esa-sceva/satcom-chunk-collection huggingface dataset:
_applied sciences_  
_Review_  
Review of Probabilistic Risk Assessment Models for Ship Collisions with Structures  
**[PERSON] 1,2, [PERSON] 1,2,* and [PERSON] 3,*  
1 Hubei Inland Shipping Technology Key Laboratory, Wuhan 430063, China; [EMAIL_ADDRESS]  
2 School of Navigation, Wuhan University of Technology, Wuhan 430063, China  
3 Hainan Harbor & Shipping Holding CO., Ltd., Haikou 570311, China  
*Correspondence: [EMAIL_ADDRESS] (Y.M.); [EMAIL_ADDRESS] (B.W.)  
Received: 17 February 2022  
Accepted: 24 March 2022  
Published: 28 March 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
Received: 17 February 2022  
Accepted: 24 March 2022  
Published: 28 March 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Abstract: Researchers have always been concerned about collision risks between ships and structures on busy waterways, as the consequences can be catastrophic. The models for determining the probabilities of these accidents, however, vary widely, with discrepancies between different model results in the same assessment. The models sometimes lack critical elements or are inherently flawed, and therefore do not represent reality. This paper aims to review the existing probabilistic risk models for ship and structure collisions. The advantages and disadvantages of each model are discussed, which leads to a better method for future model development. This paper reviews the existing literature for the probabilistic risk assessment (PRA) between nautical traffic and offshore infrastructures. This paper differentiates the existing models into three categories: statistics of collision rates, statistical models, and simulation models, as the models are evolving from statistical models to simulation models to derive more accurate results. The advantages and disadvantages of the statistical models were evaluated by comparing the details of the elements contributing to risk. Simulation models with virtual autonomous ships can better reflect the reality and include more risk elements than those described in the existing models. The cores of simulation models and the advantages of different models are elaborated and compared, thus supporting future work in this area.  
ship collision; risk assessment; statistical models; simulation models
# Confidence Measures for Deep Learning in Domain Adaptation
## 5 Results
### Classifier Reliability  
In a real domain adaptation setting, the performance of the classifier \(C\) cannot be evaluated, since a labeled test set is not available. Instead, the proposed confidence measures allow estimating the reliability of classifier \(C\) on target samples. Formally, a reliability classifier \(M\), implementing one of the proposed measures, estimated the confidence of the output of \(C\). For each confidence measure, a threshold \(th\) was employed, on the output of \(M\), to classify the network output as certain or uncertain. If the confidence provided by \(M\) was larger than \(th\), then the output of \(C\) was considered to be reliable; otherwise, it was considered unreliable. In the experiments, \(th\) was set based on the ROC, using each measure to predict the accuracy on the validation set of the source dataset (see Figures 2 and 3). Tables 5 and 6 show the accuracy and the Area Under the ROC (AUROC) achieved by \(M\) on the test set of the target domain. (The area under the ROC is a measure of the performance of the classifier. Differently from the accuracy, the AUROC does not depend on a single predefined threshold, but it provides a unique measure that summarizes the behavior of the classifier for each possible thresholds.) In Tables 7 and 8, the corresponding confusion matrices are shown. On SVHN \(\rightarrow\) MNIST, the best reliability classifier \(M\) obtained an accuracy of about 89% and an AUROC of about 73%. Notice that, in this case, the accuracy was not a significant metric because the classifier \(C\) was very accurate and therefore correctly and incorrectly classified samples are unbalanced (i.e., we would obtain an accuracy of 92.34% only classifying each instance as \"certain\"). However, the value of the AUROC suggests that the proposed confidence values are correlated to the errors of \(C\). The benchmark CIFAR \(\rightarrow\) STL was more challenging and, in fact, the network proposed by [PERSON] et al. [13] reached an accuracy of just 56% on STL. In this case, our measures allowed predicting the correctness of the output with an accuracy of about 67% and an AUROC of about 73%. Thus, the experiments suggested that the proposed confidence measures are closely related with the actual reliability of the classifier, and could be very useful where an accurate uncertainty estimation is fundamental for decision making, such as in automatic analysis of medical images.
###### Abstract  
This paper presents a novel, four-port, rectangular microstrip, inset-feed multiple-input and multiple-output (MIMO) antenna array, enhanced with metamaterials for improved gain and isolation, specifically designed for multi-operator 5G open radio access network (ORAN)-based indoor software-defined radio (SDR) applications. ORAN is an open-source interoperable framework for radio access networks (RANs), while SDR refers to a radio communication system where functions are implemented via software on a programmable platform. A 3 \(\times\) 3 metamaterial (MTM) superstrate is placed above the MIMO antenna array to improve gain and reduce the mutual coupling of MIMO. The proposed MIMO antenna operates over a 300 MHz bandwidth (3.5-3.8 GHz), enabling shared infrastructure for multiple operators. The antenna's dimensions are 75 \(\times\) 75 \(\times\) 18.2 mm\({}^{3}\). The antenna possesses a reduced mutual coupling less than \(-\)30 dB and a 3.5 dB enhancement in gain with the help of a novel 3 \(\times\) 3 MTM superstrate 15 mm above the radiating MIMO elements. A performance evaluation based on simulated results and lab measurements demonstrates the promising value of key MIMO metrics such as a low envelope correlation coefficient (ECC)! 0.002, diversity gain (DG)!10 dB, total active reflection coefficient (TARC)! -10 dB, and channel capacity loss (CCL)! 0.2 bits/sec/Hz. Real-world testing of the proposed antenna for ORAN-based sub-6 GHz indoor wireless systems demonstrates a downlink throughput of approximately 200 Mbps, uplink throughput of 80 Mbps, and transmission delays below 80 ms. Additionally, a walk test in an indoor environment with a corresponding floor plan and reference signal received power (RSRP) measurements indicates that most of the coverage area achieves RSRP values exceeding \(-\)75 dBm, confirming its suitability for indoor applications.  
channel capacity loss (CCL); diversity gain (DG); envelope correlation coefficient (ECC); metamaterial (MTM); multiple-input multiple-output (MIMO); open radio access network (ORAN); open radio unit (ORU); superstrate; total active reflection coefficient (TARC) 2025 15 7406
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 6 Conclusions  
In this paper, a lightweight 6D pose estimation network based on RGB-D images was proposed, which exhibited significantly improved pose estimation accuracy in occluded and low-texture object scenarios. The model is composed of a multiscale point cloud feature extraction module with a self-attention mechanism and a lightweight image feature extraction module. These components were validated through ablation studies on the LineMOD dataset, demonstrating their effectiveness and complementarity.  
The proposed network achieved an accuracy of 98.5% on LineMOD with only 19.49 million parameters and a real-time speed of 31.8 FPS. Comparative experiments on the LineMOD, YCB-Video, and Occlusion LineMOD datasets showed that the proposed method outperformed several state-of-the-art approaches. Specifically, it achieved a 4.2% and 0.6% improvement over DenseFusion on the LineMOD and YCB-Video datasets, respectively, and reached 63.4% accuracy on Occlusion LineMOD.  
While the method demonstrates promising results, we acknowledge that it still exhibits weaknesses in handling severely occluded and highly symmetric objects. Nevertheless, its performance on difficult cases suggests that the method also holds great potential in these areas, especially with further enhancements in feature learning and occlusion handling. In addition to planned improvements such as multi-view fusion and sensor combination, we also aim to investigate the model's potential in few- or zero-shot settings, which would further enhance its practical deployment value.  
In conclusion, the proposed method exhibits a compelling balance between accuracy and computational efficiency for 6D pose estimation and lays a solid foundation for practical deployment in resource-constrained environments such as edge devices or robotic platforms.  
Conceptualization, Z.L. and Y.G.; data curation, S.Y. and L.D.; formal analysis, S.Y.; funding acquisition, H.Z. and Q.W.; investigation, H.Z.; methodology, Z.L. and Y.G.; project administration, H.Z. and Q.W.; resources, H.Z.; software, R.G.; supervision, R.G.; validation, J.S. and J.H.; visualization, J.S.; writing--original draft, Z.L. and Y.G.; writing--review and editing, Z.L. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Henan Provincial Science and Technology Major Project (No. 221100110100), the National Natural Science Foundation of China (No. 32271993), and the Key Scientific and Technological Project of Henan Province (No. 24210211193).  
The original data presented in the study are openly available in the BOP datasetrepository at [[https://bop.felk.cvut.cz/datasets/](https://bop.felk.cvut.cz/datasets/)]([https://bop.felk.cvut.cz/datasets/](https://bop.felk.cvut.cz/datasets/)) (accessed on 31 March 2025).  
The authors thank the College of Information and Management Sciences of Henan Agricultural University and the Artificial Intelligence Research Center of Henan Agricultural University.  
The authors declare no conflicts of interest.
## 4 Results
### Bibliometric Analysis
#### 4.1.1 Q1. What Are the Spatio-Temporal Trends in TPM Research in the Era of I4.0? In the context of I4.0, as shown in Figure 2 below, research on TPM has increased gradually over time and is widespread worldwide. Between 2017 and 2025, the number of publications has increased, reflecting stronger interest in smart maintenance approaches. From 2017 to 2020, publication levels were low but consistent, ranging between two and seven articles annually. This period likely focused on early explorations and establishing foundational ideas. A clear growth phase began in 2020. The number of studies increased rapidly, from 3 in 2020 to 9 in 2021, peaking at 14 in 2023. This upward trend mirrors the wider adoption of I4.0 technologies such as predictive maintenance, smart monitoring, and IoT-based systems. Since 2021, journal publications have become more frequent than conference papers, suggesting a shift toward more in-depth and mature research outputs. A drop in the number of publications was observed in 2024 and early 2025, with only one article recorded in the first quarter of 2025. This decline is likely linked to typical publishing delays and should not be interpreted as declining interest. It may also indicate a shift toward more specialized research areas. Geographically, publication output is standardized. The next Figure 3 demonstrates that India leads with nine contributions, likely supported by national initiatives like \"Make in India\" aimed at advancing smart manufacturing [34]. The United States follows with six publications, benefiting from its strong tech ecosystem and industrial collaborations [35]. Italy and Portugal each produced five studies, likely driven by EU-funded digital innovation programs [36]. Brazil also contributed five articles, showing growing participation from emerging economies [37]. Meanwhile, countries such as Egypt, the Czech Republic, Nigeria, Poland, and Slovakia each published two studies. Although these numbers are modest, they signal rising interest, possibly tempered by challenges such as limited funding or infrastructure capacity [38; 39; 40]. Figure 2: Trend graph for publications by year. 4.1.2. Q2. What Are the Most Influential Works and Publication Sources in the Field of Digital TPM? The field of digital TPM has been shaped by a small number of highly cited papers and well-known publication venues. The most cited publications are mentioned in Table 1 below:
## 5 Conclusions  
The present study investigated CO\({}_{2}\) gas propagation and thermal characteristics in a tank of a transport ship numerically by using two commercial codes. The local variations of temperature and gas concentration behavior were examined for different crack sizes. Moreover, an effective crack area was introduced to determine the boundary conditions for CFD simulations. The following conclusions were drawn.  
1. Considering the phase change, the gas velocity exhausted from the crack was estimated by considering rapid gas expansion. According to the mass conservation principle, an effective crack area was calculated for making appropriate boundary conditions for CFD simulations. The ejected gas flow was rapidly diffused and impinged on solid surfaces. In particular, the CO\({}_{2}\) concentration changed rapidly depending on the crack size. The present study provided the evolution of a dangerous volume with a CO\({}_{2}\) concentration above 10 %, showing an increasing tendency as the crack size increased.
2. Numerical results showed that the leaked CO\({}_{2}\) gas rarely propagated toward the upper tween deck region because of the buoyancy effect. For a crack size of 30 mm, it was predicted that the entire region below the tween deck could become a dangerous volume within 300 s. Moreover, a substantial difference in dangerous volume with differing crack sizes was observed at the initial stage of leakage. The present CFD result supported the fact that, for quick detection before propagation, the gas sensors should be mounted near the bottom corners of the container of a transport ship.
3. The temperature profile from the crack to the air pocket exhibited a decreasing tendency as the crack size increased. The solid temperature of the impingement surface decreased rapidly below the design temperature when the crack size was larger than 20 mm. However, the sudden change in the wall temperature was delayed owing to the conjugate heat transfer from the air pocket.  
Conceptualization, S.H.L. and M.-S.S.; investigation, D.Y.K., C.H.J., B.J.P., and M.S.K.; methodology, D.Y.K. and C.H.J.; supervision, S.H.L.; writing of the original draft, D.Y.K.  
This research was supported by the development of human resources for design specialists in applications of functional materials (Brain Korea (BK) 21).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.2 Remote Sensing Data  
Vegetation greenness significantly influences GPP; therefore, this study incorporates indices such as NDVI, EVI (Enhanced Vegetation Index), and LAI. NDVI and EVI are derived from MODIS products (MOD13A2 Terra and MYD13A2 Aqua) with a 16-day revisit cycle and 1 km spatial resolution [29]. By combining Terra and Aqua data, we obtained composite data with an 8-day revisit cycle [30]. LAI data are sourced from the GLASS product, with a 0.05\({}^{\circ}\) spatial resolution and an 8-day temporal resolution [31]. The temporal resolution enhancement of forest greenness indicators was achieved through  
Figure 1: Distribution of forest flux tower sites across China. The map shows the locations of four flux tower stations (marked by red pentagons): QYZ (Qianyanzhou), CBS (Changbai Mountain), DHS (Dinghushan), XSBN (Xishuangbanna), overlaid on an elevation map of China. The surrounding photographs show the flux tower installations at each site.  
Cubic Spline interpolation, transforming 8-day NDVI, EVI, and LAI measurements into daily time series data [32]. This sophisticated interpolation approach effectively captures the natural cosine-like variations in forest greenness patterns, thereby ensuring enhanced data accuracy and temporal continuity for subsequent analyses.
## 2 Methodology  
The methodology followed to develop the DT-based EMS is outlined in Figure 1. The first step involves the definition of the primary objectives of the solution, along with the intrinsic characteristics of the problem. In the second phase, the key energy assets are identified, each of which needs to be individually represented by a DT. Based on the specific features of the system, the appropriate optimization layers are then defined, including the hierarchy and data flow between DT and optimization modules. In the third stage, the mathematical formulation of the DT and optimization models is determined. The final stage involves implementing the designed EMS in a real-world application, allowing the optimization of a complex energy system.  
Figure 1: Methodology for developing a DT-based energy management system, from objectives to implementation. The numbers show step sequence, dashed boxes describe each stage, and blue boxes indicate key outcomes.  
The proposed procedure can be adapted to a wide range of complex energy systems or industrial applications requiring optimization. In the subsequent sections, this methodology is applied to a specific case to demonstrate its effectiveness in a real-world scenario.
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 1 Introduction
Unlike the aforementioned works [16; 17; 18], which are devoted to analyzing the relevant sum-rate performance, this paper will investigate the average bit error rates (BERs) of the proposed relaying schemes as a function of the number of relay antennas, power splitting ratio, and energy harvesting time. The remainder of this paper is organized as follows. Section 2 introduces the system model. Sections 3 and 4 present the PS-based and TS-based multiple-antenna relaying schemes, respectively. Section 5 compares the BER performance of these relaying schemes by simulations. Finally, Section 6 concludes the paper. Notation: Bold upper-case letters denote matrices and bold lower-case letters denote column vectors. \([\cdot]_{ij}\), \((\cdot)^{\mathrm{T}}\), \((\cdot)^{\mathrm{H}}\), and \((\cdot)^{-1}\) denote the \((i,j)\)-th element, transpose, conjugate transpose, and inverse of a matrix, respectively. \(\mathrm{mod}(\cdot)\) and \(\mathrm{demod}(\cdot)\) denote the modulation and demodulation functions, respectively. \(|\cdot|\), \(\mathrm{E}[\cdot]\), and \(\oplus\) denote the absolute value, the expectation value, and the bit-wise exclusive OR operator, respectively. \(Q(\cdot)\), \(\mathrm{erfc}(\cdot)\), \(\Gamma(\cdot)\), and \({}_{2}\mathrm{F}_{1}(\cdot\cdot\cdot\cdot;\cdot\cdot)\) denote the Gaussian Q-function, complementary error function, ordinary Gamma function, Gauss hypergeometric function defined in (Equation (4.1), [22]), (p. xxxvi, [23]), (Equation (8.310.1), [23]), and (Equation (9.100), [23]), respectively. A circularly symmetric complex Gaussian random variable \(z\) with mean \(\mu\) and variance \(\sigma^{2}\) is denoted as \(z\sim CN\left(\mu,\sigma^{2}\right)\). In the case of communication over a slow-fading channel, the average BER for binary phase shift keying (BPSK) modulation is defined by (Equation (5.1), [22])  
Figure 1: System model. \[\overline{P}_{\text{e}}=\int\limits_{0}^{\infty}Q\Big{(}\sqrt{2x}\Big{)}f\gamma(x )\ dx \tag{1}\]  
where \(f\gamma(x)\) is the probability density function (PDF) of the instantaneous signal-to-noise ratio (SNR) of the received signal, \(\gamma\).
## 2 Materials and Method
### Ancillary Data  
Data on chlorophyll-a, Secchi disk depth, and total phosphorus and nitrate were obtained from national monitoring programs implemented and reported by ARPA Veneto [69]. In addition, satellite estimates of turbidity (NTU) were extracted from two areas (of eight pixels each) east and west of Sirmione from 2016 to 2019 using data from the Lakes CCI project (Figure 1) [70]. Data on bird numbers were taken from the annual census of aquatic birds for Lake Garda carried out every January [71; 72; 73; 74; 75; 76]. Herbivorous birds were dominated in 2019 by the Eurasian coot (_Fulica atra_) (13,295), mallard duck (_Anas platyrthynchos_) (1442), and red-crested pochard (_Netta rufina_) (123) [75]. Total number of piscivores and herbivores were calculated following [PERSON] et al. (2020) [77]. Data on daily lake level from the hydrometric station located at Peschiera was obtained from AIPo [78]. Tourist numbers were obtained from the province of Brescia website [79]. Climatic data were obtained from ERA5, the fifth-generation ECMWF reanalysis for the global climate and weather (hourly data on single levels) [80]. Data used for analysis included 10 m u-component of wind, 10 m v-component of wind, and surface solar radiation downwards. The wind components were used to calculate wind speed and direction which, together with fetch, allowed the prediction of wave height (see equation 15 in [PERSON], 1982 [81]). All ERA5 data documentation is available online: [82].
## 1 Introduction
### Motivation  
As previously mentioned, ACC systems, when intelligently controlled, can play a crucial role in this context. By optimizing speed control to minimize fuel consumption while ensuring safety and comfort, these systems can contribute to significant reductions in emissions. Optimization-based techniques like MPC and RL offer significant flexibility in defining a cost function, allowing for various formulations to achieve objectives such as fuel economy, car-following, safety, and comfort. Specifically, for MPC, two main approaches can be distinguished: one that explicitly incorporates fuel consumption in the objective functions, and another that attempts to indirectly minimize fuel consumption by other means.  
For example, ref. [30] incorporates a fuel consumption map in the cost function, where the fuel flow rate is obtained via linear interpolation on experimental engine data to maximize fuel economy. Similarly, ref. [31] exploits a fuel map, but a fourth-order polynomial approximation is ultimately used in the MPC cost function rather than an interpolant function. In a different approach, ref. [32] utilizes a strictly quadratic formulation of the cost function and relies on the tracking objective to maximize fuel economy. More specifically, a reference trajectory for the acceleration is defined with an exponential attenuation of its current value; thus, a smooth response for the ACC controller is favored, which in turn should minimize fuel consumption. A similar approach was developed and tested in [33], and an impressive 12-13% improvement in fuel consumption with respect to a traditional ACC implementation based on PID (proportional-integral-derivative) control was reported.  
In many ACC applications, fuel consumption is not a quadratic function of the control effort, though it is strictly monotonic. Therefore, adhering to a quadratic cost function requires significant approximations and may lead to sub-optimal performance. On the other hand, adopting a non-quadratic cost function significantly increases the complexity of the optimization problem. Thus, more sophisticated solvers and more computational power might be needed, and the optimality of the solution might not be guaranteed if the problem is non-convex.