esa-sceva/satcom-chunk-collection huggingface dataset:
# In-flight Wind Field Identification and Prediction of Parafoil Systems
## 5 Simulation and Analysis
### Simulation of Wind Field Prediction  
Substituting the identified wind field at a specific attitude into the atmospheric wind field prediction model, the lower average wind field can be predicted to provide a reference for real-time flight path optimization of parafoil systems. Since the meteorological data of the average wind fields at different altitudes are difficult to obtain, in order to facilitate the comparative analysis, this paper selects the measured instantaneous wind velocity of 36.0994\({}^{\circ}\)N, 114.9716\({}^{\circ}\)E, 12:00, 2 December 2013 (GMT+8) provided by Tianjin Weather Service [32]. The actual measurement wind velocity components are (u = 2.8361 m/s, v = \(-\)2.1750 m/s) at the altitude of 1744 m in the 14 th layer. We use it instead of the average wind to calculate the geostrophic wind, and get \(u_{g}\) = 2.6581 m/s. Substitute the results into the wind field prediction model, the average wind field at an altitude from 400 m to 1600 m is predicted, the results are as shown in Figure 9.  
The comparison results of the predicted wind speed and the actual wind speed are shown in Figure 10. It is observed that the maximum \(u\)-direction wind speed deviation is 0.7 m/s, while the maximum \(v\)-direction wind speed deviation is 0.3 m/s. We can conclude that the prediction results can reasonably track the actual wind field despite certain deviations. However, the deviation is not  
Figure 8: Wind field identification results. (**a**) Wind speed. (**b**) Wind direction.  
the exact prediction error, and the actual prediction error is smaller than the deviation. The main reason causing this is that first, the model predicts the average wind field, while the actual wind is the instantaneous wind field data at a specific moment, which is unlikely to be completely consistent. Second, in the atmospheric wind field modelling process, some parameters are idealized. We believe that the prediction results are sufficient to provide adequate wind field information for parafoil systems.  
Figure 10: Comparison results of actual wind and predicted wind.  
Figure 9: Average wind speed prediction results.
## 2 Mission Concept
### Mission Concept of Operations  
This section presents a detailed concept of operation (ConOps) during the mission operation of Light-1. The satellite operates in three main modes, which are initial, normal, and safe mode. As illustrated in Figure 1, before the CubeSat ejection from the PPOD, all components will be switched off. After the ejection, Light-1, by default, it will enter initial mode, in which it will perform a health check of all the components. In case of any abnormal activity in any of the components during the initial mode, Light-1 will automatically switch to safe mode, otherwise, initial mode operations will be conducted sequentially. After executing initial mode successfully, normal mode will be activated through a command from the ground station. In normal mode, all regular operations such as payload data collection, attitude pointing, payload data transmission, and housekeeping data transmission will begin. If any negative feedback occurs during the normal operation, Light-1 will switch to safe mode automatically. If any abnormalities are visible in the received telemetry data, Light-1 will be switched to safe mode after receiving a command from the ground station. While in safe mode, depending on the present fault, a unique failure detection isolation recovery (FDIR) plan will be executed. The transition from safe mode to normal mode can be either through an automated process or a command from the ground station.
## 1 Introduction
L-band radars due to the high levels of transmitted power, which may interfere in an area as large as \(80\text{ km}\times 700\text{ km}\) (cross-track \(\times\) along-track), for an error level of \(e_{\mu}\!\geq\!10^{-2}\) (see Figure 4), and
2. non-GSO MSS up-link transmitters (due to the low spurious rejection and their proximity to the 1400-1427 MHz band), which may interfere in an area of \(50\text{ km}\times 92\text{ km}\) (cross-track \(\times\) along-track), for an error level of \(e_{\mu}\!\geq\!10^{-2}\). This behavior was confirmed by SMOS imagery, showing artifacts as North-South stripes (Figure 2). However, 10 years after this study [6] was conducted, SMOS is now suffering also from RFI generated by the ubiquitous use of wireless devices such as surveillance video cameras, WIFI networks _etc._ which can create out-of-band emissions if their outputs are not properly filtered, and even in some cases, emissions within the protected band. \begin{table}
\begin{tabular}{|l|l|l|l|l|} \hline
**L-BAND RADARS Model** & **Applications** & **Peak Power** & **Freq. Band** & **H\({}^{*}\)** \\  & & **[MW]** & **[MHz]** & \\ \hline
**AN/FPS-series** & Long range air surveillance. & 1–10 & 1250–1380 & - \\ AN/FPS-8, 66, 88, 107 & Commercial airports & & 1215–1400 & - \\ AN/FPS-117, 124 & Military bases & & & \\ \hline
**ASR-series** & & & & \\ ASR-7, 8, 33 & Airport Surveillance Radar & 0.425–1.4 & 2700–2900 & - \\ \hline
**ARSR-series** & Air Route Surveillance Radar & 4–5 & 1215–1400 & - \\ ARSR-1, 2, 3, 805 & Air Traffic Control (ATC), airspace & & & \\ defense. & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Possible interferences from near-band emissions and harmonics (H\({}^{*}\) = number), if any [6].
## 3 Problem Formulation
### Exploring the Influence of Allocation Variables on the Objective Function  
We present here the allocation variables described in the problem formulation to explore their impact on throughput.  
**The relationship of batch size to the objective function.** Small batch sizes (e.g., 32) tend to prolong the training duration, achieve a higher throughput, and enhance the model performance. This is due to the fact that, as batch size decreases, the number of iterations in an epoch, and thus, the number of model parameter updates, increases, as illustrated in Figure 2. When considering the objective function, reducing the batch size can maximise throughput and model performance, while increasing the batch size can minimise the training time.  
**Influence of distributed training on job throughput.** Referring to Figure 3, the throughput for multiple GPUs while training a VGG16 model on the RTX2080 Ti GPU architecture declines as the number of GPUs used for the data parallelism approach in  
Figure 1: Illustration of batch size, iteration, and epoch.  
distributed training increases. An exception is observed for a batch size of 512, where using multiple GPUs in training results in slightly higher throughput. As a result, reducing the batch size while training on a single GPU can yield a significantly increased throughput compared to distributed training across multiple GPUs. This is due to the communication-intensive nature of distributed training in the context of the data parallelism approach, particularly when dealing with a large batch size, as highlighted in [22].  
**A decline in job throughput when integrating job packing.** When employing job packing for training multiple models on a GPU, the throughput exhibits variations depending on the combination of models. Figure 4 shows the throughput of sharing an Nvidia A100 GPU architecture for concurrent training of a VGG16 model with other models. The batch size of both models is configured as 32. The red line represents the throughput of an isolated training of a VGG16 model. A comparison between isolated training and job packing reveals a decrease in the throughput of the VGG16 model when packed with other models, and the extent of this decrease varies. For instance, the throughput of a VGG16 model packed with another VGG16 model is less than when packed with a ResNet101 model. Even though the throughput of job packing is lower than an isolated training, job packing can reduce queuing delay.  
Figure 3: Throughput of various batch sizes of a VGG16 model on several numbers of Nvidia RTX2080 Ti GPU architecture.  
Figure 2: The frequency of model parameter updated on different batch size.
# New Insights into the Biology of the Emerging Tembusu Virus
## 4 Clinical Features and Pathogenicity
### Innate Immune Response and Viral Evasion
This decrease is associated with a weaker innate immune response and a reduction in the expressions of IFN-\(\alpha\), -\(\beta\) and -\(\gamma\), interleukin IL-19 and IL-6. Development of attenuated viral models stimulates new approaches to study the molecular mechanisms involved in the pathogenicity and replication of TMUV. Autophagy is a cellular mechanism of recycling and degradation of cytoplasmic components to prevent cell death and promote homeostasis of cells exposed to an extrinsic or intrinsic stress, including viral infection [119]. Despite the host immunity defense against several pathogen infections, autophagy can be hijacked by a range of viruses, including flavivirus such as ZIKV [120], DENV [121] or JEV [122], to promote their own replication. Induction of autophagy by TMUV was confirmed in avian cells, acting as a viral strategy to evade the host immune response [94]. In these studies, stimulation of autophagy promoted TMUV replication, and, inversely, treatment with chemical inhibitors of autophagy led to a decrease in TMUV virions. Moreover, [PERSON] et al. showed that TMUV infection promotes degradation of the p62 autophagy adaptor, leading to a downregulation of the innate immune response mediated through the TBK1 protein and evasion of the antiviral response. Indeed, the p62 adaptor is not only a cargo adaptor but also an important player in the immune response via activation of the IFN and NF-kB pathways. p62 degradation may be a strategy developed by TMUV to evade innate immune responses [94; 123]. The endoplasmic reticulum (ER) is an important intracellular organelle in the protein synthesis pathways where proteins are folded and matured before intracellular or extracellular release. The replication cycle of TMUV, as with other flaviviruses, mainly takes place in close interaction with the ER of the infected cell [124]. ER acts as the site where the viral genome is translated in the protein and replicated in the de novo genome RNA in vesicle packets, which are formed by invagination of the ER membrane. ER homeostasis is firmly regulated by a quality control process preventing accumulation of misfolded proteins in the ER lumen. However, similar to other viruses, flaviviruses trigger ER stress. To alleviate ER stress and maintain homeostasis, infected cells activate a pro-survival mechanism pathway known as the unfolded protein response (UPR). Nonetheless, flaviviruses have evolved to exploit the UPR pathway to facilitate their own replication by preventing apoptosis, promoting autophagy and evading innate immune responses [125; 126]. Similarly, TMUV has been reported to activate the UPR via its three arms (IRE1, ATF6 and PERK signaling branches) both in avian and mammalian cell lines [127]. However, the exact role of the UPR pathway in TMUV replication remains unclear and needs to be further investigated.
# Compositional Modeling of Dimethyl Ether-CO2 Mixed Solvent for Enhanced Oil Recovery
###### Abstract  
Dimethyl ether (DME) is a compound first introduced by Shell as a chemical solvent for enhanced oil recovery (EOR). This study aims to investigate the efficiency of EOR using the minimum miscible pressure (MMP) and viscous gravity number when a mixed solvent of CO2 and DME is injected. Adding DME to the CO2 water-alternating-gas process reduces the MMP and viscous gravity number. Reduction in MMP results in miscible conditions at lower pressures, which has a favorable effect on oil swelling and viscosity reduction, leading to improved mobility of the oil. In addition, the viscous gravity number decreases, increasing the sweep efficiency by 26.6%. Numerical studies were conducted through a series of multi-phase, multi-component simulations. At a DME content of 25%, the MMP decreased by 30.1% and the viscous gravity number decreased by 66.4% compared with the injection of CO2 only. As a result, the maximum oil recovery rate increased by 31% with simultaneous injection of DME and CO2 compared with only using CO2.  
dimethyl ether (DME); water alternating gas (WAG); enhanced oil recovery (EOR); chemical solvent; miscible gas +
Footnote †: journal: Applied Sciences
###### Abstract  
One of the most common power problems today is communication and control delays. This can adversely affect decision interaction in grid security management. This paper focuses on communication signal delays and how to identify and address communication system failure issues in the context of grid monitoring and control, with emphasis on communication signal delay. An application to solve this problem uses a thyristor switch capacitor (TSC) and a thyristor-controlled reactor (TCR) to improve the power quality of the Rwandan National Grid (RNG) with synchronous and PV generators. It is to counteract the negative effects of time delays. To this end, the TSC and TCR architectures use two methods: the fuzzy logic controller (FLC) method and the modified predictor method (MPM). The experiment was performed using the Simulink MATLAB tool. The power quality of the system was assessed using two indicators: the voltage index and total harmonic distortion. The FLC-based performance was shown to outperform the MPM for temporary or permanent failures if the correct outcome was found. As a result, we are still unsure if TSC and TCR can continue to provide favorable results in the event of a network cyber-attack.  
communication and control; Rwandan National Grid (RNG); fuzzy logic controller (FLC); power quality; modified predictor method (MPM) +
Footnote †: journal: Electronics
## 3 Results and Discussion  
### Rotational Viscosity  
Figure 2 and Tables 5 and 6 assess the viscosity of modified asphalt binders at 135 \({}^{\circ}\)C and 180 \({}^{\circ}\)C, focusing on the use of the warm mix additive LC for high-performance applications in pavement engineering. Warm mix asphalt technologies, incorporating additives like LC, aim to lower production and compaction temperatures, offering advantages such as reduced energy use, lower emissions, and improved workability.  
The base binder, PG 64-22, has viscosities of 755 cP at 135 \({}^{\circ}\)C and 285 cP at 180 \({}^{\circ}\)C, serving as benchmarks for modification effects. Adding TLA and CRM increases viscosity, enhancing high-temperature performance but also requiring more energy for mixing and compaction. For example, 10% TLA raises viscosities to 858 cP and 300 cP, while 20% TLA results in 865 cP and 310 cP at 135 \({}^{\circ}\)C and 180 \({}^{\circ}\)C, respectively. CRM has a more pronounced impact, with 5% CRM increasing viscosities to 1148 cP and 298 cP, and 10% CRM to 2550 cP and 685 cP.  
Adding LC to binders with TLA and CRM generally reduces viscosity. For instance, 10% TLA with 5% CRM and 1.5% LC results in viscosities of 1125 cP at 135 \({}^{\circ}\)C and 287.5 cP at 180 \({}^{\circ}\)C, compared to 1275 cP and 375 cP without LC. With 20% TLA, 5% CRM, and 1.5% LC, viscosities are 1450 cP and 537.5 cP, versus 1562.5 cP and 500 cP without LC. The viscosity reduction, especially at 180 \({}^{\circ}\)C, highlights LC's effectiveness in improving binder workability at high temperatures.  
LC decreases binder viscosity, enhancing workability during mixing and compaction, and reducing energy requirements. Despite lower viscosities, LC-containing binders maintain high performance, with sufficient rigidity to resist deformation at elevated tem  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{3}{c}{**Viscosity at Original Condition at 180 \({}^{\circ}\)C**} & \multicolumn{3}{c}{**PG64-**} & \multicolumn{3}{c}{**PG64-**} & \multicolumn{3}{c}{**PG64-**} \\  & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & \\ \multirow{4}{*}{**Combination**} & **PG64-** & **PG64-** & **PG64-** & **PG64-** & **22+** & **PG64-** & **22+** & **PG64-** & **22+** & **PG64-** & **22+** & **PG64-** & **22+** \\  & **22** & **22** + & **22** & **CRM** & **22+** & **10\%TLA** & **22+** & **20\%TLA** & **CRM** & **22+** & **10\%TLA** & **22+** & **20\%TLA** \\  & **22** & **22** + & **22** & **5\%TLA** & **5\%** & **20\%TLA** & **5\%** & **20\%TLA** & **5\%** & **10\%TLA** & **4\%** & **10\%** & **20\%TLA** & **4\%** \\  & **10\%TLA** & **20\%TLA  
peratures. This balance underscores LC's role in producing high-performance warm mix binders, aligning with warm mix asphalt goals of energy efficiency and reduced emissions.
# Energy-Based Thermo-Hydraulic Performance of Roughened Absorber in Solar Air Heater Duct
## 3 Mathematical Model and Validation
\(Q_{\rm u1}\) and \(Q_{\rm u2}\) are compared and, in case of difference between these two values, an updated value of \(T_{\rm pm}\) is determined using the heat gain, \(Q_{\rm u2}\), from Equation (34). The iteration continues until the values of \(Q_{\rm u1}\) and \(Q_{\rm u2}\) come to within 1% difference; that is, \((Q_{\rm u1}-Q_{\rm u2})/Q_{\rm u1}\) <1%. \[T_{\mathrm{o}}=\left(\frac{\Delta T}{I}\right)\cdot I+T_{\mathrm{i}}\]9 (36)
10. The pressure drop and the air blower power are estimated from the friction factor correlation for conical protrusion rib roughness [26]: \[U_{\mathrm{b}}=\frac{k_{\mathrm{i}}}{t_{\mathrm{i}}}\]0 (37) \[U_{\mathrm{b}}=\frac{k_{\mathrm{i}}}{t_{\mathrm{i}}}\]1 (38)
11. Finally, the exergy efficiency is calculated for the SAH with conical protrusion ribs using Equation (17), as proposed by [PERSON] et al. [29]. The process is performed for different operating parameters and rib geometries. The flow chart of this approach is given in \[U_{\mathrm{b}}=\frac{k_{\mathrm{i}}}{t_{\mathrm{i}}}\]2  
The results for the predicted Nusselt numbers and friction factors are validated against experimental data for hemispherical protrusion ribs obtained at different Reynolds numbers (\(Re\) = 3054, 6119, 9701 and 11138), carried out by [PERSON] and [PERSON] [22]. It can be seen that the predictions are in good agreement with experiments, as presented in the authors' previous study [28]. \begin{table}
\begin{tabular}{l l} \hline
**System Parameter** & **Value** \\ \hline Emissivity of absorber plate (\(\epsilon_{\rm p}\)) & 0.9 \\ Transmittance-absorptance product (\(\alpha\cdot\tau\)) & 0.8 \\ Emissivity of transparent glass cover (\(\epsilon_{\rm g}\)) & 0.88 \\ Tilt angle (\(\beta\)) & 30\({}^{\circ}\) \\ Collector edge thickness (\(t_{\rm e}\)) & 0.1 m \\ Glass cover thickness (\(t_{\rm g}\)) & 0.002 m \\ Thickness of back insulation (\(t_{\rm i}\)) & 0.05 m \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters. \begin{table}
\begin{tabular}{l c} \hline \hline
**System Parameter** & **Value** \\ \hline Insulation thermal conductivity (\(k_{\text{in}}\)) & 0.037 W/m-K \\ Collector length (\(L\)) & 1.0 m \\ Distance between glass cover and collector (\(L_{\text{g}}\)) & 0.025 m \\ Collector width (\(W\)) & 0.3 m \\ Number of glass covers (\(N\)) & 1 \\ Collector duct height (\(H\)) & 0.025 m \\ Relative rib pitch (\(p/e\)) & 6–12 \\ Relative rib height (\(e/D\)) & 0.020–0.044 \\ \hline
**Operating Parameters** \\ \hline Reynolds number & 1000–15000 \\ Insolation (\(I\)) & 600–1000 W/m\({}^{2}\) \\ Temperature increase parameter (\(\Delta T/I\)) & 0.010–0.030 K-m\({}^{2}\)/W \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 5: Flow chart of the analytical method.
## 5 Discussion
For instance, in GC10-DET, YOLO-MAD outperforms baseline methods and shows clear gains over MSFT-YOLO on challenging categories like Cg and Wf, which are known for their low contrast and irregular appearance. Detect_DyHead leverages spatial and scale-aware attention mechanisms combined with decoupled task heads to refine both localization and classification accuracy simultaneously. Its architecture is also naturally extendable to multi-task frameworks, allowing future integration with segmentation or category-aware supervision, potentially surpassing the capabilities of single-task detectors like YOLO-BA. Nevertheless, the performance of YOLO-MAD remains limited in CR and Cr categories. These defects often exhibit weak contrast and lack distinct structural patterns (as shown in Figure 15), making them visually similar to the background. As shown in the Grad-CAM visualizations (Figure 14), the attention responses for these categories are scattered or misaligned, indicating that the model struggles to focus on the relevant areas [47]. Future enhancements such as saliency boosting, contextual encoding, or multimodal guidance could improve the model's ability to distinguish low-saliency defects. From a deployment perspective, YOLO-MAD requires only 9.4 GFLOPs, making it lightweight and efficient for real-time applications in industrial settings, comparable to or even more efficient than YOLOv8n and MSFT-YOLO. Although the NEU-DET and GC10-DET datasets are derived from real-world environments, variations in lighting and the complexity of steel surfaces still pose challenges during actual industrial deployment. The experimental results in this paper show that YOLO-MAD still has a good detection performance when there are noise points on the steel surface. However, further fine-tuning training is still needed for strong and weak light conditions. Moreover, the heatmapshows that although the model has marked the edge target as a salient object, it has not performed bounding box detection, indicating that its detection performance for edge targets is relatively weak. However, YOLO-MAD has expandability and great room for improvement. It can handle various defect shapes and sizes, which enables it to be adjusted and improved for detection in real scenarios and makes it suitable for other tasks such as pipeline corrosion monitoring, weld seam inspection, and composite crack detection. In summary, YOLO-MAD achieves a favorable balance between accuracy and efficiency. Compared with other YOLO-based variants such as MSFT-YOLO [35] and YOLO-BA, it emphasizes architectural synergy and semantic representation, showing strong practical potential and scalability. Future research may explore integrating domain priors, task-adaptive designs, and multimodal inputs to further improve generalization and performance.
## 3 Results
### Effect of MLE on Plant Pigments
#### 3.3.2 Chlorophyll b Content  
Figure 3B shows the effect of MLE application on chlorophyll b content of wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) significantly reduced the chlorophyll b content by 32% and 39%, while, on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions increased the chlorophyll b content of wheat by 10% and 9.9% compared to respective Cd treatments.