esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture
#### 3.1.3 Centralization  
Data centralization is a key process in an environment with a lot of sensors. It allows to aggregate of the data if relevant, minimizes the exchanges needed for each node of the network to be updated, and optimizes the synchronization of every node of the static part. So, the C-ITS-C cases the creation of metadata (which gathers a set of smaller data), the application of a sequence number, and the computing of a hop limit, three mechanisms that we propose in our approach that reduce the use of the bandwidth. In addition, for future work, the central node has an omniscient view and can learn and adapt its naming strategy or form a new context cloud based on the need of each service. To determine if a mechanism (metadata or sequence number) should be used for a specific service, in a specific context, we present two expressions based on the parameters presented in Table 1.
## 2 Materials and Methods
### Plasmid and Strain Construction  
The plasmids, primers, strains, and gene sequences utilized in this research are detailed in Tables S1-S4.  
The initial phase of the experiment aimed to transfer the pentalenene synthesis plasmid from _E. coli_ into _V. natrigens_ to enable production of the target compound. The plasmid pAC-6409-pents, previously constructed and reported by a research group [16], was selected for this purpose. Traditional chemical transformation and electroporation methods failed to introduce the high-molecular-weight plasmid into _V. natrigens_. In contrast, bacterial conjugation proved effective in mediating the transfer of large plasmids into the host strain. Successful conjugation required the presence of the origin of the transfer (_oriT_) sequence on the plasmid [17]. The _oriT_ gene sequence was amplified from a laboratory source using Q5 High-Fidelity Polymerase with primer pair oriT_F/oriT_R. The plasmid pAC-6409-pents was linearized by Sac I digestion. Gibson assembly was performed using the ClonExpress II One-Step Cloning Kit to insert the _oriT_ sequence, resulting in the recombinant plasmid pAC-6409-oriT-pents. A total of 10 uL of the ligation product was transformed into chemically competent _E. coli_ DH5\(\alpha\) cells.  
The plasmid pAC-6409-oriT-pents served as a template for constructing pAC-6409-oriT-mva, which functions as the upstream module for the MVA biosynthetic pathway. The construction procedure followed the same protocol as described above.  
To construct the downstream module required for pentalenene biosynthesis, the gene sequence FPPS-PENTS was introduced into the plasmid 3933-agGPPS-temp, a backbone vector previously developed in the laboratory. The FPPS-PENTS sequence was amplified from pAC-6409-oriT-pents using primer pair fpps-pents-F/fpps-pents-R. The vector backbone was amplified with primer pair 3933-temp-F/3933-temp-R. Gene amplification was conducted using Q5 High-Fidelity Polymerase. Gibson assembly following the aforementioned strategy yielded the plasmid 3933-fpps-pents.  
Conjugative transformation mediated by _E. coli_ S17-1 enabled the successful delivery of the constructed plasmids into _V. natrigens_ ATCC 14048, resulting in the generation of engineered strains [18].
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
###### Abstract  
A system has been developed to remotely, continuously, and quantitatively measure the physiological activity of trees. The developed tree physiological activity monitoring (TPAM) system is equipped with electrical impedance, temperature, and light intensity measurement functions. In the two-contact impedance measurement method used in the previous plant impedance measurement, errors due to the polarization impedance of the electrodes could not be avoided. The developed TPAM system adopted a four-contact measurement method that could avoid polarization impedance errors, and, with it, the long-term monitoring of zelkova trees was performed. The monitoring of seasonal changes was conducted from July to November, and an impedance change pattern that repeated on a daily basis was observed in the short term, and an overall increase in the impedance was observed in the long term. Impedance changes related to daily temperature changes were observed even after all the tree leaves had fallen, meaning that this effect should be excluded when using impedance to evaluate tree vitality. For this reason, the influence of temperature fluctuations was excluded by using only the impedance values at the same daily temperature of 25 degrees from July to November. The analysis results at 25 degrees showed that the tree impedance value increased linearly by 8.7 \(\Omega\) per day. The results of this series of long-term monitoring and analysis revealed that the ambient temperature must be taken into account in the evaluation of tree physiological activity based on electrical impedance.  
electrical impedance; tree physiological activity; ambient temperature; continuous monitoring; zelkova +
Footnote †: journal: Applied Sciences
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
###### Abstract  
Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, making it difficult to distinguish defects from the background and thereby hindering accurate defect detection and damage evaluation. In this study, following systematic analyses of hydraulic concrete color space characteristics, we propose a Dual-Branch Luminance-Chrominance Attention Network (DBLCANet-HCIE) specifically designed for low-light hydraulic concrete image enhancement. Inspired by human visual perception, the network simultaneously improves global contrast and preserves fine-grained defect textures, which are essential for structural analysis. The proposed architecture consists of a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB). The LAB incorporates a Luminance-Aware Hybrid Attention Block (LAHAB) to capture both the global luminance distribution and local texture details, enabling adaptive illumination correction through comprehensive scene understanding. The CRB integrates a Channel Denoiser Block (CDB) for channel-specific noise suppression and a Frequency-Domain Detail Enhancement Block (FDDEB) to refine chrominance information and enhance subtle defect textures. A feature fusion block is designed to fuse and learn the features of the outputs from the two branches, resulting in images with enhanced luminance, reduced noise, and preserved surface anomalies. To validate the proposed approach, we construct a dedicated low-light hydraulic concrete image dataset (LLHCID). Extensive experiments conducted on both LOLv1 and LLHCID benchmarks demonstrate that the proposed method significantly enhances the visual interpretability of hydraulic concrete surfaces while effectively addressing low-light degradation challenges.  
hydraulic concrete; image enhancement; luminance aware; dual-branch network; attention mechanism +
Footnote †: journal: Applied sciences  
Article
## 4 Discussion
### Implications for Future LUE Model Development  
During dry periods, vegetation growth is mainly limited by moisture factors, and appropriate moisture factors must be applied towards an accurate simulation of GPP [47]. The experimental results showed that after incorporating SM, both LUE models achieved improved GPP simulations at most sites.  
For grass-type ecosystems, LSWI should be preferred to characterize water stress, but in drought periods, better simulations can be obtained using the scheme multiplying LSWI with SM. For forest ecosystems, there is typically a strong correlation between GPP and VPD. The simulation results using VPD in both models also demonstrate better performance at most sites. Additionally, the impact of drought on grass-type ecosystems can be effectively characterized by surface SM, while it is less effective for forest ecosystems. Hence, distinct water indicators should be applied to different ecosystem types.  
When both models considered LSWI and SM as water stress indicators for grass-type, we found that the TL-LUE model outperformed VPM at many sites (e.g., US-Wkg and US-SRG). In addition, at the forest sites, the simulations by VPM after replacing LSWI with VPD showed some improvement but were still slightly worse than the TL-LUE model. Given the same input meteorological data (e.g., air temperature) and remote sensing data (e.g., LAI and reflectance), much of the difference in results may stem from the fact that the TL-LUE results considering sunlit and shaded leaves may be superior to the large leaf model [49]. Vegetation canopy consists of shade and sunlit leaves, and they receive different amounts of direct and diffuse incident solar radiation [50; 51], so they have different structures as well as light saturation points. Thus, considering the distinction between shade and sun leaves in the LUE model may also improve the simulation accuracy.
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 3 Results  
The findings revealed a significant inverted U-shaped relationship between Industry 4.0 development and carbon emissions, with the turning point occurring at an Industry 4.0 index level of 3.5 (see Figure 1). This suggests that while early-stage adoption of Industry 4.0 technologies may lead to increased emissions due to high energy consumption, the long-term benefits of improved efficiency and reduced resource use outweigh these initial drawbacks. Furthermore, the results indicated substantial regional disparities, with eastern provinces experiencing more pronounced benefits compared to central and western regions (Table 1). These findings highlight the need for region-specific policies to support sustainable development in China's logistics sector.  
Figure 1: Trend of Industry 4.0 Adoption and Carbon Emissions (2011–2019). Explanation: This area plot demonstrates Industry 4.0 adoption and the carbon emission tendency in the logistics sector in China from 2011 to 2019. The Industry 4.0 index increased steadily over time, indicating continuous technology adoption. Carbon emissions initially rose but then decreased, forming an inverted U-shaped relationship. This suggests that while early stages of Industry 4.0 adoption may lead to increased emissions, significant reductions are achieved in the long term as technologies mature and become more efficient.
## 2 System Description
### Frame Structure  
LTE supports both frequency division duplex (FDD) and time division duplex (TDD) modes. Figure 1 shows the FDD downlink frame structure in the LTE system. The reference signal (RS) is a crucial signal that is used for channel estimation and data demodulation. In an LTE system, the PSS and SSS are located in subframes 0 and 5, which are used by the UE to obtain the cell ID (CID) and synchronization. The physical downlink control channel (PDCCH) is used to carry scheduling assignments and other control information. A PDCCH is transmitted on an aggregation of one or several consecutive control channel elements (CCEs). The physical downlink shared channel (PDSCH) is a physical channel that carries user data. The physical broadcast channel (PBCH) is used to carry the master information block (MIB).
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 6 On-Field Measurement Campaigns
### Interferences in an Urban Scenario
#### 6.1.3 Case-Study C: Experiment Performed around Porta Susa Area  
During the 7 March test, other disturbances were collected in two places close to the Porta Susa train station. For example, a PSD anomaly has been observed in Via Paolo Borsellino and reported in Figure 10 in two different moments of the day. In this case, the spectrum shows unexpected spikes, however, the anomaly in this place is not persistent.  
Figure 8: Case-study A. Narrow-band interferer detected on 6 March 2017 at 15 h:26 m:11 s (**a**) and 15 h:28 m:31 s (**b**) local time, at the bus stop n. 253 (Porta Nuova) in Turin (**c**).  
Figure 10: Case-study C. PSD anomaly detected on 7 March 2017 at 12 h:29 m:04 s (**a**) 15 h:30 m:51 s (**b**) local time, in Via Paolo Borsellino, Turin.  
Figure 9: Case-study B. PSD anomaly detected on 7 March 2017 at 11 h:19 m:59 s local time, in Corso Eusebio Giambone, Turin.  
Apart from the location and address, in all the considered cases it was not possible to locate the interference sources in a more precise way. Anyway, no harmful effect on the receiver operations has been noticed for all the observed disturbances in this scenario.
## 2 Theories, Simulation, and Experiment Methods  
### Theoretical Formula of \(H\)R  
The classical theory of an HR can be analogous to an equivalent mass-spring system, as shown in Figure 1. A neck is a medium between a cavity and an external space and its geometric sizes are import factors to design the resonance frequency.  
The theoretical formula of resonance frequency for an HR is:  
\[f_{r}=\frac{c}{2\pi}\sqrt{\frac{A_{c}}{Vl_{a}}} \tag{1}\]  
where \(c\) is the speed of sound, \(A_{c}\) is the cross-sectional area of the neck, \(l_{a}\) is the total neck length, and \(V\) is the cavity volume. The total neck length is the actual length of the neck  
Figure 1: Schematic diagram of the HR and an equivalent mass-spring system.  
plus the correction length. This formula assumes the characteristic size of the resonator is much smaller than the wavelength.  
The transmission loss of a Helmholtz resonator is:  
\[TL=10 log_{10}\left[1+\left(\frac{\sqrt{A_{c}V/I_{a}}/2A_{p}}{f/f_{r}-f_{r}/f} \right)^{2}\right] \tag{2}\]  
where \(f\) is the frequency and \(A_{p}\) is the cross-sectional area of the main duct.  
The classical theory of Equations (1) and (2) clearly yields a single resonance frequency and transmission loss for a fixed neck geometry and a cavity volume. However, changes in geometry affect both the resonance frequency and transmission loss of an HR, which is why size and shape decisions required for sound attenuation performance are critical.
## 3 Results and Discussion
### Summary  
Table 7 presents a summary of the leak detection potential of the NDTs for all the types of pipes (PVC, PE, and PPR) at different soil moisture contents. It was found that all NDTs were able to detect leaks in pipes; however, the success rate of the NDTs differed for each NDT.  
Figure 17: Spectrometer reflectance obtained against different wavelengths on PPR, PVC and PE setup with 2% soil moisture content.  
The second objective of this study was to assess the factors that may affect the ability of the NDTs used for leak detection. Therefore, to assess the effect of the varying factors, such as soil moisture content, pipe material, and types of leaks, on the leak detection potential of the NDTs, it was important to fix some other factors. Table 8 presents the effect of increasing soil moisture content on the potential of the leak detection of a given NDT. It was observed that the detection capability of the GPR and IR camera was significantly reduced when the moisture content was 10%. Additionally, Table 9 illustrates the effect of the pipe material on the leak detection capabilities of NDT. The pipe material did not affect the detection ability of the NDTs. Lastly, the GPR and spectrometer could not be used on the pipe with the faulty joint due to surface ponding (Table 10). Contrary to this, the IR camera could be used. However, the presence of water on the surface was an indication of leak. Between the other types of leaks, there was no effect observed on the leak detection capabilities.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of**} & \multicolumn{3}{c}{**2\% Moisture Content**} & \multicolumn{3}{c}{**5\% Moisture Content**} & \multicolumn{3}{c}{**10\% Moisture Content**} \\ \cline{2-9}  & **GPR** & **Spectrometer** & **IR Camera** & **GPR** & **Spectrometer** & **IR Camera** & **GPR** & **Spectrometer** & **IR Camera** \\ \hline \multicolumn{9}{c}{PVC} \\ \hline Crack & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & �\({}^{*}\) & �\({}^{*}\) & �\({}^{*}\) & �\({}^{*}\) \\ \hline Hole & ✓ & ✓ & ✓ & ✓ & �\({}^{*}\) & ✓ & ✓ & �\({}^{*}\) & �\(
## 4 Discussion
From a clinical perspective, the ability to distinguish between in situ and invasive BC preoperatively has significant implications. A reliable, non-invasive radiomic classifier could assist radiologists by providing a second-read tool to support image interpretation, particularly in ambiguous cases or high-volume settings. For surgeons, early differentiation may inform surgical planning, such as the extent of resection or sentinel lymph node biopsy decisions, while potentially reducing overtreatment in patients with in situ disease. Moreover, this approach could facilitate more tailored patient counselling and streamline multidisciplinary team discussions in breast tumour boards. Compared to previous studies, our results are competitive, particularly given the limited sample size. The use of 3D augmentation and feature selection enabled performance comparable to more complex approaches. Nonetheless, some limitations should be noted: the dataset included only 71 patients, with class imbalance between invasive and in situ lesions, which may affect generalizability despite mitigation strategies. Manual segmentation by an expert radiologist, while high-quality, introduces potential operator dependency and is inherently subject to intra- and inter-observer variability. This variability may impact the consistency of feature extraction and thus affect the model's reproducibility and generalizability to external datasets. Future work could benefit from automated methods to improve reproducibility, reduce observer bias, and enhance scalability. Moreover, only post-contrast DCE-MRI sequences were used--adding other imaging modalities could enhance accuracy. Finally, relying solely on radiomic features may not fully capture image complexity; integrating clinical, histopathological, or genomic data, along with DL techniques, could further improve performance and support personalised decision-making.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 4 Analysis
### Kinematic Analysis
#### Ejecting angular velocity  
Throughout the movement of the MF satellite, the guide rail will be critical in guiding and adjusting the satellite. The angular velocity variation curve of the MF satellite during the separation process is depicted in Figure 14. As illustrated, the angular velocity of the three axials after they escape the separation mechanism completely is 2\({}^{\circ}\)/s, 0.7\({}^{\circ}\)/s, and 2.5\({}^{\circ}\)/s, respectively. The separation angular velocity meets the index requirements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Location** & **X-Response/grms** & **Y-Response/grms** & **Z-Response/grms** \\ \hline A1 & Camera mounting point & 4.3 & 5.6 & 6.3 \\ A2 & Camera backplate & 4.2 & 5.0 & 5.7 \\ A3 & Primary mirror of the camera & 3.7 & 5.1 & 5.5 \\ A4 & Secondary mirror of the camera & 5.5 & 5.5 & 5.6 \\ A5 & OBHD & 4.9 & 4.4 & 5.4 \\ A6 & Solar panel & 5.6 & 9.3 & 11.6 \\ B1 & Cover door & 14.9 & 8.7 & 12.0 \\ B2 & Side panel & 6.5 & 4.6 & 12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of random vibration analysis.  
Figure 12: Separation process.
## 2 Related Work
### Capability of Resource-Constrained Devices  
Due to the increasing capabilities of low-cost, single-board computers and their benefits in terms of cost, low power consumption, small size, and reasonable performance, several works [37; 38; 39] have studied the capabilities and performance of low-cost edge-layer devices for deep learning and machine learning interference. The works reported in [37; 38] focused on the inference of deep learning models in various low-cost devices, such as the Raspberry Pi 4, Google Coral Dev Board, and Nvidia Jetson Nano, and evaluated their performance in terms of the inference time and power consumption. Their main focus was to implement the inference parts of deep learning models in edge devices in order to achieve real-time processing. The authors of [39] conducted a comprehensive survey of design methodologies for AI edge development, emphasizing the importance of single-layer specialization and cross-layer codesign, which includes hardware and software components for edge training, inference, caching, and offloading. Several insights were highlighted with respect to the quality of AI solutions in the edge computing layer. While the deployment of low-cost edge devices is still a relatively new paradigm for advanced applications, it has found widespread interest, particularly in function processing. Some approaches [40; 41; 42; 43; 44] have been developed for edge-layer serverless platforms. Specifically,all of these works attempted to develop edge-layer serverless platforms that can support edge AI applications. Moreover, increasing attention has been paid to the capabilities and viability of single-board computers; several works [45; 46; 47] have explored the characteristics and possibilities of deploying different big data applications in resource-constrained environments. In contrast to our work, these works did not investigate the deployment of big data processing platforms using the collaborative power of local nodes in edge, fog, and cloud layers.
## 5 Discussion
### Robustness Analysis of the ML and the HML Models  
To validate the model's generalizability and real-time applicability, out-of-sample testing and cross-validation techniques were used. Out-of-sample testing assessed performance on excluded data, while cross-validation partitions data into multiple training and validation subsets to estimate the model's ability to generate reliable predictions when partitioned randomly [39]. Additionally, perturbations were introduced through Gaussian or uniform distributions to evaluate the model's robustness under changing conditions [40; 41]. In this study, a comprehensive robustness evaluation was performed using a Gaussian noise distribution.  
Figure 12 displays the noise robustness analysis results in terms of R\({}^{2}\) values for the FV predictions of the four models evaluated with various levels of noise added. As to be expected, an elevation in the noise level added results in a decline in the R\({}^{2}\) value for the training data for all four models. However, the rate of R\({}^{2}\) decline ranks the models as RBFNN-GO (best) > MELM-GO-RBFNN > MELM (worst). These results identify the RBFNN-GO as the most robust of the models evaluated when confronted with noisy data. Notably, the performance of each model declines more rapidly as the added noise increases above \(\sim\)20-25%.