esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a tropospheric tomographic method has been proposed, which considers the weightings of multi-GNSS observations as well as that of different constraint information. The data from GPS, GLONASS and BeiDou is used to carry out the experiment, and the improved Bartlett statistics is introduced for the small sample based on the principle of the mean equality in order to determine the final weightings of different input information of tomography modeling. The experimental results using the observed multi-GNSS data show that the posterior unit weight variances of different input information converged to a stable value after three to six iterations for the most epochs. Internal and external accuracy testing are both implemented for two tomography modeling based on the conventional and proposed method under the conditions of 10\({}^{\circ}\) and 15\({}^{\circ}\) elevation angle masks. For the internal accuracy testing, the RMS error and Bias of Scheme 2 under two kinds of elevation angle masks are both smaller than that of Scheme 1, with the values of 1.6/0.1 mm and 1.3/0.1 mm, respectively. For the external accuracy testing, the same conclusion is also concluded that the slant wet delay residual derived from Scheme 2 has a small discrepancy than those from Scheme 1, with the RMS error and Bias of 5.0/0.9 mm and 4.6/1.1 mm, respectively, under two cases of elevation angle masks.  
In addition, the radiosonde data is also introduced to validate the proposed method over the tested period of Doy 303 to 317, 2015, and the statistical results show that the RMS and Bias of the proposed method are both smaller than those of the conventional method, with the value of 9.0/3.1 ppm. By analyzing the relationship of tomographic modeling error with respect to altitudes, we find that the accuracy of tomography result using the proposed method is improved evidently, especially for the height of 0-4 km.  
The authors would like to thank IGAR (Integrated Global Radiosonde Archive) and ECMWF (European Centre for Medium-Range Weather Forecasts) for providing access to the web-based IGAR and layered meteorological data, respectively. Guiyang Institute of Surveying and Mapping is also acknowledged for providing multi-GNSS data from the continuous operational reference system (CORS) network. This research was supported by the Excellent Youth Science and Technology Fund Project of Xi'an University of Science and Technology (2018 YQ3-12) and the Startup Foundation for Doctor of Xi'an University of Science and Technology (2017 QDJ041).  
[PERSON] and [PERSON] participated in the design of this study, and they both performed the statistical analysis. [PERSON] and [PERSON] carried out the study and collected important background information. [PERSON] and [PERSON] processed the GNSS observations.  
The authors declare that they have no conflict of interest.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 2 State-Of-The-Art Review  
CM has transformed the manufacturing sector by shifting from traditional models to flexible service-centric frameworks. This paradigm enables the integration and efficient utilization of distributed manufacturing resources within a cloud ecosystem [10]. CM excels in resource sharing, coordination, and scalability, which are critical for multi-location operations involving diverse stakeholders. However, its implementation poses challenges, such as handling vast data volumes, real-time analytics, security, and collaboration across dynamic manufacturing environments. Effective CM systems require robust data ingestion, processing, and application layers to meet these demands efficiently. Technologies for CM frameworks include Apache Kafka and RabbitMQ for data ingestion, Spark Streaming for real-time analytics, MongoDB for data management, Kubernetes for orchestration, Flask-SocketIO for real-time communication, and microservice architectures for application scalability. We review these technologies as follows:  
Apache Kafka is a distributed event streaming platform. It enables high-throughput fault-tolerant data pipelines and is a pivotal system for managing large-scale real-time data streams in distributed CM environments. The log-based architecture of the Kafka platform ensures durability and order preservation, which makes it ideal for sequence-sensitive applications [11]. In the framework proposed by [12], Kafka was used to capture and manage IoT data streams, thereby significantly enhancing the scalability and resilience of the CM systems. This design leverages the strengths of Kafka in sequence-sensitive large-scale data management, enabling real-time decision making and operational efficiency within a cloud-based manufacturing context.  
RabbitMQ offers low-latency messaging, making it effective for real-time notifications and anomaly detection in CM. However, its scalability and durability are limited compared to those of Kafka, which handles continuous, large-scale data streams. The strength of RabbitMQ lies in its latency-sensitive applications, which complement those of Kafka in the hybrid systems.  
Spark supports real-time analytics in CM, enabling predictive insights and anomaly detection through continuous data processing [9; 13]. Unlike Hadoop, which relies on batch processing, Spark's in-memory architecture delivers low-latency performance essential for quality control and operational intelligence [14].  
MongoDB is a NoSQL database, which is able to managing unstructured and semi-structured data such as machine logs and sensor outputs. Its scalability and JSON-based architecture make it ideal for diverse CM datasets [15]. MongoDB ensures reliable data storage and retrieval across the distributed cloud resources.  
loring cybersecurity strategies and enabling CM systems to balance flexibility with rigorous security requirements.
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 4 Fault Line Selection Model Based on MCECA-CloFormer
### Efficient Channel Attention  
Attention mechanisms can significantly enhance model performance, yet most of them increase model complexity and computational cost alongside optimization. The efficient channel attention (ECA), however, employs a lightweight architecture that requires no dimensionality reduction, enabling effective cross-channel interactions locally with minimal parameters [23].  
The structure of the ECA module is depicted in Figure 4, where W, H, and C denote the width, height, and channel dimensions of the input feature map X, respectively. Initially, global average pooling (GAP) is applied to the input features, yielding a feature vector of shape 1 \(\times\) 1 \(\times\) C. Subsequently, a one-dimensional convolution with kernel size \(K\) is employed to efficiently facilitate local cross-channel interactions, where \(K\) is adaptively determined by Equation (14) based on the total channel count C. The convolution outcome is normalized via the sigmoid function to obtain weights for each channel. Finally, these weights are element-wise multiplied with the original input feature X to yield the weighted feature X\({}^{\prime}\). The computation formula for the kernel size \(K\) is as follows:  
\[K=\psi(C)=\left|\frac{\log_{2}(C)}{\gamma}+\frac{b}{\gamma}\right|_{odd} \tag{14}\]  
where \(\left|\left|\left|\right.\right.\right._{odd}\) denotes the nearest odd integer to \(t\); \(\gamma\) is the scaling coefficient for the global average pooling output; and \(b\) represents the bias value when adaptively determining the convolution kernel size.
## 2 Results
### DEGs and DEPs Associated with the Generation of ROS  
Studies have shown that _H. pluvialis_ scavenged harmful ROS in cells by up-regulating genes involved in the removal of ROS [10]. Although the ROS is not beneficial for cell survival, it plays an important role in the production of astaxanthin [43]. Besides the generation of a large amount of astaxanthin, cells of _H. pluvialis_ also prevent oxidative stress based on the involvements of a group of ROS-scavenging enzymes, including ascorbate peroxidase (APX), catalase (CAT), glutathione reductase (GR), peroxideroxin (PRDX), monodehydroascorbate reductase (MDAR), ferritin, superoxide dismutase (SOD), glutathione peroxidase (GPX), glutaredoxin (GRX), and monothiol glutaredoxin (MGRX) [29; 44]. Our results showed that on day 7, the expression levels of _sod_, _mdar_, _grx_, and _gr_ in _H. pluvialis_ treated with blue light were significantly higher than those treated with white light (i.e., pairwise comparisons of B02 vs. W02 and B07 vs. W07), the expression levels of _mgrx_, _cat_, and _gpx_ under blue light were significantly higher than those in white light, whereas the expression of _apx_ on day 2 after the exposure to blue light was significantly lower than that under white light (Figure 9). The expression level of gene encoding ferritin on day 7 of the treatment with blue light irradiation was significantly lower than that of white light. However, the expression patterns of _cat_, _ferritin_, _gpx_, _mgrx_, and _prdx_ varied on day 2 after the treatment with blue light irradiation. On day 7 with SA added, the expression levels of _cat_, _gr_, _mdar_, _prdx_, and _sod_ were significantly decreased. The expression levels of genes encoding ferritin and sod were decreased significantly on day 2 after SA was added. However, the expressions of related homologous genes were down-regulated. With the addition of SA, the expression levels of most ROS scavenging genes were decreased significantly and the down-regulated on day 7 after the treatment. Furthermore, the expression profiles of these genes were also changed with the treatment time of SA (Figure 9).  
A total of nine proteins related to ROS clearance pathway were detected based on the proteomic sequencing (Figure 10). On day 2 after the treatment with blue light irradiation, the expression levels of GPX, MDAR, and PRDX increased, whereas the expression levels of CAT, SOD, and PRDX decreased (Figure 10). On day 7 of blue light irradiation, the expression levels of ferritin and GPX were slightly increased, and the expression of PRDX was significantly increased. However, no significant effect of the addition of SA was observed on the expression of related proteins in the ROS clearance pathway. With the addition of SA, the genes related to ROS clearance pathway were significantly down-regulated at the transcriptional level, whereas no significant changes were observed at the proteomic level.
# Degradation Characteristics and Reliability Assessment of 1310 nm VCSEL for Microwave Photonic Link
###### Abstract  
The long-term reliability of the commercially available vertical-cavity surface-emitting laser (VCSEL) at 1310 nm wavelength is investigated. To do so, a high current accelerated life test is used to evaluate the 1310 nm VCSEL reliability. Variations of properties that depend on the operating condition are characterized by the light-current-voltage, leakage current and low-frequency noise. When the aging current is 6 mA, 8 mA and 10 mA, the maximum output power reduces by 5%, 6% and 15% of the initial value, respectively. It is demonstrated theoretically and experimentally that the leakage current increases and reverse bias breakdown voltage decreases after the accelerated current aging test. The current noise power spectral density increases after the device ages, and the noise increases with the augment of the electrical stress. When the bias current of VCSEL is below the threshold, the frequency index factor and noise amplitude gradually increase with the bias current increase. Further, lifetime fitting curves of the devices at an accelerating current of 6 mA, 8 mA and 10 mA are obtained, and the median lifetime of 67 years at the operating current is extrapolated.  
vertical-cavity surface-emitting lasers; accelerated life test; light-current-voltage; leakage current; low-frequency noise; reliability assessment +
Footnote †: journal: Applied Sciences
## 3 Probabilistic Topology Control Algorithm
### Communication Network Architecture of SWARMs Project
#### 3.1.5 Network Model  
In the use case of detection, inspection and traction of plumes, the underwater AUVs are deployed approximately in a 2-dimensional plane. An AUV can move based on a predefined path, as shown in Figure 2. Each AUV in the network can communicate with other AUVs whose distances to this AUV are smaller than its transmission range. For instance, as shown in Figure 8, AUV \(s\) and AUV \(d\) can communicate with each other when \(\|sd\|\leq r_{s}\), where \(\|sd\|\) is the Euclidean distance between AUV \(s\) and AUV \(d\), and \(r_{s}\) is the transmission range of AUV \(s\). The AUVs in network can adjust their transmission power from 0 to \(P_{\max}\), which can be found in Table 2. The coverage area of AUV \(s\) is a circle where the centre is AUV \(s\) and the radius is \(r_{s}\), denoted as \(C(s,r_{s})\). This is shown in Figure 8. The number of one-hop neighbor AUVs in the coverage area of AUV \(s\) is defined as the degree of AUV \(s\). For instance, in Figure 8, the degree of AUV \(s\) is 7.
## 4 Numerical Examples
### Benchmark Problem: Simple Inversion Analysis Based on the Results of Forward Analysis
#### 4.1.2 Forward Analysis and Assumed Monitoring Data  
Forward analysis is carried out to obtain assumed monitoring data for inversion analysis. Point 2 (P2) in Figure 3 is selected as a monitoring point and the calculated vertical displacement results are listed in Figure 4.
# Continuous Autonomous Ship Learning Framework for Human Policies on Simulation
## 1 Introduction  
### Background  
Artificial intelligence for autonomous ships (unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs)) has been extensively investigated in the private sector (including bathymmetric measurements, subsea pipelines management, marine geography surveys, and safety management) and the military sector (including patrol, security, intrusion detection, blockage, and defense). Artificial intelligence has proven its benefits in solving control issues related to the operation of ships under an unstable natural environment, reducing risk, and enhancing efficiency under collaborative or competitive conditions with other ships in the real ocean [1; 2; 3; 4; 5; 6].  
Operating autonomous ships is more difficult than self-driving cars because it is difficult to obtain explicit decision-making data from the environment. Self-driving cars learn the driving rules from definite environmental data, such as lanes and traffic lights. Dynamic obstacles around self-driving cars are considered when making decisions regarding the actions to be taken. However, in the case of an autonomous ship, it is difficult to obtain data regarding rules from the environment, and the rules must therefore be learned fromthe vessels sailing around them. Therefore, a learning environment that considers the ships operated by humans is required, and not only ship control but also human rule learning is required [7; 8; 9].  
In board games such as chess, human-level odds of winning have been achieved under the rules set by humans, and real-time video games such as Atari have achieved high scores through human-level control [10; 11]. In Starcraft (a real-time strategy game), multiple agents can be controlled at the same time to outperform human players [12], while in Minecraft (a role-playing video game), tiering problems are solved based on decision-making. This hints at the possibility of solving problems similar to a human [13]. In this way, the reinforcement learning algorithm can establish not only the rules of the game but also the controls, strategies, and rules to solve problems like humans [12; 13]. However, there are several factors, such as the natural environment and harbor entry/departure rules that have been defined for each harbor in the self-driving vessel's learning, and this learning must be learned through surrounding vessels. This makes it difficult to establish an environment capable of learning human-level rules through learning [14].
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 3 Results and Discussion
### The Measurement Results
#### 3.1.2 Measurements of the Doppler Power Spectra Density
To put it simply, Doppler shift is maximum when signal reflection is minimum. During the measurements discussed in the paper the opposite scenario was applied, though it was our intention to maximize the reflection by sending the signal perpendicularly to the rotors' planes. This condition was surely met for the turbines located at the front rows; however, for the turbines in back rows, the signal orientation could gradually \"move away\" from perpendicular towards parallel. This is the reason why the low power components in Figure 9 exhibit higher Doppler shift compared to the high power components: the former simply represent the turbines further back, while the latter represents those in the front rows. The analysis of the performed tests confirmed the legitimacy of using the model of determining the Doppler power spectral density specified in [11]; however, for frequencies from about \(-10\) Hz to \(10\) Hz, it is recommended to use the variant for low variability and outside this range to apply the high variability variant. This approach most faithfully reflects the shape of the signal spectrum recorded during measurements for the conditions prevailing at that time and the measured configuration of wind turbines, as shown in Figure 9.  
Figure 9: Doppler power spectral density measured on scenario 1 (a), scenario 2 (b), scenario 3 (c) and proposed theoretical model for each case.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
## 6 Challenges of Adoption of Blockchain in 6G Network
### Computation Overhead  
The 6G applications are bandwidth- and resource-hungry; they require real-time data transmission. However, the increasing number of network users hinders the QoS of the developed applications. Therefore, blockchain integrates with 6G technologies, reducing the complex computational cost. A lot of research has been carried out to overcome the computation overhead issue in blockchain-based 6G applications. For instance, the work of [PERSON] et al. [71] offered a diffused-PBFT approach to reduce computation overhead and consensus latency. Likewise, in [72], the authors proposed an LSTM-based scheme to optimize the computation offloading in a satellite-UAV-based IoT system. Then, [PERSON] et al. [73] introduced blockchain in VANET for vehicle authentication and emergency message transmission between the Internet of vehicles (IoVs). They encrypted blockchain servers using RSA-1024, a digital signature algorithm, to minimize the computation overhead. However, each participating block of the blockchain and consensus algorithms, such as proof-of-work and PBFT, require more computational power for calculation. On top of that, the continuous growth of IoT devices and smart applications leads to high congestion in the network and computation overhead, where it is infeasible to properly apply a blockchain-based consensus approach.
## 4 Model Verification and Noise Prediction  
To validate the accuracy of the proposed environmental noise prediction model for Maglev trains, a comparative analysis was conducted between the field measurements and model predictions at monitoring points 1.2 m above the ground level along the Shanghai Maglev line. Figure 14 presents time-domain comparisons of the measured and predicted A-weighted fast time weighted sound pressure level \(L_{A,F}\) at 45 m from the guideway centerline (1.2 m height) under different train operating speeds. Close alignment was observed between the predicted and measured time-domain trends at 235, 300, and 430 km/h.  
The predicted time-domain noise curve of a Maglev train passing at 600 km/h was analyzed at a measurement point located 45 m from the guideway centerline and 1.2 m above ground level. As illustrated in Figure 15, while the temporal characteristics maintained similar trends across different speeds, the 600 km/h speed exhibited significantly sharper peak amplitudes in the waveform compared to those of lower speeds. Furthermore, Figure 15 presents the equivalent continuous A-weighted sound level (\(L_{Aeq}\)) contributions from individual source intensities (\(L_{P1}\)-\(L_{P5}\)) during train passage at 600 km/h.  
Flow field analysis revealed that acoustic energy was primarily concentrated in two critical regions: (1) the near-surface boundary layer along the train body and (2) the  
Figure 14: Time-domain comparison of measured and predicted SPLs at 45 m from the guideway centerline (1.2 m above the ground).  
Figure 15: Time-domain prediction curve and \(L_{Aeq,T}\) when the noise source intensity acted alone at 600 km/h.  
wake flow zone downstream of the trailing vehicle. The aerodynamic noise generation mechanisms can be attributed to two dominant flow phenomena: (1) multiple separation vortices along the streamlined shoulder of the leading car, induced by flow separation at the critical geometric transition, and (2) large-scale trailing vortices extending over considerable distances behind the trailing car, resulting from similar flow separation mechanisms at its shoulder region. These observations confirm that vortex-dominated flow structures serve as the primary acoustic sources in high-speed Maglev operations. The intensified vortex dynamics at 600 km/h significantly amplified both peak noise levels compared to lower train operating speeds.  
Figure 16 compares the measured and predicted equivalent continuous A-weighted SPL attenuation profiles in logarithmic coordinates during Maglev train pass-by events across multiple speeds as a function of the lateral distance from the guideway centerline (1.2 m height). The experimental and modeled decay trends at identical speeds agree closely, showing consistent distance-dependent attenuation. The cross-speed analysis results reveal maximum absolute deviations of 1.3 dB(A) across all ground-level monitoring points, validating the accuracy of the model while providing extrapolated profiles for 600 km/h operation.