esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 1 Introduction
In previous studies, parking vehicles are mainly used in edge computing for content delivery [5], which may face resource limits for all the participants. As vehicular intelligence applications become more and more popular, the limited edge server resources will not be able to meet the growing demand for computing services. Deploying more lightweight edge servers requires significant costs and results in wasted resources during off-peak hours. Moreover, it is impractical, costly, and unsustainable for traditional fuel vehicles to engage in computing tasks while parked. Electric vehicles are well-resourced, inexpensive, and continuously rechargeable, making them the best choice to assist edge servers. In general, the electric vehicle commercialization services have a charging management system, which manages the charging piles and vehicles, and collects various information about charging vehicles in the parking lots. Therefore, the management system can aggregate a large number of parking vehicles to participate in VEC. In this paper, we propose a framework in which the vehicles assist the VEC servers to complete the offloaded tasks. The framework establishes an edge crowdsourcing platform (ECP) near the parking lots, and the ECP integrates the functions of the charging management system, which enables the ECP to provide charging management services for the vehicles in the parking lots and to act as a crowdsourcing service platform to assign computing tasks to charging vehicles. The main contributions of this paper are as follows. 1. We propose a VEC framework that uses electric vehicles in parking lots to assist edge servers in processing computational tasks. An edge crowdsourcing platform (ECP) is built to manage and integrate the idle computational resources of electric vehicles in parking lots, so that it can provide computational services for requesting vehicles. 2. To maximize the utilities of all participants in VEC, based on the Stackelberg game, we analyze the interactions among the requesting vehicles, the edge server, and the ECP, and theoretically prove the existence of a Nash equilibrium for this Stackelberg game. Then, a game strategy-solving algorithm based on a genetic algorithm is proposed to find the optimal strategies for the edge server and the ECP. The rest of this paper is structured as follows. In Section 2, we discuss and analyze the latest related research works. Section 3 presents the system model and definitions. Section 4 analyzes the interaction between the VEC server and the ECP based on Stackelberg game. Section 5 shows the simulation experimental results. In the last section, a summary and outlook of our work are given.
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 3 Results and Analysis
### Analysis of Ice Rate of Water in Evaporator  
In order to indicate the evaporator performance during the freezing process, it is necessary to firstly analyze the ice rate of water in the evaporator. With the help of software, the numerical results of the ice rate in the seven operating conditions and their fitting curves are indicated in Figure 7. According to the Boltzmann model and Figure 7, prediction Equation (9) of the ice ratio \(y\) in water is fitted. The coefficients of \(A_{1}\) and \(A_{2}\) and the initial time of \(t_{0}\) and time step of \(dx\) are given in Table 2.  
\[y=A_{2}+\frac{A_{1}-A_{2}}{1+e^{\frac{x-x_{0}}{dx}}} \tag{9}\]  
When the water at low temperature flows into the evaporator, its sensible heat is firstly released. Then, its latent heat follows and the ice is formed. The ice rates in the evaporator under various conditions sharply increase firstly with time. Then, they tend to smooth, of which the change in the ice rate in the C5 condition is the most obvious.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{2}{c}{**Evaporation**} & \multicolumn{2}{c}{**Inlet Water**} & \multicolumn{2}{c}{**Inlet Water**} & \multirow{2}{*}{\(\mathbf{A_{1}}\)} & \multirow{2}{*}{\(\mathbf{A_{2}}\)} & \multirow{2}{*}{\(\mathbf{x_{0}}\)} & \multirow{2}{*}{\(\mathbf{dx}\)} \\  & \multicolumn{2}{c}{**Temperature/\({}^{\circ}\)C**} & \multicolumn{2}{c}{**Temperature/\({}^{\circ}\)C**} & & & & & \\ \hline C1 & \(-3\) & 3 & 0.5 & \(-0.44\pm 0.19\) & 3.77 \(\pm\) 0.06 & 3.81 \(\pm\) 0.38 & 9.01 \(\pm\) 0.47 \\ C2 & \(-5\) & 3 & 0.5 & \(-0.49\pm 0.31\) & 3.79 \(\pm\) 0.09 & 8.41 \(\pm\) 0.73 & 3.80 \(\pm\) 0.57 \\ C3 & \(-7\) & 3 & 0.5 & \(-0.43\pm 0.36\) & 4.01 \(\pm\) 0.10 & 8.21 \(\pm\) 0.79 & 3.57 \(\pm\) 0.62 \\ C4 & \(-5\) & 3 & 0.3 & \(-0.53\pm 0.44\) & 5.31 \(\pm\) 0.13 & 7.55 \(\pm\) 0.67 & 2.86 \(\pm\) 0.52 \\ C5 & \(-5\) & 3 & 0.1 & \(-0.92\pm 0.51\) & 9.35 \(\pm\) 0.15 & 8.17 \(\pm\) 0.48 & 3.32 \(\pm\) 0.37 \\ C6 & \(-5\) & 2 & 0.1 & \(-0.86\pm 0.57\) & 5.90 \(\pm\) 0.19 & 9.68 \(\pm\) 0.96 & 5.00 \(\pm\) 0.82 \\ C7 & \(-5\) & 1 & 0.1 & \(-1.27\pm 0.72\) & 3.81 \(\pm\) 0.15 & 7.16 \(\pm\) 1.83 & 6.35 \(\pm\) 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Seven operating conditions for heat pump simulation.  
Figure 6: Curve of _COP_ varying with terminal water supply.
## 2 Materials and Methods
### Magnetic Resonance Imaging (MRI) Measurement  
All breast MRI examinations were performed on a 3-T magnet (Discovery 750w, GE Healthcare, Waukesha, WI, USA) using an 8-channel dedicated phased-array breast coil. The patients were examined in the prone position with both breasts symmetrically positioned in the coil. The PDFF [23; 24] pulse sequence used for this study was provided by the manufacturer of the MR unit (IDEAL IQ, GE Healthcare). The pulse sequence used the multipoint Dixon technique, which uses a low flip angle to limit T1 bias, acquires six echoes to correct for T2* effects, and uses a multipeak fat model. The parameters of this sequence included: Repetition time, 8.3 ms; shortest echo time, 3.2 ms; field of view, 35 \(\times\) 35 cm; matrix, 160 \(\times\) 160; bandwidth, 111.1 kHz; flip angle, 4\({}^{\circ}\); section thickness, 7 mm; and a single dimensional image with 24 sections. The images were processed using the software provided by the manufacturer (IDEAL IQ, GE Healthcare) to instantaneously create water, fat, R2*, and fat fraction maps.  
To measure the fat fraction of fibroglandular tissue in a normal breast, a ROI was placed over the entire fibroglandular tissue shown as an intermediate intensity region at the level of the nipple on a transverse image of the fat fraction maps (Figure 1a). The fat fraction was calculated using a medical imaging system (SYNAPSE version 4.1, Fujifilm Medical, Tokyo, Japan).
## 4 Experiments
### Ablation Study  
Ablation studies were carried out to determine the impacts of secondary subdivision and dynamic weighting, we conduct experiments on the MNIST dataset with Symmetry-50% noise and the CIFAR-10 dataset with Symmetry-20% noise. To eliminate the influence of secondary subdivision and dynamic weighting, we train all samples obtained by the small loss sample strategy. To verify the effect of dynamic weighting, we use fixed weighting coefficients for the pure sample and the complex sample. According to the previous analysis, these two methods should play their respective roles in the training process.  
Sample selection: The state-of-the-art performance of JoSDW is largely due to precise and dependable sample selection. We use graphs and tables to show the accuracy of sample selection. The graph shows the accuracy of the clean sample selection to study and verify the advantage of the sample selection method. It can be seen from the figure that JoSDW is efficient in accurately and reliably selecting clean samples. In every case, JoSDW outperforms the most advanced sample selection algorithms in terms of picking clean sample data. In addition, in demanding scenarios (i.e., Asymmetry-40%), although all other methods have an impact on finding clean samples, the accuracy of JoSDW's clean sample selection steadily improves as training progresses. These findings support the validity of our clean sample selection method. The last period in this table shows the best periods and last period's selection accuracy, respectively. The results confirm the effectiveness of JoSDW in selecting pure samples and complex samples.  
Tables 8 and 9 show the impact of various phases in our strategy. In training, the JoSDW-S denotes the case where pure samples and complex samples are used. In training, JoSDW-SD represents the utilization of clean samples and complex samples with dynamic weights. Finally, the JoSDW denotes the suggested approach in its ultimate form. The experimental process is shown in Figure 15.  
Figure 14: Results on CIFAR-100 dataset. Noise settings, Asymmetry-40%. **Left**: precision accuracy (%) vs. epochs; **Right**: label precision (%) vs. epochs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Methods** & **Label Precision** \\ \hline
1 & JoSDW-S & 81.12 \\
2 & JoSDW-SD & 84.96 \\
3 & JoSDW & 86.28 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average precision accuracy (%) on CIFAR-10 Symmetry-20% over the last 10 epochs.
## 3 Materials and Methods
### Method
The final representation is formed by concatenating the pooled sequence and the IP embeddings:  
\[\mathbf{c}=[\mathbf{z};\mathbf{v}_{v};\mathbf{v}_{a}] \tag{7}\]  
where \([\cdot;\cdot;\cdot]\) denotes vector concatenation. The output is computed as  
\[\mathbf{\hat{y}}=\text{softmax}(\mathbf{W}\mathbf{c}+\mathbf{b}) \tag{8}\]  
where \(\mathbf{W}\in\mathbb{R}^{|\mathcal{A}|\times(d_{model}+2d_{\mathcal{W}})}\) and \(\mathbf{b}\in\mathbb{R}^{|\mathcal{A}|}\) are the weights and bias of the final linear layer and \(|\mathcal{A}|\) is the number of attacker classes. Notation: Bold symbols (e.g., \(\mathbf{v}_{v}\), \(\mathbf{S}\), \(\mathbf{h}_{i}\)) denote vectors or matrices; normal font denotes scalars or indices. The operator \([\cdot;\cdot]\) indicates vector concatenation. All dimensions are explicitly defined at first use. The overall architecture and the interaction between its main components are illustrated in Figure 7, which visually summarizes the flow from input embeddings and positional encoding, through the Transformer encoder, to the final classification layer for attacker attribution. This architecture efficiently captures both the local features of each alert and the global dependencies within the sequence. The proposed model integrates the following fundamental components to achieve effective attack attribution:  
* IP and Alert Embeddings: Dense, learnable vector representations (\(\mathbf{v}_{v}\), \(\mathbf{v}_{a}\), \(\mathbf{S}\)) enable the model to capture semantic relationships between IPs and alert types, facilitating the generalization of attacker behavior patterns across diverse scenarios. * Positional Encoding: Sinusoidal positional encoding (\(PE\)) is added to the alert embeddings to preserve the temporal order of alerts, a critical aspect for modeling the evolution and characteristic patterns of multi-stage attacks. * Transformer Encoder: The core of the architecture, composed of multi-head self-attention and feed-forward layers, models long-range dependencies and complex relationships between alerts, regardless of their temporal distance in the sequence. * Aggregation and Concatenation: Temporal aggregation (mean pooling, \(\mathbf{z}\)) condenses the sequence into a global representation, which is then concatenated with the con textual information of the involved IPs (**c**), allowing the model to jointly consider temporal patterns and attack context. * Classification Layer: A final linear layer transforms the aggregated features into a probability distribution over the possible attackers, enabling robust attribution in multi-adversary settings. This design adheres to the core principles of the Transformer model while specializing in attack attribution. Its modular structure and clear separation between embedding, encoding, and classification stages facilitate efficient adaptation to a wide range of attribution scenarios, while preserving the model's ability to learn intricate patterns across variable-length alert sequences.
## 2 System Modelling
### Modelling of EV SOC and Availability  
The proposed management technique also considers the EV battery's SOC which is a significant metric because it indicates the amount of energy stored. Equation (13) is a typical assessment of the EV battery's SOC based on power charging and discharging.  
\[SoC_{EV}=SoC_{int}+\frac{\Delta Pr_{g2v}+\Delta Pr_{v2h}+\Delta Pr_{v2g}}{CAP_{ EV}^{full}} \tag{13}\]  
\(SoC_{int}\) is the starting SOC and \(CAP_{EV}^{full}\) is the EV battery's power capacity. Given the EV battery's lifespan, several limits have been placed on the power produced by the EV through the V2H unit and the battery SOC. In addition, \(\Delta Pr_{g2v}\) represents when the EV battery is charged from the grid, \(\Delta Pr_{v2h}\) is the energy supplied to the home, and \(\Delta Pr_{v2g}\) is the energy returned to the grid.  
According to Equation (14), EV power is limited between the lowest operational power (\(Pr_{EV,min}\)) given to the EV and the largest power range (\(Pr_{EV,max}\)) to be provided to the home or grid (V2G or V2H).  
\[Pr_{EV,max}\geq Pr_{dis},Pr_{ch}\geq Pr_{EV,min} \tag{14}\]  
Equation (15) similarly indicates the inhibition of EV battery deep discharge and full charging by setting a minimum and maximum SOC., i.e., (\(SoC_{EV,min}\)) and (\(SoC_{EV,max}\)).  
\[SoC_{EV,min}\leq SoC_{EV}\leq SoC_{EV,max} \tag{15}\]  
Equation (16) [57] considers the charging and discharging efficiency (\(f_{EV}\)) of the EV. Where, (\(C_{EV,Pr}\)) is the cost of power loss, (\(R_{EV}\)) is the EV range, (\(D_{EV}^{Home}\)) is the distance between the EV and home, (\(CAP_{EV}^{total}\)) is the total EV battery capacity, and (\(C_{pr}(t)\)) is the power price at time \(t\).  
\[f_{EV}=\frac{C_{EV,Pr}\times R_{EV}}{D_{EV}^{Home}\times CA_{EV}^{total}\times C_ {pr}(t)} \tag{16}\]  
The ability to ensure if the EV is parked at home and available for V2H connections is defined as follows by Equation (17):  
\[Loc_{EV}=\begin{cases}1\ EV\ available\\ 0\ EV\ not\ available\end{cases} \tag{17}\]
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 4 Evaluation Framework
### Datasets
#### 4.1.2 CamVid  
CamVid [56] is an image dataset containing road scenes. At first, it was recorded as a video of five sequences. The resolution of the images that CamVid is consisted of is 960 \(\times\) 720. The dataset provides in total 32 classes. Some of the most important ones for road-scene understanding are: car, pedestrian, motorcycle, traffic light, traffic cone, lane markings, sign, road, truck/bus and child.
# Wheat Ear Detection Algorithm Based on Improved YOLOv4
## 2 Materials and Methods
### YOLOv4 Object Detection Algorithm
#### 2.1.1 Feature Extracting Network  
Feature extracting network, also referred to as the backbone, plays a key role in object classification and object detection, and highly determines the model's performance. It encodes the input image into a certain feature representation. In order to mitigate the problem of heavy inference computations, [PERSON] et al. proposed a Cross Stage Partial Network (CSPNet) [27]. CSPNet separates the input feature maps into two parts. One part goes through the following convolutional neural network block and transition layer, and the other part bypasses the mentioned block and transition layer and combines with the transmitted feature map to the next stage. Cross Stage partial connection technique was used on ResNeXt50 [32] and Darknet53 [23], referred to as CSPResNeXt50 and CSPDarknet53, individually. In pursuit of the fastest operation speed of the module, CSPResNeXt50 and CSPDarknet53 were compared on GPU in YOLOv4. Although CSPDarknet53 is not as good as CSPResNeXt50 in terms of object classification, it is better in terms of detecting objects [24]. In order to find the optimal balance among the input network resolution, the convolutional layer number, parameter number, and number of layer outputs, CSPDarknet53 was chosen as the backbone. Additionally, Mish activation function was used on CSPDarknet53.  
YOLOv4 takes some measures to modify the backbone further, such as Mish activation function [33] and Dropblock regularization [34]. In the neural network, the weighted sum of the input from previous layers is transformed into output by the activation function, which is usually a nonlinear function. The activation function has a considerable impact on the performance of the model. In the YOLOv4 framework, several activation functions are used to compare the effects, and the Mish function is used for CSPDarknet53. The Mish function is \(f(x)=x*tanh(softplus(x))\)[33]. Dropout is an effective tool to regularize convolutional networks. DropBlock was applied to regularize convolutional networks, where units in a contiguous region of a feature map are dropped together [34].
## 3 System Development and Design
### Flowchart Algorithm
#### 3.2.2 Rest API  
The Rest API will be slightly more complex than the flow of the mobile application because the Rest API is the backbone of the system and handles the communication between the mobile application and server along with its data. The mobile application will call the PHP Rest API and collect all the parameter information, which is raw voice data, selected STT, and original text. Then, the raw voice data is validated to ensure they are in the correct format and size. If the validation is failed, then it will return an error message. If the validation is successful, the raw voice data, selected STT, and the original text are converted into JSON to prepare to call the Python Rest API. When the Python Rest API is called, the raw audio file has to be converted into the correct format and specification for the STT engines. Otherwise, it would not transcribe the text. Voice data is given to the specific STT engines according to the user's selection (i.e., if the user selects Google, then the voice will only be given to the Google STT). After that, the transcribed text and the original text are fed into the Sentence Transformer to obtain the similarity score. Once done, the transcribed text, original text, and similarity score results are converted into JSON format and returned to the PHP Rest API. The PHP Rest API then parses the JSON data into a readable string to be shown on the mobile application.  
Each Rest API present in this system has two different purposes. The PHP Rest API is responsible for getting the raw data, validating the data, formatting the data, and executing the Python Rest API. On the other hand, the Python Rest API is responsible for converting the raw data into the required specification, executing the STT engine along with the similarity checker, converting the final result into JSON, and returning it to the PHP Rest API. Based on the description, the PHP Rest API directly connects to the mobile application and the Python Rest API. In contrast, the Python Rest API directly connects to the PHP Rest API and the STT engine, along with the similarity checker. The flow of the Rest API will be shown in Figure 4.  
The developed system requires two Rest APIs because the Sentence Transformer, which conducts the similarity checker, can only be run in a Python environment. In contrast, the PHP Rest API is required because the PHP environment offers a more manageable, lighter, and more optimized way to get the raw voice data from the mobile application and do the validation. The downside of this scheme is that the device requires an active internet connection for the mobile application to connect with the Rest API.  
Figure 4: The Flowchart of Rest API to Illustrate the Algorithmic Flow.
## 1 Introduction
The insights and suggestions obtained with this study can guide the development of time that not only increases customer satisfaction but also reduces delivery costs by reducing total travel time and provides a win-win situation for both logistics companies and customers. In this study, a hybrid adaptive large neighborhood search (HALNS) algorithm is proposed for the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW), considering a full charging strategy in last-mile delivery. Efficient (HALNS) operators are designed to minimize tardiness by taking customer time windows into account. The proposed algorithm is tested using datasets designed for the Eskisehir Osmangazi University campus environment. To the best of our knowledge, there are no papers that address task scheduling of delivery requests by minimizing total tardiness. This paper also includes an analysis of trade-offs between the number of vehicles in the fleet and tardiness. The effectiveness of conventional cost-oriented routing and customer-oriented route optimization strategies for the last-mile delivery of electric vehicles is evaluated. The contributions of this study to the literature are summarized as follows:* Unlike the cost-focused objective functions in the literature, the objective function of minimizing total tardiness is studied. It takes customer satisfaction into account by considering customer time windows. * Since the ALNS operators proposed in the literature are not effective for the total tardiness objective function, new operators are proposed to serve this objective function. The rest of this paper is organized as follows: Section 2 briefly reviews previous research on electric vehicle routing problems. The materials and methods are given in Section 3. Section 4 discusses the results and performance of the proposed system. Section 5 presents the research results and provides an outlook for future work.
## 3 Results
### Sources of Mercury in Soils
[37] demonstrated that mercury retention and mobility in irrigated soil systems were influenced by the presence of dissolved organic carbon, which increased mercury solubility and potential bioavailability. Despite these observations, the mechanisms driving mercury transformation in irrigated agricultural landscapes remain poorly characterized. Many existing studies are limited by short-term durations, site-specific variability, or a lack of detailed chemical speciation. As a result, future research should prioritize long-term monitoring and integrated soil-water-plant assessments to better understand the risks associated with irrigation-induced mercury mobility and transformation, particularly in regions practicing water reuse. Overall, the synthesis of studies revealed that both land use intensity and proximity to water bodies are major drivers of mercury accumulation in soils across diverse landscapes. The studies collectively show that mercury accumulation in soils is predominantly influenced by land use type, industrial legacy, and human disturbance. Apart from lakeside and downstream areas, mining activities consistently yielded the highest soil Hg concentrations, while coal-related and urban-industrial zones exhibited moderate levels, especially near traffic and industrial hubs. Forestry practices such as clear-cutting and peatland drainage significantly enhance mercury leaching and MeHg, particularly in organic-rich toposils.