esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ultra-Wideband Microwave Photonic Channelized Receiver with Zero-IF Architecture
## 4 Conclusions  
An ultra-wideband photonic microwave channelization for a zero-IF receiver was proposed and demonstrated by simulation in this paper. Owing to all-optical techniques and I/Q balanced detection, it exhibited advantages including ultra-wide operating band, low conversion loss, accurate I/Q amplitude and phase balance, and large dynamic range. Meanwhile, due to balanced detection, the DC offset and IMD2 were well suppressed. Simulation results show that the RF signal with a bandwidth of 3 GHz can be divided into five sub-channels with a bandwidth of 600 MHz. The measured SFDR was 107.5 dB-Hz2/3. The scheme only needs a set of optical frequency combs and narrowband OBPF group to realize channelization processing of wideband signals, and can also be applied to analog radio over fiber transmissions in uplink communications [20,21].  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the article. [PERSON] and [PERSON] reviewed and edited the manuscript. [PERSON], [PERSON], and [PERSON] responded to the reviews and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Nature Science Foundation of China under grant 61701412; in part by the National Postdoctoral Program for Innovative Talents under grant BX201700197; in part by the Project funded by China Postdoctoral Science Foundation under grant 2017M623238; in part by sustainably supported Foundation by National Key Laboratory of Science and Technology on Space Microwave under grant HTKJ2019 KL504011; in part by Shaaxni Provincial Key Research and Development Program 2018 NY-158; in part by Xianyang Normal College Youth Key Program XSYGG201716.  
The authors would like to thank the editors and the reviewers for their comments on an earlier draft of this article.  
The authors declare no conflicts of interest.
## 3 Simulation, Results, and Discussion
### Complex Permittivity Extraction
\begin{array}{c}0.3124\\ 0.1867\\ 0.1189\\ 0.0514\\ 0.0365\\ 0\\ \Delta f_{50\%}\\ \Delta f_{60\%}\\ \Delta f_{60\%}\\ \Delta f_{70\%}\\ \Delta f_{90\%}\\ \Delta f_{100\%}\\ \end{array} \right]\text{and}\]  
\[Y_{2}=\left[ \begin{array}{c}\Delta Q_{0\%}\\ \Delta Q_{10\%}\\ \Delta Q_{20\%}\\ \Delta Q_{30\%}\\ \Delta Q_{40\%}\\ \Delta Q_{50\%}\\ \Delta Q_{60\%}\\ \Delta Q_{70\%}\\ \Delta Q_{80\%}\\ \Delta Q_{80\%}\\ \Delta Q_{90\%}\\ \Delta Q_{100\%}\\ \end{array} \right]=\left[ \begin{array}{c}-4.5466\\ -4.7513\\ -4.0621\\ -2.7325\\ -1.6591\\ 0\\ 1.4484\\ 4.0915\\ 7.3011\\ 13.3947\\ 18.0188\\ \end{array} \right]=\left[ \begin{array}{c}-4.5466\\ -4.7513\\ -4.0621\\ -2.7325\\ -1.6591\\ 0\\ 1.4484\\ 4.0915\\ 7.3011\\ 13.3947\\ 18.0188\\ \end{array} \right]\]  
After having obtained the complex permittivity, the resonant frequency, and the Q factor for the different samples of the water-ethanol mixtures, we had only to apply the initial matrix model given in (1). Thanks to a detailed algebraic analysis, Equations (5) and (6) were generated [32]. Thus, the unknown coefficients can be determined as given in (7):  
\[\left[ \begin{array}{cc}m_{11}&m_{12}\\ \end{array} \right]^{T}=\left(X^{T}X\right)^{-1}\cdot X^{T}\cdot Y_{1} \tag{5}\]  
\[\left[ \begin{array}{cc}m_{11}&m_{12}\\ \end{array} \right]^{T}=\left(X^{T}X\right)^{-1}\cdot X^{T}\cdot Y_{1} \tag{6}\]  
The final matrix providing data on the different water-ethanol samples is described by (8). These calculated coefficients are used to determine the complex permittivity of any given sample as a function of the change in the sample's resonant frequency and Q factor:  
\[\left[ \begin{array}{cc}m_{11}&m_{12}\\ m_{21}&m_{22}\\ \end{array} \right]=\left[ \begin{array}{cc}-0.0057&-0.0187\\ 0.2734&-1.1230\\ \end{array} \right] \tag{7}\]  
\[\left[ \begin{array}{cc}\Delta\varepsilon^{\prime}\\ \Delta\varepsilon^{\prime\prime}\\ \end{array} \right]=\left[ \begin{array}{cc}m_{11}&m_{12}\\ m_{21}&m_{22}\\ \end{array} \right]^{-1}\left[ \begin{array}{c}\Delta f_{res}\\ \Delta Q\\ \end{array} \right]=\left[ \begin{array}{cc}-97.4829&1.6231\\ -23.7329&-0.4953\\ \end{array} \right]\left[ \begin{array}{c}\Delta f_{res}\\ \Delta Q\\ \end{array} \right] \tag{8}\]Table 4 shows a comparison of the proposed sensing structures with the literature in terms of sensitivity and Q factor.
# A Shearlets-Based Method for Rain Removal from Single Images
## 6 Some Discussion for the Proposed Method
### Description for Parameters  
The proposed method includes two kinds of parameters, the model parameter and the algorithm parameter. For the model parameter, \(\gamma_{1},\gamma_{2},\gamma_{3}\) are used to constrain the sparsity of Shearlets decomposition coefficients in high frequency, thus the recovery is sensitive to their changes. But different testing images with directional rain streaks are not sensitive to the parameters \(\gamma_{i},\alpha_{i},i=1,2,3\). In the testing, we set \(\gamma_{1}=\gamma_{2}=1e-3\), \(\gamma_{3}=1e-2\), \(\alpha_{i}=8\).  
For the algorithm parameter \(tol,M_{iter}\), they are chosen to be \(tol=1e-3\) and \(M_{iter}=250\) (for some images, parameters are chosen different to obtain the best result). The default setting for the 15 ICCV, 16 CVPR, and 18 UTV methods parameters can be found in [21]. We give the average quantitative performance of 20 images of UCID in Table 3.  
Figure 10: The corresponding local enlarged view of four methods for real rainy images.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Background**} & \multicolumn{3}{c}{**Rainy Streaks**} \\ \cline{3-8}
**Rain Type** & **Methods** & **PSNR** & **SSIM** & **RMSE** & **PSNR** & **SSIM** & **RMSE** & **Time(s)** \\ \hline \(den=0.04\) & 15 ICCV & 28.74 \(\pm\) 2.49 & 0.8293 \(\pm\) 0.0513 & 11.57 \(\pm\) 2.895 & 26.19 \(\pm\) 3.42 & 0.4702 \(\pm\) 0.0527 & 13.042 \(\pm\) 3.521 & 144.27 \\ \(len=40\) & 16 CVPR & 29.64 \(\pm\) 2.01 & 0.8846 \(\pm\) 0.0312 & 9.142 \(\pm\) 1.259 & 27.41 \(\pm\) 1.39 & 0.5327 \(\pm\) 0.0932 & 9.741 \(\pm\) 1.692 & 1152.64 \\ \(theta=0\) & 18 UTV & 30.74 \(\pm\) 0.63 & 0.8811 \(\pm\) 0.0737 & 9.747 \(\pm\) 0.994 & 27.94 \(\pm\) 0.53 & 0.6017 \(\pm\) 0.0574 & 9.226 \(\pm\) 1.726 & **1.59** \\  & **Ours** & **31.02 \(\pm\) 1.722** & **0.8842 \(\pm\) 0.0397** & **8.117 \(\pm\) 0.692** & **28.52 \(\pm\) 0.63** & **0.6064 \(\pm\) 0.0827** & **8.017 \(\pm\) 0.923** & 3.94 \\ \hline \(den=0
# Exploiting the Value of Active and Multifunctional Facade Technology through the IoT and AI
## 4 An IoT-Based Multifunction Facade Prototype
### Heat Pumps and Mechanical Ventilation  
A heat pump and mechanical ventilation system were integrated into the prototype to provide a fully autonomous system. A commercial product for nZEB installation has been selected and integrated directly into the facade. This was selected from the market according to several parameters such as cooling/heating power, cost, and the possibility of not installing an external machine. The development of a fully customized solution was not considered, as this would require a long development time. Specifically, a thermodynamic controlled mechanical ventilation is selected to ensure double flow, heating, and cooling dehumidification. Although the physical integration will have to be rethought according to the building type (e.g., the presence of the ceiling, slab), the prototype allows for an evaluation of the potential benefits of a decentralized system with a good degree of approximation. In the MEZero MF, the HVAC system takes and ejects air directly from the hidden holes provided in the perimeter of the spandrel frame. Specifically, one of the two modules is responsible for air intake, the other for extraction. From an aesthetic point of view, this solution means that there are no visible air vents in the facade or external units. A smart thermostat is generally used to control and set the indoor air temperature, but for the MEZeroE prototype, the smart thermostat is replaced with a dedicated IoT infrastructure with edge-AI. Data gathered from IoT sensors are communicated to an on-board PCB (printed circuit board) to manage the adjustment of the different machine components (e.g., air velocity, air temperature, etc.). In this case, data are communicated in the Modbus protocol.
## 5 ARF Is Involved in Regulating Fruit Development and Ripening
At present, there are few studies on the regulation of auxin in fruit shedding, and some economic fruit trees, such as lithti, are suffering from fruit shedding in actual production. Studies conducted by [PERSON] showed that the ARF family genes in lithti might play opposite roles in the regulation of lithti fruit abscission, with the majority of the members acting as repressors. The members that repress fruit abscission include ARF2D/2E, 7A/7B, 9A/9B, and 16A/16B, while members such as LCARF5A/B may promote fruit abscission in lithti [15]. It has also been reported that LCARF1 is negatively correlated with small fruit abscission [48]. These findings fill a gap in the research on ARFs regulation of lithti fruit abscission and provide new insights into the function of ARFs. Figure 3: A model of ARFs regulating tomato fruit development and ripening. Several members of the auxin responsive factor family influenced the development and ripening of tomato fruits by regulating the expression of genes related to ethylene biosynthesis and chlorophyll accumulation. SIARF2 regulates ethylene synthesis and tomato ripening by regulating the expression of ethylene biosynthesis genes ACO1/ACS2/ACS4. GOLDEN2-LIKE transcription factor SIGKL1 plays an important role in chloroplast formation and chlorophyll accumulation. SIARF4 inhibits the expression of its encoding gene SIGLKI, while SIARF10 and SIARF6A promotes its expression. Moreover, SIARF6A also targets the promoters of CAB (genes encoding chlorophyll A/B binding) and RbC5 (gene encoding ribulose bisphosphate carboxylase small chain), genes that affect chlorophyll accumulation, and positively regulates the expression of these genes. SIARF6A is also a negative regulator of SAMS1, which in turn negatively regulates ripening and ethylene biosynthesis in tomato fruit. EXPENSIN5 (EXP5) likely promotes cell expansion during fruit development. SIARF7 inhibits tomato fruit set and early development by directly regulating EXP5 and ACO4. It is well known that anthocyanins have antioxidant and free radical scavenging functions and are increasingly sought after by people for their role in reducing fatigue, anti-aging, and beautifying the skin. Studies have shown that ARFs are involved in the regulation of anthocyanin biosynthesis in apples, and MDARF2 and MDARF13 are considered to be inhibitors involved in anthocyanin biosynthesis and metabolism [49; 50]. However, similar regulatory effects of auxin in fruits with high anthocyanin content, such as mulberry, blueberry, and blackcurrant, have been rarely reported. It will be very valuable to promote related work in the above-mentioned fruits.
## 5 Conclusions  
Monitoring of crop diseases at a regional scale is of practical importance for agricultural management and insurance claim. This study has contributed to the detection and mapping of different rice disease species at fine spatial scales. Firstly, a set of normalized two-stage VIs were developed to characterize the progressive development of disease infestations, subsequently, based on these satellite-derived spectral features, three kinds of rice diseases (i.e., rice dwarf, rice blast, and glume blight) were classified and mapped. Additionally, the study involved the first use of combined bi-temporal PL imagery. Our findings suggest that the normalized two-stage VIs can be used to identify the difference between the progressive developments of the various infestations on rice. Furthermore, this method can make optimal contributions in fitting the PLS-DA model to produce a reasonable disease map with an overall accuracy of 75.62%. The potential application of the normalized two-stage VI PLS-DA approach facilitates loss assessment for the agricultural insurance industry by examining spatial damage information caused by diseases. This includes the identification of suspected areas and deploying experts to direct prevention operations. Future studies should explore the use of VHSR imagery with short-wave infrared band (e.g., WorldView-3), which can produce more information for characterizing the pathological progress of disease infestations and for developing strategies for early detection and monitoring of diseases before the symptoms become visible. In this domain, more efforts and studies are needed to improve the performance and robustness of disease mapping techniques.  
[PERSON] and [PERSON] developed the concept and research plan. [PERSON], [PERSON], [PERSON], [PERSON] were the co-workers to conduct the campaign and field working. [PERSON], [PERSON], and [PERSON] provided expert knowledge about methods, interpretations, participated in discussion, editing and revisions of the paper. All authors discussed the results and implications and commented on the manuscript at all stage.  
This work was supported by Supported by Free Exploration Project of the State Key Laboratory of Remote Sensing Science at Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (17 ZY-01), National Natural Science Foundation of China (61661136004, 41601467), the STFC Newton Agritech Programme (ST/N006712/1), the Hainan Provincial Department of Science and Technology under Grant (ZDKJ2016021), Hainan Provincial Key R&D Program of China (ZDYF2018073), and the Youth Innovation Promotion Association CAS (2017085).  
The authors would like to thank Dr. [PERSON] for providing the field investigation data used in this paper.  
The authors declare no conflict of interest.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 2 Related Work
### Clustering-Based Methods  
Clustering is mainly an unsupervised technology. Although clustering and anomaly detection seem to be fundamentally different, several anomaly detection technologies based on clustering have been developed. These methods are based on the following assumption. Normal data instances are closer to the nearest cluster core, while abnormal data instances are farther from the nearest cluster core. When using clustering algorithms to cluster data, for each data instance, the distance from the nearest cluster centroid is calculated as its anomaly score. For example, [PERSON] et al. [16] proposed Self-Organizing Maps (SOM), K-means Clustering, and Expectation Maximization to cluster training data and then use the clusters to classify test data.  
However, if the exceptions in the data themselves form a cluster, the abovementioned methods will not be able to detect such exceptions. To solve this problem, people propose a density-based anomaly detection method. For example, [PERSON] et al. [17] assigned an anomaly score to a given data instance, known as the Local Outlier Factor (LOF). For any given data instance, the LOF score is equal to the ratio of the average local density of the k nearest neighbors of the instance and the local density of the data instance itself. The anomalous instance will obtain a higher LOF score. [PERSON] et al. [18] proposed the CLAD algorithm, which obtains the width from the data by randomly sampling and calculating the average distance between the nearest points. All those clusters whose density is lower than a threshold are declared as \"local\" outliers, while all those clusters that are far away from other clusters are declared as \"global\" outliers. [PERSON] et al. [19] proposed the FindCBLOF algorithm, which assigns an anomaly score to each data instance, called the clustering-based local outlier factor (CBLOF). The CBLOF score captures the size of the cluster to which the data instance belongs and the distance between the data instance and its cluster centroid.  
Such techniques can often be adapted to other complex data types by simply plugging in a clustering algorithm that can handle the particular data type. However, these methods are unable to capture temporal correlations.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 1 Introduction
In summary, the key contributions of this study are the following:  
* We propose SAM2-UNet, a novel hybrid deep learning model that integrates Segment Anything Model 2 (SAM2) and U-Net to improve the precise segmentation of tumors. * The model effectively leverages the zero-shot segmentation capabilities of SAM2 along with U-Net's fine-grained spatial localization to enhance segmentation performance. * We implement a parameter-efficient fine-tuning approach using Hiera pre-trained models, which helps in capturing multi-scale features with minimal computations. * The proposed model is evaluated on the BraTS 2020 dataset, demonstrating superior segmentation performance across multiple evaluation metrics, achieving an mDice of 0.771 and an mIoU of 0.569 with minimal hardware resources. These contributions provide a significant advancement in medical image segmentation, offering a more efficient and accurate approach for brain tumor detection. The remainder of this paper is organized as follows:  
The paper's outline is as follows: Section 2 discusses the background. Section 3 presents the dataset used in this study and the methodologies that were applied to achieve data pre-processing. Section 4 introduces the proposed SAM2-UNet architecture. Section 5 reports the results that were achieved when performing the experiments on the proposed model. Sections 6 and 7 provide a detailed discussion, studies, and conclusion.
## 2 Methods
### Gated Recurrent Neural Network  
#### 2.3.1 Long Short-Term Memory Unit  
Recurrent neural network (RNN) has units to memorize long-term dependencies and was initially proposed for sequence data. However, with time lags increasing, gradients of RNNs may vanish through unfolding RNNs into deep forward neural networks. In order to solve this problem, the long short-term memory (LSTM) unit was initially proposed by [PERSON] and [PERSON], which has a similar structure and better sequence processing capabilities to traditional RNN [31]. The form of LSTM is shown in Figure 2, where \(x_{t}\) is the input matrix, \(h_{t}\) is the output of the hidden layer in the network at time step \(t\), \(c_{t}\) is the cell state of the memory cell at time step \(t\). For the next time step, the LSTM cell accepts the cell state (\(c_{t}\)) and hidden state (\(h_{t}\)) of the previous time step. To better learn long short-term dependencies of sequence data, three special structures called gates are introduced in LSTM network. The gates are input gate (\(i_{t}\)), forget gate (\(f_{t}\)), and output gate (\(o_{t}\)). These gates act as filters, serving different purposes.  
Figure 1: One-dimensional convolutional neural network architecture.  
Input gate (\(i_{t}\)) takes the new vector and combines it with the existing vector to create an updated value. Whether it is necessary to input new data is determined by the sigmoid layer of the input gate layer. The existence of forget gate (\(f_{t}\)) enables memory cells to have the ability to delete attribute information, thereby storing information that can better characterize time lag. Output gate (\(o_{t}\)) determines output value of LSTM model according to the cell state. Thanks to the design of these gates, LSTM model has a significant improvement in capturing the long- and short-term dependencies between sequences compared with the traditional RNN [32]. In dam deformation predicted model, a rectified linear unit (ReLU) layer is applied on output layer of LSTM model. Moreover, the recurrent dropout mechanism is designed in LSTM model to avoid overfitting during the training process.  
In Figure 2, the cell state (\(c_{t}\)) and hidden state (\(h_{t}\)) of the LSTM cell are calculated as follows:  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}) \tag{2}\]  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{3}\]  
\[\hat{c}_{t}=\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{4}\]  
\[c_{t}=f_{t}\otimes c_{t-1}+i_{t}\otimes\hat{c}_{t} \tag{5}\]  
\[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{6}\]  
\[\hat{c}_{t}=\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{7}\]  
where \(\sigma\) is the logistic sigmoidal function, \(\otimes\) is element-wise multiplication of two vectors, and \(W_{xi}\), \(W_{hi}\), \(W_{xf}\), \(W_{hf}\), \(W_{xx}\), \(W_{ho}\), \(W_{xc}\), and \(W_{hc}\) are the network weights matrices. Similarly, \(b_{i}\), \(b_{f}\), \(b_{o}\), and \(b_{c}\) are bias vectors. \(f_{t}\), \(i_{t}\), and \(o_{t}\) are vectors for the activation values of the forget gate, the input gate, and the output gate, respectively.
## 3 Results and Discussion
### Morphology Study
SEM images confirmed that there is a relationship between electric field intensity and fiber diameter (scaling law), which allowed for precise control of fiber dimension. Thus, it was possible that they would remain in the nanometer range for increased surface area and bioactivity, even when modified with additional nanoscale additives. In performed theoretical studies regarding the voltage applied during the process, as shown in Table 2, it was found that this voltage directly affects the maximum field intensity, which influences the polymer jet's stretching force. The application of higher voltages increases field intensity, leading to thinner fibers obtainment but, on the other hand, may also introduce instability if not carefully controlled. Our research showed that electrical field simulations lead to the preparation of the material, which is characterized by uniform morphology and high  
Figure 8: Nanofibers morphology containing periclase (\(\times\)20k magnification): (**a**)—sample 2; (**b**)—sample 3; (**c**)—sample 4 (**d**)—sample 4 with extra magnification (\(\times\)50k); (**e**)—sample 5; (**f**)—sample 6.  
porosity, which is crucial for biomedical applications, especially in terms of tissue recovery. Successful integration of periclase and PDA nanoparticles enabled potential bioactivity enhancement via promoting osteogenesis and cell adhesion. To confirm MgO and PDA presence inside the nanofibers, EDX analysis has been carried out. As shown in Figure 9, MgO crystalline particles are clearly visible inside the polymeric matrix composed mainly of carbon and oxygen. No additional impurities are present. It can be noticed that nanoparticles are uniformly distributed inside the material and exhibit a homogenous shape. Such distribution is very important from the future application point of view since it will provide good mechanical durability and bioactivity, which results from MgO presence. Moreover, it shows that electrospinning parameters were well-adjusted since ready material is characterized by repeatable architecture. Such NPs distribution will help to provide good coverage of new bone formation areas and will be able to interact with cells and biomolecules. Superficial placement of MgO particles will also help to prevent infections due to bacteriostatic effects due to direct interaction with pathogens. Such elemental distribution is especially important in the case of highly porous, nanofibrous membranes, which are characterized by low thickness, and their primary mode of action strictly depends on the fibers' alignment and nanoadditives presence at the superficial layers.