esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Polarization-Insensitive and Wide-Angle Triple-Band Metamaterial Absorber
## 5 Conclusions  
This paper proposes a triple-band metamaterial absorber based on metal-dielectric-metal. The simulation results show that the structure achieves 97% absorption efficiency at 4.23 GHz, 7.403 GHz, and 14.813 GHz. By changing the polarization angle and incident angle of the incident plane wave, the MMA exhibits polarization-insensitive and wide-angle absorption characteristics. The absorption mechanism is explained by the equivalent medium theory, impedance matching, and the equivalent circuit. At the absorption frequency, the structure satisfies impedance matching and electromagnetic parameter matching. The analysis of the electromagnetic field and surface current distribution shows that the absorption process of MMA is mainly completed by the magnetic response formed  
Figure 16: Absorption comparison of the simulated and measured response under oblique incidence: (**a**) theta = 0, (**b**) theta = 10, (**c**) theta = 20, (**d**) theta = 30, and (**e**) theta = 40.  
by the reverse current between the upper and lower surfaces and the electromagnetic resonance on the upper metal surface. Then, the influence of different parts of the structure on the absorption performance is analyzed, and the three independent parts on the upper surface of the structure correspond to three different absorption peaks. Finally, the MMA's performance is validated through experiments, and the experimental results align closely with the simulation outcomes. The MMA proposed in this paper can be applied in the inter-satellite communications, achieving functions such as electromagnetic shielding and stealth.  
Conceptualization, S.Z. and X.J.; methodology, T.Z.; software, S.Z.; validation, S.Z., M.G. and G.F.; formal analysis, S.Z.; investigation, S.Z.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, X.J.; visualization, M.Z.; supervision, X.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Innovation Leading Talent Project of Special Support plan for high-level talents in Zhejiang Province (2021R52032); in part by the Natural Science Foundation of Zhejiang Province under grant No. ZCLZ25F0502; and in part by the Zhejiang University Students Science and Technology Innovation Activity Plan-New Talent Plan (2024R409C054).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 5 Biological Activities of Rice Phytolexins
### Bioactivities of Diterpeneoid Phytolexins
#### 5.1.2 Oryzalexins  
As mentioned above, oryzalexins A-F are distinguished from oryzalexin S both by both the biosynthetic pathway and their molecular structure (Figures 3 and 6).  
Contrary to what has been reported for momilactones, the literature concerning oryzalexins' biological activity is surprisingly limited. The most widely known bioactivity associated with these diterpenoid compounds is the antimicrobial activity against _M. grisea_, the rice blast fungus [20; 187]. Although many reviews from the last decade report this activity [1; 199; 200; 201; 202], they typically refer to literature from the late 1900s that merely scratched the surface on this topic, focusing on _M. grisea_ spore inhibition [33; 185; 186].  
Interestingly, recent reports suggest that oryzalexins may have other potentially valuable capabilities. [PERSON] and colleagues [43] recently reported a potential anti-inflammatory activity of oryzalexin A, which has been shown to possess an inhibitory activity on NO production by mouse macrophage RAW264.7 cells. Furthermore, [PERSON] and [PERSON] [188] observed that oryzalexin B, in combination with other natural compounds, seems to be able to bind six potential receptors in estrogen receptor-positive breast cancer, suggesting another potential use in medicine.  
Oryzalexin S also shows a mild allelopathic effect in lettuce and bamyard grass [190] and, along with other oryzalexins, seems to be able to affect stomatal closure, playing a role in drought resistance [189].  
Regardless of their role, oryzalexins have been shown to accumulate after exposure to fungal proteins [203] and oligosaccharidesad [204; 205; 206; 207], as well as fungal [208] and nematode [209] infection. Abiotic factors also seem to induce the accumulation of oryzalexins, such as heavy metal ions [54], salicylic acid [210], and UV radiation [53; 211].
## 5 Results
### Classification Results
#### 5.3.4 Total Profile Deviation  
The accuracy values of the classification of the profile total deviation with different classification methods and feature selections are shown in Table 8. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature Selection** & \begin{tabular}{l} **Number of** \\ **Features** \\ \end{tabular} & \begin{tabular}{l} **Accuracy** \\ **Bayes** \\ \end{tabular} & \
\begin{tabular}{l} **Accuracy** \\ **SVM** \\ \end{tabular} &
\begin{tabular}{l} **Accuracy** \\ **KNN** \\ \end{tabular} \\ \hline MANOVA RS + STFT & 4 & 0.69 & 0.88 & 0.69 \\ \hline MANOVA RS + CWT & 5 & 0.57 & 0.86 & 0.78 \\ \hline MANOVA RS + DWT & 8 & 0.79 & 0.83 & 0.67 \\ \hline MANOVA RS + WPT with 15 coeff. & 6 & 0.74 & 0.78 & 0.79 \\ \hline MANOVA RS + WPT with 128 coeff. & 5 & 0.67 & 0.84 & 0.72 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + STFT & 60 & 0.76 & 0.81 & 0.60 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + CWT & 60 & 0.59 & 0.72 & 0.59 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + DWT & 60 & 0.52 & 0.43 & 0.45 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + WPT with 15 coeff. & 60 & 0.57 & 0.53 & 0.36 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + WPT with 128 coeff. & 60 & 0.72 & 0.64 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification model evaluations for OTE classification. Figure 13: Best two features and confusion matrix of SVM to classify the OTE (feature selection: MANOVA, features from raw signal, and STFT). Both the SVM with features selected by ANOVA and the rank correlation coefficient from the raw signal and the WPT with 128 coefficients, and the Bayes classifier with features selected by MANOVA from the raw signal and the STFT, achieve the highest accuracy of 0.81. The SVM, with features selected by ANOVA and the rank correlation coefficient from the raw signal and the WPT with 128 coefficients, achieves the highest accuracy of 0.81. For the two best performing of the 60 features selected from the raw signal and the WPT, the training and validation data and the SVM confusion matrix are shown in Figure 14. The SVM that selects features from the raw signal and the DWT using ANOVA and the rank correlation coefficient is the worst suited for the classification of the profile total deviation. It misclassifies 84% of the data points with an accuracy of 0.16. In general, the results have demonstrated that employing a support vector machine (SVM) leads to the minimum number of classification errors. However, the precision of classification predictions significantly relies on the chosen features, which are determined by the applied feature extraction and selection techniques, that should be tailored to the specific parameter of interest. For future research, we propose exploring smaller parameter ranges and additional influencing variables through a long-term experiment that should run parallel to the production process. This long-term experiment wo  
Figure 14: Best two features and confusion matrix of SVM for classification of profile total deviation (feature selection: ANOVA+rs, features from raw signal and WPT with 128 coefficients).
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 6 Quantitative Classification of Ineffective Circulation Channels
### Application of Ineffective Circulation Channels
This phenomenon is more likely to occur when there are high-permeability channels between the injection and production wells, leading to rapid response, or when there is a high injection pressure that induces a strong response in the surrounding production wells. Uneven pressure distribution and imbalance in injection-production configurations can also contribute to connectivity coefficients exceeding 1. In order to further characterize the changing trend of connectivity parameters, various examples of well groups are listed in Table 8 according to the statistics of different curve forms. By plotting the inter-well connectivity volume and connectivity coefficient in the early, middle, and late stages of block Nml3 in the South Area, NmIII, and NmIV1 in the North Area, it is found that the curve can be subdivided into three forms: continuous rise, increase-decrease-increase and decrease-increase-decrease, as shown in Figure 21. The continuous rise category accounted for 58.8%, such as the well groups C03-H03H and C03-B26 HS. The increase-decrease-increase category accounted for 19.7%, as well as groups C31-H14H and B21-B16. The decrease-increase-decrease category accounted for 21.5%, such as the C31-H02H and B21-B15. These classifications and examples help understand the dynamic variation characteristics and laws of connectivity volume of different well groups during waterflooding development. For well groups falling into the category of continuous rise, the development of ineffective circulation channels is significant, and the connectivity volume and connectivity coefficient are increasing. Maintaining a high connectivity volume and connectivity coefficient would help to improve reservoir recovery efficiency. For wells H02H and H14H in well group C31, there is a trend of increase-decrease-increase. The connectivity volume and connectivity coefficient increase first, then decrease and rebound. Such patterns indicate that inter-well connectivity fluctuates over a period of time, possibly due to changes in injection-production conditions or geological factors. For such fluctuating trends, it is recommended to increase the injection rate during periods of decreased connectivity to stabilize inter-well connectivity. Additionally, more precise monitoring at the production well end is needed to assess the effectiveness of the adjustments. If necessary, dynamic regulation of injection pressure can be implemented to mitigate fluctuations and ensure a quick recovery of inter-well connectivity. Figure 21: Variation trend of (**a**) inter-well connectivity volume and (**b**) inter-well connectivity coefficient in typical wells.
## 3 Results and Discussion
### Changes of Histamine  
The changes of histamine content of the tray packed sturgeon inoculated with different strains during ice storage are shown in Figure 2G the histamine content of the fish in each experimental group showed an upward trend during storage. In the early stage of storage, histamine production was less. On the 9 th day, the histamine content of fish in each treatment group was significantly different from that in the blank control group. This was mainly due to the small number of microorganisms, less histidine decarboxylase production and slow change of _histamine_ content. However, with the gradual adaptation of microorganisms to the storage environment, microbial growth and metabolism were vigorous. The increase of histamine content in each experimental group was accelerated. After 9 days of storage, the histamine content of _Pseudomonas fluorescens_ group and _Pseudomonas mandeli_ group increased significantly. On the 15 th day, the histamine content of _Pseudomonas fluorescens_ group was significantly different from other experimental groups, reaching 24.170 \(\pm\) 58 mg/100 g, while the histamine content in _Pseudomonas deceptionensis_ group and _Pseudomonas_ sp. J-10 group increased slowly during storage, and reached 15.650 \(\pm\) 0.41 mg/100 g, 14.76 \(\pm\) 0.39 mg/100 g. This indicated that the ability of different microorganisms to promote histamine production in fish was different, among which _Pseudomonas fluorescens_ was the strongest and _Pseudomonas_ sp. J-10 was the weakest.  
During the storage of fish, the free histidine is decomposed by histidine decarboxylase to produce histamine under the joint action of endogenous enzymes and microorganisms. Among many biogenic amines, histamine has the greatest impact on human health. Histamine poisoning caused by eating aquatic products occurs from time to time, so it has become an important index to evaluate the freshness of aquatic products [17]. Both _Pseudomonas_ sp. and _Shewanella_ sp. inoculated in this experiment were psychrophilic bacteria, and the activity of histidine decarboxylase was also inhibited due to the low temperature of ice storage, so the histamine content of each experimental group was still lower than the specified limit even after complete spoilage. However, the overall results still reflected the difference of histamine content among the experimental groups.
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Mathematical Models
#### 2.1.1 Attenuation Due to Atmospheric Gases  
Nitrogen, oxygen, argon, and carbon dioxide represent the main constituents of the atmosphere. Table 1 shows their percentage distribution for four different concentrations of water vapor [18].  
The specific attention due to the atmospheric gases \(\gamma_{g}\), is given by [6]  
\[\gamma_{g}=0.1820\times f\Big{(}N_{\text{Oxygen}}^{\prime\prime}\ (f)+N_{\text{Water vapor}}^{\prime\prime}\ (f)\Big{)} \tag{2}\]  
where \(f\) is the frequency and \(N_{\text{Oxygen}}^{\prime\prime}(f)\) and \(N_{\text{Water vapor}}^{\prime\prime}(f)\) are the imaginary parts of the frequency-dependent complex refractivity of oxygen and water vapor, respectively.  
Attenuation due to atmospheric gases along a specified line-of-sight path is subsequently determined by substituting the value of \(\gamma_{i}\) in Equation (1) with the value of \(\gamma_{g}\) for the specific path segment.
# Sensing Performance of a Vibrotactile Glove for Deaf-Blind People
###### Abstract  
This paper presents a glove designed to assess the viability of communication between a deaf-blind user and his/her interlocutor through a vibrotactile device. This glove is part of the TactileCom system, where communication is bidirectional through a wireless link, so no contact is required between the interlocutors. Responsiveness is higher than with letter by letter wording. The learning of a small set of concepts is simpler and the amount learned can be increased at the user's convenience. The number of stimulated fingers, the keying frequencies and finger response were studied. Message identification rate was 97% for deaf-blind individuals and 81% for control subjects. Identification by single-finger stimulation was better than by multiple-finger stimulation. The interface proved suitable for communication with deaf-blind individuals and can also be used in other conditions, such as multilingual or noisy environments.  
cognitive assessment; vibrotactile glove; communication by concepts; deaf-blind people; mobile health +
Footnote †: journal: Applied Sciences
## 4 The Results
### Decision Algorithms When Controlling a DG Shipment  
The complexity of DGA regulations and the number of necessary data, together with the significance of the result of wrong verification, make DGA a very difficult process that is responsible for many elements. In order to support the work of a DG operator with IT tools using AR technology, all the decision algorithms, together with the data necessary to make a decision in the process, have been identified during the study. They have been used to learn which stages of the DGA process are the most effortful and error-prone and which of them reveal the highest potential for AR support for the operator. A fragment of an exemplary decision algorithm, being only a small part of the general decision algorithm when controlling a non-radioactive DG shipment with the chosen \"full DGR consignment\", is shown in Figure 3.  
To answer all the questions presented in the algorithm shown in Figure 3 (and in a corresponding acceptance list), a DG operator must thoroughly know general, as well as special, DG regulations regarding air transport. Using this knowledge, they can control visually all the required shipment parameters and compare these parameters with law regulations. The same important task is to confirm the compliance of the data declared in the shipment documents and printed on a shipment's outer packaging. The complexity of the problem arises even more when a DG shipment contains more than a single packaging of DG material and/or more than a single kind of DG material. It should also be noted that, when checking labelling and marking of the shipment, the operator must check if the shipment has any irrelevant or unnecessary labels and text on its outer packaging. The observation of DG operators' everyday work, as well as the development of decision algorithms, lets us identify the operations which have been considered as the most prone to AR support.  
Figure 3: A fragment of the decision algorithm for a non-radioactive dangerous good transported according to “full DGR consignment”.
## 4 Discussion
### Comprehensive Discussion of Pulse Waveform Datasets  
In order to fully consider all types of pulse waveforms and maximize the extraction of waveform features, the dataset covers the waveform categories of full waveforms, rising edges, and falling edges, and downsampling, noise addition, and random cropping are used to reduce the model's complexity and enhance its robustness. In subsequent studies, when multiple particle identification is performed, the waveform data can be further converted to the frequency domain, and dual-channel data in both time and frequency domains can be used as input data, which are expected to further improve the identification accuracy. In addition, possible wave stacking and variations in temperature should also be part of the training data.
## 3 Methodology
### The Proposed Ensemble Cost Function  
Ensemble learning is a widely recognized approach that involves combining the predictions of multiple learning models to improve the overall performance [39]. Using three diverse loss functions balances individual strengths and weaknesses. Averaging these losses can enhance the model's ability to address a wider range of prediction errors. For instance, if one loss function is sensitive to outliers, including others with distinct characteristics can mitigate their impact on optimization. This strategy promotes a more holistic optimization landscape, potentially improving the generalization and robustness of predictions. To identify patterns for prediction tasks, discriminative deep-learning models are employed for supervised learning. This article introduces a new ensemble cost function that leverages three distinct loss functions, namely, L1 loss, L2 loss, and Log-cosh loss, to enhance the performance of CNN in regression tasks. The proposed ensemble-learning approach exhibits a key strength in enabling the effective fusion of multiple sources of information. Moreover, the method offers a robust solution for handling unknown noise distributions commonly encountered in real-world applications.  
Based on Equations (1) and (2), the aggregation of the proposed ensemble average loss functions is formulated as follows:  
\[\text{Ensemble }\cos\text{t function}=\frac{1}{3n}{\sum_{i=1}^{n}{\sum_{j=1}^{3}{ L_{j}(\overset{\rightarrow}{x},y_{i}^{i},y_{p}^{i})}}} \tag{7}\]  
where \(n\) is the number of data samples, \(L_{j}\) represents the loss function, and \(j\) denotes the number of loss functions. It could be formulated more specifically as follows:  
\[\text{Ensemble }\cos\text{t function}=\frac{1}{3n}{\sum_{i=1}^{n}{\left|{y_{t}^ {i}-y_{p}^{i}}\right|}}+\left({y_{t}^{i}-y_{p}^{i}}\right)^{2}+\left(log(cosh( y_{p}^{i}-y_{t}^{i}))\right)\]  
The computational cost and time associated with utilizing multiple loss methods warrant consideration. While a single loss function might appear to be computationally efficient, combining multiple functions could introduce added complexity due to calculating and merging multiple loss terms. Nevertheless, potential performance gains might offset the heightened computational demands.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Binary Classification of Breast Cancer
[84] tested a model using two widely used datasets, DDSM and MIAS. The DDSM dataset has 1500 mammography pictures, 479 of which are benign, 519 normal, and 502 of which are cancers. MIAS includes 326 images. There are 207 normal images, 119 abnormal images, and 68 normal and 51 cancer types among the abnormal photographs. Furthermore, the MIAS dataset has a precision of 0.99 (normal vs. aberrant) and 0.98 (cancers vs. noncancers) for the DDSM dataset. Finally, there were constraints such as reduced learning speed, local minimum trapping, and more learning epochs. [PERSON] et al. [85] advocated employing characteristics taken from the Hough transform to classify mammograms. A two-dimensional transform is the Hough transform. It is used in images to isolate the attributes of a specific shape. SVM was used to get 95 mammography pictures for the dataset. Getting images, preprocessing them, and extracting features using the Hough transform and SVM classification are among the approaches employed. The precision is 0.94.
## 3 Results
### Limitations for External and Internal Mass Transfer  
From the data provided in Tables S1-S3, it was found that the model that best fits the experimental data of the kinetic process of Cr (III) removal is [PERSON]'s; however, there are also other models that provided information on limitations due to internal and external mass transfer. As such, it was necessary to carry out more extensive analyses on these phenomena in the Cr (III) removal process (Table S4), for which the graphs shown in Figure 4 were constructed. It was observed that, for all samples and at any pH, a straight line is constructed with the experimental data in the first 3.25 h of contact in the adsorption, except for WTOP at pH 0.91, where this event occurs at 4.5 h; the presence of this straight line suggests that there is an external mass transfer.  
This result indicates that it is important to establish whether there are internal mass transfer limitations to determine which phenomena could significantly influence the overall speed of the adsorption process. Figure S2 shows the behavior of q vs. \(t^{0.5}\), based on the intraparticle diffusion model (Table 2); in this figure, three stages can be observed: external diffusion, internal diffusion, and equilibrium [22; 26]. Defining each of the stages, it is also possible to determine the mass transfer rate constants in such a way that the process that dominates the removal of Cr (III) can be known. Table 5 shows the speed constants of the different treatments applied, using the \(k_{1}/k_{2}>1\) criterion, which indicates that mass transfer by external diffusion dominates the global adsorption process, where \(k_{1}\) represents external diffusion and \(k_{2}\) internal diffusion [22; 26].  
The results obtained show that in the adsorption of Cr (III) in OPTmW the external diffusion dominates, which causes the Cr (III) ions to take time to arrive from the core of the solution to the surface of the adsorbent, and therefore, more energy is needed to capture the ions on the surface, and this causes the adsorption capacity to be lower compared to the other two treatments. In MTOP, there are internal diffusion limitations at a pH of 0.91 and 1.78, but at a pH of 2.72 there are external mass transfer problems, which is why it has a lower adsorption capacity compared to WTOP at the same pH. Finally, in WTOP there are external mass transfer problems at pH \(<2.72\), which explains the fact that the maximum adsorption capacity is present at pH equal to 2.72, due to the fact that less energy is required to remove the Cr (III) ions from the solution.