esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Local Learning Unit  
To represent the semantic features of entities and relations, we use internal initialized embedding vectors, \(E_{(s,o)}\in R^{n\times d}\) and \(E_{r}\in R^{r\times d}\), to stand for entities and relations, respectively. Here, \(n\), \(r\) stand for the number of entities and relation types, respectively, and \(d\) is the dimension size of each embedding.  
Since static embedding vectors are not able to reflect the evolution characteristics of facts over time, two types of representations, the local temporal feature and the global vector, are proposed to reflect the evolution of facts. The local temporal feature \(h_{s}^{t}\) summarizes the local information around a central entity until timestamp \(t\), reflecting the change of relationships between these linked facts in the past. The global vector \(g_{t}\) focuses on leaning the trend of background information of entire facts on the current knowledge graph. The two types of dynamic representation capture different aspect of informative knowledge from TKGs, which allows us to verify the reasoning process in different ways.  
To capture the local structural information around the fact, GCN blocks are proposed to aggregate neighbor information and transform them into a single representation standing for the main feature of the central entity. The problem is that previous GCN blocks used in knowledge graphs ignore the semantics of relations, and some recent models only regard relation types as a part of entities. However, classical knowledge graph embedding studies show that semantic features from entities and relations have different effects on the performance in the model. To illustrate this divergence, we introduce a new GCN algorithm, which uses the full semantic information of facts to create representations of facts, named ERGCN. The aggregator is formally defined as follows:  
\[h_{s,t}^{l+1}=W_{0}^{l}h_{s,t}^{l}+\frac{1}{n}\sum_{r\in N_{r}^{l}(s)}\sum_{o \in N_{r}^{l}(s)}(W_{r,1}^{l}e_{o}+W_{r,2}^{l}e_{r}) \tag{3}\]  
Here, \(h_{s,t}^{l}\) stands for the neighborhood message of entity \(s\) at the \(l\)-th layer. \(W_{0}^{l}\) and \(W_{r,1}^{l}\), \(W_{r,2}^{l}\) are trainable parameters for self-loop and aggregating features at the \(l\)-th layer. \(e_{o},e_{r}\) represent the embedding of entities and relations. \(n\) is the number of neighbor of entity \(s\).  
Therefore, the local historical representation of entity \(s\) at time \(t\) can be illustrated as a sequence of the neighborhood message in an observed length \(m\):  
\[h(s,t)=\{h_{s,t-1},h_{s,t-2},\cdots,h_{s,t-m+1}\} \tag{4}\]  
Then, we update the state of the local temporal feature for query and its sub-query via a GRU block:  
\[H_{s}^{t}=GRU_{1}([h_{m}(s,t):T_{m}(t)]) \tag{5}\]  
We use the final hidden state vector \(H_{s}^{t}\) to represent the local temporal feature of entity \(s\) at time \(t\). \(T_{m}(t)\) is the sequential temporal features trained in the global unit and it will be discussed in the next part. The symbol: represents the concatenation operation.
# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 1 Introduction
Both datasets show considerably fewer samples from older patients, who may have a high likelihood of skin lesions. The solid lines show the fitting of the curves and are only included for visualization purposes. representation for sensitive attributes [14, 15, 16]. While effective for tabular data, this approach is not well suited to handle image inputs. On the other hand, an adversarial network can also be appended to the end of an encoder or classifier to forecast protected or sensitive attributes (e.g., age, sex), creating a minimax game (see Section 3.5). This game aims to maximize the network's performance in predicting the class label, while simultaneously minimizing the ability of the adversarial network to predict the sensitive attributes [15, 16]. However, if a model is required to ignore features pertaining to a protected attribute, this may have a negative impact on its classification accuracy, as those features could potentially contain vital information necessary for accurate predictions. Furthermore, most related works consider sex, gender, or race as the selected (binary) sensitive attribute [14, 17]. These models are limited in their ability to handle continuous sensitive variables, such as age, which cannot be arbitrarily divided into categories as there is a lack of consistency in the labeling of age groups [18]. Manually binning data into age groups can also introduce human biases and inconsistent categorization [19]; thus, previous approaches are not suitable for this problem. Age as a continuous attribute could be incorporated into an adversarial loss function for bias mitigation purposes (e.g., BR-Net [20], AMAT-Net [21]); these are based on Pearson and/or cosine correlations between the true and predicted values of a protected variable. Furthermore, it is challenging to directly use the previous fairness criteria for continuous attributes (e.g., age). Therefore, alternative approaches are required to address fairness concerns related to continuous attributes. In this paper, we present a new method, called Mitigating Digital Ageism using Adversarially Learned Representation (MA-ADReL), to handle a continuous sensitive attribute in an adversarial learning setting for the mitigation of age-related bias in skin lesion problems. We introduce a mutual information penalty term that can mitigate bias for sensitive continuous attributes to improve the fairness while maintaining the accuracy. To further improve the performance, we utilize the information fusion of both low- and high-resolution inputs for medical image encoding in order to learn a more transferable latent representation. Various experiments on two skin lesion datasets demonstrate the consistent effectiveness of our method in mitigating bias and achieving comparable performance.
## 4 Results and Analysis
### KAN-BiLSTM Model Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Test**} \\ \cline{2-5}  & **Region** & **RMSE** & **MAE** & **R\({}^{2}\)1** ** \\ \hline RNN & \(\surd\) & 0.1532 & 0.0971 & 0.7285 \\  & \(\surd\) & 0.1141 & 0.0777 & 0.8495 \\ GRU & \(\surd\) & 0.1350 & 0.0954 & 0.7893 \\ LSTM & \(\surd\) & 0.1207 & 0.0794 & 0.8315 \\  & \(\surd\) & 0.1245 & 0.0744 & 0.8208 \\ BiLSTM & \(\surd\) & 0.1210 & 0.0766 & 0.8306 \\  & \(\surd\) & 0.1043 & 0.0673 & 0.8543 \\  & \(\surd\) & 0.0907 & 0.476 & 0.9049 \\ KAN-BiLSTM & & 0.0482 & 0.0352 & 0.9731 \\  & \(\surd\) & **0.0283** & **0.0240** & **0.9908** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy metrics of various models on the moisture sequence test dataset. Figure 16 illustrates the data comparison and error distribution of the KAN-BiLSTM model. Compared with the basic BiLSTM structure, the KAN-BiLSTM model shows improved performance in capturing subtle variations in moisture content, with only minor deviations from the actual drying curve. Notably, in the moisture ratio range from 0.2 to 0--which is typically challenging to model accurately--the nonlinear prediction capability of the model is significantly enhanced. Figure 14: The actual values and predicted values of test data using different architectures. (**a**) RNN + Region, (**b**) GRU + Region, (**c**) LSTM + Region, (**d**) BiLSTM + Region, (**e**) RNN, (**f**) GRU, (**g**) LSTM, and (**h**) BiLSTM. Figure 15: The error probability distribution curves of different models. (**a**) RNN + Region, (**b**) GRU + Region, (**c**) LSTM + Region, (**d**) BiLSTM + Region, (**e**) RNN, (**f**) GRU, (**g**) LSTM, and (**h**) BiLSTM. The experimental results demonstrate that the KAN-BiLSTM model significantly outperformed benchmark models in terms of nonlinear prediction ability; its RMSE, MAE, and R\({}^{2}\) metrics all surpassed those of the competitors. Consequently, the KAN-BiLSTM model was adopted as the efficient solution for predicting the tangerine peel moisture ratio.
# Topology Optimisation in Structural Steel Design for Additive Manufacturing
## 3 Topology Optimisation--One Concept, Various Fields
### Discrete Optimisation and Optimisation of Continua in the Context of Structural Steel Design  
Topology Optimisation applications can be divided into Discrete Optimisation and Optimisation of Continua, based on the Topology considered for the optimisation problem (Figure 9).  
The optimisation of discrete structures has a history of its own. It has been present in structural engineering from its early days, even if with a different scope, and this reason justifies why some researchers find the roots of TO in the classical theory of elasticity. As suggested by its name, Discrete Optimisation is directed to discrete structures. Its objectives lie in finding the optimal number, location, shape, size, and connectivity for structural elements and nodes coordinates. Therefore, one can understand that most early Discrete Optimisation applications have been limited to Shape and Size Optimisation, rather than TO, due to significant limitations on the optimisable variables' domain. For the very same reason, Discrete Optimisation has been used under the names of Truss Topology Optimisation (TTO) or Topology Optimisation of Skeletal Structures (TOSS) and its early applications to finding optimal layouts led to the name Layout Optimisation, regarded by many as a former name for TO.  
However, the Discrete Optimisation scope exceeds one-dimensional members. In fact, since the advances by [PERSON] and [PERSON], which used grid-like structures, TO has grown as a merger of techniques and approaches, deemed to find optimal solutions for specific cases. Continuum structures can be modelled as ground structures--complex systems of one-dimensional elements--and therefore, be subjected to Discrete Optimisation.  
A major issue of Discrete Optimisation can be attributed to considering nodes coordinates in or out of the optimisation domain. If coordinates are not possible to optimise, solutions can easily result in a plethora of thin, one-dimensional structural element. Conversely, if the nodes positions vary, node superposition will likely occur, leading to significant computational problems.  
The Optimisation of Continua is usually applied to solids, shells, or design envelopes (which, in fact, are solids) and is deemed to optimise the design space external boundaries shape, as well as the internal boundaries shape. The latter refers to the newly created boundaries between the material and void.  
Figure 9: Example of (**a**) problem statement and discrete optimisation [94] and (**b**) problem statement (**left**) and optimisation of continua (**right**) [94].
## 2 Materials and Methods  
### The Methodology  
The method described exploits the capability of some TLS models to function as a line scanner and to provide the timestamp of each detected point, synchronized with the GPS time.  
It is necessary to identify a significant line to be surveyed, which allows to obtain the difference between the elastic curve of the unloaded structure (dead load) and that of the structure subject to mobile or static loads. To this purpose, it is generally chosen to scan a line at the bottom of the deck, on the longitudinal axis or parallel to it.  
For this reason, the TLS must be correctly aligned. Since the instrument does not project a visible beam, which would allow to check the alignment, it is possible to use a total station and/or exploit the features present on the surface of the structure or specifically positioned targets.  
As a preliminary step, a line scan of the unloaded structure is performed, in correspondence with the selected section, which allows to have the reference shape. This shape is given by the best fitting line of the points measured (usually 10 to 100 points per meter at a 100 m distance from TLS). To obtain instant displacements, line scans are performed during the normal activity of the structure, with a sampling rate of up to 120 lines per second. For each line scan, the interpolation line is obtained and the scan time t, provided by the instrument timestamp, is memorized. The displacements at time t, are given by the deviation between the interpolation line at time t and the reference line. These displacements provide the elastic curve due to the load present at time t.  
TLS is equipped with a GNSS receiver, which allows you to transform the timestamp into GPS time. The instantaneous position of a vehicle can be obtained from a GNSS receiver with which the vehicle is equipped. A video of the structure during the activity can also be used to determine the position of moving loads. Nowadays, several cameras are equipped with accessories that can provide Coordinated Universal Time (UTC) and, therefore, allow us to synchronize TLS and camera acquisitions.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 2 Exploring the Options
### Option 3: LEO Plane-Based Integrity  
This option assumes that the LEO constellation is equipped with inter-plane intersatellite links. The LEO satellites on each plane share their LEO-MEO observables. Within each plane there is a selected LEO satellite for each MEO that acts as an MEO monitor. The selected monitor collects all the observables for the MEO, runs the monitoring algorithm, and returns the flag to the rest of the LEO satellites in the plane as their downlink to the user.
###### Abstract  
Several studies have focused on modeling and analyzing the impact of rotor faults in conventional low-pole-count machines, while related research on low-speed high-torque (LSHT) machines with a high pole count remains limited. In these machines, the combination of low speed, high inertia, and high torque levels presents a critical application for advanced diagnosis techniques. The present paper aims to describe and quantify the impact of rotor faults on the performance of LSHT machine types during the design stage. Specifically, 10-pole and 16-pole synchronous reluctance machines (SynRMs), permanent magnet synchronous machines (PMSMs), and squirrel-cage induction machines (SCIMs) are assessed by means of detailed 2D simulations. The effects of eccentricity, broken rotor bars, and partial demagnetization are studied, with a focus on performance variations. The results show that LSHT PMSMs are not significantly affected by the partial demagnetization of a few magnets, and the same holds true for common faults in SynRMs and SCIMs. Nonetheless, a significant increase in torque ripple was observed for all evaluated faults, with different origins and diverse effects on the torque waveform, which could be hard or invasive to analyze. Furthermore, it was concluded that specialized diagnosis techniques are effectively required for detecting the usual faults in LSHT machines, as their effect on major performance indicators is mostly minimal.  
c 2020 accepted: 6 February 2025  
Published: 8 February 2025  
**Clitation**: [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Comparative Analysis of the Effect of Rotor Faults in the Performance of Low-Speed High-Torque Machines. _Appl. Sci._**2025**, 15721. [[https://doi.org/10.3390/appl5041721](https://doi.org/10.3390/appl5041721)]([https://doi.org/10.3390/appl5041721](https://doi.org/10.3390/appl5041721))
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 6 Conclusions  
This paper proposes a very low-complexity adaptive Volterra filter-error least mean square algorithm for the active control of nonlinear noise processes after considering a decomposable Volterra model and filtered-error structure. The computational complexity analysis shows that the proposed DVMFELMS algorithm can significantly reduce the complexity, especially for the NANC system with the NSP case. The simulation result shows that the proposed algorithm can achieve comparable performance with the VFELMS algorithm and other state-of-the-art nonlinear filters in simulation conditions, while the decomposable error of the Volterra kernel may inevitably be introduced. In addition, the proposed DVMFELMS algorithm shows potential in a better convergence property  
Figure 8: Amplitude and phase response of measured primary and secondary path.  
Figure 9: Performance comparison using measured primary and secondary path with saturated reference signal.  
due to the fewer parameters used in each sub-filter. Further work should concentrate on studying the step control to improve the stability of the DVMFELMS algorithm for practical applications.  
All authors have contributed to the work presented in this article. [PERSON] derived the algorithm and conducted the analysis and simulation; [PERSON] proposed the scheme, directed the work, analyzed the obtained results, and revised the manuscript. [PERSON] conducted the simulation and interpreted the obtained results; [PERSON] analyzed the the obtained results and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.  
The following abbreviations are used in this manuscript:  
FXLMS - Filtered-Reference Least Mean Square  
FELMS - Filtered-Error Least Mean Square  
FIR - Finite Impulse Response  
IIR - Infinite Impulse Response  
NANC - Nonlinear Active Noise Control  
LSP - Linear Secondary Path  
NSP - Nonlinear Secondary Path  
FLNN - Functional Link Neural Network  
GFLNN - Generalized Functional Link Neural Network  
EFLNN - Exponential Functional Link Neural Network  
EMFN - Even Mirror Fourier Nonlinear  
LeNN - Legendre Neural Network  
VFXLMS - Volterra Filtered-Reference Least Mean Square Based on Decomposable  
DVMFELMS - Volterra Filtered-Error Least Mean Square Based on Decomposable Volterra Model  
FXGFLNN - Filtered-Reference Functional Link Neural Network  
FEFGLNN - Filtered-Error Functional Link Neural Network  
FXEMFNL - Filtered-Reference Even Mirror Fourier Nonlinear Filter with a Linear Part  
FEEMFNL - Filtered-Error Even Mirror Fourier Nonlinear Filter with a Linear Part  
NMSE - Normalized Mean Square Error  
SNR - Signal-to-Noise Ratio
## 2 Methodology  
### Methodology for the Assessment of Data Quality  
#### 2.1.1 Measurement System Analysis (MSA)  
Methodology Overview  
Measurement system analysis (MSA) is generally a set of procedures used to determine the variation caused by a measurement system and whether the measurement data are valid before being used for the construction of services delivered by a DT. MSA aims to separate the variation among devices being measured from the error in the measurement system [16]. Understanding and minimising the sources of variation in the measurement process allows organisations to make more informed decisions and improve the quality of their products or services. An MSA process was defined to determine the level of repeatability by a water distribution testbed's measurement system to aid in identifying the impact of the data quality on analytics as a service. The analysis starts with defining a process for capturing healthy and faulty condition data over one month by different operators. A relative pooled variance, which computes a combined measure of variability by considering both the sample sizes and the variances of the groups, was used to determine whether there was variability in the healthy and faulty condition data over the period in which data were recorded. To determine the source of data variation when more than 10% relative pooled standard deviation was observed, a gage repeatability and reproducibility (gage R&R) approach was adopted to determine the variation in the process data associated with the process and the measurement system. The earth mover's distance (EMD) metric was also used to complement the gage R&R analysis by determining the similarity between datasets by measuring the distance between the probability distributions of the measured process data. A data-quality (DQ) score between 0 and 1 was then defined as an average of the scaled outcomes of both the gage R&R analysis (repeatability) and the EMD score (similarity metric), where a DQ score of 1 represents a highly repeatable measurement system.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 2 Wide-Area and High-Resolution System Design
### High-Resolution Camera Design  
#### 2.1.1 Optical Analysis  
This paper uses the GMAX3265 CMOS image sensor developed by Gpixel. The sensor has 65 million pixels, and the size of each pixel size is 3.2 \(\upmu\)m. Its characteristics consist of a large pixel array, low readout noise, and a global shutter function. The main technical indicators are shown in Table 2 and the quantum efficiency curve is shown in Figure 2.  
In order to improve the adaptability and performance of the high-resolution camera, the optical part is designed as a zoom lens. The optical system needs to design a set of high-resolution imaging lenses, and the primary optical parameters are calculated according to the requirements, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Data** \\ \hline effective number of pixels & 9344 \(\times\) 7000 \\ pixel size & 3.2 \(\upmu\)m \(\times\) 3.2 \(\upmu\)m \\ photosensitive area & 29.9 mm \(\times\) 22.4 mm \\ image mode & monochrome camera \\ image data bit & 12 bit (max) \\ maximum of frame rate & 31 frame/s \\ dynamic range & 66 dB \\ peak quantum efficiency & 65.3\%@500 nm \\ number of saturated electrons & \textgreater{}9.0 ke\(-\) \\ read noise & 2.0 e\(-\) \\ dark current & 5.3 e\(-\)/p/s@40 \({}^{\circ}\)C \\ nyquist frequency & 156 lp/mm \\ consumption & \textless{}2.1 W \\ \hline \end{tabular}
\end{table}
Table 2: GMAX3265 Image Sensor Index Parameter Table.  
Figure 2: GMAX3265 Quantum Efficiency Curve.
# Super-Resolution Remote Imaging Using Time Encoded Remote Apertures
## 3 Reconstruction Algorithm
### Core Finding  
The first step of the buildup algorithm is to find the core. The core of the embedding point-cloud is an over-constrained set of five points, including the source, see Figure 2c. The core can be broken down into three pieces: the base triangle and two tetrahedra. The base triangle (Figure 2c (\(p_{0},p_{1},p_{3}\))) is constructed using two first (Figure 2c dashed green line) and one second (Figure 2a solid red line) bounce. Each tetrahedra (Figure 2a (\(p_{0},p_{1},p_{2},p_{3}\)) and (\(p_{0},p_{1},p_{4},p_{3}\))) uses one first and two second bounces. Because the bounces are not labeled, one has to exhaustively search over all possible first and second bounce pairs to build the base triangle and tetrahedra. Finally, we loop through all of the remaining bounces to find one second bounce in order to test and check whether it fits into bridge bond \((p_{2},p_{4})\) between two vertices of the tetrahedra. If a correct second bounce is found and the bridge bond is satisfied. We found a core structure and can move to \"adding a vertex\". If the bridge bond is not satisfied, then we restart \"core finding\" and choose another base triangle and tetrahedra by exhaustive search. This process is repeated until the core structure is found.