esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Hardware-in-the-loop (HIL) simulations of power converters must achieve a truthful representation in real time with simulation steps on the order of microseconds or tens of nanoseconds. The numerical solution for the differential equations that model the state of the converter can be calculated using the fourth-order Runge-Kutta method, which is notably more accurate than Euler methods. However, when the mathematical error due to the solver is drastically reduced, other sources of error arise. In the case of converters that use deadtimes to control the switches, such as any power converter including half-bridge modules, the inductor current reaching zero during deadtimes generates a model error large enough to offset the advantages of the Runge-Kutta method. A specific model is needed for such events. In this paper, an approximation is proposed, where the time step is divided into two semi-steps. This serves to recover the accuracy of the calculations at the expense of needing a division operation. A fixed-point implementation in VHDL is proposed, reusing a block along several calculation cycles to compute the needed parameters for the Runge-Kutta method. The implementation in a low-cost field-programmable gate arrays (FPGA) (Xilinx Artix-7) achieves an integration time of 1 \(\upmu\)s. The calculation errors are six orders of magnitude smaller for both capacitor voltage and inductor current for the worst case, the one where the current reaches zero during the deadtimes in 78% of the simulated cycles. The accuracy achieved with the proposed fixed point implementation is very close to that of 64-bit floating point and can operate in real time with a resolution of 1 \(\upmu\)s. Therefore, the results show that this approach is suitable for modeling converters based on half-bridge modules by using FPGAs. This solution is intended for easy integration into any HIL system, including commercial HIL systems, showing that its application even with relatively high integration steps (1 \(\upmu\)s) surpasses the results of techniques with even faster integration steps that do not take these events into account.  
hardware-in-the-loop; floating-point; fixed-point; real-time emulation; field programmable gate array +
Footnote †: journal: Applied Sciences
###### Abstract  
The rise of autonomous vehicles necessitates advanced communication networks for effective data exchange. The routing protocols Ad hoc On-Demand Distance Vector (AODV) and Greedy Perimeter Stateless Routing (GPSR) are vital in mobile networks (MANETs) and vehicular ad hoc networks (VANETs). However, their performance is affected by changing network conditions. This study examines key routing parameters--Maxlitter, Hello/Beacon Interval, and route validity time--and their impact on AODV and GPSR performance in urban and highway scenarios. The simulation results reveal that increasing Maxlitter enhances AODV throughput by 12% in cities but decreases it by 8% on highways, while GPSR throughput declines by 15% in cities and 10% on highways. Longer Hello intervals improve AODV performance by 10% in urban settings but reduce it by 6% on highways. Extending route validity time increases GPSR's Packet Delivery Ratio (PDR) by 10% in cities, underscoring the need to optimize routing parameters for enhanced VANET performance.  
routing protocols; AODV; GPSR; bio-inspired algorithms; autonomous vehicles; BACM (bio-inspired adaptive chameleon method)
## 3 Results  
### Wine Metabolite Profile Can Distinguish Vineyards  
The volatile compounds of the resulting Cabernet Sauvignon wines (E stage) were analyzed using HS-SPME-GC-MS to characterize the metabolite profiles of the wines from different vineyards. A total of 70 volatile compounds were identified in these wines, of which 51 exhibited significant differences between at least two vineyards (see Table S2 in the Supplementary Material). Subsequently, \(\alpha\)-diversity and \(\beta\)-diversity were used to elucidate the complexity and regionality (the differences between wines from different regions) of the wines. As indicated by the Shannon index, the \(\alpha\)-diversity of the wine metabolite profiles varied with regional origin (Figure 1a, ANOVA, F = 65.092, \(p\) = 0.001). The Shannon index of the wine metabolite profiles from the JZ vineyard was the highest (1.42 \(\pm\) 0.02), followed by the WZ vineyard (1.41 \(\pm\) 0.02), WW vineyard (1.39 \(\pm\) 0.01), CL vineyard (1.35 \(\pm\) 0.01), and YC vineyard (1.25 \(\pm\) 0.01); among these, the Shannon index of the WZ vineyard did not significantly differ from those of the JZ and WW vineyards (Figure 1a). Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarity revealed that all wine samples from different vineyards were clearly separated; the first two principal coordinates (PC) accounted for 92.69% of the total variation; PMANOVA also confirmed significant differences in the metabolite profiles of wines from different vineyards (R\({}^{2}\) = 0.983, \(p\) = 0.001); among them, the YC samples exhibited shorter distances to the WZ and WW samples, and longer distances to the JZ and CL samples (Figure 1b); geographically, the YC vineyard is closer to the WZ and WW vineyards and farther from the JZ and CL vineyards (Figure S1). Linear Discriminant Analysis Effect Size (LEfSe) analysis further confirmed that this distribution pattern was significantly associated with the metabolite profiles of wines and vineyard locations (Figure 1c). LEFSe analysis revealedthat YC had two characteristic metabolites, namely 3-methyl-1-butanol and ethyl butyrate; WW had two characteristic metabolites, namely ethyl palmitate and 3-methyl-1-pentanol; WZ had seven characteristic metabolites, including phenethyl alcohol, 1-butanol, and isovaleric acid; JZ had ten characteristic metabolites, including isobutanol, ethyl lactate, and isobutyric acid; and CL had three characteristic metabolites, namely ethyl acetate, 1-hexanol, and phenethyl acetate.
## 5 Experimental Results
### ANN
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Theil U Decomposition** & & & & & \\ \hline
**Stock (Avg. Price in US5)** & **ARIMA** & **GLM** & **LM** & **SVM** & **ANN** \\ \hline AAPL (116.29 \$) & 0.0263 & 0.0205 & 0.0217 & 0.0211 & 0.0263 \\ \hline GE (29.23 \$) & 0.0107 & 0.0149 & 0.0102 & 0.0103 & 0.0195 \\ \hline IBM (142.25 \$) & 0.0591 & 0.0270 & 0.0267 & 0.0264 & 0.0504 \\ \hline MSFT (50.81 \$) & 0.0496 & 0.0337 & 0.0316 & 0.0316 & 0.0424 \\ \hline ORCL (37.89 \$) & 0.0146 & 0.0119 & 0.0118 & 0.0117 & 0.0184 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Theil’s _U2_ decomposition results for the different algorithms and stocks. Figure 7: SVM and LR are outperforming all other models, with ARIMA and ANN having significantly worse performance. For reasons of space economy, Table 4 tabulates the DM test between all models for the AAPL stock. The results for the other companies are almost identical to AAPL. We could observe that based on both the \(p\)-values and DM-statistic metrics, LR and SVM can be considered as having almost the same accuracy, while all other pairs of comparisons do not follow this trend, with the small exception of the GLM method.
## 5 Simulation Experiments
### Determination of Weighting Parameters  
By reading the relevant literature, we set three different sets of weighting parameters, namely \(\omega_{1}=0.3\),\(\omega_{2}=0.4\),\(\omega_{3}=0.3\), \(\omega_{1}=0.2\),\(\omega_{2}=0.5\),\(\omega_{3}=0.3\) and \(\omega_{1}=0.35\), \(\omega_{2}=0.3\),\(\omega_{3}=0.35\). By observing the changes in network coverage brought about by different weighting parameters at different numbers of iterations, the analysis of Figure 10 shows that the network coverage with weighting parameters of \(\omega_{1}=0.3\),\(\omega_{2}=0.4\), \(\omega_{3}=0.3\) is higher than that of other two groups, so we determine that the parameters of the multi-objective fitness function are \(\omega_{1}=0.3\),\(\omega_{2}=0.4\),\(\omega_{3}=0.3\), which can effectively improve the network performance.
## 4 Experimental Validation  
In this section, the results of the computational model were compared with the experimental results, and a computational model was developed, verified, and validated. Firstly, the computational model was verified with tests conducted in the swimming pool (a calm water area) of Osaka Metropolitan University, Osaka, Japan. Secondly, we compared the predicted results of the DP computational model with the actual sea test conducted at Nanao Bay, Ishikawa, Japan.
## 4 Data Processing
### Preprocessing  
As a preprocessing step before the ship's magnetization correction, we applied a 4.5 Hz cutoff Butterworth low-pass filter to the original 250 Hz raw data before downsampling it to 10 Hz. Considering the USV's 1-3 knot operating speed, the resulting 0.12 m spatial resolution from 10 Hz sampling was sufficient for resolving geomagnetic anomaly signals in shallow waters of 20-30 m depth. This filtering process eliminates high-frequency noise and enhances the accuracy of subsequent correction procedures to reduce loss of signal quality.  
To integrate data from asynchronous sensors, both IMU (50 Hz) and GPS (10 Hz) datasets were harmonized to a 10 Hz sampling rate through linear interpolation. Temporal synchronization between sensors was achieved by calculating the vessel's azimuth from the magnetometer's horizontal magnetic components and aligning it with GPS-derived headings and IMU yaw data through cross-correlation analysis.
## 4 Problem Definition and Analysis
### Markov Decision Process Modeling
To effectively guide the UAVs' behavior, we design a distance-based reward function that provides continuous feedback during the detection process:  
\[R(d,r_{p})=r_{p}\cdot\left(1-e^{-3\left(1-\frac{d-\sigma_{\text{min}}^{m}}{d_{ \text{max}}^{m}-m_{\text{min}}^{m}}\right)}\right), \tag{16}\]  
where \(r_{p}\) is the preset detection reward value, \(d_{\text{max}}^{m}\) is the maximum cross-range distance of the key protection area (or the diagonal length if the area is square), \(d_{\text{min}}^{m}\) is the minimum distance, and \(d\) is the distance calculated from RSSI using Equation (11). By using this method, we link the successful detection reward for an individual UAV to the distance, creating a dynamic reward mechanism. This guides the UAV to approach the anomalous radiation source for better detection results. The total reward for a corresponding action is then calculated. For the first successful detection and successful three-point localization, additional rewards are assigned to each UAV. * Penalty: Due to the sparsity of rewards caused by the large spatial range, we implement penalties to encourage the red team UAVs to perform mobile detection behaviors. A penalty is applied at each step when a UAV fails to detect the anomalous radiation source. Additionally, a penalty is imposed if the UAV loses track of the detection target in the time step immediately following a successful detection, and when collisions occur between UAVs or with obstacles. MDPs provide a powerful and flexible framework for modeling decision-making problems. In this study, we have applied an MDP-based framework to the challenge of detecting and localizing anomalous radiation sources. Building on this framework, we showcase in later chapters the implementation of highly adaptive reinforcement learning techniques. These techniques allow us to derive optimal strategies in environments that are either unknown or only partially known.
# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 3 The Proposed Method
### Coordinate Attention Mechanism
#### 3.3.1 Coordinate Information Embedding  
For channel attention, global pooling is commonly used to encode spatial information. However, it is difficult to preserve location information during compression. To attain the desired spatial long-range dependency information for more precise location information, global pooling is disintegrated into a one-to-one encoding process. Specifically, for input X, each channel is encoded horizontally and vertically using pooled kernels of dimensions \((H,1)\) and \((1,W)\). Therefore, the output of the \(c^{th}\) channel with a height of H can be expressed as below:  
\[\mathrm{z_{c}^{h}(w)=}\frac{1}{W}\sum_{0<i<W}\mathrm{x_{c}(h,i)} \tag{5}\]  
Correspondingly, for the \(c^{th}\) channel with a width of \(W\), the output can be expressed as follows:  
\[\mathrm{z_{c}^{w}(w)=}\frac{1}{H}\sum_{0<i<H}\mathrm{x_{c}(j,w)} \tag{6}\]  
The above transformations combine features along two different spatial directions, resulting in a couple of direction-sensitive and location-sensitive attention maps that benefit the ability of the network to track down the target object more accurately.
###### Abstract  
Cross-document relation extraction (CodRE) aims to predict the semantic relationships between target entities located in different documents, a critical capability for comprehensive knowledge graph construction and multi-source intelligence analysis. Existing approaches primarily rely on bridge entities to capture interdependencies between target entities across documents. However, these models face two potential limitations: they employ entity-centered context filters that overlook relation-specific information, and they fail to account for varying semantic distances between document paths. To address these challenges, we propose CLEAR (Cross-document Link-Enhanced Attention for Relations), a novel framework integrating three key components: (1) the Relation-aware Context Filter that incorporates relation type descriptions to preserve critical relation-specific evidence; (2) the Path Distance-Weighted Attention mechanism that dynamically adjusts attention weights based on semantic distances between document paths; and (3) a cross-path entity matrix that leverages inner- and inter-path relations to enrich target entity representations. Experimental results on the CodRED benchmark demonstrate that CLEAR outperforms all competitive baselines, achieving state-of-the-art performance, with 68.78% AUC and 68.42% F1 scores, confirming the effectiveness of our framework.  
cross-document relation extraction; entity linking; attention mechanism; path modeling; text compression Article
## 3 Results and Discussion
### Cytotoxicity
The plant extracts, which showed significant activity against the tested cancer cells, were prepared from organic solvent (acetone), and similar results have been observed in other scientific studies where organic solvents were found to possess more antiproliferative activity than aqueous extracts [40; 41]. The type of solvent used for extraction clearly plays a crucial role as it determines the class and polarity of compounds which may be isolated. The extractant used influences the biological activity of _P. obliquuum_ extract, particularly against the normal and cancer cell lines used in the study. A wide variety of secondary metabolites are produced by plants, typically as a coping mechanism against attacks from microbes, insects, viruses, herbivores, and other plants [19]. Due to seasonal shifts and geographic location, a plant's chemical composition can alter over time [42]. Geographical location appears to have affected the antiproliferative activity and perhaps phytochemical composition of _P. obliquuum_. It was interesting to note that SANBI acetone leaf extracts showed some toxicity against normal cell lines tested, while other acetone leaf extracts collected from different geographical locations were all less toxic. It is likely that the plant leaf material collected from SANBI had a higher concentration of toxic compounds compared to plant material collected from other different geographical locations. Moreover, two acetone extracts from Hatfield and Walter Sisulu, which had the best anticancer activity against HepG2 and HeLa cells, were less toxic to the normal cell lines. This further indicates that there was a difference in the phytochemicalal concentrations from this plant species based on the geographical location.
## 4 Discussion
### Challenges Faced by DA in Agricultural Application
#### 4.1.1 Deep Fusion and Uncertainty Management of Multi-Source Heterogeneous Data
This necessitates emphasis on steps such as data cleaning, deduplication, transformation, and validation to eliminate or reduce noise, outliers, and errors in the data [237]. For example, when studying the impact of climate change on crop yields, sensitivity analysis of parameters and uncertainty analysis should follow modeling to ensure decisions are made within controllable ranges, enhancing the accuracy and reliability of the fused results [238; 239]. Nonetheless, the deep integration of multi-source heterogeneous data and uncertainty management in CGMs assimilation also confronts numerous challenges [240; 241; 242]. Some studies strive to optimize data fusion algorithms, theoretically providing higher quality data for assimilation, but many existing methods overlook complementarity and potential conflicts among datasets, potentially leading to failed fusion in practical agricultural contexts [243; 244]. Hence, the development of more sophisticated inter-source relational modeling algorithms, such as those based on ML and DL fusion algorithms (e.g., SW-PanGA super-resolution, MSFF, DB-CNN, etc. ), to deeply reveal and capture the complex interactions among datasets, holds promise for future DA [82; 106; 143; 144; 245; 246]. Meanwhile, other studies focus on quantifying and managing uncertainties in both CGMs and the data fusion process, employing methods such as Bayesian Model-Data Fusion (MDF) and Semantic Segmentation Networks [247; 248; 249]. Furthermore, building an end-to-end  
Figure 11: Variation and comparison of spatial, temporal, and spectral resolution of different satellite images [70]. uncertainty model based on systems engineering represents another major focal point for future DA research.
## 3 Results
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Length** & **Model** & **PRE (\%)** & **REC (\%)** & **SPE (\%)** & **F1 (\%)** & **ACC (\%)** \\ \hline \multirow{4}{*}{6 s} & 1 BCNN-BLSTM & 67.34 & 67.72 & 83.59 & 66.84 & 74.89 \\  & MCNN-ULSTM & 77.23 & 81.81 & 89.68 & 79.10 & 82.60 \\  & MCNN-BLSTM & 76.81 & 80.99 & 89.81 & 78.94 & 82.96 \\ \hline \multirow{4}{*}{10 s} & 1 BCNN-BLSTM & 71.45 & 73.49 & 85.77 & 71.84 & 77.80 \\  & MCNN-ULSTM & 79.20 & 81.17 & 89.49 & 80.10 & 82.96 \\  & MCNN-BLSTM & 85.24 & 80.80 & 89.94 & 82.57 & 85.99 \\ \hline \multirow{4}{*}{15 s} & 1 BCNN-BLSTM & 78.92 & 76.59 & 87.71 & 77.61 & 81.50 \\  & MCNN-ULSTM & 80.38 & 82.74 & 90.84 & 81.45 & 84.78 \\  & MCNN-BLSTM & 82.93 & 80.18 & 91.21 & 81.41 & 85.63 \\ \hline \multirow{4}{*}{20 s} & 1 BCNN-BLSTM & 74.90 & 81.71 & 89.23 & 76.45 & 81.69 \\  & MCNN-ULSTM & 79.74 & 84.12 & 91.11 & 80.92 & 85.39 \\  & MCNN-BLSTM & 80.37 & 84.44 & 91.84 & 82.17 & 85.63 \\ \hline \multirow{4}{*}{25 s} & 1 BCNN-BLSTM & 75.67 & 81.84 & 90.22 & 77.63 & 82.84 \\  & MCNN-ULSTM & 81.89 & 85.63 & 91.84 & 83.59 & 86.17 \\  & MCNN-BLSTM & 85.38 & 83.79 & 92.01 & 84.56 & 87.57 \\ \hline \multirow{4}{*}{30 s} & 1 BCNN-BLSTM & 74.40 & 82.18 & 90.84 & 76.95 & 82.47 \\  & MCNN-ULSTM & 79.69 & 85.10 & 91.51 & 81.51 & 85.57 \\ \cline{1-1}  & MCNN-BLSTM & 84.27 & 84.20 & 92.03 & 84.15 & 87.51 \\ \hline \multirow{4}{*}{61 s} & 1 BCNN-BLSTM & 83.22 & 82.11 & 90.53 & 82.08 & 86.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance using MCNN-BLSTM on the PhysioNet/CinC Challenge 2017. Figure 2: PR curves of the three models under the six input designs.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 3 System Description  
The main goal of the system is to get the widest possible frequency range as possible and the best time accuracy for the transmission. This involves both the hardware design of the sounders  
Figure 3: (**a**) The wooden discs support the four Helmholtz coils in a specific distance from the proton magnetometer in the center; (**b**) A specialist manipulating the fluxgate theodolite in the ASJI; (**c**) In the Figure we can distinguish two of the three adjustable levels holding the fluxgate magnetometer in a flat position. More details on the fluxgate sensor can be found in [15].  
and the data transmission system of the OIS. The initial system was deployed on the first survey (in 2003-04 campaign) [6] but 6 years later (in 2009-10 campaign), an upgraded, faster and more reliable system was placed in Livingston Island to increase the range of the sounding intervals [18]. In such way, a higher bandwidth up to 40 kHz and a sampling frequency of 100 ksps is reached. In addition, concerning the soundings, this system is more flexible in terms of frequency and bandwidth selection.  
The following subsections describe the hardware of the transmitter and the receiver.
## 2 Methods
### Bessel Curve Optimization  
The ultimate route derived from the aforementioned algorithm is still a broken line composed of nodes, which does not conform to the kinematic law. Ignoring the curvature of the path will lead to a large tracking error when tracking the path, causing system oscillation, and even affecting the stability of the robot system. Therefore, we need to further smooth the planned path. Polynomial curves, B-spline curves, and Bessel curves are commonly used path-smoothing methods.  
Because the Bessel curve has the advantages of simple control, strong description ability, and that it is easy to generate complex smooth curves, this document employs the Bessel curve technique for path smoothing. The expression of Bessel curve can be defined as follows:  
\[q(\tau_{i})=\sum_{k=0}^{m}C_{m}^{k}\tau_{i}^{k}(1-\tau_{i})^{m-k}P_{k},\tau_{ i}\in[0,1] \tag{4}\]  
where \(m\) presents the order of the Bessel curve, \(q(\tau_{i})\) is the interpolation point at the parameter \(\tau_{i}\), \(C_{m}^{k}\) is the coefficient of binomial theorem, and \(P_{k}\) is the kth control point.  
Through the value of parameter \(\tau_{i}\) between [0,1], multiple interpolation points can be generated between the first control point \(P_{0}\) and the last control point \(P_{m}\). When \(\tau_{i}\) = 0, \(q(\tau_{i})\) = \(P_{0}\), and the starting point coincides with \(P_{0}\); when \(\tau_{i}\) = 1, \(q(\tau_{i})\) = \(P_{m}\), and the endpoint coincides with \(P_{m}\); when 0\(<\)\(\tau_{i}\)\(<\)1, the curve point is the fitting line. In this paper, the third-order Bessel curve is used for smoothing. At this time, \(m\) = 3, and its expression is as follows:  
\[q(\tau_{i})=(1-\tau_{i})^{3}P_{0}+3\tau_{i}(1-\tau_{i})^{2}P_{1}+3\tau_{i}^{2}( 1-\tau_{i})P_{2}+\tau_{i}^{3}P_{3} \tag{5}\]  
where \(P_{0}\), \(P_{1}\), \(P_{2}\), and \(P_{3}\) denote four consecutive control nodes on the path to be fitted.  
The third-order Bessel curve is determined by these four control nodes. The starting point of the fitting path is the first control node \(P_{0}\) of the third-order Bessel curve, and the end point of the fitting path is the last control node \(P_{3}\) of the third-order Bessel curve. The fitting result is shown in Figure 4. To prevent the smoothed path from colliding with the obstacle, only the smooth processing is performed near the inflection point of the path.  
The Bessel curve mainly determines the shape of the curve by the position of the control point. By adjusting the position and number of control points, the radian and shape of the curve can be changed. The path without collision with obstacles is selected as the optimal path, and the following two points are the algorithm judgment logic:  
* The generated path has no collision after obstacle collision detection.
* If there is more than one path that meets the previous condition, the path with the shortest length is selected.