esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin-destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems.  
Keywords:public transportation systems; emergency passenger flow prediction; casual inference; deep learning; big data
## 1 Introduction  
The demand for water rises along with the global population. The need for water to irrigate land, which will produce more food and clean water for drinking purposes, is crucial to coping with the world's population growth [1]. The connection between energy and society growth is dynamic in that the volume, nature, and value of economic progress are jointly dependent variables on the quantity, sort, and cost of exiting energy. There is increasing consent that effective progress needs a strong agricultural basis. That is why the basic worth of life must be developed for and with the dynamic involvement of the poor bulk of the people living in the rural villages [2].  
Furthermore, the availability of power sources to pump water is a great problem in most developing and developed countries. Constructing an electricity network system is often costly because remote villages are commonly far away from existing network power lines. Also, relying on imported fuel stock is difficult and unsafe; foreign exchange rates oscillate, and the economies of many developing nations can then collapse [3]. Even if fuel exists within the country, transportation to these remote and rural villages is very tough, as there are no roads or supportive infrastructure where animal transportation is still common. It limits load capacities, and some loads--diesel generators, for example--may be impossible to bring to such locations [4].  
Renewable energy is a promising option for water-pumping applications in remote areas of many developing nations. Moving these systems, like wind machines and photovoltaic (PV) pumps, is much easier than conventional ones because they can be conveyed in parts and reconstructed on site. Traditional windmills have been used to pump water in many developing countries in the last century [5]. Another relatively new technology harnesses solar energy. This technology, called photovoltaics, transforms the sun's energy into electricity via electromagnetic means when the PV panel is exposed to the sun [6]. Solar energy is converted into direct current (DC), which an inverter needs to convert to alternating current (AC). PV has been used to power rural communications. This technology is ideal for water pumping applications because energy storage is not required for night pumping as the energy is stored in the form of water [7]. As a power source, PV systems can be a smart approach to effective community growth in developing countries [8]. Solar power becomes especially interesting considering the reduced cost of solar components and the high yearly radiation in Iraq.  
This study investigates the feasibility of a more sustainable solution for agricultural irrigation systems. Due to the low operating costs and environmental advantages, the study will concentrate on a system using solar power via PV technology. The focus will be comparing the solar pump systems in use today with a PV-powered option, showcasing their advantages and disadvantages. It will explore the irrigation methods that would best fit a solar-powered system [9].
## 1 Introduction
From the top down, previous studies focused on using satellite-based remote-sensing red-edge VIs to model GPP. The medium-resolution imaging spectrometer (MERIS) provides vegetation red-edge bands. [PERSON] and [PERSON] [47] first used satellite-based red-edge reflectance to build the MERIS terrestrial chlorophyll index (MTCI) and linked it to terrestrial chlorophyll. Thereafter, [PERSON] and [PERSON] [48] utilized the MTCI as an example for evaluating the relationship between GPP and red-edge VIs. The satellite-based MTCI that was retrieved from red-edge reflectance showed a robust relationship with GPP in deciduous forest and croplands [48]. However, because the global MTCI product has a spatial resolution of only 1 km, it cannot specifically describe the spatial variance in vegetation characteristics. Sentinel-2 provides three red-edge channels with 20-m spatial resolution, which can be used to retrieve red-edge-related VIs [49]. The European Space Agency (ESA) first launched Sentinel-2A in June 2015 and Sentinel-2B in March 2017. Both satellites carry the multispectral instrument (MSI)sensor, which has 13 spectral channels from visible to shortwave infrared. In particular, the sensor has three vegetation red-edge bands centered at wavelengths of approximately 705 nm, 740 nm, and 783 nm [49]. The MSI sensors onboard Sentinel-2A and Sentinel-2B acquire images at 10-day intervals at similar viewing angles; thus, the network of Sentinel-2A and Sentinel-2B has a five-day revisit period. The MSI scans land surfaces with a 290-km field of view and a 20-m spatial resolution, which is a much finer spatial resolution than that of MERIS (300 m) or MODIS (500 m). The VIs derived from high-spatial-resolution remote-sensing reflectance can reduce carbon flux estimation uncertainties because of spatial heterogeneity [50; 51]. Thus, Sentinel-2-based vegetation red-edge-based VIs are a useful dataset for high-spatial-resolution mid-term GPP estimation. To the best of our knowledge, most previous studies showed that ground-based observations of red-edge VIs have a high correlation with GPP in croplands [42; 46; 52; 53]. However, there is limited research on the use of continuously high-spatial-resolution red-edge data to estimate GPP in natural vegetation types, especially in forests and grasslands. In this research, we evaluated how much GPP variation can be explained both temporally and spatially by the red-edge variation derived from Sentinel-2 reflectance in forest and grassland sites in southeast Australia. The objectives of this paper were (1) to establish and compare the relationships for GPP from eddy covariance (GPPEC) vs. red-edge VIs and GPP vs. non-red-edge VIs in grassland and forest sites, (2) to investigate the patterns of spatial and temporal GPP change by mapping GPP data with continuous Sentinel-2 satellite-based VIs in a natural environment, and (3) to evaluate the performance of GPP estimation with red-edge-based VI and LUE model-based GPP products. Based on these results, we expect to reduce the large-scale spatial and temporal GPP mapping uncertainties by using the vegetation red-edge information.
## 4 Implementation Strategy
### Phase I--Monitoring and Data Acquisition  
The initial phase establishes the foundational capabilities of the digital twin: real-time monitoring of crop development and environmental conditions within the greenhouse. The robotic platform acquires data through its multimodal perception system, enabling spatial understanding and a high-resolution 3D reconstruction of the cultivation environment. Key tasks in this phase include the following:  
* Seeding assessment and scheduling, based on tray analysis and current environmental conditions;
* Fruit detection and tracking, including estimation of key phenotypic attributes such as diameter, color, maturity stage, and spatial coordinates;
* Environmental monitoring, via embedded sensors measuring CO\({}_{2}\), relative humidity, temperature, and pH--see Figure 2;
* Planter identification and localization, achieved using QR markers to establish contextual links between plant data and specific hydroponic units; Figure 3 shows this labeling strategy based on QR markers.  
All data streams are semantically structured and synchronized with the Virtual Layer, forming a digital representation of crop lifecycles and environmental dynamics. This representation is accessible through a real-time dashboard, which integrates irrigation layout, environmental sensor data, historical trends, forecasting tools, and system status indicators. Figure 4 illustrates the interface used to monitor the irrigation and environmental conditions within the hydroponic greenhouse in real time.  
To contextualize the capabilities of DigiHortiRobot, Table 1 provides a comparative overview with respect to conventional smart farming monitoring tools, based on technical information commonly reported in the literature [20; 25].  
Figure 2: Hydrogenic greenhouse sensors installed for monitoring.  
While conventional systems rely primarily on environmental sensors and dashboard-level visualization, DigiHortiRobot introduces semantic spatial mapping, real-time synchronization, and closed-loop integration with robotic agents. This foundational layer enables predictive task planning and execution, surpassing the passive role of traditional monitoring systems.  
Figure 4: Real-time monitoring dashboard for the hydroponic greenhouse, showing irrigation layout, environmental sensor readings, and system status.  
Figure 3: QR-based crop identification system.
## 3 The g-Extra Connectivity of the BCCC Network
: According to Lemma 3, when \(|N_{BC_{n,k}}(V(C))|=(g+1)(k-1)+n\), \(V(BC_{n,k})-N_{BC_{n,k}}V(C)\) has exactly two connected components: one is V(C) and the other is \(V(BC_{n,k})-N_{BC_{n,k}}[V(C)]\), where \(\delta(BCCC_{n,k}-N_{BC_{n,k}}[V(C)])\geq g\). Meanwhile, we have \(|C|=g+1\) and \(|V(BC_{n,k})-N_{BC_{n,k}}[V(C)]|=N-(g+1)-[(g+1)(k-1)+n]>g+1\). Thus, \(N_{BC_{n,k}}(V(C))\) is an g-extra vertex cut of \(BC_{n,k}\). The upper bound of g-extra connectivity is \(\kappa_{g}(BC_{n,k})\leq(g+1)(k-1)+n\). Lemma 9: _For any integers \(n\geq 3\), \(k\geq 2\), \(n\geq k+1\) and \(0\leq g\leq n-1\), the lower bound of \(g\)-extra connectivity is \(\kappa_{g}(BC_{n,k})\geq(g+1)(k-1)+n\)._  
Proof. : For the sake of contradiction, suppose that \(F\) is an \(R_{g}-\)cut of \(BC_{n,k}\) with \(|F|\leq(g+1)(k-1)+n-1\). By Lemma 7, \(BC_{n,k}-N_{BC_{n,k}}(V(H))\) has the largest component containing at least \(N-|F|-g\) vertices, where \(0\leq g\leq n-1\). After excluding the largest connected component, the number of remaining small connected components have the most \(g\) vertices. Hence, \(F\) is not an g-extra vertex cut of \(BC_{n,k}\) with \(|F|\leq(g+1)(k-1)+n-1\), a contradiction. We have the lower bound of g-extra connectivity \(\kappa_{g}(BC_{n,k})\geq(g+1)(k-1)+n\). According to Lemma 8 and Lemma 9, we have the following Theorem. Theorem 1: _For any integers \(n\geq 3\), \(k\geq 2\), \(n\geq k+1\) and \(0\leq g\leq n-1\), the g-extra connectivity is \(\kappa_{g}(BC_{n,k})=(g+1)(k-1)+n\)._
# Polyp Generalization via Diversifying Style at Feature-Level Space
## 4 Results
### Experimental Results of Kvasir-SEG
#### 4.2.3 Generalizability on ETIS Dataset  
The results of our proposed method, trained on the Kvasir-SEG dataset and tested on the ETIS dataset, are presented in Table 5. Our method achieves a Dice score of 0.779 and a mean IoU of 0.694, along with FM, SM, maxE and MAE values of 0.739, 0.853, 0.904 and 0.011, respectively. Among the models evaluated, SwinE-Net and TransUnet rank second and third with Dice scores of 0.758 and 0.733, respectively. Notably, our proposed method significantly outperforms U-Net and UNet++, demonstrating its robustness and generalizability on unseen datasets.
###### Abstract  
Future autonomous vehicles will interact with traffic infrastructure through roadside units (RSUs) directly connected to transportation field cabinets (TFCs). These TFCs must be interconnected to share traffic information, enabling infrastructure-to-infrastructure (I2I) communications that are reliable, synchronous and capable of transmitting vehicle data to the Internet. However, I2I communications present a complex optimization challenge. This study addresses this by proposing the design, implementation, and evaluation of an automated management model for I2I service channels based on multi-agent reinforcement learning (MARL) integrated with deep reinforcement learning (DRL). The proposed models efficiently manage the routing and scheduling of data frames between internet of vehicles (IoV) infrastructure devices through time-sensitive networking (TSN) to ensure real-time synchronous I2I communications. The solution incorporates both a routing model and a scheduling model, evaluated in a simulated shared environment where agents operate within the TSN control plane. Both models are tested for different topologies and background traffic levels. The results demonstrate that the models establish the majority of paths in the scenario, adhering to near-optimal routing and scheduling policies. Recursively, for each individual request to create a service channel, the system establishes online an optimal synchronous path between entities with a limited time budget. In total, 71% of optimal routing paths are established and 97% of optimal schedules are achieved. The approach takes into account the periodic nature of the transmitted data and its robustness through TSN networks, obtaining 99 percent of compliant service requests with flow jitter levels below 100 microseconds for different topologies and different network utility percentages. The proposed solution achieves lower execution delays compared to the iterative ILP approach. Additionally, the solution facilitates the integration of 5G networks for vehicle-to-infrastructure (V2I) communications, which is identified as an area for future exploration.  
IoV; RSU; I2I; TSN; TMS; TMC; smart road side infrastructure; MARL; DRL; routing and scheduling optimization; SDN +
Footnote †: journal: Applied sciences  
_Article_
# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 4 Visualization Results and Bibliometric Analysis Based on CiteSpace
### Country and Institution Analysis
Obviously, even if we shorten the time interval to the last decade, China and the USA are still the top two countries in terms of the number of publications, and HEC Montreal also maintains its central position in the collaboration network of institutions. Among the top 10 high-published institutions in Table 7, universities from China (i.e., Chongqing Jiaotong University, Beijing Jiaotong University, Huazhong University of Science and Technology, and City University of Hong Kong) have issued a total of 52 documents since 2011, which is more than the total 51 publications produced by Brazil and Turkey. Coupled with the visualization results from Figures 7 and 8 and Tables 5 and 6, we can see that three countries, China, USA, and Canada, not only have deep professional knowledge accumulation, but also have strong academic innovation capability necessary to promote the development of PDP research. Table 8 presents the top 20 keywords with the high frequency of co-occurrence. Among them, \"VRP\" is the most dominant keyword, which appears 255 times in total with other associated terms. Keywords linked to \"VRP\" include \"a ride problem\", \"system\", \"time window\", \"transportation\", \"tabu search\", \"vehicle routing\", \"delivery\", and \"network design problem\". Followed by \"algorithm\" with the frequency of co-occurrence of 232, which has a close relationship between \"model\", \"optimization\", \"metaheuristics\", \"scheduling\", \"tabu search\", \"genetic algorithm\" and \"branch\". Moreover, according to the distribution of keywords in Table 8, we can further draw the following conclusions: (1) Scholars have placed great emphasis on the relationship between routing selection and optimization through formulating mathematical models under realistic backgrounds and assumptions. (2) Most of the findings in PDP research are related to design computationally efficient algorithms and methods to ensure the calculability of formulated model, and further obtain the optimal strategy for logistics managers' reference. (3) Most commonly used algorithms include genetic algorithm, tabu search, and other heuristic algorithms, which are also the important foundations for proposing more innovative methodology that performs better. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Freq.** & **Year** & **Keyword** \\ \hline
1 & 255 & 2000 & VRP \\
2 & 232 & 1995 & Algorithm \\
3 & 169 & 1997 & Time Window \\
4 & 143 & 2003 & Pickup \\
5 & 139 & 2000 & Pickup and Delivery \\
6 & 130 & 1995 & TSP \\
7 & 115 & 2001 & Vehicle Routing \\
8 & 103 & 2002 & Optimization \\
9 & 88 & 2003 & PDP \\
10 & 82 & 1995 & Model \\
11 & 82 & 2000 & Heuristics \\
12 & 76 & 2002 & Search \\
13 & 73 & 1995 & A Ride Problem \\
14 & 71 & 2000 & Tabu Search \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The top 20 keywords with the most co-occurrence frequency. Figure 9: Co-occurrence network map of keywords in the research on PDP.
## 2 Proposed Cone Cell Identification Method
### AO-SLO Image Denoising  
The human eye has a limited resilience to light exposure. Therefore, to prevent optical damage, the light of AO-SLO imaging should be set to a low-power mode. However, low light might lead to a low signal-to-noise ratio in AO-SLO images. To increase this ratio, multiple registered AO-SLO images can be averaged. To this end, we adopt TV-L1 optical flow estimation [37] for image registration and then average the registered images after screening. The flowchart of image denoising based on registered images is shown in Figure 2. First, the middle image in a time sequence is selected as a reference for the registration of other images. Then, TV-L1 optical flow registration is applied to each image to be registered. Third, image registration is evaluated. Registered images whose structural similarity index [40] with respect to the reference image is below 0.5 under masks are discarded. If the number of remaining registered images is below one-fifth of the number of acquired images, the algorithm selects the next image closest to the middle in the time sequence as the new reference image. When the number of successfully registered images is sufficient, they are averaged along with the reference image under masks to obtain the denoised AO-SLO image.  
Figure 3 shows an example of image denoising on a representative AO-SLO image patch using the proposed method. Note that image noise is markedly reduced after denoising, indicating the high effectiveness of the method.
## 4 Results and Discussion  
Simulation studies are carried out on a grid-connected WECS with DFIG wind farm in Simulink/Simpower systems. The stator is directly connected to the power grid, while the rotor's terminals are fed through a variable frequency via the converters in Figure 2. The simulation study of DC voltage overshoots is shown in Figure 3 and Table 1, where we can see the effectiveness of the proposed FTC  
Figure 2: The block diagram of the doubly-fed induction generator (DFIG)-based wind power system.  
The operation of the controller is implemented in Simulink and summarized in the proposed scheme's flow chart depicted in Figure 1. The value of overshoots DC voltage is calculated by applying this method [25]. With the combination of PAC and MPPT, an FTC scheme is proposed and depicted in Figure 1 to enhance the fault-tolerant of the DFIG-based wind farm operating under balanced short-circuit and wind speed variations.  
\[\text{Overshoot }\%=(\text{V}_{\text{dc-max}^{*}}\text{V}_{\text{dc-rated}})/ \text{V}_{\text{dc-rated}} \tag{14}\]
## 4 Experimental Evaluation
### Discussion
Secondly, adopting NOMA technologies with IBFD is an emerging and popular issue for 6G communications. For example, using IBFD in NOMA relaying [3] or cooperative NOMA systems [4] are proposed to essentially improve system performance. In these NOMA scenarios, the proposal in this study can also be applied because the SI source information is known at the receiver, and the detail analysis and implementations are left as our future works. \begin{table}
\begin{tabular}{l l l l l} \hline
**SI Cancellation** & **Proposed** & **Proposal in [5]** & **Proposal in [6]** & **Proposal in [31]** \\ \hline Antenna Type & 46.9 dB & 30.0 dB & NA & 31.4 dB \\ Analog Type & 22.0 dB & 20.0 dB & 62.0 dB & 28.2 dB \\ \hline Antenna + Analog & 68.9 dB & 50.0 dB & 62.0 dB & 59.6 dB \\ \hline Digital Type & 30.0 dB & 10.0 dB & 48.0 dB & 24.0 dB \\ \hline Total Cancellation & 98.9 dB & 60.0 dB & 110.0 dB & 83.6 dB \\ \hline BER & \(10^{-3}\) & NA & NA & \(10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of IBFD implementation proposals.