esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WFGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFHIC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method's superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.  
power quality; wind power; battery storage; fuzzy logic; total harmonic distortion; power factor; artificial neural network +
Footnote †: journal:
## 4 Case Study
### Fuzzy Clustering Method for Identification of the Source of Water Gushing
\begin{array}{cccccc}1.0000&0.6515&0.1944&0.3967&0.1295&0.1707\\ 0.6515&1.0000&0.2510&0.3846&0.1178&0.1706\\ 0.1944&0.2510&1.0000&0.7234&0.6194&0.7506\\ 0.3967&0.3846&0.7234&1.0000&0.6783&0.7527\\ 0.1295&0.1178&0.6194&0.6783&1.0000&0.8268\\ 0.1707&0.1706&0.7506&0.7527&0.8268&1.0000\\ \end{array} \right]. \tag{19}\]  
The transitive closure \(t(C_{2})=C_{2}^{4}=C_{2}^{8}\) was found for the 4-th time, which was shown as follows:  
\[t(C_{2}){=C_{2}}^{4}{=C_{2}}^{8}=\left[ \begin{array}{cccccc}1.0000&0.6515&0.396 7&0.3967&0.3967\\ 0.6515&1.0000&0.3967&0.3967&0.3967\\ 0.3967&0.3967&1.0000&0.7506&0.7506\\ 0.3967&0.3967&0.7506&1.0000&0.7527&0.7527\\ 0.3967&0.3967&0.7506&0.7527&1.0000&0.8268\\ 0.3967&0.3967&0.7506&0.7527&0.8268&1.0000\\ \end{array} \right] \tag{20}\]  
The optimal classification threshold \(\lambda\) was set at 0.6515 and the classification results were shown in Figure 5.  
From Figure 5, it could be seen that \(0.3<\lambda_{min}<0.6\), so there was a weak correlation between the samples inside (S1 and S10) and outside (S11-S14) the tunnel, according to Section 2. As already mentioned in the previous section, samples S1 and S10 had been classified as one. When \(\lambda=0.6515\), the samples could be classified into two categories. The new samples S11, S12, S13, and S14 were grouped together as a new category, and the S1 and S10 were grouped in the other category. According to the fuzzy clustering method, the results of analysis showed that there was a certain difference in the physicochemical composition between the samples from the tunnel and the water system outside. In the early stages of tunnel construction excavation, the water gushing into the tunnel was mainly derived from water in the bedrock fracture, partly from surface infiltration of precipitation.
## 2 Related Works  
### Modbus Gateway  
The frame structures of the three Modbus protocol variants are illustrated in Figure 1. While these protocols achieve transparent application-layer communication, their incompatibilities at the physical layer, data link layer (frame formats), and transport layer necessitate protocol conversion for cross-domain interoperability. Current research has predominantly focused on Modbus TCP-RTU interconversion. [PERSON] et al. [20] designed a Modbus TCP-RTU gateway to communicate between the S7-1200 and the DSC and Controllogix parts of the system. [PERSON] et al. [21] developed an STM32F107-based hardware platform with an RT-Thread real-time operating system, implementing protocol conversion between Modbus and Transmission Control Protocol/Internet Protocol (TCP/IP) to interconnect fieldbus networks with Ethernet. [PERSON] et al. [22] created a C library and socket programming framework for Modbus RTU-TCP/IP conversion. [PERSON] et al. [23] employed Node-RED as a protocol conversion middleware, where Modbus nodes were used to acquire field device data (e.g., PLC register values), followed by data processing (such as unit conversion and JSON formatting), ultimately publishing the transformed data as standardized information models via OPC UA nodes. [PERSON] et al. [24] designed a dual-line Ethernet protocol conversion gateway that converts Modbus TCP to MQTT protocol, transmits data to the Alibaba Cloud IoT Platform, and enables Modbus TCP data reception with MQTT communication. [PERSON] et al. [25] proposed an architecture for a hierarchical sensor network using the MQTT protocol for sensor data acquisition, which interacts with the MODBUS protocol to enable information transmission to SCADA systems. [PERSON] et al. [26] designed a multi-protocol gateway supporting bidirectional conversion among Modbus RTU, ASCII, and TCP.  
However, there is still a big gap in the research on the interoperability conversion between UDP transport and serial links and their reliability enhancement mechanisms. In particular, the research on how to maintain industrial-grade communication reliability under weak network conditions, such as high packet loss rate or out of order rate, has not been fully explored.
# Subcooled Flow Boiling Heat Flux Enhancement Using High Porosity Sintered Fiber
## 2 Experimental Setup and Procedure  
### Test Loop  
The experimental apparatus was similar to that used in [27]. As presented schematically in Figure 1, the experimental apparatus was designed to analyze the subcooled flow boiling heat transfer at a low system pressure. It consisted of a degassing tank with a capacity of 40 L that was installed to store deionized water that was used as the working fluid. A heat exchanger was connected directly to the tank to reduce the working fluid temperature and keep the subcooling conditions. Just after the heat exchanger, a magnetic pump \"IWAKI\" with a maximum liquid inlet temperature of 80 \({}^{\circ}\)C provided the forced convection on the system. A preheater was used to keep constant the inlet subcooling temperature, and the mass flux was regulated by a control valve. An oval Coriolis flow meter monitored the mass flux just before the inlet part of the test section. The water flow was rectified with a porous aluminum body, and the deionized water flowed on the test section. In the inlet and outlet part of the test section, a pressure gauge and a differential pressure gauge were installed, respectively. A proportional integral derivative (PID) controlled the temperature on the test section, and a data logger collected the obtained data. A detailed explanation of the test section was given in Section 2.2.  
Figure 1: Schematic diagram of the experimental apparatus.
## 3 Model Construction and Parameter Analysis of Space-Based Information Network  
SBIN is an integrated space-based and ground-based information network with a certain ability for independent operation management and network reconstruction. It includes spacecrafts with various types of payloads and corresponding operational control systems and application systems. According to the principle of maximum comprehensive utilization of information resources, it takes the spacecraft platform as the hub and adopts the centralized and distributed methods. Through connectivity and information exchange, SBIN makes information acquisition, processing, promotion, transmission and distribution uninterrupted, real-time, safe and reliable. Its functions include navigation, positioning and information transmission, etc., which can be connected with land-based, sea-based and space-based multi-element information systems.
# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 3 Results
### Option 1: ML Applied to Statistical Features  
The SVM achieved a cross-validated accuracy of 0.9528, while the RF recorded an accuracy of 0.9407, and the MLP scored 0.9518. Among these, SVM, with optimized hyperparameters (C = 1, gamma = 0.1, and kernel = rbf), was identified as the best model. Its final accuracy on the test set was 0.9438, as indicated by a confusion matrix comprising 434 true negatives, 16 false positives, 30 false negatives, and 338 true positives. The F1-score for this model was 0.9437, and the Area Under the Curve (AUC) was 0.9872.  
Furthermore, the stacking model, which combined the predictions from SVM, RF, and MLP, also achieved an accuracy of 0.9438, matching that of the SVM. This stacking model maintained consistent F1-score and AUC values, demonstrating its effectiveness in enhancing predictive performance.  
In terms of computational cost, the extraction of statistical features took on average 0.0034 s per 15-s sample. Training times varied significantly between models, with SVM requiring 31.26 s, RF 257.60 s, and MLP 104.96 s. However, once trained, inference times were much lower, with SVM requiring 0.000081 s per sample and the stacking model taking 0.017837 s per sample.
# Auction-Based Behavior Tree Evolution for Heterogeneous Multi-Agent Systems
## 2 Three-Way Handshaking Communication Protocol
### The Formats of Auction Information  
To enable efficient cooperation for heterogeneous MASs in multi-task environments, we adopt an auction-based three-way handshaking communication protocol. The fixed  
Figure 1: Collaboration in heterogeneous MASs within a multi-task environment. The entire allocation process is controlled by the same auction BT, adhering to a three-way handshaking communication protocol based on the auction algorithm. Upon the completion of task allocation, robots evolve heterogeneous execution BTs tailored to their tasks.  
information formats specified in this protocol are described as follows. Let \(R=\{R_{1},R_{2},\ldots,R_{n}\}\) represent a set of \(n\) robots. Each robot \(R_{i}(i\in[1,n])\) is modeled by a set of state information \(S_{i}=\{V,C,L,E,Tk\}\). In order to bid for tasks, bidders are required to report their \(S_{i}\) to the auctioneer. \(V\) and \(C\) are two constant attributes that represent the traveling speed and capability of robots, respectively, to execute different tasks. The location and energy level of robots during the task execution are recorded by \(L\) and \(E\), and \(x\) number of tasks \(Tk=[Tk_{1},Tk_{2},\ldots,Tk_{x}]\) are allocated to robots. Each task \(Tk_{m}(m\in[1,x])\) consists of attributes \(Tk_{m}=\{l,t,p,\tau,\mu,\sigma\}\) and after the bidders have received allocation result \(Tk\), they are ready to execute these tasks. In these attributes, \(1\) is the coordinate of a task in a two-dimensional plane. Different types \(t\) of tasks have different attributes, such as Excavation, Communication, Transportation, Search, and so on. A task is cast with a priority \(p\), which is reflected in the time constraint \(\tau\). The last two attributes \(\mu,\sigma\) represent the energy consumption per unit of time and the total completion level required for a task, respectively; thus, the energy and time for a robot \(R_{i}\) required to execute a task \(Tk_{m}\) depend on the capability \(C_{im}\).
## 2 Entrainment through the Input Pathway
Therefore, now CikA and oxidized quinone are added into the standard KaiABC in vitro mixture in order to test whether CikA contributes to a phase delay in response to quinone [46]. During KaiC's nighttime dephosphorylation phase, KaiA and CikA compete for the same binding site on KaiB. When the bound CikA becomes inactivated by oxidized quinone, it becomes released from the KaiC-KaiB complex, and now the empty spot on KaiB can sequester KaiA, leading to further dephosphorylation. This creates a delay in the oscillation and shifts the oscillation to peak later. In sum, CikA indirectly alters the phosphorylation profile of KaiC by modulating the effective KaiA concentration. Finally, with both CikA and oxidized quinone in the mixture, both a phase advance and a delay of oscillatory KaiC phosphorylation can be generated in response to quinone in vitro, and this gives a more accurate representation of the cyanobacteria's phase-shifting response to darkness (Figure 4) [47]. It is also worth mentioning that the temperature-compensating property of the cyanobacterial circadian clock may provide a further advantage in survival. This is because the central oscillator should be invariant to minor temperature changes in the environment, whereas almost every chemical reaction speeds up with the increase in temperature and vice versa. It is crucial for cyanobacteria to  
Figure 4: Schematic diagram of quinone entrainment. Oxidized quinone (“Q” in a black rhombus) becomes acutely abundant at night as a proxy for darkness. As KaiC autophosphorylation takes place, quinone application at this point inactivates KaiA, leading to premature dephosphorylation and a phase advance (lime arrows). On the other hand, in the in vitro mixture containing CikA and KaiABC, as fully phosphorylated KaiC enters the autodepphosphorylating phase, quinone application at this point inactivates CikA, adding an extra time frame for the KaiA sequestration to take place, delaying the phase (yellow arrows). ensure that the molecular timekeeping is unperturbed. Previously, the standard KaiABC oscillator mixture has been shown to be relatively immune in the biologically meaningful range of temperature between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, with a Q\({}_{10}\) value close to 1.0 [13]. In a similar manner, quinone entrainment with the addition of CikA into the mixture also turned out to compensate for temperature [46].
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
###### Abstract  
The presence of B-line artefacts, the main artefact reflecting lung abnormalities in dengue patients, is often assessed using lung ultrasound (LUS) imaging. Inspired by human visual attention that enables us to process videos efficiently by paying attention to where and when it is required, we propose a spatiotemporal attention mechanism for B-line detection in LUS videos. The spatial attention allows the model to focus on the most task relevant parts of the image by learning a saliency map. The temporal attention generates an attention score for each attended frame to identify the most relevant frames from an input video. Our model not only identifies videos where B-lines show, but also localizes, within those videos, B-line related features both spatially and temporally, despite being trained in a weakly-supervised manner. We evaluate our approach on a LUS video dataset collected from severe dengue patients in a resource-limited hospital, assessing the B-line detection rate and the model's ability to localize discriminative B-line regions spatially and B-line frames temporally. Experimental results demonstrate the efficacy of our approach for classifying B-line videos with an \(F1\) score of up to 83.2% and localizing the most salient B-line regions both spatially and temporally with a correlation coefficient of 0.67 and an IoU of 69.7%, respectively.  
lung ultrasound (LUS) imaging; b-lines; spatiotemporal attention; classification; video analysis Article  
B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], et al. B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention. _Appl. Sci._**2021**, _11_, 11697. [[https://doi.org/10.3390/appl12411697](https://doi.org/10.3390/appl12411697)]([https://doi.org/10.3390/appl12411697](https://doi.org/10.3390/appl12411697))  
11 November 2021  
Accepted: 30 November 2021  
SubSubject: 9 December 2021  
Subject:MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Three-Tier Architecture of LS-WSNs
### Implementation of the Architecture  
The heuristics and construction algorithms of the different clusters of our architecture are taken from the work of [PERSON] et al. [35]. We are interested here in the behaviors of the different sensors of the network as well as in the arrangement of the tasks that can allow us to save the energy of the sensors and to maximize the life span of the network. It should be noted that sensors are generally not autonomous in terms of energy, they are powered by external energy sources such as a battery, for example, the exhaustion of the battery puts the sensor out of use and disconnects it from the network. The problem of the lifespan of the sensors, and therefore that of the network, is closely linked to energy management. Our architecture model relies on the logical structuring of the sensors and the management of the radio component in order to maximize the life of the network.  
Indeed, considering the low energy autonomy that characterizes sensors in general and those of level 1 in particular, we propose a heuristic based on the management of the radio component of the sensors to reduce the energy consumption. To do so, we opt for an adjustment of the periods of activity and inactivity (sleep) of the sensors. The sensors must obey the principle of event-driven transmission as shown in Figure 4 as well as the interactions between the different levels, the exchange of wake-up messages between \"predecessor\" and \"successor\" nodes in the communication chain. In order to respond to alarms that may be triggered, it is intended that nodes (CH), (N2), and (P) remain in listening mode, and upon receipt of wake-up messages, they must perform the programmed tasks without any restrictions.  
With regard to data detection and transmission, interference due to the presence of walls and obstacles may influence the quality of data transmission. In addition, the quality of processing depends on several factors such as: the type of processor, memory, the robustness of the calculation algorithms, the ability to process multimedia content, etc. Finally, the transfer periodicity (transmission interval) of the sensors deployed at the first level of the architecture (N1) can also have an impact on its energy consumption at the sensor level. These aspects will be demonstrated in the simulation part.  
The communication arrangement heuristic is done in two phases: the modeling of the communication within a level and the modeling of the communication between the different levels of the model. In both cases, we will use a states-transitions diagram to describe the behavior associated with the different sensors and the programming of the tasks to be executed according to the realization of the application scenario. The second phase is the definition of inter-sensor communications (inter-level interactions).  
Figure 4: Inter-sensor interaction between global network levels.
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 4 Results
### Model Training and Verification
The results showed that the RBF neural network model is more advantageous than the BP model in reflecting the change in the average insured loss rate of rural houses in each township for the purpose of disaster loss assessment.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 4 Data Collection Scheme Based on Opportunistic Node Connections  
In the scheme, the mobile sink remains stationary and can determine the collection scope during the data collection. Once this data collection is completed, it can move in the network randomly and launch another data collection task anywhere in the network at any time. The proposed scheme can be presented in detail in the following four phases:
# 3D Printing with Mixed Powders of Boron Carbide and Al Alloy
## 1 Introduction  
Using neutron scattering measurement, materials at the atomic level can be probed, which is essential to develop innovations like faster computers, tougher armor, and effective medicines. Radial collimators are used when a neutron scattering instrument employs a large area of detectors to cover a wide range of scattering angles [1; 2]. A radial collimator consists of a series of absorbing septa radially oriented from a fixed point. These collimators will often oscillate several degrees at the sample position. Conventional neutron collimators use thin foils, typically made from mylar, aluminum (Al), or stainless steel, coated in an absorbing material, such as gadolinium (Ga, melting temperature 1312 \({}^{\circ}\)C, thermal conductivity 10.6 W/m.K) or enriched boron (B, melting temperature 2076 \({}^{\circ}\)C, thermal conductivity 27.4 W/m.K), to absorb divergent incident neutrons, or those scattered from unwanted sources. The foils are held together and tensioned to ensure uniformity, using bolted frames. The unit for gripping and tensioning the foils limits how closely they may be placed together, their angular separation, their proximity to the sample, and therefore the efficiency of the collimator. Modelling software, in combination with ray tracing simulations, shows that there are designs that can greatly improve the performance of the collimator but are not possible to implement using conventional assembly or machining techniques.  
Additive manufacturing (AM), especially laser-based AM, becomes a powerful tool to replace conventional methods such as casting and hot spray, due to its cost effectiveness and capability of making complex structures and compositions [3; 4; 5; 6; 7; 8; 9]. Specifically, AM shows significant advantages in parts fabrication with mixed powders, metals, and/or ceramics, to achieve desired performance [7; 8; 9]. However, direct thin-wall (2D and 3D) fabrication for radial collimators for neutron scattering instrument is still a challenging field [1; 2], especially for multimaterials such as a mixture of boron carbide (B\({}_{4}\)C) and Al alloy (AlSi10 Mg). This is mainly due to a limited understanding of composition control, laser melting control, melted structure formation, and phase transition.  
In this paper, research is extended from the fabrication of functional ceramic components to directly make radial collimators for neutron scattering measurement without binders and post process. To the best of our knowledge, this is the first publication demonstrating additive manufacturing using mixed powders of B4C and Al alloy.