esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Augment Data Through Transfer Learning  
Transfer learning has emerged as a powerful paradigm in deep learning, where knowledge gained from pre-trained models on source tasks is effectively transferred to improve performance on target tasks. This methodology offers two key advantages: (1) accelerated model convergence during target task training and (2) significant reduction in required training data volume. The transfer learning methodology employed in this  
Figure 5: Comparison between the prediction results of the PINN models and the results obtained from the analytical solutions (27) and (28) for three points \((c,v)=\{(2.0\times 10^{6},10),(2.25\times 10^{6},10),\)\((2.5\times 10^{6},10)\}\). The remaining parameters are derived from Equation (25) in Section 2, while the magnitude of the moving load is adjusted to \(P=10^{3}(kN)\). (**a**) Comparison of true and predicted values for point \((c,v)=(2.0\times 10^{6},10)\). (**c**) Comparison of true and predicted values for point \((c,v)=(2.25\times 10^{6},10)\). (**d**) Prediction error for point \((c,v)=(2.25\times 10^{6},10)\). (**e**) Comparison of true and predicted values for point \((c,v)=(2.5\times 10^{6},10)\). (**f**) Prediction error for point \((c,v)=(2.5\times 10^{6},10)\).  
study involves pre-training a PINN on a source domain. The resulting weights from the pre-trained model are then used to initialize the weights of the target-domain PINN. To retain learned features from the source domain, the shallow hidden layers of the target-domain PINN are frozen, preventing them from participating in backpropagation and parameter updates during subsequent training. Weight updates are confined to the deeper hidden layers, enabling fine-tuning of the target-domain model while leveraging pre-trained features. A schematic of this transfer learning process is illustrated in Figure 6.  
The three pre-trained PINN models discussed in Section 3 serve as the source domain. The migration paths of these models within the \(c-v\) parameter plane are shown in Figure 7, along with the demarcation curve \(g_{\mu_{0}}(c,v)=0\) defined in Section 2. Through this process, 15 target-domain PINN models are generated. After transfer learning, the target-domain models undergo additional training. During this phase, the number of sensor-acquired data points included in the loss function is reduced from 20 to 10. The predictive accuracies of the trained models are evaluated and summarized in Table 3.  
Figure 6: Process of a transfer learning strategy with layer-wise parameter freezing.  
Figure 7: The migration path on the \(c-v\) parameter plane. Arrows represent the direction of migration paths.
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Application of Constraints Programming
#### 3.2.2 Problem Definition  
There are \(N\) tasks and \(M\) workplaces. Each task \(J_{i}\) can only be executed in a specific workplace \(\text{W}_{j}\), and each task has a start time \(S_{i}\) and a duration \(d_{i}\). The goal is to schedule all tasks across the workplace and minimize the total completion time.
## 4 Discussion
### Remote Sensing as Means of Detecting AMD
The handheld spectrometer was able to export spectral signatures directly, with three out of four spectral responses bearing resemblance to the baseline goethite example (Figure A1d, Fe(III) iron occurrences 1 to 3); however, the fourth spectral signature did not, even though it had been previously identified by the Halo handheld spectrometer as goethite, using its real-time prediction based on its internal spectral library. This emphasises the importance of exporting raw reflectance data and interpreting the spectral response curve with an external verified library of spectral signatures. Sentinel-2, with its 12 available bands across 490-2200 nm, captured a response curve more like to that of goethite (Figure A1a) and the Halo handheld spectrometer. The sensors of the PS2.SD and Nano-Hyperspec were limited in available bands (4 and 3 bands, respectively) yet showed absorption in the blue and green band (490-560 nm), and increased reflectance around the red band (660 nm), which is indicative of the start of a goethite signature (Figure A1b,c). Past research also shows that the spectral patterns of secondary iron oxides show strong absorption in the blue, green, and NIR bands, and strong reflection in the red bands [47; 48; 49]. These findings reinforce the capabilities and versatility of a multi-sensor chain in AMD detection processes, as goethite was discovered within the area of the four training sites [26]. However, the spectral resolution of the sensor can be a limiting factor when the entirety of the spectral response is of interest. Through increased spectral and radiometric accuracy, enhanced spectral graphs can be achieved with the Nano-Hyperspec, as it has an additional 264 bands available. PlanetScope DOVE-R fleet has up to eight bands (431-885 nm) and has increased radiometric sensitivity compared to PS2.SD, yet it is still not as spectrally sensitive as the Halo handheld spectrometer, Sentinel-2, or UAV Nano-Hyperspec. Studies on the spectra of iron have discovered absorption features between 430 nm and 1300 nm [59], which are known as either Fe(III) iron absorption features or iron feature depth. These absorption features have previously been used to delineate areas where mine waste is present and active AMD generation takes place [16; 19]. Using similar Fe(III) iron band ratio operations, it was possible to distinguish between the grey tailings to the west of the lower lagoon, which had less Fe(III) iron, and the marbled tailings surrounding the lower lagoon (see Figure 2 tiered rasters), which had a higher abundance of Fe(III) iron minerals as a result of the weathering of pyrite [26]. Ground samples that were taken within areas successfully delineated by Fe(III) iron bands across the globe have also observed the presence of mixed ferrous- Fe(III) iron minerals such as copiapite, and Fe(III) iron-based minerals such as jarosite, goethite, and hematite [16; 19; 60]. Likewise, this study was able to identify spectral signatures resembling goethite within our outlined AMD deposits.
## 4 Experiments
### Experimental Settings  
#### 4.1.1 Datasets  
To validate the long-term forecasting capability of the DESTformer, experiments are conducted on six real-world datasets. (1) The EET dataset (Electricity Transformer Temperature Dataset) ([[https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)]([https://github.com/zhouhaoyi/ETDataset](https://github.com/zhouhaoyi/ETDataset)) (accessed on 12 July 2023)): This dataset is commonly used for long sequence time series prediction and contains data from two different regions in China, recorded at 2 h, 1 h, and 15 min intervals from July 2016 to July 2018. Each data point includes the oil temperature and six electricity load indicators. (2) The Electricity dataset ([[https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)]([https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)) (accessed on 12 July 2023)): This dataset contains hourly electricity consumption data for 321 customers between 2012 and 2014. (3) The Exchange dataset [36]: This dataset contains daily exchange rates for eight different countries from 1990 to 2016. (4) The Traffic dataset ([[http://pems.dot.ca.gov](http://pems.dot.ca.gov)]([http://pems.dot.ca.gov](http://pems.dot.ca.gov)) (accessed on 12 July 2023)): This dataset is a collection of hourly data from the California Department of Transportation and includes road occupancy rates measured by different sensors on highways in the San Francisco Bay Area. (5) The Weather dataset ([[https://www.bgc-jena.mpg.de/wetter/](https://www.bgc-jena.mpg.de/wetter/)]([https://www.bgc-jena.mpg.de/wetter/](https://www.bgc-jena.mpg.de/wetter/)) (accessed on 12 July 2023)): This dataset contains 21 meteorological indicators recorded every 10 min throughout the year 2020, including temperature and humidity. (6) The ILI dataset ([[https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html](https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html)]([https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html](https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html)) (accessed on 12 July 2023)): This dataset contains weekly data on influenza-like illness (ILI) patients recorded by the Centers for Disease Control and Prevention in the United States from 2002 to 2021, describing the proportion of ILI patients to the total number of patients.
## 2 Related Works  
[PERSON] et al. [11] suggest a prototype that includes an independent system named Intelligent Diabetic Assistant (IDA), that determines the prognosis and the treatment prioritizing relies on the observation manifested on a screen. The IDA comprises a knowledge-related module for a severity level-related classifier, and clinical decision support. In [12], an ensemble-based ML method containing ML techniques such as Logistic Regression (LR), Random Forest (RF), Adaboost, KNN, and Decision Tree (DT) can be tested on the DR dataset. In the initial step, normalization can be performed on the DR dataset by the min-max normalization technique. [PERSON] et al. [13] introduced an automatic DR detection technique for smartphone-related retinal images by utilizing the DL technique with the ResNet50 network. This work primarily used familiar ResNet50, AlexNet, and GoogLeNet structures by making use of the transfer learning (TL) method. Secondly, such structures are retrained with retinal images from numerous datasets involving Messidor-2, EyePACS, IDRiD, and Messidor for investigating the impact of utilizing images from the cross, multiple, and single datasets. Thirdly, the suggested ResNet50 method can be implied to smartphone-related synthetic images for exploring the DR detection accuracy of smartphone-related retinal imaging systems.  
In [14], a method for automatic detection of DR was suggested with the help of a low complexity image processing approach and a modified Convolutional Neural Network (CNN) having superior precision for helping an ophthalmologist via identification of variation in retina features. [PERSON] and [PERSON] [15] project a robust diagnostic mechanism via a compilation of state-of-the-art deep learning (DL) methods for automatic DR severity detection. The idea of deep CNNs was used and revolutionized various branches of CV such as medical imaging. [PERSON] et al. [16] provided an approach that directly classifies and identifies the DR severity in digitalized fundus images by employing CNN. The methods employed are CNN built and integrated with Keras. The dataset which is trained is divided into two kinds; they are categorical and binary datasets further trained with or without pre-processing and their results are compared.  
Although diverse DR classification models exist in the literature, it is still required to boost the classifier results. Due to the incessant deepening of the model, the number of parameters of DL models gets raised quickly which leads to model overfitting. At the same time, different hyperparameters have a significant impact on the efficiency of the CNN model. Particularly, hyperparameters such as epoch count, batch size, and learning rate selection are essential to attain an effectual outcome. Since the trial and error method for hyperparameter tuning is a tedious and erroneous process, in this work, we employ the AOA algorithm for the parameter selection of the BGRCU model.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### System Model
#### 3.1.2 Trust Metrics Identification and Trust Index Calculation
Figure 2: System Model of SMTrust. be forwarded for routing decisions. However, the nodes included in _t3_ are in the middle of _\"No Trust\"_ and _\"Complete Trust\"_. Hence, SMTrust tends to consider the nodes in _t3_ only when there is a shortage or no nodes available in the range of _t4_ and _t5_ tuple for communication, following the strict calculation of the trust index. This is also to overcome the non-availability of nodes for routing. Hence, the trust threshold is kept at 0.46.
## 3 Typical Applications of IoT in Smart Agriculture
### Smart Precision Farming  
The advent of the GPS (global positioning system) has created breakthrough advances in many fields of science and technology. The GPS provides the most important parameters for locating a device, such as location and time. GPS systems have been successfully deployed in many fields, such as smartphones, vehicles, and IoT ecosystems. However, GPS is only good support for outdoor systems and the sky. Meanwhile, the demand for the locating and navigating systems in the home and on the streets of smart cities is growing rapidly. Aiming to solve this problem, an advanced global navigation satellite system (GNSS) is being deployed [100]. Based on GPS and GNSS systems, suitable farming maps have been established for fields and farms. As a result, agricultural machinery and equipment can be operated autonomously [101]. Figure 6 presents an illustration of the typical cloud-assisted, IoT-based precision agriculture platform.  
In smart precision farming, one of the most important applications is the use of drones in monitoring and farming activities. Some common farming tasks using UAVs include spraying pesticides, fertilizing, sowing seeds, evaluating and mapping, and monitoring crop growth. In [102], the authors presented a detailed survey of drone applications for smart agriculture, including applications, control technology, and future trends of the UAV application for smart agriculture. In [103], the authors designed an automatic agricultural product classification system based on camera systems, image processing algorithms, and  
Figure 6: Cloud-assisted IoT-based precision agriculture platform.  
mechanical actuators. The experimental results for agriculture products such as oranges and tomatoes present a classification success rate of over 95%, and the sorting time for each product is less than 1(s). This solution can be adapted and applied to the classification of different agricultural products. In [104], the authors proposed a solution to estimate grape production. The proposed solution combines an RGB-D camera mounted on a mobile robot platform and size estimation algorithm for a bunch of grapes. The experimental results present an average error in the range of [2.8-3.5] (cm). The results demonstrate this solution is a feasible method for evaluating the productivity of large-scale grape farms.  
The survey results show that smart precision agricultural equipment, such as irrigation systems, unmanned aerial vehicles (UAV), and smart agricultural equipment, etc., are configurable in an autonomous-control mode based on certain conditions or can be controlled remotely by the farmer via the Internet [105,106]. Smart precision farming helps to improve productivity and production efficiency and is suitable for large-scale farms. Nowadays, suppliers of precision agricultural equipment have IoT modules built into their machines, allowing machines to operate autonomously and remotely via the Internet [107].
# TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform
## 4 Evaluation  
### Environment Setup for Development and Performance Verification  
A simple web game server scenario has been devised to prove the operability of the proposed system and to verify its auto-scaling ability. It is required to develop a game server in this scenario that offers the game \"Pacman\" worldwide online. It assumes that the developer has defined a game server for a single Kubernetes cluster in a TOSCA template.  
Figure 4 illustrates such an execution environment setup across multiple clusters. User interactions with the game are being handled by the load balancer to automatically add or remove web pods in both the Kubernetes clusters in Tokyo, Japan, and Oregon, US according to the workload and number of online users in the system. Figure 5 presents the skeleton code of the corresponding TOSCA descriptions of the game server federation. In the figure, Kubernetes Federation and cluster components are defined at element 1 through element 3, and HorizontalPodAutoscaler is defined as a TOSCA Node at element 6.  
Cloudify Manager 4.2 (Cloudify, New York, NY, USA, 2017) was used to run in a virtual machine configured with CentOS 7 x64 as its operating system hosted by VirtualBox in the Ubuntu 16.04 LTS x64 environment. Kubernetes 1.8 was used run on Google Cloud Platform, and Kubernetes clusters were built in the Tokyo, Japan and Oregon, US areas. Each cluster contains two nodes; the node type is n1-standard-2 (vCPU 2, RAM 7.5 GB), and Nginx is used as Web daemon, and MongoDB as the database server by default. Each node has a web pod, database pod, and a persistent data volume. Each web pod and DB pod have assigned a unique IP address through which it can be accessed accordingly. The number of nodes is later scaled up or down according to the incoming traffic.  
Figure 4: Web game server scenario to demonstrate the operation of the proposed system architecture and verifying its auto-scaling ability. GCP: Google Cloud Platform.
## 4 Discussion
### Limitations and Future Research Directions  
While our work has addressed core issues in the standardized validation of ML models for medical classification, several avenues for future research remain. First, the development and evaluation of advanced model architectures that further mitigate overfitting and improve domain generalization should be explored, particularly in more heterogeneous and multi-center clinical datasets. Second, the real-world deployment of such validated models (integrating them into clinical workflows and assessing their ongoing impact on patient outcomes and care efficiency) warrants thorough investigation. Third, the creation of standardized benchmarks and open challenges for external validation in diverse clinical settings would further advance the field. Finally, additional work is needed to align composite utility metrics with evolving clinical guidelines and regulatory standards, ensuring models remain relevant and actionable as healthcare practices progress.  
Additionally, while the composite evaluation metrics and their associated formulas (including weighting schemes, penalty functions, and aggregation strategies) were designed to be consistent with established clinical reasoning and to reflect priorities commonly recognized in medical decision making, we acknowledge that these formulas were not formally validated through direct physician input or structured expert consensus in this study. Future work could incorporate expert clinician feedback or employ a formal Delphi consensus process [49] to systematically refine and validate these formulas and weighting strategies, thereby ensuring that all aspects of the evaluation framework are optimally aligned with real-world clinical priorities and expert judgment.  
Furthermore, benchmarking composite clinical scores against baseline models (such as random or majority-class classifiers) and, when available, against human expert performance would provide valuable context and further strengthen the clinical relevance of these findings. Incorporating such comparisons represents an important direction for future work and would help to more clearly position the utility of the evaluation framework within real-world clinical practice.
## 3 Results and Discussion
### Comparison of Zr-EPRC Preparation Methods
#### 3.1.4 Determination of Optimum Load Time  
Figures 7 and 8 show that the equilibrium adsorption capacity of both modified steel slag and fly ash gradually increases during the initial loading period. When the loading time reaches 8 h, the loaded adsorption capacity of modified steel slag reaches 49.03 mg/L. Beyond this point, further increases in the loading time have minimal impacts on the  
Figure 5: Effects of different loading pH on phosphorus removal effect of steel slag.  
Figure 6: Effects of different loading pH on phosphorus removal effect of fly ash.  
adsorption capacity. By the time the loading duration reaches 12 h, the adsorption effect of steel slag and fly ash stabilizes, with no significant changes in the adsorption capacity.  
This phenomenon arises from the process of zirconium being loaded onto the adsorbent material through condensation reactions. Initially, a surplus of active sites exists on the material's surface, allowing for the rapid loading of active zirconium onto both the material's surface and within its pore structure. However, as time progresses, the number of available active sites on the material becomes saturated, leading to a plateau in the adsorption effect. Consequently, the optimal loading time for the material is determined to be 12 h.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 2 Machine Learning Techniques for Time Series Analysis
### Unsupervised Learning Frameworks  
The Auto-CM framework represents a significant advancement in unsupervised learning for satellite imagery cloud masking. By leveraging spatio-temporal dynamics, Auto-CM outperforms traditional physics-based methods and supervised ML models on diverse datasets [22]. The framework employs contrastive learning to identify cloud patterns without explicit labels, making it particularly valuable for regions with limited ground data.