esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Extraction and Reconstruction of Summer Maize MODIS EVI Time Series Curve in the Reference Area  
Based on ETM+ data, the spatial distribution of summer maize (\(38.5\times 10^{3}\) ha) in Ruzhou is shown in Figure 5. The result of summer maize extraction in Ruzhou City was verified by the statistics of the total surface area of summer maize (\(43.4\times 10^{3}\) ha), and the accuracy was higher than 88%. It was revealed that the way of intersection operation based on different spectral characteristics in maize critical periods can acquire the maize cultivated area with high precision in the relatively single area.  
The summer maize MODIS EVI time series images of the reference area were obtained by masking the spatial distribution area of summer maize and multi-temporal MODIS EVI images from the reference area. The average value of summer maize MODIS EVI images in each period was calculated, which was regarded as the summer maize EVI value to obtain the original MODIS EVI time series curve of summer maize in the reference area. Smoothing the original MODIS EVI time series curve of summer maize with Savitzky-Golay filtering, the smoothed MODIS EVI time series curve of summer  
Figure 5: The spatial distribution of summer maize in the reference area from Landsat ETM+.  
maize was gained in the reference area (Figure 6). The Gaussian function was used to fit the smoothed MODIS EVI time series curve of summer maize in the reference area. The formula is as follows:  
\[EVI_{i}=a_{1}\times e^{(-\frac{i-b_{3}}{c_{1}})^{2}}+a_{2}\times e^{(-\frac{i-b_ {3}}{c_{2}})^{2}}+a_{3}\times e^{(\frac{i-b_{3}}{c_{3}})^{2}}(i=145,177\cdots 289) \tag{5}\]  
where \(i\) is the day of the year (DOY), \(EVI_{i}\) is the simulated EVI value of summer maize in the reference area on DOY \(i\), and the parameters of the simulated EVI function are shown in Table 2.  
It is thus clear from Figure 6 that the value of summer maize MODIS EVI increased first and then decreased with time in the reference area, consisting of the growth trajectory of summer maize. From DOY 157 to 225, summer maize was in the periods of souring to tasseling, which was the rapid growth stage, and the EVI value gradually increased. It entered the period of tasseling around DOY 225, which was the most vigorous growth stage, and the value of EVI also reached the peak value. From DOY 225 to 258, summer maize was in the periods of tasseling to maturation, and gradually matured with the decrease in EVI value.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 4 Experimental Results and Discussion
### Behavior with the BRACOL Enhanced Dataset  
Initially, the models were tested using the enhanced BRACOL dataset to assess the impact of the dataset on disease detection in coffee leaves, and the results are compared with those achieved with the original BRACOL dataset. Table 4 shows the quantitative results obtained with the YOLOv8s model for the mAP, recall, and F1-Score metrics.  
In order to more comprehensively evaluate the behavior of the models with the enhanced dataset, two samples were chosen from the test set for a qualitative analysis. Figure 6 shows the detection results in the samples using the model trained with the original dataset and with the enhanced dataset. In the predicted images, there are differences in the model's detections. In Figure 6a, the model detected only two larger regions. On the other hand, in Figure 6b, the model detected smaller areas and a greater number of affected regions. In Figure 6c, the model correctly detected two regions. In Figure 6d, in addition to detecting the two regions found in Figure 6c, the model was also able to correctly detect the other diseased regions.  
Quantitative results show a significant difference in the metrics achieved with the datasets. The improved dataset increased the difficulty of the task because it has more marked regions and the model needs to detect them correctly. Specifically, higher mAP and F1-Score values achieved with the original dataset are justified, because originally some affected regions were not marked and the model was not penalized for not detecting them. In the improved dataset, any omission error is accounted for, thus considerably reducing the F1-Score. The mAP also tends to decrease in the results with the enhanced dataset because the model may not be detecting all the new labels correctly. From the qualitative results, although some regions are more difficult to detect, resul  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**BRACOL Original**} & \multicolumn{3}{c}{**BRACOL Improved**} \\ \hline  & **mAP** & **Recall** & **F1-Score** & **mAP (\%)** & **Recall** & **F1-Score** \\  & **(\%)**\(\uparrow\) & **(\%)**\(\uparrow\) & **(\%)**\(\uparrow\) & \(\uparrow\) & **(\%)**\(\uparrow\) & **(\%)**\(\uparrow\) \\ \hline YOLOv8s & 69.1 & 94 & 63 & 54.5 & 93 & 54 \\ \hline \hline \end{tabular} Note: \(\uparrow\) indicates that higher values are better.  
\end{table}
Table 4: The results of the YOLO model on the BRACOL original and BRACOL enhanced datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Input size & \(640\times 640\times 3\) \\ lr0 & 0.01 \\ lrf & 0.01 \\ Optimizer & Adam \\ Weight Decay & 0.0005 \\ Batch Size & 16 \\ Epochs & 300 \\ Patience & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters used to train the localization models.  
detections, the model trained with the enhanced dataset has a superior ability to detect affected areas, ensuring that fewer diseased regions go unnoticed.
# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
###### Abstract  
Due to the limitations of traditional retinal blood vessel segmentation algorithms in feature extraction, vessel breakage often occurs at the end. To address this issue, a retinal vessel segmentation algorithm based on a modified U-shaped network is proposed in this paper. This algorithm can extract multi-scale vascular features and perform segmentation in an end-to-end manner. First, in order to improve the low contrast of the original image, pre-processing methods are employed. Second, a multi-scale residual convolution module is employed to extract image features of different granularities, while residual learning improves feature utilization efficiency and reduces information loss. In addition, a selective kernel unit is incorporated into the skip connections to obtain multi-scale features with varying receptive field sizes achieved through soft attention. Subsequently, to further extract vascular features and improve processing speed, a residual attention module is constructed at the decoder stage. Finally, a weighted joint loss function is implemented to address the imbalance between positive and negative samples. The experimental results on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that MU-Net exhibits better sensitivity and a higher Matthew's correlation coefficient (0.8197, 0.8051; STARE: 0.8264, 0.7987; CHASE_DB1: 0.8313, 0.7960) compared to several state-of-the-art methods.  
deep learning; retinal vessel segmentation; multi-scale information; selective kernel; attention mechanisms +
Footnote †: journal: Medical Image Processing  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 Results and Discussion
### Selectivity Test  
The origins of molecular recognition in MIPs are generally believed to arise from pre-organization of functional groups within a shape selective cavity [33] The molecular recognition of MIP- and NIP-coated chips were examined using three substituted indoles as structure analogues--namely, IAA, IPA, and TA (see the corresponding molecular structures in Figure 5). The binding of IBA on MIP was higher than that of all the other indole compounds (see Figure 6). All the compounds tested in this study exhibited stronger binding on the MIPs than on the NIPs. On the IBA-MIP-coated chips, the extraction amounts of IBA, IPA, TA, and IAA were 31.9, 15.2, 13.3, and 8.6 \(\upmu\)g, respectively, proving that even with high similarity between the homologues (IAA, IPA, and IBA) and the template, the imprinting was sufficient for the discrimination of the different compounds. As the homologues concerned, the template IBA was found to have the highest binding on MIPs, followed by IPA and then IAA, which shows excellent shape selectivity of the MIPs. TA is identical to IPA in structure but features an amino group instead of a carboxyl group. TA had lower binding on the MIPs than IPA did. This evidence suggests that MIPs have functional group selective recognition as well. NIPs showed the lowest binding to TA, but could not discriminate the homologues according to the shape of the template (see Figure 6). This indicates that there was only functional group selective recognition in NIPs. We propose that the shape selectivity appears to be the dominant mechanism for selectivity by the imprinting effect.  
Figure 5: Molecular structures of IBA and its analogues IAA, IPA, and TA.  
Figure 6: Fluorescence intensities of IAA, IPA, and TA adsorbed on the MIP/NIP coating (10 mg\(\cdot\)L\({}^{-1}\) target solutions in buffer pH = 2.5, \(\upmu\)x/em = 294/370 nm).
# A Multidimensional Data Collection and Edge Computing Analysis Method
###### Abstract
This integration significantly bolsters the accuracy of target detection, ensuring more effective and precise results;
* The calculation of image pixel-level spatial utilization and the dynamic monitoring of image 2D mapping are seamlessly executed. This two-dimensional intuitive calculation provides comprehensive insight into spatial accessibility, privacy considerations, and the connectivity of the objects under scrutiny. The rest of this paper is organized as follows. In Section 2, we introduce the designed image acquisition module, wireless module, and selection of edge computing. Subsequently, the overall structure of this system and the improved target detection model are proposed in detail in Section 3. The calculation of the utilization of public space under the improved target detection model is in Section 4. Lastly, we conclude our work in Section 5.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 3 Post-Quantum VerITAS: A zk-SNARK-Based Framework for Secure Image Provenance Verification
A summary of runtime benchmarks is provided in Section 10. By combining the privacy-preserving properties of post-quantum zero-knowledge proofs with the practical requirements of image editing, Post-Quantum VerITAS provides a robust solution for ensuring the authenticity of digital images in a trustless environment. Its ability to handle high-resolution images efficiently positions it as a viable alternative to traditional provenance frameworks, offering a new paradigm for secure and scalable image verification. To highlight the improvements introduced by Post-Quantum VerITAS, Table 1 compares it against C2 PA and the original VerITAS system across key dimensions.
## 3 Results and Discussion
### ANN Model Results  
Figure 23 provides an examination of the ANN model's predictive capacity for \(\sigma_{min}\) and \(\gamma_{max}\) across both training and test datasets. The training set exhibited a coefficient of determination (R-squared) of 0.9946 for \(\sigma_{min}\) and 0.9963 for \(\gamma_{max}\), along with an RMSE of 6.01 for \(\sigma_{min}\) and 0.073 for \(\gamma_{max}\), and a low MAE of 4.625 for \(\sigma_{min}\) and 0.051 for \(\gamma_{max}\). Although displaying slightly less accuracy than its predecessors in the training set, the ANN model demonstrated notable predictive capabilities. In the test set, the performance of the ANN model stands out, ranking among the top models and closely approaching the performance of XGBoost and GBR models. The ANN model achieved R-squared values of 0.9917 for \(\sigma_{min}\) and 0.9954 for \(\gamma_{max}\), with an RMSE of 7.92 for \(\sigma_{min}\) and 0.086 for \(\gamma_{max}\), along with an MAE of 5.872 for \(\sigma_{min}\) and 0.061 for \(\gamma_{max}\). These results underscore the proficiency of the ANN model in delivering reliable predictions for unseen (test) data instances. A comparative analysis with previous models revealed that the ANN model holds a favorable position, offering near-perfect accuracy and minimal degradation, as evidenced in Table 5. This high level of performance positions the ANN Model as a valuable tool for predicting applied stress and dry unit weight.  
Figure 22: Comparing the actual and predicted values for the training and test datasets in the SVR model for (**A**) \(\sigma_{min}\) and (**B**) \(\gamma_{max}\).
# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 2 Related Work
### Generative Adversarial Networks  
Generative adversarial networks [14] are unsupervised generative models that contain two deep neural networks. The two neural networks are named as the generator (\(G\)) and discriminator (\(D\)) and are trained parallelly during the training process. GAN training can be considered to be a two-player min-max game where the generator and discriminator compete with each other to achieve each other's targeted goal. The generator is trained to learn a mapping from a random noise vector (\(z\)) in latent space to an image (\(x\)) in a target domain: G(z) \(\rightarrow\) x. The discriminator (D) learns to classify a given image as a real (output close to 1) image or a fake (output close to 0) image from the generator (G): D(x) \(\rightarrow\) [0.1]. Both the generator and decimator can be considered as two separate neural networks trained from backpropagation, and they have separate loss functions. Figure 1 showsthe high-level architecture of the proposed conditional GAN model. The generator will try to generate synthetic images that resemble real images to fool the discriminator. The discriminator learns how to identify the real images from the generated synthetic images from the generator.  
The widest adaptation of GANs is for data augmentation, or that is to say, to learn from existing real-world samples and generate new samples consistent with the distribution. Generative modeling has been used in a wide range of application domains including computer vision, natural language processing, computer security, medicine, etc.  
[PERSON] et al. [34] used GANs for synthesizing image data to train and validate perception systems for autonomous vehicles. In addition to that, [35; 36] used GANs for data fusion for developing image classification models while mitigating the issue of having smaller datasets. Furthermore, GANs were used for augmenting datasets for adversarial training in [37]. To increase the resolution of images, a super-resolution GAN was proposed by [PERSON] et al. [38], which took a low-resolution image as the input and generated a high-resolution image with 4\(\times\) upscaling. To convert the image content from one domain to another, an image-to-image translation approach was proposed by [PERSON] et al. [15] using CGANs. [PERSON] et al. [39] proposed a TriGAN, which could solve the problem of image translation by adapting multiple source domains. Experiments showed that the SeqGAN proposed in [40] outperformed the traditional methods used for music and speech generation. In the computer security domain, [PERSON] and [PERSON] [41] proposed a GAN-based model to generate malware. For private product customization, [PERSON] et al. [42] proposed GANs to manufacture medical products.
## 3 Results and Discussion
### Pile Movement Monitoring
As an example, saturating the soil can cause soil shear strength reduction, which will influence the pile settlement as well as reducing the pile bearing capacities [48]. Another critical issue that can increase the complexity of soil-pile reactions, as well as load-settlement responses, as highlighted by [PERSON] et al. [49]. In the study provided by [PERSON] et al. [49], the influence of dynamic and static loading conditions (e.g., for identification of the primary material properties of a base-isolated bridge) are investigated. Their results showed that static identification is much less complicated compared to dynamic analysis. Although the current study covers the static load test, future work could evaluate the effect of a dynamic loading test in real-time monitoring of \(Y_{c}\).
## 3 A Framework for the IoT-Edge-Cloud Continuum
### The DevOps Layer  
A comprehensive continuous integration/continuous delivery (CI/CD) framework allows for automating and streamlining the software development and deployment process. Well-known open-source tools are used for better cost-efficiency, interoperability, transparency, and flexibility:  
* **GitHub** for source control and tracking, code repository, code versioning.
* **Jenkins 2.470** for automated building, testing, and deployment.
* **Docker 27.0** and **Docker Compose 2.26** for bundling the developed services and components into containers using _de facto_ standards.
* **SonarQube 10.6** for performing static analysis of code to detect bugs, code smells, and security vulnerabilities.
* **Kubernetes 1.30** for orchestrating the deployed services.
* **Harbor 2.7** container registry for managing, storing, and distributing the produced Docker images.
* **Portainer 2.20** for providing logging and health monitoring information for the developed and deployed applications, offering easy Docker container management through a GUI.
* **Keycloak 21.0** for handling access and identity management of the users.
* **Microsoft Teams 2024/Mail** for receiving notifications of the whole CI/CD process.
* **Ansible 2.17** for automating the deployment of the CI/CD stack.  
The CI/CD platform allows users to connect and utilize tools by signing in through their GitHub account via Keycloak, serving as the single sign-on (SSO) method required for triggering deployment workflows. These workflows, initiated after the developer logs in, vary based on the chosen approach and any code-sharing security restrictions imposed by technical teams.  
* In the **GitHub approach**, the process starts with a developer committing code to GitHub. This action triggers Jenkins automation via webhook, which is followed by SonarQube code analysis. If successful, an image is created and uploaded to the Harbor registry. Finally, deployment on the development server is carried out using Jenkins and Kubernetes.
* Alternatively, the **Harbor approach** begins with the developer pushing a Docker image to the Harbor registry, activating Jenkins automation, and deploying it on the development server using Jenkins and Kubernetes via webhook.
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 5 Potential Applications and Future Work
### Alignment with Emerging Regulations  
Because SecureLLM integrates cryptographic controls with differential privacy, it naturally aligns with the stricter requirements set by frameworks such as the EU AI Act. For \"high-risk\" applications affecting employment, healthcare, or public services, encryption and privacy guarantees can serve as proof of due diligence and compliance with data protection mandates. Future refinements may include formal audits, transparent noise parameter logs, and real-time privacy budget monitoring.  
Another regulatory shift gaining traction is the EU Cyber Resilience Act, which places strong emphasis on software supply chain security. SecureLLM's cryptographic safeguards and MPC-based training protocols can generate provable evidence of secure data processing throughout the AI pipeline, helping organizations demonstrate robust compliance during audits [61].  
Additionally, SecureLLM is engineered to comply with standards like the GDPR, HIPAA, and CCPA. Technical measures such as data minimization, strong (e.g., 256-bit) AES-GCM encryption, and effective key management support this compliance. At the organizational level, audit trails and continuous monitoring mechanisms ensure transparency and traceability. Collectively, these measures uphold stringent data protection and accountability requirements.  
As regulators refine the guidance for AI in critical infrastructure--e.g., energy or transportation--further research into secure API endpoints, traceable model outputs, and real-time threat detection may be necessary to keep SecureLLM aligned with emerging standards.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
Therefore, we tried to use three different types of satellite images, Gaofen-1 (GF-1), Gaofen-2 (GF-2), and WorldView-2 (WV-2), to conduct the test to answer the question 'Is there a universally good method for high spatial resolution images?'. We carried out image fusion and evaluated it, hoping to find the best image fusion method for subsequent information extraction. This article evaluates the quality of the fused satellite images and compares the results with seven traditional statistics indexes of gray mean value, standard deviation, entropy, average gradient, correlation coefficient, spectral distortion, and bias index. Then, we apply the \(\mathit{CQ}_{\mathit{max}}\) index to the same work to determine whether \(\mathit{CQ}_{\mathit{max}}\) can replace these seven statistics parameters. A brief description of the main acronyms used in this paper is listed in Appendix A.
## 3 Results and Discussion
### Net Present Value  
The net present values discounted net annual benefits of systems and subtracted the net investment in the PV system (project cost \(-\) federal income tax credit) using a discount rate to account for the time value of money (Figures 11 and 12). All system sizes and billing arrangements resulted in a positive NPV, indicating projects were capable of paying for themselves over 25 years. In contrast to NEM, a lower NPV was observed with NEB as expected, given the avoid cost rate remuneration penalty with NEB. With higher self-consumption rates, the NEB pricing penalty played a smaller role, and, hence, both poultry farmers and policy makers can expect a slowed DG expansion of PV systems with NEB  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**PV System Size, kW**} & \multirow{2}{*}{\begin{tabular}{c} **Billing** \\ **Arrangement** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Annual** \\ **Electricity Cost** \\ **without PV** \\ **(USD)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Net Electricity** \\ **Costs with PV** \\ **(USD)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Net Annual** \\ **Benefit (Net** \\ **Savings) (USD)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Capital** \\ **Expenditure** \\ **(USD)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Simple** \\ **Payback (yr)** \\ \end{tabular} } \\  & & & & & & \\ \hline
35 & NEM & 15,254 & 8448 & 6806 & 49,000 & 7.2 \\  & NEB & 15,254 & 9051 & 6203 & 49,000 & 7.9 \\ \hline
70 & NEM & 15,254 & 2167 & 13,087 & 98,000 & 7.5 \\  & NEB & 15,254 & 5329 & 9925 & 98,000 & 9.9 \\ \hline
105 & NEM & 15,254 & \(-\)4290 & 19,544 & 147,000 & 7.5 \\  & NEB & 15,254 & 2314 & 12,940 & 147,000 & 11.4 \\ \hline \end{tabular}
\end{table}
Table 4: Simulation results of annual electricity costs without and with PV installation (USD/yr) and net annual benefits of three system sizes under two billing arrangements (NEM: net energy metering; NEB: net energy billing).  
Figure 11: Average net annual benefits over a system’s useful life of 25 years as a result of two billing arrangements with three system sizes. NEM, net energy metering; NEB, net energy billing.  
compared to NEM. Utilities, on the other hand, likely will continue to invest in utility-scale projects as they have greater control over pricing. Whether and to what degree utilities pass on federal income tax credit savings springing from PV installations to electricity users in the state is at their discretion but are usually also governed by public service commissions.