esa-sceva/satcom-chunk-collection huggingface dataset:
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 5 Conclusions  
We investigated the performance of GWRFR in predicting corn yield at the county level in the US Corn Belt using different sets of features in this study. Our results show that GWRFR can outperform all other five non-spatial models in corn yield prediction irrespective of the features used. However, the predictive power of machine learning models varies significantly with the use of different sets of features. In addition to GWRFR, SVR and RFR have a better performance than other linear models. The models trained using full length features can yield better results than the same models trained using a subset of the features. GWRFR can model the spatial heterogeneity and produces the lowest Moran's I value, which means GWRFR has the capability to address spatial heterogeneity and can be potentially used to address spatial problems in other applications. Although GWRFR outperforms other models in this study, more research needs to be conducted to test its effectiveness in predicting yield for other types of crops or in other areas.  
**Author Contributions:** Conceptualization, S.N.K. and D.L.; methodology, S.N.K., D.L. and M.M.; software, S.N.K.; validation, D.L. and M.M.; formal analysis, S.N.K.; investigation, S.N.K.; resources, D.L.; data curation, S.N.K.; writing--original draft preparation, S.N.K.; writing--review and editing, D.L. and M.M.; visualization, S.N.K.; supervision, D.L.; project administration, D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was jointly supported by the Department of Geography and Geospatial Sciences, South Dakota State University and the Higher Education Commission (HEC), Pakistan. The APC was funded by the Department of Geography and Geospatial Sciences, South Dakota State University.  
**Data Availability Statement:** The data used in this study were mainly obtained from publicly available sources. The details about the datasets are available in Section 2.2.  
**Acknowledgments:** The authors would like to acknowledge the information technology division of South Dakota State University, who provided the computing resources to process data and run models. Specifically, we would like to thank [PERSON] for her technical support.  
**Conflicts of Interest:** All authors declare no conflict of interest.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges
### Integration of Satellite  
Satellite and non-terrestrial networks are also envisioned to provide global coverage with the necessity of HO between terrestrial and space networks seamlessly. The traditional techniques of HO based on RSS are insufficient to handle such heterogeneous mobility scenarios. Instead, predictive and AI-driven HO mechanisms must consider several factors, including speed, path, and environmental conditions, to ensure that seamless connectivity is offered [55].
## 1 Introduction
### Related Works
#### 1.1.1 Equipment Control  
In the application domain of end milling, a solution to perform condition monitoring on cutting tools is presented, by exploiting a microphone whose data are processed by a convolutional neural network (CNN) [7]. The project implements a detector for the state of the tool in use to alert the operator before an imminent breakage. Other applications of modern technologies to Computer Numerical Control (CNC) machine monitoring have been proposed by [8], who apply deep learning techniques to data collected by an accelerometer positioned on the tool head in use. CNCs are the object of study also of [9], which implements an early detection machine failure based on vibration signals. The system implements two components: a signal analysis system, based on a fuzzy neural network, and a decision maker, whose goal is to classify the machine state as normal or in alarm or to shut it down More generally, [10] analyses the methodology that should be carried out in order to retrofit or connect existent CNC machines in order to make them smart and fool-proof, with a special attention to human factors. The authors of [11] consider a generic maintenance process, which can be potentially applied to any workstation composed of machines or mechanical or electrical equipment. The goal of the project is to standardise the data acquisition phase nearly after the breakdown occurrence in order to improve the data quality and consistency, avoiding missing or incorrect parameters during the fault description. As a consequence, average maintenance scheduling lead times have been drastically reduced, resulting in better data organisation and therefore better performance tracking. In [12], the authors implement a system that is able to automatically generate customised CNC programs compliant with specific requirements. The need for such a system lies in the constantly increasing demand for customised products, which makes it challenging to have a manual CNC setup for each of them. The outcome of the study is a system based on decision trees.
## 2 Materials and Methods
### Isolation of APs from MPTH  
The isolation process of APs from MPTH was performed according to the previous method [26]. MPTH was fractionated using 3 and 5 kDa molecular weight (MW) cut-off membranes, and three fractions termed MPTH-I (MW \(<\) 3 kDa), MPTH-II (3 kDa \(<\) MW \(<\) 5 kDa), and MPTH-III (MW \(>\) 5 kDa) were collected and lyophilized. MPTH-I solution (5 mL, 40.0 mg protein/mL) was injected into a DEAE-52 cellulose column (1.6 \(\times\) 80 cm) pre-equilibrated with deionized water (DW), and stepwise eluted with 150 mL of DW and NaCl solution (0.1, 0.5, and 1.0 M, respectively) at a flow rate of 1.0 mL/min, respectively. Each eluate (5 mL) was monitored at 214 nm. Six fractions (MUA-1 to MUA-6) were pooled on the chromatographic peaks and MUA-5 solution (5 mL, 10.0 mg protein/mL) was separated on a Sephadex G-25 column (2.6 \(\times\) 160 cm) eluted with DW at a flow rate of 0.6 mL/min. Each eluate (3 mL) was collected, monitored at 214 nm, and measured the DPPH- and hydroxyl radical (HO) scavenging activity. Two subfractions (MUA-4-A and MUA-4-B) were collected and MUA-4-B with higher DPPH- and HO- scavenging activity was isolated using a Zorbax, SB C-18 column (4.6 \(\times\) 250 mm, 5 \(\upmu\)m) in an Agilent 1260 HPLC system. The sample was eluted with a linear gradient of acetonitrile (0%-40% in 0-40 min) in 0.1% TFA at a flow rate of 1.0 mL/min. Thirteen APs (MMP-1 to MMP-13) were isolated on the absorbance at 214 nm and lyophilized.
## 2 Dynamic Lognormal Shadowing Framework
### Transition Probabilities  
The process \(\mathbf{X}_{t}\) as a solution to (18) is a Gaussian Markov process. The transition probability densities \(p(\mathbf{x},t|\mathbf{y},s)\) are defined by:  
\[P[\mathbf{x}<\mathbf{X}(t)\leq\mathbf{x}+d\mathbf{x}|\mathbf{X}(s)=\mathbf{y}] =p(\mathbf{x},t|\mathbf{y},s)d\mathbf{x} \tag{30}\]  
These can be derived by using the SDE solution and the properties of the stochastic integral or by solving the corresponding Fokker Planck Equation (FPE):  
\[\partial_{t}p(\mathbf{x},t|\mathbf{y},s)=\sum_{i}\partial_{x_{i}}[\beta_{i}xip (\mathbf{x},t|\mathbf{y},s)]+\frac{1}{2}\underset{i,j}{\sum}\partial_{x_{i}} \partial_{x_{j}}\Big{[}G_{ij}p(\mathbf{x},t|\mathbf{y},s)\Big{]}. \tag{31}\]  
Using the assumption of stationarity we have:  
\[p(\mathbf{x},t|\mathbf{y},s)=p(\mathbf{x},t-s|\mathbf{y},0) \tag{32}\]  
The expression of \(p(\mathbf{x},t|\mathbf{y},s)\) is given by:  
\[\begin{array}{l}p(\mathbf{x},t|\mathbf{y},s)=\frac{1}{\sqrt{(2\pi)^{n}} \Delta}\exp(K)\\ K=-\frac{1}{2}(\mathbf{x}-e^{(t-s)\mathbf{B}}.\mathbf{y})\mathbf{C}_{\mathbf{X} }^{-1}(t)(\mathbf{x}-e^{(t-s)\mathbf{B}}.\mathbf{y})^{T}\end{array} \tag{33}\]  
where \(\Delta\) is the determinant of the covariance matrix \(\mathbf{C}_{\mathbf{X}}(t)\) and \(\mathbf{C}_{\mathbf{X}}^{-1}(t)\) is the inverse matrix assuming that it exists.
## 3 CPS Architecture, Layers and Components
### Network Layer  
The second layer of CPS architecture is the network layer. This layer is used for the communication of the physical layer (real world) and the application layer (cyber world). Due to the network in the CPS, it is possible to control physical systems remotely. A local area network is used for local data transmission and communication protocols involving 4G, 5G, ZigBee, Wi-Fi, and Bluetooth. This layer assures data transmission and routing using cloud computing, firewalls, gateways, and an intrusion detection method. These features make CPS cost-effective and better than the previously point-to-point controlling system. Before transferring data to the next layer, it is important to secure the transmission by avoiding malicious attacks including malware, denial of service, distributed denial of the surface, and unauthorized access. For example, wireless networks such as WirelessHart and ZigBee remotely enable the controlling of industrial processes in real time. Moreover, in the smart grid, the distributed network protocol (DNP) provides a system of centralized monitoring and control. There is a great challenge for power-restricted devices due to the overhead of power and processing.
## 5 Experimental Results and Evaluation  
### Results and Discussion  
The learning curve of our model (accuracy and loss graph) is shown in Figure 8a,b.  
According to Figure 8a,b, the \(x\)-axis represents the number of epochs while the \(y\)-axis represents the accuracy and loss, respectively. From Figure 8a, it can be noticed that the validation accuracy increases from 0.22 to 0.65, and the training accuracy increases from 0.2 to 0.64 after the first 50 epochs. After that, it reaches 0.87 for validation and 0.81 for training in the last epoch. From Figure 8b, it is seen that the validation loss decreased to 0.9 from 1.99 and the training loss decreased to 0.95 from 2.00 after the first 50 epochs. After that, it decreased gradually and became 0.4 for validation and 0.55 for training in the last epoch. The accuracy learning curve follows the upward trend while the loss one  
Figure 8: Learning curve: (**a**) Accuracy vs. Epoch; (**b**) Loss vs. Epoch.  
follows the downward trend. These trends help the model to achieve an average accuracy of 93.23% for training and 87.17% for testing at the end of the training phase.  
The evaluation metrics for this model are shown in Table 8. According to Table 8, it can be observed that the accuracy of each class is equal to or more than 80%, while the accuracy of joy and sadness is equal to or more than 90%.  
The integrated framework is designed to use real-time validation. For real-time validation, we first select an image from the local storage. After that, the model calculates the overall sentiment of the image by analyzing the values of \"Positive\", \"Neutral\", and \"Negative\". The process of the real-time validation is shown in Figure 9a,b.  
We have performed 5-fold cross validation to validate the model performance. From our previous study [11], we have seen that the deep learning model works better with a 70:20:10 split ratio. Therefore, we split the dataset into a 70:20:10 ratio, where 70% of the total images are used for training the model, 20% of them are used for validation, and the remaining 10% are used for testing. These images are selected randomly from the dataset. The results of 5-fold cross validation and the average accuracies along with the standard deviations are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Anger & 0.87 & 0.85 & 0.86 & 0.87 \\ Sadness & 0.92 & 0.79 & 0.85 & 0.92 \\ Joy & 0.95 & 0.76 & 0.84 & 0.94 \\ Love & 0.83 & 0.94 & 0.88 & 0.82 \\ Surprise & 0.80 & 0.95 & 0.87 & 0.80 \\ Fear & 0.86 & 0.86 & 0.86 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation metrics.  
Figure 9: Real-time prediction: (**a**) select an image from directory; (**b**) sentiment analysis of the selected image.  
From Table 9, it can be seen that the highest training accuracy was found in the fourth fold, which was 0.95. However, the highest validation (0.89) accuracy and testing (0.87) accuracy were achieved in the third fold.
# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 5 Proposed Algorithm
### Power Allocation Algorithm  
The Uplink power allocation scheme is used to ensure fair data rates between users sharing the same RB [34]. Consider two users share the same RB, if \(g_{k,n}>g_{k,f}\) then according to (8) the SNIR of near and far users respectively are given as  
\[\Gamma_{k,n}(t)=\frac{p_{k,n}(t)\big{|}g_{k,n}(t)\big{|}^{2}}{p_{k,f}(t)\big{|}g _{k,f}(t)\big{|}^{2}+\sigma^{2}} \tag{37}\]  
\[\Gamma_{k,f}(t)=\frac{p_{k,f}(t)\big{|}g_{k,f}(t)\big{|}^{2}}{\sigma^{2}}, \tag{38}\]  
Assuming that the UAV will decode the signal of the nearest user first then decode the signal of the far user using SIC. To achieve fair data rates for both users, then,  
\[\frac{p_{k,n}(t)\big{|}g_{k,n}(t)\big{|}^{2}}{p_{k,f}(t)\big{|}g_{k,f}(t)\big{|} ^{2}+\sigma^{2}}\ \frac{p_{k,f}(t)\big{|}g_{k,f}(t)\big{|}^{2}}{\sigma^{2}}, \tag{39}\]  
Let \(\big{|}g_{k,n}(t)\big{|}^{2}=g_{N}\) as the gain of the near user and \(\big{|}g_{k,f}(t)\big{|}^{2}=g_{F}\) as the gain of the far user. Thus, the power allocation coefficients \(a_{N}\) and \(a_{F}\) of the near and the far users respectively can be derived as  
\[\frac{a_{N}P_{max}g_{N}}{a_{F}Pg_{F}+\sigma^{2}}=\ \frac{a_{F}P_{max}g_{F}}{ \sigma^{2}}, \tag{40}\]  
Let \(\rho=P_{max}/\sigma^{2}\), then divide both sides by \(\sigma^{2}\)  
\[\frac{a_{N}\rho g_{N}}{a_{F}\rho g_{F}+1}\ =\ \frac{a_{F}\rho g_{F}}{1}, \tag{41}\]  
\[g_{F}^{2}\ \rho^{2}a_{F}^{2}+(g_{F}+g_{N})\rho a_{F}\ -\rho g_{N}\ =0 \tag{42}\]  
\[a_{F}\ =\ \frac{-(g_{N}+g_{F})\pm\sqrt{(g_{N}+g_{F})^{2}+4\rho g_{F}^{2}g_{N}}}{2g _{F}^{2}\ \rho} \tag{43}\]  
where \(0<a_{N}<0.5\) because \(a_{F}>a_{N}\) and \(a_{N}+a_{F}=1\).
## 5 Conclusions and Future Work  
This study demonstrates a deep reinforcement learning solution for autonomous driving tasks using 3D-LiDAR sensors, achieving both simulation and real-world implementations. By integrating the DDPG algorithm with a PointNet-based point cloud processing approach enhanced by self-attention mechanisms, the research provides a scalable framework for unsupervised autonomous driving systems. The study addresses several key challenges, including the limitations of discrete action spaces in traditional reinforcement learning algorithms and the complexities of processing irregular and dense 3D point cloud data in dynamic environments. Moreover, by eliminating the need for labeled datasets, the approach reduces the overall cost and labor compared to supervised learning methods for autonomous driving. The system demonstrated consistent training performance through simulation experiments conducted in the CARLA environment. Real-world tests using F1 tenth cars further enable the completion of basic autonomous driving tasks in a real-world environment. The effectiveness of reinforcement learning in  
Figure 11: Results of the experiment. (**a**) Initial stage (**b**) More than 200 steps trials (**c**) Around 600 steps trials.  
Figure 12: Reward diagram of the experiment.  
real-world applications was demonstrated, bridging the gap between virtual simulation and actual implementation.  
There remains considerable room for improvement in this research. Future enhancements could explore more complex autonomous driving tasks and scenarios, particularly in adapting to highly dynamic or previously unseen environments. The reliance on pre-defined reward functions may also limit the generalization to more diverse driving tasks. Applications in complex road environments require well-developed reward mechanisms as well as longer exploratory training processes. They also place higher demands on hardware such as battery capacity. In terms of sensors, the point cloud processing framework proposed in this study allows for a higher density of information input in order to cope with complex road environments. Future research can be flexible to further improve the feature extraction capability by adding different feature processing modules. More comprehensive environmental information from multi-sensor fusion can also be considered.  
In conclusion, this study lays a foundation for advancing unsupervised learning techniques in autonomous driving, promoting safer, more efficient, and cost-effective solutions for future smart transportation systems.  
Conceptualization, Y.C., C.T.L. and G.P.; methodology, Y.C. and G.P.; formal analysis, W.K.; investigation, C.T.L.; writing--original draft preparation, Y.C.; writing--review and editing, W.K. and G.P.; supervision, C.T.L. and G.P.; project administration, W.K.; funding acquisition, W.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Macao Polytechnic University (project code: RP/FCA-14/2022).  
Not applicable.  
The authors declare no conflicts of interest.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
###### Abstract  
Pseudolite positioning systems offer precise localization when GPS signals are unavailable, advancing the development of intelligent transportation systems. However, in confined indoor environments such as kilometer-long tunnels, where vehicles move at high speeds, traditional pseudolite algorithms struggle to establish accurate physical models linking signals to spatial domains. This study introduces a deep learning-based pseudolite positioning algorithm leveraging a spatio-temporal fusion framework to address challenges such as signal attenuation, multipath effects, and non-line-of-sight (NLOS) effects. The Enconv1d model we developed is based on the spatio-temporal characteristics of the pseudolite observation signals. The model employs the encoder module from the Transformer to capture multi-step time constraints while introducing a multi-scale one-dimensional convolutional neural network module (1D CNN) to assist the encoder module in learning spatial features and finally outputs the localization results of the Enconv1d model after the dense layer integration. Four experimental tests in a 4.6 km long real-world tunnel demonstrate that the proposed framework delivers continuous decimeter-level positioning accuracy.  
s 2025 17 858
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
## 5 Experiments
### Performance Comparison Experiments  
To validate the model proposed in this study, performance comparison experiments were conducted after selecting nine models that showed good performance in SOTA methods and the ATLAS dataset in past semantic segmentation studies. The SOTA models used in the experiments were typical U-Net-based models (U-Net, SegNet, PSPNet, and HarDNet) in the segmentation field with a CNN-based encoder-decoder structure; segmentation models (Swin Transformer and Swin UNet) using vision transformers; a model (TransHarDNet) that combined the CNN and vision transformer; and X-Net and D-UNet constructed to analyze the ATLAS data.  
Table 3 shows the experimental results. The performances shown in Table 3 were obtained from the validation dataset after training the model with a separated dataset. In Table 3, the column \"input type\" refers to the input image shape of the model, \"2D\" refers to 2D grayscale images, and \"3D\" refers to 3D grayscale images. A 3D image was created by combining consecutive MRI scan images and the \"output type\" of every model was a 2D image. The 2D output corresponding to a 3D image input was based on the target value of the third image in the four consecutive MRI scan images that produced the 3D image and was the same as the 3D input/output used in D-UNet [2].  
\begin{table}
\begin{tabular}{c c} \hline
**Device** & **Specifications** \\ \hline OS & Windows 10 \\ CPU & Intel Core i9-9900 KF 3.6 GHz \\ GPU & NVIDIA GeForce RTX 2080 Ti \(\times\) 1 \\ RAM & 112 GB \\ Storage & 1 TB SSD \\ Language & Python 3.7, PyTorch = 1.5 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental setup.  
As shown in Table 3, the STHarDNet proposed in this study resulted in a Dice of 0.517, IoU of 0.387, precision of 0.622, and recall of 0.498 when the \"input type\" and \"output type\" were both 2D. These performances were higher than those of existing SOTA models and stroke diagnostic models. Furthermore, when a 3D image was input and a 2D image was output, the proposed STHarDNet showed a Dice of 0.5547, IoU of 0.4184, precision of 0.6763, and recall of 0.5286, showing higher performances compared to when a 2D image was input. (The shape of the 3D input image of STHarDNet is 224 \(\times\) 224 \(\times\) 4).  
The experimental results show that the Dice, IoU, precision, and recall performances of the HarDNet built based on a single CNN or a single Swin Transformer-based segmentation model were lower compared to those when the two models are all combined and used. This proves that, if the ViT-based Swin Transformer model and the CNN-based HarDNet model proposed in this study are combined, the performance can be improved compared to when a single model is used.
# The Accurate Measurement of Students' Learning in E-Learning Environments
## 6 Results  
### Percentage Scores  
Figure 4 presents the percentages of correct responses to the items and the persons in the pre- (colored blue) and post-tests (colored orange) using box plots. The plots clearly show that the percentages of correct responses were higher in the post-test. For the measurement of students' learning, the key question was whether students' higher proportion of correct answers on the post-test should be attributed to students' actual learning or to easier items in the post-test. The two factors (i.e., students' abilities and items' difficulties) were confounded in percentage scores. Therefore, percentage scores cannot be used to answer this important question.