esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO2 Geological Storage in China
#### 5.1.2 CO2 Storage in Depleted Oil and Gas Fields in China  
Depleted oil and gas reservoirs have ideal geological characteristics to retain hydrocarbons and prevent their leakage for millions of years, so they are regarded as another promising site for CO2 storage [175]. Additionally, since these reservoirs have gone through the production stage, a variety of historical data such as logging data, drilling and completion data, formation parameters, and production history, is sufficient to provide a detailed basis for assessing the suitability of CO2 storage [175].  
Oil and gas fields in the eastern and northern areas of China that have reached late production phases and are no longer economically feasible for production are expected to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Operation Start Year** & **Capture Technology** & **Transport** & \
\begin{tabular}{c} **CO2 Storage Capacity** \\ **(Tonnes/Year)** \\ \end{tabular} \\ \hline
\begin{tabular}{c} CHN ENERGY Ordos deep \\ saline aquifer project \\ CHN ENERGY Guohua Jinje \\ CCUS full-process \\ demonstration project \\ \end{tabular} & 2011 & Pre-combustion & Tanker truck & 100,000 \\ \hline \hline \end{tabular}
\end{table}
Table 9: CO2 storage projects in deep saline aquifers in China [50].  
be excellent depleted oil and gas field sites for CO\({}_{2}\) storage. It is anticipated that the overall CO\({}_{2}\) storage capacity of these areas can reach 30% of the total demand for CCUS. The obvious benefits of CO\({}_{2}\) storage in depleted oil and gas fields include less additional costs to implement, mature technical processes and ease of management. However, it is worth noting that the leakage of stored CO\({}_{2}\) may occur due to fractures created by the design and production of oil and gas fields [176]. Up to now, there are still only a few large-scale projects worldwide using depleted oil and gas fields to store CO\({}_{2}\)[177]. Although China possesses the required advanced technology, there is currently no demonstration project for storing CO\({}_{2}\) in depleted oil and gas fields since such projects are often launched after the completion of CO\({}_{2}\)-EOR and CO\({}_{2}\)-EGR projects.
###### Abstract
Therefore, it is difficult to determine which index provides the best performance [39]. To overcome these problems, several studies have integrated the information given by each index separately by combining them together into one model using multivariate models to provide more information than an individual index. In recent literature, partial least squares regression (PLSR), the most common multivariate method used in spectral studies, has been combined with different SRIs to provide an important approach for better predictions of target traits compared with standalone SRIs. For instance, [PERSON] et al. [52] found that coupling two-band SRIs with a PLSR model estimated the biomass of wheat satisfactorily (R\({}^{2}\) = 0.89). [PERSON] et al. [53] mentioned that the prediction of the grain yield of durum wheat under normal conditions was generally stronger when based on PLSR models than when based on previously assayed SRIs. [PERSON] et al. [54] also reported that SRIs coupled with PLSR increased the accuracy of predictions of wheat grain yield under drought conditions. Other studies have reported that SRIs coupled with other multivariate models, such as artificial neural network and support vector regression, exhibited better performance for predicting the biomass of wheat and maize at different growth stages [55; 56]. These articles show that the predictions for plant biophysical traits could be significantly improved by coupling SRIs with an appropriate multivariate model. Owing to the limited available knowledge on the use of hyperspectral reflectance data for characterizing the impacts of salinity stress on plant biomass of a large number of advanced spring wheat genotypes at various phenological stages under field conditions, this investigation was undertaken with the following objectives: (1) to evaluate the potential of full-spectrum range (350-2500 nm) wavelengths to accurately detect genotypic variation in plant biomass at different growth stages, (2) to investigate the performance of published and modified vegetation- and water-related SRIs in estimating plant biomass at each distinct growth stage and across all stages, (3) to evaluate the performance of SRIs coupled with the PLSR model in predicting plant biomass at each distinct growth stage and across all stages, and (4) to identify the most effective combination of SRIs and PLSR models for early detection of genotypic variation in plant biomass.
## 2 Data and Methods
### Data Collection and Processing  
#### 2.2.1 CCI Soil Moisture Product  
CCI soil moisture product data were the basis of this study. The CCI project was a part of the ESA global essential climate variable monitoring project. The product consists of three surface soil moisture data sets: active products, passive products, and combined products. The active and passive soil moisture products were derived from two microwave scattermeters (ASCAT and European Remote Sensing (ERS) Active Microwave Instrument (AMI)) and four microwave radiometers (Tropical Rainfall Measuring Mission Microwave imager (TMI), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)), respectively. The combined products were merged by the first two datasets [27]. The spatial resolution of this daily soil moisture data was 0.25\({}^{\circ}\) with a reference time of 12:00 a.m. UTC (Coordinated Universal Time). These products measure the soil moisture at 0.5-2-cm depth. The soil moisture data of passive and combined products are in volume units (m\({}^{3}\)\(\cdot\)m\({}^{-3}\)), while the active products are expressed as a percentage of saturation (%) [28]. In this study, daily soil moisture data from April to October for the period of 2007-2015 were accessed at [[https://www.esa-soilmoisture-cci.org/](https://www.esa-soilmoisture-cci.org/)]([https://www.esa-soilmoisture-cci.org/](https://www.esa-soilmoisture-cci.org/)). Then, eight-day soil moisture time series were generated using the maximum value composite (MVC) method. The images were subsets of the study area in ArcGIS.
## 4 Experimental Verification
### Case Study I: Depending on Different Fault Components  
#### 4.1.1 Test Equipment and Data Introduction  
As shown in Figure 4, the designed experimental equipment mainly included the motor, coupling, planetary gear box, bearing seat, rotor, and so on. Aside from the normal fault-free vibration signal, six different fault types were set respectively for different fault positions of the shaft, bearing, and gear. Depending on the location and depth of the shaft crack, the six fault types were three cracks on the left side of the larger rotor (1.2 mm, 2.4 mm, and 3.6 mm) and three cracks on the right side of the smaller rotor (1.2 mm, 2.4 mm, and 3.6 mm), as shown in Figure 5. The six faults were referred simply to as L1, L2, L3, R1, R2, and R3, where L represents left, and R represents right. According to the location and depth of the bearing fault, the six fault types were three inner ring faults (0.2 mm, 0.6 mm, and 1.2 mm) and three outer ring faults (0.2 mm, 0.6 mm, and 1.2 mm), as shown in Figure 6. These six faults were also simply referred to as II, I2, I3, O1, O2, and O3, where I represents inner, and O represents outer. On the basis of the location and depth of the gear failure, the six fault types were three kinds of sun wheel failures and three kinds of planetary wheel failures: crack, pitting, and wear, as is shown in Figure 7. In the same way, these six faults were simply referred to as P1, P2, P3, S1, S2, and S3, where P represents planetary, and S represents sun. Therefore, there were three groups of seven health conditions each, and a total of 21 sets of test data needed to be processed. However, the 21 sets of test data to be processed actually only represent 19 health conditions since the failure-free data in the three sets was the same.  
The data settings are shown in Table 1. During the test, the motor speed was respectively set as 900 r/min, 1100 r/min, 1300 r/min, and 1500 r/min. The output speed was three times that of the motor speed since the speed ratio of planetary gearbox was 1:3. It is necessary to collect test data at all speeds for the all 19 cases above-mentioned. Therefore, 78 sets of test data needed to be collected. For the collection, a three-way vibration acceleration sensor (PCB315A) was installed on the surface of the bearing seat. Vibration signals on the upper surface of the bearing seat were collected by the LMS data acquisition instrument, and the sampling frequency was set at 12.8 Khz. A total of 200 samples were collected for each health condition, and 600 data points were taken for each sample at each rotation speed. By stitching together signals at different speeds for the same health condition, 200 samples of each health condition were obtained, each with 2400 data points.  
Figure 4: Rotating components fault diagnosis test bench.  
Figure 5: Shaft crack.  
Figure 6: Bearing fault.  
Figure 7: Bearing fault.
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Group Mobility
#### Protocols for Specific Mobility  
Until now, protocols for specific mobility focus on soccer players, patients in hospitals, and others. First, a new approach FAtigue MEasurement (FAME) [51] was presented to report the fatigue value for soccer players and soldiers. The protocol assumed RPGM and measure fatigue through vivo sensors on the leg. When the player's fatigue value exceeds the threshold, its value is transmitted near the base station in direct communications. This protocol can be used to include three different exercising modes for soldiers: Soldier Walking Model (SWM), Soldier Slow Running Model (SSRM), and Soldier Fast Running Model (SFRM). Their velocity is set to around 5 km/h, 15 km/h and 24 km/h, respectively. In addition, there are few special vibrational pads attached on the leg to reduce the fatigue.  
Second, two outstanding protocols were presented: Distance-Aware Relaying Energy-efficient (DARE) and Mutual Information based (MI-DARE) [52] for eight patients in the hospital. In DARE, each patient possesses multiple data monitoring sensors, event-driven data monitoring sensors and one Body Relay node (BR), whereas one sink node locates at the center in a hospital room. Both sensor and BR check their alive status. If the measured value is lower than threshold, a sensor node measures the distance between them. Based on this distance, it computes energy consumption cost and transmission delay. Upon completing computations on all sensor nodes, BR aggregates all information and then sends it to the sink. An extended MI-DARE focuses on continuous data monitoring sensor. Especially, in order to prevent multiple transmissions, the value is transmitted only if the difference between two consecutive values are beyond the threshold. In addition to two papers above, [PERSON] et al. [53] introduced an architecture to combine WBAN MAC protocol and Cognitive Radio Technology without aiming at any specific environments. The proposed architecture consists of three levels. At Level 0, sensor nodes and actuators are embedded into patients. Each sensor node and actuator has different designs in cognitive approach. In addition, Cognitive Radio Technology (CRT) based Network Coordination Units (NCUs) play a role in sink nodes on the body. The functions include data fusion, spectrum sensing, configurable networking, energy efficiency and reliable data routing, in addition to wireless cognitive connectivity with Central Controlling Units (CCUs). Next, Inter-WBAN communications at Level 1 are accomplished by more than two NCUs that are connected to CCUs. The CCUs are responsible for spectrum sensing, energy efficiency and highly reliable routing and QoS awareness. Finally, CCUs make wireless connections to external hospitals and doctors in Level 2. The architecture is illustrated in Figure 8, where NCUs and CCUs are additionally embedded into existing modules to support CRT.
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 3 Proposed AICHE Transformation  
In this study, we propose the AICHE algorithm to segment the image into two sub-histograms of target and background by improving upon the Otsu method, and then perform the histogram equalization process separately, which ensures that the average brightness of the original image will not be shifted. Additionally, the algorithm segments the histogram of the image based on the adaptive threshold. This effectively avoids the phenomenon of image over-enhancement and also prevents detail loss to a certain extent. The flowchart of the AICHE algorithm is shown in Figure 1.
# Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration
## 2 Materials and Methods  
### System's Optimization Algorithms  
#### 2.1.1 Objective Function  
Minimizing the total energy consumption (\(Q_{total}\)), total energy cost (\(C_{total}\)), and total carbon emissions (\(E_{total}\)) represents the ultimate goal of this system. The load rate (\(lr\)) of each component serves as a critical decision variable for these parameters, allowing for the optimization of energy structure, component capacities, and load rates to achieve the best possible outcomes.  
\[minQ_{total} =f_{1}(lr_{1},lr_{2},\cdots,lr_{n}) \tag{1}\] \[minC_{total} =f_{2}(lr_{1},lr_{2},\cdots,lr_{n})\] \[minE_{total} =f_{3}(lr_{1},lr_{2},\cdots,lr_{n})\] \[0\ll lr_{i}\ll lr_{i,max},i=1,2,\cdots,m\] \[g_{k}(lr_{1},lr_{2},\cdots,lr_{n})\ll 0,k=1,2,\cdots,m\]  
where \(f\) is the objective function, \(i\) is different energy components, \(g\) is the constraint function, \(n\) is the number of decision variables, and \(m\) is the number of constraint functions.  
The overall gain of the system, which serves as the economic objective, aims to enhance the system's competitiveness by maximizing its economic efficiency while fulfilling the ultimate goal. The optimization of the objective function leads to the maximization of the overall gain of the system.  
\[G=max\Big{[}(INCOME_{bene}+INCOME_{resi})-\Big{(}COST_{intve}+COST_{oper}+COST_{ depr}+COST_{fuel}\Big{)}\Big{]} \tag{2}\]  
\[INCOME_{bene}=\sum_{b\in B}\sum_{t\in T}k_{b,t}^{bell}\times P_{b,t}^{bell} \times\Delta t_{t} \tag{3}\]  
\[INCOME_{resi}=\sum_{c\in C^{end,start}}\sum_{t\in T}k_{c,t}^{resi}\times\epsilon _{c,t} \tag{4}\]  
\[COST_{intve}=\sum_{c\in C^{end}}\frac{k_{c}^{inv,prod}}{pvf_{c}}\times P_{c}^{ prod}+\sum_{c\in C^{end}}\frac{k_{c}^{inv,stop}}{pvf_{c}}\times P_{c}^{Stor} \tag{5}\]  
\[COST_{oper}=\sum_{s\in S}\sum_{t\in T}\sum_{c\in C^{end}}k_{c}^{ prod}\times P_{s,t,c}^{prod,load}\times\Delta t_{t}+\sum_{b\in B}\sum_{t\in T} \sum_{c\in C^{end}}k_{c}^{Stor}\times P_{b,t,c}^{Stor,load}\times\Delta t_{t} \tag{6}\]  
\[COST_{depr}=\sum_{c\in C^{end,start}}\sum_{t\in T}k_{c,t}^{depr}\times\varphi_{ c,t} \tag{7}\]  
\[COST_{fuel}=\sum_{b\in S}\sum_{t\in T}k_{b,t}^{buy}\times P_{b,t}^{buy}\times \Delta t_{t} \tag{8}\]  
where \(G\) is the system's overall gain, \(INCOME\) is the system's total income, \(COST\) is the system's total expenses, \(C\) is the total equipment, \(pvf\) is the present value factor for specific components of the equipment, \(P\) is the flow of energy, \(k\) is cost, \(\varphi\) is the depreciation coefficient of the equipment, and \(\epsilon\) is the equipment residual value coefficient. The superscript \(prod\) is production, \(stor\) is energy storage, \(buy\) is energy purchase, \(depr\) is fixed asset depreciation, \(resi\) is equipment residual value, and \(sell\) is energy sales. The subscript \(c\) is each individual piece of equipment, \(b\in B\) is the system's energy demand, \(s\in S\) is load intervals, and \(t\in T\) is time steps.
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 5 Discussion
### Threats and Validity  
* Internal Validity: The datasets used in this study were derived exclusively from the Eclipse and Mozilla open-source projects. While these datasets are widely recognized and provide a diverse range of bug reports, they may not fully represent the characteristics and complexities of other open-source or enterprise-level software projects. The reliance on specific datasets introduces the possibility that the observed performance improvements may not be generalizable to other contexts.
* External Validity: The study's findings may face limitations in applicability when they are extended to other domains or projects with different data structures or reporting formats. Many open-source projects have unique ways of structuring and prioritizing bug reports, and proprietary or business-oriented software projects often follow distinct processes for bug tracking and resolution. Additional verification on datasets from other open-source and industrial projects is necessary to confirm the generalizability and robustness of the proposed model.
* Construct Validity: The study assumes that priority-based feature selection effectively captures the most relevant aspects of bug reports for priority prediction. However, this assumption may vary depending on the quality and consistency of the bug reports in different projects. Variability in how bug reports are written, such as the inconsistent use of terminology or incomplete data, could impact the model's ability to generalize across different datasets.
* Data Imbalance: An imbalance in the distribution of bug report priorities was observed in both the Eclipse and Mozilla datasets, particularly for certain priority levels such as P3 and P5. This imbalance could bias the model's predictions and limit its ability to accurately represent underrepresented categories. Addressing this issue through techniques such as oversampling, data augmentation, or normalization could enhance the model's performance and improve its robustness across diverse datasets.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 3 System Model
### QMix Integration for Multi-Agent Reinforcement Learning  
Our work seamlessly integrates QMix, a potent algorithm designed for cooperative multi-agent systems, into our spectrum allocation framework for vehicular communication. V2V agents, functioning as independent learners in an uncertain environment, collaborate to dynamically explore and determine the optimal spectrum choices.  
The challenges introduced by multiple agents actively exploring the environment extend beyond traditional learning paradigms. To address these complexities, we model the environment as fully cooperative, thus fostering collaborative adaptation among V2V agents under dynamic conditions.  
QMix, also known as \"Deep Q-Mix\", strategically addresses the hurdles of cooperative learning by introducing a central mixing network [24; 25]. This network efficiently blends the individual Q values of agents, thereby enabling seamless information sharing and coordinated decision making.  
In Figure 4, we illustrate the decentralized Q values integrating into a centralized mixing network. Each node represents the Q value estimation network for an individual agent \((Q_{1}(s_{1},a_{1}),\dots,Q_{N}(s_{N},a_{N}))\), where \(o_{1}\dots o_{N}\) represents the agent observation, and \(a_{1}\dots a_{N}\) represents the corresponding action. These networks estimate the Q values for each agent's actions in a given state, and the Q values flow through the central \"Mixing Network\" block.  
Agents actively share their Q values, thus contributing to the mixing network. This network utilizes hyper-networks to assign mixing weights to each agent's Q value, thereby determining their influence on the final joint action value function. The output of the network, depicted as the node, represents this joint action value function \((Q_{tot}(s,a))\). It encapsulates the combined value for all agents taking a specific joint action in the current state.  
This collaborative decision-making process guides agents toward optimal spectrum allocations, thereby ultimately enhancing communication and network efficiency.  
\[Q_{\text{tot}}(s,a)=\sum_{i}w_{i}(s)\cdot Q_{i}(s_{i},a_{i})+V(s) \tag{10}\]  
where:  
Figure 4: Generalized illustration of the QMix Structure [26].  
\(Q_{\text{tot}}(s,a)\) is the joint action value function for state \(s\) and joint action \(a\).  
\(w_{i}(s)\) are the mixing weights for agent \(i\), which are determined by the hyper-networks. \(Q_{i}(s_{i},a_{i})\) are the individual Q values for agent \(i\) in state \(s\) and action \(a_{i}\).  
\(V(s)\) is the state-dependent bias term in Equation (10) respectively.
## 2 The Bridge-Borne Noise Prediction Model  
The bridge-borne noise prediction model consists of the vehicle-track-bridge coupled dynamic model, the track-bridge finite element model, and the bridge boundary element model, as shown in Figure 1. The dynamic model is used to compute the wheel-rail interaction forces excited by irregularities. The time-domain wheel-rail force is further used as the input of the track-bridge finite element model to solve the vibration responses of the track-bridge system. Finally, the bridge vibration responses are imported into the bridge acoustic boundary model as the acoustic boundary condition to calculate the bridge radiated noise.
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 3 Simulation and Verification of Real World Signal
### Setting of Simulation Signal
We use linear frequency modulation (LFM) as the transmitting signal of active sonar. The LFM signal can not only improve the anti-interference ability and target recognition efficiency, and more effectively carry out underwater target detection, but also the time frequency image of its echo signal is suitable for convolutional neural network training and recognition. The time function of the LFM signal can be expressed as,  
\[S(t)=\left\{ \begin{array}{c}A\mathrm{e}^{\mathrm{i}2\pi(f_{0}t+\frac{1}{2}kt^{2} )}-T/2\leqslant t\leqslant T/2\\ 0\end{array} \right. \tag{23}\]  
In Formula (23), \(A\) is the amplitude of the LFM signal, \(f_{0}\) is the center frequency, \(k\) is the frequency change rate of the signal, and the pulse width of the signal is \(t=[-T/2,T/2]\). We set the transmission frequency modulation signal \(f_{0}=2500\) Hz,\(k=2500\) Hz/s, and the transmission duration of the transmission signal is 1 s. The time-domain waveform and frequency-domain waveform of the transmitted signal are shown in Figure 11.  
After transmitting the signal, active sonar waits for 1 s to receive the signal, and the receiving time is 20 s. Waiting for 1 s reduces the effect of reverberation.