esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Setup
### Neural Network Architecture and Training Configuration
The entire training process is handled offline. Table 1 summarizes the key experimental parameters and configurations used in this study. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline System Configuration \\ \hline Modulation format & QPSK, 250 MHz bandwidth, 8 sps \\ Frame synchronization & 32-symbol Zadoff–Chu, sliding correlation \\ Tx/Rx filters & Root-raised-cosine, roll-off = 0.25 \\ Communication distance & 1.5 m/2.1 m (Indoor LoS) \\ Air data rate & 2 Gbps \\ VST output & 2 GHz IF, \(-10\) dBm, 1 GS/s \\ LO frequency (MSG) & 16.4 GHz, 9.5 dBm \\ Upsampling/Downsampling factor & 8/8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key experimental and training parameters. Figure 5: **(a)** BER performance of the two-stage equalization model with different numbers of LSTM units. As the number of units increases, the BER gradually decreases. However, beyond 128 units, the performance gain becomes marginal. Therefore, 128 is identified as the most cost-effective configuration based on our experimental results. **(b)** Comparison of BER and total computation time (including training and inference) under different up/downsampling factors. When the sampling factor is less than 8, the BER is significantly higher. When it exceeds 8, the BER improvement is negligible, but the total time increases substantially. Hence, a sampling factor of 8 provides the best trade-off between accuracy and efficiency. Both parameters were selected based on empirical evaluations to achieve a favorable balance between performance and computational cost during offline training.
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 4 Discussion
Therefore, the user can be directlywarned about the zones within the buffer zones that need attention, which is the main objective of this system. If this system is deployed as a commercial service for external customers, it must be able to handle multiple concurrent tasks, so it is important to identify potential performance bottlenecks. All operations related to displaying map tiles and orthophotos are handled locally, using JavaScript in the browser, and by a scalable backend managed by the map provider (Mapbox). Therefore, adding concurrent use cases does not overload the FIRECOM server, as the code runs on clients and the scalability of map tiling is ensured by the map provider. Regarding AI segmentation, which is carried out by the FIRECOM server, one advantage of using YOLO is its performance in inference operations. In fact, the time required to segment an image with a resolution of 640 \(\times\) 640 pixels has been measured, and it takes only 260 ms on average to complete the task, using a server with ordinary specifications. As this operation is normally carried out only a few times per use case, we do not foresee any performance issues related to AI segmentation. The real bottleneck of this system is the generation of the orthophoto and 3D point cloud files by ODM. As a matter of fact, a large use case can easily contain hundreds of high-resolution images, which can take several hours to process on a single server. This means that if several use cases have to be processed at the same time, a horizontal scalability architecture must be adopted to guarantee a reasonable response time.
## 2 Materials and Methods
### Target Microorganism and Inoculation  
_Escherichia coli_ WDCM00013 (ATCC 25922, a surrogate for the pathogenic _E. coli_ O157:H7) was used as target microorganism, taking into account the likely contamination at the industrial level. _E. coli_ is one of the most common causal agents in fruit products associated with several outbreaks of foodborne illnesses [13].  
Frozen stock culture of _E. coli_ was activated following the procedure provided by the supplier BAControl--5 (Alicante, Spain). One tablet was dispensed into a sterile tube containing 20 mL of sterile distilled water. Afterwards, it was let to dissolve for 10 min at room temperature, shaking it gently every 2 min. Before _E. coli_ inoculation, AP samples were pasteurized at 72 \({}^{\circ}\)C for 15 s to eliminate vegetative microorganisms. Then, AP samples (100 g) were inoculated with 3 mL of the _E. coli_ solution to obtain an approximate final concentration of \(\sim\)10\({}^{8}\) cells/g. After that, the already inoculated samples were shaken for 2 min to ensure that the inoculum was uniformly homogenized throughout the sample. Afterward, inoculated samples were aseptically manipulated to be subsequently subjected to HPP, US, PEF, and thermal pasteurization.
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 3 Isfl-Sys Architecture
### Effl Federated Learning Mechanism  
Prior to the ETFL training iteration, the server utilizes a benchmark dataset (an assuredly secure dataset) to maintain the server model. Throughout the training iteration, clients follow the procedure to carry out updates on their local model parameters, taking into consideration the contribution of their local model parameters using the server model. Existing FL methods focus solely on updating the global model through the local model updates from clients [22; 23; 24; 25], excluding the process of assigning trust scores to client local models as in our approach. In contrast, ETFL simultaneously considers updates to the server model and client local models to update the global model.  
In model attacks, malicious clients alter the direction and magnitude of model parameter updates, resulting in a detrimental impact on the global model in the opposite direction. Therefore, we arbitrarily define the magnitude of all model updates to be a fixed unit length, focusing solely on the differing directions of model updates. This mitigates the impact of local model attacks on amplitudes. ETFL utilizes the server model to evaluate the credibility of local models for allocating Trust Scores (TS), and ultimately updates the global model through a TS-weighted average. Specifically, ETFL iteratively follows the four steps outlined below, as depicted in Figure 4:  
1. Parameter Initialization and Sending: The server receives the initial model parameters \(\omega_{0}\), such as weights and biases, and sends them to the client;
2. Local Model Parameters Training: In the \(t\)-th round of training, each node \(i\) trains the previous round's model \(\omega^{t-1}\) using its local dataset \(D_{i}\) and calculates local model parameter updates \(\omega_{i}^{t}\);
3. Trust Assessment and Uploading: Local model parameters are sent to the trust assessment model, and the model parameters that have undergone evaluation are uploaded to the server;
4. Global Model Aggregation: Based on trust assessment results, allocate aggregation weights to local model parameters, and maintain the global model.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 4 Polymer-Inorganic Hybrids as Linkers
These polymers extend in only one dimension, but by altering the structure of the ligand, more complex architectures, such as zigzag coordination polymers, can be achieved [129]. This tunability highlights the power of metallosupramolecular chemistry in designing materials with desired structuraland functional properties. One of the most appealing aspects of metallosupramolecular chemistry is its simplicity and efficiency compared to traditional synthetic methods. While organic synthesis often involves complex, stepwise procedures with limited yields, the formation of metallosupramolecular structures typically occurs quantitatively through spontaneous self-assembly. By simply mixing the appropriate metal and organic ligand building blocks, the desired product can often be obtained in a single step. This self-organization process is driven by molecular recognition, where the coordination geometry of the metal ions and the spatial arrangement of donor atoms in the ligands determine the final structure [129]. Thus, MSPs boast a combination of processability and functional properties, offering a powerful platform for developing advanced materials with applications in fields such as smart materials, catalysis, and drug delivery systems. Metallosupramolecular polymers, in general, are classified into three main categories: metal-organic frameworks (MOFs), coordination polymers (CPs), and supramolecular coordination complexes (SCCs). Historically, the terminology surrounding coordination polymers (CPs) and metal-organic frameworks (MOFs) has often been used interchange  
Figure 9: (**a**) Synthesis of the first polymers with pendant [Ru(bpy)\({}_{3}\)] complexes. This complex is synthesized via radical polymerization of a polystyrene backbone, followed by bromination, lithiation, and functionalization with 2,2′-bipyridine. (**b**) Left: preparation of star-shaped polymers by the addition of metal ions to monofunctionalized bipyridine polymers; right: GPC analysis showed a significant shift in the molecular weight after complexation. Adapted with permission from [PERSON] Wiley and Sons [135]. ably, leading to some confusion. While they do share certain similarities, such as being composed of metal ions coordinated to organic ligands to form extended structures, they typically refer to small-molecule linkers, which are not the emphasis of this review. For the sake of accuracy, we will briefly discuss each and highlight the distinctions between them.
## 3 Structure Design and Optimization
### Junction Points: Lightweighting  
The original design of the junction points is based on traditional manufacturing technologies. They can be assimilated to parallelepipeds with threaded holes and machine-shaped joints (Figure 9a,b). To exploit the advantages of AM, an effective design strategy based on topology optimization was provided to generate lightweight designs for AM [60]. The upper junction joint was modified in order to have self-supporting surfaces and connected to its skin with a 3 mm-radius chamfer as shown in Figure 9c. The lower junction joint was lightened using gussets and reinforcing arms (Figure 9c).  
The new design functionality was proven via Siemens PLM Femap(r) v11.3 simulation platform. The upper junction point (Figure 10a) showed a maximum Von Mises stress of 31 MPa at the bottom hole which is much lower than the fabricated material resistance. Minor values are reached at the wall connection. The lower junction point is more stressed by the local load as shown in Figure 10b: the reinforcing arms exhibit about 25 MPa at the top and nearby the hole housing; the maximum value of 79 MPa is observed at the  
Figure 8: Self-supporting tree structure for upper thermal plates to overcome the problem of fabricating horizontal planes by PBF-LB/M.  
Figure 9: Original design (**a**) and AM redesign (**b**) of an upper junction point. Original design (**c**) and AM redesign (**d**) of a lower junction point.  
connection between the back plate and the hole housing. Additionally, for this junction point type, the simulation easily meets the requirements.
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Surface Templating  
_Surface templating_ is the principle of utilizing the geometry of DNA origami nanostructures to orient or position other molecules that do not have the intrinsic capability to organize to the geometric complexity of the DNA-based template. This most commonly entails using the addressable surface of a designed DNA nanostructure to specifically position guest molecules. While this can generally describe any application of DNA nanostructures interfacing with other molecules, we reserve this categorization for applications that do not have discerning features that would otherwise more appropriately describe them as encapsulating or exhibiting nanomechanical dynamism. In our proposed categorization, surface templating DNA nanostructures differ from those categorically identified with encapsulation functions by the fact that permeability is no longer an essential property of the DNA nanostructure itself. Although as with DNA-templated lipid nanostructures, the DNA nanostructure can still serve to template another material into an encapsulating structure. The first demonstrations of DNA origami included a world map of the Earth designed by attaching physical moieties to specifically addressed locations on the DNA origami surface, creating a pattern that could be imaged by AFM. This principle has since been exploited to arrange a widespread selection of other molecules. DNA nanostructures are addressable by binding functional groups, which can encompass organic or inorganic molecules, to nucleotides that are at the desired locations within the design. These modifications are usually done at the 5' or 3' end of a ssDNA segment, although there are situations that may call for modifications to be made mid-sequence as well, such as phosphorothioate for hydrocarbon modifications [116]. The ssDNA segment can be an extension of staple strands already included in the structural design of a DNA origami nanostructure or be a separate strand that hybridizes to a complementary domain extended from those strands. The latter option is typically chosen to be more economical and versatile as it decouples guest molecules from each unique staple sequence. For placement of the same guest molecule to multiple locations, a universal sequence can be used, which reduces costs, as functionalization can be an expensive process to repeat for many distinct strands. It is instead more economical to synthesize a universal complementary sequence extending from the staple strands. If multiple unique locations are desired, then the sequences can also be chosen with the necessary orthogonality. As such, DNA nanostructure surfaces can place guest molecules with a resolution similar to its own per base resolution of approximately 3.4 A along a strand or 2 nm between helices [117].
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 2 Theoretical Model of Specular Returns from Inland Waters
### Very Small Water Bodies  
The smallest possible target is a point specular target. The integral in Equation (1) is eliminated since the radar echo is from only one elemental scatterer. The echo power is constant for the duration the target is within the antenna beamwidth. The target does not even have to be near nadir. In the (_n,r_) plane, all the energy falls on a hyperbola manifold [13; 14; 15]. It is not uncommon to see such hyperbolic patterns in radar altimeter data, especially from small vessels near coast. [PERSON] and [PERSON] [16] give a further example of the CryoSat-2 radar pass over the Svalbard transponder, which was designed as a point scatterer calibration for radar altimeters. Figure 3 in the [PERSON] and [PERSON] paper is the \(\mathcal{Z}\) for a point specular target.  
Figure 3 shows the results from the model calculated for a 10 m river. The river is orthogonal to the satellite ground track, straight and 10 m wide (blue line in the left panel). The white line is the nadir track. The center panel shows the power and phase \(\phi=arg\mathcal{Z}(n,r)\) of a complex radar echo obtained by the model. Here and elsewhere in this paper, the echo number \(n\) is replaced with the equivalent distance of the satellite nadir from the water centerline (along track distance); to convert to echo number, simply divide the distance by 3.8 m. This makes more apparent the relatively short distance at which water can be detected before or after the satellite nadir passes over the water surface. The range dimension is plotted as range in meters relative to local Earth ellipsoid, after de-spinning the phase of _z(n,r)_ for the component of the satellite velocity perpendicular to the ellipsoid.  
The 10 m river is very similar to a point specular target, except that the echo power is lobed and extends \(\pm\)1000 m centered on the closest point of approach (CPA). Figure 3, right panel upper plot, shows the maximum power for each received waveform. For a point specular the main lobe extends to infinity. The bottom right panel is the \(d\phi/dn\) scaled to Doppler velocity in units of m/s. As expected, the Doppler velocity is negative on approaching the first river shoreline. It is positive on receding from the farther shoreline.  
Figure 3: Model for the narrow river example (dimension is approximately 10 m).
## 5 Land Use Planning: Using the LBCS Ontology
### Automatic Categorisation  
A method similar to the one described above can be used to make automatic categorisations through reasoning. Equivalence relations can be established between categories of land use, allowing new categories to be inferred for parcels, based on the categories that have already been asserted (or inferred) for them.  
This is accomplished by defining subclasses of parcels and declaring them equivalent. For example, the user might want to state that all the parcels with the Structure category \"Utility and other nonbuilding structures\" also have the category \"Developed site--nonbuilding structure\" should be also classified with the Site category \"Developed site--nonbuilding structures\" and vice-versa. In this case, the following would need to be declared:  
_\"EquivalentClasses:_  
_hasSite some SI.DevelopedSiteWithNonBuildingStructures_  
_hasStructure some S.UtilityAndOtherNonbuildingStructures\"_  
Note that this inference works both ways because an OWL equivalence has been defined, but it is also possible to define a one-way inference. If the intention is to state that if a parcel has the category \"A\", it also has the category \"B\", \"having A\" must be declared a subclass of \"having B\". For example, if the user wants to declare that if a parcel has the category \"Utility and other nonbuilding structures\" that also has the category \"Developed site--nonbuilding structures\", they would need to declare something like:  
_\"hasStructure some S.UtilityAndOtherNonbuildingStructures_  
_SubClassOf: hasSite some SI.DevelopedSiteWithNonBuildingStructures\"_  
These constructs are very useful when dealing with the LBCS standard because of its multi-dimensionality. In this way, relationships can be established between the various dimensions of the standard, allowing for complete categorisation with less effort on the part of the user.  
Figure 4: Matching between NLUD and LBCS. Matching between the NLUD class “ReligiousBuildings” and the LBCS ontology concepts through existential restrictions in the dimensions properties.
## 4 Materials and Methods
### Data Engineering and Preparation Framework
#### 4.1.2 Data Transformation  
Data transformation refers to the encoding and normalization of the data parameters or features to make them suitable for the model. Encoding transforms the categorical parameters into a numerical format to make it easy for the models to interpret. For the purpose of this research, two types of encoding are used:  
* Label encoding: It directly converts categorical values with numerical substitutes.
* One-hot encoding: It ensures that no ordinal associations are identified by the model through converting categorical variables into a sequence of binary columns. Each category is encoded by a binary vector with just one member assigned the value '1' and the remaining components set to '0'.  
Each parameter has a different range of values. So, it is necessary to make all the parameter values follow a common range. Normalization is normally used to scale up the numerical values to a common range. Min-max normalization is mainly used in this study.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### General FSM Synthesis Method
The Quartus Prime and Vivado tools have also their own optimization procedures for performance, area, and power parameters, but they mostly operate in the phase of fitting and routing. The equivalent conversions of FSM do not consider this phase; they operate only in the pre-synthesis stage. For this reason, we decided to use default compiler parameters for all examinations. Three output values were carried out from report files for further analysis: total logic elements, maximum clock frequency and total power. The authors chose for the implementation the EP4 CE115F29I8L FPGA device from the Cyclone IV E family (Intel) and XC7A35 TSCG324-1 FPGA device from Artix-7 (Xilinx). The example results for MS strategy are shown in Table 6, where \(C_{0}\), \(F_{0}\) and \(P_{0}\) are, respectively, the cost of implementation (number of used logic elements), maximum frequency (in MHz) and dissipated power (in milliwatts) of the initial FSM (without transformation); \(C_{1}\), \(F_{1}\) and \(P_{1}\) are, respectively, the same parameters after power direction transformation with JEDI encoding; \(C_{2}\), \(F_{2}\) and \(P_{2}\) are, respectively, identical values after using speed direction variant with one-hot encoding; and finally, \(C_{3}\), \(F_{3}\) and \(P_{3}\) are, respectively, the same parameters after synthesis, using the balanced variant with binary encoding. The comparison of average values of all investigated parameters for all scenarios and variants in the case of using the Quartus Prime tool for implementation is depicted in Figure 3. It can be noticed that the proposed strategies can be successfully used with the Quartus Prime tool. The worst results were obtained using the power optimization direction. Poor results in terms of power arise from the fact that the optimized power parameter (dynamic power) is significantly less than the static device power and has minimal influence on the total device power. Although all considered scenarios were optimized for speed or power, the most significant gain was noticed for the area parameter. Figure 2: Comparison of average results for different encoding styles. A similar comparison of average values of area, speed and power parameters for all scenarios and variants in the case of implementation using the Vivado tool is presented in Figure 4. It can be noticed that the proposed strategies can be successfully used in most cases also with the Vivado tool. The worst results were obtained using a balanced optimization direction in terms of area, but it was not the optimization goal. Similarly, as for the Quartus Prime tool, the lack of significant gain in terms of power is due to the fact that the optimized dynamic power is considerably less than the total device power.