esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Mobile Networks Support for the Sectors in Focus
The fundamental issue lies within the 3 GPP definition of the basic service requirements for rural macro scenarios ([83], clause 7.1), as the maximum user-experienced data rate is 50 Mbps for the DL and 25 Mbps for the UL (traffic capacities that are equal to 1 Gbps/km\({}^{2}\) for the DL and 0.5 Gbps/km\({}^{2}\) for the UL). For comparison, the urban macro scenario ensures 200\(\times\) higher data rates and 100\(\times\) better traffic capacities for both the UL and DL. Consequently, according to the current 3 GPP standardization, some of the AFBW use cases cannot be implemented due to capacity impairments. The improvements promised by 6 GS are expected to be more than adequate in terms of the data rate, latency, reliability, and network capacity in both urban and rural environments. It has to be noted, however, that 6 GS can contribute notably to several AFBW use cases via introduced mechanisms and features, including, in particular:  
* _Network omnipresence_--Coverage extensions achieved through integration with NTN will provide massive benefits to use cases that involve large amounts of spatially distributed sensors (e.g., UC6, UC10). The exploitation of satellite systems will enable the building of AFBW systems with lower overheads regarding infrastructural costs, contributing to the profitability of both MNOs and service providers. Although the 5 GS roadmap gradually includes NTN feature integration (Release 16--satellite access in 5G; Release 17--IoT/NR over NTN and satellite components in the 5G architecture; Release 18--IoT/NR NTN enhancements and IoT NTN management architecture/mechanisms [147, 148, 149]), it is 6 GS where terrestrial and non-terrestrial access will be unified [133]. * _Positioning with an accuracy of 1 cm in 3D_--The accuracy assumed by 5 GS (30 cm precision with a 1 s latency [83]) is still far from sufficient in the most demanding AFBW cases. The built-in 6 GS location mechanisms eliminate this issue by providing better performance, thus allowing for backup measurements (e.g., for security purposes) or resignation from the RTK solutions. Moreover, embedding location capabilities into the terminal allows for better power management and the extension of battery lifetime (network-side localization). * _Flexible service deployment_--The support for deployment over heterogeneous infrastructure will contribute to service availability and resilience, which is especially important in remote edge environments. In the context of mobile network support for the AFBW sectors, the application layer support offered by edge cloud computing platforms such as ETSI MEC cannot be neglected (e.g., UC3:S1, UC4, UC13, and UC14). However, the standardization of their integration with 5 GS is not yet finished, raising multiple questions regarding functional overlap, scalability, and redundant complexity [128].
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 3 Finite Element Method
### Algebraic Formulation  
Let us now denote with \(\mathbf{\phi}_{i},\ i=1,\ldots,N_{u}\) a basis of \(V_{h}\), and with \(\psi_{i},\ i=1,\ldots,N_{p}\) a basis for \(Q_{h}\). The unknown functions \(\mathbf{u}_{h}^{(k+1)}\) and \(p_{h}^{(k+1)}\) in (7) can be written as a linear combination of these basis functions, as  
\[\mathbf{u}_{h}^{(k+1)}=\sum_{i=1}^{N_{u}}\mathbf{\phi}_{i}u_{i},\qquad p_{h}^{(k+1) }=\sum_{i=1}^{N_{p}}\psi_{i}p_{i}.\]  
At each iteration \(k+1\) of the Picard method, a linear system has to be solved, which takes the following form  
\[\begin{bmatrix}\
u A+N&B^{T}\\ B&0\end{bmatrix}\begin{bmatrix}\mathbf{u}\\ \mathbf{p}\end{bmatrix}=\begin{bmatrix}\mathbf{f}\\ \mathbf{0}\end{bmatrix}. \tag{8}\]  
where \(\mathbf{u}\) and \(\mathbf{p}\) contain the unknowns \(u_{j}\) and \(p_{j}\), i.e., the values of velocity and pressure of the approximated solution on the nodes of the grid. Linear systems with this particular block structure are usually called saddle point systems.  
In the following, \((\mathbf{x}:\mathbf{y})\) represents the element-wise product of two vectors \(\mathbf{x}\) and \(\mathbf{y}\), while \((\mathbf{x}\cdot\mathbf{y})\) represents the Euclidian scalar product. The matrix \(A\) is the discrete vector Laplacian  
\[A=(a_{ij}),\qquad a_{ij}=\int_{\Omega}\
abla\mathbf{\phi}_{i}:\
abla\mathbf{\phi}_{j}, \qquad i,j=1,\ldots,N_{u},\]  
the matrix \(B\) is called the divergence matrix and is defined as  
\[B=(b_{kj}),\qquad b_{kj}=-\int_{\Omega}\psi_{k}\
abla\cdot\mathbf{\phi}_{j},\qquad j =1,\ldots,N_{u},\quad k=1,\ldots,N_{p}.\]  
The matrix \(N\) is the discrete vector convection matrix, which depends on the current estimate of the velocity \(\mathbf{u}_{h}^{(k)}\) and has the following definition  
\[N=(n_{ij}),\qquad n_{ij}=\int_{\Omega}\left(\mathbf{u}_{h}^{(k)}\cdot\
abla \mathbf{\phi}_{j}\right)\cdot\mathbf{\phi}_{i},\qquad i,j=1,\ldots,N_{u}.\]  
The \((1,1)\) block of (8) will be referred to as matrix \(F=\
u A+N\).  
**Notation.** In the sequel, we will denote as \(n\equiv N_{u}\) the size of the \((1,1)\) block in (8), while \(m\equiv N_{p}\) will indicate the number of rows of \(B\).  
We will now define some other matrices that will be useful later: the velocity-mass matrix \(\hat{Q}\in\mathbb{R}^{n\times n}\) and the (pressure-)mass matrix \(Q\in\mathbb{R}^{m\times m}\) as  
\[\hat{Q}=(\hat{q}_{ij}),\qquad\hat{q}_{ij}=\int_{\Omega}\mathbf{\phi}_ {i}\cdot\mathbf{\phi}_{j}\,d\Omega \tag{9}\] \[Q=(q_{ij}),\qquad q_{ij}=\int_{\Omega}\psi_{i}\psi_{j}\,d\Omega. \tag{10}\]  
They are both square, symmetric, and positive definite. From the point of view of operators, the mass matrix corresponds to a discrete identity operator.  
Figure 1: Linear and quadratic triangular elements, used in the Taylor-Hood approximation.
# Novel Reliable and Energy-Efficient Routing Protocols for Wireless Sensor Networks
## 3 The Model
Besides that, the proposed algorithms reduce the consumed energy in overhearing reception; the node receives just from the relay node specified by BS deterministically, which makes the algorithms beacon-less because the nodes don't need to exchange routing information. In the proposed protocols, aside from the necessity of determining the optimal energy consumption values that maximizes the residual energy of the path, it is also necessary to guarantee that the packets arrive at the BS with a given reliability \((1-\varepsilon)\).
# The Construction of a Digital Agricultural GIS Application Suite
## 2 Design Considerations and Methods
### Implementation Method
#### 2.2.3 Microservices
In the realm of digital agriculture, users are primarily concerned with the spatial distribution of crop plots and crop growth conditions, emphasizing the terminal expression of thematic applications. The Spring Cloud framework [48], a prevalent microservice framework, boasts a robust ecosystem. It organically integrates the functionalities of various microservice components, enhancing the development efficiency of the microservice architecture. Consequently, this paper adopts the Spring Cloud framework as the implementation technology for the microservice architecture of the digital agricultural GIS application suite. This paper employs containerized deployment for the digital agricultural GIS application suite's microservices. Containers represent a technology for rapidly packaging and running applications on cloud servers, providing an environment for the independent execution of geographic models and algorithm programs in digital agriculture. Containers enable the deployment of multiple independent and isolated applications on a host server. Currently, Docker [49] serves as a concrete implementation of container technology, characterized by high utilization, resource isolation, and enhanced security. Docker's containerized deployment aligns seamlessly with the microservice architecture, offering capabilities for independent deployment, elastic scaling, and resource scheduling for the digital agricultural GIS application suite's microservice architecture. To achieve an automated container orchestration, this paper utilizes the Kubernetes [49] open-source framework to handle the automatic deployment and orchestration of containers, enabling seamless replications and automatic layouts of containers, as well as the cross-node deployment of clusters, supporting the elastic scaling of containers and facilitating the continuous integration of services for the digital agricultural GIS application suite. The continuous integration of microservices is depicted in Figure 4.  
Figure 3: The microservice architecture designed for the digital agricultural GIS application.
## 3 Routing in Satellite Networks
### Routing Protocols in LEOs
#### 3.3.5 Dynamic Detection Routing Algorithm (DDRA)
In light of the uneven distribution of the world population and the continuous movement of the satellites, an imbalanced traffic load occurs on the satellites. The aim of this investigation is to produce a remedy by using the ant colony optimization algorithm, which has a distributed routing structure, to solve the imbalanced traffic problem in LEO networks. To improve the performance and reduce the complexity of an ant colony optimization routing algorithm with window reduction (ACORA-WR), they also used shrinking windows to restrict the scope that the algorithm will search. They showed the LEO satellite network as a graph, as in Figure 5, and represented it with \(G=(V,E)\). \(V\) represents each satellite or node, and \(E\) represents the connections (ISLs). Here, \(N_{i,j}\) depicts the logical address of the satellite, and \(i\) and \(j\) represent the orbit number and the satellite number in its orbit, respectively. They compared the efficiency of the introduced algorithm with LBRA-CP, SPR, and LCRA by modeling the iridium-like system in the NS2 simulation environment. In the simulation results, the proposed model performed well with regard to data delivery rate, average delay, throughput, and transmission overhead [119]. In 2022, a study of routing in large-scale low Earth orbit (LEO) satellite networks was conducted. The authors' goal concerned how to provide minimum latency routing in time-varying topologies where the number and position of satellites are continuously evolving. For this, they focused on stochastic geometry-based approaches, which are more suitable and powerful for dynamic large-scale satellite constellations. This is the inaugural investigation of satellite routing utilizing stochastic geometry. They obtained output results close to the minimum delay in real scenarios. The assessment of this article's reliability is limited to a particular algorithm and does not offer an analytical formulation for the probability of disruption, and only latency performance is considered [120].
## 3 Establishment of Ship Emission Calculation Model
### Determination of Ship Main and Auxiliary Engine Power  
The engines on ships are divided into the main diesel engine that provides power and the auxiliary diesel engine that is used to generate electricity. Because the rated power information of the main and auxiliary engines is missing from the AIS ship data published on the Internet, in recent years, some researchers have obtained the main engine power of the ships from the Lloyd's Register database. The Lloyd's Register database counts global ship information. Considering that there are some differences in the main engine power of ships on the Yangtze River trunk line in my country compared with other countries, this study uses the ship MMSI provided in the AIS information as a key factor to find and obtain the main engine of the ship in the National Maritime Traffic Safety Management Information Service Platform (AIS Information Service Platform). It records the power, the ship design speed, shipload, and other information. However, a small number of ships have not recorded the main engine power in the AIS information service platform. Some researchers have fitted the main engine power of the ship by combining the basic dimensions of the ship, the maximum design speed or the deadweight, and other data. For example, [PERSON] et al. [3] proposed a fitting function between the main engine calibration power, the ship's design speed, and the ship's deadweight in the calculation of marine exhaust emissions in the vicinity of ports in Liaoning Province. The main urban area of Chongqing City in this study area belongs to the Three Gorges Reservoir area. For this reason, this paper refers to the captain classification in the \"Main Scale Series of Standard Ship Types for Transport Ships in the Chuanjiang and Three Gorges Reservoir Areas\"(2016 Revised Edition) and selects the recommended power value of the main engine according to the ship type and captain.  
Ship auxiliary engine power is seriously missing in the ship static database. At present, most researchers calculate the rated power of the auxiliary engine through the ratio of the rated power of the main and auxiliary engines according to the ship type. The California Air Quality Commission obtained the power ratio of main and auxiliary engines of different types of ships by investigating the power of main and auxiliary engines of a large number of ships [25]. After comprehensive analysis and comparison, the ratio of the rated power of the auxiliary engine to the rated power of the main engine is comprehensively determined according to the characteristics of the power ratio of the main and auxiliary engines of the ship in the field research area and the actual ship measurement experiment process of the ship's shaft power. The power ratio of the main and auxiliary engines of various types of ships is shown in Table 1 below.
# Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
## 3 Proposed Empirical Models
### Approximation Using Polynomials  
The first model proposes a piece-wise fitting of both the storage and loss moduli using polynomials. As a first step, the two moduli curves (storage and loss) are divided piecewise into sections that will be described by different polynomials. Typically, both the storage and loss moduli curves include regions that can be described by linear functions, as shown in Figure 4a. These linear functions are estimated first using least squares fitting of all data points between the start and finish of each straight line. Linear regions are  
Figure 3: A typical complex modulus of a tyre. DMA material testing was commissioned by the authors for a tread rubber compound.  
typically connected via higher-order polynomials--see Figure 4b. The requirement for any non-linear section is continuity up to--and including--the first derivative with the preceding and succeeding curves. In this context, greatest flexibility is offered by fourth-order polynomials whereby continuity at the start and finish points offers four equations for four parameters. A fifth equation is obtained by requesting that the function passes from a selected midpoint between the start and finish points. A pair of typical first and fourth-order equations used in the process is shown in Equation (9), below. The superscript \(i\) indicates the number of the piece-wise curve, with counting starting from the left of the frequency range. This method offers robustness against global optimisation methods and very easy tuning.  
\[\begin{cases}y=\alpha^{i}x+b^{i}\\ y=c_{4}^{i}x^{4}+c_{3}^{i}x^{3}+c_{2}^{i}x^{2}+c_{1}^{i}x+c_{0}^{i}\end{cases} \tag{9}\]
## 4 Experimental Investigation
### Results and Discussions  
The original signals were all processed with band-pass filters. Figure 14 shows the signals at 85 kHz when both the actuator and sensor were or were not covered by the SMPs. Comparisons were made between the ratios of the flight times in the signals and propagation distances from the transducers to the near end and far end. It can be confirmed that the wave packets at 2.25 \(\times\) 10\({}^{-4}\) s are echo waves from the near end and those at 5.48 \(\times\) 10\({}^{-4}\) s were from the far end. The far end echo waves were observed in the experiments, of which the peak values with the SMP were about two times of those without the SMP. The magnifications of signals at different frequencies were calculated by using the peak values of the far end echo waves in the envelope signals, which are shown in Figure 15. The SMP provided the enhancement for the MPT used in wave generation, which were even more than that in wave detection. The signal strength could be improved by 9.8 dB at most with both of the transducers covered by the SMPs.  
Figure 16 shows the results of the MPT covered by different layers of SMPs at different frequencies. The enhancement was promoted by multiple layers of SMPs, which reached a maximum amplification of 12.7 dB at 130 kHz. However, there were no significant changes when the layer number of the SMPs was over two. The step change at the frequencies of 90 kHz and 95 kHz may have been caused  
Figure 13: The diagram of the experiments for transducer improvement with the SMP.  
by two different types of FPCs in the experiments. Considering these results, the SMP with the thickness over 0.3 mm should be recommended. The typical results for different lift-off distances of the SMP are shown in Figure 17. The decreasing trends at different frequencies match the prediction. The magnifications decrease faster at higher frequencies. A slight enhancement of 2.8 dB still exists when the SMP is moved 3 mm away from the FPC. However, for better results, the SMP should have a closer fit to the FPC under allowed conditions.  
Figure 16: Amplitude magnifications for the MPT with multiple layers of SMPs at different frequencies. T1 was an actuator and sensor.  
Figure 14: Original signals of the cases in which T1 and T2 both were with and without the SMPs at 85 kHz. T1 was an actuator and T2 was a sensor.  
Figure 15: Amplitude magnification for the MPTs in wave generation and detection with the SMPs at different frequencies. T1 was an actuator and T2 was a sensor.
## 5 Discussion  
As explained in Sections 3.2.3 and 4.2, different areas can have different characteristics with respect to road networks. Some areas are covered with more forested areas, fields and farmlands than others. In addition, some areas can include a greater number of one-way streets than others. The proposed benchmark sets and the corresponding analyses may not represent the entire picture and characteristics of a country, or even a city. Although they only provide partial images, the fact that the benchmark instances are realistic remains unchanged.  
The analyses in Section 4 are constrained to the maximum distance range of 10 km. This is because when creating road distance and road time matrices, the cost of links that exceed 10 km in terms of air distance are estimated rather than precisely defined, as noted in Section 4.3 using the values of boundary [9; 10; 11] in Figure 11. Asymmetry is applied to the estimated road distance and road time using the ratio values of boundary [9; 10] provided in Figure 10. We believe this is applicable since we are focused on creating benchmark instances for last-mile deliveries and the distribution of goods in urban settings. Additionally, since 72.6% of the four million pairs are within 10 km of air distance, and 27.4% are not, we are able to conclude that using the majority to assume the remainder is reasonable. More detailed information on the number of cases and the ratios for pairs of nodes within individual air distance ranges are provided in Figure 12.  
Figure 11: General conversion ratio for air distance: (**a**) to road distance; (**b**) to road time.  
Road distances and road times between nodes change over the time of day and over the day of the week. This is because the distance or time from the map APIs are based on the shortest or fastest path between nodes, and the greedy paths in the road network change over periods. This is also because there are peak hours and off-peak hours in urban transportation environments. Road distance and road time matrix creation is based on a random time of day to ensure that we accommodate both peak and off-peak hour characteristics in the new benchmark instances and in the analysis.  
Even with the limitations discussed above, the newly proposed benchmark instances have merit for a number of reasons. The previously available [PERSON] [18], [PERSON] [22] and [PERSON] [20] instances are all based on European regions. The new instances are the first to be based on an Asian country. In addition, for the purpose of testing cutting-edge VRP solving algorithms on single or multi-objective real-life problems, the new instances are the first to satisfy multiple important qualities simultaneously. Specifically, there are sufficient numbers and dimensions of problem instances, real data are used in the generation of depot and customer node locations, both road time and road distance matrices are present, and best-known solutions (BKS) are available. With these qualities in mind, a comprehensive comparison of the existing and new benchmark instances is provided in Table 4 below.
# Encrypted Client Hello Is Coming: A View from Passive Measurements
## 4 Results
### HTTPS DNS and Query Volume  
As Figure 3 shows, HTTPS DNS queries alone cannot be used as a replacement for A and AAAA queries, as they are typically incomplete. Thus, the clients supporting HTTPS DNS must issue two or three queries at the same time (of type HTTPS, A, and potentially AAAA) to accomplish a resolution. We now quantify this phenomenon by investigating how often an HTTPS DNS query is followed (or preceded) by another A or AAAA query for the same domain issues by the same IP address. Figure 5 quantifies these fractions in the DNS-Incoming dataset, considering windows of different durations. The x-axis represents the size of the time window in milliseconds, centered on the HTTPS DNS query, within which we search for other DNS queries. The y-axis shows the percentage of HTTPS requests associated with at least one subsequent DNS query. The results distinguish between queries of type A (blue line), AAAA (red line), and cases where both A and AAAA queries are observed (green line). The figure shows a growth in the occurrence of duplicate DNS queries as the time window increases. For a 1 ms window, 5.3% of HTTPS requests are followed/preceded by a repeated A query, rising to 18.8% at 10 ms and reaching 26.4% at 100 ms. AAAA duplicates are less frequent--starting from 1.6% at 1 ms and growing to 8.4% at 100 ms--consistent with the still-limited adoption of IPv6. Combined A and AAAA duplicates start at just 0.4% for a 1 ms window and reach 6.2% at 100 ms. Overall, A duplicates are approximately three times more frequent than AAAA across all windows. Notably, at the 100 ms mark, the share of combined A+AAAA duplicates (6.2%) nearly matches that of AAAA alone (8.4%), suggesting that clients issuing AAAA queries often also perform A queries--possibly due to dual-stack fallback strategies. We repeat the same analysis on the DNS-Outgoing dataset, while we do not report the figure for the sake of brevity. In DNS-Outgoing, we observe significantly higher proportions of duplicate A  
Figure 4: DNS request type from the top 10 ISPs (DNS-Incoming dataset).  
queries--up to 46.2% within 100 ms, while AAAA duplicates remain extremely low, never exceeding 2.7% (The low presence of AAAA queries is explained by the lack of IPv6 support in the campus). The x-axis is truncated after 100 ms, as the Round Trip Time we measure between the passive monitor and the campus DNS servers is in the order of 2 ms; thus, we do not expect correlated queries to be captured with a temporal separation larger than 100 ms. Indeed, all three curves significantly flatten after 50 ms. We conclude that although HTTPS DNS queries are already a 5-10% fraction of DNS traffic, they cannot be used alone; thus, they are accompanied by A or AAAA queries to the same domain, increasing DNS traffic volume and imposing extra load on DNS servers.