esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
## 3 Seeded Fault Testing
### Seeded Fault Tests  
To observe the changes of the operating parameters under the faultless and faulted working conditions and validate the performance of the proposed fault diagnosis method, seven common seeded faults were introduced into the reciprocating compressor test rig to obtain real faulted and faultless data. The seven seeded fault cases and the faultless case were listed in Table 3.  
The ring valve plate may crack like a small crevice or a bigger breach due to material defects and corrosion environment. They can both increase the gas leakage through the valve, leading to the decrease of flow rate of RC. The small cracked crevice may expand to a bigger breach in on-site application. As Figure 6a shows, a small crevice was made on the discharge and suction ring valves to induce the cracked valve plate faults in level 1 in experiments (Fault 1 and 5). Additionally, in Figure 6b, a little piece of valve plate was cut off to simulate the bigger breach in the discharge and suction valve plate faults in level 2 (Fault 2 and 6). Besides, the stiff of the valve spring may degrade over time due to fatigue damage, so one spring was cut short to simulate the fatigue fault of the spring set (Fault 3 and 7), see Figure 6c. Further, the piston ring may wear gradually because of the uneven stress, which can increase the gas leakage between the piston ring and the cylinder. Figure 6d shows one side of the piston ring was scuffed to induce the wearing piston ring fault (Fault 4).  
In faulted tests, the healthy components were replaced by the associated faulted components, respectively. In each faulted and faultless tests, the compressor was operating at the same suction pressure and the same regulating valve opening. Additionally, the data were sampled continuously more than 10 s in each test and were downloaded onto the laptop.  
Then, MATLAB R2020a was applied to plot \(p\)-\(V\) diagrams based on the raw data. In this article, 96 groups of \(p\)-\(V\) diagrams were selected in each case; therefore, there were 768 samples in all, with eight classes. In each class, 70% of the samples were used to train the ANN, 15% of the samples were applied to do the validation, and the other 15% were used to test the performance of the ANN.  
\begin{table}
\begin{tabular}{c c} \hline
**Seeded Faults** & **Cases** \\ \hline Cracked discharge valve plate in level 1 & Fault 1 \\ Cracked discharge valve plate in level 2 & Fault 2 \\ Broken discharge valve spring & Fault 3 \\ Wearing piston ring & Fault 4 \\ Cracked suction valve plate in level 1 & Fault 5 \\ Cracked suction valve plate in level 2 & Fault 6 \\ Broken suction valve spring & Fault 7 \\ Healthy condition & Faultless (case 8) \\ \hline \end{tabular}
\end{table}
Table 3: Seeded fault cases.
# Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
## 1 Introduction
[25] presented a method equipped with Cascade Mask R-CNN [26] to tackle the problem of table detection in document images. However, the capabilities of Cascade Mask R-CNN have not been investigated yet in the domain of mathematical formula detection in document images. This paper presents an end-to-end data-driven approach to detect both isolated and embedded formulas in document images. The main contributions of this paper are as follows:  
* We present an end-to-end trainable framework that operates on a Cascade Mask R-CNN equipped with a deformable composite backbone to detect both isolated and embedded formulas in document images. * Unlike prior work, our formula detection pipeline operates on a lightweight dilation method as a pre-processing step. * We accomplish state-of-the-art results in detecting isolated formulas on a higher IoU threshold in the ICDAR-2017 POD dataset [27]. Furthermore, on the Mar-mot dataset [9], we surpass previous state-of-the-art results on embedded formulas with a huge margin and achieve identical results with prior state-of-the-art on isolated formulas.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 5 Networking for Cloud and Fog-Based Multi-Robot Systems
### Fog-Based Multi-Robot Systems  
While cloud computing can provide advanced and powerful services for MRS applications, it usually cannot provide provisions for distributed MRS applications that need real-time, mobility, low-latency, data streaming, synchronization, coordination, and interaction support services [55]. This is mainly due to the transmission delays imposed by the large distances to be covered between the robots and the cloud platforms in addition to the usual issues of jitter, packet loss, and broken links that may occur across the Internet connections used. In addition, it is difficult for cloud computing to manage and deal with a large number of heterogenous robots, sensors, actuators, and other devices distributed over a large-area. Fog computing was lately introduced to offer more localized, low-latency, and mobility support services. Fog computing allows moving some functionalities from the cloud to compute nodes situated closer to the devices such as the robots [56]. This approach aims to enable different Internet of Things (IoT) applications over distributed fog nodes that provide localized services to support these IoT applications. For MRS applications, fog computing can complement cloud computing to support MRS applications [57]. While cloud computing can provide powerful and scalable services for MRS applications, fog computing can provide more localized, fast-response, mobility, and data streaming services for these applications. The integration between the MRS, fog computing, and cloud computing, as shown in Figure 5, can provide a powerful platform to support different MRS applications by taking advantage of the best features on each platform. Yet this platform becomes more complex and highly distributed, thus creating more issues for the supporting communication infrastructure that needs to keep up with the imposed demands on the network services. This leads to the need for well-designed networking and communication support to efficiently and reliably handle the communication between all components. Several aspects in the network need to be addressed to create efficient communication infrastructure and minimize network problems across all platforms. This also includes introducing strong network security support to avoid any threats and vulnerability possibilities in the integration and in supporting MRS applications.
## 3 Proposed Methodology
### Attention Mechanism  
Nowadays, attention-based neural networks have shown tremendous achievement in query answering, machine paraphrases, speech acknowledgment, and image/video description generation. Various attention mechanisms have been proposed previously; among them, we have considered the soft attention model inspired by [25] to obtain a dynamic weighted sum of the extracted features from the video frames. Instead of utilizing a frame as an input to the LSTM, we used weighted image characteristics that compensated for attention in soft attention. By multiplying the related segmentation map with a low weight, soft attention discredits unimportant parts of the frame. Therefore, high attention zones retain their original worth, whereas low attention areas approach zero. As a result, the LSTM can generate more accurate predictions.  
If we consider that the feature vector \(F\) consists of \(\left(F_{1},\ \ldots,\ F_{n}\right)\) features, we can denote \(F_{i}\) as \(F_{i}\ \in\left(F_{1},\ \ldots,\ F_{n}\right)\) and the importance score \(s_{i}^{t}=\left(s_{1}^{t},\ \ldots,\ s_{n}^{t}\right)^{T}\).  
\[s_{i}^{t}=W_{s}\ tanh\Big{(}W_{f}F_{i}+W_{h}h_{t-1}+b_{s}\Big{)} \tag{9}\]  
\[\alpha_{i}^{t}=\frac{exp\big{(}s_{i}^{t}\big{)}}{\sum_{i=1}^{n}exp\big{(}s_{i} ^{t}\big{)}} \tag{10}\]  
\[F_{t}^{\prime}=\sum_{i=1}^{n}\alpha_{i}^{t}F_{i} \tag{11}\]  
Here, \(W_{s},\ W_{f},\ and\ W_{h}\) are the weight vectors for training and the bias vector \(b_{s}\). However, \(\alpha_{i}^{t}\) is the attention at time t. The previous state of the hidden stage \((h_{t-1})\) and the i-th features of the video frames proceed the standardized weight score \(s_{i}^{t}\). Then, the features of the visual frames feed into the bi-LSTM encoder at time t. Finally, the weighted sum of the frame features is calculated through Equation (11). Figure 3 illustrates the bi-LSTM [53] with an attention mechanism for generating meaningful sentences from the visual frame.
## 2 Materials and Methods
### Mathematical Models
#### 2.2.2 Two-Dimensional LNSE-FEM Model  
The 1D Riccati model presented in Section 2.2.1 is computationally very efficient; however, it does not fully resolve the internal structure of the SASs depicted in Figure 1; therefore, it does not account for the effects such as the evanescent coupling of the adjacent slits, non-plane-wave propagation, etc. Therefore, its numerical predictions should be validated via a comparison with a more detailed model. As a symmetry with respect to the \(z\)-axis is assumed (see Figure 1) and the incoming wave is a planar one, all these effects can be accounted for by a 2D model--represented by the \(xy\) cut of geometries shown in Figure 1. Moreover, due to the symmetry, the computational domain can be limited to \(y\ \geq\ 0\).  
The acoustic field is described using the linearized Navier-Stokes equations formulated in the frequency domain (see, e.g., [32; 33]), which have the form:  
\[\mu\bigg{[}\mathbf{\
abla}^{2}\hat{\sigma}+\frac{1}{3}\mathbf{\
abla}( \mathbf{\
abla}\cdot\hat{\sigma})\bigg{]}-\mathbf{\
abla}\hat{p} = \mathrm{i}\omega\rho_{0}\hat{\sigma}, \tag{6a}\] \[-\rho_{0}\mathbf{\
abla}\cdot\hat{\sigma} = \mathrm{i}\omega\hat{p},\] (6b) \[\kappa\mathbf{\
abla}^{2}\hat{T} = \mathrm{i}\omega\rho_{0}c_{p}\hat{T}-\mathrm{i}\omega\hat{p}, \tag{6c}\]  
where \(\hat{p},\hat{\sigma},\hat{T},\hat{\rho}\) are the acoustic pressure, acoustic velocity vector, acoustic temperature, and acoustic density complex amplitudes, respectively, \(\kappa\) is the coefficient of thermal conduction, and \(c_{p}\) is the specific heat at constant pressure. Equations (6) are supplemented with the linearized state equation for an ideal gas \(\hat{p}/p_{0}\ =\ \hat{\rho}/\rho_{0}+\hat{T}/T_{0}\), where \(T_{0}\) and \(p_{0}\) are the equilibrium values of the gas temperature and pressure, respectively. Equations (6) are solved with the isothermal (\(\hat{T}\ =\ 0\)) and no-slip (\(\hat{\sigma}\ =\ 0\)) boundary conditions on the rigid walls.  
The mathematical model is implemented in COMSOL Multiphysics v. 6.0 (Acoustic Module, Thermoviscous Acoustics Interface, frequency domain, 2D geometry), where Equations (6) are discretized by employing the finite element method. The computational domain was discretized by employing a rather fine free triangular mesh, and the thermo-viscous boundary layer along the rigid walls was properly resolved using mesh boundary layers. The MPP in the case of the MPP-SBH was modelled as an internal impedance boundary with transfer impedance, as given by Equation (3). The tangential acoustic velocity vector component was set to zero at the MPP surface.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 3 Results--Radiometric Comparison
### Overall Radiometric Differences  
To summarise the average radiometric differences between SLSTR-A and B, we considered a single orbit of night-side gridded data. The selected orbit is from 3 September 2018, a day late on in the tandem phase such that SLSTR-B had stabilised following the initial commissioning activities.  
The comparison is restricted to pixel values in the nominally linear region of the instruments' dynamic ranges, defined here as measured temperatures greater than 250 K. The location of this linear region is largely governed by the set-point temperatures of the instruments' hot and cold blackbody calibration targets, which are held at \(\sim\)303 K and \(\sim\)265 K, respectively. Temperatures close to, and especially between, these points are the effective dynamic range of the sensor, so chosen to optimise performance at sea surface temperatures (\(\gtrsim\)\(-\)2 \({}^{\circ}\)C). Outside of this range measurement uncertainties increase considerably, particular for colder temperatures where the Earth radiance is very low, especially for the S7 channel (3.742 \(\upmu\)m). An analysis of the sensitivity of the differences as a function of scene temperature is given in Section 3.3.  
The results of the comparison, given in Table 2, show small mean differences for channels S7, S8 and S9 in both the nadir and oblique views, of order 10 mK, with slightly lower standard deviation of differences in the nadir view, particularly for channels S8 and S9.
## 1 Introduction
Moreover, tunable biasing allows for dynamic adjustment of inductor characteristics, enabling adaptability to varying frequency and bandwidth requirements in 5G systems. In this work, an active inductor circuit is proposed, offering a high quality factor and wide tunability, designed to meet the performance and efficiency demands of 5G NR (FR1) applications. Although many CMOS-based active inductor designs have been investigated, relatively few report measured results. This study introduces and experimentally characterizes a grounded CMOS active inductor based on a gyrator-C architecture. The proposed design is optimized to operate within the sub-6 GHz FR1 spectrum, covering key frequency bands used in 5G. The focus is on addressing the core challenges of active inductor integration in 5G NR systems, with emphasis on achieving a high Q-factor, wide tunability, and low power consumption. This paper is organized as follows: Section 2 reviews the fundamental principles of grounded gyrator-C active inductors and key design considerations. As well, it presents the proposed circuit topology and outlines the techniques employed to enhance its performance. Section 3 details the measurement results and performance analysis. Section 4 concludes the paper and discusses future research directions.
## 2 Problem Definition and Related Work  
### Problem Definition  
In educational data mining, the prediction of students' performance is regarded as a classification task of supervised learning. Researchers collect a training dataset containing students' demographic features, assessment scores, online learning behavior variables, and corresponding performance to train the models established by algorithms based on machine learning. After training, the prediction results of a student's performance can be obtained by inputting the new student's features into the prediction model. A training dataset \(\mathbf{D}\) containing \(\mathbf{m}\) students can be expressed as \(\mathbf{D}=\{\mathbf{(S_{1},P_{1})},\mathbf{(S_{2},P_{2})},\ldots,\mathbf{(S_{m},P_{m})}\}\); \(\mathbf{S_{i}}\) is the feature vector of the \(\mathbf{i}\)-th student; and \(\mathbf{P_{i}}\) is the academic performance of the \(\mathbf{i}\)-th student. The prediction model obtained by training can be regarded as a function \(\mathbf{f}\). For a student with an unknown performance of feature vector \(\mathbf{S_{x}}\), we can obtain the prediction result of performance \(\mathbf{P_{x}}\) by using\(\mathbf{f}\) based on \(\mathbf{S_{x}}\), which is then expressed as \(\mathbf{P_{x}}=\mathbf{f}\)\((\mathbf{S_{x}})\). If \(\mathbf{S_{x}}\) is a sequence of features, an early prediction means that using the part of \(\mathbf{S_{x}}\) in front can predict a student's performance.
# UAV Support for Mission Critical Services
## VMCS Concept
### VMCS Foundations  
In the last decade, cloud technologies began to be used not only for the implementation of service platforms but also for the virtualisation of network functions, i.e., the implementation of distributed networking solutions atop the virtualised infrastructure. The approach allows for deploying in-software-made networking solutions that can be combined with services. The description of network virtualisation concepts and the benefits of the technology is out of the scope of this paper. However, with some modifications, network and service virtualisation can be exploited for a new generation of MCS. Thus far, such a solution has not been yet proposed except the use of Fog for natural disaster management [48], limited to real-time IoT applications only.  
The Fog computing infrastructure assumes that nearly all network physical nodes (including terminals, routers, base stations and UAVs) provides computing capabilities; however, their resources can be constrained. Usually, the term cloudlet is used to describe such a small-scale data centre used for Fog [49]. In most Fog approaches, the Fog is only part of the solution, and the computing infrastructure is split into Fog resources (micro data centres and cloudlets), Edge resources (mini data centres) and Cloud (large data centres). In VMCS, the following nodes with computing capabilities can be identified:  
* Terminal nodes. VMCS end-users operate such nodes. Some may be dedicated to VMCS, whereas others are just commercial terminals. Most have cloudlets; however, vehicular terminals may provide mini data centres.
* Edge nodes are nodes between the terminals and the core part of the network. They typically may have mini data centres.
* Core nodes. These nodes typically are connected to a reliable infrastructure and clouds by high bit rate links. Their functions can be implemented using Cloud. In Scenario 2 (Section 4), such components may not exist, and the Edge nodes take their role.  
In VMCS, a node may belong to Fog or Edge category depending on their computing capability and stability of resources, as Edge and Fog use different orchestrators tailored to these properties. This is shown in Figure 4.
## 1 Introduction  
Thermal insulation coatings are thermocoramic coating materials that function on an acrylic base and are available in various color shades. These coatings consist of three main components: binder, filler, and pigment. The binder is a highly elastic acrylic resin that, when exposed to UV radiation, forms a dual-network structure. The filler is in the form of microceramic hollow spheres, which contain an inert gas filling. The pigment is typically white and in the form of titanium dioxide, which can be tinted to any desired shade if necessary [1].  
The thermal insulating properties of the coating are provided by the microceramic hollow spheres with the inert gas filling. The thermal insulation coating primarily ensures high reflectivity of solar radiation that acts upon it. A total of 86% of the coating is capable of reflecting solar rays into space, thereby reducing material overheating. The binder and filler of the thermal insulation coating prevent the penetration of atmospheric water and air moisture into the material while allowing evaporation, thereby regulating the moisture content of the substrate with absorptive and diffusive capabilities and increasing the thermal resistance of the entire structure. The high elasticity of the coating prevents the formation of cracks when applied to any surface. The application itself can extend the lifespan of the material [2].  
The typical product based on this technology is the IndustrySpecial coating. It is an industrial coating ready to use, based on the thermal ceramic coating technology. IndustrySpecial for tank coating is a resistant protective coating suitable for use on almost all surfaces in industrial sectors. The special composition of ThermoShield IndustrySpecial provides a wide range of energetic application areas for thermal shielding in summer [3].  
The key properties of this coating are variable diffusion, high resistance to aggressive environmental influences such as smog and ozone, low emissions, free of solvents, high UV and weather resistance, reflective, very high color resistance, waterproof, expandable, and thermally comforting.  
The material data are as follows [4]:
* Sd values according to DIN 52615: dry area: sd = 0.58; wet area: sd = 0.42;
* Density of 1.04 kg/dm\({}^{3}\), according to ISO 2811-1;
* Fire behavior: C-s1, d0;
* Sunlight reflection: 84%, according to DIN 67507;
* Chemical resistance: available upon request.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 5 Results and Discussion
### Clinical Implications and Practical Considerations  
Our method's success in eliminating complex preprocessing steps represents a significant advancement toward practical clinical applications.  
Traditional approaches often rely heavily on sophisticated signal processing techniques and manual feature engineering, which can be both time-consuming and expertise-dependent. In contrast, our approach directly learns from raw EEG signals and their time series images, significantly reducing the preprocessing overhead while achieving superior performance.  
The successful knowledge distillation into a lightweight model addresses a critical need in clinical settings. The student model, while maintaining 96.8% accuracy, requires only 40% of the original computational resources, making it suitable for deployment on portable devices and integration with existing clinical systems. This efficiency is particularly valuable for continuous monitoring applications, where real-time processing and battery life are crucial considerations.  
Figure 11: Distribution of EEG features between ictal and interictal states in the CHB-MIT dataset, demonstrating the feature characteristics of seizure versus non-seizure periods.
# Facility Layout Problem with Alternative Facility Variants
## 7 Discussion  
### Applicability of the Proposed Approach  
The proposed FLP, with the realistic definition of workstations together with the finite set of possible fixed-shape realizations available for each workstation, fills the current gap in the literature. Existing FLP formulations involving loosely-defined facility shapes and dimensions rely on the concept that the facilities can freely vary their shape and dimensions within some limits. However, this is not a general case. In many practical situations, facilities can only be realized by a small number of fixed-shape variants. One example is the automotive industry, with assembly lines composed of dozens of interconnected workstations. Every time a new car model is to be introduced to production, the workstations and assembly lines must be re-configured and reorganized within a production hall accordingly. Importantly, the design of a feasible workstation realization is a highly-constrained optimization problem itself. It involves various constraints related to the specifics of the technological procedure implemented by the workstation and to the workstation's interface with other workstations it interacts with.