_{k}\cdot\sum_{t=1}^{T}P_{VPP_{k},t}\right),w_{k}=\sum_{j=1}^{m_{k}}\delta_{k,j}\cdot w_{k,j} \tag{20}\]  
where \(n\) is the number of VPPs; \(w_{k}\) is the generation cost coefficient of VPP\({}_{k}\); \(P_{VPP_{k},t}\) is the output of VPP\({}_{k}\) in period \(t\); \(m_{k}\) is the number of DERs inside VPP\({}_{k}\); \(\delta_{k,j}\) is the capacity proportion of DER\({}_{j}\) inside VPP\({}_{k}\); and \(w_{k,j}\) (constant) is the generation cost coefficient of DER\({}_{j}\) inside VPP\({}_{k}\). Therefore, \(w_{k}\) would be a constant if the types of DERs and \(\delta_{k,j}\) are known.
## 4 Discussion
### Timing Delineation  
Our study showed that automatic and user-independent segmentation was much faster than manual segmentation for RPS volumetric analysis. Manual segmentation required an average time of 3887 s, while automatic segmentation required a few seconds. This would suggest good viability for the use of automatic segmentation in clinical practice, where time constraints may restrict which methods are used. ERFNet had better performance than ENet in terms of training times (4.16 days vs. 5.31 days) and in terms of inference times (58.08 s vs. 113.10 s, using a dedicated GPU, and 13.49 s vs. 14.64 s, using CPU). On the other hand, ENet had fewer order-of-magnitude parameters than ERFNet (363,069 vs. 2,056,440, respectively), requiring lower disk space (5.8 MB vs. 25.3 MB, respectively). However, in our experience, both algorithms can be used in a PC equipped with simple hardware and in portable devices, such as a tablet or smartphone.
## 3 Energy-Efficient Optimal Power Allocation
### Energy-Efficient Optimal Power Allocation for Real-Time Applications
#### 3.2.1 Average Transmit Power Constraint  
With ATP and AIP constraints, the EE maximization problem for real-time applications can be formulated as  
\[\begin{array}{l}\mathop{\max}\limits_{P_{t}}H\left(P_{t}\right)=\frac{R_{th }\mathbb{E}\left[1-\chi_{s}\right]}{\mathbb{E}\left(\xi P_{t}+P_{t}\right)},\\ s.t.\left\{\begin{array}{l}\mathbb{E}\left(P_{t}\right)\leq P_{av}\quad(a1 )\\ \mathbb{E}\left(P_{t}\right)\leq I_{th}\quad(b)\end{array}\right.\end{array} \tag{26}\]  
where \(R_{th}\) is the prescribed OC of the satellite user and \(\chi_{s}\) is an indicator function for the outage event of the satellite user at each fading state, which is expressed as  
\[\chi_{s}=\left\{\begin{array}{l}1,\,\Big{[}\log_{2}\Big{(}1+\frac{P_{t}}{N_ {s}}\Big{)}\Big{]}<R_{th}\\ 0,\qquad\qquad\qquad\text{where \(P_{th}\geq 0\), which is the minimum transmit power required for the satellite user to guarantee \(R_{th}\). It is notable that \(\chi_{s}=1\) when \(P_{t}<P_{th}\) whereas \(\chi_{s}=0\) otherwise. We can conclude that the maximum of \(D\left(P_{t}\right)\) is \(-R_{th}\) when \(P_{t}=0\) or \(-\left(\eta\xi+\tau+\mu G_{1}h_{1}\right)P_{th}\) when \(P_{t}=P_{th}\). Let \(P_{t}^{*}\) denote the optimal transmit power for (28), which depends on the relationship between \(-R_{th}\) and \(-\left(\eta\xi+\tau+\mu G_{1}h_{1}\right)P_{th}\). Therefore, \(P_{t}^{*}\) can be given as in Theorem 5.  
Theorem 5: _The energy-efficient optimal transmit power with ATP constraint for real-time applications is given as_  
\[P_{t}^{*}=\left\{\begin{array}{cc}0,&P_{th}>\frac{R_{th}}{\eta\xi+\mu G_{1}h _{1}}<P_{th}\leq P_{th}\\ P_{th},P_{th}\leq\frac{R_{th}}{\eta\xi+\tau+\mu G_{1}h_{1}}\end{array}\right.. \tag{31}\]  
For a particular \(\eta\), (28) can be efficiently solved via (31). Additionally, we can address (26) by modifying Algorithm 1, where replacing (14) with (31) while calculating \(P_{t}^{k}\). For brevity, the details are omitted here due to space limitation.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 2 Effective Sensor Architecture
#### Full-Attitude Determination with Sensor Measurements
The magnetometer and Sun sensors are weighted in this statistical implementation according to their quality, defined as the mean of the measurement's variance computed in the sensor postprocessing functions:  
\[a_{\mathbf{b}}=\frac{1}{\frac{1}{\frac{1}{\sum_{i=1}^{S}\sigma_{ \mathbf{b}}(t_{S+1-i})}}},\text{ and }a_{\mathbf{\hat{s}}}=\frac{1}{\frac{1}{\sum_{i=1}^{S}\sigma_{ \mathbf{b}}(t_{S+1-i})}}, \tag{6}\]  
where \(a_{\mathbf{b}}\) and \(a_{\mathbf{\hat{s}}}\) are non-negative weights respecting \(a_{\mathbf{b}}+a_{\mathbf{\hat{s}}}=1\). The outcome of the statistical attitude determination is the best estimation of the spacecraft's orientation, in terms of the quaternion, \(\mathbf{\hat{q}}\), and angular velocity, \(\boldsymbol{\tilde{\omega}}\). The dynamic attitude determination complementary filter uses the output of this static attitude determination section, combined with the angular rates' measurements, to obtain the updated bias values, as described before. At any time of the mission, the output of the full-altitude determination functions is the best estimate of the attitude state vector. The orbit determination algorithm is based on a loosely coupled GNSS and inertial navigation system (INS) integration. Namely, the accelerometers and the SGP4 orbital propagator are exploited to update the orbital state vector generating the INS preliminary estimate, which is then compared with the GNSS measurements to calculate the measurement error. This measurement error signal is fed to an extended Kalman filter (EKF) [29], which outputs an updated estimate of the position and velocity errors, as well as the accelerometer bias term estimate. Eventually, the output of the EKF is used to correct the INS preliminary estimate, whereas the bias term is fed back to the integration block of the accelerometer measurements. The orbit determination section's algorithms provide a full orbital state estimate in terms of inertial position, \(\mathbf{r}\), and velocity, \(\mathbf{v}\), using positioning and acceleration measurements:  
\[\mathbf{r}_{M},\quad\mathbf{v}_{M},\quad\mathbf{a}_{M}. \tag{7}\]  
Note that the accelerometer bias term estimate is continuously updated to calibrate the accelerometer measurements:  
\[\mathbf{a}_{\mathrm{C}}=\mathbf{a}_{M}-\epsilon_{\mathbf{b}_{\mathrm{a}}}. \tag{8}\]  
Whenever the GNSS is experiencing an outage period or a fault, the on-board SGP4 orbit propagator is used as the synthetic measurement element for the INS input. In this case, the EKF exploits a different measurement model and covariance matrix with respect to the nominal mode with GNSS and accelerometer coupling. Obviously, the approximate orbital model introduces propagation errors, and an inherent position drift cannot be avoided. However, the coupling with the EKF helps in accounting for the orbital error dynamics, and it determines a slower divergence rate with respect to a pure on-board orbital propagation including the same perturbation terms.
## 4 Application Examples
### Landslide Prediction Performance
This phase belongs to the acceleration phase which has been introduced in Figure 8. At this stage, a warning should be given and the slope should be reinforced. Therefore, the entire disaster model can determine the early warning level according to the change rate, which is of great significance for the evaluation of landslide dangerousness. Compared with the results of the TOPSIS-Entropy weight theory, the results are more stable and less likely to be affected by abnormal data fluctuations.
# Probiotics and Beneficial Microorganisms in Biopreservation of Plant-Based Foods and Beverages
## 4 Biopreservative Properties of Probiotics
### Vegetables  
Post-harvest spoilage caused by fungi in tomatoes results in a greater economic loss for the food industry. [PERSON] et al. [72] screened 9 LAB strains isolated from tomato for antifungal activity against 33 fungal strains and then used as biopreservatives of tomato inoculated with _Penicillium expansum_ and _Aspergillus flavus_. The highest antifungal activity was observed in the cell-free extracts of _Lactiplantiobacillus plantarum_ TR7 and _Lb. plantarum_ TR71. Organic acids, phenolic acids and volatile organic compounds were identified as responsible compounds for the antifungal activity. Biopreservation of tomatoes with the cell-free extracts of the above two LAB strains decreased the microbial counts by 1.98-3.89 logs and 10 spores/g compared to those which were not fermented [72].  
Lacticasebiucillus casei V4B4 and Lb. plantarum V7B3 showed excellent adaptability to the minimally processed lamb's lettuce to control spoilage microorganisms and inhibit pathogenic microorganisms. More importantly, the appearance and color of the products were not affected by the addition of these potential biocontrol agents [65].  
Application of the cell-free supernatant of _Pediococcus_ spp. (15 mL/g) showed enhanced preservation of strawberries, corn, tomatoes, and button mushrooms. For example, although untreated tomato and corn samples remained fresh only for 6 days, the treated samples of tomato and corn remained fresh for 13 days and 20 days, respectively. Moreover, this study suggested that 100 g/L of cell-free supernatant of _Pediococcus_ spp. demonstrate antimicrobial potential against _E. coli_ and _Shigella_ spp. More interestingly, the treatment with the _Pediococcus_ spp. demonstrated enhanced preservation in these products compared to the chemical preservatives sodium sulphate and sodium benzoate. The microbial quality of food samples treated with _Pediococcus_ spp. showed significantly lower total bacterial counts compared to those treated with chemical preservatives. These results suggest that bacteriocin-producing _Pediococcus_ species provide enhanced shelf life to certain food varieties and can be used as biopreservatives [73].
###### Abstract  
A vehicular ad hoc network (VANET) is a mobile ad hoc network composed of communication between vehicles, between vehicles and roadside units, and between vehicles and pedestrians, in order to achieve traffic safety and entertainment services. The design of the routing protocol is very important for the realization of the service function of VANET. Local optimum and network congestion problems are restraints of traditional geographic routing protocols for VANET. In this paper, a software-defined network (SDN) based unicast routing scheme in an urban traffic environment is proposed, which uses [PERSON]'s algorithm to find a global optimal anchor path. The RSU neighbor discovery protocol is proposed, through which each RSU can discover its neighbor RSUs, and then each RSU periodically sends ant packets to its neighbor RSUs, evaluates the communication connection quality of each street segment according to the statistical data of the ant packets received, and sends the evaluation value to the SDN server in time. The SDN server has the connection quality evaluation values of all street segments in the global scope, from which an optimal anchor path can be calculated. The simulation results show that the proposed scheme has better packet delivery ratio than other related schemes.  
software-defined network; routing protocol; vehicular ad hoc network; ant colony optimization
## 1 Introduction
However, the abovementioned studies did not consider the uncertainties of the properties in each constituent at the micro level of fiber/matrix combination (tow) and macro-level of tow/matrix combinations (laminae) in the fabric composite, respectively. Several studies have been conducted to evaluate the effective properties by considering the uncertainties of the mechanical properties of fabric composites. For example, [PERSON] et al. [20] used the MCS method to consider the uncertainties related to the elastic properties of plain weave composites and evaluated the effective properties of the composite. [PERSON] et al. [21] developed a multi-scale homogenization technique to predict the elastic behavior of fabric composites. The abovementioned studies analyzed the effective properties of the fabric composites by considering the variability at the macro-level. However, the variability of the mechanical properties of composites comes from the uncertainties of the fiber and matrix at the micro-level. The effective properties of composites should be, therefore, evaluated under the multi-scale condition from the micro-level to the macro-level. Furthermore, these studies have the limit in that the linear correlation of the properties at the micro- and macro-levels was not analyzed. Figure 1: The assume structure composites of periodic; square array [16]. In this study, the multi-scale probabilistic analysis on the mechanical properties of plain-weave carbon/epoxy composites using the homogenization technique was performed. First, the effective properties of tow (meso, Figure 2b) were evaluated through homogenization analysis at the micro-level with fiber/matrix combination (Figure 2a). In addition, the homogenization analysis result was verified using the ROM theory and the reliability was verified by comparing the tensile test result and volume fraction measurement with the homogenization analysis results. Next, the probabilistic homogenization analysis was performed using the MCS to consider the uncertainties of the properties of the constituents. Finally, the effective properties of the fabric composites at the macro-level with tow/matrix combinations (Figure 2c) were obtained by using the mechanical properties results derived from the homogenization analyses at the micro-level. Furthermore, the linear correlation between the basic constituents (fiber and matrix) for effective properties, and the probabilistic analysis result were analyzed.