esa-sceva/satcom-chunk-collection huggingface dataset:
## Theory  
### The TSSA Algorithm  
The TSSA method mainly contains two stages, the embedding operation process and the tensor decomposition process. In the embedding stage, a one-dimensional time series \(\mathbf{x}\) with length \(n\) is mapped into a 3D tensor \(\mathcal{X}\).  
In the embedding stage, two works need to be achieved. Firstly, \(\mathbf{  
where \(J\) and \(\tau\) can be determined by the False Nearest Neighbor algorithm (FNN) [31], thus we can obtain a 3D tensor with the size \(I\times J\times K\).  
In the second stage, the obtained 3D tensor needs to be decomposed. The CP tensor decomposition method, which factorizes a tensor into a sum of component rank-one tensors, is used here. It can be considered as a generalization of bilinear principal component analysis (PCA) [32; 33]. The fundamental expression of the CP based on outer product of the three factor matrices is given as [34; 35]:  
\[\mathcal{X}=\sum_{r=1}^{R}\mathbf{a}_{r}\circ\mathbf{b}_{r}\circ\mathbf{c}_{r}+\mathbf{\mathcal{ E}} \tag{3}\]  
where \(R\) is the rank of tensor \(\mathcal{X}\). \(\mathbf{a}_{r}\in R^{I\times 1}\), \(\mathbf{b}_{r}\in R^{I\times 1}\) and \(\mathbf{c}_{r}\in R^{K\times 1}\) are the vector elements of factor matrices \(\mathbf{A}\in R^{I\times R}\), \(\mathbf{B}\in R^{I\times R}\), \(\mathbf{C}\in R^{K\times R}\). Tensor \(\mathbf{\mathcal{E}}\in R^{I\times J\times K}\) is the residual term. Hence, the CP model can be approximately expressed as:  
\[\mathcal{X}\approx\left[\begin{array}{cc}\mathbf{A},\mathbf{B},\mathbf{C} \end{array}\right]=\sum_{r=1}^{R}\mathbf{a}_{r}\circ\mathbf{b}_{r}\circ\mathbf{c}_{r} \tag{4}\]  
The above-mentioned decomposition model is shown in Figure 2.  
The TSSA algorithm uses the iterative least squares method to seek the factor matrix, namely CP-ALS, and the main ideal of the algorithm is to make the following error function to reach a minimum:  
Figure 1: The construction process of the slices of tensor \(\mathcal{X}\).  
Figure 2: Illustration of an R-component canonical decomposition and parallel factors (CP) model for a 3D tensor.  
\[f(\mathbf{A},\mathbf{B},\mathbf{C})=\frac{1}{2}\|\mathcal{X}-\llbracket\mathbf{A},\mathbf{B},\mathbf{C} \rrbracket\|^{2} \tag{5}\]  
First, \(\mathbf{A}\), \(\mathbf{B}\), and \(\mathbf{C}\) should be given an initial matrix which is generally the random factor matrix. Then, \(\mathbf{B}\) and \(\mathbf{C}\) are fixed to solve for \(\mathbf{A}\); \(\mathbf{A}\) and \(\mathbf{C}\) are fixed to solve for \(\mathbf{B}\); and \(\mathbf{A}\) and \(\mathbf{B}\) are fixed to solve for \(\mathbf{C}\) in an alternating fashion until reaching some convergence. However, the convergence of tensor decomposition may be poor using the iterative least squares method, which will lead to an unstable or even wrong result. Thus, we develop an improved TSSA algorithm based on the CP-WOPT model in this paper.
## 5 Data and Results
### Test Environment and Multimodal Sensor Setup  
Both sensors (visible-light and thermal infrared camera) are placed in parallel and focused to a common point of the same scenario, since the objective is to obtain two similar views of the same scene. Back and front views of our installation can be observed in Figure 6a,b.  
Simultaneous and synchronized acquisition from both cameras is possible thanks to a video encoder capable of grabbing frames from two cameras in the same instant. The frames acquired by the cameras connected to the encoder are separated into channels (one channel for each video input) and added to a buffer, which streams the video.  
Figure 6: Installation for simultaneous acquisition on the thermal infrared and visible-light spectra. (**a**) Back view; (**b**) front view.  
The chosen test environment is an outdoor environment. The two cameras are placed in a window of a building, 6 m in height, looking down at an angle of about 45 degrees. It was decided to work in an outdoor environment, since such an environment shows a high variety of temperatures (over a year) and lighting conditions (over one day, for example). Instead, an indoor environment does not usually admit such a large range of changes. The scene has no defined entrance area for humans. In the lower part of the scene is a concrete platform, which easily stores the environmental heat. This same property exists in the buildings surrounding the scene. The detection of humans in the thermal infrared spectrum will be problematic in the building in the background of the scene, since the infrared thermal camera automatically performs a thermal attenuation, resulting in a lack of precision in obtaining the temperature of humans near the building. Specifically, attenuation makes humans merge with the building.
# Design and Implementation of Simulation System for Multi-UAV Mission
## 2 General System Framework  
Considering the mission simulation requirements of multi-UAV, the general framework of the simulation system designed in this paper is shown in Figure 1 below.  
It contains six major parts: collaborative planning module, ground station module, onboard logical task module, execution module, scene display module, and flight parameter setting/state display module. The system is based on the idea of hierarchical progressive control and modular design, with the overall architecture divided into a decision layer, a control layer, an execution layer, and a display layer. It has the characteristics of centralized management, centralized decision-making, and decentralized control, which can meet the basic principle of adhering to the task-oriented upward and compatibility with heterogeneous platforms with different dynamic characteristics downward.  
The decision layer is the collaborative planning module that can plan and generate task parameters based on specific task requirements. The control layer is the ground station module and the on-board logical mission module, which are mainly responsible for monitoring the status of the multi-UAV system, sending control commands, and reading, loading, and interacting with mission parameters. The execution layer is the open-source flight control PX4 firmware and virtual external sensors, which are used to track the desired state of the multiple UAVs sent by the control layer and to execute the relevant actions of the external sensors. The display layer is a Gazebo 3D physical simulation platform and the QGC (QGroundControl) virtual ground station; in addition to completing two major functions of loading different models of virtual UAV types and quantities, and real-timestatus display, the QGC virtual ground station can also be used to set the speed, attitude, power, etc. of the UAV platform.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 2 Methods
### Channel Attention
#### 2.1.1 Motivation  
Because we concentrate on learning features rather than designing features, it is necessary to study how to improve the learning capability. In the CNN, feature maps on the inner layers hold the core information about the original signal. In the conventional CNN, the convolutional neurons equally \"observe\" the feature maps on the upper layer, in which \"equally\" means all the feature maps on the upper layer assign the same weights to the neurons. This implies that those features would play equally important roles in achieving the classification results. We show an example in Figure 2. From the figure, we can see that the black dominated feature maps, for example, the 2 nd, 3 rd, 4 th, 6 th, 7 th, 9 th, and 10 th feature maps on the first row, contain little information for detecting the singing voice; they thus need to be restrained or neglected. Therefore, we can draw the conclusion that some feature maps need to be restrained or deleted so that the neurons can focus their \"attention\" on the important feature maps.  
To measure the \"attention\" of a feature map that the neurons should impose, we can design a mechanism in which the attention is adjusted by learning. Afterwards, the convolutional neurons can distinctively \"observe\" the feature maps in terms of importance. In Figure 3, we illustrate the learning mechanism for CNNs. It is supposed that there are two adjacent layers--the layer n1 and the layer n--in a CNN; the layer n \(-\) 1 is above the input, and F = (F\({}_{1}\), F\({}_{2}\),... F\({}_{m}\)) are the output feature maps at the layer n \(-\) 1, where every channel F\({}_{i}\) presents the corresponding feature map, and is actually an image with the same height \(h\) and width \(w\). In this architecture, we try to learn the weight w\({}_{i}\) as the attention of the channel F\({}_{i}\), and then feed the weighted channels F\({}^{\prime}\) = FW to the next layer n, where W = (w\({}_{1}\), w\({}_{2}\),... w\({}_{m}\))\({}^{\text{T}}\). After imposing the attention values, the feature maps at layer n \(-\) 1 can be treated more distinctively for the results. It was hoped that this mechanism can improve the overall SVD performance.  
Figure 2: The conventional CNN neurons equally “observe” the feature maps on the upper layer. (**a**) An image from the log-mel spectrogram of the music “you are”, and (**b**) a group of the feature map examples from a CNN inner layer with 64 channels.
# Additive Manufacturing in Underwater Applications
## 3 Materials
### Metals  
Only a few metals have been investigated for application in underwater 3D printing [22; 49]. The most advanced work was performed with titanium alloy Ti-6 Al-4V for the site repair of marine equipment in underwater environments, using laser metal deposition (LMD) [49]. For this purpose, the researchers used a specially designed drainage nozzle, which ensured a local dry chamber for this process [49]. The obtained results show that this technology can be useful for the fabrication and repair of tailored components or parts with complex shapes in underwater environments [49]. The same material and LMD technology were also used by [PERSON] et al. [22] for the production of thin-walled Ti-6 Al-4V alloy parts to check the possibility of repairing underwater structures [22]. In the case of this technology, the backflow of water was prevented by the improper nozzle construction and the shielding gas flow, creating a stable local dry cavity for printing [22].  
The titanium powder--the Ti-6 Al-4V alloy--was also used for the 3D printing of hemispheres dedicated to underwater vehicles [50]. The elements were produced using an electron beam melting (EBM) system [50].  
A less popular metal used for underwater purposes is stainless steel. It finds application in AM technology for the parts of underwater robots and in propellers for an outrunner motor composed of 316L steel [51]. Stainless steel is also used as a simple and fouling-mitigating membrane, created using 3D printing technology for underwater applications [52]. This material was also applied for the manufacturing of miniature marine structures for crashworthiness verification [53].  
For underwater applications, aluminum was also tested as a material for powder-based 3D printing. This material was tested for an underwater acoustic transducer [54]. The usage of 3D printing technology allows the production of elements with complicated shapes that are not possible with traditional methods. It positively influences the efficiency and size of the device [54].
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## Particle Deposition Model
### Particle Migration
#### 2.2.1 Mathematical Model of Flow Field  
In this paper, the re-normalization group \(k\)-\(\varepsilon\) (RNG \(k\)-\(\varepsilon\)) turbulence model commonly used in the field is used to simulate the time mean flow field, and its higher adaptability to the strain rate and the applicability of the streamline bending degree is used to fit the flow characteristics of particles affected by the insufficient development of near-wall turbulence during the migration and sedimentation process. In the RNG \(k\)-\(\varepsilon\) model, \(k\) and \(\varepsilon\) can be described as [30]:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho kv_{ i})=\frac{\partial}{\partial x_{j}}\left(\alpha_{k}\mu_{eff}\frac{\partial k}{ \partial x_{j}}\right)+G_{k}-\varepsilon\rho \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} (\rho\varepsilon v_{i})=\frac{\partial}{\partial x_{j}}\left(\alpha_{k}\mu_{ eff}\frac{\partial\varepsilon}{\partial x_{j}}\right)+\frac{C_{1}\varepsilon}{k}G_{k}-C_{2 \varepsilon}\rho\frac{\varepsilon^{2}}{k}-R_{\varepsilon} \tag{2}\]  
where \(\rho\) is fluid density, \(v_{i}\) is the velocity vector, \(\mu_{eff}\) is the effective dynamic viscosity, \(G_{k}\) is the turbulent kinetic energy generation term, \(\alpha_{k}\) and \(\alpha_{\varepsilon}\) are, respectively, the reciprocal of the effective Prandtl number \(k\) and \(\varepsilon\), \(C_{1\varepsilon}\) and \(C_{2\varepsilon}\) are empirical constants, and \(R_{\varepsilon}\) is \(\varepsilon\).
# Two Interval Upper-Bound \(Q\)-Function Approximations with Applications
###### Abstract  
The Gaussian \(Q\)-function has considerable applications in numerous areas of science and engineering. However, the fact that a closed-form expression for this function does not exist encourages finding approximations or bounds of the \(Q\)-function. In this paper, we determine analytically two novel interval upper bound \(Q\)-function approximations and show that they could be used efficiently not only for the symbol error probability (SEP) estimation of transmission over Nakagami-\(m\) fading channels, but also for the average symbol error probability (ASEP) evaluation for two modulation formats. Specifically, we determine analytically the composition of the upper bound \(Q\)-function approximations specified at disjoint intervals of the input argument values so as to provide the highest accuracy within the intervals, by utilizing the selected one of two upper bound \(Q\)-function approximations. We show that a further increase of the accuracy, achieved in the case with two upper-bound approximations composing the interval approximation, can be obtained by forming a composite interval approximation of the \(Q\)-function that assumes another extra interval and by specifying the third form for the upper-bound \(Q\)-function approximation. The proposed analytical approach can be considered universal and widely applicable. The results presented in the paper indicate that the proposed \(Q\)-function approximations outperform in terms of accuracy other well-known approximations carefully chosen for comparison purposes. This approximation can be used in numerous theoretical communication problems based on the \(Q\)-function calculation. In this paper, we apply it to estimate the average bit error rate (ABER), when the transmission in a Nakagami-\(m\) fading channel is observed for the assumed binary phase-shift keying (BPSK) and differentially encoded quadrature phase-shift keying (DE-QPSK) modulation formats, as well as to design scalar quantization with equiprobable cells for variables from a Gaussian source.  
Q -function; approximation; Nakagami-\(m\) fading; modulation formats 2022
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 5 The Deep Learning-Based Log Parsing Method
### Online Parser  
Since our online parser focuses on directly consuming streaming log records (i.e., one at a time), it has to learn as quickly as possible. To address this issue, we use attention-based BiLSTM to construct our online parser.  
Specifically, as shown in Figure 4, our online parser has four major components, i.e., _a word embedding layer_, _a BiLSTM layer_, _an attention layer_, and _a decision layer_. Two _dropout layers_ are placed before the BiLSTM and decision layers, respectively. The explicit component descriptions are as follows.  
Figure 4: Deep learning-based log parsing framework: online architecture.  
To start, the input log record \(x\) is converted as \(e^{\prime}\) through the word embedding and dropout layers. Then, let \(\overrightarrow{h}\) and \(\overleftarrow{h}\) denote the forward and backward hidden layers, respectively. The BiLSTM layer in our online parser can be formally described as follows:  
\[\overrightarrow{h}_{t} = H_{lstm}(e^{\prime}_{t},\overrightarrow{h}_{t-1},b_{\overrightarrow {h}}), \tag{10}\] \[\overleftarrow{h}_{t} = H_{lstm}(e^{\prime}_{t},\overleftarrow{h}_{t-1},b_{\over h}),\] (11) \[h_{t} = \overrightarrow{h}_{t}\bigoplus\overleftarrow{h}_{t}, \tag{12}\]  
where \(\bigoplus\) denotes the element-wise sum operation.  
Then, an attention model (or mechanism) is added between the BiLSTM and decision layers. Specifically, our attention model consumes \(h\) and adjusts the weights of the BiLSTM output as follows:  
\[g_{t} = \tanh(W_{hg}h_{t}+b_{g}), \tag{13}\] \[\alpha_{t} = softmax(w^{T}g_{t}),\] (14) \[o = \sum_{t=1}^{T}\alpha_{t}h_{t}. \tag{15}\]  
Finally, the decision layer takes the re-calculated and filtered output \(o^{\prime}\) from the attention model to determine the log event type \(\hat{y}\) (similar to our offline parser).  
One thing that should be noted is that, since the training of BiLSTM is quite time-consuming, we train our online parser only based on falsely predicted records to reduce the training time.
###### Abstract  
There is a growing demand for robust and accurate positioning information for various applications, including the self-driving car industry. Such applications rely mainly on the Global Navigation Satellite System (GNSS), including the Global Positioning System (GPS). However, GPS positioning accuracy relies on several factors, such as satellite geometry, receiver architecture, and navigation environment, to name a few. In urban canyons in which there is a significant probability of signal blockage of one or more satellites and/or interference, the positioning accuracy of scalar-based GPS receivers drastically deteriorates. On the other hand, vector-based GPS receivers exhibit some immunity to momentary outages and interference. Therefore, it is becoming necessary to consider vector-based GPS receivers for several applications, especially safety-critical applications, including next-generation navigation technologies for autonomous vehicles. This paper investigates a vector-based receiver's performance and compares it to its scalar counterpart in signal degraded conditions. The realistic simulation experiments in this paper are conducted on GPS L1 C/A signals generated using the \(\mathsf{S}\)prientTM simulation system to create a fully controlled environment to examine and validate the performance. The results show that the vector tracking system outperforms the scalar tracking in terms of position and velocity estimation accuracy in signal-degraded environments.  
GNSS; vector tracking; scalar tracking; GPS; autonomous vehicles +
Footnote †: journal: _Acticle Journal of the Acoustical Society_  
1
Footnote 1: [[https://doi.org/10.3390/sigmals1020007](https://doi.org/10.3390/sigmals1020007)]([https://doi.org/10.3390/sigmals1020007](https://doi.org/10.3390/sigmals1020007))
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 1 Introduction
### Power Electronic Applications  
Managing output voltage in a DC-DC boost converter poses a complex challenge for control engineers. The authors in [37] have presented a PIDD2 controller design for a non-ideal DC-DC boost converter to address this issue. The proposed method employs the IMC approach to derive the controller parameters. The practical implementation of this approach demonstrates a significant enhancement in maximum sensitivity, rise time, and total variation, surpassing the performance of its predecessor, the IMC PID controllers. All the above literature review has been briefly summarised in Table 1. From the literature review, it can be summarized that the performance of PIDD2 can be enhanced using the fractional-order concept. This is because fractional ordering allows for increased flexibility and robust performance [38]. Recently, a PLC-based FOPID controller has also been designed for industrial process control application [39]. From the review findings, this paper presents a novel PIDD2 controller that combines the advantages of FOPID controllers and PIDD controllers. The main contributions of this work are as follows:  
* A new controller structure for PIDD2 has been proposed, which includes the second derivative term from PIDD2 and utilizes fractional order parameters exclusively for the second derivative term. * The controllers' robust performance has been tested in both simulation and experiment compared to PID, PIDD2, and FOPID controllers regarding transient response characteristics. * The controllers' robust performance has also been tested for fixed and variable set points and in the presence of external disturbances.
## 5 Conclusions  
In this work, metal oxide semiconducting (MOS) sensors were integrated to an Arduino board to assemble an array capable of selectively quantifying acetic acid released by films undergoing degradation, regardless of the presence of other volatiles. To do so, different MOS-based sensors have been tested and two were selected for being responsive only to acetic acid and butanol, each with its own calibration curve. An algorithm was devised which uses the calculated calibration curves to precisely determine the acetic acid concentration in butanol-acetic acid mixtures. The system demonstrated a LOD for acetic acid of 30 ppb and an upper limit of detection of 30 ppm. Finally, the sensor duplex with the developed algorithm was used to quantify the acetic acid released by a total of 48 film samples, made of cellulose acetate or other supporting materials. The results obtained were validated using the acetic acid concentrations found with the official method (A-D-strips). It is possible to foresee an application of the described system as an early-warning tool for incipient degradation of cellulose acetate-based films in archives.  
Conceptualization, M.T.G. and D.Z.; methodology, J.B., G.V. and D.Z.; software, G.V.; validation, G.V. and D.Z.; formal analysis, G.V. and I.I.; investigation, I.I., J.B., G.V. and D.Z.; resources, M.T.G. and N.W.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z.; supervision, M.T.G.; project administration, M.T.G.; funding acquisition, M.T.G. All authors have read and agreed to the published version of the manuscript.  
This research has received funding from the European Union's Horizon 2020 Program: NMBP-35-2017-Innovative solutions for the conservation of 20 th century cultural heritage (Grant agreement ID: 760801), and MSCA-RISE-2017-Research and Innovation Staff Exchange H2020-EU.1.3.3-Grant agreement ID: 778157 (CanBioSe).  
Not applicable.  
**Data Availability Statement:** Part of the data presented in this study are available on request from the corresponding author. The data are not publicly available due to \"Non-disclosure agreements\" between authors.  
Figure 5: Comparison of detection limits between commonly used method for evaluation of degradation level of cinematographic films (A-D strips) and the developed BIOS1 + BIOS2 sensor duplex.  
**Acknowledgments:** We would like to thank NEMOSINE project partners and heritage institutions Deutsches Filminstitut & Filmmuseum, Institut Valencia De Cultura (Filmoteca Valenciana), Osterreichische Akademie der Wissenschaften and [PERSON] of Fondazione Alinari per la Fotografia 2018 for providing the film samples used in this work. Furthermore, we would like to thank all NEMOSINE partners for the precious discussions and information exchanges concerning film conservation and degradation that led to the results here published.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.