esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model and Problem Statement  
The system model for V2X technology selection is described in this section. Consider two V2X nodes, referred as On-Board Units (OBUs), and named as OBU1 and OBU2, as in Figure 3. There is a direct V2V communication link between OBU1 and OBU2. Also, both OBUs have a V2I link with a 5G gNB. Following assumptions are considered in this regard:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Case** & **Reliability** & **Max. Latency** & **Max. Range** \\ \hline Vehicle Platooning & 99.99\% & 10 ms & 350 m \\ Advanced Driving & 99.999\% & 3 ms & 700 m \\ Extended Sensors & 99.999\% & 3 ms & 1000 m \\ Remote Driving & 99.999\% & 5 ms & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: V2X use case requirements.  
With two short-range and one long-range V2X technology at hand, the problem lies in the real-time selection of the most suitable single/multiple technology(ies) for sending the C-ITS messages. This technology selection should be such that the Key Performance Indicators (KPIs) are maximized. Let \(t_{generation}\) be the timestamp added to the CAM by the sending V2X node. Similarly, \(t_{reception}\) is the current timestamp of the V2X node where CAM is received. The one-way end-to-end CAM latency (\(L_{X}\)) is given as:  
\[L_{X}=t_{reception}-t_{generation}. \tag{1}\]  
where \(X\) represents any one of the three technologies or the hybrid approach. Let \(N_{DSRC}\), \(N_{\text{PCS}}\), \(N_{\text{5G}}\) and \(N_{Hybrid}\) be the total number of CAMs received via DSRC, C-V2X PC5, 5G or hybrid technology, respectively, during a decision window on a V2X node. The decision window is a time sliding window during which CAMs are received and their statistics are observed. Similarly, \(T_{DSRC}\), \(T_{\text{PCS}}\), \(T_{\text{5G}}\) and \(T_{Hybrid}\) represent the total number of CAMs sent via respective technologies. Packet Delivery Rate (PDR) via a particular technology or the hybrid approach, represented as (\(PDR_{X}\)), is defined as:  
\[PDR_{X}=\frac{N_{X}}{T_{X}}*100. \tag{2}\]  
Similarly, CAMs are considered reliable if they are received within the latency threshold of a V2X application. CAM transmission reliability for a particular technology or the hybrid approach, represented as (\(REL_{X}\)), is defined as:  
\[REL_{X}=\frac{N_{X}\text{ within }L_{thr}}{N_{X}}*100, \tag{3}\]  
where \(L_{thr}\) is the latency performance threshold of the V2X application. This threshold can vary depending on the QoS constraints of the V2X use cases.  
Figure 3: System model for technology selection with two multi-technology enabled V2X nodes.  
Rather than statically using short-range for safety related and long-range for non-safety related V2X communication, this work aims to formulate an intelligent hybrid V2X technology selection algorithm that optimizes latency, PDR and reliability.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 3 Modern Internet Transport Protocols
### Congestion Control Algorithms
#### 3.3.1 Cubic  
TCP Cubic congestion control grows its transmission window (_cwnd_) according to a cubic profile instead of the linear profile in TCP New Reno [23, 25]. Its core idea is to fill the pipe very quickly to increase utilization in high BDP links but to prefer stability over utilization when operating near the congestion point. This means it gradually increments  
Figure 4: The protocol stack layering for HTTP/2 and HTTP/3.  
cwnd as it moves toward the critical operating point that caused the last packet loss (concave region of the cubic function) and then increases cwnd more aggressively as it moves away from the critical operating point (convex region). This serves to maintain stability while improving network utilization.  
The algorithm operates in three phases. The first phase is the slow start where the transmission rate is increased exponentially as in standard New Reno or as in other slow start algorithms such as HyStart until the cwnd reaches the slow start threshold (_ssthresh_). Then, it will move into the congestion avoidance phase which will increment cwnd every acknowledgment received according to a cubic function of the time elapsed since the last congestion event. When a packet loss is detected, the algorithm moves to the multiplicative decrease phase which decreases cwnd and ssthresh by the multiplicative decrease factor [23,25].  
It is worth noting that the cubic function is disabled when operating in a TCP-friendly environment such as small BDP networks, where the standard congestion control performs better. Thus, the Cubic algorithm ensures that the throughput in these networks achieves at least the same throughput as in the standard New Reno congestion control [23,25].
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Ship Arrivals and Departures
#### 4.4.8 Known Uses  
The ports of Los Angeles and Long Beach in the USA use this process for ship arrivals and departures. Most ports worldwide, such as the port of Shanghai and the port of Singapore, also use this process for ship arrivals and departures.  
Additionally, Port Everglades and the Port of Miami use AIS for vessel tracking [53]. AIS tracking websites (e.g., Marine Traffic) operate over 2000 AIS stations in over 165 countries across the globe.
# Cancer Treatment: An Overview of Pulsed Electric Field Utilization and Generation
## 9 Pulse Generators  
Pulse Generators (PG) aim to generate high-voltage (HV) repetitive pulses in different shapes, widths, repetition frequencies, and plateau voltages for delivery to loads similar to biological cells and tissues, as well as for insulation material testing. As illustrated in Figure 10, energy is harvested and stored in passive elements such as inductors (for magnetic fields) or capacitors (for electric fields) over a relatively long period of time compared to the pulse duration [68]. The process then involves releasing the stored energy using switching devices in a very small fraction of a second in the form of HV or current, indicating high power and low energy, using fast switching devices. The delivered energy is in the form of pulses, which reduces the energy delivered to the cell medium and enhances energy efficiency [69].  
There are various applications that utilize pulsed power technology, for instance, food sterilization [70; 71], military and defence applications, energy, material processing, and medical treatment [52]. The development of PG has led to the growth of interdisciplinary sciences such as bio-electrics. Low-voltage long pulses are used to increase cell permeability of RE to facilitate drug uptake as a tumour treatment in electrochemotherapy [27], or can be used to suppress cancer growth and enhance immune system response to detect and attack tumour cells. First, electrical energy is harvested from a source (i.e., steam generators, wind, and photovoltaics). The collected energy is stored in fast-response passive elements, i.e., inductors and capacitors, as these can provide the energy in zero time compared to energy generation sources.  
PG topologies can be categorized into two main categories based on their operation methodology, namely, classical PGs and power electronics-based PGs [29; 64]. Figure 11 illustrates the main categories and inherited features.
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 3 Analysis of the Aerodynamic Interaction between the Forward and Aft Blades
### The AB Flow Characteristic Influenced by FB  
The baseline AB was simulated in single-blade and tandem configurations, and its behavior, influenced by FB, was analyzed. It should be noted that the inlet boundary conditions of the AB single-blade configuration were extracted from the min-plane in the tandem configuration. The AB flow status in the tandem configuration can reappear in the single-blade simulation by using this method, except for the gap flow and the diffusion of the FB wake. The Mach number and airflow angle in mid-plane, corresponding to the FB incidence, are shown in Table 3.  
Compared to the single-blade configuration, the AB isentropic Mach number distributions in Figure 10 reveal two significant characteristics in the tandem configuration. Firstly, the acceleration of the gap flow in the tandem blade induces a low-pressure region near the leading edge of the AB SS, which leads to a tendency for an increase of AB local incidence (Figure 11). Secondly, the FB wake blocks up the AB passage, especially for a large positive-incidence condition, which will change the aerodynamic load and diffusion process of AB. The AB isentropic Mach number increased to about 0.1 in the tandem configuration. In addition, as shown in Figures 12 and 13, the local aerodynamic load near the AB trailing edge decreases due to the blockage effect of the FB wake; consequently, the separation near the AB suction side is restrained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(i\) (\({}^{\circ}\)) & \(Ma_{21}\) & \(\beta_{21}\) (\({}^{\circ}\)) \\ \hline \(-\)5 & 0.705 & 43.863 \\ \(-\)4 & 0.659 & 43.852 \\ \(-\)3 & 0.630 & 43.911 \\ \(-\)2 & 0.607 & 43.970 \\ \(-\)1 & 0.585 & 44.081 \\ \(0\) & 0.566 & 44.234 \\ \(+\)1 & 0.549 & 44.373 \\ \(+\)2 & 0.533 & 44.605 \\ \(+\)3 & 0.519 & 44.850 \\ \(+\)4 & 0.509 & 45.157 \\ \(+\)5 & 0.503 & 45.577 \\ \(+\)6 & 0.501 & 45.936 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The aft blade (AB) inlet conditions.  
Figure 9: Comparison of static pressure contours in single-blade and tandem configurations; incidence \(-1^{\circ}\), inflow Mach number 0.8. (**a**) FB single-blade configuration; (**b**) tandem configuration.  
Figure 11: Tandem blade gap flow detail.  
Figure 12: Comparison of Mach contours in single-blade and tandem configurations. FB incidence \(-1^{\circ}\); FB inflow Mach number 0.8. (**a**) AB single-blade configuration; (**b**) tandem configuration.  
Figure 10: Comparison of the AB isentropic Mach number in single-blade and tandem configurations. (**a**) Incidence \(=-5^{\circ}\); (**b**) incidence \(=-1^{\circ}\); (**c**) incidence \(=+5^{\circ}\).
## 2 Materials and Methods  
### Location, Treatments and Olive Leaf Sampling  
The experiment was set up at an orchard in Sikovo, Zadar County, Croatia. Trees in full maturity were selected within two olive cultivars, Leccino (L) and Istarska Bjelica (IB). Standard agricultural practices according to Integrated Pest Management (IPM) were implemented, including soil fertiliser application in the form of 1 kg NPK (7:20.28) per tree in autumn (2020) and 1 kg of KAN per tree in spring (2021) to ensure optimal growth conditions. The following foliar treatments were conducted until runoff, at 15, 30, and 45 days after anthesis, with each application of approx. 5 L solution per olive tree:  
- Control treatment (C): water  
- Si1 treatment: water and Silitec (Kimitec Agro(r)) at a concentration of 4.25 mL per litre of water (0.55 g Si/L)  
- Si2 treatment: water and Silitec at a concentration of 8.5 mL per litre of water (1.1 g Si/L)  
- Si3 treatment: water and Silitec at a concentration of 17 mL per litre of water (2.2 g Si/L).  
At harvest time, during October of 2021, leaves were collected from both cultivars from the middle portion of the current season shoots as described in our previous research [25].  
All samples were transferred to the laboratory and rinsed with tap water, a 1% acetic acid solution with deionised water, and, finally, two times with deionised water. Plant material was then air dried until constant mass and milled.
## 3 Results
### Training, Testing, and Evaluation
Figure 13: Close-up samples from the orthomosaic with red outlines of the yellow rust areas, classified using the unequalized DeepLab v3 + ResNet34 (**a**), unequalized DeepLab v3 + ResNet50 (**b**), equalized DeepLab v3 + ResNet34 (**c**), and equalized UnetClassifier + ResNet34 (**d**) models. Figure 13 shows a close-up area from the orthomosaic, with the classified yellow rust areas marked with red outlines. It can be seen that all models were able to classify correctly most of the areas which are obviously under the influence of yellow rust disease. However, the UNet-based model also identified other areas, which are also yellowish, but not that obvious. These areas might be in an early stage of infection (Figure 13d). Another such example is shown in Figure 14, where very similar results can be observed. The UNet-based model classified a significantly higher percentage of the area as infected by yellow rust. From the histogram equalized basemap, it can be seen that these areas are yellowish, which again might be an indication of the early stage of the disease (Figure 14d). The performed analysis allows us to conclude that the UNet-based model, used on the histogram equalized images, has the best performance. This also indicates that training accuracy is the key metric to be considered when choosing the optimal model.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications  
Telecommunications, a heavily regulated sector with high-performance expectations, is still in the early stages of realizing use cases for blockchain interoperability. One critical benefit of blockchain and its interoperability needs is its ability to make decentralized decisions. Below are the predominant applications in the research domain that require decentralized decision-making processes.
## 4 Discussion
Table 5 compares the advantages and disadvantages of the OOA-VBO, traditional FDM, and MCFA-AM methods to highlight the basic differences between them. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Sample Type & & & & & & & & & & \\ \hline Control & 0.82 (sp) & 0.82 (sp) & \(56\pm 1\) & 1.06 & N.A. & 120 & 62.60 & 3\% & S (4), C (1) & N/A \\ CFRP/OOA-VBO & 0.82 (sp) & 0.82 (sp) & \(55\pm 1\) & 1.37 & 1.0 & 120 & 70.61 (*) & 4\% & T (5) & +12.8\% \\ \hline CFRP/No-pressure-3 DP & 0 (sp) & N.A. & \(53\pm 1\) & 23.94 & N.A. & N.A. & 53.99 & 6\% & S (4), S&T (1) & -13.7\% \\ \hline CFRP/MCFA-AM & 0.78 (rp) & N.A. & \(53\pm 1\) & 1.36 & N.A. & 45 & 62.83 & 3\% & S (4), C (1) & +0.37\% \\ \hline ZT-CFRP/MCFA-AM & 0.78 (rp) & N.A. & \(53\pm 1\) & 3.87 & 1.0 & 45 & 69.73 (*) & 1\% & T (4), S (1) & +11.34\% \\ \hline \hline \end{tabular} Note: T = fiber-tension failure, C = fiber-compression failure, S = interlaminar/intralamin shear failure; (*) indicates the ILSS for shear failure could be higher; (sp) indicates the pressure/vacuum is steady-state pressure; (rp) indicates the pressure/vacuum is rolling pressure.  
\end{table} 
Table 4: ILSS values for all different kinds of sample, along with their processing-pressure settings, FVFs, void contents, processing times, and z-threading-CNF concentrations in the matrix. Figure 23: Parameters for achieving improved ILSS. Based on Table 4 and the discussion above, the ZT-CFRP/MCFA-AM samples clearly stood out from the rest of the sample types. According to a simple comparison of the ZT-CFRP/MCFA-AM samples with the benchmark of the control CFRP/OOA-VBO samples, the ZT-CFRP/MCFA-AM case had an improvement of 11.34% in the ILSS mean, a 2% reduction in the COV of the ILSS (i.e., it was more reliable and consistent), a 2.81% increase in the void content (this void increase is not desirable, but the ILSS performance was not affected, thanks to the ZT-CFRP material's mechanically-interlocked microarchitecture), prevented the interlaminar- and intralaminar-shear-failure mode, increased the composite parts' production speed to 2.67 times, and reduced the costs of the labor, tools, and consumables. Furthermore, the CFRP/MCFA-AM provides a more effective approach to using materials and consumables, and it is potentially a more sustainable and greener composite-manufacturing process, although future studies on this topic are required.
# Fault Investigation of Circulation Pumps to Detect Impeller Clogging
## 3 Results and Analysis
### Motor Current Signature Analysis--Impeller Clogging
#### 3.3.3 Discussion  
It is shown that the faults have almost no influence on the component \(f_{BPF}\). This component is not suitable for detecting the fault impeller clogging. The same result is obtained with a similar application in [15]. A decrease of the amplitude at \(f_{BPF}\) caused by impeller clogging, as described in [14], cannot be confirmed here. Whether \(f_{BPF}\) in this application is suitable to detect the fault cavitation, see [16], is part of future work.  
Based on [23], the hypothesis is that \(f_{r,pump}\) can be influenced by the fault impeller clogging. Although, in [23] only the faults misalignment and unbalance are mentioned. This hypothesis is confirmed by the results. Especially with half-sided clogging, with three and four of seven clogged channels, there is an increase in amplitudes at the frequency \(f_{r,pump}\). Smaller faults show differences to the healthy variant. This is especially true for higher speeds, in this case greater or equal 1800 rpm. That the fault of clogging of the impeller causes an increase in the component \(f_{r,pump}\) is new compared to the state of the art. Whether this is the same for other types of pump units is part of future work. In particular, it should be examined whether the components of the circulation pump, particularly the plain bearings used, have an influence on this result. In addition, there is an increase in amplitude at the frequencies 791.9 Hz and at 875.3 Hz, which are sidebands of the 5 th harmonics (\((5\pm 1/p)f_{s}\)). This increase cannot be explained by Equation (1) or (2) and could argue for an adaptation of Equation (2) for the fault of clogging. It is also necessary to investigate how to distinguish between bearing faults and impeller clogging, as they affect the same frequency in the MCSA [17].
## 3 Modeling the GNSS Observations with the Proposed HFM
### Constructing Tomography Observation Equations Using Integral Signals  
To reconstruct the three-dimensional water vapor field, the tomographic area is divided into a number of uniformly distributed voxels over a GNSS network. Generally, the WVD within a voxel, assumed to be constant for a tomographic epoch, is estimated as one parameter. GNSS signals, from the GNSS sites to the top or side boundary of the tomographic region, provide the distance traveled in each voxel. Accordingly, Equation (1) can be discretized to a linear equation with the following form:  
\[SWV_{j}=\sum_{i=1}^{n_{j}}d_{ij}\cdot x_{i}. \tag{18}\]  
Note that the SWD of GNSS rays was converted into SWV using Equation (3) for the tomographic system. \(d_{ij}\) denotes the distance traveled by the \(j\)th ray in the \(i\)th voxel, whose WVD is represented by \(x_{i}\). \(n\) is the total number of voxels intersected by the ray \(j\).  
For integral signals, passing from the top boundary of the tomography area, the observation equation can be written in matrix notation:  
\[SWV_{integral}=A_{integral}\cdot X, \tag{19}\]  
where \(SWV_{integral}\) is the observation vector of integral signals and \(A_{integral}\). represents the observation matrix, including the distance traveled in each voxel. \(X\) denotes the unknown vector for the three-dimensional tomographic model.  
Figure 6: A comparison of the flow chart of the traditional tomography model (**a**) and the improved model (**b**).
# Study on the Hydrodynamic Performance of Typical Underwater Bionic Foils with Spanwise Flexibility
## 5 Conclusions  
In this paper, the hydrodynamic mechanism of bionic foils is studied via numerical simulations. During the simulations, two typical bionic foils are chosen as research objects. Specifically, the NACA foil represents the general form of locomotive organs and the crescent-shaped foil is derived from the caudal fins of thunniform swimmers. Additionally, the self-propulsion model of the thunniform bio-inspired model is adopted to verify the numerical model and solution approach and to analyze the hydrodynamic mechanism of the thunniform mode. The numerical results show that vortex rings exist in the wake of the bionic foils or thunniform bio-inspired model. The ranks of vortices can induce a jet stream and thrust forces can be obtained in return. In addition, spanwise flexibility is considered in the simulations and the results indicate that spanwise flexibility can influence the shedding of vortex rings, further influence the arrangements of the vortex rings and eventually lead to a change of the propulsion performance. Furthermore, the thrust force is enhanced significantly when the flexibility phase difference is positive. Additionally, the NACA foil can achieve both higher mean thrust force and higher propulsive efficiency when the flexibility phase difference is between \(10^{\circ}\) and \(20^{\circ}\). When the flexibility phase difference is positive, the crescent-shaped foil can achieve higher thrust force and maintain relatively high propulsive efficiency. To verify the reliability of the numerical results, an experimental verification platform is designed and built. The experimental results compare well with the numerical results, which indicates that the numerical results are reliable and the analysis of the paper is reasonable.  
The research has been financially supported by the National Natural Science Foundation of China (No. 50905040) and State Key Laboratory of Robotics and System (HIT No. SKLRS200801C). All authors discussed the contents of the manuscript and contributed to its preparation. [PERSON] contributed to the research idea and the framework of this study. [PERSON] and [PERSON] contributed to the research plan and the result analysis.  
The authors declare no conflict of interest.  
Figure 17: Comparison of experimental results and numerical results: (**a**) NACA foil; (**b**) crescent-shaped foil.