esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
###### Abstract  
This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor model was developed to determine autocorrelation and partial autocorrelation in training data, accounting for dynamic changes and external factors, achieving a prediction accuracy of up to 97.9%. A modified LSTM-based predictor model with Dropout and Dense layers predicted sensor data, with a tested error margin of 0.218% for predicting the TV3-117 aircraft engine gas temperature values before the compressor turbine during one minute of helicopter flight. A reconstructor model restored missing time series values and replaced outliers with synthetic values, achieving up to 98.73% accuracy. An anomaly detector model using the concept of dissonance successfully identified two anomalies: a sensor malfunction and a sharp temperature drop within two minutes of sensor activity, with type I and II errors below 1.12 and 1.01% and a detection time under 1.611 s. The system's AUC-ROC value of 0.818 confirms its strong ability to differentiate between normal and anomalous data, ensuring reliable and accurate anomaly detection. The limitations involve the dependency on the quality of data from onboard sensors, affected by malfunctions or noise, with the LSTM network's accuracy (up to 97.9%) varying with helicopter conditions, and the model's high computational demand potentially limiting real-time use in resource-constrained environments.  
anomaly; predict; sensor; LSTM network; preprocessor; reconstructor; anomaly detector; gas temperature in front of the compressor turbine; aircraft engine +
Footnote †: journal:  
0000-0001-5000-0001-5000]S.V.V.  
predicting and detecting anomalous data is crucial for ensuring the reliability and safety of sensor systems.  
Modern approaches to anomaly prediction use machine learning methods [11; 12] and artificial intelligence [13; 14] to identify the hidden patterns and trends in large amounts of data. These methods adapt to changing conditions and provide timely warnings of potential issues, allowing for preventive measures and risk minimization. This article's scientific relevance stems from the need to improve algorithms' accuracy and speed, necessitating research on model optimization and data-processing enhancement. In the context of the increasing digitization and reliance on automated systems, anomaly prediction is becoming a key element in maintaining their stable and efficient operation.
## 5 Solution Space Management for Data Farming in Strategic Network Design
### Solution Space Structuring  
A detailed overview of the solution space structure is shown in Figure 4. The first sub-step (1.1), identification of fixed degrees of freedom, classifies the literature-based solution space into fixed degrees of freedom, already defined by strategic network scenarios (e.g., plant locations, customer sites, and product structure), and variable degrees of freedom (e.g., number of production lines, number of warehouses, and inventory levels), defined by operational decisions. The fixed degrees of freedom set boundaries for the solution management approach, while the variable degrees of freedom can be changed by planning decisions. Several authors provide detailed descriptions of production network design parameters for the literature-based solution space [21; 39; 40; 41; 42].  
Figure 4: Detailed overview of step 1: Solution space structuring.  
Figure 3: Overview of the solution space management approach.  
The second substep (1.2), clustering of variable degrees of freedom to input data groups, deals with the clustering of all variable degrees of freedom to input data groups based on VDI3633-1:2014-12 [43]. The VDI3633-1:2014-12 [43] describes organizational data (e.g., shift models), technical data (e.g., production line output, production line failures), and system load data (e.g., production orders, bills of materials) that is required to simulate any technical system, such as production networks. The solution space management approach aims to enable data farming in production networks by providing input data sets for production network simulation. Therefore, the input data sets must contain the required data according to VDI3633-1:2014-12 [43].  
The third substep (1.3), composition of the solution space structure, analyzes the interdependencies between different variable degrees of freedom. The final goal is to derive a set of sub-solution spaces (solution space structure) that can be either modified independently or have clear interfaces to each other. As a simplified example, the sub-solution space to describe the production capacity is composed of the two variable degrees of freedom shift model and line output (c.f. substep 1.3 in Figure 4). This sub-solution space results from their interdependency and from the fact that the working hours (shift plan) are multiplied by the line output to calculate the production capacity.
## 4 MSaaS Reference Architecture
### Orchestration of MSaaS-Based Distributed Simulations  
According to a MSaaS perspective, a distributed simulation is an application built as an _orchestration_ of existing M&S services available in the cloud. Specifically, the term _orchestration_ takes into consideration both the specification of how services have to cooperate to provide the required functional behavior and the management of several components that contribute toward providing the execution platform. Thus, the development of a MSaaS application is typically undertaken through the following steps, as summarized in Figure 1:  
* **Service discovery**: In order to satisfy the MSaaS application requirements, a service discovery is performed to identify the set of candidate M&S services providing the required capabilities. Specifically, as detailed in Section 5.2 the functional and non-functional services characteristics are described in terms of _metadata-based descriptions_ stored in a services registry, while the related services implementations are stored in a services repository.
* **Service composition**: The service composition or, in other words, an _application orchestration_, refers to architectures, methods, and tools, used to coordinate the execution flow and the messages exchange among the M&S services identified in the previous step, in order to meet the functional requirements of the needed simulation.
* **Service deployment**: Each M&S service implementation requires to be deployed on top of the given execution platform, which consists of computing nodes, systems and application software, databases, network connections, etc. The term _infrastructural orchestration_ includes those activities needed to set up the execution infrastructure and deploy, configure and eventually start the required M&S services. In this context, machine-readable formats for specifying the service deployment description, along with an orchestration engine able to compute such descriptions, can be introduced for easing the service infrastructural orchestration.  
With regard to infrastructure orchestration, this work exploits the TOSCA standard to improve the portability and interoperability of M&S services. This is achieved by specifying YAML-based and vendor-independent orchestration descriptions that can be automatically processed by any TOSCA-compliant engine.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 4 Attitude Determination Using the Optimal-REQUEST Algorithm  
Based on the above calculations, models of the observation vector are acquired. However, the results of coarse alignment are inevitably affected by GPS measurement errors in the process of constructing vectors. Thus, an Optimal-REQUEST algorithm, based on position loci (OR-PL), is proposed to reduce the impact of errors and to determine the initial attitude matrix \(\mathrm{C}_{b(0)}^{n(0)}\). The Optimal-REQUEST algorithm utilizes the measurement vectors and reference vectors to construct a K-matrix. Moreover, an iteration algorithm, which can adjust the gain according to the measurement error, is applied to update the K-matrix. The optimal attitude quaternion can then be obtained by calculating the K-matrix eigenvector corresponding to its largest eigenvalue.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 3 Materials and Methods
### R-Cnn
#### 3.2.2 Faster R-CNN  
While Fast R-CNN improved the accuracy and speed of R-CNN, the selective search algorithm was still hampering the success of R-CNN architectures due to it being a static algorithm. The selective search algorithm was unable to learn the key features of an image, slowing down the speed of Fast R-CNN. Faster R-CNN fixes this issue by replacing the selective search algorithm with a Regional Proposal Network (RPN) algorithm [32]. This speeds up the computational calculations of Faster R-CNN, since the RPN network learns the key features of the image and proposes regions of interest that only consist of key features.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics were presented using means and standard deviations. Shapiro-Wilk test was used to check the data normality. Systematic bias between sessions and tools was evaluated using the paired samples \(t\)-test [18]. Standardized differences in mean (with 95% confidence intervals; CI) were calculated to determine the magnitude of the change across and between tests. According to [PERSON] et al. [16], Cohen d effect size (ES) magnitudes of change were classified as trivial (\(>\)0.2), small (0.2-0.5), moderate (0.5-0.8), large (0.8-1.60), and very large (\(>\)1.60). Reliability between test-retest was analyzed using intraclass correlation coefficient (ICC), typical error (TE) expressed as coefficient of variation (CV%), and smallest worthwhile change (SWC) according to Excel spreadsheet provided by [PERSON] (2007) [23]. Regarding the ICC analysis, a single measure, two-way mixed, absolute-agreement parameter was used [24]. The highest jump from each subject on both testing sessions, retrieved from the My Jump 2, was used. ICC was interpreted as \(<\)0.1 = low, \(<\)0.3 = moderate, \(<\)0.5 = high, \(<\)0.7 = very high, \(<\)0.9 = nearly perfect, and \(<\)1.0 = perfect. A good reliability was considered if following criteria was fulfilled: CV \(<\) 5% and ICC \(>\) 0.69 [25]. Test usefulness was determined based on the comparison of SWC (0.2 multiplied by the between-subject SD, based on [PERSON]'s ES) to TE [26]. The following criteria were used to establish the usefulness of tests: \"Marginal\" (TE \(>\) SWC), \"OK\" (TE = SWC), and \"Good\" (TE \(<\) SWC).  
The concurrent validity of the app was tested with Pearson's product-moment correlation coefficient (r). Additionally, the agreement between Optojump and My Jump 2 data was then examined  
Figure 1: Take-off and landing phase frames on My Jump 2 app.  
graphically using [PERSON] and [PERSON]'s plots in which the difference between both devices was plotted against the mean of the two devices [27].
## 3 Results and Discussion
### Proof of Concept: Quantifying Real Noise  
After a literature revision, we noticed that some datasets were subject to visual inspection in their respective papers, so we applied our method to quantify the proportion of noise embedded in those datasets and to test whether they may lead to appopenia. We choose two datasets from literature for two main reasons, first, the data should have a high number of items that are way above our Z-score significance threshold (\(>\)13) and, second, we wanted contrasting orderings of the partitions to have one dataset that looks very disordered and other that looks somewhat ordered to compare the noise proportions. Last, we assessed the behavior of the score in highly ordered data. This also matches our threshold mentioned above.
## 3 System Model
### Optical Link Blocking Model  
In [24], blocking incidence and occupancy are used to model VLC optical link blocking. Blocking incidence probability can be used to characterize optical link blocking at a point in time [25]. The random movement of MTs in VLC heterogeneous networks introduces random masking effects in VLC LoS links, resulting in link blockages. The occurrence of link blockages can be modeled as a Poisson distribution process (PDP) with a link blockage recovery rate parameter \(\beta\). The cumulative distribution function (CDF) of the blocking probability is expressed as a function of a blocking duration of \(\omega\) seconds or less [3]:  
\[P_{block}=CDF(\omega;\beta)=1-e^{-\beta\omega}, \tag{10}\]  
The average duration of link blocking, denoted as \(\gamma\), is inversely proportional to the link's blocking recovery rate, \(\beta\), which can be expressed as \(\beta=1/\gamma\). By incorporating the recovery rate parameter, we can construct a specific model for VLC link blocking.
## 2 Materials and Methods  
This section provides an overview of the network intrusion detection classification method based on feature engineering and model optimization. The structural framework of the paper is divided into four main parts: data pre-processing, feature engineering,training and validation of the classification models, and evaluation and analysis of the experimental results. The framework is depicted in Figure 1. The main objective of this study is to enhance the performance of network intrusion detection models through the adoption of feature engineering and model optimization methods. Firstly, we conduct data preprocessing on the NSL_KDD, UNSW-NB15, and CICIDS-2017 datasets, including data cleansing, one-hot encoding, and min-max normalization, to improve data usability and robustness. Subsequently, the preprocessed data undergoes a dual strategy of feature selection based on mrmr and feature engineering based on SMOTE. This approach enables the selection of relevant and non-redundant features and effectively addresses the issue of class imbalance, thereby bolstering the model's ability to handle the intricacies and diversities of network data. The feature-engineered data is then fed into the CatBoost model, which undergoes hyperparameter optimization using the Optuna framework, followed by training and classification. Evaluation metrics such as accuracy, recall, precision, and F1-score are employed to validate and assess the results.  
1. The raw data is subjected to cleaning, normalization, and type conversion in the pre-processing stage to ensure data consistency and usability.
2. The feature engineering process involves further processing the pre-processed dataset using the mRMR algorithm. This algorithm selects highly relevant features, reduces redundant information, and enhances the representation of the classification model. Additionally, the SMOTE algorithm is employed to generate new minority class samples, addressing the issue of class imbalance, and improving the overall class distribution of the dataset.
3. The next step involves training and validating the classification model using the feature-engineered dataset. The dataset is divided into a training set and a test set, with  
Figure 1: Schematic diagram of the structural framework of the study.  
a ratio of 7:3. The Catboost model is optimized using the Optuna framework, which selects the best hyperparameter configuration to enhance the model generalization ability and performance.
4. Finally, the results are evaluated and analyzed. Performance metrics such as accuracy, precision, recall, and F-value are calculated to assess the classification performance of the model on the test set. These metrics provide insights into the effectiveness of the proposed approach.
## 3 Results and Discussion  
Intensive simulation runs were performed to understand the effect of many parameters on drop size distribution. For hybrid surfaces, the effect of DWC region width \(w_{\text{DWC}}\) is analyzed, since it plays an important role in surface renewal. To demonstrate its effect, the ratio of maximum drop diameter \(D_{max}\) to \(w_{\text{DWC}}\) is introduced (\(R_{D}\)), where its value ranges from 0 to 1, and 0 implies a full-DWC surface. Drop growth and surface renewal processes are based on dimensionless parameters, and \(r_{max}\) is not necessarily definite. When the comparison is made with full DWC, \(r_{max}\) is set at 0.5 mm. Work is also performed for total temperature drop, \(\Delta T\), of 2, 4, and 8\({}^{\circ}\)C, and values of the effective radius are obtained from Equation (2) for variable nucleation sites \(N_{s}\). The contact angle \(\theta\) is constant throughout the analysis and equals 120\({}^{\circ}\).  
The water-saturated temperature \(T_{sat}\) is 373 K, and enthalpy of vaporization \(H_{fg}\) is 2257 kJ/kg. All properties such as thermal conductivity, density, and surface tension are evaluated at average temperatures. A run period is set at 40 s with a time interval of 20 ms.
## 2 Structure and Working Principle
The fabrication steps using this method would consist of: low-ion-current FIB milling of a grating in the Si\({}_{3}\)N\({}_{4}\) layer over the fiber core region; milling two trenches with a depth h and their inner walls in the middle of the side grating bars, as shown in Figure 2a,b; ion-beam assisted metal layer deposition on the inner trenches' sidewalls to form an FP resonator using appropriate sample pre-tilt with respect to the ion beam. To achieve good reflectance for the mirrors, Au layer thicknesses of 150 nm would be sufficient. The designed RWG sensor has a small footprint of about 15 \(\times\) 17 \(\upmu\)m\({}^{2}\), such that multiplexing of many sensors on a facet of a multi-core fiber could be achieved. An example of a multiplexed RWG sensor with 19 parallel sensors integrated on the facet of a commercial 19-core fiber is shown schematically in Figure 2c. The operation principle of the designed sensor is as follows. When a guided waveguide mode impinges upon the mirror, it will be reflected, and the leaked energy can be coupled back to the core fiber mode with high efficiency. The use of mirrors is equal to the application of periodic boundary conditions in simulations for waves propagating inside the FP cavity. This kind of restriction is much stronger compared to the bare Bragg reflection offered by a uniform grating, thus helping to achieve strong resonant peak intensities with a limited number of grating periods.
## 5 Case Study: Problem Formulation
### Needs Definition  
The growing number of elderly people is a global problem, and many local authorities, also of the Polish region Silesia, acknowledge its importance and are working on it. The general needs and targets introduced by the stakeholders and future users represented by elderly people and their families have considered possibilities to support elderly people, especially those of limited mobility, living alone or patients with the first symptoms of dementia. The support can be yielded by means of an autonomous system monitoring the target's position, their vital signs and able to recognize different activities and even classify human behavior.
## 3 Operational Requirements
### Technical Specifications
#### 3.4.3 Perception Systems  
The perception systems in a fruit-harvesting robot are critical to its ability to detect and pick fruit efficiently. Among these systems, cameras play a crucial role in providing essential visual data for accurate fruit-harvesting and detection of obstacles in the environment. The RGB cameras capture color images, allowing the robot to differentiate between ripe and unripe fruits based on color. For example, in apple picking, deep red indicates ripeness, while green may indicate that the fruit is not yet ready to be picked [61]. Multispectral cameras capture images at different wavelengths of the light spectrum, allowing crop health to be assessed. They can detect variations in leaf and fruit reflectance that indicate water stress, disease, or nutrient deficiencies [62].
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
###### Abstract  
Spectroscopy in the terahertz (THz) band has been discussed as a promising tool for identifying substances such as mold in food, narcotics, or explosive materials. Other than the technological limitations, the most important difficulty is the presence of water vapor in the atmosphere, which affects THz measurements. In this paper, we present a systematic approach to the challenging subject of remote identification. We start with a brief analysis of the technical capabilities of the THz components and report the choice of devices for designing an experimental setup for reflection spectroscopy. We follow with the presentation of the transmission THz spectrometer working in an open atmosphere. Research conducted on the transmission configuration provides findings that are implemented in the experimental setup working in a reflective configuration. The final phase is an experiment providing data measured in the reflection configuration with the presence of water vapor, allowing the use of spectra in the identification of the measured samples.  
THz radiation; reflection spectroscopy; remote identification; optical parametric oscillator; hot-electron bolometer; water vapor; lactose; PABA; RDX 1
## 6 Conclusions  
This paper presents a deep learning-based ultrasonic diffraction data analysis method for the automatic interpretation of ultrasonic data, with the objective of accurately measuring defects. A series of defects in weld joints of varying depths, heights, and lengths were created to test the efficacy of the proposed method. In terms of length measurement, the lengths of defects can be automatically determined by the designed convolutional neural network (CNN) architecture in conjunction with a connected-region-based method. The mean measurement accuracy is \(\pm\)0.065. In the case of height measurement, the arrival time of the diffracted waves can be accurately estimated by the proposed intelligent denoising architecture in conjunction with the Hilbert transform. Furthermore, the height of the defects can be calculated by means of the corresponding geometric relation. The mean measurement accuracy can be estimated to be \(\pm\)0.061. Consequently, the proposed method can be employed for the automated analysis of ultrasonic data and the precise estimation of the ultrasonic diffraction technique in practice, even in the presence of noise and a weak signal.  
The study presented in this paper demonstrates that the proposed convolutional neural network (CNN) model is capable of accurately identifying the boundary of the tip diffraction signal and noise reduction through signal classification. This enables the realization of high-precision quantification of defects under noise interference conditions. In other instances of common weld seam defects, such as porosity clusters and imperfections, the characteristics of defect signals undergo significant alterations. Consequently, the generalizability of the proposed method and the capacity to identify defect types based on comprehensive defect signal feature extraction and quantification require further in-depth investigation.  
Conceptualization, W.X.; methodology, W.X. and J.Z.; investigation, Q.F., J.C. and L.J.; writing--original draft preparation, Q.F. and J.C.; writing--review and editing, J.Z. and J.Y.; supervision, H.D.; project administration, J.Y. and H.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China, grant number 2018 YFB1106100.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] for her assistance in the research of this paper.  
The authors declare no conflicts of interest.