esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Set-Up and Procedure  
The experiment was performed in a fixed-bed reactor system, as shown in Figure 1. The quartz reactor tube was inserted in the tubular furnace, with a porous sintered quartz plate in the middle of the reactor. During experiments, char samples (0.02 or 0.2 g) were previously mixed with 5 g quartz sand of the same size as the char. The main purpose of the sand was to prevent the aggregation of char particles. A shallow layer of quartz wool tiled on the porous quartz plate was used to hold the mixture. Once the desired temperature was reached, the inert flow of N\({}_{2}\) was switched to the reactant gas mixture (NO/N\({}_{2}\) or NO/CO/N\({}_{2}\)) passing through the char samples. For each experiment, the total flow rate was 450 mL/min. An infrared analyzer (BRUKER, Nasdaq, GA, USA) was used to measure both the inlet and the outlet concentration of gas species (CO, CO\({}_{2}\), and NO).  
For studying the effect of the CO concentration, experiments were carried out at 900 \({}^{\circ}\)C, with CO concentration varying from 400 to 4000 ppm. In order to evaluate the influence of reaction temperature, experiments were conducted in the range of 750 to 900 \({}^{\circ}\)C at the same NO concentration (400 ppm). The influences of both the initial NO concentration and the char loading on the NO reduction of char were also examined with the concentration ratio of CO and NO (C\({}_{\text{CO}}\)/C\({}_{\text{NO}}\)) ranging from 0 to 10.  
The reduction ratio of NO was calculated as  
\[\text{X}_{\text{NO}}=(\text{C}_{\text{NO,in}}-\text{C}_{\text{NO,out}})/\text{ C}_{\text{NO,in}} \tag{4}\]  
where C\({}_{\text{NO,in}}\) (ppm) and C\({}_{\text{NO,out}}\) (ppm) are the inlet and outlet concentration of NO, respectively.  
A blank test without char sample was conducted with a high concentration of CO (1000 ppm NO, 6% CO) at 900 \({}^{\circ}\)C. No detectable reduction of NO was found, indicating that the quartz reactor, quartz wool and silica sand were all inert under the present conditions. This agreed with the experimental results obtained from previous investigation [19].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surface Areas (m\({}^{2}\).g\({}^{-1}\))** & **Pore Volume (cm\({}^{3}\).g\({}^{-1}\))** & **Pore Size (nm)** \\ \hline
135.83 & 0.031 & 6.382 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific surface areas, pore volume, and pore size of rice husk char.  
Figure 1: Experimental setup for NO reduction.
## 2 Related Work  
### Graph Neural Network  
Graph neural networks (GNNs) [19; 27; 28; 29; 30] learn the embedding representation of nodes using node aggregation propagated separately on each node, and node attribute information is not consider in the model learning process. Graph convolutional networks (GCNs) [20; 21; 31; 32; 33; 34; 35] optimize the embedding vector of a node by making full use of sufficient attribute information and leveraging inductive learning to generalize to new nodes. LightGT [36] predicts user-item interactions by using modal-specific embedding, a layer-wise position encoder, and a light self-attention mechanism. Attention mechanisms [37; 38] and feature interactions [39; 40] are used for feature learning of node embedding representations. LightGCN [33] simplifies the framework of GCNs and removes nonlinear activation processes and feature transformations. Neighbor aggregation and message propagation are used to update the embedded representation of nodes and the weight matrix. A message propagation strategy is used to aggregate messages delivered from other directly linked node types (such as users or attributes) to enhance the representation learning of items and users by integrating relevant attributes, and it also naturally solves the problem of missing attributes [41]. An adversarial GCN [42] leverages a GCN-based autoencoder that encodes high-order and complex link pattenes to enhance interaction data and utilizes a GCN-based attention mechanism to obtain the social relations of each user. BLoG [43] uses an online encoder and a target encoder to encode user-item bipartite graphs to learn user-item representations and uses local and global regularization to facilitate information interaction between these two different graph encoders. Although GNNs have achieved remarkable success in recommender systems in the past few years, most existing GNN-based algorithms are researched on homogeneous graph with only one node type and one edge type. Meanwhile, most real-world graphs consist of different types of nodes and edges with related attributes in different feature spaces, and these nodes have sufficient attribute information.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 3 Architectures and Protocols for IoT Communications via Satellites
### Architecture
This enhances the spectrum efficiency and contributes to latency reduction and bandwidth optimization. ML is fundamental in proactively detecting anomalies, allowing predictive maintenance that ensures connectivity continuity for IoT devices [37; 38]. In addition, ML is used on the satellite to perform inference and training tasks of AI models, such as neural networks, with the objective of processing and analyzing the data collected by IoT devices intelligently and autonomously, without having to send all the data to a data center on Earth [5; 39]. For its part, Blockchain technology implements a decentralized and transparent approach, which guarantees the authenticity of the data transmitted and stored by IoT devices [40]. Therefore, its use focuses on access control, privacy, and security authentication [41; 42].
mapped aquaculture ponds over coastal area of China using Landsat-8 images and GEE platform [41] and further analyzed their dynamic changes from 1990 to 2020 [42]. Existing studies were mainly focused on designing artificial image features or training deep leaning models to map a specific type of aquaculture area, such as aquaculture ponds. However,many different aquaculture types are found over a large area, and how to simultaneously extract multiple types of aquaculture areas over a large area has not been well studied. This study proposed a novel approach for mapping aquaculture areas with multiple types over large areas. With the Pearl River Basin (Guangdong) of China as a case study, time series Sentinel images were used as a data source to overcome the accidental factors of single-time observation. The spectral indices (including normalized difference vegetation index (NDVI), normalized water index (NDWI), and normalized built-up index (NDBI)) derived from Sentinel-2A multispectral images, the VV and VH polarized data of Sentinel-1 SAR images, and their derived texture features were used to map aquaculture areas using machine learning algorithms implemented in Google Earth Engine. The proposed method holds great potentials in simultaneously mapping different types of aquaculture areas over a large area.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Machine Learning and Deep Learning-Based Techniques
#### 3.7.2 Supervised Classification Algorithms  
In machine learning (ML) applications, the most frequently used supervised classification algorithms are SVM and MLC, which offer insightful information through cloud computing using geographical data. After machine learning models are trained using training data, their performance is assessed through cross-validation [51]. A classification function reduces the prediction error and often learns the complex input-output connection patterns to link the input data (features) with the output class or (target labels). The inputs are represented by n-vectors (X1, X2, \(\ldots\), Xn), while the results are given as finite k class labels (Y1, Y2, \(\ldots\), Yk). Training and testing sets are created from datasets. The lithological units in the study area were classified using two machine learning techniques, MLC and SVM. The used algorithms are briefly explained in the subsections that follow.
# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 4 Experiments and Results
### Results
#### 4.2.4 Necessity of the LSTM Module  
SSSGAN includes an LSTM module to explicitly capture the relationships between the noise codes, which plays a crucial role in the subsequent compositional synthesis steps. While GANs have the capability to generate diverse images, one might question the need for the LSTM module. Could SSSGAN achieve similar performance without modeling these relationships? To address this question, we conducted an ablation study by removing the LSTM module, resulting in a simplified version called SSSGAN-s. In SSSGAN-s, the noise codes for the parts were independently sampled from a Gaussian distribution, while other aspects remained the same as in SSSGAN. The experimental results on the CelebAMask-HQ dataset are presented in Figure 9, revealing that SSSGAN-s failed to generate the semantic mask, indicating a lack of awareness of the semantic part structure. To further examine how SSSGAN-s encodes the part noise code, we evaluated its performance on the sequential part composition task, as shown in Figure 10. It is evident that SSSGAN-s treated each part code as a whole face, completely disregarding the semantic meaning of each part. These findings clearly demonstrate the necessity of the LSTM module in SSSGAN, validating the effectiveness and novelty of our approach.  
Figure 8: Correlation analysis between noise codes and latent codes using cosine similarity. The stochastic calculation is based on 10,000 samples.
## 2 Materials and Methods
### Method Validation
#### 2.4.4 Limit of Detection (LOD)  
The method's sensitivity was characterized by determining the limit of detection (LOD) and limit of quantification (LOQ) through matrix-matched calibration. Ten independent blank feed samples (representing all validated matrix types) were processed following the complete sample preparation protocol to establish the baseline noise level. A matrix-matched standard working solution (10 \(\upmu\)g/L) was prepared by spiking the blank matrix extract with appropriate dilutions of the mixed standard solution.  
An instrumental analysis was performed on both the processed blanks and matrix-matched standards. The signal-to-noise ratios (S/N) for all six target analytes were calculated at their respective retention times using the following criteria: LOD: defined as the analyte concentration yielding a signal 3 times the standard deviation of blank responses (3\(\times\) S/N); LOQ: established as the lowest concentration meeting method accuracy (80-120% recovery) and precision (RSD \(<\) 20%) criteria with a signal 10 times the blank noise level (10\(\times\) S/N). The final reported LODs and LOQs were verified through an analysis of samples spiked at these threshold concentrations, with confirmation across three separate analytical batches.  
Blank samples of compound feed, concentrated feed, premix, and bovine concentrate were spiked with appropriate amounts of standard solution to achieve target drug concentrations of 25 \(\upmu\)g/kg and 50 \(\upmu\)g/kg, respectively. For each concentration level, three to five replicates were prepared. The spiked samples were processed according to the established sample extraction and purification protocols and subsequently analyzed using UPLC-MS/MS.
## 3 Network Model
### Failure Model
#### 3.3.2 Transient Failures Model  
In the transient failure model, the failure occurs, but the system reverts to its former status after a time interval, or fluctuates between normal and abnormal status. The work in [47] takes into account when the network is intermittently disconnected and connected. Each time an unstable node awakens, it remains connected for a time interval chosen uniformly at random in the range [0, 120]. When it is disconnected, it remains disconnected for a time interval chosen uniformly at random in the range [0, 60].
## 10 Future Research Directions
### Support for Real-Time Applications  
There are many real-time application areas for WSN, e.g., in industry automation, chemical process monitoring, and multimedia data processing and transmission. Schedulers have been designed to support soft as well as hard real-time operations in some operating systems, but the effort is far from complete. In future, there is a need for scheduling algorithms that can accommodate both soft and hard real-time requirements of applications. Furthermore, with the emergence of WMSNs, a networking protocol stack is required that provides implementation of protocols that support some kind of differentiated services.
# Outlier Detection and Prediction in Evolving Communities
## 1 Introduction
In particular, we propose a method for addressing both community and outlier detection simultaneously based on COTILES [6], which is an evolutionary community detection algorithm that leverages both structural and content information to form more cohesive communities of densely connected nodes with similar contents. More specifically, we extend COTILES so that it also identifies outlier nodes while incrementally forming overlapping communities in a streaming context, where each new interaction may update the formed communities. To identify outliers, we define a novel measure called the _outlier score_, which again is based on both structural and content criteria. The outlier score is dynamically evaluated for each node and captures its \"outlierness\", i.e., the degree to which a node can be considered as having outlier behavior. The extended algorithm can be leveraged into assigning higher significance to either structure or content depending on the application requirements concerning both community and outlier detection. Furthermore, we argue that the outlier score can be used as an indication of a node's future behavior, that is, the current outlier score of a node can be exploited as a feature for predicting whether the node will remain an outlier or become one in the next time instances. We evaluated our algorithms on two real datasets regarding social networks. Our experiments show that the proposed methods can be used to study both current communities and outlier nodes while predicting outlying behaviors with higher accuracy compared to other predictive features. The rest of this paper is structured as follows. In Section 2, related work regarding both community and outlier detection is presented. Section 3 includes basic definitions and describes the original COTILES algorithm. In Section 4, we present our method for outlier detection. In Section 5, we present our experimental results on outlier detection in two real social networks, while Section 6 tests the proposed prediction capability. Finally, Section 7 consists of our conclusions and directions for future work.
## 3 Case Analysis  
The Shuangjiangkou hydropower station is an important hydropower project located in Sichuan Province, China, as shown in Figure 6. The maximum dam height is 314 m. The hydropower station has complex geological conditions. The diversion and power generation system is arranged on the left bank. The main rock type of tunnels is granite, and the rock uniaxial compressive strength is 60-70 MPa. The maximum principal stress is 37.82 MPa. The total length of the access tunnel in the main powerhouse is 1473.31 m, and cross-sectional dimensions are 11.24 m \(\times\) 8.7 m (width \(\times\) height). The powerhouse caverns are mainly buried horizontally at depths of 400 m to 640 m and vertically at depths of 320 m to 500 m. The excavation dimensions of the main powerhouse are 132.56 m in length, 29.30 m in width, and 63.00 m in height. The layout of the main powerhouse and the access tunnel in the main powerhouse is shown in Figure 7. Due to the comprehensive influence of high in situ stress and structural plane during the tunnel excavation, a series of serious damage problems occurred frequently. Among them, the occurrence of rockbursts is the most prominent problem, as shown in Figure 8.  
Figure 5: Waveform analysis of a rockburst signal in the time domain and the HHT spectrum.  
Figure 6: Geographical location.  
Figure 7: Layout of tunnels.  
[PERSON] et al. [39] introduced the MS monitoring system of the Shuangjiangkou hydropower station in detail. The main powerhouse (early excavation) and the access tunnel in the main powerhouse are monitored by six accelerometers. At least four accelerometers must be triggered simultaneously to determine the source position. The Paladin data acquisition system has an acquisition frequency of 20 kHz.
## 1 Introduction
### Contributions  
Addressing the deficiencies in existing research, this study proposes a comprehensive framework for traffic parameter extraction and analysis based on vehicle trajectory data. The main contributions include:  
1. Established a systematic trajectory data processing methodology framework. This study designs a complete processing workflow including data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. Each module has been carefully designed and optimized to ensure data processing quality and efficiency. The framework exhibits excellent modular characteristics with clear interfaces between modules, facilitating customization and extension according to specific requirements.
2. Proposed a trajectory point \"labeling\" algorithm based on regular polygon coverage. This effectively solves the technical challenge of mapping discrete trajectory pointsto road network nodes. By constructing regular polygon coverage areas centered on nodes and utilizing geometric determination algorithms to assign trajectory points, high-precision vehicle path identification is achieved. The algorithm demonstrates excellent stability and efficiency when processing large-scale data.
3. Established a multi-source data fusion method for traffic parameter calculation. Addressing the uncertainty in trajectory data, this study adopts a dual calculation method based on vehicle instantaneous speed and arrival time differences, improving parameter extraction accuracy and reliability through multi-source information fusion. This method effectively reduces errors that may arise from single data sources and enhances the robustness of parameter estimation.
4. Developed a highly reusable and automated data processing toolchain. Based on Python and MATLAB platforms, a series of comprehensive data processing functions and modules have been developed, including automated data cleaning, feature extraction, parameter calculation, and result visualization. These highly reusable and automated tools not only serve this research but can also provide technical support for other researchers and engineering practices.
5. Validated the effectiveness of the method through actual data. Using actual trajectory data containing over 2.48 million records, the performance of the proposed method was comprehensively validated. Experimental results demonstrate that the method can effectively process large-scale data, and the extracted traffic parameters accurately reflect the operational status of the road network, providing a reliable data foundation for signal control optimization.
###### Abstract  
Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters (\(\hat{\alpha}\), \(K\)) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).  
variational mode decomposition; waveform matching extension; grey wolf optimization; rotor fault diagnosis +
Footnote †: journal: Applied Mechanics and Applications  
1
## 3 Preliminaries  
### Kappa Architecture  
[PERSON] introduced the Kappa Architecture (KA) in 2014 to reduce the code overwhelming of the Lambda Architecture [29; 43]. Lambda architecture is a scalable, fault-tolerant framework with three layers. The batch-processing layer aggregates historical data but is time-consuming, while the stream-processing layer handles real-time data for quick insights. The serving layer merges both outputs for efficient querying. KA consists of one pipeline machine learning process, where all the data is considered as a stream. KA eliminates the need for the batch layer of lambda architecture, which consists of stream and service layers only. KA as represented in Figure 1 works as follows: real-time data will be processed by a message passing system such as Kafka to create the real-life streaming and send it to the streaming layer. In the streaming layer, Apache Spark ML-lib can be utilized to build machine learning models for real-time predictions. The results of machine learning will be sent to the service layer where all the results can be stored in most of the well-known storage management systems such as Hadoop Base, Cassandra, and MySQL. In the service layer, real-time analysis and decisions will be provided to users. We use KA in our research to build a real-time prediction system. Apache Spark and Spark Streaming component can be used to implement KA, which is a big data tool engine that includes different Application Programming Interfaces (APIs) such as MLlib, Spark Streaming, Spark SQL, and Spark Graphics [44]. Spark has been used recently to build big data applications for ITS due to its ability to process large scale distributed data in real-time. Spark is known for in-memory computation and the capability of processing the data 100 times faster than Apache Hadoop. In every Spark application, a Spark context will be created to manage all tasks. Once Spark context creates Resilient Distributed Datasets (RDDs), the cluster manager will distribute the RDDs among the available worker nodes in the cluster to be processed in parallel and will provide the results in real-time. Figure 2 demonstrates Apache Spark architecture.  
Figure 1: Kappa Architecture.