esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry
## 2 Materials and Methods
### Multichannel Singular Spectrum Analysis (MSSA)
#### 2.2.3 Reconstruction  
The singular triples are located in matrix form. The reconstruction stage consists of transforming the singular triples into a new time series of length \(N\). For this, diagonal averaging is applied to the singular triples, which approximates the elements of the matrix along the diagonals. In the MSSA, the singular triples are in grouped form, which should be partitioned into matrices of the size corresponding to the time series sizes. At the horizontal method of grouping the trajectory into \(L\times K_{p}\), at the vertical method--into \(L\times K\). Then, for each obtained matrix, diagonal averaging is performed according to Equation (4), which converts it into a time series of length \(N_{p}\).  
\[\widetilde{f}_{k}=\left\{\begin{array}{l}\frac{1}{k+1}\sum\limits_{j=1}^{k+ 1}x_{j,k-j+2}\ \text{where}\ 0\leq k<L^{*}-1,\\ \frac{1}{L^{*}}\sum\limits_{j=1}^{L^{*}}x_{j,k-j+2}\ \text{where}\ L^{*}-1\leq k<K^{*},\\ \frac{1}{N-k}\sum\limits_{j=k-K^{*}+2}^{N-K^{*}+1}x_{j,k-j+2}\ \text{where}\ K^{*}\leq k<N, \end{array}\right. \tag{4}\]  
where \(L^{*}=\min(L,K)\) and \(K^{*}=\max(L,K)\).  
Thus, the outcome of the MSSA for a univariate series (when s = 1) and for a multivariate time series is its representation as a sum of time series, which are called components of the spectral singular value decomposition (Equation (5)).  
\[\widetilde{F^{(p)}}=\widetilde{F_{1}^{(p)}}+\cdots+\widetilde{F_{d-1}^{(p)}}, \tag{5}\]  
where \(d\) is the rank of the trajectory matrix \(X\).
## 4 Overview of Research Projects on AI/ML for Communications and Networking
### Europe--H2020 Research Framework
#### 4.1.1 Arialadne  
Artificial intelligence aided D-band network for 5G long-term evolution (ARIADNE) is a three-year research and innovation project that started in November 2019 and is funded with EUR 6 million under the EU framework, Horizon 2020 (H2020). The overarching goal of the project is to investigate techniques and technologies that can efficiently support high-bandwidth communications in the D-band (130-174.8 GHz). This band is a promising spectrum candidate for implementing the fronthaul and backhaul of small cell networks operating in mm-wave frequencies and for enabling very high data rates over short-range connections in the RAN [114]. Three use cases and six corresponding scenarios are selected by the consortium and are summarised in Table 5 below [115]. Concerning the selected scenarios, the project sets the following KPI targets: an aggregate throughput (or an E2E throughput where applicable) of 100 Gbps, nearly 100% connectivity reliability, and 10 times energy efficiency improvement in the RAN, as compared to public 5G RAN deployments.  
The opportunities brought forward by AI/ML to achieve the objectives of ARIADNE are vast. For instance, in the D-band, highly directional antennas should be used to overcome the adverse signal propagation conditions. However, with very narrow beams, the capacity of a long-range (<500 m) backhaul link becomes sensitive, even to small beam misalignments (due to winds or other physical phenomena), calling for continuous channel estimation to correct the beam steering. In short-range scenarios (10-30 m) involving mobility, accurate user localization for beam tracking is required. Therefore, ML techniques for real-time channel estimation and prediction (see Table 1), and ML-based mobility and beamforming prediction become relevant [116]. For NLoS environments, implementing highly reliable and high data rate backhaul and fronthaul with RIS imposes many challenges, especially on the optimal design of phase shifts (see Table 1). While the focus of ARIADNE is the real-time RIC (timescales of less than 10 ms), several near-real-time closed-control functions, such as load prediction at the backhaul, power control, and user scheduling based on AI/ML are also investigated. Finally, channel modelling in the largely unexplored D-band, including, for example, models for the link gain and the channel impulse response under various weather conditions and user mobility, is another potential area for applying AI/ML techniques.
## 3 Results
### Power Optimization of System under Inertial Load
#### 3.1.2 Anti-Resonance Solution  
Figure 8 shows the values for \(\gamma_{opt\_AR}\), as a function of \(k\) and \(\xi_{E}\), at \(\eta=\{5,\,1\}\). For \(k\gg 1\), the value of \(\gamma_{opt\_AR}\) tends towards \(\sqrt{1+\eta}\). Contrary to the resonant case, \(\gamma_{opt\_AR}\) has a significant dependence of \(\xi_{E}\) even at large values of \(Q\). As stated earlier, there is no valid solution for \(\gamma_{opt\_AR}\) at \(Q<k_{crit}Q_{crit}\). For \(k=1\) and \(\xi_{E}=0\), we have \(\gamma_{opt\_AR}=\sqrt{1+1/Q_{crit}}\), i.e., the resonance and anti-resonance peaks merge at \(k=1\), and there is a single global optimum for \(\gamma\).  
Figure 8: Normalized angular excitation frequency at anti-resonance and power optimal load. \(\eta=\{5,\,1\}\), from left to right. The color scale is linear and independent for each plot. Y axis is identical for both plots but only specified in the left plot.  
Figure 6: Power-optimum normalized load resistance, at resonant excitation frequency. \(\eta=\{5,\,0.1\}\) and \(\xi_{E}=\{0,\,0.1\}\).  
Figure 7: Dimensionless power at optimal load and resonant excitation frequency. \(\eta=\{5,\,1,\,0.1\}\), from left to right. The color scale is linear and independent for each plot. Y axis is identical for each plot but only specified in the left plot.  
Figure 9 shows \(\xi_{C\_opt\_AR}\) as a function of \(k\) and \(\xi_{E}\). Contrary to the resonant case, \(\xi_{C\_opt\_AR}\) has an overall nonlinear relationship to both \(k\) and \(\xi_{E}\) and decreases with increasing \(k\). At \(k=1\) and \(\xi_{E}=0\), we have \(\xi_{C\_opt\_AR}=(Q_{crit}+1)/Q_{crit}\).  
Figure 10 shows the power at anti-resonance with optimal load as a function \(k\) and \(\xi_{E}\). Again, the maximum power over the load resistance is a factor with \(1/8\) of the reference power for \(\xi_{E}=0\). Compared to the resonant case, we can see that there is a larger decrease in \(\overline{P}_{IL}\) at increasing \(\xi_{E}\). Contrary to the resonant case, as \(k\) is increased, \(\xi_{E}\) has a larger negative effect on the output power.
# Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
## 3 Sloshing Frequency
### Results  
The first sloshing frequency for a rectangular tank is given by the formulation below [28]:  
\[\omega^{2}=\sqrt{\frac{5}{2}}\frac{g}{l}tanh\sqrt{\frac{5}{2}}\frac{h}{l} \tag{19}\]  
where \(h\) = height of water in the tank (m), \(l\) = length of the rectangular tank along the direction of excitation (m), and \(g\) = acceleration due to gravity (m/s\({}^{2}\)).  
Four element sizes, namely 10, 7.5, 5, and 3 mm, are considered along with a 50 mm liquid height. The Fast Fourier Transform (FFT) results of sloshing frequency and simulation fluctuation results are then compared for different element sizes to determine the optimal choice. Figure 3 shows the SPH-FEM simulation of sloshing with different element sizes at 2.0 s. Element sizes of 10 and 7.5 mm are eliminated due to the amount of air entrapment as shown in Figure 3, as well as inaccurate FFT results as shown in Table 2. On the other hand, the FFT results of sloshing frequency for an element size of 5 and 3 mm are identical as seen in Table 2. Therefore, 5 mm is chosen to ensure accuracy while maintaining calculation efficiency. The ratio of the liquid element size to the side length of the container base is 5:270.  
The theoretical result of sloshing frequency is obtained by [PERSON]'s formula in Equation (19), and experimental results with the same container size are obtained from [PERSON] et al. [29]. The sloshing frequency results with different liquid heights from the SPH-FEM simulation exhibit good agreement with [PERSON]'s theoretical results and experiment results [29] as shown in Table 3, especially in shallow water. Thus, fluid-structure simulation using the SPH-FEM method is feasible. The liquid element size can be scaled up based on the optimal ratio 5:270 and applied to the large LNG tank to ensure simulation accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Results**} & **Housner’s** & **Experimental** & \multicolumn{2}{c}{**3 mm**} & **5 mm** & **7.5 mm** & **10 mm** \\  & **Result** & **Result** & **SPH-FEM** & **SPH-FEM** & **SPH-FEM** & **SPH-FEM** \\  & & & **Result** & **Result** & **Result** & **Result** \\ \hline value & 1.23 & 1.28 & 1.22 & 1.22 & 1.42 & 1.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sloshing frequency results of different element sizes obtained by the SPH-FEM method compared to theoretical and experimental results.  
Figure 2: Displacement vs. time graph for the new loading condition.
## 3 Methodology
### Objective Functions  
Selection of the objective functions is a significant task to obtain an optimum solution in an optimization problem. It also necessarily affects optimization behavior as well. In this study, two objective functions are considered for optimization as shown in Equations (1) and (2).  
\[min:F_{1}=\sum_{i=1}^{N}a_{i} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Line Number**} & \multicolumn{2}{c}{**Bus Code**} & \multirow{2}{*}{**Length (km)**} & \multirow{2}{*}{**R (pu)**} & \multirow{2}{*}{**X (pu)**} \\ \cline{2-2} \cline{5-6}  & **From** & & & & \\ \hline
2 & 2  
\[min:F_{2}=\sum_{i=1}^{N}{(V_{i,t}-1)^{2}} \tag{2}\]  
where, \(F_{1}\) is the objective function, it represents the total number of installed ATCTr, and \(F_{2}\) is another objective function represents overall voltage deviation of nodes. \(a_{i}\) represents the number of introduced ATCTr at each node \(i\), \(V_{i,t}\) is the voltage deviation on each node \(i\) at time \(t\), \(N\) is the total number of nodes.  
Constraint inequalities are as follows:  
\[V_{min}\leq V_{i}\leq V_{max} \tag{3}\]  
\[T_{min}\leq T_{i}\leq T_{max} \tag{4}\]  
where, \(V_{i}\) is the distribution voltage of node \(i\); \(V_{min}\), \(V_{max}\) are voltage lower and upper limits, respectively. \(T_{i}\) is the tap position of node \(i\); \(T_{min}\), \(T_{max}\) are the tap position lower limit and tap position upper limit, respectively.  
Equality restriction is as follows:  
\[g_{A}:\sum_{t=0}^{q}{x_{t}}=5 \tag{5}\]  
\[g_{B}:\sum_{t=0}^{q}{x_{t}}=10 \tag{6}\]  
\[g_{C}:\sum_{t=0}^{q}{x_{t}}=15 \tag{7}\]  
where \(g_{A}\)-\(g_{C}\) are the constraints of the number of tap change position, and \(x_{t}\) is the tap change position number at time \(t\).
## 3 Materials and Methods
### Models and Indicators of Operational Effectiveness Assessment
The \(\textit{MTBF}\) (_Mate Time Between Failure_) indicator indicates how often a specific technical device fails, i.e., this is the average time between failures, which is calculated as the sum of the values of \(\textit{MTTR}\) and \(\textit{MTTF}\). In practice, it is used to plan the frequency of preventive inspections. \[\textit{MTBF}=\textit{MTTR}+\textit{MTTF},\ h \tag{8}\]The _MTBF_ factor affects both reliability and system availability. Reliability means the ability of the system or component to properly act in certain conditions for a given time, i.e., the likelihood that the system will accomplish its task without failure. However, availability describes the system's readiness to act when needed. This means that the system will be functional and capable of acting at the moment. The _MTBF_ coefficient is a key indicator of the system reliability, usually given in hours. A higher _MTBF_ value means greater reliability of the device or product [48]:  
\[Reliability=e^{-(\frac{\zeta\textit{MTBF}}{\textit{MTBF}})},\% \tag{9}\]  
Like _MTBF_, the _MTTR_ factor is usually expressed in hours. It affects the availability of the system, but it has no direct impact on its reliability. The higher the _MTTR_ value, the less effective and prone to emergencies the system becomes. \[Availability=\frac{MTBF}{(MTBF+MTTR)},\% \tag{10}\]  
For the above equations to be true, a certain assumption should be taken when analyzing the _MTBF_ coefficient. Unlike mechanical systems, most electronic systems do not contain movable components. For this reason, it is assumed that the failure coefficients in electronic systems are constant at all times.
## 4 Future Directions
### Build Energy-Efficient MAC and Routing Protocols to Reduce Energy Consumption in UIoT Networks
#### 4.8.3 Block Multi-Data Transfer in UIoT Routing Mechanism  
UIoT devices are sparsely deployed in UIoT environment, since it is difficult to avoid multiple repetitions of sending and receiving data of UIoT devices. In this case, it is necessary to adopt a multi-data transfer prediction and block mechanism to prevent the wastage of energy and data loss.
## 1 Introduction
In the previous study [27], the author clarified the characteristics of AE signals propagating in the plate with the stiffener and quantified the scattering of AE signals at the stiffener with a different height. It can be learned from these studies on the characteristics of signal propagating in a plate with a stiffener that using scattering coefficients to quantify the scattering characteristics of a signal is an important technical method. The characteristics of AE signals, such as the existence mode, the propagation characteristics, and the evolution mechanism, in a stiffened cylindrical shell and a stiffened plate are different due to their structural differences. The propagation characteristics of the signal in the shell are extremely complicated compared with those of the plate, and therefore, the results obtained from the stiffened thin plate do not hold for cylindrical shells. Hence, it is essential to investigate the characteristics of the AE signal propagating in the cylindrical shell with a stiffener and to summarize the interaction mechanism between the AE signal and the stiffener on the cylindrical shell. It is well known that the AE signal generated by faults consists of different frequencies and multiple modes. It is difficult to acquire a unique characteristic of a certain frequency or mode propagating in the structure due to the complex and confusing waveform if the acquired AE signal is analyzed as a whole. Therefore, from the perspective of the single frequency and the single mode, this paper carries out a fundamental study on the characteristics of the AE signal propagating in the cylindrical shell with a stiffener based on the determined monitoring position according to specific criteria. This study proposes the multipath propagation model about the AE signal propagate in a stiffened cylindrical shell and derives the constraint conditions for monitoring positions to separate the direct signal, stiffener scattering signals, and other signals in the time domain. On this basis, the interplay between the single-mode AE signal and the stiffener on the cylindrical shell is summarized from the numerical simulation results, and the scattering coefficient is employed to characterize the propagation characteristics of the single-mode AE signal at the stiffener with different heights. Then, the numerical simulation is validated by experiments. In this paper, the theory and methodology are discussed in Section 2. Then, the numerical simulations and the experiments are, respectively, dealt with in Sections 3 and 4. Section 5 shows the results and discussions. Finally, the conclusions are provided in Section 6.
## 4 Doppler Frequency Estimation in the Presence of Noise
As before, they can be generalized to \"R\({}_{n}\)-\(\overline{n}\)\" and \"C\({}_{n}\)-\(\overline{n}\)\". In the following two subsections, we discuss two terms of comparison worth to be considered for the frequency estimators proposed so far, firstly a least squares solution and secondly the CRLB on the variance of the estimator. Performance comparisons obtained in simulation are presented in Section 4.3.
###### Abstract  
In astronomy, physics, climate modeling, geoscience, planetary science, and many other disciplines, the mass of data often comes from spherical sampling. Therefore, establishing an efficient and distortion-free representation of spherical data is essential. This paper introduces a novel spherical (global) coordinate system that is free of singularity. Contrary to classical coordinates, such as Cartesian or spherical polar systems, the proposed coordinate system is naturally defined on the spherical surface. The basic idea of this coordinate system originated from the classical planar barycentric coordinates that describe the positions of points on a plane concerning the vertices of a given planar triangle; analogously, spherical area coordinates (SACs) describe the positions of points on a sphere concerning the vertices of a given spherical triangle. In particular, the global coordinate system is obtained by decomposing the globe into several identical triangular regions, constructing local coordinates for each region, and then combining them. Once the SACs have been established, the coordinate isolines form a new class of global grid systems. This kind of grid system has some useful properties: the grid cells exhaustively cover the globe without overlapping and have the same shape, and the grid system has a congruent hierarchical structure and simple relationship with traditional coordinates. These beneficial characteristics are suitable for organizing, representing, and analyzing spatial data.  
spatial grids; spherical framework; area coordinates; spatial data +
Footnote †: journal: Applied Physics Letters
# Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm
## 4 Discussion
This approach reduces biases in keyword selection and improves the accuracy of data categorization by leveraging natural language processing techniques, including TF-IDF vectorization and cosine similarity analysis. By systematically identifying and consolidating semantically similar terms, this method ensures more reliable and consistent results while minimizing the risk of false positives or redundancy. It is also important to note that bibliometric studies are subject to limitations due to the introduction of bias resulting from language restrictions. The exclusion of research published in languages other than English may result in the overlooking of valuable research, thereby limiting the scope of the analysis. It is also the case that citation biases can affect bibliometric analysis. This is because highly cited publications may not always reflect the most creative research, particularly if they are published in journals with a relatively small readership. Furthermore, it can distort the perceived significance of research when self-citations or citations among collaborators, supervisors, or editors are exploited to artificially inflate citation numbers. Additional limitations specific to this study include the exclusive use of WOS as the sole database. Although this choice was methodologically justified to avoid integration difficulties, it potentially excluded relevant publications indexed exclusively in other databases, such as Scopus or specialized repositories. Additionally, the COVID-19 pandemic introduced significant temporal distortions in research and publication patterns in the post-2020 period, which could overrepresent pandemic-related themes in our recent data while simultaneously creating publication delays for other important research. Finally, our analysis primarily captures trends in academic publications and may not adequately reflect important developments in medical waste management occurring in practical contexts that are not widely documented in the specialized literature. Despite these constraints, bibliometric studies remain a valuable instrument for the scientific, academic, and student communities, offering a pragmatic approach to grasping research tendencies and academic performance. As machine learning and artificial intelligence (AI) tools continue to evolve, many of these limitations are likely to be addressed, including those pertaining to language barriers, false positives, and biases in citation analysis. These advancements will enhance the accuracy and depth of bibliometric analyses, improving their ability to assess research quality and trends.
# Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Presurized Hydrogen
## 3 Results and Discussion  
### Yield of Pyrolysis Products  
The yield of pyrolysis bio-oil is an important index to determine the pyrolysis effect. Based on material balance, the experiments were repeated three times, and the average values of the three experiments were used. The error of pyrolysis bio-oil yield was controlled within \(\pm\)2.00%. The yield of pyrolysis bio-oil in the hydrogen atmosphere was 59.23%, which was higher than that (40.88%) in the nitrogen atmosphere. Considering that the volatile free radicals and molecular fragments produced by the thermal decomposition of biomass in a hydrogen atmosphere are stabilized in combination with hydrogen, it easily escapes from the inside of the particles, thus remarkably improving the yield of pyrolysis bio-oil. In addition, in a nitrogen atmosphere, the pyrolysis bio-oil is a dark brown liquid with a unique smoke smell and free fluidity Dulong-Berthelot formula (2010). In a hydrogen atmosphere, the color of pyrolysis bio-oil is dark yellow and clearer because hydrodeoxygenation and hydrogenation occur in biomass pyrolysis under hydrogen.  
The solid product of pyrolysis in the nitrogen atmosphere is 26.02%, while the solid product in the hydrogen atmosphere is 20.41%. In the raw material, the lignin methoxy group of pine wood can be decomposed into small molecular free radicals to further stabilize the macromolecular fragments produced during lignin pyrolysis to prevent them from polymerizing into carbon Dulong-Berthelot formula (2010). In a hydrogen atmosphere, the decomposition of the lignin methoxy group is inhibited from producing a large amount of polymerized carbon. Moreover, the methanation reaction of pyrolytic carbon is promoted in the hydrogen atmosphere to improve the carbon conversion rate. The oxygen in biomass is also removed in the form of water, thus reducing the conversion of carbon into carbon monoxide or carbon dioxide and improving the utilization rate of carbon.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 4 Functionality Comparisons and Security Analysis
### User Data Protection  
In our proposed scheme, we use the sign-cryption method to generate the encryption data with the corresponding signature within. In this time, the signer cannot know what the plain-text is without the corresponding decryption key. Only the receiver is aware of the corresponding decryption key to decrypt this cipher-text. Thus, our sign-cryption scheme could offer privacy protection of the user's personal sensed information.
## 5 Experiments
### Preliminary Exploration and Training Details
#### 5.2.5 Algorithm Hyperparameters and Technology Stack  
All autoencoders of the five different embedding dimensions were trained with an initial learning rate of 0.05 and momentum 0.9. For both the generator and discriminator networks across all embedding dimensions, an initial learning rate of 0.1 and a momentum of 0.5 were employed. The scale factor \(\lambda\) of the generator adversarial loss (Equation (6)) was set to 0.1, and the discriminator was trained once for every five updates of the generator. We utilized mini-batch gradient descent optimization for all training, employing a batch size of 64 for each network update. The entire system was implemented in Python version 3.5.2. Machine-learning-related code was implemented using PyTorch library version 1.1.0. The training processing of all models was run on a SupermicroTMSYS-7048 GR-TR server allocating 160 gigabytes of RAM and two IntelTM Xeon E5-2670 processors at 2.30 GHz. The execution was accelerated using a GeForceTM Titan Xp GPU accessed via a CUDATM platform, version 9.0.176.