esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 2 Materials and Methods  
### Test Site  
The study was carried out at Biebrza Wetland test site, which is located in North-East Poland (UL coordinates: 53 deg50''N, 22 deg10''E; LR coordinates: 53 deg05''N, 23 deg10''E). Figure 1 presents the localization of the test site in Poland on the orthophotomap (Figure 1a) and the Biebrza Wetland area on Landsat 8 OLI (Operational Land Imager) RGB (Red, Green, Blue spectral bands 4,3,2) image registered on 3 August 2014 (Figure 1b). The Biebrza Wetland was designated as a wetland site of international importance in the NATURA 2000 Network and since 1995 has been under the protection of the RAMSAR Convention on Wetlands. The test site contains Biebrza National Park (BNP) established in 1993 with a total area of 59.233 ha which includes 15.547 ha of forests, 18.182 ha of agricultural land, and 25.494 ha of wetlands--the most valuable habitats of the park. It is a flat area with an average altitude of about 105 m above sea level, situated close to the Belarus border. The main river is Biebrza, which flows out near the eastern border of Poland and whose recipient is the Narew River.  
The soils are mainly hydrogenic, with a dominant majority of peat soils in various stages of mouldering. The peatland complex covers about 90.000 ha and is the largest and the least disturbed of its kind in Central Europe. Wetlands floods occur annually in the spring and, besides precipitation, are the main supply of moisture into the peat soil. The site is characterized by large homogeneous fields of the following non-forest habitats: reeds, sedges, sedge-mos, grass, grass-herbs.  
The weather is the coldest in Poland, with long winters (up to 120 days below zero) and a short growing season. The length of the summer ranges from 77 to 85 days, with daytime temperatures averaging 15 degC. The level of precipitation is one of the lowest in Poland--around 510 mm. The dry year was 2015, and normal years were 2013 and 2014.  
Figure 1: Localisation of the test site in Poland on the orthophotomap (**a**); and the Biebrza Wetland area on the Landsat 8 OLI RGB (4,3,2) image registered on 3 August 2014 (**b**).
# Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device
## 3 Results
### Reconstructed 3D Images of Large-Sized Branch Structures  
Voxel data was developed to reconstruct a 3D image of a cultured NHBE bronchial tree, which was stained for actin and fibroblast growth factor receptor 2 (FGFR2). Various sophisticated algorithms have been developed to reconstruct 3D images from multiple plane data [24; 25]. In this paper, a simple algorithm, that intensity data from six datasets were averaged, was employed to demonstrate the capability of the hardware and the method using the gel cube device for large-scale imaging. The objective lens used was of 10\(\times\) magnification with NA 0.45.  
Figure 4A and Supplemental Video S3 show the 3D image reconstructed by \(z\)-stack imaging data from one side. Half of the bronchial tree was not properly imaged, and the other half had an elongated signal, although actin and FGFR2 were partially imaged. By contrast, the 3D image reconstructed by \(z\)-stack imaging data from six sides revealed the entire 3D bronchial tree structure through actin staining, with FGFR2 expressed at most branch tips (Figure 4B and Supplemental Video S4), confirming the results of imaging frozen sections [26]. The sample did not have to be physically sliced for the gel cube device to enhance image quality with multi-directional scanning. The entire sample measured 1090 \(\times\) 1090 \(\times\) 1544 \(\upmu\)m, at a resolution of 2.27 \(\upmu\)m/voxel.
# Robust and Efficient Corner Detector Using Non-Corners Exclusion
## 1 Introduction
By developing [PERSON]'s idea, [PERSON] and [PERSON] proposed the famous Harris corner detection algorithm, which is one of the most important corner detectors based on intensity [6]. It computes the SSD in any direction. Simultaneously, it enhances the stability and robustness of the algorithm by introducing Gaussian smoothing factor. However, the computational efficiency of the technique is observably low. [PERSON] et al. developed the FAST algorithm by constructing the optimal decision tree [19]. [PERSON] et al. further proposed the binary robust invariant scalable keypoints (BRISK) algorithm by constructing a scale-space with the FAST score [20]. [PERSON] et al. proposed a fast multi-scale detector in non-linear scale spaces [21]. [PERSON] et al. introduced directionality to FAST in order to avoid repeatedly searching [22]. These FAST-based algorithms aim to speed up detection efficiency and retain good performance. In this paper, a robust and efficient corner detector improved from the Harris corner detector, named the robust and efficient corner detector (RECD), is proposed. It is worth noting that the number of corners of an image is generally far fewer than 1% of the whole pixels. Thus, a key idea behind our method is to exclude a very large number of non-corners before detection. Because the non-maximum suppression (NMS) of the original Harris corner detector is complex, using an improved efficient NMS to reduce the complexity is another key idea. The original algorithm detects corners by three major steps. First, the image gradients are computed by the convolution of Gaussian first order partial derivatives in x and y directions with the image. Second, the CRF of each pixel is computed. Last, the maximum points are retained by NMS. In order to reduce the complexity, we analyze the three steps carefully and conduct many experiments. The experiment results show that the RECD possesses good robustness and efficiency. The RECD can be used for many visual processing domains because of its high speed and good performance. The remainder of this paper is organized as follows. In Section 2, we discuss briefly the Harris corner detector and two criteria for performance evaluation of corner detectors. Section 3 introduces our detector, with a description of the algorithm and its implementation details. Section 4 illustratesthe results of comparative experiments and gives an analysis of the results. Finally, we conclude our paper in Section 5.
# Performance Prediction of Composite Marine Propeller in Non-Cavitating and Cavitating Flow
## 2 Methodology and Results
### Flow Simulation  
#### 2.2.1 Governing Equation and Boundary Condition  
For the analysis of the unsteady flow around the composite propeller, each cell in the control volume satisfies the governing equations, including the continuity equation, expressed as:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\rho\overline{V}\right)=0 \tag{1}\]  
and the Reynolds Averaged Navier-Stokes (RANS) momentum equation, expressed as:  
\[\frac{\partial}{\partial t}\left(\rho\overline{V}\right)+\
abla\cdot\left(\rho \overline{V}\bigotimes\overline{V}\right)=-\
abla\cdot\overline{p}I+\
abla \cdot\left(T+T_{RANS}\right)+f_{b} \tag{2}\]  
where \(\rho\), \(\overline{V}\), and \(\overline{p}\) are the density, mean velocity, and pressure, respectively, and \(I\), \(T\) and \(f_{b}\) are the identity tensor, viscous stress tensor, and the resultant of the body forces. Here, \(T_{RANS}\) is a tensor quantity, known as the Reynolds stress tensor. A commercial program, STAR-CCM+ ver. 14.06.013, was used for the flow analysis of the submerged body with the composite propeller. The diffusion and convection terms of the governing equations were discretized to second-order accuracy, and the velocity and pressure were analyzed using the SIMPLE algorithm. A turbulence model was applied to the realizable \(k\)-\(\omega\) shear stress transport model, and a wall function was used for the boundary condition of the submerged body with the composite propeller.
## 3 Blocked Distance Estimation  
In contrast to the conventional location-routing problem (LRP), the location-routing problem concerning the transportation of cruise ship cabin materials necessitates the consideration of actual deck constraints. The presence of fixed obstacles like stairs and functional compartments on the deck renders the Euclidean distance matrix, derived from the positional coordinates, incongruent with the practical scenario. This incongruence can impede the selection of subsequent path plans. Consequently, it becomes imperative to ascertain the likelihood of encountering obstacles between each position and identify the specific obstacles that may be encountered. When employing path planning techniques such as the A* algorithm [33; 34; 35], the escalation in the number of distribution centers and cabin points leads to a substantial increase in the quantity of paths that require computation. This surge in computational load poses significant challenges in the practical implementation.  
Hence, this article introduces a method for estimating the shortest distance by utilizing the Euclidean distance and obstacle blocking coefficient. The obstacle blocking coefficient is used to depict the influence of various obstacles on the material transportation process, thereby offering an accurate and rational distance matrix for future planning. This approach employs Gaussian process regression within machine learning to develop a predictive model for obstacle distances and to compute the blocking coefficient for individual obstacles.
## 5 Comparison  
Table 3 systematically compares encoder and comparator designs employing quantum dot (QD-SOA), conventional (SOA), and carrier reservoir (CR-SOA) semiconductor optical amplifiers across 10-160 Gb/s data rates, with particular attention to extinction ratio (ER) and quality factor (QF) metrics. The QD-SOA comparator [22] establishes performance benchmarks, achieving 10 dB ER and 9.0 QF at 160 Gb/s through simulation, demonstrating exceptional signal integrity for ultra-high-speed applications. In contrast, the SOA-based comparator [23] operates at 10 Gb/s with slightly lower ER (9 dB) and lacks QF data, reflecting relaxed performance requirements at lower speeds. Experimental studies using SOA technology at 40 Gb/s [24; 25; 26] consistently achieve 10 dB ER (with [24] and [25] focusing on encoder designs, while [26] examines comparators), though the absence of QF reporting limits comprehensive performance evaluation. Notably, our CR-SOA implementation advances the field by operating at 120 Gb/s while maintaining 10 dB ER and a competitive QF (8.33-9.05), representing only a 7.4-11.1% reduction compared to the QD-SOA benchmark despite 25% higher speeds than 40 Gb/s implementations. The CR-SOA results reveal three key characteristics: (1) maintained ER parity with top-performing designs, (2) a slightly reduced but stable QF (8.33-9.05) at elevated speeds, and (3) clear optimization pathways through phase tuning, injection power adjustment, and noise reduction. This analysis yields important insights: QD-SOA maintains superior signal quality (QF = 9.0) at extreme speeds (160 Gb/s), while CR-SOA demonstrates excellent speed-QF compromise (8.33-9.05 at 120 Gb/s). The consistent ER = 10 dB across multiple studies [24; 25; 26] suggests reliable signal contrast at moderate speeds, though the prevalent lack of QF reporting
## 3 Environmental Conditions and Buoy Modeling
### Numerical Analysis with Buoy Modeling  
The numerical analysis was conducted using OrcaFlex 11.2b, a commercial software package operated in Windows that incorporates buoy geometry and Morison force calculation [51]. In this analysis, we utilized a 4-ton buoy with a maximum diameter of 2.8 m, as illustrated in the left panel in Figure 3. The buoy is moored using a single mooring line consisting of chain-nylon-polyester-chain, as shown in the right panel in Figure 3. A mid-line buoy is positioned at the midpoint of the mooring line, approximately 340 m away from the ocean buoy, where the current velocity is noticeably reduced. Due to the numerical modeling of the buoy, the x, y, z coordinates of the ocean buoy are not fixed but instead vary over time, either increasing or decreasing. These time-dependent ocean buoy responses, corresponding to different sea state conditions, are utilized as simulation conditions in Section 4.
# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
## 3 Our Proposed Methodology  
Our proposed method introduces the Gaussian noise removal technique as a foremost pre-processing step and integrating auto-encoder with a convolutional neural network. In a deep convolutional model, classification techniques give flexible lung nodule prediction, as there are multiple features present in the dataset. In this section, we also define the correntropy measure with equations. The autoencoder used in our proposed model and how our model deals with the Gaussian noise in the lung images are given in detail. Figure 1 provides the overall flow of our proposed lung nodule detection framework in five stages.  
Figure 1: Overall flow of our proposed lung nodule detection.
## 3 Workspace Analysis  
### Forward Kinematics  
Through kinematic analysis, the relationship between the position of the end-effector and the angles of each joint can be easily defined for a robot. The kinematics can be conducted using two analyses, namely, forward kinematics analysis and inverse kinematics analysis. The forward kinematics analysis allows one to obtain orientation and translation of the end-effector on the basis of the given angles of joints. Therefore, the workspace of the fingers is easily acquired using the forward kinematics analysis.  
Before analyzing kinematics, the problem must be formulated using a mathematic tool. [PERSON] and [PERSON] in 1995 introduced the convention for two spatial coordinates [8]. From this convention, a matrix, which contains the information on the translation and rotation, can be obtained. To access the values of the D-H parameters, the coordinate of each joint must be defined first. The long axis of each cylinder, as shown in Figure 1, represents the rotational direction of each joint for the index finger and the thumb. Tables 1 and 2 give the geometric parameters of the index finger and the thumb, respectively. The forward kinematic model of the index finger or thumb is given through D-H parameter convention, and the transformation between adjacent \(i\)-th and \((i-1)\)-th joint coordinate systems can be written in the form of the following homogeneous transformation matrix:  
\[i-1T_{i}(\theta_{i})=\left[\begin{array}{cccc}cos\theta_{i}&-sin\theta_{i} cos\alpha_{i}&sin\theta_{i}sin\alpha_{i}&a_{i}cos\theta_{i}\\ sin\theta_{i}&cos\theta_{i}cos\alpha_{i}&-cos\theta_{i}sin\alpha_{i}&a_{i} sin\theta_{i}\\ 0&sin\alpha_{i}&cos\alpha_{i}&d_{i}\\ 0&0&0&1\end{array}\right], \tag{1}\]  
where \(\alpha_{i},\ a_{i},\ \theta_{i}\), and \(\theta_{i}\) are D-H parameters. From Tables 1 and 2, one can observe that each finger has six joints from the base to its fingertip. Starting from joint 0 to joint 5, the homogeneous transformation matrix \({}^{0}T_{5}\) can be written as  
\[{}^{0}T_{5}=\ ^{0}T_{1}\cdot^{1}T_{2}\cdot^{2}T_{3}\cdot^{3}T_{4}\cdot^{4}T_{5}= \left[\begin{array}{cccc}n_{x}&\alpha_{x}&a_{x}&P_{x}\\ n_{y}&\alpha_{y}&\alpha_{y}&P_{y}\\ n_{z}&\alpha_{z}&\alpha_{z}&P_{z}\\ 0&0&0&1\end{array}\right], \tag{2}\]  
where \((P_{x}\ P_{y}\ P_{z})^{T}\) is the position vector of the fingertip measured from the base coordinate system. By giving many sets of joint angles, the workspace of the finger can be collected through the forward kinematics analysis.
# Focused Ultrasound in the Rehabilitation Setting:
## 4 Discussion and Future Directions
### FUS Controversial Aspects  
FUS is a promising non-invasive therapeutic technology, but its clinical application is hindered by several controversial aspects. The heterogeneity in the parameters used across studies complicates standardization and reproducibility, limiting the comparability of results. Furthermore, the scarcity of randomized controlled trials (RCTs) reduces the strength of evidence supporting its efficacy and safety. Anatomical variability, such as differences in skull and scalp structure, can affect targeting precision and therapeutic outcomes. Additionally, adverse events, although generally mild and transient (e.g., gait disturbances, numbness, or headaches), raise concerns about consistency in safety profiles. These issues underscore the need for further research to refine protocols, improve standardization, and expand the evidence base for FUS applications.
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 5 Discussions  
The performance of the proposed ADC at the Nyquist input frequency is compared to other state-of-the-art SAR ADCs with similar specifications in Table 3 based on the test results.  
For SAR ADCs, the CDAC occupies the most significant area. The incorporation of the 4-reference voltage method provides a significant advantage in terms of area efficiency, surpassing other designs with similar FOM values that utilize more advanced processes. Remarkably, the proposed ADC occupies only 0.008 mm\({}^{2}\) of core area. The design of a high-linearity bootstrap, low-noise comparator, and low-mismatch CDAC enables the proposed ADC to achieve impressive SNDR and SFDR of 55.3 dB and 66.6 dB, respectively, at a sampling rate of 200 MHz and Nyquist input frequency. These results meet the system's requirements for ADC sampling rate and effective resolution. The measured SNR results also closely align with the SNR values calculated based on the noise budget in  
Figure 21: Measured performance versus input power.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Ref & [24] & [7] & [25] & [26] & [27] & This Work \\ \hline Technology (nm) & 40 & 65 & 28 & 90 & 22 & 55 \\ \hline Supply (V) & 1.1/1.3 & 1.2 & 1.05 & 1 & 0.8 & 1.2 \\ \hline Sampling rate (MS/s) & 100 & 160 & 250 & 100 & 200 & 200 \\ \hline Nyquist SNDR (dB) & 56.3 & 55.6 & 52.4 & 55.4 * & 48.7 & 55.3 \\ \hline Nyquist SFDR (dB) & 76.3 & 69 & 68.3 & 68.1 * & 62 & 66.3 \\ \hline ENOB (bit) & 9.05 & 8.9 & 8.4 & 8.91 * & 7.8 & 8.9 \\ \hline Power (mW) & 1.4 & 2 & 3.23 & 1.25 & 4.3 & 2.8 \\ \hline INL(LSB) & \(-\)0.71/ & \(-\)0.97/ & \(-\)0.86/ & \(-\)1.02/ & - & \(-\)1.1/ \\ \hline DNL(LSB) & \(+\)0.82 & \(+\)0.93 & \(+\)0.96 & \(+\)1.8 & - & \(-\)1.1/ \\ \hline DNL(LSB) & \(-\)0.73/ & \(-\)0.75/ & \(-\)1.02/ & \(-\)0.93/ & - & \(-\)0.69/ \\ \hline Nyquist FOM (fj/conv) & 26.2 & 25.4 & 38.2 & 26 * & 96.5 & 29 \\ \hline Core Area (mm\({}^{2}\)) & 0.027 & 0.023 & 0.068 & 0.0144 & 0.0168 & 0.008 \\ \hline \hline \end{tabular}  
* Due to a lack of testing data for the Nyquist input frequency, only low-frequency input data are available.  
\end{table}
Table 3: Comparison with previous works.  
Table 2, reflecting excellent design matching. In addition, in the subsequent large-scale testing (results not shown), the deviation of the SNDR was concentrated within 0.5 dB, further demonstrating that the proposed ADC can meet the requirements of large-scale production effectively.
## 3 Methodology  
### Formal Task Definition  
Given a set of posts \(\mathcal{P}=\{p_{1},p_{2},\ldots,p_{n}\}\) and a set of targets \(\mathcal{T}=\{t_{1},t_{2},\ldots,t_{m}\}\), the goal of stance detection is to identify the stance \(s_{ij}\) expressed in a post \(p_{i}\in\mathcal{P}\) toward a target \(t_{j}\in\mathcal{T}\). Each stance \(s_{ij}\) is categorized into one of three possible labels: **favor**, **against**, or **neutral**. The goal is to accurately classify each post-target pair into one of these stance categories.  
Beyond the direct relationship between \(p_{i}\) and target \(t_{j}\), stance detection benefits from integrating relational information involving entities linked to posts and targets. To enhance stance detection, we extend the task to incorporate multi-hop relationships among posts, targets, and intermediate entities \(e\in\mathcal{E}\) (e.g., users, hashtags, organizations, and topics), represented as \(p_{i}\to e\to t_{j}\) or more complex paths. This heterogeneous multi-hop relational structure can be effectively captured by constructing a heterogeneous stance network \(\mathcal{G}=(\mathcal{V},\mathcal{R})\), where the nodes \(\mathcal{V}\) include posts, targets, and intermediate entities, and the edges \(\mathcal{R}\) represent the relationships among these nodes.  
Thus, stance detection can be formulated as learning a function \(f:(\mathcal{P},\mathcal{T},\mathcal{G})\rightarrow\mathcal{S}\), where \(\mathcal{S}=\{\text{favor, against, neutral}\}\), predicting the stance \(s_{ij}\) by leveraging both the semantic content of the posts and the relational information in the heterogeneous network \(\mathcal{G}\). By leveraging multi-hop relationships within this network, our approach enhances stance prediction accuracy, enabling a deeper understanding of contextual dependencies between posts and targets.  
Our framework, illustrated in Figure 2, consists of two key components: (1) heterogeneous stance network construction, where we utilize LLMs to process posts and targets from a given dataset, forming a heterogeneous stance network that incorporates multiple node types and diverse relational edges; and (2) multi-view stance detection, which integrates textual and graph-based representations for stance classification in both supervised and zero-shot scenarios. In the supervised setting, we employ BERT and relational graph convolutional networks (RGCNs) to jointly model post-target textual information and structural relationships for classification. In the zero-shot setting, we enhance LLMs' ability to perform stance detection on implicit targets by encoding post-target connection paths within the network.
###### Abstract  
With the arrival of 5G wireless communication systems, there has been increased interest in exploring higher frequency bands above 6 GHz, up to millimeter-wave frequencies. Radio wave propagation at these higher frequencies can suffer from substantial Doppler impairments. The linear dependency of Doppler shifts with carrier frequencies make them challenging to use in high-mobility 5G cellular scenarios. Therefore, the Doppler power spectrum (DPS) characteristics and radio channel coherence time (CT) of the received signals are of great importance for 5G wireless systems. In this way, this paper presents the effects of a narrow beam phased antenna array in reducing the DPS (due to user movement) and, simultaneously, increasing the coherence time (CT). Functional and complete descriptive assessments of beamwidths versus the DPS and CT, through different elements and geometries of the phased antenna array, are analyzed. Moreover, in terms of CT and the DPS, better performance on the 5G cellular scenarios was obtained.  
n  
article  
Effects of Narrow Beam Phased Antenna Arrays over the Radio Channel Metrics, Doppler Power Spectrum and Coherence Time, in a Context of 5G Frequency Bands, April. 20211, 11, 10081. [[https://doi.org/10.3390/app112110081](https://doi.org/10.3390/app112110081)]([https://doi.org/10.3390/app112110081](https://doi.org/10.3390/app112110081))  
19 August 2021  
22 October 2021  
27 October 2021