esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Sentinel-2 Data Pre-Processing  
For this work, we apply our DSPEB method to two different types of inputs, corresponding to the wave kinematics-based DSPEB approach (W-DSPEB) and color-based DSPEB (C-DSPEB). Throughout the article, we make a distinction between two types of information included in raw satellite imagery over coastal areas. Signals and information corresponding to ocean waves are referred to as \"wave kinematics information\". These signals are pre-processed (described further in this section) and used as inputs to the W-DSPEB model. The remaining signals, termed \"color information\", are presumed to represent ocean water color as affected by the optical properties of water, water constituents, seabed reflectance and depth. These signals are filtered and used as inputs to the C-DSPEB model.  
The inputs to both neural networks are \(40\times 40\times 4\) px satellite images. We noted that model training was sensitive to different dates with varying meteorological conditions. To construct a training dataset, we select dates based on high estimation correlation from the S2 Shores method, which depends on visible waves. We provide a list of all images used in this work in Appendix A. While the focus of this work is to develop on our previous work on DSPEB, a less expensive and more general date selection method is a topic of on-going work.  
The first step in our pre-processing workflow is cloud detection. We make use of a simple cloud detector based on the percentage of blue pixels in each subtile by looking at the RGB Sentinel-2 bands. We discard all subtiles where the percentage of blue pixels is less than 80%, allowing for a margin of noise in the input data.  
We apply a pass-band filter to our input subtiles in the range of ocean-specific wavelengths (periods \(T_{min}=5\) s to \(T_{max}=25\) s). First, we create a frequency filter based on \(T_{min}\) and \(T_{max}\). Then, a discrete FFT is applied in two dimensions to the signals of each Sentinel-2 subtile band. The original filter is then used to filter the resulting frequencies, discarding all wave signals with periods outside of the specified range. For W-DSPEB, we further process our filtered subtiles by calculating the two-dimensional normalized cross-correlation (NORMXCORR) of each band, in order to extract the most consistent and recurring wave signals, which we presume correspond to the crests of actual ocean waves. For C-DSPEB, we subtract the filtered signals from the raw input image in order to retain the background color rather than the ocean wave signals. Figure 2 demonstrates our pre-processing workflow on an example 400 \(\times\) 400 m Sentinel-2 subtile. For C-DSPEB, we scale all input images such that the minimum and maximum pixel values over the full dataset are equal to \(-0.9\) and \(0.9\), respectively.  
Figure 2: Pre-processing of a single 400 \(\times\) 400 m Sentinel-2 subtile band (10 m resolution). (**a**) Raw subtile. (**b**) Passband-filtered subtile. (**c**) Final NORMXCORR result used as input to W-DSPEB. (**d**) Color-augmented subtile.
## 4 Discussion
### Advantages and Disadvantages  
Analysing Table 2, we observe that, in general, the most common task performed in the medical field is classification in all application areas exemplified. However, another common task is regression in the infectious disease area. This task is little used in areas, such as Alzheimer's disease, Parkinson's disease, and hepatic diseases. On the other hand, the clustering task is also slight considered in hepatic and heart diseases, however, it is often used in Alzheimer's disease and Parkinson's disease. Finally, the combination of neural networks and other supervised algorithms is widely used in cancer, Alzheimer's disease, Parkinson's disease, and renal disease application areas, but is seldom applied in metabolic, hepatic, infectious, and heart disease application areas.  
The author's decision regarding the technique used was motivated by the pros and cons of each tool in the particular area of application and according to the own experimental conditions. The following tables analyse some advantages and disadvantages of the techniques and approaches previously described. Based on [190,191], Table 3 shows some advantages and disadvantages of ML and DL. Based on [192], Table 4 analyses some advantages and disadvantages of unsupervised learning and supervised learning. Finally, based on [44,193], Table 5 details some advantages and disadvantages of each algorithm described previously. In any case, it should be noted that the various ML techniques cited in this review have been successful in their particular area of application. In this sense, no technique can be ruled out a priori, but its applicability must be carefully analysed according to the purpose of the investigation (classification, prediction), scope, data typology and their relationship, experimental resources, etc.  
Figure 3: Number of references for each goal and algorithm.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline ML & Algorithms are often easy to be implemented. & Complex relationships between dependent and \\  & Algorithms are flexible enough to handle complex & independent variables are not identified easily in \\  & problems with multiple interacting variables. & high-dimensional databases. \\  & Input and output are not necessarily fixed. & High computational cost. \\ \hline DL & Complex relationships between dependent and & Input and output are fixed. \\  & independent variables are identified easily in & Overfitting problem possibility is high. \\  & high-dimensional databases. & Implementation is not so easy than in ML. \\  & Ability to handle databases with high noise. & Training process requires a higher computational cost than ML. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Advantages and disadvantages of Machine Learning (ML) and Deep Learning (DL).
## 3 Experimental Results and Analysis  
This section presents the results and analysis of our comprehensive experiments, which were designed to validate the effectiveness of the proposed gradual de-noising method which combines I-SOBI and FFT with A-CFS for simulated signals and GB-SAR bridge deflection measurements under various conditions. We conducted a series of simulation experiments to explore the theoretical performance of our approach, followed by real-world experiments to assess its practical applicability and effectiveness.
## 3 Results and Discussion
### Activated Carbon and biogas Upgrading  
Activated carbon was used to eliminate the last traces of biogas contaminants before biogas entered the membrane-based upgrading unit. Two types of commercial activated carbon were placed in series: the first one represented by the classic nonpolar activated carbon for the removal of organic and inorganic compounds and the second one impregnated with 10% sodium carbonate for specific removal of H\({}_{2}\)S. It is, indeed, known that activated carbon impregnated with alkali compounds is effective for H\({}_{2}\)S elimination [41]. At the outlet of the activated carbon treatment unit, the concentration of contaminants such as H\({}_{2}\)S, silicon derivatives, and VOCs was below 0.1 mg/Nm\({}^{3}\).  
Contaminant-free biogas was then compressed to around 10 bar and passed through the polymer polyimide membranes system. Selective CO\({}_{2}\) permeation through the membrane surface allowed us to obtain biomethane with a purity > 98%.  
Upon leaving the upgrading plant, biomethane had the characteristics (Table S2) such as to satisfy the requirements established by the national legislation and was introduced into the natural gas distribution network. Both plant and natural gas distribution network managers had continuous monitoring systems at the point of delivery of the biomethane to verify its quality and compliance. The economic value of the biomethane injected into the natural distribution network varies daily based on the market price. The average price for the thermal year October 2022-September 2023 was 56,789 EUR/MWh while in the thermal year October 2021-September 2022, the price was higher (121.727 EUR/MWh) due to the Russia-Ukraine conflict.
## 3 Results and Discussion
### Effect of 3D Model Resolution  
To determine if the low-resolution 3D coral models were sufficient, the modelling of reflectance over coral shape area (Figure 1g, results in Figures 3 and 4, and Table 3) was duplicated using the low-resolution versions of the coral 3D structures (activity 2, Table 1). Across all corals and illumination treatments, the difference in any band when using the low-resolution 3D models was not greater than 0.51%, apart from coral 16 which had a maximum difference of 1.33%. In most corals, the maximum difference was less than a third of a per cent (Table 2, final column). Visually the outputs were very similar (Figure 5). Therefore, in general, using the low-resolution structures made almost no difference to the reflectance averaged over the coral area, and there seemed little need to continue using the high-resolution models, which with up to \(\sim\)100,000 polygons cause the 3D radiative transfer model to run substantially slower. For this reason, the remaining modelling activities used only the low-resolution models.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Species**} & \multicolumn{2}{c}{**Shading Scale Factor**} & \multicolumn{2}{c}{**Shading Scale Factor 50\%**} & \multicolumn{2}{c}{**Max \(\boldsymbol{L_{\text{a}}/E_{\text{d}}}\) diff. with**} \\  & & \multicolumn{2}{c}{**Coral only**} & \multicolumn{2}{c}{**Coral-Substrate Mix**} & \multicolumn{2}{c}{**Interstitial IOPs (\(\leq\)690 nm)**} \\ \hline  & & **Range** & **Median** & **Range** & **Median** & \\ \hline
03 & _Montipora capitata_ & 0.71–0.79 & 0.76 & 0.83–0.88 & 0.85 & 2.6\% \\
08 & _Porites eermanni_ & 0.57–0.69 & 0.63 & 0.68–0.77 & 0.74 & 2.8\% \\
10 & _Porites eermanni_ & 0.71–0.82 & 0.75 & 0.75–0.84 & 0.81 & 1.6\% \\
11 & _Montipora capitata_ & 0.75–0.84 & 0.79 & 0.80–0.85 & 0.84 & 1.0\% \\
12 & _Porites eermanni_ & 0.71–0.77 & 0.74 & 0.79–0.85 & 0.82 & 1.7\% \\
16 & _Porites compresa_ & 0.39–0.66 & 0.50 & 0.51–0.64 & 0.59 & 3.1\% \\
17 & _Porites eermanni_ & 0.69–0.75 & 0.72 & 0.75–0.79 & 0.78 & 1.0\% \\
18 & _Porites compresa_ & 0.50–0.65 & 0.54 & 0.55–0.68 & 0.65 & 3.0\% \\
19 & _Porites eermanni_ & 0.69–0.85 & 0.77 & 0.78–0.86 & 0.83 & 1.9\% \\
31 & _Porites compresa_ & 0.54–0.65 & 0.59 & 0.64–0.71 & 0.70 & 2.8\% \\
32 & _Montipora capitata_ & 0.72–0.78 & 0.75 & 0.79–0.83 & 0.81 & 1.2\% \\
33 & _Porites compresa_ & 0.51–0.74 & 0.67 & 0.71–0.81 & 0.78 & 2.6\% \\
35 & _Pacillopara sp._ & 0.62–0.68 & 0.66 & 0.70–0.81 & 0.74 & 2.1\% \\
38 & _Porites eermanni or lutea_ & 0.76–0.88 & 0.82 & 0.82–0.87 & 0.86 & 1.5\% \\
39 & _Montipora patula_ & 0.81–0.88 & 0.84 & 0.83–0.88 & 0.87 & 0.7\% \\
40 & _Porites eermanni or lutea_ & 0.75–0.79 & 0.76 & 0.87–0.89 & 0.88 & 1.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Shading factors for coral area only and coral with surrounding substrate in 50% mix. Final column shows the maximum difference in the reflectance of any band up to 690 nm, when interstitial attenuation and scattering is included in the canopy model.
## 2 Calculation Model and Method
### Space-Time Neutron Diffusion Model
#### 2.2.2. Spallation Neutron Source Calculation  
The space and energy distributions of the spallation neutrons \(S(z,t)\) in the target for the ADS reactor are calculated by the Monte Carlo transport code, which could simulate the interaction between an accelerator proton beam and a metal target [29]. The beam footprint at the target is circular with a diameter of 0.16 m, and its height is 0.9 m. According to the geometric arrangement and material composition of the spallation target, a three-dimensional model of the target was built using the Monte Carlo code and the neutrons tracked in the spallation target were tallied into an output file, then the distribution of spallation neutrons could be obtained, which are used asa shape function of the spallation source during the process of ADS neutron diffusion calculation. The distributions of spallation neutrons along the axial direction of the core and its energy spectrum are shown in Figure 4.  
In the process of transient simulation, the space and time-dependent spallation source term \(S(z,t)\) in the neutron diffusion is factorized into two functions; the shape function \(S(z)\) is shown in Figure 4a and amplitude function \(S(t)\) of the spallation neutron source could be calculated by:  
\[S=\frac{\eta_{p}}{e}I_{p} \tag{4}\]  
where \(\eta_{p}\) is the number of spallation neutrons released per proton, \(e\) is the electron charge, \(I_{p}\) is the intensity of the proton beam that can be derived by the following formula [30]:  
\[I_{p}=\frac{P_{f}\
u}{E_{f}}\frac{e}{\eta_{p}}\frac{1-k_{eff}}{k_{eff}} \tag{5}\]  
where \(P_{f}\) is the total thermal power of the subcritical core, \(E_{f}\) is the averaged energy released per fission, \(k_{eff}\) is the effective multiplication factor, which is an important concept in reactor physics. When neutron disappearance does not equal neutron regeneration,  
Figure 4: Space and energy distributions of the spallation neutron in the target.  
Figure 3: Spatial discretization in the axial direction.  
is introduced to the steady state diffusion equation for criticality calculation, and the subcriticality \(1-k_{eff}\) could be obtained, which means the distance from a criticality. After an external source is added into the subcritical core, the neutron population is perfectly balanced; thus, \(k_{eff}\) should be eliminated in the ADS steady state calculation.
## 5 Architecture, Optimization, and Technical Challenges in LLM4 Rec
### Methodologies in LLM4 Rec  
LLM4 Rec systems employ advanced methodologies to address scalability, personalization, and interoperability. For example, pre-trained LLMs are fine-tuned for recommendation tasks.  
* Domain-Specific Fine-Tuning: Models like Clinical BERT have been adapted for healthcare recommendations by fine-tuning on clinical datasets [85].
* Lightweight Fine-Tuning: Adapters and LoRA (low-rank adaptation) reduce computational overhead while retaining performance [86].  
Figure 2: LLM4 Rec architecture. A modular design integrating user-item embeddings, extended LLM token vocabularies, attention mechanisms (self- and cross-), and prompting strategies (soft and hard) to generate adaptive recommendations or explanations. Source: Author’s own design.  
In addition to fine-tuning, optimizing prompts enhances LLMs for recommendations through manual prompts. A structured query, such as \"What are similar products to X?\", guides the model in generating recommendations [87], and prompt tuning using gradient-based tuning of soft prompts improves performance without full model fine-tuning [88]. Moreover, mutual regularization techniques enhance the robustness of LLM4 Rec through  
* Cross-Modality Regularization: Aligns text and metadata embeddings to improve multimodal recommendations [89].
* Adaptive Aggregation: Dynamically combines the characteristics of the user and the item according to the interaction context. To address sparsity in LLM4 Rec, data augmentation is used through 1) synthetic data generation by generating reviews for underrepresented items mitigates cold-start problems or adopting masked language modeling (MLM) by pretraining tasks like ELECTRA's replaced token detection to enable more efficient learning of recommendation patterns [44]. Lastly, multitasking learning and knowledge distillation improve scalability through
* Multi-Task Frameworks: Jointly training on ranking, classification, and explanation tasks improves the model's generalization [90].
* Distillation: Compressing large models into smaller, task-specific ones, such as DistillBERT, improves scalability [91].
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## 4 Numerical Simulations
### Data Simulation  
For the presented numerical results, we use \(R=0.4\), \(T=1\), \(L=1.5\), \(N=300\), \(M=300\) and collect FFD-data for \(N_{\theta}=300\) projection directions. As initial pressure \(\mathbf{f}_{*}\) we take the sum of three solid spheres, as show in the left picture in Figure 3. The used sound speed \(\mathbf{c}_{*}\) is shown in the rightpicture in Figure 3, and consist of two Gaussian peaks with opposite signs, added to the constant sound speed \(\mathbf{c}_{0}[\mathbf{i}]=1\). The top row in Figure 4 shows the simulated FFD-PAT data \(\mathbf{g}_{\star}=\mathcal{XW}\mathbf{f}_{\star}\) (left) and the noisy FFD-PAT data \(\mathbf{g}\coloneqq\mathbf{g}_{\star}+\text{noise}\) (right) at projection angle \(\theta=0\) for both cases. To generate the noisy FFD data we have added Gaussian white noise with a standard deviation of 10% of the maximal value of \(\mathbf{g}_{\star}\), resulting in a relative \(L^{2}\)-data error \(\|\text{noise}\|/\|\mathbf{g}_{\star}\|\simeq 85.3\%\). For the first reconstruction step we apply the inverse Radon transform to the FFD-PAT data, resulting in the approximate 3D final time pressure \(\mathcal{X}^{\sharp}\mathbf{g}\simeq\mathcal{W}\mathbf{f}_{\star}\). The reconstruction of final time pressure from noisy data is shown in the bottom right image in Figure 4. For comparison purpose, the simulated final time pressure \(\mathbf{h}_{\star}\coloneqq\mathcal{W}\mathbf{f}_{\star}\) is shown in the bottom left image in Figure 4.  
Figure 4: FFD-PAT measurement data and final time pressure. (**Top left**): Simulated FFD-PAT data \(\mathbf{g}_{\star}\) for projection direction \(\theta=0\). (**Top right**): Noisy FFD-PAT data \(\mathbf{g}\) for projection direction \(\theta=0\). (**Bottom left**): Simulated final time pressure \(\mathbf{h}_{\star}=\mathcal{W}\mathbf{f}_{\star}\). (**Bottom right**): Recovered final time pressure \(\mathcal{X}^{\sharp}\mathbf{g}\simeq\mathbf{h}_{\star}\) from noisy FFD-PAT data.  
Figure 3: Phantom and sound speed used for the numerical simulations. (**Left**): Initial pressure \(\mathbf{f}_{\star}\) (the phantom to be recovered). (**Right**): Spatially varying sound speed \(\mathbf{c}_{\star}\).
# Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
## 4 Experimental Results and Discussions
### Single Image-Based Classification
Therefore, it becomes difficult for models to correctly identify an object of which the label has never been trained, and the illumination condition may also affect the result. From these observations, we need to find and use the common characteristics of PET bottles and aluminum cans other than the appearance or label and the shape of the neck or bottom part of the objects to achieve better interference results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\#Params**} & \multirow{2}{*}{**PET**} & \multirow{2}{*}{**Can**} & \multicolumn{2}{c}{**Non-Target**} \\  & & & & **Fraud** & **IAO** \\ \hline ResNet-18 & 11.2M & 89.39\% & 88.64\% & 44.29\% & 88.33\% \\ DenseNet-121 & 7.0M & 100.00\% & 79.55\% & 47.14\% & 96.67\% \\ SqueezeNet & 0.7 M & 90.15\% & 90.91\% & 52.86\% & 63.33\% \\ MobileNetV2 & 2.2 M & 93.18\% & 88.64\% & 41.43\% & 85.00\% \\ EfficientNet-B0 & 4.0 M & 95.45\% & 66.67\% & 37.14\% & 75.00\% \\ EfficientNet-B1 & 6.5 M & 79.55\% & 67.42\% & 47.14\% & 90.00\% \\ EfficientNet-B2 & 7.7M & 85.61\% & 71.21\% & 48.57\% & 90.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of CNNs trained with top view data, classifying objects into PET, Can, and non-target classes. As shown in Figure 10, we can find that a common characteristic of most PET bottles and aluminum cans is the shape of the lid, which corresponds to the front view in our approach. Using this common feature, we tested the classification accuracy with only the front view images and show the results in Table 5. Since ResNet-18 has the largest model size among the candidate CNNs but is not showing acceptable classification performance, we excluded the ResNet-18 from subsequent experiments. The experimental results in Table 5 show that, as expected, training with the front view images is more accurate than the top view image training. The general accuracy of 100% on target objects is not surprising. Previous work by [PERSON] et al. [14] also presented the 99% classification accuracy of pet bottles, cans, and glasses, but they did not consider the fraud objects. Figure 10: Front view of five different aluminum cans. Figure 9: Grad-CAM visualization results using MobileNetV2 for target objects: (**a**) Properly classified objects; (**b**) Misclassified objects. The predicted class and its probability are shown below each Grad-CAM figure. According to the classification accuracy of target objects, SqueezeNet and MobileNetV2 can be regarded as the best-fit models for the RVM application, as in the previous experiments. However, their accuracy for the fraud object is still low. Furthermore, creating an intentional fraud object of the front view is more straightforward than the top view.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 4 The Requirements of 6G
### Network Architecture
The 5 GPPP Architecture Working Group proposes an architecture based on a multi-dimensional approach, with five horizontal layers [59]: infrastructure and environment, user plane network functions, control plane network functions, the management plane, and vertical service providers. It has a vertically designated orchestration profile which deals with responsibilities related to: the network intelligent stratum, security stratum, sensing stratum and network stratum. The network intelligence stratum coordinates functions across the entire network, managing network functions intelligently and autonomously through data and analytics gathered from the infrastructure and environment layer. The security stratum oversees all cybersecurity and data privacy aspects throughout the network, coordinating functions across planes and domains, up to the vertical service provider by distributed data and AI/ML pipelines, with automated closed-loop network operations and orchestration to meet end-to-end service KPIs. The cloudification trend is anticipated to persist in 6G, introducing innovative network designs, such as cloud-optimized procedures that involve network functions (NFs) capable of accessing network information with minimal hierarchical interactions [90]. AI is projected to optimize 6G communication and operate as AI-as-a-service across various network entities and layers of the radio stack. The potential applications of AI include optimizing the physical layer, mobility algorithms, network management, QoS, and more. As 6G approaches, the trend involves deeply integrating AI as a fundamental enabler for communication, influencing the behavior and communication of network components, in contrast to its role in previous generations as a tool for parameter optimization.
## 4 Experimental Setup
### Evaluation Metrics  
To assess the quality of enhanced images, we use both full-reference and no-reference metrics:  
* PSNR (Peak Signal-to-Noise Ratio): \[\text{PSNR}=10\,\log_{10}\left(\frac{\text{MAX}_{1}^{2}}{\text{MSE}}\right)\] This measures pixel-wise fidelity between output and ground truth.
* SSIM (Structural Similarity Index): This captures perceptual similarity in terms of luminance, contrast, and structure.
* NIQE (Natural Image Quality Evaluator): A no-reference metric assessing image naturalness.
* LPIPS (Learned Perceptual Image Patch Similarity) [27]: This computes perceptual distance in deep feature space (lower is better).  
These complementary metrics allow us to evaluate the method across both quantitative and perceptual dimensions.