esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
1. Based on the basic principle of the SINS, a real-time algorithm for solving the attitude, velocity, and position of the shearer is constructed, and the specific process of the coarse alignment and fine alignment of the SINS is studied. The main error terms affecting the accuracy of SINS are analyzed for the shortcoming that the accumulated errors of SINS at long endurance are difficult to be effectively corrected.
2. The initial alignment of SINS is carried out based on the optimized unscented Kalman filter algorithm. The influence of installation deviation angle on SINS error is clarified, an effective installation error compensation algorithm is proposed, and the lever arm effect error compensation method is optimized, the influence of lever arm effect on  
Figure 17: Shearer SINS sculling error.  
Figure 18: Contrast with or without sculling error compensation.  
the system accuracy is analyzed through simulation. Given the influence of the strong vibration environment of the shearer on the accuracy of SINS, the coning error and sculling error caused by the shearer vibration are compensated based on the rotation vector method.
3. This paper conducts an in-depth study on the real-time and accurate shearer attitude sensing and provides a complete set of theories and methods to improve the attitude sensing accuracy of the underground shearer, simulation results also verify that the theories and methods can significantly improve the perception accuracy, which not only helps enrich the research results of intelligent sensing at fully mechanized mining face, but also can provide theoretical and technical reference for production state prediction at fully mechanized mining face and intelligent control of underground mining equipment.
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 4 Data
### Feature Extraction
#### 4.4.2 Discrete Wavelet Transformation  
Discrete Wavelet Transformation (DWT) is a linear transformation that breaks down the original signal into smaller parts called wavelets. There is an infinite number of possible wavelets, but they need to fulfil admissibility and should fulfil regularity conditions [28]. Hence, over time, several waves fulfilling these criteria were created, called mother waves, that are typically used to generate the wavelets. Child wavelets are generated from these mother wavelets via translation (\(k\)) and scaling (\(j\)) as shown in Equation (3).  
Following the notation by [PERSON], [PERSON], and [PERSON] [29], any square-integrable signal/function can be described as follows:  
\[f(t)=\sum_{k}\sum_{j}a_{j,k}\psi_{j,k}(t) \tag{2}\]  
where the mother wavelet, \(\psi\) can be described as follows:  
\[\psi_{j,k}(t)=2^{\frac{i}{2}}\psi(2^{j}t-k) \tag{3}\]  
and the \(\gamma(j,k)\) are the DWT coefficients.  
Compared to Fourier transformation (FT), which breaks down the signal into a list of sine and cosine waves lasting the whole sample, DWT considers not only frequency information but also where this frequency occurs (temporal information) in the signal. We try to exploit this property, as the indications of faults can be very localised events or cause sudden changes to the time series.  
As our samples are considerable in length, we have opted for a discrete, not continuous, version that is more computationally expensive while providing better feature extraction.  
DWT has been used in the domain of PdM in the past; see, for example, [30,31].
## 1 Introduction
As a result, a new unsupervised damage detection method based score-guided regularization strategy is proposed in this study. Figure 1 depicts the basic flow of the score-guided network. The representation learner, which maps the data to the hypothesis space, is connected to the score-guided network, as shown in the diagram. The representation learner is trained to extract more valuable information using the score-guided regularization technique, widening the gap between undamaged and damaged data. This method's representation learner employs an auto-encoder to extract damage characteristics, while the score-guided network serves as a damaged detector. Finally, multi-task learning is applied to make parameter modification easier. The following are the novelty and significant contributions of our proposed method:  
1. The proposed method requires contaminated data as training data, which is more realistic than current unsupervised algorithms that solely employ undamaged data. 2. The model can learn more representative information using a score-guided regularization technique. The difference between undamaged and damaged data is widened by scoring the input data, allowing undamaged and damaged data to be distinguished. The model takes an anomaly score as output. Proposed method selects the anomaly score, that is, the score of the input sample, as the damage indicators, which does not require relevant background knowledge or pre-defined, but is directly output in an end-to-end manner. 3. Using multi-task learning to simplify the hyperparameter tuning procedure increases the model's performance and adaptability. There are four sections in this paper. The \"Introduction\" section introduces and evaluates existing unsupervised structural damage detection algorithms. Section 2 explains the unique unsupervised structural damage detection algorithm. The suggested algorithm is validated in Section 3 using two benchmark datasets. Section 4 summarizes the entire text and suggests future research areas.
# User Experience, Functionality and Aesthetics Evaluation in an Academic Multi-Site Web Ecosystem
## 5 Results
### Quantitative Analysis
The main representation of the sharing request feature in the ecosystem's database is the \"requests\" module, which records every sharing request and information about it. Table 4 presents a statistical overview of the requests module, which includes the total distinct values and average values for each of the aforementioned information. Figure 6: The number of authorized users of the content management platform of the ecosystem. Figure 7: The number of unique news items and total news posts across all websites. Out of 10,540 unique news items in the years after the introduction of the sharing request feature, 2126 were part of a sharing request (20.17%). Moreover, out of the 99 websites integrated into the ecosystem, 57 were involved in a sharing request (57.58%). Finally, in terms of the people involved, 42 out of 92 users made a sharing request (45.65%), while 45 out of 92 users accepted or rejected one (48.19%). These numbers show a very strong engagement of the content editor community with the collaborative request feature. On average, every news item that was involved in the sharing process requested to be reposted on 3.4 different websites. Figure 8 presents the total number of requests in the four full years the feature has been active. Each year, the number of requests increased dramatically as the feature gained even wider adoption. It is also important to observe how content generation has evolved in other informational entities. Table 5 presents the number of new item entries in three entities before and after the introduction of the collaborative features. The content generation rate for informational pages increased dramatically, while at the same time, a small increase was noted for courses. On the other hand, staff entity items were not equally affected. This is an indication that the streamlining of the content generation and sharing process for news items has had a strong effect on the growth of informational entities that are more closely related to the institution's online presence while having a limited effect on the content that is somewhat related to actual institutional activities and no effect on the content that is closely related to the institutional reality.
# Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
## 2 Related Work  
The provision of counterfactual explanations in machine learning models is becoming increasingly important in the field of XAI (eXplainable Artificial Intelligence) because it resembles human reasoning, where we often use what-if statements when providing an explanation for an event [8]. However, most classical methods for generating counterfactual explanations involve solving an optimization problem for each input datum \(\mathbf{x}\) to generate the counterfactual explanation \(\mathbf{x}_{cf}\), which can be time-consuming and result in explanations that are not straightforward.  
One of the first methods proposed for this purpose was CEM [9], which generates minimal changes to an input that change the model prediction to its nearest class. Similarly, [PERSON] et al. [10] introduced a term regarding class prototypes to speed up the explanation process and allow for the specification of the target class. However, these methods still involve solving optimization problems and thus, the explanations are not straightforward. More recent proposed methods, such as PIECE [11], generate more plausible counterfactual explanations and also produce semi-factual explanations, which are the largest possible feature modifications made to the input without changing the classification [12]. However, PIECE is only applicable for CNN-based models. Another recent method, DiCE [13], generates a set of \(k\) different counterfactual explanations for each input, but is limited to binary classification problems.  
The limitations of these methods in terms of time-consuming and non-straightforward explanations have led to the development of _amortized inference methods_, which consist of models trained to generate counterfactual explanations instantaneously. These are typically generative models, such as Generative Adversarial Networks (GANs) [14; 15] or Variational Autoencoders (VAEs) [16]. However, these methods also have their own limitations, such as the difficulty of training GANs, instability or non-convergence [17], or the lower quality reconstructions of VAEs. Recently, it has been proposed to use a simple AE to generate counterfactual explanations. [PERSON] et al. [18] propose a simple method that considers binary classification problems. In this method, the counterfactual explanations are obtained by minimally changing the latent representation of the encoder so that the prediction given by a classifier \(f(\cdot)\) to the decoded sample reaches a predefined target probability \(p\). Additionally, most of these methods have focused on image or tabular data rather than time series data.
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Software Design of the System  
The software design of the system includes the heuristics of leaf/router and border nodes. The most notable feature of the software is that the algorithm design can mainly focus on sensor processing. The network signaling is strictly treated by the Wi-SUN protocol stack, making communication very transparent from the perspective of the designed algorithms. The transparent communication process is possible, due to the Wi-SUN FAN self-forming and self-healing features. It means that adding new devices to a network is easy, and it automatically re-routes to the gateways when a pathway fails. Thus, the destination and the forwarding of the messages are within the Wi-SUN's protocol functionality, and it is outside of the software scope to handle network signaling.  
Figure 10: The weather station sensing elements: (**a**) rain gauge, (**b**) wind direction, (**c**) wind speed, and installed sensing elements on the mockup tower, and (**d**) all sensors installed in the tower.  
The main difference between leaf and router nodes is that the router keeps a \"passive\" state by handling the messages of other tower sites flowing through, while the site leaf node's devices can go dormant during the timeout period. Next, we describe the internal process of the program and the initialization of each part of the system.
# Deflection Reduction Shaping Commands with Asymmetric First-Order Actuators
## 2 Materials and Methods  
In this section, two approaches for generating deflection reduction shaping commands are developed to reduce the transient and residual oscillations for control systems with a single flexible mode operated with asymmetric first-order actuators. First, an analytical input shaping technique is presented with a simplified response expression to approximate the final limited impulse magnitude of the input shaper and an exponential expression to mimic first-order actuator dynamics. Second, an optimized deflection reduction shaping technique is proposed for an alternative technique with a parameter optimization and for justification of the approximated analytical technique.
## 3 Shadow Encoding Scheme for Power Spectrum Density
### Bandwidth Efficiency  
Bandwidth is a useful resource for network communication. The proper use of bandwidth can increase the throughput of transmission. Bandwidth is a critical asset to evaluate the performance of network communication. The network communication system transmits the chain of bits from a communication device to the entire network. Some line codes use extra bandwidth to send data through the channel. RZ and Manchester coding schemes require double bandwidth to transmit a bit over the network [21]. SES does not require extra or double bandwidth to transmit digital bits. SES needs one \(\mathbf{T_{b}}\) for each bit of transmission.  
Figure 2: Hierarchy of the existing modulation techniques.
# Parameter Optimization for an Accurate Swept-Sine Identification Procedure of Nonlinear Systems
## 3 Results
Figure 12: Function \(h_{2}(t)\): Comparison of (**a**) the reference trend with those obtained by (**b**) identification Procedure 1 (swept-sine range from \(f_{MIN}\) to \(f_{MAX}\) and matched filter range from \(f_{MIN}\) to \(f_{MAX}\)); (**c**) identification Procedure 2 (swept-sine range from \(f_{MIN}\) to \(f_{MAX}\) and matched filter range from \(f_{MIN}\) to \(OrdMax*f_{MAX}\)); (**d**) identification Procedure 3 (swept-sine range from \(f_{MIN}\) to \(OrdMax*f_{MAX}\) and matched filter range from \(f_{MIN}\) to \(OrdMax*f_{MAX}\)). Figure 11: Function \(h_{1}(t)\): Comparison of (**a**) the reference trend with those obtained by (**b**) identification Procedure 1 (swept-sine range from \(f_{MIN}\) to \(f_{MAX}\) and matched filter range from \(f_{MIN}\) to \(f_{MAX}\)); (**c**) identification Procedure 2 (swept-sine range from \(f_{MIN}\) to \(f_{MAX}\) and matched filter range from \(f_{MIN}\) to \(OrdMax*f_{MAX}\)); (**d**) identification Procedure 3 (swept-sine range from \(f_{MIN}\) to \(OrdMax*f_{MAX}\) and matched filter range from \(f_{MIN}\) to \(OrdMax*f_{MAX}\)). The considerations can be made cumulatively with reference to the three figures considered. Identification Procedure 1, which involves a significant limitation in the bandwidth of the identified function and an abrupt jump in the amplitude response at \(f_{MAX}\), leads, in all cases considered, to an increase in the duration of the initial peak and to a strongly oscillating trend in the response over time. In the case of identification Procedure 2, it is not so much the band limitation that makes its effects on the time course, but the irregularities, which are observed in the amplitude response, are reflected in irregularities in the time course and, again, in oscillations in the response. Only identification Procedure 3, in all the cases considered, provides an adequate ability to regain the time course of the functions considered, and thus, a correct identification of the Hammerstein model in both the time and frequency domains. It can be added that the trends of functions \(h_{4}(t)\) and \(h_{5}(t)\) lead to essentially equivalent considerations to those of the three functions \(h_{1}(t)\), \(h_{2}(t)\), and \(h_{3}(t)\) shown in Figures 11-13, respectively.
# Federated Learning for Edge Computing: A Survey
## 4 Frameworks
### OpenFL  
Open Federated Learning (OpenFL) is a software platform for FL that was initially created as a component of a collaborative research project between Intel Labs and the University of Pennsylvania focused on FL for healthcare and medicine. Intel and the community that provides this framework are still developing OpenFL for general real-world applications [88]. OpenFL uses certificates to secure communication and provides scripts for deployment in bash. All participants must provide a valid public key certificate signed by a trusted Certificate Authority (CA) to establish communication and connection. The library consists of a collaborator or FL client that uses a local dataset to train global models and an aggregator or server that collects model updates and combines them to create a global model. OpenFL includes both a command-line interface and a Python API. The documentation describes that OpenFL is only available for Ubuntu operating systems version 16.04 or 18.04 and artificial neural networks built using TensorFlow or PyTorch might serve as an illustration. Additional ML model libraries and model training frameworks may also be supported through an extendable interface.  
The first use of the OpenFL framework was directly in medicine in collaboration with the most significant international medical institutions, the aim of which was to gain insights and create a model for the detection of tumors in large and diverse patient populations [89]. [PERSON] et al. [90] address these challenges and propose SecFL. It is a confidential FL framework that uses Trusted Execution Environments. SecFL performs global and local training inside enclaves of Trusted Execution Environments to ensure the confidentiality and integrity of computations against strong adversaries with privileged access.
## 5 Case Study
### Parameter Analysis  
We analyzed the relationships between maintenance cost and maintenance cycle, as well as between acceptable failure rate and maintenance cycle, in the maintenance interval optimization model. As illustrated in Figure 8, the initial growth rate of maintenance costs was relatively high. However, as the number of maintenance cycles increased, the growth rate of maintenance costs decelerated and began to increase uniformly with the maintenance cycle. Figure 9 demonstrates that as the number of maintenance cycles increased, the failure rate continuously decreased, with the rate of decrease gradually declining.  
By examining the cost changes in Figure 8 and correlating them with the failure efficiency curve in Figure 9, we can draw the following conclusions: Initially, the total maintenance cost increased rapidly due to a higher acceptable failure rate \(\lambda\), longer intervals for imperfect maintenance, and a predominance of corrective maintenance. This resulted in faster growth of total maintenance costs. Subsequently, as the acceptable failure rate \(\lambda\) gradually decreased, the tolerance for product failure diminished. Consequently, imperfect maintenance intervals continued to shorten, and corrective maintenance decreased while preventive maintenance became dominant. This shift led to a slower and more constant growth rate in total maintenance costs.  
Figure 8: Curve of total maintenance costs over time.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Defective** \\ **System** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Defective** \\ **Components** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Number of** \\ **Maintenance** \\ **Intervals** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Average** \\ **Intervals** \\ \end{tabular} } & \multicolumn{2}{c}{**Maintenance Cost before**} & \multicolumn{2}{c}{**Optimized Maintenance**} \\  & & & & **Optimization (\(\boldsymbol{\mathsf{V}}\))** & & **Cost (\(\boldsymbol{\mathsf{V}}\))** \\ \cline{2-8}  & & & & **Intervals** & **Reparative** & **Preventive** & **Reparative** & **Preventive** \\  & & & **Intervals** & **Maintenance** & **Maintenance** & **Maintenance** & **Maintenance** \\  & & & & **Costs** & **Costs** & **Costs** & **Costs** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Cooling \\ system \\ (strong oil \\ circulation) \\ \end{tabular} } & Fan & 7 & 52.14 & 123,250 & 24,525 & 112,500 & 11,605 \\ \cline{2-8}  & & Control box & 9 & 40.56 & 32,875 & 299,751 & 28,750 & 18,312 \\ \cline{2-8}  & & Cooler & 9 & 40.56 & 16,600 & 519,385 & 15,400 & 228,049 \\ \cline{2-8}  & & Submersible & & & & & \\ oil pump & 10 & 36.5 & 40,000 & 410,385 & 32,875 & 198,407 \\ \hline \multicolumn{8}{c}{Overhaul cost} & \multicolumn{4}{c}{513,918} \\ \hline \multicolumn{8}{c}{Total maintenance cost} & \multicolumn{4}{c}{1,980,689} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of maintenance costs before and after optimization of the maintenance cycle.
## 2 Materials and Methods
### Data Processing
#### 2.4.2 Image Orthorectification and Mosaicking  
After transforming all the images into hyperspectral reflectance and land surface temperature, the Agisoft Metashape software (Agisoft, St. Petersburg, Russia) was used to generate orthorectified and mosaicked (orthomosaic) images that covering the whole experiment rice field. In hyperspectral image processing, we pooled both RGB and hyperspectral camera images for camera alignment to guarantee cloud points dense enough for an accurate reconstruction of Cubert hyperspectral camera locations. To avoid processing of large volume of hyperspectral data iteratively, only panchromatic gray images were used in the alignment process. This allowed reducing the computational time on a desktop computer with a 16-core 3.40 GHz CPU and two GPUs (GTX1080 Ti, Nvidia, Santa Clara, CA, USA). The DEM and orthomosaic hyperspectral images were generated. The DEM was used to estimate rice canopy heights by subtracting the average surrounding soil background elevation from the canopy elevation. The hyperspectral images were used in the subsequent analysis.