esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, MODISEVI time series have facilitated the mapping of winter wheat-summer maize rotations in the North China Plain, and Landsat-based analyses have captured rice intensification in southern basins [29; 30; 31; 32]. However, research in the farming-pastoral ecotone of China (FPEC) has largely prioritized land-use conversions such as cropland abandonment and reclamation, using MODIS or Sentinel data to differentiate broad categories like cropland versus fallow, rather than detailed crop-type mapping [5; 33; 34; 35]. Recent localized efforts have applied deep learning models to Sentinel-2 imagery for early-season crop identification [36], and satellite-based studies have begun to detect specialized systems such as green depression agriculture [37]. Yet, several critical challenges persist: (i) most existing studies focus on land cover change or single-season crop identification, lacking long-term, multi-crop rotation analysis; (ii) systematic assessments linking crop distribution to topographic gradients (elevation, slope) remain limited; and (iii) these issues are further exacerbated in the FPEC due to highly fragmented sub-hectare plots, frequent cloud cover during critical phenological stages, and substantial intra-class spectral variability arising from microclimates, varietal differences, and heterogeneous farming practices [38; 39]. Consequently, existing frameworks exhibit limited generalizability across the FPEC, particularly in complex and fragmented terrains. Zhangjiakou City, a representative region within FPEC, is characterized by significant ecological fragility, pronounced climatic variability, and fragmented agricultural landscapes. Agricultural practices here include complex and diverse cropping systems with both staple crops (maize) and regionally significant specialty crops (oats, potato, sesame, vegetables) [40]. Although large-scale maize mapping products have enhanced the understanding of dominant cereal distributions, comprehensive spatiotemporal analyses that capture major crops, along with rotation dynamics across heterogeneous landscapes, remain scarce. Addressing these gaps, this study aims to: (1) develop a robust crop classification framework by integrating multi-source satellite imagery to accurately map major crop types in Zhangjiakou (2019-2023) at 10 m resolution; (2) identify dominant crop rotation types and assess their temporal frequency and transition structure using transition matrix analysis; and (3) analyze altitudinal and slope gradient dependencies influencing crop distribution. The results could provide critical insights to optimize cropping strategies and improve agricultural management in ecologically fragile regions.
## 2 Configuration and Design Scheme of the Feeding Structure
### Compact SR Phase Feed  
The topology of the proposed radial SR feed is shown in Figure 2a. The input port of the SR phase feed, denoted by \(P_{1}\), has impedance \(Z_{p1}\) = \(Z_{f}\) determined from testing connector or RF circuitry. \(P_{2}\) through \(P_{5}\) are the antenna feeds in which power is distributed equally with an incremental phase delay of 90\({}^{\circ}\), connected to the respective antenna elements. The lengths \(l_{1}\) through \(l_{7}\) are quarter wave (\(\lambda_{g}\)/4) impedance transformer at the operating frequency in which \(l_{2}\), \(l_{4}\), and \(l_{6}\) provide the impedance transformation between the junction points of the circular section of the SR phase feed. The remaining  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Type** & **IBW/ARBW** & **Polarization** & **Gain (dBic)** & **Angular Width** & **AR (dB)** \\  & **(GHz)** & & & **Width** & \\ \hline
4 \(\times\) 4 Array & 8.05–8.25 & LHCP & \(>\)12 & \(\pm\)10\({}^{\circ}\) in two & \(<\)1.5 \\  & & & & planes & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the 4 \(\times\) 4 array antenna of the satellite application.  
Figure 1: Configuration of sequential-phase (SP) feed and 2 \(\times\) 2 SR CP array.  
lengths transform the impedance at the junction point to the impedance at the output ports. The initial widths of these lines are chosen by conducting some calculations for equal power distribution, as discussed in the following section. The overall layout is simple and compact, occupying \(\pi\)(0.35\(\lambda_{S}\))\({}^{2}\) area. Moreover, an open stub is added at an arbitrary location in the circular section of the feed to optimize the performance of the SR phase feeding network that will be explained in the next section.
## 2 Problem Statement
### Comparison of the Packet Loss Rate  
Figure 4 shows the packet loss rates of SCTP and TCP under different PERs. By analyzing the trace file, the number of packets lost and the total number of packets sent could be counted to calculate the packet loss rate.  
In the case of different packet error rates, with the increase of PER, the probability of packet loss of TCP and SCTP increased. However, the packet loss rate of TCP was higher than SCTP at the same PER. In order to ensure the reliability of the data, both SCTP and TCP needed to retransmit the lost packet data. Therefore, the resource consumption of the retransmission packet of SCTP was lower. With the increase of PER, the trend of TCP packet loss rate increased much more rapidly than SCTP. The retransmission mechanism is a way to ensure data reliability. In the process of packet retransmission, the retransmission mechanism increased the burden of the link. Low packet loss rate is helpful to improve data transmission speed. Therefore, using SCTP for data transmission could effectively improve the transmission efficiency. It also showed that SCTP had unique advantages over TCP.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 4 FESS Applications
### Power Quality  
As part of the power quality requirements, the system frequency and voltage need to be maintained to an acceptable level and deviations should be avoided. When the loads are added or subtracted from the grid, the system voltage and frequency will also be increased or reduced. Energy storage systems, especially those which are fast performing like flywheels, can quickly add or take power from the grid, to keep the system voltage and frequency within range [64]. Flywheels provide ride-through applications for interruptions of up to 15 s long and provide a means of switching between power sources without any service interruptions [2]. Flywheels can operate for up to tens of minutes for reactive power support, spinning reserve, and voltage regulation, to supply reliable electric power and improve the power quality in applications such as communication facilities and computer server centres [2]. As long as run down losses are kept low, durations can be extended to several hours, without losing excessive amounts of energy. In North Western Australia, a flywheel system has been integrated into a town's power supply to support the increased power demand during the tourist season [65]. Coral Bay, a wind energy operated power station, consisted of seven 320 kW low-load diesel generators with three 200 kW wind turbines. In 2007, the integration of a 500 kW flywheel virtual generator into the system allowed the wind turbines to provide up to 95% of Coral Bay's supply at peak times. The reported data shows that for nearly 900 h per year, 90% of the power station's total supply comes from wind generation. In addition, while maintaining the grid standards and improving the power quality, 80% of this total power is wind-generated for one-third of the year [65]. Another flywheel-based stabilisation system has been planned for the Marsabit wind farm, a remote community served by an isolated microgrid in northern Kenya. A 500 kW flywheel-based system will be integrated into the existing two 275 kW wind turbines and diesel generators. The PowerStore flywheel to be installed by ABB will stabilise the grid connection to maximise renewable energy penetration [66]. PowerStore is a flywheel-based stabilising generator which is mainly used for improving the power quality. It enables the integration and control of renewable wind and solar energy in the electrical grid. Acting like a static synchronous compensator (STATCOM), it combines an 18 MWs (Megawatt second) low-speed flywheel with solid state converters that absorb or inject full energy in 1 millisecond [67]. The range of models from 500 kW to 1.5 MW allows the configuration of either a grid support mode for MW scale grids, or as a virtual generator for use in smaller isolated grids.
## 3 SD-QGrid Routing Platform and Algorithms
### Experimental Map  
This project uses the data of Taipei City's bus movement trajectory [30]. First, we planned a latitude and longitude range in Taipei City, and the vehicles appearing in this range were used as research data and recorded in tables. The first table records the name of the route, the license plate number, the direction of departure and return, and the time when it first appeared in the data, which is used to record the departure sequence of the license plate number corresponding to the bus route, and records the return time in the direction to and from this vehicle node. The other table records the longitude and latitude of each vehicle node at each time point, and the coordinates of the grid where the vehicle node is located, which is used to record the actual trajectory of each vehicle. The map of this experiment is shown in Figure 5. The longitude is from 121.5083335 to 121.5677824, and the latitude is from 25.0283751 to 25.0644845. The length of this map is 6000 m, and the width is 4000 m.  
Figure 4: SD-QGrid network architecture.
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 2 Proton RFQ Accelerator Design
### RF Design Studies  
The 2-dimentional cavities, as shown in Figure 6, are designed to satisfy the requirements of the RFQ physics designs and to understand the cavity properties and to estimate the power loss in the cavity, using the CST MWS code [42]. The cross-section, average aperture radius and vane-tip radius of both RFQs are kept constant along the full cavity length for an easy tuning and the possibility of applying a shaper-cutter in the machining. The adoption of a square profile in the 162.5 MHz will benefit from easier openings of holes for tuners, power couplers, vacuum port and so on in the mechanical processing. However, if a similar square profile is used at the frequency of 325 MHz, that will limit the available spaces for tuners and couplers. Thus, an octagonal profile is more favorable to a 325 MHz RFQ.  
Figure 5: Simulated beam transmission efficiencies, transverse RMS envelopes and input transverse particle distributions (x-x) of beam dynamics in the 162.5 MHz (**a**,**c**,**e**) and 325 MHz (**b**,**d**,**f**) RFQs.  
To have a better understanding of cavity performances in both designs, a comparison of high-frequency parameters between the square 162.5 MHz RFQ and the octagonal 325 MHz RFQ is presented in Table 2. The half-width of the 325 MHz RFQ inner wall is 101.66 mm, which is about 60% of that in 162.5 MHz RFQ, leaving a slope width of 76.6 mm, which is adequate for tuners and couplers. The mode spacings between the fundamental quadrupole mode (Q\({}_{0}\)) and dipole mode (D\({}_{0}\)) are 5.0 MHz and 10.6 MHz, respectively, which should guarantee sufficient RF stability during operation. Besides, the square 162.5 MHz RFQ has a larger unloaded quality factor because of its higher area to perimeter ratio. Furthermore, the corresponding specific shunt impedance is about 2.7 times higher, reaching up to 272.1 kf\(\Omega\)-m. Taking account of a factor of 1.3 for power loss scaling to 3-dimentional cavity, the estimated total power in the 162.5 MHz RFQ is evaluated to be one-third of that in the 325 MHz RFQ.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 2 Related Work  
The literature survey begins by exploring various traffic forecasting problems discussed in existing research. Initially, these problems are categorized based on the traffic state they aim to predict, such as traffic flow, speed, demand, and other related issues.  
These traffic problems are categorized by the level of traffic states: road-level, region-level, and station-level, each with distinct modeling needs to capture spatial dependencies. Road-level issues use sensor or GPS data mapped onto road networks via map-matching, viewing the network as a graph of numerous road segments connected by spatial proximity. Station-level issues involve metro or bus station topologies, with dependencies defined by transit routes. Region-level issues treat areas as graph nodes, with spatial dependencies influenced by factors like land use from points-of-interest data. In the context of smart cities, these modeling approaches can be further enhanced by integrating real time, enabling a comprehensive understanding of urban mobility patterns. By employing machine learning techniques within smart city infrastructures, traffic forecasting can leverage additional layers of information, such as weather conditions, event schedules, and demographic changes, resulting in more accurate predictions. This holistic approach addresses current traffic challenges and paves the way for sustainable urban planning and intelligent transportation systems that can adapt to the evolving needs of urban environments.
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 2 The Durability Prediction Method and Research Method  
### The Durability Prediction Method  
Succinctly, the principle of the method is using the durability life of critical components to represent the durability life for the entire historical square rebar RC buildings. The durability prediction method is structured based on four main phases: the in-situ investigation, the finite element model simulation, the component importance analysis, and the structural durability prediction (Figure 1). The former step is necessary for the subsequent step. In-situ investigations primarily consist of structural surveying (mainly including load condition, structural arrangement, component geometry, concrete cover depth, reinforcement arrangement, reinforcement geometry, etc.) and material properties testing (mainly including concrete compressive strength, etc.), which can provide the essential data for the subsequent step of constructing the finite element model and computations. The finite element method is used to accomplish the component importance analysis, which can provide the order of all structural components according to importance. The vertical loads for the daily condition are loaded in the finite element model. Then, according to the importance order, all the structural components can be divided into four levels, and the first level components are critical components for the entire structure. Ultimately, the averaged durability life of critical components is calculated to represent the durability life for the entire structure, enabling the durability life evaluation to progress from the component level to the structure level. The durability life in this paper is represented by the corrosion-induced cracking life of the component, including the time before rebar corrosion and the time from corrosion initiation to corrosion-induced concrete cover cracking.
# Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio
## 2 System and Channel Models  
The CR model comprises an ST, an R, two SUs, and a PU. Within the SN, the ST employs soft-switching FSO/THz links to propagate signals to R. As shown in Figure 1, R takes control of the spectrum band belonging to the PN using the CR and transmits signals to the two SUs via the AF-NOMA RF link. Without loss of generality, the channel gains are assumed to be \(\left|h_{RU_{1}}\right|^{2}<\left|h_{RU_{2}}\right|^{2}\) according to the inverse of the link distance because the greater the distance traveled, the greater the attenuation by atmospheric turbulence.
## 2 Materials and Methods  
This section describes the proposed system, called Double-Step U-Net, which is based on the analysis of Sentinel-2 satellite data through deep learning models. Double-Step U-Net is based on the joint exploitation of a two-stage approach that combines a classification and a regression algorithm to address the post-wildfire severity estimation process. In this section, we initially describe the used data (Section 2.1) and how they were collected (Section 2.2). Then, we formalize the addressed problem (Section 2.3) and we describe in detail the proposed system (Section 2.4) and how we implemented it (Section 2.5).
# Improvement of Boeing Bump Method Considering Aircraft Vibration Superposition Effect
###### Abstract  
Pavement evaluation is critical for the decision-making process of pavement preservation and rehabilitation. Roughness is a key airport pavement characteristic that has been linked to impacts such as safety and service life. The Boeing Bump is one of the few roughness evaluation methods that has been developed specifically for runways. Although it is superior to the widely used International Roughness Index (IRI), it does not take into account the superposition effect of continuous runway bumps. Based on the ADAMS/Aircraft virtual prototype platform, this paper establishes and verifies five typical models (B737, B747, B757, B777, and B787) and then analyzes the most unfavorable speed (in terms of aircraft vibration) of each model and the dynamic responses caused by multiple bumps. The original Boeing Bump is improved and optimized by determining dynamic response thresholds for the various aircraft types. The results show that the revised Boeing Bump is more realistic than the original version, especially with regard to medium and long wave bands.  
airfields; pavement engineering; runway roughness; Boeing Bump; aircraft dynamic response; virtual prototype +
Footnote †: journal:However, despite its superiority over the IRI and other roadway evaluation methods, the Boeing Bump method has some inherent deficiencies. First, it is based on a single bump and thus cannot reasonably address the superposition effect of multiple bumps on the aircraft's vibration response [13]. Second, its standards are based on a single aircraft type, the B727, and thus it may not be applicable to other aircraft [14]. Therefore, for runway roughness evaluations specifically, the Boeing Bump method is theoretically less realistic than actual situations can support, which clearly is unfavorable in terms of the safety of runway operations. Boeing Bump method evaluations can be improved if these two shortcomings (i.e., based on a single bump and a single aircraft type) are corrected and optimized.  
To this end, in this study, we carried out aircraft dynamics simulations by first establishing five typical virtual prototype models. Next, we quantified the superposition effect of the aircrafts' vibration responses under multiple bumps. We then backcalculated the modified Boeing Bump's height settings at different wavelengths according to the threshold of each aircraft's dynamic response.
# Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems
## 4 Results and Discussion
### Discussion of the Optimization Blades
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Design Results**} & \multirow{2}{*}{\begin{tabular}{c} **Blade** \\ **Length (m)** \\ \end{tabular} } & \multirow{2}{*}{**Cp (-)**} & \multirow{2}{*}{\begin{tabular}{c} **Capacity** \\ **Factor (-)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Blade Root** \\ **Mf (Nm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Blade Mass** \\ **(kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Hub Fx** \\ **(Nm)** \\ \end{tabular} } \\ \hline Optimum NPV & 70.1 & 0.463 & 0.342 & \(1.58\times 10^{7}\) & 17,930 & \(7.44\times 10^{5}\) \\ \hline Optimum LCoE & 65.4 & 0.457 & 0.312 & \(1.27\times 10^{7}\) & 14,552 & \(6.80\times 10^{5}\) \\ \hline High Cp & 65.4 & 0.487 & 0.321 & \(1.40\times 10^{7}\) & 17,303 & \(8.63\times 10^{5}\) \\ \hline \multirow{2}{*}{**Design Results**} & \multirow{2}{*}{\begin{tabular}{c} **Tower** \\ **Height (m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Tower Mass** \\ **(tonne)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CAPEX** \\ **(1000 S)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **LCoE** \\ **(cents/kWh)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **NVP (1000 S)** \\ \end{tabular} } \\ \hline Optimum NPV & 105 & 369 & 2725 & 6.62 & 1295 & \\ \hline Optimum LCoE & 93 & 270 & 2377 & 6.54 & 1223 & \\ \hline High Cp & 93 & 346 & 2625 & 6.84 & 1096 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Turbine properties of designs produced for optimum NPV, LCoE and Cp. 0.457 may seem as unacceptable compared with 0.487, a decrease of 6.2%. However, the AEP is only degraded by 3%. Meanwhile, the design loads and structure masses were reduced significantly, accounting for a reduction in capital expenditure by 9.3%. The result produces 4.1% less LCoE and the NPV is increased. The LCoE design could be a more reasonable choice from the economic point of view.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 1 Introduction
### Related Works  
TSF has been extensively studied, with traditional methods such as hidden Markov models [6], Kalman filters [5], and statistical models such as ARIMA [25], exponentially weighted moving averages [26; 27], and vector auto regressors [28] demonstrating notable performance. In the field of deep learning, RNNs were initially prominent for TSF due to their effectiveness in modeling sequential data [29; 30; 31; 32]. Subsequently, TCN architectures gained a decent amount of popularity [8; 9; 10; 11; 12; 13], with TCNs and RNNs often used in conjunction with graph neural networks (GNNs) to capture both spatial patterns and temporal patterns [13; 33; 34; 35; 36]. Transformer architectures have emerged as dominant players in sequential modeling tasks, largely replacing RNNs [14]. The success of transformers is attributed to their self-attention mechanism; however, the quadratic computation and memory complexity of self-attention pose challenges for handling long sequences. Consequently, recent efforts in transformer-based LSTF models have focused on developing more efficient architectures, often by proposing sparser query matrices for computing self-attention [4; 15]. Incorporating classical concepts alongside modern deep learning concepts has performed reasonably well [20; 21]. For instance, Autoformer [20] decomposes the original time series into seasonality and trend components, extracting trend information through multiple decomposition steps using average pooling and treating the difference between the original signal and trend as seasonality. Additionally, Autoformer uses an autocorrelation block to extract the dependencies [20]. Whereas SCINet [21] leverages multiple resolution analysis in deep learning, employing a unique downsampling technique alongside convolutions and an interaction block [21].