esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Least Squares for Estimation of a Damped Sinusoid
#### 2.2.2 Least Squares Approach  
The bridge's response to a vehicle crossing can be modelled by a sinusoidal function with an additional damping term:  
\[y(t)=a\cdot e^{-\lambda t}\cdot\sin(2\pi ft+\varphi) \tag{3}\]  
The sinusoid is defined with the natural frequency \(f\) and the phase \(\varphi\). An enveloping exponential function determines the damping with the initial amplitude \(a\) and the decay rate \(\lambda\). From the decay rate, the damping ratio \(\zeta\) can be determined:  
\[\zeta=\frac{\lambda}{\sqrt{\lambda^{2}+4\pi^{2}f^{2}}} \tag{4}\]  
A least squares approach minimises the error distances between this model and the measured displacements. Since the model is non-linear, it has to be linearised at the approximate values of the parameters. The approximate natural frequency and phase values are easiest to determine with a Discrete Fourier Transform. Although the frequency resolution is limited with the typical time window of a few seconds, the quality of the approximate values is generally high enough for the convergence of the least squares. An approximate value for the decay rate is calculated from the logarithmic decrement, which is defined as the ratio of adjoining peaks \(x_{i}\) of the sinusoid:  
\[\lambda^{0}=f\cdot\ln(x_{i}/x_{i+1}) \tag{5}\]  
The amplitude \(a\) can be approximated from the highest peak of the sinusoid. The approximate parameters are iteratively improved until the least squares converges.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 5 Conclusions  
Detecting high-impedance faults is a challenging task for conventional protection systems due to their low current magnitude. However, advanced intelligent classifiers are capable of detecting repetitive patterns that characterize such faults. In this work, we present an HIF detection method based on intelligent classification, using a feed-forward neural network as the classifier. The FFNN is trained to distinguish normal operating points (knowns) from unknowns, which in this case are identified as HIFs and treated as outliers. The key conclusions drawn from this study are as follows: (i) the number of classes in the dataset can be reduced by focusing on normal operating states; (ii) HIFs can be detected as outliers using a neural network trained according to open-set recognition principles; (iii) the architecture of the neural network does not need modification, only the training procedure; (iv) FFNNs are highly effective for HIF detection, with an accuracy of 99.99%. Additionally, the dataset used in this study is made publicly available, as no similar datasets exist in the literature. This method offers a promising approach for improving fault detection in power grids, especially in microgrids and other low-current scenarios.  
Conceptualization, I.G. and H.P.; methodology, I.G. and H.P.; validation, I.G.; formal analysis, I.G.; resources, H.P.; data curation, I.G.; writing--original draft preparation, I.G.; writing--review and editing, H.P.; visualization, I.G.; supervision, H.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Alternating current \\ BESS & Battery energy storage system \\ BPNN & Back-propagation neural network \\ CNN & Convolutional neural network \\ DC & Direct current \\ DER & Distributed energy sources \\ DWT & Discrete wavelet transform \\ FFNN & Feed-forward neural network \\ FFT & Fast Fourier transform \\ GAN & Generative adversarial network \\ HIF & High-impedance fault \\ kNN & k-Nearest neighbors \\ LIF & Low-impedance fault \\ MLS & Maximum logit score \\ MPPT & Maximum power point tracking \\ NN & Neural network \\ OSR & Open-set recognition \\ PV & Photo-voltaic \\ ReLU & Rectified linear unit \\ RNN & Recurrent neural network \\ RES & Renewable energy sources \\ SVM & Support vector machine \\ VSC & Voltage source converter \\ \end{tabular}
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology
### Non-Simultaneous Imaging
#### 3.1.1 Rotary Polarization Imaging  
Rotating polarizer and wave plate are the most basic polarization imaging methods. By means of rotating polarizers, arbitrary polarization states can be detected, as well as arbitrary complete polarization states can be produced (rotating polarizers and quarter-wave plates can produce all points on the Banga sphere). Because the modulation mode of rotary polarization is more intuitive, easy to control and difficult to implement and low cost, rotary polarization devices are widely used. Moreover, the rotating polarizer collects an intensity image at a specific Angle, which has the advantages of medium volume and simple system and is mainly suitable for static or quasi-static imaging applications.  
Traditional rotational polarization imaging has low imaging speed and insufficient dynamic detection capability due to its inherent non-simultaneous working mode. In order to improve this problem can be used in a rapid continuous rotation partial detector [15] ways of working, polarization image decoding process was optimized at the same time, improve the polarization rate, make full use of the intensity of each image, makes the polarization image acquisition speed and intensity of the image acquisition speed, so as to meet the dynamic target detection. As shown in Figures 2 and 3, the pictures of rotary polarization imaging in the study by [PERSON] et al. [15] and [PERSON] et al. [16] are quoted here.  
Figure 2: Structural design of rotary polarization imaging polarization camera [15].
## 1 Introduction  
The occurrence of extreme rainfall in the last four months of 2015 and the first two months of 2016 in north-central and northwestern Parana, a state located in the southern region of Brazil, was particularly problematic due to its impacts on the local human activities. This was related with the El Nino event of this period, classified as \"strong\" by the Oceanic Nino Index (ONI) values and by some authors [1, 2], causing abundant rainfall for consecutive months [3, 4], with impacts on several geosystem elements.  
In order to analyze the connections between the El Nino event of 2015/2016 (EN 15-16) and the respective geosystem responses in the northern region of Parana, the following objectives were set: (i) to characterize the El Nino episode; (ii) to evaluate rainfall databases for the period of the episode, seeking to determine which statistic(s) reflects the process intensity; (iii) to calculate return periods for the recorded rainfall, as a means of identifying rarity through frequency; (iv) to map the distribution of these extreme rainfalls; and (v) to describe examples of disturbances caused to geosystem features, using various sources.  
The geosystem is a traditional theoretical approach in physical geography, resulting from the application of the General Systems Theory [5] in this branch of geography. A system can be understood as an organized whole, composed of several parts or variables that interact with each other, so that the whole is different from the sum of the parts [5]. The geosystem is composed of four spheres: atmosphere, hydrosphere, lithosphere, and biosphere [6], as well as their interactions, exchanges of energy and matter between them.  
There are two specific systemic principles that support our exercise--the hierarchy among variables and the concept of threshold. The former relates to the resilience of landscape components, which refers to the ability to experience an adverse event and self-reestablish the characteristics (forms, structures) and functions [7]. Thus, in this study, we evaluated that, during EN 15-16, an expressive variation in the state of one of the controlling variables (climate) led to a series of adjustments in landscape response variables. In addition to the hierarchical organization, it is assumed that a geosystem threshold was exceeded, since the input of water into the system through the volume and intensity of rainfall altered many variables of the geosystem, and without anthropic action, such changes would be irreversible. This assumption presumes that the variables would be in conditions of relative stability.
## 5 Mitigation of Pharmaceutical Pollutants by Nanomaterials
### Biochar-Based Adsorbents
#### 5.1.3 Metal-Based Nanoparticles (MNPs)  
Metal nanoparticles (MNPs) are another class of inorganic material, which are employed to remove different pollutants, such as heavy metals, organic pollutants, dyes, pesticides, pathogenic and other hazardous bacteria, fungus, and other microorganisms from polluted water [108]. Because of their numerous uses and structure-dependent characteristics, metal particles with variable size and form are extremely important. Many  
Figure 6: SEM (**a**) and TEM (**b**) micrographs of the magnetic chitosan Sorption adapted with permission from [58].  
researchers from all over the world have been drawn to MNPs because of their potential applications and novel properties. Some pollutants, such as heavy metals, have been removed using silver and gold nanoparticles [109]. A range of metal-based adsorptive materials in the form of metal oxides/hydroxides, metal oxide/hydroxide modified adsorbents, and metal-ion-coated adsorbents have previously been explored for the efficient remediation of pharmaceuticals [110]. Carbamazepine [90, 106], ibuprofen [111], tetracycline [112], and other key pharmaceuticals have been successfully adsorbed utilizing metal-based adsorbents. [PERSON] and colleagues, 2019, studied commercially available powdered activated carbon (PAC) as a control for the magnetic-organic framework (MOF) to check the practical feasibility of MOF adsorption, because PAC is a widely used adsorbent for the removal of pharmaceuticals. The characterizations of MOF are shown in Figure 7, and a detailed explanation is provided in the supporting information. The effects of contact time on the qt and removal ratio of pharmaceuticals using PAC and MOF are shown in Figure 7a,b. After about 2 h of contact time, both adsorbents reached equilibrium, with MOF outperforming PAC. This is due to the larger surface area, as shown by the Brunauer-Emmette-Teller (BET) results (Figure 7a). Adsorption of ibuprofen and carbamazepine was performed under four water chemistry conditions: (i) solution temperature, (ii) solution pH, (iii) ionic strength/background ions, and (iv) the presence of HA. The adsorption mechanisms of CBM and IBP were primarily determined by hydrophobic interaction with supplementary hydrogen bonding effect and both hydrophobic interactions and electrostatic interactions, respectively, based on experimental results of four water chemistry factors.
## 4 Experiments
### Study of ATKGRM  
#### 4.3.1 Selection of the Number of User's Recent Interactive Items L  
Since L is the number of recent interactive items of users, and the choice of L will affect the modeling of users' short-term interests, the influence of diverse L on experimental performance can be explored. The ATKGRM is evaluated on three datasets with different L values. The results are shown in Table 7.  
As Table 7 shows, on the MovieLens-20M, the effect of the model is the best when L = 7; on Book-Crossing, the AUC of the model is the best when L = 5. When L = 5, the F1 of the model is the best; on Last. FM, the effect of the model is the best when L = 5. If L is too small, there are too few user's history items to effectively mine the short-term interest  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MovieLens-20M**} & \multicolumn{2}{c}{**Book-Crossing**} & \multicolumn{2}{c}{**Last. FM**} \\ \cline{2-7}  & **AUC** & **F1** & **AUC** & **F1** & **AUC** & **F1** \\ \hline ATKGRM-AT & 0.981 & 0.946 & 0.750 & 0.687 & 0.821 & 0.746 \\ ATKGRM-P & 0.983 & 0.949 & 0.768 & 0.705 & 0.845 & 0.762 \\ ATKGRM & 0.984 & 0.954 & 0.782 & 0.723 & 0.857 & 0.776 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of ablation experimental results of ATKGRM.  
Figure 4: Comparison results of adversarial training recommendation models. (On two different datasets).  
feature of users, which makes the effect of the model poor. If L is too large, it will lead to overfitting and inaccurate features of user short-term interest.
# Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
## 1 Introduction
Clearly, the wave penetration loss of typical building materials should be studied in light of the need to improve future 5G mmWave indoor coverage. However, the existing millimeter-wave research mainly focuses on the millimeter-wave frequency band below 100 GHz, and the 100-300 GHz millimeter-wave still needs to be developed. D-band (110-170 GHz) electromagnetic waves (EMW), with a frequency range of 110-170 GHz, are located in the cross-band of millimeter waves (30-300 GHz) and terahertz (THz) waves (100-10,000 GHz). The atmospheric window frequency bandwidth of the D-band millimeter wave is about 26 GHz, and its center frequency is about 140 GHz, and the propagation loss in the air is smaller than THz band. Compared to lower millimeter wave frequencies, D-band electromagnetic wave signals have a wider bandwidth, with narrower beams and shorter wavelengths, resulting in greater transmission capacity and higher resolution. Research on the D-band channel propagation characteristics will be helpful to the research of new technologies in the physical layer of 5.5G and even 6G systems. To this end, this paper studies the penetration characteristics of the D-band (140-160 GHz) to typical materials, such as glass, slate, vegetation, and wood, and finds that the penetration loss of D-band millimeter waves is not very dependent on high frequencies, while the masking loss of vegetation is as high as 10-20 dB. D-band millimeter waves can hardly penetrate 5 cm-thick slate and 2 cm-thick wood, and the penetration loss is positively related to the thickness of the masking material. mmWave is known to increase the capacity of 5G networks and reduce latency. Wider implementations of high-definition video conferencing, teleoperation, and industrial automation will benefit from the wider bandwidth of the mmWave spectrum, especially those applications that require high precision. 5G mmWave will also enable each automated robot to generate or receive large amounts of data, as well as the high-density deployment of these robots in confined areas. From this point of view, good mmWave indoor coverage is necessary. Some current measurements and modeling efforts are still well underway, and work is expected to be carried out in several areas [9], including the following: current measurements focus on a few hotspots, with additional measurements in other candidate frequency bands; existing models claim that they support high bandwidths, but rely on systems that typically have smaller measurement bandwidths and lower angular resolution, so they will also enhance measurements and data analysis in large bandwidths and large antenna arrays. In addition, the statistical parameters provided by the model are all in the form of large table data lists. If the size scale parameters and frequencies, connection types,including antenna height and environmental parameters, can be established, this requires a rethinking of the modeling method.
## 2 Related Works
### Laser Shock Peening and Remanufacturing  
LSP is a surface modification technology that enhances the mechanical properties and durability of metallic components. [PERSON] et al. [22] validated the effectiveness of LSP in improving the high-temperature oxidation resistance of Ti2 AlNb alloys; microhardness and compressive residual stress were significantly improved, which contributed to the formation of protective Al-rich oxidation layers. [PERSON] et al. [23] explored the impact of LSP on the high cycle fatigue properties of laser-welded 2A60 aluminum alloy joints, and LSP introduced compressive residual stresses and grain refinement, by which fatigue strength and lifespan were enhanced. [PERSON] et al. [24] provided a comprehensive review of LSP applications in various metallic materials, emphasizing its potential to generate deep compressive residual stress fields and refine microstructures, which are critical for improving fatigue resistance in extreme environments. [PERSON] et al. [25] combined LSP with laser cladding to enhance the fatigue properties of remanufactured Ti-6 Al-4V alloy, and there was a 2.64-fold increase in fatigue life due to the beneficial microstructural modifications and removal of tensile residual stress. [PERSON] et al. [26] studied the effects of femtosecond LSP on the fretting wear resistance of Ti6 Al4V, in which the optimal laser energy parameters improved surface hardness and wear resistance. [PERSON] et al. [27] attempted a combination of direct energy deposition (DED) and LSP on titanium alloys, which resulted in refined microstructures and a 12.46% increase in ultimate tensile strength. LSP was proven to beeffective in repairing and enhancing the mechanical properties of DEDed components. [PERSON] et al. [28] explored LSP performance on silicon nitride ceramics with different sintering additive ratios, and LSP improved both surface hardness and residual compressive stress. [PERSON] et al. [29] introduced a laser non-uniform shock peening (LNUSP) technique for SUS 304 stainless steel welded joints; more uniform residual stress distribution and improved fatigue performance were achieved compared to traditional LSP methods. [PERSON] et al. [30] investigated a hybrid approach combining laser-directed energy deposition with LSP for remanufacturing Ti6 Al4V alloy. Interlayer LSP significantly improved microstructural features and mechanical properties, including tensile strength and hardness.  
LSP has shown good effectiveness in improving the surface and mechanical properties of different materials, but the application in bearing remanufacturing is still rarely researched. There is a gap in extending the life of LSP remanufactured bearings; therefore, the relationship between LSP parameters and remanufacturing needs to be further researched so that optimization can be applied to find the optimal remanufacturing parameters.
# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
## 4 Multivariate Chemometrics
In addition to these linear discriminant analysis methods there are several nonlinear equivalents that effect nonlinear mapping from input \(X\)-data (spectra) to output \(Y\)-data (the classes or sample objects). These are often referred to as machine learning techniques and perhaps the most popular are support vector machines (SVMs), random forests (RFs) and kernel PLS (kPLS): these are reviewed in [PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] et al. [59; 60; 61]. The recent resurgence of interest in artificial intelligence and artificial neural networks (ANNs) has given rise to deep learning. In these convolution neural networks many different layers are used [63], which are fundamentally different to the single layer neural networks developed by [PERSON] and colleagues [64]. These have predominantly been used for image analysis and speech recognition and they are very data hungry (that is to say require lots of input data) and may have a role in the analysis of chemical images generated from SERS and Raman microspectroscopy as illustrated by [PERSON] et al. and [PERSON] et al. [65; 66]. As the old saying reveals--\"you will reap what you sow\"--if a supervised model is incorrectly calibrated, there is a possibility of overfitting and subsequently false classification or regression. For accurate prediction, quality control and assurance in disease diagnostics, it is always important that the number of latent variables is selected carefully and supervised models are validated through bootstrapping or \(n\)-fold cross-validation, etc. [67]. Examples of where chemometrics has been applied to SERS and related techniques include detection of cancers using PC-LDA and SVM [68; 69; 70; 71], microbial pathogens by various discriminant and cluster analysis [72; 73; 74] and drug and their metabolites by PLS and ANNs [75; 76; 77].
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 4 Discussion
Fifth, one ongoing challenge in the use of STDecoder-CD for change detection is how to automatically set the threshold for binary \"change versus non-change\" classification. If the threshold value is large, precision is higher while recall is lower, and vice versa. In this study, the optimal threshold values (-0.70) were determined through a process of trial and error. In the future, we plan to modify the \"FCN\" module of the three decoders as a two-class object classifier so that the threshold values can be determined automatically by the \"argmax\" function in Python/Pytorch. Sixth, compared to other SOTA models, STDecoder-CD has obvious advantages in terms of accuracy metrics, visual quality, and generalization capacity. It has disadvantages too: e.g., STDecoder-CD is very data-hungry, and long training epochs are required for good convergence. To ease the effort of acquiring high-quality annotation data and reduce the training time, [PERSON] et al. [34] developed a non-\"data hungry\" transformer termed CCT (Compact Convolutional Transformer), which can perform head-to-head with state-of-the-art CNNs on small datasets, often with better accuracy and fewer parameters. For future research, we are planning to replace the H-MHSA module in TransCNN with CCT so as to make the model more lightweight, more robust to small datasets, and more competitive in engineering applications. Seventh, our new findings that differ from previous studies include: (1) Siamese TransCNN is a powerful change feature extractor; (2) different decoding strategies can moderately affect the final CD performance; (3) presumably due to the multi-scale design, the performance gain yielded by the Type I or Type II decoder is significantly larger than that obtained with the Type III decoder; (4) the transformer encoder and decoder usually come in pairs, and using the transformer decoder alone, like Type III STDecoder-CD, does not produce optimal results; (5) the proposed decoding architectures are stable and not sensitive to ablation studies; and (6) for small datasets, the introduction of transfer learning is necessary, but it is better to initialize all of the parameters of STDecoder-CD using pretrained weights and then fine-tune them, rather than freezing and only initializing the encoder part. With these innovations, the STDecoder-CD series model can be greatly helpful in real-world applications; for example, it can assist in land use/cover management, urban planning, and monitoring of landslides. In particular, the large-scale application of our methodology is also feasible due to its ability to identify small changes and its relatively low computational complexity.