esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Mobile Vision Transformers for Land Cover Classification
## 3 Materials and Methods
### Proposed Lightweight Transformer Modifications
After these changes were made, the proposed model had 20,598,679 fewer parameters than ViT on average (a 95.03% decrease). Also, it had exactly 234,272 fewer parameters than MViT across all datasets (a 17.85% decrease). For the SWIN model and its variants, the standard SWIN architecture from [PERSON] et al. [37] was used as a reference. Detailed dimensions and configurations play a crucial role in achieving balance between model complexity and accuracy while ensuring the parameters remain lightweight. In the SWIN-Regular model, the PatchExtract layer processes input images into 2 \(\times\) 2 patches, reflecting the defined patch size. These patches are embedded with positional information in the PatchEmbedding layer and then processed through SwinTransformer layers, which are characterized by a window size of 2 and a shift size of 1. This design reduces computational complexity compared to global attention mechanisms. Figure 4: (**a**) Architecture of the entire MViT block and (**b**) architecture of the ShuffleNet block that replaces the final MViT block to create the final MViT-combined variant. The embedding dimension is set at 64, with the number of multi-layer perceptron (MLP) nodes at 256. Two different SWIN variants were also explored. In the SWIN-ConvBlock variant, the introduction of a convolutional block before feeding into the SWIN model enhances the feature space and is beneficial for performance improvement. This block comprises two sets of layers: a convolutional layer, a batch normalization layer, and an activation layer with a ReLU function. By replacing the third patch extract layer in the SWIN model with this convolutional block, the model achieves an increased feature space and a reduction in the number of parameters. Secondly, the SWIN-ShuffleNet variant features a ShuffleNet block, which is designed to create a more efficient architecture through a channel shuffle operation. This block consists of separable convolutional layers, group normalization layers, and activation layers (ReLU function). By replacing the third patch extract layer with the ShuffleNet block, it is expected that SWIN-ShuffleNet maintains or boosts accuracy while reducing the parameter count, capitalizing on the efficiency of the channel shuffle operation. The total number of training parameters for each model architecture trained on the four GIS datasets AID, EuroSAT, UC-Merced, and WHU-RS19 is summarized in Table 2. None of the hyperparameters except the number of classes and train-test splits used by the model influence the number of training parameters, so there are only four entries for each model. An observable trend in the table is that the number of training model parameters for each architecture increases for datasets that have a larger number of image labels. This shows that each model's output layer, which maps to a differing number of image classes for each dataset, slightly influences the number of training parameters of each model.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 1 Introduction
The network integrates RGB and thermal imaging modality features and introduces a novel AEFF to optimize cross-modal information interaction and improve detection accuracy. Furthermore, to address the limitations of existing networks in multiscale feature extraction, this paper introduces the C3-PSA module to enhance the capacity for capturing multiscale spatial information and improve the network's adaptability to targets of varying scales. Specifically, the main contributions of this paper are as follows:  
1. Dual-stream backbone network and midway fusion strategy: The YOLOv5 feature extraction network is restructured into a dual-stream backbone, employing midway fusion as the default setting to integrate multimodal features from the C3 to C5 layers. This design effectively leverages multispectral information to enhance detection accuracy and robustness. 2. AEFF: The AEFF framework is introduced to address the limitations of convolution-based feature fusion in modeling long-range feature dependencies. By incorporating an attention mechanism, it further optimizes both cross-modal and intra-modal feature representations, thereby enhancing overall model performance. 3. C3-PSA module for multiscale feature representation: The C3-PSA module is introduced to enhance multiscale spatial feature extraction using the PSA mechanism while strengthening cross-channel attention. This improves detection accuracy and adaptability while reducing false alarms and missed detections under complex imaging conditions. The structure of the present study is organized as follows. Section 2 briefly reviews the related works in multispectral object detection and identifies existing challenges. Section 3 presents our proposed YOLO-MEDet model incorporating the AEFF and C3-PSA modules. Section 4 describes the materials and methods, including the datasets, evaluation metrics, and implementation details. Section 5 presents the experimental results, covering comparative analysis, ablation studies, Per-IoU AP analysis, and qualitative evaluations. Section 6 summarizes the key contributions and outlines future research directions.
# Mass Processing of Sentinel-1 Images for
## 4 Results and Discussion
### JEODPP Results  
The main SUMO processing has been conducted on 11,647 Sentinel-1A products that constitute a data volume of around 19 TB in uncompressed mode. One single core has been assigned by product resulting in 472 concurrent jobs; this was the full cluster capacity at the time of workflow execution. The total elapsed time of the SUMO processing was approximately 2 h. Such fast processing enables the exploration of the parameter space in view of determining optimal settings.  
Some scripts to extract and aggregate meta-information from the Sentinel-1 products were written in Matlab. An additional container having the Matlab Runtime Environment installed was deployed  
Figure 5: Average revisit time (in days) between consecutive: (**a**) Sentinel-1A acquisitions; and (**b**) Sentinel-1A + Sentinel-1B, from 29 November 2016 to 9 February 2017 (72 days, 6 complete orbit cycles, absolute orbit number from 14,160 to 15,209 for Sentinel-1A, and from 3177 to 4226 for Sentinel-1B; 1381 Sentinel-1A images and 1523 Sentinel-1B images). Calculated with a grid size of \(0.05^{\circ}\).  
for their execution. Due to the lightness of the specific process and in order to avoid the overhead of launching and stopping many containers in a short time range, a number of 25 products was associated to every job, resulting in 466 concurrent jobs. The duration of this process was 15 min and the respective volume of the output data (csv files) reached the number of 30 MB.  
Figure 7 shows the CPU workload of the JEODPP cluster while processing the two aforementioned processes. Note that as indicated by the very low wait time, no bottleneck is experienced to access the 11,647 Sentinel-1 products thanks to the CERN EOS distributed file system deployed on the JEODPP.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 3 Procedures, Material and Methods
### Monitoring Landforms
#### 3.2.2 Solutions Based Entirely on Feature Tracking Software  
There are several solutions to monitoring landforms based entirely on software image processing, such as CIAS, Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) and image georectification and feature tracking toolbox (ImGRAFT), among many others. The photogrammetry software CIAS correlation image analysis [54; 55] is used to derive the terrain feature displacement between two multi-temporal orthophotos, and is based on the normalized cross-correlation.  
The COSI-Corr method was created at the California Institute of Technology (USA) for precise geometrical processing of optical satellite and aerial imagery [56]. It permits an automatic and accurate ortho-rectification and co-registration of satellite or aerial images [57]. The precise ortho-rectification process depends on the automatic generation of accurate GCPs, which are created from the correction of the viewing geometry of the detecting platform allowing accurate images with ortho-rectification and co-registration [57]. The horizontal ground movements are obtained from the subpixel correlation of the ortho-rectified images. The ImGRAFT method is a toolbox for georectification and feature tracking [58], and is a complete, yet adaptable, opensource package developed in MATLAB. This toolbox addresses and combines georectification and feature-tracking processes. Although ImGRAFT was primarily focused on terrestrial oblique images, several authors have used it with satellite imagery, with good results (e.g., [59; 60]). There are also other methods which do not use automatic calculations, such as theodolite techniques, differential GPS and TLS (terrestrial laser scanner). These methodologies provide high accuracy, but their main drawback is the low automation of the method and the time required for its application.
# Maize and Sorghum as Raw Materials for Brewing, a Review
## 2 Maize and Sorghum: Raw Materials for Brewing
### Chemical Structure and Composition
The content of starch-free polysaccharides (NSPs) in sorghum grains suggests their possible ability to lower cholesterol levels and improve bowel function in humans [43]. The total soluble sugar content of sorghum cereals ranges from 0.7 to 4.2%, while reducing sugars range from 0.05 to 0.53% [30]. As many studies have indicated, sorghum grains are a good source of energy, as well as vitamins, minerals, carbohydrates, polyunsaturated fatty acids (PUFA), and some essential amino acids. Linoleic acid and oleic acid are the main fatty acid components of sorghum lipids. The grains are usually eaten with the tests, which retain most of the nutrients. Sorghum is a good source of minerals; however, the wide spread variety of its mineral composition is generated by environmental settings [42]. Sorghum contains both essential and nonessential amino acids, including alanine (7.34-9.62 g/100 g), aspartic acid (4.83-7.06 g/100 g), glutamic acid (17.5-28.12 g/100 g), leucine (12.02-14.48 g/100 g), phenylalanine (4.03-5.62 g/100 g), proline (6.66-12.34 g/100 g), and valine (4.22-6, 86 g/100 g). It has reduced values for tryptophan and lysine. However, it has bioactive peptides and beneficial protein fractions--e.g., \(\alpha\)-caffeine, kafirin, protease, amylase and xylanase inhibitors. Sorghum also contains cationic peroxidase, which has anticancer, antiviral, and antioxidant properties, and can lead to decreased cholesterol and reduced risk of hypertension. Available studies have also shown that sorghum contains a number of minerals and vitamins, which are part of the vital nutrients that humans need in order to perform the functions that sustain life. Sorghum contains relatively high levels of potassium (K) (900-6957.67 mg/kg) and phosphorus (P) (1498-3787.25 mg/kg), minerals known to facilitate muscle movement, maintain a healthy nervous system and build strong bones and teeth. B vitamins (0.1-19.9 mg/100 g) and vitamin E (1.38 mg/100 g) are also among the main essential vitamins reported [43]. Sorghum grains are known for their toughness compared to other cereals. The hardness of the sorghum grain is owed to the increased protein and prolamin content (3.6-5.1%). The lysine quantity varies from 1.06 to 3.64%. Studies of proteins in sorghum have indicated that the allocations of albumin-globulin, prolamin, and glutelin are approximately 15%, 26%, and 44% of total nitrogen, respectively [43]. [PERSON] et al. (2013) analyzed current knowledge on the development of functional non-gluten sorghum products, such as non-gluten bread, noodles, baby food, and beer [30]. The energetic value of 100 g of sorghum is around 400 calories, similar to corn and wheat. However, sorghum has a higher content of resistant starch. It also contains micronutrients like B-complex vitamins and minerals (potassium and phosphorus) [65].
## 1 Introduction
For example, the proposed regulation of the European Parliament and of the Council concerning batteries and waste batteries, is to promote \"an electronic system that maximises the exchange of information, enabling tracking and tracing of batteries, provides information about the carbon intensity of their manufacturing processes, as well as the origin of the materials used, their composition, including raw materials and hazardous chemicals, repair, repurposing and dismantling operations and possibilities, and the treatment, recycling and recovery processes to which the battery could be subject to at the end of their life\" [7]. As all IoT devices and tracking technologies generate big data, there is a rise of new business models, as well as more reliable data flows. A prominent example of this trend is SAVANT, a consortium by Earth-i, Marex, and ESA, who developed a business by taking video monitoring data from several different Earth observation satellites, to show and analyse smelter activity levels and to detect periods of shutdown or inactivity, and feed their daily assessments into global and regional indices to give consistent, insightful and dependable results [8]. Physically tracking the flow of raw materials from mines to smelters, and eventually to end-consumers, in combination with a blockchain system, can reshape global supply chains toward more responsible sourcing and enable circular processes.
## 2 Fabrication Process and Filter Design
### Fabrication Process
The fabrication sequence culminated in Au-Au thermocompression bonding between the cap and bottom wafers, a critical step that established both structural stability and functional electrical connectivity throughout the device, followed by precision dicing to separate the individual filter chips.
## Nomenclature  
\begin{tabular}{l l l l} \(b\) & Width of the Piezoelectric Layer & _Greek letters_ & \\ \(c\) & Stiffness & \(\varepsilon\) & Permititivity \\ \(\mathrm{c_{a}}\) & External (air) Damping Coefficients & \(\theta\) & Piezoelectric Coupling \\ \(c_{S}I\) & Internal Strain Rate & \(\theta_{\mathit{eff}}\) & Modified coupling \\ D & Electric Displacement & \(\delta\) & Location of the Piezoelectric Layer \\ \(d_{31}\) & Piezoelectric Strain Constant & \(\lambda\) & Natural Frequency \\ \(e\) & Piezoelectric Stress & \(\xi\) & Mechanical Damping \\ \(E\) & Electric Field & \(\omega_{r}\) & Undamped Modal Frequency \\ \(E_{p}\) & [PERSON]'s modulus & \(\sigma_{r}\) & Dimensionless Frequency Number \\ \(h\) & Thickness & \(\eta_{r}\) & Modal Coordinate of a Clamped-Free beam \\ \(i\) & Current & \(\phi_{r}\) & Orthonormal Mode Shape \\ \(m\) & Mass Per Unit Length & & \\ \(M_{p}\) & Moment & _Superscripts_ & \\ n & Number of Slits & \(eff\) & Effective \\ \(P\) & Power Modulus & \(L\) & Load \\ \(R\) & Resistance & \(p\) & Piezoelectric Layer \\ \(S\) & Mechanical Strain & \(r\) & Ratio \\ \(T\) & Mechanical Stress & & \\ \(T_{c}\) & Circuit Time Constant & _Abbreviations_ & \\ \(V\) & Voltage & FEA & Finite Element Analysis \\ \(w\) & Displacement & FEM & Finite Element Method \\ \(w_{b}\) & Base Motion & FRF & Frequency Response Function \\ \(w_{rel}\) & Relative Transverse Displacement & MEMS & Microelectromechanical \\ \(w_{slit}\) & Width of each Slit & PDMS & Polydimethylsiloxane \\ \(\chi_{r}\) & Modal Coupling & PVDF & Polyvinylidene Fluoride \\ \(\gamma T\) & Bending Stiffness & PZT & Piezoelectric Transducer \\ z & Distance & & \\ \end{tabular}
# A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator
## 6 Conclusions  
In this paper a 160-GHz radar target generator with a bandwidth of \(20\,\mathrm{GHz}\) based on a flexible dielectric waveguide for tank level probing radars is presented. With different targets representing a certain medium, the functionality of a radar can be evaluated simply and in a low cost manner.  
The RTG has a rectangular waveguide interface, which feeds the radar signal into the extruded dielectric waveguide made of HDPE with a mode transformer. Two targets, representing water with a reflectivity of \(20\,\mathrm{GHz}\) and palm oil with a reflectivity of \(-16\,\mathrm{dB}\), are clamped on the dielectric waveguide and manipulate the field distribution. In order to arrange the flexible dielectric waveguide in a space-saving way, holders made of Rohacell are used. Dependent on the dynamic range of the radar, distances from \(13\,\mathrm{cm}\) to at least \(9\,\mathrm{m}\) can be simulated with the RTG.  
The frequency-dependent group velocity of the dielectric waveguide spreads the target peak and decreases the signal level. Inhomogeneities in the dielectric waveguide material causes reflections in the range spectrum which are, however, below the radar noise level. The lower accuracy of the target distance results only from the decreased SNR.  
[PERSON]. ([PERSON]) and [PERSON] conceived and designed the theory, simulations and experiments; [PERSON]. ([PERSON]) analyzed the data; [PERSON] and [PERSON]. ([PERSON]) contributed research and development supervision; The manuscript was written by [PERSON] and reviewed by [PERSON] ([PERSON]), [PERSON], and [PERSON] ([PERSON]).  
Figure 12: Range spectra of the RTG for the minimum measurable distance (**a**) and with two targets in a distance of \(1.5\,\mathrm{cm}\) from each other (**b**).  
**Funding:** This work was partly supported by the Ministry for Science, Research, and Arts Baden-Wurttemberg, Germany, through the project ZAFH MikroSens.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Description of Test Machines, Fatigue Tests Setup, and Specim Geometry
#### 2.2.3 Test Machine for Uniaxial Fatigue Tests  
The machine used for the fatigue tests of the base material is a Rumul Mikrotron resonant machine (Russenberger Prufmashcinen AG, Neuhausen am Rheinfall, Switzerland) with a maximum load capacity of 20 kN. Uniaxial fatigue tests were conducted in load control mode, at 30 Hz frequency, with load ratio R = 0.1 and a sinusoidal load function. In this case, the complete fracture of the specimen was assumed as the failure criterion. Otherwise, tests were stopped at 2 \(\times\) 10\({}^{6}\) cycles and considered as runouts.  
The test machine and a detail of the test setup are represented in Figure 10.  
Figure 9: Preliminary mesh sensitivity analysis of the finite element model of the specimen. The axial stress distribution is measured on the outer surface of the specimen, along its centerline.
## 6 Discussion  
The findings of this study underscore the significant potential of integrating multispectral and hyperspectral satellite data for the detection and quantification of methane super-emitters. By leveraging the complementary strengths of Sentinel-2, EnMAP, and GF5-02-AHSI, we have demonstrated a robust methodology for identifying and monitoring large methane point sources, particularly in regions with high methane emissions such as coal mining areas in China and oil and gas fields in the United States. This approach not only enhances our understanding of methane emission patterns but also provides actionable insights for mitigating climate change.
## 2 Materials and Methods
### Sorption of Cesium Ions  
The sorption of cesium ions was carried out according to the method presented in [25]. A sample of the sorbent weighing 0.1 g, weighed with an accuracy of 0.0001 g, was continuously mixed with 20 mL of the test solution for 48 h. After that, the sorbent and solution were separated by filtration through a \"white tape\" filter paper, and the concentration of cesium in the filtrate was determined. Each experiment was repeated at least three times.  
As a liquid phase, solutions of sodium nitrate with a concentration of 0.1 and 1 mol/L, pH 6, and in seawater of the composition given in Section 2.1 with a cesium concentration of 10-20 mg/L, were used. According to [24], these sodium nitrate solutions are recommended for assessing the sorption properties of sorbents concerning cesium.  
Quantitative determination of cesium concentration in solutions was carried out on a KVANT-2 atomic adsorption spectrophotometer (Kortek LLC, Moscow, Russia) in the emission mode in an acetylene air flame at a wavelength of 852.1 nm. The error in determining cesium averaged 1% and did not exceed 2%.  
The cesium distribution coefficient was determined by equation [9; 47]  
\[K_{d}=\frac{C_{0}-C}{C}\cdot\frac{V}{m}, \tag{1}\]  
where \(C_{0}\) is the concentration of cesium in the initial solution, mg/L; \(C\)--cesium concentration in the filtrate, mg/L; \(V\)--volume of solution, mL; \(m\)--sorbent mass, g.
## 5 Materials and Methods
### Validation of Control System Integration  
Ensuring seamless integration with existing control systems is essential. The evaluation focused on validating protocol support, system modularity, scalability, security, and long-term adaptability. The prototype was tested in a real industrial environment to assess its ability to connect with multiple control systems without requiring extensive manual configurations. The results demonstrated successful integration with industrial communication standards, confirming its robustness and compatibility with various protocols [82,83]. An overview of the setup is also provided in Figure 6 (Section 5.3).  
Further validation was conducted on the modular architecture by dynamically adding and removing modules under operational conditions. The integration of OPC-UA, Modbus TCP, MQTT, and native protocol interfaces confirmed the system's seamless adaptability. While a minor requirement for measurement restarts was noted, overall flexibility was preserved [79,84]. Additionally, the prototype's ability to integrate system updates and new functionalities without disrupting operations was assessed. Successful software update implementations demonstrated its capacity for future adaptation, with user feedback playing a significant role in ensuring long-term relevance and maintainability [79].  
Security and reliability were evaluated through stress tests to determine system stability under high loads, particularly concerning CPU utilization and data transmission security. The results confirmed that the system maintained consistent performance and robust security measures, even under maximum processing loads [79,85].