esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 2 Related Works  
### Liver Volume Segmentation  
Liver segmentation has attracted many algorithms with various approaches [8], such as graph-cuts, region growing, variational approaches (level-sets, etc.), or model-based techniques [11; 14; 15]. In recent years, many researchers developed DL methods to solve liver segmentation problems. Among the most widely used methods, various Convolutional Neural Networks (CNNs) have been proposed to learn feature representations for liver or liver lesion segmentation. [PERSON] et al. [16] proposed using Fully Convolutional Neural networks (FCN) for liver segmentation and liver-metastasis detection. They used three adjacent CT image slices to form a three-channel image to serve as input. [PERSON] et al. [17] trained an FCN for liver segmentation, then made a comparative study on the postprocessing step to refine their results. [PERSON] et al. [18] proposed two cascaded FCNs to segment liver and tumors simultaneously. The final output was refined using a 3D conditional random field. [PERSON] et al. [19] designed a multichannel FCN to segment liver tumors, the probability maps were generated by the feature fusion from different channels. [PERSON] et al. [20] used a 3D CNN and a graph cut to refine their results. [PERSON] et al. [21] proposed a superpixel-based CNN, which divides the CT-image into superpixels by aggregating pixels according to their intensity. They classify them into three classes--interior liver, liver boundary, and nonliver background--and utilized the CNN to predict the liver boundary. [PERSON] et al. [22] proposed introducing multiscale features in the CNN that extract global and local features at a more granular level. [PERSON] et al. [23] used a U-Net (architecture based on a FCN) in two models and filtered the false positives of tumor segmentation results by a random forest classifier. In [24], the authors proposed an improved U-Net model with a graph cutting. [PERSON] et al. [25] presented a basic architecture as U-Net with the extraction of contextual information, which combine low-level feature maps with high-level ones. A hybrid method, namely, H-DenseUNet, presented by [26], combined 2D and 3D models to segment livers and liver tumors. [PERSON] et al. [27] also used a 3D and 2D U-Net network to segment livers. The 3D encoding phase is for capturing 3D surfaces, while the 2D decoding phase reduces the complexity of the process.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 2 Existing Studies and Literature Review
### Edge Computing and Blockchain Integration  
Edge computing deployments enable substantial efficiency improvements. [PERSON] et al. (2021) utilized ARM Cortex-M4 microcontrollers to implement Fast Fourier Transform algorithms that can detect anomalous vibration patterns during transportation, reducing data requirements by 87% while maintaining real-time alertingcapabilities [18]. Signal processing was further refined by MOKOSMART (2024), which implements Kalman filtering on ESP32 microcontrollers, achieving 94% noise reduction with sub-200 ms response times [24; 25].  
Blockchain integration with these sensor networks presents promising traceability solutions. [PERSON] et al. (2022) developed Ethereum-based ERC-721 contracts that integrated sensor data as transaction validators. By requiring temperature readings within 19-21 \({}^{\mathrm{o}}\)C ranges before allowing ownership transfers, they were able to achieve 3.4-s validation times with minimal gas consumption [26]. [PERSON] et al. (2020) addressed scalability challenges with a permissioned Hyperledger Fabric network utilizing Practical Byzantine Fault Tolerance consensus, achieving 3500 TPS while processing data from 5000 concurrent sensors [27]. Data provenance was enhanced by [PERSON] (2023) through elliptic curve cryptography authentication, generating digital signatures with 15 ms verification times while adding only 64 bytes of overhead per measurement [28].  
While these studies demonstrate significant technical advances, our work uniquely addresses several critical gaps identified in the literature. As shown in Table 1, where a \(\surd\) means presence of topic and \(\times\) means its absence, existing implementations typically focus on either IoT integration or blockchain utilization but rarely combine these technologies within a comprehensive dual-layer architecture. Furthermore, none of the reviewed works offer process verification capabilities or transaction cost analysis, which are essential for practical deployment in agricultural settings. Unlike existing works, which have focused on either blockchain or IoT in isolation, our framework introduces a dual-layer integration with smart contract-triggered decision enforcement based on simulated IoT sensor inputs such as RFID, GPS, and environmental data. Furthermore, we uniquely model process verification, batch-wise traceability, and mobile validation across multiple stakeholder roles, which are either absent or loosely defined in prior implementations. Our approach integrates blockchain-based ownership transfers with IoT-driven quality assurance in a unified framework, providing both technological innovation and ethical supply chain transparency that previous approaches have not fully achieved.
## 2 Materials and Methods
### Methods
#### 2.2.1 Collection of Scientific Data and Models  
We used the wildland fire-induced risk map for the Fort McMurray communities, the primary basis of this study, that was prepared after the HRF event (the fire entered from the southwest on 1 May 2016) primarily using a WorldView-2 satellite image acquired on 6 June 2016 [1]. Additionally, we collected remote sensing-based forecasting models to understand wildland fire danger conditions in the area, which were primarily based on the meteorological and biophysical variables of vegetation, such as land surface temperature, precipitable water, normalized difference vegetation index, normalized difference water index; and historical ignition cause-specific static fire danger (SFD) maps [44; 45; 46]. We considered all the recommendations of these models for preparing the survey questionnaire in this study. It would be worth noting that we obtained the comprehensive dataset from the integrated analysis of satellite images and GIS (geographic information systems) to make the generated maps available for potential use in this research.
# Decision Support Application for Energy Consumption Forecasting
## 2 Material and Methods
### HyFIS  
HyFIS was proposed by [PERSON] and [PERSON] [30], and it is a combination of fuzzy logic with neural networks. As Figure 1 presents, the learning process of this method is based on two phases. The first phase is the structure learning, where the method uses the knowledge acquisition module to find and create the rules. The second phase membership functions will be tuned to achieve a desired level of performance, which is the parameter learning phase.  
Figure 1: A general schematic diagram of the HyFIS.  
When a new set of data is inserted into the method, a new rule will be created for the new data, and the fuzzy rule base gets updated. Having an easy updatable fuzzy rule base can be named as one of the advantages of this approach [31].  
The neuro-fuzzy model in the HyFIS is a combination of fuzzy systems and a multilayer ANN. As Figure 2 shows, this structure contains five layers, where the input state and output control/decision signals, respectively. Also, the nodes in the hidden layers represent the membership functions and rules.  
The nodes in the first layer represent the inputs of the method. The nodes in the second and fourth layer, act as the Membership Functions (MF) to express the input-output fuzzy linguistic variables. The fuzzy sets in these layers are presented as large (L), medium (M), and small (S). However, in some cases, this distribution can be more specific and be shown as large positive (LP), small positive (SP), zero (ZE), small negative (SN), and large negative (LN). The third layer includes the rule nodes, every node in this layer is a fuzzy rule. The connection weights between the third and fourth layer represent certainty factors (CFs) of the associated rules, i.e., each rule is activated to a certain degree controlled by the weight values. In the end, the nodes in the fifth layer present the outputs of the system.
# Edge Effect Analysis and Edge Defect Detection of Titanium Alloy Based on Eddy Current Testing
## 2 Edge Effect Evaluation
### Modeling Geometry  
Three-dimensional (3D) finite element method (FEM) models with different sensor parameters were built with ANSYS Maxwell. The simulation model contains a test piece, an EM sensor, and a computation region. The test piece used is a cuboid plate with the material of titanium alloy. The EM sensor used is a cylindrical copper coil placed above the test piece, as shown in Figure 1. The modeling parameters are listed in Table 1.
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 2 Materials and Methods
### NWP ZTD Integral Method  
Two methods: the integral method and model estimation method, are commonly used to derive the ZTD from the NWP meteorological parameters [28]. In this study, the results of the integral method and model estimation method was compared by computing the tropospheric delay of one reference station for one year using these two methods. Treating the tropospheric delay gained by GAMIT as the precise value, the tropospheric delay residual of the two methods are shown in Figure 2. From this figure, we can see the residual of the integral method generally vary between \(\pm\)40 mm, while the residual of the model estimation method can reach \(\pm\)60 mm. The standard deviation of the tropospheric delay residual of the integral method is 17.5 mm, and is 33.2 mm for the model estimation method. As a result, the accuracy of the tropospheric delay from the integral method is better than the model estimation method in this experiment. Although the integral method gets a better results in this experiment, we also agree that the accuracy depends on the level of integration.  
The integral method is applied in this study. The physical conditions on each 3D grid point of NWP products (e.g., pressure, temperature, relative humidity, and height) are used to calculate the atmospheric refractivity \(N\) of each grid point (Equation (1)), and the ZTD is estimated by the integral method using Equation (2).  
\[N=k_{1}\frac{(P-e)}{T}+\Big{(}k_{2}\frac{e}{T}+k_{3}\frac{e}{T^{2}}\Big{)}=N_{ \text{dry}}+N_{\text{wet}} \tag{1}\]  
Figure 1: Distribution map of the experimental stations.  
\[\text{ZTD}=10^{-6}\sum_{i=1}^{n}N_{i}\Delta\text{H}_{i} \tag{2}\]  
where \(k_{1}=77.6890\) K/mbar, k\({}_{2}=71.2952\) K/mbar, and \(k_{3}=375,463\) K\({}^{2}\)/mbar are empirically determined coefficients [35], \(T\) is the temperature (unit: K), \(P\) and \(e\) are atmospheric pressure and water vapor pressure (unit: mbar), \(N_{\text{dry}}\) and \(N_{\text{wet}}\) are the refractivity of dry gas and refractivity of wet gas, \(N_{i}\) is the atmospheric refractivity in the \(i\) th integral area (it equals the average of two adjacent layers' grid points), \(\Delta\text{H}_{i}\) is the height of the \(i\) th integral area, and \(n\) is the level of integration, which depends on the number of levels of meteorological data from the observation station to the top of the meteorological data.  
Since the observation station would not be located at the grid points, the ZTD of the observation station are obtained using bilinear interpolation (horizontal direction) and the Gaussian function model (vertical direction) in this study [36].
## 3 Types of Supplementary Dampers and Their Characteristics
### Tuned Mass Dampers
#### 3.5.3 Key Characteristics  
Tuned mass dampers (TMDs) exhibit several key characteristics that define their behavior and applications. They demonstrate frequency-tuned behavior, which allows them to be precisely calibrated to the structure's natural frequency. This tuning results in their effectiveness being concentrated in a narrow frequency band, making them highly efficient for specific vibration modes. However, this narrow-band effectiveness also means that TMDs are potentially susceptible to off-tuning due to structural changes, which can reduce their efficiency. Another notable characteristic of TMDs is their large space requirements for the auxiliary mass, which can be a significant consideration in their implementation.
## 3 Design of Main Working Parts
### Seedling-Casting Device  
In order to ensure the alignment of the heads of the seedlings when they are placed manually on the inclined plane of the seedbed, it is necessary to reasonably design the seedling-casting device. Currently, the commonly used methods to control seedling spacing include pre-treatment of seedling belt method [9], hole disk seedling belt method [19], and artificial seedling-feeding method [20]. The pre-treatment of seedling belt method requires a lot of preparation work and would consume much time, while the hole disk seedling belt method is suitable for transplanting seedlings with small plant spacing. Therefore, in order to improve the transplanting efficiency and meet the subsequent outcrop cultivation mode of the membrane edge, this design adopts the method of artificial seedling feeding and mechanical auxiliary seedling casting. As shown in Figure 7, the seedling-casting device consists of a seedling protection plate, cylindrical pin, connecting block, fixed pin, seedling separation plate, conveyor belt, driving shaft of the seedling-casting device, driven shaft I, and driven shaft II. They are fixed on the frame, relying on the rotation of the ground wheel to drive the driving shaft of the seeding device which rotates and drives the conveyor belt to move at the speed of \(V_{c}\). Driven shaft I and driven shaft II support the conveyor belt and cooperate with it to move. Positions \(m\), \(n\), and \(e\) are the seedling protection area, seedling-casting area, and seedling-dropping area, respectively. At point \(e\), the seedling  
Figure 6: Spatial force analysis at the moment of soil particle slipping under the action of shaper.  
protection plate opens due to its dead weight, and seedlings can be placed manually. When seedlings are transported to point \(m\), the seedling protection plate closes under the action of gravity to protect seedlings. When seedlings are transported to point \(n\), the seedling protection plate opens to drop seedlings. The seedling-casting device flexibly uses the self-weight of the seedling protection plate to realize real-time seedling protection and precise seedling casting.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 6 Conclusions  
While there has been notable progress in the development of UAIS and wireless mobile communication technology in recent years, the current focus of UAIS is primarily on applications that depend on terrestrial network infrastructure. To resolve the challenge of ubiquitous network coverage for various UAIS applications, we proposed insights into 6G UAV NTN communication-enabled UAIS capabilities, which is a promising inter-discipline field for future research in distributed computation, human-machine interaction, and multi-agent cooperation. The 6G NTN concept along with edge/fog/cloud computing and artificial intelligence will be a major driving force for the evolution of unmanned autonomous intelligent systems. Integrating 6G NTN capabilities, UAIS is capable of enabling a variety of applications including intelligent transportation system in smart city, disaster relief and management, smart agriculture, oceanic surveys and other intelligent applications to come. In this paper, we proposed a comprehensive review of the challenges and opportunities that non-terrestrial networks may bring about in the field of unmanned autonomous intelligent systems. A case study of UAV NTN communication was presented to demonstrate the application of UAV NTN technology for UAIS in regions where terrestrial network infrastructure is unavailable. From the literature review and case study, we can conclude that UAIS in non-terrestrial network technology is an emerging field that holds great potential in enabling space-air-ground-sea integrated ubiquitous coverage, which holds various application prospects and research opportunities. Much research effort and industrial implementation work are urgently needed to promote the realization of more efficient, human-friendly, self-organized UAIS integrated with non-terrestrial network concepts and technologies.  
Conceptualization, [PERSON] and Y.G. ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); writing, original draft preparation, [PERSON]; writing, review and editing, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant No. 62222121 and grant No. 62341110).  
The authors declare no conflicts of interest.
## 5 Simulations Results
### Case Studies  
Several simulations are carried out to study the process behavior with the different controllers and their effect on process economics and removal efficiency. The performance indices provided by the BSM1 platform are used to evaluate the process performance, with the different controllers in the operating period under characteristic storm weather influent variations. In total five case studies are contemplated in this paper for comparing the control and the performance of the proposed one layer optimizing control strategy. First we present four case studies and then we present another case called case 5 to address the lack of the degree of freedom.  
* Case 1 (NGPC): Unconstrained Nonlinear Generalized Predictive Control (NGPC) that minimize the following cost function that takes into account only the control objectives, without considering closed loop predictions: \[\begin{split} J(k)=&\sum_{i=1}^{n_{y}}||w_{1}(r(k+i|k )-y(k+i|k))||_{2}^{2}\\ &+\sum_{i=0}^{n_{u}-1}||w_{2}\Delta u(k+i-1|k)||_{2}^{2}\end{split}\] (57)
* Case 2 (NEGPC): Unconstrained Nonlinear Economic Generalized predictive control (NEGPC) that minimize the following cost function which accounts for economics, but without considering closed loop predictions: \[\begin{split} J(k)=&\sum_{i=1}^{n_{y}}||w_{1}(r(k+i|k ))-y(k+i|k)||_{2}^{2}\\ &+\sum_{i=0}^{n_{u}-1}||w_{2}\Delta u(k+i-1|k)||_{2}^{2}\\ &+||w_{3}\xi_{u+\Delta u}^{T}||_{2}^{2}\end{split}\] (58)
* Case 3 (NCLGPC): The one layer optimization and control based on nonlinear closed-loop GPC presented in this work that minimize the following cost function without economics.  
\[\begin{split} J(k)=&\sum_{i=1}^{n_{y}}||w_{1}(r(k+i|k))-y (k+i|k)||_{2}^{2}\\ &+\sum_{i=0}^{n_{u}-1}||w_{2}\Delta u(k+i-1|k)||_{2}^{2}\\ \text{subject to}&(4)-(8)\end{split} \tag{59}\]
* Case 4 (NECLGPC): The one layer economic optimization and control based on nonlinear closed-loop GPC presented in this work that minimize the following cost function which accounts for economics. \[\begin{split} J(k)=&\sum_{i=1}^{n_{y}}||w_{1}(r(k+i| k))-y(k+i|k)||_{2}^{2}\\ &+\sum_{i=0}^{n_{u}-1}||w_{2}\Delta u(k+i-1|k)||_{2}^{2}\\ &+||w_{3}\xi_{u+\Delta u}^{T}||_{2}^{2}\\ \text{subject to}&(4)-(8)\end{split}\] (60) Those controllers are summarized in the following Table 5:  
Those control strategies are evaluated and compared by means of simulations of the process model (Equations (35)-(42)) implemented in Matlab. The simulations have been carried out considering the influent profile described in Figures 3-5 (storm weather scenario), and analogous influents for rain and dry weather described in the BSM1 specifications.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments  
To verify the validity of SSTGL and explore the reasons behind it, in this section, we conducted extensive experiments to answer the following research questions (RQs):  
* RQ1: How does the proposed SSTGL method perform when compared with the start-of-the-art baselines?
* RQ2: How does each component of the SSTGL contribute to the overall performance?
* RQ3: How do different hyper-parameters influence the performance of SSTGL?
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology
### LEO Satellite Tracking Radar System
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**City, State** & **Longitude (*E)** & **Latitude (*S)** & **Elevation (m)** \\ \hline Perth, Western Australia & 115.86 & 31.95 & 32 \\ Brisbane, Queensland & 153.02 & 27.47 & 31.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The geographic description of the study sites. geostationary (GEO) and medium-Earth-orbit (MEO) constellations, owing to the shorter proximity between the terminal and the satellite. To effectively analyze the performance of the LoRa Uplink, it is essential to possess a deep understanding of the satellite-to-end-device positioning, as illustrated in Figure 3. Let E symbolise the elevation angle caused by the sudden oscillations of the LEO satellite, let S denote the slant distance impacting the total distance travelled, and let \(\beta\) stands for the elevation angle of satellites. Then, by applying the geometric principles, individuals can ascertain the slant distance (d) through a designated elevation angle (E), and vice versa (as shown in Equation (1)). \[S=\sqrt{Er\sin^{2}\beta+2H*Er*\sin\beta+H^{2}}-Er\sin\beta \tag{1}\]  
Figure 3: Satellite-to-end-device positioning. E symbolises the elevation angle caused by the sudden oscillations of the LEO satellite, S denotes the slant distance impacting the total distance travelled, and \(\beta\) stands for the elevation angle of satellites. Figure 2: Radar detection of Australian study sites and the anticipated paths of the GlobalstarM087 and ISS NAUKA satellites’ movements on the ground within 12 h. The previous trajectories are shown through continuous yellow lines, and dotted yellow lines distinguish upcoming trajectories. The key factors considered in this study for the satellites include bandwidth, application payload, low-data-rate optimisation, spreading factor, elevation angle, average inter-arrival time, loss due to static Doppler shift, loss due to dynamic Doppler shift, and LEO pass duration. Additionally, Figure 1 showcases the names and orbital paths of the satellites. Figure 2 illustrates the anticipated path of the satellite's movement on the ground within 12 h. It distinguishes between the previous and upcoming trajectories by utilising dotted yellow lines for the latter and continuous yellow lines for the former.