esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
## 5 Computational Studies
### Results on the VRPD with Unlimited Flying Range  
1. Single vehicle with two drones
2. Two vehicles with one drone assigned to each vehicle
3. Two vehicles with two drones assigned to each vehicle  
For a small-scale problem with 8 nodes, the RL sampling model exhibits a 1.45% optimality gap. It demonstrates an almost 70-fold increase in speed within the first configuration. It outperforms all other methods in the second configuration and achieves a 2.14% optimality gap in the third configuration. In a small-scale problem involving 10 nodes, the RL sampling model surpasses all alternative methods in the initial two configurations. The third configuration achieves a minimal 0.92% gap compared to exact methods. Since no results were reported for big-scale problems with 20 nodes, we only reported the RL model results. The fast results from the RL sampling approach mostly come from its learned policies that use the problem's structure effectively, such as grouping similar tasks. The proposed \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{1},|\mathcal{D}|=\mathbf{2}\)} & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{1}\)} & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{2}\)} \\ \cline{3-11}  & \multicolumn{1}{c}{**Methods**} & **Costs** & **Gaps, \%** & **Time, s** & **Costs** & **Gaps, \%** & **Time, s** & **Costs** & **Gaps, \%** & **Time, s** \\ \hline \multirow{5}{*}{\(\mathbf{\mathcal{O}}_{\text{test}}\)} & Solver & 238.45 & 9.55 & 601.18 & 223.54 & 13.66 & 602.9 & 217.45 & 15.23 & 601.86 \\  & Solver Lazy & 246.84 & 12.89 & 601.27 & 223.58 & 13.95 & 602.1 & 215.81 & 14.33 & 601.22 \\  & Heuristic & 218.01 & 0.28 & 304.48 & 196.10 & 0.00 & 62.82 & 188.96 & 0.00 & 81.43 \\  & RL greedy & 232.24 & 6.51 & 0.03 & 205.70 & 4.77 & 0.07 & 198.47 & 5.14 & 0.02 \\  & RL sampling & 221.72 & 2.04 & 0.63 & 202.76 & 3.30 & 0.50 & 195.46 & 3.52 & 0.80 \\  & Ensemble RL & 219.78 & 1.04 & 4.54 & 200.16 & 2.03 & 3.88 & 193.17 & 2.30 & 5.44 \\ \hline \multirow{5}{*}{\(\mathbf{\mathcal{O}}_{\text{test}}\)} & Heuristic & 264.50 & 10.14 & 268.97 & 213.90 & 0.00 & 326.69 & 196.59 & 0.94 & 498.83 \\  & RL greedy & 252.18 & 5.09 & 0.02 & 231.80 & 8.30 & 0.03 & 218.36 & 12.02 & 0.03 \\  & RL sampling & 244.32 & 1.68 & 1.40 & 222.74 & 4.06 & 1.60 & 201.79 & 3.73 & 1.98 \\  & Ensemble RL & 240.01 & 0.00 & 9.67 & 220.63 & 3.09 & 11.36 & 201.63 & 3.58 & 11.96 \\ \hline \multirow{5}{*}{\(\mathbf{\mathcal{O}}_{\text{test}}\)} & Heuristic & 475.87 & 35.46 & 141.93 & 320.66 & 6.51 & 128.64 & 295.68 & 15.84 & 151.92 \\  & RL greedy & 391.58 & 11.37 & 0.04 & 318.29 & 5.71 & 0.05 & 276.61 & 8.32 & 0.05 \\ \cline{1-1}  & RL sampling & 357.77 & 1.73 & 4.76 & 305.68 & 1.48 & 6.79 & 261.61 & 2.49 & 7.66 \\ \cline{1-1}  & Ensemble RL & 351.63 & 0.00 & 33.18 & 301.95 & 0.25 & 47.65 & 255.74 & 0.20 & 54.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The VRPD results with Limited Flying Range of Drones with \(\beta=1\). Averages of 10 problem instances.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 5 Conclusions  
In this work, we have investigated the VANET connectivity probability where a generalised analysis framework has been used to capture impacts of big vehicle obstructions. The channel effects from channel models and vehicle obstructions statistics were reformulated to derive the dynamic transmission ranges for two traffic models. Parameters affecting VANET connectivity like pathloss, vehicle density, transmit power have been analysed using generalised dynamic range distribution model. The benefit of such scheme would be in designing VANET traffic models for different random environments such as urban highways with high traffic. Therefore, unlike the conventional derivation of connectivity probability from channel distributions using static communication ranges, we used statistics of dynamic communication ranges in the connectivity probability analysis of synchronised and exponential traffic models. Also, the approach we took provides a compact and simpler way of VANET traffic modeling with cases involving multiple lanes as well. Using simulations, the results agree with the theoretical model.  
In future work, we will consider evaluating the framework to understand its performance in terms of PDR, and throughput. We will also consider a model to capture weather conditions (such as fogy, snowy) and how the new dynamic range models affect VANET performances.  
Conceptualization, K.O. and A.H.A.E.-M.; methodology, software, formal analysis, writing--original draft preparation, investigation, visualization, project administration, funding acquisition, K.O.; validation, resources, writing--review and editing, K.O., E.M. and A.H.A.E.-M.; supervision, E.M. and A.H.A.E.-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Summary and Conclusions  
A series of small scale laboratory tests for buried pipes in reinforced and non-reinforced sands were conducted by measuring the peak uplift resistance to calibrate advanced numerical models employing neural network methods. Many studies have employed ANN modelling techniques to predict the peak uplift resistance. However, a few studies hybridized the ANN with other modelling techniques. Besides, the ANN capacities were not explored completely in previous studies. Thus, this study investigated the feasibility of employing ANN modelling techniques while considering different capabilities of ANN. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types were used to train two neural network models, which were then developed using bagging and boosting ensemble techniques.  
The main conclusions of this study are the following:  
* The results of the numerical modelling revealed that boosting and bagging techniques can significantly improve the performance prediction (both the accuracy level and the system error) of the MLP model. However, these techniques were not able to improve the performance of the RBF model, which may imply that both ensemble techniques are more suitable for the MLP-ANN than the RBF-ANN model.
* The boosting technique performed better than the bagging technique when applied to MLP and RBF models. In addition, correlation coefficients in excess of 0.954 were achieved between the measured and predicted peak uplift resistance.
* The proposed developed models reveal the complicated non-linear response of the peak uplift resistance of buried pipes in reinforced sand. Furthermore, they can be useful tools for researchers, engineers and for supporting the teaching and interpretation of the peak uplift resistance of buried pipes in reinforced sand.  
It is important to mention that the proposed models in this study should be utilized within the condition and variation of the input variables. The peak uplift results of the other situations may be different and unreliable. While this is common in that the geo-technical database is small in size and these datasets are used for predicting the variables of interest,further studies can expand the database with more datasets to achieve better accuracies and fewer errors.  
Formal analysis, J.Z. and D.J.A.; Conceptualization, D.J.A. and A.S.M.; Supervision, P.G.A.; Writing--Review and editing, D.J.A., P.G.A., A.P.M., E.A.G., A.S.M. and J.Z.; Data curation, K.F.; Resources, M.H. All authors have read and agreed to the published version of the manuscript.  
The first author would like to acknowledge the Science and Technology Planning Project of Chongqing Education Commission (KJQN201804305, KJQN201904307) (JG-KJ-2019-006).  
The authors declare no conflict of interest.
# Assessment of a Mid-Fidelity Numerical Approach for the Investigation of Tiltrotor Aerodynamics
###### Abstract  
The study of the complex aerodynamics that characterise tiltrotors represents a challenge for computational fluid dynamics tools. URANS numerical solvers are typically used to explore the aerodynamic features that characterise the different flight conditions of these aircraft, but their computational cost limits their applications to a few vehicle configurations. The present work explores the capabilities of a new mid-fidelity aerodynamic code that is based on the vortex particle method, DUST, to investigate the performance and flow physics of tiltrotors. With this aim, numerical simulations were performed in DUST while considering XV-15 tiltrotor configurations with increasing complexity. The study started with the investigation of a simpler configuration made up of a single wing and a proprotor. Subsequently, the full aircraft was studied in steady-level flights and its major operating flight conditions were explored--i.e., hover, conversion phase, and cruise. A thorough assessment of the code capabilities was performed by the comparison of the numerical results with high-fidelity Computational Fluid Dynamics (CFD) data. This thorough comparison showed that the mid-fidelity numerical approach implemented in DUST is suitable for capturing the flow physics related to the complex aerodynamic interactions between the proprotors and the wing along with the entire flight envelope of the tiltrotor. Moreover, a good representation of the aerodynamic performance of the vehicle was obtained, particularly for the flight conditions that are characterised by limited flow separations. The good accuracy obtained for both the performance and flow physics, combined with the relatively lower computational costs required by the mid-fidelity solver with respect to the URANS simulations, indicates that DUST could be considered a valuable tool for use in the preliminary design of novel tiltrotor configurations.  
rotocraft aerodynamics; tiltrotor; computational fluid dynamics; vortex particle method +
Footnote †: journal: Applied Sci.  
**2021**, _11_, 3385. [[https://doi.org/10.3390/app11083385](https://doi.org/10.3390/app11083385)]([https://doi.org/10.3390/app11083385](https://doi.org/10.3390/app11083385))
# EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection
Hence, the number of test data is reduced by extracting key frames for movements that occur by considering the characteristics of pigs that have been lying down for a long time. In addition, this study proposes an accuracy measurement method that does not require additional annotation cost by modifying CorLoc [34], which is used in weakly supervised object detection (WSOD). The validity of this measurement metric can be verified by comparing the pig detection performance in an enclosed pen on all video frames without annotation with the proposed performance metric. This is possible by visual verification of a small number of 13,997 key frame images. The contributions of the proposed method are summarized as follows:  
* For real-time deployment, a deep-learning-based pig detector should handle unseen data. An ensemble-based pig detection method is proposed in this study to improve the detection accuracy in overexposed regions (as an example of unseen data), presumably for the first time. The detection of pigs from such overexposed regions is very challenging because the pixel distribution of such regions caused by strong sunlight through a window is different from that of other regions. Without using training data, including those from overexposed regions, image preprocessing for diversity and a model ensemble with different preprocessed images can robustly detect pigs from overexposed regions. * Another practical issue in applying deep-learning-based techniques to pig detection is the annotation cost for large-scale data. Experimental results for pig detection with large-scale test data have not yet been reported because the box-level annotation cost for this data is very expensive. Accuracy metrics for pig detection in a closed pig pen are proposed to evaluate the accuracy of detection, without box-level annotation. Presumably, this is the first report of large-scale pig detection in a pig pen with 216,000 test data, without any box-level annotation. It is also indicated that the detection accuracy with 216,000 raw video frames is very similar to that with 13,997 key frames. Thus, reducing the number of test images using key frame extraction is effective in reducing both the evaluation cost (with very large test data) and the inference time (with the model ensemble). This paper is organized as follows: Section 2 summarizes previous pig detection methods. Section 3 describes the proposed method to efficiently detect pigs using the model ensemble method. Section 4 explains the details of the experimental results, along with the new accuracy metric, and the paper is concluded in Section 5.
## 4 Results and Discussion
### Requirements Analysis

Table 6: Analysis of the RSC prototype with respect to requirements. The prototype RSC we designed and implemented features a student-centric design and incorporates remote large language model (LLM) services packaged in a visually appealing manner that supports interactive conversations. However, the RSC still needs to deliver a fully personalised learning experience. Videos demonstrating the prototype RSC in action can be found in the Supplementary Materials [95]. The RSC prototype developed in this study successfully implements several user-centred design elements derived from our background literature review (Section 2). However, while the prototype has made significant strides in design, affordability, and open-source availability, there remain challenges in achieving optimal performance, learning support, and reliability, indicating substantial opportunities for continued development. As an open-source project, the RSC offers global accessibility and the potential for significant market impact without traditional barriers, such as cost or market availability. Teachers' expectations (Section 2.1) were met because the prototypes improve motivation, act as information sources, and are classroom-ready robots with voice control. The multimodal, portable, and modular design was created to empower students, giving them greater autonomy by serving as a study support robot. The RSC distinguishes itself from other existing social robot companions (Section 2.2) by using readily available components to create an open development platform. It can support isolated study sessions where motivation is critical and provide a more engaging and interactive physical presence compared to conventional screen-based methods. This enhances the potential for reflective engagement and supports a richer variety of interaction modes, delivering additional value in educational settings.
## 1 Introduction  
Surgical morbidity related to inadequate perfusion is a major challenge after oesophagectomy with gastric conduit reconstruction for oesophageal cancer. This type of surgical morbidity includes anastomic leakage and graft necrosis, and remains high even after recent developments in minimally invasive surgery, enhanced recovery programs, and the centralisation of cancer surgery, occurring in up to 20% of the patients [1; 2; 3].  
To achieve digestive continuity after oesophagectomy, the replacement of the native oesophagus by a constructed gastric conduit is a potential solution. The perfusion of the gastric conduit is mainly based on the right gastroepiploic artery, which usually terminates before the future anastomotic site, causing the future anastomotic site to be at risk for inadequate perfusion. Surgeons assess the perfusion of the gastric conduit by observing the colour of the serosal surface, the bleeding of resection margins, or arterial palpation. To aid surgeons' decision-making, different optical imaging techniques have been introduced to assess perfusion intraoperatively [5; 6].  
Optical imaging, which is established by the interaction between light and tissue, is potentially able to image changes in perfusion real-time and at high resolution. These characteristics of optical imaging have the potential to analyse a perfusion intraoperatively and in a quantitative manner [7; 8]. Some optical imaging techniques are already Food and Drug Administration (FDA)-approved and Conformite Europeenne (CE)-marked systems, but others lack clinical evaluation [5; 9]. Not all techniques have an adequate specificity for patient outcomes and are so far unable to objectify perfusion in quantitative values for clinical usage [5]. The comparison of techniques in the literature is hampered by the large range of available imaging systems and the heterogeneity of quantitative parameters and perfusion endpoints [5]. Therefore, there is scant evidence on which optical imaging technique and related quantitative parameter is the most predictive for inadequate perfusion and patient outcomes.  
In this explorative study (IDEAL phase 2a), we aim to compare four optical techniques using different wavelengths of light on performing a quantitative perfusion assessment of the gastric conduit in patients undergoing oesophagealomy with gastric conduit reconstruction for oesophageal cancer. The four optical techniques are Optical Coherence Tomography (OCT), Sidestream Darkfield Microscopy (SDF), Laser Speckle Contrast Imaging (LSCI), and Fluorescence Angiography (FA). We hypothesised that the quantitative parameters of the optical techniques show decreased perfusion (A) towards the fundus in the gastric conduit and (B) in patients with anastomotic leakage. By comparing these four techniques, we aimed for a recommendation of an optical imaging technique for a quantitative perfusion assessment of the gastric conduit for future studies and clinical intraoperative use.
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 3 Results
### Volume of Rice Production Lost  
From 1970 to 2018, the average rice production that was lost per typhoon event was higher in Vietnam than in the Philippines (539,150 vs. 190,227 tonnes). The Philippines had 20 data points, while Vietnam had 4 (Table 2). Production losses ranged from 800 to 2,000,000 tonnes in Vietnam. The lowest production loss was due to Typhoon Cecil in 1985, while the greatest loss was due to Typhoon Ruth in 1980. Cecil was a Category 1 event, while Ruth was a tropical storm. In the Philippines, the lowest production loss of 299 tonnes was by Typhoon Conson in 2010, while Typhoon Nalgae in 2011 resulted in the highest volume of rice production lost (760,207 tonnes). Conson was a Category 1 event while Nalgae was a Category 2 event (Supplementary Material S2). Typhoon Rosita in 2018 was excluded from the calculation of the mean for the Philippines as the most recent report for Typhoon Rosita had a maximum value of 116,000,000 metric tons of rice lost [26], and there may be a reporting error as the next highest value of 800,000 metric tons was reported a day earlier by another press agency [27].  
For Vietnam, although tropical storms (TS) are less intense events with weaker winds, the average quantity of loss associated with TS was 900 times higher than the production loss that resulted from stronger Category 1 events (718,600 tonnes vs. 800 tonnes). The limited data points for Vietnam may have skewed the results; there was only one data point for the Category 1 event and three data points for TS. More data were available for the Philippines, and the relationship between typhoon intensity and production loss was also not direct as well. The average quantity of rice produce lost was ordered as such: No LF (6402 tonnes) < Category 4 (52,303 tonnes) < Category 1 (53,203 tonnes) < TS (178,638 tonnes) < Category 3 (269,2019 tonnes) < Category 2 (346,080 tonnes) (Figure 4b; Table 2). Likewise, there was a weak relationship between landfall intensity and the volume  
Figure 6: Relationship between Accumulated Cyclone Energy (ACE) and the area of rice damaged in (**a**) Vietnam and (**b**) the Philippines. (**c**) Relationship between ACE and the volume of rice production that was lost in the Philippines. (**d**) Relationship between ACE and the value of rice crop lost in the Philippines. The ACE index of each storm is the sum of the square of the maximum sustained 1 min surface wind speed every 6 h whilst the storm is at least tropical storm strength (\(\geq\)39 mph).  
of rice produce that was lost (Figure 5c). Similarly, the correlation between ACE and the volume of rice produce lost was equally poor (Figure 6c).
###### Abstract  
The study aimed at assessing effects of partial replacement (0-40%) of wheat flour with roasted flaxseed flour (RFSF) on the quality attributes of biscuits. Nutritional, antioxidative, volatile and sensory properties, as well as texture analysis and the contents of macroelements and microelement were studied. Increasing RFSF content in biscuits resulted in a significant increase (\(p<0.05\)) in protein (from 8.35% to 10.77%), fat (from 15.19% to 28.34%) and ash (from 1.23% to 2.60%) while the hardness and spread factor of the biscuits decreased with the increased level of roasted flaxseed flour. Moreover, the addition of 40% RFSF registered a positive influence on the fibre content of the final baked biscuits, increasing its value about 6.7-fold than in the control sample. Total phenolic content, antioxidant activity and biscuits' aroma volatile profile increased their amounts with RFSF addition. The nutritional, textural and sensorial results of the present study demonstrated that 25% RFSF could be added in the biscuits manufacturing without affecting the biscuits aftertaste, offering promising healthy and nutritious alternative to consumers.  
roasted flaxseed flour; GC/MS; aroma; antioxidant activity; sensory evaluation; fiber; macro and microelements; biscuits +
Footnote †: journal: Applied Sciences