esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### MATLAB Simulation Results  
The proposed AFTCS for the AFR controller was implemented in a MATLAB IC gasoline engine model. The FDI block is used to identify, separate, and substitute inaccurate parameters with expected values. As shown in Section 5.1, the NLR provides more accurate results by consuming less computation power; thus, the observer model in the FDI unit was built with the nonlinear regression relationships. The AFTCS phase is simulated with failures, based on input from the other functional sensors in the FDI unit; the FLC observer develops a new estimated value, which is delivered to the controller. The defect is injected into each sensor, one at a time by the FIU, which keeps the others healthy. The mixture AFR ratio is kept at 14.6 in normal circumstances, while it decreases to 11.7 under faulty situations (rich mixture). Despite the degradation, the system maintains stability, which is compatible with AFTCS design philosophy [45]. Figure 6 shows how the proposed AFTCS performs in both normal and abnormal scenarios.  
The AFR deteriorated to 11.7 in the steady state with a fault in any one sensor with the AFTCS portion alone, as shown in Figure 6. It can be noted from Figure 6 that the engine response is less oscillatory as compared to results reported in [12], especially when the fault is introduced in the MAP sensor. This is because the estimated values generated by NLR are very close to the actual sensor values, which validates the analytical redundancy and proves the effectiveness of NLR.  
Figure 6: AFTCS performance for AFR control of the IC engine.
## 3 Results and Discussion  
The results of measuring \(E_{\text{corr}}\) of a copper electrode in a neutral chloride solution in the presence of sulphinylalkyl derivatives of 5-amino-1\(H\)-1,2,4-triazole are given in Table 2. The change in \(E_{\text{corr}}\)with the increase in the concentration of the inhibitor was different for the studied compounds. Thus, with higher concentrations of inhibitor A, \(E_{\rm corr}\) shifted to the cathodic region. While the higher \(C_{\rm inh}\) of inhibitor B results in a more noble corrosion potential. The concentration of inhibitor C has hardly any effect on \(E_{\rm corr}\). Thus, with concentrations of 0.01 and 0.10 mM, the value so \(E_{\rm corr}\) stays practically the same within the margin of error. With the maximum concentration of 1.00 mM studied in this paper, \(E_{\rm corr}\) slightly shifts to the cathodic region (15-20 mV). The dependence of \(E_{\rm corr}\) on the concentration of inhibitor D is not symbatic. It is thus impossi ble define the whole group of sulphinylalkyl derivatives of 5-amino-1\(H\)-1,2,4-triazole as either anodic, or cathodic inhibitors judging by the values of \(E_{\rm corr}\). Inhibitor A can be defined as a cathodic inhibitor and inhibitor B as an anodic inhibitor. Polarisation curves of copper in neutral chloride solutions with the compounds under consideration are given in Figure 1.  
The experiment carried out without the derivatives of 5-amino-1\(H\)-1,2,4-triazole demonstrated that the peak current density of +0.35 V was present in the anodic region of the polarisation curve, which then shifted to the passivation region with \(E_{\rm act}\approx+0.71\) V. For inhibitors A-C, the potential \(E_{\rm act}\) shifts to the anodic region at any concentration studied. This fact is considered in [32; 33] as the inhibition effect of the additive. The potential \(E_{\rm act}\) increases with the additive concentration. For inhibitor D the potential \(E_{\rm act}\) does not exceed the value observed without an inhibitor, when \(C_{\rm inh}=0.01\)-1.00 mM, and practically equals the value without an inhibitor when \(C_{\rm inh}=1.00\) mM. Pits on the surface of the electrode at the activation potential point were only formed in the experiment without the derivatives of 5-amino-1\(H\)-1,2,4-triazole. When the inhibitors were present, no pits were observed on the metal surface. The value of the maximum current density on the anodic curve decreases with higher \(C_{\rm inh}\) and the growth of the sulphinylalkyl chain (Figure 1a-d, curves 1-4). The value of the maximum current density was greater relative to the experiment without an inhibitor at \(C_{\rm inh}=0.01\) mM in the presence of inhibitors A-C, and \(C_{\rm inh}=0.10\) mM in the presence of A. There is no active dissolution area for B,C at \(C_{\rm inh}=1.00\) mM and for D at \(C_{\rm inh}\geq 0.10\) mM. The inhibition effect is also demonstrated by the fact that either there is no anodic current maximum on the polarisation curve, or it decreases when the inhibitors are used [34]. The current density in the passivation region is also lower when the compounds are added to the solution.
## 3 Results and Discussion
### Application of the Body Dimensions Estimated by RBF ANN Model  
In order to verify the model presented in this study, tight-fitted active leggings patterns making was taken for use as a case study. Due to tight fitness, the outputted body dimensions could be used as pattern dimensions of active leggings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model No.** & **Parameter \(\sigma\)** & **MSED** \\ \hline
1 & 0.001 & 2.7423 \\
2 & 0.005 & 2.7395 \\
3 & 0.008 & 2.7293 \\
4 & 0.010 & 2.7283 \\
5 & 0.011 & 2.7280 \\
6 & 0.012 & 2.7279 \\
7 & 0.013 & 2.7284 \\
8 & 0.015 & 2.7292 \\
9 & 1.000 & 2.8055 \\
10 & 1.250 & 2.9829 \\
11 & 1.670 & 2.9254 \\
12 & 2.500 & 3.0759 \\
13 & 5.000 & 3.4778 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The MSED of the models with various parameters of \(\sigma\) in the hidden layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measurements**} & \multicolumn{3}{c}{**MSED**} \\ \cline{2-4}  & **RBF ANN Model** & **BP ANN Model** & **Linear Regression Model** \\ \hline Waist Height & 3.0814 & 3.1185 & 3.1854 \\ Abdomen Height & 3.0197 & 3.7809 & 3.7631 \\ Hip Height & 1.8514 & 2.0029 & 2.0887 \\ Croch Height & 1.0450 & 1.1139 & 2.0705 \\ Knee Height & 1.9307 & 2.0918 & 2.0084 \\ Croch Length & 3.9251 & 4.0531 & 5.9871 \\ Thigh Girth & 1.4631 & 1.7397 & 2.5929 \\ Knee Girth & 1.0238 & 1.1137 & 1.4043 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The MSED of various predictive models.  
The application process in this study in illustrated in Figure 6, as follows:  
Initially, anthropometric data of a subject were selected randomly. The subject's stature was 171.8 cm, waist girth was 68.9 cm, abdomen girth was 77.2 cm, and hip girth was 88.9 cm. Therefore, the input vector \(x\) = (171.8, 68.9, 77.2, 88.9). The desired output vector \(d\) = (105.8, 94.7, 82.8, 77.4, 45.2, 77.2, 49.2, 35.4) was composed of the real dimensions of the subject, which represented waist height, abdomen height, hip height, crotch height, knee height, crotch length, thigh girth, and knee girth, respectively.  
Secondly, the RBF ANN was utilized to estimate the body dimensions related to making the patterns of active leggings. After the vector d was inputted into the predictive model based on RBF ANN, the output vector \(y\) was generated, as follows:  
\[y=(106,\,94.1,\,83.2,\,77.6,\,45.9,\,74.9,\,48.8,\,35.2)\]  
Sequentially, the body dimensions generated by the RBF ANN model were employed to making the patterns of active leggings. Then, the patterns were joined together into 3D active leggings by virtual stitching technology.  
Finally, the stitched leggings were tried on by a virtual avatar to evaluate the fitness.  
The available evidence lent support to the view that the proposed RBF ANN model could estimate the body dimensions related to active leggings pattern making efficiently and precisely. The predictive accuracy would be further improved by more training data.
# Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
## 5 Convergence Analysis
### Synthetic Data
Under this circumstance, the relative errors versus different \(\boldsymbol{\lambda}\) of MRPCA and GLRTR are shown in Figure 1. We take \(\boldsymbol{\lambda}=0.01\) in the second case and the relative errors _versus_ different \(\boldsymbol{\tau}\) of GLRTR are shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**(p, \(\boldsymbol{\lambda}\))** & **(30,0.03)** & **(50, 0.035)** & **(70, 0.03)** & **(90, 0.03)** \\ \hline RE of MRPCA & & & & \\ (\%) & 88.38 \(\pm\) 0.25 & 78.94 \(\pm\) 0.34 & 65.06 \(\pm\) 0.59 & 40.03 \(\pm\) 1.34 \\ RE of GLRTR (\%) & 62.51 \(\pm\) 1.72 & 20.09 \(\pm\) 2.63 & 9.14 \(\pm\) 0.12 & 8.58 \(\pm\) 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results on incomplete 20 \(\times\) 20 \(\times\) 20 tensors for \(r=4\). Figure 1: Comparison of relative errors between Multi-linear Robust Principal Component Analysis (MRPCA) and Generalized Low-Rank Tensor Recovery (GLRTR) with varying \(\boldsymbol{\lambda}\). It can be seen from Figure 1 that the relative errors of MRPCA and GLRTR are about 9.90% and 4.62%, respectively, if \(0.02\leq\uplambda\leq 0.07\), which means the latter has better recovery performance than the former. Furthermore, their relative errors are relatively stable when \(\uplambda\) lies within a certain interval. Figure 2 illustrates that the relative error has the tendency to increase monotonically, and it becomes almost stationary when \(\uptau\geq 1\). At this moment, the relative errors lie in the interval (0.037, 0.080). This group of experiments implies that, for our synthetic data, GLRTR is not very sensitive to the choice of \(\uplambda\) and \(\uptau\).
## 2 Infrastructure, Instrumentation and Calibration Methods
### Calibration and Validation Methods
#### 2.2.4 Crossover Analysis  
This technique aims at providing relative calibration between two or more contemporaneous satellite altimeters [19]. The analysis usually takes place on a global scale and at locations where the ascending and descending ground-tracks of altimeters intersect. A crossing event occurs when the time difference of the intersecting passes of two (or more) missions is commonly less than two days [20]. Under the tenuous assumption that the variability and signature of sea level has not changed significantly in the two-day lag between measurements, the sea surface height at the crossover location, as determined by the two missions, can be compared. A significant advantage of this relative performance approach is that it does not require ground measurements and can be performed throughout the missions' lifetime and on a global scale [19].  
At the PFAC, the crossover analysis has been modified and applied to retrieve the relative bias of Sentinel-3A Pass No. 14 and Jason-3 Pass No. 109 about 20 km south of Gavdos (Figure 3).  
Figure 2: Transponder responses as captured by Sentinel-3A (left) and CryoSat-2 (right, Credit: ESA), [PERSON]) altimeters during the transponder calibrations carried out on 3-May-2020 and 20-Sept-2019, respectively.
## 3 Materials and Methods
### Transcriptome Sequencing, De Novo Assembly and Unigene Functional Annotation  
Total RNA were isolated from the rhizome and leaf tissues using a plant RNA Prep Kit (Tiangen Biotech, Beijing, China) following the product manual. Each tissue was sampled from two individual plants, and after quality determination RNA from two biological repeats was mixed in equal amounts into a single pool for RNA-sequencing. The RNA samples were sent to Novogene Company (Beijing, China) where the cDNA libraries were constructed and RNA-sequencing was performed on an Illumina HiSeq2000 platform. Raw reads were cleaned by removing adapter containing reads and low quality reads. To monitor sequencing quality, the values of Q20 and Q30 of the clean data were determined. Clean reads were _de novo_ assembled by Trinity software with default parameters [29]. Gene function was annotated by BLASTx (_E_-value \(<\) 1e\({}^{-5}\)) search against NR, Nt KOG/COG, Swiss-Prot, KO (KEGG Ortholog), Pfam, and GO databases. To calculate gene expression levels, clean reads were mapped back to the assembled transcriptome to get read-counts for each unigene and gene expression levels were normalized to Fragments Per kb per Million fragments (FPKM) [30].
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 4 System Design
### Fragmentation and Routing  
Data fragmentation is carried out using the same mechanism in the three technologies defined in the hybrid network architecture. It is based on the use of a flag defined by a bit of the HNF, as mentioned. When the data to be sent exceeds the maximum size established by the frame, it is fragmented and identified by activating the corresponding flag. This flag is set to a low logical level during the transmission of the last fragment of data, or if the amount of data to be sent does not exceed the mentioned maximum size.  
On the receiver side, the original frame is re-composed by concatenating all the incoming frames marked as fragmented, and finally adding the last one marked with the inactive flag. Figure 5 depicts the procedure in a ZigBee-to-LoRaWAN gateway communication.  
Regarding routing, in this work, it was assumed that each node possesses knowledge about the whole network. Each node must know the MAC address of the coordinator module, and it must know the MAC addresses of each leaf node, which will be stored in an address table.  
When inter-technology routing is needed (from a ZigBee node to a VLC endpoint), e.g., this action is performed by checking the HNF's header (DN field). The access point decides the output gateway depending on the destination network, address, and purpose field. If the address corresponds to a node within the access point's coverage, it performs internal routing. Otherwise, it relays the packet through its default gateway (LoraWAN in this work). The master coordinator (LoraWAN Gateway from Figure 2) of the system will decide, attending to the purpose field of the HNF, if the packet has to be sent to another access point or to a server on the Internet (sensor data). The opposite data path is also considered for interaction with actuators. In this case, a command arrives from the Internet and is properly delivered to the corresponding node.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Purpose** & **Description** \\ \hline
0x01 & Board sensor data (LoRaWAN) \\
0x02 & External sensor data (LoRaWAN) \\
0x03 & Data Frame (ZigBee) \\
0x04 & Interaction with actuators \\
0x05 & Data Frame (VLC) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types of frames considered in this work.  
Figure 5: Fragmentation in a ZigBee-to-LoraWAN communication.
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 2 Design of the Digital ENFMS for APR1400
### Logarithmic Power Signal Generation
#### Hardware for the Digital Signal Processing  
The digital signal processing processes the captured waveforms in real time using fast digital signal processors (DSPs) or FPGAs to perform all necessary data processing functions digitally. These functions include pulse shaping, summing, signal-to-noise discrimination, pulse counting, frequency band-pass filtering, MSV calculation, the logarithmic power and rate of power change signal generation of signals from the pulse and MSV mode processing, data communication with other interfacing systems, and, if required, D/A conversion.
## 3 Overview of ICN Architectures  
There are four major ICN approaches: Data-Oriented Network Architecture [20], Named Data Networking [21], Publish Subscribe Internet Routing Paradigm [22], and Network of Information [23]. While these projects vary in architectural specifics, they converge on several common assumptions, objectives, and structural properties as they approach ICN.  
Among the architectures listed above, only the Named Data Networking (NDN) project [21] (shown in Figure 1) is in active development [24]. NDN expands upon the content-centric networking (CCN) [25] paradigm, offering a topology-independent naming scheme and exploring greedy routing for enhanced router scalability. Please note that although CCN is now known as NDN (both are part of the same research initiative), this manuscript uses the original terminology as presented in each respective article when discussing the existing literature.  
The details of all four major ICN architectures, including their working principle and differences, can be found in [2; 6]. To provide a general understanding for the subsequent sections, particularly to clarify the specific contributions of ICN-focused studies in Section 5, the following discussion outlines the fundamental principles of the NDN architecture. Table 1 lists some frequently used abbreviations in the following sections.
## 1 Introduction
### Aim of This Study  
The hypothesis behind this work is that the recent deployment of OCAC in Sweden provides unique insights into the practical application of oxygen carriers in biomass combustion technologies, which could serve as guidance for future developments. The specific aims of this study are to  
* Provide an overview of recent use of oxygen carriers as bed material in industrial-scale facilities in Sweden, many of which have not previously been reported in the scientific literature. This includes commercial operation of OCAC and research performed at a semi-commercial scale e.g., in Chalmers University's Research Boiler/Gasifier.
* Briefly present what main conclusions can be drawn from this body of work concerning factors such as material handling, procurement, and logistics. These conclusions are of high relevance for biomass combustion in general, but also for the development of future technologies such as CLC. Especially since hands-down experience of large-scale handling and logistics is difficult to provide in lab environment.
* Present an overview of current material flows for fluidized-bed boilers in Sweden and compare the demand for bed material with existing flows of potential oxygen carriers. This includes reviewing flows of intermediate products, by-products, and waste products generated in metallurgical industries in Sweden and neighboring countries. The goal is to provide input about what would be a realistic material choice for affordable and sustainable large-scale application of CLC and OCAC.  
Ultimately, the overreaching goal is to provide guidance concerning the choice of bed material for future development of technologies such as OCAC and CLC.
# Towards a Universal Semantic Dictionary
## 3 Proposed Method
### The Objective Function  
The objective of the proposed method is to learn linear mappings in the form of translation matrices that are obtained by maximizing the cosine similarity of gold word translation pairs in the above defined multilingual space. Therefore, for each language, one single translation matrix is searched that maps the language from its original vector space to the multilingual one. The method tries to bring the translation pairs as close together as possible in the multilingual space. Therefore, it is not only applicable for language pairs but for any number of languages as well. The main advantage is that by introducing new languages the number of the learned parameters remains linear to the number of languages since instead of learning pair-wise translation matrices, for each language, only one matrix is learned, the one that maps directly to the shared, multilingual space.  
Let \(L\) be a set of languages, and \(TP\) a set of translation pairs where each entry is a tuple of two in the form of (\(w_{1}\), \(w_{2}\)) where \(w_{1}\) is a word in language \(L_{1}\) and \(w_{2}\) is a word in language \(L_{2}\), and both \(L_{1}\) and \(L_{2}\) are in \(L\). Then, let us consider the following equation to optimize:  
\[\frac{1}{|TP|}\cdot\sum_{\begin{subarray}{c}L_{1},L_{2}\\ \in L\end{subarray}}\sum_{\begin{subarray}{c}(w_{1},w_{2})\\ \in TP\end{subarray}}cos\_sim(w_{1}\cdot T_{1},w_{2}\cdot T_{2}) \tag{3}\]  
where \(T_{1}\) and \(T_{2}\) are translation matrices mapping \(L_{1}\) and \(L_{2}\) to the multilingual space. Since the equation is normalized with the number of translation pairs in the \(TP\) set, the optimal value of this function is 1. Off-the-shelf optimizers are programmed to find local minimum values, thus, during the training process, the loss function is multiplied by \(-\)1. Word vectors are always normalized, thus the \(cos\_sim\) reduces to a simple dot product.  
At test time, first, both source and target language words are mapped into the multilingual space, and from the most frequent 200K mapped target language words a look-up space is defined. Then, the system is evaluated with the Precision metric, more specifically with Precision @1, @5, and @10. The distance assigned to the word vectors when searching in the look-up space is the \(cos\_sim\).  
Previous works, such as by [PERSON] et al. [4] or [PERSON] et al. [5], suggested restricting the transformation matrix to an orthogonal one. From an arbitrary transformation matrix \(T\), an orthogonal \(T^{\prime}\) can be obtained by applying the SVD procedure. Our experiments showed that, by applying SVD on the transformation matrices, the learning is significantly faster. The best results were obtained when applying the SVD only once, at the beginning of the learning process.
# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 2 Related Works
### Particle Swarm Optimization (PSO)  
The particle swarm optimization (PSO) algorithm imitates the swarm social behavior, which refers to the group of applicant solutions, and each solution is considered a particle. In References [58; 59], a new method for feature selection using the PSO algorithm, namely FSPSOTC, aims to solve the problem of feature selection in text clustering by building a new subset of informative features. These feature subset can reduce the computational time and enhance the clustering performance. The proposed method shows superior performance in feature selection comparing to the feature selection in text clustering by harmony search algorithm and genetic algorithm.  
In Reference [60], a hybridization of the Spectral Clustering algorithm and PSO algorithm for text clustering, namely, SCPSO. The randomization is implemented with the initial population by considering the local and global optimization methods. The performance of the SCPSO compared with the basic PSO algorithm and K-means algorithm and show high accuracy in text clustering.  
A novel approach is proposed in Reference [61] for text clustering based on the PSO algorithm and latent semantic indexing (PSO + LSI). To achieve better performance in the search space the adaptive inertia weight (AIW) is used to balance the exploration and exploitation. The proposed approach proves a high performance comparing to other algorithms such as PSO with k-means.  
In Reference [62], a hybridization between binary PSO with a chaotic map, fitness based dynamic inertia weight, opposition-based learning, and mutation, is introduced for feature selection in text clustering. Further, the opposition-based algorithm used, to begin with, diversified solutions and a set of promising to accomplish the best result. Besides, the opposite position of the global best particle (gbest) generates by the opposition based learning algorithm.  
A new method is proposed in Reference [63] to improve the feature selection process using the PSO algorithm. An improved PSO version was introduced to optimize the feature selection based on constant constriction factor and functional inertia weight. Then, a functional constriction factor is created based on the constant constriction factor, and combine this factor into the classic PSO method. Moreover, two improved PSO models were proposed called synchronously and asynchronously PSO. Finally, the chi-square test applies for feature selection. The proposed asynchronous approach shows a significant result in terms of the balance of various dimensions and text classification.