esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection of Material and Technology
In particular, the ABS material has very low potential antimicrobial activity. Regarding the PA12 material, this activity is noticeable for the _S. aureus_ and _C. difficile_ strains, while for the _P. aeruginosa_ strain, this activity is at a level similar to ABS. This fully indicates the need to sterilize the adapters, which can be done with different techniques [16]. Sterilisation of ABS products produced with FDM technology can be performed with ethylene oxide (EtO) or Gamma radiation [17; 18]. On the other hand, sterilization of PA12 products produced with SLS technology can be performed by the autoclave (121 \({}^{\circ}\)C) and ethylene oxide or steam formaldehyde at 60-80 \({}^{\circ}\)C [16; 17]. Taking into consideration that the adapters were manufactured using the process parameters recommended by the manufacturer of the material/device, it was assumed that the mechanical properties were in line with the material sheet. Furthermore, the mechanical properties of ABS material processed in FDM [19] and PA12 material [15] processed in SLS have been described in the literature [20; 21]. The description of the mechanical properties contained in the above-mentioned material data sheets and articles leads to the conclusion that the above materials are suitable for the production of the adapter. The mechanical properties of PA12, however, are higher than those of ABS [15; 19]. Furthermore, the products made of PA12 material after the SLS process are characterised by closed porosity at the level of about 5% [20], while in the case  
Figure 5: The assessment of the surface colonisation capacity by the microorganisms/potential antimicrobial activity: (**a**) Survival of bacteria on the ABS (FDM) and PA12 (SLS) substrate in the TSB microbiological medium; (**b**) survival of bacteria on the substrate made of ABS (FDM) and PA12 (SLS) in artificial saliva (AS). of ABS products, after the FDM process, the products are characterised by open porosity. It is important, particularly in the case of the manufactured adapter, where it is necessary to maintain an air-tight connection with the mask filter. Based on the conducted analysis (Table 3), previous research, and literature review, it was confirmed that it is possible to use FDM and SLS technologies to produce a prototype and small series for the medical industry.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Results Validity
#### 4.1.3 Long Term Validity of \(f_{\Theta}(\theta)\) and \(F_{\Theta}(\theta)\)  
From a few days \(f_{\Theta}(\theta)\) can be observed very similar compared with the same PDF is a much greater period, e.g., one year. Figure 12(b) shows the functions \(F_{\Theta}(\theta)\) for three different simulation periods ranging from one week to one year. The CDF curve is almost identical for all the simulation periods. The orbit configuration and ES's location were choose to be the same that for Figure 12(a).  
Figure 12: PDF and CDF for two orbit configurations sharing the same altitude, eccentricity, and orbit inclination, but with the remaining orbital parameters being different.  
Figure 13: CDF \(F_{\Theta}(\theta)\) for the same orbit configuration and ES latitude but for (**a**) different ES longitude and (**b**) different simulation period.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### Estimating Speedup for Larger-Scale Quantum Circuits  
We used the data points obtained in the range 8-15 qubits to extrapolate the speedup for larger VQC circuits. A polynomial trend line is fitted on the data points using regression and extrapolated up to 50 qubits. The fitted trend line follows the quadratic equation in Equation (14).  
\[y=0.0935x^{2}-1.7308x+13.2957 \tag{14}\]  
The regression trend line estimates a speedup of \(\times 160\) for 50-qubit circuits, see Figure 10. Therefore, for larger circuits, the benefit of the FPGA processing pipeline becomes even more pronounced as the complexity of parameter optimization increases with the quantum circuit size.  
While our extrapolation provides a theoretical estimate, the practical evaluation on larger circuits remains an area for future exploration. Based on available hardware specifications, the on-board memory capacity (64 GB) could feasibly support simulations of circuits up to 24 qubits, given that each amplitude requires eight bytes for complex number representation. Similarly, although our current experiments used real datasets restricted to 8-15 qubits, larger synthetic datasets could be generated in future studies using techniques such as Gaussian mixture models. These synthetic samples can be projected into higher-dimensional PCA spaces to match the required input dimension for larger quantum circuits. While promising, this dataset generation strategy was not explored in this paper and is left as a direction for future work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Resources Used** &
\begin{tabular}{c} **Utilization** \\ **(\%)** \\ \end{tabular} \\ \hline LUT & 13,515 & 1.23 \\ \hline REG & 12,000 & 0.66 \\ \hline BRAM & 0 & 0 \\ \hline URAM & 0 & 0 \\ \hline DSP & 71 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Resource utilizations on XCU200-2 FSGD2104E (Alveo U200 board).
# Experimental Methods to Evaluate the Carbonation Degree in Concrete--State of the Art Review
## 2 Qualitative Methods
### Phase Analysis
#### 2.3.1 Thermogravimetric Analysis (TGA)  
After the conventional method mentioned above, the TGA technique is also now commonly used in a wide range of studies concerning carbonation of concrete [25; 26; 27; 28; 29]. These studies investigated using profile ground powder from the outer to the center parts of carbonated concrete and reported that the carbonation reaction induces the varying amount of calcium carbonate (CC) and portlandite (CH) during the carbonation process. Theoretically, the quantifications of CH and CC are evaluated based on the mass loss of sample at temperature ranges from 450 \({}^{\circ}\)C to 550 \({}^{\circ}\)C and from 550 \({}^{\circ}\)C to 850 \({}^{\circ}\)C, respectively [63; 64].  
Figure 4: X-ray tomography observation of sample cross-section after 136 day-period of exposure to CO\({}_{2}\) saturated solution [36].  
Figure 3: SEM observation of carbonated specimen with the red line showing the edge of non-carbonated and carbonated areas [60].  
At the depth at which both C-H and CC exist, this part is considered as the carbonated area. The content of CC changes remarkably along with the grinding depth owing to the decrease in the penetration of carbon dioxide [65]. When CH remains and does not convert into CC, a negligible amount of CC has been found at the higher depth of the specimen, from which carbonation reactions hardly occur. For instance, [PERSON] et al. [26] investigated the modifications in the pore solution composition of carbonated and non-carbonated concrete (as a reference) based on TGA data and concluded that, contrary to all reference samples, the CH and CC content in the carbonated sample showed a significant variation along with the depth of concrete, as presented in Figure 5. Additionally, the data result from the mass loss also clearly reflects the rate of carbonation. At the first steps of the carbonation process, the CH consumption occurred quickly. Further reaction, however, slowed down significantly owing to the creation of a continuous and protective layer of CC surrounding CH [66, 67].  
The TGA technique would show more accurate values compared with the conventional methods owing to the high-sensitivity detector (the thermal scale of this method almost has 0.001 ug accuracy) [68]. Nevertheless, it should be noted that the results from the TGA are restricted because of problems associated with the sampling. TGA technique requires a typical small range mass (only from 5 ug to 10 mg, depending on the type of sample). All the sampling was performed on small positions for the powder samples to be representative of small regions in the sample, not of the complete sample.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 4 Methodology  
As shown in Figure 3, our architecture consists of two main phases, namely, data processing and data reconstruction. During the data preprocessing phase, the input data obtained from several sensors will be collected and combined into a two-dimensional form. This procedure facilitates the incorporation of temporal attributes and the interconnection of sensors within the incoming data. During the data reconstruction phase, the processed data serve as the primary reference. The proposed model is used for the purpose of extracting intrinsic features while efficiently eliminating both internal and external noise.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods  
### Framework Design Motivation and Method Comparisons  
The core challenge in video editing tasks is to ensure temporal consistency while preserving spatial accuracy. Among existing approaches, single-modality learning methods rely exclusively on visual information: traditional GAN/CNN frameworks excel at learning local features for high-quality regional editing but fall short in temporal modeling, leading to inter-frame inconsistencies and imprecise control over editing regions. In contrast, Implicit Neural Representation (INR)-based methods maintain strong spatio-temporal coherence, making them well-suited for style transfer and global modifications. However,they struggle to achieve high-precision local edits and exhibit suboptimal performance with complex user inputs (e.g., text-guided editing).  
End-to-end Transformer and large-scale diffusion-based video editing methods offer robust global feature modeling to maintain video consistency, applicable to tasks like style transfer and reconstruction with long-range temporal dependency capture. Yet these approaches suffer from high computational costs and poor real-time performance. Moreover, constrained by the global nature of Transformer modeling, their fine-grained control over local regions remains insufficient, complicating precise localized editing.  
To address the gap where existing methods fail to simultaneously meet the dual requirements of \"video spatio-temporal consistency\" and \"precise local-region modification\", this study proposes a region-aware cross-modal learning framework. The framework integrates the strengths of CoDeF and diffusion-based local editing: it leverages CoDeF for temporal consistency modeling to enhance video-editing coherence, employs a dual-branch diffusion architecture for high-precision local editing control, and incorporates cross-modal information from reference images to mitigate the controllability limitations of single-modality approaches. Key indicator comparisons show that traditional GAN/CNN methods achieve high spatial accuracy but low temporal consistency, with editing controllability dependent on training. Transformer/diffusion models exhibit high temporal consistency but moderate spatial accuracy, limited editing controllability, and high computational costs. By contrast, our method achieves superior performance in spatial accuracy, temporal consistency, and editing controllability with moderate computational overhead.
# Error Modeling of Fiber Optic Gyroscope Universal Time Measurement
## 2 Analysis Model for FOG Measurements of Earth's Rotation
### Noises in the Measurement Process
#### 2.2.3 Correction of Precession and Nutation Error  
The polar motion has an effect not only on the FOG, but also on the variation in orientation of the instantaneous rotation axis. The change in orientation of the instantaneous rotation axis is mainly embodied in the celestial motion as precession and nutation. The term 'precession' describes the long term, while 'nutation' is the name given to shorter-term periodic variations. The rotation axis of the earth is moving in space at a velocity of  
Figure 6: Polar motion observed with VLBI as reported by the IERS. The dataset covers June 2023-June 2024.  
Figure 7: Angular velocity effects on FOG produced by polar motion diurnal and semidiurnal high-frequency terms on 32-day days.  
1.5 km/year due to precession and has periodic variations with amplitudes at the level of 600 m (as seen from space in a plane tangent to the terrestrial pole). Figure 8 shows the schematic diagram of precession and nutation.  
We apply a theoretical model adopted by the International Astronomical Union (IAU) in 2000 (nutation) and 2006 (precession) and by the International Union of Geodesy and Geophysics (IUGG) in 2003. The adopted model depicts the earth as a deformable object with a deformable inner core (viscoelastic central part of the earth composed of a solid iron alloy), a liquid core (also composed of iron alloy), a deformable viscoelastic mantle (composed mainly of olivine and perovskite), as well as oceans and an atmosphere [31]. The adopted theoretical model is almost perfect, as seen from the observed residuals, and has a good long-term prediction accuracy, which can differ from the VLBI observations to within 0.2 mas.  
The full model calculations are based on the standard of fundamental astronomy (SOFA) [32], using the coefficients given in Tables 5.1a and 5.1b of the IERS Convention 2010. The angular velocity effect on the FOG caused by the precession and nutation from 11 January 2024 to 12 February 2024 is calculated [33]. As shown in Figure 9, the fluctuation peak-to-peak error is mainly caused by nutation, while the curve constant value error is caused by precession, which is in the order of magnitude of 10\({}^{-13}\)-10\({}^{-14}\) rad/s. With the improvement of the measurement accuracy of the large optical gyroscope, the necessity of the study of precession and nutation will be gradually reflected.  
Figure 8: Representation of precession and nutation of the earth.  
Figure 9: Angular velocity effects on FOG produced by precession and nutation.
## 3 Results
### System Distributability  
Section 2.1.5 describes the distributed design of the system, with agents allowed to freely move between nodes during the interaction phase and two different methods used during the swarm phase, one that is less compute-efficient, but produces results identical to swarming on a single node, and a second method which is more compute-efficient, but may not produce the same predictive performance as swarming on a single node. WoC-Bots, and the meta-swarm (depending on number of inputs) both can have a large number of participating agents with many decisions directed by a random number generator (which way to move, which node to be distributed to, etc.). Additionally, large multi-agent systems will have variances in their outcomes based on agent-to-agent interactions and agents interacting within their environment [33]. The results presented in this section set a seed to the main random number generator used by the system to guarantee that each run of the system produces the same ordering of interactions and to hold predictive results consistent across multiple runs.  
The following tests were conducted using virtual machines with the following hardware:  
* CPU: AMD Opteron 6376 (Released November 2012); four virtual CPU (vCPU) cores per virtual machine
* RAM: 12 GB DDR3 per virtual machine
* Ceph-based network storage,  
This was with all virtual machines configured with identical virtualized hardware, and with no control over how physical hardware was divided by the virtualization software. For example, when running with four virtualized nodes, those four nodes could be powered by a single hardware CPU, or with each node on a dedicated hardware CPU, or some other possible combination. We found that 12 GB of RAM was enough to allow all agents to maintain themselves in memory without swapping to disk during all phases of this system. Running on more modern, single-node hardware, the interaction step takes an average of 19 ms for 1250 agents and 96 ms for 5000 agents (Threadripper 1950x CPU and 64 GB RAM).  
Figure 7: Cast, Crew, Production Features—System Accuracy.
The paper is specifically proposing and evaluating a new non-intrusive load monitoring (NILM) instrumental asset, keeping the needed capabilities for monitoring and control of energy consumption while reducing instrumental complexity and cost of the current load monitoring solutions. The proposed NILM solution is based on: (i) disaggregation of whole-house consumption into the single portions associate to each consuming device; (ii) exploiting information of users presence and hourly use of appliances; (iii) enabling a constructive behavior of final user for energy saving; (iv) addressing monitoring for total active power measurements, which can be sampled at a much lower frequency thus reducing their data set size. As shown in the paper, positive results have been obtained through simulations fed through and experimental open dataset. The eighth paper--by [PERSON] and [PERSON] [8]--is concerned with emerging network technologies based on the paradigms of software-defined networks (SDN) and network function virtualization (NFV), which are aimed at enhancement of network operation flexibility and cost reduction. Such an evolution is particularly relevant for the current roadmap of the 5G network and for its impact in smart city contexts. In such a context, criticalities and limitations currently emerge for cross-virtualization and dynamic integration of network services among different network operators. The paper contribution is concerned with such a problem. A new approach is proposed and discussed, which is based joint exploitation of the service-oriented architecture (SOA) paradigm, to cope with the multi-operator virtual network integration, thus complementing the current NFV and SDN approaches. A new network architecture is thus worked out and discussed. Preliminary results of prototype implementation and testing activities are also presented, which highlights also benefits for network service providers. The ninth paper--by [PERSON] and [PERSON] [9]--is concerned with advancements of telecommunications technologies for wireless communications. The topics faced pertain to the current roadmap of the 5G network, which are particularly important for related critical requirements arising within the smart city context. A specific contribution is indeed made about a new methodology to be adopted to improve usage of all potential spectrum resources. Resorting to cognitive radio technology is prospected and discussed, in order to support context-aware dynamic optimization of the spectrum usage and sharing. A review is made on such a subject, by exploiting two relevant new network paradigms: heterogeneous networks and Machine-to-Machine communications. The guest editors wish to thank all the contributing authors, the professional reviewers for their precious help with the review assignments, and the excellent editorial support from the _Future Internet_ journal at every stage of the publication process of this special issue. The authors declare no conflict of interest.
# Temporal-Guided Label Assignment for Video Object Detection
## 2 Related Work
### Label Assignment  
Current CNN-based object detectors perform dense detection by classifying anchors and regressing boxes. The process of setting classification and regression targets for anchors is called label assignment.  
Traditional label assignment methods usually employ predefined rules to assign ground-truth boxes or backgrounds for anchors. RetinaNet [12] exploits the intersection over-union (IOU) ratio to label anchor boxes. FCOS [13] defines the anchor points within a certain center range of the gt as positive samples. Such predefined rules rely on prior human knowledge to allocate samples in space and scale and cannot adapt well to changes in data and the emergence of new instances.  
In view of the shortcomings of fixed label assignment strategies, the latest detectors propose adaptive label assignment strategies. GuidedAnchoring [14] dynamically changes the shape of anchor boxes according to the distribution of objects. MetaAnchor [15] randomly samples anchors of any shape during training to cover different types of object boxes. Moreover, in the adaptation of anchor priors, some approaches focus on the sampling method for each object. FSAF [16] embeds an anchor-free branch as a label assignment guide for anchor-based practices.  
The label assignment methods mentioned above are still somehow dependent on human knowledge. The latest works bring forward a fully-adaptive strategy for label assignment. AutoAssign [18] takes all points in a gt box as positive samples and then calculates the weights of these sample points. The weights are completely learned from the data, so they are entirely data-driven. OTA [17] believes that AutoAssign still has shortcomings because it only finds the optimal label assignment strategy for a single object and cannot consider context information from a global perspective. Therefore, OTA proposes a global optimal allocation strategy, which defines label assignment as an optimal transfer problem (OT). However, fully data-driven label assignment also cannot adapt well to video object detection when object appearance deterioration occurs in videos. We propose temporal-guided label assignment to enhance the labels. This enables the label assignment to perform well even when the object's appearance deteriorates.
## 3 Proposed Method for Defect Shape Estimation
### Preprocessing and Feature Extraction  
Saturated images or confocal microscopy images are certainly affected by noise. Image preprocessing is necessary. The images are mainly contaminated by additive noise, including background and Gaussian noise, as shown in Figure 6. Other abrupt changes in regions are caused mainly by manual marking or dust on the surface. Background can be corrected through differentiation from the fitting plane. Gaussian noise can be reduced by smoothing. As shown in Figure 6, preprocessing effectively filters out most of the noise.  
Figure 5: Framework for polarizer defect shape estimation based on manifold learning and regression. ROI stands for region of interest and SV represents support vector.  
Figure 6: Preprocessing example. The first and second rows show the preprocessing of the saturated image and the confocal microscopy image, respectively, from left to right.  
Considering that the useful information of the saturated image is the change of intensity along various directions, this study chose the histogram of gradient (HoG) method for feature extraction. The essence of HoG is the statistical information of the gradient direction of the image. HoG operates on the local grid unit of the image. Thus, the optical deformation can be kept reasonably invariant. The resolution of the defective region used in our experiments is \(48\times 58\). To avoid missing the details, we set \(4\times 4\) cells with step size 2.
## 5 Discussion
### Analysis of Limitations and Scalability  
#### 5.2.1 Scalability  
Our approach is built entirely upon the Transformer architecture, which provides better scalability compared to previous methods based on CNNs, RNNs, and hybrid networks. Transformer enables efficient parallel processing and distributed computing, allowing it to handle large-scale datasets and adapt to increasing workloads. Additionally, the modular and hierarchical structure of Transformer allows for easy scalability and the integration of additional components, improving the overall system's scalability.