esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporation of UV Filters into Oil-in-Water Emulsions--Release and Permeability Characteristics
## 3 Results and Discussion
### Release and Permeation of UV Filters from Oil-in-Water Emulsions
It should be highlighted that the action of the active ingredient after topical application occurs in two different steps. Initially, the active compound should diffuse from the formulation to the skin surface, and after this stage, depending on its physicochemical character, it can permeate the stratum corneum barrier [53]. Based on the results shown in Figure 6B, it can be stated that the rate-limiting step in avobenzene release was not the diffusion from the vehicle but the penetration through the Strat-M membrane, simulating the skin layers. This membrane can act as an additional barrier for lipophilic compounds, such as avobenzene to penetrate. It was calculated that in the case of OMC, around 2% of the UV filter dose was detected in the receptor medium, while for avobenzene ca. 0.2% of the dose permeated through the Strat-M membrane. Very low level of OMC and Avo was also determined by [PERSON] et al. [23] who performed permeation studies through human skin. The UV filters were not detected in the receiving phase in the first 8-10 h of measurement. The cumulative amount of Avo, which permeated through the skin was much lower than that of OMC. As it was mentioned above, the difference between the amount of UV filters permeated is attributed to their diverse lipophilicity.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Localization Comparison
#### 3.3.4 Comparison Method Using Machine Learning  
Localization qualities are compared by a decision tree model as shown in Figure 7. Specifically, we use a gradient boosting framework, LightGBM [38]. The effectiveness of the gradient-boosting machine (GBM) algorithm has been verified in a wide range of tasks, such as binary classification [39], multi-class classification [40,41], and fault detection [42]. Additionally, we considered the efficiency of the algorithm since a mobile device is used to run the model.  
We compared two localization processes, \(A\) and \(B\). The features are extracted from each process and concatenated to make input features for the model. The label value zero  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Features** & **Descriptions** \\ \hline \multirow{7}{*}{Turns and intersections} & * Average distance from each turn to the nearest intersection \\  & * Maximum distance from each turn to the nearest intersection \\  & * Number of times an intersection exists within 1m of a turn \\  & * Number of times an intersection exists within 3m of a turn \\  & * Number of times no intersection exists within 1m of a turn \\  & * Number of times no intersection exists within 3m of a turn \\ \hline \multirow{7}{*}{Disappeared particles} & * Number of particles disappearing per step \\  & * Whether all particles have disappeared or not \\  & * Number of steps in which over 40\% of the have particles disappeared \\  & * Number of steps in which over 60\% of the particles disappeared \\  & * Number of steps in which over 80\% of the particles have disappeared \\ \hline \multirow{3}{*}{Statistics of particles} & * Standard deviation of position at the final location \\  & * Standard deviation of scale at all steps \\  & * Standard deviation of heading direction offset at all steps \\ \hline Others & * Number of steps \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features extracted from the localization process.  
Figure 6: Evaluation metric using sampling points of the ground truth path.  
is assigned to the input features if the metric of the process \(A\) is smaller than \(B\). Therefore, the model is trained by solving a binary classification task. The output of the model is a value ranging from 0 to 1.  
The model is trained on only the synthesized walking data. Two localization processes are performed on the same map image and \(mpp\). We allow no encounters between the processes. Thus, the model compares independent localization processes.
## 2 Materials and Methods
### Numerical Method  
Spatial discretization of the differential equations was performed on a uniform mesh for the fluid and solid domains, incorporating the second-order central difference scheme for the diffusion terms and the Quadratic Upstream Interpolation for Convective Kinematics (QUICK) scheme [17] for the convective terms. Temporal derivative terms in the differential equations are treated using an implicit approximation with second-order accuracy. The algebraic discretization equations were solved through the line-by-line application of the tri-diagonal algorithm. Steady-state solutions to the governing equations were obtained numerically by a pseudo-transient approach. The temperature field was considered convergent when the maximum variation between the value computed at the present and previous iterations was less than \(10^{-5}\). All computations were performed with double-precision arithmetic. Moreover, the global energy balance over the computational domain was checked for each simulation and found to always be within \(0.1\%\).  
In this paper, the grid convergence index (GCI) supplemented by the extrapolated relative error is used to search for the grid point and determine the data error. The grid point test is performed for pure water and a fluid with \(20\%\) PCM particles by volume. The various errors in the local \(Nu\) of the inner and outer wall surfaces, the local heat fluxes, the average cross-sectional solid-liquid volumetric fraction, and the average cross-sectional temperature are calculated using different numbers of grid points to determine the number of grid points. Figure 2 shows one of the obtained results. For a grid point test, obtained with \(c_{p}=0\), \(Bi_{o}=1\), \(Pe=1000\), \(Ste=0.1\), \(t_{w}=0\), \(k_{wf}^{*}=10\), and \(\theta_{in}=0\), three cases have been tested, and the recommended numbers of points are \(501\) in the axial direction and \(50\) in the radial direction. For \(c_{p}=0.2\), \(Bi_{o}=1\), \(Pe=1000\), \(Ste=0.1\), \(t_{w}=0\), and \(\theta_{in}=0\), four cases have been tested, and the recommended numbers of grid points are \(2001\) in the axial direction and \(250\) in the radial direction, which are used in this paper.  
To validate the present formulation and numerical method, simulations were performed for selected cases of the forced convection of PCM suspension in a circular tube for comparison with the results that were presented in previous studies [10]. The results showed that there was no significant difference; thus, the reliability of the simulation results was confirmed.
## 5 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classifier Algorithms**} & \multicolumn{3}{c}{**Accuracy Using Reduced Data and BSSID**} \\ \cline{2-5}  & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline
1 & KNN & 82.10\% & 92.17\% & 92.17\% \\
2 & SVM-poly & 90.11\% & 95.63\% & 98.96\% \\
3 & MLP & 93.67\% & 98.00\% & 99.15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Localization performance through classification. As shown in Table 5, the computational time of MLP in Scenario 2 and Scenario 3 are better than Scenario 1, since LDA reduced complex features into simpler features. Additionally, the appended BSSIDs help to provide extra information without affecting computational time. In general, the training stage of regression scheme required high computational time, while the testing stage needs very little computational time, and is relatively straightforward. The proposed method shows that it can be applied in indoor environments to provide better and steady localization accuracy. Applying LDA for feature reduction and the appended BSSIDs having the five strongest RSS values help the proposed algorithm achieve the best localization accuracies. Additionally, collecting numbers of signal values from a 1 m by 1 m grid helps the proposed system adapt to the environment easily for better localization. Filling the missed RSS values using regression is also used to improve localization performances, rather than filling them manually. Above all, the MLP network outperforms with respect to indoor localization accuracy because of tuning with back-propagation, which helps to update the widespread, and control overfitting problems. MLP has several parameters or features that could be updated according to the nature of the data [34]. MLP also has good performance in pattern recognition in deep and complex studies. The projected data from complex to lower dimensions can be more easily understood by MLP than KNN and SVM-poly because MLP has a nonlinear activation function over the others. Therefore, MLP can distinguish data that is not linearly separable, easily using its nonlinear activation function. Moreover, MLPs are fully connected so that each node in one layer connects with the following nodes with a certain weight \(w_{ij}\), which allows MLP to learn better. Generally, the simulation result shows that the proposed technique can provide accurate indoor localization in complex and hierarchical environments. The results also indicate that the location estimation errors changed very calmly between consecutive tested points, due to the superior robustness of MLPs in uncertain, as well as complex, situations.
## 1 Introduction
### Problems and Challenges  
Specifically, urban road travel time prediction faces the following important challenges:  
(1) Intersection signal control timing: Unlike highways, urban roads contain intersections of different scales on arterials, collectors, and local roads. Signal-controlled intersections cause vehicles operating in the road network to form interrupted flow rather than continuous flow, resulting in periodic fluctuations in average segment speeds, thereby exacerbating the volatility of segment travel times. This characteristic makes urban road travel time prediction more complex compared to highways.  
(2) Dynamism and uncertainty of traffic demand: Highway traffic demand exhibits relatively stable changes with strong periodicity. However, urban road segment traffic demand is highly dynamic with intense variation. Although periodicity exists, it appears less obvious than highways due to strong volatility, significantly increasing the randomness of segment average speed changes and consequently the randomness of segment travel time variations.  
(3) Traffic incident impacts: Compared to highways, urban road traffic conditions are more susceptible to various traffic incidents, particularly non-recurrent traffic events. Recurrent traffic events mainly include rush hour traffic congestion, while non-recurrent traffic events primarily include traffic accidents, construction, and weather changes. If occasional mobile bottlenecks or different types of traffic conflicts (such as vehicle-vehicle conflicts, vehicle-bicycle conflicts, and vehicle-pedestrian conflicts) are also considered as traffic events, it can be understood that non-recurrent traffic events affecting traffic conditions in urban roads have diverse sources, strong randomness, varied development trends, and different impact ranges, causing urban road segments and networks to experience sudden traffic congestion spanning different spatial and temporal distances due to the occurrence of multiple non-recurrent traffic events.  
(4) Randomness problem in prediction models: In deep learning models, particularly recurrent neural networks such as long short-term memory (LSTM) networks, there exists a randomness problem--even when using the same network structure and hyperparameters, different prediction results may be produced in different training iterations. This randomness stems from random weight initialization and randomness in the optimization process, increasing the difficulty of model selection and evaluation.
## 2 Related Work
### Heuristic Routing Protocols  
Researchers have come up with some heuristic routing protocols, which have primary intelligence and can appropriately optimize and adjust the network communications temporally. The authors of [30] integrated Tabu-search meta-heuristics and simulated annealing to improve local-search results in discrete optimization for geographical routing protocol in the Delay-Tolerant Network. The results show the Tabu list improves the packet delivery ratio, and if Tabu is used, a better performance can be obtain with the simulated annealing routing strategy. In [30], an ant colony optimization algorithm and the Dynamic Source Routing algorithm are proposed. The pheromone level is calculated by the distance, the congestion level, and the stability of a route. A new pheromone volatilization mechanism is proposed; judging by the results, the proposed algorithm outperforms traditional algorithms in terms of network performance for a battle environment. In [31], the authors extended the Gaussian-Markov mobility model into the fitness function of the bee colony algorithm (BCA) to predict the link expiration time, and then used BCA for the route discovery of FANETs in the 3D environment [32]. A genetic-algorithm (GA)-based ad hoc on-demand multi-path distance-vector routing protocol is proposed [33], which utilizes a fitness function to optimize the path based on energy consumption. The proposed routing protocol extends the network's lifetime. Although a heuristic algorithm can employ simple intelligence to manage an ad hoc network, it is more suitable for solving static optimization problems. A heuristic routing protocol will relax when nodes join and leave, and which lack of scalability and consume considerable computation time for high mobility UAV networks.
## 2 Materials and Methods
### Finite Element Analysis  
Finite element analysis was employed to determine the shear bond strength under both static and fatigue conditions (Figure 2).  
Figure 2: Von Mises stress and displacement values obtained using FEA. Finite element analysis (FEA) results showing von Mises stress distribution (**left**) and displacement values (**right**) for composite repair specimens treated with different adhesive protocols. Blue regions represent areas of minimal stress or displacement, while red regions indicate peak values after a static load of 300 N similar to the shear bond tests was applied in the direction indicated by the blue arrows.  
Mesh validation was performed using a mesh convergence study while tetrahedral meshing was applied with an average element size of 0.2 mm. The model was re-meshed with finer elements until further mesh refinement resulted in a von Mises stress value change of less than 5%, ensuring computational accuracy and stability. To simulate the virtual bond strength tests, Fusion 360 (v2603.0.86, Autodesk Inc., San Francisco, CA, USA) CAD software was used. Physical properties of the study materials, including Young's modulus, the shear and tensile strength, and Poisson's ratio, were collected from manufacturers' technical data or FEA studies, as shown in Table 2.  
Subsequently, static and dynamic FEA analyses were utilized for calculations, with a 0.05 mm automatic contact detection tolerance, and three sides (both sides of the 3D resin block) were selected as constraints. The load (max. of 300 N) was then applied in accordance with the load-to-failure test to each specimen, considering the ultimate tensile strength (MPa) data as the main safety factor, and data were recorded for each model. In the present study, all materials in the finite element analysis (FEA) were modeled under the assumption of linear elastic material behavior under static loading condition. Boundary conditions were also simplified: three sides of the resin composite blocks were constrained with fixed displacement boundary conditions to simulate immobilization during shear bond testing, while a static load of 300 N was applied on the opposing surface, in alignment with the experimental setup. These simplifications allow for effective stress distribution analysis while maintaining computational efficiency and relevance to clinical composite repair scenarios.
# Underground Mine Road Detection Using Deep Learning Technique
## 5 Conclusions  
Aiming at the semantic segmentation of underground mine roads, an improved network detection model BiSeNet using deep learning technique is proposed. It integrates the backbone network of the STDC model with the agreed attention fusion module UAFM, thereby enriching the feature representation and improving the learning ability of the network model. The improved BiSeNet mine road detection model is tested on the dataset constructed based on the underground mountain road image, and compared with other  
Figure 15: Comparison of the recognition effects of curved roads and sandy roads: (**a**) bend; (**b**) wind and sand; (**c**) SegNet_1; (**d**) SegNet_2; (**e**) ENet_1; (**f**) ENet_2; (**g**) BiSeNet_1; (**h**) BiSeNet_2; (**i**) BiSeNet_improved_1; and (**j**) BiSNet_improved_2.  
methods. The experimental results show that the improved BiSeNet network increases the mIoU and PA of the mine road to 89.34% and 98.34%, respectively, and the frame rate to 23 f/s. It provides a reliable reference for the environmental perception of unmanned mine trucks on underground mine roads.  
Although the semantic segmentation network proposed in this paper can achieve better detection of mine road areas in the context of underground mines, this study only recognizes the road areas in the image, and does not take into account the interference factors such as road workers and rockfalls on the mountain, which will be studied and solved in our future work.  
Conceptualization, Z.T.; investigation, Z.T.; resources, Z.T.; data curation, Z.T.; writing--original draft preparation, W.Z.; writing--review and editing, X.Z.; visualization, W.Z.; supervision, X.Z.; project administration, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Laboratory Project of Shaanxi Province Department of Education (20)S063).  
Not applicable.  
The authors declare no conflict of interest.
## 3 The CLICP Construction Scheme
### CLICP Deployment Model  
The CLICP deployment model is shown in Figure 6; the CLICP adopts a hybrid cloud mode, with the data and core business information stored on private clouds belonging to the railway enterprise for core asset management and monitoring. Non-core business functions, such as value-added services, can be placed on the public cloud of the cloud service provider. The elastic computing resources of the public cloud are shared, minimizing the operational cost of the platform and improving its working efficiency. Cross-border logistics transport schemes can be determined quickly by using cloud resources, and the order and dispatching order are distributed to the production department and third-party branch highway carrier enterprise.  
The purpose of CLICP is to attract more third-party carriers and attract more third-party carriers. In the promotion stage, the platform can distribute orders to carriers or individual drivers free of charge, attract carriers, and cultivate usage habits. At a macro level, the operation of CLICP will go through early, mid-term, and final stages. At present, the CLICP's operation focuses on introducing the platform into the market and successfully gaining a foothold; in the mid-term, it will quickly occupy the corresponding market share and resources. As soon as possible, it will enter a stable final stage with an active user base and profitable income.  
Figure 6: CLICP deployment model.  
As shown in Figure 7, the CLICP acquires historical transportation production data through the opening of the ports of its own production information system by railway and highway transportation enterprises as data support for sending transportation schemes, dispatching instructions, order distribution, and statistical analysis. According to actual transportation demand, the consignor can obtain a door-to-door logistics freight product and its corresponding attribute information from the CLICP, including price and time, and then choose the optimal freight product within the acceptable price or transportation time range. The cloud-based cross-border logistics transport scheme has the advantages of accurate marketing, efficient feedback, and mutual benefit.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 1 Introduction
For example, [PERSON] and [PERSON] compared several \(K\)-d tree variants in the context of music similarity search [22]. They found that the combination of a tree variant for approximate search, known as spill trees [23], with principal component analysis (PCA) [24] gives a favorable trade-off between accuracy and complexity. As an alternative, in our contribution, we explore a modern graph-based index structure called Hierarchical Navigable Small World (HNSW) graph [25], which provides an approximate search solution, and already has been successfully used for, e.g., image retrieval [26]. In our paper, we use a cross-version music retrieval task [9] as an example application to explore the HNSW graph in practice. We conduct systematic experiments using databases of different sizes to analyze the impact of the graph-based index on the quality and speed of our retrieval system. As our main contribution, we show that we can increase the efficiency of our music retrieval application by several orders of magnitude through the usage of an HNSW graph as an index structure. Although the graph-based search belongs to the category of approximate nearest neighbor search approaches, our experiments show that, by and large, the retrieval quality is not negatively affected by using the graph in our scenario. We also analyze several further aspects in our retrieval system, such as the feature computation and the construction, saving, and loading of the index structure. We want to emphasize that our results demonstrate huge advantages of the graph-based indexing approach compared to previously used index structures. We expect similar benefits for other MIR problems involving nearest neighbor search. Beyond cross-version music retrieval, such search problems occur in diverse MIR tasks, such as cover song retrieval [27, 28], music similarity estimation [19], query-by-humming [20], or symbolic music genre classification [29]. To make our results reproducible [30], we use open-source implementations of the discussed index structures and provide code that shows how we use them, along with pre-computed features for an example dataset ([[https://www.audiolabs-erlangen.de/resources/MIR/2020_signals-indexing](https://www.audiolabs-erlangen.de/resources/MIR/2020_signals-indexing)]([https://www.audiolabs-erlangen.de/resources/MIR/2020_signals-indexing](https://www.audiolabs-erlangen.de/resources/MIR/2020_signals-indexing)), accessed on 12 May 2021). In this way, we enable a straightforward usage of the index structures in future MIR applications. The remainder of this paper is organized as follows. In Section 2, we outline our music retrieval application. Next, in Section 3, we describe graph-based search procedures in general and the HNSW graph in particular. Then, in Section 4, we present our systematic experiments where we apply the HNSW graph for our music retrieval task. Finally, we summarize our main findings in the concluding Section 5.