esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 3 Materials and Methods
### MDVA Method
#### 3.2.1 Multi-Dimension Interaction  
Traditional methods for calculating channel attention typically involve strategies for aggregating spatial information and then reweighting channels accordingly. Although these methods have achieved remarkable success, particularly the convolutional block attention module (CBAM) [8], which effectively integrates channel attention and spatial attention, they also have certain limitations. CBAM has demonstrated superior performance in various visual tasks and is widely used in deep learning applications. However, traditional attention modules primarily extract global context information through global average pooling (GAP) [45], which can result in the loss of important details. In CBAM, the channel attention mechanism derives the importance weight of each channel through both global average pooling and global maximum pooling. The spatial attention mechanism, on the other hand, determines the importance weight of each spatial position by performing average pooling and maximum pooling across the channel dimensions of the feature map. Despite their efficacy, these methods overlook the connections between channels and spatial dimensions and fail to capture the interdependence between these dimensions, thus ignoring the relationship between the two.  
Figure 2: Comparisons with different attention modules: (**a**) Squeeze excitation (SE) module; (**b**) convolutional block attention module (CBAM); (**c**) Vision Mamba (Vim) module; (**d**) multi-dimensional Vim-like attention module. \(\ominus\) denotes the “nn.ReLU”, \(\otimes\) denotes broadcast element-wise multiplication, and \(\bigoplus\) denotes broadcast element-wise addition.  
Inspired by the construction of multi-dimensional attention and Mamba's linear complexity operations, we propose using the concept of multi-dimensional fusion interaction. By employing three branches to capture dependencies between the (C, H), (C, W), and (H, W) dimensions of the input tensor, and interacting with the left branch akin to Vision Mamba [22], we introduce multi-dimensional interaction in the new attention mechanism. This approach allows us to more comprehensively capture the interrelationship between channels and spatial dimensions, thereby enhancing the overall efficacy of the attention mechanism.
## 2 Materials and Methods
### HPLC Analyses  
The glycyrrhizic acid in the extract samples were determined by using HPLC. HPLC analyses were carried out on an Agilent Model 1100 high performance liquid chromatography (Agilent Co., Ltd., Qbiogene, CA, USA) which consists of an auto sampler, a degasser, a quaternary pump, and a DAD detector. Before analysis, the extracts obtained from UAE method were centrifuged at 12,000 rpm for 10 min to remove the fine suspensions on a MIKRO 22R frozen centrifuge (Hettich, Tutlingen, Germany). Separation was achieved on a Kromasil KR100-5 C\({}_{18}\) column of dimensions 150 mm \(\times\) 4.6 mm. The column was eluted by a mobile phase which consists of a binary mixture of 3% CH\({}_{3}\)CN and CH\({}_{3}\)COOH-H\({}_{2}\)O (41:59, \(v/v\)). The eluent was monitored by a DAD with the displayed wavelength set at 254 nm. The flow rate was constant at 1 mL/min. The total time of the chromatographic separation and the retention time of glycyrrhizic acid were 20 min and 10.5 min, respectively. The extraction yield of glycyrrhizic acid can be calculated using Equation (6).  
\[\text{Extraction yield }\left(\text{mg}/\text{g}\right)=\frac{\text{amount of glycyrrhizic acid }\left(\text{mg}\right)}{\text{sample weight }\left(\text{g}\right)} \tag{6}\]  
where amount of glycyrrhizic acid represents the average mass of three subsequent sample determinations, sample weight represents the average weight from three samples before extraction.
## 2 Methodology
### Ionospheric Correction Evaluation Method
#### 2.2.2 Ionospheric Correction Error Calculation  
The ionosphere measurements for code and phase can be written as [34; 35]:  
\[\left\{\begin{array}{c}PI=P_{1}-P_{2}=\gamma\cdot I_{1}+c\cdot DCB_{r,P_{1}-P _{2}}+c\cdot DCB_{P_{1}-P_{2}}^{*}\\ LI=L_{1}-L_{2}=-\gamma\cdot I_{1}+\lambda_{1}N_{1}-\lambda_{2}N_{2}\end{array}\right. \tag{21}\]  
where \(P_{i}\)_and_\(>L_{i}\) are the raw code and phase observations at the \(i^{th}\) frequency, \(I_{1}\) denote the slant ionospheric delay at the first frequency, \(\gamma=1-f_{1}^{2}/f_{2}^{2}\) is the scale factor and \(f_{i}\) denote the \(i^{th}\) frequency value, \(c\) represents the speed of light, \(DCB_{r,P_{1}-P_{2}}\) and \(DCB_{P_{1}-P_{2}}^{*}\) are the differential code biases (DCBs) at the receiver and satellite, \(\lambda_{i}\) denotes the wavelength of the \(i^{th}\) frequency and \(N_{i}\) represents the corresponding phase ambiguity which has absorbed phase bias \((i=1,\,2)\). Generally, the precision of phase ionosphere measurement is much higher (approximately 2 orders of magnitude) than that of code ionosphere measurement; however, there is an unknown ambiguity in the phase ionosphere measurement. On contrast, the code ionosphere measurement has significantly larger noise and multipath, but it can provide an absolute value of the ionospheric delay after correcting the satellite and receiver DCBs. The ionospheric delays can be extracted from phase ionosphere measurement by adjusting its level based on code ionosphere measurement. This technique to extract ionospheric delay is called \"levelling carrier phase to code\" and is widely adopted by IGS analysis centers for global ionosphere map modeling, e.g., JPL [36], WHU [37], CAS [38]. The code-leveled phase ionosphere measurement can be written as [36; 39; 40]:  
\[\overline{LI}(t_{i})=\frac{\sum_{i=1}^{n}(PI(t_{i})+LI(t_{i}))}{n}-LI(t_{i})= \gamma\cdot I_{1}(t_{i})+c\cdot DCB_{r,P_{1}-P_{2}}+c\cdot DCB_{P_{1}-P_{2}}^{*} \tag{22}\]  
\(LI(t_{i}),\,\,PI(t_{i})\) are the phase and code ionosphere combinations at the epoch time \(t_{i}\)\((i=1,\,\cdots,\,n)\), and \(n\) is the number of epochs in the continuous arc where all phase measurements of this satellite share a common phase ambiguity. Accordingly, the slant ionospheric delay derived from code-leveled phase ionosphere measurement is given by:  
\[I_{1}(t_{i})=\left(\overline{LI}(t_{i})-c\cdot DCB_{r,P_{1}-P_{2}}-c\cdot DCB _{P_{1}-P_{2}}^{*}\right)/\gamma \tag{23}\]  
When there are no P1 observations, C1 observations can be used instead. The satellite and receiver DCBs are obtained from the ionosphere products or monthly DCB products provided by the Center for Orbit Determination in Europe (CODE). In this paper, we use slant ionospheric delays derived from code-leveled phase ionosphere measurements as the references to evaluate the SBAS ionospheric corrections.
## 3 Results and Discussion
### Structural Characterization
Raman spectroscopy was able to overcome the XRD limitation, allowing the precise identification of the TiO\({}_{2}\) phases present [53] (Figure 4b). The pristine Au paper Raman spectrum is presented for comparison, and no contribution was detected. Raman spectra were recorded in the range of 100-700 cm\({}^{-1}\) and confirmed the presence of anatase phase. The Raman bands associated to anatase can be assigned to 155 cm\({}^{-1}\) (\(E_{g}\)), 205 cm\({}^{-1}\) (\(E_{g}\)), 397 cm\({}^{-1}\) (\(B_{lg}\)), 510 cm\({}^{-1}\) (\(B_{lg}\) + \(A_{lg}\)), and 636 cm\({}^{-1}\) (\(E_{g}\)) for anatase [54; 55; 56]. This Raman band shift observed can be associated to the presence of structural defects in the TiO\({}_{2}\) lattice [57] or minor deviations from stoichiometry of the TiO\({}_{2}\) films [58]. Moreover, the smooth aspect of the TiO\({}_{2}\) thin film spectra has been reported before [57; 58], and detected especially for the films synthesized with nitric and hydrochloric acids. Figure 2: SEM images showing the Whatman paper with an Au film and the TiO\({}_{2}\) nanostructured films grown under microwave irradiation. (**a**) Whatman paper with a 140 nm Au film deposited by ebeam to act as contact. (**b**) Synthesis of the TiO\({}_{2}\) nanostructured films using nitric acid, (**c**) hydrochloric acid, and (**d**) acetic acid. The insets demonstrate the TiO\({}_{2}\) film thicknesses. An Au/Pd sacrificial layer has been deposited on the top of the TiO\({}_{2}\) films. Figure 4: **(a)** XRD diffractograms of the TiO\({}_{2}\) nanostructured films grown on Au paper together with the pristine Au paper. For comparison, the simulated XRD diffractograms of rutile, brookite and anatase are presented. **(b)** Raman spectra of the nanostructured films and the pristine Au paper. Dashed lines indicate the anatase bands. Figure 3: SEM images of the TiO\({}_{2}\) nanostructured films produced using nitric acid **(a)**, hydrochloric acid **(f)** and acetic acid **(k)** together with their corresponding energy dispersive spectroscopy (EDS) maps of Ti, **(b,g,I)**, Au (c,h,m)**, C (d,I,n)** and O (e,j,o).
# Low-Photon Counts Coherent Modulation Imaging via Generalized Alternating Projection Algorithm
## 2 Methods
The specific details of implementation are as follows:  
Updating \(x\): assume \(z\) is fixed, Equation (6) can be simplified to  
\[x=\operatorname{argmin}\frac{1}{2}\|y-|Ax|\|_{2}^{2}+\|x-z\|_{2}^{2} \tag{7}\]  
and be solved as  
\[x^{k+1}=z^{k}+A^{T}\Big{(}AA^{T}\Big{)}^{-1}\Big{(}y-Az^{k}\Big{)}. \tag{8}\]  
Updating \(z\): assume \(x\) is fixed and \(g(x)\) the total variation (TV) regulation, Equation (6) can be rewritten as  
\[z=\operatorname{argmin}\|x-z\|_{2}^{2}+\lambda\|z\|_{TV} \tag{9}\]\[z^{k+1}=\text{TV}(x^{k+1}). \tag{10}\]  
The TV regulation term features a good edge-preserving property, and Equation (9) forms a denoiser. The anisotropic TV defined as  
\[P_{near}=As\{M\{As(x\cdot s)\}\}. \tag{2}\]0  
was used in this work, and the accelerated implementation method in [53] was adopted. The symbols \(D_{x}\) and \(D_{y}\) denote the first derivatives along the horizontal and vertical directions, respectively. Figure 1 shows the overall flowchart of the CMI-GAP algorithm. Both numerical simulations and optical experiments with similar parameters were carried out to evaluate the performance of the proposed algorithm. For both cases, the image quality was quantitively evaluated via root-mean-square (RMS) [54],  
\[P_{near}=As\{M\{As(x\cdot s)\}\}. \tag{2}\]1  
where \(\phi\) denotes the reconstructed wavefront, \(\phi\) denotes the ground truth, \(F[\cdot]\) denotes the Fourier transform operator. The peak signal-to-noise ratio (PSNR) was also used.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 2 Improved A* Algorithm
### Improvement of Heuristic Function
#### 2.3.2 Manhattan Distance  
The Manhattan distance allows only horizontal and vertical movement. The calculation speed is faster and has a good performance on the grid map, but it may cause the path to be too long. Its calculation formula is  
\[h(n)=\left|x_{2}-x_{1}\right|+\left|y_{2}-y_{1}\right| \tag{3}\]
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model
### Integrated GPS/Galileo Code and Phase Observation Equations  
In the construction of multi-system double-differenced equations, it is mainly divided into loosely combined mode (LCM) and tightly combined mode (TCM), which depends on whether each system selects a pivot satellite independently or whether all systems share a common pivot satellite [29].
## 2 Proposed Simulation and Experimental Methods
### Model Training and Evaluation Metrics  
The YOLO11 model was trained using the PyTorch framework (version 2.7.1) on an NVIDIA RTX 3090 GPU for 200 epochs, with an initial learning rate of 0.01 and a batch size of 128. The performance was evaluated using the following metrics: (1) precision, recall, and mAP (mean average precision) at IoU thresholds of 0.5 (mAP50) and 0.5-0.95 (mAP50-95); (2) FPS (frames per second) to assess real-time detection capability; and (3) params (number  
Figure 4: Examples of data augmentation applied to simulated thermal images: (**a**) random occlusion with artificial noise blocks simulating partial visual loss; (**b**) random rotation combined with Gaussian noise injection to enhance model robustness to image variability.  
of model parameters) and FLOPs (floating-point operations per second) to evaluate model complexity. These metrics provide a comprehensive assessment of the model's accuracy, speed, and computational efficiency, supporting its application in high-speed industrial defect detection.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 3 Y-Network Using MBLT Algorithm  
In this section, we propose using the MBLT algorithm as a coding technique in the Y-network (Figure 1) in order to enhance the performance. The encoding and decoding processes of the MBLT algorithm are shown in the following subsections.
## 3 Construction of an Evaluation Indicator System for Spare Parts Support Capacity
Service capacity indicator. Service capacity indicators include the spare parts supply time, average number of missing spare parts, and spare parts turnover rate. The supply time of aircraft spare parts refers to the time from when the demand for the spare parts is proposed until the spare parts are available. A short supply time of aircraft spare parts means that spare parts can be timely available and indicates a good spare parts support capacity. The average number of missing spare parts indicates the average number of spare parts lacking by the aircraft within unit time, and a small average number of missing spare parts manifests the sufficient spare parts in stock, which can meet the aircraft maintenance needs and reflects a strong spare parts support capacity. The aircraft spare parts turnover rate means the ratio of the frequency of use of spare parts within unit time to the inventory level, and a higher turnover rate reveals a higher circulation rate of spare parts in stock and a higher frequency of use. This generally means that inventory management is more efficient and spare parts can respond to maintenance needs rapidly, thus reducing the possibility for spare parts to be idled and wasted.
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 2 Analog Predistortion MMIC Topology
### Independently Tunable Linearizer Design for Improved Nonlinear Indices
#### 3.1.3 Simulation for the Independent Tuning of Gain and Phase Expansion  
A layout-level simulation of the APD MMIC was carried out based on the 0.15 \(\mathrm{\SIUnitSymbolMicro m}\) GaAs pHEMT process. The simulation platform integrated all the circuit components needed for the proposed circuit, such as the Schottky diode, capacitor, resistor, etc., and  
Figure 4: Schematic diagram of (**a**) gain expansion and (**b**) phase expansion independently tuning.  
especially the large signal simulation model for the Schottky diode. The Lange coupler and Wilkinson power divider were obtained from the process library. The output amplitude flatness of Lange coupler was less than 0.3 dB, and the return loss and isolation were both less than \(-25\) dB in the bandwidth range of 24-28 GHz. The output amplitude flatness of the Wilkinson power divider was less than 0.3 dB, and the return loss and isolation are both less than \(-24\) dB. A two-finger 20 \(\upmu\)m Schottky diode with a bias resistance of 4 k\(\Omega\) was adopted as nonlinearity generation component of the gain nonlinear branch. When the power supply increased from 0.4 V to 2.0 V, the DC current was less than 1 mA. A four-finger 40 \(\upmu\)m Schottky diode with a bias resistance of 1 k\(\Omega\) was adopted as the nonlinearity generation component of the phase nonlinear branch. When the power supply increased from 2.2 V to 3.8 V, the DC current was less than 3 mA.  
The APD MMIC simulation results for the independent tuning of gain and phase conversion are shown in Figure 5. For a clearer comparison, the gain and phase under large signal excitation are normalized to 0 dB and 0, respectively. When the bias voltage \(V_{\mathrm{p}}\) of the phase branch remained unchanged at 3.4 V, the bias voltage \(V_{\mathrm{g}}\) of the gain branch gradually increased from 0.4 V to 2.0 V. The phase expansion remained steady at 4\({}^{\circ}\), while the gain expansion increased from 3.2 dB to 10.5 dB. The gain expansion change was mainly realized through the decrease in the small-signal gain. When the bias voltage \(V_{\mathrm{g}}\) of gain branch remained unchanged at 1.1 V and the bias voltage \(V_{\mathrm{p}}\) of the phase branch gradually increased from 2.2 V to 3.8 V, the gain expansion remained unchanged at 4.5 dB, while the phase expansion increased from 28.2 to 50.5\({}^{\circ}\). The phase expansion change was mainly realized through the decrease in the small-signal phase. The gain and phase expansion can be independently adjusted by altering the bias voltages \(V_{\mathrm{g}}\) and \(V_{\mathrm{p}}\) of the two nonlinear branches.
## 4 Conclusions
\begin{tabular}{l l} AC & Analysis Center \\ ANTEX & Antenna Exchange format \\ BDS & BeiDou Navigation Satellite System \\ CAO & Cabinet Office \\ CAST & China Academy of Space Technology \\ CODE & Center for Orbit Determination in Europe \\ CLS & Collecte Localisation Satellites \\ CNES & Centre National d'Etudes Spatiales \\ CSNO & China Satellite Navigation Office \\ DD & Double Differenced \\ DOY & Day Of Year \\ ECOM & Extended CODE Orbit Model \\ EGSC & European GNSS Service Centre \\ ESA & European Space Agency \\ FOC & Full Operational Capability \\ GEO & Geostationary Orbit \\ GFZ & GeoForschungsZentrum Potsdam \\ IGS & International GNSS Services \\ IGSO & Inclined GeoSynchronous Orbit \\ ILRS & International Laser Ranging Service \\ IOV & In-Orbit Validation \\ ISB & Inter System Bias \\ JAXA & Japan Aerospace Exploration Agency \\ LRA & Laser Retroreflector Arrays \\ MEO & Medium Earth Orbit \\ MGEX & Multi-GNSS Experiment \\ ON & Orbit Normal \\ PCE & Precise Clock Estimation \\ PCO & Phase Center Offsets \\ PCV & Phase Center Variations \\ POD & Precise Orbit Determination \\ PPP & Precise Point Positioning \\ QZSS & Quasi-Zenith Satellite System \\ SECM & Shanghai Engineering Center for Microsatellites \\ SHAO & Shanghai Observatory \\ \end{tabular}
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
## 5 Independent Source of Localization
1000 bits, an integration factor of 1000 is obtained (considering one measurement for each bit of the preamble), \(k_{m}\) was posed equal to 1.6 (according to [41]), obtaining \(\sigma_{AOA}=0.0077\) degrees or, according to Equation (27), \(\sigma_{AOA}=0.002\) degrees in case of MUSIC angle estimation and a range error standard deviation below one meter. As mentioned above, with respect to angle error, for close distances, the predominant error is due to the glint error, which reaches values of almost up to 0.06 degrees at 100 m from the station, but it becomes small as the distance from the beacon increases. For large distances, the predominant error comes from the limit of the angle measurement noise, which at 20 km reaches 0.0077 degrees. Finally, by imposing a minimum distance from the local sensor of 600 m, the total angle error can be bounded to a maximum of 0.01 degrees for every possible distance (between 600 m and 20 km). Hereafter, for simulation, a constant value of 0.01 degrees will be assumed and an additional margin to take into account other secondary error contributions such as multipath effect on the angle estimation, errors due to digitalization, etc., will be considered. The same approach is used for the measurement error of the range where secondary effects, such as multipath, station position error, and transponder jitters, are taken into account doubling the receiver range measurement error, obtaining \(\sigma_{R}=1.66\) m.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **LSTM** & **LSTM-AM** \\ \hline Accuracy & 94\% & 96\% \\ \hline Recall & 90\% & 94\% \\ \hline Precision & 84\% & 90\% \\ \hline F1-score & 87\% & 92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model evaluation indicators. Figure 15: Histogram of RMSE between predicted and actual waveforms for healthy and faulty samples. system without damages. Of course, the selection of the set of damaged samples has an impact on the results of the model evaluation indicators in Table 2.  
Figure 16: Original and reconstructed time waveform (**a**) LSTM, (**b**) LSTM with Attention Mechanism. To minimize anomaly detection errors, the measuring path should be calibrated regularly. The strain gauge sensors inherently possess general limitations, including restricted directional measurement, environmental sensitivity, susceptibility to overload, nonlinearity,  
Figure 17: Identification of signal anomalies by (**a**) LSTM algorithm, (**b**) LSTM-AM algorithm. surface finish requirements, calibration demands, and potential for interference. A more comprehensive discussion of these issues has been presented in one of the authors' doctoral research papers [31] and further detailed in a separate publication [19]. Therefore, this topic is not the primary focus of the present work. In the current study, these limitations were mitigated through a controlled experimental setup and robust data preprocessing steps. The research was conducted on a \"test stand\" (a conveyor model), which provided a controlled environment, significantly reducing external environmental interference during data acquisition. The described application concentrates on detecting and localizing mechanical damage (cuts, cracks) by analyzing changes in strain, an area where strain gauges prove highly effective. The concern regarding limited directional measurement is addressed by the strategic placement of three strain gauges (T1, T2, T3) on the drum to effectively capture changes in stress distribution. Furthermore, the collected data underwent extensive preprocessing, which included synchronization, denoising, detrending, and normalization. These steps are crucial for alleviating noise, nonlinearity, and environmental influences, ensuring the high quality of data fed into the deep learning model. The presented method allows to locate the damage. With a known tape speed, the distance of the damage area from the beginning of the tape can be determined.