esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
###### Abstract  
Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries.  
agriculture; remote sensing; Segment Anything Model; cropland; parcel extraction
## 2 Classification of Breast Cancer by Histological, Molecular, and Morphological Characteristics
### Histopathological Classification of Breast Cancer
TC is recognized as a well-differentiated invasive carcinoma and is further classified into two categories: pure TC and mixed TC. Pure TC refers to tumors with a tubular content of more than 90% and a low nuclear grade, with few-to-no mitoses [20]. On the other hand, mixed TC has a tubular composition of less than 75% [20];
* Mucinous carcinoma is characterized by low-grade tumors composed of cancer cells that are situated within the mucin substance found in mucus. This subtype accounts for fewer than 2% of all breast cancers [21];
* Papillary carcinomas are tiny cancerous cells that have finger-like appendages. Less than 1% of all breast cancers are papillary carcinomas, making them extremely uncommon [22];
* Cribriform carcinoma is characterized as an unusual subtype with a Swiss cheese-like pattern of holes. This type of cancer accounts for fewer than 1% of breast cancer cases [23];
* Adenoid cystic carcinoma: In contrast to typical ductal cancer cells, adenoid cystic carcinoma resembles cancerous cells found in the salivary gland. This subtype constitutes less than 1% of all breast cancers [24];
* Metaplastic carcinoma: This takes place when ductal cells transform into new cell types. Less than 1% of all breast cancers are metaplastic carcinomas, which are typically more aggressive cancers [25].
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
## 3 VNF-FG Problem Formulation
### Constraints  
#### 3.1.1 VN Mapping  
As we know, each VNF-FG is a graphical representation of a set of VNFs and VLs along with its resource requirements. Depending upon the service type, each VNF requirement varies. Some VNFs like Firewall and Deep Pack Inspection demand more central Processing Unit (CPU) and RAM resources, others comparatively less. Likewise, these requested resources can be CPU core, RAM, Memory, etc. One of the critical aspects for a successful VNF deployment is the provisioning of sufficient resources by the substrate nodes, according to the requested resources. Thus, the first constraint will check the availability of the resources for the VNFs in the substrate network (Here, onward 'Substrate Network' or 'Topology' represents the Telecommunication Network.),  
\[\sum_{v}Y_{h}^{v}p_{v,r}\leq a_{h,r},\forall h,r, \tag{1}\]  
where \(p_{v,r}\) indicates the amount of requested resource \(r\) by the VNF \(v\), and \(a_{h,r}\) represents the availability of resource \(r\) on the substrate node (i.e., the node present in the substrate network) \(h\). \(Y_{h}^{v}\) is a binary variable, which indicates the placement of the \(v\) VNF onto the \(h\) substrate node. If \(Y_{h}^{v}=1\), this indicates the successful placement of VNF onto the selected substrate node, else \(Y_{h}^{v}=0\) represents an unsuccessful placement of VNFs, leading to an eviction of the corresponding VNF-FG.
## 1 Introduction
This paper presents a methodology that is used to evaluate previously treated alternative solutions in the literature [3; 4; 5; 6] and optimize a given design of multirotor UAVs to tolerate actuator failure and perform maneuvers required by post-failure missions. The needed moment force to track a predefined mission trajectory is denoted as a time history of required moments that can be obtained from simulation and analytically converting the desired course into control input. The system requirement that imprints these required moments derived from the designed mission and disturbance rejection was geometrically represented as standard deviation geometry (SDG) [9; 10; 11]. Similarly, the maximum capacity of a given multirotor configuration in generating moment force is represented by the attainable moment set (AMS) as a convex polytope whose shape is influenced by design parameters, such as the number of actuators, position, orientation, and propeller-related parameters. For a system to be capable of fulfilling its task, the AMS should inscribe sufficiently scaled-up SDG to ensure the system requirement is below the system capability. Therefore, the proposed approach focuses on formulating the optimization problem that considers actuator health status and a related algorithm to evaluate the enclosure of required moment points within the AMS up to the enforced marginal requirement. The proposed method was applied on a Hexarotor type of UAV designed for urban parcel delivery and developed by a PNU drone to optimize its actuator tilting angle and arm installation angle and grant the system actuator fault tolerance. Furthermore, the model of the selected UAV employing an active tilting mechanism was simulated for its fault tolerance at hovering and following a preplanned path. A brief structure of the paper is given here: Section 2 comprises a theoretical and mathematical overview of multirotor UAVs as well as an introduction to the assessment tools and the assessment of the effect of actuator failure on the system; Section 3 elaborates controllability criteria and their geometrical representation of system requirements; Section 4 introduces an overview of the approach and mathematical formulation of the optimization problem and the Point-in-AMS checking algorithm; Section 5 discusses implementation details; Section 6 comprises the results and discussion; and Section 7 briefly concludes the paper.
## 2 Description of the Proposed Method
### Simulation Model
#### 2.2.2 Hybrid Model of the Milling Process
Equation (13) is used for time domain simulations of the cutting process. When the tool (structural subsystem) and the workpiece (modal subsystem) are in contact with each other, this hybrid model becomes non-stationary and time-varying due to tool rotation, causing a continuous change in the geometric position of the instantaneous contacts of the tool edge with the workpiece. However, contrary to the previous considerations [30], due to the invariance of the position of CE no. \(l\) in relation to the rotating system \(x_{r1}\), \(x_{r2}\), \(x_{r3}\) of the RFE, this time the matrix \(\mathbf{T}_{l}\) has constant components. The latter significantly reduces the required simulation time. To simulate the hybrid model of the cutting process of the workpiece in accordance with the planned tool path, a computer with the proprietary AMIKRO4 software version 4, developed in FORTRAN using the free MSYS2 MinGW 64-bit toolset, and the free GNU compiler, were used. The results were analyzed using the MATLAB 2017 and 2022 (MathWorks, Natick, MA, USA) package. To identify the modal model of the flexible workpiece, the matrix of normal modes \(\mathbf{\Psi}\), dimensionless damping coefficients \(\mathbf{Z}\), and angular natural frequencies \(\mathbf{\Omega}\) of the modal subsystem must be determined. By isolating the modal subsystem, the model size is significantly reduced to a few essential modes. These modes remain unchanged during machining, allowing for identification through Experimental Modal Analysis (EMA) on the workpiece installed on the milling machine table. It must be noted, that in the case of large or complex workpieces, different dominant vibration modes can be observed in various zones of the milled surface. Thus, it is needed to perform EMA for a set of points distributed on the structure, especially on the machined surface. The dominant modes may be identified based on the frequency response function (FRF) and a chosen modal identification method.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 2 Materials and Methods  
Figure 1 illustrates the research methodology flow conducted in this study. The yellow rectangular boxes represent the sequence of results derived from the analysis using each technique. The process began with an exploratory data analysis (EDA) of the heavy metal concentration data, followed by data preprocessing. Subsequently, a grid was established to define the options and parameters for both the machine learning model and geostatistical techniques. This facilitated hyperparameter tuning to identify the optimal combination. The interpolation techniques utilized in this study include the RF model (indicated as number 1 in Figure 1), OK technique (number 2), and combined RF and OK models (number 3). The interpolation (prediction) errors of each model were compared and evaluated, and a map was generated based on the prediction results.  
This study utilized ArcGIS Pro 3.2.1, a commercial GIS software, for data visualization and analysis, and Python 3.11.5 was used for machine learning analysis. Additionally, geostatistical analyses were performed using the Python library PyKrige 2.7 [26], which implements Kriging techniques for both two-dimensional and three-dimensional data.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 5 Conclusions  
This paper addresses the critical issue of speed control in autonomous vehicles and proposes a genetic algorithm-based PID controller parameter optimization method. The primary objective of this approach is to tackle the challenges associated with precise speed tracking in autonomous vehicles operating in complex and dynamic road environments. Speed tracking accuracy is essential to ensure that autonomous vehicles respond accurately and promptly to changing road conditions, thereby enhancing system safety, operational efficiency, and overall driving experience. This precision directly impacts passenger comfort and vehicle operational stability.  
The proposed solution leverages the global search capabilities of genetic algorithms to achieve fine-tuning of PID controller parameters, significantly improving both speed control accuracy and the system's dynamic response. Through this optimization, the autonomous vehicle can better adapt to various environmental conditions, such as road surface variations, further enhancing the vehicle's stability and responsiveness, making it more reliable in real-world applications involving uncertainties.  
Experimental results have clearly validated the effectiveness of this genetic algorithm-based optimization approach, particularly in terms of improved speed tracking accuracy, demonstrating that this method meets the complex control requirements of autonomous driving. However, to further clarify the applicability and potential prospects of this method, it is recommended that future studies verify its scalability and robustness in a broader range of real-world road conditions and multivariable control systems, thereby supporting its application in complex driving environments.  
J.C.: conceptualization, methodology, validation, writing--original draft preparation. G.T.: supervision, writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 2 Background Theory
### Edge Server  
An edge server is a server on the edges of the network [29]; it is located where the corresponding function is required and distributed processing is performed [30]. The edge server performs compute offloading, data storage, caching, and processing. Further, it distributes request and delivery services from the cloud to the user [31].  
Figure 1: Wireless mesh network topology.  
Figure 2a shows the network distance between the client and cloud server, and Figure 2b shows the network distance between the client and edge server. The physical distance between the edge server and client is shorter than that between the cloud server and client. The edge server simplifies the network structure, and the time required to send and receive data is short [32]. The edge servers in the LAN layer can communicate data with clients in a stable manner. In the case of a cloud server, the data are transmitted from the client to the destination server via a metropolitan area network (MAN) and WAN. Latency such as bottlenecks and intermediate node systems can occur depending on the amount of data transmission [33]. [PERSON] [34] conducted research on 5G, edge caching, and computing. Edge caching has been studied in the existing literature to minimize latency and load.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 1 Introduction
[17] introduced FACNet for high-precision segmentation of pumpkin seedlings, incorporating a dual-branch feature extractor and an adaptive multi-scale fusion module to handle overlapping leaves and morphological diversity. [PERSON] et al. [18] developed an automated pipeline using CAFPoint for accurate tomato organ segmentation and trait extraction by fusing geometric, normal, and color features. [PERSON] et al. [19] introduced Cott-SegNet with enhanced attention and region-growing for precise cotton organ segmentation. [PERSON] et al. [20] proposed Plant-MAE, a self-supervised framework that reduces annotation needs while ensuring accurate trait extraction. [PERSON] et al. [21] combined PointNet++ with Transformer to build TPointNetPlus for high-accuracy cotton segmentation. [PERSON] et al. [22] introduced a dual-phase method integrating PointNeXt with Quickshift++ to perform plant organ instance segmentation, achieving accurate and generalizable results across monocot and dicot crops through integrated semantic segmentation and spatial clustering. Despite these advances, 3D plant point cloud analysis still faces several challenges. Existing methods often treat semantic and instance segmentation as separate tasks, lacking synergy and leading to inconsistent results. Local geometric details and spatial contextual information are underutilized, limiting performance in complex structures such as intersecting stems and occluded organs. Moreover, there is a lack of 3D deep learning models specifically designed for tomatoes--a morphologically complex dicot--making it difficult for general-purpose models to accurately capture their phenotypic characteristics. Therefore, developing a segmentation framework that integrates semantic and instance information, is structure-aware, and is tailored to the unique features of tomato point clouds is of great importance. To address these issues, we propose TSINet, a dual-task deep learning network that runs end-to-end for 3D tomato plant point clouds. TSINet is designed to perform both semantic and instance segmentation to achieve fine-grained recognition and separation of tomato organs. The main contributions of this work are as follows:  
* We design a Geometry-Aware Adaptive Feature Extraction Block (GAFEB) within a shared encoder, incorporating EdgeConv, PAConv, and residual connections to enhance the extraction of local and contextual geometric features. * A Dual Attention-Based Feature Enhancement Module (DAFEM) is introduced, combining spatial and channel attention mechanisms to model salient regions in decoder features, improving perception in structurally complex areas. * We propose TSINet, a point-based dual-functional segmentation network tailored to tomato plants, capable of segmenting stems and leaves with precision in both semantics and instances.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 7 Conclusions  
This paper systematically mapped out 3D path planning algorithms in UAV-enabled communications systems. UAVs with communication technologies are promising wireless connectivity solutions in remote and disaster-stricken areas. However, integrating UAVs into real scenarios requires diverse technologies and algorithms, with 3D path planning algorithms crucial in determining optimal obstacle-free paths.  
Through analyzing 37 articles from the Scopus database, we provided an overview of UAV-enabled communications systems, examined objectives to be minimized or maximized, investigated optimization variables, and explored algorithmic strategies for solving the 3D path planning problem. Unlike existing literature focusing on 2D environments, our study specifically addressed 3D path planning solutions, filling a critical research gap and offering insights into recent advancements.  
The study highlights the need for further research and development in several critical areas of 3D path planning algorithms in UAV-enabled communications systems. These include the consideration of multiple UAVs to enhance network coverage, incorporating dynamic obstacle avoidance capabilities, and exploring multi-objective optimization approaches beyond altitude and distance minimization. Addressing these challenges can advance UAV-enabled communications systems' efficiency, reliability, and effectiveness, unlocking their full potential for wireless connectivity in challenging environments and contributing to various application domains, including disaster response, remote areas, and beyond.  
**Author Contributions:** Conceptualization, J.C.-R., M.M. and C.T.; methodology, J.C.-R., M.M. and C.T.; software, J.C.-R. and M.M.; validation, J.C.-R., M.M. and C.T.; formal analysis, [PERSON]-R., M.M. and C.T.; investigation, [PERSON]-R., M.M. and C.T.; resources, [PERSON]-R., M.M. and C.T.; data curation, [PERSON]-R. and M.M.; writing--original draft preparation, J.C.-R., M.M. and C.T.; writing--review and editing, [PERSON]-R., M.M. and C.T.; visualization, [PERSON]-R., M.M. and C.T.; supervision, [PERSON]-R., M.M. and C.T.; project administration, [PERSON]-R., M.M. and C.T.; funding acquisition, [PERSON]-R., M.M. and C.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Escuela Politecnica Nacional has supported this work in the project PIS-22-09.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Acknowledgments:** [PERSON] acknowledges the support provided by the Escuela Politecnica Nacional for his doctoral studies.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Datasets and Preprocessing
#### 2.2.2 Remote Sensing Products  
Remote sensing technique has provided massive products for extracting and analyzing natural features [49]. Six types of remote sensing products were utilized in this study:  
(1) Vegetation indexes, closely associated with ecological status, were selected from the GLASS (Global LAnd Surface Satellite) remote sensing parameter dataset [50]. Leaf area index (LAI) represents the sum of the one-sided area of green leaves per unit land area. It plays an important role in monitoring and measuring material and energy exchange of land surfaces [51]. Gross primary productivity (GPP) is the amount of organic carbon fixed by organisms (mainly green plants) through photosynthetic pathways per unit time, which is closely related to the quantitative description of the terrestrial ecosystems and carbon cycle [52]. NDVI data was obtained from the MOD13A1 v6.1 NDVI product of MODerate resolution Imaging Spectroradiometer (MODIS). It reflects the magnitude of vegetation cover and the land cover class of the ground background [53]. Specific information of spatial and temporal resolution can be found in Table 1.  
(2) Temperature data (Tmp) and precipitation data (Pre) were sourced from the 1 km monthly mean temperature dataset for China (1901-2021) and the 1 km monthly precipitation dataset for China (1901-2021) [54], respectively.  
(3) PM2.5 data [55] were obtained from the ChinaHighPM2.5 dataset, part of China High Air Pollutants (CHAP), offering long-term and full-coverage datasets of ground-level air pollutants in China.  
(4) Soil nutrient availability data (SNA) were derived from the Harmonized World Soil Database (HWSD) v1.2 dataset. We used sq1 data in soil quality, i.e., nutrient availability data.  
(5) Urban built-up area data were obtained from the dataset of urban built-up area in China (1992-2020) v1.0, providing support for understanding the urban expansion process and its impact in China [56; 57].  
(6) Land cover data used were sourced from the China Land Cover Dataset (CLCD) of Wuhan University [58], containing annual land cover information for China in 1985 and 1990-2022.
## 3 Design of SOCA  
SOCA is designed to mitigate the impact of VDE-TER data exchange on the AIS message transmission in coastal areas with a high density of ship stations. To achieve this, SOCA focuses on effectively allocating slots for VDE-TER data transmission by selecting candidate slots that have a low probability of being potentially used for AIS message transmission. SOCA is a MAC layer enhancement of the VDES standard and, therefore, uses the VDES protocol as its baseline MAC scheme. Specifically, SOCA comprises four steps: (1) construction of the NIT and VDES frame maps, (2) construction of the candidate slot list for VDE-TER, (3) TDMA channel selection, and (4) collision avoidance slot selection. In the following, we provide a detailed description of the operations involved in SOCA.