esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Architecture Search for a Highly Efficient Network with Random Skip Connections
## 1 Introduction  
Sequence learning is a promising field for modeling sequential data [1] and is widely used in time-series predictions, video analysis, information retrieval, etc., where models must learn from sequences, such as those found in speech, documents, videos, etc. Using a recurrent neural network (RNN) has become the standard approach for processing sequential data. Although RNNs have shown remarkable performances in many areas, long-term sequence learning remains challenging due to the gradient vanishing problem. Hence, many improved cell structures have been proposed to manage this problem, such as long short-term memory (LSTM) [2], gated recurrent units (GRUs) [3], and the Just Another Network (JANET) [4]. Others focus on the networks' architectures, such as, the skip RNN [5], dilated RNNs [6] and hierarchical multi-scale RNNs [7]. However, these networks depend on human design, which demands a lot of expert knowledge and it is usually hard to obtain ideal frameworks. The neural architecture search has emerged for solving this problem, which aims to find good architectures using a controller network, such as an RNN. Investigating neural architecture search (NAS) [8; 9] methods has become an important research direction nowadays, which comprises three parts: a search space, a search strategy, and a performance estimation strategy. A search space defines which architecture can be discovered in principle, such as chain-structured neural networks [10]; many different search strategies are used to explore the search space, including random search, evolutionary methods, reinforcement learning, etc.; while performance estimation is used as a feedback to the searchstrategy. In this paper, we propose to utilize a neural architecture search for managing long-term dependencies in sequential data. First, we designed a dense neural network to construct the search space, which was composed of a novel resource-saving cell structure, and then we selected a network architecture with skip connections via random sampling on the generated probabilities. Next, the architecture was chrono-initialized and trained. The network's high efficiency lay in three aspects: (1) the remote skip connections directly capture long-term dependencies and can shorten the path length of information propagation; (2) the cell structure demands minimum computational and memory resources compared with other algorithms like JANET; and (3) chrono-initialization can alleviate the gradient vanishing problem along the propagation path.  
This paper is organized as follows. Section 2 presents related work. In Section 3, the first part illustrates our proposed method of architecture generation, while the second part presents a special initialization scheme that can capture the dependencies more effectively and alleviate the gradient vanishing problem. Section 4 analyzes the advantages of our network in terms of memory and computational power consumption, and then provides the experimental results. Section 5 gives concluding remarks and prospects for future work.
## 3 Analysis and Clustering Methodology
### Spectro-Temporal Analysis  
The set of audio signals gathered from each location during \(H\) 1-h periods within the predefined analysis period (e.g., one day sampled at every hour) are represented using an MFCC-based parameterisation [12]. The spectrum of each signal frame is computed considering \(B\) energy sub-bands, following the approach described in [29]. Then, the mean spectrum obtained for each analysed hour of the day, taking into account only those frames labelled as RTN, is computed to obtain the STP of each \(n\)th-location (for \(n\in\{1,N\}\)), and denoted as \(\mathbf{STP}_{n}\in\mathbb{R}^{B\times H}\). \(\mathbf{STP}_{n}\) is a matrix of real values that contain logarithmic energies of \(B\) frequency subbands at \(H\) 1-h periods of the analysed day, i.e., \(\mathbf{STP}_{n}=(STP_{i,j})\), for \(i=\{1,2,\ldots,B\}\) and \(j=\{1,2,\ldots,H\}\).
# Reasoning Algorithms on Feature Modeling--A Systematic Mapping Study
## 4 Methodology
### Protocol Definition
#### 4.1.1 Aim and Need  
In this SMS we aimed to collect reasoning algorithm proposals for FMs, present in the literature from the past ten years. We established some parameters regarding the application domain, underlying model, origin, and degree of empirical validation of the proposals.  
We see this study as the foundation to generate a proposal for reasoning algorithms based on model-driven development approaches. To accomplish this, it is necessary to understand in detail the proposals that exist today within the area. This analysis will allow us to:  
Figure 4: SMS Process and stages.  
Understand the requirements to create algorithms of this nature.
* Understand what technologies, tools, approaches, etc., are used for building these algorithms, as well as the justifications for using them in each case.
* Avoid activities or processes that have already been carried out by other authors.  
Moreover, the last study that collected this information is ten years old [14]. A similar and more recent state-of-the-art report has emerged, although it has a different focus than the one we wish to address in this paper [28; 37]. In particular, [37] is an extension of [14] and seeks to answer questions related to FM reasoning algorithms that can be applied in _configuration modeling_. On the other hand, [PERSON] presented results focused mostly on bibliometrics [28].  
The importance of this study lies in the systematic gathering and reporting of an updated view of the state of feature modeling tools for SPLs in terms of their origin, development process, underlying modeling notations, and empirical validation. We also included other aspects in this study, namely, the origins of the papers, as well their context of application, year of publication, publisher, and target audience.  
Providing a clear picture of all these characteristics may help professionals by diminishing the risks associated with choosing a tool, and it may also contribute to the field by providing a framework against which new tools may be compared. Furthermore, we aim to foster a discussion among the community about the qualities that feature modeling tools for SPLs should have to facilitate the creation of high-quality specifications.
# Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images
###### Abstract  
To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).  
Keywords:image enhancement; noise filtering; mixed Gaussian and impulsive noise; fuzzy logic +
Footnote †: journal: Applied Sciences
## 2 Structure of _n_-4R Compliant Parallel Pointing Mechanism  
The _n_-4R compliant parallel pointing mechanism is a class of 2-DOF parallel micro-motion platforms with two rotational DOFs around _x_- and _z_-axes that can realize quasi-sphere motion. As shown in Figure 1, it is composed of a mobile platform, a fixed platform, and \(n\) (_n_\(\geq\) 3) similar branches.  
For convenience, the 4-4R compliant parallel pointing mechanism is used as an example to introduce the mechanism structure. As shown in Figure 2, the 4-4R compliant parallel pointing mechanism is composed of a fixed platform, four branches, and a mobile platform. Each branch consists of four right-circular flexure hinges (hereafter referred to as flexure hinges) and three links connected in series. The four branches are similar and equally distributed around the mobile platform by \(90^{\circ}\). \(O\) and \(O^{\prime}\) denote the distribution centers of the flexure hinges directly connected to the mobile and fixed platforms, respectively (hereinafter referred to as the mobile platform center and the fixed platform center, respectively). \(O\) is defined as the intersection point of the axes of the four flexure hinges connected to the mobile platform, and \(O^{\prime}\) is defined as the intersection point of the axes of the four flexure hinges connected to the fixed platform. We define the distribution radius of both mobile and fixed platforms as \(R\), and the distance between the centers of mobile and fixed platforms as \(L\). For the flexure hinges \(R_{2}\) and \(R_{3}\) on branch 1, their axes pass through \(O^{\prime}\) and \(O\), respectively, and intersect at point \(J\). From the geometric characteristics, it is known that \(OJ=O^{\prime}J\). Define the angle \(\angle O|O^{\prime}\) as \(\varphi\). Define the geometric centers of flexure hinges \(R_{2}\) and \(R_{3}\) as points \(D\) and \(F\), respectively. The horizontal distance from D and F to \(OO^{\prime}\) are both \(l\).  
Figure 1: A class of _n_-4R compliant parallel pointing mechanisms, (**a**) \(n=3\); (**b**) \(n=4\); (**c**) \(n=5\).  
Figure 2: Structure parameters of 4-4R compliant parallel pointing mechanism.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Propositional Logic
#### 2.4.3 Conjunctive Normal Form  
We provide the following definitions about propositional formulas.  
**Definition 23** (Literals, Clauses and Cubes).: _A literal is either an atomic formula or the negation of an atomic formula. A clause is a disjunction of literals, whereas a cube is a conjunction of literals._  
**Definition 24** (Conjunctive Normal Form).: _A formula \(F\) is said to be in Conjunctive Normal Form (CNF) if is is a conjunction of clauses._  
For instance, \(a\), \(b\), \(c\) and \(d\) being propositional variables, \(a\) is an atomic formula, both \(\
eg a\) and \(b\) are literals, \((\
eg a\lor b\lor\
eg c)\) is a clause, \((\
eg a\wedge b\land\
eg c)\) is a cube and \((\
eg a\lor b\lor\
eg c)\land(\
eg b\lor\
eg c\lor d)\) is a CNF formula. Given a truth assignment \(\mu\) is easy to see the following:  
* A literal \(a\) is satisfied by \(\mu\) iff \(\mu(a)=\top\).
* A literal \(\
eg a\) is satisfied by \(\mu\) iff \(\mu(a)=\bot\).
* A clause \(C=l_{0}\lor\dots\lor l_{n}\) is satisfied by \(\mu\) iff \(\mu\models l_{i}\) for some \(0\leq i\leq n\).
* A cube \(Q=l_{0}\land\dots\wedge l_{n}\) is satisfied by \(\mu\) iff \(\mu\models l_{i}\) for each \(0\leq i\leq n\).
* A CNF formula \(F=C_{0}\land\dots\wedge C_{n}\) is satisfied by \(\mu\) iff \(\mu\models C_{i}\) for each \(0\leq i\leq n\).
## 3 Results
### Poultry Litter and Its Biochar Effects on Soil Fertility Indicators  
Soil pH, Organic Matter Content and Cation Exchange Capacity  
Figure 1 depicts the pH of the soil. A significant gradual increase of soil pH was observed with the increasing application rates. Generally, PL-treated soils had the highest pH level ranging from 7.67 to 8.24, followed by BC350 treated soils (8.20) and then BC600 (7.86) (Figure 1a). The control treatments increased soil pH from the initial 6.36 to slightly alkaline pH range of 7.31 to 7.82 (Figure 1a). A combination of biochar and poultry litter (PLBC350 and PLBC600) displayed an increased soil pH (Figure 1a).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Available plant nutrients (mg/kg) & \\ Phosphorus (P) & 0.80 \\ Potassium (K) & 3.58 \\ Calcium (Ca) & 10.07 \\ Magnesium (Mg) & 3.69 \\ Iron (Fe) & 9.31 \\ Manganese (Mn) & 2.77 \\ Copper & 0.04 \\ Zinc (Zn) & 0.24 \\ Exchangeable cations (cmol/kg) & \\ K & 4.13 \\ Ca & 22.0 \\ Mg & 4.13 \\ Na & 6.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial soil elements concentrations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Available** & & & \\
**Nutrients (mg/kg)** & **Poultry Litter** & **Biochar 350 \({}^{\circ}\)C** & **Biochar 600 \({}^{\circ}\)C** \\ \hline Phosphorus (P) & 33.01 & 33.37 & 33.98 \\ Potassium (K) & 826.79 & 1714.08 & 855.20 \\ Calcium (Ca) & 813.48 & 724.32 & 748.65 \\ Magnesium (Mg) & 349.14 & 526.35 & 314.46 \\ Sodium (Na) & 137.34 & 354.75 & 162.26 \\ Iron (Fe) & 12.75 & 8.53 & 28.83 \\ Zinc (Zn) & 41.37 & 31.53 & 25.56 \\ Manganese (Mn) & 28.39 & 51.35 & 36.67 \\ Copper (Cu) & 0.73 & 1.22 & 25.56 \\ EC (\(\upmu\)S/cm) & 13.90 & 365.33 & 278.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Plant available nutrients concentrations in poultry litter and its biochar.  
Figure 1b shows that the increased application rates of soil organic amendments significantly increased cation exchange capacity. In all treatments, the highest application rate of 120 g/kg has shown the highest CEC (Figure 1b). However, PLBC350 exhibited a higher CEC of 6.70 mmol/kg. In terms of soil organic matter content, increasing the application rates of organic amendments effectively increased organic matter content when compared to the control (Figure 1c). The organic matter content (OM) in PL- and BC350-treated soils increased with application rates (Figure 1c) compared to BC600-treated soils.
# CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
## 4 Experiments and Discussions
### Ablation Study
#### 4.4.1 **Q1**: Is Our Method Efficient on a Limited-Sized Dataset?  
To ascertain the performance of our method on constrained datasets, we undertook evaluations using just 20% of the available training data for two benchmarks: 2D-Gesture and ECG. The results were promising. As presented in Table 6, our method delivered an \(F_{1}\) score of 99.93 on the 2D-Gesture benchmark and 90.64 on the ECG benchmark. Such high scores, despite limited training data, suggest that our approach is resilient to data scarcity and can be effective in real-world scenarios with limited training datasets.
## 4 Experimental Results and Analysis
### Analysis of the Two-Way Partitioning Results
#### 4.1.1 Analysis of Minimum Edge Cut Results
average stability, reflecting the variability of its results across different runs of the experiment. This phenomenon is closely related to the inherent randomness of the tabu search algorithm. The inherent randomness is effective with respect to avoiding local optima, but leads to result inconsistency. To obtain more reliable optimal results, it is recommended to execute the algorithm multiple times independently.
## 2 Principle Analysis
### Imaging Procedure  
The imaging system of the TSOM is the same as a bright-field microscopy. The complete imaging system includes an illuminating path and an imaging path. These two paths can be described by their corresponding optical transfer function (OTF). The OTF of the illuminating path is [6]  
\[H_{ill}(k_{i,j})=\exp\left[\hat{i}D\sqrt{\left(\frac{2\pi}{\lambda}\right)^{2}- \left|k_{i,j}\right|^{2}}\right], \tag{14}\]  
where \(D\) is the illumination spot defocus and a variable here. The OTF of the imaging path is  
\[H_{ima}(k_{k,m})=\begin{cases}1&\left|k_{k,m}\right|\leq\frac{2\pi}{\lambda} NA\\ 0&else\end{cases}, \tag{15}\]  
where \(NA\) is the numerical aperture of objective lens. The complete system-structure interaction is described by  
\[\mathbf{E}_{k,m}^{out}=H_{ima}(k_{k,m})\cdot S_{i,j,k,m}\cdot H_{ill}(k_{i,j}) \cdot\mathbf{E}_{i,j}^{in}, \tag{16}\]  
where \(S_{i,j,k,m}\) is a four-dimensional scattering matrix, and the two pairs of indices \(i,j\) and \(k,m\) describe the in-plane components of the wavevectors for the illuminating plane waves \(\mathbf{E}_{i,j}^{in}\) and scattering plane waves \(\mathbf{E}_{k,m}^{out}\), respectively. Finally, as each illuminating plane wave component corresponds to a specific source point in Kohler illumination, and the light source is incoherent, the subsequent scattering plane waves are assumed as incoherent as well. Thus, each pixel of the image is generated from the incoherent sum of output plane waves scattered from a given location of the structure, as depicted in Equation (17).  
\[I(r)=\sum_{k,m}\left|\mathbf{E}_{k,m}^{out}e^{i\mathbf{k}_{k,m}r}\right|^{2}, \tag{17}\]  
where \(r\) denotes the location vector toward the CCD plane.
# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 3 Data and Methods
### Data  
#### 3.1.1 Data Collection  
Sina Weibo is one of the largest and most widely used social media platforms in China, similar to the foreign social media, Twitter, where users can post text, pictures, videos, and other content, interact and communicate with other users, and the platform is publicly accessible. Despite the existence of content monitoring mechanisms on Weibo, there are still many users who post discriminatory remarks by some means to avoid monitoring, so in this study, we crawled discriminatory and non-discriminatory remarks on Sina Weibo as a dataset.  
By analyzing the composition and characteristics of Sina Weibo's microblogs, it is found that among the three ways of accessing Sina Weibo, the mobile website m.weibo.com is the most convenient and easy way to access and analyze the data of Sina Weibo [28]. The textual information of Sina Weibo is stored in the webpage, which can be obtained through web requests. Requests 2.32.3 is an HTTP client library for Python 3.10, which supports a number of HTTP characteristics. By setting web request parameters such as cookies, headers, and keywords, the Requests library is used to simulate a browser sending requests to the Sina Weibo server to obtain microblog web page data. By analyzing the data in JSON format, it is possible to obtain the text of microblog topics, microblog content, microblog comment content, and save them locally.  
We set up keywords to search for microblog topics and microblogs that contain keywords, as well as the comments of the microblogs. In order to protect users' privacy, we only collected the content of posts and comments, and other information were not collected, such as the microblog's ID, user's ID, posting time, and so on.  
We collected a total of 26,542 posts and comments data, and the size of the dataset after removing duplicates and gaps is 25,726, of which 73.1% are discriminatory and 26.9% are non-discriminatory. The large difference in sample size between different labels indicates a serious sample imbalance in the collected data.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 2 Multi-Robot Formation Control
### Formation Control Algorithm  
The CVT algorithm is usually used to solve the optimal distribution of static resources. If the iterative speed of the CVT evolution process is slower than that of the motion planning algorithm, the algorithm can be used for motion control of multiple robots. Through the static or dynamic communication topology between robots, the multi-robot formation uses a consistency algorithm to drive robots to achieve a consistent state. The desired formation of the robots is composed of the centroids of the Voronoi region divided by the CVT algorithm. The purpose of the consistency algorithm is to drive all robots to their corresponding centroids.  
Assuming that \(p_{i}=(x_{i},y_{i})\) is the actual position of the robot and \(p_{i}^{*}=(x_{i}^{*},y_{i}^{*})\) is the desired position of the robot, the robot is driven to the desired position via Equation (16):  
\[p_{i}^{*}=\frac{\sum\limits_{j=1}^{n}g_{ij}q_{j}}{\sum\limits_{j=1}^{n}g_{ij}} \tag{16}\]  
where \(n\) is the number of robots and \(g_{ij}\) is the parameter in the robot communication topology matrix. The Algorithm 1 flow chart is shown in Algorithm 1.
## 2 Materials and Methods  
### Renewable Energy and RO Desalination  
RO desalination needs electrical power to operate. Figure 2 presents the various options for powering an RO desalination plant. Generally speaking, the option to connect to an electricity grid can decrease the cost of powering the desalination unit in comparison to an autonomous system. Given the decreasing cost of renewables, scenarios using distributed renewables are going to be investigated under schemes such as net-metering in terms of cost, apart from the environmental benefits this approach presents. Distributed energy storage in grid-connected systems is not investigated in this paper, since no regulatory framework for distributed energy storage exists in Greece yet. In any case, the investigation in a storage system for a grid connected plant has to take place on a per-country basis based on the regulatory framework and the motives given for it.  
Figure 2: Powering RO desalination.  
In many cases, islands are powered by isolated grids. There is a possibility that this grid is not able to meet the electricity demand of the planned desalination plant. In those cases, usually in collaboration with the distribution system operator (DSO) various scenarios are investigated in terms of upgrading the grid to be able to meet the needs of the desalination system. This is the only scenario this paper does not investigate given the complexity and multitude of decision parameters that go beyond a techno-economic study.  
In cases where either the grid cannot meet the power demand of the desalination plant or the grid extension infrastructure investment is not cost-effective due to distance and/or topography, autonomous systems can be considered. The main topology used by autonomous/off-grid systems as the power demand is increasing is based on microgrids. According to the CIGRE Working Group C6.22, microgrids are defined as electricity distribution systems containing loads and distributed energy resources (such as distributed generators, storage devices or controllable loads) that can be operated in a controlled, coordinated way either while connected to the main power network or while islanded. This ensures that this infrastructure could be interconnected to the main grid seamlessly in the future, if this makes economic sense.