esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Imaging Evaluation of Therapy Response in Metastatic RCC
### Immune Checkpoint Inhibitors
Thus, iRECIST could capture atypical response, but it is important to underline that pseudo-progression is uncommon, so clinical decisions about the treatment strategy should be based mainly on patient medical status. Currently, in clinical trials iRECIST are the most encouraging criteria to assess response rate and PFS [35]. Figures 2 and 3 illustrate two examples of patients with metastatic clear cell RCC that have undergone ICIs treatment showing PR and iUPD, respectively (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & **RECIST 1.1 [17]** & **irRC [29]** & **irRECIST [33]** & **iRECIST [34]** \\ \hline
**Dimension** & One. & Two. & One. & One. \\ \hline
**New measurable** & & & & \\
**lesions** & Designates PD. & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline
**Complete Response (CR)** & No evidence of target and non-target lesions; Short axis of lymph node(s) \(<\) 10 mm; No new lesions. & No evidence of target and non-target lesions in two consecutive exams (at least 4 weeks apart); Short axis of lymph node(s) \(<\) 10 mm; Short axis of lymph node(s) \(<\) 10 mm; node(s) \(<\) 10 mm; No new lesions. & No new lesions. & No new lesions. \\ \hline
**Partial Response (PR)** 1  & Decreased (\(\geq\)30\%)))))))))))))))))))((((DecFigure 3: CT scans in a female patient, 80 years old with metastatic RCC in the right lung before (**A**,**C**) and after medical treatment (**B**,**D**). CT images are reported in coronal views (**A**,**B**) obtained with multiplanar reformation and in axial views (**C**,**D**). The patient underwent right nephrectomy for the primary tumor (clear cell RCC) (**A**,**B**) and was medically treated initially with a flat dose of nivolumab (480 mg/4 weeks). CT images before medical therapy showed the presence of a lung nodule in the superior right lobe (**C**, white circle) that showed a 60% increase in size (10 mm vs. 16 mm) after medical treatment (**D**, white circle) reflecting an immune-unconfirmed progressive disease according iRECIST (Table 2). Figure 2: CT scans in a male patient, 69 years old, with metastatic RCC in the right adrenal gland before (**A**,**C**) and after medical treatment (**B**,**D**). CT images are reported in coronal views (**A**,**B**) obtained with multiplanar reformation and in axial views (**C**,**D**). The patient underwent right nephrectomy for the primary tumor (clear cell RCC) (**A**,**B**) and was medically treated with a flat dose of nivolumab (480 mg/4 weeks) after he developed the secondary adrenal lesion. CT images before medical therapy showed the presence in the right adrenal gland of an inhomogeneous hypodense metastatic lesion with irregular margins (**C**, black arrow) that revealed a 32% decrease in size (28 mm vs. 19 mm) after medical treatment (**D**, black arrow) reflecting a partial response according to iRECIST (Table 2).
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Data and Pre-Processing
#### 2.2.2 Winter Wheat Vector Data and Phenological Periods  
The reliability of crop yield prediction using agri-environmental variables depends on the spatial aggregation of environmental variables. The use of annual masks for specific crop groups can effectively improve the accuracy of yield estimates compared to the use of general cropland masks [40]. The winter wheat vector data in this study were extracted from 10 m resolution Sentinel-2 satellite remote sensing images via an object-oriented deep learning method. In order to ensure the accuracy and quality of the data, the satellite images used underwent rigorous pre-processing, including atmospheric correction, radiometric correction, and geometric correction, to eliminate possible sensor errors and atmospheric effects. At the same time, in order to ensure consistency between the extracted winter wheat planting distribution data and the actual planting situation, the confusion matrix was calculated by combining the ground survey sample data, and the Kappa coefficient was 0.82. Considering that Henan Province is the main production area of winter wheat and its crop cultivation structure is relatively stable, the winter wheat vector data in 2021 were selected as the mask data for extracting the model feature parameters. Table 1 shows the winter wheat phenology calendar in Henan Province.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Phenology**} & \multirow{2}{*}{**Emergence**} & \multirow{2}{*}{**Tillering**} & \multirow{2}{*}{**Overwintering**} & \multirow{2}{*}{**Green-Up**} & \multirow{2}{*}{**Jointing**} & \multirow{2}{*}{**Heading**} & \multirow{2}{*}{**Milk**} & \multirow{2}{*}{**Ripening**} & \multirow{2}{*}{**Maturation**} \\ \hline \multirow{3}{*}{Time} & Late & Early & \multirow{3}{*}{Mid-} & Mid- & Mid- & Mid- & Mid- & Mid- & April & \\  & September & to late & December & December & to & February to & March to & late & May & Early June \\  & October & December & mid-February & mid-March & early April & April & April & May & to Late June \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phenological calendar of winter wheat in Henan Province.  
Figure 3: Statistical graph of production data.
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 6 Conclusions  
In this study, we developed a full-waveform joint-inversion scheme for the Rayleigh wave phase and the Z/H ratio simultaneously. First, we developed an approach for calculating the 2D sensitivity kernels of the Rayleigh wave phases and the Z/H ratio based on the adjoint-state method. A numerical experiment was carried out to investigate the sensitivities of the Rayleigh wave phase and the Z/H ratio to the S-wave velocity structure. The experiments showed that they were both mostly sensitive to the shear-wave velocity, but their sensitivity patterns were different. The phase kernel was concentrated along the propagation path, with a much broader sensitivity range than the Z/H-ratio kernels. The Z/H ratio was more sensitive to the shallower structure compared to the Rayleigh wave phase-velocity dispersion at the same frequency. In addition, the Z/H ratio could deal with the lateral heterogeneity better, which was more helpful in constraining shallow structures, such as sedimentary basins or near-surface structures.  
The joint-inversion method was applied in two synthetic experiments involving large- and small-scale inversions. By comparing the tomographic results of the adjoint tomography of the Rayleigh wave phases, the adjoint tomography of the Z/H ratio, and the joint inversion, we demonstrated that the joint inversion outperformed the separate inversions because it combined the complementary sensitivities of both, resulting in a more S-wave velocity unified model. Also, joint inversion could deal with the cycle-skipping problem in FWI in near-surface cases. The proposed joint-inversion scheme offers a computationally efficient and inexpensive alternative to imaging fine-scale crustal structures or near-surface structures beneath a 2D seismic array.  
[PERSON] made the main contributions to this research, providing the ideas of modeling and inversion; [PERSON] was responsible for the simulation; [PERSON] provided suggestions on the feasibility and innovation of the solution. All authors have read and agreed to the published version of the manuscript.  
This research was funded by financially supported by the Natural Science Foundation of Jiangsu Province, China, grant number BK20190499; the National Natural Science Foundation of China, grant number 42004037; and the Fundamental Research Funds for the Central Universities grant number 2019B0071428.  
Not applicable.  
The data of this study is available from the authors upon request.  
We gratefully acknowledge [PERSON]'s constructive comments from university of Toronto.  
The authors declare no conflict of interest.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
###### Abstract  
Loop closure detection plays a very important role in the mobile robot navigation field. It is useful in achieving accurate navigation in complex environments and reducing the cumulative error of the robot's pose estimation. The current mainstream methods are based on the visual bag of word model, but traditional image features are sensitive to illumination changes. This paper proposes a loop closure detection algorithm based on multi-scale deep feature fusion, which uses a Convolutional Neural Network (CNN) to extract more advanced and more abstract features. In order to deal with the different sizes of input images and enrich receptive fields of the feature extractor, this paper uses the spatial pyramid pooling (SPP) of multi-scale to fuse the features. In addition, considering the different contributions of each feature to loop closure detection, the paper defines the distinguishability weight of features and uses it in similarity measurement. It reduces the probability of false positives in loop closure detection. The experimental results show that the loop closure detection algorithm based on multi-scale deep feature fusion has higher precision and recall rates and is more robust to illumination changes than the mainstream methods.  
loop closure detection; convolutional neural network; spatial pyramid pooling Article
## 2 Proposed Method
### Spatial-Channel Mutual Attention-Guided Transformer Module (SCATM)  
The SCATM is the central part of the proposed method, which contains a Spatial-Channel Mutual Attention Guiding Block (SCAGB) and a Channel-wise Multi-head Transformer Block (CMTB). Based on the need for multi-scale feature maps to apply different suitable blocks for better feature extraction and enhancement, SCAGB is further divided into two distinct blocks: the Spatial-Channel Mutual Attention Guiding Block-Top (SCAGB-T) in the encoding-decoding stage, and the Spatial-Channel Mutual Attention Guiding Block-Bottom (SCAGB-B) in the bottleneck stage. SCATM-T aims to promote both local facial details and global facial structures, while SCATM-B seeks to optimize the encoded low-level features. Moreover, unlike the usual spatial-wise transformers, which are limited to position-specific windows, the CMTB utilizes feature map channels to achieve image-size receptive fields. The combination of SCAGB and CMTB is complementary, and can simultaneously promote both local facial details and global facial structures.
## 5 Conclusions  
Our study is an encouraging step towards an early warning approach that can predict the risk of hantavirus infection because fluctuations in the abundance of the rodent reservoir can be estimated from satellite imagery, which provides an effective method for monitoring environmental conditions on a large scale. The trophic cascade hypothesis has been validated in our study area by using satellite and filed surveillance data. Vegetation productivity (farmland land condition, represented by PsnNet) was shown to act as an important driver for increases in the abundance of hantavirus reservoir (_A. agrarius_), with a 3-month lag.  
In conclusion, we have shown that the risk of HFRS is closely related to environmental variability and remote sensing could be critical for effective risk prediction and public health applications. This approach offers a valuable basis to develop a less costly but effective warning system to prevent HFRS outbreaks in our study area, Hu County. Additionally, the utility of remote sensing for real-time environmental condition observation provides health authorities plenty of time to develop targeted strategies for the control and prevention against the risk of HFRS.  
**Acknowledgments:** We thank the hundreds of CDC staff and local health workers in Shaanxi Province who collected data during 2005-2012. Funding has been provided by the National Key Research and Development Program of China (2016 YFA0600104), Health industry research special funds for public welfare projects (201502020), the National Natural Science Foundation of China (81673234, 71473264), Shaanxi Provincial Projects for Serious Disease Prevention and Control (0617-15240415), the Science and Technology Project of Shaanxi Province (2014-E1, 2013K12-04-02), and the Fundamental Research Funds for the Central Universities.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] performed analyses, [PERSON], [PERSON], and [PERSON] conceived the study, [PERSON] and [PERSON] wrote the paper, [PERSON] and [PERSON] collected and analyzed the remote sensing data, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed materials. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] collected field data during 2005-2012. All authors commented on and edited the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
# Robotic Coverage Path Planning for Ultrasonic Inspection
## 5 Ray-Tracing Based Process Model  
We propose to estimate the coverage of a linear phased array ultrasound sensor via ray tracing. This provides a good trade-off between physical accuracy and computational complexity. The main goal is to predict at which locations defects are detectable.  
Common defects in carbon fiber re-inforced plastic (CFRP) parts are delaminations and porosities [30]. These fault types cause two different effects on the captured ultrasound data, which are utilized for fault detection: (a) delaminations cause reflections of the sound, and (b) porosities scatter the signal and, therefore, cause an attenuation of the back wall echo. We use a ray-tracing approach to judge whether both fault types can be detected in a volume element from a given sensor pose (position and orientation). For each sensor element of the phased array, we simulate the propagation of the wave vector. If a ray passes a specific volume element (of the discretized specimen), we assume that delaminations can be detected. If the same ray is reflected at the back wall of the specimen and afterwards detected by the sensor, we assume that an existing porosity in that volume element can be detected by detecting a back wall attenuation. Volume elements that are passed by rays that meet these criteria are marked as captured; see Figure 3.  
The ultrasound sensor used throughout this paper is a linear phased array (PA) that consists of 64 transducers and has a width of 114 mm. This is a simplification of a real sensor with 114 transducers at 114 mm width. The discrepancy allows faster ray tracing computations. The sensor is modeled as a rectangle where the single transducers are spaced equidistantly along the longest edge of the sensor. Rays are emitted at the center of each transducer in a range of different angles with a pre-configured maximal aperture. The number of rays per transducer is fixed. Ray tracing is performed with a recursion depth of four. All rays that are reflected by the geometry in such a way that they hit a phased array element are marked as captured by the sensor. As illustrated in Figure 4, the modeled PA sensor is placed at a given working distance relative to a streamline that is calculated for the mesh surface. Emitted rays and reflections/transmissions are shown. Only rays emitted near the center of the PA sensor are cast back to the sensor, due to the challenging position of the sensor.  
Figure 2: Surface normal \(\mathbf{n}\) (blue arrow), tangent to first principal curvature \(\mathbf{t}_{1}\) (green arrow) and second principal curvature \(\mathbf{t}_{2}\) (red arrow) form an orthogonal system. The box shown above the surface \(s\) represents a line-shaped sensor. For linear phased array ultrasound, we claim that the long edge of the sensor should be parallel to the maximum curvature.
## 3 Proposed Methodology  
Creating a multimodal method for diagnosing PD by analyzing EEG and MRI data entails a comprehensive strategy that utilizes various machine learning techniques. The combination of EEG feature extraction using Flexible Analytic Wavelet Transform and MRI feature extraction utilizing Functional MRI (fMRI) with the General Linear Model and classification through algorithms like LightGBM is pivotal in advancing medical diagnostics and cognitive neuroscience. By extracting intricate patterns from EEG and fMRI data, it enables early detection and personalized treatment. This synergy offers a comprehensive insight into brain function and connectivity, fostering innovation in neuroimaging and signal-processing techniques. Ultimately, this integrated approach holds promise for improving patient outcomes and deepening our understanding of the complexities of the human brain. The proposed work, as depicted in Figure 2, is an attempt to diagnose PD from multimodal data. The proposed model begins with data collection, which includes MRI and EEG images. Next, data preprocessing removes distortion from these images. Following that, EEG features are extracted using wavelet transformation, and MRI features are extracted using functional MRI and the General Linear Model. Both extracted features are fused using PCA. Finally, LightGBM is employed for classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Core Methodology** & **Dataset Used** & **Accuracy** \\ \hline [PERSON] et al. [29] & CNN with LSTM & PaHaW and NewHandPD & 89.23\% on PaHaW and 91.11\% on NewHand PD \\ \hline [PERSON] and [PERSON] [30] & \begin{tabular}{c} Vision Transformer with \\ ImageNet \\ \end{tabular} & Hand Written Dataset & 92.11\% \\ \hline [PERSON] et al. [31] & \begin{tabular}{c} Mann\_Whitney test, SVM and \\ KNN \\ \end{tabular} & \begin{tabular}{c} 31 patient data of Casa di \\ Cura Le Terrazze \\ institute \\ \end{tabular} & 71.1\% for SVM and 75.62\% for KNN \\ \hline [PERSON] et al. [32] & \begin{tabular}{c} Chi Test, Extra Tree Classifier, \\ RNN, and Boosting \\ \end{tabular} & \begin{tabular}{c} 8 patients with 233 instances \\ and 23 features \\ \end{tabular} & ROC Accuracy 98.7\% \\ \hline [PERSON] et al. [33] & XGBoost, Decision Tree, SVM & \begin{tabular}{c} UCI dataset with 195 \\ instances and 24 features \\ \end{tabular} & \begin{tabular}{c} ROC Accuracy 96.2\% and \\ Sensitivity 100 \\ \end{tabular} \\ \hline [PERSON] et al. [34] & \
\begin{tabular}{c} Deep learning and \\ Neuro-fuzzy, PCA, and Deep \\ Belief Network \\ \end{tabular} &
\begin{tabular}{c} Real World Dataset 5875 \\ record with 16 features \\ \end{tabular} & ROC Accuracy 88.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Analysis of Core Methodologies.
# Half-Elliptical Resonator Lowpass Filter with a Wide Stopband for Low Band 5G Communication Systems
## 2 LPF Design Process
### Initial Low-Pass Filter  
Figure 2 illustrates the layout of the initial LPF. This filter consists of five resonators, one half-elliptical resonator in the middle, two elliptical resonators on both sides and two rectangular resonators, which are used as attenuator cells. The dimensions of all applied cells will be described in separate sections. Microstrip technology was used for implementation of the proposed filter. This technology has several advantages, such as low fabrication cost, planar configuration and ease of integration with other Radio Frequency (RF) components; hence, it has been widely used in microwave component designs, including filters [30; 31], metasurfaces [32; 33; 34], power dividers [35; 36; 37], phase shifters [38] and antennas [39; 40; 41; 42]. The LCE circuit is used to locate the transmission zeros (TZs) of the resonator. Each resonator generates a transmission zero, which uses a transfer function (TF), and a specific LCE circuit can be applied.  
Figure 1: The design process of the proposed low pass filter.
## 4 Mechanism Discussion
### Formation of Ettringite  
The initial formation of ettringite results from the hydration reaction in cementitious materials like cement. This reaction provides some strength to the specimen during the initial curing stage. However, as SO\({}_{4}\)[2]- ions diffuse/or absorb in the interconnected pores reacting with existing hydration products, the equilibrium state of the pore solution with the minerals in the CGB is disrupted, and monocarboaluminate reacts with sulfate to form ettringite in the presence of calcium hydroxide and water [30; 31; 32]. The formation of ettringite leads to a local decrease in porosity [33], which results in a denser internal structure of the CGB and a corresponding increase in mass, leading to a short-term increase in the strength of the CGB. This explains the decrease in the acoustic emission rate in the early part of the immersion. However, with the increase in the erosion time, the volume of ettringite exceeds the pore space, exerting significant expansion pressure on the microstructure and causing cracking [34]. Due to the microcracking-induced damage to the microstructure, a decreasing threshold of microcrack expansion can be seen in the acoustic emission experiments on CGB immersed for 120 d and 150 d. The specific reaction equation is as follows:  
\[\begin{split} 4\text{CaO}\cdot\text{Al}_{2}\text{O}_{3}\cdot 19 \text{H}_{2}\text{O}+2\text{Ca}\left(\text{OH}\right)_{2}+3\text{SO}_{4}^{2}- 14\text{H}_{2}\text{O}\to 3\\ \text{CaO}\cdot\text{Al}_{2}\text{O}_{3}\cdot 3\text{CaSO}_{4} \cdot 32\text{H}_{2}\text{O}+6\text{OH}^{-}\end{split} \tag{3}\]