esa-sceva/satcom-chunk-collection huggingface dataset:
# Mass Spectrometry: A Rosetta Stone to Learn How Fungi Interact and Talk
## 2 Fungi and Their Specialized Metabolites  
### Taxonomical Fungal Groups and Their Specialized Metabolites  
#### 2.1.1 Lower Fungi  
Lower fungi are an unnatural group that comprises diverse phyla of basal fungi [16]. Two unnatural groups are found here: zoosporic and zygomyectous fungi. Zoosporic fungi are the most basal ones and are characterized by producing zoospores that bear a single flagellum, so they are able to move, whereas zygomyectous fungi are characterized by developing a zygospore by fusion of specialized hyphae during sexual reproduction and the absence of septate hyphae [17]. Zoosporic fungi embrace diverse phyla such as Chytridiomycota, Blastocladiomycota, Neocallimastigomycota and the Opishtsoporidia group, whereas zygomyectous fungi embrace the phyla Glomeromycota, Mucoromycota and Zoopagomycota [16; 18]. Lower fungi are highly diverse in regard to their nutrition and lifestyle from heterotrophs and saprobes to symbiotes and parasites, and they can be found in terrestrial and aquatic environments [16; 19; 20]. Members of lower fungi are not well represented in mycological studies compared with those of higher fungi, and they have been believed to be incapable of producing specialized metabolites [21] because some previously reported compounds have been found out to be actually produced by endosymbiotic bacteria (e.g., rhizoxins and rhizins; [22; 23]) and some screened lower fungi did not have enzyme-encoding genes, which are key in specialized metabolites production (e.g., polyketide synthases, nonribosomal peptides synthetases, tryptophan synthases, and dimethylallyl tryptophan synthase; [24]). However, recent studies involving clusters of orthologous groups of proteins would indicate that zoosporic and zygomycetes fungi produce specialized metabolites, but their capacity is underdeveloped compared with higher fungi [10]. In fact, sterol compounds [25; 26] have been recorded for them and zygomycetes fungi are known for their diversity of carotenoids [21; 27]. Moreover, the identification of VOCs in some _Mucor_ species (phylum Mucoromycota) also confirmed that they release volatile compounds [28; 29; 30]. Therefore, it is likely that many other lower fungi produce some specialized metabolites, and in particular VOCs, but they have not been examined or the appropriate sampling technique has not been used (e.g., extracts are not optimal for volatile collection, but instead headspace sampling; see below).
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 1 Introduction
To this end, this work initially examines the design and architecture of WESCS in wind energy, followed by an exploration of ML, particularly focusing on three regression models and their capabilities. The rest of this paper is organized as follows: In Section 2, the incorporation of IoT in the wind energy sector is analyzed, and in particular the composition of WESC, the cyber-physical integration of a wind turbine, the SCADA systems, and machine to machine (M2M) for internet of everything (IoE)-enabled wind farms. Section 3 is focused on ML in wind energy prediction. To this end, the data and preprocessing steps are described, with an emphasis on regression analysis. In the same context, Linear, Random Forest, and Lasso models are then analyzed, with their basic characteristics and results on various key performance indicators (KPIs), including cross-validation results. In Section 4, KPIs' performance is presented on the test set and cross-validation, concluding with the complexity and interpretability of the models. Finally, concluding remarks are outlined in Section 5.
## 1 Introduction
In this paper, we report recent advances achieved by the EU H2020-funded consortium entitled Large Scale Silicon Photonics Matrix for Low-Power and Low-Cost Data Centers (L3 MATRIX) [11], which was comprised of leading companies and universities in the field of SiP. L3 MATRIX focused on the development of a 2-dimensional 4 \(\times\) 16, 1.6 Tb/s (64 \(\times\) 25 Gb/s) silicon photonic high-radix transmitter matrix and co-packaging with a switch ASIC to mitigate the on-board bandwidth/distance product limitations of traditional SerDes. The footprint of each individual cell size encompassing the modulators, lasers, rib/strip transitions and grating couplers is 1000 \(\upmu\)m \(\times\) 1750 \(\upmu\)m. By using a 1-cm\({}^{2}\) 1.6 Tb/s silicon PIC interposer flip-chipped onto the ASIC chiplet (1.6 Tb/s/cm\({}^{2}\)), L3 MATRIX aims to propose a potential compact solution to feed 25.6 Tb/s Switching ASIC. The discussed technology has been developed, having in mind two strict requirements. First, in such a tight space, it is critical that each individual component releases as little heat as possible; therefore, low-voltage and low-current operation has been set as a high priority, together with low-cost solutions in order to accommodate future DC priorities. Most importantly, all the parts of this technology, as will be discussed through this paper, are being developed without recurring to CMOS-incompatible materials and utilizing only processes that are compatible with the AMS AG foundry standards in order to allow for low-cost mass-production when the technology reaches the required maturity. The paper is structured as follows: In Section 2, we propose a new system architecture and analyze it. We also introduce the mask design and layout tools that we employed in the following sections. Section 3 reports our advances in SiP fabrication for this application and relates them to key performance improvements. Section 4 discusses our achievements with respect to integrated laser sources, in which we have worked with both quantum well (QWell) and quantum dot (QD) based active media. Improved modulators of both conventional and slow-light design are analyzed in Section 5, while the 3D system integration, chiplet assembly and matrix vertical fiber coupling are discussed in Section 6. Section 7 discusses the future perspective and open questions arising from our main results, and Section 8 concludes the paper. We emphasize that all steps that will be presented have been designed for high-volume manufacturing and tested for compatibility with existing industrial production lines. This is particularly important for the problems that are addressed, as very often, lab demonstrations may exhibit impressive individual performances, but it is impossible to scale them up for production.
## 5 Implementation Details of Curve Arithmetic
### Endomorphisms  
In [25], the formulas for the endomorphisms \(\phi\) and \(\psi\) are described. To reduce the number of representation conversions, we represent the results of endomorphism operations using extensible coordinates. Let \(P=(X_{1},Y_{1},Z_{1})\) be a point in curve Ted127-glv4 represented by homogeneous projective coordinates. Then, \(\phi(P)=(X_{2},Y_{2},Z_{2},T_{a},T_{b})\), where \(T=T_{a}\cdot T_{b}\) can be computed as follows:  
\[X_{2} =-X_{1}(\alpha Y_{1}^{2}+\theta Z_{1}^{2})(\sigma Y_{1}^{2}-\beta Z _{1}^{2}),\] \[Y_{2} =2Y_{1}Z_{1}^{2}(\beta Y_{1}^{2}+\gamma Z_{1}^{2}),\] \[Z_{2} =2Y_{1}Z_{1}^{2}(\sigma Y_{1}^{2}-\beta Z_{1}^{2}),\] \[T_{a} =-X_{1}(\alpha Y_{1}^{2}+\theta Z_{1}^{2}),\] \[T_{b} -(\beta Y_{1}^{2}+\gamma Z_{1}^{2}),\]  
where \(\alpha=\zeta_{8}^{3}+2\zeta_{8}^{2}+\zeta_{8},\theta=\zeta_{8}^{3}-2\zeta_{8} ^{2}+\zeta_{8},\sigma=2\zeta_{8}^{3}+\zeta_{8}^{2}-1,\gamma=2\zeta_{8}^{3}- \zeta_{8}^{2}+1\) and \(\beta=\zeta_{8}^{2}-1\). We also utilize the fixed values for curve Ted127-glv4 as follows:  
\[\zeta_{8} =1+Ai, \sigma =(A-1)+(A+1)i, \theta =A+Bi,\] \[\alpha =A+2i, \gamma =(A+1)+(A-1)i, \beta =B+1+i,\]  
where \(A=143485135153817520976780139629062568752\) and \(B=1701411834604692317316873037158840\) 99729. The endomorphism \(\phi\) can be computed by using \(11M_{2}+2S_{2}+5A_{2}\) or \(7M_{2}+1S_{2}+5A_{2}\) operations in the case \(Z_{1}=1\).  
Similarly, \(\psi(P)=(X_{2},Y_{2},Z_{2},T_{a},T_{b})\), where \(T_{2}=T_{a}\cdot T_{b}\) can be computed as follows:  
\[X_{2} =\zeta_{8}X_{1}^{P}Y_{1}^{P},\quad Y_{2}=(Z_{1}^{P})^{2},\quad Z_{2}=Y_{1} ^{P}Z_{1}^{P},\] \[T_{a} =\zeta_{8}X_{1}^{P},\quad T_{b}=Z_{1}^{P}.\]  
The endomorphism \(\psi\) can be computed using \(3M_{2}+1S_{2}+1.5A_{2}\) or \(2M_{2}+1A_{2}\) operations in the case \(Z_{1}=1\). Because the endomorphism \(\psi\) requires fewer operations than the endomorphism \(\phi\), \(\psi(\phi(P))\) can be computed on the order of \(\phi(P)\) with \(Z_{1}=1\) and \(\psi(\phi(P))\).
## 1 Introduction  
Obstructive sleep apnea (OSA) in adults is a condition with a high public health impact [1]. OSA is characterized by recurring episodes of cessation (apnea) or reduction (hypopnea) in airflow during sleep caused by obstruction of the upper airway [2]. OSA affects 4% to 6% of middle-aged men and 2% to 4% of middle-aged women and its frequency increases with age [3]. People at higher risk of developing OSA include those with enlarged tonsils and/or adenoids, a family history of OSA, excessive weight/obesity, actively smoking and jaw problems, such as micronathia or retrognathia [4].  
The sleep arousal caused by OSA induces excessive daytime sleepiness (EDS) [5], increases the occurrence of driving accidents [6], induces reductions in concentration in both adults and children [7] and induces sleep fragmentation, which in some cases may induce depression [8]. Moreover, the intermittent hypoxia caused by the apnea/hypopnea events initiates a sequence of systemic alterations, including activation of the sympathetic nervous system, systemic inflammation, oxidative stress, endothelial dysfunction and atherosclerosis [9; 10; 11; 12]. As a consequence, OSA seems to determine cardiovascular comorbidities, such as alterations in heart rate (HR), systemic hypertension, myocardial infarction, cardiac arrhythmias and stroke [13; 14; 15]. HR variability, in particular, is a clinical sign measured easily and non-invasively but it has been demonstrated to be directly associated with survival (higher HR indicates lower survival) in the general population and in patients with impaired cardiovascular function [16; 17]. In fact, an increase in HR by 10 beats per minute has been associated with a 20% increase in the risk of cardiac death, which is comparable to the increased risk correlated to an increase of systolic blood pressure by 10 mm Hg [18].  
To reduce the number of apnea/hypopnea events and contrast the onset of other comorbidities, continuous positive airway pressure (CPAP) is the most common treatment of patients with OSA [19]. Other suggested treatments are surgery [20] or the use of a mandibular advancement device (MAD) [21]. In 1995, The American Academy of Sleep Medicine (AASM) stated that a MAD was indicated as a first-line therapy for mild OSA and a second-line therapy for moderate to severe OSA [22]. However, the studies that investigated the effectiveness of MAD therapy in improving cardiovascular parameters compared to CPAP reported contradictory findings [23; 24].  
Therefore, the aim of the present retrospective cohort study was to evaluate the effects of MAD therapy with a particular fully customizable appliance on respiratory and cardiovascular parameters in an adult population with OSA.
## 2 Materials and Methods
### Evaluation Methods
#### 2.3.3 Positive Matrix Factorization Method  
PMF is an improved factor analysis method proposed by [PERSON]. This method has been successfully applied to the source apportionment of environmental pollutants [36]. In the PMF model, the sample concentration data matrix (\(X\)) is decomposed into two factor matrices (the source contribution matrix (\(G\)) and the source profile matrix (\(F\))), as well as a residual matrix \(E\). According to the USEPA PMF 5.0 user's guide, it is expressed as follows [37]:  
\[x_{ij}=\sum_{i=1}^{n}g_{ik}f_{kj}+e_{ij} \tag{4}\]  
where \(x_{ij}\) represents the concentration of the \(j\) element in the \(i\) sample; \(g_{ik}\) denotes the contribution of source \(k\) to the \(i\) sample; \(f_{kj}\) is the concentration of the \(j\) element in source  
\begin{table}
\begin{tabular}{c c c c c} \hline \(I_{geo}\) **Category** & **Description** & \(E_{r}^{i}\) **Category** & \(RI\) **Category** & **Description** \\ \hline \(\leq\)0 & None & \(<\)40 & \(<\)95 & Low \\ (0, 1] & Light–moderate & [40, 80] & [95, 190] & Moderate \\ (1, 2] & Moderate & [80, 160] & [190, 380] & Strong \\ (2, 3] & Moderate–strong & [160, 320] & \(\geq\)380 & Very strong \\ (3, 4] & Strong & \(\geq\)320 & & Extremely strong \\ (4, 5] & Strong–extremely & & & \\  & Strong & & & \\ \(>\)5 & Extremely strong & & & \\ \hline \end{tabular}
\end{table}
Table 1: The classification of indexes.  
\(e_{ij}\) is the residual matrix, that is, the part of the sample concentration matrix \(x_{ij}\) that is not explained by the PMF model. The PMF method is based on least squares for constraints and iterative calculations to obtain the optimal matrices \(G\) and \(F\) by minimizing the objective function \(Q\), which is expressed as follows:  
\[Q=\sum_{i=1}^{n}\sum_{j=1}^{m}\left(\frac{e_{ij}}{u_{ij}}\right)^{2} \tag{5}\]  
where \(u_{ij}\) represents the uncertainty in the concentration of the \(j\) element in the \(i\) sample.
## 6 Discussion  
In this section, we compare the computational complexity of each algorithm. We compare the total calculations by the number of times that the multiplication and addition operations were used. We assume that there are \(N\) UEs with \(G\) generations and the number of population sizes is \(M\). Thus, for the complexity of one-generation PSO with an object function of (13), \(4N\times M\) multiplication operations and addition operations are needed. In updating the local best of pbest and the global best of gbest, in the objective function (15), it requires \(117N\times M\) multiplication operations and \((26N+38)\times N\times M\) addition operations. Moreover, \((3M-1)\) addition operations are required for the loop operation. Thus, the total computational complexity of applied PSO can be obtained by \(((26N+42)\times N+3)\times M\times G\) addition operations and \(121N\times M\times G\) multiplication operations.  
Similarly, the complexity of one-generation of GA can be found. For the complexity of objective function for all genes, you need \(117N\times M\) multiplication operations and \((26N+38)\times N\times M\) addition  
Figure 8: The comparisons of (**a**) system capacity and (**b**) throughput for different weighting \(\alpha\).  
operations. For the selecting operation, an addition operation is required. With the crossover operation, 0.9 (crossover rate) \(\times\)\(M\) addition operation is required. With the mutation operation, 0.07 (mutation rate) \(\times\)\(M\) addition operation is required. Thus, the total computational complexity for applied GA is \(117N\times M\times G\) multiplication operations and \((0.97(26N+38)\times N)\times M\times G\) addition operations.  
According to the above analysis, the computational complexity of the two applied algorithms mainly depends on the execution number of the objective function. When the execution number of the objective functions is the same, the computational complexity of the two algorithms can be considered to be nearly the same. Compared to the comprehensive search method, it is necessary to perform \(13^{N}\times\) (\(26N+38\)) addition and \(13^{N}\times\) 117 multiplication operations to find the optimal solution. The computational complexity of these two applied algorithms is far less than the comprehensive search method but a sub-optimal solution can be obtained.
## 3 Three-Dimensional Finite Element Model of Composite Foundations
### Stress Distribution  
In order to investigate the stress diffusion patterns of friction piles and end-bearing piles, stress values for the central pile body and the soil between the piles were derived from the results of each condition. Stress distribution curves along the depth direction of the soil were plotted. Taking a pile length of 20 m as an example, Figure 7 shows the stress distribution of pile bodies and soil under an upper load action, while Figure 8 depicts the stress distribution of the two types of piles when the soil modulus was taken as 50 MPa or 100 MPa.  
From Figure 7, it can be observed that both friction piles and end-bearing piles exhibited three distinct stages in the vertical stress of the central pile and the vertical stress of the soil between the piles along the depth direction.  
Specifically, in the first stage, the additional vertical stress of the pile body sharply increased within the depth range of 0 to 0.25 \(L\). In the second stage, within the depth range of 0.25 \(L\) to 0.9 \(L\), there was a gradual decrease. Compared to the friction piles, due to the effect of end-bearing, the vertical stress of the end-bearing piles was 30% to 80% greater, and the decreasing trend of the pile stress was slower. In the third stage, from 0.9 \(L\) to 1 \(L\), the pile stress decreased sharply until it approached the stress value at the pile top. The stress diffusion pattern in the soil was completely opposite to that of the pile body. Additionally, under the same conditions, the vertical stress of the pile body increased with an increase in the elastic modulus of the pile body. This indicates that the higher the stiffness of the pile, the greater the load it can bear.  
Figure 6: Isoline of initial geostress field/Pa: (**a**) vertical stress; (**b**) horizontal stress.  
Figure 7: Stress distribution in piles and soil under upper load: (**a**) pile stress; (**b**) soil stress.  
From Figure 8, it is evident that the smaller the elastic modulus of the soil, the greater the additional stress on the pile body, indicating a higher load-bearing capacity of the pile. When the elastic modulus of the pile decreased, the change in the pile body stress due to variations in the soil modulus became more pronounced. For instance, when the elastic modulus of the pile body was uniformly 5 GPa, the interval between the stress curves of the pile body was most distinct when the soil modulus increased from 50 MPa to 100 MPa.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach
### Pre-Processing  
It is firstly preprocessed by zero averaging before the wavelet decomposition of the signal. The purpose is to remove the DC component in the signal and make the amplitude of the zero-frequency signal very small. In the subsequent power spectrum analysis, the influence of DC energy on the spectrum analysis of low and intermediate frequency signals can be reduced. The zero averaging formula is given as follows:  
\[X=X-mean(X) \tag{1}\]  
where \(X\) is the original input signal, \(mean(X)\) represents the mean of \(X\).  
The algorithm defaults a tolerance value of 3. Tolerance value is an empericial value, which is adopted to determine the maximum boundary value of the energy change points. For example, if the tolerence value is 3, the position value of the energy change point cannot exceed 3. That is, if the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ECG Signals**} & \multicolumn{5}{c}{**SNR**} & \multicolumn{5}{c}{**MSE**} \\ \cline{2-9}  & **db5** & **sym5** & **haar** & **coif5** & **db5** & **sym5** & **haar** & **coif5** \\ \hline
100 & 22.87 & 21.38 & 19.53 & 20.82 & 0.00015 & 0.00021 & 0.00033 & 0.00024 \\
101 & 23.70 & 23.63 & 19.30 & 21.00 & 0.00018 & 0.00018 & 0.00050 &  
minimum point of energy change occurs at the fourth position, the coefficients of the forward three decompositions are only considered.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design
### Hybrid Analog-Digital Equalizer
#### 4.3.1 Optimization Problem  
A general optimization problem for a hybrid receiver can be formulated as  
\[\begin{array}{ll}\left(\mathbf{W}_{a},\mathbf{W}_{d,l}\right)&=\text{arg min BER}\\ \text{s.t.}&\sum\limits_{l=0}^{NM-1}\text{diag}(\mathbf{W}_{d,l}(\mathbf{W}_{a })^{H}\mathbf{H}_{l})=N\text{M}\Pi_{U}\\ &\mathbf{W}_{a}\in\mathcal{W}_{a}\,,\end{array} \tag{26}\]  
where \(\mathcal{W}_{a}\) denotes the set of feasible analog coefficients. As shown in Appendix A, for the particular case wherein a maximal ratio combining (MRC) approach is adopted for the analog equalizer, we can simplify Equation (26) to  
\[\begin{array}{ll}\mathbf{W}_{a}=&\text{arg min }\sum\limits_{l=0}^{NM-1} \left\|\mathbf{W}_{d,l}(\mathbf{W}_{a})^{H}-\mathbf{H}_{l}^{H}\right\|_{F}^{2 }\\ &\text{s.t.}\sum\limits_{l=0}^{NM-1}\text{diag}(\mathbf{W}_{d,l}(\mathbf{W}_{a })^{H}\mathbf{H}_{l})=N\text{M}\Pi_{U}\\ &\mathbf{W}_{a}\in\mathcal{W}_{a}\,,\end{array} \tag{27}\]  
where its solution gives us the analog equalizer matrix.
## 2 Related Work
### Traffic-Based Methods  
To identify devices, some researchers have collected and analyzed traffic data. [PERSON] et al. [10] used machine learning to distinguish the types of smart devices. The fingerprint tS is represented by n data packets and 23 features (such as packet length, port number, and protocol used by the packet) as binary features, which can achieve high accuracy. [PERSON] et al. [12] designed a port scanning strategy that combines multiple weak classifiers into multiple classifiers. Each classifier is responsible for analyzing specific port data, which greatly shortens the cycle of device identification and increases the identification accuracy by 46.67%. [PERSON] et al. [11] used Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) to extract and construct the characteristic fingerprints of HTTP and TCP cross-layer data packets to achieve high-precision and fine-grained IoT device identification. In [13; 23; 24; 25; 26; 27; 28; 29; 30], an inspection of data packets was used to extract device features.  
The authors of [10; 31; 32] proposed mechanisms for analyzing encrypted traffic. The mechanism proposed in IoT Sentinel [10] uses a flow attribute vector of 276 dimensions (12 groups \(\times\) 23 features), which increases the excessively high computational cost [33]. The mechanism proposed in [31] requires 49 traffic attributes and 30,000 frames to identify the device, and it takes a long time to capture the traffic.  
Although the automation of device identification is improved when applying machine learning to analyze network traffic, the device model can not be identified (coarse granularity).