esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Study
### Time-Frequency Analysis
#### 2.4.1 Short-Time Fourier Transform (STFT)  
The STFT is used to describe the frequency spectra of local signal sections changing over time. The original signal is first divided into several sections with equal time length by an appropriate sliding time window function \(\eta(t)\). These sections are approximated as stationary processes. Then, by applying the Fourier transform from \(t\) to \(f\) to each section, the frequency spectra vary with time are obtained. The STFT can be expressed as [17]:  
\[STFT_{z}(t,f)=\int_{-\infty}^{\infty}z(t^{\prime})\eta^{*}(t^{\prime}-t)e^{-j2 \pi ft^{\prime}}\,\mathrm{d}t^{\prime}\,, \tag{8}\]  
where \(z(t^{\prime})\) is the original signal, \(\eta(t)\) is the window function centered around zero.  
The width of the time window should be properly selected. For example, if a wide time window is selected, the frequency resolution will increase, but the time resolution will decrease, which means these frequency components can only be determined to appear in this wide time band, not a certain instantaneous moment.  
Figure 10 shows 3-dimensional (3D) graphs of the two conditions calculated by the STFT. For condition A, three energy peaks can be observed at approximately 700, 900, and 1200 Hz. For condition B, the number of energy peaks increases, and these peaks are all higher than those of condition A. Among them, the energy peak at approximately 700 Hz is the highest. On the low-frequency band between 0 and 100 Hz, the energy distributes widely along the time axis.  
Figure 9: Dynamic stiffness analysis.  
In summary, when the void exits, the vibration energy becomes stronger, which is indicative of increased reflections of the stress waves from the tunnel-air interface. In addition, more energy moves to the frequency of approximately 700 Hz, which in turn becomes the dominant frequency. It can also be seen that the low-frequency energy attenuates slowly when the void exists.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 2 Materials and Methods
### Error Analysis  
#### 2.2.1 Simulation of the Nonuniform Absorption Signal  
In most practical applications, the overlap of absorption lines interferes with the measurement. Generally, isolated absorption lines should be selected as far as possible [30]. For the isolated absorption line, it is seen from the line-shape function [Equations (3)-(10)] that the absorption signal is affected by eight parameters (including flow field and spectroscopic parameters). However, the absorption signal can be characterized by five line-shape parameters, namely the \(A\), normalization coefficient \(x^{\prime}\), line damping parameter \(y\), absorption line center wavenumber \(v_{0}\), and frequency shift \(v_{c}\).  
In the case of a nonuniform flow field, the absorbance is the integral of the absorption length \(L\). Mathematically, the line-shape function is nonlinear, and therefore, the nonuniform absorption signal cannot be characterized by a single uniform absorption signal, i.e., the nonuniformity of the flow field leads to the distortion of the line shape. Equation (1) is rewritten as  
\[a=\ln\frac{I_{0}(v)}{I_{t}(v)}=\int_{0}^{L}PX_{m}(I)S_{m}(T(l))\Phi(v,l)dl. \tag{11}\]  
The integral of Equation (11) can be regarded as the linear superposition of \(n\) quasi-uniform flow field absorption signals. When an isolated absorption line is determined to be used and the pressure and velocity of the flow field do not change greatly, changes in the center wavenumber \(v_{0}\) and frequency shift \(v_{c}\) may be disregarded. The remaining three line-shape parameters are used to write the absorption signal as  
\[a=\sum_{i=1}^{i=n}A_{i}\frac{1}{\sqrt{\pi}}x^{\prime}{}_{i}K(x^{\prime}{}_{i} y^{\prime},y_{i}). \tag{12}\]  
Equation (12) is the functional expression of the absorption signal of a general nonuniform flow field. This means that for the measurement using any isolated spectral absorption line in any flow field without a frequency shift, the spectral absorption signal can be modeled using the line-shape parameters (i.e., the line damping parameter \(y_{i}\), normalization coefficient \(x^{\prime}{}_{i}\), and \(A_{i}\)) of \(n\) quasi-uniform flow field regions.  
Therefore, the theoretical truth of the IA is  
\[A_{S}=\sum_{i=1}^{i=n}A_{i}. \tag{13}\]
# Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes
## 3 Results and Analysis
### Resistance to Ageing by Thermal Shock  
Table 7 shows the variation in mass of the specimens, in grams, after having subjected them to 24 h cycles with a stage of immersion in water (8 h) and another stage of drying in a stove (16 h).  
\begin{table}
\begin{tabular}{c c c c c} \hline SCC HR-30 & footprint (mm) & \(\alpha\) (mm) & SCC HR-45 & footprint (mm) & \(\sigma\) (mm) \\ \hline HR-30-0\% & 19.32 & 0.11 & HR-45-0\% & 19.22 & 0.18 \\ HR-30-20\% & 19.33 & 0.09 & HR-45-20\% & 19.51 & 0.44 \\ HR-30-50\% & 19.95 & 1.13 & HR-45-50\% & 19.92 & 0.38 \\ HR-30-100\% & 20.58 & 0.05 & HR-45-100\% & 19.97 & 0.59 \\ \hline \end{tabular}
\end{table}
Table 6: Abrasion test results and standard deviation \(\sigma\).  
Figure 10: Compressive strength results (**blue**) of the specimens after the freeze-thaw test compared to reference (**red**) specimens, as a function of the effective _w/c_ ratio.  
The results do not show notable modifications for the different SCCs, even a certain weight gain is observed, certainly produced by some water retained in its pores. Thus, it can be concluded that that deterioration does not occur in the samples due to alteration in the concrete surface layer.  
Figure 11 shows the results of the compressive strengths obtained as a function of their effective water/cement ratio, carried out before and after the thermal shock test. Although a slight increase in strength is observed, for control SCCs and 50% and 100% substitutions, there is no clear trend in the results.
## 4 Conclusions  
The study reports the energy and economic analysis of a multi-source (solar and ground) absorption heat pump plant for the heating and cooling of a retrofitted school building. The main objective of the analysis is to define the size and the type (sensible or PCM or enhanced PCM) of the three thermal energy storage systems (for DHW production, for heating, and for cooling). As a matter of fact, the operating temperature of the storage depends on the solar collectors' area, on the volume of the storage tanks, and on the kind and entity of the thermal loads to be faced. For this reason, only a transient simulation allows a reliable comparison between different layouts and sizes of system components. From the energy point of view, the best solution is given by 40 m\({}^{2}\) evacuated tube, 300 m ground probes, 3000 L hot tank and DHW tank capacity, and 1000 L cold tank capacity, the former tanks filled with PCM RT47, and the other as sensible storage (water). Such a configuration features the higher primary energy ratio and solar ratio between the different scenarios considered, because the mean operating temperature of the hot tank is longer near the melting temperature of the PCM during the year. The use of Al-foam enhanced PCM does not yield an additional advantage from the energy performance point of view. Instead, a more constant hot water temperature is guaranteed. This allows a higher level of service for the hot water produced. Further development of this research could address a wider economic evaluation of the proposed study, as the greater investment cost of PCM and enhanced PCM technologies with respect to sensible storage means their viability must be carefully evaluated.  
[PERSON] and [PERSON] contributed to the idea and funding support for this paper, [PERSON], [PERSON], and [PERSON] contributed to the methodology, [PERSON] and [PERSON] and [PERSON] contributed to the writing of this paper, [PERSON] contributed to revision of this paper.  
This research was funded by the SID2016 Research Project (\"Utilization of PCMs (phase change materials) in short, middle or long terms thermal storages\") by the University of Padova.  
The authors would like to thank [PERSON] and [PERSON] for supporting the simulations.  
The authors declare no conflict of interest.  
Figure 20: Temperature and flow rate of water at the outlet of the DHW tank in the case of sensible (water), phase change materials (PCM), and the enhanced PCM DHW tank.
# Instantaneous Frequency Representation Used for CPA Laser Simulation
## 1 Introduction
The simulation of this term is straight forward in the spectral domain. As for the nonlinear Schrodinger equation, the nonlinear term must be simulated in the temporal domain [8; 9]; a fast Fourier transform is usually used to pass from the spectral to temporal domain and vice versa. As already mentioned, the huge temporal excursion, due to the stretching ratio of the CPA, will introduce a huge number of points that tend to overload the memory of standard computers. In this paper, we propose an alternative method that keeps the advantages of both domains without needing any direct relationship between the spectral and temporal resolution and excursion. It also has the advantage of being intuitive as a graphical representation of stretched laser pulses with huge time-bandwidth products.
# Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters
## 4 Results and Discussion
### Effect of Corrosion Location
The curve in Figure 19 shows the limit landslide displacement for various axial corrosion positions on the pipeline. The pipeline is most susceptible to yield when the corrosion is at the center of the landslide, with the smallest limit displacement. This increases to a displacement of 0.825 m at 6.5 m from the center, and there is a slight decline then rise between 11 and 12 m, peaking at 0.832 m at 13.5 m, and remaining stable after that. The region impacted by the limited landslide displacement spans up to 13 m. Beyond this, the corrosion impact is negligible. Figure 10:1 Axial pipeline stress distribution. In practical engineering, landslide simulations under various scenarios can be combined with the distribution patterns of corroded pipeline stress under landslides for modeling, monitoring, forecasting, and excavation repairs.
## 5 Discussion  
The results presented in this study highlight the significant potential of neural networks (NNs) in optimizing wireless power transfer (WPT) systems. The compact NN architecture developed for distance prediction and impedance matching demonstrates high accuracy and efficiency, making it suitable for real-time applications. The ability to predict inter-coil distance and optimal capacitance values with low error rates is a notable achievement, particularly given the complexity and nonlinearity inherent in WPT systems. One of the key advantages of the proposed NN architecture is its compactness, which facilitates potential deployment on microcontrollers. This is crucial for real-time operation, where computational efficiency and speed are paramount. The NN's ability to generalize well, even with a relatively small dataset, further underscores its robustness and suitability for practical applications. The two-stage NN approach, which first predicts the inter-coil distance and then uses this information to determine optimal capacitance values, represents a significant improvement over direct capacitance prediction from impedance alone. This cascaded architecture not only enhances prediction accuracy but also provides explicit distance estimation, which is valuable for adaptive WPT systems that adjust coil separation to maximize power transfer efficiency. The NN's performance on the phase dataset, despite the presence of noise and measurement errors, is particularly encouraging. The ability to predict the optimal frequency for minimizing the phase angle with errors consistently below 3% suggests that NNs can effectively replace or enhance traditional PI control methods. The significant reduction in the number of measurements required for frequency adjustment, from approximately 200 in conventional control systems to just 1 in the NN-based approach, offers a substantial advantage in terms of speed and computational efficiency. This makes real-time applications and enhanced WPT power efficiency achievable.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 4 Cooperative Multi-Objective Optimization Model
### Particle Swarm Algorithm
#### 4.8.2 Coding  
In the inner-loop, the coding about the economic dispatch of RESs, EVs, and DC/AC converters can be represented by a real-valued matrix. \(k\) is the index of the particle of the inner-loop. \(M\) is the number of MG. \(T\) is the dispatching cycle.  
\[I_{\text{MG}}^{k}=\left[\begin{array}{c}I_{\text{MG1}}\\ I_{\text{MG2}}\\ \vdots\\ I_{\text{MGm}}\\ \vdots\\ I_{\text{MGm}}\end{array}\right] \tag{48}\]  
\[I_{\text{MGm}}=\left[\begin{array}{cccc}P_{m,1}^{PV}&P_{m,2}^{PV}&\cdots&P _{m,t}^{PV}&\cdots&P_{m,T}^{PV}\\ P_{m,1}^{WT}&P_{m,2}^{WT}&\cdots&P_{m,t}^{WT}&\cdots&P_{m,T}^{WT}\\ P_{m,1}^{G}&P_{m,2}^{G}&\cdots&P_{m,t}^{G}&\cdots&P_{m,T}^{G}\\ P_{m,1}^{EV}&P_{m,2}^{EV}&\cdots&P_{m,t}^{EV}&\cdots&P_{m,T}^{EV}\end{array}\right] \tag{49}\]  
\(P_{m,t}^{EV}\), \(P_{m,t}^{PV}\), \(P_{m,t}^{WT}\), and \(P_{m,t}^{G}\) are the power outputs of EVs, PVs, WTs, and DC/AC converters in the \(m\)-th MG in the \(t\)-th hour, respectively.  
In the outer-loop, the coding about the reactive power output by DC/AC converters can be represented by a real-valued matrix. \(s\) is the index of the particle of the outer-loop.  
\[O_{\text{MG}}^{s}=\left[\begin{array}{c}O_{\text{MG1}}\\ O_{\text{MG2}}\\ \vdots\\ O_{\text{MGm}}\\ \vdots\\ O_{\text{MGm}}\end{array}\right] \tag{50}\]  
\[O_{\text{MGm}}^{s}=\left[\begin{array}{cccc}Q_{m,1}^{G}&Q_{m,2}^{G}&\cdots&Q_ {m,t}^{G}&\cdots&Q_{m,T}^{G}\\ \end{array}\right] \tag{51}\]  
\(Q_{m,t}^{G}\) is the reactive power output of DC/AC converters in the \(m\)-th MG at \(t\)-th hour.  
However, the dispatch range of the outer-loop variable also changes when the variable of the DC/AC converters changes in the inner-loop. Therefore, a dynamic range adjustment algorithm is added to the outer-loop model.  
\[\left|Q_{m,t}^{G}\right|^{\text{max}}=\sqrt{S_{m}^{2}-\left(P_{m,t}^{G}\right) ^{2}} \tag{52}\]  
The inner-loop and outer-loop cooperate to generate the optimal optimization results.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Physical Processes  
Physical technologies permit removal through physical mechanisms such as physical separation, soil replacement, thermal treatment, or vitrification (Figure 2). These methods allow us to have a high removal efficiency, but they are rather expensive.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **Studies** & \begin{tabular}{c} **Maximum** \\ **Removal** \\ \end{tabular} & **Advantages** & **Disadvantages** & **Location** \\ \hline _Physical processes_ & & & & & \\ Removal through physical operations & Physical separation [63; 64; 65; 66] & & & & & A, B, C \\  & Thermal treatment [67; 68] & 95\% & \begin{tabular}{c} High efficiency \\ Simplicity \\ Rapidity \\ \end{tabular} & Cost & \begin{tabular}{c} A, B, C \\ A, B, C \\ A \\ \end{tabular} \\ \hline _Chemical processes_ & & & & & & \\ Stabilization [70; 71] & & & & & \\ Treatment with nanoparticles [72; 73; 74; 75; 76; 77; 78; 79; 80; 81] & & & & & \\ Removal through chemical operations & Stabilization/solidification [82] & 90\% & \begin{tabular}{c} High efficiency \\ Simplicity \\ Rapidity \\ \end{tabular} & \
\begin{tabular}{c} Cost \\ Changes in the \\ physicochemical soil \\ properties \\ \end{tabular} &
\begin{tabular}{c} A, B, C \\ A \\ B, C \\ A \\ \end{tabular} \\ \hline _Biological processes_ & & & & & & \\ Removal of pollutants by the microbial activity of microorganisms, or plants, or their combination & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Removal techniques for heavy metals/metalloids.  
Figure 2: Physical processes to remove heavy metals and metalloids.
# Topology Optimisation under Uncertainties with Neural Networks
## 3 Neural Network Architectures  
In modern scientific and engineering computing, machine learning techniques have become indispensable in recent years. The central goal of this work is to devise neural network architectures to facilitate an efficient computation of the risk-averse stochastic topology optimisation task. In this section, we develop two such architectures. The first one described in Section 3.1 is based on the popular convolutional neural networks (CNN) that were originally designed for the treatment of image data. In Section 3.2, a classical long short-term memory architecture (LSTM) is adapted to predict the gradient step.  
The usage of deep neural networks with topology optimisation tasks have been previously examined in [4; 10]. However, in contrast to other approaches, our architecture aims at a single NN that can be trained to handle arbitrarily fine meshes in terms of the requirements warranted during the topology optimisation process. More precisely, we seek an NN that predicts the gradient step \(\varphi_{n+k}\in\mathbb{R}^{|V(\mathcal{T}_{m})|}\) from Equation (6) discretised on an arbitrarily fine mesh \(T_{m}\) at an arbitrary iteration step \(n\in\mathbb{N}\) with given \(k\in\mathbb{N}\), for \(\mathcal{N}^{k}:\mathbb{R}^{1+d\times|V(\mathcal{T}_{m})|}\to\mathbb{R}^{|V( \mathcal{T}_{m})|}\) such that  
\[\mathcal{N}^{k}([\varphi_{n},u_{n+1}])=\varphi_{n+k}. \tag{13}\]  
Thus, the total number of iterations required for the topology optimisation iteration should ideally be reduced, resulting in improved practical performance. For the sake of a convenient presentation, we consider all other coefficients of Equation (6) as constant in the following analyses. Alternatively, one would have to increase the complexity in the number of degrees of freedom which are the weights describing the NN as well as the required training data. It can be assumed that with more information in the form of coefficients provided to the NN during training the accuracy of the resulting approximation of \(\varphi_{n}\) increases. Within the optimisation procedure, the actual calculation of the gradient step given in Equation (6) is performed on the basis of variable coefficients (e.g., \(\tau\) and \(\gamma\)).  
Since the discretisation \(\varphi_{n}\in\mathbb{R}^{|V(\mathcal{T}_{m})|}\) can be rewritten rather easily in tensor form, which represents the input of a CNN, this is the first architecture we consider in the next section.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Infrastructure Deployment in the City of Barcelona
#### 4.1.1 Core Tier  
The core DC in Barcelona infrastructure is deployed in the _OMEGA-DC_ at i2 CAT. This DC hosts two compute servers providing the core NFVI. The fiber infrastructure connects this location to the on-street components deployed in the _22@ area_ and the city hall using L2/L3 network devices. To add resilience, two end-to-end fiber connections of 10 Gbps were deployed. Each fiber provides end-to-end redundancy in case of interruptions.
## 3 Results
### Decision Procedure Design
#### 3.2.2 CNN Training and Results  
The training of the selected architectures was performed in order to obtain a CNN-based decision element. The ADAM search technique was chosen for use as an optimizer based on its generally acceptable performance [44]. Initial weights were set randomly, with Gaussian distribution (location = 0, scale = 0.05). The training instances were performed 50 times. See Table 2 for all parameters of the training processes. The resulting values of the categorical cross entropy loss function computed over the testing set for each topology are shown in Figure 15. Categorical cross entropy loss function is calculated as follows.  
\[E=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{K}t_{i,j}\log(y_{i,j}) \tag{1}\]  
where \(N\) is the number of samples in the testing set, \(K\) is the number of classes considered for classification, \(t\) is the label of the target class (0 or 1) and \(y\) is the \(j\)-th scalar value in the neural network output (between 0 and 1).  
In addition, accuracy of the decision element when using the data in the testing set is depicted in Figure 16. As training is a stochastic process, the results are depicted as box graphs. Accuracy is defined as follows.  
\[Acc=\frac{n}{N} \tag{2}\]  
where \(n\) is the number of correctly performed decisions and \(N\) is the number of all decisions, i.e., the number of samples in the testing set.  
Figure 15: Final values of loss function (1) over testing set.  
As mentioned in Section 2, we have tested three hypotheses in our research. The best final accuracies of the decision element designed in this contribution are compared to our previous results in Table 3. In addition, we include other important metrics--confusion matrix, precision, recall and F1-score--in Appendix A.  
\begin{table}
\begin{tabular}{l l} \hline \hline Number of experiments & 50 \\ Input shape & \(110\times 110\times 1\) \\ Training algorithm & ADAM algorithm \\ Initialization & Normal distribution (mean = 0, std = 0.05) \\ Maximum epochs & 100 \\ Stopping criterion & Maximum epochs reached \\ Learning rate \(\alpha\) & 0.001 \\ Exponential decay rate 1 \(\beta_{1}\) & 0.9 \\ Exponential decay rate 2 \(\beta_{2}\) & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of CNN training for decision procedure design.  
Figure 16: Final accuracy (2) of the decision element over testing set.
## 3 Ground-Based Instrumentation and Data Processing
These physical measurements are important constraints for profile retrieval. Neural networks (site specific) derived using the Stuttgart Neural Network Simulator and long-term record of radiosonde profiles are provided by Radiometrics. A standard back-propagation algorithm was used for training and a standard feed-forward network is used for profile determination. Although the number of independent measurements (eigen values) are less than the 58 retrieved layers, the finer resolution provides better displays and easier processing in subsequent data processing steps. In our application, we are interested mainly in the LWP but validation of this parameter is very difficult in the absence of aircraft measurements. On the other hand, direct channel calibration for our system can be tested. To do this, we have made some cross validations against the two instruments against existing radiosondes and AERONET at the Brookhaven National Laboratory (BNL) as part of an overall calibration exercise. The results are given in Figure 4 for the 2 days when all measurements are available. For both days, the MP-3000A was a better match against the AERONET retrievals than with the WVP-1500. In addition, there is clear improvement (especially for day 2) in the comparisons against the Radiosonde launches. This comparison illustrates the general consistency in the calibration of the radiometers. However, we do note a near constant bias between the two systems, which is likely due to differences in the algorithm training datasets used by the different systems.