esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Compared to other studies on AI-based DTs in engineering, our review highlights a stronger emphasis on personalization, particularly in tailoring simulations and predictions to specific users, environments, or operational profiles. Several studies, for example, on aerospace and manufacturing systems, have used experimental data--such as sensor logs and operational feedback--to train adaptive DTs that could adjust maintenance schedules or performance parameters in real time. These findings showed that personalized DTs in smart manufacturing reduced downtime by 18% using adaptive learning models. Other studies, particularly in energy systems, have shown that AI-assisted DTs using user-specific consumption data can optimize energy efficiency and predictive control, improving accuracy by up to 25% compared to static models. A recurring theme in the scientific literature is the use of ML algorithms--especially reinforcement learning and deep neural networks--to dynamically adapt DT operation to individual conditions. In comparison, our review indicates a growing shift toward hybrid models, combining physics- and data-driven approaches that offer both interpretability and adaptability. It is worth noting that personalization was less emphasized in earlier studies, which focused more on general system modeling and simulation fidelity, suggesting a recent methodological evolution. Furthermore, research in biomedical engineering has highlighted the use of personalized DTs in diagnostics and patient-specific treatment simulations, confirming the versatility of this concept across various domains. Although technical implementations vary--from cloud infrastructures to embedded edge computing--the trend toward the user-centric personalization of DTs is gaining traction. Our synthesis shows that personalized DTs consistently lead to improved outcomes in terms of accuracy, efficiency, and system responsiveness, reinforcing the value of integrating adaptive AI frameworks with DT engineering (Table 6).  
The answer to RQ1 highlights state-of-the-art AI methods (deep learning, generative AI) used to customize digital twins for individual users, resources, or environments. The answer to RQ2 highlights technological gaps and societal concerns that impact the adoption and responsible implementation of personalized digital twins. In response to RQ3, infrastructure readiness and limitations in current DT deployments were assessed, particularly in distributed or resource-constrained environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature/Area** & **Emerging Trends** & **Research Gaps** & **Dominant Themes** \\ \hline \multirow{2}{*}{Signal-level data} & Integration of multimodal & Standardized preprocessing & High-resolution, real-time \\  & sensor data for high-fidelity & pipelines for noise reduction & signal acquisition and \\  & digital replicas & and synchronization & interpretation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Emerging trends, research gaps, and dominant themes related to the personalization of AI-based DTs in engineering.
# Coupled Mode Sound Propagation in Inhomogeneous Stratified Waveguides
## 4 Application to Waveguides with Penetrable Infinite Half Space and Internal Wave
The internal wave shape is  
\[h_{in}(x)=50+50\mathrm{sech}^{2}\bigg{(}\frac{x-0.5x_{R}}{1200}\bigg{)}, \tag{37}\]  
where \(x_{R}=16\) km. The water-sediment interface is sloping,  
\[h_{1}(x)= \begin{cases}200-0.005x,&0<x<x_{R},\\ 120,&x\geq x_{R}.\end{cases} \]  
In numerical implementation, a false rigid boundary is placed at \(h_{2}=200+5\lambda_{2}\) beneath the water column, and the sound speed in the bottom layer is replaced by \(\tilde{c}_{2}\) (Equation (36)) with \(\alpha_{0}=10\) dB/\(\lambda_{2}\). The point source is located in \((x_{s},z_{s})=(0,30)\) m at a frequency of 50 Hz. The source condition, which is represented by the modal amplitudes of the Green's function in the waveguide of Figure 4, is  
which fall in the interval \(\left(\omega/c_{2},\max(\omega/c_{1}(z))\right)\), correspond to the horizontal wavenumbers of the trapped modes. At the chosen frequency, there are six trapped modes at \(x=0\) and four trapped modes in the transmission region. Figure 5a presents the computed sound field using the present two-way coupled mode method. The truncation number is \(N=30\), which has been proved to be enough for numerical convergence. Figure 5b compares the transmission loss predicted by the two-way CMM, one-way CMM, and COMSOL. The numerical model used for COMSOL is a waveguide with a PML beneath the water column. The receiver depth is \(z_{r}=60\) m. The transmission loss is defined by  
\[\text{TL}(x,z_{r})=-10\log_{10}\frac{|p(x,z_{r})|^{2}}{|p(x=1,z_{s})|^{2}}. \tag{39}\]  
We see that the agreement between the two-way CMM and COMSOL is excellent. Differences are attributed to the deviations in spatial discretization. The results of the one-way CMM are accurate for the range internal from \(0\) m to about \(6\) km, implying that the backscattered energy is small in this region. As sound propagates through the internal wave, waves interact with the sloping interface and internal waves, and differences in the results between the one- and two-way CMM increase gradually. Since the computation model used in COMSOL mimics the real waveguide with a penetrable infinite half space, one can conclude that acoustic pressure in the water column of a waveguide with a penetrable infinite half space and internal waves predicted by our two-way coupled mode method is accurate.
# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 2 Proposed Method  
The schematic concept of this people-counting system is shown in Figure 1. The angle type of the camera was a side view and the angle was between 30 and 60 degrees. The height of the camera was between 3 and 5 m. We used two IMX 185 cameras [13] to construct a stereo camera. The camera was connected to the NVIDIA Jetson TX2 [11] and attached to the ceiling.  
The flow chart of the proposed method is shown in Figure 2. The camera calibration process is a step used to acquire the camera parameters of each camera. The camera parameters were used in stereo rectification [17] and view projection. We applied the stereo-matching algorithm [18] to the rectified stereo image to obtain the disparity map and convert it to the depth map. The background subtraction algorithm was applied to the depth map to only extract the moving object. The height map and the occupancy map were obtained by projecting the moving object onto the top view [15]. By using these maps, we generated the likelihood map based on the Gaussian distribution and found the detected head using the local max filter [15]. The detected head was tracked using a Kalman filter-based tracker [16] and determined whether it had passed the counting line. We will describe the main algorithms of our method in this section.
# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 2 Proposed V2X Communication System Model
### Small-Scale Fading in V2X System Modelling
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**NLOSv Cases**} & \multicolumn{2}{c}{**NLOSv Additional Blockage Loss**} \\  & & \(max(0,\alpha)\), \(\alpha\)**-\(Lognormal(\mu_{a}\sigma_{a}^{2})\)** \\ \cline{2-3}  & & **Mean \(\mu_{a}\)** & **Std. Deviation \(\sigma_{a}\)** \\ \hline \multirow{2}{*}{\(max\{h_{T},h_{R}\}<h_{B}\)} & \(\mu_{a}=9+max(0,15\text{log}_{10}d-41)\) & \(\sigma_{a}=4.5\) \\ \hline \multirow{2}{*}{\(min\{h_{T},h_{R}\}>h_{B}\)} & \(\mu_{a}=0\) & \(\sigma_{a}=0\) \\ \hline \multirow{2}{*}{else} & \(\mu_{a}=5+max(0,15\text{log}_{10}d-41)\) & \(\sigma_{a}=4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NLOSv additional blockage loss. environment, both in 4G-LTE and 5G-NR V2X system modelling, the parameters of the Rician channel are also random. Therefore, the K factor of the Rician channel is determined as random following a log-normal distribution in dB. The mean and standard deviation of the K factor are presented in Table 5. For the NLOS state, a Rayleigh channel is assumed to be more suitable. In the V2V system model, the transmitter and the receiver are highly mobile, so a dual mobility model must be considered with different Doppler impacts. The model must consider the relative speed between the transmitter and the receiver. Specifically, the Doppler shift is expressed according to [8] (p. 31):
## 4 Simulation Examples and Results
### Scenario Parameters  
To evaluate the performance of the proposed approach, a real application case is proposed. The case study is based on the rural community of San Miguelito, in the town of Quimistan (Honduras), that lacks access to the electrical grid because of the geographical limitations. This community was chosen by the Honduran Foundation for Agronomic  
Figure 10: Example of the exchange of nodes between two individuals during a crossover operation. Note how the highest and lowest nodes (A1 and B3) are directly inherited by the offspring, while the internal nodes (A2, A3, and B2) are randomly distributed among them. End nodes A4 and B1 are thus not used.  
Research (FHIA) as a candidate for the installation of a micro-hydropower plant. In a first evaluation by the technicians, a minimum generation of 8 kW was established as the power required to supply the approximately 40 families that live in the community. An aerial drone topographic survey was performed to provide the dataset, composed of a grid of 2900 terrain data points and a set of aerial images, based on which an additional set of 59 points of the river profile were determined. Regarding the problem constants, those from [23] have been considered in this work, for the sake of comparison. These parameters are summarized in Table 1.  
For a further analysis of the performance of the method, two additional study cases have been proposed by modifying the example presented in this section:  
1. Modified problem 1 is based on reducing the power output constraint. This models the design of a plant for a much smaller village, with a consequently lower power supply requirement. In particular, the required power output, \(P_{min}\), has been set to 4-kW.
2. Modified problem 2 is based on (i) increasing the required power output of the plant and (ii) modifying the costs associated with the pipe and its deployment. The first of these modifications represents the application of the method to supply a more populated village. In particular, the required power output, \(P_{min}\), has been set to 14-kW. The second of these modifications is based on considering a low quality terrain that strongly makes the transport of the pipe difficult, translating into an increase of its cost per unit length, but eases the excavations labor involved. This has been modeled by using a 1.5 multiplier for C\({}_{p}\), on one hand, and reducing the required cut angle of the excavations, \(\beta_{exc}\), to 10\({}^{\circ}\) and the unitary volumetric cost, \(K_{exc}\), to 2 c.u./m\({}^{3}\), on the other.
## 4 Experiment
### Experiments in Outdoor Real-World Scenarios
#### 4.5.1 Preparation Phase  
In the preparation phase, the autonomous vehicle traversed the site, capturing RGB images with a stereo camera. Concurrently, the RTK system recorded the vehicle's real-time coordinates to ensure precise location data. The stereo camera images were then processed by the depth estimation module to generate depth information for each frame. The primary objective of this phase was to gather both visual data of the site and accurate location  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Pose Estimation AUC\%**} \\ \cline{3-5}  & & **AUC@5\({}^{\circ}\)** & **AUC@10\({}^{\circ}\)** & **AUC@20\({}^{\circ}\)** \\ \hline \multirow{4}{*}{Sparse} & ORB [31] + GMS [48] & 5.2 & 13.7 & 25.4 \\  & SP [32] + NN & 7.5 & 18.6 & 32.1 \\  & SP [32] + SG [35] & 16.2 & 32.8 & 49.7 \\  & SP [32] + LG [38] & 14.8 & 30.8 & 47.5 \\ \hline \multirow{4}{*}{Semi-Dense} & DRC-Net [45] & 7.7 & 17.9 & 30.5 \\  & LoFTR [36] & 16.9 & 33.6 & 50.6 \\ \cline{1-1}  & MatchFormer [44] & 15.8 & 32.0 & 48.0 \\ \cline{1-1}  & TopicFM [47] & 17.3 & 35.5 & 50.9 \\ \cline{1-1}  & OURS & **19.1** & **36.4** & **52.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pose Estimation Results on the ScanNet Dataset. Bold values represent the best results among all methods.  
information, which will serve as the foundation for subsequent map construction and localization tasks.  
ResNet-50 was employed as the feature extraction network to derive features from the left-eye images. These extracted features, along with the RTK coordinates, depth information, and original images, were stored in the database. Serving as the core of the map, this database stores both the visual features and associated geometric data for various locations within the site. This process ensures the database is complete and accurate. It provides a reliable reference for image matching and localization during the testing phase.
### Questions of Research and Contribution  
Proven experimental methods and validated data analysis are a prerequisite to systematically assessing the cellular network for logistic applications to control and enforce their further expansion. For this study, the authors focus on the German highway, driving at speeds relevant for logistics, to find out if existing or slightly modified methods for cellular network analysis deliver reliable and reproducible results with sufficient confidence. The effects of the time of measurement, the vehicle speed, and the direction are evaluated. Furthermore, a method for comparison of two network operators is proposed and implemented to assess the quality and create a ranking based on objective data. In summary, this publication describes the method, its validation, and the first tests performed to assess the usability and performance of cellular LTE networks for smart logistic applications. It addresses the following questions of research:  
1. Does the experimental setup used in this study provide reproducible and consistent measurement results?
2. How can different measurements, with spatial variation in vehicle position along the road under analysis, be compared?  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Service Specific, Complex Characteristics** & **Simple, Directly Measurable Parameters** \\ \hline Quality of Experience (QoE) & Reference Signal Received Power (RSRP) \\ Quality of Service (QoS) & Reference Signal Received Quality (RSRQ) \\  & Received Signal Strength Indicator (RSSI) \\  & Delay, Latency \\  & Packet Loss \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typically assessed parameters, based on [21; 27; 31; 32].  
What is the level of consistency assessed by the spatial correlation between different measurements with variation in time, speed, and direction?
* What is the level of consistency based on the confidence per measured position?
## 3 Enrichment of Sediments by Soil Organic Carbon  
Accelerated soil erosion, due to water and wind, is a predominant process with a strong impact on the global C cycle and budget. Indeed, accelerated soil erosion, both by water and wind, affect properties of the land, atmosphere, and rivers (Figure 2), and erosional risks must be minimized through the adoption of judicious land use and of site-specific best management practices (BMPs). Climate change and the ever-increasing demands of the growing and progressively affluent human population will aggravate the soil erosion hazard. Globally, between 0.57 and 1.33 Pg C may already be transported by inter-rill soil erosion [8] because of its selectivity. Indeed, soil erosion leads to a preferential removal of the SOC fraction because it has a low bulk density (0.6-0.8 Mg/m\({}^{3}\)), and it is concentrated in the vicinity of the soil surface [9; 10].  
It is widely recognized that sediments have a high SOC enrichment ratio (SOC-ER) and soil organic nitrogen enrichment ratio (SON-ER), and thus are enriched in SOC/SON contents and other light fractions, which are selectively transported by hydric and aeolian processes. Based on a study of a grazed watershed of South Africa, [PERSON] et al. [11] observed that eroded sediments were enriched in SOC by a factor of 1.85 and in lignin by a factor of 3.7. The high lignin concentration indicated that it was less decomposed in eroded sediments than in the original soils. Further, a negative relationship was observed between the C:N ratio of eroded sediments and soil CO\({}_{2}\) production, indicating that the microbial biomass could be N-limited. [PERSON] et al. [11] concluded that the SOC in eroded sediment consists primarily of undecomposed plant material, and it is mineralized to a greater extent, regardless of the landscape position. This is an important observation indicating the mineralization of eroded SOC and the emission of GHGs.  
For dryland calcareous soils, [PERSON] et al. [12] observed that eroded sediment was significantly enriched in SOC. For a study located in the Tertiary Hills, about 40 km north of Munich, Germany, [PERSON] et al. [13] observed that sediments are highly enriched in SOC compared to the parent soil. In northeast China, [PERSON] et al. [14] also observed that SOC was enriched in sediments, indicating a high selectivity of the erosion processes. [PERSON] and [PERSON] [15] also observed that eroded sediments were enriched in C and N by 51.3% and 50.6%, respectively, and that both elements are stored in soil fractions that are selectively eroded. Both SOC-ER and SON-ER are affected by slope gradient and soil texture. However, the C:N ratio increased by 9.9%, indicating the preferential movement of C-rich particulate organic matter (POM) compared to N-rich mineral-associated organic matter (MAOM).  
Figure 2: The soil erosion process impacting the exchange of Carbon (C) between the land, atmosphere, and rivers after the global C budget, which must be duly accounted for.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 2 System Overview
### The Sensor Node  
The sensor node we used for the on-site data collection (Figure 5) is designed based on the off-the-shelf module Sodaq SARA AFF [32] featuring the NB-IoT module from uBlox ATSAMD21J18 and the 32-Bit ARM Cortex M0+ microcontroller. The shield is designed to support various onboard and external sensors as specified in Table 1.  
It is planned to include a leaf wetness sensor and an anemometer in the next version of the sensor node.  
Figure 3: Algorithm for primary infection alarms.  
Figure 4: Algorithm for secondary infection alarms.  
Figure 5: Sensor device: (**a**) physical appearance; and (**b**) block diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type** & **Operational Range** & **Unit** \\ \hline Air temperature & \(-40\) to \(85\) & \({}^{\circ}\)C \\ Air pressure & \(-300\) to \(1100\) & hPa \\ Relative air humidity & \(0\) to \(100\) & \({}^{\circ}\) \\ Ambient light & \(1\) to \(65\),\(535\) & \({}^{\circ}\)K \\ \hline Soil temperature & \(-55\) to \(125\) & \({}^{\circ}\)C \\ Soil moisture & \(0\) to \(100\) & \({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor specifications.
## 4 Conclusions  
This study evaluated the effects of zirconium-based KZC series with different zirconium contents and polyamine-based PBC crosslinkers on coated paper properties, focusing on viscosity, wet rub and pick resistance, dry pick resistance, gloss, brightness, ink set-off, and print mottle. Results indicated that crosslinker type and concentration significantly impacted viscosity, with the KZC series showing consistent performance across a wide zirconium content range. Both crosslinker types improved wet rub resistance at higher concentrations, with the KZC series demonstrating superior wet pick resistance, particularly KZC7 at higher concentrations. All crosslinkers maintained excellent dry pick resistance, and gloss and brightness were mostly unaffected except for a slight reduction with high zirconium content in KZC20. In set-off was better at lower concentrations for the KZC series, and print mottle remained consistent across all crosslinkers. Overall, the zirconium-based KZC series significantly enhanced wet resistance and maintained high performance across various properties, though it might slightly reduce gloss and brightness and increase ink set-off at higher concentrations. Conversely, polyamine-based PBC offers balanced performance, underscoring the importance of optimizing crosslinker type and concentration to meet specific application requirements.  
Figure 13: SEM images showing the surface and cross-section of the coated paper with different crosslinkers.  
**Author Contributions:** Research and investigation, C.-H.K., J.-H.P., H.-H.P., T.-G.L., M.-S.P. and J.-S.L.; tables and figures preparation, J.-H.P. and H.-H.P.; data curation, T.-G.L., M.-S.P. and J.-S.L.; writing--review and editing, H.-H.P. and C.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program via the National Research Foundation of Korea (NRF), funded by the Ministry of Education (grant No. 2022R11A3053045). It was also supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) and a grant funded by the Ministry of Education (grant No. 2022R1A6C101B724).  
Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Tree Growing Algorithm  
We choose a CDS-based approach, since exploiting a CDS provides us with the most benefits in regard to network flooding. Finding a MCDS is considered NP-hard [8]. Therefore, we need an approximation algorithm that keeps the set of forwarders as small as possible in order to reduce flooding redundancy effectively. There has been a lot of work done on finding good approximation algorithms for the MCDS problem [11]. The work of [11] provides us with three types of CDS construction algorithms: subtraction-based, MIS-based, and tree-based algorithms. Compared to subtraction-based and MIS-based algorithms, tree-based algorithms produce generally smaller CDSs and cause less message overhead [11]. Hence, we choose to implement a tree-based algorithm for  
Figure 6: Network clustering. Colored areas around nodes represent clusters. Cluster heads are colored in green. Gateways or distributed gateways are colored in grey. (**a**) Clustering with overlapping clusters. (**b**) Clustering with non-overlapping clusters. (**c**) Clustering with distributed gateways.  
CONE. We use the tree-based algorithm suggested by [PERSON] [20], referred to as \"tree growing\" algorithm. In the following, we describe two centralized variations of the tree growing algorithm.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis
### Attention Module Comparison Test  
In order to verify the effectiveness of the C2f structure combined with the EMA module, three typical attention modules, i.e., CBAM [23], GAM [24], and ECA [25], are selected to be embedded in the same position in the C2f structure for a side-by-side comparison of the improved algorithm proposed in this paper; the experimental results are shown in Table 2.  
Based on the results shown in Table 2, it can be seen that the P, R, and [EMAIL_ADDRESS] values obtained by training with the module combining C2f with EMA are superior to those obtained by training using C2f combined with CBAM or ECA. The [EMAIL_ADDRESS]:0.95 value is improved by 0.3%, with an equal [EMAIL_ADDRESS] obtained by training of the embedded GAM attention module, which proves that the improvement proposed in this paper is more effective for remote sensing target detection.