esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
The set of real numbers is denoted by \(\mathbb{R}\). The set of nonnegative integers is denoted by \(\mathbb{N}_{\geq 0}\). The transpose of a matrix \(A\in\mathbb{R}^{n\times m}\) is denoted by \(A^{\mathsf{T}}\in\mathbb{R}^{m\times n}\). \(|s|\) represents the absolute value of a scalar \(s\). The Euclidian norm of a vector \(x\in\mathbb{R}^{n}\) is denoted by \(\|x\|\), and let \(\|x\|_{\infty}\) denote its infinity norm, i.e., \(\|x\|_{\infty}=\max_{i=1,\ldots,n}|x_{i}|\). \(tr(A)\) represents the trace of a matrix \(A\) and its induced two-norm is denoted by \(\|A\|:=\sqrt{\max\lambda\{A^{\mathsf{T}}A\}}\), where \(\lambda\{A^{\mathsf{T}}A\}\) denotes all of the eigenvalues of \(A^{\mathsf{T}}A\). An asterisk \((*)\) in the matrix is used to present a symmetry block. For a symmetric matrix \(P\in\mathbb{R}^{n\times n}\), \(P>0\) (\(P\geq 0\)) denotes that it is positive (semi-)definite, and, similarly, \(P<0\) (\(P\leq 0\)) means that it is a negative (semi-)definite matrix. Let \(I_{m}\in\mathbb{R}^{m\times m}\) (\(0_{m\times n}\in\mathbb{R}^{m\times n}\)) represent the identity (zero) matrix, and, for brevity, we sometimes omit the subscript of \(I_{m}\) (\(0_{m\times n}\)) if there is no confusion from the contexts. The saturation function for a scalar \(s\) with upper bound \(c\), \(sat_{c}(\cdot)\), is defined as \(sat_{c}(s)=\text{sign}(s)\min\{c,|s|\}\), where \(\text{sign}(\cdot)\) denotes the sign function. In addition, the vector saturation function is defined as \(sat_{c}(x)=(sat_{c}(x_{1}),\ldots,sat_{c}(x_{n}))\) with \(x=(x_{1},\ldots,x_{2})^{\mathrm{T}}\). For a group of points, \(p_{1},p_{2},\ldots,p_{l}\), the convex hull of these points is defined as \(\alpha\{p_{k},k=1,\ldots,l\}=\{\sum\limits_{k=1}^{l}\alpha_{k}p_{k}|\sum \limits_{k=1}^{l}\alpha_{k}=1,\alpha_{k}\geq 0\}\).  
To deal with the saturation property, we introduce some lemmas for later use. Define \(\mathcal{D}\) as the set of \(m\)-demision diagonal matrices whose diagonal elements are either \(1\) or \(0\). Obviously, there are \(2^{m}\) elements in \(\mathcal{D}\). Label all elements in \(\mathcal{D}\) as \(D_{j}^{+},j\in\mathcal{M}:=\{1,2,\ldots,2^{m}\}\) and denote \(D_{j}^{-}=I-D_{j}^{+}\). For a positive definite matrix \(P\in\mathbb{R}^{n\times n}\), define an ellipsoid \(\Gamma(P)\) by  
\[\Gamma(P):=\{x\in\mathbb{R}|x^{\mathrm{T}}Px\leq 1\}.\]  
For a given matrix \(H\in\mathbb{R}^{m\times n}\), denote the \(j\)-th row of \(H\) as \(h_{j}\). Then, we define  
\[\mathcal{L}(H):=\{x\in\mathbb{R}^{n}|\,|h_{j}x|\leq 1,j\in 1,\ldots,2\}.\]  
With these definitions, we introduce the following lemmas.  
**Lemma 1**.: [27] _For a given \(u,v\in\mathbb{R}^{m}\) with \(u=(u_{1},u_{2},\ldots,u_{m})^{\mathrm{T}}\) and \(v=(v_{1},v_{2},\ldots,v_{m})^{\mathrm{T}}\), suppose \(\|v\|_{\infty}\leq 1\). Then,_  
\[sat(u)\in co\{D_{j}^{+}u+D_{j}^{-}v|j\in\mathcal{M}\}.\]  
**Lemma 2**.: [28] _For a given \(\Gamma(P)\) and \(\mathcal{L}(H)\), if_  
\[\left(\begin{array}{cc}1&h_{j}\\ *&P\end{array}\right)\geq 0,j\in\{1,\ldots,m\},\]  
_where \(h_{j}\) denotes the \(j^{\mathrm{th}}\) row of \(H\), then \(\Gamma(P)\subset\mathcal{L}(H)\)._
## 4 Results and Discussion
### Performance of Polymer Curing Agent
#### 4.1.4 Hydrophobic Performance  
Measurement results of the surface contact angle of the specimen before and after curing are shown in Figure 10.  
It is seen in Figure 10 that, as the polymer-cement ratio increases, the contact angle gradually increases, which then tends to become stable. In addition, the polymer emulsion increases and the cement decreases. The hydrophobic layer of polymer latex is mainly formed on the surface of polymer curing agent so that the wetting angle increases. When the polymer-cement ratio further increases, the hydrophobic performance of the polymer curing agent is dominantly affected by the polymer emulsion so that it tends to be the maximum. Through the comparative analysis of the wetting angle before and after modification, the polymer curing agent can improve the hydrophobic performance of the weak rock surface. The main reason is that a part of water in the polymer emulsion is volatilized, so that polymer particles are dehydrated and bond together to form a continuous elastoplastic layer. On the other hand, after the cement absorbs the remaining water in the emulsion, it is hydrated and polymerized. After the reactions, it is cured and forms a waterproof film structure of an interpenetrating network together with the organic polymer chain. Some gaps exist in the molecules of the solid polymer. The width of the gap is about several tens of nanometers. It is reasonable that a single water molecule can pass through these gaps, but water by nature is usually in an association condition. Due to the action of hydrogen bonds, a larger cluster of water molecules is formed among the polymer molecules.  
Figure 10: Comparison of contact angle before and after modification.
## 2 Possibility of Generating Renewable Energy from Biogas Using Sugarcane Processing Wastewater  
Sugarcane is used for sugar production at the business scale and contributes about 80% of the world's sugar revenue [22; 23]. In South Africa, the provinces of KwaZulu-Natal and Mpumalanga are the major sugarcane producers contributing to the prosperity of the sugar industry's economy [24]. The same sugarcane that contributes to countries' economies is characterized by the generation of large quantities of organic wastewater, with excessive chemical oxygen demand that pollutes the environment [25; 26]. Therefore, the sustainable advancement of the sugarcane industry requires reducing and treating sugarcane processing wastewater. One way of treating this sugarcane processing wastewater is by discharging it into wastewater-treatment systems where there would be physical or chemical nutrient removal. Nevertheless, such methods present disadvantages of secondary pollution, high operation costs, and limitation of nutrient reusability [27; 28]. Sugarcane processing wastewater is an attractive substrate for bacterial cultivation to produce beneficial products, such as biogas, biomass, enzymes, and organic acids, due to its high carbohydrates, minerals, and sugars [26; 29]. Many researchers [30; 31; 32] have studied the conversion of biogas to electricity. [PERSON], et al. [33] analyzed the efficiency and sustainability of biogas to electricity production from a large-scale biogas project in China using pig manure. Even though they obtained lower yield results compared to traditional coal and natural gas power plants, electricity generated from biogas still brings more advantages and reduced antithetical environmental effects, as opposed to fossil fuels [33].
## 3 Results
### Comparison with XMed-Dry Measurements
For the measured dust deposition rates in parentheses the lower and upper limit of the central 95% confidence interval are shown. In the last three columns total deposition rate, non-dust fraction and the average PM\({}_{10}\) concentration for the relative period are reported. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Latta** & **Run\_2** & **Run\_3** \\ \hline April & \(\upmu\)g m\({}^{-3}\) & \(\upmu\)g m\({}^{-3}\) & \(\upmu\)g m\({}^{-3}\) \\
12 & 56.7 & 73.5 & 57.5 \\
13 & 43.4 & 49.8 & 161.9 \\
14 & 48.6 & 77.0 & 221.8 \\
15 & 158.2 & 115.8 & 944.3 \\
16 & 40.4 & 14.3 & 29.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Daily mean PM\({}_{10}\) at Authority’s station in Gharb (Malta) and predicted with the WRF-Chem configurations run_2 and run_3. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Lece** & **Run\_2** & **Run\_3** \\ \hline April & \(\upmu\)g m\({}^{-3}\) & \(\upmu\)g m\({}^{-3}\) & \(\upmu\)g m\({}^{-3}\) \\
12 & 32.2 & 41.0 & 124.8 \\
13 & 35.5 & 20.3 & 25.1 \\
14 & 35.0 & 17.2 & 36.3 \\
15 & 42.3 & 23.3 & 51.5 \\
16 & 184.0 & 50.2 & 214.5 \\This permits a comprehensive comparison between XMed-dry measurements and model outputs. The model results (dep_rate) for the run_2 and run_3 configurations are shown in the second and third columns of Table 6. For each XMed-dry sampling site, the variables of Equation (3) are extracted from the WRF-Chem netCDF output file in correspondence of the appropriate (lat, lon) coordinates of each station. To have a proper comparison between experimental data and model, dep_rate values are normalized using the same units (mg m\({}^{-2}\) day\({}^{-1}\)). If we consider the mean values that are calculated by averaging over all periods and stations and reported in the last row of Table 6, it results a positive bias (under-estimation) equal to 2.5 mg m\({}^{-2}\) day\({}^{-1}\) for run_2 and a negative bias of -4.6 mg m\({}^{-2}\) day\({}^{-1}\) corresponding to a considerable over-estimation for run_3. The non-dust fraction (sixth column of Table 6) represents the abundance of particle mass not classified as dust. For the presented days, this non-dust fraction was mainly composed of sea-salt, but contained also sulfate particles (sodium/calcium/ammonium/potassium/magnesium sulfate and mixtures thereof) to a lesser extent. As can be seen, for all the samples the dust component is dominant except for the SLS_149 sample (Malta site). In fact, for all samples non-dust fraction ranges between 0.09 and 0.01, while for SLS_149 one non-dust fraction is 0.49, i.e., dust always dominates the total deposition. Observing average PM\({}_{10}\) concentration values, obtained by daily values of air quality monitoring networks in Lecce, Athens, and Malta, good site-specific relative agreement with XMed-Dry total deposition rates is found. However, it appears that the same PM\({}_{10}\) concentration leads to different deposition rates at the different locations, which might indicate local conditions.
# Grammar-Supervised End-to-End Speech Recognition with Part-of-Speech Tagging and Dependency Parsing
## 2 The Framework of the ASR System  
The baseline framework of the ASR system considered in this paper is presented in this section.  
The attention-based encoder-decoder ASR system has enabled state-of-the-art speech recognition performance over a wide range of tasks. In particular, it consists of a \"conformer\" encoder module and a \"transformer\" decoder module [25] as shown in Figure 1. In the conformer module, the input sampling rate is reduced from 10 ms to 40 ms with a convolutional subsampling block, and the rate is kept constant through the entire multi-blocked stacked architecture. Each encoder block consists of the following components: a sequence of feed-forward modules, a multi-head self-attention module, a convolution module, and another feed-forward module. The conformer block gives consideration to the diversity of both global interactions and local features, and obtain better accuracy with fewer parameters.  
During encoder-decoder training, the CTC criteria, whose non-autoregressive decoding method benefits the inference latency, is used to maintain the training robustness and achieve a fast convergence as the following loss function formula. In addition, the tunable parameter \(\lambda\) is set between 0 and 1:  
\[Loss_{asr}=\lambda Loss_{ce}+(1-\lambda)Loss_{ctc} \tag{1}\]
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 2 DME Interference Model  
DME operates across the 962-1213 MHz frequency spectrum, utilizing 252 discrete channels with 1 MHz inter-channel spacing. The system employs two distinct operational modes: X-mode and Y-mode. For airborne equipment, the transmission frequency for channels 1X to 126X ranges from 1025 MHz to 1150 MHz, while the reception frequencies range from 962 MHz to 1024 MHz (for channels 1X-63X) and from 1151 MHz to 1213 MHz (for channels 64X-126X) [20]. The B2a signal completely falls within 1151 MHz to 1213 MHz, and the DME interference is mainly from ground beacons [13]. Consequently, this study specifically examined the interference from DME ground beacons operating in channels 64X-126X (1154-1213 MHz).  
The DME signal consists of repetitive pulse pairs, and the time interval between the X-mode pulse pairs is 12 \(\upmu\)s. The signal expression for the DME X-mode can be expressed by  
\[S_{\text{DME}}(t)=e^{\frac{\Phi t^{2}}{2}}+e^{-\frac{\Phi(t-\Delta t)^{2}}{2}}, \tag{1}\]  
where \(\alpha\) is \(4.5\times 10^{11}\,\text{s}^{-2}\), indicating the width of the pulse; and \(\Delta t=12\)\(\upmu\)s is the time interval between two pulses. The time-domain waveform and spectral characteristics of the simulated signal are shown in Figure 2. Pulse pairs arrived randomly at the aircraft, and the output peak power was 1 kW.  
To describe the pulse pairs' transmission rate of the ground beacon, the number of pulse pairs transmitted by a ground beacon per second, i.e., the pulse repeat rate (PRR), was usually used. As aircrafts may experience interference from different ground beacons at the same time, we designate the beacons as 1, 2,..., I. The DME radio frequency signal as interference can be defined as follows [15]:  
\[j(t)=\sum_{i=1}^{I}\sqrt{P_{r}^{i}}S_{p}^{i}(t)\cos(2\pi f_{i}t+\phi_{i}, \tag{2}\]where \(S_{p}^{i}(t)\) denotes the complete baseband signal transmitted by the i-th DME beacon; and \(P_{r}^{i}\), \(f_{i}\), and \(\phi_{0}\) denote the peak power, carrier frequency, and carrier phase of the i-th DME signal, respectively.  
If only the influence of DME interference is considered, the signal \(r(t)\) received by the BDS receiver antenna can be expressed by  
\[r(t)=s(t)+j(t)+n(t, \tag{3}\]  
where \(s(t)\) denotes the BeiDou signal, and \(n(t)\) denotes white noise.
# On the Existence of Solutions of Nonlinear Fredholm Integral Equations from Kantorovich's Technique
## 2 [PERSON]'s Technique  
As mentioned in the introduction, this paper has two main aims: to obtain conclusions about the existence and uniqueness of a solution of (1) by using the theoretical significance of [PERSON]'s method and to numerically approximate a solution of (1).  
It is clear that solving (1) is equivalent to solving the equation \(\mathfrak{F}(x)=0\), where \(\mathfrak{F}:\Omega\subseteq\mathcal{C}[a,b]\to\mathcal{C}[a,b]\),  
\[[\mathfrak{F}(x)](s)=x(s)-f(s)-\omega\int_{a}^{b}\mathfrak{F}(s,t)x(t)^{p}dt,\quad s\in[a,b], \tag{2}\]  
where  
\[\Omega=\left\{\begin{array}{l}\{x(s)\in\mathcal{C}[a,b]:x(s)\geq 0\}\text{ if }p= \frac{q_{1}}{q_{2}}\text{ with }q_{1},q_{2}\in\mathbb{N}\text{ and }q_{2}\text{ even},\\ \{x(s)\in\mathcal{C}[a,b]:x(s)>0\}\text{ if }p\in\mathbb{R}\setminus\mathbb{Q}. \\ \mathcal{C}[a,b]\text{ for any other value of }p.\end{array}\right.\]  
For solving equation \(\mathfrak{F}(x)=0\), [PERSON]'s method is  
\[x_{n}=x_{n-1}-[\mathfrak{F}^{\prime}(x_{n-1})]^{-1}\mathfrak{F}(x_{n-1}), \quad n\in\mathbb{N},\quad\text{with }x_{0}\text{ given in }\Omega.\]The method has already been applied to approximate solutions of nonlinear integral equations [16; 17]. However, the novelty of this work is in using [PERSON]'s technique to obtain a convergence result for [PERSON]'s method when it is applied to solve (1) and, as a consequence, us the theoretical significance of the method to draw conclusions about the existence and uniqueness of a solution of (1) and about the region in which it is located, without finding the solution itself--this is sometimes more important than the actual knowledge of the solution. A solution is found by constructing a scalar function ad hoc which is used to define a majorizing sequence instead of using the classical quadratic polynomial of Kantorovich.  
[PERSON]'s technique consists of translating the problem of solving equation \(\mathfrak{F}(x)=0\) in \(\Omega\) to solve a scalar equation \(\varphi(t)=0\) and this is done once \(x_{0}\in\Omega\) is fixed under certain conditions. In addition, the domains of existence and uniqueness of a solution for Equation (1) can be determined from the positive solutions of \(\varphi(t)=0\).  
The idea of [PERSON]'s technique is easy: once a real number \(t_{0}\) is fixed, we define the scalar iterative method  
\[t_{n}=N_{\varphi}(t_{n-1})=t_{n-1}-\frac{\varphi(t_{n-1})}{\varphi^{\prime}(t _{n-1})},\quad n\in\mathbb{N}, \tag{3}\]  
such that  
\[\|x_{n}-x_{n-1}\|\leq t_{n}-t_{n-1},\quad\text{for all}\quad n\in\mathbb{N}. \tag{4}\]  
Condition (4) means that the scalar sequence \(\{t_{n}\}\)_majorizes_ the sequence \(\{x_{n}\}\) or, in other words, \(\{t_{n}\}\) is a _majorizing sequence_ of \(\{x_{n}\}\). Obviously, if \(\{t_{n}\}\) is convergent, \(\{x_{n}\}\) also is. Therefore, the convergence of the sequence \(\{x_{n}\}\) is a consequence of the convergence of the sequence \(\{t_{n}\}\) and the latter problem is much easier than the former one.
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Dataset Selection
#### 3.1.1 Lung CT Scan Dataset [81]  
The lung CT scan dataset consists of a total of 267 medical images. These images are representative of lung CT scans--a common modality used in pulmonary imaging. Lung CT scans provide high-resolution cross-sectional views of the chest, making them essential for diagnosing various respiratory conditions and diseases (Figure 2).  
Figure 1: Overview of the proposed PSO-HE approach for enhanced medical image segmentation.  
To ensure comprehensive evaluation of our method's performance, we purposefully chose datasets with unique characteristics and challenges, such as the lung CT scan dataset. This dataset primarily comprises high-resolution CT scan images of the lungs, which are known for their intricate anatomical structures, including lung parenchyma, blood vessels, and airways. Challenges encountered within this dataset encompass the identification of subtle abnormalities, the management of variations in lung density, and the requirement for precise segmentation of lung regions.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 2 Datasets and Methods
### Method
#### 2.2.2 Convolution-Transformer Adaptive Fusion Kernel  
The artificially specified CNN-Transformer hybrid strategy has poor generalization ability and is difficult to satisfy the recognition requirements of fine-grained objects in complex scenes. To overcome this challenge, a feature extraction unit called the convolution-Transformer adaptive fusion kernel (CTAFK) was designed, as shown in Figure 5b.  
CTAFK captures the local high-frequency features using a convolution block (Conv block) and extracts the global and sequential low-frequency information using a Transformer block (Trans block). Afterwards, the local high-frequency and global low-frequency features are adaptively weighted and fused to provide a more generalized and discriminative representation of the HSI data. The details of Conv block, Trans block, and CTAFK workflow are shown, as follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Train** & **Test** \\ \hline Acer negundo & 140,953 & 84,694 \\ Willow & 119,010 & 61,756 \\ Elm & 12,523 & 2830 \\ Paddy & 342,682 & 109,462 \\ Sophora japonica & 305,611 & 169,980 \\ Frazinus chinensis & 123,224 & 46,118 \\ Goldenrain tree & 19,126 & 4178 \\ Waters & 122,790 & 42,857 \\ Bare ground & 27,526 & 10,883 \\ Stubble & 134,439 & 59,391 \\ Corn & 43,796 & 15,369 \\ Pyrus & 764,745 & 261,768 \\ Soybean & 6682 & 469 \\ Poplar & 62,489 & 28,583 \\ Vegetable field & 20,822 & 8326 \\ Grass & 315,130 & 106,660 \\ Peach & 43,220 & 22,294 \\ Building & 18,452 & 11,164 \\ \hline Total & 2623,220 & 1,046,782 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of samples in train and test sets for the Xiongan datasets.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis  
Numerous classification algorithms are used in the related works for the HAR and classification of these activities; some of the well-known ones are SVM, KNN, and random forest [3; 11; 12; 42; 43]. In previous sections, CSI data were preconditioned, denoised, and a feature matrix was prepared to classify the activities. This section covers the implemented machine learning and neural networks algorithms used for the human activities classification. There are several algorithm tuning parameters used for optimizing machine learning and neural networks. The achieved results of classifications are also discussed.
# Automatic Segmentation of Ethnomusicological Field Recordings
## 2 Materials and Methods  
The segmentation algorithm presented in this paper was designed to robustly label and segment ethnomusicological field recordings into consistent units, such as speech, sung, and instrumental parts. Resulting segmentations should be comparable to manual segmentations researchers make when studying recordings. Field recordings are documents of entire recording sessions and typically contain interviews with performers intertwined with actual performances. As these are live recordings of amateur folk musicians, they usually contain lots of \"noise\" and interruptions, such as silence when performers momentarily forget parts of songs, false starts and restarts, dancing noises, interruptions by other persons, or cars driving by. Performances may also change character; singing may become reciting, a second voice may join or drop out of a performance, etc.  
The described nature of field recordings calls for a robust segmentation algorithm that would not over-segment a recording at each interruption--for example, we are not interested in each boundary separating speech and sung parts, as only some of them are actual segment boundaries. We would also like to distinguish between several different classes of segments and would like to take some prior knowledge of the classes into account. Last, we are not interested in millisecond-exact segment boundaries or exact labeling of each small recording fragment; sometimes placing a boundary between two performances is a very soft decision and accuracy of a few seconds is good enough. Taking these points into account, we propose a three step approach to segmentation:  
* First, a deep neural network is used to classify short audio segments into a set of predefined classes;
* then, a set of candidate segment boundaries is obtained by observing how the energy and class distribution change within the recording; and
* finally, the recording is segmented with a probabilistic model that maximizes the posterior probability of segments given the set of candidate segment boundaries with their probabilities and prior knowledge of lengths of segments belonging to different classes.