esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## Problem Formulation
### Multiple Access Network Coding (Lossless Second Stage)  
After proposing the optimum admission control, the next part is developing multiple interfaces based on random linear network codes to decrease packet completion time by taking advantage of network coding routing. The first stage as lossy and lossless second stage is considered before developing the algorithm for a complex channel, which will be extended for lossy after developingthe algorithm. The packets are lost in first stage during transmission from the base station. However, in re-transmission, there is no loss of packets. The primary aim of this approach is to evaluate the strength of our network coding without considering noise. In MANC batch, the packets re-transmitted from source/satellite and D2D are designed so that end of re-transmission, the packet completion time is minimum. First, the source determines the packets received by all the admitted devices and updates the acquired matrix \(A\). The linear combination of the packets is sent by satellite (if all packets available) or by the source over a large field. Once packets are received, devices update their deficient \(D\) matrix until the \(D\) becomes a null set. The D2D devices with a max number of acquired \(A\) are selected for the transmission on the D2D link.
## 3 Results and Discussion
### Unconfined Compressive Strength  
The maximum peak strength (\(q_{u}\)), measured in the unconfined compression strength test, of the treated mixtures defined in Table 3 is shown in Figure 13, along with the \(q_{u}\) results obtained for the untreated tailings. The full dataset is also available as a supplementary data file. For comparative purposes, three different compaction degrees were used in the untreated tailings, whose results are also presented in Figure 13. The specimen that exhibited the highest strength corresponds to mixture 8, which attained the extreme values of each variable. Specimens with lower strength were specimens 1--with the lowest limit values of variables--and 9, which has the lowest \(t_{FA}\) value. All specimens showed \(q_{u}\) values higher than those of the untreated tailings compacted at 90% and 95% of the Standard Proctor, and only two mixtures (numbered 1 and 9) presented values below the strength obtained in the untreated tailings compacted at 100% of the Standard Proctor. Therefore, it becomes clear from these results that there is a significant improvement in strength due to the stabilization with alkali-activated fly ash in comparison to the untreated tailings. In mixture 8, this improvement corresponds to approximately eight times the untreated strength.  
Figure 11: Molding parameters for the mixtures on the day of specimen preparation: (**a**) dry bulk density (\(\gamma_{\text{d}}\)) and (**b**) porosity (\(\eta\)).  
Figure 12: Mass variation in the specimens along 7 days inside an insulated box.  
Since the results presented in Figure 13 showed a trend of increasing strength with greater values of the mixture parameters, the possibility to expand the experimental plan by increasing the upper range of the input values was considered. However, this option was not reasonable for the following reasons: (1) Increasing the fly ash content (\(t_{FA}\)) would increase the precursor amount beyond reasonable limits, and, therefore, it would increase the volume of the material in the dry-stack pile. (2) Increasing the liquid content (\(t_{L}\)) makes it unfeasible to control the liquid content of the laboratory specimen since the liquid comes out from the specimen at the base and top of the mold during compaction, indicating that the mixture is close to saturation. (3) Increasing the concentration of the SH solution could lead to operational issues in situ, requiring additional care to perform the mixture, and a higher carbon footprint. On the other hand, it should be noted that, for the purpose of slope stabilization, very high unconfined compression strengths may not be required [30].
## 4 Statistical Quality Assessment of GNSS Signal
### Time Domain Features  
The features in the time domain representation are identified based on the statistical parameters computed below:  
1. Mean:  
The mean value of the samples of the GNSS signal is calculated as  
\[Mean_{j}=\frac{1}{n}\sum_{i=1}^{n}X_{ij}\]  
The mean is calculated as the mean value of the ith sample, where \(X_{ij}\) is the jth feature (or data point) of the ith sample, and n is the number of features (or data points) in each sample.
2. Median:  
Similarly, the median value is computed as  
\[Median_{j}=\text{Median}(X_{i1},X_{i2},........\ldots X_{in}).\]
3. Standard Deviation:  
\[Std_{j}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}X_{ij}-\mu_{j}\bigr{)}^{2}}\]
4. Mean Absolute Deviation:  
\[MAD_{j}=\frac{\sum_{i=1}^{n}\bigl{|}X_{ij}-\mu_{j}\bigr{|}}{n}\]  
where n is the total number of data points in the dataset.
5. Root Mean Square Error:  
\[RMSE_{j}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}X_{ij}\bigr{)}^{2}}\]
6. 25 th Percentile:\[P_{25}=percentile(X,25)\]
7. 75 th Percentile: \[P_{75}=percentile(X,75)\]
8. Inter-percentile Range: \[IPR=P_{75}-P_{25}.\]
9. Skewness: \[Skewness_{j}=\frac{\sum_{i=1}^{n}\left(X_{ij}-\hat{X}\right)^{3}}{n\sigma^{3}}.\]
10. Kurtosis: \[Kurtosis_{j}=\frac{\sum_{i=1}^{n}\left(X_{ij}-\hat{X}\right)^{4}}{n\sigma^{4}}.\]
11. Entropy: \[H_{j}=-\sum_{i}P\big{(}x_{ij}\big{)}log_{2}P\big{(}x_{ij}\big{)}.\]
12. Maximum-to-Mean Ratio (MTMR): \[MTMR_{j}=\frac{\max\big{(}x_{ij}\big{)}}{\mu}.\]  
Moreover, frequency domain features were calculated to examine spectral characteristics. These include the normalized spectrum bandwidth, normalized spectrum kurtosis (NSK), normalized spectrum flatness (NSF), the ratio of the variance to the squared mean of the normalized spectrum (RVSM), single-frequency energy aggregation, and the ratio of the maximum peak to the second maximum peak of the normalized spectrum (RMPS). These features provide valuable information regarding the distribution of the signal power across different frequency components, a measure of information, the shape of the spectrum concerning the centre frequency, and a measure of the spectral shape and concentration. The spectral domain features are listed in Section 4.2.
## 4 Experimental Results
### Subjective Measures  
In this section, user preferences such as the level of immersion, perceived engagement, comfort, and desirability of the IVR-SDO distance education system are examined, similarly as in [14; 26], especially [26], who researched the efficacy of VR in distance team-based learning for students as an engaging platform. To acquire a subjective evaluation on the VR-TBL experiences, we utilized the well-established Likert-type questions. We modified the survey to suitably fit our research and performed two pre-tests with the volunteers before the user study to verify that the questionnaire reflects the intent of our research.
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 5 Visual Tracking Based on Object Proposal Voting  
Object tracking is performed for the object candidate areas proposed by the object proposal voting method. It is formulated based on the assumption that the position of an object in a frame changes minimally in the adjacent frame. Thus, if the object position in the current frame is known, the area closest to the object position in the current frame is selected from candidate areas for object proposal in the next frame. In addition to tracking the object by the similarity of the object positions in adjacent frames, more accurate results can be obtained by assessing the similarity of the object appearances in adjacent frames. For this purpose, we adopt a Markov chain Monte Carlo data association algorithm [37] that can be run in realtime. We associate similar object proposals in terms of positions and appearances across consecutive frames:  
\[p(O_{i},O_{j})=exp\big{(}-d(O_{i},O_{j})\big{)}, \tag{7}\]  
where \(O_{i}\) and \(O_{j}\) are the \(i\)-th and \(j\)-th object proposals in the current and next frames, respectively. The dissimilarity function \(d\) is designed as follows:  
\[\begin{split}& d(O_{i},O_{j})=\\ &\big{|}g(O_{i})-g(O_{j})\big{|}_{2}+\big{|}a(O_{i})-a(O_{j}) \big{|}_{2}+\big{|}w(O_{i})-w(O_{j})\big{|}_{2},\end{split} \tag{8}\]  
where the first and second terms measure the \(l2\)-norm distance between two object proposals in terms of their positions and appearances, respectively. In (8), \(g(\cdot)\) returns \((x,y)\)-center positions of bounding boxes, which are obtained by the proposed object proposal voting method. \(a(\cdot)\) returns appearance feature vectors of image patches described by the aforementioned bounding boxes. We obtain appearance feature vectors from the 14-th feature map of the VGG-19 network [38], which is pre-trained using the ImageNet dataset [39]. Each layer contains different features with various sizes, in which early convolutional layers typically extract local (low level) features and later layers exhibit global (high-level) features. Based on this observation, we use the 14-th feature map of the VGG-19 network, because it can contain both local and global properties. \(w(\cdot)\) returns voting weights computer by (4), in which voting weights have high values if we vote the corresponding regions many times. As a result that identical objects have similar voting weights across consecutive frames, we also measure the \(l2\)-norm distance between voting weights using the third term in (8). For visual tracking, we choose the best pair of object proposals, which maximizes \(p(O_{i},O_{j})\) in (7):  
\[(\hat{O}_{i},\hat{O}_{j})=\underset{i,j}{\text{argmax }}p(O_{i},O_{j}). \tag{9}\]  
Algorithm 1 illustrates a whole pipeline of the proposed visual method based on our OPV. Table 1 summarizes notations used in this paper.
## 2 Materials and Methods
### Aerobic and Anaerobic Capacity Assessment  
A VO2 max test was used for the assessment of aerobic capacity. Firstly, the heart rate and the maximum oxygen uptake in a resting state were measured. Then, the volunteers began the test which initially included running at 8 km/h for 2 min, and after every other 2 min, the speed increased by 1 km/h until the athletes reached exhaustion. The exhaled gas of the participants was recorded for each breath during the run via an open spirometry circuit (Sensormedics vmax229, SensorMedics, Yorba Linda, CA, USA). At the same time, during the test, the heart rate was measured with a portable heart rate monitor (Polar H10, Polar Electro Oy, Kempele, Finland). The test was considered successful when (a) the participants showed the inability to continue the protocol due to exhaustion or when (b) their heart rate reached at least 10 bpm lower than the maximum heart rate for their age, as calculated using the equation (220--age). For VO2 max, we recorded the mean value of the three highest reported values of VO2 [15; 16]. Furthermore, anaerobic capacity was evaluated through the 30 s Wingate test in a cycle ergometer (Monark, Monark Exercise, Vansbro, Sweden). The volunteers started the test protocol after 2 min of trial cycling at a slow and fast pace followed by another 2 min of resting. Initially, they cycled for a short period of time without any further load in order to minimize the phenomena of inactivity and wheel resistance. Afterward, the researchers applied the chosen load for this protocol (0.075 kp-kg-1 body weight); as the time began to count, the participants began to cycle at a maximum level for 30 s. The frequency of leg rotation during cycling for every athlete was recorded every 10 s. The recorded values were written down on a specific spreadsheet so as to calculate the maximal work performed over the period of 30 s and thus the anaerobic capacity for each athlete [17]. The VO2 max test was conducted at the specialized Exercise Physiology laboratory of Harokopio University, while the 30 s Wingate test was performed inside the basketball gym.
## 1 Introduction
However, most of these methods are designed for detecting and analyzing a single wearable object, which limits their effectiveness in the multi-specification wearable application environment of power systems. To improve the detection accuracy of targets in power systems and to more effectively locate illegal wearing, advanced target detection algorithms are often combined with traditional or binary classification algorithms. However, these models tend to be relatively complex and struggle to achieve a balance between speed and accuracy. Additionally, there has been a lack of emphasis on the development of real-time detectors for low-cost edge hardware, such as the Jetson Xavier NX. To address these limitations, it is urgent to propose an advanced multi-wearable object recognition and defect location method that can effectively solve the aforementioned problems. In this paper, we investigate an improved intelligent detection method based on Edge-YOLO for detecting instances of illegal wearing among on-site power construction workers. Our proposed approach embeds lightweight models into the edge devices, achieving a balance between precision and real-time network architecture. Our main contributions are summarized as follows: (1) Our proposed Edge-YOLO model enhances object detection accuracy by integrating the SE [6] module, which adds a channel attention mechanism to the bottleneck structure of the backbone network for improved feature extraction. This module prioritizes informative features and suppresses less relevant ones, enabling it to perform well on edge devices for automatically detecting complete compliance with wearing requirements for power construction site workers; (2) We present a novel embedded real-time detection system, leveraging Jetson Xavier NX and Edge-YOLO, for the purpose of identifying and alarming instances of illegal wearing behavior among power construction workers. To achieve a lightweight implementation of the embedded model, we adopt a strategy of designing suitable detection heads that take into consideration the size and distribution of the targets. This approach aims to reduce the model's parameter count while simultaneously improving detection speed, all while minimizing potential accuracy reduction; (3) A data augmentation method has been proposed to increase the number and diversity of the dataset. We collect original images from real power construction sites or networks and enhance the resulting dataset to be used for training our models. The remainder of this paper is organized as follows: Section 2 introduces the related work. In Section 3, we present our improved detection methods for various objects. Section 4 provides detailed explanations of our data augmentation methods. Section 5 outlines the experiments we conducted to evaluate our method. Finally, in Section 6, we present our conclusions.
# First Theoretical Realization of a Stable Two-Dimensional Boron Fullerene Network
## 1 Introduction  
Fullerenes were originally zero-dimensional (0D) carbon-based polyhedral cages consisting of hexagonal and pentagonal rings, which, depending on their number of atoms and topology, can appear in diverse stable forms [1; 2; 3; 4; 5; 6]. In 2022, [PERSON] et al. [7] reported the first successful synthesis of a quasi-hexagonal-phase C\({}_{60}\) fullerene 2D lattice. Shortly after, two other experimental groups also fabricated 2D forms of C\({}_{60}\) fullerene networks [8; 9]. These latest experimental advances [7; 8; 9] offer novel fabrication approaches to forming nanoporous and light-weight 2D systems made of 0D fullerene cages connected by 1D bonds with remarkable stability [10; 11; 12] and low thermal conductivities [6; 10]. It is worth mentioning that after the discovery of C\({}_{60}\) fullerene [2], it took almost two decades for the experimental realization of the full boron B\({}_{40}\) fullerene counterpart [13]. On the other side, it also took almost a decade after the experimental realization of the single-layer graphene [14; 15; 16] for three different 2D borophene lattices to be first successfully synthesized using epitaxial growth over a silver substrate [17; 18]. As an interesting matter of fact, the possibility of the synthesis of borophene nanosheets and their metallic electronic nature were theoretically predicted before their experimental fabrications [19; 20].  
Boron atoms, similar to their carbon neighbor, show outstanding capabilities to form various bonding architecture, and in 2D form they can appear with buckled or fully planar structures. As it is clear, the recent experimental accomplishments for the synthesis of full carbon 2D fullerene networks [7; 8; 9] might be extendable for the case of boron-based counterparts. In fact, B\({}_{40}\) fullerene can be considered as the basis for the new experimental and theoretical endeavors, taking into account that it is experimentally producible [13] and is already predicted to form bonding interactions with neighboring cages [21]. In this short communication, on the basis of the experimentally fabricated B\({}_{40}\) boron fullerene, and by the screening of diverse possible 2D configurations, we could successfully predict thefirst 2D boron fullerene network. To this aim, we conducted extensive density functional theory (DFT) simulations to perform the energy minimization for detecting the stable atomic configuration and, furthermore, investigated its corresponding bonding, structural, stability, mechanical, and electronic features. The presented DFT results confirm the remarkable energetic, thermal, dynamical, and mechanical stability of the herein predicted isotropic B\({}_{40}\) fullerene 2D lattice, which makes it highly appealing for further theoretical and experimental endeavors.
# Removal of Methylene Blue from Aqueous Solution by Bone Char
###### Abstract  
Bone char was prepared from bovine bone for the removal of methylene blue from aqueous solution. The effects of particle size, contact time, and adsorption temperature on the removal rate of methylene blue were investigated. It was found that bone char particle size had an insignificant effect. The equilibration time was found at approximately 80 min. The removal rate decreased with an increase in temperature. The intraparticle diffusion was the main rate-limiting step. The experimental data was analyzed by kinetic, isotherm, and thermodynamic equations. The results show that the pseudo-second-order kinetic model and Freundlich, Temkin, and Dubinin-Kaganer-Radushkevich isotherm models are true of the adsorption process. The spontaneous and exothermic ion-exchange adsorption process was certified by the negative values of free energy change and enthalpy change, and 13.29 kJ mol\({}^{-1}\) of adsorption energy.  
bone char; methylene blue; adsorption; ion-exchange; kinetics +
Footnote †: journal: Applied Physics Letters
# Image Completion with Hybrid Interpolation in Tensor Representation
## 2 Preliminary  
Mathematical notations and preliminaries of tensor decomposition models are adopted from [31]. Tensors are multi-way arrays, denoted by calligraphic letters (e.g., \(\mathcal{Y}\)). Let \(\mathcal{Y}\in\mathbb{R}^{I_{1}\times\ldots\times I_{N}}\) be the \(N\)-way array. The elements of \(\mathcal{Y}\) are denoted as \(y_{i_{1},\ldots,i_{N}}\), where \(\forall n:1\leq i_{n}\leq I_{n}\), \(n=1,\ldots,N\). Boldface uppercase letters (e.g., \(\mathcal{Y}\) ) denote matrices; lowercase boldface ones stand for vectors (e.g., \(\mathbf{y}\)); non-bold letters are scalars. The vector \(\mathbf{y}_{j}\) contains the \(j\)-th column of \(\mathbf{Y}\). The symbol \(||\cdot||_{F}\) denotes the Frobenius norm of a matrix; \(||\cdot||\) denotes the 2-nd norm. The sets of real numbers and natural numbers are represented by \(\mathbb{R}\) and \(\mathcal{N}\), respectively. The symbols \(\lfloor x\rfloor\) and \(\lceil x\rceil\) stand for the floor and ceiling functions of \(x\), respectively.  
Let \(\mathcal{M}=[m_{i_{1},\ldots,i_{N}}]\in\mathbb{R}^{I_{1}\times\ldots\times I_{ N}}\) be the \(N\)-th way observed tensor with missing entries, and \(\Omega=[\omega_{i_{1},\ldots,i_{N}}]\in\mathbb{R}^{I_{1}\times\ldots\times I_{ N}}\) be a binary tensor that indicates the locations of available entries in \(\mathcal{M}\). If \(m_{i_{1},\ldots,i_{N}}\) is observed, then \(\omega_{i_{1},\ldots,i_{N}}=1\); otherwise, \(\omega_{i_{1},\ldots,i_{N}}=0\). The locations of positive entries in \(\Omega=1-\Omega\) correspond to missing entries in \(\mathcal{M}\). The number of observed entries is equal to \(|\Omega|=\{\sharp(i_{1},\ldots,i_{N}):\omega_{i_{1},\ldots,i_{N}}>0\}\).  
**Definition 1**.: _Let_  
\[\hat{\mathcal{Y}}=[\hat{y}_{i_{1},\ldots,i_{N}}],\quad\text{where}\quad\hat{y}_{i _{1},\ldots,i_{N}}=\left\{\begin{array}{ll}m_{i_{1},\ldots,i_{N}}&\text{if} \quad\omega_{i_{1},\ldots,i_{N}}=1,\\ 0&\text{otherwise}\end{array}\right. \tag{1}\]  
_be a zero-filled incomplete tensor._  
**Definition 2**.: _Let_  
\[\mathcal{Y}(\Omega)=[\tilde{y}_{i_{1},\ldots,i_{N}}],\quad\text{where}\quad \tilde{y}_{i_{1},\ldots,i_{N}}=\left\{\begin{array}{ll}m_{i_{1},\ldots,i_{N }}&\text{if}\quad\omega_{i_{1},\ldots,i_{N}}=1,\\ \emptyset&\text{otherwise}\end{array}\right. \tag{2}\]  
_be a subtensor of \(\mathcal{M}\) that contains only the entries pointed to by \(\Omega\)._  
Thus: \(|\mathcal{Y}(\Omega)|=|\Omega|\). Let \(\mathbf{\omega}=\text{vec}(\Omega)\in\mathbb{R}^{\mathbb{I}_{n=1}^{N}}\,t_{n}\) be a vectorized version of \(\Omega\).
# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 2 Digital Filter as Bio-Inspired Optimization Problem
### Cuckoo Search Optimization  
The cuckoo search (CS) algorithm is a metaheuristic global optimization approach inspired by the breeding habits of cuckoo species that steal eggs from other birds' nests to hatch them [21]. Algorithm 2 describes the CS algorithm. The host bird will either quit the nest or discard the foreign egg if it is discovered (probability = \(prob_{a}\)). The cuckoo search (CS) algorithm is expected to decrease the number of nests over time. Still, abandoned nests are believed to be replaced by new nests built in different locations, thus maintaining the same number of nests over time and introducing diversity to the algorithm [23, 24]. The CS algorithm requires a well-defined objective function, single or multi-objective, and constrained or unconstrained. The algorithm optimizes the objective function, starting with a random population for the nest location at \(itr=0\). [PERSON] et al. [23] applied the CS algorithm to design a digital filter for medical image denoising. The iteration of the CS algorithm starts at \(itr=0\) by selecting a random population for the nest location. Then, updation of the nest location is performed with regard to time, and mathematically, it is represented as:  
\[nest_{itr}=nest_{itr-1}+\alpha*Step_{itr} \tag{4}\]  
where \(nest_{itr}\) and \(Step_{itr}\) represent the nest location and step size for the current iteration, respectively. The scaling factor is expressed as \(\alpha\), whose value is greater than 0 and generally lies between 0.001 and 0.01. This value depends on the search space's largest value. For designing digital filters, the CS algorithm searches the system coefficients having the following objective function:  
\[objective_{function}=\min\{\int_{0}^{\pi}\left(\omega-\left|F_{D}(e^{i\omega}) \right|\right)^{2}d\omega\} \tag{5}\]  
The optimization problem dimension (represented as \(Dim_{M}\)) is also represented as the number of system coefficients, such as \(m_{i},\ n_{i},\ 0\leq i\leq O\). Here, system order is represented as \(O\). Mathematically, it is represented as  
\[Dim_{M}=2O+2 \tag{6}\]  
Each nest location in the \(Dim_{M}\) is considered a solution for the filter response.