esa-sceva/satcom-chunk-collection huggingface dataset:
# Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification
## 3 The Proposed Method
### Extracting Features Using Transfer Learning  
Transfer Learning (TL) is a technique that builds a new model using the knowledge learned from previously trained models. To address complex issues, most deep learning and machine learning methods necessitate a large amount of data. Transfer learning techniques handle issues like having insufficient data for the new task. In this technique, we first train a base dataset, and then apply the learnt features to a target network. This strategy will work only if the characteristics utilized are generic, meaning they are appropriate for both the base and destination datasets. Machine learning and deep learning algorithms are typically trained to solve specific problems. As a result, if the feature space distribution changes, the models must be rebuilt from the ground up, or they may suffer a considerable performance loss, if not outright failure. This solitary learning paradigm is overcome through transfer learning.  
Transfer learning can be used as a feature extractor in deep learning. Deep learning models have a layered architecture and learn different features at different layers. These layers are then finally connected to a last layer (a fully connected layer) to get the final output. In transfer learning, a pre-trained network without its final layer is utilized as a fixed feature extractor for other tasks. The key idea is that in the case of training the model with new data for a new task, the pre-trained model's weighted layers are used for feature extraction rather than updating the weights of the model's layers. That is, it allows the extraction of features from a new domain task and the utilization of the knowledge from a source-domain task.  
Spark ML provides fast, distributed implementations of common learning algorithms. In our case, we need to perform transfer learning on a huge set of images. So, to facilitate quick transfer learning, we used the parallel processing power, the Apache Spark. The pre-trained CNN, VGG16 and ResNet50 models, which are two pre-trained architectures on ImageNet, were used in this study. The transformer peels off the final layer of a pre-trained neural network and uses the output from all previous layers as features in the classification method. The number of features given as output of VGG16 and RESNET 50 constitute the input to classifiers after applying a feature reduction approach (please see Section 3.4). For classification, a precision-based ensemble model is used.
## 6 Experimentation
### Discussion  
This section discusses the limitations of the IRRINOV(r) method, the limitations of the Arvalis dataset and the limitations of the DSS system.  
First, the IRRINOV(r) method is not a complete method for irrigation decisions. IRRINOV(r) provides guidelines for irrigation decisions based on crop variety, soil and region in metropolitan France. This method is only dedicated to climate events that have a greater chance of occurring in metropolitan France. For example, the IRRINOV(r) method ignores the fact that in summer, some daily rain events with high intensity may occur for a long period, such as in tropical climates. Moreover, the IRRINOV method provides no guide about the amount of water that must be used for irrigation. It does not take into account that a farmer may have some water restriction usages. For example, one farm may be allowed to water a plot only each week on Monday morning.  
Second, the irrigation decision in the Arvalis dataset is a human decision, and humans can make errors. For example, the first watering decision in the Arvalis dataset occurs on 08/07/2013. Following the IRRINOV method, farmers should not water the crop on this day since the crop is at the V7 state, and moisture sensor measurements do not reach the threshold. Maybe the farmer has made an error, or maybe he has decided to water the plot sooner because he knows that he will not be able to water the plot in subsequent days due to water restrictions.  
Third, the DSS provides automatic results that seem to contain no errors. Unfortunately, the DSS is limited. The IRRIG ontology and rules decision is dedicated to a maize crop on a specific type of soil. If the irrigation decision concerns another crop or other soil, all the thresholds for crop growth, root zone moisture level and crop watering property should be changed. However, the rain intensity rule is the same for all the IRRINOV guidelines regardless of the observed crop and type of soil. Thus, rain modeling and associated rules can be used. Fortunately, CASO contains the threshold definitions associated with the state of the property. This means that a new ontology should be defined for each type of crop and soil as long as some threshold values change. However, our rules are generic, and their updates should be easy to implement. Please note that in other systems, such as PLANTS, the thresholds are declared inside the rules. Therefore, it is more difficult to modify them.
## 3 Results
### Friction and Wear of Liquid Base Lubricants  
To obtain comparative data about the tribological effects of GNP and TiNP additives to fluid lubricants, friction and wear tests were conducted using a pin-on-disk tester. For semi-liquid samples (grease), a shaft rotatory shaft-on-plate test was used as described in the experimental setup. For liquid base lubricants, anti-scuffing and friction coefficients were calculated using in situ conditions. As demonstrated in Figure 6, a higher concentration of graphene nano additives reduces the friction coefficient; however, wear preventive properties are sacrificed. Comparing titanium dioxide with graphene illustrates that it can significantly lower the wear; however, a higher concentration of TiNP  
Figure 4: Viscosity index (**A**) and Kinematic viscosity (**B**) of liquid lubricants.  
Figure 5: Rotating pressure vessel oxidation test (RPVOT) anti-oxidation test.  
additive does not significantly affect wear preventive properties. As explained, a 0.05% concentration reveals less friction for the graphene sample. For titanium, it has lowered wear, which led us to prepare the 0.05% concentration using three different weight ratios of the additive. As illustrated, S-8 shows a lower friction coefficient and S-9 proves the lowest wear.  
The results of the liquid lubricant samples enhanced by GNP and TiNP were compared separately at different concentrations. In addition, the S-2 and S-4 nano additive concentrations were found comparatively effective for hybrid nano additives of GNP and TiNP; thus, three ratios and nano additives at this concentration (0.05%) were prepared and tested. The results displayed in Figure 6 manifest that GNP has a higher wear rate compared to the coefficient of friction. However, the TiNP samples reveal a lower wear rate and higher friction coefficient. This motivated our research to evaluate the tribological effects of these additives together. S-8 illustrates moderate wear as well as friction. Along with that, S-9 shows the lowest wear but is compromised by friction. In addition, S-7 demonstrates better control on wear but it gains higher friction.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 1 Introduction
* Implementation of an automatic thresholding method to increase efficiency in obtaining final binary mask prediction. * Implementation of morphological operations and dense Conditional Random Field (CRF) methods for binary mask refinement. * Creating an annotated shadow image dataset using existing aerial images from several online sources. This paper is organized as follows: Section 2 discusses the related studies; Section 3 describes the methodology; while Section 4 discusses the results. Section 5 outlines conclusions and future works.
## 3 Infrared and Visible Image Fusion  
The process of multiscale image fusion is organized into three steps. First, the image is transformed at multiple scales to obtain decomposed images with different resolution levels. Then, the fusion rules at each level are designed to obtain the transform coefficients used for reconstruction. Finally, the fused image is obtained using a multiscale inverse transform. In this paper, we use the dual-tree complex wavelet transform to perform the multi-scale decomposition of the image and design the fusion rules for the low-frequency subbands and high-frequency subbands of each level according to the characteristics of the image at different scales.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 1 Introduction
### Related Works
#### 1.1.2 Spectral Decomposition  
Spectral decomposition of time series data finds applications in speech and music recognition [46; 47], machine health and mechanical vibration monitoring [48], river and oceanographic tide modeling [49], and power demand prediction [50]. In TSF, spectral decomposition is gaining traction. A new decomposition method based on Koopman theory [51] and comparable to Fourier transform [24; 52; 53] has been suggested for long-term forecasts [54]. Furthermore, methods such as StemGNN utilize graph Fourier transform and discrete Fourier transform to exploit spectral dependencies for intra- and inter-series correlations in time series [17]. However, StemGNN's complexity, combining gated RNN, self-attention, and GNN, may hinder its application in real-world datasets. Moreover, StemGNN has not been evaluated in LSTF settings.
## 6 Conclusions  
This paper explores the use of calibration targets with known positions to refine the inaccurate transmitter/receiver positions and thus enhance target localization accuracy in MPR systems. We start our research by evaluating target localization CRLB in the presence of calibration targets, which justifies the potential of calibration targets in enhancing localization accuracy. Then, in order to fulfill this potential, a novel closed-form solution was designed for target localization using BR measurements from the unknown target as well as the calibration targets. The proposed solution was shown both analytically and numerically to attain the CRLB under some mild conditions, and verified to outperform existing methods in terms of localization accuracy. Furthermore, from the view of engineering practice, if the employed calibration targets are off-the-shelf, such as the commercial aircrafts broadcasting an ADS-B signal, the use of calibration targets would bring little added cost or complexity to the MPR system, but could bring a significant enhancement to target localization accuracy.  
Conceptualization, Y.Z. ([PERSON]); methodology, Y.Z. ([PERSON]) and D.H.; software, Z.L.; validation, Y.Z. ([PERSON]); formal analysis, Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]); supervision, D.H.; project administration, C.Z.; funding acquisition, D.H. and Y.Z. ([PERSON]).  
This research was supported by National Natural Science Foundation of China, grant number 61703433.  
The authors declare that they have no conflict of interest to this work.
# Fire Tests of Load-Bearing, Light-Steel-Framed Wall Systems Insulated with Polyurethane Foam
## 1 Introduction
Depending on the test method used, the heating rate of the sample, the air flow and the weight loss, the ignition and decomposition temperatures for polyurethane foams are between 260 \({}^{\circ}\)C and 500 \({}^{\circ}\)C and between 400 and 650 \({}^{\circ}\)C, respectively [24]. The combustion of PUR produces large quantities of smoke that obstruct visibility, with carbon monoxide and hydrogen cyanide being the most important toxic combustion products. The addition of flame retardants causes PUR to form a small protective layer of charcoal on the material [27; 28], which can have a positive effect on fire behaviour for a certain period of time. Flame retardants have an influence on the smoke and toxicity development of PUR. There are some contradictions in the literature regarding the effect of flame retardants on the overall toxicity of PUR foams [25]. A literature search revealed only one study that addressed the fire performance of assemblies with PUR foam and gypsum fibreboards in the form of structurally insulated panels (SIIPs) [29], which showed that that such assemblies can only withstand 30 min of fire exposure. In the scientific project \"Composite Light Steel Framed Panel with an Integrated Load-bearing Structure\", led by the University of Zagreb, Faculty of Civil Engineering, and industry partners, an innovative load-bearing LSF system has been developed to improve construction speed and energy efficiency. In addition to lightweight steel members, the LSF system consists of gypsum fibreboards (GFBs) as sheathing and PUR foam as thermal cavity insulation. Furthermore, spacers are added to physically separate the wallboards from the steel members and, consequently, reduce the thermal bridging effect. The idea behind the development of such an LSF system with the chosen components is described in our previous paper [30]. In the current paper, the details of the experimental study on the fire resistance of the aforementioned LSF wall systems exposed to the standard ISO 834 are presented. For this purpose, three test specimens were made from the same components differing only in the number (double and triple) and type of gypsum fibreboards (regular--A2 board, and with improved fire properties--A1 board).
## 4 Discussion  
As shown in Equation (10), the IR energy distribution can be obtained by the superposition of different eigenstates. This section discusses the contribution of each eigenstate with the single-surface heating in the 40 mm focal length. Figure 12a shows \(q_{\text{mm}}^{n}(x)\) for the single-surface heating case with the 40 mm focal length calibrated in Section 2.3, while Figure 12b-d represents the independent 1 st, 2 nd, and 3 rd eigenmodes, respectively. As shown in Figure 12b, the 1 st eigenmode is dominant in the total heat flux due to the value of \(\left|C_{1}\right|^{2}\) (0.987) in Table 1, consistent with the design purpose of the elliptical reflector to focus energy as much as possible in a narrow area. Theoretically, an elliptical reflector can collect all IR rays emitted from a focal point to the other focal point. However, engineered conditions have imperfections: the center of the lamp cannot perfectly be located at the focal point of the elliptical reflector; the lamp size also affects the distribution of the IR rays. Owing to these imperfections, the influences of the other eigenmodes exist. Figure 12c shows that the 2 nd mode has little effect on the total heat flux distribution in this case; the value of \(\left|C_{2}\right|^{2}\) is 0 in Table 1. The 3 rd mode has a certain degree of influence with (\(\left|C_{3}\right|^{2}\)= 0.013), as shown in Figure 12d. The 3 rd mode can be interpreted as the IR rays being distributed over the entire length of the entrance rather than being concentrated in one place due to the imperfections of the engineering condition.  
Figure 11: Temperature distribution of the focused IR heating.
# Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data
###### Abstract  
Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels at frequencies of 0.5-32.5 Hz with multitaper spectral analysis using 4 s windows with 3 s overlap. For each resulting 1 s segment, we computed segment-specific correlations between power levels for all pairs of leads. We then averaged all pairwise correlation coefficients involving each lead, creating a time series of segment-specific average correlations for each lead. Our algorithm scans each averaged time series separately for \"bad\" segments using a local moving window. In a second pass, any segment whose averaged correlation is less than a global threshold among all remaining good segments is declared an outlier. We mark all segments between two outlier segments fewer than 300 s apart as artifact regions. This process is repeated, removing a channel with excessive outliers in each iteration. We compared artifact regions discovered by our algorithm to expert-assessed ground truth, achieving sensitivity and specificity of 80% and 91%, respectively. Our algorithm is an open-source tool, either as a Python package or a Docker.  
artifact; EEG; polysomnography; correlation; movement; lead popping Article
# Frequency Response Analysis of Perforated Shells with Uncertain Materials and Damage
## 2 Preliminaries
### Perforated Domains
#### 2.2.1 Penetration Patterns  
The quantity used to characterize perforated sheets of metal is the ligament efficiency \(\eta\). Let us assume that the holes are ellipses with \(a\), \(b\) as the horizontal and perpendicular semi-axis, and the separation of the centres is \(P_{x}\) and \(P_{y}\), respectively. Following [5; 14; 15], we define horizontal and perpendicular ligament efficiency, denoting them \(\eta_{x}\), \(\eta_{y}\), respectively. For regular arrays of holes  
\[\eta_{x}=(P_{x}-2\,a)/P_{x},\qquad\eta_{y}=(P_{y}-2\,b)/P_{y}, \tag{22}\]  
and for triangular arrays, allowing for alternating layers,  
\[\eta_{x}=(P_{x}-4\,a)/P_{x},\qquad\eta_{y}=(P_{y}-4\,b)/P_{y}. \tag{23}\]  
For circular holes, the radius \(r=a=b\), of course, and further if the pattern is regular \(\eta=\eta_{x}=\eta_{y}\). Both pattern types are illustrated in Figure 2. Notice that the triangular pattern in the figure has a tighter packing than that implied by Equation (23).  
An intriguing option is to replace a regular pattern with one induced by a quasi-random process. Notice that quasi-random means that the process is deterministic which has implications to manufacturing. One of the simplest sequences of points generated by such a process is the Halton sequence [16].  
Figure 2: Penetration patterns.  
**Definition 1** (Halton).: _Let \(i\in\mathbb{N}_{0}\) and basis \(b\in\mathbb{N}\) be such that \(b\geq 2\). Then, the inverse representation function \(\phi_{b}(i)\) is given by_  
\[i=\sum_{k=1}^{\infty}i_{k}b^{k-1},i_{k}\in\{0,1,...,b-1\}\longrightarrow\phi_{ b}(i)=\sum_{k=1}^{\infty}\frac{i_{k}}{b^{k}}.\]  
_Let \(q_{1},q_{2},\ldots,q_{s}\) be the \(s\) first primes. The \(s\)-dimensional Halton sequence is_  
\[H(q_{1},q_{2},\ldots,q_{s})=\{\phi_{q_{1}}(i),\phi_{q_{2}}(i),...,\phi_{q_{s}}( i)\}_{i=0}^{\infty}.\]  
The choice of the primes is crucial, and, in the context of this paper, only two have to be chosen. The effect of this selection is shown in Figure 3, where the local structures of two sequences are compared. As can be seen from the definition of the Halton sequences, in 2D, the points populate the unit square \([0,1]^{2}\) and have to be mapped to the actual computational domain. We do not attempt to define averaging ligament efficiencies for such penetration patterns.  
However, a useful concept used to relate different kinds of patterns is the cell size \(\epsilon\), which is the size of the cell containing exactly one hole on average. In the case of a regular \(n\times n\)-grid on a shell of revolution over \((x,y)\in[-\pi,\pi]\times[0,2\pi]\), we have \(\epsilon=2\pi/n\). In the limit as \(\epsilon\to 0\), we expect regular and quasi-regular penetration patterns to converge.
## 5 Open Problem and Research Challenges
### Open Challenges
#### 5.1.4 Void Problem  
If the sending node cannot find a node in the higher region that can receive the signal, it creates a vacuum known as the void region [93], and the routing node is called a routing hole in the underwater sensor network. Unfortunately, most of the proposed works have overlooked this void problem as they assume the nodes to be next to each other [94]. This void reduces the capacity and affects the performance of the network as more energy is lost to the void. Thus, it shortens the lifetime and prolongs the routing propagation. Future works on routing protocols of UWSN should consider this problem and minimize it.
## 5 Bench-Marking Models
### Lstm  
The LSTM technique is a type of Recurrent Neural Network (RNN). The RNNs [42] are capable of processing data sequences, or data that must be read together in a precise order to have meaning, in contrast to standard neural networks. This ability is made possible by the RNNs' architectural design, which enables them to receive input specific to each instant of time in addition to the value of the activation from the previous instant. Given their ability to preserve data from earlier actions, these earlier temporal instants provide for a certain amount of \"memory\". Consequently, they possess a memory cell, which maintains the state throughout time [43]. Figure 7 illustrates an overview of the simple LSTM Model.  
As noted in Section 4, the LSTM [44] model has the ability to remove or add information to decide what information needs to go through the network from the cell state [44]. Different from the M-LSTM model, the LSTM model has just one LSTM layer, with the input sequence \(\mathbf{x}\). Therefore, the hidden state (\(h\)) and cell state (\(c\)) for the \(i\)th LSTM cell are calculated as Equation (9).  
\[(h_{i},c_{i})=C(h_{i-1},c_{i-1},\mathbf{x}_{i}) \tag{9}\]  
In the experiment, we compare the performance of the proposed model with the univariate and multivariate LSTM models. The univariate LSTM takes the first input _(i)_ described in Section 4.1, and the multivariate LSTM takes both those inputs.