esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion
PER can be reduced by not utilizing spatial diversity since each Sigfox node transmits to three gateways, and if a message is not acknowledged, the same is re-transmitted, which increases the number of collisions. It can also be seen that LoRa has better performance than Sigfox in congested environment, while Sigfox has a better performance when spatial diversity is not utilised, as seen in Figure 10.  
In Figures 6-9, it looks that Sigfox has fewer collisions; however, the same is due to Sigfox using half the packet size in comparison to LoRa and also a small BW and data  
Figure 6: LoRa packet collision simulation with 1000 devices transmitting randomly. Figure 7: LoRa packet collision simulation with 5000 Devices. rate. LoRa, on the other hand, does not utilise spatial diversity and thus has a lower PER than Sigfox; in case of the non-receipt of acknowledgement even after two reception windows have passed, LoRa transmits again with incremental SF, thus increasing ToA while sacrificing data rate and contributing to re-transmissions and further collisions, thus increasing devices above a certain limit may end up in choking the entire BW. As Hata propagation model with urban correction factor is taken, \(-123\) dBm is the sensitivity level of a device [15] for SF 7 with an estimated range of 0.80 Km and 1.7 Km in semi-urban setting. Sigfox on the other side, with a sensitivity of \(-130\) dBm and transmitted power of 14 dBm, the estimated range in an urban setting with the Hata model will be up to 3 Km, and with a reduced transmission power of 7 dBm, it will further decrease to 1.9 Km. Figure 8: Sigfox simulation for 5000 devices with three gateway. Figure 9: Sigfox simulation for 10000 devices with three gateway. Table 5 presents the number of collisions calculated during simulation by traffic generator. Equations (23), (26), and (27) have been used to calculate the delivery ratio and cases where no collision occurs. LoRa devices below 100 and Sigfox devices below 200 can safely coexist without collision if the SF and TP assignments are uniform and there are at least two gateways present; the delivery ratio in this case is close to 99.2%, owing to shadow fading and other multipath effects. In case only SF7 is utilized, which has a lower ToA of 36 ms, results can be seen in Figure 11, while if the highest SF12 is used then the PER increases, as shown in Figure 12. It is recommended that PER be maintained below 10% and  
LoRa has a better delivery ratio than Sigfox, as in the case of the former, gateway can dictate transmit power to devices, thus limiting ranges while also increasing the downlink traffic from gateway to device. PER in case of Sigfox reduces by 14% as gateways are reduced from three to one. Table 6 compares this work with previous work carried out on scalability of LoRa and Sigfox.
## 2 Preparation and Characterization of GAF  
The GAF is fabricated by a high temperature annealing graphene oxide (GO) film method. Firstly, the GO suspension was diluted to \(15\) mg/mL by ultrapure water. Then, the GO suspension was coated on the polyethylene terephthalate (PET) substrate and evaporated at room temperature to prepare GO assembled film. The thickness and area of GO-assembled film are controllable. After that, the GO assembled film was annealed to carbonization at \(1300\)\({}^{\circ}\)C for \(2\) h and graphitization at \(2850\)\({}^{\circ}\)C for \(1\) h in argon gas (Ar) atmosphere. Finally, the GAF obtained after rolling compression with the pressure of \(200\) MPa. Figure 1a shows the transmission electron microscope (TEM) image of GO sheet. Figure 1b is the digital photo of GAF showing that GAF has good flexibility and does not need any substrate. GAF has comparable electrical conductivity to metals measured by four probe method (1.1 \(\times\) 10\({}^{6}\) S/m) [32], which lays the foundation for the design of high-performance antennas and other electronics. Furthermore, the density of GAF is only 1.48 g/cm\({}^{3}\). Figure 1c illustrates the X-ray diffraction (XRD) pattern of GAF. The extremely graphitic peak (002) located at 26.5\({}^{\circ}\) indicates that the GAF has a regular stacking of graphene sheets and the interlayer spacing of 0.34 nm. Moreover, the characteristic peak (004) proves the GAF has high degree of graphitization. Figure 1d shows that the weak D peak and strong G peak in the Raman spectrum demonstrated the GAF has few defects. All the characterization results elucidate that GAF has high conductivity. The cross-section image in Figure 1e shows that the GAF with a thickness of 21 \(\upmu\)m is composed of regular stacks of graphene nanosheets.  
Figure 1: (**a**) TEM image of graphene oxide (GO) sheet, (**b**) digital photo, (**c**) XRD pattern and (**d**) Raman spectrum of graphene assembly film (GAF), (**e**) the cross-section SEM image of GAF, (**f**) thermal conduction comparison of GAF and copper.  
Additionally, compared with copper, the temperature of GAF increases rapidly under the same heating condition, as shown in Figure 1f. GAF has better thermal conductivity, which is more suitable for the design of miniaturization and high integration electronics.
## 1 Introduction
### The Need for Cooperation Among Autonomous Underwater Vehicles  
Cooperation among AUVs may significantly improve the execution of maritime and offshore operations and missions. Multiple AUVs can work as a team minimizing the need for divers in dangerous missions, reducing the need of costly equipment, saving energy and allowing the achievement of mission objectives of higher complexity than the ones that would be attainable with a single vehicle. There are multiple scenarios where cooperation is useful, such as AUV navigation, ocean surveying or seabed mapping. In the first case, as the cost of small AUVs has significantly decreased, so if maritime vehicle fleets are used, expensive sensors previously required for precise AUV navigation will no longer be necessary in all vehicles, as navigation sensors need to be installed only on one AUV that serves as the guiding vehicle for the fleet [1].  
In addition to this, cooperation is being used in several real applications. For example, the WiMUST project uses a team of underwater vehicles acting as intelligent sensing and communicating nodes of a reconfigurable moving acoustic network [2]. Also, mapping the seabed is a vital operation with great importance to industrial, military or biological applications. Unfortunately, due to the imaging geometry of the mapping devices, a map obtained by one single vehicle may not depict the seabed topology correctly. This is why a solution based on map fusion and vehicle cooperation is proposed in the SWARMs project [3].
## 4 Experimental Results
### Data Preparation and Transformation
#### Skin Disease Data Stream Pipeline Preparation to Simulate Concept Drift (CD)  
In this study, the authors selected a challenging real dataset created by the International Skin Imaging Collaboration (ISIC). ISIC released this dataset to the research and professional communities for open competition (skin lesion analysis toward melanoma detection) in 2019. The ISIC competition provides a challenging task to the research community with the aim of obtaining optimized solutions worldwide. ISIC is developing proposed standards to address the technologies, techniques, and terminology used in skin imaging with attention to the issues of privacy and interoperability (i.e., the ability to share images across technology and clinical platforms). In ISIC 2019, the skin disease ([[https://challenge2019.isic-archive.com/](https://challenge2019.isic-archive.com/)]([https://challenge2019.isic-archive.com/](https://challenge2019.isic-archive.com/))) dataset contains imbalanced classes. Furthermore, the samples in the classes have several similar features, which makes this dataset more challenging. ISIC 2019 is an archive repository of dermoscopic images for clinical training and supporting technical research to assist in automated algorithmic analysis. In total, the dataset contains 25,331 dermoscopy image samples consisting of nine classes, namely, melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), squamous cell carcinoma (SSC), and unknown (UKN). Class UKN is the ninth class of the ISIC 2019 dataset, containing the image samples that are unrelated to the other eight class types, which helps increase the generalization of the developed model. A few random image samples are depicted in Figure 7.  
The maximum number of samples was 12,875 (for class 1), and the minimum number of samples was 239 (for class 5), which represents the highly imbalanced classes. In addition, for each class, the available number of samples was 4522, 12,875, 3323, 867, 2624, 239, 253, and 628 in classes 0, 1, 2, 3, 4, 5, 6, and 7, respectively, as shown in Figure 8. This class imbalance problem can cause overfitting issue (bias toward classes for which the number of samples is greater). Therefore, image augmentation techniques were used to handle the overfitting issue, such as image flipping, random cropping, random scaling, central zooming, and increasing/decreasing brightness and sharpness, to balance the number of classes in each class. Figure 9 depicts the random samples generated using image augmentation. Python libraries were also used with appropriate parameters to increase each class image sample. The image pixel intensity values were also normalized from 0 to 255 to 0 to 1 to reduce the computational complexity.  
Figure 7: Random image samples from International Skin Imaging Collaboration (ISIC) dataset; (**a**) melanoma (MEL); (**b**) melanocytic nevus; (**c**) basal cell carcinoma.
## 4 Applications of AI in Modern Communication Networks
### Traffic Prediction and Load Balancing
#### 4.5.1 Traffic Prediction  
AI-based predictive models, particularly recurrent neural networks (RNNs), analyze historical traffic data to forecast future traffic patterns. RNNs are especially effective for this task as they are designed to process sequential data, enabling the capture of temporal dependencies in network traffic. This predictive capability helps network administrators prepare for potential traffic spikes and plan resource allocation more effectively, thus optimizing overall network performance [124].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **AI Integration** & **Application Area** \\ \hline Federated Learning & Localized model updates & Mobile Networks, IoT \\ Homomorphic Encryption & Computation on encrypted data & Healthcare, Finance \\ Differential Privacy & Anonymization of data sets & Social Media, Healthcare \\ Secure Multi-Party Computation (SMPC) & Parallel secure computation & Collaborative Cloud Services \\ \hline \hline \end{tabular}
\end{table}
Table 6: AI-based privacy-preserving methods and techniques.  
Recurrent neural networks (RNNs) are ideal for this use case because they excel in handling sequential data, making them well suited for traffic prediction tasks. As illustrated in Figure 7, the predicted traffic closely aligns with the observed traffic over five hours, demonstrating the accuracy and reliability of RNN-based models. This alignment underscores their ability to anticipate variations in network demand, providing network administrators with actionable insights to proactively manage resources and avoid congestion.
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 3 Results
### Evaluation Results of LSTM-CNN-Attention
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Properties** & **Metrics** & **LSTM** & **LSTM-Attention** & **CNN** & **LSTM-CNN** & **LSTM-CNN-Attention** \\ \hline OC & R\({}^{2}\) & 0.769 & 0.863 & 0.913 & 0.926 & 0.949 \\  & RMSE & 35.670 & 27.490 & 21.970 & 20.160 & 16.687 \\  & MAE & 17.950 & 14.850 & 11.020 & 10.820 & 8.599 \\  & RPD & 2.081 & 2.701 & 3.379 & 3.683 & 3.940 \\ \hline N & R\({}^{2}\) & 0.723 & 0.769 & 0.829 & 0.883 & 0.916 \\  & RMSE & 1.805 & 1.648 & 1.419 & 1.174 & 0.993 \\  & MAE & 1.087 & 1.014 & 0.777 & 0.704 & 0.580 \\  & RPD & 1.899 & 2.079 & 2.415 & 2.919 & 3.737 \\ \hline CaCO\({}_{3}\) & R\({}^{2}\) & 0.611 & 0.811 & 0.889 & 0.915 & 0.943 \\  & RMSE & 87.400 & 60.910 & 46.770 & 40.780 & 33.370 \\  & MAE & 35.830 & 24.610 & 18.420 & 15.920 & 12.909 \\  & RPD & 1.604 & 2.301 & 2.997 & 3.437 & 5.377 \\ \hline pH(H\({}_{2}\)O) & R\({}^{2}\) & 0.284 & 0.409 & 0.888 & 0.906 & 0.926 \\  & RMSE & 1.132 & 1.028 & 0.448 & 0.410 & 0.364 \\  & MAE & 0.922 & 0.786 & 0.343 & 0.308 & 0.265 \\  & RPD & 1.181 & 1.301 & 2.985 & 3.262 & 3.352 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment results for soil property forecasting. high computational efficiency and prediction accuracy compared with the full LSTM-CNN-Attention model. This finding indicates that, in scenarios with limited computational resources, the CNN model can serve as an effective benchmark. Its capacity to efficiently extract spatial features makes it a practical choice for spectral data prediction tasks, offering a balance between predictive accuracy and resource constraints. Additionally, the scatter plots in Figure 9 further validate the model's predictive capability, with predicted values closely aligning with the ground truth for OC, N, CaCO\({}_{3}\), and pH(H\({}_{2}\)O). These findings highlight the robustness of the proposed framework in capturing both temporal and spatial patterns, reinforcing its effectiveness as a reliable tool for soil property prediction.
## 2 Related Work
Similarly, the data rate maximization of an ABS-assisted downlink cellular system using RL was presented in [24]. Here, the Q-learning technique was used to optimize the ABS location, where simulation results revealed that RL performed better than a \(k\)-means algorithm to find the optimal ABS positions. Similarly, in [25], a Q-learning approach was proposed to solve the resource allocation challenge by considering the user's fairness and several other quality-of-service (QoS) constraints. We also summarize some of the key papers with their contributions in Table 1.  
As the deployment of ABS is a challenging task considering that it should provide a proper coverage to the ground user, maximize user association, maximize sum rate as well as optimize power consumption. To optimize the ABS deployment, the 2D location as well as the altitude of the ABS to cover a maximum number of ground users are considered important factors during deployment. Another challenging task is the deployment of multiple ABSs compared to a single ABS, because of increased cochannel interference from using the same frequencies to improve the spectral efficiency. It is difficult to deploy an ABS in such a way that interference between the ABS to the ground BS is minimum. The position of the ABS should be flexible to cover more users with less interference. Recently many conventional schemes have been proposed to improve the coverage, maximize user association, and maximize sum rates. However, recently, machine learning has made remarkable progress and RL is considered to be most effective in tackling ABS-assisted communication challenges. RL is the machine learning technique that interacts with the system and optimize its performance by finding the best possible action to maximize the given reward. RL gets the raw data and uses a trial-and-error method to detect the errors at the output, and then feeds it back to the system to enhance the system efficiency. Moreover, the RL is usually categorized into two main categories: model-free and model-based approaches as shown in Figure 2.
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 5 Adaptive Data Rate Algorithm Design  
LoRaWAN leverages the advantages of the LoRa physical layer by optimizing the communication performance of EDs through adjusting data rates (i.e., LoRaWAN ADR). In actual network deployments, adjusting the data rate requires balancing communication quality and network performance. When the environment where the ED operates is subject to interference, communication quality should be prioritized. Additionally, since LoRa networks use the pure ALOHA protocol, data rate allocation must also consider network performance optimization. A new ADR algorithm based on link state and network performance optimization is proposed. Based on the link state, the transmission power and a list of selectable data rates are first determined, followed by the optimal data rate allocation in combination with network optimization. Figure 9 shows the overall flowchart for designing the adaptive data rate algorithm.  
Figure 8: Relationship between the number of EDs and average latency at different SFs.
## 3 Materials and Methods
### Dataset Preprocessing  
After completing the data collection, it is vital to focus on any missing values in the dataset. The prediction efficiency will be affected if missing values are present. In our dataset, there are some missing values. We applied numerous imputations (MI) to fill up the missing variables. A missing values is handled in our approach by replacing it with the mean or average value of the considered attribute. As a result, more accurate and actual forecast results will be obtained. The current nominal variables are then transformed to numerical values ranging from 0 to 1. These processes will aid in the acquisition of a preprocessed dataset. Any classifier model can now be fit to this dataset.  
In our view, Figure 2 represents correlated features with the predicted class attribute (classification). The attribute values define the strength of the correlated features at the right portion (range from \(-0.6\) to \(0.6\)), in accordance with the lightness of color. The Figure represents 'pcv' and 'rc' as having a strong correlation with 'htm', having the value of \(0.74\), \(0.68\); whereas'sod','htm' has a lesser correlation with 'hemo'. having the value of \(-0.62\), \(-0.5\) approximately.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Attribute** & **Datatype** & **Description** & **Unit of Measurement** \\ \hline
1 & Age & Numerical & Age & Years \\
2 & bp & Numerical & Blood Pressure & mm/Hg \\
3 & sg & Nominal & Specific Gravity & 1.005, 1.010, 1.015, 1.020, 1.025 \\
4 & al & Nominal & Albumin & 0, 1, 2, 3, 4, 5 \\
5 & su & Nominal & Sugar & 0, 1, 2, 3, 4, 5 \\
6 & rbc & Nominal & Red Blood Cells & Normal, Abnormal \\
7 & pc & Nominal & Pus Cell & Normal, Abnormal \\
8 & pcc & Nominal & Pus Cell Clumps & Present, Not Present \\
9 & ba & Nominal & Bacteria & Present, Not Present \\
10 & bgr & Numerical & Blood Glucose Random & mgs/dL \\
11 & bu & Numerical & Blood Urea & mgs/dL \\
12 & sc & Numerical & Serum Creatinine & mgs/dL \\
13 & sod & Numerical & Sodium & mEq/L \\
14 & pot & Numerical & Potassium & mEq/L \\
15 & hemo & Numerical & Haemoglobin & gms \\
16 & pcv & Numerical & Packed Cell Volume & 0, 1, 2,... \\
17 & wbcc & Numerical & White Blood Cell Count & cells/cumm \\
18 & rbc & Numerical & Red Blood Cell Count & millions/cumm \\
19 & htn & Nominal & Hypertension & Yes, No \\
20 & dm & Nominal & Diabetes Mellitus & Yes, No \\
21 & cad & Nominal & Coronary Artery Disease & Yes, No \\
22 & appet & Nominal & Appetite & Good, Poor \\
23 & pe & Nominal & Pedal Edema & Yes, No \\
24 & ane & Nominal & Anemia & Yes, No \\
25 & Class & Nominal & CKD, Not CKD & CKD, Not CKD \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data variables for patients’ CKD dataset description.
## 3 Materials and Methods
### Method
#### 3.1.2 Stage 2 Training
There are two situations where we need more padding to cover the ONH (Figure 3). The first one (Figure 3b) is if the predicted bounding box has a portion outside the ground truth bounding box and the reset overlap the ground truth. The second situation is when the predicted bounding box lay entirely inside the ground truth. In the second situation, for a given \(IoU\), the maximum padding is required if the bounding boxes shared a corner (Figure 3b). If the predicted bounding box is to move or change in size to require a larger padding, it will no longer have the same \(IoU\). To calculate the minimum \(IoU\) required for a given padding size, we can use Equations (4) and (5). \[IoU_{outside}=\frac{dim-x}{dim-x} \tag{4}\]  
\[IoU_{inside}=\frac{dim^{2}-2 dimx+x^{2}}{dim^{2}} \tag{5}\]  
where \(dim\) is the dimension of the ONH ground truth bounding box in pixels, \(x\) is the padding size in pixels, \(IoU_{outside}\) is the minimum \(IoU\) for the case where a portion of the predicted box is outside the ground truth, and \(IoU_{inside}\) is the minimum required \(IoU\) when the predicted bounding box falls entirely inside the ground truth bounding box. The dataset contains images with varying resolutions and varying ONH diameter sizes. The largest ONH bounding box diameter across datasets is 401 pixels. Figure 4 shows the required padding vs. \(IoU\) for a fixed ground truth bounding box with a dimension of 401 pixels. For a padding size of 100 pixels, an \(IoU\) of 0.56 minimum is required, but in our calculations,  
Figure 3: Overlap between ground truth and predicted bunding box (**a**) with overlap outside the optic nerve head (ONH) and (**b**) with overlap inside the ONH only. we will use a larger _IoU_ threshold of 0.60 to determine the success of localization. If the optic disc is not detected, but the optic cup is detected, the same criteria for success localization will be applied. However, when cropping the image, padding of 150 pixels will be used instead of 100 pixels. The segmentation accuracy can be measured using five values: the optic disc Dice score (Equation (3)), the optic cup dice score, the cup-to-disc area ratio (CDR), the horizontal cup-to-disc ratio (hCDR), and the vertical cup-to-disc ratio (vCDR). Figure 5 shows an example of vertical/horizontal cup and disc measurements used for hCDR and vCDR calculation. The proposed method will be assessed based on three measures: the optic disc Dice score, the optic cup Dice score, and the absolute error in the vertical cup-to-disc ratio (vCDR) to compare the approach with the reported results in the REFUGE challenge.