esa-sceva/satcom-chunk-collection huggingface dataset:
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 1 Introduction
In conclusion, there is a scarcity of studies on EEG evaluation in China, and most of the existing studies are limited to specific regions and time periods. Furthermore, there is a dearth of research on continuous, long-term monitoring of EEG in China since the twenty-first century, which is necessary to comprehensively analyze spatial and temporal changes at a nationwide scale from both horizontal and vertical perspectives. Additionally, many current studies on EEG evaluation rely on statistical data, which can introduce subjectivity due to the uncertainties associated with different statistical sources. Furthermore, traditional EEQ evaluation methods based on statistical data lack the ability to offer insights into the spatiotemporal changes of future EEG. Conversely, remote sensing data possesses distinct characteristics of objectivity, scientific rigor, and timeliness. Hence, there is a pressing need to develop a regional EEQ evaluation system that integrates multi-source spatial data, thereby addressing the limitations of current research approaches. This will require a continuous, long-term series of EEG data for China to analyze trends and spatial variability objectively, scientifically, and reliably. Such data support is crucial for informing China's ecological environmental protection and pollution prevention policies and mitigating potential ecological risks resulting from urbanization in the future. Here, based on the evaluation guidelines proposed by the National Environmental Monitoring Centre of China, namely, comprehensive principles, representative principles, scientific principles, comparability principles, and operability principles, we integrated multi-source spatial data to develop a comprehensive evaluation system for EEQ, known as the Modified Remote Sensing Eco-Environmental Quality Index (M-RSEQI). This evaluation system is designed to possess spatial and temporal universality, achieved through the application of the entropy method to ensure objectivity. By utilizing this system, we aim to monitor the continuous, long-term spatial and temporal variations of EEQ across China. The primary objective of this endeavor is to accurately and objectively depict the actual ecological environment of China based on scientific principles.
## 4 Conclusions  
This paper investigated the hydrogen evolution in glycol-based coolants used in battery electric vehicles, focusing on the influence of copper corrosion inhibitors and coolant electrical conductivity. The hydrogen evolution was evaluated by analyzing the cathodic current evolution in a linear sweep voltammetry test.  
Results revealed that the use of common copper organic corrosion inhibitors (TTZ, BTZ, and MBT-Na) had no effect in suppressing the hydrogen evolution, regardless of concentration and synergetic combinations. This observation suggests that the effectiveness of these mixed-type corrosion inhibitors may be limited to lower overpotentials, and their behavior may be more complex in glycol-based solutions. In contrast, the coolants' electrical conductivity has a significant impact on hydrogen formation. When comparing coolants of equal TTZ concentration, the cathodic current density at \(-5\) V (vs. Ag/AgCl) was 96% lower in a low-conductivity BEV coolant (86 \(\mu\)S/cm) than in a high-conductivity ICE coolant (2577 \(\mu\)S/cm). Further, progressive dilutions of both BEV and ICE coolants showed that as electrical conductivity decreased, the cathodic current decreased linearly in low-conductivity ranges and exponentially at higher conductivities. These findings confirm that ohmic polarization, rather than activation energy or mass transport, is the dominant mechanism limiting the hydrogen evolution reaction (HER) under these conditions.  
Therefore, while the use of azide-based corrosion inhibitors offers little benefit in suppressing HER at high overpotentials, reducing the electrical conductivity of coolants emerges as a clear and effective strategy to mitigate hydrogen accumulation and the associated safety risks in BEV cooling systems. As in this study all measurements were conducted under static and controlled laboratory conditions, they do not fully replicate the complex and dynamic environments present in operational battery systems. Future works should focus on evaluating hydrogen evolution under realistic conditions and further characterize HER in glycol media.  
Conceptualization, L.S., S.C., G.B. and H.T.; methodology, L.S., S.C. and H.T.; investigation, L.S.; writing--original draft preparation, L.S. and S.C.; writing--review and editing, T.K., G.B., S.L. and H.T.; supervision, A.H. and H.T.; project administration, S.C., G.B., S.L. and H.T.; funding acquisition, G.B., S.L. and H.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Flanders Innovation & Entrepreneurship (VLAIO), grant number HBC. 2022.0011, CERMIT Project.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] were employed by the company Arteco NV. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss
## 4 Experimental Results
### Noise Experiment  
The key to solving the problem of masked-face recognition is to ignore the invalid features of the mask occlusion area and pay more attention to the effective features. In order to prove that our method pays more attention to the unoccluded facial area (the upper part of the face) during feature extraction, a noise experiment was designed. First, each entire image was divided into an upper part and a lower part according to the key points at the bridge of the nose; and then salt and pepper noise, Gaussian noise, and random noise were added for recognition. The results are shown in Table 4. The noise part \"Up\" represents the upper area of the face. \"Down\" represents the lower area of the face. \"All\" represents adding noise to the entire image.  
The addition of noise destroyed the facial feature information and caused a decrease in accuracy to a certain extent. In the experiment, MFCosface was compared with the original method FaceNet. It can be seen that on different datasets with different noises, our methods were much better than FaceNet and showed strong robustness. The recognition accuracy after adding noise to the lower half of the face was high, which shows that our method can still extract reliable facial features for recognition after the features in the lower half are destroyed. This proves that our method pays more attention to the facial features in the upper half of the area, and is less dependent on the facial features in the lower half. In summary, our method pays more attention to the upper half of the face that is not covered by the mask, and has strong robustness in the face of noise.  
Figure 9: The receiver operating characteristic curve. Facenet represents the original network, masked represents the use of images generated by our method for training, Cosface represents the use of large margin cosine loss as the loss function, CBAM means training with an attention mechanism, and MFCosface represents the method we proposed.
## 3 Results
### Chemical Composition  
#### 3.6.1 Fatty Acid Content and Oxidative Susceptibility  
To better understand the findings so far, we examined the chemical composition of the oils, starting from the % content of fatty acids (Table S2). Our results verified that the avocado oils were rich in monounsaturated fatty acids (MUFAs), as also the EVOO, with a composition in accordance with quality specifications [40] and previous research [6; 41].  
Figure 1: Antioxidant activity (AA) of oil samples, using both DPPH\({}^{\bullet}\)**(a)** and CURAC (**b**) assays. C-AO = avocado oil; C-VAO = freshly extracted virgin avocado oil; AL5-AL10-AL20, O15-OL10-OL20, and OP5-OP10-OP20 represent C-VAO enriched with avocado leaves, olive leaves, and olive pomace, respectively, at 5, 10, and 20% (_a/w_) employing ultrasound-assisted maceration. Results expressed as mean value \(\pm\) standard deviation (_u_ = 3). Darker colors per used plant material indicate higher enrichment levels. Different letters above the bars indicate significant differences (_p_ < 0.05) within the whole sample population, per assay, employing Duncan’s test.  
Regarding the fortified avocado oils, the FA composition was also similar to that of the base AO (C-VAO). The relevant data in the literature indicate that other fortification process parameters may be significant. For example, [PERSON] et al. [42] found that maceration of olive oils with olive leaf material did not affect their fatty acid composition, while [PERSON] et al. [43] reported a significant increase in the oleic/linoleic acid content ratio upon ultrasound-assisted extraction. The increase in oleic acid was attributed to extraction from the leaf, whereas the relatively small decrease in linolenic acid was due to ultrasound-mediated oxidation. The latter may occur due to thermal effects and/or the formation of free radicals in edible oils [44]. In our study, fortification did not induce any appreciable effect on the % MUFA content (Table 4). On the other hand, the ratios SFA/PUFA and MUFA/PUFA became slightly higher but only at the lowest addition level (AL5, OL5, and OP5). As a result, the oxidation susceptibility (OS) index, which relies heavily on the content of highly oxidizable FAs, followed a similar pattern of changes.  
These observations signify that during the ultrasound-assisted maceration at the lowest addition level, PUFAs are not efficiently protected against the oxidation that is expected to be promoted by the applied technique. It is interesting to note that a similar pattern of variance among the studied oils was also evidenced through the PCA-based analysis of the ATR-FTIR spectral data. As these changes may take place at room temperature, we cannot explain the performance of the fortified VAOs under thermally induced oxidation conditions, such as in the Rancimat test.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 1 Introduction
Dishonest users commit fraud by submitting multiple fake reports over a short period of time while honest users operate under the scope of consumers who view adverts at a balanced pace while engaging in typical social activities. Social activities such as making online purchases, moving through space and interacting with other users are exploited by our system to verify that a report was submitted by a honest user. More specifically, when users perform a particular social task (e.g., visit a designated location or meet other users) they obtain a **Token** which works as proof of the user performing the action at a specific time. To better comprehend this concept, think of a game of scavenger-hunt where players prove to have visited a location by recovering some type of artifact from the area. In a very similar fashion, users of our system collect a series of Tokens and submit them along with their Ad-Reports in the form of a **blockchain-inspired construction** which we term as the Ad-Report Chain (_ARC_). Tokens work like time-stamps which allow Advertisers to calculate the rate at which the submitted reports were composed and identify suspicious activity (dishonest users who create large volumes of reports over short time). This results in the ability to verify that an Ad-Report claim is the outcome of real user activity without need for knowing the identity of the particular user. Our contributions are as follows:  
* To the best of our knowledge, our system is the first to exploit user behavioural patterns (Behavioural Verification) for the purpose of exposing advert fraud. * Behavioural Verification does not need user identities and therefore preserves privacy. * A major component of detecting fraudulent behaviour is the rate at which the use creates reports. We simplify the process of calculating this rate compared to the literature. Traditional methods compute this rate by filtering to identify reports that originate from the same source while the approach that we use logs Ad-Reports in a chronological order directly at the user's device and submits them as a single message (_ARC_). The rest of this article is organized as follows. In Section 1.1 we present the scope of our research and in Section 2 we offer insight on some of the related work in the field. In Section 3 we provide preliminary knowledge which is required in order to completely understand the functionality of our system. In Section 4 we provide a high level overview of our model by presenting the system's architecture and formalize the addressed problem. In Section 5 we provide a detailed analysis of our system and offer insight into our approach for solving the addressed problem. Finally, we evaluate our approach in Section 6 and state our conclusion in Section 7.
## 3 Cable Models and Methodology
### Transition from 2D to 3D: Extruded Models  
Each 2D cross-section is extruded along the \(z\)-axis to form a 3D cable:  
* Single-Segment 3D and Three-Segment 3D: Extruded by **1 m** (Figure 5a,b);
* Five-Segment 3D: Extruded by **4 m** (Figure 5c).  
This difference in extrusion length (1 m vs. 4 m) helps determine whether longer axial spans cause deviations from 2D results when all conductors remain strictly parallel (no twisting).  
Although real cables often twist either the entire cross-section or each segment's conductors, these baseline 3D models omit such helical pitching. This isolates the effect of finite axial length alone before adding the complexity of twisting.  
Figure 5: Three-dimensional models of 1-segment (**a**), 3-segment (**b**), and 5-segment (**c**).
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 4 The Proposed Deep Learning Framework  
In this section, we present the proposed DL framework based on the DNN model to obtain the optimal solution, as shown in Figure 6. More specifically, the proposed DL framework is used for solutions to these problems in this paper, as follows:  
* _Problem I_: Finding optimal fitness function value for clustering.
* _Problem II_: Detecting whether the next node is a blackhole node or not.
* _Problem III_: Finding the optimal fitness function value for routing.  
We consider the general DL framework with two phases: phase 1 is for training the DNN model, and phase 2 is for testing the DNN model, as shown in Figure 6. Figure 6a shows the DNN learning from the generated dataset in the training phase, where the DNN model learns the dataset generated from the conventional method. The conventional method can guarantee convergence at the global optimum. The optimal solutions obtained from the conventional method serve as the target parameters of the DNN model. The DNN will be trained to learn the relationship between input trainable parameters and target parameters. In the training phase, an error will be obtained from comparing the DNN output with the target based on the optimal solution of the conventional method. Then, this error will be minimized by updating the weight and bias on the neurons by using backpropagation, which continues until the iteration is satisfied [38]. After training, the trained DNN model can predict the optimal value with new input data variables, as shown in Figure 6b. By using these processes, the DNN learns to predict the optimal value. However, it can be noted that each DL framework to solve each problem is totally different, which can be explained in the following subsection.
## 3 Results
### Model Development  
As noted above, incorporation of all wavelengths also includes unrelated information for modeling and noise, which can cause multicollinearity and data overlap problems [27]. To avoid these issues and improve the FWC model predictability, EW regions were defined and applied to both PLS and LS-SVM models. These EW regions were selected using a baseline that was lower than the average RMSE values of the FWC but with higher \(R^{2}\) values than other wavelengths. Figure 4 shows the calculated baseline RMSEV values (black dashes, 12.4) for the FWC of the mixture samples. An EW region (475 to 775 nm) was then selected.  
Figure 3: Spectral data of mixture samples with increasing FWC concentration (0, 5, 10, 15, 20, 30, 40, 50, and 100%): (**a**) whole and (**b**) average spectra.  
Figure 2: Workflow of HSI analysis for qualitative and quantitative evaluation of OF FW components.  
The beta coefficients of the developed PLS model are shown in Figure 5. Because the high peaks of the absolute values mainly influenced the model development, four wavelengths (470, 625, and 675 nm) were selected.  
Table 2 lists the results of both the PLS and LS-SVM models produced using five preprocessing methods within the selected EW region. The overall results of the LS-SVM model were superior to those of the PLS model. Based on \(R^{2}\), SEC, and SEP, the LS-SVM model was selected. The correlations between the FWC concentrations, given by the mixture sample and by the LS-SVM model with the range normalization method, are shown in Figure 6.  
Figure 4: Selected effective wavelength region using FWC RMSEVs.  
Figure 5: PLS model beta-coefficient plot for FWC detection in OF mixture samples.
## 2 Materials and Methods
### The Selection of Waveguide Cross-Section Parameters for Calculations with EIM  
In paper [29], the etching technology of waveguides based on bulk LN was developed in order to further its transfer to LNOI substrate. To determine the parameters of the waveguide cross-section, calculations of the mode composition of optical radiation were carried out. However, slope angle of sidewalls and anisotropy were not taken into account in [29]. The waveguide cross-section might be adjusted, taking in consideration the slope angle of sidewalls and anisotropy. Therefore, the target structure is a shallow-etched waveguide with a width of 1.1 \(\upmu\)m, an etching depth of 0.3 \(\upmu\)m, and a slope angle of sidewall of 75\({}^{\circ}\) (Figure 2).  
In this paper, the applicability of EIM for calculation of the waveguide, shown in Figure 2, as well as waveguides with varying parameters of the cross-section was evaluated. The limits of applicability of the EIM to the calculation of anisotropic waveguides based on the X-cut of LNOI for a set of etching depths from 0.1 \(\upmu\)m to 0.6 \(\upmu\)m were studied by  
Figure 2: The scheme of the cross-section of the studying waveguide. Slope angle \(\alpha\) is 75\({}^{\circ}\).  
comparison of the calculation results of the optical mode characteristics obtained with FDFD and EIM methods. The waveguide width for each etching depth was chosen as slightly less than the width, at which the first-order modes appear. Additionally, the variation of the slope angle of the waveguide depending on the technological parameters should be considered. Therefore, the numerical study of the mode characteristics of waveguides with slope angles from 60\({}^{\circ}\) to 90\({}^{\circ}\) in steps of 10\({}^{\circ}\) was also implemented. The parameters of the cross-section of the studied waveguides are given in Table 1. In each simulation iteration, when the slope angle of sidewalls varied, the rest of the parameters were fixed. This was done in order to take into account the influence of the slope angle of sidewalls on mode hybridization and the additional error in the accuracy of calculating the effective refractive indexes using the EIM.
## 4 Conclusions  
The results described in this work demonstrate the viability of using new or existing Wi-Fi infrastructures for the collection of agronomic data with low-cost nodes in deployments for taking measurements over long periods of time. Although some authors argue that Wi-Fi is not the most suitable technology for WSN in PA, the application of techniques to reduce consumption and the use of energy harvesting methods (solar panels) allows for virtually unlimited node operating autonomy. Consequently, this work demonstrates that, properly managing the energy demand, WSN deployment using Wi-Fi is feasible and even advantageous in installations where Wi-Fi coverage is already available or a major investment in the communications infrastructure is not desired. In addition, the use of Wi-Fi is particularly convenient when using multi-node deployments on the same plot (high density), e.g., in research trials where it is necessary to replicate measurements in several locations to minimize variability, and also to perform them with different parameters to evaluate the performance of irrigation strategies on a given crop.  
In addition, time synchronization techniques have been implemented to ensure time alignment of the data measured by the sensors. This facilitates its subsequent analysis for the realization of agronomic studies that relate the different irrigation treatments with the state of the plant and the crop yield.  
Likewise, the experimental installation described in this work has demonstrated the reliability of the nodes, sensors and communications infrastructure used. The experimental results have shown that, although wireless networks require less installation and maintenance tasks than wired ones, they do need programmed servicing interventions and occasional troubleshooting.  
Next steps would involve the integration of nodes with other communication technologies such as LoRa to carry out studies to compare the advantages and disadvantages of each technology.  
Conceptualization, R.T.-S. and M.J.-B.; methodology, R.D.-[PERSON] and [PERSON]-R.; software, [PERSON]-M., [PERSON]-V. and [PERSON]-[PERSON]; validation, [PERSON]-R., [PERSON]-V. and [PERSON]-M.; formal analysis, [PERSON]-V. and [PERSON]-R.; investigation, R.D.-M. and P.J.B.-R.; resources, [PERSON]-S. and R.D.-M.; data curation, M.J.-B. and [PERSON]-M.; writing--original draft preparation, M.J.-B.; writing--review and editing, M.J.-B., [PERSON]-R., [PERSON] and [PERSON]-[PERSON]; visualization, [PERSON]-B.; supervision, [PERSON]-[PERSON] and [PERSON]-M.; project administration, R.T.-S. and R.D.-M.; funding acquisition, R.T.-S. and R.D.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Science and Innovation Ministry (MICIN) and the European Agricultural Funds for Rural Development, grant numbers: AGL2016-77282-C3-3-R and PID2019-106226 RB-C22/AEI/10.13039/501100011033.  
Figure 18: Evolution of the battery voltage of a node with a dirty panel (node 04).