esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 2 Stacked RBM-AE Model
### Details of Stacked RBM-AE Training
#### 2.2.1 Pre-training of standard RBM
By summing all the states of the hidden layer unit, the edge distribution \(\mathbf{P}(\mathbf{v}|\theta)\) of the visible layer unit set can be obtained:  
\[\mathbf{P}(\mathbf{v}|\theta)=\frac{\sum_{\lambda}\exp(-\mathbf{E}(\mathbf{v},\mathbf{h}|\theta))} {\sum_{\alpha,k}\exp(-\mathbf{E}(\mathbf{v},\mathbf{h}|\theta))}\]  
\[=\frac{\sum_{\lambda}\exp\left(\sum_{i=1}^{n}\sum_{j=1}^{m}v_{i}W_{ij}h_{j}+ \sum_{i=1}^{n}v_{i}b_{i}+\sum_{j=1}^{m}h_{j}c_{j}\right)}{\sum_{\alpha,k}\exp \left(\sum_{i=1}^{n}\sum_{j=1}^{m}v_{i}W_{ij}h_{j}+\sum_{i=1}^{m}v_{i}b_{i}+ \sum_{j=1}^{m}h_{j}c_{j}\right)} \tag{5}\]  
The representation based on the energy function is simply an alternative to the product representation of the factor [33]. According to Equation (5), we can get \(\theta\) by maximizing \(\mathbf{P}(\mathbf{v}|\theta)\). As \(\ln(x)\) is increnebted, arg \(\max\limits_{\theta}\mathbf{P}(\mathbf{v}|\theta)\) and arg \(\max\limits_{\theta}\ln(\mathbf{P}(\mathbf{v}|\theta))\) are equivalent. Thus, we define the train goal function \(\mathbf{L}(\theta)\) as:  
\[\mathbf{L}(\theta)=-\ln\prod_{l=1}^{T}\mathbf{P}\Big{(}\mathbf{v}^{(t)}\Big{|}\theta\Big{)} =-\sum_{l=1}^{T}\ln\mathbf{P}\Big{(}\mathbf{v}^{(t)}\Big{|}\theta\Big{)} \tag{6}\]  
\(T\) is the number of train set. arg \(\min\limits_{\theta}\mathbf{L}(\theta)\) uses the gradient descent method [34]. In the direction of \(\frac{\partial\mathbf{L}(\theta)}{\partial(\theta)}\), \(\mathbf{L}(\theta)\) becomes the fastest, we can find the minimum value of function by finding an optimal parameter adjustment stride in this direction. We use \(\alpha\) to represent the learning rate of the algorithm, and the parameters update formula can be defined as:  
\[W_{ij}=W_{ij}+\alpha\Big{[}\mathbf{P}\big{(}h_{j}=1\big{|}\mathbf{v}\big{)}v_{i}-\mathbf{P }\Big{(}h_{j}=1\big{|}\mathbf{v}^{(k)}\Big{)}v_{i}^{(k)}\Big{]} \tag{7}\]  
\[\mathbf{P}\big{(}h_{j}=1\big{|}\mathbf{v}\big{)}=sigmoid\Bigg{(}c_{j}+\sum_{i}v_{i}W_{ ij}\Bigg{)} \tag{1}\]0  
\[\mathbf{P}\big{(}h_{j}=1\big{|}\mathbf{v}\big{)}=sigmoid\Bigg{(}c_{j}+\sum_{i}v_{i}W_{ ij}\Bigg{)} \tag{1}\]1\(\mathbf{v}^{(k)}\) is the state of \(\mathbf{v}\) after \(\mathbf{k}\) step Gibbs sampling.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### The Tracking and Aiming  
Tracking and aiming research is a major challenge because some UAVs are flexible and small in size, which creates difficulties in tracking. Moreover, laser beam transmission in the atmosphere causes linear and nonlinear effects. These effects decrease the beam quality and aiming accuracy. Aiming error will cause misalignment; consequently, it will decrease the efficiency of the laser beam and PV cell conversion efficiency. Thus, there is a need to develop high precision tracking and aiming for UAVs.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 3 Results  
### Experimental Setup  
To evaluate the effectiveness of the GCSG model, the experimental data used in this study were collected from the multi-region joint simulation test system of the Digital Twin Logical Range, jointly developed by the Chinese Flight Test Establishment and Xi'an University of Technology. This system primarily processes aircraft flight status data obtained from devices such as GPS, theodolites, cameras, and radars during flight tests. It integrates six high-performance servers to form a central processing node cluster that is responsible for data processing, digital twin model rendering, and command forwarding.  
Scripts were developed to collect and store various load metrics from each node in the central processing cluster. The data collection spanned 144 h, with a sampling interval of 90 s, resulting in 5760 records per node (Logical Range task processing device). The dataset contains timestamps, Logical Range IDs, device IDs, and CPU load measurements (0-100%).  
To ensure consistency in model training and convergence, all input features were normalized using standardization (i.e., zero mean and unit variance) based on the statistics of the training set. This preprocessing step improves the numerical stability of the model and ensures fair comparison across different methods.  
To ensure the accuracy and reproducibility of the experimental results, all experiments were conducted under consistent software and hardware conditions. The detailed hardware and software configurations are summarized in Table 1.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 3 Preliminary  
This section provides a foundational overview of key concepts and notations crucial to understanding the proposed methodology in the subsequent sections. It encompasses fundamental topics such as graph structures, various clustering types, the metric \"edge degree\", and the concept of relative density as a user-specified density metric. Furthermore, it introduces the equations associated with these concepts and provides a visual representation through a practical example. The section serves as a solid basis for comprehending the subsequent sections.
## 2 Principle of Simplified Second-Order Volterra-DFE Equalizer
### Second-Order Volterra Transmission Model for EML-IMDD Systems
 \tag{11}\]where linear kernels \(\overset{\wedge}{h}_{L}(k)\) and nonlinear kernels \(\overset{\wedge}{h}_{NL}\) can be expressed as:  
\[\overset{\wedge}{h}_{L}(k)=R[h_{k}+jah_{k}], \tag{12}\]  
\[\overset{\wedge}{h}_{NL}(k,0)=-\frac{1}{4}\Big{(}1+a^{2}\Big{)}R(h_{k}-h_{k}h_{k }^{*}), \tag{13}\]  
\[\overset{\wedge}{h}_{NL}(k,w)=\frac{1}{4}\Big{(}1+a^{2}\Big{)}R\big{(}h_{k}h_{k +w}^{*}\big{)}, \tag{14}\]  
In the nonlinear kernel described in the formula, the second-order term mainly represents the beat noise caused by square law detection at the receiver. In order to study the nonlinear kernels, optical fiber parameters are substituted into Equations (12)-(14), and the kernel values of the signal square terms \(\overset{\wedge}{h}_{NL}(k,0)\) and non-square terms \(\overset{\wedge}{h}_{NL}(k,w)\) can be obtained, as shown in Figure 1. The values of the parameters are dispersion \(D=3.5\,\mathrm{ps/nm/km}\), wavelength \(\lambda=1310\) nm, fiber length \(L_{f}=10\) km and EML chirp coefficient \(\alpha=1\), respectively. As can be seen from Figure 1, for the EML laser, when \(w=0\), that is, when the corresponding beat terms are the signal square terms, its kernel coefficients are much larger than the nonlinear kernel coefficients when \(w\
eq 0\). This indicates that in second-order nonlinear terms, the signal square terms have a greater influence on the received signal than other nonlinear terms. This is because, in the O-band, the crosstalk between the signal numbers caused by fiber dispersion is small. After detection by the square law in the receiver, the beating noise is dominated by the signal square terms. According to this result, Volterra's nonlinear kernels can be deleted to remove unnecessary second-order nonlinear terms to simplify the structure of the equalizer.
## 6 Conclusions  
During the study, emotion recognition methods suitable for monitoring the emotional states of software developers in a laboratory environment were examined. Analysis of the collected data has allowed the research questions to be answered.  
In response to RQ1, it can be stated that most of the tested channels can be used successfully during programming. Only audio channels are completely useless in a laboratory environment. Although in the survey the participants reported that they often express emotions verbally while programming, this was not confirmed during the study. In the case of tools for recognizing emotions on the basis of facial expressions, attention should be paid to the appearance of the subject. Studies have revealed that recognition results may be seriously compromised when a participant wears glasses, has a long fringe or has thick stubble.  
Among the methods that can be used to monitor the emotion of programmers in the work environment (RQ2), non-invasive methods were indicated first and foremost. The suggested data channels that can be used in daily work include the eye tracker, typing patterns, mouse movements and video recordings. Most respondents also agreed to the use of the EDA sensor. Combined with the results on wearing a wrist watch while programming, it can be claimed that smart watches can be successfully used to monitor the emotions of developers.  
Finally, emotion-inducing methods in a laboratory environment were evaluated (RQ3). The malicious plug-in to the IDE proved to be the best approach for triggering negative emotions. Of the remaining methods, shortening the task time did not meet the assumptions of creating time pressure. This incorrect test case, on the other hand, can be used in more complex tasks.  
The study has revealed that, while most methods of recognizing the emotions of programmers can be used in laboratory tests, only those that are non-intrusive, such as an analysis of facial expressions or typing patterns, are accepted in the real working environment. In practice, this means that physiological sensors can be used to monitor the emotions of programmers only in the least invasive form--for example, biosensors built into smart watches. In addition, inducing the emotions in the laboratory environment proved to be challenging. Among the three evaluation methods, only the malicious behaviour of the IDE had an impact on the majority of the participants. To conduct research on the emotional states of programmers in a laboratory environment, it may be necessary to develop and validate more approaches to the problem of inducing emotions.  
The author declares no conflict of interest.
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
## 2 Experimental Conditions  
### Materials  
A commercial metakaolin (hereafter MK) was purchased under the name of ArgicalTM from AGS-mineraux(r), Clerac, France. According to the producer, MK is mostly amorphous and almost exclusively composed of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) at 55.0 and 40.0 wt.%, respectively. A common local red clay (hereafter RC) was provided by a local supplier, Mota Ceramic Solutions(r), Oia, Portugal. RC was calculated in a laboratory oven at 800 \({}^{\circ}\)C with an isothermal step of 2 h (heating rate of 3 \({}^{\circ}\)C/min up to 500 \({}^{\circ}\)C, and 10 \({}^{\circ}\)C/min up to 800 \({}^{\circ}\)C) followed by natural cooling. Similar thermal activation procedures have been adopted elsewhere [40; 43]. The calcined clay (hereafter CRC) was used to gradually replace MK in the alkali activated binders and mortars (the mix proportions are further detailed in Section 2.2). Densified silica fume (hereafter SE; Elkem Microsilica(r) Grade 940, ELKEM, Norway) was used as a secondary source of reactive SiO\({}_{2}\). SF has been characterized in previous works and determined to be composed of 95.0 wt.% SiO\({}_{2}\) with minor elements such as CaO, Fe\({}_{2}\)O\({}_{3}\), and Al\({}_{2}\)O\({}_{3}\) being considered negligible (<0.5 wt.%). The BET specific surface area was determined to be approximately 22,200 m\({}^{2}\)/kg [17; 48].  
Activating solutions were prepared by mixing sodium hydroxide and sodium silicate solutions. Sodium hydroxide solutions (8, 10 and 12 M) were prepared in the laboratory by dissolving sodium hydroxide beads (M = 40.00 g/mol, 97% pure NaOH pellets, CAS [1310-73-2], Scharlab, Sentmenat, Spain) in distilled water. A commercial sodium silicate solution (CAS [338443-3L], 10.6 wt.% Na\({}_{2}\)O, 26.5 wt.% SiO\({}_{2}\), and 62.9 wt.% H\({}_{2}\)O, Sigma Aldrich, Burlington, EUA) was employed without further elaboration. River sand (\(\leq\)2 mm) was used as the fine aggregate to prepare the mortar formulations.
## 3 Results and Discussion  
### Temperature Rise with Excitation Frequencies in the kHz Range  
Figure 4 shows the experimental temperature rise curves for stainless steel, for ferritic steel and for CFRP at an induction frequency of 10 kHz. Calculated curves based on Equation (17) and the material parameters from Table 1 are also shown. For better comparison, all curves were normalised at \(t=5\) s.  
The curves for CFRP show an almost linear temperature rise, as \(\mu_{T}<<\delta\) holds for the full time of 5 s. The curves for ferritic steel from the beginning have dependence proportional to the square root of time. A calculation based on the material parameters in Table 1 shows that the thermal penetration depth exceeds the skin depth very early at  
Figure 4: Normalised thermal signal after begin of induction heating obtained for ferritic steel, stainless steel and CFRP at an induction frequency of 10 kHz. Solid lines: experiment, dashed lines: theory.  
\(t>34\) ms. The curves for stainless steel lie in between the curves of CFRP and cast iron. For the curve of stainless steel, the thermal penetration depth equals the skin depth at \(t=1.4\) s. It starts with a more linear part and has a more square-root-dependent part for later times. There is deviation between the theoretical and the experimental curves. This is mainly accounted for the fact that the one-dimensional wave propagation from theory cannot perfectly be realised in the experiment. There is also some uncertainty about the actual thermal and electrical properties of the materials.
## 1 Introduction
An early-warning system directly monitoring the change of the activated sludge activities can represent the real performance of WWTPs and provide a basis for emergency measures of toxic substances in industrial wastewater, for effectively evaluating the influent toxicity and alarming the shock load of industrial wastewater [3,40]. The oxygen uptake rate (OUR) is a theoretical indicator for characterizing the activated sludge activities, representing the in situ changes of microbial activities in removing pollutants [41, 42, 43]. The OUR has been used for several cases of toxicity assessment like organic compounds, PTEs, and complex wastewater [14,44]. For instance, the OUR is able to estimate the kinetic parameters in a model including substrate hydrolysis, biomass growth and endogenous metabolism [45]. In addition, the degree of the OUR also indicates the toxic and inhibitory level of pollutants [46,47]. Studies have shown that the sizes and concentrations of pollutants inhibit the OUR of the activated sludge to different extent [46]. Other operation parameters in WWTPs, like the activated sludge contents and hydraulic retention time (HRT) have distinct influence on the OUR [48]. From the OUR of the activated sludge in WWTPs, the influence of toxic substances on the biochemical treatment units is intuitional. However, as the activated sludge have unique compositions across WWTPs, it is difficult to apply one type of early-warning system in different WWTPs [49]. Therefore, a robust, reliable and universal model simulating the OUR response to different environmental variables is needed to calculate and predict the influent toxicity across WWTPs. The aim of this work is to assemble an online early-warning system to monitor the activated sludge activities in WWTPs affected by industrial toxins via the relative OUR (ROUR) and develop a model to discuss the impacts of environmental variables on the ROUR, with a case study in a typical WWTP in the Taihu Lake of southern Jiangsu Province, China. With the typical toxicity and inhibitory sources in industrial wastewater as the target toxic substances, e.g., Zn\({}^{2+}\), Ni\({}^{2+}\), and phenol, we also studied some influential factors, e.g., types and concentrations of toxins, the activated sludge contents and HRT, to comprehensively explore their influence on the ROUR and predict the ROUR under different conditions. Our findings help in better understanding and applying the online early-warning system for the effective operation of different WWTPs response to the shock load from industrial wastewater.
## 2 Materials and Methods
### Modeling of the Composite Structure
#### 2.1.1 Transfer Matrix for the Porous Metal  
Transfer matrix \(P\) for the porous metal can be calculated using Equation (1). Here \(k_{p}\) is the wave number in the porous material, which is obtained using Equation (2); \(Z_{p}\) is characteristic impedance of the porous metal, which can be achieved using Equation (3); \(d_{p}\) is the thickness of the porous metal [8; 9; 27; 36; 37]. Transfer matrix of the preposition porous metal and that of the postposition porous metal in Figure 1e are symbolized as \(P_{f}\) and \(P_{b}\) respectively.  
\[[P]=\left[\begin{array}{cc}\cos\bigl{(}k_{p}d_{p}\bigr{)}&jZ_{p}\sin\bigl{(} k_{p}d_{p}\bigr{)}\\ jZ_{p}^{-1}\sin\bigl{(}k_{p}d_{p}\bigr{)}&\cos\bigl{(}k_{p}d_{p}\bigr{)}\end{array} \right], \tag{1}\]  
\[k_{p}=\omega\sqrt{\frac{\rho(\omega)}{K(\omega)}}, \tag{2}\]  
\[Z_{p}=\sqrt{\rho(\omega)K(\omega)}. \tag{3}\]  
Figure 1: Schematic diagrams of the investigated composite structures and corresponding labels of their structural parameters. **(a)** porous metal + cavity + microperforated panel + cavity (PCMC); **(b)** porous metal + cavity (PC); **(c)** microperforated panel + cavity + porous metal + cavity (MCPC); **(d)** microperforated panel + cavity (MC); **(e)** porous metal + cavity + microperforated panel + cavity + porous metal + cavity (PCMCC).  
In the Equations (2) and (3), the symbol \(\omega\) is the angular frequency, which can be calculated using Equation (4); \(K(\omega)\) is the complex effective bulk modulus, which can be calculated using Equation (5); \(\rho(\omega)\) is complex effective density, which is derived by Equation (6) [8; 9; 27; 36; 37].  
\[\omega=2\pi f, \tag{4}\]  
\[K(\omega)=\gamma P_{0}\Bigg{[}\gamma-(\gamma-1)\Bigg{(}1-N_{u}\Bigg{(}j\frac{8 \omega\rho P_{r}}{\sigma\phi}+N_{u}\Bigg{)}^{-1}\Bigg{)}\Bigg{]}^{-1}, \tag{5}\]  
\[\rho(\omega)=\rho\Bigg{[}1+\Bigg{(}3^{2}+\frac{4\omega\rho}{\sigma\phi}\Bigg{)} ^{-0.5}-j\frac{\sigma\phi}{\alpha\rho}\Bigg{(}1+\frac{\alpha\rho}{4\sigma\phi} \Bigg{)}^{0.5}\Bigg{]}. \tag{6}\]  
In the Equation (4), \(f\) is sound frequency, and its range is (2000 Hz, 6000 Hz) in this study. In the Equations (5) and (6), \(\gamma\) is specific heat ratio of the air, 1.40; \(P_{0}\) is static pressure of the air, 1.013 \(\cdot\) 10\({}^{5}\)Pa; \(N_{u}\) is the Nusselt number, 4.36; \(j\) is symbol of the imaginary number, \(j^{2}=-1\); \(\rho\) is density of the air, 1.21 kg \(\cdot\) m\({}^{-3}\)\(P_{r}\) is the Prandtl number, 0.71 [8; 9; 40]. Meanwhile, static flow resistivity \(\sigma\) and porosity \(\phi\) of the selected porous metal in the proposed composite structures for this study can be measured and identified as \(9.2\cdot 10^{3}\)Pa \(\cdot\) s \(\cdot\) m\({}^{-2}\) and 0.9, respectively.
## 3 Results and Discussion
### Separation of the Droplets Using the Microchannel  
Prior to sorting the Ag nanowire micro-ring structures, hollow glass particles with a diameter of 10 \(\upmu\)m were utilized to test whether the microchannel would be suitable to efficiently sort the Ag nanowire micro-rings (Figure 3). DCB was used as a solvent for the glass particles to disperse as in the micro-ring formation process. DCB with and without glass particles was released at different flow rates (A:B = 1:1, 1:4, and 1:10) simultaneously at each inlet. In this feasibility test, almost all the glass particles were collected through outlet 1 (downstream) when the flow rate ratio was fixed to 1:10 (Figure 3d). It has been reported that moving the particle close to the wall can initiate a dispersing effect, resulting in sorting the particles by size distribution [10]. This indicates that the ratio of two different flow rates can determine the separation of particles in various sizes using a pinched channel.  
Following this feasibility test with hollow glass particles, the same experimental setting was implemented to apply to the immiscible emulsion with the Ag nanowire micro-rings (Figure 4). Figure 4 demonstrates that approximately 50% of the Ag nanowire micro-ring structures with 2-3 \(\upmu\)m diameters could be collected at outlet 1. Further detailed evaluation of the size distribution revealed that the Ag nanowire micro-rings with a relatively smaller size (<2.5 \(\upmu\)m) could be collected in outlet 1 compared to outlet 2 (Figure 4c). Using this separation process with the micro-ring particles, we were able to collect Ag nanowire micro-ring structures of a similar size dispersed in the organic solvent. Next, we applied this Ag nanowire micro-ring material to printing ink, and investigated a printing pattern with the Ag nanowire micro-ring structures to enhance the surface plasmonic scattering for biological applications.  
Figure 2: (**a,b**) SEM images of coiled silver nanowires, (**c**) droplet diameters of a single Ag nanowire, and (**d**) droplet diameters of entangled Ag nanowires.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 4 Discussion
### Uncertainty Analysis  
In the process of up-scaling GPP and assessing the accuracy of the results, the uncertainty of the observed data and the reference data would have some influence on up-scaling and validating the results. Firstly, the ways to derive GPP from the eddy covariance systems would bring some uncertainties in the up-scaling process. The method to process the missing carbon flux data, such as the look-up table and ANN, would induce some errors when interpolating the eddy covariance data. Second, the footprint of field observation data was related with the heights of the eddy covariance instruments, air conditions, and spatial heterogeneity. The errors of calculating the footprint of GPP at the flux sites would certainly bring some uncertainty in validating the up-scaled results. Third, the errors of the input data (NDVI, FVC, SWR, RH, Ta) used in the machine learning models to up-scaled GPP would also have some influence on the precision of the results. The high accuracy of the machine learning methods relied on the high quality of the training data. Although MODIS LSR products, and land-cover products and meteorological datasets have high accuracy, the errors of the MODIS LSR products, land-cover products and meteorological datasets would have some impact on the accuracy of the training data, and also have some influence on the up-scaling GPP using machine learning models.