esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sample Formation and Feature Calculation  
The training sample was formed based on the results of waveguide modeling, considering the data from experiments on cyclic loading of specimens of dissimilar welded joints. The set of AE signal waveforms was calculated with the help of the FEM model by placing the AE source in depth along the fusion line and around the circumference of the weld joint. The amplitudes of AE signals in the data set were modeled in accordance with the parameters of the amplitude distributions obtained as a result of the experiment on cyclic loading of the samples.  
Long-term signals recorded in the field conditions were used as noise. To ensure greater reliability of the results, two types of process noise were considered. The first type of noise was measured on the body of a chemical reactor (Figure 6a); it has a nonstationary character with an impulse component of short duration and a broadband frequency spectrum (Figure 6b). Noise of this type is often recorded when monitoring heat exchangers, chemical reactors, and process pipelines, and it can be caused by the presence of solid dispersed particles in the flow of liquid or gaseous medium.  
The second type of noise (Figure 7b) corresponds to the noise of a loading device (compressor); it was measured on the body of a process pipeline (Figure 7a) with a turbulent flow of gaseous product and a high vibration level. The noise has a quasi-stationary nature with slow trends in the change of the standard deviation and is characterized mainly by a low-frequency spectrum.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range of Values** \\ \hline AE sensor axial coordinate, m & 0.1; 0.15; 0.2; 0.25; 0.3; 0.35; 0.4; 0.45; 0.5 \\ AE sensor azimuthal coordinate, \({}^{\circ}\) & \(0^{\circ};45^{\circ};90^{\circ};135^{\circ};180^{\circ}\) \\ Source depth below the surface, mm & 0; 1.5 (middle-depth) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Variable model parameters.  
Figure 6: Photo of the chemical reactor (**a**) and time realization of impulse noise (**b**).  
Filtering such types of noise seems to be the most difficult since nonstationary stochastic noise processes have a form and frequency spectrum similar to AE signals. However, the use of the FEM model allows us to estimate the parameters of the AE signal and bring the signal detection procedure closer to the parametric version.  
Classification features are calculated based on the continuous wavelet transformation [24], which allows us to clearly identify the features of signals with impulse components simultaneously in time and frequency domains.
# Bio-Sourced and Biodegradable Membranes
## 2 Green Membrane Materials
### Bio-Sourced Polymers for Membrane Fabrication
#### 2.1.1 Cellulose and Its Derivatives  
Cellulose is one of the first materials used for membrane production. Thus, the first membrane for hemodialysis was produced from cellophane, which is the product of cellulose treatment. Cellulose is one of the most prevalent polysaccharides that occurs in natural sources like trees and plants. Saccharide monomer units in cellulose (see Figure 2A) macromolecules are arranged predominately in a linear manner, which results in its fiber structure and high mechanical properties.  
Due to the presence of active hydroxyl groups in the saccharide unit, cellulose is widely used for its modification to obtain new artificial materials with improved properties. Some of the structures that resulted from cellulose modification are presented on Figure 2A.  
Thus, the most common cellulose derivative is cellulose triacetate, which is, along with other cellulose derivatives, used as the fabrication material for membranes applied in many industries such as oil-water separation [14; 15; 16], antifouling improvement [17], virus removal [18], heavy metal adsorption [19; 20], desalination [21; 22; 23], hemodialysis [24], and gas separation [25; 26; 27].  
Figure 1: Classification of biodegradable polymers.
## 4 Performance Evaluation
### Number of Helper Nodes Used at Different PRR  
In this subsection, we show simulation results of the number of helper nodes at different PRR between sender and receiver nodes, when every node transmits data packets to the sink. That is, we correlate the number of helper nodes used of one node to its next hop PRR. We observe that most of the helper nodes belong to those nodes with weak links (PRR \(0\sim 0.1\)) as shown in Figure 6a. That is, DRDT enhances reliability by cooperative retransmissions of helper nodes over links with low PRR. In Figure 6b, however, most of the helper nodes perform the retransmission task over links with good PRR since GF-ETX has already selected fairly good paths through built-in PRR based measures. In this case, helper nodes on good links can still work when the backward PRR of the receiver node is very low. This also helps enhance GF-ETX performance.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline \multicolumn{4}{|c|}{**Radio**} \\ \hline Modulation & NCFSK & Encoding & Manchester \\ \hline Output Power & -5 \(dBm\) & Frame Size & 50 \(Bytes\) \\ \hline \multicolumn{4}{|c|}{**Transmission Medium**} \\ \hline Path Loss Exponent & 3 & \(PLD_{0}\) & 55 \(dBm\) \\ \hline Noise Floor & -105 \(dBm\) & \(D_{0}\) & 1 \(m\) \\ \hline \multicolumn{4}{|c|}{**Deployment Configuration**} \\ \hline Area Height & 300 \(m\) & Area Width & 300 \(m\) \\ \hline Number of Nodes & 2,500 & Radio Range & 25 \(m\) \\ \hline \multicolumn{4}{|c|}{**Baseline Routing Schemes**} \\ \hline \multicolumn{4}{|c|}{Original Greedy Forwarding (termed GF-HOP)} \\ \multicolumn{4}{|c|}{PRR\(\times\)Distance Greedy Forwarding (termed GF-ETX)} \\ \hline \multicolumn{4}{|c|}{**Performance Metrics**} \\ \hline End-to-End Transmission Cost / End-to-End Transmission Delay / \\ End-to-End Transmission Rate \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 3 Composition of Fast Protection System  
The _FPS_ mainly protects the internal components of the accelerator from beam damage. Prior to designing the _FPS_, an investigation was performed on the _FPS_ of major accelerator facilities. The _FPS_ consists of two distinct parts, namely, _FPGA_ and a central processing unit (_CPU_), which play a role in parameter management, fast protection, fault data, and fault analysis. Figure 2 shows the block diagram that protects equipment in various modes of operation, integrating subsystem interfaces, software development using PLCs, hardware configuration and control interlocks, and _FPGA_ control-based timing systems.  
1. Mitigation node
2. Acquisition node  
Figure 1: _RAON_ central control system architecture.  
Figure 2: RAON machine protection system architecture.  
Basically, the _FPS_ consists of a mitigation (master) node and an acquisition (slave) node. A proper firmware configuration design of the mitigation and acquisition nodes is required to enable the nodes to transmit and store fault data for fault analysis.
# Laser Welding under Vacuum: A Review
## 1 Introduction  
Welding, a joining method by heat or pressure to make the materials reach the connection between atoms, is the most versatile and realistic joining technology in every industrial field [1; 2]. Like the arc, plasma and electron beams, a laser beam can also be used as heat source in the welding. Laser beam has been applied to welding since the first ruby laser was invented by [PERSON] [3; 4; 5]. In the last 20 years, laser welding has experienced rapid development. Because of the advantages of high quality, high precision, high efficiently, high performance, high flexibility, high speed, low distortion and low deformation, laser welding has been the most advanced and the best developing foreground welding method [6; 7; 8; 9]. In the field of laser beam welding, constant attempts to alter and broaden the application possibilities have been made for years. New laser beam welding methods are appearing constantly, for example, laser welding with filler wire, remote laser welding, laser scanning welding, and so on [10; 11; 12; 13; 14; 15; 16; 17; 18]. Among these new laser welding methods, laser welding under vacuum or under reduced pressure is one of the most promising methods. Although there are some different opinions about laser welding under vacuum, the exceeding expected laser welding phenomena and excellent weld quality under vacuum or reduced pressure both attract the interests of researchers.  
This paper makes a short overview of laser welding under vacuum. It begins with a short introduction about the research status of laser welding under vacuum. Next, the equipment of laser welding under vacuum are introduced. Then, the fundamental phenomena of laser welding under vacuum, including penetration depth, weld geometry, plasma plume, molten pool and keyhole behaviors, are presented in details. Finally, the applications and prospects of laser welding under vacuum are proposed.
## 3 Experimental Results and Discussion
### Development of Hardware for the Dual-Axis Device  
To make it shockproof, robust, and waterproof, the outer material of the dual-axis device of this paper is made of Teflon. It is equipped with two sets of step motors (including its step motor driver). These two-step motors respectively control the up and down elevation angle and the movement of the left and right rotation of the dual-axis device. The pixels of full-color image used in this device are \(640\times 480\); that is, P = 640 and Q = 480, and a two-phase step motor \(S_{R}=10,000\) is used; that is, \(\theta_{m}=0.036\). The test height of drone tracing is 10\(\sim\)100 m, and the radius of the dual-axis device is a = 0.25 m. The maximum flight speed of the drone is 20 km/h. As a result, the maximum rotation speed of the dual-axis device is \(V_{wave,max}=\frac{a}{R_{min}}\times V_{uav}\approx 0.139(m/s)\).  
Figure 10(a) shows the dual-axis tracing device and Figure 10(b) shows the hardware structure inside the dual-axis tracing device. Figure 10(c) shows the focus fine-tuning structure diagram for the laser ranger.  
Figure 8: TE–EQ1 infrared thermal imaging device.  
Figure 10: Laser Ranger LRF 28-2000B.  
Figure 9: Single-lens reflex camera.  
The main control board of the dual-axis tracing device is shown in Figure 11(a) and the motor control unit is shown in Figure 11(b).
## 3 System Model and Problem Formulation
### Computing Model
#### 3.2.1 Local Computing Model at Active Users  
First, consider the local computing for executing \(l_{i}(t)\) input bits. Assume that it can be known through off-line measurement that it takes \(C_{i}\) CPU cycles to process one bit at \(MD_{i}\). The wireless device can adjust the CPU clock rate by the Dynamic Voltage and Frequency Scaling (DVFS) technology. In the \(t\)-th time block, let \(f_{i,m}(t)\) denote the adjustable CPU cycle frequency, \(m\in\{1,2,\ldots,C_{i}l_{i}(t)\}\). This frequency cannot exceed the limit of the maximum CPU frequency; it is denoted by \(f_{i}^{max}\), which depends on the chip architecture. This constraint is as follows:  
\[0<f_{i,m}(t)\leq f_{i}^{max},\forall i\in K,\forall t\in\mathcal{T} \tag{1}\]  
Figure 1: System architecture.  
Figure 2: The time allocation.  
As local computing should be accomplished within the block, we have the computation latency constraints as follows:  
\[\sum_{m=1}^{C_{i}l_{i}(t)}\frac{1}{f_{i,m}(t)}\leq T,\forall i\in K,\forall t\in \mathcal{T} \tag{2}\]  
Thus, the consumed energy for the above discussions at a mobile device can be expressed as:  
\[E_{i,\text{loc}}=\sum_{m=1}^{C_{i}l_{i}(t)}\xi_{i}f_{i,m}^{2}(t),\forall i\in K,\forall t\in\mathcal{T} \tag{3}\]  
where \(\zeta_{i}>0\) represents the effective capacitance coefficient and the microscale relies on the chip architecture at user \(i\)[45], we \(\zeta_{i}=10^{-28}\) according to the actual measurement. To minimize the consumed energy as much as possible while satisfying the delay constraints, it is optimal for each mobile device to set the CPU frequency to be identical. By satisfying strict equality of the constraints in (2), we obtain the following equation:  
\[f_{i,1}(t)=\dots=f_{i,C_{i}l_{i}(t)}(t)=C_{i}l_{i}(t)/T,\forall i\in K,\forall t \in\mathcal{T} \tag{4}\]  
The maximum CPU cycle frequency is \(f_{i}^{max}\) for each user, so the number of computation bits by local computing is constrained by:  
\[0\leq l_{i}(t)\leq\frac{Tf_{i}^{max}}{C_{i}},\forall i\in K,\forall t\in \mathcal{T} \tag{5}\]  
As the total computation task is composed of local and offloading computing, we have \(Z_{i}(t)=l_{i}(t)+q_{i}(t)\), the following constraints can be obtained:  
\[Z_{i}(t)-\frac{Tf_{i}^{max}}{C_{i}}\leq q_{i}(t)\leq Z_{i}(t),\forall i\in K, \forall t\in\mathcal{T} \tag{6}\]  
Accordingly, the energy consumption of \(E_{i,\text{loc}}(t)\) can be expressed as follows:  
\[E_{i,loc}(t)=\frac{\zeta_{i}C_{i}^{3}(Z_{i}(t)-q_{i}(t))^{3}}{T^{2}},\forall i \in K,\forall t\in\mathcal{T} \tag{7}\]
## 3 Results and Discussion
### Enhancement in Cellulolysis by Reversible Assembly of Catalytic Module and CBM
Therefore, it is reasonable to interpret that P1-Cel9**:P2 and P1-Cbb9A*:P2 repeatedly associate and dissociate with the substrate, enhancing the catalytic turnover of the cellulose hydrolysis compared to the stable binding. Especially in large-scale applications for the refinement of bulky biomass, the accessibility of the substrate for enzymatic breakdown and the turnover rate is challenging. Our approach could be beneficial for cellulose degradation in industrial applications, such as the biofining and processing of cellulose-containing waste from woodworking industry, household organic waste, or wastewater. Furthermore, our results provide insight into the engineering of enzymes processing polymeric substrates. Incorporating a separate substrate-binding module into the enzymes can be beneficial for processing polymeric substrates by enhancing substrate binding. However, the excessive enhancement of the binding may not maximize the benefit, as it could prevent the efficient turnover of the release and binding of the enzymes to unprocessed parts of the substrate. Therefore, the substrate binding module introduced into the enzymes would need to be tuned for optimal substrate binding properties. The tuning would be attempted by mutating the substrate-binding module and discovering variants with moderate binding properties. Linking the enzyme and the substrate-binding module via a reversible connecting unit, as shown in this study, and controlling the connection should be a potent approach for processing polymeric substrates, which can be applied to enzymes for the degradation of plastic waste, contributing to environmental remediation. Figure 5: Cellulose degradation by single catalytic moieties in the excess presence of P3-CBM3a. The cellulolytic activity on crystalline cellulose is presented as the amount of reducing sugars. Avicel (8% (\(\omega\)/\(v\))) was incubated with (**A**) P1-Cel9*, (**B**) P1-Cel9**, or (**C**) P1-Cbb9A* (0.1 uM) at 55 \({}^{\circ}\)C for 21 h in the presence of PCNA (0.1 uM) and P3-CBM3a (0, 0.1, 2.5, or 5.0 uM), except that P1-Cel9* was incubated for 18h. Error bars represent the standard deviation of the mean for three replicate experiments, except that the reaction by 0.1 uM P1-Cel9**, 0.1 uM PCNA2, and 5 uM P3-CBM3a was evaluated in duplication. ND, not detected under the assay conditions.
# Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization
## 1 Introduction
Their proposed TD feature sets were utilized to classify the gait patterns of healthy subjects and of patients suffering from Parkinson's disease. [PERSON] et al. [25] calculated TD features from accelerometer signals and conducted a principal component analysis to detect the freezing of gait of patients suffering from [PERSON]'s disease. In addition to the aforementioned studies, several gait phase estimations with TD features have been adopted and performed satisfactorily [26; 27; 28; 29; 30; 31]. Because gait patterns vary across subjects, it is not appropriate to use the same TD feature set and an identical LTW for different subjects. For example, the SD value of the thigh angle is a critical feature in the estimation of the gait phase of a specific subject, whereas this feature may be redundant for another subject [32]. Furthermore, the TD feature values depend on the LTW. Even if gait patterns are similar across subjects, the feature values may be different across the same subjects owing to variations in stride time [33; 34]. Therefore, the selection of an optimal TD feature set and assignment of the LTW are crucial in gait phase estimation. This paper proposes an optimization approach to effectively determine the TD feature set and LTW for MLP-based gait estimation. The TD features were determined using a binary genetic algorithm (BGA). A BGA is an effective feature selection technique that is widely used in many applications [35; 36; 37]. This algorithm not only selects important features but also minimizes the dimension of the feature set. After the TD features are selected, the LTW can be optimized via Bayesian optimization (BO), a hyperparameter-tuning method that can rapidly converge with a small number of iterations [38]. The LTW was optimized by minimizing the gait phase error, which is estimated from the MLP. The BGA/BO combination is realized through iterative processes, where the BGA chooses the candidate TD feature set, and BO searches for an optimal LTW value for the candidate feature set. This method repeats this calculation until the BGA finds an optimal TD feature set. The remainder of this paper is organized as follows. Section 2 describes the thigh IMU signal used in this study for gait phase estimation. Section 3 describes the MLP-based gait phase estimation model used in this study. Subsequently, the proposed method for the feature set and hyperparameter determination is explained. Section 4 presents the optimized results for each of the walking and running cases evaluated. Finally, the paper is concluded in Section 5.
# An Open-Source Algorithm for 3D ROck Slope Kinematic Analysis (ROKA)
## 1 Introduction
Recently, different authors showed that new remote sensing techniques permit overcoming the limits of the traditional field survey-based and direct-contact sampling approaches (e.g., [18; 19; 20; 21; 22; 23]). This is possible thanks to the possibility of acquiring data from inaccessible portions of the outcrops and making analysis using the so-called Digital/Virtual Outcrop Model (DOM/VOM). DOMs can be developed using the Digital Photogrammetry (DP), which is a low-cost and user-friendly methodology [24; 25] that has been recently widely applied in geosciences [25; 26; 27; 28; 29; 30; 31; 32]. The principal advantage of DOM-based rock slope stability analysis is the possibility of performing digital measurements, mapping all the visible discontinuities of the rock, and extracting their information, such as position, orientation, and size, with high precision and accuracy. The knowledge of the exact orientation and dimension of the measured discontinuities, but, in particular, of their location onto the rock slope, allows an easier verification if each discontinuity is critical for the portion of the slope where it outcrops. In this paper, we present an open-source algorithm, written in MATLAB language, aimed at performing the KA using the discontinuity measures collected onto a 3D Digital Outcrop Model. This code performs the KA of four MOFs (planar failure, wedge failure, flexural toppling, and direct toppling) automatically for all the different portions of a rock slope, highlighting only the real possible MOFs caused by 3D mapped discontinuities (with a defined position, orientation, and size) that really intersect the calculated different portions of the slope. The main advantages of ROck Slope Kinematic Analysis (ROKA) are (i) the identification of the discontinuities responsible for the real possible MOFs in the different parts of the slope, (ii) the evaluation of their criticality and the visualization of the critical discontinuities, and (iii) the critical portions of the rock slope on the digital outcrop model in a 3D environment. In this way, KA can be easily performed even in complex environments to evaluate the local stability conditions, allowing focusing an eventual detailed in situ analysis only on limited portions of the slope.
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 1 Introduction
Such correction can be carried out by the use of growing stock volume (GSV) [20], which is the volume over bark of all living trees with a diameter at breast height (DBH) \(\geq\) 75 mm [21]. Previous studies have produced the data needed to use bio-geochemical models in the study area despite the difficulty of obtaining them at a spatially distributed level. The methodology for the production of the principal daily meteorological series (minimum and maximum air temperature, precipitation, and incoming solar radiation) by means of ordinary kriging and artificial neural networks (ANN) was developed in [22; 23]. Uncertainties of 0.93 \({}^{\circ}\)C, 1.94 mm, and 3.16 MJ m\({}^{-2}\) d\({}^{-1}\) were respectively obtained for air temperature, precipitation, and incoming solar radiation. Other required meteorological daily series (day length, daylight air temperature, humidity) can be obtained through the use of microclimate simulation models, such as MT-CLIM [24]. Ecophysiological parameters for the main vegetation types in Mediterranean areas were set by [17] taking into account the typical water stress effect. Soil texture maps (with standard deviation generally below 4%, although it can rise to \(\sim\)100% in mountainous terrain) [25], land cover maps (with geometrical error \(\leq\) 3 m) [26], and digital elevation models [27] are also available for the study area. A rooting depth map was obtained as a result of a calibration of Biome-BGC in the study area [19]. The rooting depth is the depth at which plants are able to grow roots [15] and is a crucial input in Biome-BGC for the simulation of the site water balance. A GSV map with RMSE = 64 m\({}^{3}\) ha\({}^{-1}\) was produced for the study area from a combination of remote sensing and forest inventory data [28]. Following the previous research efforts described above, and based on the capacity of NPP to track the changes in vegetation due to the climate change, the objectives of the present study were: (1) to apply a modeling strategy to assess forest annual NPP at the peninsular Spain scale using remote sensing and ancillary data and (2) to simulate forest annual NPP in future climate conditions (increases in air temperature and reductions in precipitation). The modeling strategy consisted of the combination of remote sensing and bio-geochemical modeling (Monteith approach and Biome-BGC) driven by information layers currently available over peninsular Spain and mostly derived from remote sensing data. It was applied to the five main forest ecosystems in the study area and validated against forest inventory observations. The simulation of future NPP was based on Biome-BGC simulations using meteorological time series modified on the basis of future climate scenarios.