esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
###### Abstract  
The optimal delivery route problem for truck-drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck-drones. The results indicate that truck-drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck-drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck-drone delivery route and improve the delivery efficiency.  
optimal route problem; truck-drone delivery; mixed-integer nonlinear programming model; variable neighborhood tabu search algorithm +
Footnote †: journal: Applied Sciences
## 3 Variable-Thickness Domain
### Domain Thickness: Model Accuracy vs. Utility Trade-Offs  
The thickness parameter \(T\) plays an essential role in the nested kriging framework. On one hand, it determines the volume of the domain \(X_{S}\). Increasing the value of \(T\) by a factor of two enlarges the domain by a factor \(2^{n-N}\), which may be orders of magnitude for highly-dimensional space. This directly affects the number of training data samples required to yield the accurate model. In particular, keeping \(T\) at low values is beneficial for computational efficiency. On the other hand, reducing the domain thickness leads to keeping a part (or even a majority) of the optimum design manifold \(U_{F}(F)\) out of \(X_{S}\). This compromises the design utility of the surrogate because the true antenna optimum becomes unattainable for certain regions of the objective space as indicated in Figure 3. Thus, the trade-off is to set \(T\) at the minimum value ensuring that most of \(U_{F}(F)\) is in \(X_{S}\). Clearly, this leaves a very limited room for reducing the cost of training data acquisition given a target level of the model error.
# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 1 Introduction
[17] discussed and compared the identification accuracy of the apple flower in the field based on the You Only Look Once v4(YOLO v4) algorithm and YOLO v3 algorithm. Deep learning is a new and efficient method for intelligent cultivation in sugarcane planting. In 2020, [PERSON] [18] used deep learning to study the furrow mapping of sugarcane billelet density, and [PERSON], [PERSON] [19] proposed an approach based on deep learning for sugarcane disease detection. These research studies demonstrated that deep learning has a stronger identification ability in the natural environment and sugarcane field. In 2019, [PERSON] et al. [20] introduced the object detection technology for sugarcane stem node recognition based on deep learning, which was applied in the sugarcane cutting process for the first time. It used an improved YOLO v3 network to establish an intelligent recognition convolutional neural network model. However, the sugarcane samples were pre-processed by removing the leaves manually first in a pre-processed single-colour background environment. Table 1 shows the relevant studies by the above-mentioned scholars on sugarcane stem node recognition. Although deep learning has been adopted in other crop recognition applications, it is still rarely used in sugarcane stem node recognition. The visual identification of sugarcane stem nodes in the complex natural environment still goes unreported due to the following difficulties: (1) The complex lighting conditions in the natural environment and unstable sunlight during the day reduce the image quality and affect the accuracy of the detection algorithm; (2) sugarcane s grow in clusters, and some of the sugarcane stem nodes are more or less covered by leaves; and (3) the diversity of the biological characteristics of sugarcane, including different stalk diameters and peel colour, increase the difficulty of identification. In order to solve these problems, this paper proposes a sugarcane stem node recognition algorithm based on deep learning driven by big data in the natural environment. The big data acquisition experiments were conducted at a real sugarcane farm, and the big data samples of the sugarcane stem node consisted of different light conditions and different shooting angles using the data expansion technique and images lighting conversion. The object detection algorithm based on deep learning can learn and understand the characteristics of different sugarcane stem nodes in the natural environment by learning big data. The rest of this article is organised as follows. The second section introduces the experimental procedure and data processing, including image acquisition, data expansion and the creation of image datasets. The third section introduces the sugarcane stem node detection model based on the YOLO v4 [21] algorithm in the natural environment. This algorithm is currently the best one-stage detection algorithm. The fourth part is the experimental part, which mainly discusses and analyses the experimental results. The last is the conclusion and prospect of this article.
# Development of Screen-Printable Nafion Dispersion for Electrochemical Sensor
## 2 Materials and Methods
### Electrochemical Sensor Performance Tests  
Electrochemical measurements of Nafion printed on a commercial sensor were performed by a CHI-655E potentiostat (CH Instrument) with square-wave anodic stripping voltammetry (SWASV).  
The sample solutions of Pb(II) at different concentrations were sequentially measured by the commercial sensors, with or without modified Nafion, using the SWASV technique. Prior to testing, sensors were presoaked in sample solution for 24 h for the Nafion to reach equilibrium. The analytes were first deposited on the working electrode at \(-0.9\) V vs. Ag/AgCl reference electrode for 120 s. The potentials were applied in the range of \(-0.9\) V to \(-0.5\) V, with a frequency of 5 Hz and a pulse amplitude of 25 mV.
# Experimental Characterization of the Engineering Properties of Landfill Compost-Biocover
## 2 Materials and Methods
### Methods Employed
#### 2.2.2 Moisture Density Relationship Tests  
Standard Proctor tests were performed at different water contents to obtain the compaction curve of compost material, following the guidelines as discussed in ASTM D 698-07 standard [34]. First, the desired quantity of compost was passed through the #4 sieve and placed back in the covered pair to preserve the existing water content. Before performing the test, each sample was mixed uniformly in a large mixing pan with the specified water content. Each addition of water resulted in clods of different sizes in the beginning. However, each test was performed after assuring uniform mixing of the sample with water. The compaction tests were carried out for a wide range of water content on both sides of optimum water content, which was due to examining the behavior of compost material (porosity, free air space, and relative diffusion coefficient) for a lower and higher degree of saturation. Replicate samples were prepared at each water content level to ensure the repeatability of test data. Each sample was used only once and discarded after the test. The compost material showed different behavior at a high and low degree of saturation. For wet optimum, the specimen started to jump out of the compaction mold while hammering, and this situation arose for samples with more than 100% water content. Similarly, the finer particles of the sample were also observed coming out of the compaction mold during compaction for the dry optimum segment. However, no issues were noticed for water contents ranging from 55% to 100%.
## 4 The Evaluation Results and Discussions
### The Experimental Setup
#### 4.1.2 The ASR System Preparation  
The speech features in the training set were used to learn context-dependent (CD) tri-phones as acoustic models, which were arranged to be two different structures, i.e., GMM-HMM and DNN-HMM, respectively. Stating in more detail, GMM-HMM and DNN-HMM used GMM and DNN, respectively, to represent each state of the HMM. As for GMM-HMM, each mono-phone of speech signals and the silence were respectively characterized by an HMM with three states, having 1000 Gaussians in total, and each tri-phone was characterized by an HMM with three states, having 2500 leaves and 1500 Gaussians in total. Furthermore, LDA, MLLT, SAT were applied to the speech features during the tri-phone model training. On the other hand, for the DNN structure, five hidden layers, each containing 1024 nodes with a dropout rate 15%, were connected totwo independent output layers for tri-phones and mono-phones, respectively, in the DNN-HMM. The training process of this DNN underwent 24 epochs with an SGD optimizer and used the log-likelihood loss function for tri-phones and mono-phones. The losses of tri-phones and mono-phones were added up for minimization in the model training. The Kaldi toolkit [32] was used to create the GMM-HMM and the Pytorch-Kaldi [38] toolkit was used to create the DNN-HMM.  
In addition, a set of tri-gram language models for training utterances was created via following the standard recipes of Kaldi.
###### Abstract  
Limited drainage tunnels face pore water pressure, water inflow, and permeability challenges. At present, there is little systematic analysis and research on the lining water pressure of sub-sea tunnels, and there is a lack of verification of relevant engineering examples and field monitoring data. Based on the numerical simulation method, this paper discusses the lining water pressure and its reduction coefficient of the horseshoe section tunnel, which is verified by an engineering example. Based on many numerical simulations, the recommended value of the water pressure reduction coefficient considering the thickness of the grouting ring and grouting effect is put forward. The stress law and safety of tunnel lining under different water reduction coefficients are studied, and the safety of lining is evaluated combined with the measured data of lining stress. The results show that the numerical simulation has been well verified. Considering the water pressure according to the water pressure reduction coefficient method proposed in this paper can ensure the structural safety of tunnel lining. The method of lining water pressure reduction coefficient proposed in this paper can provide a reference for the subsequent lining design of the sub-sea tunnel.  
sub-sea tunnel; water pressure on lining; reduction coefficient; numerical simulation; field testing +
Footnote †: journal: Applied  
Shin [18] determined the relationship between water pressure and the inflow rate of laminar and turbulent flow in underwater tunnels. [PERSON] [19] and [PERSON] [20] adopted another analytical method, conformal mapping, to study this problem under different boundary conditions along the tunnels, such as zero water pressure and constant water head. [PERSON] et al. [21] verified this method with the FLAC3D software. [PERSON] et al. [22] proposed a simple model to calculate the lining water pressure with controlled drainage.  
Previous studies are mainly limited to theoretical analysis, numerical simulation, and model test methods, and most studies mainly focus on the analysis of the seepage field. There is a lack of systematic analysis of the external water pressure of tunnel lining and the verification of relevant engineering examples and field monitoring data. Therefore, based on the sub-sea tunnel project of Qingdao Metro Line 8, through the combination of numerical calculation and field test, combined with the actual situation of the project, the factors with significant influence effect are selected, and a selection table of underwater tunnel water pressure reduction coefficients considering these factors is proposed. The stress and safety of lining under different water pressure reduction degrees are studied, and the safety of tunnel lining is evaluated combined with field monitoring data.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Kmeans++ Initial Anchor Frame Clustering  
The Kmeans clustering algorithm first initializes K cluster centers before formal clustering. As a result, cluster center initialization heavily influences convergence. If multiple cluster centers are initialized in the same cluster, then the results will be incorrect. The Kmeans++ clustering algorithm improves the way of initializing cluster centers: K cluster centers are selected one by one, and the farther the sample point is from other cluster centers, the higher the probability that it will be selected as the next cluster center. This is conducted as follows.  
1. The first cluster center \(c_{1}\) is selected at random from the dataset.
2. Calculate the shortest distance between each sample and the currently existing cluster center, denoted by \(D(x)\); then, calculate the probability \(P(x)\) of each sample point being selected as the next cluster center. Finally, select the sample point corresponding to the maximum probability value as the next cluster center \(c_{i}\). Where  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistic Items** & **Value** \\ \hline Number of images & 1333 \\ Resolution (m) & 0.4–2 \\ Number of training set images & 1067 \\ Number of validation set images & 133 \\ Number of test set images & 133 \\ Aircraft Carrier & 163 \\ Merchant ship & 2285 \\ Number of targets count & Warcraft & 2106 \\ Submarine & 415 \\ Total & 4969 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset details.  
Figure 7: PR curve.  
\[P(x)=\frac{D(x)^{2}}{\sum\limits_{x\in X}D(x)^{2}}. \tag{16}\]
3. Until K clusters are selected, repeat Step 2.  
The data set was clustered using the Kmeans++ clustering algorithm to obtain a priori frames suitable for the ships assigned to the three detection scales of YOLOv4.
## 5 Results  
### Endmember Spectra  
The spectral signatures of the five endmembers selected for each dataset are presented in Figure 4. The endmembers of the two primary volcanic products (rhyodacites and basaltic andesites) generally showed low reflectances and, in the case of ASTER and Landsat-8/OLI, a similar pattern as well. However, they seemed to better differentiate in the NIR region of Sentinel-2 MSI between 0.7 \(\upmu\)m and 1 \(\upmu\)m. Compared to them, the alluvial deposits endmembers showed higher reflectances and, in general, the same pattern. They seemed to differentiate in the SWIR bands of ASTER and the NIR bands of Sentinel-2 MSI.  
The hydroxyl-bearing alteration endmembers showed higher reflectance values than those of the endmembers of the two primary volcanic products [(34)] and alluvial deposits. In general, their spectral pattern differed from all other endmembers. Specifically, in ASTER it was differentiated between 0.55-0.8 \(\upmu\)m, in Landsat-8/OLI in the SWIR region, and in Sentinel-2 MSI it differed in the whole spectral range. Finally, the sulfur endmember exhibited very high reflectance, compared to the other endmembers. In addition, it exhibited a significantly different pattern from all other endmembers in all three sensors. More specifically, it was featureless for the range of wavelengths between 0.6 \(\upmu\)m and 2.4 \(\upmu\)m, while it presented a characteristic steep slope between 0.4-0.6 \(\upmu\)m. However, the latter was detected only by Landsat-8/OLI and Sentinel-2 MSI, since they had an adequate number of spectral bands in this spectral region.
# Fire Performance of Self-Tapping Screws in Tall Mass-Timber Buildings
## 3 Results and Discussion
### Effect of Fasteners on the Timber Charring Rate
This effect was greatly observed when comparing the bolt and the STS after two hours of exposure, as illustrated in Figure 9a,b. For the specimens with the head exposed (i.e., unprotected), the charring depth was limited along the first few threads of STS shank while the bolt shank created a charring depth along the entire shanks after two hours of exposure. This difference was caused by the larger heated area of bolt shank that conducted heat faster within the specimen and consumed timber surrounding the steel shank, whereas small, long, and slender screws had limited heat transfer into the timber. Although the STS limited thermal penetration, localized charred threads were observed along the first few threads (see Figure 9b,d). The characterization of this locally charred penetration area was measured and is also presented in Table 3. Due to the high thermal conductivity of the steel shank, the heat conducted through the fastener heats up the timber inside and the wood fibers around the fastener, as previously discussed. The same phenomenon was observed for the STS screws initially protected by 20 mm of gluam after two hours of exposure, as shown in Figure 9d. Frangi & Fontana [41] reported that the thermal penetration depth, defined as the distance from the char layer to the wood that is still at ambient temperature, increased with a longer fire duration. Under long fire durations, the cracks formed in the char layer can accelerate the  
Figure 9: Charring of fastener under constant heat flux; (**a**) Unprotected \(\mathcal{O}\)15.9 \(\times\)140 mm bolt head exposed for two hours; (**b**) Unprotected \(\mathcal{O}\)10 \(\times\) 160 mm screws head exposed for two hours; (**c**) Protected \(\mathcal{O}\)10 \(\times\) 160 mm screws (tip exposed) for one hour; (**d**) Protected \(\mathcal{O}\)10 \(\times\) 160 mm screws (tip exposed) for two hours. temperature rise through the charred layer and warm up the timber inside. Assuming the charcoal starts glowing at 500 \({}^{\circ}\)C [42], the cracking in the charred layer allows the heat to pass through the char layer and affects the temperatures into timber. Therefore, the temperature profiles through wood members exposed to fire depend majorly on the relative exposure of the wood and steel elements, as well as the fire duration which affects the temperature distribution along the fastener shank.
## 2 Extension and Refinement of the Ocean Disturbance Simulation System Model
### Method Analyses
Assuming unidirectional current, some parameters are defined as follows:  
\(v_{c}\) is the ocean current velocity at depth \(h\);  
\(v\) is the tidal current velocity at the still water level, which must be measured in the actual experiment;  
\(z\) is the vertical distance from the still water level (positive upwards, typically negative);  
\(h\) is the total water depth measured downward from the still water level. \(\alpha\) is an exponent, and typically, \(\alpha=1/7\); we can calculate the speed of the ocean current:  
\[v_{c}=v\times(\frac{h+z}{h})^{\alpha} \tag{10}\]  
To improve simulation efficiency while maintaining modeling accuracy, the tidal current velocity profile was simplified based on the operational depth range of the robot (0-25 m below the still water level). According to Equation (9), the velocity variation within this depth range was moderate and allowed for approximation. For example, assuming a total water depth \(d=100\) m, the distance from the still water level \(z=-60\), and an empirical exponent \(\alpha=1/7\), the velocity decreased gradually from the surface downward. The velocity was approximated to be \(v_{c}=0.14\times v\). These representative values were derived from the theoretical profile and effectively reduced computational costs while preservingthe fidelity required for motion simulation. The corresponding value was selected based on the robot's real-time depth in actual implementation.
## 4 Framework Implementation and Validation Strategy
### Integration of Framework Components  
The framework's five core components align with the visualization implementations in the following ways:  
* Data collection: The hospitality sector implementation draws from multiple data sources, including booking records, pricing information, and geographical data, demonstrating the framework's capacity for multi-source integration.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Decision Support Systems (DSS) [24, 25]** & **Evidence-Based Policy (EBP) [26, 27, 28]** & **Institutional Intelligence (Ours)** \\ \hline Integration of AI and LLMs & Partial & No & Yes (AI-driven modeling, LLM-powered recommendations) \\ \hline Historical Memory Preservation & Low (data archiving only) & Medium (case studies) & High (institutional memory through structured AI inference) \\ \hline Predictive Capabilities & Medium (basic forecasting) & Low (focus on retrospective data) & High (machine learning and graph neural networks) \\ \hline Adaptability to New Policies & Medium & Low & High (AI-assisted scenario analysis, policy simulation) \\ \hline Visualization and Explainability & Low & Medium & High (interactive visualizations, LLM-generated narratives) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of policy analysis frameworks.  
* Historical analysis: The temporal visualization techniques, particularly the violin plots and cumulative heatmaps, directly implement the framework's historical analysis component by revealing patterns across different time scales.
* Policy impact assessment: The interactive visualizations, especially the geographic mapping and sunburst diagrams, enable stakeholders to assess the impact of various market conditions and policy decisions on key metrics such as average daily rate (ADR) and cancellation rates. ADR is a key hospitality industry metric that represents the average revenue earned per available room per day.
* Predictive modeling: The temporal pattern analysis and clustering visualizations support the framework's predictive capabilities by highlighting recurring patterns and trends.
* Intervention design: The multi-layered visualization approach facilitates evidence-based intervention design by providing comprehensive views of market behavior and performance metrics.