esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 1 Introduction
In the urban rail transit ad hoc network, the network nodes need to be used for a long time and have a long-term power supply system, so there is no need to consider the loss of node energy, and the network nodes are randomly distributed around the trackside, which differs from VANET networks in that they contain many static trackside nodes. So, the existing routing algorithm cannot give full advantage to the characteristics of urban rail transit networks to improve the network communication performance. Considering the multipath routing technology, it not only enhances the link fault tolerance but also relieves network congestion, which improves the reliability of data transmission and the transmission latency and improves the QoS of urban rail communication services. Consequently, it is important to study the multipath routing algorithm of urban rail transit ad hoc networks based on low-latency and high-reliable communication. The main purpose of this article is to provide a routing algorithm suitable for urban rail transit ad hoc network scenarios, namely, the reliable low-latency multipath routing algorithm (RLLMR). First, based on the ad hoc network scenario of urban rail transit, a static configuration routing strategy is proposed to shorten the time for on-demand routing discovery. Second, in response to the issue of different communication requirements for different services in the process of urban rail transit vehicle-ground communication, a multipath routing algorithm that can adaptively adjust parallel routing based on services requirements is proposed to meet the latency requirements of different services as much as possible while saving routing resources. Finally, a local routing repair scheme based on maintenance nodes is added to enhance the reliability of the communication process by introducing a routing maintenance mechanism. This method has a faster repair speed compared to traditional backup routing repair methods and can reduce maintenance costs. In addition, the performance of RLLMR was compared with that of AODV and AOMDV, as shown in Table 1.  
The rest of this paper is organized as follows. Section 2 introduces the urban rail self-organizing network model. Section 3 discusses the multipath selection routing algorithm and the route repair scheme. Section 4 shows the simulation results. Finally, Section 5 summarizes this paper.
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 6 Managing Complications
### Breathlessness  
Respiratory symptoms are frequently observed in FD, including breathlessness, wheeze, and cough [53]. These may reflect both pulmonary and cardiac manifestations of FD. Lung disease associated with FD is likely due to sphingolipid deposition in bronchial cells and smooth-muscle hyperplasia with resultant bronchial constriction and airflow obstruction [54]. Biopsy studies have demonstrated smooth-muscle hyperplasia, as well as peribronchiolar fibrosis [55; 56]. In a study of 17 patients with FD, cardiopulmonary involvement was assessed using exercise stress, spirometry, high-resolution computedtomography (HRCT), and CMR. Self-reported pulmonary-related symptoms correlated with a lower EF and longer QRS duration on ECG, suggesting an overlap with cardiac manifestations relating to FD causing respiratory symptoms [57]. Left ventricular outflow tract obstruction (LVOTO) may cause breathlessness, and obstruction is seen in around two-thirds of patients with HCM [58], although incidence is low in FD [59]. In a recent case series on 14 symptomatic adults with Fabry disease, 6 patients developed dynamic LVOTO on exercise stress echocardiography that was due to a combination of systolic anterior motion of the mitral valve, in conjunction with relative reduction in LV cavity size. Dynamic stress echocardiography should, therefore, be considered for investigation of the breathless patients with Fabry disease, to confirm normal systolic response to exercise, assess for change in E/e\({}^{\prime}\) as a reflection of LV end-diastolic pressure, and exclude LVOTO [60].
## 2 Thermodynamics and Operational Conditions of the Proposed System  
### System Components  
Figure 1 shows a schematic of the proposed heat driven cooling system in the paper. As shown in the figure, the ORC expander was coupled with the VCC compressor by using a transmission unit. The combined system aimed to produce cooling for refrigeration cabinets by waste heat recovery from engines. The thermodynamic states of the combined organic Rankine cycle-vapour compression cycle (ORC-VCC) are shown in the Temperature-Entropy (T-S) diagrams in Figure 2. Here, a 12-cylinder Wartsila RT-flex96C engine with 68640 kW rated power was selected. The exhaust temperature and heat energy contained in the exhaust gas of the engine is presented in Figure 3. It was observed that the temperature peak appeared at 40% contract maximum continuous rating (MCR) and that heat content increased with the engine load. The exhaust gas temperature was in a range of 528.5 K to 597.9 K, and the energy contained in the gas varied from 11,860 to 35,787 kW.  
Figure 1: Schematic of proposed organic Rankine cycle-vapour compression cycle (ORC-VCC) systems for waste heat recovery.  
Figure 2: Temperature-Entropy (T-S) diagrams of both the ORC and VCC.  
The system was subject to two working conditions: (1) a belt drive was adopted, and the transmission ratio was adjusted by changing the diameter of the pulley when the transmission ratio was not equal to 1; (2) the ORC expander and the compressor shared a common shaft and the transmission ratio was set to 1.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 5 Discussion
Among these, SPHINCS+ has an advantage derived from short key length, but as mentioned earlier, the signature size with a kilobyte unit is unfortunately not suitable for V2V environments where broadcasting-based communication is the primary focus. Therefore, for PQC migration on a V2V communication environment, Crystals-Dilithium and Falcon are more suitable. In this paper, we report simulation results for Crystals-Dilithium and Falcon via V2 Verifier. Based on the results shown in Tables 4 and 5, we believe that the most important consideration for PQC migration on V2V communication is communication continuity for each remote car. In the results of Table 4, Falcon looks very inefficient compared to Crystals-Dilithium because of the high latency. On the other hand, Table 5 shows the opposite result. Falcon has a high computational load for generating NTRU lattices and deriving a lattice-based problem; therefore, the reason for the performance decrease is mainly related to the key generation process. In other words, in an environment where the subject of communication frequently changes, Crystals-Dilithium, which has a performance balance for key generation, signature generation, and signature verification, appears efficient, and in a situation where communication is maintained for a long time, the initial key generation cost is high, but Falcon, with fast signature generation and verification, looks efficient. The V2V communication environment is ultimately aimed at an environment where autonomous vehicles communicate. In an autonomous environment, the local car moves in real time, so information about the surrounding remote cars can be updated every moment. This means that the number of messages the local car receives from the BSM from one remote car is variable, which can be low or high. In a high of a high number, the local and remote cars have the same destination. Although the initial key generation cost is high, it is most desirable to use Falcon, which is most efficient for continuous broadcast situations. Of course, statistically, an investigation of the average of session hold time between one local car and a remote car should be preceded. For the simulation of Crystals-Dilithium in the reference code-based previous research, it was difficult to achieve the recommended latency limit of 5 m/s in an autonomous driving environment. In this study,we used the optimized PQC-DSA code to achieve a latency below 5 m/s at all security levels of Crystals-Dilithium and to further satisfy correctness for the WAVE protocol. Therefore, we believe that Crystals-Dilithium can also be used efficiently in a V2V communication environment.
## 2 Computational Model of External Flow Field  
### Fluid Model  
To carry out the computation for under low vacuum environment, the suitability of the continuum model should be considered. In the tube, as the tube pressure decreases,the density of the air in the tube decreases, that is, the air in the tube becomes thinner. The Knudsen number (denoted as \(Kn\)) is usually used to indicate the thinness of the air. The air flow can be divided into three areas according to the thinness of the air, namely, the slip field (0.01 < \(Kn\) < 0.1), the transition field (0.1 < \(Kn\) < 10), and the free molecular flow field (\(Kn\) > 10). When \(Kn\) < 0.01, the air flow can be considered to be in the continuous domain, in which the continuum model can be used to describe the air flow.  
The Knudsen number, \(Kn\), is defined as the ratio of the average free path of the molecule to the characteristic length of the flow, and its expression is:  
\[Kn=\frac{\lambda}{L} \tag{1}\]  
where \(\lambda\) is the average free path of the molecule and \(L\) is the characteristic length of the flow.  
The average free path of the molecule can be expressed as:  
\[\lambda=\frac{k_{B}T}{\sqrt{2}\pi d^{2}p} \tag{2}\]  
where \(k_{B}\) is the Boltzmann constant, \(k_{B}\) = 1.3805 \(\times\) 10\({}^{-23}\); \(T\) is the temperature; \(d\) is the molecular diameter; and \(p\) is the pressure.  
When the temperature is 298 K, the average free path of the molecule under the standard atmospheric pressure is \(\lambda\) = 6.11 \(\times\) 10\({}^{-8}\) m. In the present paper, the tube pressure in the low vacuum tube is 0.3 atm, and the corresponding average free path of the molecule is \(\lambda\) = 1.02 \(\times\) 10\({}^{-7}\) m. The height of the high-speed train can be taken as the characteristic length of the flow, which is usually about 4.0 m. Therefore, in the present paper, the Knudsen number is:  
\[Kn=2.55\times 10^{-8}<<0.01 \tag{3}\]  
The air flow in the low vacuum tube in the present paper is in the continuous field, and the continuous medium model can be used to describe the air flow characteristics in the low vacuum tube.
###### Abstract  
The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 \(\times\) 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices.  
agricultural robotics; real-time detection; YOLO; embedded system; greenhouse +
Footnote †: journal: _sciences_  
Article  
Embedding a Real-Time Strawberry Detection Model into a Pesticide-Spraying Mobile Robot for Greenhouse Operation
# A System to Improve Port Navigation Safety and Its Use in Italian Harbours
## 2 Materials and Methods
### Bathymetry Updating and \"Virtual Traffic Lights\"
In Figure 4 the bathymric map of Bari harbour is represented, with threshold levels equal to 8 and 9 m in Figure 4a and 9 and 10 m in Figure 4b. After this, sea level values can be added to the starting bottom depth to recalculate moment by moment the georeferenced bathymetric map and update the so-called dynamic bathymetry (variable over time). Mareographic signals can represent past measurements loaded from a dataset (to analyse bathymetry variations during past events of particular interest, e.g., vessel stranding or refloating) or real-time values transmitted by monitoring stations described in the Section 2.2 (if we are observing the real-time evolution of the bathymetry), but it is also possible to load expected sea-level values resulting from the forecasting procedure described later in the Section 2.3, in order to forecast a future bathymetric map. Figure 2: Bathymetric maps of test sites: (a) port of Livorno; (b) port of Bari; data have been provided by local Port Authorities. Figure 3: Virtual traffic lights in the port of Livorno: (**a**) thresholds equal to 10 and 11 m; (**b**) thresholds equal to 11.5 and 12.5 m.  
Then, the division of the port map into green, yellow and red areas is updated maintaining the same threshold levels (e.g., for the same vessel considered in Figure 4b, threshold levels remain constant at 9 and 10 m). Therefore, by varying sea level, an area that initially was prohibited (red), can become a warning (yellow) or allowed area (green) for that vessel, or vice versa. This represents the so-called dynamic analysis, because the real bathymetry varies over time, based on sea-level values loaded. The processing procedure and the graphical interface of the application, running under the Windows operating system, have been developed using the C# language. Figure 5 shows the block diagram of the algorithm. Figure 4: Virtual traffic lights in the port of Bari: (**a**) thresholds equal to 8 and 9 m; (**b**) thresholds equal to 9 and 10 m.
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 3 Model Construction of Engineering-Scale Jointed Coal Mass
### Calibration of Parameters for SJM-Based Joint Model  
The coal mass in the direct shear test contained only a single joint, and the laboratory-scale joint did not differ much from the engineering-scale joint. In the numerical model, the particle radius was enlarged in synchrony with the model size, and the joint roughness was consistent. Therefore, the engineering-scale direct shear test model could be directly used to match the experimental results. The direct shear test simulation model was divided into upper and lower disks, with the same aspect ratio as that of the standard specimen in the uniaxial compression test (with the aspect ratio of 2:1), and the size of the specimen was 2.5 m \(\times\) 1.25 m. The particle contact parameters of the upper and lower disks were kept the same as those in the compression test. The calibrated smooth-joint material parameters are presented in Table 4. The numerical simulation curves of the shear stress-shear displacement of the coal specimens and the calibrated shear parameters of the joint plane are illustrated in Figure 4. The calibrated friction coefficient and cohesion were 0.74 and 1.99 MPa, respectively, which differed from the experimental values by 0.05 and 0.14 MPa. The small difference proved that the simulation parameters could be used for the follow-up study.  
On the basis of the above calibrated parameters of the coal matrix and the joints, construction of the engineering-scale jointed coal mass model could be completed by embedding the joint distribution law in Section 3.1 into the model. The constructed model is shown in Figure 1, and the parameters for the coal matrix and joint model are provided in Tables 3 and 4.  
Figure 3: Loading curves of numerical model of coal samples of different sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & **NT1** & **NT2** & **NT3** & **NT4** & **NT5** \\ \hline \multirow{8}{*}{Model} & Model size (m) & 0.1 \(\times\) 0.05 & 0.5 \(\times\) 0.25 & 0.5 \(\times\) 0.25 & 2.5 \(\times\) 1.25 & 2.5 \(\times\) 1.25 \\  & Minimum particle radius (mm) & 0.30 & 1.50 & 1.50 & 7.50 & 7.50 \\  & Contact Young’s modulus (GPa) & 2.15 & 2.15 & 2.27 & 2.27 & 2.37 \\  & Ratio of normal to tangential & 2.16 & 2.16 & 2.24 & 2.24 & 2.44 \\  & stiffness (/) & 14.25 & 14.25 & 13.97 & 13.97 & 13.83 \\  & Tensile strength (MPa) & 9.76 & 9.76 & 9.56 & 9.56 & 9.47 \\  & Number of particles (/) & 8753 & 218,825 & 8753 & 218,825 & 8753 \\ \hline \multirow{3}{*}{Test results} & Strength (MPa) & 27.91 & 26.39 & 25.76 & 26.45 & 26.16 \\  & Elastic modulus (GPa) & 3.72 & 3.80 & 3.89 & 3.98 & 3.98 \\ \cline{1-1}  & Poisson’s ratio (/) & 0.28 & 0.29 & 0.29 & 0.29 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Step-by-step matching results of coal sample model parameters of different scales.
## 5 Conclusions  
With the acceleration of industrial Internet technology development, OTA technology is widely used. With the increasing complexity of industrial software, upgraded files must be reduced using a differential compression algorithm to reduce the transmission of wireless communication networks. However, existing algorithms have the characteristics of insufficient differential efficiency, considerable time consumption, and easy interruption, which increase the risk of an OTA upgrade. This study proposes a lightweight and fast differential SAIS-BsDiff algorithm. By solving the efficiency problem of suffix sorting in the BsDiff algorithm, the time consumption of the entire difference process is reduced, and the efficiency of the OTA upgrade is improved. Compared to the original BsDiff algorithm, the SAIS-BsDiff algorithm can reduce the time consumption by more than 35% through a hardware-in-the-loop simulation experiment. In the future, differential compression algorithms can be used for data cloud storage and intelligent transportation. Based on this study, we propose the use of a greedy algorithm to quickly determine the approximate matching parts of old and new files. The advantage of this method is that it avoids the memory consumption of the computing equipment caused by suffix array sorting.  
Conceptualization, X.Z., Y.W., Y.S., J.H. and L.K.; Methodology, X.Z. and Y.W.; Software, Y.W.; Formal analysis, H.Z.; Investigation, X.Z.; Resources, Y.S. and L.K.; Writing--original draft, X.Z. and Y.W.; Writing--review & editing, Y.S., H.Z., J.H. and L.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported in part by the Key Research and Development Plan of the Ministry of Science and Technology under Grant 2020 YFB1701900. This study was also partially sponsored by the NSFC Program (No.52102439) and the China Postdoctoral Science Foundation Program (No.2021M701886).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing  
[PERSON]  
1 National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China; [EMAIL_ADDRESS]; Tel.: +86-451-8641316  
actual applications [4; 5; 6; 7; 8]. Quartz-enhanced photoacoustic spectroscopy (QEPAS) is a modification of the conventional microphone-based PAS, which was first reported in 2002 [9].  
In the QEPAS technique, a quartz tuning fork (QTF) is used as an acoustic wave transducer. The QTF is a commercially available millimeter-sized piezoelectric element, which is usually used in watches, clocks, and electronic circuits to provide clock rate [10; 11; 12]. The high \(Q\)-factor (\(\sim\)100,000 in a vacuum and \(\sim\)10,000 in a standard atmosphere pressure) and narrow resonance frequency band (\(<\)1 Hz) of QTF improve the QEPAS selectivity and immunity to environmental acoustic noise [13; 14; 15; 16; 17; 18; 19]. Due to the merits of high selectivity and sensitivity, low cost, compactness, and a large dynamic range, QEPAS sensors have been widely applied in gas detection for atmospheric monitoring [20; 21; 22; 23; 24; 25; 26; 27], chemical analysis [28; 29; 30; 31; 32], biomedical diagnostics [33; 34; 35; 36], and trace gas sensing [37; 38; 39; 40; 41; 42; 43; 44]. Different QEPAS sensor architectures were developed to meet the requirements of a large number of applications.
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### 3D Reconstruction Algorithms
#### 2.1.4 Algorithms with Proposed Modifications
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Variant of 3D Reconstruction Pipeline**} \\ \cline{3-6}  & **Reconstruction Step 1** & **1** & **2** & **3** & **4** \\ \cline{3-6}  & & **(Baseline)** & **a** & **b** & **a** & **b** & **a** & **b** \\ \hline
1. & Load frames & + & + & + & + & + \\
2. & Camera initialization & + & + & + & + & + \\
3. & Head detection & – & + & + & + & + \\
4. & Image quality estimation & – & – & + & + & + \\
5. & Frame selection 2 & \(N\)-th & \(N\)-th & Best from \(N\) & Best from all \\
6. & Feature point detection 3 & n & n & h & n & h & n \\  & (Normal–n; High–h) & & & & & \\
7. & Feature point selection & – & + & + & + & + & + \\  & (remove KPs outside BBox) & & + & + & + & + & + \\
8. & Feature description & + & + & + & + & + & + \\
9. & Image matching & + & + & + & + & + & + \\
10. & Feature (descriptor) matching & + & + & + & + & + & + \\
11. & SfM (sparse reconstruction) & + & + & + & + & + & + \\ \hline \hline \end{tabular}  
* reconstruction steps correspond to the order of the generalized 3D reconstruction pipeline in Figure 1; 2 frame selection strategies: “\(N\)-th”, selects every \(N\)-th frame; “Best from \(N\)”, selects the frame with the highest quality from consecutive \(N\) frames; “Best from all”, selects a certain number of frames from the full set of frames exploiting image quality and image similarity information; 3 Describer Density preset in Meshroom’s Feature Extraction node.  
\end{table} 
Table 1: Summary of 3D reconstruction algorithms tested: baseline 3D reconstruction algorithm (1) and its variants (2a, 2b, 3a, 3b, 4a, 4b). All variants introduce head detection and discarding of feature points outside the bounding box of the head. Variants 3a, 3b, 4a, and 4b additionally utilize image quality during the frame number reduction but differ in the applied reduction strategy.