esa-sceva/satcom-chunk-collection huggingface dataset:
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
###### Abstract
In this work, we abstracted the lane lines with geometric characteristics as a series of discrete key points and proposed a lane detection method of parallel multi-scale feature aggregation based on key points, FPLane. It focuses on the precise location of key points in the global lane line, and the global detection results are aggregated into the local geometric modeling of lane lines by using the idea of association embedding. Moreover, the proposed parallel Multi-scale Feature Aggregation network (MFANet) in FPLane can integrate the context spatial information of multi-scale feature mappings. In addition, MFANet incorporates the newly designed Double-headed Attention Feature Fusion Up-sampling module (DAFU), which is able to facilitate the network to accurately recognize and detect objects under extreme scale variation. Finally, the main contributions of this paper can be summarized as follows:  
* Through abstracting the lane lines as a series of discrete key points, we propose a novel lane detection method based on key points, FPLane. Different from previous detection methods, the proposed method can accurately predict key points of lanes directly from input images or videos, which not only eliminates the need for an object detection box and complicated post-processing, but also reduces the influence of background pixels, thus simplifying the output of the model. Therefore, it is suitable for processing road scenes that include arbitrary numbers or any structures of lanes. * We propose the parallel Multi-scale Feature Aggregation network to take full advantage of the prior information of adjacent lanes. MFANet can not only extract more discernible representations in complex scenarios with a weak appearance (e.g., vehicle occlusion, road degradation or weak light, etc. ), but can also alleviate the information loss in the feature sampling to a certain extent. * We propose the Double-headed Attention Feature Fusion Up-sampling module and integrate it into MFANet. This module can precisely cast the sampled feature mapping to the higher resolution representation, and can effectively integrate the spatial information of depth features.
## 2 Wind-PV-Thermal-Storage Scheduling Mode Considering Demand Response  
Further development of the energy revolution and the digital revolution has let the smart grid and the Internet of things continue to merge and develop. Among them, the smart grid is characterized by informatization, automation and interaction, and the Internet of things can realize intelligent identification, tracking, monitoring, and management of goods based on the Internet and traditional telecommunication network. These technologies make demand-side resources can be flexibly scheduled, which is conducive to the interaction between users and the power grid. In addition, it is helpful to achieve peak load cutting and valley filling and improve the absorption rate of renewable energy. The wind-PV-thermal-storage scheduling model considering demand response consists of two stages, as shown in Figure 1.  
The day-ahead scheduling is implemented one day in advance, and the total dispatching cycle is 24 h, unit time of 1 h. The power grid department informs the price demand response users in advance and predicts the system load of the next day. In addition, the dispatching center receives the predicted output values of each wind farm and photovoltaic power station on the next day and arranges the output of each thermal power unit, wind farm, photovoltaic station, and storage station in the next day. Considering that the thermal power unit start-stop is time consuming, high cost, and time price cannot be changed after the release, the thermal power unit start-stop combination and price demand response are determined in the day-ahead stage, which cannot be changed in the intraday stage.  
The intraday scheduling is carried out 15 min in advance, and the whole dispatching cycle is 1 h. The output of units, energy storage power station, and IDR at each time period are revised in a rolling manner. The power grid department informs intraday demand response users in advance and gets the ultra-short predicted values of the wind farm and photovoltaic power station. In addition, the intraday system load considering the day-ahead demand response is calculated at this stage to correct the output of the started thermal power unit and determine the intraday demand response and energy storage power station output.  
Figure 1: Wind-PV-thermal-storage scheduling mode considering demand response.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Secure and Resilient Communication Channels
#### 4.1.3 Transmission Delay  
Many smart grid control system actions (like real-time state estimation and communication between the power supplier and consumer) require time-sensitive data. Many operations and services have max latency of the data; for example, the protective relay is in 4 ms, and the situational awareness monitor system based on PMU is in sub-seconds [19]. In this case, data transmission delay is also considered critical to data availability and smart grid operations. A Quality of Service (QoS) routing protocol can effectively reduce the transmission delay [44].  
The transmission latency and reliability analysis in the distribution automation area was studied in [32; 45]. In [38], an SDN model was developed collecting real-time network flow to create static network topologies and calculate QoS to guarantee the transmission delay. In [46], the authors used a method that combined code division multiple access (CDMA) and compressed sensing to solve the transmission delay problem in the meter reading process under sparse data arrival scenarios. [PERSON] et al. studied fog computing's performance in response time, transmission delay, and energy management cost in 5G smart grid distributed network communication [47].
# An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
## 4 Results and Discussion
### Recognizing the Existing Obstacles in the Development of SCs
#### 4.1.4 Street Lighting  
In SCs, the maintenance and control of streetlamps can be more cost-effective and straightforward through the use of the IoT [44]. In particular, IoT systems can be paired with sensors that connect to a cloud-management solution [45], providing confident monitoring of illuminated transport paths such as streets and the movement of people and vehicles. Measuring the environmental conditions can also allow for a more accurate analysis regarding the need to improve the lighting schedule and indicate if lights should be brighter or dimmer [46]. On the other hand, IoT systems have a remarkable effect on energy-saving associated with urban street lighting as using warmer lights and increasing light uniformity can result in a 30-50% energy saving on street lighting, and for medium-sized cities with populations around 200,000-400,000 residents, energy savings on street lighting it can reach 8-23 MWh per annum [47].
## 2 Integration of Fiber Communication System and BOTDR Sensor System
The measured optical spectrums after the multiplexer and BPF are illustrated in Figure 1 inset. Figure 1: Experimental configuration for the integration of Brillouin optical time-domain reflectometry (BOTDR) sensor system and fiber communication system. (DFB-LD = distributed-feedback laser diode, PC = polarization controller, MZM = Mach-Zehnder modulator, DD-MZM = dual drive-MZM, EDFA = erbium-doped fiber amplifier, ASE = amplified spontaneous emission, PBS = polarization beam splitter, PMF = polarization maintaining fiber, PBC = polarization beam combiner, CIR = circulator, B-PD: balanced photodetector, ESA = electrical spectrum analyzer SMF = single-mode fiber, ISO = isolator, BPF = band pass filter, LNA = low noise amplifier).
# Development of Ground Special Vehicle PHM with Case-Based Reason Model
## 4 Application Validation and Analysis
### Results and Analysis
#### 4.2.2 Analysis on Result of Degree of Health Performance Determination  
In this case, the results obtained by the three methods are shown in Table 7, and the results are plotted in Figure 8. For the five data sets, the SA-CBR method is the smallest, indicating that the SA-CBR method has the strongest weight optimization ability. At the same time, except for data set B5, the RMSE value of the CW-CBR method is smaller than that of the EW-CBR method. CW-CBR has a certain weight optimization ability for numerical prediction problems, while EW-CBR has the worst reasoning effect.  
From a comprehensive analysis, SA-CBR shows the best reasoning performance for both normal/abnormal state classification problem and health performance determination. In the problem of normal/abnormal state classification on data set A1 to A5, the state classification accuracy of this method increases by 4.24%, 4.92%, 0.95%, 4.31%, and 1.57%, respectively, compared to EW-CBR. In the problem of degree of health performance determination on data set B1 to B5, the root mean square error also decreases by 1.953, 1.559, 1.431, 0.328, and 0.328, respectively.  
In summary, it can be seen from experimental results that for the allocation of feature attribute weights; SA-CBR is superior to the CW-CBR, while the CW-CBR is superior to EW-CBR. SA-CBR has the strongest weight optimization ability and can adapt to various types of data sets. SA-CBR has the highest reasoning accuracy in both normal/abnormal state determination and degree of health performance determination in the study. Although the CW-CBR has some capability of feature attribute weight optimization, it has some limitations on data set requirements. In some cases, it is not even as good as the EW-CBR method. Therefore, it can be inferred that the water injection principle method for weight distribution based on data correlation also has limitations.
## 4 Conclusions  
This study developed an artificial intelligence home monitoring system using CNN and LSTM based on the Android development platform. The system monitors and predicts indoor temperature using the LSTM and CNN-LSTM models. The experimental results indicated that the performance of the proposed LSTM model was superior to that of other models [14; 18; 19; 20; 21] in terms of MAPE and RMSE. The proposed home monitoring system uses the QoS 0 messaging service \"at most once\" to speed up communication. The MQTT protocol cannot confirm that the network is connected successfully in the communication layer. A KNN algorithm was employed to impute missing sensor data. The experimental results indicated that the system effectively performed data imputation. The LSTM model is more suitable for indoor temperature prediction because it performs with a small MAPE and RMSE. The CNN-LSTM model takes 50% less time for training compared with the LSTM model. The proposed hybrid CNN-LSTM model can be used in portable devices to reduce the computational burden. Its main feature is the development of a home monitoring system that integrates ZigBee, KNN, CNN, LSTM, min-max normalization, MQTT, Node-RED, and a mobile app. In the future, additional mobile platforms and communication protocols will be explored to enhance compatibility and broaden the scope of applications, with particular emphasis on improving the application interface and user experience. Furthermore, edge computing will be integrated to alleviate computational burdens and improve communication accuracy. Statistical tests, such as \(t\)-tests or ANOVAs, will be employed to validate performance differences between models.  
Conceptualization, G.-M.S. and T.-H.C.; methodology, S.D.K. and Y.-J.C.; software, T.-H.C.; validation, G.-M.S., S.D.K., T.-H.C. and Y.-J.C.; formal analysis, S.D.K. and T.-H.C.; investigation, Y.-J.C.; resources, T.-H.C.; data curation, G.-M.S. and S.D.K.; writing--original draft preparation, T.-H.C. and Y.-J.C.; writing--review and editing, G.-M.S. and S.D.K.; visualization, S.D.K.; supervision, G.-M.S.; project administration, G.-M.S.; funding acquisition, G.-M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council (NSTC), Taiwan, grant number NSTC 113-2622-E-027-001.  
Not applicable.  
No new data were created.  
The authors would like to thank the National Science and Technology Council (NSTC), Taiwan, R.O.C., for financially supporting this research under contract no. NSTC 113-2622-E-027-001. They are grateful to the Taiwan Semiconductor Research Institute (TSRI) for fabricating the test chip. This manuscript was edited by Wallace Academic Editing.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Simulation Results  
The simulation results showed that, when \(T_{I}\) was \(-2.5\)\({}^{\circ}\)C and cabinet air temperature was 0 \({}^{\circ}\)C, \(T_{differ}\) was \(-2.5\)\({}^{\circ}\)C, which did not meet the design requirement. Thus, further reduction of the temperature of the cold radiation source was needed. The refrigerating fluid used in the chamber was Freon and its lowest boiling point was \(-50.0\)\({}^{\circ}\)C under normal temperature and pressure. While taking the load of the refrigeration compressor into consideration, \(T_{radiation}\) was set to \(-40.0\)\({}^{\circ}\)C. The power of the heating wire was set to 40.0 W based on the theoretical calculation, and it showed a condensation degree of 0 on the upper and bottom films of the isolation layer. As shown in Figures 6 and 7, \(T_{differ}\) then reached \(-3.2\)\({}^{\circ}\)C, meeting the design requirement.  
Figure 5: \(T_{differ}\) in the three observation nights, where (\(\blacktriangle\)), (\(\blacklozenge\)), (\(\diamond\)) is represented for the dates of February 4 to 5, March 1 to 2, and January 26 to 27, 2016, respectively.
## 2 Components of the Digital Twin System for Wind Turbines
### The Construction of the Digital Space  
The digital space of a wind turbine is a virtual world created from the physical turbine entity and environmental data, processed through modeling and digital techniques. Its main function is to provide a visual display, addressing the issue of background operational data that cannot be intuitively shown. Based on the wind turbine's actual parameters, the 3D model constructs the digital space, incorporating elements such as lights and cameras to visually display the real wind turbine entity. The digital space structure of the wind turbine consists of four main parts: geometric modeling, geometric assembly, format conversion, and scene construction, as shown in Figure 2.  
Figure 1: Intelligent monitoring system framework for wind turbines.  
Geometric modeling is the foundation for visualizing wind turbines. When constructing the geometric model of the wind turbine, it is essential to ensure high consistency between the physical entity and the model, using 1:1 scaling based on geometric dimensions. Using the 750 KW wind turbine from a wind farm in Xinjiang Uygur Autonomous Region as a reference, the spindle bearing and wind turbine models are created using 3D modeling software Solid Works 2021. The spindle bearing model of the wind turbine is shown in Figure 3, while the wind turbine model will be presented later. Additionally, the component models are optimized for lightweight processing, retaining only key parts while simplifying irrelevant ones to reduce computational load and improve processing speed.  
Geometric assembly involves assembling parts according to their technical specifications and the wind turbine's solid geometry constraints. During assembly, it is crucial to adhere to the assembly relationships of the wind turbine, including position and motion relationships.  
Since the virtual model created using 3D modeling software cannot be directly used in Unity 3D, third-party software is required to convert the format. The model saved in SolidWorks is first imported into 3D Max, where it is converted to FBX format. Finally, the FBX model is imported into Unity 3D for further operations.  
Figure 2: Construction of the digital space.  
Scene construction focuses on the geometric model of the wind turbine, integrating it with the actual working environment to create a simulation closely resembling the real-world setting. In Unity 3D, plug-ins like Camera, Light, Terrain, Button, and Text are used to integrate the wind turbine model with the environment, enhancing the digital space's resemblance to the real world.
# Effect of Inense Ash on the Engineering Properties of Cement-Based Composite Material
## 2 Materials and Methods
### Methods  
The incense ash was mixed following the procedure specified in ASTM C305, specimens with incense ash replacing up to 30% (volume) of cement were produced, and two types, namely W/B 0.3 and 0.4, were used. The newly-mixed mortar subsequently underwent tests to determine its fresh properties, including the water requirement for normal consistency and setting time (ASTM C187). After setting, the specimens, which were made in various replacement ratios, were cured under different temperatures (25 \({}^{\circ}\)C and 75 \({}^{\circ}\)C) for 3, 7, 28, 60, and 120 days and then tested for compressive strength (ASTM C109) [33], heat conduction (ASTM E1225) [34], and water absorption (ASTM C642) [35] to determine their engineering properties. Afterward, a thermogravimetric analyzer (TGA) was used to explore their thermal behavior, and XRD and SEM were performed to determine the differences in microstructures and hydration products, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Composition (\%)** & **Cement** & **Incense Ash** \\ \hline SiO2 & 21.31 & 12.95 \\ Al2O3 & 4.58 & 6.03 \\ Fe2O3 & 2.87 & 1.86 \\ CaO & 65.37 & 47.35 \\ MgO & 1.18 & 3.14 \\ SO3 & 2.13 & 3.62 \\ K2O & 0.62 & 5.09 \\ Na2O & 0.26 & 3.77 \\ LOI & — & 15.2 \\ \hline \multirow{2}{*}{Physical property} & Fineness (m2/kg) & 321 & 688 \\ \cline{2-3}  & Specific gravity & 3.15 & 2.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and physical properties of cement and incense ash.
## 4 Key Application Areas for Passive Wake-Up Radio
### Medical Applications  
Medical application in the form of wireless body area networks (WBAN) is a recent revolutionary sensor network technology used to monitor the human body for both medical and non-medical purposes. High energy efficiency, reliability and variable data rates are thus required in WBAN since emergency and life-critical data is handled. The passive wake-up radio application in the medical field is shown in Figure 9b.  
Communication in WBAN is either on-demand for sending normal data, usually initiated by the external sink node or event-driven for sending emergency data, initiated by the sending node. However, long ranges are not necessary for medical applications [116] since the sink node is mostly within the same facility with the sensor nodes, but reducing energy consumption while idle is very important. These make the passive wake-up radios suitable for the medical application. The passive wake-up protocols proposed in [100; 101; 102] are designed specifically for the WBAN. Other protocols, such as the passive wake-up radio protocol described in [64; 108] have the criteria that match the WBAN requirements with ultralow power consumption and addressing capability. A recent design presented in [117] called W2B-IoT, implemented and validated a prototype of a WuR-enabled IoT with a two-tier end device to IoT server connection via BLE and LTE. The solution is targeted at a 5G IoT scenario where battery powered massive IoT devices do not support direct 3 GPP connections. This fits well the data transmission range of BLE for indoor applications and can be applied in health-care scenarios.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 2 Related Works
First of all, the works in [33; 34; 35] introduced AI technology to routing protocols for wireless sensor networks in order to improve the energy consumption of each node. On the other hand, the authors of [36] implemented an intelligent routing protocol in a particular, simple SDN topology. Their proposed intelligent routing protocol was based on the reinforcement learning process to choose the best data transmission paths according to the best criteria in terms of weights periodically rewarded by each node on the current path. However, a formula associated with the cost function was not specifically defined and the impact of rewarding periods was not considered. [PERSON] et al. [37] proposed an application-aware multipath flow routing framework (AMPS) by enabling the controller of SDN to prioritize each flow using machine learning techniques and to assign one or more paths based on its classified priority even if the flow are between the same pair of nodes. The main contribution of AMPS controller in comparison to SDN with traditional routing is its ensuring high availability of an unloaded path for high priority flows even in a heavily loaded network. [PERSON] et al. [38] presented a routing strategy based on deep Q-learning (DQL) to generate optimal routing paths autonomously for SDN-based data center networks. However, they aimed to provide different quality of service guarantees for mice-flows and elephant-flows, designated in a data center network. The aforementioned works have obtained considerable improvement on network performance by introducing AI technology. To avoid network congestion that may cause serious packet delay, packet loss and throughput degradation, this paper proposes an AIER mechanism by introducing an ANN model to the controller of SDN that uses flow load and link load as the feature data, and queuing size of an OpenFlow switch as label data. The proposed AIER mechanism consists of three stages: (1) collection of a set of adequate data for model training, (2) establishment of an ANN model in the control plane using the training data, and (3) application of the ANN model for path selection.
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
###### Abstract  
Accurate segmentation of medical images is vital for disease detection and treatment. Convolutional Neural Networks (CNN) and Transformer models are widely used in medical image segmentation due to their exceptional capabilities in image recognition and segmentation. However, CNNs often lack an understanding of the global context and may lose spatial details of the target, while Transformers struggle with local information processing, leading to reduced geometric detail of the target. To address these issues, this research presents a Global-Local Fusion network model (GLFUnet) based on the U-Net framework and attention mechanisms. The model employs a dual-branch network that utilizes ConvNeXt and Swin Transformer to simultaneously extract multi-level features from pathological images. It enhances ConvNeXt's local feature extraction with spatial and global attention up-sampling modules, while improving Swin Transformer's global context dependency with channel attention. The Attention Feature Fusion module and skip connections efficiently merge local detailed and global coarse features from CNN and Transformer branches at various scales. The fused features are then progressively restored to the original image resolution for pixel-level prediction. Comprehensive experiments on datasets of stomach and liver cancer demonstrate GLFUnet's superior performance and adaptability in medical image segmentation, holding promise for clinical analysis and disease diagnosis.  
medical image segmentation; swin transformer; ConvNext; GLFUnet; cancer