esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 5 Conclusions  
This paper presents a sequential design procedure for decentralized robust control of a network of IBDGs which are connected together through inductive and resistive interconnections. Using the overlapping decomposition method, the state space model of the network is expanded with respect to the shared dynamics among the inverters. Then, the network is clustered based on the norms of the off-diagonal blocks of the extended system matrix \(\bar{A}\), which are proportional to the distances among the inverters as compared to the norms of diagonal blocks. The proposed sequential \(\mu\)-synthesis robust controller design process is then utilized for each subnetwork (cluster). Moreover, \(\pm 10\%\) variations are taken into account for the LC filter parameters of all the IBDGs in the design process of the robust controllers. The time response analysis illustrates that the proposed sequentially-designed decentralized controllers outperform the independently-designed (benchmark) ones, even under strong interconnections among the IBDGs of each subnetwork. The main reason for this is that the influence of interconnections are taken into account in the sequential design process, while they are taken as external disturbances in the independent design scheme. Moreover, transient overload and nonlinear load analyses illustrate that the proposed sequentially-designed decentralized controllers are able to keep the load voltage within \(\pm 10\%\) of the nominal value (IEEE 1250 standard) and the harmonic voltage distortions to less than \(4\%\) (IEEE 1547 standard). This study recommends that the proposed sequentially-designed decentralized controllers provide a valuable solution to stabilize a network of IBDGs when their distances become short, as is the case in nanogrids. However, the sequential design of decentralized controllers becomes more challenging in cases where there are many generation systems in a subnetwork. Moreover, this case requires plug-and-play operation of the system, which the proposed sequentially-designed decentralized controllers are not able to provide. Therefore, potential future work could involve the independent design of decentralized robust controllers such that their plug-and-play capability is preserved.  
Conceptualization, S.M.A.; methodology, M.S.; software, M.S.; validation, M.S.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S. and S.M.A.; visualization, M.S.; supervision, S.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Time response of the sequentially-designed decentralized controllers for inverter \(\#4\) (\(Inv_{4}\)) for 10% parameter variations.
# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 5 Possible Application Scenarios with Digital Twin
### Overall Scenario of Digital Twin in Agriculture
This integration includes real-time data from IoT devices distributed across the farm, which monitor key factors such as soil moisture and nutrient levels. Weather patterns are important in making decisions about farming activities. The data coming from local weather stations can offer valuable insights in terms of how the weather patterns could change. Additionally, the DT utilises high-resolution satellite imagery to monitor larger-scale changes in crop health and environmental conditions. The DT improves its predictive accuracy by accessing agricultural databases to collect data on crop varieties, pest/disease patterns, and historical data. This can result in guiding farmers towards more informed decision-making in modern agriculture. The DT not only simulates and predicts the outcomes of different farming strategies under varying environmental conditions but also continuously updates its model to reflect real-time changes on the farm. This dynamic model ensures that the virtual farm environment is always in sync with the actual farm, allowing farmers to visualise the consequences of their actions before implementing them. By acting as a decision-support tool, the DT enables farmers to efficiently manage risks, such as pest infestations or disease outbreaks, and test different scenarios, including best and worst-case situations. Ultimately, this approach supports sustainable and environmentally friendly farming practices, empowering farmers with the tools needed for success in the ever-evolving world of agriculture. In the following section, we will explore a few use cases that demonstrate the potential of DTs in agriculture: irrigation systems and early disease detection. Specifically, we will compare the processes for these use cases with and without DT technology. Figures 4 and 5 illustrate these processes and highlight the benefits of using a DT. It is worth noting that with DTs, it is necessary to consider all related factors before making a decision instead of only considering a single factor. This feature enables farmers to make more accurate decisions that consider the potential future effects of the decision rather than simply deciding whether to irrigate or manually identify diseases. This is one of the key advantages of DTs compared to other modern digital systems. Figure 4: Irrigation Systems.
## 4 Target Performance
### Detection Performance for Low Resolution  
We set a performance target for detecting small markers in the image that exist at a distance, i.e., low-resolution markers. The relationship between the size of a marker, \(M\), and the size of the image, \(D\), is shown in Figure 6 and expressed in Equation (9).  
\[D=M\times\frac{L}{r}\times\frac{I}{S} \tag{9}\]  
where r is the distance to the marker, \(L\) is the focal length of the camera, \(I\) is the size of the image, and \(S\) is the size of the image sensor (obtained by multiplying the image size by the pixel size).  
Considering the flight time, we assume a case in which markers are detected from a small fixed-wing UAV circling and staying at an altitude of 100 m and a speed of 25 m/s at a maximum distance of 2000 m. The marker size is tentatively set to be 2 m, which is large but realistic for operation. The marker sizes for the surface distance between the ground and the UAV are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Focal Length & 50 [mm] \\ Pixel Size & 0.00345 [mm] \\ Image Size & \(1280\times 720\) [pixel] \\ Field of View & 5.1 [deg.] \\ Exposure Time & 0.003 [s] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Camera parameters assumed for wireless relay system.  
Figure 6: Relationship between the size of the marker M and the size D of the image.  
Figure 7: Relationship between the marker size D in the image and surface distance.  
Figure 7 shows that the marker drawing size will be 14.5 [pixels] at the assumed surface distance of 2000 m. As an example, if the turning orbit of a small fixed-wing UAV deviates by about 10 [m], the marker drawing size changes by only about 0.05 [pixel]. Therefore, without considering the margin, the detection performance required of the proposed method for low resolution is the ability to detect a marker of 14.5 [pixel].
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Adaptive Filtering Methods
#### 2.3.1 Kalman Filter  
The Kalman filter is a recursive algorithm used to estimate the state of a system from a series of noisy measurements. It is particularly useful in the real-time processing of ECG signals due to its ability to follow the dynamics of the signal and improve its quality by removing noise. A relevant example of the use of the Kalman filter is presented in paper [38], which proposes an algorithm for discriminating ventricular fibrillation (VF) signals from ventricular tachycardia (VT) signals through the adaptive filtering technique. This algorithm first adopts the VF and VT signals as the primary input of the adaptive filter, and the reference signals can be conveniently obtained by a suitable delay. The energy of the error signal (EE) acts as an eigenvalue, and the decision thresholds are determined by the learning period. To test the proposed method, sequential testing is used, which identifies with an accuracy of 97.4% VF and 100% VT after three tests.  
The paper [39] presents an innovative method of using the adaptive Kalman filter to enhance ECG signals. In this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. Using a Bayesian framework, this paper develops a sequential averaging filter that adaptively adjusts the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter is designed as an adaptive Kalman filter. Adaptive estimation of process and measurement noise covariances is achieved by maximizing the Bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between multiple simultaneously recorded ECGsignals. These noise covariance estimates allow the filter to give more importance to newly arrived data when it exhibits morphological variability and to reduce this importance when there is no morphological variability. The filter is evaluated by applying it to a variety of ECG signals.  
The Kalman filter can be applied to enhance the real-time ECG signal by removing background noise and artifacts. In [40], the use of the Kalman filter for the separation of ECG components and the removal of background noise was demonstrated. The filter was able to improve the ECG signal, allowing more accurate detection of episodes of ventricular fibrillation.
## 7 Towards a Simplified Optical Transceiver
### Full Coherent Optical System
#### 7.1.2 Optical Coherent Detection Principles
Heterodyne detection can be considered as a scenario in which \(|\omega_{IF}|\gg 2\pi/T\), where \(1/T\) represents the symbol rate that determines the optical carrier modulation bandwidth. Based on this, it can be inferred from Equation (30) that the signal's electric field is down-converted to an IF signal. Moreover, in this scenario, the signal phase can be expressed as:  
\[E_{s}(t)=A_{s}(t)\exp(j\omega_{\text{s}}t), \tag{25a}\]4  
where \(\theta_{s}(t)\) represents the phase modulation and \(\theta_{sn}(t)\) denotes the phase noise. As a result, the output of the receiver can be defined as [10; 135; 134]:  
\[E_{s}(t)=A_{s}(t)\exp(j\omega_{\text{s}}t), \tag{25a}\]5  
where \(\theta_{n}(t)\) represents the total phase noise, which can be expressed as:  
\[E_{s}(t)=A_{s}(t)\exp(j\omega_{\text{s}}t), \tag{25a}\]6  
From Equation (32), note that the complex amplitude of the current can be defined as [10]:  
\[E_{s}(t)=A_{s}(t)\exp(j\omega_{\text{s}}t), \tag{25a}\]7  
In addition, for the synchronous demodulation of the IF signal in Equation (32) that is obtained through heterodyne detection, a phase-locked loop circuit is required at the IF stage. The employment of the electrical phase-locked loop helps the estimation of the phase noise and decoding of the symbol on the phasor expressed by Equation (34). Homodyne Detection  
This detection scheme applies in a scenario where \(\omega_{IF}=0\). The photodiode current for this detection scheme can be expressed as:  
\[E_{s}(t)=A_{s}(t)\exp(j\omega_{\text{s}}t), \tag{25a}\]8  
With reference to Equation (35), the homodyne receiver determines the inner product between the signal and the LO phasors. Furthermore, for effective symbol decoding, the LO phase, \(\theta_{\text{LO}}(t)\), has to track \(\theta_{sn}(t)\) to ensure the condition \(\theta_{n}(t)=0\). This can be satisfied with the optical phase-locked loop implementation in the conventional coherent receiver. On the other hand, in the digital counterpart, it can be fulfilled with the aid of DSP. Nevertheless, this brings about an increase in the system complexity in practice. Based on Equation (35), one the in-phase (cosine) component can be detected, while the quadrature component (sine) is not considered. Consequently, full information on the complex amplitude of the signal cannot be extracted as both IQ components of the signal cannot be detected simultaneously. Phase Diversity  
Assume another LO with a phase shift of \(90^{\circ}\) in the homodyne receiver and a signal path with two branches, as illustrated in \[E_{s}(t)=A_{s}(t)\exp(j\omega_{\text{s}}t), \tag{25a}\]9 This configuration is known as a _phase-diversity homodyne receiver_ and is capable of the simultaneous detection of both IQ components of an optical signal. The required \(90^{\circ}\) phase shift can be achieved by a \(90^{\circ}\)-optical hybrid. It should be noted that this approach allows the beat of the signal with a \(90^{\circ}\) shifted version of the LO.
## 3 Results
### Aneurysm under FDS Treatment: RRT Distribution  
From the stenting-efficiency point of view, the RRT is of the most interesting variable as it is a measurement of the relative increase of blood residence time within the aneurysm sac. This variable allows for the global analysis of hemodynamic changes and thrombus formation in the sac [22]. In the Figure 6 it can be seen that stenting leads to an increase of RRT within the aneurysm sac. STENT 2 with a low porosity shows a similar spatial distribution with respect to the control case; while STENT 1 has higher values. The biodegradable stent seems to provide the largest increase in RRT (by comparing the third row of Figure 6d with Figure 6a to Figure 6c). This increase may be explained by the large high OSI region caused by the biodegradable stent due to the lower diverted flow and the induced change in flow structures (Figure 4a-d). As reported by [PERSON] et al. [55], high RRT is characterized by low velocity with low WSS and oscillatory flow with high OSI. OSI is insensitive to WSS magnitude; locations with low WSS could not necessarily be identified as sites of high OSI because low WSS may simply results from a decrease of the flow velocity without any local flow recirculation. Our results shows that the high RRT in the case of biodegradable stent is reached with low, but not oscillatory, WSS and high OSI around the aneurysm dome. From the third row of Figure 6, it is seen that the highest RRT values are reached in very small locations near to the aneurysm neck. This is due to the local oscillation of the flow, as seen from the streamlines (Figure 4). As a result, at the same location, all stented arteries have higher OSI values than the one without stent. The variation of peak RRT for the different stents is shown in Figure 7d. It is seen that PRT reached  
Figure 7: **(a)** Flow reduction promoted by FDSs, **(b)** peak TAWSS, **(c)** OSI, and **(d)** RRT **(d)** in the aneurysmatic sac for the artery without treatment and in the presence of the three considered stents.  
its maximal value at the dome rather than the area near the neck and among them, STENT 2 has the highest RRT. These results suggest the necessity of computing the RRT so as to provide the information on the flow motion near the aneurys wall as well as the information on the FDS-induced flow blockage inside the aneurys sac as reflected by OSI.
_Texins_**2012**, \(4\), 1261-1287; doi:10.3390/toxins4111261  
_toxins_  
**ISSN 2072-6651**  
www.mdpi.com/journal/toxins  
_Review_  
**Shiga Toxins and the Pathophysiology of Hemolytic Uremic Syndrome in Humans and Animals**  
**[PERSON] \({}^{\dagger}\), [PERSON] \({}^{\dagger}\), [PERSON] and [PERSON] \({}^{\ast}\)**  
Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA; E-Mails: [EMAIL_ADDRESS] (C.L.M.); [EMAIL_ADDRESS] (C.S.L.); [EMAIL_ADDRESS] (S.K.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-617-414-7092; Fax: +1-617-414-7073.  
_Received: 17 September 2012; in revised form: 1 November 2012 / Accepted: 2 November 2012 / Published: 8 November 2012_  
**Abstract:** Food-borne diseases are estimated at 76 million illnesses and 5000 deaths every year in the United States with the greatest burden on young children, the elderly and immunocompromised populations. The impact of efficient food distribution systems and a truly global food supply ensures that outbreaks, previously sporadic and contained locally, are far more widespread and emerging pathogens have far more frequent infection opportunities. Enterohemorrhagic _E. coli_ is an emerging food- and water-borne pathogen family whose Shiga-like toxins induce painful hemorrhagic colitis with potentially lethal complications of hemolytic uremic syndrome (HUS). The clinical manifestations of Shiga toxin-induced HUS overlap with other related syndromes yet molecular mechanisms differ considerably. As discussed herein, understanding these differences and the novel properties of the toxins is imperative for clinical management decisions, design of appropriate animal models, and choices of adjunctive therapeutics. The emergence of new strains with rapidly aggressive virulence makes clinical and research initiatives in this field a high public health priority.  
**Keywords:** Enterohemorrhagic _E. coli_; Shiga toxins; hemolytic uremic syndrome; animal models
# Hybrid Fibres as Shear Reinforcement in High-Performance Concrete Beams with and without Openings
## 3 Experimental Results and Discussion
### Inelastic Parameters  
#### 3.2.1 Maximum Load and Over-Strength Factor  
All the inelastic performance parameter values of the beams are shown in Table 9. The over-strength is specified as member capacity and is usually defined using over-strength factor. This factor is a parameter of the strain hardening, number of structural redundancies, deflection constraints and fibre content volume, which may be defined as the ratio of ultimate load to the yield load of the beams [65].  
The first comparative parameter for the beams is the increase in maximum load, which reflects the effect of different fibre volume contents on increasing the load-bearing capacity. Beams B3 and BO2 with the inclusion of 1% ST fibres and 0.05% PP fibres and beam BO3 with 1.6% ST + PP fibres content presented the highest increases in maximum load, 17%, 12% and 14% respectively, comparing to the beams with stirrups. The beam with the lowest fibre content exhibits the greatest decrease in maximum load, 26%. Figure 8a shows the values of maximum loads for all the tested beams. The FRC beams have lower over-strength factor values in comparison with RC beams B1 and BO1, Figure 8b. The over-strength factor is not calculated for the B2 beam because the yield load is not reached. This shows the dominant action of stirrups in the first stage of strain hardening.
## 3 Results
### Mitigation of Odor and Targeted Gases as a Function of UV Wavelength
#### 3.2.3 Measured Odor, VOCs, NH\({}_{3}\) and H\({}_{2}\)S  
There was no statistically significant odor mitigation, VOCs, NH\({}_{3}\) and H\({}_{2}\)S for all UV wavelengths tested at relatively low doses used for direct comparisons (Tables S5-S8). This result is due to insufficient UV dose in one chamber used for side-by-side comparisons and this study's limitation. This limitation could be addressed by refurbishing the entire UV mobile laboratory with one type of lamp, effectively allowing a more extensive range of doses to be tested (e.g., lower variability reported for UV treatment using an entire mobile lab with 12 chambers facilitating treatment). Therefore, further research is needed for the accurate evaluation of UV-C photocatalysis at a farm-scale.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**UV** & **UV Dose** & **Light** & **Control** & **Treatment** & **\% Reduction** \\
**Wavelengths** & **(μ)-cm\({}^{-2}\)** & **Intensity** & **(μ)-cm\({}^{-2}\)** & **(ppm)** & **(ppm)** & **(\(p\)-Value)** \\ \hline
185 + 254 & 0.03 & 0.01 & 0.232 \(\pm\) 0.004 & 0.221 \(\pm\) 0.002 & **5.1 (0.02)** \\
222 & 2.55 & 0.59 & 0.221 \(\pm\) 0.006 & 0.209 \(\pm\) 0.005 & 5.4 (0.20) \\
254 & 1.60 & 0.37 & 0.205 \(\pm\) 0.005 & 0.201 \(\pm\) 0.002 & 1.7 (0.26) \\
367 & 1775 & 410 & 0.200 \(\pm\) 0.009 & 0.188 \(\pm\) 0.007 & 5.8 (0.20) \\ \hline \end{tabular}
\end{table}
Table 7: Mitigation of N\({}_{2}\)O with different UV wavelengths irradiating gaseous emissions inside #2 chamber. Airflow = 0.28 m\({}^{3}\cdot\)s\({}^{-1}\), inlet air temperature (influent of chamber #2) = 28 \({}^{\circ}\)C, inlet air RH = 67%, outlet air temperature (influent of chamber #3) = 31 \({}^{\circ}\)C, outlet air RH = 61%. Values in the table are mean \(\pm\) S.D. Bold signifies statistical significance.  
Mitigation of Odor and Targeted Gases with UV-A Photocatalysis as a Function of PM Size and Concentration  
This experiment allowed the examination of the effect of PM on UV-A photocatalysis. The UV-A was selected because the entire mobile lab uses UV-A photocatalysis. Thus, three different airborne PM conditions were achieved via air filtration before the UV treatment (Table 8). Sections 3.1 and 3.2 report on results using the 'best-case' scenario where the MERV 8 and 15 filters removed 98% of incoming PM (to the Total PM = 0.004 mg\(\cdot\)m\({}^{-3}\)). Then the MERV 15 was removed and the MERV 8 filtered out 77% of incoming PM (to the total PM = 0.06 mg\(\cdot\)m\({}^{-3}\)). Lastly, MERV 8 was removed, and unfiltered swine barn exhaust discharged from the manure pit fan were subjected to the UV treatment (total PM = 0.22 mg\(\cdot\)m\({}^{-3}\)). This last experiment represents the 'worst-case' scenario, where there is no PM filtration to swine barn emissions. The subsections below report the UV effects on air with three different PM conditions (considered 'control' for UV treatment) (Table 8).
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 3 Proposed Method
### VGG-Ear Model
#### 3.1.1 Transfer Learning  
Convolutional Neural Networks (CNN) have shown impressive performance in various computer vision tasks in recent years, such as image classification, face recognition [28], object detection [29], etc. It is well known that deep CNN networks require abundant training data to achieve better results. Although ear recognition has grown in popularity in recent years, unlike the field of face recognition, ear datasets are limited to thousands of images and hundreds of identities. Therefore, how to utilize the concepts of deep learning to identify limited ear datasets is a big challenge.  
Transfer learning is a CNN architecture trained on a large dataset and then reused to train other datasets. Transfer learning leverages the knowledge gained from previous training to improve its learning ability in new complex tasks. This method dramatically reduces the depth of traditional deep learning models, better alleviates the common overfitting problem of small samples and has remarkable achievements in the field of medical images.  
[PERSON] et al. [30] proposed using transfer learning to use active data augmentation and selective learning on existing models to significantly improve the recognition rate of ear images. [PERSON] et al. [31] proposed to train different networks by randomly initializing the weights and fine-tuning the pretrained model to build the best model and improve the  
Figure 3: An example of the AWE ear database [25].  
recognition performance. The above methods fully demonstrate the effectiveness of the ear recognition model based on transfer learning.  
This paper compares the existing commonly used pre-training networks VGG19 [32], ResNet50 [33], InceptionV3 [34], Xception [35], and MobileNet [36] on the AWE dataset, and the optimal model VGG-Ear is obtained.