esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 3 Methodology
### Travel Behavior Analysis
#### 3.1.2 Methods to Analyze GPS Data
Therefore, a minimum period for a stationary state was selected to distinguish trip ends from stationary periods in an area within 50 m from an intersection stop line. In order to separate most cases of stopping at an intersection from trip ends, a value corresponding to the 85 th percentile of the distribution of time stopped near an intersection was selected as the minimum stationary period for classifying trips. The statistics regarding stationary periods near signal intersections and minimum stationary periods by service type are shown in Table 5. A stationary reference time that was too short was likely to result in an overestimation of the number of trips, and a value that was too large was likely to underestimate the number of trips. Therefore, the stationary reference time values were selected as near the 85 th percentile of the stationary periods due to traffic signals. Moreover, trip chains were classified based on whether there was enough of a time difference between trips. Multiple destinations can be planned before a trip begins. Since a trip occurs for a single purpose, travel involving multiple destinations between the initial departure point and the final destination can be classified as a single group [29]. Therefore, in this study, multiple trips were grouped into a trip chain. It would be ideal to know the multiple purposes planned by the driver before the trip begins, but GPS data do not include the driver's trip purposes. To differentiate between trip chains, the time gap between trips was utilized. A suitable criterion was needed to distinguish between trip chains, and the 30 min criterion, used in previous studies [30], was applied. Figure 2 shows the concept of how trips and trip chains were classified. Squares and circles represent the positions of the micro-EV every second, and each shape represents a stationary state and a moving state. Stationary states above the minimum stationary period classified trips, and temporarily stationary states that did not meet the minimum stationary period were not used to distinguish trips. In addition, stationary states of a period of 30 min or more were used to distinguish a trip chain. The last step was to extract the average values of the travel behavior variables. Trips and trip chains were classified based on the stationary state defined in the previous steps. This step derived trip characteristics from the divided trips and trip chains, as shown in Table 6. The number of trips per day, travel distance, and travel speed were derived for each trip. In addition, the number of trip chains per day, travel distance per trip chain, and travel speed per trip chain were derived for each trip chain. Finally, the usage time per day and the travel distance per day were derived to understand how much micro-EVs were used during the day.
## 4 Results and Discussion
### HOMO-LUMO Analysis  
The frontier representation of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of the L-dopa molecule is shown in Figure 7. The energies of these two MOs and their gaps are helpful in calculating the chemical parameters like ionization potential (I), electron affinity (A), chemical hardness/softness (S), chemical potential (\(\mu\)), electronegativity (\(\chi\)) and electrophilicity index (\(\omega\)) [27]. The HOMO, LUMO and their energy difference as well as related parameters are collected in Table 8. The energy difference between the HOMO and LUMO of the L-dopa molecule suggests that the C-I monomer and the dimer D\({}_{1}\) are relatively more chemically soft as compared to the dimer D\({}_{2}\). From the HOMO plot of L-dopa, it can be seen that the localized charge density is spread over the phenyl ring and both the OH groups attached to the ring, while for the LUMO the charge density is shifted towards the 2-aminopropenoic acid moiety. Perusal of the MEP and LUMO plots shows that the charge density is shifted towards the electron-deficient region. This suggests that in going from the HOMO to the LUMO the charge density is shifted from the electron-rich region to the electron-deficient region.  
Figure 6: Pictorial representation of the MEP plots of the C-I monomer and the dimer D\({}_{1}\).  
Figure 7: Frontier representation of the HOMO and LUMO orbitals of L-dopa.  
The observed and computed UV-vis spectra of L-dopa are shown in Figure S3. The computed and observed absorption bands with related parameters and possible transitions are given in Table 9. The computed UV-vis spectrum of L-dopa has three absorption bands peaking at 259, 246 and 241 nm with the major contributions from the transition H \(\rightarrow\) L, H \(\rightarrow\) L + 1 and H \(\rightarrow\) L + 2, respectively. The highest wavelength was observed due to the transition H \(\rightarrow\) L, suggesting that the observed absorption band 271 nm corresponds to the computed wavelength 259 nm. The computed wavelength 246 nm is correlated with the observed band 235 nm reported by [PERSON] et al. [12], which arises due to the transitions H \(\rightarrow\) L + 1 and H \(\rightarrow\) L + 2 with 68% and 32% contributions. The computed absorption band 241 nm is a result of transitions H \(\rightarrow\) L + 2 and H \(\rightarrow\) L + 1 with contributions 60% and 30%, respectively.
# Recent Advances in Tunable External Cavity Diode Lasers
## 1 Introduction
For ECDLs, the performances are influenced by many factors, such as cavity length and reflectivity. We have summarized the performances of external cavity semiconductor lasers with different structures, focusing on key parameters such as tunable range, output power, and threshold current density.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 5 Experiments
### Comparison with State-of-the-Art Models  
To further validate the segmentation performance of the proposed BEAC-Net, we compared several current state-of-the-art models, including U-Net [8], Deeplabv3+ [15], M-Net [12], and Swin-Unet [35]. The same experimental environment is set up for all model training, while the same pre-processing operations are performed on the fundus image. Figures 8-10 show a visual comparison of the segmentation results between our BEAC-Net and other methods of OD and OC segmentation on three datasets. The segmentation results of the OC and OD show that the boundary part of the BECA-net is closest to the ground truth, indicating that the OC boundary pixels are correctly categorized, and it enhances the feature capture to collect the boundary contextual information via the EBPA module. The segmentation method based on the U-Net network with its U-shaped structure and jump connections can effectively transfer the high-level and low-level information, but the number of network layers is small and cannot capture boundary features. Deeplabv3+ predicts the feature map directly bilinear upsampling 16 times to the desired size, which does not have enough detailed information, and the segmentation results are affected by blood vessels and bright lesions. Swin-Unet uses the Swin Transformer to extract image feature information, and the structure can better capture different levels of features, but there are difficulties in preparing segmentation for OD with large noise backgrounds. The reason that these methods do not give the expected results in segmenting OC and OD is that the boundary features and the contextual information of the boundaries are not extracted completely.Our proposed BEAC-Net can make the boundaries of OD and OC regions more sensitive by introducing boundary awareness into the network, make the AFF module focus on fusing the multi-scale features, and eliminate the influence of blood vessels and background noise on the segmentation results. In Figure 10, BEAC-Net produces segmentation results that are closest to the ground truth.
## 4 Results  
### Results of Static Simulations  
Static simulations allowed us to obtain the distributions of reduced Huber-Mises-Hencky (H-M-H) stresses in the implant and main bone stresses. The aim of this part of the analysis was to indicate whether the destabilization of the implant-bone system may occur in individual load cases. In addition, the distributions of nodal displacements on specific surfaces of the bone scar were obtained, which were then used to prepare the boundary conditions necessary for calculations regarding bone remodelling in the area of the fracture gap.  
First, examples of stress values obtained for bones using anterior stabilization for various load values are presented (Figure 14), then lateral (Figure 15) and anterolateral (Figure 16).  
The obtained values of H-M-H stresses during static analysis do not indicate that the bone proportionality limit is exceeded. Stress concentrations occur at the edges of the holes where bone screws are placed. These values, however, do not exceed the critical value, except for the case of lateral stabilization and 2400 N load (Figure 15c). In order to systematize the obtained values and present them in a more transparent way, the rest of the results of the maximum stress values obtained as a result of the simulation are presented in the table below (Table 2).  
Figure 14: Examples of obtained H–M–H stress distributions for anterior stabilization: (a) 353 N; (b) 483 N; and (c) 2400 N.  
Figure 16: Examples of obtained H–M–H stress distributions for anterolateral stabilization: (**a**) 353 N; (**b**) 483 N; and (**c**) 2400 N.  
Figure 15: Examples of obtained H–M–H stress distributions for lateral stabilization: (**a**) 353 N; (**b**) 483 N; and (**c**) 2400 N.  
What should be emphasized is that the force values do not exceed the critical values for cases 353 N and 483 N; therefore, the tests on the bone remodelling process were carried out for these load values. The highest values, significantly exceeding the yield strength values for the stabilizer material, were read for a load of 2400 N; therefore, no analysis of the reconstruction process was carried out, but only the damage propagation process was examined in the Abaqus/Explicit program. It is also worth mentioning the maximum stress values that were read during static tests. They most often occur in single elements in the screw-bone contact area, which may cause local stress concentration, but also be related to the tetra-type finite elements used.
## 3 Proposed Method  
In this paper, we introduce the DMCCT model, which is specifically designed for the effective integration of CNNs and Transformers in hyperspectral image classification. An overview of the proposed DMCCT model framework is illustrated in Figure 1. The framework consists primarily of a spatial information extraction branch, a spectral information extraction branch, and a fusion classification module.  
In the spatial information extraction branch, we begin by compressing hyperspectral pixel blocks using 3D convolutions, significantly reducing the computational burden for subsequent operations. Following this, the spatial multi-granularity information embedding module is employed to perform pixel-level patch embedding, generating token vectors enriched with multi-scale and multi-level spatial information. This process facilitates the comprehensive capture of spatial features across varying granularities. Subsequently, positional embeddings are added to the tokens, which are then processed through the  
Figure 1: Overview illustration of the proposed DMCCT model framework.  
convolutional cross-substitution attention module. This module further extracts spatial information characterized by both long-range dependencies and local features, skillfully merging the robust local feature extraction capabilities of convolutional operations with the Transformer's aptitude for capturing long-range dependencies. Additionally, residual connections are incorporated to enhance the extraction of contextual information.  
The spectral information extraction branch employs a similar strategy, initiating with the compression of hyperspectral image blocks via 3D convolutions. The spectral multi-granularity information embedding module then conducts spectral dimensional embedding, resulting in token vectors that encompass multi-scale and multi-level spectral information, which aids in the capture of spectral features across different granularities. In alignment with the spatial branch, after the inclusion of positional embeddings, the tokens are fed into the convolutional cross-substitution attention module to further extract spectral information characterized by long-range dependencies and local features. Residual connections are also utilized in this branch to strengthen contextual information extraction during the spectral retrieval process.  
Lastly, the fusion classification module integrates the extracted spatial and spectral information for classification purposes. This module serves a pivotal role by effectively merging the distinct feature types obtained from the two branches, thereby facilitating the accurate classification of hyperspectral images. In summary, the proposed network framework employs a dual-branch structure to extract spatial and spectral information independently, successfully leveraging multi-granularity embedding modules, convolutional cross-substitution Transformer modules, and a fusion classification module to combine the advantages of CNNs and Transformers, offering an efficient and reliable solution for hyperspectral image classification.
## 4 Use Case Study: [PERSON]'s Healthcare Monitoring
### Scenario Overview
It suggests that the middleware should contextualise heart rate readings with concurrent physical activity data, thereby helping to reduce unnecessary alerts. Meanwhile, NLP techniques are employed to improve the semantic annotations. Based on the feedback and the identified pattern, NLP suggests refining the annotation schemes of physical activity data to better capture the context in future data collection. On the other hand, clustering and classification techniques help to recognise patterns and classify them into actionable segments. This makes the feedback more structured and insightful. RL methods foster adaptive learning and continuous improvement. They incentivise the system to adjust its decisionmaking strategy based on the feedback and the outcome of the decisions made, leading to a system that evolves and improves its alert generation strategy. Incorporating the insights and suggestions from the AI Feedback and Analysis Module, the Service Integration and Composition Module adjusts the alert system to consider [PERSON]'s physical activities in its decisionmaking process. The Semantic Annotation Module improves its annotation schemes as suggested, and the Semantic Analysis and Inference Engine incorporates the enhanced data for its analyses. Lastly, the Semantic Visualisation Component reflects these updates, and [PERSON] is informed that the alert system now considers physical activities in its analysis. Through this AI-driven iterative process, the system becomes smarter, providing more context-aware and personalised services to its users. Figure 9: AI module: usage of deep learning to implement adjustment for alerts. To demonstrate the operation of the AI Feedback and Analysis Module within the scenario discussed in this work, Algorithm 3 is provided. It delineates the module's process of responding to and incorporating user feedback for system enhancement. Algorithm 3 illustrates how the module employs advanced machine learning techniques, including deep learning, natural language processing (NLP), clustering and classification, and reinforcement learning (RL), to analyse user feedback, similar to the insights provided by [PERSON]. It details the iterative process of identifying patterns, refining semantic annotations, and making informed adjustments to the system. It also details the steps within the rest of the SIM modules, such as the analysis of feedback content, the application of various AI techniques for pattern recognition and contextual understanding, and the subsequent system modifications based on the insights gained. This includes adjustments in the alert mechanisms to consider factors like physical activities during heart rate monitoring. Algorithm 3 also shows how these refinements are integrated back into the system, culminating in enhanced personalised user experiences.
## 3 Materials and Methods
### Methodology
#### 3.2.3 Stacking-Based Ensembling Methods  
In learning-based methods, the ensembling model is a technique that improves the overall performance of the final output predicted by combining the decisions from multiple models. The stacking algorithm is utilized in this work among the different ensembling techniques. Each base model has a different performance for each encoder and produces different segmentation masks, \"\(P_{ij}\)\". In order to aggregate the advantages of each model prediction, ensembling the outputs is required for this work. These predictions from each model are assigned some fixed weights based on their performance in the training dataset. The fundamental problem in using this ensembling algorithm is calculating the optimal model weights that produce better performance than any other combination of weights contributing to the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Resnet50 (1)** & **VGG19 (2)** & **Inceptionv3 (3)** & **Efficientetb7 (4)** & **Mobilenetv2 (5)** \\ \hline U-Net (1) & M\({}_{11}\) & M\({}_{12}\) & M\({}_{13}\) & M\({}_{14}\) & M\({}_{15}\) \\ LinkNet (2) & M\({}_{21}\) & M\({}_{22}\) & M\({}_{23}\) & M\({}_{24}\) & M\({}_{25}\) \\ PSPNet (3) & M\({}_{32}\) & M\({}_{32}\) & M\({}_{33}\) & M\({}_{34}\) & M\({}_{35}\) \\ FPN (4) & M\({}_{41}\) & M\({}_{42}\) & M\({}_{43}\) & M\({}_{44}\) & M\({}_{45}\) \\ \hline \end{tabular}
\end{table}
Table 2: Representation of each basic segmentation model with different encoders.  
Figure 3: Overview of the framework for stacking-based ensemble approach of Segmentation Models.  
A simple grid search algorithm determines the optimal weights of each model to overcome this challenge. A grid-search algorithm is a parameter optimization technique that selects the best possible combination of different parameters from a given range. In this case, the parameter used is the weights, \"\(y\),\" assigned for each model prediction. The grid search algorithm starts by forming a grid search space with the given weight values. For each model, the initial weight values are assigned between zero and the randomly specified range, and the error is calculated for each combination of values. After each iteration, the weight values are updated, and the error value is calculated again. Finally, the optimal weights \"\(W_{opt}\)\" are selected for five encoders based on the minimum error values. \"\(W_{opt}\)\" for each basic model is represented as  
\[W_{opt}=[w_{1}\ w_{2}\ w_{3}\ w_{4}\ w_{5}] \tag{1}\]  
The weight values range from 0 to 1, and the assumed weights are commonly expected to be constrained, as in Equation (2):  
\[w_{j}\geq 0\ and\ \sum_{j=1}^{5}w_{j}=1 \tag{2}\]  
These weights are multiplied by their corresponding model output predictions, \"\(P_{ij}\)\" to form the final ensembled meta-model, \"\(E_{j}\)\".  
\[E_{j}=\sum_{j=1}^{5}P_{ij}\times w_{j} \tag{3}\]  
The final obtained ensembled model (\"\(E_{j}\)\") generates the segmentation mask for the test image with better prediction accuracy than other models before ensembling.
## 2 Methodology  
### Study Area  
This study was carried out on part of the WDN of the city of Sharjah, UAE. The Sharjah WDN is shown in Figure 1 below. The network details were obtained from the SEWA. This network was selected as it represents a large WDN and is suitable for the application of this model. The pipe network consists of approximately 70,000 nodes, and the pipeline extends to about 3152 km through the entire city. Water in Sharjah is obtained from ground water and desalinated seawater and distributed to the pipe network from three main treatment plants: Layyah, Hamriyah, and Saja. Chlorine is further injected into the network as a secondary disinfectant. The WDN is subdivided into districts, each of which contains one or multiple pressure, flow, and water quality sensors. Sensor readings are recorded from the 13 monitoring zones daily to ensure the best quality of supply and to allow for anomaly detection. In this study, the proposed method was tested on data collected by SEWA over the span of a year.
## 5 Results  
Analyses were conducted using the data acquired by TLS and MLS to compare point clouds based on their classification, shifts in the selected objects, details, and density. Before the actual comparisons, the data were processed into a form where unwanted and redundant points were filtered out from each point cloud. The program Trimble Realworks 12.2 was used for segmenting the point clouds from TLS and MLS, while Leica Cyclone 3 DR, Trimble Realworks 12.2, and CloudCompare 2.13 were employed for processing, filtering, visualizing, and comparing the data. Figure 7 illustrates the optimized workflow diagram from measurement through processing to analysis of the point clouds.  
After processing the data from TLS and MLS, dense point clouds were generated, which served as input for further comparisons and analyses. For TLS, the resulting dense point cloud (Figure 8a) was generated with the Leica Cyclone Register 360 software and contained 916,014,221 points. This point cloud was subsequently classified and filtered. Three different mobile laser scanners were used for the MLS method. The point cloud generated by the mobile scanner Lidaretto contains 119,383,840 points (Figure 8b) with an overall RMSE accuracy of 3.4 cm on CPs. The resulting point cloud from the mobile laser scanner Stonex X120 GO comprises 199,096,585 points (Figure 8c) with an overall RMSE accuracy of 2.1 cm on CPs. The iPhone 13 Pro with an Emlid scanning kit and a GNSS antenna Reach RX generated a point cloud containing 14,384,860 points (Figure 8d).  
Figure 8: The resulting point clouds obtained by methods under study—3D view and top view of the TLS (**a**), 3D view and top view of the Lidaretto (**b**), 3D view and top view of the Stonex X120 GO (**c**), 3D view and top view of the iPhone 13 Pro with Emlid scanning kit and GNSS antenna Reach RX (**d**).  
Figure 7: Diagram of the optimized workflow.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 3 Modeling  
### Problem Description  
According to the terminal distribution demand points in a certain urban area, two types of co-distribution centers are set up, namely air and ground. The associated distribution tasks are accomplished by UAVs and land-based vehicles, respectively. Through the connection between co-distribution centers and terminal stations or other intelligent receiving points, full coverage of regional distribution demand points is realized. To minimize the construction and operation costs of the regional distribution network and the overall distribution time, the site layout of the distribution centers must be optimized. This paper proposes a method for determining the optimal combination of air-ground cooperative distribution centers. Airspace management requirements are fully considered, and certain no-fly zone restrictions are set within the study area to improve the applicability of the planning method. Figure 1 shows a schematic diagram of the model's proposed solution scenarios and problems.