esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Membrane Characterization
A prominent adsorption peak at 3411.94 cm\({}^{-1}\), which is assigned to the stretching vibration of the -OH groups of GO is seen in the spectra of the PES/GO membrane. Another peak, though less pronounced, is located at 1713.6 cm\({}^{-1}\) and corresponds to the stretching vibration of the carbonyl C=O functional groups of GO. Finally, the adsorption peak seen at 1110.6 cm\({}^{-1}\) is attributed to the stretching vibration of the C-O bond [33]. These results, in comparison to the spectra seen for M1 further indicate a robust oxidation of graphite into GO and exposes the rich oxygen-containing functional groups that are present. These groups increase the hydrophilicity of the PES/GO membranes [45]. Figure 7: pH scan of selected PES membranes with resulting zeta potential measurements. The mechanical strength of the synthesized membranes was tested using the DMA (Dynamic mechanical analysis) technique. Figure 9 shows a clear trend of increased stiffness of the membranes as the GO loading increased up to 3 wt. % loading. The stiffness, inferred from the Young's modulus values, then decreases at the maximum loading concentration of 5 wt. % GO in M8. The higher the modulus, the more stress is needed to get the same amount of strain, rendering the membrane stiffer or less stretchy. The seemingly surprising result seen for M8 can be explained two-fold. Firstly, we can assume that at a higher loading concentration of GO there will be agglomeration of the GO in the matrix and, secondly, the presence of high quantities of GO may impact the integrity of the membrane's mechanical strength due to increased porosity as a trade-off scenario [50; 51].
## 4 Anomaly Detection Method Based on DeepESN
### Key Benefits of the DeepESN Architecture  
Compared with ESN, the DeepESN constrains the non-linear time series modeling capability of ESN by incorporating efficient deep learning functionality. The main advantages of using the DeepESN architecture are  
* A deep layering of DeepESNs allows for the effective diversification of temporal representations in the layers of the hierarchy, amplifying the effects of the factors influencing the time scales and the richness of the dynamics, measured as the entropy of recurrent units' activations [59].
* The short-term memory capacity of DeepESN is higher than that of ESN. The short-term memory capacity is used to determine how well the most recent data (input) can be remembered based on the current context [60]. The number of recurrent units is quite similar in both the DeepESN and ESN mechanisms. However, experiments have demonstrated that the DeepESN mechanism exhibits a higher short-term memory capacity than the basic ESN [61].
* The proposed DeepESN solution can be seen as a reduced form of the basic ESN model. Specifically, influences greater than one are eliminated from the initial layer to the reservoir, and connections between the higher layers and the lower states are removed, thereby ensuring fewer recurrent non-zero connections [62]. Furthermore, for both ESN and DeepESN (\"layerization\" of the ESN), the costs of updating each state are,  
Figure 6: The sub-phases in the global architecture of the proposed AD methodology.  
respectively, \(O(N_{T}\cdot L\cdot N_{H}^{2})\) and \(O(N_{T}\cdot L^{2}\cdot N_{H}^{2})\)[63], where \(N_{T}\) is the number of time steps, \(L\) the number of layers, and \(N_{H}\) the size of the reservoir. Given that ESN is a \"layerization\" of DeepESN, its reservoir size is \(L\cdot N_{H}\). DeepESN demonstrates significantly lower costs compared to the basic ESN system. In fact, DeepESN surpasses the basic ESN in both computational efficiency and accuracy.  
Overall, the proposed DeepESN overcomes the standard ESN by increasing its computational efficiency, short-term memory capacity, and reservoir state richness. Accordingly, in this work it is proposed to use DeepESN to generate short-term predictions of electrical parameters. The general architecture of the proposed method, along with its associated input/output components, is depicted in Figure 7.
## 4 Results and Discussion
### Shap Analysis Across Reduced Features
#### 4.4.2 Results for Quadrant 1  
In Figure 11, we observe that 'Number of Micro-Saccade' is the most significant feature for predicting emotions in Quadrant 1 of the CMA, associated with low arousal and positive valence, indicative of emotions such as relaxation and calmness. Lower values for this feature have a positive impact on the model's decisions, indicating that individuals tend to perform less rapid involuntary eye movements while fixating on a particular point when they are calm. Conversely, higher values of 'Micro-Saccade Horizontal Amplitude', 'Micro-Saccade Vertical Amplitude', 'Micro-Saccade Direction', and 'Micro-Saccade Amplitude' cause positive SHAP values, suggesting that individuals experiencing these emotions tend to exhibit longer micro-saccades. This indicates that during states of calmness and relaxation, the eye travels greater distances between fixation points from all angles.  
Figure 10: Summary Plot for Quadrant 0 for 15 features.  
Similarly, higher values of 'Number of Saccade', 'Saccade Amplitude', 'Saccade Length', and 'Saccade Duration' generally cause positive SHAP values, indicating that people feeling these emotions, such as relaxation and calmness, are likely to display longer saccades. However, lower values for 'Saccade Direction' impact the model positively, suggesting that smaller angular movements in saccades are indicative of intense emotional states in Quadrant 1.  
Additionally, lower values of 'Fixation Duration' have a negative impact on the model, while higher values impact positively. In contrast, higher values in these fixation duration metrics are linked to states of calmness or relaxation. This pattern corroborates the hypothesis that individuals in relaxed states tend to have longer fixation durations, signifying fewer eye movements and a steadier gaze. Yet, increased 'Number of Fixations' impacts the model ambiguously, indicating both positive and negative effects.  
Figure 11 demonstrates that lower values of 'Number of Blink' and 'Blink Duration' impact the model ambiguously, indicating both positive and negative effects. This variability results in insignificant alterations in SHAP values, suggesting their minimal diagnostic significance for emotions in this quadrant. All these findings for Quadrant 1 generally correlate with the scenario with all 49 features.
## Materials and Methods
### Microalgae Growth Monitoring  
Two samples were withdrawn from the bioreactor on a daily basis. The first ones (circa 3 mL) were used to lead transient chlorophyll fluorescence signal (OJIP) assays (AquaPen 110-C) after 15 min of dark adaptation. These tests were conducted daily to verify the proper functioning of the cells' photosynthetic system. The three main outcomes of these assays were the absorbed energy per reaction center (ABS/RC, which indicates the ability of the chlorophyll aggregates to capture light), the trapped energy per reaction center (TR\({}_{0}\)/RC, which evaluates the transfer from the chlorophyll aggregates to the complexes using light energy to release electrons), and the transferred energy per reaction center (ET\({}_{0}\)/RC, which indicates how efficiently the recovered electrons are transported further). Together, they assess light energy capture and transfer toward biosynthesis by using a PhotoSystem II (PSII). One should note that these indicators are intended to detect problems or evolution in the photosynthetic apparatus status. They do not relate directly to carbon capture but ensure that the cells are performing optimally from a photosynthetic point of view.  
The second samples (50 mL) were used to obtain microalgal cell density. To do so, the samples were passed through pre-dried and pre-weighted filters (0.45 \(\upmu\)m pore diameter). Loaded filters were then extensively washed before being dried at 105 \({}^{\circ}\)C overnight. Finally, filters were weighted once again to measure the retained cell mass and compute the cell density.  
Homogeneity of the culture within the PBR was assessed during the run. Three samples were withdrawn from the top, middle, and bottom of the bioreactor and analyzed with a spectrophotometer. Comparable values of absorbance at 680 nm were found: 0.021 \(\pm\) 0.02. This was consistent with our visual observations of the culture inside the photobioreactor, the absence of flock in suspension, and fast dissipation of biofilm taken from the edges. For the sake of pragmatism in operation, the samples used for the analysis were withdrawn from the top region of the bioreactor (between 0 and 10 cm deep). Results might be marginally underestimated due to residual concentration heterogeneity.
## 2 Literature Review
### Multimodal Classification of Conversations  
There are hardly any investigations that jointly explore the audio signals and the text of automated transcriptions in the context of education. Most research in multimodal approaches are oriented toward estimating the speaker's emotion in an audio conversation [33; 34], using the sound and spoken content of an emotional dialogue to obtain a better understanding of speech data. Since the focus is on classifying the emotional content of speech, these approaches typically work with a fixed vocabulary of words that identify an emotional category (e.g., \"happy\", \"sad\", \"angry\"). Therefore, the scope of the content analysis achievable with these tools is significantly more limited than with the techniques exposed in Section 2.2, as the language used in these applications is delimited to particular words with an emotional burden.  
Textual-acoustic feature representation has also been applied to sentence-level speech classification for detecting intention in the speech of a medical setting [35], and to music genre classification as in the work presented in [36], where authors showed that the learning of a multimodal feature space increased the performance of pure audio representations.  
It is more common, however, to find multimodal educational systems that use human rather than automated transcriptions. Some studies have evaluated the performance of the LENA system (see Section 2.1) applied to native French-speaking young children using audio recordings and their manually transcribed files [37]. For systems devoted to the language development of small children (children from birth to 3 years as in the case of LENA), it is affordable to have a professional team to produce accurate and reliable transcription of the audio recording files [38].  
Broadly speaking, we can conclude that multimodal classification is based on the audio signal as the primary data source and it is complemented by the extraction of text features. Furthermore, we can also affirm that this type of classification has mainly been explored in particular contexts of application (emotional, medical, music) that feature their own professional or technical jargon.
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 7 Conclusions  
This paper aims to optimize the location and volume of DG in the AC/DC distribution network. Based on the consideration of DG and load timing characteristics, a DG planning model is established. The model is solved by the GA-ACO algorithm. The following conclusions were obtained from the case analysis:  
* It is necessary to consider environmental costs in the problem of DG access to the distribution network, because this reflects the environmental advantages of PV and WG, helps to achieve a balance between system economics and environmental protection, and greatly improves the utilization rate of new energy, and this makes the distribution network cleaner and more environmental-friendly.
* Timing characteristics models are critical in solving the problem of DG access to the distribution network. By considering the timing characteristics, the model can more accurately reflect the operation of the distribution network, and the resulting planning scheme is more realistic.
* The GA-ACO algorithm combines the advantages of genetic and ant colony algorithms. It is superior to the two algorithms in terms of convergence speed and convergence results, and effectively avoids the objective function falling into the local optimal solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DG Type** & **Consider Timing Characteristics** & **Disregarding Timing Characteristics** \\ \hline MT & 2(12), 6(27), 10(11), 14(7) & / \\ PV & 21(8), 24(10), 32(15) & 21(13), 24(18), 32(20) \\ WG & 22(18), 25(8) & 22(5), 25(3) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Planning schemes considering load growth.  
Figure 11: Optimization algorithm comparison chart considering load growth.  
**Author Contributions:** Conceptualization, D.Y.; Formal analysis, F.M.; Writing--Original Draft, D.Y.; Writing--Review & Editing, F.M. and J.Z.  
**Funding:** This paper is supported by the 2107 Key Research and Development Plan of Jiangsu Province BE2017030 \"Research on Custom Power System in Photovoltaic/Battery microgrid with VSG interface\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Bio-Based Polymers with Potential for Biodegradability
## 2 Bio-Based Polymers
### Cashew Nut Shell Liquid  
The liquid extracted from cashew nut shells (CNSL) is a mixture of phenols [56], with the most important constituents being anacardic acid, cardanol, and cardol [57]. High temperature processing of CNSL converts anacardic acid into cardanol [58]. CNSL finds use in flame-retardant applications due to its chemical structure, which includes an aromatic ring [59]. Other applications include synthetic polymers [57; 60; 61], resole and novolac resins [62], free radical and ionic thermosets [63; 64], and novel CNSL-formaldehyde resins [65]. Overall, CNSL-formaldehyde resins prepared by various ways have shown inferior mechanical properties [66]. It has been shown that an optimum performance is obtained for resins with a composition of 40 wt % cardanol [62]. Blends of CNSL-based and conventional resins have also been studied. Novalc resins from cardanol have been used in epoxy resin formulations with higher tensile strength, toughness, and water resistance [67]. Currently, there is no evidence that CNSL-based materials are biodegradable. Their aromatic content and crosslink density are features that may compromise their biodegradability potential.  
One of the applications of CNSL outside of the realm of strictly biodegradable materials is the synthesis of cardanol-based polybenzoxazines [68; 69] with good thermal and flame retardancy properties [70; 71], which can also compromise the biodegradability of these materials. Due to their versatile molecular design, different alternatives have been developed for the preparation of CNSL-based polybenzoxazines, including blending with bisphenol A-based polybenzoxazines [72]. Other studies have reported the use of furfural-modified cardanol [73; 74], or cardanol-modified bisphenol A-based benzoxazines [75; 76] in order to enhance the performance of these resins. Alternatively, different amines, such as aniline [72], and ammonia [68] can be used in the polybenzoxazine formulation. Bisbenzoxazines have also been prepared through the reaction of mono-phenols and diamines with formaldehyde [77].
# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
## 4 Algorithm Simulation Verification  
Two algorithms were used in this study: the neighbor point cloud segmentation algorithm and the Kalman filter. The MATLAB software was used to combine calculations for the two algorithms. The filtering algorithms were written for the following:  
1. Simulation space;
2. Simulate the interference of rain or snow, which will cause noise in the space;
3. Neighboring point cloud segmentation algorithm;
4. Simulate normal weather LiDAR measurement;
5. LiDAR measurement disturbed by adverse weather;
6. Kalman filter reconstructs the LiDAR measurement results disturbed by adverse weather.  
In the calculations, the size of the space was first established, which was set to 800 cm on the \(x\)-axis and the \(y\)-axis; the height of the \(z\)-axis is 50 cm, as shown in Figure 4(a). Some point cloud noise was added to this space to test whether the two algorithms are appropriate. Nearest neighbor segmentation was tested first, then some point clouds were added for noise processing, as shown in Figure 4(b). Using radius filter for filtering, we set the radius to be within 2 cm and the number of neighboring point clouds to be at least 10. Anything less than 10 would be filtered out as noise. The filtered result is shown in Figure 4(c).  
Figure 5(a) shows the simulated LiDAR signal. First, the LiDAR signal was simulated using the software. Gaussian white noise was simulated. The mean parameter was set to 0, and the parameter for the random number of standard deviations was set to 0.03. Real LiDAR will also produce a little error during measurement, but the value of the error will be within a certain range. Figure 5(b) shows the LiDAR signal with noise. In future, we will add Gaussian white noise for testing. The mean parameter was set to 0, and the random number of standard deviations was set to 0.1. Figure 5(c) shows the simulation results after Kalman filtering. The selection of \(Q\) and \(R\) is related to the statistical characteristics of the measurement and system noise. The current theoretical approach is to make an estimation based on experimental data. The parameters of the Kalman filter were set to matrix [1,0,0,0; 0,1,0,0], \(Q\) was set to a 4 \(\times\) 4 matrix, and \(R\) was set to [2,0; 0,2]. The five steps comprising Equations (1)-(5) of the Kalman filter process were calculated to restore the original data. The input was noise. We first calculated the estimated value and the estimated value of the noise, then the Kalman gain. The original data were restored after the Kalman gain was used to calculate the best result.  
Figure 4: Smoke-making machine.  
Figure 5: (**a**) Simulation space; (**b**) neighboring point cloud noise; (**c**) filter results of neighboring point cloud segmentation algorithm.  
Figure 6: (**a**) LiDAR measurements; (**b**) LiDAR measurements disturbed by environmental noise; (**c**) Kalman filter reconstructed environmental noise.
## 2 Materials and Methods
### Data Preprocessing  
GSR and PPG signals are highly susceptible to noise distortion, such as motion artifacts, breathing, and 50 Hz power line interference. Such artifacts can significantly affect further calculations, so the use of noise reduction methods is extremely important. A zero-phase second-order Butterworth filter with a bandpass range of 0.5-5 Hz, as recommended in [33], was employed to eliminate noise and movement artifacts from the PPG signal. This bandpass filter, specifically designed for PPG signals, operates in two stages:  
1. Low-frequency filtering: This stage removes baseline wander and slow drifts, typically achieved using a high-pass filter with a cutoff around 0.5 Hz.
2. High-frequency filtering: This stage attenuates noise and artifacts by applying a low-pass filter with a cutoff around 5 Hz.  
The PPG signal is shown in Figure 3.  
Having skin resistance and skin conductance signals available, skin resistance signals were selected for further analysis. They were then filtered using the interquartile range (IQR) method [33]. The IQR method is a widely used technique in data processing for outlier removal, helping to eliminate extreme values that may distort the analysis results. The IQR is defined as the difference between the third (Q3) and first (Q1) quartiles, which represents the range covering the middle 50% of the data. To identify outliers, the lower and upper limits are calculated: the lower limit corresponds to Q1-1.5 IQR and the upper limit corresponds to Q3 + 1.5 IQR. Values that fall outside these limits are considered outliers and can either be removed or treated separately to ensure more accurate and reliable analysis. The IQR method helps preserve the core structure of the data by eliminating only those values that significantly deviate from the majority. The skin resistance signal before and after filtering is depicted in Figure 4.  
Since skin resistance is very individual and can vary from tens to hundreds of kOhm, the data were normalized using the StandardScaler [34]. This enables uniform scaling across records, which is crucial for achieving accurate model performance. To facilitate further analysis, the segments of the data record corresponding to the measurement without a stressor (3 min) and the measurement during the air raid signal (3 min) were truncated. Further, in the text they are called \"no-stress\" and \"stress\".  
Figure 3: PPG signal.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 2 RFI Classification and Formulation
### Interference Classification  
When SAR emits high-power electromagnetic waves to detect targets, its echo signals are inevitably doped with electromagnetic interference signals of the same frequency band from the surrounding environment. According to the energy source, SAR interference can be divided into two categories: passive interference and active interference (see Figure 2).  
Passive interference is generated by the reflection of SAR signals by metallic objects such as chaff and reflective nets. Active interference is the interference generated by external electromagnetic radiation sources, which can be divided into two types: intentional interference and unintentional interference. This article mainly introduces a detection method for active SAR interference. Unintentional interference mainly refers to radio frequency interference caused by communication, television, ground radar, and other operating frequencies within the frequency band of the SAR receiver. Unintentional interference usually does not completely spread over the entire image but exists in the SAR image with the characteristics of a scattered distribution and block aggregation, forming multi-point and local occlusions on the real image. Intentional interference is primarily caused by enemy jammers. Intentional interference can usually achieve high-power suppression of SAR signals and appears as a patch of bright occlusion on the SAR image, and ground targets are completely submerged in it. The existence of interference signals affects the results of SAR image processing and information extraction, thereby reducing the application efficiency of SAR image products.