esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Introduction and Analysis of Experimental Results of the Demonstration System  
### Introduction of the Demonstration System  
The structure of the demonstration community microgrid system is shown in Figure 2. The entire community includes a typical community microgrid system, a public building sub-microgrid system, a family sub-microgrid system, a rooftop photovoltaic installation capacity of 100 kW, a container-type 100 kW/210 kWh battery ESS, and three sets of 5 kWh distributed lithium battery ESS; the total load in the community is about 130 kW. Photographs of key equipment in the microgrid system are shown in Figure 25, including rooftop photovoltaics, energy storage batteries, electric heating facilities, air-source heat pumps, and monitoring systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategies** & **Electricity** & **Loss Cost of the** & **Comprehensive Cost** \\  & **Cost** & **ESS** & **(yuan)** \\ \hline Basic real-time control strategy & 22,108 & 18,024 & 40,132 \\ Overall optimization parameters & 21,540 & 16,214 & 37,754 \\ Optimizing parameters by scenarios & 20,538 & 15,649 & 36,187 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Annual operation effect of different strategies.  
Figure 24: Comprehensive cost value of variable parameters.  
The microgrid monitoring system interface is shown in Figure 26. The monitoring system realizes the monitoring and control of photovoltaic power, the ESS, the load, and other equipment in the community microgrid system. The monitoring system can display the status of photovoltaic power generation and the status of energy storage batteries. It can monitor the real-time data of load power consumption and the weather data. For controllable equipment, the monitoring system can send instructions to control it. The monitoring system includes a local database function to record and store operating data. Historical data can be displayed in the form of curves, lists, and so on. The strategy of energy management is realized by programming in the monitoring system.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 1 Introduction
### Motivation  
In body-centric communications, usually the achievable data rate is not of primary concern. Even though THz communications have shown to achieve very high data rates, for these specific applications energy efficiency is more crucial. In the pioneering works of [PERSON] et al. (2016); [PERSON] _et al._ (2016); [PERSON] _et al._ (2016), the authors proposed the use of Time-Spread On Off Keying (TS-OOK), where \"1\" and \"0\" are represented as a pulse and silence, respectively. Furthermore, a link budget analysis was also presented in [PERSON] _et al._ (2016). In addition, molecular absorption noise, which is a particular noise that exists when this frequency band is used for communication inside the human body, has been shown to significantly affect the pulses in TS-OOK. Therefore, there is a need to explore more reliable modulation schemes, suitable for reducing the effect of the molecular absorption noise.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 4 Progress of CO\({}_{2}\) Utilization in China
### Development Status of CO\({}_{2}\) Chemical Utilization in China
Shanghai Advanced Research Institute, CAS, achieved a highly selective synthesis of gasoline fractions with high isomeric hydrocarbons (C5-C11) by direct CO\({}_{2}\) hydrogenation in 2017 [120] and direct conversion of CO\({}_{2}\) hydrogenation to aviation fuel under milder conditions in 2021 [121]. In May 2022, [PERSON] et al. achieved the highly selective preparation of long-chain olefins by ambient-pressure hydrogenation of CO\({}_{2}\)[122]. In June 2022, the world's first 10,000-tonne CO\({}_{2}\) hydrogenation to aromatics industrial pilot project jointly constructed by Tsinghua University and Jiutai Group started construction [123], further promoting the production process of high-end chemicals from CO\({}_{2}\). \begin{table}
\begin{tabular}{c c c} \hline
**Project** & **Operation Start Year** & **Technology** & **Productivity** \\ \hline Lanzhou liquid solar fuel synthesis demonstration project & 2020 & Methanol synthesis from CO\({}_{2}\) hydrogenation technology & 1440 tonnes/year \\ CNOOC methanol synthesis industrial pilot plant Guangdong Huizhou Daya Bay carbonate synthesis project & 2021 & Carbonate synthesis from ionic liquid catalyzed CO\({}_{2}\) & 100,000 tonnes/year \\ Erdos aromatics industrial pilot project by Tsinghua University and Jiutai Group Shanxi Lu’an CH\({}_{4}\)-CO\({}_{2}\) dry reforming project & 2021 & Aromatics synthesis from CO\({}_{2}\) hydrogenation hydrogenation & 10,000 tonnes/year \\ \hline \end{tabular}
\end{table} 
Table 5: CO\({}_{2}\) chemical utilization projects in China [50,113,123]. Syngas production from CO\({}_{2}\). As the \"cornerstone\" of the synthesis industry, syngas has a huge market demand [113]. The efficient production of syngas can be achieved by employing recent technological advancements such as CH\({}_{4}\)-CO\({}_{2}\) dry reforming [124, 125], CH\({}_{4}\)-CO\({}_{2}\)-H\({}_{2}\)O ternary reforming [126], and reverse water gas shift reaction (RWGS) [127]. Moreover, these technologies promote the effective utilization of natural gas while lowering CO\({}_{2}\) emissions. In August 2021, the world's first 10,000-cubic-meter-level CH\({}_{4}\)-CO\({}_{2}\) dry reforming facility for syngas production [128] was completed in Changzhi, Shanxi. After achieving stable operation, it realizes the daily conversion of CO\({}_{2}\) up to 60 tonnes [129]. In addition, this facility can realize the efficient utilization of CO\({}_{2}\) and the flexible adjustment of H\({}_{2}\)/CO of the product syngas, and it is ready for industrial application promotion [130].
## 2 Problem Description
### Aiming Strategy Problem
#### 2.1.2 Optimization Strategy  
The heliostat field is partitioned into different sectors to reduce the computational load of calculations and each corresponding to a panel on the central receiver, as depicted in Figure 3. Each sector is associated with a receiver panel, forming an entire heliostat field. The full field can be seen as a collection of multiple sector heliostat fields. This paper proposes a real-time aiming strategy optimization method for heliostat fields affected by continuously dynamically changing clouds, which mainly considers the real-time aiming strategy calculation for continuously similar heliostat field scenarios. The shape of the heliostat field has little influence on real-time calculation. Therefore, the whole heliostat field is divided into several sector heliostat fields, and selecting one of them is the application scenario of the subsequent study. We assumed that these sectors were independent and would not affect each other in the calculation process. Then, the whole heliostat field real-time aiming optimization problem can be reduced to a collection of multiple sector heliostat fields real-time aiming strategy optimization problems.  
Figure 2: The structure of the receiver geometry. The eastern and western panels are numbered from the entrance (north) to the exit (south) and depict the flow paths in the west (**right**) and the panel and the optional aiming points (**left**).  
Let \(M\) be the full heliostat field, and \(M_{l}\) (for \(l=1,2,\ldots,L\)) represents the sector heliostat fields; \(L\) denotes the number of the sub heliostat field. Let \(F(M)\) be the aiming objective function of the full heliostat field \(M\), which can be simplified, and \(F_{l}(M_{l})\) be the aiming objective function of each sector heliostat field \(M_{l}\).  
The full heliostat field real-time aiming optimization problem can be described as:  
\[\max_{M}F(M) \tag{3}\] \[s.t.\;\;M\]  
The problem of full heliostat field real-time aiming strategy optimization can be simplified as a set of \(L\) sub-problems, which is the main concern in this paper:  
\[\max_{M_{l}}F_{l}(M_{l}) \tag{4}\] \[s.t.\;\;M_{l},l\in\{1,2,...,L\}\]  
After solving these sub-problems, the optimized sector heliostat fields \(M_{l}^{opt}\) can be combined to obtain the optimized full heliostat field aiming strategy \(M^{opt}\):  
\[M^{opt}=M_{1}^{opt}\cap M_{2}^{opt},\ldots,\cap M_{L}^{opt}. \tag{5}\]
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 3 Problem Statement and I3 Mote Scope
### Full Product Coverage  
I3 Mote integrates most of the components needed to be deployed as a product, avoiding in some cases the need to build a custom solution. Being specifically designed for this purpose, the I3 Mote features the most advanced low power components to cover industrial sensing, on board computing and industrial wireless/wired connectivity, in an attempt to cover all the specifications and weakness described in Sections 1 and 2 (see Table 1). In short (see Figure 1):  
* The I3 Mote features a multi-source power solution. It supports energy harvesting from thermoelectric or solar/indoor light sources. Energy can be stored in rechargeable batteries or just drawn from primary batteries. It also supports the industrial grade power lines over 8-36 V, being essential when there is already available power in an existing industrial network. For maximum flexibility, the platform is designed to provide full software control over different power management options.
* Sensor fusion is built around a high performance, ultra-low-power Advanced RISC Machine (ARM) Cortex-M4F core, the MSP432. The platform includes several on-board sensors (temperature, humidity, acceleration, ambient light, pressure) as well as an Smart Sensor Interface (SSI), a \"de-facto\" standard to enable multimodal extension.
* Communications rely on an advanced low power wireless SoC, the CC2650, featuring an ARM Cortex-M3 core and a 2.4 GHz-compliant radio able to support IEEE 802.15.4 on 2.4 GHz, Bluetooth and BLE. It is also perfectly suited for the new wave of industrial Internet of things (IIoT) deterministic standards such as IEEE 802.15.4-2015 time-slotted channel hopping (TSCH) and the IETF 6 TiSCH stack.
* Thanks to its Highway Addressable Remote Transducer (HART) interface, the I3 Mote provides a perfect solution for a wired-wireless bridge, wireless redundancy of a wired-channel, HART sensor wireless replacement and the existing 4-20 mA/HART connectivity.  
This deployment-oriented approach is reinforced by a robust design to meet the industrial-grade reliability.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 3 The Proposed Network Framework
### Mitigating Overfitting  
Networks with more complicated functions can perform better generalization performance as they learn different representations in each of their layers. However, complicated networks can easily over-fit the training data, producing poor generalization capacity and testing performance. Over-fitting occurs when a network model with high capacity fits the noise in the data instead of the underlying relationship.  
Therefore, to avoid over-fitting in the proposed RNN model, three standard techniques were used. These were Gaussian noise injection into the training data [32], using a ReLU activation function in the hidden layers [20], and subsequently applying the dropout technique [29]. Dropout leads to big improvements in the prediction performance of the model. When a dropout technique is applied, the generalization capacity of the model is improved because the network is forced to learn multiple independent representations of the data. A probability of 30% was used in the dropout technique. This is because low probability has minimal effect and a high probability results in under-learning by the network.
## 6 Conclusions  
Hence, this article introduces a new CEAGA to solve the task allocation challenge in IUWSNs. Firstly, the model for the allocation problem was determined. On the basis of introducing the CEAGA into task allocation, a result function for evaluating network efficiency was designed to maximize the efficiency of IUWSN. In addition, numerical simulations were conducted on different numbers of sensors and targets using the CEAGA in IUWSNs to demonstrate its effectiveness compared to the HGA, MBPSO, and ISA methods. Simulation experiments have shown that this method has higher network efficiency compared to the other three methods, and as the number of sensors and task targets increases, the optimization effect of the CEAGA will become better and better. Therefore, the CEAGA has a significant contribution in the task allocation of IUWSN.  
However, this article also has its limitations, primarily stemming from the lack of consideration for latency and bandwidth in the context of IUWSNs. This may result in overly idealized or impractical task allocation schemes for IUWSNs, which might not perform effectively in real-world deployments. Simultaneously, overlooking issues like latency and bandwidth could potentially impact the network's stability and sustainability. In future research endeavors, scholars will concentrate on optimizing latency and bandwidth aspects within IUWSN task allocation.  
H.F.: conceptualization, methodology, software, writing--original draft, visualization, validation. B.Z.: data curation, software, writing--original draft, visualization, validation. Y.L. ([PERSON]): writing, investigation, project administration, validation. M.Y.: writing, investigation, project administration, validation. Y.L. ([PERSON]): writing, investigation, project administration, validation. J.Z.: supervision, writing--review and editing, funding acquisition, validation. All authors have read and agreed to the published version of the manuscript.  
This article is supported by the project \"National Key Research and Development Program for Group Intelligent Autonomous Operation Smart Farming\", grant number 2022 ZD0115800, National Key Research and Development Program of the Corps \"Key Technology Research and Application for High Penetration New Energy Grid Dispatch\", grant number 2023 AB010, and Eighth Division Shihezi City Science and Technology Plan Project--Research and Application of Intelligent Inspection Platform for Substation Equipment, grant number: 2023 GY06.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Tasks** & **Number of Sensor Nodes** & **CEAGA** & **HGA** & **MBPSO** & **ISA** \\ \hline
30 & 16 & 0.13 s & 0.31 s & 0.21 s & 0.43 s \\
45 & 24 & 0.18 s & 0.45 s & 0.35 s & 0.51 s \\
60 & 32 & 0.24 s & 0.63 s & 0.49 s & 0.66 s \\
75 & 40 & 0.33 s & 0.81 s & 0.56 s & 0.84 s \\ \hline \hline \end{tabular}
\end{table}
Table 8: Running time of each algorithm in 100 generations.
## 4 Leak Detection Technologies
### Software-Based Methods
#### 4.2.5 Support Vector Machines  
The support vector machines (SVMs) have recently become a valuable tool in water leak detection, providing robust performance in handling complex, high-dimensional data from pipeline systems. The primary mechanism of SVMs involves finding the optimal hyperplane that separates different classes, such as \"leak\" and \"no leak\", based on input features, such as pressure, flow, and acoustic signals. The SVM's effectiveness in leak detection is derived from its capacity to handle both linear and non-linear relationships through the kernel trick, which projects data into a higher-dimensional space where they become linearly separable. This flexibility enables SVMs to adapt to complex, real-world water distribution systems, where leak detection can be challenging due to environmental noise, variability in operating conditions, and the presence of non-leak signals [79; 80]. The SVM was utilized to detect leaks in a simulated water pipe network, analyzing pressure data from various junctions. The system used data from sensors in a water distribution network and the SVM algorithm was trained using the radial basis function (RBF) kernel to classify and detect leakages in the network. The system achieved a root mean square error (RMSE) of 0.06785 for leakage size prediction and 0.1382 for location prediction, indicating a high level of accuracy [81]. Another study focused on the application of a one-class SVM (OCSVM) for anomaly detection in water distribution systems. The OCSVM was used to detect leaks in a cold climate using a hydrant-mounted sensor. The system was designed for minimally invasive monitoring, and the results demonstrated 97% accuracy in classifying leak events from acoustic data [82]. The main advantage of using the SVM in water leak detection lies in its strong classification ability, even when data are noisy or when the relationships between variables are non-linear. However, the limitations of the SVM include its reliance on high-quality training data and its sensitivity to parameter tuning, particularly with kernel selection, which can affect model performance. Additionally, SVMs can be computationally intensive when working with large datasets, which may restrict real-time application for large-scale water networks [83]. Future improvements in SVM-based systems for leak detection include hybridizing SVMs with other algorithms, such as random forests or neural networks, to improve robustness and accuracy. Furthermore, the integration of IoT technologies and advanced sensors could enable more comprehensive data collection, enhancing the precision of SVM models in identifying and locating leaks.
## 4 Conclusions  
In conclusion, we have demonstrated the capability of an MRR configured as notch filter to bypass the limited modulation bandwidth of an RSOA and directly modulate the RSOA nearly three-and-a-half times faster than possible without the MRR. The analysis of the results obtained through modeling and simulation of the RSOA and MRR response reveals that if the MRR critical parameters are properly selected, which is technologically feasible, then the MRR allows one to enhance the RSOA modulation bandwidth with improved performance and encoded signal characteristics. Employing the MRR in practice allows one to leverage the benefits of free spectral range fine adjustment, controllable finesse, sharp spectral selectivity, feasible and versatile detuning, bandwidth and wavelength tunability, enhanced peak-to-noth contrast ratio, the existence of many different material systems and corresponding fabrication processes, integration compatibility with other photonic devices and platforms, and off-the-shelf availability.  
[PERSON] and [PERSON] conceived the paper theme, applied and adapted RSOA and MRR modeling, ran the simulations, derived and interpreted the obtained results and wrote the manuscript.  
The authors declare no conflict of interest.
## 3 Control Strategy Design  
In the previous study, the pump/valve coordinate control strategy for the NIMV system was chosen under the fixed load condition [31; 32]. Therefore, the control method was not too complicated, such as velocity feed forward control (VF) for the valve and PFC for the pump, or VFPB for the valve and PFC for the pump (VF + PID). However, in this study, the variable load and the switching mode are applied to the NIMV system during the boom moving up and down. Therefore, it is necessary to include two factors, load and speed, of the cylinder into the control strategy. Moreover, the PFC method using the proportional integral derivative (PID) controller is not suitable when applying it to the variable load condition. This PID controller is operated based on the displacement error that is feedback from the cylinder, wherein the gain parameters are selected appropriately and fixed setting in the controller in the fixed load condition. However, when the system suddenly changes the load during operation, the displacement error is quickly changed. At this time, the PID controller does not respond in time, causing large errors in the pump and valve control. Hence, the VLFPB method is proposed to apply to the valve and the VFPB method to apply to the pump (VLF + FLC), shown in Figure 3. In this case, the PB method is replaced by the Fuzzy logic controller (FLC).
## 2 Related Works
### Science Gaps in Existing TSR Systems  
Despite significant progress, existing traffic sign recognition systems have made significant progress over the years in detecting and classifying traffic signs. However, there are still some scientific gaps that need to be addressed. One of the main issues is the limited robustness of these systems, particularly in real-world scenarios. Environmental factors such as lighting, weather conditions, and occlusions can significantly affect the system's performance, making it challenging to generalise to new scenarios. A study by [41] finds that existing traffic sign recognition systems have limited robustness when tested under various lighting conditions. The study showed that the systems' performance dropped significantly when the lighting conditions changed from normal to low or high.  
Another science gap is the limited scalability of existing traffic sign recognition systems. These systems often struggle to handle large-scale datasets and generalise to new and diverse scenarios. This is due to the lack of high-quality and diverse training datasets, which makes it challenging to train the system to handle various situations [42]. For example, traffic sign recognition systems struggled to recognise certain types of traffic signs that were not present in their training dataset.  
A third science gap is the limited interpretability of existing traffic sign recognition systems. These systems often lack transparency, making it challenging to understand how the system makes its decisions. This is a significant issue as it makes it challenging to debug and improve the system's performance, and it can also lead to a lack of trust in the system. A study by [43] considered that existing traffic sign recognition systems lacked interpretability and provided little insight into the decision-making process. The study proposed a new approach to traffic sign recognition that used attention mechanisms to provide better interpretability.  
Finally, existing traffic sign recognition systems are unable to accurately recognise and classify rare or novel traffic signs that may not have been encountered during the training phase of the system. In fact, most traffic sign recognition systems are trained on a limited set of traffic signs, which are commonly found on roads. These systems work well for detecting and classifying these common traffic signs, but they may struggle with rare or novel signs, which may have different shapes, colors, or symbols. This can be problematic in real-world situations where drivers may encounter new or unfamiliar traffic signs, particularly in different countries where traffic signs may differ significantly. One of the contributions of this work is to address this science gap through our proposed TSR system.