esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 5 Conclusions and Outlook  
In this work, we propose a model to enhance the accuracy of generated medical reports based on prior knowledge. We validate the proposed model experimentally, and we validate the effectiveness of our added prior knowledge on the Open-I and MIMIC-CXR datasets. The experimental results show that our model achieves relatively excellent performance in the indicators of natural language generation and clinical efficacy. Ablation experiments show that our model can learn visual features and text features better after adding prior knowledge, so it can generate medical reports more accurately. In addition, the establishment of our knowledge graph is built according to the dataset, which does not need additional experts to build, so it can be more convenient to apply to other datasets.  
In our work, we have not considered the influence of location information on the generation of radiology reports, which is important. In the future, we will explore the impact of including location information in disease classification on improving the accuracy of generated reports. Next, we will explore how to improve the accuracy of our classifier, which is related to the accuracy of our automated reports. Specifically, we will pre-train our  
Figure 3: Examples of three visual reports selected from the Open-I dataset. The same color emphasizes the same description of the chest X-ray image. Additional findings of our model for images are highlighted by underlines.  
image encoder and text encoder using a public dataset and then try to incorporate location information into the classifier.  
Conceptualization, D.Z., A.R., and Q.L.; methodology, A.R., Q.L., J.L., Y.M., and D.Z.; investigation, A.R. and Y.M.; visualization, H.W., A.R., and J.L.; project administration, D.Z. and Q.L.; writing--original draft preparation, A.R., D.Z., J.L., and H.W.; writing--review and editing, Q.L., D.Z., A.R., J.L., Y.M., and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (i) Natural Science Foundation China (NSFC) under Grant No. 61402397, 61263043, 61562093 and 61663046; (ii) Open Foundation of Key Laboratory in Media Convergence of Yunnan Province under Grant No. 220225201. (iii) Open Foundation of Key Laboratory in Software Engineering of Yunnan Province: 2020 SE304. (iv) Practical innovation project of Yunnan University, Project No. 2021z34, No. 2021y128 and 2021y129.  
This research was supported by the Yunnan Provincial Key Laboratory of Software Engineering, the Kunming Provincial Key Laboratory of Data Science and Intelligent Computing and the Key Laboratory in Media Convergence of Yunnan Province.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Engine Setup  
A naturally aspirated, single-cylinder, four-stroke, direct injection diesel engine with an eddy current dynamometer was used in this study. Technical specifications for the tested engine are listed in Table 4. The experimental layout of the test engine is displayed in Figure 1. Engine tests were carried out in full load conditions in triplicates, and the engine speed varied from 1000 to 2400 RPM with an interval of 200 rpm. The exhaust emission parameters (smoke opacity, NOx, HC, and CO) were analyzed using an AVL exhaust gas analyzer (Model: DiCom 4000, AVL Ditest, Austria). In Table 5, the technical specifications of the used gas analyzer (AVL exhaust gas analyzer) are listed. First, the neat diesel fuel was utilized to bring the engine to a stable operating condition. Once this condition was reached, the biodiesel blended fuel was used for investigation. The engine was run for a few minutes, and then the residual diesel was drained. Data acquisition was performed after the drainage of residual diesel. This practice was repeated for each biodiesel blend. After one test was completed for the biodiesel blend, the engine was operated via diesel. This practice helped to drain the residual biodiesel blend used in the previous test from the fuel line.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **Unit** & **ASTM Standards** & **Diesel** & **MOB10** & **MOB20** & **MOB100** \\ \hline Density at 15 \({}^{\circ}\)C & kg/m\({}^{3}\) & D4052 & 856.9 & 860.1 & 861.7 & 877.6 \\ Density at 40 \({}^{\circ}\)C & kg/m\({}^{3}\) & D4052 & 828.4 & 831.2 & 834.6 & 860.7 \\ Kinematic viscosity at 40 \({}^{\circ}\)C & mm\({}^{2}\)/s & D445 & 3.2525 & 3.5572 & 3.6772 & 4.8338 \\ Viscosity index & – & – & 91 & 101.5 & 112.1 & 185.2 \\ Oxidation stability & h & EN ISO 14112 & 35 & 33.5 & 32.7 & 26.4 \\ Cetane index & – & D4737 & 48.9 & 48.3 & 49.6 & 58.5 \\ Flashpoint & \({}^{\circ}\)C & D93 & 68.7 & 80.1 & 82.9 & 151.2 \\ Cloud point & \({}^{\circ}\)C & D2500 & 8 & 7 & 8 & 18 \\ Pour point & \({}^{\circ}\)C & D97 & 0 & 3 & 6 & 18 \\ Cold filter plugging point & \({}^{\circ}\)C & D6371 & 5 & 6 & 6 & 18 \\ Calorific value & MJ/kg & D240 & 45.86 & 44.18 & 43.61 & 39.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physicochemical characteristics of _Moringa oleifera_ biodiesel and their blends.
## 3 Design Method of MCRWPT System Based on Simulation
### Ferrite Shielding Structure Design
The special-shaped ferrite shielding, Case4, has the largest coupling coefficient and PTE. It can be seen from Figure 6 that after the introduction of the magnetic shielding structure, the magnetic induction intensity under the winding structure is smaller than the magnetic induction intensity of the side of the coil with a gap. Although the inertial sensor is installed under the winding structure, circuit components are inside the winding structure, as shown in the prototype of the uniaxial rotation module in Section 4. Therefore, the configuration of two red measuring lines located 1 mm on both sides of the ferrite shielding structure with a length of 18 mm is shown in Figure 5.  
The two maximum magnitudes of the B field simulated along two red measurement lines are shown in Figure 7 to compare the magnetic flux density differences numerically. Taking the Line-in measurement line as an example, the magnetic flux density on the measurement line increases first and then decreases from bottom to top. Analysis of Figure 6 shows a maximum magnetic flux density value near the gap between the primary and secondary windings. The maximum value of the magnetic flux density obtained by the simulation based on Case1 is 1154.1 uT, while the maximum value of the magnetic flux density obtained based on Case4 is 309.5 uT. The maximum magnetic flux density on the Line-in and Line-out measurement lines decreases gradually from Case1 to Case4, as shown in Figure 7. The smaller the maximum magnetic flux density on the measurement line, the better the magnetic shielding performance of the corresponding winding structure under the condition of the same output power. Compared with the magnetic shielding structures of Case1 and Case2, the performance of the magnetic shielding structures of Case3 and Case4 is better, and the maximum magnitudes of the B field are close. Moreover, the magnetic shielding performance of Case4 with the special-shaped ferrite shielding is better than that of Case3 for the same distance between two windings. Compared with Case1, the magnetic leakage of Case4 decreased by 76.44%. Case4 has the best magnetic shielding performance, the highest efficiency, and the largest coupling coefficient under the condition with the same output power. The following will take Case4 as an example for designing circuit and excitation frequency.
# The Long-Range Biomimetic Covert Communication Method Mimicking Large Whale
###### Abstract  
Short-range biomimetic covert communications have been developed using dolphin whistles for underwater acoustic covert communications. Due to a channel characteristics difference by range, the conventional short-range methods cannot be directly applied to long-range communications. To enable long-range biomimicking communication, overcoming the large multipath delay and a high degree of mimic (DoM) in the low-frequency band is required. This paper proposes a novel biomimetic communication method that preserves a low bit-error rate (BER) with a large DoM in the low-frequency band. For the transmission, the proposed method utilizes the time-dependent frequency change of the whistle, and its receiver obtains additional SNR gain from the multipath delay. Computer simulations and practical ocean experiments were executed to demonstrate that the BER performance of the proposed method is better than the conventional methods. For the DoM assessment, the novel machine learning-based method was utilized, and the result shows that the whistles generated by the proposed method were recognized as the actual whistle of the right humpback whale.  
underwater communication; biomimicking communication; covert communication +
Footnote †: journal: Sensors
## 4 DT Use Cases and Application in Healthcare
### Capacity Planning  
During COVID-19, the demand for hospitals surged, highlighting capacity and resource issues such as the availability of ventilators, intensive care units, and healthcare personnel. DTs of hospitals or departments can offer a comprehensive resource management and planning view, enabling more targeted activities based on needs. In DTs, machine learning and artificial intelligence techniques facilitate predictive solutions to optimize processes and resource utilization. Insights gained from these predictions can be tested on the hospital or department's DT, with the resulting scenarios being examined by authorized experts. As a result, appropriate methods can be applied in the hospital or department, leading to more efficient resource management processes.  
Siemens Healthineers used DT solutions to enhance hospital procedures in Mater Private Hospitals' radiology department in Ireland, responding to high patient demand, extended waiting times, and other issues. A DT of the radiology department was populated with actual data, various predictions were generated, and multiple scenarios were tested. Informed by the insights from the DT, adjustments to the planning and organization of the hospital's radiology department were identified. DT solutions are anticipated for numerous hospital systems and resource management applications, like the one in this study. They could be beneficial for addressing healthcare resource shortages during events such as the COVID-19 pandemic [57].
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Energy Efficiency  
Energy efficiency is a critical concern in edge-cloud computing due to the resource constraints of edge nodes and the high power consumption of cloud data centers. Unlike cloud environments, where centralized power management can be optimized at scale, edge nodes operate in distributed, often energy-limited environments, making fine-grained energy control essential. Smart-city applications, including real-time surveillance, autonomous transportation, and industrial automation, require continuous data processing at the edge, leading to high energy demands that must be optimized without sacrificing performance [249; 250; 251].  
One of the main challenges is energy-aware task scheduling and workload balancing across cloud, edge, and end devices. DVFS has been widely adopted to adjust processing power based on workload demands, but its effectiveness is limited in latency-sensitive applications. A more adaptive solution involves RL-based power management, where an AI model continuously learns optimal CPU/GPU scaling strategies based on real-time workload variations. However, training such models remains computationally expensive, and their deployment at the edge requires lightweight inference models to minimize processing overhead [252; 253; 254].  
Another issue is energy-efficient communication between edge nodes and cloud servers. Frequent data transmissions over wireless networks consume significant power, particularly in mobile environments. Emerging solutions leverage adaptive edge caching and data compression techniques to reduce redundant transmissions, thereby lowering energy consumption. Additionally, energy-aware network slicing can optimize resource allocation at the network level, ensuring that only the required computing resources are activated while deactivating idle components [255; 256; 257].  
Furthermore, heterogeneous energy consumption in multi-tier architectures presents optimization challenges. While cloud servers can leverage liquid cooling and advanced thermal management systems, edge devices must rely on low-power hardware accelerators such as ARM-based processors, Field Programmable Gate Arrays (FPGAs), and neuromorphic chips to achieve energy efficiency. However, the integration of specialized hardware into existing edge frameworks remains a challenge due to compatibility issues and software-hardware co-design constraints [258; 259].  
Dynamic energy-aware workload scheduling should be implemented to optimize power consumption without compromising performance. Future research should explore low-power computing techniques, including neuromorphic processing and green energy integration, to enhance sustainability in edge-cloud environments. Additionally, predictive task migration mechanisms can be employed to minimize energy-intensive operations [260; 261; 262; 263].
## 3 Results
### Sebum Level
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Sebum Level (\(\mu\)g/cm\({}^{2}\))**} \\ \cline{2-7}
**Time** & \multicolumn{2}{c}{**Unwashed**} & \multicolumn{2}{c}{**Washed Only**} & \multicolumn{4}{c}{**Washed**} \\ \cline{2-7}
**(h)** & **(Nontreatment)** & **(Control Treatment)** & \multicolumn{2}{c}{**Toner Base**} & \multicolumn{2}{c}{**Emplica Toner**} \\ \cline{2-7}  & **Forehead** & **Forehead** & **Forehead** & **Cheek** & **Forehead** & **Cheek** \\
**(F\({}_{0}\))** & **(F\({}_{\rm C}\))** & **(F\({}_{\rm B}\))** & **(F\({}_{\rm B}\))** & **(F\({}_{\rm B}\))** & **(F\({}_{\rm E}\))** & **(F\({}_{\rm E}\))** \\ \hline \(-1\) & \(66.8\pm 1.41\)\({}^{\rm\,A\,A}\) & \(66.8\pm 1.37\)\({}^{\rm\,Ba\,}\) & \(66.4\pm 1.28\)\({}^{\rm\,Pa}\) & \(55.9\pm 1.47\)\({}^{\rm\,K\,b}\) & \(66.5\pm 1.36\)\({}^{\rm\,Pa}\) & \(56.3\pm 1.44\)\({}^{\rm\,Vb}\) \\
0 & \(67.3\pm 1.40\)\({}^{\rm\,A\,C}\) & \(2.5\pm 0.21\)\({}^{\rm\,Cd}\) & \(2.5\pm 0.22\)\({}^{\rm\,Cd}\) & \(2.0\pm 0.19\)\({}^{\rm\,Cd}\) & \(2.4\pm 0.21\)\({}^{\rm\,Q}\) & \(1.9\pm 0.19\)\({}^{\rm\,Wd}\) \\
1 & \(67.8\pm 1.46\)\({}^{\rm\,A\,C}\) & \(34.1\pm 0.80\)\({}^{\rm\,D\,f}\) & \(30.9\pm 0.98\)\({}^{\rm\,H\,g}\) & \(21.4\pm 1.19\)\({}^{\rm\,Mh}\) & \(26.9\pm 0.81\)\({}^{\rm\,R\,g}\) & \(18.3\pm 1.05\)\({}^{\rm\,Xh}\) \\
2 & \(68.1\pm 1.40\)\({}^{\rm\,A\,A}\) & \(61.4\pm 2.07\)\({}^{\rm\,E\,i}\) & \(53.4\pm 1.98\)\({}^{\rm\,I\,i}\) & \(40.3\pm 1.92\)\({}^{\rm\,N,Al}\) & \(45.1\pm 1.82\)\({}^{\rm\,S\,k}\) & \(33.9\pm 1.58\)\({}^{\rm\,Yl}\) \\
3 & \(68.8\pm 1.20\)\({}^{\rm\,A\,m}\) & \(66.5\pm 1.64\)\({}^{\rm\,B,mn}\) & \(61.4\pm 1.74\)\({}^{\rm\,Lm}\) & \(50.4\pm 1.88\)\({}^{\rm\,Q,pq}\) & \(54.6\pm 1.84\)\({}^{\rm\,Tot}\) & \(44.4\pm 1.76\)\({}^{\rm\,Z,p}\) \\
4 & \(69.9\pm 1.35\)\({}^{\rm\,A\,n}\) & \(67.8\pm 1.73\)\({}^{\rm\,B,n}\) & \(64.1\pm 1.63\)\({}^{\rm\,F,stu}\) & \(53.5\pm 1.78\)\({}^{\rm\,K,vw}\) & \(58.2\pm 1.64\)\({}^{\rm\,U,uw}\) & \(48.1\pm 1.57\)\({}^{\rm\,D,v}\) \\ \hline \multicolumn{7}{c}{Different capital letters (\({}^{\rm A\,-Z,\,O}\)) in the same column indicate statistically significant differences (\(p<0.05\)) when compared within the same column. Different lower case letters (\({}^{\rm A\,-w}\)) in the same row indicate statistically significant differences (\(p<0.05\)) when compared within the same row.} \\ \end{tabular}
\end{table} 
Table 2: The sebum level on the forehead and cheek skin (F\({}_{\rm C}\), F\({}_{\rm B}\), F\({}_{\rm E}\), C\({}_{\rm B}\), C\({}_{\rm E}\)) at \(-1\), 0, 1, 2, 3, and 4 h of 30 volunteers. Figure 4: Sebum level on forehead and cheek skins of 30 volunteers at different periods. Different capital letters (\({}^{\rm A\,-Z,\,O}\)) indicate statistically significant differences (\(p<0.05\)) in the same treatment among different periods. Different lower case letters (\({}^{\rm a\,-w}\)) indicate statistically significant differences (\(p<0.05\)) in the same period among different treatments.
###### Abstract  
The application of carbon nanotubes to enhance bitumen properties is relevant due to the need to increase the durability of asphalt concrete pavements and reduce maintenance costs. Key areas requiring further study include the processes during ultrasonic dispersion, the selection of the optimal medium, and the stability of the resulting dispersions. This study examines dispersions containing multi-walled carbon nanotubes (MWCNTs) Taunit M (from 5\(\cdot\)10\({}^{-4}\) to 5\(\cdot\)10\({}^{-2}\)%) and various hydrocarbon plasticizers. For the first time, the change in Gibbs free energy, enthalpy (interaction energy), and mixing and disordering entropy was calculated based on experimental data (surface tension, average cubic diameter of MWCNTs, molecular mass, etc.). The data were compared with the storage stability of polymer-modified binders (PMBs). It was found that mixing entropy plays a key role in forming thermodynamically stable dispersions, while the contribution of disordering entropy is minimal. High dispersion enthalpy of MWCNTs can reduce dispersion stability at high concentrations despite entropy growth. Systems with selective purification extracts showed the best PMB stability despite thermodynamic instability. The property changes after 3 days at 180 \({}^{\circ}\)C were no more than 5%. This suggests structural changes from component interactions are critical, highlighting the need for an integrated approach considering both thermodynamic and macroscopic properties.  
thermodynamic compatibility; Gibbs free energy change; enthalpy; entropy; multi-walled carbon nanotubes; hydrocarbon plasticizer; polymer modified binder; storage stability Article
## 3. Freestanding Position-Based Routing Protocol  
As previously explained, the constraints imposed by highway channels must be considered when developing a routing strategy for highway VANETs, this in order to achieve a satisfactory routing performance when deploying VANETs in this kind of scenarios. Therefore, the different modules of the routing protocol (e.g., the beaconing period, the next-hop selection algorithm, the location discovery service, and location prospection) should be designed with the aim to overcome the drawbacks imposed by the highway scenarios. The next subsections introduce the different modules that comprise the Freestanding Position Based Routing (FPBR) protocol proposed in this paper. These modules were specifically developed for highway VANETs. For the development of FPBR it was assumed that the nodes are equipped with digital maps, a global positioning system (GPS), an IEEE 802.11a radio transceiver or an IEEE 802.11p radio transceiver with a single traffic class enabled. It is considered that all the nodes in the VANET use the same radio transceiver (either IEEE 802.11a or IEEE 802.11p). Additionally, a distributed strategy for traffic density estimation like the proposed in [56,57] is also assumed.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 4 Results
### Map Projection Accuracy  
Figure 7 shows the map projections based on the satellite attitude \(\hat{\mathbf{M}}^{\prime}\) estimated by the proposed scheme. Because the map projection procedure considers the elevation, the projections are orthogonally corrected. The miss-registration seen in Figure 2 was successfully corrected.  
Figure 6: Histograms showing the differences in angles between \(\mathbf{V}_{\text{C}}\) and \(\hat{\mathbf{M}}^{\prime}\mathbf{V}_{\text{E}}\) estimated from all pairs (both correct and incorrect) extracted from the images in (**a**) Figure 4a, (**b**) Figure 4b, and (**c**) Figure 4c. The satellite attitude \(\hat{\mathbf{M}}^{\prime}\) was obtained in the proposed scheme.  
The accuracy of the map projection was assessed by the registration errors, defined as the differences between the detected feature points. More precisely, the latitude-longitude coordinate of a base map feature extracted from the OLI image was compared with that of the matched feature point in the projected satellite VIS image using the estimated rotation matrix. As the VIS and OLI images have different GSDs, to find the corresponding points from a pair of VIS and OLI images involves difficulty by template matching, because a scale adjustment is required. In contrast, feature  
Figure 7: Map projection of UNIFORM-1 images onto latitude–longitude coordinates using the satellite attitude determined by the proposed scheme for (**a**) the less cloudy (Kanto, Japan), (**b**) the moderately cloudy (Yosemite, US), and (**c**) the many cloud-patch (Kyushu, Japan) cases as shown in Figure 4. Background base images are from Landsat-8 OLI. Contrasts in the VIS images are enhanced.  
detection with SURF outputs the locations to sub-pixel accuracy, regardless of scale differences. The mean displacements of feature points, which indicate the absolute geometric accuracy of registration, was calculated in the east-west and north-south directions of the three images in Figure 7. The results, together with the root mean square errors (RMSEs) for indicating relative miss-registration errors, are presented in Table 4. As the GSD of the VIS images is 85 m, the registration error of our proposed scheme is approximately two pixels or less, even in VIS images with large cloud coverage. This error is sufficiently low for applying conventional map-registration techniques such as ground control points (GCP) matching or template matching between projected and base-map images, which would further improve the accuracy of map registration (e.g., Landsat-8, [32]). Here, feature pairs were eliminated if their distances exceeded 1 km, sufficiently larger than the expected magnitude of the miss-registration error (200 m, measured from the 0.02\({}^{\circ}\) accuracy of the satellite attitude and the 628-km observation altitude under the nadir condition).