esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Distribution Characteristics of TCWV in China
#### 3.1.2 Seasonal Spatial Distribution Characteristics of TCWV  
In this paper, the average value of TCWV from March to May is selected to investigate the distribution of TCWV in spring in China. Similarly, June to August, September to November, and December to next February were selected as the seasons of summer, autumn, and winter, respectively. The distribution of the TCWV in each season is shown in Figure 2. It can be seen from Figure 2 that the TCWV is the lowest in winter and the highest in summer, which is more obvious in South, East and Central China. In winter, the coastal provinces of Yunnan, Guangxi, Guangdong, Fujian and Hainan have higher TCWV values due to the influence of tropical ocean water vapor. The TCWV in most other areas is low, and its distribution is higher in low latitudes than in high latitudes, higher in plains than in plateaus, and higher in coastal areas than inland areas. In spring, the TCWV increases in most parts of China, among which the increase is more obvious in southern China and its coastal areas, and is somewhat faster in the central China and the coastal East China than in the same latitude regions. Entering summer, due to the influence of East Asian summer monsoons, the maximum value of TCWV in China is located in the southeast, and the northwest is relatively dry, while the TCWV shows a southwest-northeast trend. Specifically, starting from May, with the establishment and development of the East Asian summer monsoon, the TCWV begins to increase over the southeast of the Tibetan Plateau and southern China in China, and the maximum value of TCWV keeps advancing to the northwest with time; in June, the maximum value of TCWV pushes to the position of 30\({}^{\circ}\) N. In autumn, with the weakening of monsoon intensity, the high-value areas of TCWV in China starts to retreat to southward, and the TCWV over southeast China and the Tibetan Plateau gradually decreases. The distribution of TCWV is very similar to that in spring, except for southern China where the TCWV is lower than that in spring, and all other areas are higher than that in spring. Generally speaking, the spatial distribution of TCWV in China is not uniform, with more in southeast China, less in northwest China, more in coastal areas, less in inland areas, more in mountainous areas and less in plain areas. The TCWV in the same area is generally greater in the summer and less in the winter.  
Figure 1: Spatial distribution of TCWV during 2002–2022 in China. (**a**) Longitude distribution. (**b**) Latitude distribution. (**c**) Longitude and latitude distribution.
## 3 Algorithms for Managing Complex Traffic Scenarios and Violations
### Types of Complex Traffic Scenarios and Violations
#### 3.1.2 Aggressive Driving and Sudden Lane Changes  
Aggressive driving behaviors, including sudden lane changes, tailgating, and speeding, pose significant challenges to autonomous systems. [PERSON] et al. (2018) reviewed deep learning-based vehicle behavior prediction methods, emphasizing the importance of anticipating aggressive maneuvers to maintain safety in autonomous driving applications. [PERSON] et al. (2018) proposed a lane change intention prediction model based on long short-term memory (LSTM) networks, which demonstrated the potential to anticipate lane changes up to 3 s in advance with accuracy of over 90%. However, the challenge lies not just in predicting these behaviors but in responding to them safely.
## 3 System Design Overview
### Intellectual Property Protection  
SPARK-IT offers both protection of intellectual property and efficient matchmaking between innovators and experts, ensuring the protection of intellectual property and building trust among participants. Innovators often fear that sharing their ideas and business plans with potential mentors could lead to unauthorized use or theft of their intellectual property. In addition to struggling to find suitable mentors, innovators must also ensure their intellectual property is safeguarded and secure access to funding opportunities. The innovator-expert collaboration protocol described in Section 3.1 is a structured process. It enables innovators to get feedback from experts while protecting intellectual property through NDAs and secure communication channels. The protocol aims to build trust, ensure accountability, and enhance the platform's credibility and attractiveness.  
User information, including the NDA and expert feedback, is stored in a custom encrypted format that leverages blockchain and decentralized storage, as outlined in Section 3.2. Proposals and associated data are encrypted and stored on IPFS, with proofs stored on the blockchain. This ensures that only authorized parties can access the data, providing trust, traceability, and verifiable transparency. The main motivation for this functionality is to ensure the security and privacy of intellectual property and sensitive data, fostering a trusted environment for collaboration. Innovators need assurance that their ideas and business plans are protected from unauthorized access and potential theft. By providing robust security measures, SPARK-IT fosters a trusted environment where innovators feel safe to share their intellectual property. Ensuring data privacy and transparency through blockchain technology builds trust among users, encouraging more participation from innovators and experts. Experts and mentors are more likely to participate in a platform that guarantees the protection of their contributions and maintains a high standard of data privacy.  
The primary advantage of utilizing blockchain for intellectual property lies in eliminating reliance on credit from third-party intermediaries. This enables more individuals to store data on distributed blockchains, ensuring that the information remains immutable. Blockchain-based intellectual property solutions offer key benefits, including traceability, tamper resistance, and resource efficiency.
# A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks
## 2 Background  
In the past few years, data traffic in mobile networks has seen an explosive growth. The Long Term Evolution (LTE) network architecture has been adopted to meet this evolution and nowadays the LTE architecture has been widely adopted by mobile service providers around the world [9; 10; 11; 12; 13].  
Figure 2 illustrates the LTE architecture proposed by the 3 rd Generation Partnership Project (3 GPP). It is composed of the LTE core network called the evolved packet core (EPC) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN). EPC includes the packet data network gateway (P-GW), the serving gateway (S-GW) and so on.  
Although it significantly improves network performance, LTE creates some new problems. Because all traffic goes through the P-GW in EPC, the P-GW becomes a bottleneck to extend the mobile network, and because each device in this architecture uses specialized hardware and software, it greatly increases the time and equipment costs of the operators when introducing new network functionalities into LTE.  
In order to address these challenges, many recent researches have proposed and discussed new mobile network architectures for 5G based on Software-Defined Networking [9, 10, 11, 12, 13].  
For example, MobileFlow, illustrated in Figure 3, is proposed as a SDN-based mobile network architecture in [10]. The key components in the MobileFlow architecture are the MobileFlow controller and the MobileFlow forwarding engine. Similar to the SDN architecture, MobileFlow separates mobile network control from all user plane elements. MFFEs are interconnected by the IP network and are fully software driven. MobileFlow uses the OpenFlow protocol for communication between controllers and switches and support network layer tunneling. This makes MobileFlow much simpler than traditional EPS elements.  
In [9], the authors propose a SDN-based architecture of mobile networks named Software-Defined Wireless Networks (SDWN). In the SDWN architecture, programmability is widely  
Figure 3: The MobileFlow network architecture.  
Figure 2: LTE network architecture.  
introduced into Radio Access Networks (RANs) and the core transport. An API is enabled in order to allow third parties to control the network.  
In [13], the authors propose another SDN-based mobile network architecture named Cellular SDN (CSDN). In the CSDN architecture, radio access networks allow the orchestration of resources using the SDN and NFV techniques. Additionally, these techniques are leveraged for service orchestration.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 7 Application Control Policy
### Local Policy: Threshold-Based Policy  
In the threshold-based scaling policy, the OM monitors the CPU utilization of the operator replicas. Let us denote by \(U_{r}\) the utilization of replica \(r\), which measures the fraction of CPU time used by \(r\). When the replica utilization exceeds the target utilization level \(U_{\text{s-out}}\in[0,1]\), the OM proposes to add a new replica. Conversely, the OM proposes to remove one of the \(n\) running replicas (i.e., a scale-in operation), when the average utilization of the remaining replicas would not exceed a fraction of the target utilization, i.e., when \(\sum_{r=1}^{n}U_{r}/(n-1)<cU_{\text{s-out}}\), \(c\in(0,1)\). This avoids system oscillations with the OM executing a scale-out operation just after a scale-in one.
###### Abstract  
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area.  
Streamflow forecasting; AutoML; rainfall data; Amazon; water-resource management Article  
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## 4 Experimental Section
### Experimental Design  
A simulator is used to examine the proposed cluster formation techniques in dynamic and obstructed environments typical of industrial environments. Selecting a set of parameters of interest for further studies, statistical design of experiments is also used to analyze the sensitivity of these parameters to changes and fluctuations.  
In this paper, we take into consideration the following ambient and medium related factors as well as a selection of design factors for further investigations into their impact on the WSN clustering techniques: number of Sink Nodes, number of Anchor Nodes, signal blockage level, and cluster formation techniques. Table 3 summarizes these factors and the levels that they can take. The choice and level of the factors used for these experiments were determined through experimental work with a 433 MHz MICA2 based Cricket platform, which is comprised of six Cricket motes (at least one of which was mobile). This work is reported in [25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Levels** & **Description** \\ \hline No. Sink Nodes (A) & A1 & 4 \\  & A2 & 7 \\ \hline No. Anchor Nodes (B) & B1 & 400 \\  & B2 & 676 \\ \hline Signal blockage \% (D) & D1 & 0\% \\  & D2 & 5\% \\ \hline Cluster formation technique (E) & E1 & _Ad hoc_ \\  & E2 & Dynamic \\  & E3 & Static \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental factors and their levels.  
To study the adaptability of the cluster formation techniques, we limit the effect of ambient conditions to different levels of signal blockage. In a previous study, we tested the effect of other ambient conditions such as varying temperature on the wireless sensor networks, and concluded that air humidity, pressure and temperature have measurable but negligible impact on the wireless sensor networks [8].  
Given the factors introduced in Table 3, we can devise a statistical scheme for the experiments as follows. We have four factors in total, three of which have two levels and one has three levels. With a full factorial design and no blocking (one block), we will have the following design [26]:  
\[2^{3}\times 3^{1}\]  
This design has 24 base runs; with only four replications for every treatment and two additional replications for every run of E2 and E3 levels, the total number of experiments is 160.  
To reduce the effects of external factors--either in the form of noise or disturbance--randomization of the running order of the treatments are widely suggested [26]. In this study, we randomize running order of the resultant treatments via Minitab 16 software. The design table is constructed in Minitab to produce a standard run order; however, to reduce the potential influence of any external factors, the run order is randomized to result in a randomized run order to be used to execute the experiments accordingly.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 1 Introduction
The spectrum handoff decision chooses on a suitable handoff class between reactive and proactive with regards to (w.r.t.) the overall service time of a CRU. The overall service time of a CRU comprises of sensing, processing, waiting and transmission times. Spectrum handoff takes place when the PU appears on its bandwidth which is temporarily engaged by the CRU. Apart from maintaining its transmission CRU must have to sense for a new free channel. When a free channel is sensed, the CRU can continue its transmission on the recently vacant spectrum, packet failure can occur throughout this handoff method [2; 18]. Moreover, A preemptive resume priority (PRP) M/M/1 queuing model is used to manage the transmission and incorporated to differentiate the spectrum usage behavior of PUs and SUs. The research aims to develop a PUTPSH algorithm with various handoff modules that permit a CRU to detect patterns of PU's traffic and select a reactive or proactive scheme accordingly. On the arrival of PU, the CRU intelligently decides whether to do handoff or to wait for the on-going channel depending upon the overall service time (in both cases). A simulation which included PUTPSH was setup in Matlab to validate and show the performance of the presented approach by comparing the results with some of the existing reactive, proactive, and hybrid schemes in terms of the overall service time, the number of handoffs and the channel utilization. This paper is organized as follows: the literature review is presented in Section 2. The PUTPOSH approach is described in Section 3. It comprises of three sections (a) the presented approach, (b) the queuing model, and (c) the timing diagram of CRU transmission. Section 4 describes the simulation setup and some of the results. Finally, the conclusion is given in Section 5.
## 7 Discussion  
Theoretical analysis and experimental simulations confirm that the CCFD scheme can significantly enhance inter-satellite ranging accuracy, with its technical advantages manifested in two key aspects: Firstly, regarding observation precision, the CCFD scheme doubles the inter-satellite measurement duration, allowing the system to achieve narrower  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ranging System** & \(\mathbf{C/N_{0}/dBHz}\) & \(\mathbf{1\sigma}\) **Ranging Accuracy/cm** \\ \hline CCFD & 61.6 & 1.9 \\  & 69.3 & 0.8 \\ \hline TDHD & 61.6 & 3.9 \\  & 69.3 & 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of pseudo-code ranging accuracy based on CCFD and TDHD schemes.  
Figure 15: Comparison of ranging accuracy between CCFD and TDHD modes at a carrier-to-noise ratio of 69.3 dBHz.  
code loop equivalent noise bandwidth at the cost of moderately reduced loop convergence speed, thereby markedly improving pseudo-range observation accuracy (with improvement exceeding 50%). Secondly, in terms of timestamp reckoning, the CCFD system can effectively eliminate the \"spatial reckoning\" errors inherent in the conventional TDHD system caused by the difference between transmission and reckoning timestamps. After ephemeris and almanac corrections, residual errors can be maintained at negligible levels.  
As a critical metric for BDS-3 ISL construction, ranging accuracy directly impacts system performance. Given that both orbit determination and clock offset calibration rely on pseudo-range observations, accuracy improvement is essential for achieving sub-meter precise orbit determination and nanosecond-level time synchronization, which will significantly enhance the navigation system's service quality and constellation autonomy.  
It is noteworthy that CCFD technology has applications extending far beyond BDS-3 ISLs. It demonstrates considerable potential in large-scale heterogeneous constellation networking, satellite formation flying, space station TT&C, and new technology demonstration satellites. While this study focuses on optimizing pseudo-code ranging methods, CCFD's unique long-duration measurement capability creates opportunities for implementing higher-accuracy techniques (such as carrier-phase-smoothed pseudo-range and pure carrier-phase ranging), which will constitute our primary research direction. Furthermore, CCFD implementation not only enhances measurement performance but also improves inter-satellite data transmission efficiency and routing reliability; these derivative advantages warrant further investigation.
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 7 Conclusions  
This paper presented a comparative analysis of the three U-Net-based models in the liver vessel segmentation task. The analysis results clearly show that the leader in the considered task is a MultiRes U-Net, which provides the most accurate results in terms of the Dice coefficient.  
When the 3D setups used for liver vessel segmentation are considered, the best performer is the full 3D approach, which provided the highest Dice scores for two of the three considered U-Net-based models. However, in our experiments, the slab-based setup used with the MultiRes U-Net performed the best.  
The accurate vessel segmentations obtained in our work has several medical applications. For example, we could investigate the automatic and personalized Couinaud zoning of the liver. This process requires precise extraction of the first branches of hepatic vasculatures (portal vein and hepatic veins), which are very well reconstructed, as the visual inspections have shown in this article.  
The results of liver vessel segmentation using the MultiRes U-Net are significant and promising. However, they could probably still be improved. The possible ways of improvement include adaptive thresholding applied for postprocessing of the predicted vessel probability maps. Additionally, instead of segmenting liver volume and liver vessels separately, as we did in this study, one model could be used for three-class segmentation to detect liver and vessels jointly. These issues will be considered in our future works.  
Conceptualization, A.V. and A.F.; Data curation, A.A. and A.K.; Formal analysis, A.A., A.K., A.V. and A.F.; Funding acquisition, A.V. and A.F.; Investigation, A.A., A.K., P.C., S.F. and M.-A.L.; Methodology, A.A., P.C. and S.F.; Project administration, A.V. and A.F.; Resources, A.A. and A.K.; Software, A.A., A.K., P.C. and S.F.; Supervision, A.V. and A.F.; Validation, A.A., A.K., P.C. and S.F.; Visualization, A.V.; Writing--original draft, M.-A.L., A.V. and A.F.; Writing--review & editing, A.V. and A.F. All authors have read and agreed to the published version of the manuscript.  
This work was first financially supported by the \"PHC Polonium\" program of the French Ministry of Foreign Affairs and the Ministry of Higher Education and Research and the Polish National Agency for Academic Exchange (NAWA) with the Campus France Code 44833 NE and NAMA code PPN/BFR/2019/1/00006/U/00001. This work was also funded by the French Agence Nationale de la Recherche (R-Vessel-X, grant ANR-18-CE45-0018).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Results
### Differential Pressure Inside the Building
In the simulation results shown in Figures 16 and 17 for scenarios S1 and S2, the values recorded after door opening indicate that although pressure is partially maintained in the upper zone, the lower level no longer meets the required minimum threshold, partially compromising the effectiveness of the smoke control system in the lower section. A comparative analysis of the three ventilation scenarios (S0, S1, and S2) highlights the critical influence of the pressurization strategy on aerodynamic behavior in the stairwell under fire conditions. In scenario S0, the absence of active pressurization leads to the development of pronounced negative differential pressure due to thermal buoyancy, which promotes uncontrolled smoke infiltration and completely compromises the evacuation routes. Scenario S1, with unidirectional top-down pressurization, ensures the most stable  
Figure 17: Differential pressure distribution in the sectional plane Y = 5.75 m for scenario S2 at different time moments. pressure regime and effectively maintains a downward airflow, preventing smoke ingress into the stairwell. Scenario S2, although characterized by globally positive pressure, suffers from local imbalances caused by the interaction between opposing jets and fire-induced flow, reducing the protection of the stairwell, particularly in the lower and middle regions. A critical aspect identified is the transition from differential pressure control to airflow velocity control upon door opening toward the fire compartment. In such cases, effective protection is governed not by the maintenance of static pressure but by the direction and magnitude of airflow velocity at the opening.