esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
## 5 Conclusions  
In this paper, two new methods to decrease eddy current loss have been established by analyzing the circuit model of the underwater WPT system in air and seawater. It is viable to reduce the eddy current loss and improve the energy transmission efficiency of the system by controlling the current in the primary coil and the secondary coil. In addition, the underwater WPT system of planar dual coil model was established by adopting FEA software to verify the correctness of the two methods, and the simulation results are in good agreement with the calculation results. The study shows that, within a certain range, increasing the phase difference between the primary-side and the secondary-side current or adjusting the current modulus ratio of the two coils to the optimal modulus ratio can reduce the eddy current loss of the underwater system and improve the energy transmission efficiency of the system.  
Figure 14: AC-AC efficiency varies with \(\alpha/\alpha_{0}\).  
**Author Contributions:** Conceptualization, J.W. and B.S.; methodology, J.W.; software, J.W.; validation, J.W. and Y.W.; formal analysis, J.W.; investigation, J.W.; resources, J.W.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], B.S., and Y.W.; visualization, [PERSON]; supervision, B.S.; project administration, B.S.; funding acquisition, B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Microwave Characterization Methods
#### 2.1.3 Rectangular Waveguide  
Rectangular waveguide is one of the most commonly used instruments for the measurement of dielectric properties [26; 27]. A rectangular sample of MUT, with a specified length, is placed in an empty waveguide, provoking a perturbation of an electric field, which is frequency-dependent. By measuring the resulting \(S_{21}\) and \(S_{11}\) parameters, the di  
Figure 1: Resonant Coaxial Bireentrant Microwave Cavity: **(a)** schematic of the resonant cavity; **(b)** detail of the actual cavity before being inserted into the measurement equipment.  
Figure 2: Measurement Equipment: **(a)** Single-Post Reentrant Cavity Measurement Kit at 2.45 GHz; **(b)** Split-Cylinder Resonant Cavity Measurement Kit at 6.25 GHz.  
electric properties of the MUT at a given frequency can be extracted. For the measurements of the work, the WR42 (18-26.5 GHz) rectangular waveguide is used jointly with a VNA, as shown in Figure 3. The MUT size must fit the X-band rectangular waveguide aperture, which is 10.668 \(\times\) 4.318 mm (corresponding to WR42 rectangular waveguide size standard). This measurement setup was used for the MUT characterization at 24.5 GHz.
## 3 Results and Discussions
### Scanning Electron Microscopy (SEM)  
SEM images obtained on the GO powders before (a, c, e) and after the silanization process (b, d, f) are presented in Figure 7, at magnifications of \(\times\)500 and \(\times\)5000, respectively.  
Figure 6: UV-Vis spectra of GO, GO-Ag, GO-Ag-APTES, GO-ZnO si GO-ZnO-APTES powders.  
Figure 7 shows the multiple lamellar aspect of the GO powder, which allows us to distinguish the edges of the sheets that are superimposed on each other. Figure 7a-d shows that the GO layers have sharp edges that are raised in places. In Figure 7c-f it can be observed the dispersion mode of the Ag (in the form of white dots) and Zn nanoparticles on the surface of the GO sheets and between the GO layers, especially in the case of GO-ZnO (Figure 7e,f) where zinc oxide is abundantly attached to the surface of the GO sheets. In addition, there is a difference between the images taken on the non-silanized powders and those recorded on silanized powders. On the silanized powders from Figure 7b,d, it could be observed how the agglomerations were reduced and some separate layers are not observed. The GO-ZnO-APTES (Figure 7e,f) were also observed in the same figure. The GO-ZnO-APTES (Figure 7f,g) were also observed in the same figure.  
clearly visible due to the attachment of the siloxane group on the surface of the GO sheet. In Figure 7f were ZnO is in excess, the GO layers are hardly noticeable, as if they were wrapped in a protective coating.
# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 2 Related Work
### Contrastive Learning and Siamese Network  
Contrastive learning is a self-supervised learning method designed to map similar samples to nearby points while pushing dissimilar samples apart. In contrastive learning, a key concept involves training a model by maximizing the similarity score of similar sample pairs and minimizing the similarity score of dissimilar sample pairs. Typically, contrastive learning could employ Siamese network structures to learn the similarity between samples [33; 34; 35; 36; 37]. Siamese networks are a type of deep neural network structure, usually composed of two or more identical sub-networks, which handle different input samples but share the same weights. By training Siamese networks to maximize the similarity scores of similar sample pairs and minimize those of dissimilar sample pairs, the goal of feature matching can be achieved. Furthermore, the Siamese structure offers a method for learning from unlabeled data, learning knowledge by comparing instead of directly using labels [38]. Training with a Siamese structure helps to address the challenge of insufficient labeled data.  
In the task of object pose estimation, contrastive learning and Siamese networks can be employed to measure the similarity between input images and templates. This aids in identifying the template that is most similar to the input image, thereby facilitating the estimation of the object's pose.
# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
###### Abstract  
The proposed model may enable the development of a device for the real-time field monitoring of plants. Such a system would be capable of assessing plant health and physiological needs, thereby preventing stress-related issues--such as wilting and yield reduction--and ultimately preserving product quality.  
The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research.  
hyperspectral imaging; baby leaf; precision agriculture; water content +
Footnote †: journal: Applied sciences
# Limitations of Large Language Models in Propaganda Detection Task
## 7 Discussion  
Having obtained the results from the first experiment, we can raise the following open points for further discussion:  
* As a general observation, we can say that _gpt-4-0125-preview_ was often unable to output an accurate span for a propaganda technique--some selected fragments were too long and the additional text did not include any valuable context to better understand the detected propaganda technique.  
Although the temperature was set to \"0\", which should provide more deterministic results, for the given prompt and task, various GPT models were unable to generate the same results; therefore, the blue method should be considered not reproducible.
* In the original paper by [PERSON] et al. [58], it was not mentioned whether the models were run several times, but we can assume it was done only once, and thus, the results are not trustworthy.
* In the same paper, there was no mention about error analysis nor any specific mistakes that the models made when predicting the propaganda techniques.
* Reformulation of the original SemEval2020 Task 3 and the use of the annotated development set as a test set was an example of data contamination--there could be a high risk that the models were trained on this data and it was the reason for significantly better results. The experiment should be conducted on the original test set for which the golden labels were not released to the public.  
The second experiment was also limited in one aspect--golden datasets were not publicly available and the submission system had a strict input data format requirement; post-processing was required in cases where the models did not detect any propaganda technique. In such instances, we decided to replace the missing values with the most numerous technique from the training dataset, that is, _loaded language_. This also raised a problem of a scarce number of annotated datasets in this field.  
The methodology proposed by [PERSON] was not implemented within the scope of our experimental procedures [59]. Due to the complicated nature of the task of propaganda detection, we can expect that the results of his study are also not reproducible. What is more, the author used the annotated data that was possibly used for pre-training GPTs by OpenAI; therefore, we could have another example of data contamination.  
The third experiment gave some hope for LLMs, such as _gpt-4-0125-preview_, to be a possible propaganda technique detection tool, but the results are limited on some levels. First of all, a larger number of examples than 100 should be manually checked by human annotators (preferably experts) to verify the credibility. Additionally, more annotators would allow for comparing the results and reducing the bias, for example, by calculating the inter-coder agreement. Second, fine-tuning the models with examples of Polish propaganda in news could enhance the results of detection and classification.
# Interaction Strength Analysis to Model Retweet Cascade Graphs
###### Abstract  
Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph that is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users' interactions. Initial findings showed the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.  
social media; network analysis; interaction strength; retweet graph; retweet cascade
## 5 Verification and Comparison of the Developed Lower-Depth Soil Temperature Evaluation Model
### Experimental Verification of the Developed Lower-Depth Soil Temperature Evaluation Model
Previous studies used solar radiation, air temperature, and albedo in combination with soil characteristics (such as thermal conductivity or diffusivity) to estimate soil temperature [26, 37]. However, in this study, an efficient lower-depth soil temperature estimation model was developed, in terms of the number of parameters of the model, analysis time, and the accuracy of estimation. However, despite of simple and convenient use of the developed model for shallow-depth soil temperature prediction, there are limitations. The procedure of the mathematical model development is valid, independent of location; however, for the determination of three model fitting parameters, \(A\), \(Z\) and \(CRCST_{i,mmnx}\), at least two time histories of soil temperature depth profiles are required at the target location. The three fitting parameters are functions of air temperature variation and soil characteristics (such as thermal conductivity, soil density, water content and others). Future study  
Figure 12: Regression analysis for measured and predicted temperature curves for additional data at depths of (**a**) 10 cm; (**b**) 15 cm; (**c**) 20 cm; and (**d**) 30 cm. is recommended to quantify the effects of air temperature variation and soil characteristics on the developed soil temperature model. The soil temperature model development requires continuous time history of soil temperature-depth profile measurement. Omission of several days of soil temperature measurements will ruin the accuracy of the proposed model. In addition, the soil temperature prediction model may induce high errors in prediction, if the spatial variability of soil characteristic (soil structure, texture, or fabric) within shallow depth (depths within 30 cm from the ground surface) is significant. However, in most of the cases, the soil's spatial variability within shallow depth can be assumed somewhat homogeneous.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 4 Datasets
### Other Datasets  
In addition to the publicly available datasets mentioned above, there are many documents that use self-created datasets, which are created by simulation software according to their needs. For example, [PERSON] et al. [41] used Matlab to generate the required dataset; [PERSON] et al. [59] generated datasets by GNU Radio simulation, and so on. The parameters of different datasets are different. The parameters and formats of the datasets are simulated and set according to different requirements for the data.
# Recovering Texture with a Denoising-Process-Aware LMMSE Filter
## 3 Recovering Texture via Statistical Analysis
### LMMSE Filter for Texture Recovery  
We use an LMMSE filter to estimate each texture patch \(\mathbf{t}_{i}\). The objective function of the LMMSE filter, \(\mathbf{W}_{i}^{*}\in\mathbb{R}^{L\times L}\) is formulated as  
\[\mathbf{W}_{i}^{*}=\underset{\mathbf{W}}{\operatorname{argmin}}\,\mathbb{E} [\|\mathbf{W}(\mathbf{t}_{i}+\mathbf{n}_{i})-\mathbf{t}_{i}\|_{2}^{2}], \tag{6}\]  
where \(\mathbb{E}[\cdot]\) denotes the expected value and \(\|\cdot\|_{2}\) is the \(\ell-2\) norm. Because this formula is quadratic, we can easily find the minimizer as follows:  
\[\mathbf{W}_{i}^{*}=(\mathbf{R}_{\mathbf{t}\mathbf{t}_{i}}+\mathbf{R}_{ \mathbf{t}\mathbf{n}_{i}})(\mathbf{R}_{(\mathbf{t}+\mathbf{n}_{i})(\mathbf{t} _{i}+\mathbf{n}_{i})})^{-1}, \tag{7}\]  
where we adopt the following covariance matrix notation: \(\mathbf{R}_{\mathbf{a}\mathbf{b}}=\mathbb{E}[(\mathbf{a}-\mathbb{E}[\mathbf{a }])(\mathbf{b}-\mathbb{E}[\mathbf{b}])^{\top}]\) (where \(\mathbf{a}\) and \(\mathbf{b}\) are some random vectors). The proof is given in Appendix A.  
Unfortunately, the covariance matrices \(\mathbf{R}_{\mathbf{t}\mathbf{t}_{i}}\) and \(\mathbf{R}_{\mathbf{t}\mathbf{n}_{i}}\) cannot be directly obtained because \(\mathbf{t}\) and \(\mathbf{n}\) are not observable. We solve this problem by using [PERSON]'s lemma and introducing several assumptions.
## 3 Results and Discussion
### Biochar Characterization
#### 3.1.3 BET Surface Area  
The surface area and pore volume of biochar are very important factors when BC is considered as a potential filler for polymer composites. The higher surface area along with the increased pore volume of BC samples might induce a good mechanical interlocking between the filler and matrix. It can be achieved by the infiltration of the molten matrix into BC pores during the melt mixing process which, consequently, improves the mechanical properties of the composites [12; 27].  
The BET surface area and pore volume of biochar samples are illustrated in Figure 2. It can be seen that both the BET surface areas and pore volumes of biochars were significantly increased with pyrolytic temperature. The surface area and pore volume of biochar produced at 700 \({}^{\circ}\)C was found to 249.13 m\({}^{2}\)/g and 0.031 cm\({}^{3}\)/g, respectively. On the other hand, biochar made at 300 \({}^{\circ}\)C exhibits a surface area of 2.04 m\({}^{2}\)/g and pore volume of 0.0057 cm\({}^{3}\)/g. This increase of surface area is due to the removal of carbon mass as volatile matters from the biomass surface which creates pores in the resulting biochars structure.
## 2 Materials and Methods
### Experimental Tests  
#### 2.2.1 Sample Preparation Plan  
To prepare the soil samples, the collected loess was oven-dried at 105 \(\pm\) 2 \({}^{\circ}\)C for over 8 h until weight stabilization. After cooling, the dried soil was sieved through a 2 mm mesh. Based on the experimental design, specified amounts of CL at varying dosages and MgO at a fixed 10% were weighed using a high-precision balance (accuracy: 0.01 g). The soil and modifiers were thoroughly mixed for 5 min to ensure uniformity. Distilled water was sprayed onto the mixtures to achieve the target moisture content, then sealed with plastic wrap and conditioned for 24 h under controlled temperature and humidity to ensure uniform water distribution and minimize evaporation. The mixtures were molded  
Figure 1: Particle size distribution curve of loess.  
Figure 2: Improved material of FT-IR: (**a**) CL; (**b**) MgO.  
into ring-knife specimens (61.8 mm diameter, 20 mm height) using a two-layer compaction method to achieve 95% relative compaction, ensuring sample consistency.  
Table 2 outlines the experimental protocol for investigating the synergistic improvement of thermal conductivity in loess using CL and MgO. The experimental framework involved several geotechnical tests: liquid and plastic limits were determined according to ASTM D4318-17 [32], compaction was assessed using ASTM D698-12e1 [33], collapsibility was evaluated based on ASTM D5333-14 [34], carbonation followed ISO 1920-12:2015 [35], freeze-thaw cycles adhered to ASTM D560-16 [36], and thermal conductivity was measured under controlled conditions following ASTM D5334-14 [37]. CL dosages of 0.5%, 1%, 1.5%, 2%, and 3% were selected to capture the full range of modification effects, while MgO was maintained at 10% based on its optimal carbonation efficiency and soil stabilization performance [38]. According to the preliminary experiments, the strength was highest when the CL content was 1%. Therefore, the thermal conductivity tests were conducted using this dosage as the standard. Moisture content levels of 8%, 12%, 16%, and 20% were chosen to represent conditions ranging from near-dry to near-saturated states, while dry densities of 1.4, 1.5, 1.6, and 1.7 g/cm\({}^{3}\) spanned loosely packed to highly compacted conditions, allowing for a comprehensive evaluation of their effects on thermal conductivity. Freeze-thaw cycles of 0, 2, 8, 15, and 20 were applied to simulate seasonal environmental fluctuations in loess regions, providing insights into the durability and long-term stability of the modified material. The experimental design integrated the physicochemical interactions between CL and MgO, while accounting for environmental stressors, generating reliable data to elucidate the mechanisms of CL-MgO synergy in enhancing thermal conductivity in loess. The findings offer a foundation for developing improved soil stabilization techniques under challenging conditions.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 3 Results
### Evaluation Index  
The approach proposed in this paper achieves the detection of infrared multi-scale small targets through semantic segmentation. Therefore, the F1 score, intersection over union (_IoU_), and normalized _IoU(nIoU_) are used as quantitative evaluation metrics for semantic segmentation.  
(1) F1 is the harmonic mean of precision and recall, which can be used to comprehensively evaluate the performance of the model. The formula for F1 is shown in Equation (8):  
\[F_{1}=\left(2*Precision*Recall\right)/\left(Precision+Recall\right) \tag{8}\]  
where _Precision_ refers to the proportion of samples predicted as positive by the model that are actually positive, while _Recall_ refers to the proportion of samples that are actually positive and are predicted as positive. Higher values of _Precision_ and _Recall_ indicate better performance of the network model, and their values are calculated with Equations (9) and (10), respectively:  
\[Precision=TP/\left(TP+FP\right) \tag{9}\]  
\[Recall=TP/\left(TP+FN\right) \tag{10}\]  
where true positive (\(TP\)) refers to the true examples. This is the number of pixels where the predicted box intersects the true box. \(TP\) represents the number of samples that were correctly predicted as positive by the model. On the other hand, false positive (\(FP\)) is the number of false positives that the predicted box incorrectly contains outside the true box. \(FP\) indicates the number of examples that the model incorrectly predicts to be positive. False negative (\(FN\)) refers to the false-negative examples. It represents the number of pixels in the true box that are not covered by the predicted box or the number of samples that are incorrectly predicted as negative by the model.  
(2) _IoU_ is one of the pixel-level metrics used to evaluate the performance of image segmentation models. It is used to evaluate the ability of an approach to describe the contour. It represents the ratio of the intersection area to the union area between the predicted segmentation results and the true segmentation results. It is calculated by Equation (11).  
\[IoU=TP/\left(TP+FP+FN\right) \tag{11}\]  
(3) _nIoU_ is a normalized version of IoU and is commonly used to calculate the average degree of overlap between multiple predicted boxes and multiple true boxes. It is the index obtained by calculating the intersection over union (_IoU_) for each class and then averaging the values. nIoU is an evaluation index specifically designed for infrared dim- and small-target detection. It can be calculated based on Equation (12):  
\[nIoU=\left(\sum_{i=1}^{N}IoU_{i}\right)/N \tag{12}\]where \(N\) represents the number of classes, and \(IoU_{i}\) represents the intersection over union (\(IoU\)) value of the ith class.