esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Isolated Dual-Mode High-Pass Birdcage RF Coil for Proton and Sodium MR Imaging at 7 T MRI
## 2 Materials and Methods
### Single-Tuned Birdage (STBC) RF Coil  
A 12-leg high-pass tapered birdcage RF coil, as shown in Figure 2, was designed to enhance signal intensity at the apex of the brain for both \({}^{1}\)H and \({}^{23}\)Na resonant frequencies at 7 T. The legs and end-rings were constructed using copper sheets with a thickness of 0.15 mm and widths of 20 mm. An acrylic plastic frame was used to fabricate a support coil frame, consisting of a large cylindrical tube (250 mm outer diameter and 180 mm length), a partial hemisphere (250 mm diameter and 110 mm length), and a small cylindrical tube (115 mm outer diameter and 120 mm length) with a thickness of 5 mm. The diameters of the RF coil at the bottom and the apex of the RF coil were 250 mm and 153 mm, respectively. The length of each leg was 210 mm, and the legs were attached along the outer surface of the acrylic frame.  
Each \({}^{1}\)H and \({}^{23}\)Na STBC RF coil port was tuned to the homogeneous mode at 298 MHz and 78.9 MHz. High-quality factor (Q-factor) and high RF power capacitors (DLC70E, Dalicap Tech. Corporation, Dalian, China) were attached at the end-ring to tune the RF coil to the desired resonance mode and trimmer capacitors with high Q-factors (Q-factor > 3000, capacitance range = 1.0-14.0 pF, working voltage = 1250 V, non-magnetic, Voltronics, Denville, NJ, USA) were attached at the end-ring of each feeding points to fine-tune the RF coil. For the \({}^{1}\)H STBC RF coil, capacitors of 4.3 pF and 3.2 pF were connected to the upper and bottom end-ring, excluding the ports. In the case of the \({}^{23}\)Na STBC RF coil, capacitors of 102 pF and 55.2 pF were similarly attached to the upper end-ring and bottom end-ring, excluding the ports. Shielded coaxial cable traps were connected after the matching circuit to mitigate the propagation of common-mode currents along the coaxial cable shield [41]. For the bench test of both the \({}^{1}\)H and \({}^{23}\)Na STBC RF coils, the scattering (S) parameters  
Figure 1: Simulation design for tapered birdcage RF coil for \({}^{1}\)H and \({}^{23}\)Na in (**a**) front view and (**b**) top view; Simulation set-up with the 3D human head model in (**c**) front view and (**d**) side view.  
Figure 2: (**a**) Constructed \({}^{1}\)H STBC RF coil; (**b**) constructed \({}^{23}\)Na STBC RF coil; (**c**) schematic of the STBC RF coil, shown in an unfolded view; x-axis is the perimeter. Trimmer capacitors are utilized to fine-tune both \({}^{1}\)H and \({}^{23}\)Na STBC RF coils. The depicted section of the circuit includes key components; the remaining four legs are omitted.  
and Q-factors (at 3 dB bandwidth) were measured and analyzed using a vector network analyzer (N9913A, Agilent Technologies, Santa Clara, CA, USA).
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 3 Methodology
### The Cascaded Group Attention Mechanism  
In the AIFI module of RT-DETR, a single-scale transformer encoder is utilized to perform intra-scale interactions on high-level features. However, traditional multi-head self-attention (MHSA) distributes attention equally across the entire input, leading to a significant computational burden, particularly with high-resolution remote sensing images. Additionally, high-level feature maps that undergo multiple downsampling steps may lose information about small targets. This study replaces MHSA with CGA, which splits inputs for each head and concatenates output features across heads. This approach enhances intra-scale feature interactions, improving small-target detection performance while reducing computational redundancy. The architecture of CGA is depicted in Figure 2.  
As shown above, CGA divides the complete feature map into different parts as input, with each part being processed by an attention head to produce an output. The outputs from all heads are then concatenated and projected back to the input dimensions via a linear layer to form the final output. In this approach, each attention head focuses on a part of the input, and the combined attention from all heads provides a comprehensive feature representation. CGA enhances model computational efficiency without adding parameters. Additionally, through concatenation, each head's output is added to the input of the subsequent head, gradually refining the feature representation.  
Figure 1: The architecture of RS-DETR.  
Figure 2: The architecture of cascaded group attention.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 7 Conclusions and Future Work  
### Conclusions  
In this article, we presented a novel approach based on machine learning for algorithm selection to compute the treewidth of a graph. Given a set of algorithms and a set of features of a specific problem instance, our system predicts which algorithm is most likely to have the best performance, according to a previously learned classification model. For this purpose, 13 features of graphs were identified and used. To evaluate our approach, we compared three state-of-the-art solvers, our hybrid algorithm and a hypothetical, perfect 'oracle' algorithm selection system, on thirteen publicly-available graph datasets and three performance measures. Our intentionally stripped-down machine learning approach outperformed all individual solvers on all datasets and on all performance metrics, which clearly demonstrates its utility. However, the hypothetical perfect algorithm selector still significantly outperformed our approach, which indicates that there are further improvements to be made. Based on our results, we argued that size, density and degree variability are the most important characteristics of a graph that determine which algorithm would be fastest on it. We also identified specific solvers' strengths and weaknesses based on these same characteristics.
## 5 Experimental Results and Discussions
### Capacity  
The watermarking algorithm embeds data through a dual domain technique targeting specific frequency components in both DCT and DWT transformations. This implementation applies distinct channel-specific embedding operations across Y, Cb, and Cr components in disk image JPEGs and R, G, B channels in stand-alone colour images. The approach delivers measurable improvements in attack resilience with \(\mathrm{BER}=0\) against JPEG compression (QF \(\geq\) 60) and preserves visual quality (PSNR \(>\) 46 dB) and maintains fixed embedding capacity regardless of image dimensions.  
The watermark payload consists of a 64-bit pattern, embedded twice:  
1. In the (4,4) coefficient of DCT blocks;
2. In the HL (high-low frequency) subband of the DWT decomposition.  
This results in a total payload of 128 bits for all images, regardless of size or colour depth. The embedding capacity is expressed as  
\[C_{\mathrm{effective}}=N\times(C_{\mathrm{DCT}}+C_{\mathrm{DWT}})\]  
where \(C_{\mathrm{effective}}\) represents the maximum capacity in bits, \(N\) is the number of colour channels (one for greyscale images and three for colour images), \(C_{\mathrm{DCT}}\) refers to the 64-bit watermark pattern embedded in the DCT domain, and \(C_{\mathrm{DWT}}\) refers to the 64-bit watermark pattern embedded in the DWT domain.  
The adaptive embedding process adjusts the embedding strength based on image content and size. The embedding strength for each block or coefficient is determined by an adaptive threshold:  
\[S=JND\times\gamma\]  
where \(JND\) is the Just-Noticeable Difference and \(\gamma\) is a scaling factor that varies based on image characteristics.  
Compared to other techniques, the proposed method offers significant advantages. The adaptive nature allows it to optimise capacity in parts of the image that can accommodate stronger modifications, in contrast to fixed-strength methods [74]. It provides more embedding opportunities than single-domain methods [9]. Additionally, adaptively embedding across all colour channels in RGB/YCbCr images yields a higher capacity than techniques restricted to the luminance channel. The adaptive embedding strength further ensures that imperceptibility is maintained.
# Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
## 1 Introduction
Through the corresponding research, comparison, results, and analysis mentioned above, the feasibility, availability, and efficiency of the LEO POD using onboard BDS observations have been sufficiently proved. The LEO orbit quality is very susceptible to the amount of the available BeiDou observation data and to the poor accuracy of orbit and clock products for BeiDou satellites, especially for GEO. The simulated experiment demonstrated that the BeiDou GEO with orbit accuracy several times inferior to IGSO and MEO would still improve the orbit quality for GRACE-A [10]. The FY-3C POD using practical BDS observations measured by the onboard GNOS receiver finds that including GEO will significantly decrease the precision of the FY-3C orbit to several decimeters. Fortunately, the FY-3C orbit less than 10 cm level can be easily reached by rejecting GEO. The contribution of BeiDou GEO to precise point positioning (PPP) [17; 18] represents a positive correction when involving GEO with the current orbit and clock quality of GEO [19]. In contrast to the rough exclusion of GEO satellites this study proposes a new data processing scheme which incorporates GEO satellites through in-flight calibration of GEO orbit errors on the basis of a former study. The numerical results indicate that the proposed method improves the precise orbit accuracy by 20.2% as compared to excluding the GEO satellites. The structure of this paper is arranged as follows: In Section 2 we introduce the current BeiDou precise products we adopt, then we assess BeiDou orbit quality using day boundary discontinuity (DBD) indicators and analyze the characteristics of orbit biases in GEO. In Section 3 we design and describe three kinds of comprehensive POD configurations for FY-3C using BDS to validate the proposed assumption that orbit biases can be estimated in the POD process together with the LEOorbit. Then we give a brief introduction to the distribution of onboard BDS measurements collected by FY-3C. Then we validate the precision of the three kinds of final orbits for FY-3C by comparing them with the GPS-derived reference orbit. The results support the rationality and correctness of the orbit bias calibration method proposed in this paper. Phase observation residuals derived in the three orbit solutions are analyzed further to support the method. Finally, the quality of the reconstructed GEO orbits by calibration of daily systematic biases are evaluated with the outliers removed. The discussion and conclusions associated with the experimental results and analysis are organized in Sections 4 and 5, respectively.
## 2 Samples and Analytical Procedure  
### Site Description and Aerosol Sampling  
Aerosol samples (PM\({}_{1,0}\), \(n=28\)) were collected during a field campaign from 17 March 2008 to 13 April 2008 using low volume air sampler and pre-combusted (450 \({}^{\circ}\)C, 4 h) quartz fiber filters (Pallflex 2500 QAT, 47 mm in diameter). The sampler was installed on the roof top of the first story of the Cape Hedo Atmosphere and Aerosol Monitoring Station building (CHAAMS, 26\({}^{\circ}\)9\({}^{\prime}\) N, 128\({}^{\circ}\)2\({}^{\prime}\) E) [48], which is located at Cape Hedo in the northwest coast of Okinawa Island, Japan, an outflow region of East Asia (Figure 1). The flow rate was 16.7 L/min and sample volume is 24 m\({}^{3}\). Each sample was collected for 24 h. Before and after sampling, the filters were stored in a preheated glass vial (50 mL) with a Teflon-lined screw cap. After the collection, samples were stored in a freezer at \(-20\)\({}^{\circ}\)C to prevent the microbial degradation of organics. More details are described elsewhere [28; 49]. Possible artifacts on the filters due to the evaporation and adsorption of semivolatile organic compounds during sample collection could not be eliminated.
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 6 Current Collector Shape and Structure
### Foams  
Foam inserts, essentially a highly porous expanded 3D mesh, are a solution to avoid pressure drops and flow disturbances that anode side meshes can have while achieving a large cross-sectional contact patch for current collection. Loss of contact between foams and the cell wall from sagging has not been reported/studied in the literature and is unlikely due to the ease in shaping, allowing the foam to fully fill the tube bore, analogous to a filter inside a cigarette. The filling of the tube will reduce the conduction pathway within the current collector in the radial direction compared to a mesh, for example. Foams are characterised by a high mechanical strength and low weight, favourable for a portable fuel cell device interconnect [123]. The foams are typically made of silver, nickel or steel alloys and have been shown in recent literature to exhibit excellent current collecting capabilities [124]. In 2017, [PERSON] et al. [125] reported a current density at 0.7 V and 750 \({}^{\circ}\)C in H\({}_{2}\) of 0.95 A\(\cdot\)cm\({}^{-2}\) with a peak power density of 1 W\(\cdot\)cm\({}^{-2}\), achieving 24 W from a single cell. The cell had an outer diameter of 9 mm and was 100 mm in length. They performed a degradation study over 4000 h, operating at 18.5 A with a fuel utilisation of 64.38% observing a degradation rate of 26 mV.kh\({}^{-1}\). The ASR estimated from the polarisation plot at 750 \({}^{\circ}\)C was 0.27 \(\Omega\cdot\)cm\({}^{2}\).  
The high surface area to volume ratio of foams positions them as an ideal candidate as a catalyst support, increasing the functionality of the current collector. The catalyst can either be alloyed into the base material during fabrication or added postfabrication by doping. Typical dopants include metals such as copper, cobalt, nickel, manganese and tin. Recent studies into doping have shed light on the possibility to control anode side degradation from coke deposition and increase tolerance to sulphur derivatives [126]. The improved tolerance leads to improved fuel flexibility for operation on lower purity hydrocarbon fuels such as syngas and sour gas [127]. [PERSON] et al. [128] developed a phosphorous doped nickel foam for current collection. When compared to a bare mesh, they observed an improved electrochemical performance and durability under operation under syngas for coatings with phosphorous contents above 6.4%. Weight loss was negligible for the plated mesh sample, compared to the approximately 250% weight gain observed for samples exposed (ex situ) to syngas for 50 h at 750 \({}^{\circ}\)C.
## 2 Materials and Methods
### NDVI and LST
#### 2.3.2 \(Ndvi\)  
The \(NDVI\) is an indicator that highlights vegetation and determines the relationship between the absorption of spectral radiation; in the red band, by the chlorophyll is present in plant cells, and the reflectance of leaves is found in the near infrared region. Equation (8) shows the calculation of the \(NDVI\)[27].  
\[NDVI=\frac{\rho_{\lambda IV}-\rho_{\lambda V}}{\rho_{\lambda IV}+\rho_{ \lambda V}} \tag{8}\]  
where  
\(\rho_{IV}\) = Reflectance of the near infrared band;  
\(\rho_{V}\) = Red band reflectance.  
For the Landsat-8 images, the reflectance of band 5, in the near infrared, and the reflectance of band 4, in the red, were used. We adopted five thematic classes that stipulate different types of soil situation. The \(NDVI\) intervals are identified as shown in Table 1[28].
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 4 Network Layer Security
### Secure Routing Service--SCOTRES  
After authenticating a package with TESLA, SCOTRES evaluates the sender's trustworthiness and its contribution to the network [62]. SCOTRES is a secure routing system for wireless ad-hoc systems that is based on trust computing and is designed around the intricacies of CPS solutions. It maximizes the information that is inferred regarding the network state, based on the knowledge that a node already processes. It safeguards communication against Internet-originating attacks or compromised equipment and jammers. The overall setting is utilized for real-time monitoring of IoT and CPS applications and their management through the cloud.  
SCOTRES consists of five components that rate different aspects of the networking operation: (i) The topology-aware component improves the traffic load-balancing and defends distant entities from being isolated: (ii) the energy-aware component estimates the remaining energy of each node, defending the network against energy dissipation and other relevant threats; (iii) the channel-health component identifies jamming in the wireless medium, constraining its effects by routing communication through unaffected paths; (iv) the reputation component ranks a node's fair use of the network resources for routing, forwarding, and recommending activities; and (v) finally, the trust component aggregates all these pieces of knowledge and evaluates the trustworthiness and overall cooperativeness of network entities. Performance and security analyses for the five components have been conducted in [62].
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 2 Literature Review
### Traditional Code Search Approaches  
Traditional code search methods primarily view source code as either plain text or a sequence of tokens, disregarding its inherent structure and semantics. These models commonly involve manual feature extraction or NLP-based vectorization to identify clone pairs using text similarity algorithms. These approaches are prevalent in industry due to their simplicity, low complexity, and cross-platform applicability.  
In their initial research, [PERSON] et al. [20] identified distinctive sample features, including function names, expressions, and control flow, through manual extraction from the source code. The similarity between clone code pairs was assessed using 21 function-level metrics derived from these features. This method heavily depended on expert domain knowledge due to the manual generation of features. However, the model exhibited limited adaptability, particularly in handling the varying structural features across different programming languages.  
A well-known Eclipse platform plug-in, SDD [21], utilizes token detection to find cloned code snippets in the source code. Through the implementation of inverted index and N-nearest neighbor algorithms, it efficiently minimizes detection time, enabling developers to swiftly identify clone code within the IDE environment. SDD excels in detecting code but falls short in recognizing clone pairs with significant syntactic dissimilarity that still serve the same function.  
Deckard [22], a renowned model frequently employed for comparative experiments by subsequent researchers, conducted an analysis on the abstract syntax tree (AST) generated from the provided source code. The vectors in Deckard are manually generated using predefined rules during tree traversal. Following vector processing with the local sensitive hash (LSH) algorithm, the model determines code similarity based on Euclidean distance and provides the probability of clone pairs. Deckard is effective in detecting clone code pairs with statements in different orders, as the corresponding AST preserves the fundamental structure of the source code and is adaptable to various programming platforms with corresponding AST parsers. However, the model faces challenges in large-scale clone detection due to the intricacies of clustering operations.
## 3 System Design of Mobile Robot Person-Following System
### Position Estimation Using Depth Information  
The distance is recorded in the path history of the target so that the trajectory of the person can be predicted when the person is lost. To facilitate the estimation of distances from the RGB-D data, we converted the coordinates from 2D to 3D using the point cloud after determining the centers of the people in 2D images (\((c_{u_{i}},c_{v_{i}})\)) and the camera point. Consider the detection of many people \(P_{i}\), \(i=1,2,3,...,n\) by the robot. The relationship between the 3D coordinates of a person \((X_{i},Y_{i},Z_{i})\) and the center of the boundary box of the person in the 2D RGB image coordinates \((c_{u_{i}},c_{v_{i}})\) is given by:  
\[\begin{bmatrix}c_{u_{i}}\\ c_{v_{i}}\\ 1\end{bmatrix}=\begin{bmatrix}f_{x}&s&u_{0}\\ 0&f_{y}&v_{0}\\ 0&0&1\end{bmatrix}\begin{bmatrix}X_{i}\\ Y_{i}\\ Z_{i}\end{bmatrix} \tag{1}\]  
where \((u_{0},\,v_{0})\) is the center point of the image, \(f_{x}\) and \(f_{y}\) are the focal lengths along each axis in pixels, and \(s\) is an axis skew that arises when the \(u\) and the \(v\) axes are not perpendicular. Once all of these parameters are known, the distance \(d_{i}\) between the person and the center point of the camera in meters can be estimated using the following equation:  
\[d_{i}=\sqrt{X_{i}^{2}+Y_{i}^{2}+Z_{i}^{2}}. \tag{2}\]  
We adopted the transform library (tf) in [28] to transfer the 3D coordinates of the persons and join them with the SLAM algorithm and the coordinates of the rest of the system, as depicted in Figure 4.  
The pose of the robot in 2D plane navigation is represented by only \((x_{r},y_{r},\theta_{r})\), where the angle \(\theta_{r}\) indicates the heading direction. An Orbbec Astra camera was used to provide synchronized color and depth data at a resolution of \(640\times 480\) over a \(60^{\circ}\) horizontal field of view. The angles of the persons relative to the center of the camera are dependent on the camera specifications used and are computed as \(\varphi_{i}=-0.09375\times c_{u_{i}}+30\), where \(c_{u_{i}}\) is the center of the box on the u axis. Then, from the previous equations, we obtain the real-world positions of the persons as:  
\[\begin{array}{l}x_{i}=d_{i}\cos(\theta_{r}+\varphi_{i})+x_{r}\\ y_{i}=d_{i}\sin(\theta_{r}+\varphi_{i})+y_{r}.\end{array} \tag{3}\]  
Figure 4: Coordinate transformations of the mobile robot system.
## 3 Data and Methods  
### Data  
We combine the Sentinel-1A/B and RADARSAT-2 satellite imageries with different geometries to study the spatiotemporal characteristics of surface deformation over the entire Shandong peninsula. Sentinel-1A/B images with five tracks are all considered, including three descending tracks and two ascending tracks. The descending tracks include Path 76 (frame 467 and 472) and Path 3 and ascending tracks include Path 98 and Path 69. A set of 137 descending Sentinel-1B scenes from Path 76 and Paths 3 and a set of 122 ascending Sentinel-1A scenes from Path 98 and Path 69 are utilized to identify the distribution of surface deformation and to derive its evolution over the entire peninsula from 2016 to 2019. To monitor the surface deformation over YRD, ascending Sentinel-1 imagery (Path 69) is utilized, which is also combined with descending data to decompose the vertical and horizontal surface deformation. To this end, a total of 66 scenes of ascending and 33 scenes of descending Sentinel-1 imagery are used. Lastly, 12 scenes of descending RADARSAT-2 images from April 2012 to June 2016 are also involved to restore the historical deformation over YRD.  
One-arc-second (\(\sim\)30 m) SRTM DEM is used as an external DEM to remove the topographic phase from the interferograms [30]. Detailed SAR parameters are shown in Table 1.