esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Finite Element Analysis of Stump-liner Contact Interface  
Interface pressures, shear stresses, and slip/sliding distances at the stump-liner interface were analyzed from the developed FE model. Interface pressures and slip are critical parameters for evaluating the coupling and load distribution in lower limb prostheses, while shear stress contributes to fatigue in the soft tissues of the residual limb [47].  
Figure 5 illustrates the pressure distribution for both loading phases. The stump end exhibited significantly high-pressure levels, attributed to the lack of relief in that area. The maximum pressure was observed in the region of the stump where slipping occurred within the model. Under FBW and HBW conditions, the maximum pressures at the stump end were 72 kPa and 34 kPa, respectively. Conversely, the distal portion of the anterior side of the stump exhibited the lowest pressures at the stump-liner interface, with values of 33 kPa and 16 kPa recorded during FBW and HBW, respectively.  
Figure 6 illustrates the resultant shear stress distribution at the stump-liner interface. The resultant shear stress combines the circumferential and longitudinal shear stress components at the contact interface. The highest shear stress under both loading conditions was observed in the lateral region near the fibula head. The patellar tendon region (anterior side) and the stump end exhibited shear stress values of 8 kPa and 7 kPa, respectively.  
Figure 4: (**a**) Amputated limb and (**b**) Sensors position.  
Figure 5: Anterior and lateral view of pressure distribution map at stump–liner interface (**a**) FBW and (**b**) HBW loadings.  
Figure 7 depicts the pressure and shear stress distributions at the stump-liner interface under two loading conditions: FBW and HBW. For FBW, pressure values range from 33 kPa to 72 kPa, with the majority of the interface pressures falling between 33 kPa and 52 kPa, except at the stump end, where peak pressure was observed. Under HBW, most interface pressures range between 16 kPa and 27 kPa.  
Figure 6: Anterior and lateral view of shear stress distribution at stump–liner interface (**a**) FBW and (**b**) HBW.  
Figure 7: (**a**) Pressure and (**b**) the shear stresses at different regions of the stump–liner interface.  
Shear stresses for FBW and HBW range from 2 to 11 kPa and 1 to 5 kPa, respectively. In both loading phases, shear stresses were more pronounced in the anterior, lateral, and bottom regions of the stump.  
The third parameter used to evaluate the proposed FE model was the frictional slip or sliding distance in the non-linear FE analysis. A non-uniform and localized slip is always present between the skin and liner in the stump-socket assembly. The maximum sliding distance of 0.68 mm was observed on the lateral side, just below the fibula head region, while the average slip across the entire model was 0.48 mm.
## 2 Theoretical Background
### Computer Vision and Education
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Element** & & **Description and Related Research** \\ \hline Stage 1 & Data Collection & \(\bullet\) & Gathering necessary data for a project from various sources, including structured data (e.g., databases) and unstructured data (e.g., text, images). \\  & & \(\bullet\) & The importance of data collection is emphasized in many studies, as data quality significantly impacts analysis results [12]. \\ \hline Stage 2 & Data Preprocessing & \(\bullet\) & Transforming collected data into a suitable format for analysis, including data cleaning, handling missing values, and data transformation. \\  & & \(\bullet\) & Data preprocessing is essential to ensure data accuracy and consistency, highlighted in various research [13]. \\ \hline Stage 3 & Data Exploration & \(\bullet\) & Visualizing and analyzing data to discover meaningful patterns and insights. It is crucial for understanding the characteristics of the data. \\  & & \(\bullet\) & Data exploration is foundational in data analysis for understanding data structure and distribution [14]. \\ \hline Stage 4 & Modeling & \(\bullet\) & Building predictive models based on data using machine learning algorithms such as regression, classification, and clustering. \\  & & \(\bullet\) & Modeling is the core of data science projects, where predictive models are constructed using various machine learning techniques [15]. \\ \hline Stage 5 & Model Evaluation & \(\bullet\) & Assessing the performance of the built model and validating its accuracy. It is important for confirming and improving the model’s validity. \\  & & \(\bullet\) & Model evaluation is critical for measuring model performance and identifying areas for improvement [16]. \\ \hline Stage 6 & Model Deployment & \(\bullet\) & Implementing the final model in a real-world environment to perform real-time predictions. It is the last step to make the model usable in practice. \\  & & \(\bullet\) & Model deployment prepares the model for use in practical settings, emphasized in various studies [17]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stages of data science process. education, aiming to help learners progressively acquire knowledge from basic concepts to advanced applications. Computer vision education is generally divided into four main stages: image recognition, object detection, image segmentation, and video analysis [24]. In each stage, learners engage in hands-on activities to directly apply technologies and learn problem-solving methods. By integrating data science and computational thinking into this process, learners not only use technology but also develop logical problem-solving skills based on data. For instance, in the image recognition stage, learners can start with simple exercises using no-code platforms and gradually advance to higher-level technologies using programming languages like Python [25; 26].
# Antinociceptive Effect of an Aqueous Extract and Essential Oil from _Baccharis heterophylla_
## 2 Results and Discussion
### Chemical Constituents of the Aqueous Extract  
Chemical investigation of the aqueous extract afforded eight known compounds (Figure 2), identified as chlorogenic acid (**1**) [16], 3,4-di-O-(_E_)-caffeoylquinic acid (**2**) [17], 3,5-di-O-(_E_)-caffeoylquinic acid (**3**) [16; 17; 18], 4,5-di-O-(_E_)-caffeoylquinic acid (**4**) [16; 17; 18], 3,5-di-O-(_E_)-caffeoylquinic acid methyl ester (**5**) [17], apigenin (**6**) [19], genkwanin (**7**) [17], and acacetin (**8**) [20]. The structures of the known compounds were identified by comparing their spectroscopic and spectrometric data with those previously reported or by comparison with authentic samples (Tables S1 and S2, Supplementary Materials). Compounds **1-5** and **8** are reported for the first time in this species.  
Figure 1: Antincociceptive effect of an aqueous extract (31.6\(-\)316 mg/kg) and an essential oil (30\(-\)177 \(\upmu\)g/paw) from _B. heterophylla_ in the formalin test in mice. (**A**) AUC from the time course curve of phase 1 and AUC from the time course curve of phase 2. VEH: vehicle (0.9% saline solution). DIC (50 mg/kg, p.o.). (**B**) AUC from the time course curve of phase 1 and AUC from the time course curve of phase 2. VEH: vehicle (0.9% saline solution). DIC: (100 \(\upmu\)g/paw, i.p.l). Each measurement represents the mean \(\pm\) SEM 6 mice per group. Significantly different from VEH group (* \(p\) < 0.05, **_p_ < 0.01, ***_p_ < 0.001) determined by ANOVA followed by [PERSON]’s post hoc test.  
As with other plant's traditional preparations [21; 22; 23; 24; 25; 26; 27; 28; 29], the antinociceptive effects of AE could be related to the high content of chlorogenic acid (**1**), di-O-(_E_)-caffeoylquinic acids (**2**-**4**), apigenin (**6**) and acacetin (**8**). Thus, in vivo studies demonstrated that compounds 3,5-di-O-(_E_)-caffeoylquinic acid (**3**) and 4,5-di-O-(_E_)-caffeoylquinic acid (**4**) showed antinociceptive action in the acetic acid-induced writhing model and the hot plate assay in mouse [21; 22]. Compounds **1**, **3**, and **4** significantly inhibited carrageenan-induced rat paw edema [23; 24]. In vitro, compound **4** inhibited hypoxia-induced cyclooxygenase-2 (COX-2), expression and cell migration via a TRPV1-mediated pathway [25]; pretreatment of RAW264.7 macrophage cells with a mixture of di-caffeoyl quinic acids or pure **3** suppressed the production of NO, PGE2, and pro-inflammatory cytokines (TNF-\(\alpha\), IL-1\(\beta\), and IL-6) by inhibiting the NF-\(\kappa\)B and MAPKs pathways ([26; 27; 28] inter alia). On the other hand, flavonoids **6** and **8** showed antinociceptive effects in different models [29].
## 3 Results and Discussion
### The Influence of Power Frequency on NOx Removal Efficiency
When the discharge frequency exceeds 8 kHz, as the frequency increases, a larger discharge power and energy density are required to obtain the same NOx removal efficiency, which will cause the temperature of the discharge area to rise. The main chemical reactions for NOx removal in the DBD reactor are shown in Formulas (3)-(10), where the reaction rate of Reaction (9) is much smaller than that of Reaction (10). The increase in power frequency will increase the collision rate of active particles with NO, thereby accelerating the reaction rate of Reactions (7) and (9). However, the Reaction (3) is an exothermic reaction and the rise of temperature in the discharge region will shift the balance of the reversible reaction to the left [22], so that there is not enough O\({}_{3}\) for Reaction (10). The discharge frequency is not the only factor that affects the NOx removal efficiency. In this case, as the power frequency increases, more energy is consumed on the high-voltage electrode of the DBD. As a result, the reactor temperature is increased and Reaction (3) accelerates to the left, so that the reaction rate of Reaction (10) as well as the NOx removal efficiency are reduced [23]. Therefore, when the discharge frequency is greater than 8 kHz, the NOx removal efficiency is reduced as the discharge frequency increases. Since the maximum regulated voltage of the plasma power supply is 250 V, the reaction is ended with a NOx removal efficiency of 34.19% when the energy density reaches 2.313 kJ/L at the power frequency of 10 kHz. Overall, under the same energy density, the NOx removal effect is the best at the power supply frequency of 8 kHz. In particular, when the energy density is 3.105 kJ/L, the NOx removal efficiency reaches a maximum of 85.24%. \[3\mathcal{O}_{2}\rightleftharpoons 2\mathcal{O}_{3} \tag{3}\] \[e+N_{2}\to e+\bullet\bullet N+\bullet\bullet N\] (4) \[e+O_{2}\to e+\bullet\bullet\mathcal{O}+\bullet\mathcal{O}\] (5) \[\bullet N+\bullet\mathcal{O}\to N\mathcal{O} \tag{6}\]  
Figure 5: The influence of discharge frequency on NOx removal efficiency at different SEDs: (**a**) NO removal efficiency; (**b**) NOx removal efficiency. \[\bullet N+NO\to N_{2}+\bullet O \tag{7}\] Figure 4:0 (8) Figure 4:1 (9) Figure 4:2
# Silicon Integrated Nanophotonic Devices for On-Chip Multi-Mode Interconnets
###### Abstract  
Mode-division multiplexing (MDM) technology has drawn tremendous attention for its ability to expand the link capacity within a single-wavelength carrier, paving the way for large-scale on-chip data communications. In the MDM system, the signals are carried by a series of higher-order modes in a multi-mode bus waveguide. Hence, it is essential to develop on-chip mode-handling devices. Silicon-on-insulator (SOI) has been considered as a promising platform to realize MDM since it provides an ultra-high-index contrast and mature fabrication processes. In this paper, we review the recent progresses on silicon integrated nanophotonic devices for MDM applications. We firstly discuss the working principles and device configurations of mode (de)multiplexers. In the second section, we summarize the multi-mode routing devices, including multi-mode bends, multi-mode crossings and multi-mode splitters. The inverse-designed multi-mode devices are then discussed in the third section. We also provide a discussion about the emerging reconfigurable MDM devices in the fourth section. Finally, we offer our outlook of the development prospects for on-chip multi-mode photonics.  
integrated photonics; silicon photonics; mode-division multiplexing +
Footnote †: journal: Applied Physics Letters
###### Abstract  
The tropospheric delay is one of the main error sources that degrades the accuracy of Global Navigation Satellite Systems (GNSS) Single Point Positioning (SPP). Although an empirical model is usually applied for correction and thereby to improve the positioning accuracy, the residual tropospheric delay is still drowned in measurement noise, and cannot be further compensated by parameter estimation. How much this type of residual error would sway the SPP positioning solutions on a global scale are still unclear. In this paper, the biases on SPP solutions introduced by the residual tropospheric delay when using nine conventionally Zenith Tropospheric Delay (ZTD) models are analyzed and discussed, including Saastamoinen+norm/Global Pressure and Temperature (GPT)/GPT2/GPT2w/GPT3, University of New Brunswick (UNB)3/UNB3m, European Geostationary Navigation Overlay System (EGNOS) and Vienna Mapping Functions (VMF)3 models. The accuracies of the nine ZTD models, as well as the SPP biases caused by the residual ZTD (dZTD) after model correction are evaluated using International GNSS Service (IGS)-ZTD products from around 400 globally distributed monitoring stations. The seasonal, latitudinal, and latitudinal discrepancies are analyzed respectively. The results show that the SPP solution biases caused by the dZTD mainly occur on the vertical direction, nearly to decimeter level, and significant discrepancies are observed among different models at different geographical locations. This study provides references for the refinement and applications of the nine ZTD models for SPP users.  
residual tropospheric delay; model corrections; SPP solution; vertical influence 2021 13 1202[[https://doi.org/10.3390/rs13061202131](https://doi.org/10.3390/rs13061202131)]([https://doi.org/10.3390/rs13061202131](https://doi.org/10.3390/rs13061202131)) 1
## Appendix A Radiative and Convective Contributions to Membrane Heat Loss  
The radiative contribution to the heat transfer coefficient is \(\alpha_{r}=\varepsilon\sigma_{t}\big{(}T^{4}-T_{t}^{4}\big{)}/(T-T_{t})\), where the Stefan-Boltzmann constant \(\sigma_{r}=5.67\cdot 10^{-8}\) W/(m\({}^{2}\)K\({}^{4}\)) [33] and the emissivity of the silicon nitride membrane \(\varepsilon\leq 0.2\) [36; 37]. Therefore, \(\alpha_{r}=16\) W/(m\({}^{2}\)K) at \(T=1000\) K and \(T_{t}=300\) K. Thus, \(\alpha_{r}/\alpha\) is about 0.01% at \(a=\lambda/r_{0}\), which is about \(1.56\cdot 10^{5}\) W/(m\({}^{2}\)K) for helium gas \(\lambda=0.156\) W/m\({}^{2}\)K) and \(r_{0}=1\) um. Then \(\alpha_{r}\) is negligible compared to \(\alpha\).  
The convective component \(\alpha_{C}\) of the heat losses from the membrane decreases with a decrease in the characteristic length of the problem [21; 22; 35]. Convective heat losses arise due to the temperature dependence of the gas density in the gravitational field in the presence of temperature gradients. The ratio \(\alpha_{C}/\alpha\) is equal to the Nusselt number \(Nu=(Gr)^{1/4}f(Pr)\)[38]. The Prandtl number \(Pr\) and \(f(Pr)\). for gases are about 1 [33; 38]. The Grashof number \(Gr=\frac{r_{0}^{2}}{v^{2}}\frac{(T-T_{t})}{T}\)[38], where \(g\) is the acceleration due to gravity. Thus, the Nusselt number \(Nu\) decreases as \(r_{0}^{3/4}\) with decreasing \(r_{0}\). In fact, \((Gr)^{1/4}\) is about or less than \(\left(r_{0}^{3}g/v^{2}\right)^{0.25}=5\cdot 10^{-3}\) at \(r_{0}=\)1 um and \(v=1.24\cdot 10^{-4}\) m\({}^{2}\)/s [33], that is, \(Nu<0.005\). Convective heat loss only acts on the upper side of the membrane. However, heat losses due to thermal conductivity \(\lambda\) of the surrounding gas act on both sides of the membrane. Thus, the convective contribution to the membrane heat loss \(Nu/2\alpha\) is about 0.25% for \(r_{0}=1\) um.
## 4 Experiments Analysis and Discussion
### Comparison with Other Frontier Methods
Compared with conventional individual MLP-based and transformer-based models, TSNN-ESTD achieves relatively lower forecast error in the metric of mean squared error (MSE) and mean absolute error (MAE) in most situations with the long-term bias mitigating trick. Furthermore, TSNN-ESTD additionally introduces the encoder-decoder forecaster on the basis of the encoder-only iTransformer model. The mean squared error has been reduced by 10.14%, 46.51%, and 25.86% on dataset ECL, Exchange, and Weather datasets, respectively, where the relative error is calculated as the quotient of the reduced error and the original error. However, the traffic data does not have such a strong periodic law, so the short-term deviation prediction correction effect designed by TSNN-ESTD is not so significant. Meanwhile, TSNN-ESTD reduces the MSE and MAE by 12.4% and 5.4%, respectively, on the four composite temperature datasets of ETTm1, ETTm2, ETTh1, and ETTh2. In summary, there was a reduction in MSE and MAE by 9.17% and 2.3% on the five benchmark datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ETTm1**} & \multicolumn{2}{c}{**ETTm2**} & \multicolumn{2}{c}{**ETTh1**} & \multicolumn{2}{c}{**ETTh2**} & \multicolumn{2}{c}{**Average**} \\ \hline  & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline iTransformer [28] & 0.334 & 0.368 & 0.180 & 0.264 & 0.386 & 0.405 & 0.297 & 0.349 & 0.299 & 0.347 \\ Rlinear [18] & 0.355 & 0.376 & 0.182 & 0.265 & 0.386 & 0.395 & 0.288 & 0.338 & 0.303 & 0.344 \\ Dlinear [19] & 0.345 & 0.372 & 0.193 & 0.292 & 0.386 & 0.400 & 0.333 & 0.387 & 0.314 & 0.363 \\ PatchTST [29] & 0.329 & 0.367 & 0.175 & 0.259 & 0.414 & 0.419 & 0.302 & 0.348 & 0.305 & 0.348 \\ Crossformer [30] & 0.404 & 0.426 & 0.287 & 0.366 & 0.423 & 0.448 & 0.745 & 0.584 & 0.465 & 0.456 \\ TiDE [31] & 0.364 & 0.387 & 0.207 & 0.305 & 0.479 & 0.464 & 0.400 & 0.440 & 0.363 & 0.399 \\ TimesNet [32] & 0.338 & 0.375 & 0.187 & 0.267 & 0.384 & 0.402 & 0.340 & 0.374 & 0.312 & 0.355 \\ SCINet [33] & 0.418 & 0.438 & 0.286 & 0.377 & 0.654 & 0.599 & 0.707 & 0.621 & 0.516 & 0.509 \\ FEDformer [34] & 0.379 & 0.419 & 0.203 & 0.287 & 0.376 & 0.419 & 0.358 & 0.397 & 0.329 & 0.381 \\ Stationary [35] & 0.386 & 0.398 & 0.192 & 0.274 & 0.513 & 0.491 & 0.476 & 0.458 & 0.392 & 0.405 \\ Autoformer [36] & 0.505 & 0.475 & 0.255 & 0.339 & 0.449 & 0.459 & 0.346 & 0.388 & 0.389 & 0.415 \\ \hline TSNN-ESTD & 0.305 & 0.360 & 0.221 & 0.307 & 0.380 & 0.395 & 0.144 & 0.251 & 0.262 & 0.328 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance on one composite dataset.
## 3 Results
### Characterization of the PLG-GRAPE Conjugates
#### 3.1.3 In Vitro Diffusion Studies by Vertical Franz Cells  
The results were presented as the cumulative percentage of the diffused amount for the tested samples, with the corresponding diffusion profiles illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** &
\begin{tabular}{c} **Total Polyphenol Content** \\ **(mg eq CA/g)** \\ \end{tabular} & **Dpph Inhibition (\%)** \\ \hline Grape pomace extract & 18.00 \(\pm\) 0.6 & 65.0 \(\pm\) 0.8 \\ Blank & 0.05 \(\pm\) 0.4 & 0.2 \(\pm\) 0.9 \\ PLG–GRAPE 2.5\% & 17.91 \(\pm\) 1.0 & 64.1 \(\pm\) 0.6 \\ PLG–GRAPE 5\% & 17.30 \(\pm\) 0.7 & 64.9 \(\pm\) 0.4 \\ PLG–GRAPE 10\% & 18.11 \(\pm\) 0.5 & 63.7 \(\pm\) 0.7 \\ PLG–GRAPE 20\% & 17.90 \(\pm\) 1.1 & 65.2 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total polyphenol content expressed as mg eq CA/g of the liquid extract obtained from the Navigio extraction procedure and DPPH inhibition expressed as a percentage (mean value \(\pm\) standard deviation, based on three replicates).  
Figure 1: In vitro diffusion profiles. Results are expressed as mean \(\pm\) standard deviation, based on three replicates.  
The results demonstrate that the grape pomace extract exhibits a rapid rate of diffusion, reaching 50% diffusion within 2 h, 80% at 4 h, and 100% at 6 h. In contrast, polymer conjugates demonstrate a slower and more controlled diffusion rate. PLG-GRAPE 2.5% reached 38% at 2 h, gradually increasing to 66% at 6 h, and finally reaching 100% after 24 h. PLG-GRAPE 5% exhibited a comparable trend, reaching 33% diffusion at 2 h, 54% at 6 h, and 100% at 24 h. Conversely, PLG-GRAPE 10% and 20% exhibited the slowest initial diffusion, reaching 32% and 28% diffusion at 8 h, respectively. At 8 h, PLG-GRAPE 10% reached 53%, while PLG-GRAPE 20% reached 50%. Both samples finally reached full diffusion at 24 h.  
These data demonstrate that polymer conjugates facilitate sustained and gradual release, in contrast to the rapid diffusion observed for the extract.
# Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
## 3 Methodology
### Hierarchical Prompting Module  
The questions in structured medical VQA are typically hierarchical, such as those in the Rad-restruct dataset, as shown in Figure 3. In structured medical visual question answering, questions are often hierarchical. The question hierarchy, as illustrated in the figure, consists of three levels. The highest level asks about general findings, such as the presence of signs, diseases, abnormal regions, or objects. The second level focuses on specific elements, such as a particular object or disease. The lowest level asks about specific attributes. Current models often struggle to differentiate the fine-grained visual details required by the questions due to the lack of clear hierarchical reasoning guidance. Our approach addresses this by using a hierarchical prompt module to guide the model's attention to different fine-grained image regions. One point that needs clarification is that in the Rad-Restruct dataset we used, each patient sample corresponds to a medical image, and each image is linked to a pre-filled structured report template. In the hierarchical prompt module, we employ the Deepseek API ([[https://platform.deepseek.com/api_keys](https://platform.deepseek.com/api_keys)]([https://platform.deepseek.com/api_keys](https://platform.deepseek.com/api_keys)), accessed on 8 January 2025) to input different prompt words for each level, summarizing these structured report templates. The diagram illustrates this process.  
As shown in Figure 4, for low-granularity tasks, we provide the prompt: \"Summarize the low-granularity findings, directing the model's attention to the overall image\". This corresponds to Level 1 questions. For mid-granularity tasks, we give the prompt: \"Summarize the mid-granularity findings, focusing on disease elements\". This corresponds to Level 2 questions. For high-granularity tasks, we provide the prompt: \"Summarize the high-granularity findings, guiding the model's focus on specific organ lesions and other detailed aspects\". Deepseek outputs disease findings at three levels, each with its own focus. These three levels of disease findings are then passed into a text encoder for encoding, followed by subsequent feature alignment and feature fusion modules.  
Figure 3: Hierarchical questions overview: The questions are organized into three levels, representing a stepwise refinement of inquiries regarding the patient’s medical imaging condition. The first two levels employ binary “Yes” or “No” response candidates, while the final level contains multiple-choice candidates primarily describing pathological attributes.