esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microalgal Growth and Biomass Productivity  
The extreme turbidity of the wastewater, caused by the presence of many suspended particles, made it challenging to estimate the biomass concentration accurately using direct dry weight measurement or spectrophotometry. Cell growth was assessed by observing cell density with a microscope (Olympus BX41 TF, Olympus Corporation, Tokyo, Japan) and a hemocytmeter. The dry cell weight (DCW, g L\({}^{-1}\)) was determined by establishing a linear link between cell density and dry cell weight using Equation (1) and biomass productivity (P\({}_{\mathrm{b}}\)) using Equation (2):  
\[\mathrm{Dry~{}cell~{}weight(DCW)}\Big{(}\mathrm{g~{}L^{-1}}\Big{)}=1.98\times 1 0^{-8}\times\mathrm{Cell~{}density}+0.0446~{}~{}~{}\Big{(}\mathrm{R}^{2}=0.99 \Big{)} \tag{1}\]  
\[\mathrm{P_{b}}\Big{(}\mathrm{g~{}L^{-1}d^{-1}}\Big{)}=\frac{\mathrm{DCW_{x}- DCW_{0}}}{\mathrm{t_{x}}} \tag{2}\]  
Cell density is in cells mL\({}^{-1}\). DCW\({}_{0}\) represents the initial dry cell weight in g L\({}^{-1}\), whereas DCW\({}_{x}\) represents the dry cell weight in g L\({}^{-1}\) at time t\({}_{x}\) in days.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Unit** & \begin{tabular}{c} **Raw Whey** \\ **Wastewater** \\ \end{tabular} & \begin{tabular}{c} **Autoclaved Whey** \\ **Wastewater** \\ \end{tabular} & \
\begin{tabular}{c} **Litterature Range** \\ \end{tabular} &
\begin{tabular}{c} **References** \\ \end{tabular} \\ \hline pH & - & 3.80 \(\pm\) 0.00 & 3.83 \(\pm\) 0.02 & 3.5–7 & [4,6,9] \\ Turbidity & NTU & 40,5142.9 \(\pm\) 2359.77 & 55,535.71 \(\pm\) 4005.30 & 94–1000 & [9] \\ Total solids & g L\({}^{-1}\) & 119.62 \(\pm\) 4.07 & 121.45 \(\pm\) 0.25 & 55–71 & [8] \\ TN & g L\({}^{-1}\) & 2.81 \(\pm\) 0.14 & 2.54 \(\pm\) 0.12 & 0.13–2.02 & [6,8,9] \\ NO\({}_{3}\)-N & g L\({}^{-1}\) & 0.51 \(\pm\) 0.01 & 0.32 \(\pm\) 0.01 & 1.5 & [4] \\ NH\({}_{4}\)+N & mg L\({}^{-1}\) & 74.53 \(\pm\)3.44 & 109.04 \(\pm\) 3.16 & 60–270 & [8] \\ PO\({}_{4}\)-p & mg L\({}^{-1}\) & 795.30 \(\pm\) 22.58 & 756.31 \(\pm\) 22.80 & 340–430 & [8] \\ COD & g L\({}^{-1}\) & 100.36 & 96.00 \(\pm\) 2.47 & 0.73–100.2 & [6,8,9] \\ Total carbon & g L\({}^{-1}\) & 39.85 \(\pm\) 1.77 & 36.73 \(\pm\) 0.93 & 19.34–43.26 & [9] \\ Organic carbon & g L\({}^{-1}\) & 39.07 \(\pm\) 1.78 & 35.84 \(\pm\) 0.90 & 18.89–42.07 & [9] \\ Inorganic carbon & mg L\({}^{-1}\) & 782.80 \(\pm\) 33.31 & 897.07 \(\pm\) 100.40 & 450–780 & [9] \\ Proteins & g L\({}^{-1}\) & 13.99 \(\pm\) 0.58 & 12.91 \(\pm\) 0.52 & 3.41–8 & [8,9] \\ Lactose & g L\({}^{-1}\) & 66.75 \(\pm\) 0.10 & 61.57 \(\pm\) 0.12 & 10–92 & [4,8,9] \\ Lactate & g L\({}^{-1}\) & 9.03 \(\pm\) 0.03 & 9.57 \(\pm\) 0.02 & - & - \\ Acetate & g L\({}^{-1}\) & 0.83 \(\pm\) 0.00 & 0.82 \(\pm\) 0.00 & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Whey wastewater characterization.
## 5 Slice Evolution  
The concept of slice phase space is a by-product of the \(SASE\)\(FEL\) physics. It is indeed associated with the fact that, in these devices, the combination of mechanisms such as gain, slippage and finite coherence length, determines a local interaction, because the radiation experiences only a portion of the beam, having the longitudinal extension of a coherence length (see Figures 7 and 8). The interaction is therefore sensitive to the slice-brightness, which is characterized by the relevant six dimensional phase space distribution [52; 53; 54; 55; 56; 57; 58].  
The analysis associated with the transverse phase space and with the relevant electro-beam transport properties has already been accomplished in a number of papers, which have clarified a significant deal of the physical and practical issues regarding the evolution and interplay between slices and \(FEL\) power output and performances.  
In this section we clarify the effect of longitudinal phase space, by the use of the formalism we outlined in the previous section. We sample the optical bunch as indicated in Figure 7 and characterize each slice with a progressive number [59; 60].  
Figure 7: Slice number sampling and shape, using the e-bunch distribution as the reference frame.  
Each slice is assumed to be Gaussian with an amount of charge \(Q_{s}\) and an associated current  
\[I_{s}=\frac{Q_{s}}{\sqrt{2\pi\tau_{c}}},\qquad\qquad\tau_{c}\simeq\frac{L_{c}}{c }\equiv\textit{coherence}-time. \tag{59}\]  
A natural assumption is that the amount of charge follows the Gaussian shape of Figure 8, namely,  
\[Q_{s}=Q_{s^{*}}e^{-(s-s^{*})^{2}\delta^{2}},\qquad\qquad\delta=\frac{2\pi L_{c }}{\sigma_{\zeta}} \tag{60}\]  
with \(\delta^{-1}\) being the number of slices inside the bunch and \(s^{*}\) representing the slice with largest current. With these assumptions we can write the slice Pierce parameter as  
\[\rho_{s}=\rho_{s^{*}}e^{-\frac{(s-s^{*})^{2}\delta^{2}}{3}}. \tag{61}\]  
As we already stressed, each slice carries its own energy spread, and therefore we get a corresponding inhomogeneous parameter specified by  
\[\bar{\mu}_{s}=\bar{\mu}_{s^{*}}e^{-\frac{(s-s^{*})^{2}\delta^{2}}{3}}. \tag{62}\]  
The energy spread corresponding to each slice may be completely random and that with largest current may carry the worst spread.  
By taking into account these effects, we obtain the results reported in Figures 9 and 10. The first refers to the case in which the slice with maximum current coincides with that at the center of the distribution, and the energy spread of each slice is chosen to be completely random. The second accounts for a configuration in which the maximum current is associated with a slice belonging to the rear part of the bunch.  
Further comments including the elements for a more accurate computation of the slice distribution, are discussed in the forthcoming section dedicated to concluding remarks.  
Figure 8: Bunched envelope, associated slices and superimposed radiation.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 5 Experimental Validation
### Experimental Results
#### 5.3.1 Master-Remote Position Controller  
In this assembly experiment, the master-remote position control was implemented; the remote robot followed the exact trajectory sent by the operator. The touch force and the trajectory were recorded. As observed in Figure 8, the biggest contact forces on the measured direction amounted to 11 N--a hazardous force that might cause damage to the robotic system or the assembled components. Tiny position deviations can create large collision forces, and the performance of teleoperation largely depends on the operator's experience.  
Figure 8: The contact force and the trajectory using the master–remote position controller.  
Figure 7: Position tracking accuracy in the X direction.
# Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals
###### Abstract  
When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time-frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.
# The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice
###### Abstract  
Standard safety assessments of civil engineering systems are conducted using safety factors. An alternative method to this approach is the assessment of the engineering system using reliability analysis of the structure. In reliability analysis of the structure, both the uncertainty of the load and the properties of the materials or geometry are explicitly taken into account. The uncertainties are described in a probabilistic manner. After defining the ultimate and serviceability limit state functions, we can calculate the failure probability for each state. When assessing structural reliability, it is useful to calculate measures that provide information about the influence of random parameters on the failure probability. Classical measures are vectors, whose coordinates are the first partial derivatives of reliability indices evaluated in the design point. These values are obtained as a by-product of the First-Order Reliability Method. Furthermore, we use Sobol indices to describe the sensitivity of the failure probability to input random variables. Computations of the Sobol indices are carried out using the classic Monte Carlo method. The aim of this article is not to define new sensitivity measures, but to show the advantages of using structural reliability and sensitivity analysis in everyday design practice. Using a simple cantilever beam as an example, we will present calculations of probability failure and local and global sensitivity measures. The calculations will be performed using COMREL modules of the STRUREL computing environment. Based on the results obtained from the sensitivity analysis, we can conclude that in the case of the serviceability limit state, the most significant influence on the results is exerted by variables related to the geometry of the beam under consideration. The influence of changes in Young's modulus and load on the probability of failure is minimal. In further calculations, these quantities can be treated as deterministic. In the case of the ultimate limit state, the influence of changes in the yield strength is significant. The influence of changes in the load and length of the beam is significantly smaller. The authors present two alternative ways of designing with a probabilistic approach, using the FORM (SORM) and Monte Carlo simulation. The approximation FORM cannot be used in every case in connection with gradient determination problems. In such cases, it is worth using the Monte Carlo simulation method. The results of both methods are comparable.  
reliability and sensitivity analysis; Sobol indices; first-order reliability method; Monte Carlo method +
Footnote †: journal: Applied sciences
## Interferometry processing
### Deep Learning Using the Convolutional Neural Network (CNN)  
Deep learning is a type of machine learning that relies on multiple layers of nonlinear processing for feature identification and pattern recognition, described in a model. Deep learning models can be used on different tools or by performing Python codes related to libraries such as PyTorch, Keras, TensorFlow, Onnx, Fats.ai etc. In this case, open-source libraries and Python scripts were integrated with ESRI ArcGIS Pro v.2.8 for object detection, object classification and image classification. In order to extract the building and roads, deep learning techniques using convolutional neural networks (CNNs) were adopted starting with the ortho-rectified images acquired by air flights over the Aosta Valley region. In particular, the two images regarding the AGEA (Agency for Disbursements in Agriculture) 2020 and the Aosta Valley 2018 orthophoto were used.  
An inferencing process was performed to extract the roads and buildings. This phase was crucial because the information learned during the deep learning training process was put to work in detecting similar features in the datasets. ESRI ArcGIS Pro uses an external third-party framework and model definition file to run the inference geoprocessing tools. Therefore, the library and dependencies were appropriately installed. In this case, the two models provided by ESRI and edited accordingly considering the alpine areas were adopted. It is worth noting that the model definition files and (.dlpk) packages can be used multiple times as inputs for the geoprocessing tools, allowing for the assessment of multiple images over different locations and time periods using the same trained model.  
The main settings adopted to perform CNN deep learning on ArcGIS Pro are reported in Table 7.  
It is worth noting that filtering and threshold is normally not present in the parameter settings. In fact, to avoid deep filtering out features, in this case buildings and roads, with a surface less than 100 m square, a script was realized to include this command and perform this analysis during the building extraction phase.
## 3 Data Description and Preparation
### CF Events Extraction  
The dataset was deemed satisfactory for analysis due to minimal noise and missing data. To further enhance the quality of the data, Kalman filtering and cubic spline interpolation techniques were employed for noise reduction and inpainting. An automatic search filter was then utilized to extract CF events based on specific thresholds such as time headway and lateral distance. The extraction criteria were informed by previous research [34; 35].  
Following data extraction, a random sampling methodology was implemented to ensure that the sample accurately reflected general driving behavior. A total of 100 CF events were randomly selected for each driver from a range of traffic flow facilities and conditions. This resulted in a total of 4000 samples collected from the 40 drivers for subsequent analysis, with a total recording time exceeding 2500 min.
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 3 Results and Discussion  
The antenna was fabricated using an LPFK machine and the fabricated prototype was tested to validate the simulated results. The measured S-parameters are shown in Figure 5a,b. The measured and simulated results are nearly alike. The minimum measured isolation that was achieved was 23 dB, and the slight disagreement in the S-parameters can be attributed to the cable and measurement set up losses [33]. The return loss from Antenna 1 and Antenna 3 covers the desired resonance frequency ranges from 4.55 to 4.9 GHz based on \(-6\) dB criteria.  
Figure 4: Simulated S-parameters of proposed Antenna (**a**) Reflection Coefficient (**b**) Ports Isolation.  
Figure 3: MIMO configuration: (**a**) simulated model, (**b**) fabricated prototype front, (**c**) back.
## 3 Results and Discussion
[PERSON] et al. [29] argue that ventilation, humidity and temperature are the main factors influencing fungal evolution. This assertion explains the variations in behavior observed in vitro and ex vitro between each strain of _Trichoderma_ spp. Also, the antagonistic activity of _Trichoderma_ to biologically control pests is severely controlled by their individual mycelial characteristics. These interactions influence both the production of antimicrobial compounds and the ability to parasitize other fungi. [PERSON] first delved into the genetic delimitation of _Trichoderma_ spp., emphasizing the relevance of morphological characteristics, especially the presence or absence of specialized structures. In the particular case of _T. viride_, this species' conidiophores are sparsely branched, a condition that reduces the dispersion of conidia, and with it, the effectiveness of _Trichoderma_ as agent of biological control. On the other hand, _T. harzianum_ develops conidiophores of a verticillate form that propitiate the dispersion of the fungus and the capacity to colonize new substrates [30]. In this analysis, the findings showed that individual mycelial characteristics of _Trichoderma_ spp. do not directly correlate with a high index of antagonism when coming into contact with phytopathogens. The _Trichoderma_ E9 strain grown in MEA medium developed greater mycelial growth but did not stand out as an antagonist agent. Of the more than 400 recognized species of _Trichoderma_, _T. harzianum_, _T. asperellum_, _T. atrovriide_, and _T. atrobrunneum_, among others, have been widely recognized as BCA. However, each species deploys variable mechanisms of action in response to the pathogen to be inhibitedand the surrounding environmental conditions. A frequently underestimated factor is the health and composition of the soil, which, through its chemical and physical properties, whether altered or typical of certain soils, can lead to negative interactions between the biodiversity of beneficial soil microbes, thus minimizing the effectiveness of biopreparations. For this reason, it is important that future studies address a more detailed exploration of the relationships between soil parameters, the mycelial growth of _Trichoderma_ spp. and the antagonistic capacity against pathogens.
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
## 2 Methods
### Direct Downward Continuation from Topography  
Direct downward continuation is a favorable method to solve the problem of irregular surface. The process is simple and it is also applicable to the situation of large fluctuations in the surface elevation. Ref. [4] first proposed the concept of wavefield downward continuation based on accumulating step by step. According to this concept, we achieve the full-wave-equation depth extrapolation for migration by using direct downward continuation from topography. The specific steps are as follows:  
(1) First, mesh the model, select the height of the highest point on the surface to establish a datum; then use the surface velocity to fill the space between the datum and the surface; and set the initial value of the source and receiver wavefields to zero.  
(2) Starting from the datum, at each depth, first check whether the receiver or source point exists, if so, add the recorded wavefield or source wavelet to the corresponding wavefield; then use Equation (9) to downward continue the source and receiver wavefield.  
(3) Filter the wavefield calculated at each depth to eliminate the wavefield energy above the surface [4].  
(4) Update the image using the cross-correlation imaging condition.  
Our method uses common shot data. We perform the above steps for each shot, and superimpose the results of all shots to get the final imaging result.
# Strength Analysis of Cylindrical Shells with Tangential Nozzles under Internal Pressure
## 3 Limit-Load Analysis
### Strength-Weakening Coefficient and Its Influence Factors  
In order to quantitatively characterize the influence of tangential nozzles on the ultimate load-bearing capacity of cylindrical shells under internal pressure, a so-called strength-weakening coefficient is defined as follows:  
\[K=(P_{0}-P)/P_{0}\times 100\% \tag{1}\]  
In the formula, \(P_{0}\) and \(P\) are the limit load of the cylindrical shell without a nozzle and the cylindrical shell with a tangential nozzle, respectively. Obviously, the greater the strength-weakening coefficient, the greater the influence of the tangential nozzle on the load-bearing capacity of the cylindrical shell. The strength-weakening coefficient of the cylindrical shell having tangential nozzles with different diameter ratios \(d_{o}/D_{i}\), thickness ratios \(t/T\) and diameter-thickness ratios \(D_{i}/T\) under internal pressure is shown in Figures 8-10.  
It is seen from Figure 8 that the strength-weakening coefficient of the cylindrical shell with tangential nozzles decreases monotonously with increasing relative thickness \(t/T\) for different \(d_{o}/D_{i}\) and \(D_{i}/T\), meaning the thicker the nozzle is, the smaller the weakening effect of the nozzle on the load-bearing capacity of the cylindrical shell is.  
It can be seen from Figure 9 that with the increase in \(d_{o}/D_{i}\), the strength-weakening coefficient \(K\) shows an increasing trend. But when \(t/T\) is small, i.e., the thickness of the nozzle wall is small, with the increase in \(d_{o}/D_{i}\), the increasing trend of the \(K\) value gradually slows down, meaning the influence gradually becomes less significant.  
It is seen from Figure 10 that, with the increase in \(D_{i}/T\), the strength-weakening coefficient \(K\) increases monotonically. With increasing \(d_{o}/D_{i}\) and decreasing \(t/T\), or with the increase in the diameter of the nozzle and the decrease in the wall thickness of the  
Figure 7: Change in the limit load value of the cylindrical shell having a tangential nozzle with \(t/T\) for different \(d_{o}/D_{i}\) and \(D_{i}/T\).  
nozzle, the \(K\) value increases significantly, i.e., the load-bearing capacity of the cylindrical shell decreases greatly.  
Specifically, when \(d_{o}/D_{i}=0.30\), \(t/T=0.50\) and \(D_{i}/T=125\), the strength-weakening coefficient reaches 61.52%, meaning the load-bearing capacity of the cylindrical shell is significantly decreased. But when \(d_{o}/D_{i}=0.10\), \(t/T=1.75\) and \(D_{i}/T=71.43\), the strength reduction coefficient is close to 0, meaning the load-bearing capacity of the cylindrical shell is not obviously affected by the tangential nozzle.  
Figure 8: Change in strength–weakening coefficient \(K\) of the cylindrical shell structure having tangential nozzles with \(t/T\) for different \(d_{o}/D_{i}\) and \(D_{i}/T\).  
Figure 9: Change in strength–weakening coefficient \(K\) of the cylindrical shell structure having a tangential nozzle with \(d_{o}/D_{i}\) for different \(t/T\) and \(D_{i}/T\).
###### Abstract  
Microgrid integration and fault protection in complex network scenarios is a coming challenge to be faced with new strategies and solutions. In this context of increasing complexity, this paper describes two specific overload control strategies for four-wire inverters integrated in low voltage four-wire alternating current (AC) microgrids. The control of grid-tied microgrid inverters has been widely studied in the past and mainly focused on the use of droop control, which hugely constrains the time response during grid-disconnected operation. Taking into account the previous knowledge and experience about this subject, the main contribution of these two proposals regards providing fault current limitation in both operation modes, over-load capability skills in grid-connected operation and sinusoidal short-circuit proof in grid-disconnected operation. In the complex operation scenarios mentioned above, a hybrid combination of AC droop control based on dynamic phasors with varying virtual resistance, and voltage/frequency master voltage control for grid-(dis)connected operation modes are adopted as the mechanism to enhance time response. The two proposals described in the present document are validated by means of simulations using Matlab/Simulink and real experimental results obtained from CENER (The National Renewable Energy Centre) experimental ATENEA four-wire AC microgrid, obtaining time responses in the order of two-three grid cycles for all cases.  
microgrids; control strategies; three-phase four-wire systems; fault current limitation